From 5098ca648782797842d83a017cd931a5ff09e9bc Mon Sep 17 00:00:00 2001 From: chanyub Date: Fri, 4 Jun 2021 12:46:41 +0900 Subject: [PATCH] =?UTF-8?q?chanyub=20=EC=A0=95=EB=A6=AC?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .DS_Store | Bin 10244 -> 6148 bytes ...ference.ipynb => my_model_inference.ipynb} | 0 ...l_bestmodel.ipynb => my_model_train.ipynb} | 0 chanyub_seg/.DS_Store | Bin 6148 -> 6148 bytes chanyub_seg/code/.DS_Store | Bin 0 -> 6148 bytes chanyub_seg/code/0_aug.ipynb | 1 - .../0_aug_pan_effb0_focal_madgrad_cosLR.ipynb | 1 - chanyub_seg/code/1_aug_horizontalflip.ipynb | 1 - .../1_aug_pan_effb0_focal_madgrad_cosLR.ipynb | 1 - .../code/2_aug_CLAHE_Horizontalflip.ipynb | 1 - .../3_aug_horizontalflip_Rotation90.ipynb | 1 - chanyub_seg/code/3barrack.ipynb | 1 - chanyub_seg/code/FCN32s.ipynb | 1 - chanyub_seg/code/UNet++ baseline.ipynb | 1017 ----------------- ...etpp_effb3_noisy_focal_madgrad_cosLR.ipynb | 1 - chanyub_seg/code/adamp.ipynb | 1 - ..._pan_effb3_noisy_focal_madgrad_cosLR.ipynb | 1 - ...oisy_focal_CE_madgrad_kwparam_stepLR.ipynb | 1 - ..._pan_effb5_noisy_focal_madgrad_cosLR.ipynb | 1 - ...lv3p_effb3_noisy_focal_madgrad_cosLR.ipynb | 1 - chanyub_seg/code/madgrad.ipynb | 1 - ...cal_madgrad_cosLR.ipynb => my_model.ipynb} | 0 chanyub_seg/code/mybaseline.ipynb | 1 - ...an_effb3_noisy_focal_adamp_coswarmLR.ipynb | 1 - ..._effb3_noisy_focal_madgrad_coswarmLR.ipynb | 1 - ...resnet101_imagenet_focal_adamp_cosLR.ipynb | 1 - ...et101_imagenet_focal_adamp_coswarmLR.ipynb | 1 - ...snet101_imagenet_focal_madgrad_cosLR.ipynb | 1 - ...101_imagenet_focal_madgrad_coswarmLR.ipynb | 1 - ...oisy_focal_CE_madgrad_kwparam_stepLR.ipynb | 1 - ..._pan_effb3_noisy_focal_madgrad_cosLR.ipynb | 1 - ...3_noisy_focal_madgrad_kwparam_stepLR.ipynb | 1 - ...pan_effb3_noisy_focal_madgrad_stepLR.ipynb | 1 - ..._pan_effb7_noisy_focal_madgrad_cosLR.ipynb | 1 - 34 files changed, 1044 deletions(-) rename 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satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug>=0.4.0->albumentations==0.5.2) (2.4.7)\n","Requirement already satisfied: decorator<5,>=4.3 in /usr/local/lib/python3.7/dist-packages (from networkx>=2.0->scikit-image>=0.16.1->albumentations==0.5.2) (4.4.2)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.826930Z","start_time":"2021-04-18T10:34:45.406686Z"},"scrolled":false,"colab":{"base_uri":"https://localhost:8080/"},"id":"HiN9b-Ly0I3p","executionInfo":{"status":"ok","timestamp":1620130644359,"user_tz":-540,"elapsed":3420,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"1492ea99-071d-4b79-ae2f-5f0cacafc466"},"source":["import os\n","import random\n","import time\n","import json\n","import warnings \n","warnings.filterwarnings('ignore')\n","\n","import torch\n","import torch.nn as nn\n","from torch.utils.data import Dataset, DataLoader\n","from utils import label_accuracy_score\n","import cv2\n","\n","import numpy as np\n","import pandas as pd\n","\n","# 전처리를 위한 라이브러리\n","from pycocotools.coco import COCO\n","import torchvision\n","import torchvision.transforms as transforms\n","\n","import albumentations as A\n","from albumentations.pytorch import ToTensorV2\n","\n","# 시각화를 위한 라이브러리\n","import matplotlib.pyplot as plt\n","import seaborn as sns; sns.set()\n","\n","plt.rcParams['axes.grid'] = False\n","\n","print('pytorch version: {}'.format(torch.__version__))\n","print('GPU 사용 가능 여부: {}'.format(torch.cuda.is_available()))\n","\n","print(torch.cuda.get_device_name(0))\n","print(torch.cuda.device_count())\n","\n","device = \"cuda\" if torch.cuda.is_available() else \"cpu\" # GPU 사용 가능 여부에 따라 device 정보 저장"],"execution_count":5,"outputs":[{"output_type":"stream","text":["pytorch version: 1.8.1+cu101\n","GPU 사용 가능 여부: True\n","Tesla P100-PCIE-16GB\n","1\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"Uuj6y7Ra0I3r"},"source":["## 하이퍼파라미터 세팅 및 seed 고정"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.841930Z","start_time":"2021-04-18T10:34:47.827931Z"},"id":"k-febRcn0I3r","executionInfo":{"status":"ok","timestamp":1620130644360,"user_tz":-540,"elapsed":2097,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["batch_size = 8 # Mini-batch size\n","num_epochs = 20\n","learning_rate = 0.0001"],"execution_count":6,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.856930Z","start_time":"2021-04-18T10:34:47.842931Z"},"id":"YA3jAi2a0I3s","executionInfo":{"status":"ok","timestamp":1620130644361,"user_tz":-540,"elapsed":1618,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# seed 고정\n","random_seed = 42\n","torch.manual_seed(random_seed)\n","torch.cuda.manual_seed(random_seed)\n","# torch.cuda.manual_seed_all(random_seed) # if use multi-GPU\n","torch.backends.cudnn.deterministic = True\n","torch.backends.cudnn.benchmark = False\n","np.random.seed(random_seed)\n","random.seed(random_seed)"],"execution_count":7,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"ePFcujAe0I3s"},"source":["## 학습 데이터 EDA"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.381961Z","start_time":"2021-04-18T10:34:47.857930Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"Ds0jp-pz0I3s","executionInfo":{"status":"ok","timestamp":1620130648016,"user_tz":-540,"elapsed":4325,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"db7c3e72-985b-4bef-d0b9-ff4ffb5aba4d"},"source":["%matplotlib inline\n","\n","dataset_path = '../input/data'\n","anns_file_path = dataset_path + '/' + 'train.json'\n","\n","# Read annotations\n","with open(anns_file_path, 'r') as f:\n"," dataset = json.loads(f.read())\n","\n","categories = dataset['categories']\n","anns = dataset['annotations']\n","imgs = dataset['images']\n","nr_cats = len(categories)\n","nr_annotations = len(anns)\n","nr_images = len(imgs)\n","\n","# Load categories and super categories\n","cat_names = []\n","super_cat_names = []\n","super_cat_ids = {}\n","super_cat_last_name = ''\n","nr_super_cats = 0\n","for cat_it in categories:\n"," cat_names.append(cat_it['name'])\n"," super_cat_name = cat_it['supercategory']\n"," # Adding new supercat\n"," if super_cat_name != super_cat_last_name:\n"," super_cat_names.append(super_cat_name)\n"," super_cat_ids[super_cat_name] = nr_super_cats\n"," super_cat_last_name = super_cat_name\n"," nr_super_cats += 1\n","\n","print('Number of super categories:', nr_super_cats)\n","print('Number of categories:', nr_cats)\n","print('Number of annotations:', nr_annotations)\n","print('Number of images:', nr_images)"],"execution_count":8,"outputs":[{"output_type":"stream","text":["Number of super categories: 11\n","Number of categories: 11\n","Number of annotations: 21116\n","Number of images: 2617\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.546964Z","start_time":"2021-04-18T10:34:51.382969Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/","height":355},"id":"SVmavtk00I3t","executionInfo":{"status":"ok","timestamp":1620130648910,"user_tz":-540,"elapsed":4896,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"636e9c5c-1a98-4fd8-aa1f-b10f0191c634"},"source":["# Count annotations\n","cat_histogram = np.zeros(nr_cats,dtype=int)\n","for ann in anns:\n"," cat_histogram[ann['category_id']] += 1\n","\n","# Initialize the matplotlib figure\n","f, ax = plt.subplots(figsize=(5,5))\n","\n","# Convert to DataFrame\n","df = pd.DataFrame({'Categories': cat_names, 'Number of annotations': cat_histogram})\n","df = df.sort_values('Number of annotations', 0, False)\n","\n","# Plot the histogram\n","plt.title(\"category distribution of train set \")\n","plot_1 = sns.barplot(x=\"Number of annotations\", y=\"Categories\", data=df, label=\"Total\", color=\"b\")"],"execution_count":9,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.561965Z","start_time":"2021-04-18T10:34:51.547969Z"},"id":"xK3EYdyX0I3u","executionInfo":{"status":"ok","timestamp":1620130648911,"user_tz":-540,"elapsed":4262,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# category labeling \n","sorted_temp_df = df.sort_index()\n","\n","# background = 0 에 해당되는 label 추가 후 기존들을 모두 label + 1 로 설정\n","sorted_df = pd.DataFrame([\"Backgroud\"], columns = [\"Categories\"])\n","sorted_df = sorted_df.append(sorted_temp_df, ignore_index=True)"],"execution_count":10,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.576961Z","start_time":"2021-04-18T10:34:51.562964Z"},"scrolled":false,"colab":{"base_uri":"https://localhost:8080/","height":421},"id":"9UQEjg8r0I3u","executionInfo":{"status":"ok","timestamp":1620130648930,"user_tz":-540,"elapsed":3601,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"12e63608-6080-442d-d07a-33bb205047f9"},"source":["# class (Categories) 에 따른 index 확인 (0~11 : 총 12개)\n","sorted_df"],"execution_count":11,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
CategoriesNumber of annotations
0BackgroudNaN
1UNKNOWN128.0
2General trash2225.0
3Paper7448.0
4Paper pack527.0
5Metal449.0
6Glass488.0
7Plastic2472.0
8Styrofoam1074.0
9Plastic bag6114.0
10Battery50.0
11Clothing141.0
\n","
"],"text/plain":[" Categories Number of annotations\n","0 Backgroud NaN\n","1 UNKNOWN 128.0\n","2 General trash 2225.0\n","3 Paper 7448.0\n","4 Paper pack 527.0\n","5 Metal 449.0\n","6 Glass 488.0\n","7 Plastic 2472.0\n","8 Styrofoam 1074.0\n","9 Plastic bag 6114.0\n","10 Battery 50.0\n","11 Clothing 141.0"]},"metadata":{"tags":[]},"execution_count":11}]},{"cell_type":"markdown","metadata":{"id":"AHvcEEXh0I3u"},"source":["## 데이터 전처리 함수 정의 (Dataset)"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:52.693328Z","start_time":"2021-04-18T10:34:52.681328Z"},"id":"tBr2oTea0I3v","executionInfo":{"status":"ok","timestamp":1620130661645,"user_tz":-540,"elapsed":892,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["category_names = list(sorted_df.Categories)\n","\n","def get_classname(classID, cats):\n"," for i in range(len(cats)):\n"," if cats[i]['id']==classID:\n"," return cats[i]['name']\n"," return \"None\"\n","\n","class CustomDataLoader(Dataset):\n"," \"\"\"COCO format\"\"\"\n"," def __init__(self, data_dir, mode = 'train', transform = None):\n"," super().__init__()\n"," self.mode = mode\n"," self.transform = transform\n"," self.coco = COCO(data_dir)\n"," \n"," def __getitem__(self, index: int):\n"," # dataset이 index되어 list처럼 동작\n"," image_id = self.coco.getImgIds(imgIds=index)\n"," image_infos = self.coco.loadImgs(image_id)[0]\n"," \n"," # cv2 를 활용하여 image 불러오기\n"," images = cv2.imread(os.path.join(dataset_path, image_infos['file_name']))\n"," images = cv2.cvtColor(images, cv2.COLOR_BGR2RGB).astype(np.float32)\n"," images /= 255.0\n"," \n"," if (self.mode in ('train', 'val')):\n"," ann_ids = self.coco.getAnnIds(imgIds=image_infos['id'])\n"," anns = self.coco.loadAnns(ann_ids)\n","\n"," # Load the categories in a variable\n"," cat_ids = self.coco.getCatIds()\n"," cats = self.coco.loadCats(cat_ids)\n","\n"," # masks : size가 (height x width)인 2D\n"," # 각각의 pixel 값에는 \"category id + 1\" 할당\n"," # Background = 0\n"," masks = np.zeros((image_infos[\"height\"], image_infos[\"width\"]))\n"," # Unknown = 1, General trash = 2, ... , Cigarette = 11\n"," for i in range(len(anns)):\n"," className = get_classname(anns[i]['category_id'], cats)\n"," pixel_value = category_names.index(className)\n"," masks = np.maximum(self.coco.annToMask(anns[i])*pixel_value, masks)\n"," masks = masks.astype(np.float32)\n","\n"," # transform -> albumentations 라이브러리 활용\n"," if self.transform is not None:\n"," transformed = self.transform(image=images, mask=masks)\n"," images = transformed[\"image\"]\n"," masks = transformed[\"mask\"]\n"," \n"," return images, masks, image_infos\n"," \n"," if self.mode == 'test':\n"," # transform -> albumentations 라이브러리 활용\n"," if self.transform is not None:\n"," transformed = self.transform(image=images)\n"," images = transformed[\"image\"]\n"," \n"," return images, image_infos\n"," \n"," \n"," def __len__(self) -> int:\n"," # 전체 dataset의 size를 return\n"," return len(self.coco.getImgIds())"],"execution_count":12,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"PBcB4oQh0I3w"},"source":["## Dataset 정의 및 DataLoader 할당"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:58.823175Z","start_time":"2021-04-18T10:34:54.106233Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"LxAXSS-c0I3x","executionInfo":{"status":"ok","timestamp":1620132524447,"user_tz":-540,"elapsed":6262,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"24834cdb-4db3-4083-a434-f733c6038dbb"},"source":["# train.json / validation.json / test.json 디렉토리 설정\n","train_path = dataset_path + '/train.json'\n","val_path = dataset_path + '/val.json'\n","test_path = dataset_path + '/test.json'\n","\n","# collate_fn needs for batch\n","def collate_fn(batch):\n"," return tuple(zip(*batch))\n","\n","train_transform = A.Compose([\n"," A.augmentations.Resize(256,256),\n"," ToTensorV2()\n"," ])\n","\n","val_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","test_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","# create own Dataset 1 (skip)\n","# validation set을 직접 나누고 싶은 경우\n","# random_split 사용하여 data set을 8:2 로 분할\n","# train_size = int(0.8*len(dataset))\n","# val_size = int(len(dataset)-train_size)\n","# dataset = CustomDataLoader(data_dir=train_path, mode='train', transform=transform)\n","# train_dataset, val_dataset = torch.utils.data.random_split(dataset, [train_size, val_size])\n","\n","# create own Dataset 2\n","# train dataset\n","train_dataset = CustomDataLoader(data_dir=train_path, mode='train', transform=train_transform)\n","\n","# validation dataset\n","val_dataset = CustomDataLoader(data_dir=val_path, mode='val', transform=val_transform)\n","\n","# test dataset\n","test_dataset = CustomDataLoader(data_dir=test_path, mode='test', transform=test_transform)\n","\n","\n","# DataLoader\n","train_loader = torch.utils.data.DataLoader(dataset=train_dataset, \n"," batch_size=batch_size,\n"," shuffle=True,\n"," num_workers=4,\n"," collate_fn=collate_fn,\n"," drop_last=True)\n","\n","val_loader = torch.utils.data.DataLoader(dataset=val_dataset, \n"," batch_size=batch_size,\n"," shuffle=False,\n"," num_workers=4,\n"," collate_fn=collate_fn,\n"," drop_last=True) \n","\n","test_loader = torch.utils.data.DataLoader(dataset=test_dataset,\n"," batch_size=batch_size,\n"," num_workers=4,\n"," collate_fn=collate_fn)"],"execution_count":43,"outputs":[{"output_type":"stream","text":["loading annotations into memory...\n","Done (t=4.17s)\n","creating index...\n","index created!\n","loading annotations into memory...\n","Done (t=0.93s)\n","creating index...\n","index created!\n","loading annotations into memory...\n","Done (t=0.01s)\n","creating index...\n","index created!\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-aaTrvBk0gKc","executionInfo":{"status":"ok","timestamp":1620132530452,"user_tz":-540,"elapsed":3791,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"cd7afeac-35f8-4ae0-8cb6-425599fe30d1"},"source":["!pip install wandb"],"execution_count":44,"outputs":[{"output_type":"stream","text":["Requirement already satisfied: wandb in /usr/local/lib/python3.7/dist-packages (0.10.29)\n","Requirement already satisfied: six>=1.13.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (1.15.0)\n","Requirement already satisfied: configparser>=3.8.1 in /usr/local/lib/python3.7/dist-packages (from wandb) (5.0.2)\n","Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.7/dist-packages (from wandb) (2.8.1)\n","Requirement already 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(2.10)\n","Requirement already satisfied: gitdb<5,>=4.0.1 in /usr/local/lib/python3.7/dist-packages (from GitPython>=1.0.0->wandb) (4.0.7)\n","Requirement already satisfied: smmap<5,>=3.0.1 in /usr/local/lib/python3.7/dist-packages (from gitdb<5,>=4.0.1->GitPython>=1.0.0->wandb) (4.0.0)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":306,"referenced_widgets":["8520fca572ae41fba4b06d5cd7b146a0","cf4555b1edb145b0bd1eb1179c9c2c1e","ffb958f347a94d94a432dddf89f86fb5","0baf14a1025a4633af5f3370bee4584e","81c71acdd8034290808173d195460292","be1a7cf3b214418e9253ffa4c70f5461","05c337aa1d3a4e9984d97b724c788d22","a90fba3ce66f4196927f1e8fd031380b"]},"id":"V6tsUEOy2HFb","executionInfo":{"status":"ok","timestamp":1620132534928,"user_tz":-540,"elapsed":7671,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"9a05f4a2-f826-4b1a-9f3a-514558cf43ef"},"source":["import wandb\n","\n","proj_name = '0_aug'\n","\n","wandb.init(project='chanyub',name=proj_name)\n","\n","config = wandb.config\n","config.learning_rate = 0.01"],"execution_count":45,"outputs":[{"output_type":"display_data","data":{"text/html":["Finishing last run (ID:18jgj601) before initializing another..."],"text/plain":[""]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/html":["
Waiting for W&B process to finish, PID 1529
Program ended successfully."],"text/plain":[""]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"8520fca572ae41fba4b06d5cd7b146a0","version_minor":0,"version_major":2},"text/plain":["VBox(children=(Label(value=' 0.00MB of 0.00MB uploaded (0.00MB deduped)\\r'), FloatProgress(value=1.0, max=1.0)…"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/html":["Find user logs for this run at: /content/drive/My Drive/Trash/code/wandb/run-20210504_121812-18jgj601/logs/debug.log"],"text/plain":[""]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/html":["Find internal logs for this run at: /content/drive/My Drive/Trash/code/wandb/run-20210504_121812-18jgj601/logs/debug-internal.log"],"text/plain":[""]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/html":["Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"],"text/plain":[""]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/html":["\n","
Synced 0_aug: https://wandb.ai/pstage12/chanyub/runs/18jgj601
\n"," "],"text/plain":[""]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/html":["...Successfully finished last run (ID:18jgj601). Initializing new run:

"],"text/plain":[""]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/html":["\n"," Tracking run with wandb version 0.10.29
\n"," Syncing run 0_aug to Weights & Biases (Documentation).
\n"," Project page: https://wandb.ai/pstage12/chanyub
\n"," Run page: https://wandb.ai/pstage12/chanyub/runs/1n7umjip
\n"," Run data is saved locally in /content/drive/My Drive/Trash/code/wandb/run-20210504_124849-1n7umjip

\n"," "],"text/plain":[""]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"71t0S3di0I33"},"source":["## baseline model\n","\n","### [TODO] 코드 구현 UNet++ \n","\n","- 출처 : https://jinglescode.github.io/2019/12/02/biomedical-image-segmentation-u-net-nested/"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"wPRySrgMK3oT","executionInfo":{"status":"ok","timestamp":1620132537575,"user_tz":-540,"elapsed":9093,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"7af8043c-7d20-48da-f03c-5326aa53b9ac"},"source":["!pip install segmentation_models_pytorch"],"execution_count":46,"outputs":[{"output_type":"stream","text":["Requirement already satisfied: segmentation_models_pytorch in /usr/local/lib/python3.7/dist-packages (0.1.3)\n","Requirement already satisfied: torchvision>=0.3.0 in /usr/local/lib/python3.7/dist-packages (from segmentation_models_pytorch) (0.9.1+cu101)\n","Requirement already satisfied: pretrainedmodels==0.7.4 in /usr/local/lib/python3.7/dist-packages (from segmentation_models_pytorch) (0.7.4)\n","Requirement already satisfied: timm==0.3.2 in /usr/local/lib/python3.7/dist-packages (from segmentation_models_pytorch) (0.3.2)\n","Requirement already satisfied: efficientnet-pytorch==0.6.3 in /usr/local/lib/python3.7/dist-packages (from segmentation_models_pytorch) (0.6.3)\n","Requirement already satisfied: torch==1.8.1 in /usr/local/lib/python3.7/dist-packages (from torchvision>=0.3.0->segmentation_models_pytorch) (1.8.1+cu101)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torchvision>=0.3.0->segmentation_models_pytorch) (1.19.5)\n","Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.7/dist-packages (from torchvision>=0.3.0->segmentation_models_pytorch) (7.1.2)\n","Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from pretrainedmodels==0.7.4->segmentation_models_pytorch) (4.41.1)\n","Requirement already satisfied: munch in /usr/local/lib/python3.7/dist-packages (from pretrainedmodels==0.7.4->segmentation_models_pytorch) (2.5.0)\n","Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch==1.8.1->torchvision>=0.3.0->segmentation_models_pytorch) (3.7.4.3)\n","Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from munch->pretrainedmodels==0.7.4->segmentation_models_pytorch) (1.15.0)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:11.634792Z","start_time":"2021-04-18T16:16:05.875817Z"},"colab":{"base_uri":"https://localhost:8080/"},"id":"MJ2vs-Y_0I35","executionInfo":{"status":"ok","timestamp":1620132541349,"user_tz":-540,"elapsed":12349,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"82f75c82-795a-4af2-fde2-8c3ea5f541fe"},"source":["# 구현된 model에 임의의 input을 넣어 output이 잘 나오는지 test\n","import segmentation_models_pytorch as smp\n","\n","model = smp.UnetPlusPlus(classes=12)\n","x = torch.randn([1, 3, 512, 512])\n","print(\"input shape : \", x.shape)\n","out = model(x).to(device)\n","print(\"output shape : \", out.size())\n","\n","model = model.to(device)"],"execution_count":47,"outputs":[{"output_type":"stream","text":["input shape : torch.Size([1, 3, 512, 512])\n","output shape : torch.Size([1, 12, 512, 512])\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"SgM4SGqL0I35"},"source":["## train, validation, test 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.104200Z","start_time":"2021-04-18T16:16:18.093174Z"},"id":"Dl6skKCT0I35","executionInfo":{"status":"ok","timestamp":1620132541350,"user_tz":-540,"elapsed":11355,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["def train(num_epochs, model, data_loader, val_loader, criterion, optimizer, saved_dir, val_every, device):\n"," print('Start training..')\n"," best_loss = 9999999\n"," best_miou = 0\n"," for epoch in range(num_epochs):\n"," model.train()\n"," for step, (images, masks, _) in enumerate(data_loader):\n"," images = torch.stack(images) # (batch, channel, height, width)\n"," masks = torch.stack(masks).long() # (batch, channel, height, width)\n"," \n"," # gpu 연산을 위해 device 할당\n"," images, masks = images.to(device), masks.to(device)\n"," \n"," # inference\n"," outputs = model(images)\n"," \n"," # loss 계산 (cross entropy loss)\n"," loss = criterion(outputs, masks)\n"," optimizer.zero_grad()\n"," loss.backward()\n"," optimizer.step()\n"," \n"," # lr_scheduler.step()\n"," \n"," # step 주기에 따른 loss 출력\n"," if (step + 1) % 25 == 0:\n"," print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(\n"," epoch+1, num_epochs, step+1, len(train_loader), loss.item()))\n"," \n"," # validation 주기에 따른 loss 출력 및 best model 저장\n"," if (epoch + 1) % val_every == 0:\n","# avrg_loss = validation(epoch + 1, model, val_loader, criterion, device)\n","# if avrg_loss < best_loss:\n","# print('Best performance at epoch: {}'.format(epoch + 1))\n","# print('Save model in', saved_dir)\n","# best_loss = avrg_loss\n","# wandb.log({'best_loss': best_loss})\n","# save_model(model, saved_dir)\n"," avrg_miou = validation(epoch + 1, model, val_loader, criterion, device)\n"," if avrg_miou > best_miou:\n"," print('Best performance at epoch: {}'.format(epoch + 1))\n"," print('Save model in', saved_dir)\n"," best_miou = avrg_miou\n"," wandb.log({'best_miou': best_miou})\n"," save_model(model, saved_dir)"],"execution_count":48,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.285795Z","start_time":"2021-04-18T16:16:18.267686Z"},"id":"Yw_3xbyj0I36","executionInfo":{"status":"ok","timestamp":1620132541351,"user_tz":-540,"elapsed":11032,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["def validation(epoch, model, data_loader, criterion, device):\n"," print('Start validation #{}'.format(epoch))\n"," model.eval()\n"," with torch.no_grad():\n"," total_loss = 0\n"," cnt = 0\n"," mIoU_list = []\n"," for step, (images, masks, _) in enumerate(data_loader):\n"," \n"," images = torch.stack(images) # (batch, channel, height, width)\n"," masks = torch.stack(masks).long() # (batch, channel, height, width)\n","\n"," images, masks = images.to(device), masks.to(device) \n","\n"," outputs = model(images)\n"," loss = criterion(outputs, masks)\n"," total_loss += loss\n"," cnt += 1\n"," \n","# print(outputs.shape)\n","# print(masks.shape)\n","# wandb.log(wandb.Image(images, masks={\n","# \"predictions\" : {\n","# \"mask_data\" : torch.squeeze(torch.squeeze(outputs, 0),1),\n","# \"class_labels\" : classes_dict\n","# },\n","# \"ground_truth\" : {\n","# \"mask_data\" : torch.squeeze(masks, 0),\n","# \"class_labels\" : classes_dict\n","# }\n","# }))\n"," \n"," outputs = torch.argmax(outputs.squeeze(), dim=1).detach().cpu().numpy()\n","\n"," mIoU = label_accuracy_score(masks.detach().cpu().numpy(), outputs, n_class=12)[2]\n"," mIoU_list.append(mIoU)\n"," \n"," avrg_loss = total_loss / cnt\n"," avrg_mIoU = np.mean(mIoU_list)\n"," print('Validation #{} Average Loss: {:.4f}, mIoU: {:.4f}'.format(epoch, avrg_loss, np.mean(mIoU_list)))\n"," wandb.log({'Train Loss':loss.item(), 'Val Loss':avrg_loss , 'Val mIoU':np.mean(mIoU_list)})\n","# return avrg_loss\n"," return avrg_mIoU"],"execution_count":49,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"b92qGwBc0I37"},"source":["## 모델 저장 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.909918Z","start_time":"2021-04-18T16:16:18.898918Z"},"id":"H50hk0za0I37","executionInfo":{"status":"ok","timestamp":1620132541351,"user_tz":-540,"elapsed":9826,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# 모델 저장 함수 정의\n","val_every = 1 \n","\n","saved_dir = './saved'\n","if not os.path.isdir(saved_dir): \n"," os.mkdir(saved_dir)\n"," \n","def save_model(model, saved_dir, file_name='0_aug.pt'):\n"," check_point = {'net': model.state_dict()}\n"," output_path = os.path.join(saved_dir, file_name)\n"," torch.save(model.state_dict(), output_path)"],"execution_count":50,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"UwzGmX190I37"},"source":["## 모델 생성 및 Loss function, Optimizer 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:19.698902Z","start_time":"2021-04-18T16:16:19.694902Z"},"id":"hOKlPrNn0I37","executionInfo":{"status":"ok","timestamp":1620132541352,"user_tz":-540,"elapsed":7951,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# Loss function 정의\n","criterion = nn.CrossEntropyLoss()\n","\n","# Optimizer 정의\n","optimizer = torch.optim.Adam(params = model.parameters(), lr = learning_rate, weight_decay=1e-6)"],"execution_count":51,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"start_time":"2021-04-18T16:16:20.331Z"},"scrolled":false,"colab":{"base_uri":"https://localhost:8080/"},"id":"MSReHpkI0I38","executionInfo":{"status":"ok","timestamp":1620134665306,"user_tz":-540,"elapsed":2131014,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"40a34daa-cd88-475b-e8e6-92c4bde29f58"},"source":["train(num_epochs, model, train_loader, val_loader, criterion, optimizer, saved_dir, val_every, device)"],"execution_count":52,"outputs":[{"output_type":"stream","text":["Start training..\n","Epoch [1/20], Step [25/327], Loss: 2.0580\n","Epoch [1/20], Step [50/327], Loss: 1.7738\n","Epoch [1/20], Step [75/327], Loss: 1.4771\n","Epoch [1/20], Step [100/327], Loss: 1.3583\n","Epoch [1/20], Step [125/327], Loss: 1.0827\n","Epoch [1/20], Step [150/327], Loss: 1.1390\n","Epoch [1/20], Step [175/327], Loss: 0.9211\n","Epoch [1/20], Step [200/327], Loss: 0.8288\n","Epoch [1/20], Step [225/327], Loss: 0.8539\n","Epoch [1/20], Step [250/327], Loss: 0.7062\n","Epoch [1/20], Step [275/327], Loss: 0.7350\n","Epoch [1/20], Step [300/327], Loss: 0.8364\n","Epoch [1/20], Step [325/327], Loss: 0.5529\n","Start validation #1\n","Validation #1 Average Loss: 0.6592, mIoU: 0.2416\n","Best performance at epoch: 1\n","Save model in ./saved\n","Epoch [2/20], Step [25/327], Loss: 0.5634\n","Epoch [2/20], Step [50/327], Loss: 0.5116\n","Epoch [2/20], Step [75/327], Loss: 0.4335\n","Epoch [2/20], Step [100/327], Loss: 0.8705\n","Epoch [2/20], Step [125/327], Loss: 0.6355\n","Epoch [2/20], Step [150/327], Loss: 0.4825\n","Epoch [2/20], Step [175/327], Loss: 0.6041\n","Epoch [2/20], Step [200/327], Loss: 0.5511\n","Epoch [2/20], Step [225/327], Loss: 0.9910\n","Epoch [2/20], Step [250/327], Loss: 0.3393\n","Epoch [2/20], Step [275/327], Loss: 0.3954\n","Epoch [2/20], Step [300/327], Loss: 0.4037\n","Epoch [2/20], Step [325/327], Loss: 0.4578\n","Start validation #2\n","Validation #2 Average Loss: 0.5583, mIoU: 0.2668\n","Best performance at epoch: 2\n","Save model in ./saved\n","Epoch [3/20], Step [25/327], Loss: 0.5841\n","Epoch [3/20], Step [50/327], Loss: 0.2514\n","Epoch [3/20], Step [75/327], Loss: 0.5333\n","Epoch [3/20], Step [100/327], Loss: 0.3442\n","Epoch [3/20], Step [125/327], Loss: 0.3650\n","Epoch [3/20], Step [150/327], Loss: 0.5552\n","Epoch [3/20], Step [175/327], Loss: 0.3525\n","Epoch [3/20], Step [200/327], Loss: 0.4024\n","Epoch [3/20], Step [225/327], Loss: 0.7308\n","Epoch [3/20], Step [250/327], Loss: 0.4013\n","Epoch [3/20], Step [275/327], Loss: 0.3475\n","Epoch [3/20], Step [300/327], Loss: 0.6860\n","Epoch [3/20], Step [325/327], Loss: 0.4112\n","Start validation #3\n","Validation #3 Average Loss: 0.5023, mIoU: 0.2786\n","Best performance at epoch: 3\n","Save model in ./saved\n","Epoch [4/20], Step [25/327], Loss: 0.4805\n","Epoch [4/20], Step [50/327], Loss: 0.4694\n","Epoch [4/20], Step [75/327], Loss: 0.3710\n","Epoch [4/20], Step [100/327], Loss: 0.2313\n","Epoch [4/20], Step [125/327], Loss: 0.5873\n","Epoch [4/20], Step [150/327], Loss: 0.8141\n","Epoch [4/20], Step [175/327], Loss: 0.4452\n","Epoch [4/20], Step [200/327], Loss: 0.3481\n","Epoch [4/20], Step [225/327], Loss: 0.4394\n","Epoch [4/20], Step [250/327], Loss: 0.7286\n","Epoch [4/20], Step [275/327], Loss: 0.2855\n","Epoch [4/20], Step [300/327], Loss: 0.3746\n","Epoch [4/20], Step [325/327], Loss: 0.2712\n","Start validation #4\n","Validation #4 Average Loss: 0.5006, mIoU: 0.2985\n","Best performance at epoch: 4\n","Save model in ./saved\n","Epoch [5/20], Step [25/327], Loss: 0.2290\n","Epoch [5/20], Step [50/327], Loss: 0.1945\n","Epoch [5/20], Step [75/327], Loss: 0.2322\n","Epoch [5/20], Step [100/327], Loss: 0.2755\n","Epoch [5/20], Step [125/327], Loss: 0.2903\n","Epoch [5/20], Step [150/327], Loss: 0.2316\n","Epoch [5/20], Step [175/327], Loss: 0.3088\n","Epoch [5/20], Step [200/327], Loss: 0.4538\n","Epoch [5/20], Step [225/327], Loss: 0.2953\n","Epoch [5/20], Step [250/327], Loss: 0.5207\n","Epoch [5/20], Step [275/327], Loss: 0.2394\n","Epoch [5/20], Step [300/327], Loss: 0.1895\n","Epoch [5/20], Step [325/327], Loss: 0.2858\n","Start validation #5\n","Validation #5 Average Loss: 0.4780, mIoU: 0.2815\n","Epoch [6/20], Step [25/327], Loss: 0.2125\n","Epoch [6/20], Step [50/327], Loss: 0.2348\n","Epoch [6/20], Step [75/327], Loss: 0.2957\n","Epoch [6/20], Step [100/327], Loss: 0.1504\n","Epoch [6/20], Step [125/327], Loss: 0.2319\n","Epoch [6/20], Step [150/327], Loss: 0.2661\n","Epoch [6/20], Step [175/327], Loss: 0.2755\n","Epoch [6/20], Step [200/327], Loss: 0.4063\n","Epoch [6/20], Step [225/327], Loss: 0.2676\n","Epoch [6/20], Step [250/327], Loss: 0.2780\n","Epoch [6/20], Step [275/327], Loss: 0.2310\n","Epoch [6/20], Step [300/327], Loss: 0.3630\n","Epoch [6/20], Step [325/327], Loss: 0.2417\n","Start validation #6\n","Validation #6 Average Loss: 0.5133, mIoU: 0.2714\n","Epoch [7/20], Step [25/327], Loss: 0.2114\n","Epoch [7/20], Step [50/327], Loss: 0.1803\n","Epoch [7/20], Step [75/327], Loss: 0.2803\n","Epoch [7/20], Step [100/327], Loss: 0.2441\n","Epoch [7/20], Step [125/327], Loss: 0.1847\n","Epoch [7/20], Step [150/327], Loss: 0.2810\n","Epoch [7/20], Step [175/327], Loss: 0.2763\n","Epoch [7/20], Step [200/327], Loss: 0.3655\n","Epoch [7/20], Step [225/327], Loss: 0.3188\n","Epoch [7/20], Step [250/327], Loss: 0.4754\n","Epoch [7/20], Step [275/327], Loss: 0.3008\n","Epoch [7/20], Step [300/327], Loss: 0.4240\n","Epoch [7/20], Step [325/327], Loss: 0.4239\n","Start validation #7\n","Validation #7 Average Loss: 0.5242, mIoU: 0.2811\n","Epoch [8/20], Step [25/327], Loss: 0.1457\n","Epoch [8/20], Step [50/327], Loss: 0.0734\n","Epoch [8/20], Step [75/327], Loss: 0.3162\n","Epoch [8/20], Step [100/327], Loss: 0.3723\n","Epoch [8/20], Step [125/327], Loss: 0.1865\n","Epoch [8/20], Step [150/327], Loss: 0.1142\n","Epoch [8/20], Step [175/327], Loss: 0.2599\n","Epoch [8/20], Step [200/327], Loss: 0.3028\n","Epoch [8/20], Step [225/327], Loss: 0.2822\n","Epoch [8/20], Step [250/327], Loss: 0.1889\n","Epoch [8/20], Step [275/327], Loss: 0.3458\n","Epoch [8/20], Step [300/327], Loss: 0.2681\n","Epoch [8/20], Step [325/327], Loss: 0.1320\n","Start validation #8\n","Validation #8 Average Loss: 0.4910, mIoU: 0.2797\n","Epoch [9/20], Step [25/327], Loss: 0.1672\n","Epoch [9/20], Step [50/327], Loss: 0.2069\n","Epoch [9/20], Step [75/327], Loss: 0.1503\n","Epoch [9/20], Step [100/327], Loss: 0.2303\n","Epoch [9/20], Step [125/327], Loss: 0.1953\n","Epoch [9/20], Step [150/327], Loss: 0.1467\n","Epoch [9/20], Step [175/327], Loss: 0.2809\n","Epoch [9/20], Step [200/327], Loss: 0.1771\n","Epoch [9/20], Step [225/327], Loss: 0.2305\n","Epoch [9/20], Step [250/327], Loss: 0.1718\n","Epoch [9/20], Step [275/327], Loss: 0.2117\n","Epoch [9/20], Step [300/327], Loss: 0.3196\n","Epoch [9/20], Step [325/327], Loss: 0.5398\n","Start validation #9\n","Validation #9 Average Loss: 0.5088, mIoU: 0.2753\n","Epoch [10/20], Step [25/327], Loss: 0.2272\n","Epoch [10/20], Step [50/327], Loss: 0.6097\n","Epoch [10/20], Step [75/327], Loss: 0.1983\n","Epoch [10/20], Step [100/327], Loss: 0.2899\n","Epoch [10/20], Step [125/327], Loss: 0.1894\n","Epoch [10/20], Step [150/327], Loss: 0.2300\n","Epoch [10/20], Step [175/327], Loss: 0.3173\n","Epoch [10/20], Step [200/327], Loss: 0.6058\n","Epoch [10/20], Step [225/327], Loss: 0.1141\n","Epoch [10/20], Step [250/327], Loss: 0.2055\n","Epoch [10/20], Step [275/327], Loss: 0.3062\n","Epoch [10/20], Step [300/327], Loss: 0.1689\n","Epoch [10/20], Step [325/327], Loss: 0.2212\n","Start validation #10\n","Validation #10 Average Loss: 0.5132, mIoU: 0.2754\n","Epoch [11/20], Step [25/327], Loss: 0.1976\n","Epoch [11/20], Step [50/327], Loss: 0.1127\n","Epoch [11/20], Step [75/327], Loss: 0.0959\n","Epoch [11/20], Step [100/327], Loss: 0.2674\n","Epoch [11/20], Step [125/327], Loss: 0.1417\n","Epoch [11/20], Step [150/327], Loss: 0.1495\n","Epoch [11/20], Step [175/327], Loss: 0.2440\n","Epoch [11/20], Step [200/327], Loss: 0.2582\n","Epoch [11/20], Step [225/327], Loss: 0.1502\n","Epoch [11/20], Step [250/327], Loss: 0.2312\n","Epoch [11/20], Step [275/327], Loss: 0.2031\n","Epoch [11/20], Step [300/327], Loss: 0.2414\n","Epoch [11/20], Step [325/327], Loss: 0.1941\n","Start validation #11\n","Validation #11 Average Loss: 0.5931, mIoU: 0.2516\n","Epoch [12/20], Step [25/327], Loss: 0.1380\n","Epoch [12/20], Step [50/327], Loss: 0.1000\n","Epoch [12/20], Step [75/327], Loss: 0.3298\n","Epoch [12/20], Step [100/327], Loss: 0.1872\n","Epoch [12/20], Step [125/327], Loss: 0.1886\n","Epoch [12/20], Step [150/327], Loss: 0.5476\n","Epoch [12/20], Step [175/327], Loss: 0.1349\n","Epoch [12/20], Step [200/327], Loss: 0.0646\n","Epoch [12/20], Step [225/327], Loss: 0.1624\n","Epoch [12/20], Step [250/327], Loss: 0.2600\n","Epoch [12/20], Step [275/327], Loss: 0.2717\n","Epoch [12/20], Step [300/327], Loss: 0.2226\n","Epoch [12/20], Step [325/327], Loss: 0.2428\n","Start validation #12\n","Validation #12 Average Loss: 0.5184, mIoU: 0.2824\n","Epoch [13/20], Step [25/327], Loss: 0.1745\n","Epoch [13/20], Step [50/327], Loss: 0.1103\n","Epoch [13/20], Step [75/327], Loss: 0.1949\n","Epoch [13/20], Step [100/327], Loss: 0.2069\n","Epoch [13/20], Step [125/327], Loss: 0.0726\n","Epoch [13/20], Step [150/327], Loss: 0.1322\n","Epoch [13/20], Step [175/327], Loss: 0.2192\n","Epoch [13/20], Step [200/327], Loss: 0.3137\n","Epoch [13/20], Step [225/327], Loss: 0.2129\n","Epoch [13/20], Step [250/327], Loss: 0.1418\n","Epoch [13/20], Step [275/327], Loss: 0.2125\n","Epoch [13/20], Step [300/327], Loss: 0.1206\n","Epoch [13/20], Step [325/327], Loss: 0.1580\n","Start validation #13\n","Validation #13 Average Loss: 0.5239, mIoU: 0.2841\n","Epoch [14/20], Step [25/327], Loss: 0.1147\n","Epoch [14/20], Step [50/327], Loss: 0.1828\n","Epoch [14/20], Step [75/327], Loss: 0.0758\n","Epoch [14/20], Step [100/327], Loss: 0.1479\n","Epoch [14/20], Step [125/327], Loss: 0.1616\n","Epoch [14/20], Step [150/327], Loss: 0.1611\n","Epoch [14/20], Step [175/327], Loss: 0.1382\n","Epoch [14/20], Step [200/327], Loss: 0.1758\n","Epoch [14/20], Step [225/327], Loss: 0.2107\n","Epoch [14/20], Step [250/327], Loss: 0.2447\n","Epoch [14/20], Step [275/327], Loss: 0.2019\n","Epoch [14/20], Step [300/327], Loss: 0.3618\n","Epoch [14/20], Step [325/327], Loss: 0.1022\n","Start validation #14\n","Validation #14 Average Loss: 0.5575, mIoU: 0.2849\n","Epoch [15/20], Step [25/327], Loss: 0.0756\n","Epoch [15/20], Step [50/327], Loss: 0.0894\n","Epoch [15/20], Step [75/327], Loss: 0.1293\n","Epoch [15/20], Step [100/327], Loss: 0.0769\n","Epoch [15/20], Step [125/327], Loss: 0.0697\n","Epoch [15/20], Step [150/327], Loss: 0.1887\n","Epoch [15/20], Step [175/327], Loss: 0.1076\n","Epoch [15/20], Step [200/327], Loss: 0.1266\n","Epoch [15/20], Step [225/327], Loss: 0.1572\n","Epoch [15/20], Step [250/327], Loss: 0.1651\n","Epoch [15/20], Step [275/327], Loss: 0.1713\n","Epoch [15/20], Step [300/327], Loss: 0.0522\n","Epoch [15/20], Step [325/327], Loss: 0.1333\n","Start validation #15\n","Validation #15 Average Loss: 0.5444, mIoU: 0.2800\n","Epoch [16/20], Step [25/327], Loss: 0.1690\n","Epoch [16/20], Step [50/327], Loss: 0.1977\n","Epoch [16/20], Step [75/327], Loss: 0.1002\n","Epoch [16/20], Step [100/327], Loss: 0.0978\n","Epoch [16/20], Step [125/327], Loss: 0.2736\n","Epoch [16/20], Step [150/327], Loss: 0.1549\n","Epoch [16/20], Step [175/327], Loss: 0.2405\n","Epoch [16/20], Step [200/327], Loss: 0.1077\n","Epoch [16/20], Step [225/327], Loss: 0.1265\n","Epoch [16/20], Step [250/327], Loss: 0.2101\n","Epoch [16/20], Step [275/327], Loss: 0.1529\n","Epoch [16/20], Step [300/327], Loss: 0.1347\n","Epoch [16/20], Step [325/327], Loss: 0.0968\n","Start validation #16\n","Validation #16 Average Loss: 0.5866, mIoU: 0.2852\n","Epoch [17/20], Step [25/327], Loss: 0.1476\n","Epoch [17/20], Step [50/327], Loss: 0.0721\n","Epoch [17/20], Step [75/327], Loss: 0.0757\n","Epoch [17/20], Step [100/327], Loss: 0.0549\n","Epoch [17/20], Step [125/327], Loss: 0.1081\n","Epoch [17/20], Step [150/327], Loss: 0.0735\n","Epoch [17/20], Step [175/327], Loss: 0.0904\n","Epoch [17/20], Step [200/327], Loss: 0.0777\n","Epoch [17/20], Step [225/327], Loss: 0.1692\n","Epoch [17/20], Step [250/327], Loss: 0.1331\n","Epoch [17/20], Step [275/327], Loss: 0.0884\n","Epoch [17/20], Step [300/327], Loss: 0.0940\n","Epoch [17/20], Step [325/327], Loss: 0.1148\n","Start validation #17\n","Validation #17 Average Loss: 0.5850, mIoU: 0.2732\n","Epoch [18/20], Step [25/327], Loss: 0.0750\n","Epoch [18/20], Step [50/327], Loss: 0.1091\n","Epoch [18/20], Step [75/327], Loss: 0.2212\n","Epoch [18/20], Step [100/327], Loss: 0.0459\n","Epoch [18/20], Step [125/327], Loss: 0.0581\n","Epoch [18/20], Step [150/327], Loss: 0.0951\n","Epoch [18/20], Step [175/327], Loss: 0.1381\n","Epoch [18/20], Step [200/327], Loss: 0.0966\n","Epoch [18/20], Step [225/327], Loss: 0.1952\n","Epoch [18/20], Step [250/327], Loss: 0.1369\n","Epoch [18/20], Step [275/327], Loss: 0.2116\n","Epoch [18/20], Step [300/327], Loss: 0.1269\n","Epoch [18/20], Step [325/327], Loss: 0.0948\n","Start validation #18\n","Validation #18 Average Loss: 0.6264, mIoU: 0.2673\n","Epoch [19/20], Step [25/327], Loss: 0.3032\n","Epoch [19/20], Step [50/327], Loss: 0.2935\n","Epoch [19/20], Step [75/327], Loss: 0.1573\n","Epoch [19/20], Step [100/327], Loss: 0.2442\n","Epoch [19/20], Step [125/327], Loss: 0.1116\n","Epoch [19/20], Step [150/327], Loss: 0.1775\n","Epoch [19/20], Step [175/327], Loss: 0.0856\n","Epoch [19/20], Step [200/327], Loss: 0.1986\n","Epoch [19/20], Step [225/327], Loss: 0.2461\n","Epoch [19/20], Step [250/327], Loss: 0.1410\n","Epoch [19/20], Step [275/327], Loss: 0.0809\n","Epoch [19/20], Step [300/327], Loss: 0.1459\n","Epoch [19/20], Step [325/327], Loss: 0.0752\n","Start validation #19\n","Validation #19 Average Loss: 0.5772, mIoU: 0.2640\n","Epoch [20/20], Step [25/327], Loss: 0.0766\n","Epoch [20/20], Step [50/327], Loss: 0.0686\n","Epoch [20/20], Step [75/327], Loss: 0.0470\n","Epoch [20/20], Step [100/327], Loss: 0.0669\n","Epoch [20/20], Step [125/327], Loss: 0.0707\n","Epoch [20/20], Step [150/327], Loss: 0.1155\n","Epoch [20/20], Step [175/327], Loss: 0.0688\n","Epoch [20/20], Step [200/327], Loss: 0.0738\n","Epoch [20/20], Step [225/327], Loss: 0.1446\n","Epoch [20/20], Step [250/327], Loss: 0.0783\n","Epoch [20/20], Step [275/327], Loss: 0.1173\n","Epoch [20/20], Step [300/327], Loss: 0.1586\n","Epoch [20/20], Step [325/327], Loss: 0.1155\n","Start validation #20\n","Validation #20 Average Loss: 0.5678, mIoU: 0.2812\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"jOE-56p5MkXM","executionInfo":{"status":"ok","timestamp":1620134875031,"user_tz":-540,"elapsed":209694,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# Sleep for a few seconds.\n","import time\n","time.sleep(2)\n","# Play an audio beep. Any audio URL will do.\n","from google.colab import output\n","output.eval_js('new Audio(\"https://upload.wikimedia.org/wikipedia/commons/0/05/Beep-09.ogg\").play()')"],"execution_count":53,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"6IqKsZ4u0I38"},"source":["## 저장된 model 불러오기 (학습된 이후) "]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:21.050200Z","start_time":"2021-04-16T19:44:20.802200Z"},"scrolled":true,"id":"dl5dIeHB0I38"},"source":["# best model 저장된 경로\n","model_path = './saved/UNetPP_best_model.pt'\n","\n","# best model 불러오기\n","checkpoint = torch.load(model_path, map_location=device)\n","model.load_state_dict(checkpoint)\n","\n","# 추론을 실행하기 전에는 반드시 설정 (batch normalization, dropout 를 평가 모드로 설정)\n","# model.eval()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:24.939227Z","start_time":"2021-04-16T19:44:24.518228Z"},"id":"mOUcjOP20I38"},"source":["# 첫번째 batch의 추론 결과 확인\n","for imgs, image_infos in test_loader:\n"," image_infos = image_infos\n"," temp_images = imgs\n"," \n"," model.eval()\n"," # inference\n"," outs = model(torch.stack(temp_images).to(device))\n"," oms = torch.argmax(outs.squeeze(), dim=1).detach().cpu().numpy()\n"," \n"," break\n","\n","i = 3\n","fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(16, 16))\n","\n","print('Shape of Original Image :', list(temp_images[i].shape))\n","print('Shape of Predicted : ', list(oms[i].shape))\n","print('Unique values, category of transformed mask : \\n', [{int(i),category_names[int(i)]} for i in list(np.unique(oms[i]))])\n","\n","# Original image\n","ax1.imshow(temp_images[i].permute([1,2,0]))\n","ax1.grid(False)\n","ax1.set_title(\"Original image : {}\".format(image_infos[i]['file_name']), fontsize = 15)\n","\n","# Predicted\n","ax2.imshow(oms[i])\n","ax2.grid(False)\n","ax2.set_title(\"Predicted : {}\".format(image_infos[i]['file_name']), fontsize = 15)\n","\n","plt.show()"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"pmZEjwGE0I39"},"source":["## submission을 위한 test 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:27.469285Z","start_time":"2021-04-16T19:44:27.456021Z"},"id":"X4s-Ng1_0I39"},"source":["def test(model, data_loader, device):\n"," size = 256\n"," transform = A.Compose([A.Resize(256, 256)])\n"," print('Start prediction.')\n"," model.eval()\n"," \n"," file_name_list = []\n"," preds_array = np.empty((0, size*size), dtype=np.long)\n"," \n"," with torch.no_grad():\n"," for step, (imgs, image_infos) in enumerate(test_loader):\n","\n"," # inference (512 x 512)\n"," outs = model(torch.stack(imgs).to(device))\n"," oms = torch.argmax(outs, dim=1).detach().cpu().numpy()\n"," \n"," # resize (256 x 256)\n"," temp_mask = []\n"," for img, mask in zip(np.stack(imgs), oms):\n"," transformed = transform(image=img, mask=mask)\n"," mask = transformed['mask']\n"," temp_mask.append(mask)\n","\n"," oms = np.array(temp_mask)\n"," \n"," oms = oms.reshape([oms.shape[0], size*size]).astype(int)\n"," preds_array = np.vstack((preds_array, oms))\n"," \n"," file_name_list.append([i['file_name'] for i in image_infos])\n"," print(\"End prediction.\")\n"," file_names = [y for x in file_name_list for y in x]\n"," \n"," return file_names, preds_array"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"GnldifLS0I39"},"source":["## submission.csv 생성"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:45:42.235310Z","start_time":"2021-04-16T19:44:30.499016Z"},"scrolled":true,"id":"uV_ZSnqT0I3-"},"source":["# sample_submisson.csv 열기\n","submission = pd.read_csv('./submission/sample_submission.csv', index_col=None)\n","\n","# test set에 대한 prediction\n","file_names, preds = test(model, test_loader, device)\n","\n","# PredictionString 대입\n","for file_name, string in zip(file_names, preds):\n"," submission = submission.append({\"image_id\" : file_name, \"PredictionString\" : ' '.join(str(e) for e in string.tolist())}, \n"," ignore_index=True)\n","\n","# submission.csv로 저장\n","submission.to_csv(\"./submission/Baseline_UnetPP.csv\", index=False)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"LIRbR3Ro0I3-"},"source":["## Reference\n","\n"]},{"cell_type":"code","metadata":{"id":"ZUxotp6d0I3-"},"source":[""],"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/chanyub_seg/code/0_aug_pan_effb0_focal_madgrad_cosLR.ipynb b/chanyub_seg/code/0_aug_pan_effb0_focal_madgrad_cosLR.ipynb deleted file mode 100644 index 6954a2c..0000000 --- a/chanyub_seg/code/0_aug_pan_effb0_focal_madgrad_cosLR.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"nbformat":4,"nbformat_minor":0,"metadata":{"hide_input":false,"kernelspec":{"display_name":"Python 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google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Mounted at /content/drive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xDwpp4Lk-gSH","executionInfo":{"status":"ok","timestamp":1620108753763,"user_tz":-540,"elapsed":6665,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"233b9eb0-ca17-4248-a8e0-05c7fb5b7b0a"},"source":["ls"],"execution_count":2,"outputs":[{"output_type":"stream","text":["\u001b[0m\u001b[01;34mdrive\u001b[0m/ \u001b[01;34msample_data\u001b[0m/\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sItrVDeh-iYC","executionInfo":{"status":"ok","timestamp":1620108755140,"user_tz":-540,"elapsed":7634,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"e0849325-7367-45f2-da14-bd3c3762527e"},"source":["cd drive/MyDrive/Trash/code"],"execution_count":3,"outputs":[{"output_type":"stream","text":["/content/drive/MyDrive/Trash/code\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"a54v4-kN_LVO","executionInfo":{"status":"ok","timestamp":1620108763370,"user_tz":-540,"elapsed":15416,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"99ac4b49-af13-40eb-e26e-19dd0bdb81ea"},"source":["!pip install albumentations==0.5.2"],"execution_count":4,"outputs":[{"output_type":"stream","text":["Collecting albumentations==0.5.2\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/03/58/63fb1d742dc42d9ba2800ea741de1f2bc6bb05548d8724aa84794042eaf2/albumentations-0.5.2-py3-none-any.whl (72kB)\n","\r\u001b[K |████▌ | 10kB 24.1MB/s eta 0:00:01\r\u001b[K |█████████ | 20kB 29.2MB/s eta 0:00:01\r\u001b[K |█████████████▋ | 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(1.3.1)\n","Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug>=0.4.0->albumentations==0.5.2) (2.4.7)\n","Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug>=0.4.0->albumentations==0.5.2) (2.8.1)\n","Requirement already satisfied: decorator<5,>=4.3 in /usr/local/lib/python3.7/dist-packages (from networkx>=2.0->scikit-image>=0.16.1->albumentations==0.5.2) (4.4.2)\n","Installing collected packages: imgaug, opencv-python-headless, albumentations\n"," Found existing installation: imgaug 0.2.9\n"," Uninstalling imgaug-0.2.9:\n"," Successfully uninstalled imgaug-0.2.9\n"," Found existing installation: albumentations 0.1.12\n"," Uninstalling albumentations-0.1.12:\n"," Successfully uninstalled albumentations-0.1.12\n","Successfully installed albumentations-0.5.2 imgaug-0.4.0 opencv-python-headless-4.5.1.48\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.826930Z","start_time":"2021-04-18T10:34:45.406686Z"},"colab":{"base_uri":"https://localhost:8080/"},"id":"l_LPA4XD-NwC","executionInfo":{"status":"ok","timestamp":1620108767473,"user_tz":-540,"elapsed":18243,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"bf105e0c-fea2-4e6f-b9f8-d7d465411dcd"},"source":["import os\n","import random\n","import time\n","import json\n","import warnings \n","warnings.filterwarnings('ignore')\n","\n","import torch\n","import torch.nn as nn\n","from torch.utils.data import Dataset, DataLoader\n","from utils import label_accuracy_score\n","import cv2\n","\n","import numpy as np\n","import pandas as pd\n","\n","# 전처리를 위한 라이브러리\n","from pycocotools.coco import COCO\n","import torchvision\n","import torchvision.transforms as transforms\n","\n","import albumentations as A\n","from albumentations.pytorch import ToTensorV2\n","\n","# 시각화를 위한 라이브러리\n","import matplotlib.pyplot as plt\n","import seaborn as sns; sns.set()\n","\n","plt.rcParams['axes.grid'] = False\n","\n","print('pytorch version: {}'.format(torch.__version__))\n","print('GPU 사용 가능 여부: {}'.format(torch.cuda.is_available()))\n","\n","print(torch.cuda.get_device_name(0))\n","print(torch.cuda.device_count())\n","\n","device = \"cuda\" if torch.cuda.is_available() else \"cpu\" # GPU 사용 가능 여부에 따라 device 정보 저장"],"execution_count":5,"outputs":[{"output_type":"stream","text":["pytorch version: 1.8.1+cu101\n","GPU 사용 가능 여부: True\n","Tesla P100-PCIE-16GB\n","1\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"HxgRSL_M-NwF"},"source":["## 하이퍼파라미터 세팅 및 seed 고정"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.841930Z","start_time":"2021-04-18T10:34:47.827931Z"},"id":"rV3JmGP5-NwF","executionInfo":{"status":"ok","timestamp":1620108767475,"user_tz":-540,"elapsed":16534,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["batch_size = 8 # Mini-batch size\n","num_epochs = 20\n","learning_rate = 0.0001"],"execution_count":6,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.856930Z","start_time":"2021-04-18T10:34:47.842931Z"},"id":"Z6LOuJXQ-NwG","executionInfo":{"status":"ok","timestamp":1620108767476,"user_tz":-540,"elapsed":16316,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# seed 고정\n","random_seed = 42\n","torch.manual_seed(random_seed)\n","torch.cuda.manual_seed(random_seed)\n","# torch.cuda.manual_seed_all(random_seed) # if use multi-GPU\n","torch.backends.cudnn.deterministic = True\n","torch.backends.cudnn.benchmark = False\n","np.random.seed(random_seed)\n","random.seed(random_seed)"],"execution_count":7,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"iWF_EJDu-NwG"},"source":["## 학습 데이터 EDA"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.381961Z","start_time":"2021-04-18T10:34:47.857930Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"lg0x0D0a-NwG","executionInfo":{"status":"ok","timestamp":1620108779451,"user_tz":-540,"elapsed":26202,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"e176fc18-8091-4ea7-d912-924b8d17caa2"},"source":["%matplotlib inline\n","\n","dataset_path = '../input/data'\n","anns_file_path = dataset_path + '/' + 'train.json'\n","\n","# Read annotations\n","with open(anns_file_path, 'r') as f:\n"," dataset = json.loads(f.read())\n","\n","categories = dataset['categories']\n","anns = dataset['annotations']\n","imgs = dataset['images']\n","nr_cats = len(categories)\n","nr_annotations = len(anns)\n","nr_images = len(imgs)\n","\n","# Load categories and super categories\n","cat_names = []\n","super_cat_names = []\n","super_cat_ids = {}\n","super_cat_last_name = ''\n","nr_super_cats = 0\n","for cat_it in categories:\n"," cat_names.append(cat_it['name'])\n"," super_cat_name = cat_it['supercategory']\n"," # Adding new supercat\n"," if super_cat_name != super_cat_last_name:\n"," super_cat_names.append(super_cat_name)\n"," super_cat_ids[super_cat_name] = nr_super_cats\n"," super_cat_last_name = super_cat_name\n"," nr_super_cats += 1\n","\n","print('Number of super categories:', nr_super_cats)\n","print('Number of categories:', nr_cats)\n","print('Number of annotations:', nr_annotations)\n","print('Number of images:', nr_images)"],"execution_count":8,"outputs":[{"output_type":"stream","text":["Number of super categories: 11\n","Number of categories: 11\n","Number of annotations: 21116\n","Number of images: 2617\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.546964Z","start_time":"2021-04-18T10:34:51.382969Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/","height":355},"id":"PjLyVHVY-NwH","executionInfo":{"status":"ok","timestamp":1620108780301,"user_tz":-540,"elapsed":26140,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"011cc49f-9194-420d-ae8f-b2974abcb8e7"},"source":["# Count annotations\n","cat_histogram = np.zeros(nr_cats,dtype=int)\n","for ann in anns:\n"," cat_histogram[ann['category_id']] += 1\n","\n","# Initialize the matplotlib figure\n","f, ax = plt.subplots(figsize=(5,5))\n","\n","# Convert to DataFrame\n","df = pd.DataFrame({'Categories': cat_names, 'Number of annotations': cat_histogram})\n","df = df.sort_values('Number of annotations', 0, False)\n","\n","# Plot the histogram\n","plt.title(\"category distribution of train set \")\n","plot_1 = sns.barplot(x=\"Number of annotations\", y=\"Categories\", data=df, label=\"Total\", color=\"b\")"],"execution_count":9,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.561965Z","start_time":"2021-04-18T10:34:51.547969Z"},"id":"34Tw5__i-NwI","executionInfo":{"status":"ok","timestamp":1620108780301,"user_tz":-540,"elapsed":25663,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# category labeling \n","sorted_temp_df = df.sort_index()\n","\n","# background = 0 에 해당되는 label 추가 후 기존들을 모두 label + 1 로 설정\n","sorted_df = pd.DataFrame([\"Backgroud\"], columns = [\"Categories\"])\n","sorted_df = sorted_df.append(sorted_temp_df, ignore_index=True)"],"execution_count":10,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.576961Z","start_time":"2021-04-18T10:34:51.562964Z"},"colab":{"base_uri":"https://localhost:8080/","height":421},"id":"CQk4vV5N-NwI","executionInfo":{"status":"ok","timestamp":1620108780302,"user_tz":-540,"elapsed":25037,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"a31aa422-6d3b-4782-94fa-c90afbe52238"},"source":["# class (Categories) 에 따른 index 확인 (0~11 : 총 12개)\n","sorted_df"],"execution_count":11,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
CategoriesNumber of annotations
0BackgroudNaN
1UNKNOWN128.0
2General trash2225.0
3Paper7448.0
4Paper pack527.0
5Metal449.0
6Glass488.0
7Plastic2472.0
8Styrofoam1074.0
9Plastic bag6114.0
10Battery50.0
11Clothing141.0
\n","
"],"text/plain":[" Categories Number of annotations\n","0 Backgroud NaN\n","1 UNKNOWN 128.0\n","2 General trash 2225.0\n","3 Paper 7448.0\n","4 Paper pack 527.0\n","5 Metal 449.0\n","6 Glass 488.0\n","7 Plastic 2472.0\n","8 Styrofoam 1074.0\n","9 Plastic bag 6114.0\n","10 Battery 50.0\n","11 Clothing 141.0"]},"metadata":{"tags":[]},"execution_count":11}]},{"cell_type":"markdown","metadata":{"id":"KXU0zmZs-NwI"},"source":["## 데이터 전처리 함수 정의 (Dataset)"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:52.693328Z","start_time":"2021-04-18T10:34:52.681328Z"},"id":"QFnTI8_Z-NwJ","executionInfo":{"status":"ok","timestamp":1620108780303,"user_tz":-540,"elapsed":21633,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["category_names = list(sorted_df.Categories)\n","\n","def get_classname(classID, cats):\n"," for i in range(len(cats)):\n"," if cats[i]['id']==classID:\n"," return cats[i]['name']\n"," return \"None\"\n","\n","class CustomDataLoader(Dataset):\n"," \"\"\"COCO format\"\"\"\n"," def __init__(self, data_dir, mode = 'train', transform = None):\n"," super().__init__()\n"," self.mode = mode\n"," self.transform = transform\n"," self.coco = COCO(data_dir)\n"," \n"," def __getitem__(self, index: int):\n"," # dataset이 index되어 list처럼 동작\n"," image_id = self.coco.getImgIds(imgIds=index)\n"," image_infos = self.coco.loadImgs(image_id)[0]\n"," \n"," # cv2 를 활용하여 image 불러오기\n"," images = cv2.imread(os.path.join(dataset_path, image_infos['file_name']))\n"," images = cv2.cvtColor(images, cv2.COLOR_BGR2RGB).astype(np.float32)\n"," images /= 255.0\n"," \n"," if (self.mode in ('train', 'val')):\n"," ann_ids = self.coco.getAnnIds(imgIds=image_infos['id'])\n"," anns = self.coco.loadAnns(ann_ids)\n","\n"," # Load the categories in a variable\n"," cat_ids = self.coco.getCatIds()\n"," cats = self.coco.loadCats(cat_ids)\n","\n"," # masks : size가 (height x width)인 2D\n"," # 각각의 pixel 값에는 \"category id + 1\" 할당\n"," # Background = 0\n"," masks = np.zeros((image_infos[\"height\"], image_infos[\"width\"]))\n"," # Unknown = 1, General trash = 2, ... , Cigarette = 11\n"," for i in range(len(anns)):\n"," className = get_classname(anns[i]['category_id'], cats)\n"," pixel_value = category_names.index(className)\n"," masks = np.maximum(self.coco.annToMask(anns[i])*pixel_value, masks)\n"," masks = masks.astype(np.float32)\n","\n"," # transform -> albumentations 라이브러리 활용\n"," if self.transform is not None:\n"," transformed = self.transform(image=images, mask=masks)\n"," images = transformed[\"image\"]\n"," masks = transformed[\"mask\"]\n"," \n"," return images, masks, image_infos\n"," \n"," if self.mode == 'test':\n"," # transform -> albumentations 라이브러리 활용\n"," if self.transform is not None:\n"," transformed = self.transform(image=images)\n"," images = transformed[\"image\"]\n"," \n"," return images, image_infos\n"," \n"," \n"," def __len__(self) -> int:\n"," # 전체 dataset의 size를 return\n"," return len(self.coco.getImgIds())"],"execution_count":12,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"cp2aIOlP-NwK"},"source":["## Dataset 정의 및 DataLoader 할당"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:58.823175Z","start_time":"2021-04-18T10:34:54.106233Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"P7zFlRn6-NwK","executionInfo":{"status":"ok","timestamp":1620108787895,"user_tz":-540,"elapsed":26427,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"518e5197-50a8-4d68-9528-815f732dccc7"},"source":["# train.json / validation.json / test.json 디렉토리 설정\n","train_path = dataset_path + '/train.json'\n","val_path = dataset_path + '/val.json'\n","test_path = dataset_path + '/test.json'\n","\n","# collate_fn needs for batch\n","def collate_fn(batch):\n"," return tuple(zip(*batch))\n","\n","train_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","val_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","test_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","# create own Dataset 1 (skip)\n","# validation set을 직접 나누고 싶은 경우\n","# random_split 사용하여 data set을 8:2 로 분할\n","# train_size = int(0.8*len(dataset))\n","# val_size = int(len(dataset)-train_size)\n","# dataset = CustomDataLoader(data_dir=train_path, mode='train', transform=transform)\n","# train_dataset, val_dataset = torch.utils.data.random_split(dataset, [train_size, val_size])\n","\n","# create own Dataset 2\n","# train dataset\n","train_dataset = CustomDataLoader(data_dir=train_path, mode='train', transform=train_transform)\n","\n","# validation dataset\n","val_dataset = CustomDataLoader(data_dir=val_path, mode='val', transform=val_transform)\n","\n","# test dataset\n","test_dataset = CustomDataLoader(data_dir=test_path, mode='test', transform=test_transform)\n","\n","\n","# DataLoader\n","train_loader = torch.utils.data.DataLoader(dataset=train_dataset, \n"," batch_size=batch_size,\n"," shuffle=True,\n"," num_workers=4,\n"," collate_fn=collate_fn,\n"," drop_last=True)\n","\n","val_loader = torch.utils.data.DataLoader(dataset=val_dataset, \n"," batch_size=batch_size,\n"," shuffle=False,\n"," num_workers=4,\n"," collate_fn=collate_fn,\n"," drop_last=True) \n","\n","test_loader = torch.utils.data.DataLoader(dataset=test_dataset,\n"," batch_size=batch_size,\n"," num_workers=4,\n"," collate_fn=collate_fn)"],"execution_count":13,"outputs":[{"output_type":"stream","text":["loading annotations into memory...\n","Done (t=4.27s)\n","creating index...\n","index created!\n","loading annotations into memory...\n","Done (t=3.21s)\n","creating index...\n","index created!\n","loading annotations into memory...\n","Done (t=0.32s)\n","creating index...\n","index created!\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"8rqJiHb_-NwM"},"source":["# wandb"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"H1kHXm0uAX3R","executionInfo":{"status":"ok","timestamp":1620109134636,"user_tz":-540,"elapsed":8274,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"956bad23-6074-4c00-88c4-d77596af12c6"},"source":["!pip install wandb"],"execution_count":14,"outputs":[{"output_type":"stream","text":["Collecting wandb\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/67/5a/b037b50f9849212863a2fed313624d8f6f33ffa4ce89dc706e2a0e98c780/wandb-0.10.29-py2.py3-none-any.whl (2.1MB)\n","\u001b[K |████████████████████████████████| 2.1MB 14.4MB/s \n","\u001b[?25hRequirement already satisfied: requests<3,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from wandb) 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requests<3,>=2.0.0->wandb) (1.24.3)\n","Collecting gitdb<5,>=4.0.1\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/ea/e8/f414d1a4f0bbc668ed441f74f44c116d9816833a48bf81d22b697090dba8/gitdb-4.0.7-py3-none-any.whl (63kB)\n","\u001b[K |████████████████████████████████| 71kB 10.9MB/s \n","\u001b[?25hRequirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from protobuf>=3.12.0->wandb) (56.0.0)\n","Collecting smmap<5,>=3.0.1\n"," Downloading https://files.pythonhosted.org/packages/68/ee/d540eb5e5996eb81c26ceffac6ee49041d473bc5125f2aa995cf51ec1cf1/smmap-4.0.0-py2.py3-none-any.whl\n","Building wheels for collected packages: subprocess32, pathtools\n"," Building wheel for subprocess32 (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for subprocess32: filename=subprocess32-3.5.4-cp37-none-any.whl size=6489 sha256=e302878a0161329f4a83ce97d85709b508d416c944896e5dced2c577067c5b39\n"," Stored in directory: /root/.cache/pip/wheels/68/39/1a/5e402bdfdf004af1786c8b853fd92f8c4a04f22aad179654d1\n"," Building wheel for pathtools (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for pathtools: filename=pathtools-0.1.2-cp37-none-any.whl size=8786 sha256=994217558276f73823cb6b596125ae7d6aef5d143a8fe340760dc48175c23c9e\n"," Stored in directory: /root/.cache/pip/wheels/0b/04/79/c3b0c3a0266a3cb4376da31e5bfe8bba0c489246968a68e843\n","Successfully built subprocess32 pathtools\n","Installing collected packages: smmap, gitdb, GitPython, configparser, docker-pycreds, shortuuid, subprocess32, pathtools, sentry-sdk, wandb\n","Successfully installed GitPython-3.1.14 configparser-5.0.2 docker-pycreds-0.4.0 gitdb-4.0.7 pathtools-0.1.2 sentry-sdk-1.0.0 shortuuid-1.0.1 smmap-4.0.0 subprocess32-3.5.4 wandb-0.10.29\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":153},"id":"aMW4VV9V-NwM","executionInfo":{"status":"ok","timestamp":1620109148898,"user_tz":-540,"elapsed":11321,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"67e19b7a-3f59-40e6-81e1-edd0353a5c86"},"source":["import wandb\n","\n","proj_name = '0_aug_pan_effb0_noisy_focal_madgrad_cosLR'\n","\n","wandb.init(project='chanyub',name=proj_name)\n","\n","config = wandb.config\n","config.learning_rate = 0.01"],"execution_count":15,"outputs":[{"output_type":"display_data","data":{"application/javascript":["\n"," window._wandbApiKey = new Promise((resolve, reject) => {\n"," function loadScript(url) {\n"," return new Promise(function(resolve, reject) {\n"," let newScript = document.createElement(\"script\");\n"," newScript.onerror = reject;\n"," newScript.onload = resolve;\n"," document.body.appendChild(newScript);\n"," newScript.src = url;\n"," });\n"," }\n"," loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n"," const iframe = document.createElement('iframe')\n"," iframe.style.cssText = \"width:0;height:0;border:none\"\n"," document.body.appendChild(iframe)\n"," const handshake = new Postmate({\n"," container: iframe,\n"," url: 'https://wandb.ai/authorize'\n"," });\n"," const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n"," handshake.then(function(child) {\n"," child.on('authorize', data => {\n"," clearTimeout(timeout)\n"," resolve(data)\n"," });\n"," });\n"," })\n"," });\n"," "],"text/plain":[""]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"],"name":"stderr"},{"output_type":"display_data","data":{"text/html":["\n"," Tracking run with wandb version 0.10.29
\n"," Syncing run 0_aug_pan_effb0_noisy_focal_madgrad_cosLR to Weights & Biases (Documentation).
\n"," Project page: https://wandb.ai/pstage12/chanyub
\n"," Run page: https://wandb.ai/pstage12/chanyub/runs/1x53p8hs
\n"," Run data is saved locally in /content/drive/My Drive/Trash/code/wandb/run-20210504_061905-1x53p8hs

\n"," "],"text/plain":[""]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"uQRiIVGX-NwM"},"source":["## My model"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"S0xCGpNeAqeD","executionInfo":{"status":"ok","timestamp":1620109153846,"user_tz":-540,"elapsed":11292,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"6f864946-4f42-4cf5-9c85-557f70eac99c"},"source":["!pip install segmentation_models_pytorch"],"execution_count":16,"outputs":[{"output_type":"stream","text":["Collecting segmentation_models_pytorch\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/65/54/8953f9f7ee9d451b0f3be8d635aa3a654579abf898d17502a090efe1155a/segmentation_models_pytorch-0.1.3-py3-none-any.whl (66kB)\n","\u001b[K |████████████████████████████████| 71kB 3.8MB/s \n","\u001b[?25hCollecting pretrainedmodels==0.7.4\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/84/0e/be6a0e58447ac16c938799d49bfb5fb7a80ac35e137547fc6cee2c08c4cf/pretrainedmodels-0.7.4.tar.gz (58kB)\n","\u001b[K |████████████████████████████████| 61kB 9.6MB/s \n","\u001b[?25hCollecting efficientnet-pytorch==0.6.3\n"," Downloading https://files.pythonhosted.org/packages/b8/cb/0309a6e3d404862ae4bc017f89645cf150ac94c14c88ef81d215c8e52925/efficientnet_pytorch-0.6.3.tar.gz\n","Requirement already satisfied: torchvision>=0.3.0 in /usr/local/lib/python3.7/dist-packages (from segmentation_models_pytorch) (0.9.1+cu101)\n","Collecting timm==0.3.2\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/51/2d/39ecc56fbb202e1891c317e8e44667299bc3b0762ea2ed6aaaa2c2f6613c/timm-0.3.2-py3-none-any.whl (244kB)\n","\u001b[K |████████████████████████████████| 245kB 44.0MB/s \n","\u001b[?25hRequirement already satisfied: torch in /usr/local/lib/python3.7/dist-packages (from 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munch->pretrainedmodels==0.7.4->segmentation_models_pytorch) (1.15.0)\n","Building wheels for collected packages: pretrainedmodels, efficientnet-pytorch\n"," Building wheel for pretrainedmodels (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for pretrainedmodels: filename=pretrainedmodels-0.7.4-cp37-none-any.whl size=60963 sha256=f019a89f684098d3718c71a6a432cb2f2b9002601c05fdbbb5662a7251d88e1f\n"," Stored in directory: /root/.cache/pip/wheels/69/df/63/62583c096289713f22db605aa2334de5b591d59861a02c2ecd\n"," Building wheel for efficientnet-pytorch (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for efficientnet-pytorch: filename=efficientnet_pytorch-0.6.3-cp37-none-any.whl size=12420 sha256=94b5674e4b4e4c27a9abdcfb043bf39288f78e189e6b9ad299cc7f17834f607a\n"," Stored in directory: /root/.cache/pip/wheels/42/1e/a9/2a578ba9ad04e776e80bf0f70d8a7f4c29ec0718b92d8f6ccd\n","Successfully built pretrainedmodels efficientnet-pytorch\n","Installing collected packages: munch, pretrainedmodels, efficientnet-pytorch, timm, segmentation-models-pytorch\n","Successfully installed efficientnet-pytorch-0.6.3 munch-2.5.0 pretrainedmodels-0.7.4 segmentation-models-pytorch-0.1.3 timm-0.3.2\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:11.634792Z","start_time":"2021-04-18T16:16:05.875817Z"},"colab":{"base_uri":"https://localhost:8080/","height":137,"referenced_widgets":["48559cce7c6e44529933416904fed1d4","9778a208cd29448d9e319b7bfb70a996","b88d267d06724d83b8bef4de4a1f1442","be629e4ecf8040fb9058fbbe51867bf1","b7ae1a01de524d07bbd9cd4854c8cd81","25bb6ca00d7645129a8c64d94f7bd64c","da2d770c69d440819ddcdc0a4f0b22b7","c46bfe9dbb684a30a05ff7cc55189181"]},"id":"a8IfZfiM-NwM","executionInfo":{"status":"ok","timestamp":1620109172459,"user_tz":-540,"elapsed":12021,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"f7d86029-cd58-4259-a77a-eea7158e76fa"},"source":["# 구현된 model에 임의의 input을 넣어 output이 잘 나오는지 test\n","import segmentation_models_pytorch as smp\n","\n","model = smp.PAN(encoder_name='timm-efficientnet-b0', classes=12)\n","x = torch.randn([2, 3, 512, 512])\n","print(\"input shape : \", x.shape)\n","out = model(x).to(device)\n","print(\"output shape : \", out.size())\n","\n","model = model.to(device)"],"execution_count":17,"outputs":[{"output_type":"stream","text":["Downloading: \"https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b0_aa-827b6e33.pth\" to /root/.cache/torch/hub/checkpoints/tf_efficientnet_b0_aa-827b6e33.pth\n"],"name":"stderr"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"48559cce7c6e44529933416904fed1d4","version_minor":0,"version_major":2},"text/plain":["HBox(children=(FloatProgress(value=0.0, max=21383997.0), HTML(value='')))"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\n","input shape : torch.Size([2, 3, 512, 512])\n","output shape : torch.Size([2, 12, 512, 512])\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"zvDnI7_T-NwN"},"source":["## train, validation, test 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.104200Z","start_time":"2021-04-18T16:16:18.093174Z"},"id":"RA3oAapJ-NwN","executionInfo":{"status":"ok","timestamp":1620109186153,"user_tz":-540,"elapsed":1131,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["def train(num_epochs, model, data_loader, val_loader, criterion, optimizer, saved_dir, val_every, device, lr_scheduler):\n"," print('Start training..')\n"," best_loss = 9999999\n"," best_miou = 0\n"," for epoch in range(num_epochs):\n"," model.train()\n"," for step, (images, masks, _) in enumerate(data_loader):\n"," images = torch.stack(images) # (batch, channel, height, width)\n"," masks = torch.stack(masks).long() # (batch, channel, height, width)\n"," \n"," # gpu 연산을 위해 device 할당\n"," images, masks = images.to(device), masks.to(device)\n"," \n"," # inference\n"," outputs = model(images)\n"," \n"," # loss 계산 (cross entropy loss)\n"," loss = criterion(outputs, masks)\n"," optimizer.zero_grad()\n"," loss.backward()\n"," optimizer.step()\n"," \n"," lr_scheduler.step()\n"," \n"," # step 주기에 따른 loss 출력\n"," if (step + 1) % 25 == 0:\n"," print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(\n"," epoch+1, num_epochs, step+1, len(train_loader), loss.item()))\n"," \n"," # validation 주기에 따른 loss 출력 및 best model 저장\n"," if (epoch + 1) % val_every == 0:\n","# avrg_loss = validation(epoch + 1, model, val_loader, criterion, device)\n","# if avrg_loss < best_loss:\n","# print('Best performance at epoch: {}'.format(epoch + 1))\n","# print('Save model in', saved_dir)\n","# best_loss = avrg_loss\n","# wandb.log({'best_loss': best_loss})\n","# save_model(model, saved_dir)\n"," avrg_miou = validation(epoch + 1, model, val_loader, criterion, device)\n"," if avrg_miou > best_miou:\n"," print('Best performance at epoch: {}'.format(epoch + 1))\n"," print('Save model in', saved_dir)\n"," best_miou = avrg_miou\n"," wandb.log({'best_miou': best_miou})\n"," save_model(model, saved_dir)"],"execution_count":18,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.285795Z","start_time":"2021-04-18T16:16:18.267686Z"},"id":"EulIikmq-NwO","executionInfo":{"status":"ok","timestamp":1620109188796,"user_tz":-540,"elapsed":1580,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["def validation(epoch, model, data_loader, criterion, device):\n"," print('Start validation #{}'.format(epoch))\n"," model.eval()\n"," with torch.no_grad():\n"," total_loss = 0\n"," cnt = 0\n"," mIoU_list = []\n"," for step, (images, masks, _) in enumerate(data_loader):\n"," \n"," images = torch.stack(images) # (batch, channel, height, width)\n"," masks = torch.stack(masks).long() # (batch, channel, height, width)\n","\n"," images, masks = images.to(device), masks.to(device) \n","\n"," outputs = model(images)\n"," loss = criterion(outputs, masks)\n"," total_loss += loss\n"," cnt += 1\n"," \n","# print(outputs.shape)\n","# print(masks.shape)\n","# wandb.log(wandb.Image(images, masks={\n","# \"predictions\" : {\n","# \"mask_data\" : torch.squeeze(torch.squeeze(outputs, 0),1),\n","# \"class_labels\" : classes_dict\n","# },\n","# \"ground_truth\" : {\n","# \"mask_data\" : torch.squeeze(masks, 0),\n","# \"class_labels\" : classes_dict\n","# }\n","# }))\n"," \n"," outputs = torch.argmax(outputs.squeeze(), dim=1).detach().cpu().numpy()\n","\n"," mIoU = label_accuracy_score(masks.detach().cpu().numpy(), outputs, n_class=12)[2]\n"," mIoU_list.append(mIoU)\n"," \n"," avrg_loss = total_loss / cnt\n"," avrg_mIoU = np.mean(mIoU_list)\n"," print('Validation #{} Average Loss: {:.4f}, mIoU: {:.4f}'.format(epoch, avrg_loss, np.mean(mIoU_list)))\n"," wandb.log({'Train Loss':loss.item(), 'Val Loss':avrg_loss , 'Val mIoU':np.mean(mIoU_list)})\n","# return avrg_loss\n"," return avrg_mIoU"],"execution_count":19,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"C_7CtFfH-NwO"},"source":["## 모델 저장 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.909918Z","start_time":"2021-04-18T16:16:18.898918Z"},"id":"gpCM5BFO-NwO","executionInfo":{"status":"ok","timestamp":1620109199953,"user_tz":-540,"elapsed":805,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# 모델 저장 함수 정의\n","val_every = 1 \n","\n","saved_dir = './saved'\n","if not os.path.isdir(saved_dir): \n"," os.mkdir(saved_dir)\n"," \n","def save_model(model, saved_dir, file_name='0_aug_pan_effb0_focal_madgrad_cosLR.pt'):\n"," check_point = {'net': model.state_dict()}\n"," output_path = os.path.join(saved_dir, file_name)\n"," torch.save(model.state_dict(), output_path)"],"execution_count":20,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"0D3rsEd2yJfV"},"source":[""]},{"cell_type":"markdown","metadata":{"id":"FyKGeg8T-NwP"},"source":["## 모델 생성 및 Loss function, Optimizer 정의"]},{"cell_type":"code","metadata":{"id":"ORugl8s1-NwP","executionInfo":{"status":"ok","timestamp":1620109204482,"user_tz":-540,"elapsed":1007,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["from torch.autograd import Variable\n","import torch.nn.functional as F\n","# ref : https://github.com/clcarwin/focal_loss_pytorch\n","class FocalLoss(nn.Module):\n"," def __init__(self, gamma=0, alpha=None, size_average=True):\n"," super(FocalLoss, self).__init__()\n"," self.gamma = gamma\n"," self.alpha = alpha\n"," if isinstance(alpha,(float,int)): self.alpha = torch.Tensor([alpha,1-alpha])\n"," if isinstance(alpha,list): self.alpha = torch.Tensor(alpha)\n"," self.size_average = size_average\n","\n"," def forward(self, input, target):\n"," if input.dim()>2:\n"," input = input.view(input.size(0),input.size(1),-1) # N,C,H,W => N,C,H*W\n"," input = input.transpose(1,2) # N,C,H*W => N,H*W,C\n"," input = input.contiguous().view(-1,input.size(2)) # N,H*W,C => N*H*W,C\n"," target = target.view(-1,1)\n","\n"," logpt = F.log_softmax(input)\n"," logpt = logpt.gather(1,target)\n"," logpt = logpt.view(-1)\n"," pt = Variable(logpt.data.exp())\n","\n"," if self.alpha is not None:\n"," if self.alpha.type()!=input.data.type():\n"," self.alpha = self.alpha.type_as(input.data)\n"," at = self.alpha.gather(0,target.data.view(-1))\n"," logpt = logpt * Variable(at)\n","\n"," loss = -1 * (1-pt)**self.gamma * logpt\n"," if self.size_average: return loss.mean()\n"," else: return loss.sum()"],"execution_count":21,"outputs":[]},{"cell_type":"code","metadata":{"id":"yfeFQknz-NwP","executionInfo":{"status":"ok","timestamp":1620109204483,"user_tz":-540,"elapsed":1004,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["import torch.optim.lr_scheduler as lr_scheduler\n","import math\n","class CosineAnnealingWarmUpRestart(lr_scheduler._LRScheduler):\n"," def __init__(self, optimizer, T_0, T_mult=1, eta_max=0.1, T_up=0, gamma=1., last_epoch=-1):\n"," if T_0 <= 0 or not isinstance(T_0, int):\n"," raise ValueError(\"Expected positive integer T_0, but got {}\".format(T_0))\n"," if T_mult < 1 or not isinstance(T_mult, int):\n"," raise ValueError(\"Expected integer T_mult >= 1, but got {}\".format(T_mult))\n"," if T_up < 0 or not isinstance(T_up, int):\n"," raise ValueError(\"Expected positive integer T_up, but got {}\".format(T_up))\n"," self.T_0 = T_0\n"," self.T_mult = T_mult\n"," self.base_eta_max = eta_max\n"," self.eta_max = eta_max\n"," self.T_up = T_up\n"," self.T_i = T_0\n"," self.gamma = gamma\n"," self.cycle = 0\n"," self.T_cur = last_epoch\n"," super(CosineAnnealingWarmUpRestart, self).__init__(optimizer, last_epoch)\n"," # self.T_cur = last_epoch\n"," \n"," def get_lr(self):\n"," if self.T_cur == -1:\n"," return self.base_lrs\n"," elif self.T_cur < self.T_up:\n"," return [(self.eta_max - base_lr)*self.T_cur / self.T_up + base_lr for base_lr in self.base_lrs]\n"," else:\n"," return [base_lr + (self.eta_max - base_lr) * (1 + math.cos(math.pi * (self.T_cur-self.T_up) / (self.T_i - self.T_up))) / 2\n"," for base_lr in self.base_lrs]\n","\n"," def step(self, epoch=None):\n"," if epoch is None:\n"," epoch = self.last_epoch + 1\n"," self.T_cur = self.T_cur + 1\n"," if self.T_cur >= self.T_i:\n"," self.cycle += 1\n"," self.T_cur = self.T_cur - self.T_i\n"," self.T_i = (self.T_i - self.T_up) * self.T_mult + self.T_up\n"," else:\n"," if epoch >= self.T_0:\n"," if self.T_mult == 1:\n"," self.T_cur = epoch % self.T_0\n"," self.cycle = epoch // self.T_0\n"," else:\n"," n = int(math.log((epoch / self.T_0 * (self.T_mult - 1) + 1), self.T_mult))\n"," self.cycle = n\n"," self.T_cur = epoch - self.T_0 * (self.T_mult ** n - 1) / (self.T_mult - 1)\n"," self.T_i = self.T_0 * self.T_mult ** (n)\n"," else:\n"," self.T_i = self.T_0\n"," self.T_cur = epoch\n"," \n"," self.eta_max = self.base_eta_max * (self.gamma**self.cycle)\n"," self.last_epoch = math.floor(epoch)\n"," for param_group, lr in zip(self.optimizer.param_groups, self.get_lr()):\n"," param_group['lr'] = lr"],"execution_count":22,"outputs":[]},{"cell_type":"code","metadata":{"id":"AG1oQeu7BX1M"},"source":["# !pip install adamp"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"eH2PpwF9a-Os","executionInfo":{"status":"ok","timestamp":1620109208512,"user_tz":-540,"elapsed":3087,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"bdfc887c-2a4d-4ded-f9ef-e006b685368d"},"source":["!pip install madgrad"],"execution_count":23,"outputs":[{"output_type":"stream","text":["Collecting madgrad\n"," Downloading https://files.pythonhosted.org/packages/65/f0/4584f18202a2fb8903d456bf907b80e7cb54ad8fcba68604084ff41b7cf8/madgrad-1.1-py3-none-any.whl\n","Installing collected packages: madgrad\n","Successfully installed madgrad-1.1\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:19.698902Z","start_time":"2021-04-18T16:16:19.694902Z"},"id":"9Dly8KZj-NwQ","executionInfo":{"status":"ok","timestamp":1620109214703,"user_tz":-540,"elapsed":1213,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# from adamp import AdamP\n","from madgrad import MADGRAD\n","# Loss function 정의\n","# criterion = nn.CrossEntropyLoss()\n","criterion = FocalLoss()\n","\n","# Optimizer 정의\n","# optimizer = torch.optim.Adam(params = model.parameters(), lr = learning_rate, weight_decay=1e-6)\n","# optimizer = AdamP(params = model.parameters())\n","optimizer = MADGRAD(params = model.parameters(), lr = learning_rate, momentum = 0.9, weight_decay = 0, eps = 1e-06)\n","\n","lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=50, eta_min=0)\n","# lr_scheduler = CosineAnnealingWarmUpRestart(optimizer, T_0=150, T_mult=1, eta_max=0.1, T_up=10, gamma=0.5)"],"execution_count":24,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"start_time":"2021-04-18T16:16:20.331Z"},"colab":{"base_uri":"https://localhost:8080/"},"id":"cMSiFA-3-NwR","executionInfo":{"status":"ok","timestamp":1620117725762,"user_tz":-540,"elapsed":8504721,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"d830568f-017c-4bed-e465-ac122fd3d36f"},"source":["train(num_epochs, model, train_loader, val_loader, criterion, optimizer, saved_dir, val_every, device, lr_scheduler)"],"execution_count":25,"outputs":[{"output_type":"stream","text":["Start training..\n","Epoch [1/20], Step [25/327], Loss: 0.6025\n","Epoch [1/20], Step [50/327], Loss: 0.9703\n","Epoch [1/20], Step [75/327], Loss: 0.7496\n","Epoch [1/20], Step [100/327], Loss: 0.6444\n","Epoch [1/20], Step [125/327], Loss: 1.0047\n","Epoch [1/20], Step [150/327], Loss: 0.5725\n","Epoch [1/20], Step [175/327], Loss: 0.6761\n","Epoch [1/20], Step [200/327], Loss: 0.5220\n","Epoch [1/20], Step [225/327], Loss: 0.6070\n","Epoch [1/20], Step [250/327], Loss: 0.7197\n","Epoch [1/20], Step [275/327], Loss: 0.5434\n","Epoch [1/20], Step [300/327], Loss: 0.4413\n","Epoch [1/20], Step [325/327], Loss: 0.4280\n","Start validation #1\n","Validation #1 Average Loss: 0.4854, mIoU: 0.2718\n","Best performance at epoch: 1\n","Save model in ./saved\n","Epoch [2/20], Step [25/327], Loss: 0.4143\n","Epoch [2/20], Step [50/327], Loss: 0.5028\n","Epoch [2/20], Step [75/327], Loss: 0.2997\n","Epoch [2/20], Step [100/327], Loss: 0.5638\n","Epoch [2/20], Step [125/327], Loss: 0.4103\n","Epoch [2/20], Step [150/327], Loss: 0.3281\n","Epoch [2/20], Step [175/327], Loss: 0.3733\n","Epoch [2/20], Step [200/327], Loss: 0.3063\n","Epoch [2/20], Step [225/327], Loss: 1.1834\n","Epoch [2/20], Step [250/327], Loss: 0.5493\n","Epoch [2/20], Step [275/327], Loss: 0.3540\n","Epoch [2/20], Step [300/327], Loss: 0.3379\n","Epoch [2/20], Step [325/327], Loss: 0.3644\n","Start validation #2\n","Validation #2 Average Loss: 0.4334, mIoU: 0.3047\n","Best performance at epoch: 2\n","Save model in ./saved\n","Epoch [3/20], Step [25/327], Loss: 0.3520\n","Epoch [3/20], Step [50/327], Loss: 0.5596\n","Epoch [3/20], Step [75/327], Loss: 0.3243\n","Epoch [3/20], Step [100/327], Loss: 0.2455\n","Epoch [3/20], Step [125/327], Loss: 0.2309\n","Epoch [3/20], Step [150/327], Loss: 0.2213\n","Epoch [3/20], Step [175/327], Loss: 0.4014\n","Epoch [3/20], Step [200/327], Loss: 0.4144\n","Epoch [3/20], Step [225/327], Loss: 0.2481\n","Epoch [3/20], Step [250/327], Loss: 0.3728\n","Epoch [3/20], Step [275/327], Loss: 0.5070\n","Epoch [3/20], Step [300/327], Loss: 0.4201\n","Epoch [3/20], Step [325/327], Loss: 0.4621\n","Start validation #3\n","Validation #3 Average Loss: 0.3758, mIoU: 0.3304\n","Best performance at epoch: 3\n","Save model in ./saved\n","Epoch [4/20], Step [25/327], Loss: 0.3674\n","Epoch [4/20], Step [50/327], Loss: 0.5638\n","Epoch [4/20], Step [75/327], Loss: 0.2501\n","Epoch [4/20], Step [100/327], Loss: 0.3354\n","Epoch [4/20], Step [125/327], Loss: 0.3481\n","Epoch [4/20], Step [150/327], Loss: 0.3641\n","Epoch [4/20], Step [175/327], Loss: 0.1896\n","Epoch [4/20], Step [200/327], Loss: 0.3649\n","Epoch [4/20], Step [225/327], Loss: 0.2745\n","Epoch [4/20], Step [250/327], Loss: 0.4847\n","Epoch [4/20], Step [275/327], Loss: 0.3973\n","Epoch [4/20], Step [300/327], Loss: 0.3030\n","Epoch [4/20], Step [325/327], Loss: 0.2587\n","Start validation #4\n","Validation #4 Average Loss: 0.3610, mIoU: 0.3454\n","Best performance at epoch: 4\n","Save model in ./saved\n","Epoch [5/20], Step [25/327], Loss: 0.2730\n","Epoch [5/20], Step [50/327], Loss: 0.7241\n","Epoch [5/20], Step [75/327], Loss: 0.5081\n","Epoch [5/20], Step [100/327], Loss: 0.3683\n","Epoch [5/20], Step [125/327], Loss: 0.4433\n","Epoch [5/20], Step [150/327], Loss: 0.2112\n","Epoch [5/20], Step [175/327], Loss: 0.5972\n","Epoch [5/20], Step [200/327], Loss: 0.2656\n","Epoch [5/20], Step [225/327], Loss: 0.3537\n","Epoch [5/20], Step [250/327], Loss: 0.3016\n","Epoch [5/20], Step [275/327], Loss: 0.3040\n","Epoch [5/20], Step [300/327], Loss: 0.4046\n","Epoch [5/20], Step [325/327], Loss: 0.4539\n","Start validation #5\n","Validation #5 Average Loss: 0.3353, mIoU: 0.3682\n","Best performance at epoch: 5\n","Save model in ./saved\n","Epoch [6/20], Step [25/327], Loss: 0.2844\n","Epoch [6/20], Step [50/327], Loss: 0.3986\n","Epoch [6/20], Step [75/327], Loss: 0.2882\n","Epoch [6/20], Step [100/327], Loss: 0.1761\n","Epoch [6/20], Step [125/327], Loss: 0.2685\n","Epoch [6/20], Step [150/327], Loss: 0.2731\n","Epoch [6/20], Step [175/327], Loss: 0.4491\n","Epoch [6/20], Step [200/327], Loss: 0.2135\n","Epoch [6/20], Step [225/327], Loss: 0.1313\n","Epoch [6/20], Step [250/327], Loss: 0.1760\n","Epoch [6/20], Step [275/327], Loss: 0.4074\n","Epoch [6/20], Step [300/327], Loss: 0.2555\n","Epoch [6/20], Step [325/327], Loss: 0.3107\n","Start validation #6\n","Validation #6 Average Loss: 0.3384, mIoU: 0.3678\n","Epoch [7/20], Step [25/327], Loss: 0.2355\n","Epoch [7/20], Step [50/327], Loss: 0.2809\n","Epoch [7/20], Step [75/327], Loss: 0.2807\n","Epoch [7/20], Step [100/327], Loss: 0.3637\n","Epoch [7/20], Step [125/327], Loss: 0.2503\n","Epoch [7/20], Step [150/327], Loss: 0.3974\n","Epoch [7/20], Step [175/327], Loss: 1.0051\n","Epoch [7/20], Step [200/327], Loss: 0.3923\n","Epoch [7/20], Step [225/327], Loss: 0.2732\n","Epoch [7/20], Step [250/327], Loss: 0.2699\n","Epoch [7/20], Step [275/327], Loss: 0.2118\n","Epoch [7/20], Step [300/327], Loss: 0.7830\n","Epoch [7/20], Step [325/327], Loss: 0.3693\n","Start validation #7\n","Validation #7 Average Loss: 0.3212, mIoU: 0.3711\n","Best performance at epoch: 7\n","Save model in ./saved\n","Epoch [8/20], Step [25/327], Loss: 0.1637\n","Epoch [8/20], Step [50/327], Loss: 0.3532\n","Epoch [8/20], Step [75/327], Loss: 0.3440\n","Epoch [8/20], Step [100/327], Loss: 0.2397\n","Epoch [8/20], Step [125/327], Loss: 0.2474\n","Epoch [8/20], Step [150/327], Loss: 0.1994\n","Epoch [8/20], Step [175/327], Loss: 0.2294\n","Epoch [8/20], Step [200/327], Loss: 0.2710\n","Epoch [8/20], Step [225/327], Loss: 0.2273\n","Epoch [8/20], Step [250/327], Loss: 0.2559\n","Epoch [8/20], Step [275/327], Loss: 0.2449\n","Epoch [8/20], Step [300/327], Loss: 0.5486\n","Epoch [8/20], Step [325/327], Loss: 0.2646\n","Start validation #8\n","Validation #8 Average Loss: 0.3455, mIoU: 0.3765\n","Best performance at epoch: 8\n","Save model in ./saved\n","Epoch [9/20], Step [25/327], Loss: 0.2191\n","Epoch [9/20], Step [50/327], Loss: 0.1772\n","Epoch [9/20], Step [75/327], Loss: 0.2696\n","Epoch [9/20], Step [100/327], Loss: 0.3008\n","Epoch [9/20], Step [125/327], Loss: 0.2653\n","Epoch [9/20], Step [150/327], Loss: 0.2680\n","Epoch [9/20], Step [175/327], Loss: 0.3054\n","Epoch [9/20], Step [200/327], Loss: 0.4096\n","Epoch [9/20], Step [225/327], Loss: 0.6189\n","Epoch [9/20], Step [250/327], Loss: 0.2564\n","Epoch [9/20], Step [275/327], Loss: 0.1488\n","Epoch [9/20], Step [300/327], Loss: 0.1525\n","Epoch [9/20], Step [325/327], Loss: 0.2160\n","Start validation #9\n","Validation #9 Average Loss: 0.3327, mIoU: 0.3702\n","Epoch [10/20], Step [25/327], Loss: 0.1905\n","Epoch [10/20], Step [50/327], Loss: 0.3048\n","Epoch [10/20], Step [75/327], Loss: 0.1970\n","Epoch [10/20], Step [100/327], Loss: 0.0878\n","Epoch [10/20], Step [125/327], Loss: 0.4035\n","Epoch [10/20], Step [150/327], Loss: 0.2150\n","Epoch [10/20], Step [175/327], Loss: 0.2788\n","Epoch [10/20], Step [200/327], Loss: 0.1959\n","Epoch [10/20], Step [225/327], Loss: 0.2102\n","Epoch [10/20], Step [250/327], Loss: 0.1938\n","Epoch [10/20], Step [275/327], Loss: 0.1849\n","Epoch [10/20], Step [300/327], Loss: 0.2986\n","Epoch [10/20], Step [325/327], Loss: 0.1821\n","Start validation #10\n","Validation #10 Average Loss: 0.3204, mIoU: 0.3704\n","Epoch [11/20], Step [25/327], Loss: 0.2472\n","Epoch [11/20], Step [50/327], Loss: 0.1364\n","Epoch [11/20], Step [75/327], Loss: 0.2443\n","Epoch [11/20], Step [100/327], Loss: 0.2462\n","Epoch [11/20], Step [125/327], Loss: 0.2468\n","Epoch [11/20], Step [150/327], Loss: 0.2706\n","Epoch [11/20], Step [175/327], Loss: 0.2242\n","Epoch [11/20], Step [200/327], Loss: 0.1574\n","Epoch [11/20], Step [225/327], Loss: 0.2048\n","Epoch [11/20], Step [250/327], Loss: 0.2322\n","Epoch [11/20], Step [275/327], Loss: 0.4434\n","Epoch [11/20], Step [300/327], Loss: 0.1453\n","Epoch [11/20], Step [325/327], Loss: 0.1527\n","Start validation #11\n","Validation #11 Average Loss: 0.3231, mIoU: 0.3941\n","Best performance at epoch: 11\n","Save model in ./saved\n","Epoch [12/20], Step [25/327], Loss: 0.1542\n","Epoch [12/20], Step [50/327], Loss: 0.2102\n","Epoch [12/20], Step [75/327], Loss: 0.1363\n","Epoch [12/20], Step [100/327], Loss: 0.1267\n","Epoch [12/20], Step [125/327], Loss: 0.1352\n","Epoch [12/20], Step [150/327], Loss: 0.1370\n","Epoch [12/20], Step [175/327], Loss: 0.1678\n","Epoch [12/20], Step [200/327], Loss: 0.2559\n","Epoch [12/20], Step [225/327], Loss: 0.2780\n","Epoch [12/20], Step [250/327], Loss: 0.1351\n","Epoch [12/20], Step [275/327], Loss: 0.2063\n","Epoch [12/20], Step [300/327], Loss: 0.1633\n","Epoch [12/20], Step [325/327], Loss: 0.1627\n","Start validation #12\n","Validation #12 Average Loss: 0.3325, mIoU: 0.3763\n","Epoch [13/20], Step [25/327], Loss: 0.1750\n","Epoch [13/20], Step [50/327], Loss: 0.2008\n","Epoch [13/20], Step [75/327], Loss: 0.2296\n","Epoch [13/20], Step [100/327], Loss: 0.1366\n","Epoch [13/20], Step [125/327], Loss: 0.1922\n","Epoch [13/20], Step [150/327], Loss: 0.1667\n","Epoch [13/20], Step [175/327], Loss: 0.1558\n","Epoch [13/20], Step [200/327], Loss: 0.3808\n","Epoch [13/20], Step [225/327], Loss: 0.0974\n","Epoch [13/20], Step [250/327], Loss: 0.2382\n","Epoch [13/20], Step [275/327], Loss: 0.1916\n","Epoch [13/20], Step [300/327], Loss: 0.1367\n","Epoch [13/20], Step [325/327], Loss: 0.1786\n","Start validation #13\n","Validation #13 Average Loss: 0.3285, mIoU: 0.3901\n","Epoch [14/20], Step [25/327], Loss: 0.1501\n","Epoch [14/20], Step [50/327], Loss: 0.1887\n","Epoch [14/20], Step [75/327], Loss: 0.2404\n","Epoch [14/20], Step [100/327], Loss: 0.1708\n","Epoch [14/20], Step [125/327], Loss: 0.2601\n","Epoch [14/20], Step [150/327], Loss: 0.1553\n","Epoch [14/20], Step [175/327], Loss: 0.2324\n","Epoch [14/20], Step [200/327], Loss: 0.2333\n","Epoch [14/20], Step [225/327], Loss: 0.1088\n","Epoch [14/20], Step [250/327], Loss: 0.1098\n","Epoch [14/20], Step [275/327], Loss: 0.1352\n","Epoch [14/20], Step [300/327], Loss: 0.2577\n","Epoch [14/20], Step [325/327], Loss: 0.2224\n","Start validation #14\n","Validation #14 Average Loss: 0.3259, mIoU: 0.3807\n","Epoch [15/20], Step [25/327], Loss: 0.1823\n","Epoch [15/20], Step [50/327], Loss: 0.1979\n","Epoch [15/20], Step [75/327], Loss: 0.1184\n","Epoch [15/20], Step [100/327], Loss: 0.2011\n","Epoch [15/20], Step [125/327], Loss: 0.1587\n","Epoch [15/20], Step [150/327], Loss: 0.2005\n","Epoch [15/20], Step [175/327], Loss: 0.1933\n","Epoch [15/20], Step [200/327], Loss: 0.1654\n","Epoch [15/20], Step [225/327], Loss: 0.1653\n","Epoch [15/20], Step [250/327], Loss: 0.1773\n","Epoch [15/20], Step [275/327], Loss: 0.1909\n","Epoch [15/20], Step [300/327], Loss: 0.1252\n","Epoch [15/20], Step [325/327], Loss: 0.1453\n","Start validation #15\n","Validation #15 Average Loss: 0.3296, mIoU: 0.3908\n","Epoch [16/20], Step [25/327], Loss: 0.2906\n","Epoch [16/20], Step [50/327], Loss: 0.1583\n","Epoch [16/20], Step [75/327], Loss: 0.2115\n","Epoch [16/20], Step [100/327], Loss: 0.2293\n","Epoch [16/20], Step [125/327], Loss: 0.1252\n","Epoch [16/20], Step [150/327], Loss: 0.2705\n","Epoch [16/20], Step [175/327], Loss: 0.1138\n","Epoch [16/20], Step [200/327], Loss: 0.2056\n","Epoch [16/20], Step [225/327], Loss: 0.2145\n","Epoch [16/20], Step [250/327], Loss: 0.1604\n","Epoch [16/20], Step [275/327], Loss: 0.1138\n","Epoch [16/20], Step [300/327], Loss: 0.2513\n","Epoch [16/20], Step [325/327], Loss: 0.1379\n","Start validation #16\n","Validation #16 Average Loss: 0.3181, mIoU: 0.3995\n","Best performance at epoch: 16\n","Save model in ./saved\n","Epoch [17/20], Step [25/327], Loss: 0.1679\n","Epoch [17/20], Step [50/327], Loss: 0.2071\n","Epoch [17/20], Step [75/327], Loss: 0.3042\n","Epoch [17/20], Step [100/327], Loss: 0.1119\n","Epoch [17/20], Step [125/327], Loss: 0.1421\n","Epoch [17/20], Step [150/327], Loss: 0.1515\n","Epoch [17/20], Step [175/327], Loss: 0.1803\n","Epoch [17/20], Step [200/327], Loss: 0.2220\n","Epoch [17/20], Step [225/327], Loss: 0.0761\n","Epoch [17/20], Step [250/327], Loss: 0.1158\n","Epoch [17/20], Step [275/327], Loss: 0.1531\n","Epoch [17/20], Step [300/327], Loss: 0.1776\n","Epoch [17/20], Step [325/327], Loss: 0.2160\n","Start validation #17\n","Validation #17 Average Loss: 0.3422, mIoU: 0.3862\n","Epoch [18/20], Step [25/327], Loss: 0.1127\n","Epoch [18/20], Step [50/327], Loss: 0.1279\n","Epoch [18/20], Step [75/327], Loss: 0.1922\n","Epoch [18/20], Step [100/327], Loss: 0.0719\n","Epoch [18/20], Step [125/327], Loss: 0.1419\n","Epoch [18/20], Step [150/327], Loss: 0.2133\n","Epoch [18/20], Step [175/327], Loss: 0.1278\n","Epoch [18/20], Step [200/327], Loss: 0.1288\n","Epoch [18/20], Step [225/327], Loss: 0.1753\n","Epoch [18/20], Step [250/327], Loss: 0.1456\n","Epoch [18/20], Step [275/327], Loss: 0.0745\n","Epoch [18/20], Step [300/327], Loss: 0.1401\n","Epoch [18/20], Step [325/327], Loss: 0.2428\n","Start validation #18\n","Validation #18 Average Loss: 0.3199, mIoU: 0.3916\n","Epoch [19/20], Step [25/327], Loss: 0.1457\n","Epoch [19/20], Step [50/327], Loss: 0.1943\n","Epoch [19/20], Step [75/327], Loss: 0.1201\n","Epoch [19/20], Step [100/327], Loss: 0.1361\n","Epoch [19/20], Step [125/327], Loss: 0.1300\n","Epoch [19/20], Step [150/327], Loss: 0.0969\n","Epoch [19/20], Step [175/327], Loss: 0.1219\n","Epoch [19/20], Step [200/327], Loss: 0.1357\n","Epoch [19/20], Step [225/327], Loss: 0.1565\n","Epoch [19/20], Step [250/327], Loss: 0.1877\n","Epoch [19/20], Step [275/327], Loss: 0.1242\n","Epoch [19/20], Step [300/327], Loss: 0.1455\n","Epoch [19/20], Step [325/327], Loss: 0.0906\n","Start validation #19\n","Validation #19 Average Loss: 0.3318, mIoU: 0.3783\n","Epoch [20/20], Step [25/327], Loss: 0.1061\n","Epoch [20/20], Step [50/327], Loss: 0.1044\n","Epoch [20/20], Step [75/327], Loss: 0.1230\n","Epoch [20/20], Step [100/327], Loss: 0.1404\n","Epoch [20/20], Step [125/327], Loss: 0.1241\n","Epoch [20/20], Step [150/327], Loss: 0.1554\n","Epoch [20/20], Step [175/327], Loss: 0.1202\n","Epoch [20/20], Step [200/327], Loss: 0.1735\n","Epoch [20/20], Step [225/327], Loss: 0.2036\n","Epoch [20/20], Step [250/327], Loss: 0.2238\n","Epoch [20/20], Step [275/327], Loss: 0.1189\n","Epoch [20/20], Step [300/327], Loss: 0.1578\n","Epoch [20/20], Step [325/327], Loss: 0.1379\n","Start validation #20\n","Validation #20 Average Loss: 0.3263, mIoU: 0.3803\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"C6ClcO0J-NwR"},"source":["## 저장된 model 불러오기 (학습된 이후) "]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:21.050200Z","start_time":"2021-04-16T19:44:20.802200Z"},"scrolled":true,"id":"KQPtUDzd-NwR","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620102899370,"user_tz":-540,"elapsed":882,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"72d54b02-20cf-4ea6-991d-52fd5331c0a6"},"source":["# best model 저장된 경로\n","model_path = './saved/pan_effb3_noisy_focal_madgrad_cosLR.pt'\n","\n","# best model 불러오기\n","checkpoint = torch.load(model_path, map_location=device)\n","model.load_state_dict(checkpoint)\n","\n","# 추론을 실행하기 전에는 반드시 설정 (batch normalization, dropout 를 평가 모드로 설정)\n","# model.eval()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{"tags":[]},"execution_count":16}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:24.939227Z","start_time":"2021-04-16T19:44:24.518228Z"},"id":"0LQqrDAp-NwR","colab":{"base_uri":"https://localhost:8080/","height":502},"executionInfo":{"status":"ok","timestamp":1620102907649,"user_tz":-540,"elapsed":6266,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"b34da4ee-1fe9-4960-f841-9627d644c50b"},"source":["# 첫번째 batch의 추론 결과 확인\n","for imgs, image_infos in test_loader:\n"," image_infos = image_infos\n"," temp_images = imgs\n"," \n"," model.eval()\n"," # inference\n"," outs = model(torch.stack(temp_images).to(device))\n"," oms = torch.argmax(outs.squeeze(), dim=1).detach().cpu().numpy()\n"," \n"," break\n","\n","i = 1\n","fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(16, 16))\n","\n","print('Shape of Original Image :', list(temp_images[i].shape))\n","print('Shape of Predicted : ', list(oms[i].shape))\n","print('Unique values, category of transformed mask : \\n', [{int(i),category_names[int(i)]} for i in list(np.unique(oms[i]))])\n","\n","# Original image\n","ax1.imshow(temp_images[i].permute([1,2,0]))\n","ax1.grid(False)\n","ax1.set_title(\"Original image : {}\".format(image_infos[i]['file_name']), fontsize = 15)\n","\n","# Predicted\n","ax2.imshow(oms[i])\n","ax2.grid(False)\n","ax2.set_title(\"Predicted : {}\".format(image_infos[i]['file_name']), fontsize = 15)\n","\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Shape of Original Image : [3, 512, 512]\n","Shape of Predicted : [512, 512]\n","Unique values, category of transformed mask : \n"," [{0, 'Backgroud'}, {'General trash', 2}, {'Paper', 3}, {9, 'Plastic bag'}, {11, 'Clothing'}]\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"evYjR2F3-NwS"},"source":["## submission을 위한 test 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:27.469285Z","start_time":"2021-04-16T19:44:27.456021Z"},"id":"nhMLnV5d-NwS"},"source":["def test(model, data_loader, device):\n"," size = 256\n"," transform = A.Compose([A.Resize(256, 256)])\n"," print('Start prediction.')\n"," model.eval()\n"," \n"," file_name_list = []\n"," preds_array = np.empty((0, size*size), dtype=np.long)\n"," \n"," with torch.no_grad():\n"," for step, (imgs, image_infos) in enumerate(test_loader):\n","\n"," # inference (512 x 512)\n"," outs = model(torch.stack(imgs).to(device))\n"," oms = torch.argmax(outs.squeeze(), dim=1).detach().cpu().numpy()\n"," \n"," # resize (256 x 256)\n"," temp_mask = []\n"," for img, mask in zip(np.stack(imgs), oms):\n"," transformed = transform(image=img, mask=mask)\n"," mask = transformed['mask']\n"," temp_mask.append(mask)\n","\n"," oms = np.array(temp_mask)\n"," \n"," oms = oms.reshape([oms.shape[0], size*size]).astype(int)\n"," preds_array = np.vstack((preds_array, oms))\n"," \n"," file_name_list.append([i['file_name'] for i in image_infos])\n"," print(\"End prediction.\")\n"," file_names = [y for x in file_name_list for y in x]\n"," \n"," return file_names, preds_array"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"r1YKHBf4-NwT"},"source":["## submission.csv 생성"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:45:42.235310Z","start_time":"2021-04-16T19:44:30.499016Z"},"scrolled":true,"id":"Bz79_g8K-NwT","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620103458747,"user_tz":-540,"elapsed":512460,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"36e4f410-35c7-4b9c-9e4a-e66269f73292"},"source":["# sample_submisson.csv 열기\n","submission = pd.read_csv('./submission/sample_submission.csv', index_col=None)\n","\n","# test set에 대한 prediction\n","file_names, preds = test(model, test_loader, device)\n","\n","# PredictionString 대입\n","for file_name, string in zip(file_names, preds):\n"," submission = submission.append({\"image_id\" : file_name, \"PredictionString\" : ' '.join(str(e) for e in string.tolist())}, \n"," ignore_index=True)\n","\n","# submission.csv로 저장\n","submission.to_csv(\"./submission/re_pan_effb3_noisy_focal_madgrad_cosLR.csv\", index=False)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Start prediction.\n","End prediction.\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"Zgl7Ye7F-NwT"},"source":["## 제출까지\n","\n"]},{"cell_type":"code","metadata":{"id":"022E8AC7-NwT","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620103464861,"user_tz":-540,"elapsed":5218,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"1b00da55-62cc-4fa6-c823-83dcbaa871b0"},"source":["import json\n","import requests\n","import os\n","from urllib.parse import urlparse, parse_qsl, urlencode, urlunparse\n","\n","def submit(user_key='', file_path = '', desc=\"\"):\n"," if not user_key:\n"," raise Exception(\"No UserKey\" )\n"," url = urlparse('http://ec2-13-124-161-225.ap-northeast-2.compute.amazonaws.com:8000/api/v1/competition/28/presigned_url/?description=&hyperparameters={%22training%22:{},%22inference%22:{}}')\n"," qs = dict(parse_qsl(url.query))\n"," qs['description'] = desc\n"," parts = url._replace(query=urlencode(qs))\n"," url = urlunparse(parts)\n","\n"," print(url)\n"," headers = {\n"," 'Authorization': user_key\n"," }\n"," res = requests.get(url, headers=headers)\n"," print(res.text)\n"," data = json.loads(res.text)\n"," \n"," submit_url = data['url']\n"," body = {\n"," 'key':'app/Competitions/000028/Users/{}/Submissions/{}/output.csv'.format(str(data['submission']['user']).zfill(8),str(data['submission']['local_id']).zfill(4)),\n"," 'x-amz-algorithm':data['fields']['x-amz-algorithm'],\n"," 'x-amz-credential':data['fields']['x-amz-credential'],\n"," 'x-amz-date':data['fields']['x-amz-date'],\n"," 'policy':data['fields']['policy'],\n"," 'x-amz-signature':data['fields']['x-amz-signature']\n"," }\n"," requests.post(url=submit_url, data=body, files={'file': open(file_path, 'rb')})\n","\n","\n","####################################################################################\n","test_dir = \"/content/drive/MyDrive/Trash/code/submission\" # 수정 필요 : output 파일 폴더 \n","desc = 're_pan_effb3_noisy_focal_madgrad_cosLR' # 수정 필요 : 파일에 대한 설명\n","output_file = \"re_pan_effb3_noisy_focal_madgrad_cosLR.csv\" #수정 필요 : output 파일 \n","user_key = \"Bearer 7bb5f96452751a238ffaf91a93c4242bf9b72abe\" # 수정 필요 : Authorization \n","\n","\n","submit(user_key, os.path.join(test_dir, output_file),desc)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["http://ec2-13-124-161-225.ap-northeast-2.compute.amazonaws.com:8000/api/v1/competition/28/presigned_url/?hyperparameters=%7B%22training%22%3A%7B%7D%2C%22inference%22%3A%7B%7D%7D&description=re_pan_effb3_noisy_focal_madgrad_cosLR\n","{\"url\":\"https://prod-aistages-private.s3.amazonaws.com/\",\"fields\":{\"key\":\"app/Competitions/000028/Users/00000099/Submissions/0021/output.csv\",\"x-amz-algorithm\":\"AWS4-HMAC-SHA256\",\"x-amz-credential\":\"AKIA45LU4MHUJ7WLDQVO/20210504/ap-northeast-2/s3/aws4_request\",\"x-amz-date\":\"20210504T044420Z\",\"policy\":\"eyJleHBpcmF0aW9uIjogIjIwMjEtMDUtMDRUMDU6NDQ6MjBaIiwgImNvbmRpdGlvbnMiOiBbeyJidWNrZXQiOiAicHJvZC1haXN0YWdlcy1wcml2YXRlIn0sIHsia2V5IjogImFwcC9Db21wZXRpdGlvbnMvMDAwMDI4L1VzZXJzLzAwMDAwMDk5L1N1Ym1pc3Npb25zLzAwMjEvb3V0cHV0LmNzdiJ9LCB7IngtYW16LWFsZ29yaXRobSI6ICJBV1M0LUhNQUMtU0hBMjU2In0sIHsieC1hbXotY3JlZGVudGlhbCI6ICJBS0lBNDVMVTRNSFVKN1dMRFFWTy8yMDIxMDUwNC9hcC1ub3J0aGVhc3QtMi9zMy9hd3M0X3JlcXVlc3QifSwgeyJ4LWFtei1kYXRlIjogIjIwMjEwNTA0VDA0NDQyMFoifV19\",\"x-amz-signature\":\"f10073e85a92d49dacd44915305fdc789f99edede496d9b4f5029a16e422a52f\"},\"submission\":{\"id\":14867,\"phase\":\"Created\",\"type\":\"File\",\"local_id\":21,\"hyperparameters\":\"{\\\"training\\\": {}, \\\"inference\\\": {}}\",\"description\":\"re_pan_effb3_noisy_focal_madgrad_cosLR\",\"final\":false,\"created_at\":\"2021-05-04T13:44:20.427630+09:00\",\"updated_at\":\"2021-05-04T13:44:20.427663+09:00\",\"user\":99,\"competition\":28,\"image\":null}}\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"wPYl39uVqxL8"},"source":[""],"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/chanyub_seg/code/1_aug_horizontalflip.ipynb b/chanyub_seg/code/1_aug_horizontalflip.ipynb deleted file mode 100644 index 7d38b36..0000000 --- a/chanyub_seg/code/1_aug_horizontalflip.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"nbformat":4,"nbformat_minor":0,"metadata":{"hide_input":false,"kernelspec":{"display_name":"Python 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google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Mounted at /content/drive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"CHnx6ACo0SSR","executionInfo":{"status":"ok","timestamp":1620132706726,"user_tz":-540,"elapsed":6361,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"217ac587-f003-4a45-b7bf-5fb4779f2df9"},"source":["ls"],"execution_count":null,"outputs":[{"output_type":"stream","text":["\u001b[0m\u001b[01;34mdrive\u001b[0m/ \u001b[01;34msample_data\u001b[0m/\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rfBXtbCI0TPa","executionInfo":{"status":"ok","timestamp":1620132707310,"user_tz":-540,"elapsed":6522,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"b295ea79-eb8a-49b0-e504-4e791510358f"},"source":["cd drive/MyDrive/Trash/code"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/content/drive/MyDrive/Trash/code\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"X6b4uOxD0Vkp","executionInfo":{"status":"ok","timestamp":1620132716044,"user_tz":-540,"elapsed":14905,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"5825ba4b-81b5-4fab-eced-c52b5f8d6ab4"},"source":["!pip install albumentations==0.5.2"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Collecting albumentations==0.5.2\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/03/58/63fb1d742dc42d9ba2800ea741de1f2bc6bb05548d8724aa84794042eaf2/albumentations-0.5.2-py3-none-any.whl (72kB)\n","\r\u001b[K |████▌ | 10kB 19.5MB/s eta 0:00:01\r\u001b[K |█████████ | 20kB 26.9MB/s eta 0:00:01\r\u001b[K 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(37.6MB)\n","\u001b[K |████████████████████████████████| 37.6MB 1.3MB/s \n","\u001b[?25hCollecting imgaug>=0.4.0\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/66/b1/af3142c4a85cba6da9f4ebb5ff4e21e2616309552caca5e8acefe9840622/imgaug-0.4.0-py2.py3-none-any.whl (948kB)\n","\u001b[K |████████████████████████████████| 952kB 52.4MB/s \n","\u001b[?25hRequirement already satisfied: numpy>=1.11.1 in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (1.19.5)\n","Requirement already satisfied: pillow>=4.3.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations==0.5.2) (7.1.2)\n","Requirement already satisfied: PyWavelets>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations==0.5.2) (1.1.1)\n","Requirement already satisfied: imageio>=2.3.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations==0.5.2) (2.4.1)\n","Requirement already satisfied: matplotlib!=3.0.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations==0.5.2) (3.2.2)\n","Requirement already satisfied: networkx>=2.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations==0.5.2) (2.5.1)\n","Requirement already satisfied: opencv-python in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (4.1.2.30)\n","Requirement already satisfied: Shapely in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (1.7.1)\n","Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (1.15.0)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.16.1->albumentations==0.5.2) (1.3.1)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.16.1->albumentations==0.5.2) (0.10.0)\n","Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.16.1->albumentations==0.5.2) (2.4.7)\n","Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.16.1->albumentations==0.5.2) (2.8.1)\n","Requirement already satisfied: decorator<5,>=4.3 in /usr/local/lib/python3.7/dist-packages (from networkx>=2.0->scikit-image>=0.16.1->albumentations==0.5.2) (4.4.2)\n","Installing collected packages: opencv-python-headless, imgaug, albumentations\n"," Found existing installation: imgaug 0.2.9\n"," Uninstalling imgaug-0.2.9:\n"," Successfully uninstalled imgaug-0.2.9\n"," Found existing installation: albumentations 0.1.12\n"," Uninstalling albumentations-0.1.12:\n"," Successfully uninstalled albumentations-0.1.12\n","Successfully installed albumentations-0.5.2 imgaug-0.4.0 opencv-python-headless-4.5.1.48\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.826930Z","start_time":"2021-04-18T10:34:45.406686Z"},"scrolled":false,"colab":{"base_uri":"https://localhost:8080/"},"id":"HiN9b-Ly0I3p","executionInfo":{"status":"ok","timestamp":1620132720207,"user_tz":-540,"elapsed":11000,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"c9b4f6ef-683f-4d7e-f780-729ab6575981"},"source":["import os\n","import random\n","import time\n","import json\n","import warnings \n","warnings.filterwarnings('ignore')\n","\n","import torch\n","import torch.nn as nn\n","from torch.utils.data import Dataset, DataLoader\n","from utils import label_accuracy_score\n","import cv2\n","\n","import numpy as np\n","import pandas as pd\n","\n","# 전처리를 위한 라이브러리\n","from pycocotools.coco import COCO\n","import torchvision\n","import torchvision.transforms as transforms\n","\n","import albumentations as A\n","from albumentations.pytorch import ToTensorV2\n","\n","# 시각화를 위한 라이브러리\n","import matplotlib.pyplot as plt\n","import seaborn as sns; sns.set()\n","\n","plt.rcParams['axes.grid'] = False\n","\n","print('pytorch version: {}'.format(torch.__version__))\n","print('GPU 사용 가능 여부: {}'.format(torch.cuda.is_available()))\n","\n","print(torch.cuda.get_device_name(0))\n","print(torch.cuda.device_count())\n","\n","device = \"cuda\" if torch.cuda.is_available() else \"cpu\" # GPU 사용 가능 여부에 따라 device 정보 저장"],"execution_count":null,"outputs":[{"output_type":"stream","text":["pytorch version: 1.8.1+cu101\n","GPU 사용 가능 여부: True\n","Tesla V100-SXM2-16GB\n","1\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"Uuj6y7Ra0I3r"},"source":["## 하이퍼파라미터 세팅 및 seed 고정"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.841930Z","start_time":"2021-04-18T10:34:47.827931Z"},"id":"k-febRcn0I3r"},"source":["batch_size = 8 # Mini-batch size\n","num_epochs = 20\n","learning_rate = 0.0001"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.856930Z","start_time":"2021-04-18T10:34:47.842931Z"},"id":"YA3jAi2a0I3s"},"source":["# seed 고정\n","random_seed = 42\n","torch.manual_seed(random_seed)\n","torch.cuda.manual_seed(random_seed)\n","# torch.cuda.manual_seed_all(random_seed) # if use multi-GPU\n","torch.backends.cudnn.deterministic = True\n","torch.backends.cudnn.benchmark = False\n","np.random.seed(random_seed)\n","random.seed(random_seed)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"ePFcujAe0I3s"},"source":["## 학습 데이터 EDA"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.381961Z","start_time":"2021-04-18T10:34:47.857930Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"Ds0jp-pz0I3s","executionInfo":{"status":"ok","timestamp":1620132729909,"user_tz":-540,"elapsed":17407,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"3a45d987-cefc-457e-979a-22d2adb475cb"},"source":["%matplotlib inline\n","\n","dataset_path = '../input/data'\n","anns_file_path = dataset_path + '/' + 'train.json'\n","\n","# Read annotations\n","with open(anns_file_path, 'r') as f:\n"," dataset = json.loads(f.read())\n","\n","categories = dataset['categories']\n","anns = dataset['annotations']\n","imgs = dataset['images']\n","nr_cats = len(categories)\n","nr_annotations = len(anns)\n","nr_images = len(imgs)\n","\n","# Load categories and super categories\n","cat_names = []\n","super_cat_names = []\n","super_cat_ids = {}\n","super_cat_last_name = ''\n","nr_super_cats = 0\n","for cat_it in categories:\n"," cat_names.append(cat_it['name'])\n"," super_cat_name = cat_it['supercategory']\n"," # Adding new supercat\n"," if super_cat_name != super_cat_last_name:\n"," super_cat_names.append(super_cat_name)\n"," super_cat_ids[super_cat_name] = nr_super_cats\n"," super_cat_last_name = super_cat_name\n"," nr_super_cats += 1\n","\n","print('Number of super categories:', nr_super_cats)\n","print('Number of categories:', nr_cats)\n","print('Number of annotations:', nr_annotations)\n","print('Number of images:', nr_images)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Number of super categories: 11\n","Number of categories: 11\n","Number of annotations: 21116\n","Number of images: 2617\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.546964Z","start_time":"2021-04-18T10:34:51.382969Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/","height":355},"id":"SVmavtk00I3t","executionInfo":{"status":"ok","timestamp":1620132730805,"user_tz":-540,"elapsed":17112,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"e74c7d8f-d14e-4de7-8e62-fc361e9bf631"},"source":["# Count annotations\n","cat_histogram = np.zeros(nr_cats,dtype=int)\n","for ann in anns:\n"," cat_histogram[ann['category_id']] += 1\n","\n","# Initialize the matplotlib figure\n","f, ax = plt.subplots(figsize=(5,5))\n","\n","# Convert to DataFrame\n","df = pd.DataFrame({'Categories': cat_names, 'Number of annotations': cat_histogram})\n","df = df.sort_values('Number of annotations', 0, False)\n","\n","# Plot the histogram\n","plt.title(\"category distribution of train set \")\n","plot_1 = sns.barplot(x=\"Number of annotations\", y=\"Categories\", data=df, label=\"Total\", color=\"b\")"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.561965Z","start_time":"2021-04-18T10:34:51.547969Z"},"id":"xK3EYdyX0I3u"},"source":["# category labeling \n","sorted_temp_df = df.sort_index()\n","\n","# background = 0 에 해당되는 label 추가 후 기존들을 모두 label + 1 로 설정\n","sorted_df = pd.DataFrame([\"Backgroud\"], columns = [\"Categories\"])\n","sorted_df = sorted_df.append(sorted_temp_df, ignore_index=True)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.576961Z","start_time":"2021-04-18T10:34:51.562964Z"},"scrolled":false,"colab":{"base_uri":"https://localhost:8080/","height":421},"id":"9UQEjg8r0I3u","executionInfo":{"status":"ok","timestamp":1620132730815,"user_tz":-540,"elapsed":16415,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"de4031e6-f8a6-4bb2-b6c7-7a1d6feb7722"},"source":["# class (Categories) 에 따른 index 확인 (0~11 : 총 12개)\n","sorted_df"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
CategoriesNumber of annotations
0BackgroudNaN
1UNKNOWN128.0
2General trash2225.0
3Paper7448.0
4Paper pack527.0
5Metal449.0
6Glass488.0
7Plastic2472.0
8Styrofoam1074.0
9Plastic bag6114.0
10Battery50.0
11Clothing141.0
\n","
"],"text/plain":[" Categories Number of annotations\n","0 Backgroud NaN\n","1 UNKNOWN 128.0\n","2 General trash 2225.0\n","3 Paper 7448.0\n","4 Paper pack 527.0\n","5 Metal 449.0\n","6 Glass 488.0\n","7 Plastic 2472.0\n","8 Styrofoam 1074.0\n","9 Plastic bag 6114.0\n","10 Battery 50.0\n","11 Clothing 141.0"]},"metadata":{"tags":[]},"execution_count":11}]},{"cell_type":"markdown","metadata":{"id":"AHvcEEXh0I3u"},"source":["## 데이터 전처리 함수 정의 (Dataset)"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:52.693328Z","start_time":"2021-04-18T10:34:52.681328Z"},"id":"tBr2oTea0I3v"},"source":["category_names = list(sorted_df.Categories)\n","\n","def get_classname(classID, cats):\n"," for i in range(len(cats)):\n"," if cats[i]['id']==classID:\n"," return cats[i]['name']\n"," return \"None\"\n","\n","class CustomDataLoader(Dataset):\n"," \"\"\"COCO format\"\"\"\n"," def __init__(self, data_dir, mode = 'train', transform = None):\n"," super().__init__()\n"," self.mode = mode\n"," self.transform = transform\n"," self.coco = COCO(data_dir)\n"," \n"," def __getitem__(self, index: int):\n"," # dataset이 index되어 list처럼 동작\n"," image_id = self.coco.getImgIds(imgIds=index)\n"," image_infos = self.coco.loadImgs(image_id)[0]\n"," \n"," # cv2 를 활용하여 image 불러오기\n"," images = cv2.imread(os.path.join(dataset_path, image_infos['file_name']))\n"," images = cv2.cvtColor(images, cv2.COLOR_BGR2RGB).astype(np.float32)\n"," images /= 255.0\n"," \n"," if (self.mode in ('train', 'val')):\n"," ann_ids = self.coco.getAnnIds(imgIds=image_infos['id'])\n"," anns = self.coco.loadAnns(ann_ids)\n","\n"," # Load the categories in a variable\n"," cat_ids = self.coco.getCatIds()\n"," cats = self.coco.loadCats(cat_ids)\n","\n"," # masks : size가 (height x width)인 2D\n"," # 각각의 pixel 값에는 \"category id + 1\" 할당\n"," # Background = 0\n"," masks = np.zeros((image_infos[\"height\"], image_infos[\"width\"]))\n"," # Unknown = 1, General trash = 2, ... , Cigarette = 11\n"," for i in range(len(anns)):\n"," className = get_classname(anns[i]['category_id'], cats)\n"," pixel_value = category_names.index(className)\n"," masks = np.maximum(self.coco.annToMask(anns[i])*pixel_value, masks)\n"," masks = masks.astype(np.float32)\n","\n"," # transform -> albumentations 라이브러리 활용\n"," if self.transform is not None:\n"," transformed = self.transform(image=images, mask=masks)\n"," images = transformed[\"image\"]\n"," masks = transformed[\"mask\"]\n"," \n"," return images, masks, image_infos\n"," \n"," if self.mode == 'test':\n"," # transform -> albumentations 라이브러리 활용\n"," if self.transform is not None:\n"," transformed = self.transform(image=images)\n"," images = transformed[\"image\"]\n"," \n"," return images, image_infos\n"," \n"," \n"," def __len__(self) -> int:\n"," # 전체 dataset의 size를 return\n"," return len(self.coco.getImgIds())"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"PBcB4oQh0I3w"},"source":["## Dataset 정의 및 DataLoader 할당"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:58.823175Z","start_time":"2021-04-18T10:34:54.106233Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"LxAXSS-c0I3x","executionInfo":{"status":"ok","timestamp":1620132870296,"user_tz":-540,"elapsed":9147,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"fc5ee34a-3232-4401-e308-07e5996d498e"},"source":["# train.json / validation.json / test.json 디렉토리 설정\n","train_path = dataset_path + '/train.json'\n","val_path = dataset_path + '/val.json'\n","test_path = dataset_path + '/test.json'\n","\n","# collate_fn needs for batch\n","def collate_fn(batch):\n"," return tuple(zip(*batch))\n","\n","train_transform = A.Compose([\n"," A.augmentations.Resize(256,256),\n"," A.HorizontalFlip(),\n"," ToTensorV2()\n"," ])\n","\n","val_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","test_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","# create own Dataset 1 (skip)\n","# validation set을 직접 나누고 싶은 경우\n","# random_split 사용하여 data set을 8:2 로 분할\n","# train_size = int(0.8*len(dataset))\n","# val_size = int(len(dataset)-train_size)\n","# dataset = CustomDataLoader(data_dir=train_path, mode='train', transform=transform)\n","# train_dataset, val_dataset = torch.utils.data.random_split(dataset, [train_size, val_size])\n","\n","# create own Dataset 2\n","# train dataset\n","train_dataset = CustomDataLoader(data_dir=train_path, mode='train', transform=train_transform)\n","\n","# validation dataset\n","val_dataset = CustomDataLoader(data_dir=val_path, mode='val', transform=val_transform)\n","\n","# test dataset\n","test_dataset = CustomDataLoader(data_dir=test_path, mode='test', transform=test_transform)\n","\n","\n","# DataLoader\n","train_loader = torch.utils.data.DataLoader(dataset=train_dataset, \n"," batch_size=batch_size,\n"," shuffle=True,\n"," num_workers=4,\n"," collate_fn=collate_fn,\n"," drop_last=True)\n","\n","val_loader = torch.utils.data.DataLoader(dataset=val_dataset, \n"," batch_size=batch_size,\n"," shuffle=False,\n"," num_workers=4,\n"," collate_fn=collate_fn,\n"," drop_last=True) \n","\n","test_loader = torch.utils.data.DataLoader(dataset=test_dataset,\n"," batch_size=batch_size,\n"," num_workers=4,\n"," collate_fn=collate_fn)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["loading annotations into memory...\n","Done (t=3.95s)\n","creating index...\n","index created!\n","loading annotations into memory...\n","Done (t=3.38s)\n","creating index...\n","index created!\n","loading annotations into memory...\n","Done (t=0.60s)\n","creating index...\n","index created!\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-aaTrvBk0gKc","executionInfo":{"status":"ok","timestamp":1620132881653,"user_tz":-540,"elapsed":8535,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"7712ec3d-9d83-4238-825c-e5d11642d38c"},"source":["!pip install wandb"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Collecting wandb\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/67/5a/b037b50f9849212863a2fed313624d8f6f33ffa4ce89dc706e2a0e98c780/wandb-0.10.29-py2.py3-none-any.whl (2.1MB)\n","\u001b[K |████████████████████████████████| 2.1MB 15.7MB/s \n","\u001b[?25hCollecting sentry-sdk>=0.4.0\n","\u001b[?25l Downloading 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/root/.cache/pip/wheels/68/39/1a/5e402bdfdf004af1786c8b853fd92f8c4a04f22aad179654d1\n"," Building wheel for pathtools (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for pathtools: filename=pathtools-0.1.2-cp37-none-any.whl size=8786 sha256=ad71e82f0d7faa8583f26ec7f94040e0d95e64a0fcc85dcad77ad98de4a05925\n"," Stored in directory: /root/.cache/pip/wheels/0b/04/79/c3b0c3a0266a3cb4376da31e5bfe8bba0c489246968a68e843\n","Successfully built subprocess32 pathtools\n","Installing collected packages: sentry-sdk, shortuuid, smmap, gitdb, GitPython, subprocess32, docker-pycreds, configparser, pathtools, wandb\n","Successfully installed GitPython-3.1.14 configparser-5.0.2 docker-pycreds-0.4.0 gitdb-4.0.7 pathtools-0.1.2 sentry-sdk-1.0.0 shortuuid-1.0.1 smmap-4.0.0 subprocess32-3.5.4 wandb-0.10.29\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":153},"id":"V6tsUEOy2HFb","executionInfo":{"status":"ok","timestamp":1620132893825,"user_tz":-540,"elapsed":11417,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"c7939dcf-6ed7-4a9d-ca13-c3a5480bc295"},"source":["import wandb\n","\n","proj_name = '1_aug_horizontalflip'\n","\n","wandb.init(project='chanyub',name=proj_name)\n","\n","config = wandb.config\n","config.learning_rate = 0.01"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"application/javascript":["\n"," window._wandbApiKey = new Promise((resolve, reject) => {\n"," function loadScript(url) {\n"," return new Promise(function(resolve, reject) {\n"," let newScript = document.createElement(\"script\");\n"," newScript.onerror = reject;\n"," newScript.onload = resolve;\n"," document.body.appendChild(newScript);\n"," newScript.src = url;\n"," });\n"," }\n"," loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n"," const iframe = document.createElement('iframe')\n"," iframe.style.cssText = \"width:0;height:0;border:none\"\n"," document.body.appendChild(iframe)\n"," const handshake = new Postmate({\n"," container: iframe,\n"," url: 'https://wandb.ai/authorize'\n"," });\n"," const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n"," handshake.then(function(child) {\n"," child.on('authorize', data => {\n"," clearTimeout(timeout)\n"," resolve(data)\n"," });\n"," });\n"," })\n"," });\n"," "],"text/plain":[""]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"],"name":"stderr"},{"output_type":"display_data","data":{"text/html":["\n"," Tracking run with wandb version 0.10.29
\n"," Syncing run 1_aug_horizontalflip to Weights & Biases (Documentation).
\n"," Project page: https://wandb.ai/pstage12/chanyub
\n"," Run page: https://wandb.ai/pstage12/chanyub/runs/3lm5iyar
\n"," Run data is saved locally in /content/drive/My Drive/Trash/code/wandb/run-20210504_125450-3lm5iyar

\n"," "],"text/plain":[""]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"71t0S3di0I33"},"source":["## baseline model\n","\n","### [TODO] 코드 구현 UNet++ \n","\n","- 출처 : https://jinglescode.github.io/2019/12/02/biomedical-image-segmentation-u-net-nested/"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"wPRySrgMK3oT","executionInfo":{"status":"ok","timestamp":1620132899027,"user_tz":-540,"elapsed":14131,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"52051499-a702-4aff-b50c-c32ff1b5f50d"},"source":["!pip install segmentation_models_pytorch"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Collecting segmentation_models_pytorch\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/65/54/8953f9f7ee9d451b0f3be8d635aa3a654579abf898d17502a090efe1155a/segmentation_models_pytorch-0.1.3-py3-none-any.whl (66kB)\n","\u001b[K |████████████████████████████████| 71kB 3.3MB/s \n","\u001b[?25hCollecting pretrainedmodels==0.7.4\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/84/0e/be6a0e58447ac16c938799d49bfb5fb7a80ac35e137547fc6cee2c08c4cf/pretrainedmodels-0.7.4.tar.gz (58kB)\n","\u001b[K |████████████████████████████████| 61kB 8.1MB/s \n","\u001b[?25hCollecting timm==0.3.2\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/51/2d/39ecc56fbb202e1891c317e8e44667299bc3b0762ea2ed6aaaa2c2f6613c/timm-0.3.2-py3-none-any.whl (244kB)\n","\u001b[K |████████████████████████████████| 245kB 41.5MB/s \n","\u001b[?25hCollecting efficientnet-pytorch==0.6.3\n"," Downloading 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efficientnet-pytorch: filename=efficientnet_pytorch-0.6.3-cp37-none-any.whl size=12420 sha256=1bad12b8fc0ee313fde7abbae5c39e3c509dff3916c1876d36cae1d42d9fd629\n"," Stored in directory: /root/.cache/pip/wheels/42/1e/a9/2a578ba9ad04e776e80bf0f70d8a7f4c29ec0718b92d8f6ccd\n","Successfully built pretrainedmodels efficientnet-pytorch\n","Installing collected packages: munch, pretrainedmodels, timm, efficientnet-pytorch, segmentation-models-pytorch\n","Successfully installed efficientnet-pytorch-0.6.3 munch-2.5.0 pretrainedmodels-0.7.4 segmentation-models-pytorch-0.1.3 timm-0.3.2\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:11.634792Z","start_time":"2021-04-18T16:16:05.875817Z"},"colab":{"base_uri":"https://localhost:8080/","height":117,"referenced_widgets":["cf7a87e3dff04544a0ba8c6dd1a83a78","2a3dbbedbe344c3a8ae744a4edb0da86","985402c5c3c84836b82366764e2358ba","30f00f6bdb554af4a8ef3dc8229df621","cab1f7b1556f4600aaf639bd020ab75a","b10c8b0947764856a2a717c8cb108d5a","2956a264e74e402facea808eb7c5c4a7","8bd12f43c92d49fd93350b6e95a11f99"]},"id":"MJ2vs-Y_0I35","executionInfo":{"status":"ok","timestamp":1620132911480,"user_tz":-540,"elapsed":23755,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"53865aea-0505-4b28-a1f7-9aee739c65ec"},"source":["# 구현된 model에 임의의 input을 넣어 output이 잘 나오는지 test\n","import segmentation_models_pytorch as smp\n","\n","model = smp.UnetPlusPlus(classes=12)\n","x = torch.randn([1, 3, 512, 512])\n","print(\"input shape : \", x.shape)\n","out = model(x).to(device)\n","print(\"output shape : \", out.size())\n","\n","model = model.to(device)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Downloading: \"https://download.pytorch.org/models/resnet34-333f7ec4.pth\" to /root/.cache/torch/hub/checkpoints/resnet34-333f7ec4.pth\n"],"name":"stderr"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"cf7a87e3dff04544a0ba8c6dd1a83a78","version_minor":0,"version_major":2},"text/plain":["HBox(children=(FloatProgress(value=0.0, max=87306240.0), HTML(value='')))"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\n","input shape : torch.Size([1, 3, 512, 512])\n","output shape : torch.Size([1, 12, 512, 512])\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"SgM4SGqL0I35"},"source":["## train, validation, test 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.104200Z","start_time":"2021-04-18T16:16:18.093174Z"},"id":"Dl6skKCT0I35"},"source":["def train(num_epochs, model, data_loader, val_loader, criterion, optimizer, saved_dir, val_every, device):\n"," print('Start training..')\n"," best_loss = 9999999\n"," best_miou = 0\n"," for epoch in range(num_epochs):\n"," model.train()\n"," for step, (images, masks, _) in enumerate(data_loader):\n"," images = torch.stack(images) # (batch, channel, height, width)\n"," masks = torch.stack(masks).long() # (batch, channel, height, width)\n"," \n"," # gpu 연산을 위해 device 할당\n"," images, masks = images.to(device), masks.to(device)\n"," \n"," # inference\n"," outputs = model(images)\n"," \n"," # loss 계산 (cross entropy loss)\n"," loss = criterion(outputs, masks)\n"," optimizer.zero_grad()\n"," loss.backward()\n"," optimizer.step()\n"," \n"," # lr_scheduler.step()\n"," \n"," # step 주기에 따른 loss 출력\n"," if (step + 1) % 25 == 0:\n"," print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(\n"," epoch+1, num_epochs, step+1, len(train_loader), loss.item()))\n"," \n"," # validation 주기에 따른 loss 출력 및 best model 저장\n"," if (epoch + 1) % val_every == 0:\n","# avrg_loss = validation(epoch + 1, model, val_loader, criterion, device)\n","# if avrg_loss < best_loss:\n","# print('Best performance at epoch: {}'.format(epoch + 1))\n","# print('Save model in', saved_dir)\n","# best_loss = avrg_loss\n","# wandb.log({'best_loss': best_loss})\n","# save_model(model, saved_dir)\n"," avrg_miou = validation(epoch + 1, model, val_loader, criterion, device)\n"," if avrg_miou > best_miou:\n"," print('Best performance at epoch: {}'.format(epoch + 1))\n"," print('Save model in', saved_dir)\n"," best_miou = avrg_miou\n"," wandb.log({'best_miou': best_miou})\n"," save_model(model, saved_dir)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.285795Z","start_time":"2021-04-18T16:16:18.267686Z"},"id":"Yw_3xbyj0I36"},"source":["def validation(epoch, model, data_loader, criterion, device):\n"," print('Start validation #{}'.format(epoch))\n"," model.eval()\n"," with torch.no_grad():\n"," total_loss = 0\n"," cnt = 0\n"," mIoU_list = []\n"," for step, (images, masks, _) in enumerate(data_loader):\n"," \n"," images = torch.stack(images) # (batch, channel, height, width)\n"," masks = torch.stack(masks).long() # (batch, channel, height, width)\n","\n"," images, masks = images.to(device), masks.to(device) \n","\n"," outputs = model(images)\n"," loss = criterion(outputs, masks)\n"," total_loss += loss\n"," cnt += 1\n"," \n","# print(outputs.shape)\n","# print(masks.shape)\n","# wandb.log(wandb.Image(images, masks={\n","# \"predictions\" : {\n","# \"mask_data\" : torch.squeeze(torch.squeeze(outputs, 0),1),\n","# \"class_labels\" : classes_dict\n","# },\n","# \"ground_truth\" : {\n","# \"mask_data\" : torch.squeeze(masks, 0),\n","# \"class_labels\" : classes_dict\n","# }\n","# }))\n"," \n"," outputs = torch.argmax(outputs.squeeze(), dim=1).detach().cpu().numpy()\n","\n"," mIoU = label_accuracy_score(masks.detach().cpu().numpy(), outputs, n_class=12)[2]\n"," mIoU_list.append(mIoU)\n"," \n"," avrg_loss = total_loss / cnt\n"," avrg_mIoU = np.mean(mIoU_list)\n"," print('Validation #{} Average Loss: {:.4f}, mIoU: {:.4f}'.format(epoch, avrg_loss, np.mean(mIoU_list)))\n"," wandb.log({'Train Loss':loss.item(), 'Val Loss':avrg_loss , 'Val mIoU':np.mean(mIoU_list)})\n","# return avrg_loss\n"," return avrg_mIoU"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"b92qGwBc0I37"},"source":["## 모델 저장 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.909918Z","start_time":"2021-04-18T16:16:18.898918Z"},"id":"H50hk0za0I37"},"source":["# 모델 저장 함수 정의\n","val_every = 1 \n","\n","saved_dir = './saved'\n","if not os.path.isdir(saved_dir): \n"," os.mkdir(saved_dir)\n"," \n","def save_model(model, saved_dir, file_name='1_aug_horizontalflip.pt'):\n"," check_point = {'net': model.state_dict()}\n"," output_path = os.path.join(saved_dir, file_name)\n"," torch.save(model.state_dict(), output_path)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"UwzGmX190I37"},"source":["## 모델 생성 및 Loss function, Optimizer 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:19.698902Z","start_time":"2021-04-18T16:16:19.694902Z"},"id":"hOKlPrNn0I37"},"source":["# Loss function 정의\n","criterion = nn.CrossEntropyLoss()\n","\n","# Optimizer 정의\n","optimizer = torch.optim.Adam(params = model.parameters(), lr = learning_rate, weight_decay=1e-6)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"start_time":"2021-04-18T16:16:20.331Z"},"scrolled":false,"colab":{"base_uri":"https://localhost:8080/"},"id":"MSReHpkI0I38","executionInfo":{"status":"ok","timestamp":1620134990016,"user_tz":-540,"elapsed":2074957,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"0072e153-da32-4aef-c03e-d553555c7dcc"},"source":["train(num_epochs, model, train_loader, val_loader, criterion, optimizer, saved_dir, val_every, device)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Start training..\n","Epoch [1/20], Step [25/327], Loss: 2.2053\n","Epoch [1/20], Step [50/327], Loss: 1.8790\n","Epoch [1/20], Step [75/327], Loss: 1.6039\n","Epoch [1/20], Step [100/327], Loss: 1.3640\n","Epoch [1/20], Step [125/327], Loss: 1.1161\n","Epoch [1/20], Step [150/327], Loss: 1.1771\n","Epoch [1/20], Step [175/327], Loss: 1.1231\n","Epoch [1/20], Step [200/327], Loss: 0.9199\n","Epoch [1/20], Step [225/327], Loss: 0.8309\n","Epoch [1/20], Step [250/327], Loss: 0.9420\n","Epoch [1/20], Step [275/327], Loss: 0.8516\n","Epoch [1/20], Step [300/327], Loss: 0.7088\n","Epoch [1/20], Step [325/327], Loss: 0.7155\n","Start validation #1\n","Validation #1 Average Loss: 0.7119, mIoU: 0.1655\n","Best performance at epoch: 1\n","Save model in ./saved\n","Epoch [2/20], Step [25/327], Loss: 0.6291\n","Epoch [2/20], Step [50/327], Loss: 1.0634\n","Epoch [2/20], Step [75/327], Loss: 0.4929\n","Epoch [2/20], Step [100/327], Loss: 0.7236\n","Epoch [2/20], Step [125/327], Loss: 0.6230\n","Epoch [2/20], Step [150/327], Loss: 1.2509\n","Epoch [2/20], Step [175/327], Loss: 0.9349\n","Epoch [2/20], Step [200/327], Loss: 0.6692\n","Epoch [2/20], Step [225/327], Loss: 0.4141\n","Epoch [2/20], Step [250/327], Loss: 0.8065\n","Epoch [2/20], Step [275/327], Loss: 0.5188\n","Epoch [2/20], Step [300/327], Loss: 0.4020\n","Epoch [2/20], Step [325/327], Loss: 0.4123\n","Start validation #2\n","Validation #2 Average Loss: 0.5514, mIoU: 0.2294\n","Best performance at epoch: 2\n","Save model in ./saved\n","Epoch [3/20], Step [25/327], Loss: 0.4447\n","Epoch [3/20], Step [50/327], Loss: 0.3934\n","Epoch [3/20], Step [75/327], Loss: 0.3741\n","Epoch [3/20], Step [100/327], Loss: 0.5393\n","Epoch [3/20], Step [125/327], Loss: 0.4034\n","Epoch [3/20], Step [150/327], Loss: 0.4362\n","Epoch [3/20], Step [175/327], Loss: 0.5671\n","Epoch [3/20], Step [200/327], Loss: 0.4820\n","Epoch [3/20], Step [225/327], Loss: 0.9861\n","Epoch [3/20], Step [250/327], Loss: 0.4899\n","Epoch [3/20], Step [275/327], Loss: 0.6173\n","Epoch [3/20], Step [300/327], Loss: 0.3655\n","Epoch [3/20], Step [325/327], Loss: 0.3882\n","Start validation #3\n","Validation #3 Average Loss: 0.5135, mIoU: 0.2928\n","Best performance at epoch: 3\n","Save model in ./saved\n","Epoch [4/20], Step [25/327], Loss: 0.3325\n","Epoch [4/20], Step [50/327], Loss: 0.5056\n","Epoch [4/20], Step [75/327], Loss: 0.3833\n","Epoch [4/20], Step [100/327], Loss: 0.3128\n","Epoch [4/20], Step [125/327], Loss: 0.3688\n","Epoch [4/20], Step [150/327], Loss: 0.3940\n","Epoch [4/20], Step [175/327], Loss: 0.4049\n","Epoch [4/20], Step [200/327], Loss: 0.2225\n","Epoch [4/20], Step [225/327], Loss: 0.8314\n","Epoch [4/20], Step [250/327], Loss: 0.6908\n","Epoch [4/20], Step [275/327], Loss: 0.2589\n","Epoch [4/20], Step [300/327], Loss: 0.5707\n","Epoch [4/20], Step [325/327], Loss: 0.3162\n","Start validation #4\n","Validation #4 Average Loss: 0.5078, mIoU: 0.2746\n","Epoch [5/20], Step [25/327], Loss: 0.5859\n","Epoch [5/20], Step [50/327], Loss: 0.4822\n","Epoch [5/20], Step [75/327], Loss: 0.5563\n","Epoch [5/20], Step [100/327], Loss: 0.2045\n","Epoch [5/20], Step [125/327], Loss: 0.2608\n","Epoch [5/20], Step [150/327], Loss: 0.2802\n","Epoch [5/20], Step [175/327], Loss: 0.2051\n","Epoch [5/20], Step [200/327], Loss: 0.5182\n","Epoch [5/20], Step [225/327], Loss: 0.4202\n","Epoch [5/20], Step [250/327], Loss: 0.4205\n","Epoch [5/20], Step [275/327], Loss: 0.3791\n","Epoch [5/20], Step [300/327], Loss: 0.2353\n","Epoch [5/20], Step [325/327], Loss: 0.5300\n","Start validation #5\n","Validation #5 Average Loss: 0.4799, mIoU: 0.2760\n","Epoch [6/20], Step [25/327], Loss: 0.3078\n","Epoch [6/20], Step [50/327], Loss: 0.4254\n","Epoch [6/20], Step [75/327], Loss: 0.4703\n","Epoch [6/20], Step [100/327], Loss: 0.1232\n","Epoch [6/20], Step [125/327], Loss: 0.3178\n","Epoch [6/20], Step [150/327], Loss: 0.2442\n","Epoch [6/20], Step [175/327], Loss: 0.5558\n","Epoch [6/20], Step [200/327], Loss: 0.2966\n","Epoch [6/20], Step [225/327], Loss: 0.5579\n","Epoch [6/20], Step [250/327], Loss: 0.1997\n","Epoch [6/20], Step [275/327], Loss: 0.2556\n","Epoch [6/20], Step [300/327], Loss: 0.2713\n","Epoch [6/20], Step [325/327], Loss: 0.3453\n","Start validation #6\n","Validation #6 Average Loss: 0.4834, mIoU: 0.2828\n","Epoch [7/20], Step [25/327], Loss: 0.1998\n","Epoch [7/20], Step [50/327], Loss: 0.3151\n","Epoch [7/20], Step [75/327], Loss: 0.4902\n","Epoch [7/20], Step [100/327], Loss: 0.2670\n","Epoch [7/20], Step [125/327], Loss: 0.4886\n","Epoch [7/20], Step [150/327], Loss: 0.2189\n","Epoch [7/20], Step [175/327], Loss: 0.2341\n","Epoch [7/20], Step [200/327], Loss: 0.1849\n","Epoch [7/20], Step [225/327], Loss: 0.1911\n","Epoch [7/20], Step [250/327], Loss: 0.2837\n","Epoch [7/20], Step [275/327], Loss: 0.2381\n","Epoch [7/20], Step [300/327], Loss: 0.3363\n","Epoch [7/20], Step [325/327], Loss: 0.2232\n","Start validation #7\n","Validation #7 Average Loss: 0.4462, mIoU: 0.2779\n","Epoch [8/20], Step [25/327], Loss: 0.3818\n","Epoch [8/20], Step [50/327], Loss: 0.3407\n","Epoch [8/20], Step [75/327], Loss: 0.3566\n","Epoch [8/20], Step [100/327], Loss: 0.2639\n","Epoch [8/20], Step [125/327], Loss: 0.3061\n","Epoch [8/20], Step [150/327], Loss: 0.2257\n","Epoch [8/20], Step [175/327], Loss: 0.7492\n","Epoch [8/20], Step [200/327], Loss: 0.3509\n","Epoch [8/20], Step [225/327], Loss: 0.3424\n","Epoch [8/20], Step [250/327], Loss: 0.4519\n","Epoch [8/20], Step [275/327], Loss: 0.6323\n","Epoch [8/20], Step [300/327], Loss: 0.3155\n","Epoch [8/20], Step [325/327], Loss: 0.3296\n","Start validation #8\n","Validation #8 Average Loss: 0.4694, mIoU: 0.2671\n","Epoch [9/20], Step [25/327], Loss: 0.3578\n","Epoch [9/20], Step [50/327], Loss: 0.1441\n","Epoch [9/20], Step [75/327], Loss: 0.3057\n","Epoch [9/20], Step [100/327], Loss: 0.2268\n","Epoch [9/20], Step [125/327], Loss: 0.1979\n","Epoch [9/20], Step [150/327], Loss: 0.6256\n","Epoch [9/20], Step [175/327], Loss: 0.3125\n","Epoch [9/20], Step [200/327], Loss: 0.1649\n","Epoch [9/20], Step [225/327], Loss: 0.2530\n","Epoch [9/20], Step [250/327], Loss: 0.2548\n","Epoch [9/20], Step [275/327], Loss: 0.1905\n","Epoch [9/20], Step [300/327], Loss: 0.2293\n","Epoch [9/20], Step [325/327], Loss: 0.2053\n","Start validation #9\n","Validation #9 Average Loss: 0.4622, mIoU: 0.2736\n","Epoch [10/20], Step [25/327], Loss: 0.5379\n","Epoch [10/20], Step [50/327], Loss: 0.1715\n","Epoch [10/20], Step [75/327], Loss: 0.3082\n","Epoch [10/20], Step [100/327], Loss: 0.2744\n","Epoch [10/20], Step [125/327], Loss: 0.2901\n","Epoch [10/20], Step [150/327], Loss: 0.1068\n","Epoch [10/20], Step [175/327], Loss: 0.2132\n","Epoch [10/20], Step [200/327], Loss: 0.2004\n","Epoch [10/20], Step [225/327], Loss: 0.2180\n","Epoch [10/20], Step [250/327], Loss: 0.3251\n","Epoch [10/20], Step [275/327], Loss: 0.1358\n","Epoch [10/20], Step [300/327], Loss: 0.2626\n","Epoch [10/20], Step [325/327], Loss: 0.1492\n","Start validation #10\n","Validation #10 Average Loss: 0.4555, mIoU: 0.2896\n","Epoch [11/20], Step [25/327], Loss: 0.1867\n","Epoch [11/20], Step [50/327], Loss: 0.4728\n","Epoch [11/20], Step [75/327], Loss: 0.1192\n","Epoch [11/20], Step [100/327], Loss: 0.2495\n","Epoch [11/20], Step [125/327], Loss: 0.1964\n","Epoch [11/20], Step [150/327], Loss: 0.3213\n","Epoch [11/20], Step [175/327], Loss: 0.0853\n","Epoch [11/20], Step [200/327], Loss: 0.3326\n","Epoch [11/20], Step [225/327], Loss: 0.1236\n","Epoch [11/20], Step [250/327], Loss: 0.1677\n","Epoch [11/20], Step [275/327], Loss: 0.0998\n","Epoch [11/20], Step [300/327], Loss: 0.3528\n","Epoch [11/20], Step [325/327], Loss: 0.1217\n","Start validation #11\n","Validation #11 Average Loss: 0.5132, mIoU: 0.2735\n","Epoch [12/20], Step [25/327], Loss: 0.2013\n","Epoch [12/20], Step [50/327], Loss: 0.1395\n","Epoch [12/20], Step [75/327], Loss: 0.0870\n","Epoch [12/20], Step [100/327], Loss: 0.2687\n","Epoch [12/20], Step [125/327], Loss: 0.1004\n","Epoch [12/20], Step [150/327], Loss: 0.4187\n","Epoch [12/20], Step [175/327], Loss: 0.3621\n","Epoch [12/20], Step [200/327], Loss: 0.3244\n","Epoch [12/20], Step [225/327], Loss: 0.1667\n","Epoch [12/20], Step [250/327], Loss: 0.2107\n","Epoch [12/20], Step [275/327], Loss: 0.1215\n","Epoch [12/20], Step [300/327], Loss: 0.1936\n","Epoch [12/20], Step [325/327], Loss: 0.1555\n","Start validation #12\n","Validation #12 Average Loss: 0.4916, mIoU: 0.2786\n","Epoch [13/20], Step [25/327], Loss: 0.1517\n","Epoch [13/20], Step [50/327], Loss: 0.2444\n","Epoch [13/20], Step [75/327], Loss: 0.1703\n","Epoch [13/20], Step [100/327], Loss: 0.3786\n","Epoch [13/20], Step [125/327], Loss: 0.1960\n","Epoch [13/20], Step [150/327], Loss: 0.1218\n","Epoch [13/20], Step [175/327], Loss: 0.2644\n","Epoch [13/20], Step [200/327], Loss: 0.1507\n","Epoch [13/20], Step [225/327], Loss: 0.3827\n","Epoch [13/20], Step [250/327], Loss: 0.2961\n","Epoch [13/20], Step [275/327], Loss: 0.1476\n","Epoch [13/20], Step [300/327], Loss: 0.3425\n","Epoch [13/20], Step [325/327], Loss: 0.2503\n","Start validation #13\n","Validation #13 Average Loss: 0.5190, mIoU: 0.2910\n","Epoch [14/20], Step [25/327], Loss: 0.2233\n","Epoch [14/20], Step [50/327], Loss: 0.1177\n","Epoch [14/20], Step [75/327], Loss: 0.2174\n","Epoch [14/20], Step [100/327], Loss: 0.2842\n","Epoch [14/20], Step [125/327], Loss: 0.2908\n","Epoch [14/20], Step [150/327], Loss: 0.1361\n","Epoch [14/20], Step [175/327], Loss: 0.1516\n","Epoch [14/20], Step [200/327], Loss: 0.1475\n","Epoch [14/20], Step [225/327], Loss: 0.2228\n","Epoch [14/20], Step [250/327], Loss: 0.2225\n","Epoch [14/20], Step [275/327], Loss: 0.3283\n","Epoch [14/20], Step [300/327], Loss: 0.3217\n","Epoch [14/20], Step [325/327], Loss: 0.1748\n","Start validation #14\n","Validation #14 Average Loss: 0.4932, mIoU: 0.2830\n","Epoch [15/20], Step [25/327], Loss: 0.0922\n","Epoch [15/20], Step [50/327], Loss: 0.4184\n","Epoch [15/20], Step [75/327], Loss: 0.1957\n","Epoch [15/20], Step [100/327], Loss: 0.1116\n","Epoch [15/20], Step [125/327], Loss: 0.0879\n","Epoch [15/20], Step [150/327], Loss: 0.2783\n","Epoch [15/20], Step [175/327], Loss: 0.1048\n","Epoch [15/20], Step [200/327], Loss: 0.1541\n","Epoch [15/20], Step [225/327], Loss: 0.1685\n","Epoch [15/20], Step [250/327], Loss: 0.1462\n","Epoch [15/20], Step [275/327], Loss: 0.1694\n","Epoch [15/20], Step [300/327], Loss: 0.1323\n","Epoch [15/20], Step [325/327], Loss: 0.3003\n","Start validation #15\n","Validation #15 Average Loss: 0.5168, mIoU: 0.2875\n","Epoch [16/20], Step [25/327], Loss: 0.3233\n","Epoch [16/20], Step [50/327], Loss: 0.1288\n","Epoch [16/20], Step [75/327], Loss: 0.1541\n","Epoch [16/20], Step [100/327], Loss: 0.3170\n","Epoch [16/20], Step [125/327], Loss: 0.1183\n","Epoch [16/20], Step [150/327], Loss: 0.1345\n","Epoch [16/20], Step [175/327], Loss: 0.1607\n","Epoch [16/20], Step [200/327], Loss: 0.2143\n","Epoch [16/20], Step [225/327], Loss: 0.1646\n","Epoch [16/20], Step [250/327], Loss: 0.4013\n","Epoch [16/20], Step [275/327], Loss: 0.2261\n","Epoch [16/20], Step [300/327], Loss: 0.2334\n","Epoch [16/20], Step [325/327], Loss: 0.1216\n","Start validation #16\n","Validation #16 Average Loss: 0.5013, mIoU: 0.3027\n","Best performance at epoch: 16\n","Save model in ./saved\n","Epoch [17/20], Step [25/327], Loss: 0.1628\n","Epoch [17/20], Step [50/327], Loss: 0.0997\n","Epoch [17/20], Step [75/327], Loss: 0.1981\n","Epoch [17/20], Step [100/327], Loss: 0.1692\n","Epoch [17/20], Step [125/327], Loss: 0.1136\n","Epoch [17/20], Step [150/327], Loss: 0.3007\n","Epoch [17/20], Step [175/327], Loss: 0.1423\n","Epoch [17/20], Step [200/327], Loss: 0.1782\n","Epoch [17/20], Step [225/327], Loss: 0.1196\n","Epoch [17/20], Step [250/327], Loss: 0.3560\n","Epoch [17/20], Step [275/327], Loss: 0.2081\n","Epoch [17/20], Step [300/327], Loss: 0.3163\n","Epoch [17/20], Step [325/327], Loss: 0.1000\n","Start validation #17\n","Validation #17 Average Loss: 0.5243, mIoU: 0.2916\n","Epoch [18/20], Step [25/327], Loss: 0.2045\n","Epoch [18/20], Step [50/327], Loss: 0.1023\n","Epoch [18/20], Step [75/327], Loss: 0.0987\n","Epoch [18/20], Step [100/327], Loss: 0.2046\n","Epoch [18/20], Step [125/327], Loss: 0.2846\n","Epoch [18/20], Step [150/327], Loss: 0.2421\n","Epoch [18/20], Step [175/327], Loss: 0.1370\n","Epoch [18/20], Step [200/327], Loss: 0.1809\n","Epoch [18/20], Step [225/327], Loss: 0.1713\n","Epoch [18/20], Step [250/327], Loss: 0.1793\n","Epoch [18/20], Step [275/327], Loss: 0.1736\n","Epoch [18/20], Step [300/327], Loss: 0.0845\n","Epoch [18/20], Step [325/327], Loss: 0.1728\n","Start validation #18\n","Validation #18 Average Loss: 0.5289, mIoU: 0.2931\n","Epoch [19/20], Step [25/327], Loss: 0.2348\n","Epoch [19/20], Step [50/327], Loss: 0.1033\n","Epoch [19/20], Step [75/327], Loss: 0.1203\n","Epoch [19/20], Step [100/327], Loss: 0.1976\n","Epoch [19/20], Step [125/327], Loss: 0.1121\n","Epoch [19/20], Step [150/327], Loss: 0.2003\n","Epoch [19/20], Step [175/327], Loss: 0.1442\n","Epoch [19/20], Step [200/327], Loss: 0.1589\n","Epoch [19/20], Step [225/327], Loss: 0.1452\n","Epoch [19/20], Step [250/327], Loss: 0.2423\n","Epoch [19/20], Step [275/327], Loss: 0.2779\n","Epoch [19/20], Step [300/327], Loss: 0.1064\n","Epoch [19/20], Step [325/327], Loss: 0.0948\n","Start validation #19\n","Validation #19 Average Loss: 0.5740, mIoU: 0.2829\n","Epoch [20/20], Step [25/327], Loss: 0.0916\n","Epoch [20/20], Step [50/327], Loss: 0.1461\n","Epoch [20/20], Step [75/327], Loss: 0.1733\n","Epoch [20/20], Step [100/327], Loss: 0.2311\n","Epoch [20/20], Step [125/327], Loss: 0.0852\n","Epoch [20/20], Step [150/327], Loss: 0.0530\n","Epoch [20/20], Step [175/327], Loss: 0.1867\n","Epoch [20/20], Step [200/327], Loss: 0.0915\n","Epoch [20/20], Step [225/327], Loss: 0.1110\n","Epoch [20/20], Step [250/327], Loss: 0.1633\n","Epoch [20/20], Step [275/327], Loss: 0.0825\n","Epoch [20/20], Step [300/327], Loss: 0.2299\n","Epoch [20/20], Step [325/327], Loss: 0.1036\n","Start validation #20\n","Validation #20 Average Loss: 0.5799, mIoU: 0.2901\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"jOE-56p5MkXM"},"source":["# Sleep for a few seconds.\n","import time\n","time.sleep(2)\n","# Play an audio beep. Any audio URL will do.\n","from google.colab import output\n","output.eval_js('new Audio(\"https://upload.wikimedia.org/wikipedia/commons/0/05/Beep-09.ogg\").play()')"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"6IqKsZ4u0I38"},"source":["## 저장된 model 불러오기 (학습된 이후) "]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:21.050200Z","start_time":"2021-04-16T19:44:20.802200Z"},"scrolled":true,"id":"dl5dIeHB0I38"},"source":["# best model 저장된 경로\n","model_path = './saved/UNetPP_best_model.pt'\n","\n","# best model 불러오기\n","checkpoint = torch.load(model_path, map_location=device)\n","model.load_state_dict(checkpoint)\n","\n","# 추론을 실행하기 전에는 반드시 설정 (batch normalization, dropout 를 평가 모드로 설정)\n","# model.eval()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:24.939227Z","start_time":"2021-04-16T19:44:24.518228Z"},"id":"mOUcjOP20I38"},"source":["# 첫번째 batch의 추론 결과 확인\n","for imgs, image_infos in test_loader:\n"," image_infos = image_infos\n"," temp_images = imgs\n"," \n"," model.eval()\n"," # inference\n"," outs = model(torch.stack(temp_images).to(device))\n"," oms = torch.argmax(outs.squeeze(), dim=1).detach().cpu().numpy()\n"," \n"," break\n","\n","i = 3\n","fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(16, 16))\n","\n","print('Shape of Original Image :', list(temp_images[i].shape))\n","print('Shape of Predicted : ', list(oms[i].shape))\n","print('Unique values, category of transformed mask : \\n', [{int(i),category_names[int(i)]} for i in list(np.unique(oms[i]))])\n","\n","# Original image\n","ax1.imshow(temp_images[i].permute([1,2,0]))\n","ax1.grid(False)\n","ax1.set_title(\"Original image : {}\".format(image_infos[i]['file_name']), fontsize = 15)\n","\n","# Predicted\n","ax2.imshow(oms[i])\n","ax2.grid(False)\n","ax2.set_title(\"Predicted : {}\".format(image_infos[i]['file_name']), fontsize = 15)\n","\n","plt.show()"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"pmZEjwGE0I39"},"source":["## submission을 위한 test 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:27.469285Z","start_time":"2021-04-16T19:44:27.456021Z"},"id":"X4s-Ng1_0I39"},"source":["def test(model, data_loader, device):\n"," size = 256\n"," transform = A.Compose([A.Resize(256, 256)])\n"," print('Start prediction.')\n"," model.eval()\n"," \n"," file_name_list = []\n"," preds_array = np.empty((0, size*size), dtype=np.long)\n"," \n"," with torch.no_grad():\n"," for step, (imgs, image_infos) in enumerate(test_loader):\n","\n"," # inference (512 x 512)\n"," outs = model(torch.stack(imgs).to(device))\n"," oms = torch.argmax(outs, dim=1).detach().cpu().numpy()\n"," \n"," # resize (256 x 256)\n"," temp_mask = []\n"," for img, mask in zip(np.stack(imgs), oms):\n"," transformed = transform(image=img, mask=mask)\n"," mask = transformed['mask']\n"," temp_mask.append(mask)\n","\n"," oms = np.array(temp_mask)\n"," \n"," oms = oms.reshape([oms.shape[0], size*size]).astype(int)\n"," preds_array = np.vstack((preds_array, oms))\n"," \n"," file_name_list.append([i['file_name'] for i in image_infos])\n"," print(\"End prediction.\")\n"," file_names = [y for x in file_name_list for y in x]\n"," \n"," return file_names, preds_array"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"GnldifLS0I39"},"source":["## submission.csv 생성"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:45:42.235310Z","start_time":"2021-04-16T19:44:30.499016Z"},"scrolled":true,"id":"uV_ZSnqT0I3-"},"source":["# sample_submisson.csv 열기\n","submission = pd.read_csv('./submission/sample_submission.csv', index_col=None)\n","\n","# test set에 대한 prediction\n","file_names, preds = test(model, test_loader, device)\n","\n","# PredictionString 대입\n","for file_name, string in zip(file_names, preds):\n"," submission = submission.append({\"image_id\" : file_name, \"PredictionString\" : ' '.join(str(e) for e in string.tolist())}, \n"," ignore_index=True)\n","\n","# submission.csv로 저장\n","submission.to_csv(\"./submission/Baseline_UnetPP.csv\", index=False)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"LIRbR3Ro0I3-"},"source":["## Reference\n","\n"]},{"cell_type":"code","metadata":{"id":"ZUxotp6d0I3-"},"source":[""],"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/chanyub_seg/code/1_aug_pan_effb0_focal_madgrad_cosLR.ipynb b/chanyub_seg/code/1_aug_pan_effb0_focal_madgrad_cosLR.ipynb deleted file mode 100644 index 33bd2c4..0000000 --- a/chanyub_seg/code/1_aug_pan_effb0_focal_madgrad_cosLR.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"nbformat":4,"nbformat_minor":0,"metadata":{"hide_input":false,"kernelspec":{"display_name":"Python 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google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Mounted at /content/drive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xDwpp4Lk-gSH","executionInfo":{"status":"ok","timestamp":1620109308766,"user_tz":-540,"elapsed":5259,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"38459d3f-07a9-4479-dc44-9c647f1a105b"},"source":["ls"],"execution_count":2,"outputs":[{"output_type":"stream","text":["\u001b[0m\u001b[01;34mdrive\u001b[0m/ \u001b[01;34msample_data\u001b[0m/\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sItrVDeh-iYC","executionInfo":{"status":"ok","timestamp":1620109308769,"user_tz":-540,"elapsed":4804,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"613e216e-bba9-4f93-edc7-6a1b80b38475"},"source":["cd drive/MyDrive/Trash/code"],"execution_count":3,"outputs":[{"output_type":"stream","text":["/content/drive/MyDrive/Trash/code\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"a54v4-kN_LVO","executionInfo":{"status":"ok","timestamp":1620109316970,"user_tz":-540,"elapsed":11915,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"3377bc4f-8393-426e-b3c5-98bbee6c3801"},"source":["!pip install albumentations==0.5.2"],"execution_count":4,"outputs":[{"output_type":"stream","text":["Collecting albumentations==0.5.2\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/03/58/63fb1d742dc42d9ba2800ea741de1f2bc6bb05548d8724aa84794042eaf2/albumentations-0.5.2-py3-none-any.whl (72kB)\n","\r\u001b[K |████▌ | 10kB 23.2MB/s eta 0:00:01\r\u001b[K |█████████ | 20kB 14.2MB/s eta 0:00:01\r\u001b[K |█████████████▋ | 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albumentations-0.1.12\n","Successfully installed albumentations-0.5.2 imgaug-0.4.0 opencv-python-headless-4.5.1.48\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.826930Z","start_time":"2021-04-18T10:34:45.406686Z"},"colab":{"base_uri":"https://localhost:8080/"},"id":"l_LPA4XD-NwC","executionInfo":{"status":"ok","timestamp":1620109321444,"user_tz":-540,"elapsed":4559,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"63080fea-b08d-4228-947e-afb548f0f1c8"},"source":["import os\n","import random\n","import time\n","import json\n","import warnings \n","warnings.filterwarnings('ignore')\n","\n","import torch\n","import torch.nn as nn\n","from torch.utils.data import Dataset, DataLoader\n","from utils import label_accuracy_score\n","import cv2\n","\n","import numpy as np\n","import pandas as pd\n","\n","# 전처리를 위한 라이브러리\n","from pycocotools.coco import COCO\n","import torchvision\n","import torchvision.transforms as transforms\n","\n","import albumentations as A\n","from albumentations.pytorch import ToTensorV2\n","\n","# 시각화를 위한 라이브러리\n","import matplotlib.pyplot as plt\n","import seaborn as sns; sns.set()\n","\n","plt.rcParams['axes.grid'] = False\n","\n","print('pytorch version: {}'.format(torch.__version__))\n","print('GPU 사용 가능 여부: {}'.format(torch.cuda.is_available()))\n","\n","print(torch.cuda.get_device_name(0))\n","print(torch.cuda.device_count())\n","\n","device = \"cuda\" if torch.cuda.is_available() else \"cpu\" # GPU 사용 가능 여부에 따라 device 정보 저장"],"execution_count":5,"outputs":[{"output_type":"stream","text":["pytorch version: 1.8.1+cu101\n","GPU 사용 가능 여부: True\n","Tesla P100-PCIE-16GB\n","1\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"HxgRSL_M-NwF"},"source":["## 하이퍼파라미터 세팅 및 seed 고정"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.841930Z","start_time":"2021-04-18T10:34:47.827931Z"},"id":"rV3JmGP5-NwF","executionInfo":{"status":"ok","timestamp":1620109322782,"user_tz":-540,"elapsed":1332,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["batch_size = 8 # Mini-batch size\n","num_epochs = 20\n","learning_rate = 0.0001"],"execution_count":6,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.856930Z","start_time":"2021-04-18T10:34:47.842931Z"},"id":"Z6LOuJXQ-NwG","executionInfo":{"status":"ok","timestamp":1620109322782,"user_tz":-540,"elapsed":1326,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# seed 고정\n","random_seed = 42\n","torch.manual_seed(random_seed)\n","torch.cuda.manual_seed(random_seed)\n","# torch.cuda.manual_seed_all(random_seed) # if use multi-GPU\n","torch.backends.cudnn.deterministic = True\n","torch.backends.cudnn.benchmark = False\n","np.random.seed(random_seed)\n","random.seed(random_seed)"],"execution_count":7,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"iWF_EJDu-NwG"},"source":["## 학습 데이터 EDA"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.381961Z","start_time":"2021-04-18T10:34:47.857930Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"lg0x0D0a-NwG","executionInfo":{"status":"ok","timestamp":1620109330542,"user_tz":-540,"elapsed":8480,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"c13b0d90-87f7-4615-c51b-a222fd8ec20d"},"source":["%matplotlib inline\n","\n","dataset_path = '../input/data'\n","anns_file_path = dataset_path + '/' + 'train.json'\n","\n","# Read annotations\n","with open(anns_file_path, 'r') as f:\n"," dataset = json.loads(f.read())\n","\n","categories = dataset['categories']\n","anns = dataset['annotations']\n","imgs = dataset['images']\n","nr_cats = len(categories)\n","nr_annotations = len(anns)\n","nr_images = len(imgs)\n","\n","# Load categories and super categories\n","cat_names = []\n","super_cat_names = []\n","super_cat_ids = {}\n","super_cat_last_name = ''\n","nr_super_cats = 0\n","for cat_it in categories:\n"," cat_names.append(cat_it['name'])\n"," super_cat_name = cat_it['supercategory']\n"," # Adding new supercat\n"," if super_cat_name != super_cat_last_name:\n"," super_cat_names.append(super_cat_name)\n"," super_cat_ids[super_cat_name] = nr_super_cats\n"," super_cat_last_name = super_cat_name\n"," nr_super_cats += 1\n","\n","print('Number of super categories:', nr_super_cats)\n","print('Number of categories:', nr_cats)\n","print('Number of annotations:', nr_annotations)\n","print('Number of images:', nr_images)"],"execution_count":8,"outputs":[{"output_type":"stream","text":["Number of super categories: 11\n","Number of categories: 11\n","Number of annotations: 21116\n","Number of images: 2617\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.546964Z","start_time":"2021-04-18T10:34:51.382969Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/","height":355},"id":"PjLyVHVY-NwH","executionInfo":{"status":"ok","timestamp":1620109332251,"user_tz":-540,"elapsed":1700,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"1c524e4f-bef7-462f-9d76-2446ec5f0f8e"},"source":["# Count annotations\n","cat_histogram = np.zeros(nr_cats,dtype=int)\n","for ann in anns:\n"," cat_histogram[ann['category_id']] += 1\n","\n","# Initialize the matplotlib figure\n","f, ax = plt.subplots(figsize=(5,5))\n","\n","# Convert to DataFrame\n","df = pd.DataFrame({'Categories': cat_names, 'Number of annotations': cat_histogram})\n","df = df.sort_values('Number of annotations', 0, False)\n","\n","# Plot the histogram\n","plt.title(\"category distribution of train set \")\n","plot_1 = sns.barplot(x=\"Number of annotations\", y=\"Categories\", data=df, label=\"Total\", color=\"b\")"],"execution_count":9,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.561965Z","start_time":"2021-04-18T10:34:51.547969Z"},"id":"34Tw5__i-NwI","executionInfo":{"status":"ok","timestamp":1620109332252,"user_tz":-540,"elapsed":1698,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# category labeling \n","sorted_temp_df = df.sort_index()\n","\n","# background = 0 에 해당되는 label 추가 후 기존들을 모두 label + 1 로 설정\n","sorted_df = pd.DataFrame([\"Backgroud\"], columns = [\"Categories\"])\n","sorted_df = sorted_df.append(sorted_temp_df, ignore_index=True)"],"execution_count":10,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.576961Z","start_time":"2021-04-18T10:34:51.562964Z"},"colab":{"base_uri":"https://localhost:8080/","height":421},"id":"CQk4vV5N-NwI","executionInfo":{"status":"ok","timestamp":1620109332545,"user_tz":-540,"elapsed":1982,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"ad8123a8-f81c-4771-edd4-d5f2217bc64f"},"source":["# class (Categories) 에 따른 index 확인 (0~11 : 총 12개)\n","sorted_df"],"execution_count":11,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
CategoriesNumber of annotations
0BackgroudNaN
1UNKNOWN128.0
2General trash2225.0
3Paper7448.0
4Paper pack527.0
5Metal449.0
6Glass488.0
7Plastic2472.0
8Styrofoam1074.0
9Plastic bag6114.0
10Battery50.0
11Clothing141.0
\n","
"],"text/plain":[" Categories Number of annotations\n","0 Backgroud NaN\n","1 UNKNOWN 128.0\n","2 General trash 2225.0\n","3 Paper 7448.0\n","4 Paper pack 527.0\n","5 Metal 449.0\n","6 Glass 488.0\n","7 Plastic 2472.0\n","8 Styrofoam 1074.0\n","9 Plastic bag 6114.0\n","10 Battery 50.0\n","11 Clothing 141.0"]},"metadata":{"tags":[]},"execution_count":11}]},{"cell_type":"markdown","metadata":{"id":"KXU0zmZs-NwI"},"source":["## 데이터 전처리 함수 정의 (Dataset)"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:52.693328Z","start_time":"2021-04-18T10:34:52.681328Z"},"id":"QFnTI8_Z-NwJ","executionInfo":{"status":"ok","timestamp":1620109332546,"user_tz":-540,"elapsed":1981,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["category_names = list(sorted_df.Categories)\n","\n","def get_classname(classID, cats):\n"," for i in range(len(cats)):\n"," if cats[i]['id']==classID:\n"," return cats[i]['name']\n"," return \"None\"\n","\n","class CustomDataLoader(Dataset):\n"," \"\"\"COCO format\"\"\"\n"," def __init__(self, data_dir, mode = 'train', transform = None):\n"," super().__init__()\n"," self.mode = mode\n"," self.transform = transform\n"," self.coco = COCO(data_dir)\n"," \n"," def __getitem__(self, index: int):\n"," # dataset이 index되어 list처럼 동작\n"," image_id = self.coco.getImgIds(imgIds=index)\n"," image_infos = self.coco.loadImgs(image_id)[0]\n"," \n"," # cv2 를 활용하여 image 불러오기\n"," images = cv2.imread(os.path.join(dataset_path, image_infos['file_name']))\n"," images = cv2.cvtColor(images, cv2.COLOR_BGR2RGB).astype(np.float32)\n"," images /= 255.0\n"," \n"," if (self.mode in ('train', 'val')):\n"," ann_ids = self.coco.getAnnIds(imgIds=image_infos['id'])\n"," anns = self.coco.loadAnns(ann_ids)\n","\n"," # Load the categories in a variable\n"," cat_ids = self.coco.getCatIds()\n"," cats = self.coco.loadCats(cat_ids)\n","\n"," # masks : size가 (height x width)인 2D\n"," # 각각의 pixel 값에는 \"category id + 1\" 할당\n"," # Background = 0\n"," masks = np.zeros((image_infos[\"height\"], image_infos[\"width\"]))\n"," # Unknown = 1, General trash = 2, ... , Cigarette = 11\n"," for i in range(len(anns)):\n"," className = get_classname(anns[i]['category_id'], cats)\n"," pixel_value = category_names.index(className)\n"," masks = np.maximum(self.coco.annToMask(anns[i])*pixel_value, masks)\n"," masks = masks.astype(np.float32)\n","\n"," # transform -> albumentations 라이브러리 활용\n"," if self.transform is not None:\n"," transformed = self.transform(image=images, mask=masks)\n"," images = transformed[\"image\"]\n"," masks = transformed[\"mask\"]\n"," \n"," return images, masks, image_infos\n"," \n"," if self.mode == 'test':\n"," # transform -> albumentations 라이브러리 활용\n"," if self.transform is not None:\n"," transformed = self.transform(image=images)\n"," images = transformed[\"image\"]\n"," \n"," return images, image_infos\n"," \n"," \n"," def __len__(self) -> int:\n"," # 전체 dataset의 size를 return\n"," return len(self.coco.getImgIds())"],"execution_count":12,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"cp2aIOlP-NwK"},"source":["## Dataset 정의 및 DataLoader 할당"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:58.823175Z","start_time":"2021-04-18T10:34:54.106233Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"P7zFlRn6-NwK","executionInfo":{"status":"ok","timestamp":1620109351303,"user_tz":-540,"elapsed":7501,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"03081937-08d0-43c0-b3c1-3dd40995e0d3"},"source":["# train.json / validation.json / test.json 디렉토리 설정\n","train_path = dataset_path + '/train.json'\n","val_path = dataset_path + '/val.json'\n","test_path = dataset_path + '/test.json'\n","\n","# collate_fn needs for batch\n","def collate_fn(batch):\n"," return tuple(zip(*batch))\n","\n","train_transform = A.Compose([\n"," A.transforms.RandomShadow(),\n"," ToTensorV2()\n"," ])\n","\n","val_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","test_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","# create own Dataset 1 (skip)\n","# validation set을 직접 나누고 싶은 경우\n","# random_split 사용하여 data set을 8:2 로 분할\n","# train_size = int(0.8*len(dataset))\n","# val_size = int(len(dataset)-train_size)\n","# dataset = CustomDataLoader(data_dir=train_path, mode='train', transform=transform)\n","# train_dataset, val_dataset = torch.utils.data.random_split(dataset, [train_size, val_size])\n","\n","# create own Dataset 2\n","# train dataset\n","train_dataset = CustomDataLoader(data_dir=train_path, mode='train', transform=train_transform)\n","\n","# validation dataset\n","val_dataset = CustomDataLoader(data_dir=val_path, mode='val', transform=val_transform)\n","\n","# test dataset\n","test_dataset = CustomDataLoader(data_dir=test_path, mode='test', transform=test_transform)\n","\n","\n","# DataLoader\n","train_loader = torch.utils.data.DataLoader(dataset=train_dataset, \n"," batch_size=batch_size,\n"," shuffle=True,\n"," num_workers=4,\n"," collate_fn=collate_fn,\n"," drop_last=True)\n","\n","val_loader = torch.utils.data.DataLoader(dataset=val_dataset, \n"," batch_size=batch_size,\n"," shuffle=False,\n"," num_workers=4,\n"," collate_fn=collate_fn,\n"," drop_last=True) \n","\n","test_loader = torch.utils.data.DataLoader(dataset=test_dataset,\n"," batch_size=batch_size,\n"," num_workers=4,\n"," collate_fn=collate_fn)"],"execution_count":13,"outputs":[{"output_type":"stream","text":["loading annotations into memory...\n","Done (t=3.76s)\n","creating index...\n","index created!\n","loading annotations into memory...\n","Done (t=2.77s)\n","creating index...\n","index created!\n","loading annotations into memory...\n","Done (t=0.31s)\n","creating index...\n","index created!\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"8rqJiHb_-NwM"},"source":["# wandb"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"H1kHXm0uAX3R","executionInfo":{"status":"ok","timestamp":1620109360806,"user_tz":-540,"elapsed":8254,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"30dbc849-8a44-4d0c-d4c1-4dc9d7b5cca5"},"source":["!pip install wandb"],"execution_count":14,"outputs":[{"output_type":"stream","text":["Collecting wandb\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/67/5a/b037b50f9849212863a2fed313624d8f6f33ffa4ce89dc706e2a0e98c780/wandb-0.10.29-py2.py3-none-any.whl (2.1MB)\n","\u001b[K |████████████████████████████████| 2.1MB 8.4MB/s \n","\u001b[?25hCollecting GitPython>=1.0.0\n","\u001b[?25l Downloading 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sha256=e7cd5afb8941a3cd74c959eb9e035a12cc52bfebbda69f9ad6a40dc50296d898\n"," Stored in directory: /root/.cache/pip/wheels/0b/04/79/c3b0c3a0266a3cb4376da31e5bfe8bba0c489246968a68e843\n"," Building wheel for subprocess32 (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for subprocess32: filename=subprocess32-3.5.4-cp37-none-any.whl size=6489 sha256=061504c9dbd879f03d5651b3f8d7f9bb4bbd0eb75daf53b2985e342283184b72\n"," Stored in directory: /root/.cache/pip/wheels/68/39/1a/5e402bdfdf004af1786c8b853fd92f8c4a04f22aad179654d1\n","Successfully built pathtools subprocess32\n","Installing collected packages: smmap, gitdb, GitPython, pathtools, subprocess32, configparser, docker-pycreds, shortuuid, sentry-sdk, wandb\n","Successfully installed GitPython-3.1.14 configparser-5.0.2 docker-pycreds-0.4.0 gitdb-4.0.7 pathtools-0.1.2 sentry-sdk-1.0.0 shortuuid-1.0.1 smmap-4.0.0 subprocess32-3.5.4 wandb-0.10.29\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":153},"id":"aMW4VV9V-NwM","executionInfo":{"status":"ok","timestamp":1620109380329,"user_tz":-540,"elapsed":11412,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"7205070e-eb9c-4866-9b1a-7dfc4a88f2e9"},"source":["import wandb\n","\n","proj_name = '1_aug_pan_effb0_focal_madgrad_cosLR'\n","\n","wandb.init(project='chanyub',name=proj_name)\n","\n","config = wandb.config\n","config.learning_rate = 0.01"],"execution_count":15,"outputs":[{"output_type":"display_data","data":{"application/javascript":["\n"," window._wandbApiKey = new Promise((resolve, reject) => {\n"," function loadScript(url) {\n"," return new Promise(function(resolve, reject) {\n"," let newScript = document.createElement(\"script\");\n"," newScript.onerror = reject;\n"," newScript.onload = resolve;\n"," document.body.appendChild(newScript);\n"," newScript.src = url;\n"," });\n"," }\n"," loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n"," const iframe = document.createElement('iframe')\n"," iframe.style.cssText = \"width:0;height:0;border:none\"\n"," document.body.appendChild(iframe)\n"," const handshake = new Postmate({\n"," container: iframe,\n"," url: 'https://wandb.ai/authorize'\n"," });\n"," const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n"," handshake.then(function(child) {\n"," child.on('authorize', data => {\n"," clearTimeout(timeout)\n"," resolve(data)\n"," });\n"," });\n"," })\n"," });\n"," "],"text/plain":[""]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"],"name":"stderr"},{"output_type":"display_data","data":{"text/html":["\n"," Tracking run with wandb version 0.10.29
\n"," Syncing run 1_aug_pan_effb0_focal_madgrad_cosLR to Weights & Biases (Documentation).
\n"," Project page: https://wandb.ai/pstage12/chanyub
\n"," Run page: https://wandb.ai/pstage12/chanyub/runs/37cp7ao4
\n"," Run data is saved locally in /content/drive/My Drive/Trash/code/wandb/run-20210504_062256-37cp7ao4

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sha256=1ee51de4ebb5ec3d6725fa0a81bdbad5c1171143151a9d33746641db6467d9b4\n"," Stored in directory: /root/.cache/pip/wheels/69/df/63/62583c096289713f22db605aa2334de5b591d59861a02c2ecd\n","Successfully built efficientnet-pytorch pretrainedmodels\n","Installing collected packages: efficientnet-pytorch, munch, pretrainedmodels, timm, segmentation-models-pytorch\n","Successfully installed efficientnet-pytorch-0.6.3 munch-2.5.0 pretrainedmodels-0.7.4 segmentation-models-pytorch-0.1.3 timm-0.3.2\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:11.634792Z","start_time":"2021-04-18T16:16:05.875817Z"},"colab":{"base_uri":"https://localhost:8080/","height":137,"referenced_widgets":["c5417817737a4daeb2dd6500390daed6","bea992c11f794d3ab87878142fcb57ff","e7e89be93882419092e2b66931e6ff4a","62c32123e4da4250821f6762323f4eb9","339745946d2e4d8b80b8812083302124","e425ecdc8f364d1db56b1da73d022853","accffcfc045d4bb2b61676285f1642d5","d6a6258bb07248c98b137e4ad44ece59"]},"id":"a8IfZfiM-NwM","executionInfo":{"status":"ok","timestamp":1620109395760,"user_tz":-540,"elapsed":18434,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"545942dd-89e5-438e-be14-f5d3a16d01e3"},"source":["# 구현된 model에 임의의 input을 넣어 output이 잘 나오는지 test\n","import segmentation_models_pytorch as smp\n","\n","model = smp.PAN(encoder_name='timm-efficientnet-b0', classes=12)\n","x = torch.randn([2, 3, 512, 512])\n","print(\"input shape : \", x.shape)\n","out = model(x).to(device)\n","print(\"output shape : \", out.size())\n","\n","model = model.to(device)"],"execution_count":17,"outputs":[{"output_type":"stream","text":["Downloading: \"https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b0_aa-827b6e33.pth\" to /root/.cache/torch/hub/checkpoints/tf_efficientnet_b0_aa-827b6e33.pth\n"],"name":"stderr"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"c5417817737a4daeb2dd6500390daed6","version_minor":0,"version_major":2},"text/plain":["HBox(children=(FloatProgress(value=0.0, max=21383997.0), HTML(value='')))"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\n","input shape : torch.Size([2, 3, 512, 512])\n","output shape : torch.Size([2, 12, 512, 512])\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"zvDnI7_T-NwN"},"source":["## train, validation, test 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.104200Z","start_time":"2021-04-18T16:16:18.093174Z"},"id":"RA3oAapJ-NwN","executionInfo":{"status":"ok","timestamp":1620109397604,"user_tz":-540,"elapsed":1843,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["def train(num_epochs, model, data_loader, val_loader, criterion, optimizer, saved_dir, val_every, device, lr_scheduler):\n"," print('Start training..')\n"," best_loss = 9999999\n"," best_miou = 0\n"," for epoch in range(num_epochs):\n"," model.train()\n"," for step, (images, masks, _) in enumerate(data_loader):\n"," images = torch.stack(images) # (batch, channel, height, width)\n"," masks = torch.stack(masks).long() # (batch, channel, height, width)\n"," \n"," # gpu 연산을 위해 device 할당\n"," images, masks = images.to(device), masks.to(device)\n"," \n"," # inference\n"," outputs = model(images)\n"," \n"," # loss 계산 (cross entropy loss)\n"," loss = criterion(outputs, masks)\n"," optimizer.zero_grad()\n"," loss.backward()\n"," optimizer.step()\n"," \n"," lr_scheduler.step()\n"," \n"," # step 주기에 따른 loss 출력\n"," if (step + 1) % 25 == 0:\n"," print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(\n"," epoch+1, num_epochs, step+1, len(train_loader), loss.item()))\n"," \n"," # validation 주기에 따른 loss 출력 및 best model 저장\n"," if (epoch + 1) % val_every == 0:\n","# avrg_loss = validation(epoch + 1, model, val_loader, criterion, device)\n","# if avrg_loss < best_loss:\n","# print('Best performance at epoch: {}'.format(epoch + 1))\n","# print('Save model in', saved_dir)\n","# best_loss = avrg_loss\n","# wandb.log({'best_loss': best_loss})\n","# save_model(model, saved_dir)\n"," avrg_miou = validation(epoch + 1, model, val_loader, criterion, device)\n"," if avrg_miou > best_miou:\n"," print('Best performance at epoch: {}'.format(epoch + 1))\n"," print('Save model in', saved_dir)\n"," best_miou = avrg_miou\n"," wandb.log({'best_miou': best_miou})\n"," save_model(model, saved_dir)"],"execution_count":18,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.285795Z","start_time":"2021-04-18T16:16:18.267686Z"},"id":"EulIikmq-NwO","executionInfo":{"status":"ok","timestamp":1620109397606,"user_tz":-540,"elapsed":1837,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["def validation(epoch, model, data_loader, criterion, device):\n"," print('Start validation #{}'.format(epoch))\n"," model.eval()\n"," with torch.no_grad():\n"," total_loss = 0\n"," cnt = 0\n"," mIoU_list = []\n"," for step, (images, masks, _) in enumerate(data_loader):\n"," \n"," images = torch.stack(images) # (batch, channel, height, width)\n"," masks = torch.stack(masks).long() # (batch, channel, height, width)\n","\n"," images, masks = images.to(device), masks.to(device) \n","\n"," outputs = model(images)\n"," loss = criterion(outputs, masks)\n"," total_loss += loss\n"," cnt += 1\n"," \n","# print(outputs.shape)\n","# print(masks.shape)\n","# wandb.log(wandb.Image(images, masks={\n","# \"predictions\" : {\n","# \"mask_data\" : torch.squeeze(torch.squeeze(outputs, 0),1),\n","# \"class_labels\" : classes_dict\n","# },\n","# \"ground_truth\" : {\n","# \"mask_data\" : torch.squeeze(masks, 0),\n","# \"class_labels\" : classes_dict\n","# }\n","# }))\n"," \n"," outputs = torch.argmax(outputs.squeeze(), dim=1).detach().cpu().numpy()\n","\n"," mIoU = label_accuracy_score(masks.detach().cpu().numpy(), outputs, n_class=12)[2]\n"," mIoU_list.append(mIoU)\n"," \n"," avrg_loss = total_loss / cnt\n"," avrg_mIoU = np.mean(mIoU_list)\n"," print('Validation #{} Average Loss: {:.4f}, mIoU: {:.4f}'.format(epoch, avrg_loss, np.mean(mIoU_list)))\n"," wandb.log({'Train Loss':loss.item(), 'Val Loss':avrg_loss , 'Val mIoU':np.mean(mIoU_list)})\n","# return avrg_loss\n"," return avrg_mIoU"],"execution_count":19,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"C_7CtFfH-NwO"},"source":["## 모델 저장 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.909918Z","start_time":"2021-04-18T16:16:18.898918Z"},"id":"gpCM5BFO-NwO","executionInfo":{"status":"ok","timestamp":1620109401666,"user_tz":-540,"elapsed":726,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# 모델 저장 함수 정의\n","val_every = 1 \n","\n","saved_dir = './saved'\n","if not os.path.isdir(saved_dir): \n"," os.mkdir(saved_dir)\n"," \n","def save_model(model, saved_dir, file_name='1_aug_pan_effb0_focal_madgrad_cosLR.pt'):\n"," check_point = {'net': model.state_dict()}\n"," output_path = os.path.join(saved_dir, file_name)\n"," torch.save(model.state_dict(), output_path)"],"execution_count":20,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"0D3rsEd2yJfV"},"source":[""]},{"cell_type":"markdown","metadata":{"id":"FyKGeg8T-NwP"},"source":["## 모델 생성 및 Loss function, Optimizer 정의"]},{"cell_type":"code","metadata":{"id":"ORugl8s1-NwP","executionInfo":{"status":"ok","timestamp":1620109405899,"user_tz":-540,"elapsed":1143,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["from torch.autograd import Variable\n","import torch.nn.functional as F\n","# ref : https://github.com/clcarwin/focal_loss_pytorch\n","class FocalLoss(nn.Module):\n"," def __init__(self, gamma=0, alpha=None, size_average=True):\n"," super(FocalLoss, self).__init__()\n"," self.gamma = gamma\n"," self.alpha = alpha\n"," if isinstance(alpha,(float,int)): self.alpha = torch.Tensor([alpha,1-alpha])\n"," if isinstance(alpha,list): self.alpha = torch.Tensor(alpha)\n"," self.size_average = size_average\n","\n"," def forward(self, input, target):\n"," if input.dim()>2:\n"," input = input.view(input.size(0),input.size(1),-1) # N,C,H,W => N,C,H*W\n"," input = input.transpose(1,2) # N,C,H*W => N,H*W,C\n"," input = input.contiguous().view(-1,input.size(2)) # N,H*W,C => N*H*W,C\n"," target = target.view(-1,1)\n","\n"," logpt = F.log_softmax(input)\n"," logpt = logpt.gather(1,target)\n"," logpt = logpt.view(-1)\n"," pt = Variable(logpt.data.exp())\n","\n"," if self.alpha is not None:\n"," if self.alpha.type()!=input.data.type():\n"," self.alpha = self.alpha.type_as(input.data)\n"," at = self.alpha.gather(0,target.data.view(-1))\n"," logpt = logpt * Variable(at)\n","\n"," loss = -1 * (1-pt)**self.gamma * logpt\n"," if self.size_average: return loss.mean()\n"," else: return loss.sum()"],"execution_count":21,"outputs":[]},{"cell_type":"code","metadata":{"id":"yfeFQknz-NwP","executionInfo":{"status":"ok","timestamp":1620109405899,"user_tz":-540,"elapsed":666,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["import torch.optim.lr_scheduler as lr_scheduler\n","import math\n","class CosineAnnealingWarmUpRestart(lr_scheduler._LRScheduler):\n"," def __init__(self, optimizer, T_0, T_mult=1, eta_max=0.1, T_up=0, gamma=1., last_epoch=-1):\n"," if T_0 <= 0 or not isinstance(T_0, int):\n"," raise ValueError(\"Expected positive integer T_0, but got {}\".format(T_0))\n"," if T_mult < 1 or not isinstance(T_mult, int):\n"," raise ValueError(\"Expected integer T_mult >= 1, but got {}\".format(T_mult))\n"," if T_up < 0 or not isinstance(T_up, int):\n"," raise ValueError(\"Expected positive integer T_up, but got {}\".format(T_up))\n"," self.T_0 = T_0\n"," self.T_mult = T_mult\n"," self.base_eta_max = eta_max\n"," self.eta_max = eta_max\n"," self.T_up = T_up\n"," self.T_i = T_0\n"," self.gamma = gamma\n"," self.cycle = 0\n"," self.T_cur = last_epoch\n"," super(CosineAnnealingWarmUpRestart, self).__init__(optimizer, last_epoch)\n"," # self.T_cur = last_epoch\n"," \n"," def get_lr(self):\n"," if self.T_cur == -1:\n"," return self.base_lrs\n"," elif self.T_cur < self.T_up:\n"," return [(self.eta_max - base_lr)*self.T_cur / self.T_up + base_lr for base_lr in self.base_lrs]\n"," else:\n"," return [base_lr + (self.eta_max - base_lr) * (1 + math.cos(math.pi * (self.T_cur-self.T_up) / (self.T_i - self.T_up))) / 2\n"," for base_lr in self.base_lrs]\n","\n"," def step(self, epoch=None):\n"," if epoch is None:\n"," epoch = self.last_epoch + 1\n"," self.T_cur = self.T_cur + 1\n"," if self.T_cur >= self.T_i:\n"," self.cycle += 1\n"," self.T_cur = self.T_cur - self.T_i\n"," self.T_i = (self.T_i - self.T_up) * self.T_mult + self.T_up\n"," else:\n"," if epoch >= self.T_0:\n"," if self.T_mult == 1:\n"," self.T_cur = epoch % self.T_0\n"," self.cycle = epoch // self.T_0\n"," else:\n"," n = int(math.log((epoch / self.T_0 * (self.T_mult - 1) + 1), self.T_mult))\n"," self.cycle = n\n"," self.T_cur = epoch - self.T_0 * (self.T_mult ** n - 1) / (self.T_mult - 1)\n"," self.T_i = self.T_0 * self.T_mult ** (n)\n"," else:\n"," self.T_i = self.T_0\n"," self.T_cur = epoch\n"," \n"," self.eta_max = self.base_eta_max * (self.gamma**self.cycle)\n"," self.last_epoch = math.floor(epoch)\n"," for param_group, lr in zip(self.optimizer.param_groups, self.get_lr()):\n"," param_group['lr'] = lr"],"execution_count":22,"outputs":[]},{"cell_type":"code","metadata":{"id":"AG1oQeu7BX1M"},"source":["# !pip install adamp"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"eH2PpwF9a-Os","executionInfo":{"status":"ok","timestamp":1620109411008,"user_tz":-540,"elapsed":3928,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"261ea7d4-86ae-42f6-a052-a1910aacdb31"},"source":["!pip install madgrad"],"execution_count":23,"outputs":[{"output_type":"stream","text":["Collecting madgrad\n"," Downloading https://files.pythonhosted.org/packages/65/f0/4584f18202a2fb8903d456bf907b80e7cb54ad8fcba68604084ff41b7cf8/madgrad-1.1-py3-none-any.whl\n","Installing collected packages: madgrad\n","Successfully installed madgrad-1.1\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:19.698902Z","start_time":"2021-04-18T16:16:19.694902Z"},"id":"9Dly8KZj-NwQ","executionInfo":{"status":"ok","timestamp":1620109411008,"user_tz":-540,"elapsed":1364,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# from adamp import AdamP\n","from madgrad import MADGRAD\n","# Loss function 정의\n","# criterion = nn.CrossEntropyLoss()\n","criterion = FocalLoss()\n","\n","# Optimizer 정의\n","# optimizer = torch.optim.Adam(params = model.parameters(), lr = learning_rate, weight_decay=1e-6)\n","# optimizer = AdamP(params = model.parameters())\n","optimizer = MADGRAD(params = model.parameters(), lr = learning_rate, momentum = 0.9, weight_decay = 0, eps = 1e-06)\n","\n","lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=50, eta_min=0)\n","# lr_scheduler = CosineAnnealingWarmUpRestart(optimizer, T_0=150, T_mult=1, eta_max=0.1, T_up=10, gamma=0.5)"],"execution_count":24,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"start_time":"2021-04-18T16:16:20.331Z"},"colab":{"base_uri":"https://localhost:8080/"},"id":"cMSiFA-3-NwR","executionInfo":{"status":"ok","timestamp":1620117861945,"user_tz":-540,"elapsed":8445636,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"c9bfb383-2794-4cd9-fd48-21a84ffe5746"},"source":["train(num_epochs, model, train_loader, val_loader, criterion, optimizer, saved_dir, val_every, device, lr_scheduler)"],"execution_count":25,"outputs":[{"output_type":"stream","text":["Start training..\n","Epoch [1/20], Step [25/327], Loss: 0.6333\n","Epoch [1/20], Step [50/327], Loss: 0.9969\n","Epoch [1/20], Step [75/327], Loss: 0.7567\n","Epoch [1/20], Step [100/327], Loss: 0.6494\n","Epoch [1/20], Step [125/327], Loss: 1.0670\n","Epoch [1/20], Step [150/327], Loss: 0.5928\n","Epoch [1/20], Step [175/327], Loss: 0.7160\n","Epoch [1/20], Step [200/327], Loss: 0.5127\n","Epoch [1/20], Step [225/327], Loss: 0.6442\n","Epoch [1/20], Step [250/327], Loss: 0.7644\n","Epoch [1/20], Step [275/327], Loss: 0.5439\n","Epoch [1/20], Step [300/327], Loss: 0.4138\n","Epoch [1/20], Step [325/327], Loss: 0.4641\n","Start validation #1\n","Validation #1 Average Loss: 0.5000, mIoU: 0.2681\n","Best performance at epoch: 1\n","Save model in ./saved\n","Epoch [2/20], Step [25/327], Loss: 0.4377\n","Epoch [2/20], Step [50/327], Loss: 0.4807\n","Epoch [2/20], Step [75/327], Loss: 0.3008\n","Epoch [2/20], Step [100/327], Loss: 0.5893\n","Epoch [2/20], Step [125/327], Loss: 0.4139\n","Epoch [2/20], Step [150/327], Loss: 0.3343\n","Epoch [2/20], Step [175/327], Loss: 0.3589\n","Epoch [2/20], Step [200/327], Loss: 0.2838\n","Epoch [2/20], Step [225/327], Loss: 1.1851\n","Epoch [2/20], Step [250/327], Loss: 0.5690\n","Epoch [2/20], Step [275/327], Loss: 0.3869\n","Epoch [2/20], Step [300/327], Loss: 0.3684\n","Epoch [2/20], Step [325/327], Loss: 0.3984\n","Start validation #2\n","Validation #2 Average Loss: 0.4250, mIoU: 0.3082\n","Best performance at epoch: 2\n","Save model in ./saved\n","Epoch [3/20], Step [25/327], Loss: 0.3373\n","Epoch [3/20], Step [50/327], Loss: 0.6174\n","Epoch [3/20], Step [75/327], Loss: 0.3301\n","Epoch [3/20], Step [100/327], Loss: 0.2623\n","Epoch [3/20], Step [125/327], Loss: 0.2387\n","Epoch [3/20], Step [150/327], Loss: 0.2390\n","Epoch [3/20], Step [175/327], Loss: 0.4276\n","Epoch [3/20], Step [200/327], Loss: 0.3856\n","Epoch [3/20], Step [225/327], Loss: 0.2676\n","Epoch [3/20], Step [250/327], Loss: 0.3823\n","Epoch [3/20], Step [275/327], Loss: 0.5312\n","Epoch [3/20], Step [300/327], Loss: 0.4299\n","Epoch [3/20], Step [325/327], Loss: 0.4867\n","Start validation #3\n","Validation #3 Average Loss: 0.3873, mIoU: 0.3309\n","Best performance at epoch: 3\n","Save model in ./saved\n","Epoch [4/20], Step [25/327], Loss: 0.3770\n","Epoch [4/20], Step [50/327], Loss: 0.5306\n","Epoch [4/20], Step [75/327], Loss: 0.2418\n","Epoch [4/20], Step [100/327], Loss: 0.3566\n","Epoch [4/20], Step [125/327], Loss: 0.3308\n","Epoch [4/20], Step [150/327], Loss: 0.3738\n","Epoch [4/20], Step [175/327], Loss: 0.1935\n","Epoch [4/20], Step [200/327], Loss: 0.3266\n","Epoch [4/20], Step [225/327], Loss: 0.3359\n","Epoch [4/20], Step [250/327], Loss: 0.5600\n","Epoch [4/20], Step [275/327], Loss: 0.4422\n","Epoch [4/20], Step [300/327], Loss: 0.3236\n","Epoch [4/20], Step [325/327], Loss: 0.2473\n","Start validation #4\n","Validation #4 Average Loss: 0.3657, mIoU: 0.3516\n","Best performance at epoch: 4\n","Save model in ./saved\n","Epoch [5/20], Step [25/327], Loss: 0.2975\n","Epoch [5/20], Step [50/327], Loss: 0.7861\n","Epoch [5/20], Step [75/327], Loss: 0.5389\n","Epoch [5/20], Step [100/327], Loss: 0.3791\n","Epoch [5/20], Step [125/327], Loss: 0.4483\n","Epoch [5/20], Step [150/327], Loss: 0.2682\n","Epoch [5/20], Step [175/327], Loss: 0.7083\n","Epoch [5/20], Step [200/327], Loss: 0.2589\n","Epoch [5/20], Step [225/327], Loss: 0.3822\n","Epoch [5/20], Step [250/327], Loss: 0.3002\n","Epoch [5/20], Step [275/327], Loss: 0.3312\n","Epoch [5/20], Step [300/327], Loss: 0.4295\n","Epoch [5/20], Step [325/327], Loss: 0.4900\n","Start validation #5\n","Validation #5 Average Loss: 0.3414, mIoU: 0.3722\n","Best performance at epoch: 5\n","Save model in ./saved\n","Epoch [6/20], Step [25/327], Loss: 0.2834\n","Epoch [6/20], Step [50/327], Loss: 0.3861\n","Epoch [6/20], Step [75/327], Loss: 0.2889\n","Epoch [6/20], Step [100/327], Loss: 0.1820\n","Epoch [6/20], Step [125/327], Loss: 0.2611\n","Epoch [6/20], Step [150/327], Loss: 0.2657\n","Epoch [6/20], Step [175/327], Loss: 0.4770\n","Epoch [6/20], Step [200/327], Loss: 0.2334\n","Epoch [6/20], Step [225/327], Loss: 0.1501\n","Epoch [6/20], Step [250/327], Loss: 0.1585\n","Epoch [6/20], Step [275/327], Loss: 0.4086\n","Epoch [6/20], Step [300/327], Loss: 0.2494\n","Epoch [6/20], Step [325/327], Loss: 0.2702\n","Start validation #6\n","Validation #6 Average Loss: 0.3406, mIoU: 0.3662\n","Epoch [7/20], Step [25/327], Loss: 0.2836\n","Epoch [7/20], Step [50/327], Loss: 0.2793\n","Epoch [7/20], Step [75/327], Loss: 0.2840\n","Epoch [7/20], Step [100/327], Loss: 0.3990\n","Epoch [7/20], Step [125/327], Loss: 0.2625\n","Epoch [7/20], Step [150/327], Loss: 0.3694\n","Epoch [7/20], Step [175/327], Loss: 0.9400\n","Epoch [7/20], Step [200/327], Loss: 0.3474\n","Epoch [7/20], Step [225/327], Loss: 0.2874\n","Epoch [7/20], Step [250/327], Loss: 0.2436\n","Epoch [7/20], Step [275/327], Loss: 0.2054\n","Epoch [7/20], Step [300/327], Loss: 0.7486\n","Epoch [7/20], Step [325/327], Loss: 0.4279\n","Start validation #7\n","Validation #7 Average Loss: 0.3252, mIoU: 0.3726\n","Best performance at epoch: 7\n","Save model in ./saved\n","Epoch [8/20], Step [25/327], Loss: 0.1828\n","Epoch [8/20], Step [50/327], Loss: 0.4592\n","Epoch [8/20], Step [75/327], Loss: 0.3080\n","Epoch [8/20], Step [100/327], Loss: 0.2600\n","Epoch [8/20], Step [125/327], Loss: 0.2981\n","Epoch [8/20], Step [150/327], Loss: 0.1920\n","Epoch [8/20], Step [175/327], Loss: 0.2781\n","Epoch [8/20], Step [200/327], Loss: 0.2909\n","Epoch [8/20], Step [225/327], Loss: 0.2203\n","Epoch [8/20], Step [250/327], Loss: 0.2681\n","Epoch [8/20], Step [275/327], Loss: 0.2655\n","Epoch [8/20], Step [300/327], Loss: 0.5932\n","Epoch [8/20], Step [325/327], Loss: 0.2275\n","Start validation #8\n","Validation #8 Average Loss: 0.3414, mIoU: 0.3779\n","Best performance at epoch: 8\n","Save model in ./saved\n","Epoch [9/20], Step [25/327], Loss: 0.1528\n","Epoch [9/20], Step [50/327], Loss: 0.1906\n","Epoch [9/20], Step [75/327], Loss: 0.2800\n","Epoch [9/20], Step [100/327], Loss: 0.3188\n","Epoch [9/20], Step [125/327], Loss: 0.2681\n","Epoch [9/20], Step [150/327], Loss: 0.2451\n","Epoch [9/20], Step [175/327], Loss: 0.3149\n","Epoch [9/20], Step [200/327], Loss: 0.4608\n","Epoch [9/20], Step [225/327], Loss: 0.4416\n","Epoch [9/20], Step [250/327], Loss: 0.2429\n","Epoch [9/20], Step [275/327], Loss: 0.1790\n","Epoch [9/20], Step [300/327], Loss: 0.1570\n","Epoch [9/20], Step [325/327], Loss: 0.2582\n","Start validation #9\n","Validation #9 Average Loss: 0.3228, mIoU: 0.3705\n","Epoch [10/20], Step [25/327], Loss: 0.1840\n","Epoch [10/20], Step [50/327], Loss: 0.3643\n","Epoch [10/20], Step [75/327], Loss: 0.1864\n","Epoch [10/20], Step [100/327], Loss: 0.0940\n","Epoch [10/20], Step [125/327], Loss: 0.4118\n","Epoch [10/20], Step [150/327], Loss: 0.2226\n","Epoch [10/20], Step [175/327], Loss: 0.2251\n","Epoch [10/20], Step [200/327], Loss: 0.1902\n","Epoch [10/20], Step [225/327], Loss: 0.2217\n","Epoch [10/20], Step [250/327], Loss: 0.2288\n","Epoch [10/20], Step [275/327], Loss: 0.2048\n","Epoch [10/20], Step [300/327], Loss: 0.2497\n","Epoch [10/20], Step [325/327], Loss: 0.1952\n","Start validation #10\n","Validation #10 Average Loss: 0.3149, mIoU: 0.3735\n","Epoch [11/20], Step [25/327], Loss: 0.2550\n","Epoch [11/20], Step [50/327], Loss: 0.1985\n","Epoch [11/20], Step [75/327], Loss: 0.2418\n","Epoch [11/20], Step [100/327], Loss: 0.2564\n","Epoch [11/20], Step [125/327], Loss: 0.2629\n","Epoch [11/20], Step [150/327], Loss: 0.2966\n","Epoch [11/20], Step [175/327], Loss: 0.2395\n","Epoch [11/20], Step [200/327], Loss: 0.1625\n","Epoch [11/20], Step [225/327], Loss: 0.2134\n","Epoch [11/20], Step [250/327], Loss: 0.2513\n","Epoch [11/20], Step [275/327], Loss: 0.4312\n","Epoch [11/20], Step [300/327], Loss: 0.1548\n","Epoch [11/20], Step [325/327], Loss: 0.1885\n","Start validation #11\n","Validation #11 Average Loss: 0.3148, mIoU: 0.3945\n","Best performance at epoch: 11\n","Save model in ./saved\n","Epoch [12/20], Step [25/327], Loss: 0.1438\n","Epoch [12/20], Step [50/327], Loss: 0.2216\n","Epoch [12/20], Step [75/327], Loss: 0.1544\n","Epoch [12/20], Step [100/327], Loss: 0.1512\n","Epoch [12/20], Step [125/327], Loss: 0.1995\n","Epoch [12/20], Step [150/327], Loss: 0.1367\n","Epoch [12/20], Step [175/327], Loss: 0.1824\n","Epoch [12/20], Step [200/327], Loss: 0.3596\n","Epoch [12/20], Step [225/327], Loss: 0.2992\n","Epoch [12/20], Step [250/327], Loss: 0.1347\n","Epoch [12/20], Step [275/327], Loss: 0.2011\n","Epoch [12/20], Step [300/327], Loss: 0.1510\n","Epoch [12/20], Step [325/327], Loss: 0.1700\n","Start validation #12\n","Validation #12 Average Loss: 0.3235, mIoU: 0.3789\n","Epoch [13/20], Step [25/327], Loss: 0.2046\n","Epoch [13/20], Step [50/327], Loss: 0.2263\n","Epoch [13/20], Step [75/327], Loss: 0.2796\n","Epoch [13/20], Step [100/327], Loss: 0.1668\n","Epoch [13/20], Step [125/327], Loss: 0.2408\n","Epoch [13/20], Step [150/327], Loss: 0.1850\n","Epoch [13/20], Step [175/327], Loss: 0.1720\n","Epoch [13/20], Step [200/327], Loss: 0.4034\n","Epoch [13/20], Step [225/327], Loss: 0.1211\n","Epoch [13/20], Step [250/327], Loss: 0.2274\n","Epoch [13/20], Step [275/327], Loss: 0.1997\n","Epoch [13/20], Step [300/327], Loss: 0.1265\n","Epoch [13/20], Step [325/327], Loss: 0.1926\n","Start validation #13\n","Validation #13 Average Loss: 0.3150, mIoU: 0.3962\n","Best performance at epoch: 13\n","Save model in ./saved\n","Epoch [14/20], Step [25/327], Loss: 0.1707\n","Epoch [14/20], Step [50/327], Loss: 0.2253\n","Epoch [14/20], Step [75/327], Loss: 0.2499\n","Epoch [14/20], Step [100/327], Loss: 0.1783\n","Epoch [14/20], Step [125/327], Loss: 0.2652\n","Epoch [14/20], Step [150/327], Loss: 0.1504\n","Epoch [14/20], Step [175/327], Loss: 0.2612\n","Epoch [14/20], Step [200/327], Loss: 0.1846\n","Epoch [14/20], Step [225/327], Loss: 0.1167\n","Epoch [14/20], Step [250/327], Loss: 0.1133\n","Epoch [14/20], Step [275/327], Loss: 0.1306\n","Epoch [14/20], Step [300/327], Loss: 0.2935\n","Epoch [14/20], Step [325/327], Loss: 0.1873\n","Start validation #14\n","Validation #14 Average Loss: 0.3206, mIoU: 0.3838\n","Epoch [15/20], Step [25/327], Loss: 0.1859\n","Epoch [15/20], Step [50/327], Loss: 0.2410\n","Epoch [15/20], Step [75/327], Loss: 0.1211\n","Epoch [15/20], Step [100/327], Loss: 0.2031\n","Epoch [15/20], Step [125/327], Loss: 0.1582\n","Epoch [15/20], Step [150/327], Loss: 0.2057\n","Epoch [15/20], Step [175/327], Loss: 0.1880\n","Epoch [15/20], Step [200/327], Loss: 0.1716\n","Epoch [15/20], Step [225/327], Loss: 0.1925\n","Epoch [15/20], Step [250/327], Loss: 0.1875\n","Epoch [15/20], Step [275/327], Loss: 0.2245\n","Epoch [15/20], Step [300/327], Loss: 0.1323\n","Epoch [15/20], Step [325/327], Loss: 0.1391\n","Start validation #15\n","Validation #15 Average Loss: 0.3459, mIoU: 0.3889\n","Epoch [16/20], Step [25/327], Loss: 0.3228\n","Epoch [16/20], Step [50/327], Loss: 0.1547\n","Epoch [16/20], Step [75/327], Loss: 0.2454\n","Epoch [16/20], Step [100/327], Loss: 0.2671\n","Epoch [16/20], Step [125/327], Loss: 0.1225\n","Epoch [16/20], Step [150/327], Loss: 0.3258\n","Epoch [16/20], Step [175/327], Loss: 0.1632\n","Epoch [16/20], Step [200/327], Loss: 0.2180\n","Epoch [16/20], Step [225/327], Loss: 0.3088\n","Epoch [16/20], Step [250/327], Loss: 0.1822\n","Epoch [16/20], Step [275/327], Loss: 0.1527\n","Epoch [16/20], Step [300/327], Loss: 0.2664\n","Epoch [16/20], Step [325/327], Loss: 0.1474\n","Start validation #16\n","Validation #16 Average Loss: 0.3337, mIoU: 0.4004\n","Best performance at epoch: 16\n","Save model in ./saved\n","Epoch [17/20], Step [25/327], Loss: 0.1714\n","Epoch [17/20], Step [50/327], Loss: 0.2490\n","Epoch [17/20], Step [75/327], Loss: 0.2950\n","Epoch [17/20], Step [100/327], Loss: 0.1522\n","Epoch [17/20], Step [125/327], Loss: 0.1430\n","Epoch [17/20], Step [150/327], Loss: 0.1576\n","Epoch [17/20], Step [175/327], Loss: 0.2000\n","Epoch [17/20], Step [200/327], Loss: 0.2298\n","Epoch [17/20], Step [225/327], Loss: 0.0839\n","Epoch [17/20], Step [250/327], Loss: 0.1202\n","Epoch [17/20], Step [275/327], Loss: 0.1744\n","Epoch [17/20], Step [300/327], Loss: 0.1728\n","Epoch [17/20], Step [325/327], Loss: 0.2281\n","Start validation #17\n","Validation #17 Average Loss: 0.3326, mIoU: 0.3928\n","Epoch [18/20], Step [25/327], Loss: 0.1338\n","Epoch [18/20], Step [50/327], Loss: 0.1307\n","Epoch [18/20], Step [75/327], Loss: 0.2076\n","Epoch [18/20], Step [100/327], Loss: 0.0797\n","Epoch [18/20], Step [125/327], Loss: 0.1360\n","Epoch [18/20], Step [150/327], Loss: 0.2392\n","Epoch [18/20], Step [175/327], Loss: 0.1266\n","Epoch [18/20], Step [200/327], Loss: 0.1301\n","Epoch [18/20], Step [225/327], Loss: 0.2301\n","Epoch [18/20], Step [250/327], Loss: 0.1679\n","Epoch [18/20], Step [275/327], Loss: 0.0914\n","Epoch [18/20], Step [300/327], Loss: 0.1254\n","Epoch [18/20], Step [325/327], Loss: 0.2304\n","Start validation #18\n","Validation #18 Average Loss: 0.3141, mIoU: 0.3991\n","Epoch [19/20], Step [25/327], Loss: 0.1443\n","Epoch [19/20], Step [50/327], Loss: 0.2198\n","Epoch [19/20], Step [75/327], Loss: 0.1209\n","Epoch [19/20], Step [100/327], Loss: 0.1296\n","Epoch [19/20], Step [125/327], Loss: 0.1129\n","Epoch [19/20], Step [150/327], Loss: 0.1056\n","Epoch [19/20], Step [175/327], Loss: 0.1393\n","Epoch [19/20], Step [200/327], Loss: 0.1295\n","Epoch [19/20], Step [225/327], Loss: 0.1853\n","Epoch [19/20], Step [250/327], Loss: 0.1774\n","Epoch [19/20], Step [275/327], Loss: 0.1588\n","Epoch [19/20], Step [300/327], Loss: 0.1575\n","Epoch [19/20], Step [325/327], Loss: 0.0904\n","Start validation #19\n","Validation #19 Average Loss: 0.3210, mIoU: 0.3881\n","Epoch [20/20], Step [25/327], Loss: 0.0841\n","Epoch [20/20], Step [50/327], Loss: 0.1029\n","Epoch [20/20], Step [75/327], Loss: 0.1236\n","Epoch [20/20], Step [100/327], Loss: 0.1406\n","Epoch [20/20], Step [125/327], Loss: 0.1357\n","Epoch [20/20], Step [150/327], Loss: 0.1692\n","Epoch [20/20], Step [175/327], Loss: 0.1385\n","Epoch [20/20], Step [200/327], Loss: 0.1617\n","Epoch [20/20], Step [225/327], Loss: 0.2095\n","Epoch [20/20], Step [250/327], Loss: 0.2132\n","Epoch [20/20], Step [275/327], Loss: 0.1184\n","Epoch [20/20], Step [300/327], Loss: 0.1695\n","Epoch [20/20], Step [325/327], Loss: 0.1597\n","Start validation #20\n","Validation #20 Average Loss: 0.3265, mIoU: 0.3862\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"C6ClcO0J-NwR"},"source":["## 저장된 model 불러오기 (학습된 이후) "]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:21.050200Z","start_time":"2021-04-16T19:44:20.802200Z"},"scrolled":true,"id":"KQPtUDzd-NwR","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620102899370,"user_tz":-540,"elapsed":882,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"72d54b02-20cf-4ea6-991d-52fd5331c0a6"},"source":["# best model 저장된 경로\n","model_path = './saved/pan_effb3_noisy_focal_madgrad_cosLR.pt'\n","\n","# best model 불러오기\n","checkpoint = torch.load(model_path, map_location=device)\n","model.load_state_dict(checkpoint)\n","\n","# 추론을 실행하기 전에는 반드시 설정 (batch normalization, dropout 를 평가 모드로 설정)\n","# model.eval()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{"tags":[]},"execution_count":16}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:24.939227Z","start_time":"2021-04-16T19:44:24.518228Z"},"id":"0LQqrDAp-NwR","colab":{"base_uri":"https://localhost:8080/","height":502},"executionInfo":{"status":"ok","timestamp":1620102907649,"user_tz":-540,"elapsed":6266,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"b34da4ee-1fe9-4960-f841-9627d644c50b"},"source":["# 첫번째 batch의 추론 결과 확인\n","for imgs, image_infos in test_loader:\n"," image_infos = image_infos\n"," temp_images = imgs\n"," \n"," model.eval()\n"," # inference\n"," outs = model(torch.stack(temp_images).to(device))\n"," oms = torch.argmax(outs.squeeze(), dim=1).detach().cpu().numpy()\n"," \n"," break\n","\n","i = 1\n","fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(16, 16))\n","\n","print('Shape of Original Image :', list(temp_images[i].shape))\n","print('Shape of Predicted : ', list(oms[i].shape))\n","print('Unique values, category of transformed mask : \\n', [{int(i),category_names[int(i)]} for i in list(np.unique(oms[i]))])\n","\n","# Original image\n","ax1.imshow(temp_images[i].permute([1,2,0]))\n","ax1.grid(False)\n","ax1.set_title(\"Original image : {}\".format(image_infos[i]['file_name']), fontsize = 15)\n","\n","# Predicted\n","ax2.imshow(oms[i])\n","ax2.grid(False)\n","ax2.set_title(\"Predicted : {}\".format(image_infos[i]['file_name']), fontsize = 15)\n","\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Shape of Original Image : [3, 512, 512]\n","Shape of Predicted : [512, 512]\n","Unique values, category of transformed mask : \n"," [{0, 'Backgroud'}, {'General trash', 2}, {'Paper', 3}, {9, 'Plastic bag'}, {11, 'Clothing'}]\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"evYjR2F3-NwS"},"source":["## submission을 위한 test 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:27.469285Z","start_time":"2021-04-16T19:44:27.456021Z"},"id":"nhMLnV5d-NwS"},"source":["def test(model, data_loader, device):\n"," size = 256\n"," transform = A.Compose([A.Resize(256, 256)])\n"," print('Start prediction.')\n"," model.eval()\n"," \n"," file_name_list = []\n"," preds_array = np.empty((0, size*size), dtype=np.long)\n"," \n"," with torch.no_grad():\n"," for step, (imgs, image_infos) in enumerate(test_loader):\n","\n"," # inference (512 x 512)\n"," outs = model(torch.stack(imgs).to(device))\n"," oms = torch.argmax(outs.squeeze(), dim=1).detach().cpu().numpy()\n"," \n"," # resize (256 x 256)\n"," temp_mask = []\n"," for img, mask in zip(np.stack(imgs), oms):\n"," transformed = transform(image=img, mask=mask)\n"," mask = transformed['mask']\n"," temp_mask.append(mask)\n","\n"," oms = np.array(temp_mask)\n"," \n"," oms = oms.reshape([oms.shape[0], size*size]).astype(int)\n"," preds_array = np.vstack((preds_array, oms))\n"," \n"," file_name_list.append([i['file_name'] for i in image_infos])\n"," print(\"End prediction.\")\n"," file_names = [y for x in file_name_list for y in x]\n"," \n"," return file_names, preds_array"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"r1YKHBf4-NwT"},"source":["## submission.csv 생성"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:45:42.235310Z","start_time":"2021-04-16T19:44:30.499016Z"},"scrolled":true,"id":"Bz79_g8K-NwT","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620103458747,"user_tz":-540,"elapsed":512460,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"36e4f410-35c7-4b9c-9e4a-e66269f73292"},"source":["# sample_submisson.csv 열기\n","submission = pd.read_csv('./submission/sample_submission.csv', index_col=None)\n","\n","# test set에 대한 prediction\n","file_names, preds = test(model, test_loader, device)\n","\n","# PredictionString 대입\n","for file_name, string in zip(file_names, preds):\n"," submission = submission.append({\"image_id\" : file_name, \"PredictionString\" : ' '.join(str(e) for e in string.tolist())}, \n"," ignore_index=True)\n","\n","# submission.csv로 저장\n","submission.to_csv(\"./submission/re_pan_effb3_noisy_focal_madgrad_cosLR.csv\", index=False)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Start prediction.\n","End prediction.\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"Zgl7Ye7F-NwT"},"source":["## 제출까지\n","\n"]},{"cell_type":"code","metadata":{"id":"022E8AC7-NwT","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620103464861,"user_tz":-540,"elapsed":5218,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"1b00da55-62cc-4fa6-c823-83dcbaa871b0"},"source":["import json\n","import requests\n","import os\n","from urllib.parse import urlparse, parse_qsl, urlencode, urlunparse\n","\n","def submit(user_key='', file_path = '', desc=\"\"):\n"," if not user_key:\n"," raise Exception(\"No UserKey\" )\n"," url = urlparse('http://ec2-13-124-161-225.ap-northeast-2.compute.amazonaws.com:8000/api/v1/competition/28/presigned_url/?description=&hyperparameters={%22training%22:{},%22inference%22:{}}')\n"," qs = dict(parse_qsl(url.query))\n"," qs['description'] = desc\n"," parts = url._replace(query=urlencode(qs))\n"," url = urlunparse(parts)\n","\n"," print(url)\n"," headers = {\n"," 'Authorization': user_key\n"," }\n"," res = requests.get(url, headers=headers)\n"," print(res.text)\n"," data = json.loads(res.text)\n"," \n"," submit_url = data['url']\n"," body = {\n"," 'key':'app/Competitions/000028/Users/{}/Submissions/{}/output.csv'.format(str(data['submission']['user']).zfill(8),str(data['submission']['local_id']).zfill(4)),\n"," 'x-amz-algorithm':data['fields']['x-amz-algorithm'],\n"," 'x-amz-credential':data['fields']['x-amz-credential'],\n"," 'x-amz-date':data['fields']['x-amz-date'],\n"," 'policy':data['fields']['policy'],\n"," 'x-amz-signature':data['fields']['x-amz-signature']\n"," }\n"," requests.post(url=submit_url, data=body, files={'file': open(file_path, 'rb')})\n","\n","\n","####################################################################################\n","test_dir = \"/content/drive/MyDrive/Trash/code/submission\" # 수정 필요 : output 파일 폴더 \n","desc = 're_pan_effb3_noisy_focal_madgrad_cosLR' # 수정 필요 : 파일에 대한 설명\n","output_file = \"re_pan_effb3_noisy_focal_madgrad_cosLR.csv\" #수정 필요 : output 파일 \n","user_key = \"Bearer 7bb5f96452751a238ffaf91a93c4242bf9b72abe\" # 수정 필요 : Authorization \n","\n","\n","submit(user_key, os.path.join(test_dir, output_file),desc)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["http://ec2-13-124-161-225.ap-northeast-2.compute.amazonaws.com:8000/api/v1/competition/28/presigned_url/?hyperparameters=%7B%22training%22%3A%7B%7D%2C%22inference%22%3A%7B%7D%7D&description=re_pan_effb3_noisy_focal_madgrad_cosLR\n","{\"url\":\"https://prod-aistages-private.s3.amazonaws.com/\",\"fields\":{\"key\":\"app/Competitions/000028/Users/00000099/Submissions/0021/output.csv\",\"x-amz-algorithm\":\"AWS4-HMAC-SHA256\",\"x-amz-credential\":\"AKIA45LU4MHUJ7WLDQVO/20210504/ap-northeast-2/s3/aws4_request\",\"x-amz-date\":\"20210504T044420Z\",\"policy\":\"eyJleHBpcmF0aW9uIjogIjIwMjEtMDUtMDRUMDU6NDQ6MjBaIiwgImNvbmRpdGlvbnMiOiBbeyJidWNrZXQiOiAicHJvZC1haXN0YWdlcy1wcml2YXRlIn0sIHsia2V5IjogImFwcC9Db21wZXRpdGlvbnMvMDAwMDI4L1VzZXJzLzAwMDAwMDk5L1N1Ym1pc3Npb25zLzAwMjEvb3V0cHV0LmNzdiJ9LCB7IngtYW16LWFsZ29yaXRobSI6ICJBV1M0LUhNQUMtU0hBMjU2In0sIHsieC1hbXotY3JlZGVudGlhbCI6ICJBS0lBNDVMVTRNSFVKN1dMRFFWTy8yMDIxMDUwNC9hcC1ub3J0aGVhc3QtMi9zMy9hd3M0X3JlcXVlc3QifSwgeyJ4LWFtei1kYXRlIjogIjIwMjEwNTA0VDA0NDQyMFoifV19\",\"x-amz-signature\":\"f10073e85a92d49dacd44915305fdc789f99edede496d9b4f5029a16e422a52f\"},\"submission\":{\"id\":14867,\"phase\":\"Created\",\"type\":\"File\",\"local_id\":21,\"hyperparameters\":\"{\\\"training\\\": {}, \\\"inference\\\": {}}\",\"description\":\"re_pan_effb3_noisy_focal_madgrad_cosLR\",\"final\":false,\"created_at\":\"2021-05-04T13:44:20.427630+09:00\",\"updated_at\":\"2021-05-04T13:44:20.427663+09:00\",\"user\":99,\"competition\":28,\"image\":null}}\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"wPYl39uVqxL8"},"source":[""],"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/chanyub_seg/code/2_aug_CLAHE_Horizontalflip.ipynb b/chanyub_seg/code/2_aug_CLAHE_Horizontalflip.ipynb deleted file mode 100644 index ba845ae..0000000 --- a/chanyub_seg/code/2_aug_CLAHE_Horizontalflip.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"nbformat":4,"nbformat_minor":0,"metadata":{"hide_input":false,"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.1"},"toc":{"base_numbering":1,"nav_menu":{},"number_sections":true,"sideBar":true,"skip_h1_title":false,"title_cell":"Table of Contents","title_sidebar":"Contents","toc_cell":true,"toc_position":{"height":"calc(100% - 180px)","left":"10px","top":"150px","width":"297.278px"},"toc_section_display":true,"toc_window_display":true},"colab":{"name":"2_aug_CLAHE_Horizontalflip.ipynb","provenance":[]},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"kO9JcCvC0Qq_","executionInfo":{"status":"ok","timestamp":1620137005505,"user_tz":-540,"elapsed":1183,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"89b67bdc-7d93-4a43-db78-bf021ac853f7"},"source":["from google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"CHnx6ACo0SSR","executionInfo":{"status":"ok","timestamp":1620137006169,"user_tz":-540,"elapsed":1823,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"770419be-62ea-4ffc-f636-de1d95f384ad"},"source":["ls"],"execution_count":2,"outputs":[{"output_type":"stream","text":["\u001b[0m\u001b[01;34mdrive\u001b[0m/ \u001b[01;34msample_data\u001b[0m/\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rfBXtbCI0TPa","executionInfo":{"status":"ok","timestamp":1620137006170,"user_tz":-540,"elapsed":1810,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"0a290e17-d1f4-491c-84e8-e253fac84cb7"},"source":["cd drive/MyDrive/Trash/code"],"execution_count":3,"outputs":[{"output_type":"stream","text":["/content/drive/MyDrive/Trash/code\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"X6b4uOxD0Vkp","executionInfo":{"status":"ok","timestamp":1620137008146,"user_tz":-540,"elapsed":3774,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"2a713050-08ce-4b70-8d86-26fdfc9a513e"},"source":["!pip install albumentations==0.5.2"],"execution_count":4,"outputs":[{"output_type":"stream","text":["Requirement already satisfied: albumentations==0.5.2 in /usr/local/lib/python3.7/dist-packages (0.5.2)\n","Requirement already satisfied: scikit-image>=0.16.1 in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (0.16.2)\n","Requirement already satisfied: PyYAML in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (3.13)\n","Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (1.4.1)\n","Requirement already satisfied: numpy>=1.11.1 in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (1.19.5)\n","Requirement already satisfied: opencv-python-headless>=4.1.1 in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (4.5.1.48)\n","Requirement already satisfied: imgaug>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (0.4.0)\n","Requirement already satisfied: imageio>=2.3.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations==0.5.2) (2.4.1)\n","Requirement already satisfied: pillow>=4.3.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations==0.5.2) (7.1.2)\n","Requirement already satisfied: PyWavelets>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations==0.5.2) (1.1.1)\n","Requirement already satisfied: networkx>=2.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations==0.5.2) (2.5.1)\n","Requirement already satisfied: matplotlib!=3.0.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations==0.5.2) (3.2.2)\n","Requirement already satisfied: opencv-python in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (4.1.2.30)\n","Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (1.15.0)\n","Requirement already satisfied: Shapely in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (1.7.1)\n","Requirement already satisfied: decorator<5,>=4.3 in /usr/local/lib/python3.7/dist-packages (from networkx>=2.0->scikit-image>=0.16.1->albumentations==0.5.2) (4.4.2)\n","Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.16.1->albumentations==0.5.2) (2.8.1)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.16.1->albumentations==0.5.2) (0.10.0)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.16.1->albumentations==0.5.2) (1.3.1)\n","Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.16.1->albumentations==0.5.2) (2.4.7)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.826930Z","start_time":"2021-04-18T10:34:45.406686Z"},"scrolled":false,"colab":{"base_uri":"https://localhost:8080/"},"id":"HiN9b-Ly0I3p","executionInfo":{"status":"ok","timestamp":1620137009509,"user_tz":-540,"elapsed":5122,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"1488a35b-17c5-44ba-f965-509365844714"},"source":["import os\n","import random\n","import time\n","import json\n","import warnings \n","warnings.filterwarnings('ignore')\n","\n","import torch\n","import torch.nn as nn\n","from torch.utils.data import Dataset, DataLoader\n","from utils import label_accuracy_score\n","import cv2\n","\n","import numpy as np\n","import pandas as pd\n","\n","# 전처리를 위한 라이브러리\n","from pycocotools.coco import COCO\n","import torchvision\n","import torchvision.transforms as transforms\n","\n","import albumentations as A\n","from albumentations.pytorch import ToTensorV2\n","\n","# 시각화를 위한 라이브러리\n","import matplotlib.pyplot as plt\n","import seaborn as sns; sns.set()\n","\n","plt.rcParams['axes.grid'] = False\n","\n","print('pytorch version: {}'.format(torch.__version__))\n","print('GPU 사용 가능 여부: {}'.format(torch.cuda.is_available()))\n","\n","print(torch.cuda.get_device_name(0))\n","print(torch.cuda.device_count())\n","\n","device = \"cuda\" if torch.cuda.is_available() else \"cpu\" # GPU 사용 가능 여부에 따라 device 정보 저장"],"execution_count":5,"outputs":[{"output_type":"stream","text":["pytorch version: 1.8.1+cu101\n","GPU 사용 가능 여부: True\n","Tesla P100-PCIE-16GB\n","1\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"Uuj6y7Ra0I3r"},"source":["## 하이퍼파라미터 세팅 및 seed 고정"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.841930Z","start_time":"2021-04-18T10:34:47.827931Z"},"id":"k-febRcn0I3r","executionInfo":{"status":"ok","timestamp":1620137009513,"user_tz":-540,"elapsed":5118,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["batch_size = 8 # Mini-batch size\n","num_epochs = 20\n","learning_rate = 0.0001"],"execution_count":6,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.856930Z","start_time":"2021-04-18T10:34:47.842931Z"},"id":"YA3jAi2a0I3s","executionInfo":{"status":"ok","timestamp":1620137009518,"user_tz":-540,"elapsed":5114,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# seed 고정\n","random_seed = 42\n","torch.manual_seed(random_seed)\n","torch.cuda.manual_seed(random_seed)\n","# torch.cuda.manual_seed_all(random_seed) # if use multi-GPU\n","torch.backends.cudnn.deterministic = True\n","torch.backends.cudnn.benchmark = False\n","np.random.seed(random_seed)\n","random.seed(random_seed)"],"execution_count":7,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"ePFcujAe0I3s"},"source":["## 학습 데이터 EDA"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.381961Z","start_time":"2021-04-18T10:34:47.857930Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"Ds0jp-pz0I3s","executionInfo":{"status":"ok","timestamp":1620137013393,"user_tz":-540,"elapsed":8982,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"fe4ae56f-08c9-417c-bd2e-8b87bd7997c5"},"source":["%matplotlib inline\n","\n","dataset_path = '../input/data'\n","anns_file_path = dataset_path + '/' + 'train.json'\n","\n","# Read annotations\n","with open(anns_file_path, 'r') as f:\n"," dataset = json.loads(f.read())\n","\n","categories = dataset['categories']\n","anns = dataset['annotations']\n","imgs = dataset['images']\n","nr_cats = len(categories)\n","nr_annotations = len(anns)\n","nr_images = len(imgs)\n","\n","# Load categories and super categories\n","cat_names = []\n","super_cat_names = []\n","super_cat_ids = {}\n","super_cat_last_name = ''\n","nr_super_cats = 0\n","for cat_it in categories:\n"," cat_names.append(cat_it['name'])\n"," super_cat_name = cat_it['supercategory']\n"," # Adding new supercat\n"," if super_cat_name != super_cat_last_name:\n"," super_cat_names.append(super_cat_name)\n"," super_cat_ids[super_cat_name] = nr_super_cats\n"," super_cat_last_name = super_cat_name\n"," nr_super_cats += 1\n","\n","print('Number of super categories:', nr_super_cats)\n","print('Number of categories:', nr_cats)\n","print('Number of annotations:', nr_annotations)\n","print('Number of images:', nr_images)"],"execution_count":8,"outputs":[{"output_type":"stream","text":["Number of super categories: 11\n","Number of categories: 11\n","Number of annotations: 21116\n","Number of images: 2617\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.546964Z","start_time":"2021-04-18T10:34:51.382969Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/","height":355},"id":"SVmavtk00I3t","executionInfo":{"status":"ok","timestamp":1620137013879,"user_tz":-540,"elapsed":9459,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"a8ef6d56-5ad9-4725-b1ce-88788d196b85"},"source":["# Count annotations\n","cat_histogram = np.zeros(nr_cats,dtype=int)\n","for ann in anns:\n"," cat_histogram[ann['category_id']] += 1\n","\n","# Initialize the matplotlib figure\n","f, ax = plt.subplots(figsize=(5,5))\n","\n","# Convert to DataFrame\n","df = pd.DataFrame({'Categories': cat_names, 'Number of annotations': cat_histogram})\n","df = df.sort_values('Number of annotations', 0, False)\n","\n","# Plot the histogram\n","plt.title(\"category distribution of train set \")\n","plot_1 = sns.barplot(x=\"Number of annotations\", y=\"Categories\", data=df, label=\"Total\", color=\"b\")"],"execution_count":9,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.561965Z","start_time":"2021-04-18T10:34:51.547969Z"},"id":"xK3EYdyX0I3u","executionInfo":{"status":"ok","timestamp":1620137013880,"user_tz":-540,"elapsed":9453,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# category labeling \n","sorted_temp_df = df.sort_index()\n","\n","# background = 0 에 해당되는 label 추가 후 기존들을 모두 label + 1 로 설정\n","sorted_df = pd.DataFrame([\"Backgroud\"], columns = [\"Categories\"])\n","sorted_df = sorted_df.append(sorted_temp_df, ignore_index=True)"],"execution_count":10,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.576961Z","start_time":"2021-04-18T10:34:51.562964Z"},"scrolled":false,"colab":{"base_uri":"https://localhost:8080/","height":421},"id":"9UQEjg8r0I3u","executionInfo":{"status":"ok","timestamp":1620137013884,"user_tz":-540,"elapsed":9448,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"98ab585d-e3eb-44c4-bf36-5dc37ed32e50"},"source":["# class (Categories) 에 따른 index 확인 (0~11 : 총 12개)\n","sorted_df"],"execution_count":11,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
CategoriesNumber of annotations
0BackgroudNaN
1UNKNOWN128.0
2General trash2225.0
3Paper7448.0
4Paper pack527.0
5Metal449.0
6Glass488.0
7Plastic2472.0
8Styrofoam1074.0
9Plastic bag6114.0
10Battery50.0
11Clothing141.0
\n","
"],"text/plain":[" Categories Number of annotations\n","0 Backgroud NaN\n","1 UNKNOWN 128.0\n","2 General trash 2225.0\n","3 Paper 7448.0\n","4 Paper pack 527.0\n","5 Metal 449.0\n","6 Glass 488.0\n","7 Plastic 2472.0\n","8 Styrofoam 1074.0\n","9 Plastic bag 6114.0\n","10 Battery 50.0\n","11 Clothing 141.0"]},"metadata":{"tags":[]},"execution_count":11}]},{"cell_type":"markdown","metadata":{"id":"AHvcEEXh0I3u"},"source":["## 데이터 전처리 함수 정의 (Dataset)"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:52.693328Z","start_time":"2021-04-18T10:34:52.681328Z"},"id":"tBr2oTea0I3v","executionInfo":{"status":"ok","timestamp":1620137013885,"user_tz":-540,"elapsed":8116,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["category_names = list(sorted_df.Categories)\n","\n","def get_classname(classID, cats):\n"," for i in range(len(cats)):\n"," if cats[i]['id']==classID:\n"," return cats[i]['name']\n"," return \"None\"\n","\n","class CustomDataLoader(Dataset):\n"," \"\"\"COCO format\"\"\"\n"," def __init__(self, data_dir, mode = 'train', transform = None):\n"," super().__init__()\n"," self.mode = mode\n"," self.transform = transform\n"," self.coco = COCO(data_dir)\n"," \n"," def __getitem__(self, index: int):\n"," # dataset이 index되어 list처럼 동작\n"," image_id = self.coco.getImgIds(imgIds=index)\n"," image_infos = self.coco.loadImgs(image_id)[0]\n"," \n"," # cv2 를 활용하여 image 불러오기\n"," images = cv2.imread(os.path.join(dataset_path, image_infos['file_name']))\n"," images = cv2.cvtColor(images, cv2.COLOR_BGR2RGB).astype(np.float32)\n"," images /= 255.0\n"," \n"," if (self.mode in ('train', 'val')):\n"," ann_ids = self.coco.getAnnIds(imgIds=image_infos['id'])\n"," anns = self.coco.loadAnns(ann_ids)\n","\n"," # Load the categories in a variable\n"," cat_ids = self.coco.getCatIds()\n"," cats = self.coco.loadCats(cat_ids)\n","\n"," # masks : size가 (height x width)인 2D\n"," # 각각의 pixel 값에는 \"category id + 1\" 할당\n"," # Background = 0\n"," masks = np.zeros((image_infos[\"height\"], image_infos[\"width\"]))\n"," # Unknown = 1, General trash = 2, ... , Cigarette = 11\n"," for i in range(len(anns)):\n"," className = get_classname(anns[i]['category_id'], cats)\n"," pixel_value = category_names.index(className)\n"," masks = np.maximum(self.coco.annToMask(anns[i])*pixel_value, masks)\n"," masks = masks.astype(np.float32)\n","\n"," # transform -> albumentations 라이브러리 활용\n"," if self.transform is not None:\n"," transformed = self.transform(image=images, mask=masks)\n"," images = transformed[\"image\"]\n"," masks = transformed[\"mask\"]\n"," \n"," return images, masks, image_infos\n"," \n"," if self.mode == 'test':\n"," # transform -> albumentations 라이브러리 활용\n"," if self.transform is not None:\n"," transformed = self.transform(image=images)\n"," images = transformed[\"image\"]\n"," \n"," return images, image_infos\n"," \n"," \n"," def __len__(self) -> int:\n"," # 전체 dataset의 size를 return\n"," return len(self.coco.getImgIds())"],"execution_count":12,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"PBcB4oQh0I3w"},"source":["## Dataset 정의 및 DataLoader 할당"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:58.823175Z","start_time":"2021-04-18T10:34:54.106233Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"LxAXSS-c0I3x","executionInfo":{"status":"ok","timestamp":1620137060788,"user_tz":-540,"elapsed":5714,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"58c39944-346c-4cde-effa-929ba90e4ba4"},"source":["# train.json / validation.json / test.json 디렉토리 설정\n","train_path = dataset_path + '/train.json'\n","val_path = dataset_path + '/val.json'\n","test_path = dataset_path + '/test.json'\n","\n","# collate_fn needs for batch\n","def collate_fn(batch):\n"," return tuple(zip(*batch))\n","\n","train_transform = A.Compose([\n"," A.augmentations.Resize(256,256),\n"," A.FromFloat(dtype='uint8'),\n"," A.CLAHE(),\n"," A.transforms.ToFloat(),\n"," A.HorizontalFlip(),\n"," ToTensorV2()\n"," ])\n","\n","val_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","test_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","# create own Dataset 1 (skip)\n","# validation set을 직접 나누고 싶은 경우\n","# random_split 사용하여 data set을 8:2 로 분할\n","# train_size = int(0.8*len(dataset))\n","# val_size = int(len(dataset)-train_size)\n","# dataset = CustomDataLoader(data_dir=train_path, mode='train', transform=transform)\n","# train_dataset, val_dataset = torch.utils.data.random_split(dataset, [train_size, val_size])\n","\n","# create own Dataset 2\n","# train dataset\n","train_dataset = CustomDataLoader(data_dir=train_path, mode='train', transform=train_transform)\n","\n","# validation dataset\n","val_dataset = CustomDataLoader(data_dir=val_path, mode='val', transform=val_transform)\n","\n","# test dataset\n","test_dataset = CustomDataLoader(data_dir=test_path, mode='test', transform=test_transform)\n","\n","\n","# DataLoader\n","train_loader = torch.utils.data.DataLoader(dataset=train_dataset, \n"," batch_size=batch_size,\n"," shuffle=True,\n"," num_workers=4,\n"," collate_fn=collate_fn,\n"," drop_last=True)\n","\n","val_loader = torch.utils.data.DataLoader(dataset=val_dataset, \n"," batch_size=batch_size,\n"," shuffle=False,\n"," num_workers=4,\n"," collate_fn=collate_fn,\n"," drop_last=True) \n","\n","test_loader = torch.utils.data.DataLoader(dataset=test_dataset,\n"," batch_size=batch_size,\n"," num_workers=4,\n"," collate_fn=collate_fn)"],"execution_count":14,"outputs":[{"output_type":"stream","text":["loading annotations into memory...\n","Done (t=3.80s)\n","creating index...\n","index created!\n","loading annotations into memory...\n","Done (t=0.85s)\n","creating index...\n","index created!\n","loading annotations into memory...\n","Done (t=0.02s)\n","creating index...\n","index created!\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-aaTrvBk0gKc","executionInfo":{"status":"ok","timestamp":1620137063293,"user_tz":-540,"elapsed":3825,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"ae685b71-c12c-4934-b43f-a8eedab69440"},"source":["!pip install wandb"],"execution_count":15,"outputs":[{"output_type":"stream","text":["Requirement already satisfied: wandb in /usr/local/lib/python3.7/dist-packages (0.10.29)\n","Requirement already satisfied: shortuuid>=0.5.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (1.0.1)\n","Requirement already satisfied: psutil>=5.0.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (5.4.8)\n","Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.7/dist-packages (from wandb) (2.8.1)\n","Requirement already satisfied: protobuf>=3.12.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (3.12.4)\n","Requirement already satisfied: Click>=7.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (7.1.2)\n","Requirement already satisfied: subprocess32>=3.5.3 in /usr/local/lib/python3.7/dist-packages (from wandb) (3.5.4)\n","Requirement already satisfied: docker-pycreds>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (0.4.0)\n","Requirement already satisfied: sentry-sdk>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (1.0.0)\n","Requirement already satisfied: promise<3,>=2.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (2.3)\n","Requirement already satisfied: configparser>=3.8.1 in /usr/local/lib/python3.7/dist-packages (from wandb) (5.0.2)\n","Requirement already satisfied: pathtools in /usr/local/lib/python3.7/dist-packages (from wandb) (0.1.2)\n","Requirement already satisfied: GitPython>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (3.1.14)\n","Requirement already satisfied: PyYAML in /usr/local/lib/python3.7/dist-packages (from wandb) (3.13)\n","Requirement already satisfied: six>=1.13.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (1.15.0)\n","Requirement already satisfied: requests<3,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (2.23.0)\n","Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from protobuf>=3.12.0->wandb) (56.0.0)\n","Requirement already satisfied: certifi in /usr/local/lib/python3.7/dist-packages (from sentry-sdk>=0.4.0->wandb) (2020.12.5)\n","Requirement already satisfied: urllib3>=1.10.0 in /usr/local/lib/python3.7/dist-packages (from sentry-sdk>=0.4.0->wandb) (1.24.3)\n","Requirement already satisfied: gitdb<5,>=4.0.1 in /usr/local/lib/python3.7/dist-packages (from GitPython>=1.0.0->wandb) (4.0.7)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.0.0->wandb) (2.10)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.0.0->wandb) (3.0.4)\n","Requirement already satisfied: smmap<5,>=3.0.1 in /usr/local/lib/python3.7/dist-packages (from gitdb<5,>=4.0.1->GitPython>=1.0.0->wandb) (4.0.0)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":136},"id":"V6tsUEOy2HFb","executionInfo":{"status":"ok","timestamp":1620137068011,"user_tz":-540,"elapsed":5400,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"e79062fd-895a-4fa6-91f4-506b609cf203"},"source":["import wandb\n","\n","proj_name = '2_aug_CLAHE_Horizontalflip'\n","\n","wandb.init(project='chanyub',name=proj_name)\n","\n","config = wandb.config\n","config.learning_rate = 0.01"],"execution_count":16,"outputs":[{"output_type":"stream","text":["\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mpstage12\u001b[0m (use `wandb login --relogin` to force relogin)\n"],"name":"stderr"},{"output_type":"display_data","data":{"text/html":["\n"," Tracking run with wandb version 0.10.29
\n"," Syncing run 2_aug_CLAHE_Horizontalflip to Weights & Biases (Documentation).
\n"," Project page: https://wandb.ai/pstage12/chanyub
\n"," Run page: https://wandb.ai/pstage12/chanyub/runs/3so7ty83
\n"," Run data is saved locally in /content/drive/My Drive/Trash/code/wandb/run-20210504_140424-3so7ty83

\n"," "],"text/plain":[""]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"71t0S3di0I33"},"source":["## baseline model\n","\n","### [TODO] 코드 구현 UNet++ \n","\n","- 출처 : https://jinglescode.github.io/2019/12/02/biomedical-image-segmentation-u-net-nested/"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"wPRySrgMK3oT","executionInfo":{"status":"ok","timestamp":1620137070660,"user_tz":-540,"elapsed":5548,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"60c16a76-0e35-4db6-deeb-f817526c5caf"},"source":["!pip install segmentation_models_pytorch"],"execution_count":17,"outputs":[{"output_type":"stream","text":["Requirement already satisfied: segmentation_models_pytorch in /usr/local/lib/python3.7/dist-packages (0.1.3)\n","Requirement already satisfied: pretrainedmodels==0.7.4 in /usr/local/lib/python3.7/dist-packages (from segmentation_models_pytorch) (0.7.4)\n","Requirement already satisfied: torchvision>=0.3.0 in /usr/local/lib/python3.7/dist-packages (from segmentation_models_pytorch) (0.9.1+cu101)\n","Requirement already satisfied: efficientnet-pytorch==0.6.3 in /usr/local/lib/python3.7/dist-packages (from segmentation_models_pytorch) (0.6.3)\n","Requirement already satisfied: timm==0.3.2 in /usr/local/lib/python3.7/dist-packages (from segmentation_models_pytorch) (0.3.2)\n","Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from pretrainedmodels==0.7.4->segmentation_models_pytorch) (4.41.1)\n","Requirement already satisfied: munch in /usr/local/lib/python3.7/dist-packages (from pretrainedmodels==0.7.4->segmentation_models_pytorch) (2.5.0)\n","Requirement already satisfied: torch in /usr/local/lib/python3.7/dist-packages (from pretrainedmodels==0.7.4->segmentation_models_pytorch) (1.8.1+cu101)\n","Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.7/dist-packages (from torchvision>=0.3.0->segmentation_models_pytorch) (7.1.2)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torchvision>=0.3.0->segmentation_models_pytorch) (1.19.5)\n","Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from munch->pretrainedmodels==0.7.4->segmentation_models_pytorch) (1.15.0)\n","Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch->pretrainedmodels==0.7.4->segmentation_models_pytorch) (3.7.4.3)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:11.634792Z","start_time":"2021-04-18T16:16:05.875817Z"},"colab":{"base_uri":"https://localhost:8080/"},"id":"MJ2vs-Y_0I35","executionInfo":{"status":"ok","timestamp":1620137077530,"user_tz":-540,"elapsed":11879,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"59ace9c4-c92e-4371-da73-850b8ae49613"},"source":["# 구현된 model에 임의의 input을 넣어 output이 잘 나오는지 test\n","import segmentation_models_pytorch as smp\n","\n","model = smp.UnetPlusPlus(classes=12)\n","x = torch.randn([1, 3, 512, 512])\n","print(\"input shape : \", x.shape)\n","out = model(x).to(device)\n","print(\"output shape : \", out.size())\n","\n","model = model.to(device)"],"execution_count":18,"outputs":[{"output_type":"stream","text":["input shape : torch.Size([1, 3, 512, 512])\n","output shape : torch.Size([1, 12, 512, 512])\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"SgM4SGqL0I35"},"source":["## train, validation, test 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.104200Z","start_time":"2021-04-18T16:16:18.093174Z"},"id":"Dl6skKCT0I35","executionInfo":{"status":"ok","timestamp":1620137077531,"user_tz":-540,"elapsed":10558,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["def train(num_epochs, model, data_loader, val_loader, criterion, optimizer, saved_dir, val_every, device):\n"," print('Start training..')\n"," best_loss = 9999999\n"," best_miou = 0\n"," for epoch in range(num_epochs):\n"," model.train()\n"," for step, (images, masks, _) in enumerate(data_loader):\n"," images = torch.stack(images) # (batch, channel, height, width)\n"," masks = torch.stack(masks).long() # (batch, channel, height, width)\n"," \n"," # gpu 연산을 위해 device 할당\n"," images, masks = images.to(device), masks.to(device)\n"," \n"," # inference\n"," outputs = model(images)\n"," \n"," # loss 계산 (cross entropy loss)\n"," loss = criterion(outputs, masks)\n"," optimizer.zero_grad()\n"," loss.backward()\n"," optimizer.step()\n"," \n"," # lr_scheduler.step()\n"," \n"," # step 주기에 따른 loss 출력\n"," if (step + 1) % 25 == 0:\n"," print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(\n"," epoch+1, num_epochs, step+1, len(train_loader), loss.item()))\n"," \n"," # validation 주기에 따른 loss 출력 및 best model 저장\n"," if (epoch + 1) % val_every == 0:\n","# avrg_loss = validation(epoch + 1, model, val_loader, criterion, device)\n","# if avrg_loss < best_loss:\n","# print('Best performance at epoch: {}'.format(epoch + 1))\n","# print('Save model in', saved_dir)\n","# best_loss = avrg_loss\n","# wandb.log({'best_loss': best_loss})\n","# save_model(model, saved_dir)\n"," avrg_miou = validation(epoch + 1, model, val_loader, criterion, device)\n"," if avrg_miou > best_miou:\n"," print('Best performance at epoch: {}'.format(epoch + 1))\n"," print('Save model in', saved_dir)\n"," best_miou = avrg_miou\n"," wandb.log({'best_miou': best_miou})\n"," save_model(model, saved_dir)"],"execution_count":19,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.285795Z","start_time":"2021-04-18T16:16:18.267686Z"},"id":"Yw_3xbyj0I36","executionInfo":{"status":"ok","timestamp":1620137077531,"user_tz":-540,"elapsed":9793,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["def validation(epoch, model, data_loader, criterion, device):\n"," print('Start validation #{}'.format(epoch))\n"," model.eval()\n"," with torch.no_grad():\n"," total_loss = 0\n"," cnt = 0\n"," mIoU_list = []\n"," for step, (images, masks, _) in enumerate(data_loader):\n"," \n"," images = torch.stack(images) # (batch, channel, height, width)\n"," masks = torch.stack(masks).long() # (batch, channel, height, width)\n","\n"," images, masks = images.to(device), masks.to(device) \n","\n"," outputs = model(images)\n"," loss = criterion(outputs, masks)\n"," total_loss += loss\n"," cnt += 1\n"," \n","# print(outputs.shape)\n","# print(masks.shape)\n","# wandb.log(wandb.Image(images, masks={\n","# \"predictions\" : {\n","# \"mask_data\" : torch.squeeze(torch.squeeze(outputs, 0),1),\n","# \"class_labels\" : classes_dict\n","# },\n","# \"ground_truth\" : {\n","# \"mask_data\" : torch.squeeze(masks, 0),\n","# \"class_labels\" : classes_dict\n","# }\n","# }))\n"," \n"," outputs = torch.argmax(outputs.squeeze(), dim=1).detach().cpu().numpy()\n","\n"," mIoU = label_accuracy_score(masks.detach().cpu().numpy(), outputs, n_class=12)[2]\n"," mIoU_list.append(mIoU)\n"," \n"," avrg_loss = total_loss / cnt\n"," avrg_mIoU = np.mean(mIoU_list)\n"," print('Validation #{} Average Loss: {:.4f}, mIoU: {:.4f}'.format(epoch, avrg_loss, np.mean(mIoU_list)))\n"," wandb.log({'Train Loss':loss.item(), 'Val Loss':avrg_loss , 'Val mIoU':np.mean(mIoU_list)})\n","# return avrg_loss\n"," return avrg_mIoU"],"execution_count":20,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"b92qGwBc0I37"},"source":["## 모델 저장 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.909918Z","start_time":"2021-04-18T16:16:18.898918Z"},"id":"H50hk0za0I37","executionInfo":{"status":"ok","timestamp":1620137077531,"user_tz":-540,"elapsed":6985,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# 모델 저장 함수 정의\n","val_every = 1 \n","\n","saved_dir = './saved'\n","if not os.path.isdir(saved_dir): \n"," os.mkdir(saved_dir)\n"," \n","def save_model(model, saved_dir, file_name='2_aug_CLAHE_Horizontalflip.pt'):\n"," check_point = {'net': model.state_dict()}\n"," output_path = os.path.join(saved_dir, file_name)\n"," torch.save(model.state_dict(), output_path)"],"execution_count":21,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"UwzGmX190I37"},"source":["## 모델 생성 및 Loss function, Optimizer 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:19.698902Z","start_time":"2021-04-18T16:16:19.694902Z"},"id":"hOKlPrNn0I37","executionInfo":{"status":"ok","timestamp":1620137077532,"user_tz":-540,"elapsed":5849,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# Loss function 정의\n","criterion = nn.CrossEntropyLoss()\n","\n","# Optimizer 정의\n","optimizer = torch.optim.Adam(params = model.parameters(), lr = learning_rate, weight_decay=1e-6)"],"execution_count":22,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"start_time":"2021-04-18T16:16:20.331Z"},"scrolled":false,"colab":{"base_uri":"https://localhost:8080/"},"id":"MSReHpkI0I38","executionInfo":{"status":"ok","timestamp":1620139361041,"user_tz":-540,"elapsed":2288558,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"62915a78-b2c8-4b27-8c43-2f535d79b083"},"source":["train(num_epochs, model, train_loader, val_loader, criterion, optimizer, saved_dir, val_every, device)"],"execution_count":23,"outputs":[{"output_type":"stream","text":["Start training..\n","Epoch [1/20], Step [25/327], Loss: 2.2279\n","Epoch [1/20], Step [50/327], Loss: 1.9054\n","Epoch [1/20], Step [75/327], Loss: 1.6353\n","Epoch [1/20], Step [100/327], Loss: 1.3676\n","Epoch [1/20], Step [125/327], Loss: 1.1504\n","Epoch [1/20], Step [150/327], Loss: 1.2282\n","Epoch [1/20], Step [175/327], Loss: 1.1436\n","Epoch [1/20], Step [200/327], Loss: 0.9132\n","Epoch [1/20], Step [225/327], Loss: 0.8202\n","Epoch [1/20], Step [250/327], Loss: 0.9189\n","Epoch [1/20], Step [275/327], Loss: 0.8905\n","Epoch [1/20], Step [300/327], Loss: 0.7087\n","Epoch [1/20], Step [325/327], Loss: 0.7812\n","Start validation #1\n","Validation #1 Average Loss: 0.7169, mIoU: 0.1692\n","Best performance at epoch: 1\n","Save model in ./saved\n","Epoch [2/20], Step [25/327], Loss: 0.6470\n","Epoch [2/20], Step [50/327], Loss: 1.0615\n","Epoch [2/20], Step [75/327], Loss: 0.4947\n","Epoch [2/20], Step [100/327], Loss: 0.7281\n","Epoch [2/20], Step [125/327], Loss: 0.6435\n","Epoch [2/20], Step [150/327], Loss: 1.2607\n","Epoch [2/20], Step [175/327], Loss: 0.9460\n","Epoch [2/20], Step [200/327], Loss: 0.6814\n","Epoch [2/20], Step [225/327], Loss: 0.3966\n","Epoch [2/20], Step [250/327], Loss: 0.8192\n","Epoch [2/20], Step [275/327], Loss: 0.5379\n","Epoch [2/20], Step [300/327], Loss: 0.3907\n","Epoch [2/20], Step [325/327], Loss: 0.4359\n","Start validation #2\n","Validation #2 Average Loss: 0.5806, mIoU: 0.2191\n","Best performance at epoch: 2\n","Save model in ./saved\n","Epoch [3/20], Step [25/327], Loss: 0.4536\n","Epoch [3/20], Step [50/327], Loss: 0.3817\n","Epoch [3/20], Step [75/327], Loss: 0.3921\n","Epoch [3/20], Step [100/327], Loss: 0.5139\n","Epoch [3/20], Step [125/327], Loss: 0.3747\n","Epoch [3/20], Step [150/327], Loss: 0.4941\n","Epoch [3/20], Step [175/327], Loss: 0.6076\n","Epoch [3/20], Step [200/327], Loss: 0.4663\n","Epoch [3/20], Step [225/327], Loss: 0.9735\n","Epoch [3/20], Step [250/327], Loss: 0.5224\n","Epoch [3/20], Step [275/327], Loss: 0.5431\n","Epoch [3/20], Step [300/327], Loss: 0.3846\n","Epoch [3/20], Step [325/327], Loss: 0.3273\n","Start validation #3\n","Validation #3 Average Loss: 0.4979, mIoU: 0.2719\n","Best performance at epoch: 3\n","Save model in ./saved\n","Epoch [4/20], Step [25/327], Loss: 0.2910\n","Epoch [4/20], Step [50/327], Loss: 0.4762\n","Epoch [4/20], Step [75/327], Loss: 0.3866\n","Epoch [4/20], Step [100/327], Loss: 0.3536\n","Epoch [4/20], Step [125/327], Loss: 0.3519\n","Epoch [4/20], Step [150/327], Loss: 0.3808\n","Epoch [4/20], Step [175/327], Loss: 0.4220\n","Epoch [4/20], Step [200/327], Loss: 0.2109\n","Epoch [4/20], Step [225/327], Loss: 0.7979\n","Epoch [4/20], Step [250/327], Loss: 0.6778\n","Epoch [4/20], Step [275/327], Loss: 0.2823\n","Epoch [4/20], Step [300/327], Loss: 0.5464\n","Epoch [4/20], Step [325/327], Loss: 0.3352\n","Start validation #4\n","Validation #4 Average Loss: 0.5121, mIoU: 0.2887\n","Best performance at epoch: 4\n","Save model in ./saved\n","Epoch [5/20], Step [25/327], Loss: 0.6161\n","Epoch [5/20], Step [50/327], Loss: 0.4452\n","Epoch [5/20], Step [75/327], Loss: 0.5551\n","Epoch [5/20], Step [100/327], Loss: 0.2074\n","Epoch [5/20], Step [125/327], Loss: 0.3224\n","Epoch [5/20], Step [150/327], Loss: 0.3175\n","Epoch [5/20], Step [175/327], Loss: 0.2231\n","Epoch [5/20], Step [200/327], Loss: 0.5790\n","Epoch [5/20], Step [225/327], Loss: 0.4442\n","Epoch [5/20], Step [250/327], Loss: 0.4613\n","Epoch [5/20], Step [275/327], Loss: 0.3609\n","Epoch [5/20], Step [300/327], Loss: 0.2471\n","Epoch [5/20], Step [325/327], Loss: 0.5040\n","Start validation #5\n","Validation #5 Average Loss: 0.5345, mIoU: 0.2799\n","Epoch [6/20], Step [25/327], Loss: 0.3519\n","Epoch [6/20], Step [50/327], Loss: 0.4085\n","Epoch [6/20], Step [75/327], Loss: 0.5204\n","Epoch [6/20], Step [100/327], Loss: 0.1426\n","Epoch [6/20], Step [125/327], Loss: 0.3081\n","Epoch [6/20], Step [150/327], Loss: 0.2972\n","Epoch [6/20], Step [175/327], Loss: 0.5622\n","Epoch [6/20], Step [200/327], Loss: 0.3031\n","Epoch [6/20], Step [225/327], Loss: 0.5905\n","Epoch [6/20], Step [250/327], Loss: 0.1824\n","Epoch [6/20], Step [275/327], Loss: 0.2470\n","Epoch [6/20], Step [300/327], Loss: 0.2820\n","Epoch [6/20], Step [325/327], Loss: 0.3209\n","Start validation #6\n","Validation #6 Average Loss: 0.4524, mIoU: 0.2864\n","Epoch [7/20], Step [25/327], Loss: 0.2173\n","Epoch [7/20], Step [50/327], Loss: 0.3023\n","Epoch [7/20], Step [75/327], Loss: 0.5520\n","Epoch [7/20], Step [100/327], Loss: 0.3875\n","Epoch [7/20], Step [125/327], Loss: 0.5115\n","Epoch [7/20], Step [150/327], Loss: 0.2062\n","Epoch [7/20], Step [175/327], Loss: 0.3446\n","Epoch [7/20], Step [200/327], Loss: 0.2078\n","Epoch [7/20], Step [225/327], Loss: 0.2157\n","Epoch [7/20], Step [250/327], Loss: 0.3313\n","Epoch [7/20], Step [275/327], Loss: 0.2458\n","Epoch [7/20], Step [300/327], Loss: 0.3265\n","Epoch [7/20], Step [325/327], Loss: 0.2675\n","Start validation #7\n","Validation #7 Average Loss: 0.4820, mIoU: 0.2571\n","Epoch [8/20], Step [25/327], Loss: 0.3995\n","Epoch [8/20], Step [50/327], Loss: 0.3361\n","Epoch [8/20], Step [75/327], Loss: 0.4349\n","Epoch [8/20], Step [100/327], Loss: 0.2928\n","Epoch [8/20], Step [125/327], Loss: 0.3124\n","Epoch [8/20], Step [150/327], Loss: 0.2031\n","Epoch [8/20], Step [175/327], Loss: 0.6244\n","Epoch [8/20], Step [200/327], Loss: 0.3778\n","Epoch [8/20], Step [225/327], Loss: 0.3423\n","Epoch [8/20], Step [250/327], Loss: 0.4407\n","Epoch [8/20], Step [275/327], Loss: 0.4840\n","Epoch [8/20], Step [300/327], Loss: 0.3855\n","Epoch [8/20], Step [325/327], Loss: 0.3374\n","Start validation #8\n","Validation #8 Average Loss: 0.4998, mIoU: 0.2468\n","Epoch [9/20], Step [25/327], Loss: 0.3350\n","Epoch [9/20], Step [50/327], Loss: 0.1565\n","Epoch [9/20], Step [75/327], Loss: 0.3212\n","Epoch [9/20], Step [100/327], Loss: 0.2352\n","Epoch [9/20], Step [125/327], Loss: 0.1988\n","Epoch [9/20], Step [150/327], Loss: 0.5720\n","Epoch [9/20], Step [175/327], Loss: 0.2807\n","Epoch [9/20], Step [200/327], Loss: 0.1623\n","Epoch [9/20], Step [225/327], Loss: 0.2533\n","Epoch [9/20], Step [250/327], Loss: 0.2528\n","Epoch [9/20], Step [275/327], Loss: 0.1712\n","Epoch [9/20], Step [300/327], Loss: 0.2193\n","Epoch [9/20], Step [325/327], Loss: 0.2176\n","Start validation #9\n","Validation #9 Average Loss: 0.5133, mIoU: 0.2556\n","Epoch [10/20], Step [25/327], Loss: 0.5948\n","Epoch [10/20], Step [50/327], Loss: 0.2418\n","Epoch [10/20], Step [75/327], Loss: 0.4442\n","Epoch [10/20], Step [100/327], Loss: 0.3194\n","Epoch [10/20], Step [125/327], Loss: 0.3456\n","Epoch [10/20], Step [150/327], Loss: 0.1076\n","Epoch [10/20], Step [175/327], Loss: 0.3061\n","Epoch [10/20], Step [200/327], Loss: 0.2477\n","Epoch [10/20], Step [225/327], Loss: 0.2634\n","Epoch [10/20], Step [250/327], Loss: 0.3243\n","Epoch [10/20], Step [275/327], Loss: 0.1506\n","Epoch [10/20], Step [300/327], Loss: 0.3162\n","Epoch [10/20], Step [325/327], Loss: 0.1391\n","Start validation #10\n","Validation #10 Average Loss: 0.4712, mIoU: 0.2763\n","Epoch [11/20], Step [25/327], Loss: 0.2027\n","Epoch [11/20], Step [50/327], Loss: 0.3069\n","Epoch [11/20], Step [75/327], Loss: 0.1147\n","Epoch [11/20], Step [100/327], Loss: 0.2717\n","Epoch [11/20], Step [125/327], Loss: 0.2345\n","Epoch [11/20], Step [150/327], Loss: 0.3298\n","Epoch [11/20], Step [175/327], Loss: 0.1040\n","Epoch [11/20], Step [200/327], Loss: 0.4001\n","Epoch [11/20], Step [225/327], Loss: 0.1685\n","Epoch [11/20], Step [250/327], Loss: 0.1584\n","Epoch [11/20], Step [275/327], Loss: 0.1103\n","Epoch [11/20], Step [300/327], Loss: 0.3767\n","Epoch [11/20], Step [325/327], Loss: 0.1055\n","Start validation #11\n","Validation #11 Average Loss: 0.4931, mIoU: 0.2788\n","Epoch [12/20], Step [25/327], Loss: 0.2177\n","Epoch [12/20], Step [50/327], Loss: 0.1401\n","Epoch [12/20], Step [75/327], Loss: 0.1312\n","Epoch [12/20], Step [100/327], Loss: 0.2970\n","Epoch [12/20], Step [125/327], Loss: 0.0771\n","Epoch [12/20], Step [150/327], Loss: 0.4864\n","Epoch [12/20], Step [175/327], Loss: 0.3337\n","Epoch [12/20], Step [200/327], Loss: 0.2846\n","Epoch [12/20], Step [225/327], Loss: 0.1547\n","Epoch [12/20], Step [250/327], Loss: 0.2169\n","Epoch [12/20], Step [275/327], Loss: 0.2121\n","Epoch [12/20], Step [300/327], Loss: 0.2075\n","Epoch [12/20], Step [325/327], Loss: 0.1814\n","Start validation #12\n","Validation #12 Average Loss: 0.5052, mIoU: 0.2690\n","Epoch [13/20], Step [25/327], Loss: 0.1762\n","Epoch [13/20], Step [50/327], Loss: 0.2482\n","Epoch [13/20], Step [75/327], Loss: 0.1737\n","Epoch [13/20], Step [100/327], Loss: 0.3731\n","Epoch [13/20], Step [125/327], Loss: 0.2103\n","Epoch [13/20], Step [150/327], Loss: 0.1200\n","Epoch [13/20], Step [175/327], Loss: 0.2618\n","Epoch [13/20], Step [200/327], Loss: 0.1664\n","Epoch [13/20], Step [225/327], Loss: 0.2711\n","Epoch [13/20], Step [250/327], Loss: 0.2803\n","Epoch [13/20], Step [275/327], Loss: 0.1705\n","Epoch [13/20], Step [300/327], Loss: 0.2465\n","Epoch [13/20], Step [325/327], Loss: 0.1827\n","Start validation #13\n","Validation #13 Average Loss: 0.5281, mIoU: 0.2906\n","Best performance at epoch: 13\n","Save model in ./saved\n","Epoch [14/20], Step [25/327], Loss: 0.2255\n","Epoch [14/20], Step [50/327], Loss: 0.1586\n","Epoch [14/20], Step [75/327], Loss: 0.2203\n","Epoch [14/20], Step [100/327], Loss: 0.2207\n","Epoch [14/20], Step [125/327], Loss: 0.3068\n","Epoch [14/20], Step [150/327], Loss: 0.1359\n","Epoch [14/20], Step [175/327], Loss: 0.1283\n","Epoch [14/20], Step [200/327], Loss: 0.1734\n","Epoch [14/20], Step [225/327], Loss: 0.2205\n","Epoch [14/20], Step [250/327], Loss: 0.1662\n","Epoch [14/20], Step [275/327], Loss: 0.3010\n","Epoch [14/20], Step [300/327], Loss: 0.4078\n","Epoch [14/20], Step [325/327], Loss: 0.2545\n","Start validation #14\n","Validation #14 Average Loss: 0.5037, mIoU: 0.2837\n","Epoch [15/20], Step [25/327], Loss: 0.1091\n","Epoch [15/20], Step [50/327], Loss: 0.4058\n","Epoch [15/20], Step [75/327], Loss: 0.2117\n","Epoch [15/20], Step [100/327], Loss: 0.1015\n","Epoch [15/20], Step [125/327], Loss: 0.1202\n","Epoch [15/20], Step [150/327], Loss: 0.2201\n","Epoch [15/20], Step [175/327], Loss: 0.1799\n","Epoch [15/20], Step [200/327], Loss: 0.1801\n","Epoch [15/20], Step [225/327], Loss: 0.1905\n","Epoch [15/20], Step [250/327], Loss: 0.1681\n","Epoch [15/20], Step [275/327], Loss: 0.2123\n","Epoch [15/20], Step [300/327], Loss: 0.1070\n","Epoch [15/20], Step [325/327], Loss: 0.2155\n","Start validation #15\n","Validation #15 Average Loss: 0.4859, mIoU: 0.2885\n","Epoch [16/20], Step [25/327], Loss: 0.3159\n","Epoch [16/20], Step [50/327], Loss: 0.0908\n","Epoch [16/20], Step [75/327], Loss: 0.1421\n","Epoch [16/20], Step [100/327], Loss: 0.2557\n","Epoch [16/20], Step [125/327], Loss: 0.1019\n","Epoch [16/20], Step [150/327], Loss: 0.1718\n","Epoch [16/20], Step [175/327], Loss: 0.2116\n","Epoch [16/20], Step [200/327], Loss: 0.2298\n","Epoch [16/20], Step [225/327], Loss: 0.1914\n","Epoch [16/20], Step [250/327], Loss: 0.2731\n","Epoch [16/20], Step [275/327], Loss: 0.2244\n","Epoch [16/20], Step [300/327], Loss: 0.2308\n","Epoch [16/20], Step [325/327], Loss: 0.1430\n","Start validation #16\n","Validation #16 Average Loss: 0.5112, mIoU: 0.2972\n","Best performance at epoch: 16\n","Save model in ./saved\n","Epoch [17/20], Step [25/327], Loss: 0.2028\n","Epoch [17/20], Step [50/327], Loss: 0.1265\n","Epoch [17/20], Step [75/327], Loss: 0.3223\n","Epoch [17/20], Step [100/327], Loss: 0.1684\n","Epoch [17/20], Step [125/327], Loss: 0.1240\n","Epoch [17/20], Step [150/327], Loss: 0.2615\n","Epoch [17/20], Step [175/327], Loss: 0.1761\n","Epoch [17/20], Step [200/327], Loss: 0.1991\n","Epoch [17/20], Step [225/327], Loss: 0.2799\n","Epoch [17/20], Step [250/327], Loss: 0.3890\n","Epoch [17/20], Step [275/327], Loss: 0.2273\n","Epoch [17/20], Step [300/327], Loss: 0.3350\n","Epoch [17/20], Step [325/327], Loss: 0.0902\n","Start validation #17\n","Validation #17 Average Loss: 0.5132, mIoU: 0.2957\n","Epoch [18/20], Step [25/327], Loss: 0.1896\n","Epoch [18/20], Step [50/327], Loss: 0.1068\n","Epoch [18/20], Step [75/327], Loss: 0.1554\n","Epoch [18/20], Step [100/327], Loss: 0.1536\n","Epoch [18/20], Step [125/327], Loss: 0.2393\n","Epoch [18/20], Step [150/327], Loss: 0.1489\n","Epoch [18/20], Step [175/327], Loss: 0.1603\n","Epoch [18/20], Step [200/327], Loss: 0.1516\n","Epoch [18/20], Step [225/327], Loss: 0.1144\n","Epoch [18/20], Step [250/327], Loss: 0.1621\n","Epoch [18/20], Step [275/327], Loss: 0.1968\n","Epoch [18/20], Step [300/327], Loss: 0.0953\n","Epoch [18/20], Step [325/327], Loss: 0.1921\n","Start validation #18\n","Validation #18 Average Loss: 0.5234, mIoU: 0.2910\n","Epoch [19/20], Step [25/327], Loss: 0.2444\n","Epoch [19/20], Step [50/327], Loss: 0.0981\n","Epoch [19/20], Step [75/327], Loss: 0.1428\n","Epoch [19/20], Step [100/327], Loss: 0.2254\n","Epoch [19/20], Step [125/327], Loss: 0.1224\n","Epoch [19/20], Step [150/327], Loss: 0.2009\n","Epoch [19/20], Step [175/327], Loss: 0.1329\n","Epoch [19/20], Step [200/327], Loss: 0.1514\n","Epoch [19/20], Step [225/327], Loss: 0.1469\n","Epoch [19/20], Step [250/327], Loss: 0.2274\n","Epoch [19/20], Step [275/327], Loss: 0.4301\n","Epoch [19/20], Step [300/327], Loss: 0.1016\n","Epoch [19/20], Step [325/327], Loss: 0.1043\n","Start validation #19\n","Validation #19 Average Loss: 0.5746, mIoU: 0.2800\n","Epoch [20/20], Step [25/327], Loss: 0.1058\n","Epoch [20/20], Step [50/327], Loss: 0.2731\n","Epoch [20/20], Step [75/327], Loss: 0.2120\n","Epoch [20/20], Step [100/327], Loss: 0.1592\n","Epoch [20/20], Step [125/327], Loss: 0.1200\n","Epoch [20/20], Step [150/327], Loss: 0.1060\n","Epoch [20/20], Step [175/327], Loss: 0.1675\n","Epoch [20/20], Step [200/327], Loss: 0.1123\n","Epoch [20/20], Step [225/327], Loss: 0.1562\n","Epoch [20/20], Step [250/327], Loss: 0.1165\n","Epoch [20/20], Step [275/327], Loss: 0.0825\n","Epoch [20/20], Step [300/327], Loss: 0.2827\n","Epoch [20/20], Step [325/327], Loss: 0.1282\n","Start validation #20\n","Validation #20 Average Loss: 0.5363, mIoU: 0.2847\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"jOE-56p5MkXM","executionInfo":{"status":"ok","timestamp":1620139363914,"user_tz":-540,"elapsed":2197727,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# Sleep for a few seconds.\n","import time\n","time.sleep(2)\n","# Play an audio beep. Any audio URL will do.\n","from google.colab import output\n","output.eval_js('new Audio(\"https://upload.wikimedia.org/wikipedia/commons/0/05/Beep-09.ogg\").play()')"],"execution_count":24,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"6IqKsZ4u0I38"},"source":["## 저장된 model 불러오기 (학습된 이후) "]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:21.050200Z","start_time":"2021-04-16T19:44:20.802200Z"},"scrolled":true,"id":"dl5dIeHB0I38"},"source":["# best model 저장된 경로\n","model_path = './saved/UNetPP_best_model.pt'\n","\n","# best model 불러오기\n","checkpoint = torch.load(model_path, map_location=device)\n","model.load_state_dict(checkpoint)\n","\n","# 추론을 실행하기 전에는 반드시 설정 (batch normalization, dropout 를 평가 모드로 설정)\n","# model.eval()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:24.939227Z","start_time":"2021-04-16T19:44:24.518228Z"},"id":"mOUcjOP20I38"},"source":["# 첫번째 batch의 추론 결과 확인\n","for imgs, image_infos in test_loader:\n"," image_infos = image_infos\n"," temp_images = imgs\n"," \n"," model.eval()\n"," # inference\n"," outs = model(torch.stack(temp_images).to(device))\n"," oms = torch.argmax(outs.squeeze(), dim=1).detach().cpu().numpy()\n"," \n"," break\n","\n","i = 3\n","fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(16, 16))\n","\n","print('Shape of Original Image :', list(temp_images[i].shape))\n","print('Shape of Predicted : ', list(oms[i].shape))\n","print('Unique values, category of transformed mask : \\n', [{int(i),category_names[int(i)]} for i in list(np.unique(oms[i]))])\n","\n","# Original image\n","ax1.imshow(temp_images[i].permute([1,2,0]))\n","ax1.grid(False)\n","ax1.set_title(\"Original image : {}\".format(image_infos[i]['file_name']), fontsize = 15)\n","\n","# Predicted\n","ax2.imshow(oms[i])\n","ax2.grid(False)\n","ax2.set_title(\"Predicted : {}\".format(image_infos[i]['file_name']), fontsize = 15)\n","\n","plt.show()"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"pmZEjwGE0I39"},"source":["## submission을 위한 test 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:27.469285Z","start_time":"2021-04-16T19:44:27.456021Z"},"id":"X4s-Ng1_0I39"},"source":["def test(model, data_loader, device):\n"," size = 256\n"," transform = A.Compose([A.Resize(256, 256)])\n"," print('Start prediction.')\n"," model.eval()\n"," \n"," file_name_list = []\n"," preds_array = np.empty((0, size*size), dtype=np.long)\n"," \n"," with torch.no_grad():\n"," for step, (imgs, image_infos) in enumerate(test_loader):\n","\n"," # inference (512 x 512)\n"," outs = model(torch.stack(imgs).to(device))\n"," oms = torch.argmax(outs, dim=1).detach().cpu().numpy()\n"," \n"," # resize (256 x 256)\n"," temp_mask = []\n"," for img, mask in zip(np.stack(imgs), oms):\n"," transformed = transform(image=img, mask=mask)\n"," mask = transformed['mask']\n"," temp_mask.append(mask)\n","\n"," oms = np.array(temp_mask)\n"," \n"," oms = oms.reshape([oms.shape[0], size*size]).astype(int)\n"," preds_array = np.vstack((preds_array, oms))\n"," \n"," file_name_list.append([i['file_name'] for i in image_infos])\n"," print(\"End prediction.\")\n"," file_names = [y for x in file_name_list for y in x]\n"," \n"," return file_names, preds_array"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"GnldifLS0I39"},"source":["## submission.csv 생성"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:45:42.235310Z","start_time":"2021-04-16T19:44:30.499016Z"},"scrolled":true,"id":"uV_ZSnqT0I3-"},"source":["# sample_submisson.csv 열기\n","submission = pd.read_csv('./submission/sample_submission.csv', index_col=None)\n","\n","# test set에 대한 prediction\n","file_names, preds = test(model, test_loader, device)\n","\n","# PredictionString 대입\n","for file_name, string in zip(file_names, preds):\n"," submission = submission.append({\"image_id\" : file_name, \"PredictionString\" : ' '.join(str(e) for e in string.tolist())}, \n"," ignore_index=True)\n","\n","# submission.csv로 저장\n","submission.to_csv(\"./submission/Baseline_UnetPP.csv\", index=False)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"LIRbR3Ro0I3-"},"source":["## Reference\n","\n"]},{"cell_type":"code","metadata":{"id":"ZUxotp6d0I3-"},"source":[""],"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/chanyub_seg/code/3_aug_horizontalflip_Rotation90.ipynb b/chanyub_seg/code/3_aug_horizontalflip_Rotation90.ipynb deleted file mode 100644 index 995b651..0000000 --- a/chanyub_seg/code/3_aug_horizontalflip_Rotation90.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"nbformat":4,"nbformat_minor":0,"metadata":{"hide_input":false,"kernelspec":{"display_name":"Python 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google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Mounted at /content/drive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"CHnx6ACo0SSR","executionInfo":{"status":"ok","timestamp":1620193209755,"user_tz":-540,"elapsed":22733,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"9a553407-ed8f-451c-dfa3-b7d9e5809b24"},"source":["ls"],"execution_count":2,"outputs":[{"output_type":"stream","text":["\u001b[0m\u001b[01;34mdrive\u001b[0m/ \u001b[01;34msample_data\u001b[0m/\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rfBXtbCI0TPa","executionInfo":{"status":"ok","timestamp":1620193209757,"user_tz":-540,"elapsed":22725,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"0f9255dc-99a6-465d-8425-277c225d30be"},"source":["cd drive/MyDrive/Trash/code"],"execution_count":3,"outputs":[{"output_type":"stream","text":["/content/drive/MyDrive/Trash/code\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"X6b4uOxD0Vkp","executionInfo":{"status":"ok","timestamp":1620193217886,"user_tz":-540,"elapsed":30841,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"9040e332-7e19-4490-b184-2d8f9bb7745f"},"source":["!pip install albumentations==0.5.2"],"execution_count":4,"outputs":[{"output_type":"stream","text":["Collecting albumentations==0.5.2\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/03/58/63fb1d742dc42d9ba2800ea741de1f2bc6bb05548d8724aa84794042eaf2/albumentations-0.5.2-py3-none-any.whl (72kB)\n","\u001b[K |████████████████████████████████| 81kB 3.9MB/s \n","\u001b[?25hCollecting opencv-python-headless>=4.1.1\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/6d/6d/92f377bece9b0ec9c893081dbe073a65b38d7ac12ef572b8f70554d08760/opencv_python_headless-4.5.1.48-cp37-cp37m-manylinux2014_x86_64.whl (37.6MB)\n","\u001b[K |████████████████████████████████| 37.6MB 1.2MB/s \n","\u001b[?25hRequirement already satisfied: numpy>=1.11.1 in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (1.19.5)\n","Requirement already satisfied: PyYAML in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (3.13)\n","Collecting imgaug>=0.4.0\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/66/b1/af3142c4a85cba6da9f4ebb5ff4e21e2616309552caca5e8acefe9840622/imgaug-0.4.0-py2.py3-none-any.whl (948kB)\n","\u001b[K |████████████████████████████████| 952kB 56.6MB/s \n","\u001b[?25hRequirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (1.4.1)\n","Requirement already satisfied: scikit-image>=0.16.1 in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (0.16.2)\n","Requirement already satisfied: Shapely in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (1.7.1)\n","Requirement already satisfied: imageio in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (2.4.1)\n","Requirement already satisfied: opencv-python in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (4.1.2.30)\n","Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (7.1.2)\n","Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (1.15.0)\n","Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (3.2.2)\n","Requirement already satisfied: PyWavelets>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations==0.5.2) (1.1.1)\n","Requirement already satisfied: networkx>=2.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations==0.5.2) (2.5.1)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug>=0.4.0->albumentations==0.5.2) (1.3.1)\n","Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug>=0.4.0->albumentations==0.5.2) (2.4.7)\n","Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug>=0.4.0->albumentations==0.5.2) (2.8.1)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug>=0.4.0->albumentations==0.5.2) (0.10.0)\n","Requirement already satisfied: decorator<5,>=4.3 in /usr/local/lib/python3.7/dist-packages (from networkx>=2.0->scikit-image>=0.16.1->albumentations==0.5.2) (4.4.2)\n","Installing collected packages: opencv-python-headless, imgaug, albumentations\n"," Found existing installation: imgaug 0.2.9\n"," Uninstalling imgaug-0.2.9:\n"," Successfully uninstalled imgaug-0.2.9\n"," Found existing installation: albumentations 0.1.12\n"," Uninstalling albumentations-0.1.12:\n"," Successfully uninstalled albumentations-0.1.12\n","Successfully installed albumentations-0.5.2 imgaug-0.4.0 opencv-python-headless-4.5.1.48\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.826930Z","start_time":"2021-04-18T10:34:45.406686Z"},"scrolled":false,"colab":{"base_uri":"https://localhost:8080/"},"id":"HiN9b-Ly0I3p","executionInfo":{"status":"ok","timestamp":1620193222569,"user_tz":-540,"elapsed":35513,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"859c2491-5812-40ae-80ed-c41e7011bdfa"},"source":["import os\n","import random\n","import time\n","import json\n","import warnings \n","warnings.filterwarnings('ignore')\n","\n","import torch\n","import torch.nn as nn\n","from torch.utils.data import Dataset, DataLoader\n","from utils import label_accuracy_score\n","import cv2\n","\n","import numpy as np\n","import pandas as pd\n","\n","# 전처리를 위한 라이브러리\n","from pycocotools.coco import COCO\n","import torchvision\n","import torchvision.transforms as transforms\n","\n","import albumentations as A\n","from albumentations.pytorch import ToTensorV2\n","\n","# 시각화를 위한 라이브러리\n","import matplotlib.pyplot as plt\n","import seaborn as sns; sns.set()\n","\n","plt.rcParams['axes.grid'] = False\n","\n","print('pytorch version: {}'.format(torch.__version__))\n","print('GPU 사용 가능 여부: {}'.format(torch.cuda.is_available()))\n","\n","print(torch.cuda.get_device_name(0))\n","print(torch.cuda.device_count())\n","\n","device = \"cuda\" if torch.cuda.is_available() else \"cpu\" # GPU 사용 가능 여부에 따라 device 정보 저장"],"execution_count":5,"outputs":[{"output_type":"stream","text":["pytorch version: 1.8.1+cu101\n","GPU 사용 가능 여부: True\n","Tesla P100-PCIE-16GB\n","1\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"Uuj6y7Ra0I3r"},"source":["## 하이퍼파라미터 세팅 및 seed 고정"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.841930Z","start_time":"2021-04-18T10:34:47.827931Z"},"id":"k-febRcn0I3r","executionInfo":{"status":"ok","timestamp":1620193222569,"user_tz":-540,"elapsed":35504,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["batch_size = 8 # Mini-batch size\n","num_epochs = 20\n","learning_rate = 0.0001"],"execution_count":6,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.856930Z","start_time":"2021-04-18T10:34:47.842931Z"},"id":"YA3jAi2a0I3s","executionInfo":{"status":"ok","timestamp":1620193222570,"user_tz":-540,"elapsed":35502,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# seed 고정\n","random_seed = 42\n","torch.manual_seed(random_seed)\n","torch.cuda.manual_seed(random_seed)\n","# torch.cuda.manual_seed_all(random_seed) # if use multi-GPU\n","torch.backends.cudnn.deterministic = True\n","torch.backends.cudnn.benchmark = False\n","np.random.seed(random_seed)\n","random.seed(random_seed)"],"execution_count":7,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"ePFcujAe0I3s"},"source":["## 학습 데이터 EDA"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.381961Z","start_time":"2021-04-18T10:34:47.857930Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"Ds0jp-pz0I3s","executionInfo":{"status":"ok","timestamp":1620193235714,"user_tz":-540,"elapsed":9137,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"bffc365b-22a1-40f5-a89b-2de7803c05d3"},"source":["%matplotlib inline\n","\n","dataset_path = '../input/data'\n","anns_file_path = dataset_path + '/' + 'train.json'\n","\n","# Read annotations\n","with open(anns_file_path, 'r') as f:\n"," dataset = json.loads(f.read())\n","\n","categories = dataset['categories']\n","anns = dataset['annotations']\n","imgs = dataset['images']\n","nr_cats = len(categories)\n","nr_annotations = len(anns)\n","nr_images = len(imgs)\n","\n","# Load categories and super categories\n","cat_names = []\n","super_cat_names = []\n","super_cat_ids = {}\n","super_cat_last_name = ''\n","nr_super_cats = 0\n","for cat_it in categories:\n"," cat_names.append(cat_it['name'])\n"," super_cat_name = cat_it['supercategory']\n"," # Adding new supercat\n"," if super_cat_name != super_cat_last_name:\n"," super_cat_names.append(super_cat_name)\n"," super_cat_ids[super_cat_name] = nr_super_cats\n"," super_cat_last_name = super_cat_name\n"," nr_super_cats += 1\n","\n","print('Number of super categories:', nr_super_cats)\n","print('Number of categories:', nr_cats)\n","print('Number of annotations:', nr_annotations)\n","print('Number of images:', nr_images)"],"execution_count":8,"outputs":[{"output_type":"stream","text":["Number of super categories: 11\n","Number of categories: 11\n","Number of annotations: 21116\n","Number of images: 2617\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.546964Z","start_time":"2021-04-18T10:34:51.382969Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/","height":355},"id":"SVmavtk00I3t","executionInfo":{"status":"ok","timestamp":1620193235734,"user_tz":-540,"elapsed":7552,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"193b7427-1133-4fe9-81b7-cf294c3bd009"},"source":["# Count annotations\n","cat_histogram = np.zeros(nr_cats,dtype=int)\n","for ann in anns:\n"," cat_histogram[ann['category_id']] += 1\n","\n","# Initialize the matplotlib figure\n","f, ax = plt.subplots(figsize=(5,5))\n","\n","# Convert to DataFrame\n","df = pd.DataFrame({'Categories': cat_names, 'Number of annotations': cat_histogram})\n","df = df.sort_values('Number of annotations', 0, False)\n","\n","# Plot the histogram\n","plt.title(\"category distribution of train set \")\n","plot_1 = sns.barplot(x=\"Number of annotations\", y=\"Categories\", data=df, label=\"Total\", color=\"b\")"],"execution_count":9,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.561965Z","start_time":"2021-04-18T10:34:51.547969Z"},"id":"xK3EYdyX0I3u","executionInfo":{"status":"ok","timestamp":1620193235735,"user_tz":-540,"elapsed":7083,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# category labeling \n","sorted_temp_df = df.sort_index()\n","\n","# background = 0 에 해당되는 label 추가 후 기존들을 모두 label + 1 로 설정\n","sorted_df = pd.DataFrame([\"Backgroud\"], columns = [\"Categories\"])\n","sorted_df = sorted_df.append(sorted_temp_df, ignore_index=True)"],"execution_count":10,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.576961Z","start_time":"2021-04-18T10:34:51.562964Z"},"scrolled":false,"colab":{"base_uri":"https://localhost:8080/","height":421},"id":"9UQEjg8r0I3u","executionInfo":{"status":"ok","timestamp":1620193236248,"user_tz":-540,"elapsed":7254,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"8a82b225-17f1-4228-d288-1d9b7a948e42"},"source":["# class (Categories) 에 따른 index 확인 (0~11 : 총 12개)\n","sorted_df"],"execution_count":11,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
CategoriesNumber of annotations
0BackgroudNaN
1UNKNOWN128.0
2General trash2225.0
3Paper7448.0
4Paper pack527.0
5Metal449.0
6Glass488.0
7Plastic2472.0
8Styrofoam1074.0
9Plastic bag6114.0
10Battery50.0
11Clothing141.0
\n","
"],"text/plain":[" Categories Number of annotations\n","0 Backgroud NaN\n","1 UNKNOWN 128.0\n","2 General trash 2225.0\n","3 Paper 7448.0\n","4 Paper pack 527.0\n","5 Metal 449.0\n","6 Glass 488.0\n","7 Plastic 2472.0\n","8 Styrofoam 1074.0\n","9 Plastic bag 6114.0\n","10 Battery 50.0\n","11 Clothing 141.0"]},"metadata":{"tags":[]},"execution_count":11}]},{"cell_type":"markdown","metadata":{"id":"AHvcEEXh0I3u"},"source":["## 데이터 전처리 함수 정의 (Dataset)"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:52.693328Z","start_time":"2021-04-18T10:34:52.681328Z"},"id":"tBr2oTea0I3v","executionInfo":{"status":"ok","timestamp":1620193236250,"user_tz":-540,"elapsed":3424,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["category_names = list(sorted_df.Categories)\n","\n","def get_classname(classID, cats):\n"," for i in range(len(cats)):\n"," if cats[i]['id']==classID:\n"," return cats[i]['name']\n"," return \"None\"\n","\n","class CustomDataLoader(Dataset):\n"," \"\"\"COCO format\"\"\"\n"," def __init__(self, data_dir, mode = 'train', transform = None):\n"," super().__init__()\n"," self.mode = mode\n"," self.transform = transform\n"," self.coco = COCO(data_dir)\n"," \n"," def __getitem__(self, index: int):\n"," # dataset이 index되어 list처럼 동작\n"," image_id = self.coco.getImgIds(imgIds=index)\n"," image_infos = self.coco.loadImgs(image_id)[0]\n"," \n"," # cv2 를 활용하여 image 불러오기\n"," images = cv2.imread(os.path.join(dataset_path, image_infos['file_name']))\n"," images = cv2.cvtColor(images, cv2.COLOR_BGR2RGB).astype(np.float32)\n"," images /= 255.0\n"," \n"," if (self.mode in ('train', 'val')):\n"," ann_ids = self.coco.getAnnIds(imgIds=image_infos['id'])\n"," anns = self.coco.loadAnns(ann_ids)\n","\n"," # Load the categories in a variable\n"," cat_ids = self.coco.getCatIds()\n"," cats = self.coco.loadCats(cat_ids)\n","\n"," # masks : size가 (height x width)인 2D\n"," # 각각의 pixel 값에는 \"category id + 1\" 할당\n"," # Background = 0\n"," masks = np.zeros((image_infos[\"height\"], image_infos[\"width\"]))\n"," # Unknown = 1, General trash = 2, ... , Cigarette = 11\n"," for i in range(len(anns)):\n"," className = get_classname(anns[i]['category_id'], cats)\n"," pixel_value = category_names.index(className)\n"," masks = np.maximum(self.coco.annToMask(anns[i])*pixel_value, masks)\n"," masks = masks.astype(np.float32)\n","\n"," # transform -> albumentations 라이브러리 활용\n"," if self.transform is not None:\n"," transformed = self.transform(image=images, mask=masks)\n"," images = transformed[\"image\"]\n"," masks = transformed[\"mask\"]\n"," \n"," return images, masks, image_infos\n"," \n"," if self.mode == 'test':\n"," # transform -> albumentations 라이브러리 활용\n"," if self.transform is not None:\n"," transformed = self.transform(image=images)\n"," images = transformed[\"image\"]\n"," \n"," return images, image_infos\n"," \n"," \n"," def __len__(self) -> int:\n"," # 전체 dataset의 size를 return\n"," return len(self.coco.getImgIds())"],"execution_count":12,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"PBcB4oQh0I3w"},"source":["## Dataset 정의 및 DataLoader 할당"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:58.823175Z","start_time":"2021-04-18T10:34:54.106233Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"LxAXSS-c0I3x","executionInfo":{"status":"ok","timestamp":1620193249185,"user_tz":-540,"elapsed":9007,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"f737e78d-c00d-45be-f2f3-589e45591058"},"source":["# train.json / validation.json / test.json 디렉토리 설정\n","train_path = dataset_path + '/train.json'\n","val_path = dataset_path + '/val.json'\n","test_path = dataset_path + '/test.json'\n","\n","# collate_fn needs for batch\n","def collate_fn(batch):\n"," return tuple(zip(*batch))\n","\n","train_transform = A.Compose([\n"," A.augmentations.Resize(256,256),\n"," A.HorizontalFlip(),\n"," A.RandomRotate90(),\n"," ToTensorV2()\n"," ])\n","\n","val_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","test_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","# create own Dataset 1 (skip)\n","# validation set을 직접 나누고 싶은 경우\n","# random_split 사용하여 data set을 8:2 로 분할\n","# train_size = int(0.8*len(dataset))\n","# val_size = int(len(dataset)-train_size)\n","# dataset = CustomDataLoader(data_dir=train_path, mode='train', transform=transform)\n","# train_dataset, val_dataset = torch.utils.data.random_split(dataset, [train_size, val_size])\n","\n","# create own Dataset 2\n","# train dataset\n","train_dataset = CustomDataLoader(data_dir=train_path, mode='train', transform=train_transform)\n","\n","# validation dataset\n","val_dataset = CustomDataLoader(data_dir=val_path, mode='val', transform=val_transform)\n","\n","# test dataset\n","test_dataset = CustomDataLoader(data_dir=test_path, mode='test', transform=test_transform)\n","\n","\n","# DataLoader\n","train_loader = torch.utils.data.DataLoader(dataset=train_dataset, \n"," batch_size=batch_size,\n"," shuffle=True,\n"," num_workers=4,\n"," collate_fn=collate_fn,\n"," drop_last=True)\n","\n","val_loader = torch.utils.data.DataLoader(dataset=val_dataset, \n"," batch_size=batch_size,\n"," shuffle=False,\n"," num_workers=4,\n"," collate_fn=collate_fn,\n"," drop_last=True) \n","\n","test_loader = torch.utils.data.DataLoader(dataset=test_dataset,\n"," batch_size=batch_size,\n"," num_workers=4,\n"," collate_fn=collate_fn)"],"execution_count":13,"outputs":[{"output_type":"stream","text":["loading annotations into memory...\n","Done (t=4.02s)\n","creating index...\n","index created!\n","loading annotations into memory...\n","Done (t=3.19s)\n","creating index...\n","index created!\n","loading annotations into memory...\n","Done (t=0.95s)\n","creating index...\n","index created!\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-aaTrvBk0gKc","executionInfo":{"status":"ok","timestamp":1620137394540,"user_tz":-540,"elapsed":8200,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"da7ed297-8d84-4063-8e90-8371cd0af49e"},"source":["!pip install wandb"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Collecting wandb\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/67/5a/b037b50f9849212863a2fed313624d8f6f33ffa4ce89dc706e2a0e98c780/wandb-0.10.29-py2.py3-none-any.whl (2.1MB)\n","\r\u001b[K |▏ | 10kB 22.0MB/s eta 0:00:01\r\u001b[K |▎ | 20kB 28.1MB/s eta 0:00:01\r\u001b[K |▌ | 30kB 22.6MB/s eta 0:00:01\r\u001b[K |▋ | 40kB 17.3MB/s eta 0:00:01\r\u001b[K |▉ | 51kB 12.2MB/s eta 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wandb-0.10.29\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":153},"id":"V6tsUEOy2HFb","executionInfo":{"status":"ok","timestamp":1620137414345,"user_tz":-540,"elapsed":11093,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"244d7f86-aac4-40f9-c546-487ba6407f6a"},"source":["import wandb\n","\n","proj_name = '3_aug_horizontalflip_Rotation90'\n","\n","wandb.init(project='chanyub',name=proj_name)\n","\n","config = wandb.config\n","config.learning_rate = 0.01"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"application/javascript":["\n"," window._wandbApiKey = new Promise((resolve, reject) => {\n"," function loadScript(url) {\n"," return new Promise(function(resolve, reject) {\n"," let newScript = document.createElement(\"script\");\n"," newScript.onerror = reject;\n"," 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\n"," Project page: https://wandb.ai/pstage12/chanyub
\n"," Run page: https://wandb.ai/pstage12/chanyub/runs/39s3jkwq
\n"," Run data is saved locally in /content/drive/My Drive/Trash/code/wandb/run-20210504_141011-39s3jkwq

\n"," "],"text/plain":[""]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"71t0S3di0I33"},"source":["## baseline model\n","\n","### [TODO] 코드 구현 UNet++ \n","\n","- 출처 : https://jinglescode.github.io/2019/12/02/biomedical-image-segmentation-u-net-nested/"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"wPRySrgMK3oT","executionInfo":{"status":"ok","timestamp":1620193255877,"user_tz":-540,"elapsed":6682,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"992c94ab-ea03-4bcf-e70e-e2c8115843f5"},"source":["!pip install segmentation_models_pytorch"],"execution_count":14,"outputs":[{"output_type":"stream","text":["Collecting segmentation_models_pytorch\n","\u001b[?25l Downloading 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Stored in directory: /root/.cache/pip/wheels/42/1e/a9/2a578ba9ad04e776e80bf0f70d8a7f4c29ec0718b92d8f6ccd\n","Successfully built pretrainedmodels efficientnet-pytorch\n","Installing collected packages: munch, pretrainedmodels, timm, efficientnet-pytorch, segmentation-models-pytorch\n","Successfully installed efficientnet-pytorch-0.6.3 munch-2.5.0 pretrainedmodels-0.7.4 segmentation-models-pytorch-0.1.3 timm-0.3.2\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:11.634792Z","start_time":"2021-04-18T16:16:05.875817Z"},"colab":{"base_uri":"https://localhost:8080/","height":117,"referenced_widgets":["bc3889631e79409d9abcdd9f67dfec27","04fe55a93e7f470ca76e3d270b58ef43","943aa127770d49d69049f44707a8dc45","48044b89ffdc4f0aa6bc2b25640668e2","44b83bef50a1475397fff6ae6beb5464","311ec03867874c2fa9a85b2a930ca2d4","8e75300921cd4492915341a5f52aef0f","ac3ea57705c3405696aa20122f82c45a"]},"id":"MJ2vs-Y_0I35","executionInfo":{"status":"ok","timestamp":1620193267039,"user_tz":-540,"elapsed":17803,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"30dc670e-2038-4eb0-f72a-a33f751f40aa"},"source":["# 구현된 model에 임의의 input을 넣어 output이 잘 나오는지 test\n","import segmentation_models_pytorch as smp\n","\n","model = smp.UnetPlusPlus(classes=12)\n","x = torch.randn([1, 3, 512, 512])\n","print(\"input shape : \", x.shape)\n","out = model(x).to(device)\n","print(\"output shape : \", out.size())\n","\n","model = model.to(device)"],"execution_count":15,"outputs":[{"output_type":"stream","text":["Downloading: \"https://download.pytorch.org/models/resnet34-333f7ec4.pth\" to /root/.cache/torch/hub/checkpoints/resnet34-333f7ec4.pth\n"],"name":"stderr"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"bc3889631e79409d9abcdd9f67dfec27","version_minor":0,"version_major":2},"text/plain":["HBox(children=(FloatProgress(value=0.0, max=87306240.0), HTML(value='')))"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\n","input shape : torch.Size([1, 3, 512, 512])\n","output shape : torch.Size([1, 12, 512, 512])\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"SgM4SGqL0I35"},"source":["## train, validation, test 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.104200Z","start_time":"2021-04-18T16:16:18.093174Z"},"id":"Dl6skKCT0I35"},"source":["def train(num_epochs, model, data_loader, val_loader, criterion, optimizer, saved_dir, val_every, device):\n"," print('Start training..')\n"," best_loss = 9999999\n"," best_miou = 0\n"," for epoch in range(num_epochs):\n"," model.train()\n"," for step, (images, masks, _) in enumerate(data_loader):\n"," images = torch.stack(images) # (batch, channel, height, width)\n"," masks = torch.stack(masks).long() # (batch, channel, height, width)\n"," \n"," # gpu 연산을 위해 device 할당\n"," images, masks = images.to(device), masks.to(device)\n"," \n"," # inference\n"," outputs = model(images)\n"," \n"," # loss 계산 (cross entropy loss)\n"," loss = criterion(outputs, masks)\n"," optimizer.zero_grad()\n"," loss.backward()\n"," optimizer.step()\n"," \n"," # lr_scheduler.step()\n"," \n"," # step 주기에 따른 loss 출력\n"," if (step + 1) % 25 == 0:\n"," print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(\n"," epoch+1, num_epochs, step+1, len(train_loader), loss.item()))\n"," \n"," # validation 주기에 따른 loss 출력 및 best model 저장\n"," if (epoch + 1) % val_every == 0:\n","# avrg_loss = validation(epoch + 1, model, val_loader, criterion, device)\n","# if avrg_loss < best_loss:\n","# print('Best performance at epoch: {}'.format(epoch + 1))\n","# print('Save model in', saved_dir)\n","# best_loss = avrg_loss\n","# wandb.log({'best_loss': best_loss})\n","# save_model(model, saved_dir)\n"," avrg_miou = validation(epoch + 1, model, val_loader, criterion, device)\n"," if avrg_miou > best_miou:\n"," print('Best performance at epoch: {}'.format(epoch + 1))\n"," print('Save model in', saved_dir)\n"," best_miou = avrg_miou\n"," wandb.log({'best_miou': best_miou})\n"," save_model(model, saved_dir)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.285795Z","start_time":"2021-04-18T16:16:18.267686Z"},"id":"Yw_3xbyj0I36"},"source":["def validation(epoch, model, data_loader, criterion, device):\n"," print('Start validation #{}'.format(epoch))\n"," model.eval()\n"," with torch.no_grad():\n"," total_loss = 0\n"," cnt = 0\n"," mIoU_list = []\n"," for step, (images, masks, _) in enumerate(data_loader):\n"," \n"," images = torch.stack(images) # (batch, channel, height, width)\n"," masks = torch.stack(masks).long() # (batch, channel, height, width)\n","\n"," images, masks = images.to(device), masks.to(device) \n","\n"," outputs = model(images)\n"," loss = criterion(outputs, masks)\n"," total_loss += loss\n"," cnt += 1\n"," \n","# print(outputs.shape)\n","# print(masks.shape)\n","# wandb.log(wandb.Image(images, masks={\n","# \"predictions\" : {\n","# \"mask_data\" : torch.squeeze(torch.squeeze(outputs, 0),1),\n","# \"class_labels\" : classes_dict\n","# },\n","# \"ground_truth\" : {\n","# \"mask_data\" : torch.squeeze(masks, 0),\n","# \"class_labels\" : classes_dict\n","# }\n","# }))\n"," \n"," outputs = torch.argmax(outputs.squeeze(), dim=1).detach().cpu().numpy()\n","\n"," mIoU = label_accuracy_score(masks.detach().cpu().numpy(), outputs, n_class=12)[2]\n"," mIoU_list.append(mIoU)\n"," \n"," avrg_loss = total_loss / cnt\n"," avrg_mIoU = np.mean(mIoU_list)\n"," print('Validation #{} Average Loss: {:.4f}, mIoU: {:.4f}'.format(epoch, avrg_loss, np.mean(mIoU_list)))\n"," wandb.log({'Train Loss':loss.item(), 'Val Loss':avrg_loss , 'Val mIoU':np.mean(mIoU_list)})\n","# return avrg_loss\n"," return avrg_mIoU"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"b92qGwBc0I37"},"source":["## 모델 저장 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.909918Z","start_time":"2021-04-18T16:16:18.898918Z"},"id":"H50hk0za0I37"},"source":["# 모델 저장 함수 정의\n","val_every = 1 \n","\n","saved_dir = './saved'\n","if not os.path.isdir(saved_dir): \n"," os.mkdir(saved_dir)\n"," \n","def save_model(model, saved_dir, file_name='3_aug_horizontalflip_Rotation90.pt'):\n"," check_point = {'net': model.state_dict()}\n"," output_path = os.path.join(saved_dir, file_name)\n"," torch.save(model.state_dict(), output_path)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"UwzGmX190I37"},"source":["## 모델 생성 및 Loss function, Optimizer 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:19.698902Z","start_time":"2021-04-18T16:16:19.694902Z"},"id":"hOKlPrNn0I37"},"source":["# Loss function 정의\n","criterion = nn.CrossEntropyLoss()\n","\n","# Optimizer 정의\n","optimizer = torch.optim.Adam(params = model.parameters(), lr = learning_rate, weight_decay=1e-6)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"start_time":"2021-04-18T16:16:20.331Z"},"scrolled":false,"colab":{"base_uri":"https://localhost:8080/"},"id":"MSReHpkI0I38","executionInfo":{"status":"ok","timestamp":1620139867814,"user_tz":-540,"elapsed":2431181,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"f7a57a4d-5b44-49f4-b009-33a1862e33ce"},"source":["train(num_epochs, model, train_loader, val_loader, criterion, optimizer, saved_dir, val_every, device)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Start training..\n","Epoch [1/20], Step [25/327], Loss: 2.1958\n","Epoch [1/20], Step [50/327], Loss: 1.8974\n","Epoch [1/20], Step [75/327], Loss: 1.6173\n","Epoch [1/20], Step [100/327], Loss: 1.3770\n","Epoch [1/20], Step [125/327], Loss: 1.1435\n","Epoch [1/20], Step [150/327], Loss: 1.2088\n","Epoch [1/20], Step [175/327], Loss: 1.1111\n","Epoch [1/20], Step [200/327], Loss: 0.9145\n","Epoch [1/20], Step [225/327], Loss: 0.8583\n","Epoch [1/20], Step [250/327], Loss: 0.9290\n","Epoch [1/20], Step [275/327], Loss: 0.8707\n","Epoch [1/20], Step [300/327], Loss: 0.7044\n","Epoch [1/20], Step [325/327], Loss: 0.7578\n","Start validation #1\n","Validation #1 Average Loss: 0.7049, mIoU: 0.1660\n","Best performance at epoch: 1\n","Save model in ./saved\n","Epoch [2/20], Step [25/327], Loss: 0.5990\n","Epoch [2/20], Step [50/327], Loss: 1.0162\n","Epoch [2/20], Step [75/327], Loss: 0.5238\n","Epoch [2/20], Step [100/327], Loss: 0.6918\n","Epoch [2/20], Step [125/327], Loss: 0.6649\n","Epoch [2/20], Step [150/327], Loss: 1.2740\n","Epoch [2/20], Step [175/327], Loss: 0.9146\n","Epoch [2/20], Step [200/327], Loss: 0.7144\n","Epoch [2/20], Step [225/327], Loss: 0.4082\n","Epoch [2/20], Step [250/327], Loss: 0.8300\n","Epoch [2/20], Step [275/327], Loss: 0.5287\n","Epoch [2/20], Step [300/327], Loss: 0.4127\n","Epoch [2/20], Step [325/327], Loss: 0.4350\n","Start validation #2\n","Validation #2 Average Loss: 0.5796, mIoU: 0.2453\n","Best performance at epoch: 2\n","Save model in ./saved\n","Epoch [3/20], Step [25/327], Loss: 0.4367\n","Epoch [3/20], Step [50/327], Loss: 0.4050\n","Epoch [3/20], Step [75/327], Loss: 0.4351\n","Epoch [3/20], Step [100/327], Loss: 0.5569\n","Epoch [3/20], Step [125/327], Loss: 0.4771\n","Epoch [3/20], Step [150/327], Loss: 0.4607\n","Epoch [3/20], Step [175/327], Loss: 0.6087\n","Epoch [3/20], Step [200/327], Loss: 0.5008\n","Epoch [3/20], Step [225/327], Loss: 0.9955\n","Epoch [3/20], Step [250/327], Loss: 0.5537\n","Epoch [3/20], Step [275/327], Loss: 0.5702\n","Epoch [3/20], Step [300/327], Loss: 0.3540\n","Epoch [3/20], Step [325/327], Loss: 0.3704\n","Start validation #3\n","Validation #3 Average Loss: 0.5003, mIoU: 0.2674\n","Best performance at epoch: 3\n","Save model in ./saved\n","Epoch [4/20], Step [25/327], Loss: 0.3595\n","Epoch [4/20], Step [50/327], Loss: 0.5102\n","Epoch [4/20], Step [75/327], Loss: 0.4325\n","Epoch [4/20], Step [100/327], Loss: 0.4053\n","Epoch [4/20], Step [125/327], Loss: 0.3739\n","Epoch [4/20], Step [150/327], Loss: 0.4187\n","Epoch [4/20], Step [175/327], Loss: 0.4577\n","Epoch [4/20], Step [200/327], Loss: 0.2353\n","Epoch [4/20], Step [225/327], Loss: 0.8061\n","Epoch [4/20], Step [250/327], Loss: 0.8364\n","Epoch [4/20], Step [275/327], Loss: 0.2999\n","Epoch [4/20], Step [300/327], Loss: 0.5897\n","Epoch [4/20], Step [325/327], Loss: 0.2916\n","Start validation #4\n","Validation #4 Average Loss: 0.4844, mIoU: 0.2890\n","Best performance at epoch: 4\n","Save model in ./saved\n","Epoch [5/20], Step [25/327], Loss: 0.6780\n","Epoch [5/20], Step [50/327], Loss: 0.5407\n","Epoch [5/20], Step [75/327], Loss: 0.6524\n","Epoch [5/20], Step [100/327], Loss: 0.2229\n","Epoch [5/20], Step [125/327], Loss: 0.2861\n","Epoch [5/20], Step [150/327], Loss: 0.3554\n","Epoch [5/20], Step [175/327], Loss: 0.2796\n","Epoch [5/20], Step [200/327], Loss: 0.5562\n","Epoch [5/20], Step [225/327], Loss: 0.4751\n","Epoch [5/20], Step [250/327], Loss: 0.4572\n","Epoch [5/20], Step [275/327], Loss: 0.4107\n","Epoch [5/20], Step [300/327], Loss: 0.2533\n","Epoch [5/20], Step [325/327], Loss: 0.5409\n","Start validation #5\n","Validation #5 Average Loss: 0.4864, mIoU: 0.2986\n","Best performance at epoch: 5\n","Save model in ./saved\n","Epoch [6/20], Step [25/327], Loss: 0.3452\n","Epoch [6/20], Step [50/327], Loss: 0.4707\n","Epoch [6/20], Step [75/327], Loss: 0.5286\n","Epoch [6/20], Step [100/327], Loss: 0.1377\n","Epoch [6/20], Step [125/327], Loss: 0.3500\n","Epoch [6/20], Step [150/327], Loss: 0.2828\n","Epoch [6/20], Step [175/327], Loss: 0.8834\n","Epoch [6/20], Step [200/327], Loss: 0.3087\n","Epoch [6/20], Step [225/327], Loss: 0.5734\n","Epoch [6/20], Step [250/327], Loss: 0.2085\n","Epoch [6/20], Step [275/327], Loss: 0.3064\n","Epoch [6/20], Step [300/327], Loss: 0.3325\n","Epoch [6/20], Step [325/327], Loss: 0.4335\n","Start validation #6\n","Validation #6 Average Loss: 0.4705, mIoU: 0.2930\n","Epoch [7/20], Step [25/327], Loss: 0.2783\n","Epoch [7/20], Step [50/327], Loss: 0.3314\n","Epoch [7/20], Step [75/327], Loss: 0.5874\n","Epoch [7/20], Step [100/327], Loss: 0.4972\n","Epoch [7/20], Step [125/327], Loss: 0.5647\n","Epoch [7/20], Step [150/327], Loss: 0.2562\n","Epoch [7/20], Step [175/327], Loss: 0.3767\n","Epoch [7/20], Step [200/327], Loss: 0.2362\n","Epoch [7/20], Step [225/327], Loss: 0.2321\n","Epoch [7/20], Step [250/327], Loss: 0.3952\n","Epoch [7/20], Step [275/327], Loss: 0.2618\n","Epoch [7/20], Step [300/327], Loss: 0.3964\n","Epoch [7/20], Step [325/327], Loss: 0.3818\n","Start validation #7\n","Validation #7 Average Loss: 0.4573, mIoU: 0.2680\n","Epoch [8/20], Step [25/327], Loss: 0.5679\n","Epoch [8/20], Step [50/327], Loss: 0.4003\n","Epoch [8/20], Step [75/327], Loss: 0.4279\n","Epoch [8/20], Step [100/327], Loss: 0.3616\n","Epoch [8/20], Step [125/327], Loss: 0.3383\n","Epoch [8/20], Step [150/327], Loss: 0.2446\n","Epoch [8/20], Step [175/327], Loss: 0.8409\n","Epoch [8/20], Step [200/327], Loss: 0.3677\n","Epoch [8/20], Step [225/327], Loss: 0.3445\n","Epoch [8/20], Step [250/327], Loss: 0.4389\n","Epoch [8/20], Step [275/327], Loss: 0.6308\n","Epoch [8/20], Step [300/327], Loss: 0.4019\n","Epoch [8/20], Step [325/327], Loss: 0.4930\n","Start validation #8\n","Validation #8 Average Loss: 0.4830, mIoU: 0.2618\n","Epoch [9/20], Step [25/327], Loss: 0.4391\n","Epoch [9/20], Step [50/327], Loss: 0.1555\n","Epoch [9/20], Step [75/327], Loss: 0.3504\n","Epoch [9/20], Step [100/327], Loss: 0.3233\n","Epoch [9/20], Step [125/327], Loss: 0.2313\n","Epoch [9/20], Step [150/327], Loss: 0.8070\n","Epoch [9/20], Step [175/327], Loss: 0.3554\n","Epoch [9/20], Step [200/327], Loss: 0.1979\n","Epoch [9/20], Step [225/327], Loss: 0.3132\n","Epoch [9/20], Step [250/327], Loss: 0.3120\n","Epoch [9/20], Step [275/327], Loss: 0.2165\n","Epoch [9/20], Step [300/327], Loss: 0.3335\n","Epoch [9/20], Step [325/327], Loss: 0.2325\n","Start validation #9\n","Validation #9 Average Loss: 0.5173, mIoU: 0.2385\n","Epoch [10/20], Step [25/327], Loss: 0.7178\n","Epoch [10/20], Step [50/327], Loss: 0.2616\n","Epoch [10/20], Step [75/327], Loss: 0.4867\n","Epoch [10/20], Step [100/327], Loss: 0.3907\n","Epoch [10/20], Step [125/327], Loss: 0.5234\n","Epoch [10/20], Step [150/327], Loss: 0.1616\n","Epoch [10/20], Step [175/327], Loss: 0.4809\n","Epoch [10/20], Step [200/327], Loss: 0.3340\n","Epoch [10/20], Step [225/327], Loss: 0.2696\n","Epoch [10/20], Step [250/327], Loss: 0.3001\n","Epoch [10/20], Step [275/327], Loss: 0.1711\n","Epoch [10/20], Step [300/327], Loss: 0.3171\n","Epoch [10/20], Step [325/327], Loss: 0.1993\n","Start validation #10\n","Validation #10 Average Loss: 0.4933, mIoU: 0.2545\n","Epoch [11/20], Step [25/327], Loss: 0.3599\n","Epoch [11/20], Step [50/327], Loss: 0.4035\n","Epoch [11/20], Step [75/327], Loss: 0.1486\n","Epoch [11/20], Step [100/327], Loss: 0.2414\n","Epoch [11/20], Step [125/327], Loss: 0.2816\n","Epoch [11/20], Step [150/327], Loss: 0.5126\n","Epoch [11/20], Step [175/327], Loss: 0.1345\n","Epoch [11/20], Step [200/327], Loss: 0.4752\n","Epoch [11/20], Step [225/327], Loss: 0.2084\n","Epoch [11/20], Step [250/327], Loss: 0.2258\n","Epoch [11/20], Step [275/327], Loss: 0.1317\n","Epoch [11/20], Step [300/327], Loss: 0.5722\n","Epoch [11/20], Step [325/327], Loss: 0.1360\n","Start validation #11\n","Validation #11 Average Loss: 0.4316, mIoU: 0.2847\n","Epoch [12/20], Step [25/327], Loss: 0.3258\n","Epoch [12/20], Step [50/327], Loss: 0.2543\n","Epoch [12/20], Step [75/327], Loss: 0.1990\n","Epoch [12/20], Step [100/327], Loss: 0.3040\n","Epoch [12/20], Step [125/327], Loss: 0.1676\n","Epoch [12/20], Step [150/327], Loss: 0.4720\n","Epoch [12/20], Step [175/327], Loss: 0.3987\n","Epoch [12/20], Step [200/327], Loss: 0.3946\n","Epoch [12/20], Step [225/327], Loss: 0.2980\n","Epoch [12/20], Step [250/327], Loss: 0.2851\n","Epoch [12/20], Step [275/327], Loss: 0.1730\n","Epoch [12/20], Step [300/327], Loss: 0.2182\n","Epoch [12/20], Step [325/327], Loss: 0.2195\n","Start validation #12\n","Validation #12 Average Loss: 0.4350, mIoU: 0.2824\n","Epoch [13/20], Step [25/327], Loss: 0.2580\n","Epoch [13/20], Step [50/327], Loss: 0.4609\n","Epoch [13/20], Step [75/327], Loss: 0.3894\n","Epoch [13/20], Step [100/327], Loss: 0.4332\n","Epoch [13/20], Step [125/327], Loss: 0.2973\n","Epoch [13/20], Step [150/327], Loss: 0.2216\n","Epoch [13/20], Step [175/327], Loss: 0.3887\n","Epoch [13/20], Step [200/327], Loss: 0.3394\n","Epoch [13/20], Step [225/327], Loss: 0.4260\n","Epoch [13/20], Step [250/327], Loss: 0.3684\n","Epoch [13/20], Step [275/327], Loss: 0.2014\n","Epoch [13/20], Step [300/327], Loss: 0.4668\n","Epoch [13/20], Step [325/327], Loss: 0.3273\n","Start validation #13\n","Validation #13 Average Loss: 0.5064, mIoU: 0.2959\n","Epoch [14/20], Step [25/327], Loss: 0.2515\n","Epoch [14/20], Step [50/327], Loss: 0.2020\n","Epoch [14/20], Step [75/327], Loss: 0.4674\n","Epoch [14/20], Step [100/327], Loss: 0.2799\n","Epoch [14/20], Step [125/327], Loss: 0.3881\n","Epoch [14/20], Step [150/327], Loss: 0.2481\n","Epoch [14/20], Step [175/327], Loss: 0.1896\n","Epoch [14/20], Step [200/327], Loss: 0.1818\n","Epoch [14/20], Step [225/327], Loss: 0.2867\n","Epoch [14/20], Step [250/327], Loss: 0.2588\n","Epoch [14/20], Step [275/327], Loss: 0.4291\n","Epoch [14/20], Step [300/327], Loss: 0.4819\n","Epoch [14/20], Step [325/327], Loss: 0.3365\n","Start validation #14\n","Validation #14 Average Loss: 0.4560, mIoU: 0.2840\n","Epoch [15/20], Step [25/327], Loss: 0.1880\n","Epoch [15/20], Step [50/327], Loss: 0.4899\n","Epoch [15/20], Step [75/327], Loss: 0.4261\n","Epoch [15/20], Step [100/327], Loss: 0.1468\n","Epoch [15/20], Step [125/327], Loss: 0.1626\n","Epoch [15/20], Step [150/327], Loss: 0.5053\n","Epoch [15/20], Step [175/327], Loss: 0.1891\n","Epoch [15/20], Step [200/327], Loss: 0.2097\n","Epoch [15/20], Step [225/327], Loss: 0.2359\n","Epoch [15/20], Step [250/327], Loss: 0.2718\n","Epoch [15/20], Step [275/327], Loss: 0.3026\n","Epoch [15/20], Step [300/327], Loss: 0.1795\n","Epoch [15/20], Step [325/327], Loss: 0.3078\n","Start validation #15\n","Validation #15 Average Loss: 0.4819, mIoU: 0.2735\n","Epoch [16/20], Step [25/327], Loss: 0.3700\n","Epoch [16/20], Step [50/327], Loss: 0.1390\n","Epoch [16/20], Step [75/327], Loss: 0.2323\n","Epoch [16/20], Step [100/327], Loss: 0.3706\n","Epoch [16/20], Step [125/327], Loss: 0.1645\n","Epoch [16/20], Step [150/327], Loss: 0.5024\n","Epoch [16/20], Step [175/327], Loss: 0.2263\n","Epoch [16/20], Step [200/327], Loss: 0.2845\n","Epoch [16/20], Step [225/327], Loss: 0.4304\n","Epoch [16/20], Step [250/327], Loss: 0.3275\n","Epoch [16/20], Step [275/327], Loss: 0.3559\n","Epoch [16/20], Step [300/327], Loss: 0.2994\n","Epoch [16/20], Step [325/327], Loss: 0.1688\n","Start validation #16\n","Validation #16 Average Loss: 0.4315, mIoU: 0.2963\n","Epoch [17/20], Step [25/327], Loss: 0.2397\n","Epoch [17/20], Step [50/327], Loss: 0.2541\n","Epoch [17/20], Step [75/327], Loss: 0.2774\n","Epoch [17/20], Step [100/327], Loss: 0.2901\n","Epoch [17/20], Step [125/327], Loss: 0.2060\n","Epoch [17/20], Step [150/327], Loss: 0.4640\n","Epoch [17/20], Step [175/327], Loss: 0.2384\n","Epoch [17/20], Step [200/327], Loss: 0.2345\n","Epoch [17/20], Step [225/327], Loss: 0.1888\n","Epoch [17/20], Step [250/327], Loss: 0.3335\n","Epoch [17/20], Step [275/327], Loss: 0.2749\n","Epoch [17/20], Step [300/327], Loss: 0.4724\n","Epoch [17/20], Step [325/327], Loss: 0.1137\n","Start validation #17\n","Validation #17 Average Loss: 0.4596, mIoU: 0.2936\n","Epoch [18/20], Step [25/327], Loss: 0.3253\n","Epoch [18/20], Step [50/327], Loss: 0.2375\n","Epoch [18/20], Step [75/327], Loss: 0.1921\n","Epoch [18/20], Step [100/327], Loss: 0.2530\n","Epoch [18/20], Step [125/327], Loss: 0.3028\n","Epoch [18/20], Step [150/327], Loss: 0.1467\n","Epoch [18/20], Step [175/327], Loss: 0.2271\n","Epoch [18/20], Step [200/327], Loss: 0.2652\n","Epoch [18/20], Step [225/327], Loss: 0.2019\n","Epoch [18/20], Step [250/327], Loss: 0.1940\n","Epoch [18/20], Step [275/327], Loss: 0.2651\n","Epoch [18/20], Step [300/327], Loss: 0.1338\n","Epoch [18/20], Step [325/327], Loss: 0.3314\n","Start validation #18\n","Validation #18 Average Loss: 0.5036, mIoU: 0.2828\n","Epoch [19/20], Step [25/327], Loss: 0.2482\n","Epoch [19/20], Step [50/327], Loss: 0.1713\n","Epoch [19/20], Step [75/327], Loss: 0.2165\n","Epoch [19/20], Step [100/327], Loss: 0.4057\n","Epoch [19/20], Step [125/327], Loss: 0.1531\n","Epoch [19/20], Step [150/327], Loss: 0.2609\n","Epoch [19/20], Step [175/327], Loss: 0.2873\n","Epoch [19/20], Step [200/327], Loss: 0.4082\n","Epoch [19/20], Step [225/327], Loss: 0.2845\n","Epoch [19/20], Step [250/327], Loss: 0.3069\n","Epoch [19/20], Step [275/327], Loss: 0.6281\n","Epoch [19/20], Step [300/327], Loss: 0.2710\n","Epoch [19/20], Step [325/327], Loss: 0.1846\n","Start validation #19\n","Validation #19 Average Loss: 0.4981, mIoU: 0.2779\n","Epoch [20/20], Step [25/327], Loss: 0.1982\n","Epoch [20/20], Step [50/327], Loss: 0.5019\n","Epoch [20/20], Step [75/327], Loss: 0.2711\n","Epoch [20/20], Step [100/327], Loss: 0.3240\n","Epoch [20/20], Step [125/327], Loss: 0.1472\n","Epoch [20/20], Step [150/327], Loss: 0.1595\n","Epoch [20/20], Step [175/327], Loss: 0.2342\n","Epoch [20/20], Step [200/327], Loss: 0.2094\n","Epoch [20/20], Step [225/327], Loss: 0.2385\n","Epoch [20/20], Step [250/327], Loss: 0.3449\n","Epoch [20/20], Step [275/327], Loss: 0.1320\n","Epoch [20/20], Step [300/327], Loss: 0.3735\n","Epoch [20/20], Step [325/327], Loss: 0.2344\n","Start validation #20\n","Validation #20 Average Loss: 0.4867, mIoU: 0.2804\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"jOE-56p5MkXM"},"source":["# Sleep for a few seconds.\n","import time\n","time.sleep(2)\n","# Play an audio beep. Any audio URL will do.\n","from google.colab import output\n","output.eval_js('new Audio(\"https://upload.wikimedia.org/wikipedia/commons/0/05/Beep-09.ogg\").play()')"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"6IqKsZ4u0I38"},"source":["## 저장된 model 불러오기 (학습된 이후) "]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:21.050200Z","start_time":"2021-04-16T19:44:20.802200Z"},"scrolled":true,"id":"dl5dIeHB0I38","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620193274421,"user_tz":-540,"elapsed":4092,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"3518160a-6034-4b4a-c5ce-61db2add7567"},"source":["# best model 저장된 경로\n","model_path = './saved/3_aug_horizontalflip_Rotation90.pt'\n","\n","# best model 불러오기\n","checkpoint = torch.load(model_path, map_location=device)\n","model.load_state_dict(checkpoint)\n","\n","# 추론을 실행하기 전에는 반드시 설정 (batch normalization, dropout 를 평가 모드로 설정)\n","# model.eval()"],"execution_count":16,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{"tags":[]},"execution_count":16}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:24.939227Z","start_time":"2021-04-16T19:44:24.518228Z"},"id":"mOUcjOP20I38","colab":{"base_uri":"https://localhost:8080/","height":542},"executionInfo":{"status":"ok","timestamp":1620193290091,"user_tz":-540,"elapsed":15639,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"28a10452-5f97-4416-a7b7-d3743d7deffb"},"source":["# 첫번째 batch의 추론 결과 확인\n","for imgs, image_infos in test_loader:\n"," image_infos = image_infos\n"," temp_images = imgs\n"," \n"," model.eval()\n"," # inference\n"," outs = model(torch.stack(temp_images).to(device))\n"," oms = torch.argmax(outs.squeeze(), dim=1).detach().cpu().numpy()\n"," \n"," break\n","\n","i = 3\n","fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(16, 16))\n","\n","print('Shape of Original Image :', list(temp_images[i].shape))\n","print('Shape of Predicted : ', list(oms[i].shape))\n","print('Unique values, category of transformed mask : \\n', [{int(i),category_names[int(i)]} for i in list(np.unique(oms[i]))])\n","\n","# Original image\n","ax1.imshow(temp_images[i].permute([1,2,0]))\n","ax1.grid(False)\n","ax1.set_title(\"Original image : {}\".format(image_infos[i]['file_name']), fontsize = 15)\n","\n","# Predicted\n","ax2.imshow(oms[i])\n","ax2.grid(False)\n","ax2.set_title(\"Predicted : {}\".format(image_infos[i]['file_name']), fontsize = 15)\n","\n","plt.show()"],"execution_count":17,"outputs":[{"output_type":"stream","text":["Shape of Original Image : [3, 512, 512]\n","Shape of Predicted : [512, 512]\n","Unique values, category of transformed mask : \n"," [{0, 'Backgroud'}, {9, 'Plastic bag'}]\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA6sAAAHJCAYAAABjQa/9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOydeZwlSVXvvxGRmXepW1W9zg7MMDA5MDxAZFhFNjdEBBFFXBA3lEVc8CGoKIMLCiqgPsEFRdwYQMEVERAVFGQQkHWCAWbfp7da7r25RMT740Teyr5d3V3Ty3Qj+ZvpT1XljcyMjMybJ37n/M4JFUKgQ4cOHTp06NChQ4cOHTp0OJ2gT3UHOnTo0KFDhw4dOnTo0KFDh3l0ZLVDhw4dOnTo0KFDhw4dOpx26Mhqhw4dOnTo0KFDhw4dOnQ47dCR1Q4dOnTo0KFDhw4dOnTocNqhI6sdOnTo0KFDhw4dOnTo0OG0Q0dWO3To0KFDhw4dOnTo0KHDaYfkVHeggyDPcwV8L/Ac4BLAAx8DfsNa+7dbPMbLgOdba3fdyXO/EbiftfbBd2a/E9WnY+33lzryPA/Aj1prf+c4j3MG8Fzgjdbaa05RH54M/BJwb+CLwGXW2stbn2fALwMPAx4M9K216njOeZh+fB1wX2vtaw7z+bcDvwWcba0NeZ7fF/ht4OHAfuAPY99dax8FvAT5bu4CrgBeYK39eKvN04CfBHJgAbgW+FPgldbaMrY5O7b5OuBCYB/wL8BLrLU3HeW6/hW4w1r7tDs1IB06dDitEO3dL7Q23Qx8EHiRtfYLJ+mc3wT8HXCBtfaaPM/PB64GnmSt/fstHuPbgaG19o0nqE8jYBX4vuM9Zp7nzwL+GFi01q4d57GOaENOdh/yPO8BvwJ8D2JL/hV4Xtu253n+YOD5iN26N/Ama+2zjvWcR+jLi4APW2v/9TCf/yPwKWvti+LfPwS8CLgb8GnkmX7v3D7nAr8DfA1QAG+O7catNq8HHgOcB1TAJ4GXW2vf02rzNcAPIXOK3ci84/XA69r2e5M+n8+dfPY7nHp0kdXTB7+LTJT/C/gW4OnANcDf5Hn+01s8xh8CX38M5/5F4FnHsN+JwrH2u4PgDGTyc/6pOHme518F/BXwPuAJwD8AfxmNfoMh8IPAGPjPk9idrwN+/AifPxH4x0hUtwPvAQLwZODlwAuBy+b2eTHwUuDXgCcBa8B78jw/q9VmJ0I8fxAZgz8Cfhb4zVabr0S+238Zj/N/gYcC/xknbkfCcxHC3KFDhy99HECIxsOBnwIeCLw3z/OFu+j8N8dzf+BO7PPtnNp5wl2Fo9mQk43fQsb5p4CnIQ7Sd+d53m+1eSTwVYjj9JaT2JcXIaTxEOR5PgQeC/x9/PsZCFl8E2IDPw38fZ7n92vtkwLvAu4BfAfwY8C3Ab8/d/gBQmi/Bfhu4A7gnXmeP6zV5tkImf854BsR0vsbwCuPck3H8ux3OMXoIqunAfI8fwrwI8BzrLWvb330zjzPbwF+Jc/zd1trP3qY/VPAW2tvAG64s+c/Wd7cO3H+Y+p3h9MGLwX+3Vr7gvj3+/I8vwT4eeCfAay1+/M83xFJ4vOBx93VnczzXCNG9Dlx048gRvGp1toVZEKwBLwsz/NXWmtX4gThxcArmuhznucfRBxJz0cMJdba35s73fvisZ6X5/mPWmsDYhwvttbWrT59FLDAtwJ/cri+W2s/c3xX36FDh9MItbX2Q/H3D+V5fh3wfmTS/db5xnmeD6y1kxN1cmttAXzoqA073KXI8/w84AeA77fWvilu+wQSCfxuxLEP8NvW2tfGzz9yKvqK2PApG87nlwF/Yq39xdivfwO+ArGf3x3bPA24D3Ava+3VsV0FvDnP88ustVcBWGu/t32iPM//CRmD72TjuX2utfaOVrN/jQT6J/I8/5n4jB+C7tn/0kRHVk8P/BjweeAPNvnsV4AfRibG3w8bkkCECPw0ElE7P8/zH2BOTpvn+f0Rb9eDgC8g0ZyXI9KNZ8U2b6QlA27JWe6PRIYeAVwP/Iy19q9bx34i4oF8ANAHPgP8vLX2n+/Mxc/LgPM8fwwSpfsa4AXx503A84D3Ar8KfB8iIfkNa+1vto71cCQCdSmwBFwFvMpa++dz53wM4sG8CJGYPA/4R+B3rLUva7V7MkLG7ofIRN8E/Ky1troz13gEZHmevxaR/GhEOvrCOenoLyPezbOR+/AWRBJTRknLJ+Ox3pfnOQCNxDbP853IM/TNwHZEnvq6OYmTyfP8VxBJTUAmSz95uJd9G1Gy9FjkPrXxZuCP8zxfttYeiH0KWxyTzc5zASLz+SZr7T+0thvgRsSI10hktJE3gxjPZ8XfLwW2Ae+Ofz8BeFckqu1+/xrwaEQ29wjkOXpL08Bau57n+d/F/X/uCN3eA2St/fbPN7DWfi7P8zFwzhGOc4gMuPnOIBHh3wbuC1yJfI8+0NqvB7wGeAbgkIjvjcCrT4YMu0OHDseE/44/zwfI8/waRK2yH7H/ZwJpdLi9CFFw3A15n/+ytXbm6IppC7+AqDEGwNuBf2qf7HBSyCjjfAEiLT2AEOgfAF6LONTa79bLGlu5FTuZ5/m3Aq+I/b4CSYk40bhPnue/iaSaXA/8tLX27a0+HHHOEt+rh7UheZ5/NaK8uRR5n34M+Alr7cdafbgg9mHTedNR0KiRZu2ttTfmef4BxN78Ydzmt3i8TRGv80eAc9rHiuPz98j9fw+iGPqFPM8b2fpjW5LgJyL2s87z/J7IXOrHWv32eZ6/tb0tXsMVDVGNeAdQAt+AzNcOgbXW5Xm+n4Pt6R2bNP0Ycl93IBHUza79fOae/fh9exvyzD8PGAF/gxDiA619jzqf7nBy0MmATzHyPE8QScLfbaazj1+U9wFfPffRI5EI0U8jksIDc583Mo13IQbrGUhO4auBu2+xe38B/C0ixbgK8X6d1/r8AmRC/z2IIftPJBr8yC0e/2j4PSQa9S2IUX4bIg1ZRDxsbwN+I8/zh7b2uQfwH4iBfRJi8P84SlSAWc7EPwK3IZ6+3wP+HBknWu2+HTEaH0bI3mWI9OQVR+p0nufPyvM8xJfi0fBCJC/ju5D782yEnDbYBexFDPs3AK9CiPpvx89vjvuCvGQbaRl5ng+QfJenIFLvb0RkMvPE6IVx23fH4/8wBxuYI+FCIEWIUhufRd4vF23xOEdENG4fRqRobTwamci9GTHkf4HIoppx+MVW2ycC72+R04vn+22tvQ6RKl/cauM41Ih+ttVmhjzPTZ7nwyiNfgHiGDgsSY/Gbwh87nBtjoAh8GeI8fw2ZJL4zjl58isRSdllyHNyd+JkrEOHDqcNzo8/25LO70Teb89F0oJA3vs/h8gmn4gQ0T+KOakNXoCoWn4fsW8Tji6NJM/zn0Ns4b8hNuM5yLxihLxH34eQgebd+odxv6PayTzPHwRcDvwP8FRk3jBzAB6lX/8anXVbweUIyXgq4sR9a57nD2h9frQ5y2FtSHRwvxfJofxe5J68Hzh3rg9HmzcdCRcDN2yS87qpvTkOXI7YzUfPbX868N/W2s8j/T8AvIGNsWir+56IpPw0/YbN5wE78jzf3Wo3b3NLhPgddH15nqs8z5M8z3fmef4TCIH+o6NcV1N74rajtNsMz0ACIz+EzLeeyEYk+0TMpzscB7rI6qnHLqCHkLHD4VqEqLSxDXigtfbWZkMTVWvh+xDP2IOttTfGNl9A8mK3gldba/8o7vffwK3ANyGTY9pFeaLH931IcagfQAjj8eJPrbWvise/AcmByK21j4vb3oO8XJ/aXJO19s2tPing3xEy+ENIriCIZ3WMeNYmse0K8gJv7/sqpHDBc1vbC+D/5Xn+CmvtnsP02yMEZyuRxFXg26J3850xEvaz8fh7rbWfRHJXmvP/B7COTFB+1FpbRJkQwGda0jKAZyL340GtYkD/skkfrml5Bd8VDfdT2cIEB4nWghiINvbNfX4i8GbEy9trRX2fDnzaWvspgDzPbwaKuXFo8ETEKdFgO4f2G6Tv21tt1jZxJO0DhnmeZ00UPGId+T6DRBf+7+EuJn5nXotMaLZURG0OAyR68RfxeO8DrkOe7xfHqPqzkcjBq2ObdwGfOoZzdejQ4QQiOqoB7onUrFhFolltfJO1dhrb3wshkN/XiqS+J6pvfgHJDzSIA/v3rLWN6uNdeZ6/m0NJVbsv24CfAV5jrW1HPNtKqr2Abr9b74SdfDHikPv26Lx7Zy5F937pyKMEiC3dKv7QWvvrsQ/vQiKnL0HyI486Z7HW3nAEG/IKhGx/fcsB+U8ciiPOm46Crdik44a19rNx3vB0ZAwaFc6TieTcWvuxPM9rhDwfNBZ5nv8f5Hlqrn8r84DbuXPX93Q25mzrwNOttR8+3DXlUizxOcCvbRb42QIGwBMbR0Ge5+vAn+Z5fh9r7Wc5/vl0h+NAF1n90sV/t4nqYXBpbHdjsyF+2Y+2X4OZnDcanNsQ4gdIfkWe53+S5/mNiASzQmQsJySahngxG3w+/pyRrUjwvkjLCOd5vj3P89/K8/za2J8KmbC3+3Qp8O65HKB5snAR4jF7S/TuJXFy8S+IzOR+HAbW2jdZaxNr7ZEcEA3+Zk7S89fIS/N+8XpUnuc/nuf5Z/I8n8Tr+XOEEB3No/c44GMtono4zMu2P0PrPp9GeAsiyf0GmE32nkrLyXA4xAndV7DhCT5ZeATwKCR6+WRECXA4vALxBH/PccjKZxK3aGTfDTwkbvo/yLP6t602AYksdOjQ4dRhJxv2ySKE9enW2rZ08b0NUY14POIIffucTXov8MBIVO+GpIv8zdz5jiZDfThid/74Tl7HVu3kQ4C/nVOZbEkaa619vLX28VvsT/t96JFxaN6HxzxnyaXw1UMRSfDRnNBHnDedRrgc+NaW0+QJiGptKxHvJyJVgm8/WZ1DopiXxn69HYlQP2azhrkUS/wr4BNI2tOx4N1zEe23Ayr2AY5/Pt3hONCR1VOPO5Dcy3scoc09kDyzNrbyBTkL8WbNY6svmHkPWIkYoMYr+bfI5PznkbzFS4F3Nm1OAGbnb0WvDtuniDciHrlXIUboUkQ60m5zyLjESUH7RdXk/f4jG5OKCsl1AJkUnAjMy1Wav8+OP38c+HXkxflkxPA+L352tHHeyWHyNuZwtDE9EhrP6fLc9u1znx83opH4ABuSuMcj9+nNh91pA98IfNFaa1vb9nFov0H6vq/VZhQngvNtxnNRVay1H7XWfsBKHvULgOfkeX7h/AnyPH8uEnX9XmvtsXpm1+yhRVduY+PZaeTA89/3kznB6NChw9FxALFND0aIzPnW2nfOtZm38bsAE/dt26Q3Iiq5s9n4zh/OrhwOO+PPrdiL+T7B0e3kWcfQp2PBZuc4G457zrIdIS53hT09mk06UbgcuX9NscOnAx+MqTBHQ1sCDFufB2z5+qy1+6y1H7HW/pO19nuQ5Z1ePr9jLkUQ/wZx4H/zvE2+Ezjo2bGylM4aB9vT45lPdzgOdDLgU4yYnP5B4Il5nv/UfOJ8LhVFH0PLYxixFYnpLci6j/PYvcm2O4t7IZGqJ1hrZ1KYmCd5ShBfWt+ErEn2+tb2eafMLcyNQdy3vXzI3vjz2Uiezjyu3mTbseCMw/zdGMVvA95mrf3ZpkGUu2wFe5D7dDLxBWRycjGS69TgYiQKcCy5mEfC5cCvxufs6UjkeNOiDHOYN64guTPzeTJ3Q3JBr2y1Mcg4tonuIbk3m6DJ77kAGafmHN+K5J69yLbWoj0GjPJDq4Sewcaz0+S/7WbjeW7+7tChw6lDba09WhXXeRu/F4kGPhJ5t87jNjbmdIezK4dDk9JyNuJA3yq2aidvOYY+HQvOYONamr+b9+HxzFn2IWN+9tEaHieuBO6W5/mCtXa9tX0r9uZOwVr7hVwqCT89lwJOT0Kk4EdEjGI+nIOLKjZ9u5iDU9ouBva2IrCb2dwMURYcTSb9MaKcu7WvQXKE7ws8cgtqwyPhoOcx5qiOONienqz5dIejoIusnh54LSJD+cFNPnsxIn08kpzwcLgC+MpcCgoBkOf5Q5DE+uNF84KfVYzN8/weiCE9Veghz3S7T4tI0Yc2rgC+ds5IzbexSDT7/Ojdm/93uHzVO4snz5HppyIFMZq8wgGt64n4rrm/G0/ivPf2vcBXxCI+JwUxd/R9CKluo/HSHlL46zjxVmRMviX+m4+qHuLFjsbwazmUrL4T+Pr4jDR4OjL+DfH+T2CF1vVFI/akuP+R0HwXZo6NKGP6c2TpgV8/yv5bwbe0jj1CrrPJ6/kksrTAk1ttFNL3Dh06fGnhXxDH2fJhbFKJVJ+9hdZ3PuKpRzn2B5H33vceoc1mEcKt2skrgG+O75+t9ulY0H4famQcmvfhVucsh1xnJI7/BTxz7hpONBoJcfs6zkFSS45mb44Fb2bDlg44dNmkze75NwC32lYFZGvtFxHHdNtO6vh3u9/vBC6N497gm5G522b5v82xFEKQ54MEvxv7881zqqljwdfmB693/i2Iw6hxKp3M+XSHo6CLrJ4GsNa+I8/z1yMFCe6LlA5PkInzs4CX2MOssXoU/DFSOfDv8zy/DHkZXYbIFo6r9DniIbsBqcb7UiTX4TIOlSvfZbDWHsjz/Arg52PBJI+Q/QMI4W/wGkRK+3d5nr8akXe8GCm65OOxfJ7nL0QS7JeQl2yJeACfAjwtykQOQZ7nz0SkxxduIW91EalY+AdIoYeXAv/PWtt4rN8NvCDP8/9ConPfxaHR0uuIE408zw8AVfTavyle5z/nUqreIlG+i6y1Lz5Kv+4MfhFZ4+w1SBn6b4z/DioKluf5E5BFvB8Y/35a/OiKLeb3Yq29LZfKkL+OFBmbz6+5Ejgzl+WXPoVECe6FSLj+ba7t6xHv8F/nef5ryL19GfCbNlYMttZO8zz/VeCleZ7vi8f/ScQp0lRkbtaBew9SBMwhE6AXApfbuI5xnuf3ieNzJXB5fvAC57e32j0acTQ83lo73+c2JsAvRwN7E1KIK0OcX1hr98Tn6rJc1rJrikQssTVlRocOHU4TWGttnCe8Oc/zVyKT6D5iNy6y1v6glSU+Xgn8ep7ndyDVar8VWdvySMfen+f5LyLvkwyR9fYQRcplMQXjSsS5+hTE9t9krb1pi3by1xCy95Y8z9+A5LL+wFauO8/z98Y+biVv9QfzPC+Rd/8PIu/+ZiWArc5ZDrEh1tprkDnCe5DiUL+PFP15OPAR21r+53gQCzy9AXhNJGi3IzbpWqTyOwC5VNdtKvluB+7R2FNr7dvuxCnfgqRMvQpZK31e5nwlovr7J0QSa5Fn4h83OdbLgD/LZRmY/0AcH/dGqlo3eBvws4jNfSkiCX418BeNQirP80chNvbtyNxmZzzWw2g5WvM8/xk2qk77OXv6mcaGx/F8tLX2aCqzCfAPeZ6/Comgvwp4u91Y4/xkzqc7HAVdZPX0wXORirUPR/T3b0Ve+E+21v7qsRwwGolvQL6ElyMvkxchORUrh99zS8cuEM9ojbyAfhF5aRxpcn1X4DuRoktvQibtfxV/nyEa3iciso+/Bn4UWcPW0BqXKNF8MkKu3hrbPheRdx4pL0LHY23FA/sbiMzkL5E8mjdwsBTn5fGzX4o/S+bWNI35tj8EfCUy/le0tj8OKajzcmQi8SKE2JwwWFnX82lI2fd3IZ7S77SHrrf7OmQcm0nKW+O/x97JU74ZMSYfipOINt6C5HC9EhmHlyH3+j12bt1Ya+0+JO/VIGN0GWI4f4GD8avIckIvQRxJS8DXzkmOrkAcS2+NfXhSbP89rTYPRYzzA5CI7Qdb/17aaqfY/PmZJ5hjpOLzc5HnfDvwjXMTjhfF8XgZ8vzcijxjx/X979ChwynB8xBb+0yEMLwReb/9e6vNa5AiMz+CvBdGyHvgiLDWvgKppvo1yBzk9xCH4Gps8rtI5O+PkPfds+N+R7WT0Xn6HYgM9x0IkW1qDxwNJv7bCr4DiYi9A3nPPr2JAN6JOctmNgRr7b8jypVmybDLEcJ4wxb7tlW8AJmz/CZy//YCXzdXbOsSNuznPZFUsebvLcNaez1ii85m89oP/xch5f+AjMWlyJzykEKF1tq/RJ65ZyFR0vsj1aw/1WpTxf2b9eJ/J17js1uHuh5x+P4KMp94LTKn+iprbZskN2vSvoSDbekHkXVQGxg2D8zN29M3IyqxNyDfoXfScqiczPl0h6NDhdA52L+ckOf5BYhc49nW2jtb+e9/LXJZF/P9wOOste871f3pcOKQ5/nngFdZa//gVPflWJHLEggfsdb+cPz7ZcDzrbW7jrjj5sd6D5Baa+fX2OvQoUOHDh02RZ7nj0AI3U576FqwXxLIZdmdTwCPsNZ+MG67BqkN8lNH2HWzY3Xz6bsInQz4fznyPH8JEkm7Fikx/xJEtvBXp7JfpxpR9vkxNpLmX4q8wE51ZLjDCYa19kQtpXSXI8/zs5Do+P3Z2jp98/s/FonofhRIkWjG4zk0x7hDhw4dOnQ4LKy1/8nGOuJfcsjz/CuA5yOqpM8ew/7dfPoUoSOr//sREFnjOUhhgfcDP9Xo+b+M0UNyEs5EZE7/DPzkfDXmL3fEanuHkzMHe2yLb292nkb6ejg4e/T17f434juQ7++fA396DPuvIZK7lyD5bVcBz7qTeU0dOnTo0OE4sAUb50/U/OModhtrbX0izvMliDcg6TI/ZK2dX2JoK+jm06cIJ1UGnOf5RcCfIAnSe4BnbnGZiQ4dOpwGiPKYw60BfK219vwTdJ5nceQF6b/PWvvGE3GuDh2+3NHZ5g4d7lrESvBHSjG6zFr7shN0rn9lowDTIbDWnsyKxh06nHCc7Mjq65HKpn+W5/l3Iwn7jzvKPiBRr0uRwjMnJHLToUOHO4+nPOUpPzyZTLLNPhsMBiVw/ok4zy/90i99/P3vf//88kEzPOpRj7r+RJ2rw5ctDFJI5AoOXQ7qyw2dbe7Q4S7E6173utvf8Y53HNbG3fe+972VE2TjnvGMZ7x87969C0dockLO06HDCcJRbfNJi6zmeX4Gkni8M5ZTN4gH996tBYIPh6bYTYcOHTp06HAi8SjgA6e6E6cKnW3u0KFDhw6nIQ5rm09mZPVuwI1NTls0ijfF7UcziDcDfMd3PoGbbrmRVCdoFJlRLA37DBNDzyQM+j1Asbq6xmpRM/WBtfUxVVkxGPRJkwSjYNDvMehnpEaTJYZUK0yiUUGzb+8+HIrawcr6hKAUy9t3gNaUVc14vEpV1fRSg9YGraHf71GWFWVRUftAXftZEezFpRFaKdbWViF4jAoM+z2yTHLSi7ImTRN6aUJdF7jaU5YlSa9Pb7DApKxYG6/hnGeQ9Rj0ekynU0ItTmzvfVzYQjFcHGGMYTyZsG/ffkCxOBqxvLiIIlBOC1ztCAR8gGlZUAeP1gZCYNTvQwgEPM55AgFQ8r9SBO+p6hpX12hj0ErjnZdxzFKCd/i6Jk01w8EArTXOe0AxLQrW1idMKg/GoFAohXzuPYkxGG2oncPVNSZJSIzBO4dJU7JMgnnO1fjg8N4RvEcbHfsWCEH6qVAYZciyjLquKcqSLEnI0owQAsH72diFEFBKUQHr0ymuqjFa0cvknM55jNH0Bwusrq0zKQoccp4EUFqjUPjgZ+cGDvo7xIfBKI1u2ihQCkJQbPzh8T62VjI+SinkYdpwIsk2aBxLITC7jvhRHAvp3/x+DWb7aC19DBvtjNJorQh4FArdPAMhEDxoA1mvB0rOJcdrpdcohY7nS7NMnqugyLKMqqrw3mOMoa5ryrKc3Ys2tFJoAh65GK31rA9KBk/utdazcw0XRpxx5pns2bcP5x1lVeCrGoPi3ve8J/00IzjPpJigtGLbjm2E4KirirL2VM5T1hWVr1EKqrrknDPP4tIHPYQs6aGD4fbb97Bnz35CUGg8RblKWZQUVaCsAwvLAx78kEvoDxbwaj/3uHDMaKlAUVCHCVrV4DR+vJu3/flH+dQnV/G9Pmv1KhdeeAH3vueFbB8sccet13LWuRn3feC51PWQK6/cy/U338H97n8/LrzXvUnMNnl2QgHBo7zn2muv5+/+7u/Zv38/aZLivUcrgwrgnefMXWdwnzxHBeilGVXt2bNnhf1rByjLkmldcGDlAEU5pQ4Vu844g3tfdC92n7WbCy+8kMFgQKIytElY2bOH977nXdx2680sL464/oYb2La0nXtdlDNYWGQ42sZF+X3pD3p4X+F8yXXXXsMVH76Cm264CaM03gWKosQ5jw+glSbNMh7wwAeQ3ydnedsyJjGkvR5oJe1cTZal7L1jL2+9/HL27d1LYhISowk+4ILDu5oQAosLCzzkKx7MMOvhHSRpRlE7Pn/tdZQ+8P0//JyZffkyxnHb5kc9+snceOOX+zB26NChQ4fjxbnnns37/+1v4Ai2+XQtsOQA1vfcwdrtt6NNIhNoAuVwgPYOowKDXp+yLFhfH6OSHrXSrK6uMZ1O6fV6JGmCJrBj+zZWNCRKMcgS0sSQGEM/61OXBZOiZlJWXH/jzUxLR380QpsE5z1VXaIIQp6Cpz/IWFxcYnVllem0xEWyKpN9zepqn7KYsnJgP1mSsLgwYGk0YLSwiDaG9fGEuqpIjGbQywjBM55MMWmG4w4OrI6FwHlPakw8b2BpNEKhKIqCAIynE8LtQk6qumZ1dRWFYnU4ZHVhgVQpnHMzAhMCrKytUwfHwmhE8IFiTVNXNc4JEVZKkaYptXfUtUz+qqrCOTcjDP1+n9RogncE59AaEq0xRpEkCc45tE4oq4q18Zhx4fBKE0LARMLrqgoQ8ljXkuefpikhBOq6ppdlLC4uYoyhchXeC1lVSuG9J01TGieFc4407QmZTOU5cc7JAsIBjBFymWgN8TPnHVUIrI7H4DyDfo+FhSH9NKN2DmU0SZKxf2WFaVGB1iitZeFLpWYEqhmzEMJBY9iGiuOmtYxBCIrEJHiE4Mq46hlJ1VrNfp8/h/d+RuCa8WtgzEbdBr0JYW0TQ200ITojmvYbpNpH8i/7NXTcKXEUNMcxxhx0bGPMrA9Nf3u9AcPhkPF4LA6ZJKGqKvXEcR4AACAASURBVHmGZ8R7jrAGj499POQ6AuA9WimMMYQQ2H3GmSgCe/btJRCYFhNcUTEcDFhb3cnYB4LzrE/XWRiN0DrgXCVEraqpXHTI4AjBU5QT7nneedTlBF+U4BSre/dSrozxtce5ktKvMy0qylqxVkwYLZ+D1p7pZC9J7wCDvkGWXlvHBI8KAXzKdV+8mluvu4mUbRRFTTUeM1lZY7yyzihJOGOX5pJLdpMmY/bceoC1A+tkWnHGzl0kKoFgosPD41yNUoprr72am2+6nrKsZEx8QKHRKHCee5xzLnVZ4GuHL0vW1qfcftsdlK6iKAvGxZh9+/YxLicEHLvP3EVVFSwuLpD1UozR8l3yjsFoSL+fsXfvHowO7Nt7O+PVFc6723niACprJuur9AcZdV1x1ZWf5oorruDmm26inBaUZU1dO0BRVTWgyXo9HvKQh3HJ/S5hMOwTgqeqPWiFMjo+4wHvaq787Ke56nOWNE1RIaCVlmcz1PKeqmt23PsiskQzXl0jMSkKxcr+FW6+8UZMNlOzd/LVY4cDuPHGm7n22hO9vGSHDh06dPgyxmFt88kkq9cD5+Z5blpSo3Pi9i1h0SRkXiJGSa9HYhKqqibVhixNcAHK2hOUIctS+klKahL27dvL+vo6WsNoOAQvpCsYRaqVRJB8QFOSZhm1h6J2JCYBPJPJhOHCCAjiufceVIVzFZNiyt59B+hlPUySUhYFCoNJE5IkkYjNdEo/67FtecRo0KOXGMBBAF9XHNi/DwhsX1pieWmR7ctLKJOyOp6Sao3RGucluuadwyhNqiMRVIDSQKAYT0ErTJIyGgxJE02aJISqolaBLE0JQFUWpGnGaDigDo5emlBVjrqsqGNUs9/v471EebXWJMZEbuBnJLEsS+q6pp8NQGsczcS/IMsylDYEpXDBo7Si3+sTVE1ZO5xzJFoikrBBorROZEKsTSQ9GwRIa0USDLUX4qaNIQSHc56qdpS1ExKoNaWrGY+FBGulSBMhrlkQgjpxJXg5htEyyU21oXI+klsVI6MSuPbO452fHU+pSNwaEtcipQ0ZbaKFbZIV4rUQwHsIBBRhg3wpCCoSN4IQG+YjqRs/G8I6O/7c+VQkct77g8jsPPFtIqyzawlCnoWsAvFatdbQulZjzCERz4aozx/fe09RFLNnyDk369Nm6QcqbldaHbQxyA6yn4q/ey/fDa1nDhznHMHLGCVJshGNjf3PUok8OueovceHqCVoIuIBEpOyfcd2aVd76sJRliUkitp5ylDh0XilcaFGm8AZu3fQN4ZpsY8d2xVJ8KiqJFCg9DJMe1x39T7e/ldXcO2NMFrazWjUow4r9HxNHwVFyTnnLxLKFSalYn2/o5o4zjnzXLYv70SRElCiMMDhQk1wmi9e/UWqqpZ7EohOEXm/JcawY8cOXO3QShGCoqoqlFb4ylOVFevr67joCOr1BuzcuYvl5WXOOvNssqQXx84QvMcHRVnVs3u6tLyNlZUV1tfXGA4XqeuK8doqi8tLfPYTn+AjV3yY8XiMUQm+nhJ8wJiEEAJJmrKwsMgjH/VVPOCBD8IkmtrXaCVOr8CGIsJoTTEt+PznP4/WGqMU3s9kARiT4l1AJ4rzzj2PunKgNC4E6mnBnv0HKKuafrpp6vWXI47bNnfo0KFDhw53FU4aWbXW3pbn+ceBZwB/Fn9+bAs5MTPc+x73oJqMufrGm1lbmzAYjtAKChzrY8+w3yNNEpIUkZIqxUIvxWzfxqjfw3lPlqYYBVVV4ytPosC7mvH6OoujEQuDBZyryYxmaXFE0OvUPuDqMk6yg0z0nEOrhCRNGI8nrE+meD8lSzN6vYy0l5IkmnoikrXRwoBhP0P5GoIi1BASmYh5J9Ja7yrqukK5mtpNKKYVwdUEpWXCFgLBOZQKOF8TCIxGQ8qykmOHQFEVhLpiMOwz7A3QCspCJoZJYoBAXYmc1miZlLuyoi4rpuPpTLJZFEUkZUIqjDE4LxLd2rmZ5HR9fY1yOmE4HGC0onYBHwKT6XRGlEKAuq7xIbAwGKCLksl0KgTQu4MihFo3E08huEKG5H463chQI0HzgSRJKIqCyWQqE3M042kxk46G4AnO46LUuK6F0Ihs2KM9sd1MyypRp0i4QgjgPDpFZMmhaR9J5QaXnCH4g4mgEFI1I0JBye/ohhS1iFdDTKMm14cwi8bO+sMGIW4IaxPhbbdrfm+TwgbNPk1/FYdGR5WaaZVF1hwah4KORFb2SZJkFhVvR3qbtiGEWZuyLKIEOJn1qXUaNuTOIpXWxghZFe/AxhjHewUbUuckSUjSlLquDyLDVYzOJ0lC7St8FZ+5GKnzgAuK6A+KDgUZo36/T5IklGVJqjPKoqCoSlxQ1NrjjaKuAw5F4RyBiqWFPr4sScOEHf0e5YEJQSt8meALze23TXjfv3+Oq68vKfUivl5l+2KfbOwwwaFKR21q1tdLtu1cYOWAY31NM53A3c6/AJWY+AzWBEo8U0wKd9yyh6uvvmb2fY2jI89y7VnYvp00TSnrin7ao3aOoiypnaN2NUVVUJTl7BnbvUuI6uLiEv3+EHHPGAJyj9fX9zOZFNQ+MC0qRqMRe/fuY8+evSwv7yBJ+9x6803ceP31fPyj/y3faR/E2Zam+BConUMnCYP+gMc87vFccsn9hFjiNiL3ipkMvJGP33Dbrdx6y61o5N0g3yOPC+K7Cz6we+duztx9FtWkIDEJShnKyjFen8Tnkg6cGNvcoUOHDh063FU42TLgHwH+JM/znwf2Ac+8Mzs7VxKCxyhFlmZUUd6ZGEWihUhUtcPVjiwxZIkBPIlRLI4WWuQp0MtSghN5a1VWrKyskmhFlhjJGwyKhUFKVSWMpwVKa0bLy0yKgmJaCAlCJkVLi8vUrsb5wMLCSCZirgYcSitSnZJoI+TKGIxW6MTgaplUjkYjjFIsLo5ItKKqHXVVQ/DUVcWkqhiORiQmweGjPLZAawXKYAyMFgb0einr4yhRNZo00SgVCC6SM19jjGFh2Kd2ToiQDxRliasrmQw6RzGdYoxhMBxikg05aVmUQnpj1DPrD1BaiGhZ1yIPjFmOGiGuDcGoYo5tmkKWGEKagJa837Iscd6jlMZ7OV6apqS9HibmLarg4zEdvnZkvR46MRRFSVXWBA8uxOiYVrhIEgkB7wOq9jgXWBgMqUuRMvcjgfHeRyJp6GU9iSIHhatqIZ4aNpIzPUjc9WDC2oIPkXxrvREBJP6qdGwjEVCttcizaZHb+K+R/85HbRsy0RDBZjswiyK2t1VRZj2LjMLsHDoSd906R3BRjty0bfqLyJib/ZpIadOHhgA3pLe9XX4Ps0imMZok0YhPIMz+zefeekCFTeTUWuGdR0dVgfcSnZOo20S+n60I9HA4JHj5PlVVFa9BHDCNMiGEMIuuEu/voD+IuZ8BjxcnSyRZztfUvmJtMiUEjSewuDRgadsCeIcOU1b37GX9i7fTH20jGZ3N+h7Hv3/gf/j8tWvUDChNjVcHqOiT9RXj1TWm05Ik61O6BJKdVHXF+njMaHnEjt27UDh8aJwfBb5aJzEpX7jqSvbv3TdzWiRJKmqA6FjYtXNXvDKND8h3p66p6oraOcbTqdyfKLXdsXMnw+ECi6MlyXtVIp2HQOVqbrn5FqZVjUlSqrpmOBgASqKzzpGYwJ477uC2W2+lmhYiC3eQ6pQ1N545I5a3bePhj/wqLrr4YhwBJTdcHCTRUdFWdDjnuObqa1g5cACjDSHmlQtLDeADKijO2LUbHx1VWgW0kdz5yXQyc9B0mOG4bHOHDh06dOhwV+GkklVr7ZXAQ491fxdqdCqFhLxJmUxLJuMJRVmxkKUsDvv0+z0hoVXJeHWFNE3JehkhaKqqZDpel/xVY9BJgvc14+k6SczFUjhSI4V+RoOMNFlmdbyONimLS0s4H1hdWWFtfZ2irFgfT2EASht6WUYxmTAej6nKgiw1pEqhVaCYAr1EirSYKEtVnuCgPxhSFVOqqiIdDEgziV7oJOCCIkwVEkisQYkOuqoLlPJU9VSuMctIEhgOsiiPlmJHWkOapDHK5EjTJBKNQFUVMqnVBm+85E7GaFeapmRpKhzNeSHPPszyWEXqKflwWZrJ8Z3kn4VY3SegcAGJDkfiUNf1jFClWYaOub2Su1jifSCNBDl4T39hiKtKXOVnJEMpRV1VaC/bqqrCt4or0Uhqg+Qn6hgBTE1CL81Q8TOjTcx9VgQFg8GA1MiEN0TZ7IY8WYihq2sCGpMkhHieeSIlY9COckLDaJsoq2xvEcaDCGo8ilLyTB4mYtq0aX62yVk7d7a5jvZ52seZ5YMGDiF57aJJQSHR4BjlVAh5qKpq04iqMQbn3Oy+pWk661dVVbOc5vniSu1x2RhRIcxtom5MMpOGAvR6PUzMZ/e1SICVkqJOw4WFWTGyqqoYDAZAoHY1dQjU3s2i5jT3Lih2bNtBlvYgSMS/rCqUD7hJSQgViVEo53E+4EONyfp4FShdwWhgGJ21i6qoWDzj/izuuJh3feRd3HDTlBAWMLpgkAaqekqxVrLQ286+1QNMVc2gH5h4ze17HOvFkLViyvkX3p20n+KUOGe00oRQoTWooLj6i1ej4rPunIMkRfKexSFx7jnnzCKTIQTKssI5j44y8bp2M7/LYDBk+84d9Pt9du7cSVMEzAcpFFYUFTffcgvLS0ucedZZ3HHbLQz6fZI0ZXVtFVdXmIFmbW2d9dU1gg9UrsKYhLKaipRaK5a3b+NRX/3VQlRDQBk1i9yH+Gz6IMQyeJGkl0XJ5z93FXVZiuMsPnseubeJNvSzHmedcRZ1WUlUVRsq59h3YD9lVVLVFYnv0UFwvLa5Q4cOHTp0uKtwuhZYAqAKjgPrq0zqCleWeC+yUW1S+lmKUZpEa7Jehqs0+6drKJWQGoNShjTRG3lqdc20kuV7tNaMRosMhxkGDz5gCBgVyIZSOdh5j9Yekxp62xYYZIrJtKafVZReKuuWU5EI+liJ1egevSwTAagCoxOqusLVkmNp0t4sSjMtK9I0oaxrkiTBJIlUAB4MUEbyrXzwUtwnQF2XM/JpjKIofIukxOJDzs3IVFOsqCl8U9c1dcxtS9IsEhkdc/3SWS6iRP0CVVlKdVYjj4iOUY6qLmeFdBqS4Z300wTwteTCKhViISQ2iE0I+CBRRYlGe6ZFAcjkuZxOGQP9XoYxG1FXIYygnQPvpRosmiA1ZFCx71VZSjs0Bk2WyPl7WUaiJBe4qiRPVycGkyRoYyT/MRI9kxiyrMdwOGBlZUUmzjoWM9JNBHH+SW0KIM3lYs7I3ME5p/M5rm1Jr4rR4c0wH1VtopoH9aQV6WxHbpvPDtqOj4R17nyqRcjnyXQklI2Uu3F2zMi9c4TWM9gcuyGpTeGuNhlvk2XJkWVDDhq3O+fAEMlqzEXVmhA2HAxqdgw1q0wtfXTyvMGssFRonBvx/sj1KJaXl9E6wdWeoiiZjAuCCyRKE5xE1hWaEBw+eM4+5xySfoqbVgwWhwwGffoXPwCyC3j3e/6bD3/8KialxpPRzwwTt4qpwE8Nve3bmVQHuH3lDtJdCVm1gFkNlEXAJwlLu7fjqES7oDTBl/K90j327x1z/bU3o6Lzpx1pD86zbXEbo9GIoqgxJsHVjvF4QlHXUlhpOp09m7WvOW/3uSwuLjIcDukPBvgQSDExn1qxZ88d3HTzTVxyn4u55JL78sdv+APQmsFgyOqBFdbWJ2zbtkuqhIfAdCp57M57HGDShJ27dvFVj/5q7p1fROW8KCi0wvlYwE1vPF+Ng6eXZlx1zee44frr5BnwAaPl3eCRwl51VXPOGWeyvLRMVRQkkbSvrK2y98B+6vidO1nLtHXo0KFDhw4dTh5Oa7K6sj5mdX2NtL9AKD1lOSExhkRB8DXOFQSnqXyNr2uWF5cwiUEZQ1lWM6KRJoZQOIwWqdxwOCDRir5REIQIJWg8nlCXZElKUBoXPKH2qODppwmJTgDNdO8BXDFFmYRBP6Moq1ggyON8LdG82TIugV4vIRhN6R3jScG0rFBJwrisQWl6QaG1yO5C8DIRC0GKLKlAkmgSnQIJSRJzBp0se6JisREJiCnZJ0hOWlVWNMuHJGkSZZ5N9EqKnRRFgXcVWon8Nfhalv8INUaLdFmks7Lch3MOnySRxIqcUib7UNcy4U/TdBZpa4iTixJspRSpyYRU9RU6RoWU97Por/c1Cqky3BC3fq8nEaTgSbIU54PkEnqH8hpfOfCKQW8BrRRVWTGpJrjMkSZJnARrlDKYRKONTOynRYnyHkWQaHYtRZiSRIMKOO9IEkPtHdpvTKYbtKOLhxBD+SWOdkDNEcONti0pbDj0WPPYLLraPl673XzO60xKHMIsanqQhDdsLMPUHNMpieRJk+bcUjAKmkrIevYMCHSsDK1nBZ/axHyz8Ws+k0WUNiLSTT+88zP5tBQE6kl151aOrveezCRyTudidWiPSZMN5w4bP330NAUPw8GQ7du3S8EiJdLZynl87UlSgw+Koi4pXU3la7QOLC9tQweFDzULCyOqtQl7brqJL958Nf/xH58S2WzWI3hN5daopiU7t48kJ7tWDIcjVlb3cXbYhQsJ4yJQViULywO27xoS8BjE+eaCR+seipTrr72WvXesY7TknKdGlqsRSSyce+65G2OHoqy9VD+uasbTKdPpVBxpQe7R2WefRZZlLC9vI0t7hCgBDtERc9111zHo9bn73e/Bysp+KueonWc4WmDPnr2srq0C0Ov1GQyHjNcmErmO9/2e97yQhz/qkew6YzeVd1SujtWLRfqrY8XwmRrAR9m8MXzOWtbW1sT55MVJ4ZzDBU+v18O7mnPOORuNfH9VIp+vj8cURSHv4RCgvdRShw4dOnTo0OFLAqc1WV0brxMCGKXoZynUFf0sZZglZNqQGE1qhBQGJBfNh4D2HqVENtfMd5PEoFWPLJX1WRVgVMC5QF1VgJPcLpCcTucwJolFP8Cg0Immnxi2Ly6wtLjAuCipfIA0YNKUfq+HIsRKpLA+ntLLNEF50kxTuZrVyZTptCDREhVOkoRQOYwBk6QUVcnK2hilNWmWkpqMxGhZUzDIGpXBQ5KkKAV1FVBK8io3ZKAOVIiy3EBRVehaJp/GNDmMUFeyLIxIQWU5jBCEEGSZFK4Zj9cZDodoLZWGm/zIZu3WEMDHiWWTN5clJlYf9bMgoXdCNkyM1DYVZHtZNiPBKklAKVyMwHklha2cE8KYpglaSZXXyjmqSqG0kGWjFYlJydJEbrmXPGCTaJJUKg7XdS1Fo5xD++bZiLmZBHAiBfYqFjrSTeTZxQrH5qDiRg3miyE125Q6mNi2WWDwXibqCggbOZRtgrbZMee3tbcfkufZ6ud8n+f3bR93ngQ3uYMHtz94v7reWAKpidCH4JFbunm/2/06KMI817eGbHgkqu6DRNf6/X58/uJ9i5Lm/mBAEp0g3je502nMOZdnnRjlDpH84AO9rM9gMBQnkFZMi4rKe5TRTPFUCgofKL2j9hWjhQGLi8uU0xLlK9b2rbJejvn4FVfx2Wtux1UJaVzbtKprFOucf7cRj330Q/jsp69j395VFkcL3L73dtb3lowyqJWjLAvO2LkTY6LMngTv1UwG7Su4/rqb5L2UJHhXyjjFQmi9fo+zzjwzLgslY1hHBce0lFSApvKzd56lpRHbtm3DGMPi0pJU3W4KhKFYW13hjttv48wzz2RpeYkPfeg/kYJLYxaGA1CaAyurFGVJmmYMFhYIeh9KG2pfc48LLuChj3gYo+UlnNywmSQclOShRtm4ic+OnFmxf98+PvmJTzCdFoQe6BCXXgLQirIqWRotsn3bdibjMUbLu2dSFEyLAm00riyZTCckXTXgDh06dOjQ4UsOpzVZ1RiWR0tUzjMY9Dlj2xJ1OUUHj1EiTdWauC5mD42seepnk25iFc0m9zKRYkdxX5RC6wQVyVFiNMF5iTpUFb1ej36/Ty/rx+igJ2RQVyXKGNI0pfKBSVmiTMKu3bsZj8fs37tHcmS9p3YwWZmCntBfWMSkGb4oKZ0jSVImRUXtAmmaEKqaoqpxnkj2NM4rqCTXjuDQMYKllORfVtVUooWGGJms8cGT9VJ6/RStZbmK0pVopTFOokk+Fr3xvsZ7x3RazSKhg34PFfPIkmRDUmq0yAldJAe1B2U2ljiRiKqSpSOCLEMTgoyzUrJ2abuiLchU2kQiTJAcQeecyKONIUukAnOaJkiMCJFgesmh1UidlWzQRwo2CRFJEhOvvcQYjUl7EsFJNGhiBBqaVVJ8CPi6onY1aS+jCSKaxEhu3VyUsvl5pNxS4cItgqjanzX/xWrTDZFrN58jefNy3gbzJHCz6O/hZLcN+Z7dk1Yfj3TM+c/axZaAWCkYnNsgu00UdLMxPGjs5INZ9LPJlwW5T3iPScwsMhcvJlZ8DgyjjBWgrmLxrjSdVbUWwirPTzwoChgOh7FIkaga1sdTnAsoA5X3eKVwSqo7ezzLyyOydEAIE/qZYWFxCfs/13P11bfiXUZiUiauQic9dFWyY1nzTV/3AO7zgPOoigNc8aGrGfTOQNeaxPdwkwA9T2Y8u3eNCJQohrigqStHZjQEz3g85qabrkepsHENzf0LgcFgQJKkcb3pBZyXd5pzGznNVV1GGXVgaXmJXq/H8vIyS4uLoHSUOsv9ufaaaxmvr3PeAx7AgQMH2LNnD1mWsba2JmoWk7J3337W1sfs3DlgMBywsLjI+vqYCy68kEsf9hCWdyyLHDd46pjTHgKyrjAKEyPw8pcoNPCez191Ffv27qPXk3xTkf77WP0bptMpF979Agb9PtWkiDJkKQQ2Ho+jvL+mKkvqujrsM9yhQ4cOHTp0OD1xWpNVg2aQZaiigLpksNAn6JRyvC5rB2ap5Bwq8czjHUpv5HA1kbG6LGX5k0xymXyMaDmkOmWvN8QkUqClqosone2TZT3SNJPlVQJ471DKM+xlBK1ZP7DCgbUxSX9Aos2M5Drn6WUZWb/P+niVspziUKyVFdqk1C5QFgWgSBSyfmPtqL2DIMux1FWgKMZoLTLirJeSGA1aU9WOqppQFRVlKcVu+oOBrB8rDB2lDHUI4IKQSjTapISgKMopVTll0OuRpAbVKlwkE91ktnROL+sxLUQ2qJWQARPH2ASFTpJY2yhW2/SSk0uMywQUHiXL0qiNHEeI0lSlSVIdl7DxuJBIJWBvohxXSGpVVQTvYiSpjstXSOVT72rqoGdR22pu6ZbKOahr6qqKAtOAikVnnKslD1GBSswsMg4hrjlr2CA1RyaJzTUd7jNUU1e4TdQAvyFvDTGPcn4tVVrb5slde53T9n7NGMznsG7WxyY6GXAb358Wid0sKhtv8cGbDjqmOBaOFBFuX4sPXiLYSnJSZSxCdJS0KiEbRb/Xi4W64tIrsVgUCOmsqwod5Lnp9Xsz1UEdnw3vpdo0akMZsG15GWNMXPpJonZojatqNIFQe4wLGA+194xGI8rSEajZvnvEvsmUD378Mzg9YFwpaqUJRhF8wfaR4UmPfQj3vfsSet9N7Mo8qZuiVEk6CEzLNXTYhXGa0eKAHcuLpCg0KcEncv4g75/b9tzMLXdch0oqQojS7tn4BkajBeq6oqpqFhYSppOS6XTKtKioazcreua8k1zSnbvQWstP0zzvAaU06+vrXH31F9m+vI27nXceH/nIFayurGCMYW1tjaAUo9GI1dVV1tfX2bVzF71eH5Mm3Du/iAc86IGMFkd4D2VVkfVSqZaeJLO1UoPz6ETjY7V0Hd8L48mET/zPJw4qtCapAHK/aidFu84792y8q0kTg9GGaVWysrbKpJxS1nWUArtGt96hQ4cOHTp0+BLCaU1W0zSFAKk2uKpgsr5KEufivUwqjbqqQhuRl6okITGSB1k7R1mJB7/ynslkjJqK3G8wqOj3eiRGY+ISKs1ETxstEUml6fel0E81KwoDWZYRqCmco6hrxsWUnkmEgE5LhsMBu7bvIEkNZVUyKQrSrEev12f/gRV8kGIvRVkRAiwuLFA5WRpCaYMKSkhvWaMUmESTJilGJbIcjpIIUlmWlGUFyJIpZVXFisMGhcbVnmlVEbzIewmKLEMqfAImyfAEsl4vRpQSprHoSghCgBoZdgiBsqpRSgoSNWsWer9Bjrz3s2Uzer0MpQLFtMA5TxUnlyqIHDjL0iiRDZi0yaN0QhqUkiV/tJaqvc35y5oiOLJM1s/1TvKBDTJ5dbXk4zWFdZoqqGmSifQwRk03Ck6JZFQmxxKFV0GRJIbRaEiWJATv4mdaImyqTUZjoZ+ZtFeI5yxAqg7mcqHVehalje1rhGwZczARnidzmxLG1vbNIrFKKZqCybPCSQeRxhD7RHTK6IPOu1G4pyn81OQpI78rkdVu5Nx6lNatsWjLpts/OWi7sM0YSZUW0a3QGsPoENHKzIp3yRqoFQ6JKqba0OtluKoSohkcSZZS1RVFVcbvsooRbx+v32O0YTAcxOVxDEVZMVmfMAs2hyAEsCz4/+y915ck6Xnm9/tcRKSpLNO+pxtDgOASJAFwaZYid7mkaFYipXN0Vjr6T1e3eyMaiSSWIAkMwCEcMQBmpm25zAzzOV28X0RmVfdgddlzTr4XM93VmeEysk483+NSFomzqyqyymjt+PTZJd/8xt+y7izRZ6KpSUbhGFjNFb//G1/h4cLy/DsfSjrupsLmTNYZN694efWa+/feww8DVahIIeN9QulEJqFN8QznzCcff8z15RVKS+XSFHSWE1YbTo5Pyu/IaierVeV8VSaW30FD5zk9OuHevfsYV7NcrshJle1mUImXr16w3a756q/+Cv3Q8+mnn0j3bNcTgwQerVYrLi8u2GzWpByp64ovfemL/NIv/zKr0xP6oceHAet2YWDBSw9zVVVTuNl0P6eMsYbLpPpTcgAAIABJREFU1+f8+Ec/onEVg/cTw56ihOIRAndOzzg7OaVdb3HGSUXPENisN6SYCb2n63qRpb/963OYwxzmMIc5zGHe4Xmnwaq2hhi9dDSamhwTKUNlHbPZTOoQvMdpM1WPoDXKGayybLuOmDJDCAwhEXOUWhY0IWvqyuCyEsmulqRho+SBPXjPMEAza9AaCRtKmbb1XG9btt6z7nrcfM7p2R2cseisqIwhpUjbdbRDi61rtJFAoDGQRhc2Qqg2RwgiCc7Cn0i4iVYibfM9Q9/T1I7GOUnOVAqMIWkzBfiM6HFkjLRSGLSkn5JJGukiHatqKkdthemMSkkicWHghsEzptuiFL33xCzSWWOteOcyGLPzKwrrLIDLWV1kliL7zAkw0icbY8KaCuvEU9gPHmslsTUE6Tm15fxTqUERjx3EmNExobXB6MKIGVBZPIHCnCmUtqAMIWaMFSZzBKcpRXIK5FSqXrIiFTl5ZQ1NZVk0Nc6AM0qe7wtwlblZuzICsH1/7lv9qkX2iCohRoz8VdmGVqWv9k3J7yiz3U/evZ22e3NXN+XCSo0dtDsAu19rMyZIa63QmDfkznKEukjqp73IyWRd+HNVOlCZpM+ZEdCrSVKqigoiT2FNI8Au1yGXBYuySLB7rZy3RpFjmr7zPgayhqQSaFjMZlTWClhF5MJNU02LBErJd4KS8JxyqXtyFavlESlIoFHoN+QQJQU7J3wK+Djgo2dIA/V8Tj2bE3Pg6uKKb3/rH9lce3SqMdbJOafI0dzytV98wP0jaC9eoYOCfMR8Nme1uuKl77Gu5vXVmk2/xVWGtI10fcLNK1kPUKVWKSu61vPTjz4leiVJvbqg6STpxM7VnJ6doZSmqWqGrqfdbqfvfQyBvoQrGaM5vXOHqmk4uXOH2eIYbV25TyXQ7qc//THWah6995gXL57xkx//mBfPnnN1cYVCamWcsfRdz3p9RYgDRlvmdUNVO1IOKKexiNIlKSVBUIjVQeTiGqXVFJzksiL7wL988F2uzy9vqQKkSgegqWb88pe+jPRlKYy1hJRou0EkzyExDEEW31DEA1g9zGEOc5jDHOZzN+80WM2ZiSU7ms8EtCpValTKA64aWb5MCJFtt6GeB6yR1fj1egNaoa3B9wPeB6zL9IPH6IwzUttitCaGoVRriA/WVBZtDToL/5JTYr1ec932rPuOTddiq5rNdkNTNThlIPjCmInUuHISGhRCoq6dMDVa40r/pwSdiLfS2Yp500AKDGTms5rUWLq25fp6Tc5SdO+9Z/CSQGxKR2kKYQoR0jAl4Cqlxe9pdmFBPgTyEDlaHouMNmeUMlhbkXNiGIYJvEZJMiHlxBCEDbE5CThXmmEYaNsWkL7UajEvxxGl37aShOK26+l9T8qZOs8wSPVEDJFuiGhTFg3UjkvLxUs8gibnKkBNQTLiY4UcQwnWKgnKSUJYjC2LGEh/6KiQlfcbwVRKgGImycMtA5nMYr7EWcfQ9QKgSletgNN9kKgKuyV/k8TdHZDU+k1W9G2hSfv3/Mg63n7duM399+z3aL7N07r7/5ve19tA982AqN02MsJG7q+NCHAfWVk9sewjwBxZ0v3jv72/t3l9d3j4FuimMGs546oKH4Jcg3Kdc4amaTBKo6wj+B5jDNY58R3rkgIdBnxMk9Q4pcxs1VDXlSyAZNhs2wnA5yRp3bpsy7dr5kdzmmZG37Z8+9sfcH3dorG7c06BWQW/9itf5jd+7Quo/hwTFfP6DjE6Znff42We8dNvfMDq7JTzqxbvvfyuiJFhCDT1Ail+SSgNOmsuLy/5+GcfT1LxSRKbEgrFanUiKcleFu68Dzsf+NAz+KEsIMn3Yrlcoq3UyhgnIDumgDWK6+s1n376Ce+//wVyCPzNX/01n3z8MS9evMAaQ85weXHJ0y884ehoQdtt6bqW1dGJ9Nl6j20qtNGloiaJ5D2PSdlM3nDG+yOJoub85Su+/73v7TzQMVDVtdzv1pJ8oKkbnjx+QtvKZ2Wt5ep6w7bdMPS9KCOMLJpkZLHyMIc5zGEOc5jDfL7mnQarXT+grcNZi6lqDAJgYmFEjLFobaZuR2sd5JbkI7oy1FVN7wYSGeuEwcxOmMah7yBpUjCkNJM+1hL84axD2xqjDSElchJQMAwBZY2834uMV2tN13X02w5nNItmxmI+p3E1Jon/y4eEV4GUrIBVYyWhNyPJwaMfLQ0smiPm1ZLr60zlVKmqyGw2W67Xa5qZ1HV472kaI6RKlrTcpqlle+UhP0UBzM5VmMoRYqAfEoRMSJGMPOAlHwjBTx2YKAEEI9tlrSXmRAieoZc00bqqqawrvaUehUiztRbAHGKQBF/bEFJis23xhUXxwaO0oh9ETqi0oq7FT5xL9QYweetGJnEflO2DKaM02ZZ7IUNKipgodTkeHwJNU3N6eopzlvX1FV03CB+oCthEEVUWNjoLy0NWshiSVDmOXb8sjODwzYCgnCectfezN9nKt00mo5W+ART35b1jSNFY8/HzGNZxi/vzWaB3/zjHfd7wriZV0p/z3vtH0LsLa9q/Pvvk8pts783KnB3bG8viwk5Prcr7c9mZUoqmadi2LTHvUoBTElmpLEZACBFt9/tf5RgT8nuEVBKzY+Do6Ii6biRdG0PbduV7oKakYQF8A0oplvMFKme+9a1v8eLFc+azOdF78XhvtzS15nd+89f59//+6zjdYnJNDlvabct67em7jvd+4ReIf/NtQjdQK0NfQFfwnvPzcx4/fVqkr5oUpdDn448/5vXr11LNM35WucimY+TRw4fy5/L9DUEC18aAKz/0RC+VNcvVktOzU1xVcXx6AuQC/DUxBv71hz+k325ZX17xf/2X/8KHH37I1dUVs9kMjZKFu+tr6rpmsVywXq/ZbDacHJ8VP/1Aw2L/JhDHtlZloS5P0l5dFilMSXH++Gcf8+zZs+nn+17s4D3RB+7du79LFa9rBGjLgmLRpOODB5WxTmHsgVo9zGEOc5jDHObzNu80WL283nJ13XJ2fCwAJA7EEETiqhQ6C4hUCHiCxMnpqcgry4PwfD5nCMJCzppGAn9SZusHcu8hGsia1AhjRrF2mSypuz7GEu6ipNy+cixXR0Sjcb1H20qAScpFQpxEYqdL1YpWaAbCkLBKMeREilESR2PCWYv3PbXRGCK+W3O0XOBWC5EOK81JvcLVFevNhpAyymiUFW4yxihhQ07A3mJWo5Swr13XlQocgzIK7yVF1zkDGNabzSTjNaqk02pbanu0APwYyKUvk5LWCYhsVWWM0VRVhVZKHmK1SKitNgXsRwbvpafUSlUPOhFzIKZAImKUwRfWtnIO3/fkEr4yMrz7YGgEUWN3J0jysNYanSEESU8OMQojXoBGCpFUpIc+RpTQLihVvJDaUFcV1lbEJIFXO4/lLuDnbeBw37s79ovuH+f+/PcA6/7cTu/dDz0aPbe3a2V2rK6U8rwNsN7+89vY1nGhACBzc9v773/zemRQeZJCf1YQ1f656NHnuicTHT2GI1hJpe5nNmuoZg3rdiuMXNqFQFlrJ2kpSC3KCFRHCbYESYksXpfjODk9K8cgCgIfxuRt6T32wZPKd00rkeh/+M8f8vzFc+qmIoSBpq4IfmC+mPPrX/0l/sMf/DuO7i7JqoXcotIxtTEsvSbHiuPgWJ2sMMYyM5pXz55x7949tFWcn58TfI9rKmIJQHPG8eMf/1iUDGWhjHI/g3xXTk/PGHqPLQtJ2+2WrtS4hOgLqyrM6tnZCbN5w/JoQeWcyLaVSMG7YeD5J5/QXm/4p4//iefPntG3Ha6EmEn4mCxMdV2Lc47Xr19zdXXF3Ts9YOj6jtXuRpZrvXdvxRSlJieXRaqiQkgx8d3vfoeLy8tpkcpaV35PiZqjqSqePnki1oIMIWV8P3BxcSFWCSKD79lsroRRt+JlPsxhDnOYwxzmMJ+veafB6rbvubi8IsbI0bxh1lRYbYqkK4ts1FhSSljnhP3wAzGJ36rrOqq6xhpD2/USSKQ0dV0zn80gCUhLQO89xliMtfisSFGkpL3PgKfvezabDbPZAmOcdI8aLym+QYJaYkps2oFu6Fgs5syXMwzSCzmvHYvFgov1lq4PaKXJGnkwTwltFfOmZt5YLBFXGTCW602LDwIyQ4qkMDKWFmMNmowpEs+h65g3jQQtjamzStgKX9hY7z3KjDUzwiB5P1C5SgBrCcsx1lA3NXErbBJaeh6tFf/uCJIS8jlUlRNpYJKAGm0KiMiJFCKVsxJoZaQnNYYiCRzlnUqAhbVOHl5jlDMoYM97qZ0YA5KUEjkwWUKxKInN0fvJ+2q0YvCxvDZwdXVVqi0QSWEeGT7IKRJ85DqsaZqaBw/uYZ2jHVpSDqB1CXWisM7CgGot/secmYDtqG9UJUFJmMH/f/f8xCLusZ/74PM2IzlKom9Idvd3JulAN/Z/G9Te2P/e9m+w2JO/NE9S4FGuLaBp58ClsKRvnNstKfRtkB2j+E4zo+c3F19vnqTWKYOr6uIBz9Muc8o4V+16gMuxW+ukqiknQmGl5XMU9YBWYIzj5ORMALnSbNbXhJjQSklVU5TwLh8CwQesM7x4/kwUAqMaQGlClJTpx0+e8NWvf4VsPN3mnGqm2HZr7GyG1ZpkhMm3xvDg8T2ef/KS5cLx4vUV3g/MmwWb62u2my0ndUUYBhSK7WbLRx99JItJ2qKVkWuW5PzP7t6lqmpReTiN9xE/+Om7sm1b/OCnxYHTkxOsNZzcOaGUDaMyZCLPPvmY81eveP3yFeevzmUBQMli07bd0nYtOWfWmzXnFxccrVY8e/6c9WYz3UPb9YYUo9TTiLZdFqFGpptyb2lFTBGVFM5YXr58wQ9++MOyeCX+eF3OYWTrH9x/IEx4KMF0ShOCZ9u2KK0YvKftW3zwhW0PzGfNf+/rd5jDHOYwhznMYd6xeafBas6ZqmlYLBZYa+i6gTAMVJVjMWsk+bHvGAbxYTnnyCicq9i0Ld4P1E2D0aakeYqstGlqqroiR4PK0A8Dbb9FaU0zm+PqmllT4fuB84tLoHQTDgNgUGogpozSlspaVBYWD6UgMrFeo2TZWsWiWdKHTPRBABbQDgOLppbKDCLzecXRYkYO0g0ZleL6+oLXl9dgLBhXHvATs9rhjMZpTdKKTdux6Xtms1nBDZm6qqYHdHnQE+Zp9GrKOWisq4rEVR6gnXOThywV36i2kgJsjMUaOzGyOSapjDDysJjTzt85DD25JIvOm6b04mqM0vT9IMC4zyhtqKoKpQxd22OdRroe5R7wXiSFI0iFnWxUZfEAKyNg1Q8DwXs5l6ouwVW5BEDJe621JDRDlM/HKEVKkaEfSKFndbQUcJQEkEag7zqMtjfBnSngMGVhiQpAG4OGUko32CQYseNneVZHULn7930f6LjN2++9LUO+wXYmGKnKfbA73aP55r+9bZ9yjpGU1MRCZ3beVAEcTJJOSpgTN87t5jGP8s199jllYddke+UajonAOUMBrMbYSYY/yn9jTNi6LGToHdtaVbUA35wkXCtnUFpCnMqCjtYwa2blXKSSJYSI1hS/p59k8toI29puWzCSoGy0LgFekfeePOWP/vRPefr+XYbNM64319xZ3EVXFm0dvQ+kCE09R+uah0/f4/s/+FcW8xX90DEMPU2s2W47NpsNq5Nj/OCpqpoXL17w6cefoJSWzuMC2I02DCFy7959FKrYIywxDIVRlWvrQxD/vjbU85o7Z3eYzWecnZzJ9cgJpQzEzA/+5Xs8+/gTtusN0XsUClfVDL5n224JMfL06VPatuX8/Jz3338fay2bzYbrzZrVfMV2s5F045IgnvLoE97ds6K0KGx6ylTW8t0PP+T169c0TV1ALQJmy31SO8fjR49w1rLpe46OFvR9z+XVJSEGrjcb1tsN3dBhrcU1FSF6Ts5O3rgfD3OYwxzmMIc5zLs97zRYtdrQNA3z+RxSottuSTGiqEhZsd60tNstPgQ2bcd83rBcLqmc4/T0jMViSdd1DDFKr2lONHXFmLuqrUWhUTHjo6RmXrUDxljqqsE6zaYdgIyrHKd372FyQqtM8BGlLUobXrfn4tvUujBD8pDth4h2mrqu8H5g6CND1+KTPPjdv3uHWe0IfYszmqauiSGQSwKycRVnJydcb1rmRyuutlus1VTWUlc1Rgkz1NRzQorMZjWkSNdtxbuqDDkHhmGgD9LHOpvVRDLeR4YYgFIbMfr9yKgYRHbXD2ilWCwW4kP1gegDqmmmLtaEJ+UIOGEcESAefAQ0MWQ0WkBtLQFORsN8VpFjYFHXxCzJrykrhq5n6zuplNEalXZgCth1forSVMB3CWrSWmOtQSkBLzF6lDLUdY1SbmInY5RzjyFJ36oWZqpyDlNbnBMWPFMWOJSE6+SUp8CXncz2pg/zNhs5Bm2NQUv7QHQ/6XY/CGmc8eF8nwl9Mzjp7ZLi2wD2xrXbe83b3nebcZWEaY1RN38+cq1aFXCaR/Bm5Cz1mwzu/vtvSoDVlALM3jGIpH93Yay1NE0zpURrJfeWzpQFD31ju1oVRjYJEB1Tncdr0Q4djx8/plkuiEMEBZfXV7RdK8ANCffJJaZHJMkCyHWpjUoJjHU8ffqU//x//J88evoFlAI3u8ciRTCGea0heZQeiDlicMIk2oeEvMLWd8Ccc35xztHxkmEY+PFHP+Le/TtTWNq3v/Ut1us1zlREFadFo1Eq+/4XvsjQ9eQE3gfatmPwnn7wImMuCoWUEovFnNXxiqOjJVVty8JQIoTIB9/6Fv/wD9/k/PwC33cYZ1FK0/e9+M6N4ze+/nX+/H/5c77x3/6Of/zHf6Rqambzmq7fsl5fsagXpJiko3YhKeejEmKvaZhdGjUYa9hut3znO9+h7/up1iYjtgOUwlmLM4blYkHb9jhXA5JUHkJms24JUerIYoy4yjEMA3fv3uHu3btv/a4c5jCHOcxhDnOYd3feabBaVYZ+C8EPSP9Jpp41kvKKMGohJXyIzJyjqhoycHl1NTFHwXvWmw3kTFOLnzOEgDUGaw1aCcvWhERAEVImpMzQtjhvhOFTEELi8nLN6dEM5yyVrcR3FTKzugKl8ElK7U0J+2nbju16IMxrmtlcWEtrUSnjrNTc5FJm74wWOaHVkG2RIQaOT474UlXhU+ZotaLru/KwJ8ytspYQIov5AvLomQzCuJhEiIEYk7CCZLquI+QszMvod83i5QteGJycMilKCq8GrDFy/bPQZznJa43RuFquA6owyimTtSWlTNfLw6QPHsjgLOQ0+TmNypCTpLdqjUHhKkfow+T9FDCkJvnvCBYpYKYtsr+6qWS7QRKYsyseU2PIOZGKVDOlRPBhelzOIZKMACVrDEYl/OB5/eqleE+NKgnOqiwAyPmnneJVGMHxpt3hLbRRaGWm2pnbwVA/D2jeDkHaBRB9Fis7Xpab/lZ5zdulvnAzuGa85uPPxwUD+ceboPPmdm8exw5ovj0k6rNSiJXWNzaVdy+YJKO6sHFpLzAJmPzO4yJBSmGSnqYiOU05l05P6YbNJR327t07ssiRNZvrjsurK4JPWCcMrQ8BH3pyEgn+6K9VRf5trOPxe0/4k//0P/PwvfdR2ZT6qRpldPneACpTOQ9VApXoN2vQhnq+IAJ1U3NxccGDh/eJ0fPi02e0bUvTzPDDwEcffVSkzRajTLmnFd4Hjo9WVNaxGdY09Qw/eJEIl9Ch3nt8lOCzmCJPnjxBaSVSYCPBVsEn/ukf/om//ou/5PWrVwx9R2U0Shv6YZB722r+h9/8Pf7z//6/sVod8eOf/CvDIAxuXddcX1+z3WzQ90T1sF6vma+OCoAcIerIxu/+DgqjNP/60U/46F9/PN6opGJx0OU+aduWX/ra1zg5OWGz3lJVFf0gyotu6NHOEkNP7wdyls9fK8PZ2Z2Sa3CYwxzmMIc5zGE+T/NOg9VFU7Mh0vdbrNFUldTMhJTIMYh0VRsSHmn1zIShp9u2aK2pq4qmaaYUT1PAhjaSFKyUIpGwRpJshxDw25YhRKyzxKionC0hLKBzJqcASWOM9EvGFKidpW5qNoNn8IPI1mKSvtLoSfMaYw1pSKyWS7SxbLZbrBL/Vl3XGEUJdkmowvJZnYlDYNbU0PfiU8uOwXtUln2EHERKV9ShUq+yq/NRymCMKtsO+OCl5kJ7jHFoY3DaSDJqSqQQyNMDfpyul9EKVZhoqb8ZSFajKgktCaMXMIt3txsGNtsWoz2ucvKwWc5LkfCDVEsorXC2fI4J2U/xp421Nc7K+1NK1HWNRhFiwHuPNprFVJcjHsL5bIarakKMbNq+BMeM16cko6YxAEh8pcLCidzUGkNMxd+YxBMbY9qxo1oJ46vGopJcAOtN0BZTnBJbR6B523O6P29jTsefv23eBlzfYFNzKvLON0Hy7eP5eQwobz2ENxOFx22kAuj32efb88ZxsNvNBGApP1RMDGJlHZt+YAzXKm+gbhrGl8eYWC5kgcjHIIwrgVQ8tzv5s+LOnbsFdBq2Xct6u8WaCvaSl30IaLXzEEvwl3S2Hp+d8vt/+Ed86d/8Ckpb8tSWq5jMoFkWmFAGcgQCz1/8lM6vsVXCpy2nZ8e8evmKvu/JGV6+fMnl5SXz2YLnL1/w7NNnk6w5jdcNSc8+PTnDDwGjxa/e917SzzPEEIUVjYGQAvPljKPjFUrBYj6nMho/DPzd336Db/ztN3j94jXbzYaj5UIULb30LCuj+f3f+w/80R//McenKzKZh48eYZxls93gnGO73XJ1JR2s2hiur6558PjRDVZfTWnXRTmAxmpDCIFv/v3f0/WdBK0VRtvsLU5Ya3l47z7BB0IYpcFSRSaS50jb9WzbTuTP/cDx8bGA1frgWT3MYQ5zmMMc5vM27zRYbWrHYt5I8E/xocWYSCmUBxdVgnLk4fh6sxGfalWzXCxxxhCD1EmkENFKUTkn/ktKXExJxqxrjdYztIL1tqXrO+qq4Wh+xHI+BzJ9u8WoBDlitcXZmtpYhpTpQiSnviTqmgkIKW0xxpEyDKGXUCdrqIxG54S1lso6UgrEJEm6m+tr+q7n0cOHHK2W8vAfAjEntptrlNI0zZycEn3v8T4ya2oJMi7BRACJJEm9RqO8FxGjguilG7Wuoa6q4vNLklCcJHFz9AlqqzBaCZOjBATkNCYaB3ISYG+MxTpTpK+lK1NpYgaHLuAyUDlbFJ2JqrKgSliTEmCVKVLbAvC01hNYFRbJQ8o0TUNVVRitmTUNw9DR9730MDqNKLIVJAlHQukJqFqtyTGgjIRFgYBkoxXOaAH3g3R0mgwhC8AZU36BGyznZ6b7viHx3YGz2+/7rHTh/eTffbnxG7vKN4Hj9Oecb+DMt0mD989l/8/jYoHWGo2+sQ3ZTmEYb52XsJapJG/fTHC+DVDfAOIlDCnvvYe9c3HGolGk4v3OaRc+VVWV/CxKqJexrvgdE1lJlU1McfK65izfj+PjI1QBoufn56w3a5bLY4hKOomzyIO1NShjcEbJYpWCejbjD//HP+ZXv/p1rKlBiZdUfMtJNMx5P5VZrmMMgZcvX3B1cUHtLOv1msV8Sd93XF5esljO2W63XLx6zXsPH/PjH/2IdrPBGlcqqcqCVMxY5ThdndJ3PZWriHG38BBTkkWqweP9QEqRew/uUzc1R8sli8WcjOZv/9+/5K//8m+4vlrTbltmsxkoJRVb1mJz5rd++7f4T3/2ZxydrAhBFgvu3rvH2dkp23bDarHEOUfbtoTBM1tUDF1HzlIHFmK4cQ+JNFgWUzRweXHBRz/5iSQ6x1CSgscFpkyKkceP3+NoeUS33VJXlYRIec/l5RXbbSsLhKIbRxtLiJ67d++xWp2wXBy98b05zGEOc5jDHOYw7/a802C1riuc00X651DAZtuSgwTmpBK2UtU1QxhYt1uiD4VdhNVywayumdUNRusiJQZQOGPIOeJDR/QRbStmdYU1hnlTE3wgZ4VKgeR7KmupZjVVLY+blXFY7dAqkLzHaIW1mtYHhrU8VFbaUtcNWWlCjCyXS1IjUlyjFTkmsk50bUvKWSpllANdgQooI55YoyRQ6fziApNjYVIjOWaMsRLkFBJNU+GcyGEHP5QKDyOAuARJ5Sxet6aZSTBNiPgMRoncVhuFGn1w2mJLeFJMSXJztBa5rGnIOYEWsWffdzTNDKV1kVmKBzYnTYyS4murWlhOranqSmTcPtFHj9hmFSFF+hKmZErYkylBSt57uraVsBfnppqSzWZDCAMATV2jrRF2OKQ9RqcsdMSIcW5KBdZKSTqygcpatE5UleP09BTz0U9IfhC9p9aFvc5T36oAvdG5+eYoJYD5tnz2s5Jyf15g0u3/v01We5sNFbCnJ6z388DuyFre7Erdew9vO543t1H2Dtxkmm+D6dv7GP8+JkyP+91nZnNKk1JiOrYxTKyuccZOkvbxPHwM02LWLpxKOmCTTxwdLWmaetr/y5cv2W63NM188j3GOGCshIalSXofmC8X/NGf/Am/9Tu/S1a2uLUVWjuUSjeCtFBqkkWjDJcX1/S9p91uee/hYz788F9w2lI5J12mc0m6/eSTT3j69At8//vfZ+hD+X0oCz45ASkzX845Pj4mhog1jmHo6bqObdvSd93kubXWELPC1hUxZ45WS8iJv/iv/5W/+9u/o2t7uraVpHSl2GzWaKNo6or/+Id/yH/8gz+gmtUT+G/bjtliwYOHD/jB97/PndMzlosl3bbl8vKSejajHwaurq44PjtFpbI4oUapuyxsFM6ZH/7gBzx/9qzYCtRUXRRSKrJrxaNHj4oYQn73dX1f6nmESW2LrzYrTUyRup5x5+49SYBfLN+47w9zmMMc5jCHOcy7Pe82WK0My/kcW/r8NpsNQ9+RoSReCggxxlCbipgDbZBuwXa7pTJG2MHCyGnImHudAAAgAElEQVR1M/UUEoqMQeR0uXRxqiygp21buk3L3In00GjNYlZJMqqP9N2WEPPkWaxcRZ1UYTvH5E0J8pVwITUlktrC4qGgH4TBCkpAobGO+XJFQrHtOqwBozXOGsKgi1dVycNnQvxxSk2JvqAIPuJ9QGsJWEkpSTVPFjZ5VkBt9L50rAIpFV+tmZJ4yTt5r8paXmuMBCkZR4yetm1Ll63F2kxIBdRaS0qqpLVKpc8wRKwV5nO+mJNNJgwif05KC7hIwjg7J7JvjVw3YwyLxRJrdj5QozX90EOOzJcLkQzmjI+pMEkZaxTWCYDoYyAEjyuVRyhF7SqsUejRd5sjy+UMpYTNwehbix37/y8VK/vAayTRimfuTebx7YzsZzGtE7jN+a3b+6xtTK97y3frNhjd39c+kzsmME8e38JIipT2bfu8CdBuA+T9a3AbwE7sLhKctB9mBRQftmEYhhs/I0m4kjDAoi7QRk8ssVaSfD0ea0zC/KaUOD4+EWmttmy3HeeXr5GgoVFGKx2+FLCEVkQy8+WS3/293+O3/93vSdAaBpQhRoWEjxeJLpBvfM6KGALnr18Tgme1XPGlX/giLz59QSpS5u12WyT5kdevX/PJJ5/wySefMt5aIn0ttT/AvXt3aZqa7bolpcRms6HrBryXlPJQaqtiijSzGavVMdoajo5W/D9/9df8t7/7Bn3X07c9TVVjjWW93WCdo5k1/P4f/CF/9Kd/WsLMoO8HUAltDQbN/YcP+fDDDxkGj9aKMHjaVrz1MUZevXzF8nglYViqLKJojcQuyeJPu9nwnW9/QNd1k6JCPkeDztJze3K04u69e2w3m8k/jlJcXV2J3J1M8IGh78kKYkicPTxjtTpmPj/iaLHiMIc5zGEOc5jDfL7mnQarOSZUkk7VWIrvndFkRIanlQIjnYxKa5pqSessfSdVCzlltttWHjy9x1rLfD7HaouPZfXeOCo3Z73p6boWa2uu1xsgs1zOmZ3UVLWjmdVYrcjBo3Kk71rabkBpC8aSlZZ0zjQIY4AmG4upKsgelSIhJ8IgoDlr6Xkcn+1TgtDLA6UPgcEP6MpS5wprNNF3gCVrJzJjbVAmSbJxFpCQcgAUQzfQ9VthhpIhFZBTuQqfAt22o6pq5rMKnQrfkxPGGGa1kwTVFCTNszwUGyVhJzHJAya5PBh6LzU0pYc0KWHUrBZfnbEGYmDwke1mwLrMXDfCACtNTIHNdktMoGwlHZLGEEOgz5lZXQsDGzxGaayzkDODH8T/pyCFQUKu5DQIIbJtBRgrpTHG4YzF6ozTEIt82ZV7yJConCX5wNB3xODQKlE5jdMWEACHFnl0SolMkus2ej7TCFiFJ1KjD3gPu92uhJG3qxsAbt9Dqsp7tNZToFSxb75V2iuhNeVnJYn45ox1NfvM6L6ceTwGisx1tyetIYU8+V/fVnkDO2CmzJtJybtzH7taRwmvubFvGLtvd37UVKTF1lpiEu+l1pqsEj555vMZxmriIPet0aaApVTeu+dvTfL7RGfF/bv3sbZCYejaC7abDZlE17WgtHQZa0cK0MwqtFLMFnO++tWv8Zu/87u4+RKFBeWkt3dMQJ4+qZvMMIhCYLNZ0/UdJ2envPf+e5zcO+Wjjz6imTc8e/6cfvBkpbi8vOaDD77L5eU1zlWyTVUWahA/+enpsXxvrKUfetquE0lsjPR+9HYbSJ7KWWZ1w2p5xF/+33/FP//zdwghlPTdhhgy274lZVguj/jFX/wy//Y3fxtjK8YKKKD4ueVeePToiXQcx4y1DZvrC9q+KzcUdJsWnTVZl3RsbSQ1vXy+jbV8/PwFH//sp7KIhHxvyq9oclI45Xj/vS9Qu5ptN1A5TciBPg4M0eOjZxh6Ug6MOV2VNZwen7BczDk7O8FVjsMc5jCHOcxhDvP5mncarKaYJgZNSunVlOg7Pu9bqwtrJimhy2ZO2/Xk0nc6DANHxyu0EabGaEffDbvORAybbc/5+TV9l5jPQeuK+azGGEBlhuBp+56mqqiVIsWEVpq6qcnKElFYNNtNR7veCMOp7MQAW5WIIeCMxTkLKAYfiGHAWglMCeVYQ0j0vqfttmhrSCiqquL6Wmp7KmsxJkGQRFtrdXmINyUIKBKTMBzWGlIKhBRRypADpBTRWpjK66trbAEKtbHUjZNk0CQpopAxgDGKnCBGL0yWli5LlSR4R8CGEk9g3AGvBLhKpH7CdIE1eqq9GX13fTcwxExdjx5FAUHOGDlfrVE5yX60JmZhs7fthpgzjTM0CrpuQOlIRhMioExJjpWE32KFxRi1kzWnSApSk+TmNeRAXVcslzOs1SQfcc5grWZIaufRLPhsDF2apLZjhwuldzfv2MHPkveO//62nytA50KqlqjhPdj15nsm9vVN9lJ2kdl/6cgS775Tb01SQmVposnjZ1tA6z5YzeMB707qhpT3JrM8Xg+5hvtsIUruU/bYUGE45fP0XrzNiUhWciyucgQfSCVMrWmaogxIexh1rDMS9raezTg9PkNnQ8zw+tU5bdtKcBKRqnICEFOWhOmcsNbx9OkTvvbrX+Pk7AyFhmzI7OTW40mMvDsUuXIJ81pfXdB1HT547j96n9lywcMnj/nBj37I8mjB8xeKbdtSVRUX51c8f/aKzXo7nVOMkTGQSiqvGgbfs1wcc3FxSd8PtMOAj55+GBhCmHqGT05PqZzjm3//95yfnxN8YLvdsFod0feeruvpQ+Tk+JSnT3+Bp+9/ieXqBFnUiMVjOvqo5UwfPX7McrGi63oWiyM++dkz1psN27ZFKc3mak3wAVtC7ULO0yKZMZqcIt//3odcnl+IakRJmJshycJKSqyOjnn04DH9tqUyYgnpg2fTrYk5EoJn8F3x7MtteHK84sGDB8znM1FxqLff24c5zGEOc5jDHObdnXcarHaDp+0GQpQQJG00la1wZidTNcZglIAY0AxDR7ttyaoU0Re5Yl03KAWbbcf5xQVd16O1SNKurreEoHCuoR1arDHoIVEpi0FCVlKC+XwhnZIoKlNjk8hNjVb0IZXXSXdnTNJZ2Haa+Uo8cSHK/pyrqOrM9bDB9z1KG3wfGZKAYGGAZ8xnDU1VobTCFDChtSGWCgpJPJaqmRB6rA3ShxqCVORoAZ5a68J8pFJJogkhQhb2l5RQ1qCMpfNe3oMELsnrJfDEh4SrKpLaJaL6IUj4EpSqjHEfAixGZkspsMZS1w7nxC/qvccay/HxEUOIpKzo+0FeV9XSL6kyqFw6NAVkmnKNlO5Z1HMW84ZZ8aButi3d0BOSHF/OGV9qLKQ3UwB2XTlAoZWjqkRiXddSi2ScYdO2aG2omkaSg3MB5iXJVEKkJMwHKB2he2ilePF4CyD9efUzo2/zs4DsCBbz7Z9PoHRM1X3TDzqCwxEYTgzuHlO6nww8vjelRFQRlfc8pKhp0SEU1UMh0qaZunLL/m7v47YvlZHJzXnnZyzHMAYoiUw5knMiJ1A5U1uR6QfvJ4n4bDbDGGH7c1n0Gtlvay0xxCmkK0X5zl6cX9K1vdRjGV2k8SPgVrja8vT9J/zKr3yFxWIh2FPlIn1O4llVQKnEGWlulcfPKpFyZH19zWazpmlmPHz4CKUdX/jCl/jL/BdFWSB+0dXqMVeXa16/fl1A6u67RVb03cC9B/eYz+ZS4aSg6zrW7Ya26/A+kCjgTyua2Zyqrvj+97/Pdn0tn3PKLBdH+CESYsa6mnsP7vDg0WOUNjx89IiqrhhZ8JQkwG3/Mzw5OeH+/Qc8+/hjzlYnNE3D1dUV6/Wao6MVV9fXrK+vObkrv8tyjuTCNFdVxfmrl3zw7Q/wwWONhGKRRaGRyvdrsZhTVzXb9TWrxYKcM8PQs7leS5f24BmGMC1sKqW4f/8+x8crZjPx0ofidT7MYQ5zmMMc5jCfn3mnwWog48kkJTUI1lrxVFYVVu1VgWSIOQuL4D0og9bibQvR08yq8nAz0Lbb8tAqsrX5fEbdHNF1nhAz3ocSAoQwrzkRYiaEgav1lrPViqiyhJZkVSTAOyBYuYohBAGAKIauIy4qSSRO8kCYcxAGtZe6BWECE7aqmc9qKm2wpuFoPpfKm5iYNw25Eba5C1KRY43FGociE2MA8uT1FCAg18hHSQO1xmCVIccknlpliF03pSL7wZPCIP5YJ2E1OotXLyTwIaFNIlH8g1ZSj+umRmlT6mYkcdX7gAryUCpg21I5S11XkgJcQInKsFws8SGx2WzZBo9zRjzEKYqUWGu0EQluTkmcxjlRVxVHywXOWVKKDIOXh/QgUsN932XOCa1UAcoJTZEI6wprM4PvGYLHD4HT6pjl0ZKQE0MIWOeIRSb9ZjjRTeC1CzwqwPEt4A/eBJpv+zNQfLWjL7a8Zg8UvjVZWP7hM3yiIxP8ZsjTeD4jOLtxHOWzUoX11uyA7LTPWzN6RvdDoPbnNlDWakyRVgXg3WShq6oqYCQyBkhloK5qURHEiFLiZazrWtJkYyCW/ljxR0pQUsyJ5eqIetbI9UyJ6/W6sLGyKEEBzLPZjMpZnjx9zNe+/lVm8wbX1IUoHWuRBIyqXADslDeupu0olUjR48NA13W8995DZvMlWhtOTs5YLlds1lcYY1iXY5Hz9Vhb7V0zCc6y1vHgwf2iorBsN1vWmw3r9ZqYRH2QEY/xuCDw+vVrwjCglaQ9V1VNzpm+H4hZWNKHj5/w+vyCh4/f470nT6bzSzlOC0/GGNJeZ/Lde3f49Gc/JcbIYjFjvV6z3W5ZHR+jlWKz2XBy52zyHatyMznn+OEPf8SLFy/K91R+nnOWhQIyKSaOj1YMQ0dVFqBSiHRbWZhMMRJDnBZNcs7M5nPu3ruHtY75YoE2Nzt8D3OYwxzmMIc5zOdj3mmw2vY9IaXCsOmSEKknOSgxoYu/LaRA1/d0fSBnMCA9eymzbSWUKQRPzpnl0YLZYomzhuVyiXUN19cb1tstfd/T9S3VrMZWFj8MhJjwQ2R49gqFwVlLSuBDwBhhSq0DhUbljFEa6eAUQBaj/FuKgb4fJpbJOccQIyEmlNaS/GsttYXKSJ2DyqNPkPJAJg9mZFUA9y6RdpTQGWNKErBsM3kvbIMWMDB2FqYUGaIX72ZOxOCpxi7aAvZiymSfiDGTshKQywgwhBWzzuFDnPSpY2pqzpmshBFzzmCMSH+9DygRTpJ16VHVubDAEHySzlMFpgTtKJj8szFGgvdFCrxFtUBK9N4zDNIfu2PkFGDwPhBjoGlqtDZoNFqJLFzpSEqFFdfC+L6+uCghUgYfo3hv800wqZWaQPqboyZwfeOn6iYr9TYQB7cAproJ9gqeuvHafW9o5rb8dz8x+OfX3+ynCO8D7/Lu6c8jWzpKUiev6t7/415P6eS9fcv+RpZ3Yn6nntIdGE4p4Zwrf8/T8aWUmC0X5X2qeDdFei2+yri7F8vrQaON4eT0lLppUFqx3bRcXlyUOiRLTqCNwllZZLn/4C7/5pd/iVkzI6N4/vwZ1+uOu/ceMJtrjHWM8bXjf3ORjaMEdKEyQ7/l+uqKnDPHxydoLQz/fH7Evbv3OH/9kqqqePXqNaEoNcaPXuvC6Cfp8G3qhrOTM7q2o6obtu0uBVh+lxgyomwwWnzgftCQEhGpf/JeQF5MmV/44pf4n/7sz/nWB99BXax59Pg9UaSAANUUpntAKSUsdgmpe/jgPt+S9lfxqXYtV1eXPHz0EGstbdsW1YmeEpubqiKEwAff/oC27aTfOEacc4ScyVl+pzRVzXuPHqFSZt40Iuv34r2PIZXfi373OQPHpyc0s9m0IJFyRt36Lh7mMIc5zGEOc5h3f95psBrLw9r4oBt9pGvXhOCFOUHhjC2JsIHzyyvatif4xGK5kjoLN0eZLMGsWsDBZrshZ0Nuaq6ur7F2QBtLXdd0Q09GAnyMk4farDToTMjws+cvmVUii9tuWzKR46OTSU5YOYcuvrKcM8F7ri8vGdotw9AD8iBvrGW+WGIqS9v2dP0gEmadmR3NqK3FCloV2WMKpOiFVdEKqyXNtvceZ62k9KqdV1RCmCyZLGmlytB3AyBgelY7QsrEkFBqDE5R2MbQVMLiRB+IIRARgGqtnWCL1tIJm9FoLWxJHD3GhV1NWRKDQQCWMB8JUmToO4IfqOuaxepIQpCsxeg5wcnnHpMsRmjyjpHRUmnhKoeOaUpXNtpgnQOlian0xSIs2uhdzCGTQ8TUDq0kJZaUSMmDkm7MOL42JUk9BZxzxBiETdwzfCoUKquJ5Z/YzKJmLZbeW4wr04KCbOZmrcr+z6fPEgQM5pudqeO23hZgBFoUBG/hPG8fy/529kHdDdlu8THvA84xjTWVVQrFTfB9ux/2bcdwI1xKK0ldHs853wylks8hTttMORFTZD5r9oK5Is2sIZMFhJXX718JrTXKKVbHRxObt91sWa838ntFi3zZWvmdcPfuXb7ylV9muVigteHs7B737j8kJECZcr3KdVdyT8g5lBsBQCVy7Hl9/oqLy9ccHR1xfHIqrHkGV1W89+Q9vvnNbzCbzQghstls6PsB56ry/ZF7TGMIPtEsa6qqptt2KCWJ6a9evaRtt1R1jTJVocLVxHLH4AnDwPHxCd4H/CCWgkePnvBnf/6/YuuGT5+94Gi14he/9ItF0ZCIaQTOAp5zWYwTxjpx7/59WXzzPc2s4fz8ktfn5wzDQFVZXr14ztP3n1DP5yQiFDn2j37wQ773ve9PC5HKyAKWCpGchFV9/4tPOFou6LcizU9R/P1jr3LX9bRtX66zYTarefToEbPZjMePH1PP68mze5jDHOYwhznMYT5f806DVacNhDSl5G43W66vr6lcxXK5oK4qrLUYpemGge22RSlLVTuWyyV1XZGSJ6tEygFjLZlA23UoLN4PwvbpFmMd/eDph5bl6gjrZJVfgFGNUongI0prLq63hDBM3aTrzQbTtdRVxfHxiu22JWYBV9v1mspZFvMZx8dHOOdEIqwl7WcWAwZFDAMpeoxypDCQLVKT4QXwgoCmlKWGQ+pgxIentRbwZQQ8iHdUKjLQGusqjPf0fY82WiphXCU8SEyonEkx4IxmNquxyjAMHTF6qfPJYIsUN8ZdII/3nqw0uUhD+74v/bSS8Gq0MLRalaTglMnZYDXyWaQk1RqDJ1kmYDEGY3nvSdZRuQqlJHAJZTE2kYuXePTjpizgxDmFilkY8lKDYbQq3ubCtqIwSk++Z6Xk/eSMM5ZZPWPRzJhVNf3WQwSDeHdH8DGBrXQTDCpEYpvJuz/fkvlOEtZboHHfv7nPNlLCdMpqxLSdXfCQLSBix6iOYG48v9vzNkZ3et+t6prxzPYB8Vj7M/ohb293fP/oWb2denwbxOacyTHv5Md5J1Ue63Scc3RdRwji6xQjttQXeT9gqoYYA84tpiCqlDMhRSK785Hk24rFYoF1Fj9Erq7W5f6piFGkv845Tk5OqOuajz/5hOOTU3zI2KqhWR6z80HvhWjty38neByBSN9vOT9/SSZz//59jlanpQ1J7oUvvP+lssgk39HXry5Yr7el/zZTWUuIiRAHQPPgwUNRkRSf7rbd8smzT8ko6vmsAECK2kEWYKT+aUEIgWEY0Nrw6METvvKrv8bDx0/4h3/6Fn0/8P4X73J25065T0eGOgJlIaNIg40xDF3PcrFgsVhycX7OvbP7fMwzqf7qOuaLOS9ePGfoB5rFfPpe+N7z3W9/wPr6GmudfC9KSJzKudR1Wd5//32GrivXODF4T9cPZTEi0XYdwzBgKlnwOlqtOFod08xnzGYzssqFsR3v5cMc5jCHOcxhDvN5mXcarDZVQ5ofgULqTUKirmegYAgRVKAdfAktcswXc4x2xJiFlRg6jNXM53WRzcqD/XyxICYBLDlnut6jgoAyCdSJWAxVJQ+uxhhQAzFJ3UzMwvpVs4bFfEYOET/0WDMCI41BsVqtmNcVfScSt1lTE/wweTUBNJmmNvStZt4smTUzrM40tZsCZ+raoYLIhZ21WFsxeE+kJB57z9FiJvt3FYMPUn0BOGMIOUuXq9a4SipjqhLAJD2r4GzDrBb/KxkMjqqAU3lQLeA4R0BPwNJYjR4fiGMURlNbAV2F7Y1hICPpxNoKQIkpQ5H9XV6vASa/rfc7SZ91NQnwwzBV4YQo1zvlDIi82ChTgFZG6R0IHB/InXMYVKn/AJXFF5liRGmwRgKmMtKXe3Z6h3ndsFYJtPjzUhTfni7gd2REd8BrJ71VSkKxErskW9ixjWMIzG3AOgKzG8FD3GRAb0uI0wSYb/lXs7oByPf3tx98BDsGcx9A7if9Kr2/LTm2WBjLm9Wqb5cX3/xzfmuNz/j33TkyKSuMMTRNMwUJaSMLKtYaSYsunlHUDgTHnIq3PBd/6a4jVhKfl8IS5kzbtjTNDO/DFBx1584dmqbmww8/5Dd++99KfUtSHK/uoLKBCZiCgHk9/VnOhd1xEdhur1hvrlFacXxyIixhGs83cnbnjHv3H3B+fs7R0THXV9cY425cq7Gr1FrLo0ePWa+3OGfROXN+cUnbdWVRS9Qk42c9foZjJZEPgZQzT54+pWnmPHz0mN4HfvDDH2Ks4ctf/jJVZYlZFB0xBvYDr0a5bRwiOUPTNNy9d5dXz55PEmHvB7q+pW4qYop03ZYTdUqMUaTO/x97b9YkyXWeaT5nc/fYcq29sBGUSO2ksTWiKKmb0zNmMuv53W3i9EjWEkmNSIISSYBYClWFyi0ifDlbX3zHPSKzCpy5LJrFZwagkBnp4Vtk+Xve7eVLfvzjH2Otu7UIE0Og0rKY+OjRIxazGV3bolC0fc/NZs3NZkNI0A8lgE8p/BCwtuLk7Iz5fMHTJ0/L75r4xnvtMIc5zGEOc5jDvP3zVoPVWVXTqUzlKsxygVaw7Vv6fmAIg0jTin+tyYkHDx7w7jvv8fz5S37zm0/wPpKSJs8kRTTGiNaa5WIOWIwxXF5fM3jxpVrjaKoFV1dXWGuo61qknmjICqNLm0Z5WJ81DavVCt/1KDUm52rms5p+8KQYqCpHjB4fPLQZQ5aU0iQg22qFqgz6ZCWps2ScVoWZk95MnaXiwQdPzoammaGMkQCgYRCmOCXquqbGMPhA5wMxRUwUH9kQIlpptLI4KxLHymiGFAvABlX6RomZrMUvmrRCGyedtV2RMWtNShL6A5kcI1klqspNfkljRYYqibDyIO+ckwdrLdmpKUWR7EYJemmahn4QaZ8xFuuqkaoU5tsH/LYlxoAEzBjICe8HYkgMxTtqjMVZkQXnIje0tiKFwKbtMArmtcMohVYWrSNaS5DWZrPh5OSI46PjSVZqnRa/XxTQY5SZQrqgSHQVgKaQ2ow4c2Re74K4u4m/d72sUxLvKLu+lfG7g0gjALu9DfaAhWJX36LES12Y0X2Aeve/+0nAE2gdAQXiOUyFWb7Fnqq9Y023vbOvh1DdPnZVFmcmFhmmROfjY+kSHRcf8riYYS1aUYBfOQ9GMwTpFw0hymEXD+0olT8+Pma5XE5pszHGUr2yoKoqVqsVTdPwL//yz8QYeFBkrlU1p65nZCX9u28eoXXFpypdyMEPXF1fsd1uqOuG5fJYgpy0JiYJf5rNFzx+/JRnz55TuZqsNsWTblEKQoyTL/f09Iy6rtler5nP53T9wOdffI5zjtlijqusKCMyOCMLNFqJv7xrW3LO/Pl3voPSFq0d3/yDP+CTTz/jxYsXPHn6hHfffUc6S6HYCsQrm1Kezv8YqEbOGOO4f/6AX6ifkXOmntVcr2+4ubnh5OSEGAI3N9c8Uk9kcUEpPv74Y16+fIkfBuqqLotv8daCzdMnT+TzrhVaafreM/jAtu8YQqTvPRLEpaD8Xjk6OqKZzXb1RSnLccTwNdfrMIc5zGEOc5jDvK3zVoPVpqklxEZBU1XiO9JqqrCwlZvkbArpAb25uSJGz2Ixk7L7ocWHvtSoRLTOVG4mD4CmeLlQgKbvA5iB4BM312uOT444OT5G5UxOYK1A17p2qJxo6ppZU1EZTVVJX6nWkLOmqWtiDGw2G7wfpsRarRUL01BVjjRWfpBxVosHMyZ0YeGUguADPkhHYV1XZDQpB9p2O6V5gpqYx6g0Q0z0Xh7s0cLgRR/kKNUAuQI8yiiaugB532NKmnFSgRD0ayBD6njSBGCcs4wVLhJbo2iaZmJdXeUm1ts5N6XpGiMM6DAkxmRTpTWurglZ6j1CjFilSTlLqE2G6+vrCcg654qXWTCUJ8r+pVSYNz0BE/G66uILHljMZ+jlnOi9HKM2BcspUOJz7bqWYfB4P5BSJhRGWRnD2A87+hR3bGNhOyWD5jUP5/7c9XGOgG8f3I1prqgCDvcCYsY03hGc7s/IFu6u3w6U5jEEhwl7ynKMkrTU/UqbCfwVlnKscVFaiw/yzv6Ww/7/nP39e5PnVhY8StcqTCmvIYTpfHovEnD5HTFKhcviiNETcz0C1Yl7LkD5/v0H1HVNDHmqtDk5Oebk5FSsBdbw05/+lC+//JK/+O6fcnZ2AsB8tcRYUWpQgtT2464mllkJUIUABNpuy/X1JW3b8uTJU+bzxaScyIjE1qB49933+ad/+p/oxqCNQeeSSjwuCqBQKvHw4QNi9NRNQwYurq54/uIF2kpqej/00+ctBI9WGuMc0QdSTvzgb/6WP/7TP+NHP/p/+Na3v4WrGz766COsNfzJn/wxs6YiI+c/Jb+3wDCmjI+pzJCjLKbdf/AAoy3DMOCqiu6rr9hut5LK7j0vnn/Jh9/8JklJj+zPf/Yz+V2iND7GsjyHhEH5gZOTE+7ff0DXtcK+lms8RJF1+xDohoGYkyTGa83Z2Rknp6c8fPSQxXIh9zNi19i23f+Pu/MwhznMYQ5zmMO8TfNWg9VN13Kz3bBaLlFpx2bWOU8AKMRI3/cCDNc3XF9fy0p7Fg+lqxwqQxg8QxhYLFasVnBuOHMAACAASURBVCupjvE9q6Mj+iHQdgFtFDFkFI7ZbMG8npNCQGUBzhmo6xk+BIauw/ctm1gCjrQiZZHfKgSAJRLaGkiGpDRtP6CKzyspMBqM3gtkUWCtE8aQXSiUKgFTkuYroDHnWIADIn3UmrpucFXD0HYMPtKHKMFBWhffqNS7OCNsVFSGlBTbzZroeyp3MoEIa22RDI4gQZjTfvDi/6qE8YoxE6KAcLI8yPqoJo+Y0VoCi2LAOFM8aZnBB9puKAm8hpglH7iZz8kq47dtOUcWlBbgnRVNPSs9kwKkKltJ/24OWGsYBl+qhGKRG9rCZCcJylIGlGHwEWccGYtPUpeTUqKPgZAzaI2yInWNMUj1BTsP5phKK+cg3pLMAqQI6rUU39v+1HFuhyNx6/W5QMCs8lSxMvo5d9u5DRFlc+o1QAiQk3w2lNIFGoyvLSbPLMdZLImFUS2gzJQAoTcA63x7F3Y/e+d45L/plhT5LlCNpaM2510a8NizKx7KNBHGzlkB4Hm83jU5RZH/TqA/SdhVkSBbBecnJ6iUqYzlt7/5hJQyH3zwIVdXV2QFv/n4N7x89YJ6VvPBNz6QhaIcRJUx+WpzQad7KbNKyf+PKC57sk5sNjesr68xSvPo0RPE6wqhhHtlEkpb3nn6Lov5kq7tSDFPvbEpSXhbzpnZvObxowf4QUAkWnOzvhEJe12D2Xl8R4DpjCX5gKkdf//3f8/f/Zcf8s//8hOsc3zjQznuTz/7lLPzM9555ylZCcjzviflfan46KmWa2mMIZaFm+OjE+bzOTfrNcfHx3z18iVffPEFjx49IKXAyxcvuby8YHl0xJfPnvGr//iPAlZNkfTL3R68sLWPHz3Cai29zsCm3bLebFm3GxKZPni891CUG9ZVPHz0mGY24/j4mKqqyDlxfbXl+ZfP+PkvPuJbf/Q9DnOYwxzmMIc5zO/PvNVgtes7vrp4Rd8LqCSXBFeliN4T++JXylLfMmukM3F9swWQsCBncc7S9x0xhIlFCkECOo5Pjgk+EcIVKEfOCq17nFXUVU3b3mCNQqsKtMIPHZlMXVly1ITQ48MoXRMmQmsJH4opYmyFKw9jvpd+w5gVPkR8TlgjD3/ehyKZNYSY0DpLLovWaCPfT+QCtpSA8JiJSaophhBoFGhryUq8tRQfZwbQBq2U9NW6Cq1EYqm0xrqa7WbNq1eXmPuGylqsc4WdAgqTEmNgGISdcNmitQESOStSATHiqbM7EAdYZ6mcAwVd1zEMga7rpLPRCIOUcubq5oaUJV20ahqMcWzX8oAao/SkLqsa5ywxBmKQhQoUWKuxRhONJiQBM8YYmrohZxiGIOwM4pft/EDXgdYBbSKx7UoyskJbSx8827YVAFC5wvxkYdnLwztKwPwoBn2NQ30DUIWdd/T1l+fXgBtKmOspVFaN8uv97b4u370d8PP1fr27ktw3hUEB4k1+g6912me1O7b9t38jAN8DsnfDl6BIp9WOEa6qitlsRt/3E+AkM3kfNUp81aVGxwcvgV7lDMQSBkV5ndWak+MjUozYquL5ixesNz33799ns9nw7Pkzrq6vaWYNKSWpX3EGpSzLo9Xe2kC+8w/I3SAgVuUkvzP8wPX1BdfX15ye3uPo6Hjktst/ExmpYFkdHXN+fo9f/vKXtxjo0SMNsFwscM7RbnsWzVJ839fX1I2oL1LO1HWN957aOmrn8IPn3vk5/+mv/pK/+sH3icCvf/Nrzu+dszo+5h/+4UcorXnvvfeYzxsUUh2Tki/XoSxSoIp3fbwGY/I5LBZLHtx/yC8/+oUkHaPYbrdcXV3hB8/Lly/ZbDbcf/CAjz/+mOvra5Fmo271KMcQWC3m3Ds7w/thui+8D3RDz7br6PtBPp9FAp5TYj6fszo64uz8nNVqJR7XtuOXv/iIf/3pT24pEw5zmMMc5jCHOczvx7zVYDXm8k9hE7XWhBjF81Q8ajnEwgRqTGXxIXJ6cowxjsFLLYP3fvJXbTcbAYQhie/1InN1vSaGKOmgtmLbZhQJoxWL+QKjEkPfooywcnXViL+y3XJ5tZWUzeJxtcYSgtS29P3AEDtsM8doTVLCeAwpk5Sm3/ZI2IzCDz2z2Yyq0uTomTUOjCaHRIiJwUescygUISZizoTgiSkJ+8hA3w/EvCkspHSwKi3yTq0yRhfG1GpS8KSkmS+OGWxL18/IOfHs2XOsNZycHJGLD7aqazabDSjFbDYrUlwjIUmAdo6h68gJVqsjamvYbiXFtLKWmAI+DOgkIFYbAT4hiVRZKYWtnNTQhIjSGWcdOYtPr21bhiFyvFyijCpya41rHJDoupaUJGFYIZU7SUl68sgSh5BRSuBLTJFhSFhjSTGQY5BzGRNVVRFiYr3Z0g2ekCI6RWJOaGOnoJ4dy6cwege2dpirsKh7rOc+0zm9nhHX7QJmoDCK5Tgze5JcJLhp3O6+VHdfSrwPCN8EQN8MIt/MAo/vk1LpENaaMUtIqb0jvMOk3t3+7mWq+JjvAF4o/kOpRxll1dZKYNd4r4zHVtcVy8USTSKGVFQEpiRWF8lqYfbH900hszw5ZrFYodB47/ns8y+YzRbMZjO6ruXy1QUxBDKwWM6ZL+SzYazGOE0OLSgjTPcEVEu1D6rQzAk/dLhKs7m54vLigrZt+bM/e1wC24Tx1UoV6bpUAM0WCx4+fMQvf/nvWGMn1n6siqpcxcnRCSnKeUuAL92kq9WqVG5JSJlI3BV9P9DMGv76b/6G7//nv6WpK/7t//03tm3Ld773IcMw8JuPf8Pp6Ql/8M1vMmsaULtKqMhY/yQLhWHs19UaXX5H55Spqorjk5PSV91hrKLtWzabDevthqEwoX3X8ZOf/ERSmV0FqdyzlO5iazk7O2O5WuH7AaUMfpDFo/V6XXz6vaSBF8tETJH5YkZVV9y/fw/nLMH3/Oi//3f+/ZcfcXN9TdPMvvb+PMxhDnOYwxzmMG/nvNVgdQiJerakGzx5vSlS1sDRakUIA84amlmDUsK+JEA7Wan3MZDyyLRkUJZmviTGzKura/GwpszF1QU5g7WGvtugm0DyraSMqtJTWjyQQwhoZbhsN8RX1/R9z+DF+2mSIqrEYi6VLb4Vz2NImZQ7fN71dm6HyOAH6rpCIUX36IouKkLvUbFnPqtJGbbbjpAUPmaSljqSdrulqiqqqsF7T9u21EYzryzbfku36YhokdDlTGUdBrAqYwnoNJBzYNP2aKeZ1TWzxZzoA9Q1V5cXtF+23Ds/R8dI6FpSFiBnlCkPq4lMJOZM3w3EKA/O204Cktq2ZTabsVqsuLq8oOta6aC1kuy6nDd0g+ZmsyXkRN91ZBS1q2iMFstfTOgsAU5G4lfQWpFylOtjMhpNysKYppxRRsBpIpNTYr1ZY7QhJQjDIKBDGzQC4CvniBGSN6XDMdK2PTc3PWDBKiJI1RC7wKF9CavgxTudoWoMJSquxLxjCjNIp+QIBLkDDHP5SlbkuA965V7M5TUg6cg55iI5juV9dwFP4zZHH/G477v9fF2qe5fpTCmhUsZqhTYKlQWhjAA5ja+95V/dAebb52WULQtbt2MP5ZzpDBMBluXIranRypISU0hSCIGmqcjKog2kHDHaUjVztDZ0o5Iig4oCZrQSg/OD+/dFIqoUV9c3wqLO5zSzCmPBmERdW/ph4MGDR5yc3cfHzHJZkcKGIW+IvhdAPV6PGMnKEH0v3cTBk2Kkqhqub7asry6Zz2bcu3+PrCKZSFZ5L7BIPJhhCLz3wfvU//RPdG2Ls/IrOoaANY6U4Pj4rPSv1mij6TcDm01LCAmtRR1ibfGip8Tq+Ij/9t/+L77/g79Gk1nf3PDzX/yChw8f8vTpO/ziFx+RUubhw4ec3zsn5ogtUvfxOqIMiUzwAzHE6XqTM1abqR7m/NEjTF2Tklyfq/UVN9trtl2H956Li1f0/cDzZ89w1srv5xhxzkKWXuWYE48ePUHy0gxkzWa75eZmLex6SuRSq6VQRBLWao6PFzx8fM7xyZyvvnrG//gf/zc//ecfk0LE9x2rxeJ3/n1zmMMc5jCHOcxh3r55q8Gq1Y7lYokfBvzQM4QeUqavBunoDBq7tFR1Q4gBCtsYYsRHCduR52QtD4KFDTBG2JcQMs5W4n0Nns16g1WZphYPqrN6knrGFFE6cnW55fpmLf2r1qKMLmmzEjhUOYczGqOU9CKmxOVmy9BLV2rKCu0Mp+cPpC8xJVzTEL0n5IhCi/x1CMQEHsMQI/3gCUouV0bjfSSGgLOWxWxGZRVn5+ds256YvmLd9lPITKUr8cFGDzliVEZbQ+gCN9fXhPkcZ0RanFPm7OyMlBMxQ9cPbLdrmrrBaGEjc4bBe3xhfdu+p6oaUghst9sSVCSSyxAGlos5y0UDKTH44tutDHW1IOeET5khZamh8J6QNY2zAjhipK4qhiEQo0g9R5+gUmYndC3A0VpHxhBTLGFBFElyQBmYuRqAOHic0dS1JQQ99Uj6IPvo/YC2VryKBShpbstWb7GgZcavf51f89bXGEHdCFbl0HIBabd/dl9qenu7Sr1ZTrs7NTsZ6Zv2a78i5+7Pjd8zSKDS6GUdpclZSTzZqEbeB6a7/dtJfvcDnG7vB4y1SWqSG0s/r9bCgI5geKwIclU9SciH4CcZ/Sj5HaWqSqmyYCFy4EePH02saNt19H4o1Tg13/jGB3z2yW9EMt/UPH36FKc0NicuXjzj5z/9R9rtNcH3dF2LM1Yk+llqVHzwGCvMaUzwJ3/y59x78IT1zTXn9x5incMPLbkAf0kAL6A9ikJj6AdUkemHUs0laeaJ5WLJYiGVO9aJ5PfZs2dcX19TNbVU0qQ0sdHn9+7xF9/9Dt/9T98TFjInXn71FTc3N3znm3+I0YZPfvspi8WCDz74kKaekZEgq5SkoislBTqV/4/T4sp4fYX5z4BmuTpmsVjS+Z7FaoG7uuCri6+oqpqUEp99+hnPvvySvuvl+pZ9zVm6nsmK5WrJ2dlZ8f5D33s2246+Hei6nk27lfuB0mesYblccO/ePd55/JBf/8e/80//+I88+/xzUUwMXgLe7Fv9191hDnOYwxzmMId5w7zVf3s7A5XVVKahBayxaKWIMXNzs4Ys4Tyz2ap4N4OASy0P1ylnWbWvLClp/OAlYVNrQii1C2jpzlSlB9QoFvMZlXPiBR06jBFPlO8HYuipnCGTMRrqeS3SSDK1VTROS22KzlQGQkL8qb10jRqrQSVyCmiEVWjbVo4lZ0iaZCwmW4YY2baeIQjwishDtVEarTKazKyumM8alJIH86qquHd2ir68oisPafP5HEXGDxGjlDy0ZZHt1XXNfD5nGDq6toOccU68rCFIaM1m2zFfrCBLb+kwRIYYCFEA4+roWIKWUsQoJ/saPa6qIAVcYTsVmbrWqCze2YSi7QyxH3AofBap8tFSQGwIA7PZTHojC8hRWtN2G4zWWLsoLKM8NIcgct2UpRM2xIgxZnpwF+migZxZLBdTYA0kqsriQ8bHBEi3bPQ9IQwoLQsEmZ10dWQqXwsa2gNpEhacp/Chu0BRgPTte34fBO+DvP1t3w1nMntgcwQZXweWp/P4Bsnu3e2O/aYC+tJuO8UHrfX+Nl5nUvf/LJLp3bl5M6DfSaIzYIpftyqp32Ow07jtpm4wxhCKzN85h3FFOms0DkfwgRHkx5xwVcXJ6amkTTvLer1hKL2+PgQePXrEfD7n8uoKlObxQwn5cUQuXr3kt7/6FatFg3OG06MjKtdgreNodYStHFVdUzc1OIuuHBrHZ58/x/vA2dkZmczgPdpK2NcI8kWyLDTtF59/Ttd2UzevKjdKiIGjoyOqqqLrerRSDD5yfX2DMYZhGDDOyoIO8PDhQ77x4Yd8+OGH1HUDwNAPfPLxJ1jreP+9D/jyy+dsNxtOz854+vRpkU0rUgzSsZylRIvxvsySRJ33FyYQpl+hOTo64ujoiKuLlyyXc4yRdOCmkWv1b//2b3z16lW5V+S6e+9FFo4ihMA777wjicbdgFZafKrbLZt2y7btCDGXiii5J502PH3ymOOjIz76xS/58Y//me1mI8F7oagwrKPrd/7XwxzmMIc5zGEO8/sxbzVYrZyFFLHaYLXCKAkHEq9TTVVVWDdjCJnNpielQcJUGOWJ8kDVth3NTLpJRUqYcFWNMRljBLgpMjon8TDmTF1XtO2Wm5sbqsqyXC6xixk5ZYKPZAXzWc1ydUS73dB1LYYiT1MJYkSlgFGKo+UMazTr7ZZ2kBoOP3QFDAaGDCrJQ9WgAF2x9Yp+6On6fgIOMQ4C0hTMagtkgu9JlTCMISS6vscPgVllWc6bqTpGAbkWZkVrRdd1kCT0KVU1zlbomVSyhBAY+m56GHXVDIokOhC53mwYQuD09BzrKvGj+UhKmZz91C0aQqB2DU4DWcCdneSpWVjhpiKGSJciTukSilSX/kxNpnhurTBj2hrqZk6KwoKSha2OUUKmxroX+X9P8p7j4xMWi4a+7aRGyFrqukarjPeBTERpqUoJUR6c5/OmVPPIvZFCIOYSSqVuV7vc7Svdn9GL9zpQ3Xkvx9fBzrt697XymteB3i4xWCaV/tHXXnPHk3oXCN/d5/F7E2NbgIsqNOqt5OM37vXr+7+/j8BrjO5Y07Pv8zXGUFXV9Hr5HIiKorauXH9JpDbO3gLIIWVRV5Ttigd1ydHJCUpLYvXzl89vZSnP6oaTkxNeXVxwcnbKvfNzctboZsmffu8HfPcHf4utHUpJt6hSUmWktd0ldocBbQECH/3sIwkVun+fR0+eYLSRupm8XyGUSVESkDfbDZ9++ultMC+6cYyy3Du/D0BV/Njee4ZhmM5pKqFTDx484MMPP+Tk9JTz83tFtg4vXr7kyy+f8/TpuzSzOR//+Kd4H3jv3feYz+fT/brzQmup6c2ZHFPpt91dx0lVUBaSVkcrHj16xPNnn7FczKeQthDE57peX2GNxadEihFd6nXEPx4xxvH06bsMQ2CUi/eD5/Lqmpv1Bh8DWY/22Sy+X+2YNTN++fNf8OriK8iZzXpNSsJE55zZti3qwKwe5jCHOcxhDvN7N2/1397zumJWSWjOUKo7FIm6rlgsl2htabsOv14z9ANDL15T5xyqSH3bbpAH3AxKO6yGvmsJ3SBhQwpiiGgljKIu3qm22zKfzcgnJ4QURHYcJbyp73tJ1bWaGHpiGHDO4JxB5UT0wsyhwBqDtpqqWuCcwW46+sEz9NIPaK3FWYdRIoNUWhOV5eJ6w2bbMps1BWyWYvsQiSRU40SGDPR9Tz9IHys5M5/PmVtXJJ7i6gpBeilVCTdyztG269L9qjlaHVHPFyil2G7bEmo0lIqgis2mlZCbEOi8R2lNAjZdx7YT/95Quh2dlXPvg6euaxqrSCmIXDJFYWEyGK05XR2hjWPd9iwWO/AygkLvg0gnQ+DVqwucsywWCyrriqd3KzLKySsJMQmjHqKEAkk9jp62pxQkqslvGIJU7zRNTd04jo+P5D1chdYlDMhK5c4uWOl1byfcSbmFcp5uS2NhDFDapeiO3x89p/vzdQm9u33YsaX739v3pk77tA9A38Dippx2Mt+IgBCYvLK51MpM+6JeFydP+1HkzKksIOVbXbHlPLGXdlyYVK31tA/jfSAsowQSDQWsjtVVqSRijwx8jLJwEmOUzRbvZkqJxWKBNpaMZrPZ8uLFS5rZjK7r8F1P8D3vvvMOn3zyCWenp9RNQ0yBqplz9uCcrAIqSwAbKIYhYGyN73uMk4Tsm5s1q+UMP7RcX6/Zth2PnrzH8ui4BEtZYukunc6Cgqp2fPTRRzx//ny6f6y1pJiJITKr56xWK/zgqauanOHm+kak/pWklRtrWB0fT73Ss9mM4+MjQpDApRfPnxNj5lvf+jbrzZZnz77k6OiI995/HwVlQaJUGaEhR25fYTVJ62/fW7I4pVGc379P27bEeETO0LYdq9WO8Y8hkpMsFKYseQEpivrkwYMHaKXpS5hU23Zsthsur6+IOZM19F6k0VYruk3H/fN7fPzxxwQ/oMn0nVSW1bVIj9uuI6TI6fwQsHSYwxzmMIc5zO/bvNVgdVZb5k1FTomUKprZHOsqrtdbrm7WpKzwRYoKmWq2kAd9rUloYgkm0oCL0mWYc+bi6gbvPcerFcdHRzgnMt+YAigIYSDFINUTSAKqQlE78YSNaGIYPORUkmtl2zFFSaSNQR7IUsK4CoVmXtcMQyCEyGrVoJSm63ucMyxmc6L34sFyDUNIRD9AiqQg/ldjNK6qMAWwV8aiVcL7gavra0CxmDU0lcVoCRSSqM4semRrCFG8fk0zI4ZACAmjJGnUTGDWUNczum4gxEzyAa0N80UldlFtyFpzs93S9QO2qhiGgaw12kmKb8qRqq6F3QyhAB7xsgoA1JMfzvtA3w8slkfiO1SZEAIhBLqux0Q558cnJ8QYAUkkDSEUv2yN1ZLUK4xrLPU/hqqypJTYbrfE0kvZdb302yqpwyBlqspibYV1mpQy220nLH1W5JRQylFUma9JW7++EkNErbADg79rlFISCHSHbd39/Js9qfsM5bhPb0r83e+CzYrbwUgjAAVhB1MmjeJnLRLoXFjbEWNLlc+bWWORrupJPpoKI5ezfE8rg2I6odPZGn2vsUhZ5/OGqrYSrBUjKu8SwPfrXLSWz4YUH8nixT4oT1lY+dOzM7k/Q+TVxQU36zXL5bIka8t7Pn78iOVywfn5GYv5HK0C+DXbV59iFzN8uyGFKBJXn8hZalessSyOlzQ2oQiSXOsDOcODR48EqGpNiH46TzmmwlDKQsEvfyFMrCnfk+sor1kuFzjrCIMnGam76TpRQMznc7KCumm4vLykrmu++93vcnZ+gi0Laa9eXfDZ55/z4P5Dzs/v87Of/Yy2bfnwwz9guVhO12/0e5MpQWq/m4kfJ6UEWnP//n1c1XCz2WKtK4tU0snsy/kw2gFain5SJqeMRvH+ux8QfCTFjCfSdj0vX74kpIgyhhBl0S0rCClxdHTEzc0Ns6YmB/EOL+ZLWYzrOzbbLVnD8ckpf/wnf/a1+36YwxzmMIc5zGHeznmrwWpdWSqnMabCWOnyhIhzmvX6iiFk0JaEQmtL5Wr6ocd7z3w+o7KWpAw5K0LMuFJtM/hQJLiZm+trUvDUVcVi1mDdDrhZ6wgx0Pcix7XWoUudxOgV1MagUQx+wCldukelgsH3HlLGFF+kUrqAWyOVG9pMPi3IuNoJ42gt5njFwhnariPGRF0Z6qpmPmvQJWCGFNHKYNFU1QCj3LE8dFujpIIjSw2PgBBw1lE3jTDIbYv3A13XoY2kK2stMllXQGhKko6qtWU2r2i7juv1GpNK3YTRzGbCWiilCP3AyckJs7oiRNkn5yxGy6JCRqG0kfO/bbm4vOF625GwzGYzjM4CTLQp1TsKW1grW/y2w+Dph4HK1dT1jJzBB88weAYvwVKudgB0XU9KW3SROFO8sRPLmEErg1YG7yOXlzd0XU9OCq3Ey2p0nhJ494OHvi7QiHKkFJnufm3N9Jo9ZnXazhtZqx3ovQtMhYG7HcZ0l3m960Xdf483vW48volt3WNDiw1X+o7R08JNnv5VDkKcjrcY3Lte1v2wpQlYcnv/jdEYoyURWsvnOMbI0dERzjliClOV0rgAMvo39xcRlDIYozg5OUUhNTEXF5dc3VzzwQffoN9s6PuWGAOz2YwH9+/z8OED8QNrh2kWZLukbQMxOmw1B1fTzCuUMvK7QRtsbWWRSyXa7hlXV9esjk44OTktLH54jU2X8CjNuu351a9+JcevJFSqcq54wxUPHz4qiz0GrS39EGjbjuVyRd93oODFi+fcbDb8+Z//OTFGTk5PSyha5vPPP+P66ppvf+/b5AQff/wJTdPw+MkjtFEj/0+KsXiydz7l3YKDmGzTtHCh9u4TOaYHDx5wfHzM9fUVVdWwvtnI56mw9laLIkSVzIEYEs44Vsen3L9/b+qcTlnCzi6vrshafLMpSeK31tKrXNeOHBNd22KULPpZ67i5WTMEj9KG+w8f8P2//j7vf+NDDnOYwxzmMIc5zO/XvNVgNcYgCZHsWNGMwTgtvZw60SyWDCEzDIHeR7ohiHy1kpTfqq6JMTJ4T9t1pJSZzeYlgTOz3bRE3+OdSIKP6iWr1QpnDcYYur4npszcWG7WGzIJ6yy1q7DGEKPIb9u2I9U1qtITy2qdw1VSGxNiQitJEA5xoN1uCXHnM+vaFussThsMAxUZN3MczZoR3uFTJIYBU0kthY+hgKpEU3xwqtSX5BzRiMQuZ5GwpiTeuDEsp67rwmJt6LyH4EGBMZKcKVJSU6pABtBbZos5ytqRL6SZNTRNTfACGnJKzOdzzs7O6Lcb2n6gdopIIKUBa6xIo9G0Q+Dlqys2bY8yjm4I+Lihthrvh8IU1RhnxXOmooRsaV1SnS1WG0Dhg6cr3YugcK6irhq0Fu9vjOXr1pYFgiJnVZCD1JrkrBlCTzOrWa1OUGWhwxknXtg9RHaX9bwL9vaTaFWRtN6V444+ylvMbH4To/r1k4tf8y4bux9o9Lt8qW/+3MVpG7cDmdR0L0r6a0Kzq8IZJcS6RBprffs4Xgc3u+MeO2tH9+v4OgGhiqEPGK0JZd/HxREQ76ksYlD2i6mfdGJelaau6wLselLOXF5e4rsBlTPWarbtlphEOvvOe+9xcnJCIjOfH/Hg6TepmhpU+WwbB5gizpcantFDqVRku/mKi8sbNtuOP/yjb8jvHGPJOYqtIGcokmetNM5VfPL553z55Zfl2HfXSWkFSXF0dCxBUtZRVzVdP0znaRgGrm6uubi85N6D+zx8+JD5Ys7pySkouLy85LPPPuP4+IT7Dx/z/Plzrq+uefT0CefnZ7dDukB+35InOfK+F3p/3lSDNJstuHf/PhevLnDWoZSm3bZYG5wRNQAAIABJREFUZ0ptlNxLCkUKsVTyZB4/flJqfDzWGlTOPH/xnJiFwU/sqpfqusJZjVaUsCzHopkDcL1eA1DVNR9885v83Q//Myenp0zlwIc5zGEOc5jDHOb3Zt5qsBpyJqSMypGYkvgSlcK6inv37/Hl81d0XU/MCm0s2jrxiVqDNZL62tSO4OH65kYeJLVBG4OzDp0yM2tQeY5SeUqNVVoTUmTTdmzaltl8QddtGYJ440xVk1Bs2gHve/GSJvBDRBGF7YmJZlZjUxavVUpgNCGId9Mp8dZWVSVJpEOPylA7K9LLDNoKm5lLENAwBOmHTIneD/T9wM31NUpr7p/fw2rFMIjXtHIaYw0pK1JMJKR2IvgwsTDj8WqtqeqKUWZqjDxOaqVI5aG5qSuquiLntJcSG1HkqXcx+oA1GmsU15cXtJsNTW1JSdjmGCNN0+CMwseWrh8YQkRb8RBuewGz3bZnGHoymWY2Y75YQBYGOoZY3t+W3tc9dq88MFtrMUYRk9Tr5JSorUOjsMZIFVJMGGvpe2HgtJcApZQ9DQ0xJHRhyrU2qJFJ2nve3Qdfb2QwYfJ7vjGBaPSGssempjcDzDeB4/HrYw2LMJMFGI4y38KEjaxlSvGWt/Xue+zYMybGEpiCq6bjZhewY0twzeRJzHI/5Hj7/EzJwnvvt+/9lWPRaMPk0a7rSn4OAW3j6+u6FvlyhuAji/kSrTQqlYqqXKTOKTP6ZVeLJZV1dN2AD4HLV1copdhuttw7PykdnhnrNO+//x7NTHo5m2bBbHHOjhLNoBQpj57bwiVnZCFIGa6vtlxcXGNsxZMn76CMFU+nHaXJO4+yMRpnLb/6j19xc3MzBUdprQk+oZWlmTXFv5owTs7j5eWlKCNCEBlxWSj68MMPOTpacXZ2Sj1ryDnz8ccf8+XzL/mzP/4L5rM5P/qHH9H3Pe+88w7z+bwcVSZmkQBrBVmVqiHGeqIx8Xl3D+7fM3lcnDGahw8f8a8/+VckfEpqd5yx0yKNLA4xqTaaqubpkyfkUlUEcH1zzbMvvigLPQJwjdbYSvIClIKu9DlbY+n8AFkTErjK8Z3vfY//+n/+H1RVRdt3uKp+w4fwMIc5zGEOc5jDvM3zVoPVlDXKVGhgiMJUJTIxSKBPojCWxpY0yYQtwMtqhc6JyijSELAkCbrJGQPoJFUxYyCQ1iMLmafAlovLS9Zty3wQBlNpgzGWnBKDDwxdR9/31M4UpqUE2yiDMrpI2UCVgBnvA+u1sI0LZdBZahustZg9/6NWClV8soOXvtSUIafAMLYvaEXlHLP5jKqqpGonRYYh0KXAct5AAaLKaEkqJpKzJ/hATG05x+JJc66S4JSyeQlkUjgt59ZaS9aKTdcSBi+JnsHjB1/qIRzOGlQJrApJgq0GH4lJ2CDpusy0oS8saMRYhzaGrg8MneyTSpmsFdZWKG3ovZ98ezFKzVBVgbWSiCopxCX8SJXInpSIWQCnVJxo4hCkbzYnsgLrDF0nPNLIdAmo0sQQyk2YyKoASL1jknZpqW8Gq1/HXN5iQNWb/Z77f74tn3297uXu+37te71h+7cSZ/de9yZ/Ys4776Lgjd334h1mdwIxgMq3e1zv7tPd/RemUd7PGAkCkwApAUkxxSkxel+eWtd18bYnYvFI50RZzNCE4FnMF8JEe+lUXt/cYLSma1uOj99nfXNDLJ+Ao2MJ/lJKcXR8DNmMGVDlhAifOv5ZDiajlSalQLtteXVxyenZOSen0hmKUkh3aPG3x4gu57LrOn7161/v4HA5P8YYYog8ePCgeIsRprLtePHlc3wMDH4o1T0Vrqo4Pj7GGM2jhw8hS8fwV6++wjnHoyePefHiBV988TmnZ6c8eHBfFgEQ2W+MnpzjPvqePl8UtUDm9sLGKA1WZWVGKcWjh48n5YazrnjR9+qLyCg0Rhu6tuPdJ09x1tG27SQBfvHl8929m0FrMFp+n8ZhIClYLuag9FRTllLk8ZPH/OVf/W985zvfEZlximJB+B1qgsMc5jCHOcxhDvN2zlsNVp+/uKDdtDR1RYxMQS/ee7yPaGVRKqKUPAhpFck6Uxl54FU54EjYxjGvjvAh4WMBSMoSQqLvepHgWmF+3CByQeMcaENMmW3bo62BwhIMwwBJqhyayrGYzwUABl+ApsKHQPAB49zO56c18/kCbSuMNYQYSw2P4/ToSCpj/AC2Kg9Wo2dMWAStTQn9ydSukQ5SYyYp6TB4Qgll6XzAF/9d5Zx0z+qA9QGjhTEZQpoYVtQOgKUUMUZTVXOsFaAaYmTTtvRdR0pMPbQCEDLKSABUzglNRhcAPpQOTGtAGzOxKRQG1CpD1gYf5KG37wcWswqdjVTHFJTSNDPpTcyJnITNVVmRlLB4Ku+krwoBDSMDN5/PRC6oBUAbrVBBGPSqshNjaowmplInNHSFJdZkDSmJbPEuK/km8LX7fp6ej98IKAslNwK2lNLE0u1vU37+daB5a1N7wPNNAPp3yX5/1+smb6lWt0Dnnk71NdA7/Tnv/fzXgOpb/t9y7KlsU2pr3OQVjjESgseVe3Jido0pkvxYXiMLDQIMSxpxyiwXC1kMynB1ecnQDxht8N5PcuOuFyl5VTfiHbcVq+Pjwi7uZMoFnt06FfJ5jfh+Q9duAM17772PcxU5yc/HFG6dV600Rmu++PxzfvvbT6RKa/88Zvnc37t3b5LMykLWyDayO0YUi8WC5XLJ0dERx2enkOHq6oqXL19wfn7G2ekJP/mfPyWmyAff+IDjkxMJhEsJNS7KlOMZz+8IVmURT458v2cYSueu2oV7nZ/fY7U6YrNZM5/NSt/t/ucnQ0mWDt5zfHzMMHSEEHDOsV7fcHHxCq1K0vdk75bPt1KKppKFTB+jKE9C4Nt/9Kf88L/+77zzzrslFVrOMUT84F+/+Q9zmMMc5jCHOcxbPW81WO2HyLYd8CFT1w6dNVdXV2SlRfqrLSqJP1EeWIEIs9pRW0OMA9bCvFkAItvzMdJ2HRnNrJkRfZSOVGNJKdK2vchom4b5YoGPkYgmhPGh2NL7lr7d0lQ1904fsFotubm6YvCjfy8TyfhhwBpDLhUaVT3j/v37xJzphsDNek0MQRgWY7CAH3q6HOh7j1Gapqll32MixiwsSvBoo1gu5sznDf3Qc3N9Q9sWZlIr1tsOUiQrA+WhnVSYQ61RWaFUwjphhEPZDxCw2jQNoBmGjpQs2hjati2dtQ1V5aic2wXbhEhO8jCttSJ6SVeV9xMpaU4ZZXcPtNY6jKtoe08/tOJ1VJq2HTg5PaaZzeiHdgIgMUrSsta6BMJEctxJTCsMUfTL5aE4MQwdzmicEdl1LimnlXNkDc2sRgH90IOS5NV+6Li8/ApIWKcnIC3n5nby75tA6j4b9HWO07us6e8aAXSiJc5vAIf777sfjPQmlnb/Z/b3/U0pwncBcMFGt49JSXDXdLx7IDmXJNkRrO730d6S/u6zsSkVf3oSRUCpYBlZ9ZwzVV2J3LjcF6NUebw/RKKcy/XMU23TarkkRwF1L1++nLyuCkVbPOOXl19xerQUj2lOLGYzjLNkFcuWc/F0judij2rFA4Gbmxe8ePkFs1nDg4cP5dtaY4CYd8eR8y6069e//jVXV9eM4Vfj58YPgdOTU/k8xox1FQrFrGk4Ozvl+auX07kbhoH7c+lhffToEc5VeD/wyScfMwwDH3zwAe12y6effsZivuDdd9+V30/liEKUaim5z6R+S/7Z3S85pluyeyg9u7lA+fK6+XwufbWvLlBpB2wn73IeWdbM6ekpq9WSEEJJ745cX1+z3qzJOYoMvyQpawVWa6koM5Kmvul65osl/+Vv/44f/O3fMV8sycjiWCQSxgqjr1EgHOYwhznMYQ5zmLd33mqwOpsvefH8S67XG2azBlTm+nqNqRrQBqUMxjpShHZoqVSmtprZrGY5mxF9J46yHHFVRdsPxJL0m0HkoETIicpZXGXJSF+j0hpFYrWYs217qlp8ZIva0RhFq8XHebyaoU0mpoG23ZBiwlW1+GdjJCdFTuUhN0WUiWgUMQxcXV0JS6ItIYu8LyvNMAS2bY+zwu4KCBEvWPSKkMGHTF9CjdY3GzIiGTTGoAtrm1MQKXEJEBJgLGFVvR/YdD2L5QJbGFtTUkNHIOC9MBG6SOlyTszmDbNmNgWyaCX+1soZjBUfYQgRU1cMQ88wSL+j94lea6qqYvADg/do7UH3UmcRE/PZrPTOWrneWpGygN/NZjOlo5oxZKlAhZwzpDg9xIoUMxGTl+P2nuil8xUAlaf+01H67b0vICeyWMyp6xmD92y7FmMqSWR9Q2/p1zGcwvDuMUm5cHLlX8qMkvPStanGPlN1y8u6/4B/1/g6sVpqP0xJl6Te0pkJBUyqiTmD0Wu528dpH+7Iee+yrjoXiqswvXrU5+4ObvceWlJ883TsUnujlS6gjImZLdri8todGwulXojdQkFd1cK4JZH0usrtWD9FSQWW+3U8vqZpWMwXKGAIiYuLa2KUe56cWV/dcHx6xOXlFe2jnkVOqASmcrKPReKap2uTBUgphcKgKMAzR15dPOfq6pLHjx4zXyyBTMqhgMFQgGAmZ0kvjiHw209+W9jX8UYCqU3KHB0dkzMYW2GdqCCq2nF0tOLFq5cELzVPSsGTJ484Ozvl3v17oGCz2fDJx59ycnzKe+9+wK/+42O+urzgvXff4/TsTPYtSYid94NIgKFIgvN0vGm6ThptpHImZUkOHuXCqDHMSrFarXj48CE/+/nPCX6gdhUoJVFUykAW2XCMiXeePqVpGjbrNVVV0Q2DVE2lSF1LXZgq2x0VE1kptv2AyoZ75w/5ux/+kL/8/l+hioc9p0Q/DBLMFBM57u6nwxzmMIc5zGEO8/szbzVYVSRC9CQSQ4oSfKQ16+0a5xqcq0hJWEeVI1klNJaUEz4GYoo0zpCiIgRJyQUlnX3aYpRiOZ9TV5b5vCTH9p14Ew0YpWisY1lXKCX+uThEVO04P1pgnUFpYR+aec2mXdP3nnYIzGYrqqpGmarIlCO1NTirCSkThx5jDaaaMfjEx58+o7KGo+UCZy2rpUgWb9Y3hJiYzWYsV0uqWpOVYTsEhq+uUMV76azFVBUpCmgzSqOV1MWkEMkalJKU3857rjcdIQZM7zk5XgCD+OiCAFRhf0Qau9m0+CCgw2mNJtE4CVsSNtWOsJGsQFlNiOItVAgjut1uJcipCcJ6aIvPmb5tyQlmTcN8PsdoBSRiHCApdM6lukMSUxfLJaEf6LserbSAW612D8wZtBJJo3MVKYufcZRKphTRVkNJUwZF33u8D+gMs1mDVVZ8x0pLOFUOEvdzh4mE2ymp49dfC1til1I7To5JMN8e+ypAVc4j3K6pyWUxY5SGy9fyxFJNP5+YmGU9gsc0gt4dmwWlP1iVVNYSbHNXSrw7BmlFzcW3qPSOU9znFsf9mvaxMN3jCzPI528EwJTe1hGoqp1keLFYyUJGyuSYCV6k342rSCFOXSlN04CGHDNDkQBHCjhRohqYzWYCjkJivWlZb9pSd1VhjCIMUl/VdVu6Xj4bzUzYeLQlZ0mLRoEmkxn7lhWgJWA7W7xv+ezTz4gx8PDhY1ELIOAv5UgiFVA7XjvF9dUNn3/6OWVNo9wTkvBrjWVeUm5RJTRLJbRVrI6WxJhK9zPM5zPO752xPFpQNTUpJT7/4hkXl1d85zvfJQbFr3/9CWjLh3/4h4UxFuly33ekHCbuOMZMyiJ/lyNOoGVxIuU03UepsODjvss117im5vHTx7jKAgIuE3J/W2NJIZJSpmlmPH70ED8M1JX8rhyGIOFUpsL78ns7K2LM2MqSdWaICaMdT5++z3/54Q/51h99WwLkgp+CvBQQvNSHkfO02HSYwxzmMIc5zGF+f+atBqvGaVarBX30hBBJZGbzGSElvB8wxhY2EEiZyjnmC6m0GIYeUiAqJR5MK4mU/TAwxIxxmmwSVV0xX85QhVlQSjxUaSjJta4k8qZE8FJzoZSefF0xpsK6GVZHp4R4yXrTcbNZs8jyFF7XlqZpsBq87yXlOPjCiCmRy2pN3TTUdSMhIkoYiPVmgy/VK8cnx8SY2G5b0ggGlWKu5hiDdEvGgHJuqnfQWrPZbNBGgk66vqfvBwG3WuGsxmhhkNq2Zei7kgCqmM3nxJRYr9doLZLkpqlwlZvyVwRASfoxxdmX0SglXaazpibl0pdpDcZZvI+knCUMq6oAsFrShUWJnKYE0b4fCEHYaOssOoPRBldY4Jwi2lZYY4lRKkGsscUnnKTqRmlCDHjvSSlJBQkCjoIXBlkr6XB1riJn8CGQyRhrGUJm/zn3rlQWbntXJ6lwCaO56wcdQaGEcan9De8Yx+Ihvg1Y855V9HXf7Dj7rOT+z+6/dmJ/78iJ9/f1Fks8sae3v3d3+3d/7m6Q0psCovQdEKEAWyTmMe7CiKY+VWMkuTdLkNYoQx4ZwDddj8ViUT6rgYvLyyI5rairmhD6qdvTOfmMjNLyk1NhH7VKt/2qSpOyEUAUKIssiucvvmS9XrNaHXF8fIwq97UqC0jJi8rBxwKqYuLl8xe8fP5iOn6tJQzMKFmEWq1WE+McUyCE/8XemzVJkqXnec9ZfIsl98pauruqZwFIjAAIgIABIIk0A00mijBRxLWkX0ZdSqDMaIR0IawDGWS6IRZyNAPM9IBAo7unt+qqriUrMyI83P1suviOR0RlZQ1wWW0Wn9lMVkZGeHq4e2T7e95NQOtkOqEsK4aL54Tkeef+25RVxdnZGVVdMwwDjx49oqpqHtz/Gs+fv+CLLx5x+/Yd7t69uwGeozc1BJ+B4cvXuKyzqC0YjRJ0NZ4tlZN9N0nh1hBj5Pz2bebzOZcvFvn6kc+mcwNWy+f3nXfeoaoa1m0rQXFlyWKxYNmuCHlhRudfXBal/A3zPcoafuZbP8s/+43/hntvvZX/9iViSBTWbMLVlJJO3eus/372s5/97Gc/+/lqzBsNVgfn6IYepRV1XWFyZ19VlHTrnqg0fT8wuIG6KJk0FZOqJgZHigGjEkOSuo71spVezqomOr/hp0KMRBeJwaOzhNB5t5GHDs6h2GGCQhDZrAuEPgpLGlNe9a9opoeooqFbd/iUWLQtLhR475jPJoSQWPc9677H+0SKHUTER6oUpIjWkobpnYMkXtm6qQneCfOZwcB4k7lYLOg6Q11XNE0j3rggfbPW2swqKZbLJX3fZ/aiA8CoCdE7QGG1YjadopSkk47hJ8YYZrMpZVlgjJbUYTdgjaa0hhAERKUo4CGqMSlXemVjjJimFnm10bSxxwUvgUYh4n1g8AI0i6LI8kC3YSOVEubPu4RGURYltq5xQydhSyFIsmgG79ZaXPC4IZB8Yhhc7lmVbfX9AEZuYgVUa1RZ5oAoCfZZrVYb4KutwWdmedzGdf/nWAE0ej8FsKYM5l9lKncB5UugL8t1087zdp8zKmZ35yZwOs5NwUkji7m7/xvwqV99b6++/mZwfB2o7h6r6/7UbTCP3jBhIfitXBooy5LVcknwToS2UST61koNjLEWW5Yb8Bpi3OzrbkIxwGw2y8As0HUdZVlSV7UsbGSgNvQDWisuLy/x2b9tjUZFUXegEkoZQtLEKP3FfR9YLju+fPQxMa64WnzJkCJ3jo9pZlO2gu7R36o2iya+H2jqKR988AHL5VKiblMiKmEfScIa23x8rDUMbmB5teLs5ITJdEpV18IohsDZ7XOm8zlHxycYY3ny9BmPHz/h/NYdmsmU9977a5xzvP3OWzRNTYxBOOL82duC1DFcSc5XiDddL9trQY6zXOs6g3JrLKvlirZtNywnIOxyDEQUZVly7969/DdJM5lMiCnx8NEX9EMvMvIkf1em0ylo8cdOZjP+yT/9p/zqr/waVS31QsYYBid/x/p+yDYBNuy6QqGyd34/+9nPfvazn/18deaNBqvKSuGKTYpZ3WALYfTOjo+5vFry5bNnFKWhKCck73DDwFBoDInSGoxSBO/ovWPdtiQ0ZTNBWQto2q5jtVxS1yXz2TTLyAb0SG1CDjbyGwaoKmy+GY7CigZF2/Ykpamt+CDRFmULUJrgAot2TYiVBAoZg7YFVVmhTMInkRD6EFi3LaHvKCvxaXrvURqK0nB4OGe9XkGCg9mUrutou57JRBJ7UwrMZ3PquqRdLnMthqGpK4pCQmqq8kh6XgfPuhMJbVWVrFvx2lpjMrtRMQpOG90wz0xt17UUhaEqC5Qq0EpJp601G9AZU5DqGRDvGAFtFEZpnOsZ1o4x8MkNLt8Qy2u1zlLbuPUeKiUS0BATIQZ6HzAkrDZolEh6lSakCEnqKaIPm7CoEdQoKx2PzjlC3xOJOVxHPHiFMYj8WPajqWu01jkAR78EvuDlm/fd719iTwHSliHdDREan/uK71WN/tJXf8eNnxHURt54E2C9Dg7HsCapg3k1Bfh1v/N1DOn4fm5iTK9v73pw000hU2PKr9aa9arl3QcPuLh4zsNPPwPISd2WEIVpRSuCz1Lsnf3ZHmNFUVRMxyRgbej7jqqsNuyeMRJktLhaMj2Y8Pz5BRfPnzObHeL7gbJuIEnw2rrr6QbFoyctH338jA8++JinTx7Stl/y7V/5GQ4PClCK0/Nb4qUVDbZ4wL2A1BiC9Lmaku999/v82b//M8gsu9KG6OXfMQTu3LmTF18kDOrFixc8ffqUn/7mNynLkuPjY9KHMJlMJAH44IjDw2NS0nzy6WcMzvONn/om667j408+4+z8nHv37ubO2jH9ezxe2/7XGHc7dzWgNwswgmrVS2z7GIKktUJrw9A7/vpH/4l21VHlLmmTE5uNMQx9z+npKVVVsbi84vjoCGMMDz/7nKvLhTw3L0RNqlrY2LJgPj/g2//lr/Ebv/HPMKak6xwJYapjIis3HAKeDSipPUrETUDYfvazn/3sZz/7+erMGw1WC2ulsH5MqAxegMkw0LUr/NATlQKtKYxCESAGuRGLkSF4YggcHR8xm815cbVk0a5AFUSliUGCl4qiwIcgIlatMcpuVuW11ngfNvUWRWHouo7Vak0ICrSl7wM+JhateMdcFBCmjZJKjQSrtbA5s9kEnRJHx8cMQ8CFyLobiK1DpSjJmzEJmzcMKOQGfT6dUpclq+Uq+0cjJrOIwOY9eO9fYkVQY9JrJISIVomyNBR2hi2km9YbLUyzgpQl1tYakTBKRBVXiwVXC/HINk3NZNJQFSW9C8ToUUo6ZVM+H2OgDlkqbKylH0TOPZvNSEpxdbWQE60MPjiskqobAxSlSJiFcRXw4oYeFyNVYamaEpvlhj77a9u23dwMo8ZKHtl/732uPHKglARmkQFUTEQUWgkL3/ddTmGW4xEIG8b0phmB2QiQR7ZQKQnIEZW6yBJzNM+OlDy7BOWAyTWYA4iuS3VHGfArwJCXv7/Ogr7KBL+8jevg83Xy3utM8N8Hpq8nJ+/+rl3p75iOG1MkukBZllhrJZW7LF8COTFGvJdAob4XAFPVtVRLIR2/UrUj5yv4wHw+p6rr/HcBvJN6lMJIh2tKCh8jy3bFwfEchWa5bOm6NY8fP6JZLHj+4ikvlpc8e3HF5TLw+RcLnj7vcEMkhjWn5yec375Fu3pOVZWcnpwQ0zZBeCObzTL2vu/4j9/9D3znD77DarXCZLWGLU3ez4S1luPjY8iSZJAamq5f49xAw5TDowNsUXB+5w5V3TA/OKCsKq4WCz777CEnJ2ccH53w/t/9HZeXl/z8uz/L8fGReLe1JACnNPqut2B/lF3Lec8sJdeux40XOl+DUaGUQWF4/OVDPvzwQ1kQ0goVcxhTTCgt7+f05IS+E8l1UZR0/cCPP/6EwQ0kJaBXwpSgqmru3rvLg699jX/0j34GlKbrB5Qy9EO/SVQPMWCsJZFIedGLMVhsD1b3s5/97Gc/+/nKzRsNVjGauqrQJKxSuXolUWhFaQWghuxJKq2hKQvqUthLlSJY6Uyt6pqIJijDEOFqtcJl9mK8GR57FhNyE+36Hh8ChRYGUilFVVf44FHGoIxm3UooiTEl5GAQWxWAQZuEMlBNGlQShne17jDWYLUSD6eJKGVYhYG+bzFaANQwDMQUSDFKBcThAcTA4XxKYTTPn19Ij6wClRJlXTMMHVeXVzCfSbCTxObQdz191wGSeKyQZGG0ghgpjKY5PsZ7z7pdA3pzMxmi+OREgmipqoZuWLPuHElpus4JKDSalKQeRGmFwQiTkcTLF2ISr6ktKFAoI4m9yoicM8SA856qakR2mQLWGElq1Tontso5X6/XIoNGPLjOOVLMgCNXlIB4iZUWTzJIIFBRFPTDIK/J8sLNooTSFFYApNKKdt3mm3XxI19nR18nCYZrnlZSZrG2fkqdZZ3sgNERuI7doEq/ut3rADP/gpe+392f19XswHb/ditrbgKjN8mFx+fo1zC6PwnE3nTsbpIVF/kz9xd/8Res1y2+HyiKgqHvReqegUlTTISp8357bpBwIK0NMTrxnlelhGVFCREaw6ViFCtAAtbrjqGX9OrVcknbtjx99oTu4UMulwtWfYeLiqcXKxbLJb/wi7+EMQWXL75Eqx4/DAzrjpPbZ5SlfJa1GpOJ5VoTwAZ/9qd/zh/+3u/TrwdRCWiFKkqSrBgRvOfw+IS6riWsLEufl4uF9MF2a6bhgKZpODw85N69e5RlxfHxCSklPvnkMy6eX/DL/8W3UUrxwQcfMJlMMlMrioWYAsPQbxYAdiXsu19T9Bs5e77aMsG6FThLZlTMNVWaD/72fR4/fpwXmQbKwkrtTb5mZrMZZ2e38M7RNA0hRq4WS5bLpfjPSfkcR2bzOXfv3uXBgwfMDw64c/uO9NMajXPZ15qfyyZpOmRJedos/Gwqlvazn/3sZz/72c9XZt5osPr0yTOeffklVsHh/IDToyO883TrFqPYyIEtAAAgAElEQVSgtBpVFChjmJQVE2shxk2ipTEGWxR4F/HJY20h3X+Xl4CA0MViQUwN8+lU/JBlicuAZuxttEZ8cgIwy3xTZOi6QNdLKJC2BqVHRlNkkYcHB9LfGsSPF5WSIJeqEPmhUqTkMSlmMKeIUWpUQlQcHxxyeDCXACQDbujou5bF4oqDw2OOTyasuz6zTD3WWlarFqYNtiwojaEoNIurK8rCUBQWUqTr1hSFJBzbosg3pQpj8r9jAishQ8MwSN+lMTSTCclohn5g2XY5SVXTDX4jRa7KEp9CDsAy2MwIDesOZQyrdc968JzeOmcyP+Dpk6cidzYFQ0gUVUGppcPVWkupRUK5Xq9JwHw+hyShOtZqyqLGFpbeeULcVpzEFDA2A1WlNonAm8TbzNaqXA8iycYKXQibMwwDwyDvwW9uyuPGRyvbudlDqvLNckqjpPfVYCGtNdflwOMIQHidLPdlWW8ivVRds7vt8Tnjv3f9heO2dwFtSltgfR38XpdB7+7v9WTk63NTiNP17lUg19FEiInDg0PxpjqHToleaZxz1E1DVVWUZclyudzWEbELzsdzkzDWcHx8LNtKjqtVS4qJsizxPuBzQFMKUrm0WrdUVcXV1ZXI1IPE/LRDoB00CcvDR0/5xk/9NGUT+Pij9zk9PuL+W+/g10tUSDx4+20KK9fR2KOqAOcCIST+5I//hD/6wz9kWHcSLEZC5URtFwI2Vy8dHx8DSipivOfy+XMSMPQ9q7bl3BjmBzPe/doDptMp8/mc+fyAvnd8/PHHNM2EO3fv8Nnnn7NarTi/dZu337qH0Qof/QaobvuVt2FLLy1g3LCoMP58t+O2yHYB5xzvvffeRlLvvUcrhVGSUB1j5M6dO5RlybpdU1UNKcHjx48l7MzI36UQImcnJzR1nWXUnm9846cwpmBwnhATMSpCGq9xQKXN4kAKPgfZ/cOUAPvZz372s5/97OfNmzcarAbn8U6YzL7ruby8ZOgleGMynXJ6dIxLgYRmUpXgAygJ45EbFpH8hURO4E10g9velGeprNFmc1MlICNSlWWuWJHKGqN1Bqly01aWFae3buGGyNWyy1JXTUzgfMQWGlJkubyk63K3a4wkPxBDTVMWqBSIwdNUBXE6wceEnTTUdZmDXRIhOMqiotCavu8hBpqqRCsIbsjAsUUh4UbWWFbtisODA/HBdm0OammyHzBQVnVmoKROYhgkMGmUUAJ4El3XE1JkUk1QWtN7T0oi0a0yGx1CIISB4DxGa/pBbhBH2XTKVSLKGrQ2rNdrdFHy5OkzhsHhfBDmVQs7FpcrDie1BDOlhBscq9WKrusgRJq6xhoBm8731GVFUdocmNWQkiQtJ5dZrJTBm96CN/GqJlRmV3MjiYCL4IkaTk9P+ezh5yyXaxJSm+NzrY9MYvSW3qQv3A1Euimc6CYAKFtNGUve/PNRMrwrz91G+LB5bPffuwBTwOe4//xEELrL0u7W2txUz/MKwLn2fnd//ziiZniZzRu3X9c1SinmsxlNVeLzwsno4YwpbT6XG744s9eJLcC2tmAynQjLbywpRawtSAmsFfm7y8E8Li/6TCYNL1684NnTJ1RVjSobFquBdkh88egx89kR99+5x7PnD5lOE66/IPljXLdGJcWtkzOMNgI+o9+EPbWrlu9854/5v//ojxm6XvqKjd2k56I0SmliTBhjOTg4kM+LMXRDx/Pnz5lOJlhjWC6XpBSYzSbcvn0OSnH33l3KsuSjjz/my8eP+ZlvfYvJZMJ3v/tdnHN84xvfoGkaIsPmMyq+VbVZ4NiC1Z1rQks90q7HelxoGP89yt6LouCv/vIv+eCDD1FK1A7ia7dE73HeM51MeevePUxWrVRlydViSbtquX33Duuhx4VAWVa07YonT57wi7/4i9y5c4ez27fp+h42SdH5XMfsSVVjkFeUPtio8Lmndy8D3s9+9rOf/eznqzdvNFgtipK6ajiYzwhuoOt7yqKAlHB9v6ldAIVX0gO5kTVGSErYjKTEDTiGslRVTYjixaqqiqqQsCC0xhqN1hZdVZsb3kkGdy8uL6USwRi0shS2EilxN5BQ2KKQ7JHoSD4QBpc7LsWzGLwnDB4VIzrVHDQVhYaQpXMBjalqYXA1rBcL+jBQFxpjK6xRNFVFmsGQSaS6aegGR1VYimL09mna9ZrVasXQd5SF5eBwjilKQt9hizL7XjXrfqDruhyQYnDZzwcabQ2llYRhHwNljBhb4GKgHwbBO9oIK5QXCVLYhhsV1pKUwue05qppKMqCdTcw9B3OiWdXDpB4aiksq3ULKlGmcrOI4JzDKM0I1hQSchW8IyoJZrJ2TAM2memVoBifO3dHsGSNQSXZR2WspAEL3CdGD4i/TkCGJDW/DFRlrrOZ8DK7qJTmZaD5+iCjzWsYAe42Bfhlj+hP9pbeFLI0zsvbullWfNM2r29j/NkueLkOvq/7Wm9il0fAs8vujo9Zazc+RB1E/j16gUdftrFWGLjMNifUSGtvtldW0s2bosiC+6wCiGJ/x+jEkAZCZmFDrmCy1rJcLvFuwNgGZQuWz6/o1wO/9Iu/yONHD3lx+QVVCXfv3EUnT7decfv2OUZr8UHrSN93aKVZXq34g9/7A77znT+WLuRcuZWUIibpf43ZGx2HwMmh+Oy999iyoOs6FosF57duMZlMWK2WXC2uOD074/BghvOB05NjfPA8fvSIuq555537PHv2jCdPnnJ+fs47998iJE+MHueGTVXX9hySg5W27L0wljr7Pkdv9fZaGYOTxnPd9z3f//736fuBqiqlWkobvHcb6+jp2Snz2Zy2XVNVFX5wrJZLyrIiaXApEdLAs4vntKuWrz14wK1b5xyfnFFXDS7J35+YsqM+hk2ycUwhB+LlntgUkdw2qbnaz372s5/97Gc/X615o8Hq+dktGHrxJpHoWglc0UYTnHisSmtJMRGdo6ybXK2SA0JSpHeD3GzlkCFrpbt1ve4hCqvqvSd4T1laVGkZWS2VQCOpnDEXy5OQ+pmhRSuHNgUKSRkNYcD5gNEJbQxWa3Rh6fsu39hvWYijgzmzSphON4hEOGBJpiQlv2EHYvAMXYcFCms2TFJ/tcYFjy4nlEXJdNpQlgLQtNE8v7hg1a6p65KQEk+fX4gkTmvqqsQ5STYd3EAMgclkirUGnKcfhuy11JRGE6KAA2sNOinSkHBKoVLuvcwJnMPQYbTBKg1W2O6yKHCup6qbfGMuwKQfpILHFgalDCkmvArMpxPqUo7Zulvmig05bkVZbBKCjTbCqmxLXnICsoBGa7V4/ZQmBGG3jNaSnJsSSQkoMRpsBtvBOxKSaNp3nSw8jN2RilfqUK4zmOPXrez2VWD6OjC5fc5WpnvTbP2t29dsg3BelWveDJZfrZf5+3ynf59Hd3c7PymA6frxe5mRlcettRufeFkUBDeQUqKu682x3XiIjUFlLztsAf64zbpuqCvpXg4h0PU9GvFsj93Acu4jB0cHFFXJat2ijRYpsHMUE/GeXjx/zj/+6Z+mKUsefX6FSolh1UOMDK4lxIH7795H2wKlDd73BOe4Wq35P37n/+Qv/vw/ELwnRXDR00ymeXEiYQuLD9t03tOzU2DLYg/DkP3dkelkQrtccnn5guPTE7RWnJ4cMZ3UXFw848vHX3D79m2Oj4747nf/P/ww8O6DB0wnM1CeqORYX+/wfZ2MWwKYNt9tFmES2x7g8e/aw4cP+eCDD4TBzTJgTcLHiC0KkoI7d+5s6nKm0yld23N1tUAbjTIa36549vwZQ5YEn56ecnR0xJ27d2T/UvZ2IyqKMa1dFgVlvcIonX3/4l2NIbDHqvvZz372s5/9fPXmjQarKcAweFTu7TS2IMSIMVaCkTKDgUq4TRKu2qSLjr2BEiaUE0cjFMbQKbBFkUN6ekLwaA3eCXunURTWkJIkjBZFwenJCRdXS9Z9z9A7wDOZaKrKIhZDAcRaSQptoaUqR1vD1WIBCWbNhNmkoa4KkfIGTwiObtVyuR7Q1RStQeOwQFkWhOBxrkfrihhSTgE2tF1PCm2WNNoNUHlxeUnbtnKjXldAwocktS8qMLiei4srVusBrYWRtW4gpUKSRC9bCZcqC8pUkJzclJZFwXrdM3QrNFkWHSPNpMYAViuaUqpyYvDikzWGulASEBMjlBaj5OZRY9FG3ofVhqqpmE1qrDV03Tr3L6rMjgZqrVDWYJQwKilEFAatEp0XtqgsxYurjUHFIEFLSm0CezYePSWBN0klFJJGaq1FRQUm+x+VyjkyUl+zC8ZeH160K5N9faXM7uMvAQTZKUZA+crPr8mAQUivm3ymrwegI0v2aoDT65jf6+9jd/uvY3lvlDlfe911gKSUommaDeOqd4KmqqrKslK79eRuXjr6g8fsH5EEz6YzjLFoJTL67ULCuD8iJQWYTmfM5g0XL54xn01ZXC24urpkcnhKaTTnp4e8dfeMH/3ge6TkmUxKzs/POZof8uzZE45ODji/e07AgJfO2Ha14t/89v/Od7/7PaIfZc7CRHrvpbc5y5qjnBaKQrz1If9sGAYuLy83/axlWbKKka5do4HSWm7fvg3a8MnHH7O4uuKXfvmX6dYdH37wIfP5nJ/65jdRCkL0G9Z+95yM50Frvfn7ucu4bmXvu4spCm00McSsxoC/+Zu/4eLiAispR5tkdmss3jumkynz+Yy+7/MCX2K1XHF1dQVacbm44qpdoTK7fXh4yN27dzk+PqYqa0JI9M4Rk3hUtVIkPbLDkZSD6eSxXIs1yPML+0b/524/+9nPfvazn/3cMG/0f70vLy9ZLK7QUqUpKZba4kPAak1lS3w/oBMURiSn4mNiAxSM0cJyxJBDOqRfs7KWoihzPUIiRQMkhk6kxrawkCD6iC0LCluCgqaZUFYNVSmyPO8HQG4wm1mzA2QksMiWBUrD4Abadg1AXVZYU5BCT/Ce1WrF5dUlV52HdkCphNWJk8OZJJ8qeR8pBrSxrNcDxmjxdA2BYXC07ZKqLokxMQyD3OiWBdP5jLquWLcr1l2XU3QtKE1R1bKf1hCB9dBTWkvVVNT5ZrGqKhJRWKiUsBpUigyuR2lDWRRooKlLCit+OlLE+0T0DucHisKiCBitqKtCKmdS4mqxIqVEVRRMJhO8k0oSkfKWlKVUBnnv6HvPpBGQ60niW0VutEMIdN2awTlh3rVCpURwIh9WWtjbEajuSmFRo0QwQpb8aisyVDc4yVQOceN3U0oye/8+ADru23X26qbnjSM9s2oDoG963k2BRzeB0puA6D9kbgKrN732J/lwX7dv17d907FRWks6rPe4rqfvO7pOlAkj4zo+dzqdClOeZeeb7QMxH/v5wQFa6U0q7Qhu4ngdKAFnxlqaZsLxyQnPLp6hcmjVYrnk3HsOZ1POf/6MDz98n3Z1SWFLViEyfXBEDIq+7/na1x/k2qSCEBxPv/yS3/5ff5sf/OA9UXEYTczVOVW2GdhSQsBCjEhwraKpGwpbCOMbQvbPPiOpxDD0whxay+XlC5arBVXdYK1h6Ds+//xzTk5OOT464b0f/QjvPe+++y5lWQJjB22QtG62bONPlpVvFzmUGgPF5PGUAkVRUpYlT5484b333pPPWRrV/QkVIzFJ8NG7Dx5gMjifNhNZpKoqlNY47zfnzWfLxv3796mqmvPzc6azGc7L/o9KGVLMLGoUltVLx6oLIg/23pFS2HjS97Of/exnP/vZz1dr3miwenY4JbkT1sNA160Zo2Rc8Kik8cOABgpbEBVEoSqBRAjbG2fxUQrTYYxCG0UzqQUwRqlXAfE0pZjEe6q1+DLREBXJCdDxMWKMln7WDIyMEemw0rmeIgW0MmgtHkirLccHh1hliD6w7tdYq/CDx3l40Q44DAeHcyKavm+prKFpGsqykBoeIkPMTGJZ49uBde9o1z3GWJbtmt57ylJuHJ1zVNYyaxom0wmlMbSrBd45Ukj5fYsv0HtPU1cUVryyZdmQfJBUTo2EIA0DSTuU0pRVSe8cRVGgjEh7rS2o61oktsGTlKJtO4J3VE0tbJm1JKAoK6qoGC6u6PqOsqrRRSCGiFWR3q0lGCuCcyIXrEqLNVYknAnxICoLKAbn6F2gHzzF4JjWuXM2ZNYuSt+lVmAKqcKJCSJKro0YUCFtvJBlVUJSmFxZkxQUpSH4gEq5VzJ7+GS7EoqTorD4IxucqX0BnioDzXxt3yy33GU+X318G460ZW0BqeQYPagqB4e9ZN8UllEL0mYMcEKrXHG0+8vyNtkylOPvvr4/4+frdYB8bDjZ/P+O0nmsOUpprBeRndXKYI2l63u86+mHAZ/Ed24Ku2EaY4wURSU+6TjKP+ValP8FmrJmNp2gSFitCYNHJ4VH2M2UAt73pOQx2nJ0fEwznYMytP0AWnN1eUXftcwOzzg5O+X09IDPPn/E558/ZDadcXg8Z7F4zqSZcn5+NydMw48/+oh/92//LT/60Y825yuh6J1U1ZjM8sUo/buFMRClF/bo8FAWlJLCB8/FxRXrrkcbjXMen5Ueq+WKyxdX3L4zYXG1pO0cq1XPz/7n/5iQ4P33P2A6m3LvrXvYQpOSIwaPj2Oqtc7s41jzE3fO7c5J3DDYSVYN8xUTk6g8jNUYrXj8xUMeP/x883lTShF9kNTzmJhNZtw6u0W/7qjKRhaRjObg8BBIrNYr1n0HiJrl5OSE8/M7HJ+ccXxytqnSUhp89MQMTBUChL33kEPwUgwEL2FvbK7hvQ54P/vZz372s5+v2rzRYHXWGBalYXCJxWpBSpHDgwNKY4gp0rkOixHZ5Ei/wqbOYmTRfAys2h5lNGVV45PI1gpriB5ClqWOtTF93+fApIp+6Hj+4ku0tsznc5rKADHX4mQ5Yq5XcfnGyDmHVhofBoJPOC834ypKQmVIibVzdJ2k4fZJY+sJRVERQqCczTiYTpg0lr5fQwqSjoshJri8WnBxuUKrQsKiQkBbQzf0wmQQM7vpxCPnPSk4YT1DpB9lzGaUuEaCG8AkQgrU5QxlxZNK8FgSngwQrclsq6Ko6g3QUFrYjeA8Md80KhQxaYYhorWjNhYUdH3P1arFJwW2oA8JtR7Qmlz9IzeXZVXJ+3ADVVVTFnYDBjLviA+ObvAMIRJRdL1DqR6VJGFYIYsRhRXZtXTpKnonIDU4T2E1yYikESXyzLZdSyqrlt6gFCNWrI4bZmmLvQQIxw2gy1AvIV2ueleuyiuppC97THeBwvbnW9ZyZCzlWh/lrinlzljY+Hiz2FcApUo7ScMv+0vH4JtxG6/zpN60T5vX7UiQN69jB+cwHred96xe7p/dDZDyXkCVCx4fvLDdxRbgGSMhWs5LF24U4j8fC6mvquuK2UTYOzyE3mOSIZBIKsjnMziUhelswnQ+p6objs/Oef7sCVVR0a7XDEOHblf8+KMr5kcH/Pwv/Bw/+wv/GZeXVwztiu5Jz+nJGUcHZxgMf/v+3/Jv/rff5pOPPialrb9z8AFblvzcz/4s7arls08/ZWJLkbMr6UU2RnN6fIxGkoGdi7y4vMrXdWIYHMMgnbNtSpKOrjRPLy64uFhgy4pbt+7w4Qc/put7vn7vXY6PjxA/r4SNjbVc4/Eer60QwjX/8/bEpTFcKUaU1uicIJyAFCPduuWvvvc9VpdX1FXJkFPAtWJzXd46PZOFCNdRFCUoxdmtW1xdLSirCn8pC1MhJoy2nJ2eM53OObt1vkkwR8s1rdR4vSRiiqQYUTlgKYaROY6MV99NCoT97Gc/+9nPfvbz5s8bDVat0WgiVWGZ1HVmi1Rm9ypJaR08XYpYpUjBE1PEKENdCmBVSuGCxznPOjMyZe4WHfoeawuRCiPxwd4Lm6G1MDjrrpMKFhLrrkWlgtlsQkpJuj9joqlr8ZkqJeFESfpCnXN0vWMYRu8klE1JCJ71OmwkeZNJkxMzfQ4e0bgUiWiKssa7XlJztaFd91wuVnT9QFFq6tIwnx9QFIp+6Igh4gaPVYroPJcXz2kXl8JOVtWmR9VoRcwARoFU2hRFroYRSXQKIt9D6SxXVCSlCUMgRNAh5WPrqIqS6DNQDuJl1cagYyLEiHMOtxDGddX1XCxakjJ5QWDAESWlV0FViTy7LC2FrojBUtpik/YLkYRsd71eM7iAtSVaR2KCdt1hlKIqS6nsMRJM5dyAD3LM1+sO8nNmk0n2RnqU1qQERWEzCBKPNEplsGtgzA5OiGx3ZFi1kj3bgLebPak3YL/Ntbr79Sc9Z3e7u9Lb60FINwHN3e2kXFNys+x3+9jfC0zTy/2vSsmx+Env5/r+JUVOlh1D0gQ4ucEznU3ExxrkWmqaZlOxkpJcvyEEIjEzt4rpdEpd15IgPLhcUSMLIs6HTQ8oHk5OT7BWpP/33nqLZ0+foI3h8vIFXdsymx8TouLjH3/MX/7gB9y/f587d+7g+g6F4ujgGJUU77//d/wv//pf8/Czh6ikKaqK5WoJSuT23/7VX+XXf/3X+b3f/d3sr05ElZnOXJk1nU7x3lMUJc552m4t142CrutZr9eUsxnDMHBxccHb99/Fx8Dl1RVv3b9PUVj+5m//E03T8M79+0ynU5xrc+K2z6B0e75Gz+p4PAXA7pz33QWR/DmQBRj526GV4vNHj/jwww+JMeCdl89nEoVBoeUauH37NjEkUhzVKNA0DX03UJRF7liVXtm6brh1fs7B/IA79+6O0Vl4L923xhiSUfhct6UYr52Yk9dloVLnpG/59x6s7mc/+9nPfvbzVZs3GqzGGNAk6qri1tkp666HJP2KtihQKIKWfsJqOkUrSYf03tEPQ2YjJXwIlfv2lIT9jDe9xXjjE8XLaowVeSKA0jhvcrJnyOyrZrVcyE2ymMzQWpJqFbDybZZGGqpSY02FD9B1nfgxyf2feb9MZSTZUitSrOiLgvV6zWKxIAZPVVjaZYv3jqqZsFz3hJCTNaMA26YpsBrqYkLf9yzdgKkKAWtVhdYilZXe0ZRZ4QKbw4isNZRWUVpDVdnsUZXaEIX0Txojx9z5wOA6ur6XG/6U5AbYeQYzUJclhdHCthpJBh6ltT546W1tGgYfWTuP0YrSGAGCRLRSRD8QFZAs1hjqqiK4kCmanJIcRbY7eE+ICZSwzhpkv8WkJuczs0QhppwAKzfYRVFQV7nTVieImqQU2hjxCWewqcmMHbu+yLQ1vbILEsf/exmMveTr5FUAd5Pv9HrA0uvA7PXHb/Ifph2wobTaMFPbd3DD617a35v37SfNKH/efd04Y5rv7n4ZbagaYeuDH1OCyaFCFTDWTyWqqs4Aa+f9bd6rgNDJZApoSZrOEu+Uq1lSTEhQrOzTfD4Xj2WKnN+6xbSZEnyP94EXl5fcuv02R4cHfP2b32DVtTx/8QLX9wzdQAyR05NT/vL7f8nv/M6/4+EXX1BWNdHL3xijDWVd8V/9k/+a//af/3M++eQTnnz5BGMMQ9dvQHJKMGmm0l/sIyoGej/gQ0BbC0QGL1VTs8mEmBJPnz1jtV4xOzjk9OSYB/fv8/TpU9rVijt373D37l1SkgWXtl+KN/ba7J4D+V7O/vXjOib+aq3Ro5oie73/9q//E0+ePKFuGnwIWW2g8DESUNw6PWM+P8C7wHQyxRpL3w+0q5bj42Pms7l4dKMs6p2enjCbTjk/P6dpJrkTFpz3gMrnfvx8pawQefWztvv5fN3CzX72s5/97Gc/+3lz580Gq0DKN9eFLemS+DPLQkJJfJRqGh/AlpamLKT6YIjE6Dc3UzEl2nXHatViXYE2VgCvyXKy7NtM+UYueEdIYExBWRY0RSVgxzvqsmC1uhK5blmyXq+56DuOj0+YTecczOXmyBgjdRMh4H3KLGsHSJpnVVUS+LJT/TCZTCkLy6pdEWIQ4JbDmkJUrNsBrQyTyZR110FKHMwmWJ0otPjChuDBO7Q1EEUeZ2yJUSHLQSVAKAQB30VpKQsD0dP3jtXKUZZWgp1sQUwI4AzQ9w4XAl0/MDiHCuLNFcZVbaR/qqqwOoNVoxmc9NBqI5JtW1YcKYNetcSoKJsKDRA91ipSDFRVtZF6VlVJ1/UMw0BKJrM9ipASSpvcs5gEmLKVCCttcD6IV1WLZ1BlafC0qSlL6ewNrpNanhE85Y5Vow1GaVISFil/2QI3tfWKXmcmY4wb1nqc8cYfxTbNlpuB6vj1enjR7vVyU1rw+Pj4vJE1292eQoGGHLKN2nntK6N2tsvL7/M6m7r7WMrHi9fgg5t+l9IS6uVz7VRKKTNnibqqAeHUlZZwMfGmkv2XeVeTyEKNNjTNJIMqlUPHBLSpIIAnxMgwOJppw+HhISALGIfzAybTKc+frJhNZ1w8u6Dv1iRgUZYcnhzT1A3tcsXzJ0+ZT2f89Xs/4o//+I/48vFjCltuzrVzjsl0yr/4zX/Bt3/t16jqivfff18WrtLIBsp45zk9PZNrTMvnZRicBAopka/7IB2jPgTqpqbtOlbLJUfHp0ynU05OjvmP3/0eCXjnnXeYTBpikkWqlGSxZpc1HRnV8XrcBXjXFz+01puu21Eu3tQNn338Cf/+T/9UGOxRXqyEITXGoBK8884DFJroHdVMgPzjLx5xfHjMrdMzDg8PKMsC165RSnHnzu1NEnBKW3tASuK1d27IKpQovluS9FgHqbJJ2zfJ6J/dz372s5/97Gc/X715o8Hq2kU6LyFF7Xqdw0gkFEeCNkTa1a6ucH5NaQzWShenNQacRivN4B113dD3A8pophORFAajsTZXZDgBGG7wItnNQKk0hgRobaEqUTFwfutc+kmjeF+99xgjASVjvcZqteJycUUIkUkjrEFVVqAErDZNg85MkfeOFANWQ0JTlZbalBSFpWtXmZFVOOcoy4aqbojeE0NPU1usjlTWEoMiFBpfFcIke4fTijJLC0EY3hAC1hgiSjyhwyC+2Ko+Kj8AACAASURBVEJYyK4bMFZAvdIGow390DP0A1FpuUkEqUzJCwIp5JoR5CY2JElmttZii0IkofkGMuaAmLqqGIYBrRXWaJIHaxTKasqywBrxBPscsrTuhpE7wdpCpJEo0CYzc3oLzpIwaD6ztQIehA1SKEoLlRW5s8qsTMzBUCiRDUsFTyRFL0z+jldUbdic8W1tWanxcSFZX3+jfB2M/kNuqG963nVmbHzs9c+PxJA2lTeyrztBSyNAzQlJu/zry/7a1wNVNp7HsSbm+uvHoJ5x2wqjrbCK2TsZQqDv8wJVWeN9lstqjbYm+y7ZSK/Fy6jQSVPYgvlstjk2fd/npFmzlb4GkRofHx8zm83ovccUBRE4PDzk0eefM2sqLl9csV61lFXF4uoFRSWS+KsXl4TB8dHDD3nvhz9gsVwg9VUBpTQhRWbzGf/9v/yX/MqvfpuyEk/6w08/I3qR3RuVNrJcU1gOjo4IyH6iDKvlEmstWilC8vgYWHdr2n5N1VS8WFxx8eKCd+6/i7WWTz/9lEePvuDo6JAHDx5sQOUw9PhskxjB23XGffT9CgDcni/90lkapcFASvzovff4vf/rd3ny5ZcopSSsrZnQ9b0EfcXE0fExpydnuMFRljXWFjx79owXL6RiyxaWg4MDJpMJi+WKW+fnnJ2d8fY7bzOdzYQNVlKBMy4oba/xrYxdFjc8IbqXPhcm98LurAvsZz/72c9+9rOfr8i80WD1YrHiycUVzgUunj1nPptyfusEYsT7XsJ+Suk5NcaScqJrUZY471l3PUZlFtUWnJ2dUdYVVVXJKrwRCa9WmrqqhLUwgzBmSqONFh9iTOJ3SpGU6ShrLKbUlKWwKNaW9F1P13X0vXxNJMqyJEZPURQS4EPcMBRlUdB3PSQIIbJatcQoDEFdlNRVQWUP6HuXk4MjRVlhC0txOEelmnlTiP90cBTaYidT8eSmxOACaGEiu64TgMcWVAQvQFkpAYnFBpAp2S+UyBmDMMMhs1ZJgVYSALNhXHLI0AgWlZau07ZtKUqbgZGTRNocHqOzgTOliEoabQS8lEVF8AFS7nIlkZTGeQEXKEWI8rtDSPiYxCuH7Jd4kMmJ0MIqSRKuoixKOX5WQQo5MGYbLqONIUTPerXEe5GTBth0Xu7eKAthua1KGms+tgzoywbVTQ3IDSAPttJYefJWRvtSjY3avmYMTLrOdu5+HV/3Cgur1DYcixGsbqnQlM/l5udq84s3r9/KnUdAu8ve7o56iRF+WZIpYFZrjbGGIvvJnZPeXOci02mD0QbnPc6JF1F6SXOEzg7oGpnBqqyZTqbSqRsVgxdpavDbECEBt4nDo0O0MUTvcucpnN065wP7PiCS3BcvXjCZTXFdEODlpFrn808+5YP3/47laoHPFTjkIKTT01v8q3/1P/BzP//zRMQz/tc/+hGPHj2ShZA4LpKIPPns9Jy6rnGDQ5uCtm25uHyBysnFKYnXdtW1tOsVTSOhQ8+ePpMgNl3yV9/7Puu+4+vf+Dqz6ZSYZGFvPF5jUvV4HkIIL52H8byEENBK54W68XOa+5ETrNuWH773Hr/7u7/L4sWlAGrE2tD3HSB/O3303LlzB6MNQxxAy4LZRx99hDGW9XqN94HpdMqkaYDEO2+/zexgzp07d4U9j1ItNTK123ori/cBHzx5mQxjFdFtu7ZjCKTN36k9Wt3Pfvazn/3s56s2bzRYbT2sQyImRTM/5OjokJOTY8LQQScdnsoapnUBGHRdQBLGxQfPYikVKLPplLqpCMFTuoHgHF3XokhSbWEtZVmhvGdwnqIsMssQWa8lYddah9EaazR9ZidBbr59DKTY4r08VpalpHWu1xhr0NqitcK5YXOTNfpobSlMsPeexXJB33WUdcls0mByOm5pDYXSGCNpwlopSTa2BZqIyh2oUsURXpJ6joAtkvBhgCytVAq8l/1pmoayMBloiN8zhMRqtWaVq3FAwFQEDuaHgNx0DoP4cEMM+CTsalVVGxARFITBURhFkfc9xIh3HX0vYVSVLSmM3UiAY1LEKFLlVdsSooB0Y0qKUpjuYZCU2KIsqQqDc26TzCyVLOBjwGrxA4Yofuaud8xnU2qbk4Z9xOjsPTaaEAO2qDbgS2uNUoYQroHC/HW0AKaUgaYStn8DTK/Btt3vd9mrDWBN6SWgugvs9AgO2Ulr3eBfdaMsd9z+LliNiZwAnOFp2n2e3vyOlEIGK2arfd71oG7+t5VajlJNvfPedoHqeEyBlz5Do9w9xriR/rrsUy6rWo5zUgQfmR/MXgHoctgEssQYmUwnIkFVmmFw9P1AREKfYgikIMytLQoOjg7wMaKtRVkjabS2wFaVVFmVJavFEmJitVzSrzv6wfH5J5/yyUcf07UtWqlNEJuPkfvvPuA3/7vf5Fvf+paEdKHQCT758ccsl0tKW2x+f8yf0WbS5M+upigKrq6usLYk5MUsgdeyENT3PSlFiqLMydWRupYFsbquuP/O28hCiiQAb9J8d0Dp7oLBeCFd9xUrJb71mKIsOCm4ePGc//dP/h/+/M//nL6XXurr4VrBO5RSzKYNd+/epe/7XBcET58+xfuAtQLIV6ulpLwXBYeHR5yenXF0eMRkOsnXRtjIi3eVE9v9zXLmzBy/4oXOvvnduqf97Gc/+9nPfvbz1Zg3Gqx2IeACKGXAJFZdx3LVcjCrgBrVR5rJhNnBAZ8/esJ68Bij6Z2n63qS0piiwIfIYrHAGI13AzrXuqQYaM5ON2zKarXi6bPn1PUkhxCVKOdxw4DrB6qqopo2EAPGGnwUeWGIo7ROU1UV1krPpwsid63KMjMVaiOP9C7kHlbpifQh4mMCLdUwRhliiCK99UH2RSVhDTUQI1ZrNOJ1M6YQdi8nXnoXiECAzE5kBhA2El6lJDG1qirZltZ0XbdhnmKMqJS2oUPZq1YUBUUhLG2MXWY4w0bOG0LAOQm6CjFKP6otMEbhnWMYPBEywyxhT8YYYnDECG3biVxTCyM2uIALIv2NG4OlwRjpTNVGY5G+xfFGNQbx+W1ucmPEeYcCut5QqERlcy0NkRDdBmymJADFuQEgH9uUE4NfvqEfH7s+1wHm+JhSW6B7Ewsq7PvLXtPr29wFprs+1uvPG5+7/d03SHbZXhNb4DmC2hsY32tzHRzvMqev89S+Kj+NGy9kjCn7NLOUWhkBOaOEW0loVkpq89kZq0x2+eH5dAaRjV81RjmezoesZJBrdHY0ZTKdiS++qCjrms45lC2ZzA64ePIUpQ0vLi/p1h2FLvj0009ZXF7x+WefQa7B6t2AUgkfA1/7+tf4rd/6Lb7+ta+LLDXlELeU+PGPfyxMv9abuqxR1n52covgIoUp0ChWy1Y+c14+JzF3Pc/nB9SVAD2tNVeLK66urpgfHTOZ1MwPDjk8OkIpjevX+OBeYb5FOjuC1J2FjCB/y+SYy3jnsFbqwj75+GO+850/4q9/+CNijLm+K2yvA0HC0l2d4OTwmKaqef70OZN6wnK9pOs65vM5IQSWywXtasXZ7XNOTs5oZnPmBwecnJxtPL+Dd5InoMSXL+y4Z6ymGeW/MXhQMS8IamLMgBuV/e97ZnU/+9nPfvazn6/avNFgFSAoMAp0YemD53K5ABWorZKQlZhYLVd0Xc+yHQhpTd8NuEHqLSaTCSolnIsi9y0LJpOGws5RGuazGYvlknXX03e9MDARkeO5wMWLF2iELdRGYbWWcBqlwEeGfHM2rtqPfk6x1omH1miFd2MPILSrFud8Th82qJyeW1YNwXq0KQkhYY2maWq5gUywbFuG4CnLkrIowCAJuzHJTZoxmMKSInTtwLJthTHSBmsKlE7ozF4poKrqLbiLAlU2VRCZAdZ67IkUCV5hBPxfXl5mECDHoiwLlCJXxDhSijkkBzo3CDtiNCl6RMkrYSyDHzD5OHX9QNv1OJcos9w5KfG7Oh+JeIzK3lQSkYgPQfpPjRLgkuWdJrPXq9UKNwijPMpv+8FRGUNR1BTWYDSooDEaXPAkDfODA0lg1Vkmbe018JheAl67j2/8mbABv5ufpl1m9FWQS0obdnAXnF4HqLssluJVUDj+7OVNX2OGx+/zwkVMcbMtYa+24HU3EGp3+9f35ScB95v2aXfbAujYhCuNQNzaMgNnWRAqbJFTfSMxqXxqXwbJhweHGxnrMLgd4Cz1LYkAOjGbTbGFxafIyeEhhycnPHr0JeuhZzI94OFnjziYViwWV7TtmrNbt3j/g7/j8aPHVIUkFJdlSUiB9eB48PV3+R//5/+Je/fu0XcdIAsQicQXj77g4cOHIqFHPitkRvhgfiCVNYOnakQGP/QDZVHQDaKAMLpAFYm33rpHu7ii73qayYwXl5d8/vnnvHX/AZPphLfffpu6rkhJmOtRabIbtDUCufyx2D6eJIQNFMPQYwtDUViGvuOHP/whf/D7v8+TL79EIwtT4ge/zlomrNZUZc29u3dZt61kCKRIt17jvefgYM5yuSSEQLteUxQF9+7eo/OOw4Mj3n77bVwQWb61hhhlQcy7cSEjkXKw0lgBtlUWbPcj7bzBfcjSfvazn/3sZz9fvXmjwWphzKYGxWgFyrBcr9EqUZ8ckSIMzoOLnJ6es+oec3HxgpQEXBZVjbElVaGZ6wmKRFNJ5UvXCWvx9OlT1t0AStM0E27dKlgsliJFDZ66rDbpvcfHhxRaAlQkXTRRlxV9BkBjKqnLdSpaa6qyFDCkVO5M7IVtCZGoAgaRySagKAtWy5V4JWczUmHQWjCF1sISd4Nj2a6pq5KmqrAqoRGQZQqNKS0BRTs4rlYtISapqTEi/bVGS12MtfgQ0SGhNHRDR0Z7KCRgyBY5IZd8I6s0KUaCd3gnbIfNQVS7HYZaK4wpMdbSdT1d1+O6jliWNNkzPDjPcrVk8IG69pRRmOWUQJc1Hgg+4uXNE1LEWkNRCoDWIeCG/5+9N/uRLLvv/D5nuUtsGZlZe3dVL1xUEkWJWqihKI1mRFEzGnggG14gwC/z7Kf5E/wX+MnwAPbjAIaFeTDsAcYDSSY5mpElkaOFlLgWuyn23l1rbhFxl7P54XfuzcjqLC56oJpC/IBGdkVk3LwRcSPzfM93C6TgCYj8d/CdKqWoywqtNXVdC/sdAnVdUxQFKUb6GLHOgwJTlFhboVWkbyUJuHc9zrsxAIrsyx0mXcKyylxcGG8D3JSp7eH1vBy4pREgqmcssi/8ewv8Xpqw+zSA3GZXGeTAW4e74P3Mj3+fB3Xr+Tz1s54+58ue59OS5QGoWmsJSWSuIXqCl4CuwhYEHyXVVuvMrAobG3IXsEpxDCub1rJJJYx6wPcOeV/E++mDI3iPtYYrVw6p6oI+RQ6uXqGaTEFblCmY7S3pfUQrkeqeHJ9y7doNKluRfMSlnsIaNpuOqqr4+Cc+zn/9O/8d1azGhZ5s1EaqYwq+9a1v8uTJI0orYFTnpOkYE4vFkhgiWoE1hrPVevQSG6VYdx3OO/b35+ztLbl57Spf/9rXx9fuvffek+Th6TR7xCPB+xGAej8k5Aq4304A3n4/rbV0bT++H0rB6mzFH//xH/GHX/gCZ6en6K0NA2Py9TFsqAzvvUpM6pLl3px2vckbi4Gzs1OU0iyXS0IKtK7l+PQIFzxXr17l4eMjnn/+DlU9JXatdGunc1m9eHfjKA+WIC6XJeUBFeX5DZ3MKYUcKqXx/v21PbvZzW52s5vd7OaDPR9osJpCT2k02hZS1+AdpfSQ4KIYqHRREZ1IRU1RUtVTQggorfE+kKpEUVa52sUBeQHre7zXdH2PMQX1pEZpS1GKZy6hCErjeqm5qKpqq0rmvMqhKMQ/mlC0GYiidF5IB5p2Q5mDebTWzOdzQHO2WuODLMCdlwVV1OBipAiR3gXatsW5jhiDpAcXBbbS+ARoA9rivKNrWozRFBGKpMFYjC0oygnJB/GIBmEICyNhUtVkglbgun70e6bMuNZ1RVFYjNWE4IVBRdjitnOEmKjKMrMqgALXO0xhRkYwBJ/TV3WWWQtQMyazyT6ijaU0BUpbmrYX9lQZ8df6kNmzgDEaawpiDHRdK55QQGlZhCYiJGF4nO8lCIksDcygxlrLbDYTybKTjYW294Tgc0+uAaVRWpNi4OT0dOykHMKGxN+Z5ZRbbOYFOa9SoxSbxFiHdA7eeL+N9al5WlY7fH2axXzW4y5lbM8/VdtUGoPX9PITYWSjf5B5n9w3Dj/rPMRp1JaixtdUa0NRlBhjx9qRYeNh7PXUGu8EYKJE+p5UHBOB5atsNMznc6qqAuS6HNJ2Yzp/L0Jw2MKwt1wgfmVRJZxtVnSuxxQF08WCxfKAPjjKqubxk8c839xmuVyOlTNFIb87fuYTP8Nv/rN/yvLggMa1hOAZvJQA3jtee+2742sUk8jWY97Ums3mxBCoCgls67tOnnOWKwswNOwfHHDt2jU+/lN3efWVV2jblqqqWK1WHB8dsX9wwHq1IqQkmy25X3lQR4Ss0thm7odr2OcQqrquaZqGsix59913+MLnPsdXvvKXRO+xxo4hVcNroBC5++iAzWqT69euEZzUWWmtWa3XnJ6eUtd1DveCpm05Ojmi7zqKomRS1Vy7dlVYaSUy4JjOK6BEji6/F2IKI2CVep5ISsPzyrYIJf29bIV87WY3u9nNbnazmx+f+UCD1WZ1htFI7YqCEBw+RVatAzaUZYnViul0ISxiUtiipHcbQvTEkEa/qOu9MGe99G6GCCmG7N3yRAwp9YSosEUtTCiO+VzShEmKo6MTrIaiMGOIUt/1EBNm6B/M/k6yLG6oygBZFBZGPKld7zAxESKgRc4KhulswWIyo7SGZi1eOxK0XYdyjrKu2VssRNqrNFFp+t6Lr0tHovKQE3JdTIQMTsS3JWC774U1rMoSnX2oAzhIUZJ/tRb/q/eBtusklEdLOq/30kE6mU5JCZqmIcVIZSegJGDGe9kYMFYzUTU+s+PWmuwzlcWuyT7R3vXZmxoI6jx4JylQRmOsJvqYfbABq1Wu2skJwIWksMYuoJTFFBaForCGsvLEvOgX2bUhJXnNg/coraiCpSg0XefpnWO1WoNSIrFGnq8kXmWZagSeYkC3F//nAUtcuE1lALh9u1wnepTixqfA5tMA9QcBj9uS4Yt3DF+Gc3+/7/QCG/x9fs62RPnp55SSgJhzXlYga0rnwDXl526UXIeklCtPhD0bEqoBQshgdZAIG3k91fB88gbS3t4eVVmTQiKEQaJ/ngAsPtJEXQnQ9CEwme0xncyIubbJtY56MuXOC3f4zre+wdQamqZlvV5zPVernK3O6PqOT37ik3zms7/ObLmH8538XskAcai3Oj095dHDR4z+bs43H8qyZFLX+XeH9Mz64fFDrY0xeN9xeHDAcn+PTbOhaVtsUVEUBWdnZ7z7zjscHh6yOjujb1tJ8I5BkoBjlOAxNQQtnb9XcUtynRL0rmNS13zta1/lc5//HK9/92+IMX9WkG5Y2SYS0G3RW9eAvN6VKTjY28c7R1GUrNZrHj5+JBsBpcUFSV5u2obT42PapsGWE65evy7PNSZcVj74EFAk6b8OkjUQQpAe33yFGi1hWt57YvR5E2D72t4B1d3sZje72c1ufhznAw1Wq9mU7vSY09WpSElDIoVE03Qcn2Sm0BZYc0rv3HmtTFnI0iQlNs2KybSClDg+XaGNzeE9RuSBKbKYL1h1ka6VsKRpXUOWxGmr8d7RtC0peFRwwqwoC1rhVJb/JkmNFVZIFkooQ7NpKOZTysLS947gHJqEHTpJlSKFKGmnQRbbPgZmZcm0vio9q66nsMIohJQoVBTfZgwkFTFW0XSBftOiXUQZy+l6g48BHyOltUTv8B4mVTmm8nrv0dagYpJAEoxUU6ghlVXR9YGmFQktSVEXCoeAv5DBt1ZQlQUphlzl42lDQBtDVWR/obUIt6Nx3uFch/c92lpCsFJBk8hSYHntIY29qColCmMxGrxXQvMGMIWRgCWtUMZQFaXIdWOgKEsAjNEE7+m6DsgL2CieYE2k6yRIyQWIIRIxtL0nRnA+oI2EtfTOS4+vMVkxLX2a592P59LWlINz1HBfyj2vI7uY799mNhMoNEZtyYsTF6Ae+bq+MM+Q3A4yz4FRejpUaZiQYq4iygByS6o8oNXvF5b0NBscY8yS58j2M9DoEbAZPfSdeqxWuUMZCEm+T0eMVvRdi7GG4ByTvQUpify0z4yjdJsOLLxhuTyg7z1WF4Sg6PpIRBFIuOhoYkfQkcliJv25KIyyhD6homU+XbAKZxRaMZ+WnK2OWFy5ymqzzqFGiqvXr9KHjk9++pf41X/4adCQCKgY0UGCyaRaJxFi5MGDR5ycSg/r8J7GnIa8d2VGUWi0sZiikvor7+lzwnZSiRAD+/tLbj13i+X+ktfeehMKQx96JtUEHxxnJycQIn3b8eTxY+YL6XcOuYJpuOqcl77pGCMhRYqypOs6Cm1IMZJi4A8//5/4o//4h5wen5CCEzCrJBSqcx3zxVwuDaNJWuVNMXmP+7bl+efuMJ8upB8bxZOTU45OzvAxq16Cp7AGgif0og5Z7F2hzwnE1kDn3MjshxBwwdNs1vIsMrhGgcaQlAaVsgddNji00iPYTclji5Ld7GY3u9nNbnbz4zXfF6zevXv3fwL+W+Al4Gfu3bv3tXz7TwD/GrgCPAb+xb179175fvf9MLOYzzk5fiJBHFHYUB8hBiAFgk8o5ShLSZo1RkGKGCNAzmiYVBORBSuRzwbnSUlqExQK7yNptcnSO0ez2XCiNYqUvaplDpuJTCc1e/tXhFENstPvvCyujAWbpXcpRQlWAhrX4XIvYIwhS1glLdM5YQiGACXvvTCb7YYiBZZ7C6qqpC4LVAyE6Eh9T6EKjIY+9ETvSdFjjcaHRN+2eORYShvKwjKd1GgmlFYzrSuS98JKqKFr0ZO8gNSyLDNTpUTqqgzKWpKykCLRxwzki5GRmUwmWC2VQX3bsm4a+ugpqgqPIuSAm7Is8d6x2TT0TpKCjbLUVUFRlqRNS+98rqGIxCgSXYwmRYhaWKqyKHB9T4oCZgapqFKGqrqYMuu9F39tXtwOXr3SiPeVCNpYbFHI44yGEDC2ou3Ey5wyuIxJmNY0BLcwMH7nqaQDQzrMheRbngaUW8APlUHnlld0Gxh+j8/JZT/vsvtGZlb+ceEYF8KcnvppzwyDesbjL7tvBMJcPDedb9daS0L18Ji8aTHKxgU+58qnhNLCqsYcyATC4lZVRVXWUn0TAl3nCCERiKIKiCLxjykxmU4pqoqm7bl2ayap4adrgovM6gnf/ubX+Isv/Qm+b/FJ0qSPj4+JMbLcX/LCh17g53/h5zGloe1bTGZLrTH44DPLBy44vvPqqzlN2JKCeKy11gTvONgXWbG2JqsMHC6Dsz5ImjBErlw9YG+5pOk73nv4Hs/feZ6333gLaw1WWx7ev0+zXlMWJevViuliLqxk/lmDTL0sK9peNm5sUYiEP9sWHj14yH/8wn/gr7/yV/SbJr9/EKJsaIQQ2dtb0ns3WiMGqffAqtbVhOdu3abvepQytG3HerNm0zYk5D1o21Y20UKka9acHB9z7dYdfN/n310XN1gAYpKgKEgXvNZjcnQO4BoULsOpDdfv3zcZ8N/l3+bd7GY3u9nNbn5U84P89f6/gX8EvP7U7f8r8K/u3bv3E8C/Av63H/C+H3iW0zl79ZSbV6/z0p0X2N9bYlIiBSdeJRUIyaOMoq4ryrLIBfYRTc43iRGNou962k3DZr2m79ssEw4YBcbqLE8VqarRIj+cTGoKa6mrwcNZ0PrEqgu0HnzSBDQhE1CTyYSD/SVXDg+4crBkOZtkJjCwaVpWqw3rzQbnPcYYyrLIskfxihIlMbgwGmMUIXiUgrKq8CTWmzaDJXKwimY6KblyuOTa4T7XrhxwsJwzrQsqq7EGJlXB3nzO3t6CsiyJUXoX+76n63pJQO578c3GwY95vlAc0EPbdVIxYg1FWVBVJUqRK2sEoE3qkumkZjqpWcymTOoSswUQjVZM6or9/X0O9vdZzGdMp9PMosrC0ijNkNg6SA519sCNICqKlNsYTYyBvuto2z7X5QhI6LqOtm2liielc4m2lnqhspLQrLKeoLQR+XGKxHROXIqPssheVjMu+MNW5+PA6A0y6uG/4fV72tN6eSgT+flvSYfTRXnus8KMngWML5MBj/d9n8/d9rF/2ATVy57j08d4Wvpsci1SjGnsXpX7Tf5Mn/e0GiPs3+BDTfmazfw7k3pCURSEGHF9T9O2BM49sMEHlE9YLPP5gqgVXkVmB3sEFfHBsz4745tf/Rpf/fJfcfrkmNXJGX3fUZQlJycndH1HXVd85CMfparrLLG34+aP9x7p5hVQFYLnrTfezEm2589PKamsWS73c+2TJE53XZeBroQHxSh9rLdu3WJvb8HJyTHHxyd89rO/yUsvvox3gUk9Yb1ec//+fYrCcnR0lI+RrQ7qPOG5d57ClpAUoQ8YZTAYvvPKd/i3/9e/5Stf/iuR+pclEYUpSlGNoFjsLaknU6wpsk9cbXXgJoILHB5eYX95gM/dsM45uq4bP58SYObO/ewxcnp2OsrA27bF5ec/bDiBfB7ruqYqK/Glb8mXh2tk8OJue7yH1PW/h/N39rd5N7vZzW52s5sf1XxfZvXevXv/H8Ddu3fH2+7evXsd+AXgn+Sbfhf4X+7evXsNgTeX3nfv3r2HP8zJmQS1LdCmIGlNoSQgKASRh5baYrRhNp9QWIvvO5QxkMRjqBVE74jBMZ1UxLjg+OSUzWZNcJ6qsJK+qSV0qJhOIXvFjLEYrZnNZsxmM/GB9T1HZ6dERPZqlEarhNKJ0PcUhWU+q7FGk4KC6NEkQkwE73EuoLTFGEtZFsTNhhSCsKNJ4UL2RioFaQiMUQQbhSWtK5xzOO+Z72mcCAAAIABJREFUTCZM6pJExLkeW4lPtioNttU416FSEptl6Ak+0TYt5E7EoeM05GAlm2mIkETCHFPK4UxkebNHKfGIJhi9cMF5egV78zla5RRhY/JCFoxRTCZVZsrE6WYLjbYVphQgGGLC+57geyBhMisynUh6bwiOhGxABNcTQ8Aa8Q2HEOicH0HLACRjFNli76RbVRflyKzKwlYAaErCYHW9eFcF1Gq0tmyaDpTGaKnZMdpcCjQvA4TD/1/GbAJb0uGnjrPFPl6ewXs5W3rZuTx928h+ygMv/Z7LWNSnn8dlwPlZ5zkCzUvuG8GGUaCVhORs/QwBGcKAOxcwxmZJaJTApJC7gAfGLEFdTdBagIkPQYBODmCSoDEPEcqyYrncp+lEErzpG7QKpOj47iuvcO/rX6ddnTIta7okn53pdMrRyTGr1Yp6UvPkyRMOrxxKwJpLY+iQ7510EyNJvw8fPOa99+4TQhxra2L25R4eHGTAJqDLZV9mTF6UFPm1399fMpvN0Frz7rvvcu3aNW7euMXR0TGb9YbZZMrjh495+PARL37oQ6xXK7z3FGVBSCEzkWm8/r0TNnV43b70pS/yHz73BZ48fjy+v877TJgmXPDcuXOH3/jMZ/jzP/tzmqaR35P5OtZKUoFTjNy8cYOQ35cIWFvQtf3onfUhjuFnSiu6vufk9IQQPCbXTc2UmAZivj5CCPK+h4ger6uLHu4hnOucQRWv6qiy/z7qgB+3+bv827yb3exmN7vZzY9q/rae1TvA2/fu3QsA9+7dC3fv3n0n366+x30/1B/E5mzFyePHJG3QpqDQhv3FnMaVsgAd+hhDT9s3hL7PoMlijaYqC5SSxUs9mVBXhfR0niAAMnQkpXBeURYWq2VhvL0LP5lM8N5zcnJMiIneQ+s8vfMYBdZKiA8xYRm6Va30ijqHUuJ3VNpiC3LiaSRGR9M0xBAojEhYB6ZlMZMuWGsM3suC24dATApjC5E61oXUtniHVVI/k5R4JH2SsCCTpKOwd0PVjEgUtRZPZdyqeimKgqosRxDhnKfre/qBcdUDA20JnDN3Ji98T09OqHLoFEAKgaQVzofRc9a2EnBUVCXa2BzapCisZTrNICOtadteamoKi9Z50UxCKyOPG7thhcE0WpOUSDu3/Z8JCV+KUSpQptOpgP0c3NL3Dh+cfL9KxN6jdGD/4ICP/+wneOvd+5yt1qAi9WRCdAKmBnZyAO4qe+PgPOxni5Q+l99+D8B3gX3lgiJ4PHBWcY5ftx/3NAv7vQAkTx97OPHheE8F8Fw0uV5kWweZ5WXP6dypmn/gU2bZ4T0cZNznQULCjtd1LbJ5rXDOUVblOZudhj7QXMGSJaizqQA6kdWL9DclMksZxsTboigoy4qA5+atW8ymFe+985B7f32Pb3/1m/RNg0XRZ2DcdC3L5R7Bex48eMC169dYr8WPGZ0wqTEGQgqYwtI10rFqjOXNN9/k5ORUnqs1o181xsjVq1fFv2sk5di5npjiqA4IMYBKXL9+lcViTtd1HB8f81u/9U957fXv8t579yFErhwcElPi9PQ0s9OKk5MTrl67dg7WM2APIWUpvaNvWr7w+c/zn7/0JbqukwgspXBe+pybriXFwCd+7uf4nd/5HTbrNf/+//n3oxpivKZSwqCYzOYs95Z0Oc1YKQGjq81m9O9umobFfCohc9bStQ2r1Yr1Zs3e/iHr9ZrJdAr6HIQO559SysFKg7Li4kZMURTnv4NSEqn42Cf7w6kEfkznR/K3eTe72c1udrObH9V8oAOWfF6cWGMk9CUGKmso7ISUoOulYD55hw6e2aRkb1pT2LzbrwWsWCvdqpbE/mLKfFLSO8fJ6QneeyprmE5EWibgRZGiLPCOj47wrkcrxd7BkubhkSyetCS3KqwA3aIAFM4nlIoiQW02uJBQtpDz0ANL5Eb57HQ6FWkjit4HyqJkUmticPTBicRORZGiGkNhBewZY+lcLxLBDMiSBp/ZJums1Dgf6PrsDzVGeixBwHWWzw0sa0oCYkOKOJ+lws6jtcnAILNaKp0H8Ch5nZ8cHVGXFYcHBxhrca6XjYGiGJ9viMJihgjVRGpiQhBQ3DkvMsHgmU6qETjHGCiH+hIl/YnACGxSSoS2wzlhcowuLshg5T89yjJDECDpo7xOIQ5+SkXCoBJ0neOVV76DjzF7c6HrHTYnF28pdbNdT409s88Coc9KzH0/Mzr8nzpHpJCZ46dB8oCQ39+zellC8fBV8VTNCAxRTxdBbGL8dwzD+V4ExtuP2WaCBwAzMHqXvTbD90lPr8Y5TwwCMGNKlFUlSgmlcMEzLWYSwJVBjzxeiohTItcTzVFKUqx7P9TgxAzSpOokxMCNwz2UEt/5Rz/0Movpgr/8oy/xrb/6Cv2qwyhDiB6UhDJVGSBPplOOj05oNi2RI86OT5ntzcbrre9l82lA5TrBu2+/g8+1L/I6KBLyuVvMl5BysJr3bDYbCRNyPX3fo1Siqirm8znGGN566y0ODw9ZLvf5/d/7fZbLJauTM6wtscZyenrG2emKqq548uQJ+wf7KE2uvwmkJOqBrut48O57fP7//RzfvneP5AODM1hYUUUfPIu9PT796U/z27/925Rlyb/53d8Vz3dZ0jQN2lp0BsMaxfWr10ap/KQsccHz1ttvj5YCpST5OyYJjarqmrbt6Jzn5OSE67ducbY5k35pnX3OLqBSxCrADuy6H6+pbRnwAJC3U6qFtTYi6d/Nbnazm93sZjc/VvO3BatvAs/fvXvX5N1ZAzyXb1ff474fasq6op5OUJlhjFEku7awwl6GguCdhO9EL8E/hByek5fOKRC9sGdaKaqhesYqrF7mABKD0efsV9/3EsCkwHmRkVpr6dqWwigmlYUhiVdbCQXJ0rQQEyoJWFVKYcqSIQ02BM+0nozBMXVV45wXSajSgDCKRitIfuydbNsOa0uKsiSEyGq9QamEUSkv7AMoSThGCYurdSGsg0q4kBnoJMxKZQvKssAqYTVB2AeXHK6X1yohDIa1RoC4kh5TCT5WkuqZn3M9nbIXAjbX9HRdl6t2hMEapIdFUUqAEmkMWBqCcKST1VBVBSqSfashA2+NteeLUYCisBRFSUoQoqLr15JkvBVwJB5IYY6FUXUjk3eBFc2UnzwWVusNX//GN4gKUBpthcHZZiyfBpuXeTTltov06MCMbh9rZDvz96QRJW5RoGo4ntym1PB9g1Lzco/o9rmce0XNRSaVi7Lcy6TFz/KvXvb9349B3j6nbSVDCJ7g47iRM4R9hSznHRgzlTdO4hZgjVHur6o6d/Rquq4XJjVvevjgCNHjCUymNc73LJYzJsbwB//u3/GVL/4ZftNhEd90JEKp+akP/zTz+YwH796HBKvViq7rsIXl7GzF8mCfTduScqdv7zxKaQqrWZ+tefONNxmaZkUeLz7n5d6SspKkcq1EceFdGL2d0iUamc3mLBYLuq7j6OiIX/ylX+Lk5JQHDx9x9dpVqqKi63uKoqDZNBwdHXHnpRc526xyErDKvniFNYYQE1/76lf5wue/wOMHD7FKkfImWoziM+2D56UPvcxv/dZv8Qs//wsURcGrr7zCd7/7GoAEJBUFxIg1BcF7yqri4OAA1/XyO6iwnB4fc3p2lutoQu6fjjjn6F1PXVegFZvNhtXqDGvlte/7jmoyHTfZvH+/F3VgVYdr81wavCU/z5sh5/L/v/fzI/nbvJvd7GY3u9nNj2r+VmD13r17D+7evfsV4L8H/vf89cuD7+V73ffDTOs6AgmdIiF6NuuWxXxOOamprCHkZMmYIlgtC7IYIIgHdACgpCwHBFAqeyIVdVnSB2Ee15sNxkinqrWWuq5ReWEafRj9kdPKcPP6AWVZSihMSNx/8JCu64lK6hZiCAJataJrWrSWMBfpT/Q4JyxLUHGUGbddj7VWekFLKzUSSlOUFcZ0OBfo2yaHjjgUgdmkxtqSJ0dP2LQ98719AJou0PWeqDRFVVGFSNv2koIaAz4mpsYyn9TZLysgKIQgrFWQuhZtDEVpKIpi7CR1KWKLQtJOM/CtipL9w0PIATYhB6MEL7UlAxhLcSDrFL3zUgcEW2mlIjO2auhtlIWrtZrCFqQs+5aqmZ6qChhdZMlwQb8FRsnJrGZks9IY6CIdspLym7TOZ8QoVxZvscEoJQxskGtn6MZ8/6QLC+cLMwAztkjZUan8FOAd7huVuM8IKVLnrOvoF3yKTYZzYP/+cz7X454v/OOFf19gT7e8p5fNZZ5W+fdTTO14/vJlkKAPP39gZvu+H3+eMKJ+fO/kupCDpCSM8+BFnkwmlGWFQjaN+q4fge8gLQ4xkoxClxKo1q/W/J//x7/hm1/7BskrkkuUkwnrrgWreenDL/LpX/1l3nv7Xd579z2m8xnHj59wfHRMVdU8fPiQK9evopXB580XHwIqKazV3L//gIcPH0ESH7jRGq0VPsFyucxsowB21/kxZEo+5/J5XCwkHK3rOqaTKbdu3eI73/kORVHwUx/7GJvVhr/8s7/g8OoV3nn7Hd597z0+9JGP4FpJN59MJ7iuF+mv6/mjP/4TvvTFL9JuGhSILD6dd7GmEPm5n/s5fvu/+i+5feeOKFtS4vXXX+fk5IQYYpapD6Fn8vXg4IDZdEoMkWoyQSnNyelpZlXzZl4IGK1pmgaTZdsJOFudcXx8hOvlPNu2w5blGKblsxVgCLAaruGLQPX8s7KtZBgCvIz5+w9Wf1R/m3ezm93sZje7+VHND1Jd8z8D/w1wE/jc3bt3H9+7d++ngf8B+Nd37979H4Ej4F9sPex73fcDz3xvzmYz4exsTdf3+NBztjoheUeYTES0FiIZb9AHL2LGokAnRYrid7PWYMiLY2QR72IUmXGE4JOkhkaR+c0XC9CWGD3VpKZvO9quQWvFcj5hUmqKQklXoUksl3PW6w3Oi1yXoiCERNe1bJqOspQ6ClMUdL6n6Tpi2GCMoa5rjBFfqQqGsqpkoao1hdZ45zEalNUkq2nbBlNIIMvJ2YrpdIpH0/lI3DS0XU+MMJntSTdoH4jxvHNQZ4/owcE+RYr0RLwfKnQCxCwTjRKw5GMgBD961NquZ2YLYVOiA6Np+g7fS0iVtgXTqSGmhPM9tqiy3HZgTy2mKMWzN0gljRH/qkqUpaU0ZU7Y9cQoDErve0L2z7q+F0DiE0p10qEZUpb7DuxKwtpzL+S5XFDYXx8HH5wEtlgj4VFhSJc1Ami986P/9FkyVrUFvp4Ge9+LZfxBbn8Wmzn8bDiX6MqNINrbc6mvELznst2ULppWnxUINfz72aD3e5/r5QFREoyktdQRYRRKC9tnrM01QKJkKIpCvKcuUJU1WhkBfSHKc07nx48+MKklWVonhes62bwIib7vpeM4enyS97OeT3F9zyvf/CanR0foaHAxoYqSVe+gKvm5X/wEv/6bv879+++hsxR9Utc8ifDkyTHXb9zk4cPHPHd6xnQ+IykIw2sJKDSvvvIqXduKDDXT6jGJN/3g4AohSDdoSnKe3ntc77PMWTGdTrh69QoAq9Wa27dvU1cTXn/tDfYWS1760IdYnZ7xn7/4pxws91Fa8/jRY1KMzOdzjh8fU9qSylaszlb8/u//Ad+6921OT0+lrsYnjCkE1BcWWxb8yq/+Cv/FP//n8pyV2ANOj0/46le/Tt87EhIelaJsMgTn0Npw7doNJLhMUxQVZ6sVp6sVIQZMUUDIvcMK2t5RFD2999JlHTzHR0dSuTOd0ntP23ViXcihd/miz9ypgqyi0Ftft73sg2f1XDXwzMv3x3L+Lv8272Y3u9nNbnbzo5ofJA34XwL/8pLbvwV86hmPeeZ9P8wMrJFWSqpj9JQUIl3fQUrUVSl+qQyw+t6DUpQ1lMZKom3MbIbRKHQme4QJkyXXMIrOOdzZGT5EZvM5hTWo/FiTLEYn9vZmRC8pv0aDC4G9+ZS6Kjk+OSPGRFlPROrWNXjviCnRtBsW8zkxOELwVJmZbdpWAlBionOOru+YTyv2FhNU1FlODLN6Qj2dsDqztH2PLUt6Hzg6XdN2nsl8L4Myh/OB1LSkqDILoSiMZTqrpRLHeVzbYIzBKo1PTip+tIC+mIFljEFeI5XQShJUbVGCUpyuzmj7jk2rsNpwsNgjAt458RBmeXBdTqSOwzmatsN7R/DiRTa529SHIBJua+QY3udQFWHDBgYyhpgXpuJBTUgtUEQRk6SODuwNbPvXhjCfxBDIE2Kgdz3Bi9xaleePH1mZNPDA2wDkosT2HJDGS+Wzw/ddlOHq98lhnyW93f562TEv/9wI8bq9aOepc78s0fdZwHgbLPwgUsrxdeGy1+OijNkYkfQ7l73i+flOp9NRzeC9Zzqdjsd4uh5oCN6p6xqAGGG92tB3PSAeSed6XL6e5vt79F3HN159lebsjBg0IRmCLVi7QDWf8dOf+BnufPgFmk4qkUKM7O3vs15vmE6nnJyc4rz4qderDWVdjQFfCfHR9l3P33znOyilxa+alLD0MbJYLFgsFvI8FLgs/+37nvV6Q985UoLpdMp8Pqfretq24+WXP8Trr79B3zt+8ic/zPO3b3NydMRkNuP49ISiLDg9OeHB/QdcvX6d4+Njrl25xqvffZUv/umXeO/+fbzzaDX0rsomn9aa6zdu8Ouf+XU+8fM/T4gRW5UoFEZpvvmNb/DmG29AgsKK7Hf4XKYk7O9yuZTNNStVUA8fPWS9WaOtJSXJIDBGErqvX7uONSI9NtYCifV6zWp1xs3lHqv1Gm1N9t7HUUyglBIQLRcj2wqB7etTWHvGa2pgq/8+zd/l3+bd7GY3u9nNbn5U84EOWOrblhQTk7oipRLnAtEHmqah7XuMNWgNBk1hLUU1ERlnTKiQ0MbmMJZELgNEIemxpqho+5b1pqHpOlKSHj8fPGfrNU3XMZ9NWe7t4V2H847pYi6SwhQ4OV0RE9T1RP4rCwqj2PQtKhYUSjGrK1lclRWrszM2mw0QKYsCZQzaWkpt6NoeF3u00Vkem6hL8XyVhaUqC0pbYID5dCIJmjnp9HTTCivpRKp85cpVVpuW09MzjC6Y1DVlITU8zktoS2ULJmWJCo6m3RBjpLDlGNrTdZHgpOewrkrqugSU+Flzfctqs6F3PbawVIsFpiiIShGRRaULntWmQ6WVsLJa40Ok9xF8T4S8zlQ4J32yNkS6rsdFIEXxDW4tMAdpaSJ7MHN9iTIiF+66TmTEuYNx8CsCo2dtWy5rrcXm9GaRBxt0Snit6V2PMnoErCklVDpP/R1GGNvM9VwC9obvOT//i8DwaVbz6RCmp5nO4f5BQiuv4OXhStvH3/65Q1rxZee1fdynz2/bB3iZf/fp5ybq7/z6P/VcYpblDxLwgZUejluW5YXXUJKB9VY1URxOMAf+SAiRMP6Rpmnoeycdurnb0/vsyYyR777yN/imJTpPUdZ0PrFBoff2+YVf/8d8+MMvc/roPutNz2bVUJYlH//4T/OlP/kidV2zXq9Zr9YURcnZ6Yrl4T4xDT27Aastjx894sGDh6JqiOTNEmFX9xZ743PTyuQ+UWH6xdPp8cGzXO5TVTVHR0dcvXKNwyvX+PO//DJVNeEjH/0JbGHZ29/j2o3rfPeVv2E+n3NyfMrDh4954YWXeNQ/4o//0x/zxuuv0WwaCT0DJlVF07aYosBoxYc+8mE++9nPcvv2HbTRFHkjSWlN07b81Ze/Qt92Ut+UP0uDl78PHdeuXcsXAFR1jfOek7OzXD0jmxHCHjustVy/cZ316oymaVjuL0QKfHbGyfExd156EZRUXTnvs9dVeqUHuT758z+oJSRk7OLGUMyy7GFzZLAE7GY3u9nNbnazmx+f+UD/9fZdT3BOpJpGfInRGFRetBglibxRaYI2YMCHyPpkJQxnVY2di7K4jZB9Y3U9wfmANgV1pWldT/JSQaENKKPZNA3NZkNhFLPpjKquRAaXxHPpnKeqavq+p21aUnBM6xIVBegtpiWzxYzGJZqmpet7DpZ71Fnqa62l63qKqsxMqcc7R9c7NpsN04N96rLAKoFowfWkBM1mw/0nx/RRkbQlaeh6Se2dTAyTqsLX4kVVMeD7iC4LqhxmU1qNRhaA3guDI3jSYI1UP/R9x6bZoFLM/LMs+oIPaGupqpqYEihNQuFjxPVSkROTBBsNyaMoYbW11lij6HOFhjYGpcEWJVqRfaQSGvU0fzcGqshuA1oZqd7wHp0Xpl3XUVVVliyrkREcQFjM6b4D82iNxhT2ApsqabnC4iqQRXE+BxFmnicha5RsfyRh65/mOdVWbtL3kgRvA9XLGNdtwHgZI7r9vc8CzBcB8vltzzres+f8PJ8lU5bvOn9dnvWch9d9m/UavKmTyWT8XmMMVVUB5xU0gxeVfB6LxYK9xR7WGJp1y8nJCWgDSYLNQhI23ShFt+lw9EzLCq016y7gqgl2scdLH/9Z2FvyztEx+5MZwSvaTcvh4T53P/JRvvHVr9OvOwBOT09ZLJc8OTri1p1bxDSkDUeMSrz22us0TQOQnyNjcu18Ph+B9/nzyv2x2b9blhVXDq9K8JL3fOxjH+fB/QecnJzxwp0XuXnzFglJC37xxRd45Vvfpi7l99Fbb77JJz/5Say1vP32Wxht2JvNcX1Ppzt676jqmqThU5/+NP/wH/0aZV2hrYB75XNKs1K8+sorvPbaa+KNzpsMg+87hEBZV+wfHFz4/B2fHAlrmntnh00Q5xw3blzn8PCQ2XTCa9/9rry/dU3TNDx+8iQnf8uxlR5+d0uG9RiglgYZ+Ja6Qb0/zGsAqhLw9oH+c7eb3exmN7vZzW4umQ/0X2+tDd55XAioEFlnT+aknjCdTkTmmxRWK1rvcQECmj5EXN8RU6IoDCSF9wL0UBrvA20Gd8PyprCWejLB2IKoEs45To6PsQom1QzvPY8eP2Yxqamrktl0PrJCTdOwXgmDOK1rCSMCFJG+belcwACzSc00e21LU2CMJeVFepnB32a9JvpOUniN9KSqKNUNzgtQXixmeKV48OQUU1hMUVAVOTE3RXzfoZKXGpqBBUmGsipRTlKRg08oo5jMZrStk7CkQs7JIP2ys9lUFq4I6prXhbwHrkdp0eVppWi7HtKK0oicz/W9+NbKUnxxMeKDSLSttSijxC+M1M/URSXS45jQKnfBZgYwjEnBmpSkIzMmgYZKa4y1Od1XjeFYKi+qB8YOtv2kAzCNSDOGLL5TBsPCPGZHZ0yyGcIQaBSz5zODRq0zYz8AtkFuOwDLLEHOtw25w2MoUv5/BpZzxH0RqdJJsgBPTzGuWSEw3DQw4rAFCAdWUw3k8FbdEIj3L4f7DDLpgUkbmaunjp0NsVuSaoVS8hgBphGVFEpLRWZEowagnV/n8fUBjDbje6RyTYnP/uiqqmTTIL9v2uTrKIqPmqfOazqbUU9qQoycnZ3x+PiI5XIflVOlh3fGmJLCSC1SHyJJRUJtqPZrfvIXf5ZiseDs9CFHbUOYLykWSzoXuHJ4hZu3bnJ4eMAbJ2+gtOLR48c8f/sOm9WatmkpSqmvIsFmveHet+5JUJCxqCT1RlprSlswnU7H9FqlJOTIOce6WdO0jXze5gvmizmbzZrJdMJLL7/A7/3e72Gt4eUPvURVlYTkcNHz/AsvjJU/AOvViqMnR1y/eo39vSVnp2ekELCFBa3QhWWxWPCZ3/wsP3H3rry+KRGdMJ/GFGilWK82/PWXv8J6vUbnzUEJavIU1uK858aNG8xmM/quo65rEvDk8WOcc/n9DfltShijuXnrFrdv32Y6mfDee+/RtQ5rC5p2w5PHj+maRsLcfKCoK1CZlU550yVfx8ZkD+ygwFDnnzetNVYp6YdW8j3fdx9mN7vZzW52s5vdfODmAw1Wy7pi07VsNkONAWhTEpVm3fTj4sP1HU3vaUOgKgsO9hbs2wVER9+1lMbilCYkWOztc7Zq6DpHJIy78SFBmaA2Fo0i+oBOAJq27SVJMgZSNYTURIwZKhFUXqhCIBFSwico0KTkaFdrlNaS3qtEvub6lqC1sHtWk2JHcA5ST11olvMJZakJwSExuhFtpMfRdZ7ZtGbedUQ0xmisBqsgRYfRCe86tLEUhaWuJ3RdR3PSUhjLbDYjpMx2FjU2itQv9Q5rpK+07brMYpic7BvYdJ2AcKuwUVOWFqUFLGptCElWisYUGYQpnA+SMDp6PwNWK0pbkqKkDltrJQQndJikKY3JIF6keymzrlpLENMQKyzAV9gobQyFyu9BiKQoHZ+DHFCAqvTZigdZYbQEKVlToLWh836s7yCpsfInZlAdshxxYCTjCIizzDBftwM7i4KoMkesFGG4H4gj9rxYQaNUkusyn2dSaQTnRmWYl+Q2rQQk+iTeT/EhBoZgmXGyfJoRdJ//7G3JbkqJkGQzY2C5B8ZYoQhjkJUCZcZzV0mOm4ksVAS0pHGfi7jPufKYBJzGFKnqiqouMV6zWq1wuaJmYPBc31NW4pP2KeJSIGmFsgJeSeJXns0WGFsSY+R0fUbnWjrfSv0TEqRmdElRFBhliSSc6vG6Z//mAT/7D36KG7f3SSiOHjd0Z5obB3N84yjLilvP3aCsLD/1sZ/iW9+6R1XUnJycsDpbU08rjh494vnbz+NyGvWT0yPuP3iE0QWgc090JEbPcr6PwhADlGVBDLI51vYdLgaUNfjgObxygLHgNj0fu/sxHjx8j7fffYvbt1/g9p3nSPmKCj5x8+Yt9g8OadYbqrrm7OyMo8ePOVguKcuK1eo9CY6zBqfgxZdf5jO/8Rtcv3FDNhsQ9UFZyAaTVrKR8ODBO7z22ut411OWBUolfJBApbbrSClx5fBaTtC2GFuy2Wzouh5rLD74LIaQZO35Ysbe3oI7d+6IyqOqiV5k3Kt0xpOjxxwfPWFvvuDJ8QnWTGDYqPAswuhPAAAgAElEQVRePgN5Y0qhcheu+FhF5ZGvMSWfwRADUUUUEaO+v996N7vZzW52s5vdfLDmAw1WlbFU9YT1RmpXkjLY0lBPJlneF2nbhtW6wQNN36EVFEXBZFJCsJTWYLXBWAFQtpyQ0GyaJ3R9TzmpSEkWziEl2t4J+CtKprM56/UaFSJVXTOb1hQGyOBFIczikFza946+9zk5VBGSSBgX89lY2aCROo4YgohIjcJqDUoRvaewmuViwWxaE7zL2CLhvGdSTgi9p+07TALvPOumoywr9uYTooriu9Sa2WxG27kMRlKuxZHXwAVP5yJxI1U64lPtxLtaCKMyMJpKKWFLUqLtOwIRlRnpoiiFnR6lsrn3EvEFK62wWSYsXlaRBWulsBmQhJhfl7KUyhrAKkUcpX3CpA5eWKNAYzKrDikkUoiE5AmZeZEyWPnPKAF8CUDpsX+3KArq0hITYwItDAyXzwFT0iuL0luBXGpQRDMs8Ee2Jx8jpeFVEIbyMrnt5eFIGUim4bEqs0UigczSgAseU2Gbvkda7zad+fRdF9haRi/rAL6HH6mUIqZzUHtBeskAzhUKM0LbhCYGRrbr6RMS6bmoByQYTJQU2iQm9UQ2JrJ/s5qWUukSJUk35U2k4XrTWrNYLFBKAsVOTk+IKdC0zei9tLbAaEkTVtoCPWjHzdtLfvHXPs70QOHi6ywWe1w9PCQ2JZujwP0HR1w5PGBvuQAiL778AsZI6FrbNKxWp2i7x9HRMTdu3gAt53P//gOOnhxD/h2RsvY8hMBisRjlzkM1S9/3wkDGgPfiBT843CdkVvLFF1/gS3/+Z2hjeP72cyzmM/G5BtlgMVpz+/bzfP2rX2c2m/Lw/iOOj4+p65rr169z/8EDeu9Q1vLLv/IrfOpTn2IyneKDRyfkd55SWCObFEUOvfrWt77Jk6MnsjGUlQTDZohzjquHh+zt7Y0hWFprTk9PgdxN3Xd5Y0fClW7evMGNG9e4fv06MUb29va4/+59ZtMJWmu6tuX05JSDwyt470cWNaaIKez4O2EITQIl2QXKyMdme6NGiVVExPryfbvZzW52s5vd7ObHaz7QYDXkpNeyntBnP6m1ls1mQ1EUdF3Her3GFiUxeKqiZDqZUBbFKDGsbIFS4hnbNB2x9RhrKcqCAFSTqQT/9A6fEs1mg9ESMKJSlpYaAWe2KLAqMSySvXMiZ7QmS1E9znvsAFTGYA+RwkXvCXmxX1iLMpCyTFEbqa2pTc1kNiOmwGrTSBOOFumyLSJJaerpDG1KitqjXWA6m1PWJd47jC2xSdG0HUVRjB4yAReSLtz3/eiX67oOneshfAhEyEBcYEXfi0RYAT5FWYyjxIe6BZJiBp3GGAptsFZjszzX5de2KAqMNdJDCbLojImIMJRlWWJQUqGj4nh8qSqJI0MaI5ltZPQsBueBQW6rxm5FwXdKmK3sh+07jwoBTSQh3rjWSb3JpmkFoBkLxMwIK0KIWGuyAHcYNfpfs9J2vH17LgORzwookn8PTNdwzCTM+nhdDUzouS/v/BjncuLtFODxOFunOEiayVf0INglxe3v3Dq2GoHKEGazfcxtWW6MA4DN7OsWYh185BJ4o2g2zdiDqpShKEpiEAAr1S6FANUoqoRcroRSOoeDWZbL5dinenx8LJsymSWXsKbcH6yN9JeayEsvP8/PfuqjVMtItGuWswprOlI8oa4OOAtrSA3P3fywhLzFwPPP3eLq1WucHJ8QggDj2WLK6ckJ3jtsWRBj5J133qbvxNsaQkAPGzUZWMN5lVbf9/LZC4Gm2WCNYj6fsLdc4Jzjzp3bcsy33xFW8oU76Fx307Udrus5OznhnbfepmtalvM9SHD//n2c98zmc7mSFXzql3+ZD9/9KJPJhN45bCEJ2INkd+td5+joCd/+9j2apqGw5n1pugq4evXq6EU1xtB1HWdnZ6OsXN5T2XBaTGdcvXqVF154YdxYODg44M033mTGhJQSZ2dnPHj4gA9/9CfEv991pCxbBvDeMa0n+L4fP3sxsVULlEhRfr9UVUmXK8dsYXnju69z6/pL7GY3u9nNbnazmx+f+UCD1U3Xc3yyQqHwEcrKsGk72raVdFAURltsYdFGUykotKHbNFR6Sj2tRWqqNb7tQGuCD3gX6X1P43pSV4zpm8OiS2sNSssCDZGYKaXou47ZtJbqmsJydnbGe/fvc3B4QFnmftQoi9OiKEWiGBMKR10VwhyOQTpawB9KQp2UIZLoOodzJ0wnJfV8RsrVHVEluphwKdE1PQmPMhZTVKzbjqZpmUwrBAdoYoS9vXkGbImmaXB58Sfy4BIfIzrKAg9tKWwpybYJfIwYLUx2UsKUjuBNKbwPmR2zAhhzMmdlDWX2jhqjic5jyoKUO099CKgEKrOfZfYPDkxJFxw++NHhOKT6Bh9HwAn6AvgxCvHrMnjb1LjhgFL0fc9ms8F5P0qWfQg0rbCAIUXarqPrHNpIeJQPCe96koaiEDl0SHGEoXpwoOY+30Hmew4nk0h64/nzGOZZFTfboJA0qHfPQd/ALsv3bn+N43GVGuHs6IVNW7U6aZDs5nMcGNwLQJMLsHY42vhDB7ZfqeHf5xszjMcFjBlZrWwCzl5DQyJSFSXaFITO5fdUPmfW2hEYDWz8kOI7POnB4ptSYjKZMJlkZq7raJouf/byppL3lGWZE58VLrT8g1/6eT7zT36NWHa8+/h1Vl2icYnDgxmGmhQgug5rHC+/9Dww1C2V3L37Uf70T76EUomTkyOuXD1g3ax58uQJh9eusl6f8eabb114T4eQr7qumU6no8/aGEN3eopzDu978WEqzdWrV7HG0DYb7ty+zb1792jblo989CPcvHGTFBN936ESfOeV7/AHv/f7vPrKqyymM4hQVQVHR4959OgRy+Uezz3/PB//+Me5dusmAanJGTaTQghjj7L34hkOIfDGG2/w7jvvjhsTMUucUVLBM5/PuXLlCt47FosFpS14/PgJ7aaVDaC2lRqqJH7Sg4MDDq4ccuO5W/R9j9aa5557jr/8iy/T9z3WWpqm4cnjJxAkgM51vfhWk/xerYuSpmmwWku+QDyX+qNApexR95513zGZVDx59Igv/umf8Nrrr/OpT/1jdrOb3exmN7vZzY/PfKDBaog5VRaFNpZN09C2LTazqmUhybXeO+q6zkAhcXJ8zPr0mMMrB8ymk9zVlxkZLUEiAz0kHYpbVRhIZULbNMTColKg6wKqLCnnU4zW2LIkIovka9euUU9qQEKDYi8pvD4Egpfk4aos0cPOfzzv/JP6Fk3vZHEXUhTvaIrUs4rD/X2qskCXJShP0/W0Xc/jJyesmxZligwFBDr5GFitG6L3+WfF8We1bYtzDucdJkZiiqAEcKmUQIssUyH+0Nh1xBSEKbWZEY2BkEIGAtsgS8BsmZncsihyHpGwkTEEktYChr3H+4aYk4IFFLqcHiwMrQs9MMhPEzHIYldrWVwPX4dqCp0RWsiM7QCmYgy5AiQQchWIzoyrtnbIMYIkxywrkxfj8nitNTEDsLFaJS/c02BK/f/Ze9NYS870vu/3LlV11rvf3ptNcjjD0Wj21bE82gxlEluQEThGrNhBDNgxssFAYMBOHDhOYMQfDGdBAgE24GxAECTIl8hSAjhWJNuSZjQSZ+Es5HBvkk2y+3bf/SxV9W758LxV5zZnxpZtSWxJ5wGI7r7LqfUc1v/9bw8xlgLaOiCo8ut/LwnwQ1JeLvxMymxlDlZayW3FJ/1QEFNixfxGAY9yfvJ+dMZYxYWgo3zBks7HsAprWm3nQoZvZnBVt9kLx7MKX334+C4C3+6vF/F5SkHOrzI9cE6ZjbVW90oAAU4GncHqQ2nKrADr5uZmL/edzWY5kOliJZGESBmj0Rqeevp9/PhP/AiPPXGLSGR7+zL3z+5zcnLC7GhBaFumoxLXOG5cv8JoVPbBRSE4Hn/ycZ75ja/grGE2nzFfSCjSvbv32N/f58HBAw7u3iPkROkuwbZtWi7tX+r/3VXxNE1D0zQsFguR4QI721uQEru7u4zHE46OjhiNRjz55JOiKmlbFvMFX/7ir/GPfukfcnh4SLNYgg/sbO0ymU54cHTI8ckJjz/xONdvXGfv0iVSiqBFDt8FmnX70gFXozWz2Tkvv/Qis/kMEEVC93OiVkjs719iPJ5QLxoG1QDvPYvFQo6rDbi27RehxuMRu3t73LhxA2ssy0VNWRRsbW2hjSxilUVBvVhwcnLMyckJpS1onGM4GeO8z4uNntlsxuZ0Kp5tTb/YpZAQJY30crdt4Jtf/Sq/+qu/wu3br7G5tc161rOe9axnPev53TWPNFi1tqQoK5QWiexisWC+XEDwjAbCpKT8EJViYDAYSI+gUrT1krPTc5bLugc7IUXKqmI4GmNsgTqbUbcOlSJKxb6uRJEYDYdsbWywmJ0zn50Tc2jRPDRYrQkpUlUV29vbRCIhgq9rlsslKWmUFmntcDDA+0hhNMbYXtrqgiclLcwlSRJOs3cVrWmalrPzGcaKp88oTd06nPPMlzU+rkBZShGrNadnc4yRdORBWTKfz/tz2UuNje4ZZPFCyoOdUhLU1OlLi9GAri6mqkqKoqR2LY1vSVEhhGqCmGi9x1Zl9uGJXzfFQAiyjQ76+OBZtk0fzCIAX6SCKKmr6JJhu6RllZk4Y4qetb4oMSTJA2tCAli0ls5dYzqp6QrsxFbCgwTIJqwpUECIHqVNDm8R6NWB/JQDlICH2LxkoPth8Xgix013TZIcw7skubB6uP5eozLaXuEyQYpyLd79wx3Z2cmx8/51FGwGFST1LrCIBBj1QNLI/nfAvNM05/5Y8Qhe2J0OYH+f4+nucR9yt3Fme/vakfz3oigoigpjnSzWxBaTUv/7nWy3Uwegup5YenYVpZhOpxKAFQKz2UzY2QxWhak1pBSpqpKnPvA+bty6zGRDPJ8hKgbFDjuTIdPqGiF42uWSk6MHqHDMrZu30CqIXDzKdd3Z2Wa8MaZ1juPjY07PTqnrmoODA2KMvPnGGxwfH2F12YN8Adtw9cqVvu8XoGkaQox5IUK87BtbG4xHI4Lz3Hr6AxwdHXFwcMDla1e5eeMmWmnOTk75v3/+/+HbX/8mpyenONdK8naILJYLNjY2iClx7+AeH/zQhyRpfLFkOB1J+JhW/fvbFqZnVAtr0QoO7t3l1VdfIcaQF/jkMyZmOXZVlGxtbdHUwpAavVK9dDVRF6uXNje32N3fY//SJZqmQWlN6xyTyYTpZErb1pS5SmixWHB0dMRkY4PaO7nu2eN7nEG71jqnhBtM7lmOQdKGVYq889ZbfPnXvsS3vvkN6uUCFRNWrT2r61nPetaznvX8bptHGqzGKL2eG9vTnGg5E1+k0mxMxhhjODs7EbCZEtPxCGJkUBYwmdK0Da1r+ofMBLTOEeYzvJeAo8IoVBCPosNJxYRRTMdDxoOKQkUqKw/7vlkKozoYkkgslkuMNShtcCHS+oDzAWtzTYwy1Ms5bVMzGY/ZmkwJ3lPZAlNUwmIU4p+d2hIXvPhnQ8tgNCJqLfJkNE1oCRECClsNKHQhEsJW5MuFNpDEG2eNusBGCYgyRtPVvQBoXVAYYReLqqQwVpjhFCltIfvSOqzRlNbQGSh96yUVV2lSlKCirc1NyGy0SpHhcIj3bfYMihexS5ENMZI6z2kOzPE+iDTZWkyhUUSsJFkRwooh7v7uvSf4mGWUVmSJMfTdtZ03MCUwBnzu5OxYNu89RSmJsMvlUmTGIaKtSIS1MkSliCRhhbPf9oIpNQNCAXQqe28v9rqmBCmEnrF8d19q5w/sHuohg0AFJBEU9568DFIvvoa6IEhOF8XHF0Bl91PqXSmoCUms1p0sPYt1UwcAu2CnDAZBISrwh/tVL4LPdx8f+WV69K/kmouyOVEUFYPBiOFwxHy+JEZh/YeDEpBFBucco/Go93tG1/bnMiKA2xrNzs5Wz7Sfnp73XaYSGCaJ2OPxmA996IPsXd5jMCwYjQeE6HA+UjtHigmrYViUtCROY2J3a5ftrV3kYzIIIIqJ8WTMpf19To5OiTFyfHxMjJG33nqLxWzOW3feXm3fSFdpiInJeCISc+coSkkuPj8/p2kb6rbGBYdWsLExpaoqvPfs7u7yG8/8BmVZ8qGnf4DxeMpLL77Mz/38z/PSd17A1Y6UEoPBiJP6hJQ8jasZT68S70YOj45ofYsxhmXTMNqYQvK9pFfn89a9N2IU//YLL7zAgwcPJBk9SE1NSqu+4qKQ+p0YI8OhVFydnp7inKN1jrqp+yTo8XjM1atXuXr1KtPptFdLKKAsS65cvcLLL73I5vY2IUbmsznHR0dMNzZom0YWkaxlPp9zeHTEpUuXMB0jn2n/tpFFxOV8ybNf/ypf/JVf4eTwsO/i9c79Jv+vs571rGc961nPeh6leaTBqm9b6nqJv+/Y2N7m0qVLbLstTo4OWSzmjMcjppMRwUcKrRiUBSGK/9T7mEGqzqCwQJFoXUtq28wkCdM4mIywxnB2foZCekwHZUEKjuGwZDQoSdETvCd6z2A4wBYWHyN1KyEqKKjrBmMLJptT5vMlTb0UGKANyhic97StQysjAU5WUm1DkrCm2jUEEj4mfIhYtDwgJggRUMISVgMJDtLaonVBUqBTQisrHFvypBQw2lCUNj9ki7zPZh9nigkDJJ3QRLxv8wOsxvuW4B0gTIVWIn+1RguIdFnKCn3SaVvXONfSNEuRAhpF8B5UzIxfLt9UClMYLEqYtyBeRAl3chitKAyEIIsV4o3NktwOtGlNh798CKiQUJr+gbtjRSXIRwKbOi9kB85CiHgvTFDnNPXeA6H3Dyut0UmY6xBCD/Q7j2a6ABmzAvj7zm8uDRje7f1MafW1mFIP/Ho2FbJnVpKKuxRf1CoKKuXgmawblm8r8Yh2+54yuA2h8/51Ul/VS+b1are6vcvgNrOeFw9DScK2oktKlnOs6Bh1SbldLJZZCtuKgqCrCMqAvyhKQoi9h9l76Q/WOeV5NBoxmUx6qXuMonjoaoVGoxGj0ZBr165w+fIlkk5cu36DqhyybGpa15BC6MO4UoST4wfUywVXL19lONwUb24S+bL3nuFwwJWrl3nhhRepqorj42Pphj0tefnll3nn7bflqDPITnmhZ3NjE0VePPGeBFma73s/dUyByXRK07Y8+cQTzOcLDg+P2Nne4datWzzz5V/nZ3/27/HWW2/n97+lLC0hM6AqQd3UhBgZjca8ffcd7t69x87ODqenp2xvb0mgmelY6pyUawxayefD67dv88ILL+Jbh1YaY0sh6qOkinvvuX79hiwUZeVD61a+cB+8+OG1wvvI9tY2ly5fYn9/X3yoOfhKAcZarly+zEsvvSjKl6JgNp+xXC7ZnG5wNp9LeJoV0Hlpfx/nHElrtLW0bcNs1jIoCt46uMc//MX/j5defFFClpJkACgl2/knvD3Xs571rGc961nPIzqPNFit6wWaRFUW+KbhaD4ThkyLdHU0KCiMdE2WZYExirPjc2azBT7kBhPkgaxe1kynYzbGQ2bzc0LwkrRrTQ9yhgOpYikLSwyegKEyBVYbFBZrjISaKIWP8oC9qJeMRmPKsmRrZxulDa0LuDgHI4CiKCuSsdQ+kFDUTQONgEOfhNFzzrFs6h5oNY0j2NT7MlOSECEBGqpnG3uPZUjCIlmLSp4UA6UxlKUFEqGwqNxXCBpdaHxbZ1lr7BmTLrxIPH4mg1fxlLZBZIqkzDj6QO1dH36TUpJtpEiKSpjK3NdoTNH3OcoDb6A+P2exrDMjadApEpXOvYtlzwjGGHFOQmASAtzDBRYvxYgldy8qTQgJnRnlmMRvh1IUxub7IRBiyh5c8TLmF8MHT8JD9lD2wFE97DWlx3Ado9hJbbMDM7OKnc/03ezq959E5xvNu4SAVnUB8HXe0izpRaplVL43lO7jn0gp5MTldEEnnA8hAwqFki7ctPK0dvdZPtRV+jKgdAc58/7mc8275cCsZLtd5UhWOUvdiNa02deYUsw1UCUpCciRBOYSEL9niiKFNsrkc6oZjyf9IkTbul4KboxhMBoyHo+JMeC9EylpYbh85QbWDimTAHbnHDE4gveYwuJdwGjLzs4eRhlZyMnxWXKvaq5evUY1GFAvG05PTxkOh5yfz3jmma/w4MEhSukVE5nZ+c2NDblXc4CUy+FDMS8m+JQkgGkyphxUXLt5g6997WtopdnZ3uEXf+GX+OIXv8SD+4f5vaBRWtMET9u2XL1+nU988uO8+cbrnJ2fS0/1oubo6IjLly9z950DmrbBlpaUkFTkfEHkvZ9YNg2vvPwqB+/cg5SvXXftk9REDasBu7t7OO+pqgpjDPP5nMa1YmkIEuCktEIZzaXLl9jd3WU6nUpYXLZuhBAwwO7uXmbppXLr/Pyc8/NzqSCrKs5OT5nubDEYDPoO3qZpsCFQFAXniwVf/vrX+NY3nuXk6AiR+UdSFJVL6+T8uxBYz3rWs571rGc9v7vmkQar4+GQ8WhIWRUAJJ8wGqrhQJiAXPQuOUECeKrBAOcjy9NzEpqyGub6CpOrMjSjwZAQHKAoilKYpxAobIktJbTIuVbSLINnPBxRFCKFrdtGHjZT4nw+43w2oxoMGI7GuLhkvligdEFZDXqw1TQ1rpGH8kIb5rN571XDaMbjkYAfrRkUxYWwoQQZdnSBKDGHQXVARGupwUmAsZqiMBhliL7FqoQ1wpbF6ClMiTKGunbELiXZ+d4/qI34BF3HXmnwoaVps6Q3dQpRI8yszknJCFuijaGqKpJOvQSTEKTrUGligpgiLodQFdpglCYq8esRNaY0FMaikgA9hcpdrpHWOQGfQR7utRGfq+5CeFConEosXtTYH4+2BoNd+V2Dz4ydbEOYYo3OXlfvg/j1TO5m1dLHqpRGK7Xyd+aH/A4DruCdzCr9eQVYLybdvnsEgF4AtEnle6CT8uZam6RIHWjPXkIhOCPGatCysBFTIoWU+2JX+9eBxoT4RzUSzNR3zvbpSZkX7RnZh/f5oi/xogy4S/1V+VoIgO/6bmE4HIrMumP9jYEkzF3TNEQvbHhZFj2rGtPqvHnvUSmyMd2gqgYoBefn5xhj2N7e7k/d2dkpZ2dn3HzsOpDY2d5hc2MXzYCqqChNJFWxB29NUxO9xhYDysGAkDzGGpo69JLt1ju2tncYj8fMZnNQiqZpKWzJnTfu4NuAsbrfT1KiLEoGw0FfgdQ0DYvlUmprvM+dxprNzS3KcsD2zg5oxYMHRyil+drXvs5bb77NfL7AaotRouxoQwAFt554gh/90c/z+K1bfPFX4Vvf/Daj4YiTkzOODw+pioJBWTGfzZluT1FRroopcghV9q8fHB7yyiuviIfV5FTm1NUVCcDc3dnpZc7GSPDa+XxGGzyuZ8kjKSb2L+2ztb3N1tZ2n+qssse1ez9sbm9RlRXLuqasKsLxCScnJzRtw3g85uz+ARuo3Mkr98BgIEnvr774Il/80he5/dprxODRSth32a+I83J+fEhMN7e+6/22nvWsZz3rWc96Hu15xMHqgO3NTUKUDlA7GiBhQorCSiBQURisUrgMLozRVIMBZr4khA7oSKejMZaysKRCUy8DHQCQh1Com5ZKKekgbB0+wnLZ0LYeYzRlYagK2zMh1WBA0kokviEwr5ccHp9ibMGybgheiuwHAwGuPntSPQldFhQ5OKbzXnUpwjqHDaFNlsCm3msZQxKZpOnYJXmIbH1DCC2xMFLrQEBbTUoSnmONkb5YpTEmZHZD40MktA4fEsaIf1RYxUD0gRgiPrOnEXk4tUXBYFhRFmV+eFSUpQQsLZZLzpczvPe0py2boxGDogQVSHEF6GIS39v2VkXbCiDWylBVpfjRMmMa8znxHUMKaJuPMT+8hhBoW0dK4s2rbNHLgducNGwgJ+aqDGIvJOtm1rTzu8p5FRl0l+YrLJTuLJz99ZLrQy8RJvHQdeG78ehDATvdrIBsZmnlq4ABNCnpDNlMD2B7gJjEX9spfaXMV/Y1pZRBYda45m0ZLYsHKspOC1hLmV3N7DCgL4DWi4fyUDLvBc/txeNKeZsiNI29n1aRKMsye5nlv5SS3FeDAYW2zGez3P/pQQvoS6T+3KUYKcuCzc0NjJFrfXR0yMbGBpubWxwdH3J0fMzp6QlFYdjb3ctVK3tYOwQK6a3VKq/AJCDw4OCUo6NTrl67xGg0ACKNazO77/M9rBiNRmxubvLGG2+KdNlHvPMsFwvKopBzqxQhSTja1tYW1sjHbQiB5XLJbDajcS3OO1nE8Z7JxpQEPHbrFi+++CIHBwe41nH/wX1841EorLGyIEWiGg746Mc+yr/0Bz7H1pYwt1evX+OrX/0au7t7hBC4d+8ebdMymUxZLhZs7W5lZYDK4UjC0KYQuXv3Hm+88QZtK4FNrN4ico2MYXdnR+qAckVVXQvw7t6HLi80aKW5fPkyk+mUza1NupCs7t7o63yGQ7Z2tjl4520u7ezinOPs7IzTk1N2L+1Tv7EUMK01KJH7n54c8/WvfIVnfv3LLOsao0WyXpaWGD3OteKTt9KRfe3mTW7euvXdb8b1rGc96/kXnGk1YlIM+n9/8cnL7P/Mn3/4h84O+eCf/NtS5QXcm5/0C7DrWc96/snzSINVCUfRJBcoVA5NCUEkrlaCU1KUWhSdDXVN0+CdADGTuzeVsdRtSyKgqNBKGNmkOi1n9u4Zi7IFGIutBmhToEnMZnOapmYyHrK1MSJ5Ac/GGIwtOD+f0bgTCa3RRgBSjNiyIKZE6yNGS/JtjAGUpigLSDEH6ERsUWALK9+PEANZDtyglGE4LIXJLBI2CUPoU8AahXeNhBQpMFqJbC8FjJY+RGs0uigIIeCDJK8moHGeEBPOB5xf9qE/ItWVFFZhpIW5a32D9y0KkemOhhTnnscAACAASURBVKPsE0y9L1KAnSTBeu/xLpC0+HZjEJ+eJPUKwFJKUxUlZVGIXDX7V23ucwzeSwBSjFhboI104JJZweBj9poiMt5G6mpMThb2mbXTKZEiaKNRWlEUCp8iJsW+89IWOsueRTYYcnejSHwTSa0qjlQPSDv2OwO4DjGqDPoylfhuOfCqkmY1gp1NL+1dIWmNOIzz/YpBofNpyBJfpXofb0ITcgKwOJITSWdACRKshITpdAslWuf91t3xPgxI1ffx5Hb3zHcnAosEW0XQOvXsqGb1ukolvJfgG+890+lUQI6RazcYDMR/mSLaGIbVAG0N9WKJqiqsMQxHQ0KQe+T8/JzRaEJTtyzmdfZGBnZ397l06RIAu3u7aFUgiwCrY0o+4H3N8eF9zs5OeP8HbhGTALZE6FOou0UOYwzXrl3n17/8TPZSa9rGZR/waqHAaPESb29vC6OIovGeJi/QRFby6qqq2N7ZZXd/n6Zuefbr3+TwwSGudUQXsbrAaINznjY6Nra3+OyP/CE+9dlPQ4qE4NBGceXaVUaTEc5L2u69gwPeeP0Nblx7jMXxnBAcZVXma5/VCyjO5nNefvll6mUtPdFRvOApelJm3Le2dxgOxhAi5XCIUpqzszOapqF1jsa1EjoWFZtbm2xtbXHjxg2GwyEhBkyWbIOA2RgiWkeuXLnC22++iS1KlNbM53PuvnOXW088QfKB+fmMydYG3gW++Y1v8I1nv87bb7yBUQmrxINttKK+kP6OUVy+cpUnn3o/R0cnnM+X330Dr2c961nPP+eMygF/Zv8z/Jf/yinDv/4z/9Sff/mFL/R//68+9Z/xn77zS7+du7ee9fyemUcarGrTsUAiTwzO9ZK0pmlEamoLlLYE5/DeSViP96QY0UYTgqPxnuFgIA9ByyVE3/srh8MxSgtAITNb9bLJacGSohuSog2Js3kNSmR8CUhOQpy6Splewqk0VTXA5lClpBIxSZqoUDOSQjyoxiglyZi2KIBEiBprS5wLLJbL3DNpsYXBWgmf6eR4yoHJISvWVivvZcygtA34KAmjPogcsX/gRoKbJKwGYX2EPqGqBoxGQ4xcAJSCtvVEFVm2Db5tQS9IMQkw1JoUVjLJQmtcTOiksLagGg77YBUfggCEqCAmfBSwYrQh6iRhKkrROGFoUoqkC4m3EiYkQFLAsBOgb7r04CihNa4BJcy67iqD1Iqx1FpjIuJHVhptJD1YkoFT7t4FlHTIZjoVEN8eWgBfV/9DUv31V5D7X2Rb2Xaa/5ke6oYEcktu53Y1xCz1Jb+ugB8DOb03ZnABHfOZt9n5WjPuTFH4UKM6oJuBNhKw1XmTpcZkBWbpf4r+dS8uEHT7tvLvqpXkOKb+eHVXhaQUpJDlxlB0tTIXjjOhGA4GWGvw0eNTYLIxxVgr9VM+cusDt7hy5Qpf+rUvcX5+xsb+Phubm2hjqOdLQOGc5zyckQiQIoOqYntrG6010+kGg2qMdB11vtsESkDZ8ckRRyeHjCdj9nZ30UrjYxBJeFoxwxIOpLh+/RpFaVkul6gE0RRoq1dMNpKoXFrLsBrKeVeKthWvexuc9BYDIQhYH1QDlrMlv/yPf5WDd+7hWtl+UuL/dN7TJsetW7f4iX/1C9x88glCDOLlNRrnHFU14Nq167zx+h2GwxH37z3gzTff5n1PPE0KCd84ynKA2AssAfkMuHv3HV5+6UWCb8WXnpIAViIgi0W7ezsX1AWRZrnk7PRU3o/O4Vwrd7ERNnljusHe3p4wzdpeWIRZ3Ucxwfb2LjFC6wOj4Zi6rjk8fIA2hslwTD1bsFws+da3v8WdO3dYLueY/N7sEsKNNeiiwKMYjkZ85jOf5aMf/TjfeeElbr9+hyvj6ff9f8161rOe9fxm589d+yH+fGjZ3Ztz/R/8jX+u1/iPfvkv8hM/NONvm4L/8e0v/hbv4XrW83trHmmw6rwnJi/+Q+eY5dCN6cYGiSzRtYGRGuUKlIi1FQNV4HwkKvmasZqQAsF5Qg4IAkjBE6kFlKHwzuNaYR6dk/AOpTV13ZCSxiU4W9RYo3sApY0lRnOhs1LCZrQW5qAaVGCkzD64QFPXDMqS8XDIdDKWx7UsBa7rmtq3kqrrPD44jJXk4K6aRWcWWSkJLwLxrMbsu6zrmuAcKQVUCtTK545XKK0kfpaFyHZDlO5FpSzWFjRNAwqic7S1gBXTA4skgFYXYCSsZL6shWG0cht1XlqjFIUxeBStc7ic/En2L3rnqdum97yBPKxrrRmNhyQ0i8VSQm2UeM+a1gNS09P9jnRYqv76yblZdSl2UmcBaroHiBe7Oq0xpHzxvI8rGa5avZZSIofsanAFkHaVNfm6604Om1nODIbIv9uxbZ0XdMVC9vA5e4ItKPsQKMx8qHiFO/8mKTO3OfSpe7m0kih3MmQBlTqDTuG0yT5i+ZlIl3Ac82KI+Hj73SPpi0zrar9iluSujkn1ck+Vw7+61GClFAZ6ybhrnfhuMxMmITuJGD2JVZVQUZS0bcvx4THOOU7PTvHeYUvxK6MUxhbs7O4RfaJuFqQY8iKMYnd3h6qqGA5G2HIg+8jF1OVISJ6T02POzk957NYNBsNhv7iUYq7RCrEH+ClGdna22dvb5ez0VLy/tpNmR0AqraLzVGWFyeFePkRmizmns3OU1sTke4Zxe2uH06NTnnvreerlkvn5jMFgQFlWxOzZRsNn/uDn+PEf/zE2tzbxMaIIKCI++MycG65cvsZLL7zKZHNMinB4eILzEZKirR2TiSJqWcxx3hFD4Pbt1zg/P0WRcK7BWCtAPoeRjaoRk+kYH5x4RlNkuajz+yD2/lyypHtnZ4fdvX107joOUbYv2QHqwnXQ7O3tM5lu0DrHaDphOZtxNjvj7PiIq5eu8Mu/8ivcefstfHDCpIbIoKxwrpVjNoZl22LLiqc/8EE++7nP8fitJ3hweMjrb9xBqYLBYPTd/5NZz3rWs55/hvljVz/Ff/t3/zD24z/xL/Q6erTJR7/2X/Pffe3vc//PNfzcO1/5LdrD9azn99480mC16zVUgC0KiqrKUlDpBG1doJ3X0jWqFd61jMdTNjamxATn8wVkkOWDPBSKf1QJy+cdbVujlJH0WaMIPuKCJxJpfUIbi7YWo414Ot2S2D2ox0RMXuTECLNUFobxaCzVGzmgp/WeajCkNJYUItPJhPFwRJVTiLsqh8JaWm9o2pYUYVBWaK0pS+merOtaHp6DBC35EIghUGdgJJ6xBoX4KAttpMN1PGI8HmLyQzYkTFHgQiIi9RnGaMqyyEFKEeda8RCWFVppYVDm5xyfnWOVJpaJZBKF0eiBRSewhQAVizys6kzBNE3zXSmgcAH8pESzrEVCPBImVQKohPlRiQzYBRR57/sQFfESur5HVaTLpQSseE9fz5LPj8t9i0VZZPAqEt0QAi73ooLKKakpE+EddOzkq3r1b4Vck4foU4hKvKQdzdiTkGQfYAeYUZlJFX40KU3swWcn+1VcrMnpFblpBVh7kEjne135Anu1e663kVcIF37ne+h7AaUMvbL5e74/O9l3B+Iy26ulpkhrnQOU5J5LMZKUoiwKSPTSWuccSnVpzipfWwn+EXBjiAru3T/gztt3GFQVTium02nvV92YTNna3OLo8Pgh+bItC/YvS2XKZDqlyO+lnEvVX7e6rrn/4ADnWq5du77yKSeyzFj84lqJJD6GyGg04tq1a7zy8qviG86LSSmKvzJmz/VoNJIFn5x+fHR8xPnsnNF4nPc/oZTh9PSMu3fvifrBCdPa3bPLesnG1iZ/5I/+UT780Y+gjMqLAKue3ovz+BNP8KUv/Vr/+XF+fspiPmM4HDA7n7Gzu5MBpKhMDu7f5/nnnpP+U6VlESql/D6QW2Q6nVKWJU0j3vC2dcxmM6wxNIsloXUo5HNwb/8SV69eY2tnWxjsh+TveeEorKTxk8mUzc0tzk6PqQYVx4cPuH9wwPHxMZf2rwp7H+Wz3OVFvKYFn7uak4LdnT3+0I/8MJ/53OdISdQ4zz77LGdnp1hb8tRT7//eN/J61rOe9fxT5upkh5f+t38H/eQn0TvXfste137iC/wf/+AjxJef4f1/+n/gndnRb9lrr2c9v1fmkQar3vs+QMbags3NLaQSArxvsbYg5ZCg6IRJcd6zbNqcfioCNjKgEbmn6sGGJOwqYvQ0bcohPYDK3sbcaxpz9UcKMf87YK1IR5u2zWyVwjUNMRgGVYUymhg8KkqKpTEGqzTV7i6llX7CEEL/H0j/onee0lhUJR7OEAOubfG5noIchNNRXsbIfoQYMCiqociBUwwooCoNg9JSWUOKAZ+CJB03DT4p6jon/RaWGIS96R4gywTWtAQfmM3n1E2LDwoXPXXdUhjDZDiQUJyylAfVGEnRE7OnV6pkAm3b9kzqxfTYGEQeWuXAHd84ktKkKL7ikK9dQmGLgrIocN7hZtJNqVIi5odgnZFV16mqtYQR+SCycO88bdNItYu1KCWsXggitQz9Q788/HaJppCyTDj19x+kHI6Qe0Qz6FNZHklmyxUZb+br1bOVmW1NSmVPqjgHUy9Ppd+X7n698CV6O2ye1L+w6v3b8t7Jv9CFQ6HeBW4zgO5kwBmgdlUi+dW/5/vzYrDSuwOXlJCL/ZFolZFhDt5JXZJsXngYDCqqqgISzgWqShZJWu9zJ6vK3lojadIkLu9fkW5QZdnZ3uXk+JR779zrFyRAsbGxwfb2toQRjUf9IsFqdwXwL+YzTk9P2dreYjIZZW85xCxv70LYnHfECDFEjDLcuHFTAHAUxUCRWeMOoFml2dzcBCB4T+NaTs/OVr7oXAelUJycnFCVlXwGFIWcm+DxMfDBH/gg//IXvsCNx2/1CcLarFKgLy5QxBTZ2tpic2ODxXxBNSg4PHzAO3ff4uq1K5wcHkuadqKv1nnppZe4f/++fBaFiDFdJ3GCCIOqYm93j5QkHMtog3dLlstlL71PefFEa83lK1cYTSdMphOKQlQbYnUgLw51i26y0DEcDtjd3eXOnTe4ce0qZVlwdn7OW2+/zY2bT1BVFSFF2mVNNRzQXURlLcOq4umnn+bTn/0M125c7xewbt9+nZdffhlQ7O3t8vGPf+x73sfrWc961vP95sZ0j39r+mH+yl+7jv30T/62bEPvXEN/9qd48W+9xR/+q8/w6/df/G3ZznrW87t1HmmwGkL3AAbOiQRUayU9fj5gixKbpZrOtwJcEpyenvWANQEuuJyGGlgs6/51pIszd3IaRVEaAQ4pZRChSFplWR8rpKE0JrN4bdviXZfSKcytyG0Ba4URLivqusY5z2g0QqUM6KLqmSlAUnGzFBfVZItkDvrJYS3GdPJU1cvfisJKP2sIApDzvmslfjlFJLgGrZVU2ThofMu8DiyWDV1NjoAPCSBKKeFazyzMJXE3RoajIakshSmtWwEdCJMiiwLy0H2RwXSZUU0pCducwaRIdjVEAawdwBQpsSSextRJwbsalAwiU8qLCYroM0sL/XaAXiKdUuzDYRTizTXWCLOXk4SDF6kn7wJcXVKzeDp7hNl/v7tu3WurTgJMktskdffOw97OjlntKmlS7uRUaGHtO8Cq9Eqq229lBRBXdTYxg+TOTEh/7N1tq1L+fva1dp7X7nUVCaUvHtP3SPbtt8t3ff3iv7vFiBBCD5yNzgsoSmGs6b/fhRWVZQXIez7GyHAo3tIoWCl/L+bXRuqihkOIkIg9mDLmZdzM9QqC3d1dyqpCW8toPF5d4yTnUjzjidPTE9q24dbjNyiKAlTEuYa2bVeMoFYSGJavn3OOmzdvSoXN2QxtrPiE9eq8jEYjptNpn1q9WCxYLJfS55oX40TKrmVxxjkGmf1tXYspDX/gk5/h8z/yeS5fucKirgX0he4cuwvya7Vi+BXcuHGT57/9HOPJhPuzexwc3OXq9cvMF3Pq5ZLJYEOsA8slr9++3XvAU4j5c0NBCsSY2NjeYjKe4p1nMpmQAjTLhhACdd3I/uZztbO3y+bmJqPxiKKUxHCbrQLCBMu510ahtc3yYZhOJ33vblEU1HXN0eEhGrh05TJfe/brJBKTqiQuFa0P3Lz1OJ/4xCe49cTjTKZjXNvivOzTN7/5TZbLJcPhkI989AfZ29tlPetZz3p+szMqBzz7R/YY/zd//Xdke8VP/Xv8wvHf4PN/y/Hs4Wu/I9tcz3p+N8wjDVYBAaUme/iSwnmX2bBEwhMy29W0La6tGQxGOBdovEfwhwTDFHlVv5sUI4pEVRaUxlJUJSk/HC+bhrZxtK2kiaaw8jma0lJVI+kTTZHgPfO5AMXpZMLOzg6FNT1wTBGWswXLxUJYhCQ/GzLz2MkguzTdkFkcMiuTgLKwDKsBtiwkgVhbYZDnCwGFIWC0Rl/wrRVWvKZFIcnBddtSZmmu856mbqmXgYTKqbeawaCSupgMKoRRSvgsNWzblrppUSiqqqSwUv+xbBqRBioyEJFzG0LMfaXysGqLou9AVQAx9vgvhIDOgLV76A5OUkhTTARC/4DfP+Sv9K045zI4MhLWpNIKKOXz3LOUWtjRGCIxrPyUq7wgYduh86kmIJKSgMvOt3rhbiImAVKrqCQBNyoC+Tp2xuaIRmFQSnp/pXpGAHbH3HYs6EP7LXuU/4wrKbHKx9dLdvOB5OPsgG/vn1X5Z2MkJQkiit2XMwOab0HehU1XR9yB+Qw23z3d/dP5fTsPrEi1bX/+pAO5ZDgcysJHCIQQqaqSkM9rtw0BZ54YA+PRNoOiJMXE6ekxG5NNtjY2mY7GHB0+IGaIu7m5SUJ6OUfDMV0jruq9tBHnW+4d3KMoCna2t3qpetM08v7q8G2MOS06opUmENjc3OT69Ws8d/w8hTX53MhCkveerctbFLYQpYG1nJ6dyXu/KGidR6OkjxiRO2sli2WLesFoPOInf/In+dRnPg1aUzdNf+61lrClzm998XaU92rDlauXeOH557GFBgP3Du4ym7+Puq05OT1hY3cL13ruvHmHu+/cxbVt/lzs7mpRtSil2Nvdk79LIy+LpXRKe++zpULyAAaDAZevXGFrZ5ut7e1e/j8cjfo6qZWnWrYSgiNFYWytkQ7a0WTM0YNjDo+OWDY129s7TDc2sIWlcQ3D0ZBPfObT/OBHPsLW1hbLekGbFzRRijt37nBwcA9rDXt7e/zABz/I/fsPuHHj5ve+odeznvWsJ49Siq/f+DhP/qWnKP74X/gd3Xb5b/8VPvrf/yWeZQ1W17Oebh5psHp2ds5svmA6HmdwsWLpekYvRmKKhBiwRUGbE2d1LrrvHgKdc7Rti9bCfuqiwBIYDwbZEyrhJwrxeupKS3JmlgPKA6wHLNPpRHoinZcKncxCbG1OKW2Ba1sBuVEYRtd4KazXhvlsBsjr1XUtQUchYK1lPBz1LF5hLU3TcH5+TvSeVEZ5kNQakiQjN/US5zxFUVBOhlg7wNoClaQzVWmd99sRfEQJcmLZOOrWg9ZoFFUlEsyUJXmdVFdYzo7VFPBrijInHycJNoqJEDJLYoyk7BpLNIbFci6M3bslojnAp7uW3faCF3AaWbF6woJKWvNyuZSgnOGQsizlmsYWYw0qWVIS36/Wtt9W5yPs2DGtBZyntAJ2HaiNPRPZheSs9lmu2erefFj6upLgZn61f8hX+kKScaKX/SpVQK45kVgqWSjRvVyXh167B+f5axfBSaRHpBnAdt5a1ZFsqAziUwbBRiWUilkGnxnpJKFGwmV+H6PqhfPRb+PC17vvdf2uJlfbdGC6A51danIHXrp04O7fWpsMgGIPelcybMX+3r6kxi4WPHhwxNbmHo/dlLTgO2/fwbctRVky3dhAKcV4PMUU5QW5tgIiMXpmszMePDhgZ3eLjY2Nfr9ArAId4AohYE1BF28lnyUFTz/9QV556VWCj2ht+nvDKM3u9s5DwPz4+Kj3r4YYxROvTS8l98ETU+TW47f4kz/90zz5/qdYLhfio/cpJ317XPBo04Hjd98t4snev3SJyXQikmVrOHhwwNHxEcEHTs9OUUrRNDWvvPIyp8fHuKbFKFFvXAzLqsqKrc0tXOsoikISl8/PcW0L0APVsirZ3Nzk8uXLjCcTNjY2cjqxdClbY/r96y0ewGIx5+07b/HMM8+wXC6om5rRoMJ7L5+Bsxnbu7tsb+9yfHrE/qVLfOzjH+P6YzfxKVG3DWUlzLxzjqZpefXVV6nrGmMMn/zEJ7h06TI/+3/9Pf7NP/Wnv+99vZ71rOf39/zAzk1+avA+/tov/AeoyQ7Klu/JfvydX/rLvPr5/4RfPXj+Pdn+etbzqM0jDVabEOUBUxuiDyQFpiikZib67GUEtMoeN2kMqZTGFCUohffil2xb8WYOh0OKDDDHwxJLzBLJhFEKqw261DgX0ENonaOuG4zVJFMyHOaApralni8EIGkBX+2yZt6e4pxI2STgR5EC2UOpSIhktwsriTFyfn6O1pZBNZTAleh7Ns9aLSnBtWakNS4lnFsI6+MFqFWDApIWEIQ88DofKKsCF4M8tCkBnK3ztD6RlPhotRaA2bE18pAuLGro/KRVJSBSKYaDirIoWS6lx7IwNnuHIz4vHChlSR1EU1r6X43pvbvduQkpYrgQuKS1SHy1QcXVE3i3MJFYhfGsmDsJFLLaopUk+3ZfB/FeSh3NRb9sx8zzkH/2oV5RVnUuF6WtF8Ev/e/rzEKufl9nCa9gI6ksQWuMtii568itow8BzI5V/V49rP35uMieIoxszH2ZqzO2kgF3IVMxdmyWIgDGFqSg0ASUku8Zo0jJZxltFxwVV8dyYbp9vAhSL454NwWwpiRATpjVnATsg/ThqkRVVZk1DwyqIUqJOiGhQBnxrSpJnVUpMR2Pmc/mBO95cP8BmxvbOOe4fPkyw9GQ1tVMJmM2NqZopbPfvef083WUU3VwcA9tFJcvX6IoC0J0uWpKpMqd1zqFlXQ5hIBGpOzBB1IEa6v+XvHOUdqC0WjUg/Gz83N8CBRliY8JY00P5q210iVcWD758Y/xr/+JP87O7g7LZkFMuYoqBrE2pIRRhuDyZ1c+nocW8rxnOBywsb3Bg7v3mUwmHB8fc//BfZKCN998k6c+8H7u3r3La6/dZj5f9AnW3ftHK4WPkUt7l9DaEGNLQuS+dV0TSdR1I13QOVRre2+XyXTKzu4OJh9TjLG3AEiXckKjaNqGw8NDnn32WZ5/7jnm53OCd5yenjIZXaGqKo6Ojrh37x5PfeBp9vb2ePypJ3jyqScoBxUuBHyKFEUhVVN5MfP267e5c+cOIUSuXrnKxz/2cb7x7LO89tqr3/P9tJ71rOf393xq7yn+jeIx/v3/849hn/zke7076Oku/+/P/QX+7L/2P/O/v/3l93p31rOe93weabBqywp/fsr5fEGhDYmI0VFK30lUVYnO3aMpJYaDiqKsSAlcEEmtgKPIeDzCGks1qERuaixG0ydSdgBAGQlEEs+n/L3IVQshioR3MZ8RclhPyl9LIaCN6ZnEjgUaj0aMR2PKqkQpxWK5ZLFcsrm1hXOO8/NzFPLwPp/PKQqL8y3WaFwjgUrj0UgAYQzZE4kE+ISI0kbY3Mz+1HXde8dcqISpCRFrFYuzGYtljVZKfHxZN2ozI9oBMe8di8UCo0Xua7T4dCNSR6KsgLPFQhJDlc5BRTFgtSYa+X5ZFFgrcsXOo9p1InYP/fLwLaDK9GA2g9Mk6atGQTRJjp+VN9V7L55VAykDyz5Flsy6Zy+qyoxqJxPUVoBP7Pys7/KkKq0xagUgBcx/N4Dsfa1KzlH38xGV/ZQCtrTSKG3lP2Vy5WkHUrPPtfe2CnenOgCoHoahHfWqLuzDd88KfHdyy5WUufu9vJhgNUbnSpbkAU0irkCL/n6geeVjffd5UUp1ttAMpBPGSJ+uSmCUpo1SmVJWJaWVnmFZUBqvXjOlPjwo+EgInuFgSFUNWS6XuLplb2+f8/NzTk9P2d/fZzqZcnxyyGQylpocYxiNR/1+xSgLMxCpmyWHRw8orGF7ewtrDc7X/eJWSgK6Q5RVBO+cyHwza+2d56233iaEiNbde1+Oc3dnV+75IKqPphEfelfx1CXjFkVJWzdsbG7w6U9/ih/+kR9md29PpMz5PKZsEZDFH1k46Dpa5dqu2H8BhZGiLNjc3OTOG3eoyorlcsnde3cZj8ecnJ9zdnrG7duvc3R4CCCLRKlbZJLar0FVsbm1RQiRoqwoioLz8xkhJZqmZbFcgla44JkMJ2xtbzPd3GBra4vOm64A75woJ2KiLAuWyyXPPfcczz//PO+88w6z8xnBh9wVnZnoXNczn88xSnH95k129neohgNC9GhrKbTJ3lr5PKgXS77x9WepFzVWG374859HKc1XvvI1vOvO13rWs571yOf0yx/+ALt/4ibln/2r7/XuPDTm5of5u3/7R6n+Xc3/8vaX3uvdWc963tN5pMGqKSxJa4w2VIMKaySQx+U0zJQrQzonYAge2ixHRVG3+WFKSa/ooKp6QGeUolWSpjsoB4QsrytQPcMolSuhB0cxCXPYJJH3Rh8oK8toMOh9e0VR9OEgMcJ4MqEqyhy0VFANBpSDASFGzmcz5vMFKayksGRGQ3dSQq2xxuY+UJHeJi1/6gsSS62k07RpJPQEBTQKZcSr2LTiK0MplLG0zmNzSJHJYFUYQ4hRfItVPmdVWawAkTbECMOyIDpH3ba0rWxTpUQxqPoFAmMMOomf+N1yXGOMpPBekHbHlEgWjC56CS1KozNgIHfNdgnK3oce0MX8p87prl1glM7+yK6eptu+LQpUDPhOUpt6w2c+px0TuarbuMioPhQ41AHVvlM1s61aS/WOkr+rLlAJOT/d73UMtEwOyUmRHFsl97d6OPAIenjykPT2u0Hj6idTd2DZsxkyUNda9YfeS6OVEcCsLoL5C72nxAAAIABJREFU72NgfWh7FyTIom3t/YkaWfxRZNYu+x2HwyEK+oWMsiiyxD/7uDO72cnHR8MRhbUSADafY9CE4Dg4OOAHf/BDPHbrMV557UU2Nzf7KqPpxiZdD0u3MJFIzM7POT4+ZDqReqfEKqG7W1ABcsjZhVsk+5wPDh7w2mu36ZJ9uzqllD3sWilM9sufnJzKfaq1vEcQgLhcLLl+7Tof+eiHuXb9GvuXLxMV1LUk6DauJXbyedVvPi9xdEC12zFRBATks2p/f58YI9VAgObBwX2Goxnz5ZIXXnyRN15/nflsDlEWvjQiT0mIFHqysct4PME1jrIqaVvHfLnAB0/jHSF7irUxbGxusr+/z5WrV7HW0mYfuYRr9doB3rh9m+efe57Xbt9mvpgTXPaHG8kXaNqGiLDM88Wce/fuYQtLOSiZL+cMJkOUMRhrpa85RGKQjtfvfOc7HD44JMbIY489xoc//BGee+55bt9+XXy361nPen7fT2Esv7T9aT72N3+A4gt/pl9oftTGfvon+Zn/dZvXfvqMf3zv2+/17qxnPe/ZPNJg1aeEMgWj0YDgPb5pKAtLMSiwXuNcC0hAi9LyUFfX4qsqygFFoSm8wZiiT6PUKTHM4Ms5hwuBkESmGHzABfGjNXXT161clMkqpP90b3eHyWgooFGBtdI76IOEEjkXcN4xX9bM5/M+6bb1nuFwSEiRoqgYDBOnJycUZClljBS2yLJMRVUNcvovdGxh5/MsioIQIovFUuTJRIaDEmsLfPSkIGm69XJJ4z3WFgyHI2xRUNdLQADCcrmEHI4CChUDZWEYjUaU1mIUwiyRMGiwmtgmvGto6hpbSK9p9K5/yFfk9N+YMNmfSoiZsZSH0g6EJFYSYOc9FAKU0PL7Pr+m7oBGTBATVmsJljK2B6tKK0KKgBYvq9LC6ADK6OypNXRM40XAn5xUpCilMygWgCKM7+rnesCKgOS+fxUF5EUEZdDGoqxBJamoSUlJ6nBmXjvQpi4A0YugsJMEP1RF89Co7/qdjlHtvJkrcjYzt0qAceKCXDpA0jkZ13uUimiVMtjuAO9qoeHivrx73x8Cq518GoXRUlljre13tbuXi6LowV6KoG0hNU4h4kMi5BRj8jGMx2MkdTr2SbTj4Zg333qLp55+P08++STf+vYem5ubhJBrcPoQqG7/Bczdf/CAtmm58tSTVFVJDL4PceqmA9ESxpXDp5KiKAtefPElDh88oLAVMYpXPOWql/Fk2Hs2nXM9MHchEAFr5X3z1Pue4uaNGxRlxa0nn6AaDiQoTWtm8xmtb0k5UIuUfb6RDCq7/Yz5Gss1L6zF+8Du7i5VJWnkEvB0ymx+jjaWr331q5zPZ/l3Ur4jBcZ3Xbebm1u9dz2lxGw+Y7lcUjdN7kKOfbDS9u4OG9tb7O7u4rynsJbgPcF7dFaVfOub3+KVV17h+PCIulninHS2TicTAcHOidzfy7k6a8+ZzWYsm4bxdMrp+Qndm8M5l9OLA4W1HB8d88Lz3yHFyGAw4Md+7McJIfGlL35ZGHL7cMjeetaznt9f87HdJ/j84AZ/8x/9ZVQ1RplH+hEYAPvBH+IL+u/zy+q576OiWs96fu/PI/1OjQla76hbTfQencTDWdqKoipxTjpPuz5PpRXOBGEFosdozXQ8Rgik1DMAKUYW9YLW59Tg+WLFoEHuMxUmoCorhhmUVlXFqCrRCqpBSWEszrXE4FkuxadqbEldL5jN5yhlKMqCCDw4PGQ2m6GNYWd3h8l0ikpS41GUJSolmqahbRLD4QCtu7qeVVepPEDKQ7cxlqKIpCQP122zxChFNSgpq0H/cOxci1IwrCpMUVIUlrIssEazXCwkIdhoio7p9OLXG1QVhTVZ+ipgRytFCvLQrkHAurH4mLLUt/MkZuYw+yirsuofeLtqFBc8y7oWD19RYKztgY4E2giz5rxIAQUIrMJrtBb2V2U0lYLU63SVJQoDSuHaZe7plPoU1dXthND31V70t64qfB4ODgohoLR5CJwBFxhPJcyULiTgSWnQVryqSqNSBoq5FsiYlTe1lz3mBYEuNfjdltWHgeIFf63SPZjojkMRe/ls92K9mFh1nlzd3/MKST7WBcTgiNFlxpeHzvlFn+r3motgVRZ65NV1BtFlWUJKhCR+bmutSHX7BSHTKxvkHlipJ7rFg/FoTAiRtm1ZLpbYosBuiDz1wYMHbGxMuXbtGuPxmBgjGxsbkjytTA94AWazc+7eewdtNLu7u73UdsWqrq4/dInVK9WA9773QRpjBWxn0Lcx3aAsyv5c1HUtwUpK3vOlLRgMR3zoA0+zvbWNNYbp1ia7e5dQWuNa8al2926MAZIwlCEEkZinJHJtOivDhYCrfO3GkwlXrlzh9muvUxQl88VckpZTonWiitBKSxq0VuBjf31HkzGj0Qjn5bM0xMhiuaRtW+bLRc+cKq0YjEfsX7rE/v4+tixQ0eBaAaJN63jj1dd47rlv886dt6TGq22JKTKsKhIwWy6o65rRsJIguvmcjckW9b373Lt/wNHxMRsbG9w9eEeSlQtDSJGqLCXFOMErr7zCyekpKUae/sAHeN9TT/Ebv/4Mb7/9DhubW/31WM961vP7az6y+zj/oXmSP/U//UHsJ77wXu/OP/P8xWf+c8yn/wv+43d+8b3elfWs5z2ZRxqs2sy4uNZRFpbSlvJnUTKoCsqyIMaVTLcoS3TraHJfqXO+l/71D3FBWJJEImqD0lo8qgiDQmZQOxbVWEOZQd54PMakRIqetm44XRyyXC4ZDIdUZSlM0KLm5GyG857NzSnDQUWInsa1lFWFLSzns5ls1xY0dSNMqHM0rmVQVvK9qsC6SN06aJ084GeWqAMOCd0/oIYYGY2HaP3/s/fmsZpm953X5yzP8i53r7Xb3e2tHS/x2nbaS5yAwBMgkZCAPwiLkGBGIDQMCtKwa8SwSAgE0jAQzYghEkIBjQQBJEiY7IMd2xOP3XYSt93uvauqa7v7ve/7LGfjj995nvdWO9EMmSRV7bw/qbuqb9e991lvne/5boYUg0h8sYRaQqHqyZSQEstlKwtUralLkSxba3M3ag8qoZLC2ixnTBGNISVZqJsRI2kmlXiEnQ90vYMUx2uplCJFkfKW1uC9SJRjIqcUG6wtQXmUsZI/O8huQZhIlQgxEGJAoUegOwBikwFOBHwC790KRKWAzgB/YMVTjPjoMDFhjcpsqbrgSR1k0JktfQseewCg5Q5ehZyLMRZjK5FpM9wbJQBjZD5VBo0rf6FgO4WwvOKnTDFlUDV4EFnd8yypfRAwi89P/KZZjZzUBWWTessvCtCSGC362yw5V0ynM7p2QdNKmnXSQ7UP4319IIjq92WGV7/qoac4ShdsURREH+gvvGfGarnPud5Fa42PkbfeAoXIt+vJBOd6vHeE6PGNy+FMjjt37vL4449x/bHHqMoSayxXLl/KFzLLeLVsCpyeHtO1S/Z2d9nc3MA5T4huBKpy2BHnQ67QEUAXYyQqODk64v79fWxOGVZqCAuTwDcS2ELksGdnZ5LMaxSLpmFja4tr166ztbUlMt3pjMcfe4zNzTk+OBQJ7x1kljMEkRaTRG1AkmclIHLxt4ZchRAoy5Kk4er167z40stjKrpSCVuVLBZnLM7PcE685ilK9dUQhLa1uUVVVcQMYLu+G1lV59yYHq60Zmdnh+2dba5ev/YAY36wf8C3vvEcr7/6KsdHh/g+A1ylsUa8tz7L9Z9+3/v4xMc/xuuvvsprr77CpR1JSj89P2V//z5Xrl5ZvTdao8KqV3n//n1uvP4GpMTW5hbPfPwT+N7xtd/+mlR4WctisWQ961nPn665Ot/ht/7Npyn/7F962Ifyhx+l+fP/7cf4d//pNVhdz5/OeaTBavJBwGWCsiooS2H7Eoq+8xij0LnuwodE53uc95ycn9P1PUYbvAsja5dixGpNWYpkr5zUlNlj6b2ndx1927JcehSRwliCdxzt32c6mUD20xmjaRrpTlVagnOiNpyfLTk6OqF3EW0tkz4ymVhQkd29PZRSHB+e0HeOo/6EyWQ6SnpDgoQmasPB6TnT+ZR6OmV5fo53HoXChUjftlRlhckEUVVXxOiFNdBC1w2eUKUUk1qYHGsL2l6Cm2JEQpYmFcpoQogsmgV931OUxRgKE5WiKEym3QwuM6raGFzvaLtOgJq1bM9nGCMesqZp0FqPTFKIIuUMSUm3pMlhLqbC6EKKQDIYd64Tv2RMOTiL7G+NK99bkoRZrTUqQVQKHxJJLiLarLylGki57kRYU5H3Klvgc5UPF8GfygRchDiwitnTywVGM6DQyVKUFdZWoDUxKvpIZsOzzzUzuQNDKXUlOsuWLwBYrSAq6eFVA0Mn31LI48xuosfQopRdrYogHsrhvQmM33/1Mq0kzDr7WIWxFA8zSuVCFoO2pVQUdQ1W6/ELrICqIDml1Qg4kkJY7QysFRqtEloNDCBszDeY1hOC86iY6LsGpTSF0VitOOtblBH2M8ZIyOwhKl8vJRtYRVGsNi6KSN93tH1LSpbbd/dRWnP12jVOTo6lw3U+hxQIFHJvUk/wDUcHdwl9x9WrT40bAcEnvEukJPctpCTAP0matZyiJiR48+59Ts7O0abIVuA0pl5vb28TYqK28jNEF4ZLl3ZJh+LhvnPrTZ56x5MYI13Fm5ubPPXOxwEHeNl4SHI9dZJaI8HbCqUDIaR8v2RjAmBVtiR/bgDbTz31BL/1pUTTNRgjXuGiLDk5ORm7ieVdCxhrCPk9297aQkVJSQ8xcXpyxmLZEmKkcz4nd8u7tbt7iWtXH5NnmITvWr7z/PN885vf5OjwkMViIenoKKrJhKbrICW6rmU63+Dzn/tR3v/BD0gl0WyDmzfexPvI1tY2vu+5d+82H/3Yh9FalC8bk1rSf/PfES+99BKHx0copXjf+57m3U+/hy996be4c/dNtrd3CLHj8PDoH/SvpPWsZz1vkymM5c1/4l1M/tw/hf3kTz3sw/kHHvOJP8Pz7/5VPvzatwlxHRa3nj9d80iD1d2tHdrFGcF7SQNOIo2NweOznzR46dVT2hAQMKSMxijpWZ3NpWplWteUpbCSMXd2ohSFMRhraJcLDvbvS4F9XaMyUxdDoDQFPlfVuMJirUEp2NzawnnpPGzPJczJ5VAW7wKnp2d431NWhrIs0EJ5Makm+ThiDmQqAUfTtRyfn9F2Heddx2QyRSlF73LYk3NYq9nQhlIbSJHCGmxREWPAOWGyrDUUmeHsnaNfdqA6CZ5SSha1KtL3nqKSMCMU+BjwXcjSzAxkE/iuBxIxCMAyKnB2dkbbtsxnc8qqomtbyqqm6zratqMoS5IyFNkqmJQiovAhkHwEFQiZEQakGohEiIqo4hhOZLJ/NCWHUSIzLopiZLE0iqRyPY3PFTj5fLRSY62F82GsEjLGSJhTTDlEaFVdM8h0k1KjhFWOK2bvbUBrS1FUFEWJLasVw62EsRRvrB1ltMSVPBed5bRR/JmwYskxcsxkLy9pSBrWGXiqMQxJI0BEWlGFbZUApwvs5oV/S5VQZq2T+HUVWVGQVjVDvROv4mQyk3qovqMoNCEkQuix1uZgrswHxxVEWm0QKAE4+c9opWTzpq5JMY4BaQDTaU1RWLSGpCJVNRFpNZGQEBBFJOUE8LIqhDlWsjdTlgZtStq2oywtR0fH3N2/z6VLe5yfnbK5OcHQgLZZIQAxeQ7273Hn9psYq7i8t4fLklbvo4BThmckIj7kdEHma3C95+WXXyUlJR2iOoegxUhR17mqSmMKS7s4Z//wkEXbcn5+zquvvsr1649Lp2uSDacrVy8zm04Rfa+cs9TiJECPtVwxxfzehFwTld+vdDFiSWTJQyjTbD5nd2+Xlw9eXIWZhYBOkHT+2gr5GeplU2h3a5v5bEZwHq0tTbPk5OQU57z4aZWkKYcYecc7Hmdvb4+rV65QlyW3bt7ga7/9VV584XvEmCQwKSUJzIuJs8UCjGxmvf/pH+Yzn/sse3uXZBMJzd7uJWbzTUIITCYT7t27x+H+gahV5hscHx9JurpWOOe4e/cut27dous66rLiE888w8HBAV/72m/n58twcP++9EKvZz3r+YGewlh+4spH+O9/6JSNv/5zD/tw/shGVTPe/cW/ys9+/C/zr95bM6zr+dM1jzRYTdGjUiKFQNM1tIOOL0r64+Ch00qJv9JaZpOaqpKOVa0FiGplKAuL0pq2aembJTolmq5n2SyYTCZYrZnVE1yQAKTCiJcuhEBdVtKr2jSklOh68fOZohBPbfajlUXB5oYmhcx2pITvW9rGEaMEQU2rKRuzGc45jLECII0mpUAIGhXAaUMIiaZpGbo9hWG0FLYgRQhEjFIj+6OVeDe9Dyu2NoebdH2Pc9LviDL4GNBESmtzpYr4ccXrK8BNUpC7nEw8AECdA5ta2rYVBhZouhatDL4VCfRisaDI6aOzqsJaA4inGKXwLkjqahKJowQSibS3yEFYv1+QzxCONMhOY4zCxOnV/wNWz0X+dag2WiX7Dit8LvhoBwmyGlnP70voNYKSTFkwqScjI651pmMzk6VIMNQMaQG74uc1qzCpHOAkocASPoQ4TbN0PY6+34vXIWm5jkmBHoHKSv751jTgt358+DXG9JYARHUB7GqsrYjVROp9YkDlAB+RPevcu/n9QDVfVkK+noPxUxvxZjvncN6NKdBlWWFMMVYPTSaTC/c3jRVGWavNdDod/bBEMLrCmIrFIlAUsGyWvPbKa3zus5+iLDQqtcB5BoAtSs1ISXFwsM/R0RHXH79OWddjMvEgG0/5ul5MWh7sBEYbWtfx5q1bnJ6eMZtOR690IrE5n1NVFSbf57quKMuS09Mzjo+Pmc1mXLt2VRQdMTCdz7h+/TrGWmLs8V7C3gbv7HCdH5D6DgT3heO7uCEhcvaIUZq6qnjiiSf49u/87ghW5eePweZz0qjsoS8gJa5du5ZBr7y7JycnnJ2dgdE450Z1QEyByaTmiScex2rFl774//Lcc89xcnyE1lr8/4A1BpdrtACuXLnCRz7yEZ5++mmms6lYNtB479nY2GA2m3F+esLGfM7+/j6Hh4ecHB2xs7PD9156kd3d3fzzzvPGG2+wf38f3zs+8enP8I53vINf/MVf5Pj4mPl8Ttu2nJ6esLO9+/u+G+tZz3p+cOZ/3fosP/H1/+xhH8YfzyjNP/Pjt/iPfmmX2+eHD/to1rOeP7F5pMHq0cEBJ4cHQCK4nqoqxT9aFlgjoLQoijFlVOfd+r539H0HWuODI0bP6eJsrD1pm4a+d6ANurAsF+e50qYhJQjlAPgsdT2RZOCuR/b9FUkpQvA0jfhiTWZobSWXUwKIdA4I8oRUcn52RgyBSV0xnU7pXY9Wms4JkKUq0VaYOZ+DhbKIVaS5SP+p8x7vHEYrJlWJUoWED2WPqCz6MiOHyAEFZHb4GMVfB0QitTWZxQJjCupaj3UmbWZINVCWhYQmqYhHdKnaWvF8aoNPArN85/ExkbSh7XvazAaLdNrmLtYoklEYF67W2txlK1LGsVoks58pRllM58TglBfwIfe1aluMsmN4C3DKi/mhw3X4/zGHMWUFZa6pET3rCqjE1VVUGuelTqOsauqJMIDKB4EIGZipUWKbwaqyGKtyhYqVXlkx7gpQVgA6pxJnEDoevc7BOpk1y+bR0b+az9OkwQX7lrlItY0gJ6cQ64u+WT2GLw0srtT7TKiVpm+XpNChiKTgSSrXGzFSp4yS5jHgJ0uAM+AECbgaAVjIPa5aqqacd6QIZVkx1BgxflUFRqD0fCZy86QCMSWsneR03I6+b9EKbrx+E//JT7C7vU0Mx5wcv8FGmlNNL0GMdK3i3v59Otextb2V+1QDIfjxe0tlzsoTSVJYrUg+oq3h+PiYe/fuEYMXqXFV47qOxfk5O9tbYydvXdXs7u5x9959jo6O8c5R1xN2cwer1oatrU1mG/MRoA9hakN1zvC8DkBztYHDGAg2bhwogawXN2estTz55JMYa4idMKfBB2xmGle1MhC8Y1JPuLx3adXX7BxHx8f0wUtNUPTZMxvZ2trk2tUrnBwf8Qv/+zd447XX6Z3L70Jm2q3BO0/f9Uzncz709Pv48Ec+zPbONkprfAhjtZNSivnGBpcuXeLg4D475TbWWs4X5xwcHPD+D3yA+N3v0nUdaMXR0REvv/gSbduyvbXNJz/5SW7cuMl3vvPd8Z0/OT6WoLlHs51iPetZzx/R/Mru5/jcb//bD/sw/lhn9lf+Bt+5+jPs/nfHxBT/3p+wnvX8AMwjDVatgVktAUCut1hr2d7eZlLXGK1y6mzCBy91KCkS+o6+aTlbLEiIf1FrkYkqpdHIotloRUwBqyw+ePFxZUntpK45XZxjTUkRIgeHh7I4QgJi6lqAsjC6BWVZY40k55ISQQdU9owZY0kUdG1L33UsFudZqmtZLhecnZ3l0JgaqzUuhNwzKYvxsqqljsV5AdI5ETX4INUVMVEWVqpeEoTMOIcAVWlRWjy+IUZ650UyqjUkYWKHWo5s9yRGYVUlxEX6JfveU5aKmBd7RV2BFtBjyhKtDT5Elu2ZdKpmqGysFfY0hFw/I8xPUZZjqqm1IpFWSuSUgzxUwq3syHaiJBE1hAcrRQZ5bmLFrMKDLJTObOTw8RACwUsvI0aPIIHh2iiVwYp8zNqhjkZRFCVFUY7JwEWhs8pXZamoehA85L5VpXL9R045FsDKgEKzN1WJVFGpVXhU1BeY0weZzFWFzqpW561zkYl7gF1mwJUroCyEcEIVBmGWrchslaFdBlLoCSkKPh06ZLV+AFiuMLEaDzdlj7HSUiPkvafve7TWlEWJsSanZ4sawnk/fq54hdVYAVOWlYBtYySDqtB415ISLBbnzGYTDg8PuXfnPlevbNF0p+zffZ2+q9ncWjDdeIJbt465fec2RVmwubmJ1joD1iTPWBxA48o7rNIgt5brdePGDXl3jWVjNuML/+gXODo45Ktf+TJVWcoGS2GZTadcv36db3/72/Rdh7WW+XyD7e1tnHNsbk14xzseZzaf0fVLfBhSvH0OFjPjNYwx5q7nNG688dYkYLXyYMuzJO/J1atX2dvd4+6du5IAnlUHw+cppVCZwN7Z3s71U3Ifj46POFucy8+VGGSTJYhdYHNjg9tvvskL3/0uzjlRRuSv572nKApcDlV673uf5qMf/xjXH3tMNpcK8Uq3bUtV1RS2HOX9ly9f5oXvfodB57w4l77VD/3wD2MLS9M0FEXB888/z8nJKcEHPv7xj3Pp8hX+55//eU5PT5nNpsQYWZyf/4GKg/WsZz1v/zHa8Avbn+NzX/m30NOth304f+xT/cx/zM7P/bMcLE8f9qGsZz1/IvNIg9UnHr/G1sSitaHrpNOTGHB9h8/sEgwATcnufS8LPec8OvtRu94Rg/gelVJoa5iYicjfrEE7qEoBID4mrLVszbfwMbF/eMSiaccEWp8inXdM5jOIUdicoCiseNaIURJzs0QupUQfkrAYtpAk4a4hxQLvWs7PT6knE2aziYT/dA6AqqoJIYo0MQMyrQ1VPSF6j6THGjCGlJll53pSBiMxRnoXCFE6I20poTkhiuGxsIVUZHipkBGZcTGCyhjJFSyZNe09ySiSkVoUbSxFUaJtSdd2NE1LCAlrS4oMAEPwWKMxRYGKCkXIAK8cfY1d10mQFhKopceAHrD5vEKM4qfNlTbDgjwk8fXFzGhe7ExdLeZz2Ayr1NqBpRrCmh6oZdGZ8YzCPw/yWK0thS0pq4qirNHG5PqbmIGbcIASOBQJXuFCgORRyRB99mCmJOA2qeyNZASuwixJIFdQkvKKATv4WlMiRj+yq8P5aHUh7foPAKxv/biEQPHA5yjUuFmhdYGxFUqJhLnvzvB+8Mrm/tachjwwuBfrcwYWeDi+aVVRWUvjnACoJIxkVU9IUbE4X1JPJxlUyPlqn++RktTbybSmLErxao6nowleUL/3PSEUdE3Pa6++zjuufZxeGzSes9N7nB6fsrHleeP1Y87Pz3jqqaeYz+fj8xBTys9ZZOU4kN/HnMBrbcH5yTkvvfAiVmmU1ezfu88Xf/Nv07UNVVmINxfxSzvn2drc4sqVK7z8ymvEENja2srea8vlK5fZu7JH13f0rsc56S91wY8Kg+FZjtGz2hbImw9ZATB4v1eJ0SJ/HzY9tra2eOKpJ7l//z7W2PHZUUm6k4e07fl0xrUrV+m7jrKs6HvH7bt38TFQmNUmydCXe3p2wvFJlMAzBa4PWFuA0pA90LPZjB959lk++MMfGtUPcs0TqDSqY+R6OZqm4fKVKxRlQd/3zDY3OD4+5ubNmyyXSzY3Nui7jv39fV556WXKsuTy5ct8+tOf5qUXX+R7L3yPwhjmkyk3bt6gaRqsFavFetaznh+sMdrwN3c+z0/87n/ysA/lT2zUZIPX/so/ycaf+58e9qGsZz1/IvNIg9W93R36xSmLRYPrewn3GDyOkIGPyFGdT3RdTwyJmBS2qEApuj7Q9cLkFIVlUtUS5hMC1moKa3FGQF8ICdf2uD6D3hBwzlGWJTHGUWpsjJEFvJNwJ8aU2kLSUxN45/CuJyn5s3VdkUrxwYpiVLxeW5tzVF48VrbEu8jp2bmE7Rg7njNF9qsS8yJUUVVlZnFkUeoz2DJFkX2aERdclnSWg24QU8hxJu9Gtta5gLEifS7LIktwI4UtCN6zXErqqcqpp9ZaiNA1HW3b0rW9sJcJjNIUVUEKKffdmswuXkiTVWALg/N6lORqpTBFIexM9iIba3HOEVPEGAusvKAxs6TwIHgbFvdSkaFyMM2DbOSQ7jtgnqFGRVjV7M9UQ4Lwg/JbYyToyTOk8mZQq7J/FQGvavAukxlHJT5jlVSWWyvi2AGsiNEPrlHxqmaf5iC4AEQ6AAAgAElEQVRVVkqhjR09lWpIKM7ndpE5fesMEtJx1Ftku/lzB7+y0jkUzGgwlhAlzTl/O5RKRALWVhSFzTUryDkoqVdJWny/Rism06lcYyMhUcYYqrqSd9c5eu/YqnckJVqFB+6n1OoYNmZzqrKUhOXs4x4Sb0MOYHLeYUzBzRt36J+JRKfwPZhKum7v3b7HwcE5IUT2Ll1CaUXTtkDKPto0fs1BkquVhrwRc75Y8pWv/B1efe11Oda84XDz5k2M1rz3ve/JfchSB3V6eoo2hitXrlJYCT26cumy+LlnU65dv8ZkOkErhQ/d+OymmC5snMS3+IMZn7Xo8/2/AGDHZ/nCRkxRFFy7fl16ptOQBC0bCUrJzyytFNPJhLIoCV7u42JxzsnpCdqa8bkyOR1ba3lHw3CPjMm+dOh7R1GWvPOpd/LMM8+wu7ebf36a0Rs8hIUNj7z8PC9QwPbONrPZXEKT6pq+7zk8POTs7Izd3V1efPF7vPTKK3jvKcuKT3ziE8znc/63X/iFEZw3bcPp6SkQiTF31a5nPev5gZr/Y/tzfOFPEVAdRr3vo/zrj7/Gz9764sM+lPWs5499Hmmwun/3LidHhzg/eNgUZVnhMkhUSmSqw4Kp95G+l/qFMjOlECgqQ1FPmE4mmMw6Bt9jVKKwhrqu0abg5PScrutpmg6UJqSIMYaNjQ36vpf+1rZla3tLwj1CkEoXH0hNSwyeuqwgiS82xEhdGep6ggK870kp5gWlQanEzs7WWK2i1cDQJSm6V47gPTEEglZM6lq+V4rCjGlZ+DsfOTk9zr7IEp3raIrSooJIgGOUmohIYmosIQZc12fwZfFhWNia3DGrCCmHWJWWypeUdU0fHK4PRB8l+bdzGRhG6rqmriXdNaVEUAFdyCOWMkAV0BRkIQ3YDL4ijCzcEKIEK6YohThKAq212MKSkBqLECJaPyiHvLhojzl0CnhgwZ1SIqT4wP9T+dxj9uEq5PpUZYUtyrGOR/4sueJUGE9hZ3U+zIBW4u8VOamAU2MsOuVFfiCz72TP5ODj1BmAPAgkY4z47PMEcijTym+o1Or8LgZUybVYfXyovElR5VCe1de4GGykcwBY1zZ4D6gi941qNBqdDNaU+VqSfZpiYY0hZdZZjc9lShFjVsxfWVVjSI42lqqqx3s8HOu4MQVM6joHAQ1y7kgIQ+2RJ6RI27ZszHbYPzxm/+CYzc2a228esXu5Zj6bslj0HB2doIsZ8405zntJ2M2bG957tLIopYXJTBI6VmjL/f0DfvmXf5Xnnvum+LCz51RqfDRVVTGbClNblIazszO6rsc5x5XLV5lNZxSuZ29vj6os2dra5Pr1a+LlzSFgFz2yQ9WSz9Jp2TQZ5PrxARCr5FPG+x6yTFc2OAw+BBaLhYS55cRfo7Xkd2XfkzGGS3uXpCorb2YcHR0TgkMZhfdpTDke3kOFYlLV47PjvciEL1+5xqc/82ne/e53f98zrI3GsArostaOVVQX1RGbW5vcunmTS3t7FEXB2dkZhwcHXL16lbt37nLvzh0AdnZ2+PCHP8wLL7zAjTdusrGxQVmW3L13J1+HVf3Setaznh+s+Yf/5p952IfwUMY+/Sz/xf9wzuJf9vyPb37lYR/OetbzxzqPNFglV4rElCWvKJpWejxDkIVv0zthNq2lc4Guc/TeM5nO2NzcxNoSHSPO9RwcHhNjYHNjzrSumE9KrEooY2nanrOzM5plI35ErbFVycbGBpNpjfcVZ2fndKedeOq6lq53skI0hqQULihKJfUVPgmbqYwdJadamyxTFtDqnGM6nTLb2EQpCSExWlKFfZTaHK1FHmqUAIG6qqmLEmsVpbUUVqOVxfUK7x1FYUghYXOYTcz+zN4Flk0zyvMmVZ29rGCt1LuIRE/ldGNDYQ2KSFlYyo25LOC7iEue4AREqyj9plprjNJMqxqlJJU1EjG6IMRBtinHGKNsAhilMFZl1kOtwOIFoDUEzUTRJQtQtRajDWVRkoL0cQ5/Psa4YnlG4COs2zADM6mVGu/P8Lk6Q8SYwYLWRmTLuYs0EbOfWO6H1RJ8lRgYRzKgyCB4YEdjIHg5Dq0txiikMmdQzMbMcgdU1JmJHYKLICWRF5MSBgHqKcYL4FhmYH7H3tALDDNkMKOUdNKqBwF81vZmb7OirKQqRtuALScEnwhOAp+MLajqKbaYEKIT6SwBoyFm+XWOzKEoxHcek3RiphhxXmTAw3FaK2Fm0tij8mdn7zdSRzCpJ/n+Sg+vSN2dJEvHmBOsA9NpJPrIGzfv8swz78GFmv2Djtt37nJ45Dg+bXjPDz1FWU1wTpjTvuvy8xZJqc/gymB1gXOBF156ib/1t36F55//DhsbG7KZNeRvxQQxsrGxydBvmyIsFksWiwWL83OuXL7MlStXWS4bJpMJZVly/fp1NjY3CCngg8sy93z+6oJPNV7ciFnJrS8+7w88BDA+/y54cHB8dMxLL71EWVZ0oQOV/dkhjtVFdV0znU5xvWO+ucGyadg/3KdpWzbKcpTn6/yeaiXPqfNh7EKuJzUf+vCH+dHPf566nmYWVVhW7wM+1x8Nm0WDSkGPFVVpPP7Lly/z0osvYqwoUpqm4e7du1y5ciUrVzxVVfHss88yn8352le/RmEs8+mctu04PT29IHGXjbD1rGc9PxgzLWte/4knMO//3MM+lIc29iP/CD/7S09z5ycavn76KvvLk4d9SOtZzx/LPNJgNeVFsUXYnJhCTuCNoDTO+cwMaZILYEpmm1PioqFpe2I6AyRB1HvPsu0IfYc1ms35DKUUbdugbZmrPDR1WYkvU2nqekJVlpltUdRVSTupcbncHqUIROlXRNE5B21HDJ62cxQJur4hhSWTugISbbukLAuKouDk5ER6UF3E2oKUFH0IlGWJLWvxj0YBGimC0YqitJSFJcVACoGitNSmghRZLs8JwWFUMbJpAxAISVFVVaYCc/UHavR19n2f2TFN27aUpWU23RJZ6rhQDnjvJCimLJnP56SkRUapFDEIIA7B41wnLJuNo6R3YP5E3hmFUb4QZDMwK0MK6sVUWGstAZG+Dl7R4eOy4F7JdAewOnwtrRUxrVg6yIBK6zEoCRiBnST1itRYmCRD9CEDA0sIHjP0xpBW55NCZmQz65okSElrTYyMabPaBJE0J5XlxjncSQlIFvwj90eArCZGSTadz2eklKQneOBDM8NK/veQAK2UbFoIc+7He56yjJoLlShqgNuZ3Y8p0XUdZVUxnc2pysfpuyVN0+C6HmsM9WTKdDalKi1HR/ucnR6JqVVFrDXSkZvk/QAIuR4mjjJ2Sf7tnVt5wnvHkAI97C9okOPItTZDkFOMIjP3QXzdLiRsafEpgFa88vobPPMjH2Q6v8qi2Sck6LwnJsVjjz2GMYa+6zPQCiNbO9S1kOD4+Ixv/97z/PKv/Ar79/ep6wnGlsSYPb45dMkWls2NzfGZHeqsQHFwcMiTT76Lxx97nKZtUUoznU659th1jDb0vqPr2nxt4oVzXIHVizLgQRo8sJXDD0vZ31hJ1VNKVFWFRvGtb32LO3fuZDY+M+wDu4pI/q9evYY2BlsIWNzf3+f07IytrS2xF+RQJKPla8cQsUUhzz7wrve8h8985rP80Ac+SNM0+ODRRlPoauzWDVEkzkPw3SD1HnqYhqArBVy+coUQhRGua+lwvnnjJh98//vZ2d4BEtevP8ZHP/pRnvvGc9y+fZu6qrDWcvPmDWJIGKMzm589sutZz3p+IObn58+y8df/04d9GA99zOUn+T+/8Vdxv/m/8O/8xd8dP/5GXPB/3f7GQzyy9aznj24eabCqlMaHmBfrCFD1UdgwgGSZTicUtuBsseTg8ARblFSTCcYWxCAS4fPzcyZ1yc7WFsH19P2S05MjmE0JvscmhTYF08mUhMFnlsb1PZ1Wo9x2uVzgvMPakpQUShmM0fgAMXic6+mzd877RFQStlQbWbQ3zRLvOiYT6V3c29uj7x0hRHxwEmiEwhojPlaXiL0AZVsV2Z9r8H3PcnGWgVzEGkVKgel0khlYRdM01PUMATQKtIDekEGAc54Uw7jodU48h2VZ5jCSgdGREKmBNvQ5ZScMSby2oKwqSfz1nqZrMyEo9T4+9rmTU+TLQ7JuCGFMY5Z7vQKMQ2BQyv89+jBjXAX3DGxoCMSQxkTci57NUSKZwSusZLHDIj14P36OsHxy7EZpsAXGrJJTbSE9sJI8PYDLlNnh7KHNgUtKKzQaNDnRNeWk4yjVRorMiqYHFtHWGGETM7MmcmmFVpL6XJUlXU7S1doIm6hhBK5JpMUDUy4+QvERR+eERUviHU15gyFfmPwlJP3YFAVKK3zw4DRGlxSlQZsJaRqwRjOpK6rCUlrZ3OmdA6NAiefX2BKVEoUxY7Ks1iIXnkwmFEVBTInee+pqMjLL8mzElTQZ2NjcpCzL8VhXrLGncz197wlJsTHbwCdPYQr2j4+4dWefja1djs/P6HrH+dJR1VPm843M9AkrK5Li4X2QBOr9/UO+9nf+Ll/84pdoO4exJb0LTKbZg519npDkvSlKAbtasVgsqScTptMZBwdHKDRPPvEkd+5KGu/Vq1eZb2zIMx4DLrgs71/VIA1g9a3hWSOzmr3yaXiuL/zsHDZryrLk8OCQ733ve3LN8juktSb4gMnpvUVRjB23Jr+jbdfKx4zGBU9Z1Dl4SkBw8KIW2N7Z5lPPPsszn/oU8/kGTdviYxzBc0oiXa+qihgLnOtXGQAXOpGHDauUAfvO7i71dErTNEynU5bLJfv37nGwf4D010745Cc/SfSB3/nWt1BKsb29jVKKk5MT8cDmUDsJCvv/9/fPetaznvW8Xab4h36a//prPz3+t//mr/AX/mzNz7355Yd4VOtZzx/NPNJgNWUPZVKadtlwcnpG1zlsWYlkbVJRVCVKazZMgY+as9yZqpWh73tiCFSFhJ2YqqCoS2JoidFLGEtmr4KXRXLTNISYJPW1EG+kJHLmHsYsYXROPIZFUZCi1K6E6DGZKUsxEIBJWVCUJTE4UImdnW02Nzcz46fFg+sTzieUNoSY8M4RXU8KHpUiKUR0aZlOa9q2pW0bWfwZK57bSUXXLFEKqrLKjGqksOKF9VkW7JTC9R1KSaVFWdic6ilM3Ci3zfg2BC9+RiVMZFQKbTRlVbJsO9pzR1XJgtd5R0gJnftEIYm0MghrE72AU0gYO0hXhZ3WRkvScEy4ECSkCkZQC4N8VYvk0siCfgA2KYn3cliuP1AdI5+NuiCrFCZHo1HZg7mSHo6iQZUBZ+4dtdZklkblY88eXyPnIDbVMB6HGkKf8vc2VkGWr6bsRUQNAHFgOAXAShCTfJWoVvLilMQn7JzLGyUSwOVG8PrganyQUI/sFQOoiQJYlRJPbXRyX5QmMBKuo7Q5pITGSAdsYSDmDtVkaFuH15GudQQf8zMimwhBeUwO9xm+3nAM0+kkb5KIH7PenmSGnQfBWb5EW5ub6MFvnoPPvAsjYHW9x9Qlk+mMpj2jD9Lze/P2XX7kyY9w886bLNqWZed57zvfQ1XWI4sZQhC5bALnAoW13Lp1k1/91V/jxRdeEjm4McQotUtd7yiLDJw1qBgpiko88jk06OT0FOc9W1s7HB4ccHR0xGOPPcbB4QGbG5tcu35dvN0xjozqcO5DuNlFRnX0bg+68QtXVF4ltdqvyEB+YFdfe+01jo+PpfImBIzKagKtiEglz6XdXaZTqXqZTCY47zg+PhmfK2MqYhRLQF3V0olbVHzkox/l8z/242zv7hJR9E7CuVLqATUy6VpLn6o2mqIskTTzFbsqr0/IGz3CDG9tbXH16lXevHGT3Z0dnHOcnp1y584dFufnXL16lQ+8//184+vPcf/+febzDXZ29njhhe+OrLMGfEwo831K6fWsZz1v0/nMlffzhb/2yYd9GI/02I99gf/mb8C9f6VdM6zredvPIw1Wz0+XOanT4GNgvrlB5QPHJ6eEJlFNpzgvzJ9SIvea1lXuQp0yqUpOz04zc5IIKTGZTFFESBHnEx5FtzwXhjNGqTyY1UQYkzN9SjRNz9HJgqKq6btOQHRKdL0f2QatNT6JFzDGhHKOwkh3Klnu2jlP04hvTWsDOnK+POfw6ARpzFCS7ms0JjNjvZPu0qqqWC6X9H1PVZX0Xc9ycc58PqXQCqMVqixHPy/I14jJY40loOh7nxeTkb7vqes6yxU1bdOMfbND72mKwqINKz1J14045+n6QEJTUBKRVFxtLLYwWfKbSE4WoYW1oIX140LSro8hy2FXdTdd43K1R/GA1FErNbJIFwOYVPbeEmXhfTH1NiVyYJEaU4dTjALArBkljcMCX4KvJA4fXWT2UxbPVVVizIMBQGoM1L3o+5R034EdG5JaBR9kAJoipCzfVRIeNgAnpeL49QbmDLLP14h/1YdIjI4htXilBo0joHsgTTd7GGMGQioD9ouhPlFJUrHSjJVAKUaE/BZvsVJSUWJLw6Sq8b6l75a5PkjhYsDEiE8JpzyTqsK7yGKxJCaH7z1d17O1U+B9kPfOe8rSEkMYr+XIKudbWdd13piIKx9zCmNyboyJWVVSFEbqa0JHUZa8/MqrfPZzz1JXG9w636coJrzzne/GFgUhSKVV1/V41xMDGF3w3HPP8Wu/9hsSxKSMSNrztZJk8IQtCqLzIqtViemkzqw4NG3D0dERXddx5co1Dg+PuH37Nh/60IcoS8vu7jbb21sYY+l9QyKAinlDLD7Apl7sQh1lwSlmkYFsMIyyYbVKxR6kzcvlkm9/+/dYLBZj4FUiorQVRUneLLq8t5c9s/KcHRwciITbWvqweh+ruqZ3gXc8/gSf+9HP80MfeD+z6ZTOyUZUiom27UikUdJL/v2w+VQUxfjejZVMYfBfq3yOAVtYLl26xMvfe5G0s0MInvPlkv39+7RdyxNPPUmI8JUvfwVjLHt7e3Rdw61bN4VZVhL4ZK0lxrCWAa9nPT8AMy1rfvWX/33MlXc97EN55Md+7Av8GF/iF7MlZz3rebvOIw1W2y5werZEGwNGYwqLiw6lE1Vd0vY9Z6fngDBfMXlIEVMVbNQFi9iTgkPbgqbv8acSjFMaS0yRw+Nz2r6nLizTumJSKCCCclS2IqBoXcAFRRcMtt7EuR7Xiz/RFOJnNNasgn2UkkVn8ujsq/Nh8HdV2KKicx5tpQs2hEhSEBV4BgliQqs0spz1dIq1BctlS9N0wvD2YABtC2KEsixQKdAslhKcFCT9V2uD85Fle5zBhkITx3TYGANaFVijmE1rXC8smwJhAKPCFJaiLPAh0nQdfdtJOuukkAWh64hD2kxcgW0VIyEFlF5JcxWaIFpbjJbjvBioNCxYB2lgWdc478RHelHeq4YgpCHcCflerPpWY0oChFOWDfsAQ4iSEjklGQBoJQFRcubiWVSDgtoaqomkHIfgBHQOLGZmdYXFykxY9s8OAG6V7qsFKGQEtvLmCiRdSZhX4B2FfFZwJDwJhXMtSsliv64KlI90fT8u/jNhTT797Em8wFAnSN4T9RBqJYnMPkg9kJybbP7EGFG5ymeonUlR7pOkKWmUMWxsbVPVlsX5CcF1wshnea3WBtd7Qoy0bSdAE40LkaZpSSGgUsKFDucHr6qWZxOoqpK6qvBern0IDu/73EvqaJYdwXu2NmcQHfNZhVUa3ysO7x9y5/Y9rly+zssv3+DK5SvMN7boeyd1MU6SrYma85NTvvGN5/jqV39bfPFJ0/uUE389hTGkGNBKOna1EoG3VrA5m0IKkDTHpycslg2oYSMkcuPm63zs4x9hc3POzu4W1aQkJkeKwpbHnCisssc5oYh+AKW5MiiRNynk/QEtGwwXJOBDundKirIoeP3V1yQ1NwV0itL3DBClt1URubS7x+bGjL5zzKYTUgw41zObTGi6Fq1EASK1WBUf/ND7+cd/8qfY2Nik6TpaH4kJghc7A3mTwyhJOZYfYxqVQ8JSiCQkMC+GQQ7PCFSNMYTosLbgySef4Ktf/Qqd6ygqy6I55827b8pGhw+8/votmqbl+vXrbG1t8uUvfxnnOmxhUWY414RKCp3WOuD1rOftPhqF3nviYR/G22b+wtf/Mu6Zv8R/cPs3HvahrGc9f+h5pMHq5uac0+NKgFKSDkSRrhq6bgmuhxxw0rsWY2BSVUxKi7UwmZTYQuO8Q1uDD46mXWKnteR5aKl16dqO6aSmrEoUkaos8AFc5+jbnpNFS9PJ10i5BmZY9MusKmdiEjmvMRY1VMb0TkCzUlRlgTGaruvoe0fX9iRtsEWBix1FUVBYTfCeFBEWwpgsRZVgFu8lYVMrzXQ2ZT6boHyP73q6tsNWJcqWdE1LTIG27TlfLFBGS+cpIme1VqEzmLbG0PuAywmbiUgKQdgaYzFFScRlqXFBPakJUcJxho5UkAWn63pcDm1RYx3PiiEirYKTVJYkjpJHRa7fEd2ez6FEYlmVncEBrI4SSaVRWRqckJ7dEIMwgwhxmWAEAYNgWMBqHEEpuSeWHPBijKUoK5F4jqynEiB+4fsPkl9hPs3ojx3A6AqEroD5xXMJYZXqOlShDCzQwDTG5Ekpp8UicsnCWuqqQrXyPEit0bDoF0AjYTz5KR08uYBRStTHF9bvSg0AfOWXHepcjCnGKpSQhKXqnSM4DyisLbBmniX3Pa5r6XOqdOck2dp7x2K5oChFFuy9p+s7TGHxIdD1buxyHX2pSSqRhiTcGCNtDiPqXU/vXQ4hQ4BY6tnc2ODK7g6vvHSLrnO8+tIr/OiPfYZLe5fYvXQFUu5B9n48/puv3+Dvfu3rvPLyq2PYGjEBIlN+11OPM5nOuHHzlshpowRHxRSZVGWWs8rmT5Nrq0KMNG2L1prDw0PatuHSpUsUVSV1NYiEeQg7SvnnilzzQeK8eoZiupgenW/VA0Fcq3dwkLS/8MILnJ2djeB69fxJz6nNYU/BS2hYVVYcHR/Tux5jDakFWxRUVcXG5jaPveMJPv3Zz7K5vZ1Zy1LCuPqOLodKyYZCGIOeVu9K+j4p7sWNqlXqcWR4FOfzOfP5nK5rmc1mHBwc8Oad2xhlODg4YjqZc+nyHjs725yenrC/f1/eT4AoFpC+6yisRbEGq+tZz9t9/u+Nj8nP5/X8fc/P/PqfZ+/HFf/avV9/2IeynvX8oeaRBquzWcWlS1sobTg5PSXpxGQ6ZTKZ0LSOo5MTuuioK4NVFh8cxip2drdEmtY7fPDcuX9/rEyYTko0kWZxTnKBjemU6Ht83+OMoiwsYOj7Fu891ioKC23nCc6hjJFal6LMEtuV720AY9nSCEgCqk+RyupcB2OpJxUgC7S+d1hjKDfq7JMtiVHCnfrOSTIvUBR6DCWRkvuANZYip9X2LpJQFPVEul9DJKElJTVGMAatLSFFgncUFExnM8pKGNOua+g68Zkp5zMDKOfgvCcuE67vcK5DY6hzJ2bXS1VQ17qVrC97X40ZekffCu4Yw16GHk5g9MxOStmgSAB9yDUlfrXQHgDv6EvNLlGTQ5ZiyownAmrTykc6dIxGIirKLu3gaR2ZWK3R2oovVOeajQjaDl2iKw/oxd+PQUy5Xsd7T5crUbLz9PsAhbCYqyAorUVuS7ooMxYP8gBsVPa5phRx3lGUhqLXYyKyPFsZ0LzVA5o3DWRTRUEaek8frAwaPm+oDyrLNN4nTJYN52feaI21FcFDDI5JMWU2mbJcLkgEOudolg3LZoHRislsOnpvl8slm1tbOC/g0UfGBGWixGZJ6nQa+0a7vh/95a73NG2LtbV0DruWzfku25sbvP5yhwFee+VVfuzHfpTLe5epJxNh9lKkXTZYU/DSiy/xt3/9Nzk4OEA2niT9GQJNs+BHfuRTfOZTn+Sbv/M73HrzNmVR582VAMEz29umLIuc+izyerTUxpyenrKxscHZ2Rn37t3jscevs2waUpAk494JExlDxPsh+IuxV/UBGfDIvg8ebi54fFdqhIH1Pzk54caNGwIcjSGlMN5fbQxE2QjY3toiAVVd0XYdN27e5Nr16yJ1RlFPJxyfnHBtNuP6tWvs7e3l7xFGn7hCntG2bRi6lC/6UYeAtYvP/8V/5M9c2LBRUks0n2+wvb3N/v37o9pkcX6O0ZbpdE5Vl2xublBVJd/85nMUpSX4kHuMHdboVWDbWga3nvW8reeLe5/mma/8e4y7Wev5+xq9dYV/8Zf+Bf7535gD8MR/+OucdIsHLFPrWc+jPI80WNU60PctIUas0TgfWC4WhCikx+VLe0znG3R9z9HRISYoyklJUJ7ONSybJQcH90gpsrU5Z2M2ZTapMETmtWHZeIpqBimMPrhF09E5+e+YEoW17GzNKazm9OwMtPSSFmXJmIBKBl5KJJjeOVnAmwJFoCjMGMyTAKM13vVoFHVdkZKk/aIzMAmJuqywxrJYLuiahmbZ0LtefGNFQWkLUEk8rF0ngTwJZvMN2q6jaTqiUkQUuiixSeXU4YSxBZP5jLKuaZpGKkqslYCUvicEIyzupBJmBEhBJJnTqhSWrbDiT9UW1ba0oRXAnpkUndllMsM3eA/HBWoCo634TXPysstgrA8eFQxDZ+WAkYaFuyTYigwSkIRjskdTybcMMRLy4ngMUbrAPumBhSWNfZGJhFYGY61UCSmFDwG8z6FTcjwxvtUXK5LRgVECqdQpywrQF4KOVgBQFvsD4+wvAO8MMvVF16rK/uguH6NG6wREkSWniDGKMpns9xsYLMXQzSoyTEb2WQ4ciIl0cXclrQAGORVXandWDN9wZCEz4RGVr1Wk7RzBasrScPWxxzFG0XUti7Mz4mFicXbGYrmEAyWSUWAWA03TSiKvGHpHaWtZWCaTiaT2pkTTdzSdE7DaObq+5Wx5ztXLWyKfT47NmaYuOmZ1Aqc4uHufW2+8yeZ8k3me5YMAACAASURBVDv37xJSJ+cc4eu/93W++dzvsFw2GFMSQqDrOtlwKAw/+VP/GJ985hnOjk54/fXXMyhLWXJr8K5nOp1moCoeSe8F2Dvfcf/wgMlsRtf3fO/Fl3jv0+/jzt27LJdLkooE78dUXR8CKZkMysNbNhmkzihdeA9WLKVshihlHqhuev7559nf38c5uV7WyLOqtbzTvfNML29gq4qmaSjLmuVySVVVVHUtfnKt+Oa3vsXe3h57ly5x7fp16rqW8Lq8gWCtoSgtKdUcHR3hnGMyqQTMswLcFwOjRH4uygqUJK6rYeOjMDgnUuTpdMLly5e5eeMGk8ywO+9x0WNtQYyBre0tlssFt++8KZ2+hQBxreTnWWnzfaonf++/dNaznvU8svNDzx6g5zsP+zDelmOuP4355/4iAG/+5L8EKfL1z/9X/BvqHl10fOfwxkM+wvWs5w+eRxqsdn0nfsWkcD6CMngfScpgC0tR19jSglZsbm3StA3eOc7Pz2kVLBYLQuyZzebs7WxhtLgG67JEWQOppfe91LgkkTV2fU+RJX7CWiqM1dTFnFlVgrGgNShN3zuITqSlIfsiSVidF2JkIWYKKGWp64qytKQUOT8/z3JizWLZYNsStMbagrIocoKq1Eicny9o8wJ6kAWvQFsiBfEfSldrlr5qJdfNdfhRvip+TKPFe9c2DX3fMQCVoZPQeydAMkteDUhyqJb6B5XrZqL34jfMDGUgh74YAaHCZAaRTg/HhRrV04N8dui3tNqM8uIYV1UTF3sjfVgFpYzSwfF8Jbk2hIjPYTWDnW9IlU2KcVNB0omFzVRoUAatDdpYlDUCYhGWSxuN94zVK8MM35/M3FxckJdlOR77+GfT6uyHGQD88LkrRnXFcA4VIMF7AiKPtsbIDnPuzlViks5pw+K11EqRtMpybC2+2TEQbDC3MuiNSerC9yRLpWMieAkyUkajjXhwUwy5bkkkr7aomE6R/06ee/cP2NrepjAlG1u7TOdznOvp2iVts2RxvswBS5HNeU9Z1XK9sq9y8F9qY+i9x3nP0ckJy+WSEMTvvWyXdL5nOpuRomc+K5lNwKiGvd0JXdPQto7XXn6ND3/8o/RNy93b5/Su5+aN29x68zbBR1KULmOlLCTN5SuXefbTn+Jd73oXCsXde/eEMc2bD0YpvIvMJhNm0+kI4pdtS9KKoi7p+46mb+l66SW+desWzonC4/TkhOlsSnQShkUGaheZx+EZh1zpkyW/4wuU5FoPVoiLEtumafjud77DcrmkWSyARDGbike27ymsbAJsb2+jlWY+m0s9UFVxeHLM0fER9/b3uXPnDkVR8M53vZvNzU129/YoqpK+l+evbVtC8CvZdggI6z8Ejb3lH1IOO5KfD56EThf6VofwMfJ7imI2m4+qi7Zt8c5RVzVNs8S5jqoqeO65747AWd71hLHS2yuSbculy3u/z98y61nPet4O88yl91L/+Pse9mH8QIzeugLAp373v+SrgH/hK/y1n/5/APjPT7/O/vLkIR7detbz/fNIg1WUyFkn9YyYAn0fCUGTtKJvek7OG0IOt6mqQhZMgDUFJkvG6rqmMDoHzEiqbfBeamGQCg6toMvJlbYoQCmKqmJSlaTk8a5HacX2xoSIJuZkNZ0CMWSAQ6RpGlRKVEWZwaTDFpppXVMWhtlsSp39al3d0vfClHrvcyhSQFsD0RODLIaX5wtSikynU5TWWGsJXnyis+kUazTnZ6d0nWdzc1NSXhOQIs1yCTklmZhA597BFHFdS9TiJzWFMA+khCaijHSKWquxkrQkCbHo1e+1oXfCCGpjcgVOXmAmAUESriNdng9WcOSFOCuvZllmoB3imMKslPn/2HvTWNvS9L7r9w5r2sOZ71S3qofq6nZVD+6u7jiO7U5ARHEiIogCnxEEGRKFBAgEBAIxKEhhEhAikkixhKLwIYkcWcJE4AQTDyKd2G53tXuo7uoabtWd75nP2Xuv6R348Lxr7X2rQ4Ki4Hsdn0e63XXPsPe7115r3/V//xPWyk3s0MkpzJIfTxGRtEoVi3g7Y/ojrDKJxVRaI5HBkhgsmwjDo6zTjtHSoUliWGNkZGjz3CbAHzdYxsR4JXA9gurEjg1JwpuevE12cmDBNr+3CVSelumIdDTRmQzS4uGcVQqUjiTSFYV4hqMeHs+nn5GNgtFLHNbAWKkgYVYxVTBFYdX7rsPlOZkST25UYR2OIw+OzQqKLBfppVY0XSuMpVYUZUYMmslkxnw6Ryu43F1wfn5O27Ys647lqqXI89T3CVkm3mUfIfjA5eWC+/fv41MVitaay9WCLMspihzvOubTksw4FJ7ZLEObFVob7t57wOde/zzz+Rbv3XmbBw8f4Fwgzwq0atHK0vuOEAOf/vSnef1Ln2f/YJfedUQPTw4PBdRGRr9U8I7ZdJcszyVQyBqatsVYK8nfzqGtZ9nUzKoJx8fHPHlyyHw+5+zsjLzIpSc3bVppzcjaq03kqdYp18P5MWwmyAbMIAFWo0z9vXfe5eGDhyjk2M1nU2bTOdev3+DycsHdu3eZTWbM53OCj5Sp9/b27dscHh9z54P3OT45QbpL9zjYP+Da9Zvs7OxK+nKMuFSZ5JxUekGgLHNCsMkTq9edyiFV1SRGFTWAW5469/M8H5UHylouLy+5f/8+1mbUdS09087jrVTztG3DW299l/v375HnsrlIeiznulFOPt/eYmd75x/hH6GruZqredbzys4L/O0/8Qr5v/QfPOul/BM59gd+hD/2az8CwB/6E/8a9Z0b/KUPbl+FMl3NczPPOVjNQOV0TpEVU5bNgqbtQVuariMo6NxQjxDY255z8/oN2raG4NjfO+D84oK26el7j8kMUUPb9qgobIZ3nq5r6XthyAYxZtd1aBUpMiPJpoDCY4lEJTdrTd8S+waHJjrHpMioqorMWJqmpa5XZFqRZZbptBqBqcmzEaSAYjabEUJIN64iB3S9Z7FaEpWiKEravhfwEQTw5QlM40MSxAqjm+c5IXr6zFJYS+ccwUlKblFWZNbi+o5oVGIjI33fSUJyCHTOk2VW2F1tMDoBSiCzKTU2yUKdc9KjmCpyeudT6IoewVmR27X01ViBiXHoFd1gXAYAoCQGRfy8HUopYZmVwmhh1jfBn8kSy8tGXyYgab4boU4MrA2oAMoI0yzwfABvJoFVYZWd9yLbTqFJ4ifuvw9MiOJ2DWKBxJLGJBsOo49wSPkNwX+fV89aC1GNfZghrvtaN0TH4/ELQWGtwbuegceXDRiVJOVSBRSjSky5sFYDw6yUIctsUi54SXQeAbiHMIBvkjy2Iy8q8QG7OB57lCIGTV13dG1DWRTs7uxw/cY+i8tL2nrFbFLhfc/lxSm5tUynE65fu8X1a7fouo7lquH46JCuqWnaC4oipyjg4KDEefFqn19ecHxyKptK1pIXGU3bsr2zJ5U1rhepKD1Gw3xrilbnGGN5/OSQs9MLppMJTx4/plk1aCP+xhjTXo42vPzyK3z6s69is4zVaoXJDKvFig8++CBtbqiRRTRGM5lOhb1WCm0ErCqtaesepbWoPNqG+XSGD57Hjx/x2c99hidHh8y35qJY8EH+P53zfd+P568LfvSvDueaMJBSOaMiKW1bk9mcGKFpGt544w3Z4EkyfO8j21s7/N4f/32cX5zzv/zlv8y169cl7TsFhnkf2N7ZYXt7m8UibXRZw7XrN5lv77K3t0dRSDr34NMf/KkoUiK4KEnk+g74tH5Qcv0PNWJJFWLSRtewYeW9pywLuk4UK2+++V1OT0/Fq7pcyiZZkGOU5TknZ6c8OTocN/5AZO/iUxUxeVEU3LxxA++Gz4aruZqr+c00nyivk/+h/+hZL+O3xEz/+7/IFPi3773Jv/nNj41f/4U/+Q7/3OkvfWgD/Wqu5jdmnmuw2nQBFzWrZQ2xYbVquVzURK1xLmDzDB+R6JwAJ8fnrC4XzKcTtmYTPGBNQcwlXKjtPV0M5AZhn5SEhEj/lCdGTVmWTGdTvOs5OTpka2vKfDZDxUjwDowwKn3X0dRL+rZF6YwiL5jP5xRFRd91hL5DlTk2dZa6BOQmVSkBMYM01nuRzSolXaQKshDxAYp8F2UMy6bhcrUce1GLLGM+mWCUou5ayjzHm4jvW5ZdzWrVorRhPi3pnBdG1kj9jELR92ZkMX0IEuoSI9ZmzOcz+l5Sf/u+JRqN0WASGHQxEjy0vaNpOyErjRGA7b3IQ1kHp3ifbqw3Oz03/JRD9NDApgx/1pJCBAyBBEQlEDfKnQeqMIj/VG6eARUJkQ3JcKpuQeqFVPIHJzpY/H42x2ib1pYksBtMZ9d1aB1TCNJa0hvVGkRs+gwHX+H3f319jocQngKJMcbEdA43/6yZYdaSYK0lsGvY8AghEnxMaFx8rSGoxI76QegNiPR5XRdCCu6JazCb5M8Day2scZaeZ5ApD+BbjqHWBl1ovIscHZ3x/vt3OTg44Mb1A3Z3donB07UNvo+0rsM7z2pZU1VTrM3Z2p4wmczxfcPR0RGrekVdtyyWNadnFxijOTw8oe8dxmi6rqPtWoIS4Nq2Kywd02mF1rBqaiITlM1QXjpHH9y/zxde/xxWZ5yfn1OUE0yWy2aEDXzsEy+zt7fLkyeH+EeO+XyCzjRHT444PT3D+5hYfzmPijxnWlXEIKCrXtZ0XY/rxYcK0g9bVRW96+m847vffYvXPv0qwQcWiyVFVaTjz7o/NoR0bYjce5N5XCuAY+qF1kk+LO9HnufcvXt37DUNPpJnJd4HTk5O+d9+5mc4PT/DGsvezh7ee3KbjUnXOzu7TCYTQuqFzrKc69evc+3ada5dv07X9wLKl0vyPE/e1SCbKwmAaqNxSXXhg3vqOhhWvymXl80gqbWRrlePtRkffPABb731FjaFyBHXx2MEy1Fk1PIzWhjeGIlhMLqTPpeLtKlzNVdzNb+Z5keuv8pP/8J/9qyX8VtuzIuvYV58bfz77/49jr/yhf+cP2ePxq99/fwOZ83iWSzvan6LzXMNVo9OLzg9u6Bergg+pPoMRUi75gAqakl2Tck6IbGTi8VSehidIytL6HqRmBLxRqNTEi1aMZvPUUrjXKoEMRqD4eBgj7Is5HGDlzCQICmo3kvabVlWZFkhbJhzBNNhjKIoC2ITcaHHNZ6maSBGTk6OxQ+bZSNIVCqkwCGN0gqPdKRGkgwX8XN6YyiKQjpFvcNgMFqTlzlWC1vb9QI8vOsIIWKspSxzYSW9l9eRmFIUaKspy5y2c/Te0fadSIVDoE/pq8YYUjMPKNkoWNYNXe+Tp1BhbY7JYvLWpeoXrVPYj5Gb6/R4mDXrqbXZ8HyupbIDKAWSfDeglKQ7x8GACuMN/FOySRQJo0pno18DyzV41KP9T6NQqdIHJUxrTMynGSqJgsenEJvhyYeb7TiuZXiOAayvVyTPPyQYPt0tG0ZJL+N5PTzHU6nDG/zq8BxrxlaAafBxlJKKbXkIUBqOQfo92VVg2A8Y/YIhJhmlRumw4TGW/+3aljyXJOwY4uiH9dFDVGRFRVGJkuHs9JSL0yO25jNuXr/G7vYMC5yfndG3jq6RjlRtcqrpVEKNdM716y+gtJLwKw110+Kc4+TsjM55TPBrP61RdH3D2fkxu3MNKtI0PV2vOL/sUbakWTi00tx5/w5f/NIXODi4xtvvvos2PXXTkZcTbr34EuWkou07QnA417NaLaibmsePHuOdfP6IRDqgtGI2m5Plkt5trGa5kk7omD6DVs2Kz7z2GQFKvaMsSh4+fkRdt8zmWywWC7IyF8l8L8nOgzc1JN/2JlCV8zKsE38V6OSzBpH4N6ua7333exwdHXN+fkEIkSzLIQbapuPOnQ+IMfLSC7cxyhDwlEXJ5cUlC7Nga77F9nxHmE6bsbO7x+7ePh/5yEeZzbekEzaslRGjbD2d71pLoJlPCoTNnwlhnRD8/T5tuX6KopSgu+WSd999l8ViQVmWXLt2jXq5JESfXq9cy+s6KflaTF7YsPEZdLC/j1Gajcvnaq7man6TzN/8r38UPb/ymz/rUcbyB77xp/gDG1+797v/CJ97+23qvn1m67qa3xrzXIPVrusknTK3lHmRGCND1IbFYkXT9eQ2Q5kcAxTWkFuN9x0hBPJc5KNd1+NckloaQ9d1RO8kLMcY8kwqYIThBNc3GKXJs2wMlsmzHO8dbdvRe/G/5nklslal6ZqWVbPALFZU04nUaDgnQT/BkxmLNYbFqmY+m7E1mdKs6uSpjFJ0nYCH63uI4Hw/AkiiZ2trLimjTcPZ2RmlFQldlWeYzBKCp6pKymrC+cUFl5dLLBFigfeBru3kpnADAyqt6eqWy+UKk1mKsqDtHTF6fOfw3mGNJjfSjeljpOsdddMSlQa7DuyRhM/EoA6gkwS0YhDQSBRfLYyMKTD+nuv7EbwKI2tGKe36y2r8WggptCV5Lo02wuwm5nVYW4yQ23RjHyNCX4IyEqgkdRnpJld+Sfy8DIxmGNmrTVA9gjxAAPWaRWbj+QcwuTnDMobHHMDk8Dukc2LsP92YgXU2xgjYRhjYQMQTUkJyTFLnkDyFCaDHIdFWWFzpDZXnkz8hHfs1ABles4QMQb4pg04S7RAkIThiCCis1hS5heDJDOxuz7m2v0N7sMfx8QkPHz1C2wwfOuqTDq1EkpoXBWVVMptPyfOMqippu5rZfI73jma1FMUCakycbpuafH8bgFXtiCrnfNnz+PicxcLxwo2bHB0fc3FxwSc/9Spf+dVfZbFquHHzBrPtbYqypGnbxOBLwnRTNxJuVrdYYxOjL+BKIyqMAXQ55+RzhUjTSlJuVVXM5zNu3rjJyeExru85Oz3l3oMHfPRjH+Hd946Y7WyjtaZ3srEmHbYhnQNPs/JDz+oI9hDlgLXrzZ/337/LW9/9Hg8fPhYfberWtSZLoWXiMd7e2SHGSFlIwu79+/e5/eJLOO954fZtdnZ2OT8/59atWxwcHHDz1k0555QmBPETy7kQx7VEGJlO59xT1TVDH6xzbrxGBo+t1mpDgSLs8sOHD3n8+DExRubzOT/yO36Yn/prf43HTx6TZbnUKVE8dQ6OGzti/iVGxXy2xWwyw/XSFXw1V3M1V3M1/3jmxZ/7C9z74z+Bu/T8F996gT97/xef9ZKu5p/Qea7B6mw2ZXdnizLPmFUFWW5FAugiVkcOj88gKnJtqcqSTEsmbfQRazRFVVJOFeeLJSEIEPEJBBiboU1Ku80sSktybd/1BO8p8yKlVGr6XsJyUPLcTScJxcZaTFZI26DuQWuikm7T4AJ9EOawzCUAhhjZsbvJJyo+ruHmDqVR6SZvuVhi8pyyyFHWYl1GXK2w1tC2Lb53EAIOkRHPSklR7VwvlQ1GE4m44ImOJOtVo0/Mp1oMY3PyLEcRwGg6H2gXK/rkcQ1OuhGrqiIvhAEKfY+PDoyASJ98rn0nErsx/VYoTAEA6SZS+mmzBObSzW2Q129S5Q1Kjd6yod7lwx6JzS7RkYlNTIo1NmUPDUztIJuVJ4wjUzV4AJWwUyol67ImYIY8XFlWqsnxHu8/5FlNbNswo3yZ9c8MHZObjNIASNY/E9NrElC0BqprWfIAAIZjorVGR9a/l1iqAWACGK3wCWih1o+lEloWWCs+P5W+nyy4xDHYZw3Ae+fGUK0RnAdwLqU6Z1K7tDef8MXXP4trG1aLc+rVgklVsr015fYLt9jf3+X9D+7RuYBBix9dKZbLhqbriCiKIqdte+ZbFa+88klc33J8+ISTk2NOjo5x0UlKsQpUiaVsusjJxTnfffuYx4cr+h7293dxIfL22+/yhS+9zgsv3KZ3PdO5+Eabtk5ALNB3XoDq5ZLlckXXeXKjCV4YVYVsehVZLuydMdRNjY8B13u6tsM5x97eNlVV8oUf/EHe+LU3ODs7A6W4d+8er772KiD+0qzIU9WSHyuC1iz84D32xA0GcQB9eZ7Le9L3fOUrf5e/9Tf/Fqen52Nq+HCNGm1wnSOzmu2tOdPJhK7pJAwuRPq+p6kbVquamzdvcm3/QDys29u8+OJLlFU1hoYNYHH8PAlSMxOc9DM/VU+zca5+ODBM1ifnWogBrUibiYE7d+5wenJCnhd88Ytf5OMff3l8DK0UjCFsTz/H8MEwnJfXrl0XhvpD8vuruZqreb7HaMMv33gd8+V/4Vkv5Wr+ATP9sz8JwH9595v86Q++yKf+1b/MRbsCJBhw1TXPcnlX80/IPNdg1XcdmdFMpzllYfF9S+9EbjibWNomp+0CmYbtmQSdGBXpcsOqWVLXK0xeUFUTfITF5YK2bymyTMI+jCYoYVeNVjT1iuAcZZFTr5acnJ5js4yinJDZjIDi7PycrncUeclsS6S3ddMKy1vkWG3pvRNpqrGghqRPCRrKsyxJ5aLEEAeFTzfbISVrOudAG6bTKXk1oYiRqBTLpsY5Jyyw1kzyDBUFwPgYUdrQOY/2irKash0tTdfi/FBTolNwTxBpblQsliuarqX3EecjQavEgEqYU5kXlNUUjMgd0QaTl5Q5OOdpG+lH9T4I+NdqBDHeuXQjGlN4lUhSQ/LbEdN/x5BqQ1Sq51jfoMO6+iUOvjcv7KDzXpJxFOIpZh0aNYBVrTV5LiDE9xIaJIAvSQlZ950CI6s7yBblvltASkhspdxgqzVwhFQX8/QN+SYjuvZ6bkqI1feB1YHh3ASpQ8XM4IvdBMryXCatPA5fRJPCwtRaIiy/FhMIJzHCERWG5ClFjMNrBmUkKRil5VQNEm4lzH9ET2YJpAfZhDCGtu1S5yi88srL3Lqxj+s7mtWErllx+OQRZ6eKlz/xST760Y+yvb3LB/cf8OTohLIs8B65NqNntarpXU9R5jjfkueWssh45ZVXyO2rrJYrjk5PCMoRuwV5mdN0PfcfHvKdt+9zcgY238aFnvPFOTd29nnvvff4kS//GJ/61Ku8897bNM1KEri1XIdai2y8bTu6tqdrPUZnKIUEhyES8flstu5XdR6fUn2bFApmjOXawTXmE/G813XN2dkZRVFw9+5dmqZlNttiuVwy1QqfFBhy3qiNcwHZwIkSumVMqmZR4nHvup779x/wsz/7s3zj1781njOZyQdDMlZbfB/Gx93b3aNrRe7vncf3jv3dPZbLJcdPjvjUa6/x0Y9+FJ1Zrt24wa1btxJjrlFKQqBcFL97DB4feqn/CuJ1DyFgrfnQtSDnnFzLwvgPwU5q49z2IfDkyWMODw8x1nD79m2++MUv8iu//MsslyuIKZTJ2FH+vymFj8jnhlaKvd0DptM5XddjjWW1qv9h/+RczdVczXMwVVbwt7Zf5zO/+l+Nm8hX83yPeemz8NJneefbG0Jh1/Kv/Nh/yio6vrZ4n3uXR//vD3A1V/MPmOccrLZE19M3CuU7CFI3g9LkxrC7NeVisaTrappmRe8lbCXGQN1JQqVVnqyQlxmVpm5aur7HpBvwMs+ZlCXT+QxtFKEXT9Sjx2dcLHuq0qAzyMsco5D0z1q6BV3fURYFs7IA16Mi1Ksl9WpFMZFUYBccVue0bYf3kdB7qkkpHtDgsZlIKK21wuCGSFZWWJujTIbzkd57qcwJiuig9R1VkZFlk5ScG7BaJG7OdbSuEVlt8KgYMMoKkLcZ2IxIjrEZAY12ji548L34UzPDtCroe4fVhqqaYK3BeUfbNHLjl4lkWhgtUh2FME56YEKjSE8lvMYyhqyEmBJC/RgcFHxIj6VGD9oQ8EMMKJJPNMbUwQrEQEiM56abM/iQQoGEMRR1b6qj0Z4Y1VizI72qFhBvLSGmsKSBqQmpWkM8rfJ69NizqrVGG5XSWsOGJ+5DvtOQ4GGM40qH6pG17JcNEKlJiGTE0UqBClE2PzallyGAMeMN+yDv9V6SWKvJhLbtkv8zeYdDFHDNAFAjKgoIGxFuBMIA4iNETwxJPRAirvf4XphQm1li8OnxItYatnZ2+PZ3voPWju35jL5ZMSlzPvPZHxRW31jaruX09JiqMLz8kVs0bYuP0LQdddtRFBVaW1zwGC2+69WqJThPkeVMpzNu3XwRbSH6jryc8vZ7j/nOd9/n6GSBsXNMv8THwOViwcHuLo+Pn3B4eMiLL7zAt771DVxweAJByZq8j9R1Q1O3+F5YPG0sPjhhl1MnapHlZCmMy0fo+0Dfe+q6xXlHXmTYzOCC5877d4gIyNve3ubw8JCjo0O2tubc+eB9yqpMXmwBeWP9UsomH88LeRuwmWwGnR+f88bXv8ZXvvIVTo5PJcwMyLJMNlcSO49S+NBR5BlFlrE9m9M7j841F5cXeOfZ3d3h4aPHPHnymM994Qt89OMfZ9m23L59m7KqRI3B4BN16XwTtn68tpO/OYI8vx+80wMglet5UH4YI2nDpOAu30cUnrfffofj42OqSclv+6Ev0rueb337O0ymO1xcLBMQls9Ta3M5LlEqr9Aa7xxZnrOzuysMtJFNhvPLqyCQq7ma3wzz6vaL/LZf/2+e9TKu5h9hlNmAFcbyl371vwVg+W/9BL/wc5/hrcLwf8Yj/mi3+32/+y8vf4XLxMpezdVsznMNVpXW9F1HjafcnmOzgpACdrTW7O5MKfKMk9Mz6mbJ45NLQkA6VwnkeY52PVMt3tVJNaXvRb6mtaK0lqooaOqGsswoypJO9/Q9KFti80Af4OTsEh9haz6lLHKsVjRNg1VgooQ2zcpcbsiCR1cFRVkAiraVUvq27enanuih73omVcZkklGUNvm1NHXT0DlHlpVEpblYrFApIGhZ1/jgiQH63jGpCqzNUARhWq0iuEDwjqauyTK5ubc6gbkQkzfMinQ3Vc70m0mayXNZaE2WZZRFSV7kKbHW4Z2j66QmRUk3CtYYgvf0wWOUJno3hpuIWs9sSPXWftPN/lHX98QA2ugkWQ0JbIc1E+s/JJtVepQN5KjaiQAAIABJREFUyw3z4CFNYUwIu6jT9xVgrBzLEBxoizUWY7Pk4UT6KhM6HDpGx3MxMUOaTRZSfHEh+IQ2h5v15D0Nw+07o9xxYIfHrtWI/CyymcIGgytkuPhQiR5FQCudBKJKNlxQaGvTMQwjI5dZy/Zki62tLR4fPsG7Xs4DRfIx6sSaxvTer4+t1WnzQSVTbUiAH0UIfXrvwfUd2hiKaQlE8feWOaCZzWf0jeetd9+nsJqd+YRJkdM0HXmeU5Qld957j4cP7/PKyx/jIy/eIi8zFrV40U9OL2jbQEBRlJUcoxhQWc5kMqVe1bjzhRzXlNDc99A0hvn8Y0QuWCxXo4+0bRtWXUv0gXfeeYdPv/Ya02rCydkptWtSoFOD9wHXJcZetNUJpJl0HmmsValLOR2eAG3Xj73D3nvm8x3KqqRuG+4/fMB0NhHveqrEefL4CV944fO0dU2zrKVWS5E6lwGd/J1pc0BrjVUWqwy+87z79rv8/M//PO/fe5/e9Uk6TpLZm5ENly0Jh7EiVd+ez6XuBmFAzy8uCM5z/fp1lILjkyO6ruHg4ICdnT32D66PzIZKEl9rDc6Jl72PiphAqU/hT2G8DiNKKzlnlUrOgPTZoFMiNxFlFBqDsoZHDw+5895djM148aUX+YHXPskv/eLfoa5b9vdvcnpyhlJSXWOzYryWYkLJOjGt+/v7zGYz+q6nKEpOT065XFyB1au5mud9lFL8X39w9qyXcTX/mGf6Z36Sfxb4vY/e4d/46s+R/f5//ft+5s4f/wn2//p3f+MXdzXP/TzXYDUvJ2AKqV7JSkQNmvx3ww1K8nHl2jApck7PLgiuZTKt5KacQFPXOCcsUGYUVhu6tsNkOVuzKU0jFTTWlADid81zGtPLzW4UuRsqMptORIqb2CyRR0ayJFWdVgWTSQFDvY6ZEHxAZ5rowQ0BKoBN/k2tNMZYtra2saahaTsiInfu+46+l6qcoSKjKEu8czRNQ1UVSaIqAE/bDFSH88IMOh/JUoAJWpEVOaUx9JeXdHUHSjGrJhIaEyXUyvVSkWOtIgaPTjUng6xvYD+BVKGSqneMyJM3PWupeXH8u9Z67FGFQTK4Dn/RWqOCH79PAlIh9TOOoS1xIDL/fn7WtTdUJ2lxTABbj6SnHuW1a+ZKVirPgYDPuO6CHVJGBasKeHb+Q681ikR5eLz1wvT4pVEeOTCqUdhX2JQRD/7VlJRKTLz14AfefD/EtymbMJrpbMqkrJjP5+N7ZTfZ7cREpYUxVPgoKe0c5cJDYFSEJB2PshmTjp1zDqsgBEeW2dRzGfFevIfbBzcoco3VgWZxznt3PuCtrmN/f4/Xv/Q6H3/lZT756ivkRp5zuWwIwbK/s81kWlI3S3x09L1jcdmQ5yXOKVzX0jcSVNZ1LXlV0HYdZ3UjcmmTM9vaY7a9T9M0EtqU5zRNQMfId77zFq9/4XWuXbvOk6PDERSGJG11TnzJcWxO0kneKulk1WTCdDpJ53mkbVtWTU1drxIDq5hMJrJB1VzQrFquH1wjz0v63qG14d133+WHfui3UVVT7t79gGo6YWtnm6wQD2ropQoqS58bxijyLOfJoyf8yi9/lTfe+PWUGM4IUkFsBNZaBCimDZ+oMFpDDGxtbY3y+KZpODk9kXNASe3N48dPWCwWXLt2jWvX9tne3h7ltiFs1EkRUuXXwK7Kn5CqY1TarZINpfW5DeKvLW1FiBJgpjEEIovFkjfffJOmrdnamfPlL3+Zuml4442vsz0/wJqc48Ntjk8eps+nnHrVocw6Gds5R1lVXL9+fXyd3nueHB4+JZ+/mqu5mudztospxb/znzzrZVzN/09jbn4C8/s/8ff93uR/+Asc6z/CCz/9Hq3rfoNXdjXP8zzXYPVsseRyVdNlspM/KTOMlV5BazP63hODZjbbxmQFk8kWu1tzWtehUdg8w2YZbddRlXJD23ZSg1FVJfv7u2zP5xSFomlWnJ+dAQprSqbTCd55utxgraaaFBitxiChoXrGp4TUARDoJN0MMaCjxxiNUxCDhkLRm0Dw8hgRYUn7PqLoMCZHG4vzdWKxpCokKiXJxAmMZFaPnszgA71ySVrbA3JslssVMSLSxhRe4p3DLZbkRU5Mj62NIeTCxmzN5yyWS+q6Tn2HcuNutMhojTHkeS7rdj0xRMqylK8lllWUfk+HrIS4rqcAuWkdvLlyk2lG/5rWGhX1+PwD8DTGJA/pGmQpvdEXOuaRAhs3pWuJbdxgTZMnVEl348ZvEkPAI+Bg8Pg5F0F5bGK5BLiRknZZe2rDGtiOXr2YQppiHCtu1osTYLvZGzsAyRBEsmyUkVRehJ0CZPNAr9fnfRhZ82HzAGCxWLBM/bykdWoFfXTjjfvoYx3WuxYqA2ZUIocQUAggyXOD1nLuojzLZaQqS6y1GKNwrqdzgbjyNHXkYG+L2y9+lL3dXU5PjkFFeufY2d0Wz6NryfOCo4ePyWzJ1vaEvZ0tlJkQ8ZyenBC6GqUcJkYIHa49g0pxbX8nJQpXnF0scAFm8y1Ozi5YLpdonVEUks7d1C1WwYMHjzk+OePjL7/CN958E6Mz2r5DaZL3NBCcMNQGOWYusYYxhtTbmYvkPERW9Yqmbqibmq7rqCZT8rxgtVqxWqyYVBVFVkhFzOWSra05d+/e5/z8kps3b/Ktb38bmxk+XhTMM7s+55XGdyI/XtYrvvrtr/G1r73B44dPcE4CmbQCa236DDJUVTWeFzHE8Zxxfc/e7i55ntOHAAoOj4+4XC4oipLFckk5qVgsFjx6+JDP/ODn2dndkfc9xqQOaej7XtQSOuJ9T9/3wjirOKZHrzd01hdhSFtCPgSskdcYvHid+74nBs3hkyPu3bvHZFry4ou3uXXzBf7G3/jfiUExnVa88olXOD1+yPHJAxQ21QmZ8XPRGIN3jp3tbbIso121TCZTjo9POD095eDatX/YPzlXczVX84zn7p/+Pejt6896GVfzDEYZy/TP/CR/++f/JD96+Pee9XKu5jma5xqsruqOZdNRt4GVhnZSUOYZZVFgdEbvPV3XY0xG9AGrI5Myxzqhj0wm0l5r5YambRtUjNJnijCofbsCH7Ba49JOfNfUaJ2xuzMnxIDre5EaIiEnY3WJ1vS9sK8qRsgLtFVjAIpUdIoctfd96orVKGWT5FTWtaqXdK28Du+lj9QM/aNEXPKLlWVJVVXEIKnBPgRWdUOe25Hl0EZjsoxyMsHoDG0NPrGuRkmacL9qMMZQlnJD3Xc2AS41SntJXjOtNK4XcKOMQRkJaRoYyMEruZb/iXRyAJXargNhRm9a+v4QOmSMTWA0SBdsYv2MMUSlk9RyYG6SX9Oo5IsL4zEe75AHzyepc3RgLRHwixKvLBvgGSW8pU/vrVFSwwNDum9E2TVDJGBWem7X6b5h7HQdmFiltHSdrqng5N/T6W5ewGxUyX/K00Bbfl7k0ZoNr6xeh05orSHEkeHuWvF12tSFaoyh73t5jxKwV3rzeQanIWMFkNqszFEwBC+pBGAlOCyxy31LQ8BahdL2KQCstOLs7IzF+TGzScF8a05ZFVIrhUi/Y5Tz8tYL15jNZshbE3Fd4OLinAcPHjCpKra3K9q6JrcFL73wSfrOEaKm7XvatiezlsIW+KDw0RDJaFtP2y5RUdKoVWa5WK74pb/zFV76yEvkeUm77CUt3Dlhs52kKuuoRn+yHKiAVoqqKJKiQ6VNok5qppz4vicTqbSpVw1952lVz9nZOfPZNkfHp+zs7NI0C+7evcern36Vqqzofc9isaScTCiyLIFEAZ0P7j/gq1/9Nd59905KBQaVuqK9d+gIVVVxcHCd1Uqkz4NHWq7lQFCKvf29sSKqbTsuFpdEwMfAxeKS3a1t2q7l7t27fPZLP0RZlLRNwzTLcd6vWVUVk9LB4dPGm2wHhaQG8E9thsTIxoaQxmY5SmlsJh7hGGQj4Z133qN3LYXJ+NLrr/PwwSPuvv+A6WSbvb09yirj9PSQLM/G5xGfsXxeNE3DtKq4tr9P33ZihTCGhw8f0nbtuA1zNVdzNc/n/MQLP4b67Jee9TKu5hnPZ/9wxef//Mf5+vF7z3opV/OczHMNVq3NsNYyrQp2tqYYFeibmq5tIaQKFqUoK0kobbtWQlpCIC9KgvfUqxV9CNLXqbUAFUKqiLmkNwqlBDh1bYfWBtc7skzArFFgiwyl5aY/eKjrmqZpGBJvCXJTqLXCqgSClKTThgghsWvGKEyWjx2Dl4slSkHXSzdl5zxt3RC8Z1JVzLZmRDTLVUPXddJJqBS2LJkkUH1+foa76JlOKvKiwGZG2OcgMmkXRKbsXJ9CVwSEVlUFIXDW9BSZwya5XGYMmRHJqMlyrPE0sRGmMwUcDdUvUl8ivlNrTZIJOpwX1kcA+zoVdF058/Tf+76XaiAELCk99DBafJS1S1XGkKgr7LqgosDQ4QgDEBwkuYNscWAhRfardepVFa5wzbqqiDFrZmh4vEHe6D0bIFHWoRO7HmKUAK3kGx2qR6JCanHYAMsoSS4ejKyeJKMc2Kg1Y7xZXwMbMmgEBAx/L8pihLnWWGGIvR8TfAVgC/OUkAwK/RRQHV7ryEGrwQ+4Tkc2xgqYVGH0QUYC3nfUjaLIq8ScQWYgzwy+62iaJbkNeAd1vWC1uqQsS4xRbG/NqYqctm1o+5pqUhGc9JzW9ZK27sF7drYm7O/OKfICazKODs9QZFR5QV5U1G3g7sPHXC47uiCstbUZhqEOSZFZCGh+5de+xgf379P3Ani1lmTdGAPBR4KPWC0+3ZiOhXeO2XRCkWdjoFTX97gQcMnTbTPDbD6nrmsWiyUqamJU5HXDzs4BMSrquiFG+OCDu3z+81/g5vWbPHj0gLaRzy6VPJ6XiwXf/OY3efPbb3J+fjlu5kRAG0XT9mRGcevmLW6/eBuU4Zvf/CZD+BakTZ7gmc9nVEUxJoGv2pq6bUFrur5nWa842N9HacX9+/fxfcdsNqNznuHRsiyTDZmnFA5y7jrvUrcqSdGhWa9iOL906oTVaG3IbC7eU5vx4MH73Lt3D2stH/v4R7h16yZ/82d/HqKmmky4dv2AN7/zTU7PjiBElLajlF0lRBy8Z39vjzwv8M6R5zknJydcXl4ynU6pJpP/z//2XM3VXM1v/Pz7W+fYz/xTz3oZV/OMJ/+jf4rf8T//h3ydK7B6NTLPNVjtepEvTqpS5LoWiHN814lEt5OwF02k7lt619H3HWhNluSqXWKYfJBu067ryPOCosjR2pBnhq5rhIHSRjybThKBFdlT3ZPBe7wfwJVUyFhrybShKIoECoQZlHRY8WNmxmKsMAAh+crqpme1lGTL3vUUuXQ2DpK66XTCbDbj8nKB0ZDn2Vpem9i/4CNKGfLcUFZV8sIKIL1YLHHOU1WTJP3zNMsVXddQliXbW1sD+ZhqLOzog0SlPtHQbbCfAygVgNa2LW3bYI0hzzKc88nXl26oYxhByxqcrkHRZj9i30to0yBjVVqvwWQCXIPfUv6oMcRJm8SSJnZlkD0S11Uy0qGbQMcABMdQoc01JX/ihowXNjysqI3/JlX8bDCSadTA3o6/b5L3M9XCxMQ4J+C69vSl16AFWOsUTjMeQ9a+XjneQ8UNkkBrxQPddR0xhLHT1ns3Sq+7vnuKlRXgm47MBhu2rtlJJ4kaqoAY5Z42BVb54NLzdPhgx00V7x1OdeQZaA/t6hxrRaqq8dSrS/b39tjd2ibPMxYLxd17dymKnNlsilaRLDNcu7ZPVQpjG4Hj4zOOj06xOmdn7zo3r9/ibLHk0ZO7tE031v8YY0Y/ap7nDL5rrTOMtpyeXpAVWXrPETlwWwtbHxirTmN603zwzGazEbT1fcdyuaTvOtquo3c95WRCVVWcn5/L5oXrZdPIeeq6RilF20nVy/t3PmC1qrl16zZHx0esFjWXZxeYbcWjh4/4u1/5CkePnxACBB9T6rRc+y5IKNI//bu+zGq5out73rvzPt57keWnNUuljGdra0tqamIgKyxd76ibJikfpA85EMmLkieHh5ydnDCbTDk6PZUAMdbqCd+7lAgs56jzg1LCo7VIwfUQgBZHxzpK6eQ1zcZz2lpL3TR873tv0/cdkzzjyz/2YxweHXLv3j3yvOL6tRuUZcmb3/yG1ONoSwykjRNROBAj89mM6wfXqeuayWSCUnB0dJTY+vUmz9VczdU8f1PYnMn2lU/xamT+u5/5Cf7ejz/gjaN3n/VSruY5mOcarOrQYaJjUmRkOmISw6O0IstzyiwjIgyEUVAWJd4rOu9xAwuoFG3vMCZDm4yi1AlQJTlq1FITozRa9ygM06kVVglhZEPwY/ei0hIkU1UV3gVhZoCL5QqlIvPJhFHwppTIi1MqrDaKtmlZNQ113chjo3AuUBaa7e0t9na3RcJshB8sigxrDecXl6zq5inghJHf10rRdk68dT6wXK5YLlcobQm0LFa1sF+po3ZrPkdrRZ7nVFWegKik7iqt0criOodSHpPlGKXo+x7lpRKmSYBAKUPXOwFmcQhB8omV1IlpdCPwGzyokPyTA5ubZUjQz8BMktg7tQFUBTwNab7GSk3FcBMqksmQKj8GWpQUGJRY36jIrHTsKmUToxo3brQNQ/0OZi0BjQPTOrDFWqP1IFNOwN4PGwlJ+pv8tFYLEymvfSOhNSUQa6Uw2oLRxOhEaquk73SQPRutntpEAAHVVVXJ2pJf1VqbNhk8KkqyrPzsOnxmeB8205+NFjn64HkWcBqQbmBS0qwwsyhwvsdom6p7BoY7bVS0NTpGlDVo5YleWDerPNYEwONaT5nPmU5mXJyecfTocWLhMoLKaVvP5fIx1jpu3thlvjORcDIXefDwlOWiZlJNuXnzBXa294lRk5mC+XSbk/MV9F2SZ0eMVeSZRQF5JgFgaJM8v+Dl9JXjWUwgQrOs04aBnD8xpPTwvGBSVclrDV3f471nuVrRti1KKba3twkh0NQNMQWChRBYLFZ03QM595uG3Z1dnjw54t69+1y/dk223Lzj4b1HvPXtt7h37x5HR0doZQjREyKSLlzXVJOCz3zuc/y+H/9xjg+PeOPXvobWmpOjY6qilHMibRxlxpBXBVtbW5KObTKc95ydXch5SsR5x6quWSyXFGXB6fEpd+/d47XPfIZ4ckSMIoN3zm1sWij6ZA8wxm5co0l9kFhtSOds+txWSeGSZ1myPljefusdjg6fkBnDD3zqB7h58xZ/9a/+FH3n2T6Y8/LHPsavf/1Xefz4MTEGMiPXl1bijzZK0q9v33qB4D1WG/Z2djk8Oh49tsaap6TvV3M1V/N8zb93/Uc5+Ok/9ayXcTXPyZibn+D//tn/mH/x9/+P/B+P3njWy7maZzzPNVidVSV6a4rB47oGpZHUXe+ShyoQoqRYts7R9Y6iKpkWBQHN+dGxVL74SFmVFHlOnucQB1mspnMOayw6SnqvNhllWULKsQ3eoxOYE7ZGJ6Bl5Ge0pm97vBeAibEYJf6tvu/onaPz4IOn6TvOzy9Z1bL+7a05ShmKoqSsJlRliVYi20thmuSZJWaKtu1oW2Fy6rpZs2Yh4FzApD6KqOwIGAfWzfuA0pH5fEqRWfIiY1WvUGi2trdBKbquE6Y2RKIKkrgcIq1rRrbTJTAKjIxrCIG6roVJSe+b2gAvw88Lm5e+vsFmwtOyYK01Pq5Bmf8QWB0eQ5J8Y2KiE2gcvakpJVfQBj5KBUpCJUgzTPhQEFNKYlID/7rpsxsYWmE0jYkU5YQsy2jaOiVGJzFxkoEP0l15bfLfPqy9uCiVajYMkBKUlR6ltyPrOjx3ev15XgAwnc4oioK+72ld/9SxDD6kepq//3EeUlKHFzdU7azXDCQwPYzS6zRYow0h1QMZqxOI7tHKJt+1E6BovIBunPyJARUDYFlcntK3rfx83xIjTKYVfVB0bYPNA3u787SRlNH2LUdPTjk7XXDz1m1eevEjTKczoofTk3OOTk45OT1FE5lWGSbLZUMlpU/3vSMEx2QyoywnyTfthYVNKcCBICDcZMToMcqs+4CBqirJ82KURHvvkwzYi5w1l1qdxWJB0zRyiumBPe/H97AJ0jHqnOP+vbu88vLLbM23uf/db3N8diqhQykUy2Y5vW8ggcOPfPQj/M7f9Tv55Kc+hdGar/7yrwDCIIoMd/Azy4aK946trX3ZEEo1VYvzS5x34mX2PcZaVk3NxcUFB3sH9H3PnTvv8YOfl07cpm3JihyVNl2GFGS5xte+cJ3CwCThXM7tNRuv0ckyEEKgaRq8C1xeLvje976H9475fMqP/uiP8d3vvs39+4/Ymu3x4osvoLXija+/wVAdFFO10+a1mef5uFkzSXLf05PT0XLR9T1ZlnM1V3M1z9/sT7b4d3/P0bNextU8Z2NufZKf+p/+GX7ijxf8lQdXgUu/lee5BqvzaUmp5xSZQQVP33R4tQ6SSbE1ZNawahtsltF0HaumwUdF13djimy9WtHWwpiURcF0UmGMpqlrFI4YFW0tPtS6aShyTVFISq7Wmt73+BDR0RNdIOLwPt0MJ/ChTIbNS4ge5xoSdqJzEojU9h3OO7LcUGUTimpCkecE5/C9Y7m4YDatUET6rqP3LiXlZiNj0bU93rd0nR3lsmu2T6cKkzACFGMMWZYRokvesh7v5G33BLKiIMRIl9ghlEm9kR0hyC2pzSx5no8Jvpsz3Lx2XgJuMruW/n54Rpnph76mtRoZxcFvt5kmPHS1yuMOIS/D728AgrAON/JBADdKSeUIGmPEKyrAVDpLTTp2kDoaB48ma6ns8JjBx9QNKbLOQcI8rDWqjd8JEvrkEVAjfakDmPQjqJS+2UwY4RA/dGyG4yhySqUNZSlpr0VRjMe+73v6rqPZOOTer+t/olofe3jad7vp9R3OJQmsWR+XQVa7Br/CZlujyayRSqG0ceC8g6jIsgxlxNuq0uZAiA4dg7x+r2i8x2Y5WmmyPMPanhgcRWko8xKjSuplpFk1XF6eo3XG9Rs3aduew+MTAobDJ4cEF3jh9i0Oru1zcnbK0ekJNrMcHFxnb3+f3nnOzy85Ozuj63qc92hlxENuGAEUMdA5nyTcAtZ9eo+IgaqshEX0Hpeu6d5JwJrzjlm5TZblnF1cJFBvBVAl77RLwWmdE3BbliUf3Hmf+OXAwf4el+cXECIaeX/LsqJuW5quYzKp+OEf/iF++Hf8dmazGcYYHj14yNHhEVmWcXp6un4d8q4BAuK2UoVRbgwKWF0u17sw6Qzo2o6mkY2pLMt4+OA+fdeRF4XU/1jxiA6vY31dMKosjDHpWpXXPF5XG13LbFzbfd/zzjvvcHJyAgRee+019nb3+Jn/9W9gdMHW1i6vvPIKX/k7v8Tx8eEY+Ka0gqSMkY0ExbVr17HW0tYNu7u7XF5eyvE2hqZtRQFwFbB0NVfzXM5BsUX1p//8s17G1TyHY3/kD/KTf87w4A8v+MXH33rWy7maZzTPNVgti5yMEqs1KjpsleO83BzarGC2s82qbolojLX0wVOWBb7uaOsm7e4rgndUZUmZFzjfc3F+RrM8R2mVQoVK+t7je890WqF0pG49s1BRlgWh6zk7vyR6uDbdp3dd8qC1dC6QZbmwjq5n1TSQJJxD36ZzHh8CxmTMt+ZkeUGWVRiTsVzUNMuaTENmC4bAIB8cruvJMkXUAWsMVVlghsoeoOscxoq0UWupt2k7qXzJ8pwQBdDnZYFWOV1b0zUtVZ5RlBXLpmXZNDjnWdTCdOSFBWNQJvVqAkobXIg4H3EJCGfWjgEqxloUDmMsIUpFziAbHWW6HwoFgk1ApZLsMgUi6fXN7eBrld/dCGkKMTGYKW4UhU41Ft5H2ShIoUoxktKD1wm3Q1XMmI4LY/rvMEOK8DAueDIjfZZt245fNzpLWaggoE442YAAj6jVKDceXnNmc4qiwtoM5wN904o8d/SJymNthtMopVitVlRVRdcJuGjaFpsSsoPzIkdFJc9ikvxaM9brDL22IldWT7038n/rm/pNZnYTwA6e1uG9jcimSNu2WC3nOYCPmhB0Ymgj0tsapEpJW7QNxNgTY2S1WrKojyknBagJq2XGydEZ08mcLLfM53t8/OWPYqzh9Oycd965w/nlEqOUbDgYz3yrZL51g4ODGdoYDvavYbICHzWzyZSu6Wjqc7quw+gMY3TyNXuIka7rePLkkKIomEwmUg1j87RuqIoKg8ZFP55nfe+lFzkKwGzbVpQOxorkW6W6o9HLLZLVuq7Z2d7h/v37PHz8iGvXrpEbS9fUqCyXALnoMdbyuS/8IK+//gU+8pGXyK1J64XvvPkm3jnatmWxWIzvh9Q/ges75tvbTKsJhEBe5ZJQ3Lbk2tD6gFFrz7ykBDcoHXn86DGPnjxmOpmxOj7C+W706o/MPDptZAzPazDJp65jRCvxhYuoQZj+LJNcAJFPL3j7nbfwrmNvb4cvfunzfOMb3+TwySl7u9d4+WMfR6vIr371l1FKpPMo8WSXeY7CEoJ0x25tbdG3LS/cvImOcHJ0zHw2Sye1wuQZtrhiVq/map63eWnrGl/963/sWS/jap7jsb/9n+eW+cVnvYyreYbzXINVYqDILFZLkIvNDDroZGg09D6CNrRtR1Qwn03RqcMvBs/EWJSxBCe/m9uMrqsxQVJ/675jVbc0bS+etemMalLivACHOiULW5MDGpOZZBU14nZUPSE4UIpqMkVpRe9F/guR6P0Y7OSDI8szTGbxIVBfXqIx1KsWg9TsGKVQUVJ2Z7MZoRKZ86puCFFSXXEem4mXsHcpkCdJCsWzKt2uShuaztH3jsJ7bG4py5JoDRBp2obFYpVSaQXso4W98z7ik6NTawVG40PKj9UCerQ2iYUU8JnnudyQ904sj0gajqSPPi3hhQ+HLAGs/akxrGWvYQCwMDKsQ9jHk2w0AAAgAElEQVRQSD5RrRITmr4XUhVMQgvCoNpM3sdNQJrWp1O4UBR69ymAvGYk1Sh7ltf/NFuJGqB7ep0fYjkl9dQynU4py5JJNZWuYCf+Qe9XsqSUIjQ89CiR1hvseYysVvLzk6qinE4IzrNqpEN46Fkdzo1NZloeU/yzHw6cGUDpEDw1JCmnXYHENkac91g0zkUGG7JsziRW23ku2kuyMkfjKXQkN/JhE5QnsxofHaH35FmqsNERa6CrF7i2Q8UM5xQUFuVLzk9P+N73eoqypOs9N2/e5oWXXiSzmqPDhzx4+AF5nnH79i1eevGGXCtRs1gu+eD+Ez744CEuaPKiki0EpZIEWLztwTsWiwWnp2dcS32cMYBXERc88yons9LtKfJfR9v11E2T0sMzjLG0rag5jE6bKNo8VZ9EOqfrpuFgX4Db44eP+NIXX2dvZ5vjwycUZYXWmhs3bvDJVz/Ny598melsiguO0PWUef7/sPduMZLl933f5385t7p290zPZWe0Sy5pDkVKpGVJiCJbMaQ8BIlhxMqbg+QhfrGd2EAeAgTJQ14MOI6Rh8AJ4khBAuSCCI6DAHEQBIIuFpSIpiVb1I3kDnfJ3ZnZmdmZ6Xt11bn9L3n4/c+pntWFpBVplnT9gN7Z7uqqOnXOqerz/X9vvHj+nEcPH1AVEx4/eZoWcrYebjmHFJPJRKwL2qKVpl5vUIhPWXzQmuAD+8u9sa+1qiouLzccHx3xvZ+9xaPHjzC5Hc+R4Rwc/OUxDotGKqnrFdZkRFI9lVaj9D1EqdHyXcejR494/vw5mbH8wJ/6PHlu+NVf/VXm8z329q5x5+4dfvmXf4GLizPyzEIQyX+Ra0Lo5Thqy80bN/C99D4vl3u88/bbBO+ZLZfCYjvH8dkpeVWym93s5qM1X/73vx/76T/9qjdjN7vZzUd4PtJg9eJ8RWFhOqkAaHtHJKCNJS8nwhxqz/rsnL4TT5JvO3zfSfZrDOB7fN/jHeiiBO8kBEmROlgb+i6Q2YI8LzAabNQYU9K0a/HBljNmswzvJThFaQkyGnxlwwW8VoaAXIiG4DEpSKQsS3rvyHOLNpqm7+ldI8A7iieu7zs2G8dkkkvCbQJvvfdsNht8YGQuIuJfiwkTaa1ZzBdoItZJIvB601K3/Xjx6ryD4Ine03ctnfdEnWOsFTlj71FK0+Ol1zRdW/sIOqrEjqgE3McIKYbE0RDMCCqzTCSqXd+hVBjB0kiCphkuemGQmiZ2M1XMoIYE5iFUaCtZlUCnlJ6cPJfDdgn2FZAVAa002li0tfI8g9x3lBoPj/27A1hGNndryE2PHcd9H1J1zlWv6SjHTOC3LEv29vZYLBYAdF3Ppt7QO5FnW2vkGBFfAqohBFQAzNabN4DCqqooioL1Zk3TtKmeJbGnaVHBGIO2ZpREZkkirfRWxnkVtCrFFrwnRlSnjteAhDbFAEZbQkBCmVTa65FU6SQ+XNcojAadSYURscNHT4iyoGKUxgc/ssnORZSyRB+IqkXryMXqKVrn+GA4W51TFFMm0zm9h+69B+wfzFke7PHanRvST6zEFuCcY71uefbshJOjU4yxUmcVr7yOBPBUBO8ibeuwtiDLC/E/p77ezFjKPMcau5Vee0/vE5OtVZJnS3hTnknwT0z9xkrJ4s+wgBF8YLNZE6IkNj969Igf/uEf4uNvvsnjx0/w3vOxN9/kjU+8yXx/D5Np2q4hM5JyHpznvW+8R1WUgGK1Wo3n3BDoFL1nOp0yn88hLSh572m7bgxV0ko870ZpDg8Pmc/nXK5WlEVB8+KEp4+f8LnPf25cMFJKSYVV2C5ACfC16RySmh6FePRHfcCwAKQgRlkQWa0u+fo3voExhmvXDvjMZz7DV77yVc7OLjjYu83rr7/O6ckJv/Zr/xjXd5BCw8gU2sj5YrRib29JWVb0bcfh4eE2kE0bzs6ko3dT16ybmo+/+Ynf9f7ezW528+rmr935MfSP/cSr3ozd7GY3H/H5SIPVpo+cn684CBBCx3RSpcCdgC2FDTw+PaeuOzCa04sNXSsX7UWRM5tKXcUmdEka10N0GJsJYImRZZWjJhbnAk2zJmpNURYQI5nOsLkwu8FGyiIjuF7K7AP0Hi4uLjm/uCAC1WRCbq3Ifl2PyTPyLCNojSaTNE6tcfWGDJhOC5TS1JsNMUjf6vPn5+SZpaxK+l7kcm3ryPICY3KM1qm7FbIYUqAJBOdwwRPQtJ3j8nJN3/dEpYgbjQ89KkJVljgHnYOs0Pg+SO9jgCxLXtYYMEhSqPjtejRJbplCVrzzBK3GDtG2bUdgHFJXpDUWVEjg60pgT4xXLmRTUNUV6WlIMtFx1NAZG4QRUuBCxDPebSuYTVLWGFKrqdbpObYBRwp5SoUawWdUA3uYAqAiY1rwUEUTk09akoN1AlmKqJK8WA2gO0I6LjFErBEwsFwu8d5T1w1Ns5HzJNX8GDME14h4eAikUmx7KU2WS++lAmstTdNQ1zVtW0v6LVESfEMkqogxwuR2XU/UEZMbgttgrMEHkZKiIERJa9XaSPKtkaTgGN0o90RZiHoEsX3vwJjUo+tkX6mYAMlItxKCou+NAEKlybUmqoAxmkznRAlQBvwIgohpvSJGfHDgRYIe+5ZNvWG1uaCczJg0U87OTrBas3ew5LVbt5jPpjw/WvPs2QuOjk8RlG/I8hytTArdCuRlRQiwvqxxSYGwXnUU+ZQim5BphVIeaxQ6hZNZo4kqst60dF0nrKoP9CGyKCe4AFZrjJEFFx+dLDrFgE5qAALEENjUDfWmZlLNeP/RYy5Wa+6+8TGWb73Fcv+A1998k2o+S+ebI8sMRsvyy+pizcP3npLbKccnxzR1BxFRZiS22GjDwf6+VPZoqYw5OTmh6Voi0HS9sKrBM18uuHn7Fp/4xCf44he+iLE5xliePX1G23Qs53POVudUkwkxSaB1lpKzlZyj1hqsGUBrei8zyNrlOz0YvoPi4XuPODk+IcbA93/u+yBqfvM3vsq0mrFczPjYG3f45V/+RU5eHKGULFZNpzPKMmezvhwtBtf291EhktmMyWTGZz77fbSd47d/53f44NkHbOpaVDeLOXfu3Ppn+TO0m93s5o9o/p1Q73pVd7Ob3XzT+UiDVY/hbLWh6TushqgNWSbdenXb4V1D3TmUlQRPozO0ySE6qiJjUhV435Mv5iglbGjXSbJt8GB0JFdqrFSMuaZuWi7ONhRlRVkUIrlcr5PnL6fILE3Tsqkbuq6XagxjQSnKMmNaTphNK/quI47yVUnSdb2nq1ua9ZosM5SZFX9cmYl/dd3w/PkRRmtuV1MWi33Ozs9TWFMP2mKSd9GnsCNjoAcu2hprDdqmsJ4kX/Qx4mrxnlpraXtP10rCsUvVO20jQNaabPSA2kHqh0gYIyJPhi2gjE7qfRQfDkESB6caWcjfO3BJfj4wkIwA0XsvEFFvvXCjNJVEmIfUo5qCkMz4iEPykk6E3zaFONGIcvMVNlE6WxNAVam6Rg2/P4DWAMpsE0i5wuDG4XcFqAg2F4Y0Kwz7+wcsl3tcXl5ydnaW5Lbb/litFdZqQlCiFoiJmUr7zGhzZf9GWdhwnSQ4h4BNlRxaa8qiJIRA24qU3bky1fykxOcYsNoCEviklJZjnPzVgicGebXCWtme6MWDHcOQ/hvRMYJNKcIJjA3scoyR4OQ4xtwQghL/qAoo30vqtPYYpdHSQpSO83bf+6jkOAeHC5GIJ9CxbtecX55jTwv2l4dMyimb+gVHR2cURYHrA5tNQ+8iZTFBa8hzCeaxSmN1TtN1tI0oCkDkuNpkzOcVRZajlcNgMCaSZ4aqKIgx0LQtTd9RNzVt29IH8awrbSCSJNgKoyxeO/q+QytDViTPsJf3ZIiRs7Nz3rj7PZydnfHs6VPeePNNrt28IZ9hV8K6ZBECyiLD95EnT57iXaDIMl4cnaDQyX/rRyWAUpHFYsEQWOaco2kaUIqm7/Ax4IOnqEpu3rrJxz7+cW7dvs1sMUdHxWQy5fnz55y9OOHgYJ9nL56R5dkVhcS251hrqYaxVz8fZMlFztcYJOTMySLU6mLFW2/dRwGz+ZR/4Ud+mF/8hV/m/GzDtf0DPvnJN3jy5AFf+JX/B6KHELCFZm8x47Of/Qx93/GV3/kys9mM/eWC9brm8PAGVVVx8+ZtDm885ez8V6ibBpNZlNG89tptST7fzW5285GYv3Lnz/CZX/lPXvVm7GY3u/kOmI80WFU6w6NpOi+M3vE5eZGRWUvvzug7T4jS3xf7QNet0USqKmdiSlSquUFpetfTth197ymKEmsMKoUZRR/pnadz4kVzPlAZI4E5SnG+WgMwn89Yd63UdihFNSmotISMREQmiwpMqpJYFmw2G7q+p9AK13T4XrosizyjKgu50DNgokhSjVLMphNCGOS0WQIsUrGjug5ttkma3kdhOvoe7T1VVaIJKSVYLs5zkyUmrEeh6KMA80lVCXhvpXYleJGM5nlO27aj1FTqPcIYDnQ1ITYKgQgJYI6e0gQ2gg8I8fp7ANU4AD4/AsdATJ62BApjJCRmyjkvQDL5TOW1D4rcxLKm+1yV9G5Bsmz3h8W+4tu7IofdKopHcDhKHpPEcnye9K8Zfh4HCa8wW3meyTHRiqOj56nzUepvhn0qYNyiVPIK96lrNb68jYMM02g5V5xz489CkGMXfGBT1yKl1jqlY7cCIo0mqIAtLFFoZTTbMJ4BuIJN8miRUmotjGqMAuJlcUB2UB8k1TezH5JPK1BoASsxgg6go9TUOPF4h+AI2pFlGu0juLQvRsAznGMCqkIU9jtG8Zo639N0LW3dMZ0smc1mlL4Yu3w719PUjsxK3YzWNp03KT3ZBfrOo9GSht3WZJliNptQFtIha3VEqUBZ5vIZQ6R3TuqC2oaub+j7nslkKueBMWQpYKgPPVqr1CG8PYY+BJTVYBRn5+d8z527eO958PAR3/u57+P6teu8ODmjaRqmixkS7qRF/h+g73oev/+Yoihompqz8/OtrTgFa0Uis+mUsqyI0ZOl93Rd17jk4VRagPNkMuHw8JDDw0OMMSwWCz548gFFmbNaXXB6esJnPnaHrpPQuizP0MYwFFXJ/vYEr/BX33Naar5kvUpOZqnZavnyl79MXYuy4Cd+/Ce4XF3ylS9/mclkxnw+4+Lign/6T/8JXduKysWLF95ay61bt1lfXmKtZW9vn7qWfts8zzk7O0NrzeH161hryfIcHwLzxYKbt29zeHPHrO5mNx+VydCovHrVm7Gb3ezmO2A+0mA1anAx4r0wfeeXNf7sYpRbSi1LIYm4xiQvngalcSHS+1SpoMG5JE1EgFsX+tSZKkX12lh8aNEmoyoszks9zRA+JEmzAWMt86qgmkwwNslNo8j6zi9WeN+PxkznPV3f0TSbVLFgJDDKliwXM7QGazVea2KQgJLeOZq2ZnV5IR2OTrxnffBYm1E3DetNTd/3oHW6QDZoa8mKUgKSUPJ9npNlOTFCkWdkNsNojXeOEDxtIwmfAhLkwjrLMjabzejpGzyXV9nAbRgSqW8TAZ/pgl2plLI6XEC/BFavMJoMrGUK70HhExhRmlFm62MUZi1GlE4e15hYwRhRCbRuPZhbBjUmVKyjSIL1lVCneGWbFFsf6tVwpZeCoJIsN8aQ5Jlh+1pCSBfmQ+ppJoE1fUfT1NjMSsKs9zi3Sd5CqRmK0W23KSmit4B7GzClAYwewaTWQzcqScYr4V3DPo8KTGYw1uJDTwwKq4RRN1qk0UobVBj2oR6rWuIgp07gVGudwKoixm3Cb4iQIqqSZHkL+kMAmxmKwpDlWvpZowICPtQoWjTSK4yX+7krCczDsYvJGxliwCdWfaj6aVwjgWXtJWVVsrfcI8tLuq4nyNINIWoBp66jdx58er95AdLBezKjWCz3uH5tSWYQCTQB73uMFfaZxIgO3bq9c3jfjwsUmTXCMMcwBk4NXcg+ygKBsYaua4kh0LQNZxfn2CzjwcNHdG3HnTt3efbimL7rRIifmEwVpWv56OiI09MzlvN9Hj9+MvqnQd4vQSmUjly7di3J1RXGGs7O5T3dda28j72kQu/v73P79msUZUkEDg4OeO8b77G/3OPs7IwHjx7x2R/8PGVZSojb0BE89rmmYzX4q8f3nYDTLAW6eSedts+ePeftt9+m7zru3n2Nz37mM/ziL/w8bdOgouG3f/tL/NqvNWwu1ygF0+mU6BwKOD+74B/+w1+i61qqoqTICpqm5caNG6wuLtlsakCxf/2QxWLJ2cUFymgOb9zk9mt3eO3u3T/w781udrOb3exmN7v56M03Bav37t27BvxPwCeADngb+Mv3799/ce/evR8BfgqogPeAf+v+/fvP0/1+39u+1VFGgJfrOmzQuL4VBsMaFss9ZrMFbdNxuVkTCFRVRZkL4BJQECiLnC6lpAY/hPkE+s6Bkgv+rKhQxmCzHFRIEllH07bCNhYlVVUlX6ATwOc9IUqgjVaK3GomZY7WFhc8F5drlDIURYHG0jYNIQRmkxlFnmGslgRLDUYLIFPJy6eUomlbus5TFAUmsxiVU5YlSmuaroMhOMk5XAhp2w1BKWHu0mvVqYs1eIFjRmtUqs7YbDaEFNpjlLymzWbzckWFkR7Zl+tjtv2uw/40SqfOT8eQSqrFZDfOIDVFCQCRHwooiVFdqZsRejPEocc1JmmswKdRaspWjqsTUE0PmVCfSoBOvgZIpQapKfBS8u8AWOPvBqpJD8z4AojjBfpw/6F+JyTms+/BWkM1EXbPu5Aqb2IKy0qSZu9lf6JTcNEAlofXdAUwh623leRnNCYjoqSSxnsBmiqxW4UlzzO8F1AJlr5XZCZDpbCh3gVcL4sPgj80Q+OrLKIg0tUgX1ptvauDF3nYpwNYFT2AhJD1ecbt174HouHF81MgYnJhcF1sMXF4TCPy8ihHNbmOhaU1Wphn3+NcYtaVxvUdXddKZ3Kb0fcdRT5Bm4I7d99Eq4y+E0VD13dEH1HBCPNtM/YP9tBG8d5732A+K9jfn+O6BteHsVd3eD2dd6w3a05PT+lcT58CrQa1u1aKvmuTTH57ugwJurLY4xMTWGC05vLykls3b/L8+TMuLs65desWMf7GyJxba8msqCNiVDx8+D7eB9qm5fjkRHygibl0aRFqsZyzWMxxrmcymVA3DavLS/m88B4fhF1dHuxxeHjIjZs3ROavFJPZdPR2+xB49OgRbdNy+9YtHj95IioGv1VDDIA1ps8BrTXoFLLkPUYLiz6oO9566y1WqxWzyZQf+qEf4vj4lF/71V/j9PSM1cVmXJxzzlFNSkknzvO0GAYnxycYY7h98xZt01HkBUYbHj38BpPpjIvVJbO9JdeuH/Leo0fsHRxw7fCQ6zdvDjKQ75p5lX+bd7ObP8zcnO3zn/70v/SqN2M33yETvcPF8M1/cTfftaO/+a8Qgb99//79e/fv3/9+4OvA37p3754G/mfg37t///6ngF8G/hbAH3Tbt7VxBqxV5AmAKm0k0CQl19okuVMxQog0bcvF5SWbekPnPW3fs97UdH1P2zp654lBYZT4qzKbo02Gc4GuE++a1ha0eD+Vsdi8QNuMpuvpncdkFTorRKoaIDiR4dabDZerCzbrDfVmQ103gKIoSqZlxbW9ffYWC7qmpamb1IUqVTYxRlarFZeXaxSRqqyYz5bYIkdnwvIaK2mm1uaU5YSqmpBnJUZngMEFuFjXXFysOT+/oK5r+t5Lf+XlBa7vR/bRdR11XaONJssycptRFAV937PZbEaG1FiR43ZdT5v8h8YIY5jnAp7LsqQoCkh+w4D0kfbeCaMUPC6CixLc5GLAeRJDJl+9C7R9R+c8LnWk+hDpQ6BzQcJ1AGNEJhoFQVw5QQXWxEEOqUxiHKWiwygj3kiUBNz4LZDYevzkzoqrAHX7OzEGqc6IwjUaNDqKLNmgpAs4RHSEMsup8pwiy5lPJ5R5TrNeU69XklZrLBoobEZmDCpAdD7FGwtAFNA7bMF2uwZwMPz/wGQrrSgmBeW0xBaWvMzJywKbW3SmKKc5s+WE6V7F8mDOdF4yXVbMFhWTWU5WajASwiR5VEPi6xXgmqThAsZEEhwieB9S1dF2fxEDSkuPalO3ZFlJ5yJN5+m9IuicaEp8sPig8WgciFdT61ThI0FFIfUOy2eCdAoLoRzRVhN1pPM9l/Wa47MjXpy84Oz8jMvLS1YXl6zX8l7QypLZnKosWUynfO+nPsWn/sSbXF6cUm8uqKoc73s51lqB1mRZiTE5UYnq4fjkhPOLcwlXGpNx5XiMizx+u4gxSOkHIKm1ZjKdMlvMKacTXPCYTBQTX/3qV9nbW7JYzGhbYUKVkhCzGBTnZyvef/8JVTXh+OSYtm1FIj30uKbtXiwW4s+1Imder9d472m6jt71QCTLLYeHh9y8dchkOmGz2VA3jSyuaViv10ynE45PXnB2esLt27eJCYwOC1ODskApnWzGMS0SuvR+NQx1SM71PP3gMd/4xjtYo5jOplhj+Pt/7+/x7rvv8vzZc+p6jbUaCJL4GwKFzV5ihLXSHOwfYG1OiJKIvV5vaOqG6D2PHz+mKEuuXbtGWU25dfsub3z8ExzeuA06+3b/BH3U55X9bd7Nbv4wkymD/YF/5VVvxm6+AyYcv8//+IN/g//9g3/yqjdlN69wvimzev/+/RPgl6786IvAXwV+EGju37///6af/zfIKu1f+ia3fcvTNQ1dU5NlOWWRURY5WinariU4x9nxEd57cmuku7CuU6dlx2w6IYZAvV4TnMMqyGxGWRQYK0mSKEXvPc4H6qal63uUMSkrRzOZTMiyjL7ruby8pOtabt+8zXRa0fctrnf4vsVq6RUsiwrnI03T0XU9SrX4vmdSKqbVlCzL0Kald57NuiHlMiV5qybLDcGL1LBPLGXTSNfrpO9pu16+937swyzKUnpWeyfSxP4K42OMAJ4Y6BJbl6UnzfMCN1xQJ7byao+nMZK6Kx2xwmMaY1PQ1JaRHFhE57YM43Cbcw70Vga8lQhGohMpbQxSpTKwicLQJP/vwNpd8YyOwuIrgU2DDFn+GXymid3VwzZvZbURIKQu1uExrgQvXVERf2iE8X0ZNDLuk7IS4F6WJXmegY4417FerwkhkuUZwlqKTzpItJGAeu9FVh3FlzkELyllpOs2sXtKKWxmkw8wgWol0nebZ+R5PsrPtVXkRUZZZehMEaKTZFoVif0g2TREFeldT4gKr7bgPECqlhEmVYWBZRz5bCD5FlOglN4iVgiOIrNEbXnx/JSL9YaYFi5CF4TdpUxhQh6jhjNF5MRRpe2IUbyYw4KCRFITfJB0cCW+Y2utJP56STeeTac0tUvnjwCn3FpuXrvO63fvMpmWPH7yPg8fvkteWIoiw/WdSHiDsP1aW6LSaCJt23J6diZS5eDF9ztI3pMKQA2yaSV9qr3r8cGjtKaqJhRFgffbiqG+d7RdC0rx9Xe+wZ/+sR/jtVu3eOfdbxC8S7U6HmMsjx4+oa47quWMk5NT2VdKjaS/LCJlzGczYgjYTHy6wuaLWqNtO5TWTOdzrh9eZ//gYHzPe+8py4L5csHl+SXVZMLpySnPnj3j5muvYa2k/Vpr5by4slgyyMS99wzLPsZIwJJzPXW94e23v0bTNAQfePr0KT/9U/8t52cn+DCAcpWCokJSKaSFSSJ5luOcoywKbty4kfzb0rHd9T1lWVLXNefn5xRZxp27d3j9jdc5vHmDm7duY/MCZT7Srpdve17l3+bd7OYPM/9Z8f2vehN28x0y3X/9t/nLz7/0qjdjN694vhVmdZy0KvtXgX8AvA48GG67f//+EaDv3bt38E1u+5anyAyZ1ago3aCZ0RitmFcVe/M589mEG9f3OdxfUhU5WSYXa8EnmW8c7KOaopxQVhPyPCfPLEWZYzOLVhprLUWRCzvS9XJR1/VsavGHnp2dc35+Tl03fPD8BS+OTnn+4oTLdSPyyqCIQZFlJZktsNpgtcH1PU1dU2/qxHQ68qLEZBkuRlAGHyWlWOpowujHGwBznepJjo9PefL4KZebdWI4LXlRSAAJKh1KYbqGPlatxSdoUufmGJQUxK+WZVkCXVu/mdZ6DCcpioKiLCVRUwnDNQQtyaJAT9/3Saa7BbwjmFNqrNkZSmAiQ0BOoOv9S2xqat1Ivki55B0qKuIgQ70CiId/R7yWUOtV0HzVW6cSqNu6/Phd26zThfdVCfDwGIM/dLhI//DXfD7n8PCQxWKBNnK/kHyHWZFTTadMZzPyokBbM3Z1RiSReBQjKy3+T7bPY60Vz621ZEVBVpbkVUFelWRFgc2shIj1HYFIVubkZYmxlqjlXLKZxWYCfpVJJKYBYxUm0xgrfkd0wEcn7Hh0o5R6kOduR34ekGND8tzGEOR/lEcbyLOC1cWa4CPlJEdb8NHhowU1wccCFzUuKnwKc0obvfUQq+HciOMixCA1jynYavjX+8B8PufGzRsvnUPz+ZxPfvKTfOpPvIm1cHT8jGfPHnO5Omc+n6VaJrELdJ3YCITdlec4Pj3l9PwMkJRi5wNFUWKMpHlfPW7OeeqmJUSptNrb22MymQjIMnasFSqqknVdU00qPnj2nNX5ihs3bqKBrmuxJiMzOc2m5eHD95nPFqw3G85XKwl9ii8z2pOqkrqsEDBa07a1JBi3Hc6HcdHj2rVrHBwcMJvNpCPaGAkQi5HpdMpqtSLLM0IMPHr4EK2VVC85N35GjIFqceurHt4fCkXwnrreQIx88PQpDx484PLykpOTUx6//5izszM5Zuk8spn0UCst8v3hAzyG7efT3t4eQGKdNc+PjtjUNdPZDOccm/Wavm25fuMGN2/e5LU7d5jNFqjk6/9unT/uv8272c0fZv71f/BvvOpN2M1udvMdNN/uX+//ErgE/huo+uMAACAASURBVCvgJ///35yXZ7GYsbeY03cirzNKwkJm1US8nVGK4b13LKaVyEydx/U9hEjf9hDl4tCHbdVCnifGYV3TO8e6ln+lLkVJtUfycA7AJ89zQoh0feDs/JLCavIspygqVGIPiIrg5SJxPpnSO0cIDm01PioihkBgXbfU9ZqiyCjKnDzLUviPxlhJ/o3O03YdeVHQtB3rdS9BNbYQD2Hw2LzA+0jX9XgnSacqvYbhAh1Ifa0Ra0XSGRODoaKAoRgj0QvDapNfN3iPwibgnIKAQiQmeajIHNVLIDUi3saY0ouG/TmwmiGBDQG8YQTlkICmgm3YkcxVT6ggIcY7jL+lXoKf45esU6gU0DP83tbnqhgkw1fvPz7oKO8cRnpWRf4ax8eKeN9hjaXte1yQ1NKm6zBG5JLT6RRrs/S6tsBPQGiGmii6tqNp5DgR9UhRRgRoysW2JPya5AUWsGau7BKdgG/AxJSqy8AqmgQKBiY9bkG6NWhrUD6iXEgrPPKoZVFibIZveoaQKdDEmFjVtK9iWiQY9vXwHJNJRZbNcF48n5mV92vveqJXYDVWFfTB07kam+zEmU3sajqkcahBSd8LYAWiwnmHtZnIkX0gLwpu3LjO86dP6buGMp+QFzmv373L7Vs3qS/PODr6gM2m5sGj9+hdx97eEgGhoGJaMFAabSzGKNq+4ezslJgUHFmekxc51WRKlhVoZVLnsJJE8RDJspxqWlGWpaQ1x8CYqawVVTVhOp1yfnbOdDLl+OiYd999lzuv30EpRdu0si9D4MF7D7g4X3F4/ZDnz16IckEkD2IpsJbgPXt7y7F72VhNfV4TQqBpGpq+AwVlWXD9+gEH16SHNRCTnFjY0tfu3OGrv/Pl0YP93oMHuK5nb7Hk7OwM03Vyv5QOLn5rNcqCJcU8qQOU4vEHH/CPv/iPef/ho5TeK+emDwFjxacdg/hzGXqKBz98DNjkj59Np1y/fp2+68iynK5rOT45Zm9/j7LaA6N49uwpq8sV+wcH7B/sc/d7vodqUhEUL32ufBfOH+vf5t3sZje72c1u/rjmWwar9+7d+8+BPwH8+fv374d79+49BN64cvt1INy/f//kD7rt29m46By+70Tu5xzeOyZVCTHSNTV5ZvAxYpSmzA3zaUWMmraRtM0sz1kul3Rtw/ryEp8bub2VC+/Nek3n5EJOG0tRFjSdMIU+gaTMWibLBV3Xcnm5QetcLrSVpekkUVgrRZFbMmuBDdZaqqoi+EDdNdRdnTyYkabtuFhvBLApRxs8KtYYrZlUUwEHWQ7KUYRAUVZkRc90FqSCQxmapqVumiGHd5TjDmFEQ0CsTpUqWpuRSVVK4Xrx8w7sH0AIfpSiioy0J6BwiYl9yY/4IQ/j0KcaR5OlGhS3AqD1FZA4BM8kUDMwYoy3bZNpfr9u1uGXx9tTwM0A74SZlR5QpbUAygiGKH7ICCqGD4FRkR0O8+H6mxghJjksSiXZsuzrGGURRFKha5bLBVVV4YPHZGasmhnkoqvVpQQVGam3KYqSsnRYW+NcxHtwvt8yimkTtTECTvU2FEqn7TepZiYM54KHPobkG46UlaIoMqzJiMrh6VNYkzD8khqtwSu6VpqHhe1K0uUkxyZu/b1DjnNUcn4EIiamflzAGGGDlY5E78lthOhSxnCk73u0KjBZMe5HpRwEL72cSPq0NnJjSD7emBKZB+VEZhNwUprgA3vLPZaLJU8ePWc2XWKtoSwKyqLgxfNnnF8c4dqGdb3mYnXJ3sE+s/kC768EOERJnjZazuW2bdhsNmOdVFWWzOYLsixP/bCa3sWEsyyz6YxyUox1VvK+1FgroE7sCMNnhiysuBj42v2v8elPf5q95ZKmbenbnqZd8967DymyEu8CJycn8t6OnohBKanjWcynzKZTWYAwWgBqCnZzzjGEYF27do3l/h6LxULStEcmUxbM3vzEm3zp8Dp105DnOc+fP+f87Iz9/X1eHL3YAsn0mWOt+FKjD5i02KXQPHnyiH/0xV/h7Xe+xosXLwSmxwgYYavlDKaua6bTyagekHfjywFOwqouX1q4Ojo5kfqirmNIXt5sNpycHHP95i3u3r3LdDLBGksftszvd9u8ir/Nu9nNbnbzRz39//XTfPDz7lVvxm4+AvMtyYDv3bv3NxGvy1+4f/9+m378T4Hq3r17fyZ9/1eAv/8t3PYtz8DgDYBmfbnh8nKdekAlOTfPLETH8dFzNqsVwTn5GdIjaI0hyzKR+RLHUJSYLoKz3CZQ50dmwBgJEXFdLxUSWjGdTpnPZ9gU/BFCEBlnkHAZFyOBSFkVzKYlVgWs6plVBVleULctq/WaTdPiYiQrKrK8AGXofaAPkd4HNk3L2cUFq82auuu5TDUy+3sHlEVFIFK3DV3vRrAogSYf8oWm24wxVFVFVVWgFM4HXAg0XZs8rt0o4RseSy5uPW3r6DqpC4mJ0hqBW9weIwEkeux3laTgISX0SmpuuqAd/KlXj7OwLRKoczXIZ/j68FwFuAO4VQNqVQNTq1BGE7VUmPhRkiye2O2+Gr7S5XNiHMdHVen1KbWV5KqBPVTMZtORye77PnlU5RwxJqOsptgsH1nSxd4ey/09ZvMZZSUe1+lsxmJvQTWpMDY9v067BGGsfRBAI9JiJLo3ya2jEgiolEXrnEgGZLhe0W4czcbRdRHfK7xT9E7Re4UPBqVyjCkwOkOZfCu/RdE7T9+LFNhaM6ZVk24fmNXtMYkj8AnR0HY9XV/jfY1SnmazhuAxWnyqzvf4qEBVaD3F2hKb56A1IUhFVfDxpWMk+5aR9Rd6Eaw2FHnO/nKf0+NTYepiRCsocsv68pzV6py2bQkKXhydsF7X7B9cx2ZFYm91YvMDRkVQDuelSio4R5nJAthkOiPL8iT/tWmBAZS2TGZLymqS2FmF0Rk2y8c+3Swr0MZgbQ5o8qKkd55JNeHRo0dcri85PDykaxuc6zk6Oubk+IzFYo/1ekO9acYFpJDYbOc9e/t7V98hrFYr2lbe58778bmvX7/O/v6+SMSjLJoEEJmskgqrwxs3WNcbpvMZ56dnY7fr8Jks7xMzKjOGCiznHA8ePOD//D/+AT/zv/wMv/Wbv8XJ0THmSio36XMJIMtzPnXv0ywXS2nmVfKlYkwLHuJBLoqcsizoe0nTvrxccXx6NHYzm0yAbtPWnJ2eMKkKhgohH3z6QPi2XC/fEfOq/jbvZje72c0f9fz8f/iQT7/92wD8jds/Tm6+60LydvMtzrdSXfNZ4D8CvgZ84d69ewDv3r9//yfv3bv3bwM/de/evZIUgQ+QVnd/z9u+nQmJzcqLArRls95Il16MdM0at5ixXMxQWi6gVpfnKBSz2ZImtqxWK1arFWVZUOTppUYJ34kxSdCUZTpVo59La6nL8F5AqvdeZH9ZRggelCRpBjzRR9Z1LV2tm8hiVrGcTVDeo1Ug05pN19F2HW3X0zphFawtMFmGyQy+a1JXqBgvex9FPuzDmAbatj1Zdsl0Ohm9osDoH23bVqR0I9PJSyBPJSaw66S31acuT+lb3AboQPJZJh+lHnygya9pFJAAxFWvp1IqyX+3zzsEB41ABkbvGSmkJ5J8pENoESK1vVqNsQWqVwDrlf8dIGUiIcfXGxOLtGV+B/lvZEjPVVdA8B/E4g4X2CTZrSQSB5HNjt7c1KUZYVPXWGspq1LOXaXJlCYrCpzzqcYkASJjyPNCWPi2wRMkeEnFkQkb2FUXPMoLeDbWpn7hlIiNIcYkY0W83fLCRHbb1I7gG5HOKgG8INVE1hpC0DgvF/QS6mQJXrHZSKdlluU4H9AxEsKV9GQFMUooVfBRQpGSh1kjlSvetwTvwECz2WBMBsoQo8e5SKczbDbBqEigT7JPDUYTgqbvOgFmYajPCWNidPBh9BQ759lbLrhz5zWevP+CzFZSy2QMk0mV0G7AaMvl5pLTswuyvGB/7xreRYZqJaUixkoSudKevmnYbNZUVSnn1lDXEiVgKQSR2BdlRVlOUFESbQc/NUlSnEh5bAp401rqe4qiYrNZszdfcH5+wdMnT7h+7Rpf+crvcHx8woMHD7CZJHA/ffoBIUYyq+lcx1AhVFYVi/mCEAOkkKnBU942rXQrozk42Ofg4IDlcpneNBJi5ZKkN7MZzjtu37rFw/ceJPtD4OHDB3zu85+jKIrx/Pbe0zUtJnlk33n3Hb761a/yzjvv0KxqXOgwRtQpMXpRgkTFpq0pTcbHPvZx/rU/96+igP/t7/+vL3/2B5GRy+KQZm9vSZZlkKqNzs5PaZsWnWf0riMEL7LnZsNqdY41etwHOsuIWs6376Z5lX+bd7Ob3ezmj3reKrYLjO+oRq6xdvPP5XwracBfZosDPnzbF4DfM9btD7rtW944a5hMJkymU9qmS7UQga7ruVxtWC4XGGswWvPGG6+zqVs2dY93jrpuaJueED3r9SWL+ZT5tCS3uYC9mC5yraXSmr4PdCm0QyV2Icsy+r6na1t8J8xBmWXk0wm9d2zqmrZtJHXT9fiuJrqO2aTEphTcpm1o65qiKAhACFpqTrSib/vEvhnyFHbk+g4VIsvZlLpu6Jz0Sl6sLlBaOlSHw7Fer8ewE0nhFC+qBABJuBIJ/HknUsGm7ggxYqwmzwzBe3xwaPQVaW56HFL9RWbThbUX4NB7hloTpQbfolz7iqx0AL5GWKqUjjNWXaT/xivfbeV9VzywI3uaeOOrgHR4TrbMqFJXvK3JF9r3DpslxJfQgtJDFUzyw8Y4vh6tVUp43cqb5QkH4DjAbA3p/l3vmE5mFGVGlklYF8BkNiXPC+qmlaqaoqDebMQbrA19L/dXWpJNtVVEZEFEmy0gInWQxqgkzCuFD0lglMEoAa0hQOg8QxeubHaGVoroPW3tQYWU0OyTBBTyPKXXOiAatM4wOoD3uN5hpznWGrzvXloAMdoggUYxeRSFvdbjokSAaPDeEVNAjjU5rvcoI88Z08KF1obcTpiWBVWhCKFFaUff1VxcnNG24ucd0rJDjPggrKlW4lst8pK7d+9y+9Ydnn1wRpGXlEWFMRZrDU1T03UNtig5OT1ns2mYTudMp/Mkgd/6dbVWUp9CoOsFDMmiGGgURV5isxylLJPJjDwrAE0MUSqWrEHpOALpONiilXg681wC3aJSzGYzzk5OMUvNpt7w/vuP+VM/9CcBxfPnz3j2wTP2lte4vFxzfn4ui0MhUKTFjxAje8uFfMa4Dq0Ul5cNfe/oWieATYs39PDGIfsHB5QpRXxgSofPkWGB7uD6NTk/0ufJ19/+OnVdM5/NaVvx0uZZhtOaZ89e8Ou//ut85StfFksFmhglxTcE6YpywaPQ9M5z585dbtx8jR/90R/l85//Af7O3/kv2GzW5Ma+tDgV0zGelCXz+WyU+nrnOLu4oG4bci3sfoheqnaOjzk9OaHreq5fP6DrOyZ6RlCiiPlumlf5t3k3u/nDzJ//yZ/i//7S333Vm7Gbj/j89b/zef763r8Mq1N+8t/9eVHJ7Oafy/lIxyNqBdf2l+IJc54yywkqkOcZs9uviX8V6bl0vhNPni04Ojqn7yLK5Bg8ucnI8wxjFCH6BIYyvO9xvqd3gYvVWrpTUQSksqJvOzJrMZnFGkuRW4xS9F1LnllmxQKl4PxixUW9IWZSFdO5QDApdVZr9hZTQtRSjxM9Pkjf68AmLhd76AgXq3PaumZSlVSZZlou2bQtZVGyrjsuLi6T31MLaApOJHVKC8iJAY0i06mPUpF8fpHgxStY5AIgBTiTQI/8f/ACKIzNsKmKQhgyDVHYLKUEVA1MbYjSAauVGQHdCFD1wI4lz6s2o3Txw/MSML0KbEd/3JXAoZe8pinyJ7HHMaFapbwA5L5FWStSxxhfCoPRaoDLEptktJGEVSXPZzMzMk4AMXjyvERpQ920KJNRlCUHBwdMZxXeO4xVlGUuF+saSftVAXSk6xoUAd/3ktYcg1QxbdYCQLoueSVBhYHlTKFYITG4XqFygzWFSI+VQZssBSkprBWmLyjksRAJsTEZEPChF7bMeamr6QPrpkMBwTlAoYKBIIxdlmc0Xc3ETJOfUGh5lep0Bna9bz0xOHwQD7fWCu96ukb+uHTeYQzYvKBztXTWpiTuzGpiqOk6y2J+yJ/8oc/zW7/1RZ4+/TqZDbSup+sdYAgpxCxE8UIPXs+sKLFZwd27b/DgwftYnVOUU4zJqaqKrnOpRibQ1Bsevv8+bddw8+YhmdH0fZfYaHlMozTWFLggtUzz2QLvIm3XU05maJ2R5xVlWYlvVZlEvqvUU0uSU4tiIn2L0RKxNIQGhehQCkym2XQNOiv42jvv8P2f+xxEy5PHTzBaFu0ePnpEiA5tFZ6AC9IJmxnDtYM9uq5hUpZE76nrlpBqtFySUc8mJdeuX2e2mGHznM712GQWNkoReg9aarP2lvvsH1yjd5HZZM7ZyRknz09ZLpccPzvCt56jsyPu37/Pu+9+ndPzM7zrMBq86yFGXHBEHD4BycVyH6Nz5rMDbt+6w8c/9ia/+Rtf4snjR9gsE3CskLwBI4BXq8id27eSRzeyt3/AV778FTZdT1aVeOSYtJ1nNplilOboxQtWqxXX9vZ5dnSMRdP2jpCkx7vZzW5e7by9+eBVb8JuvgPmz/21n+dnv/R3+aXv+4/5uZPffNWbs5tXOB9psNrWGzbrFcZmbNbrEaTEqFI9Q+Ty8pLZpEw1CyLzFOYio/c+yRmFlXVekWcWhcL3jouLFa1zmKyQLsQIyojcTqPBCvOoomJSFmSZwRDxfUsMHrle15RFgZ9MMNakOhhDiEhnY4CqKqibXiSYLojsFrBZRmYrJmVJ33X0bcekLNhbzDFa0TkJl1KJ7dt09cj8FnmO1nJhGqMEwYiHTlhW1zvxhSmNTx7U3kmdjzKaTJkrjKjIAVFDl+kABhPrGUKS0abuyL5PrAej1DYkeV4cQ3AkcuelNF22YHT0rwEfluBevc/wfdqsl0Dth39fa8VVP+zQxxq8S7U0ZmRQRf4oF8cqhWTF5EPViX0dQCZWHttqhbLilZ6XE6piQllWTCYTjBHqTOTbHmsFmHvvxtqNQYhsraFtm1RLo+h9xHknLLp3L7GpyZwqrK/RiSkX8GG03b6eKEFS4sVN2bkJrA7sntYmJUBnGBXwPuJ8h3cC1oOXY+Z6N0qxhzTmyWRCvalZr7vkqR0YZpLc1G8XDLTCZBacG5n24GGzqQXYJn+jsRJOBJG+b/Gu49mLY37ny29xdHpB1weaRtQLwoWLgXdcYhhIfHmVzGYLgofjozPyfAIYMptjTS4AN4DNcp48eYzzDm00s/lc9mGMSRrvyHIzJjnXTUuMisViSYiwXtdokzGZzjFW3m9yXkla8VippPX4eTWco0qJz9NmWwZxUBFUVSWfZdMZj5885etf/wbee87Pztnbv0bf97x48QIUY7fs8B5bzJeyUJeUDE3diCKkS9VSURaLDg8PWS4XTGZTYhIKRB/S+yYkybgoN1Sm2d/f59kHL5jOppy8OOLZB0+5c+c2F+cXfO2ddzg+Pqb30v3sUhesDy3aKJzrBXgCN27e4C/+xX+Tr33tG7z11tsYa/jUpz5FkRd84QtfwPvh/SL3G8Ojguf64aEsNvQ988WCi4sVJ2dn4kVXGtf3eO9Zr9fsL5fp/zdcrla88fobPHr8mM1mg8ky9Hd3GvBudrOb3XxXTPc//E30536Q//66XKX+i3/Jce+/u8v90/df8Zbt5lXNRzpxYrlYUBUFOkbyPKMsipG5a9uOoxdHnJ6eE5CAHuccZ6enXFxcEINDEem6lk29oe87tNHSi6g1TdsIO+g8TdOQ50WqGJFQFtd3tG2Lcz1GSXVNZm26KJOQnQh0fScdjosF0+lsrCMZgN58PqcsKvK8oKoqASipyzOGgDVWmJOqYrlcMJvNAEXb9RBU6g5sgFQjktiBEMUbOMhUh7AmH+LI5gSk4sYHj/Neqj2CVNAMl/wDizkEK8HWC+uDJxJTtcW2X3UItRlTfuOVDlfvR4Bz9eulChp+b9D5zzpXn2dQ7l7tBnXOQ3w5WdSOnk/DWPlCEC+qVdjSUExyltfmHN464PadG1y/ueTGa3vcef2Qj338NrfuXGe+nBBCT9dt/YOujygsIR2/CBityfOMqpIqE6UUl5eXNE1DJNL1wvr13uPCAGuRBYS0iKCUQRs79tGKbHmopEksZ+rLvDrDfhB5e47R5ncdH1KQlHOOvu9e6t3N85yiyJlMJhTFAPxkIyXlWPa7TmqCEKSiZXgNMW1D3/fj7w+LCt57WUhyPT44mmbNgwcP2awbvFM4p8izCpQh4EdPbxiDuwQgZ7bg9q07eA9d59HK4Hqf1AB6/GraltOTU5xz4+vq+m6bohzC6L2W+piGrpVqG2szmqYlL0qszRKrnM6fK+BTKTUek6sLL0pJ4Jexdvt7aR/PZjPaRvJxmqbht377tzg6OiICZVlycnLCxcXFyFprrURuHGExm6GSHt478bDLZ2Qrxx6RoB/eOGS+mEuNTgjSrco2ERxI/cyGvu9ZLvdo25Yss/jgePr0Mdpaeuf4+tffoXMS3CTp5Sp5t2XhxeYZ0+mEw8Mb/PhP/Di3X7vN48ePCT6wv7/PvU9/ivv33+LRo0cURT768we2Xup4MubzOX0vqeuT6ZSTk1N8YuV9kM+iGCJt247v8fV6zfGLFxhr5e8FacGE7y4Z8G528506L+oLfv3z/8Gr3ozdfERH3/sM6vod7vzcTwHQ/MYLzvr1K96q3bzK+Ugzq5Oq4jgENpdrIgqjc6zSOO9oXBBpoHJcrC5ZrVY45+g6qV3xXlGUFVWVk+dLisJQFlIVYZJPjKyAtoN00S8ZS5J62vZS7ZEXOcYa+q4laJ1AXcRYRZEXxLalcw6tJCnWh8B6UxODRxgjR7tuAQkvya3DJy2rdCX2nJ+32BS0QxAPWu9cuv6TUJ++F++cTheTg4fUuT6BTCUXZ5CAqU/MagqCscIshBDwkRSCswWX3g8gL6QAIEkPNck7OQDdEAIx+SGDD8lHtg1XgkQGJhnqVUkvvMw0DfMSYE2+2d8vCXj4drj7h7Hu1Z+rtLChEjOslZbU5pCkrIA2arxPnhdUs5Ll3lzYtcygsqHTFIpiIhUoeYXROd4p2sbRGiOySxR5USVmHtreCYtpVPJsamxm8d4xWy7YtC1112ODBCNleYluAzAAIMaEX4xBW5sYP4cLQeSv2kg4F0oAXAKRRtvx2Gqtx4t2wyCpHjzHBvH6DmA/dbKqIdAoebuVHhOPRYWgUSaMYVsi51XEIP5Ol0LCjNEvAbctqAMIY2XSsJjRBU/b10wnOXk+x8Yc7xq0qQmhI3g/bj+pe1VrTZbl3L37BhenKwk5UuKzVlqn/uWeru05Oj5Ji1mRg4PrlEWFSh5v13VyXgePMdkIrlerC7761RUffPCc3nkOb95OHbcWIle2R16bTqzqy+ftwEOnQCqd2Psovb3VZIbJcuqmBuCt+1+j73tmsxm9czx//lzk21HUESF6VIxURc5sUhG9yKTlM7AFAn3fJu9oSL2qBxwcHFAVVbJwi6WBqAabN33X07YdRV5yeHgDYzRZkaM0PH7yhNXqnI+/+QbVP5rQti2T2QznPUVZ4TeXVOWEO3dvszo/p643lGXBn/2zf5af/dmf4/j4mEk15ZOffBPvO371n3wRCKgYKTIr7LTOZMHDw8HBNapqQgiR5d4+5+dnPP3gqSzGBOmUtjZP9UyBruuYTqdcXq6F9Y1S9xUTMP+97Ae72c1u/vindR0/kxf8qVe9Ibv5SI79kb/w0vdf+vXbPLt85xVtzW4+CvORBqvRhwTiMuq6hegoy/+PvTeJkSxLr/S+O7zBBjefIjwyInKIiBwqs7KSVHHsRrMlUk1BAiRAC0LQrleSVhK00EKAIDTV0EqAAC21lDaCNhIa6lZDoIoSVGQVi9Us1pTJ6qrKqMyMyhgyw2d3m967kxb/fc8sIrOK5KIZUaD9gMMj3d2ePXt2n+U9/zn/OSWz2QJbluxf2adp5ySCuGUWFXWVsKahaYLE0ljD1mQkG/IoUkutFGVVU2uLU4bWeVwrc4RVWVJXNSolgjOUpUGlxPnZKbawDOoBtigIIXJ5ecn55SXOB4rCUtcDkaFNLymsYTCoaZoWawxVXWOUobAFyXuJkPGe4DwKaDN7bDMoULqgdQ7nEyEIkySb1W6GMxJ9yHJQkft672VDlg1o2sUSld1eO+ZDCJgoQLPfZIt0dOWYKwyJEl2kmNmEkOEdPbAIMuIozOo6sHwKiK6zTuvf/1pr4WfsM3/2oVLPPnZxKyJVlt/lqVpUzg1NSsxk6mHNlWtXsIWh8QtccllariksFJXBlEZckX02p7JGNutO8km9T7l5UYDu2DoFSDxPSgrvI3U9ZLFo8D5SVgO891gbgCIzVDnvVIuBkjQfVJbgamKAaMToCCSqyXtpMlSFxVjDYrEUMGkMkSguxJmaTUn1EuGsPQZEqhqQ99gYhTGWoixxTpgsawtihOBDPh95jNIJolxn5xxlYfr18iRQVWvvkVybGAKRgNIWqwzeAUlRlDVFZQlLB1GaU0qrDLBEy1rXFXVdsbU14eHHjzG6RGEosty2aZa9SmI6neK8ZzKZcHBwQFEWYhzmnKgvlMIqyYYNIXL79m2m717y0/v3cC5w/cZNiqJGDK8SnXNSJ/Ndyb0/pxmT792uERTCSjZsrWU8HnN6coL3LdPLC3H9LUoeHx5xfHJCVVW9ZFuj0Ap2t7cpbSHPmRJt09C2TQ+0nfcMhgMOrl3j4No1dnf2xNzNe3E3Z02+nBtOVVmKBPn8nOVyIT+rK+4/esDZxTlXrx2QFMyXS+rhkMa1DIdDXrn1El94801OT4/51je/SVFYfvu3f5ujo2O+ZtXGmgAAIABJREFU/effJsbIiy/d5Jd+6W3effddHj28j1KJEFw2Y8vmGSEyqGu2d3aQaB+LMpr7Dx/R5s84mW+Vho41EqEzm80YDQdisnR6is9g/+T8ksF4jFHPtZBoU5va1KY2talNfU4912BVa0td1f28XYqyeXU+sGxaIomirCgrw2Q0FJegpCjLOcfHp4TgKCtL9L5nj0gJn2TTvnSR+WLB5eUM7wK7O9sMxlsMBwNKazg+PGQ2XTAeDiTTtctkTYn5YsF8sWCxWMgG2yqaRpgZW5R5frWCJIBbK5HWOe9YLpYsl62AghCo6xKjNfP5gmFVE4HWeWZzObZWWsyUJHdEQG4IELP8stAoLZtfl3/XMZpGiYNpVInQZXSS5xizC2sn/xSGAnzw+fqDT7Jp71heHzsGTmNMB0bJx+2mCaXW51P/yrXGQD3Nvj7Npj499/okC6ty7mvORUVl2XT3a90D8k4GHGIU0yESRV3gmobQekL0GFUQkbnoECIkLbmtOZuzLAs8HudiloprirIkEnIecEEMXuTCaMpySIgapUpsUVJWFfP5guk8kJKR65DnRo21mflPpCTH08ZCblKgBXRa1TnzCkAejoa0TiTKq+zblOcBuxgYAZPkPFOSANfUzx2rfjbcO5+dp/P7qru5UYVRCujyLBUhRIrhgKYRCbu1Nss5BVh47wXsmk6eLSBaKUhK0TQBY2TuNKSIjznTVWm01WIAhUZrmQEej7dEArts0bpCa5kfb9tGjpmigMDpFKMtOzt7GGNYzBfyuyAu4nVVoK3Fx8jO7ja/+w/+AR//9B4/vttQVTWTybasI43M0PJkJi9yhSHJnHm3TmUufLWWvZd7tCiKbOqW2N7Z4cH9B9hCs2gatFZM53Pc2QV9lqlRmfmVa74zmUgmqVK0bUuzbGR0YD7v+lBs7+yws7vDlf2r1PUAHzzOh/7cYpTmgkq6B6l3737ABx98wPnZGaPBgN0r+xwdHvLo0UNefuUV7ty5w/ffe5cQI2+88QZ37txhPBkxnU75+tf/WOJvrl/j7bff5v/85/+ctnVcvXrAO++8Q4ieb/3Zv6BZLmQ+XK3ym3WWvO/u7vZxSTu7E6bTGdPpHFsULJYLUoKQRLZ87do1Smtp25ad7S2U0pyenrKczdjd3eHo7Jzgg+T3bmpTm9rUpja1qV+oeq7B6nw+4+j4lGUzlygZW+O9w7Uti/mMw+CwhWF3d5u5WlIVIssbDQds70w4OjqhaVtCkPk0rTugJ3Ej59M586WjKAq881xeXjIejgCoyoLtnQnz2YyqqhiOBmJEkhSz2Yz5YkFRVlwZbVEUJUorptMZbdMIgC6KXi7bOo/zEecDPsg8n4Tdx7wJDejKYIuSkBS+DaANxspmLSaJ6ykKMEW5AmgI0PBNwAbJnZRj5jm9HM2jjUiGFWIk1G2aQ/DZaMb082IC+GRmLMTQb3jJbKtswk3+uWySOwOmtT/+XAlv93NYAc2/LN90fRb1yeN0V2D1t+t/rzOAU110CIkYAynPxIUQKMuClESeOxhUJC2zo/PFnGpoMYWmwOIWSxZLzzI44likuJW1OKfwHtplQ1mI+Y+xliIzlWK8Y/FOXFK1LnvjHpnrLHPMUEWImvnSC1BVAlaV6iJo1k16sjw4IcziE2CJft7Pe0/TtH0kifxuxV510ls5rhbn1iwnTinPAiaF0Za29Zwcn4rDb9RZZg5JmdV5aWleKJVwUaJqOrOc7j3u/t0x89337n3TmTWWWCmTn0tAaYga10aMSljb68wpCo1rF7z5xmscHx8REaZNGY0yGiK0vqGuKx5+esz59JzRaMzO9rZEOXmXM3flfIejCmssIbaUZcmt27e4c+cO3/rOt6mqOkfUrN6Hbs33M6RazKe6OdCOVZb7RKTzUedM506aGhNGCeC2ZcF0ekHr5PkXS4mgEdmwsOeohGsdO/t71JXIXEGzXC77RpYP0nwaDEZsb+9ycHCdnZ0dYmbrZbrYCFudFEZZPvn0E+7evcvDhw85Pj5lsViSQuTs7IzRQPJV7927x6/82m9w88UXcSHwhTe/wO7ejig52ob3f/I+i+UcbRRf/vKX+eTTT7l79wPqesjLL7/MK6+8wg9+8APu3r2LNbpff10kjQaqumJ7sgNkeXlV8eiDn+Rmkgc0SSW00tR1za1X74AP3PvJByilKauSs/NzDg8PufXG67l/kjB24wa8qU1talOb2tQvWj3XYNXnzrmYkGSmxAXatmUxn4uDpDFMpzNSCPjCUhYF1hqmZzPOL85EtlhU2RVTgIzzjmbZMFs2aFsKGK0HYpoUI/PZDNdaysKwtbWFUWJU5LzMIA4GQ6pqQD2ULNSz8wva1hGCz/OkmcXKpi7By4aUpPLcnxjleO9ZLpfZuCgDjRydEYPM5DatI8SEd9lZNTu1duxph/WWfb6m6nNby8wkxDwvqbVBsiTJhkniEqqNQWeJacgzj1prQlrNMvYS2s6oKD9vH0vTg8W1+TylntDvPs2wfp7hEqwMedYf96T772ePsQ565L/l95IdqXsJs4Cx1LN71hquXNlnsr3FfDll3kyZzmaUtUEVgE59Y0GnkvPTJUWRWOjEsBYWPoaFMJQ+Yq2w1M1yKXOlMZKCxC0JuxtFlo3COU9IiaVb4lzA+YTStmc7u008edbYZmOeGGNvcmS0YTQaZZMi2ciHbKbVzT+uy6+LopBj+JABl8hwu+vXz6hqnRsfIg8NKeB9JCY5d2M0kQ6Iqgy6FbBqXDStROL05k9KPcH6P2mMleXEKjO/KFrviBiUjmhVgWpI+J4BJCW8j+xsb/HG66/z1T/6M2xRoo1lvLWFsZaqtig95P33f8zDRw9AJfb3r2CLQoykEiTvUSlK/rAxoBMpJE5PT5lPZ3z5y1/mD77yFQbDcT/frtDZyEr3YPTzXKzVZ5oxnexcvke5iUgxUQ5qtrcnPH78yYrlTyLztrYgBofWErRkjObKlb1+LjylyHwxZ9E0LNtGPkMS1MMhO7u73Lx5M5u6ydxyCAnvHaA4Ojzivffe5d69e7Rt25tegbDBh48PuX7tKgCPHj2ibRvu3LlDXddMtifEzOQfHR3y6NEjlsslN6+/wJ1X7/AHf/CHxJgYj8d86Uvv4L3n61//Wm7EdCA+iXpBsrYYDgYopWnblmvXr/Pp48ecX1zStG0WxwjYV1pzcO0a167fQKfIw/v3aVzDaDiiWS45PDzk9ptfwGiN9w7rHZva1Kaefd3Y2ue/+8p/9qxPY1O/ABWnpzw2zzVU2dTfQD3XK0AZy9bONls7Y1zbMp831PWAGzeu93LCwaBGmc7JNKKVJxFpmgXiw6FQxrBoHLP5HKUUVV0w2d1llBStD1ycnZNiZGs0xuEx2lBog1bCUqGhrCpCsHgvZkbaihFNTOB97F00iwwSxdClFQCcTaGCz7EQeZMLmoQRF1VT4GMiBC9mOCkRxQVFrGRsziHMzKcY4GRnXqUIUeYLC1v051BVFW3b9g6ssvlMYk6SIkVV9uyaRNHEPCdJjtdRInXNoDiPta6AjYKkhWFSK/Hvk+/hU6Czq59poLT+76eA7l/FObh7DmPy7K/KcSqqMy7qgFvqQWvbOo6OjpgvZ6ATgUBZWYqBxZZ5JjQZbKqpqorlsmU6n9HUsDWeMKhHtE2Lb0VyG2PEBSfvbwRtLM6HHC0CvnFoI3OooAW0xtCfX+d2mx2g8hpYbe51vuYxpt4dumsqpO4aqs6RtmMzMwAxGpVyhIoBnw17VJK/j9FngGmynHgVkxPxxCBSzYTOJl1ZupukoUL0pJieiBFSCoocv7TuGL3uUgydgzMYBT5GQmhRymLQKFNRmCEpLsVUzHusFqMw5xx/8o1v4FpQeshksktKitFoiLEK50SKXJYyUrC1JZJhEv36CDFSVQVFXaNyL+bk5IS7H7zPm198i9u370i2br6mHbtKEo6yc5/WWoFa3VPda4zd61SS45wnEuReR+6xpm3Z2dmVZkRKhJSQ6Ncn135Kia3xiK3RiNA6tNJMZzMu53NaL7nR3sv1HY8nXL9+k8lkp//s8t7jmpbTs1P+5Q+F5ZxOp8QosUmd47nWhqZtRH2hNbvbexwdHnN+esre/g4PHt2XZoePuNBy796HPP70ETFGfv03fo2Hjx5x9+5djLa8/NItDq5e48+//S0++OADqrJYGanl9aIBUxRsb++QUmK0tYU2hk8++aR31RYxgTCyu/t7vPr661y5epXQLEV2HxNlXXF2fsGjhw/FhKquabNp1KY2talnXxqF3nnhWZ/Gpn4Bqv0ffp9/ePidZ30am3rG9VyD1aXzXCwWYoy0XJJSoq5rtiZjAC4vpxRFgS0Ny/mCQVXTNI5mOsMYMVhxLggARAxvEtC4gFouMbYk+IDWIm1sXcOgqrHW9KY8jRNDpsFwJHOO0bFYLmlaz3whEj1tDD4EXOsE7GVpbIzi/KmVhiRyXkikEEkRfEzEhEgVc3apzE9mx1qlsvsvWKV7h9+U8xDbRuJOUKAIveQVyBEkTgAtKWPU/PydDDVHWHSOrnTALqUc76n6mBTZwad+443KwCh9Fkh+hktamyt9utYBS0qZ//wZDOz6w7s/efrYT8/IhiD5uamTRWcpbX5zSDGyWCzIyAWVYD5dMB8N2Km3UEmjTYE1FhuG+BYG9ZDxcJuT4zNO2lO2J7too7BlKdechDaahMYg7K73DmU0g7pAK01Z1bTOMZ3OxdUaRUgrmXLM50cGgx0zCfnfuT0QY2SxmGe2Tq5+956ss7Ii4ya/bnK+qUhYvY8knY17MrgVht6uwO7anHCMa0x7ZqrFpdehku8bKF3FGAm+Y5SfbFSozML22cPGoHWB1olFE/AhoXSFChqta5qmoV0s0coRTSSlwOXllNPjP+X69VcZDa9Q2S1u3LzJeDzm+OQxKXnGWzskDEkGX/Oaz8ywIEKKqszX3vfn+dGHH/Ibv/mbvPPOl/j+u3+R1QlZ5tvZXnevoXMB/pw1mdZed4wRlaXpnVNyTIll2+Jjgr7BII8PUYB1WVhCaLFas7+7J5JeJfPBhyfHzBeL/jNFjJIqtibbXLl6gHOe4XDIbDbn0aNH/OhHP+TDjz7g4uJCTMK0uE7HGClL0+e0HhwcUBeGs7MLJlvbHB0f8+jRJ7xz7Zclyss5tNUcHh7y0b0PiTHw6p1b3Lp1i//9f/snxBjZGm/xhS+8yWw251t/9i2Z000x20RlCbg2KCJ7u7vUdUWKiu3tbe4/eMDZ+YXkDyuVZdCKwaDm2vUbvPjKLQaDIakoGG1tcXF6wng4onUNJ6cnNIsFo+GQeZaxb2pTm9rUpja1qV+seq7B6qJtuJjO0CphEJdKHwLn52cs5k12+VxSlIbzs3NGwxHj8QAAZRQGiykqYlJcNEtckM1fTIGmvcTakhSzyUmOPhgPRxRFwWI5zzJYqdOzc3HWzRmETdPi5wuRM2oxUBpvTUgpMZ/PSFFAsMwNml6CLPOEgRgEEEp8TSREASy6G1FUwpyFII6dxtDLQDvpYYoKgjAOdVkIqFlz7BUH32yOlOjZuZiBoctRHSnPuGqlsVaJ+QvdZlmtvrLRaSR9FpHyuT9a/W5t4/6zSq0Q6Gf+/i8jVT9XgpkBuAAquQbdWWqlIAk4TTFhiwKItH6Jjx63aDFYCi2yUGsqUlAU1mBMQVmUVNdrjo5OOD09pa5r6kFFUZYMiyFlWdK2jtlsxmAwyIzsEpVgOByANiyOl4QY0EYAo9IaldSKMc9gmqeiUJRS2I7594EAFGUhzG0AWGWkds0HpXXOnZRjCqYSwCkGVGLopLUh+K65Y3oWt5cvZ5WANFPME+ekjUZFTUyKEHz/nsQoM9cdqF1n1PvmQgd4VULbDMVT6O8bhcFoDakQllo5UnTEJBLhra1tptMF+3tjynLEfNby4Yfv0rRztE3ZddkxHIyQWcj8fi8bkorS3LI2m5LBfC5z8oeHhyzmc95664v8+P0PCEEkvFrpHCtkcjMhr0EV8xr7/JnsThbdqSf0OoBNicePHxNSFJO2rA5ISdhXcRHWVJVla2tM61rqquJivuD0/KJ3ySUz2LvDETs7uxwcXMMWJffvP+C73/0uH330EZeXl8QYZF2l1dlpbWiahv39fb70pXd4/fU3ePzoAd/8k29wcPUFQgi8//5dvvyrv8JoMOT49JjJzhYfffQhFxfnGKP4+//6b3H37vs8ePAArSxvv/0OL798m29/+1scHR0RgqewRgy1ENDvnWc4GrC/v08IkcFgSNu2kjUbZX46poQ2hkTi6sEBt2/fZmtngiksZV0y3tri04cP2dvZxVrD5eUFZ6en1KMtUjjC6s3M6qY29dxUitIE39SmNrWpv6Sea7CKNrTeMahLrl7ZpypKdFLMp3OahWzUnHMQE+PRiJQkW3VSjVi2C3wMhJCYTpdMZzPAYMuCuhqyXC6IeXO+NRwS64qz0zOm5xewNWY8HrFompxLKqxHUZS0vqHJG++QXTWVSZBkIxlzzqqG7JwaMLoAZJPqfcAHYdBSXBnoxCTASiS7kbquAJktg845tFxz5lXowmB1l/O4Nl+6Ng9Ikr8ns65aa4nlUbrfYPc5qCrLf40cU/axim70VIx0ZKP9swyU/rr1tNT36Z+tfvezH78+L9iBNGOsXP+42ouHGCAKAPB55tEYg9UGg4AQsuTVtZHgAnpQYbQiuCCOtwoqawjBE11ib3uX49MzFoslxlomkzGDgcgN54s5y3aJUhKvUpYFxlhCShw//pT5YiEsJZIF2jUiUqcA7t+zLLJOGWR3brSqM5R68rXrNZderZU0XfI1VbnR0e3buxlnYiImRUruM8ZHMaYcgWRJvmPoDVopfAjyfJmxttYSiNmwSZx6u7XifegbJt3PjNHYjjVOEe+8XFulM3tr5T41BURFYWvqakgMHoUTsKMMKWlGwwlVNeL8bM63v/09nF8w2Rnh/JK6qimKEmNKuQpaGgTee5IKFIVIvIXxg6OjI66/8AJn5xf85Cc/4datW+xs73B6dklEzq1TMeQr+fTK7HNqe/MyZFZcpSebPd31aJZLFosFg8GQpl2gtKJxLdYU2Dx3qTUMhxO5zj4RIjw+PJT5YGWQjxHZBG5t7/Dyyy9zeTnlD//w/+GjDz7sZ5tjkqZZUUiclqyTyNbWFm+88Qavv/4Gk8k2ZVlSKsW3lDiXj4YjPr73U5bzJbu7u5yen/Lxxx/zox/+CNc0vPXFN9nb2+Of/R//FK0NN67f5Jf/tS9zenrKn/7pN1k2TpyirQWEWTfZLX1nZwdrLFpFhsMhnx4eijImJVxuyqmomEy2eOnFF7n92h2GoxFGi1R/b3+fD/O9XZYlF2fnHD0+5LW3DrDWoM2GWd3Upp6Hun95xD/+9d/n97/13z7rU9nUpjb1C1DPNVi1BEZVwXg4oNQGTcoZkVEiK0yJUhCiZ1AOaVrPomnRpsZ5hTYVxipskairQFGUoBVFYagLCzGilWZQlSJ5TInFfCkSSSXRM+LGmTMLXQBEZmeKEoumdUu8C2gl0TMxiqRNZ2mf0kayAbNcN2ZTI2MkUiVBD2C11iu5bQag3XFcnoUUok2vMje74IvOOVYBcWW8lJD4GZvBizCjKpsYdTOEAohRyDxtyhmr5PnEjlR9AljK9/W5xKdLZaDbbUrX1aFpjeXsqjPr6cDwZ4731MzrE//OB+9MniQbVhySlaGPYkmhu1bC/olEXLI2ffDEkCiqEpU0bglqXOHawHzWYHVDXQ2Zzhd5VlFTVQUHB9c4Pz9nsVgQvGNeVRijcCFQ1zXeB46OjoV1HQy5uLykaRxaWwFmMVJYk98P1bUdZK7VZgfnLhhIdaZWIjWOKWHQGG0x1q69pyL9BXntZCkxmWU3WR7cOQwrrUndY3LO6LokO2YnVqUkdzimiFIhNzvAq4RFkbrZTZMdh4Gk8nrO0TQqrz15obKCxaVapKAhenyS2deYIq5VJKtIypLQVPWYxWKJD07WqyoIqiDqkuPTKY8eHtM4x87OhEBEW0siUVYWa8H5Fmvlc6O7nsYYrLHEGJjN55xfXvLCCzfwAX7ywT3uvPY6+3u7nJyeQwarqLha10nn15G91GKSqKEoCgq5rSOkAMFgtDCmJhuxpZS4f/9jFss5VV3ifJuNwYTRVlGigjSJ7ckWKgbqquLk7JzHR8f4zJJ298h4a0BZGN57710OHz8W0zBb9WZIRVlSFAUhSFNhPN7iC2+9yUsvvciVK1fwXtzAvXdMdrbZ2tmmaVuqqub09IyT41MGoyHz2Yyf3L2L0pHJ9ha/+uu/xh9/7Wscn16wM9nji2+/w2g05I+++kc8eviAFAJ1UdHpyjuz68GgZmtrC+edsKrOcXxyzGK5IHSNAaUoCsuNmze4dfsO29u7qNxowRiu3biJi5HpfEZRFCyXc87PTqmspjAmKys2talNPQ81zbP9m9rUzyvzK1/itw4avvb4B8/6VDb1DOu5BqsDAzZ6JlUN3kMUWWPbLLFZIte6hA8BYxPVUCTA00XLfN6ijaYoC4y2VFVJXddYLQyO1YVID1NEp0RVGNJoyLJpmC8XNN4Rs3lL7CSaWuSUSUVckK8QMzhS5PgKmf0SJkk2gq2XGbiiKDDaPDF/SGaf1hSwKGvROkGAwlpCiIQkpigxAxBrLUVhMZl1C0BKIR9yLX8UmZf1cUUxppjyz9fBpurlnimuMhhVdg+O0M+rqidYpDUAu/bTjtV7ElTGJwDr6kErRrWfW32qPk/m+0Q9JRcOPhG1GFrpjpbsvsU8P6zEhTRGmYOLIWCVQUWNbxPTacNoArOl4/JigVYtg9pTllWWpRqcX6B1m82+BqTocU1DS8o5wAWD4RDXiuHSYuFomwjYnvkurLj0+hw7hErCVBphshNpFftCB350lkR2RksWq02fJSvsbCd1FnmnsYaUAjpBYSxkwNpF1kj2Zmf+lXrlt1IKazVFUTAablHamrPzKd4HdFGRUm5taC0S5iistdIIw5rfg0SCJCBNKwNGXltMYOgyYAW8ppQl7ERS0oTYoq3KM5U12g9Z+AaTFJPxhHIwZrZ0nF4cMV96yromGEsIDVpHSmNARRJewKQuaJetvPcJ6rISox8Uh8cnLJaOy9mC8WSbjz6+j2s9r7zyCj+++wExz4crRc6aTUQiKqnMUIfMmK8YVSXdIcS0TKJqZCFrfIhMLy+4nJ7TNHOuXrsmWc6zBdYUxBDROdJne2dLlCCuxRQlnzw+ZLZc5vtNmOqqLDBa8fjxJ6SQDbKMISaPtSUKhXeOROLmzRvcvn2L6zeuM97aQilwzvWsfkqRpAxXrx/w6MGnjAYjTj/9lAcfP+CXv/xLXJydcfjpJ6QUuXX7VXz0/PDH72OKmt39q9y6fZvLi3O+8+1vEWOW/4aAsV20jyhShoNaZniVoqxKDo9Omc3mmV1fNaJ29na4fuM6Vw+urta3saA1+wfXqIZDfAzYwjKbOk5PjlAxYJWmXSw/55NlU5va1KY29bxW8e/+J/zX/+V/xb/zrE9kU8+0nmuwuj2ZMD0fk5KwNItWWM6iKCnKSpgerUQKrDQmyqyZz0ZExhhxuQwBqzWFNoy3Rn28SLuUmAZVGubzOWfn51xeTlFKY2yRZXwJa6zEPcSA1gldWIKPBC8gwvsus1IYsM4Z1BhFjKqXC65/XweznRSzN9ZhJbE1RqIyfEwk58TJt5e9ykZUnHxD/xitxDynN3TpJburWbrVua6bzGTWrseX6TPqxp8XI9NVB8Y/a360+upAandeKxnv56+Fnzvrmh+4hnsFPKXOITdAlj0DeQ5YTiJFRcjzw+TNbwgCOC4uppjiVFyPkZxPRUsMCq0tKYo7bCc57mJFAglCBCM5pUZbvBJWXCHMe9u6fL1lZroqB9C2tG0DvVnPau2s3ssVUF+PmYkx0TQtbdtSGANBwKdSwrZqrUXqnGdRq6rC2pgjSuijfHwemu7yQldy4PzmpNTP1HYsrlJiXBSVMPL9W5WXT3dpVSdX1hqlTU/5piTyWBBQJ42R/Jx06oHuz4VNtsUQ24ozsA+K2XxJswzEWKNtgbFGZsF9y6BCzMRA2MG6AqVYLpdApMyNrC4r9Pz8nKZpOTo54ZVbtzg9PeXBw/vcefUO9de/sRYTJQ0flddeSl0cVHbzjVmmnmWq/ei3Ftdlawsxc0pweXEhM81aMRqNuDi/7O+h7nrGGNnb20Nrha0qvHN8+skjmuUcpTV1VVMNKkajEUWWTUNukpncQHMO7wNXr13lrbfe4pVXXqGqyxVINVqk/rlJEmOidQ0vv/IK9z9+RF3XuNbz4OOP+bVf+zLj8RjnHHVd8+Zbb/Heez+gdZ7xaMIXvvAm29vb/OH//RU++fQTqlK8AbrPHGs1zjm2t7fY3dsjpcT2zo4YRh0dsmza/jMihsBoPObKlQNefvkVMVVKEWvq3thqsjXh6sEBj376EdujIW3TcHx0xOVsRlVXLObzn/kZsqlNbepvtgKJFLw0mza1qZ9Tv/VP/n3+rf/wgq988r1nfSqbekb1XE+3SwbqMMvkJPLF5ExQ2eSXbE0mDAYDtBaJWF3XwjT6PPsWAiolqqpEa3Cu4fLyksPHj5nNZsI4qpw7GiPWFhgrMTR1PWA4HFKUhYC5pFg0juVCZMEhZ112URwdCOqceEHY1j7rM8+TdjmG3VeXPQlrc5txNXsKslH1QRhPa20/C9i2LU3T4J2XCJQ1A5u4Nr+6fp4pD0V2gN5Y04OiJ6W2rAx6MlOqsxGTWkeFCUwHrrL5zGccglOWgHYMMp+XTdlDm/75n/76vOq52I4dzn/8hBtr/xX719mDvj5KRffHADF9mc8XpBApCokC6tjITmob8nVtc2yN95EYOpWjrNngA23T5hlNYdS896BUv9ZAwELjTC6UAAAgAElEQVTj2hUQQhoVPRDu51DFGdplB2mjbf43VFXFZGs73xOrNdetD1mTun+O1d+EzzgOm7yJ6AyBqrLEWJtNfKAsS0xnwJRS/x7JOcprUtnEqpNgrgNwYVLztYxd/irEoAgBUhTzHYUhJpEbK1UQo8GYAWUxwZoxTZtom5j/JmALjbEa71tQKbOgGq0t2pZobWmbFufkPi3LEpvNlS4uL5nNpOF1fHLC+cUlzkd+cvce1164wdWrB3ROyb3SoF/vco7drGoIHh8kfzmLuPvmTNeUkp9FZrNLmmbJ7s4OKgNp3eWDWkOKkeFwwHgwJIZAVRYcnxxxeXnGoK7Y3d5mf2+XrfEYm1nzLvO561NZW3L16gG/8zu/w+/93u/xxbffpiiL/nOkn2nP66CTozdNy9Z4jFIysjAcDrl//z5n52fs7O5htOXO7dewpuKjD+9TlQNu3brN66+/zuHhId/85jf7fN+uMaeUIgbJCd7Z2UFrjbUFg8GABw8ecXk5lSZcEHWEMYbt7W2uX7/OjRs3WeVZiyqhKEtQir29fUIIDIZDqqri5PSE4+MjhqMhbpOzuqlNPTf1Pz78Gj/8O//Fsz6NTf0ClH39N9nTm+ixv831XLe0YlLCKHrXgzqjRTYXgqcwWmYyMwCTSITEbCagRGGoqxKrNHVVgSIHyyfqupLomiDRJSEmtC4oCmGknPN0OZZaW5SSrdtisVz7uc6zgSvGMaUsHY4Rpaon2EWfZ1fX/7abP+1+323o5HFhDRgFUoKisNkVmJzPGcFoUkiQIEVF0hkUZullN+u6fmydXfgS9Mzr6pxUDxw6fKiz/Fel1SxjSv0kZT8HCbkDkoFLXMOiHRus1p579XNYMa0/zwjl8363DgTycVhJWFcoWWYIjckS5fy4mFLftVF5Djjm9aM7dstLDippDfySAXSQCBWRy0ojIUaFd4HgInVd9iA1RAGZ1hbYopDIoxiJbUuTWS+tbX8crQwhuB5odrOVKcj7A7LpV0mh8syq8w5jNIW1OOf76yL3kLBUrnUoZdaaJ7mJYTTKaLTqDJ4EaQowsNSDIfu2pGk/pW09AZ1Z0ERUcuVVkllnlT7Lwq8UAbFvrESlZO0oDTGbjSWR7co8rcxtJwXKFKgks61FCVBmKa2YkxltM6hqAI8x4ratlAEMShcopTk7O5X7TUNd14QQaFrH6dk5rQ+kpJjNF9x/+IiXX3yRn378gBhFCvz+T+4xHteEp+a1O8AuDQ1pAETkvllvRoUQKMsClKyzy4sLzs5OSSlx5coBJycnBB9k3t5ocVCOkf39vTy3Ktm8x8dHXNnbZWd3N2eMkteEzgBa7siiKNnd3eWVW7d58aUbDAYDLi8vKctSHMlj6GXlqpsX16uoJ2s0mprt7QlNs6Sua+4/esDJ6Sn7e/vs7u7xpS/9Et/53veIEUbjLd5550tUVcmffuNPmM4ucySRyfP0Irf2PrC1tcVwOMQ5z2iyw8nxGYeHh2JGFQIqO61PdrZ54foNbt26Q1UPCTFKI6foTMjAFJabN2/yF98vSCiqumY+m3F5ccHNmy9KDvCmNrWp56JSSvwvaYv/5odfx77595716WxqU5t6juu5BqsoRVKa2XyWWchAYS1VWWKLgghiTGRk8zafz1cRFEFMa+q6IoUoMRBaE7xkE66iIHKmoPf4ILmOJksGfYzy37qL32jFTCZvfkUyJ5t6awzOe3wMWZ6sKaoCkiZkMx3vHc6FfkMoQFX32Y193ikZNGmd4yhCZhCsAFvkHGLHNlsReCa9YlRXhKUwPh2s7CN0UuiNmFKKaxttUCajXb0257o279od90mpbwdEFJ258Or511hRpVaAL/++/8vMHvahnn+NWoHcFTvbXeeUIkqvna9KIuMlQVKiO9VynVKMRKXy+ko0ywatLWVZos2KzZHXI+9Zd127J1VaGhxaqSfOqW0cSgl41NZkwJ8IIccXhY6l05n1FsMf1/osbdd07tHdczeNo9KFHC/LPBeLBVqZ/j0RoCCPCVHuI6MTEEkpZploxDlPUWTTp+ABcQEWh1xF61qWyyVFnnsE0EbjvaydGJJIh2NEE55obMh60f1iWneTjrmJgE5oiYunNwvr5eqyTrXWWF1Ks6osgJIQE20TCDFR1RZtNW3bYHQU12NlUNoymy9RynJ174BPHn0CJAaDCmstTdsyX8xljMB3DDdcXE4xxnJ0dMKjR4d84c23+Ooff6Nn4tPa/SH3byKkNVl+XoNRxM0CNrU0T7z3RO85PT0mhMDe3j6j0Ygf//jH0qjQSgzBknyWbY1HQKSqaubTc4qi4LVX7+Ccp3UOHyNFKRmlzgeqsmZ//wo3b77E/pV9rC0JAWbzOSF6QvfeF0ak3Z1MuZ93X71XtjBcu3aNex/eYzCs+eijR3z6yafcee0Ob+YM1Z/eu09dDbl16w7Xrh1wcnrMu+9+X9afgugls1rufcnM3tvbwxpDaUvKquTjj+9np26VzeQURmv29va4efMme3v7MpphC5Q2lLYgRLmHoo5cPbhKXQ9YNg1lWTKbzzk7PZUZ7bQxdNnUpp6n+u8ffpV/9P8Z2IDVTW1qUz+nnmuw6jKwLOsBFXB+fi6yzATDokBFAXzjrS0W84Vs6r2naVsKbdDIRt1kBgvopWVt43A+oG2By5JMpQ2+aUh4QkpYIxshv2xE2hsCaDE2KooiM2h5XrWTuGlDWZWAgOHlcknwwgKEIDOCnfQyhJhnKc2KxYK8EZaNLUHA0DqoDSFkKeYqy9GYFUsWUxRz345F7aSe+St5L0AmrtiudYntClfqNcCXmVW1Ple72th2rrLd+feM2tNxEakz1VmB3l4Wqjpo8lmw2kmRV//dneNT8uBeZrwCRDEmjFVo272euJqh7IG0ysZTK2lwIGFNfELCarIEXYCT6VnolKXdMcq8ZV3XYgSUZ5qtsURDdtRV+ByJFKK4Q8cMWLWyeeZYrmPMjLrMxeoMlqVJEnykbRy+EJAZfUQXBpOSsLoh9bJk1UV3KNDayN/HSIziWKy1zG2nFPHesfRO8ljVClg5r7m8nKK1wXlpwLR5bccoC0oAv4B3k5CIHa1WwC6JeRTQz/iKiY6YgJXGZumwpnPbJSFzoFlSa21B9BqdFFVVsVg2aC2mPUVREJMnRLkmSiVQGucTbeM5ONhm/8oLpB/9COcco9GI4BMLt+Dk5JTL6TR/ViiU0bgQObucYlLk7gcf8bu/+ztcO7jO48NDbFE+cX90DZMQozDVchOSSCQvkm9LJ9GVe9M3nvOzM5xvePW1X+bs7Kz/jOiAWoyR7cmEyhZiTqQVl5eXjAYDjFIELRLtlAIaia95+eWXeOWVO+zu7JLyPeu9F4WAToQg6g9rLdWgQhtNciI5VtnNvDP3Mlb+N3Hl4Arv//gu25NtrDXcvXuXF1+6ydHxMfe+/12sKdje3eett97GGMP3vvddzk5Pc5PQY9RKgp5SYjgcMhgMWMyX7O7ucTmdMZ3NaJ1DaUtRVXgfGG2NuXLlCjdu3ITsJ1DXtURRdXJ2pVguF4y3Juzu7nJ+ciwy4ONjjo4OaYIXNntTm9rUpjb1C1U/+Xv/Kf/s6EfP+jQ29QzruZ5ZXbZtNkspODw8kjlQrdDW9FJOpRRN0zCdTZnNZiyXC8rSonRCm8xc+YDuDYc0bSP5qCGmXu7rfGC5bAlROBCS7ufPfEokrSnKMm8iV7N8RVH18mOQ5r3Ms8F8vhRAQCIk+a6M7r/bssAWlpAirXfCygZhPFyQqJwQBAAISEy4IO7HIXSOvVrONT9HSKuMTK0txsi8bMhM5pOZpHKdPwsEV+hvFW0jIPLpOdROpvyk3HN1rH6GljxbmmcHrS2xtsQUFpWlp0pbrC4wRr5Efm2Exc7/XrnVrtg6bTRoTcae3Vn0QN1accolg6IQAsv5EoV6YpZOW0MHV0O+xm3bslwsUPlaeO8wpjOusRizYmz7vMymYTab0TYtTSMuwGVZk4CyrEQCnCW7Shm8TzStB6Ul17Is8rFiP0tY5qgR0iq6KOX3wHnfs/kqXxeRludM1Jy12jovYLcD2P37nnKGp2Y8Hgvgy8ZJ0oexMstthGFWKCSJJht0oQgx5Pc6Ny2UyQusu49EKp1SEKCUfDYi8qQUiFGiZHxoJVrIZOOlIIAaEsbq/Dch30eKiMGYiqZ1aCPztG3TCFDVch5aF0ynC1yA07NzPrp3n5AMLogrts/31LJpckMp4ELAmIK2bTk5OSUlzb/84Y+JyXPntVezc62oLkiBEB0xSQMi5aynELsGmcwjp5jQysj7mI3YTk5PcG7J7vY225MJH354T+5bpQmtE7mzNezv7aG1pigsrWsk0iUEqrJiNBwCirqs2d3d5e/+nd/iV3/1N7iyf0DKTYJudtV7z3K5ZOlapos5l/MZZ+fnzGazJ9exQmTAea1Za9nb281RN6JUuXfvIx48eMDl5aWoXsqSV199jWvXrnH4+DHf/853STHifZs/r7PLNNJcG41G/ZystQWPHn3C5eUcbYq8PgP1oOTg4ICbL77I9vZuP8MdUlbIRFlD0jRMlNnHoLtvjDEcHR0xm065cvXqX/n/PZva1Kb+Zurknz4k+fZZn8amnuP6+HzCvN24uf9trueaWRXDGo8Ctre3KQqLcxITUhQF2mgW8znOtcQgm5eqqqiqihj8atOdnprXNAYjhIvMRvmV8ZCxFpQW0MfK1KabDUxoQnBiAJMSRbGSm66b38DKlTeq2LOf3WxqV845jDF59jXlx2cJpupkp7LhjP2c38oFtjdPSivAuC5JjEk24itgItmnXY5qB7C6+btsa5qP081Fkje8OZOzY1XXnF8ltUNYwk4qnBQS0bFm3NNdz89nQ4XFM0/LEFcPFJluJy9lzXxpTVT8tHFTZ/iUKTp5iSrhXIvSUBYlxgjYNWZ1TRWyuW+WC1wzQBcFKTPYw+Eo51TmmJJoadrsDB0EQE5nM1SezbOFlfcO0NZQYFE+4Fwjc6pk0K3AB49SEIPHGMNwOKBpG7QuSJBZVJEFt00jzLs1lFWFMoqyriQv2HVuu3LtjC1yA0fWe2d0E0LAGMtgULBYLNFGwDGIa3bHJpNgPB5zcSEziD4pYuikriKtjyR0koiWhEE01msycbUuz87Nj9S9LyobPWUVuta9cjMRc/xNxAdHVQ+JURMC2KpiuDVGK03rW0JwGFsARmZRG4e4NoMLkUefHnJ6dklVWpIyNK2naUVpIU0fOa+qKkkpcXl5wZXdfQ6Pjnj48IQ33nid733vu72bdyc17yThIfgMVOW/C2sxhQD+3sQIWMznXFyckVLi5Zdf5OTkTNzOjUVBdnkOjAYjRsMBikhVVWiEcS9rySa9mE4JPnH9xnVu3niZ3b2rBJfoXIk7sbUPYrLlo8NH33/eSHMEZrMZ4/FIFCN5TtYW0hQJRMq6Ymdvh2W7ZDQecXR8xN2f3GW+XKC15sqVq9y4cZMUI//im9/k+PhYxi/yOltXLmxvbzOZTADY2dnh8vKSi4tLWteKgR7S4Li6u8vLr7zMzZs35RoWVpjvGHtTPa3kszlKN44bN27y0d33qeoaay3T6ZTZ5QUvvvQKm9rUpp6vuvXdHzI7eYA5uP2sT2VTm9rUc1rPNViNUaSVg6pmMBjgXMt4PMZa6a4rnWhbyRVkNGS+aHCZPYJIJ/rqwaPuXIUN2so8mF+T1GqtxdTJB5JMztGZ5pAUTWh7uW3H0AnzKC6TZVn2clijbQ8w1+fzhFFT/fksl8snwKoA05g36ytg57w4z6rMVoHqX9fTTsLyM3pwFJ9AhtAhBrMWi9JFhzydjdofUkNSa7/r5Lcd0MxzwPK3+fUphdL2CfC4fq6pP8jTp7d6fPeYDmxHQMWYzWC6+bcIdPLCDHrXZMr9dYmZ31WqBz3KZYl1Ur3z8bprcEwJ78VF2hqN8569vT3KssgMWYE0AAzzZcNyKexcZ2yklWbZNMTFgm5WtaprFEYAXkySo6m7+JdubUVUitSDAfWgIqaOoU7CroUskVWiCnA+9jJbEOmtKQwmRJwL4pJbFiJRTxFyQ6Su6x6wzmYzlq2T9aIFiAo7KZJU76M4AFsrzKEPWFvjQ8oItG+BiJRXrZi0bk2inmTn1RPf5b0MMaGT6WXl8qtVzmz0jhIw1uKDw1jLcDTC+RbfuCyZ72KipPnUOV8rrTk7O8cFqG2FC4nL+RzXLpgvGwGtzlEUBXVdEVNiNpszm89RJN577z1++7f/LpPJFscnR3IpgygifJDmUAiBkFq0gaQjVkFhy9VajgJopxdTlouGvf09rl+/wZ/92bexxsj8u2tRyDqfTCaQItooRsMhr965zXw246cff8z7H3zA2dklV6+8wGg4oR6MSUmM5zrZPilmafTKdZmsiIjI2uhcmVOKOCdqkaIoCFrYy8J0cTOGxyfHTLbGnJ4dcffu+zS+pagqXnzxJlevXuHk7JQf/OA9jMm502u3ePd5uru7SwghX+cBH354T+S/SuHaFoymqmuuX7/Oiy/dZLIzIcaU1Q+g+7ltMQdTShQUISSuZMdmm1UTJycnnJ6ccPu11z/7WbOpTW3qmdf/9G//z/xH3/nHz/o0NrWpTT2n9VzLgFNSzGYzUow0bcunn37K5cWFME5K4lLqumIyGTMc5ogbpSjKAmtLvPPZFKljGESGSjapUZm11CobNGWw672wJCGEPFOo0NZS1hVKyUazbVucc9mkaRVPQ28opHEusFy0ecaTnonpnISdc0wmE1566SVeeOFanh3sJLq6Px7QSz5hlWPalcrGOzEiRiM5OqMzjeoAdBex8YQbsGAMmY9bY0FWB0fmDvPP+rnXfE797GxmjHSOFtJrx1lntZ8+lydfi8oy0mxQtPZlzEoS3MmErS0pihKbpc5Gr+Y95T1I+flAre2YVwy0RHbM53PaHAPjvMiw+3iXBCpB0zQ45xhvjdnZ3cZYYb2UBhccLrT4PAcock1xnU1okjIoU6ALi/Oe5VKOFUM2ucpfMQhj1mWcDkcjBoOq39TDultzwPsWpRSLxYKmbVFGUxQly6bBx4ApLOWgxpQlKam8nnO0SkpUVdXLfn0ILNsGrREZfZZYGlNk2boYitmikDlwY7NUuHMN7hhDWTBJm16WHZPK62bF/isFRmmMEkslld80YSYTIUAIipg0EYtSlhhX0uCmFUbZFgXGZKm3MVirMaXNzFzKbKYFZYgJWudBF9SDMaYocT7ROM90Nme+WOQIoNhH/wTvaF3L2dkZSmve/Yv3sLbmxZdu4FyDcw3LZsFiMWO+mDJfTFk0i7yeyC0v00u+RSlh8M5zeXnBZDLh7S++jbWWi4tzmQNuW2x25hVp9ghSoi5LmsWSmzducPPmTc4vLnj4yWNCVEx29hlNdqjrLWI0xKQJSSKAvA/ZCdr3c9DdDGvbtrTLhmaxYLFYcHp6ytHRERcXF6JsceLEHlPk7OyM84tzTk9PMPnz6/HjT2gakWft7+0RgudrX/sjzs/P+8acKEGUzFnHwM72DpUtiD6wNRzx+PFjjo9PCEFYdW0U1mr29nbY29/l4OBKfnznIh5RRu7rFFPftFNalAYyL17SNi2jrS288/n4G4OlTW3qeaz//PCr/K+//I+e9Wls6jmtv////sf8e9d/5VmfxqaeYT3XzKowjoGUXTu3tiTrL3iH9wYVVY5WMNjCMhyPmE9nLJZLSlugEbaTlIgZxMkmX0BciBnYAcumlTD7yQRrxLk19hsh188Mxti5+Eo2ozi0roxDZMZO3ClTFOBVFCaD3NDPUXUARGvdy507ELvOPsqmMvSZrutglz4CpnORXbFWSmv0U3OkKzfczGzmud2YAdA6Cxk/57Hdv6GbgVvNn2m9ckYFehZwHfeuHv95PZJOcr16/NO/13plFrXK6sx5khno5oSY/niwAsQKvWJPoX9P2lYYrKoSwxydzaxkPi8z/M6xMxwwGNQ8fvyYqpLc1bYN0iQhrTHiwo5rY3rmLZFEdp5W17C7Hv1rykykzPBV7OxuE7xjOp32a0XknKFvKiilCEQWy4bFconStRj0ZFMcaZRIE6GbWVQEdOoAzIqdL8uyz7WUWVSR1qcsu65KiWJSWosxzrKlbR1FWWNMZrtCIIlGXuYKe+GnrBWJDtJrTslyvTrjLZ+ivE9JoSK56WFlLjvb1SYUjfNo2zIebWFNQds22NKAKulmRLvGhsx9C1hu2hZjLFVZY21isViwXIorb6amsdYyGg17B3BQXFxesre3y8OHD/no3j1ee+1V/vzPv8VsMRUXcO/x2VgqodAavNX9B2yMibLsZtnnXF6cY4zl4Oo+Winee+8v5LMl30/WWmazOfs7u4wGAxSJ4D3LtqWsKm7cvJmznA07e1cYjSeMR9s5azb1wCymdea0MxyLuNbhg89r0JCiXAujFW2j+ibfeDzElgXet3z005/2zuyz2SUomM1nRJWZeDz37n3Id7/znX4EwxjT39QpJQpr2d7ZlmOPRgD89N69/r5pncPmvOwXX3yRGzdeEAVKu8xuxh6DzKwm70VjsmbsZow8djzZ5uTxp4xHQ85Oznj44AFt21J9zifPpja1qSfr37z2Di+bLQD+vHnIu8cf/St9vhADXykW/Ad/8VXs2//Gv9Ln2tQvXpmD2/zDdsL/pU3vBbGpv131XIPVjkmazWdsjcfs7u3inROQplWWfXmWzZLpdEbCUlYV89kM7xxVWVLVNYWxzOZzlvMFbesEqGgrTJpzqGwcY+wKPHZMkTEWl/MvxWRGEWPHJCmJlUmqN/twPuC9mDqlBMbqHkjaDCA6xs8Yg/ee09PTNRbwScmsD8KQxpiFfUr1eawmZwx2c30dgOvmLa21/ea0YxoFpHWzu5rgVzd+71DcCWnVGsjN0mHxjlEZ/ORZwwxcu+PrDoDJQemzTntTpB6R5tete0lvdx6QDXyeRrt8DrOcZ3pVx0R346lpZbIkrJ+4N/sgsSqdqzEq0bQLUJEis5Axdtxxyg0KkU03TUPTNFirSamQ+dGkVpmbupsBjgIQslRaTLcSpIh3AVPJWujkqd7lRkSeU6zrAYW1BN/mhsiKVXdty2gwlHzUsCApMSObzudZ/mv76+qjyMeF/RYkrzKQXy6XPQvcnUvnECzuw+CjxMlIcyIymy9ISmFsnkVOK8CpkbnEmOXvHfhTmhV7mgGrPKx7TTk7FvDNQo6LRkVDShrdGT1lozCVGyNt60gjadb44DFGYW0h7KtPKGOJGaAXdWbjiwKtSmazOe7/Z+/dYy277vu+z3rsx3ndx7yHQ3KGzyEp0qJkyZYt27Lr/JEGTus2gZ2msI38ZQNBkTgtkBRpmjptghRtg6JIirRxggJBgaINnKZ22iBIbcuybIkmaZJ6mKSGQ3I473vv3LmP89h7r0f/+K21zx1ZreRayozk87Mpkpf3nrvP3muf2d/1fR3M8W5BVSi09klqLx7TsiwT4BVWuOlaZos5wXt+99XX+MT3fS/D4ZA7d+7QuS71wOb7VhHT58G9q1cn1tgzn89RRN59913efPP3aNsOkCThQSU1V1VZcfLkcYhSHdMu5ngvGxHnz19gfWOD5s4hGxvH2dg8TlUPcM5DNL2aA5W9q8vO5uC9dPNCz1bmIDSMIiRGPKdDl3XJbDbl/fffTxspht27dwkx4IJLSpOOa1evcuWDG8xmc0ojXuYQvHhKQ0ArzcmTJ2XNoyjLkq2tLdrUSx1RfX3O5uYGJ0+dZHPzGJ332KIUkXiQpOTgHCqKHz974rUSP/d82qCVous6qlKK5Le3bnOwu8vk2Inf93mymtWsBipb8i/XvxtN5CN/9xPYT/4pANq//9d5/e+d4RY1f3rn17/pv/e/PvNv8L3dDBqIr78EK7C6mq8x/9Ybv8D4iT/Bh9cvsKYrfuXGq/f7kFbzr3EeaLCqjTBCewcHDAZDjLEQI4uuw80ayjKmByVwQWGsQhWGoiySdLNjb/8W4+GQsiyxVYWLUaSWCkKSBbetg+Sx69qO4AJK61Q4nytPFNYacsqnDyFVzwi7G9F03rNo2sSCCqdUaLDoe4BYZgazN+6r03NTlk3vwRSv3b0gDiJ41T+U5h7KXFGjAJ0AnIIe0IYYwSe2Ky4tozEBydiXLYLRpmfkll7IHFCke1DoI8kXF3v5cjgiWfUxJg9jYoFJADMDS6XyG06RNOmhP9W7KC2y5RAzkM4HrlItivg/5T8vw5uMteLb61no9PcImiVgynJcp6QHUidwrUjHpqBzHQf7e4zHEwojMk5feKwp0Khebp1TeHXI7z+z3RGSfDGfzgykjZFgJqVlU8A7R13WVFVF08yT90/ep/cSlLNoW5Gxawt4CaWJ8rAfkxRZoRhUA3wnIMuWNqXXSkKwwqbOYDmvVlu8CigM1kAw4H2bzp8CbVg0HcoY2hgJaQMg9syWT/eLpWsdWi3DqkgebI2AP5+AnDFWmE8Cw+GQxnk5JoIA2OS3VMqmTSaTgqY0rvNMZ3PqugZt5OdSEq9OibGz+ZzhUIKIuq6lKAp27+wznzd41yEr0lCUFuyYrokM6yFRl0QV0hqCDsf+9JBRPeSdS5f5no9/nFPHT3L58jtSBYMEqilBaGgMhdFYrVAxoFVEIeBqb++AW7e3cV3LfHZIXVUiZTYlRqv0GeBZn4wYj2q866gKzebGMW7cuEa7aHjk4XMcP3aSw3nk+LGTbKxtopRFa9lccd4RlVzbGJee1BC87Ez3sviQ0pqTVD5tHBVK4dqWaQh0XcdXLl1iOp1SVyVPPPUEX/7SF0FFWtcxTID385/7PLu7+xRKE6Ow/zF5ZGMIDAc1a2sTuq6hGo5ou5Y7d+9SFCXOzcmdwOPxhFNnznL85Cmq0Vg+awHfOQiRoggU1gKSYi0hS5pmPuOtdy9z6a03eSWif0wAACAASURBVO/yJZQPGK0oraGZzdi7s8NDT6x8q6v5zp2HJsf5J/WTfP/W57/u917/gacYfc/x5Reqguo/+Fu/7/vKn/sFPv5zEGZ77P33KZE1BH7gH9/mN3/mzB/6mO2f+TOYh5/9Q7/Oar7DR2mu/PxH0U89CfUA/znDqb/3Gm3KjFnNd/Y80GDVFgXGGmIL87ZhUNfMG5HMWh2YN10Ca0qAbArPyfLLopKH2tmipXVBgprqEd63aCXVFYezOYcHM6lQScyTMTYxpV4SQpOcTeoRSKBMHt5DDHikrqRruz4IKXjQtqBQwpaY5EXN4DLLLvtu1PR3AHQOlxGAJ57b3L8qckkiRySbAmQyEw0JGgliFt9tSMxojl4Rw1cfiNObUyF5Q1UCyWYp7+0RLulhV6VwI9Cm6BM6lbYCiMS0l4BlAjwJTOsMUjM4TOckhGWQVBKOirxUKxTmCBt7lIWWY1fGCLjN8lgFVhtCPpeIrNRoRds2xLRWAIIXyaewmwZldB9QpbTCO5ECx+CxRh7ojRZwEqL8fH9t0f1aRMUUiLuUdpOYWwGJ2fN5tKplee2KoqBp5sLgawVBXtd5T/CA0vLPRJpGwoEE8IRU8qP7SxCCdJQaW9C1IYWUycbCaDiiaRpms7mQzWoZUkSE4KGaDKnqWrzQQYBN5wNWiXQX0iaGNhhr+00O1Xucl8x+H+ql5F4JQZJqi6JGa6kMCtGj0z0GgVxdFINKq9jQNg5jgoQBhU7OEyp5VRWz6SHPv/A8o9EoMZgtTdMkZYJIhFsn9VRaVdgKBqMJnoKAw9hSJLgK5m3L+nidm9dvsrt9hwsXLvDbL/2WqBiKZWKyVYpCafKqG6RU2v2DPbZ37nD79jaL+RxiwGpLjDoFkaVEaBxawdraGO9aCemaz9lc22RQD9nZ3uG5Z5/huWefw3nLxto6dT1MnyOyNlAQY/Kp5nCumNn+fN8fnXx1kv3Ae9oQMN6ztbXF3t27RCKPnn+Y848+ysuvvsRoOEwVTxIodrB3SNt2lGUlEvCsvEgbP+PxWDYMrKUsC27d3mL/YL/fuEm6DE6cOsWFx5/g+KnTci8ZWePiS5dj8ypSFRalFbs7W1x+5x2+8vZX2L2zTbdoWMxmFErRNGdZW5swn085PLj7Nf+cWc1qHvQZlwM263H/7/+TfZpP/PM/+/u/0RTo4w8zvf3u131Nfe4ZVAp++0ZGD9ep/qP/qv/3z/3UVzBnV5s/q/nXN0c3U4of/vfYeezv8E//1l2uWfiPb/zafTyy1Xyr54EGq1m+WZYlTdMwTAxpn1TpHM2iSZ5Pj7bL/knvA1GncBsVcC4wp6UqCxSGzvkkr5U6iPwQ3rYtSgtQiCGn5Ib+Absoqj6chKygNVqkjTH2bLBXkaglDMS5iFBaR+R2RybLMHM4jw8io8xexhz0Y5T0kebvPcrIZqkvZEyZAm0SWyq+Q9U7CIlZBijAWLxly0AnAXxm6fdMwDIg56VX8qb/VSCsnMqy3nSciA/vqPSXDDZ6OfIy+TWEAK5L37pM583npve/9UcqYBJtxCcco6yPskzs7jIFWif5qtGGOg5wXSvrZ9EQQ0gy08CibaQ7V06ESCaVl+8dtP36W/pmlweTq1jknMY+4VfI48yqRlzwwsQpLWyhsZJC7QPWJgbVOVEE2IKua9BGwI/U68jrGGOwJtL5jrZpmE4j49GQsqwILuBdDtiS9F5Vyf3TOScdnOm6lnVF6zraLjH7yhBVIJB6Oo3phbwKCIlJztLfzNQJqx/ThpETpj4aFCFtjiwrnkIIiQUWoLdYNBRVLceXAq9yGJgPMckDtIBYJcm+3nmapknrNK1Xvdy0GY0nbG3vcHf3Lk3Tsr21I2tBW9lsUCEBLkcgYk1BjJrZtBHJqVLS+6oV83mDUoamabh27TovfuQFzp49y/Vb17DJF9w6h1UWZTLrDft7++wd7LN7d5/5YiGbbdbiXCREj7EV2eMdg9xDVVUyGgkYREW2bt9mUA2ZTCa8++67fPwT38vzzz/P1s4h4/FEpOQ+oHUBqQtVArVcUlxkr37XfwblOqd7Kq3Usn7LGMN8Pmd7ewsfHFVV8tRTT/H6a69JB20nvbrifRZvvdyeQc5b8JA++qrRgMlkIpt0VcW86djeuZOS1xNYDTAeTzhx/CQnT55mOBgTYqTznsKk1RdECTGbz/hg6zbvvvMOV69epW0bsWEsGuazuSgUiDSuA2s4nM/Z3dtnNav5dhqtNH/+oR/gLz98kxP/9B9+wz9nzn/Xt/Co0u9YAdXV3OcpfvIv8RM/Cf/oI3/9fh/Kar7F80CDVVtYilJ2/nLybvaSzWazlFYpD5SD4ZCyKqW2JiqmfpZSMD3z2VweDr3He8+grgUgKJEeKiUME02TQkjoWQaRhC6BplJdH3QESb16pBpHkf2s0l0q/k6dSMVl12oGX7maxjlhbWMCmNkDm19XfldiIo+EMC0ne0sjQS0rXHI4jk5sceJOjxxvlqKKB5Ek01RH/KVamyS3vRfMyjkJxOgSeHLLY5FvQidPaAY6CLHYv4bWJlWNJIbzyHnx3vVpy9kzmftj+59XmqiFeS2sgFSbym9DFBmxrAk5npwaGwlYZ9DWYKymbST5NPhA27WEGATYWisP4DHSNg2LxYLBYNCntWbWe8kgCSDVRqTNIQq4zOx1DB6XWKeQAplUSlGOTvx9ObDKe09hDHVd4X3X+5a1NgQPMTGPRQndvBXm3EuPb1XV5DCtZR+vdBf7INVMPoj/0xQlbZLfRrWUg/u0vjMQ7Zq2vydNYqTv8VgjVSIie83nIwU1LdE8MYK1hhgFpOaf9t4T27b3WoOw8DbVlYQo94dCL18OuXfatqXK1z4GfFh2Ge/t7eOdZ3d3R66N1pnUT4oHhYrSLdo6x8HhjIhHq4A1ClsYjNbEEJjNZhyfrPPe++/zIz/yA5x/9FG2d26LcABFVRWoIAFqoQns7e0zm82YLxq63LOrtXRDh8BwNFyqBADvO5SOrE3W5WtGM58v2NreYTJe4+mnn2Zre5v9/T3OnTvHxuYmg8EgfbYk2XSQWibnOkLoQCXFgpcgqJA6YPMmRv58UckCENJmlFKKw8MD5vNDogo8+ugF7t69y/b2NuPxhNl0itaGpmnTxkWyAAQnG0hRQKtSis2NzT6grusct29vSaev1n3QnbKW48eP88gjj7KxsUEMgbZp0EZTpSyCWzdvcOPaNa5dvcJsb5/oRXGgfaRz0qcrfdwOD7TeMRwOwGp29lfM6moe/Hl07RS/vH4OgKLwPP6Z/7x/BlnNalazmj+K80CD1bIoBeglCe1isWA8HlOWpTCbZFCpKMpK2KlOevfaTmSFIUTqwbAPF5kvFiwWC+q67hmypvP4tuulnU3TYop0arKEMTF8bbsEqzmt1yPhSWVZEqKi6yRwRCuNLSuKXgG7ZBhdYnUz5pxM1hgOhwLEUxXJUQZkma67TIHtZcEpiCh7XOXBPsl0Q2b5bM+a9AFF6t56GbT4SFU+zoQqBcgK8FAswYkcv8f77MdNJta4TNs11vTXL0/PmPWy6+VxSICLTedIo7Xvj0+yVI7IabUcUdDi1dRlQTkcoHSSQiKS3+xTBQhK4aJ4JLVRlIMSYzXGGuazKJ7lGGm6Bu1UD8xMKZsMEpwlQUga8FFAXpZk9+dTzkTyEuv+vxkrybDxiCRaJW8xCnxKoM6SYO0VZVnTdp1IR9MGRA6AUgomaxOadpF+zhKC+CejUsTgQMV+Q0IpLRs81tAFYfi0EbbQh4ixIo0VI3Ly+0YBGNFKp6xrO7wLcu5/312b2OPM0sXEAofQM2h5tE49s3nzR9FL4vM6cV1Hri+KqbpG5VRoSAnKirbthGUuCgHIWWWg8s/I2g4xYnSW1+e1Ja+jtaLrIp1LcnZjCSi0LbFWYwvF4XTGyY1jXL9xk9n8gItPP82bb32ZqIT1nc0aQvC0XUeXNsckQChilMYjqbt1XTEY1DjXUZaFrNOYfbyG8Xjcg8i7e/vsHx6ye/cuKMV8vuDaB1d55rkPsbGxjjaSPm2tlr5d0T8Im+q7HpyKTHm5brI+Yfm5tJSoGyuJwHt7e3SuYTCsePiRs1x57wO00kzGE+bTBdYkyblOKc9aNlEkCFo2iqqqYjQapTR0y8HBITs7O31QVl5n4/GYY8c2OXnyRNrsiAzrkr29fd7+vS/z7juX2dnZwrUtKkbxhad6sRCDbChoTeOlT9YYw87uXUbjEV3wzFPFzmpW8yDOLx37FH/s5yvU8WMUP/7n7/fhrGY13zbz03/3u/j3X+3Y/NufTbah1XynzQMNVrXRknyrxcvlgme+aFAo9vYOaNuW0WjU79gvpgvm85T4m+SVpRW2E+/ROtD1vkjACxO0WCzwPvneYqDzDlPYBOp08npFoopEH4k+SPdkAo2F0lhbAorg3VK6isd5hwqSUpkBZmauMkumtWZ9fR1jjFRlJJ/f0f5RdWRnNdeHCChM4CBV0OQHfQGDCluIjDRLjfMxx4h0Yfa81hGWLC7ZFmDZb5hAaEYCwlzKX8J4qR6Y5iRUTQpqOiInzim8Sn0thljGe9/30kZINSjLY1RaEqFBArC0NSg0jeuk59UmNgwEtPW+W0nbNdpASMDZGkxZUMa6ZzRdJ3UmTdNQGIs1FlsUIm1MoTEuPSj7tOmgUT1rLCzT0jssEnFJpFXa0LZuuQkSgnCPWqSzPgRc+lpMft2irGjaLrG1MfXjyrWqBxWDekDTzGlbR1GUFEWFxrOYN8TETpqUeu28yEFVTKnAsyYBXdmsEX+reAStMWkzQtaZ7z3Zcj2MkX5fuTYiwQ0p5EhhUMS01kkbHbLa8saKVgo0xCD3a0Tfs4Zzmq+xBp3u6aWfW85NloZ3nSgfrBW2WKefbRYCZAtb43IYQ0pIBgGq+brJPkkkqkBUBufBe0thpWM2OM+ildd48+33+PB3fZjP/Oan2bmzTedaYggYbamrGr9Y0LYOoy3RQOsaiJGHzpziU5/6FNvbW7z88suygaQ0wXcoFRgMhhSl3K+LxYKDg32KomC6aJi3LeWg5tI7l3nho9/NuYce4vrNbWyRPfuyakLoUqiSTxVMshmgtMJgyH3OR7ub8z2fq5f29/dZLGZA4OLFJ9Fac+XKFdbG65w5c47DgznNYo5PMuxl/ZUmetdbFzbXN3qlRdMs2NnZwfmAIXm1lVT9bK5PuPjkEzx09jSz+YzL77zHe++/x+V33qNt5xilKaxFRZHLQw6+s4QY6IJsnBlbcGxtwonjJ7h96xbBR4aDMbu7e1/zs2Y1q7kfc2q0walqnc/9s7+IWjuFHm+iBpP7fVirWc233dhP/in0R/4YT/79n+Xt3Wv3+3BW8y2YBxqs1imYxHuPsbZnYaqyYjAYMBwOGQwGHB4cMp1N6VxgvpgnaatIRDNbWiSgYaztQ3pyh6n0mGYDqlR5ZMmuVMUIO+Gdp22k+qZMtTjLsJhI0zZ0TiRwVSUA2nsnJfdkEBD7KBPvRUIanWP37l1c55jNZ4ktQtjMVMtCAp/55+XvLCm8JInVehmOlKt3jC2EfUnnL4QgSciCPOV3JQnrPUxr8rQtQdUy3Ajyg678lcGoPO+q9DCslqE6KfVWKY0KgZxcnL2XOTIHwHWdAKKQ/JbGoKMwkVkGrY0EKmkgaIUuCowtiSoxtcYIoMuyatI5USmgx0tCbUSsx0HJX1ErSXJVCgd45/DO0bmOsqzx3tOlqg1FTDLopdRWKZWkx1qYsnQeMyjIvZM+pFCmJL1V6RzlGg7vcgemMKFFUci94PKuoUiIQwzMZlNsYXDO4L3gMOc8JiVBC6Ml/WRd16VjQ/57hHaxSOdGYbXGe9l40GiMthBc8o0KUB6PJxxOZ7RdCjMyNqX7aqIK6Rw7lJH6GQFQmZ0Xlq/3Yuc1jpxDH9U951Ij9wzpM8BaC9H090QG0XJviH8VSow2ktCstcibgaqu8bMA0feKjHvWezqGiARKuUjy4zpi1BhdUhc1B/MGWxi++MUv8f3f9zEeOnOa9967jPeRzePHMabmcDbvN6K6rsV7R1mUnDl7hg9/+MNcOH+eV199pd9Ect5B8GgdGQ5rFLKuFk3uz1XMF3P29vYZj4dcvXaNZj7noUfOcXNrB2M0reuS3LfFeYf3HTEuWWrZhBL/cQarR89BBpvWatquZTo9JITAxsY6jz/+GK+88hplWbK2ts73fM8n2N66wzuXLonyIN3vqIhJ95NWmrquGI/HsqaU4uDgkLt7e/1nobUWtGZQ1Tx09hST0YCXfvu3eP/997l9+zZN21AWFcOyIHpPcC0qOIyRzcSm7UApRuMRNA0bgw0GwwEXzp/nhz71KX7tX/0rrl69Smkt04PDb/BPntWs5lszf/ahT3CRAQB/4d++S/3X/rv7fESrWc13xujhOq/87R9m8rP/8/0+lNV8C+aBBquDwYC1tTV2d3cpbMFi0dI2LXVVMx6PiTEynU7Z3d0jRkXnA85HiB6TAF90kfGwFFCpRLjoE3OTgUeuksnA86jMVb5PApe0Uj141gkEqxQUE1IIU9bhapPBmTzIpUZP0IaYqjyiUr0n83A6Sx2nmr6dUf51+WCPyGadT5UuSqe6E2F2s9RX5KYmK3IRHxoJPCeQmHyYeXQCDJn9yh2h+XdDsi6m82OtSa+r+mAjkVpm2XGSXxrbs6ASzpQ2BVSqMMmSjXQoWmthiKNIZbM8FpYML1qB0ejCYJTU+ujsb1YpyCgzeEfkjSj5eggBfK7viP21SHpQQoi9144ocL3rOnzwhE5knM61FGUl3xNCUgFkVktCcrzzaBVBGVDitw1RJKHWGLzSoHIEFQloLD3MXeeS5N0zqobU1YCpm0sdUErQFWl6I4ynD0l3rOlajyoEHMdWKlqUin1wjlGGQVlSVRXOFlhjMday66IgDQ+O0F8XlMIUkg5dlKWAXSM9qIUtRSYcA14Jk966VgKcSEFdJm1w+KMhS0kG7AO5yuhoqFi66AKCk4RVp/eX17EEeh1VDIR+E0jbQuT2kOTwwlAH1+Jdl5dVul663+AJiATYO0/UkaYTT6zVBaUtWbSeiOba9Zvc2d3j4tPP8NJLLwnrp+S+Lwrxj2qtmUzGVFWFMYbTp05z+uRJbty4zv7+PsaY9BkAisCgrhmPhoTgsYXlcDZN3aNiL9ja2mJj4wl2D/a4ceMmx0+cpqoKfNrYgZjsbcuNKZ/kyPL5IH7dZY900Z9z+RoUpWH/1h6LxQytFRcvXmRra5s7d+6wNt7k8ccf58yZM5w8eZp3Lr1DUZT0aXMxkrbCKKxhc31N2GZjmM3m3Lx5SzZSjMG5jrZtGAxGGK24fesGv3rzBvsHh30A16AsZS142WQojEI+4h3RGM48co7z589z+vQZXn/9dZqmYW0y4cUXP8r5CxfEHuE8w2rAYjr7+n/orGY13+SZVEN+9/HHADjz3/4J7As/cp+PaDWr+c4c9cIn+E8eusZ/cf3X7/ehrOabPA80WB0Oh2xubjKdTvu6CeccIUaKohRG9XCW2KICrQyFFnChExgLPrBYLISATNJNouzoi9cp9t4p3wfYaLxPPr0o3qmjzGGf2us8qWEx6T3zkase9OUwlzxHPag6SS4FpGb/nO59p5kB8yFF1KQ0Va0MNtU2SCqqwRqLOuJBjenBPXey5p7FmLpK0/8vZbWJsQQg1aiEI/7BJdMKUUU8sZdwKpNCdLQARWVUkmsCUQy7QfSrklAcY8/ixMSykQBHJMr3pgd0lEoMcjo+axKzKlLfoiyFsTPCiIcILvj0/oSxjOn8xSTHDV5YUZXOM0pCsIpC+nld7NAhoozIi0kMaPABm/yd0+mUdSvfnxl8Qbah9w9n0K11RGkJ8HHeJU9gmXyn9NkZgsNUf5yuy5spskbqekjX+T5NNnuaY6om0SkgazqdMR6PGY+GeOcok2og4z/XdYSUkmoiGFMwHA2ZTWdE79BaQqGMAt91iY7ThMRyuxBQRmTFwSuKokQpYRCNsnjfEnzERZe6Ri1EUTDEkFlM1bP1pLWulb7HA6uUeKRzsFgkpo0i3WNoshBeAannNEZP17Uip03JwiEGtDFYLXaCaJaSeW2zDzxL7A0GSXPunKPr5DgPZy2gmIwqPJq9/QMuXXqHZ595lhPHj3Ptxk2KukabEh88w+EAay3D4ZDDw0OqsqCuSjY3N3n11VdxnaOsyiObIx2bGxuUZUFItTNt20qHrodF03J7Z4vHnrhA23VcunyJh8+fZzCouLt3SIj5syamjYysXpD3aoykIFur+lAzY2wKy5J7sigsITi2tm6jteLkqVOcO/cwv/GZ36AsBxzbPMaTTz6FNZbJZCLWCe/6axFixCjxko+GI4bDEV2S/O7s7HBn9w5FUfYhWkVRUJYC7Pfu7EgAmpaKKm00itgfX9N2NE3H8WPHeOT8eS4+/zwnzz5EVVW88sorNF1LBJ588kmefuYiV959n3cuXSb6QFEXKM9qVvMtH600P3r6BX7prz4tXyhLij/5c/f3oFazmj8CYy+8yF/5XwO3fqLjH1z/7P0+nNV8E+eBBqsR6eYbjUbc3b2LTlUQbdtSljVlWQEqhS3FVAGRfzb2VSVd28kDehCJYlEWwrIUhbBmresfoLuuS6+njwBUdQ8jkX2nIB6+EEMfmFQUxREQFogh0h1JuJWuyCP+UBRK5ZTbDFazdNbigk8P8PIQbW3y7SW5r4AG07NP+WeXITy9mlekvv1xaEkI7mcpVc1IwOhlnUzfbarl+5xz/esUZUkiIfvQmghHmOEA/l6WNocxZTN8TguOpI2EHtQKMFVGy7HbFBaV/h0t7LJIGw0qsbVRUNFSJR0iyybHfB0kyEgRlyClSF7J1qG1F19vImCdcxRVKaE5rfgTO+dRxmIgAciAUcL+KKVwXgCPeDuDhBhpk85nIuJjSKFBCrQWv2oING0jwNGaHoAWRSn1Ssr0gDXECCbgnZzzECJaWwlmahbUtdwnTeMIyX9aFGVfjzMaj9Bac3BwIAA4gW1rLRiB/MKE+X6zSOT0DaAwWhPSurWJbVUAPqY9HAn68j515rKU3ub9EaWEQc8MaO99TGvR2oIQ872Ra5iAdI8FomBqJee26zqKoqOuByilBMjECEpTDUd0izmLRsKQCmP7zROtNUEBKoqsPqSEb1vgQmTRBfzBjPF4yMaw4I0vfJHvfvEFHj73KLdubeOdhLhpYzi+vkGMkaZZEGOgrioeffQRDg4OuHr1KlrJ540xFtd1jMqCjfUxioAxmtlsKnYEbWmjl8+yzjGdTjHW8s47l/jUp36YzWPr3N7aRtuyDzsSwOpTYFUkd9SqFLRlrUlg1SzTgJV4m6988AFd11DVJc88c5Fr124ym7YcO3aM8+cfoyxrDg4Pee+99wRM62UdEUSC7yiNZW1tHRA1ymw24/r168lPLZL2wWDAeDSiKku0chCXsvIQpR9aGUPjHFVVce6RM1x89hkuXrzIxuYmjkhUmmvXrvHW22+DgrMPneWjH/8Yzjl+/dOfpmk7Qtuxsb7xNcLAVrOaP/wopfj4iWWNy6/+w38H86FPrfynq1nNfRj7+Ee5EP93nj92ni/eef9+H85qvknzQIPVxaLBhcDa2ho3zA2CD3gfmc8bBgNPWVXYsqIsa0gy4Fwcr7MMlIgqLK5rca6jLirKJIcLKkvf9BHppU9hS/IgZ0yuucgyQ5G5LsFdFMYoPdwrK945YcRCX/ERUp9kZlkzs5rB79FU1BAhoJGsFJEV2qJYJhQjkmaUwhbZx6fld/a1OiqlhC4Bq2H5u9M/9AxV9rYBqcIig8slUJVjthQ2JeOmNOLc+wnygN+fIxXxcelPzL/jnvAmFfvvV8ljqo0Vj29iPFUCqzqHKolmMjG8ARUVKqbeznzcR+o3CClxN2aYpNPv1egkFdZKg86sncZ5CC6I7zWxnV3XUSXvXUh1KT4ENAKmcl9oukJoYyAk8NxXIokiuUiJx1mqma+FMiYx7aAQSbRVKgHFQFlWLBbSKWm0dLMCaa1mdYCmbbtUhyTJttaWuC4SEgvmg6fpOmxZ4KLjYO8ApzyqSLUrKqUrVyVdFxJ7LueqHgwYjUYcHsxSiJaitJauUWkNJA+uMgmMyzmXeybdlzGvgxzehYSXcXQjhx60CjC/R7zQy3jz+hXZeZINx4hzHagB2hpiGwleEms9YKsBA1vQNXNCcGRPc75XlnIIqfyxZSFKBTSN7ziczTmxOebSO5fZPzjkQx96nt97820cYKxI0nOQWNe11KVlMKg4eeI4r7zyCtPpYa+saHwHITAerWOtpXMN2moODw9Sz6h0wBpbgIK9gwNOnzzBzZs3uXHzOsdPHqd8731CRCpyuq6vqZFNpdR7LMS/bFQpI7aCmGuV5PPr4PCAra3bGKtZX59Q1xWvvPoao9EaJ46f5uGHH6UoCt566y0uX/6KXO+jHmOtCTEwnkwoS+mPLcuCm/v7FGXFqCwpy5JBXVOWRbqV8+ewnI9FJ55UZUvWNja48NjjnH/sMU6fPs1kfY0IzH2XNuQ6Xn311f4e+PCLL7KxvsFLn/sc23d2mGyssX1rS9btcMhqVvPNmu8+8SQ/bR+lDvDTr/w1lHmgH6dWs5o/MvNTD13nL/yNn2LyY39z+by7mm/reaA/XY01hBAoq4rReMze7n4vBW6ahroeyIPPYEDbOqbzKWVRYLTtZataMj+ST0/1rFhRFkL8KZF/SsiS9AXWdZ0e/k1a6BI4Q/aOqpSme4QpzMzpUY+Y+OmEwZCJfZDTV4PeJeMoD/LGGGwlQTFiykxy1uzfzGFKKdE3xCAsbAKQqucRuYe9zHLoGELPoPVzBCDkYwGp3FBWY3WBtVUKhYk9C+2dT2xeDlnJP788Rxn0Hn3d/LuWlToJzOR69ulm4QAAIABJREFUncz6pGoa8a+qxBCr1JEqvamSbCvBSTGK5DghKfTSmirMqlIEwj2/P6pU/5N6U7UxPdgMSLCQSusHBfP5HGMMg9FQNhOsxRYlISyE9wtpTTgNLK9LZqXL5HdVWqN8kihrerAr5zP39i7ZwuV6b3Gd76+VQlGWBXPX4p30gc7ng/Rakgjbp7VqBVGjjAKrmDcLprOZVNh00gcasqS5sBjlUzjSUr5eVpWcb2StVmUFMdB2czSSOqySH5WIsHjapOtzdC3kxZc837lWKW8raI2xZS+zzsxqDyb7jaTM1ObEaQhI92beFHJBOkadd1RlRT0YYK1mMZ8KQ0veTJHQo5j3RVRO7xY/a2EL5k1L5zzNYsabb73NuXPnWF9fY3+2QBnxc2cvqveeelBz9uxZnHO8/fbb6LSWbFni2o7CWjY2N5LsVdO0TS/z7lL1jTaGejBksZDu0a7ruHbtKhefeQ5jNIf7Uwlpm837z6C+t5j8mSV/yefH0XMtm267d3bxXgLFnn76aT744ApN41iv17lw4QmsLWmals985jNJjr5kZUPaKKmqmvWNDZTW1FWNS59pp0+fPiKXl9gzoyXUS2mRurfOMZqs8fjjT/DY409y+uxZ1tY3ZBMnBjqS911bKqv5wuuvs3XrFoPBgMcff5xnnn2Gq9ev8cprv0tUiuF4DDt38ApGK7C6mm/CfPr49/HUs9uM/80nKP/cX73fh7Oa1azmq+bUL/8isZnyf23+AH/8zmfu9+Gs5pswDzRYtabA+0jbeap6iNZTjLE0TcPh4Sz5rywhlccXKdE0eGGytFYCZJSiKkvwgaZpRbpmUydj1BhDz4qFELDp4XgJOiEq3wMvbeR7hcGQyo8MVKMWkFFV8hrWio9OQOzSQ6aUxjmffi55vYx4TwtTYGyRZLU5y1aY4Mz6aKNBWyL0PZ8uAWGdmEciKWhILZm7fHLVvSD5qyfLAzOgikokliq9Xp/sqwxd1yTfWqaN5YHYB4+AkIwUBVTnUKll1YXIeIvCJAdieog2eglgkvzYWIuxpg+d0VqkjcEFfOcTuyrA0qQamhiSzFhJumwvVwxH2GTSucrgx0jicHAJ4CuFS+vHBvHbaWvYPH6M4WhEUdYYW9C2TnyVIIFeaV3IurI9YG3bRjpOtRYVgMnGVTAm9tUxKoVAKaVYLBaEEBimh+4Q2uTTDSm0qWBBhw8ieW9dR2E1vgs416BV0W/AaGMobIUPgc5FynpA07ZJJgpRizw+ImFhIQjr3LQts/mMrpP3Kb2cgboq8a6maaZopShtgVaKJrHACo1SBcpqvG+JyiVAvFyVKm1SZKbOJya0rEraNkj1K8vcaKWXYPfoBoukD8s456nrgrIs8V4CxpSRtFutoKoqtIq0zbzvJFYxM7gxbUQYIiLzDt4TjWa+6JjOO8blkLcvvcczF5/l+MlTzK5+kDbYJPX58PCArlkwOrHJI488zJe+/Hvc3T/E2gL644yUlWVQVRLcVRXsHeyjtSWENiXpasqy5MyZswTfsVg0TCbrvPfuu3z0I9/N8ePHuHb9JjHK5kTeHMndugDaLEPDQAkDrkUBoZSibRt2drbw3nHq1ClOnDzFG298gaIoOHv2LMeOnUArw+uvv8alS5cYDQe4riNGT5E91CFQjoZU9QAdYTQac2d3l9FoJOu+68hBWCpGtLHiyXeB9c1jPPvch7j47DOMxxPqwVCUAtpIwnbq3Q5R1BS7Ozt84bXXqIqS9ckaH33xIxSm4HOf/zyLRirATp08xfr+AdPFnI0Tx/9f/6xZzWq+3vylhz7FL/yXFzHf+2Po8eb9PpzVrGY1/x+jqhE/9Nm/yK98UvEndz+zYli/zeeBBqvNvME1jugjla0xpiC4gFEW1zh01KigmB7OGNR1AmkpUdSLnDek2oqIIihFUUsqp6T4+iXDEwKF1YSoku9SXieDYUnQVfgYcEkKqoxOr0yP03ySBPfy2ACaiEFYwOAlAEjYUJEYkr1jxkhITJKiJhWw0DtaY5RKLGkuFlH9Q734EIvEmiT/WJaXpvd4NP3YGrM80UfYzfxga6zFlvKQb7ShaRtc2xFcS9N1SUabmRqPtRptlsBMXjZKKFTwyT+qiASUWr7fsq4oy5KAVAOFJJkV22AUEKcTk6o0WC3hTlFRFZJk2nqHc6EH6ybBe+8T+DBHBaMQVToGbRLL5yXoR6ffqyNlacF7uqYVGbAXZq3tPNqKlziDvqZr8CHStofU9YBBVTOfN2AKOueJMYdnSW+oMhajrYQQtR02g3aUVNB4OSbnHFU9kOsYPJrIdHqAtZp6MMJ5T+cSYx4Do/GQ2WyO8pFFM6dzY4pygPPJaw2YopAOWUQSGqLGO4eLTtZRlC5LXEc0BuccuXczxii1PrrADArGwxHNvEGryNpkiHdz9kKHLS0qpjAyr3GdIgSNMRU+NgQCUUVQLoFxhfLia1U6EDy9t7qqBpw7d4F3332fEDqS+h0XPSqK/1fuBlkfVuXu4JBUGB3D4TF8UCyaXYie4DusEdBM0EyGE+xknel0KtJuAlqZJHOHojJ0jcc72SxqFp5hXbF34KiPrfHW5evM2sD5849z88Y1isKgtKJzgWY+YzKuGY9qrNW89sYbKFPig3iOu3ZOZTWnj60RfEtVS7Ky6wLeRRaLtv8sOnPmDIUt8Eqzd3fGw+ce4voHt9jbO+DMyZN80Xf4KLLsoBS2rOWzKwSMBmsiWjn5TFLy32wK2zYmcuXWVdpONkQ+/JGP8eZblzk47FhbW+OJJx4HPNs7t/md3/m8yOdDoLBG6pR8wCgojOHMqdNEFyiqmq5p0RHKlHoctPi5bVGhjKEeDDh95gzPPPssDz18juFo2NfczJ3U0tj8eaSV9Lc6x2Ix57VXX4boGA/HvPj8C5x/6BFee+NLXP7KFcbjMQHP93//Jzm2uclLn/tt6uKB/uNuNQ/w/Oy5H+Bv/vZ/irLl/T6U1axmNd/g6PVT/Ojrv8DPf+9/xt+5/un7fTir+UPMA/2nd9c6mqbrfVdlWTFrZ3gnLNVisUgeVU3XdgzrWoBiYkh8zMzlUvIZScEd3h95CKdn/FzyveokJdba0HWSDNwleRxKQkOyVPion9V7nwKasjTYCNgg9oxjpoaUEtAsYUEiF05CRAKSoJtZx6MJwkcTfJee1BRI4xODmxJv75UXL8Nr+j2mxEgZYyjLUhifNIEo4VS4JJMOIpeMy1oMbTTG2h5EKC0wOrN9RiOhPzGAknAkawuKsqSoJGxFW0PbdXQ+iD/QphfTwhBrLa8rKcWhZ9989EQfBKgYSbaVjYW8d5CA0BGfcH7fOn0xBPG3hijAXimwhYUQKQcS2LWYzUXmGMEYm6S8kmQaiHSLBWUZ0EYkunU9wPmAC5GqqmjahrbtUt+pJYaALtK6BYIXQIiSOiRbGILvcL7FOemh9F4SgIeDAdPDaTqWxJIq1W94bGxssLW1RYiBplkwmYxTjYrC2oK2EQZeG01ZlHDkWi7XrEhd27bLJ66/j3TqYbVGWFpjxdfdtg2KQFUUwg4WmVWeo1SBLsoU9GPRtiBEn6S3y9dX/TWRDQvpzdU0TSPgOl3foGTDJtcLqSNXO0uE870RQmRvbx9jS5FTy4ljOKxxnUvsrWU4HLA2WedwOmV37y5t12FSQrGJGq1s38ccPDRt4HDuYGefvQO49O4Vzj/+JC/9zksYreh8pEsdvPVgwGNPPM67773L9HCGMTUupDCyGKiKkvF4iNLiA889vvPFAiJ0XcdkMmEynpAtCAeHh8QI0+mcm9eu89RTTzIej7i7tw9KAqNi+vwJOJQKqBggLkOrTGLMB8OaO3e2uHv3Ds61PP74E8QQePPLb+F95NSpM6xNJjjvePutN9nZ3mZQ17LZp5BNNC+hUJubm7KplK7p1vYWhS1Ym0w4nE5xMVDYgmIw5KmnL/LU00+zefwkIUpVjw/gQkTbXGWUNg2NxvuOMvnXr966xa1bN7HWcvrkKS4+fZG7d/f5zG/8JnVVo7Thxe/6MI9euMD21u1kf1hFLK3mDzY/fvZj/Ltuwp/+9H+4AqqrWc234Shj+flzN/kHO0MOmlV92bfr6K//LfdvwhGAVVUVk8mE7FnN3r+qqpisrSVA0OKco+vkIbv3OZK8ign0Oedok4S3fwANUmsjUmCR+HZdS9fJa/rgRQpplvUwGTQdDYOx1vZpl0oLUMp1LKH33AEIeDC2SMBYfG455TRlHAkYN8LC9LJRveyEzf5PpVQfbEMIfe1MCKEPWun9rBm9pclywXzs/YO+83RNQ7OY410nVFyCBMJKS4qtSdUfPgY5T8jXbWHxQNCaoioZDIcM18ZMNteYbE6ohzXKKFrv6EIgao22NgX5xMS+ZY9i9r3KMUTkd8UYlu89g50crNV7DjM5vQSu2ceZO2dFhlyINFzr3otZVpXIU61IkUlJtGVZUtU108ND2aRwgUXTMEspsz7G5CH2jEYj6rpKstkU9qRUv06yJFs2F3IPrVQHxcQWK60pq0LSU6tSAsO6FmPE1x2isIDr62u9GqBpGowxFKUlC1u9z52raQNAIex5kn1nCe5gMKCsqnvWB5AkzG0v45aaG5GH54TXtASx1lKVScmQUmHz5pI1BoW91/udgLN0FMvmzXw258qVK73X++iaza/VS4Ah9erSr2EfPPsHBxweHqZ+42XKcFlWkrBcluzcucPO7h3miznZNlBYK4BV5yom8Q9rY4lKs2g9dw8XHM4d//dnXqKjYrxxilmn8RTs7c9QpmRj8yTjyQa/9+bbyb8uzboxscPDYY01GmssEGka8as2iwXeecqiYHNzs/fwF7YgBKnkstby/pUr1KORsIkhYFQC2ak71hibfLum/5zJ3bJaS4L11tZW8kRXPProo7z80svs7e5ybGOD5565SIyBdrHgi1/4AiErGLR4uolgrWE4GLI2GhO9p6pKFs2Cu3t7mEJqwpq2pW07iqri6aef5sMvfpj1jY20eWL6zb6jKeyylkK/1kDS4L/85S9BVBRlyXPPf4jBaMRvfvY3U9WP5uTJEzz15BMoFVlbn+CjsLGrWc03On/8zIv84//me/iJ1/8Gerh+vw9nNatZzf/POfnPfpF3fvTc/T6M1fwh5oFmVnvflRZh56CqMYlBykFGzjk2Nja4e2cX5+RhyxgJBPKtxyUfXogRlHgvO+9RXlgGBZB2742RMKX8wJ7HFgZtNRUFnpjAMqAkjEceguV7IxFjpf8UJYyihsQiSVclqASAiiQHTnJagMSowjI1Nz+oL9OIl/68zIgZREZ5D4scl/+cR+SgAsg19O+zbVu89xjTkNOEvxYgzg+UKNDWpo5a6apVISb2M/WpAsoU1IOS4XCItQa0whQ5IRnpVAxA0xFixHVdqiBJPtV0Vn0CGtn36b0X8FMUJOEjSkeiD+LBy75XQu+FTJcBYt4IoWfmlE5hRyhhuJTGoFDKS5pskCTq4D26KqiKkq7tmB4esraxiS9AG0vnHPuHBywWnWxsaEtd1wAsFm1/zSRkqcRYi/edyNVjJAaP0VBa6Z703mONoSosw6qW0JihBIotmgVGK0JKVJbNGwlacq6j6xwheuq6TsnAPjHcCucdta6oyyFKwcHBHiFEyqJO60ASpnXq7tVKAKaw3rLGbWFpm4ZU70o9qJnNp1R1zaCuCMFR1wO0jiwa1/u/lbKAS8ApuUOVIqYNB59CmEpTYlOHa+uELs+9wv0mkZZ7OIaQwPdSOeDDUgXQuBYXgigHQmC+aFmbrOFCRLUdRhdy//gkkU+bLwR57z4G8BK0pIwmak0XoaoGeK159Y032d65w7CyoGpC55kuPKdOHWP92Gneu3Kd9z+4jtLSzyyp2Z66LDh5bJMieZfbrmM+n9M0HdlSvba2zmSyjjG293uXZcndg32Or29w/fp1mvmchx56iKtXr2NMSlnWYiuIWhODEoCMQOXOBWwRsYVhe2eb21vbzGdTXnjhQ8xmMy6/c4mN9WOcO3eOY5ubTGdTPvdbn+XWjRuykZBkIpnZt0qzubaO1QatNMeOHePOnTtorXnvgysEH5g3DZPJGmvrGzz8yKNIgrmXjaDsY1XiNc++WqVl0yOmz6qo4I033mBra4uqKnnssSd49rnn+fIXv8S7ly8zGEj687PPPcNkbczh4QHD0ZDBcEDXtd/oHz2r+SM+zx17lH/yv/0M9omP3e9DWc1qVvNNmMn/8D9y+Lv/gn/+5z7PT+78+v0+nNX8AeeBBquuc7jOpYdRSasU2aHpger+/j4nT57k9JnTdAvpM2zalmbeSX1Hergj1dMI+JKHdaUUpSl6ICOsh+rZT/GiCpAxJBlij2GXlSzZZ5kfjLXWYDJPSmIHs7tVOj6VsfIwmWTBkSRf1apnuJTWfRVN9g5yBHj2XtoELr86nTgny/b1ODE/8GsiITEkiqybza8nb1slNmaZfCzfmlhLayissLASlONFlhsTS1mV1IllK6uSuipBRZp2jo8eYzTGKNrO0zQtXZukqEonhkwjabMx+R9FQpylr0rRs28xxvSAvqyD6TOdYr5WyeubmVak6kXO57LmRgJoRG5NjCgf0NbQNA3E5YO0c4693bugYTAcY4sy1as4mlbkpaWu0VHCvzK4Jnmi87UyxlAUJY5OmMUkF5f3Sb+eBoMhVVUxW8yJITAc1BTWsmgWKFUSQoPzjq5rU1pwQ4iO2WzKiRMnhPldND3Tb62hsJZISBsEitFojFaWECKz2ZzFYoFSOllmU/JrYqi1lhRtHxzjyZhTp06yd3eX0WjAwcEu586dRRG5dvVmkjAbuk6k4bkpFUReKwtO96lJWum0gZNCzmLov1c6hu9VNCTafPnBoXKgWPpNabPKOVFclIWlqpJXOjjmbYNJ11ZrCSxTRGxiU6NSBLR4Sb2EThW6AqWZzluK9TGmHPHBjTso5amyf9tF0BWLNnL1yhW6NvahWOKTdoxH6wwHFcQOpQvapBBZNIueZdzY2KQsyrSJYKgr6Y7d2dni1PHj3Llzh5s3b/LU00/zxS9+GReUsL9Rg7KgA8EpYnByH/R2BUlGv37tBgcHh6yvjTlx4jhvvP46g7pmY2Od48c2+PznPsebb7/Ju++8Q11VOOcYloV8tiJ9zGVRMBoOMVozHA44f/48586d49LlyxwcHLBoW4bDEQ898giPXrjA8ZMnCVHROUehjMjjoz+ifAjYJDGPUfy71lquXfmAL3/pS1grPa6f/OQPsbt7l5dffhUXAr5refbp5zh//lG0UeCkJmoymXBwcPAH/0NoNX/k5pOnnuVf/ou/jDnzxP0+lNWsZjXfpFHGYj/2Y/zQh36JEy+vsz3bu9+HtJo/wDzQYFUjrFvPOPrAZDyhS166XOvQNA1VVRKcyMy6rmXRSt2DD8KsmfQwq4zFFCU+ykOp0aHv6AR5SHLeCeOVvHMqHPF99hK15Jv8KnniUQ9rOuwERhPrgyYqgzKFPLgrSbaNy5zexGalh28lIU7JjYdJsuB8vBlcHmVU89ePMqOeBKoTm6yUeNrSDy2TWFFHXverQLCWlFiRhSq0XYI7UJiixBQWY21fsRKi8DkuSsdnToCVLlFF9EhPqlJLpk3pbFSVRGcrftrCFngvkuyisJIeHAAVezmqtRIKFLose16CcBWPSLd1JFG4/bnu2fR0vYIPuBgp0wO68058xalSpO0c1hYsFguKqkLpjtm8IaAoqxJIHZS9VNLiXEhgRTYgiqLAGEvXOSISHiYQK0nTncNoTVFY8W5HIIiMtLSa4A1KQ9squrahNdJjWdcVi2bOdDrl+PHNvnPXGIPzXuTeGrquRSupa6qqkuChc21/bE3TpPcgEtgsmQeoBpVUGhWG4XjE3sFdqrrCWMPNWzd49uJz7O/PaZo7NO3yPgkhJnBqJPwqLuXxMSVMS9WKrIGyKOlc6CW+PeuWNjb6a9fLwJfqBLmWsd908sHhvKEejggxigLCKFRIadpRuoLlRAvDXVQVylrKwrJ/MKVtXdrjiJii4O7ePoNBxageYI1kgnnf0ga49N4HXLt2lcX8EK1zGrMcoVaRY5trEAX0Oe9YNJKe7JPVYTAYMR5P+nNvjKJI/bc7d7ZZNA2d63jvvff4wQuPsbG+zu7egaRXJ2m51YZgkcCxKOoSk/p8t7e32dnewTvPE088wfRwyvbWNkoZtre2+JX/45fZ2d3BaJWSkyXMynuRMFtjUTFybPMYWkmoXF1XrE3WmGysYazBOY82ls0TJxmvrXPukUdxAXKAVogSXkdcfm7qtOZFHi6+9MO9PX7npc8Tgqcsal588SNMJmv82q/9Kjdv38Jow7HNdV544XlMacQmoMAWBRsbm1zZ22c1q/l683/+zOYKqK5mNd+hs/m//CN+/GN/hV+cffZ+H8pq/gDzQIPVGANGa9quYzAY9BJU6UV1vUdUfG4GZQxRKYqqRBlL07bgPKawJNqGqDRV8uIF1/VezuzJk5oNdQ+gyD7CZcDREVlpXDKqfQrvPW8CfP7dKS1Ipe5Un6S/ORwp90XG5An96iCl/P4z+BKgLIm0xKVEOHtVsy8xJI1xBmr53Gqz9PdpIbqWzGnyM4b0f1FJJ6KKKTwlSSWF6S6kmgKRXoKkdwaEhQshSkqyiv17zV+HBCRRFNZSlZZoLMro5M3zlFWxvP5OujKl09TRekeyAeKDQ6dr0wZhafLDtdTXLL2+mWWztkClapKu6yhtIRsKSMWJSQmiI6VYhBm+k4oeYW5FuriYLSirmpgSZEkMuLaysYAQ9nQu+fHShkDXdam6JocBQVnZxAhbYhTPdNN2NF2HNTaFFAnw7NqO45vHuLN/l7lucM7jXIe1BVWqQcl1N0I+amJ0vQd1Mp70wCn3D0+ns5SivJTkKhLrqGQtSU1RxBhFVRbCmrqWxWJO08wZT8bc2dnh5s2bVGXJZDLG3T2U+7koCC4mOXYEFeSvGHr23xrZuAgAyUsucl/d3y850ftocBgoqTqJvj+fLO/8flNlUNcMB0O861jM5hitEqMvjLOEg3mMtgn41xhr6dqGMKxprWPedITgcU5ksK7tiGXFcG2MigEfK3yMLKb7xM5jlAYVsDkV13V9SrACCmu4ezil7TraTkLctLaMxxMG1QCVEn7LsuxlwOPJhPlihrUFly9f5lM/+IOcPnWKnd27KGXQaPGTJ6Zegp9Ebl8UlsVizvb2Fk3bMBoNWV9f4+WXX2J3904KEleS9KsCg8G4VzTIunAUxhKCZzwaM5lMiMFTlgXTw0OU1hw7foIzZ85w89ZtykHNyVOn/x/23iTGsuzM7/ud4Q5viBcRGTlUZtbEodisYpHFapJqUm253dZG8MIT4IW9bMuyAXtnC/DGgL3wAAgGvDIgL7SQAQMWDNiyBA+S0Wq51ZK7xeZQHJpFssiqysqsHGN6Ee/de8/kxXfOfS/ptk11NztL5PsKyWJEVkTcd4cX53/+Ex/7xCexdUM/uPE9wpRKp/KGOW6Qbd4bSYkfvvND7n14j8XeHrdv3+LTn36Vd99/n9//2jeygkbzy1/4ZQ6u7NMNPS54kvegFDeee47337/zR/1VtJtfkPlPbv065l/5V5/1YexmN7vZzW625iMNVmHjWy3gtK7F/9hnBqIEjTRN/RTjCIyLneClOqWqaqytcVp6MAcSLocylZ8DImMFtgBsYSq1lNNnwFimsKkbkKh+AswCaGGNio+MIukVECv/V43e18KzbsuR2ZLoloAecvhQ8CKVHnno8g22AmeeSlxNCGMxBshkTlUhoCilMUgFI/2kJaSqaRvathUwl4OkyteQq3GMkYUyQQBjynJKHwIhd46KzBVZWOtEXdVMZnsMKWGrmknb5uPN4AlQtXxsrHR+Du4C78XjGGOkbWsWiz3UlQNilFAY75xIa3U1Bladnp7Su2FkxpMqzOrGU0xJvy1MuhHvaiHtVIIYIkGJlNnWjZwjcqpuvoIl4MaHQNcNxBDHVGcApXWWEIctfl3G+8CQAXLMjLR4YKU6RDpQY2bKIISI1vKxrYSJ9j5gbYW1HmMktCnGKGFSwiFnn2RPVVmMkftJdR0oxvu5AEW5z8O4WYJKLM/PuFwtZQMph3Tdv3+fo6OrLC+WhOBR2hJJUkGlFERFSoGYfNm1EaYzX4OYZfFJKYyuQKmxN1hrufeeenbV5nlRRd6ez2hREyilcH7g9PSUpq5RWu5JU2T3+auNsfn5kvcPazSHiwWVXXO56okxcbFaU+uGdtJS203nc20tYXAE75lPpwzdJSoGVALvB0geoxP7+3vYSqPyuez7jnUnf7xP1HUjjKXeXOMCVpWCxWLB8vyUg7097t69y/nZKbefv8kPf/SObIwYjXe9nA2Ve00VVLVBm8QHd+7w+PEjtIbl+Tl/+2//bZbLE+r8ur0PWS6vx07W8W1KSY2WAo6uXCHGQNvUxBh59OgxYRg4XCy4cf06Wmtu3LjJ7Rde4MbNm2NAlTZF0m2yj7ckTm/uM0jUlbC8b7/9NrPplMoa/syv/BmapuZ//7t/l9VqhTWGlz/2ErdfuE039AxeFDZ935O89HRvWyh2s5ufnH/j1pf5D/7Xv4i59uKzPpTd7GY3u9nN1ny0wWpUpKgwyjJ0bmSl2qYZvYvA6O1qmpa+H1iv17LoyV42rbUkBScn6r4UpftUa+JWmmqMIpPUxZs5MjY6p9MySkB/0h9ajmMb9EIGuXrD7IqZ1ZC0keThVDCq/GNIpFECvPGQQmFANwxqtlUK6Ath7HjVRoBwAcBloT6e1syewmiZFeCTJc7l76OSxahU62gBPHVF27ZjbUtJ6PRRmEqlBUSF6EkOKl2NgUJaaypdE7TIY2NKVHWNTYl11+GioyFQNVPqZkIzmdA0NUO3pu86UJvKIACtAiEkkbJqNQJHoyySAAAgAElEQVSopmkkfdc7XF3jhiGHdBncICFHh1f2M9BhlIt36yGrkEuSbMwsdmRwDp9EUh5DwAUvHlotbGrfdUymM7SVYC2tsk841/6I73SC1obVaj1eq1JTkvL948omRJLzHkl0Q08/ePm5zjGdz8fE1/Pzc7yLuRJIwIQA8obKDSKbDpH9xRw3eIbBS/BSENBtK4vRir7vSClwcHDEcnlBCA5jDTarFeRmUZR05pCTqow1VJVl1a1x3tM2NcZUtNOGs+MzpuupgB1TwId0DytlwRhScGXHAqMUaPl5KRV/t85y3yLzzRsumV0tQWvlZlblOJFtoPL0FMCtRBfO5fKc0DQsFgvq6RTvssoiiZ9WIT298p6QxmCjFCN1VbG/WDCdTscQpxQi3jv8oIjDwNnymMMrc/ZnM95955RKRYyRBF1JiJ5z5XB/rFDq1h2Ddwy+z6oJz/7iek74lS2QyWRKVdWEFBi8YzqdcnZ6TNXUnBw/4cfvvssnPvUp5rMZT07OCCGMrwXA5Lefrhu4ePyIk5Mn8oxEz/lSOmhN9oCX9xhrzagyUUqNtoqiUCjnoagEalvhnePD+3fR+ku8/PLHODq6yvPPv8ALL7zAbDbDiW0WYzKjGgIuRozVWKs2n09JPMTe8+1vf4uT42PauubNN9/khdu3+Af/8He4+8EHEAP7+0e88fk3REKf/fOBhPMeHeHo6jWOrl7/6X/37OYXbmaYHVDdzW52s5uP4Hykwep6tRJwxqb/MYQ4VqyUihoBQ4HKWqq65ny5xLkcSCS0qITJJAmKAQF3xmiqaiKeSu8JMWYWTI+yyJQXr9mklv12jGm/IQZiSPkYDVplEJ1XyeJ/05lJ1SPDWFI5ZfFflpOMnk5QYxBS6Ra1xqBBFsU+oPQGVKeoxoW7SqX6Q21YwPFnkhd0jEBzszjdsMpyQJLI204nTKcT6jrXuOR0VJUXrtvVI2W89+A90UonpzZG2Nrc97m3t0AbLd2LMTCrrHj2/MCknWErA1oW7UMGXUpJOmjK0mEfHCnFscqonKf1ukPrTZWKVgo3OGL0I7tmtM7AQQ47xEDfuQx81JgMW2pXvB4wVupAhk5AZkoJa+W8un7AO0dtDaUX0pqKxd4+zgeOT05I6x6fgbtCApS8D3nhLhsMIUh6b8qcZ103eB+5uMhyVRLeBULyTCYNXR+pqhpClHohNjJra2vU0NN1HeWiquwJdc5zebliPp+grMF7N94zpdZH7gdNiFkdoLbvl5hZVEMksl5fEoIjxOw1NgalFScnxxweHpGA1WrAVjVDcBlQitxXmxpxmiaiyjJpnRNtM7hNRqHSVk/wKIPf9M2m9LR3++lUb7nzhaFP2UvpWa1WtG3LbDZjtV5LH66Xa+uTyMTbtiUlWK3WuBDR+T1iNm1JStQZKYYsKw6k6Aiu59rR85w9OWboV1LTlKR3uLIVVw4XNK0lpiBJueuevu/xzst1b1quXruafZ0NSmmm06k8lyHgvEdVFTHJZs10NuEH3/8+n3n9NRaLGQ8fPcJH8QDLpopmvV5xfPqE49MnXF6uWF12uCyrV+SU4+wdVvm9oygRyjncFmwYY9nf38cHhzGa5XLJczduMJ/NOH78hPVqza2bN3n++ec5unrE/sEhCZ0l6ZvNA5UVIfKz9GjzAAnH+vE77/DOD3+IVopbt27yxS98gXv37vL1b3ydkAJ1Zfns5z7H4ZXD7P8vEn2xJcQATdMym+799L98dvMLNdO65T/93INnfRi72c1udrObP2Q+0mB1eX4uXrPMXAoL5YUN2vaYKiVprcgCqniettnX+XwOwGq1gphy7YlUrwzeEWJ4KlzIOTeCVW3rp4KTJAE4BxuFTV2MBPc8LQdW2mSmNPvikirVhJjMWEj4j/j3BEimTcjSFntrrSWFIAm9FJ9jZi3ThhGJPowevgI4yR+NwTM/MVprtDXoSkBUUolKCdibL/aomyoDhDTWvkjOjdqE22yB1cJAW1vjoifERNtM2Ns/ZDqdERJcXl4Q0UQiWLNh30xCVwLk192Kdd9BCFLTktzIqA1Dh9Ype1sFvAJcrlYMQ89k0kAUdjgGqSEhCYNprNSwVLnnNITIhVkL6xgTKWwlO5OkcsdorLYEG7K81pOSGiW83XqNyRsmtjJUOb1YawELLgQqW2XZY/HmyZVRymBt8T5rCrCsm5aEo+t7rDG0TY0PkdpoqqoWaXDShDQAihhlM6MANYWm6zpCFFlpt3YS6OQdy4sLKmtItYBXpRSXl9lbanTu6pWKGcGpxZ8tDG+IsiEUYmTdrRjcgNJp9JPWdcXpySnGaKazBV3eDJDkV/FNo2xmQ11m+BMYPTKq5RwphAHXelPd9HRN09P39TZYlb8TX2yKaXweQ0gsl0vW63Vm7C1VVaONldCnKHL59eBwFys5vyhU8MTEKFOu6hqVEn0MWXIemE0n+KHj+PEDFtMJlQFrhFVVWrpVlZLX64On9wHnvIBQJem1s9lcQNZsTzqZK81qvZJ7MkmIVFXVXK7WHCz2eP/O+/R9x+HRAf13V8wXB6y6gYuLJWdnJzx+8ICQBlbdihRhGIpKJGKMAq3oe/GdPv/8C1y7eo3vf/+H+bnOGxTIhorRiul0mgPCJETs+PiY2WzGZDbl/PSM8/Nzrl69yu3bz/Oxlz/GbDojImzttiWh9B8LeNVbx2RYrS752te+Rt91zGdT/tyv/irWGr761a9yenZGP/S8/NJLfPrVT6O0wgWpZ3JZZaG0xihDZQxHV6/+v/6u2c0v9nzwr32c6V/5q8/6MHazm938jGf5l36D/2V5/KwPYzf/hPORBqvrroMUme/tcXl5Sdd1aKOoYqCua/by57dlpk3TMJ/PWedde2MMRmlqYzOokwWiqayEqShQMf93Gdx6L52QgwskwCKeQWstsGF0RuC6BU43LIRMiomkyUwZxYCKdIOKvFi0p+RgHQExUVCVLBD1pjsSskdW51qPEEYANkokM8uZEqjRM5t/7BbTqrTIinX2nhprJDVWQdM21E1D3Yj0V35WzOyxlpCcDYE8ggUBkQLyjbWYShOSoq1rrl9/jr39Q86Xl5ydn+NDoJ7NqQms+xUpDNRNQyDQuR5NYn25EoCuFCopovNAnRnXngBgFD4UyW7u1Rzk3NXWbDYhooC3qq6prAA/bSv6weF9J+AsBKmtUcKudl0n1xCyRzLgMztfzrdCgN3Q97TTKSn7qINNDP0xwyAAuK5rUBrXdWAk6TpXg2ZmW1hZH2ShLoFcFmXy5olzeO+YTiYsDg9Gv7aP8tq01njv6Tqf2TiVJfDC3s1mM5bnqyzvrHDDgPOeppEql66L9EOH1pZN1WVmwDJxLuFfcfxjjMZ7Rz/0430FUhcynU7o1iuWy3MmkzlaK7puoK4mKCUa9hAdIQGxhIvJhkLxRYqjlvwsyF/EsrkDW4BUJLolWVqjIKlRfD8aWoHoA9FLlVBV1/gQWK1WxMiYzqzyZkGMEXVJ9vIaBicKjJh95pXWVJW8lsqUjttISo4H9+8SwsB00uL7PouSA9N2QmUtSmlCGOiHQTYdYsiMeMXhwQGTyZQrV65mJUmNjw4zGDn+LJ1tmoaL5TnXr17h4ePHfHDnDjefu0nTNNy9d5ez8yUhRIahJ0SHUonaGAKRaBQxKel0BSZNwxtvvMGXvvRFrl+/wW//9j9gGAbx1RYPsJL8gHrSstgTpjIBJycnnJycMJ/PuXnzJo/uP+D+3bt85o03uHbtGteuXSdpjUaSvlMs71P5vUOXe2cjuQ7B861vvcX9+/fZn8/53Oc+x4svv8TXv/pVvvPt77DuB64cHvKVr3yZqq7yefQkrfFBntPx/Xxw7B8c/LS/enbzCzb1X/qNZ30Iu9nNbv4U5ge/e4V7yx8868PYzT/hfKTBqvMBN3TsHx4UYycJkU7WdU1VVXRdj3NdZikk9XQ6neL6ARD6I6VE1/eQmQivAz4G+pXHR0eIIsOUzs5ICHGU+24TkUVyGmJAq408TgBSScpMm9TdIiFGZMBSxZErNxTZUzYa7UbpbUIYoOJnNehR9qiLRzWmDM5AkoWF7Rg7Y7OvTwlNWlbsEmJS0IBRaGupmnpM5FQamknLdDpDW03whe2R7xdSwiBBPtGLz2zDSG/OBYjcUT4XCDFwsbxAGflc2zYZfAWqxoydnSlGVPQMfinAtx+orJFgKoRdttbk1Oac9mukfqQPTlzBRhNjEFllr7HG0jYt2mjcMIwBT+gS5lO8xVJlE5Mw3MoqlJPzVFuLCoGVGwTUpoQm1x2lmK9nJIZICpHVxQqtB5FAa0vbtujMslprWa1WpCy1LGylMEuMYTpaQ3SSRq1Io5f2YrXCGAEQMUZ8iiIl1xqQ6qWZnWFti/eRbt2xXF5weHiI0sJOiiQ+0Q8D89mEqqplM0jZUVJrraVtYHAu34ujCDT7CSWd+PJyRd8NWbhcXo/HWJhMaoZhIPiBpqoYukCKWcJeOnUTGbXrvKWjx+tSpLtalQ0eSCHgo9CeOgcOlQ2acnzlf8unUsp+1S1pa0qJoe9IbDpWSYlhGGBUZsQcbGWFAUS82+U+D7H0QHtUbalsDmpKEe8G2qaCFJhOa2KQsKP9xR61tegEnfN5MyNKaFyK2MoynS/Y29tnb7Fg6LNfPxqMNnQ+MnROrrX3XK4uCF5Yz/d+9C7//J//81TG8OTJI+Z7B3gv/bt13XB5eT6eIhc8ShsWBwuapuGzn3udf/bX/hykxAcffMBbb31TgLgCnWRTSzzRsLc3ZzJpCCGwXvc8ePiA5eUFq27NdDZFG/jww3u8/ubnadqadd9jG41t6vG6ylteJCFqAlHMKIL3aJW4/+FdvvPWWzSV5drVI774hS+yXF7y9//B73CxWmGblk+/+iovvviiBCllxUvKHdwpSOUNWhQlB0eHP+2vnt38As2/8NybqOluI2M3u/l5n3Dn2/yfdvasD2M3f4T5SIPVmAKX65VU10wnY/KpgDRJqywywSLXjCEIuEklECSM7FBEFjPaWlLQUoQxRFIKpKQYhiL9hbAVAGLtduCHGkFZSSIVNaKA0G3PawqJqJLICnP6b6mtUSMLCgURl+9b/l0AYkygUwkuSmOQUAhBwB2ZYSpesPx6S3hLApKKAo4AqwxYTTKaqm2Zzabo3L+ptMY2Fq8iyQeImyTejEFzPYh4aokbn6sLjhSynxeE2UrkDYPA+fkp1lYsDq4QY+RiuWS1XjOdTyXAKkBwHqtCrttJVFquU5GjTibzDBbk+seY5ZoqYrWRTlJbYW0rgUVrh/eJdXKZwYxAZDgdOD05lddbV8IyWwPBE5V4ZQWwGmHrSPh1T3SuqNLlOqQw+i+HwdN1A9PZPDOHiradMJ3ORsa5BISRZKMhhCgdqylh9CZUJqVADB6VIkaBNgprpJImxsiT4xOqus5sbb7GKaGtJrkcXGVqFGu8S7hBApWqSrPuA2hDZSoGN9D1Dms10rGT65oi1HUjKcbeE/WmTkleu4D7FFVWNwgIdoMTUKMii/0plYH15SUEx+H+FYZOmNRIIiQvIV7WIFzo5nvDhq03WqFUGp8ZpeRZSgGC96O/ckxXLr5koxl9wZEx8VmP0uskoUMZF+fmYmzxUubzqhNolaC894SI0RLEplA0xqLrCmUSvetxrqexDqsi2bJOiJICPG1r9udzrDI451hdrLi4uKTruxxepJjO5uzvH7DYPyQlja2ks7e857jBoZLK3ndJiD49PeFgccDdO/dwzvPKJ1/hR++/J4ncSrzP64tL2azzjpTg5s2bXK7W6MpycHjICy+9zHotYWXf+MY3WC6XTNuJ9KrmzYEYI23TcrDYAyJtW7FaOVbdCmU0p8tzfPQ0TcXDRx8yBMfR9WusXU8z3xvfRxWp7D8SdUIZ2fSpTY0bOoa+45tf/zoqRmZty5tvvsnhlUP+x//pb3L3wwfUk5YXX3qJL33pi2LjSBGXFQ/eS9q7Nkb2PfK9ZM1H+tfdbp7R/Hf/UsLc/qVnfRi72c1ufsbj/4f/lv/ww3/4rA9jN3+E+Wj/9layED49PeXKlStjdUMJEgpBGFaR7fqRcbRG5MCr1Uq+j1iuiFHSIUcWDekorXI1RN9LyInSGls1I0gtABmK7FA8isVvWDxY24wNQEgCLnUIaCs+PvJCWqXiqSsuUpH/bv5A6SCVn5852iz5FdDF6Bk1xozhTJuaGi1AJkuUY5bx1W2Drix20mCrCltVJKTWIgE+BnSKAjTza1YJkYKC/AyyxFlJr2gB1qN3UCmG4IkuYavsC/WBi4slxlbZh5zwfuDiQjpRI7JxQGatjTES1JPTka2t0KZGW4uJYH0UyaVOVGiSkk7YYRiIMbPeWljWGMIouRU7b+7HDB7fRUk3zue6aRqsMQz9MG4o+NzbGHMKrpyGlIOIzMjqDMPAarWiaiYsFvvizy0bCzB6ncV3K729AMMwyCnJXuXitZQQLAXaUFf1xouqNc65TUVNDhvTSmO0yvUjmpAS676ndwO2ruVaZ+Rm6wajxNO62JuPbGG5r7XasJAhB0qllAhFCloLSyap1tKB2zYVPjishcXhgvl8wunpGZerS2Z7bpS7yx8FGGHWUpZaG2E4Y4xy32Z/JmpT9QTy/AAEH8Vr/ROdq8YIW19eT0C+X4wBFxAmz251GCuVmeosF9eMlU46qbw54iTJOAWSTrRty2TSYisjDKsKnF4sMRpmjc27O4oQPaiAVYn9/QVt20hImhs4Pj6m63tiBG0NTTNlPtljsXeY7yu5z4ahZ3Bd3jBIONez6kRivbq84PFjzY1XrvLhhx/y6OEjbt68yTAMVNqQotynTdNQY3Gux9YVk9mc5eUlfdfx+vPPc/3Gc2ijOT875e2332bSNBLqFTeMudKKo6MjbC0Aum1b3nv/DhGwVUXvPMuLS9q25fj4mLOTE27cuM7bP/wxRhUNhiJpkW2j2aoNYrQQ/Oidd7h37x6KxLVr13jt1Vf53ve+x7e/8x3qtmFvb8EXvvhFFouFqBTyczUMbrwHyp+iNqls9VP80tnNL9L85zd/nerf/Nef9WHsZje7+RlPePQ+//JfP3/Wh7GbP+J8pMFqYSjPzs7Y39/PXZLhqUWpzf2GRSbrnEMpCT0Jy+XYZxkT+BAZBkeMQdi0nwhmKYtcXYDSTyyAS99hObaUNouhkmy5XWkzyhfzxykiIEmrERyhNDr7WGMsAHg8IkZgFEuNzaZ/cJQa50WkVmoUUJbeSZBETLQAJWMMbWZSsdmnGyOR7eCaDBgLAM8GRvEEssW0phHgCTOYq4BgI3POskY5JlivV/gQM/sItjISSqPSCDrGV683QTvaWJrJVMJslMEY0MZDcJCyjNDWKBUB6c6MIeaU5LxboRQouX5G0Igw11HuC0kglfNujSFakTqHEHCDBN9oEwDpFw0hbyYEjzbVeB+uVitmyuDcwHJ5jjFG0puNRsfI8fEqb4ToEcTHGEfPYmHRfrKvF4o3WcBuuddV/riEjOlRDWCZTqecnJywXC5x3tFOWgG3OeUrEnFu4/nermUq/b+FZSzHEUKgHwaqyo4bRTEFYnT4YPFh4Or1a+wdzOm7jmpSse4cl+sltrH0PcIKRrKcQOX2GmGbt/uNdd6oKNVOchBb3cTk8KyRQWOUUktaeHnGBYCq7G31wSEe4XxjKo1RFq0gqjQ+axR1cVSAXF/yfe+Ggb35LPfWDiTviW6gbqoRkFujJC08RWxtOTg4GN+nHj5+xPnFUjYtjEUHA8kwmy2Yz/aoqxo3iFVAFB2R1eqC4+MnnJ2d0fVrYkpcXl6i8j2UgPfee48v/9mvsL+34Pzikv3DQw4PD1lfrOn6FeteM5lNuf/gAf0wcPXaNT75yU+O71lf/9rXCS7QTCek4DOjLVdhbz5nPpeNjaZpeHx8zOMnx3kjSbqVT8/PeeHGVe7evcudO+/y+ptfIrghs+Mp7x0mIqLcsHnDplR/datLvvPdP4AEe4s5f+Ev/AVWqxW/+Zu/yWq1Yjqb8cYbb/Dyyy+P8t8y29757ZAtpbbun93sJs+bvcc8/+qzPozd7GY3P+OJb/19/t6Dbz/rw9jNH3E+0mAVZOHadR1d17G3t5dZszim4w7DAJAZ0ESIEJPC2JpIqTHRUsuAsCw+JpILWGueWpxLgBKAEsmffONNxYKSBFEJUNl4UwugKAys804AIpBUljgqLcnASHVNJGVmbZPWS5Y3bwNkqSFJEHWW/JY+VDZePTbAWCnGLtYSSKO1JmlZz5umompq6bjcYggL42UyIC+A12RpbcyS4CLWTJnhjZk11MZIwrLREMMmTCoDhxBFiul9ZN31rNYrkpJAG2uFXZOU4zQmKMfsaTRKY2xNVTXSw5kQBlJbyAAnxiAhQ1ZL7Q0JTxAZtorEIN2gKvuDVUpEL8A2xkhIAvyapgWUVIgECTZy+Z5TGcBFNim+5TwbrdBWQ4ry30+kE6fvu5FFJcg9a6ycY1tpQhT/tDLSO5tywq7CjJsHWsu1T2wkx8BYr1RVDfP5DO+9BOLYvBFDyudXPLJ93zOdtnKsOeFXEo41gxueUgaUQK2YfeA6Pz8ivRYGLLQNzjl89Pg4YDToOjExDUfXD7ATS8CiG4NuNEMYaJqWVT9IIm8GiWUTJOaQJWNU3mTabACNCclsNjPId2MCQtiwaduBZxuwkr8ie2MjCZ8itakyu12+txb2XIXxPDP+bEUIAwZQWpjW1eUSa6BqZIOktgmtpM7KqAySMzybTFrquiJEOWcPHj3E+UAEDAqrDG3dcv3oOrWtSCGRYmC5POPs/JjHjx9wcvoE7/ucXGxZrTvqqqYfeo5PRRr+zo/e4Vf+7Ff4zGuf4e79++wfHvL48WOe9CeklJhMJzjnpBt4MuHll1+mbVui97x3533e/t73mEzazETH8ZwbY9jb28vnVZQaDx8+Fr97DgZLg+Ps7IyXbl4nxMD9u/f4wpcbJk1NdA7b6FGOnWLKG3XyOklgreH3v/b7nJ6c0jYVv/IrX+H69Rv8nb/zd3j0+JjpZMatm7d59dVXsZWlu+hQSlQGEgC3Aafje3kqqdI7sLqbzdzaO+ILv3r/WR/Gbnazmz+F+dy/9z8/60PYzR9jPtJgNabE4AQonC+XzOYzjDVEV6pjpC/RWCt+S7UJqKmspZ20XKwuIShUkO5JtELloCOtFSnmons2Xa5lcTwudmEL/AnXWdg6peJT7FdiwzaiJJSE7FWVSgyJ5YkxZl8m6FQYUp4Cz2VRqLXUuESE3dyAZ0bZ7zYLtn3+FIBWEtqkNdpagkBmgBFUw6YKRMBOZuh0ZjtVlMqdFFHGZBlwHNlIbQwhRkJhW9nIXRXZZ+vF3xtTwofCTksi8njOsz9OQAsobTC2ompaqrqV859A6YoQwQdIcSCpQEhs5KRay9fn8zbWnIz+Rrn1vQ/4VHpnc2hRCPRdTwiR4D3eScCRd8LWFk8lebOgAFeVJNFVwK4TplMpJpOJSD9DlH7fnLy8YfY3rKlcP5tBVWHJEXCXyT6tpGoHFM556qpmPttjvVqP/ZSr1QqUbPQopej6jrPzc5q2ltdaNjdGOa2T58koUhJFg/cus/0xd7xqQhCGL+XzOQw9w9CJbNaAj57Z3oyqNZhWUemK2eGMs/MLYdsR2bb8I4BZ+oAVSgVh7INcp5JAW2qYtpmxsUZKqfFSlGdwW/5ZnocN+ybS6xg36dnSLUtO5PV5MyqiTb5/lYDdFD3RD6DAmAprgOgwjYEUmTSaGCyJID5jlYjBofN53dubE6P0mj45fsLy8gKjRRKvdMJaw9GVQw4OFoTgOD4+5smTJzw5fkzXXTK4FUpFqionCcdAVVUMIeB84MnxMS+98ALHx8csz895+aWXePTkMW4YOD8/l9duFLPZjHffew9rLUdHR7z4wgu4YcBqzbe//W2Rl+e9BGMMMYgcu25q2omkTNd1zZMnT1hmv3JwDtvUJGDd96y7Dmsr7n94H9etOTo85PJyybzSWeoux2KMzt3REhD34x+9x4/e+RFNU/Oxl1/i8298nvfee5+3vvUttNZMJhPeePPzzPf26Na9/OwwjJ3bRht88JTar6QSSadyc/zhv2h28ws5v7r3Cfb+6n/5rA9jN7vZzW528/8zH2mwKiybxhrDultzdn7OZDLBWJMlY4mmbbOUMzD0cQzdsdZK5c1qRe98ZgGFaTQ2919mhlNnb2eRhApzu5EBx7QBU86FLDWURbv3WyyOoCNKzYzWIl9VuqL4LuOWJbWECKUQRFoY/5+LqbGPcOvrNlJZAWabTtUtUM3Wz8iAG6Mxtc3Jq2SJY8zppZu+2KapyhePoIQi940bRrnIsnWWTruQz3MG7UoJ8EtWgHRM8ncSPiTAr4BWQOSkMYIyVLWlqiqquqWqGmbTGc1kSqkpIQmock4SeHUIeWMgSRVKiBloqdGXLMAkEUMUr2OuqBGWXhJeh37AD7L4lb0JYcVjDHlDQGTRSW3k2WTAmUowELBerVFKM53PRjk7KY0hYVprnHdjinTKrO9GQr5hvAuIJpVrDClXB/X9wMnxCUYbCTdSGqVMlvUKGG7blq5fc3J6yuHhgfi8B4/JEnnpzJyhFFxeXgISXiXJ2AKgJRG3dMuKiDPGyOAG+r6nbrQEJpGYLabYxlC3FZ7A4sqCD+8+JMWICw5jDc6V+1aOV2l5vsr3FflvGsF5YVBz5NHWx2QWNj11729v2mx/XJjXzTNcNo4K+5ZrecbKqNIbC8En6kajYqKyiemkxuhEZSMxDly7dp36PHB2+gSjLUYrgpNU5Mpa5vOpKAxS4OHDh7IRQkRlFnlvb0bbGu5+8C5Pjk9ZXlzm+zIAMStBihQ8SkhWZTm6coXDxT5PHj0kkLi4uODDDz/kuRs3aOuaxyfHXCyXtJMJSSVW62NuTHMAACAASURBVDUhBNp2wgsvvMB8Pscaw/HxsfhE8/tp2fxKiHJgMmnHvukYE0+Oj0W2bSsigRAjk+mU2XyPy/Wa6XTK/fv3OXn4iGtHhzz+wTu00xZVVaByyrDR6ARGw+XFBd/4xjeorGUxn/PlX/kyTd3w937rt7i4XFE3NZ/81Ct8/BOfwPs49vyW58l7j1HS+VoM9yo/MNu9rrvZzbRu+Wt/5Zef9WHsZje7+VlPivwfn/2PuHvx5FkfyW7+GPORBqtWGyZNC0C/7ujXHdN2QgyRppJgJTL7N2knBB9Gpm/wDltZZntzVo+eoJSid24MhTHGCOOhK7yXRXhZ1MScXltGAygpnA8+jDROYWN1UTQSUdpiq0qkxii0sdiqBWUyiBNgGZPIXgtAMxkApqSBsOWZzcmrSdJQtakYE6Pykj1m2e022CkjgUgSQqPGHtmA1jGn5toNE5zH9QPWWOmCBFKIBOelRiUm6ZjMslVbVcznc3yKDN5Jt+HWzw8p4rLvU0C9BCGFDPBxAbLPVQXZSLBW2FitFZWtaOuW2WwuMmAU626Qig9rR7AuIU7ZT5oY2VqR+aYRpHjvRSatshw8SeozwPrikq7vsabKgHgTMAQwhAGSsLY6s9rFdwwCIrQW32NwEqDT1PtURuG8B2mZJGRAlMofNqSPVtILq8ZPJqnhzVJ2qSxKKCM1Oy54zpbnsgGRwXBVVSPLr5T4o+uqZn2xInjpDE4pEX1gOpvy/PO3sFrx+NEjrNKE5KiqDCC1bOakCDF6Jk3NxcWlbMIgPbYXbsAbDWHg6nML5nsty9Upq3AOSVHVhrZtOD/tqE0Nyso2SwpyRtSm8zfkTmPZUMnwNIPlMgpG5n7Dvm7d82kTlFXX9ejvLZ5epaSWx4fCJhtsqa7yCZWEGS0tr1YHiBFrPcH3VJXh4GCK1RIq1VSWK1eucO3aPnc/+AFNrTEmEZ2ntgalI9evHlEZQ0qB49NTludnpJQk+KqqmVmD8yvuP7iDNTUpaYyqSCmHUimRCsttZNjbW3D16nXme3tMJq3UAvVr+qGnqmu+//bb3Lp1i/3FPj969z3qppFzrDWr9Yq6bdhf7PPKK69QVRUKxVd/7x/TrdZorcYwpJIAPZlMWCwWeB+Yzyacn59zebkGFL3zVHVNUnD7+Rd54dZz3Hv3Ha4dHTEMp9z74C6/9NnXUSmiVfaphyhqDB/G94vvfOc7nB4/YX9/wWuvvcaLL73E3/vN3+TOnQ+YTqfcvHWbr3zlK9LFnHoUhhg8MUCKCoUhhDQ+zymC0TaLu3PH6252A1TaUP1zu2Cl3ezm53ZSxP9ff5Pf/bd+l3/x5B/lcMzd/NM6H2mwWlilAqRWqxXz+Zy2bUcpWgmGqSqLMRrvNkxo6as0VuO8MAU+J6h6AK1z+uhWOFLcTmHd+FBTEj8smW0QhgEBCCpl31wQ+JjUmG4pPiktwaA6LwBTXlCpwmAJqNoA1OKD3SQB68z8bnMD257UMKa4Pj1aqbGypqS9xhRRmD/8nGfmRitFisIIBu9JGXBqVbyLfsOuFk9qCTGKEiiTALPlCy7+x4IsttncAjqVUkQdqLWkqaqUqCuLHVOB5Rw4N9B1a7wbCN6hVcRmX6VSArC0EnlnCIkYUt6UyH2gWuqEYghFhYvznuADKve5ls2PEKLUGKFpJy3z2QTXDyyXZzmgCkIM1FWDMVYqlZLwf9aYXAUTx9TofBtJV2qW9Bb/8DbSzypjiaKJOZwmPh36ZfNxee/HmpKYEovZTGTSLmC0yQnaUUCnF5+xSonJpKHvOi7dQNd11E0t5zAIqJfrLKC5qixDJx2kwflMfMpmCTFhGrhx/SraRqIKOD+gsdRVxdHVQ06evI9nQJF9x0mBjsKsRrI0uKgDnpb2lsqocpJSPlcpxVwlpJ96ryjha9uhUeMzk+9xay1tKxsgAnZKOFVmnC1okwBP01iqSjbP2qZiPq1I0eF14urVPY6uHrJcHkNygEElg7WKGDzzSUvb1hirCcFzfHyMdw4NHC72WRwcMJ1OaGqV/cTyekDqdLQmpz7XzGZz9g8Omc32RKKbyoZHYjKfs7q4oK0q3v7+9/n1X/915tOppHinCMrigmfdd9RNwyde+SR1XdM0De+99x4ffPABkAOu8qaTyudjOp1KmJ2pCCHy4OEjqdHKEviY4ODwkFc+9UtcOzrk3vvvY2yFMZYf//jHvPH5z9PYCu8GsAZdVeN7afKBe3fv8fb3vodRihvXr/OFX/4Cd96/w1tvfYu2FaD8+mc/y3xvQUi5Nsml0VddrnmKm/f9uhLvdqnwKptOu9nNbnazm5/vWf47f5Frf+sHz/owdvMnNB9psOoGt8XIKYZh4PJSahGAcSFaQI41mqFIWoMsYOq6Bq1y+qciBelZhXHJO4Y1FdZNZxZurBwpC10kSVSZjaRQeNLsG1Wa4MU7GZPKdTVVXnUXQLyRDJdk1xgTST+dMrztZSxSzSJpK8zqeG5ShnAqA9ayqs/yYZG/bfm2ovi4QoqjD3DsfE3SVRtRRKUJTlhnFeXzaPCjt9VirJWKnpy+q0ZAseWdjYmilhVwId7iAkw2flIB8xoBqE1VoVLCDwPd5Yq6jihjicHTd2suL87p+w4/DKgUmE6acQOh3BfCkOZNiIhIwQEVRUoZQpCgpZSZGLOpK5JU6QaTD35xcEBT1yIVTYmqqolewnKC99SmGq+jtRZSYr1aM5vNMEoLQNFRWKgkctaMTFDZ48wYArO59uIAluPfnFeNVikfr3TkamNQWuG8ox8GmmqCQmG0ZdJMGYY1fdeJ/DcJwBbmXDYkZDEv3aBWGeqmYrVe4b2TzysBXyTxHw9dR/BerscQeP7mda4cLXCxI6SIQRFcwirN4cEBlf1QQK3V4wYC2fddvMgbz/bmtSqlKKpgnno25L6WGqdN2Fm5n7quy7f8tkQ4e24TVNpSVcK8Bp/wzguTaoSNbluLNpBiT9sYrLFYq6mtJFiv+xVtU7FYTKksnJw8wlqFViWGC1IKTCYNVWXENuAc/XrNjevXmUyn1PUEWzXS8YtspKQkm2uQmM8X7C0W7C3mzGYzka4n2fBw3mOMBEIlBCy+f3bKdNLy4O493n33Xa5du4ZWUFWGqmk4fXxGUvDcrZt84pVPYpDNlLfeeothGGjqaty001o2NprphOl0IqFgreXB/QdcXFzkDQUJaptMZzx36xbXbjzHbNpg24beebS2PH74iBQih4sDLlxPVdncCS2J2d3lmm998y1WF0v29xe8+fk3qeqa3/3d38P7QN00vPzyx3nppZcJUd63Ejm4LYfdbTbENsFNIck9ratq8x63m93sZje7+bmd4b/5j/nv/+vEXz7/8Fkfym7+BOcjDVb94Bj6gRSjgE6VWF1ccrDYp65r8SalklArTEpdeZwPpOhJytC0DdO2JQxOWCekpzB6j7aGtJX0W0ArKPp+kLL5EDZSUKFeJM2SKOmlRIzKXkYj3z96SW3VyqIzg2eMdLOSWdmUJGm2LKxDEL9tZavRaygYKYjMN0gYicmLvML8hsz8KaP5/1qKja+x+P+iMI2FfZKe1lL3EnHeC/CMAjoK6I0xokG6SKtKYFTYBBiV0WJ4y6BjU8PyVFVQ2sg5x0kirSRGrFIQI91qDUln+avFe1nwd+tL/ODxvhfAPt3exMgVNs7naphEClEktUBI4mtLIYPlJKnDVe6EHQaHzjU/MQS6rsf3nrY1BBcY3CCsq3OQgbwzDpsSYLC1QaHwfU9qa1Ca+WzOZDqldwP94KVeJ4pXc+NDVlubC8WzmdlBGDGa1pqoyiaJAH2yH9b7wHK5ZG360XMrqc5GzgHCLFZGC3g0mqatadpqDOJRWjZ6rDV0XZ9BlMjnfHJoFEPfYbXKmzVw8+YNrFESopTkvmhtg06WWWu5eeMGD+6dQIqYnMibUqlN2lTUpK0XqrRs1JTCEwp7P/435BCywsSmHJi0DXpBmMqUAY5IW13X0yvNfD4XAO3y+U4Jo5MAT52IGqwNaA0aQ0wB118SQ8/B4oC92YzlxTnriwtsvlYheKrKYitL09TSJZoS3WrFc9dvMJ1NWQ8DzkWMrYTp1tLta2zFtWtXOTq6zmJ/f6wIckHCnRLkFO6IQnqMk1LM9xb5fRKquuLO+3f4zGdeYzqbErTibHVBJDKbz/n4Jz6BtRXBBd57913u3vmA2lbCOjuPUUkktErqalSSXuKh73n8+HGubZLzXdcNR0fX+PgnXhEA3lRcvX6Ds0ePmEymnJ6c8PDhQ5qm4rxfYbTGhcQwOFzvePedd7l75w6z6ZTXXn2NT33qU3z961/nzp07VHXN0dERr732GlVV4WOkdwNoRdf3WcWRFSMxymakkk3FlN/XjLL44In+D5Ge7OYXchqz69zdzW5+3sb9b3+NN/+r7/LO6Q6o/rzNRxqs1nWNNQIotRZZp3OOi4sLDvb3t6SAAlqrqso1KWthbqIwl/PZnKEfcs2HdCAW+9I2yCqAypjiaQsoWzorc6DLJpQVMksqcrwiNbNYDD4C2ubldal72WgbpWohbCTFiVHC9lT1TP46n9k9bfX4c0DYoJTZhJS9bYXxHQOiYsopyKAL85oYF3glNKV0ucaUiM6RnEcDla0kXTfXU5QeW601wbun2O1Ss1Lqb0iQsqS5SK1H6W8ScIDSOcxGvr4yAqpcPwgzqoTVShGUkRTUfljhhp6w1fFqrPwcoxUxKoJPmVXNjGqMwsyRRvaIKFdHPJOlrHMr9CkGAdRKo23FxcUlMXhKcFNtJ/TrTpKDswzcWi2MY/TEODBpG46uXaWuW1IIeRNAgrUEpFiUlhRkrXL/6ghUyxbE9lbEJsVW5OmJumkwxjB06/G6ezdgTA1JniU39Ay9YzptCSFI1YxWdF1HCOKNdE76ZkMKhODzdUM2MNyAxeZQKbBKoUxFDJ7b129w/dpVmtZg1QRUwlLTrzytnRIay2K+5IPhATbWRCoh+Qm5Rienakfh1kvfrNweagyvolhZR2BqRjm1MKY5gIvS0xxHYCWg1hDy9w0hsF6vaZqGpq6z7Drmiiidn+9IYzR1lTJYLRVGjklbM5tPgcTpyTF9L5Lp0t8aY6CpLUeHB8L0Bc/QD+wvFpkR1wQL637AmgprK27euMqNm7fZPziEJCnOznc453EhZPWEvD5rLWQfeLk/prM9ussL5vM97t29y2c+8xo3n3uOu48eSB+r1ty8eZObt25htKZb9/zBd7/7lLJC582SGCPz+YzpdEYInmnd8PDR4yyNF3+2Mob9w0Ou33yO27dvYypLVdfceO45HnxwlxuHV7j3+An379/n4596hfW9uxxevyabZEQeP3jI137/axg0169d59d+7dc4Pj7mH//e71F6pF9//XWOrh7hcnhbiIGL5SW+H9AlAyCDVqP0qNCwxmK0KFjqzKDvZjcAP/rrvzFmDexmN7v5p3fC3beJb/02XFxw9d//W/R+eNaHtJufwXy0wWplsVvdjyWZ9uzkhDZ7rTQCOK0xGAXB+FHxGqOwl21bY61mvXZY05KihKpIxkvMvjC9xfxt/JUlFbh4Da1SwspFWTHrzKpqhXS85mAlY3NVTQlWCnHEG877sWqhgNXCeAK5lqSwAKNxb+NrZBMSI57JmLshMyOXtrx/5WsDJJ2TVjNYfYpnKB/k8xycE2CaQLVg6wZjLRjZFDA6Lwp/AoCq8XXkUJwsWyzyy+L/HSuCEIlnAbtGG9q6kbTcoSehsbZhGDp0EOCYYsJlCSpZHm20pqnq7O/TXK4uGYYeg87srSKkDfAzSkKuktpKm00pA5sM2FMkkP/tI9qrHBKTZdxJek7F02rGBGGtDS45jNbUlWXSTiAmjp88YXADyhi0rrKcUUEK4tlMiZhBu9x7arz+OfeZmAIKYdYD5ONMEljlJTSqtrnqKIJViIxbyfd3g6c5bIkx4gZht+bzKeu1nC9toK4MvYukKIDVuYHoHckHvEpURmTHk8kUH3pmkwmf+PjH2V8swHQQJfjm8uKS85NLkj8hOMPy5EwCdZIjKUUapfEgZUoiDy/gM0Yv/alazkDx8MrOTr6SWipQtt3cJZhMnrEsDQ4QfAQClZaNHpUrdPphkI0KSZEC5HtqIpqEKLoDKgpSjkECl2bzGW3TMAw9l5crAKqqGcOzSIGjg2uA1BpVpuL4yRPaumZ2OMvp2ZH9vQVHRzeYzA+o2wmVrRmGmN/DpH9WG4PJ96LgaHlvq3Iyug6adfS0bcvpk8dc2dvjzp07PHj4kLppePLkmBA8+4eHvPzxl0kxUtUNd+9+wIMHD8qJHc9z2TCZTqcCVKcT+n7N2dkZAMZaYoRJO2F/f59bt25R1w2mshhbc3h0FbR49RWah/fu8/rrn0EpqTtSpsb5wPf+4A/ouzUHh/t84Qu/zP7+Pn/jb/wNlsslk8mET77yCi+99DG8D4QU6IeBdbemW6+JPlH0JCr7w4tKxOaEb+m3lnvq/PyM27dv/mG/anbzCzT/9u1/BvX8K8/6MHazm938Ccz5X/4vuPlbP3zWh7Gbn/F8pMFqCpnFCGH0ZaE0g3N0w4DNyb6leiUpAUeJksyqR8Z1PpuxurzEDQ5jbAYVm53V8rE2yM8clYYFuApYTZllUsK9CMDxgZggKo2P8m9ja0xTU8KVSmJvjEFASg4u0VpjlB4XXTFIx2MBptthOuKOzcdQMCyR8l/rvNjfUM4alSWZJMRr6yJJBXRtQAn7Zo2mMhVKa3yKUqUTYw7OibhB0dQVWhvqtpVNAm0AAZfRdyJ9Lq8hIY49VWp8NsFV27Pdgakyw1mClHxmbKuqwZhEcB3BD0QkWXcYBvH/ZuCWUqTvew4O9gFFDJd03UBtJGQFlc993NwXRb4KjHUmuZRlrC+JfT7umDCID9U7h3ODJCQHT/SS3pxCIqRA1AFrNZWtmU1mQOJ8eULX9zRti0+BEHLXrLFoW+4PkZSjTK7VgRFAUABrZoWVFt8x5b5KBCX1PUrr3HUbGXyPc5KwG2IgKUUzmaF1hRs6+n5gfzEjeU/0PSn7SIkJ163o1xckH4hDj0pZ4ouVBOcEbdPw/O3n2WunXD5Z0g9rLlZL1l3H8sk5fgh0naPvAoNTWGXRSovPOSZhVZHu46Tz6y01NEh1TCx7N+rpgDGynD2F4oPWGJsZyxBJFqk4ycFSRW5tlMn3qZNnSSeGFPAx4kkYSjp3wmgBrAZIOWnaOYfSMJ9PsVZxdnZG161ARbn+0UN0zCY1+/t7pBBop1NeeOF53n3vx3TDwJPjY84vLjm8co2bt19kNj8gIUnibHmmJVxLNg1C9ITgKSnPWsl1Jkaij4ToRImSIlVTs/Ydd+59AFoRoqO2lpdffonr16/lwDTHD97+A4ahw2qNTko2KIg0tqKdtDSNPD+T6ZwPfvgD+r4TVt5YIpG9vRm3b9/i5s3n0FreFV0ILPYO2Jvv0w8DprLc+/BDXAhUTcX5+Smz/X3ee/c93n33x0ynLS+9+CJvfPazfPubb/GD73+f/f1D9vcP+fSrrxFIXF5e4oKny7kFIUiFUlFDVNaiTT0G38mmh8LmjYwYAl/9x7/Hq69++qf99bObn9P5z778CPPC68/6MHazm938Mcd/73f4d98+fNaHsZs/hflIg9UQXA7kERY0RI8Uymu6rmMymciCLks4U9DUNUwmU2K8IISAdx6tNJN2QlM3LJeXo0+wSFOhMKtmXNSWipvCGhYGS7yhKfs+ZSGslSGhc4eqQZsKU1VoUwloCyqzIRG/1V2pRhbAjOm1KYDzAz66MYBJGMfM/OZ/yOyHMhoVlMSGFkMb+e8ywNUF6AcISXpiK6XQlXgWCRFMEhZbqRyglNg0W4o0UFlDVVdUdSVdpSGKZzbX/hQv4YgtCptauiszq7p9XgEBMEZTWQm8GQZhnafTifQnug5b2dzBuumAlMW6/JwQPKvVarwnvBc55sb/WM7Zlic0S8hjDkwSybF8P2MNJpn8OgPJh+wfFp+lTkU1qUbgXu6JjRcYlhdLumHFzdvXaaYzLtZrCYmJFqUqSYP2Hmu1gJ2QQ2G0FZl2iKO82Q25ekVbEM53PJ9KIZ7UpOR4czBYwOXrFPDB0/U93knisRs8J0+OmVQaP/T060vOTo5x3qG0kloVo7F1ZlpDlI2IJBVKIYKparxf8c2vf5OT40dYU0v1Cgqdvea2amlrjdYRbcB78EMgRek3VqbIWNW4QUT24arxOhXsLjLhcodJlVIGlFaeE9DZN1u+TgmILzL1vK8SkBoVW1dEJWFQAfGzx+iojKW2ipQkwRiEpR5cR1NV1HWF1ol1d0nCo4x0CaMSPvRMp3tUVucuZrhy5Sqf+tSn+Yf/6HeIKbK3OGQynTPfO6SqZwLec72NSJozy5sf03EjgfwCVGGgI971GJPPp4LT5Rmmrnjn3R9xNctu9/YXvHD7lihWGsP3336bux/cocpe56JykDC4xJXDfapKU9dTTs/OWJ4vcc5nBYKjbSfsHyx48aUXsJXFVJVYNpRBW83Rtes8+uADptMZDx894PT0hNl0wuPlOVErvvu979K2NdOq5ctf/jKXFyt+67d+C2NEMfJLr77K4vCQ3kl+QDf0/zd7bxprW5red/3eaa09nvGeO1bV7a7q9ulO247txhBjrIQ4QBQw/mDJUhB2vqAIJKIoFvABKUEREILCt8TBxhEkSiIUgnCCIoUh2J20B+we3el21e2hpjsPZ9zDmt6BD8+71j7XwnZ1G/pWt89Turq3zj7DOmuvvfb7f/8TbdvbJ/Qgg+/ryspCOqCNlo1AZ80QdPbLn/wlPvOpT/ETP/mT3/D70eVczuVczuW8Pyb5lh/68Z/jc8++9qIP5XK+CfP+BqtA0gJGI1JBokgQgwCTyZiiLElR2FMQEFiWI9q2GcBRl/tV5/N5Xjj2dRdsWNP8tw8i7ewXQL0EOQRhXowSL5RWhpg6SGEAjylGMEZ2+ZXN3jKDUhtQrFIcKmqkCsJilMkpxFm+maIEguRj6gOVjOkX6Vn6bK2waORqjyierl5OKwt3hc+JmTGGfExK2EArC/noPRiDsU7CqiIieUyaRBi8tr3nK4SQZYgiZ04ARg/y2efSOYmDlxA2bGr/775ipA96ijGgjc1MrizGrRP/sOQEq2EhH2OUa0QJG962Lefn5yhlaNtWns+UQVaQBb5WZpCTp+x/G7pLM5hRSqEydZ0znug6LwnTF8nhxCBn7tnvnq3vn99IIqmIKTQ3bl7n5OyM45NT2gaCb1FJnn+6JGFChc4BV13uunUoJde+1WaQNJMl1H16tUH8yLJ47/JGiqdtGkajCdPxGMKK42dPuHntgMmo5PSoYaUCi0WB1YnpeIxvJ9y9fxdIaOtwusRZjRmP6ZoOHyxd0Jmplmvg7PSM0FUif247mhgkLEwrVss1aIsyDmUcIRliVPgAoAlZIt8n/vZy503IlMqgbXO9yKU0SAvEj2zAOZevodyjHKVWigEMk1/TipCkZ3g0zl/jRfasVQa9NskfI79jQqHRoiAIidnONPuAW5bn50PwGDkwajwes7u7I3J9Y3j69ClvvfUWt2/f5rOf/yxV3XD16jX29g4oilLqrGxOyU593Y4kAvcvmf41HHMHtFaQjLCKKGHfV9WaZbWm8y07Ozs8OXqKcZYQA9euXWN/fx+fAp3v+NSnPkXbtmKh0EYUFXkDbz6fUY5GKCOvz7feelvSklW+TrXl4OCA27dvs7W1JR7gGNBKVBVOG7Z2drj31pvMRyMeP3zG0dOnfOA7XuPeo4c8e3bE4uSUwhZ89Ds/xsu3b/MP/+H/yvHJGdP5jJsvvcStl1/K3v6OdbVitV4P50E2L1OWVxvGZUFZWAwS0FYtl3z18Rt87Wtv8uabX+P+3XvcvHHzd33PuZxv7/ng9nXsd1x90YdxOZdzOb/H8f/73+RLJ+++6MO4nG/SvK/Bqs+S2RgSnZcdfavFLxVipK4bXFFIFQcCFLz3GYxu/JQ9Qzkej2lbT13XmenzmRBUz4UEmVwr09fXSM+jxipZCGOEBdXWIj2TssjuWRxjHGhLjHqQwoYorJSkBmsKV8jiWgk7FHwgZJlhL/O7yJQO/0ySYBt0xBj5mTFGksrS1p6RUko8pmzAZW9rjUrAmQ4JNLJQNwlUIrR+ICL7ROIY03OeXp+7SUMPjo1G567RRGah+02AvLCGi6ymTP89n+u5TYmyKHCFzZ8jNSJSySI/24cWEPZV998zB0lVVY1Sm/qSwberdA7GSZlxy12w+d/yyeIF7PsaN/LknvdTF75OFsxaqSzSRUKa8uaGDyLQdaUl4Vmsl1zXka2dKdoqjp+eU8UO37YkjLBRSUOImMJRlqUAiJjo2pYY5NyGEEBLsJBGPKmkSPQNPkqPp2+bDLIDXV1hUoLgCbkmZLU4pyxHGK2oqzWLM8PO7hznHDs72zx99pimyz8zBOJGW45WGmscEWGdfQdV1RB9B0mJt1cbynKMNeILDrFnBiNRKWIyJKVR6Jx5JfU1KqgN+32hwue5jlQuws4MXvNzbYyRDmUvF7pNvQ118zoB6QZNOlFokbgGHwZ1gFaJsjCMS4tKOWl6eBHKdV0UJfO59JyenR6zWCwkIKyv0gLmszll4QjRo5Wjbir++Zf+OVcPrgCKyWTGzs4e8/kc5woSZpD298cp9544bKb0ncvyEyIRDwFc7m9tG8/54gxInJ+fMxqP6LqOk9MTYoiUZSHBaCnwG5/7PPfevct4NMZ3Hog5kCpgrWFnd4+UoLAlp6cnOSUadO52HU0mXL9xQwBl8IxHJTGJyiTGQDCa2dacqCSZWCnFwwcP+Nh3fyfLs3O+8tbbzKczrl2/wQ/8wA/wxS99kS/+5peYzKZs7Wzz6odeI5Fou4ambeRajGHYRHTaUDonAzyHSQAAIABJREFUXl6l8F3L3SdPuPvuuzx88IBHDx/y5PEj+fwE89lssFZczu/f+UvuI5R/5j9/0YdxOZdzOb+Haf/6n+df+Omv0IbuRR/K5XyT5n0NVuumps2yyE3XacqySEXbtcQYKV0xsKgpA7Z+4dpLbqFnXqSvVUYNPrYBNJGI+Ws3zKaAzpjUIEdMIaKIApS1LCBT1MJQGkNSlpi04L4Y8bm8XrxmBmsl/ZOU8OH5PlkJ19FY0wfBZG0f/cI7onUaQLTWZggLMrnDVLpctfgDvcfHXrosX+8b8QEbI+A56IAzFqMtXepI8QI4UBpjnfSKKglslZ+XUCazlSD+zSSuz4RUx/xWnyowhKD81p7c/nkyRs6PsVIH5JwEpdgQaVpP3XYbGbExqJQG2fNG9stzjLnUrW42HwCUNhI2hRrqa5LoioVhVpK4izFErUm+B94XgdQmsXdgr+lThCOmsBnpRnxoGY1Lrk33KIuCB/ces2xrrNGSGZQi3keUF7a0DRD6n6n6oCePcQXJCGOcgs/HYDOwrWmbKoNuSDHQrD2LsxNiTFhTUK+XFM5RFo6qqqjqNdO2wOiAUYnSOWIMNG1HtV7lUC4garTRYBwps82yQRTomlairOIm4MiHiM5SzJgUISCviSzljdkYrvpNFlIO28rXRC957892791Ow/ZArzwfZOb9xodGamf6r5PrLm85qISxFldsOnWtMTgtigNNwBgEQCef7wubpOTZ1jxbDSLn5wtJmzWFAN8kkvnxqERn1cByucLHwPHxEXfv3UUpzdWr1xlPZjhbyGaQkURqeU2rAZzKfSEM9wfZMiFLgOVS9L7D+5amqVmulrJRQWS5WuK95/z8nMlkwtHREaRIXdV85jOfEXY8RAprCZ1HGY21hu3tbZSWxG+A4+Pj3BOrZGMqaa4cXOHWrVtMJhOSkq7WYjIiJun77UJg58oepnD4ECiKgof3H+B9YHs6hxApSsfHv+d7McbwK7/yK3J9p8hrr73GwcEBnW+JJLqupa1ruqbBak1ZlDjnWCwWPHv2lPv37nP//n2ePX3K6ekpvuuE6Q0BrRQ729sUhcvKlMu5nMu5nMv5Vp5//N/Cl0/uv+jDuJxv4ryvweqqqVnVFdPJBOvkUHvWSqtE09RUVYXVkk4qC9ve5yWdmSpXYMQYKIqSspRk4Bh7ltMOoCmEQFKy8PVeQHJRFFkuKoBTSwpM9k1K0qQmCZA1BmVMTjrNjC3kWhudgaHFWrNhc/vPu0Ck9sxdv/DuwVf/2w0sZLyQtKTUAGqjIOTs38q+Vp9TV/vU2dSROpESGyu+V6cd42JE1zQE76U/NSWiTgP4VTl9tweBcsyyyAwZMIs+UQ7rYrdqD1Kdc5s6oJQG4BqCyLtTgqLcQSnDeDxmOp2yWq2omlpk3D3jlCLRS22NvdCX258sH3JvbN7EIPS+WbLfDfq0KoW+AIwkrEfF3OGrFdhE11XDtcBv8d6Kn1imT2nWWjMaj/GhxntP1awZTR2eGjuKfPgjt3n65JRHD55mBk2eH79sWfgzFMLAF0WRGURhl0LXCbsekoQ8xYgm4GOgrSu8l/Cp0hWE2NF68SMaBYVTtE1FYa9QlhatJyg8pIBRjqapadsKYzSz6YR1VZGUzmFSUVjdiHht644UFTGA0Q5SIBLovCQmmyL3PGWva0gaMUXqgX0nS9b7zRjVW5+zz/oCES/qief2Pjb/079m+6ClHsCG6PHBo1X/nCjGkxHG6sHfq5KAcJ0UxjkKLT2jWvWbQZJs3eVNru3tLcqyYLE4Z7laYW1Bn4KtUThrmE4mw+u3rtYsl0uaXMVknWNre5fZbAtTFALojRUVSQ5TijEMG3AbVrXfPNvcE5RK+OgJKUpKblMxHo/Y29vl0eMHoBJN01KOHI8fPuLo2RFf/dpXefLwEeNyNAS6GSvy87IsmU6nAj6LgpOTE1bLpTDsiHl2vr3FjRu3uHLlCijZTOqif+71oFRiMirZ2ppz8uAR0+mUZ8+esTo949a163zJvc6HX32N1157jV/4hU9w79495lvb3Lhxg1dffVXsFUGeP6cUwRi81tTrNW9+5Su889bb3L1/j9Vqhc+qm9TXR6lI1/kMbB0QSSmgzfPxXJfz+2uMNuzG8Lt/4uVczuW8b6f6z/5D/vTqMv3399u8r8Fq03UsFgtSEg+YMzbLLnsfV6KqKlwGB1wAK/2iqQd+4vF0zGYz6rphva7oZYQXWc2eCSrLcjiOftFojJbSea0xVtJ0RVaY00ZR4t3MsEfYPAVaYZTFWPGz9mA1eL/xsiolIDfl5NwYUZnR7SW5G2aJQdYYcviTNmYAWwJGGRjhjcxVmECUIoWU+ysjvoksG5HLjSeT7Ldth0VyR0e1rjDOYUuH6pNrEKa78132lPLc76MgewjluJ1zebPADedVa0WRq2qapma1aoWpKhzT6ZSzs5bj42ORX5ZjWXQqhTUaSkcM+XrISD8kqdsJMeaalDScP2HbJAyp7xDtH4f8/Cl5WlMIsimCwhlDWZRYpfBtS9M00sELQ0ftRUa89/YqBIg1dQQibdOBSlhnuLZ7hRBh62CL/eu7vPPOfapVQ1t3EBSFk2qQGAJt08g1FYM83zEBBnKSbtO2dJ4haEerhCss8+0JiilVVWMwbG3toLWmbTxaK0Zlie/W9OnTiYgrDKPRaKhWGo9GtE1HbP1m4+ECi52Swtoig2aP1k6OTefKpth7UbWA3gQ+RWIUZlWjhLFTOntTsy87W3Ofu6bYsKsXr7OhWuqCEiL6nKDbM5IqZIZb0XYdJmpiSHkDCqxT6BQoHUxGBSYz4VYLUxi8J4bAeDRie1sSp9frNU3dSDhWLzdPkVE5kcRsBXVdD8nV+Tdge3uPre1dynIi94d00Yft83F7YvL5d9x4oLXObHKWeGgtAUPedyxXC7TR3Lx5kxQDv/n6MdPplLZtmfgJdV3z5dff4DfvvAFIWJQzIudWCpyxTDLItlaqmI6OjnP1j8i7rbVcPbjG7du3mc23RGVBohiNJGzO6EHNEkLg5q1bPHjzLa5sX+XBvbvce/cdPvpd38VLN27y8Y9/H+++/Q6f/OQnKYqCyXjMRw8PmZQFy+UKrRWLs1OePnvK44cPeeedt3ny6BHL5RLfdWhraYMXtUmMoibQCZXAWk3ykdGoZDSS+4u+xKq/r+ePXf0u/shn/9KLPozLuZzL+QYnLk/4wv8y4axevehDuZxv8rwnsHp4ePgPgA8ixN4S+DN37tz5/OHh4XcAfwvYB46An7xz585X8tf8to+911HK0HaBk9NTVtWardmc2WS6CcFRiq5raFrHbDanL4Z3zpGS+J16oOqsy52rIyaTMVVV08tUL4bjXJSiPicjTbIgskYPi/seCAbAp5xKmqSYXgpgVf4cjXV2+N4xSzQHMMWG/Rt6SDPbJCBlU2ejsiI4IovNJAY8WfACWoMyAkat7X2fInH2MftvjSWEDqcUKqosN/QszheEmCgKkXmSJEjF+45nz47pfGD3YA9tDGRGNLYNbdcOP6eXwmrd+3c1ZSngvz+e/pxaa/PvGzKT4xiNSpbLNYvFuVRPZAZ2AAjH6+xfi4NXWY51U4/TK4GlEqVHPD1wzuxLylLLDGR7IDT0+cb+Y0hQTw7vMcZgncuBX0F8o9aKVzqfk973nEKkXTcUtkSZQFO3GG3Ex6oDbiR1QNNUsu93aGvP4mjN6mRN1wSISlJoY8reaAnxiSmisp81pIixitZLn/B47FAqMZtPKIuStmmZuZkAkdGI1XotXvDYMRmPaZolMUbppe0qdrZm0P9uCnzniT6yNZ/T+cj5skPrgMKSIrkWRhMi2a8o51r1GwOK7OnuZfRZDaB0fv7M4BGGRFRBHjPDfogAtP7x/MH+PG9qnTZSYKVMBvUSGNRvHsToAU23Xuf0bQk267KKwJmIdoaikNdHDJ6U/bPRdygi+3s7FIXj/PyM09OTIahNjkWu6yv7exRaep+b9Tld29L5li50FG7Ezs4ue/sHuHKEou+RjgQv6edCL6fh3tArRawtB1a13xwRC4EXAOc9s9mU8XTCm1/9Cq4oqHIfcdM0kDSf+/znOV8sMMqIQiIECWJLck6nU7m/FkXJ+fk5TdehIlhXEGJia3uH6zducnBwDaU2tVF917OkCUdIYj84ODhAW9m4IMH9e3f52B/8br77Oz9GaR2/8E/+CUYpZtMJH37tVeaTCW+/+SYnpye88/bbPHr8kKOjI5q6FmVB9lErRP4cgiRv+yg/O7SdbAYiGzajUYnpz9nX8+bzLTIv6r35ci7nci7nmz3xK7/OHz761Rd9GJfzAua9Mqt/6s6dO2cAh4eHPwr898D3AT8D/PSdO3f+zuHh4b8L/CzwR/PX/E6PvadxOSU2kSApVqsVGsVsOkVZRQriNQsh0DR17k9NKJWyZ3IzIQOoEEKWoRrqumWTPNoHEQlDeNFTCQwrZ2V636d8LCadF8lKUk+1G6SOaCULuiShULI4FBYkxggDGynsxCYMSA0AM4RASMIWGGvyAjwDr5Qy+9qHIOUKn7xo7OWoXSdSUFQkEiHG3G+psy5YiZfQR6p1jffdcF5CCKgcpnNycoodlWxvz9FGpNdt09KLEgcGOgNRYe/GjEZjCufwedEsLKhIWH3wRGMZlSWj0YiyKIFjurahWq8pXMF8a45WipOTM6rVGm2c1NaElBNw5SmKSTx4/bEbJex3f2xkqWAMwrKK5pvhvKsLsmCtNVYbLCp7Sb2kJqMwrsApDV1Hf6VtYpjEQ6yUBP3UVc1kMhIpcdS0TaBNDRNKSp0YTx2mNNixYTqbsTvb56Q85dmTY9q6k+oaI6nR3ossW0BzKd+/a9FKMZmMGY9HhNBhbd5wsRrtoa5qWhSd7ySAKHasVkuuX7/GqjrD2ZKyNCyX53QhUI5GrFYr2WtBcWVvn3JUcHxyhg81yndExMfchw7F2Ot3kWs/J2GTPd4Cs7NH3ChU6ut+LiT1Jg0IQ5Y02SsaCZ2w6VqrAcTFBDoDXXl5ioxfGGE9fKxnNLXeMLFWi5c7dpLcq1UkdB2FEYmwtYmmrjOjabJ8N7K9PWd/f4+maVitVqyrtcjugwetSTGwu7PF1nQCCbrWs1ysqOsG7zu6tmM+32Vnb5/JZIoxDpPvbxJWFolJKpIkCTgOQWH95pywm/29KWI0rFbnrNcriqJgf/8K5+fnrOuayXTK8dEJSmkWixV6y3JycorSmnKQ4YusXmvN1tYca/VQgXN0crxRpbgC6yzbO3u88oEPghKmvJcpEwO1b8m2YEblCGUM09mUoixZNzXFqODx48eEtsV3Hf/4H/0j3n37bdxohE6RR3fv8uadO7x7/y7r9Zqu6zBW09S19KrGPtUbVL6/W+eo1qIOKMuCD33oVcqy5P79+3Kfz9kDKcUczPZtNy/kvflbbQrj+Pt/7taLPozLuZzL+UYnRf6jP/2JF30Ul/OC5j2B1f7NMM82EA8PD68ib4r/Wv74/wj8tcPDwwNk9fn/+tidO3eevteDS4P8VqoVILJarUgxMpvNMFoTAhfCR3xePCm8FwlgL0kbqmmyF7UsC+q6GZJuhz7VzFr0PsR+eklt5ztZOGoDSTpJOw9KOYwbY4oxShf4KF7VvnqF3hMberGueu77K1k2C9C+sEDtA5T6bldJ7VRY60gpCougNW3bbfyaMWV/rBkky8JiZt9mEgAUAaIagEZC4bN80roNEOilx8TE+fGJ1ERMxiyXS1KKEuKUj0VrQ2GNMKnGYI2jaVqWTTMw1yEnNpdlyXg83tTWBDnn08mE46pitVyhZoqt2ZzOSw2L7zwuGXSS5wL6BFadPbkbpryXIpPZ0hBz8muIaONQGLTK51VnsNo/3yrXwgh3K0y4NiIfTuAKjTbCDHddi9ISrCMs3sY3qyK0dYv20I0joVNMphNCF4hFwlnHdOqYLGu6yjOfzCiuO4w2LM5WdG0Ygp9iCFirhc1MIi222qCdIanEarUkRM/+3g5NU6O1Yns+p3AlbSt1NsYarPGcnZ1y8+YNJtMJdbXEh8Te3i7T6ZjlYsFkMsH7yPbWDi/fehmloXAlXXtEGxytl00bnwOlyP5lpVX2cltyZBKZ40arjXQ5Jp0VAf0le0GurSFFTdLy1X2AVRyYxt7Tqof7w9A36j1aGxIqB3XZ59QTwugbnHVE7enaBpU8k4ljf3/CqIS2ld5UrU2+DxiUgq3tOdZpFucrFsszYWpTAkJmVRO7O1solYghUdctTdNRVzWd91hn2T+4wny+jXVSV2OVwgdP8C0h9T7VTaDSxdA4+b1FHz30jEZNtV5jjGZra4tr16/xhS/8Blorrl2/TrWW5PO2bVksljhnscblmpqNX945y2w2HWqkHj95IpuDRu4vCZjO57z0ym129q4QclBbTImm7bBOY5TIh41WOGWInae0hitX9nny8D5aK7765pucLxaURcE7b70lCeshsF6tWJ+ds1yv6doMTlNkvaqJydNly4Sxhq5r5TVnLPPZjFdffZUPvfYat2+/zJXdPX7xF3+Rhw8fDkCVdPG1+e01L+q9+VttXpkf4P7kT73ow7icy7mcb2Di0T3+7L/x1/i5B7/8og/lcl7QvGfP6uHh4d8A/nXkze6PAy8D9+/cuRMA7ty5Ew4PDx/kj6vf4bGvC6wqpKdQgI5BI72pTdMwnUyH1N6+d7QHqdLPJwu+i4uUruuGBdnF8KKeyRQ2VA0LRejBWsrfjyyDzbUbyaKNJSkLlMRgCSERlZYKmT48qPOEEHOAjMpJnHKmkpIFqNSqRMhVHwKYDNaKhLHvUE05lVh+bwmK0SqnzsaQQZqWDtRMICaCBM1YSfUMKWHQskBOPTBWWWYsoTnaMCzYRU4pya9np6e4umLd1CQl7Ebp7CZxM0tohanebBxclFn3rHGfsFoURQ7FclhjGZclTVNDlPCf89NjfNsxGY+YTrdpW8/R0UmWe8qCVCmNSmqARz2rJ7hODefFKKQvs7er6sywgiy0M3MFQA5Z0koRQysp0FpR5KRU771IJHvfMz1Ali8PQXycIcByUXHy7Jzx9CpKR3Sw0GpMSOgazp6csWSNxohSwBq0sazOV9RVnX3MwuJa4zBak4j5NSDpvdZqpqMxzXrN+ekp88kEnaDQGjcaYYxFBUNVS9emJPUm2q5DG8VqteT05EzOXWbfm6albSpWqxVt1xCSQusRxjiUMtTrHPZlssK3R1Zy5oYYJJX5sF62zfDxPpMr0adzK6UQbTFoI32/PcuolcrpyX6QcV8M7BLlgmwKXexJlteyMP+xawneYzU4azi4Mmd7u6SuT/F+nSXDBcaMGZUlWkdGo4KqWtG0NauVyKd7EKRSZFyO2JrPJN0YTb1uCSFR1TVd6Nja3mN//4DpbJavrx6ESzBVjH6Qqfd/LoatXax46mXA6/WKpmkxxvHKK69wenrKuq7Zms/5/u//F6nrhrvvSgKx917qkJDzYXKStjWW+Xw+bOi1rfjEdZa8E0Ebx9Wr13n55VdEyp0TgBObRPKUgmz4RNBKNle6XA9WNzUH+/ucHbXcvXuX1w4/ytZ8i6p+htOWwjnW3VJYZSI+dMJsO03XJLAiKbdlwe6VfV55+RVuf/ADXL1+jZ2dHYzSjMdj/s//43/j1z/9KebTmTz3Ue65iZSl5N9+8yLem7+V5o9d/27+wd/+cZQtXvShXM7lXM7XOf7Nz/Jf/fjPXwLV3+fznsHqnTt3/j2Aw8PDnwD+CvDn//86qH7EXzqhcG5gXSDiu46qqiiKnJRqhGXrus2f3suVUmI0KukXhn1XpnjAHBAGBhO4EK77fO2K0hoNaGtFNosCbXCmAFOQsCRliFhZIFmLdUWW7gqQVpALJ8XX2XtSBwCHJqreN5kyS6QGyk8BSpvs1/JZLpgIQaS8EhAkNRs9GIwkprMprizpnj4jBEFoMSTQStJco4QtaSXhNr3vVCmDtrnWRykRvKZEXVVoY9jZ3sklNUk+T0lwUej8RraoeA749+zX5jmww2aDVNTIxsDW9harpWZxfs47b7+TvcgF0/GErfmMqmo4Ojre9MfmEzT0rtL3VcrvFHuGvQcBQTpDtZEkVknzlXNjtMEVdujATTHKRoOxuAugoWs72s7n51P+KKQ+SIKcoG0brDVYHNEnzk4WTKZjXAHVomZ5vEJhOX52yv237wOK8XSCwmBUQYyywVKOpE+47621pqBtI11VEVXAOEPhCqJvWa9WWC1+b2JkMplwdnpGiaK0BY3yAkhOTphOywxQOra3t1j7joPr11icnRO8sN/ed9R1hSusyO59XwOTXyfyytlUq+g+CKyX/qqen86y601Q0vD6yuCrl+xmcW/elEGkwxeY/hQ3EBjYKDDyxlSIQbpc1aY7uf+8FAPWKnzXYaxh/8o244mmbRek1GBsJPmIzrVJkhAeWK+WoBSLxZKu6+Sekbt6VYrMppOczgVt27JaranrhuAlDXl3T3pVR+NxTiHPlVyC3gd1xyYYauNR73+Hi39733F0dIRU4ewzmU750uuvUxQlL7/8Cjdv3eLWzZd4eP8hTdegjKHvILbG9olVw31WMgA6Tk9PxcuaX5sKzWQ248atW0xmc1BKAsayFFmh8Z2k8BbOYZSiaxrqasXjRw94+913pHLJWuqm4Z133uajH/0Ye7u7vHP3PmNtMOOS6XRM3VbEWu57VdNQTsZc2d7i4NpVrt+4wUsvvcR4MqEoRJYcfJBEeGN48OABn/nMZynLUe5mloRjUW2EYQPp221exHvzt8r8ievfy9/7y9+L/dgfftGHcjmXczlf58Sje/zlH/95/ssHn3jRh3I5L3i+7jTgO3fu/O3Dw8P/DrgH3Do8PDR5d9YAN4G7yDLot3vsPU9ZFkwmYwrrSDHSNA2dF3YlRpEEK6UYjUuCCoRuw6iSuxp7OWwfvkRekDVNg1IMoT/DInfo4pQFpCwi+wWvBW3wIZGSwdgSY0aEZMSrah0higQwafGt+symGq1Ba3z2Xgnw1PTWSSAv/CShN2YmKQRh00SFrDIb2IcYqQEgQGZEpdsGk0NSWt+yt7fPZDal6zzPjo5zEJDOcuOUmV3oV3M9uwWQopLKh/x9tVYcXDtge3eHddMQUsIHny26CoUVP2DPeKo01A7FnNDbA4jRaMT+/j4nJydUVUXbttJ3aS3OOba2togxcHp6xqgssaZgva5ISdH5RNfFLC/tgUzMXmIBMyEEvI8ZbG7k4CklnClyjZDF5GtAEljlnJsgANwY6QUNuWbFOQF3dV2zqir5fiEOi+L+81NOeQoxsl5XOBcoxyOWZyve7e4xHhcUhWE6GWO0Y7VYk9oEOrFcLElRMRrNchBUZDSaUhQuB2VFQhep21rk19qQQqJwBcoYusYjcnHN8dEJL788Zz6dU1cVJEXhSoxqaJqWre05o9GYpl2jlGZnd5/V8hyNoqlaYkqEKMyrcwURAUlWl6QUsFmJ0IcmKSXXiWwMRMibKypfT329SxqAqhqYr4sbGtqI51ipPtH7Qhdv3NQF6ayO6APILnaUKqMGmX//+vZtS0qe0EYmY8fe7haTiSHFmpRavG/wocG5gnJUkFKgrhusjpyeNrii4OlTIaAkmGgT+LS1tSUqAeNYrpasqxXr9RK0ZuRKrhxcZzad5yCqnhGXay4NgDUO7Gr/WpSKLcNFZULbtlTVEu8Dzllu336Zr37tazRtw5UrVzj8yEdAaW7cuEGfxB18pLRm6BoOPlCWBdPpdNg0Oj8/59Gjx3LD0ZoUE+WoZDKbcfOllwem2hmDz+qMpFL2kHcsz844evqE++++w+nJCcvVkqapUPn3MkZzdHxMjIH9g33arkW1hlEYsVgsWK0rxtMZO5MRu/v7XL95g9F4zO7+PtYairIUWXPX0tZ+894QAr/0S7/Eel2xu7UlOpWYNxL6fl2+vXtWv5nvzd8K8517t/mf/uofxv7gj73oQ7mcy7mcb2B+6o//ND/z4Jde9GFczvtgflewenh4OAN279y5czf//48Ax8AT4PPAnwT+Tv77c73v5fDw8Ld97L2Os4bSWWGqCkMiIoSVo65rfOhYVyt86JjNZlir0coRo0EZTVEUw+KqZy0Gma+KVE1DTBFri8xiiN8qBVk4hhQkbVU7YQRjxJQTglIoY7GjKUobfBdpvSTMBpUwzuXkUwEy1hpi8ANQ09YOslGtcu1FEtZUpJ0enxIpShppygE21mYmJgffpL4yTknCb0LY0pgSTec5XywZT8bMplvc/sAHsKbkV3/t16iqioik6ZauwHRKmCjI7GIaWAgVEyZJsEtSgenOlCs3rgj4KAwhRNkgSAL2eimnUhFj5DsqDUYZQsjeUORHKKPR1rB/cIXHj59S1zVV04r0NrOuPipC0pyvarQZg44oM6JrPBaRWvvgiVmCqBCQ1LbtEA6FuFNRyuCcACrbd9DmoKoUEyoprLbiBTYWkxSkIMFKwWNQjDLwXtQN0edeXnnyhmoWkaBKrZCOEFUkhIYQI7YwhNDR1g3TyQTlLVoHooeD3atElXjy9IgQAlW7YjQei/y0ruiipyxGFOWIsrQ0PmI6kSH30nKVEjF5UtIUhc5AM8g1WdUY65hNt2nqSFe3EBVFOcGaNWenS2bTktQliDCZTJDOWcdovM1yXYFSlKWjbRtGoyld14iM3DiMURl0GQF75YjOB7wPdEOyr0Yj0vUYeg+qQiWRCye9ScL1GV5ohHw01oqfVUfZYMnBPiRQ1mCwpOgJMSIZ3WQGsw8LE4BYKM+0LLh6dR9XGJarE7puBamDFBjZCaNxSYyJrpXe3y5ERqMR52dnxCBBVUQBaaFt2TvYxxmLU4629SyrJWu/okuBiGJ7+wrzyTbT0ZzURdyoQCNyfDKQi1F0Cn2QUt8JnGO75D9x+oLzAAAgAElEQVQtKbvWWM5OT7E2ce3aPnW95ujoKeNRye3bt9meyc+5snfAbDJncXom5z1GtJVzkUgUo5LRZEKX+4ifPjuiqltRrCDXjRtN+Oh3fw/FZEogYbTCdy2lK2jahmq15M2vfY2nT59wenZC29QE32CMoa4bmqrGWU3XeIqi5OTomKOjZ+zu7uKcpYsBD9z64Kt8/MZ1tre30U7S0FdVRcTjY5BgNJ83DrWCKLVSCcXnP/957r77LnvbOxIYtRGkyH1ASyrAt9O8yPfm9+vMywmvf89L7P2NvwjGoneuv+hDupzLuZyvc8KTt/iP/82f42fuXwLVy5F5L8zqFPj7h4eHUyAgb4Y/cufOnXR4ePjvA3/r8PDwLwAnwE9e+Lrf6bH3NM5JJ6lUhFgmkzEwHoAIMEhKy6JEOTcwZdZtfKmJhO8kpKOX3ZZFybWDK5yenMjCTSm8b7OHT4KWfIoSOhQC0+kW1/evs6wDZ8taJLxKs6pqQHb8XTmi9R6fmQtJXhV2DxCZamaDlILOp4Hx60OGesOjSH2ztywn68bYB0b1QTbCKpJDUkCYmJ55kv5Nzxe/+EXu3r/H9Rs3uHHjBnfv3sN3ufPTSnhMbGKWG+rnZLuAJB0rAd37B/uMJmNh2rRCh4APHbHrhgCUSEIlCYCJXvo3ffSZ0dlUyDRtw4OHD0XS2HkJTMr4pW07oBWWKUqA1flyzWQyxXthvsuRgLTUiMcNpIYkdB6VwGmDMS5T14qoclQpElgU8OJjU2aoROnlqUaJt7XrgqQjp4S1wmy2bUtZlsQU8SEMFTr9iLQzZbOwyIu9j8TY0HlJw7XWEH3EKMt0OsNoYRi1MswnWywW53JOtFyTW1tbcm3FSLtaMSrHQyWIj5HJeMp4XKJUoq7WErBkDPt7u+zs7FKWJWVZcvTsiOl4C+ccJ6dneB+ZbW3TdR316pyyGGOUHoKoRpMJRTkGrSmDAPEYImXhsFbRtn3qts6dqlJ3JFdxvt4NqND3w0patxCwPQNNFpP30mDxcDM8lobKll5WbK0VRjamHBaU8uuj93RumE9nLVpbYmgonMahuXZ1l/l8TNWsMDoRtCL6iHMlo5HLLGYkaEXbNBhr8F2X5fHIBhPimx2NS3a2dwbWr65rqrqiatZ0PuJsybVr19nb3Udri9GiPkBJR3MvIY8xEkN87vXX+7i9l85Vaw0Yw9OnT2jbhrIw7O5u88adL5NiZHt3j5deegkfJFVZKmvy9zKyUSM/K1AUwqr25+vBg4c8ffos34tkI2prNOHq1avceukmMXZ4H1gtzlgvV9y/e5fjo2csl4uNkkLuGKQUaduAtZZOa0IXWK1W7G7v8u7bb/HkyROuXL3G9378e/jghz/CeDbFFSVNW4vvdr1GW5NDsgyd7yh1KZYHyDJf+WnHx8d89rOfpbCWtm6YjEe5BigNmQZKbXzm30bzwt6b348zLyd8/kMf4MrP/+yLPpTLuZzL+QYmVQvW/+lP8Vc/eYO//uCTL/pwLud9NL8rWL1z585j4A/9No+9AfxLX+9j7/ngjKUoiiGcBxgkbINsN3sQm6bGWct4OmE2mz0npfMxyM582kiIIaFSYDouWK0qYlRoDXXTgFYEpYnK4kNie/cKN26+zGg85ezeI7SzwmxmeahC2LX9K/sU5YjHjx9zfn5OiBIGZbX0EfaeuhSjhB3pjV/U2I3ELyWN1sL0yuJewkVSCgJGSRtJm9rUdPTeNqUNIQezhBBo25aHDx9yfHxCUgImkpHuzh6cOeeGBefFOpD+e8QIujCMR1OU0UyKSa7VCYynY87Pz1lVldRNZG9c1wV0ShRFSeFK6rpmvV7jvRdpY05qBSjHJUWRJX51B6kTCTTShdkv5uu6wRjLdDxGKzm2Uemo65DZZy0S1bxYF19hBkWJLLnsJacZOOk0yFSBvLnRYkqXk5fFb+kKKzVAbctoVKC1YrVa5+ssDr7qFKOEEPVy2D5US7559iMHzs4WBB9pmg5rDIV1hOzplMW4VActl0u2t3eYT+cslmuc0RjjsDpXN8XIaDzCGE3XtYL3Y+Tg6j5XDw4EMLQdzjqcc5wvloCmaTwnJ6cib7YFdr5NTElkwXVDl4N7lssVZ+enGGUpihF17bOsftOtKyFoSiTrShQA6/V6SObdeEw35xguAtb8BA2fkLKsXCZEP6RrS6CP+GBVroeSOhsxuMpegfCR49FIrp/kMSoSfWBnf8b29ly+Z+xwzuSeWCvqjF7One81SmuUhrZrcq1Tf+309ylDWTr67uXHjx+LDHhdkYJiPttmd2eH8Xgsn+9kg6iv1ukl0L3sv2dUN+dH7ndFIVVBVb3ifHFGCJ79K1fx3rNerzHW8tLLL7G1tQUBfPK8/vrrLBZL+Z10n7och/Tf8XiMUonVasWjR49o25aYK6m2t3fY3ppz89pVHt57hycPH3N+dsrpkch427qR83QhbXm4tyrZkKmqmvF4TOg6lqsVVw+uEFLk4eOHvHT7A1y7dp2rBwesm4aqWtO0onYhnx+de62tscN9YJNOLtaJT3/607R1TekKlEIk+2xec/39TZQC3z7zIt+b32/zP+//EX7o4/fY/puXQPVyLudbdeLxA/b/3hvAGy/6UC7nfTZft2f1mzld7uWMMVIUhYSdXKig6FlJpRR1U6OAorBMJqNhkay1RkVomhzUopWwDikyKiy+WZOCx0dQ2qGdZV03dDGSlGYy32G+f40qKp49PqYLiXI8oSyKwTtZNx2d95ydnTGdRabTCVW1pmqaDDgkEbbMXaJd14n0NCVUjEM1RAwSugQ5h0lptCsIMUCIKONwrpdaZvYpy3bNxU7YnsFFZU+uMFOdDzRNi1IisSVtGJy+4qPLDOnF2gw5nkjbBdZ1y1ZM7O6Kn3RZLWjbltl8jDKwXFW0TSdsLApbOJQ22KJgezxiNJ3QrMWfmhIURTmEGNnS0XUeM7IQEqGriQFxOWa2MvrI4uwcqxU7O1uE0OVwFwhePKxdF+hCO7DPPZuqhQoT9ehQcRMwvY9S958njFcionVCawsZdLa+AyMbC2VZ5LAvCbsiIj45IjqJgFXqiHLpUBR/sEoJnTQxwGpZUa1qJpMx169dBw3ni1NJbrUGrROrVcX9+w+4evUqk/FEfKTeC1jLAUdt20Ih4LptW6bTCdPJjOOjE7QGkgRozadbKOWI0XFysmSxrCjHE2azkXhQrcOVjmI0JmbP9flqyWQ6pTAFdRMIYU3sqXxyH6oRObVOefNF97L9fB6UqBQSWhjWAZeqAfRtPpL6nNzhuQM213eMIrsG0Xpq0NrmiqesDEChEclsUom2a1Apsrsz4+WXrhF8TdtVOKNYty2aSDEqMzvr0dpmz3MfBmZofU2MPm8KiT43hcDO7vaweXa+OOfZ0RFN12QFgGb/4IDZfJ4D3XINVmaCL26e9JtFMW5Y1f7+1gPVED3r9RLvW8YTx/UbN3jjjddJKbK1tc2rr74m8tu25uz0lDtffgPvGyajsYQcZdmsKxzjUYnKQPjp4yecHh/J74liMpowLkt82/DGF79Akyu7gu9wxtK1rWyaaHk6YopDMrfKdTTGar7/+7+fH/7hH+affeITvPGlL5KA0WjC/fv3+YGsYjk+PkZbw7qu8EE6da2V15w2hhDkue9az7gcyWMRptMJv/zJT/LW196kdEW+DxqiD5j8so9BQp/mszmT8bcXWL0c+Hdu/iF+9i+8ivlXfhS9d/NFH87lXM7lfCOTIil4/uyP/g8v+kgu530672uwKhJVCUQiszeFczhnmUwmdF1LDCInXS4XRC91B9PplPl8RvKRzrcS3LFaS8BH/p5KKZroUVphy4LQJaoukJRl58pVZjt73Hr5ZU7OVxyfLmi6SHIFFo1vWzovKZhdDOjcdXp2dsZqvWY0Gm0YBjbr8h4E9nK5Hnj3ILGvptH5Yyr7KkNQA2uos6dWWFbYsDzPA0ypl9gEMBVlQduJVFYpRdO0OLsJnukZVUl/9c8dZ89mhRA4enbCdDpmNpuyvTNnPHUcHYvHMiYB1rKwFBlpVVeEEFit15vNhsySGmOYz+eQkngBYyAFj0ZTlk7Ym0YW/JCkq7IQGW3btKzXK3Z3dykKx9GzExbnKwl/cU56WLsuywYzE5P6RGgkBCj/zQDK5d/G6uxtTfjoKZxBG8Nq7dFGOmTJoUqls/gWggcBV4EYAimJTFwZx1Df0u8tIMfWh0PFKKFK7zbvsn/l6sAIVet17v3VVOuKJ0+esLuzz2Q2Q6Gwman3rSfEQEgiXR6PpUJFpNSgemY5/6yidBAso8mU88U5T9IRsMfW1pQudIR1QzmeYFzJ6fmSiGI6mxMa8YYnLFXVEjwicU8qy1UtotbdSHjlmhSZvTbioSUzkCRhtjeAVW1eKxfuA0NoU/7/lFLu5M2yYyvMsDFOkqFN7jSOkagiMXhiqBD8U9C0KxSBlDxd10DyWCvXmDEapROd74agtbZrMHYs4WgpPZdW7ArHfD4fOoY/97nPsa4qQk4y3tnZ58aNW2zNt57rVY5JUrFDDMNrLYQ4yFyfr3lSJERd0qwrzhcnhNjwwQ9+mM53PDs+wljHRz/2BxhPxoO64Qtf+A3Oz89wZUmI4tvsfIuxhrJwjMej4bX34MEDptMp4/GEyWTKbDylKAqi9/gMzn3nxQtKZDIuiSFQtw3OWqIC5wpm0yl1XTObzXjttQ/xoz/2Yzx9/Jj7Dx7gytHw2OnpGdW6YlxMWC2WTLZmIrHWyPOooAuB4APWWDn+XvERE6Oy5NH9B3zmU5+msBZnZLNN5feJmPuQtVKMx2Omk4nIwi/n22I+uvcyO3bKn2jHuH/7P3jRh3M5l3M53+DExRG/+gN/hR9ZfoZ1W7/ow7mc9+m8r8Fq6SyTUYlRkrZpjcjzQgiSDGnH8m+jIbMUPnacnp+wXC+osiy1DzMCJLAIkYfGrmM2nWHKMSG0mLJk7+A6N1/5AGeLFfcePcUnhRvPCDFRrRuUtiiT6PJCVltDimoASQB1U+FDi1YS3FOU5RD01B9TDw6VUuKN7YErfRXLhmG1TrxWKeXQpxhyFUuSpF4SKqSNhHdgDqUORthVjXMlKQnzNh6N6Hsd+zqMvn/WX2C0h8e1xjjNelVRrWvOzxYoldjembI1n9G0Lc458W8qS2s7us7T+UDIdTU9CC6MXHZd23L09ImAS61RJKxRpBAYFZboLfW6IQaFUnYIyYEc3NJUdG3H3t4+21vbOFNycnJC11TClSqAyNC8qpAdPLIcEoXU2gqbpaPIPVOC0HmMMjgjC/suJLQxaCVpxZNZQb2uqNcLgaIqV/ikJN7LDFZEwh6zXzAn4wJK9QnECavl+g4h8OTxY9LgrxPvbESY87Zpefr0CVtNy2y+xXgypXAFnY+D9ziEwCz3eBptGE+nqCTebgm8qXPQkBOmuAuYLrBYriVwRyeMkSTus8WSk7MzynLE2dk5TVWzNd9lZ2dEDGfUsdvU+GSA5FwxbJiMRo4Qg7Bl2mCMJfmwkQRreX5gyLMe/gZyBY58at9PvJHPpkHaHZJw2KI4kG5Xq5QAUt8RQo3RARUDy8UR9+4u2JpNGE9GaB0oR3Jted+BEglujBIclpDaqa5rBr+40XmDI0Smcwl2QyUeP3nI4ycP8SGhtAFl+OhHvpOdnR3KsiSlSFGUgNTTeC9e7pjEptBLoy/2O/e/b4wJ5zSLxRltW7O3t8PNm9f5tU/93yRgZ2+XW7deom1FOfHs8RNef/31/P1EJaIVJJ8wSjMZj9Barpe6WfPSy7cYlWOstpRFmaXUaWBMFQmdFRtN2+CNZTadsn/9GlcPrrK3v8fBlQPeefdd7t59l63ZjD/4vd9DYS2/+E//GUUpLG7dNIwmE87Pznj27Ijd3V3e+PKXKadjUpKKH5QA08I5eSX5gFVmCLUyVnN+es4//cVPSPiZ0kSCqBf6TYy8UderWVLK/t3L+Zae13Zu8OfKj/Kn/psP4X74J1704VzO5VzO72FSW/Gpf/m/5oePf/VFH8rlvM/nfQ1Wu66l65osC5TFklIpszeyyDVaMRmPGJUFPnqppalrzpuGpmkw1g4hG30vYtM0kuLrA1W3ZDrfZevKNa7feIWA5cnJguVqhTKG6XxOTIp2VWOtI3rxBLZdH6S0YR6FDZVj76V+2tnM+mxY4pDlvj6nVIr3rpcGRmFYL1R1pJhyd6eEwCg0KAGBsfeqJnIVjfSHWmMJncd7AQqajXcrJgH2IrHzJMBYg8vVKMZJsnBMka4Tb602FqUhdJGz0wXj8RhjBFDs7m1hrKVpGuq6YTISzyhJWKHgO+mMVRpnbE4DFsmeNQIIQwh03qNTYlRYdra3Qe3z5PExT5+c0LYepy1JaalEicKCLc4rqvUD5tMZ29s7XLt6lfOzMxaLFQDW6Axwk3hku5T9xhB8yDUrsgHigxdQrhNuUjKdTZhPx3jfcHq2ZDqeEJNcc85YOhIqRYySPtmgEsYIwAwxorQROTcx+2Z7EGJIyW+6P2Mkhk3Vkpz3LiciG2yuKBKpeGRxfkZV1RwcXM2KA6kNKdwY7TTeBwls6oOIlJIqkiQBWsEH6rbC+4R1BQlFVTXEEBgXGmNguVrRdB3TyRRXFDTrCoDFYkFCak6arpU029zX27YtKSfuGmOG8CBjDNoWF3zC8FyIUpJO396K2P+9+fyeU31eLyyfE3Mqdoc1CiMBzsLSJY/3FUZ52mZN0yyYjB2hUSzONLPZjO2dLWaziWwApQvS6vxchRAoy1IqaC6oGFKKWGfZ3t6SAKCu462330YbQ2E066phNp9ycPUa08lMvOK5IinSd9JufOVDnUwIQ51W30Xc/8zF4pzzxRnWGj784Q/x+PEjnjx9RlGWvPLKKzkpOLBerfj0pz9NtV4zKksUYDKT7axlNh1TOAtZfr8932J/bw/vE03dQE7UBvAhUTc1gUTpCuY721y7epWbN29x48Z15ltzinKE9x2PHj3i/sNHKGO5ceMG3/Hh7+DXfv3Xefb0KUU5Yjad8uzRQ7bmM548rnnn3Xf5wR/8IZq6xbcdiST3nkTuFJZ6nQ0RL/d+awxf+fKXefTwIc6YHE4mmxXWmuyMUDgn4WXWOkKIrKvLXftv1fm7V/5V/ugfuMfk+3Yp/5O/+KIP53Iu53L+P5j/6/v+C/6tS6B6Oe9h3tdgNXgvNQRaIwyMhK6Yi72dfQhKZl/7PsKyLGnadthRd85RVVVe8AhQtOWInb1ruHLKbGufyieOTo5JWsk+fYSm7TJD1hE6YTQ77wfJrlIMTFGfZNoHO2mtcRks9CylSP8iykhKbOEEyBorALGua9q6IyEsoopJgKHW1NUa77scyrIJi0opYtLm96eXEw9dqReTfYV56MNxlBaAap1Dm41EWuVal/+HvTeLsSRL7/t+Z4nlLnlzz6ylq5fpHg57SApcRpTIkS3LCyUL8IOtBYIBG/DyIMCyIcAADQjWg2zYEGTBsA0/yaYM2QZkQTRMyaJFESQ1lDRcZqGGMy3ODHup7q7qrKxcb+a9N24sZ/HDdyKymrIt0SJQ3T35AVXV1ZVL3LgnIuN//puSHYJeO4tGs1pUXE+WWCt+XKUiGxsbXK+vBZxozWxjivdRQKrWVFVFdAIyIyLP0zFKcnHnIQRsSN8qBggdG7Mpu5/5NJuzE54+OaNpRCbZpTRg7wIuOlwTadcN11fX7O7usru7y3SywcnJKSE9/IcQaOo2satgk2RUfHayhkg+6Hrt2dyccffOPTY3Jjx85y2pC7EiM8RDV69pm5roOlRfqRQjTSPy3tGoROuM1nl5L1NPqzDs4UOARCHvh/ha/eBZhCiAMvmJ0yrDOY/zNavlkq3tbcJikRKJGda3rEcGdsnaDOc8eYhUVcdyVaU1J17TPB9hM4UyklwTQqTMCorxGK01Xd3Qek+1qugcWFOQZRlt16X3U4B1ZrMB7PkQhpTrEPvrQmp9buzVaZOHG+92LxHuz88w8cMeViIpaCylatMBARUVBJfUDZ51Nefq8gRrPKGzFEVGWY5QBDpXs15PmEzHIgFWCuccbdsOazem7mWiMOQheIiR0UgAmFaKddtyfXWFMRbnA6PxlJdf/hR7u/uUo1EC4bIOAlHAcUoJir1U+reEUPVBUiE6QLNYXrFcLnjhhbtMN8Z8+StfQivNweEh9+/fH9bxydNj3n/vvUHpQPLhGqUo8kwqk5C05GrViCokz4nRobSm64StdN6T5QV7dw7ZOzjgwYMH7O/vMxqXZDZLqg6B8M16zdsP30VpTZEX/PDv+RHOLy749V//OtoYtra2+L7veZ2//4tfEKbdGD744IPhHC8XC/JpmZLCxSNsVNrIUyZZIzzjsuTk5IRvfO1rZEaTJVsHMRKDGurAFIrpZEpRlBTliPnVnKq+ZVY/rvOH//ULRn/uLz/vw7id27md36H5he/9M/wbV7/yvA/jdj4m85EGq+IdvAFQ3nfE6P+xJOAeoOo+tChCWeaUZZ7SYnNC8FijaJMX7fd+7of5sT/8r3G5bPkbP/XTXF7OWVQdPmq0tWR5zmhUoIF1vcY1XWo6VBilCBrAoJT0rw7JuV3Pdkowj9IK1/nhWH0MqAQqizITX1h6qDfGiKcv6CHJVilFVdU06wqtBcwAGMOQIBxSxY4kDuuBFRmqcNQNUAix/3wzMG9Zlg1/7/2zzwLuvvPUh4DrHMF3XJ1fUVgNYcRlvGZSTtne3GG9fkKIge3tbUA6XzfrGScnTzk7PSc6jzE2hRxB26xQMVIWo3Qc0NY1F21LtVyzu3uIIWJ1RJeWrpO4XO8jKhp8cINHd93WPF59QLWq2d/f56WXXmK5uOL6+lrAO8LyaqPwEWLU+EYCmgRISlWSMZqT4xPaes1LD+6zXKyoVzVN7SEqjIZMa+r1Ctc2eC/1PFprjFVYK2FaKAmWWq5WhODJdJJiKkmqld/DM7VDJG8ekmqrDCEwPNATAyEBPWsyVssl+wf7jMcjsjxLITyByWQCIFJipVA2o+s6qqYmBuhCZL1uqOuWar1mNJZqGu+cdAiriEYlgF+TZTmz2QZd06CMJVOGvvlTNoq0AHaJAk4bIYYssyitabuOrmtvNkLUzTqOSSFBDIRokss7MZvxt3Cp/1gQk5wzAScafJBeZBQoj1YOFVqWV+d4X6OVeKidszTtmropKOqCarWkXk+ZTCZkWUbnHc51ONdRliV1vU6AWq4H33VYpRiXku7bdR1HR0dU64Ysy8nLjNFoyksvvczW1tawieTTtdez+CGKOqJP7u6Z1P5+ID5a+Z51vWZ+OWdnZ5tPf9enee+991gsF8w2t/jUpz4lx7mqUUrx1ptvUq8r8ixH80xKslaUecG4HBO848UHD1gsl3z9jTfY2t0RBYlzlJMx9+7cZ/fwkBdffJG9vQNiUoU41xGUrCGFqF9slnF0/JTzM2F5f/fnfjeHd+/yf/zk/05VVYzGY+6/+ECCpmabnD89ZjKdDKnpezu7zFdXjLY2klxco5VJ/52hogBr8c22/OzP/G2ur+ZsTMdpDQlDTLzZECjLEZPplLwoqNYVT09OKG8Dlj62c/KLnns/+z+R/di/87wP5XZu53b+Gce/93X+1gjai+55H8rtfEzmIw1Wi9SL10vjnq1U6VnKvtZh6BtNErsYVWIZNfl4RJcePvMsQ5clZ6dP+eqXv4TORlyenXC9bAgqJ6iMTClJeQ3CorZ1k3yuhsxK6qSKEpLSPyANHrcUwJIlxjQEYRedF0+a7tlfYyjLEmOkQzDEiO9ELouKFEVOCJHF9YJ63dBLEKGvp0mpqVoqN7Kh2scRvISMoOQhT3o+JXwqJBnms6nKvVe1Z6m7dBz9w3Oey2vRPoCT77Ferrm0Bu8iZdvxQTzmhQf3Odg54MnTYxbX12xvbYt3NrMc7O0xzksuzy9ZLZd0rZNOSScgx+SaQmdYrcBY8tyileHpkyPW605kv1oRTCCzSSfq+ooLAVohnfvLiwuqqmI222B7a5OtzU3pacwyri4vhgAgqSgxeH/D4rnOEYOAsdPTc9bLij59WWsngUgKotFE35FbTdSW1kWKshxYVKW11MnYjGpdYaIaunJ7BpvEqvYARSTZEZ26hY1S6WNlc6Hr2iQ7F7avbWsuzy/YmM1wXYcpCrKsGIJkrDFkeUHnHNfXktoMGuekzmlVr2UDwnvqdUNZaPn66fODdwTnqDuHM5qtrS3y3HF5uZCv0/l0TDGtrXStGstoNKIYlSxXq+G6UKn3NF2qw+fFPhw4SpjWAN7T76pf80k+3P9dK4he5MT6GS9rIKCiIzORjc0J4/I+JyePqFZzYci1oqk9XdvStjVFUeC9+LTzPCcvi8HD7b0fErK1EnCuETBelmViYj0nJyey2RAjpc2YTKdsbe9QFOKX7NnxZ5UgMYRUp3XDpPevU0KVJBDOGMvJ6SWRyO7uDmVR8u6771COcg4P77C/v0/XtmilODk55dH7j4aqF++8eE0VFFnGeFSma8lwsH/A/v4hv/nW2xw/eYoHdrZ3+f4f/CFefPFljM0IwPWqIioS4wm5zYZ7SpbnXF/N+dZvvEGMkTsHB/zQD/0QX/3yl3n48CFRaXb393j55ZfJipK79+7x/sN32J7NmF9ec3Z6yvbWNmdXF2hjhvupUnroLxbWXlMWOT//s3+Hp8fHbG9tQgzDBluvUACpfdrc3JSNGq04Oz/nerGgSJsLt/Pxm8/85hsc/AePef/NW7B6O7fzcR7/3tf5i3/kJ297VG/ntzUfabDay1h7QNUzjf3D/bNeVKkHiShjCDF9HumhLwZ5mDcGm6SY69WKX/ni30PpDF87xnkBOmNZO0KniMHT+kDn3PBw7JwnuOQtC9z0PiqNjz1IjtKBaTO0gbZx6UFdGKOeBY7PfBP/7D0AACAASURBVLxPIUYhSCBSMc5pm4bVYjUkIUvaqqGXQysdEtghhfcwSEubdYvzLoXSiAQ2qpSEzE0djdZ2OL/9OX22ruam01KncyzyOu8NwTkuz66o1y2TjZK27ggh8vLLD9jZ3OL45CnBe7a3t9FEqZqZzShNxglwfXlNiJGAIbjAelmhfcRbQ5YbsrIUhjBcY3VLjAqFxqPousB63VBVa7ouim8yMJzLfuOgrmvq9Yq7d+5QFCMMiq6paFuHDjp1mpoEIiIoLb2lLnW2moKmEYCqdURHjzICuEKUDtksy1AKRmbE3v4+Sluenp1xdb3kermQzYrO0Sfd2rxI55bhwdwkxtVFdePDjAEJlJagHqVBaUXsPCiJcMqynMX1nI2NDdq6odYaPRmzdm5g6o210oFbVRRFkWpGZNMky3IJwHISghSCqACsMiK5TRjKuY6mcXSdo248xhrqpmO5XFPkJXUjoFeHQJEX5FmOUoqmbmjqWiS6WkkFU7omQZPSdIgKSYim9/c+k9isU4BY/6dS0lYzqNOVbD4QRaarQOEJviGoiFGG7e0NpuMXmc9HnDw9omkaAY9E1tWN5Lf3CW9szhiPx1irU2CRkvWnxMuZG8tkMqYoCpRSnJ2dslyuiFGhtQGl2d8/YHt7G5Nlg2UAPixrlo01/1uuuV7Or+mDz6pqxeL6GmM1Dx484IMPPqCuG8pRyauvfgqrpWLIGMu3vvlNlosFmRVPqmSMyb1iPCoYlSUKaJqWGODeC/dREY5PTjk4vMPBnbvsHx4SEEm8sZao5Lry3qOC3G+s1WijcV3DP3rjGzR1zbgo+b2/54e5vrzgF7/wixL2tTnllVdewVpLXa/Z3dsDJZtE0QeefHDE933fQbpG5AT0FgWtNDoF05VFyTtv/SZf+cpX2ZhMiN7R1zb1567fuByPxxRlCQrOzs+5nM/l338LM387H6+Z1yt+43N/ms9+5b953odyO7dzO/8/JrZr/swf/Wv8t0d/73kfyu18zOajDVYVjIpc2EhtUlpkCrXRCqsNkKpXoiTvhlQpAvKcb4zBhcTMak3bNCLDjJFxZum6lnFuCEgq76Sw1J0k7mptyK1JPkJhAF3bDbkwWhm0yggo8Z31nak98xulfiG9mvQ8rYhK5LFt67CZJUYBstbmRGC9rnBNm3xlinxcYE2GDy6F84hns8+d6UGaMWqQHsdU2dL7etERHRRoMEZTFCUpjnU43v7Xs7UZH3rIjuIhM9rgvDCB7bolBI/rPOtqRbNe88qrL7G3s8vV9ZzLs1M598B0POXgwX3GRcn56ALfeoILLK8WXF7MWXVy/vIsY71syfJrbKZxriUgzKUxBiw4C5mBrnEQhOkKDkL0ZFmOMiJbnM/nuK5ld2eb3Z1tDg8OWK1WrKqWtg3pHCiaBLZd8oxabXBtC0goTYzCNFplUwgTGG0pixxtNI3vWCwWOB+4vLjkYj4nJNB4Iz8U9qffFOjFwCFKt2SWZQQ+HK5kjJK065gCwhDgCwx1Qk1dQQys12uK1EeslQCtVVVRN52ARSLOBzov378HAv373jYNxUh6b/tQM2LaFPKepm6pm44QNMZk5EVBvV4nMEdK3ZavV1UVnXcpNVuuTTVseqT+45QuG5NM1UeVmNIbxlnWoJw9UbTHmxQmbiTAMQSRLyepaFVd07VLop8yHWdszSbcv3PIbFLy6OiEqlrjnGzgeB+pqoqmacSf61u83yTLMrz35HkxACM5LsPGxkzSj43l+OgJddOwXrfYvGBza4/DwzuMx+P0PvUbIv7mfMcgyoJnrjujk6zZCGi3tqDrWs7Pz/DBc/fgDtvb23zpS7+KMZqXX3qZvd092qbFWsvj9x/z1ptvyroSqhqDIgBFUTAZT8R+YAvWdU1dN+zu7mBNxmxzi52dHR68+IDRaEzUAuaj0mhDur8Y8jyTQK/gcF3LB4/f5/TkKcF3fPdnvpfveu0V/te/8j9TLa/JizEPXnzA5mwrhdFFptMpo9Eohctpnjx5wuuvfy8hRrq2Yzwphw0/pZR4oIHlYsEX/8EXZeNOafLcStr4wHrLZsJ4PGY8GYvMOgSOnz5lVa2GcKvb+fhO6zv++bPf4Fc//6d45af/U/TWned9SLdzO7fz25hwccR//+SLz/swbudjOB/pn+AqJolgCEjMroIY8KGvqVCJpWFgIWKMyRMoE59hLRUMfteua8m0wereHxokBVYZFJ667ahWDZ1PD9nKoIx46mIQL6hSEFXAu54lJbFukbaTlN2u8xilh1CZKE/o8tCLEl9ZehB2zrFaLYldl8KlxLsWQ6RtO2FQdcqZiRplQOsbdqZpHEpLeqbJMpEZBqlUIQTaNmCsppyKP6+uW7pOKjRADYwiSXrXd3yq/tynpNQQ+4AcAQq+9TQ0qGB5enRM2zTs7m4T2gBaczmXBFO8ol13dE1LZg1NtabIC3b3dkSO20kl0HQ8JURP2zbyOmKQVF/XAtJbmmeGOOorKbwwo1Hhgki4lVfkeYYisFispI6nbXn5wYvYPCfLF6yqCtuCdxqtIuu1g6gSY+RwPpLnGS4EjAoJOGaMyoI8txijyXKR7EZvWa5WzK+uuJzP8a7DZLLREoL4lNGKQPLshjjIebvOCaOmTWLJxXucFzlFWaCNoe06NBFl+tAZcE5qShaLa0bjEaHzaC2dujHCallR1WvyYoTNMtrGERKo7FyHtdKNKinOa4qNMSGkdYQbwru0tRAjXefI84y2CTRNnaT3wtoZLcnSbZKjRmSt9x5CkcY/EyqVMrtsChrDqxS+lTqCiaDCYFMdGMmok/Rbzp8gyADRoQkE17BeXlBX12jjOTu9piotwe2wt7vN9s4eWTnlyZOnzOeXQ6I26bwANG0LQJ4XFEWOMZY8s/SJ5MYYxmkD4vz8nCdPjmmaDoJcI3cO77C7vUtucqyy4iWO3PQXxzAkcvd/T+JlDBqiJEIrFVksFlTrJVpHPve538VXf+3XePz4iLt37/LKK59Oa0hSut/4xjdo1jXjohDGOd0njdZsbGwMdUit6/Ah8MHRB+w9PGB7dw+H5pVXX2Nv92C4j5lebQGJqdfim/eOPDesVzUP33kbgmd/Z4fP/77P88u//Mt8+603MSZjc2eLl156ER8dTdUQgiO3hnxcUl0vMLnl6dkpy9U1WsFqsWRUToQINrLZl2mRT3/ly1/i8aP3KYqMvLA0bYPRsomJkp8V1oo0uyhymtZxfHLCYrHAZvmH7v238/GdZbvmex7+Oup7/jjz//j3on/4d5P9vj/2vA/rdm7ndv4J477yt/h9/+5fHxRWt3M7v535SINVYzMym9F3KjrnUtfhTVVM8HFgYIAbaVhibZRShLT7HhLzKeyhISagajKbQGQE5Yh5j5I1dZvSTo0mRE8XAGVFDhukMzIiPr8YBOwobUH1FQodIUovrHgks+F4vfcs5ldDD6z3kjbsnUMRxTPmA13nAY0B6fvkRvIrX0oRQupojZK0eyMxdvjUpwowycbkNk9fTRN8xBGI6WO07sNNLAzJmiLJUxqiUegonlm6KPJqq3GNo40QnOb40THaIVLKIie6NRFFu3bYsWUymYgcMUbOzs7JTE5W5KzX13Q+sm4kKEZZzXK1Ii8sxvTMyYi29QTfMRmPJPkVCcBp6g4cBK2FwXaycWFtgfNwcXlN2z7k4GCXF17YY71ecTVf0jaByahkfrliuawJXqSpWZHTOQcJJLadwxpPXoDSAmKvzxeJNA+s1y113YiXLskYrc3J8xydGHTvPFhh2IX+ukkREmAQwYAtMkbTMaNRSSBAoyTNWkmCrDCLKoXwBAmO0pqubWiamrqWWhmTZSIRXjcYkxOiJE6rVHNiEpOt02ZFq0CrkNahXDfKSQquMfI5MQa0ipIwKxdduvhks6jtWkSMm6TPmcVYAeJG9RVNXvozTUr0NRD7zszYBy/1ADWm6yxJgRGAkr4SKkaM9lgdaJs1bT3Hd8t0/XmaxvL09ITlei1ptmXJ/ft32dqacXJyynx+RRfkNdoso21bruYLphuR6XQDoy3Re4xReOfYPjxIHno4On7CqqrQWtj3zdmMw4MDZtMZOmoMFh2T11Vpguol3uK3lvuGvJ8+BlFGGEnCda7lan6Ba2s+9eqLQMfbb/8m1mbsH9xlOt2ia1qssjx+/Jh33nqbIrOoGIYNjRAjRV5Q5gVKGTrnePT4Meuq4fTikjffeZeI4uDwDvfuPSBL9wZRqTiptwoREWXchGlFF3nv4dssruYQAz/6oz9C8JEvf/mrcg+ymte/5zOMxgWt6zBGQrXqpmX3YJdvn54xHY+5vrzm9OKcGAInT46ZTWcoleGVl40V7Tl68oivf+0folTAZoqma9IakgRlleTiGxtTRpOSuqlZrSrm87lsakT5ebGztfs7+NPpdp7nxBjZ/Iu/zOcP5vwP0y/w4l/6E9jP/nPP+7Bu53Zu5/9hup/7K/yb/8k/5Gtn7zzvQ7mdj+l8pMFqH4DTAy9gAKtDwmYfWNJLBrVGxZsqmz6Ao5fnxhgHhuPGJxZkax55SFZ48gzQBpOJXy0G6LxIFX1wKG2kQzS4VPmqMVr6Neu1MIZZYk2FjbEJYN5Ic5umwTmPd6m6IvbARcCyb+X7GJtBAuh5Jr5ToyMxelRinwQvqCH1lhgHgErye1prKYoSpTRt6wbPZH9Ob/osb/zCN35VhTYqHafFGpVqeCLKR9BIv6fRtG3H8ZNjtra2eeXVV4lRcXU1Zx0rmqbm3t07WGOZzWY456nXNV0T0Nago2N+fUWMgTzPyDJFCAaf+jslYEjepyyzTDfGtF1L23UUZQZNxHvApNCiIH5HraTCY7lc0nU1q+U59+/dYWdnl9VqTfBS1+NcS9d5fONQUTpgQcmDO5qmbfGLSNt5rNX45En1vkNpzXQ6w4VA0zapOkRjtMWaXHpIQ8R1khSdZzlaCxMsFU0GVCTPC8bjMWVZYjMJuVHaisfXR6k04kaqPR6PKMsRdd1weXkJKIyVepbOe1qfmHMkhCq4IAx0IxUsuc0wStE2LZkuUCYKsNT5EBAWoycahffST+t9pA97Ugm4hgT4Iir5kXuZr4CzIi+wVhhYiHgtiomopD/WJwZap40iSNL+JCFWfdgSvWc1JmDmJR1bB8rCsDkbs7iuqesVNpPE6K72dM5TrWu2tzfZ291lc2uTvBwxmpxz8vQpTdvivBcGMrGLmTFYIz7Z4ANlkTMejTBGs1qtOD5+KnLqELFZxu7OLod3DimKQgKClBz/cH3HVLkVxGogfbDJ06pk40DWjGa5vKKu10w3xvzAD34/v/zLX6RpWu7eu8tLL72Mc+Lv9SHw7W99m+A9Svebez1gVUynU/HXollVK+q6oQsOt1xQ1Q13773AK698isl4Coh8XlQcUk0VlQEl4WBd59AqcvzkKe+//z4xBl575VO8/tnP8rf/r5/h4uKCECIvvfIK+wcHdM4RI7RtS9e11OuKoiyHALdI4OjJY3yEuuuoVhWz2RbBiwJhXVf83V/4Ba6vr5hMR5I+bK0ER2lNIGCMZjIZMxqN0EpRNzUnZ6dDUrzSio3ZjL29/d/pH1G385zniyff5LMn8Cf+7RH/kvs5/q2v/bnnfUi3czu388y43/j7/NEf/wp/5/hrz/tQbudjPB9psPps0E8PULMsAxgCZHowatLDFfomPbRnX3vAG+JNPYSksYr8LvY+Qhw+KJrW4T1EJTJhYU0UqhNZbeudeAhTFYxWFt0HxShhENqmSUywoyyLQU4bk6fPp/5YoyPK3gRJ9UFHdVMTYyTPLFrbIZU3hI7oJdk3pu/fywkHfyl9uFQPOEXCpwDvAnXdCvAjDr5H59xwPp89lmdZ6xCEcYtRmAyRePaS7EiM8j2yvCCEwOnpGcbm7B0cAIosK1mvl5ydnbMxnZDbjPF4wuJqRdc58ZomIO2cyIC1lkRo5zzeB9pW2L4e6I/HYzZmY5FhIv2m1aphva6FydLCEIXgBo9k1zqu5zWLq3fZ29vlxZceUJYFk+mCotCcnJwRERm27wIxaIwyItf0AecbXOeEkdTCMvsAMXh8BGNzxjanKAryPGc62WC9rll3a/JCXo/NC0ajMUZDnTYMjNa4KEE58r4EYuvEg4ys4yzP0c6jUqekc448z9nZ2eH6esHp6SnlaCQhYuu1bCAkVr9tU89k1PjGE7x0U/roiVpCpFzXB9yAGTYvEmiJCqXkugsx4JW7YfPTunXek6fr1KW1kWXZ4FXsXEfwBu8tnWrRxg4rVnufJMOpcinttWglgKMHqkqBVhGlAhoPrktdpZ680Mxmh+zsTLk4e8rZxQUEeU/atiVEmF8GqtWKg4MDptMZh4eH5HnOSZKNCpg2jMoSELBe5Bk+BMZj6WP13nN6espyuRikzuOi5N7d+0wmU4w12MzedOQmANmrHHyS7ituOpGzLCOzBmsVrgtcXswx1vDpT7/G1dUV7777iMl0gwcv3WdvfzdJ8uHJkyc8fPhQ6pL6sKYEgI0xg0fUKMXFxaXUNgFFUTIaTbl39x67e/tU64rRaIzSiq5zgxdemwybG1GrEKmqJd/4+tfo2pbZbMYf/EN/iDfffJM33ngDay1bW1u8/vrrdK3DB0+1XtO5juVySVNXFEleDXLvPjo6YjSd0HSOLE/KEyUbjr/0xS/y8OFb7O/v4LxsAA1S8xDQSsm1OxE/7mK5lBqyxRLSPbccjTi8c4fd/b3fkZ9Lt/PRm//t6Ff569rw5z/7x/n5lzfZ/4kfx9z99PM+rNu5ne/o8afvE7/x5Vugejv/zPOxAas9yByNbuoH+ge82NNtZvjED7GEPVDtAd0AZBNITXQHwfeVOF1K6JX6FE1iZq0cg9XycAoi15WITI1Lx6gzS+cCBI8GXOvQdAQnWv0eXIIwwT0c7D1zKEUxmgyv32ZZqo9RNHUrdRchEnxLiG4IaOk/3qSEYqU+nEAsry0Ik5ykepJmq4Zu1ZvwG8WzbOsNK0Q6X4jDTvUJxcJeOpcqehLTe3Z2ziR5ZK3WtNpwcXFB2zTkNqNet7KJoKBrWrquI88tIIFBWWZRWJSOxACr5VpCs5yj7RqKQh7wq1WbPIyKIjdYO0UpzXq9pusY/JfRd3RegTeUxYir+Zr3ecwLDw7Z3ppQ5GC041H7hOANwRjaJqTwpZB8bxJUFGKH6eWxIUqfqPdEJL2aqFFBvINKKawxIint/10pXJC12//dJsa0aTo611eXyPvoU81Pv877DZDj4xOMuenKDd7TBfFdBt8Rg0ty0yTjjAGV5LMhgI4KoyU4qm0bdCdAEHqfX6qyCRFjRBKvQ59kLCRoCOljjRYpb/JLTyYTtre3KUsJC9Ja45whRgk6i4kv7TtFXfAYJQy667wwZ1oToqTFCljtr+OAjh5tHNLUJB9jlSIb50wevEBRFhyfnNA2LcZanHMsl20C8w17e3tsbm6ytbVFWZZcXJxzdXmJMZrZdIo1Gq2VJD+XBZtbM7TWVFXF8fExIAoDm0sty+HhoVgXgKAinp5BTeFwSBqy9sI+92O0psgKYYl15Hq1lM2YScGLL73AF3/pl8iyEVvbmzx44QXxMdcdrq15441vUNdryjwfApyslrSmrZ1tlNFopbiaX7OsVqC1WAwi7O7vcefeXfKikGOOAe/CjfpEKWwUtYIPnug73n/3IavVilFZ8Lkf/CHyLOPnf+7nhE3Ocl77ru/GdYH1eoXSmsVyRV3X0q0bxQoxmYxp6hqlI5fzc+qupnWBdbVkY7pBjJF333ubr371y2xtbdJ2Ddaqwe/cH1uei8y+v49dXl5xcXGJUpLKnBcFh3fucf/BS0w2Zv80P3Zu52M6Pnjenj/h5a89QX/u32f+n/3L5P/en33eh3U7t/MdO3/kD/5X/MwtUL2d34H5SINVSUMVaW1K/RB5m9b0HKLNJGnWxyD1mc/UQADPsBrxQ0zh0MunReZJ8sMB5FYP9TdRRYxEFGGQ0BNlU6qq69BJyqajJGcGIhgj8l1jCVEN/Z8h+EG2nJyuqbswMcQpoEVpQ54XoMB1jqIoKMsC5yWdVthgCWmJIaB4RmaYclyV7kGqvD7nnARJKYPrhNnNlP3Q+YgxdYLEniWWvkPpgpQ4JWvtAET6zQJ5TR7nAtYoCYgJklbcdY63337I4eEBGxsTCTBSFucCwQugKsuS1arC+Y68yMRLmUCCpMwGcpNhixytSf5fuF5csa4rtre32dzawLvA1dWStu0AAZDeO8bjEUZrmramaZokS/R07Zo8F19xDC1b2xMmkxH37x8ym045enzG5cUCMmF7BZgKUoxBArR8iDjfpepWk1i/5PPzAr6btiUvCmyWy1pOHbjOy3roWdWYfIxSw5O+nxdWfFjPN5dCCrmK5HlO23ZMpwUxwmKxZDKZsr21xWq5EgBKJESHdwEfQQVNdAGiwlg5rzF4iI6QfLHPpqyqdG0551LglnijoxYZcN/bazPxcjZNw3g6ZTbbwDnH+fly8I3H9AKCj4QeeCJVKMmCK0FfCdz116pWsgmidEQrj1KRTHu0NLxiNFgNIbQYLZVAdw4PmG7MODo65mI+R2mR9TZNi3Oep08dVVWlDtlcekzznBidSHidoxyPsKl31WhN0zScnZ1RVRXlaITzgfF4wv7envgy61rAk87F2x1I16xPwUnpfCbqWDaIFESPzTRtu+bi8hxjFa+99ipVtebs7ByU5c7d+0ymG0Pa7dHREY8ePUrXYV+zJBse1lrG4zE+yPk+OTsVD7aWwLTZ1hZ7B/uU4zHrpiYvcppWNsOU1kMtjE9KkhA8V/MLjo6OyPOc+/df4Hu/53v5hV/4uyyWK6bTGXfv3qMoCgkvs5amrqSfNiRZctpoGk8nXM3nRAWraolXkc4Fzs7P2NvbZ7FY8otf+AV5T61GnPqBtm3J8xzficR3MpkwGo8JIXC9XLBcLmUTQGmMtWzv7HL/hReYzTYlR+B2viMmxMBn/8Kv8cbRf0T5Z/+75304t3M733HT/Nc/zpv10+d9GLfzCZmPNFg11mCsIbooD9hKYayRwJokX+1Z19AFgXDPSGz7h+M+AOZZICvP+QKGem+XMaTPuQG4oZfqEQgEonMSEqPk4VMpSXiNAUyUKh3vPdrkWANt5xG5WxCw4B09lhZPay9L1sQAShvQmrbrMMZSjkqsNanKQv6MwRGUMMJaC74sypF0aIaIjr0EWF5fH1DlvQBdjEIjXYv9OQQghVMFbhhttEKlgJiIGhKaexZY0m49SmmRQidGzFqTakakm/Ls5IwYAjs7m3gfWCyWImvVhvF4wu7uLmVZUlUVMXroPJJ465jPrxiPx+S5J8tEhjrb3EQbqKqK9bqirlu0SkFMWt7Tosjp+r5RYyiKGXVWs1wuIQHhum5puzVdVxFCx7qq2N7c5O6du+xu7/P2W+/y9MmZvFZEDh68l62GJP/sgVpEoZQZPLVyEjVBKTrnUdqijEJbTZF8j13XoZww9yH1+A7nHpE6dimAS0CbRut+EyIOEu4YI9ZmxChrQJhdcF1HZg3j8YhqvSJ4h1EGFyUwSEclgTkJ0Gjdryup0smyHO8lECn4kKTjcgEFL9LsfpMnpH5YrXu5fpRznXyQAnT7TRSVNj1E4uyDyPKVAu/EB260ST0uUkklPlWf2nadeD11wGhhissiJ8u0+IeHaw5m0wmj1z7F7OScD46OcCk4CiJd13F5eclisWA0KpnNNhhPRqKYCJ7dg30ODw84Pz1LYFXA1sXFhaRoK/FLzmYzptMp19fXLNdrtra32dzcEkWBFalziCElIcYkAw70yc/CukP0nqurK7quZjIZ89qnP8Uv/cov0XWO7Z0tHjx4QOdCYrI1b731JqvlkslkIt5hJzVIyhi2trbknBvL9fWCar1GGSOJ29MpW9vb7O7uDtL00EuVU62MhGP1iemBtmk5fnI8rLUf+MEf5IOjJ3zzm99iPJ5QlCN29/YEOMe+m1VYWoUe1oZSgb29fT54/IgQA9W6QhnZELq8vCAvcn71536Fs7NTxuOR3PPS2jLG4LoOjaYoC/KiQGtN3TTM53NW60o8/mhG4zF3793nzp276Xq5ne+kebw443N/NefH/uaf4S/85X8F+31/4Hkf0u3cznfEtD/xn/Mjf/kD3p4/ed6HcjufkPlIg1WltXjpMouy5sbTqeTBGK0GH6oykm/bg9EPgdQkue0f+HXPVCkFmMQ8MIBUa/Ikg/Mp0iWKJ845AWvBCRjKNCGm0Jko/ZWQeilxz3RS5iiCHF/ymkqYTkg+1Ei9Fo9q9II+tRUpZEwPXVW1IviOEBwhelQMZNaQWZHBogwhirczODk+eT3P9DxyA3qskp5L72+k0T3bG2MvvRbmMCRm+9ngpp4dI958Tt84AuA6ARe+68gLS1XVdE9OsNayubmFVK4siCqSO8/m5hbGGA4P9zk9OaZpCpRasFhUCXArVqs1ZVlwdXXJ1taMjdlYfL15wf7eHc7PL1itVoTgmU4nwzqy1lJV1ZC2LFJMkU7blPK8XKxRQeE3p0S3oFq2bGxs8OnXXmZ7a4PffPMd6s6IvFtWRGJRwbnU76skq1aqV3Rv/MQFSVwu84wss2hryMoRk8mYul6zWq0Gttg3HdGnbtwERoegGCXAf+gmhQTEM6pqzWQirFO1qsBDtarQWrMxnaAUqBhTR2YUptU7jM1RQcJsZP150ttM1ErUAs6LvFtJ34wPHoWEBxF7ICHJsUYpegF+76fNsoymqQFYr9dDcJrWch3rXgUgNGqSEyt01AwLK0Txp8aOEFoiHSo68JGssBRFhtYSNpY926mZ2PXMZhwe7jObbfDmW2/SNGvxBKf13HWKplmzWi3Z39vhYH8fqxUv3LvH5z//eb7whS/QdR3GGK6urri4vKRzjizPC+T9lgAAIABJREFUcc6zubmJD5627ShiyenJU+bXV0ynUzamU0Zl+SHQKmm2vbQ5EjR45bHGsLxeoFTH9//A6yxWS959+D4xwmuvvZpYdI/NDe+8+RZvvfUmo6Ikek/wyd8co3hrswyS/P/46TF5UVKt1+RlwXRjxv7BIds7eyiVFANRDZt0fdWL1irdJzznZ2dczS/JjOWz3/069w7v8JM/+ZMUozExwr1797CZxQXZoOulxDcBbmZY5xuzDbIsY72uiArarkUpw/GTI771G/+Ib3/rm6KISKFuKq0nm9QiRltG5QRjMryPnJ6ds1xWsvnRebTV7O3t88qnXiFL99hnrSG3850xb82PeGt+xN/6Y9/m2298HmXz531It3M7n9jxj97gc//qf8mT6pJ5vXzeh3M7n6D5SINVnvFM/tY/nx0JeYmDL4zf8jFSbaBTzUb6rf8zpAdiRZLTqhTEYxJLJd2j/Z+9NDWESEzVNRK2KnU2Kor/TBGHsKcYOmxWYouMEITlG0/GNG0nINNHvJNQoRA8WVbgomdUlhRlxnq9ZLG4wif/oSZSlDmjMhfQERKQiDdhLT4FEsUhsbVPZiWBiz5b9ZnzhPrQOY8I09NvCPgQBYwkNijGmHpsE6aIN18JhMk1maWpO7RWtG3Huw/f5+7dOxzekT7Hi7MzMptx/PQpk8kErTWvfOoV1us1x09OsNkVbeMkMAVFXog37fp6ybJaQgxkWU3beIqiJM8LvG9wrmM8ntC2K/I8YzzZY35xKbUteJx3aJPJ++MVREvbRBbXDd5BW8hGRZFnzDYnvPbpV3jv8SmrqsY50IMPVgGyNqT+yCSmHEmYVqnmRyuiBpPnTKdT8jIHrSnHI0xm6boW17UUZUFTrQXEqZtQIZfql0zaeIjxZpOhl51aK7VAV1fXAkoyS5ZZ2q4jeEfbdik8SNantYYYPFqZQb7dRYBADJ6ukfoa0YRrtHT0iHhVBYwCr0nrLEhdUIwYxDdtjGU8HlEUBfP5FdWqSr5XYbm0MsMK9N4RFJK8S5L5RwHsSgWM9sTQEkOLosNojzZIt2rsADt4GWUZ9wnWfT+zdLpOJiM++/p3c3L6lNPTM+q6QRs7sOFNU4svPSkD3nn4kHfffZdROWJnZ5sYAk+OnrBcLokxUjcNs9km040pKNkI896BMbRtw9WVp6lrRqOS0agUD3a6HpViSKtNwmiOn55QNw17B9t813e/xk/91E+jtOaF+y+wt7croCsa6nXN17/x67L5Yi1E6WTuug5rLXlRyHWsFGfnZzifkoeNpixHzGab3Llzd7g/xpR2jErvj5IE7BjECnF9fc3T42Occ2zNNvn9v/9f4Mtf+hLn5xdoY3nw0ots72wTEYdz398sgDUOlgKtBbAGpZhMp5ycngjj7D1FkfH48fucPD0dpNJKC1usCEkSLjem6WyDrMhxIbBcVVxcXsr31iL/nW7MePW115iMJ7h+Q+6f8OPmdj6582R1Qfc//hfkf/LPPe9DuZ3b+URO94W/yh/603+Xb148et6HcjufwPlIg9Vhl///Baj2EuD+Y/+/xmhNNPpDHxekuUP8cdz8dw98e0avByW9Vw9U8uklL6eORCQVOFMabS0eTecjPiBdpr4hL0bpNXisVUwmm1ibsagqVtUKYzLxehrxJgbXsFyscb5DK7C5RkWRV+ZZhtXyYOy9J5BY1RBQSaYbUiCQ1GcI6xtTR6fWHw5QkvPZ/8Yz0tabNOYYn5VUp8/RYNCEIKAmooaH354NGdhvoc44PT1DKcXe/p7IQ72jWzc0XUvT1cyaMXmes7G5wapa07QtNhfmb72uMVaR5xlGiSew64JUiGg7yGevrq6RBGLLxeUFRVEwnYwxWuHybAhLCkH8i23j8R7qqqVtOqaTEQpFXTeMJyU2z3nxpfs0Tcv52QWnp+eoGJIMNkitT3rtA+BPiaZKa/mlFFobinIEKgxMXWYzYe5DkvQSRcLcthAimdF0CVQa3beLBpSSlGutDcvlUjyUxRiiokmJqVprFoslSoO2Uo0DGptpmsaLh1FFvJMkZa0DXVvjvJPXphRlUcrmTVoXUi2T1kgf1kQQYOM9QZAmwbXUq5X8P+dQgE0MsVES+pOu9AG8uSSzVwDJjy2BSp5IC7FJ0l+wVoLEgnesqiVlWZLn+SAB1tykW8t1IZtNRWZ58YX7bG9tcnT8lPn8Cuc6QghsbGywtbUl6dRG4zqRLu9sFqgIi8WC07NTmqYBFBjN1s4ONs+4WlxRliNUB8pabJaLf7eNdK6ladapjkk2FsqyvKmG0pG2a1gur0FHvuf7XufNt77N8ZMjRuMZL7zwgPF4Kiy+Vrz3zkOOHj/CGjuw2yHBsfF4LMFzwLpuuLycpxAzRZGXzDa3uHfvvvg4kVqtPpmXKAFazyYYd97z5IMPWFcV43LEv/gH/gCnZ+d85StfJctyitGIw4NDRuMxTdMSQjfI07uuQ0KVTKoPE0BpTcHO7i7f+va3UMaInD4EvHc0sWZUTpIkOYL30vGcvMuTyZTRaIKxOdeLBUdPnuKDJAhrrdDG8Oqrr3J4eDhsqGlleHJ0K0n7Tp3OO/7YX7rgb/zJ530kt3M7n8z5pT/1db548c3nfRi38wmdjzhYDR8Cq6SaDLgBqs/6LT/8uR8OWVIDIxoGD2cMJD9eLxO+8bQ+C+RcCBLgFHqJ3LOgFZGq9f9PK7QVGWjAiY8vBKzNIXSDr6+qFuRFzvnFnK5zjMcFXReo65quadBG0dTyZ2alZ1TCZXoprqNtfTpenWS/YUgCtdYO/kAQ5qpzLcSINX3liubDGwA3jCp9UIvWIg+OArqMUilVNn1kYtr690OY1h6ICMBXvQcW8Xu2TcvZ2TnWGnZ396jrmsvLc9q2SUnA0DQ1Sim2tnq54JrVqqZtWrS2tI1jXbWJqRI5d0QLOE4VKHVds7W1xe7uLvP5nPPLC3kNxmCzm85WkWK3uFaSfEOA61XFYrliY2NMUY7JlCHLFRsbM/b3t9nanPLo/UdUVU1wgaCMBA6RFpYyksishcU3xqDQhM7R1DVZnhF8EDAUvWw6uI4YA63rMErhupa2aRiNRuR5IWmq6RrQxgiLF5JEPSrm82t2dzKszXCuo247SO+fRaOtRIWpIbCItO6DfC8F4FHBY40ikPoxI9gsip9aDcJ4kdfHm18CLlLtiBbm/np+gdLSizkqCpwLeOckJCkBtYhP4FfYWZFAiB9VwKpHhRalnYSZKY9RDB7pgGye1E1L0zmKLKMsyoFNE++1UP+yRgX4b27OmG1u8uTJMY8/OKLtWspRSVGWEm8cI03TsbU1oyxHEOHs7Jy6btI15WXzw3W8++5Drq+v2d7aoixKsmJElheMxyNG4zF5ltF2gda1kpprtFCQGtkkMIblsqH1jsPDfQ4P9/mb/+dPYTLDweEeh4cHdJ3Ir13b8K1vfkuu5cwSnMNohXdeNmWm01RNBfP5nLppJHVca0bjEQf7B9w9vJvWZ+9tj+m+4pO8u/fvBy4vzjk5fYoGXnv1u/nMZz7DT/zETxCC2BheeuklJpPpsEGGgs7JJlRfzyPnPQ4Sc2Mt29u7iW0F3znW1RqlDFtbm3StwxgrKcBawtYIYG3GeDxGa4NzgcWyYlVV6VoIlFnJ3Xv3efmVV5K8u6EoSo6Ojri+XvwTfuLczu3czu3czm932v/lz/MfxofP+zBu5xM8H2mw2oOvIS1zmB5UqmdY0J4J/TBIBQaP6rN+1j4gKISetZXAkvDMx/SpvUqDkpJBkQ2nUb2cOEr6pIQJOank0BnaQJkVbO5vMplMWVULrhZXhBBpmoonT1ZkWUHwiqg8VVVxcHBI1zZ0bU0kcufeXbLM8vTklLqpUkqquZGIJlAZlRy7cw6t9MBghRBSJ63GuQ7XdQlM3ADynkHtdXLPpokWo5LOOek2FBNrkhTLa3edMITS+eiHjQCUQsW+6/Ym8EkAF9R1zaNHRzRtw+HhIYeHhzx69D5Nu8b7KV3XYm3GxmyDnZ0dTk7OKIqSs7MLutZhrcVa8aJ1XSf9qc6TWYuxQtOt6xp3dkae52xtbbFcLlmv1wksmOQTlr5J8bWupb+19eR5gdGGxaLm6cmcwzu75KpDG48xGXfu7HL3zgGnpxc8fOc96rolpAAmlEalDYEQBTirKOejbVuWV9eUpQRneedwXSMyWO/w0dN2LcF1WGPY2dlmPJ5QVVUKUhJAnHJyJcU3JnYONfiTnRPQIdLgkcgpvUvhS4bcZgQfWS0rtDaMxmOMkjXhYq9YEEmv910CyFm6lhJLHCNET/QOgjie+3RqYXXlfW+aWs6LyRKo7X2iHcqL/N5kIhuWXZA+tCkmj2qHVVJPg1FIq47H+4gyEqA1qCKcI3qpocpTZ2dm7U13a4zCwCVAFom8+OIDAa3HT1lVK05OTtje2iLPMjamG2xMZihE7nx9fc3+/j777HNxeUnV1JyenlLXIh9eryus1mzv7jPb3MJ7x3K5xFrLdLbBxsbGkJTdNHUKXJOO2MX1NUVh+YEf/F289/57XF0tGI2nTDemNE1LkY8xxvDe++9wfHREbovkx4+D53gymQw1LvV6zXw+FxkuEXRkY2OTO4d3GY1Gsllg7JBQ3nuhY+/vjLCuKh49fow1mtl0gx/50R/lH3zxH3BycsJ4PGZzc5OdnV3pz033n7Zt5e9pE/BD92PVd0rD5uYWm5tbzK8upAcWzXQykeCq/t6WfsUYGY3GjMdT8nyEUobziwveeeddVLrOInB4eIfXP/tZjBVptwLm80sePXqfra2df+qfPbfzyZt17PCPv4l54fXnfSi3czufiHHv/Brxa1/kzp/9eaq2ft6Hczuf4PlIg1V5aA0ISpSHc7iRB/cytT4ohd6n9kwdSwhSwzCkqT4L0KJ4ooR51dwkuCYQHAM++eYk6EMTtAQXycN5Oh4Y2K04pJpGrLYorVnXFZ1rpU5GC2jUJjFvyZfVuY7RuEwPrx7vWybTCS/cu8tkY4KxmnfeeRut0kM5WkBxCsW5qZq5kfF678S7OdliMtlEqcji+vqGFX129D8utdZas7OzQ+ccV/M5KgTauh4kk7qPhSUM1TjEvuYknZNOsoV9jGgdE5gKidHuODk5pShy7ty5w507d5jPz4cHZ+8dl5crkXcWGaPRmK7zXF8vCT6mihqFD9B0ckwhD8PDrbWWECPVep2AXki+xIbJeMJoNB4eqK3VbGxM2NycAorlYk1VSQhP/fSMxWrJS6/sMpmUbGxMCZ2naTqKwrK7t0O1qqkaR/BSPURKP41oXGLvVZA11dYN0UmyMUTathZPKoGoQRHY3Jxx7+5dNqYTzk7PaJs1xmQUZY5SinXTglKMyhEKQ1XVKEUCwVmq2jF0XUfbWrJcKoxGoxGjogBgcbUa5OBGa8bjEt81eN/h2g4SwxoH5s2n1OBA7CXxov0VwIkWyS7ibc7ynOl0g+tr6QwVcCQyYm00eZYhGDKCEa+k7oPBgnw/kZTWROXJLFitkiQU6ZxVEmQVgxIfbUp9bZ3ImK0xkCthhmHwXKcMYrQy1PWa2WzKbHPGkyfHPD1+SgyB3Z0dciuS3RgVi+WCxXLJ3sEB040pW7s7zK+uOD8/p+s6ikJkv13XcXFxTlXV7O3tMZ1OAVgtF9R1xXg0YjKdJN93YLkKXF9f0TX/N3tvFmzpetb3/d7hG9a4p967Z50GIWSEmSTMYDO5nEqVuUhsX6VSyZVzgyuVkXJCJRcpp3JBUg5xElNOnFRiKDDYQAGZLoyFmCSBsCRLaDjqcw7dp3t39+7uPa7pG94hF8/7fWv3kRywHNMNZz2qrt06vdfa7/qm/f6f5z+s+MAH3sd0e8wnPvk7GFuwt7dHZg0nx8dsTUUf8Huf/QyuqTF5QYgxyQE81koeMyi89zx99qx3YI7AZDLh6tUDptNpP0mNPqSJc+gn1CJ98LRtw5MnRyzmFwzLnA9+6EPU1Yrf/cTvsL29hTEZ16/dELqvc/jgqZuGuhHNuLyZxGhpLXRjrXRqSmhGozEHV69yfHqM0YYyyynLsiNs4BMlPMaIMZqiKBkORdf+/Pkxh4ePWDV132jc3dnhGz/wAcbjEW3TUC+XXFxc8PDhI1aLBaOy/Op+DW3qT0T9+tHn+OUf/Gn+8mf+y5e9lE1t6o99tb/6U/zr/9Fv8eGjz77spWzqXVCvNFh1LhKCGKMI8OzAatLHIS6s3odLmkCdwFIX1xKILkXcsHayjckpt9NThuRgCd3myhJjosQl6mVHjyNKBIxKk5rOmMgqjVMySZRsRfn5XWxHVlhCymfNC0VRloSosFZjcsVyWTFfnmM1ZJmAp/OLE45Pn3F2dkaeZ2L2pCUfs5tYdlov0RDST0jatiVEyc4sC8P21pjgG1arihAcRoFkD5r1RLqbZChAK1rXYKwRvWxQhGjxIVFtnSPLcgbDIednZzS1gyhOs3SUZUDFdDBC0sBqk0jTAdcEnhweQVDs7uwwKMZczOZoDYNygDGGuq6ZTCZMxlNAsjZlouuoVg2ZtZJZ6rxkwRrzQjapuOh60Xh6ed1yuSLLMow1/WZdpemb0Zqt7RGoyHK5gigmRm+88Tbn50tee02xv3eFEA0+XpDlhsJnuOBp09S5bV0y0hFjr76RkBooEp8i191qtcJoTQgOFyqu37jKzZu30CiOjh5z/OwYASBBQF5epHsCMmuwWU7dVOmYQzkoqaqmnzZKrE9OWeaUhWgml/MF3rdChw70rsN161LzJxkdqTQt7ZoYEbk3EPaBDqTPJ+dbp+igqMEFGGQDhpOMEBXjyZTlasnFxTkxekZbEwZlQXROpq84hGmscU2gqVYEXxFdjcejgsIWcs6CT5PXZIomUFzu345+HYOca1CEKDmuWCtU9pj8rxBXbQ0QA++5eZPpaMTjR495+uQxBwdXmZcDtDa8+dY9Ts/nmLxEWcvO7g7lYMBgMKAsS05OjsXsymYE71kt5hy1LavplO2dbYpSzkFdrWhdw2q5YO/KHqH1nJ+fEEPL137dHd5+8DZPnz4nLwa8dudrGY0mRC+uzIePHnD8/DmDouife9I7CwwGBdbmGGM4ObmQpk4I+BjJyoKDgwP29vYwVo6FS1pQuUm4RDOPONdyfnbC2ekJZZ6zv7/P+9//fn7lH/0KTSOGcddvXKUcDtJkNtA0NVVV4UPnun1JD49ORl4Gk7TXZZFz+8YtvvSFL8q0Py8JTppgQlVPp1hBWQzIswKlNMvFgvv373F6PiPLM9q2IStydre3aOuKu1/8PKvlCte2rFYrcSNG01TLf/FfSpva1KY29S4v91s/z7/1w5/YANVN/ZHVKw1WtRJqYOfGK/ommbJ2hhuyy0yTVF40A1qDVpnUiZwwTbmQjMxOp3qZaiweNN0Etpu4xuRqqfscz45VaJBsVB8UrpHNoVYCqjpNaVSauvG9IY4xGRGNzYSi2LQtEY9zLVlZMN3aYjIdsarEYKhtm/5ziOuxTCdVl1eZNoddZqz3Hh88me3yDRU7O1s0dcVquSQGR+MdShm0kegbZQ2ZtqJrVBptNcvlApsZvK+F1mskSgjvcI0jItEw5WAIVLjWo5I2WIx0OjlxN3VVyQAqyFRNK5aLimdHz8ltzpW9fU7Oj2nqiu3tHZkGzy7Y2p5ilOHK3g7EyOnJGVlmmU6n1FXD2dkFypDyPUNqYvg1tbGnh8t5a5pGGgi5FdCaAK61cr15F5hMhkBgPlvQNo6cgqOjc2azu3zNnZrbt2+yvbNL2ziausHYRMX2YiplrcRqyGTepJ9Of50JLVfoi1XTkBcZt2/d5vr1A7wPPH32jMV8gVIdLV3iaNrWkSVN8moxEyMfAtrCYrlkVVWEKAZMSttEzdQUuZXGRuto6wajjUzEvDQ2qqq7tpIGW5GyTQUMhSDZrMJ+l+OoVcQCQYGxGjJDTJ/PZAVt1Iy29jBZznA8Ri9mNCpS1wuCUUQdUSYCjhAbskyTZQrvG+pqRnQVVsv95NtAqxwKAS0Yi46pOdQ1jeJlgzC5hn3w4AK5zoSS3Te11JpqmnSUrm3YGo+Zvu/rODk54eT4OU+fPiPLSp4cPaV1DU+OnjJfLqjqmqtX99ne3mYymbCzs8Ph4UPquib4yCAvcU3DyfNntM2KwWiEcy1bW1O0UrRtw9Ojxxhraduar//6O9y8eZ2//zM/y+nZOTdvTdm7chVR52rmZ+d8/nOfJXgnRkI65UArTVkMGI3GknHqIycnp4SuiUdgMBhwcHDAZDIBBT4EQPWTZu89qJC05hHX1hwdPZEJrlb8K3/hL/D2/fs8ePCALMsZTyZc2d8ngjgNJ1ZKiNKcs+n5KTEzGcZarLGURQkqYNEYZdi/ckBwjnI4JLN5amrFrm2I0iJH6MBqXdc8ffaUql5JBmyeMRqWWKNZLRfcff0LaIWYdKWGhQLa1lMtN2D13V5/vfkcP/hj/wnFf/ijL3spm9rUH8vyj+/yA3/t/+J3n9192UvZ1LuoXmmwKm4c8qUDqpfdf0PacEHEB9EGCuhIwCDpLrupUaeB7WNufPyyzatEtKy/97LuSgyHOofRlNkaQaOJqOR+6fBobHJPFR1j0hMqhbGGvMiESppASNvWNHXFcFDQeoPVhrIoqVY1F7MZLvg06aWn2JKAY0/7VR0td222pFhnJBKEEmmtgDPnfNKGBXQMGKOw2KR/lciPsizS61MEhQtkxlLklswMaTNxzl0uFpI/62Wye9mkqjtXUt25k3PbgewYA2dnZ3jvWVUrpjsT0daCaEidp1rVoqfs8m+VAjyLxZyqatL58GkaH/pzefmc9+cwGU/FGGkbJxNZo8kym7Sect1ordnb22Vvb5fFYsXz4xMCgfl8wZde/xLz2QV33vMaw9GEs/M5Wlt8aFAYijKnrluhlCNsgA74CRXaywa6qlBJQzkoclzjePr4iBAirvVkJgcNdduIMZGKYMCT2ABK0TRzbF7i25blfE5wvqdCE6VpopBpbrV0GK0xWmOzjCbpjX1yfTXdPYesUymhzoakI+w+i1IaoxUoiwoel7STVim0zVBZjilzghYAO5pO0daQhwHjMCGc17hQsayXZEbhdY1zFdYZXONZzC6o6wUaj1E6gavuegjkRUHWm39115s0hrpnR6dlN0Zossulw2aGIrNJ82yFkpp0wE1dp2tDsnJv3LjBzs4O999+yLPnz2hdIy63RrNcLXn48CGz2Tn7+/s9WN3fv8KDBw94dvRcnI2txQfPbDZjUa1YLBesVku2d3YYT8asVgvqumYwLPmBH/gBPvWpT/LG3Te4ev0GX//1X89isUQhcTNvv/02b735JqNBmZy+Nd7JuRgNh2SZ6FZPT08FMIeA0ZrJZIsb12+xf+UqRVGSZYU0coyhaZrkiu2l2ZIaYQ8ePKCpa4wxfPd3fxdbu1f4B//w51FKqOSvvfYaRVGIXtxokUWkay3PrVyDyejNaIM1qSmkYDgcJR104PXXv0hRFAzKAQRSk0X357SLYxoORyilmJ3OOD+X/FplMkyWUeQZRsvzNc9kautdi0LM8VBiLlYMiq/2F9Gm/oTUg4tnuLd22VwJm9rUV1ff9a/+DT57fO9lL2NT77J6pcFqXdeykUtmIJ15x2UQdDmaAviK/xaT8cdlExGJVIFuU/tOvWfkRbOmdXyOTMZCEnglIiJKK7LMEFF4tESZqNglhaCU6amxmRVTGBU8dVPTtA0xBoqiQDtNWzecn5/3n8mFgElnqm0FXAnF9JIDrUrGPsh0zydXTpnAaFrX4OYtTdvIZk75RIFNLr9a97RdYw1CtzVkucU5MNpS1zOCbilsjjUWneVEH3F1S9tpVlEpXlTiSbpj1FUHuPvjHNY/b7WqePr0GTrTDMuC05NzjBGAWVVrsNq6FmMVy2VN2zqMkRiWSOwzG3Vy/X2nycv6fGokfij2lGnnxLip6DIqUUQCxljKsmB7a5v5YkEInqZ2PDp8gm8D+1evsnflgJPjM5oWlLLUjSfLMlrv0jTfE5PhkNbiarpaLfFOMmg78BxcpJxI/MxFc8FivsTVLcaI1k8p3cfAGCPuvtYY8sxA9FTVIsUIxb7RE0MU2nar8K3HarN2pEV+tk5azxAR86buetKpCZFyipXuwGBMumtQWHztCL5FUZBnFjsYsIqGYjBiON5C2YJiOCTqjFVdYWwh5lJ1TasD+JYYWqrlinq1pFouIHi0jvi2JRJ7o7QQhIIelehWTboXei1qjIjplND51/e3pm1cP9n3HvJcYW3Kh02mZN1zomlqjNG89713GI6G3H/7AYtlTYyahN05P7+gbVv29vbY2dkhhMjt2+9hd2ePJ4ePOTs/T/RaI47iMbBcLpgt5uzs7LC1NaEsS77xAx9gd3eXn/qpj5FnOcPBkNFwnPJ1NSfHSz772c9KzFHiMHcmWFlmKYocYqSpak6eP790D1i2t7e5eeNG0nva5KQrTIyY3MqNUcmsKTCfXzCbXWC04vq1G3zHd30n/8cv/RLL5YrJZMrNW7eYTCb9cdJRTLRWqxXONWR5jraSqdrJMro/QuuvKMqcL33pdb7w+S+wtbUlDZNkvtZds1prskx0uNZaLi4uWC4XFEUh2lwt8UDSXJBoIpF+CBD3XnS03XP3xSfRpja1qU1t6g9b7a/8PRb/+0d4vDp92UvZ1LuwXmmw2nXmO4D5lUDl+qvCmEvT0eT22wXTX56mde/TTz8va/IgbeK+fGMTYwqoT/zfPotUpfgQYqLmBUJwKCPUQhUMPkBM01jXyqY84PFt27/XcrmQyU3rxOAGyPMCrc16QhhkhQDBS9SHcz6ZLdFPmjoQU5QF1soExVrJXCUEMfdxHt/F0KQNonMu6SHFNGk0HggIDwFf1wQnlFdo+slkTBOc2BsrSRRJSBvmrxSPQ6IaW2sw7RtPAAAgAElEQVTwwdGZZa1WFc+ePufgYI/xcMh0us18fpEclFe9c+mV/X0ODq5y/PyM1WrVuwLLxM98xevlMnDtXYtJplvo/u9aa/JctH9VVVFXq95Fdzoe07Q1zWpFvao5fPiI89mc1177Gra2d5gtKiJJt+okusgHnzbTsqkOQeFcg2sbbAJgg7KgzBMNMhpmFzNOnp8KgAgRhdB+lRZQ1ekxo45cu30DpTXOBerK02qhT8pEPeUBe4heQwy0CHPApk1/27ienh5VB1algRBRxEvnT655EsDWAlaDwWn5u/yJKdPUgMo4OZnRuHNu3rpFWQzJsxFGnxNVjYui1VUI/b6ploS2QiNA1WqFUYYYfAI8wqQI0aOiTARDlJgTmQQL3T8Ej9YZZTmgrmu898klV65R5wIxti+wKtq27c97N7mMyWTtxo1rFIOSw8OHnJ2dpSl+IEbDchlpmobFYsHe3h57e3tcPbjKsBzy+PFjDg8Pqesak9lk3hY4Oz2jqirOz0/Z398ny3M+/I8/wunJGdbmnJ+d8+lPforbt19jZ2eXN+++waPDR5RFjupyVUPEKM14NMRqRWYznp89Z7Vc4pF4o7IcsLsra+qeJWK0NaRtWxZz0YdPpxOIAR8cR0dH4phsNN/5Xd/J3dfv8sUvfonJZMJoPOHqteuX2Auil1cKssygdZ6aH7Y/tkaZRB+XZ6M1muPnz/ndT3yip7MHH164TxVQFAXDoTggL5dLFosFEfpzFBOFm6Q/1onxEmLAtZ62bdne2QFtWCwWGPtK/7rb1B9R/Qcf2+Xv3P8M5rVvftlL2dSm/tjUg//id/jAW2+87GVs6l1ar/Rvb20MJrPEpLGKgDJJe0oELaYzvqOFhrVGtY9TSTE17wS4MkUxCViszXg6kOMS2L0MdoB+E/vO6rr2SoOOERe8xJUgbqld2mhM0x/nhT5nOgodhogHFSmKjODEOKZtHVmm0Ghym+FUTAY4nSFKWqNO5kVpqpnZjDK35HmGUp6macjzIcNhyXI2JypNZhWhdel9hDqKUnjn0P1UUiVQaRmUA9qqomrFJTf4mI5tJLrYHSA6QPqVAP+61g0Ba1JWqhJdZLOqmZ8vGQ1GhCDAwnvHaJQ0vHXNbDZje3uHG7euc3pyjnOBxWIupjNGybm9lJt7mZosm2mdNMimO7N4H1i5irZt+ymbSyZSmdJoHJ7IoByhtyYsFnOWC9lEf+GLX+T27TtMJhNOTi9SYyGmXF2folwE/LStY7Wcy7mPkeFgwP7+HqBompaz0zNOjp9BiIxHI3GPHQ1p2pYm0TubpmU0LLl6/SpKBZaV6CRTaiYET2hF22i0JjNGsludxyXKfJblZMZQhwat1+ej03ejkuO11qj44pRc6PYCFIyWpkMEjILoHW2zwgxLjPaozJDZQlyrnaOwVlx/W48GMmUI0RF9i6+XGBXTe2tsyhl2bh0nBdKEQhuMlvsphFaOszZJqwvOtaxWi14WIHFItn9++BCSVlzySgUAgY+eSCAEucfyvMQ5x+72lK3J+3n8+DEPDw9pm0aOadPiW8e5D7R1Q7WouHnzJltbW4xGI65cucL9+7/P46MnjIw0AVzbUhFZLiROqf5wxcH+HtbmYlpWDphdzPj0pz7F7s4eh4ePUEpAukmK4RgCw0FBWRTCPqiXHJ88F9lAot3u7Oxw69Ytdnb2knuywgcB11pr9vf30jPP0dSOk5PnXJydEfF867d8O7s72/zsP/p5yuEQYzNu3b7dNwyquqKqVpLjqmJ/b/X3mNJYY1PmszSDnGsYj7b49V/7NBfn5wwGZTJAEmOsDnhmWSYu4Hnen++Onh/S74IYhbqdW0s5KFI8V0MIgbIcUAxKPvjtH+KNN96UNaX32tS7u37y0cc4/Etz/u9P/o+sHcY2talN/bOq/YX/gR88PnrZy9jUu7heabDatI24a2ZZDzo7LVY3dYW1DrHr9l+OqenooE3TALIJkiiKKDTgS+65vUZVyfddBlvde2ql0Uq0ct0Ut896ldWkF3ha36KcOAejhNKWZTnKCiW4aVsgorViMhhgMstyucC1MtXsKIzdNDHPC3yiMHbuyP1nj8lsJWWbGmuwyTjIGkPwMuXL85wsz1hVLXlZ4kIktCnWBfrjZ6wY81SVZEG2TYVrG9qm6X9+b1zUH6Z1/u3lYeplc6N+kqmEotm9xnsHKqYNZ+DiYgZKcXE+ZzwZ0DQNq6rCWs3u7h6rVcXz42OUgvnFPEV32ATMHRD7CWt3rNZxO7KWLDP95B4EeIc0UT49Pe3p51CTZTllblmFFq1kErW1PWUxX+ATnXw2O8NmBXt7W5yenhNCiwpRKNbJZCoGj/ct21tT2qYhEtnamgASYbNaVVycXzAeDLj9HnEEDt5T5Bnn5+eYQc58Pmc6HbGzs4vVWvS+MaKUFQ1uU2NNjs4zcYMOgRg8MaSJaDK6komj0Cc7kx0BdgqtDCiJYJLcVJsm9qQp9PqcS55quv9iAO9Ag1YLCJHt6V7S6c5wrSG3nsK2VH6OogU8vp7jmhVWRRSBqLyA4yDmOKozK0ugOngxplK5xaRGDYnCrJVQUKOmp5aK8Zbq6bEqXZdt2+K9J4/hEtBS73iutKJtTdf1a6+9xu7uLnfv3uXifEaeZziXImBCoKkbFos5N27c5OrVq1y5ssd0a8L2w13u3b+XdLTSTDHWsFouOXx4yHI+4+Bgn62tUZqCiib1rbfeYnYxYzgs5Wc4aZiF4JmMx2TWYKzl6EhyYrWxRCLTyZQ7d+5w/doNTNLa+/Qc63SqwSNT4tDQthVPnx4RgmP/YJ8PfehDfPzjH+fs7IzxZMqV/X22trbwXmJtOj9mia5aN86653L3d5umpzEEhqMBn//C53j99S8wGJQCag20iUnSNXU6qm//3DUGbQ3KO2yWYbOMyWTKoBywt7NDlmc8f/6U+XzWr+v9f+r9/P7v3+Ph4SFb21tMppM/8HfOpt4d9U/O33rZS9jUpv5YVPuLf5vdf/8XaL172UvZ1Lu4XmmwWpZlr1XtgOTlydjlqdnl/FSg//7ue/M8/zId62WjH1hP3cQYSfWmTJcBaUcR7t6rowzKZjkmd+FAJIheVa2nloOyYDga4UJkVVUYrXBeXuecoyxzRoMBi1ChS41zQmULYa31MiZDK0VHaO11Y6ozNwKQjFFlNYFI4xy5FfOgGCArSppWjk+R5yit8elnWGsJSV/mnGO5XGKtaNzEq8RgLGJKpGUVTdNIHM0lCm7vMPoOndg7J9yS88kauNJNxh0X5xdk1hIClOUIFyTaxEdJ311WK0IQzWo5yPEusFrVaO0hSpMiz2Vy45wjDcsTaA0MBqPefKdrYDjnGA1HMlEr8qRdrFjMLxiVI8qyIAbRHRprycqcrIsRspZqVWGNZm93ytnZjOWqTqBDGgJaKcaDgRhYFUWiZsNyuaCqKpqqYjjIuXbtgN3tLbSC+cUF5+entE0DKMo8o8gsbb1iPmuoGwEOPrR4J9RQAahCz0ZFijwnz0THt3AtRhkU0hyQSapkwkJy/gVM15Ahaa/pWAgqaYOVuPQGj9UKHxUqdoZdsL2TMd4eEEPL6dkp83kFEaxR4JZkekn0NbFtwa3QvkEnmnrwkteajH6FKqyFrm2M7Sn9dbVC6w4UkbKLY6IBX3YOT+63xvT3vkr0VDE5a/tr2VqdgGtcf96wfhZ43zKZjPjWb/1WDg8Pefz4CW27pqh7L0yG+XzB+fk5165fYzgecuv2LYajAY8fP+bs7Eym9glEutZxdnbOalXROKEwb023JCc2BrLMgJdpvTQ/ZCKfZzkxROazGcfHx4QYMQiFdmdvlxu3bjIYDohRzIpa57BKy+ftQH90BO84OnqKcy15kfO93/s9nF+c8bkvfgFtNOPxmBs3bqCUomlqfAw0TUPTNC/Q7UWTLfE0Wl1qKCpFUQ44OTnhox/9aGoeQIie0EpjSUc5F3meMxgMyLKM1WrFbDajaVtaJ9r4yXTK9evXuXbtRsrmDcznc05PTyjLkhBFR35xccHh4SHbO9tkeclgMPoqfgtt6k9iVa5l+Z/+NYY/+nde9lI2talXtuq/9SN8449/cQNUN/XS65UGq8ALIKKbCMDaNKmbBnVTEVhPQTvA+k7dawdOO/3WZUDabbqC/8rGPKI7pV/DOx1nY1TJ9CXiCATfEiLoTDa51WqJCwLGtDVpQ55oeE2DNoqyKIT66lb9prnbfHemQJeBhtYKbfOkYxN3YmtN0nMJeG5cizWaPC/JC8neFPdkS2EMIUBMG2cBUJ1bbwQLWV6gtKVGA61oJNsWoxWlHYi5UpqydjmWkSC6Pi7RqTtda0za2iD/phXJ6lgRo2TeOuc5PT2jrmvGW2MBilGxWtXJRVeR5wUEMeEJwWOtAXIZ7qUpajdNr6rQX0cuZXuWZdmbuADibqo1IYr5VJ7nQqUG6uUSKNZOstaSFTlGG05Pz3DeYzTMZ+cMhkOu7G4zmy2Zz2fkZZ5iRQRcj8dj2rbBuRZrDW0rzYGiLNjf22UwKIjI9LAsM05PW6xVlMUAoVAr2rphtVoSlVCaoQOS4soq17giRCiLnK2tKc+eHhGDx+icoixQyohGOmkGo/fJaCfiFcQoOsCOXhyDxCCpNI3uXI59CNjkTh2CxwZHqT1DG2ibCt3O8KsF9aoiRE8MDa6tULEheEfsTaG6zOOOWq8S4BZQ3//cS40Nf0nvqJQ81CR3Wb63W+d64t99XdOK180VMCZLucVaALBg/xc1lUruu9dee43t7W3u3bvP6elpMm8CHyI+wtGzp8wWc65eO+DatWscHFxlb2+Px48fc//+/TU1GWjbSNM6/NsPWS0qrl27hveearVCK03wcuy99+Q2YzAYotK5f3h4yGy2oBwO0UazvbPDjVs32b1yBZMZMpvRNELvtzbDuZaiFGp207TMLi44Oz0BAt/w/g/w3vd+LT/xEz9BXTcMBiOuXb9OMSjFSA2o64q6rpJ7thiWxc7nLckAOif2wWAgDQHX8PGPfYzZxTmT8RiTYrWi9xib95PY8XjMoBxQVRWziwtOTk9TJqxkUx9cPWD/6gHGavLMcnZ6yuHjQ0xmWVUrDg6ukuc5b7z5JuPJhCwvGAwG/e+OTW2qdg3f/ItHfHr+Vxn/7f/1ZS9nU5t65ar+Wz/Cd//dt3k4e/6yl7KpTb3aYLWu6x48dBuZDmi0bduD18t03K46oLKOuFkDpu41Hb66nMV52Zine033PvI19GCr+17vPS5NVUD30xUfAyhLiFB7J+DO5miTYWyGCjpNmkxy9wxYU5AbQ1U1LELowWYHxC5/FptlAix1ZzojTrNa2RQrEtJ60+ZcKZQR19+Qcmi9b1HaYG1OVBofYk/Fg2Q+g0HpgMo0uVLEssC3DqqVRESESPInEtdZL5o/a+Vn+UAfiaFSgiLJLKdzNI39ZBwMGTEKUPPeM5svqNqWnZ1ttrYnfUTQ1tYWOzs71NWKo8ePqOYVJlGnV6tafg6636RKXIZMy5xvuZg1yV207K+tywZd4/Goz7G1WYYzVmjRIZLnDcZa9NKm9zSMR6Nk4qNEh1gtMRrGoxFFkffGSOVgwGg84OnTp2idMxgUifatmU4mDEdDirKgKHJi8LS1HLvMWCaTMd6JC21TN0LPVYqQCLpyfOX6LYqcuq6ZjIds72wznYyZnZ+xWi7IjGJ7Mqb1kYuLcwgeOpdr1YE7CEF0116cs4hKYTMtM9golF2jEffr7ntcoI4tTx6sODt+ToyGuvaUOmcynTCbnTFfrgiuJiQXYHE3lutiTdeW91Rao+L6nuxA6uV7uvs3mW6ma8t2AD6m99LooLE2T9fG+j27xo9MDsU8rJMfAJTlYE1J7bXHaYK8vcW3fMs3c+/ePd6+/7Y0JGyGD56qrnHesVjOmc/n3L59m+l0wsHBAdPplAcP7/PkyZNerpDnOfP5QvTTqxVlWTIejTrKBF2ObFGIVhUUxyfHHB4eEpVBZZa9rSvsXz3g1u3b4jBuJJImyzPqusH7luFwQAiOoALGKY6ePEGryGQy5fu+7/v5zd/8TQ4PD5lOt7h1+xZb21u0bUvT1EDSDHdnI7E5dGoQhBhR6bjleZ409Jbf++xnOHz4gPFolCbc8nyzidKslGIymTAcDAjeM51MODk+ZrVakRc5RVFw8+ZN7rx2h7wsIIr29unzZ9TJQG4ynnD16gGv371LWQrNOM/yF5g4m9oUwMPZc379176Rv+galN3omTe1KYBw/hT/j3+Wb/jxL/Bodvyyl7OpTQGvOFjtKHvrjRE9kIAXzY/eSfGFNWC9vFF5cfoK3Ya4153GZCJzCax2k9POqKmj+3V/nI/rDXJaX2cIokxyx0URlAInmlPnPXmUvEyd3DOLvGQ8HuNaL2CkkZzDzK6PQxeporXBBCd8WBRtAmJKCe0zBg3K99E1yiqZwKExNmMwGkkkSJQpkEsA09hcnI0vUfp0irUxJsPY0Ee8lMMBbVMzOz/HtbXAUKPQWsxx5H8Kk1yGhSIt6+2qmxSHoBB8olAJ4GulQFtCcKxWK5kWG8VwWLK1vUWW5yyWS1zbMBwOCT5wfj6nbTrnV02Mazp3d+6dF+OhLDMYY/HeiVuryfopvLWW1UomRz442qYh11Ycd+uW5aomRDDG9tpWY8SEKgaPUQqswjUNRVaQW02eZ/009vnz57ikn3x+fCr0x6LEE3EhUJYlbdtSVytc2zIcDFnMF5yenhGcmE4pZcmtlUiUBBqkKZCMwFIkUVEUTKcTijyjKHJ2d3fwwdHUFatG8lsBgmuJKc9Y/r9Pk1nRvIbgUcYIFTxlKcUQiVruVecaFKqn1qrWcvZ0TlW3hKiwOqMYFL2WV6dJfEg20gGQBB2d/oT1/Rw1KsX+dPevPCP0C/dud88aYwnBp+gj09/nJBbDZZfqTl/d388py1d04h0VvEm5rC9mOAt5QX72e9/7Nezt7fDWW/c4OT0nODku3ouR2qNHj7i4uOD69WtcvSrTv/e9731sb2/z4MEDVssKpwJKGVaLikE+YDAtwItRl7ES75LnOYM8RyFRVo8OH9M2nqgDVVUxnkx5z507bO/uYDKJ9QlKpuVZLrmn3stnrOuKp0dPWK0kEubPfc/3cHJ8zO/+9u+yNdliPNni+vUb/cQcwDlp8qxWq6QtLVMDMFGAjcHqjBDkWWhQHJ+d8slPfjJJDcRQiyj0cp0ZvAsUZSHsgxDQKL7pm76Jpml4fPSEPM/Z2dnh5q1bZHnWG+g9ffqUi9mMLLMMhwPu3LmTJtYtg8GAohxgjZVn2gasbuod9VdOfo3PfM8P875f/a9Qg42meVPvzmp/+r8hpn319/7Xn+czJ/c2z8tNvVL1SoPVyxPT7sbpQEc39bhM3b2sYV1TffWXvWf3VUG/obq82U1cti97jbx3R/e9rHuFyy/oQB5IlIc1AgiJQSiPKopxTpDJZ9MEtBY6c53XBN8ZLK1plVlmexMTiYsA2XgnAB19r/+MKghgVd2/g80K8qJIlEPFYDQCJyDQN4n6ik4GUxrVb/y7zb9GW01UiszqFKcSsTa5fNYVTV3jO0AdJXKl06yKnk0lwHp5MtZ9DmkeGG3wyvXnUxoVkmO7WCzw3uH8lG0zpXVLiIHt6YTx3i7n5xeE8JjFvAIMrvW93lmm8DpRjsU4SDbsCeCjeoqwgBAjmbZeNvRt2zLICkKMOBfQWYY1Bucci0WF9y2uabFafJ/n8xli6COgu44S79E5AI9GI9zFBQCTySQZzSiCc0LT9B7nWrTSlHmJaxx57mSSahSZkoaF0oaiLKmahsViSdMITVMrjfOeoijS9S1mQtYarly5xvnFGfP5guglSqR1aSIeNSqzcn0HT0y0bImIEc2t6fTgqRFirSbToPAS0ZRoxMvZkjZNjI0yuLZh6ZYoEwixRdFijFxHnfFPCDrpb0N/7Tif/q7W92JHI+/4zp02XYyy3Av0f6VJWvPOJTrdy2lC28WfXL5/YT09LIqi13cSo8TkXHrehBB6VsHuzi75nyo4fPSYJ0fPWSwWQNZrwMW0THSY73nPexiOBty8eZOiKHh69JwnT54AkCdqelmU4jpsDFrL5x8Nh+RFQQyR4+enzOcL8kFJiJHd3T2uXb/G/sGBAMgE5m1mqetaomLkUxKjZtbUPHv2lCzLeM97bnP7Pbf5pV/8pb75cvPmLfKUFyzuyXXSX0dhD9Q106lKhlDdeTDpnCTnXh35+Mc/zmIxZ5QowcYaCEGydl3EmozRaESe5TjXcnp2zkc+8hHm8zlFUTAajbly5QqDshSzPKWYzWecnp0Kzd9obt26xfHxMScnJ0wmEzG061yqtenpyZva1OX65rc/xfzh57Hv+86XvZRNbeqPpPyzt7n/l39U/u41H3z4extd6qZe6Xqlweo7J2LvBKOdocc7wanEkqzpsl9urNRNodavu/y+EQU6vvDf138PSR+4nsropNcSiqC43Fq71tTKlFAnvaaAVqIXh970Pj44VFXjnEw7jcm4eXMP51qeP3+OMgqrEg04GfpwiQ5JAl0dCCdNiNfaUxLwiGg0RZbTdMYo0JtEKQXRR2EbKiRXtXN3tRneC+grklaToBgMh6jhANdIdmhd1TRtDV5AefRi9hNCF31yGcTqtK4A6ASOSdo8eZ04iQqoWa5WVHUl9NbpBGsMq1WFUbCzs8P21i4PHz7m6Og5RFgslmmKxtqxVKt0znzKiDQYbXswK5P0y3FGkBmJ3yiLAmM8QYHRFq0tbdNiTY7zgbPTc4yOKf7G0tFNm7phMZ9hTMZoOGI4GbE9mRIVbE2nGGsJ0bNcLAjBsVgsUySJQUVwrWTqVk2DMTnDoTjGxgguRKzNsZnDuI6+27ltCXhbLmYQPWWRMxoOaJuKarlkUA6JXnExn5MbQ936dH2ma7WjBMfYX1ekOB6QiJju+GpjBLQ7hzIyBS1ySwSJU9EtgZYQHJEWYmIoIA0XMfryRNVNVVOzptPGJgp5jCnXOEh2LZco5N119Y4elawz0dqlQSEgXO6ftZZxrVtNjuFBTM6ihzwXiqt3QjnNsozOAUqle7ttagZFwfve+16uXr3OG3fvcvTsGbCeytZ1zdHRUXJ1nnDnzh0O9q9SFkMmkwmHh4fk1qbs09gbn0XECGxQlGilaXzD02dP5VgrxbAsObh2jb0rV2QtriXWqbHnE0AkkcW1ALinT56IfrXI+L7v/R7uvv5FHj96xHA4ZH//gO2tbXwIYm7Utr0ZVQiB8XjcP2/lmJmkgfYMygytFaPRkE//k0/yxptvsL09lYgao5I7tU/69shoOCTLMpx3nF9c8ODhQ2nS5TmT8ZTd3V22d3Zw3pNpzXwxTzR60eZfv36Ntm15+PAhw+EIow15LppsrbVkEvvNpGBTX7n++r/5S/zbzT/g2/7p33zZS9nUpv6l19/4i3+XH330mZe9jE1t6g9drzRYjdETokMne5duwLmmAKY4Bt9BPtmga61StEM3dRUtlUvgVwx0JHcxaplQhUTJjXR5pWtY1RsowSUq6yXAbFSK5uiAsPyMLBOaoPKiOXWdnk5neAWhiURtiAjFMcaWtpFYG+cdqBlZLjmz1WqZNl8Rk1lCVGnTKcfGqhwVNRGJp1BKYZNragiBZuWIrkYHi9GaupFpoO8my1HAXJblEME5IMsgeGxmMEYn589IMAaDJyuH2Cwn2kTdy0uyGLFtS1Y3uLaVqW3bEqhwoSH4zqW1y1TUGBT20mRMTGMCRmeSHxt9cjk1acIVWcxWGCXUv7mvmJ3NUTxjd2+PqCLlIEObgqgDy+UK78La3MmL8ZOWURXWGIqyACU5p8SIQaibvm0p85w8y6irmuGwQCnLfFWR5QVNyqRVStG2NaumhuCwxjCeDnGtp2lbySJNlG5rFDStnCetWPqAzTPyomB/Z5cQAyfHJ+iUqDmfLWlb0KZkPCgYjCYYY1lVtUzmnUzD82KAD5q6XiXAb3CuRUXNs6MnGBW5fesWo0GJClOq+Zy2dQwyTSwLApq4WNC6hq4dE33sNYcxiKmRDgHtvdBylUYbnabC4GIALVmlzlf9uUsnO5EWRF8dU+xNBxYjMdFDfVq/Xt9n6Z7UpG6GimB0f6+EEHBu7UgrxlASV2Xt2qHaWEVudDJ5QpyTAR/jugmllGh0AZTBS/gqvmpTTIwRpkTbok26dhUQPJ2nUwie7a0BH/y2P81b936ft+8/oG0rimKA9zJJrtuW+XLJbL7k5q2b7O5sYzRMx0Muzs/FGVkF+YM0nPJBQVSWqCwnZ8fMqxXayhNyOBpw/cZVBoOCqloKIG0yRqNxmtxnAtyaljLP+P379zg+fopW8Ge+/UPkRcZvf/yjZFYzGg052N8HoKkrfJTIpaZpeudkmarn/TNRRWkIqDTtHhQlR48f81sf/U2m0wkqRmkQJKMoQqcNHzMcDlBGXLEfPjokKtBZji1KhltTDq5fxxYlUUHrPM+ePROauops7+2RmYx79+4lA7kSrQ0+yLM8eE/rRD+8qU19pfrxw99gduO7+Z9mx+jJ3stezqY29S+lwuyY//zP/xg/9vjXX/ZSNrWpf656pcFqiEL/k0xTUmyF6d0zYU0LJrlQrnvnMtESOq1QLLvOfxct0XqHivrLImpeoAOTJq5Ji9q5BF/+Pp+iUDp95zvfp9PVqegIUYuZDUrwWoxEJHNRaIpRnHW1mOjs7e0yHAxF5+paMW7xaYraaywtmS1wTqijIBpTreXzWmtBG4IPzC7mMhkjYjPdf06jFMoCwRGT86lRFhU9wUWciwQnUzdvDKpziC2HGGtlEuUFEOZFhs1L0UJ6cUR2reg+V8sVy/myn2B14NUoRUxa3KCSIY5KhkFRYxCgKRPiSEvLcllhjEUVBQbRTD49ekrrWpnQoRmPh5uJkq8AACAASURBVJRFwXK1YrmoECMYg44GpXWfq1rVNVmRYzNDUzdUq1UCl5bMipHVKlacnJ4xGI1RxrBKujlblpRFjvdDXFNRrRY0zrFcrWidF+2okSxMqzXBOeZVBUGopU3b4qNk2w7HY3b29lBKs1isWMyXRB+xxqKMofXQXCxk8hwhsxlRWbTS5EVBUQ5pqkHK662xJkMFR1PVaDxNVRGcOP5qFKFt0VGRayv6aYRZGy7RtDMjNF5UirVxnjZ4jAKTlxgroBGj8Srioksu2I0gGLWOwIk95bu7j9ZUcyKpydSxHfqAJmKM+ADaq15vKs0GelOmFyj+6e+dQZtoT8XURxvTT8rb1uHasAbHURG1Slraji+s0EbiXsS912AzQ2YMJorZkklUeaOs0N0B7xqUUrz/fV/HzvY2d+++wbKqxVk46Yld6zk5OeX45IT9vV3ec+tmYldIPFLT1GxtbVGWJa4N4gCsNccnJ9x7++1kfBUpioIbt2+wqlbMZhcMhwNpWrQtbesYjcaUA40LYoh1fn7Ow8OHKAVXr13lz/7Z7+bnfu7nWC6XjMZj9vb2mEwnVJWAUxd9T2PuKPtKSROgu09imjBLlJZiNrvgN379N+QeyiTrufdn9pIVOyhLRoMhAPPlgqfHz9GZxgWZ5E+mU9GpFmXS/3qOT55T1zW5FTOzsiw5PDwUqq+R52Hnd9CZ37XO0boNzW1T/+z6yUcf41u+/7/nh37538Dc+oaXvZxNber/1/L3P8N/91d+jv/28a+97KVsalP/3PVKg9V+QxqVbCIvTTM7uuZlB9cOVK6jJUzvDioUUPm+fjqgVW+8cVn7Cp0OLa7dV+hovrr/2pXr6cr6y/Sz76Qka9UZxSTXTPlU8qOCEwpyp61zkTwryLOM5bJCYYQyiyXPSrJsgErAFCWuwCYzWKVTJmWi9YaAx6OTXtL7NQV3PJ4yHApNta6EXtsfi4gAqphomgls4ANtJd8XPGR5LpRDFNpYbJ71x0Zbg7aGokzno22ZzxfUqxXVqpK4HgQc+ETB1imqRCtpOHSRLJ0GVqVzMLuYU9UN40FJmVtGo6Ho/KJP+lYxjcqzjBgirhFdplaiuTXWisuv9zRtQ1s35Lm4jgaXslmrCmcdRV6ibUZZlBSDIUFpBkYmv3leMB4OyTJLkVtWqwUP799nuVjIhllr6koMqIoUg+Od6ynOxhjQisY1xKVE0Tjn8c4jbtGWtm7QidrYrVsnWrXJLDGIttMo1WuTq9WCul5CSJq9KDE+xhjOzy+Yz5c0dQ1onBdjp9Bv6BWRkPyF1065OiqadBxD8BgrwOTs/IyqXqFVx4iQY88lCj5RrqWOfbAWil66X8yXu7a+877sngt9BqzqzMD0C6/pynv/gnt499XaLN3PtZigxZjygrtGiRFZrNaUZU6WGVonz47QOGKWMUguoj5KkymqgInp/kvr8t6zt7vL5Ns+yOHjx9x/+yFN0whYDUKpLfKCi4sL7t1zXL26j7WWPM947bXXAAGX4/GYLMup25aHhw85Ozsjz3JG4xG7u7vieqsUz5494/T0lFu3bzOdbuNoWS7mNE3FZDSidY7DwwfM5+eMh0O+7/t/gE//089y9+5baGMpyiHbO7vUTYOPAR/kfPvWEfpnnel1wlZbgg+9AZXkrMIXX3+dZ8+eUia3a5We31qn84ZiMBiQD0p8iMxmM05OTvprdGtrm5s3b7J3Za9/Rp+fn3Jyckye54QY2d/b49mzZzRNQ1mW5OXghWulM7tzzglbZVOb+v+oH37yYdS/Bj/0a//eZsK6qT8x9eaf+3f55WqX/+zxr77spWxqU19VvdJg1Vqbokjil4HSrkTzGEVmF4Ve572YDVlrJA4japq2xaVYFZ+mowLGOopv7OmcHRi9DFj7yBvWNORuw6y07rMeL4NoWZ/qFkpUAo4J4oAqwAzAQ9SAEaCKTGus1rimpSwHqChUZpF2ZmhVolQhRksBfAClTaIF039WVEAnjagPnrZtUESKPMf7gFaG4WhEYxvJiHQe17ZEH2TqacRJVdyATVob+OBpqhXBeUxtaVrJkwVxSbVFjs2yRF0WYya0ISssE5NRDIbYxYJquRIw7QJt62QKZ1U/bSVGgpKJtrZKTG6I4tDrPTSOWjeiwaxqrLXs7m6T5wWnp2fpOgoYYxiNhtR1hfcBH+TzhRT7IxTygIoyxfQmxaBonbJiW7Ki5MrV65SDASjLYDgSIyAtmtqLs1NOTiuCa2kTiB8MSsaTCcv5grOTU5bLVaKZ+/5cWSKD4ZBRUTIYThgMRzgXWFUr6mUtYMsFijyn9RGvArawZNaKU29UkKX4D5CJuQ/Uq6XkYDoHVnNlZ4fBYMj9+/d5+PAh9apJ9OtMrkUroFcn7W5MtE56fbQYLmmd5LB46nrFYnHBqloRgkMygQNKXwacsf/T6UrX7AX5752bMeHLzdL65sk7DHKkORV646XLjaLLrwsh9HE01loc66msMZqizDFtS9M6QmzRiMFWR7M32qB0JDMalO0NnIQOG8lslo5ZwEYg06jghe4dZI3aaPLc8t6v/Rqm0yl3797lYj4XqjvQNjVRa8xkQlmWaKXIs4w7d+7gvTRf8iLDh5ajoyccHj7AOWkyjYZDtra2WM6X5HkulP265s033uDg4CrXbtzAGE21aiG0LGcznh8/YTAc8IE//Y2MJ1P+/k//DKuqYTAacnD1BnXjcCH0IK8DfV2eb+ewLIBcnICzLBN9d5ZxfnrM733u9zBWlMaE7voR/b7W0nDJi5IYFefnZxwfHwsLJEJe5BwcHLB/cKU/l1VVcXp62j+bp5Mp8/mcpmnIslxYFul8k57TAlQTIyZsNKub+oPrP37yYT7951eUaH70+08Y/s3/+WUvaVOb+qpq/kN/lR/5xD5/7+jzNL592cvZ1Ka+6nqlwWoInZnRut45qfRdrmeUGAShfTkg0Layuc1s0RvGdFS1GBXBC/2PbuMcZdsMa1OlkMxcOrAa+wzIF8GqUjJZ7V57ebMt36PQsZv8CN1X6I+hBwOqAx4YQpT4lPn5OcNywHg45WK+IM8KQrR4ryXyQ4luMLLejMWkv+vYlF6FZGjiBPQpibwgBnx7xmQySY6bE9FIzmcS08OL0yqZPKn++DnvaRvJwvUxGT8B3juZnAUBoKqLszBeNv5KJq55WcrbBllv5rxkP4bkhKy1aApDwHU6OSW6Ya0UOkRCVPgQMdpQVw0VFZcjR9q2xXuJ+7DW0Laauq6TO6y4kZKAcYiRppXmwM7ODgDL5ZKLswucd+TaUA6HibZ5xt6VA5RJmkfvOTs+pq6WQMS7lq3plCv7VxmPxxz5xwSlGE6m7OxuM7+YobSiqla4xqGtZTSZkuUDnI+gNNPpDnpLNuDnZzOWyyWN9+BarPWUZY6xGVrbFJGU9Niu5WJxxsV8QXCBMstRBJarigdvP+TJk0eicbWZ0IvRElmTNvtdcyaS4l1ixPsWlO+vZzS40LJcSfZmjEEaQ8T++7qsVKVS40anaT0qTdfWNF+SrjWE9RS1q8t5vKnP9ALlt5MApDtLAMul2JsYI3mWiSESco83bUWIjjzPyXNLURRkjRHAGoSmGpOuNgTPYlGlOBuFtRk25ajWtcP7sNbPJ/1mnmlhPZAimBAdq3OeK3u7TCcf5NGjQ954401hgaTIoCtXrqC1aJu9D/z2x38bm1mGwyExBuqm5t69e8znc4lMQrGzs8P56XlvPLa3v8dwOMR5x5Mnj4kxcvX6dUbDIav5nHv332Q+n3Pr5m3+zHd8Bx/+lY/QuIDJcvb3rzKeTqmqiuACTVP31N+uyWeSFr5bc4yQ51k6LhZjFL/10d9iuZwzGg0FqAZ5jkqmtCLPcsaTCTbLOTu74NnxCatqRZYyUa9cucLBwX7fnmiahqOjo+RobSXixjsuzi+YTCbIdJ7+d0H3e8E5R/BRLsuNG/Cm/pD1k48+BsDP/MKQyf/5l/jST/07qFvfgLn23pe8sk1t6g8ud+/TtP/b/8Kd/+cB8+ZLL3s5m9rUv3C90mBV8M5XcgNeA0XnGpxzEpPgY9JoCvXOtdLJj6FJRj3dVGCdvdnFw3QGLuHSz4FkMnOZ0ntpk3wZyMl69Atr7dyFQ6e3RaH1Ou6mmzKFGHtGpIoKdER5oVwGF6iWc0bDIVXdiK0pmtYJZTizBdEAXuOjS7RIjVI5KI/YRqmkQTSYzKBjlKzRCK33zGYL6rphUJZpI5rhXBDqdAdeAj3o0Hbtnuo6x94YUVb31EAFMqE1AWU8IUi2o0KtDXOAqCJRCwgWd2FQCbAYpcmzDKM1vnVUq5Xkc6qIUlYcdJOjs0MonJnJWC5WVFVDkWeAXAtVVfWUTO+95L8qyaC0xoAxNE2Da1rm8zl1XTMajZhMttDapv/W8Pz5c4yx7O7uEIMjprVWyyWr5QKjFGVRQCYTs2fP/1/23j1Ykuu+7/ucR/e87vvuLrB4LZ4ECVIiKZKQREuqVKLYjuiyEseynSpb+UcqpZJUqeJSlIctOYmT2HE5dpzEkWy5JMeVStlxKrIcKYoUEYplKTYjkQRJkOACC2CfF4t93sfcmenu88gfv3O6e+4CKj0YYUHNrwrYvTNze3pOn5493/P9/r7fu/iosOWIze1dtFI4D7oYEGNkOLGsn1lnfWON4GMyztKpF1nic4y2DMZjysGI2axiPp8zW1RUdaAclNhyQFkWgML7iHOexaKiqhsMkcY5hoXl+HjGoXegpV9TyCbP2lj6IPcPDwREagF0WiG9zFHkrbmHHKVQBLzPknsHKhCigFCjswN3py5QShymVVCdRDjdA33V78l7vX9/gYBI732vd723QeMDMcW7ECImsaeZlTVKY7XpRazkWB6JpxkOJ4QQqRaOxbymrgVkBdU5bccY8S4QtLgwozQ+KoKPaCOMeeUCWhUoAkaZJKlXFIUoEOpqQVmWPH7uMba3tnjt9de5des2ZTFmUJYYJbmj1ho04lQ9HIzRSuTj6+tj5vUah3cP2dnapWkarl+/jnOOw6NDDg8PxRl7Z5vhcMjt27c4Pp7y5JNPcP36Nd588xrGFHzsYx/j0qWLfPX8eSaTNba2t3jk0UcJQTZ7FvVCVAPt5lvHXmfjusKU7TXTqTf7s5/9LFeuXGFzY0020FTP8RmF1Ubkv2XJbL7g7t399v4MwbO7u8uDZ88wHo8kPsh77ty5zWIxa/uPB4MBBwdHjCZrkDbD8oyJMaZNqiBz4sRcWtWqfqt1VM04qmas/dH/ij/10Dfz499dU/zAv4c588S7fWqrWtU95X7jZ3E//4s89z9dZu/o9rt9Oqta1des7muwCsvyvvSIsJGpxHW3EHYxIyCl0MpA7DI2Y8hmR/Iin1jV5VgaWVCLjLFjSfN59M+pfe6EqcvJ85U/I1rHlJ8qvXFRXIwE2EXJwURFVJTYDq2UuJUay/FUmD0VA7VzKFVAMKAiwStCEFYsu7ViBNOmxBG0MZSFkYW5dwksaAGJSD9vu7ALmSFWkjMaArawrQQ651ualKWY43DSUlbiQVpmLoFRRPocY0hS6GReE0L3d0hgLaCM9PWS+3utFVOcckBd1dRNjXfSM4kKEJz8XlR4rTDlAKM0dSPZpkprkfxGiUOxtsA7LyxYMuBqwY/SxBBYLCrquqGqGsbjMWfPnuXO/iEqSVwHSfJYVTVVXeHrhtKIgY9LWZTVwlBVnqbx7cZFYS3O1RRlyXg0FEbPFtSNYzabU1UN3sfEWhlhkbVFaU1pS6LSDIbjJHcPLKoGv6gYDocMygJtFM5HTFEwHI+J3lFXM1BgtSZ66W8OITAcjRiPRqxN1pgvFrIP4mNi0MHqAms10XnZDFD5nhE2McaI0sI8GpOde30bZ0O+PVSS+YbYKgr6iom3y0s+GTmVN0BiDzDlqKh8L75dm0AGuVkSmnOGtUp5wYkFD1ZjBoWARa0prGV6NGc+r1FG45qANuKk7VwQd2etSR7hvc/iiUDVRAiGoIN8P2mNa5xIzJW4g4cQ2Nxc57kPPMv1t25w+9Ydbt++zebGBqPRCO89tigYjUbEGKlqR+M8Dz30MJvb21x/6wbVdMG1a9dambPzkp18eHjIYrFga3eL4XDI0dERr77yKrPZIUeHB3zi+W/m0cce4e/+1N/jeHZMUZbipB3lGkYlLsreN+01lI29Lh5Lp5xduXfkv2tXr/Li5z7PeDwGBU0j2a46fSEppbBFQVGKIdytW7c4Oj7GeXEdH41GPPDAGdbWJjhfE1Hs7+8znR5JXJaxbG5usr+/n9o8TMuYt9fAy73hGtfOgZPzYlWr+u3W39/7DH//x+Bv/PTf5Q9ObvHEP/2b7/YprWpVAMT5Eb/yif+S/3no+R/3Pv9un86qVvU1r/sarC4DPqkQfLdQXpICylI2QjJNomX5ci8rdAvizMxmSXFezIikdrn3rZ/F2q8YI7njUallyWx/8W160mUTIeCJQfIviTqxiPlzRAJOQFNMi9rG4V1FwAKWqGKChYVgNRTOJ4SqRN4a0vGMNinKB8DgUTQBIoEQBThrpcQdFY2x0n82GGTQDiRQ3y71BJkm10/avsH0wSXCIoHEKH+RY6ReRYmtQF6kIUaTckGF5crgJSgB7dIoHDHKYgrLyNq2Z9A5h4oFBAHW4nYqPXHdNXaJYRcgIWgqMbKN9OG1C958vZXIMI+OjqjrmrquWSzEJdinPmeTQG62ntIEnKsJkGTFeV5YqkrAr7NBjH0sLCqHGCxXzBdzFrOFOEur7HZrBLgj16QyDVqJBFJpI8x7WoAvFgvJdrUJSFgBBNUigLa44FMPpiVGhy0sp86cYX2yxnw+YzafCjuKE6BiLMZEYnRIpqoH5dLlDyliJksr01wmMWhREaOctzD9WTaQJ0+W9oZ7WNW3AxX9vm9rzT33WX7NSVb25PdHCAHnxQgMPFrZBL4DrlHUC40eKqyBYlRgjcZaOD5eUBYFISoa72TzAFFnYHIrgIyDc16UETqgfMBoAaVZuqra3l2VFB6Osix47NFH2N3e4eLFyyzmM86ePct4PE4y5RIfApevXME5xyOPPEI5HLCxsUl1XPHGGxc5OjqiKAqqqqJxDYvFAuc90/kx21tbrG+sc3h4yOz4gPFoxB/71/4YL/zyL3PhwgUmkw0eePABnGuYzQR8i5u27HY1TZ2+G21i/EW2bE36WSmJUprP+MKLn0epKJscMYIShYUxBo2iLEsmkwnaGA4ODrh95w6N8ygljsZnzpxmbW3SzqfZbM7+/n5yAnbs7p5iOp0mab8YiS3/+5AMoVxPsn5yHq1qVb+L+sHrL7BWjvjUx3+Iv/WvNgz/3F+XJ5T+zX9xVav6Wlba5P/iN/0Qf1nD/3bn19/lE1rVqv7/q/sarMYU7RFzXirSZxq8gI4cCUI2gTECnBrvJb7EC0AVxkwMdGQxDNkspr+AaaNmEiuUJcE5Z7WVuPbA7sn+1Pxn+x+0UrSYGCs5ldSD552wE1r3FtkIcAOCd6CMfDgsShVEJJ4makuMAjKD1yhjIfcJGsNgMBKn2BwTAolFDPiYDIYy4I6OCBTK4oNIk5XRSbaanF+1IrsyW4WYpNjQ9rT1W8JakE5m06SnLCLgIEfr5BKQqjEBFnXd9p5FH8QVt+dAaq2AVrRCW5Mk3NLX2jSN9EaaAmNFVojrXQMlHrcuKpFopszM6PobEorgXTs3jo+PmU6nS73SwuiUBKeSa28rQkwsoLBQ0Qeq2UIW/zFQ11A3gfmiblkhYwQADMeTtrfPJyAXg0JpCEQWTY2KjsYLQ5faONEpN7Sqa2IVuzzXskTcvDzBgQ8i7cVYFlXNpUuXU08luFCDCqgcQ6MjPta4ppbMYR0IJMCrQJnUFw1i1kxiFRPzLoJ7JRsZMe0P6BQtpTIwzWCyk9XnbOT+POqDzmWVBe3cXuqtZhmw9p/z3ktve4SGmL4/ophoRYk2KssBWkUGA81kvMXR9Jj9g2MIgYFJUnLn0KZIvc95c0oGMwRP4wEjpmrBB5HoK0Wpi1aO3ncw1lqzvbPJ+sYHeeONN3jtwqucffAsw0ce4fh4xu07d9nbu85sPmM6n/Hkk0+ysbGBKx2nTp3i4sWLvHrhAgaLTjLiw4MDFlXFdDplY2Odra1NRsMhf/JP/HHeuvEWP/ezPwcoBqMhw1HJ4dE+ITicq8URO0V8lYNB2wNOEIY5bwI65xhYC95x/uWXuf7mHuPJGBVDMqiTfn6NoigKxuMxa5MJ+4cH3Lh5k0VVobWlHBRsb2+zvb2NtZamEcB98+ZNGVPnOHv2LIeH+9Jznr4LTn5/u8bT1I4YkwwcWs8B2/u+WdWqfjc1ref8g73P8I9/YkD5k98FwF/d+Gb+jZ/6JOaD34EqR+/yGa7q67nc53+Bx7/nv6MJjllT0ayczlf1dV73NViVUm0/Z2bzcuXFnvTpuSUZL5CkielXe4vW2AMW2f1U9wBom7tKkrb2pIT5Nb0DC/EXuiiOPhMbYkxSwf6vqJRNaqQnK3/M3gI7s5Bymun9YsDHRoxv0EiTnPyngiFGm1b+GmOGFKZM8Rg+9ZYmgOFlIRmiIPIQHC71eRWpH7Wp61ZC60NeWNvESgljKItFndjAzpinz3DlRW2WPIsBk7jx+sR4yjhlZlxhrEkLT0fjG7xzPWOetEhN196YfD0k51KlvMeoDEHBeDwWdjU66qqiqitc3VAMVJtz2qS4HhCgoRStu3N3fBkLY0xibjUxeOrGtQxOlj/LIl0BjtrNxKQLUMagTYExRXtM7wNEB8ncKPfP6rSpolCtiZNzknkrbrS+dVHWVrXMfozgksFVaSUCCCMZl8H3erStTe7BAbSANm0tPjo8wkAqROYbYyCSZNfk/uy0L0K3OZPnbruZkzYE5PrnnkfIcoTMruYb4CQT1j9elp7271/VP9iJ38ny1HsY13ZDKR+ncwxWiOOsUjAeD9E6MFmzbG2f4tzjZ3nzzZtc27uB9EtrQmhA2bSpADF/bl3I3GpNzsQJN1RBjLrKQXdfpPHMucMQed8zz3Bqe5cbN27w+uuv43zg8GjK8WyGKQw3b95iNpvzyMOP8MhDDzEoSp5++ik2t7a4dPkyt27dJsaIDZbGHzObzTg6PGI6nbK7s8Wv//pnuXLtamJ8CxbzGS9/9SWIUFhhS7e3NtvNpMV8zmA0YjQYURQ+3Qe2N95w48YNvvSlL2ILI/MqDbV02ItD+GQ8YTAYMJvNuHnzJtPpFG00RVmwsbEh8Ttl2Roj3b17V77XFWxvb+O95+DggOFwmDYZ8uaXjLV3jibdjznrtduc7LKDV7Wqr1XNm4p5UwHw/YsX+P5PvcDrH/oAm88uq7GKDz9G+X0/+m6c4qq+jsp9/heofvIf8skXjrk1O3i3T2dVq/o9q/sarGoluaSw3Cfa71/1PrZyUEiLF+LS7+SFTZZv5j5X13h8TGY71koOKUiOYupx0wjIAAGhRVHcs/gNsXM+7bNv0isbiD2QLexqyq1MfXs+MZx9g5BA5y6cRIzyfIit4ylBJXY45a8Gmygsg68b5qFhMBpjihIDREPqxysJwTKrIsk5KcWtiPmQ6cujU6+ntRYfQoqHMICYE2kdMdriY2eElaOGIjEpo8QJ2dpC2NqehFfYOYHAKm0EWCVAMrs7xwQcs2zUezkvZTSFKhJA0MmkybZ9dcRIUJrhcERZGJq6Zl5JVE7wnqau8c7hQ8TVDRpwXrJvMxBadnU2Ii+2yedVaXSWg8qL5I+QdNIqCIsMKC3GX0Up/beujTqSSRu8sLMuNola7xj8zNYWhUX1gClJSt74pmVnQ0hOqMFTO+kLRkksTe7DVgSMNYwGJcOyABzz+TGL6hhPQ0CUCVZrRoOS4AO1a1JMTqT/tZF7sjPoizGPWwfe870TQ5S9FXQrD87sah7jvnz+7frV+6/pn8OSKdpvonxQWrebPxnICFgVU6HJeMR4XDKZDLGFYrI2oigN29vbPP30w5w//wYvvvgV6jpi7QitDFHJJozkQes0f0RG4BqP1gFtFCFIr7xzgbIsKMuiBcrZXKgoCnxTs729yfb2Fnt717l46TLT4xlohXMSJzWbL7h67Rqz4ykPP/QQ29vbnDp9itFkzJvX3+Ly5cs0TYMOsrEwm80I0VNXFf/XL36ard1tysEApcBYxWI+RQEz72kax+H+bQblgPF4wvrGpnw3hihgMIgngGxgyTj+88/8M5qmZjgc4L3DKImgyQZ5ZVkyHo0obMG1vWvs7++LaDwExoOC02dOMRgMMMbQNDVHR4c0TQ1ERqMRGxsbXLt2rf0+t9ZibJHulZyl6962ZUOrBGzfvptjVav6mtaTL70MLy0/9oFfnvGf/I0f4bt+7Bspvu173p0TW9V7t2LgF77hR/jfh56/s3f+3T6bVa3q97zua7DqkvNn31AF8gJZt+xIZinqeiHyVa2SvLLAmNQvmpgZhaxvNFpAReptNUa3C58MLIWRu3cBnRndmICqhJ3qpde0i+cWZPVBT1rAK3HWDQqCc4klyK/R6VyTrDexxjrFgwjTlIya8OgohkmgCXh841m4hsZVaFOAEXmgMbY1LBoMBimSwhMT+xa9BxWxppBePu+T1FGYzjyKESUyXC3GVsGf6P3VMp46kuTJhoBOTr+GojTYIrYsax53nXrcFMIyeudo6gbvfDv2UfCbjGGSMSulIOU9Wm2lh9DLmHnEubVJmwLaCIAdDCPWaKaHB9y5dRtXVYQQk1wwJBCq2r64qAzaFtjCUqQYo7qqKLRtpeKt2ZRSSTYtPdZaG0bDEm0s86qS+aIBIi54kfOiElMuJmKtMUzoyWGT9LewlqKw2MJgS4kDikS8IwH62EU6gUTcpP5MFT2DsmA4HBC9Y3o0ZTY/wIeGqB1RCZOqbYk2MeX1duOskmESUbXMIjD17gAAIABJREFUuNYm/V2AekwxRlLdnF9mYPXS/SBz515E0d1LdBs1/cdhiansz8N+5jHI3kwwoMkbUDFJoaW/e3t7i+3tdQZDTVlq1tZHDAaa8aRkMBjynec+yaOPPcSnf+lXmR57wKF1QYw6xVyp9rsGJYxfDBG0bSNsvPfM5zIvRiMBaNJmoJnPjxmWI2ECg+fs2bPYouTLX3mZ6fGMydqExnuMhePZDOdqjmdTdk+d5onHn2KytsYjjxSsr6/z2muvcffuXcpSHHvn8wrXeGZGM6sWjMdDtnc2WVsfCfMbPNZYhmWBijCfTTk+OuTu3btMNjbZ2d5lbW2DuqlQM413jug8n/vKl7l58wabmxupXUCniCy5XwflgNFIFA6HR4fcvn2bumnQ1jIsSzY3N9jY2sQgCpnj4xmz2QwxdYLTp09z9+4dmqZhMlmXaJ+0aRhipEnfYfkezPOrFeRkZcQJ1n5Vq/q9qpfvXOFPcoWP/dtXOW1/hR+oN/nDL/4oypbv9qmt6n6uGIje8T88/1/wQ3f/6dK/Zata1e+nuq/BqlaStRkTE5f7Ro215KWIUhFjNINBgVKRxjUtwBSDog7QlmUhC1jvE0soxiBaWVnoJ3bLe9e6BWul8D1JbtPUWGPBWpFURlq5mVTOT5WFvIkihyRGfIwYOkdNndxujXY4lPQp5mxZhWSMao0mydiS4ZFCp1gORQxBmg5RhAAuOKIHFTVaOZT3BN8IWFQKnwFrYTHFMDErGl1I1EaMEFzTShNVU0t0R1QQJb3ShYirayrfyAI8na+MnchorbVYbDI6suS4oJheq4xJPcC0oF+Anrj16l5Wp3eeylVYa3smT931aXv/ooCx2i3wLtDUFYvZrAXBRFn8DsqS8djJOJaDtg+67TWVq9gy21nKqU0USXRQjAYl1g4pyxJX1a0ba2ydbRMQI2K1wjUVsyMPSlNVdU8yqlFRYo+MNi27rI1GmQKMQhsBmoq24RlfR5pFkDgghYBznXJpE2j0KbdWkYAzJHbUU1ee/bsNKggwBU+MdZLnCvM3HJbCshtFTGZJcs+pBMJU26cs7DFC35PjmTL4VK382Hd7OEQ6BjRviOg+oEjseNcFoFr1Q8eaBvJJ9JnUDFpCYrYzgyq7QRmo5v2pJN/2gboWM6Cd3W0mkwKtPTE2gCeEOUaP+I7v+Agf/chz/Pz/8Wm++KUL1LVDqaHcg0ny3vhIsmpLHyMmybd8XxW2pKk83lWMRyPpsyYyGozSxo+YfoUQeeCBM2zv7PD6xTe4dOlSYqmlV7+ua5raMZ0uODyYcu7c40wmE3Z3d1lbW2Nv7xqXLl3C1QuKoiAGz6xxVM6xWCyYz+dMp1OefOIck8mYpq6Yz49p6horjek0zYK7t2r2b9+hsJLHvLt7ilOnznB9b48vfvELbGysAeIkrNOmijUlRVmyPlljOBxQLyreun6d+XyWAH3k9OlTnDl9iugb2VTyntn8uHVuPvPAAxwdHXF4eCT3mvOsTUpsIfFaTe6XD/2NCtrvhhC7NoRVrerdrs/eugDALyrN4Knv4sofOcfge74TAP3MN2EefvbdPL1V/S6r+ZV/kBcMS6Wf+5bfdtxR86v/kBv/6S/xoVdeY+HqFVBd1e/ruq/Bau1kca+tguBbqaNkpsqCLmTW0yhMYVAmLYq9x7kG6WU0CQAIGJhXdWtwIvEMwtoVVlPXdSvjEwkbFCkrMUZxucxMbHa7JLn9iuRTFtAtW6gUxAzUPFF1gEikvB5NxBqFCsJY+rTItbZAKZJUNrT9k9lUKoQgLKkWVgetsEGYLx2iuP2GAKpBRSPgIipwmuhKYSzT95+1lrIsKGwBxQgXArooKEdDfPAEH0GnrExo+3R9CIjxlROgFoK8v1J412BsSYyeJvUAW2vxPtA0lSzQ27FLfZOJMVFai7Q7RnRREPUCFwPKe6wSaWNIEt7oLBgrpkOJ7WyqCtc0BNfgQ0hSYjnvhVIcRDEiCkHcTlGSwwn9DkhFDMnVV0caV2OjwROYEyms3D7Sy+nRKhBVQCOAMkbVAiVNJLiaGMG2uZ0BhXyWDMx00IQYCF7RKJ3AqKEoTGJMpR9VtUAu3QdBGGPTsrE5R1h695oQJDfV6Na5VudYIeR1Rukk0xUzmkKZxM5K/JFzrr1XpMd1OWYG6LGuaYMjsbkZS6g0wN71+1zlvEzqP8+ls4t07O7FPhMr80TyNUO6Z04aLen0WU3solaUsbgQUDEIwNfChvoYuH3nDuO1ITunNwjI/WcKizGJ1Y8LXHOH3Z11vvfPfIpf+7UX+fQLn+HO3TlKDeV2o0wqgGRGlU3GYlJDOJG/am1QXrOYOXShGQ00UWVmOuLaTaMabRTvf/YptjYnXLhwgenxMVoXaFMSCSgVuHX7LsezirMPneXU7g6j4ZAnHj/H9uYaly6+zq2bN6U32paiWpk3LKqG2azi6HDGM089yTNPPw54Do/uMjs+pK4WaKWRWzHi6wV35nNu37zF68XrLKqKyWSMRPNUMpejuIsbDYWxFNbinePO/m0Ojw5kI85q1tYnbG1tMBoPRZbvao6mU/m+IbK1s0PtHPv7BxIbVAwYjSZgDI13BBcIzuEa37/i6Q7WAlTTBBNlxMqtdVX3R4UYmDcVp376FfjpVwD4K2f/Rf7ND15m46d+QtRIq/o9r9m//wNU1+rf2S8Hxblfu0jtm3ue+okzX+WPPHd56TG7plj/2z95z2ubf/Q3mf4vv8FT/89VZvXid3Yuq1rV11nd19+I3nuqqko9SkViazqpF2TG0ac8SkVRlBijJYNysSDG2ErGTrqKAu0imOQImkFsX84ri+kEeHXH4IiRTt617ySHrRw0L8ZzPE0yLBGwpxC/4Z68WN6o/dlaAbaejjnQxnRMYrp8wlhFVBDm0JgkBXUBH4Tdk/7JTr5JkAWzT5ra6BtCcFgr7sQ+Zc3axICW1rROsTEkNqsnb26qBXVVJbMlMQNyzovDbylSp5jGO6bnQwxtj7F8XjFHGpRDrDZoq3HKg48MSzFmwXmiT/3FPtDgUNqhrQVdEKLD1TWurvGuSQMRiMkcKHWTimSw7kxXrLWJWT5h8hMhpoiYqAJeRXABl1x2gcSGu2Rc5bsd0CyRVVpAk86yWWGgUhNyHkqZ26qbIfI3Ab6uqTuZo8ogNE29ZA6El75IYSjBGoVJzDVaev/KsqRpaqpqQWEtTQzJTEvGPwO3/jxWSqf4n7h075y8l2ICzyfHr1/vJMXM/aNZEpvneP7MJ6W/HUhuLyjp5mxfK/dx7I1mdw46RToJc9/QuMAwDoDIlStXibHi/c89zekzmxgTKC24pmIxX3CrcYyGc9bWtvjYx55ja2eLX/r0P+fChT2MHhMJWK0hdoZUWhvJFY4BYwuc86gYKQqNwtMsKryLrI1H2MLSSt5j7geWjYvTp0+xubnB3t4er732Bk1doa3FOY02kfl8xtUrlznYv8vZBx/gobMP8vDDD7O1ucHVK1e4vLfHvGq6axE1Te058sd8+csvc/fubZ566hwPPXQGe/YBDvbvMD2YMjteUFUer+R7Yzafs9i/y9r6OsPBmsznnKVKUjAMSobDEms1+wcH3Lhxk8Y5TNoYO3XqFGtra+LgrSR+KefPrq2toZRkrCo0RVEyGAxSTq7825D/W2o96M2xFRGxqvdS/fCbL/AfXFf8N5/4ixQR/vSfHVH+mf/w3T6tr+v63Id/iBf1pP35zx9e4c786Gv+Pt9/4wW4sfxYaQr+2kf/Aie30P5XbvLCWxe+5uewqlW9l+u+Bqt13VDXdQIxeQHH0kK636eUe7NAJclYFz/SB5B9YJRLpI3iQqkTYCyKoicpjBSFsITQWzQT24V2B24FeGZTGVjuqWudSvXywtuY5IKbnY0TIxWj5F5qrRPgWV6cxSSJVVp6cFVMa/eU6qOUMMBR5wWcAC8dEttLREWDChBdlOxIpL8vYtOCP4DXEo8aVWKsAJUAQVFIv2v6vCoxv3VdY9NnyyxqSMxz22eWDIRy7qlvAqWVfmODONeq8Zh6sZDfcQ4VTerfzC7HgYY6HUMchGPwrVGT1iTWW15rMwOXjI50AvMZ/dzjNJuAZXYedS537iqapu4cUNN7qNjrzzTJlTdJQkkseQpHIr9By0jq5CLtkklVYpIBQvRt37XSqgVsVnWGQihxMY69Y2ZTmqIoQImc3TnX3iPd/OyAqpjWNBIVorv3y/dTPq+3zyFW9wDVbiBP9p0mx+HYbRKdrH5MyXLvOq08md793UZMJblp//eqqkJr0iYWrUS5aRpC9GhdcvnKHlU957kPvo+zD+0wHBSEhRgmVSFwdFhx+9YhUSmMKfmOb/84m5uv8vkXX8U1gSL1neZ7te29T+ZatpCvXucrlIrYwuIbx9H0iMFgwHA47FzIYyQ4134fFUXB008/zc7OLl89f4GDg0Nq5xgMhxJxVHmCd1SLOYeHhzx+7jEmkwkPP/IYGzu7vPbGRW7evAXIZw8xG5l5rl7d4+7dOzz26MO8731Psbl1muFgiGsaFouGt27cpvaOEBzDUcnGxrqYjIVkIuUjpigpC8twWDIajZjNZty6dUvMobRFacPOqdNsbm2ijSF6x/H0mKYROX1ZDtjZ2eHmzZuJCZf++hyfk83v+pFg+foqdXID5e3m4KpWdX9WjJEfvP4CAP/1XzrLE3/93+Efv/AjYAeo0frXFeMavSO+HTDUGj3e/Nq/Xz0nJqZy8Rf/Iz71C54X999o3Zx/r6v2Df/uWy+8K++9qlW91+q+/uZrakfwEE2KVSmSkUmIeJ/71XJWIm3eZ1EUlGWZnCWbLuagtwjPYC+DWJsiGyCxs9a2r80LzSyXzBVjFOluDAlEL5srBaUk17O3lm8XVFreM4PtLh7Hieur6sXoqNxfa7qFZYxJItq9n1aJ5UyxNFpr6X1EJWOm1NepFEpZoKBqHMFHeV4HYqwITiV0J/md0OCUIjYC4KIyYDJQTv2SIYiLr9Ftn6CxlgB43yCw1wgzrEQIrVG4mA4SpI84AL6qccZID2eSfhICA2txRAhR/qETwhBlFNE5nHc4JwBcIcm0IQHCGHIUkUhy2ygVLWAptFm0IiHUvfdu5d1In2ZQHryX3mClEmsbRbKejqKyHJcI+CSXTqXEQTkiTFvIAF9JT6xRJsWGyDlmMBVCmos6xS3p5CJMYuXbHlHamJo8N5zzHBxUoJOEN52NGJElt+wknc9gL7Oc0ucNBp0+Vx6p5c2eTiGQ5m4QOZXubbB0Oaqq/czkkY+BEDqgke+7bHDVf69uzqdjmd9aT6JcE5+ceR1a57zgfL8HFouG4ajg5o19PnP0eT74oWd47LEzuLpmsaiITqTuwTfM5zO2d3YJ0fC+Z86xubHFF794nsODmYBvbZPEOPXxakNQntr7JAP3aKBpAipAWQ7xPnJ0dMxkMpHvpCibD3njToyYZpw6tcvzGxucP/8qb755HVfPCWjKosQBR0dH0pN6dMQTjz/G7u5pTFHwoQ8+x961N7ly5WrroFsUVjZhmsjR0ZzX37jC0XTGJ57/Jp77wLMczw65c+eAycYGd+8c8vLLF5iMx7imxugCpTRWGbxSEBSD0YByVOKj48atmxxOp21r+9bGBrs7p7BWelBnqXdWNoMUW1tbHB4etmqZ4WBIWZSoJEvP/xGWZeH3XmvS/EsxV/okh7GqVd2/9dr+m7zGm6x/6E8B8OKj38DjP/phin/l+97lM/udl3vtN4hf+Q0Abv/YZ3nqi6/e8xqjNLf+0h9CbQpgNf/C96DXd3/773X+nxFf+UL789/+C2/ywzf+CZDWgit78FWt6j1T9zVYVVoggk95mN4HykHZk+jmBUm3uJaeSM90OsV73y5COxmvTgv/0B6nD16HwwKbjE2apmllwX1JXlqrd7LidL469UWFBLxyL61kkCYzoNTvalJPZoSeqUzEKAGYxsoit2kcTdO0i31ZcOVcxk7WLPLT1EfqHZGItoXIjVsgkQc2OboGT2mCGCgpD0oRiOJSrAyRkKStrWAZoiZmrTFp/LWSvFYSA5vkvRoBptEHgnc4I/mMApYEQJr29bFj4oIA3EDO1kxMslJYIj4KwJb8T4WKihg90TtUMqcxWVad4mOkN5NkUKVaWXA291GJrsxSc/msPSm3VhTJ+Th6j7inZrAasgI14UtZlWvdbahkl+BWAB67D6uUQtx2lfSRRgcx5QTHdM4JOFsr4DWbV6vkuSQMZ2ZdMzDs7g2tdcpfzY93czrfJ2VZUtW+ndf5HhEVQjdWS4xzHsPUG4vKr3i7XGPan/Nc7KsR7rn/lVr6851e/7Zgpf+eavkcck5sjKGXzxyT2ZpBYQhetlNmxzXnX36D46MZ62tjjFGo6ARkR+k7PTo8xpiSxi0YDAwffO4xrlx+i1s3DlhUVZJtFzhIG0O27W2PIeKjF5Y+KhZVgzEyDw8OjxiNxcRLR5Lhksc5iZKq6wprDB94/zNsbW1w8eIljmdzvG/a6+q95+7+PscvHfPwI4ecfehBhoMBzzz9DNtb21y8eImDgwPqusYYjauibIzZwJWr1zmc/gp37tzkU5/6Qzz//KPcunmXn/7pn8EYzXg8Yj5fUBYlTe1wITIoB1hrhOUNkVs3b3F0dARJabG5scHW5g517QhxTlSBRbUArfGNY3Njg9lsTlVVadNxQFEUaG3a/NUYklmVj737d3mudNe7m/8n+5lXtar3Qvn0b9U3XHqR0b/1Mi9/8DNLz29964jhj/y378ap/Zaq+ms/zN1fugvAf3/jAf7q3j/5TV/v8Wz88M+2P3965yJPPXYbgN3/+A/eE/0T50e89a//2XuO85dvnuLHr/3q7/b0V7WqVd0HdV+DVR8CVV2jXZY4OpwPafFCB1azw2zOAYyxXWi3rqAnpIp54WKTQZFzGRTW6Lom5Yq0C/qc42psAjSxx+4k3o0kC/Up6xJEvpgZT60kYZJkTBRDEFMf1UmZdc4QNEYMhVTusdOJ/QjJ2VUTo2qZL61NYmmjMDFaYZQixV6m42QGLIEfPNYIU5ZNlJRWaDp5IPhen2eU1yiFChITk7lKkaymNNh0Th5Q2iS3ZVmo+7hYWjgqBbqVb2Yol/uBk9I3di67RAHEBvAxCJMZQBGwRKLJvXiA8vKkzs7LWeIa275MGbseW6eyvDr1StKXlcr5epWyYVVsH5OW1NjKgOXDdXZZkOefav/QSifJeZfFU5iSpq5oGoeOaXEdVXINllgdpSLRh3R8MQiKIaAS8M3mXy54yZ618rPVNo3lsiy2D06NNqC6fvEMVvsxNf1+7vZ+yuZUIcU5qdCCq358jFoa9+UewyV2Nm0q9c8zV/+9366Ptn3dPY/QO17eoJAddhcaQogMhyNAETwopamqBU09ZTELnDq1ydbWiLIArQKFNdiixDeRg9kB2hqKgWUwiLz/2Ue4s73Na29c5WjeoIxFRUPjYrsFI3FXlvwNAuKGG6JuWwKOZzPqpmYyGbeMtPTky/dLSH8+8vBZTu/ucvHKFS5evETwjWw4Rdl0axrHlatXOTw85IEzpzh16jSndndZm0zYu7bH5ctXOJody7UOcHg0pywL9vdn/L+feZErV67xLd/yrTx09mGuXnuLzY1tnHMYY/CNSxs7Im9f31hDK83+4SFXr15rvy+HwzFra+vs3z1AWcNoLOZtPjQoIqd2dyRDdjqVe6Eok5ldx/znjcXgIzFvJPbmTX8+9WXYfbnwqlb1Xq15U/H4i19deuyJNx7k+/7RjwLwqIM/8YX/7N04taX6+Q/9eb4ykO/3v7e4wvm7V9MzX33nX3qH+pfu/Brckb9/9w9OeJ4vLT0/V5H/fO+3f9xVrWpV7526r8GqmIrEViar0iIa6O2WZ8kkKXZCJzlvx6Ca5BCcAWe3wx6XDJWy7DAET1Qeo4t2gZMXTBLf6ZOEk+Qo2/0ueAFTiZ3sO5qSjHVE7tjlaHafV4lEMWpicku1SvrcPCGZ3HSsMCmGRCmdTHw0pjAJnKquf1IpyR5VCp8YWHF9FTmtMLkar5IUNyZDohixWqO0ODa5EIg4NNm4R4Cs1QXBJACeo0JUUtrGgIoOm6hA5x2+9sLUJoMbDSKTRCCb0ZZC65TB2qQIipAW5p1hTvJ7IrVlCpuslDjiIpsdRJ/6QwVQKaPk5JL8uAWvJHZXdf2rhdXJkVnhUqxRbgpWWoCqNlpckhODOhhYYc0bj/c1ITqMMigDTSOy7eCF6ccYdDSyyM/zWWmMKYhpl0HMjQLex949EBL7lqS/SZ6uUnZoBnPGiFOu936pfVQpMftqo6CSaZe1lkXll5jLfG/E5CXWVyTk57OUPd8j2bTMOU8n8+2OdTIPs/9n/97O93o+Zt/4rP9ZTv69vbt7myA52iiEIHOgfY3MUSHqIjGmOCuNOH/bQuSqjeLWrSMWiwU7O+sMSmFgCzsgRE9ZDjBWURSyMVEWikce2+XM2V2+8NJ5btw6YjgsCHOH95JLqxBzp8JKpnGM0tMJof2uKkqTlCIzhsMybdSpdhy1TvefcxSF4bln38cDp0/z4hdeYlFVqIAAZa2pqobbt28zPTzgcP+Ax86dY219g4cffoTJZI2rb15nb29PrmkMhNjgfBB1h7/F9Td/jslkne2tHYpCHHwVAe/lM2htKUuLsYaqqrl6ZY/9/UOGwyGj0Yj1jTUW1UyY06ZmfX2dEGMC40NihKPDQ+jl72ZvAYkTEzVDSBnCWQLe39ToNuQ65U3fq2BVq/p6qzcOrvPnDq4DYLThr3zoT7/LZwTnD67RePc1P+7PvPlZfuZrftRVrWpV93vd12A1S3tz356USMCknym0z5v2tRHnfNtvZ61lPB62/ax9RifLMwXMyFC0u/AKkcGG0DKPRGFWW1YjSW5V7lTsIYLMObYsYXpUWDzdgskeXUWOumjzYYkYbSgK26ZcSrSHPK6tRXvf9S32FvPadD2TfUOqGBIoVEHAXorkkIxZn8yZGmxRiPlJkB4/AGvEZVZ6YWMCLxFCTQ6vDj6B58yIROnFUzFLaz1ah5YxNVFArCYxckiiJ0FhiBRF2mhQEYJLQ5UlzYldC4iREhnoCIjLUtTcM5llswnZprFXyT05QtQJQEtmbYxeJLkoSACrz5yK7Lffc+lo6kBI/csoAaHWCvAejcQkZrFYEHyKzIkWm4x+5DrlzRjTSm8zIIQusik/R2YhWWY65ZRjuyGQRyuzczF0kTOZsWqaRgC/7uZSO3fCSQa0u1cgYqxps2oHgxLnGkJ0vX7cZearb5iUqwMUy47ay4Y5PRZe9cGrXP90Yu37xNhzA1aqM6VKUmliFrh37DJ4wLQxOnV0GCWmWq5xLGYNW1vrPPjAGt6DsYaiFMbb2Ii1JUZrBgPLmILnP/4hruzd5JULl1iflIRgaBqJ7zFliXcxbUCIpjtGibYggvNQpJ7dqqpwzjEcZmmsgPqmadpIqDpW7G5v8x3f/gd49dXXuHL1GlW1QBkrm3pG47Xn+ltvMT0+5rFzj7O7s8vW1haTjU02Nze5fPkyx7NjiIq68kRrmfqazY111sbbjIYT6kUjG2Xk72lRfgyGA2KM3Lx1m6OjIzGKUpHd3S3Go4LpcU0INfPZEUoFhqMhTVWx+9jDvPXWW0Qiw9GQjY1NrC2SuiK2EuB0e8h1732/9tn4bl5lFckKpK7q90f54HnpzqV3+zRWtapVreprWvc1WPUuJNlpgiWq/d+SlC87BLdSRLqFuFIqyRHD0mJepxiR7CwpIFaRTTxcMs1RmsR6mUQXLve6eu+FaVMahSb3VpJkfC0bB4ll6jNBSDZsT4bpfUgAiWT6JLE9+fkFdQJSAaWMnJtSeNcBAq0zc9j7jw74QJagyiK9dddM7FVRyMKb1AOpVRIpqpgW+6Tgazm4dwI+Q3ACTnUUA58Y0dEkUJfASJbuJrCucn+oFhl3TGAhj51RhRg46UjQof18GUSBXJ9saCSgxROVwhYqGfYkfKp6AxJj2mLIslyV8keTsFlHnIsQXYK9KQIoZlC8DJi7y+oJUQyUyP3LvusDbTc6osdYuQbeN8K2qU4RoHqZkPm6ed+5RMdIm/cb6JtDZVY9U9vdMTowlz6v6hsfBZqmoRxYsitv/j1ITHxi81A5cqiLfYox3Qda5gmtpLsPQpelx30Q0d2bHQCGTmadT727l3S6Bukq9pmz3ufKGxICzjMsTWOrpF9azjVfw05eHEJyfm4atFaU5QBrhtSV5njquV0cc/rUOtpGTGEYlGUyQsvMrGI8LnngwR22d9d55OHTfPFLX2Vv7zZaDVDGUlURrQtQBVqLYgNo53O+LsJ+SyvEfC7nNRiUxJA2CuRLAaNt+/oPvP99nNo9xYXXX+fW7TtJaaAhirPw9HjK66+/zt27d9ne3ma8ts7u7jbj8ZBr165x69YtiIFqUTPa2mJrc4fxaAJRSWyS1sQYsFpaFwbDAUVpOTg44sZb11Mru2dzc52tzRGRSFF7lKqxNqJxVPNjHj/3BE1dc3w8xRSWclAmp2qPT6ZLPuaNw25exihyeJEf3+sW3b8xs1neqla1qlWtalWrem/VfQ1Ws4QLUltqTAAnUSX9+BqjpXevLEussbhGsgRDCBwdSQ9UNpGRHtRmSba4tFjunUMGCsLWqdZUJzuUZmZKJHFd71S/r6+fEwkif+1YwM7kpnud9KCWZZmcgZP0N8Vt5HMWWTCAwpoEoFDJ/9Z3z1kDKkdldAxz8AL0gSUmrSzLDoin8yVmICzsdR5zHdPyWoFJ76G1FmjnA0qHFlDEkHs9ZQmZgVeGEEZFgo4I/BJ5a/ARHaWnz5oMUCQiQ65VTIZDwmVLL3CWRqsEUEnv2QGhfDkyUylGVNIIHRJrqXTv9USRfCcHQWEzhX1tu1FbANZqoNN7dM7TkilqL0woAAAgAElEQVQpzOJkIr190+m0fU2MJIfaTuYr72dwDpqmSTEewsY2TUOLlGMARErc3Tc5NkW3fbvE/rlK5R7JPAfbfuw0RzOTKhtE3b0ifbtZdizn671DnLMTi5vPJakUurH/zXsI+/dROxeX2F3Vw5f3yolP9rXG9s2zhNmke0T6f7uNJQHf3qcNJZvBUQJLylDXgdu3D4ix5rFzpxmMRqjoKcqSshwAHqUDIVQYO2BQ1jz15BkeeXiX8+ff4MUvnGc2q1CDgkVTE4PK3HA6twy8aAesuzbi2t3UjslohLWyuRSCp6nley2mTZLdU9tsbH2Y1167yMXLlwjBEZRpI4ucO2I2m/Lmm3s8+PBDPHDmAdbWJjzzzNNsb29x+fIVZrMFOzvb7OzstJFLMldcYoOhHAwoB0NmswXX3txLqoHAZDTgoYfP8I0feT/7d29z40ZE6QpjPbPZnDMPPIj3FTdu3kJpTdM46rphsViglCg7jCnyVhH0vmO1Tkx0WJ4r3TxYlgmvalWrWtWqVrWq917d12A1o8aY/pclXSJ/TIY+CVQ5ZLcfYDgcihTYZnMk3zI0IfVVthmfPQmZgKcMYKT6gDQvmjPIzaAVSHEpy3LF9KL2Nd2iScBSdi8GRDaZZMXiztqxAUpl5oy2xzBnmIrhSECj0T3goICYAKzR+T1cx0YmJiJLT/v9YTn+pygKnG9apsbqIrG6AkxOxvgsSTdBJIde+uqE7WIpq5NIMvvJGwQdc2uUsKIxmVHptBnRLUY7djymnkMZT+khzhm3OjtxtQt+AVxa9cFSd13E+CiDmc5UKGSZcQZFSYq7NFGhA+YxuR1bGecMKvNcGg5HEEmu0SfzSvXSHJSfxSxqbW3MxuYazkkGceNyDmuWLqseod6BXaIwYQrVGk3JZ9RLi/nGVe1c7YPW2IL07l5pTcG0at1aSZsHvi/XVGrptXl8+nVSWnxS+nuy2nmskgS6dz3a343LGbXpWdnoMDopgNMckmcQAX8+Vux641NPeIhO2DwlHcL7BzWjWwW2NGxujBmPR4zGQ5QKaAPe1YzGBRvr22ilKYoJTz3xSZ55+hw/9/P/N3vXDxmN1onRUlXNEphv5zrd+MlD4oyulSc4z9raBGM0ZVlCjDjvaJqALQrpSVfwwQ99gJ3dHV555RWm0ykhps2X4EApyrLg8uVL3Lp1kyeffJLNzU1OnznFxsY61669yfbOJkWpaOqKsjDSfxtk46MoB4wnY5yPXH/rJjdu3GZQGMrS8sCDu/zR7/7DPP/NH+Vg/xbnz7/MlatXeeONqzz77Id4/NxT/K0f/zs0zZwQNZO19QRUFd4FhsORyLGVbIDl3upsrBSDxG7152tfBvxO82dVq1rVqla1qlW9N+q+BqtBSYhIjAkcJCCh+nLg7KQahX2t66ZjIJqALQxFUXQ9T0m+mhfQmWFYYmSEPu3OowdqfYxoI8xEVVUtYBPWTxx6MxA2xmDSwiqDwhCTwZHSLTubgWLbA5veu2matu82M6oKjTIa57u+XOlN7aSggg0VMffA4qTPNwZ0crvNJkTyXh2YzkyZMUkm7LLLp8WaAtfEBNg1IXY9lnkx3XfdVEqielRm7cigoAdKdGYzM6iKqJgFuoaIO3HcLqfz3iiKDMBEHpoXrsL8npC15jHqLWT7IOnt3EP78vLQi74xPckuIcdpIOHmLYgTOXreGCmLIVVVLb2HAKxADEl63F90y6ARg2N+fMx0eoTSYG0p1wHkyqd7I6brYW2BMTZtDCxL4UMIYhAVae+L/iZGHg+d4pIyo94/32UgetJsablf+uQY58dPqhFOOgHL5ohZuk7tnypfR+55XnFCqp2eEz+tfN/IZpDWiVlX2bSs37dLez1idKAjPoAKhsZZrr91m6quefTRB5isTzClYjJeJ0SPqxWjkaXQEKNDKc9sNuOhh7f43u/94/yfv/CrfOX8JVwIoMXdWcWeCkMlc6uQ2fwcUxUks9VoDg6PMMawuTYRs7TehlZUMgeqxYydnS0+/vGP8/rFS1y9epnZYp6+n6CqA1Zrpgf7fOnFz3Pu3OM8+OCDLGbHbG2MGZSKO3dv0NQVZx84g7EDfOUZjybYYkCI8NbNm1y+clXmu9Vsbkz41k8+zyc+/hEWi0MGQ803fvgDvP8DTzI7btjZOs1XXn4VpT0o+Y4ZDoe41D8tWdqglMHYApPaIWKUzaNIUneo5Q2Xk/OgnUesQOuqVrWqVa1qVe+1uq/BahKCIlKu5KqbpZy9dYcwrQJofQj4WkLudXKUlQgTkdYqLYtulRb/OgOp/uLcmBbc9EFLCIHRYIi1RphbbU6wOB3rZ4xK/aZpka003kmPldaasizE5TZEfJBeRGssk/FkyUwnhkjwAazkQMbkZJtFbib1niY+CGKQBW8PdOV4C51/MUruaUSk0cYa+fzBE4JPgEWksUYrRsNhMksSOW6OesGLe3Aks5iJtUvxJSodPwNSjZHM3LbflRZUteMXI4F7e8v67HZf6icy6Y6J7K6XaqWtORhGzKDkeLnPc5lVv1ey3Z9jMRkWGaPbfkaZk31pa5b9CpWfN0SstUm+W7K+vs7R4ZS6FmZZ9xgh1Zop9RfbsddLLPOiLEuJXGkcyto0J9L8jeCV5MyWRSExSM7JnRRzzFJ+DwHGeTGvBLl2PcBatfMpbyj0e3D71+YkMy+S4O5antwc6ACp/Ke1umfslySfvevbMmiKdwQhIcrYZnl1THm+edMrRN9ufIWYpORKteAnH1+igzoXY22S43RQNHXAmIL9u8c4t4cxBeeKs5TGMx4VrG2WaB1ROrQsoLFQNQuMHvAvf+cnOXVqmy+99Cp7e/sYM5Q5EBNLHkGncY1ezldbjXM1VVPR+ILxaESIgcPZlGE5EEM2I991MTjqao4tB9J3a0uefeoJtjYmXLp4kbv7B7L55h3BS75zWQ64cnWPw6Njdne22d3dQmsIg5Kt9TU+8Oz72dhY56tfPS8GcOWQ/cMpr71+kTuHh0zGI8ZrY9737LN85CMfFaZUk76PG4JzrE+GHOzfglDxzR//Br740nkWNZj0upDmxHyxQBvLQCmMle9MRUw5yXK/ZDf4/O9Eaiog/WsB7zA/VrWqVa1qVata1f1f9z9YjQk8KMn9lDxJYU3yAjabi0TV6+lMi/cQFbEnA26BheoYn1ZSdoL1kZ4u18p1Y4wURobMaM1wMJDzXGKPxF04s5N5oaTS+2aQGUMkqgRU6N47sydSmpAlwCGKCVE+zwiFsZgUbZIlytLbKp/BpEVc1J3ja+YAlYqJtRZn5Xzu4oTrBRQbOW5ZivOoc7F1zo0xolXEGlnoZzGvmEtJL2tmHWVMdTt2Lnjquk6sdj5eSMY04u6cgUE+t5MgRfVASGbw2nkT+xLdzFjGlrFqmbneIjbHt/xmLrToLvZIK9P2tHa9kLTXu7/ZAQLw6rpGKUVd1xSlZRiHNFXTGnz1ZYyZHeufi7VZ/t0sAfK+CsCkvmVjipYlda7pgH7aTDgpddcpP9gqk4y2kqJBSV6vC6G9F/ugM5ty5b+/E9jvj0V2CM69sH3jnLcDs/nxdzruO4ERlTYMYuuglEXcOaO2cxIOUaESYNWmuwclSkijlIyZzCURhMcoaoq6doBlelTz6itXcQ6eePwBrAqUg4kAPRrsYAQxUlqDCzXT2RHBG55+4kEePL3LZz93ntdeuyaZx0FJZI5Xqe84daKHgELMrIqyIHjFoqqwVjaCxC14yPraJG3qRQHW3hGCl3kbPA+e2uXBM6d47fU3uPDa64QYsGWJNpaqFhdna0vW1jZSzq5nOBhglGLv2h7H002MNozHaxwcTnnl1Qu8ef06uiipnWNre5u6brh65RqndjcpS83RwQHe1YTQQJgRI+xurfGJj36Ioix46SuvUzeBiKFqfGJPwfkGNxNFih6OiEmabU3eHBG5t0j+TV+Un4TwmYl/h+mzqlWtalWrWtWq7tu6v8Fqj9XsL/ozCMoMVAaraOkNzItn70nspj0BPERWF3vvk4+TF9ttrurbZDvmx/Nxs8Q4B9Zn4JOP2V+AZxavqipGo9HS+/c/b+6Vy+ck7JXDh+6YGVT3zzufx2AwaA2lAKqqWnovpbVkn9J3IRYA1I2HYjQaph5gBzRkd9t7QJ1RLfDPYye9q8vOskopLBBt0TJykBhe02V/9se8f51zv1o3Jj69VydFzdVnADOA7cBldz598JtjfLI8vC+LbT/TCSB6UuqaKx93MBi0r6nrmtu3bye5cDKF6pkHZWBfluXStcv9y3nzJJ+TzE+9NF/lHOXxqqqW7omO1e4inPKGBUDUy+xnjAKAoloGjydluv35nYF2XVdL86T7XYDlsU/PdICwVyeBarth0LvWJ6/JyWMvvU/6wxjbmk8VxopqIPFyJ+eE6kOgdN1aYB4isRDQdPfuPs35mnoxRT37KD5GytKwtj6kaTxFYYlBNtXKMjCdLtBGsb4+5Nu//Xl2T7/G5z73Bag8aE9sIkoZrFYolb9vnJhYpc/jnE/3hiFqRVXXxEPPcDikLMvERKb849g5aqMV73//s5w+c4YXX/wCh4dTkYwr2Yjb3tpMTuopTziIbP/O3X0W84qNjU2c81y+coXzr5zH2BKrPWdOn2Exd7zxxlXeeusGFy5c4JN/4Hm2NifJ/Ax8bGgaieJZ25jw8Y99lNF4I+XDSn/3bF4zKIY4H3GNZzY9QsXAZLIm33kqopJsW7K0u+vSnxPigLysxlnVqla1qlWtalXvjbqvwWqIHQg7KQe8BwT2JJfdglkW7p3B0TJDp7UiJLOl5cflPfqgKb9fE0Ir/+2DqAxq+3/vL7L7vX59JrQPhPq/03/PbsEPPsSl42fQnP+egU0fcOcxyH2xzrkUY3EvuIIOOGbg7b1nsVjgnJc4iR4I7MszT7JuUpF+1mYGmD749rP5lM8JEHzoMcvJ7EhJ36nRWvIllcJ5L2w6y2xcN87L/ZTeezKZqFRyY87zKYpRV3uUfN1SL6cxEhHk04I/f+58/nkcl7jVKPM3xtBGDwk7KtEiMrZ6idHNY/9OkuT+HMpzuv+a/KdsJjQoZZY2EDr2vetN7R9XKdVKwweDAdm9GDqWsr9RkuehbGR0cyD/Xt5Eace8B0rvfSz/rO957qQJVP/1ufpS7pPzcJmJT1FYUbKK87W2xiQgt3x8OU5SRfR+lpxnAYrDYYn3MoeKsqSuPRcuXOLw4A4f/shznDv3iEi+Y6CqFmilaBrHomqIwTEZrzGfNfiw4NxjpxmUH+HLX36Fw6MF0UVciISokwO2mFmZtKmidMRYi8rX3YPC4pTiaDplOBgwGo5Ekl+YFG0jGzExiLT4zOnTfPu3fRsvvfQV9t7cI/jAeDRiNBqmDblAXVXisu48k8kaa+MxoLhx8yZXr11jOBjioyhA5rMFV9+4zHBYcubMLov5gqtXr/H889/EU0+fY1AIG6yIuMZx6A8YDCZ89MMfpLSGz3/hiyyqBq0KZvMFRIPWkjF9eHjAfD5nbW2DyVjM1OS6pH8rWrY89zH345numUKrWtWqVrWqVa3qPq/7GqzyNsxqy9KEZbmgZFt2jrbZJRe6HkmJfOixRtA+dnLh3F9kL7neJjku0C7Su9ON9yyS+yA2P57ZwY4VXAZzfQDXBxR5EZZ/r4sV6Vwy+4D3JNA/yZj5nrlU/78+kHHO0TQSJaG1acFEHzhlsNU/1748lLgMOFrA6jtzphglMkdr3V7bPrRRCTgTkhOrkt5i7yU6RrVsum83Itq5kc9VZZClsMa28ysEfw+o0shGRnBeGCVrsbpnaOVDPiTZKTgD5Cy/VErOKwMdY2xrstRUdQJNAobz48Z0TP1JprZ/XTIAldexNCfyGChFumbLc/JkLbGQPrQMcD6H1vG6d5+cZC37lTdEshx++XP0eqlDF5NzElD259bJftX8nm/393dyqM4bMG08UHLPDWE5g7n/md4JJIfQja305QZUik/RXsnPKnLz/2PvTXYsSbIssSODDm+wyc2H8EiPMTPCMjMyasjqJosgCf4Af6ABcsMF0UD/AHckuOo1AQJsbtkLAvyCBrggqwiQILqKVcWcLCb38PB5MjezN6iqTFxcuSKizz2ZmQ1UpntCb8BhFm/QQVRUTc4955777BJ/8zc/Q9dZfPLJhySzjXXl3jvUVYVgPEzfI3iPbruBdwHHV+b4i7/4Ce7df4ovvrgLv7VQSsN7AWuohp3ciRWEpKRItDaGDwFDTIa0bYPeWAzmAvt7e4CQGIYBSiiYpByQ2K5X0HWNP/uzP8HVq1fw8MFD9L3B0HWYty1kRfsenKHSA6VRVQ26vkfXdbh27TrqusGz52cIAnj08CFkkOh7h0ePnsM7j9V6i2dPz/CDTz/ET3/6E1y/foQAC484Xn0HO1jcunkNwGf4f3/+S1izQlMLdL2FNRZSUrum9ZpaPVW6QlU3MdEQooIgJiF2nIA999eaYooppphiiineqnijwar3SL1Aaa0RzYaQ681owR9NbUAAxoMWi8QOZgCT6kcFL3DJxESA3HF9yGZEQmK0mE5yYPnqIp0Zq8S0gcGJGn2mXJDzdl+VSI6daEsGj3uHDsNAi7Wi52p5PMyKloCTwSG/rpREJfPijU2duH+osw5KEhMbbECtaojoAAwgbY/Ps2RxS2ZQS5FrxhDZRngoRX1EB2MhJTCbzdC2DbbbbXLdFYLMbhQyEBUCVAscAow1kRWTUdZNcwDc3zMaBSkGQdEdVkoV23a4VAvI9ZNlMAPnvYO1IQJKIEul83wqx58uR0huy6nNiqIEAUJA27bpnKiWENHYZ2yO9Tp2sARwAKBYohtoG0FI+KgicM7QHC+2J4vrnhfzBNiqApBzwkRKCY/xcZTfLQEdz3naRpkcomvyKpP6+vtpV2K9C2KLE0hzcley/7p7SUqJWteQSlFtqiBmeBiGeHwYbSukfZQsd5boU8LAkVu2VmR4JSTqpoEPAWfnHf72b3+Gp8/P8MNPP8aV433M5zMsqgZ912EQA7z36PsN+u0KHhJCVmgbhU9+8CGW8z386vQ2nj+/hHfUhiogj491Dh4um63FmvO+H2CMxWIxg65qnF9eou4qLOZzBGuTazAdu4ISAs46fHDrFm7dfAenX3yF7+7eQdddx7XrN1DXDawxWO7to50v0BuD8/ML1HWLd28uMJ/PsX9wiAcPH2K92gAAhoHc1x88eIrl3gxd1+PlxQUePHiIz35ygs9+/Akky8W1Ry0tnBnwvXeuoa4V/u7vf47nLy4gBEm0B2NRVy1msxm2XY/zi5eYtXPMZnNUVQNA0r2nRKy5ftXRe4oppphiiimmeLvijQarrMskGWlp7pJBUVrwIgOzuq6xf7CH2WwGay0uLl4C4MUxG3KwBJTNV8hcRcSaJ+dMAmFlDaozuTaVQWwJDHflmyylzfvPdaa74Ja/w3LdkhniKN1VmfkqWTZjTFx45wW3MSa17RFCoK5raKnhvdtZzAkEL6C0gvUOHrkfrdaaWkq4vE+O7Li6w1CHyGZ7Mr9SgZw8yU0XkNKTVDuCuwSAwXWB43rFuq5yL08loYIkdjvK/Xgc+VgcsoyYtiOL5EZm1Ms6WN4nb4uvFY9DVVWJvefxL9uq0BjT8UghoCpNc1MAdVWTo3ABprz3cNbAWQslCGiHeO3InZZcsKl9Ed2uVVXBWotuu4Wxdty+IwQyVPIBSqsIOOm73pdsJs2jElx672PfWyS2N42HyOCPx4nnQAkKS2BZznM2wNmVjZe9bDk5UF6D17GnabbyfguWf5d9ZlaY+wZ7T07KQgiY2CYqJxNUAjkp8RA8nHeoxFipwIqNJpqs0bUnma5SEt7FVlY+oBs8vvryLs6eneFHn/0A7968geVyjs16wIuzczhrY/mCQnAeQ9/BO2rbNGsUfvjpx7h3/zHu339K8n0AxnLRPRko0WBx0oHSec57XF6uUdfUEsYYi/OX59hbLKBkNo8z1kRJOplxVVWFH//oR3j27Bm+/PorPHr8BB999DGuXruKpp1BCIlHjx/hl7/8Bfb29vDRRx/jyuERFos9LBZLnJ29wJ3b30IIBecCpKywXvXotj3atsZ20+Ps7BIPHjzFj378Cd6/dRNCGASQ5HzoOzR1jX/y0z/HL3/1Bb67/wiiraCkw3agOlclJTbbFfp+gPMOi3lAVdXxOojieryaFJliiimmmGKKKd6eeLPBahGv1PGFce2m1hpKEst3cHCApq0TAzle5GaQyb04vc99FXlfAqqofyqkrTtyxRK0MvDhmr2yJpW3XTq/8r52F9jManG9YDomacF9XLmuUEoJY0wGPi4D0L7vk2kRs2TjOtzxsTOY4XpDNm7h8981VpIJvDjoyFbyOUqQgQ2dVAQdseZzxC57j/l8nthppVQa4xJI8O9jo6DCUAg09rmXLnB4eAilFFarVRqzPEZjyShvi4NrfPu+Ty6+JXDlMdCagADX9QpB/VRDCNHVNc6ZIBJr731cWEcmMyiVzo3HwVkDxGullILwAT7WY4tAtYZKSsiCXd9sN+n6KiFS6yAC4myMRfcAA0MZ64Bz65lx39xygV/eg+V7fOw818o5LqV6hSXd/S6PfQlyswJg7Ij8umfCbpIk7zuD1VJtsO06CBCA9c7BW/fKXODnjI9JFP58rn8noF0C9gSoAyV9nAVkpRF8gPMBz16s8Ld/+3M8u3WG69evQCsJYz26rqPnheH+zhJ1PUtJpqap8e6712GNwfOzC/T9AK0ErPOxGbUnSbLIwJ/k3yEmVRy22y2ZdmmN1XoNKSWWy2W6d7zl54aAMxYeAcfHR5gvPsfXt7/Fd/e+w2q9wvGVY3jn8OWXX2Oz3mCz7rDZ9njv/fdxcLCHo8N97O8tcLC/j+++u4fz83MYa1DpCggS622Pbhiw2vR48fISd+89xOef/QiffP97mNcBSlC7Gus9dFXj5OQEzWyOr76+Q6Ue1sA6j75zcC7AS4eLC4e+77Fc7GM2m9G8TvOKSxe43c0UU0wxxRRTTPE2xRsNVkPBmGX2RRbAAYmFE0JAKIkggE23xWCHaAaUGUWIvDAWEMlcRQgF7wHnLNhV9nWZeJal0vGM61JLh9LSxXcX1DIzyoBmd5HNC/1SsptkjELCFoCNHWsBJMDKDrIsE+bjo/q8Xcdckg0G72E99Y3l7whh4ZwnCbb3sNbA2rFDSQlCyprJXYBT/kyv+ywzresaxhiYYYCOBkr8nXKMSrDDY0/MGsCtiMpEwm5dbYCH8zbVLe7KV7m9TAnq27aFlDIZd+2Cqaqq0LYtuq5DFXuaAkjnRkZQZFBUylSX8zm6rkOIjKO1FlVVJfMlBsXMbhpjoAJdNwbFi8UCbdti6AesVys0dUM9c71H6gMbqFdxTuwQM81MZwh0bYWgY1YSEJAJbKexI01zGl8+vt3ETXlP7MqFy+tGr79a5/w66fPu98r/F1KStBqvXs/XGZ1RkgXQStF8j7WsxFj6aIoVQTn3bXY+1l17qolU+dycs3GM6DnC40WgUxCgis8q7wPWK4Ovv7mH58/P8M4719E0FZwXEKKG8wMAib7rsd1QH1FjPKQy8A545+Yxjq8e4t79h3j+4gWkVJAgZYL3gdrSAKMkFEm+aS4Mw0AJDEny39V6jcV8kZIVURBAigsJABKztsUPTz7Bk6fPcf/+fTy8fx9SKmy3XWyBpHBxeYk7d27j6tVjXL9+HW3b4ubNG9jbX+Dx46f49s536PoOdVXH1knAFgb9YLHZDLg43+Drr77Ejz55H+/evIF21uLly2doZy2atsX169dwuVrh8ZNnGExsreUHGOtQ1+ThvNlcou877O8doG3n0LqmZyxJAmiOiEkSPMUUU0wxxRRvW7zxYLVc545YTDBwjAYdUTEZQoCxNhqq8CIaUd4X+6rG3pS0CA0jECLKBU2xWE6LcudHx1OCRWZyGLCWwIOluczYMaNVOqnuGicxU5UXnlR3yZ8vmVfuBVs6sLKEuWQpS6ArI2AZrIN3HlplOWyWaGYmUwhEo5df36aFj78EM7vXTxSsX1VVWC6XWK/X6KPRjNv5PP9e7ncMil91crbWYrPZJNBurUVwLgFjIbK0e5cx5LFltrGUEfLxl+ZYzGDv7+/De4/1eg0g9/wtTbYYACtF17/bdkm6Xtd1Or/yXBk4BuvTZ5xzacy4l+3ecgljDQZjoLWEcx7Oungf5VplOiYGnSLVXgLZgobGxKefVdNASjVqz1RGOZY8H15JUBTnI3burdeB1BLcjyXFY9OzVIsaEzJjue6rc7NRuZ2VrivUIKBuvcUQZaY+hFSMrKSENxbWusjUYXQedI/pnZrZEBMGiM8UAQEFC6q5fvmyw2Z9DweHe9jbm6NuGgSQVHnofQLBQz8AwkTWlHoev/feDcwXNe7fewxjHXTVEBh2VGcOJZKpGWKvasSrS22I6DlgjcPl5QqztsV8PqdkmSMmd7Amu7FLievXr+L4+Ai3b9/B7W/u0DO1UIR0XYdHjx5hu93i6OgIN9+9gYODfbRNiytHV/DFF1/h5dl5SiwOlmrVB2Ox3Q54/uwZHj54gE9+8DE+PfkBEIDN2QX2D2i8PvroQxxdOcKvfvUl+sGirhS2vYEPAgA9/63rcXF5jn4YsFwsATSQUsNaEVvyTHbAU0wxxRRTTPG2xRsNVn0Ir114SimpH2KxABUi17Lmek9y6yRWNdcppgW3IIlYls6VLURyvSoHbWfc25RBJX0nu70yy7ZerxMgZaDKkkSW1ZbSVn6trmtorUeuwMZkdi+NUTzXJD2NdZ/MFPJiGhjX05aSZT63EgCXY940DWazGYbBYBhM2lZ5TcqWKwBeMVzaBV8+Asztdgsba/YSMA9x0Y/YXgNkrMXnQ/Jheh0xocGGWHw8XH/oQ0A/bM/p4M8AACAASURBVKkGU9cJZF45OEwS37ZpoJSinqQFyArewxlDjr4MpBBrdLm1jxDw1kJVVZLnailjGxELIQJ8oLra4HNd9fn5efp96A2MMakXbpm0KOeYczYx6HyeVVUBsRWNUorej5Bz1s7gvcd26NPY6UqTi3JqnzM2AeOboZw3AKCVTvdjyfizFLpkgMt66102lIPBcpnA2K3/LudPed+Wrtd8rDxGzFJz7IJqpRSWe3vQsca7TFRVWqdnBLyHZ+MtpeDDGJATu0rMtPeAtcS80rFwggPIJlwiyoOBIQQ4P0DIgIvVFjffvYH5PGCz2ZKM1XuIkAG7MQZVTQZnnekgIHH96jFm7Qzf3XuK1WYDpSooqTAMBlpXkFJH5pOYciBKhQEMzkP4ENsTBazWawzDgPl8jqZtMBgLXdGzJwhB9a3GQAuJz370Q+wvlvjZL36F9XqDum7gEWA90JsA4yyev3iB9XqN927dQtO2ODo6xE9/+uf47rvvcPv2nVhnrSCEghkcrHHwrsZm+xL3H/5bfH37O3zyyceoawlIjStHBzDGoGkafPbZD/HF6VfYiAAvgO2WalaZZbaug9sM6PstDg4OMWvnsNaibWfou81r5+IUU0wxxRRTTPHmxu8EVk9OTv5rAP8NgM9PT09/dnJy8pcA/hWAGYA7AP6z09PTJ/Gzv/a93zZ2mVV2ExVCRldgn14jJ0huKcHusbRoLRmucvFbdjLIkjmuwRtLGPk7ldKvMEbMqDJQLRk6Zn1YTsoGSFLmFiPlfrTWqW0IA1b+rPceMu6/ZPvITbRP58e1dfw9IMtcSwaYT4P7gPLCmpnggIDlYoHlcpH6twKvl2dmhkmMQDcv/tn4JX1NkAGR8R7D0MdjjIZXYbxd/v+SJS6vCe+7BDUMGkJkh7i2NLG+sZ6UQQ8DfGYuqa+sTYC0att0Xnl+htH4MuAs5d8spaX6Z4ngPfpYM5lqdGVmN183J5xzELFWmfdX13UCqIPpUcXrzeOoq4qYVQapMZngQwDcuHavrPWVxbiX/1x0TuY5zeNdzvVdhpprBXfnSfl7eQ1/0+sMPEsGk5MHQFY3jN4vgXi8TtyjlEFt0zRYLpcwdkgO03TvRDM177G9XGO+mKOua1QVg2UPqWSs3faw1sc56viKk6zWkZcyJxGc11Bax6SHxLOnL9G0NYwZ0LYd2raBEnRewbmYrHEQEGirGoOxEYC1+PCj9/D06Qs8f34GYwZoRT1XU2ul6HAcQGZPQsp0vzvrqCVUTPRdXFxgGfagtKJerNFsyjJTbz3MMODWrVuYLZY4/eILPH78FMEatLMZnLOpNvbp4ydYXa5w7do13LhxHXVd49at72E2b/Hw4WOcnb0semJr9IMlplZKfHPnIV5eXuLdd65isVhg1jbYdmtIKVA3NY6uHCCceWp74wOMcfFeswAkOcJ7g9Xqkua3qrBaX2Lbb/HHGr/vv81TTDHFFFNM8fsK+Zs/QnFycvJTAH8J4Nv4/xLAvwbwL05PTz8F8FcA/uVveu93D5H+MQtKi3h+PTJreBXMoHwNWSr76xayHGVrj8TkFgYwLOGlhWuVvl/WmXK7mDZK7Mr+r6VBj7UWfd8nhlEphdlsNmI9c2uNMTNaMpYmuRQTSGCWqWSOSgMfdg7OzK4YuQmT6VSA0hJVrTGbt1gs5lgsFrSAnM0SYKIxk6+ADK1JMsn/pKSxVUpCKwktBbSWUZpKrWDK2s6SqS3rgvm6lGCdZaBNZEmBXAdZutoqpTD0BhcXl+j6fgRaOOFQAsFduWkJzEvJtHNuBFQFsrHWYrFAE4GyjCCdzb9oPvjInJtUh1wmOdq2TWCbnWx5n5eXlxBCoG1aVFWF+XyOK1eO0bYtmSp5O5p3zjn4gg0VQqR5HAc2zWdmvEOg3pUsVS4ZUB5rnu98HcrjfN2/XaBfyrr5M6Ucnj+nC1D+unuTr1MJ9MtjVUpiMMRkQwChaDE0DGaUpHCOjIm6bQfEvqaZXR2rNGj7cqe3LJmQUc8mah/M8uIQBIbBYbPpcXG5xeXlFv1gsO16ak0Tk29s8GQdGZ8ZY8gUyjlYa6CVwLs3b+DW925ib2+OplZQUkAK2gpCIJAaQCZfQUBIUhmEQAC/Hwz6mEBbb9YknxexLY6zsY8rKQ1C8BhMh4P9Bf70Tz7H53/yY+zv78EOQ5IeO+tgLTkRf/fdPXzxxVc4O3sJISSODo/w8Ucf4dZ738N83sJ5i8H09AyzwDAEBFHhxdkGX31zD//7X/9f+NXpV6ibGaQkpUpVady4fg17yzmqKj5LlARdGkpiaq3g3ICzs+c4vzjD48ePcHl5/spflz+G+MP9bZ5iiimmmGKKf/z4rZjVk5OTBsB/D+CfAfjf4st/AaA7PT39P+L//w+gLO1/8Rve++0jkNMsM4FA7GEZHIQq2RMJiAA2j0ksKDOq6tUFMC00LTEzkhlBluxl2Z4Z3AggSkXmLFIpWggCI0MgXoDTz3hssQVMaUjDzNRmQw6ubdtmUKkkhBTwwWOwA20rAFKR7NAYB2MstGbg4ACpYX2AliIaIdnCYIkAkg+BXEQDELzNZkzewLsAZwcYa5Pz6XyxAHzAZrXGbD5D29ToMcA5wCEypRAQWgPwI6kuOwFT3S3VADpu7SF57Ag8+hAgQ5EwKIy1pIhusiHC2dhPFSJEpkqgbRrUdUUmUMZBIstKvVdwNiDA4/JiFVt6uDQ/pCRzHW8MKoAMe7yHiXWLEtn4yjtPjqQqy3NLCToBVMA5DyWryOoGbDc9yEyIPu+8RxCCltXOIXrAYHAGdhjQNi2kIvOf5d4SALBerzAMQ+HaG5I0UkoCYI2S2D/YB4TAar0i4CtlZFKpv6quKsyaltM/2HZbuMFCBFCP2oCkUCDjKvqcMzYmA1qYeByIvWwTRS/4itC1oXFypITwDt7T2LFhEydayuSCEiRFlvH+j0WfqMqERrzffDpAriMN8MyuSbrvOIzp47UKMGaAkoJ6JgMwWwPZSRibJfBKStRKwykNoUXsA0zj5nyAlPFoAmB9ABDv7Qimg3fE+tncNooTZ8NA4+ucg5BAVWl0XQ/AY+gHiEDmWZTEkZC6gYlu01opqCqypELBuwEhGCzmNZp3r2N1ucGTJ8/gQoAQFYRUkEJDSoXgqSZ1GHpYSVJ6kuOT4mIwAgcH+/DBYXV5icV8Fq9hAIRCEFRLS86+Ho1W+OjWLdw4vopvvrmNu9/dRTdYtLMZejdA6wp26DGcvcDlao2b775L0uCGEl/HVw5x+/Y3ODt7QXNONnA+YBgEICSsF7hYv8ST5/8W3z54hp98doL5XKEfDIJ3WMxaeGtwGTYYjIeoFUKQsDbA2IESmx44f7lC3xscHe79dn933qL4g/1tnmKKKaaYYorfU/y2zOp/C+Bfn56e3ileex8xkwsAp6enzwDIk5OTK7/hvd86lJKo6wp1XaWFXgguLlJDZu1kAGReyB4eHuLw8DDVjTIby2CSGYos032VSQUyswRk0x0BAn3EdhArRz9pu9yGopQwch0dAznvPbbbLdaxhUTbtqkulGtPw2gcFOqmTswhS3iZlXMOkXUm0yhjLIzhtjMhsYcMsNq2IbavaSBEiOfo0bYtFvM5mqYhIBvPoa5rKKkQAst7XWKrWWZLjCCzWj45KwvB3NVYJhpQtBRip1kpIaSCSHWUIjJfBHQJLBJgZPl3Aozgfq/MzAKIDCK1w6HernRdMvMMkduTWGux7Tqs12v4QDV9LB2meWOTNJTPlYBWiK97SEHgIBQadppvkbWO7Cm/znMwgPa3WC5RNzWapkbd1PDeYRj6lDDZdXtO8l54rNYrPHv+fLzd+D0GtT4ygd5RXXTbtAlwa6VjLSG1nGmaJn4vM5qhcOz1O62MiMWjXq2+ALHs+lvX1UjSzlHKmxP7vCP1BbL8OB+viiz4mGkt61nLdkXMfvf9Fv3Qk6Ih3q9SySS753ujqiocHx/j4OAgjbvnORvlpuUzpe97rNdrbLttMugqa7W9Jza077ukamC1xDBYWOMx9BaXF2u8fHmB8/MVum6AsdH5XMgoJ5ZRWBLgrEHwBgIWdS1wcDDHzZvX0DYVQjDwIdZNe7p3WNrL/18+9xwCXpydJaOzy8tLrNdbQCoY5yEgoSTV+jpjIeDhrcG8bfAnn3+GP/vTP8X+3h4uzi/QDx26oaPEz2DhfMC9ew/wq1+d4vJyBe89Fos5Pv30B/j+Rx9i1lZwtoMUnhytg8AwAD5U2PbA//P3p/g3/+tf4d69Z/BBwVqqx22bBrNZS8mF6DPAzygpgK7bwBqDutZJhv1HFn+Qv81TTDHFFFNM8fuK38isnpyc/AcA/gmA/+of/3DGscuE8uIu167mmkY27Vkulzg6OooLrXWWHQp25Sy2K8YmLfxzV65YfscHDxmKfqzpc3684JYyAg1ihodhSLWqpYyUAUFiizyxRXVdQYixYU0IIfYRJMkitcOgz4cQUg9T2hQbQOU+l1LuynR1YrXYuIdf42Ni2efFxUWUB5ftb6i2lz/DMmLCLQT+XLEg1krRtYqAPY8fsXpUU+khgkwgNSATrRkoRPBLFwHeeITgRpJQ5yLrNpLu5sWqR+7XO8oMIJryeE+GSelcc5DJj0rjqZSMEl6HSldQipIJxGaNW/rwtWcH4dLgKIQAEec5J1pYlp2OC2PZrJQSldKomxqDMbi4uBglRpTWseetSvM5IMBZm8y/yvN7XfsfljuXILiM0iSsZJttcIDL9xbfH6UEv5R6IzLY5f3Bx23tUFzbLB/mYOAuhEDfk6PvcrlEXde4vLxM+6VjzIZopeEZ9y7uui711uXEQvk7z6/gfBo/Vn6ksYdIoJu/w+NZGquxo67WGlKQ1NYYB+c2UFpiGChxwUks7wP6fkjfZ/UEM9VSKSz3FpCqwrPnL3G52sI6Axnb6oSix25+ZiCdo9Qa3baHMYbaIg0Gg7lAXTdo6woueOrvqwWsM1EZYWEHg/c/uIX9o3388pdf4sHDe/A20OdVhc12Da00nj8nsH50dIijKwfQWuHajRto5ws8fvwMT589R98NEKpKyRWqOVd4+uQF/uqv/k98+P57+PCDWzjYn8PYHs4DVVUTuwpg1rYwhiTcZhjQ6Aa1rlCz1P2PJP6Qf5unmGKKKaaY4vcVv40M+D8B8CMAt09OTgDgFoB/A+C/A/ABf+jk5OQqAH96evri5OTk7q9773c5uFwfVvbblGmxVcpzGTB2XYfz83N0XZekd9mwJoNOrTUU9boZgVL+XF6kZ3fdsiZ2t24UYdyCI7EXhSNqKRMOIWA+n7+yLSllNEHRSSrIC2mSEA6ppo6OM9a/CpUMW8petMbEljOS3UuRgEzTNAghEMMW6yGZgWXGDkBko/p0Dco6wvl8joODA1hr8eLFiwTImYlWSqbVMLXXyKCHrzEdV3ZHFmla7hpZiXQNmVVn1rw0f/KemFghAFe09xl9Pjh4m8ETgyAeN4zANO2vaSpQD0sf26SwGZLE0dEhqqqGEBKb9QabTZRs6gxGy3ldtiXi6wuQDFlGYFQ6JHNorVM9aN8TqAjewzoLCGqLw4BvGAa6TvHcWBYvIEZ1uTzuDBD5vHnulQkjBlZAZobTse+A0N1kUxk838vPSyEwn83S8TBjn6/duHduCZLL97hulkE2gPQcoHmbjaLatoUQIpkzHR4eYrvd4uXLl2kON02TzjftO9A8AJCuE8+dsv42m3iNHZIzSJeQQkFpCRNBKJ1rrGvtDfpugK5UZKYVpESUVOdkAuVcmFWWaNsa168fo52tcH6+Qj901Ic1srNx0FjUkGIYBjKR8wGXFyuqn68arFcr+LZB2zZk+OQLE7CoTrF2wN5ijp/+9HNcvXuAL7/6Gn1v4KwnSbAnCbaNbZfOL85x451rOD4+htIV9vb2cO3aMb7+5g7OVys0bQvujeqsR/ACwdf48qvv8PDhM3z6gw+wWNSAlORarDW8pfvaOY9+O6BWNdqYeKvUG21+/+8Sf7C/zVNMMcUUU0zx+4rf+Nf79PT0X6IwYDg5ObkD4D8F8AsA/+XJycl/FOtf/jmA/yV+7G8AzH7Ne79zlOwmLTpf7XXqfYg1rh7bbQdjyMBmGAZiH4DItuXvVW0LKdTYSCa1qxFUA2ldWsw551DVxJqR9DCDVYEMqktX0rKFC4BR2w2tdWJOqyLr7zwvphWapoW1Ji3eSYrK7UwkrI2GTcjgdVRjK9mVddzahc+HwQfX5HlPfULz/kwyAyrHuzSh2U0cMCDXWkNqAgmp5tOXbDQbRGV2OF6d117/9BOC6isjLSQERuCAf8oICHe3VW4vJQjice8CLh4vBtRAiJJYgOslm3qGEDwuLs7B9ZKZtB0fO48793slpjyk5IqIjqg8PqVDMDOF2+02AacQcl9hTjIwSCPXYw8VzZl4XHQEI6XEHcgGRny9GVCXgLlM2JTtkV57vZB73EohcHB4iGEYsNlsRsCSwSyDTGYneY7t1p/uXssyOcJsI59HOdd4e4vFLI0p9+Jlw6mu69A0Dfb29jAMAxaLxSi5kuS7AuNxLsaBxlhByFyLG8KrTshCiHgdCFyxhIASIgEhWPTDAO8cZrMZEAR0xckBArPGZCZaSKovdY5Mk+bzJY6vHEJricvVCpt1F02wqjy34+3Gsny65lTfXFUV+r6HcwF1XcF7j81mg3bWoq0bAJzs4Xkdn6Fa45Pvf4x33nkHP//5L3D//kMELyG0gjUOQhC49+cXOL+4wDs313jve++irmscHR3h88/nuH3nLh4+fgypiPl21kEKhdVqCykUzm2Pf/jZKd65eYxbt25B6xohGlr1/QbnLy9Q65rAtsxO4X9M8Sb8bZ5iiimmmGKKf+z4d041n56e+pOTk/8cwL86OTlpES3wf9N7v0swMwOUDEVmbEpgIWU07gES46S1GvUqTYvL4BGCwdpnc6Xs6UMumkpISKmhFAO+CK5cYHouLdJCIMfNUqZI+9eJdSqPlyWefG5lP1Q6N5EAK4NYBihK0flAEGDOQBFA8Aghs1ghOISQ90vsWZYs5tYpMgGU0vwJwEj62HVdWtSXQGG73eLy8jKBhRK8MkAYgROA6kR3WMMEenbYZk5CeGbYQQYsKjLIEmXtqCdYU8yNErAmoCCJHSKDLTLhipanEIFqZTm5wdeVxtRDqSael4eQwGZziaqq4T0nElSSDwd2SI1zkOcLM6TL5TLN1105LQPPvs99UrPUOozujVJ+yzLusiVNoMJmBO8T7Ev3haIaVZKeC8x0O7qPQgiodE3GOhg77PL5vPb+RUitUYQQqQetEmN1gkDEaTFRwqCprFF1LpsfvQ54lOxteXw8TgzkeQxLxUPZl/ji4iI9M3g/XdcBGCeayGjJpWMt5zgncViWz2A1398yAVz+Xp5jAtbS601bo2kpmaGrCh4S1mVxvBAOSgIuUE9YFcp7xmO7WUFIhVlbo9L7qJTCet1jMNy6iE3KiMCUsooJk7LnLc2vrutRVVQjvF5vYK3FfD4nYzvv4T2beEl4Z7A1A5pK49//p3+Bu9e/w+npV9j2HbwXMAO14DKxTEA8eYZ+2+Gda9ewf3iAetHg+9//GPuH+/j227ux7yu1YqrrGZylcoHBWNz97gFenq9w48Y7OD4+Rt8PuDi/QHAOVUPqGW7jw0Z4f+zx+/jbPMUUU0wxxRS/rxBvaLb5QwC3/+P/8J/iwf17ALLMj3qn5sVslpOyfWkJDPNC9FWJYubvtNJQWkWpLAESBJbOcg0o0ra5VhGItWxugBQSVax/YzaSgSEzm2WtHYNQNmJhRqiqKggpYYxFF/sCEvilukViTsiJVQpiQax1CBJQUkHFxTGDXoDq+agPp0BV6STxLWv+SjDP42qtTQCVJZlVVSUGloEogORUWwJwktt6AnBKgtxLmfFzBFbBAESkpAGzmLzQ3wVjLFnl8VCSgGuqByyuTXmcXKdMJ5wZzyTb9MTMsMEOgzVnHZl9NTW5oVbk9EvsGgE9rTUuL1bo+wFtO4OUmsA1cgudEtDw8bNcl6+ZluO5UiYWGIiWoIlBFo89X6sSzO5eG77ezECy7JZ/5+1zgkQIASVz4mWXrWZWejf5IlU+xl3gWILdMsrzLIG7UuKVOVGOYZk84SjbSpVzPIQs7R/1mC1qQMskCwPqOppUOWthrIFzJs0dbjtENbAk4S9dwvk4y+Mtj4nr15kh5rZX3P5JpnNFfI0gPtmDeQgpoaSOyTce48jqAikhYAxwebnFZkMAXIpcAyyQJfnltc3A36UkCySii3mDpiEzthCyjF4KLrtQ0LrG8xcv8dVXt3H/wSM6VqVJvgvqGVwrjUppHB4d4saNa5gt5uiGHqv1Cg8ePMDl5QrOhVh7Sw7jCAZ1AwASdVXh6PAIbdPAGoN5Q6qZqqqoZxCAd2+9h//xf/qfAeAjEEib4nePDwHc/vgH/x6+/fbeH/pYpphiiimmeMvjgw9u4Zuv/m/g/+dv8xtdxOOjFLBcjHLtFzBepAohE5MBsEQQ4N6IJVCg7wGQmVnx3sNJSc7CiHLHEIgJKlw/EeW2pfzQWjsy78gsaT6XEkhw39QScPCC30Vn4QAyMOn7PgFPlkCnRbYhwymtNYIgo5/gYo2qGtfyEQgYy+EYULJJDANorjvlcylZ4RBCOqZd0MGsMS/2h2FIRjPEyDYIPkteJQS84AV77vvpvMcwDGnRDgBaN1gs5pBSjiSw1looKeFdgHUZhPD5lWBAIicvPMgZtZS46ljDeHh4iPl8DuccVqsVVpeXkLFWmsdSqlwHaa0Bs0qZ7XTo+yG3MknzNBtSlYxqGSWzyOfB4zuq8YwAZ7VavVLbWkq0S0kvjeUYeJaJgLItTlnHipCTIK8Dmdx7l12uy/nCx8Vjx+dSntNIGhtyIoH/UTumcf0sb7Ps9UvtiGR6bvB2eL7yPvkc+b2s0CApcnlfCCFQaU3SXsGtmoi9TP14hYSMSSmZzgWj+6MEv+VxsOSZwS5fBwAwJre9oeSCyiA0BGgF+CAQnIcTBlJyqyCuO/eZ8RUCdd3g6vERLqo1Xr58mVoxsWKBQW5ODEXHdUHu3NZaBACq0ggQ6HpKolURVKfrK2PbIkj0w4C9vSV+9MNPcXz1Kr7++hu8vLiErmpUdQ1nLTa9gRIaw+CwXm9x7foxrt64hj0pcO3aNSwXe3jy5Cm22y2EkKiqGlLVgIj1qYPB+fkF5OEhlrM5mqpO5yQkj/cr03aKKaaYYooppnjD440GqxzlYp4lleX/e0/tMtiMgyPL/HghPwZWgn5JLKsLHsKRAY0QHlX1KtB1LgDwaYFHtZgAdF7s0oI5L9RLdrA0rWE5ML+mFNXPDtFRV2kRjXxYZkcgVAiJWTtDJwZsNluSKifw8XpjJz5Wa016fbe+sZROlv94WwyWx7JeXgEGNA0ZNVEbj3HbH/gACQFV6bQwl1JCSwmtyTjGA8lAinrLEmsaN4K+68hciK+flCmhEXxewJcAQUoJJanlipRAXbdwzkJKOncRPytjOxtuIdTWTV7sBuD8/BxKZOdYZ6k/ZtvWCEHADAZC2HQNrfWwzkHFY2CAnd/PrHQJ/GVFrqXMcrKcHED6TgnE+r6HBwGRAMAaA4SAilvaFPOAwW/TNNjf38fl5eUIPFprE6gt56TWmuTvxT1Vbo+PabVajVylfXCvSGRL06Pd2J1/5Xf43uVjzDLVMSBm4FkeG7/OwFDKMTDmuV2aqPH9OpvNYIzBPLaV2m63o+ROYsRjIipfZ5KAl8fA51Lelyw1L1UA5fHTPe2LuV1BB2JPhSRTJTKMcvE5ZwugmRl2rl8OEqgqYG85g5QBq9Uaw2BSnb4IxMPSNrLShJzUqTew9QHeZPMvKk8QWM7nqKoKzhkIEe+7wK2JyDvgxo1rODw6wFdf38a9e/fhDKDqhtp/hUBzee3R3+/w7MVz3HrvFjGm7RaL5RyPHj3G5cUlAA9r6ZmvpEJT19jf38d8PkelqKZcxDp4JfXIdXuKKaaYYooppnh74q0Aq2P2Q0LK14AqBIhA7Wlex9qUwa/5EHu1SkUS4sBANYI+5xEKuWhm6njfmYmp6iouzkJkNtUIVJfAoaz7LCWETdMkRhEQsK5kXT28HyLAlRCSjrOqNbTSgIqteLTPrFhR8xdCgC3qIrVWyS22BAW7zB2PM++Xt8XMMAMxdqjlhT9JGDX6fijMcrZQuhptO9ckehhn0XddMq/JckYGLy6ymLHdjaQxHgYTJZC5xUoCHsXYk5xUQsoKqqLPz9sZ5vM52rYF9+Fkh10lFSqtcXx8jLqusdqsQe2IBjhnMfQ9qkoDAbFdioOUKtUclsx7CWi4Fc3u/OSfwzCg73vUdY29vT0IQc6/Za1jmsN+DHYSQNmph2TGjsd9FyzzTwabzGAmplK4keN1mYBhAF5KnL33yQm5lL/vyn/597Lesxwb/n1//wBCCFxeXr5y3Bw0F4Z0rrv75J90/2Ywy+PJ32eQPpvNsFgscHl5mUAy7ycEktTz98u2VTw+PGcz+MvsNN3X1JM4ny9GY1sy7PzdJLGWgISkllzg+9YihGg2h6hC8QFSSYRobAYJdN0WgMBiMYOUAi/PzgEIOM817DolvTJoHicCXXT2VcpiNmthrcdqtcZs1qKqKgx9VMQU36FWVuQ+/tmPf4zDwwPcvXsX201PRmCqjrWoJvoKBPz857/AtWtXcXR0iMV8gQ/efw9nZ2e4f/8++t6kXsizlkyx5nUTa/cDuaorkkfvJgCmmGKKKaaYYoq3I95osLq7iKfYlVuyNFFGq5BxH8G8rfjt4vUQqDdrEFSvGLyH54V6ELCvWYDSYo5dUH3qR6oVsT/siJn3LaB1Zm0YTDHAK3uXMmujtUbbzgAA1hooLdE0deHeS4wOMVcRLHiLoCQtYiUi+zyY4QAAIABJREFUQFLQUhHjnEAfgTylKoQw7gvLIFpKMlNiBqmUk/JCOy+aZVqsK6USaOVzFEIm8Gyth7Vdas1Ttk4BArRUqHUFKIwk0zq62XILEV7M0/tEi3vP130sBec656rKrChLVtkQyjmHruvQdR222y3JJx1dW2bXqqrC/v5+TCSs4RzNv+2GJM3U2kfEekIJwI3Gq6w93ZWylvNliKCUmdXVapXe52NlEFHeF/z9XXD2yv3jqccqS2DLNjFllGw8yYCzpH6cQMjSYmbES5BTHldZT7t7f5afLY+ZARozzMxmvu4cS8DOqoGS2eUx5GRHOZc4ocCJlfl8nmq2rbXojMFms3kFOJbXphw3eqRYOG9BdaS5lIDdfEu2l37PsuKsCsnMd9kXmY9X2NKEDghsDuZtdvot5NeIZmTGGFxe0DPo8HAfxjh0vYG1Pj7DsisxsaphZz6RMZfzDqvVBk1dQ1Ua202Preixt1gCQsI5m5Qv3nsEKWBMDyEUbr5zA8dXjvDNN7fx7Xf3ySQKGsZ5tHIGYQS8c3j84BEuXp7j6tUrODjYx/HRIZQUeHmxQtf1aNsWe3tLtE0D78nEiw3j09ggpLrfKaaYYoopppji7Yk3GqxKNXamBfDK76+yppw9d6PPZxATGVIlQZ6e2d2zZI2EoDpIB2B30Z+ZSJUWjwEBPoK2sXxxHOUiP4SQnF6B3NJkvliiqurUp9R5C+8DmqZJgJmBRPAkc5ORWWV2l7eZWD1JQNK5vHCtKpVMncrWOXzsDBJzzVxmLxk4CSEwm80SE8itR5iVM8YmVioEqsFTgmoAVVp8C3gf21kED2EDqqaFbug8K12hqqu0b1U3kQXrMUgFCMAHD3g248mJDCWJSRUCyRxKCIHNZo1h6BEtoNE0NbQmAO8sXe+u30ArDSGJ7dw/OEwg11oyYpJSYLvtIKXCdtvF5AOx8j6EuP98TXhsmJlmAJLqOUM2TbLWom3b9Hk+p5LxljLXaqd7IYRUN5nqjUGgxTIAkUOaq3yNhRCJ8WfX4QwMZZSIjsF1CRD5+zyXSawwbtmTEwi5xVJ5n5TscXmPbzbbUdKH3y/vtRLk7T4nXvevvKdL12v+Ttd16ThDZMRZus+Sek4+lKxp2n/IiaBduXJZ+57v1/w7j0s2EvPJKKt0SqbzL1ho3rbnulGSCFtLqoR+GFDVmhjfIKMKpELbNpBKY7XaxPYyVIPq4UkqH496V8KslIISVH9thyExq6v1GkJI6EpHp3SqHQ3Bo1IKxnkY4zCbtfj00x9gsb/EnW/vYr3Zoo3tuuxg4J1D0zTYrLe43z3Eer3BzZvk/OsCOYq3bZs8A6wxUHWdjpXbfFVVjaqqMcUUU0wxxRRTvF3xZoNVAQgJiMB1WSRGC54WKblakkIrBZEaO8QPC5b27tQrcT2ZD3GFl2tYQwiw3gFKJUayZHVowUhS2tzXlMCqj9K7dA47NXqprYYUuLi8JMfQCCzmsxZKKvi4YO36Dt12C6UlIEi+R5LcOjNMPjrGipBYDD7fksUynlqqQAjUdYWqqtE0VQKlzPyFWNvGr83nc/RDD+88+n5AN3QYegJaJi7eD4+O4L3HkydPIASBOGrl4kaLdJIWOnhP58NgPbdiUdCxJldJmQxf+r5H13Vo2zaBCpZLCyFQKQ0hJarYazEDIRpzYvVoYb/dbiPQtrF2VUYjJ4vDw0O0UYrtvEeldWSOqLZTSIXFYlFIdPex2awwDCbVHrJTs5CZrWeJbDmHtKbekdxDlxjyClppuJhgqOOim+ffbkuWxGoqBc/j7H1smyMgpU6fF3GuexFZz5DbFB0eHibJKgMoBqr0k3pcCiEQbGnYReC0qisMUe6dwAxphhNg42vGgK4EuNkEh+qQRTwGIQUqNjBSEmboARTmQuyKHO83ZlN5HDzLpAPf44jfz4oMrrlm2WtViTgOhbkUAgaTnW55flErlwbOWfT9kBIinGggszZqF+RA48jfVUqPgDmDWL5n6LrTdUz3ZmJts4SeX6dHV0xQiGyMVsqTWRHi+x5AgPfEkGrtATlE8BogFR2HDfFaFckPCEH3bmxVQ+yrg9Y14B22W2Ki66ZBt12jaVqo+IyBiC1/nCU3YADGkGz7ow/ex/HxFZx+8SWePHkGJStoqaC0wmAsKq2x3ZL64fz8Ja5duwaPgL3lArXWOD87g5vP0NY1BERWnDgHXWksFks0TYsppphiiimmmOLtijcarAZuzRAlcg4BPjg47yClhmTWNQRoJdMiDSEkdkwUq9RSepneCYAgcgKemVZqOALPC2lBAIoX3bTAJRdYpUgOJ0C9OoMLsN7Cu5AAn7UWPgRUdVUskiVm8xlcXKw65+BBAH0wPTYd9TL0sY9hCAE2AiLb9+iNQRXBjPUGUgh467LcESQXlkomDTRLMUtmJvjMwgoRXUWdgzMk6bxcXcIVYNtHIAAhoGty87xcrQjA6wo6MqXMzPECmvuSSkksatc56Mh06komyXDb7qPrevS9iSwdyxGBvs/SVWtdNLsKkVEWkIIYHCUkAAkhMnAYhnyOxM5l92MGZ6vVCm3bklFRlLQye8Wf2W63aNsWSqlYp+qxXC6T+/Fms0n71IrmrQgBlZKQgmqrpSSGWQoGcj72jAUQ/A5rlk2Zdg2AkjTYDmBpq+D7JpD0W0CkFkwhGpBVlUbT1KkVEbyDFLm2dIgJBHgHETxdUz4+BEgVs0gAAjzVEYv4egwtFBD4mOL8kxrODqkeUSQHXUFuwyEguEAg25PKgAy7BpLkKw3qEktA1sdtWmcBLyChIgsoULczUK3vFs7n+wJaQwgNSAUBQddBSmhNCQyAnG1tBFGd6SFldtHma+K9R9c5tK2E97l+mlrCkMMuopTWBZuuGSUuqN1U2xJ4yvXDBCglg/mkHAkRsJKUXghK7FACb1zrm2uIhxGjm56pgVhi7zyEUOhDDxsTMc4FBAEEGZUKEqn/NCXhACHJ0Egqeg5STTjNA0gBESSs9dgOl6gqjd5QXXyQkSmGQF3VSfHhfYAbHEywmFUN/uwnf4p79x/g22/vYrvtoesKQQg4kIS4qWp4D6xWa+ztLbC3XED4ABdLCL73zk3s7S3x4sVLDN2Atq4xX+7BB+DOt3cxxRRTTDHFFFO8XfFGg9XoeETmR3FVVJqTJPApYrsIjBlNDgZMr5MQS5G/Re9nIxaEEIFyZGuTjI/MS4QEsWexPQsAKKWhokEJiu947xCNQtMikpkghABjC/fcyBpXWqfaz6qqcBBrJlM7lwhizDCgqeqR5JD3za0pGKQ2TTMyxPHeo9JVBD0uAjICXM5TP9QmMpr9MEBHt9rZbIaq6McZd5jkiiUDxYv0UqqptEbTNpjP5+l4+r7HMEQnUTWWZ+46F/MCn41pmA1L7FIycBKQkvqiep8/n1mhsVssM6Bl3W1pGMVtffq+J4dgRWwrmUn1ic2qqmrk2ltel1z7nGuF81i5zID5bLLD8lMeq1HNZgB8cBAQox6sBOwthCAHYDKAIvdlrh9l9pTHWgiRaoObpkHXdZHlVCnpUcpdgVxXuTuWla7Sdks2tDSIKu9NkjRLCAQ0VQ0RgO16DR+4BjYaFgnqKSxkSNeaxjcrGficeOxTr1cAXU8mXSSZ95Fh5POS0KqKc6qU7IZU48zJl7J2OoHh9OzJtfP0HgFF56IkVkhwz2Q25Ao+QMV67RBE7Ekc5dQRKPJ1V4qfLXks+RziUy+asmVJcTwTcM0pH7NlhlsqpBRfTACFKClmllwIdlKnRAyB5VxOwTWyIQRst12qYw+BkiSqqDnm57cQAZVWSXnx4fvv48b1G7h951s8fPQIPpDzLz3DSFp8cHCA5XIO7yyaqkZ9dIhKa3zw3nv46KOP8dd//ddwhp5dxhh8c/sO6olZnWKKKaaYYoq3Lt5osFoyGSWgZMCYFs0B0RWTYrcOrtwev58WeCK3rsFoT3n/bLziYssLqtUiOa0QwDD0CEJCK0WLaK4fE7wo7RKbVJqxsAsws8Plgt4ak5xN57MZmqaBLsArgxgXW5SoKA1klrYE5qXUUEqZWM9UzyizdFNXGk1oqZY2fqasl6vqGnVVQSD3Y+VtlkJrrq1kCeNms4H3nmpy53MslksonQFbKfdVsknHn+WR47Y0JfNIr3O/UGJN2bAGoDYixH7l1kflthjMlPWI3AuWwR+5xIpXWsp477FarVKt7qsL8XFv1BLUlJJYvrZ0vCq9x9/hRX/J7FG9JKCEjH0lkb6f9yWilJQSPNy6hWXUPA58nty6h1nnlHwIHgK5X2x5L5bnWYLY0kiqZO9fl1Sh32OCyAVA+sie21j3SLJuTgqResJBqF05bAZtfK2ts4UEO5qggZJEbDLGCSXnABRy/7quEEI2OOIoey/v3m/lc4vOn9/J5mRJTeGziRgEEivP22PZ+DAM0NW4nQ0rPMoETVl2QNsfJwYSVQpOzIFKHYSA4msgRLqHyIAO+bOJwRdJeh4vRzyuDG6lVDDGwbkuJkssVhcX2N/bH7X9EaKQzwcBZwiA/vDkBNeuXcXPf/4LbLotvHfQSmJ/voc61rArSfesFBoCwC9PT/HgwUOs12uoqsZ8vsDf/cM/4PGTp/jgw48xxRRTTDHFFFO8XfFGg1XvbZTmxcUWaLEa4gKHs/wASw3HLSo4dtqv5gU218nFBa4I1OePCN1sDcOsKoMKZuWIiWImycO5MYtLTNi45ozEvnmBzu1jtKZaRWstoBSkoLYq3JqBZIcdbT/EWltJcl9RLFAZiDKLquvMbgFIzKxzDiEucAdr4SPYnLUz1HUNqRRE3yVA40ELdInMwJWgZW+xTOAbAGazGZbLZWLnhBBo2zbVfFpj0LTktvv8+fNUS6p1lXqmAmMDnBIUjfsmhlijSdLexFgjs36lEQ5vlz/Dkl+uny0ZYZYy03xEYk91TBIYY9DF/q9N0ySgWiYDdoPnRjmOh4eHsNbi4uICbDaVXYQzM8gJicyAethhAJOAWmvMZg0dp9ephjLX6joY048AbQlaASQjIU4sOOfQbzuaEwVTzMBr91rxOQ7DkNyhs2NtmWAYg/css5fpesvU2shF0Dh2DCY5K0bXvASLxlh0XY+6DpCCahilEHAhX+OcFKBtm2Az+HLkSAuRHY8TCxwZ1vLc07Yc16uODZVoDHisifn0PiT59+58YUUCg9CShaZEBPc+zv2Pd+/NEHL7JHZDLrA1Peci8PYB8bmYHZ2FVJElRnw2UhKEj4PJXLpvqXbfxmRB8KDyhUCu5lVVY9P1ENsey+UyHgHNZyUkFWA4AwnAOYMb16/h8PAv8Xd///e4OD/HrK2xXM4JTEOgqVtIBDhr0NQNttsBw3CGw8MDVFWDO9/exZ0738WWOWMTuSmmmGKKKaaY4s2PNxqssttuYl0QayYDA8m4ekpMQWbiOEIIkCLXqpZyOWIAdtgvH1LdH4DCWIT2TWAlAtAg4LyNkk6RPpsBADEju2xTpYkNMLYACbQSJ+bIezR1i/l8RtsaDKQkcOKsRYj1dhIS1hu4aBK1ux8Vmd7wGvZFSoneGDiuo4uLeKU0+qEftQhRWkMag0pr1FUdnY99rCkmoN6b3GJGCJHOu+s67O3tYbFYJNBkjIGuKlxeXmIYhiQ1lVKirioYMwYxJQOXf+Zz0lpBaVlcx1jLKiW0riOgyCyglHk+8QKf5Zj8Wk560Dyja5rNamazWQJ88/k8fb5kV6sdyWMJtAEk92Q+P2MMEEJqmcR9Ypl9ms1msNZG4EljPAwdmrZG2zaRkc1MrI2AzFiqaW6aBaqqwna7LdqlhMTWM6BXSmG5XEIIEVvyjHvulteBGcxdVhUA1ZIOcfyCj0wmf5eMvFg6CgEEx6wxS3oVEGhcZrM2JlnsyMiJrkdmDtmwKjPbMgFZksVGoAcBBTE6r5SYSXLxzMITSBbJ5ZnGOSTWtWToQ2R9aX9jcF7OY/q8hhDjz5TPsHJsdx2L82t5e8y2Zqm5gnN+tO8MzItjihLqAABexKQd9SoV3uc+zlHGwvcgP7qoNQ3Ss5nrfNmB3FoDISoM3hIrClBZgVLQWpL425OkWEmF4B2kAPpui6Zt8Bc//XM8evgQj588wfn5ORaLOZqj4yKpQ6oDpTX29/agdY2zs5fo+h4/PDnBy/OXuLi4wBRTTDHFFFNM8XbFGw1Wy4U9EPFcBBIevOiLTMLISok/TwuxUoLIP6WUsdRq/C2ZthKSAVOIAMIHD+EDrKVFWtvWqHRNzpOx9pS3z4vdsv4xudSCdq2Vwmw2yw6hkqTEvHAb+iEBlhD7iCIAPi7W61g/GkLAptsmqSJLRrXWBO6RZaf8s+v7NIYCgK7IHCmAwKmP7F8zi4DZ2lQja43BcrlEXZNz7nq1xt7e3kimymCB6zu5fynXpprYRoMBEjNxUip4n8HuLqNaMteIl4/r5li6SJeNQAUCmXL5kOWVZCwlEjtExja5hrNkwHh6eM8gN4M0bvlT1my2bZvAd+qFKXKvVZ7PzKizBJeNmZRUsIYSIEwiqopqKy8uXqYWNjyvFosFmlpDSq5L9Tg/P49ASMILj0ZWqKsG8zmx2rmuMTPOzEi3bQuWt5asKwOZklndVTHsgjGlJKwzr8jQOQElMU4a5GOK5y/5+iKB0FIOLqUkE6AQEpAdBmK+GWyXdb7WWgTBCa8x6CuTCt7mY+WkgxASZrDpWNmMSUQXIsbcgRnIHWO3cl+lLLq8V8oEze5xWWsBkWuFS9Z/zJg6sPydtx23hpJRDSHL5+mYABfrX4ndjC1ylAKEh4LKfUoFHxvde95n+S+DVRENuUKg5yD1dWY5fYhAmpJk3tPzU0cJPxCSesZ7h77bQkiJ733vXRxdOcL9+/dxdnYGEQT29vahZjMgUI1/rStIqfHs2XNcXKywXO6hbmocXjlKhnRTTDHFFFNMMcXbE280WK2qCrWuYGPLB4AWwAxayRe0bEMxrlMtQU2uj8psig+BmsVjzEoCsTYuEMNK++PWIEiLR2slKlHBwcFZC8QFrpQq/iOmg2rvaBFZVRpstALJNWw2spHRqTeavxBbCLjo8iulhA8e7awZHS+zm1VVF3V1mWH0sc0E9x91zsIFqhEjxovcW7VWrAmE1jrV6w3OQkhEtkNiPp+hbdt0LrPZDHt7ywTo+FytDZH9I4BVmkMNw5ANpTgBEWvzZOzxSO8xm8mGN8RwMYMamJWLMuxsrKRGMk9ue5OMcSK6KM2CdkFxKaskgBViQqCCEAHWDmjbBpvNBtYOyZiIQa2ARNPM4J0jtgkkdXSeHHZtdKIlRjGmW0RAO6sxDAbWxW0mJ1Y1Oq/t9gL7+/uQUqDvO7BB0GKxwHq9TvdAGlepsNls4rWr4nHTMSwWi3T8q9UqJViSG3JkrUIA6rqC8w5mYMcwOluqjyQGzgcPJRWct1RbGk2RAJLlS0ESVh53HyILKbPEm8ZdjgBqeW2klKjqCkoqDGaAMSTf9d6jN4YnBgIETMkuSnK0llHhUAJFay3gQ7x/dZSXK7gIunefLeV9WJqKsYO4EIFaEmkVj43BJIHHDPo9vB9e+8zi/ZDHVW77w8/IUvLONbJC5IQDzUlmaGWax0LIBFoRQiwtEOmZ6r2HDvTcCAhwIc7RQIkeKmUlyTA9Y7IMWwqf0Xu6HwPquoljbdEL0DMn5g0dl3tIcnZnbwDvqVWYdxaztsHJySd48vQp7t65i+16jevXb2DWtpCyhq4rPH32DLdvfwulJGbzOfYPDuCcw3JvD1NMMcUUU0wxxdsVbzRYZVfJksVhwMIuulxPmigDZGYT/B5/HmNJqQ8eYeQhHL8f/ws774QQoJVGCBJSIsoRASEpq89sIMsImf3QWqGqdGJY2NymamqSFEcGg4GB0pqYWiCxXrzwHNXzxfeklIlJ5f3yOQuEV8aDATu71gLciqODFAJ7e3vEtCkyfZGdQLfdQgqJ5Xwe6whpPwRU95IBE6CKBXs28il7hALkmtxGKS23hPHRRKeUOzKYIPOhWAcYGSG+nt6xSRBiXXGVJKRVXSVJtpQKWgs466IkFSP2szRFKk178vjKCO4yU1rXFbTeQ9/3BcCL24lmNJxgAdj91aRWNACStFxIgbZtMJu1QACc9+j7LrZXqdI82my2aXz6vkfbaOzv78exppYo6b2WmHEEpNrhsnURz6fVapXY3bZtE+MfQkDTNMRQ1Q3Ozs5G23HOxbpRSu6IKCeFRzrHkjnk4x4lW3j8VZasM0gU0VU39QsVec4rNmqKCRJiu0lW7RxLu236x67OQRDwymyqSuAS8JCK5blZ2r3bIqaU1JeML59fvtdoTlob0jHRa+P7snSmLsel3Ja1ZnTfJ4fj4pj4OOg65G3leU4MLEvLy/uIdoTEnDIDTJ8LgPOglIsAAdP48MtPSAgpoYQY+QTQs5Z+JwBKrD85jPeo6wptVUHCQAqJpm1jksJR2UEIqaezdRbWSRwfHeH44BBfnH6BJ48fY39/H/2MDNq+/fYuvCOVw927d7HarHDr1i3MZnNMMcUUU0wxxRRvV7zRYLWpm1T3572P5kQ+sW5KcuuaV6WEuws/YGx+FCKYY3YWQkRegHsHZpkpgFRz54VP0sTMjhC4dc4nk6G2bVObGCkIeJQLWq01LegCLcC1UpG5CnGx7cCGTqXzaLnoZ7DD/UD597I1CJ93KTmtqgpCR7MZNooxBkpILBcL1LpKElcgmloFMnNqmyaBBe9z3WHXdWmcSyaMgXXpmiqEQN3UI9CSF925/Up5vXghzscP5JYoAHL7HeeomlcoCCVhBpf6szJAGy/Ws1yXt1X2ouV/1H6DW7+QXJYTBJvNBk3ToK5rdF1XSJcRZZmZoa20RKNrzBWBwa7rIGRA3dC2tFJoYy0rneOcDGrinNhs+yRtZWfitp2h64bYl1Tj7OwMzjmqe54t0Am69n3fAxi7UO+6Ktd1jVXsmzubzWhs41xgA6pyrFhiW95f4+uZQVe5r/I+TdcaBJ4ICgmIkBlEHWsgAWJm+f/NMFBdZdzvMAxRpVCNADUnTrz3EBHFmUDzp67rzCAHnwyFiLnPc5nvMZ53PEdeJ0/nJBmNlYVzJTiWSKgwSnNL6W45LiXLyvt6nRHTrhybn09CjNlZds3OyoUxOxyi4oBdl4PzMMVniDlXsU4YyfQuA98QATFLuAPg6FiQko4ZnHtHLuDBB7R1TWqXrsNyMUfd1LCWjMqkliTtJ+0xHICmqvD5T36Cx4+e4OGjR3j67Bm22y1snPsueKy3a9inBqvNCqqZZMBTTDHFFFNM8bbFGw1WIWPi3he1lUolmdouIGUGh4MXmeRguVNTVxgSMTP7/7H3Zkt2ZElyoJ7F3e8SEUhkVneNyAzJJwrZwvn/bxnK9AN7eprdVdWZQCx3cz/LPNhRO+YOcJ4BEbcSCBIR9/pyfClTUzU18VuS/leyErUC1ZG5rciFgKUxC02KyuNhEgt0Nqhs2Qt09iXGqOAoNxkhfJAE0LemPSdzSZ2THk4m2857lFrFdKlt17KCZFKC7yyJXaPQ+hBjjJhiRPABx8MRoe2P5xB8wKeXF8Q2loeJMyDA595AjB3tYRN8OsuSYRWmK+s4GyvxrKViMK6dFtwTKNpjsGxod4Bd1NGWTr/fsqYi1bTFEF6X74ECAhmuyfV6xeVyURBu+1dVVuocXANKLBboPdnO6Xw+676EzfZALbjeLii5rd84quPzOEYMQ8T9/tB9ffnyRZx3hwm//PILns7P+P333+FdNW7GDsfjGY/Hva1r77nlyByug/deZ+eyh1VG+VQ9D3tO69ExvaeXRRcACuzs57b3ituYhFmwy2tjv+u9xzROmJcF1+sVIkXuvxeH7YxllntgXhaUXJAh8tMAL/3M5pltO9b7kkDX3mv2GvI4+BlKgb8HXrucvIPHLaj/XsFNGVbX10hZ3dz7qrdB+b/tmW1bXq37+jrYol67lktq70tRlLjA+bQAUBDCgOAdUqZMv5htNjVwBVyB3sdkWGMcUGoSs7kCDEOArw6X6xXDGLUwtqTU8K4oJmqteMwzAhz+9KffcDwd8T/+6f/B+/t7ex8nuBDgfMDtcUdGwb/8z//3+wu1xx577LHHHnv8sPFDg9Wclub+2V1H4YXWlAS2j4OwjA6DAIY/WTnMQnpW+UtRabZkDStVscob+3ahwGkYxAgmhqAWT2RpdB6q79JLMmQ06Nn22Yr5T8HcTF44g5FJbmzgys5KtQknk+7tudpEmZ+jbBW1YhhG6SmsMi4ELZH1zqmxChklMpg0TiKbxXPh8QnjG1fJPJN0kfO15d0k/pZFsj8fx1F/T4aKCb78vCJGYfoul4vp1exjVmq7lvO8tF7Ebo5E8CgOpnkFci3Dxj9dcul0LflvytRtz6tl4sjocX6uAPeMJSXUknB+ecYQB1yvNyxpQQhR10F6TTubfj6fcWzs6tvbe2N5Jy0QdKOgiJSizBmtaxMlC9Y49oizMe11fHp6Qq0Vt9tN708bdl0IZNeS0m97yy2LSDD2PcmwlRQrIM4e97uMWBrHSXtcOQt3WRLS0h2aMwpSLXBwiENADNFst/V7Nqk5i0xW7ssCAZntLaDm+WwZS8vSWwC7/bNlTO35k6Vf/4G+w2z/9fo4LCitKGXLyvbvdxOman7X2gnaD2xhrtZukrUqNtSiYBfa51q0X5ysq2sAVN4JBffLA9MYMY0yN3heZpTcWiRQVV5c5YARhgEpLTidjviHf/ivOB5P+Od//mcsy4KSFtR7xfnpiPv9htv1gj322GOPPfbY4+eKHxqs1lqbC67tIZTfdZZmw5i2WLEx+Vtm03kvzqAQQOb4+yr9Vq6KVFhSLX5PWE4mam2jcK0HrfnqfAPevFuzTURMtQrzpVJEL0YmBUCqabUdm8DzPG0SzeB/q0TWOYQYVBbJhPjVK6CKAAAgAElEQVR4PGJJSRJH0we6LAuWeVbGl2wuQa2rwCP1USdAA74qse1GMwJOOri080tzlt5ACzhlOTvg3QJWywSxWMB7IMbB9LpGOBca8yPzKDlqhLJI+Xfqx++cSnkJwiiV5b5DsCzrGrBSSmp7JGOMYhzTQBolzFZGTFbPOekpPY4jxmnCGCJyaS7GS8Y93eFCwP3xwOPxwDi27+eM+SGy88fjHbfbDS8vL3h6esIwTMiNnc15xu12wzRNuF6XVTFjmiYF6a+vryovJjCT/U2tGBB1jbbgyF4nPm9bKbcFrxag9XugF0N4nSn/ttvk/T1fZtzb7FsWr5yTkTsykqmAZmcsolRb8EBAyRXFF4gxVmizix28sowyL9T2ja/76L+9H763DrZv/nufsZLiLUsNQHppfb8HRfIeEJxI23OxM4H7nF7Opu0FHm7Tra6FPCvyHiicGe0By5yXUoGSTPXOI2dh5ivnVju6EgOUOqOpJsSdfKNqcW27wWMME1Je8Ha5YBoixmFAcRnH4wk1FSBWwMsYogC5Nx2aadkw4j//5/+MP/9vf8b/9d//O/7yl7/ieDoiLYsUFKNhz/fYY4899thjj58ifnCwCgDiwqqOl2SzWlJUKkeNiFRX/1OlaEzGmF9Jjymcg6t0Aub+2oxVBwGNbeZqUEMREbjVImYjLomrbksPEZp8F8AKHBazbx0k04AqQVcMZBcyaqERT1L2xvaicjSNdQG1czOtwRHHT0iiKD1lIUQgFzxunG8akJCQlXzhrEQxg7o0453oQ3N4zQriKyQBHTeMriTfQdkYpra1JfuHaZKCAWc9tnUT2XEHE0zULTCw41YEWApwXmYxwvHewzsZKdJBSm3sZ1rJUe0frrll/iwY4/Wyv1sn8u3cDofeF9qYqpwLvC96/LfbFcMQtZ8REMDrYwRaL3XKGeM0IecbbvcbfJCZmc7JynPES4xiTDSOQzNzkv1crxedwymSUGCaRtzvV+QMvY8I/mqVmbHLMmM6SH/f4/EQ9jeIq/PtdlOJMIsZtXajs1KK9oCj1sbiikOysIp0wu3MWgdRHqWI/N21ayemS6ndG0W3JbvwiMFjGoUpX+akxkglkyUk+9v7ab13CENAcRW1jaTysY1PCW14VZX3ANp93s5K5fho10feQeIuzPN2XrbBvkyrOLD/7vcXn5c1U2slxYyqYhCnBl6lyjvDGsLZ4oD8O5vf9V7ZdR+9X31fZbfOCTtbqqpabNuBgt3KcWAcJOba8RY2tyJA3nE5V3g/IESPXMSZHLliyQucd1jmBfP9jmkc8PL8jNvtJufrHVCqmGs52DPGMj+QU8JxGvB//rd/wN//6Tf8j3/6JzzuC0IM2rO9xx577LHHHnv8PPFDg1XnuuyPKZp3XhKzlkyVXNYMz4axUWDqAJmnaaR5rihIYsJdqhll0lMy/VlObU6qF3PMitbLWAtiKIi+s5hAlwSjJbXeSZ8WILMjQwjSh9uOl//NHk2a+JDlAro0cMv8WeaGDJ5laoStasxuAQbncZ/vuJculZ2mCT4EnYdIN99axQzFu7w6Biak87yo1HUYRnjfgJ/z8LUl+0WS2Gka9bzmJP2GS2PScs5AsSM95PzUSGkYME19dE/OWRnMpVZMo8hXVQaak0hBk0hZ5V7xMn4l59Ua0hGZjKf9nU3KLVNoDZ9ijKsiweXjgpwEwPKe5XUpJeF+v4JmN7fbVYoWbXzRME2AF0ffeVngmztqKTJ/dxwGHJop0+l0UlZSgPC9HTMwDFEdiMUA6qYAKOesMul5ljE5nz59wrLMuq+xjYZxEHn60syoCAIpNZb5pUWAH3skS0at3hRZ6ArMwkpBCLEx4wKKikyOEaa+PQOci9oeb5QifxQcN5c0FmQEOMn4JxfJwjY2shSEKg7Cj2WGb4ZePiXU6uFdRKDDbWUZSt41LmzAXLsvmspV2dFaK1x1ytzLiJi1fHylGnCttcA5PS/dNt9VLLAUUxioxbCk6xYA/t331XumoeOf1vtiQaMfk3wml4QQZM0qDQAKWuEEeo7Otd9RoQIH9nkH71Cc01FhJUOUA4nH7luhT0CoqzJLe1kS/vjyRYzfhhGP+0N67KepvddzB92lttebwxA8/uP/8b/j119+wf/9j/+Iv/z1r0iPBXvssccee+yxx88VPzRYlYS597l1wGYTvp5UWckpQRrApK/3VjHIcNrPrYHumoETWZwkYJTBDmNz1qylyeC6RE/30xJUyn1p/BNjxNDktgAUPMxt25TickyF916/x6SYUuHD4aAAjcwjgZMFqyJn7W6yfvbIyApUOfbk9fVVgQznb5ZcUHJnh0IIyLX02aVmf+yFHEIExoAhtPN06EZSbZspJcyPB9DY2IwuL94yl5ZtorHR09MTlnkG3B3DGDAdBsxzlUkbWa60sPOUDweV7PI+Yk8rmdVvwQXl3n2mL6+J3gutB5nrHWPEnLvZE1lM6feUczidjnh6esLLywve3t7w+++/489//jMAKEtba8X7+3vbT2fMeK3u9zuOx6MWNWzRgus1Nodhnh+vz9wk3+M44nw+C5OPzoBznYYgc4DP4YiUsho1CSMcUVCRcweRtebu1o0u297ek3yu2BvrQmcubYHm/08OvmYrqxaw2EIg35FCjW/Fo/vtjpKzFj7ULq3WZqhm3hNt/qiV4ttj+d4xeR8QTcuCvJcIttfbcE3tUTLBe9XnY9vjyveLmoK5/txtCztbqXZoI6fs+3N1HKYIQzn+siwoyHCOSpHWeqGMLI8tG+DaWilaIcs3BYV8rvcBVxi5fysIetdAvHNSGKvy79t9xjwnHA5STLter4hDxDSNQFNnhCgjiNLSCiqh4nCY8F//y3/Bn377Ffd5B6t77LHHHnvs8bPFDw1WbdiErSex7InjrMRvXTbXidxm/qrr0rkuEfTfbIPbYbJXqzCew8hxFxneyexAbssmgtyGNYgRwBFXYze4z0NjNwlKyPKteiFd7wf0jYGzIzW2wIn7Z8LNz+eWsD89PWniXmtVc52VZBFkS9ZjMnjswurdFCDVWnEYp7V5jw8q5bbHFUJAbYzw4Ifv9kNag6JxHDHPMx6PB97eXpEWmX9aSlbTp8djxryIDNY5v5LEWsMisoRcb7rc2qKDc34lk9y629Yqsm4C/lIK0iLM7PF41Dm7XOvH46Zg8ePjA6fTCfM8A+iFB67h8/MzXl5e2hxUMTciACIT/Hg8OlBt4JYsNAsTBLP8tx07AwgwGYYB0zgiZXF3Ph6PuN/vmB8LXJVizbLMWsi439v6eKezgrU4svR5uQDnyXoFwfZ5BqSIYdsKLTPoQx9dY82XuA3K5YVVTSjVq2qis+TC4GUUBBdWxRCCae9dV25wH5t3gwXHW9Bsn2N739vfrY4V/d0zThMO00Hdpi1Dys9yW1q08X079pnic26LdnKMdCnnzGrK2f3qswJKu4LFnheLAZRZ9+PqPfnKJvPca3/XlpJRi9OedbkHEgrW72/vusMyILLrW3PBHocBORc8HjNiDIhxkGJSXq9TSgnDEPGf/tN/xPH0hD322GOPPfbY4+eKHx6sMplhwsIkHvieNBMQYNITQCZtHWAJhyJ9V2vGgv9twRjDmcTXO4fT6YSn5xMejwfu9yuCk144a+Zkj5tJKgB1X2UyVUy/qTVZ4ecJUmk0Q6BkjYsej4eCODJ4TPbJmnKf3T11VMaPBkE8/0+fPq22TVkw/1AiS0fl7wH7aZowDqOeM9eVDFtu/YKTmd06TRPSUrQ/lOdAMMg+VbLQ9/sdwxBwPBxwOBxAeWmtBfP8aFJBhxCccaPlZ/o1Z5Bx5H9bieUw9MdlWwyJMSrgnaZJr/EwDICrWNKMXNLqPCUGlCJGMafTCZ8/f8bz8zPmeUZKCX/961/x+vqKv//7v8fhcMA8J/2ddZcls5tSklmTqbP/9hmwYIDyZX7uer3ier3icJhQ0Q16hmFAzSLB9zEoyF49W9WtJPWlFDEqMqDpeDyu/s01Vqa+AnlTKGIxZvt5C+C2Zkzee8QQtefUXlvvBaxyu7wOnTXtZmv6p5qGgE1h6XtjanLOqK6ilrWRkDU6Wqs3Wk9xXQNR+/f37rtapVd2e1zbfdh3Qa1u5STNdYtxWDlj15o2x9j2vzkO8+hAzMuE3czJSIqDQ3DCxhYFwCLVrrmIA52jj4DcZ22DqM6pxNo5AbqPeUEuFQcnEvFSAU7N8ugAt5aCaZRnYpln/PanI/bYY4899thjj58rfmiwaoHqquJuAJ1N5uzva1WPl29YD40maUOtcKGxhmBHV5PnMQmEuHlYBjEOASE0UFmlf4vSNs4SBYBxFKbQAj5hrQ7tOLrBD8+NTA9/TuBAUAkAt9utG/nUugJ3/OxWPsl1JQNGefXHx4f+m32VdtsWtPGYRIbYe2TtjFEevwUSzjuVUyowNmNUlGmNHimLL7LzQHDC2t0fN9RFQFReFqS0wIfW7+Z9k4x77RnOJcvoiyL9nDxO5xwejw6ev+diHAyTZ8GsnfO6lRHz/mJxwXsvBjI5mXtU+kh//fUzrtdrm1/aJd/8Prf19PSEv/3tb/jLX/7SxtwMOBwOanR0vV51zMztdluZyPDakzG3bK1lhpXZbDLoYYiIQ5edS+EAyDlhCBHnpxPm5dHOEfBBpMMyNzM09i4JWEOXTHNMD3tk39/fvynq2DmpFixZtn57P/O/9XrGgGEcEJpDNK8Fn79zA80VwMRnpFZ5huv6PdLXUV4W3jVDpVxk3nGt+sJwEGY/p4waovTXr9jO9TO4BbksIq0Z/W/7t/n9dpWbbLuKYqH9dBpHxDZHF1Vmkpbc1CSrAl9/r/6vihoODiWjAeP+PnHu+47IAJAXvhs8QuvAKLkg8bxC1F5dfX6rOR9Ic7ICV+4vtLFZS0KpDzw9nZEr8LjecJgOCDFgWXqPLWcDxxCQ5/WYpT322GOPPfbY48ePHxusujWrCvRxLSo1NGyAGrtAJGnsE7MsAjZsaktDhTXayGa3UtdaGgsASSx9cDifTwLc0iK9kN4jhNgSsNhYt570A11Cqkyf6zK9EAJiA4vso+TnmegzgSWQJKgkk2eljdzGsiz6fcpHQwg4Ho8qIyaIOZ/PAAQM55z1M/NjXs2stGtDo52te+kwDh39S0avcmmCB57bOI5NIvvAPDsF3VyL0+nYWMOEnIHRCyM8NIMl2086TRNOpxNyNUxp7mAyN7dYy15bZlt+XrGY87UsKrfJe45rG5o5Vc4Zh8OEaRp1HfkZoGqv6PF41HtDPjPgfr8rsIwx4vn5Wa/H4eDw8nJSCWspBb/88gtutxvu9zucczg/PSEtwjzLTNakx+W9XzHpZMG5DryHhlGOlWzr8XBASgkHd8D5fML1eoEYJDVQmzNqLci5s4PBSbGFfbe///47jscj/vznPyuDbWf/yvr2wpItQlWwR9O1Z6wXB2wfs1UlVHSADvRCA9niIg22q4KDdaK2gI0GTs45detWyTOqgnLfPltKwZx70UH+eF1jPjvCUAPDKPebvc/4GQsCt/+24J77AsSYCHBijqXHVOHDmu0WgF20QNKZbvO55qi0Wg++TTcs94qNbcU7gv2KiiFGpFbo4nNda0XKCXRc5/uQ7w0qMQCgNga2VmBJGa9v73I/DxH3+x2PCulrbYUENdwq3RF7jz322GOPPfb4eeLHBqvBrwDIVnJHySWAbxImK0kFsEqy+DsCWyunJGDbJl+aBI8Rj/sDtRSkZcHHx7so2NCAs6tISUCHjJfpTBllmtwmgQJDwYw57lqrGdGSViZAZKuGYWj9jj1ZJRDh2vFcyQZ+/vxZJcBkneyonPf3dwDCynFkSQzr0Rq11jbip+Dt7U1lpfZc4mANY7rLMf89tOM8nk84Ho+6/gBUGstzoWyZjKL3HqfTCdM4YmnATH7nEKOwSodmYETQTpBxPp/x5csXZbNrzSgl6Xpz3ufhMOJ2yxjHqNeAyTPBCq8L17uUguNRJIceEMl4M1Ga5weA3iM7jiNyznh7e8PlcsHT0xOOx6OyiM45Bb8xRszzA3/88Ycy2S8vLysAer1em1qgy63ZK0oJcq0Vl8vlm+dimiaUUoS1vVctfFBC/nR+wvV+0+8QMI/jiGD6lXlu3lctiLCY4ZzD6+urAlWuv9yfTkfWpJRkbFSQuaelVuQqwIk9pc65Nnf0W7nrkpYmS+7suV433+fqsreY86W27xDei5Sx67vJSJPtd7j/UtbFLjLbfDZY7ADauKbiMY2HVbvD995bdntWebJ9L/J553HrOyf3WbQCJCG25kh6PXk/67uvunW/PdhrLNc5enF0XhUXQu9VFWVBe/808Buj1+IG0JQrrBwCMppm6O8sOAHbXJcQQiswRjweDyzzLOOwamkGZiIB7utWULGD1T322GOPPfb42eKHBqu1FDFEKRnz/BB53xBb4lJWswWdB2q2LMa6Z1VYjW9ZU0k6mQACaFI+SXCc/ky2Jcc1HSZwXoYkixmxad1KqSvTHvZH6ggbWJnhOpnVxNN7mf9Y1iZDlhGx0tlSyspQiUwTE20r8yTTtR2JQ7aJQPLxeOhn7D6tNNJ7jzn1/kULYKyZk5UyAj2BPh6FKXXOYRonBC/9kDR/4rFYN1vu37LtTPwJ6vn9w0Fk1pfLxRQuHIY4YBwnvLy84H6/t+S2rkYOAQLeuM6WrSYTZ4E7j4nHLfLTBaWx2iktOJ9PmKZRpbrsE31/f8flcsGyLHh9fcXtdtPxO2SJeU2lN9mvGGcCUoLBkjOkNzEp4LK9rWS1WDyZ51nBHFkwv5FrBx+RE011gJRyG+PkMc8LAIfj8dT2l/B4PPTa89x4TiyYbNULBKw8N56P3Hvlm2dX1pxafj7bHF0DEnXgjFfvgz6vLgMZNBaSe+dwmLAs66IN14prt72XLQu8VRXY51d+V1e9zZTbp7yIbLp2t2sWuL5heLE2evre+87ej5aF5fra38lnfQPv0XzHbqt+8y6yShZpn2htEJXmdb0NQ94/4sYs6xeAXGQ+LMR0zYfQJdjfHB/QnJr0WsvJAiUnpGVB8B6zk5E14yjFu5xSW+Og84732GOPPfbYY4+fK35osFpqRsqNGUAB4OXvZqTkG0Ak4LJGIJZZ3UrXAGtIws8X0FGYrMj/KgkMmvh30NITybABPevRMZZ9C2FArWWVvHrvgVKQW2ZFsEDgYJM4m4jKvv2KQdzKV7lO0zStgKAFmZR+cj1t/xzQTWpUMo3ea2uBr03St/JqsrhkhM/nM56a9Pjy8YG3tzeddVqruOHye2RmCUQVJLRrJIC2S0TneV6xXTlnfKSLAifOkT0cDgreyIzSCZeSWXsO/CxZKK6/BbfDMMCPco3HcYD3Dl+/flU1wF//+leklHA8HvHy8qLA/XK56DW6XC56X4sU+qzXwjK64hSccTqd2iigtOp9JbjnPWLvFfs8yOcqQruGtYrMfL4vOEwHHE5HGeVSnc4LrgWIY1T2VPoEI1D6tabM3DmHl5cXfUZt/7RzbeRLm6N5uVxWfbb2meQ1hUNzje0FoFpbP3vt6gQtDlXf2Fg0kDMhtvm7/GN7lnU/m/eKfeZYnLBsu/etX7P9W4oJsz4flpUvZNCXZeVEbI/H9rjasO88G/Y4LUssz8baqZiMK2Xk8ozb7QDef4v0bCHBObS+/+Y4XJOCw/6O5faKmknxAzJZqApobUw67w99t8KLcRWPi0x16P3Vac5Ngi+FLSmMFcTjEfABe+yxxx577LHHzxU/NFi1PZjWOKjLSiVsQgdIkshE0wI7mHENkgh9O56m/86vkmP9AwFosl2Z3VmrSNoqoOMYhPmV/Ei2nxXokbHkGByYMTViNFJRGgixxjJkDi3DsnX1tGyYZf6s+RJ7XAGoiyxH1by9veF+v+NwOCjDRbDB0TXjOOo6lc3xAN1dltJTC5jt3FgAeH5+xufPnxUkv76+KuN0Pp/x9PSkktfL5YLb7abSUz2GLM6fXKuhORALgJDZn9frXR2GeX3pnEu22fab8r4CoMCW60qg/Hg8EGNUwyML/J2TETQx9BEnNEMim0pprwVEZJzFlfegn//ll1/asQy437s7s85BbZLuEAJKLirJtkUL3gd8nqz0mz2cIQSEGBDjulBRSsXHxwd8DKv+XIJ257szLo8r1/QN0GNRgLJue98757AseVX04M/z0s3JVqoIfFu4IShyfmMHDNcMu2S77A0mqElpAcev2LFNlNhb2a2NLdMq6ybFLyvB5z21BaOAHKt19rXvH3sdtr+zDD/3z+tuwer2s6VQ2bFeN3se/bkNYpbl12Z10RQ0eGyNAkUMUYEiKe5Sel+qc5W+y20fFR4FMnGoillwBWrNqI2mrTCotwDVAdG3/29IWcaAtaLZ5XLDOEacTieUClxvd5x3g6U99thjjz32+OnihwerTIjsGA0yYkz8rHEQvxd8T+oswyh5Y0/ILOjdJnVbySEAlFrgSvfEpKR1iGvpq4BVh1JzYxaqmthspXZkYjXpNudhE90tCCVgsEkppZ/8w3Oh0Q73w15JghabcJ7PZwU+7BNdGutDIyqaNtEhmduktJiupiF4lAIFitM0qWGTXqsQdPTOr7/+ityOm6NotjJfe/7ee2SfkZekay/3S5thm8oKRPGc7/c7AKwck7kOZGQJtkMIzbW34OnpCTlnvL6+rkAHGWu99lUS9nGUpP3t7W1lXmVBM9eCIGuaOtjmPd8l21DmkWtD9tHOeX08lhVgsfcRf277p3nt+XnLJs/zjBIqcsr4+LioLDuE2EY33VEbc78qLHmvcl6upXMOHx8fqzXo8vjO9l6vVy1ueO8RBtkOz7efU1WHWd5PzjnkUlDSti+99b6ndX+mZcSdw4px5vV1zjUnYDRDpXa92EdZ5WclZ3lOFHwv36wzf8bzq7VimRd4J6znek2cXvv1e6yvnQXAWzZ1qwyRf/NZKObn688QRHvv4V1s7yqoYzqlwfZYeS9779WZWO5x86xmI+em53orAjrv23ib/iyUNiMZzkHG4nQXYr9ybhZm3jLkpWSUUvUdlvIOVvfYY4899tjjZ4sfG6x6j3EYEbzHkhNiA6w2ibbzO1WmGzxQTeLUesA6y0pgKsmuZV+7XG7NlmjSH7obL5O0GCOGGNVJVHscHfu1KsZxWPXsCQhvQMWAZm7b9mdu/2biymDSuD1my8pYIxv2E1r2TdYtKPi93W4KrEIIeH5+RloS0iIsK8HldDzgGI6rwsHhcMCyLDqKo8uew4rF47pS6ppSEsA7jqtihAV2XEMm8QIiPDycAkoFrfCIUdbs06dP2ttJsEZAasfxWMaQ18ACl+v1qgCfgM6a2FjzK8s28txfXl6U3b7dbnqe7HcOQYoXXCMWZgCo0RHvId6n5/NZ1885h+PphMfjdVW04P1Ta7/eFmzzGMdxxOEwYRwHZYtl2xk5CXioFRiGsZ1vwTBMOJ6OmBsol+vyrUJhuz+y+2TJRRItfa+8v/SaDJ3N7cfeCyzWfE00pgXBy3cqZOQMAWd1fnV/2SIVa1PbnklH2WqtCM3VN5fWo9rUGt55lFrEMNx5VPTCAJ+9WotuvxtMidlTLa0l0zzXPL/vreH2c9uwhbbOknrzcwJXkcjDFb0W8l4QUAwvKhFXHeDbMbg1Y27/ljUn881jAWIc4JwUXLpqJME7MUuCef/yOso2OfuWo3lEkVLhsPAeGiLY88vnQlQqwH1eUOBQzNidPfbYY4899tjj54gfGqyOQ8DpMLakTg41Bg9UoKQFKEWGypfWB+W6C2ZFWSV5AJO3bqAkSaRTwOG9x/Pzs4IRhpUdVmS46uGK9FAFyiJdc5Qlw0I5YBGHYLhBeugcEBtIJOAAeqIXW0IezEgVm6iyP5HAgsfWx6J0MEu2zYIwC4prKZLEt5EdBNulVpxOJxyawZBvQOv97b0ztC3BJlCQfWbknBDCSeW74s57aUm4w/V604RcQGHU3scQGlOUM6JKM5PKkb0PyKUgNnmljNJZ4LwwWLc2jsd5L5LOUsDxQbL9iBAyvI/wTkBSLW32YwPamixDDL58CEi1SsFkWbC03sQhRpSc4WLEOAyoAB73O+6PhxYBxCBMrt1vv/2Gl5eX5ug7KxjjvSaMc5u7CwE1y5Jwuci1fnk5IgSPZX7gdrngdr/j08snHI6H5rhccDiMKKXiev2A82YWLnxjpRxkZA8BkKyTMIAJ3gdls79+eWtAqyAEO6onQByuCaAHBWF5mRGDR81JWMgGoqzE2AJQyvotsw5fMacHljQjxIhlngUE3gvGccLpdGgAvjHHJcOhIojeHqWxhcF5MWNrTGDJGb4CeUnKxNpjADr4LLXo9xpWQil9hBQZQziHgopIJYYsLOIQkZaEZZmRc0GMATRTK2UtFy6l4HA4oGSRDn+vUMawxQcCYBv8zpZ55WeVkW6gLcQI7xyyOvAOEEJVwKBDRclAzjNCaOqV2tj62t4fPkDaLgpyFjDvBwfvAG9Ydr6XvKcngCkSoqK6gtLWvlBCrcC6t3A4fb9n5Op0KlZu5xziAM68rZDxOQXA9THj43rDHnvssccee+zxc8UPDVanccAQxSkymGp9KUWdIR3EYKXCozo7s9S1pIuSM0gyW1t/Y+1z98hknU4nHA4HNRiyyR+Zt5EjF9BY0OAl0azsMU0rgFxqwTAEwHWDI9SKpVTE2OdcMvGk4cqA3hMmh97ZVzuux7K8lmEmMCWIpRmQZacIkrw5NvaKrsaytDEiwzjA3R2cdzgej20fXXbM/la6vz4/P8M3KSgAlXF2lqmamaQHZWTF7Go991ZAq1MAThffYRgwHSYsqY8WyaUb8oQQ1HxoGCaI7LDKZ5jgsi8yJ5VG8p4gIGfRgO65PGfOrx2GAaHdP/1+SZjnpCNdxnHE169f8f7+rvfY+XzW7y0zlEoAACAASURBVBBMMsGf50X/+48/vuB4nDAOIrU+n04IgQWGjGkacDjI6J/X1zc453E6CQs6zzJzs+TmkmuKOCFEvRdCyHh/f9e5r12ivpaLAnUlk+ba8Dmr6MzhPM/485//jNPphN9//13l1wyCVQCYlwX3L1+kCOMdUpJ7xV5b5zorJ0WnggBhNeGcghl5ZoRdh1x1lCyFiVx68YZFHT5r8l741n2XgJt/XAOlOWfppySIRFdgjG7Uz8rz2iXmvYBCQ6aIaYq4XC7qzmwN1azUdivv7efbmWbLylrwW3LR4/Xeo/q+nvY6ktEMIaCiNJDZe2e9iw20d9VBL74s8NFt9t+diIGCUmi8tGaNfes7dc7px8XhuRVYqqhhHASE1vb9UukU3G5A+DYTWwoKBdiZ1T322GOPPfb4CeOHBqtMjr0DXJR+rnUS5hCCA9DZyW+Ss1IQhwGlJc7LssA305tSpVJvHTvv9/tKHsrtKhgcgjm2dQIn+1ub7ITGApLlkh4wjxBlbEaX5wlTkVJCaOwev8NzZjJHBtAeB91IgbXREgHq4XBYSRJDCOqiaSW19nwtgK5Ver9OpxOWZdE5okysCaYJPrjdT58+4XK54HK5KPhhwr0sMvZmnmfc73ccj8f2/S61tgyRc15lsXSXBdD64HqybOXZ9/td74tleXSWqZnpSJ+pJOn3+60Ba0lta83IeUHOC5ZlVnbSFgYo6bWmUWS8hRH2+Otf/6rrY52fb7cbHo2J5ba89wpuKUcmw3m73RCbY3V33i0oKE1iyf7Xda8mFQRZLFcFsJi+7doKNynNWBYZq8P5tmTNx3HUmbsA8PnzZ5RS8OXLFwBS8OE9wTU4n88KysnIb2WsfH6895jGUR18eb/avtucZXSPvb8EkFaV/XObtYr5F6WkOj+0dFaXx01wvrR5nla6zO0V1/vLWTyxxSL7LrDS6y5p7TJ7rsvj8VAjM96XnDXMoo59F/Jv27u+LajZsPJjfVdA+mzXx9rBql3D3lfe2xvszy2w9eY9uj0O+07h3+tWhrVLb84JGWj98X395LgSVL4MJ65PzinbrZ91BXDt/f3N/vbYY4899thjj58lfmiwapM+ysByzsilO8vK5wD2/EnSJX2iXTLHvkn5ubKGOSNEAX/CuGYA3gCEzrAw6U85KxNn+0F7QlVXYFEAtV8l5z440LGY4ZyDJ9BrSS0/z6SY+2RSaXs/LbvKhJLA0H5GR9RYJsMkgwS+Fqho4l57T5oCjGnCOI4q46xVRmCcTqfVTEmCHesObN1wb7cbLpcLDodJmV1g7WYrvYxdQsv+OjmW0K5hS54JeAOQS21g0+Hp/CSsV3zW9WUPb619rI8FClzDLfPEa2yBQghBAUjOIgX99ddf8f7+jr/97W84nUQibfs0c844Ho/tOjgMw7hiw5+fn+C9x7//+99EXWCAyzzPgAeWRRhc7z2WJcE5j/vjgeDj6lhrqWI+VHqRw54jIP29XH95FhbUCh3n8/HxoaCV/aUhBPz22284Ho94e3vTWbJb8MP7hvcA58NqEca4dZN1t/c1zYZ4H2+5si0oIuHGXuecM3wIOD+dcTqdpG94WXRUiuu2P7rOuUm12XfM/fB6EzzyvOyzxL97UaAokI8xrooO9/sd5/MZx+MRdOmmQZUWunx3G+fP+H6ygMyugz0W9vBatpYA3cYKqJsZt1btYMGwMuvtvSzak/U73KpDbDGgNtk15+DyPIKPiHH9/u3vAsCFCB96UXO9zSptIk3CbXu/99hjjz322GOPnyd+aLDKoAvqsqSWOK17uWi04n1AzgJWc+6mN5SoskeSCZHz4uyZ8qJ9piklBN9diO1+vPdA7Wwnt22ZDps8eu8baFqPtyilijitlp508zuuu9ZuWWILnkUymleAT1ejdrMgoCeeluUptSI1kLDaf0uE+d1lWRS4ECQXk7yzx5GSWSb0h8OhMWlyTJTVCsO56LFSpkyGiyNqrJEWrwGBqgUrcmzi0ip4Wo49BvbozjifTzgcJmGyjgKYD9OEZV7w5csXTaB5LLyW1ohIgGNZrSE/N46j3mMcAfT+/t7YWhlBQ/BO9pGzRlNK6r48jiNSynh9fTM9osKUliLs5hhlHWkmNc8zxsMozGkzxBHG3rWiChRQe++RStL7lfdGL3ZkHZczTWPrU3SIg4wu+fi4qHQcEFb9dDrBOYf393ddA4JMSqQt+897gCwrz8F7j2EcdW4vt0+QzWvOAg1Bdgg03+lS0m9VD71PdhgGOO8QhwEhipS11CImTN41ox8gpwTUXtjJ7T1kmV7eBwSwMMdhQbptI+Df0zSt+sy5rY+PD32nEKxaBte+X+z+HJwSlFZhsVVi9HsK5j4GnBnjZX9XSkFF1u2oiiL1whV7tK26Y04PPTYtkG0AMddGwGnVd0Uv7GUsSy+0bJ871IJaHUp18NWrBLu/4wQEu6bO8bsMeI899thjjz1+uvihwSoTmS63nPGYl296ygDKHSmRK5DZp2Q/nCZVTP4k8SyocArapPc1YwgO3jORp/tlS+pNog+sGQtJIvtImZ5gdbajVnEm9V7MX7ayUrK2FX10D9fCsqPbpHIrP/yeJNb77gRMELG0HtlxHKWvlsljS5jhHB5mtI068Po+P5Qg1m5/WZbmUivmORaclCKmMpaRZXIOANM0auK8LIt+nzNyxbH2YMB1xbIk6WtmAcF7DCHi8DQh54TjNOHpdML5fFZJMZlf3g+U71qwY/f38fGBlCiFlO5M78VkiOBDmMYF1+vHinEnqON/AyJ1JSDjdU0pmxE6wLLMCME1IJtwv93wp99+w29/Erb2cv1ASgHH4xHj6FWqO88JcUgK8uZ5wTgeUIrXObG32xUhsHhQlflelhnTYcCS5FpOdcLtesf7uzCqBNeUKr+8vIib7YZtZhGDwJpMv72fLZOWlgXT8aAFAxqfOeeU8SZDymKJQ4Xtcbbsnmyjy45ZULnPD3x8fOi9qte6orcIGCVEKb2nde0q3v+2bK5l27cgke8FZQ/Ne2KaZLwKwf79cYVfPJoyenWOfMY9nLLRrgogqxUYQpcWhzaLlFGbuoTvjYIKV90KdNvxQKXKfU6jM2E9172ylj2VNXLtea2o1UFMvtZS5a1qpZS1gZRzToYko8/ute9bHwIqpKBWQnMbdx5wHq4ZbtVSmoOzwxDXcuM99thjjz322OPHjx8arOacsTQg5JqxyjLPyKU0Z9fOCPL3nKXYezFlNh8Ace4dYjNaCljmxpQEGemxreCTsQXseIZ+fJTekbUR18+efMp3qu5fP+uEwRmGzt4yGQPWx2ETbytD5T7JVPJYttI8yzAxMQd68s71I2hjUs5jtTJRuw48Vgb3wfEzlHFyxAmPkyNfnJMeYdvzejwem8nVhJyFcfv69Stutzvm+QGOGgqBvbRLc1Bds7AyXzEjpQ4On56e9NhL4aiYspJUclwN+5YtEOF68hwJTilrluNJOhKFUnLKfb9+/ao9vyklvL6+6ugbghZeO6Difr9BpM59xuo0TXh5fsbxdFIwNI4jpsNBv88eXR7bsiTE0IsLIXiM49BYU6izNAsjFRVwwNvbGz4+PvDLL7+IRPUx43A46D54/T8+PvDp0yccj0cdxXO/33WNyJbz3qm1rsbyAJ2ZzCXDO48//d1vuF6vAlTh9G9UmfUqrBqaa2xXEqykrbCsdAdABMW5UF7MIlA3ZnPO43g8IKWMZRHWl0qB7fOwVT5sj2H7zNjnC8BK4ns6H3B+OuPxeOD19VWf99IKF3bszFpZshmvtZHqrvrlN4yssKQJBV01wGKTXhtXlSnXd0ot4rjbCkpbmfIYR9QqKpdaCrK+x52+VV17DzqPpoyQa7VaPzp2bc5Z2ORqAHFBShW1FaFccwKuRQz1pBC4M6t77LHHHnvs8bPFjw1Wq4x+YY9VrgXDOGCoFRxJUlrlnKYxIjcbmkkPMLT5jN671ivavpNlfALBj/cBMQ4rgMbeKMCyCOse0rX0N6zABwCklBGHtdSWs0oFzAwKNNKyIDsno2IM6FRzGDOCxoIsy0RYgyPg2zEWlq0l0LJ/2K9qz43f5f7tvskeEoCOZkYqATQTX2sYRUDIfbCP736/436/K7j9+vUrrtcrAGAcBz3mx0NcZVmocI4y6e4Y673H6XTCNMkxWXZ3mg4qXbUsPZlAguh5nnG5XPD582c1iyLgJtjnsVoAZteWYPDxeOD9/V3Ni8i2EtxeLpfGIov5E/uaLZh1TtaB4HUcR5UW55zV0Giajk16vbR5s1GvVYwBMQY8HliZYrU7HMsyK+jlfffy/ALKaVNKCnJrrXh/f/8GzByPR10fsvq89iy+EJTwuWAx6vXrq5hJxYhaCqax9/AurYjA0UKl0mNnLdnvhR0pMJHN/LhckPKCELwabrGgUEDQWjFNI3K+otaiBRfO07VgkOfEP/Y4rGKCwc9ZRr3WihCFRRb5dZ/z65z0JSfIuBjvuySfbHB/N60ZTluYssUrq7qIMWIYByRj/mWZYvbdA9DnNYSA+bHudbZsr4BQB7iA6inZResHdjpPFc701jYRL0fkrAysghPaeBMl5zY+iuOUkoyzcUBsPaxwtYFUt1qrPfbYY4899tjj54gfGqz2+XqdvZyGPrS+1uYc2WZHhmZm5IMHKuftSe+TynyLJE7RB4RjQG2zBLtktg1EcNaYaN3LaMGNZVYsOGOPJhMym+SmZcGyJGVWlTk1cuRCcyMAc5PnOp40E0OI2U4MQecMEszKR/ux8r9tDxslokxSLXvEJJ7gjXJdskKUeFqAzHWwPZw5i+MugYxlgJn4EqgS6HMtCVz5GQFwPZm2LJVsK7bCQ5d9k8HlMeecFQy/vDwjBI/X11cAwOvrV3z58gXDIFLHlDiWJuJ8PiHGQfsuv2eApcxdWz/b40cmlmN3fv31VyMbfuByueDf/u3f8Hg81IyKQPF+vyPGIK7KH+8KBCnBhfPK6lI6O893nE5PzfDoHSF6HA4TagWen89YlrwCWU/PYvjzxx+/rwofwo6zzzSrRNXKWPlvy9BSMkuJuJUNk1m1I41sgefj4+O7cnP+bSWhpWaaw4pJEvrn2uBTDHGED7JGDhVTHFEALCWjpKwFGm53GAZx7HYe0QeUlHHfqAxsMciaQOmz1kCZq40crFWAU5TeSg9xCu+MumvFhgu8D5imYQW0BZiun2Wg/257LbbPmnWctgWWYRjw6dMnpJzx9etXPUdrMofSelrhUFLGfH8gLY2J9Q4xRAS69paKvPS2gO0zynC+zcaGFBt4rcSFWwqJxPj6Pd+ubZMWK5O9LCgpozoaxFXUmhH9gOB9k0ZX1Nxd2/fYY4899thjj58jfmiw6p11ZO1gqEvhimEAutGHA6Qar+M5cmNfKYvrLpjWWISAMoSKZVk74VrGxDIl9ntiRuQUrMls0ACW9G3f6djYIisjhtkuIMzRsiya8MuYlW5IQ0bIGtFsZbw2ObU9b5ZBtvJhykd7n2hRIGLllmRvLVNj+2m5/RAE+FljpO9thyNsyMIRFLFPTkDzra1pVMZpPTLFGYAoRlylZHWc5TVgHyzBknOu9clOCjjJLHL+K68/Gcoty8zj5jgagn/rWGzBDNdDx8+UgtPppGwrWTx+V3tDxxE555X7smWZCeyF4aUstD8LpWS8vr0iLd3d+OXlBZ9/+QVfvv6xut9tgWZJCbNxYuZ14/XgvWzZa8u+EhjZtSOwtrJP3gPn81lH+PDc2C/JZyKEAB88cslYcj8O3vO8/4cB62s1Dfr8p03fuDL/j7uey7IsbWZn3862AMSfsVgRgkcMkzLtfJfo/d/Arh6jjxgak89rKL3Y/T6gdP17rDTvbe6Dx8qw1xTAqu+d22NBwV5f+cy66EXTKrt9W8jjfWGl0vY9EoLMUtXvAIATzwE5r26Yty0GynvRjPtq79mCKqZZ5v2CIrNVgw/t/0t2anWPPfbYY489frb4ocFqqV0uN46TJC0t37CAEXTy9NDkCmC/miR1vbLvG4iR+ZSUuOVSUAuTOg/ve4JowexWCsjkkUyX9/2zgMj14rDu7wwhYohR2RSbvNPZlIn/di6plZpaMxcel01WbVg2GOjAhqCMbBcgrMqp9UUyyb7f7yvpqQUD1lCHx92lgU4Zwq00kZ+/Xq9qfsV9fXx8qBNxB94VwxBWiTq3xZ5h6WetK2OqaTrgeDyBTsO1Lng87qtzPp/PGIYB8zzjdrvpNeT5y33oNRGf51nnwtoE3QJwHsP9LmCSRQxu/+npSUHt/X5XYMu1JmiVcxpwOBzwy6dPyrby+CxYud/vOBwOOB6P+P33L3rNSpHxQMMwoGRxUaWbc60Vr6+vKyMkAfBTWyOR3xJIkY2358q5sZYxJbikLJwFA/Y1A1DWmce+Zan5nHXTqa4OeHp6wpxmfHy8K9MGrJlacQfvwImML+//cJiEMWySWi0gZFEyqCQeWB3XGsz1Z8z2bTr0Z24FrErvD2VRZpwGLGlRqbtIwBNyTqi1jexJXZ5r5fmW6eV58Xmzpkncp531m3PG5XpdPbdcc16/ktOqKCXy2nXBjvvW92IzWGJP8KoP1byPlJGGgzPFLzkfeRdbYM39VVRpEWmMNccM2XdvqQm1VEQ/YBpGBFP83GOPPfbYY489fo74ocGqBTfb/wawYhc7C0Rw2kfcdPMVM+rBOQGMoSWcNSGVPkbB9m2u/6yPD+jgUpIvkS0zqZf9roEiwIRqPRdQElzoNgkWCR74OTKVj8ejzy41v9PzM2DMMkHcFhk6rqM9XwAq44wxKuNHB10CZn5uK/XrbKus5fl81sTcMjpkVI/Ho4K3nDNut9vqsyLPjKu5k4dmLJRTAoLHeJT5pvOSxAUUIpPOOcv81VIwxAHj84B8OuF2vymjyV5briuvxzzPuF6vjSXtjK72GbdzZ4GAPaTH4xHLsuD19RWHw0GZRgAK7Mig3e93XC4XOOd03ioBnJXXEuh24LNoMk9TIzL8wtx2Zpx///rrrwAcrpdbv4/mu/ZOn89nfPr0CcuyYFlmAMK4LXNRR15bxLD3DFlwACvQahm5LXAUQy1xWiYTawEV2f+Xlxc9Dz7vvL6n0wk5V1yv128YYe/XhkHfPLvo41VQgdSKPmLS08zOYlBOzsp97VrY9xPfA2rji15Y4TgfgvSXlxcB/dHj9rjC+y7tpclUCO01bV4hzrUOUDMWy7KifBfYZ56f6/3PbW3mWcd2WfVFL6D18VxULqSl98rzs9x+LtJLLM/E2r1Y7oXONK+vR3+vy8/6e9++lxSMtnshBHEtt2OPACC0IkSuCck75JKwxx577LHHHnv8XPFDg1XvPI5NginJTUsEc2mAMGri51wDqKUgKcjpPZSlVKSU14xQS1AdAqRHislQQSkWqKYGChxK6b2sFhyKoU6Bc1gli8K49r47JmvyvbVzJ9CZH8ApQOTvu8wWOBw94nekmJYNtFJdoCfalDpqUu664Yr3XgEs18kaNvEYKHe0vaD2GDvrJD21MUZcLhe8vb1pX2CMUcfCAD2BJWtI4EJQJ/2oXkGL976NbBmQ09KTcHS3YgIH7xxqKwbklHG9X1VCLFJlYSg5Z5RgCRBpKq8fIGDz+flZ771lWXA4HJBzxvv7u0p8ub6n0wkfHx/KqN3vwup++fIFz8/PClwIji+XS5dY1qrHxIICz4v3W0q9B3iaJjU8Ilirtc+CvVwuSCnDQRxvpVjT7xf21IrZ062x7GekVDAOk96fz8/PGMcR7+/vetyy/9Zb7B3SkjBOo16vp6cnBbEcsUM1AZ/TlAVQ2P5QukS/v78DgEqxPz4+EIaAX3/9DOcC/vVf/xWpsfEEXsIOVr2nvfc4DNKzWtrYpuBD63NHl91GWXsfZPRLagUo+yzZwlC/JnxnCFPbmdio18L28UohI8N5hzkF5Lzg7e0N87zo8bNA5p1Dca7JZqUhn+UtC+T4juM6ipFbv3dilHdIaYxtKhkwfeillsboNnYTWJ1jCBGoy+oZYdQqZndLysBId+X+fquoUjiCbbGAvqtRqozSaefINdvKni27zG3nIiZ73nl4GFY8S//5ktYFiz322GOPPfbY48ePHxqsOhSgFhlvEAO8a06Yja0sRfryCACZVHnndE5oBRRYAZIgVrB675CzJJY5VzhHGS9ZyNrG0Xj5TgyY8tBYp8X0xI04HITh3QK/4D1c7SxAc1tpxyOJqIA/MQWRY26JYXP+lLEOHhVtjIX38NXBt5EtPkSQrbVMCpNpK1G1CaZlLZjM9365nowDPYlnry3Qz8nKEYdmykOWrZaMOUvCXHLSP4dpxGEaFWSz3/Z2u8Gh4nw66j7GcdQxL10iaBgWZQ45y7OvQy4FcOIs7ZyDCx4lJ0zTQSWwcvy1mUEd9DqSRR2GUftAO4CoKmclyH17e8MwDCrr5XmR8fv1119VemlNrmgwBQBvX19xu1x1bunLywsOxz4mh4Dy8XhgaGZj49hln+/v72pmFULA09MzDoejmkoJ29mKESiYpgE5C4i73+9tNq5v1/rQCg8B4xhwu11wPB5xfjri5eVF2EpXcLlcEIfGMha5v2KIeH7+hPf3dwQnplc03nEVmIYR0Qc5XudwGCeMMSLVhP/wH/4D3t7e8C//8i8YxwnH43HF5pJtzjmj5op/+59/WTkR11wwtF5puILoPVzoPat5SQgxwDnflRVFigLBe1wuFwGCreARhoj0yG2tx+7E20Ke2Yx5uct9P3jUXI0hEprioiKEQZ+Xt7c3XK7vWhCa00NBOu/9EAKCl6LanOY2h9nr81bRizeAANohihLD9kNTLRBjQKmt6ASH6gGPiJSBUAQ41ixMr/a+gkU9iJESnN6LVqXR2eUK7yqQE3IGsgNiZFHOFrU8+uigBvZdgewhdHO6UjooZyt2BTw8qsHK2jKwJMA5pNLZ7pRlDNoee+yxxx577PFzxQ8NVoMPiGT2mqRsK4tVGZ4BTmTuQojfsKwEs877BhA7cKOErMuMLTNZUXIWd1Ajz9yarQCS0LLfS5K4LsezMkbLyhA0WSdPAksL0LaSw846VGyB3Famaf+bMsMYIw6Hw4ql4HZW0r4mGRaQNKxkfOwtfXp6QgzdJZfH65zD29sbAKgZEt1xmagzMea1pWSUibhlsXj83ZmXbK7TYyED2+XWa4Mre83YE0uWtDM+615lyzIzMeYxkEXltsgap5RUgsp1tudDxpQ/G6cRf/rTnxRsdMlv0nXl92IMWJYEH9xKNsr7XNg0aGGFa8xCAtlQy7JP07SSFdMRmjNUyd5yXuyq/7MxmlQU8LjZG2vNg9irzOeZ63ObxUSL433mx4zHIHJ3e+17TzT0fPlckfm3PaV2DAwgJj32O9wmzZ3Ya1tr1b95Xckus9DiHDAO3VStlIIYBrhqmdZurCazaB+QcVqcOZuQckJFd2K2hkI0D+M92/97bZqWWz+1nflrZcKlenjzrnMiV1m9i6xxkhQX1uN49D36nXefMp9Un0Dmp4qHAFaf4X/LOzk0JhoQNCosqfUooIKG82ar6++yrPeDfCblBOTOruaccbtfsccee+yxxx57/FzxQ4PVGHvl3jvOTFz3PzGJyVXApMpfG6jwKt0E2COac27zGj1C6DMfmcAxCfN+7eKJxkAC0KTefocJowV8ockLLbgG1m6cwLejOXiOTI75XQIE/puJPhngLaglG8hEnH2sYjw0rcDS9Xr9Rtq3BdNM3mlEw/X5/Pkzaim4L10eSJaRPZ9M+kW6OyqgYRBoWdBsZ4BaJ9qc+xiVaRoxjgO8D1iWh65NbSy0GG3VFQPFPkZ7HQi2CEjv97uaIYUQWm9kN5XiMb68vGCeZ2XcCSitkRCltbxe31tf771IGNu9xDWbZ2G0yW5zFNDjIeZjt/tdgSp7il9eXtq5rgGTBVyWqeRaWKOnbW+qvQ6c58q1ITAn6K214nIVOfPz87OOcrLFCStRHYYBzy8v+PovX/GP//iPCtaWsuDLly8AoOx6KUV7ey0453PINeCx8N4iaGZBgetOmTV7in/55ZdVvycBOb9HQGr7cq0T8bIscKEbXgmg7C61fCaHIbbzEbrQh7VRkoJo1JWkeFV4Mu8ruWYVOS2rwhevG4ta1pyp1gp4vqvCal22xQB+x74fLHi17yxbAIDr7Qc1A0HfVfb7gDWzk7mqTSVj7+HSFNAE164JlYsxcfLys5SSGGXxHt+Z1T322GOPPfb46eKHBqs2kee/CRKYlDKJnB8PPIpkMktKKDof0mtFXjIbJ3LaIiNtSumMgU3EhCURxsOyABUFcSDj6MVxslaIYtchzxm5ZJTqEcOIWh1K2roXdyZ324PJZJyJMEEvk9wtkOxr0o1e+HnL6DGx5++BzrAxybNJpgXwW7dfsoY87vP5LIZA7RjpCmvZ05eXFx0h8/T0tDLSsQDSAh/+jKyQZRl5fgKWPErJ+PgQsPHy8qL3Sdb+vZ7w8rhlfmmXIZPNJiPF3lkypuxF3QJ69lXynHhelnXlz5VBbfLh7UxW5xzO57Pe8zpvNSXEofcX81rXWlFSxjROCG3e6hAi0rwAzmGMApSKD5gmGaXiQ8Bjfug6kMFlAeb19RUfHx96L9C0iSDTMt9ci9vttjKqyjkDVY7/eDxqb+vT05P0mpq+Z8vMcR8xRu0n/ni/KIDkuT8eDwVXPEY+V1tVgb2f6ThMUMZ1PB6PCuLI+PIZ3BaYeL14LSoqHo+8Ogc+n3JdRe7PAksIDjQeItM7jhHHw0HG6ZiRUjlnBB9Rcj8GPgvDMGBOi7reyu+BWptUOpK1FCmtA1BLVtk3z9d5vxorxHta34m1QEaHyXYqWtuFg8qoyYhWiK9UShnOtVnHvup9xuKGXKtv34vrdz5pVWNI52X8kgPgqiheXBUVzhZcu7YfOIfgHKbWomGVZwAAIABJREFUG7/HHnvssccee/w88UODVSZl7JvzPiBCjDSK+UN5askZxVT/h2Fs7rEJtRmK5NwlhM57ON8SL+9aorU2TdmaFI3jgFLYqxpML6NsR5Jtr7JFK+W0klrLjlh5qmVlreTRMioEWPYzYohSV/JBApBhGJQ1IiiyY0S4xmSrLHjkcdn+NMpFnXPKdl6vVywt8ebnvn79uhqDMwwDpmnS67uVD1rDIibN9li2DLNlg+73Gbn1oq5YKSMP57Z57nYf3P/j8fhmnzxfC1rYA2iB2+12w5cvXxRw8lyHYVA5MO8BOt/6BhR4L8R2PlZSrOOFxkHBFGOeZxwOB2FVG9DkDFlZ94xh6A7F1+sVdzN31l5fAOrCbE2vCDLSIqDofD4pCKU8mK6+l8sFqBXn8xmP2yz9lljPTyXDfjr17eSc8fXr19X1mmf5/vF41O9bllDZRcOM85629yEVBbxfLSAjAOWasYDAc7Z/uH3un8/xOI0IIa4KEgKmBXDXWnG/P7Ass4IwFlniEOCcsNVzWpSB5tper1dMI+DQTYVW7w19Lhpwc/3vEAK8E8Mh57vDLmfe6vkYxncr55V1FqOk0hCq9x6oMj6mtmbWYNsIvEOofeYsmVV9fjPnBEMl/LXWBj6tikOMwDgLuxdKDLvb9uchcmyRAxeZr+paqwf/Z9yZ99hjjz322GOPnyN+aLDqKlaJtE147CxPJo2lJTnjOClzNc8zyqovtSeZgmCbKVKtTW5X4TUBloSS36UhCEDZGnuooAzk4TC1pFVchWstKHkNypjQWqaGTDHQQdQ2cbSJORlGJnF0s2Uya9kd64zKfZHpJIjcAkEL0rl/C+Kdc9p/SnfbcZpwbg7GpRS8vr4qU/n09LRipex5W/DJXknbx2oBuzUm6gwagYLXe8Oea0oJj8esIIlgnvuwDLK916ys2jLf7PcEoECcI3gIiL/HJDHZtudDNlOvbYWynpw/S6b8/e0DPjg9ZgDNFCjqvl5eXvDv//7vq+ttCyRi2NOlphbYUZJLNpHM8+l0wnx/IM0LvBPzJBQx6zkdjhijHKOHQ0kZy7xgHBK860zly8sLYoz4+PhQVpTGVIfDAe/v7/h4fwcCdNRPSglpyXpd5dke8fT0pCDeOt9SYq3vAwN8bcHJFjLYy8o+dwtM7X1mZd+8H9R12DzTKr8v0qvuG9stzyra8RWE6GHHftKA7PF4YBzH1eiilDK8Wxe1eJ+jvSNz6Yw/Cx21VrjS3bFLKZiXWd+THP2UStaimh09ZO9/5xyQ20zT0J8JXRMv/aW1CHjdguqtXBiloqrhtbCjrjpU75oHXZM+Q0Cs9661hQQAZOMBMchrBT4HVFdRnEPwHktKyLVLf6szc8f22GOPPfbYY4+fIn5osDrEAdEH1FxQUeBC62ObZ9xvN1RIRZ8yNkk6Y2M/K+73u/T2zanJ9rrENucMNLaALI1NRCXRKuCcRkn4BnXEJAsbQgC89KUOzZ231NLnfMIhYw0YCP6Y7ALrHlZ+lv9tE2EGZYCW/SBQst9jwq29YOjyX/bf8rNkNK3JE9eE+7bgzq4ZQajt4eO4Ee2Nw7oP0gJw2wtot1PN9aEklkCBf8c4QNyh1yZZVhZo+wwtSCXAscfF3k1KUQkWCeIsCKXEmvNY+zFFPV4rTSVA5GfYL8m1TkvCOE64XC74448v+O23X1FrxR9//IFpmvD8/AwZw5RWxjmWVbRs7ziO+PLlC75+/Yq/+7u/wy+//KLHHWPE6+srbrcbnp6etEd2GIYVE0rwRQDDQglHK1kp7jAMeH19beBPel+XlPGY78g5qSvx4XDENI1YEpA+ZtzvNwFwofdXyzUpKvF0zq16nclu89+U1VvZPNebfbj8vGWM7dgnnhsA7eXmtuzzy2ePRYHT6QTvvY4NosnQsjwwzwt4K3ovqosQm0NyTvBe+p5T6dJqypWlwLEG3Pa5m+dZ7ysrhfZseXBrNrGWilL7+07eX06fBfu89WLQukilvaHfec4U7PuIWqooYgJQZik+UOkhnxWX3+hDm4Nc4WsbNQWgOo9sikSBxkreoVRIYdF5FPRRYs57RO/AI7TvJ2sctccee+yxxx57/BzxQ4PVZVlQWuLnvEfdGixhnTwJIyHggX2TXRJaAWRNPkutyKknoEz2xY6HyV9nJUMIqIWjUdwqQSTI06TNFPAFGEXt41u5U95uymZYcGmTTgIoC7S27I4k9nnFdHwvsWU8Hg9lAbkfbm/LMFqgqf3BjVXkcdOkaWzmTVyX4/GIj48PBQDW4Ik9kFwLe16WlbGJsJrucBYmurGVGCCN+nOeX4hRpd88r3Ecde1DY98BMfGCKRScTydMDcDZteE5q1S1bfd0OuF+v+N+v+v6LcuC2+2mnyc4cs6p03Vo65VyRs4F14vcu8/Pz7jf78pEzvOMr1+/4nw+4+n5rOt0v8nv2ZPKe03WXc7l5eVFj+fp6QmP+YGlXZPz+YxhiKYPNAG14vn5Wdf35eUFn14+fXPdeAxkBA+HA47Hoz43lMTy3wz2WPPnwzggIkoRqHZjoxACSu6zWZ1zyqhupd58Frg/nq+9/22POO/9LftnwRqvrX3G+Tza46S6QJUgVeSuwzCgAkhLXgHl+fqADw7jKD2tvoGs8/msigcex7Is8K7POrYMLq+PlTYDwt62F+XqfTAMUZUj6hYc+1xk+7xzbShjL6Vo8caCafvOoiTZtWEz8p72es9zvnJu9zo9A1z7H8PBwXnxFuA7Rf5uBUoO+KrCDMt6FHBrwTsgipt8hciDt+Zme+yxxx577LHHjx8/NFhNbT6nc2KQQbBq5WrOOYi3kchAVbrnAB8jSq4YRya1aFK4BbnJeSvoNNwGy7d/uyqGHqUUBE/jI44qiQo6mOBapsUCLcuoMMlfmmMu0HtImSha9pTnZxlOsk4AVixjCH2/BAHWmZamSAQZNoG3TB+TeWDNavJcyRRaVm9ZFlyvVzHYGQaM46hmSgSH3DfQ+yAJdvgzMj1MTC2A3QJX+/tlmeEccDye4FxYMZ/LsmCeHyu2LqWEvAhQQ5v5ac2OPBymYcTT+Um+kzIQB3WYJstq15AFANtb653D0+ms68Dz9mhS2gosjxmuNglrY4q4DjFGVQcQCAIy/meI7JUOq9m3LAoQtJER5M/pGjyOEWl+YBravTTPCrgoER2iMMycNXs8nhSwEBjz3mcvsAL/tp5kHtkjSUZXgWgpWtAYxxFx7J8rpeB6vaHkDsYI/mmyNbT7jeCJzyNns9Ld2I6SsoUl3oNW5WB/z99Z5pLrrPclKq63i0qJycjy+9M4AnVZSW1LlT73EAJ88Cg1w/leBLDPzuM+ryTkVuLPY+DxeSdzZqtfj+Gy4NuhS+upMOF7yRa4LBDmsbBgwOfevkv6mjmU3IG0vIs7889911q0zYJr9c07rxZkZGFSS0GpRs3BdyQqnBOjK/rolZzhap+17SCzXvfYY4899thjj58rfmiwOkTrflqEgQheTT6s/NB5j5QyHnNjVZwYi7BnSZLEIJ8rBT4XwK9HxnD0jfes8ldlvWQbnCcpTGYHcx7jOLV/E2yuzZO4D7JeBKkEnsB6/A2Pmd/jvy2bROAuyWtnh6xkbytvtiCCnyEQtKDHyob7+kUFPpzlyHNalgVDjPDOKajy3uN0OimbwmSZDAeltsMwKNigWdD2/C3A5xpRuijbFpbG+7UpzsOMyyF4dU7m7PK4T6eTghkywgwCfv43Cwt2JAoTd++9gnIBDwGfPn3S31uDJV5HAjxub56XzoqBJlmH1ts56fXl9c85S/8fivTopYToPQCH4XQSiXwY8OXrF1w/Lvj8+TOen54wjBFvb2+re4HXqJSi96n3vvXOJsQ4qJx2WRY1kOI6skDRZZ5ddk5WzV5T7vt0OikIjTnps8F7rRYoo27Xjj22vH6W6T2dTgC6vH4rbbdKBgvOLONqwS2/R4BtWwe889pOwJ9775AW9oL61drWWhBjaOe5BlBWmsw1GMcRh0lG7tDQrINJaVWozYa31t6+AAjDWpuxHIt9pfYRRt57Ba/fCzlnua/5fpPrCHDkDterv+ecFumq7BQVsl/+QtbT63Fu3zmrc8R6RrR9L+ln4VpLSB9PlnmNagWcR3Q7WN1jjz322GOPny1+aLDqmwRPkpOeFFnprI6+oJS3AGg/l3q6dbfNyGBvpSRBubRKv+nBZAKK2s2HyC5YxlRcKfsIHWF71v2vDCaqwHqGInsMtyB024MGrMc82IRdpKhe2SYA2g+53Qa3z31YlsaCC6AzTEzQLZtKh1oCbNsTaCWJVl7IYyETS3ZtK0u2gIIsoZWMWjlwzjJn1XsZD0JAy3OVPspxxZyx73kl8W7nQsdiy1JbVsqyvlxvstgsPhBc3+83vL9HBTns1yMrSYnvGoBE3e7xeFRjKkrSH48HPj4+tMcUAB73ewdTThxmGfO8wAF4Pj/her3idr3KeoXTSgLLXkquA8fujOOI3377DaV0SSwB3PF41L5nMrK873htyMyO44iXlxctMnD9OBuW51KbOc/lcoFzDuMwgqZolvEngHw8Hvj69SuAPvtY1v6u74gt8NyqM6y8ltu394V9Dlgk4/UqpYihUO3Fh8PhIP26lT3EHofp2AFr7YoIzlqFq7g/ugEY32185lk8WDG6tfeqS9GiKDMvYFKAKgEjQXROG5fvsH5HWDDYpe48vxned8Mj3kNb2b4F/nDSK1vR9ynvhazvPO88PNaqDl6Xba/ptojHn3kvPc+U99M1vgKI3qux2B577LHHHnvs8fPEDw1WgXWFXWRhIvHK6A66OWftdSsEei4gpdbHVKUXcc5LAyih8aZllWxtWRcZl9ONV4QN6jJd9nPZ/i+GZT4BrGSINtm24KozcuuZkZYVIngjgJ7nGY/HQ5mkLXNl5dL8HZmvrbTRJrDcbmduO/g7Ho+r+aH/X3vvFmrb1qUHff0yLnPOtda+nHOqrJR/peIlwxCKmIqBUqsUn3xR8BJiAtFIEEwU8yQEQiAiKIXmwUtSVIEIkgp5CMGYt3oT/ZGARAspAiMh5k/VX4aqc917rTnHrV98aL313sbc537+f5+1z+kfHM5e8zpGH72P2b/2fa01Pk5rSo9Uafnl90rrYGmtEnJfy13vSnH8kpjLv/n/ZFO1UErviDKfj/dErs/nc859ZFvjx42HvN7S2s1jxEEGttTKglAMUoSQC0wppfDOO+/g7u4u20T5OPj8mawygZ2mKbdGUorzG00m+yEEspiKucLXulSvLZZcDirw8d/d3WXSz5bhZVmyzRdAbrHTdW0OBkhL+cPDAy6XS64syy1gON+WFVgmtvL68Lhwruxbb7+Nw/GA8/mMh4eHdC4a21bcEfM8Z8LLa5/zj+V1W9cVDw8PUIqqVh+PR5zP57xGOfh0PY/4+l/PM/5+Jpn83dZa+OBhbZOPj9fqbt0YwOiG8jTTOBL55JZaDuvmdpWwWY0GSsElXvdl/AppM7oEU67vnwpI7WsUotUwaR2QJZn6U/N6vnaDOLdhXXV+bFkWNE2TgxLSzcHr3FiDeU6pDk3JK9coijbdSyOs0lAohbpyACXlc0eupq6LY+T6XpfvqeIepo3GFrYcAKnFgCsqKioqKt48PG6yqg08kPKQFBRow+OvlVAAIUT4EFIzehAhVfS6mBRWartB/VqDT61uArVI4OIe3IYCoEqWrOpSUSWgabqdwhkjqVe8qaWNbUxEmVWSUhRFtoWh9+93UJzbx7bWooiWAi28IQzBw1ra2GoFKG1yn0bECOd9rkocBVlVAFnyUOzPIZFNqYSyGsOvgdr3O5RWyJxLqxRcsrvapqH3Adk6zJZc7j/58v4em3PYEtln0sN5iXTp98V8uGIqK6Nke9RoWwulwo60MDHbVurDalPO3u3tLWKktipcHIjJBRMqSbS99+gaIpe662EMkbx1WbCtW7I7E4GZJ1KdSU1K1XCXGYfjEcfTEetGFVy7vkPbNri5vcEyL0kRXJLtssHlMmGep9yuxZhu117Fe495WWCUSson2VGpenZSxbYNx+MJXd9hWRd0bYd5nvDRhx9h21Zq89Tx59O4s1oqVXeedzSHtzTHya59PB5yUaV5nsA2V5Vs0cfjEcfTCR988D4pqt5lRVIpBZ0I+IsXLxBjxPmBggrBewRPlbWJuBggcg6lxbqVQlI8L5iEMnhdchDleDximuY0F9kmS1mNpHwiKZZU0A2goFXT0rpiwqtNqd5tkiIvbfZbWJMtveRkx8jqbUzBlRQyC4HvcgghQqkIa9nFEGGbBkoZrOsCTi9gksivY2t7BDL51EYDWtE8jHQXDIiIKpFxo3OAD4lMsvrKll+6vsc834pdfd3dK0wKpAWlsYaV+o6pfSqCtZYKHoUSrDNGw2p2IziEdC19DHA+wKqUPqANVExlmFQhvByQ4XtxupmT2s1jwPdhYTmuqKioqKioeDPwqMlqhAIU5ahCaXiQqupCURpDCEBgZdTmfoTObYiJQK3bRm0RFLU5oQj+hhgVtKLWEMF7pJ0eYgyAoWqUVNFSWgVjssFxQSPaJCluQB/ZLqsF4fR5E31tFQWKolf+U/CeNs3G0IaON+W8gZQKT1ZSYkRMRD4XW+HNn7DA8mOcj+sB+HXFJhQVzgfNqoxSWX2RRJDPiTfrzrk0jHR+sscjfx6TKN7AcoVR/m6uqMv5rFLB5Wsui/SkUwfnFO+KxwCJsEc01sJ7h8vljL47ZGsun69U0YBS9Ekpqv5rUosNcohHNMbCpnZFNlWAVVA4Hg7pfRrny4xlmWEbg8t0wf3DA25vb+CS4qi1hjUWsZW5fwHbZmAtW7sdHh7u0bZNqtzLbZpKGyFW9LI6n6pDb9sGvVKlYhstTrcntF2DD957H42xaFNw43g8ZrIKEBm5ub3B8eaEvu+xTDOmaRZWdpVIKRcOA6w1uL29we3tDZQCfPAwjUVApFzypNxu24bT6ZTb/sQYEfsO8zTht3/rt0jxMwZuc1DQ8M5jmUnxff7sebGET0TElnXZkULOY2aHAI8PBzhIBdVp/jWCpOmsamttwJVqjTHo+gNOpwMuU7Kvo1QWBkowhnOInXc5tx4ROxJN67aFtQabd4gesE0DH4G4OoRIAY4QAvqkvvuwIQJoW7r2bt3SZxYC5iOtx7bp83xGCHAxrUlQsSOj6Z7qrlIf2D5cAmxEpqlGOnY2eLaJc5EyxIjG0lzafAdAo+26XNGd17DSGj5wuy6brefOuaScUjux1W1UWyA5FxoLaGgYJYvY+bTuhS1Yk2PGh4ioqZdtjKD2Nvj4vNyKioqKioqKx4vHTVZzsRJRMRavNpz3ud5jsdex3ZQrlZJCUkgJb7BCDDBCTWChk9pPRGhjABQSp0TfQlJKSHlllSSKHoas0uSN2pVtjYkmEw/eNEs7J59jybst+WayP6QkWVKR5M+VJPXjqvByHioTCOBVmzCEUiktw5x/KL83K3HCEizttArANE24XC650i3nZBpjcDqdMhHjDTIry/w3q7BsMeSxkkWR+NyZvLDlVWuDgzlk0vvy5Uus64rj8Zgr0UqLKFeXlWoyHw+r4UXtsolQapwvlI/b9Tfoug7ruuL+/j5VJ2V1TWVlN8a4awPDll8A2WLL18Vauys8xfOHrwWP3/l83llbb29v4TeX58PiynewhftwOKDvD/DB48WLF/DO5+9kUsZjbYzB7e0tyKrc7gMoac5KS/rxeMzXl9+/risu04TgPPq+x+VySWMT0Hb0voeHh2xB3a2hWK4Vf78sUsZKcbF27+3du2JOQmHk+aSUSlWOy5p1bqOiPlyxd1mIoKbJzWvYU9QJUendd5Lt2GPZVjFPS9Vhzq9dgFfWOKvRssqz/Gx5r+F1V9aDhjYGSpk8JvIeS+dbPofvb7LvsTxGralAk0sBAQAwjYUx1LJnTb1weV7m78A+NYHt+SEEnC/nVCAvBba4DZXWiCI1g90uPCZAyo1VQFQlqHY9HhUVFRUVFRVvDh41WdWmEKq8wRHEizeXvHncnM/5bbxBW5aFSzMhOVsL0dJkKaYNTSlsAqRqs4oV1H2xIamISlusJIVs95NkT+ZDKiXV2bKxLaplUSP4vVLJ4fO/3qSyXU9u0qQayp8l27jIFjEyp1C+lwuVFGWtWHL5+gC0aZymqfRCTbZfaRWWhWuKMhpzkR0vNrWsyH5czi+DySIfBxM12b9WbvSZZPJ5ybYqMj+WK7FyLifnuMocPf5ePgceQ8pDjYAyOBwPsLbJG2zvA7RCKRhlmt3nsUUaQCZfTKpznqT3u+Pk8+OqugwmAPM843Q6UZ6tLf1AfQjwy5znC+eXOufw3vvvYVkWvPPOOzjdneBEEKBt2111ZBnAOJ/PRN5CmYPruu4CIfL8+P1t0yKakHNfee6xksxBBP5eHi8b7S6/WgZ7+Fykaq51KbwkST2fA19jaSNvQYGGp0+f4nw+44P3P4BNc+bu7g7vvfceIiwFIdK4RkSyZ2uDLrVvYicB5YEueU3TNaO1LPvlyuCSJLExUJ6r96XtFK8xSdQl0bXWQmkl7nnqlevB6QXFnoxMouW631mARZ/WeZ7hz55U4vQ8O0nyvS5GKFUCLvyZfG7WWGyalGqdrrPbNuhUrVwG3WIMu/Pn81ZG785R3gMqKioqKioq3hw8arLKFi8mB1YQGyYt5e+UBwcmYKn5fCK5tAlziFGSPwUoAxUpM1ZG3/k72ZYpSRKTLC5QIkmhLC7EGyq5YWLrpnxMEklSKvWOdEmVSm5aWYVsmgYxlL6jkgDzZ0iiyuqkVGv4XPjcJQHmMZbHyWPB58t5bXwOvAlF3FdevVwu9L2JVEib77X6kgulCKLJirkci7JxjXljzMclSYpUsb338M5nFZhJNP83TROmacrHIInNteIsx0vamUMM0MqgbTtYazL5YMutcx7WRCirdtearymATKBjLPm1fP2YvPZ9n1VqvjayuI+1Fjc3N7i7u6NxBhDTtfPCys1OhK7rcD6fcX44wweP9957DzenG5ikXDKB5wJGTITZzp3Va9tkEhljxNOnT6E1tcJhFU5WuD2ejlimOc/rbFc3Bo3t8PDwQO1teG7RpMjXkG28WlM7HFlhuu/7fNzzvGYVXBL9/b2nzHMOAGit8fLlA5ZlolzxROJ4rrVNV/J4D0eEmHI7xbxg1Zs+/tVCaiYFZeT9h+c+g5XVaLmFTCkOxXMnV0m/Ui/btsXqtpyzq/X+J6AQXpfXJ18nSfh4DcRIudn83LZt2JxDRMlTlSpsdpMkBwsHXfhzcz9mrfJ5SaVeBr3IvVECfPmeql+9j/P7KioqKioqKt4sPGqy6lwqXKRUVvVk+xG5AdFaozU25Y5tcG4CFVIKOTrPxJPa2aQKwkqBy7HQxoai/mzLVCDSzBum6019eV+xDcpcUkmqeZMlj5nBr+EehryJy0RdVJxlcib/HVVpryIJ6yepJ0y6r23IIYTcugVAUXLY/qcUfFJHEKloC2KEdy63GjqdTmQNTI9J6zL3Fe37Hk37akuZtm3x8sULAIX08kZekhQeZ3le0rbJz8m8VknAvduTAf5c3uxzH1mutMzHz+QaQFY7JXGX59H1lLM6TStibNG2HbWSURNujidEVd7HZJWvJ5NyeTwy2MFqMG/o2Zp7OBx2qmuMEc+fP8fpdMqWSmsNfPDwIbXdEe1QnHM4n8+Us3pzkzf+HCjiz+Rqxbe3tzgej1lpZcv04XDAex+8j3Wdc25i17XJMjtDaz4XB+833Nzc4vmzZ1jmGS9fPuyKWznn0HUW1pLqLCtO24YKBbGSz+PH48JrlYnsNE1YN1IOu65H25JdNYaA6GO28PqUKhBjzPnX1lp89NFH0FrheDhSvmyyZvP3GmMRvQeMSvnmfI0cYiQ7Mr8HIPtsjJTffDx12WbNxM6L9kh7S34JkvB1YUhVmMkbFxsyjUWjSj/XawcG31P2wTS8ck/ZpWLEABXLY421CKIQEn+mDPLEGOGdx7pt8MFTf+aO1oj3Dn3XQitgXclm772n9jaag34aRlEhJn91D0Dgysf7/rGVrFZUVFRUVLx5eNRkFSiW3RgCnLDGcXEa3pgpRZVQqWdjl1pcFMWLUYhbQFE2yvfJ3FGjdY7cs8Ini7HI45N2XWlTldZfaakFkO2N/Dl8fDGWNi/SCrw//pgtm6QSut3mkF8TxcnxdzNhlHmgfKzXlmBJVuVn7Sx3ap9zy5tYJuaSoN/e3paxScSfN7G8Ied8xuvcWLZI8rFxTqc8bh77dV3z+PE5SAUuqDIWTdNkMseKNds1OUgg1RyZn8vHvm1bsT6na9t2HWIMaBqLdd3Qtl1Rl2JEK2zj1/OqbdtMtrTWqdclBQLYZiuJLhNpma/J5xNC2OWkGq1hjcayzKn6asl/5dzB+/t7GKPRs402hlyJ9Xg84ng85mt9uVxwOJSCVaxqcmsevl6/+Zvf31kym4bG88MPP0zK6xM8efIE87zi5cuXOVARY8T5/JDfx59Lc8QTYU3XgMe+67psh+bzZ2uxaUhh5eppbDuP6XoqINt4tdHQpqQUUOAoBTM2l4ufyeBZ8AERLufSAqAKzdFnYs3Hyn1BOTDF39O2bXZMsILOa0ophejZir5vWZQJo1RXNVcd9ti2FVw4SgbYdscfPEIovWkp37/cx/h+wfdLLi4m1U/EUliNSfP1mlZa5eCACx7nc7kvkZpucXd7m9caF+eiz/XwnvI6dGN3jooQAqzWXMbglaBdRUVFRUVFxZuDR01Wg6cCPUibHuDV4kpKU89AFSJCIi6n02m32Xe+EB6Zp6m1QgAV7uDNlCRlsmCQJGFMLDZR0RYomyK2DgKlwM+16sqPSXIh8w+ZeHAemFSJgESGUg6X1hptKmjE3yMJ2nUeKrDfWMrcV2CvukiCylCbPjnhAAAgAElEQVRK5fzDaZp2Co/sN8pjzUoRE0UjrqdUoPN4p/fzOZMq1+1yXZlESns2q97LsmQLKI8hnxd/R9u0iFFlUsB9LHNun1J4++238+t588/nEkLA+XxG13WkTqa5xxtzJpiyMBOdd6RcVkuFleScYsLeNE0uMMQKK38G92Tl/N6Hh4f8NxNGHoe+77GuK9599108efIkn4NO5xtjRNs0eJgu6A/0fp4nWmtsPuB8Jttt9D4rhdM0oes6vP3225imKc/7tm1zvvI8z9i82yl2rL7y9eQ5ymr197//ffzI2+/gnXfegrU6nSutK7aHLsuCy/RQiH9al7wWeX5ysILtv/M87+YoB4q4bytfd85Jl8fGhJvXetu2cDlvtuSI8hxDiFDJuZHvVwBiVEDUUDrucsV5PZzP57w+eD3IIAw7HjjA4rYN3lOwgY4l5utf1ipVbpZpDkoBh0MP53y2A/Pcp39jRzCVQrZmy/XEa5wqn5cUALIGF5LKr5P3wX0OMfVCZsWebcOcbyxdDzINgIuUUVsbCjrKiuOOmtjmonxyXCoqKioqKireDDxusipsvzLviqxtOlenlcSHCZYs3hFRepvKjSX14tO5D2G2uqZNlU42YKSWB4W8UfXhooSl/q+ecr2oumsDrcvGka2dcjMqFRW2UHIurbTbdV2XN81cfIbfx+Njhd2Wx+vj8t1kLui1TY9J43VVYjnGbCVlFU6qPfJ8mDTyuUvFR+Ybyjy+nO8bQ6qya5OC4rBtCkqRUklj5dH3lAtKn0FVb5k4MnlipYaPg4khtzDhza3MyWNiKRVfVoTYHszPseIp7bkA0vdG3Nz1mXwaY1MV5BlGmWT53LAsE47HYy5Excc8TVO+Fkyq+RpIpY3zWqUCx0EIVqLnec4kh8+Tz2dZFlJSRTXabXNwadz6vsf9+Qzq/9ruAijOuUyc+fq9//776Drq3yqrAPPYs7LI84evB68Rnt+UA0tr5b333s9W3kJ0VFJWm1fsns45tG2blUr+HoBat/hEpFiF5Xko5478zLZp4UQxI60UvKa5yWQwxoDGNvDOwXkPYwu5CxDzK/pC6tI9R1rteT5xr1keF55r5R5UAgs0T5AU0a3Y4e0+H5e/s9yrUoXg1FOa5wW7TmSgTr5/FzAU1Y/p/2Q7Dkn9VaCep/m2e+X+IBeLydfEGIMYArZ1xTLNOBwP6LoOfdtl1ZtswfvAW4wRiNQXF7t2YymIJ+/9FRUVFRUVFW8EHjVZBfabJWkpw1X+EW8EZXXaTE4sbWyVKGgCUN/BEAJ0LG1ednmkISBE6lBYNlilET19Z7He0aav9G3kQiWsfOzyyAQ5ZCWWN9RcBAooGy25kWSFR9prr61uUgHmvyXpZMIs80B5XGSerdxYXo8rBwS4cI0kdrtiKsKeCxT1lY9HWqlDCDgeuqQCGpBdm/t68nGFpABRNVBWhvJGN40nkxRpi81W4lg22zyWUklmBYvVLSbk1lo8PDxgXVe0bZtt2LJoFRMxnXqwAqT4IFIPyca2mKY55YYuWNcZz549ozzXpDjyfGA1lAmULNjF15wVN7ZJyusl5xyfJ68VJonH45HISrJ+Uj9Vso+ySqq0ykWV+L193+d2PM65bKXu+x6n0wnaaLg09nwtLpcL7u/vcTgc8PTp06ykaa2xLiseHh4wzzQ2Nzc3OBwOePnyRclzFvnEStF4TtME7z1ubm52ARdpyedgUNM0UFpDxX2xsJJOoPLc5fkek2qHiN34Us9Pua5ZxaS5qUJKDRD2XFoTGs4l4gnk4IROQRMOMhwOB4R03Xluyirb3E5IBluojRZde3ZeSNLN9zRJOuVaLNbfCCvItryPyPVkjCE7727tILcG47VktEl9VPfF2uR9i3NTidxqNLZBNBFu3dCYpIYfDiKF4dXCb0YZIIZc4Ene042uympFRUVFRcWbhkdOVgtB482kFxuv6zykGGmTFWPJ81JK5cqz9PxeddSaNIQYKUeN9qQxFyUhBbVYy4zZFy7i75cFj6SFDkC2+PFmVdrapNLJj7FyIhU0HgfOm5PkPcZidZMkmCHVYrnRlARWqiVy88fvlRtKJpvSVswkgtU+tovy+6V6LM+dba0lD27fOkRaMCVRl/lvpGrFrBTyhp8JtKyum7/fGijsc3OlhZuLJ3E+a9M0VJxHVPTl8+Tjk2Q8hIAmkQUatxbTZc7VfI3S2BJhUUrj4eEBT58+3VlUmTAx6ZBqqpw7Ur2UVmbZzkTm1k4AGrvvXQsouOAyWVOKFCwmiW+99RaWZJkFkK+1JI+HRCS4/+39+WHXR7XrOrz11lvoui4rmKfTKRcVchvl0wLIxa2stfjOd76Dvj/i3XffxUcffZTnNZFJUtWZvPJ1ltfjet3J1lNMSOUakQGgnJusTAqKREQbyZGR1ggTcbruvP5NsscCgHolcNQ0LZzzgImZ8LOyyPNxXVfEUFoFydY6znkEQcrlmHBf1KahVjXFdst25JjXDami+2CXJNZal/QIhhwjGrOwy1mVKQW7e20KkPHffE1CjFSUKq13ow2i99lKHEHOAGMMbNtkhwrCPkUh379CBP9KyHuU0nuVuaKioqKiouLx41GTVUmosuqR1JRrZQDYV3vkTYrWGqsrhW+0KtbczW9kRU1EitQrBSARRk3tcpQyiQCuAHRSUMqGTCqe0nrGtsIYS56ntEDye4Gy0WKrJiufMrdUbnbZYps3mJ+gQPOml2y1TSZhMmeMP0eOHX8P/5/PTRZakaqMzItl8lJUu1JEKH8m9tV+y6ZW7Yi2zBmUG11J3LU2eSPPBI2Vzo9ThLZtg4ICF5qR6hTbYqXtlqvjMhnm/FBp7e66Lltj87z1AVAhq7Q0x8qcWZcVSrMNU+P+/n5n0eZzZEIi7bvyOskABF9X7gnLhJeJN30250/TcUetUgVtl4ml1gbamkzSD32XyR3n7vJ8VUrtbMbS3SCtvqxEv//++1n95DmhlMLmNtiux7Nnz/D8+fOcV0vXWWdbdN/3Odc0xnKtWdHn6yTt2dIezcGTTLp43USyiuZxBvK9RvEcThWJFYDpUvrZSpKnrYVVOpOjGGO2rJaiZby+Ys7DjOI+MM8zKawpV1cGamKM2FbOKy/voXtKzG6PGCH6saacY0vVk70jUkvzkaXQUmCOj5v7Vst7hLwXmJQqIdeadIfkgFgMu8+QiJGChJrvAexaEeo4r18VTA7GqKTeXgcYYixkVZJsJ1TxioqKioqKijcDj5qsIu4j87ypkZH+a0Wk2E7pI0KM6LseLm3YEAFrU2GThlqx8EZHtg+JMQKBCoVYq3bE5DqnVBIMabUtNjSTW+GwCsOv5c06kwveQFJFzmLfLMpuKRKldWmlIvMx+Xt508lEjr+Pv3OnOqi9VffjxpRfx/+Xiiu/3jmH+/t7eO9zoSt+jySF3jnYpkHf91l1BdgiXNQluQFmZZA/i4MBXdeD1SsupsMFhuTrOD+RVazg9xZkHj8mdU1DfULP53O26PLjsqIy903l4+RCQqxi0fGmfrhJadNa43RzwjxP8N7h5vYGXd/CpcBK3/e7651zGEPIJI6LbEkiIwvMyOvMBbH4GH1YEaMny2Yi9+oIbJvDPE9QSsO2LYyxmKcZ7737Hpqm5Cjf3NxkGzH3wmxS7igTrRAj1o2U6MPhkCsYe+93QQxWzXi87+/vMc9zDhZM04Tz+YyHh5domi7PubZrAWgYU+zmvIZlgEO2BoICbGOxeZ9zGLVS9Lec6xyA0joTokIUXQ5wZeKkirUaQQGJrIYQEARRlqDvQbEdi++XSrm8r/A6aJoGpu3Qtj20Vli3JV/bprHluxTNPQpikNrL560UOzFidiG8cgu+Ckrx2PM5A6Ccf713myild/cSlSzw/Dd/NjtljNFQIBKOCJjU2ohHTKf72s7i7UNWX/nz0i0UVinEK9txuFJiKyoqKioqKh4/HjVZjSrF+5XKmxYmVExkpCWSlNE2b+5Y3WmtQWMNlnXFtiypArCBTYpW1AaqB7akLrlAypuHR6cUKGcVuyb0xhjM8ww6vJIfxdVimZxqreGdw7osiJE2V3QODl0qGKK1StoGkWOoCK2AQ9/BGGGVi6UIjDEWxhpShgGERA5ImVA5H84YgzaRikwutU7fo3KRI6M1jU9SYZXI25PFn2QhIqCoqtKayxZUUg8XNNYC4powsU5dWlNV0o3y/ZIKRQEBvuZcwIb/jUToHb1GKRhr0jmRZXJelkyCvPdwnFdqLJT2iMFDWw0FlYhlIobJDh5igPMOy0rqbXfo0bd92rxrnM9nhEBE8Hw+48WLF1n188HBe4cQAaWLCi3zlgOICNm2QdsccDgesDmXySzPZUmG5OdIYhUAIiCK+o7yOUcEHI8nIBKhUADW1QEaaPueAgvnM4wlguP8imle0LRkC/ZuQ9Mb3NycsCwT1hQo6A+H1GJmznOACYFRCrZtMc0zKYURaJsGMQa4zaGxBk/u7sgmnUhmcB5d0yEYIoDzMgMKOB6P+JF33oGGwhY8LtOU2+SoRlM/T08tWYCIEAEfNiJFEVDQu9xWHwN0IlEKgE3qqVTm2IYcU35r37bwgYr9QEVYYxAR4aNPc+66DUxq9QLAagMfgQBqk+NjgA/UaqfvO5pjjoIaSmto8Tl8T4lXaQLcLqdtKHii09qGVlR9GYAKAV3TIqa8bq002rYHIuA3l6+j1jbfd5gE8j22zDWbXAgBZPdNCnFIiqtSUDEC2uRevGTpDZyiiphyeVWkCu8hqbhQSC2RAKTWY0hW5aBUdsRYrWFAhb5Y4Q4hwKE4aeh8SjVuvv/R/PTwgeZmRUVFRUVFxZuFz0VWh2H4HoA5/QcAf2Ycx18ZhuFnAPwSgAOA7wH4Y+M4/nZ6zyc+90VwXQ1Ybtj5cZkDJSuPctVOgNST1lr4bYP3LhHVkuuoRF6ZVC7lRlZuynlzJ3MDdza9VJyHeyV6zzZV2a8V4MrCxCj4s12y8jVQqmyGmSASMYxYtjVvPF1St2zfZ6Io+4Fm26Mq5yzz6Jic5tYUYkxZIZHFkBgcEJjnORP0PhEh7z385hDaFtoWWzMr5EyC+VgAwAfZ1kPnMaa5UFqKsBLX9z3aroEPHs5RLmrk/DmticQnFWyeyd5L7UyaYs3VCjYRJ+cdjCX1+XyhViLH45GCBIoCBawKUv7pfr7ZpD4qrRA2R5vzRHwaW/IptdbYkk3aWoPLZaJrDg2vfFYJ5VwPIWSF98WLF5jnGXd3d9BJIYYCVKA5ZVL/Tudcyc1diQxb0+Yeo8Ya9D31RgWA43HCBx98iHXdYE2bjllhjhHOu2TzXvDixQs453B3d4fb29tsCzZQyXLe4iERS7K6rpl8cz7qyxcvcyub0+kWT+6ewKR5ggjc399jW1d0TYv+dMS2rnDe4Txf6Pq3DdxKgSWdFFCtdc4153mutU65mxGb29A3ZteuyoeQrax8LbXiFAC1KxpGzgQNwMAlG++1m0Kl93LQSs3Asi37vPZEXE2a25zTe51rvaUc6awmty26puQwz8tcgj9pXTnnsS3nPN7r6vcqJ0wKnNGcMcYAjcj5FNZaOhcirCVkqJCFW6Wglc7jya6WkAIDKimcWmuoEIqTRdFnBOEK4e/jc+eCX6v3aNIXNuL+qkCK6/W9KbsZ8kFqaKvRZffKNwdf529zRUVFRUXF68AXUVb/0DiOv8Z/DMOgAfwygH9/HMfvDsPw5wD8PIA/8WnPfZGDI9vaftNJj5eiP3ljk8gC0qZHbkDZPsa5hWzfi6KaJIAdWZsmykeTFWHpGEqPUbZ8shVX5olyhdLj8QijS5EhxnXOKCBzwUo+Zz5/yIIviWBGqnGstKL2HcnyKO16/LmS7PNj14WgmGxz3umuiqh4304dvbIJ8msl8Yfev0drjeADlnXJeY1ykymtnLIPK6vWsrARt9HxnNcMnavJtm0La5tsgWR7MeeBUvVRam2yLGs6f7IKEwGJaBqLw+GIdVlz8abz+ZzU5AZc/TXbhFOrFXlN6TgspwXmljrW0rw5nx+wLDMFBlqPxjZZlecKuGyhlfOZlc2oqMiN1gohAhoKXUdW5MvlAqPLBr81qaJwCs5QvqfaBV9ub29IgY00Huu67Wzf67bBu9IflUlZ8B4aJXj01ltvw3uHh4d7OOfRtlTpuOs6OlfnM0n+8MMPcffkLhP7kIiN8x7ny0folxnH04lcEWxzTY4AtndmOzyNPFihds7RWCXlWRstiNc+r/E63zuEkOcdE+2dhV6rTKpkIE1WdGZ77069BF5Zp4xdBVtjsnU9FyxL57UsC7Q1O5KX3Qui4JS06pd8XfmdZfx4zsr5S8rn/jEi7OmeBZWJoVSFoRSCAlUuFlZopRSiogCZEvcgHtccoAoRG78HJXgj7xXSsfBxVutvCV77b3NFRUVFRcXrwlexAf8BAPM4jt9Nf/8iKEr7Jz7juc8No9VO6dNa54JIHFEHSn5ljHGXwwQUNUr+21qLNfXq5Lwt3shJZZbVIlYnu67D8XjA7e1tbukhVRDebHH+Hqt4vJGTm619fu2+d2GMgDGsDhWyGsSml1VHY6in4EYHvctDlWT4ujASk2v+m8+DczvbRBjk40z2OU9RKsk8TnIjL1VBqVDHSIVsgvdkf9SlcI8xJhdakQrPzsac/uP2NFQMh9RpKu7kESPlXyrRXiTGYi/2PsAYymOjnD2uutrBe+pxGgIVmDmfL3le7HOXNdqWyMDDwwOcc7i5ucmKVtMEKF1yToNHHjs6bgWoCOdWFrJzzh7PE1a2L5dL/puVSSaspmkzWVWJ4FnnSS22ZIllsrOqDQ+XM9q+wfF4AFd7NUJFbNsObUtVWen7qCIxq8bWGNhkT+fc0pBygKVNc5om6rfadpjnBZfLBc+eP82kj6sG0zxLxaQsWdTnaUYMAX3f4+bmBgAy+fIhQJlEUpINeJczqjiYpXPRKwBo2ga2aWBgdvOLiY8kTHKN5gCLSC3oui4HvDz8bn4DQFQK87LAXy6IMcBYvQvKRB/SddmE6ulKoAeUt260hTENgA3BR6hkd9Y0ILu1vDuHZG9n8s850LJIGb/X53vKPriV7x/EVqFN6UnNFvh8nxVujPQhoLZT5XNkwE66Vj7OLQMg1WYvn8+FqGSbKC6kxeuEz0N+Lp8n38e/Bfih/zZXVFRUVFS8LnwRsvpXhmFQAL4L4M8C+AkA/5CfHMfxvWEY9DAMzz/tuXEcP/i8X6hTbqVSpf2M0ZoylcTGKAoSxBsUSc54w8avCYGyppj08eMyQs8b8L7vd6qsLGjEGyFWAjlfk0kcAGF5e1XJkYV9+Lh5Q0wom0e2y0li7JzPj62pMi6//uPIoyyUJK2LfKx58+o9TNtCCbVYFnaSx85El89LbnJzHmAiQrxZXdcVwQcYbWAam8kwfw4rZ9KWyZ/DJI2vj7UWXUe5bKyCyw0r5fcSGSWyV6yA2+bgnE95wSZtdkPOofU+IIQN1m4AFKw2mVjTeJWgBldc5nHNgRUUQs8kk8fQNg2UAqw9wFqyZgZfAir82XIjzy2QeI4dDgdAcw4rqe4heMzLguPhSBbNZD1nIm8tkU0AmKY5j2eukr1tQFTwitXb1GKmSVbppKzycXLBqW3bEH1RwYPacnGkEByePX+WiTetsXZnsV2XFSZoWG2wzPOOaGityTZrDEJqzdL3PeY4w/urwj/aJCUdeb2QPVjnayNJqlRWpWrPa4LnOJNNns/e+3zvkWvbe4+odW6DhVQ9nCtyz/OMZSECrYzOxK9p2kQwI5Qml8C2OYQwJXVbJ+JKCrpSBsHHTKDlMedz1q/2eJakkc8vKFFY7gqUU6uAqAGEpCQXIhhSqEiJ14fUzobXsLwnK6Vy8Sm+DtKJkV0YvuTDW23Efc/lc5A9nfmeI10ku3uXcDx8w/Daf5srKioqKipeFz4vWf25cRx/YxiGDsB/A+AvAviff3iHReDiQlJNYwLCm5CPs4VJBYE3lSptFnkj2qSKrUxuYgQ4h1VW2iUSwSRMYV0WrEuxr3JBFq01lLARR1MszFClaIrcDPJmko8XSGQ6RiAEQOTJAUU1YWvsNF2yEsptabISjb2lkT+bca3s8iaej2eapp1KzZvO65YgkrxKYsBgosGvYSutQumJiYjdhpqVPqm+8DFL22T5nmIf5M+PaTMfo8K2leupFLVZkcolf6cszsWbX7bK0nE4HA4dnFvh/Yam6fMcur29Rd/3eQ6yxZgpxHSZsCw0b7uO7LA3pxv44BACqYzrukCZsmFn8st5wNM04eHhAV3Xoeu6/F08v72nXsJcBCyGWPJ62w7OhZRHbRFjwDITeWUVklRtk5XPZVkwTTO6rkXX93j58iWmacLxeETXEOlfliVXaGbr5+FwQNd1OE9L/nxjWhhtsC4b1qW4EphoHQ4n3N3dIUafK3ff3d3l3q03t7fo+w7TPOfrh5RrvCru36sQyXeKzW85kHM8HrEsG5ynYkNcBVoSVp6rPC/YBSEfv7bSeu/Jin/lMJBzlu451O5KBqL4/tToFjYp/5SxnNowJfK4rg5aB8QIWNvAmAYhADGK1jsoFu08J4SyyIEgY1SyruudghwB+PCqssrnLgNeUSlEFXOrrDw+WiNXVAoxE1d5PwYS6VUKWtyvr+9POZB2NZ58v+c1x0FIvncYcc/ldczfobUWxu9vFL6W3+aKioqKiorXhc9FVsdx/I30/2UYhl8A8DcB/LcAfie/ZhiGtwGEcRw/GIbh1z/puS9ycEoBXdfC+yA2IB5UfIhbs8S8cYux5GLy5shaSxaylHfGREva/KQFjjdHshiKJMombWSBV8mTJJVSwZEKJ0OqG5KoSjssg4/XWptbprCdVCon18d6rWzypk22qeANK29o+d/OOdg0VrKoEW9amazy5xeVs9vZ+/KFBOXYGm3Qtologex9ClQ4hceWLK2ljy1fE1a95LVhch/TNWGCRxv4Mi4x2Qjned5tfI04R3m9+LOkita2TZ5TZY6qbDM/n6kg0+FwoOuSVD5SFkPOP+WKzjxmISArSEYbvPjoRT53GTjh11xbV5fN5blDLY9S8EVpHE8nACortGxdZdWWidk0T2gai7vbJ3kcKCebjne9f4l129C2Le5u7+Cdy3OCczNjjAiby9fpcpnTeByzXXrb1jxGnG9Mr6cx5YBB27Q4nU5ZiY6IuRrw5UK27MPxAOc9nKPqvsaUdirFXeATWW+hnMv5o/L6ScVPBsd47vFcdZsTr6Hx5Wq2pDwCqZ1oUt0LYQWQ7N8rZHsql/qYxhhxOV92hKxc0711tgTYInRqfRMReZnl4y1uCmoN03XUiojzs3k9O9Em6+PcEfK+Ve4x2K1Nnssxxp19l99D7ZJCKSd37S5BzBZjGXjURoMrW/M9gL+X57RMV5BWYulm0VqjTQGebxK+rt/mioqKioqK14XPJKvDMJwA2HEcXySr0R8B8KsA/jaAwzAMP5vyX/4kgL+W3vZpz31uhOBAYg0RUm5Q3zQ254zxxo3zO4GiuOWNXlJ8mOgxCZEFf7J1MW0M2W7aiQ2O1hpBqUxmmEjw98mWIgB2n31tHZbqDH82v95cbRSZ2EiSzefGaqrMqcwWxbgvOMKkVtpkpXWZP5c3jJJkS5seHwMTGh4DaTHOwYUY4V2y3yX7IFUJJYukgdxUKlhr4Fw5brlxlvmvsgiTJLUll22DUg5Pnz5F0zTZqsuQKjtvpuUGWmudSajcxMugA8+NazWNK+UeTyeoFFQhwqLBuZyU96rQ6TZv/q838NM05ZzQ4/GYgwZ8rbdtgzY6By9oTVAwgNXdEIHLZcrElK+5UnS8XdeROrpusMbmecVzhMfJJtKaC4rlz3l1HlEv4VIoy3oLt22wjYW1RFRlb1VySzis64Lb2+dYlgUPDw9UtClGzCv1up3nGZfLJV/PZVlwuSzwPqSqxaXSNMtoZN/vRd/XDSru86p53cg5xEWwWL2LXrgUYslxhqIiQorbLLHt16T2UKntlVIGTdNScGFZQEWhinlW5vjK9SXnNlumpUPDWo0YEynVyNWpoWTuaZlfkgjmYI8I1vFzEnw8ci361MbGmlfb3cjP4ffZxiK4uLt/ZMcEYqncC3mcdOzWWsQQs2uCHRLyc+SY8Lnx+s22exEA/Cbg6/xtrqioqKioeF34PMrqjwL468MwGAAGwN8B8B+N4xiGYfh3AfzSMAw9Ugl8APi0574oZN6RJHSMbAVWGlbbnLsoyYNUS40xqZeqp0h+2hTye2TbFWl35e9Ciu5Ly19RcdxOBblWBmOM0IJwbq4UVIne00Y6q5akCvFnMVHJRKltU//KRArF83ycLVcCTcfg04Y9pHNjUsubc0kG13VNalWptNxwVVskVVlTXiYX1fFpI26MoeeS2p1zY41BaztqZxMDEIv6Ku2VkkTITTXbv/naMqHS2mQ1h3Mofcr9bFubCEuXbcLy3PkzWA3Mttqr79Va51xa5UkZXd2WciRV6uuZrMwRuMwzfIiwTZP7YSqUOTHPZK+Nc0CMXDRGwa0OIRTr5el0m4vjbJtPJEklt4ECQEWetCEl1zkH2zTwm8fLlw+JbBLBarsWWgHny4VcCEZT71HboOsPUNpgXlcYbUC9fhOhMzQOnKPKtl+2R2e1eN3QNQ2maUp21UAVj0PAtM3Y7le89dYznE6nHDg4HA6JgBDhnM4TPvzoI0zzhJjW9flywYcfUh/bm5sbnE43UErjo48+wuUywRoiM0vK29aW2h7RnOKKx9QqKIQAg6Quh5hzK1UKJGybo1YtmioGK0W5rjBlPkgXAxfVlfOY/g65Z+i6bUAsgZ9rUsX3FibQmeCl+wwHEPK9Bki9gCN0KAEqqwxiANqOyDW9PibFmR0fa/o+KoDknMPmt+xKIfIbs7vBmH2xM3nsSAEnLfL+d+MGDLUAAA+9SURBVK4LkneprdRylS+qQLbhSG3DINRaSTzzeKc1w/cfXq/Xx/ZxbhcOiCzL8jG/MG80vtbf5oqKioqKiteBzySr4zj+vwB+/yc8938A+Kkv+tznhVa62NmYpEbuJegobyopkVolwrRRUZfNFwUiAtmuyCpQVIC5KrjCuYHruuJ0OmUCJYt4MLndEdArJUSqNfz9SmsEvyef/L0lp8ykv8uxO+ehVEgb/0KmTMrFk0qqzDuVG14gbVSBTLa11vCBCV3J1dxtOIUFMUaq9nmtSMvNa4wRm/dokqKxbltWw40x2QKolIKKZAFmdVjakqU6I3Nu+VyvbbDOOUCVIkRtS/m7bZvyRlO1X+/5WMr1Vor6UnIbGLbcstWcLbtNY7I6CSDnCLPCQ8odbaKfPnuGdV0pLzP1Gu26Hi6Rba6Cay2RwvOZKv0ejzdY5hVN0+Zz5uNhcke5px4Aqf4BEW3XQG1UoCdGqiislUHwAdM2wTZ0nkprNKmQk7EWfvO5cA+UhjbU77XrWtjU77RpGspZ7Sgfd5omTJcJTSpwJK/dNE35723z6PtDsuur1HaF8offf//9dC7F6vvy5T0QFaZkDW4asv/3pwO6wwGXywXe+0w42raFgsLtzQ2WZd1Z2P26pGtX8kOttfBpfaiochshrTSRrjTv3eZw9pdiAYeiolRG70hUJkRqryoW+yxZfSMinKfAGLfLeSXwlvqShhBKb1fvwQfJtuOQbLQhJut7CIj61Vxx/hwKxqQK29rs1mxRzi2RW3HflVWEOfgi3QVpZZZ0h3SflkHEyOqzKms0Bx2NIXJKA0kKdCz56qXAXERMQYQmpXNw5V95rvx919Ztzm3me+AinBXfBHydv80VFRUVFRWvC1+ldc0PHcF7+C1ttpCC8brYNQHqKYkQ4cIGbvOi0wYlBKo8a1OfynmeYaxBkza6AcVCJnMXpaWRn2MyhxjFZiod51Uhj3j1Gn1V+IM3t33f43w+Z8WQEUEFSxrDFskNtHE1qUBOA2sMYvTwnlQurYsyyLi2HbdtW6x34dWqqEx6mRjxe7kvZh4DlA23JJNMdllhJtte6aPI1moiyA3gRTVSQfavz4OPsW1L9ViZZxxiSG1WSgVYIGCeZ8REIL0veX7TVJRBOudtVyQoRlaoFZQyAJr0mTER3jInWJ1lmy9bZVn5mec5BSccpmlOpPSIm5sbeO/QtBbbthRVCSodd8w5wDI4UMggEYqoAO8jbMM9eYHgI7QlQrdt1PJEG5VyoamyNl0jKioGgKzaUeWeruuKRFJPWXXn75SFyPjabduWep5ywCTAGovNOdzfX2CtTgo80HU9nj9/hhcvXmBdV5zPZzw8nPHkyROcTie0fQcfAi4zFZTioIb3HqfTCU/u7mjsNwfqxax3ObnLVpwRPC9dyleldal3657XSglgvFqJWgZKdvnYurxWrv/9fSEA0NBijVB+Zslv5/Y7QZccVqVZbeTcb1JsFadGqJiqO5djDREpmFXy1Pmeok2yzvqyZrXWsA0p83zt2MrNNntEKqqU718hgKsBs4rKw8HnE1Up2iTHOYRAPWAF+aeCa0DwggAnEs4VnKWazZ/DufV83WRgjoMXfN/RWn/jbMAVFRUVFRXfBjxqsmpSOxFJXKRyCJSNKG3Em2z5DDFimqe8qeHNqxX5qiGyVS2iaW0mgJyApjRVFlWp0KUPZBncWeHEcQFl08vPSzLmvc8FZZRSuFwuSXEiIpnfKzaYVDhlr8jw8W+pmA1ZM/1u472zDQoFmJU6lVRprsrKbXmYCGlVKp9K8iiLD0nrHYMLATH5y4QpXQfeOB76fledM4SQnIFkc2RbtqzweV1pOI972ryHmBSsVNmWvzMEBWN0brFCJIuvGbcNsWQNjSH9R/mz1naIsceyLMlmqrMyKwMS3of0epvtxiEp10QAKQDAQRS+1pfLBSHE1Nc1oG26HLzQWlOfXl16AcviUs45aGtgdOk72zQNvCYCfXt7ixACHh4eqMBO34JzVenzUtuhSEoe5VFG2MbgdDomdVnn/++CHr5cc+ccLpcLFIBNccEvhWUtudXr6tF2DZ48eZLnOpHiFQ8PD/B+QwwBNzc3UMbg5f1LXOYJW5pvp9MpzyN+/zRNsE2bCSIHCo4pN7TrupznyBWTD4cD3LIV0igCX7xeeH3y+wCy0F8X1uK5/3GWfy52VdafooDK1X2jrKGIrmuygspzWx4fFAUz+NiDp+AZz+UYU/9WGDi1weh9jruxZIu+znuPKOPAAZFctVspKG2A3WrFjjSS66I8rpISLAN0OSCVcnrludO/y2PlnhARQcHDxjY4ng55XXNQke8LMoVBXgOet+xwqaioqKioqHiz8FjJqgGA5+/8CKaZctJUymnqD4ds18uENVXd7PujsPNtuMwzNrdRzlbadG/J0ku7wmS1U0RMtNK55QNAqlMIaaOaKnr6rVTO5A2bUiXXjJSPlOvGrRyUgne0OadekWRR1doghEIs6Tu5P2cinT4k63FIrSt0VhGtNWgai2VZ8OGHL3A8HnO+JlUi5R6RbH+16T1EKF1SUZVSmVQFH7A5Iks2tQ6icSYT77ptMEZnlTMrKZEKDp1OR8zznAtZWdPkoIH31G6n73vqX+u2rLzGGNEYg65tMa1UYGddt2xBpU102ZDKDT0pZhtsauFjdGpH44OwV1qYlG/pvUdjbWr3Qr1WY4yZPNFYadzc3OR2NC9evID3Hn3Xo2mbbC02RsMaC+dd7uHKZJWP06ZcUreFROToWvZ9h5f3L2CtxjRN8N7heLhB0xxwOBwQY8SzZ8+IpEXK6WQCrhRV+F3WhQItbUsKWFR48dFHsI3F6XhC13Z4Mk+YlykFHnRWRo2xZAOGyXN3XVfc3p5w9+QO27YUkguDdXVwjnqs5ura1kIrhePpjiynSsM7h2lecLztoI1F8A6n0xF3d7cwlsZcawVlGiBGnM9n2KbBoevx1vO3sboNyljcPXsOTgHoug7T5YLj6YSbmxtoKJyOt3g4n7GuDje3d2jbNjkvaM3ZxuZ8Sh8ouONJzkTwESEU4g9aLfkGpHQhYyF4IMZkz0Yi9RTYCIg5+FMCaFQW2NjSt9b7gMbYXbVqSj4tll4i27RGfbYVq2xhj5AVsGk9GUUW777vEELEPE9ZaW2spXuQUtBKoT90OByOcJvHw8M9Yox0z+haoQaT3d8HqrJMY5R6xnIQjl65C1LFGGFUqcbr8n0NIjAEah9k9gGnGHJ1KhpTQcZjWj992+F4PObUBJ4X14FDvoJkvabXccDjR3/0x/grq8T65WEA4Md//Mc+63UVFRUVFRWfCfF78om/zWoXuX88+FkA//vXfRAVFRUVFd84/ByA737dB/GGov42V1RUVFT8MPCJv82Plax2AP4ggH8EwH/GaysqKioqKj4LBsCPAfg/AXzjSgO/JtTf5oqKioqKHyQ+87f5sZLVioqKioqKioqKioqKim8x9Ge/pKKioqKioqKioqKioqLi9aKS1YqKioqKioqKioqKiopHh0pWKyoqKioqKioqKioqKh4dKlmtqKioqKioqKioqKioeHSoZLWioqKioqKioqKioqLi0aGS1YqKioqKioqKioqKiopHh0pWKyoqKioqKioqKioqKh4dKlmtqKioqKioqKioqKioeHSwX/cBXGMYht8N4H8C8BaA9wH8e+M4/r2v96geD4Zh+AsA/m0APwngp8Zx/LX0+CeOWx1TwjAMbwH4ywD+SQArgL8H4D8cx/HdYRh+BsAvATgA+B6APzaO42+n933ic98mDMPwNwD8LgABwAOA/2Qcx1+tc+/zYxiGPw/gP0Nau3XefT4Mw/A9AHP6DwD+zDiOv1LH7/WhruVPR/1t/vKov81fDfW3+auj/jZ/Obyu3+bHqKz+IoC/NI7j7wbwl0AnVFHwNwD8SwD+4dXjnzZudUwJEcB/NY7jMI7jTwH4+wB+fhgGDeCXAfzHaYz+NwA/DwCf9ty3EH98HMffN47j7wfwFwD8j+nxOvc+B4Zh+GkAP4O0duu8+8L4Q+M4/rPpv1+p4/faUdfyp6P+Nn951N/mr4b62/wVUH+bvzJ+6L/Nj4qsDsPwIwB+GsBfTQ/9VQA/PQzDO1/fUT0ujOP43XEcf0M+9mnjVse0YBzHD8Zx/F/FQ38LwO8E8AcAzOM4fjc9/osA/nD696c9963COI4vxJ9PAIQ69z4fhmHoQBuCPyUervPuq6GO32tCXcufjfrb/OVRf5u/Gupv85dH/W3+oeAHPn6PiqwC+A6A3xzH0QNA+v//lx6v+GR82rjVMf0YpOjOnwLwNwH8BEQ0fBzH9wDoYRief8Zz3zoMw/A/DMPw6wD+CwB/HHXufV785wB+eRzH74nH6rz7YvgrwzD8P8Mw/MIwDE9Rx+91oq7lL4d6f/yCqL/NXw71t/lLo/42f3X80H+bHxtZrah4XfjvQbkdf/HrPpA3CeM4/gfjOP4EgD8L4L/+uo/nTcAwDP88gH8OwC983cfyBuPnxnH8fQD+IACFum4rKr6pqL/NXwL1t/mLo/42/0DwWn6bHxtZ/Q0APz4MgwGA9P/fkR6v+GR82rjVMb1CKoTxTwP4d8ZxDAB+HWQ54uffBhDGcfzgM5771mIcx78M4F8B8H3UufdZ+JcB/B4A/yAVI/jHAfwKgH8Kdd59LrC9chzHBbSx+BdR1+3rRF3LXw71t/kLoP42f3XU3+YvhPrb/BXxun6bHxVZTdWgfhXAH00P/VEA//c4ju9+fUf1+PFp41bHdI9hGP5LkGf+30iLCwD+NoDDMAw/m/7+kwD+2ud47luDYRhuhmH4jvj7XwfwAYA69z4D4zj+/DiOv2Mcx58cx/EnQZuIfxUU/a7z7jMwDMNpGIYn6d8KwB8Bzau6bl8T6lr+cqi/zZ8f9bf5y6H+Nn951N/mr4bX+dusYow/yGP/yhiG4Z8BldN+BuBDUDnt8es9qseDYRj+OwD/FoB/DMB7AN4fx/H3ftq41TElDMPwewH8GoC/C2BKD/+DcRz/zWEY/gVQNbwepZT2b6X3feJz3xYMw/CjAP4XACcAHvRj+J+O4/h/1bn3xZAiuP/aSOXx67z7DAzD8E8A+OsATPrv7wD40+M4/qM6fq8PdS1/Oupv85dH/W3+8qi/zT841N/mL4bX+dv86MhqRUVFRUVFRUVFRUVFRcWjsgFXVFRUVFRUVFRUVFRUVACVrFZUVFRUVFRUVFRUVFQ8QlSyWlFRUVFRUVFRUVFRUfHoUMlqRUVFRUVFRUVFRUVFxaNDJasVFRUVFRUVFRUVFRUVjw6VrFZUVFRUVFRUVFRUVFQ8OlSyWlFRUVFRUVFRUVFRUfHo8P8DNirIPYIvLwoAAAAASUVORK5CYII=\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"pmZEjwGE0I39"},"source":["## submission을 위한 test 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:27.469285Z","start_time":"2021-04-16T19:44:27.456021Z"},"id":"X4s-Ng1_0I39","executionInfo":{"status":"ok","timestamp":1620193290096,"user_tz":-540,"elapsed":9116,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["def test(model, data_loader, device):\n"," size = 256\n"," transform = A.Compose([A.Resize(256, 256)])\n"," print('Start prediction.')\n"," model.eval()\n"," \n"," file_name_list = []\n"," preds_array = np.empty((0, size*size), dtype=np.long)\n"," \n"," with torch.no_grad():\n"," for step, (imgs, image_infos) in enumerate(test_loader):\n","\n"," # inference (512 x 512)\n"," outs = model(torch.stack(imgs).to(device))\n"," oms = torch.argmax(outs, dim=1).detach().cpu().numpy()\n"," \n"," # resize (256 x 256)\n"," temp_mask = []\n"," for img, mask in zip(np.stack(imgs), oms):\n"," transformed = transform(image=img, mask=mask)\n"," mask = transformed['mask']\n"," temp_mask.append(mask)\n","\n"," oms = np.array(temp_mask)\n"," \n"," oms = oms.reshape([oms.shape[0], size*size]).astype(int)\n"," preds_array = np.vstack((preds_array, oms))\n"," \n"," file_name_list.append([i['file_name'] for i in image_infos])\n"," print(\"End prediction.\")\n"," file_names = [y for x in file_name_list for y in x]\n"," \n"," return file_names, preds_array"],"execution_count":18,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"GnldifLS0I39"},"source":["## submission.csv 생성"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:45:42.235310Z","start_time":"2021-04-16T19:44:30.499016Z"},"scrolled":true,"id":"uV_ZSnqT0I3-","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620193608401,"user_tz":-540,"elapsed":318219,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"776dc206-acbd-4dc1-bc73-1aed120eaa07"},"source":["# sample_submisson.csv 열기\n","submission = pd.read_csv('./submission/sample_submission.csv', index_col=None)\n","\n","# test set에 대한 prediction\n","file_names, preds = test(model, test_loader, device)\n","\n","# PredictionString 대입\n","for file_name, string in zip(file_names, preds):\n"," submission = submission.append({\"image_id\" : file_name, \"PredictionString\" : ' '.join(str(e) for e in string.tolist())}, \n"," ignore_index=True)\n","\n","# submission.csv로 저장\n","submission.to_csv(\"./submission/3_aug_horizontalflip_Rotation90.csv\", index=False)"],"execution_count":19,"outputs":[{"output_type":"stream","text":["Start prediction.\n","End prediction.\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"LIRbR3Ro0I3-"},"source":["## Reference\n","\n"]},{"cell_type":"code","metadata":{"id":"ZUxotp6d0I3-"},"source":[""],"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/chanyub_seg/code/3barrack.ipynb b/chanyub_seg/code/3barrack.ipynb deleted file mode 100644 index ee5f20f..0000000 --- a/chanyub_seg/code/3barrack.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"nbformat":4,"nbformat_minor":0,"metadata":{"hide_input":false,"kernelspec":{"display_name":"Python 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Table of Contents

\n","
"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QBSL7_LP9ONj","executionInfo":{"status":"ok","timestamp":1619944808745,"user_tz":-540,"elapsed":766,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"72482471-7b23-460b-9025-be10625b85c3"},"source":["ls"],"execution_count":null,"outputs":[{"output_type":"stream","text":[" FCN32s.ipynb sample_submission.csv 'UNet++ baseline.ipynb'\n"," mybaseline.ipynb \u001b[0m\u001b[01;34msaved\u001b[0m/ utils.py\n"," \u001b[01;34m__pycache__\u001b[0m/ \u001b[01;34msubmission\u001b[0m/ \u001b[01;34mwandb\u001b[0m/\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"40-m-lE19ewI","executionInfo":{"status":"ok","timestamp":1620059236604,"user_tz":-540,"elapsed":1207,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"090c4f6d-f18d-4c9a-9d9e-16a5f0a1ab34"},"source":["cd drive/MyDrive/Trash/code"],"execution_count":2,"outputs":[{"output_type":"stream","text":["/content/drive/MyDrive/Trash/code\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"MJojTRE39ULc","executionInfo":{"status":"ok","timestamp":1620059233224,"user_tz":-540,"elapsed":19939,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"d9996f18-c82f-4226-81c4-6443fbfc16ec"},"source":["from google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Mounted at /content/drive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Ys0WTRaJ91VZ","executionInfo":{"status":"ok","timestamp":1620059245559,"user_tz":-540,"elapsed":8716,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"aa132017-b059-475c-bb98-8f9854e3481e"},"source":["!pip install albumentations==0.5.2"],"execution_count":3,"outputs":[{"output_type":"stream","text":["Collecting albumentations==0.5.2\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/03/58/63fb1d742dc42d9ba2800ea741de1f2bc6bb05548d8724aa84794042eaf2/albumentations-0.5.2-py3-none-any.whl (72kB)\n","\r\u001b[K |████▌ | 10kB 17.2MB/s eta 0:00:01\r\u001b[K |█████████ | 20kB 13.3MB/s eta 0:00:01\r\u001b[K |█████████████▋ | 30kB 10.1MB/s eta 0:00:01\r\u001b[K |██████████████████▏ | 40kB 8.7MB/s eta 0:00:01\r\u001b[K |██████████████████████▊ | 51kB 5.3MB/s eta 0:00:01\r\u001b[K |███████████████████████████▏ | 61kB 5.7MB/s eta 0:00:01\r\u001b[K |███████████████████████████████▊| 71kB 6.2MB/s eta 0:00:01\r\u001b[K |████████████████████████████████| 81kB 4.4MB/s \n","\u001b[?25hRequirement already satisfied: numpy>=1.11.1 in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (1.19.5)\n","Collecting imgaug>=0.4.0\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/66/b1/af3142c4a85cba6da9f4ebb5ff4e21e2616309552caca5e8acefe9840622/imgaug-0.4.0-py2.py3-none-any.whl (948kB)\n","\u001b[K |████████████████████████████████| 952kB 10.2MB/s \n","\u001b[?25hCollecting opencv-python-headless>=4.1.1\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/6d/6d/92f377bece9b0ec9c893081dbe073a65b38d7ac12ef572b8f70554d08760/opencv_python_headless-4.5.1.48-cp37-cp37m-manylinux2014_x86_64.whl (37.6MB)\n","\u001b[K |████████████████████████████████| 37.6MB 133kB/s \n","\u001b[?25hRequirement already satisfied: PyYAML in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (3.13)\n","Requirement already satisfied: scikit-image>=0.16.1 in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (0.16.2)\n","Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (1.4.1)\n","Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (1.15.0)\n","Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (7.1.2)\n","Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (3.2.2)\n","Requirement already satisfied: opencv-python in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (4.1.2.30)\n","Requirement already satisfied: imageio in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (2.4.1)\n","Requirement already satisfied: Shapely in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (1.7.1)\n","Requirement already satisfied: networkx>=2.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations==0.5.2) (2.5.1)\n","Requirement already satisfied: PyWavelets>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations==0.5.2) (1.1.1)\n","Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug>=0.4.0->albumentations==0.5.2) (2.8.1)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug>=0.4.0->albumentations==0.5.2) (1.3.1)\n","Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug>=0.4.0->albumentations==0.5.2) (2.4.7)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug>=0.4.0->albumentations==0.5.2) (0.10.0)\n","Requirement already satisfied: decorator<5,>=4.3 in /usr/local/lib/python3.7/dist-packages (from networkx>=2.0->scikit-image>=0.16.1->albumentations==0.5.2) (4.4.2)\n","Installing collected packages: imgaug, opencv-python-headless, albumentations\n"," Found existing installation: imgaug 0.2.9\n"," Uninstalling imgaug-0.2.9:\n"," Successfully uninstalled imgaug-0.2.9\n"," Found existing installation: albumentations 0.1.12\n"," Uninstalling albumentations-0.1.12:\n"," Successfully uninstalled albumentations-0.1.12\n","Successfully installed albumentations-0.5.2 imgaug-0.4.0 opencv-python-headless-4.5.1.48\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.826930Z","start_time":"2021-04-18T10:34:45.406686Z"},"scrolled":false,"colab":{"base_uri":"https://localhost:8080/"},"id":"k5pVFOkJ8sQX","executionInfo":{"status":"ok","timestamp":1620059249523,"user_tz":-540,"elapsed":8344,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"19c81336-0c1b-4baa-8684-dff827180175"},"source":["import os\n","import random\n","import time\n","import json\n","import warnings \n","warnings.filterwarnings('ignore')\n","\n","import torch\n","import torch.nn as nn\n","from torch.utils.data import Dataset, DataLoader\n","from utils import label_accuracy_score\n","import cv2\n","\n","import numpy as np\n","import pandas as pd\n","\n","# 전처리를 위한 라이브러리\n","from pycocotools.coco import COCO\n","import torchvision\n","import torchvision.transforms as transforms\n","\n","import albumentations as A\n","from albumentations.pytorch import ToTensorV2\n","\n","# 시각화를 위한 라이브러리\n","import matplotlib.pyplot as plt\n","import seaborn as sns; sns.set()\n","\n","plt.rcParams['axes.grid'] = False\n","\n","print('pytorch version: {}'.format(torch.__version__))\n","print('GPU 사용 가능 여부: {}'.format(torch.cuda.is_available()))\n","\n","print(torch.cuda.get_device_name(0))\n","print(torch.cuda.device_count())\n","\n","device = \"cuda\" if torch.cuda.is_available() else \"cpu\" # GPU 사용 가능 여부에 따라 device 정보 저장"],"execution_count":4,"outputs":[{"output_type":"stream","text":["pytorch version: 1.8.1+cu101\n","GPU 사용 가능 여부: True\n","Tesla P100-PCIE-16GB\n","1\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"bGTnZqwO8sQa"},"source":["## 하이퍼파라미터 세팅 및 seed 고정"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.841930Z","start_time":"2021-04-18T10:34:47.827931Z"},"id":"dV2e6X4l8sQb","executionInfo":{"status":"ok","timestamp":1620059249524,"user_tz":-540,"elapsed":6896,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["batch_size = 8 # Mini-batch size\n","num_epochs = 20\n","learning_rate = 0.0001"],"execution_count":5,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.856930Z","start_time":"2021-04-18T10:34:47.842931Z"},"id":"lFBFwi8T8sQe","executionInfo":{"status":"ok","timestamp":1620059249525,"user_tz":-540,"elapsed":5999,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# seed 고정\n","random_seed = 42\n","torch.manual_seed(random_seed)\n","torch.cuda.manual_seed(random_seed)\n","# torch.cuda.manual_seed_all(random_seed) # if use multi-GPU\n","torch.backends.cudnn.deterministic = True\n","torch.backends.cudnn.benchmark = False\n","np.random.seed(random_seed)\n","random.seed(random_seed)"],"execution_count":6,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"d_aMBo_P8sQg"},"source":["## 학습 데이터 EDA"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.381961Z","start_time":"2021-04-18T10:34:47.857930Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"IZ8czxZE8sQg","executionInfo":{"status":"ok","timestamp":1620059262131,"user_tz":-540,"elapsed":10297,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"0dce1c95-b3c6-4da4-fb46-41fd814db826"},"source":["%matplotlib inline\n","\n","dataset_path = '../input/data'\n","anns_file_path = dataset_path + '/' + 'train.json'\n","\n","# Read annotations\n","with open(anns_file_path, 'r') as f:\n"," dataset = json.loads(f.read())\n","\n","categories = dataset['categories']\n","anns = dataset['annotations']\n","imgs = dataset['images']\n","nr_cats = len(categories)\n","nr_annotations = len(anns)\n","nr_images = len(imgs)\n","\n","# Load categories and super categories\n","cat_names = []\n","super_cat_names = []\n","super_cat_ids = {}\n","super_cat_last_name = ''\n","nr_super_cats = 0\n","for cat_it in categories:\n"," cat_names.append(cat_it['name'])\n"," super_cat_name = cat_it['supercategory']\n"," # Adding new supercat\n"," if super_cat_name != super_cat_last_name:\n"," super_cat_names.append(super_cat_name)\n"," super_cat_ids[super_cat_name] = nr_super_cats\n"," super_cat_last_name = super_cat_name\n"," nr_super_cats += 1\n","\n","print('Number of super categories:', nr_super_cats)\n","print('Number of categories:', nr_cats)\n","print('Number of annotations:', nr_annotations)\n","print('Number of images:', nr_images)"],"execution_count":7,"outputs":[{"output_type":"stream","text":["Number of super categories: 11\n","Number of categories: 11\n","Number of annotations: 21116\n","Number of images: 2617\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.546964Z","start_time":"2021-04-18T10:34:51.382969Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/","height":355},"id":"Xjp6yDNe8sQi","executionInfo":{"status":"ok","timestamp":1620059262725,"user_tz":-540,"elapsed":10589,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"c4f57630-7ce9-4947-92e4-97219781370d"},"source":["# Count annotations\n","cat_histogram = np.zeros(nr_cats,dtype=int)\n","for ann in anns:\n"," cat_histogram[ann['category_id']] += 1\n","\n","# Initialize the matplotlib figure\n","f, ax = plt.subplots(figsize=(5,5))\n","\n","# Convert to DataFrame\n","df = pd.DataFrame({'Categories': cat_names, 'Number of annotations': cat_histogram})\n","df = df.sort_values('Number of annotations', 0, False)\n","\n","# Plot the histogram\n","plt.title(\"category distribution of train set \")\n","plot_1 = sns.barplot(x=\"Number of annotations\", y=\"Categories\", data=df, label=\"Total\", color=\"b\")"],"execution_count":8,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.561965Z","start_time":"2021-04-18T10:34:51.547969Z"},"id":"F5imLAv78sQj","executionInfo":{"status":"ok","timestamp":1620059262726,"user_tz":-540,"elapsed":9766,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# category labeling \n","sorted_temp_df = df.sort_index()\n","\n","# background = 0 에 해당되는 label 추가 후 기존들을 모두 label + 1 로 설정\n","sorted_df = pd.DataFrame([\"Backgroud\"], columns = [\"Categories\"])\n","sorted_df = sorted_df.append(sorted_temp_df, ignore_index=True)"],"execution_count":9,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.576961Z","start_time":"2021-04-18T10:34:51.562964Z"},"scrolled":false,"colab":{"base_uri":"https://localhost:8080/","height":421},"id":"t7VfbZUe8sQj","executionInfo":{"status":"ok","timestamp":1620059262727,"user_tz":-540,"elapsed":9414,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"45398188-09d9-4bc2-f3d9-c475bb042f37"},"source":["# class (Categories) 에 따른 index 확인 (0~11 : 총 12개)\n","sorted_df"],"execution_count":10,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
CategoriesNumber of annotations
0BackgroudNaN
1UNKNOWN128.0
2General trash2225.0
3Paper7448.0
4Paper pack527.0
5Metal449.0
6Glass488.0
7Plastic2472.0
8Styrofoam1074.0
9Plastic bag6114.0
10Battery50.0
11Clothing141.0
\n","
"],"text/plain":[" Categories Number of annotations\n","0 Backgroud NaN\n","1 UNKNOWN 128.0\n","2 General trash 2225.0\n","3 Paper 7448.0\n","4 Paper pack 527.0\n","5 Metal 449.0\n","6 Glass 488.0\n","7 Plastic 2472.0\n","8 Styrofoam 1074.0\n","9 Plastic bag 6114.0\n","10 Battery 50.0\n","11 Clothing 141.0"]},"metadata":{"tags":[]},"execution_count":10}]},{"cell_type":"markdown","metadata":{"id":"dL-azWBK8sQk"},"source":["## 데이터 전처리 함수 정의 (Dataset)"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:52.693328Z","start_time":"2021-04-18T10:34:52.681328Z"},"id":"rKnBddei8sQk","executionInfo":{"status":"ok","timestamp":1620059263258,"user_tz":-540,"elapsed":529,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["category_names = list(sorted_df.Categories)\n","\n","def get_classname(classID, cats):\n"," for i in range(len(cats)):\n"," if cats[i]['id']==classID:\n"," return cats[i]['name']\n"," return \"None\"\n","\n","class CustomDataLoader(Dataset):\n"," \"\"\"COCO format\"\"\"\n"," def __init__(self, data_dir, mode = 'train', transform = None):\n"," super().__init__()\n"," self.mode = mode\n"," self.transform = transform\n"," self.coco = COCO(data_dir)\n"," \n"," def __getitem__(self, index: int):\n"," # dataset이 index되어 list처럼 동작\n"," image_id = self.coco.getImgIds(imgIds=index)\n"," image_infos = self.coco.loadImgs(image_id)[0]\n"," \n"," # cv2 를 활용하여 image 불러오기\n"," images = cv2.imread(os.path.join(dataset_path, image_infos['file_name']))\n"," images = cv2.cvtColor(images, cv2.COLOR_BGR2RGB).astype(np.float32)\n"," images /= 255.0\n"," \n"," if (self.mode in ('train', 'val')):\n"," ann_ids = self.coco.getAnnIds(imgIds=image_infos['id'])\n"," anns = self.coco.loadAnns(ann_ids)\n","\n"," # Load the categories in a variable\n"," cat_ids = self.coco.getCatIds()\n"," cats = self.coco.loadCats(cat_ids)\n","\n"," # masks : size가 (height x width)인 2D\n"," # 각각의 pixel 값에는 \"category id + 1\" 할당\n"," # Background = 0\n"," masks = np.zeros((image_infos[\"height\"], image_infos[\"width\"]))\n"," # Unknown = 1, General trash = 2, ... , Cigarette = 11\n"," for i in range(len(anns)):\n"," className = get_classname(anns[i]['category_id'], cats)\n"," pixel_value = category_names.index(className)\n"," masks = np.maximum(self.coco.annToMask(anns[i])*pixel_value, masks)\n"," masks = masks.astype(np.float32)\n","\n"," # transform -> albumentations 라이브러리 활용\n"," if self.transform is not None:\n"," transformed = self.transform(image=images, mask=masks)\n"," images = transformed[\"image\"]\n"," masks = transformed[\"mask\"]\n"," \n"," return images, masks, image_infos\n"," \n"," if self.mode == 'test':\n"," # transform -> albumentations 라이브러리 활용\n"," if self.transform is not None:\n"," transformed = self.transform(image=images)\n"," images = transformed[\"image\"]\n"," \n"," return images, image_infos\n"," \n"," \n"," def __len__(self) -> int:\n"," # 전체 dataset의 size를 return\n"," return len(self.coco.getImgIds())"],"execution_count":11,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Lui--J9t8sQm"},"source":["## Dataset 정의 및 DataLoader 할당"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:58.823175Z","start_time":"2021-04-18T10:34:54.106233Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"_1PmdNvf8sQm","executionInfo":{"status":"ok","timestamp":1620059279795,"user_tz":-540,"elapsed":5415,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"765af05c-08b7-4438-f8e8-4a2f2784295c"},"source":["# train.json / validation.json / test.json 디렉토리 설정\n","train_path = dataset_path + '/train.json'\n","val_path = dataset_path + '/val.json'\n","test_path = dataset_path + '/test.json'\n","\n","# collate_fn needs for batch\n","def collate_fn(batch):\n"," return tuple(zip(*batch))\n","\n","train_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","val_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","test_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","# create own Dataset 1 (skip)\n","# validation set을 직접 나누고 싶은 경우\n","# random_split 사용하여 data set을 8:2 로 분할\n","train_size = int(0.8*len(dataset))\n","val_size = int(len(dataset)-train_size)\n","dataset = CustomDataLoader(data_dir=train_path, mode='train', transform=train_transform)\n","# print(len(dataset))\n","# print(train_size)\n","# print(val_size)\n","train_dataset, val_dataset = torch.utils.data.random_split(dataset, [train_size, val_size])\n","\n","# create own Dataset 2\n","# train dataset\n","# train_dataset = CustomDataLoader(data_dir=train_path, mode='train', transform=train_transform)\n","\n","# validation dataset\n","# val_dataset = CustomDataLoader(data_dir=val_path, mode='val', transform=val_transform)\n","\n","# test dataset\n","test_dataset = CustomDataLoader(data_dir=test_path, mode='test', transform=test_transform)\n","\n","\n","# DataLoader\n","train_loader = torch.utils.data.DataLoader(dataset=train_dataset, \n"," batch_size=batch_size,\n"," shuffle=True,\n"," num_workers=4,\n"," collate_fn=collate_fn,\n"," drop_last=True)\n","\n","val_loader = torch.utils.data.DataLoader(dataset=val_dataset, \n"," batch_size=batch_size,\n"," shuffle=False,\n"," num_workers=4,\n"," collate_fn=collate_fn,\n"," drop_last=True) \n","\n","test_loader = torch.utils.data.DataLoader(dataset=test_dataset,\n"," batch_size=batch_size,\n"," num_workers=4,\n"," collate_fn=collate_fn)"],"execution_count":13,"outputs":[{"output_type":"stream","text":["loading annotations into memory...\n","Done (t=4.17s)\n","creating index...\n","index created!\n","loading annotations into memory...\n","Done (t=0.53s)\n","creating index...\n","index created!\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"4-uzBwkH8sQ2"},"source":["## baseline model\n","\n","### [TODO] 코드 구현 UNet++ \n","\n","- 출처 : https://jinglescode.github.io/2019/12/02/biomedical-image-segmentation-u-net-nested/"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"gGgEjCSsAo7q","executionInfo":{"status":"ok","timestamp":1620019288762,"user_tz":-540,"elapsed":3524,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"75105f9e-3b3e-46e5-a520-e62e807925af"},"source":["!pip install wandb"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Requirement already satisfied: wandb in /usr/local/lib/python3.7/dist-packages (0.10.28)\n","Requirement already satisfied: protobuf>=3.12.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (3.12.4)\n","Requirement already satisfied: 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wandb) (5.0.2)\n","Requirement already satisfied: pathtools in /usr/local/lib/python3.7/dist-packages (from wandb) (0.1.2)\n","Requirement already satisfied: docker-pycreds>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (0.4.0)\n","Requirement already satisfied: six>=1.13.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (1.15.0)\n","Requirement already satisfied: PyYAML in /usr/local/lib/python3.7/dist-packages (from wandb) (3.13)\n","Requirement already satisfied: Click>=7.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (7.1.2)\n","Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from protobuf>=3.12.0->wandb) (56.0.0)\n","Requirement already satisfied: gitdb<5,>=4.0.1 in /usr/local/lib/python3.7/dist-packages (from GitPython>=1.0.0->wandb) (4.0.7)\n","Requirement already satisfied: urllib3>=1.10.0 in /usr/local/lib/python3.7/dist-packages (from sentry-sdk>=0.4.0->wandb) (1.24.3)\n","Requirement already satisfied: certifi in /usr/local/lib/python3.7/dist-packages (from sentry-sdk>=0.4.0->wandb) (2020.12.5)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.0.0->wandb) (2.10)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.0.0->wandb) (3.0.4)\n","Requirement already satisfied: smmap<5,>=3.0.1 in /usr/local/lib/python3.7/dist-packages (from gitdb<5,>=4.0.1->GitPython>=1.0.0->wandb) (4.0.0)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":136},"id":"F89FhXcJ_QpU","executionInfo":{"status":"ok","timestamp":1620019333258,"user_tz":-540,"elapsed":5663,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"56b4ef9f-f6b6-4f19-a741-392996be7400"},"source":["import wandb\n","\n","proj_name = 'deeplabv3_focal_cosLR(1e-4)_random_split_mIoU'\n","\n","wandb.init(project='chanyub',name=proj_name)\n","\n","config = wandb.config\n","config.learning_rate = 0.01"],"execution_count":null,"outputs":[{"output_type":"stream","text":["\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mpstage12\u001b[0m (use `wandb login --relogin` to force relogin)\n"],"name":"stderr"},{"output_type":"display_data","data":{"text/html":["\n"," Tracking run with wandb version 0.10.28
\n"," Syncing run deeplabv3_focal_cosLR(1e-4)_random_split_mIoU to Weights & Biases (Documentation).
\n"," Project page: https://wandb.ai/pstage12/chanyub
\n"," Run page: https://wandb.ai/pstage12/chanyub/runs/2zh99oe2
\n"," Run data is saved locally in /content/drive/My Drive/Trash/code/wandb/run-20210503_052210-2zh99oe2

\n"," "],"text/plain":[""]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"mVz3IiRHAuov","executionInfo":{"status":"ok","timestamp":1620059292388,"user_tz":-540,"elapsed":6352,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"48442e36-6116-4db8-de4a-62841968edd6"},"source":["!pip install segmentation_models_pytorch"],"execution_count":14,"outputs":[{"output_type":"stream","text":["Collecting segmentation_models_pytorch\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/65/54/8953f9f7ee9d451b0f3be8d635aa3a654579abf898d17502a090efe1155a/segmentation_models_pytorch-0.1.3-py3-none-any.whl (66kB)\n","\r\u001b[K |█████ | 10kB 18.1MB/s eta 0:00:01\r\u001b[K |██████████ | 20kB 13.0MB/s eta 0:00:01\r\u001b[K |██████████████▉ | 30kB 9.8MB/s eta 0:00:01\r\u001b[K |███████████████████▉ | 40kB 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https://files.pythonhosted.org/packages/51/2d/39ecc56fbb202e1891c317e8e44667299bc3b0762ea2ed6aaaa2c2f6613c/timm-0.3.2-py3-none-any.whl (244kB)\n","\u001b[K |████████████████████████████████| 245kB 8.8MB/s \n","\u001b[?25hRequirement already satisfied: torch in /usr/local/lib/python3.7/dist-packages (from efficientnet-pytorch==0.6.3->segmentation_models_pytorch) (1.8.1+cu101)\n","Collecting munch\n"," Downloading https://files.pythonhosted.org/packages/cc/ab/85d8da5c9a45e072301beb37ad7f833cd344e04c817d97e0cc75681d248f/munch-2.5.0-py2.py3-none-any.whl\n","Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from pretrainedmodels==0.7.4->segmentation_models_pytorch) (4.41.1)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torchvision>=0.3.0->segmentation_models_pytorch) (1.19.5)\n","Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.7/dist-packages (from torchvision>=0.3.0->segmentation_models_pytorch) 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sha256=54ebdf0e20cfa1b83c4bdac03381326de6bcd80f6f0b1d38f358b960f0f11e9d\n"," Stored in directory: /root/.cache/pip/wheels/69/df/63/62583c096289713f22db605aa2334de5b591d59861a02c2ecd\n","Successfully built efficientnet-pytorch pretrainedmodels\n","Installing collected packages: efficientnet-pytorch, munch, pretrainedmodels, timm, segmentation-models-pytorch\n","Successfully installed efficientnet-pytorch-0.6.3 munch-2.5.0 pretrainedmodels-0.7.4 segmentation-models-pytorch-0.1.3 timm-0.3.2\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:11.634792Z","start_time":"2021-04-18T16:16:05.875817Z"},"colab":{"base_uri":"https://localhost:8080/","height":137,"referenced_widgets":["a778d3def411425eb4c12591b2dac363","146151198dfc4b5c935707cc46ff3aef","c5090a01c9704b7fbf03b6f5587b53d9","6923ce11e7ff4571a8f6044bff3f38e9","3e24380f3866477eb8a83aaa73f3462b","5ef4e011f2204cb9abc5c67a20d305dc","d89b519385ca47978051e720aad3d0b4","3c5ede9d1cbf4940b6ee1f7b12be89ad"]},"id":"E5-leCCF8sQ5","executionInfo":{"status":"ok","timestamp":1620059325089,"user_tz":-540,"elapsed":16514,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"88ae2622-47bb-4d0c-94d8-1f6701e01599"},"source":["# 구현된 model에 임의의 input을 넣어 output이 잘 나오는지 test\n","import segmentation_models_pytorch as smp\n","\n","# model = smp.DeepLabV3Plus('timm-efficientnet-b3', encoder_weights = 'noisy-student',classes=12)\n","model = smp.DeepLabV3Plus(classes=12)\n","x = torch.randn([2, 3, 512, 512])\n","print(\"input shape : \", x.shape)\n","out = model(x).to(device)\n","print(\"output shape : \", out.size())\n","\n","model = model.to(device)"],"execution_count":15,"outputs":[{"output_type":"stream","text":["Downloading: \"https://download.pytorch.org/models/resnet34-333f7ec4.pth\" to /root/.cache/torch/hub/checkpoints/resnet34-333f7ec4.pth\n"],"name":"stderr"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"a778d3def411425eb4c12591b2dac363","version_minor":0,"version_major":2},"text/plain":["HBox(children=(FloatProgress(value=0.0, max=87306240.0), HTML(value='')))"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\n","input shape : torch.Size([2, 3, 512, 512])\n","output shape : torch.Size([2, 12, 512, 512])\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"qLpYHkTE8sQ6"},"source":["## train, validation, test 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.104200Z","start_time":"2021-04-18T16:16:18.093174Z"},"id":"l3qdtKiO8sQ6","executionInfo":{"status":"ok","timestamp":1620059331096,"user_tz":-540,"elapsed":700,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["def train(num_epochs, model, data_loader, val_loader, criterion, optimizer, saved_dir, val_every, device, lr_scheduler):\n"," print('Start training..')\n"," best_loss = 9999999\n"," for epoch in range(num_epochs):\n"," model.train()\n"," for step, (images, masks, _) in enumerate(data_loader):\n"," images = torch.stack(images) # (batch, channel, height, width)\n"," masks = torch.stack(masks).long() # (batch, channel, height, width)\n"," \n"," # gpu 연산을 위해 device 할당\n"," images, masks = images.to(device), masks.to(device)\n"," \n"," # inference\n"," outputs = model(images)\n"," \n"," # loss 계산 (cross entropy loss)\n"," loss = criterion(outputs, masks)\n"," optimizer.zero_grad()\n"," loss.backward()\n"," optimizer.step()\n"," \n"," lr_scheduler.step()\n"," \n"," # step 주기에 따른 loss 출력\n"," if (step + 1) % 25 == 0:\n"," print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(\n"," epoch+1, num_epochs, step+1, len(train_loader), loss.item()))\n"," \n"," # validation 주기에 따른 loss 출력 및 best model 저장\n"," if (epoch + 1) % val_every == 0:\n"," avrg_loss = validation(epoch + 1, model, val_loader, criterion, device)\n"," if avrg_loss < best_loss:\n"," print('Best performance at epoch: {}'.format(epoch + 1))\n"," print('Save model in', saved_dir)\n"," best_loss = avrg_loss\n"," wandb.log({'best_loss': best_loss})\n"," save_model(model, saved_dir)"],"execution_count":16,"outputs":[]},{"cell_type":"code","metadata":{"id":"j2aKiPqjOdYt"},"source":["classes_dict = {0: 'Background',\n"," 1: 'UNKNOWN',\n"," 2: 'General trash',\n"," 3: 'Paper',\n"," 4: 'Paper pack',\n"," 5: 'Metal',\n"," 6: 'Glass',\n"," 7: 'Plastic',\n"," 8: 'Styrofoam',\n"," 9: 'Plastic bag',\n"," 10: 'Battery',\n"," 11: 'Clothing'}"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.285795Z","start_time":"2021-04-18T16:16:18.267686Z"},"id":"lDbL-1wq8sQ7"},"source":["def validation(epoch, model, data_loader, criterion, device):\n"," print('Start validation #{}'.format(epoch))\n"," model.eval()\n"," with torch.no_grad():\n"," total_loss = 0\n"," cnt = 0\n"," mIoU_list = []\n"," for step, (images, masks, _) in enumerate(data_loader):\n"," \n"," images = torch.stack(images) # (batch, channel, height, width)\n"," masks = torch.stack(masks).long() # (batch, channel, height, width)\n","\n"," images, masks = images.to(device), masks.to(device) \n","\n"," outputs = model(images)\n"," loss = criterion(outputs, masks)\n"," total_loss += loss\n"," cnt += 1\n","\n"," # print(outputs.shape) # (8, 12, 512, 512)\n"," # print(masks.shape) # (8, 512, 512)\n","\n"," # n_o = outputs.detach().cpu().numpy()\n"," # n_o_s = np.squeeze(n_o, axis=0)\n"," # n_o_s_2 = np.squeeze(n_o_s, axis=0)\n"," \n"," # n_m = masks.detach().cpu().numpy()\n"," # n_m_s = np.squeeze(n_m, axis=0)\n","\n"," # wandb.log(wandb.Image(images, masks={\n"," # \"predictions\" : {\n"," # \"mask_data\" : n_o_s_2,\n"," # \"class_labels\" : classes_dict\n"," # },\n"," # \"ground_truth\" : {\n"," # \"mask_data\" : n_m_s,\n"," # \"class_labels\" : classes_dict\n"," # }\n"," # }))\n","\n"," outputs = torch.argmax(outputs.squeeze(), dim=1).detach().cpu().numpy()\n","\n"," mIoU = label_accuracy_score(masks.detach().cpu().numpy(), outputs, n_class=12)[2]\n"," mIoU_list.append(mIoU)\n"," \n"," avrg_loss = total_loss / cnt\n"," mIoU_loss = np.mean(mIoU_list)\n"," print('Validation #{} Average Loss: {:.4f}, mIoU: {:.4f}'.format(epoch, avrg_loss, np.mean(mIoU_list)))\n"," wandb.log({'Train Loss':loss.item(),\n"," 'Val Loss':avrg_loss ,\n"," 'Val mIoU':np.mean(mIoU_list)})\n"," # return avrg_loss\n"," return mIoU_loss"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"SgQs2p6n8sQ9"},"source":["## 모델 저장 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.909918Z","start_time":"2021-04-18T16:16:18.898918Z"},"id":"h5vfGkK58sQ-"},"source":["# 모델 저장 함수 정의\n","val_every = 1 \n","\n","saved_dir = './saved'\n","if not os.path.isdir(saved_dir): \n"," os.mkdir(saved_dir)\n"," \n","def save_model(model, saved_dir, file_name='v3+_focal_coslr_randomsplit_mIoU_CLAHE.pt'):\n"," check_point = {'net': model.state_dict()}\n"," output_path = os.path.join(saved_dir, file_name)\n"," torch.save(model.state_dict(), output_path)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"f8cT79Ad8sQ_"},"source":["## 모델 생성 및 Loss function, Optimizer 정의"]},{"cell_type":"code","metadata":{"id":"neKn53b4_-2d"},"source":["from torch.autograd import Variable\n","import torch.nn.functional as F\n","# ref : https://github.com/clcarwin/focal_loss_pytorch\n","class FocalLoss(nn.Module):\n"," def __init__(self, gamma=0, alpha=None, size_average=True):\n"," super(FocalLoss, self).__init__()\n"," self.gamma = gamma\n"," self.alpha = alpha\n"," if isinstance(alpha,(float,int)): self.alpha = torch.Tensor([alpha,1-alpha])\n"," if isinstance(alpha,list): self.alpha = torch.Tensor(alpha)\n"," self.size_average = size_average\n","\n"," def forward(self, input, target):\n"," if input.dim()>2:\n"," input = input.view(input.size(0),input.size(1),-1) # N,C,H,W => N,C,H*W\n"," input = input.transpose(1,2) # N,C,H*W => N,H*W,C\n"," input = input.contiguous().view(-1,input.size(2)) # N,H*W,C => N*H*W,C\n"," target = target.view(-1,1)\n","\n"," logpt = F.log_softmax(input)\n"," logpt = logpt.gather(1,target)\n"," logpt = logpt.view(-1)\n"," pt = Variable(logpt.data.exp())\n","\n"," if self.alpha is not None:\n"," if self.alpha.type()!=input.data.type():\n"," self.alpha = self.alpha.type_as(input.data)\n"," at = self.alpha.gather(0,target.data.view(-1))\n"," logpt = logpt * Variable(at)\n","\n"," loss = -1 * (1-pt)**self.gamma * logpt\n"," if self.size_average: return loss.mean()\n"," else: return loss.sum()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"2jTZmhhz8wFC"},"source":["import math\n","from torch.optim.lr_scheduler import _LRScheduler\n","\n","class CosineAnnealingWarmUpRestarts(_LRScheduler):\n"," def __init__(self, optimizer, T_0, T_mult=1, eta_max=0.1, T_up=0, gamma=1., last_epoch=-1):\n"," if T_0 <= 0 or not isinstance(T_0, int):\n"," raise ValueError(\"Expected positive integer T_0, but got {}\".format(T_0))\n"," if T_mult < 1 or not isinstance(T_mult, int):\n"," raise ValueError(\"Expected integer T_mult >= 1, but got {}\".format(T_mult))\n"," if T_up < 0 or not isinstance(T_up, int):\n"," raise ValueError(\"Expected positive integer T_up, but got {}\".format(T_up))\n"," self.T_0 = T_0\n"," self.T_mult = T_mult\n"," self.base_eta_max = eta_max\n"," self.eta_max = eta_max\n"," self.T_up = T_up\n"," self.T_i = T_0\n"," self.gamma = gamma\n"," self.cycle = 0\n"," super(CosineAnnealingWarmUpRestarts, self).__init__(optimizer, last_epoch)\n"," self.T_cur = last_epoch\n"," \n"," def get_lr(self):\n"," if self.T_cur == -1:\n"," return self.base_lrs\n"," elif self.T_cur < self.T_up:\n"," return [(self.eta_max - base_lr)*self.T_cur / self.T_up + base_lr for base_lr in self.base_lrs]\n"," else:\n"," return [base_lr + (self.eta_max - base_lr) * (1 + math.cos(math.pi * (self.T_cur-self.T_up) / (self.T_i - self.T_up))) / 2\n"," for base_lr in self.base_lrs]\n","\n"," def step(self, epoch=None):\n"," if epoch is None:\n"," epoch = self.last_epoch + 1\n"," self.T_cur = self.T_cur + 1\n"," if self.T_cur >= self.T_i:\n"," self.cycle += 1\n"," self.T_cur = self.T_cur - self.T_i\n"," self.T_i = (self.T_i - self.T_up) * self.T_mult + self.T_up\n"," else:\n"," if epoch >= self.T_0:\n"," if self.T_mult == 1:\n"," self.T_cur = epoch % self.T_0\n"," self.cycle = epoch // self.T_0\n"," else:\n"," n = int(math.log((epoch / self.T_0 * (self.T_mult - 1) + 1), self.T_mult))\n"," self.cycle = n\n"," self.T_cur = epoch - self.T_0 * (self.T_mult ** n - 1) / (self.T_mult - 1)\n"," self.T_i = self.T_0 * self.T_mult ** (n)\n"," else:\n"," self.T_i = self.T_0\n"," self.T_cur = epoch\n"," \n"," self.eta_max = self.base_eta_max * (self.gamma**self.cycle)\n"," self.last_epoch = math.floor(epoch)\n"," for param_group, lr in zip(self.optimizer.param_groups, self.get_lr()):\n"," param_group['lr'] = lr"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"zKnYU6_OunR1","executionInfo":{"status":"ok","timestamp":1619974878129,"user_tz":-540,"elapsed":19641,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"d5c5945e-6ded-4310-ffa4-e391a84ea5fd"},"source":["!pip install madgrad"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Requirement already satisfied: madgrad in /usr/local/lib/python3.7/dist-packages (1.1)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:19.698902Z","start_time":"2021-04-18T16:16:19.694902Z"},"id":"cLuQNMO08sRA"},"source":["# from madgrad import MADGRAD\n","\n","# Loss function 정의\n","criterion = FocalLoss()\n","\n","# Optimizer 정의\n","optimizer = torch.optim.Adam(params = model.parameters(), lr = learning_rate, weight_decay=1e-4)\n","# optimizer = MADGRAD(params = model.parameters(), lr = learning_rate, momentum = 0.9, weight_decay = 0, eps = 1e-06)\n","\n","# lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size = 237, gamma = 0.65)\n","# lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.65)\n","lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=50, eta_min=0)\n","# lr_scheduler = CosineAnnealingWarmUpRestarts(optimizer, T_0=100, T_mult=2, eta_max=0.1, T_up=10, gamma=0.5)\n","# lr_scheduler = torch.optim.lr_scheduler.CyclicLR(optimizer, base_lr=0.00005, step_size_up=5, max_lr=0.0001, gamma=0.5, mode='exp_range')\n","# lr_scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer, max_lr=0.1, steps_per_epoch=10, epochs=10,anneal_strategy='linear')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"start_time":"2021-04-18T16:16:20.331Z"},"scrolled":false,"colab":{"base_uri":"https://localhost:8080/"},"id":"7fEF_a3L8sRC","executionInfo":{"status":"ok","timestamp":1620021762851,"user_tz":-540,"elapsed":2363945,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"2ae239bd-4814-4423-95c2-e7ed691e8a80"},"source":["train(num_epochs, model, train_loader, val_loader, criterion, optimizer, saved_dir, val_every, device, lr_scheduler=lr_scheduler)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Start training..\n","Epoch [1/20], Step [25/261], Loss: 1.7376\n","Epoch [1/20], Step [50/261], Loss: 1.5903\n","Epoch [1/20], Step [75/261], Loss: 1.4700\n","Epoch [1/20], Step [100/261], Loss: 0.9790\n","Epoch [1/20], Step [125/261], Loss: 0.8941\n","Epoch [1/20], Step [150/261], Loss: 0.8324\n","Epoch [1/20], Step [175/261], Loss: 0.8495\n","Epoch [1/20], Step [200/261], Loss: 0.9095\n","Epoch [1/20], Step [225/261], Loss: 0.9527\n","Epoch [1/20], Step [250/261], Loss: 0.6481\n","Start validation #1\n","Validation #1 Average Loss: 0.6595, mIoU: 0.3325\n","Best performance at epoch: 1\n","Save model in ./saved\n","Epoch [2/20], Step [25/261], Loss: 0.5805\n","Epoch [2/20], Step [50/261], Loss: 0.6245\n","Epoch [2/20], Step [75/261], Loss: 0.9356\n","Epoch [2/20], Step [100/261], Loss: 0.7549\n","Epoch [2/20], Step [125/261], Loss: 0.5905\n","Epoch [2/20], Step [150/261], Loss: 0.4980\n","Epoch [2/20], Step [175/261], Loss: 0.7962\n","Epoch [2/20], Step [200/261], Loss: 0.5571\n","Epoch [2/20], Step [225/261], Loss: 0.7302\n","Epoch [2/20], Step [250/261], Loss: 0.4946\n","Start validation #2\n","Validation #2 Average Loss: 0.4786, mIoU: 0.3652\n","Epoch [3/20], Step [25/261], Loss: 0.3371\n","Epoch [3/20], Step [50/261], Loss: 0.5009\n","Epoch [3/20], Step [75/261], Loss: 0.6754\n","Epoch [3/20], Step [100/261], Loss: 0.4047\n","Epoch [3/20], Step [125/261], Loss: 0.4897\n","Epoch [3/20], Step [150/261], Loss: 0.6166\n","Epoch [3/20], Step [175/261], Loss: 0.3410\n","Epoch [3/20], Step [200/261], Loss: 0.3549\n","Epoch [3/20], Step [225/261], Loss: 0.2303\n","Epoch [3/20], Step [250/261], Loss: 0.3202\n","Start validation #3\n","Validation #3 Average Loss: 0.4056, mIoU: 0.3883\n","Epoch [4/20], Step [25/261], Loss: 0.3574\n","Epoch [4/20], Step [50/261], Loss: 0.4231\n","Epoch [4/20], Step [75/261], Loss: 0.1970\n","Epoch [4/20], Step [100/261], Loss: 0.6231\n","Epoch [4/20], Step [125/261], Loss: 0.2503\n","Epoch [4/20], Step [150/261], Loss: 0.3202\n","Epoch [4/20], Step [175/261], Loss: 0.3331\n","Epoch [4/20], Step [200/261], Loss: 0.1935\n","Epoch [4/20], Step [225/261], Loss: 0.4101\n","Epoch [4/20], Step [250/261], Loss: 0.4815\n","Start validation #4\n","Validation #4 Average Loss: 0.3763, mIoU: 0.3992\n","Epoch [5/20], Step [25/261], Loss: 0.2627\n","Epoch [5/20], Step [50/261], Loss: 0.3175\n","Epoch [5/20], Step [75/261], Loss: 0.4045\n","Epoch [5/20], Step [100/261], Loss: 0.2752\n","Epoch [5/20], Step [125/261], Loss: 0.2498\n","Epoch [5/20], Step [150/261], Loss: 0.2561\n","Epoch [5/20], Step [175/261], Loss: 0.3714\n","Epoch [5/20], Step [200/261], Loss: 0.2385\n","Epoch [5/20], Step [225/261], Loss: 0.6417\n","Epoch [5/20], Step [250/261], Loss: 0.1808\n","Start validation #5\n","Validation #5 Average Loss: 0.3628, mIoU: 0.4078\n","Epoch [6/20], Step [25/261], Loss: 0.1983\n","Epoch [6/20], Step [50/261], Loss: 0.2292\n","Epoch [6/20], Step [75/261], Loss: 0.1712\n","Epoch [6/20], Step [100/261], Loss: 0.1431\n","Epoch [6/20], Step [125/261], Loss: 0.3528\n","Epoch [6/20], Step [150/261], Loss: 0.2282\n","Epoch [6/20], Step [175/261], Loss: 0.1095\n","Epoch [6/20], Step [200/261], Loss: 0.4081\n","Epoch [6/20], Step [225/261], Loss: 0.2408\n","Epoch [6/20], Step [250/261], Loss: 0.1714\n","Start validation #6\n","Validation #6 Average Loss: 0.3230, mIoU: 0.4385\n","Epoch [7/20], Step [25/261], Loss: 0.1606\n","Epoch [7/20], Step [50/261], Loss: 0.1531\n","Epoch [7/20], Step [75/261], Loss: 0.3841\n","Epoch [7/20], Step [100/261], Loss: 0.2203\n","Epoch [7/20], Step [125/261], Loss: 0.2668\n","Epoch [7/20], Step [150/261], Loss: 0.1336\n","Epoch [7/20], Step [175/261], Loss: 0.2193\n","Epoch [7/20], Step [200/261], Loss: 0.1981\n","Epoch [7/20], Step [225/261], Loss: 0.1750\n","Epoch [7/20], Step [250/261], Loss: 0.1338\n","Start validation #7\n","Validation #7 Average Loss: 0.3500, mIoU: 0.4429\n","Epoch [8/20], Step [25/261], Loss: 0.0916\n","Epoch [8/20], Step [50/261], Loss: 0.1727\n","Epoch [8/20], Step [75/261], Loss: 0.4069\n","Epoch [8/20], Step [100/261], Loss: 0.1890\n","Epoch [8/20], Step [125/261], Loss: 0.2303\n","Epoch [8/20], Step [150/261], Loss: 0.1504\n","Epoch [8/20], Step [175/261], Loss: 0.3131\n","Epoch [8/20], Step [200/261], Loss: 0.1737\n","Epoch [8/20], Step [225/261], Loss: 0.2245\n","Epoch [8/20], Step [250/261], Loss: 0.1199\n","Start validation #8\n","Validation #8 Average Loss: 0.3332, mIoU: 0.4673\n","Epoch [9/20], Step [25/261], Loss: 0.1895\n","Epoch [9/20], Step [50/261], Loss: 0.2233\n","Epoch [9/20], Step [75/261], Loss: 0.3287\n","Epoch [9/20], Step [100/261], Loss: 0.2369\n","Epoch [9/20], Step [125/261], Loss: 0.1268\n","Epoch [9/20], Step [150/261], Loss: 0.1489\n","Epoch [9/20], Step [175/261], Loss: 0.1207\n","Epoch [9/20], Step [200/261], Loss: 0.1441\n","Epoch [9/20], Step [225/261], Loss: 0.1399\n","Epoch [9/20], Step [250/261], Loss: 0.1182\n","Start validation #9\n","Validation #9 Average Loss: 0.3400, mIoU: 0.4558\n","Epoch [10/20], Step [25/261], Loss: 0.1719\n","Epoch [10/20], Step [50/261], Loss: 0.1276\n","Epoch [10/20], Step [75/261], Loss: 0.2066\n","Epoch [10/20], Step [100/261], Loss: 0.4540\n","Epoch [10/20], Step [125/261], Loss: 0.1557\n","Epoch [10/20], Step [150/261], Loss: 0.3746\n","Epoch [10/20], Step [175/261], Loss: 0.1052\n","Epoch [10/20], Step [200/261], Loss: 0.2068\n","Epoch [10/20], Step [225/261], Loss: 0.1360\n","Epoch [10/20], Step [250/261], Loss: 0.1058\n","Start validation #10\n","Validation #10 Average Loss: 0.3660, mIoU: 0.4170\n","Epoch [11/20], Step [25/261], Loss: 0.0790\n","Epoch [11/20], Step [50/261], Loss: 0.0884\n","Epoch [11/20], Step [75/261], Loss: 0.1909\n","Epoch [11/20], Step [100/261], Loss: 0.1468\n","Epoch [11/20], Step [125/261], Loss: 0.1399\n","Epoch [11/20], Step [150/261], Loss: 0.2740\n","Epoch [11/20], Step [175/261], Loss: 0.0614\n","Epoch [11/20], Step [200/261], Loss: 0.0970\n","Epoch [11/20], Step [225/261], Loss: 0.1096\n","Epoch [11/20], Step [250/261], Loss: 0.2604\n","Start validation #11\n","Validation #11 Average Loss: 0.3409, mIoU: 0.4679\n","Epoch [12/20], Step [25/261], Loss: 0.1538\n","Epoch [12/20], Step [50/261], Loss: 0.1199\n","Epoch [12/20], Step [75/261], Loss: 0.1239\n","Epoch [12/20], Step [100/261], Loss: 0.1782\n","Epoch [12/20], Step [125/261], Loss: 0.1175\n","Epoch [12/20], Step [150/261], Loss: 0.1081\n","Epoch [12/20], Step [175/261], Loss: 0.0762\n","Epoch [12/20], Step [200/261], Loss: 0.1495\n","Epoch [12/20], Step [225/261], Loss: 0.0903\n","Epoch [12/20], Step [250/261], Loss: 0.0862\n","Start validation #12\n","Validation #12 Average Loss: 0.3441, mIoU: 0.4589\n","Epoch [13/20], Step [25/261], Loss: 0.1194\n","Epoch [13/20], Step [50/261], Loss: 0.0941\n","Epoch [13/20], Step [75/261], Loss: 0.0717\n","Epoch [13/20], Step [100/261], Loss: 0.0673\n","Epoch [13/20], Step [125/261], Loss: 0.0767\n","Epoch [13/20], Step [150/261], Loss: 0.0889\n","Epoch [13/20], Step [175/261], Loss: 0.0962\n","Epoch [13/20], Step [200/261], Loss: 0.1113\n","Epoch [13/20], Step [225/261], Loss: 0.1556\n","Epoch [13/20], Step [250/261], Loss: 0.0833\n","Start validation #13\n","Validation #13 Average Loss: 0.3463, mIoU: 0.4474\n","Epoch [14/20], Step [25/261], Loss: 0.1080\n","Epoch [14/20], Step [50/261], Loss: 0.0624\n","Epoch [14/20], Step [75/261], Loss: 0.1116\n","Epoch [14/20], Step [100/261], Loss: 0.1051\n","Epoch [14/20], Step [125/261], Loss: 0.0700\n","Epoch [14/20], Step [150/261], Loss: 0.0760\n","Epoch [14/20], Step [175/261], Loss: 0.0522\n","Epoch [14/20], Step [200/261], Loss: 0.0545\n","Epoch [14/20], Step [225/261], Loss: 0.1538\n","Epoch [14/20], Step [250/261], Loss: 0.1115\n","Start validation #14\n","Validation #14 Average Loss: 0.3449, mIoU: 0.4508\n","Epoch [15/20], Step [25/261], Loss: 0.0982\n","Epoch [15/20], Step [50/261], Loss: 0.0860\n","Epoch [15/20], Step [75/261], Loss: 0.0741\n","Epoch [15/20], Step [100/261], Loss: 0.0975\n","Epoch [15/20], Step [125/261], Loss: 0.0829\n","Epoch [15/20], Step [150/261], Loss: 0.1174\n","Epoch [15/20], Step [175/261], Loss: 0.1056\n","Epoch [15/20], Step [200/261], Loss: 0.1398\n","Epoch [15/20], Step [225/261], Loss: 0.0434\n","Epoch [15/20], Step [250/261], Loss: 0.1063\n","Start validation #15\n","Validation #15 Average Loss: 0.3403, mIoU: 0.4469\n","Epoch [16/20], Step [25/261], Loss: 0.0804\n","Epoch [16/20], Step [50/261], Loss: 0.0920\n","Epoch [16/20], Step [75/261], Loss: 0.1703\n","Epoch [16/20], Step [100/261], Loss: 0.1447\n","Epoch [16/20], Step [125/261], Loss: 0.1027\n","Epoch [16/20], Step [150/261], Loss: 0.1182\n","Epoch [16/20], Step [175/261], Loss: 0.1103\n","Epoch [16/20], Step [200/261], Loss: 0.1581\n","Epoch [16/20], Step [225/261], Loss: 0.0578\n","Epoch [16/20], Step [250/261], Loss: 0.0785\n","Start validation #16\n","Validation #16 Average Loss: 0.3879, mIoU: 0.4441\n","Epoch [17/20], Step [25/261], Loss: 0.1487\n","Epoch [17/20], Step [50/261], Loss: 0.1053\n","Epoch [17/20], Step [75/261], Loss: 0.1282\n","Epoch [17/20], Step [100/261], Loss: 0.0917\n","Epoch [17/20], Step [125/261], Loss: 0.1286\n","Epoch [17/20], Step [150/261], Loss: 0.0805\n","Epoch [17/20], Step [175/261], Loss: 0.1733\n","Epoch [17/20], Step [200/261], Loss: 0.1216\n","Epoch [17/20], Step [225/261], Loss: 0.0370\n","Epoch [17/20], Step [250/261], Loss: 0.3494\n","Start validation #17\n","Validation #17 Average Loss: 0.3801, mIoU: 0.4286\n","Epoch [18/20], Step [25/261], Loss: 0.1590\n","Epoch [18/20], Step [50/261], Loss: 0.0664\n","Epoch [18/20], Step [75/261], Loss: 0.1923\n","Epoch [18/20], Step [100/261], Loss: 0.0606\n","Epoch [18/20], Step [125/261], Loss: 0.0692\n","Epoch [18/20], Step [150/261], Loss: 0.0662\n","Epoch [18/20], Step [175/261], Loss: 0.0740\n","Epoch [18/20], Step [200/261], Loss: 0.1614\n","Epoch [18/20], Step [225/261], Loss: 0.0957\n","Epoch [18/20], Step [250/261], Loss: 0.1754\n","Start validation #18\n","Validation #18 Average Loss: 0.4041, mIoU: 0.4313\n","Epoch [19/20], Step [25/261], Loss: 0.1371\n","Epoch [19/20], Step [50/261], Loss: 0.1530\n","Epoch [19/20], Step [75/261], Loss: 0.1203\n","Epoch [19/20], Step [100/261], Loss: 0.0800\n","Epoch [19/20], Step [125/261], Loss: 0.1230\n","Epoch [19/20], Step [150/261], Loss: 0.1023\n","Epoch [19/20], Step [175/261], Loss: 0.1103\n","Epoch [19/20], Step [200/261], Loss: 0.0800\n","Epoch [19/20], Step [225/261], Loss: 0.1062\n","Epoch [19/20], Step [250/261], Loss: 0.0736\n","Start validation #19\n","Validation #19 Average Loss: 0.3443, mIoU: 0.4685\n","Epoch [20/20], Step [25/261], Loss: 0.0549\n","Epoch [20/20], Step [50/261], Loss: 0.1008\n","Epoch [20/20], Step [75/261], Loss: 0.1048\n","Epoch [20/20], Step [100/261], Loss: 0.0583\n","Epoch [20/20], Step [125/261], Loss: 0.0979\n","Epoch [20/20], Step [150/261], Loss: 0.1180\n","Epoch [20/20], Step [175/261], Loss: 0.0699\n","Epoch [20/20], Step [200/261], Loss: 0.1280\n","Epoch [20/20], Step [225/261], Loss: 0.0595\n","Epoch [20/20], Step [250/261], Loss: 0.0860\n","Start validation #20\n","Validation #20 Average Loss: 0.3542, mIoU: 0.4765\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"8Ul-l-Ur8sRD"},"source":["## 저장된 model 불러오기 (학습된 이후) "]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:21.050200Z","start_time":"2021-04-16T19:44:20.802200Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"nz2gHKip8sRD","executionInfo":{"status":"ok","timestamp":1620059381168,"user_tz":-540,"elapsed":1589,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"8405af90-f8be-40f0-86b1-d42aebf1672a"},"source":["# best model 저장된 경로\n","model_path = './saved/deeplabv3_focal_cosLR_random_split.pt'\n","\n","# best model 불러오기\n","checkpoint = torch.load(model_path, map_location=device)\n","model.load_state_dict(checkpoint)\n","\n","# 추론을 실행하기 전에는 반드시 설정 (batch normalization, dropout 를 평가 모드로 설정)\n","# model.eval()"],"execution_count":17,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{"tags":[]},"execution_count":17}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:24.939227Z","start_time":"2021-04-16T19:44:24.518228Z"},"colab":{"base_uri":"https://localhost:8080/","height":502},"id":"HMs2G0AQ8sRD","executionInfo":{"status":"ok","timestamp":1620059410190,"user_tz":-540,"elapsed":4513,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"54dbb372-a327-4d0b-d0cf-f06b35b8a30b"},"source":["# 첫번째 batch의 추론 결과 확인\n","for imgs, image_infos in test_loader:\n"," image_infos = image_infos\n"," temp_images = imgs\n"," \n"," model.eval()\n"," # inference\n"," outs = model(torch.stack(temp_images).to(device))\n"," oms = torch.argmax(outs.squeeze(), dim=1).detach().cpu().numpy()\n"," \n"," break\n","\n","i = 1\n","fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(16, 16))\n","\n","print('Shape of Original Image :', list(temp_images[i].shape))\n","print('Shape of Predicted : ', list(oms[i].shape))\n","print('Unique values, category of transformed mask : \\n', [{int(i),category_names[int(i)]} for i in list(np.unique(oms[i]))])\n","\n","# Original image\n","ax1.imshow(temp_images[i].permute([1,2,0]))\n","ax1.grid(False)\n","ax1.set_title(\"Original image : {}\".format(image_infos[i]['file_name']), fontsize = 15)\n","\n","# Predicted\n","ax2.imshow(oms[i])\n","ax2.grid(False)\n","ax2.set_title(\"Predicted : {}\".format(image_infos[i]['file_name']), fontsize = 15)\n","\n","plt.show()"],"execution_count":19,"outputs":[{"output_type":"stream","text":["Shape of Original Image : [3, 512, 512]\n","Shape of Predicted : [512, 512]\n","Unique values, category of transformed mask : \n"," [{0, 'Backgroud'}, {'Plastic', 7}, {9, 'Plastic bag'}]\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"MtGw1aLC8sRE"},"source":["## submission을 위한 test 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:27.469285Z","start_time":"2021-04-16T19:44:27.456021Z"},"id":"BkHOlbIb8sRE","executionInfo":{"status":"ok","timestamp":1620059419301,"user_tz":-540,"elapsed":360,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["def test(model, data_loader, device):\n"," size = 256\n"," transform = A.Compose([A.Resize(256, 256)])\n"," print('Start prediction.')\n"," model.eval()\n"," \n"," file_name_list = []\n"," preds_array = np.empty((0, size*size), dtype=np.long)\n"," \n"," with torch.no_grad():\n"," for step, (imgs, image_infos) in enumerate(test_loader):\n","\n"," # inference (512 x 512)\n"," outs = model(torch.stack(imgs).to(device))\n"," oms = torch.argmax(outs, dim=1).detach().cpu().numpy()\n"," \n"," # resize (256 x 256)\n"," temp_mask = []\n"," for img, mask in zip(np.stack(imgs), oms):\n"," transformed = transform(image=img, mask=mask)\n"," mask = transformed['mask']\n"," temp_mask.append(mask)\n","\n"," oms = np.array(temp_mask)\n"," \n"," oms = oms.reshape([oms.shape[0], size*size]).astype(int)\n"," preds_array = np.vstack((preds_array, oms))\n"," \n"," file_name_list.append([i['file_name'] for i in image_infos])\n"," print(\"End prediction.\")\n"," file_names = [y for x in file_name_list for y in x]\n"," \n"," return file_names, preds_array"],"execution_count":20,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"6JUkRi2J8sRF"},"source":["## submission.csv 생성"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:45:42.235310Z","start_time":"2021-04-16T19:44:30.499016Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"BbHNVDNr8sRF","executionInfo":{"status":"ok","timestamp":1620059732432,"user_tz":-540,"elapsed":304005,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"113ea5d1-57d7-4b02-c79a-eee2ccc71b4f"},"source":["# sample_submisson.csv 열기\n","submission = pd.read_csv('./submission/sample_submission.csv', index_col=None)\n","\n","# test set에 대한 prediction\n","file_names, preds = test(model, test_loader, device)\n","\n","# PredictionString 대입\n","for file_name, string in zip(file_names, preds):\n"," submission = submission.append({\"image_id\" : file_name, \"PredictionString\" : ' '.join(str(e) for e in string.tolist())}, \n"," ignore_index=True)\n","\n","# submission.csv로 저장\n","submission.to_csv(\"./submission/deeplabv3_focal_cosLR_random_split.csv\", index=False)"],"execution_count":21,"outputs":[{"output_type":"stream","text":["Start prediction.\n","End prediction.\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"tIjoCiVp8sRG"},"source":["## Reference\n","\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ykrfzleS8sRG","executionInfo":{"status":"ok","timestamp":1620059965390,"user_tz":-540,"elapsed":8610,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"e8fdc8f2-abfd-4b9d-ed0f-9de9bda9638e"},"source":["import json\n","import requests\n","import os\n","from urllib.parse import urlparse, parse_qsl, urlencode, urlunparse\n","\n","def submit(user_key='', file_path = '', desc=\"\"):\n"," if not user_key:\n"," raise Exception(\"No UserKey\" )\n"," url = urlparse('http://ec2-13-124-161-225.ap-northeast-2.compute.amazonaws.com:8000/api/v1/competition/28/presigned_url/?description=&hyperparameters={%22training%22:{},%22inference%22:{}}')\n"," qs = dict(parse_qsl(url.query))\n"," qs['description'] = desc\n"," parts = url._replace(query=urlencode(qs))\n"," url = urlunparse(parts)\n","\n"," print(url)\n"," headers = {\n"," 'Authorization': user_key\n"," }\n"," res = requests.get(url, headers=headers)\n"," print(res.text)\n"," data = json.loads(res.text)\n"," \n"," submit_url = data['url']\n"," body = {\n"," 'key':'app/Competitions/000028/Users/{}/Submissions/{}/output.csv'.format(str(data['submission']['user']).zfill(8),str(data['submission']['local_id']).zfill(4)),\n"," 'x-amz-algorithm':data['fields']['x-amz-algorithm'],\n"," 'x-amz-credential':data['fields']['x-amz-credential'],\n"," 'x-amz-date':data['fields']['x-amz-date'],\n"," 'policy':data['fields']['policy'],\n"," 'x-amz-signature':data['fields']['x-amz-signature']\n"," }\n"," requests.post(url=submit_url, data=body, files={'file': open(file_path, 'rb')})\n","\n","\n","####################################################################################\n","test_dir = \"/content/drive/MyDrive/Trash/code/submission\" # 수정 필요 : output 파일 폴더 \n","desc = \"deeplabv3_focal_cosLR_random_split\" # 수정 필요 : 파일에 대한 설명\n","output_file = \"deeplabv3_focal_cosLR_random_split.csv\" #수정 필요 : output 파일 \n","user_key = \"Bearer 7bb5f96452751a238ffaf91a93c4242bf9b72abe\" # 수정 필요 : Authorization \n","\n","\n","submit(user_key, os.path.join(test_dir, output_file),desc)"],"execution_count":22,"outputs":[{"output_type":"stream","text":["http://ec2-13-124-161-225.ap-northeast-2.compute.amazonaws.com:8000/api/v1/competition/28/presigned_url/?hyperparameters=%7B%22training%22%3A%7B%7D%2C%22inference%22%3A%7B%7D%7D&description=deeplabv3_focal_cosLR_random_split\n","{\"url\":\"https://prod-aistages-private.s3.amazonaws.com/\",\"fields\":{\"key\":\"app/Competitions/000028/Users/00000099/Submissions/0018/output.csv\",\"x-amz-algorithm\":\"AWS4-HMAC-SHA256\",\"x-amz-credential\":\"AKIA45LU4MHUJ7WLDQVO/20210503/ap-northeast-2/s3/aws4_request\",\"x-amz-date\":\"20210503T163919Z\",\"policy\":\"eyJleHBpcmF0aW9uIjogIjIwMjEtMDUtMDNUMTc6Mzk6MTlaIiwgImNvbmRpdGlvbnMiOiBbeyJidWNrZXQiOiAicHJvZC1haXN0YWdlcy1wcml2YXRlIn0sIHsia2V5IjogImFwcC9Db21wZXRpdGlvbnMvMDAwMDI4L1VzZXJzLzAwMDAwMDk5L1N1Ym1pc3Npb25zLzAwMTgvb3V0cHV0LmNzdiJ9LCB7IngtYW16LWFsZ29yaXRobSI6ICJBV1M0LUhNQUMtU0hBMjU2In0sIHsieC1hbXotY3JlZGVudGlhbCI6ICJBS0lBNDVMVTRNSFVKN1dMRFFWTy8yMDIxMDUwMy9hcC1ub3J0aGVhc3QtMi9zMy9hd3M0X3JlcXVlc3QifSwgeyJ4LWFtei1kYXRlIjogIjIwMjEwNTAzVDE2MzkxOVoifV19\",\"x-amz-signature\":\"0996279e4ac9d4161fc6a54ed1999a9662ce1b2fb33df8aae3a7717682345065\"},\"submission\":{\"id\":14672,\"phase\":\"Created\",\"type\":\"File\",\"local_id\":18,\"hyperparameters\":\"{\\\"training\\\": {}, \\\"inference\\\": {}}\",\"description\":\"deeplabv3_focal_cosLR_random_split\",\"final\":false,\"created_at\":\"2021-05-04T01:39:19.517116+09:00\",\"updated_at\":\"2021-05-04T01:39:19.517150+09:00\",\"user\":99,\"competition\":28,\"image\":null}}\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"sVoz-PcVcvJ3"},"source":[""],"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/chanyub_seg/code/FCN32s.ipynb b/chanyub_seg/code/FCN32s.ipynb deleted file mode 100644 index 05a3615..0000000 --- a/chanyub_seg/code/FCN32s.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"nbformat":4,"nbformat_minor":0,"metadata":{"hide_input":false,"kernelspec":{"display_name":"Python 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Table of Contents

\n","
"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xxRLxDGqgGno","executionInfo":{"status":"ok","timestamp":1619624400783,"user_tz":-540,"elapsed":807,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"722a682f-48a0-4034-a509-4f0c71243394"},"source":["cd drive/MyDrive/Trash/code"],"execution_count":2,"outputs":[{"output_type":"stream","text":["/content/drive/MyDrive/Trash/code\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"aQIJ2taOgQ66","executionInfo":{"status":"ok","timestamp":1619597008523,"user_tz":-540,"elapsed":965,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"bcd6044c-afa9-49f9-edf1-fe1678bec6ec"},"source":["ls"],"execution_count":null,"outputs":[{"output_type":"stream","text":["FCN32s.ipynb sample_submission.csv \u001b[0m\u001b[01;34msubmission\u001b[0m/\n","\u001b[01;34m__pycache__\u001b[0m/ \u001b[01;34msaved\u001b[0m/ utils.py\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nH0Y2RCQghn8","executionInfo":{"status":"ok","timestamp":1619624407566,"user_tz":-540,"elapsed":5680,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"e2cba04f-9aca-4c35-b502-15516d6c96cd"},"source":["!pip install albumentations==0.4.6"],"execution_count":3,"outputs":[{"output_type":"stream","text":["Collecting albumentations==0.4.6\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/92/33/1c459c2c9a4028ec75527eff88bc4e2d256555189f42af4baf4d7bd89233/albumentations-0.4.6.tar.gz (117kB)\n","\u001b[K |████████████████████████████████| 122kB 9.2MB/s \n","\u001b[?25hRequirement already satisfied: numpy>=1.11.1 in /usr/local/lib/python3.7/dist-packages (from albumentations==0.4.6) (1.19.5)\n","Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from albumentations==0.4.6) (1.4.1)\n","Collecting imgaug>=0.4.0\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/66/b1/af3142c4a85cba6da9f4ebb5ff4e21e2616309552caca5e8acefe9840622/imgaug-0.4.0-py2.py3-none-any.whl (948kB)\n","\u001b[K |████████████████████████████████| 952kB 14.6MB/s \n","\u001b[?25hRequirement already satisfied: PyYAML in /usr/local/lib/python3.7/dist-packages (from albumentations==0.4.6) (3.13)\n","Requirement already satisfied: opencv-python>=4.1.1 in /usr/local/lib/python3.7/dist-packages (from albumentations==0.4.6) (4.1.2.30)\n","Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.4.6) (7.1.2)\n","Requirement already satisfied: scikit-image>=0.14.2 in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.4.6) (0.16.2)\n","Requirement already satisfied: imageio in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.4.6) (2.4.1)\n","Requirement already satisfied: Shapely in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.4.6) (1.7.1)\n","Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.4.6) (1.15.0)\n","Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.4.6) (3.2.2)\n","Requirement already satisfied: networkx>=2.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.14.2->imgaug>=0.4.0->albumentations==0.4.6) (2.5.1)\n","Requirement already satisfied: PyWavelets>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.14.2->imgaug>=0.4.0->albumentations==0.4.6) (1.1.1)\n","Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug>=0.4.0->albumentations==0.4.6) (2.4.7)\n","Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug>=0.4.0->albumentations==0.4.6) (2.8.1)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug>=0.4.0->albumentations==0.4.6) (1.3.1)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug>=0.4.0->albumentations==0.4.6) (0.10.0)\n","Requirement already satisfied: decorator<5,>=4.3 in /usr/local/lib/python3.7/dist-packages (from networkx>=2.0->scikit-image>=0.14.2->imgaug>=0.4.0->albumentations==0.4.6) (4.4.2)\n","Building wheels for collected packages: albumentations\n"," Building wheel for albumentations (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for albumentations: filename=albumentations-0.4.6-cp37-none-any.whl size=65163 sha256=981a842def96e96a47bc58d61a33645d7c42f6ede5b38d6d58f73576a02693c5\n"," Stored in directory: /root/.cache/pip/wheels/c7/f4/89/56d1bee5c421c36c1a951eeb4adcc32fbb82f5344c086efa14\n","Successfully built albumentations\n","Installing collected packages: imgaug, albumentations\n"," Found existing installation: imgaug 0.2.9\n"," Uninstalling imgaug-0.2.9:\n"," Successfully uninstalled imgaug-0.2.9\n"," Found existing installation: albumentations 0.1.12\n"," Uninstalling albumentations-0.1.12:\n"," Successfully uninstalled albumentations-0.1.12\n","Successfully installed albumentations-0.4.6 imgaug-0.4.0\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-25T10:43:33.736804Z","start_time":"2021-04-25T10:43:33.718803Z"},"scrolled":false,"colab":{"base_uri":"https://localhost:8080/"},"id":"n4FVru72eipV","executionInfo":{"status":"ok","timestamp":1619624411748,"user_tz":-540,"elapsed":6812,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"41e95973-8d91-44f9-db6b-96dc0b72e527"},"source":["import os\n","import random\n","import time\n","import json\n","import warnings \n","warnings.filterwarnings('ignore')\n","\n","import torch\n","import torch.nn as nn\n","from torch.utils.data import Dataset, DataLoader\n","from utils import label_accuracy_score\n","import cv2\n","\n","import numpy as np\n","import pandas as pd\n","\n","# 전처리를 위한 라이브러리\n","from pycocotools.coco import COCO\n","import torchvision\n","import torchvision.transforms as transforms\n","\n","import albumentations as A\n","from albumentations.pytorch import ToTensorV2\n","\n","# 시각화를 위한 라이브러리\n","import matplotlib.pyplot as plt\n","import seaborn as sns; sns.set()\n","\n","plt.rcParams['axes.grid'] = False\n","\n","print('pytorch version: {}'.format(torch.__version__))\n","print('GPU 사용 가능 여부: {}'.format(torch.cuda.is_available()))\n","\n","print(torch.cuda.get_device_name(0))\n","print(torch.cuda.device_count())\n","\n","device = \"cuda\" if torch.cuda.is_available() else \"cpu\" # GPU 사용 가능 여부에 따라 device 정보 저장"],"execution_count":4,"outputs":[{"output_type":"stream","text":["pytorch version: 1.8.1+cu101\n","GPU 사용 가능 여부: True\n","Tesla P100-PCIE-16GB\n","1\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"yNP38naSeipW"},"source":["## 하이퍼파라미터 세팅 및 seed 고정"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0336duRefEIB","executionInfo":{"status":"ok","timestamp":1619624395835,"user_tz":-540,"elapsed":21463,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"6e0cf662-86e3-4555-bfbe-bf84bfd6ef1d"},"source":["from google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Mounted at /content/drive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-25T10:43:34.648180Z","start_time":"2021-04-25T10:43:34.638147Z"},"id":"on1mKubpeipX","executionInfo":{"status":"ok","timestamp":1619624416711,"user_tz":-540,"elapsed":840,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["batch_size = 8 # Mini-batch size\n","num_epochs = 20\n","learning_rate = 0.0001"],"execution_count":5,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-25T10:43:35.530138Z","start_time":"2021-04-25T10:43:35.521142Z"},"id":"d6I8VRWOeipY","executionInfo":{"status":"ok","timestamp":1619624416712,"user_tz":-540,"elapsed":458,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# seed 고정\n","random_seed = 21\n","torch.manual_seed(random_seed)\n","torch.cuda.manual_seed(random_seed)\n","# torch.cuda.manual_seed_all(random_seed) # if use multi-GPU\n","torch.backends.cudnn.deterministic = True\n","torch.backends.cudnn.benchmark = False\n","np.random.seed(random_seed)\n","random.seed(random_seed)"],"execution_count":6,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"ZLhCXqupeipZ"},"source":["## 학습 데이터 EDA"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-25T10:44:05.731311Z","start_time":"2021-04-25T10:44:02.174337Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"xJSiXj8FeipZ","executionInfo":{"status":"ok","timestamp":1619624426576,"user_tz":-540,"elapsed":9283,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"b3a08d76-f6da-4ae4-b0a1-7edd95eb4b86"},"source":["%matplotlib inline\n","\n","dataset_path = '../input/data'\n","anns_file_path = dataset_path + '/' + 'train.json'\n","\n","# Read annotations\n","with open(anns_file_path, 'r') as f:\n"," dataset = json.loads(f.read())\n","\n","categories = dataset['categories']\n","anns = dataset['annotations']\n","imgs = dataset['images']\n","nr_cats = len(categories)\n","nr_annotations = len(anns)\n","nr_images = len(imgs)\n","\n","# Load categories and super categories\n","cat_names = []\n","super_cat_names = []\n","super_cat_ids = {}\n","super_cat_last_name = ''\n","nr_super_cats = 0\n","for cat_it in categories:\n"," cat_names.append(cat_it['name'])\n"," super_cat_name = cat_it['supercategory']\n"," # Adding new supercat\n"," if super_cat_name != super_cat_last_name:\n"," super_cat_names.append(super_cat_name)\n"," super_cat_ids[super_cat_name] = nr_super_cats\n"," super_cat_last_name = super_cat_name\n"," nr_super_cats += 1\n","\n","print('Number of super categories:', nr_super_cats)\n","print('Number of categories:', nr_cats)\n","print('Number of annotations:', nr_annotations)\n","print('Number of images:', nr_images)"],"execution_count":7,"outputs":[{"output_type":"stream","text":["Number of super categories: 11\n","Number of categories: 11\n","Number of annotations: 21116\n","Number of images: 2617\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-25T10:44:07.684681Z","start_time":"2021-04-25T10:44:07.442541Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/","height":355},"id":"5NwBpoB_eipa","executionInfo":{"status":"ok","timestamp":1619624427144,"user_tz":-540,"elapsed":9140,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"37585a1b-a9a5-436d-f3e5-4efb1fdd3abc"},"source":["# Count annotations\n","cat_histogram = np.zeros(nr_cats,dtype=int)\n","for ann in anns:\n"," cat_histogram[ann['category_id']] += 1\n","\n","# Initialize the matplotlib figure\n","f, ax = plt.subplots(figsize=(5,5))\n","\n","# Convert to DataFrame\n","df = pd.DataFrame({'Categories': cat_names, 'Number of annotations': cat_histogram})\n","df = df.sort_values('Number of annotations', 0, False)\n","\n","# Plot the histogram\n","plt.title(\"category distribution of train set \")\n","plot_1 = sns.barplot(x=\"Number of annotations\", y=\"Categories\", data=df, label=\"Total\", color=\"b\")"],"execution_count":8,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-25T10:44:08.441306Z","start_time":"2021-04-25T10:44:08.431289Z"},"id":"RJIS6avIeipb","executionInfo":{"status":"ok","timestamp":1619624427144,"user_tz":-540,"elapsed":8880,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# category labeling \n","sorted_temp_df = df.sort_index()\n","\n","# background = 0 에 해당되는 label 추가 후 기존들을 모두 label + 1 로 설정\n","sorted_df = pd.DataFrame([\"Backgroud\"], columns = [\"Categories\"])\n","sorted_df = sorted_df.append(sorted_temp_df, ignore_index=True)"],"execution_count":9,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-25T10:44:09.504708Z","start_time":"2021-04-25T10:44:09.495735Z"},"scrolled":false,"colab":{"base_uri":"https://localhost:8080/","height":421},"id":"iPQepr1feipj","executionInfo":{"status":"ok","timestamp":1619624427145,"user_tz":-540,"elapsed":8183,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"f5b1d512-dc2a-4b98-9bd6-c48aff46c02d"},"source":["# class (Categories) 에 따른 index 확인 (0~11 : 총 12개)\n","sorted_df"],"execution_count":10,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
CategoriesNumber of annotations
0BackgroudNaN
1UNKNOWN128.0
2General trash2225.0
3Paper7448.0
4Paper pack527.0
5Metal449.0
6Glass488.0
7Plastic2472.0
8Styrofoam1074.0
9Plastic bag6114.0
10Battery50.0
11Clothing141.0
\n","
"],"text/plain":[" Categories Number of annotations\n","0 Backgroud NaN\n","1 UNKNOWN 128.0\n","2 General trash 2225.0\n","3 Paper 7448.0\n","4 Paper pack 527.0\n","5 Metal 449.0\n","6 Glass 488.0\n","7 Plastic 2472.0\n","8 Styrofoam 1074.0\n","9 Plastic bag 6114.0\n","10 Battery 50.0\n","11 Clothing 141.0"]},"metadata":{"tags":[]},"execution_count":10}]},{"cell_type":"markdown","metadata":{"id":"49rqAvA5eipj"},"source":["## 데이터 전처리 함수 정의 (Dataset)"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-25T10:44:14.728045Z","start_time":"2021-04-25T10:44:14.712044Z"},"id":"mLGhL90neipk","executionInfo":{"status":"ok","timestamp":1619624427472,"user_tz":-540,"elapsed":7922,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["category_names = list(sorted_df.Categories)\n","\n","def get_classname(classID, cats):\n"," for i in range(len(cats)):\n"," if cats[i]['id']==classID:\n"," return cats[i]['name']\n"," return \"None\"\n","\n","class CustomDataLoader(Dataset):\n"," \"\"\"COCO format\"\"\"\n"," def __init__(self, data_dir, mode = 'train', transform = None):\n"," super().__init__()\n"," self.mode = mode\n"," self.transform = transform\n"," self.coco = COCO(data_dir)\n"," \n"," def __getitem__(self, index: int):\n"," # dataset이 index되어 list처럼 동작\n"," image_id = self.coco.getImgIds(imgIds=index)\n"," image_infos = self.coco.loadImgs(image_id)[0]\n"," \n"," # cv2 를 활용하여 image 불러오기\n"," images = cv2.imread(os.path.join(dataset_path, image_infos['file_name']))\n"," images = cv2.cvtColor(images, cv2.COLOR_BGR2RGB).astype(np.float32)\n"," images /= 255.0\n"," \n"," if (self.mode in ('train', 'val')):\n"," ann_ids = self.coco.getAnnIds(imgIds=image_infos['id'])\n"," anns = self.coco.loadAnns(ann_ids)\n","\n"," # Load the categories in a variable\n"," cat_ids = self.coco.getCatIds()\n"," cats = self.coco.loadCats(cat_ids)\n","\n"," # masks : size가 (height x width)인 2D\n"," # 각각의 pixel 값에는 \"category id + 1\" 할당\n"," # Background = 0\n"," masks = np.zeros((image_infos[\"height\"], image_infos[\"width\"]))\n"," # Unknown = 1, General trash = 2, ... , Cigarette = 11\n"," for i in range(len(anns)):\n"," className = get_classname(anns[i]['category_id'], cats)\n"," pixel_value = category_names.index(className)\n"," masks = np.maximum(self.coco.annToMask(anns[i])*pixel_value, masks)\n"," masks = masks.astype(np.float32)\n","\n"," # transform -> albumentations 라이브러리 활용\n"," if self.transform is not None:\n"," transformed = self.transform(image=images, mask=masks)\n"," images = transformed[\"image\"]\n"," masks = transformed[\"mask\"]\n"," \n"," return images, masks, image_infos\n"," \n"," if self.mode == 'test':\n"," # transform -> albumentations 라이브러리 활용\n"," if self.transform is not None:\n"," transformed = self.transform(image=images)\n"," images = transformed[\"image\"]\n"," \n"," return images, image_infos\n"," \n"," \n"," def __len__(self) -> int:\n"," # 전체 dataset의 size를 return\n"," return len(self.coco.getImgIds())"],"execution_count":11,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"wMsvNZmteipl"},"source":["## Dataset 정의 및 DataLoader 할당"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-25T10:44:48.504584Z","start_time":"2021-04-25T10:44:43.599579Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"Qi-DKVfyeipm","executionInfo":{"status":"ok","timestamp":1619624433641,"user_tz":-540,"elapsed":13648,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"df00a1aa-76b9-4464-a1bb-9c2d20ce2b15"},"source":["# train.json / validation.json / test.json 디렉토리 설정\n","train_path = dataset_path + '/train.json'\n","val_path = dataset_path + '/val.json'\n","test_path = dataset_path + '/test.json'\n","\n","# collate_fn needs for batch\n","def collate_fn(batch):\n"," return tuple(zip(*batch))\n","\n","train_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","val_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","test_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","# create own Dataset 1 (skip)\n","# validation set을 직접 나누고 싶은 경우\n","# random_split 사용하여 data set을 8:2 로 분할\n","# train_size = int(0.8*len(dataset))\n","# val_size = int(len(dataset)-train_size)\n","# dataset = CustomDataLoader(data_dir=train_path, mode='train', transform=transform)\n","# train_dataset, val_dataset = torch.utils.data.random_split(dataset, [train_size, val_size])\n","\n","# create own Dataset 2\n","# train dataset\n","train_dataset = CustomDataLoader(data_dir=train_path, mode='train', transform=train_transform)\n","\n","# validation dataset\n","val_dataset = CustomDataLoader(data_dir=val_path, mode='val', transform=val_transform)\n","\n","# test dataset\n","test_dataset = CustomDataLoader(data_dir=test_path, mode='test', transform=test_transform)\n","\n","\n","# DataLoader\n","train_loader = torch.utils.data.DataLoader(dataset=train_dataset,\n"," batch_size=batch_size,\n"," shuffle=True,\n"," num_workers=4,\n"," collate_fn=collate_fn)\n","\n","val_loader = torch.utils.data.DataLoader(dataset=val_dataset, \n"," batch_size=batch_size,\n"," shuffle=False,\n"," num_workers=4,\n"," collate_fn=collate_fn)\n","\n","test_loader = torch.utils.data.DataLoader(dataset=test_dataset,\n"," batch_size=batch_size,\n"," num_workers=4,\n"," collate_fn=collate_fn)"],"execution_count":12,"outputs":[{"output_type":"stream","text":["loading annotations into memory...\n","Done (t=4.12s)\n","creating index...\n","index created!\n","loading annotations into memory...\n","Done (t=1.89s)\n","creating index...\n","index created!\n","loading annotations into memory...\n","Done (t=0.24s)\n","creating index...\n","index created!\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"9B5QRSh4eipo"},"source":["### 데이터 샘플 시각화 (Show example image and mask)\n","\n","- `train_loader` \n","- `val_loader` \n","- `test_loader` "]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-25T10:44:53.814582Z","start_time":"2021-04-25T10:44:53.308553Z"},"scrolled":false,"colab":{"base_uri":"https://localhost:8080/","height":441},"id":"MCZE8skneipo","executionInfo":{"status":"ok","timestamp":1619624443558,"user_tz":-540,"elapsed":22314,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"412f1f25-82dd-43cd-b5d6-f33264086fae"},"source":["# train_loader의 output 결과(image 및 mask) 확인\n","for imgs, masks, image_infos in train_loader:\n"," image_infos = image_infos[0]\n"," temp_images = imgs\n"," temp_masks = masks\n"," \n"," break\n","\n","fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(12, 12))\n","\n","print('image shape:', list(temp_images[0].shape))\n","print('mask shape: ', list(temp_masks[0].shape))\n","print('Unique values, category of transformed mask : \\n', [{int(i),category_names[int(i)]} for i in list(np.unique(temp_masks[0]))])\n","\n","ax1.imshow(temp_images[0].permute([1,2,0]))\n","ax1.grid(False)\n","ax1.set_title(\"input image : {}\".format(image_infos['file_name']), fontsize = 15)\n","\n","ax2.imshow(temp_masks[0])\n","ax2.grid(False)\n","ax2.set_title(\"masks : {}\".format(image_infos['file_name']), fontsize = 15)\n","\n","plt.show()"],"execution_count":13,"outputs":[{"output_type":"stream","text":["image shape: [3, 512, 512]\n","mask shape: [512, 512]\n","Unique values, category of transformed mask : \n"," [{0, 'Backgroud'}, {3, 'Paper'}, {9, 'Plastic bag'}]\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-25T10:44:55.164584Z","start_time":"2021-04-25T10:44:54.612554Z"},"colab":{"base_uri":"https://localhost:8080/","height":441},"id":"V0KXqxnEeipp","executionInfo":{"status":"ok","timestamp":1619624449800,"user_tz":-540,"elapsed":27481,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"e69cbf0d-2c19-4b48-ef19-57a6eea143f0"},"source":["# val_loader의 output 결과(image 및 mask) 확인\n","for imgs, masks, image_infos in val_loader:\n"," image_infos = image_infos[0]\n"," temp_images = imgs\n"," temp_masks = masks\n"," \n"," break\n","\n","fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(12, 12))\n","\n","print('image shape:', list(temp_images[0].shape))\n","print('mask shape: ', list(temp_masks[0].shape))\n","\n","print('Unique values, category of transformed mask : \\n', [{int(i),category_names[int(i)]} for i in list(np.unique(temp_masks[0]))])\n","\n","ax1.imshow(temp_images[0].permute([1,2,0]))\n","ax1.grid(False)\n","ax1.set_title(\"input image : {}\".format(image_infos['file_name']), fontsize = 15)\n","\n","ax2.imshow(temp_masks[0])\n","ax2.grid(False)\n","ax2.set_title(\"masks : {}\".format(image_infos['file_name']), fontsize = 15)\n","\n","plt.show()"],"execution_count":14,"outputs":[{"output_type":"stream","text":["image shape: [3, 512, 512]\n","mask shape: [512, 512]\n","Unique values, category of transformed mask : \n"," [{0, 'Backgroud'}, {'Glass', 6}, {'Plastic', 7}, {9, 'Plastic bag'}]\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-25T10:44:56.424554Z","start_time":"2021-04-25T10:44:56.130554Z"},"colab":{"base_uri":"https://localhost:8080/","height":412},"id":"btzbEFBxeipq","executionInfo":{"status":"ok","timestamp":1619624455987,"user_tz":-540,"elapsed":32456,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"43d83577-b9f4-4425-919f-3020f51649e4"},"source":["# test_loader의 output 결과(image 및 mask) 확인\n","for imgs, image_infos in test_loader:\n"," image_infos = image_infos[0]\n"," temp_images = imgs\n"," # temp_masks = masks\n"," \n"," break\n","\n","fig, ax1 = plt.subplots(nrows=1, ncols=1, figsize=(6, 6))\n","\n","print('image shape:', list(temp_images[0].shape))\n","\n","ax1.imshow(temp_images[0].permute([1,2,0]))\n","ax1.grid(False)\n","ax1.set_title(\"input image : {}\".format(image_infos['file_name']), fontsize = 15)\n","\n","plt.show()"],"execution_count":15,"outputs":[{"output_type":"stream","text":["image shape: [3, 512, 512]\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"2YhhTzj2eipr"},"source":["## baseline model\n","\n","### [TODO] 코드 구현 FCN-32s "]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:14:30.953430Z","start_time":"2021-04-18T16:14:30.924454Z"},"scrolled":false,"id":"hIsLHtmfeipr","executionInfo":{"status":"ok","timestamp":1619624459458,"user_tz":-540,"elapsed":817,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["import torch\n","import torch.nn as nn\n","from torchvision.models import vgg16\n","\n","class FCN32s(nn.Module):\n"," def __init__(self, num_classes=21):\n"," super(FCN32s, self).__init__()\n"," self.pretrained_model = vgg16(pretrained = True)\n"," features, classifiers = list(self.pretrained_model.features.children()), list(self.pretrained_model.classifier.children())\n"," \n"," self.features_map1 = nn.Sequential(*features[0:17])\n"," self.features_map2 = nn.Sequential(*features[17:24])\n"," self.features_map3 = nn.Sequential(*features[24:31])\n"," \n"," self.conv = nn.Sequential(nn.Conv2d(512, 4096, kernel_size = 1),\n"," nn.ReLU(inplace=True),\n"," nn.Dropout(),\n"," nn.Conv2d(4096, 4096, kernel_size = 1),\n"," nn.ReLU(inplace=True),\n"," nn.Dropout()\n"," )\n"," self.score_fr = nn.Conv2d(4096, num_classes, kernel_size = 1)\n"," \n"," self.upscore32 = nn.ConvTranspose2d(num_classes,\n"," num_classes,\n"," kernel_size=64,\n"," stride=32,\n"," padding=16)\n"," '''\n"," [TODO]\n","\n"," ''' \n","\n"," def forward(self, x):\n"," h = self.features_map1(x)\n"," h = self.features_map2(h)\n"," h = self.features_map3(h)\n"," \n"," h = self.conv(h)\n"," h = self.score_fr(h)\n"," \n"," upscore32 = self.upscore32(h)\n"," \n"," '''\n"," [TODO]\n","\n"," ''' \n"," \n"," return upscore32"],"execution_count":17,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:11.634792Z","start_time":"2021-04-18T16:16:05.875817Z"},"colab":{"base_uri":"https://localhost:8080/","height":117,"referenced_widgets":["385695a2f46d45dc9d10d23180fae831","320e2c899feb44b99d41e5e184ef1f36","e447519143874612ba9d81740bdbf68c","41ca293efb60431b99bb3a289cd243b0","e09a85ad006e4b3ebf02c3e49e47b201","46e7150992df4c4588e4956dd0ae2fca","745e4ac5436e412a90963038785b12cf","a994c6abd6ae49caa76acc404f49190f"]},"id":"KlNKsftKeipr","executionInfo":{"status":"ok","timestamp":1619624473167,"user_tz":-540,"elapsed":13075,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"f5af6419-e8e3-443b-c939-62be3f4e2ad4"},"source":["# 구현된 model에 임의의 input을 넣어 output이 잘 나오는지 test\n","\n","model = FCN32s(num_classes=12)\n","x = torch.randn([1, 3, 512, 512])\n","print(\"input shape : \", x.shape)\n","out = model(x).to(device)\n","print(\"output shape : \", out.size())\n","\n","model = model.to(device)"],"execution_count":18,"outputs":[{"output_type":"stream","text":["Downloading: \"https://download.pytorch.org/models/vgg16-397923af.pth\" to /root/.cache/torch/hub/checkpoints/vgg16-397923af.pth\n"],"name":"stderr"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"385695a2f46d45dc9d10d23180fae831","version_minor":0,"version_major":2},"text/plain":["HBox(children=(FloatProgress(value=0.0, max=553433881.0), HTML(value='')))"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\n","input shape : torch.Size([1, 3, 512, 512])\n","output shape : torch.Size([1, 12, 512, 512])\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"BdFhm_-_eipt"},"source":["## train, validation, test 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.104200Z","start_time":"2021-04-18T16:16:18.093174Z"},"id":"f07z4SLYeipu","executionInfo":{"status":"ok","timestamp":1619624476038,"user_tz":-540,"elapsed":2869,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["def train(num_epochs, model, data_loader, val_loader, criterion, optimizer, saved_dir, val_every, device):\n"," print('Start training..')\n"," best_loss = 9999999\n"," for epoch in range(num_epochs):\n"," model.train()\n"," for step, (images, masks, _) in enumerate(data_loader):\n"," images = torch.stack(images) # (batch, channel, height, width)\n"," masks = torch.stack(masks).long() # (batch, channel, height, width)\n"," \n"," # gpu 연산을 위해 device 할당\n"," images, masks = images.to(device), masks.to(device)\n"," \n"," # inference\n"," outputs = model(images)\n"," \n"," # loss 계산 (cross entropy loss)\n"," loss = criterion(outputs, masks)\n"," optimizer.zero_grad()\n"," loss.backward()\n"," optimizer.step()\n"," \n"," # step 주기에 따른 loss 출력\n"," if (step + 1) % 25 == 0:\n"," print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(\n"," epoch+1, num_epochs, step+1, len(train_loader), loss.item()))\n"," \n"," # validation 주기에 따른 loss 출력 및 best model 저장\n"," if (epoch + 1) % val_every == 0:\n"," avrg_loss = validation(epoch + 1, model, val_loader, criterion, device)\n"," if avrg_loss < best_loss:\n"," print('Best performance at epoch: {}'.format(epoch + 1))\n"," print('Save model in', saved_dir)\n"," best_loss = avrg_loss\n"," save_model(model, saved_dir)"],"execution_count":19,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.285795Z","start_time":"2021-04-18T16:16:18.267686Z"},"id":"sJOOzJFieipv","executionInfo":{"status":"ok","timestamp":1619624476038,"user_tz":-540,"elapsed":2867,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["def validation(epoch, model, data_loader, criterion, device):\n"," print('Start validation #{}'.format(epoch))\n"," model.eval()\n"," with torch.no_grad():\n"," total_loss = 0\n"," cnt = 0\n"," mIoU_list = []\n"," for step, (images, masks, _) in enumerate(data_loader):\n"," \n"," images = torch.stack(images) # (batch, channel, height, width)\n"," masks = torch.stack(masks).long() # (batch, channel, height, width)\n","\n"," images, masks = images.to(device), masks.to(device) \n","\n"," outputs = model(images)\n"," loss = criterion(outputs, masks)\n"," total_loss += loss\n"," cnt += 1\n"," \n"," outputs = torch.argmax(outputs.squeeze(), dim=1).detach().cpu().numpy()\n","\n"," mIoU = label_accuracy_score(masks.detach().cpu().numpy(), outputs, n_class=12)[2]\n"," mIoU_list.append(mIoU)\n"," \n"," avrg_loss = total_loss / cnt\n"," print('Validation #{} Average Loss: {:.4f}, mIoU: {:.4f}'.format(epoch, avrg_loss, np.mean(mIoU_list)))\n","\n"," return avrg_loss"],"execution_count":20,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"d-cGmLXveipw"},"source":["## 모델 저장 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.909918Z","start_time":"2021-04-18T16:16:18.898918Z"},"id":"g3cROY1deipx","executionInfo":{"status":"ok","timestamp":1619624476039,"user_tz":-540,"elapsed":2865,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# 모델 저장 함수 정의\n","val_every = 1 \n","\n","saved_dir = './saved'\n","if not os.path.isdir(saved_dir): \n"," os.mkdir(saved_dir)\n"," \n","def save_model(model, saved_dir, file_name='fcn32s_best_model.pt'):\n"," check_point = {'net': model.state_dict()}\n"," output_path = os.path.join(saved_dir, file_name)\n"," torch.save(model.state_dict(), output_path)"],"execution_count":21,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"1XG2sAdIeipx"},"source":["## 모델 생성 및 Loss function, Optimizer 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:19.698902Z","start_time":"2021-04-18T16:16:19.694902Z"},"id":"lWXlWDU1eipy","executionInfo":{"status":"ok","timestamp":1619624476039,"user_tz":-540,"elapsed":2860,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# Loss function 정의\n","criterion = nn.CrossEntropyLoss()\n","\n","# Optimizer 정의\n","optimizer = torch.optim.Adam(params = model.parameters(), lr = learning_rate, weight_decay=1e-6)"],"execution_count":22,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"start_time":"2021-04-18T16:16:20.331Z"},"scrolled":false,"colab":{"base_uri":"https://localhost:8080/"},"id":"3dsTk_uOeipy","executionInfo":{"status":"ok","timestamp":1619630171629,"user_tz":-540,"elapsed":5698441,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"97676935-7a27-4105-dae0-b2efaa9c1e25"},"source":["train(num_epochs, model, train_loader, val_loader, criterion, optimizer, saved_dir, val_every, device)"],"execution_count":23,"outputs":[{"output_type":"stream","text":["Start training..\n","Epoch [1/20], Step [25/328], Loss: 1.1820\n","Epoch [1/20], Step [50/328], Loss: 1.3503\n","Epoch [1/20], Step [75/328], Loss: 0.9819\n","Epoch [1/20], Step [100/328], Loss: 0.9485\n","Epoch [1/20], Step [125/328], Loss: 0.7644\n","Epoch [1/20], Step [150/328], Loss: 1.1057\n","Epoch [1/20], Step [175/328], Loss: 0.6572\n","Epoch [1/20], Step [200/328], Loss: 1.2736\n","Epoch [1/20], Step [225/328], Loss: 0.6030\n","Epoch [1/20], Step [250/328], Loss: 0.5806\n","Epoch [1/20], Step [275/328], Loss: 0.6946\n","Epoch [1/20], Step [300/328], Loss: 0.6674\n","Epoch [1/20], Step [325/328], Loss: 0.5371\n","Start validation #1\n","Validation #1 Average Loss: 0.7319, mIoU: 0.1917\n","Best performance at epoch: 1\n","Save model in ./saved\n","Epoch [2/20], Step [25/328], Loss: 0.6853\n","Epoch [2/20], Step [50/328], Loss: 0.3894\n","Epoch [2/20], Step [75/328], Loss: 0.6090\n","Epoch [2/20], Step [100/328], Loss: 0.3273\n","Epoch [2/20], Step [125/328], Loss: 0.6541\n","Epoch [2/20], Step [150/328], Loss: 0.5618\n","Epoch [2/20], Step [175/328], Loss: 0.4797\n","Epoch [2/20], Step [200/328], Loss: 0.6138\n","Epoch [2/20], Step [225/328], Loss: 0.7451\n","Epoch [2/20], Step [250/328], Loss: 0.5379\n","Epoch [2/20], Step [275/328], Loss: 0.3129\n","Epoch [2/20], Step [300/328], Loss: 0.4703\n","Epoch [2/20], Step [325/328], Loss: 0.5562\n","Start validation #2\n","Validation #2 Average Loss: 0.5201, mIoU: 0.2258\n","Best performance at epoch: 2\n","Save model in ./saved\n","Epoch [3/20], Step [25/328], Loss: 0.4545\n","Epoch [3/20], Step [50/328], Loss: 0.4416\n","Epoch [3/20], Step [75/328], Loss: 0.5368\n","Epoch [3/20], Step [100/328], Loss: 0.5326\n","Epoch [3/20], Step [125/328], Loss: 0.3245\n","Epoch [3/20], Step [150/328], Loss: 0.2079\n","Epoch [3/20], Step [175/328], Loss: 0.6544\n","Epoch [3/20], Step [200/328], Loss: 0.4430\n","Epoch [3/20], Step [225/328], Loss: 0.3515\n","Epoch [3/20], Step [250/328], Loss: 1.0242\n","Epoch [3/20], Step [275/328], Loss: 0.5354\n","Epoch [3/20], Step [300/328], Loss: 0.3566\n","Epoch [3/20], Step [325/328], Loss: 0.4885\n","Start validation #3\n","Validation #3 Average Loss: 0.4637, mIoU: 0.2995\n","Best performance at epoch: 3\n","Save model in ./saved\n","Epoch [4/20], Step [25/328], Loss: 0.3990\n","Epoch [4/20], Step [50/328], Loss: 0.6789\n","Epoch [4/20], Step [75/328], Loss: 0.3127\n","Epoch [4/20], Step [100/328], Loss: 0.2455\n","Epoch [4/20], Step [125/328], Loss: 0.4331\n","Epoch [4/20], Step [150/328], Loss: 0.6214\n","Epoch [4/20], Step [175/328], Loss: 0.3414\n","Epoch [4/20], Step [200/328], Loss: 0.3154\n","Epoch [4/20], Step [225/328], Loss: 1.0064\n","Epoch [4/20], Step [250/328], Loss: 0.2673\n","Epoch [4/20], Step [275/328], Loss: 0.9435\n","Epoch [4/20], Step [300/328], Loss: 0.2894\n","Epoch [4/20], Step [325/328], Loss: 0.4956\n","Start validation #4\n","Validation #4 Average Loss: 0.4693, mIoU: 0.2887\n","Epoch [5/20], Step [25/328], Loss: 0.3635\n","Epoch [5/20], Step [50/328], Loss: 0.3409\n","Epoch [5/20], Step [75/328], Loss: 0.2658\n","Epoch [5/20], Step [100/328], Loss: 0.3597\n","Epoch [5/20], Step [125/328], Loss: 0.2995\n","Epoch [5/20], Step [150/328], Loss: 0.3438\n","Epoch [5/20], Step [175/328], Loss: 0.4735\n","Epoch [5/20], Step [200/328], Loss: 0.4887\n","Epoch [5/20], Step [225/328], Loss: 0.5967\n","Epoch [5/20], Step [250/328], Loss: 0.4430\n","Epoch [5/20], Step [275/328], Loss: 0.2599\n","Epoch [5/20], Step [300/328], Loss: 0.2235\n","Epoch [5/20], Step [325/328], Loss: 0.3832\n","Start validation #5\n","Validation #5 Average Loss: 0.4354, mIoU: 0.3013\n","Best performance at epoch: 5\n","Save model in ./saved\n","Epoch [6/20], Step [25/328], Loss: 0.4950\n","Epoch [6/20], Step [50/328], Loss: 0.2791\n","Epoch [6/20], Step [75/328], Loss: 0.1695\n","Epoch [6/20], Step [100/328], Loss: 0.3228\n","Epoch [6/20], Step [125/328], Loss: 0.6538\n","Epoch [6/20], Step [150/328], Loss: 0.4067\n","Epoch [6/20], Step [175/328], Loss: 0.2715\n","Epoch [6/20], Step [200/328], Loss: 0.2948\n","Epoch [6/20], Step [225/328], Loss: 0.2836\n","Epoch [6/20], Step [250/328], Loss: 0.2478\n","Epoch [6/20], Step [275/328], Loss: 0.2936\n","Epoch [6/20], Step [300/328], Loss: 0.2952\n","Epoch [6/20], Step [325/328], Loss: 0.2660\n","Start validation #6\n","Validation #6 Average Loss: 0.4453, mIoU: 0.3254\n","Epoch [7/20], Step [25/328], Loss: 0.2472\n","Epoch [7/20], Step [50/328], Loss: 0.2771\n","Epoch [7/20], Step [75/328], Loss: 0.1863\n","Epoch [7/20], Step [100/328], Loss: 0.2154\n","Epoch [7/20], Step [125/328], Loss: 0.2836\n","Epoch [7/20], Step [150/328], Loss: 0.3221\n","Epoch [7/20], Step [175/328], Loss: 0.5115\n","Epoch [7/20], Step [200/328], Loss: 0.3735\n","Epoch [7/20], Step [225/328], Loss: 0.2134\n","Epoch [7/20], Step [250/328], Loss: 0.4490\n","Epoch [7/20], Step [275/328], Loss: 0.3393\n","Epoch [7/20], Step [300/328], Loss: 0.3929\n","Epoch [7/20], Step [325/328], Loss: 0.2539\n","Start validation #7\n","Validation #7 Average Loss: 0.4746, mIoU: 0.3268\n","Epoch [8/20], Step [25/328], Loss: 0.1819\n","Epoch [8/20], Step [50/328], Loss: 0.1781\n","Epoch [8/20], Step [75/328], Loss: 0.3272\n","Epoch [8/20], Step [100/328], Loss: 0.2195\n","Epoch [8/20], Step [125/328], Loss: 0.3765\n","Epoch [8/20], Step [150/328], Loss: 0.1799\n","Epoch [8/20], Step [175/328], Loss: 0.4448\n","Epoch [8/20], Step [200/328], Loss: 0.2738\n","Epoch [8/20], Step [225/328], Loss: 0.3132\n","Epoch [8/20], Step [250/328], Loss: 0.4284\n","Epoch [8/20], Step [275/328], Loss: 0.2360\n","Epoch [8/20], Step [300/328], Loss: 0.2247\n","Epoch [8/20], Step [325/328], Loss: 0.2374\n","Start validation #8\n","Validation #8 Average Loss: 0.4692, mIoU: 0.3311\n","Epoch [9/20], Step [25/328], Loss: 0.2853\n","Epoch [9/20], Step [50/328], Loss: 0.0841\n","Epoch [9/20], Step [75/328], Loss: 0.1957\n","Epoch [9/20], Step [100/328], Loss: 0.1129\n","Epoch [9/20], Step [125/328], Loss: 0.1340\n","Epoch [9/20], Step [150/328], Loss: 0.4212\n","Epoch [9/20], Step [175/328], Loss: 0.2726\n","Epoch [9/20], Step [200/328], Loss: 0.2760\n","Epoch [9/20], Step [225/328], Loss: 0.2627\n","Epoch [9/20], Step [250/328], Loss: 0.2147\n","Epoch [9/20], Step [275/328], Loss: 0.1286\n","Epoch [9/20], Step [300/328], Loss: 0.1058\n","Epoch [9/20], Step [325/328], Loss: 0.2646\n","Start validation #9\n","Validation #9 Average Loss: 0.4488, mIoU: 0.3290\n","Epoch [10/20], Step [25/328], Loss: 0.1613\n","Epoch [10/20], Step [50/328], Loss: 0.1622\n","Epoch [10/20], Step [75/328], Loss: 0.1410\n","Epoch [10/20], Step [100/328], Loss: 0.1375\n","Epoch [10/20], Step [125/328], Loss: 0.2576\n","Epoch [10/20], Step [150/328], Loss: 0.2068\n","Epoch [10/20], Step [175/328], Loss: 0.1733\n","Epoch [10/20], Step [200/328], Loss: 0.2555\n","Epoch [10/20], Step [225/328], Loss: 0.1956\n","Epoch [10/20], Step [250/328], Loss: 0.2169\n","Epoch [10/20], Step [275/328], Loss: 0.1792\n","Epoch [10/20], Step [300/328], Loss: 0.1885\n","Epoch [10/20], Step [325/328], Loss: 0.2854\n","Start validation #10\n","Validation #10 Average Loss: 0.4460, mIoU: 0.3067\n","Epoch [11/20], Step [25/328], Loss: 0.1455\n","Epoch [11/20], Step [50/328], Loss: 0.1971\n","Epoch [11/20], Step [75/328], Loss: 0.1388\n","Epoch [11/20], Step [100/328], Loss: 0.1729\n","Epoch [11/20], Step [125/328], Loss: 0.0932\n","Epoch [11/20], Step [150/328], Loss: 0.1777\n","Epoch [11/20], Step [175/328], Loss: 0.1141\n","Epoch [11/20], Step [200/328], Loss: 0.2438\n","Epoch [11/20], Step [225/328], Loss: 0.1216\n","Epoch [11/20], Step [250/328], Loss: 0.1311\n","Epoch [11/20], Step [275/328], Loss: 0.1560\n","Epoch [11/20], Step [300/328], Loss: 0.3057\n","Epoch [11/20], Step [325/328], Loss: 0.2200\n","Start validation #11\n","Validation #11 Average Loss: 0.5240, mIoU: 0.3036\n","Epoch [12/20], Step [25/328], Loss: 0.1546\n","Epoch [12/20], Step [50/328], Loss: 0.2124\n","Epoch [12/20], Step [75/328], Loss: 0.0673\n","Epoch [12/20], Step [100/328], Loss: 0.0710\n","Epoch [12/20], Step [125/328], Loss: 0.0943\n","Epoch [12/20], Step [150/328], Loss: 0.1126\n","Epoch [12/20], Step [175/328], Loss: 0.1242\n","Epoch [12/20], Step [200/328], Loss: 0.1443\n","Epoch [12/20], Step [225/328], Loss: 0.1028\n","Epoch [12/20], Step [250/328], Loss: 0.1033\n","Epoch [12/20], Step [275/328], Loss: 0.1450\n","Epoch [12/20], Step [300/328], Loss: 0.1369\n","Epoch [12/20], Step [325/328], Loss: 0.1531\n","Start validation #12\n","Validation #12 Average Loss: 0.4981, mIoU: 0.3068\n","Epoch [13/20], Step [25/328], Loss: 0.0920\n","Epoch [13/20], Step [50/328], Loss: 0.1629\n","Epoch [13/20], Step [75/328], Loss: 0.0978\n","Epoch [13/20], Step [100/328], Loss: 0.0775\n","Epoch [13/20], Step [125/328], Loss: 0.1565\n","Epoch [13/20], Step [150/328], Loss: 0.0945\n","Epoch [13/20], Step [175/328], Loss: 0.0679\n","Epoch [13/20], Step [200/328], Loss: 0.0841\n","Epoch [13/20], Step [225/328], Loss: 0.0925\n","Epoch [13/20], Step [250/328], Loss: 0.1409\n","Epoch [13/20], Step [275/328], Loss: 0.1111\n","Epoch [13/20], Step [300/328], Loss: 0.0575\n","Epoch [13/20], Step [325/328], Loss: 0.1093\n","Start validation #13\n","Validation #13 Average Loss: 0.5466, mIoU: 0.3358\n","Epoch [14/20], Step [25/328], Loss: 0.0923\n","Epoch [14/20], Step [50/328], Loss: 0.1974\n","Epoch [14/20], Step [75/328], Loss: 0.0656\n","Epoch [14/20], Step [100/328], Loss: 0.0633\n","Epoch [14/20], Step [125/328], Loss: 0.0689\n","Epoch [14/20], Step [150/328], Loss: 0.0497\n","Epoch [14/20], Step [175/328], Loss: 0.1121\n","Epoch [14/20], Step [200/328], Loss: 0.0957\n","Epoch [14/20], Step [225/328], Loss: 0.0551\n","Epoch [14/20], Step [250/328], Loss: 0.1304\n","Epoch [14/20], Step [275/328], Loss: 0.0921\n","Epoch [14/20], Step [300/328], Loss: 0.1514\n","Epoch [14/20], Step [325/328], Loss: 0.2132\n","Start validation #14\n","Validation #14 Average Loss: 0.5626, mIoU: 0.2707\n","Epoch [15/20], Step [25/328], Loss: 0.1583\n","Epoch [15/20], Step [50/328], Loss: 0.1607\n","Epoch [15/20], Step [75/328], Loss: 0.1479\n","Epoch [15/20], Step [100/328], Loss: 0.1477\n","Epoch [15/20], Step [125/328], Loss: 0.1030\n","Epoch [15/20], Step [150/328], Loss: 0.0979\n","Epoch [15/20], Step [175/328], Loss: 0.0776\n","Epoch [15/20], Step [200/328], Loss: 0.1022\n","Epoch [15/20], Step [225/328], Loss: 0.0613\n","Epoch [15/20], Step [250/328], Loss: 0.1027\n","Epoch [15/20], Step [275/328], Loss: 0.1263\n","Epoch [15/20], Step [300/328], Loss: 0.0710\n","Epoch [15/20], Step [325/328], Loss: 0.0438\n","Start validation #15\n","Validation #15 Average Loss: 0.5590, mIoU: 0.3253\n","Epoch [16/20], Step [25/328], Loss: 0.0792\n","Epoch [16/20], Step [50/328], Loss: 0.0976\n","Epoch [16/20], Step [75/328], Loss: 0.0356\n","Epoch [16/20], Step [100/328], Loss: 0.0460\n","Epoch [16/20], Step [125/328], Loss: 0.0779\n","Epoch [16/20], Step [150/328], Loss: 0.0383\n","Epoch [16/20], Step [175/328], Loss: 0.1147\n","Epoch [16/20], Step [200/328], Loss: 0.1121\n","Epoch [16/20], Step [225/328], Loss: 0.1251\n","Epoch [16/20], Step [250/328], Loss: 0.0698\n","Epoch [16/20], Step [275/328], Loss: 0.0663\n","Epoch [16/20], Step [300/328], Loss: 0.0912\n","Epoch [16/20], Step [325/328], Loss: 0.1224\n","Start validation #16\n","Validation #16 Average Loss: 0.5544, mIoU: 0.3160\n","Epoch [17/20], Step [25/328], Loss: 0.0431\n","Epoch [17/20], Step [50/328], Loss: 0.0596\n","Epoch [17/20], Step [75/328], Loss: 0.0835\n","Epoch [17/20], Step [100/328], Loss: 0.0788\n","Epoch [17/20], Step [125/328], Loss: 0.0921\n","Epoch [17/20], Step [150/328], Loss: 0.0709\n","Epoch [17/20], Step [175/328], Loss: 0.0700\n","Epoch [17/20], Step [200/328], Loss: 0.0624\n","Epoch [17/20], Step [225/328], Loss: 0.0320\n","Epoch [17/20], Step [250/328], Loss: 0.1035\n","Epoch [17/20], Step [275/328], Loss: 0.0845\n","Epoch [17/20], Step [300/328], Loss: 0.0523\n","Epoch [17/20], Step [325/328], Loss: 0.0924\n","Start validation #17\n","Validation #17 Average Loss: 0.5946, mIoU: 0.3430\n","Epoch [18/20], Step [25/328], Loss: 0.0841\n","Epoch [18/20], Step [50/328], Loss: 0.0902\n","Epoch [18/20], Step [75/328], Loss: 0.0603\n","Epoch [18/20], Step [100/328], Loss: 0.0745\n","Epoch [18/20], Step [125/328], Loss: 0.0593\n","Epoch [18/20], Step [150/328], Loss: 0.0903\n","Epoch [18/20], Step [175/328], Loss: 0.0452\n","Epoch [18/20], Step [200/328], Loss: 0.0650\n","Epoch [18/20], Step [225/328], Loss: 0.0717\n","Epoch [18/20], Step [250/328], Loss: 0.0441\n","Epoch [18/20], Step [275/328], Loss: 0.0938\n","Epoch [18/20], Step [300/328], Loss: 0.0686\n","Epoch [18/20], Step [325/328], Loss: 0.1068\n","Start validation #18\n","Validation #18 Average Loss: 0.5774, mIoU: 0.3163\n","Epoch [19/20], Step [25/328], Loss: 0.0807\n","Epoch [19/20], Step [50/328], Loss: 0.0947\n","Epoch [19/20], Step [75/328], Loss: 0.0660\n","Epoch [19/20], Step [100/328], Loss: 0.0482\n","Epoch [19/20], Step [125/328], Loss: 0.0377\n","Epoch [19/20], Step [150/328], Loss: 0.0580\n","Epoch [19/20], Step [175/328], Loss: 0.0661\n","Epoch [19/20], Step [200/328], Loss: 0.0703\n","Epoch [19/20], Step [225/328], Loss: 0.0341\n","Epoch [19/20], Step [250/328], Loss: 0.0336\n","Epoch [19/20], Step [275/328], Loss: 0.0419\n","Epoch [19/20], Step [300/328], Loss: 0.0485\n","Epoch [19/20], Step [325/328], Loss: 0.0637\n","Start validation #19\n","Validation #19 Average Loss: 0.6480, mIoU: 0.3337\n","Epoch [20/20], Step [25/328], Loss: 0.0212\n","Epoch [20/20], Step [50/328], Loss: 0.0608\n","Epoch [20/20], Step [75/328], Loss: 0.0391\n","Epoch [20/20], Step [100/328], Loss: 0.0911\n","Epoch [20/20], Step [125/328], Loss: 0.0730\n","Epoch [20/20], Step [150/328], Loss: 0.0685\n","Epoch [20/20], Step [175/328], Loss: 0.0650\n","Epoch [20/20], Step [200/328], Loss: 0.0955\n","Epoch [20/20], Step [225/328], Loss: 0.0944\n","Epoch [20/20], Step [250/328], Loss: 0.0559\n","Epoch [20/20], Step [275/328], Loss: 0.0572\n","Epoch [20/20], Step [300/328], Loss: 0.0566\n","Epoch [20/20], Step [325/328], Loss: 0.0856\n","Start validation #20\n","Validation #20 Average Loss: 0.6517, mIoU: 0.3332\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"Bp_YQlBTeipz"},"source":["## 저장된 model 불러오기 (학습된 이후) "]},{"cell_type":"code","metadata":{"id":"5Xz-iEN4Wn-0","executionInfo":{"status":"ok","timestamp":1619630175753,"user_tz":-540,"elapsed":4004,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# Sleep for a few seconds.\n","import time\n","time.sleep(2)\n","# Play an audio beep. Any audio URL will do.\n","from google.colab import output\n","output.eval_js('new Audio(\"https://upload.wikimedia.org/wikipedia/commons/0/05/Beep-09.ogg\").play()')"],"execution_count":24,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:21.050200Z","start_time":"2021-04-16T19:44:20.802200Z"},"scrolled":true,"id":"tt-5ZiI4eipz","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1619630203436,"user_tz":-540,"elapsed":1969,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"3d78b929-f605-46d7-c2c8-98227983e8d0"},"source":["# best model 저장된 경로\n","model_path = './saved/fcn32s_best_model.pt'\n","\n","# best model 불러오기\n","checkpoint = torch.load(model_path, map_location=device)\n","model.load_state_dict(checkpoint)\n","\n","# 추론을 실행하기 전에는 반드시 설정 (batch normalization, dropout 를 평가 모드로 설정)\n","# model.eval()"],"execution_count":25,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{"tags":[]},"execution_count":25}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:24.939227Z","start_time":"2021-04-16T19:44:24.518228Z"},"id":"u4ReFHmzeip0","colab":{"base_uri":"https://localhost:8080/","height":542},"executionInfo":{"status":"ok","timestamp":1619630211902,"user_tz":-540,"elapsed":5670,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"6fa8a6b8-613b-4f20-8b70-91e3597dd9bf"},"source":["# 첫번째 batch의 추론 결과 확인\n","for imgs, image_infos in test_loader:\n"," image_infos = image_infos\n"," temp_images = imgs\n"," \n"," model.eval()\n"," # inference\n"," outs = model(torch.stack(temp_images).to(device))\n"," oms = torch.argmax(outs.squeeze(), dim=1).detach().cpu().numpy()\n"," \n"," break\n","\n","i = 3\n","fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(16, 16))\n","\n","print('Shape of Original Image :', list(temp_images[i].shape))\n","print('Shape of Predicted : ', list(oms[i].shape))\n","print('Unique values, category of transformed mask : \\n', [{int(i),category_names[int(i)]} for i in list(np.unique(oms[i]))])\n","\n","# Original image\n","ax1.imshow(temp_images[i].permute([1,2,0]))\n","ax1.grid(False)\n","ax1.set_title(\"Original image : {}\".format(image_infos[i]['file_name']), fontsize = 15)\n","\n","# Predicted\n","ax2.imshow(oms[i])\n","ax2.grid(False)\n","ax2.set_title(\"Predicted : {}\".format(image_infos[i]['file_name']), fontsize = 15)\n","\n","plt.show()"],"execution_count":26,"outputs":[{"output_type":"stream","text":["Shape of Original Image : [3, 512, 512]\n","Shape of Predicted : [512, 512]\n","Unique values, category of transformed mask : \n"," [{0, 'Backgroud'}, {3, 'Paper'}, {9, 'Plastic bag'}]\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"4EvIVTQieip0"},"source":["## submission을 위한 test 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:27.469285Z","start_time":"2021-04-16T19:44:27.456021Z"},"id":"G_i9Ge_3eip1","executionInfo":{"status":"ok","timestamp":1619630218547,"user_tz":-540,"elapsed":617,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["def test(model, data_loader, device):\n"," size = 256\n"," transform = A.Compose([A.Resize(256, 256)])\n"," print('Start prediction.')\n"," model.eval()\n"," \n"," file_name_list = []\n"," preds_array = np.empty((0, size*size), dtype=np.long)\n"," \n"," with torch.no_grad():\n"," for step, (imgs, image_infos) in enumerate(test_loader):\n","\n"," # inference (512 x 512)\n"," outs = model(torch.stack(imgs).to(device))\n"," oms = torch.argmax(outs.squeeze(), dim=1).detach().cpu().numpy()\n"," \n"," # resize (256 x 256)\n"," temp_mask = []\n"," for img, mask in zip(np.stack(temp_images), oms):\n"," transformed = transform(image=img, mask=mask)\n"," mask = transformed['mask']\n"," temp_mask.append(mask)\n","\n"," oms = np.array(temp_mask)\n"," \n"," oms = oms.reshape([oms.shape[0], size*size]).astype(int)\n"," preds_array = np.vstack((preds_array, oms))\n"," \n"," file_name_list.append([i['file_name'] for i in image_infos])\n"," print(\"End prediction.\")\n"," file_names = [y for x in file_name_list for y in x]\n"," \n"," return file_names, preds_array"],"execution_count":27,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"QhzNzFCleip1"},"source":["## submission.csv 생성"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:45:42.235310Z","start_time":"2021-04-16T19:44:30.499016Z"},"scrolled":true,"id":"PoDZ6y5Xeip1","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1619630539561,"user_tz":-540,"elapsed":316156,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"41eb49c6-4298-4d67-975e-7bba3d801cc6"},"source":["# sample_submisson.csv 열기\n","submission = pd.read_csv('./submission/sample_submission.csv', index_col=None)\n","\n","# test set에 대한 prediction\n","file_names, preds = test(model, test_loader, device)\n","\n","# PredictionString 대입\n","for file_name, string in zip(file_names, preds):\n"," submission = submission.append({\"image_id\" : file_name, \"PredictionString\" : ' '.join(str(e) for e in string.tolist())}, \n"," ignore_index=True)\n","\n","# submission.csv로 저장\n","submission.to_csv(\"./submission/Baseline_FCN32s.csv\", index=False)"],"execution_count":28,"outputs":[{"output_type":"stream","text":["Start prediction.\n","End prediction.\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"3C1eFc70fpGy","executionInfo":{"status":"ok","timestamp":1619630544301,"user_tz":-540,"elapsed":4722,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# Sleep for a few seconds.\n","import time\n","time.sleep(2)\n","# Play an audio beep. Any audio URL will do.\n","from google.colab import output\n","output.eval_js('new Audio(\"https://upload.wikimedia.org/wikipedia/commons/0/05/Beep-09.ogg\").play()')"],"execution_count":29,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"RtGLYptBeip2"},"source":["## Reference\n","\n"]}]} \ No newline at end of file diff --git a/chanyub_seg/code/UNet++ baseline.ipynb b/chanyub_seg/code/UNet++ baseline.ipynb deleted file mode 100644 index 5dc7c1e..0000000 --- a/chanyub_seg/code/UNet++ baseline.ipynb +++ /dev/null @@ -1,1017 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "toc": true - }, - "source": [ - "

Table of Contents

\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2021-04-18T10:34:47.826930Z", - "start_time": "2021-04-18T10:34:45.406686Z" - }, - "scrolled": false - }, - "outputs": [], - "source": [ - "import os\n", - "import random\n", - "import time\n", - "import json\n", - "import warnings \n", - "warnings.filterwarnings('ignore')\n", - "\n", - "import torch\n", - "import torch.nn as nn\n", - "from torch.utils.data import Dataset, DataLoader\n", - "from utils import label_accuracy_score\n", - "import cv2\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "# 전처리를 위한 라이브러리\n", - "from pycocotools.coco import COCO\n", - "import torchvision\n", - "import torchvision.transforms as transforms\n", - "\n", - "import albumentations as A\n", - "from albumentations.pytorch import ToTensorV2\n", - "\n", - "# 시각화를 위한 라이브러리\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns; sns.set()\n", - "\n", - "plt.rcParams['axes.grid'] = False\n", - "\n", - "print('pytorch version: {}'.format(torch.__version__))\n", - "print('GPU 사용 가능 여부: {}'.format(torch.cuda.is_available()))\n", - "\n", - "print(torch.cuda.get_device_name(0))\n", - "print(torch.cuda.device_count())\n", - "\n", - "device = \"cuda\" if torch.cuda.is_available() else \"cpu\" # GPU 사용 가능 여부에 따라 device 정보 저장" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 하이퍼파라미터 세팅 및 seed 고정" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2021-04-18T10:34:47.841930Z", - "start_time": "2021-04-18T10:34:47.827931Z" - } - }, - "outputs": [], - "source": [ - "batch_size = 8 # Mini-batch size\n", - "num_epochs = 20\n", - "learning_rate = 0.0001" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2021-04-18T10:34:47.856930Z", - "start_time": "2021-04-18T10:34:47.842931Z" - } - }, - "outputs": [], - "source": [ - "# seed 고정\n", - "random_seed = 21\n", - "torch.manual_seed(random_seed)\n", - "torch.cuda.manual_seed(random_seed)\n", - "# torch.cuda.manual_seed_all(random_seed) # if use multi-GPU\n", - "torch.backends.cudnn.deterministic = True\n", - "torch.backends.cudnn.benchmark = False\n", - "np.random.seed(random_seed)\n", - "random.seed(random_seed)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 학습 데이터 EDA" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2021-04-18T10:34:51.381961Z", - "start_time": "2021-04-18T10:34:47.857930Z" - }, - "scrolled": true - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "\n", - "dataset_path = '../input/data'\n", - "anns_file_path = dataset_path + '/' + 'train.json'\n", - "\n", - "# Read annotations\n", - "with open(anns_file_path, 'r') as f:\n", - " dataset = json.loads(f.read())\n", - "\n", - "categories = dataset['categories']\n", - "anns = dataset['annotations']\n", - "imgs = dataset['images']\n", - "nr_cats = len(categories)\n", - "nr_annotations = len(anns)\n", - "nr_images = len(imgs)\n", - "\n", - "# Load categories and super categories\n", - "cat_names = []\n", - "super_cat_names = []\n", - "super_cat_ids = {}\n", - "super_cat_last_name = ''\n", - "nr_super_cats = 0\n", - "for cat_it in categories:\n", - " cat_names.append(cat_it['name'])\n", - " super_cat_name = cat_it['supercategory']\n", - " # Adding new supercat\n", - " if super_cat_name != super_cat_last_name:\n", - " super_cat_names.append(super_cat_name)\n", - " super_cat_ids[super_cat_name] = nr_super_cats\n", - " super_cat_last_name = super_cat_name\n", - " nr_super_cats += 1\n", - "\n", - "print('Number of super categories:', nr_super_cats)\n", - "print('Number of categories:', nr_cats)\n", - "print('Number of annotations:', nr_annotations)\n", - "print('Number of images:', nr_images)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2021-04-18T10:34:51.546964Z", - "start_time": "2021-04-18T10:34:51.382969Z" - }, - "scrolled": true - }, - "outputs": [], - "source": [ - "# Count annotations\n", - "cat_histogram = np.zeros(nr_cats,dtype=int)\n", - "for ann in anns:\n", - " cat_histogram[ann['category_id']] += 1\n", - "\n", - "# Initialize the matplotlib figure\n", - "f, ax = plt.subplots(figsize=(5,5))\n", - "\n", - "# Convert to DataFrame\n", - "df = pd.DataFrame({'Categories': cat_names, 'Number of annotations': cat_histogram})\n", - "df = df.sort_values('Number of annotations', 0, False)\n", - "\n", - "# Plot the histogram\n", - "plt.title(\"category distribution of train set \")\n", - "plot_1 = sns.barplot(x=\"Number of annotations\", y=\"Categories\", data=df, label=\"Total\", color=\"b\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2021-04-18T10:34:51.561965Z", - "start_time": "2021-04-18T10:34:51.547969Z" - } - }, - "outputs": [], - "source": [ - "# category labeling \n", - "sorted_temp_df = df.sort_index()\n", - "\n", - "# background = 0 에 해당되는 label 추가 후 기존들을 모두 label + 1 로 설정\n", - "sorted_df = pd.DataFrame([\"Backgroud\"], columns = [\"Categories\"])\n", - "sorted_df = sorted_df.append(sorted_temp_df, ignore_index=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2021-04-18T10:34:51.576961Z", - "start_time": "2021-04-18T10:34:51.562964Z" - }, - "scrolled": false - }, - "outputs": [], - "source": [ - "# class (Categories) 에 따른 index 확인 (0~11 : 총 12개)\n", - "sorted_df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 데이터 전처리 함수 정의 (Dataset)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2021-04-18T10:34:52.693328Z", - "start_time": "2021-04-18T10:34:52.681328Z" - } - }, - "outputs": [], - "source": [ - "category_names = list(sorted_df.Categories)\n", - "\n", - "def get_classname(classID, cats):\n", - " for i in range(len(cats)):\n", - " if cats[i]['id']==classID:\n", - " return cats[i]['name']\n", - " return \"None\"\n", - "\n", - "class CustomDataLoader(Dataset):\n", - " \"\"\"COCO format\"\"\"\n", - " def __init__(self, data_dir, mode = 'train', transform = None):\n", - " super().__init__()\n", - " self.mode = mode\n", - " self.transform = transform\n", - " self.coco = COCO(data_dir)\n", - " \n", - " def __getitem__(self, index: int):\n", - " # dataset이 index되어 list처럼 동작\n", - " image_id = self.coco.getImgIds(imgIds=index)\n", - " image_infos = self.coco.loadImgs(image_id)[0]\n", - " \n", - " # cv2 를 활용하여 image 불러오기\n", - " images = cv2.imread(os.path.join(dataset_path, image_infos['file_name']))\n", - " images = cv2.cvtColor(images, cv2.COLOR_BGR2RGB).astype(np.float32)\n", - " images /= 255.0\n", - " \n", - " if (self.mode in ('train', 'val')):\n", - " ann_ids = self.coco.getAnnIds(imgIds=image_infos['id'])\n", - " anns = self.coco.loadAnns(ann_ids)\n", - "\n", - " # Load the categories in a variable\n", - " cat_ids = self.coco.getCatIds()\n", - " cats = self.coco.loadCats(cat_ids)\n", - "\n", - " # masks : size가 (height x width)인 2D\n", - " # 각각의 pixel 값에는 \"category id + 1\" 할당\n", - " # Background = 0\n", - " masks = np.zeros((image_infos[\"height\"], image_infos[\"width\"]))\n", - " # Unknown = 1, General trash = 2, ... , Cigarette = 11\n", - " for i in range(len(anns)):\n", - " className = get_classname(anns[i]['category_id'], cats)\n", - " pixel_value = category_names.index(className)\n", - " masks = np.maximum(self.coco.annToMask(anns[i])*pixel_value, masks)\n", - " masks = masks.astype(np.float32)\n", - "\n", - " # transform -> albumentations 라이브러리 활용\n", - " if self.transform is not None:\n", - " transformed = self.transform(image=images, mask=masks)\n", - " images = transformed[\"image\"]\n", - " masks = transformed[\"mask\"]\n", - " \n", - " return images, masks, image_infos\n", - " \n", - " if self.mode == 'test':\n", - " # transform -> albumentations 라이브러리 활용\n", - " if self.transform is not None:\n", - " transformed = self.transform(image=images)\n", - " images = transformed[\"image\"]\n", - " \n", - " return images, image_infos\n", - " \n", - " \n", - " def __len__(self) -> int:\n", - " # 전체 dataset의 size를 return\n", - " return len(self.coco.getImgIds())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Dataset 정의 및 DataLoader 할당" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2021-04-18T10:34:58.823175Z", - "start_time": "2021-04-18T10:34:54.106233Z" - }, - "scrolled": true - }, - "outputs": [], - "source": [ - "# train.json / validation.json / test.json 디렉토리 설정\n", - "train_path = dataset_path + '/train.json'\n", - "val_path = dataset_path + '/val.json'\n", - "test_path = dataset_path + '/test.json'\n", - "\n", - "# collate_fn needs for batch\n", - "def collate_fn(batch):\n", - " return tuple(zip(*batch))\n", - "\n", - "train_transform = A.Compose([\n", - " ToTensorV2()\n", - " ])\n", - "\n", - "val_transform = A.Compose([\n", - " ToTensorV2()\n", - " ])\n", - "\n", - "test_transform = A.Compose([\n", - " ToTensorV2()\n", - " ])\n", - "\n", - "# create own Dataset 1 (skip)\n", - "# validation set을 직접 나누고 싶은 경우\n", - "# random_split 사용하여 data set을 8:2 로 분할\n", - "# train_size = int(0.8*len(dataset))\n", - "# val_size = int(len(dataset)-train_size)\n", - "# dataset = CustomDataLoader(data_dir=train_path, mode='train', transform=transform)\n", - "# train_dataset, val_dataset = torch.utils.data.random_split(dataset, [train_size, val_size])\n", - "\n", - "# create own Dataset 2\n", - "# train dataset\n", - "train_dataset = CustomDataLoader(data_dir=train_path, mode='train', transform=train_transform)\n", - "\n", - "# validation dataset\n", - "val_dataset = CustomDataLoader(data_dir=val_path, mode='val', transform=val_transform)\n", - "\n", - "# test dataset\n", - "test_dataset = CustomDataLoader(data_dir=test_path, mode='test', transform=test_transform)\n", - "\n", - "\n", - "# DataLoader\n", - "train_loader = torch.utils.data.DataLoader(dataset=train_dataset, \n", - " batch_size=batch_size,\n", - " shuffle=True,\n", - " num_workers=4,\n", - " collate_fn=collate_fn,\n", - " drop_last=True)\n", - "\n", - "val_loader = torch.utils.data.DataLoader(dataset=val_dataset, \n", - " batch_size=batch_size,\n", - " shuffle=False,\n", - " num_workers=4,\n", - " collate_fn=collate_fn,\n", - " drop_last=True) \n", - "\n", - "test_loader = torch.utils.data.DataLoader(dataset=test_dataset,\n", - " batch_size=batch_size,\n", - " num_workers=4,\n", - " collate_fn=collate_fn)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 데이터 샘플 시각화 (Show example image and mask)\n", - "\n", - "- `train_loader` \n", - "- `val_loader` \n", - "- `test_loader` " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2021-04-16T17:59:26.346907Z", - "start_time": "2021-04-16T17:59:26.002907Z" - }, - "scrolled": false - }, - "outputs": [], - "source": [ - "# train_loader의 output 결과(image 및 mask) 확인\n", - "for imgs, masks, image_infos in train_loader:\n", - " image_infos = image_infos[0]\n", - " temp_images = imgs\n", - " temp_masks = masks\n", - " \n", - " break\n", - "\n", - "fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(12, 12))\n", - "\n", - "print('image shape:', list(temp_images[0].shape))\n", - "print('mask shape: ', list(temp_masks[0].shape))\n", - "print('Unique values, category of transformed mask : \\n', [{int(i),category_names[int(i)]} for i in list(np.unique(temp_masks[0]))])\n", - "\n", - "ax1.imshow(temp_images[0].permute([1,2,0]))\n", - "ax1.grid(False)\n", - "ax1.set_title(\"input image : {}\".format(image_infos['file_name']), fontsize = 15)\n", - "\n", - "ax2.imshow(temp_masks[0])\n", - "ax2.grid(False)\n", - "ax2.set_title(\"masks : {}\".format(image_infos['file_name']), fontsize = 15)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2021-04-16T13:50:43.557278Z", - "start_time": "2021-04-16T13:50:43.194005Z" - } - }, - "outputs": [], - "source": [ - "# val_loader의 output 결과(image 및 mask) 확인\n", - "for imgs, masks, image_infos in val_loader:\n", - " image_infos = image_infos[0]\n", - " temp_images = imgs\n", - " temp_masks = masks\n", - " \n", - " break\n", - "\n", - "fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(12, 12))\n", - "\n", - "print('image shape:', list(temp_images[0].shape))\n", - "print('mask shape: ', list(temp_masks[0].shape))\n", - "\n", - "print('Unique values, category of transformed mask : \\n', [{int(i),category_names[int(i)]} for i in list(np.unique(temp_masks[0]))])\n", - "\n", - "ax1.imshow(temp_images[0].permute([1,2,0]))\n", - "ax1.grid(False)\n", - "ax1.set_title(\"input image : {}\".format(image_infos['file_name']), fontsize = 15)\n", - "\n", - "ax2.imshow(temp_masks[0])\n", - "ax2.grid(False)\n", - "ax2.set_title(\"masks : {}\".format(image_infos['file_name']), fontsize = 15)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2021-04-16T13:51:11.569325Z", - "start_time": "2021-04-16T13:51:11.377327Z" - } - }, - "outputs": [], - "source": [ - "# test_loader의 output 결과(image 및 mask) 확인\n", - "for imgs, image_infos in test_loader:\n", - " image_infos = image_infos[0]\n", - " temp_images = imgs\n", - " # temp_masks = masks\n", - " \n", - " break\n", - "\n", - "fig, ax1 = plt.subplots(nrows=1, ncols=1, figsize=(6, 6))\n", - "\n", - "print('image shape:', list(temp_images[0].shape))\n", - "\n", - "ax1.imshow(temp_images[0].permute([1,2,0]))\n", - "ax1.grid(False)\n", - "ax1.set_title(\"input image : {}\".format(image_infos['file_name']), fontsize = 15)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## baseline model\n", - "\n", - "### [TODO] 코드 구현 UNet++ \n", - "\n", - "- 출처 : https://jinglescode.github.io/2019/12/02/biomedical-image-segmentation-u-net-nested/" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# 출처 : https://jinglescode.github.io/2019/12/02/biomedical-image-segmentation-u-net-nested/\n", - "import torch\n", - "import torch.nn as nn\n", - "\n", - "class conv_block_nested(nn.Module):\n", - " def __init__(self, in_ch, mid_ch, out_ch):\n", - " super(conv_block_nested, self).__init__()\n", - " self.activation = nn.ReLU(inplace=True)\n", - " self.conv1 = nn.Conv2d(in_ch, mid_ch, kernel_size=3, padding=1, bias=True)\n", - " self.bn1 = nn.BatchNorm2d(mid_ch)\n", - " self.conv2 = nn.Conv2d(mid_ch, out_ch, kernel_size=3, padding=1, bias=True)\n", - " self.bn2 = nn.BatchNorm2d(out_ch)\n", - "\n", - " def forward(self, x):\n", - " x = self.conv1(x)\n", - " x = self.bn1(x)\n", - " x = self.activation(x)\n", - "\n", - " x = self.conv2(x)\n", - " x = self.bn2(x)\n", - " output = self.activation(x)\n", - " return output\n", - "\n", - "class UNetPlusPlus(nn.Module):\n", - "\n", - " def __init__(self, in_ch=3, out_ch=1, n1=64, height=512, width=512, supervision=True):\n", - " super(UNetPlusPlus, self).__init__()\n", - "\n", - " filters = [n1, n1 * 2, n1 * 4, n1 * 8, n1 * 16]\n", - "\n", - " self.pool = nn.MaxPool2d(kernel_size=2, stride=2)\n", - " self.Up = nn.ModuleList([nn.Upsample(size=(height//(2**c), width//(2**c)), mode='bilinear', align_corners=True) for c in range(4)])\n", - " self.supervision = supervision\n", - "\n", - " self.conv0_0 = conv_block_nested(in_ch, filters[0], filters[0])\n", - " self.conv1_0 = conv_block_nested(filters[0], filters[1], filters[1])\n", - " self.conv2_0 = conv_block_nested(filters[1], filters[2], filters[2])\n", - " self.conv3_0 = conv_block_nested(filters[2], filters[3], filters[3])\n", - " self.conv4_0 = conv_block_nested(filters[3], filters[4], filters[4])\n", - "\n", - " self.conv0_1 = conv_block_nested(filters[0] + filters[1], filters[0], filters[0])\n", - " self.conv1_1 = conv_block_nested(filters[1] + filters[2], filters[1], filters[1])\n", - " self.conv2_1 = conv_block_nested(filters[2] + filters[3], filters[2], filters[2])\n", - " self.conv3_1 = conv_block_nested(filters[3] + filters[4], filters[3], filters[3])\n", - "\n", - " self.conv0_2 = conv_block_nested(filters[0]*2 + filters[1], filters[0], filters[0])\n", - " self.conv1_2 = conv_block_nested(filters[1]*2 + filters[2], filters[1], filters[1])\n", - " self.conv2_2 = conv_block_nested(filters[2]*2 + filters[3], filters[2], filters[2])\n", - "\n", - " self.conv0_3 = conv_block_nested(filters[0]*3 + filters[1], filters[0], filters[0])\n", - " self.conv1_3 = conv_block_nested(filters[1]*3 + filters[2], filters[1], filters[1])\n", - "\n", - " self.conv0_4 = conv_block_nested(filters[0]*4 + filters[1], filters[0], filters[0])\n", - "\n", - " self.seg_outputs = nn.ModuleList([nn.Conv2d(filters[0], out_ch, kernel_size=1, padding=0) for _ in range(4)])\n", - "\n", - " def forward(self, x):\n", - " seg_outputs = []\n", - " x0_0 = self.conv0_0(x)\n", - " x1_0 = self.conv1_0(self.pool(x0_0))\n", - " x0_1 = self.conv0_1(torch.cat([x0_0, self.Up[0](x1_0)], 1))\n", - " seg_outputs.append(self.seg_outputs[0](x0_1))\n", - "\n", - " x2_0 = self.conv2_0(self.pool(x1_0))\n", - " x1_1 = self.conv1_1(torch.cat([x1_0, self.Up[1](x2_0)], 1))\n", - " x0_2 = self.conv0_2(torch.cat([x0_0, x0_1, self.Up[0](x1_1)], 1))\n", - " seg_outputs.append(self.seg_outputs[1](x0_2))\n", - "\n", - " x3_0 = self.conv3_0(self.pool(x2_0))\n", - " x2_1 = self.conv2_1(torch.cat([x2_0, self.Up[2](x3_0)], 1))\n", - " x1_2 = self.conv1_2(torch.cat([x1_0, x1_1, self.Up[1](x2_1)], 1))\n", - " x0_3 = self.conv0_3(torch.cat([x0_0, x0_1, x0_2, self.Up[0](x1_2)], 1))\n", - " seg_outputs.append(self.seg_outputs[2](x0_3))\n", - "\n", - " x4_0 = self.conv4_0(self.pool(x3_0))\n", - " x3_1 = self.conv3_1(torch.cat([x3_0, self.Up[3](x4_0)], 1))\n", - " x2_2 = self.conv2_2(torch.cat([x2_0, x2_1, self.Up[2](x3_1)], 1))\n", - " x1_3 = self.conv1_3(torch.cat([x1_0, x1_1, x1_2, self.Up[1](x2_2)], 1))\n", - " x0_4 = self.conv0_4(torch.cat([x0_0, x0_1, x0_2, x0_3, self.Up[0](x1_3)], 1))\n", - " seg_outputs.append(self.seg_outputs[3](x0_4))\n", - "\n", - " if self.supervision: \n", - " return seg_outputs\n", - " else:\n", - " return seg_outputs[-1]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2021-04-18T16:16:11.634792Z", - "start_time": "2021-04-18T16:16:05.875817Z" - } - }, - "outputs": [], - "source": [ - "# 구현된 model에 임의의 input을 넣어 output이 잘 나오는지 test\n", - "\n", - "model = UNetPlusPlus(out_ch=12, supervision=False)\n", - "x = torch.randn([1, 3, 512, 512])\n", - "print(\"input shape : \", x.shape)\n", - "out = model(x).to(device)\n", - "print(\"output shape : \", out.size())\n", - "\n", - "model = model.to(device)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## train, validation, test 함수 정의" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2021-04-18T16:16:18.104200Z", - "start_time": "2021-04-18T16:16:18.093174Z" - } - }, - "outputs": [], - "source": [ - "def train(num_epochs, model, data_loader, val_loader, criterion, optimizer, saved_dir, val_every, device):\n", - " print('Start training..')\n", - " best_loss = 9999999\n", - " for epoch in range(num_epochs):\n", - " model.train()\n", - " for step, (images, masks, _) in enumerate(data_loader):\n", - " images = torch.stack(images) # (batch, channel, height, width)\n", - " masks = torch.stack(masks).long() # (batch, channel, height, width)\n", - " \n", - " # gpu 연산을 위해 device 할당\n", - " images, masks = images.to(device), masks.to(device)\n", - " \n", - " # inference\n", - " outputs = model(images)\n", - " \n", - " # loss 계산 (cross entropy loss)\n", - " loss = criterion(outputs, masks)\n", - " optimizer.zero_grad()\n", - " loss.backward()\n", - " optimizer.step()\n", - " \n", - " # step 주기에 따른 loss 출력\n", - " if (step + 1) % 25 == 0:\n", - " print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(\n", - " epoch+1, num_epochs, step+1, len(train_loader), loss.item()))\n", - " \n", - " # validation 주기에 따른 loss 출력 및 best model 저장\n", - " if (epoch + 1) % val_every == 0:\n", - " avrg_loss = validation(epoch + 1, model, val_loader, criterion, device)\n", - " if avrg_loss < best_loss:\n", - " print('Best performance at epoch: {}'.format(epoch + 1))\n", - " print('Save model in', saved_dir)\n", - " best_loss = avrg_loss\n", - " save_model(model, saved_dir)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2021-04-18T16:16:18.285795Z", - "start_time": "2021-04-18T16:16:18.267686Z" - } - }, - "outputs": [], - "source": [ - "def validation(epoch, model, data_loader, criterion, device):\n", - " print('Start validation #{}'.format(epoch))\n", - " model.eval()\n", - " with torch.no_grad():\n", - " total_loss = 0\n", - " cnt = 0\n", - " mIoU_list = []\n", - " for step, (images, masks, _) in enumerate(data_loader):\n", - " \n", - " images = torch.stack(images) # (batch, channel, height, width)\n", - " masks = torch.stack(masks).long() # (batch, channel, height, width)\n", - "\n", - " images, masks = images.to(device), masks.to(device) \n", - "\n", - " outputs = model(images)\n", - " loss = criterion(outputs, masks)\n", - " total_loss += loss\n", - " cnt += 1\n", - " \n", - " outputs = torch.argmax(outputs.squeeze(), dim=1).detach().cpu().numpy()\n", - "\n", - " mIoU = label_accuracy_score(masks.detach().cpu().numpy(), outputs, n_class=12)[2]\n", - " mIoU_list.append(mIoU)\n", - " \n", - " avrg_loss = total_loss / cnt\n", - " print('Validation #{} Average Loss: {:.4f}, mIoU: {:.4f}'.format(epoch, avrg_loss, np.mean(mIoU_list)))\n", - "\n", - " return avrg_loss" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 모델 저장 함수 정의" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2021-04-18T16:16:18.909918Z", - "start_time": "2021-04-18T16:16:18.898918Z" - } - }, - "outputs": [], - "source": [ - "# 모델 저장 함수 정의\n", - "val_every = 1 \n", - "\n", - "saved_dir = './saved'\n", - "if not os.path.isdir(saved_dir): \n", - " os.mkdir(saved_dir)\n", - " \n", - "def save_model(model, saved_dir, file_name='UNetPP_best_model.pt'):\n", - " check_point = {'net': model.state_dict()}\n", - " output_path = os.path.join(saved_dir, file_name)\n", - " torch.save(model.state_dict(), output_path)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 모델 생성 및 Loss function, Optimizer 정의" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2021-04-18T16:16:19.698902Z", - "start_time": "2021-04-18T16:16:19.694902Z" - } - }, - "outputs": [], - "source": [ - "# Loss function 정의\n", - "criterion = nn.CrossEntropyLoss()\n", - "\n", - "# Optimizer 정의\n", - "optimizer = torch.optim.Adam(params = model.parameters(), lr = learning_rate, weight_decay=1e-6)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "start_time": "2021-04-18T16:16:20.331Z" - }, - "scrolled": false - }, - "outputs": [], - "source": [ - "train(num_epochs, model, train_loader, val_loader, criterion, optimizer, saved_dir, val_every, device)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 저장된 model 불러오기 (학습된 이후) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2021-04-16T19:44:21.050200Z", - "start_time": "2021-04-16T19:44:20.802200Z" - }, - "scrolled": true - }, - "outputs": [], - "source": [ - "# best model 저장된 경로\n", - "model_path = './saved/UNetPP_best_model.pt'\n", - "\n", - "# best model 불러오기\n", - "checkpoint = torch.load(model_path, map_location=device)\n", - "model.load_state_dict(checkpoint)\n", - "\n", - "# 추론을 실행하기 전에는 반드시 설정 (batch normalization, dropout 를 평가 모드로 설정)\n", - "# model.eval()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2021-04-16T19:44:24.939227Z", - "start_time": "2021-04-16T19:44:24.518228Z" - } - }, - "outputs": [], - "source": [ - "# 첫번째 batch의 추론 결과 확인\n", - "for imgs, image_infos in test_loader:\n", - " image_infos = image_infos\n", - " temp_images = imgs\n", - " \n", - " model.eval()\n", - " # inference\n", - " outs = model(torch.stack(temp_images).to(device))\n", - " oms = torch.argmax(outs.squeeze(), dim=1).detach().cpu().numpy()\n", - " \n", - " break\n", - "\n", - "i = 3\n", - "fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(16, 16))\n", - "\n", - "print('Shape of Original Image :', list(temp_images[i].shape))\n", - "print('Shape of Predicted : ', list(oms[i].shape))\n", - "print('Unique values, category of transformed mask : \\n', [{int(i),category_names[int(i)]} for i in list(np.unique(oms[i]))])\n", - "\n", - "# Original image\n", - "ax1.imshow(temp_images[i].permute([1,2,0]))\n", - "ax1.grid(False)\n", - "ax1.set_title(\"Original image : {}\".format(image_infos[i]['file_name']), fontsize = 15)\n", - "\n", - "# Predicted\n", - "ax2.imshow(oms[i])\n", - "ax2.grid(False)\n", - "ax2.set_title(\"Predicted : {}\".format(image_infos[i]['file_name']), fontsize = 15)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## submission을 위한 test 함수 정의" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2021-04-16T19:44:27.469285Z", - "start_time": "2021-04-16T19:44:27.456021Z" - } - }, - "outputs": [], - "source": [ - "def test(model, data_loader, device):\n", - " size = 256\n", - " transform = A.Compose([A.Resize(256, 256)])\n", - " print('Start prediction.')\n", - " model.eval()\n", - " \n", - " file_name_list = []\n", - " preds_array = np.empty((0, size*size), dtype=np.long)\n", - " \n", - " with torch.no_grad():\n", - " for step, (imgs, image_infos) in enumerate(test_loader):\n", - "\n", - " # inference (512 x 512)\n", - " outs = model(torch.stack(imgs).to(device))\n", - " oms = torch.argmax(outs, dim=1).detach().cpu().numpy()\n", - " \n", - " # resize (256 x 256)\n", - " temp_mask = []\n", - " for img, mask in zip(np.stack(imgs), oms):\n", - " transformed = transform(image=img, mask=mask)\n", - " mask = transformed['mask']\n", - " temp_mask.append(mask)\n", - "\n", - " oms = np.array(temp_mask)\n", - " \n", - " oms = oms.reshape([oms.shape[0], size*size]).astype(int)\n", - " preds_array = np.vstack((preds_array, oms))\n", - " \n", - " file_name_list.append([i['file_name'] for i in image_infos])\n", - " print(\"End prediction.\")\n", - " file_names = [y for x in file_name_list for y in x]\n", - " \n", - " return file_names, preds_array" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## submission.csv 생성" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2021-04-16T19:45:42.235310Z", - "start_time": "2021-04-16T19:44:30.499016Z" - }, - "scrolled": true - }, - "outputs": [], - "source": [ - "# sample_submisson.csv 열기\n", - "submission = pd.read_csv('./submission/sample_submission.csv', index_col=None)\n", - "\n", - "# test set에 대한 prediction\n", - "file_names, preds = test(model, test_loader, device)\n", - "\n", - "# PredictionString 대입\n", - "for file_name, string in zip(file_names, preds):\n", - " submission = submission.append({\"image_id\" : file_name, \"PredictionString\" : ' '.join(str(e) for e in string.tolist())}, \n", - " ignore_index=True)\n", - "\n", - "# submission.csv로 저장\n", - "submission.to_csv(\"./submission/Baseline_UnetPP.csv\", index=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Reference\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "hide_input": false, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.1" - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": 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Found existing installation: albumentations 0.1.12\n"," Uninstalling albumentations-0.1.12:\n"," Successfully uninstalled albumentations-0.1.12\n","Successfully installed albumentations-0.5.2 imgaug-0.4.0 opencv-python-headless-4.5.1.48\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.826930Z","start_time":"2021-04-18T10:34:45.406686Z"},"colab":{"base_uri":"https://localhost:8080/"},"id":"l_LPA4XD-NwC","executionInfo":{"status":"ok","timestamp":1620107294171,"user_tz":-540,"elapsed":10172,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"57319e56-8d73-420f-edc9-75bc6b2e62a4"},"source":["import os\n","import random\n","import time\n","import json\n","import warnings \n","warnings.filterwarnings('ignore')\n","\n","import torch\n","import torch.nn as nn\n","from torch.utils.data import Dataset, DataLoader\n","from utils import label_accuracy_score\n","import cv2\n","\n","import numpy as np\n","import pandas as pd\n","\n","# 전처리를 위한 라이브러리\n","from pycocotools.coco import COCO\n","import torchvision\n","import torchvision.transforms as transforms\n","\n","import albumentations as A\n","from albumentations.pytorch import ToTensorV2\n","\n","# 시각화를 위한 라이브러리\n","import matplotlib.pyplot as plt\n","import seaborn as sns; sns.set()\n","\n","plt.rcParams['axes.grid'] = False\n","\n","print('pytorch version: {}'.format(torch.__version__))\n","print('GPU 사용 가능 여부: {}'.format(torch.cuda.is_available()))\n","\n","print(torch.cuda.get_device_name(0))\n","print(torch.cuda.device_count())\n","\n","device = \"cuda\" if torch.cuda.is_available() else \"cpu\" # GPU 사용 가능 여부에 따라 device 정보 저장"],"execution_count":5,"outputs":[{"output_type":"stream","text":["pytorch version: 1.8.1+cu101\n","GPU 사용 가능 여부: True\n","Tesla V100-SXM2-16GB\n","1\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"HxgRSL_M-NwF"},"source":["## 하이퍼파라미터 세팅 및 seed 고정"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.841930Z","start_time":"2021-04-18T10:34:47.827931Z"},"id":"rV3JmGP5-NwF","executionInfo":{"status":"ok","timestamp":1620107295529,"user_tz":-540,"elapsed":1351,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["batch_size = 8 # Mini-batch size\n","num_epochs = 20\n","learning_rate = 0.0001"],"execution_count":6,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.856930Z","start_time":"2021-04-18T10:34:47.842931Z"},"id":"Z6LOuJXQ-NwG","executionInfo":{"status":"ok","timestamp":1620107295530,"user_tz":-540,"elapsed":1349,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# seed 고정\n","random_seed = 42\n","torch.manual_seed(random_seed)\n","torch.cuda.manual_seed(random_seed)\n","# torch.cuda.manual_seed_all(random_seed) # if use multi-GPU\n","torch.backends.cudnn.deterministic = True\n","torch.backends.cudnn.benchmark = False\n","np.random.seed(random_seed)\n","random.seed(random_seed)"],"execution_count":7,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"iWF_EJDu-NwG"},"source":["## 학습 데이터 EDA"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.381961Z","start_time":"2021-04-18T10:34:47.857930Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"lg0x0D0a-NwG","executionInfo":{"status":"ok","timestamp":1620107307379,"user_tz":-540,"elapsed":13181,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"a837e439-4d76-4286-c3df-558dd42d8da2"},"source":["%matplotlib inline\n","\n","dataset_path = '../input/data'\n","anns_file_path = dataset_path + '/' + 'train.json'\n","\n","# Read annotations\n","with open(anns_file_path, 'r') as f:\n"," dataset = json.loads(f.read())\n","\n","categories = dataset['categories']\n","anns = dataset['annotations']\n","imgs = dataset['images']\n","nr_cats = len(categories)\n","nr_annotations = len(anns)\n","nr_images = len(imgs)\n","\n","# Load categories and super categories\n","cat_names = []\n","super_cat_names = []\n","super_cat_ids = {}\n","super_cat_last_name = ''\n","nr_super_cats = 0\n","for cat_it in categories:\n"," cat_names.append(cat_it['name'])\n"," super_cat_name = cat_it['supercategory']\n"," # Adding new supercat\n"," if super_cat_name != super_cat_last_name:\n"," super_cat_names.append(super_cat_name)\n"," super_cat_ids[super_cat_name] = nr_super_cats\n"," super_cat_last_name = super_cat_name\n"," nr_super_cats += 1\n","\n","print('Number of super categories:', nr_super_cats)\n","print('Number of categories:', nr_cats)\n","print('Number of annotations:', nr_annotations)\n","print('Number of images:', nr_images)"],"execution_count":8,"outputs":[{"output_type":"stream","text":["Number of super categories: 11\n","Number of categories: 11\n","Number of annotations: 21116\n","Number of images: 2617\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.546964Z","start_time":"2021-04-18T10:34:51.382969Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/","height":355},"id":"PjLyVHVY-NwH","executionInfo":{"status":"ok","timestamp":1620107307388,"user_tz":-540,"elapsed":13168,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"1bcf6b7c-687b-41e3-fd49-a034f252b984"},"source":["# Count annotations\n","cat_histogram = np.zeros(nr_cats,dtype=int)\n","for ann in anns:\n"," cat_histogram[ann['category_id']] += 1\n","\n","# Initialize the matplotlib figure\n","f, ax = plt.subplots(figsize=(5,5))\n","\n","# Convert to DataFrame\n","df = pd.DataFrame({'Categories': cat_names, 'Number of annotations': cat_histogram})\n","df = df.sort_values('Number of annotations', 0, False)\n","\n","# Plot the histogram\n","plt.title(\"category distribution of train set \")\n","plot_1 = sns.barplot(x=\"Number of annotations\", y=\"Categories\", data=df, label=\"Total\", color=\"b\")"],"execution_count":9,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.561965Z","start_time":"2021-04-18T10:34:51.547969Z"},"id":"34Tw5__i-NwI","executionInfo":{"status":"ok","timestamp":1620107307389,"user_tz":-540,"elapsed":13166,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# category labeling \n","sorted_temp_df = df.sort_index()\n","\n","# background = 0 에 해당되는 label 추가 후 기존들을 모두 label + 1 로 설정\n","sorted_df = pd.DataFrame([\"Backgroud\"], columns = [\"Categories\"])\n","sorted_df = sorted_df.append(sorted_temp_df, ignore_index=True)"],"execution_count":10,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.576961Z","start_time":"2021-04-18T10:34:51.562964Z"},"colab":{"base_uri":"https://localhost:8080/","height":421},"id":"CQk4vV5N-NwI","executionInfo":{"status":"ok","timestamp":1620107307992,"user_tz":-540,"elapsed":13749,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"3871380d-f382-4da7-f0ce-dd107d7cd5ea"},"source":["# class (Categories) 에 따른 index 확인 (0~11 : 총 12개)\n","sorted_df"],"execution_count":11,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
CategoriesNumber of annotations
0BackgroudNaN
1UNKNOWN128.0
2General trash2225.0
3Paper7448.0
4Paper pack527.0
5Metal449.0
6Glass488.0
7Plastic2472.0
8Styrofoam1074.0
9Plastic bag6114.0
10Battery50.0
11Clothing141.0
\n","
"],"text/plain":[" Categories Number of annotations\n","0 Backgroud NaN\n","1 UNKNOWN 128.0\n","2 General trash 2225.0\n","3 Paper 7448.0\n","4 Paper pack 527.0\n","5 Metal 449.0\n","6 Glass 488.0\n","7 Plastic 2472.0\n","8 Styrofoam 1074.0\n","9 Plastic bag 6114.0\n","10 Battery 50.0\n","11 Clothing 141.0"]},"metadata":{"tags":[]},"execution_count":11}]},{"cell_type":"markdown","metadata":{"id":"KXU0zmZs-NwI"},"source":["## 데이터 전처리 함수 정의 (Dataset)"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:52.693328Z","start_time":"2021-04-18T10:34:52.681328Z"},"id":"QFnTI8_Z-NwJ","executionInfo":{"status":"ok","timestamp":1620107308138,"user_tz":-540,"elapsed":13891,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["category_names = list(sorted_df.Categories)\n","\n","def get_classname(classID, cats):\n"," for i in range(len(cats)):\n"," if cats[i]['id']==classID:\n"," return cats[i]['name']\n"," return \"None\"\n","\n","class CustomDataLoader(Dataset):\n"," \"\"\"COCO format\"\"\"\n"," def __init__(self, data_dir, mode = 'train', transform = None):\n"," super().__init__()\n"," self.mode = mode\n"," self.transform = transform\n"," self.coco = COCO(data_dir)\n"," \n"," def __getitem__(self, index: int):\n"," # dataset이 index되어 list처럼 동작\n"," image_id = self.coco.getImgIds(imgIds=index)\n"," image_infos = self.coco.loadImgs(image_id)[0]\n"," \n"," # cv2 를 활용하여 image 불러오기\n"," images = cv2.imread(os.path.join(dataset_path, image_infos['file_name']))\n"," images = cv2.cvtColor(images, cv2.COLOR_BGR2RGB).astype(np.float32)\n"," images /= 255.0\n"," \n"," if (self.mode in ('train', 'val')):\n"," ann_ids = self.coco.getAnnIds(imgIds=image_infos['id'])\n"," anns = self.coco.loadAnns(ann_ids)\n","\n"," # Load the categories in a variable\n"," cat_ids = self.coco.getCatIds()\n"," cats = self.coco.loadCats(cat_ids)\n","\n"," # masks : size가 (height x width)인 2D\n"," # 각각의 pixel 값에는 \"category id + 1\" 할당\n"," # Background = 0\n"," masks = np.zeros((image_infos[\"height\"], image_infos[\"width\"]))\n"," # Unknown = 1, General trash = 2, ... , Cigarette = 11\n"," for i in range(len(anns)):\n"," className = get_classname(anns[i]['category_id'], cats)\n"," pixel_value = category_names.index(className)\n"," masks = np.maximum(self.coco.annToMask(anns[i])*pixel_value, masks)\n"," masks = masks.astype(np.float32)\n","\n"," # transform -> albumentations 라이브러리 활용\n"," if self.transform is not None:\n"," transformed = self.transform(image=images, mask=masks)\n"," images = transformed[\"image\"]\n"," masks = transformed[\"mask\"]\n"," \n"," return images, masks, image_infos\n"," \n"," if self.mode == 'test':\n"," # transform -> albumentations 라이브러리 활용\n"," if self.transform is not None:\n"," transformed = self.transform(image=images)\n"," images = transformed[\"image\"]\n"," \n"," return images, image_infos\n"," \n"," \n"," def __len__(self) -> int:\n"," # 전체 dataset의 size를 return\n"," return len(self.coco.getImgIds())"],"execution_count":12,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"cp2aIOlP-NwK"},"source":["## Dataset 정의 및 DataLoader 할당"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:58.823175Z","start_time":"2021-04-18T10:34:54.106233Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"P7zFlRn6-NwK","executionInfo":{"status":"ok","timestamp":1620107317026,"user_tz":-540,"elapsed":8864,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"fd90abdb-af49-4265-cc83-8f54ce9a25fc"},"source":["# train.json / validation.json / test.json 디렉토리 설정\n","train_path = dataset_path + '/train.json'\n","val_path = dataset_path + '/val.json'\n","test_path = dataset_path + '/test.json'\n","\n","# collate_fn needs for batch\n","def collate_fn(batch):\n"," return tuple(zip(*batch))\n","\n","train_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","val_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","test_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","# create own Dataset 1 (skip)\n","# validation set을 직접 나누고 싶은 경우\n","# random_split 사용하여 data set을 8:2 로 분할\n","# train_size = int(0.8*len(dataset))\n","# val_size = int(len(dataset)-train_size)\n","# dataset = CustomDataLoader(data_dir=train_path, mode='train', transform=transform)\n","# train_dataset, val_dataset = torch.utils.data.random_split(dataset, [train_size, val_size])\n","\n","# create own Dataset 2\n","# train dataset\n","train_dataset = CustomDataLoader(data_dir=train_path, mode='train', transform=train_transform)\n","\n","# validation dataset\n","val_dataset = CustomDataLoader(data_dir=val_path, mode='val', transform=val_transform)\n","\n","# test dataset\n","test_dataset = CustomDataLoader(data_dir=test_path, mode='test', transform=test_transform)\n","\n","\n","# DataLoader\n","train_loader = torch.utils.data.DataLoader(dataset=train_dataset, \n"," batch_size=batch_size,\n"," shuffle=True,\n"," num_workers=4,\n"," collate_fn=collate_fn,\n"," drop_last=True)\n","\n","val_loader = torch.utils.data.DataLoader(dataset=val_dataset, \n"," batch_size=batch_size,\n"," shuffle=False,\n"," num_workers=4,\n"," collate_fn=collate_fn,\n"," drop_last=True) \n","\n","test_loader = torch.utils.data.DataLoader(dataset=test_dataset,\n"," batch_size=batch_size,\n"," num_workers=4,\n"," collate_fn=collate_fn)"],"execution_count":13,"outputs":[{"output_type":"stream","text":["loading annotations into memory...\n","Done (t=3.85s)\n","creating index...\n","index created!\n","loading annotations into memory...\n","Done (t=2.94s)\n","creating index...\n","index created!\n","loading annotations into memory...\n","Done (t=0.57s)\n","creating index...\n","index created!\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"8rqJiHb_-NwM"},"source":["# wandb"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"H1kHXm0uAX3R","executionInfo":{"status":"ok","timestamp":1620107323947,"user_tz":-540,"elapsed":15769,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"9d6803d6-7949-47c7-b575-efe6e03acb35"},"source":["!pip install wandb"],"execution_count":14,"outputs":[{"output_type":"stream","text":["Collecting wandb\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/67/5a/b037b50f9849212863a2fed313624d8f6f33ffa4ce89dc706e2a0e98c780/wandb-0.10.29-py2.py3-none-any.whl (2.1MB)\n","\u001b[K |████████████████████████████████| 2.1MB 16.1MB/s \n","\u001b[?25hRequirement already satisfied: six>=1.13.0 in /usr/local/lib/python3.7/dist-packages (from wandb) 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directory: /root/.cache/pip/wheels/68/39/1a/5e402bdfdf004af1786c8b853fd92f8c4a04f22aad179654d1\n"," Building wheel for pathtools (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for pathtools: filename=pathtools-0.1.2-cp37-none-any.whl size=8786 sha256=05971a402095947efb054341ab8a90178057f3b40581a8d7507044a6dad985b8\n"," Stored in directory: /root/.cache/pip/wheels/0b/04/79/c3b0c3a0266a3cb4376da31e5bfe8bba0c489246968a68e843\n","Successfully built subprocess32 pathtools\n","Installing collected packages: sentry-sdk, subprocess32, configparser, pathtools, docker-pycreds, shortuuid, smmap, gitdb, GitPython, wandb\n","Successfully installed GitPython-3.1.14 configparser-5.0.2 docker-pycreds-0.4.0 gitdb-4.0.7 pathtools-0.1.2 sentry-sdk-1.0.0 shortuuid-1.0.1 smmap-4.0.0 subprocess32-3.5.4 wandb-0.10.29\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":153},"id":"aMW4VV9V-NwM","executionInfo":{"status":"ok","timestamp":1620107336746,"user_tz":-540,"elapsed":12790,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"f68bf0bd-f9e2-4492-d625-3899c153a4f1"},"source":["import wandb\n","\n","proj_name = 'Unetpp_effb3_noisy_focal_madgrad_cosLR'\n","\n","wandb.init(project='chanyub',name=proj_name)\n","\n","config = wandb.config\n","config.learning_rate = 0.01"],"execution_count":15,"outputs":[{"output_type":"display_data","data":{"application/javascript":["\n"," window._wandbApiKey = new Promise((resolve, reject) => {\n"," function loadScript(url) {\n"," return new Promise(function(resolve, reject) {\n"," let newScript = document.createElement(\"script\");\n"," newScript.onerror = reject;\n"," newScript.onload = resolve;\n"," document.body.appendChild(newScript);\n"," newScript.src = url;\n"," });\n"," }\n"," loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n"," const iframe = document.createElement('iframe')\n"," iframe.style.cssText = \"width:0;height:0;border:none\"\n"," document.body.appendChild(iframe)\n"," const handshake = new Postmate({\n"," container: iframe,\n"," url: 'https://wandb.ai/authorize'\n"," });\n"," const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n"," handshake.then(function(child) {\n"," child.on('authorize', data => {\n"," clearTimeout(timeout)\n"," resolve(data)\n"," });\n"," });\n"," })\n"," });\n"," "],"text/plain":[""]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"],"name":"stderr"},{"output_type":"display_data","data":{"text/html":["\n"," Tracking run with wandb version 0.10.29
\n"," Syncing run Unetpp_effb3_noisy_focal_madgrad_cosLR to Weights & Biases (Documentation).
\n"," Project page: https://wandb.ai/pstage12/chanyub
\n"," Run page: https://wandb.ai/pstage12/chanyub/runs/ywnbo8vk
\n"," Run data is saved locally in /content/drive/My Drive/Trash/code/wandb/run-20210504_054853-ywnbo8vk

\n"," "],"text/plain":[""]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"uQRiIVGX-NwM"},"source":["## My model"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"S0xCGpNeAqeD","executionInfo":{"status":"ok","timestamp":1620107341696,"user_tz":-540,"elapsed":17731,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"4cdaa094-d16c-476c-a672-08f28d75d287"},"source":["!pip install segmentation_models_pytorch"],"execution_count":16,"outputs":[{"output_type":"stream","text":["Collecting segmentation_models_pytorch\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/65/54/8953f9f7ee9d451b0f3be8d635aa3a654579abf898d17502a090efe1155a/segmentation_models_pytorch-0.1.3-py3-none-any.whl (66kB)\n","\r\u001b[K |█████ | 10kB 1.0MB/s eta 0:00:01\r\u001b[K |██████████ | 20kB 2.0MB/s eta 0:00:01\r\u001b[K 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sha256=e4effcf96511c8294885a39df94d9eb52b94cf7cc9c64af2ae4f6c16e92a0a14\n"," Stored in directory: /root/.cache/pip/wheels/69/df/63/62583c096289713f22db605aa2334de5b591d59861a02c2ecd\n","Successfully built efficientnet-pytorch pretrainedmodels\n","Installing collected packages: efficientnet-pytorch, timm, munch, pretrainedmodels, segmentation-models-pytorch\n","Successfully installed efficientnet-pytorch-0.6.3 munch-2.5.0 pretrainedmodels-0.7.4 segmentation-models-pytorch-0.1.3 timm-0.3.2\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:11.634792Z","start_time":"2021-04-18T16:16:05.875817Z"},"colab":{"base_uri":"https://localhost:8080/","height":137,"referenced_widgets":["3f609eb4a41b4aada8de72173cbdaa06","e0c454cf71f145cea19f11b623b9a33c","3c69a4c21b58464581c2a47cead001ac","ad1f23a040034694aa84afcb7f4d62b2","14944824d37645edbcb1ddda3d7b74a0","1975d6799fa14dd684d9f458a0bca716","45e488b5a70c46e1a5be58bb06119f85","2818960ce8a64567a70c1bc9e46af6cd"]},"id":"a8IfZfiM-NwM","executionInfo":{"status":"ok","timestamp":1620107380478,"user_tz":-540,"elapsed":17259,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"4f37f22c-699b-4acf-ac78-245a403a288e"},"source":["# 구현된 model에 임의의 input을 넣어 output이 잘 나오는지 test\n","import segmentation_models_pytorch as smp\n","\n","model = smp.UnetPlusPlus(encoder_name='timm-efficientnet-b3', encoder_weights='noisy-student', classes=12)\n","x = torch.randn([1, 3, 512, 512])\n","print(\"input shape : \", x.shape)\n","out = model(x).to(device)\n","print(\"output shape : \", out.size())\n","\n","model = model.to(device)"],"execution_count":17,"outputs":[{"output_type":"stream","text":["Downloading: \"https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b3_ns-9d44bf68.pth\" to /root/.cache/torch/hub/checkpoints/tf_efficientnet_b3_ns-9d44bf68.pth\n"],"name":"stderr"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"3f609eb4a41b4aada8de72173cbdaa06","version_minor":0,"version_major":2},"text/plain":["HBox(children=(FloatProgress(value=0.0, max=49385734.0), HTML(value='')))"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\n","input shape : torch.Size([1, 3, 512, 512])\n","output shape : torch.Size([1, 12, 512, 512])\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"zvDnI7_T-NwN"},"source":["## train, validation, test 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.104200Z","start_time":"2021-04-18T16:16:18.093174Z"},"id":"RA3oAapJ-NwN","executionInfo":{"status":"ok","timestamp":1620107410552,"user_tz":-540,"elapsed":830,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["def train(num_epochs, model, data_loader, val_loader, criterion, optimizer, saved_dir, val_every, device, lr_scheduler):\n"," print('Start training..')\n"," best_loss = 9999999\n"," best_miou = 0\n"," for epoch in range(num_epochs):\n"," model.train()\n"," for step, (images, masks, _) in enumerate(data_loader):\n"," images = torch.stack(images) # (batch, channel, height, width)\n"," masks = torch.stack(masks).long() # (batch, channel, height, width)\n"," \n"," # gpu 연산을 위해 device 할당\n"," images, masks = images.to(device), masks.to(device)\n"," \n"," # inference\n"," outputs = model(images)\n"," \n"," # loss 계산 (cross entropy loss)\n"," loss = criterion(outputs, masks)\n"," optimizer.zero_grad()\n"," loss.backward()\n"," optimizer.step()\n"," \n"," lr_scheduler.step()\n"," \n"," # step 주기에 따른 loss 출력\n"," if (step + 1) % 25 == 0:\n"," print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(\n"," epoch+1, num_epochs, step+1, len(train_loader), loss.item()))\n"," \n"," # validation 주기에 따른 loss 출력 및 best model 저장\n"," if (epoch + 1) % val_every == 0:\n","# avrg_loss = validation(epoch + 1, model, val_loader, criterion, device)\n","# if avrg_loss < best_loss:\n","# print('Best performance at epoch: {}'.format(epoch + 1))\n","# print('Save model in', saved_dir)\n","# best_loss = avrg_loss\n","# wandb.log({'best_loss': best_loss})\n","# save_model(model, saved_dir)\n"," avrg_miou = validation(epoch + 1, model, val_loader, criterion, device)\n"," if avrg_miou > best_miou:\n"," print('Best performance at epoch: {}'.format(epoch + 1))\n"," print('Save model in', saved_dir)\n"," best_miou = avrg_miou\n"," wandb.log({'best_miou': best_miou})\n"," save_model(model, saved_dir)"],"execution_count":18,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.285795Z","start_time":"2021-04-18T16:16:18.267686Z"},"id":"EulIikmq-NwO","executionInfo":{"status":"ok","timestamp":1620107424727,"user_tz":-540,"elapsed":783,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["def validation(epoch, model, data_loader, criterion, device):\n"," print('Start validation #{}'.format(epoch))\n"," model.eval()\n"," with torch.no_grad():\n"," total_loss = 0\n"," cnt = 0\n"," mIoU_list = []\n"," for step, (images, masks, _) in enumerate(data_loader):\n"," \n"," images = torch.stack(images) # (batch, channel, height, width)\n"," masks = torch.stack(masks).long() # (batch, channel, height, width)\n","\n"," images, masks = images.to(device), masks.to(device) \n","\n"," outputs = model(images)\n"," loss = criterion(outputs, masks)\n"," total_loss += loss\n"," cnt += 1\n"," \n","# print(outputs.shape)\n","# print(masks.shape)\n","# wandb.log(wandb.Image(images, masks={\n","# \"predictions\" : {\n","# \"mask_data\" : torch.squeeze(torch.squeeze(outputs, 0),1),\n","# \"class_labels\" : classes_dict\n","# },\n","# \"ground_truth\" : {\n","# \"mask_data\" : torch.squeeze(masks, 0),\n","# \"class_labels\" : classes_dict\n","# }\n","# }))\n"," \n"," outputs = torch.argmax(outputs.squeeze(), dim=1).detach().cpu().numpy()\n","\n"," mIoU = label_accuracy_score(masks.detach().cpu().numpy(), outputs, n_class=12)[2]\n"," mIoU_list.append(mIoU)\n"," \n"," avrg_loss = total_loss / cnt\n"," avrg_mIoU = np.mean(mIoU_list)\n"," print('Validation #{} Average Loss: {:.4f}, mIoU: {:.4f}'.format(epoch, avrg_loss, np.mean(mIoU_list)))\n"," wandb.log({'Train Loss':loss.item(), 'Val Loss':avrg_loss , 'Val mIoU':np.mean(mIoU_list)})\n","# return avrg_loss\n"," return avrg_mIoU"],"execution_count":19,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"C_7CtFfH-NwO"},"source":["## 모델 저장 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.909918Z","start_time":"2021-04-18T16:16:18.898918Z"},"id":"gpCM5BFO-NwO","executionInfo":{"status":"ok","timestamp":1620107431698,"user_tz":-540,"elapsed":986,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# 모델 저장 함수 정의\n","val_every = 1 \n","\n","saved_dir = './saved'\n","if not os.path.isdir(saved_dir): \n"," os.mkdir(saved_dir)\n"," \n","def save_model(model, saved_dir, file_name='Unetpp_effb3_noisy_focal_madgrad_cosLR.pt'):\n"," check_point = {'net': model.state_dict()}\n"," output_path = os.path.join(saved_dir, file_name)\n"," torch.save(model.state_dict(), output_path)"],"execution_count":20,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"0D3rsEd2yJfV"},"source":[""]},{"cell_type":"markdown","metadata":{"id":"FyKGeg8T-NwP"},"source":["## 모델 생성 및 Loss function, Optimizer 정의"]},{"cell_type":"code","metadata":{"id":"ORugl8s1-NwP","executionInfo":{"status":"ok","timestamp":1620107436190,"user_tz":-540,"elapsed":903,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["from torch.autograd import Variable\n","import torch.nn.functional as F\n","# ref : https://github.com/clcarwin/focal_loss_pytorch\n","class FocalLoss(nn.Module):\n"," def __init__(self, gamma=0, alpha=None, size_average=True):\n"," super(FocalLoss, self).__init__()\n"," self.gamma = gamma\n"," self.alpha = alpha\n"," if isinstance(alpha,(float,int)): self.alpha = torch.Tensor([alpha,1-alpha])\n"," if isinstance(alpha,list): self.alpha = torch.Tensor(alpha)\n"," self.size_average = size_average\n","\n"," def forward(self, input, target):\n"," if input.dim()>2:\n"," input = input.view(input.size(0),input.size(1),-1) # N,C,H,W => N,C,H*W\n"," input = input.transpose(1,2) # N,C,H*W => N,H*W,C\n"," input = input.contiguous().view(-1,input.size(2)) # N,H*W,C => N*H*W,C\n"," target = target.view(-1,1)\n","\n"," logpt = F.log_softmax(input)\n"," logpt = logpt.gather(1,target)\n"," logpt = logpt.view(-1)\n"," pt = Variable(logpt.data.exp())\n","\n"," if self.alpha is not None:\n"," if self.alpha.type()!=input.data.type():\n"," self.alpha = self.alpha.type_as(input.data)\n"," at = self.alpha.gather(0,target.data.view(-1))\n"," logpt = logpt * Variable(at)\n","\n"," loss = -1 * (1-pt)**self.gamma * logpt\n"," if self.size_average: return loss.mean()\n"," else: return loss.sum()"],"execution_count":21,"outputs":[]},{"cell_type":"code","metadata":{"id":"yfeFQknz-NwP","executionInfo":{"status":"ok","timestamp":1620107438162,"user_tz":-540,"elapsed":1516,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["import torch.optim.lr_scheduler as lr_scheduler\n","import math\n","class CosineAnnealingWarmUpRestart(lr_scheduler._LRScheduler):\n"," def __init__(self, optimizer, T_0, T_mult=1, eta_max=0.1, T_up=0, gamma=1., last_epoch=-1):\n"," if T_0 <= 0 or not isinstance(T_0, int):\n"," raise ValueError(\"Expected positive integer T_0, but got {}\".format(T_0))\n"," if T_mult < 1 or not isinstance(T_mult, int):\n"," raise ValueError(\"Expected integer T_mult >= 1, but got {}\".format(T_mult))\n"," if T_up < 0 or not isinstance(T_up, int):\n"," raise ValueError(\"Expected positive integer T_up, but got {}\".format(T_up))\n"," self.T_0 = T_0\n"," self.T_mult = T_mult\n"," self.base_eta_max = eta_max\n"," self.eta_max = eta_max\n"," self.T_up = T_up\n"," self.T_i = T_0\n"," self.gamma = gamma\n"," self.cycle = 0\n"," self.T_cur = last_epoch\n"," super(CosineAnnealingWarmUpRestart, self).__init__(optimizer, last_epoch)\n"," # self.T_cur = last_epoch\n"," \n"," def get_lr(self):\n"," if self.T_cur == -1:\n"," return self.base_lrs\n"," elif self.T_cur < self.T_up:\n"," return [(self.eta_max - base_lr)*self.T_cur / self.T_up + base_lr for base_lr in self.base_lrs]\n"," else:\n"," return [base_lr + (self.eta_max - base_lr) * (1 + math.cos(math.pi * (self.T_cur-self.T_up) / (self.T_i - self.T_up))) / 2\n"," for base_lr in self.base_lrs]\n","\n"," def step(self, epoch=None):\n"," if epoch is None:\n"," epoch = self.last_epoch + 1\n"," self.T_cur = self.T_cur + 1\n"," if self.T_cur >= self.T_i:\n"," self.cycle += 1\n"," self.T_cur = self.T_cur - self.T_i\n"," self.T_i = (self.T_i - self.T_up) * self.T_mult + self.T_up\n"," else:\n"," if epoch >= self.T_0:\n"," if self.T_mult == 1:\n"," self.T_cur = epoch % self.T_0\n"," self.cycle = epoch // self.T_0\n"," else:\n"," n = int(math.log((epoch / self.T_0 * (self.T_mult - 1) + 1), self.T_mult))\n"," self.cycle = n\n"," self.T_cur = epoch - self.T_0 * (self.T_mult ** n - 1) / (self.T_mult - 1)\n"," self.T_i = self.T_0 * self.T_mult ** (n)\n"," else:\n"," self.T_i = self.T_0\n"," self.T_cur = epoch\n"," \n"," self.eta_max = self.base_eta_max * (self.gamma**self.cycle)\n"," self.last_epoch = math.floor(epoch)\n"," for param_group, lr in zip(self.optimizer.param_groups, self.get_lr()):\n"," param_group['lr'] = lr"],"execution_count":22,"outputs":[]},{"cell_type":"code","metadata":{"id":"AG1oQeu7BX1M"},"source":["# !pip install adamp"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"eH2PpwF9a-Os","executionInfo":{"status":"ok","timestamp":1620107443068,"user_tz":-540,"elapsed":3673,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"06a0d162-f476-4b24-d2d2-15fbe703d0b8"},"source":["!pip install madgrad"],"execution_count":23,"outputs":[{"output_type":"stream","text":["Collecting madgrad\n"," Downloading https://files.pythonhosted.org/packages/65/f0/4584f18202a2fb8903d456bf907b80e7cb54ad8fcba68604084ff41b7cf8/madgrad-1.1-py3-none-any.whl\n","Installing collected packages: madgrad\n","Successfully installed madgrad-1.1\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:19.698902Z","start_time":"2021-04-18T16:16:19.694902Z"},"id":"9Dly8KZj-NwQ","executionInfo":{"status":"ok","timestamp":1620107448132,"user_tz":-540,"elapsed":895,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# from adamp import AdamP\n","from madgrad import MADGRAD\n","# Loss function 정의\n","# criterion = nn.CrossEntropyLoss()\n","criterion = FocalLoss()\n","\n","# Optimizer 정의\n","# optimizer = torch.optim.Adam(params = model.parameters(), lr = learning_rate, weight_decay=1e-6)\n","# optimizer = AdamP(params = model.parameters())\n","optimizer = MADGRAD(params = model.parameters(), lr = learning_rate, momentum = 0.9, weight_decay = 0, eps = 1e-06)\n","\n","lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=50, eta_min=0)\n","# lr_scheduler = CosineAnnealingWarmUpRestart(optimizer, T_0=150, T_mult=1, eta_max=0.1, T_up=10, gamma=0.5)"],"execution_count":24,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"start_time":"2021-04-18T16:16:20.331Z"},"colab":{"base_uri":"https://localhost:8080/"},"id":"cMSiFA-3-NwR","executionInfo":{"status":"ok","timestamp":1620111311704,"user_tz":-540,"elapsed":3859275,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"e88dc410-6fcd-49fd-c1f4-8fcc95657889"},"source":["train(num_epochs, model, train_loader, val_loader, criterion, optimizer, saved_dir, val_every, device, lr_scheduler)"],"execution_count":25,"outputs":[{"output_type":"stream","text":["Start training..\n","Epoch [1/20], Step [25/327], Loss: 1.5507\n","Epoch [1/20], Step [50/327], Loss: 1.0864\n","Epoch [1/20], Step [75/327], Loss: 1.0877\n","Epoch [1/20], Step [100/327], Loss: 1.1274\n","Epoch [1/20], Step [125/327], Loss: 0.7439\n","Epoch [1/20], Step [150/327], Loss: 0.6818\n","Epoch [1/20], Step [175/327], Loss: 0.7605\n","Epoch [1/20], Step [200/327], Loss: 0.5081\n","Epoch [1/20], Step [225/327], Loss: 0.7357\n","Epoch [1/20], Step [250/327], Loss: 0.7531\n","Epoch [1/20], Step [275/327], Loss: 0.5606\n","Epoch [1/20], Step [300/327], Loss: 0.6126\n","Epoch [1/20], Step [325/327], Loss: 0.5946\n","Start validation #1\n","Validation #1 Average Loss: 0.5732, mIoU: 0.2222\n","Best performance at epoch: 1\n","Save model in ./saved\n","Epoch [2/20], Step [25/327], Loss: 0.5337\n","Epoch [2/20], Step [50/327], Loss: 0.4886\n","Epoch [2/20], Step [75/327], Loss: 0.4272\n","Epoch [2/20], Step [100/327], Loss: 0.5655\n","Epoch [2/20], Step [125/327], Loss: 0.5422\n","Epoch [2/20], Step [150/327], Loss: 0.4709\n","Epoch [2/20], Step [175/327], Loss: 0.4189\n","Epoch [2/20], Step [200/327], Loss: 0.7500\n","Epoch [2/20], Step [225/327], Loss: 0.7974\n","Epoch [2/20], Step [250/327], Loss: 0.5967\n","Epoch [2/20], Step [275/327], Loss: 0.4761\n","Epoch [2/20], Step [300/327], Loss: 0.5499\n","Epoch [2/20], Step [325/327], Loss: 0.4251\n","Start validation #2\n","Validation #2 Average Loss: 0.4738, mIoU: 0.2785\n","Best performance at epoch: 2\n","Save model in ./saved\n","Epoch [3/20], Step [25/327], Loss: 0.4226\n","Epoch [3/20], Step [50/327], Loss: 0.3124\n","Epoch [3/20], Step [75/327], Loss: 0.5681\n","Epoch [3/20], Step [100/327], Loss: 0.6604\n","Epoch [3/20], Step [125/327], Loss: 0.5677\n","Epoch [3/20], Step [150/327], Loss: 0.4329\n","Epoch [3/20], Step [175/327], Loss: 0.4481\n","Epoch [3/20], Step [200/327], Loss: 0.3594\n","Epoch [3/20], Step [225/327], Loss: 0.5376\n","Epoch [3/20], Step [250/327], Loss: 0.3366\n","Epoch [3/20], Step [275/327], Loss: 0.3024\n","Epoch [3/20], Step [300/327], Loss: 0.3693\n","Epoch [3/20], Step [325/327], Loss: 0.3430\n","Start validation #3\n","Validation #3 Average Loss: 0.4376, mIoU: 0.2862\n","Best performance at epoch: 3\n","Save model in ./saved\n","Epoch [4/20], Step [25/327], Loss: 1.0724\n","Epoch [4/20], Step [50/327], Loss: 0.4624\n","Epoch [4/20], Step [75/327], Loss: 0.2640\n","Epoch [4/20], Step [100/327], Loss: 0.3050\n","Epoch [4/20], Step [125/327], Loss: 0.3554\n","Epoch [4/20], Step [150/327], Loss: 0.4423\n","Epoch [4/20], Step [175/327], Loss: 0.2701\n","Epoch [4/20], Step [200/327], Loss: 0.3788\n","Epoch [4/20], Step [225/327], Loss: 0.3908\n","Epoch [4/20], Step [250/327], Loss: 0.4216\n","Epoch [4/20], Step [275/327], Loss: 0.5817\n","Epoch [4/20], Step [300/327], Loss: 0.4860\n","Epoch [4/20], Step [325/327], Loss: 0.2901\n","Start validation #4\n","Validation #4 Average Loss: 0.4056, mIoU: 0.2794\n","Epoch [5/20], Step [25/327], Loss: 0.3849\n","Epoch [5/20], Step [50/327], Loss: 0.7280\n","Epoch [5/20], Step [75/327], Loss: 0.3421\n","Epoch [5/20], Step [100/327], Loss: 0.4068\n","Epoch [5/20], Step [125/327], Loss: 0.2748\n","Epoch [5/20], Step [150/327], Loss: 0.2535\n","Epoch [5/20], Step [175/327], Loss: 0.3638\n","Epoch [5/20], Step [200/327], Loss: 0.3018\n","Epoch [5/20], Step [225/327], Loss: 0.3504\n","Epoch [5/20], Step [250/327], Loss: 0.2717\n","Epoch [5/20], Step [275/327], Loss: 0.4167\n","Epoch [5/20], Step [300/327], Loss: 0.1884\n","Epoch [5/20], Step [325/327], Loss: 0.4018\n","Start validation #5\n","Validation #5 Average Loss: 0.3950, mIoU: 0.3349\n","Best performance at epoch: 5\n","Save model in ./saved\n","Epoch [6/20], Step [25/327], Loss: 0.5289\n","Epoch [6/20], Step [50/327], Loss: 0.3038\n","Epoch [6/20], Step [75/327], Loss: 0.4297\n","Epoch [6/20], Step [100/327], Loss: 0.3626\n","Epoch [6/20], Step [125/327], Loss: 0.2523\n","Epoch [6/20], Step [150/327], Loss: 0.2539\n","Epoch [6/20], Step [175/327], Loss: 0.3640\n","Epoch [6/20], Step [200/327], Loss: 0.4969\n","Epoch [6/20], Step [225/327], Loss: 0.3374\n","Epoch [6/20], Step [250/327], Loss: 0.2156\n","Epoch [6/20], Step [275/327], Loss: 0.3013\n","Epoch [6/20], Step [300/327], Loss: 0.7351\n","Epoch [6/20], Step [325/327], Loss: 0.3940\n","Start validation #6\n","Validation #6 Average Loss: 0.3796, mIoU: 0.3271\n","Epoch [7/20], Step [25/327], Loss: 0.3404\n","Epoch [7/20], Step [50/327], Loss: 0.4171\n","Epoch [7/20], Step [75/327], Loss: 0.2391\n","Epoch [7/20], Step [100/327], Loss: 0.1789\n","Epoch [7/20], Step [125/327], Loss: 0.3509\n","Epoch [7/20], Step [150/327], Loss: 0.3724\n","Epoch [7/20], Step [175/327], Loss: 0.3453\n","Epoch [7/20], Step [200/327], Loss: 0.2691\n","Epoch [7/20], Step [225/327], Loss: 0.3726\n","Epoch [7/20], Step [250/327], Loss: 0.2593\n","Epoch [7/20], Step [275/327], Loss: 0.3057\n","Epoch [7/20], Step [300/327], Loss: 0.3683\n","Epoch [7/20], Step [325/327], Loss: 0.5629\n","Start validation #7\n","Validation #7 Average Loss: 0.3467, mIoU: 0.3447\n","Best performance at epoch: 7\n","Save model in ./saved\n","Epoch [8/20], Step [25/327], Loss: 0.1969\n","Epoch [8/20], Step [50/327], Loss: 0.3253\n","Epoch [8/20], Step [75/327], Loss: 0.3038\n","Epoch [8/20], Step [100/327], Loss: 0.3426\n","Epoch [8/20], Step [125/327], Loss: 0.6711\n","Epoch [8/20], Step [150/327], Loss: 0.2802\n","Epoch [8/20], Step [175/327], Loss: 0.2748\n","Epoch [8/20], Step [200/327], Loss: 0.2294\n","Epoch [8/20], Step [225/327], Loss: 0.4963\n","Epoch [8/20], Step [250/327], Loss: 0.2142\n","Epoch [8/20], Step [275/327], Loss: 0.2831\n","Epoch [8/20], Step [300/327], Loss: 0.5333\n","Epoch [8/20], Step [325/327], Loss: 0.4405\n","Start validation #8\n","Validation #8 Average Loss: 0.3583, mIoU: 0.3629\n","Best performance at epoch: 8\n","Save model in ./saved\n","Epoch [9/20], Step [25/327], Loss: 0.2756\n","Epoch [9/20], Step [50/327], Loss: 0.3781\n","Epoch [9/20], Step [75/327], Loss: 0.3706\n","Epoch [9/20], Step [100/327], Loss: 0.3795\n","Epoch [9/20], Step [125/327], Loss: 0.1924\n","Epoch [9/20], Step [150/327], Loss: 0.2632\n","Epoch [9/20], Step [175/327], Loss: 0.3391\n","Epoch [9/20], Step [200/327], Loss: 0.2901\n","Epoch [9/20], Step [225/327], Loss: 0.1700\n","Epoch [9/20], Step [250/327], Loss: 0.1907\n","Epoch [9/20], Step [275/327], Loss: 0.2368\n","Epoch [9/20], Step [300/327], Loss: 0.2482\n","Epoch [9/20], Step [325/327], Loss: 0.2490\n","Start validation #9\n","Validation #9 Average Loss: 0.3294, mIoU: 0.3673\n","Best performance at epoch: 9\n","Save model in ./saved\n","Epoch [10/20], Step [25/327], Loss: 0.2980\n","Epoch [10/20], Step [50/327], Loss: 0.1305\n","Epoch [10/20], Step [75/327], Loss: 0.2236\n","Epoch [10/20], Step [100/327], Loss: 0.6733\n","Epoch [10/20], Step [125/327], Loss: 0.5550\n","Epoch [10/20], Step [150/327], Loss: 0.2442\n","Epoch [10/20], Step [175/327], Loss: 0.2322\n","Epoch [10/20], Step [200/327], Loss: 0.1960\n","Epoch [10/20], Step [225/327], Loss: 0.3258\n","Epoch [10/20], Step [250/327], Loss: 0.4923\n","Epoch [10/20], Step [275/327], Loss: 0.1293\n","Epoch [10/20], Step [300/327], Loss: 0.6264\n","Epoch [10/20], Step [325/327], Loss: 0.2385\n","Start validation #10\n","Validation #10 Average Loss: 0.3270, mIoU: 0.3453\n","Epoch [11/20], Step [25/327], Loss: 0.2827\n","Epoch [11/20], Step [50/327], Loss: 0.7781\n","Epoch [11/20], Step [75/327], Loss: 0.5123\n","Epoch [11/20], Step [100/327], Loss: 0.1865\n","Epoch [11/20], Step [125/327], Loss: 0.1429\n","Epoch [11/20], Step [150/327], Loss: 0.2610\n","Epoch [11/20], Step [175/327], Loss: 0.2771\n","Epoch [11/20], Step [200/327], Loss: 0.4113\n","Epoch [11/20], Step [225/327], Loss: 0.4611\n","Epoch [11/20], Step [250/327], Loss: 0.1018\n","Epoch [11/20], Step [275/327], Loss: 0.5752\n","Epoch [11/20], Step [300/327], Loss: 0.2542\n","Epoch [11/20], Step [325/327], Loss: 0.1824\n","Start validation #11\n","Validation #11 Average Loss: 0.3123, mIoU: 0.3784\n","Best performance at epoch: 11\n","Save model in ./saved\n","Epoch [12/20], Step [25/327], Loss: 0.3433\n","Epoch [12/20], Step [50/327], Loss: 0.2840\n","Epoch [12/20], Step [75/327], Loss: 0.7857\n","Epoch [12/20], Step [100/327], Loss: 0.2288\n","Epoch [12/20], Step [125/327], Loss: 0.3381\n","Epoch [12/20], Step [150/327], Loss: 0.2834\n","Epoch [12/20], Step [175/327], Loss: 0.3232\n","Epoch [12/20], Step [200/327], Loss: 0.1950\n","Epoch [12/20], Step [225/327], Loss: 0.2261\n","Epoch [12/20], Step [250/327], Loss: 0.1929\n","Epoch [12/20], Step [275/327], Loss: 0.3142\n","Epoch [12/20], Step [300/327], Loss: 0.2544\n","Epoch [12/20], Step [325/327], Loss: 0.3833\n","Start validation #12\n","Validation #12 Average Loss: 0.3511, mIoU: 0.3238\n","Epoch [13/20], Step [25/327], Loss: 0.1699\n","Epoch [13/20], Step [50/327], Loss: 0.2332\n","Epoch [13/20], Step [75/327], Loss: 0.1821\n","Epoch [13/20], Step [100/327], Loss: 0.1881\n","Epoch [13/20], Step [125/327], Loss: 0.1147\n","Epoch [13/20], Step [150/327], Loss: 0.2180\n","Epoch [13/20], Step [175/327], Loss: 0.1807\n","Epoch [13/20], Step [200/327], Loss: 0.1637\n","Epoch [13/20], Step [225/327], Loss: 0.1363\n","Epoch [13/20], Step [250/327], Loss: 0.4686\n","Epoch [13/20], Step [275/327], Loss: 0.3312\n","Epoch [13/20], Step [300/327], Loss: 0.1355\n","Epoch [13/20], Step [325/327], Loss: 0.3296\n","Start validation #13\n","Validation #13 Average Loss: 0.3135, mIoU: 0.3549\n","Epoch [14/20], Step [25/327], Loss: 0.2737\n","Epoch [14/20], Step [50/327], Loss: 0.0850\n","Epoch [14/20], Step [75/327], Loss: 0.1712\n","Epoch [14/20], Step [100/327], Loss: 0.2329\n","Epoch [14/20], Step [125/327], Loss: 0.1931\n","Epoch [14/20], Step [150/327], Loss: 0.2141\n","Epoch [14/20], Step [175/327], Loss: 0.1796\n","Epoch [14/20], Step [200/327], Loss: 0.1400\n","Epoch [14/20], Step [225/327], Loss: 0.1961\n","Epoch [14/20], Step [250/327], Loss: 0.6784\n","Epoch [14/20], Step [275/327], Loss: 0.1712\n","Epoch [14/20], Step [300/327], Loss: 0.1149\n","Epoch [14/20], Step [325/327], Loss: 0.2548\n","Start validation #14\n","Validation #14 Average Loss: 0.2940, mIoU: 0.3703\n","Epoch [15/20], Step [25/327], Loss: 0.2555\n","Epoch [15/20], Step [50/327], Loss: 0.3401\n","Epoch [15/20], Step [75/327], Loss: 0.2801\n","Epoch [15/20], Step [100/327], Loss: 0.2241\n","Epoch [15/20], Step [125/327], Loss: 0.1298\n","Epoch [15/20], Step [150/327], Loss: 0.5108\n","Epoch [15/20], Step [175/327], Loss: 0.1842\n","Epoch [15/20], Step [200/327], Loss: 0.2058\n","Epoch [15/20], Step [225/327], Loss: 0.2501\n","Epoch [15/20], Step [250/327], Loss: 0.1988\n","Epoch [15/20], Step [275/327], Loss: 0.2729\n","Epoch [15/20], Step [300/327], Loss: 0.2198\n","Epoch [15/20], Step [325/327], Loss: 0.2475\n","Start validation #15\n","Validation #15 Average Loss: 0.3177, mIoU: 0.3527\n","Epoch [16/20], Step [25/327], Loss: 0.1652\n","Epoch [16/20], Step [50/327], Loss: 0.1622\n","Epoch [16/20], Step [75/327], Loss: 0.1909\n","Epoch [16/20], Step [100/327], Loss: 0.1865\n","Epoch [16/20], Step [125/327], Loss: 0.2157\n","Epoch [16/20], Step [150/327], Loss: 0.2140\n","Epoch [16/20], Step [175/327], Loss: 0.1658\n","Epoch [16/20], Step [200/327], Loss: 0.1646\n","Epoch [16/20], Step [225/327], Loss: 0.3539\n","Epoch [16/20], Step [250/327], Loss: 0.1399\n","Epoch [16/20], Step [275/327], Loss: 0.1234\n","Epoch [16/20], Step [300/327], Loss: 0.1682\n","Epoch [16/20], Step [325/327], Loss: 0.2499\n","Start validation #16\n","Validation #16 Average Loss: 0.3365, mIoU: 0.3545\n","Epoch [17/20], Step [25/327], Loss: 0.1578\n","Epoch [17/20], Step [50/327], Loss: 0.3904\n","Epoch [17/20], Step [75/327], Loss: 0.1684\n","Epoch [17/20], Step [100/327], Loss: 0.2453\n","Epoch [17/20], Step [125/327], Loss: 0.1396\n","Epoch [17/20], Step [150/327], Loss: 0.2273\n","Epoch [17/20], Step [175/327], Loss: 0.1811\n","Epoch [17/20], Step [200/327], Loss: 0.1527\n","Epoch [17/20], Step [225/327], Loss: 0.2294\n","Epoch [17/20], Step [250/327], Loss: 0.3362\n","Epoch [17/20], Step [275/327], Loss: 0.1353\n","Epoch [17/20], Step [300/327], Loss: 0.1161\n","Epoch [17/20], Step [325/327], Loss: 0.2711\n","Start validation #17\n","Validation #17 Average Loss: 0.2963, mIoU: 0.3655\n","Epoch [18/20], Step [25/327], Loss: 0.2769\n","Epoch [18/20], Step [50/327], Loss: 0.1361\n","Epoch [18/20], Step [75/327], Loss: 0.3174\n","Epoch [18/20], Step [100/327], Loss: 0.2776\n","Epoch [18/20], Step [125/327], Loss: 0.1491\n","Epoch [18/20], Step [150/327], Loss: 0.1354\n","Epoch [18/20], Step [175/327], Loss: 0.3820\n","Epoch [18/20], Step [200/327], Loss: 0.2822\n","Epoch [18/20], Step [225/327], Loss: 0.0753\n","Epoch [18/20], Step [250/327], Loss: 0.1749\n","Epoch [18/20], Step [275/327], Loss: 0.1806\n","Epoch [18/20], Step [300/327], Loss: 0.2121\n","Epoch [18/20], Step [325/327], Loss: 0.2242\n","Start validation #18\n","Validation #18 Average Loss: 0.2923, mIoU: 0.3603\n","Epoch [19/20], Step [25/327], Loss: 0.2013\n","Epoch [19/20], Step [50/327], Loss: 0.1093\n","Epoch [19/20], Step [75/327], Loss: 0.3479\n","Epoch [19/20], Step [100/327], Loss: 0.1200\n","Epoch [19/20], Step [125/327], Loss: 0.0986\n","Epoch [19/20], Step [150/327], Loss: 0.1666\n","Epoch [19/20], Step [175/327], Loss: 0.1513\n","Epoch [19/20], Step [200/327], Loss: 0.2020\n","Epoch [19/20], Step [225/327], Loss: 0.1137\n","Epoch [19/20], Step [250/327], Loss: 0.1393\n","Epoch [19/20], Step [275/327], Loss: 0.1018\n","Epoch [19/20], Step [300/327], Loss: 0.1115\n","Epoch [19/20], Step [325/327], Loss: 0.2225\n","Start validation #19\n","Validation #19 Average Loss: 0.3469, mIoU: 0.3679\n","Epoch [20/20], Step [25/327], Loss: 0.3811\n","Epoch [20/20], Step [50/327], Loss: 0.1661\n","Epoch [20/20], Step [75/327], Loss: 0.2296\n","Epoch [20/20], Step [100/327], Loss: 0.1632\n","Epoch [20/20], Step [125/327], Loss: 0.2026\n","Epoch [20/20], Step [150/327], Loss: 0.1672\n","Epoch [20/20], Step [175/327], Loss: 0.0859\n","Epoch [20/20], Step [200/327], Loss: 0.2112\n","Epoch [20/20], Step [225/327], Loss: 0.1976\n","Epoch [20/20], Step [250/327], Loss: 0.1122\n","Epoch [20/20], Step [275/327], Loss: 0.1521\n","Epoch [20/20], Step [300/327], Loss: 0.2484\n","Epoch [20/20], Step [325/327], Loss: 0.1653\n","Start validation #20\n","Validation #20 Average Loss: 0.3000, mIoU: 0.3680\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"pJADWpckFx57","executionInfo":{"status":"ok","timestamp":1620111316850,"user_tz":-540,"elapsed":4243,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# Sleep for a few seconds.\n","import time\n","time.sleep(2)\n","# Play an audio beep. Any audio URL will do.\n","from google.colab import output\n","output.eval_js('new Audio(\"https://upload.wikimedia.org/wikipedia/commons/0/05/Beep-09.ogg\").play()')"],"execution_count":26,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"C6ClcO0J-NwR"},"source":["## 저장된 model 불러오기 (학습된 이후) "]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:21.050200Z","start_time":"2021-04-16T19:44:20.802200Z"},"scrolled":true,"id":"KQPtUDzd-NwR","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620102899370,"user_tz":-540,"elapsed":882,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"72d54b02-20cf-4ea6-991d-52fd5331c0a6"},"source":["# best model 저장된 경로\n","model_path = './saved/pan_effb3_noisy_focal_madgrad_cosLR.pt'\n","\n","# best model 불러오기\n","checkpoint = torch.load(model_path, map_location=device)\n","model.load_state_dict(checkpoint)\n","\n","# 추론을 실행하기 전에는 반드시 설정 (batch normalization, dropout 를 평가 모드로 설정)\n","# model.eval()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{"tags":[]},"execution_count":16}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:24.939227Z","start_time":"2021-04-16T19:44:24.518228Z"},"id":"0LQqrDAp-NwR","colab":{"base_uri":"https://localhost:8080/","height":502},"executionInfo":{"status":"ok","timestamp":1620102907649,"user_tz":-540,"elapsed":6266,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"b34da4ee-1fe9-4960-f841-9627d644c50b"},"source":["# 첫번째 batch의 추론 결과 확인\n","for imgs, image_infos in test_loader:\n"," image_infos = image_infos\n"," temp_images = imgs\n"," \n"," model.eval()\n"," # inference\n"," outs = model(torch.stack(temp_images).to(device))\n"," oms = torch.argmax(outs.squeeze(), dim=1).detach().cpu().numpy()\n"," \n"," break\n","\n","i = 1\n","fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(16, 16))\n","\n","print('Shape of Original Image :', list(temp_images[i].shape))\n","print('Shape of Predicted : ', list(oms[i].shape))\n","print('Unique values, category of transformed mask : \\n', [{int(i),category_names[int(i)]} for i in list(np.unique(oms[i]))])\n","\n","# Original image\n","ax1.imshow(temp_images[i].permute([1,2,0]))\n","ax1.grid(False)\n","ax1.set_title(\"Original image : {}\".format(image_infos[i]['file_name']), fontsize = 15)\n","\n","# Predicted\n","ax2.imshow(oms[i])\n","ax2.grid(False)\n","ax2.set_title(\"Predicted : {}\".format(image_infos[i]['file_name']), fontsize = 15)\n","\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Shape of Original Image : [3, 512, 512]\n","Shape of Predicted : [512, 512]\n","Unique values, category of transformed mask : \n"," [{0, 'Backgroud'}, {'General trash', 2}, {'Paper', 3}, {9, 'Plastic bag'}, {11, 'Clothing'}]\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA6sAAAHJCAYAAABjQa/9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOy9edwkSVXu/42IzKx6l+6efRhgYJAlkE1QBn+4XBBEwRFZREZcuKiACohc9SJcBRnBlV1UUAERFdkUFGQREBQVARHvFXVCRGeAYWD27neryozl98eJqMq3ennf3qZ7JB8+Lz1VlUtkZGTGOed5zgmVUmLAgAEDBgwYMGDAgAEDBgw4naBPdQMGDBgwYMCAAQMGDBgwYMCARQzO6oABAwYMGDBgwIABAwYMOO0wOKsDBgwYMGDAgAEDBgwYMOC0w+CsDhgwYMCAAQMGDBgwYMCA0w6DszpgwIABAwYMGDBgwIABA047DM7qgAEDBgwYMGDAgAEDBgw47VCd6gYMEFhrFfA/gR8F7g5E4JPAi51zf7bLYzwPeJpz7pyjPPfrgHs45+57NPudqDYda7tv6bDWJuDHnHO/fpzHOQ94CvA659wVp6gNjwBeANwZ+E/gMufcm3q/N8AvAP8fcF9g7JxTx3POw7TjW4C7OededpjfHwv8GnCBcy5Za+8GvAK4P3AT8Orc9tDbRwHPRp7Nc4CPA093zv1Tb5vvAr4f+BpgH+CAFznn/mjh/HuBnwceCZwHXAn8FvBy59xh1xGz1n4IuM4595jd98aAAQNON+T57ud6X10NfAR4pnPuMyfpnN8OvAO4g3PuCmvtRcB/AQ93zr1zl8d4LLDsnHvdCWrTKrAG/MDxHtNa+wTgd4E9zrn14zzWEeeQk90Ga+0I+EVkPlkBPgQ8tT+3W2vvCzwNmbfuDLzeOfeEYz3nEdryTOBjzrkPHeb3dwGfcs49M39+EvBM4ELgX5Ax/YGFfW4D/DrwzcAUeGPebjP/vhf4SeBhgAW2kOfjp51z/75wrPsifVVs138EfsY599EjXNNFHOXYH3DqMTCrpw9+EzGUPwo8CrgUuAL4U2vtT+/yGK8GvvUYzv184AnHsN+JwrG2e4DgPMT4uehUnNxa+w3AHwMfRCaYPwf+KE/6BcvAE4FN4O9OYnO+BXjGEX6/BHhXdlTPBN4PJOARiBP5k8BlC/s8C3gO8CvAw4F14P3W2lv1tvmJ/P3/Ar4D6Ys3WGt/bOFYrwO+D5lgvx14K/CSHdoMEox49g7bDBgw4JaB/YijcX/gp4B7Ax+w1q7cTOe/Op/7b45in8dyau2Emws7zSEnG7+G9PNPAY9BAqTvs9aOe9t8PfANSOD0iyexLc8EHnioH6y1y8A3Ae/Mnx8HvAp4PWIH/AvwTmvtPXr71MB7gdsD3w38OPBdwG/3Dn074El5u8cAPwxcAHzUWnth71gXIvN3hTj235//+33W2tsf4ZqOZewPOMUYmNXTANbaRwI/Avyoc+5VvZ/eba39IvCL1tr3Oef+8TD710B0zn0e+PzRnv9kRXOP4vzH1O4Bpw2eA/y1c+7p+fMHrbV3B54L/AWAc+4ma+1Z2Ul8GvCgm7uR1lqNTKI/mr/6EWAJeLRz7gAyye0Fnmet/VXn3IFsIDwL+KXCPltrP4IEkp4G/Gw+1sOdc9f1TveX1tpbI07sK/J+y4hT/Azn3G/3trs7MnG/9HBtd8796/Fd/YABA04jeOfc3+f//ntr7WeBDwPfBrxlcWNr7ZJzbutEndw5NwX+fscNB9yssNbeFvgh4Aedc6/P3/0/hAn8PiSwD/AK59zL8+//cCraiszhE+bB5+cBv+ece35u118B90Hmz+/L2zwG+ErgTs65/8rbdcAbrbWXOec+jVzrHfvj3Vr7YeCzwA8yDyZfAuwBHuWc25+3+zvgOuQ5euWhGj2M/VsmBmf19MCPA/8B/M4hfvtFJLL0NORBnUkCEUfgpxFG7SJr7Q+xIKe11t4LiXZ9NfAZ4H8jDNKnimxkUQbck7PcC2F9vg74HPB/nHN/0jv2JUgE8quAMfCvwHOdc39xNBe/KAO21j4QYaa+GXh6/vcLwFOBDwC/DPwAIiF5sXPuJb1j3R9hoC4G9gKfBl7onPvDhXM+EIlg3gX453zsdwG/7px7Xm+7RyDO2D0QmejrEZlJdzTXeAQ01tqXI1FBDfw+8JPOuTaf/wJEPvtAJLr4OeDNwM8759osafnnfKwPWmsBKBJba+3ZyBj6DuBMRHb6ygWJk7HW/iISzUyIsfQT+aV+RGTJ0jch96mPNwK/a63dVyaSI8lcd3GeOyDy4m93zv1573sDXIVM4h5hRou8GWTyfEL+74uBM4D35c8PA96bHdV+u38FeAAim/s6ZBy9uWzgnNuw1r4j7/+z+bu+o1rwSeA7e58Nco/3L2x3E3DbHa7/Q/RkwOWZQZzfVwB3Ay5HnqO/6e03Al4GPA4IwGuR/nrpyZBhDxgw4JjwifzvRQDW2isQtcpNyPx/PlDngNszEZXKhcj7/Becc79XDpTTFn4OUWMsAW8D3tM/2eGkkFnG+XREWrofcaB/CHg5+V3We7deVubK3cyT1trvBH4pt/vjSCDvROMrrbUvQWShn0Oko2/rteGINkt+rx52DrHW/g/EWboYeZ9+EvhfzrlP9tpwh9yGQ9pNO6CokWbbO+eustb+DTLfvDp/F3d5vEMiX+ePALfuHyv3zzuR+/9+4Gzg56y1Rbb+TT1J8CXI/OmttV+B2FI/3mt3tNa+pf9dvoaPF0c14+1ACzwU+LRzbmOxvc65G6y1VwK37n1dI3N+f/v1/N1h57ZDjf38vL0VGfNPBVaBPwWeUuyXvN2O9vSAk4NBBnyKYa2tEEnCO/p5cgX5Qfkg8D8Wfvp6hCH6aUSauGgAFybnvciE9Tgkp/CliMxiN3gD8GeILPnTSPSrb1TfATHovx+ZyP4OYYO/fpfH3wm/hUg1HoVMym9Fch32AN+TP7/YWvu1vX1uD/wtMsE+HJnwfzdLVIBZzsS7gGuQSN9vAX+I9BO97R6LTBofQ5y9y4AnIxPuYWGtfYK1NuWX4k74ScRR+V7k/jwZcU4LzgFuQCb2hwIvRBz1V+Tfr877grxki7QMa+0Sku/ySETq/W3Ai9n+wi9tuDUS/XwhYhz9OLvDHZFJ4/KF7/8Neb/cZZfHOSLy5PYxRIrWxwMQQ+6NyET+BkQWVfrh+b1tLwE+3HNO77rYbufcZxGp8l172wRk/Pfxb71tDof7A7McG+fcGuL0PtNae29r7Z6cS/ZY4Dd2ONahsAz8ATJ5fhdiJL57QZ78q4ik7DJknNyObIwNGDDgtMFF+d++pPN7kPfbU5C0IJD3/s8isslLEEf0tfk9UvB0RNXy28j8toW8B44Ia+3PInPhXyFzxo8idsUq8h79IOKclXfrq/N+O86T1tqvBt4E/F/g0YjdMAsA7tCuD+Vg3W7wJsTJeDQSxH2Ltfarer/vZLMcdg7JAe4PAB1SX+RSxJm/zUIbdrKbjoS7Ap8/RM7rbuabo8GbkHnzAQvfXwp8wjn3H0j79wOvYd4XfXXfJUjKT2k3HNoOOMtae25vu8U5t0Ucv8NeX97/TvTmU8S220RswPOs1O54KXAjh1An7AKPQ4iRJyH21iXMmewTYU8POA4MzOqpxznACHHGDocrEUeljzOAezvnvlS+KKxaDz+ARMbu65y7Km/zGSQvdjd4qXPutXm/TwBfQvLsXgXQL8qTI74fRIpD/RDiMB4vft8598J8/M8jORDWOfeg/N37kZfro8s1Oefe2GuTAv4acQafBJRiN89AXnIPL1ITa+0B5AXe3/eFSOGCp/S+nwK/Ya39Jefc9Ydpd0QcnN0wiWvAd+Xo5rszE/Yz+fg3OOf+GcldKef/WySS+Fpr7Y8556ZZJgTwrz1pGcDjkfvx1b1iQH95iDZc0YsKvjdP3I9mFwYOwtaCOEp93Ljw+4nAG5Eo76jH+l4K/Itz7lMA1tqrgelCPxRcggQlCs7k4HaDtP3M3jbrhwgk3QgsW2ubwoL3Ya19MGLw/eDCT4/PbSiR+AQ8u8+MHAWWEPbiDfmcH0SkUs8AnpVZ9ScjzMFL8zbvBT51DOcaMGDACUQOVAN8BVKzYg1hs/r4dufcJG9/J8SB/IHe++L9WX3zc0h+oEEC2L/lnCspCu+11r6Pg52qflvOAP4P8DLnXJ/x7CupbgB0/916FPPksxBH47FZYfNuK0X3XnDkXgJkLt0tXu2ce1Fuw3sR5vTZSJrFjjaLc+7zR5hDfglxtr+1pxJ6DwfjiHbTDtjNnHTccM79W7YbLkX6oKhwHkF2zp1zn7TWesR53tYX1tp7IuOpXP9u7IBrOfbrezHCmr6udw1fsNaWnNmi7LoauT/XHuFYh8MScEkJFFhrN4Dft9Z+pXPu3zh+e3rAcWBgVm+5+ETfUT0MLs7bXVW+cM59DHl57gYzOW+ecK6hJ1e01t7WWvt71tqrEOlFh8hYTgibhkQxC/4j/ztztrKD95/0JmFr7ZnW2l/LkpEu/z15oU0XA+9byAFarLh8FyRi9mZrbVX+8vnHiNzpkHDOvd45VznnjhSAKPjTBUnPnyAvzXvk61HW2mdYa//VWruVr+cPkQDHThG9BwGf7Dmqh8OibPtf2UGWeorwZkSS+1CYGXuPphdkOByyQXcf5pHgk4bMqL8BubevW/j5pcDXIhPfAxCW5HlWJPzHgpnELU+y7wPul7+6JzJW/6y3TUKYhQEDBpw6nM18fnKIw3qpc+7q3jYfKI5qxoORQOjbFuakDwD3zo7qhUi6yJ8unG8nGer9kXnnd4/yOnY7T94P+LOFVJBdSWOdcw92zj14l+3pvw8j0g/lfXjMNouVwldfi0iCdwpCH9FuOo3wJuA7e0GThyGqtd0w3pcgVYKPxSk8KlhrfxRRfT2xTxDkOf0tiIT+YfnvE8CfW2uPhe183wKj/TZETnxx/ny89vSA48DgrJ56XIfkXh6petntkTyzPnbzgNwKiWYtYrcvmMUIWItMQCUq+WdIXsZzkbzFi4F3l21OAGbn77FXh21TxuuQaOELkUnoYiRPr7/NQf2SjYL+i6rk/b6LuVHRIbkOIEbBicA1h/l8Qf73GcCLkBfnI5CJ96n5t536+Wwk0rgTdurTI6FETvctfH/mwu/HjTxJ/A1zSdyDkfv0xsPuNMe3Af/pnHO9727k4HaDtP3G3jar2RBc3GZzkVW11p6FPANXMpdnl9/uhTAjP+ice51z7q+dc7+I5JS+KD9TR4N1d3DRlWuYj50iB1583k+6gTFgwIAjYj8yN90XcWQucs69e2GbxTn+HCTvfT/b56TXISq5C5g/84ebVw6Hs/O/u5kvFtsEO8+TtzqGNh0LDnWOC+C4bZYzEcfl5phPd5qTThTehNy/UuzwUuAjORVmJ/QlwLB7O+Cors9a+x2I9H1b7nHG/0ZSkB7jnHuPc+49iLQ70FOjHQW2jR0nS+mss30+PR57esBxYJABn2Lk5PSPAJdYa39qMXHeSnXSB9KLGGbsRmL6RWSdqkWce4jvjhZ3Qpiqh+WXBDDLkzwlsFK59duRNcle1ft+0Qn4Igt9kPdd7X11Q/73ycwlm3381yG+Oxacd5jPZVL8LuCtzrmfKRtYWRt0N7geuU8nE59BjJO7IrlOBXdFWIB/P9ROx4E3Ab+cx9mlCHO8mE96KCxOriC5M9vyZKyUw19mnldzOWIg3glhQAoOyr3JOS3vBBpEvre5cL5yrkWm+5OIrP9sjm7iW7UHVwk9j/nYKflv5zIfz+XzgAEDTh28c26nKq6Lc/wNCBv49ci7dRHXMLfpDjevHA6FsboACaDvFrudJ794DG06FpzH/FrK5/I+PB6b5Uakzy/YacPjxOXAhdbalYVCQwfNN8cL59xnrFQSvtRKAaeHI1LwI8LKkm/3Z3tRxdK2u7I9pe2uwA09BvZQc26DKAtetfD91yOB6FeVdLAF3BVJAZoV8XJSdPJfkFoaR4tt4zHP56tsn09Plj09YAcMzOrpgZcjMpQnHuK3ZyHSx18/xG874ePA11gpKASAtfZ+SGL98aK84GcVY62sbXWiiisdC0bImO63aQ9S9KGPjwMPWZikFrdxCJt9kXPuHw7xd7h81aPFIxac6UcjBTFKXuESvevJ+N6Fz4XdW4zefgC4T2b0Tgpy7ugHEae6jxKlPajw13HiLUifPCr/LbKqB0Wx82T4EA52Vt8NfGseIwWXIv1fHO+/Aw7Qu748iT0871++q3Lb7gw81Dl3KNagTOJfvfD91yB5yEdjJBY8qteGVeQ6P5a/+mdkaYFH9LZRue0DBgy4ZeEvkcDZvsPMSS1SffaL9J75jEfvcOyPIO+9/3mEbQ7FEO52nvw48B35/bPbNh0L+u9DjfRDeR/u1mY56Dqz4/hR4PEL13CiUSTE/eu4NfCN9OabE4g3Mp9Llzi4MNGh7vlDgS+5XgVk59x/IoHp/jyp8+d+u98NXGy3r4P6HYjt1g8g3B1JV3kPB680UHAlcI88v5f9Roj0/IrD7HMkPCTPoQWPQgJGJah0Mu3pATtgYFZPAzjn3m6tfRVSkOBuCDtTIYbzE5ACLIdcY3UH/C6SE/dOa+1lyMvoMoS9Oa7S50iE7PNIJbbnILkOl3GwXPlmg3Nuv7X248BzrRRMioizvx9x+Atehkhp32GtfSki73gWUnQp5mNFa+1PIgn2e5GXbItEAB+JSE8WmTMArLWPR6THd9xF3uoepGLh7yCFHp4D/IZzrkSs3wc83Vr7UYTF/F4OZks/SzY0rLX7gS5H7V+fr/MvrJSqd0g1xLs45561Q7uOBs8HPmStfRlShv7b8t+2omDW2ocBK8C98+fH5J8+vsv8Xpxz11ipDPkihI1czK+5HDjfyvJLn0IcwDshEq6/Wtj2VchE+CfW2l9B7u3zgJe4XDHYOTex1v4y8Bxr7Y35+D+BBEVe0TvWb+Zr/nHgbCvFjQo+mZ36f8h/r7XWPhdhHb4BkXq/vORCWWsfgAQaHuycW2xzH1vAL+QJ9guI9KlBgl84567P4+oyK2vZlSIRe9mdMmPAgAGnCZxzLtsJb7TW/iryLhkj88ZdnHNPdM6F/NuLrLXXIdVqvxNZ2/JIx77JWvt85H3SILLeEaJIuSynYFyOBFcficz9X8hFbnYzT/4K4uy92Vr7GsSh2FWevrX2A7mNu8lbfaK1tkXe/U9E3v1lJYDd2iwHzSHOuSsQG+H9SHGo30YCjPcH/sH1lv85HuQCT68BXpad4muROelKpPI7MKuOWyr5ngncvsynzrm3HsUp34ykTL0QWSt9UeZ8OaL6ew8iiXXImHjXIY71POAPrCwD87dI4OPOSFXrgrcCP4PMuc9BJMEvBd5QFFJWqvqW8/0acD87Lx56wM3XHH81co/fZq39TWSOfyrCfpd1zMn9+QDn3E4qsy0k3/WF+RgvBN7WO9/JtKcH7ICBWT198BSkYu39kaIAb0Fe+I9wzv3ysRwwTxIPRR7CNyEvk2ciORUHDr/nro49RSKjHnkBPR+plnck4/rmwPcgRZdejxjtf5z/e4Y88V6CyD7+BPgxpGqrodcvzrk3IZHZeyP340+Q+/SPzNnMQ0HnY+0mAvtiRGbyR0gezWvYLsX5+fzbC/K/LQuRxpxv+ySEofsrJAJYvn8QEqH8ecSQeCbi2JwwOFnX8zFI2ff3IpHS73EHr7f7SqQfi5Hylvz3TUd5yjcik8nfZyOijzcjOVy/ivTD85B7/X63sG6sc+5GJO/VIH10GTJx/hzb8cvIckLPRgJJe4GHuO0Fzsr6eC9HWIr+3wX5fIE5I/vcfKzH5Tb+bO9YikOPn0UHcxOpLvwUZJyfCXzbgsHxzNwfz0PGz5eQMXZcz/+AAQNOCZ6KzLWPRxyG1yHvt7/ubfMyZG3tH0HeC6vIe+CIcM79EpJT/82IDfJbSEBwLW/ymwjz91rk3frkvN+O82QOnn43IsN9O+LIltoDO8Hkv93guxFG7O3IWqqXFgbwKGyWQ80hOOf+GlGulCXD3oQ4jJ/fZdt2i6cjNstLkPt3A/AtC8W27s58/vwKJFWsfN41nHOfQ9RDF3Do2g//G3HK/xzpi4sRm/KgQoXOuT9CxtwTEGfzXkg6zKd623R5/7Je/K/na3xy71B3Q/K4L0RUW/259Dd7x/pEPtYeZH361yP35iHOuf/bO57h0MTc4nz6xny+1yDP0LvpBVROpj09YGeolIYA+5cTrLV3QOQaT3bOHW3lv/+2sNZ+AxKFfpBz7oOnuj0DThystf8OvNA59zunui3HCitLIPyDc+6H8+fnAU9zzp1zxB0Pfaz3A7VzbnGNvQEDBgwYMOCQsNZ+HeLQne0OXgv2FgEry+78P+DrnHMfyd9dgdQGOarCTIM9ffNhkAH/N4e19tkIk3YlUmL+2Yhs4Y9PZbtONbLs85PMk+afg7zATjUzPOAEwzl3opZSutlhrb0Vwo7fi92t07e4/zchSy78I1I58VKETV7MMR4wYMCAAQMOC+fc3yHy8FskrLX3AZ6GqJL+7Rj2H+zpU4TBWf3vj4TIGm+NFBb4MPBTJSfvyxgjJCfhfETm9BfATyxWY/5yR16y5XBy5pSlrSfiPEX6ejgEt/P6dv8d8d3I8/uHiNTpaLGOSO6ejeS3fRp4wlHmNQ0YMGDAgOPALua4eKLsjx3mbZxz/kSc5xaI1yDpMk9yzi0uMbQbDPb0KcJJlQFba+8C/B6yJMP1wON3uczEgAEDTgNkeczh1gC+0jl30Qk6zxM48oL0P+Cce92JONeAAV/uGObmAQNuXlhrH4hIaA+Hy5xzzztB5/oQ8wJMB8E5dzIrGg8YcMJxspnVVyGVTf/AWvt9SML+g3bYB4T1uhgpPHNCmJsBAwYcPR75yEf+8NbWVnOo35aWllrgohNxnhe84AX/9OEPf3hx+aAZvvEbv/FzJ+pcA75sYZBCIh/n4OWgvtwwzM0DBtyMeOUrX3nt29/+9sPOcXe7292+xAma4x73uMf9/A033LByhE1OyHkGDDhB2HFuPmnMai4//e9IInbIsoTrgTv3Fgg+HEqxmwEDBgwYMOBE4huBvznVjThVGObmAQMGDBhwGuKwc/PJZFYvBK4qOW15UvxC/n6nCfFqgFf/4d+yvtkxGtU0dUVVV4xGI0ajEXVjqIxBKVEzmMqgskTfGENVVfKd1qSUUFpRmUq+NwZjFFp76jpwxp4xq2OFJndIShAT3XTK337gA1x31dWErmXv6ipGa770pavZWNvPuBlRmYbxeMzaxn62NjbRqaIyifPPHXP7O6yyuuqp64RSCa01kOi6KSkl6rqiaWpiCnStBBOUUsQE0+mUGAOjUcPK8jJL4xEr+/YRQmD/DTdy4MABJtOOrguklDA6EJPGxyWu27+XrXgOYbSEx0OIpC1FtzUl4klGEQAVAilGfIygFEkpUIq6rqlMRUwRjaauavbu2UOtQKeIrjRKa7QxdMFzw403cs3117ExmWC0oWmWGI3GVHWF0hUheUIITKYTWj+lbVvarsX7SOcD3kd8SHStx4dASJEQPD5G2q7De0+MkRgT02lL27aEzuO7Tu4tsLI0ZmV1iRg7Ki39dv6tzsP7jo3NCesbW1x/wwHaNoAyGK1RClJKxJgIMRESKKVRpqSVSH8ARAVaGeknEiklGSdKodCQmI1FpRQppdnfdsR5wfT8W9lPjhllGxRoQ4xJ2qQ0KI1WRrbTGqUUcnYFSj5rI/dOmWp2bG2gMgpFghTQKWBiB0QiiqQ06IqkapRuQFWA2VbXXXpZzqWUghRJKUr/K4VWmv5SYxrQTFG0EDs5twKSIekRiYaEIakEKqB0oFKgU0Jp6f8U5SjKVIRU7ohG52vt3yOlkrRXg07SRo20U6nEqDYoHSF5aqOAhEcRkyJ0HZWCpaahqhRLSytsbE3Zv9HiqYlKo2f3KWBSwqhAbSB1UyZbB6h0kn7VibrShJBIKd9DBT4pvKrY6jxKaVKCpVHFnlENfopJnhS9HDspKgyVqTBNzYTIeteS6gqMxneBFCIKCL4DeV3R+YTWFSFE2s4DikQeO0DKY1WGXURpMHia2rC6Z4mmqWbDUilNjLC2ts51N9xEF8NsvJXxXcaXvDbKmEfeG/kZmt+n+TppSoHRGmMMWmtSlHEUU0Qh7+5xM2bU1NS14Yw9Y/Yuj1ke1Zy9by9LTSOjsTJ0MXHjgXX+5fJP87mrr2Hqvdx7reRCEpx//gWMxmN8CLziFS+ZzS9fxjjuufkbH/AIrrrqy70bBwwYMGDA8eI2t7mAD//Vn8IR5ubTtcBSALjpwCb7D0wYjRqWlsaMRjU+aEI0VD5iTMQYMYGqam4YVZWiihHlARWyc6rpfKAKitGoIimFpiJ2ifVNj8awPNIkVY6TqOuG6CMba+vEtmP/NdcRVeTATTeQYocfrVCZiq4ZsTndYGNjA4KiqWCpatg8a8q4UqS2RWswRtO2UzY21kkpUjcVq6srVJXBdy0pRXFWY2RrY5PpdMKoaVD79sFkhPbrhBiZHFhjc/9NbG5O8cGTUmLUVCSlSWoP7WZgo1WE0R665CGBnmrazSneTwlGEVVCh4QPnhDFyUhKEVE0TZP7zDCqG7xqiT5Qq0RTaeqqIiloRiNCimxtbLBx4ADTztOMx1S6Zpq2mEzEWA4x4EPH5tYGk3bCpJ3QtnJe7wO+S3Qh0HWBrvN0weODOK5t19FlZxXAe8/GxhaTzU1858VoTUCKLK+MWFkZofGMxw0hdlSVYWNji/XNKddfv5/9a5uAxihd/FAiEELKDpECLWNKKYXSEhCJCkD3/My5K6fSfLliMd7nzmffYRWDPjLzVrOjPUfKDmB2VpUmoVBKaiUopcXZAJQ2OfhBvhZxZo2pqap65qyi5HkwWvpIk6hURPmpnA+ISoOqiapCmTFSNNaQ/dPcLjmYMUY+xzC7RqXIjmGaXZtS4gip2EFqIWXFoDKgx6BGhKRIGrSOaBWoVMKQUMqIs5TEWUUZaWO5P/QDAzr/ZWdYg4oSttKqtCkwqkRXiEgAACAASURBVM3sPE0FaI2PidZHgg/sW1mG5QjR000DBza3uHF9yjRoIiY7anJ/VAgYHWmMQtMx2VhD06GJGJ0wCmIMcn+MwSeIGILWrG9N0aZGAcujismohnaLJgXUdMooJMa6QukGVVVMtWFdB/bHjmltoDLELqBDghBpfUtIiZgUMcoN63yk7UIOaBhi//5pQ0qKlAJaRYyOLC+NmLYrOYCn83hVhJBYX9/g+psO0Hp/0Fjuj3VxEDUxRnwIOYAwH93lfuR4GEZptM5/eRyFGCAq6nrE8tIy49GI0Uijuj3QtsSlhiZF/KiRwKRSBK1Zu2mda6+9li9d8yWmbcjxC3m2KlNx0/41lNKce+65pTmDfPXYEQCuuupqrrzyRC8vOWDAgAEDvoxx2Ln5ZDqrnwNuY601PanRrfP3u0IIMTspiZij7yEE2rYlYahrYUm7rmM6naKUoqqqmRFlFOjsbJTIf9d1aK1RqqYwD2trE6ZbkfPOWqZeqoRZAEiJ8847n8/9x39BVLRTz9RPijlO23WEmJi0LZ2fElMgRfAeNieRtXXN6t6GURNJIc0MOqUU3geqKG0vDFX5C8ELA1RpmrqiMcKWrB3YwHthKbUy4oCgSAmMqcSoR1HpiKajC56YElppYTIqQ4yakCI6sw8qZscKZsyJXHqirmtGoxGTrS22tjaIxhA7ha8rdGUIMRJy28fjMdqIkeq9Z1RVaG3oQhQWlsxKxgQ9tke6Mgg7qQAdSF5YlpQSWilMdspSShhjqE1Fm43d4D0xePasrHC7292OlDrW9l9HSol22lLXK1RVTdMkxuMR6xtTQowkcQPlmoUgRSVx3opBXu5VgRA1qfd55roevLx0uTSlDvpc7nP/F/lOxrqQtnHGxifh9fIBxDlX+Vu5/5mVUqBUIEaD1vkalMKg0cVhUUDKbHC+cK3ymuvKADrzo9lTzY5zysePMeTrFrWA0kqYPXLb894GRUhgVJVb7vMoK86lzky+7FcpRWU0KkU0KjvpKvdFPo9SlEsQRjt3fWmPnvdRShBjLKM5v0ci2ihiDOK0xUjwPneq3Ju2CwS/QYwJrUDCN1rY9Cj3TSNM/DQGKq1Q9QhFBcnjU5D3QL53WilClPMareUalbRLp4SKEWJEpcgympWYGEfFkhYm0teKgGadRAwdIXQkn6ijQkUwppZxE0ErTdvNg08ySuTaA1nZkZS8i4hElTAatNGZCY29MSoDrbwXiiPaD9IUVv2gMc/2x0GX+6UUemG7OduqMEoTEXVB8IFYBVLUdF1LTCNAMZ22aBSjZoQ2Bq0VW1tb7N9/gK3JBO8LQ6tQOqFUS1zfxBjDmWeecVBbv0xx3HPzgAEDBgwYcHPhpDmrzrlrrLX/BDwO+IP87yd3kROzDSklcewqTwiGEESOhop0XTWXkqXtzqDsK8eIcXs1cO99dgIMIh42TKctG1sdK+NKDCgtjNkFF1wACdq2QyFOJogx1npPnTQhekLwwtRlFi5ExaT1tK2m0sLuKKUYjxr0nj1M2ynG6LlcLUtLtVZUlWZ1dQmtKipT0zQN00nLdLo5a3ttKtTSEiE7OFVVU9djNjYDRnUY5VEqoFWW9inEOdU6OyCg9HZ2BMSBLX0cQmBjY4ObbrwRozV7V1YY1xWRSKMaQgiE3O+FYYtZHlsco6quiSnhpx1t65lMp8Q0Z3pmrJi4A3NZYZF9FiO3MD6pMDRzKWJVVSyvLDEej9ncmBJjwnvP/gP7WV5ZlusmUVcVda3RQUmHzEcZ293Q4mT2HNWYxHjfIce7zzxtP9bBv28/g/SHyJ3jjGUvY72Mb5W91YNdhBLs2H4NqscypXwdSae5gkAXx1GjMKTs0MzbraUt0ni5v6WnlHyXsvMPCa3F8RcXt8hn5+x3KhJeFXPAIjvZGrRC3Op8frnnItWN2WHNYuY5szdjdEvfIErrHOigOFSZ0auMoanlqZ+mQMqs+NbmFEJkedRwzjlnszlp2ZhcT/QeVZt83flacvAgJQgkqnoJCKjkiaHDxwAEQgrii6vcKYDJ7whizAGH3HcxUoXIKCpGQJUSKkU8AaMUY13TmkibIsnIdRDlmSmyZzDE1mdZe+lvCCnNRndKkRQTSpTkcv09ljpGuS9VVRHbjq3phK7zs8elz6Ruk6/DQc7s9pGdZv9Vnt9tEuE8Fot013tP2ykqo5hMoG1H+FHDpPPUVc1SDkIGn5hMpxLA0xIIiFEcXhVTnh8MIURRvgw4YXPzgAEDBgwYcHPgZMuAfwT4PWvtc4Ebgccf7QG890BkNJKCpIVdqypDStB1HZU2NHUDeu7YFKe1OKqFKayqai4/U4qUNHXdsLRSMVoSGTCZbUMl6qYBBd63eN/RdR0hdhiVUNFkaWB2BBKkpKiUpqprEoauTXSqAzzRB5rKUNc1SiEMakxEEiF6YXyMoa41pq6plCFFSCHiO083FVY1pRZTi7RZZy5rZXmV5eUVuvYmVGqBqUgqTSVMYmYNUXNnJGXWVWtxQuYGLbM+akzFWWedJcwHoI2haRqaphFWTmuapqbNuaEhRsmfa1sIBlPV+BiZTjum7ZRp6zMTCMWETQqSVmhtek6aiG6TEoYrZQZRaUXdSB91bcz3GfavrfHZz36Wusr5kymgYsqMu4yFykiec4riZM/VuMUJVdu8RzHs546lONfbGaXF/z4SUt/Ry/+ler+lnkNUmM2SD1qCEoW55KB82Hnby32UPFXm975wtCnnL5bz9JypWQ7sjJPsHb/4qHreD4kkOb4pioOg+u3PbHmKpCSBCa01SkNMPjtGIlGWdipI4rBEEjolaRvzPOFMDMtJsvMqXZJmwSJl8hhGjqWVwpiE0Zq6FkVCyQUW/ygxGi+xd+8KS6MarTWbG+t07ZawzslLr+Q+CimhEyhdSQBDQ8zK7aQVUYnENoY2qzukjyUQIcGDFCIxRGKUZzz4RKWNtA9DTIouRSYxsdVGJioQGwNanoOQH+MQM/tMCehAiPl5jr2+Ujlolwozn8dKjmJIrmmiPJNdzicPPog8d2GMH4pVnQUMjREpMJmlnQ2kOJPVb1ctyDvUKAVGixqmjDcl7LEQrhHQaF3lcQFbk03WNzdFhRMSITvbEtDpB3zS7BoGACdgbh4wYMCAAQNuDpxUZ9U5dznwtce6fwiBEAKoRNe1jEb1TArcqErkoTERUyBoYUiLXE2M/DlD0zeO5gaMkiIjlUFXQu3EIoRNwvRMu5aQAiBtiFnG6jtPCp4UNdoghWoya7E1aWnWAxvLFX7fiFhHQihS5cTyytKsLUXmPJ1OCcHTNDXGNNRU2bgUh1srw2QyycwqjM0IVZmZw7l37z6qqkbrm0hpAnFEhRSRScqgTSJpyXnUSpFUNpZnjN12WWvXtVRVRb20zHg8LjcEraAZjRmNRjPbr6prcS7MOpNpS+ejGI0kcfCDJwQpBJNCoPUelBilaC3ctlIkLcEDY7QY8Snl/k5ZGCpWtdYaY6ocjAjEzKS1bct4tIymyFWhm7aMl5aJIRCDx2hFGwJKmSwXnbORMza1Nz6gGOE699UCa1oouxl12eNpew7xXP6b5c2ZKY4943/e/4URncuftSpFnOYOZMltVUryWHVxAhD5aqZPZ46JKlLz3MJIr91q3sYyBsjOocoeYiTNWLHykIgDWQIJJneFbJd0djNDJBGz4wYhdjmoIc9pyrm5Ieddxihj1Zgsv9XVLI84zX2YmWPS8+1lrKjtRa+UmisrYgyEKAWJYhQHraoqTDMiJNi/fz9f2txiMp0gPpoiBo8ydb6nzNuhFLH0WdKzYBVoYTOVJiVPlco9CrO0hK2tKbUy+CoRPRikeNIkRCYpSB611nQ6MVEJr8TBFgpa0ym5HyKrF3VB1wU6H/Izo0SaPddNk1KiMobQBbQxEnTLTnYIsXSxbJ5VGOLExpmzvk3GPlNUzJ+HWeExyAEM8nt2XpypjMW+mmHGtuYbrLUidB6jxuzZs0IzqmiahpWVZUbjmpQCKhm89xzYv8b+/QfofJT2IgoVpST1IUVJD6mb07VEw82P452bBwwYMGDAgJsLp/XsHWIQByFGptMpdS3VfL33BF8RTRCHVanMlMxlaCklKgVKa3F4yzFDoK7rWaEYnVmWtpOqmKERw79CDKil1RXOPOts1q6/PrM7QEoSyfdIBc9Kk5RHqURdjQhtYjL1TKdaDG26GcvbdR3e19khE8mn1vKb956UIpVRjJtKHBAtUmFTiVOcuoip6mw4aoyS/rjyys+ileRvGaVojGdacpWTyC5NyU0LCpUdkVJ8ZZYzmYqDJFLUGKP0cZKKuKpce0xUlc7MmKZqK3SW+BljxAxV0v4KQ6gq6qoSJzMXayGludyXeQ7tYvGW3CBi2F7UZ15Qy7C0NOLOd74TlVFcd83VtG3HZLLF0tIYtSSySVMpmsqwpSTfMm2TAmdDW83ZeRCnpjC9ckkL7MyMYZx/nh0vt3t79dQie2YWTJkb/HKS/v/Lb2nbedWs2FCfLEqz7WcM5MzZzAx8priKkytkV5SQRkKq8KqUMzTjjI1VmXErKaEpkvNDtxfkYeb4CyMeVUTr3H4lx4wxEIInIiy5LoWukso5oWbG2JMZSa01cXaF24MFxVnextLltqpylWqev+p9FJmu0uK0Rmi7wIG1dTbXIiq2EHwekQliQNcVEQks5PDXrC2FLE+pyJczo0zpMylwZPJYiFHk9aPREuOlJbl2HUCDN5qoAz4lAuCVZPq2KeHz8WNUBJ3JZGPk/oVI5yObkymTthNnn4TKAboSHAkhEICUQmZipSJ5Xdfz8Z/7N8RA53P+a8rv4hhnhdfmY/HgZyiVcZnHQpFr6949yt2xLVAmQbmKoigwWrG0tMS4aVge5yJ7tZHxmJlS0XvMlQDKaJpKFBRaQwyB1b17OPvsc/jmhzyYAQMGDBgwYMAtC6e1s6qLbFBBFwKTyUQM1xCojbBx1NXcQCJtk/+GEMQp6zkFhWX13hPRhJjwOtJUibXYQRpxxqohKfBesbx3Hxfe7nZ8/jOfkXxSo/A+m0dZmhZjQhuRnYWYl1oJHZsbgXa6hFqSZRpmuZcxEQhZ4gxNU7O8vIz3dW6j5Fh579FKkwyMRiPOP/9c6rqhbmqmkwkbW5t03ZSuC4RoiNFTadi32hC3DN00oJPCJ5FRVnVFCIo2TMviKBnCfKUiHcxSa+872naC0WOUMsLuKZEdSn5aPkKMVLowXGIEE/MyFqEsraMZj8d47zFaE5U4IikmdAh0ypO85PCVckKHkgsWB6n8VUqY4vX1da648kqWxjXEDo3CVA3ee5GKV5XIgGsxZH0puIsse9KjK2djRdB3Eg8tIyyjbxEzsrXnBCzKh4szUdQAUswnzBna4gCq+ZgT3653Pp3y9pEYfQ7A5KVDTGE7s3Q35nzVbW3Py9BEL1Ls3HopjCO/k5nKlD2RlKTS7KwJ2aFXMYoEU4EUEkqUnOSy3I3KhX1E/SA+qUGYdTCZcTekXvXlfAXbpJxqRjOnmeNTtpwHCpgx2EoWbpLuUpmv1/IOCFNPpSJVdixTDISY0JWhqmt8TKhY3PeY2fziJOVnYnbXSwGrWu6lCsQk9yaGhNEVRhu8j2BknHbRs5ZAG4VXmo5EFwNdlreCIsVIGz3TFPA5WKN9InbCUnddmLGYMRdcKu/B0UhTa8PS0pgYA207nT2bMUSCCrPgGUqKx21uTdicbBFioEjpy9jdVg27x7DKPUmzIEFxPOdqFraN/9kzlITpN5WesbjGlFSNgKk0VZWDEDkHO8ZA6zu6zmPqmnE9QhmNqStGdc2+vXu4610tF9/vftzjHncnhHmbBwwYMGDAgAG3DJzWzmrMzF5MCRUSrWKWd1oMq5ANrhAjOpksEc3rNPo5u9B3cASJKlUYk8BAqgytj2xudawsGZSRasIJ8FHywJQxIq+MgZQCoIk5NzLpQEydsH8pQQpsTTo2NjY4c0+N1j1HOXj8tGM6Fee7qjRVZWZsKyQ6H+jaLrMNU2CV0WhEVYm0TRthSrYmUybTDqWYOfCaRG1ibqtQopGEMpKjp0Gq+MbCD2WouSHar7w8ahrqusrsiM6VWWWHsl3XSbVXUWKmmTOrtM5BBEVTV6ysLNM0jawFGYIUrFLiNKUYCT2ZoZxhbuiW+6qQJSmMMcTOz4qr7L/pJrqlEavLI7SRfUIIdG1L1YzQSvq5rg1+2q+Qrbb1Q3EcZ3Jg5oze9tzSQzio2wzwsifbDP3+ubZLb8v2qVz5TNoq3ljv97R4zO1tKYy4AgkcqH6flqvOn3MOqjg5YcYGFydenGclrHtS2697Vowpu2m5QrVSiUpLHmLyWfI7W7Yn5kuKBBSkIl+uUEaW4ClsaELW4UTP773a9r95H5e+jUTUvJvY1tzEjFX1CWJSEoipQOMh5lzyFDGmwlQVKjvURYI8S/QU2p1tjLhon0myIu2sXVKVt/ffMojB5Lx6XbGZC5/5XGl42oojFkPMp4uElPCl38ixhFDymBWmqubsdr6PMQa2NjdZWlpidXWVqtLceMP1bG11tG3Lxsb6rFBdTOKgd53PaQ8irdX64LG+6LD2/3vbo5EKU58Lbh0kP+gHcopkGLzvuGn/TZy5dwwpF6RSEvjSQFCKrvPUVcVZZ57Ftfv3MxqNud3tb8dd7nwn7n63r2Rpacz6xgbveMefc84553CXO3/FQdcxYMCAAQMGDDh9cVo7q20nxpTSkgOn1TyPVdgykyu9qpnyrjivSilMZcRYLhIzvZ3hhA6SIgRhfMbjmhAM11w/xW9tyNp+Gxt8/qovElNZbiWhtEepCFqKuGgdAY8xEvWPMaJSkCqpUZYSUZltipmJbbuW6XSaWUzFaNxQVWZWECoiRYm6doqpNONxQ51qpu0WW9NNYXO1JkaYtB1GgWkMWgcSCY0XgdzMlt7OhqQ4d4C2SVTz78WJKv1d12nGrkQSbdcRQ8RoydUNIVBVFY2S4iwjnc1SpbL02SPiw3nV5iomogqSn6g1QanMpMRZoCLF4tj0HJLE7F76cn9TZDwaccEFt2I62UAlcbSVqol5iZHxeESIkabpmLYTYs+ZmbF0am54F3Z3Zogf5Fj2HMX+fjOmiW0Or2w/r+w733+OUk15G5NVZLmp1NdNPZllPkaW0/YDM3JzIylqQMaj1llunUoFX8hVpCCFvGzMPOggLGKcO4d5mZuZDLZ01IwRFmc0BnFOJa6R2cykcs6oB5PQuhb1RNKyVq2a3Yx8XJWfOTVrq0pq7hjPum57sEHGSJGdyvdy3ZrKQPSy9EyI5LWJxTmKSL/VuqEyCm0qfIDJZIuUGWetZgsGMQvOgCgFZsEJRVKSiyt0a9hWwMwHeUYbDAFF204kxzpXsW67jjY/M0rl3NF8NoUsx2XIOfLyhYyVOFc9lLQCpZQEvUJgdWWV1ZUVdKVYXzvAZJIl/2S5flWBVlR1jTay3rFpW1R+704mE+q6nuWwb2NT2f6MGtPLb435PaNTySWQe1uGTunRmcIg1w9IkY31NSaTfRi9j6Y2sixQSChTE0NgfX2dq7/4RW7c2ELXNXe84524+z3uRjud8pcf/EvW1tZYXV3lPvf5au5+t7szYMCAAQMGDLhl4bR2Vv3MWc22dK6WG4Knm26xuSl5THVVy1qBlZkZXlKVUlPVst6nVBCusrzVZ0dGDGutwBjFysoKZ565j7oyTGPHFZ+5gms+ewVq0rK8soettZbxUk2KCt914DVG10TVgdE0I0MKim7qMUSWxlLQJ8UwZ6lixFSa5dEYA3RdK3LLGFFRZ4miAiW5e2hF0zQsrywzGo1op1Om7TTn3hUDVlicGCNJSx5uQs6ZyxcBkodWpLsxRoprWqR7wsbEmbyz5LFOp1MAqqrGaFmPMcUkLG72MSJIICBKoZZRNTfuVZGnFpYU6QfvW1kWgywXzlWLjTHo4IW4Ks6Jmrc7pZiX+KnosqQ7kTjjzDM459xzuOaLLZPNqYyVTmTApdJyVRlGo4rNTT0rTlNYviIlneVhzpxM1beq53228Pkgo31huzkDuj2PddvxelTgNkf24CZkuWs2+hcKiImzmqXYZfzl4+tZBEPaksjXT0SrWYmxWeAA5TObWNZbjSiq7NT2nLcU8lEjKQRi7IhGnFHI8SSlUaYS5ttUKGUwVGhVE+cJzPn84kiGKM5nJvPngQAK815aUIIuxZncRu/JWqdGmFoVckDAiCMY8nIntVFU1ShLTjV+tiyVXKvWJhdOirPngyyL7jdepbI+biAm0LlaMkreT1vTCVM/FUl6N6XrOpRpJEWgC6QuopO806LSwoyXa40Kk4S9DUR87quYAjGkWSAqdD6rT7zkbBuDD57kRQasFDRNw2g0ZjwaSbGyBO20LFnTzfpznlOfaJrmoHHdl7QfQnAwOw4H3Rdm8uAS+CjjfjRqUEmzf/9+uvPOQLGS10KW99i0Fad/0k7ZmmxhgMsvv5zP/NdnOOuMfXzVV92Le97znrIO69oBPvKxj3Kf+9zz0I0bMGDAgAEDBpyWOK2d1aoyNE1NyYGrjKyzOtma0E7F+KnWpOhSYWV0r2hLyXsajZcYNTV1IzlNbdsJu0NLjB2VqdDKoE3FbW9zO+585wtYXl4mMab1inP2nc3epRHdxjLXX/ufqBTpplqWm6jERA4pMWoUjakwe2saDVpPqEzEd/N1KrVW1FkOu7Q0ZmuyMZNqttOWmCK6qmhGI1b3LKPjiKZqqFVNtxXY2GiZdi1d9CitaUYV59RnEKZeHLhxQ6cabrx+nauuv4Hx6h6qei+kJjOQnqQyqzYrbjOXh85lleJce+8hJXyQ9V2NzlV8M8r6mTEJe9T6Dp/lvyGIUzqdtrkicCT4QBs6JtOWza0tYW+qapujZbSmNiYvsVKcZ1kSJ8XMWmkt903LEkYJ+OI117CxvsbK0ihLGsWhaLuOrcmEuhlLsMAYjFHMU9jma5SWKqUzhqz0THGWthGh2+WM86V/+k6wOIL9DfuGfTlPiReovLRQ6h1HihnpzDTO1w2V3xCHUc0lsbMcTVUc41ISSJocKQWmmDO/Kstvgf4ar0ppiNk5QxxRMhFbKUMgUdbqlKI9wmjGwqqmQAoBohRUMsagdIXW+RojUoyJCLkqsLRBkZR0YIppzi7m6yo8pS7sNcXpKf2gMksdc0EeMElJRrTWJB1RMctOC+upIGpFUBC83LMQFUqLc6aMlnVokUDHXBKsZaxmnXBhBrexvSgCcl+iDxgjwYQi9VfK0PkgfZvkfsQoz0uKkZSr88byHpmFBcAHDykQvTxjSjw+Ut6uSOY3N7e45kvXUjcGHyJV3VDXY3yAA+tb+E7WQO66jkl2ZkEqBRujaZpmViSuquZFmfqy3+0S4L66gDlznvcr7HnpJ2GRE7JUkGI8WmZUrzDdPMDWVse09ayMKiqNrEGbAvvX11nf2pyNlS506KgZr6ywumcvVd1Qec9yM2Lfvn0MGDBgwIABA25ZOK2d1bW1AxxYW8/OhaarKpq6oqmbWa3SNkf7hTnTEIMwgSlilDhXphHpmqlrlDayLl8KGNNSmYgxNVo3KNVw3XVrbG5Fbn3eeayecTa3u8MdWbvms2ys7cdv3kRdQew6jO4wdURrYR9Miqg0xeiOSieMDuxZqVlZqlA64GOk61pUjNSVxqicX9Y0eN8RQmQ6mdAGj65qkoI9SzWVUdTKEKcB7xWTTc/aZIKuoKo1lTHUCqpKgR4Rq31ct5b45Kev5KaNDS666HzO0HtQqUaky15yXn2RNUNOppulRSpyRdgU8SESoyb5VorooPAhChOFrJGplfR7yGxoVJqQFD4kiNB1PrN/Cm3qzMVJvxWpMcgarjEKK1cpDXpeWTSlQNt1M8dPxYRRhsrUKDUlRCnQtLxnD7VRtNMpdVURg8eHROcjzWheZbSqNV0IcvxZZdvCSEZUcaboOZGhrOG4fZwehkg6zK/ZyYhpJmctLF3pj5KLWZgmkWfr3rFSJjrzPjEz6Fn6G2Ik6UhlTKEZxUFFlbRTSk0pYeU1KEM/9bUvzxSWUMaFOPMhV7dVefmZSpxJJc6Uygy1olQezo5xzBLmHHRQWqF1lSXPWvI4ZxRb6Qsw2kiAghwQUJLzKcvBpPxvcVHFYY2z88o6vRpNiAo8+CBsZCxjHyAFko6ImyT3OQaIXhGpMUaBUXQ+oBT4GNFFmzB7bvRM6ZBy3np5xEDTdkXWnv9IWXYtQbjkQ3aAs/Ose3ciJlLM1bC1psusrifStZ2cL+eualPNlkRCS0ViyfdWTCZTQqxJSbO52TKdSsGrwppWlZFAgzb5uSxr56qsapE887JMmM456fNUgnlF6Fl8JAcRYD4ONcyKXpXRXhkpzqYTjGvD6tISK0vLdM2IbprYmnQsjxp5jykpQLU+3SKoxLRrUXXN6niV8y+4Feece27O700sr64Svef6665lwIABAwYMGHDLwmntrAqDIcWAJJez5ON5TJKlZ0KI+CCFQOq6EoYhCpsSkpeiSJ0i1pGqAqMr9u7dR4yRpq5yruoyKS82b0zFNddchyJw1r4xy3s1043I2v5NNrsbMN0aI5OoFaQgLJ1WENAonTAmYkykqaCuQetiMIs523Uta2tiqC4tjcTwjrJmZFLQtS1hMkUpxXIzom4aYVUqzWikiOsT2ukGaapYWlqmGVegE1FXbMWKa6+Z8LFPfYGP/dPn2HvGbTnn3Cl7VjoqlQ1DFWUdVxPxoZtLbFNm8opt3Mvx9d4TUhIJZUqEkKjqhtFoiboypBTpJlNhmDsvOXlFTqsilRamR+fCUHQa1FiUzpMJ3nfiOKi87mYSVjQm8CHhQ6DzMa+NWYo3IQVqdZI8O1UxGjWsrKyiomc6nYgroNS8yilZquyFhVZSaUqMZ60IoSzfk8ceMRfgmTPPx4JDLe8xL16U8nYxBAAAIABJREFUtv9Wlh3p7SOObZrxvGxjZaG/HmuRpRaGWCkt15nECS/sZF5llVLpd8YUzsIVRWI886jFFVN5rdV89lxyrOewJeYli3MieZYoq3y9wtwijlpuizhFpkdGpnydxc8p1ZKzPFkpcVqSmgcwstNbuNVYnrseQxhDkPdJUsLezlhpkfkqVfpRmDqRKStUEMbTJHkGqh6DLNcwZ3sVRYKdsoxd5bErud0lj74sFRXy96X3Z13QGwNqdt9E7VAKz4UUZgXGjJGAQymWNCtMpo0EKlKaSebny2j5mWxcKVm/mOCJyWf571wJUAqcte0UrSStQu5U2n7fevdvptrIxyjvFaX0rJ+lKrSoF7RS1KaiqivqWpakGjUVGxsH2FirOWNlTNI655tLYGZ17x5udevbctvb355zzr0VK6urrKyssLwsqRPBd2xNN5hOJgwYMGDAgAEDblk4rZ3VpaVllpfz8gpJivkUeZ/ORo4xBm2amfEeuswuaFmPzxjD6soqF154IWefey6mHqGrKrMHI7o20YwaAKra0KWOAwduYnOyztJSx7jynHX+KpU5mxSuJ65vMlLisIVOCnrOWKTsDFaVwtSgtBjFpIjRhtGoplLiqLbt/8/em+3IkqXZed+ezHyI4cyZVZVZ2V1dXeouDRAESJAEQWhAvCB4QYgvQAF6GelCLyBA0KtQAAECDTVZRBPVZFdWZleOZz5xIsIHM9vDr4t/b3OPk1nFJnVzEvC/kJnnRLi7mW3b5rXXXutfa8TYgq8SWGO0B3O9XpNiwmHIyTN5j5FCFwqLheX8sifGjpI9nV9gcSQRolvy5euJf/FXv+Vf/foZL98KYx643e15IiPBVcAmlpwMKSlD2sD0d11uaw9sZVxtzbMFlTIbAzFN7HeJaZxqb24h1X8KyuDlEvHBIgIBISXY7bekXBij9s5NU6oMqiElzYzMFajGVD+vRoUIs/Jylm+WknHeA5bXL1/TBV2cl8oYtxig1reqY+2wJhFLphQzg2CMxRl3BBj0X4eYlP+YuisJPbSnHgx6juu455X5T8e9gEcU6J33KFCc3VaPLkJzTqs8uIHl+hqh1I+zM7uutF6V3lZgKKVKUhsza1RSq8xanp+D9mwqW6ugsAFc6uuau2wqGUH7nO9KzO8yyaZS7LaydJaqsZ7H9tADXI6YamM0jtRVClSlsYAUZYKl9t0aizVSHX5B43z06A6DlYJiY0PKqcLrCsiaCdQRw2urNLgZXd3pZz4CxDmn2cTMOX/nGTyONGrvbRsv7dks5mBY5uqmxcHxvN19vfactQ0g5TS/rh2nGSxpjnWsANhUE7dSWVfPYrFgGIajjGh7tKFwdMwjoPqda8oFce+8ocoIZGbllaUN3mKCpSTPfr9jt1vS+XOMCaQc+fDHP+bnf/bnhG6JcYHFYoXvgvYF7/eklNjc3vDi2TNKve5TnepUpzrVqU71w6n3GqyenV8Qi1cH0ZyglMrCMIvHSsmUlHURlhJpmkAKfRdY9gsuzi/4T3/5S558+AHL1YqYmiQ34bslBsdme8Prq9ekHLHBcv/BJffvPyTYzNXz33H17A1MicsHH+KWns3rZ0BhuVQJbykJ54zG2FiD8wbnQSTV6BnIWQjeERYaw2CtLuJadmy36PGh07VvVhZxyondJjHlCRcy5yvH2aLnyeNHWOkZR2FMhSkbPn8x8P/85W/47IsN+7KmX6/I9EyxqPTOG8qkC/AUVZrbTIXg0G8HRiWaRmMhUkr4EJQVyZlcatzMNFKKIU2ZFDPOO4UNRkFYLpksmSQJLw5EJZRZYD/EelwFNOr2bOvCHGVMc6kLf4sUQxHNjixUsxlTKBSyRIpkrHhKytzeblgtOwWsxtU+ZwUEUxwJIWCMLryDT0xRQ3Odq4Y4dY0tR2vpxuz9fetujIdFJM/jfPyzVsdGSsfgQPNQG7A7XtwfOwzfNbUxxmrvstHYGGcsd3JkAXUGzrN81tRBV6dnvScYS5k1rIJz2leqMm3BlIzxDmuaM3BlqI2CYYuAcTWaRirLLfq55AqqmV28FUiXKiGuOZtSY0wqI6d3v45Fk5qaUsdTNzLmsSuVAZYGdA8sa6noqhSoVmKVkS3vAGABkxEp2JLm7x4r4LwD64n54PKr8NXNmz3zZ8z3vQHEw32fc3TfBbRHv5+B6ZFCoG3UtR5mY5p43My/l3fAIkeMfQPB1to7DurGMEuCqRsKzXBMRDfX+r4nxTznF7eYKD2WOi/Pfdoi37muIoWUDlFeOtcN3nosDlPjvLrO0fWWzllW3X1evXrB1fUtq9UZxlnGmBmGiX6RCZ0yykUKb9++Zb8fmaaR7XbDzdu3jMPA2XrFqU51qlOd6lSn+mHVew1WgZl9SNWIpJRUQ+p1YZVzJkeVs/rgFIyILs41WkUX3NvbDdvNhpyFzW7HZrNhjCOhD+x3W759/i23m1sycP/hfRZBezzj5i15d8PCCxdd5p7f4qwC0/1+IASL83outjrXKrOhhkO56MI8WKeGJs6Rk8qWSxbGcaIJIReLhS6wjcGZQpSJMWb2U8SOBRM9a7+iD56UhGwt0Qaevx352y+2fPUis089xfVY68EIt5stkh2GQNVQYmzEWEHK9y9ajzNpnXM6hlV6Z62h63wFl8IkCSRincNYh/fVwbQkYtEYDswhgsQKmGVAozMd3mlOrLWGmBIpRbATRAUh1ogC6dIW48rk5MreNhOgkgvOO7qux3uvUTqh9nEWnSfjOM55tq4Sxbb2PYoODVknnU4+08xijpihd+bnIV7m8Hc5ev9/KBv7LsvdpLB3mNUKcGYDpXYCRlCy825czuGPVe4q+l9ENwSkgKlS0Xm3wDhq06SOj6lZo95iZjDtEOOqoNjMUvfWj1ka+GvXY0Sl8hbdoEC3ADTaNVGyAePrM3AYS2OM9t/OV9F6H+vfK5hvEVTtRYpNZb6fs1hXCg0lV2F0jQSqQNNQN3JyfU/E2EjJI+OYlJGNFuMCISzrZzuaY3KTO8/cuREO7sEHaKtDc5d1fBdgvgtS22tdc/edQWGdAnPP6NHcrOB9HoMjsAwHhnWaJnLWzRLrzPw51jYpfa4SY4dInt2CO+c53jQxdc5a0/qo7x7v99YRqxq8ow8eZ4U+WPwicHPt2G737MdE6D37MZEyxJzZ7Qbi7Y79OHJ7e8tms2MY9lhjyEl7etcnsHqqU53qVKc61Q+u3muwOo4j+/2eMsvS7LxQ896zXC5ZLHoWXaBfdJyfn7PoOqZx5OWLF2xursHA06dPERH2+z05Z/bDqFEMZUTQzM1hGvAhkGLkmy+vdYFsLQtr6SngLT2OXZxYOO3hzDnVzEOLcUcLQxFKForVbMwihZzVZZOijKYxChK8DwzTnu1mh8GyXC4oJWNNqaAjYcmoRanj8uwRRhI32x3Z97y8jvzzf/U5f/Xrl9xOgc1uoFtoP68nMA4jOVtE+sp6FKxPhM4QJ0Mpd4HNMZvjvafrVGIt1uBdNzstd1WuOfZJwabz+BAIXYcPlhRH9uOeMWYQiw8BKbU7sQjDMKpUOAk56eZDRnt81aQqYhCc6k1xyRAr6VVESOXuQrvkArjZcbYavVKKzKCgSRu7rmOKGecM3mm/ZqqxHxjA2RoFYyk0d2k7G9jclUvfrTsL8pmlM3cAwh234N+DZtUZtWZsCpWdbCik4a96HvNHNPdVuXMerf+yvVbP6OgcSgVWHpW0VqAqM4MJuThloK3DuLZZYCm4WQZbXXOquY9QSmLOWG29uACUqpaoDsPWY20Aa6oz9HHvqp6/9mM2plKBbhtT9SEySO07VkkvVepcQbKZiUJl/9vlm3qPmnHWUS9l27gwNmNswlnBC/gQmFKLodFrKvOplbrxUfNb64A3GXZzam63pF3Tsey3AfR5Hsws6mEjRNsfHCYdQOg8OYDvA8GtP9hZSwhhPk47ZmP1FazWSCJjyTnVe+BnA7K2uZVSInTuzjHnPx/FKbWftf79llc7b47R5rmp8mmNxUISi27Bou94/OgB3zx9yfXNhuWZZbsf2e52DNNELhBTZkoad6byc92MkCJYZ2dTvlOd6lSnOtWpTvXDqfcarH7y8Ufcvz/WRVHURXjLQwwKVvu+ByDGgWG7ZXtzQ0oJa2F9tqakxJurV7poq72LYPHWEvwCIbAIPfcvLjHOUGo/WRFBSqGMI14yj+5f8PiyY3i9IRTB9Qt2m8I07ukXPcF5YszklEnJ6dq3CHROe21NYZomSrBYY2pGqcavLN1SI1kQYs70LpCL5q8u+4Dkgsdz//yc0HVsbiOvrxNvpo5//qsv+Tef37AZesap0IcF3kLXOSRltpsbXrx4zdnyohrtjAomc2PBFGio+UtjZ3RxrfEa1XQGg7MB7wPBGayoK+qy74mrzDAVfAj4EIjTiBOhtwpwjHeUotJb4yw5F3VtnkacyQQn5DLRB8GljEOwJVOsgltD0mvyjphgv08EDZPFisWI1Z5Zgf1+wAenubWpYKo8OWfdIMhJe4qtNSwXC8ZhUzcHlA2nAcNS1DkXQ0G0Z/E/Yg5/H5t0kO1+/+K5SSNLUfdYa33Nt22SWGlJNkA1w6r9jiUlxDqMP8q9rFEzVJBgpBkToZ9p7RwNY+txSmWxjW2gCUQ8ueaxGqcyXzlizkppINIAGZsTDYj5Kg0tKRJlmiOmRGq8VAhk4ylSHYUNVfKrPckpyXwezunzC2q+JiVVWbGd71FjnaH2WDeAaw2xKDiVthEyS3Ob4ROI0d74UkdC6j1YdQ7B4HtlYlMSSs61zTVRUiH4HmM7ijGI5Ab7Cd5jJJNTnJlSa50eRw5jd8ykvrsxYu+Md5nvsbW2mhTJnZ+3OSRFn2fnPK6aogGEcIijOczNg7HVMXA+nLMqLqZpIqVEjHd7ZA+fcYhQaufd5vbx5qMKPvQ7UXJGfAPYmWUf8K5gSdy/OGccJl6/vWKRhNvNju12T0xJ5eWiZnXG2nnDpkmy49Sygk91qlOd6lSnOtUPqd5rsLrZ3HJzs6vmN+pOOUc/AFdvXpFL1sWiUYOWUgrGgre2mqrUHi915JmZJ0zBWY+vmZvOG7Aq2001M9SI0J8tWQZL7wz7YaOLefEIGe97PYYopOq6nhwnckxgCsUV+uzou0PvnxRDEjVoOTtbE0KgiPaBliLEFNlvd4gUlus1i2XP4rInWIMj8uLp19zu4euXkX/56Tf8zefXJHtJvxS6LjOOOz588oSPPv6Ib774mt1uZLffMsWJvtMsWZN8U2q+Izs9MC3UUQNdaIbgcS5gjCU47SNz1czHO0Pf9Uw5I2UimIyQcVZwwSBWiDFBzhgxBGsRr4ybLeqsWsQq+yEGSzczN1OO2qsqkJLw9mbL8/iGGDNWDM54nHEUyrxJYSoASzHpArhkrIWcEjFGQpUBO6dAxVYGdr5kvVUVuev9lZkhustE//tKx/WwyIfGdmWVGb/7WqNGVTKzspXlK6WCKqMM4WyOU9llmKXBTUquz8JxqEvtC0WwTbJqLMZ5sG6OPGn/OwbTzR2YO6xn2+wAQ1aghBCM9soqpZoP41kKlIiUhGAwPqjzq1dZea6XLHUsDAegY9uYm8YQap+zayAllypxdXOsj2bE6veF9xbJkRwFZzvtpUXqeasDNYA1ys6LsSpJrgJhzYI9emZQdjqliZIbULY4c7SpIV7vH0IpqiA4NkdSMHcYy2Op8GHu3GXw77CuHJjXNr/az44NkMAQU8TaoIDZqekVMDPWB7lx1nExNZnXqC9Ak9u3c3BOWy6maSLGeORGrJtezlVW9Z3NmgNjfOgvtsZqP7Qp+KDsbdd1LPsORyFYQ+8sGeH+vQuuNyOvXr9hN0ViLqQsGFsqCBZMdUpuIB5RF+RpipzqVKc61alOdaofVr3XYHUc9wz7DSI1pqKxLXKIv/DWUIwukoSCdU2CqP1mDdg6pwwbrkaTGIt1zdm2ZjE6g/UeX3vXnLV0wRNMhhxJacLlwn6zJTjorKUYQxwnRAr9otMFWxFy7RMbp8Ry2eOsx3mD5KQS2Cqfc/64L1BIMbEf9oz7SCqOvl+pu7CPClbcmpurPb/+9AVffhPJXGgMR9kiOdIFBW/b660Cy+DYj1uyjBi3VDYSj7MBYzRH8a7Ri9xZAM+RLxiVPDuLNQVHxhvdJPDekim4DNMUMbaQo4KSUhIYj/fQdYF+saTrFyqNFt1YcN4quC/VHTgmxlGzU/fDjtvbW6aUyd4yRu1jQxSYgPbSto2IEDq6rgMpDPtIynp9nbXkmJmGkT4ElZIXS9d5xilVZ2TtXy0NkLYFOxxJer9bDWTCO72HdbV/zDQdG9F85zNQhqnMr4XW63t8T8xBxKvvaY6sR7JjvZMZChQDzpr5p/omV8/XgVHpbUu9PDqrIxBf5b5G6mxoOZnMBlCGgpPKyiv80B7RIlX2myklUSTPMUTOW5wzytBK3SixtZ9UDv3Uh7zSNi8F42pvq168Kn7vjGqhuR5bq4Zhyuh11XgpqxmUiXqfKjNr8fO1aQtrQOZ5VjOJ0e+bUkQNvkzBGYf1YEyiGA/4mc2TcujfvXPf673PWbAcXIzbXLrTd/ru76yt97yB+7vvaUdQmC0Hafk8V20dy2MA3QBlFYtXE7bWB30A0ColLqWQS5zbBpo51vw6vl9dMN+h0jYZDlL5Fl8TfMBbS2cgGDBWuDxbc36+49mbp8SiwvIiQD4A7vn6RVnvUgopqerlVKc61alOdapT/bDqvQar2jF4WD5LzVqVmqXaTFRyKhTJ2n+IsqPaLwfOmyr/C3QhEFzAGpXfhuCxlQmx1uC8w3rNKhTAe6+LfMl0dkFYebbPXlNCh+SJKUasEbwL5BxJydJ5dbO0OCRnpikyDZau99pjVo8tRnty22K81BxJ68AFD5O6HKeY8D7gTI9drrn3wZ/wOm/4y1/9M15tOiZZszy7oPeOfRxB4PXLN7x6+YbNdkNKEWMNP/vZH7FeL5BS8MbSuY7kc42EkcqeSJXSFaxtWY1NqqebBOqZW3AkHCOSIy50LMKKxfqcacqQMoEzHAbXBbp+Rdf3rFbnGOdJqbCfRsZpZBhHpjQyxZH9MJDGzDhMs3tvjhPWCRInShH63rJYePZTJiaUAaubEtYadWWursKpFFw5MEilZOI0EWPCBXVS9cHXjYw8L9J1Qa/gbOaw/n+0u/19mdgGcI83D8yR3LdVmYGwglNT2dgD66vjcZDCVnBnG/N6yCKVeoD2cfUTD+fDwa32LkyWCtoyIhU0SUIkkkvESNS50aTKOZMr822tI5dCKspYxiJQTN0kuEtu6/GPXXaZn09KaXyvMnmi0l2ZgY8oc05Woy50A8C18TIFY6qE1xhKNvp9YJSdMxSdC4BY0xTi8z1ChNAtDmxkBdu5VKMne7hHM1tsjszMjuaGSGMDj2N3zCybPdyPAyBVd2M3j9G74HYGjQJ939N1qliw/h2zsprx2uZde+YxBiMWa+s1vAOMm3dAGlUOrH93db6VO0qC4zmu37f2aJ4f9xSX6tStRluqkDE4MsY6khUePbzPs6tbtlc3pCzkcnhu2vywdcMjJc2SVcXASQZ8qlOd6lSnOtUPrd5rsFpymhei1hvAaQ+fUYZPHXUzdK0nr/VZqZFOv+jo+0Co5iTW6CKxGfroWqoahlTpYM7K+ogIMSWMKRjJjFbwacew2/Hw4gJL4vbqJd46rFWZqvcOjOCcwRoPVnAuk0VISSMwFn1gvV5jKLNxSc6ZgpqaeB9YrTTP0BlLKpEpefbJcf1qxwcl8eQnv+R/+It/yK/++nOMXZJSYUIBe4yZaVLH5FwNjcZpYrffMgxLTM701mIRnHU4dzB0yY39OVrUNQCXUsa5UiWZifXKcXl2jvOFs4tzHv/4Yx795BNMhOvnL0nDnjxFcnGMY2K7H4nbfQUkgTJl4j4xTZlUDEigw5K9QYJnlIFEItZzn9JAwmLdAusNhUwSA85gnEWysoDTNGGMIXSBlDPeViBjPSVr7m1OGe9V/qgy4ErbVbbPHCMmDiBS3XL/8Jx9l/3SRf7d3zeJ73e9haEZ5cxOv8bUnsu7DJsUmbNK66nTegxLKTgn9boMkOt517xVo7EtpkE9qXJg3HwcPYdjM6gyzwtTKUxbsbCRgiXjJGNywkpESlTAbKtKwelmjfcOsZAFjPHYsMD5JRAw1JzV+RQa2Hlnr6COawGsVHXEzMweIEkD+97pM9p7B6LfAbYUBdd5JOVRZbHiICwINEl8OwcF+pTmJNwOoO+ZWWljqlxawbUUzbNtDsTvGhCJyGygdcdFmmPgJXdfb+6CxgbAj/tDj02aFDSaClaDsqyi3wvWSGUd0wxY78zRpnt+59xUPaJ/9t4TSjfHQzl3kAqX0jYC7/a06i28O59zzhrV5B19FwghKM9ddHPBCASrPcqr9YJ79855dnU7G6gBukn4zvjkXGYjr1K++7yd6lSnOtWpTnWq97vea7B6fr7G+e4gBZxdPbXPUBeAZcZXxppZDuqcrfl/EOM0536WXHMTjZudaaUyNNS/JylVggjWaJ8dJVLGG8bbazrjOVt2alSSNH/RWYuURGyGON5hjMd7XdGnlIlRWCwW9IuOnDQPNueorIoBwdF1QWWs8/k4hgmevtrxbz97xet/9js++fkvefKTj/jPxfPy6Wtubva82StTNcTIYtETjCFKxhlPwTCMkZILvTFYKUxxmmWDBwZE8z9/n8ttnCYMkcXa8ODhBR99sMYQ8V1H5ws2KPzZ727Yvr1BYiEmRymOVMD5UJkgj5Ha6+qCstTDwBQz4wS7IbEfR1JOWN/TLxKueIaUcKHDdeocm6vUFCMap4HM1+K9MsO64eAR0XxHjxoROeewRXBO5woVHCowPcgIBcVprUfy++r3RdrITMX9B7CrRy7Hldec5dmzlLPKg5vEcpYM056JKks2DRAfuRE3+lQU7LWeWmuqlJaD6Y1UNr2ehbLPHKV5zqBeMK0X1oA3FmOcbiZJzTm1+jxS+yqtNWAD1nVgVTLbeo0bU2ntgUU0zlZGWa+nxeQ0BlUqczb3WkoByZpv7KyCHe+QrAnNzqjp1pgG9rtrUio417FcXhLqBkYByKnGZdXPRGOcqLE9qQB4/btYWu+r3qtMy0Gtt28G4sbpDpdujkndaPuu7Pe4n3R20229+TWz1tQYKWPuzrO2GWadI+dMTKrcKCWTk9TMYd0MSZk7QG+ej5VBN+Zw8gpC3dwi4L2fGUzv3TzXDmrcgzzXGFPnxHeZYIyp/cvqOaBKGIs3BiMRRN2o4zhQUlQ1COYOGD6WS7cxA82O3Q/D73/wTnWqU53qVKc61XtZ7zVY3e239IuzmrHYGITa49QWbwD1Z0UKknLN1jTEKVMkz7EZOWdSdb3s+x5rPGOcGIe99q7ZFjNT80YNhBDwRg1JnFuwWF+y2b0hx4HOJEyJNQqjRqBUMAye3isjULIu0rMIu1iwUd/nSNiUMcYRS2ZKA9Yp8+FsR0FIYrjdw998+py/+c1rRvF8+MfwwU8e8803X+KCwfvAfrglJj2X/ThWSac65u6Hke1mg/nww8rSgfUeYlKQgi5GnTUkpEokDVI0M9V5jclwxuCsYUiFzX4DWbCS8M5QxklDShdnnF88ZNrDLo0U4ynGMpXEsEtkiSzXHrfoyaWwLwNDjiQDYj22XwKFVCaGPBC6JctlzxQn4m7HdjcSo8ZoeJsIruC9I8WihkrO4Z2ep7WGnIQskEVwviNlBe2aRmTJFpVoD0YX985SjNRewEO/KJgqL23M0aGX77hnVetI3vgdKlbBIRzLeetvRMGf4YCXVdGbUSoTDM24phqHiSBZ3yTV6bc9C0YOZkumXZMAcjDMKW3ThmahVBrtiaOydBh1Rq7qA7nDMAvUzFuwWBP0PtpCGW8p1RFYz1x9cTV2JWBsVzcv6vnrmc/Zp0Za9ImaF5nSEl0rpTufgTLFpTG+CEjEloQlEYrHicVQGOPIMG6xCN4m1qFwfv+M0K0Q2zNMhiRWN5ByJMc9w/62sruGEHqMDWA8PiyxbkGpclmV1TY5czN2k3mMbN3UaGygoQLEnCsgPDD7pu4TvNvrrPOkbYA07rng3cGE6lgOW2cjKUesN9qCkCHlSOgC1jsF307HsIll27GPAXZD3M2cqb3QGAWRKWYGIn0fgIyIqlncUawX9ZmYN02ocUjWq1KiFGzJkAcsCzCO0p7DuoEoNTNZ86rNvFlUpMzf9fNGE5BKZkqqNjnVqU51qlOd6lQ/rHqvweqv/vWvMLbjgyeP+dGHH3J571KZz6hsZNvFjzHroliYZXc5FV2cGKqxi4LQJncrpRBzZooT1sKyXxBCYwU0AmS33XOz2WokTU4Ek/nZBw9YXC4Ybp4TPLic2W5vCT7gfId1IKJSY3HN/MNAsfTrBcZbzXRlxJm6DJfqaGog5hGLI4vBdEvGsuTrF1t+8/krfvfNa376s5/xF//gL/jZzz7m1fNnXL3+FFxH1/e8/eabutQ0hNCxWCyYxpE0Tmw2G71231Ok9Te29WaV4Uk1j6lglZm51qgTdRs1jNmwGUditKRpw3ZzzfkjjymCJMP17cB2KOyiIw6Z5XJBtz6j5MyUMzsppN2Om+u3bG5vCKFDxDCVhPGOaAzJeEbx3FzfcnNzzes3rxmGiWGM5IyCHVR2eHFxDhhu3l5TyqS9bt5hrWFCICVSEUJwxDSS61xw3qljsTc4b5FJjkATlUU86rur/Z06Zo3VO/R76m8qWCyHXut3DW8OVRftR38+LOQPr2ssoi7Ay0GOXI8pkjBi1H3XVDMiIxVczuQnSIPfAJmDsY2ftbYNXClTqrJgayzFWMqBNKxnfThPW0FnsYZstJcVF7DH11uxTxGVHCt7aevH1MxV5/UaZpRUx65UhrYB28Yk1tgdi6nzWmOGIOP/lflTAAAgAElEQVRNpKSB3e3EJul3Rs6FXEByYhUM9y/XXFycY7s1UTqyK+QpYywsXUcflqRRN1ZS1GxZEUcujlIMOKCo2RLGzPfHHG0eaBarjmtOeW41wCh8t1Zdi1USnI6A6oFtfVcCrNJqR+s31+E6mHsdWEUdp67vdFNGraQJQXv2s+iMTTV+R+ocUclsjSma5227l3J8V/X+O4fJRR2PgzvEL+mW2Xz+R3e0TgndCCzFUKqMd9k5ghEcQgi+Zh07PZ8iSFEvgKLoGu2kP7hEt2svRb/7Y846N/6e6oZTnepUpzrVqU71/tR7DVZTTLx685ov/u7v6Bcdjx8/4uLinHv3Lvnxhx+wXq/IOVKKJ4QOQ5n7tuAAUruuI8Y4xyzshz2bzYbr2xtSzqwXCwa34+b2mqvXbxjHQfMDp0kXRzVgPpgEP/+Ajx+v6fB0nWPpPJTEFCeKZJxmp2Bq/ytiscZxdrbiwb0LvE/EIWFKRnImdB3WBiRNKu20GrOSkrDfBT7/dsOvP32N6R/yX/xXn/Dkgwu2V8/54tMt/+9f/ku++uotwpoohfv37xGnCd/3TFPk4cOH5BTZ3t5wc3PDze0ty65T1tE7SHFe2LaxasC1mcAQzWz8YkRBQQI228TNtuN8AVFuGeIrYh4IZx9ydnnJq5dX+LCglMJnv/uC3RQxPoCzrM/PwQj7YWSMmc3ulpgS2/3AZj/y4s1rbjc3jNOenDVfNyWDlAVxyOQC/bLDu4CUiRYFY60lxpYfGWa5ZBHdbBBvD0ZLMdLXzY4WzWPMNEe3WAPGFL67vNZ6Fzi0UuVva6IzdTEuR+/7/vd+Xz/jLPlsoOUYIcjhD02iacTUPFiV5Po6D49BJbT+VMhFgYs6vSpgNNURGWPIldUspsXTVJhyNBzGWDVNghloq7wYvOvqs6DvbrLgUgq+FMQ26WrFylVGW8WmiLHax1gBqmluTWh26EFqTQWRtadUMuTEuN/SecHWjGOV3FdWsxQmEW5vEiln+jNLsoZdVGbViqHzFuPV+9iLoesgpkIpajE2pibRLkCClqlqLRi1h5sZyjq+Zb63dbxN7bH9HiB16FvWPx/3oh6MmtIRODuYFkGLpSn1WNrPfceRGm1PyEWVJEUOUnc1iQJjDwD1rkS53JnH1hqsM+Ssx+n7rhqeuXm6zNfN4RrufGZRef5qtWKx6GsurJmvvzHqwzCy29VIMzG6UVnHuDkbl1LIqTDFNPfkHvflnupUpzrVqU51qh9GvddgtRTNxzPGMo0TT799yvPnz+m7wPOnj/nxT37ExeUFZ+s1q65Xx9E4MMWooHS/Z7/fA8Jut2O/38+gdYoTw35PKpnlYsFqtSTGSUPmp0EXlTXOpDEYRoTnz1/TycDDC0cZ9px1CSNJY1oQoGa3AkYMcdK+uK7vmIYd2IjESMmJaRqx557V2ZK+9OzHPSKFmAvbMfP5t2/5N7+55etXifXFGffPVlxeLFn1gdurG774uy8ZUk9YLBHJLLoOKRkpheA9Dx8+wFrDzbLDivZtKetmsO4481PmaI3GiMwOoVRWBLBkZb1cYLsrvL3OnJ+dc3l5xvreY/xyCcaSgavra8bxhlIcu2kiZiGlkYLh+nbLNA3EFNluN2y2O/ZTZMoq2d3t9gzbSeWnxTLGCZEadeIWdH3Q3FOJiBjGcWQ/bBnHcc53dNYq814ZrJwVsPTB1f7hSL9YKFD1nkXXEexAjqnG80BDZY1HkiOToe/EcTRlpCgDaxpN9XvqD8V5fJ/kE76/Z1a5XwWsYhSoWjJ27i+1R3LRylWKqaLcFtei7rhFCiJee7+xCharJ7dBZtCp/zlkeiJNetnsiKjnqiZjMhPBCSna4+hDqPE5cuefmkBTAbU5sJRHzF6T/xdUBm2KcEDyNdIqeBb+jGUwOJPZOcs4jvU7IiImYYwQc0bGiPQZ6QzYoACIQhLHlDIlCmSNSirFMaZCKplSJdliBKgO5RRsMWACxQRcZfxKrrmnpkbOlMOmRJtqDXi2Z/AASMt87cfzI2edx6139LhH9NgJugFV6w5MrXOOlBLjOKksvn7ubObVzq3dvO+Zv8fAdz7XmmfqnKXrQjVkO77Wd6N1GtCtsVjW0HWBRd8TvFOJsCkVi+rG0W6c2A4jU0qIOcqJvWNiZeaNkRm8zvnRpzrVqU51qlOd6odS7zVYBZjXFwI5axTBMOy53Wz44quv6Bcdjx495NHDh3jrKuDcsNvt2O12TNNECG7uQwWqnFWxRI6Zm+mGaZo0qsY5xHc1WuKwuJldcYthsTrHEBHZk3PBWcE6QyqJkqg0k/bZVZKF3XbD0id856Aag4zjxJTfkE1msVghxZBzAN/x8vqGv/zVl3zx3JD9JfbcUawuwu8/fMInnzzk4YMP+OblW3bDNSnDOEaaeM8Yy7PnTxn2e3KKLLxnt9uA+UB7H0XNTOZcwrrwdM7NrGopBVuKusfmjIZtOLA9hcDNreHZy0K4TcRvv+Kn+cd88NEDXrx8zbPnr7i5HtiNiTEVum5JFsN+nFT2KBplknMhFuF2N7AdRgWhKYEI3nsWqzVCjd7oe3wXKAjPX77kzdUbbNaFcJymyvZYZVJS1Oxca6sBi2EYJ7qwojRjm5xxzuOMpe86uj4wxKiAYr7zB2muUA79eu23UuW2M6bUeWKqJLQxUEfvOOop/C6bevzKY+OYd6WgMzNFE8uq1NRV+aQ3gpVcwattH8KhGbZBwQr7BGVlJaHmQXa+tozgjLqylrk/92Aq1Vpom3DXtjgcKTPQac+StaJRTM7pvBI9foU8zP20clemLk2fWpld61SibIoCW3EOJ4WcVDbtg6MzhmC143GxWBBCAClsb99SkuVsteDi8j62WzJIxz4rME+VPE5Jo228WCQnYlKAWnCqtjBV5l+/TMTkGpPjEGtwxiJVIqxxWup4W2Te/jh8udEib+4yp3PMzTtM/LF50DFgvOse3DbbdMPCucMzL6KS5GmaZjXKDJCp944qR5a7wl89xveBTpWgS27MMTNjO3/P2AOj2mS7IDgLSCFOI9O4p+QOa3pcnQRz3ziGcZoYJo3LkSoFL/Nmi+52lAIlKwN/zDaf6lSnOtWpTnWqH1a952DVzLvxKiPUSBojtrKuhbTZsbnd8tUXX7NaKkO6Wq0IoWexkMoeNOBSGYmUdOEIUFmPYTcQgg5HShkp+QAIjKmRNxFZr7Gux3eOpS+4ckuZphrZoLI/KYWSBeMsPjhWwWHKqBmfeHW4dJbLi3OGdMuUNsgEzpyTWfPibeSvf/OWT7+8ZuQeSGQ5jlxvHN8+E149v+HDj/8z/uk//V/53/6P/52rN69YrR6wXKwxzrLdDwzjwPBq0GvNhdE5rm+uGeOE7QIg88I154MDsDtiku86awrUVBH1qPI8fbHnt5/fkvLIjz96xOUHEz/6xGJ9z37MFLHgPMvec/X2ljdX1yyXa9bLFXFSFtS7jhAS9x8+4GG/wFvLvfMlnXXs9wPGOIZpJObE2dmabArb3YY3bwuYEWMTzsksN82xyX2VMdVbrIzxOCamGAnuwLp0vcfEjPee9XLFbjcgkmm9vwda6XsCT+dZesx7MR/z3zu7v08OTJWWHo19y4zUqJa74MQA3jbHXTVIci1GpvYeG6v9pCqnrf19RXQXRVRebKscuE5giiQ9D1fZ2aNrOvRCmjnCpI0DzYAK4W42atZAF0tlyUvFzUZbga1AyTPqVcBaz8+Y6lSrx3YuqFGQCFnygXWVovEyVvskW7/sYcMCKLDse0zvODs75+zikohjGGrfNg5vqkGXZCCxWgRcFxjGid2Q9D64DoMhFg7zophZWSDN6TtHdcmuWudS8syENobYzMyjOvseg8ADM5hnov44e/X3uQfPxlQzk2rnDOJpmkAsxlblivP184WMgvC5PVmEIjMeP77T3zufVWbf/AGqxLyp4l275npfjD0CroWmWM8xUtIEZQFGap5s3VwExkkjr0oRxBYkNVMxocUXNdlvjJFYXZFPdapTnepUpzrVD6/ea7CacyHGeFjsGCHnhLG6+Mo5k3LWXjFjGfcjL1+8ousDl5cXrFZLlsvlvBiK06hSwApEj4mNcRqI0RJ80AXS0YIQNN5E8FzfbPnt777ikydrWCcWZsRX9i04SyqmymjVMGlhPcYK3veUNLKbCmfLBX0XFFT4juKFKMJ2Et7eRL54NfLla0H6S1b9BUNMbLYb/u7vXvHMCjdvRv4Xe8bjj3/CP/7H/4i/+Xd/wzg6Yrb0yxXffPOUl1dvcD5UQ5dIGgfe3t6wHwb6PuCt5l26mkF7DIC8c8qkHrE8cIiscMbThR4rHVMW9jvLdmNZru7h+gUf/OhjVutz9rsrwqJXKSXCYrnk/r37XJyv8U77RW93WyLC7dtrrq+u6IPn55/8l5yv17x8+YrddoCS2G9v2G6uWJ0v6Zcd63XAvtZ7jym60BdBJOH9kvV6jXWWV2+uNGdRNHMxZQXpxnmsdSz6XmWWMbNe9dxuArthoq719V/GMq+kqWxTA1qNLeLAOB2vi7+vJ/X7ftZ+Xj9i7hM+/O4uiGmgxHnPou+g5Nkky5SoTs3O6fmXUq9B2UgxhmJqd2lR111nLM46ln1P6Hs1whozU44E1ykwMI1NVXdhmkR3jk2xB+BVQZzFKS8tUo1wlAkzM/sKUjIpFaRkgtXnUozVe1QjbRA/by5JLhRz6P9U0N5MsRr/pucoaI6oiGbqGmfwZsk07tjt9mSuKbZjLBbo8SZhBFwRnEmcrwI/fvyAs77j5nbDs1dXXG0GppgoxuNCDy4Q40SMNTu5FJ2TWEyNj9Lore+y8qXNqcoAWnvcE3qQ5GtP6l15b3smyyw/OWx+tA0oNUqSOZd6Zml1x0Dn29FnlFKqDJw6yQ89tsenf7zRclwNrI7jqN8l3s9gs51PkVQdoe08LsYYuuBYr3sWizAbQDUVRrOoylkYYiJlQeqmZSmVwZ+duu/mxzYZdYuFOtWpTnWqU53qVD+ceq/BqhFwxpJpjrQHuVqO6Yj4cvNC1XuPFHj16hUiwnK54Gy1ousDy+WC5XLBzc0NKaW6iGVejKWUaDSCqYDVOTszc33f07kFrnPsY2S7H/ELZbNMKoTQ4SsLJpKZUqQrBm8yLX1kEthNkfPzJWerM263A/uUEB+43hv++rMX/O3XG55eDWTbYSl0wRKHAaxQFoGvnj7j//y//y/++//2v2bVB86WC+KU2O12PH/5CoxltVpzee8eb6+uuLkZ8T6w2+2qRDbPbYJ3jHzqYvZ4nGd2z1lCcHgfsNYrUDCO9eU9nDM4IwzbLSVDf3bG/UePGfaRsWQePHrEo0dP+Prrb+n7wGqxYLu5Zbfb8vXTb/ntl19ysx/YTpHOW372kyfc/8V/woN795H8mmEPwVmub6/xfaHvDX2n7LR3hlwXvdkoWyNSmMZBf+/dwTkUjflBlF3PleVy3kGMSNa+33GKh0xPo7LSUkSJyDpwOiwGU3vm3q0D+1jn8ry4lwogDj8/vP4AQ4yzSjLODFr9XAPGKkPmnWW1WvLw3iVx3HO7uSaOI0bAlFzfkxHrsDZgrPb1FdBeUsWqCnSLYvNf/OyP+eijj7jZbHnx6i1fPX1OFiEp3UkzjDrmnaVmiWpMDqpWEN2sAWpPbZWYm6N+Uw6sIiKYuuFQcqoOsB3O9ajxkqgjbAPEOWOosSsIlOo6a3QUjQg5JVIamMZ99YvVeBMnBecMKWfyfsD1But6OgsxjVhslVFPmJQYb98wXUemGFkGi5z1XG8nkkWdj6VQxJCKI+ZCTgJkvBO8reZlUkg5kXOi9VM2+bhUJOgqcGtGUCGEeePjIOt1AHNva5lBrp2/yw7z6iDH1X51BfFdWAAwDKOeSyk4p5/bepO151jmOJh3DZHuznMOEuIj8BljVKOzBljred01VioY43HWslz03Lu4IHiHr72rVWZQJ5Jh2E/s9yNJqu/2fM3qKt0Y1fn7HfA+HLHNpzrVqU51qlOd6odU7zVYtU4XP1IKuS5Kcs7ViMNWKa/UBV7RHkRrNJeyLqKGYWC3uUUo9H3H5eUlq9WSe5cXTFNkc7thu92SY6Tre87Pzuj7nnv3L1iv1ZVysViwWPR4b1mEwDIkdq+/ZHr7JdrRZ8klY3OZ2QLJhZQz47jHL2pHoVGWaMqJ6+2tgmMDYnoyaz798hV/9euveD0EhuzogscJNd/UY41lGEfeXr3FuJH7D9Zsr2/Y3exI0RJj5Pb2Fms9t/sdL16+xFaQIKUQ48R+v+PRg3uYLFgjJClQ8wtBSGmqwAO8rQyaOTjOihScLZQCU4qszjo++ugxC7tlGm/IMYNzTGnk6bNveLvZ8uGPfsJqdc5Xv/sdt7c3/Omf/JyH9++xXi1YL5cEA8EKZ73HGMNus2XYjzjrQQwpqnOyc7Dfbjg/X9J5Tx8CcSxzb2pG2b1pmhiGHev1ufa8VbrShw5rHD44jCSmaWKzuSWLqGwboQv6ufthQowylWJm/gusuwM8ebePdQax5vgHd+oPLfiPXzODEaPzxx5tINga2VL0xXMcD7W/1FvBmeaoW8+z2NZ5e+Ahq6y+ea3+6Ec/5ud/8qdcXV2xXl+y2428fnuDcZZEQWTugAXRczv0+DYJRJWqcuS+apjl/DOCoomTLa4LBBtwZqJkS4wZkUyKE0UM1nZYV02daJm3dROAg6wWI3hnCN5iSmKclH03tW/Ze09nnbJ2zhL6BVjHNLXopkQcJ7IpiCQ2QyZtr7h/vuDxkyec3XvI65sd5vkVV7cjOQtihM53+HXHfhjZlQEoM5q31hIb8w21l/kgAW4jY+comjKDPpFUpcPvzqNDT2qbL01efTBpoo4RNd5G8E6/8tvr2nm0DaoD4Gx5sN/tsX63N7adS7tgay3eB2KMNS7Lzc/Esby4ScmtNRhr8M6yXi5YLQPBOZw1IPqdimSkGKao8VfGOSj6Xa/svD6jjU1NKen5V1m4bpa81/93d6pTnepUpzrVqb6nfhj/712BlBztssvMeilISzGxWi5pbFZwugiy1vDk8UOWy0VdUAkpT2xubsi5cHFxzieffMLZ2Rmr1YrLy0tC51ivV3RdoOsCfR8IXcB7Ty6JafuGb7bPic7juwW+gKlRCTGrm6y3LToiMSXtRfNdx2K5wJIokhlSwoaOsSx4ep359WeveHY9UcKSYgwFYT+OLELHoltwturo/IR/suKf/M//iP/pH/4D/vW/+Cv+3b/9HZtxgzWW0HVsNluAyhoG1qsVEhOSDddvr/mjj35KzHuFCRVo5KxMBDCDpDbWhuYWrAyj5AGKLiCxidBPeNnw4ukX/OkYCX3Pxb1L+lVHfnvFs2+/4Re/+HPu37/k888+ZRkCi2BZLnvOloE/+/kfcb3b8ezla/b7gf12xzgOrFdndF1H3wf2Yyb4QpJETnv6YHj84D7j/gUTB7Mh0Lgg7z337l/w9bdP0YWyxTrLbhywZkEX1DQqxklNlpwliAIdY2SWlmcRchGwdnbRnc1eZgkq89//4DQ+et8fnOrv9rJW0PNuL+M0TcoeTiMOkFzVBxSC05zZFsUzG0Shfa+50poGp3JK9N7+7W9/x5u3t6QYefHqNW+vb3WjpRx6DVsXuT6TlRGchZoHgCQYTNM1t2e3afrvSKXtDJIQA6LgW4fd4myoEbdt0yRjcbNCu0iTXldW0Oq8Dl1HMCty5xQIGv3sPji8d4QQiDlz9fYt+91I1/V4o/J8KFinrJx3sN8NfPP11/iXr9iOmd1YKAmcXczsqDMdixBqX3ck5VHzlntf5wrzOJYaKdPaP625G7vSAOFB5msOstrv6VnVOXF3Xilo1RxT3RgrajokqjRIOSPGgj3MZagsLofv2lbHBl/z66Tdk3YvqyrFOIxJ83t88Dpf6hxt72//dUbVE9471qslzigzHhYd6vCs82NKid0wEVNWbwE5AOqD++9B+SAcWOhTnepUpzrVqU71w6v3GqyWkigl1YW6LoLa7ngxwnp9xpMnT7i4uIAijPs90zQA4EwFrcHz8Uc/5vHjR5ydrTg/WxOC4+rqim+/fcarV69VHpsnzi8e89NPPmKxWJBSJOdI13kwwjDsMNYwpsi4uSaWRJLCMEZ6SfTeY6wl5cg47rBWOF93OO/JkiCDc4XgDME5hExxS253hlcb+PZaMP0TupVnP2WsEaY44l3A9x0+eM7WC/70jz7hjz95xP/43/03dMsFCfjm2XOudxPGd/R9T0wZkxLWeX768U/50Qcf8PTrb7i+esPmdstuu+NstSKNw4FhyeUOUJoZnlI03ERARGWvhoKYhK0ZjONu4Hxl2KdIniZW6wt+9OMfcf/+JcNujzGOexdrfvKjD/j0fM2rV88Zhhs++fhjfvrTj/jo4w8RY7ne7Hh7fcPLNy/JZURYYOzEamUR23Gz0fu+3b1GxHF+scA9UyVoFzrisAeg7zuC9+SU50WqXpNhs91hRHBW43x88bigctLW29oyPL13mCzEXJ1Mm8zVyBForUwfBzAKh5/DcZwG9Xe/TzbM3Cc4v/qOZPLw8/m6gN1+1HlllIUXKaQ04a1TFISybMUK+sgbTDE1msYhxlNqPvCnX3zN3372BQbdeFmt1pjgMaVuCFg/jwU4bGXh61Vo1quph8Wigt/WLyiYmuU6X08F+6VAlII6aFusV1mnc7pJpHEyogwhBWMKjqKgSI9QZcU65jknNTfKKo+31tItllhbzcOyUFzBW8vl2Rl5jEz7PWHluDhb47qAOEMqmTRssRYeP3rIan3G0+cvub19TYkFFwrYQiEhNtP7Bd4GJm8ZpoLEyqWKml0Vqcx2bT04bCYU7dut97j1XB4yVMvcX97A6jGw1dfnOz2s8zHaz4xuOOnP9HlujHdz0pXa/VvkABCl4Utz6BE+ZlznjQYOgNY5S2/6uX/Ve1/fX+Z+4saMt/eF4DTbVlT1YSizFF+AlDO3NxtuNht2o7LalsbiHzskt12AP7x5dKpTnepUpzrVqd7/eq/BajMDMebIEVUKZ2dnPHz8SEEqcH39ls3tLeNuT4wTgC52jOCt4fPPfoNI4exsycXFOd5ZHjx4wP37D7l//x7n5+dsNhs+++wzPv/8c7z3fPjhEz744AmLpe7sO2epS1KwhuXZmuG1Zb8fKRIxnUrsnPVMMjLFiZQtWQLGeF2QT5k87PG9ASdkAm+2mV//9jWffjtwtQ/EbEESlKwulsCQRsb9NS56tvcDcbNkc3XNvUeRIU5cb6/JEijRaK6sMQrUfMc4jnz19deUKeKMZb8bSCmTc7rDNuiiVO5EYhwviLUX2FKymeV5kMgpkEbP+t49LD05Rox13H/wiF/84hf8+Z/+Gd53LFdnLPoFv/zln5FT5Gy15Hy9RErCSObB/Xs8fHCfIT5mO32IcY5xvGYYr4l5Q5Etzo8g6nQsElj0jvXSsxsjodNeVCqjnXNms91qz3PtY2s/L7lUpklmttxW59rgHX3n1VW4sULOVcLvmNGSuX9TjsGkHPflUdno43zHu/mUx4v+499/bx/s/IcjKbAxWO8okslScPbQNyqSoVg9dzKlGO2vtm7OWBXXIbZThlX5Spy1pDjgTXVYBc0NblBGBFPUTdaaFnOi42Ft60OtgLUCHIxmkB5fvzFNfKznq4FOHqzDet00KMCUBOtaPIky3sYBxioIbGy1yAycc8nEONLZA5gy9nBMay0lZXLJdF3H/XuX3N5u6DqvmchFmJKA9bj+DJsmbreRN9cv2G42WCBYQBLeRFzfgbMMcaJIdaK2C4oz2itcdDMolawbQjNLLSCFLHcdf+9+BzaDMGb1wzGj2MDqIW/1bsxRmXtObZVSVzf13ByZmT9zJsFb76+xtUe6fc7BBOq42jm0ed02GKZpZL8fVG0SPDXmeS7NYdXvE++U7c454vsF3uvzGoI+m2mauN1umKaJuVe1XaMcf3eZecxUlvzdXttTnepUpzrVqU71w6j3GqzOu+StL844KMJ+v+Pbr7/mq1KYxpGcY+2H0kVJEY24cQZyitrHKplh2HP15g05J0IIuOoI23U9fa//5KzZg59//hnr9ZKffvIRP/3px1xcXBB6r+YnpQCOKUPAYlxgipGcMn3fs+h6Xfg7yxgTvusJRsh5ZBoSzgZc+P/Ye/NYy7Lzuu+3h3POvffN1VXdXT2zOYkUbWtgJFuz5QAJAgVyIhsZlDgyEsQBEsBAYkVOICexZRiBkggIAkMJkMF/GAESAQmcv4xEkSjJFCmxZVIkbZE9kD3VXG++0zln7/3lj2+f4b2qpqSo2V0U7teoqvfuu++eaZ/Te+21vrW2qGXG12/f5Yuv3OP2cWKdKhKWymsPoRVYrzNbHNaU7HB+vmB+XvPaK2+xtfM4jz92DeMMN15/C+snFFVFFGjbSGksN27coKlrvLFIGwhNy/HxKTtbW2CEsipzvE9NjOoEKiI453pnzxDUvRNjsstxoiwUjMTGUkdHShWPP36NEl0sODg44Ns/9iHCqsbbgrP5iqcev8KP/MD3EULEO3X/lK5f1hiW6zWuKElFw+nZCet6xbo54XxxQhvniKxpmhVFoc6w1kV2dyfcPzofkFxKtKEmxoaq3GE2m3FyvsgEjc29tgFJHslGRZ1zauEcTCaEIKxXgSYq4rLW5X7YgYFyTuWVvbyzG7OqCVUwmQHrw8DoO4z43r0VLrJOKX+e6yb3MAIjAW87KWkgBPCly/JaS1SLI5WrWoWVmhOaTXkwYBzGqFFZNIDXRRpyX6Q63GYOMCUktVjrMN6re6yQJeXdybh4zN2fcV+kji+VWSvLpvmlCZtlqh3gSCpjFwErFEXZg/KOMU9Jcm9qZvaMpZpOsKI5yM51Zkza69uEhqIoMNYSY2AymfRGXE1KBLQHelW3rOfnlCny/DNPUb/hR70AACAASURBVLjANApVvm8EMNYy250gtuDu0SkxBJyvdJHKa4+qyUxlN4a0p3WIdekk0sPYsBfAqLWmd0cfA9FuEeYyo3qR7exY0pxNa5Txvtg3i6JUMYMbcHfNrOnWGeijlS48pwfwPCzMqAS5KLyqUepGFwsyCyqSHalFsjt0d38J1nnKqsped+qsbr2lTbCuW9qgfeJ2dC9IjKNtj9zN6fqiLy8abWpTm9rUpja1qW+FeqTBagitAiWx/eRMzZaG8HcjmQ9KkUTCGsEZlZKp0UYkCv0kNlmDMQVNCzYmDInVusWYBc45Cu8pywLvHffu3ef27Tt88Xf/CU8/9SQvfOAFnnjqKlXhmFS7TKZXSKkhyBznlB0ITUNVVVjnie0a41Uy6bwwqSxJAsumJMqErx8GvvDqgltnjuC2VOacIhIS08mUne0t2tRk+d6UZDy3TwOrl+/y+d97i9ljz/PJ7/te/o1//Sf5B//nP6BNsFo3FNWUrd09Xv3a66xPV5RVSdu0+LJAnGMdIrasIDWQDL4qcGtPTDFLAnWS2uWtxpRoU8RkSbaIwUWDxyLJsU6R83DOlaJC1jcgPYWVFdLcZX16RLNsWNVCaSquH+wQk2XVNMo+EwmxJYSWuqlpY0MINc1yzro+x5oG5xsiiZgsvpgSAqToiCmxu71HWZ7jm5aqKmnbNVUpxLTG+kQ1KXJfpgOjPYptjPhqgpVI3bQICe+3sM7hAGtKrKmwDlqJiI4sbEw4q2Gz6lcjg8GNDB2+MAaag1ES+efGOCBl8XAnvNTq8kSh629kcNklS7WxPcAwRjDSKiOaDWz0R1neaxyxA4DGIza/zkjWTMKYmKXNnbuxV1dhRElRk0ik3MtLvs8C3pClv65n41QKYZTpzX2ieuS2V0mLRKLemGqg0zHKDFEkPWgSBUspBUpXMSknOeoFxOdj6bZr9Lxan2XaErCmVFbRWhJCNAZxjjYzsQZDSC2+dJzOFyznQjnZohYB4/DTLU4O72Pv3OPZJ65y/Zmn2J2WSKg5PT1lsVyzXp9SVBVPXymoql0wBl9UnC9q3rpxh1QvczxPIqL9ox0DTVIJs0VwXhUOumjUyXXpwWaMSfN0jeaRTsqST3ziE/zgD/4gv/Irv8JLL73EdDpVWbTJ49HqIkTqJOUCbUw9ay4xs/QIvltwMIPTrjG6UEGW7l521hVJytqP2UsZFoDsdIqkhBWjWbRW81e9dbqwkwKumtBGYdkkZlXJ2aJlUljNffYW5woWsWUljiZGYohqwOZslh3r+YoxG08ZXZjRG0sdjbMd2aY2talNbWpTm/oWqkcarOrEKNG2MUvMRizAyKmy61nreq00JzVP2Z3Lph4MmXx0rJRKYYui6PvAmrqhbRp84fBes/6WyyVf/eorvPba1zi4sscLzz7N89cfZzrd4/DoJmIik6lup5Pt6TRfZ5opRFxpMM4gydNQ8datOV9+fcn2/nPsrg65c/9I2ZeYKMspO9u7GJdoFitiChq/gOPO4Sknp0uuHuzya//oNzi4tssTj+3z/LPPcePWXVIyNG3Ler3m2rWr7OyqvHG9WOU8RZ1Ehhg09qWNiFHjlrZVqTDkri/T9c/lOAhajLNYyizJzJNYl5i356xqw/FRYPbkC8S44Oz0TRanh1hKJHliUuZ61QohGfBdBqhgHAowcJhYYUwFNHoNKs20tFFoQsNiuWK9almvVoCnaZICOgvTacV0VuGckELLZDrNfXmokZL16tLcBqZlQQwN0mWBpogzBWVR4H1B3bSZVU1YY7EyxG4kGTNS9HDzncSGD+ZS5n7BS+xWB0x6ufGlz+yYRgW9+WdZrhuTvhBxJBzJFIjRKXoQS0KzVrGav9oZG/V7ZSLGePp+v14PqqBNEEh6nr03ytfmPlMFXzk7te9tBNO1rebeVmdtlpBfYl7NIEnvZaw2A13TWTqpEkLjZ/XDJSnct7mXGlHwSd6OOjV3wNxiyd7Bxmq/aEpgDQ6nDK+1WFcQUgKjUtZ63RDbwPz8jLsuMvHXmBbbxNAwX80By8H+LmVZsbO7y85si6L0hJQ4OVuzni85OT6laVOv/uizYY1GIqk02feSW4NVY688JvKJ6Z95MUbatmVaVbz11lv877/0S4S2pZpMiCmztln+O3D12R1Xuv7O7hmr19q7oQ/7soFTdz3Hrw1fPzjq81DITr8eEljjtR/cGr2fbJfxbElROD49I4WW5e6Mx3a32J6WOARfTSmj5ehsxeHpOU3M/bvknF5J/fF0pex/uvT9pod1U5va1KY2talvtXqkwSrQT8x08gomSZ5HPzjxMDmPVfMdh7w/skyxj4wYTcTiSD7WrfYbAzEFBW9ZDuu9J4TAjRu3ufXWTf5pVfDhFx7nyrahKgua2FJk9qFODQWWZDU30oaGlAoaA1EKzleW1968zytfO6KxZ6zF0KwXFM7j8cwXK3VCdYkmNvgiu/CK5sDWdcN6DZ/+jV/m+N5rfOD55zg5O2O5jsRgODldYBcNuwf71Oua1XKpBkrZhOrunds8ff1xZpMix52gzqhZYjhOHNGLMEiynbVYWwItSSLGBDCBxXLJYg1CzZPLW7iJA7diFebYVNEGT0x6LLaosKIMrisM3lYkKXFti20SpnAUK0hrYbk85/g0Ml82NG1L3QRWy4YYDKtVzBJch7HCdFoyqTyTStnM6WzCRCZ4bxCTgIjzGnexXKyYTVQyHaIyrEWh7JsvLL4wyDqqodDIQKmXF17696FjVwfwMD57ECo90HyYedLl8X95O+P3ijEYWxI7Ix4BMRYTyP2SHatqwRUavWNd3rkOxJkR6Bj105q8/90iUQbr1li9Fx/St2idI5ElqSn3DAIYi7Nov3MfYJLRrFF5rkCvmhju0wxIslS7iyax1qnZmrM946duzf1J6vtErbHYLPkfPleZtpQydi4sk+k21XSHOhjmq1oxttU+3solTIzUK8Px6QmHx/ep1yvWdcP+3gHF1BFXLbNJIvlIBCKJ2XTKlf0DSn+HFNYqK8+rEyb3+eo4sPTLBpkR7UBY92Lq5OAyRBfNVyvaUa6otTa7Ko/GiKiR09gYadxfOu4h7sebfdBE6WHjb7j2D45bc2FsDXJ2oN9P7bu2OF/gnKMJkfvH52oCN6koC085afCThuP5kvP5Wq9Z7xzdLUJezC++cI/k403x8oNtU5va1KY2talNPer1SIPVmCIxqaHI4MMy9AR2MjrTyQ/zH5WDxUsTsIuTrj5jsJ9U5T6qboKbjVmUiYSyLLHWsj3dItQ1Z2fn3LyZePITz+GLxKpeUkw13D6kgElgvcNZobSOtg0IhmAnvHl/xe997ZCTuWEVzxEHlbc0dQ0GnC1Z1w3YgPWGlLNGvVfjFhNr7t29yf6O5fr1A3Z2pxjncW7Cql6C8ZyennN4ckpog0bwFAUSI23TcHR4j9VqxbTyvUTVGENRFDRNQ+LiZFQBbCLZQBKHjaLgx9jMYBvaFk7PVlhrqOOCndkVbFVQTmaE1uN9Sekm4CqwFYUpCDFRNw2rdc1itebsbM7ZvOb4rObkfM7p+Sl1s2K1XmcDGotQEsRinMWXFSFofI5JgaoqmE4NpJoYA5CyhFANZZIErDWEmFita5IYnC9pY4MLLVVVob12Vk1dbGbq8+hJecB1fXCMJuGjIZp78uQCYLgY+fHg5P4b1XicwnDOdRwbBZ8m9f2EKRkkGmIA5y3GqWmRWN8Dwwy/8751QNz291amKHs1gzHqFOvyYo6k2PfRCpLl+N2tqkAsxYAxPuf1OtygUc7nSV/QflpD1voOn9Ix1tkJ2OUe2yRJ2bq8z8YYJEasJfcwCjEmldXazsHaImIRSUjMrC36fchktcOpTJoEeB0zUY1/JnsT9rdnzKZbFK7g1q27nJ7WxCDU9QJJE64e7KqrcWgwtgDnkaRmZylEXQjTxuJ8XfN4En3+JEkqXc2s+wDawZhEGAFMlb4qM9nm/FZflsQQiJdY+bFh2ng8ds89XaApqAqfzYsGoBkvAbyHMa+DKmB4bfwZJD02zT1VybCIy+P1otpAxGK8IxnHsoVF3cC8IXDKstHc3Jif0xZVO0ga/SH2n9mpcTSnNhJi+P1vtk1talOb2tSmNvVI1SMNVrUksxBaHaszsA7Z8IXEOO/vslNmT7CO2ILLvVfdaymzuYOzpe1jJIK3TLzj4OoB6+aMN95+mw8+u0tVlogIznuVtaWISZ6ydEycoZXEMljeuHvG733tnLO2IhiH9RFMQ9u2XDk4YHfnGvPzBYvVMrMFkXodqMqK3Z09Jt5z9WCbJ67t8NxTB/zYj/0oIvDLv/Z3uXt/TUIoywnO1SBCWVQURcEzT12nXi65dfMmyWg/sPcufx2I6cFJ6dhFM8aQMx4lyymNmus4R5JEvXScHCWKwnD/zgm+3KKqriI2slitWTcNSSLL9SlNgKaJzJcr1uuauomEkGiawKpJtBiamMB5jKsQE2gDKndFVaUxCoIlSMz9eaI5s4UnBTXTOjk5ZXurwDtHky6azrRtZD5fMZl4QkgEHxUE5eMpS0dVFaxblblmiADQ9/T24HTMPuWewAcA7AWg0I/sC4D2gZE/YheHXsEB7KaUnYiTAZwaiWWEEq3LfaQqfTbWXXAtdhnKCCq97tjO7tj69QptXAVyJI3N5jzdcaL7YckKiGwUhAhOwCG4EaC5oIow+fdFVLLLAMZsBuYKrg2EfH5z1m33emdYpHBWepAXYqRw2oOLyQBeVHmhLGZnluXRkBSPNY4klpDHVge6rC9xZotlE7l5+02W8xXr+RrnS1IUlsuW5bLm5q07XNnf5vErB8xmE2bTGdOtPZarNeu67i7qBemspKzsEGVixaqjsnfDfQgX3Z/H7r+Sr0nHGnYLCOMx9zCGdLxoB6qsmEyq3Ocf8rC86Fr94DgeAO/lbXQGWvr7Kfdlaz9ptw1rDXiPjZr/ijM4V2Cdjleco6jUEbheLTldrIiS1zXy+E0yBvXaX68LkWPDqThqz9jUpja1qU1talPfSvVIg9WOMZUc9GcxfUajyuDyqnnIbOsl9hS6yZj2+KnR6sXIB+gmO9kslGHy47ILal3Xyj54R5TAOgiTcsb1p55kb8dyNj9lqwhQGKwYSl+yXre0dWCr2CIRidbTphk37i+4dRKh3KNdLYkhUBYWh2M6mVAUjigtYlLv3Kr9eI62jsS6YVpa2t0KYyy3b97m6OiE87MznKvYmkyIGKqqIIkhou6aTUwk6/CzKas2cLqY83jYx5CdbY3QBGUkUwp5oqnnpJPXJWkhWYyT3HPm1VFZSqzZIqXA/Nzyylfv8LVX7rFeLbl//4TFvKENCgCSKNAwzpOAtoUkOkH1RaI0LSnVWFqkbRGJqEeMMswpKtsc0RgT4zSTNgSBzKJZ47HGEwNgLGVZsa41g9VZBzicLzmfL3F+W89TguV6rZmuIvjCUpYF61Zze03OpOxKLo2jsfto9/NhED8ISFVd+3AA8KDMcgA3420PoKdjeU1m1QzeFIhxZP6pE5uOPsmqXLjbi/H+9psfOPbO5VeE7AwN6ryUudFR1qWxasBkrUpt1RRttDCUHZatMRpTg+l7T/Vz81bF9noJZ9UBWmw2gQIiRkGupB56i4gCODKgyz2ubQw9e2ytPgtSymPb6PWV3Ne7bmrqNiHGsVzVrJs1oVlhUg0x4pNhZ3+bF557jseuXKUJie2dfc7nK46PT7g3T1wtPLfu3qCJb9JGPZN129KmpAZDxuS2hHzuBb12osJp6ZlfXZno2XpMJmb158ZqHI7KvQVX+EHmi3lgMeSiPDYRUxwY2rYdyYMTY6nww6rbzigBq78XvPe6xJObu9UTQDN2xYx7ZrW5NSUhtA3J6eJBzGxszIZMgme50hgcjceyGF/1i1e61CGXwOuw4GjzIsCmNrWpTW1qU5v61qpHGqyKaJ9Rzxhl46KsbQMEZx3WdxNPnVxL6tigkSMml1jVbhqe2SHDwDJ0TEZo254lMMYQUyTkKJyj0zNivWT6/GPs7M8waaFSszaQmsh6tcR7Q2xbgjW0seB0YXnr1pLb91dEA844nFe5Icly5/ZdjDukR4md7NQ6Vqs1zaqmcAYTW7ZLz1uFQYLuo7OGW2+9gS8KvC8pJjOcL2ialqapeevtFW3bEpNKBu8eHvPcU09QeddP6FKe6CcRZNT/1kv9TFJ34rbBOE9M4I3F2wkxwGpV04bAZFUjYmhbIcRtlelavXbOalyNdGYv+VokgZhydIUVMBHnwYvFNobUptHE2JCsgNPxkSSCUZlnDCDJgCkQcUwmW1TVFOYN1hRYV+J9oionxBSp24i3yqbFhDoftwERh/M2T7IVIIwllV09jJkf5LWdDP0yYDWqApAHP+9yv+ZlA5seHNMBzY6J1OtoO3I3CTEIVpL261rtGe17VUd/OhZt4IoN2gc6AjvSmRyBMp7K6oqomY+YztE35Z5MMCRi0MiZhKoOvPdY58B2UtEhA7S3ShvJXyUzacZY7RtP9LLujOByLExm9IzFGgVF2YlJx3O331hl5GMgxpTvfQNiMaLXP2AJJNq2oQ6BJoJ12xg3xdia556+xoc+8CyTqmCxWPLm62+wvnWILyownqqYMt17jHJacev2HUKqaVJksVoRAec1skcAnxfduvMuaVgg0udZDl7Jl6LvXc/y637xDdExmpKaWD10PF1mVKUHlikl1jm6R/0BhvePs5cf3rt6ka3txs1lJtPmxaQxgO7vFxGI+tyMbehNzoxJBBFW6zWhDbktRMeNSMI4j2R5r25WAfhYmty1cUyqCZva1KY2talNbepbqx5psOqspXCeEBtSSpnRyXI4kWyOkugC4LtJkjEKbiVPmDq2apxDqNmHeZIs0mNg/Xn+Iss6x3gkYgltBDzHp0vefiuw7a9wZadgNptgQmR1PscamE4man5SW+ZB+Kev3ObtW+fgpohAm2pSAo/F24IQVzgrPWtcNy17O/sYLNElYtMqsGoSi1Vgcd4QHy/4yEc+zHT7Gv/wl3+Fk5MzinKCLyfgPJzNOTw+1kmvdWAcbQicLxcYDGVZ0jRNPlxlcOzofI0np84pG5ZioEkCTie6xiViMBojIR4RjZpoQ8xqzZYkbXZfzefYaiAMWTYqpA4DQVIzKImJFCIdahFRgJ8k9axcspZkRI19YqRuE6mN1HWLLCNtvMVyreDZlxOqBCJWjXmiUQbNK0iaVAosjVWnWO8M3lpSMA/p/bsU1TGqy/LyMeYbJvTfUAE8Ar0j4IvpmbYOZFpy1JAZwHEvA01CkqgLI87mCBxDMgM71xHGGa/2rtgiog6uLks9rVM3ZJvllmK0/1AUAop07sCdlDhBjJguLsSq2ZNxRoFqdzx5UWnI/hkAc39+hGyORN9D2fXvIgbTgV6ycY/JzK6xvQOscR6LXtc2JaKoODUlFMi7ArGWdV2zalrqpqFtA0ES1nqQzNhbxzoWfP3GMffu3aFtA21WfsCa/f09fGH57EufY1J6ZlszXDXBVxNc2RBCYFU3mVU0ROdx+SnsMqutZr7ZWXw0JrQ/txsb3YLc0Jdv7EWTpK5veuyAfrm6xagQAmYESrv9e5iZ0sPH6vB5QO96PP752BtAWwq6sTY6RlFpdNuiMT2Z+U9Rc1uN5D7kbB7Wti3W2rwQF8AIRemo1zVNXbO3t6f95+ad79dNbWpT718VznPrx17EXX1wMem/+If7/OLtz1x4rYnte7Vrm9rUph6ReqTB6tZswrXHH+v7m0JoaeqmZ0HbtlV2JM92Yox0sTXOakyHiM0MhfQMhkKPzG6MJnbq8ZI5GDsCFYZ+Ym1sSWgjyZR4Yzk9X3J4umRSTSi9UBjBlJ7Se4pJiThYBcPX3jrhxu0FmC1iTCTTYJ3w7NPPcnX/GvPTc9668TWENTFFpmXBhz72bThTcH52zvHRKVu7O6SmwVjDrVt3uHfnJnfv3uf+4Rnf/ic+xnd/5ye4fece8/maxWLF6fkCG1uuP/YYk9kWd+7fZ1U3TLwjNA2r5ZLdrSnOOaqq6p2RxTuNfYgj92QBSblXMGVW00CT6gyWCkJqSSlSSYGkQMyy1GQSYpPq9UhZhplIqckuvegk3eboEbFYW4AoM4dYzdK1kKlfjNjMKzoyxZZjRgISE20rtKElscK5kpDWrNcNIhbjCiVzSMQkxKBAIESVKRdeY4h8kbC2UTmhdSrT7FS3l1jRh02EB7nug4zUZaZqzICNzb8usqv6xxqXf2fYVgcKRawynh3TGbMzrQnYwmG9wRnJhs+i17Jn0SwxZOCX76GOXe96uRWX6dcd4E3dVTBZZiyJlFkwbw3OO6zVjNcgGqlivTr0dhLQjI6UMc6AbMTzqrTXOaaFxiZ1oLVfqEpCMmBSNsKyWQabF6MkCcno+bRFQds2hBTBODCWxXpN0wTW6yabjGlZBGMjRmKOefLcvHNKSOCrKW1MOGcofMKmFd4l/rl//of46u+9wmc/8xLB1kwE1m2rRmEo8Mb6zDTqwo1BSCkzkillaevFflAx6pzb9RebvBA3EpKMxth4fI7cl/N178ZUB0hDCDhGkvb8gQ9jVMdjfuwJ8DD338vvh/xstTpiJYET/TqKngeTw2VT1NznoijwTkFsTBGs9iBXpTpbd+Z3y+WC2DbUdc2f/FN/gg+9+CKvv/46x8fH3Llzh/P5+QPHsKlNber9q2d2rvL5Hz6g+pd+4KE//9vfF/nb6fkLr/2Zn/l1vnz0xnuxe+9qGWP409c++o4/b1Lgd+6/+h7u0aY29a1TjzRY9d5hrWVnZzszgHUOg7cIQgyBuqmp1zVtaIkhEqOaBUme9ClR1zmlXmS8rFXmJQ0az46suChxyyBFgBgShSux4jC2YjIrmG3vU22VtLIiIRRVRUBYBIOlYN4klqFgtZbsYJkQIg6hbdecz89YrZb99rxz7O/tE5uW+WrO6ekZkoTQRCyGNiZSbNnZ3WPVRH7n87/Lsp5TVQX1esX87ITFfIW0ian3hNAwP1qSlksqq0BneXLE2ekJu1uz3ENo+/iazhG5i5noZIZta0jJUjmTXWa17y6ENc6BGGVL29DoYoH1mp0aItYJxhsMHmMgpETKbqwppMyydhNah3cGayPGNKMJcAfmRKW1opJOMRbJQCQKmJwnKgi7+3tgC07mS+qmwbsis6eo41ayyrBFCI0QWmEyLbEp4J32KZsmjujHSxP3y8STdIshjNo/L469b8SqXpYBD68pm+ycy+DA9qoA67qFlzQAjdQZ7RhIiRBUPkl2W9Ws1cxOYun7Q0XQvt9sZIb+bop6f3QGSilLcwW03zMJPudnigQkRpxAzDJoKxrZYlXTm3tUNU+0P7li9Zp057U/zXrcMUZSXkDpQHSKyryrKjhClgRrfipZdi6aoWoEa7PsPjN2TdvQNIG6afM5zM+G0XizGO1ZdgVYXdAwoqZpplSTsRjWlMWEk9M5N968xf7+Vbb3r9CGmiYbmBnvqFyBMU7lsBiiRIRw4VgHue/lvnpBUqQHq0Ydky/KtfM/qR+sg2pkdEw9C2+Hz095/Cjb+uBiyniM9tvv2dcRcymS44VGLQSji6pMb8ySZ72ubdAFCVw+15mxx8BqvWLdNrSNsqim9PiqYjbbYntnhxSFo6NDVqsFO9vb/Ll/9kf5ge//fpbzBYvlgrfffpuj4yOqqnz4TbepTW3qPa8P7l/nVz+4y+QvfP87v8mp0dq4/iP3In+Zby2w+i9f/2f4ftnh3/07zw//j7tcR0f8lf/6Kv/rzc++tzu3qU19C9QjDVYXiwXHx4dMJhNijOpYWVa0ScEe6PyrKD1FORiLdAxh928bQnb+HFjYzuCjZ1ZzWP0lHx0u0hUqbcQaYhR2t7d44qkD8IF1gNJX1IsVzXqJc57pdIJxlpN54P5JzWJRY12FNwlrdEJ3795t7st9nFFDI+cKvLXEEFgtVxweHik4EVF20RmqwoNzxBRo2oS30MSICyqT29/ZZXe2xWpR46z2CE4mE3Z295hMdJ8K73Fu6G3rJL+9NK8/ZslMthAjIIZIxCQFi+T+y0RUEILN8j0wpaWwlhD0PDuXHVeT0OOoFqJtMcn17IwQEWOwzmK9hSjKwEpnynKR0VSZao4KMiDREEIkIVivjKh12Ugo51MmYt+vmMQQI9R1xBeBsqrwvkBIlGWBayJtSPRmRHABvA6nSnorIx05Y4bqQabpD1MmA9VO8gjqUt1tN2UjG+ccIiazcmpEJDG75YogyeDEIkUxANsOGMoAXvT+AZGI5JgQY7PRT3ds+fgU5CbNSM1SYJUYFxozEjMkTRZvPEZ0ccDEbMLkLFYsqf/d8TbymEipZ7MNQBISIb/fQo4sSTGRLIhJJBGaptFM1qIEYzWPNucJp5gITSS0cSQh7hh7fSZYqw61QqHj20BhQYwl5n20FowvcUBZFHzxi68y3doC6ykrx9nZMSEJRVEgScecyccrSRd58loZ3QnVfs9L0S6Z0UZGIDJ1+amXxgtcHKs9YuwWFB4Ewt3oTin1zs8dGP1GJmCd9Le/FzMjPLhvD0oBa32/DUEXPFJKhGgx3uKMJRld3OhGgnMWqdX52JcV1c6Mg8eusLOzQ4yRw8MjYmr54Ide5Md//Md54fkXmJ+d8eprr/HVr3yVt2+8Rds2OL9xWNrUph6F2pts8env2mLrL30DoPoO9Rf+zjPM/vqP8K8cfurd37F3uT6w9yT/1/6TPPcXPfYTL3zjN1+5wi/+5WP+k7//HRde/lttyS/d/O1v3k5ualPfAvVIg9W6rmnblhACbdNQliWytUVMGk0SsnnLGGTBEDjflS/UAbYr7blLxDYSTcxGP737jE4URzO9bp5ngKJMxLjCuIqycjjnuHv/LsslXDnYZjGH4/sLjCkoqkQbIrYomW5foZqtWK0DsD2xegAAIABJREFUO7Mpk61dluslbQjUjZq9zKYzXnzhBWaTgvn5OTdu3KDzQ9XeQeH69Ws8/sQ1rly5wuOPXeOxK1e5/uQTXDnYYWdrireOSTXBGsvy+IzDe/eZTibs7e5hrWGxmLNcLDlZnPPW7bs5s1KP3Vhog8c0RuWUdOB+kAKnZEkx0LYJ4z0Wh7cWiQLGY0yBM8ogG1NgXMK7Wp15k+TFgGzqlMjMWAc4lTkyaP+ZsYJ1AiZgbAtJVZuIRsnEKDhvcMYQWkGiMmLWqXGVl8RqtaIOsWfWo4QBYOXrmjIbGqOwWtV475huTTRj0xd419C0kTHTqV+MgOkl1v6damCleEeKdQARw/djeWVRFL0BDyioEqPgNEnuZTUuO9wqxI6ZTTRiSSbqOXUm55B2EmPdZsqqhB5wGKfyUyMX+kwTnehA8jbUzEnlpMp6G6Oy5CTKmIUOrFmDywsFRFSua9JAEAo9eBLJDByRtm2z4kJBubLLaZCqkxlbK5rTHCNWBKzTRQ4xeF/QxECKosY70xkpQYiaj2ysoLxnwluLLUrqZBWbGwX/atpkEesJUSirKSYGnBFWIbI8OVNgZiLr1RqSUDpP7OS+GU124I6eSe9g5qVxYfV+uYBAM9Pdpwt1i0z24jgdg9LuWflAH+tIRaFfmG84pi+Oyc5cS3/XOddLirvXx+Pe5LzcLtc6RnUztsaT0IilsbO7LoLAdDpl68oBO48dUM2mrNdLtrZmXL/+JNeuXuP7v+/7qOuaV17+Kl/4x5/npc99jrfeeqtXjTz11FMPPZZNbWpT721dqXbY+ks/9P/vl4uCf+HnnuTv/2d/ln/r6NcfiN17VGpvssUX/u0XsH/y43/g37Ef/iAv/M2Lr/1PN27yP977Hpb/+JgPf/bt/vV5s3q3dnVTm3rk65EGq6AZoG3bUhQFTd1wd7nAGO1fs2Zwqux6p8bfd2W97SdpznauqQZXFFD4PHFK/SQ9pYSkOJq45c+zyiSWzlJYz3x+xl2fKLyhPluzaBLOVrR+BxFH3VjWjcCqpW4WtCniS5XYTSczZtsz7ty5S2gblb4hFEWJc57zM+2vKosCYw1l6Xnh+Wf5zj/1CX7kR36AD3/s2/C+yvunfVwkBX6sak7v3WdnZwfvPKUvqPb2WK3XnN+9z9nZOesYWGeGaTabUhYFoCYvzlpwZFdcBZi9YZWJRGOxSTDBEK3VBBOjWYqar+g1WsI4jATAIaKROKCT1SgycmRVUyPTxZBYUAQTe1Mga2zuOdSeN71mKgd3xmNDoI2xj95JKSJiuHv3PgmHyQyzCESlt3J/pOmvbzKGOkRY1Igr9H+CRs13jAmDxHI0LsRk1vDCpD4zqQzY4gHjGzq5sOl+Y/QaqNsU9LSn6L3gvWdra4u2bVktV8oB2m4fXWbANXNYULMebfgUjAgRwaUEoc3bNYhV2e+wBwomdXWgc+Ie2Pbh2IROUNtJlTt3YF0scmBy3FTORY1isAmcsXmM6LhPkgBLQkGyw8AobqZTQuzt7vHBF1/Aknjj9dc5my9JYrOEVbI5mAIxbyyF1+3ErLpwrlAAawyF94AubljvwVSZvdU+VNuBcGsw1tO2kbZp8mmxakBl1NBJQtS++hQyeNR4nnVd5wUDSxta7Vc1CvUVqA6sew9cUSDXL57lv0w39tPA8UtKem5HK2oXlBH9Netcg03vRJx5a8W2zo1cdQcFQScvv1xmdF9CGhhf8rWX2C8ijGXHuvCQ7+c09GinmCBoD7GhW7wyasYVYbI94+r+Po89+TiNRE5OT9nZ3uLjH/843/bhj3Lt2jVu3rrJb37607z0uZd45eWXOT05wVrLdDplb2+P/f39B45jU5va1Htfv/tX/+AA7qE1m/HjP/8C//1fN/z7h7/xyJkuffTgGX7rJ5/8QwHVdyrz9FOYp59i+zvg1k/lJ/N6xV/8W18h5PapKIlfvfPlP/K2NrWpR7UeabAaSbRJe586QxWMmvOEpsnqtosuj+M+qt6wpokXJkxAL6nsJmK279v0GdTaB+TEKj/NMkGxVLOKYjoDaVguA2fLNbBSIyIxgEck4Z3miu7s72YWNbJc1yyXC5aLNaWvkBRJMXB8ckjlPcvVUmV/SftUnS1pVmtu3bjJqy+/ysHeLleuXWXdrFkvFszn56wWCxan5yxOzzg9PlezmLqlbgNtTNy9f8T9oxOatmW+XLC7u8XHPvJhnaxLUjfQlHA4xDowIbM0FqTV+bANudvW4vIkMzqNpDESIbW0VjDJYGOOV0ldT2QCEwkpEpPmpDrXgRmj4BclkArnNE8RgzeelFnXKJKlwB1DFCEkJGp/YhuDMnwCbRuRpGyvEQ9J82RNBtgxxD47V6whWYP3FXWKhPMVhS9Ubp57DK1SYWD0fw5dn60YBZYWq6BOdJyk7muTIUiPJzqAirJzkpDeGNlmCKvsUt83nP8OIRBDyGxmIiXBUmTWWQGSUUROVGtl/fyYMhMJxsTs4JtoQsClRFHNMNZpsmovhe8dyR4EQAJ0+abddjJjHQAjRuWy6AXV9Q87KFIlakyR0YgZEVTajlVWThKuu5eNLkyow/aU/Z0dmsUpTx7sYIHjxVol8pKl33kxykrIklaDN45oTO8wO7CNCYmRoipzX2ygDUKMgjjbL6zEoP2y1jik6xE2pl+N8AZ1JRZ1IgaVunqX845Fe7h17UCPfRzxMmZYB4fcS0y+fqPX3nTmRib3D5PjeqSjpUe/MgKLCDGZ3PKgvcZi1HldRk7Qubs+A2VVdVxwBjf2wve2GzQiYHJvbf6xNV27QB4QSQdBF7FjjVX37xB0ecB7cBoZFWMCb3ns2lWuXX8CrGG78Hzbhz/Es88+x9NPPcPR4SG//qlf4/Of/zxf/vKXuHPnDnVdU1YV0+mUg4MDtra2uHfvPpva1KYegfLu93/P71fW8K/+/AsUPy381L1f/aN/3rtY/4t/GvfJb3/3P7jrd53N+KX/8ruG19drfvbnrvDf3vj1d3+bm9rUI1CPNFiVJNqbNpqsiWTgCT2bMOSjPvAJvdJyLIXrGFj9Wepfs9ZSliXee7xzeO+ZzWbaayaCSKReLnXFXwxtaDk8OsQ7Q9vWiCQKpxOzENQIxTkDxmJzf6jzOl9r2xZjbGZSHWWOVzg6OmJSVZSTCVceu4p1OvFum4b7hycslgveevsGn/nMZ9jd2SGkBlJEYsvJyTEnR6ecnZ1T+grnKpIYVnXDfLkm5T67ECJt2/DY/g5PX3+SvZ3tPAk2eOtYSwtkMxqJkCJdX2gXP2JzFMpYem3IgDLpxDjEQOGK0bUD9Ti1dN2P1lrN3TR6HZ1VqWhZlGoK1ATN0s19vuoinBAzgCeTZd9xJE2M0klfFXCYnn2Kw3ZNJ4cUqkml7H2jMUlWLCFCjIHCl5RFxbpe5T47BRbWWAW5oqwlpjM1gtyceImN7dgnLpyTsdyXrEDvDG8u9wbGGDk9O0NPWQduIilmb2SnTGLn2mt6lKyusU3TYm3EFw7BkbCYqFE05cRgfUnHrnY7ZkQjarodv2Cwo5/cM3XCsFAUJOVrNxyvMYN5UX/fZvMjI+pUbARCinhf0Tv5pkDhHSnUnJ4cqQFWWea+a5X7dj3YXfyQSSqf100klRxnJ+VxdqjG3AgxtqMsYqOLG+QFBaNRKgYFh5hOgqz7F+lA5NDDKdI9Z1x+r0rQk4witLJUW6Xbw9i4vOD2TnXZZXr45uJ7BjWAXihJam6V6KK+pJcHD9vtFv+sZh+PPm9ggAf9QL84mHvs9b1dP+vD97uXCusHDDqD7jltoWka6twG8tGPf5QnnniCvb09rPG8+uprfPrTn+ZLX/oSb7zxBqenJ1jrKIqSra0t9vb2KMuC1WrF1mz2judxU5va1Ldm/cTP7PBTf+393ouh/sXr3823/+R73B8/mfBz/8EW1X/3w/z8zV97b7e9qU29B/Vog9WcX3hxwjZMjPrurpHU7OLE7lIf1uj1wfDD9pO0rkfWew8ifaRLWZYan+At3hdsbc9om8Dh4ZyyKli3NcvlOc89/yzPPvUUd+7c4+bNWxgB7ytla9pE02jvY4yJlGLvcGqtZX//gNlsytHREav1mhAj6zpQlAX7u3sYV7JYn3Hv6JDCG77+5ht456hKz2xace1ghxdeeJZnn3me119/k8PDE1Z1oJpuIVGIrsE5T9O0rNqa0lnqpuX09IxnnnqSsipp1nU+XzFP7Mnuq8Pk2qAsjPe+ZzeH/uAHzVjGoKAjoi53wSmzZHFOme06NIhIn8XYb9tarAjJDEZYun1lypMouDYh5mtv+zZAMzT29f9pHJK65G5tbWGMYblc6mKFL3qGtG1jli/63MfbHe7ArHGB4ded66ND33F8j8b0sKqST5I8AAi692tEky4s6CQ/4XI2b5IIkgF63hcFF1mumcgy6YRxgitU+hraFjENJbp4oAAry+xjyn2ql69cdxz52tPdi6lnlO2IgetMvIYj639Zr2+MauzjPdE4aoEUhZ2tKVf39yit5fz8hMXinCtXHuPk/oqjk1N8lt9aW2KsZbVeYk1B5ZwyoXSRO91WBzBkjb7etGs612ARSKZ7f7cA0oG+fD2yNldNqmS4ZqZz04UQUgbkg1lSJ3FN6SHPpofId8dj5XL/6Lj9oXuWda+Lufi+7vwPw00XWfJaWm+E1L2/c0a21uLM4JLcP2PN+F4fnqfGGDXD65C35Duue95ie/uAcW+2SNL83w78S8I6zcEty4qPf/xjfOcnP8mVawccHBxw7+59Xnrps/zmb/4mX/3qy9y9e1cVB8ZSVVXPpoYQaJqWutZIm01talN/zOrKY7z27R/nEy+/xqp9/+/xj5pt7Cc++J5v11y/zt/46WN+9vBH+P5ffJ0vHb7+nu/Dpjb1zapHGqySxqzpsIIPcBkUjV+7DGy7n3Wlk8+BYdU+S5UF6+SmwWcg2TRNPwnz3lI6z/HxYW/u45Pj8cef4M//+X+Hu3dv8//+8v9NU7eUZcn8bMlysaQoXZYD6n60IdC0garSvq24XnN8dEK9blgsFrQhUreB5apFUuT0dK7ZnylgfEUkQTKs1w2LZY1gWc/f5sknrvHdn/xe7h6e8PqNOwie+dk5WE8wlvlioZNBk1g3LSYJTdMiAiGqNNF7T1GWSIiE2PUApzyjzO1xmF4GaPPXJHV1NQIpxCGfk2ESq5EUQoixN0UwZJmg6L8uxxVpf5+lsI46yzo76aS5eGnzgoYatFgT1CwnodrTNAJGZnh/DEFlnVbZ7xBCZucczhWaZWoMxWTCcrlSuW029VHwPnScxn4mnzki04l2lYG/XA8unmROsusP7Y7PdIyjfi8SSaljsQ0xgbG559r0uDyziQOwks7ACgMugxLJwTFW+zUTFkmRZl2rQZXTxQhJiRQCximIHRuXmRFQ6RyaFejkhSZjSFZGTtPKVqZ8D/ZSUgPkSCdvEimAmBJBYwu2t3d48cUXqaxw84bhjbdv8PrbN7G+xHvH9qRk32+xrFuWqyWz2RRIREYGTL0jrTL7XTSPylsVwKs0HTSrtjOVyn23DMdouGg+1D1PjHSsflJFSAqE2OQ4qK6NIee/MgBAslFU55o7Hiff6Dl3eXHuYquD9HRm9753WszTC3DRcEnflxdmLv1eJyceFvzG52G0YDPaVA/OuXw8I2ZYBzkJsmoDEsL+7h4vvvhBnnjicarplK+99jqf/vSn+cIXvsA/+fKXOT097c3QtmYz9vcPqKoJzbrtPQ/W6zVXr1xlU5va1Ptb33vto/BuxkhZw+M//af57f+85M/df527i5N377P/kDUrJ/zg+hstUX+T6+AAc3DAp//jLXZ/5vX3bz82tal3uR5tsNrXsII/9Hk9jEG9/P3F93TAd+yG2fWkeu9xWfrbvQ70TEn3eUkSoY1U5YQ2KLi8h/DZz3yGs7MTlouF9rmFRMwASJoIpQKDDiC7nB3miwKJgaPjY05OTzEm4XxFiupoCoambXvQXHhH4RVM+FJjTI5PztkqAi+//CrHp3Pu3DvBOs+yjpzNl6zWtQI9a0gxYkZy1fP5gigJ5ydq9OQsZVUSpVaTGat9cerkqnLLjsXpzldZlhn0X2RWRUTzUsuiXwhQ+XO+Bpkt7UjFgbGi71EOIUBeULCigUUGLkrD+7/VfCbmhCERRxfIkbm9bkPqQpy3U5YTtre3OTo6wjnfv14UJWVZYa1juV7Rti0qQx+2lyGpfnoGbZLlvmTG8fJYvly9VFcGs52MTvO/9OdTkp6zlLqxabJ5UMRahy8KnPO0IWcNj4BRJ8nUM5EgZVlwsjivgCsmNb2KLg5SYBGV8o5k1j0L2O9bPpaeNe2kxyrj93bU49ixkna4t1JSN2py/I33FnIG5/n5nKZpaFNLGxPGFbgi0Ubh+pUrfMfHP0IEbt+9z+179zg5Pc25q3puJAmapjRmSgcm0TmH9QaXhBCFNqQMrvTYJSWc7WJjhmuurKwMcu98eMpGd+y+EELby82l63VF2doub7a/1nDh/rkwch5Y5OjGzwAw+981elGGrNPLQFWvgbLhXT/zCHSOtimjC3zxGo7lvMOz0jCA03GszeXNXwT7Fuc9xrq8WNIx+4HZbMbVx67SNoHP/c4/4qWXXuIrX/kKb7/5JvPzORbDdLbFzs4OW1vbiAj1ak3T6HkPIWBEI7A2talNvb/1vz1tME888a5/7gt/87v4f/6G4c+0v8eyWb/rn/8HqRe3n+BHf+FD78u2x2W2tvgPn/phfmEjCd7UH5N6pMGqc51EN9BJC/ueQDJ7w3gSN6alhur6U7vXU4oXgCuQgYgwm80oy5L1asVqpfE4rmeUhJAihfc0TUOILSKJerXi1z71KabTCQcHu0iCFFp84WnbVnM7M4iom4bQtmAdRVHon4n25qkkVQHldDbl2tXHkZTjV+qaFCOhVUfR/f2D3hBmb2fKte2Ca1f3WbeRs3nNfNVqXIjxGBsxKWTwaNnf36f0yro2oWWxXLFVVUwmkyxVbnsmpouCMcZoL58MWbbdeXd5cjn06Ol1iDFSZEDbmyGhUUJOoI2JhEaM9O7zVtlaawaWW1ltZRBjSoyN6q21eAetyeZLOEScAtKUGPjGUWWw1Emwp9Mps9mM09NTdnd3OTubE4MuKBRFwe7uHu70lLPFORIz49Mtdkg2l8nDT6FqytJQ/WNMtw+ZTbs88R81t/avGclaVP3Mvs8a24NlZQQTznkmk4qynGCdB+MQYzNrngE6Y4dXclSIVSMhE5VJzfdcEnLky2W2rFtQkNExG7IVNZ3rrfYWZwYygbju0qpkewC0Q2nEDYjxmrUZA5KEwjlSDLx98wb1esVisWC1rnG+QCTRNmvaZq0uw8YwqSZMpw3n5+dEIyQjOYZHXaoxyv5jIAZlD40xFEVJTImQGmLKYCtjezFA7FhPM97p/nkkGHUBztda8phN4i/0a8YYwY7NRfQap/7ZNvSMXhiyl74fZ6B2z7DOCI5uNJmLQPZib39CZOQizCWwO14MSvqJfY+vHczDOon0mGmWPGY7gDqMn47tHbZp+0UMlUZHEskKOD0nMQmruuazv/1bLJZLXv36a3zxi1/k5PgYiYnCe6pqwsHBgZ7viLpki+R4J5WAq6z/4TL2TW1qU3886sWf+05e+3vnfOrLz/Cv3f/U+707719tb/PXXvw8v3Dz/d6RTW3q3alHGqzqRHLUizXqf+z74vQb/echrNXQQ3dhXtZP9MYTuBAC5+fnVGXJbDbDewWbHTCLUfsb2xAu9sAZg8FRlgXT6ZQUE2VRkdI5Mq3Y3dlj7+CA9XrN2XyRLYboJ79tHCR12i8LhbNUhaNtEqRIaBSsWms1ciMmVus1ZenZms4oC0MMkTu37nLjrZvsHDyGKxwhaJRJDCoPffzaVZ579hnu3r1LCsJiuSaGRFGWkBLGNJnVHICWuniqHNWh+ZgWo1EoTUu0TqM7nKUsfGYAE6X3/TkUEVKWFReFxzqPiYm2VTMn49SsJmT3545Z2d/fZ7VeK7AVwUkixKjOq1Ydho1RwGiMGgvFaLCuxEQF6KoINqPIm4Tzru+vm0wmnJ+fUxQFH/nIR3n55Ve4e+c+RaG9nXt7ezz73LO8+eab3Lx1CxNBnFBnt2IRBTbWWJJoZI5kCeXAZV4EIBf7D6VXC3SOuv33ueexA6YdcxWi4LynDZrb++ST12lCiyRDXWvftbLxSTNwEcQOfaxqCgTea99r07YUGKxHAWfe1b7PNGick3GFus7aAYCrXDhDUGMg5+gmlFGVBGJVlWATOOf7e2/oj4UkBms91sDe1ozZZMJ8MWddN9y4eVujn7xXs6eYcBI4Pprzyiuv8h3f9d3cunufo8Mjqtl0YKchKydc/yww6MJJyqxoGwJxlRSsxgziyM2cpltksWCykVSSCyCw/9qQ3Ygd3hucpc+CviDdHT2bYHC1vtyb/I0AazcW7IixviDhtQ/u35hd1a/1WK11WGeJsQWT75U0uKGTyeML/bEjwNlF0DzYU0s/blNeMDDWXHgO92BbzHD85IUEp9z362++ydHZKfPlgq99/WscHx1ReM9sOmNSVUwmU1IUlvW67xPW53qX56rO3w+T5G9qU5t67+q/efJHeezfe/Kbuo3tn/ohfqyu+Xs/+2f52fWXuDE/fEdVyrtd18tHJx5r59/8Hv6Hl5/lr9z9lfd7Vza1qT9yPdJgNYbYT5bHkzxkMFdCBrbq8lxkYAo6BvYhP+967uhMR2C1WrFerzDGUJZlluxaqqrIPZvKTmkcSFAWKLY0TUtZTtiaTbl39z4xBPb29rly8BhlVXF4eMh8vugnmSpxPKeTy3rvVQ6JcHp8xOnxEcZayqLq2bG2qTltG0gBY6FpLPfuGtJ2xelx4uj4lLpp4PSMdUjMlyusywBXhKP7R5weHyuLayzr1YJ7h/e5/sRVyswgl1VFiCp3bVudoPYxIh3AF+kzIxFR6d7IrCrG2IPU7k/XpxraltS0ZOonu9dKzwIOUUHKxFlrSTnztctxHGTAl9h047BOWRTnIIoCb2PUKTeEbKATU5b6FppbGlpsayirkr29PU6OT8FAXdfcuXOLqir5yIc/TFmU3LhxQ9nnqOAGqzmfEgXjHEjowdt4At/3EvJOssws1c0DTMgT7wy6+0WDHLGUUuLKlSt88pPfgwBvvPEGJ6caWeSKYnB3Hd0LpgP1o2upClnBmICJgvUeZ31/j6ihkzJ/wcRe/u29x1mnCwYMjFpvqmWG1xTk0aP3HmQJOSPVYoxHjMVY4YXnnuPqlT1u3brJ7XtHnC9rkpTo3Sy88NTTvPjCM9SLc27fvMFv/fbnqEOiaWra0FI6g6SIxVKVJW2IWcac42PojJ4MUdTIKcSu3xYkM4aY8bVSRrjrGe7GXXdetU1An0NGVErcNE2/2NX9fNzfq+C5A5PSy2e7c9T/3mhR7Z3Y0rGZ2XBezQPvHzOhvQoixJ4N7Rj8bvwYpF886rYvcWzqdBFcm9HfwzP4YqRY9zk9O2xs7lWPGFp85YgpUTc167rm7muvcXJ6wnK1pCxLtqYzptMpzjpSgrZzco6diVSnZMiqgocILDa1qU29t7WdgMnkm7+hquIn/qsP8BN8gL/6n36F//nmb37TN2mN5f/42Y9+07fzB66q4ge37/PE8oA78+P3e282tak/Uj3SYDXE0PcWQie9zBMdGZSWl1HoRfb1wVibfrKUe+R6eVqecSdEcypjJMbQM31QYZy6U06rkqoolCU0gsSGtlmRggLQuq6ZTCqKwtOGlvtHhxwdHecs16I/HuccbdsSQmQ2nYIITVOTUsQ7jf2QzDZubc3wxnJyesJ8cZ7dikuatiFKycHBFSbbu7ii4sat2xjrmRSeoiq5dvVxjo+POTo6QkSom4Yoia3ZhOVyibFqblRKSRQFaSI5LiUEjDVZ6jtIUrvvu/5SlRUOAKef+I6AkbW66JByHE78/9h701jLsvu677eHc+69b6xXU1dXd6urm+wuEhJFUpSlWLJlUbLgELAlK05iR1KGDzJsIDASJ7GAxFAcR4otW7JhOQliOUiQwHL8wYZiw4qtBJBFk7IMybEGUopYbJJNdlfX+KrqDXc6Z0/58N/7nHNfvWoOYncVqfsvFF7VHc499wz77bXX+q8V5TVhwJQrhEVXRvUMklIdULXWyjYyE6NVzo30UXo6lckgSbI5lUqMRiOq2rBYLDoQXeTFIQaWywUxRdpmSUqBeiTs32x+jPctn/nMp7ny/BXe9fLL7G5v88qnPg0JjBOzqIAY64gUVPpwjdaZ1cwAI2PVocnM8DsX45rhRD9GuQdSSlSVyW61EgljjOb8+Qvs7Oxy/fob7O/fxydQJrOBWQYay+emrD0oUswEMUDMDk4+OZROmCRM6AoFRo5/CbEzoyqMpdHFdCrlz9QolUFbZvBUFKasB0xyPZHPa8r3XYySsXp3f59KC0gaj0b4pIkLR9M6KqU5uP+A+5MRk8mIza1d7tx7QMqLBnjH3u42G+MR89mMo+mUytagDK3vwTtZbh6TSINTlv8W52ORPIfMbq+yovmAEKPP51Z10tuUChADQec5OkkQJKVHtGcS1eA9vRnSSXB3WpX7Y+VaUsUt+zR0tsru94sGcfUzibnPPOJ8S11V1HXdvUdkuw+PrT37r7rxumxXPqfIxvt9KPdGirBYLJl6z5kzO1SjESEk9u/tM1ss8MGzMd5gMh5RVzVKWXwUxYsscBVtzSBWaLhvjzyK61rXut7qennvGf7o178GXHlbP/cn/4uL/JEf/QP8rfqQ//vWr79ln/OXLn07jEZv2fa/lHruv/5GPvwj8IF28dj6eNe1ri9HPdFgNcUhEEod8BRn2f513Yq/Vg+DgEcwWF2P3UnGcFAFjJXJedu2RMTIRiWF0ZadrQ1HiNCwAAAgAElEQVR2tjfZmNSMd3bY2twgBkdwDucCs9mUe/fuM50tOtbEh8BoNJLXeC+fpS1aGy5evECMgRtvXMe1LUprnI9MJjXnL1yEGJgt5sIEKjGQmc7njGvduaw+/ewzbO5sE0LE+cDm1hbjjS1C9GxubXE8Peb23X0sYHTFYtnggydVBoyiqmzuhxVG1efDVxx5C/tpTZ9ZKQygTMitsYxHo9xnmroe2DrLUJWx+BBw3hHcoH849y1qrQUUhUDbCgOsteSHaoS10lqTQiicTmaligGU9JKCEumrApVjTBQmgxJ538bGBsYY5vM5xhju7u8zX8yoanm99475YkY9qrh96yZ7u7u8773v5eyZs3zst36Tg4Nj2tZhCLgYCNGLu3GWYeqVa7L0j7JynarSW1iYt3L9l0zZVDIwAylJfJJSmrb1vPba61x/41YGQ4pqPOoWCroJe0pd1mmvJCigQe6lmBJRPIEJyRFUyD2ZJcpGjqfK++ycI3hPq5RkCSOusMbmrFelRPatELl4Bu/aaAG3MYoJFoj7srUoJddcTIl7Dw6ZjMdUleXoeErrPMknaqUhBM7sbHH+/AVef+M688WSxkVat0AROXd2lxevPM/ezjaQeOXTn+GNW3fByLXlMviXcUMkqDEJ01qCbgvYKgmq5bsPOjxZaUWQC7gfi7pFB92NLQKeVAa/ZUsC5mNmF4fsZWFW5TrSD41jp0lvy+NFeRBjzG7EcfVaG1xjKamV6/TE1gQImr7PNpYTx1At0H+2sOoDSTKsfK/ys/wVGbDIkUejEdO2pWkcSWvmiwVaW3bP7IFKRB8oH+m8JwRyL33ZAzX4USKH8iLaKW0i61rXut6eenF0gY3ve//b/8Fnz/IH//pZvuPXf5Nv/+l38Gv7n35LPuYHv+k6VFfekm3/TurZH/5GfvsnW57/tU887l1Z17q+5HqiwerJvr5u1T+mE6+D0zReQ2aiN64p2y39V/3rOskxPbsxBLApT+iNFklc6xx379/n/r17eLegqhQvvvA8T108z2w24/79A2KC1kWUMt02Uow0bSuMoveQEj4lmqYhBI/R0ktpbAVI/mjrPK+/fh3nWmH+6rpjSJfLBc1yxubmhOA9z4TAZDzO+a5LXvvcXY7nS6pqRFSK+XyBrSu8CyybhqOjY4IPVJUVw52UsJWhaVoxGbKWlMLKOUlZ6qu1xnbMc+5LVRGLlcm37SW7henQxkpMSIy5Ry4zQ8iE0lqLsQbftuWUYbXBKI8vUsf8Nw4AQwiBECNaWTFbSrk3Ec98MafVhuA9KSmMtbTNQjJ0s6R1c3ODplmiFNR1RQhJ2OzK4Jol3tbcvnWLF668wLvf9S7GozGf+sznuHHjBkfTYzSxY/eMyYz0Cclvka6X49Ff3XSQqLwmhGKq1F/PRouplkqKyXiCNpaERCBZK8c8abK8tL8/sii5O3bCmveCzXJ7xAx0yU7UgQjeo7MztKKXg4YYIUV8Xs0wRmOjJtrB4oLSKBVXgLMCkX5ncFZYMa08I1uRouJwOifdusvF8+eEYXOec+d25JpdLFnMF3zutTe4dfcejW9xSVHVY6xOPHXhAu+++hL7t2+xmC94/tlnmS8c9w6PSUjfZHHlTShCyDJ0dA9nlEJlZ2KJYMrMaB4fhmqOk1JbEBZP57EqZsa2XxiTM96ZMWVWOmbGvLCdQxnwcDwb1qqyYfhaOgY5Du7VbhtK9kEUCFBA9ep3yj3NoZeEl2zoIoeWx/O1sQKCV8fPlK859Oq43rG7yWC0pc497k3bglE455hMNjC1wXmHi6m7N1oXZFFqMKaXY5tSz94PHYnXta51PZ76f25/jH/0Q3t8z0+88Fg+X7/v6/jI8w/4fT8audUcfNmksRv1mH+6/X5G3/3yl2V7b0Wd/TO/l8/+TXjfJ65zsJw+7t1Z17q+6HqiwWqpvucpS39PrJCLhY6YvPQM7KNYiNSB25OTwWGGZNevNQC88nqZ+JH77IwyqBQZjcY4N+f1168Tgmc+m4uUFg3KdqxqXUvf3WK5FFBorfSD5s+YTqfd547HE2ISRso5x7JZiiRZK3zwWGuoqpoYA/PlnNlizvmzZxmNR5hK07aRZbNg0Sxku2mKj0mcVJGIGt8sWSyXzBYLzpzZFvObCKOqYpqEbVSjmrZZrsgGh+C+SJvLsSkstPceawW0KN1PcovLrzCAJbokdpNOrTV1PRJgq2Nm22L32nIuS5wNimzcotEqSpaoC5mxEpOupmkJSndgIWRGu21bjo6O2N3d5fz5c7zxxnXatmU0GhFjZDLZZHtni8P7hxwdHWG04Tc//nHOnj3HeLzB1Zfewdm9M3zylVd4cHRAE1oa14o0ObOlAggGIJuHF2KGz8UY8W2bDb0QRilJ1IwYySrqqsZWVXZ8VhgjztIK6WtNUUnubSfrXGW5ToKrhySu/Y0DhSWNQhjrLHtd7U2MhOBJSRNC3qdKTMdE+q0ycBOHV6PL/Ze/j60ZVQbfLEko6vEGsyZwa/+AkYFnLj/N5Qtn8c4xGm3yqU+/yudev0HSChclYzP4huAbrr/+GhMDO1sb3Llzh8nmFmfO7HI0W7B08iVStyilEUOswp2WMaAHfUqVSKGyCCCa1TSIvyrS2I7Yy2hRgHhhumWzpV9dSq5TrSUeqvSGDpn3ocP2w6dnFcx257S79kq/thmAy9DJzsv9XMAsrLr9WmOgqmiahiKbN8aQQlwBo0OmtFz35d+l/5xUFglW+2UpC4YxYqqKjY0NHjx4QFrKd5nP5/hZXjjIZyn6IB0cqpgoDa9t+R7G6IET+VoEvK51Pc6KKTLTj3nBaG+PX/xrH2T+0x/h2/71JtceXP8db/L7z3+Ab/hLT1Cv6mmlFef+02/ho3/hV/mf/DY/dfOXOg+Rda3rK6GeaLCaBn9IfS/fwNsEAK0MqMzaaUMRUnaukJ3srkwA86SmcHkrjE8/2dJdz50wiYpIpXsX2RgdSYkjLSmhtMVUI3xU+KRwsUzCMrgyJQtDvk9lJGJlNBZTpqZZcjw9FjCQJ1/PPHMZrRV37tyhbclusxFjKi5dukRVWQ4PD9jb3mBne4OnL11ib/cM+/v7LFuPC4rx5i5jl6NKYsBqxaXLz6JNxZ3btwlJcXg05dzZPcbWoKK4/tZa44MfKhb744NIgjU5TiYElBywfOaiRNI4RV2Pu8mxUgrnJIpIK+nRNFrOQ8znpXxOIk+kSfiEAAxtSKE4uSq0MsQASlu09sLmJUj4PCnPx744vJYrIYmbbkTcnZ+6dImNjU2U0rSNw7Weuq6xmwadYGtrC1crjmZzXr3+OebtjKsvv8y5rR22x5bD27fQzjFtLAcBfBI2XeELbiDEwnBqiMVlOcj1oyJJCQCKQUy7ZEEEVCpZmQbXenbPnOXSpcvM5gvJyY2RFD0qJbnGlMqgshBouRO2AJh83XfnNDOKeb1ApMiDHkqTFxqENfckJT3EQ0fgclmHIv3VojwIPjKqK1QtEUgR+Wu0XPsqJVQK7GzUvPTiC9y7fZO79w8ISjFtHcsUSFaxs7tLPR7j2gYXGjZ2NtEHhzjvIDosFeOtMTFolrMpv/3JVzh/7jx7Z/cIyjJdTGlDJKREiAqSzotWoWe0M8BM3fKXnCOVkkQ/5dtA1p3EpVkuw+KNqzsmva7F1ThEn/uHZVElZKa8B2sim4YiV+1GJYleydd5aXHoGMoCpnU/ViQKY0q3YNcJSlTPtut8zrTSmHwu46CvooyFsrmEtRUxiGqibR02ZxHHJNFTJrPohc3tt1FMrHpAWZ7PAVXyJ5VXyodOJmPmyzHT+UzYVBfxUVQKyZh8/CQbV+JwC2CXY1Yc043Rsm9KTLmKOmRd61rX7+7a+IFv48P+I3zzr17ktaM7X/J2zm3s8MNP7wNPOFjNdeUvfgN/BfiO/9zwb9/78OPenXWt6wuuJxqsxizD6ydgfZRKkesqZAVd6dTFhxRHUmH5IMZTVvNScZaViIkCbFOMoBRG9fmiw7LWUtc1IQQWiznOFfZLEZOmdYnjWYN3EUyF1oYYsqQy9D2Iit5gyXvP8WzKYjGXaBdjO1MX5/ooGWOFSQxeIlVms5n0aXrH1s4GW+MRvm147XOf5bXXrlONNqg3Njk8mmGrCcG3WK248twz7J05y/2jOePRiGXTcP/BIc8/9ww+JXzbELzDKgFdRf6Ysuy2M26JdMe5BepRjdIqx4LkybEyOO/RK66l0u8aAB8FLJgO5GbX256iypP3zM5lljYVyXaCFBQxaGIq0m5FIhCjF8fiDE7yiZeFCKNxvsE56f39zKc/w3K5JARPXVXyXX3kXntXjruuqOtdxhtbzJZzXr/5Okp7vu2bfy/veed7uLC1zS/881/kwXyJWYy5t5ji3BKr5fv6IEZQWmlkzqzzZN6Ur0kkEqIneYlI0tn9t8g0ta4Yjy3j8ZiUwLcBjcFHTyLiU0RHAQ4ZkUMSMFHuH5npC7uV8mckkHiZmDrwU0x2QJyTNSrH2SQgCLDLCwuFKcsnTLBTTGir8M4TQ8S5kCOhKioqQgRNxCAsq5vNONy/y8svvsDL77QczBd84pVXeXB4yMJHPvmpT/Hul19CKcWnPv0pbD3C1pqmaah04vzZs7zvve+lbRs+++qrXL/+BncfHHK0bEFpFk1LyOAvpITGIBAmoFJezMrmP9poQl7KUkkY0bJwVS78YsRkTjgDFwWI8062oMjXms/RW33rgZz3E9EzKrOpBYB1CzcM/g5Z317u21U5991Qd5JVzPdaGe9OGefKsCu7oLC2wjlZxNEjg61073IMhNi7HMshSt0ijZIXCHC1tmOdVRw4K2f2NXiPrS0bWxsslkuWse1aJpTS2LI4oMrY0HsNaK26bG5bGOooQNd736kp1rWuda1r6z/6Nn7j+V/h0t89oA3uoRaLNyuVf5d/4jufYvzHvvUt3Mu3pv7Qf3eZn/mv/gC3KsMf/7Y3APi5X7jMD9z78Bd1HNa1rrernmiwajSYbg682hNl9GCCl3uvCJFAXI2KCGHFjAn6Ff6UmYfi9ttHNUBdVRhjci+mvKdstzcrEbAr4fMRpTWtcxwcHFBVlqqqBhmE8lk+A81+u5FlM++2ba2l8BPOOW7cuEHbtlSVESBbG0iwXC65c+d2lqqO2Bxb7t1/wOayoWkEhB0e3SEZAQb1SJyJo3N88pVP4n1C27FMNEPDrVtnmb3zCnZro3PtLYqd0rcas1yxA/YqoWM+ZkIK5WNIjpCQ3k1xTDXddwpZ7miMweVYlv7kgCZhlQDYpGJmCCNaCQtnVEJbOR4hJoyxmCAySgUCEPK5KZJL2cdephh8QCmD95HxSLNcznMuqYDUYWasIrNPqcXomspWVNawXEY+8pFfwn2g5UPf9Yc4f/ECP//Rf8Hzpua3P/M5Xrv+moB2QGtQSmJAQvLCrpLNnzK3R1AEFwkRUL0cNGUJbiIwshXT2YzZoiGEiM4sVyIvJKRKDI1CJBKydFN6WkPo5dPlkCsJLc3HHVQKAlrzvhVQlk6AGTlnMUeCrJrldNLVzLhBf93HGGntkqoy1NZQW81L77zKubN73L5xXYyUNFy/fpPj2RylLSolDg+n/PKv/Cu2tjYYj8dYzMCROrBsHY33eB+w9RhlK2KKHM+XmSU1kuUb5bsmJYy2ANXcRpAALUZPBllIEdwtC026mHVlFQdJEUPK/Zr5wgNZLIkRCN139nkBwuQ84RXJLP1CXM+e9n9PSlh7cMsjJxYnt9U/JleLUpkZjZEYVb5PT3xO/hlTXJELlwWh4ec8tC/l+sj3pDD3J2J2utzfvkIMmGio65p6NOJ4PhuYjPULJHK+0gpYVUp3sUpGyffxIXQLfj6smdV1retx1UY95nnvHvdurJT94Dex/81fx2f+8jX+w8Uhv/XgNVx49KKWVpqvP3eFv5Eu8YEf+RrY2Hgb9/bLWOMx3/XjV0ROlU2hvvvfDPzt//KD/Mf3PkobnqzztK51PeFg1WCN7pmDIiUrwBEGzFNmCqD72TlVrsznBm6rebt+sOLem6D0eZ/lb+kzm8/nud+SFTDbOVvS76P3nhToIleKw3DXr2oN+NUervF4wmQ8Zjqd0ratmABVhhgDk40xla04PjrCeycSvZhYtp7KWiabW+zs7nLu7Dlu3LiJC4nJ1g7zZcNkMqZZLjk6TKAji2UrMk+jWLYNy2bJ3u4WurI0zuVeu57h0QMpIuTeyiCxNrYa5Uljf3TLJFb6FjXeZ0ls7E15yrHLR5/Sc6xSQg9/knJvL73sMe+XrSpwAe9dL9umZ61kwh+ypLMHFCF6jDFcvPgUd1Hcu3cvSxZlAzH3WMaYMCYR/BLvPVs7ewSfODpYslCJn/2/fo5mueCP/lvfQzSGf/n/fowPvO/9+DZy6/YtYmwwVoNyBN+ACuXqozB6MUZhISPEpNFZlpqSMFJaiwS6GMpoBXZUCyhMWVaZpe0CyAay6uy+WyTAcvh6oFB6wvt/9/tW7qEuNkqtArPh/ZfpLok5Un28STm7IbNbTd6fUV0J+5he4erLL3Hx0jPcP5hy885dPvv6G0RtqcciIa+NRaFYtp758ghjZ4BivLHBcjHj6HjKb3zs4/i2JaaEj1Hk0VmJEUIk+pQXD2L2QY7dX8j3rkqk5OWY52wsyey1eXzIPHdpPUjk4yQLAN4HvG+ld5rAYrEQszRjKO7BpwHVYUbqSaDa99uvVlYInwpYT+tllX8XebMs6KQk42YRnxSp+GrJk8YYiXdqGxJ2pb82xt4LoLCeZXu6/Js+C1bpVSO74X7L2C7jaMy5qbr03KpiEDX8Tqs9vuWZEALBy5hd4q/Wta51PZ769rPv5lt/7KXHvRsP18YGL/7I+/ko8Hd/6LN8zkZ+Kezzz2//JgDPbp/n39/+OgDORc2f+rErj29fv5xljPwd/P9P/NUrxB+CP333F9YM67qeqHqiwWoMrjOZOWk4QuhX6fvV/T5aQ6veAGbVkKmfsER6ud3wxoxkwDuYLA5NmIbgqjwGrEw4vfedxK2s/hdQXExHJKfSdv8vz41GI86cOZPzVx2QhF2tK7a3t6mMxTvHYpEnYdrQuECazlFqn4265pnLT3PlytewbBytl2bEjY1NYohcfuY5lsuGmzdvI6DSs1guxDG3abApSa9q/m6dcy9wMq5D4lUE1Ldtk2V4JQpDARGlBYDKuUwoDd75juUpfXip4J8oBlYgYFUpYdiTURit8X4w6c0T3rZps3ux7cCm1j17DSkbCohBlTUWbeDw8JBXP/MqbdvinMcam3ND1coiR4zSWxuJHB9N2dk5Lz1/WqbVH/7ov+D+wRHv/tqvz5PtyLtefjcxKO7s3yDGBqXlmlOa3AMtQChGCD6KpFyVPsQiu0YAYokJKrk7KmG0SClJfX9qillV0PWTKlKI+fPIk/1872SZ8bB6Q58MF7qnByLUwsoWiXbqNaMxyeqzymyj94HkfbeQo5T0GerM5AXvub2/z/2DQzZGFb5tSMrShoiuaqIyWCM9rjGlfL1rnAucP3+ed77jBV5//XPcfOMN7t69m+OcIlVdd/FFpTe0gDSIGawlzIDZ1tYQlcEH+Z6FERRAl8efzhyobDsv2niH93KPexdIyQFiJFaOFawe7t6AaPjvfrwZjicnq388nfg/D40nw/fIqcrjY4kYitJuURbSuu0MtpfysTdG50ziwHg8XpEwl4UNpaQLujuCBZzTs6ta95nM5T4uj6ckrQ6lD744SyvTs/bFC68H5P2xiVGM1YTNz73061rXutb1eer7/+oVAPxH7/PKz/4eAC49e8Ten3nxMe7V21vf95ef4xv+/Ac69cwfmX3qy+acvK51fan1RIPV4D3B+W7SWH5SJjgdk7law5X2N3PSjNkt9iQDcVKyNmRBCktSolqGzxUGtkxgU0pUtip7Pci/lL8+tND2OZrlNbdv3+bgwQNC8CuREN55bt+6S2Ul/3PoKrpsGqxWNMsFuJZ2MWd7a4vWeVxMLJaOBw8OaDMgKi6lZYI6n02FMY4ijTRa5HQhtcQE1mTgk4qkVed/606KGQIolRkXrbuTFoJHzGNyo1oCVMwMqFoFPymhtBHTlQwKBKAkdEx9hmdKKAxRQdu6AVDWhJjQxmRzm4HEMyUCIp211rJcOmJMzKaLrieuqkYZKCdQhpAUyQfwnnHpyU2J23fvkRKcPbPL9mSLUQWf+OSr/NrHrpFUBWbMufOXuHjhIi40HB7vy/lUloQY7sQEPkRilsgmJexvCfJJMRsQZVZIaTGlSVGkxCkhoLsDih3HKT2DWqENpJgjWfJtIM7UBZCpwfWvCcETQo6ayTEjKrNc6SGVQj5vWucYnEQIPoOvvke4AEy5dSXKxXufM3AVzkfadiHMe0xoo3AhkkKDD1DVFmLF5saY5XJBXVVi9hQCo7piczLCqOwyXBlZyEohXydRena1IXg51koplBHGzyglgDXH0+RklAGozKBf5Q55tcp+LhYLvM9ZwaksEICtbDb3kTYBVOyUGMOFt+EYc1K2e9r4U943HONOq5PPr4C6fPoLQEz5WhuOpSqVF5f/5CgYdDfuOOfEgfoEyzk8PqpkJ2eG9bTvV/YBVOc0HbzHhyJjt51SZrj91e/VK2K8D120lmzuYQZ3Xeta19tXW/WEv/d9o8e9G19w2d//jbz79z/uvXhMZQzv+rGv7/772z8/59L/MV1Lg9f1WOuJBquqm05BJ0vM/VYYTTHa6Cd5+ZUnZHTDiUx5HkAZ+YQhg7FidsLDk0hjTPf64uJZ+k1FUmsyc5UBpveQelBdwJ1S0LoeUKeUqKqKuq7xtJmZJfewys/5fJ5ZB421hnPnzhFCYNksUVqzmM+ICXY2N9ne2mIyrhmNag6O5yTvWcznLHOG4cbGRo6+Eclv07Y8ODxkvliwNapl0t9Ib2fKgEPFkudYImtM7g3tcyFLLzEpZkZDWFVjNOPxiKZtcY2jyI9DzNJEpaH0DGtNVNngKkuBC0NeGBa8ImUpYeuWBB86SbQAVGF8BXjk9+f4F5EaivFPSomlW8px957j42Mqa6kqy2gk2auj0QitDYv5AqUrrKkJwGzesP/giOOp4cz2Ns9cfgY7n7NcNNjRiNnimLoe88zly+jbifsPAilosjepmGOFBucTmtz32zGX+TrXwk9lv9jMQkncSggRZROm0rlHWEC6zmC+W3DJpmMdO5gSSknvc2/WowbXbwFsAsBiSl1P8vB+kvcJ6yX3QiRG3y3KaJXZsygOwwkxJOoYXaVyP3YliweNOM2qFFDGYIylaRoBthnQb22MSSlibY1rHYcPHnDuzC7HZ89yd/8OzXIhACqBKUY8+d7VGsZ1jfM+AzaVWXYx7WpcwPlATMMYnzx2REWIIfc+yv3pfd9vLde+FTZfwWhk0Cpnm+oiE+5bCvq+4V7BcRojerKndTgmlbGvGyFPAbEnF+t6tUcfFRXz9Xbytd1CxgDwgcRvlfNSpMDDxQgYeA3kkXDYz1ykvhqFsqr/rDJ0KHFN9l5ylbXW+CAAdCUrduV799+XRBcrJIsmeVHxlIXNda1rXW99Ga3RX/fux70b6/oSqvrOb+ZmG/juX1D81tHr65zWdT2WeqLBqrEKW61OgnpZX89+ijS1N/04uXp/8t8d6xoENJwEs4WlGk6uTmND3uzf3aQqppUJZ0oJWxnG4xFpGmnbtnt+b28PazX7d+8SnO/MmS5dusTe3h63b9/m/v37kCVuIYgUb3fvDLPpgp3tbc6f2WXDKrbGNc41GDRWS9xDVVcoa9nc2kYBT124wNbOFq9f/xznz54BYNE0TOqK4L1IA7Umek8ikFKQPj5F7iHNxz54UCKr1VpJ9EoM1HUNWrJMY4xsbm6xMZkQYmGZDSapzthI+is1IbuyGlPho5comhQyuARrKlrtO4mmcy4Dq3KMY86NVIQwZMJk/xQi8S2gCsT4qRhsOecyc+RpmpbNzU02JpsYOxFaWRvGWzX1ZMJ83uI8PDh2uNfvsru7zfbZc1grgKyqas6ee5bJZIRShoNDYbd98Hjf4HwgJE8kZoZUelB1lq6SQJdIlAQp51WKpNWiUfhW5NWTyQRTWZFkBw9B2EZh9XqgE0LuMaRM/k13P6WkVib4cvD661ruFQFmAgAyS6mLVFQk1ALAhMWMmTE2RhdpBFppYlYpyDEX9t13Ls8RHYO4FcfIsmkIwaFTYjKqIQWaxZzPfvpTWJM4Pj6GFKmNwTsHtZiw6fJd8iJGSEDUA9MoA8gxKSmeKQPrzhwNkbg3TUtKMWfd9gC/A+9lDEmyyNP4pSxkZTMypVIHVGW8icQ4vD5XAenJseUkOzgkKE9lK08wtzH38erCECeRnOsMpNNgzBsuZHQu0Xk7nTmac90CXqkum7rbP/nOZrBYV+4xo3uzqY71HyxMlRzAkM3RUr5ny36cXGDsFlIGvyOE7c/S+BMGUuta17renvqzZ78J1nL8r9iqP/Qt/NyH4ON/bod/cnbCj9748OPepXX9LqsnGqxqZdDKUKYvpQoT4UMxb8ls1AlQeRqYfAi4nrQKHtTJbZ2cQD5qonny/VrnLFYlWX/L5ZLlcpEnhj2re3R0QErSf6nzpFB6QVvu3r076H/TVJXhzp277OzsMlqKM+xoJExUlTT3FlMOHzyQI6ctrRM2s7Ii2TS2IqTE/QcPCCGwf+8+tdU8e/ky3gdMyvJOo8Hl/rk4dCmNaCU9oMKaDI+5/BX2Ur5bkQ0WMffQNba8PhG7rMseBIBSwoqVvkylEtZofIgslgvaZpH3RxOVRql+Ulr63VKMKC2OsCB5s6gslS0TXyVgy+a+V4knWuCc4/h4hrZj6vEYU1Vsn6m5fPkZJpMdbrxxl4MHU44Xjtlin83pmMuXL7K3u00Kgf39uyg0V668g5u37nLj1h20SSsFo1YAACAASURBVCjVkFSNjjUpNcTYovLCS0SSKAsrRwaJJE3KmbFKKWEHbUVVWba2twkxcnx8LOxhBrzGVr1CAIQhjyLRLHmX0qNNB1RU17Or5L6KoI3k2iqlO2mvLDAohky2UtnVtQC/JN9Gzqv0WMt75VzIHRz6+6VcTxi0TpmdTQQPBwcHxK1NdrfGWKWYT6dUVhZkivQzxogKETUA6eJcJUxxigmV5bjCSItbcsp5s84HfMjMnhe5sXeOlKCqsikaD4PVlLctDt8O1xYjNmku7nKfKbtUFge+8Enco6S/p21jqArpNwDFBb1/nZznIRBcMT8avLY8Vs7/kF0NvndMLuqSjnUdgPAOOIdAKpmt2dkZBTEE2rbtmOsiP8+XZrfNYX9vv5Ai4Hv4fHwoumdd61rX21l/9vubx70L6/oy1Ht+/Gt5z3LJv/dX3svfbLf5qTd+8XHv0rp+l9QTDVYlbmPV4KiTkcVAkOa4PGnPM5lUYiX6nwDD2VnHrHL6JEYNXncaK/uovqnh60KIXRyOyCf7mBzpqxIDI2sNNsewzOdzlFLUVdWZkTRNw+3btzvWoKqqQd+s5ejoCD3VaGPY2BhhUqA+swUhkFDs37sv7B2aajRmNB7jnSMsFty5ezd/n4RzSybjmhAjbeuxkCNTTmdsUop4RE6qo2ScEqL0lna9ehFlelBTmJgygfc+4HxxO+3lqSmDJJWjalL0aDwqeXFZjR6Sk15P36JVQivwIRBDAUdlAq+wVYWp6nydBFIIeUKrOgMv+WKyJOIysC/7XVgzbRTOt4QUuHfnDrPjGZefucL58+eYThcslws2NsYczWYsPv1ZLl+6wLOXL1FZS0iKre0z2PE2xwuPcxFTOyq3JMaGSEMMrfTqtfIzkjAUMJS6Pt3CbuaT0R3Lw8MjYYVJjCfjonSlrifZyTXRupbgG5TQtAM2mv7+iQJOhUXLCxYKlAFFD9QgSyuz86/WVoyGgrg+y+KRyMhVCl1fsNa551ubzOqWvtByPylEpZoA6XXWShOCI4XAjevXOfOud/D+97wH7xtidNy9e5fPvf4azjvqyaSIm1Gq70ONCQHn2hBjwseAtkactkkslw3LxrF0ER8j3mcH8CQwVr5zL5cdSl9lDNA9OEqDCK2CtHg4nmY4RJ28x97s/8PHT2NhT/v3kClf7YMthlE9aFxhLle+o1RhVpumwRhDne8vpSSqSrMy5K5IgbXWEFeZ0W5fVM5G9b2ypHewFvM74cr7sXQIVAu4Hi6qyfvXoHVd63oc9c/Ofgv65Xc87t1Y15erxmO+5i98gB+/c4f7f+Ob+Ps3fuVx79G6fhfUEw1WvfcdwOkBZm8kAwx6k4bdrYPnVf9o6YsqzxY2dlhFwlYmaCfrUexp2ceObUL6TOuqyjLXVcOmLtNVaepaJnodk5BE0nnx4kVCCNy8ebM7Hkopzl+4wKWnnuLVV19luVxKDmplGNcVdV2xubmFJjGZbDLe3Mb5iE+wubnJ3t4Zjo8OuXnrNsZULJolIXjqesTx0ZT79x5wZmMrH0Vh7lBamM0Uu77HPlIjYozKUTQCfKuq6lnTFPP/IaXe2TkEh3MOF4YT/37yLMApoAik4CA6CC2hbWjbRvYrBpJfQnSDhQ2ZMJcFgpTZO50Z4JDARzEKMCq762awFLxHmywRzRmaMWWQGDwBz3g0wSTJkZ0fHPOJBx8nYjG2YjyZ0LYQfMLUFQ8eHGMSPH/lBXbqilt39zmeN2xsbHLn3oFIp6stNBO0llxhoqdtlrRNQwgtwTm8a1ApUAgqwYkp3w3QtgG0JiFs/PlzZ9ne3pHDqBS2EmMo7z3T6ZSDcIibt/maSp1qQRg1LS7DRnXgQ9hpOHGnFO43u0V7qtowGo1Jy4gPDhccEg1DjtdJWcqsSFqTTO7x1FpAcMnRRK4rFTMQVoaYDMZarNE8c+V5UvB8/OO/wXg8YrFYMJtNCTFR12OMqeQzEIMsAaZCrMoSlcJmx2XXOjnezrFsxTk7YglAiNIvbK1BxUiIXnJg28zAWzMArGrlGIqyoAe7Q8C1Oq48PJ6cXAx7+D2n1xfy+uKIvMIIM1CAnAKSZXfKaobcK1rLuAVyvQedVsC7GoyD3TszAJZFPEPKx6cs6nWLJgiTXkBnNxxQ1DC5j3oAdkumclm4DD7QS5JXPQ3Wta51vX319N4Uqupx78a6vsylLl7kf/3RC4Q/n/iZm//qce/Our7K64kGq0U2V+SL0E9YimFGir0Tp9b95KiwNH3vlaLkIr7ZnOU0Oe+QDRmyAGXl/+T7BaiFjqEbZiUWqZ0AW4VzLSmFzlVTWEdxRC2um8P3p5RWer3EsEmRUqBZLoiuwaSIJjEeT9ja3sH5wGIpfaMH9x+QomPvzC73D2dM5zOMrTLTAU3TSr+nyuyW1lhjIQZikvxDlfNSUmblChvTA/c4OHcSIVP21xiTWZMiieyNgJQp5xMBitFDDKjoSL4l+SWuWdDOF+iqghhxi2NcGwnRAiJTLaY1hV1PKXaLGiJnFqdpukWQMpFVJ871cHEiEmODaxOYCUaNpB8yJZZugfdLWjdDqRprxrRNoJ1pmumC+bzlhXe+wHgy4XC2YNEsuhgd4U4rSBoVpafW1BNG2pOSJ/gFoW0IviH4BTHJMSH3QIp4tcqgyrK5tcXZc+fY3NwipCztVYgs2EtfqFEGr/RDjtfyU3eOy3IuM2BIhbEavl56PV3M14ypGI/HaK1YNnOaJgO1nPsqt6XciymmnO2Z0Jh8v0r/rMrMZFJJ/p1loEQx4Wrbhot755ke3OON16/TOCcKC2MwlSgIokJcpY3Bmiq7U4vcNyZPGxzBNbTLOa5psnFXRUCTjBxVieGRccNacRku12xKkhUsBmiKIZATRL56D3TAa8CulmN40tjoiwGppwHbR1V/TeuVRTVOgNWODR48e9r2jRHH8KaRsWU8HvdO6alflDspUC4AtIx3w97x8rwP/sT1CSeBfUypu64KYzvMxZZ9Hzz/Rcit17Wudf3O61svvpunviU87t1Y11tVWvG//cltfua/7R/aHW/y3We/nr9z418+vv1a11ddPdFg9SG5nFYrbpoFjMQ8CUuczFQdsqx5kkxanfOcNscbgOOTwHXVDTWtvGYYXQPSp9k0zcqEqoC7IrEkG4CIIZCmqipIieOjY6bHs46tLQyEc447d+5wcHDQZbl657Fa4YLHpZjBjWdza4vzFwwhJpwLtNMZi/mU4Fta72mDgNGUAtZqWtcwnR1DShhlMjticKoVhpvCghZQ158nkTUnQshSyeCzMYoh6NAxKiq3OLbeEUIkpoTLMTwSNxPRygpID4EYWqJvCa5Bq8io0nir8F7iamLwGGMxSqGSxoVIcDEbK1mKGVGin8QqwFY121vbwg6lxPHxEUdHRzmPVACH0sOeREgx4IIH44mqEqms0YysxSWPjwGlM1BWhhAi09mC+XLB8WLOxWcvo7ViPKmJh8cUhbVRhhA0Lkas0aRUo7BoEzCmxowjMSyZzw9wfkEITeceLBNwkdNWo5p6VAvru1x2944Lnvl8QdNkF9sQpfd0GFUyAKcx910OF0SkhzG/tvS5ZlDReofWimYp8UGTyQbj8Yjjqca7luBagm8FgJ6Qo4pBc8q9sIVBy/3LqaORsUbjfINCHJrHkzG+2eDK3jlu3r3HrVu3REpNwtRLbGVBaWxVU4/GjKqalFSONfHM51O8k+MoMnOLUtIYEAkdm+ejsHxRGnk7VrBkC/fMI/2YEFPH9HU9u6fgpJP30MPn4uHHTgxUpzy2uv2ynW4MBdAipT3NG7csrsVUFqDiqR9T7gtrLW3ju2NRxqmUQlY4FEOzfrGvgFWtZdGivEd2TeFyn77kFZeoJTVQyZx2bIqpVySG3llZ5b5vrTXGPtG/7ta1rq+6+l79NNV3Xnncu7Gut7DUpYv8zxe/gz9555/xD859O//GSzc4ujPj75zy2p8/+61cebrPbF3OK7721d94+3Z2XV+x9UT/9o4kwnCmNJB9JZlVo5CIipQniZyYxMgku/SODUqpldcOKwtgH3q0zNrkbScZVZnmwmpGa0xRoFKe9CkFSqduld8YnSNMZPXxzM4ZJhub3N2/x2w2Z1RXpBix1nLu7FnatmU+m+GaVhxnY2AynvC1L13l6PiAu3dv4WnZ3trmwsWL7GyfYTLZYn4057OvfZaQItNmjkqK2oxQlWGyPcGHJYSWFOdYIhUalRRRJbAinQ0hYNHYbPzkfcSaCmsqQvSQojCW0ZOi5OMaY9kYjweMs2RpVkYTnJO/IRAyUy4TT0fynpDEwdOYRF1rvI8YVWHShOPZgmUAqyti0qiEbMtrUlSQDAlDSiaf+8IchuxeDNVok9GoZnp0mN1JlbCAeXKttfS4xiDsJWqEMTWtV9S1JWhQSWGrMZVS2CxZTUlnFjrS+oZxNeJwPufOb/1/jMZjWidAHCzWasYjkds6l2jaViJpIjLhJ2ErhbUTJrsVaXFAbOYoAirJdaENaCXgbNk6/IPDztCqsFbeSw5uXY1oG4kN6WSbIaKVONhCEiacAiJV58Sqde8arHTMizECimM24/JNwJtEPZpwdneEaxumx4fM/DEJh9ZiahZxpKgElCdDyr3IyhqSzj2TxYApKWL0GA1tu+Czr73K/Xt3MFoTUCwaR4siJelvTRFUlO/XNp62nTHTTbkhSVm9AFaYalUTEkQXMjmqc3+uSIV9DCSVTcVMNhQCMZSK+V4ucTxZBOyU9HMqJWC2z1gujPZJVcaQTV0ZqAZA99Hg9GSPKSe3kd+vEyIpz0s4xTRtRRqsdQcilUq9dLiMioUqzlu2xsj11raMRmOMNqiU0IBBYbq4LlnmMNpQ5dgrcUlWpJAjq0KkbR3ZA0yAaicPl08VozUZU8ui33AMLV8/JAGrhtIfv84JXNe61rWuL2ttbPAn/puL/PFb34G6fAnGL7I7nXJ05zz/zt++zyI5fvZPPQWAeu6ZVUl4THzuv2/4wLWbHDYzIQPWta5T6vOC1atXr/4E8MeAK8B7rl279pv58ZeB/x04B9wD/oNr16698vme+1Kqg5lpdUV9ZaUeuniFPtKmgNvVyRz0wr1HfGD3maebKIls9DS5sPRKDvvXMvjNrEvKERoy+U0Z5Paf0TRLUjaGGWXWT2vNzvY2Tz/9NNPplLZpcNGhkViHylpCDMwXc5LRbG7ssL2zhbGWg8MDbt+6g1VVnlQr6nrEZDzConnhpZe49OwzvPraZ3DLBUrXuBA4v7tBbB1L12BQ1FUNIaKC5E3GkIF5kl4+YUkQExwPKAFaxhpScqKMFP0twYlskOiI3kkzobYUTg1inuimDNYMG6OKxWJB0zZdH2TRuKaYiCESfGZWoiEltbLkUHqRKRNzo7n/4ADXLHDNUjJttUgkASKKECAhjLetK2y1w2i8IT3IWcqa0DRergmlwBihjpWCqBLjrQ2efvpp6rrm9p07NE0rE2itiUGMh7RKGCPXQ+tllh5TlOeRSXsbIspEtN1krCuCb4jeobTBhxZjoDI1KJtl0RHvskGWArByjJQS2bEWq5oQHBrp67TWZiAqoIIExtq+nzhJ3EpC+jFLj3W5zqVfUBF8wutAZQwKy9bmDqPRiNnsgHY5o3fGTmglfdspyrKUBjDgtcEoiY3pAziFivUBjhdzie2J4IKYTCWC9ND6iJLu9t7sKy8kqaQwmY1OCWIyGZB1A4V8l8LmZfY3Cwa6hSq5TBTeS9+w9BSLU3JUZGY4R2Np1fVM9+PIUJnxyHWzwVjzqOcfrgxDe2l3/iRdFmE4YR5WPqmjecs9U+6bbqlnZZ9A8hPrumY+n+Oco7I10QTpb0fnSCbVq2FW2FFW9kHAK52rc5EerLDC9KC5vPeky3JhtOV+1Fhrca7NizFfPfUk/G5e17oeVWfGW/wet3jcu7Gut6M2NlAvXun/v7WF2triH/zYlUe9Q0orzv4n38KrwP/5517l79sj/vHNf/0W7ui6vlLrC2FW/yHwk8BHTzz+t4D/8dq1az999erVHwB+CviOL+C5L7hWHTMfwYKeAK/D169OxlZftyrnPVFDFqE8NOhlPK1KT9XDPa/9xApEUkg6CaxT1281nU5hOkdcV2O3n/v7+zRNI3LcKD2lPiac9xwcH/GJT79C6xrqUYWfB3wKHB8doRLMp3O0kpgJl02EFss547rmjTfe4PqNOzTOMZ9PuW0Vzz19macunCFZj46BKgCqBgJe9ayc5J5GYhTDIa2EbTJErJVeWpUczeIIokTLGGOoTCIFcfc1ugCzBqKwJCpLho3Ssu3kScFjlHR5KhI6AwxFysxb6aFVCOQp8t8SHRSyDDVmSa/HO4f3Dm0URqsMDEWarJVFKZPTTgxRWap6jNYVyshnVlZlqWoxhxJHXPIE2mgLCm7evIOxfXSL0aBtTfAJ5zzHx0eM6jGTjU22Ny3z5ZKldzLZ1lU2fEqkqAkJNDVVVaFtYlQbJps1i8WCtg04nwguCrxQFVF5tjY2Mabi8HBKiIqYFDoZAggrXUBUBqeyqBLRK66wq5L84nQrQLaXwooMMxC9wqWAydFB1lZsbm5jtWK5nBNzH6uYpYUuYijrkEVijO5YwhgFgNjcJ6nIYEdsoDFa42PKEt2YmU9pESjmOiWKKYRBP2PHJpLBaPm+PbhVqCzayItPMQM/JeezuMwWUCnJSb3B0lCO2/enrhr+PKpn9WQ9yvTt5OO9aRkngJwslKz2rPbj1mnjaZFnry7v9e+pqqqL2epB+uB7dVL61e9YxsyVzwOcF/O1EGOORVKD9/TjaTmew7G+M43K16a1BgatFl9l9dh+N69rXW9WRht+duvreO9PvPtx78q6vkLqe3/8Bb73+Jh/9Bd3APiR8ArXHlx/zHu1rielPi9YvXbt2i8CXL16tXvs6tWrF4FvAL4rP/T3gP/h6tWrF5CZxanPXbt27e4Xs3Oxk6h9EbTCKTV04n0zwNm/qGdWh5992n4Me/0YvKf/jJ55YvB9ejMQyRC1WVoYcvyKiDATW5ub7OzscHh0xGw2E9dYI86pZ3Z3QSke3H/A0WIm4IaaStecP3eJSW1pl3Oi9ywWC1CJ8ahm58weUUfmiyn3Dw4hjtBJ0SyO2DxjgSMWzYHkf3pF8MJyWiUurqquBQC2bT+ZTaCNRuuIxmCsRqtIii3JiQRTGYPGErwjtA2EhI6gQhBkoTQqiTmLa1sx4FFasnBjlJgaIlolcQKOLgMMnf+m/LM/PwVMRdEUChAi4UND8k5yTWOidQGjrLgBowlJE9EoaxlNNplsbDMabzGZbPLy5csYU7F/b5+U4Ph4yvF0QdMsiTGgtMlZnFqYQi0uxBK/Ir18lTYYKxJ25wJts2S5WGLrCrSiqgwpZHCi5PvFKN8notFBgAGpwtoJ9dgQYoNvHGgBczF4FktHXcHWaMyzz51nd2ePmzdvcHB4D+9DN5nXSuHbViSVSWKP1OB+GQKt3vU1CqOe6B4TUOCxleyv0cI++uCpqhFmU1OPxiwXc2azKYqcr4n0OStjJQNWd/xZByIL2FdKcnJTFFCUYsBYSzUeM6pHLJoG70NeOOh7GmOSRZVe7SALHoFEyOws5GiUAZsprHFZJonEpLpoH+jjcWJMkGQhSXdk8Ml2gc8z9jw0fnxx7y01ZE6H5zAxlNuX7dIxn0Pzp9K7XJj2IVtbxrqy2FZVBufIWcqR2hiSLosgg7G0MNQMDjD9dRaTLOBIv2pCkY2TKMtBuWLqzv8QwMoCn7DiVSW/3tq2xbmWkvb01VKP83fzutb1ZlUby3v/ytXP/8J1rWtY29t8z09sA/Chf3qT5esvdU/94U9Gfm3/049rz9b1mOtL7Vl9Dnjj2rVrAeDatWvh6tWrN/Lj6k2e+5J/IT5qAleeO/m6k2xGeW44gXv0h53OrL5ZDZ0+T3/B6mRTXGl7RmrIDkivJFhj2cxg1XtHs1yglETY7O7uspd7WI+nx7g8SY4x0Tae0AamTYtr51x86jxKKRbLBmNqdnfPMWsWOJIwjFOHnz3gnc9O+OAH38/589sQlyjGtEmMglTyqJx3WSa1PveJGSPRJtYojBnJTw1KRVRIxNCI9FVFNBGD9CcKKIhUGqxSBR4RQ8C3bTbLqUElfJ5wohQxONp2KeyLV4RoCdEQEoQIISUxd1I6s6WBmDwx+MzoRVQMRIK48kY6GWgo8mFlQFfU4022dvbY3d1je+sc585eYLK5yauvvsr+/gMSGuccrXOEmCTLMxbdqGzL+xallLil2poQPMH3UnWFgDprNJGAd0Hib3IkjlKapMXRtnUNJC8MomuYzRY0wbGxuclotIUxAZSlspamWRCS5njWELG8/PLX8uKL78RUI+ztisPDI5rFghAcVmuq8USMsnK/p1a9K+xwkQV1OnAqwCUEAeVVZfDeZYfYUTbSqhiPamo7wtqK5WIm5zVLdgGSlqghrRRK9eyu9J+LzLiwmSpFtjfG7OycwVYVIcL29g4hKpHMe493mekrYwJR+hmTyEQLmMq3MALeC4ASSXjfiZCN3RKUflRyT2co7PNAyvpo0Jn4PEPKl7RIV/r0h8xp6ReN3hOzM/aqQdGqHHcISIO0MWdQ+PBnlVYGay1iYOVwLlIZBTkTuP/OD3+n4ecmheTbBg9aYZTJYu6VN2SmPXbM+pBBLguT1kqvuigOWsRt+gsH+1/B9bb/bl7Xuk7W63/4ecimaeta15dS9Ye+hXrw/4/s75PcywD8Lz/Z8NdmH+P68f7j2bl1ve31RBssDbPzvhCGojw2zPs7KR0r9WYTwWIo8qb9sZ9nIrnCQMTUMQBl3wBUSlRaYjtiCIRhz1hSWKu5d+8ud+/eBhCnYGRStlgsuHHjDYmBSRGts5GL94TgOHpwRFUZjo4PcKFlNKpR2nJ8OOXW7UOS0XjlcLNjNtOSd71Q8wc/+C7e8eIOizZy/6DFVCO806gKXJiJhNkjZjiquJ1mti2De2MNtjKo3HeqIhI/o3XPmMVAdA0paUZVzaJpWSyWGG2ZjDcwJHwSR1KCJxIJTno00VbyWVuXHT4NqTHEqHOOZp6sZ/lhIhJiwHtH6jIvAzE6VPToBKVHWNSbCl2NMHbCaGOLi089zTveeZXnnnueo8MZv/RLv8xsOpPMz6ri+HiaAYpCaSvss4ghhfFTlno0QWvNaDymshXLxSIbOmmsBqvLdaoJSE4tIeJcwAfXuRIbbdje3oLk8csFjRMDnBAiTeuZjOvOwGneiIGRtjU2Ccj61V//dV751Ktsbm5w/sJTnD13gePjIxbzOffu3iFmIy+SFrmoinLcuu8nPYUxRqJazRQu952x4hCrlEJltlEZhTF1x1waLQsxVVVRVRWz2RHOS+5risWMLLu/ajmjZZsxKVRUjMebbG9vsr21wcZkxGKxxNoRtqo5s3cepQ1t65kvGo6PZxzNpiyXDd47AoHgXV64KFdMd6MTlc6YNQ3iqQZxPiqvbgilTOlNzStSkjkcGlw+dqfllw5lwENANxxfvhhVyXCMk/GrB4FFoutjjnA6sc3CrD48vj2iTSLvvwDufny2VjrNO5B+isz5ZItEt595PaBze1eapDX+FFOkVCTAKaJTz/KLIRnUdc1kMuHo6JBmOaeqKuo6R02ta13resvLXtp83Luwrq+2On+++z39gz8GPxie4z/74U/xMbfPL9+99lh3bV1vfX2pYPV14JmrV6+avDprgMv5cfUmz31RVbLzTgOhJydRQ2byJGsgZiqrwHP4s39tYV76DNGTn3FaPWp/CmvWSYFPTEp1/k62MrL6n2s0GlHZCqWgaZouFgagrismkzFtK26jMUaq2uKjYzLZxCZLbD3BeZrFghhhNN6QfNMcFROjOHeiHRvqmJe+pubf/dB7eP6pTZrmPs5NMFGhlacaVUSnCKoiekVCJHoxFXOoPEHWwoqG6DFRekBDpjpTSpicIYoXMyQCeN+KU2fTspzNJb4kAkGYyogipoCtLMZqXFR9L5w2oCAkATCJLCUV3ac42ipxgi6MaifrTMLaYGTC7X0iJMkpNXbMaLLF9pmzPPf8izz/wjt473vfR4jwj//hP2GxaEgoWudZLBtiShgrUTsqu5Z656nrmotPPcV4simxKjnzNAT5ORqNqaxlXNedM3QIkdl8jjKGs2fOEELkzv59FouWEBw+OUKs2NvdZHNvg/lU8+DBPZaupXUtRhsmk5q9vT3uPzhgOj1GW4sloY2h9Y7X37jOZDxm7+wZdnZ2OXPmLE9feprK1iwWM3zbEkKLSokUHT6Im3XpzyySb5A+T6NNFztijKGuK6zNbGj0+f4VGa+1tZyjKFJh7wNKWzY2tmnbBucaXAodQDRG5LnGCENdVyM2Nzc5s7fHztYWF86f48L5Pdpmzs2bt0lJwLn3kelsStPIdVrVY57a2maxWHB4eEgbHXXy4rzsPCE6SLKQYrXpoGtn9xXFCKowqIJJBbAm70larqOYs1VTjJDcqWDttHFo8EDH8nZAcfh/ssHVAOQq/TCoPW1RrcRiheDz+JYX0OSGyKqC2FsolbYGxcr3eHh3RdY+/DxxCvdYU3Xfs48bo5Nmn8TBKSXaVuKVIoP4JFbH2JOtHBLhJZ87Ho2oKkvTLFkuF7Rtk12+Dd61/C6ot+V387rW9aj608/8vv+fvTePtWzL77s+a9jDGe98a3g1vLGr32u76cZjdxwn7dgJYKSgRESKwPwBDkn+CQgiQEZCSEghFkMAIUGI/4mEFQmJP0iiRAgrVmxDZMfx1O1+Xe+9fl3zcOsOZ9zTmvhj7TPcW1XdbVvt99rv/KSquuecvffZtfc5667f+k7IG4OP+jQ29Ue9lOK//xu3sL845pf+wY/y7zdf5uns7Fvvt6nvyvp9Nau3b98+unXr1m8BfxH4cm8zZgAAIABJREFU39t/f3Ohe/lmr/1e60VI5ssa1fXXw9okLz73/LYX0dLVY5b0x4vnsL7P+fdcm1deOI/1s12nJYcA1jmEPO9grJRia2tIXddt/IWISJxSXLt2DaUUDx8+pKqqpYNrJ+/wyqXLCBcYnYyQtAhD0qGqDP1hyrDfp6lqnAykyoGb8fbrW/xbf+4HeOdmh7OnD/FINBlagQ0FUmUE41BSgVjp/SIeyWpaH+LE1rmAkxFRjFRJhdId5BponCU59CRFUWCNxTYGb2M8SDmfxVzVEJBJgvCxQUIIpFJYF0AqkiyjsBZjHda1/sFSgPNtoyrXzHXa+9fqFlsADGccQiiQEmch1zm7e5cY7uwhVMpguIOUmt/8rd/hy195l6PHR9S1Wd4n5xxCKWiNW3SSLD8Tg8GA4XALY/2yB2lqg9agdMrlS5cYDnrYxjCbTpFSUFYVUic455hOp+R5znDYx/kpdWORicKYin5/j2tX9qjLjIODPg8fPWMyLTg9eUaaFfSrhstXrrB/uM9odEYxn6KUpJ8MUGKC946TkxOOjp6xvb3NK1evkOU5WZ4z7HW5/d67OGswTR2RKh3pmIvFIilb85oWXV+gnhFplUsqrXMrE7NFjE5EzlxE5GWI6LLK0N2EEHqUZclsNmsbY02iM6SUbG1tc/nSZfI8LtTM5gWXLl/h8NIVinLKvGg4OR1RTwvq2lAbh3MhRvk4R5ql9Ho9Di9fZlpMmc3nZHkX7yxNu+jTNA3GOaSgpdSzRJXDQtPZ6lYJHr9QsXofs1lbau9SryoEIpwfMxZa9Zd2gctGdAXeLl5YoJ2LjS6OKy8bF5dmQ0mClGBtvCYrZDy0+lBxAXU9/37nFwHFc2PqOpIbxyuNUhrZOkpHo7Pz571+vKglj1nGPgq0V0yU5UIiSwMuIQXOObIso6pKBoM+eZ5RVyWnpyd4byMd2EckXbys6/4jVH+Yv5s3takX1b/nKsSVT33Up7GpT0jpL/0gP/Yl+Oo/PsJN9/n537jOf/T0F4GXA0yb+u6rbye65n8C/hxwGfiFW7dundy+ffszwF8B/u6tW7f+C+AM+HfWdvtmr33btaCEXTQKgfMN6csQ0tV+MTvzYnO6Tsk9NxHjeSrx+ntfPM5qu9W5ru/P+n4+tLEYkGYZzjmstRGJaF1Oi/kc71yLDDQkSbJ8r+PjY4pijrENUkqMNaRpiqkajo+OwDiC9QwGA4KMbqimdozuP2TQz2nKGQmeXBV8/jOX+FNf/BRvXOli5sd4VzEvHIaUIFOkakhFitEBUzlsi5IuqLNSSlyrzZNq7boKhVC6RTAlWaIIzkYDImeRQYBUSJ0QmqjdjdfHx+bOGlwIKAnKJxGpcZ7gBXVjsE4gZIYLgco4bIhaxkB0e0WI2GBYv6RJhxZRDt4TWoRZqCw2JEi2t7bYO7jC1vYeeafHoydH/Mqv/H9tg+xJdUZRVEipz6FVi8+Bcw5frRq6six58OBB26y2lEipyPMuSgqapmE0ci367fDBM51OaayLWs66pGzqFhFvcN6Rpzn7uwNOTp5i6mNeubLD7nYHKQ+ZTmtOTqecnM0Yj07wIXDl6hW01iRp1rYdim5/gKkrEh2poeOzM7TUHBzsc3C4z41XrjIc9vn1f/6reGlJE4X3lkQlBClxxkbTG9Xmca6Bgd4HvDU0rSvsQkO4pHa2ja1SAiF0238sKMbR2Kg3yMg6Q4wxaJXS7XY5ODik1+sxm825++AR1lqapubh46d88MH7SAnTyZyyrKNm2RMdj6VCyEhLnUxnTGZzev0u2zs7aJ0Sgmc8GXN4+Srb29scHx8zm83Isozx2YiiKCO9VYDUstV7rlyl4xjTxqyIhdmUI34EVhrJF41PL6P4vox2+3yj2FJnxYKO2zadIbRJPwt3axfpr+1ij1IpSsll7FD8DEMQL6Iqt++HOKf7X7BdVk0q0FogSQlJovAiNvELurtYUMPXGlzvIzodNbWthdMF1DRS+dfPZXUNYvMtaZqKV1+9yeHhIQ/u3+PRyTHWWra3hnHRxZiWRv9Hiwb8Uf5u3tSmXlSDrMtwWH3Up7GpT2Cl/+oXAfjpP1vz08W/QRhP+IH/8TbH9YSTYvIRn92m/qD17bgB/zXgr73g+a8BP/SSfV762u+3npvErD3/snoR9e5Fr1/8eUFTexGqu96wnj9unDQuJnCrRnhtm3aCtoiziGiDoG7ixDFLs2iM09QUZYGUkrwT9Y6J1oTgGY1HseFTcqnlCy5OFI1xdBKN0B7ra9KkS97p8OTZU0SwNMWY3YGmmY344uff4Me+8CZ7AxgdPUKLmrKuif4xFYIEZytQOQowrQvvgpK3mDXG6xQzO4MPIEHKhERnOBzBSYLS+CBx2PYqRUxKKY3KPNomaNNgrcO3erco25UEIXBE0yQLNFZgnKDxktoJrJctBViurvSS79tOrltEKkRAjOgsq6JGVSjytEunt4XzkqNnZ5TVU1yIdGVjbETAG4tSSduYWoRQJImOBjCtw/C6W661Fus8SqfR6VlIsixDCBnRI+NaWicY6yjLeWsKJbDOtjrDuGBhXXS7Nabh7KzC25JelpMnCQJLriVnVcHOsEeedSlrR2Ma7t65S2/QJ8+7OGvRSmOtJ01SnAg0jWFra4uimPP1D8f44NjqD3jzjbe4f+8ejx89wLqGNNUonWJdzG+Nmahyucgglu69or1/bfSMtfhgEUKQZWk0mFKKlTt3m1Xb6kaljLTvNJEMh7vs7u6TpTmnp2ecHD/CWEtt6kiR1yneOR49fRYbSKKLczT1kSBUpLV6jxeC0CLXRTnn5PSU3d1ddnd32Rpuk2c5aZLT7w0gSK7fuElT1zx4+IhnT5/ivcF6s0T0FoZDhBB9e0SMLBJEenPwC6bFCrG8uND14jpPjX05i2PR8bH8Hq62WaPcXqDN+tYMbX2RJW7Lkk78zeri4uDF94SVdCPJMkBE7eni/b1HSNUaZwmWelfABY9pF+0gotLQmiatHVcsvt/e46zDYrl8+TJXrlzh6dOnPHz4AGMM21tDdnd3ODs5bSNrAj780Yqu+bj8bt7Uphb1H+z9IFd/5vWP+jQ29UmuLIMsQ+zs8Ot/6ybP/tt/xt86+xw/d/SrlG0yxaa+++pjbbAEq6bxItXsZduu17fb3D5/zOcpeuto7UWUtT3yuX3WM/0EHtVq0BYTL6UUVVUhFyiHi1q36Jyq2uxRjxaa3Z1drl6+wmg04v6DuxACEkGWpvT7/bahklTVHG9r0kTQSXJ29rbIe31qU2LrKbksycWMH/r+1/nJn/g+dDimmE/pJikBRZZ3EEk0HbIBppVtJ+eaCIbGptmHqM9boSwCQURZ25QZIDoEx4moIAiNTHKEdnjXEAxIHdDBLfWurHl/CqkicicEQURkxjeeIBU2BGoDzmuEUgg0hEjQDBBjRPAIESnVgoBpdbZxgr+IIYE07ZBkXcraMC8nselQOqKHQiJbBDJGCkUX3iTRZFlGlmUYY6mtaY2V5Dk2gFLxPus2XxaiBhlg2B9EoygfEEojdYJomx9nHVIEjDWt5jKhrCzG+Oi0jMDUBlwgTTWiI+l3Uo5PxqTdLWSnQzOZk6gEbz0q13Q7XYKPCxrxPgW0lFRFgVASJRUPHjxiOhrzzjuf5rXXXqOuCspyhpCxoY5a5daF1UOQC7RKtGi1W+ZexuYkoHVCmiVtRM4iViR+K5TS6CRFSh23D55ON2O4tUWe5VRVzcnJiHlR4pynMY7DS5c5ODjg8ZMnjEcjhGwbxiBiM+3a76FY6c5DdPkCwjK2ZTQaMZvNGA63AEFVPcP5wGgypXzvfbI8R+uMrZ096rrAu4amqSJlOHgEqnW8DjhnCK5dzJEafHSZjs3Z83mnvxdq0jfTal481hLBX0N011kpvkWEF2j3sllVq5iX38t5fbPnhZQI2jij9vuziChCtM2nWFlbBaKDd8xYtbhApNiv6SuWi1itLjgQODg85Pr16zx9+pR79+4tmSiHBwdoraibGu9M2+huKGGb2tSmNvVJqoO//gX+BvBT/2nJ/yP7/OePf/GjPqVN/T7qY92sLiab8HKU9EXIw7dCW1804TuHpHJes/ptnu0L3zu0cJ6QCrVGEdZaRzORxiCFQKUJdV3jnFs2KUJJXIjOvw8fPeDs7CxOPlWMBulkGVcuXebZ8QnjaYULEq0UQXpqVzErp2S9DloJXDB0U8/3f/Z1fuAz13DVMfN6TFOViF4g047gajKtIpKqBVYJnNZYmeGNRUiPUh7lPd6uaIGLWtADjXVYG9A66tUCHhEkiZbgLLYJK82fVwgtkYmMpkcCvBPRZZUYZSEBpTICNR5JbRqKyuPRoATetqZKCIIE5+wy2mRxPyOq3WpoRXTWTfMuOknwQSLRsaGSmiRJqesGJQNaK6q6bPNtNUpHAx7rGmzRYBqHSpKIBIn2/7w0HZIIH6L7rPMoFd1T87wT6czWkKUpVV0wm7U5uTohSTVCBPAGHzyuMRAESZJhm5rgDU2tOD0dce3qAUJHt+TpeISfVnR6u+AFjTX0B1u8+canKIoSPFx75TqPHtxhdNLgXIVSAuc91hk0KWVZ8vjxY/r9Lo2xKB3NauqmwdqW6qliFq4QKt5z57DGtohzRMxgcS1o6aCeNFWkWdY6AWcgFGXZ0NQNvd6QwaCPD5Z5UXP07BRnPda6qHf1HqUkZWU4Pj2jKEuMcwTbNmSBpdGWFBKp4qKKtQ3Be5JELZFbZx1aa4yxjEYTJpMZW1s77B8c0u9vMZ5OGI3GJElGt9On0+uSaJhNJ0xnE+o6GlDFeBuP1klsWL1FiDb71a4tVl1gaayazgtjWfv3xe3X9zl3HMJz4+OiLrr7rt5hZYy2GK+C/+Zj3bcag4HWsXr13t7H9xJtBNHq3Nca7+X1AULUBRtrsc5G12ex0tAuGn2/kBoASZrw5puvc3Z2xp0736Ao5iRa0+102NraiuPl2jUUGzfgTW1qU5v6RNbbP/u9vH18zF/51Vv8m7+U8k+ffXW5iLupj399rJtVYKmrWvx8MTN18fPi9Rc1rRcncsuHF1btl1S25QZr6MxyfnZew3r+PV+EfMS/XjThiwYhnk6WxaiF6QTTaiy1UiSJQggo5jPGZ7FxsM6QJIpuL2dezrlz9y5V2eAAIaP+z9lAMIJ6aqjTOdLMOBhafvjzr/Mv3bpKR1XYZo51DUmicHhQkKQZzkRdJ67BNjOc3ybIHLSM4JRr0SEfo1lCWJB6o4aRpb4tRrBY7/Ct6ZHSGi8lKgSU0gRvEI0gMzVNUwGxOZFa4UO8N845nIsonPUW4wWVsdQOvExBgguOIGTb4LYDj2vNbojIrJIatETpBKUTkBKhErq9HlpnlHWDc7HxEIiIWMoEawydTh4Nd4JAqhhVUluD1indXi82u0KjW1fguqnx1iKVitEtUQUNQsSFCONoqqgbdbZhNpsBEQWODZujLAu01lhrqasKpTPwFiUCxlrORnPm0zOC91y5cgXnJU1tMb7CM6OxYFygqkqePHlCt9Oh1+lSlgXBGwgN3tkYTeM9EoGrSypneHD/PoPhgKppcLbGOkPT1C3tXCEFeBfvjRJihTz72NwrKVs9a8Bah0bQ63fY2R7S6UbN7nRaUDcN+/v7ZGmX8XjKgydPmM3n7X3QywZDa0Xe7dLUDScnY05OJ/jgSNKk/WUTm6BztFK/yNtMCEEDPpproUiSiPQuqMyNcTx+csTZaMz+/j4HBwdcvXyFk+PjaHTVSQFJ3hui0phF21QVVVkggEG/i1ZQzKZUxYwgIoreGL+MrYqk/YguLxvVsHILj2PD+SiglV9vODc+LdtaIVaf9+XzcZ/l+ESIWcKAW2KdK6nCouldNLeLd46H+dbU5ZWnwPnnY2O4itFZHmd5+HjfpJAsYoqsj8h7kAKCaM/7vP5fEoftTqfDO9/zNpPJGV//8H2qqoDgMU3DwY0bFEXBaDRCyLgQFX0LNsjqpja1qU19Ymt/n/Qn9/m/frwG9xnGf/c3+Ne/brk9frihCH/M67uiWV1oPGF9lfx5w6VFraOtop1Mn59srcLsV4aXqwmZX2s4w0KXFh8916gutonmSnGri6ZQi3de5KQaYyL1l+gEjBToNAEhYvYjkGjJtatXmE5njMeTiN6EQJLnHBxss7e/zXQ04+jRM/CCJBXkWUqWdkiEwhSWZgpjc8Sbr3X4zNvbfObWLtQTmsqSJdBJZIs4GmyQBCdBZFjvSVIQWLxzSCVRSSAYH+mVzhK8IxJf24gJ5JLGHOmWAqUVvnGxlQ3RXdVagfVt84YiyBR0gmpjNaSSWBcXB5SQKCTOBIzzTGae07lhVkucTPFBYYPDCxlNlrzH1HVEu6RAhmj4o6VGph3INE5IkAmEGBGkdY5QkryjVuiN96gkARHNYvC+vQ4K6x1BxMZXKImUCpwgODCmIQBaSHxoECG2B1pq0jRBSYUJAWNqCI6qclHTKECLAMEyGZ/h2pxRgscZT6oUUgWUaPDSIZVH65S6rnj3wwcE1aU2At9qar2PaL4MRD2zs5SmopqetauIARcswVsSJQjOkSYaISTONngJVRmdmhvnYt+iJCI4vAstPRNUkJE1INSyudRiRXVGSbrdLvu7O+zv79DpZkgh0Drh6uWMGAHkuHPnHscnp/S2d0l7A+ZliVKatM1h7XQ65FmGa9FKHwJlUba6YItpGpqmwRNa1Dx+97SMkTfWOaxzeA8+WFyIDVwE8uOiRapTENHAbHR6yuHhHof7ewy7CZPpDGMsUum4kKFz8q0eg61dvDE429AfdNnd3qOcT6mrklk1ozQFTV2BswTn2u+MiKbVrv3+COK9EhInAl6sxiqxpuk9j6LGV/1i/Lq4SPYC9giAC/HaJUovXcShXdBxvkXM4+ejHe6W3+W1JNqXyCto0XaBkhqPQCm1zF8G0FKhhFppeEWbpUvAETDexc9biAtPtqX0uta5ONC6GmvFm2+9AcFx986HTEan7XgvOdw/pNfpMhqdUbcLUPF6yigX2NSmNrWpTX2yK8sA2PqrP8IvA//3f/whv5Bb/teHv/LRntemXlof+9/e6zmrL0JT1+tF2q4X/bv8Oab50T5YoqzP1blJ4uKp8zramDV4XpO20IWJNtjCWNua7SyobZEqCTCbzTAmxqIoFRvssmioSoszi0mpJVGR6ltMS+aTOYlq0RpjSHpdtre3AUOTzKlHp/R7ns9/z1u89aog81MmhWE+L9nfG6CDjzQ9lVBVDVkaHXydd/hgIuXVGiQR6W0au0SJpYhUXefcwgg0ImprSJsx5lwDGNomVMpoguMFYA1CSpI0JThHU9exAWrNh5xxVPWcoik4mTmmDVh0S5FeoD4tciUVSVfRzXMOD/bY29ni/p07nJycMitKdJpEKm+ao6XCmIZ5i2Au3FVjFqSPpkZSoaXAto6q3gYkiqAg1xqp42RcKkmiUtIkJeukbG338Tjm0ylaaVKVIGVC8DAZTTgbjRgMcnqDAY+fPKFuaoJY6Dlla9YT0cFESWSiUUoyGPSRUmBt034WA95ZTkdjnPdknQ6icQQ8WkfHXilVi1DHxk4AUgQSAkIGdoZdtoc9tIqo79nxGOMrlBd0EmiqBpVogoifW60UzreKXyGpmwbVxrF456icReuEXr/P4eEBe/v75FlKohXD4YBONyd4z9HRMR9+4w5nowl5p8vrb36KIBPG8zkeyc72DltbQ4qipCgKzkaTuBjRRuBIIVvKfMzQZUHL9R7nQS8WFkI05xIIlABBZCv44PFNg/OxaVIimp0pKUm15NnTI86ePWMw6KFloJMKOnmHUlhmxQxrJTrNSDPF3DqOz04RQDfP6Gxto/sdOk2Btw5vDVUxZz6fUtclSsQGOQTXoouijVliqQVX7TgRFy4W17sdBhaZrywYH+sLdbSy0It044h+SiGXZkWLMVOKNid5ada0OlZsmH27qPLyMffiYuDiuFLINrM3RGdgoVBSnRvLow41apxjox0XaxZj46JRjaxgwa1PvUWnk/Pee+9y3Dr/SinJ84yDg4M2T3eCMSZmO7dj7e9d2rGpTW1qU5v6o15/5r97nT9zesq/+7M/wH8lPf/g8b/4qE9pUxfqY92sytac5CL196Le66Lm9EX6rsXz668jwvo871vWi3rZ8wZL501UFucp2smocfYc7TjmEMb/X1VFu/dOp0Ovl1OWJWejKbaJiJBUDhEanA2Mz87wFkxpSLUAb5FCM5+U7B1IesOce6e3uXSY8aUvvs2nXu2RyzNsZfBVIFGKpmlIO9GcKARN8AJBhtZQFDNsCEilCLYC4dAqo/aRCpokCQEipbV1Hg0iakyja3GgLEuUUmRpumxapZSoduIZFpRGwbLZh4A1luCj/tNZS1E7Zo1nWgYKq3AyhzZDFWKmpRaCLM8Y9AckOmpPD/f3eO3mNX7iS3+Soii4/cHX+cbdh4znBcYLbGMxVmNdRNmsNXgXsO190zohTRSp1mSDQWxK0UilSbKUoiro93rsbm9hTc2g3yVNElxwdLpdBttD+nmH4Bz3797j8eMnTCdTNIG3XrtMb7BFr9fjjZuXuXPvLuPppNW9pszKiqpskEoik6jB1InmYH+X4XBAliV0u11293ZJEs3p8SknJ6d0Oj3SrMvp2YhnR8cURUWkZTvKsmRezKjLErzjyv4+N29c5erlHQ4Pt8BbTk6OOTo65rd/56sUkwIfFB2V0xgHSqCTaCgVfEBJgfV2mXW5iF7aHu5x8+arXLt2jbKqKcs5RVlTFAUnoxGJ1pydnTGfF+ztHTDcOaRuDL3+FkVVI4XCGcuTx495+uQJzrkVuiYE1trlok7TNHHhRMkWoYznpYTEeI/ARzo5LTIsIhk3GuUqgpQEa2iMwVi3WoDaGrK9NSSRkHc67G9lvHljj52dLSrjuPvwKfcePWE8m1J5T2MDHhnp2cdjlFL0+126WYYIFq0Sup0uBweXeHb8lNHoJC5OOIFzpm2QI1U5BLtkkyxiZV6oY12ShNe19gsacDv2LP9EIyiBIMYKn3cEjqPXupY1NraLV9bHupfRgdfRViFW56NaB98FMhsR5XW39NgUx5xbg2vvw/pgG4KP7uci8NZbb7C7t8N7t7/GkyePl4iqUprhcIj3bQRU07BOifbBb7RJm9rUd7De2b3Bf/LTG134pr5La3eXt392l5+/cxd393t59e99SGVNzP3e/O74yOtj3ax673Buhc6tT3DOb9dCe5ynv70ITT1Xa2jqQuu2vq1sNa3+3L7rk7J145P17MWLsTfxZ2st6IhAEgKmadrcSbV0VSUElE4Q2lLPG/ARMTrY3ebqtUv4ALdvf4it3RKhuHb9Ovu7B9y9/5DxyQknRyfsbMGf/cnv5+Z+H19PkbpLJzH4bgm6iw0epTSEhLLyKJVQlQYhLEKCloFcCSpvCKFBiz4yqIgGLVCeC/rgxfWKjbgiyzI6eb5EPrI8xzlHWdUtJXK1ACGlJElSkjSlLOqo73SOqrFYpwhKA5pE5xgnqI1v3URDbD68ZTY5QynN1taALNf0+13quuD+/TuIYHn15nVOJ1M8iizLORudUpUVPgTqukLrlDzPMKZBSkWepigp2RoMmUzGNHVJmmb0ekN2d94g0ZpECw72tki0opzPSTtdhEqZl5bDnS2uX93j9asH3L17n8FgQJJoHjx8iEoznA/s7Ozz/d/3vXzw4YecnJxxOp7jhWY6K5jPZ4QQqOua2WzK08ePGZ2eIlrjJ611a3zkSNOMrZ1dQDGZzZlOZngfdcNJmjDIhvSGfWxTg7W8cukS165dZXuYkiSQ5yn7B2/w5ltv4AL8xm9+lcZE+qQUCoTCmEAn72CtoazmSClw1uKdpdvt0ev2wQfu33/EvfsPmVcNVVkS8PT7fZJEcbC/x872PpeudknTnF5/gJSa0WhECA1ZlrG9tcV8PqeqqoimNg3e22hEJhVJnsWGq98jCJaOy967aDAWAk1dY0xN3TQ45wi2jYpSMjIakiQyByLMivEtY0BKZrMZSsLNm9e5efMG1w+3GKgCJQOdYZ/9w0tcunKZ3/3a+9x/fNR+5h15lpFmOU3TUJY1wkO/0+ELP/xDnJ0c85WvfJn9gysMd3Y5OX0W769OwHkaZ1v0NDprEwLBx8WYFQoZM4nFwjEb8JzXc8YHoo3zifu3G8QxSgLCE3DtdziOn9bGOCIpI8oqzzXBL1/TuyixWEkwVtssI2cWY2BgOe4thlTnPGVRUtd11Dv7mIWstCJttclvvPYa165f43e/8js8evSQpmlQOt7vLMvo9/uMphNqY1pHdfmcHGRTm9rUd6Y6MkW8/upHfRqb2tQfqMSrN9Gv3uTBFz8HwP/5Mw/42+Ix/+zoax/tiX3C6+PdrIaFccuKSrputnEeMV2ZIcHzlODQ6q/Ov9bS2+KTrT5sLUvx3CQs7reIpPl23YdjA+3P0WNV60qZJEk85sI5NASapmE0nkRaZTBRE+o9iU5IVc7jJ0+jhlYGkB6pUgbDHbwXBGsJdsqlQ8u/9q98jstbntnJEdppZEeT55JeX7WavUgJRGqENAs8BoFDaQFS4b1AK0dja5zJkVLh2msQ0UeNkAJjbKRStnE83ns63W5Eh5SMSGwI5FkWrzOOqizwzhM8eAeEqHVLdEotDT4IfBtF4nw0C0IkBOQS/fR4EiFIE41Os9aFVyLwPH5wn+Mnj5hPJvE8dEJva5e8N0BnHfZ2h8wmZzybnLWLEYGtwYArV68QXODs7BQCJDohS1N63Q5p6nn70+/w+uuf4uz4jIf37jIfTfDFGa+8cpkrh7vUJvDBNx7zjbuP6eWaG69sk+h4DfJM47xDa0FTl+wdXOL6tVdAKcoiUheVTCEI+v0eaZown8+j83Onh3WOsqwIwtPrddnB6qptAAAgAElEQVTaHnCpP2AynTKZzrh77wE+gLVxISKaCxHfU2nSNCVNNLY2HJ9NGU3eZXeny6XDIdeuXWaws02e9/nUOw13Hk6oG0FjostulucgBGdnJxjjUTKhKGdtMxRjR+rGtNpdi7GWIKA76MdmUsV7czaeMprM6Xa79PoDlD6J2kIbsM5GJDVN6OQ5WZrGBlVrvG/zWpOUtP08hRCWsSjGNFhrSXTUB8vhAOcs48mEYj7HOr9s2JyPC0sqiQ1TJ89QrcO2kpIQFNY6Hj16wmQy4+Rwm+uHWySJZDY/4nQyZTorKY2h191iJ8uZzeYUZYmWrSRAapqqYe48T58c8fatW9y7d5+Hjx6QZAnD4S79wRZ1WVIVM+qyAGfWZAVhOQ7FNazWqEsERFgxQiREKnPwC+9dAlE/L84RUBZyB4+zFghLtDOEmJ0b19XEMgpoeYzlGHie8vsyzwA4v7C45J54H7Xrwp/bd6GxjY2vXOppRauXlUpx+fCA6zde4d69Ozx69JCiKFoEOppxLRy85/M5C3+oEM77B2xqU5va1KY29W1V6zHz5/+b1/jz9VX+l//yKq79bfY3z36NcTX/KM/uE1cf62Z1QYeD1Yr9xdXx85OQ5+nB6/tc1L56vzC4iZy35QQqhHPGIHHf1fu9+Dxe3rCe02ctqIbQ0tckzkQKZcztNNR1pDkKHHkedaSnpyOOj8c0xoACITxISdU0nJyNUc5TTY649cYWf/rHP8WNKwJhC4KAqjIkWqFShRKgqBCyneB7R1nWVHVJr5uQp1HTiIjXQYkAoUaKiHJ4GzNDl4sAIkbpeLeafHrvqcqSMsvodTtIGc9hXhR0u50lorIkC7bXxlpLXdfLRQOEQCcpyimCiU1GaWtCGzMTvEMpSZbFBiXLMqxzOGOovcU0gW43Q4jY4JbFlJOzM5Caxw/vU1d1NEXygcZYHpb3mE3G9Pt9Tk5OYrxNmjMejeh2Mw4Otzk5PeXBvV9iPJoy7HbZ3eribYXE0+1kPHh0l298+CHjmSHYnF73Bru7W5yenXLv/kPKqmQym+ECfP3uQ37tX/x2jOOpbaQkB0/jonNtpKor8ryP0payKmI2q3Ucn4w4PhlhrCXRCTqJSK1QMUPWWItzgaaJSLlsm3oQOBujhIJwTMqCx8fHvPeNh60xkgYSZLrFq6/e4PKVa0wn09gcOIeQiqOjR9EkzDikgiTRBCLVPUkyQpAIqch7HbTWS4TYoxgOBuzu7ZGkKUVZcTYa43wgTXOs9ThraOr63IKS1lFf7NumO89z0jQ9R/dffB/n8znzmSdv0deqLGmaZtk8OWuj7tdZEIE00Silcc5GJFMKBoNtlFLM53OOT05ivvHDbtS0ao1p3FLrrBPN3k6HneEOe8Nt6qpmMp2AgDRNkVLy4N599nd2+d7PfA+j0RmTYkpXd7E2Xs+t4Q4m71CXM6ypsdYuM1pFq8OFFjm90DyKluJK28AGWuMm2SKc67E0C/KGdzizykmGBYLbvkHMeEIISSSBnEdO18fSF41zy/Fu0VCLaNAUhF+e/7nFPrEaCxKt220FWkmss+zt7fDpt29x/OyIe3fuUMxny+3TJCXLMtI0ZTabLxfSvPeshvBIFd40rJva1KY2tanfc2UZf/W/fnX58C/+D48pJikAf2lm+H+P3v2ITuyTUx/rZlUKgU7Uucnogjt+XiO1moSs61sX2y2cNFduv9F9U8jlLGlF/Y17P3cuq5X/5w1F4r7n4xsWSGpsumLze5EuF1FA0eZRtpNGKUmUxlpDouGVq5eRQnL09IzxpESoBCEC/WHOa6/dpKkNRw8eo+o5r1/X/MSffIU3r/cI9YRUKdKhpcotSgtCohFpiq2jRjUgSRJJ0hHoLEWrQJJp8AHno3OnczXeV4hgkS1kIWVsWJ1zS9qvXJgtrV9776mq2Cw4F6J7a+uC7Fw05CGcR8tjI9xAiAYtiUowQaKVB+MRXhJERJuD9zTORBpqp4uUUE5ngMA7S6IVmkCv12N3a4vRtKTTk1jnSdOUSXAEbyibCiUkwRtOT445PjpCJQndbg/bNn1CaooiMJ2WNJUlSQaMpwWJlAyHCfcePqAyFVJpXrl+mfmHjyiaht/8ynt0OhlNU9HfGpJmKaO5ZTyZRZTYBRrrMTZgTPyselguXoQgopbPe5K0i1IOaRuktVjbcLh/SL/TZzQeUzeGuq6xbVRQY01sQmIgKHPvCc61xlTgvEUEF01oQojNJ5oQFFXd8PR0TmkDdVUxGY1RAayJBko6EXTzlMZUpGl0oTYm6jt0ltBN+nT7Hfb393nzrTfY3tnh6dERnTxHSMnToyOeHn/ArJhH5L2O977TiYsZs9mMqirodqMWeHdnmytXrvDGG6+TJDqaLp2e8u677zKZTqmq2OQtqPjzYh5NqlyLGLYxS4KI1gshcMbitCVPMxqlllrYqq7p9/vcfPV1nLM8fHzE6czFDNyk1b2mfVxw4ASnZ1NSLfG2odft8Om33sL5SE19863XefLoERL4l7/v+7h6/RX+/j/8+0xn08gEEAJTN2iVoLp9EF2qqqYo5gRv8WLReLaZw4JIyybgfXRJF7KFEi+McyEEpFYoISOaKkBJhcfjvUcrHZHkNl5Hy9V4JgTt9/28cvWied05BLV9LIWMmdKsGlcpBAh13tW41a8TAt65c67vIXgUmq3BgM9/9rPMixnvv3+b2Wy6fF0nCWmekaZZq59feRdE1H0R0dOaNm0Mlja1qU1talN/wNr7D7/IXvvzP753nzDZ5u7/UfHHnt5m3lQbjet3oD7WzarzbrlS/iIt6sXHF02W4Lym6oWOwGHZrS6P10ovz5VY/hVYb2YvGiy9TB+7oCirxTm2/zoXdXZKribLWit63Yy6mDAeTfHeUxQFyOgrnEjN4eEhg17Ok8kJzjzjzatDfvLHb/HKFcfZ0QOUT9jb3keKgk6nQWqDc5ZZYZiOG0QI9HspeEeaSqRKkXj2dncopjUnpwXGlTgXkJjY1CBbU6pF07+aHBsT0aA0TZfXwRjDbDbHmAYhFGmWLambIYSoJ6wrvGuoqwazRJQjShdjMAQi+Ojk6j34SC9FRjpx8BbjHdPJGKmTiKKJiIurNCHPc9555x06nS7fuPeAh0+eRsOfWSDvdNjbucpwuIWxhpOTM4xxOBdQicZZUDpGujTWc3w6o2mimZXEcHZyxGiU8pl3XuPTt95he3uIDZLT+Qf0Bn2KecPcwLicM9ga4HSPs3nNuIK5UTiIJjs+Zn56FRtwnShEgOm8IoTV50SqVsOXdegP0ojq7e3x6rUb3Lv/kA++/gEESSZiTFCaRpTMOoOzluB8pF47i/VrpgEuEJzAe0Ge6eiYGyzj0zPe/9rv4pwhWAgenDUIGcg7KYnOyVwGRHRPqQTwKBn1yp/73OfI8oxut8vxyQnvv/9+NL4R0Xm4rmsGwwHdTje6tTqPbQyz2YyyLKmq+IcAaZrw6NFDvvzl30YIwWw2wzuHsYYkSZeu4Yvvu1YKHxY5vTE6ZuE961ktJsUFloCQsl0ciMc4OxuR5h2UTtjd32c8nTOfF/gWMBwOemxvD5mMRjhTI9OU/taQ8ekx73/jDlevXObq1SvsDLe4cnjIfD5jNpvxY1/6EoeXD/nffu7vMJ1M0DLBhQbbOKyzSCXodvtolcTvmrfMZhMIDikUPixyi6PT80JiEF1vxUo20ep7QeIR6KwT/7/eIZRGBkBIQojsDq01jWniIlAIJFojtWwdqldt3sUFtxeVWGtUl8Pmwo1YxebYWgstxde3Ls9SxKgh5yxJokkSzec//1nKcs5XvvJlnj59im3iGJF3MjrdLkmSwkKP2toDrN/X0I5T4psJbze1qU1talOb+n2UuHEdAbz2PfDIf46//TN3+QVOeWIn/Nbxh89t/4XDT7Ml8296zGM359efvf8dOuPvzvp4N6tu1azC+Wb0RY9f1sRefG41qT3/+rp904ISt6LsiqUW6lvVefQ1UmrXm2O/iGHwnqapIUnRqcLbOGEdDntcu3aV46cps/k0UiBxpKkmzTIIMD09xcyecXJ0l9evb/GTf+odbl5JqOtnCCGo6sB47MjzDkkuQNYIJM5B2QSsacgyRZpJfMvOc8Hz7NkJxaRhOmkQ2uM1eEpUsGjVQacpSBkbRu8QPlIJjalwzpFl2dLBOTrEyraBjTS8sixpTIOS4KyLekVTUZUVtq5wxiBEm9+JQHgHziO9QIeADqHVo8klqut9YDafIVWCIOZqBu9xzvD6qzfZ399nPBrjbTyWrQ0heOqqpJzPqcqSy5ev8s6tdzgdjZhMZ1S1wSeCrNPFGBPRu9kEZwPGxpxTnedMq5Jf+40vc+fBI/b399ne2ePRk2NOxgWgcc4TgqA4m/P4dBapriEQSGP2pwMpY+6lCAsae0TkpUojNbqJ8SypkGA9Fo9zHkpoaoOrLf3+gE7WoS5rnI8ItvMxNiU6+LZU0QDBG8AgQ6vl9iCCRqHxpolGU4nC+orJ6VMQHiVztErJshQhIl3XmqiNlFKSZTlKRa3nYDCkk3f45V/+ZQKeTqdDluUUVYlzLlLZW5qp9566rCnLCt/quRcIu5QRERQCnLOApyhm6ETFz5aUJFpFinOaxmO3DsGLRnWZf6wk3tn286XaBSmPcZayFPR6PbRWNI2JnysCxycnQHRiTlLN4cGAuq4xjWE2P0UIw/bODsE7xpMx1cyQ9fr44Hn4+Amz8YhqPuP69ev0+j0ODw/pdLvcuPkqf/yP/yi/+7tf4eToiLqooiRBaGzwCBcNn/r9Lr1OB6kSymIa9eDBt40sWG+R0CKKEG2RFLR5sEiJaJFPLyRpnseM2jI6j6dKI7SK331n0UmOaWqcaxDWtvFU4rmFu4vj6XNNq1j9I9suUbbbSCGXi5DCxcgqINKyQ8BaszRVevvtTyOE4PZ7t3n06CFCCJI0QRIZF2mSIqTEubBCVdtGNaw1qEua8YYGvKlNbWpTm/pOlRT85b/5Kn+ZV7H/9J/z9/7Rjz23yb/9lzzizde/6WH873yVn//56wBMJPxnj//Jd+R0v5vqY92shuDaP/FxjDfxywlSWLLU4qRbiFV24OLfF0XarN6AJdoSXtaIhvV0vtXy/Mr1cs2kJKycJzm3z/M62hCieYhSMeNzQZE11lBVFU1TY5xrGxWPTiWDYY+rV67QFCWjZ4+YjY759M0tfuQHb9FLLMIGtvo9iqohbR04vc8wdQ/RGGTakCaG7S2JdzlpJlESgpBECZ/ACkeedpHbHWzQzK0h2ALvLKjYuCvVOtHW9RLJUi2N8uLigfeBPE/ROkG0cRlJEumUWaYxaUI5n6IEWK2oynnbwHlAobyIeZmtztCagAuL/MyYoQkB7wJaKBAS5z0ET1FETeSTx0+ZjCeMzs6oqxotW12ncXjjccYxOh1Rl4ZpWTCdzknSnCA0Z+MJk9mUuqqQLeLrQmjNp3TUB3o4vn2P5OtP2NnZZ7C9zWhcglSRniig2+3jgboyrbYzRcqEJIua5KZpqMuKuiqRMuodvQ8UxRzvPUmSYrEIETCmwXkbEcPgefbkMYlOWzMmj/MmfraFIAQXJ/KtDNk7j/eWEExcbBCxWV7mUTqLVPE74bEE7/DEe+GJ2uPoRqyX6KRSijzvtI+r1thohDEGIWAyntI0pwQRyPJOS/WGqqwpZyVaxWa91+2TZxGJlVJwcvKM8XjUIvMBUAQ8TeMAT+PiN0urQGgiBTVJE6RUSNnqdk38DEpACY0S0XnWuUgLFQJM3TAH9g/2GQyGPD06QusUayOFXyhICHgPWnl0NzpT103JwyfzGFUzGFDMC+ZVyeHBAZ1ej2I85mvvfcCDR0/QqeLGa69yNp9x6+1P84Uv/givv/YGDx/c572vvsvX33+feVVQmwbnQAjNZDKnmJdIAWnWQwjdLgq1iw8ohNQQDD7QGjDFBlXKqCNGaSBgXSDXHfr9Af1taJoa50zUvFqDFxHRRCSoSGOICHRLEWY5zl1gpKz9fLFpvTgWRo1tfM56j2sapFK4NrbGh0gD1onixo3rdHsdvva1dzk6erqgphB8QKcp3W4PKRTWxUWxdYmHX47DrLlMbXJWN7WpTW1qU384pf/ED/BTf+L3t6/87Dv81GfbB1XFX/if3wbgLzy1n1jE9WPerJ7PS/X++QnSxUmTEKvtFgYiF+MV2q2XOq925xdS3dYfr55+3hVzQXO7OEFbNLFLQ6bAUtuJhyxNkEJiWkSo2+0RvOfevUeUZdnS/eIEu6lrnKnBztB+zDtv7fOjP/xp9rdSjh49opg6bt7cIU9SBjs5RWGwNmE+E4zOZvQGjr1DGHRSnGnRK62pKs9kXJAoQSfXaw6aFp1AKsC2aIiPs+LnzFRic3U+YzY2yzHjU4gYSbGg/IXgITiwhrppotlRXVPMq4iUuDgxDULirSBYSKRGy4C0AbecnLcGMEJExNJHZ2KtEvCeyXgKCOq6Zj4r6GQ586KmqOb0+31u3HgVEMznBaPRiKqxEelMO0xmU0bTGVUd740OUNYlQiukUhhjUUKjZYrUPfLegL3Dq/SHA5zUzIo5naxP3u0hZcy01Unaxvrk1FXFdDzm7PSUspi31zEhad1Q66psKZ8S7xyVafDOYG00GxIt8m+FYOYm0QArRNRctREtIfgle937gPORDiykisZLKlJIvTd4V6NUimO1CCClitTh4BHCI5VCSLXUWUcnXsvp6WmLhoplrA4ByiIuaKhEkyYZTe3w3sT9Rfz8SRGdn+uqpmkdXa01lOWcQEDp2HBVVYnScqk/1DrSXEOLyOkkIUszkjSNelXvMSFgnUNLgVIiNnHBLr+DkSobM1tPT89461NvkaQ5dx88iHRZpTHWo0z8LNd1Q5alZGnO1atXCSFw9/59Tk9PSHRK8J5Hjx5xaXeHa69cpS4ryqYBC1977wOOzk6x7fjx7tduY+uG3mCLnd197MkxWbfLfD6jLCvSJC6+NKYB70l02n7PXJs322CaEu9lG8uTtN7mAoRuacIgpSLNNc4rqlrQGfQZ9Hbw3lBXBaapUc5gm4rgLc6AdYbQGjVFgshF9/Xz4+PFsXptZF0bm2M6rJACJdSSfeG8b9kroLXg9dducvnyJR49fsjp6Qnz+Xyphc26OVopIuXZnzO6WyDqtOe7prRtz41NbWpTm9rUpr57Ks85+OtfAOCfPH4M5XU++Lkxf3r0ARAXaE/L6Ud5hn8o9bFuVhd1ke4LL6b4gm8n6OtGI9GMRIjVJHdpCvICfelFA6XnUdIXn8NCM/b8ceRypX9xhOWkTsQJdSAsEapO3sF5mM2nSCXQiWB/fxcRJKPTE0bHx1ze1nzP597kj33fTXZ7Ad/MGW7nWFfSGEsqFMZMEcIhU49tAjM7RjSaYd2hryQyeOra4E2gbgR1KXBKoCQkUhCERWpJImFuGoypSbIhSkUNmJTy3DWKOanJ8nGkAEb70Rg5FCl6MWYioSwLnLVoFSl/plLtZFUj8HgZsE2c1mc6XsMgApUDLdrrqiTBx6bM+3gtnQ/gwWHRSlI3hvF4QlGWsakxhn6vy+7OFkprulnOnXv3qRuD0ikgqKqaZ6djZmVFEALXUmWtlaTZEBMarA10+wP6vT6msvg00BsMcMHx7OQxxlUkaXTE7XS7CKnZ3t6l2+kzm805evqUk2fPmIxHEAJKQAgWKRTOWqyJFM9UJ7E5MaZ1xLXx+rj4+oKG60WgrguU1GjVIqqtTnP1JyCUAJniW7/rIAzWNwQcKPAiNg9BKEC15jsaBBhbo0OMCXHGIHRCJ8vp5F2ct5RlGbOR21zdVKUY6YFIdzaNA6laN2hBcIEgaHWgMV80BN9SeaNJFPgWzXZIrVjkGUeddMPC5dX7hWzAk1hLmqUMh0OmBIrCtfvFa7BotKMhU4vIKoX3jnlR8MorNxjNpszmZTSnkhIXIvsg727R1A1SwtOnxxFV1ilSRnQ81xovJOPRGBngxrUbdILl9u33cTjmTc2v//pvkKQJd+/cQSPQCLa293BScDo6YTAYknc6TMZnlHWDbFvQomoQeJJEk6Yd8rzLdBL/31JKVJK27JK2cQ3En3V0PA7RowwaR0elpFkPlWSYqsbYkjSPztZNVfD/s/cmsZZk+Xnf70wRcaf3Xma+zKqsqatr6CYp0myTskXQkGUvCBiCVoa9884GbNg7G/DChkUQAkgT8LA2YMgGBK+88o6GDEiAYNGwJKNJNptkV1dXdVVW5fQy33CniDiTF/8Tce/LoViUulHV4v0DVZV5h7gxnIg63/m+//d534kpVRIWe//R91w7Bs8bMMmzdvecHVb6tJIWgsGtGOS+VRqM0ZzcvMHp6SlPnpyxXF6N/asxxNKXjKgSlB7D2veBsiySFbXF0EpQ9v2Qs3qoQx3qUIf6WS119y4A7/82fMQvA5B/+CP+w7+7AeD3nnyPZbf5yvbvp1lfa7BabDqKXLf0jA6M6hd8a2A6BSjtJMOg9v68M/4Yv/mMfPj6uwMRWyRlL1mmf2GkAxoxXJXvKoCUqSeNsAs+jDvkQ49G45RG25qEp5lUzJoZ7dWS9uISdzTl3TfvMjEdtVFMbkzQLmHcHJ09MUWMVpLnqgKLicbcnVE7Q2Wgri3Hd25wdn7J5WqJdZrJTA5QW0tWgZQcMRi8TnR9IMSOymScrSUPc8hlRCSmpIzRWq6VluiJpDLOyETZltgUYzSDEzI54nRmuzaomOiNYlJXdJstKSV6Z8kRkf6piIkCVtuoySWqpbaKzouMMatc+tMSMcmZfvzkgvsPH2ONwVlLUzccHR0znUzZdj3nTy+IIWGsQ1lL9InL5ZrlZgOm9AAWoOMmtQDzoJjUNRUKup7GWGaLuSw0RC/sonIc3bzF/OgmtqpRytC1nk8efcxqueLJ2ROi91BcjUNpHs0qFxMkAZvB96VfU6xyIJYeUso4THR9j7EKqzVKl++TRjOvIQZoRBXFnVoYf8hJQenjVEoVAMC1+02alIXBHsBm33ciV/eeqnLkLExX3/cSwdRLnqdzFtBjbvEgaBCmTVYGcxIpr9ICKLWW35Fdz8QoMSpyT1NyUYXtlIUpUz7XQw/z+Yxf+ZVfYbvZ8IMPfsC9H38iTCGaymqMcWy3mT54EpLvqZXi8eNHTKdzbt26idaXbNuOznvIBRQXoyOUoq4nbLZrYigZrmi8lwWZxXyO1pptu8E6i3WGy/MLtt2Gq4sLibUxhsY5FrM5KYM2FdZNqJzGJbnmy6srfNeRY8aYGk2m99KTrY3C1jN0KrmqSpfrrDG2JoSANhZbybhGK6yzpODpOvC9GkFjxhJzxtiGeuZw0ZODx/cdOUdiFEdqM5o5xRJxM0TTCGjef0aSU0nBMeOTEK2KkZKMrRgDXbclpcDiaM7d117hyZMzHj18QN93dG0H5XeryhWmV74XS7asLJ4pyJkUw25xRmnZD8mBGvtmD3WoQx3qUIf6l6HUe+/wd39b/vzd/0LzyWLC71Utf+/z3/9qd+wnXF9rsFq8Zp577WU9VEPf3X6cwsgp5QGM7oHTZyDvszJfisx0/7eVGljbF5uLvDSHUGZne3/PY1TDAGJFVhkAhVXS14g19F2LA3JYcWNu+PVf/XnefXNObB+x2UScmdC4SEy9gBDgarkmZZjNFkysZn5k0Ube3bZr1tuWy+UG7RLNtMIZTc4FUGrD5bLjahUwkwqlHEpLpqZ1NfTCIkkPWgIlJjc5C0iVEykutNrrIl8Up1WRAEtcinMOS5IJtbME78i+p3aark8CTnIm50CtIzEbGmvRwZLQJCXMo8maEGQRQAuxRAiR3gfaGPj080f83PvvMWumdNsN2XuW/RIfIq0XNg5liD7hY2bb98QYccbg+w5yxjpHEoEszipiu8aZzBu3bnB8fELKCp+hSwqfDdP5Kd94512OT27Q+8i9T+9xdXHB8vKSy4sr+nYr5yxG+r4TN11tUCmWWJ4oQCwltNpJyVOS8yfjD0YDoQw5JVKJZRlgoRr/rUYZJjmjELdUwX+OoW9b/MCSTPCJ4z2RI2RlCD6yTe21uKWuawnBj+Neegi1yHdLX6tSspihtC3HUST7RX6fi8pAJ7kPUs7i6mw0SkO2Euck7rHiYOucJRfjpYxCaRkHA7tbVRXT6ZSft5bHDx9JX3AMZGDmGuqmQkctrtQ5QlKEEHl8dkbViMtxU1coldm2G1zliAFsbWi7VhyEmwmbuKZt21Fy65NnMZ9R1Y6239ItO0IMzGci8V0vL9lqi3WOZc6slitm87lcJ23wIVJXjruvvUZd1Vw8lV5rrURmnaKnjxFVzp0uYHBgp7XS+BSERZd1BVxj6dqO7aZl0tR065aqacA6AfHOkXKm8z0KWEyP0CrTt1t88JATIXhC3xGix6Al9iZHUogoLYsluZgcid2wLJTkorAAMT8bc5bLNmPoaSY1t1+9Q0qRBw/us16KoRRJ2gGcc1TOlcisUMaNIivJ9B28DKCYOhVWFYobMDyXnX2oQx3qUIc61L8s9Z3//hf4DvC3PviQ//YfvM+/8cdXfHz58KverZ9Ifb3B6gvQ6jAxl3f3QeMXbWMPfO79fcRVXMeR+71O15lX9kDv/j49L1P+ohoYSe89rvTjDWxu3/eEvmNSW7793je5uLzi0cOH5JnhtVPDr//qN3n7zRl9ey4ANW9Zb3sxMMLIBC4L6eG9p1VbJhOFrgzBB5HqKmg3HQaH0Znk+wI8fJFVJqwOOGPQJLIxOFOJJNWYEaQbY9HKklNiMLdKKRbALKsKIYjzq0j4RA68Y8RkMaBpGqy6wVIp1iU31VQalzU6g0pBpKGbyGS+YFY7+jZT2YoUI7a24MQwCaXICbLqCSFhrOHs6QX3Hz7iN/7tv8F2veKzTz8l9onLyyVoyy/+/F/BNBM++WhYUI8AACAASURBVOw+P/zoI4gZqzTJB3EiVprkI6RAIlFXml/8hXd4561XcQZCCLz6xhs8vtrwwaefM50e81e+81e5ffsujz9/wNmjRzx++Igf/NkP6NqOGBJ1VRFzJgZPDAJAsxLHWl1krrmwrQLuovSbFtXAwGLupI3X8y736/rijdSwUKJQwoSPBjYUFnVQARSQK3IAyeAlkokS4aP1tb7wXFizmCIqIfuqNErZUbIr+5MJeZfLOzpnp2ERp0icjcYg3xFTJ4TBVwNQ15Bkl5XSWCuLJt73fPjhB7z+2hucP3kifZJWY2yFItKHgHMVVglDG1IENN4HlldXVH1TQBBYo5k0NW0rIN138t9uu6ZvDWTpfY0hoHIihczl5QXbtWY6bcpCQxwBvfee6bTGWUvf91xcXtK2nfT8GoXWmagyVmtOb96kthbvA+tty3qzwdpazJF0JvmAyhS5dgY9sI2pADsPMTGbzTDTGdvtmraVhSw0nMym47PCGMNmIzFVW6WonMFVdQGlmaqqoZkQQ0/Xbgh9X47dok2R2ZaFiKTNeK2vSXSLCkMZCkuqqOua4xsnNFXF/fv3uTg/J3iJNhrGV1WMs6RPtYB0rUtv9S6GSO8tyOwvAh7qUIc61KEO9Zeh9PvvcvT+u/zR2Rk5Rn7/dy/5j8KfkXPm3vLsq969f676moPVl1SJnBmh55eQd13/zGilNIKCF23ipSzpM/VC6e+17xZTohH47qTEo9FLYRtSzrjaklTk7OwBJINLEDcX/OJ33ua9b0zJ/jG3b55AVoQEAiIko1DyCRu0tvi+L86oGmNMcWEVaV7TVMSgUTpgm4bZrGG9aum2PVkHZiVHs8+ZrQ+k1BHDhj5qyYPNYK2j1z0xeIwW+S9BJpPkwpgaR4yhmOOI62vOmRjK8Zfr4FxFXU/o6xZbGYytycqg0FRGo7XhVpvZmjnTNXB2ha6n5BSJUbHatOhtTx+EadMajEukENEa7n3+gD/5sw/4xpuvY+sJjx8/RBtXzLg033z7HT740Sdcnl9KP24WwKVTxjpNXdWcnJxwNG+4e+eEOzemvPeNO7z66imXyyveeOd9/uRHn/F4teXOW+9z7/4Z3/vjDzj7/HMe3X/EcrmUbMmscEYTg7C3oYCJrAQIqgI6JF81jZNwkWrumdfk3T/Dgs3w+rPjcOeKLYsEKSVyEhb6RRP5Fw33UVqpdVkQENAq/cgFhFqDMcJ+iQlt6TUu20uAKowpSvYBCoNa3KuVimPWZ865/E4qxzLcV3rsQc0xEUMgktEqIjpiQ4wbPvzhD7HG8ujRQ7RWzGZTuq4lRcn6NQmMddyYL7i6usJ76bXt+54Qk8Q0IeepmU5wzrJarchZj5nCIfRMJlOss7TbNMpghwWd4f4WxjninGQBixtvvN4bnxLReyKRqDL9+opmMqVuGm7evEkG7n3+GVdXV2gLKWZqV5W4FlnQIGVSlkUbyS4VsLhaLpnO5hzN5qxWURx425Z2u+XOK6+yXq/p2pb5bEr0nrbdMJueSD6tMuIgnTLWVgJgraPvWjF9i57oQ1lIMBgtjtyDDFfvPfZMGW85ZbQZFiAMpMyD+/d5+vQpuRhIOedIKZU4LIlFEmZWj725MZaInTwwqpkYwgiwIRfn50PP6qEO9dMqpRT/vnvrq96NQx3qUPt1eooCfv1/eIXv8y24vOTf+50P+T8ffPer3rO/cH29werA9vA8cBwJVvViZvOLAOZ1t96Xf+6LMlyfff2lgLmwqPuK42ESNchihwncwCjVk4amNmzWLTYpGhX45W+/yb/+r7yHiQ8ImxV+KtI3Xbni8Jrp2g1VVXG0qDFa2EZjxHVT4nB6cpYs1LqaYEwmJI/KiugD66sNfReZzSphtXLAqszEwbZf0XYX9NqQc5F4UnpcS56l1QptrTBvQfoulZWexCH3Uw1MCwkfxPDHGcvR0RGv332NGD3WKY5u3KKup8X8RliVZRv59PGSP713zoX/iI2HqArTohLaKnSWHMesM8pU5NQT+sg2ef6ff/b/EVLk9q1TtH2K7wIow48/vcc/+e4f8Mm9z6mamsHJ2GlD1VTcffUu337/XW4eNSS/JaeO2sHDx/cJqef01bt8dv+MH354j7aFP/zuD3hwdsXq8hynI9OmJvQdmxgIweMLuChCRjHYCmIWo/NuvKRcJtk57Y2b3aRbgKoe/zx8ZgBKA4jMWRVWE+k/DKlIR6/HOg1jWNyBGX9rMCHSWotwuCBkrQS8DpY2ZIXOCqWNuB+rHauWsiLlQfYrWcOyfWGPlQKDgrzrKZdt5HI6iityysWsq5wPZYtbdiQUJ6xBcdF1Pf/0n/4TmqbBGI01hjYmQhQWOGZw1nF8cgOlLU+fPhnPgfcdIQlgymja7Ya6qZnPZ+U+knMjjsSF1TMKW9jSEDy6qbDW0rYtWmuapibGSNM0DAtlVVXhHDhjaZwlBU27XeP7ljb2LC8vAZhMZxzfuIErvd5ayyKKSYqMRuVMzJrMMDYSGgQoZjEW266W5GnDdDql7VpS9uI8vFlzcnzEk+AhGbIKLBYLZrMF3XbDermUfmjj5BqGTFXPmUyP8L5jvbyk267QKhcgWfr0C7s6+A2QU+lbHoXpALRty3qz5mp5Rdd1RUYufcl1XaOMkR54EBdrY0qvs/ShapXLApmAYWXM+P+EQZWwf98c6lCH+smW1Yb/7Ddf/ap341CHOtQX1fEx/+vPrbj74Kvekb94fa3B6n4UwrNQcR+0kkUEuQctr39hMDZSInncvffy5L0X9aP+ea+/cDsM8SE7XnUoVSb7UCbbIZBzQKkKlCZ0GeKGb715wq/+4tuwvWJSGcxkwna1RJkKlxu0nZBTC1mzWm0IITGbzorjqQclE7XtdjNKEUXCK7m1Pir8estm0xG9xpmEqzRWC0hQ2tOxJoY12R6jdIUpTrwjiIkywdS6yAIReWzwfelPNSit6b1HKYk3EQZNGJG6nvDq3dcFeCCusV3ngYie1GhToW1gs20xSjGpKjZ9T0yabe8JUZNx0uOYPColCBFtHDgF0bPedvzR9/+Mb38r4SZTrtYXzOYzPvz4Ey6WV7LzqrDQWhNiZDKdcuv0Fq+9epsbDfjOcH4ZaGYLrjYbrh5s+OT8cz765AGbTnH2dE3n4en5khsnMyaN4vz8KVeXl/heFia0USgS3neonLHGYnSGGEdwuD/2r2cKX18g2Z+UP1v7RmH72929Pmz3OnM5gFv5GXHQHnZAlf5QlPQ2U5hWAagObQ3KWKyt6HuP1gbrBLiGEOh7P4LfgS2OOUFIZLT0PZbbd4jdkf1LJT9U4+qKEAKb9QoNWK0LSBoWtuRpkENAa8VmvaZpKs7bjXR1GjeC595Hus5z4+YpWSkuLi/IUVQBMQRCTiijqWYz2s0Way3z2ZzNZiPnKydUFgMgZcRVWDkLOY2Zn9oYdM64yuKcSHr73hN8EHMsY8mpx6nMreNj+knFenVJiPJ43my2bJdL1leXJCWxMsM1ERO18vTLCgZpNaCM3JdkcM5hVBanX6Oo6wYUxJC4/9k9rH6LpmlY9h1N0zCfTfEh0EymbFYrQggSu6QtWUOIELIYrh3duEXfNPTtmug7Yoi4ypFLL/u4KFL6WY2W/mJyIgaJKmq7lrbfkmIeo44G6XfMEJM8K7XSI1DNOUkmbAbnLEZLD3hUgyphyBEuY9Qc5MCHOtRPo56f3RzqUIf6Otb8b/0Cv/WDN/nNz//BV70rf6H6mQGrL5X8DkyU2hPZZl4ADNX1z4//fuYzg+HLcxP73Q/uYd3dK/kl0PcZ+eZwLMAYV5Nj2vUQai2sR4joLnH3dsNf/c5bKH+O6hR11aDJRB1BWS4vWlBw95UTrDHUsaaqqpG1tU4mjErB0dExkMSd11YoZYhRQAQqUjUNSRuMUxirhKFMHmUryAHfrYl5izKQk8UHyV6lnKcQxf21trIPSinaXgxt6kmDqxzsSSNjiiRliUhGqipmPAkI3tP3nkk9pa4moAzz2ZTZ5ILTk4a7py19vGTZRTZtQhstfYdC0aF8BBWKJFKkoejM4ydP8d//U9568x0mR8c8Ojtjudlg64qUhClOWTJO0Zrz83O++wd/wPnZI/6Df/dvYo6P+fCTR1w+uM/Rrbucr7f8+N4P2Gw7+j6iUFwtr5g2DduV5/5nT2Sib8wIYNpNX2J7pO83Z4/vw9jTK8Pt2exKNY4cke4O8l7GCBZ5Tb67Hy1EYQFTTsI+GQNZjed7kBIDhdXaGZKpgYFN4nhrjJgmDZywXHsjTrbWoq1E2zhXcXR8MspTc5Ztay09zgPZ5kOJ5IlRgGuMO2m82jOGymrct6aZMJ1UkMG3HTFHWSDRwkIPx26tRCRNpjO26xXOWZRR4t6rNdY6QPP04orJbMGrd19HacPZ2ePx3OaciF72aTqd0rYtV5eXTCYTvPdE5HzOZjPqWsb9ZrOh71pi8CUP1dP3PdZpUhZZ62QyBRRt24oZWNeyXHU4DaFvCb5nNm24dfMGm/WWruvZbrc8evSIGDq0yoSc6AsY21+4ENUG6CjKCqUUMUSyUhhrUcrSbTfUzYQ2RrSCB/c/5/adVwqoFaOpGBPr1SVaWzEQ85GsZLAkNRgXZZwBZyqmU0MIrRxTiphM0Zor9Ph8zICR65wCXdcVJjqUSCBN00yIeZBZB7IyYr5lNBqRGLOX/5pzLue/Ynl5OapVUjEIs9aR1a7V4lCHOtRPtj77m29D03zVu3GoQx3qz6vTU/7z31yw/q2/wX93/x+Nyr2ve32twSpcZ4dkbnJdFgzPs5ove+/Z13euqeOL4+9c++x+8LxKz6NcXs7Rjq/n4U87SadzbgfCdTEnUhqjLb7bclzDz3/7LkeLntOmwoWeFBLKaoz4yrBebdhsW6a1oW702Nsl0t8tbduyWCxYHM3GHrChZ7Tzkb4PKBupm0Q9qVC1A1pObp3Qdp7zS4/3ieCLu6+KpOiFMY6DE2kglb7JlAJK1dRVjcIQUkvvvUzskwAqrTW+D0VumvG9F1OW0s+YQ0IliL1HVbIQMF8siFnzzltvMTvfsGojZ5dbktaE5Fiut8TQYcwODA8ITBSCIjFFO86v1vgff4LRlt739CmSe3Fh7qLfjQGyGOfEyA9//An/89/73zm9fZs+KHS94N75fR48OcfHJPLa5MmxAzpW60tSlN5TrSBGTw5hJ880wv54341jRc7HwHIOYGn3nlKmnOMsMm8KUE0KxZ4jrELktGkHeCV/uACbYqr07D0Ro/T3WTv8TllMGWJrgCE+U5Vc4ZQVKu8iUFKCvg+EkOh6X8BHw82bt2jblrNHj+n7UO5jUROEIIscRpuSMlJMcXIB02SMGdjexGq1LlE5iKw0aybTGdZZVqt1YTRFaFw3TcnqrIFITCIdjQkx4HKGFBOPHj/m9PYpd197ncViwdnZI9brFSl4yJHVagUw9lxvt1ucc8zncwBOTk545ZVXAPjRj37EZDIhRVeusR+Z5O12y3a7ZTqZcXx8wnQ6pet6clqgc8KQ6PotWSnW6w3Li0uRyc8XvPvGN7i1OOaHP/qA4D0pRqhMkf4OwtriAK0Y/0xZeBA1hTyDcspi5FZJBFTne6LvmTYTuq4nJmGdFVYMuEIk5YFBGcBxJsVIHyNRJawGrSqq2uB9X2JvxHE6pAhGj/m2FHY1llxdHz3GWAGUCqyx+BhHQKxViZEywiKnlNAqo1VmMZtxtJiz3Wzo+27MdIadfJ0C1A91qEP95Mscua96Fw51qEN92apr/pvffhf1Xyl+9/N/+FXvzZeqr/X/vZXa9XYOVaBq6ffb4cb8IjoVhMEpJi8DNB2DPVIaWdSBkXpx72kuKsi8b7p6/RNf0Ps67LfienB9CJ7KOarKEaLf9SR6j9WBt79xxJ3bCqdarLYsFjUqJULo0RiqieX01jFtP6PrOlKW/FJxz6zouo7VajW6aTrn0IXha/uebSsZqsYltNU4o0TaqwwXl1esNxvW6xafDSFOUWpCTh2m9JAqVAE4gRCFLVOmOIfWDc5BzJkQhaXRBeTknAtrq0gEUtRcLZd0fU9TVbLdEKmto7IO33u225bJZM6t4yPW646j2lEBVukC8DV1U9H3ws5YVxFjJGmRQaasUEaRtUHlzGojzKnKSF5j6aNFSd+h1ZasFBGF0cLIPdpsuXp0htKOtntC1yWRvDoHOZBjR/BbYupBxXIcjOzPEJU0ON+qEumxP3ZMOX/7oFX6I0HrPH53YBl327s+Dgegud/Pvc+2Dos1zy/06Gvb3LGzYqrko0cbi3FmXEhyrqJyNUppkhLTG+8jupgv5ZRJ8YwYE94HQogMvaUpikPssHAk+6XH/QCN1nlkekERY6IvCxyyk5qqnjBfzOlDYrttcVVFTIEUxZ1a6URKsvCgxLpWenetHEPbeR4+OuPkRuDk+IhbiAFQ17aSJ+t7tl1HDVTOEVNi07ZM53Pu3L7Nar2CR4/4tV/7NY5PTvj4Rx+yvLqUezuIm3UKAW00MUaulles1xvm8znT6VTiWaqKnAK2bggZUmoxtmIxnbOYLcgBXn/1NY7mC/70z75PHTtWoSVmVSTce8/BkY0fxlx5nqqEUQZjpUVAobCuwhrLdrtlPl8wqSestxu5T1Ukh8xscUTbdiXTVBMzVINZWvQE3+FjlDEwxFlZK+x3DDSuQhl5bwC68hxIZSSIzFcZA0qMuUo8Kmo0AstiQpalF1chPceLxYIYA5eXF3K9lZzjrHZOwCKo+eJn9KEOdah/vvrb//AWv/NveXAH0HqoQ/2s1H/9nzh+929/1Xvx5eprDVZz6UsaSo1UU+l/Gj43ANpn5iLPAs/9/tfrrOowGX7xJH7fhGbYnS/jQDxse/cb13dw+C3rjMhVkQlYCj2370z51nunqLxiu4bmxl26foNRWSSzERa1RWlwtUJh8d4Tgh9licZoptNpAS1FOooe+73qxqCMwjhN3WhUauh7RciamDwpKVxVoZLC1ZacHV0uk94USUmN4GJgvFPOtG1HU3tyMctRiHNpMtLzKL2sAVeiSJQxdH0xVmkaUhcwWUCQVoq23dL5SE6ZuprS2MzJvGFWGx6tWlQxWQlZ8jyds1itiMHje+ll1BpULjLvjORADjLYOOhgs0yYtQZjBtGtuNw6RzCZZehJoSX5yLSZc3w0Z7Ncs+43xNAhvZUSsZGTRL/kVK692o0jRjZsfzwMY/D6vPp65It8Jj0jU8978vb98fkiOfsIY54Zj4OzryzaPCsNUWNUTlIKksFqU7I/CyAtsCMOQLscU9f2LK9WRYap935XSU+j1mitZAHE7o4zxjB+NiZfJMm6gHb57wD2V+sNbR/wIWFdJW0BBcChBsZZ/okxYY2VxRsl0ToohQ+RR48es91uuH16i5s3b3Nx8ZTpdEpIwq5uNlvskaOqavrec3W1pGkmdN2Ws7Mzzi/O+Y3f+A2+/e1v8/FHP+Ls7DE5ZzE6y5G2bXHO4azD+8DFxQVd12GM5bzIxY1OVNZx88YJDk32EW0rQs400wWvvvYGTy/OefDkAbWBkIKMnZxJKu49x+QsDxExOcl9kHMkyboMIXhxIHc1ubjmNk2DtobttmWz3pBMxoeAD340ucopY0qcjKotOTWEmMo1ixg8MVgqY/BdS4wRMzwPlDyzQ4ojWB169wcGOibpYVbDc7703Wt0iadRkBNHiwXWWh48fsRms5E4IN+X4bX33FaUNoVDHepQP+n6nx78Pr8T3zyA1UMd6meplJJoxp8BKfDXGqyqIcpD7WSLY+Udw/rC7+6ByZe7BQ/gZTepeVlczf5nXvY7L2JXVdHh7fch7n93AADOOay1pBig73nvm3c5PTmmSg6Tei6vNkxrYRlCFpns2ZMzJpNGII/euZLGGFmvRbbYNM3ILHsf2GxacgbnNGhPZcX4KPrAdr3h6jzR1IqjhaW2YF2W/jQ0JEPqRV4XUxbwV45D5HbS2xZ8ZNN2EDPrdkOMkareTUhBFRdZkf6mFNluhQWez2ZoY7DWYgykJMxojIH1eokiMXGKeW149c4tPj77FGmnFOdi4wy+T/gURCYoIsJxrAzjRQ+MU2E4czFgUUqYZVVMhrRxIp/UmtZv5diD586NW8wmE1ZXT2jbLSpkTIKYxak2RmGwDbsxpdTO6GhnmrRj/ZVSJBJDPMvArhot+5TT4G66G2+7Mbtz/H0WqD43LIfXhgioPRb1Wfnxbn81GEsmlh5Y0QqYAbyEOALoGBPKGIKX/syREQ0ZlYOwY0qBGld+xo5cU679IAVORc6pYlFTqH1GOhXjHUMfEq3fyvedHcFOzFp6ZlPprS7HL+e0sNYDWMoKtOJqtaHte26cHHHr1ikpyQLLYn7E2dkTtpsOM3fMZ0ds2zUXFxfFcdjy6NEZf//v/1+8+fpd2u2WEALHx8dcXV2Bkp5k773kn2ZKFmoskmaDT0UFoDLaVsymM9ZXS9reY43js0ePeLK64qrr6WKmrqfg2zHeSGc5rpSS5LCmjNViPhRjIsdEyAEB/brIoWOJiJmyXW8w2jKdL5hO5jT1mrMnj1mv1qQc0SlCluzhdi3SedfUTGdH2LJIUznNN964Tey3hL7j0f3PeXr2mJTFVCknGVd98PjgC5iUey/mQbquygJGiUoaBqQaBz91XTObiQHUdr3GGStRRzHKGCpy4QGsHojVQx3qp1MpZ/w/+i72m3dQ773zVe/OoQ51qC9R6vXX+eAXfo53//j7X/Wu/Ln1tQar+/UcEBzooVLXDUb+nO+Orz/ff/qsC+v+9v8i277+/vOs7/BejBHvoaoqka8ax60bJ1iVuP/JOa/fWuCMZr32kIUBtVVTJMyJvvMYozAG6rou2wxst9tx4j+ASe8D6/WWnDKzeUNVaZHyKUPOHq0ylVWoLJNXlaNM4FEkIjF6+q6lqiZoBcYodBx6wgZmUEnGqw8S/xrytYWG3fktgDdFfI4oMut2WxgxVYBdJEbQ1mGs9Num4EtURcfR0QTrFLHtMFZhncN7z8Z39H1XnEclWiWVXE65EGIWNLB6O3CXdqwrIks0WqPQ5KiwwdD3LfPZHJ0N9+89kIiS4TMJog9EH0t2aDnuEUDuO0nvDV6h+pHeUL3HBDIuyjwr371+PodFkd1Y3c/4HMbutQWXtGPelFHXmNtcqKx9pcG4n5S4mRhRMaF0RO3F4MQC7FSMuHoiACTGwugLO77rQR8Adt6xoOVnsmK040mFfRsWlpQubH0u4vqcR2m/D5GYhV1XGJRKZbHCCNApwDsGjzIG5yphPcsCivdexntMnD+9QN+8yZ07p/R9T900vPLqa9z77FOePn3KfDZnvjhiubzCVRVV3dB2PZ/eu8fnn31GU1mm04aqruSaak1TVVS1sLIxCJAKMUrfr3XjmI1Enl4uyUlWPrd9R9evxZSMxLrdkqyVKBeciE0o7L0SoCcnM2CMwzlLbnsBnOVkxxTlHGlN8AFoyVeXGOswrsJax63TU+ZHCz7++EOuLi9K/3UgZenfBfCdYqM7GI2YAj/88EN8t6Fdr8nRQ4j4nHFa5PgxpVFSrK0t7RhlPBflzO5ZoEZBzTAGrLXcPj2FnLm4uBgzZUGcgYd7bFDd5GHcH+pQh/qJV0yRm//b9/l3Xq34rfQ9fuG/fBVOT7/q3TrUoQ71RaUVtvr6s6rwMwpWXybB3Z+Q71dKCbU38dl94frnngcB14HBIEP+8hLg4TcGPk/t/ZsRw8pEMRNDx6QyLGZTnIp0K8fDfsPpiSFWid5HJr3l6LimrirwUXq/YkJZx3xxA2NX9H0r7JExI0sh4MwUQxgx0bHaoJQj5kTMEWsUs5lF6YR10mNqjCMERUqa5D2b9QUp1mgt8R85peIQSpG+JnJS+NjL5FEBWVidFOOISIZzKYYqAkQ2mzW998Q+0Kc0mkipcqKsMbTdCpRB6UToW0gCXhtXEaO4+AbfyznPCWM0EZEAK7JkYIzdo3nsZVNaozFi6FJ+S5cYleALG5gUczdHBc2j+0/IZLSx9CGSgoBjjaayCpJEsiSl98DiAASLnHfgE9UgZy0SydKLqZVBm53T68Ce7felDhLha0PuBez/c3Lg54QK+dp9MoDVsgVhORGmTmlhBAW4RHL2KJ3Y4YliiMSQpwm1c3g/SFblk4oBk+yAsQ+hrI+UfdT7ObHDccTinq1Lj+/u3rZWrmGICZUy3gdSzhhly70SUMRCou4ckRMKJVpaiT9BkVTm8ZMz1ps1p6en1JMpN09vYitxRL64uODo6AjjKi6urrhxcsKNmzfhXNG3GwDW6w2b7afyOzmODCyFySfnkTHXSfpJc07EDH1IXK03kBK97+m6DhT0oSdrhdJVYQ7FfEuuWyrXriwIlRgebSw596McOCLnL5d+bWNE1eH7jidPHmOs5ejomNVqxSuv3GE2+yU++uEHPHn8qPSqe3IKApZzpltdYt0UZRzb0DNpMhpN23l0juiUx/tZ52J+pMStWBejpaEHeVg4UciYM+X5paBI6hNNXWOU4unTJ2y3W4bFEpFblwWpvQXGnLlmlHeoQx3qJ1+/9+C7/B7wn/6Pf53f/Tu3StDzoQ51qEP9i9XXG6xKaGeZaexe3s+UHFxJn5XzDkYjaniPXFw59DhPf5Hsd5i8Xuvx22daxRKTQSs5bkOIsx3hO/5IROUkofZaiyBVZVQBRTkmktEyAe+2LGYNd46OOZlbKjUhdJ6LpWepIk2dMabj6fmGV27fZDGboZLC957Lbc+qv2Q+s6UHT0yNNquWlDRV1eBqg60VxmaUigSvid6Tgd4rnNNUjUhis0rUkwlZVbTbjUyI05ZKG0LvySSSyng8SpdesKTJkSK2jcQsoCAlkSf73kukTd6dIK2l90yh2Kw3rNdrKmMIXoxxjHPYBFp5Mj2pZHtGMuTI6WLGhzwymAAAIABJREFUervEZ0Pf9yPwsUqhrKXPkZQCgSRARKfCqGcCwjJpZ4QdTNLPp41FOyvyz+LWKzJn6IMvBjJlG6GA8PIZsWHVlKyOAviGMax2MHkcz8P4LQNI3t2NNwbGKY1gd2CgBESKg+7IRFGsixLyzwA6lRnH5AjKchpBYi79vsMIlu3rEQQJo6n2wMCwY0lYeXYsqC4SW5USOXp0BmMNKULWu3tvPAMlJzTGWKS7YkSktCrOyWUfUipyzhKrVM6fUgpnBQAaY3BVXdhcaNstm81mdIKNWfqnnXGghdEb+iaNsRgF2mgoztwaxXa95t56zXwx5+gXf4m7d+9y48YNvvcnf8zDBw955dVXmM1mfPrpp1TWMT864ir0aCWxKb3v6b0c0+X5pRiCWYvRAtTM8I+RHl6FQmW5XqFc8JAykUyKAi7JIsGWcSkKEaUVWjtyjuQkEl3JQ474LqDKMEyp9O1WOwfqFAMKRYo9IXTc+2TLO998j6quefz5fY4Wx/y1f/Wv8fDhA/7wD7/Lsntawq0jpI6MISaPsVO00qw2kRsnJxzdrLk4u49TwoSHEHFW7oMUI30nsTWJYYwWdj+JdFcjz6OUI9poUow0TcV8MWWzWbO8vKJru9LHOiyUSIvBAE6VKufq2RWaQx3qUD+V+l8e/b/8bn4HOMRFHepQh/oXr681WB0lXF8k3xqYyxd8Zt9CSZVJIC+RhD3bk/qsFHj47+CoOkjLxs/mvW0OvzXKfwe5IiQlYEGlCBSGBU2MPVOTeOOVE04WFSq1YMHVFclbcel1hunEYHXg6mrL2cNzautYLI5ok+dquWU7r5nUFq0sKmeib/A+0ree6cKAsqRUpHXRsNlsiyFJZDqV9YGUMpPpnIurDSm0LJcbYgI7naLR+L7HTR0pa1L0GFVYEmXRKuNzJ72NBdTv9+YOPWSwO88CMKUPUWtFVTlhaTFoXZimELGVpmt7cIi7sVHMKoUmEKL8T9FojTWGkCRexzorbB0ZrcUwKoVAiNIDaIwhJYglmkMrU/owEzEEmd0r6atNBQAMaxWgMMWJeDzGOPRYa3QWdnVgSgcWCSQzNBVGLac9Ge8Lpe17Esk9dlSARxYH1b3bYRzaqhgYle3uGH3pBS8fIXPdxOw5pjaV13W5f2KElMkqjwBoAIdQiOIk5y0W12KL3csDFXfjGCPGmjH2BuSNNBzwHrB9UW+4pjDf0Yv7sbXkDFWtmEym1FU19oQOLL61lhCcOAX7SG3MKLnOOcr4cxbvkX0nY4uD7/n5Of/4H//f/NJ3fpnbr77CjZs3OXvyhOVqxXvvv88bb77JRx99hO86fNOQelnocFYMmVKMpCCLDtFHEqXP1BiapkYFg7Iir1VF9tx7X1oF/Kjq0NqUZ6OS/FBnR4WAMRrrao6Pj9iul6xWV1AYcecqkRGXa5MTEsUFxXgpEaPkMPu+5f5nn/LOO+9KrnMfOHv0hFduv8a/+ddP+IM//Gc8OX+I1pm6djTTKdPpEcHD4ydLQjQ8fbpmMas4OrrF9uqpgGiViCljtKLvPZvtlpAiylZobXaDGA25xE7lYZzI/dk0NTF6njwRVjWnhHZulLFrs+8m/eIxdKhDHepQhzrUoX426msNVuG6tPdFbOfLgOyLYj1e9pk/7/efkwJ/ye3lghwUQ0PeTo65A2xFfkmgmlRMJhOs1RLJQsA6A0Ym/dpAXVfMJhOS77l6+pRtjBijsLUBDMuLwIOrJdvVlvlswuJohjEJW1k2q8jyKoG2NI3DmIA2lroCbVIxtAHvFSlEUBPIgclEg0pEbZhNJ/ReGMeoRT4bgwcEtKUBkT9zDnNOYnRz7dgl61IrYVG6vsMHz+TkWGSJWaGyJsQe30e0UaQMvutQqmY2rblxPKd+eEEXweqKnkwdAzEmSKEYzWhKfKP09VqLq+aARMD4PuwApdIYJVmQOcZxP2VxIkmubGECB+Cwq2LnVMZBShmjDOgdUE3FAEaccSM5awF9ZdECtTNgGqSv0rsbr411kSir58bn/mfMPoi9dp8otNmpFdIgFd97fx81D6wqyKGrXMazGlFxAfKAtuWcCehUJUIHiiOzks+mlLDW4ipXxoYALQrTJwBzp3TICNM5HLOcT5HSuuLgrJQihMhqvUHp0rOtNbPZohgaZQF9WmFdjSrO2iFJz7RzTraRJIbJluiVEMTlWCmFtoY/+t4f8ebyire/+Tbb7Zb79+/zwQcf8K1vfYuTkxM26zXz6YSnjx8SfC/5sTEyqSdY42i3W7wXgI1SpBjZbreozmMrR+3s6IpLkb0O7D1kTGHUtYYQoxgXGYs1GmMklqb3sthTNVOsMcymU7SG8/NzuTq9F4UBOwfsGALG2KJQ0GzbNT/+9GO+8cZ75FpxcuMmq82ay6sL3njrbSazCecXj0k50vWR1eoJ7TYQsiZoYbdX6w1TA5PZjH67ImVRUoCm84G2D1jnRnCpSk+zmLGVha2iGMgxY53Eci2XyyL/3TkID/eCtBs8/9zWB7B6qEMd6lCHOtTPXH29wWp+MbP6on7S/Xr29f3JzMs+80UgdB8Yv+x7L3MRlveGlf4dfaYKcNVGTECMMdy8eULTNMTYU1dKJHZ0uNpQVaDxbDYeo6acLBa8cucOfbthPq9RJtI0E2Jv0cGBd2gU0Uec0zir2Wxbzi9a1uuIMZm33r7J0XFNVTVAIsRA1wbOzi5QGI6OjmgmFdZo0CKZNXVN2xo23gvzadUoVRTGcedOO5yXQWoaBwnjtXNamEcNMcC23RJjKL2HYlpkVVWARo+2Fo3CasVsWnE0r5g2lu1WkbMmGi29gAqGJN6qqoghStZlTBhrRhMWoy3WJKz1dG0vUtOcCrNVQErZT10yXWEn95ZomsJOKZGt63L8kiurx7xTYZXjHktvxliRcfwILSmE7vCdJL1+GeTYEMfYgbX+4rH3AmOwUaeuS49m+a4qhKbaCZJHRlX+svd9qZQySqVyvilAZOiLzWJgpRUhxdEULA5gsBKwGkIgpiCbL6y/KsCWwvymQQI8eKKV3E2nNXXpA/U+iBHTEI+TJUNUKeiWHYqMsZYUM8ZZUoporanrCt+24kyLEUlpSrvrW8C60lrMkIzms8/uUTc177//Pjlnzs/P+fzzzzk+PqZtW4yGxY0bXJ6fk5PE6Xjvmc9mVMayXq9ZbVYMZkEpKRSR4DMp+N0YGc55kZQPY8w6hzEalRPdthW2NCtZP8gKqwz1ZI6NNSkGqmZG7RxdH3CVg/UW7yWbWeKGpP86lQEgmbmwXK+59/lnvPvOgjuv3uXmzRt8+tmnfP/7f0jnE8ZOuLp6yupqTYgZa2psNSGkgNIK73vamGlsuWZZE8o9HzPitu0qWbZL+4Z3aRxu1urCKstik/eB1UrO3b7SZfe8UUXm/mL1zKEOdahDHepQf+nr7Iz/+H7zVe/Fl6qvNVh9mfvui8Djl5V5vehzLzKkefb1/cnQi15/lgHeVenmy4PUdYjrALI4f/oYiCEwnc5wriInLzhVZUKUXEaZmEIOma5tsSfHnJ7eoN04tApom6isx1hHbeYcL6biiJs7ZrOayawh5cykiVhToy2k1BODJRmL1haNJYaOzbojxUxdTwBwlcJVhpgDfb+G5LBGXEnbrisGtrueS620sElEKI6s++dolHwWIJhygpjJKfL06VPu3DghK4XRFcaIk6oxVoB9Thgtk9GmgvnUcPvmgovPLolZmM2BjdFaiSQXJSZLSnoda1sRQizRJWJc1Pf9oDkkRpF/qmL8BMK4arO7XTRyuKowU2JQJBNqbQxaS/zKEOcySKApCxMxxgJU9ci65jxklaZRWisRLNKPOMTbxJjG3tBdHNDzNfReP1vjK+OiwpBbWWTBeV+ercZzuX9fDMckCy6yGCOEubBmAwutimZ6YIZTTqNpVAjh2k7FUGJRtMYgQH4YGwNwl0ZGPbK2Simcc6AMRilsuaZDhIkPkcmk5vT0FMi0fUvbdxhnCUH6SKvK4p2j61pi8AJISdhCx6ecSUHUCQroS5/3hz/8kL7vOTo6ous6lssld+7coaprfPA4p6mnU5YXl+gUUCmRfKAyFj2dQk5sttuRYQU5vKx3WbRZqRE0K6VLj22GEAkpo62hmkwxuvT0pogylqqaSA9scIS+IwQ4OTnmtqu4vHhCzpZtZ0gpFkl6RGsj7tbKELPEESkFV6sNH/34E2LKvPbG67xy91Xe//bP870//iPOzs8hO+rJHOPFXC2GTFQRV8kiTYiBje/RRGwxzOrDzlE4l1WScveWMVZ6tMWSCWM0TV0zmTQ8fvxI2N8MqkTwDLXzKxAgOxo4xXjN6+BQhzrUoQ51qL/MlTdbfu/Bd7/q3fhS9TUHq8/+fb9f73kZ8PD+swzqi0Dki3pTh4nOLsLj+veugy2e+96z+7H/98KXCQDafblM4mUCb4ymbVuqWpNMhdEBlESBZA1WaXLM+JTZbDa4ecNkaskp4UMghi3zo4b5TLPdepZXW9brDX0fcbXFOcV0JuCjmVhm8xqtLV3r2W48s9mC6eSY9987YrW5JPiOtl2jzQTtDcqAyQFFj+9WmElT2MECVFIx4UFY0X0zKlFEF3MjBtYwl/7QoY9O8fjRGU8efk6Mmddee4u33npb5LFZJICU6WzMPbOJYzFznN6Y8vFnT4gJjEIm7FoLiE6SLal1ZjqpiWUCDpBioO962rYj+Hit91RLGCRGKzHcGYyTBha5OB2P2xpVtcJYpr2xEkIYmdJ9JijGNMbL7I+zwXBoAIQCyCqMtoVp7VFmD0x+wdgc/1viYYb+avJ+b6oG4rXtyTbleMbt6AxZFyaw9CBn6csl7u6VlEUU4LSYKg3H7Jwrxx1HabD3JU5Fa1zlsNoVuXQo95HFGkMfPF0nBlrGuZLzqgkxc3m1pPOepplQ1xMyki88dZYQPKFcf6Oll3Vx4wbaGrzv6XxH27ZkrWkmU4Lv6dptMfjRYmapFEpbYjlGaypUVoQ+8tGPPmJxtGA2m9F1HQ8fPuTo6IgHjx+y3LQczxfEELl4fMasavD9sBACx0fHNJOKy6srOh8gGzEGU3sxQnuLFErrwvxDLuZXCV1MpVwxUuqIGZabFmc0ikSOsDq/4un5FcaIZ11MYHSNNqBdWUjJCe1qckpMC9sZE5AtnQ98/Mk97j14MI57YxS2muCDmFNBout6aVmwBpPB9x1kj9KRlAMhCxMvbs7F4KncUzugumuRUFpByhhrmEwnrNcrNptNGZyaFz+/ZbzKgpAex5/+goWdQx3qUIc61KH+MtXF//HJV70LX7q+1mAVvrx062US3WfB5Atlkc9854t+96Vg4AXb2QFixonYvoZSWEVhGeu6ARSbTYuJhtpMyUbhtBjIaK1AG1xlMDmzWq3QeOZTieRwpiGEnna7xjmNtYbFokZr2Gw8jx6eYazBVopmYpjNLcYJm3Vxcc7DB+ecnt7hzp1TUpZYClTg+GRGjJknT89YLBq0qyB3rNdPsbmmjRrjHJWzKE0xkEmFnQmjLHTIdhwmjcLmJLQ2GC05neKGCrVtyMBiMcc5R9f1kDOpj8XpU3oc66ZiWhumTtFYaNteTJ+SvK+1xihFKP2QdVXT0hG8xyrpyx1MlIzRo9x3cOzVWqONOIkyMFt5YI/LdLqYJIESdgvJh83DjF6pwrSaYlDE2EdLSsKkCu079pgOTFDO0kdZ17W4/kbpuRwiiXZjWyb9IrPesakjMJZglp1hWcrCSA3fB1TpvU3lXOQkbtKajFG65JwOCuIC4JWSyzuyxhSgmdAqjeN+kLpWVYVSCu9F5mqtkQWabovWiulkwqSZklKk77sCaMRwy3g76H/R1hJSwhiHdQ7rKm7emtJMp2w2Wy4uL0VFkBvm8xnb7YarqyXWaI5PTjhaHBFIVHVFHRqMMWw2GxSJiZtRVxVd25ZebIU1ipgzKUfJBk0JbYww5ClwcX5J8J66abi8uKSqa+bzBecXFygUk6pmcXSM32wpibzEJIsUVVUxm81gsxEprh4MvHbutlnJtUtD36yx1HWFqyuqZkKMQbaXQBlDCgLSMoqUimmYEVfiru9F3j4syilAiRu2MsJ0Vq7i5MYJTVNjXUXXRj6794BIln5xrUSO2wcyBpSj7bdowBqH1gFNInQtsW9Bl8UCkyFLTJcqhmdGF9dopYgxjwx+SmKqJAtImbqpiTFwcXEhix1KNOGpLIjp0t+tjYD7ECMpJmJIkvkM15n8Qx3qUIc61KH+kta9v/NP+dc+PYDVn0q9iDWFZySLXAePL2KchtdftP0X/fmLPvcXfT9LPoowCVkiPUIM9L1ntdpgbCSsFanXzOeG2ayR7eWEsgpjLNZplE4lRsXgjEElhyvOtn3bY61BqwprHcZApf5/9t7k57YsPfP6rW7vfc7X3CZuRGRbaaczyy7jQgxgUMIqFYg/gSF/BWJCCTHxyCOmjAAhgYTEBBBCNSioTHBZVTalIm2cZDo7px3dvfdrT7Ob1bwM3rX3ObeLjEinFZHp86Zuft05++xm7R3rWc/zPg90qwbnNbKi6zxN19HvI+uzhrffeUjXefp+w77f0baOBw/PCU1gv9/TdQ7vQCTSNdC1kMlohqtOLtUAJyNZFmC2MCTmEC3inFOjGxTszXmsRYS2CXz961/j4vyC9dk5iD2AMxGmaarMnCUNPV1jeXDW8vhixc3thlS0HzUXGKeEtwaDZrZao6ySgll1Dg5OzW8W6Sssfanz5HeJvFh+zyI3LpUlQwwFJRhFCsbq8RbQmJJ6DNqfWY2mzCzvtQuwNRyck733tG1LCOqOnFN8w1i39fdg7Wz8ZBfgOOt7Z2Ats4kNNbalMsUq6XUVE1YJtDE1LkaZTGPn3luV9ObK/Fp7UEKomc3MDs8g4QCgjx2h5/20FnzwNE0gxgqYK6gSUdOqJjT4pgK0Mp9ji/eB0DRM08S+H5C6IGCdq99r53HKwv1mQz8N2OAJIVBKIqaoiwE1LsWHBovBmBWSMrthj3UGb/1idjWDSSMGZxwpJqbxnqZtuH5+xd/9e7/Jo8eP+cH3/5zNZgsp0a06hu0OSiZ4BcjtquH8/Jy269hsdozThAl2ySR2NUIpi6oEDEBOKvE/ArBSsjpaF+2z7od+icEpKRMnjYgxRqimzipLnhcZnMPXPt6URqzbYYwlZ0NKOg6GcaDtGlbrju12X52VCykVnPW0wavLcipM/YacJmVVl4WoWVki1Qk5q6pC5raA+bmpzw/vXDVFawjes9lulAUvpd6DL/WoInUpQAH6nPFY8jzeTzEapzrVqU51qlP9Xr9mO/Wf9W584vqcg9WDHOzQ5/h6efAsv3ztVo7A6otS4cr62UO/5THzOe+DLKALXnVNfZ2ceP5cfZ+atMzsagU5IuRUEJPAqBHJNE6Y0DDFwvX1jlzOVGJnBWcSXWsJZocjc3neMLpM0zrOXEvnHMbZmptomWICCrlkmsYRQsAHR5aIq3JOsjr4Pn54zqMHZ+x2A/vdhrPVirOzNc4YJEXO14H1ao0zkLMhmIbt5BnEQliTgSJJtZ+1v7GIsqam1PgWycyZna9KpBUQGVF2pfENjx49JqZEzBkXlC0yRSNrbNLt67kuBCM8vlzzXoCcDOI8uVSmxqj8M1jwToi29rlJzZaUQx8d8jJzfmC45q7cmZWaa84XzSgILLN0Fgu4F6OOqgRWZcGH8eKcqX24ajCTswKiEBpEDP1+IKW8bGcGvqASyiJCyglrLd61lY30ys6mVPdZe4PFgPUObwNgFmnrPJaNcZSSsNZjvF2u1/yyWeY7A6P5Hpnvo9l11czg38w9hUJK8QAk7QxGC0WK9p2KME0DMUZl3utn5JxJpWCswzoFma0LnF08wHnHvu/Z7weGcSSlrNmmKfL85oZV22m8srEqCe4T+2GgSMEFlSLnnJSds8r2mmr00zZN/Z1ml2rGKfiayVvhKi54rFFGPcXE5v6eH/3wR/zmb/0WX3jnHW6eX7G/v2e33xOCY7fZMUzVPGnU83l+cUYXWj589gzJBd+EKsPNR+drPp+VQTRCiVOVB+e66FKgCGkaCcEry5hUai1kUsxVxj5LZfX55L3Be0uRwjgObDZbnj59pvLs0OJcYBx2XD484+tf/3Xef/8DNpsdUmbTo4lx6hmHPWncY8qAswVvUfa0Pg/nXNecMyVFSkqAVcm1dQpmpdCEoOPICE0b2O927DdbdZee2w44LMwcP0/MfL9+wsXHU53qVL/Yequ7+Kx34VSnOtUb6p//x9/nf7z7vz/r3fhU9UsBVl81VDrIa49f+yaTozdLekEB6/GK++vkwi/3UpkXfv/iNl9B0sz6SaGaJNU/tW1DysquSnaMY6Ks2yptc+z7QoyZpoEHl+cKTIgM/R3TuKcJOtlvvvA2PkzKKRgPWHKGmMYKbizOG4xVU5ocC9v7PSlvaRqP8waRRCkDF5cdbbNmt+tJu4nVKrD2niaAN4ZUTaKCy+zHiHGGHDWWRE11VDIrhQrSK5OMGsDMrNTx2S4oc6YT/sLt7R0PHz7COKvRPQJNGygxY3IB64ipkMbIFCNl7FkHw1nrGEshVda0bQKGAkawIjR+NshJ5ClR0oQ1lq5pSFkZImMqE1eZb7MsNijwnau2MkKVdEqpaaVS5bGI9olyWCyZGcX53zEDKgilGifNfawxJsYxAqJS6SOpsAJfQUpZgPu8vdl9+LCz9Z6pQNJZV/tOwc4GTlTwI6JAdZZ027rIMjOUkpe+yeMhfvxxUoRMxjmvuZ++RrGUUvtHFZyXnKvE3VJyYb/f6ZLTAvDVoVozdltVFXgPYohJXW1X67XmdvqGZpy4u7ujAKsQlNGeQVmMi/w4eE9MUZ1zjcW7oByliBpvCeQcGYcB50yVL9tloWLuR7Z1HyUlxOp2jebz8P5f/hWUwsXZObfX15yfrXHOsL27wzUNKarztM2Z3OvC0qpd887jJ1zdXFNyxodATnFZTHPO0TYtXRMIPmCM3s9jHJjGUd2Q63iQVDAl1IWNonFBOb90/eriH5aYBOm1d34egzkrI77fD3jnCI3n+Ucf6di3jnEckKJ5tGkc2O3uKSVCHgg2ab+sKLOp4VaWIpBKIeWsJk7VYdswL2RRZcn6fdu1lJLZ7rb0/X55Nh/2/vWtGR/3/alOdaq/2frT/+ir4E5KhlOd6vNW8sEH/O+dZ7qOn/WufKr6nIPVQ7088XhV1gvH4PG4h/Vn9am+bMz0JgOnT1THWPZo+zN4waic1UAFJU7ZjWJwNhCalt04gc2MKbNaX5BLIqWAIdOuHA8ee9ogeDJ9v+f6dsuX3llVExPBuYZ21eFSIudESiNTjDSmValuZcCcVfliKYWm8Zyt1zRNgxSh6ywlB0Jj1UjFO0gZyYksA9BSSkRjRx2YXA+/aOSMzE6/h/NXSlHzlUWeOpfGozhj8Vb77JbzViNPihhKLBo9g6PEwmZzTxLBIVyuW548esj17jnGQAieGCNn6xUkS54mrBG1Ekrak3t+doYxlnGcKDljRIHn7LoqIpoZO6uATd1XI1UarO6rMgemmBrzYlRznJElH/N4LM7M6Awsl3OTs/Z4GkCqMUxl/+fImhkVSjEUUn1tzQTlAIynKS5y4hmIzW83tbd2trTRaBqzqAZU9luw3rNed+SSICZKFnXspV7z8vp7o8xxRCkjMlXZ5ovHr8esLtg4xzgocHVOjZi893X72u/65K0n6tLrA+uzC7Z9z/X1NXJzS0qZtu0opdCuOkLoak+sJ+VISpqxao9AfNs0NE2L88pUjikyTaP2KAPTaMjow3ycJrr6eiMQp4GU53OvvaxiwaPxOT54HIYPfvoe41sPQQpPnz7l4uKcdr0i50gXPLvNlrHf07UtacqMMvLo4UMePXzI3fYe7fW1NerH0HhH1zjaxqOdAYUcB/I4kuOEyQL1fqEIMRYQrwtIJavxmBHElNqGMF8LqWC2kDMUmcesXq/gHMMwYF2LM46PPvqA1foMxJJiZne/JaURyRNNsIwpIaT6vFPVQc4KVhFUaVFQUGqpYw8kF5w31cEXnLe0bcv9/S3TNOiYXJySzSvP2cPj5BANdgKspzrVqU51qlNp7f7Jn/P77//gs96NT12/NGD1dU6/8+/16yebjLzOhGnezicxVfrZH/Dq583un3O/1vw6g8G6gAjkCjCc9TTBk0pmyhNTLsQx0e972pC4ayKXZ5a3H5+xOl8zDBNX11seXTpWrSGliHNC27QLw5ZSIk7K3lgD3jqaVauy4CpdlKxMrzNgGyE0mv1oraHrVjQhMOy2ZAZynnCuwTqFUmqeZLFGEG3arOfV1Mnxm893CGH5W0HxydBP5FRwjasxMnraUkoYrGY39gOSBW8tVhKr4Hl4eY531+zHqOZUTVDnUynK8hRlzIJztG1LExz7Xc9+tyUXMOZ1K8HmhfUHzV9VqWIpaTFCEgwiDmMFsfXvYiiZI6nii06vM4O4AFWpbso1j3L++ywLdrUHc3YSPox9Uw2lZrfh4/tCXmBe5+E/m1DN12lmR49ZX+9VPu5EZZdTjst+W6s5nHP/6wvyywqDRRSk5KyrD865xUlY5bd5kUZDIee0AOacMxaDsx7nHWdna6aY2Ox69nZPypl+mDDW0XWrKk8WvZ8QphiZ4kQTAt4Fik1q5uUsbdPgnKVpAqv1iqZtuLu/Y1sKs5VR23VIaYjTWE24IMeEMaaOnUC/31dZa679oHr+c0zYIjgD18+v6LoWEeHq5pq2bbi8uNBeamPYb7ZMU6T1DeuuU6OmpuXhg4ds9juGqdcxEZxKlK0hl6QLODEx7Te6ICIFyULJEYpZDJwkuyrtzlCqysCooVF5KRNXr1PtN7fzOEkIwvn5miLKtqacqzOz06iaHPFOHZMfXHTEFdzfPqt9pQoqS1UgUCCJWb7aAAAgAElEQVTX+1nksChkiiw9ts5ZQvCERnvbN3f3xGlaxp7uszLd83g+XgyZx/TLz5xTnepUpzrVqf7W1t0d/+GfhZ/9us9hfa7BapFyZMai9bqV8k9invQ66fAsKf5k71/e9Foh8MdOhypgtctmK5MgkNOA8wFnLfebDYl36dZrdjs1E3l+9ZwutEhJSIF93+PdCpGOQodzFwz9hnGwrFpXQVmkZAUY3geapmXMULI6CxMKa9suPYZxKozDhDWGZDMuCCEYmsZRRBiGPVJ0Ih3jSEoJR4eTSCkTlg5Q185iIsXKYRKJRXKpsln7ikxVJ8o1V1QyRTLDsGeaRi7XFzjRnjuLpW0Dm5sN0xixYvDGknPBYNntdtxcX0NOUCwpq1vsNA6kYSBYg3cWB6zXK0SE65sbttt9BUhqAGOdW4BcvXQHIxeoEspCrkA150K1MKrsOTVKVyhiKwBW8Km/qz2bxi3GRTmpBFjdW426Ni/AtOCcX1jduedz3kcRMLMBVe1bVvkmVV6ckTwDVbuc9+W9y6LCYdwr6DJMU2Ka7gjBMzOvxqi0d8ldtUcSYapZlrUKiJjlxYe8VuCoZ3e+JxV0WFeBX1QHYmMdeEPJhr7vaboVMSXunl/h25b16lzPexaMh7ZpGafIMI7V6daw3/fkEjFFFvOgxlkExzAkYk48Co949PgRxhpurq9o28Bbb73Ffr/j5mqibQMlJWWLjdCYltAqAN3vtjRNgyuGFKPe68bgrWWa1DnXWMvZ2Rm7focIDMPI2fqM0LU0KTPs92z2O4LzPLi84Pr6mlgSNli8c3SrblmIKDkzjRNxmpCYsFLvLT0RGLTHs++11zVWZt85h6kLBNaZ+gbtNTevaYNw1mFsdYzOME2j9kU7h8y5pbn2pdcFnJQmhhGmca/7KqlKrQ868Xn868e4ClZ1wctaagav0DQe7x1XV89JcR7Prj5pZRk/hkMc1OFxe1h8WRjYT7iYeapTnepUpzrVr2Ll7/2YP3j63c96N36u+lyDVXj9BONlye7hNfISuDWvef+LQPRNEuHXfe5Rm9dBJvqGTb8ApCuGmXsY1bRHT721BskR5w1TztzsBp6cr3Am4MmYkslZJawYT9s9YLtL/Pint7z7xNA1LffbO25vE48fnOMbaAKkPJGiTiKDsySnLMiqDTg/UUpPa88xziNknCsYExHUyMb5gHFQYqRQ2A0bJEYEoW0cIoYwDOS0xbiGLE4n8wAuKWuHVSBV1LjHGD1FatoSmKaJKQ4VwCqYs2RiGtnvt1xcrGm8x9mGHBObuzv2mx3BBZU7G93WNI789P0P+cu/+ohsAhaHLQqCSy6E4DjvOtrQkMaR1A9cb7bs+pGcCtZ6dYNVCohSzJLROOdBBu+RdHAvLWgGpXJxFozD2gDWL0ynBr16BeJLDqmtY8CRUp6FuGD9Mn7EOMQ67fu1VmWmIljjakSNvtA6ZS2ziBpPOas9h6YgMSOmYJzG96gU12KNe8EY6UXznsOCQqnRMLZKhOccXO8tvl4rSlxkxNZbcpbq9uyP2GKVRlvryFJIRQFGEXWl9c5p5E8pxCpVFc1gIU8J10LTrri7v8cOA+IM1nssjsZ6nAvcb3ekmDm/uNB+1Jz0GpVClqSLCympbF0KuUSKRFIp5H7PME08fvKYi4cP6Iee3W7DgxR5550neFPo+z39LjEl7XkdppHOouc2eHS5QslIZ2uGbsmEdUfOmRgjTdPQhY77+zvIghToupawXpOAse+52W7YDj25ZKw3NL4heHX59t4T08hUc0wPzzkDRVlRW6/dW2+9zdDvuL29ra7b9frXB5iOVwt27p+uzLs9LGiUXJCUl17rXM2lTNEYI72u1UHaQC6JrmvJklUiXWqvel2EsPXmN1STryo1F0CM9qtbA95A4x1GhH63Z7/d1TE5A+qjRZf6/J2ziOd/6sZtlnFuapuBPbXQnepUpzrVqf6W1r/13733We/Cz12fe7D6cn0S46T5dYfJy6v9py+zsi///EZJ8Jv24yWgeuiXqu7AZpZJzi/Sjc35o0V0MtmPE/bBJc54ZeWM0Z41axBrefDgLfa7ezb7Hf1ffkgbPOSeyxW8+84ZF+eWXBJGCt4a0jSCcYTQYG3AOwOSEMlM04TxmVISzht1D60tl+v1mpQndrsdoD2gtAERhzUecKwmGDdTPQmuSlqjxsKg4HhhEr3gnQ63OZYl50xMFUxU4x3r/SIbdjUz0gDbzYZ+v0fNYpQtss5RMtzd3/HBBx8wDiPJFYxpCNZjrUbJrNoOKcL19Q277Y5chBgTgmZWWutVVrgw3gcX23kyn7JeH+cCRvXEOrkXvX7G6vUSlNEWDAVl2FTKO+fP6muzFMqsKJYyDxw1I3IeobLgi7xXWSWVm1qVMzcNIhCaoOyecxhTKEMkSoaaL2uNQYw7Or4DOwsc9QHCrP+cx7c6tGpWqzPQhIBzlpKry/AMmgSVqRpl4wXd53n9QtXh2ser+6OqVBxY9LhN7WfMUc15grFMMfLk/JywPqOvpkjTVCg5c3t7q8Y81mJDYBxHZeZQ86ZDfJLG4nhvoPY9dl3L3WaL9vdO9P3Ao0ePeOvtt7m/v+P9Dz6gpMh6veb8bE2/XnN1dcUwDKQUmSaVa3erlljNm0C3FbynaRukmkvNYCp4z6pbsdlsSDnhvePxW49qn7Mh9T1TjoRGs3XbLiBFlFVuGkQ0gmZ+tqRS8BWvitEFA2s0x/bywQOGcdBxW2OJzLxoxiyTleX6zwsUS5/zcVuEzIuAZmHMoToQWyHUvFwhITnWe/SQV4wcroOxlsBByq6vq0oLKRgr1V04st1uF4XDPCaPn7sqY3/xWf/CM/ml+tT+A6c61alOdapT/QrU9//T73A1bD7r3fi563MPVt80wfhZjOhB7lheec3L236ZRX2t7FhYzGwOhjtHQPjw62UyeWBVj7d//ErA1EzKohzbdrulPHmC5nZa1GJHZZEpCdtdjxSwPqhjac44Y4hF6KdMl+c4FzVOSWnCGGUD29bjQ0vJE+O0Y7uf8CEocxi8urQ6ndje3t1qv1plXTQrU2e7mYgwYGiI0eFcVuBVDFMUJBu8CxrvYUqdDCsTM/dfHsdNzDJXY8AaTwa6szW+CZSY2N1vmOokfe7bzNXxNGUh5olh2OukOhpwGt3Sdh4Rq3Eimx2bzZ6ShWGMpALzpF0lsRqhcXz95us0uwMb6zHWI5IRnEZoFO0VzJjah6eMqvEOv8Rx6LR9UT/CCz2nswLAea8AvPZxllzBbd1OqZmorrokGwPn52dY39I0AWP1fdOo/bylKNC31TBK/68sY9cYNTPquu7IAfZFt+L5fMyy31k6r9fKkCrYw1ratqsxKjUnVd8MvAiGMKLgu4LJIkLM2tvcdGvEZ/IUSUXl0febLZeNZgafdWsk3bHf9jjrdQHABYwxjFOV/x7JQs/OzmjqONrvtwz9nuAMoWlZrTK5fr4yzI4vf/kr3F7fsLm/5e5+gzUQguPs7AznHJvNhtvbG4Z+IHjP2fmKs/WaDff0/U7Bb4yINTjb1D5MPYchBC4uLogxstvtyDkRgufdL3yB+Cjy/KOPGPo93UoXVy4vH3J7c4MxjnGcNN6oVikK2LMBV91ziwhxikzjFf3QMUyTLlLVXONSr+0sxZ+v+euepXP/rbUWcsZbSzEWY9TRt0jBWAje4oxQUmRKPSWP5KTO5HIk8xZ5McKpVEfiA6OvRlpd16laYpoYhuGV5/nxc9ccPYdfBqonye+pTnWqU53qVEAR/kvTshn3n/We/Nz1uQarL5sevWnl/OXXvKmX9XXbn9/zM1fd59Yn8/KvzdxF9dptvwhW5eirbnBxChbwztL3A/t+1MjNYjCVLTHWkkphs9vhdD6Os16VptbSZ7jrR3xjWbfCxcNzLs/O2d7f048jw3R0vE5Bq/U6QVSHVnXedM7Sj/sq3VTw5r3Tya0IsWSQRMkDzoCzDcE5onEkSUw5YVIii8cWS7FF5bhlZlBlkQGnlMhJsDYv183aguTCbuj181JiGHqQjLdO8zcRSoEkhSR6Ho1RiW7NzMGZQOMtwxCJ48R+37Pd7Smi+aeFg+swVHMZmfs8VSypjKegRjD6PinaQynGYXGkakDDzKJTJ/IiWDyIXTisWTaZK+hcRlBF6jMQSBXQG6NsejFQcqYNgd/5nd/m2fNnvPfee4srbM6Z3S4yTntyVPdnFSiDlNnwSbGhwSJGe2eNMXRdw2q1IsbINE2kNC7MmjHucA8ag5AZxqnG31RjKLLKYV2gbTswTllrUbbPVanszDBO4zhDGJVUl4LGx1hMNcMKXYtziX67w7pAP0bsZo8JKvttm5bBjVAZcGMFF1TuHFPW3lHUITlOSc2Ugr6v36vs1+0HvA8LM26t4+7unrZrePjwIfd3N8p2lsLF6oImeJUZh6BRKpsNQz9QJBEePap9k5YQ5nMGcUxEc2CvjVe1wMXFBUJhnEbef/99uq7jS1/5MlMcMRvHNI5cnp1jrOXi4pI4jQzDnlIyzukiVcrpIHXNURc3ymyQBMPdWM9BA0nAWmW+67iaHZlfeFbx4vNzYdedXcaoAlVqj7VGNqUSkTxRZAKJOFvHXt3sIset28s5qVpkNkoyBWPURGu1WnF/f19Z1RedtF/eX6krMMf/nXh5AfIEWk91qlOd6lR/m+uP/pPv8V/d/ovPejf+WvW5B6vHX1/+/nWvPa6PA62v2+bPM7Exs4nHqx9QX8ChZ3W24ZFZjKcguVA5PasSxt12yzq4Ci6ojIrgjcG5Gp1iFXRkVF48idV/BcyYuL3dksdIzprT6C2M00hMEWsTPkDbtkvkSSnaW1eKAqqSMqHxykqVQggBawN5v0dMwpRE4wurxqoZjK0TWCsYlxGJiDiMJO3LLeqyOhudOKcmQupUnHFOjYUQdSPdbDeauXp9TewHgrPga+SNaI5nAfbDnrvtHcZkglUWlJKQNDHs7jEEUhwZhr5mPFKdc+c+TQWZxlpdiTAz2wfkyt5UbbSxgSRClqy9t7ksmZsOg63xMibPeZxZsfPsMjwzS8zS2xcVACkljbxZ4m5mNkpzY5u25eGjh/z9f/N3+Na3vsWHH7zPOEVStGSBGEcF6+jxVXXpIjdXk50ahVLHZ0qJ/X7POA4VINtFtnpgvwXrLCKm9isWjPH4YLHFg9ceSKlGO9bpz6UIzntCCLRdizEWH7wuUuRCShMZMM7RrlY0XQfGsOo6Wu+5fv6cm5sbhhgx48DandH3A85q767FEppW8/wqEW4qSppjakIIIFYXOYrQth37/Zbdfs/67AzB0LQtwTfc3d5hjeGrX/0qtzdXpDgxDhPGGJ48eUKOkefPnrHdbpXpdAosb29ulnG9sP9WTb6k6OKJFGEqUmNZLKvVCmNVsfBXf/VXWOcI64YHTx4z9QO3V9eM40DjvD4bxBCnDCYvsvG591eon1nvY6nZuaWIRtowM6l6fYJz5KTb0eeLnr/Z+EhMBYClZspS0DZluzCiepOkKgefMGScZArqOm44uFLPTOo8pubFu8WkCyrbbhjHkbu7W6YpLsD5Tc//Uvf/WD3zumf+J128PNWpTnWqU53qV6nkvff5X1aefJ0/6135a9XnHqx+0gnG61jXnwVCX7di/7Kk7Pi1x+zp3AMmvDoZMqYaiuhP8zuOP4mZhZspiIIgxhDahnHqWYcVjVc2y9Q8T2Pd4uzqnCdlBTchdGCEKQn9JDSdOtCmVN9nrPZQimh0RFE3W+89bdtijGGaMsMwUEoml0TTNvjglp4xjY2BOExkGZQhlQ5LYujvya1XhsUmoPbBAsYp2I5R98May9w46ZxKaXPOGBzOaA5jKYX7u3uur67JMeKtxdUeOTuzfBUc7LY7nj17SkmJ4BtyArwjtA1TyrSrFW1qK5hwCjq9V0xXM0xnPaHM9LlR0DwDS6NNgWpmVOZe05mhUjZN3WfnXFXdrrWAdWouVAGsgLrlyquT55QSYnQfnHMq3y1SjZOcuiWXwttvv8Xv/u4/4P/6P7/NT/7iLykSMDi8VeMr3erRBJ5jxkzB5rGD6jTpdW/bRsdMlYraahakIKdaSZkaSVMSpp6TIhnvNTJmVhnkHHHOH4a/tdpja8DEiCsFGaEQMc6B9RgXMNbimo6z8zNWZ2dgHff3dzinzKaOI43VyUljeSgGFzzOe9quo9Rz0DTKGosU9tsNMcbac5ppGqMgzqt5kXeO7BxXz5/TdZ6Liwuunj+jGMt2t+f+/p7rqys++OAD7U+t4M5imMapnl8dSrkyxnNPsjUaN5M59Ic2oQERxnoNnj57yoO3H3N2fsbbX/wCOWeunj7DpExwDsmZlKJKb8Pcy64tAKXGGqkSfZb6OkSyGlGLkGLEWYerbPHSQ2oMxcirgK/eo0bU4dlwYPyBGiVcqsS31Hs+Qsk4e7zgYcizyKSO99kNfO7VNRi62nO92+2IMWLm82tfluYftjU/Y19mUT+JAudUpzrVqU51ql/1mv74R/wX733/s96Nv3Z9rsHqJwWZ8PETkblH8k29Wa/bzsf1sS6/Y8aaB1BtX/cqc3i9HE3pvbHVpdUgheV7YwVDxDhT5aQG7w1Gsk4cjSXHWCeUjjhFMMr6pVyI0WBWAesdOSWmacQ3a858yxRnYBGXieB6vaqxHlF7GEOL945u1SJSmKZJJZsxctYGpqSyykTHbgw83/YQRs1ENcrGZMkaf6HNjZQSFznxOI4vXIsDuyhAITjY3N2z22y4aFtICVsKBaENnj6mOok2tE1TaVCdILftCt9dMCWdoEurIHBKhSwKiqQYrDNIlupEaqvsVxbWKJcDaBNBzWRq3ihUea2UJZbHBnNwOC0JY9Ud2FSZtfbYHrFClf1EpMqRbWUFtXfSWo2LUSytX7MU9kNPPw588Utf4N/93X/A3f0dNzejGjsVO7eB6uR+Hp/mePGm9pt6g3e+ggDPer0mhMButwfUxErkYDKlLsiVjZsloUXBu3MerGVKCSQvf5OqBhjHxDgNC6s4y6ZVgq7S55gzpR9Zn52TM3Trc9brFf04sR8GHj54hBFDHCfEexofGEokVyOfLqxYn53hvWeaUgXMme3mDmc1m3eKkVKlwz40C9soKdOcrUhJnxFGVHadUiJGHUf3tzeUnBagmWIkpelI0lwwRqq8dr7hRfNQZ4l3Zc+P1RHBu6pqGLm+vgKrLPrf+dqvsb3fsb+/14UREVLJmKKMpHXKUkrJYC0p52WRQ/uOS3WOLjir0mSkUJW1uKpq8N4vGbEvPxeP5bYpVjfgWSoyR+YYZThFtD96lrq/ICc+bHRRVcx/SykhqEnYNB0M3Y77/vWYDpLgBcSKLhAcxujLrsAncHqqU53qVKf6W1p3d/x7354+6734hdTnHqx+Gmnum/pbj1mkTzOJeS1wlU9g3jEzci8Asvqnl7Z/2Jao06yxCAXjDc5C8A3BWUwp5Fj7CbMgWKwLeKsA+erqilW35qJdIbYhF81PhcpspLSY/aiJkmUcdXvTNJKSgknrqnTSlAoqIcaJFCfa4Ml5wAdPMS1FWnyjDGyk4IzB4Mklk4vVDB00Q1PlxqayJm45rzq5P0RoULRPtR8GdpsNl60C5pyVsUlZsyS7tmUS7bltmo4Q6nZ9izGOXb8lJsPj1Rn98/sKMivjYwNzbAYi6sxb5t7MQ1+pLkQoAyQcJLFSDv2tMzsMOpH33te4F6qD85FBk1Fp7/G4Oriv6hasdUvMh3U1a7SCZ5M1EkW3U/jar32Ff/SP/iH/6//2bUQsbdtgKKQUa//ucR9vncRb7fEsotuyVh1rQ/A1uuQwNhUU6HhJOWFRY68FrFpLsFJjaXQs++Arq1eYYtQe2HpdcymE0NA0DTFnBFvNtWzNaU3s+h7rHNYHVutzHj95h/v7DQXYVPfehw8fknNmP4xkyazWK+3HhHouM1IB5363o20bNZvSMFLarqNtW7quI6XE2fkZXdcxTSPTNPD82VMuLi9YrVYMw0DMid1uT06Ry8tLGu/ZTFMFuwlEVRfHkUCmHlsIQZnNCgitdeQSIc0sa82vzYU4TozDSF6t6fc9f++3f5sf/uAH3F/fEONIE1ri1BNTorWBJgTGUY/Vec2/zSnVyCAdv76OJYfB1tggY201TGPpC1WgV0fx8txSVl3qP114E4wpIBkxspxTPeaXn4t1nB8992Zgqfuo56zrOpyz7DYHI7VZwu2cY71ev7DYmFJiGAamcVj6no+f8a8zvXvhIXyqU53qVKc61a94/cPf+9f8ydVPPuvd+IXU5xqsvql+nhXzY2nw8cr/C4D0ldnN6z/vdSDW6B9eKwuuf6pEmZnprvpGnehKBQBJCmIdoesgRYw1dG3AUrCrAEUzI1Mu1VQlE0vG+lhjcCxDP7IR4fztR7TtCrOHlAopZ0pW91vvLN63KvutWaLWGpom4LxGjiiYTcQ44azFuIL3ljFZJgn02fOTD55zs2t58u5jdfsthckYUs7EbJQ5yulo8lsWhm3OMRXR3E51Up2QFLGizqagbq0YIUkmI/U4CmMp3N3es98PiHEY14Br2PYjV7dbnrzzJXCB3X5gDg4S0XzWnF80fTFG1LUKZbil0kZiDGBVsshhvFjrNKIkaF+v4m8FpDO4LliKNQfAuEzbBV9DH8s8NvRT6iCxgOaWGsmYObtUEvt+v7CeIrA6a2kaRxGPt44iiZgTqTKORukuBZcoS2+qw+tspFRMoQwV5JTZ6MoqS14Brb7PYasxj3UOW4FpzqU6t0o9hSoN9U6P3Yr2IyLa3uqdVZOdnPBtQwgNmlPrFgnvzd39Ih0+OzunxEn7qJ1jvVqx3e9Ukk2pLKOylTlNi9TXWktoHDHpoot14LzKtZumoWkbcolYC23wtCHwja9/nR/96AfkklmtVoQQyGmCs45+p26/seacIvP51GObo1h0KUmwOIopy6JMrs+e+RwuTDcag9MP2qu6Ch3BBLzzfPOb3+QnP/oxV8+fse/3hOrW3fd72ocPabuOvu+ZUsaXoj2kRdlZSVmvR9EM1lJKlQVnRHI1i9IFiVnur7eDfq/PF2X+Z4nz7E4NFbBKgjLfXeXF4zJ22eYMZEvOi7lYLlll9I0nxom+35NzrM/lQkpCzvq+EEJdVAmsVqs6PmGaxup0nBfG9lgiLLz47D/VqU51qlOd6le9dv/tt/lovP2sd+MXVr+UYPV19SZA+br6OBmx4VWg+XGfacwhmuIT7OXRd4LUCaTGW1hiSphB6F1DyhbvWp0IW4cR8I6a26mRHTqJNNrH1qxYrwIGyEUYx8Sun3A+YL3HkjHFQVHpqvUKYGZn3hlgOOdw3jLuBzVm8g1t26h42SRsu0Lcmt2+5V9/90P+6bf+lK/+2r/Bo7d/A4vDiEaSFDMqc1dsZbyoElKdwKaU6ucemBmNa8mYrPEmfb9HrCGsWqahp0jBhwA+MKWCmSJ9P1TZZ4OIZ7sfeXq9Zdsnvv7obfpY6KdEKqJ9urbBW6vGPhVELJPZw4wcl6vK+nCxFzOl2ZSpiCEX6IcJbJVnGgNm7hoViqSF1ZslxrYCArP879WxY2ovH0YZOu/V4EhEz41QSKmCBQtxnEjo361zNKah5ESpElLtZVT5sqqPZ2BxyAROR+y7dQrAZvZLJbt6TILKSJumwVjLvu+VcbPaYz0vzHhbY2lKRIzFIqQ4UEqiiC4CSE6YRrNb9QMMKWWur69JOfLg4pxSCnGKOKPmPJvtliklNW/yyqhN01hzgnXBxXvHFCfiNHJxccbXvvY1fvijH3J9fcPlxSUPHlwiyDIGvXc0TeAb3/gNnj37iJimBRRCwRrhyZMn7HY7bp8/rwqHjKuu2QoGDbn2ekrtuSSC8w5jDd56NQUys/xbs3AtYJxn5QPTEOnvtwQXcM7zjW98k91uTz9qvmucRkK9Nvv9ntV6jQ8NMU6Ls7JFsKKsqNPmWnKKGGNpzlaMleU9zlM97g09bmuo61/aS2x0Uaf6vWkfdk46BiVjzWzgoAszh/v9qId0aXWwiBTatgWE3XbLFMdl7M/jrpRCjJot7Jz2Fx8Wu8ziMv26iLLjRcVP439wqlOd6q9X//h/Puf3/x0B++pc61SnOtXfbG3/m2/z739n5P3N1We9K7+w+qUCq5/WtffjXv+mbbypp/VNEuNFFvw6eXEFJzpXm502UdmgNoAp0+gdCDXyo7AfLfvJcL5uEAfFGIKzWJfxDtRs9cA4dF1AykjjigasOEcshe1+ABuxRs1unLVkyVU+ZzA1rkYq8yUCwzAgo/aphqDSXQUvDtyKfWrZ5wd864++x7f+xfe4vc+49T3f2I2ctw4rlsZ5kp+IeaRMsKquw8MwIgIhmAWstm3DMBT2+52a8hgdlAJc397w3ocf0ATHNPYIMI0TKQoxw2a748OPnpGL4JqOlC1Pnz/l6n6kXT/ANmuGuy3Z+OrMK/jG4b1lml69tmoWA2KUtVFpqizX+EWpoV7zmGuPqRhyFhJqPoQIxjs0TtTgGu2VzVFjRqhgVI6ceTEsYNFYS9N2hNCQSiblqAA152rgY3C20DQNuag78dl6fRQRM1TWadL+5lmqWYHyAXQfvrcVQCwMsswZr1YzYA2kkpfzlXKmpEiRssSbzCDYWYMVMM6S8ywvViCaS8a6gHUOyYWcMs7L8rnTOJKHjHHKUIKpbsbV6GecNMe0mgrlrE7WGr2k47zkyH6/Zb/f8vjxJd/8u7/BlEemFLm8uOTx48fENAGFYegZx5Hr6+f8f9/7Lvt+t8T5eO8RSaosMOrUnFKi5CptPjKrUlZdVziKFKwV7QsuojJqr/ExIrkCrYPRlojQrs+5ub3l7uaG1fqMe9nw//zJnzBOIw8eP6KUxPbuFkkTDpimSNMK3usYSElNzbzVRYBV29I4z1m34lRVKDEAACAASURBVOL8gi9/+cuMJfOn3/8ed/f3zP2g8zNtluYeK0+031oXIkxlUosUSo7kNIIkDGoqNkc96fbqgOZV0Dh/1oxh+76nH3o9f8gCoo/3QTNhqU7SWU2xvKsydbccx/xZH5evfapTnepvtv7rp/+S35evA+5nvvZUpzrVL7CK8GffeZfvXv/BZ70nv9D6XIPVl6W7xwzAp6k5s/JNLpGfxmTp44DyzwLRC7sllWWTg/FToRotCWx3E8/verAeWqdMSSuQNGvTuIIxpapFI7lk7R00CkZKlckaq71/6lyrealGIE4J77Sfcp5gdl3HOE1M40BeJIKZ9foMQRinCO6CZ3eG/+mf/AF//Gcfkewj3Lrl+f3I3f2O1aMWy+wCmihpwIlDkse7gDdJey6rqU2JasgkKVFSJMWRrHpkrDVc3d7w/OaKflAga41lHEYF2sZRCmomFS6ISXh+d8/dZiCLo1mdM2XDfoioUVDAO7MY2sz9mVIlxqb2/qqUU0DsAij1Gjms84t8eQZWBSoAq1JIYzGurdm42tMXQsPlxQXOe/rdnu1mS441w5W5/7X2q7r6OT6oTJqZ0S0UKcQ41d5QqyDKQdsGdvsdUwoa6ZMLoQkUUUMf6xzOe6QUcpoWNv8wpql9tra6GR9MncxszlQKxRyY5lKKXjdkOScKJJS1M6LAVEpaPk9Nn5SqUylzJAvkvpCL0LYrrA0M/Z7QtRgMd3e3rJqGvu9pQ+DBxSXOO/ZxAucodaz2fc849ngfAGq8jJo6rc/W5Jz4wrvv8NFHH3F19ZSzszMeP3qEtfD8+cTV1TPiNHH1XL82QR2hm8aTogLau9tr+v2eMkWcMfWYDkyhKRZQmaszx33qcxRMqZJ38CGwXnU0zjOOA7vtlnEYeHz5gN0wcnd9w+WTx2ANTdfx7NlTzi7O8Qa2t9eYoiqE/X5Pt1oRQoPJmeA9D87OeHB+xsOLCyRF3nr4mHfeesLDR4/40+9/v/aNG7pVxzROS0br3E/66vNM+1SlJHKeMAglj5QSsaZQxez1TTpmjBwWQpY/1G3OMvu2aSlFF6rmHtv5fpit6o77UY/dhIGqBDjI1ufxfPz103genOpUpzrVqU71S1tF+Onv/Sv+g5vvfNZ78guvzzdYpUZT1J/qtO9YTfuxNU9UjhmxF7b/ul4mc/hqXphwsjBgxx8vL79P5KXdkxe+FFFQYzHqGus9s7nQ4pBZhO1+4J23n2BDwASDOMsw7Yn7TBs8q3VD4wVCQayCUZw6hlpxUDzDaGlHsLZQykC2CSsBi6dxHiEiOWK8EKOQU8baQPArrDfkEukuLkkFrrZ3/PS9Lf/0n/+QP/n+NSU8pNBxcf6Qtx9esNntePLwAcYagnOYbLA4BENMGYzDBb9cxUIhpomUk0aniFQZqKgLMhbnG1KZVFIphpxA8z0zQsIWT86B7NZc3Yw8vdpTxNOEjuACcRiZxlEXObz2mBYMKUcwZbm4C1gzyuPllKvs0WNQA6Aseu2s0f2ax422uh5JEc083kCWaA9DEYvH4UNDt1qzSVskV+OjmlOKNdpb6yzWO2JO2otnLUUK4zRpdicwxYSl5t9KpsSenByStd94ZgJn4x1nDSWVpYevVMLfWO2XdM4jVtSMyx76ufMsxawKAZWF6nXV2+FIVZDVWMhWIK+Kb1uFBbNLstdM3hp3YrDkXDBRWf4cNRJn3XV0TcPd7Q5JEYzl7Xfe5a1Hj4kpEq+fkyTjraPrAsYUxnECDKFpMN7Trdb4YHn0+LFK3Gve8LvvPGHVBlIc2N7fEqeRzeaerm2YxoGx3+FMUkY0JShJY6JKIRhIFqRkMhC8q8d5AEdzH6suJilQdz4cGVhZLBZnrF6nek0aH9j3Pc57xrFne3vDu28/4fziHJlGnj39UF+3XhPHkSIjJUa8D6y7lmIc+32PpMzQDwz9yKptubgo7KaJj374I374ox9Q8sj5uuHBo4fc3NypA7Sp0VazK/XyuCsYibowUSW/RpdosGRd6GH+d/QwXLahzLguehwUJV3b0jSevt/XrNZSP78C/yOjp3lxaGZL1aTssLg3Pz8PJmazU3JZMmdBr9mpTnWqU53qVL+K9ef/2Xf4tz/4k1/JlpfPNVjVOl6xN7wIFV+Vmb2ujs2UDgY5h5X7F98rh8+qsrT598bO7NCLVd4wMOb3zl2JtZ2tykprgE3JFFMW1tWIMgfDsK+5pQ1RLFY8wpopZYq12GLJecITMWT6EjlfBwoORyBPgtlZVuvAatUgZWQcB6wIwTZk73FBj2ez3WC9pWRD8Ct8gc53ZIRntwNj9vzZT3f8sz/8C/78vYloLrA0FIF+u+GDfsvlKpDMF9G4UWWQDIGUIjZU+TMqPTZGcN5hvaOkSQ1rUMDtnKstnwaM04mr87iipkNJJkoZwBad/PuWzb7w4dWObZ8JbYuzHnJm3O8pMeGMpRjAGlIu2ttJNSCax0Eu1DAOmqZBcJSiNjkFjQvSmbAjlxcXOeb+OpnB7GyeJDqtn2Lm5ua29u6WZczk2XSpjmtjPT40pJwRkzDO420FsdX4CDObCVms8XXho2Ap5Kh9q8Z6+mE43D0VdOU8oRlJOh6tNXirfYDWKEgXYw4LM3M7sd4wYAzOK0t7LBulgKJ5lmPX3lqLdQpMs+Qlm1PP9dwe7LDKaWMMrNsVTWgwBc3YdY7tdkvrGqwPDNPEnPPqvaXrGrx3DENPTBPOeza7HdZ5Wu/xwZKlsNlsgMzF2QpTLG89ugCBv/jJDXf398oMx4l+t8WUxLQvCwiL04AxkEtS0yJU3mxqv6rG5KTaQ8zSr5ptwlnNMcYeIlskF3JM7Hd7ehGmaSBOka7tMNaoM7c1xP2O937yY955910eXpzTmC/w9PlThrEnW0NYrUj9QE6Rkn1lLYV9PzKOkbu7DW0IPL264fzsDGNg3+9pG8/5xTnrdccwDGx2e1KZpeB6yc3MdEpEyoiUsToHqyGXkbxgUiPzEhTMixPCYaGv/rSMi+A9bXDEaWLod8gsLZcqp34FqOo2FpdtZoXLyzE5ApQX/jtwkDXLIlE/1alOdapTnepXpfb//bf5z//fL/A/XH/viOD71apfArD6yet1ErbXrTC84OL7uu/Nm0053iQlft1nvLztRc48O4a+JGme5cFCJpeB25unPLz4ElkKU3IEF1itOpqmAQxT1AiQlCZKNOwmx/kZNM4x7ns2Q6Y5t7Tnl3jnCI1goqdEYSwNFxdvYcwIm6dYiVgSjXdcnJ+RrWPMLfu04l9+5y/5P/7gh9xswTcXNK4jA1MslXmC3X5QZtKoZNg5h80ekXh0bAdwN0uzNWalKGAxMLslz+dPsyg9WXLlbhxjtAgG6xuktHz0/JZ+SCrVtQ6wGgOEYRzHypqqGdICQcy8gCC1pzAvjKPzRhkjE+rXme0BkcxY4zW8cxjjFhnlbAAz941KEaxxy7HnnMgp1VxYndYb50DU9fbi8pLVasU4jsSs0th+GEhpeoElBSVi5/O5PjvjMhas88SUmdIc6dNoxuw4KIsrhiJUsAeLI7XVbNEU67UCPV6jRjbGGqwPKnE2qERarbz07daqYsDZGuMj5HzQQoBKmE0B5+SIfazMYjUaizFy1jpW7ZqLy3N8Y7m9vSGliXWz4vb2mn3TEJpA27ZYb2jawG/91m8SY+KP/vhf8ez5Fav1hZpKlUwWw+5+w4fjwKNHl3Rty+3VPbvtlrOzM7zVyJSuaxn3mZwT1mhck7eGUHsjF4b8qK+4HC1SKLNeI3NqX7C1B8CliyK2Sr4taYqkFKFkYpyI08Q06rhagJ6B66srbq6vefzkCV//ja/zxS9/kaur5/z4xz/m7vaWJnhKLhoFZAzGB8zMUhpDLIWb+3tu7+7pVi3BW5qmowkrvGuxNqg7uPfaRyrV5Re9H2LskTJiTeTQ7zw/046WDtU+W+9fAewhrknk8Bz0PtQ+YL03NSO6PnRfeha+/Iw97mGtv6nGbcf9rS/KgY+B7M/TQnKqU53q09dFs/qsd+FUp/pbUeVPv8vf/4PnPN394LPelb/R+lyD1VnSNdfrwOGbAOPHAcmPfY95vcz45+2Bet1kC1sn60d9WIfXKZtjEXabG3J6RNvAetXhTQGZSMkgtsPYc4zz2r8mEzkX+piJGYo5Jxf46N4TrSHYloBgi6GkTNoV4t01Dx62PLx8m5K3tC2MqTDcZUzTcr3L/OF3vss/+8MfMPIQwdFi+fqv/xoPH7/Fj3/yU54+fcrY77i5ueH65pbLi7Uyfb6tclZfzVFynSzqJLbkTKnM1CyDXCa3WLKp7Fylo6WUw8TUenCeKC0fXd1zu+0xrsMZR8EQfODBw0dkKYyTOtEWalapqeBILAWVuJasYEozVS3GWaQYrLe44Lm4eMj9ZkeOqYK9DFXq6Wq/5pxXCgfDHxF1XTbzuDG2MpTUfVD2kWogMwxqiARQsKSs/Xpt21JKJKVMcL4yoQKlMtQhINZSMDTdGidCCC2r1ZqcEvt9YBp6ooEiESgEteqllEJKRaOMQjgCmIAoE+5DQ2g7Pb76J+ss4zCw73f1uGfJvpoJaa4oiCR1gMXMHbr13DldQBCV9meU3UYK09CTz1q+9M6X6VYBZ2EalEGMUYhp5Mk7byMIX/zSF/jK3/kK19d3fOOb32Sz3TGNA+fnlyCGmCZ291vs5Zpnz54Tx4H7zS2bmum53W5JMTIZQympZqcWlYxaB9VAqZQDY/fxvesa2WKsmpI557Fu7n+e73MFklLqfVABb86xLrZAykll55WNffrhh9zdXPOVv/NV3v3Cu/z6177G8/UZ18+fkUgVOdr/n703i5VsS/O7fmvae8dwhpzzDlW3qu6tyqpuV7mrPdsqu9UtAxayGh4QIAxIFgjZFk+2wS9WW0Igy3IjIRDC2BIPxpIleMBIYIOE24Ddtts92N1dvpV157o5Z545IvawJh6+tePEyZt56xZd5bxdHZ+Uec6JiLNjxx7WWd/6T6VBzRgoyL2glX3wDMtI7QzXp1fZu3SdnBJ1PaWqavphQKtcaL4ZiITYFROlXujM6zxhzrvpfM4IKaMc56Zkm+OaWucSKyWNat8P5KwwukRE5YvMmYvDtHrq3/jY0wuUFw2izhvUH87V5m1t69NY3/73Pg9ma660rW39oCsPgUfLH56ImufVp7pZfbrWE8TfIB170/Fy/Hlz20+rTp/+3Y+jGz+vNp0wFRp9YSK3SXFWpWlyxBgZ+hXTqqK2iZxaglekNGAai7EzsqrIDChOmU4M1UThfaaa7FC5HVKlOfMJ0hKrAtPa4RpBenTMnK56Vn7AKI3OiTBkcrIslgvuPjnh7Q9OcfUemQY/eIau4/1336F58JCz5YrV8gyjoOtaFssz9vfnqKzxJdcVxDVWaY11BlCEkAnBoxD3YgrSmnMuVscZnUXvqUtUhlYycR9yJuuKkC1nHdx7sqBPjjC6glrHZDZjMp1x9/59Ink9Ec6I6ZTIBgsalqNMY0etadHHWScaW1dXvPa5V1mtOt55610EoXElPqNEaChpSgQpjhIx4sUhVRczJ601qtCEtdGkJCY0WglqmRIMheKacwZtqOqauqpKJE5N10uPkGLEGkdWCWMczXQGhwsygr5aY9fHQaHQ1rI0mqwVWffkEDHIe8cgTXUu0SA5SdMZR0fWrNFle9pachRWgLEOW0VUrzfuicwwuuWmSOUMyctztnJrVFGVbNYheJwxNHVNKIsAKYs+9ODwMZev7vHSzZto4M53vkNVW5x16BIFc3h0zGx3TjOZEuIhR8dHBdXOzOdTutWKrvPE4NmZzTk7O2a1XNJ3PdZaDg4OyFnMxZTKKCXNqfceY0badaF2mzGTVBcEfsM1mfMmdL0mYbQsJBRTrs1xJ2fJNs1Jsm+lxVelKRbGgTWi706FjmutZJG+987bPHrwgL29XSaTKdY6tLZUJTNVaY0tiwUpJnHyVQplLNoYUs48fHjA0dEZrq6onIMEOmdQiVj03CoHQujJnEfTkM2GNF/JtczYDMq4dgEWZmRTqGIgJo7O3nuGYSgLGoXOm84jocYaab8XFxfVuonVIw07b7pZn8s8NkfjnNXHjs/b2ta2trWtbf2mqpT5m39z50Xvxb+Q+lQ3q6N5xrPQVcV3n3h8Px0hx22oUXzKxQixvPH4Bfovig0WXGlWYdRdXdw3meClrAlJEWJmteyZVQomA0ZFXGWJCYxeoZUlq0DKPcr0KGvI2hBURJkGXRlCVhAypISuIq421DYKcmgmhOzoBg9KTHNSzjSTBlTLy7uvML/xBgeHLT5Zhr7HKsXgI4u2J2lN13aQI75rSSkS/EBOQnnt+x4Foh0sCCtkoT4GD/lc+7jWciZBdbIu6AwyKVYFyZTXWJYDPDpoWXpF0o6MNEG2qpnv7hFSpu17UioIk7jdyGckk0cqqxq3O7q6Cg3ZOouyipQjbbekrhvmOzMWiyWK88ifGCW/NUZpXlKh+NriwgzFGXZ8LxRGG1IKcnUoPQJt4gRsXbkGikkSGY1mMmmwTpqgFANKVeumIAwBPwS0tXgfaVzNdLZD3UykaUGRUBhXYYyTbNMojY0JVrJttaDKOlO2n1BI856VpusH/KolF7qz0R05x3VsiNaKlCIxRozR1E3NtHYslwtyTtIUlRvFWNHlhjxgjKWqG7KP+CEAAWc1fRh4cP8+n/nMq3z96z9G3y45PHgiiHgQ+nlMkUePH/HNN99kuWw5Oj4mhEBTN5Ayi7MzcopUzhJDYD6dEQdBrr0PJZcVvPe4YsDV9y3WCPXcWTFvkiZSrufxHh7jhzZv7nPmhBaqMOdxKutmFjH8SUVHHEJaXzO65IeOr5fmUxVdaMIZcXTulitWi6Xso3OywJKFVl65Gl035JRobUcMgcF7QsrkYnJEjOADXT+gjSySCAgi1OXgAzENhQJ/ntGbkiwcjVTbkeh9jnCem5adj4uZMapn/Gxjs7q+/hnH+afGSXUeW/Os2kRQNxcg1/+e8bptbWtbP9j62Zs/ifnypRe9G9va1m+J+ldfu8v7L3+ZP/doj7957x+/6N35gdWnulldr88/i7ab1/+d88WeQxl+Hu336cmLQuae63ZyNJnZ0FCqzW3l8+0817yj6LGedhzW6lkxEeMGDSEmPIrFomdvaoneU1dgGEjak6JHxYF6uotBmqWuW6ICoA1NPSGnFTlqQo4kv8KGgVSn8t6OpLS459a7rAaFT4oH9x9weHiH3f0Z1imuXb/Oa5+7QjcEKu2YNhOMrUkY6tmc3Z0d+r7j5OiAMHQYA74bpFGMkcePHrE73xH6H5mqchijCeH83GzqWVOJtRBGaRIEOiENcBrwMeOj4WyVeXzcEtQEXyi7PkZUjKA0J4szQs74GIk5F+MYDTERo+hJpfTFZiJnUojELI2hdRX37t/nxvUbzOczuq4lpVw+g2gcjbHknAhBKKC6RO+kNF4XEXKWpiSdxxdpLail0qJZnc3naCf6yOVyBWjmszmZoksOUfSuZSKulKZyjulkikbhtGVST1Aojg8OOXj8RJC7IM1o7RpyjgSvQBWNbmmix8bBOksaQOmMNuJUHWICJfrc3f0dduY7rFYrutUSrTMx+I0mocSyzCbMJw0x+tIMnceNDH4ANEpbMnrdtIUQOFssmM/nhMHTti3e9+ztzrh16w1+/ddWDIPn0uXLHC+W9H1HPWl4+OgxKWX6YaBtW1KQprCqHHu7l7l+9RpHR0946eZ14mxKCAPDIOjviJbK+RLq8qRucM7grGE+nwGZk5Nj+uIs7Zxba3lTPF9MG9E9VVD3sYlK6yzdczO3GAO+ZO6Oo1AMAVua/1SuD3G9lesl9oIIK5B83pQYuk6ov8Owjh0a9y+nXDSiTrJ6B1+ihRJKi6ZXrlm5blGKuqrJKeBDkpgnlVHKlhtStMzndOB0YRzOgg+X8fLifT3WSJHfpFWzIYl4ltfA86Qg59KCp4Zc9fTP6sLXbW1rWz+Yqm3FvzI5gL3Pvehd2da2fvhLK/b/1B8A4K8eHvLfL/4w3c+/x+s/d4+zfvWCd+77W5/qZlWwsBGHHBvTsTe9iGI+3Y5u6kGfrvVzuTj7bk6MLkyYzhtWXRCO0ZTn6W09byKUn0ErznmM7uDC5GxslhNRol1SZLFqIe8Qh0TIAWUi1ihsVWEcJN9SVw0ETQyKpCJV7bC2IwxH+F6homJ3VmEThIVm6CswNWZnyqSZENE4FemWLW+/9xZvv/Ue+3v71HXFfOcOv+f3/Q6mOzUOg86Jfmj55pu3efud96nrhklTcWlvhx/9yi1uXL2BUYYHjx4S/cByOmXVrjBGM59NCR5yDFitGbqWqmqw2jCEKJrGUaes5PyH0liK4U/GZMMwKE4XLSFXeAxRCUrqbMV8tlNQ1IzWFuuQXFdAYlKi6EZzJsUsDqe5TNZJ0nwYA0oa52Hosb3h9PQEksZag/ees8UJMQh1VLJZk6DVWijBVVVjjBhIBT9gjWY6nZBzZrFYkLNQadFCMbXOEVMi9IG1qRNKaKAKUor4oWe1Eo2fApQBYxRNVVE7h1bQr5b4JGiddRXGCKI99AFdI3RRFcliMiyNUxBTodl0itaafhhK1Ip8PxohaWfIiJ7SWkFTjVa0gzjFaq2pqoq6qTDGELwYbcUY6VISYzBlCCHgg0cpLaZTUZ6LMQopVkEi07Yt77z1Nlcv7bO3t8N8PiUrePmll1i9/x28D4QYMc5gs95A5wMxGCZ1jcqZe/fucnZ8yGpxSibTdx1DCKSYNzTT0lA2VUXT1FTOrjN5vR84OzsrxkBCWY8pjk5K2DEjd4zIMoLygzTnFB26GdG9PF5z6oLWU2i18phKotNWKq2ptaY0oEKcFe35qANPPqwXXIaYSDEWoyzZblYythklCOsQh/U9Jui4xg8BV9d8+ce+xNnZGR988B5d146D3nrhMKXSjCL3kRHoV5pTMloJw2DdqENhWrRlYUK0quP49zRy+jQK+vQ4/vGMmVFK8fTYn57792Bb29rW96f+7PXfz2d/5gsveje2ta3fenX5MuryZSaf/QzffPwP+LP39/nfnvwqi6F90Xv2falPdbO6SfFSTzet42u4YMlx8X/17FfJtp+PuG42n+PXj0bcfPS1m/s9Pve9VEYmlBlP1olExMdE2w+EiSCktpIIkKpoIENKWB2YuwnZANagXCLmBZ4WawxomDQTdqYGQ0tjIaAwNuCTpx0ybVDouuarv+PHuf7Kq6iiqUwhMGt2mZgKZzVaKWpt6PuO45NjaUTaJZPacWl3zks3rmMqjTWGd995h1gm18466sphR2OZJCih0Yq4RuW06ErlQMh5y4qcdWlsBrIyKG0ZgiJmRwCUkfzSumlKPE0otGBNTFGQVErGpRKtpkIRywR2jQsJrF6aCKFv5yyRLl3XYZToD7uuo++96FRJVFVBm3TZbgwslwu0pmgTxSlWteeLG9raNY88FPQ3D6CMIHDOVcX8aKCpawya6WTCpKkgncf8xNJ0xmHAk9HalEUWSHiIiRwDpChuv0n0qKLJ1NS1xUwm+KGn7bqSFSvxPWhddJNyvyglaNZiscDIDYQqTZTKmfl8hivN6tHRIXVTF6qtwseAo0IZg4oJ6zR1PRX3ZFfjqkbyXq3F1TUzbVgsFzx48JC7d+/ylS+/weVLe5yendL1K/zQE2OkqiquXL7KyfGJNJIhQEw0VUXfdSzPTkjRo1WmXbW07VKMfrTQqVOKcp6VlmYSad6HHBkGMT3yflh//nO0cNPpt8QJSaiNaIPHhkqP15MgmaloVmMI5BjX45IxBivdoPzuGvAuY2CS91Rr6u04aKTzhnfNys2FOiyMhMxIWYZcVLIKQflDlCiZqqnRChaLFe+99x1+6qd+kp2dPR4+eMDR0QHtcgUqSUZuGjWsEVQqxmWglAEysRhHAWst+phDLHrmRM5jfNjmOHuxQX16/Pw4hPQCM2XzNettbFHVbW1rW9va1g9/XfqP/wB/DfjlP635e3XDz9z7uRe9S7/h+lQ3qynlC6vuz2sKzx8oXz/SJOaPcsPKw0/Xs0yXnhWjsPn6p1f6v5cm9eLvCjKRCKATICZFbdcTYk3SiqqqmdSaFHvy6B7qW1ylEYMaBUGQolnVkJwgYJVr2d2fUVlF9oE+BBZd5P07p/z67Q+xs6s0ezvUswl7Vy9RaYNJmlk1oXFzckgMITDbm1E5y+/9/b+f17/0FVarlqFvaazhM6+8xGgOszvf4drVq3znzl2MFppsLudzRIaMczhjiFpQIc0YgVIm2WO2Kbpo7sQ11EfFauXxsSajcNagjRgXjTRD6yoozUTaoN4CBaVl7eCbC9Ilmtb8kbWNEBJt2zKbzC/k8yqV2Nvdpapquq5nGHxpUPUagRO2prgh+6EXiieQgxf0skTX5JKdSgoYo6i0InpPnxKVNUCi7zsqJzE7EmETgIQ1hroSg50EUBYIVNEHqyz05uglJxSFRPso8CEQkaYiZdFIjqY5WmucqxiGgZgiGo0xmhgCISZi6Klcw7RpCoLssVYDDmsNbddeoMdnVdBDY7HGMJ/PSRlioeLWTYNzgjL3QyDGTAiRhw8f8sXXP8N0WvPw4ZIrV64XfWwihohSQiWOxYHZh0DftaQQiL7HGM3OzqzE6gCIY7EPQ6E+ODROFnrK81Jy3obBE6MvKKA4QRtjipbzvFPMeXM82GiWSoNKEtOwGAMpBHEcXh9nS+McoVCDYwoS15Rkoe7CuttmU5Y3Hy4Xbha38Uxem5vnlMpTcn+Na0HWSbTScrmgqiqqqmK17Hj/vQ9R2vHSK5/h+vWXOD4+5O7dO/ihlxzi6Ek5kEvMTYheGmetMYVSPVJ9nRMddoxx3bx+1Ln3nM779DEdx8fvypTh2cP8tra1rR98fenSK/yZP/rDs40oDgAAIABJREFU70q6rW39Zqkf/9mv8ONPnvAf/i9f5I+/PePvPPinL3qX/n/Xp7pZfdq1d6wC6mz8PDZ6mz9/onf4aF/7nNqkpj2th91EXz9CXxshme+h1ggghpgiy7ajDzMqnVl1HmedOMlqg1FC81zFDlTG6JqMuO3mrKjrBkvG5Q6bIA+JSu9iqpr37x/zC7/0q5y0EI+XxIeWS9eucHx4QuwSJhp0zMynjmkN1166wcHpCVU9BSMxJ03dcGV/H6Myd+89oF2t2JntMAw9+/tX6PrIarUSnVpMEnOSPN4H1kY1pZFUWmHQJYc2FaOaSmjRyQuEmBWDD/S+THptxlqFtXpN0ZYJclWMfzRGI1rV0jjpvOkaytoI5zxncn15CCU1RvquIwyRpplIQ2WrNTrU9z1d1xFjQmtxcy09DUYbcWhVCmNF06hBnGALY8BVFYmML3Rc55w0iDHi6gmVc+zuTFFph92dCU1VAZkYpMmeTidM5xO6gozqJDTMkCIoIwBukngebc2FhnvoeznehW48xu+M955zFhTEFMmqIMSIDtcYw/WrV1kuFpydCcU2hSiZrSHQLZdklQk5YqyVvk2Xz5sSy67F2RrnajlmOWOcY2dvH7RjsVgRYuTk5ISz01NuXr/G0eEBTeOwWlE7R993YhCVMsH70qQV0yBksaJpGmazGd4P4gyNLOaspQDFOMiUaBVjNMaUxTISMYe1c+14j6sNRHBcmJDrScuCkTyLKU1jzoKkjos2EkRboNAszWQMgRgGyUhNBTEtaGjMxdyIgv6X9x+drOWaTetmWyKaRlYKpJyFuqySuEDHhB96mqahrmu8FwdraVgN77z7PpcuXyrU9cR0usMbb9zi8ZOHHB8dokyNygajwRmNVpoQA23b0rYrrNJUriaEgb7v11m1FHdv6U3zxTEyf3ScVOvm/OKC4bgwcBGZ5cL34/k9v5m3ta1t/SDqs7vX+cX/5Guol1560buyrW1ta7OuXmXnP/gG/9PJCenBDr/9v/02APeXR/RheME798nrU92sPsvB8Vwj+v17n2c1n89CTIE10iFmInARQYF1hzN+u/nzM+qjbsAKhVsjgTEpFsuOVe+Z1g5dW/oYsBrJUvSJqnZExDgpjdxVFDkHaQxTwFWJtvVoZVl2iTfff4d/+OYHnHSGpCZEr7HaUCvL7mRKT2B10hHJ3D865dKlKbZtOVl1fPjtd1HaobTFDx6rYXdac/PKZaa145VXXl0DQCEnlBVjI4mKCZiNGIlYnGgvHsK0hoyUEmRY24oQPSElum5AoTHKyEFQBYGPog8MIaC0wjpLXgm6l885kiilJPuyHHNdmpCcMrE0EEJdzOvM1Yz0yt6LxtKUvNO+7/E+oLXBuaogccM6zmakGCt9jh6lnElZEFJnNK5yNNOpIL4ErNWsVi2nJ6cslwuInklTM6kt7sru+hq11qI1otl0ltxJpIpElEgMidKJGIR2baxE7qAKPTlnXFWhyXgvBj0xxNKMW1ASE+KcQ0XwUeJ4JpMaoxTt2RlXr17h6uVL/Nqv/xqTyQSlFZcv7dP3Ld3qrJxD1ppOyTEdtb4ZCGgt9OqqrrGuIoRUTKdqUhYjojt3PuRzr73MzZs3SElxdHQk+lhXEXwo2lM5JhSqsbNCS51Op9R1LdmlJZJnvViBWiPf1lpUccAdWR2qLDhgzxfOxviZzMVxYhw/Uk6sAWWlEFnp2HlKhnLK5866KWdyCLK4UG6EkVGyid6Oy3GbDuOZDZRSUQx5z+8nynPi9Dwu5BiqSpETDL1E3OScsc6K6VOKWBz3Hj7AaLV2U66cY3d3B1c5Tg4PiUkXhFvylJtmymx6CYgM7Smnp0eEkDAlhxfO42hkFz/aaD6b+vssVPXiuLp5btZsnLyJ3H5vjJdtbWtbn6x+z7Vb/J///uVto7qtbX2aa28PvbfHr/1XXwTgb//pd/lbdcffuPePXvCOfbL6VDerm5PK74qW5k1s9ROiqxu6u+e5Bz/TnZJzZAPOlWQjSrL51p+gX72w3RFRTfEcaBi8x8dMUpog/jOkFHDWYbSSSXPWqFxiUMhUFoxO+NiC1pz1kVzN0WrOL/7iB9z+zgmLesJgK+JgUCh0n1FDZGYMq/ZIzGWyIjnDg7NTHpyccnn/MtVkJpEbGUHqlKLziUcHx1zanbO/6sTwR5eJcoYUU3FfzdjiKKrK8bXWFrdgOQoi2SuIVfBFM6lAGULKrPpBMKQykRYqaZBuMmWGoSclT1VZZPZeNHVIw7g2whl1gEqtdYw5y+eS+B3JgbUb7q4hiPuv1gajDdW0QakRkRPzpdWqFcdbBWTRzZJBGfmstTVMJlOMEX2mNpZYNH3GSENw9doN7n54h4MnT9ZN49lyweGhZfAeay0hRKpynOe7O8ScWCwX0lg7DV5iQ2IKZBRVbVFK0/U9McsigSm6VIVQUY2RY+Mqg7H1urntBlBBFmiapqEyhsXxMTEELl+6RO0clTN0Q8/BwROGvifGLA21Koi2lgxUHyKuqtHp/D5SStE0Quteti0hJOpmQvQdq1XHw0dPuHHjKq6q8EGQU2013gceP3xc3HsdqspE79fH01qhLg9+YBj6oh9OhRa7SUctsUobdNUYpXEbG8J1A5kzKUocktZK4PlSozaTsqgVyaAV1hqqukErRbtaoJQ4SlvjhKZOxhqNNbosiMTzMQpEZ63Ox8L1aKQAlc/HGX0+FqWcyFkhuyD7AEK3d6bCzoUVkHLCGo2kKGVSCuSsUUS8D/hemuy+U4Q4sLe3x97+JRaL0zWDoWs7Fksxe9vf2+H61Uso9VkeP37Iw8cPyUko60KjFtRYFXOop4fqzTH/6e8v6lbPkdXz39ugZm/IN8YmeVvb2tb3t/5Suoz+bV950buxrW1t63uoP/KzX+CPnJ3x7/yMxAr+ad7nzcMPX/BePb8+3c1qmfiNGsBNmu1H4mJKXqVkFuai6Suuu3kDpT2332Rc3L+I1H6UTrapmxXa6Ei2G7WQkp04bnuNf4zstiRGKkqfZwqOb3gx0kaVfc0yuTOKrDJZK5btgE9Tui6ja4M1shdaJWLqaDvY3d2FGLF63FQAbUha0acJxweKX//mW9z5cAX1PklZjNXUdUUKkZQGDk/OBK2rG0weMNpi6orlckEKnsXJgpQSziiayZTdvT1OT884OznlZBjo+p69vX2uX7mMLU1hHyKRLNq/EMh1BaY0cWWSbop2MyVQxom7LGBUMWpBkdWUVd+x7KKgZwpxuw2Sd6q0GLsYMxCCx1qDdZrshcY6UiP1qI9TihgE7bXW4moHHvphICMxO9Y6oQKX6yGkiFGKvb19qqrBuUIHNgprJD+1bVesVkuGqAhZmum6qqjqmqpysl2tOTg8Yli1aGuEJpsyqz7Qh8zVK1fYu3wVpQ05RfYv7/Pg/h0eHBzQ9oMglQgF0yjFtK5p65o6SRNV1TW2XuKHgbhaEXwkK4l30cYwmVZUlaWqKqypSUko2av2jMpZjLVMplOaZsJkUqEMPH78mHbV4UNiuVqRNCy7lldnL/Hyqzc5ePJ4jSJaa6mribgGK0dWCmscbd8TU2anaWjbXhxih4GdGzdQKdN2LSHBdD4nxYQ2DdGvOF31PDo65bOvvoKPHcpIBm7Okbt3PmRnZ6foQb1QbvMg3AflSDmWaJyBtQlTFlBeTl5GadAGsorEkNYNK0pRVw05Blar1VpzGccxQdliilUWkdiQL5RGLKGJEfosNN2YE9ZZZtMJOWV8P5AzxeU5oXLCakE+k3DNy1ij1mPIGqmlLMKMjkwpi957XOjLqdCa5dpXWRgI/eAxRmG1IiWEJp7kOFijCX0nCKu1aGNJIVA7x7xucEpxcnZK9AMqRfavX+erX/1RVm1Hu2pxznLnzgdorfnMF77E9Vdf44P338f7ThYSoqddLsQIq67JOci4rYSxgtLr5ltvaFs3h8lNKcim/nVzgdNovT5eSrF2aN7Wtrb1/Su9pdhva1u/OWtnh2/8lzsA/Pz/e8Ln/sYhp/3qU8lC+lQ3qzyDBvz8mJiMBDGkC/RhMe1J69fkMgv6OOT1454b4xk2f37W92uI9cJD55Pa5237/HeFKijOm4nDsyW78ynV3oScDUpFUvT4LKY29WRK2/Y0tS1NfQZTkYwj6QmHR55f/tW3OTjKGLcHWBwabRTTmSNFw+lpy7IbIKVidGPZ29sXZ9puYNV7tFIYbQjRc2lvh6/+9q/xzW++yXKxwodA1w8sVy1hz2NyFuRFa7JxGxTKc/Q5K9b00BBimbBqyKloHGNBRC1aN5wtFixaT0ThaocYkwryGELEWlOouD07u7vs7e3y+MlBoXeuTzDELJpgREc7m83o+64gTRkUGGsxxjKKAkOITCZT9vcvc/Pmy1jrOD05lVgTJ/papRW2MtSpRmdLNdljNp9LDm4riGt7uiCGgLGWpq7ph462bcUsSxmWbU9+csB8OmF3b5+UAtP5nMvXrhNDy2Q2I2eo6wqdwSpBmPd2dvExM53Pmc1nHD55IrmwMeBmFZPZDHA0k4aUPD50eO/p+whINud0NiUmT900VHUjJknKoxQ0k4bZTJrIk5NjlMocHB3w7vuwv7fLdFZz9+59stZYKvzRKX3vyUDdTMhaMZnN6PuhZMwKGh29NPg+ROrJnLhqOTs7xRhDVVuMm6Ks5cGjQ7rec/Olm8zmMxbLFnJicbagMpYcI0PfQRaXX7Sm1o6cA10nua0heKIPZXEmYrSYJRmrME5ROSeIaDHHapqGurLcv39XtL2pLEytGRkJoty3KZ8vgBVfYGkik+ilQ2moamdxxtA0DSplFiEwaRqUgsXijKFvycg9obJe3zOpLMidDy8aq5WgyDEIu6KwBHLOqISwCpCFP6E4ZFROZf9U6XnHjUZSSJDEwVrlBARC8rz++htMmor79+/z6P4DZrOJRAElw/vvvs39B3f5zGc/x0/8xE/wb/3bP823bn/Iz/zMX+Cf/dqbvPqZV9ndv8Lp6TGzmcEYRRwGDg8esVouqJwpQ2WJmFKsaSVKaRkDPzJYyn8jxXdkSIxjaPrIedr4mNva1ra+L/UnX/kGP/YXPveid2Nb29rWb7DsN34nd37sFo//yq/zO9+5w2F79qJ36UJ9qptVvRFA/12rNECCqJ7Tv4D1hCY/vUL/DM3Ud32b79OM5+OyWQVNsSVj05CVoesjiy6wNzeEZPFZEDWSKlTCESVJaGvxQdEPiiFbnpyu+GdvfsDBaUDbKSp7Ls2nTGtLt1qxPHwsNEyjme/tMvQDbdfSr5Y8Xi0YIzkmdUXIMAw9WhsePXrML/zCP2HVtoQYUMaidWbVtgyDp5rYtW7OlAn04D22nKMYE9a6NQ14GARhSlBiRGTyGnMm5ETrO46OT4kxoYxMyofekzCAJoVERDJJF2dnTKdTdnd3aNuWs7OVIMKVw2jIWTH0HUop6mpKSpEQRPtIQSfJEOOIoIlZ1dWr19nb21sjb/3QEaInY4o2UzSu2jh0hOAH+raDQmlWOdO4Co+i6zqWp2cYY6jrGrJ84spaFEqORzLs7e0wnU5JKXB24gEw1pD8QFXVOGPIMeG0ZnV6KhmdIRD6njT0GJRQr1GsOmkEtVGlCVXEqFA5FM2oo3E11jaCNrsZ2gndeGdnl93dfQ4PDrFO8+TRI7q+ox8GvvD51zg9O2FvuaKezuj6gYcPD0kqyzZnU+qmQRSbmr39y9jlkuMo5kvG1YW6GzBW0fceSBhTgYKHDx9ydlaj1ctCu9YO6LFWdMKr1arEosS1gZDK5xT/YRjWplUhRnKSRYrppGY6bajrismkZjJp8MPA8fEJTkuu8MnREUPbkYqhFVnclbUu1NyNRagxZutcPrBh2KUkqkmudxnbdnfmhVpeNL22YjIVOvlo6iSbv0g1HmNzciz7UpDVnEWzbQpNHQx5Tb9lrVvVRT4gDZ9e/5xTEOQZQTUFhVY8fHCfz3zmVXZ3d6gqR7s8ZTppqCcNcxJnywW3v/VN3n3vbX7u5/4vvvjFL/HKSzeJfuDb3/oWk+lEUPfKEVPEacW1a9dZNBWnJydobcT9+JnU3qfHzvJ14/+na2zYN39/PAbb2ta2fuO1U0/5N7sAzr3oXdnWtrb1/aidHa79md/Hz/35X+EPH77Ho+Wnx937U92sCt1LfWTSsVkXkE1yMY55lt71WZOezCdvUz/J/j7blOl5TenzGlYxbjEyeQSy1vTB07YRnyyrAWKCpq5Q2mCTxWlFIuBTou8Crp4TouGt9x/x7fcec9pB0lMq29D6ltcuvUKdFUO7RGWh4mqtubS3h9aa4+NjYspY42Tyax2TnT1CgpPjYw6PnhDCwKPHj0gpExPUVY3T0PU9bT8waWqcymJgVFAoP0SCleiVcdINFOOdMfdUi2urYv1zyorj0zNOz5ZoOwFbM/iBECUXdKQLxhBAZVYpcnJyxGc/+znmsxmrVUddV2VCnvBezJiUUvRdSyaXvMhNmrnQKLXSGGuYTmbkmOnabh17Q06l0fV0XZn4G120mZmYBpanZ2szprZry6WoivEMhQItx8EHifGJRtMj2ZzOGnbmU3Z2djg6eMjx8TFf/MIrZBVE52gFYZtMpkzrmtj36KZmbzrl3vEx0XtOVysa76nqKbpuQIuGVNtM8JmcDZNJIwijEfpzyon9/R32L03ww4BWlr3dOcYaUImT4yO6dsnpYkHWmslsjnYO4yxTVzPbnbNatYAi5oRxFh8izbThyrUrKK04ODzAGotxhjREcvYolalq0W5qI87OOWUOnhyQQuD69RvkDJWrS2ZuZrVc4n1HCAGnFdEHTGUKNTiurzPRmxYasFIYLXzS4Ae8UfiuY7VaEoputCtUZaVEz3x+XUAWNyOA9aJazlnQ17FZLVRgbbTQ0q24M2ulZBFjNqepa0LwTKZT0aeX+JpRlykuvudGQ09Hu4TiwKzWJkKKRF7nv5YgWRlNlD6Hf9fIpEGpjFZGzM5yXlOgRVoRWS4X3L17ly++8TqPHj1gNpsRo2foWnZ2d7h06VUODg/o+oFvf+tN3n37HV56+WWs0Vy+fImua/GDsESauqZtVyhqmsmM07MFPgpN/eJ4eG5y9bzaPB6b5ngfN75ua1vb+o3Xy9PL/PjPbrWq29rWD1t94T/7On//L3p+4cnX+WNPfu5F7w7wCZrVW7duXQH+OvA6MABvAf/R7du3H9+6dev3An8FmADvA3/s9u3bj8rvPfe5T1ofB2I+3byKMUhkE4GATWOOZ/x+vvi6ze0+b5LzcXl/z/q95wXYb06wnn6dIJl6lKCRi0nPqk8kKhKKIXpMcuTkyWTJ5nQWtCErOO0U73zwmDfffshqqEh6AkbowvWs5v3vvMvMNFTWCX1RG5yryUpz+eo1uiFwtlzhU2YIiaFd0obMbGeXqq4Y/CB5kSU7Q8E6GmTQij54CTgpOZ8UjVlK8cKkMsZI13UXjkXOmZiFOqm0BqNBW7ohEBLoSmOrikXrxUk1ejBq7XALol09Oz1lcXbGlcuX8T6wXLaEGAlBMjpFI5suvKfkwCqhIUdBubMWvfFqsYSsCN4TYlijWF3Xld8FbUUHGkJkGASVkpyOQkEv6KtSZm0wNTYH3nuUNvgo2aeiXRb6sFamLE4IMqdQ8r0xOGfxvmc6neGMYdmt2JvNuX71KicHRxx2PVrrcn4s2tU4U1HVjunMApoY5BoQ1M0QvETT/MiP3OL1N27wD/7+P6ZrB+4/vEdKcO3mdYZh4O12Qdt3HJ4c8frrr/PhvXsMMXHjxnV2dndZtZ0gp9ayu7vDquvkuMWemD0o8HEo+xaxDpJPtIsVGbBOcePGdb76lVv8nb/9v3N6espsNsfamsXihL4dSFEoqzklgveFQRExRtyPx2iZ8asg+wpj1Pr4QqRfLSVDthhY9X0vzylNyhepqKpQyRkze8v1nFM817PKC0sTBmDIGfquYyiU4f3dXarKsVicQqEfX2zAyn1TFk02JQgkZNtQTL/OM4UlczcXzfyI9sq9CqCVLg7VrBdbnNUobdYLRxT0VilB+U9PT3jrrW8Lolo7Pv/5z3Pnzoc8efyYvWEXnSH6gflkwnK1om9bJk1Du1wwnUxYLBb4wVM5OS+LxQrnDPOdPdp2SQjDBY3Ed2tSxxd8UqnIpineD0O9yL/N29rWtra1rR/eeunP/W5+erXif/7zP8G/cfh/v3Ad6yfh2GbgL92+ffvW7du3vwq8A/zFW7duaeB/BP7U7du3vwT8P8BfBPi4576X+l4WxbVSQh0dKXoU3VgWjdaYW6hFDoouWq3v/QRcXLl/emX/WY/JZ1HPfe3m60BJnGiMYmRbtGna1gwh46Oiqndw1S7oCSk3xFhzfNKxWCZOF4lFq3nr3cf8+pvfYdkpfFSgLdY6rly6xKyuqWwNxqKrGttMuXz9JZqdPd7/8B6/8Eu/wrff+4C7D59w//ERi87TDol79x/w7du3eetbt0k+UFvL7myCVhmtpWn0fig6yIFMafY4N7iSBi+WJlEm533fk3MUKenoxJqS/G6GmBWDzyxWA6ZucPWEZjKRiXSZ9KcQ1g6sOYlOLwyehw8eoICb129gjCb4nugHIBPiQEwepcWcSZFYm4aO10eS7Es/DGigcY4UPCdHRzx6cI+joycsV2flM5R8Sy20VmMsWmUqIwZIO7MZO7M58+mMytlCiBWDnTB4rLFimoU4EJMSMXhOT0+ZTKf8rt/1u7l54yYqg7OWyli0gvnOjGEYqCY18/kcpRTdqqWpa1566SWGoccYg3UOV1cY49C6oveetu9BZ7RTeD9grcUaVxrCirffeod79x9hrWI2q1kuT1muFhwfH/MH/+A3uPXlWyxWKx4fHHB4ckw9nVJNGqY7c5ppTc6RnAOz2YSvfu1HuX71CjlGjo8O6dsV1kDlNK7SkCPWGb74xdfZ3ZuREQOupqmYz2fUTYUxlg8/vMO9e/c4PT1lsRRmACkV/W7Ra+aMUZrKGrTKDEPHMHTkHPGhJ/iWvmtpl0uGtiUOntAPWK2prGW1OKVvl+icSKGHFMkxkIKHFFE5ocr5W3/N8pocvfzLAXIgp0jwnm61om/FvGA0i+u6FSEI5ffw6PCCidwYqUPOMl6lBPF8HEsxEgbRmFfO0NR1uW7EgVtlyGvm8mgGB6CJqLXLti0xTGNjaozDFXdulZTk+SKLLIvFgrOzM548ecJyueJrX/vtXL16hYcPHnB8dEClNUPfUTnHcnGKIhGGHp0Ts2bCpK7o27bsh6IfPCEmtJHoG2crMZbaiBh6Om5oXRtj67Nizr4nGclvznphf5u3ta0fm7zyondhW9va1g+yplP+5b/0Gn/9yk9Q2+qF7sp3RVZv3759CPy9jYf+EfAngN8BdLdv3/775fH/Dlml/ePf5blPXOMka0S/NuMLPopwniNrH6XK5TWCJa/ccCnZeK/xtePy/jmtbnOfvjsdedzX5z3/8cis7JbKScxFgJxEy+WHQLvq4PIljDJolcgacUZVidNlC0rR+46zZWL/8g2GZFi0npA8Knruf/A+TW2onUUrQxgCmczR4SG9H9Yon7NOzIWUuAV7H3HGQI7U0walZYJ+89pVDo+OOVssCQgtNoTAarXEe09jrbicZrV2RU4xiZkS567NY0nuqinupkZcgNEsuoGzZYexNc10RgJSDmvad4oFWVfqfMKvFO2q5f69+7zyyqtcuXKFtm3poyfFiLWOnDPee6qqKuhjoYgWlFG0hLJNaxTLxQmr0nBojRglVRXWObRxNJMp9WRKiILGhq4v+ZS78nMI1HXD0dHxWhtorcFYjdbgrKNWDqM1y6VHJ0Xbtjy4fx9y5ktvfJEUljhrUdmii/5wSAOznTnXbl7n5OyM47NT3vvwO9TThtnODkOMpJyojEJpgzaG2k3Z2a3Y2dlBYbjz4X2UgrqpiEFcuB8/fsI7byluXLsEGVarFU8Oj/DDwNvvvM2XvnyLSVPxS7/0T5jOpkznuzw+OKDtOnbmM6aTmm7ocCbzI1/5Ejl6jg4fcfnKPg/uPyT4jqaZsLcz5cB31LXjC1/4PAenx6zaDu97+r7jn/3qP0UrXXTFhhiyODXnLJrMGAnRF92wKvsv7rlxkPOdR8QzJURLmklZUbmGyhpcuVa76KXZK45JRpm1y/ioqNTqHJUkJ3y5tkOQ3FI1XvNrOruMLylFcopYY4kx8+jRI5xzJSopMhSDrxhj0V2mQt89Zw3kFAt6KkZRWik6QGtfKL+yf3psemMSCXZmvUikVRZqvlKoQrUmScwRxawu53G8NUWfq0Er2q5nMmm4/+A+B4cH7OxMuXz5Mqvlgna1xFgHZPzQ065WzKYzVquVxBUpgzXy+VLOaG3IZJyri3uynONQnh91wOOYutYEF6rz0wuOz4shU2rDSfmHpF7k3+Zt/dYupRR/7T999UXvxra2ta0fdBnDT//lz8OfgX/34O+9MIT1e9KsllXZPwH8r8BngQ/G527fvv3k1q1b+tatW5c/7rnyB/YT1RpZ4KPI5LM0oc9afX++Zik/1Yxufi26r3RxEjS+x7O2/bwT+HSD/XRtat3ONxYFXSVh1s8nUlZ0/UCMGWcVORuM1hgDxiqcmxECaKu4fmOXaduBEVrw4D3ERBw8OUgzF3IiFU3f0J6tdXJOaXwIGCRztO1XqAyVVgW5jOQUSCnw4MMP5DglQXt00fbFGAQxihatrORSpoS2xYe0mLooldfHoKocfd8J0qIk3zOWRYjj0xUhCY1Za8fZ2YoQIkZZWGuVz2OKcsrFBTpwfHQEKFzTUNe1oL4prynJoDeyNRPWOJQC5yxVLW7GxyfHtO3pmo7ZdR0xJoyx1NbimgnaVszmM7St6BbibquNwdaVNDMxrM2oxMxInJtDjNR1JQ7M1pZ8UEtKgWU3434PAAAgAElEQVSM+BA4PDri7t27WGNEk6yt6JWdZTKbYCsnVGhryUYzn8149XOv0fc9k/mM7vSE6aTB1A5jKvZmezRTMDbhB89v+20/QlXVPDl4xPUbVzg+WnB6siTljNGGmzdusFqcksJAu1xgXMPx8SGPHz/gR7/yFW7evMEv/8ovM5vv4pzDWCsoX47UVr6uFmd85ctv8ODeHa7fuMHi5JgcPfPpJV595WXIiTfeeJ1r166yXC7JObFYLGjblqE4CNd1g/eBFEVjrI00O9KMlegVbUnJ45xcG10nGmPnHEPfkYMX3agVGvWkqXFakMQQvCzg5I385eImrss1q/UYSyUofsqigR6jtEp/unYOHjOEIxlSxg8BVWVyMb0a+oHgPcpYhjysF9akKS79Z2muc0EZhaYr9xRal/gbv84A1lpjnSzG5KQkwoiMs46cED2rktga0eKKAZjEcKU1VV7yYSt8GMT4zFhyzvTe09QVISYePniENoq6loaz9z1gqZuaoe+4euUaVhtOTs9wVUVIQT5fWVjKqrBdkOa1rht00KQwCEvi6YU/MuRipKf4SKTZ8yQdP8z61X/Rf5u3ta1tbWtbv3Xqp//zl7F/UmRqL6K+V4Ol/xpYAP8N8K9//3fnu9dHabPPrs15yflq+/jv2dscX/us77/bY98Ngd3UUY3PjbS7p5GA4sspyDIjGqzIWdH3HX4YqE0t+IcudD4qMRqyCqssykaUtWirmM0bMXZJCh0VBCX6RyuoVIyJEMUt1XtPzpm2aPXIxeAlQ8yalBFUJgz0fUdMmRAjylSgNIOX5mpSO1IMDENfaJpZJt55pBqaDZMl1k1aSomuH0BplHZoWxP7wGLVo11F1Uw5XfWEmKiqiugDSkszfJ57q6Cgszkr2m6gf/iIejIVdChvIvCQszSqKokJTUg91jSCPpPw/YBKgb39XZpJzeHhEZf3rzHERNsOVE2Daxps1VBVFaaqqbxH6ylGabRSuLoixUgDLFdLJnXDdD5jNptxcHAgjsIhkRDqb6aiaWpSjMTlkr7r+PA7H5Jjy7wRyntV12gNly5dYm9vj5OTU4Ycme7ucO3mS7z2xhvcvXOH6e4ux6sVrpmws7tH00y5sn8ZrTyr1QmLruP61Stcu3qZv/t3/w+08uQ8MJvVrFaewydHmAw6R1T0rM5OuXS14cnjR0wnU/7JP/4Fftfv/p18/etf5+133mUynXLzxg3ef/st6qZhcXaKtYZ3336LlCRe5ld+6RepXMWrL7+McxU/8uWvYIxQ1fu+ZzadcnqyoC2NZsqJwfv19RJiICMu00aLi621VqykkQWFyjmMVkQ/oBVYrRmyLFIoMq6pmU4mNJVDoQnF3KjvB2JMayQyRtHvqgsGQHLvZCQCRxY+NhkginNDJnlMF0MjY0RznGMilHtfEM+wzpVWZGmOyzuFGNb3x7oJKwtuRTiO1sJs8ENgMp1grabrepSiRAQNkDKJSMyQ0tjEZVBpzTzJKGLKZBTaVEymU5Tv8X1PRmPrRmjNg2c6bdjZ36NbLYje4yoHWtG1nqHv15T1n/rJn+SXf+Wf8u5772GcZWg7dNEOq9EUr7y71obKOXxO+NijjRoP+fprzvmCiOXjzJTyhWP8Q1sv/G/ztra1rW1ta1s/iPrEzeqtW7f+MvBF4I/evn073bp16zvAaxvPXwXS7du3Dz/uue9l5y5kBj61Ov5cE43nLJ6PKOrTjenT2/skk57nGXo8q4H+JNu78Fr5TvZVSSOl0GQFIUWJhfEePZuSU0A4hmPci0VrmQybrEjJklWgMgpXUExnDMoadqY1Va3RenMfMjknrDEFKRJaHoA2BkyFMo7aGfzQsViIa2rvPashktCcnJ7Rti0+BFIM5OSKGRBrivEwDGitsM4AMmlWahCKp5HHvPcoA1pZzpZLOh+xVcMQE4MPKKWpKovPRco3HjME/TFO9I29HzDWoijustqgjSP7XpoWJc1zikliQLTCGE3lLJUzLJcL2q5lOq34w//SN/hDf+gb/NW/+j9QVzOOjxc8eHRI7yMqBGytUMbQzCY0O1O0tvje0/c9ly9fIufIyfEJpnZiqJQD+5f3aKY19+7elbigXgyIuq5dI9ZKiVbw/Xff5aVrl8DVxH5guttgnKGpFvR9z9HpPYYEQ4w8enLAr/3zf07Kib0rV2hDYDKd0UwmXNrfxWhPU2v296/z4Yf3ef+D9/ja136EL3/l86xWKy5dqnn84JhJ5VieLQjDQBpEi9mvFqwWDTkbGldhjeEf/vzPo41GW8fepSm3bt3iw/c+4O6du0xmcxbLlhAib37rTeqqZjabUdcNqJbZbIe+79nf3+fugwecnCzo+4EbN27w8NEjzs7OqKuGyjlaL03hdCoxOApxsk4hEkMmZqHJSnOpaJcr2tUKW4yrUvAYBM2vq4rpdILRGj8EcWtue0FuS9OotSCVxuqRcLFGNnO5XzYXwQT1Ox+HchaHam0Mxpr1fo3jxYgKaq2JBcUV8yBIShrmGAIhCHKbU14blqEUqmg/M4LCiqGSKqZlmaqqBZkeBqw1hOBRSgu6unHfo5D80zHiphg2KWNAG1xdgzJkJcfFakMYes4WK5zVWK3FSEtrJpMZbfdkvRh3//597tz5kNdee407d+7Q9x1NVROS0H2NNoQQZIFKFZScc1d3+YRKEFhZxlubJT0dR/as8Xk8D+Niwg9bvYi/zdva1ra2ta1t/YuqTyTiuXXr1n+BaF3+tdu3b/fl4V+C/4+9NwmSLDuv9L47vMmHmHOqzBpRQKFQAAhwAommjKRavWhpoaZpI8mkvXYy00bWG+2kjUzaaaGNZNJC1jLKJNJo6k2zJYHsZhNGEiRRQAE1ZVXlPEWEj2+4oxb3uYdnVmZWFUmrLBD+m0VmhPvz59fD/d24557zn0P12muv/Ub/838G/O6nuO9T1+OMMx4np10tRGIMTwSMjzXo2DjfphHHJ+3Af9K5Hv1a3bd53Op5Pma0xCNL39jzN/3/TddSdy1Ryv72JKOLQSZXUCHINOSZolASjUR4gYwKrfIUqxINbbfAtA2ma+namq5dYk2N7WpMV6NwZMKTi0ClYZQrdivNTqEoRUS5DmlqfDPHLufo6BDeomIgU5IyUyh62SJpZbx63Snr0j0k3zMmgdWySECmLCuq4RClc+bLOjFBQrGsWxAKpVK2m1Y6MUeZIi8KqqqiLHuGU2m0zolB4AI4F7EuIKVmPN6hqgYAuM4R7ErqmT4LtuuYTac4a1AShAhAx3PPHfEPvvsrTCcPWC7mDKqKPMtw1lHXNTEGlJYUVYHMFLuHh+wdHdJYQz4Ysnuwz6Url3n1tVfZ2duhMQ0ykwx3RwzGQ3Sm17mzmc5SJmhMfYfz+RyI5FJTZhkXjo7YGQzZHY37vtgx5y9c5OKl58gHA1RRkFcVu/v7vPKlVzk6OkeeFVjbkOeBaqjY2R3xq7/6y3Rtzflzu/zjf/e3+MY3X+Xo3BClPBcuHDGbTJmenCCjRxGpipxmsQTvmJycIIAyz1kulsyncx7cf8Aff++PWCxrimJI1wV8UPz0nfeJMQHA4+Mpp5M5xnqMDfzRv/oT/vwHf8W1j65z/foN6rpjd/cAYxx13SaJdJZRVQVZlq1l2zEGoogrLUJiOX0gzzXeGubTKaZp6dqWrmmJLvWY5lpR5lkCscbSNA1N3SYXZKnJiwqd5egsJyuK1EetZG8alvp/Y0xS8ygiyNT7iSCNJwYkAiVkn3kqe4l87Mfu1tf/ar5JPOOZIZwzFtu1BO9QAhQCJRITKgjE4BAElATdS5OVEhRlRlXm63MMqpKdnVGaVWJAhAAhEtdfyUnZGEcMArG2jUpfnfUEIZF5DlLjgkCoHF2WCJURokjXZwTfv6YLF1K8kDFJqfHBBx+ynC945aWX8T61CWzWev7tpcGp/SOmWKc0E/YM8EqOLTfR9ifOzX9f61n9bd7Wtra1rW1t6/OqTxNd8wbwT4F3gD957bXXAD54++23f+e11177T4H/8bXXXivpLfAB+t3dx973WerRhcdTzY16A5M1OFzvvEOvDaT3LnpsgMGTpMB/F/VJsTgfY1gTsiOIxK4SJVEkR9DOWmaLJdbZ3jGF5GQbST9H2/d+CbQUhKiwXepNyzKQwhGjhejRIkN6EEh0JpECrOuIBPIs73sAJSrPkEKCd0gZ0QJ8W9MtZgAMiowgNRhHriUhSKIUSaoZEqhAyiSJlTL12kVI8S2JzQnBpyiWLGOQ5WRFAJmx7JJ7sBAK6zwh9tEtURBiSNEkgBISITRCpkxWH0IPXMB5D0IhlSYEsNYTvCfPc8q8pGs6jDFrAKRkYnGapgYRyDJNlkn2doecntzll37xm8ymS/7yB28xX1p0NsALjZeCvf09dJFTDgcY61FZxs5wwK1bNzmZTjjc30sO1RL2DvYJwTMoS8Y7I46Pj7l14xbWOqqiZDgYMZvOqOsWSSS4ZMDjXEnbtIwGBXlRsPQtMUR293Y4mTcM9vYxpM/KcDAgtoY8S5LpK5cukRee557b4f6921y/dpW9vQu07ZKrH7zHf/BP/gGHBwXBdbzz1rvs7g7RWtHUS6pxSbusGRQF07rFmI4YLffsXYoip8hzWuNomprr12+SZSUHhxdoO0ckcHI6Y293h6ZzjHb3mM+WlGWFj9BZjzFJhrtYNqiixBhLnhV4H/p+SNGzdX2f4sr0B5FMlmLqYw3OEaMieE+3ikXyAdG7RbvgqKoxo+EgSevbbp1tCiBkMq5y3mG9S33V4oxFPet1JslnV1ftphIE+v5W1R/X952GXtgvzxhW+nlO9Js7PqZjvfcE7xLDq1KWboyCGMCHnoXtpe8g0zQnkqw2xNRDOp8vMFYxHA3TtRIC0adrjdWUIfr5UwiUVsmUqjd1E1LROY8Q/bUmJIgU75TpAiUETb3EdB1FpiiLDEQC5lU1oKlrrLVMJxOOh8e8/vpXqZsl165fQwhBnkmsM0m6DxAiPiQwH8Nq/t+Ymx+eWdmc8zfn07/P/amrepZ/m7e1rW1ta1vb+rzq07gB/5jH4zvefvvtPwG+8Vnv+7T1uF3yJwLWXhTY/9D3ep5lW67uSuq5xxsjPdpX+rSs1ceN9WnHPel5Vset+trOFsG952VvsLJaTFrnmS+W1J2h0BIhk6RPSRLrQjJeESQ2R3iRHG5FWvCa4CFafJRo5ZLZkoRhNkBpBc6jtSTLS7x3KKl6djL0fX/gjKHrGpyzyL7/zltL1zTYzmJaSwSctRSDYTIz6mNhokiyZec8umcNrTUolaSFK2muFBITAp31dA5cFBjvKfIKIWQy/okaoQUq5oSYQKrr805DTE6vKRYjg15WCMnMhQC27YgRlNSMx2OsqVku0+LaWpBapv5I56iKglE5IljLzt6Il199ic7Bj9/8KdZB6zxltcPXvvY1do8OaFzNtRs36VrPaLTLCy++yGwypa5biJFMCZwzCAJltUs1KLl3fB8XHRBou6Z3s03RLV3XsmxqZoslFw52WTQNjXWUo5yqLBgNR9y9cw9VDdjb28H1ebHz6Yx6vuCNN16naRryvMDHOa999UW++91v87u/+wd8+P5VnHN8//sLvvsb32A8HnL+/BGDYYm1LcPRgBihKCrarkVnmkxp6qZlMBglpty5BHKcRwiPrARKKrIsJ89y2rZGKcntuw+oqoovf/lVnL/DYrFEqAyEoiyH1PUD6qYl98lZt6oq5tMJ3eE+nUl9kyGE3qHaYboWQurHjH0vasQRg8baFAkDiZmWCHwMFFnG3u6Y3d0x0+mcpm16BjD1OwfvkH18Soip1zqxn2EtTV0xoissylr+2xsw9cBaInF9z7cQqzngYeVFyvaNPeuaQBpxBYpDn52a3METo5jirXwPWpOiJG3opGBmm/KOTZtcc32gbVJGcAzJPfgsd7VXcggQSvbP2cvjSRtFaeMpEnXqExdKAQHn06ZTUZZ4rWjbGuuTC7QQguFoRNu2/WaRYDab8cGHHzAaDamqkrZL+cSily8TUp5sCCuVzFkG8tncGDjLLf644d7PC1CFZ/u3eVs/3/XuV78G451nPYxtbWtbn0ctFvwn//X7z8xcCT67wdIzrxR8/7BMd7VAXEVTxI2d+LRhv3bveKg+q1zsb7IISiYtKzfgzdtXrNDq3ABn8TsryV70K/OU5EQalOZ4NufGvQe8+NwlNArvAwJHzBJosx58jFif2Jo8EwjpEpBQAkSBc462tYxHA4oiXxs6yT5eZVkvE1uZg/cOLVWKaokR6xw+StA5UelkrhSSe6gUK9AZiFITjCdKj8wyogAbUtwMIa1QtVasYmBiCEmX3pvEWFFw77SmDZIgNJnWlHmeWF4BLiQWzFgIdiVBTL9kpTJ0rojCJRYKkD3fLiLJ/VVEvIwkLOH55jde5fqHt+g6QeMdNgTyvMC2EybHM259eMIbrwnq5TFyqHjpq6+wWEy5+s5P0VExKA7pZjW7zz/PaJhhwpz5SY3AsLczZFQNmE3m1LMZ0XfQGYQMLBcTlq3i+ZeuMBiU3Lj6Ic50zGYPAEleVKhMgRbMmwZRlSy95fr9+1BcQOaS8XjMvdMZiEC9mJDriCJjeTKn1JqTB3dARWS+z3A8YDQsefHF83zn179D0/xr7t+7z7Jp+L//+f/Ht771NS5dfhGpCzpnuX33Ns3ylH/427/BaH+fe1c/xLlI2y4ZjUeU1QApU7yLCBBdyv9slw3WeTItmbRNYukzzWA0phqMKao5D44nHBxW1ItlMtzqgVdVFOAdmRRJUdB15DpncTojyzWmnuOsS462vmfrrSVaQ6EVmRK07RJvbQ8cNcSIVprhIGc8GjAYlHx0/RrGtH3UkEaodC4XPCEmd+y+PbTfUDrTZqyuaUGKTlJa9XNEH1vke8feXgIv4KEe1yS/7QF2iARnU593D3ozKQH9EDimn0sIAhHTJ1rEdO5V3JJb/UFZjVlKgiUZF2nVy+9BaYkISZafWgk8q6Bh0c9RRBBeoJPeg4BDZ5q8KMlyjZAC7w3WGqLU1PUSt+jYGydH6nI4YrlcIhDcP3mAi46LFy4wGA4Se9u1Pducfi+rnNq04ZbGvprPU09674r8yIT+JLf1bW1rW3+39frB8xz+mlzPFdva1rb+ftd//9/c5/dv/8UzHcPPHFh9XKbeGRv52Ac89RxPkgU/jT39tC7Bm7d/Wpb2IbYAeqfMtEDzIfVbBuDuvbsMi4Kj3R2qTNF6Q9O15FlGWRRIEYHeQEkkgLYyIEo9kBCkp8hzMq3p2hqpFXmu6RqHdw6hJFLkZCqteoNP7qPWB4xzRJGyGOu2ozOJEZJElBQpegYJQq5dTIUUK7V2Yl69TyA1BrxPmauJwU25j62VLJYNJEsc8qxMUTBSorO8H0fAeYfxiU1d9c5JIcl0RqULjLHpdxsCzjqU1hRZjvOGl7/8MtYEPvrwKi++cBktNLfvzBgI8Ei8jyxCQ7Q1P/npe7z+xhV+4Ve+RDlvISq+/o3XWc6PuXr1BqZZ0M5nvPPmD/n2v/0t7gjYGQ+pqjHGpXzL11+/wvT4mJ+8+dcUOSwWS4KXVIM9Dg4O+NpXXuPm81f44+99j9ncEEIgL0pUprHWMFsuOZ3PiFJyupgzXu5QN4G8qhiOd6htiw+O0XDEuf2L+MbhXUO9nLPs5mRFxc7uDtdvHvPSK5e59tEN2s5w+crz3Lp5g+XC8OEHd7Emo6pGtK3BB1gsU05mWQ6YzRdU5ZDhKPVBVmXFYrmkqdt1buZyPkNnBfP5nM505HlBUZa4GKmGQzpncSEk6alShBgQvcS7LCsuXryIQDCbTGnbNjH2nSHGiGk7gnd4Z1MvqNJAWPdeBi8xxtK1ppe+xp4ldBSFZv9gnywvuHnrFk2dooSkyghxpcYIG6By1WEQ14B1dY2KnuGMMblZryTAAN75fhNG9SxlOtPKIIgeMMbg8b15UkbKAl2fWwpgc3PujE1cbSzJ3pxosw9+NY71PJcG3QNn0cun+7lHxH6O2VCiRNLGRq/G6HXLyZjJe2zoI22yAfuHB5RlzmIxZbGYM5mcsphNWDYNWmsODg9p2hYfknHb6WTC7t4uO+MdqrLizp1bWNtL9nvp/lPVNL3C5NG59dF59PFz9N+vnNVtbetZ1H/Ly+jffPVZD2Nb29rW51RfBBeInwmwuimPfZwr8N/q3H+7oT393L28d9O86dNE2qxvW62OOQPVIQSCkIxGYyanpzSzCc9dvIDMQKgUadI5R5HnKJkkhkrKfqGWDGG89ThvsSJlUAafJcYmgrMpP1WrlAEZnKUNaTGc6YwYoWkaul42qbRESigKhQ+BpmkT4FSKXClcL6d0ziG1gv69FMFjnEWp9LsJ3ifHVZlYsFJXzEyXHidLApKirFL/qEqOvt2yoTMdxjs8kkAibEOPzn2/cNdZ6rk1xiIkFGXFcFDy+te+ja5K7t0/JsszJvMZu/u7TGYGESJBSOplnSTmOqMLjjff+hFfef08o0JRz2tGwyG/+uvfZdF8jw8+vIsMkrzQzO0xR1ee48UXv8K5c+d474N3GY4Kvv3tS5zbf5Wrv3yF2zdu8ed/9le89dZVDlGEw8DOaMw3/73f5NatGyzrJctlw3A8pKyG3L/3ANt1OOexPtBZx+lkiosZQUn2Dg8xp8cUZUGZ52RKMh5WTKdLpqfHyExS5JrxcMwHV69z/vwF3n//GvfvHaNkwbmji5yezJBCoVXJ5ede4Id//WPKvGJ2eo83f/gW0/mMoiipqgqZZQQfmE2nWB+omxqtNUVekuU5bZdMs7TOkhRbSqJLhjtaa7IspyhKYoy0XUdVVcQYGY/GxAiTyWmfrVqwXNZ0XcoRbZYLondA+szaaBGk99tHwCcDIu+Tc24CcALwDEc77O/tYazh3r37GOuInMUZpf7R8NC1SN+zunnNrq5RJZMyQKkEGlemYWtn75g2KYQU6555JSTB2f5zn1yzhRD9RtLHN8RSF4NY8bEPjS0E0YNimUD/amNIiIfmzZVpkRAimUGtxhg3DOV65B1JRmNKyeTOHVZ9sayZ28npKZPJhAfH99nZHVEUGUpJ9vf3ONgb8+DeXWbzOVJILl68yI3r19ctGNPZnG987Wscnxxz5+6dfk4QieHlbJNgcx49K7m+7XEGdpuqm83f5ZZ13da2trWtbW3rs9Xif/pj/pfl/FkP42cDrMIZYN0EdpuLxk+Tdfp3MYbPeuzqIQ+PL0HPJzkXb/zAqr/tjE05c8u8ePkys9Njbty4yehgzGh3TJWlvFWPSD1sQhBFcviUkiQfFAHZy4eTV2/EWIMPjj76kLXDMg7vWS9YjXHMlzXBAzK5heZ5hvPJgEVKSZ7L3oxFILygM5YQIjomyeL6NUqbXHv7vtsYI0oq6Mfdmo6sLPAhI5MFUmucT+AjEySzF5khVJLxJqJOIYUkhIjxjlzq3jAGQJDlyRF5OB7ylde/yjtXr4KU6Dyj7Tpeffl5bt4+QXnQOmPZ1AQS+xdzzf2T+8xOHyDJKQR4IRjs7PDd3/4t5v/8e8wfPGB/b4/7N25hHRTZHgA6Exyf3uZkfsiFSyNe+dIl3njtMpevXGIy+T84PZlz7+ZtxsMKncN3fv3XuHDxAt//N98HAc9dvkxZDfjw/ascn57SGIvKcpadIR+WoBTGNYxGI7rOoBDUas7B3pjJ8W3a5YKjC+e4ffMWo8GAZdPxb/7VD7h94z7N0nLj2i0OD/fZ29vhnbevcv2j2+zvHRCjQGc5ddvx5o/eQinFYlFTFgN2D8bcuXMX70JymQ4erUuklnjvWSxqrPdU1YAYfMrwJbJc1pycTLDWgoDZbEae5xjT4UOkaVum8xnGecpqQIwNi0VNnmcMd8achEDXtPhgCSFlhkqSFFf0wNO7SPAiRaooiNGjtWBnPCDLcu7eu4d1fr2ZFFbS0vgIs9eDVCnl2oBJ6zRtxg036xRR44kh9XWG6NMmRy91VyqxksGn/lQXklRXSYFApb5Y9whIJo17JczdBGhnc19/rfWANcsyjDFr0Pbo8atNr5Ur8dr0DJKzeD87xZj6VQkBLSXOOlxMm1CR2Lv5RpZzx2x6gtaCPM8piozd8ZhLFy8ynU6Yzubs7+8z3NllOV+gteD0dMJHN64Tgqc1hjzPWdRL9FpWuIL1sZcxb2zm9Xc9Ovdvzrs/T27A29rW51n/8XO/xm/+0wvPehjb2ta2Pqe6/d4OH0zffdbD+OKD1UcXIqtF2GbsS1psne2w/20XK0+T+372ehwD/HHp7+PHcLYYEz249d6Ta0XXG828/PLLHD94wL2T+5zO58x3Fuzv7jEclIyHI4TSeGPweGSUSSOLQqqIj5ZlawkIQogEPBkiOaFaS4iSsirRUtLUDV3d0LQOY5K7b4SerZG93DGNdZ0lqdLi2XmP6+V9K+dRHwLChSQFFkkqGQJoLZFC4aKgbS2gIMp1T6TWmqoaMhrvkDUd5vgUZ+yawU2GUzEB8eCJvv/M9EC4KAqUlrSm5ff+4Pd58UuvUg0GHJ47YrZYkuUFUgmC9xRaI6UmywqKQuEQzOcLXNOgtef41jH7V17HSYEaVPzKd36Zt37wJrPTCYNql8m9E37Svcl8foVXvvwCUxt484c/pVnWvHD5IrvDgqOjC4zHexw/uMViOeWdd37K1Q/f5blLlxiNRvzir/wKP/7RT/joow8ZDMYUVUndNHTG4ELEG4NTHaOdHWaNYTwcAJ5xVeKWlsV0julqnrt4npdffYV/82d/zVvWURYZTdPStZbZdMH+/h7HD07wLtA2BiUymqZLTJqzZFlOXTeMRmOstcznC3b39wk+ufgWVUmW6fVn13mPUJKubomxXufnms4wsRMEgmVd0zUNPnNn8S1SoXS27vN2ztO2Ld46ikzjrV+7N8cQiPHupWEAACAASURBVML1fdiiB2KJh0yX1MpZLSBE5Py5Iy5cOGKxqJnOFpA8llk5/CTlQt8NKfpz9WA1xDPQugKrwXtCcEki38uEsyxLfdhIYkwZokJC9A7bGawzKCF6ICsRUiAJBOeJ8eFMaVhlp6bP/8rASSmFVrqXT6fPfdu2eO97afAZsF7POKJ3Kt5gZvs2+b5PHlQP2lkDwDTfiNg7AMeU+erjGRNMSLE63llM9Ji2Zj45ZXd3h6oq2d/fx3vPzs4OXdsRY6Czho8+urZ2Hl7WNVmW9Wx5et6zOfiM1V0b5z20n7cxRz6m5WLLqG5rW383Veicf78rYTx+1kPZ1ra29XnUdMofh91nPQrgZwCsPqk2+7LOFiqfbmHySRLiv+0C50lRCp/EpG4uuB5mYVcMbb8QDYGua7G90+n58+eoRgMmsxnT01NmpzOGwwF7e7vsDEcopVJuZx/zkcxjEjvrZgusryhyRUYCZMEY6FmUECXGBdrOpkxMF3tw228WELDOIUSKxcgzjdQKay0iCsqqoDWGYJLrcOzXmlGkL2NtH7OT+hYjAhfBhIANkc44ZDFA5yURidI5SEXdGhbLhtY5IDFXQsTEqgV6R1UwbYeUIi3EvYEgGQ93ODjYQ+UFg+EYY22/EQBN2zEajWmmC4TSRCFRWUk5KDk4Ok9hBaZ26DLyzls/4qDJqC5ewEvJ+PCQN771Td78y79mOl+QD0fExZzb164jgufKi1e4/+AuP5x+wJ2bc4ZVxXK+wIdkGNV2SybzE4qyJAbHaDgmz0t+6Zd/iR+/9Q6z6YIQApPJklu37/CVV19AykhjWgKevCh46eVXePftt4hVgXWG6fyUBw/usn+ww9HhAYOyol00BOu4/+BBAvXe451H5Rpn0/MulwtOT6cMBgOUUhRFSbAdsneHbtsO07aMR2OsnZApTVBnMkylJNZ6TO9EnZE2LrRM8UDL+QIhBFolA6GulwHv7OzgnE8utDGuQZhWCaw1dY2xrgdwJAUBq0zQ1bUne+OiPt5ICqqq5Nz5A4w13L57F2MSMBKrnlLRRyjFFchbyXbTvy4kcAlnr3GzL9S7FIWU5xmxzwhNitWQQGqXopGkSAkzSkmInuBSH2tCZGduw0Af09OPou9BX0l88ypnNBql68U5lFIsl0ustSilqKpqLb9fXaurc602wdJzpDkmxhVoTz2sSqY7pZAQEmPcw/kEmnt/lbRRGAgurlljISKnkwmzuaIsCsqypCgK8rJgsVgQQrrucWa9sZGpFD+06QK8eh/OwOnqPfm4JHuz1eJx82uSTPvHHrOtbW3rk+uf7fw6/85/98qzHsa2trWtz6n82x/wn9/5wbMeBvAzBFY32c7HAbxV3MHmsav7P825Pi1A/TTHP8yOrBZLDy9Ez8YOrGMkVtK3s3PH9OB+IXnmL2K9Z7lcEuMhIgrG1ZCd4Yju8IjpdMLp6Sm3btzitky9Z6PRKMnxhkPyIifXFfVyjnUW4yI+espSowAldJJFypzOx5TXisQ4i3VpLGEFwPuFsPcWISR5rtMC2Tu0SqySzhS+cQkYyNQHF0LAWouWClGKXgIs8L2m07pAaxwqy9FlhVA53kdOJjOIM4KQoDKcDeTFEGcsIYJQEoGHmGJ3bDRAQASHEIGumRHsgK7LsXVDMRjjvacqBwjXYFrDufNHPJjVdMZRNx3eeHQ24ODwPEOf85Uvv0GlDLb5f3nzRz/mog9Q5IyzETrTvPDlL/HjH/2UIi8gRELbcfXt9zCNYTAcUqiS2zdOCeE+TVOTlSX7R3tMJlO0lhw/eIAiUOYFdV1TFiMuXbzAg/sn1HWDjJ6T00mSsGpFoRIYe/DuR7zxzW8ilerfi4z5YsFyMWNvtyLTiv2dHd59/yMuP3+ZqiixzpDrHZwzyDKjrpcsFoHFYk6WZWRZcoEuy4KY59R13efewny+YDQao/p+yTLPMdbRGYNwcd0jKaVc96NKBMPRmLJKzz2bpR7cqioYjYaMRiPu3UvxNt730uGYzLeC97R1DXElr9UE5xESvE8y3ExnRFL/IzGBMSFgZ2eHLM+4desWs+kcH5OBUWJ80+c5hE3gkzY7kmxdJLAoSJFA3veXY3LxhXSNiBjw1hKcwwdL7CXFKY4mgUwpkokY8Qx6JfCY2FupBLJXUcQQiD0Is9aipCL4SK4zqrJMAF4obGcgJKfj4D0iJoMxKQV5maXHO4v3AaQCEddqgzMzJ5lkwH2P7mreCTGihFhHyqRRh14FodazlJS9zJlkHCVler/quqZtW7IsoyiK3s3d42LqJ7bOJkfnEB6aG88Mrj7Okq42Jlfz/aNSZ+Cx4HW12bCtbW3rs9c//C9Hz3oI29rWtj6v8p7/8P8Mn3zc51RfaLD6NKC5uv9sgRI/dv+T6klg9uFesKc/95OOedyxG7eux7kZZfOx04jV0b136Mb9UYDsTUZWrFOuFFnPwpSDIeNqwIWjc/gQmC5mnJyecO/4ATdu3URnGePxmJ3xmMGgoipLlp1huVjgvUVKkDEwHpa0ozHD0iC8ZbFYYI3FO8gynRbCef7QIjMEjzGJOVNSIoWgXS4wrWFQlkShcD3bk+f52sVUCo3AI/oFcUBQdx7jYLizT9QVUlU0bYeQiqOjIzobmCxqUJKIRKuSIBL7I5QmeoGzhkFVQi9PLHLFL37r2+zv73P91m1OJyf81lf/Eddv3EAGz+LE8M7b7/DcC88nN+QswzmH85EsK2kby2JyyjvvfMQ//kff4Xd+53f4H/73f8E771/luZdfwdYLhHOUZcaXv/Y6N65dQ5HY3UxnYCxdXCKiYGd/j/lyzulkitYwGA1YLGYMqjF5VjCfTqh3lzz//IvUy47bt+4xHFRMJ6cIBKenpyzqhnPVmNYsOdgd0LU1IgT29g/Z3x2h9wUfXv2AtuswznJ6csx8NmNyckKInrarOXfuiP39PW7duU2MnsPDIx48eEBZlmRZRts1BFwCKz4ZcKW3O9J1huEwgaq2bTHOJZV53wfpvEdrjRCCuq4TQ9pHMNX1AtN16bFNjek6pJQs5ksQEmctxiYDrmZp2BuNqXvmUEpJphVZpvHC44ODCKqXGofgiAKUStdzUZbs7O5wcjLh+OSUPhQFH2OS34ok/4W44X6brsMkUU9gNkVjpeNW2agCIER8SJ/7GD3ReyIJ0PYNor3ZUnqcEqnfdb0RtZoPEOvJYGXOtgnAQp9LLHtTsvl8ju/zaEPvthtWyoy1rFquQaXAEUh96lKeyRxWvayr17w5D4YVkF47eqd5JjkKy97Nu1eECLGe4jZlyKvxp/dntSEXN1hsCP1tYj2Sx8/zZwoa8ZD78qYiZdPfYBPIhvjF+cO7rW39LNXVr7+OOHf+WQ9jW9va1udY3zv+ybMewrq+0GAVng4+Hyfn/SSG9EkmTI9jbJ8k2/3bsLCP9lU97nkBohDIR8BxAIiJuXAuMF8uqJuGYhXPIQRBpMiZQVUilGS8O+LipYvUzSJJ86ZTFosF16enaJWhswytNfv7e1RVwWw6YTwYYBw8ODmF/T3KTFMNx0CNsTV4z63ryZDn0sWL5Fn+kKELnLmPWufJvKdpOlyAcjgieo8xZg0uYvJjJUbwEWyEeWuQOicgUSpDKIVUmr39A0KEZV2nGBqVYzuPjMlQyvYuxStWUMQUhTMeD3n5xct84+uvU+QZJ5MTpIjc+OgaOzsjdLTs5Bk3rr+PaZcUuaYJAakERVEghKBrWgSak1nHrRu3uPLCFS6cO8fb9+9z/cNrPHf0PHmeYZuWc3t7XLQXuXntJm2zYH9nhGkmzO/PuXdyzEtfepWsLJjPTtjZHbO3c4DZaVguWkbVkN3BENdZbl6/zuHBeaJ3ZEKyN96hbeecnBxT1zUcDim1YG9YYNsld+7c5sLFSxzfv0e0nsZ0dM5ireX4+Ji7d25T10uyImMwqgjBpziX3R3KsqKta5bzBVmWIfPE/K1IqmSelYNIgDBTmvFoRNu2LOoGfCDPK3ReEEKkqkrapsWaFq11L8V2TCen/eclIqWgKgps7zA7qIacP3+ByWSC95Gu6djf2+fcuXPcMh2RmCSoWiFVkqm6kNxrtdIpnigmyTmAVpqdvV1aa3lwckJnLFLmG0ZCJHMvREpaEj0U6k+wMjeK0RMiKKlRpGxRYspJTdFSvgelcS31jfRsqhbrDaYYY983fvY7Xd0eI4iQNqfERi8pkTXLKoTAmJbp7DT1Cudlf93Rmx6xNlxKJkkGaxOgjSSgu4rPiStN/mqO2ZAHrwGsoM++jfgYUf2YhZK9k3diP1dgdSVp9iQWVkq1Pr/3/uEYHuJZtq7z67ltU6b8qHneChh/XLny+Hl887YQnvy3ZFvb2taTq9xz21zVbW3r56jm//Of4MMXZ4P3Cw9Wn1Z/EyOlTwtmH3fupzlPfhrA/OjtTxyL+Hj3bVz/uzKCSWxW27YwHuO8I9eSECFaTxQRRYaQkjxTDAdHHO7vY62lbRoWyyXTyYy2bXExUBYlB/v7dG3DV77yFaxpmZ2csLd/wKDIIQR2xju0XUtwHmNaqqrixs2bXH7uMkeHh2vZp/cBYwxaR4SwDEdjQojcunsf07bovFgzSN4HvHMoLXvAILEOms6isgKDYDAY0hpPQNB1lslkgtAapUuMD708OrkRZ2VG9Ku8SEkMFiE03lvu3zvmz//sB/zGb3yXUVXiuoZ7d24hxAWCaSkyxc5oCNEiZKBZNhjbMB6MGY3HNG1DaC3vXbvF11874kc/eY/FZIL2AekCi+mCvMzJ8shgUNKNhoz3Djg+OcbZhrKUOFPjuwUfvPsThjt7yCyjEPuEzjE/mVLXlkuXLnNwsMvdO7f56P0PmBzPKKsR9aJlWJWUhSSGluPj+7x05ZBBLvAGSgX3bt/myosvcTKZcnL/mOPTGaPdPfYPD5FSMZ1OEgNfFpRFDoS1RFNrzWQyRwpFDMnIJxcZbVvTNA2ZysmKHB8c1iTjoxgig2rAbLFc90ZDYipTTqdCxYhWCilFMtuyBu8dWmu0znHBryN5YlHSNg3WplxV7y1FmWOtoe1aIIE46wxd6xI730vd19eOSOZaQgpG4xFSKe7fP6btLDIrkpP1Snq/Blmb1+WK6aR3wY6JYVWSPMsSW24C3tteWeAhOKJPRk4SkqH1uq/+DAgmNvDhPwCbrOGjG1xCfHwu8v1mj7WWGJI78cqJO8ZVVE/fh7txrFQSLTKkkmljyCeOeQWYE2Mq19fmmuPsew9WgFT2kTZpvB9nUAFWybJi9XpiwDuLWLHMpNic1CYbCDF8bC38tM3IlZpmc/5dveYnPW5rtrStbW1rW9va1ifXf/RugfH2WQ9jXV9osPo0MPrkBQxPvP1xxkeb32/e/6Tnflxm6pPY2ieN+RPlw08Fu2d5kCvAKnqDFOOSRFNIgUIldkZAcBbjLJlSDIuC3cGAc/v7LA8bZrM5UdBnkYJC8MEHH1DlmqyXCrZNgwiRvMzJi5xFO+ONN97gwoUL/N7v/R6z2YyXXnwx9S2WJcZYJpMpzjmqCr717W+TlxX/1+//AbNlA8heopnYV+8CQmukiAipcQGMi5gQGB/toXRBN59S1y3OWsqyQuoME+mdVR1SQJZnOBdp64YQIZM6AVYREUJRd4a79445Pj7lm7/wdRb1kus3bzMc5Bzsjrn10XX2dgu+9OWXuP39N5ESQnR0pkVryWwxQwu4ev02uwe/zdHRCRePpsi6ZekFvvV46cg0FL2jsCor9s9dYH58JzG+UlMqjc5yunlN0xnuXrtLlmUIQOcly+USQiC6wP7uHvfv3UeICUU1RIrkslwNhjTLGms6Llza53Bfc+nCOYy33Lx5i/mypjWORdeRFwVZUTBfLmialqIYYI1BDEuIEWsMdb0kxob5fE7bJMOevMiA5Ha7yupNrB0QkylS27bpcxki3kXqusEDSmmM2cgdDR7rQjIfCoFgHVEkp+HpdIqxhtF4JwFRa/o+Rp8iX2LAWkOMAaUVRE90YS01lSI5SIfgenffCCJQVQOKImcyndB2HalNU/RS3+Rwuymxl0JtSG9XDOsKNKZrQQjZs6ms3alFTLE5EQs9m4rYELT2xmEJnOv0u/JJNrx6jsRkrpjKzfnokb5S7xkMBgyHQ6bTKV3bJdm9Umit14DVGLN2BtZar+X6KcuUlZKWGM4iwKTuAWh6wUhBkkr3LKuUyb14LQUWaxXzukIPItX6dxfXwD/6M/Y5/b5BEM4ky4+wop80/wvxccnvpjR40wirf0nb2ta2PmP9V8/9NuN/snUA3ta2fl7qX/8X7/IXi/ef9TAeqi80WP3YSggeWpg87dBP6jl9+tM+Gbhuhs4/SSr8ac6/eY5HQfXZ/fGM8RFnj13FN3jvmc1mzOslVZ4jicjkF4PzFo1GAL7v81NapzfcOrx3KGA0KGmtYT6dcjo5Sb1pzhDyjCLTzKZThlUJ3tOZFpVpikGJ9Y6TkxOOjo4Yj0ccHx8nWWWAtm0wnUFJRVUVfP9P/xSdF7RNi1IKY21aWPZyRR88CTWk122sJQC6qCiKitPZnOWyWW8USJVey7isMC4yHA8Yjyu6znLv7gOWTcA5jzEdMkoyJdFCEgTM644fvvkWv334Hb762qucni4hGKpCce5wjyz3HB6M0QqqSjMaluRZSTUsuHu/TpJMF3jv2jVe/tIr/LPf+yNmNqCH+wzyfeq6ocg1Fw73uHPvlDYExgfnMO0SKXJa0xK9pp5ajk+nWJeYzVgEiiLHR8vNj67RNi1SRsqyQiCYTCYUrWE4HjIYFAyHGVVV0TZLhuV5BsNdzh3s8hc/vcbpsmPZdFjjcCFleR5Ppty/f5/BcIjWFd4Fjo9P0RmMx0NGwyEffXiDtvM4F1BKM5/N0ZlEotBZRvAx9b+a1F9qTMd0OmF3bz9JTk2SblsfcL5NRjrWUuQFeW+w1DYtpmsTqAiKZlmjpGRnPKasKqq8QAK2TexqnuecOzrH9PQYaw2d6QjOIkViK7XWEB8GJlIKiqIkrwqWXcOiqRPI74/NdW+qFAI++DWolH3e7wpgS6EQGz2dkfS5ktGjdW8+1Zt5eWtwNqSNl0jfGfqwCkMpRVmU5FmBtRbnHN669L1PETwPGQPFZOAkSMzxOt+1B4RFUaBktjYrc9amjYksW7OqK7VDipBySdYjV0ZPadNrsy82yYPPNsYgsdQp0kaikAih+ozlx2ysRdZAmN6dewUiQ0gbCXFDAp7AczLMekiT/IQ5M8bQS7TPgPDm7/jh+ZOHXtu2trWtz16vGeDo6FkPY1vb2tbnVD/OMxamedbDeKi+2GB1I2YhrTc2WYeNoz5Db+mnYTcfd/xnve+h51ovWs8WZOLsoPTfxs+i73g7G+NZNI+UChECSE0ksmwNyyYtfKtcU/SS3YDHdYZ8MEBkeXLL7dkVJQWZ0HTW0HUdArh4/oi9nUFi0IgMhyUxBLwxafGfl1RVgfWWECNdU1PPZ1y5cinJb3vGzRqLwJNpSGYzLmXC1jUSWC5r8rIiCtkD1ZSJ6mMynPE+0lqP0DmD0RBjLXVbE6LHeIfKNarIyfKczliUzvit3/xNjo72+Rd/+IcsFnNiCCgpMTZFkUgp0ZmmGA6xpuXeyYyrH3zEV7/6Jb759dd47/33mM0yqmHO5StXKMoBL7/yAncfLDg+eYBWaaF9cHDEZDrBesOP37vOxcvP46KjrQ0lFtoJeSUoQuTKxfP8+V+9TfAKqeC551/g+fP7fPDe2+ywx51b91Ao8rJEZwqdycTM+cQUxhhQUmPahoAizxRtU7O3v8v58+exZpmifWzgdDLj8Ogcz1065I+//yZSD4g2MD2dkGc5RZbhUSwbx2C0Q9d0dF2LsR0Hh7tMJzOOjs4hpcTaBiGSA3Bdt+zkozXjKPqewjzLEUrS1jWLpmVnF1xIsteqLJHGYkzXG2ixZtGdNThrCN6nLF/nEAiGgxFZkQES6yLedWhdUETBcDhECcF8tsDZZKQk+v5PKdP3IQSiSP3FxIhSgrLKQST2V+uMGCJ5lqfv6Z1tQ0BEtYaVkWQcFENAqwyBJPpIcMmYqBc0ILWmyDMGZY6Ska6rcabDGJXyX71FbRoJ9b2azlmaJtI2KXdWSUVe5BRFTogpAsp7T/AhyYYj0BsZCZEYSa0lxrQ0jeiZ7pj6s7Wkazpc16EEjAYVddNgjAUBSuv0fy+XDs4n5+AedEbZA+yVWRIBYmK3Q28GtZqOQgTZby6FKCDIfs4S69luJcMW/RQXQlKCxPDw/B2iJwbXS4AjZw6/D28Gnn0fe8D6cZXM5tfqMZu1lQFva1vb2ta2tvXkCj/+KX8q3bMexsfqZwKsbjKOT5PyPsp0Pk2e++i5HjX0eNy5N+tJ0uPHHbNaXK+FgQ+xpB8fjxT9Ii4KEHLNdMRAkrTGtCTMyyHj3UNQOdZbsgDSOJRIjr1d2yJFkozKQiGUwMfk6Om8Q2c5RYiE4CB6Dvd2UErgbYp78dFjg2M4HDAaDQneErtkKDPc3133zlprIQa0lKhCoUQBMeJsyl9VSoHMmM1rRO+kKnViVUMAFxIQLbIMHwVBaPIiJyLo2hbnLC54UBobPcNcI5TEuTbFlUhJ27TcvH4T73wClz71Ngqh0HnBYDSiNYaIZmkcs0XN3v4et27dYXd3SAiO0e4+Ki8IKEajER9eu401hp2dI6TSaJUxKAcUuuLG/QUzG/EyYo1HRctwP1Jk8G/96rfRSnH39l3y0WWEcOg8oktNXmkmkxOm81MyWQGBvCg5PL+LEHD71n2axiT2WCZn48Y4rPUMh2MOD44QQrNsDPO6I8iM43nDS9Fz7mifKlNgLHdu3KHuOobDCuugGuwTfcbMThHKoiRoJfDWI/rQWyklO+Nh2hARglYk52lnHTIK8iJb9z46H9B5QYwpF5eeOXTO4myHJJLpJE1VAkxXY0zX9xBHIBA9CNVPQVGidYHzEed6I54IzaLh/Xffx5gOJTVReqxPmy6yZ9iklL3aIJJlmixLfbNt26aMXaXIdOrJRSRGNTGZ6TMSehQakb0cXqWYJRTWOpaLedq0EZIgAy6CDQEPVGXFYDhgPpuhi5KhgOViQabAdw3GtIT+Ogne0znTg8wk2VVKkWV5Mq/KNLoqU/6xtXjryLKMsixZLBYY06K0QipB1zVpn8tHqjK5ehda0XUGQsB1HdF7tEq5yt7bPnNWpflIRKJShBhxPdvqg0tj6t//EJLpUYwQV1Je0sZh8D3X3LOzaY6Ta0Mzv+5xlQn0x3g28a3mvugJzhJiD2zjZnzN2Xy8qaZJj9sAxo8oYDaP/6wbk9va1ra2ta1t/TzXnd+f8bu3fvSsh/Gx+oKD1cf3hT7J5OiT6knHPk7y+6hr76P3f9oSCKKIqwc/8TzrJdpqjFGgdM82+bSwR0qCi+Rac/HSBQgxgZZly/4oRyndL0ojxEBRFqwMV1Smk4zReyQR5x3WR6RSGNMSQ2BnPGBYFShR0bYNzjls1zGfTiiKjOATA7LK8PTO0dRJKjAYDBJzYkMPEDRKK7yFIsvxUVKWBdNljVKqdxiFiMdacC4n0wrjPcYGvAKFojWWujEInSOkQgqBcY66SbE9mco5PT3hp3/6U+7duYfWiXGWvdmP95Es11TDiqapU9+gzLh5+x7LuqMcDJEqp24bsqLi4PxFdg/2GR7PCQj29g/Ji4rpfMF8OmVQVuwf7HF8csz3/+xNFnWbgESm8NExtInd/sM//Jfcun2L3YsVRa5QMWDskIOjQ2bTGXlVkqmCtjE471gskhzWWp8YRAfEkIynIuR5zs7OGGLgwYP7tN0S01lCiNy9d8wHH95BqgLnLMJZTNdgjaXTkt3RgEznaeMjpPc+RIfONZ3tQAl0rtCZou3S+2mNw1qHcyblcwrwUVC3Boh0xpDneZLRxkCeqRTzYy3WplxRpSQEj+vZyWRAJFGqz+EkgcyuM/gAw2GGiBFvDZ0xPSAV6L5X0zsLMaL7rE76jRch5TqWJLHUGh8MSNAqI8/z3jSKvhdTkmc5K83qqv8yRIGQmqoa4qwjy4rkYu1skthLQaTvUY2Opu0I3vPC85fZ29vjow8/4PDwkHOH51hMT2gkdF2zYXzkkiNySNLas7nGoqSGmKS9eZ7jdYZVZg1Wsyzj9PSUtuuSjFf2rr4xJuOv4BmNRhRlkWKEOkfXtil3WEqss5RVhSR9npCC6MA7u24riBvGTGebdknNEVaKkJ4BDX3frpQCSH28K4OmGD1SJXArI0kOTJo3EhseCd71PcdnoFc+Rta7kg9/stnSw/PqQ67G/dfWDXhb29rWtra1rSdUiHj3xcwj/8KDVXg6QwofB69PO/bRepqp0t+0J/Xhk8Aaim4MbZMB2KwYI0pleC8wJkU7SKlAJofW/b1dLl28iHeO2emUsqjwXiCERgiJjwJJktaKnkVq6hq0YjQcorXsIyNS76i1CXzkeY6SSboLgjzPyLRGS4k1BiUFWiik6ONm+gVkWZbkeU6e53Rdl1xcrUX1pi9CgjEdPgqGg4r44AGdTXmpQiZ21fUyyRBj717qUMWAGAVN3eE9yYXWBYROTFhe5Bg6ikJjbceN6zfQfYSP1hLvUy9oWWiGgwExRrSSfbRJ5P7pgv/1f/td9vcPyLQm0xnXb91nsHfIg+mS+8cTApq6dcyWxyg5Rau0yB6NKoqs5NqNeyhd4sKcTERcMFTDHX7wl2/yL/+f75EPDrh94yqDUcGXXrqC94HdvQO+8Qs7VOWI6ekCZz1t22G7wLRe9IxVyoj1ziK1StE9UjA5OWU+neOjpRoWTCcTmqYlrwLvXv2Q2cwxn8/pJg1SQJlnRGdp25o7t24zm0wI1vWsnCNYT1GWhJDcm2ezCV1n+txOKPIybVgYs3aV7bpu/bla1f/P3pv8WJbl932fmcr6BgAAIABJREFUM977hhhyrqweq7vZ3WqSTVMSAUkWZHhtrQ1v9C9YK29sWPBGC8uAAC0MeGdAK+9teGfJIkQIINEcJIoyax6zKqfImN57957Ri9+5LyKjIqqSFKmsBt+vkZ1REe+9uG+4N8/3fKfVaiWsW8ki+S4irFX1Ip22lrIN85nYxJgLuYDVIp/dbJ5jVJP1NvBZgJhFllqLpNcaayhFo1XFWCcC1KqwztD3DqWKPDdnJbVWSwCT9uJVFcb95dNUNmYCcZTAor6fixTbKJbLBaMxhHGgFjBWkUJpntfK23/6LnfuHPLmw29RShaQqDUhBtkoasm5kxJhSq611jXFRCXmiJpeS6XwLeF3eq27ruPu3bt8/vnnZOo2XMpqS60IuD8/5/DggDcePsQouf/jp084PTvDVY91lhgiMPlHwVpDzFk2HZQ87hTMdAFYL97rWoqYFOoU+mTEP78Fq2Z7bPKcL/fFtg0MZVpIVb3wBlO2CcLb+96wiXgViF6+z+W/X/Li7ryru9nNbnazm93cOOWdd/m1D755rCp808HqlcXHdbvlr1JJMO3MXx/MdP0C5iZ58dclD1/93qtI0a4+ZsoFhcV7h9IWYx2L5Z4s8KNIWA2wWO5BrcI0DiNWWygZp0VwHMZAVQrrPAoIMaAwOGuBtnCsVoCdsY2BknRY7y1Vg5v3rKrUjfSdR8N2MbqYz7edjhOIUUoRc2KMIjn01qONRaPRztP1npAKICxLKfLcc84NjCgBIUZRirBuWltUlQV0qQVjHbdvHfLk8SNu3z7g008+5vz0RPyeWlOiAO5KAhQpjLiWKiu9joBynJwNrIfnzLqexXLGi7M161DRRhNLoVTFMESM6ehmHVpnUo48e/aMu3f24TRQq0NbzxAHFvs9ftbz7nsfUrIljCORgVU2GPUdUkoMQbHoZ7z53W/z/Ojf8uL0hBwrFOkMHTcCllAImC+KVBJQiCFJT6aGGDd8+N57/PD7DzF3PaMLDEFz69Yh73zwKaValJENjGGdGdcDw3qFrrDZbLDe4JwjZwGY6/Wa1WrFMAzkXJjP58xmPbUWcgqkVMilYK0lpdQCloRdPTs7I8dIGEdCDC01VvpPL0DC5DuXypSSA7mxdrmIHFyATcGo2lJlaR5qRa1aaLoinmtr5POgrSUWYWz7mcc5Q62JzvZbCXAFnBWGtTbgnFLaAmeQa4TvPMu9Dq0t8/myeUUVYYzU5ZIYIsdHzxmGlbCDtRJjZL6Yszpf4azm8OCA50+fcPLiGeOw3p7XCuS5ti7UycOpkaqnq1aHknKTNTuMMdy+fZu33nqL/+df/As24yBybK1JUYCkqAgy56sV1jmc9SwXS/b29lgsFpyensr7bqxsJFChQE6peUDZXm9Le32mr3PO0ADs5MGdwGypReS72zgpeR66SrqvXAPFg4+qLawpiXe5CLOqdHO6XvGbThsdl6+Tl/8tuMmfet1191X+ndjNbnbz8nz/4AF/9zufA2+97kPZzW52859gJpXaN22+2WCV65nP6xYpV9mer3qcq4/3H+ODfZXjry0w6dJ3uZAfXgXkUquhlMX5nr6fs7d/wGwmYUOzvsU01SoL3RjZDCMzB52teGsoRRaA4xhkkdkkuSEEKCKlTVlknt5ZqLJgFlYFjBUZgNHiW6s5c3Z6ilouRQrZFtHTMW82G5xzgMhVnfeUBmycMjjXUasmoZjNeoaTVbu/rI8naWqIEbTG6kopAYqFnPDdTNJ0UNy//wDvDauzY6xSdM7y6LNPcNagjGk+14BuXkgJiMlNbjpgrMb7DqUM/XwPhWYImXw6sFguePr0BSEGYs4Y1zHrZoQofsOUBqyxrDcDz58rrK6cnYonsess8+WcMUQ++vgzctEYFKpIaNLq7JwwBg5v7eO9Z7G34Ge//tf4xe/+gudnL+j9cvuaKdXSbQvUKfRGV5SulJSxTkOpnBy94NGnn7HsH9J5S8qWg4N9UhhRRhFjEuDixGs6bgZqkToZkoQXQeHk5Iyz03O5vfFQIjVXxmHEaIM1jphH+r4nxthkrJJ6a7VmbJLjlGJj3Gkpuqr5Gdvnur3htUhIT0UAubCNwtKXFJvEV2E0GG0kACiXbfKvNlIDU2ulqNK8p1JRVCjoFviltQQmWWsltArQ3pJDlB5ia+i6HqU0rhPfZNf15FQvwsiMwzpNP5+jlCKlkVyCyFhjBiWbCYrC0dFzzk9fcHx0RAwbNAXbwHCFVi2jtuCVUsm1yPl6yZMprKfegmqtNavVinfffVf84e08k47Z1pdaxDaQcubk5IQUE9575g2wHh4ekmJkM44Y50SFEPP2eSpzATVzUznI+dN+PmUHqIt0X/H7tk5bNQHOVu+lpg2K6bmBaum/KSRyEZBcyS2siW2FzeXr7JcDll4Gqpclwtddey+Pvlrkupvd7OYr57cW3+P2P/y1130Yu9nNbv4TzP/0z7+5/0Z+o8Hqy6EaX5Z83QRkL9/m8t9X5+uSfm8CsTcdx3X3Qb2k/hVvWPOIXQXCsgA0eD9jPttDO8v5+Yqnz57R9+fUorDWMJvNOTw85PzsjPP1Gm/F15oKOKW2rFHJGdd10rlYC/OZSD5TTChjsBZCCKQUySmyXhsOlnMJksmZfibgpO87YsztPYC+7+n7frtBML033ntmsxnzxUKAr1J4Y0UaWjXKddw6O2e1HvHOkZqHrFKJKWGiwXhL3xk2aYRsUTWjaqJkkUI7rfkbv/mf8dv/6l8y8x2b83PCMPC973yHkgqff/GEqCo5BRaLPay15FJZnZ/SzzxKKazV3L59m+VyyRdfPEEpAS3OCQBIKXPr8Dbnm00DnJUUIr7XKAU5Zo6en4qUsaQGxGCxt2zVJ5pSFDEknFfszZesz9Y8+uID7jy4ww/Sd7l9+4DF/oK//jd/gz/6/X/H86fHONORUiBGYbWMESknyojfWYkEtlZhoVfDyCcffcoP33rI+Xqk7x37B0tyDuiqUBjGdSS0z1dKiZoTxhq879DaYRpISSmRUyUlCcuiJlI4Yz6f03Uds37G4a1bHB0dkXNiHDPeOazVrFYiJ1eANfIalZxQmC2bqlrNSWlVLM5aSmMurXdMPabT8/be0HWWWirjMFBq6zRt54lzrrF/Ge8N2mgqGWMNfe8xVmNbiFeMgfsP7nP37l2ePHnCyfELrDPMZlKNFGMkxkitcj5416OUfK5TGun7OVYJ0Dq4tYf3mvPzU8ZBk4Y1m83AzDs0RfprVZF03Cs+SfF6y2eM2upjqgQN3eTJrLWyWq14//33pYZGa4yz25+bFmKVUyHEIJ7YJqsfg8jtYwzcunWbW7duU4+PiO0znos8Ho0lLuXCAyzbadKbqvTFsekGVLdKlXb+mvY4tTaPaykvPRdj5NpllKLkQK2KikJqdwum/Y7L1T2XPavXKVu2myZKbRUe08+v27jceVZ3s5vd7GY3u7lmSuV/f/77r/sobpxvNFi9PDdJvqaf3XSfr5PfXpYTX/7+dSmTkwzuJv/Udb+/fdXSVs02tRSgFLaLLNs8avP5nOXeAd53jCFx65anVJr8Mm8X6Z89+kTYKyqbYWBvscd6GOi9yBdLFYYpp4RSCtf3AhaAVAtOSXcmCrSuqDpJ/xRTQAqAdw6tFJ0XyaT3HhBwMx3LxLKmlLa3iTHK19bhfUdIuQGqDq0V681a5L2t8qZWsM5jvcPlxGqzkpRdnVEqU2vEasPx0TP+zb/+19QQ2dtfMqzPeXj3Dnfv3uXF0TGdM+SgyAqMqiwXPav1Bus8q9WK+WJBTInN+lyCpsIonlAl7LIxht53nJ+ekEolxVHeqFxYzPbIWTyvOSYWyz1yHEh5he81Ry+O2QyRqizKSDemonJ6vOL50Zmksn7xlBAGvvudN3jw4DZ37t7i7/69v8Uvfvf3+fTjR1JfUipaGUqRRbhRmoIAkBiy+AxDIMXERx98whc/eYsHD++xWq15+PAN7j+4yxefPwUsRntSlGCfg709ptqPXCGGDM5Ra2UYI2CwSmOsRindPpOOlApjHDk5/YgQhFW0RpNSJYaBOI4oJi+qI8XYwnoMRtE+95dSrYGqFUabSwCiNvAk7Kv1Dtf1QEVbkbhOclKjtGxqLDoymVpFHm2t1MBIyFZuALRwcLCPJBBXbt26RUmJvu9Esh4T4LZf7+/vs1gsePr0KUpB5x3eG6zWpATa9GgtMvXQd5wfFzZrCbTyTlQBVlfSpZqtacYxNJmzpAFXLl9XLsCZEmodYAvW5Pm3Ghwu2R9UY4+dJufaqm/qFlienh5jrWW5XHDv7gP6Wc9nn3/KenWOaex0nQCp1i1y6uIaO0nvBfxlajWtxuZC1q2m/6ui6NBKUau68NU6qY6iFGKaKrSKXKO0RpUsVPuVa/bV6+y0OTb1x1721r6K3WI3u9nNbnazm918edLv/IL8DZUAwy8BWJ0WIVeB4k1zk3f0VW9/XVjTl+Vk+muB8HYq1GmBXktLB1ZM3atKa+azOYeHByilGYYNJyfHCCVrGpM5xzuHs4bz8/Mmu9M4b3HOotWMUqV6o1QBOLSApcpULyNA0lotQUZ16lGssk6sEsqUcsJQ0Nq2xFE5RnWFVZ6CdLTWW5A6ed1E7ijf10WqSNZDYAzC+FKn2zXfW6lUCjFltKugMsbAsBlJKYNyYIWRCSUSxzXLvqOEkd45Sgx89smHrNZrShpxVuO1YX9vzp27t9kPkc2wodbCnTuHxBR59uQpm82aMES0dgybAWtNY5GqMI25HZOyWCPhNLVWum6GVp2Aj1xaVyrElNmsAzGUJr12qCrSak2BnBnWA8clE4c11IR9qLhz6xa/+Zu/hqLwwQcfE2IA41FIxVCtNK9jpVIJYdM2ITQhJD75+DPu3bsrYTfAj37wfV48PybEjNHAJEWtU/KqAqUpBTbDuPUtamUxjgbcJVApxijsZhjIVSSpmkpO4juUsCDVFAEtaEsp6jiSc6KfzVBKU1GULFJRYx3Oe3IujONITlleqyYLd85ijCa3WhPxGousWJhxg3Om1ftUlBa/ddd5dGMSwzhuP6shCLf86NFnOOebLF9kqV3fE0Js54fefm699xdAiEyp4HuHtTP6vuO4VnIO+N6RRk3KlTiOpJqEnZ5O/3oJWLZNmdrqm4yZriONsc95e1utDYoLf/hLl5R6qZqlVvIlT76wyYiUuQHWGCOfffaZyNBv3+beg/vkx5XT83OqUmjTOm+NBFBdPs8nNcX2uIxuIUls5fVSIdSuk1VRJql5+57RGlUhlUSMIykFSklNLn6xWaHa43xdoB5wEVp1ZWNxmus8rTsZ8G52s5vd7GY3X55/8H9bzsb16z6MG+cbDVavLlImluGm270KePwqsHvdjv5N9/mSx+yG319B/GdTN+C0sNOaxWxB1wnDsxlGQNidmit95+jmC87Ozlmvzzk/P0UrjWqMaMqRYVixXC5Fjtl3oCGmhO+cAExVqY2JUCkRo0EpK7USuaCU2bppL8JQQDf/aa0VYy1Oqe3jTB66y9JfAcESvDO2NFWlFCUmVnHDGCKbIZCrahJQR93E7WIbhFgJMeH6iYlLrIcNYwCrM12v6WZNQlkqvZcwKdM6NrPKWFPxTlMNzOYL7j24S8yZzlti0tgmDb19W9i14+OTtoAtpCjSWmGaFbUlJDsH6/XAsBkYxg2FQgxPydmiMBijMUPEuorWljAKGNQGjLY40wk4CRtqaNLFXDh+ccr773xIbz2dsyyWM37+Gz+lkPnow0cMmwjVkFMlk+U4FShVRGKr5HVUKD755BHff+t77B/so43iW2++wdv7S548OSLEDbWKvHzMWeSxzlGrIuVCTCNGia+zlNwqSAQ0CotVmhexoKsEAk1duaXkrSRXvLaKcRwZhkHkmUA/E09oKZUQMrXV4MhjC7BTFXQ7J4wx1BJJUaShckpJMI/rZHPGaIV10HUOZSzaVGxj98M4EsJIKZJGrbV8LheLBVrrln4NMScMRkKkgPV6Q6miGFBK/LPjKLU13nvQihDF5+2so+s6xsFhrWugVjyXOU6+3ZdVG/JHT6f/JRVD3XauWmu3511pvtabNuiuXnNeYiQRIE5FkrmVhpT4/IvPeXZ8xGw+l0obJSm+KAQ46+mCdQWstkimLeBTIl2eznORpsuVRCtFKe20rpJ6rJWm1Nzk1oGcI0ZdPCa68hWX5ZeO5+r197qfbY/70rVZ/u3YyYB3s5tXnYN+wf/686PXfRi72c1u/pLng3/0B/ze2Yev+zC+cr7RYJUrC7VXCTe6blHz5//1X82evgrTK7dTWOslpCcm+n7WPJN7bDYDMYa2kFLM50s675j3PX0/Q1VYrzakXKRHtRRyA4wTmxlj5CxF9vqOZAy1Y+txtNZug21oIKPVVWKssJoUQE+LadUCiArGeYw2GCPHllJo/lN5zjElbIxbX930WuUQ2aSVSBdzIddKCJFcFVUZrDXthQGFQSlhd0NIuFSput/KPPXcklWPsr6F58B80WOdwhuFzoWQItSMt1KDEWIm55HT0xe8+a3vMIyR49NjQhx4/OQLNsMaYzSLxRx/OGMYIi+OT0k5Y53FOk8pCRT0/Qy0YbMeqCWzv7dkpVaszoJU7tgmk2xBMzVbQGqBjBkx8z36mUNZiHFgDIHYQOvjR8/p3Uf0nefuvX36ecev//xXQRk++fARMUJtacmpJKRst7a3c/L2KU5Ozvjwg4/5yU9/Be8t877n4YN7PP7iKTEklLaUCsZajLX4ztO1sKT1GmpO5BIZxwFvNNZ5ur6jZKk9CmFE6ykwZ/Ioly3AusykhyB1LVOKrXOWnKv06lKYcpZEDgudsw20yAdC1VZzUxNKG+wUlKQ0XW+wTvyuXedwzqKsgB3VOluN8vSdbLaEEGRDplTmsxnz2Zxnz59tZaTOddvqpWEzbiXzpRRu375N18nmyMnJKWGUOp9SMtZ0zOdLxmFkWK0oVepnapPhTjU9oLbyfo2iqNSA3CVmtFSUKuSUwejt79c0RvJLzOFFUnKtVVJ3m3w31dbdquQ80Nrg+w6rDeMwElMkq0LMAaU1uSSUMuT2nqL0Fqx+lULlsl9Ua928uQUt+mFAtWpVvb2PfDbGS92q0wWgts8UW4n6VzGlL19Xb7ZvXJYxX779bnazm1ebzjhm/83fe92HsZvd7OYvc8aRfxlu8fj8D173kXzlfLPBKtenQk7zF+lP+jr579Vd/JvY1qsjNR7iS+37GV3XA7BarRmG0NiUthjvejabDUfPjhALmSz2nBNJaE4Z5z2VymK5JISR07NTFJXeGubetQ5EYamk77KleVZIjRUB0wCDHNeUwplKIcTEYiYhN0qprRRU2NTSHpMWqCKAdQo4mW47rjcMm414D7XUp6RSKRjQilm/wLuBfAnsS5VHphaDcnMqAeMKpjpiquScWM4X7O0tKHEghxGMo+rCYt4RU2YYR2azGaVqYioM63M+++wTjPWsVytqLhgvnkZrtACWzqK0JebCej0IS+wcpRqRRFvL7cUe43xkHEdCWBPGDUprrNb4rmc2t5SaOD/bNJ+pRpGE8S2ZlBWogvOKXDTkQo0VrRwfffA51mm6/sfM54Z+Zvn5z3+VxWzJe+99QhiSgKEwuSBl00IrC0h/aAqBjz/+lG9/+yH2cJ+uc7z55hu8884HnJcRbRwVTd/3zOadeHY3m/YJLaQcKFWk18Zpak0CMHMGEtYKS7f1Qmph+FVjZLWW13JKM9bGYI1l1nfUUiXEK0v3qnFOJK5Ko6vUKKkGwIUFK9vUXEVF64L1Gm8t2hacMywWjvliAbUQcyQDtWascXTzGVoZYsrb4CHvO7RqsnRJqSI3KfpqtcK22qZNEuY058TJyQkPHtzn/v371Kr44vOnW09oGM/pO4fW8sdYT1YDuYCqIt8tqmyvE7XIxs/e3gLvPTln1usNZ2fn1JzBSAJ0uuTDnDpHv3zdu2C2BTReXIdKA51Ka4pESVOTHIv1roVQla1nVECuQlVh7YtolF+6nom09+KaV8oEMC+fu20Dg3b3l66NIqmPMZKzpCZP3691AvUTE3t9HsHlxN+brv+T33f7ml+jjtmB1d3sZje72c1uLubof/sF//CL/+91H8bXzjcarE4BJFfZ1Vfxmb7qfB1zet3tr0sDvlmKLGDz8PAWXdexXq8Zx4BzvskUNc51W5ZKa0PnvKSlNuYiZwncmRaEMUVW5+dNCurbwr6QcpPaZcPMGwGhl1JQ6tbfBrpqVBXP2SRHjDEzDIG8LJAqZuufEzASU2wsl8hknRMJZGm/d9wMxBC2qaWUKrJmhF2myQy9s1hjSCFRVKEqI1JTbTBuxmqA0/PMmCuhRiLg+o7D27d48+F9PnrvHdAVrWE5m3Pn9m3Ozzc8f3EsNTktZXTWV0LKlDTircJoB7UwDhvUbEaupfkZhW0cQ0QpzRhGZvM5d+7cZbMZODs7QytLiuIb9d5RSm7JwZFxFLYoxoRC46xqqamVMWxIGVynWO7NWS7mpKFwdrYmjFIV8+EHn1Br4Mc/eYvFckku8N3vfRfrZrz37oeszlZoq0kpN8ZKWHNFq2uqcPrilKePn7G/tySnzOHhAffv32WzeQRIivDkC+y6jpRyY/QTlQw1NylrIpZCzm3hT0VbjSrSN1O5YKsufICXQ3FaWq/3aCMMs0hdDUYbVEvopeaWAFuxWtN5L0x/TtSSyNNnVxVKLGg3o3MO7xVWF8jChMYcWxWLvCclJYwzWKWoxhJLxhqLa77drusYU0IpAajjEAgqUkolpQu2MITAkydPaRZrrPWMwwZQDIP4bHNMjGMB5dC2R8VE1RVVkDu1mTpVQeG95+DgkPPzc1LKpCBBZFMC9GV/67QJNDHB7TSW87dJ9ptggqqa3HgCbErYbKNtu3+SQCsEPIsMWJNylq9RN/arvSQ1bteLi2AoAbBaX1wbywQMVd0ypjlL2JXZ+kYvpL8Xz/nLst6bpL6Xr7lX/0246d+IXfjSbnazm93sZjdtTk74Z48fADuw+h81V3fIr1uc3CTV/So/06v83kmOBy8HKqnGCCgAdSFzk9socmM0vPd0vpeFaM4cHR03SaAkfxqj2NvbwxjD6ekZ5+fnzOfzBgwl6KgC3nXt+yKp3Awjno5SK85atGlpoeMINZErDCHR+RkVSdClSS6NkcVuKYVcMlaLh1T8XJmaIIRCiIXeGXIu9F1HLYVh0wJrZK0qYTzakmPkxYsX5JQvSUMlvCfmgLVeFuK1QskSsGMMvfdsxkguBd1qTGrRnJwFnp1nhmxIJJJKKGfovMZqOD86whbo/IyDvQV78xlaK0IMOGdxXlFywWqF7gxViQx1MTOkJMxxTBGyp+97UpIApF4bQjCUVMTLOY6cvHjBMIwSvmMtKUVCHIhxpFaFMoaUB9JGFvHCBE6exUxNCutNW6R7Dg8Omfcznj15xvHJKaVGjBLZ8McfPma9CvzkJz/i4HAfa40wpdbwp3/6NqenpxinoEiKbs6ZxouRcqEA773/Id/61hvU6jFK8aMffI9nz58QoyCanCMnx6d0naeWQE4RVSumFnJjx0It7XNqqVWRGrGmKSK7LRlURRvxuBYyqqoGYitGGwkDcwalG3OqFLlkSs5oJVJrVQUYawWaDCWQNiPjcN7qUlrqrDWSIG0drvN4J7LTPAZCDIQUMTnjvUJrR4qJWb9ksb/kxckpXW+oauIpwfczyBKelWulKkhZjiNECQ3zzlOrYtgEHj9+JkC4CpZKMZNrZWihTJtcqaanuEIOiZKLhGlt62vkejKOIzFGTk5Oefz4GblkxmGUvtcGEiuKXKqw3EqJJN1aapD6nQm89t4RwkiqGbQj54p3nmEcxT/akplLLlsmXB6fbd9sqfJ7psqgUisaKI0frapJ9RsYvuFK2eTbE9BtsXFKEopL87uWnMkpoinUmpuXtW1ECqULVy7hNylYXpJQX3Pdvu7rSaFijP7SfXazm93sZje7+as49eSUf/roX73uw3il+UaD1ctzE5t5HZj9umCkr/I+Xber//LXkn569aFznjyijq7vmc/mW0+pyZXq6wXD1BjVEAL37t3j4cOHfPbZZ5yfn6O1EoYHkQMOw8BmMzAMIlEtjZGYpHHCWhZUSty5tY8G4jiwmHV456GWJrOU8BMpqpxYhoo1RgKPUpEU1SRhQ4Eiva5K5Mc1Ckgx1mPaca3OzrYyvQsfmzxPYYIUISVqKQKs0aA02mhm3nGiIOSE0k5kprUyrgfGOCeiGVJGdQavLeMQiUMk5MrB3iHeKJaLOV1nGWOkKOmftNaiTN0yUJJoo8hFQGvKhTFmQm7SU10JYY1WlvnMEkKmU46cYAxB6lQU1AmkaYNSjpQTpYr3zyqDVtLfKixSoCRhbedKwnpyzHz+2RdQC6vVilwKxlaULlAMMRSePDrm9PgP+dGvvMUbbzzAec+3vv2Qrre88/a7PH9+RM2yOeGsJY4J5zxWOVIOfPH4Kf/hP/wpv/7zn5Fz4tatPb7/3W/z/gefoLWlhMzB3qGk7AKVQipZpKiloKpIsbV1UC78l1o37yWFki595rQA0Rgjzlh8A6akyBgj8+VC2FQKNQdyHMXbmzQ5BcZxTZ08nhQqCWML1nkUFm0d3hmcVzinoCZSKmgESIcxiUqhgC4W24k0+fz8nPv3H2Jcz3ozCOhtkvaSIt70OGcZhoGoIsM4YFpQkKkGYyzWOEATxsw6B3KuFzL3MZBSJqRMyOJHl00hiKpSQ4GUROJ66Rolm1CZ0FKlp9dRGEsF7ZzXRjy8uWTikCi5opQhxggoaq4t5Kmn6xccvTghpiy9vNqQSm4qAJlJWjxdvybms0JTXLx8ja2S7CQ/K9crRl66hqp2P6a/JaRMFZEmpxzlc6UmrW8Lm2MLb+UYXpH4vLyZeFPQ0uXgP1fGAAAgAElEQVTr/HS7XcDSbnbz6vN0fcJ/9z+8zf/yj3/8ug9lN7vZzV/C/Of/7J3XfQivPN9osHpVYnvT9y7P1QTOr0qRvHz7r5IXX/7+VNOilJFF36Uglb7v0dYRG9so99Vb6aW1ltBkslO/akqJL774gvV6vQWzEnCk8d43dkTkg9oIayJgUBZg3nsJoakVZypeKayG09NT9pcznJlqZxQlZ6mqQEBF13UCEpuU1BgtMj2lyKXgtGk1OQpjJal4s15va02mNNApxXQCrZNscXqtnbPMuxlaKULK5FrQUxppTmgtYUZKWWIaiBWqmeG6jqIdIYis9vQswdKgZ7LCXo0Dm6CIKZOLpiL+U/H8QaGgkNAoXcFWjasKlwtDjMRcmM/mrDcDIUSM7elmBq0MZ6uBsRQqkzdUUYLU4mhlUAzkPHViImyzkToVcOBloV5yZNgkapMNb/shW0hSSomaWxprLYxD4E/+5E958vQ53/nud7h3V3Pv7gOWi33effddPv7oY0qIoCr9zBODAE+l5H37+JNPePDGXRaLOSlF3nhwn08+fcRqtabvl8w6j+o956tE0iLvdc5itcJoea+okFIk1oxr8s4cM0NMuL7DWkdMSWTQWUBRTIFUCpqKNZK6HFdnBCR4KsdITCM1Z3IKpDhScpJkX2Nw3mGskrRhBQpRDvS+xypHGSJVadysQ2srkmuj6Wc9Wsv5pYzB93O6fsZ6fYbxHUpXkey2JGuAO3du4azj5PiY1WpFSYlcWwWLUpRKq1kSgBNjYrVek2Ik58w4RHKppMmrWeVzoK0VlUC2LWhLKpmmEKF2VRFQpwSmTcFQUgEjmwWl1ob/Gq1ZNFZ78eS6HmolpYjFcH623gJRkeonYa6vbLRdSHSbJP+yJ3Z7nZsk2jBBT7kuTpU2ilpbCJO6kAOrBlZpfnYBhw2gNw+w0lNdl4DyK+bYryZwb5ibZL03AeudZ3U3u3n1sdrwW9G/7sPYzW5285cw4f/8HT5ZPXvdh/HK880Gq1+z1X4ZlL7qXAWyN3mhrmVsAW2sMJtNXiedkHbr34wptTRUvw2fUUpvgSggcsjGdqQYJYjIWrquI6ckHZYpM6zX4jHVpi1qAaqA1uYF22w2QkvkSO8N+/M5y3nPOAZYzDDaQK0ojAhV66VF4XaN2VhfJaxOKRWt5PvDMEolR0t6nUDq9NqUVmkDtPRXt/XZgTAz1hi8sw3kR9ZjbIxvEa/kJK3WFa0T3gfGXPFmCbpjPSTxs2ovVSS54HpDakDL+Q50gVqwTm8rT0qWjQWUknCgIl4/YzSd78i1YJ3j8KDn/HzDEDJKGVIB7xWpGHRqMtuU0aZJFlH0vWwklFLQTU4qnw+p3sm1oItIY2OKxNA2OVBNGsrWIzgxaVSF0oacMkdHJ8IEx8zDhw9xzvLjH/8Y7z0fvP+BhPYojTZe0pCRntNaEm+//TY/eOt7eC+M9Vvf/Q7vvPsBisQ4nKO0RmuF9w5rFbb5aw1ATZSS6Z1i0XeUKgx5zI5hzOQqoEgSeb30mRap09FV/JqlRMJmTSCRUySmQG6VJRTxLWqtcFSMMhgqOtfWtWogV1xnmbser7Qk3qJwVaGLhG2lELGul+fY2MLD/T1m82WTKEOMEaWkt7XrXJPfG/b3lsQofcDbTao69f3K88utZ1ek45EYIymJzDclAZQofSHbT5GaktCSymyDhl6+xlxzrUFwX23n4QTypk0uigZlAI9GY/Scv/bTn5BS4t33/xTrMsSBEIIA3knN0TbD2oO99LvlM6im/9gyzy9dCrcWhwu2tF10LvylavutbSXPFN6US0Yh9USyOSe/d3t7dXE9vvSCvPTafOnnN8x1jOrlFODrNh13s5vdfPXc6pf81//k+6/7MHazm938Bc/qn/82/+UfjhwP56/7UF55vtFgdWIR4eWFxnVM6E2Lmutuc91O+02Lo5dAK9NCS+Od3wIzgHEMF4C0rca0Nq3bMW4fT2tN10sisDXCzjrn8N6zXC5Zr9eEIHI/rZRUjXhPybIQTClJWM4lUF1ygpLwRhb+Silc5zHOCaNRbZOyXqxRaxUgOS1SK5WqRGI6jIHOKdKYyWGkFvHQXX1tJiZ1el5T56ZSassiVkrraE0NpAAI86KNakAngBafoDOafZ9J1ZBSJOcRYx0FDTEy8wv29jtmM8vZ6TGpZDrr8E6T0+QZVPjOgdWoBggLlarUdlFdS8I5R64JbR2LbsHp2UDOEDJoRB49pkwIiZQqTjso8r5573FGQqwmnkiSWBWpFIYwEseBUhIld5ydrahVKkVyFia7ZHkvjdPkHLdMeRgTKWaOj08JITAMA/fu3WW5t+CHP/oBe/tz3n/3PekzTYXeenJOoAQovHjxnM87y7379+j7ngf371BL4dmzI3LKFFVRRuGtlRygWtEUVAs9Mp3DO7tlFa1z+NmSWC1jCKAVh4eH7O3tk8LI+fkZzx5/zotnT9msV8RxbDU+A7Wkdq4iPswG+GvMaKOxxuGUwWpJqC5UnNbszebMuw5dxQ9plKWOgTFnqrKNTU6EJsuf5OlKgTYaYzSpgqlVErRrZRgGoPL82TNSyuRc2vckBC3EzDgGxjGiMIBmDC10rPXjTjU6k4hVOkRL60XVspmVLco6tEHYxi1gfbkb9abkW62VsPTKoKtHK0cpmu9/+wf87b/zd/jhD3/EH/7+H/Dk2TOGeIK2opLYjMNLYG2qlxF/an3pXKXKBlqefKdKyXvW5METgN5qc6ucP3Wqq7l8HaCiGwqVfl1JdRaWOMst2gafHEN9CbbfHE53/Vx3u+v+PbjJ0rGb3exmN7vZzV/V+e0/+jb/4ej/fd2H8WeabzRYvTpf5U26vFi56mOaApKue7zp/lcrDm6SIDsncl7vfWMvRBrcNwBai9om+04dqJc9YxPgBBgbuB2GgVIKT58+FYBXX/ZbCQMroKGW1umZM6otIOWGwlDuLxY8vH+XWdc3L5ws4EvJsgg100J1eq7T62nIObEZNmxWZxwsZygypIiiYJrU+SLZlAumuPVqKqW2Mtfp9TK29W/GAWqWSCCt8M4y844X5Zy0DSJSeFOxamSxnBOSYr0eCaWAmXH7cM5Pf+UHfPu7b3J0/Iy3316Tg0hKqarJNCNJg649SkswkLEWowAzGeMkAEfXkZorlIzvevYXlpgqKMN6XViZJFLf5YyqNDFLOIwqoCioWhoYbp2YRqp5cql0nSXPZsSQiCERBkNKFWM6QJGrEgm0USgDKU1BV1UY0lpRupJC4rNPPuXF0TO+9a2H3Lt3lwf3bjPvHR+8/z7HxyekMcjzqwVrFL7rWa/OWZ15lrMeauLhg7sYKk+fPAONyF61BIKpxnwZragl0/tOdjYoLGYd3WyGn/e4viflzBBGNpsj3nn0Dscvjjk9OWZ9fkZJQUTTCmzz8Cp94WdWpVKKyMu0BqsqTssGhTUiJ9XOslwsWHiPSomaZDOgDANFK7T36L5HGSebGY1FvHP3kM16jdKGue/YDBsJeOq8sN1KPp8hhCar79hsNiilMdoyxgRojHZSJeTnaG2ZzUU58Pz5c8IwIp51CSMCLQoLY1DVSd9rltdN6YIqBlXTl8Dq9vyQk2R7rnddL5/hJP2nzjl6t+DW4T1+9rPf4Nd++uvUqnjn7Xd59PkXzPoZiRVhGKRmyRhSEsZ4un4VKjVfBswixW25SMJ2qnYtbJ3H7eTdXve2vHCZ1BQTWdt8rc0BLSWypT3PTCl5+1BTKJ30A5dLj3rRE/t1c520GfjStf+6uU5Fs5vd7OZinv79X+He//XL42HbzW5282ecnHn0P/+Cf3Dy9us+kj/z/FKB1Wm+bgf9q3bRv2ph81WPIwmpBmsFSGw2G0q5CEwyjW1LqTBshm3vpNLSLQkvB53M53Nu3brF8fHxl3ys4lftAAHCU5pl13XboCU5vgs2RlVhML3VTSbcgKjSbQGpGmNjtyAzNXYLRAI5DoGz0xNUjTgDvVPNsQl1AtlN+ji97lrrbWDU5MG11m4XzDGMUMXHGisYZ+msAWPoOi/hOyWjkRRVi3hKfRqYO8ViAZusqDbzrfsH3D7YZ295yEeffMZqE+m9gBBN63w1RlJd12ucNfjOomrzI7akU4XapvuKTw9iCCgtzHQpCacKM6fBtQRVI55YYZ0qJclmgdYKq82W7c4lk4oWD23V5FShKJy1HB2dSfqwsfTOY9wcYzUxBTabQkwRo+T1zCVBBa1Bq0rNkRfPn6BV4u7dOxweLPjJT97iww8+4snjZyKLNRrfWRazDmM0m/WKLx59wmK5pHMdy5mFO3usNxvZcFCKFCq5Jqwx0p1rDKVGUoiSmFscq82azRefsg4bXrx4wcnZGWEcxQvdGNNaMq75Xqfvlywg3motrFyj62qTFmtl8d7hrMVqQ9WKWeeZO4uKkRpK63oFKO3YCrkWzGyGtR3GWrST7tJcJNX47OQU3/f4rsM68dcap5kvFpyenZOibACdna1IqWBdT0gDmyGQYsaYjloVIURKkU2Z2WxBLVXYe7J41ZWcc1Ib0xTiVRQVpSpylLqWWlLjJ6U2arsJ1nyp07Umxc1FqJXJpJiJpgKWjz/+iMePHvP06TPOz8/x3pGJnJ+fE/NIzpm+77l9+zY///nP+d3f+11evDgmlwvJfruyNTmu9LEarUjtNnJtmlhYtqTq9TP5TzNURa0TUK2gRO0hrGranncwXbMUlbIF69PPrv0tXwE+r359eePxzyIj3s1udgP2Tve6D2E3u9nNX+a8eMFP3v7j130Uf675RoNVxcvs5tdJfa9b2FxewNz0OFeTI6fvTX+m8KSu6wghNW/ZhRTushw2Zwm/cc5t+0bVZaaxsR+r1YoQwja0ZQo6kecgC+XJE5pSeknaJxUMX5YrG6VwRjHzDutm7XikmsUo3ZJsm/wvVUBqOGLMxJBJKQjosdLRqLXHGoVVUFuS6XXvwQTAJzZ5+gMCcq21IgUuGZXl9ai10nUds1nPeoiUmDDeYNHorNC54mxivrDcWy7Qs1sc3l3y5OljPnz0mMfPnzNGSR2lZkqK5JTZ218SSyXULNU6WGpRGKUuAVbIFdDCctL8uVOtDinTedc2CWgLd4UytlVxFJTVeGOb/FN6TGvJxASlVcKkkuicxyiLv38Hby1HL06oBRaLjq7viDlSSqHvDL1X5FSwSmNtLxsdpv2x8mc8PyEuPGbpONyf86u/+mNu7S158vhx69uVWiTaJkAKmfVZIvsObz2L3tA7CSXabDa82GwI6xWrGNgMA5thIyA158Y2IhLYmohpBK1IKeOdo8SIsY6a0xZUa4RZnQDI9GlRSokMWz6saGOwzqO1hPeUIvftlKbGQBgSThnMlOCqxA/cdTNU36H6jm6+xPVLqrasNxu0tQybDXv7B3SdZwwi6dXGENLADIU2jrMXR6SYGUNiAovDEFivB2oROWzO503GLnJwtqm9YLRIfyuQk/TUWqNRtb1WOQvrGxNay+dFqwbS2wYTVUKUlJKNJ62NcP5FNoMUIpuNKXJ8fEQpcP/eG2hbMTaTSybkUYK5lN36xIdh4L333pMNiemYv2SFkI2m3ntc33F6dibeeKYQpJbwTfN+tvtfUf9u5cOqmbArUw/rJP+VOis1XTNUkwO3ZG3VenblHJqCl159vipvYOdT3c1udrOb3ezmYv6Pf/LL41G9Ot9osHrZs3oVcMKrB2dcDUy6/PeWmWxA0XvfFo+SxjsBxJQS4zhifNcAmMO35N/p55O813snflXl8X1PTBnnHNZa1uv1S48763vsJQltSkmSaY1t/apSDZJzvlgol4uFszBYhZAiXefRrT5mChaqtVKLQhlFLVUAp0ooKrpmYi7ENJlZhXmVRaZUeVhj8EbYsmEMjA1ET+zs9LpOKcnWWgE6o/Syusay1pxIMUoica7EnFn0c+7evs1HnzzCWHBaUTOMobDoDXfu3Gbv1oKsHW6x4GxzwtFZ5cUqM0ap9xlDJIwbtFJ4b3nj4ZscHT1ntT4XFg5hjrcBVS3gpZRKLgLwrTUoI8/JWYvyAkK6XlFyIiV5jzUCLrQXObSwihqjNGMYJU1XTJNSM1SLsG1kut7i7u6xP3eE0OphlGIYM37pmc322mdPo9AXyTVIH2YlU2sm5UgcVqxPxZdbq3TI3rm1kPchJ4wSr24qCVUrVmsWvWV/uQRVef7sKf/uj/4tT774or13EaUkdGnv8IA0DqxXa4zzdP2MMYzoAtZ4ilI4l9HaELUEJCkr1T2U0hJhK9Y0tg0BXdvNH0BpRa2Kvp/L/RrE2d/bYzmbMazWkDLGitG6lIKyhuVij707h+B7TDfn8N59tJ9zfLYm18IYwjZJe5Kr51LJIbEZA+dnawBirIRUyEWJj7smSpUAr9zkukppAf0tdEwri9agSoFsqFU6g62SihmtJX2aaihZkqGxVt63UiiIfN9oLWzzpHBo6bpaGYzxTamgiXGU21knhCVSlbRY9owjhDACEaXBKAlV2tvb43vf/x7vvf9+U35MCcQv+/QrScK7UkJni9aSgDxZBS6Si6frrGykTfwoFcx0DW27OYoqucY1U2ui5gRkdNsM4lLer1Iaqa+52Bxk2si45tp9+Xp93Uyfq3p5o3G6/+Xb3fgIu9nNX+2Zzi/pYZb10h/8xr3XfFS72c1u/sKmVP7x+O9f91H8uecbDVaneRUp11Uge/X+18mDL//3lGTrvd+mhF5O1JweP5ZKZuoPTFvmdbr9xMQZ09J+U9wuDI0xhBBektFelzw8joFSKqenp9QqQSWqyXhrS6BVSjpdBeQmqtacnhV0LezPZ9y/fYDVWmCA1owpk3ImF0mu1apiVUUZCV/KWSR8tbGMORdQEg6liqJrst6cMkq/XFczJQJP6ce1Tj2rCDgGSoyUlOjaZoBq0tF519M7h6oF3QDgnXv3+d533uTwoMfYiHYd2YBaOt758COGaFHGo0Gqekyl85aURzqT+f63H/D4MWyGdUv/tRglnshpcyLEQudlA0ErhXUWZ6ywfhropM5FuhkLJRXiOLauzChqRyPpvaJqdnTOMWw2EngUEou9BVVP6bHg9ucYfdBqXyrrccC7JbPZrNX9JAqQUiaGsE1prkW6d3PJzOYWoytpOCeN8jm0ynDnYIFzXlJsiwQFAVgtmycHe0tu37rFcrnE/uQH/PVf/Snvvfsub7/9NkdHR4zjiHOWqhUOx6I/YLHc4+DggKrAWc8wBJ48fcoQIilnSjcj5cJmM4CmAW35X8m1pRw3xQACbNR0niCqAdfqmZz3LBYL6V1NGac1tRRm/YwHDx4w318SauJ0GAnDwP5inxAiw/qYs9VAqgIwu66j1kqIIuveWyyxtuPp8yOOjl6QcyWmSslN7aA1JRes1eSkyDlg7RScJr28tVZq84hL0llq/mugJpG85kqt4u+21pCKoRq9ZSr7rt8y3yJ9lt9Rcgs6w2C0a+nCAe+a5DmJ5HiMiU8//YjlYo73jhg3hBxkE6XZAh4+fMisnzEMw3ZD6TJI3XYzV0gxcZ7PsXEkX2Zft3+zPfbpv9lGB1+JSLrEqtaWAAyZy1BRbQHrxMiaL4Hi6+Y6kHoZhOsrIPdq1oB+BWXObnbzV31+/83fxP4Xv8HJb/2M3/tHn/Lf8hl7/9UPX/dh7WY3u/kLmv/+f3yPD08ev+7D+HPPLwVYvTw3Ac6vWohcTq+8ervJazoFBE2hSJfve/l3TIzhxKI66xrjJmBSQB8s53OMMRydnAhAbEFGplVLOCes7LSInKprtNbMZnNq8ytuwWy+kPSVkqiI1HhKFE6l+UepmFpYb9ZQe3prKM0/OzGyFcgqg1Y4bVrdiIBVYb0qYwjkJJJGqzShgSdrjXjdLjGrk4xZblO2C2WtNWHq4wwRAzgnskGjNCFGeudZ9D2np2v2lwfcv/eQh9/+LjNvWcwMi36B9Z5ie/TsFupnij/447cxLnPr4IA337iPd5pSBUSXOnD3cI+lOuDZ8whUXDsWpSRheQsC60W4S0oRrTLWWWHLa2XmO5GpVoXb69F1LgA+J4YUyZTm96tY7Tl58YLeG3rr0XMnoLJmqkE+X1pJ8rFTWNcRs8EYAfibdWCzCZLOqjPGNla1CNg1tZDbJoPRFW2FgVMYvPMNlBtAmHqAcRhZrc4oObE+PyMOa9bLBXduHfLmG/f4W7/1N9jf3+OLzz/jo48+5NGjz3j06BHn5+dNzipAE6UYQ2Zvvs+333xAATZj5I///Z8wjIHshMmkqiaPr+RSqFmqWHSrEtIt7AvEf51zJGWFd47l3lw6Y0PEuZ75bE7nPH3fob1DOcudwzvctpaj8zWbVDhfb1gNkZAy0o0imyvGOrquYz0Enh+9QBtHzgqqkQqmWCThNxepGcptgyhdyNxF+ZC3ygnZccmQA6rVspSWpq0orUfUoAwYDMWBqh6lKjmLtzqXyHq9bnJ4udY0u6h4Xot4q0PY4LwTqW8Y8G6GdxZNO9ezKDWGOMg56TR937ParHn89Anr9fpla0Nt70edQpAqxqrtRldpCNQYu33udfKfylUBGhCdvKrNBY+mNP9poVYJVaql+VEvuFjE5aC2X08Kh4uNuhsv3y/NS7LmKxJnePnfg8sqmh1Q3c1urp/fuPMW3/mb5+LfWC75rX/6U/4NP33dh7Wb3ezmL2jS7/yCP2qY5Zd1fmnB6qsuPr4qBXiS5t6UFjwB0kkWDBfgdbqP1rr1VIps0FpHzokYR1AeZw2mSlfnlJQ7AdSJ+UgpNSZNZIK1XiwTt4u5ybuHdJLmkgnjKAtMBaUqrHfYxo6Ow8DMe0mgvboSVLLMrEX6JGudEj6VVFY0ibFzlpSlX7ICxnmRJcKlkKeLBOBpUTg9N2GdHTkljC2Y5kmbeBiRNRuW8zmHB7f51pvfYbncY7Zw9M5wMOuZN8ltTJE6nPK9ewvu/u1fI+XCrb19Fr1nMe9JNWOsIZVMSoXv3bnP+K0Dcs7Muh6N1OmY/5+9N4uVLM/zuz7/5Wyx3C33zFq7ama6e3o809NeGWtsGRvLkh8AI3hB4gEJWfgBZCT8ABIWDwbLQsJCQvgBYSS/YCGMjHgAxCAbe+SxPZ6enl6qe9rVVdVVWbnfvPdGxFn+Gw+//zkR92b2jLF7pqqr4yelKvNG3IgTESf+db7/75aTmkMIbLpWpMqlsHHOC1N++vyM52dn1LXm4OiAlBRVVdA0c8q6ovMD3373Xb7/8f2cXrvg1o0b3L29pNKGzdkFOircMFCWC+qmnvpKAfq+p64blNZ4L5sjg23YlIqzzZpucEQjEmOFQVXCuMUU6foOFxxDN9AFT0ryXldlSdPMmM3m1JXI0A+Xc85Kw2Z1gRml3JsVj4aObr3G9S23b17n2sk13vyFP0SIntX5OefnZ7TthvV6Q9tu6PqeVefoQuL07IzHz07ph5Y7t6/x7nvv41w7+aFDnFBI9mUmkopoo1FWZYmynJOKBDFlX69ivV5Tasu8qSmKCrQhacOm77kYOp5tVthmRh8UQwRbGZzztF1PINHM5hJWFmEzOBKatvPE1OOGxMXFir73ufpGNku6dkPwQdh3PQaTxaxoiCJv1QqVlPgs0dhCo5G+15gSCYOx8n0igU4Kkia4RIyO4AepcQmD9LEGRdABow1aFZnJtagktUjWNiQidd1w9/ZtmnrB06entJuOFAPnFyupAELTD5vJW/r40WMS24Rx4FJ1TV4QIcXtdzVJ1Y+gxWy1UGkKYRqdx3kVkr+lEQCKtFkxMrchS9bH+poXg5N2/eyoFyvI/v+s378Vvn3Z/yf2/tX97OfyvH10l//rD1iqf+0Pf9KHsp/97Od3YOLXvsmf+duRv/vwR1cCDD8iYPXFgJDtz18eIHL593ZDk8Yu0FHGGkLIdRZp8lyOwGs33Gh87LCT3jleeFXWUNc1bdvmVF9Lt2kxRvogRVErTGTbttMxjKCuqqopdCalJMzLzsXWGJ5kjASxjLLjqZICCCS0gnazIQ4OEz3L+RydtIT/ZFA8AUlrsh8Qkko7NTQeDfTDwDB4rFLUdQMhoJQmaQ1ZimxzCvLEpmo9hTAJi2ly2o6wNha107UoDGcMkTu373JweMSsmbNYzlEqUGiIvselSFmUWGsomxLTFBw1Gq00s7oE72iMdJUmlYhJUSwbUIpY1yglnt8YPNWsoSgMXdfT+g23bh5iy1KkzlqTUGy6juOZYXM84xvvfIvjheW1N96g6zq6do0KnvsfvMf33v0WyRqWsyXWOJ4/f8Br9+5hQuLgxgGVKZjPFiyXB8znM6wxDEPPZrNhvV5zdn5ODElqdWzJZrPh7FzTNAYXI1oJeIq57iWEiHMedXhAJHG2umCzXqEUON8zdC3tasXzohQ/pDZUdY02isV8xnIxpywLVGY8g/c8Pzvn2ZMnaAXXrx1z49oJ168fc+/OLaqqxPvA02dPePToERddz2YIfP7zb2OrmsdPnvBrX/0at26d8PDxE97/4ENOn51ijKWw4us2SkNm2lI2OxqtMNrg/UBVVlMacLvZELxHvpaRqq5Ztx3nT58TjUYXltj2RLMh6oKimWN8x7obcCFIiFJI9OsWYz0BhY8QkyFFRdsOtG2P90lAsy0pypoQFcoExsYWndOhp40bJWAsxCSvQwpe5fxKTEqKbJoU37Itxb8ZJGVZlTWJgHNQlVUOVhIW2uRwJK0tVmtc38trcdIDnJLiydMnDL0A4a7rCT4y9F7As9L0vQOlcD7gXI/WJu8V5PUhCnc66ktG4DnZSZN4nYuiYEfnexkopiwJTirLfsd+VQH0KgP8FANqBLGaKSTv6tos65Cawueu3r67hu8GJ+3+3Oxs/MFon03549GXfue3S4nfz35+XKY0BX/j6Bf4E3/6Ceqwwfz8z3zSh7Sf/eznd2j6dzEaA1IAACAASURBVJ7ySw9/9CupPvVg9eqFzssCN3Y9qVcvasZ/j72oY9XKeN/dv4+PMYLVXc/XmLZZ7Dy3UgVKS7hM0zTU9WzypI7esBQTKW1lsS6zqArFYr6YjmF8/BFQjq8xRgnVkSoclVnBXGdjRnZBsdmsRaCXIlon8ZbuMMIqJTQRVCImR0xgcuiQsCnZG6sTBoNSBucDi8WS+ayiW68ILqCSkpTdGHB+YAQgpCwbjQmjRLIotTBymeyDB62pypoYwcfIvClo6jnz+YLZbI4tCqqyRCsBwlUpIVbGiETalgWDd8zqKvv8AvPFnBAjPkhK8ryZZd9fIBlFVZXQ1ATnBChoxbwq4fiAMvsbnXPSRxlhVpcMzsO1Y1577S7f+va3efz4Y44Pj7h5eIiyhiezGW/cu8f3H32M6zrWzvPavVdZFg0nJwccNHNUTNRViTWauswS73nDqrBcaMWN5RKfq1aMtTh/wOOnlnXniFnu6QbHMAy4IB7iTie6rqOwBSfLOYuqBC3BN4Nzuc/WMfQ9zjnWF7IJ09mCzcWaqixYzucsFktms6V4qPsON/Q8OV3x0YPH2VMcUSoxDI6iFK/y4mDJyckxMXjW56fcu3WdO3/8j/HOd77NL/2dv8vzuuDo1XsYW8u5ZS2Lec3Q99y/fx/X9VRVSXSelEQWrbWintWoQtO1PVZF0ImgBobYY+uag+UxQVk+fvKYLgSSBbSm1mBiJCSLLhqUUfQhA8hBzrWuG0gYUtQ4FwhBo00BusB5kWIvD+dSqzQMDENPGAY0Cp3AOU8Yegk5817sASky5DAquMwqFkbCk/p+oB863OBkcwCRQxe2pO16jLaUWZ4fdED7CDhUFHk8LgCazUXP5uIxRsvmgzFaNiC0JoRRURGIJJyX5OMI0rmrDUQFSdQCJq8NSWnAZNDJdPxG5ZKqEaQmpnCklEb/KpAl9EqTU34j6EiMjhQdmpBfr+Iq97m7vu6u06MkfxvAdHltf9naL5t6PmuKd9fk8X5bJYo8bw6K2s9+fkzniyev8fvrV/hv/rM3wVown/ukD2k/+9nP7+C4//tXeOX/eO+TPowfynzqwerLmNTducpA7kpRdZZ8yoXxdhd+9IqOMt4R0O16UiepWX788XeAqUM0BJ99b5rVapUZRj2B3Mnrhp4YyPE5VZY9pizJG59fqa1HVmojRlmiPPcw9NnzarHWTOxvSFI9YfIF6Cj/gy2AT1NEZg4Nigq0QpyuWe6IEmYkiistxMzoKUNRKIzJ7EtOEA4p4p2fGGmp1dGg9HRBDMIIhwSDD1hbMG9mzOYLqqqiqmqqSryWTS0ATynZIKhnM8qyzJJFCaqZzyX5NsYIRp5z/Iydc8JcW8tYm1FVFSnfPqY8jz0c4+ZC9D53ZyrxB2Zv7s0b1/je++/j+55X776Kioq7d+5wcuM6MQSapuHtz73Fq3fuUSpNe7EmdgPLxSJ7SiMhOIZOzpOysBwdHqCUJqbsDY6RuqqYzWcMLjDkrt62a1lvOpTSNPM5CcVqs2Gz6WjblsFJT6wubfZrDnT5/I0xsdm0uMGRQpDE6G7gWT9wdnaO0YaiKIGYN0kc3g9yzEq8ybNZw8nRCQcHSwH9pOm+jx58TDOb89bn3sQ5x70793jw6BkPnzylKGu0sSgCz549ZT6f0ylNN/SUxqLzxsx8sUBby6btpGVXGyhm9LFAO+lmVVHRB0cfS4pZQ+ccPiRssMyXh9iioh0G6ew1mk3Xosd04WpB33vaTUcMiuATLjk8EZcStGy/qyEQvSe4AR0DOkV83+P7AVIkpDCpLFSSyqSUIlplR2euapFAH09KAW1E1ssO21fXc8hrQ93McG6YvvvayOc4qUWUrA0SoKQpypLaSBjX0A8MbqD3AzpGjCoplMY7JwFgY1ha/u7brByQxFyzAwhlM2273rIbqzRiwSkhNEVhVU2S44MI0QujujWiZgJ2ux7vAtDddXU3LOnqmv6ydX83KErl937Xm7o7u3kDl+TH+9nPj9n8yds/x9/8dxboL/zkJ30o+9nPfn6X5hf/9xVDcL/9HX8E5kcGrL7soufq7bC9+CmK4pLcdwSruwzsVUC6DTDa+klHeez4GGP6r1LSN+kzgzl2jIYgYUkjUxpCFJ9blu6Oj7N7rONzX2WE5YUJI7AbRDImBA/DMPlqdSFJtipL+FyQqp3KWmBbOyF/srRYG9IUVpIDXrSwHM554iiRHnoKo4jeUxSBuq4B8HErkfbeY63KICwSkwMk9MYYS1Xa7GsUFrqZzSjLepJeV1Uln5mVFNa6ri59ZnVmlHZ/tgXIWT4dAmVZUlVVZntflIT7zJD5nbqh8fOy1ko6bZLgpLZtOVwe8Nor97hYtdx/9JBr12/Q1DXubOD3fvH3cOf2LQ7nCwptCINneVzK73Y97WYzeR6N0tT1jOOjI2xR4IaBfhjwMaKSQmnF4BwpDRgNTV2yXM6ISTyuxpZSgWQNi/mMGGOWDq/wWVJpVSHsnjEkFIeLOZtNy+pihXcOCoP3YfJWtps1ITgJp1JQFAatFQcHS+bzOXVTURTi723bzeRNLsuSmAJn5+coveK111/nziuv8bWvvwP2eywPjiiKkl/9x/+QJ0+ecvvWLYzWPH92SiQRRgbNWHyEPiRs2RCV5sKVlLpk6KWP2Kc13eAZQiBuBmxZYsqK9aqna59SNg1D8BSFBa1o+w6Ta4EUCpdZyhGQVVVFYUoOmxlFUTC4QTqP+4HCFqSioF2vGPqekHtmNaKSmLztMWVmP39PNRNAJokqQBtL1ZSUef1RKmG1omlqbN7cKoqCIhSQmLzjY6WN9PZu1zbvPTGBtYaqrkhG06+8MKIpoSmwIdAZTewjQ98LQJaUNnyStF6ltt23TH/fAsGXSWXHjaLt+pQg6cxUBumWTiL/FT315Y3Dq2Bz9/u4+7PdILzd2b3vpfvshED9oJksCTuhcPvZz4/T/PHbv4f/6V/Xe6C6n/38GM1X/6Nv8v3Nk0/6MH5o86kHq7/VvAzcjaBjBJ2bzeYSm3k1COgqczt1mO6A2t0LHe/9DhPLlAg6eUuz3PfyTv5W/hZD9nMp8XFK8Moug8IEOIWNyVJetZXsOT8wBuuYfCyS3pqlv0n+3Xc9sWkIekzzHP+7fc9SvvC8esk3BsyEEIlhQBWF+A1Lgy40+KsXmSOjatBqfK8taIPWhqqqKUuR9NZ1PQHWLeCUz8xYi8nXo7PZjIODA1JKkx+4LEvxCWdwuxtSNUq6y1JAwnq9om1brLXCpjKGZkVcEB9lWZbUdS11NoMDtZXUFk1NSolbN25x/VriwZOnfP/DD/jC57/AT3zuTZLzVIVlXlYE79hEz/P1BZSGYDXGVlgURilWqzXr9hn9MFDXNXU9w9gSYwJu6DFK5OR1WZFUZLNqcYMXH+3FuaQqB5E7l3XNYrGkMJqh7yRgyA1T/dJIaJe2pCoMTVmy2awZ+l5AXEp0m5akI4W1kr47q1ksFrIJUFiqsqCe1dIXCnRdt92YCIqirKlCYtN1PHz8mGax5Ge//GWSMnz1a7/BT33+C3z5K7+Xk/feZbNe8/jxY1wMFKZEGUNVVgSlaX0EWxGUJakCZea00XJ23uNcQBmNNoaiqlAp0cwXaFvhfaIsG1yuBXJeKMGikGPW2tC7gZCkhzgFRfAR7zuKRUFZV8xnM2GntaazHa7vcd6Tc59QY4p0Spgk6oMYIyn4nHg7bg7FvImkQUnVjCktdV3lDQCNRqSodVNTFBZlJGE7KSv9w0oRyfJao+Q+Sj5IrTUFlag5fGDjBlzwmKqktrV4VBV411MMBbWrOHsuIVnkbteYpbZWqZ1VYLsBJt/5kNcqne8ht8fseZWVjNw7m0BFYvCk6FFIF/C2suZFm8au+mVcb8f147cCs1cZ0e1tLyptXra5+YNA+H7281mfL528zv/yZ2+g3nj9kz6U/exnP79b0/f8z3XJ2dP1J30kP7T51IPVH3SRsbtbPwLL8WJ9ZCJ2/aC7fqkRTE6s5I4UGHhBHjw+R4xxYhXlAsi8cFG1+7vb5zTTBZeAwG2Fzfg7u8cuQS7pUodpCH4CZUohlSrjcWgtnaAKSVmNEY2i7Vp6N6cui0ssY0bBk9xvy5xEYaBSQCOexcpYdAa1ktRbT2xkURS5tkYeU6o3tu+5MQXalhhjKctiC1KbZqoLGsHQKIeuqpKqLGiahqIo2Gw2OOeo63qqGTLGMJvNAFitVtsNhMyuPj89FU9hvjhu25z6mztVUwKbWWWRc4fMsjqcG1BaUxYFrh+EkWSgHxwnR4dcOz6mu7hgKEqWswZFYrNZEUg8ePaYX/mNX2N2csTJ7Rs8vf+IN27c5dbJdeq6JkXxxw7DwHrdSuepMTRVjbEWEO9jCJ5hveH5+QVBdkQojMYa8Twn7wh9S12W3L5xjbPzFev1Gh8CxIQLImkOIRCSnAtVYQheAsVUSigVqCqTwboAt9m84eDgkJSkWilpxDfrhksS+KIs8CGgjMGWFfOq4Xy14fR8w7UbNzg9O+N/+Ot/nbqsyK2r2Z86o2oatNIUzRwXFcPg0bqUyhNlScaRkkcbw/U7NwkxMrgeSFw7ucZP/uRP0feRzcaxXnc8PXtOWTT4kPBRQoKKsqJpGubLJd5FHj58xNnpOVVVoXRB5xyPHz3iURQv+2K55GC5IMwawtCzsYa1FrZTFsgk4WD5uxiDI8YAMUqQUpQNEK0MiQgpy+KVxtqCMm+qFNZQVSUheQpVExQUWr5DfdeLdDrLgMOYfKSlK9coLfU4KqGTwVCgosd5T2E0SmuU0diqpKgqbFFwevqM1epCKo+0Fjk4Cc0VPnJnLcgrICM7uv1R9tEblcFtgpQ7Z6V5epJCKxAbwG+Z17v79OoFcLl729X77h63VnorVb78oi497lgBtZ/9/DjMrKz52aM3+D//k5+Gw8NP+nD2s5/9/C7Ob/yn3+WvPv6VT/owfqjzqQaru0mScJlJhZ0Kgx3GdASpu/KvLVDcMoFXPa4hhOkxx4Ae59yl26/6W7f5mtvH3K1xuXqc498h93rm3xFpKoQg7OBu8MkoVR17UCcf2w4bIcxr3En7TCSV6IZewKQqs5QxTQxLDImkY2Zbt6EqZG+dUuAGTygDSgmIarsN7bCZ3geTuy2VNtLJGhNaW4qiFJZYG5pmRl012ZsqIKKqi/weyOtaLpfMZrIJUJbl9P6NKc0jiB9l3SklLi4upt/XWk++Xa01Pm39yN572s2aTQbXApA1xhZSsBG3su2RfX327JmcfVqjsx/2+vGJ9HfWNe5g4P333mc4Oebo+IioItEovvPR92hN4MHjD3lv9ZhjK127R4dHQMq9p2sBfNoSnQel0Qpc36OArtsw+B5NYt5UhNy9qbQmIsC6HXpicAQHpS04OZijUuD52Rl9PxDyxsN04a801kBpNMoaFvM5s9k9Fos5i8UcW+R06RSJCXxwE9u8TpHBJZqmZsiBTyH7oX1mNROwWosk+VvvfIfHTx7TzOYYTP4uObRRFFVFWdXCohcFvg/0PqF0pGmWFFVD0prgA0cnx/zsz/40T58947vf/U2quqIbLvja13+NduOpqgXHJzdpmhnrtkdZiwbatqcfHOfnF4SPH2WWUM4d7wJ9ewHWEtUYPGZRMdD3nWx6TB5xSbYVrjER0TnkLKddM4lnd77XsrEwYjQfArWSc80YRT1rqJsapRVlVTL0PU+ePKbUimY+p2377HNPOxtsSZQi41ONIgYH0SuRIFudn1OjguxFzcoZtrJUz0vOz84J3qOytDhmu8BknRh51nRFJnx1NdYxe9qTsKjJZYAaQUXxw6YowPnKOn3171fX8nF2GdaX/d7ufeRnI3jeHuf435S2j6OUmSwh+9nPZ3kWZcMfvfYF/sa/mvZAdT/7+TGb9P0P+R/L4pM+jB/6fOr/770rzR2B5y6TevUi5qoPdQQ5uym7u4+9y56OgUxKqQmo7t4XIISUQ2jUdCwA3g+kNAa2jCFO5MAhpgunEeRpLd2mct0l4KrvRUKnchflKL9TiPdMXpfdeT9A/HgRpbMvS2lUVPl3DGH0dWkNUdjglISR0UZCbEbAqZRCK7mrTkEk1YWltgWlkYvxEASo2KxLFqCs0VZjlJHeV62pm4aiKGnqBU32B47eVGPk85nNZsxmDfP5HGsNfd9LtQ9Q1VLnY5SamFi5kE6S2uoD2mhhiWJAK42xdpIVey+e3Rh8BqwbiqJgsVhQllLLkjJDHZQA567rGIaBrutYLpcYI4/V1A1NVQngd55KG1579RU+uP8hffLcvnObqCKvvfUmm+9F2ueRzg0cHN3kzq2bFEYJMVVatJrhnWfoe/q+o++20mVSoqkrZqomxEDX9XT9IEy70gK8SjX5oWOIuepEc7iYYTWcnZ/TuR6tZLGKEhmL0pobJ0fcunWLw8MlIOyyMpouAzWlNE9PnwlL651sLFQFIZT0g5dwLaU5v7gAremdY9MPDD7w6PETvvGtb/Od73yXi9UGkiIZLYxwWQsIVJqIpiwr+sHTDx5lCg6OTjg4OGGxXNK1a7z3fO6tN7h+/YhZY9lsnqO0Zrk84Pyi5cnjc5zz8hnmSpWmqaibhpB7dlerFX7T0rtBZMAJUpROXR8DKThA47rEhR8gqxi0AkKg0Cqz8zFj0JjXCJEDS4VNBn1GaqBSlHPK6mwFSFJ5E1MS1lUpeucwRmfJr+LajRv0Q08MgbJq6NqWoe93NsWipIcrnataxBdqrJYAJ8xU/6KNQT5uCRaLKbI8OCD4wMXFRX68bd/vCLavhhdpraeApAkY5l8Y+1NTchA9So1yaEha3q5RNDyunrvr7lWwelUhszs/CNC+7PZdFnW87era/ts93n7286M+hbH8y9e+yG3dAJ8dCeB+9rOff7YJ373PX/vo65/0YfzQ51MNVmOKE+M5jrFmqkSYLqayz3M3fMcYCWgZhmECq7vM6CjDhW1w0gh+py7SHQnZrk9qlODC1ldqjM7HKoE6Va5FuQqIybLd0QN7FWyL5FeAyOQuzazNy6+1JGlUK3l+pRJFvqh1bqBtW+qqzMFFOxd1IZD0lkUZmRalcmVFysmnSC8mKqcLj78vz0yK+TGNwWjL0dERZSn+uroWwDpr5iLBVAptmCTA8/mcqioEVEbxng5DT12WFNbSNM22UzYluq4FtoxsSjqzojaHTV1mwo0xqBSZNQ2prjk8PGSxWNB1Hf3QSwpxXeF94GK9mTyxTdNAkkTc0pZYrYnOo5D04945bFNx+/Ztnq/OOT17ztG1Y24cHePuvYLVirP1CtX1rM6esyxrUop0bZt7Uz1a5dqRGDHaUljpjtVGYbPvFHKIF7keSGliTBTaUGSWyDmXpbyScNz1Het2I52bg8NYS1M34pOtquzNHUgp4LUhOei7lpA3RjYXF6yzz7vIjOswDPiQ5cUkotas1hsGL32m33jn2/zqP/kqT5+cEhIURYkPCTBoXVAUIo+NUdMPgZCE/SyrBWVZc/vOXZYHR1ituXa4wBjDcjajNBY7X/C5N97gYrVisTgkxTOsmQMljx+dMoREO7T4lBUNWtNuNrSbVpKhlcHkYCmlFd5Jiq4LjhQk8TqR0Pk8t1rOHz/0+CBJysIYGiYWL+VdphQhBYzWGCN+a50DkpSBoioJYn5FKUOMCqXBliXeOYwynJycTNJw1/XStZp92bJOyWbDqLBIiVybo9CmzDL8tBP65iSpOKRpw62qa1arlXwn8qbVuN7szu4m3/jvLcMZM4MZRfacAmJcl0Rg8auOa9X4559tXmbp2D2+F4KXXnLcV1/PVSWLWCr2YHU/n605rOfT9ZBWin/6b38O88YNANT1vU91P/vZz2djPtVgVUi7LeDbXkiN8t2tvFdaR4pJPgqXJb0jkO26bmI3R8A6DMMlxnb83d3d+m2Ak8XllFCpf9l2oo5zdZd/lPWNx3p113/39+U45JpPay69Znm88fhg94IweJ/ZVPDBk4L0Pobg8kV7DkcR+IlSIQP9rYxOKbmTSALB+/H17cimtdTXKG1wPhIBqy0nx9cm9rQspYamqsQLKQxdNUklZ7MZs6Ym+kDwgaaqcG6gMJo3X399AkhjiJJU/Ii/UHpmwxaM5oMfZaubzUY2D7RsOszy84+VRX3fUxQFs9ksV4D0+BCoCoOuqksp0FPdUAxoayAydbL2fU89m3Ewm/HhBx8yrNacnBzx1o1bHJkSFzx3bt6hKatc96GZ25kAUi0gtN20BO8BTUgObQqUMbgwECNYq1ku51IrEwIxKWJKzGY1VVVOAMUUIlfWxpAAlzdpdGaGxUssn1MIYWKcjbWZ6RN/8/OzM1ApP7bUAHnvCcHT9wEXkvS4hkBEsVp3fONb7/Crv/ZVVqsNtqhIIRIjEBPRQNKaqATohQTRR5JK1PUSMrved2uc63nlzivcvXWHZ8+ecfr0Obdv3qUsZhwsoKkPeH52wSv3Xuf+R4958vSc1WpDO/T0vsOdPuP8/Dl13WQps8j4g4/onAY8dD0hCjOpNJPP2Xsnry3GrD5Iua9YYRQkpUl6/Cpf9n4rBAwbo1EpkFLABfFtVsZO0nttJWys63ucj4Tg8WGQpObg5XwG6iyXJyW882gNphCJej+0eC/nNTF7sHXKr0n+KC1J3imIHNf3Q2bNs4c7X9rurmm7a9a0biqIjIxkzJtYZHl0RBEyzN8GNZHltld1xLuPu/u8lz2xW2A6gc/xPlclwS9JU9+dXQnx7nPtmdX9fJbm9974CX7pz7+NunPnkz6U/exnP/v5HZ1PNVgVLPXiTvsIJHa7U2OUKpZdhnQXNI4X9i+rsxkv5n6Qz/SSHE6ZnYuebUjTCIiLwpLIHth8/CaD492LtRFEj3+2LILaQlC1haNCIG+B6uRhy/fTWoKViLLDaoqC0l5+TqnCUOiMhlNCmiYYgWqOw4kBnf2ccZfx0AbBIdJ/qbWlLEqaZsZsNqfI4Ufz2VxSfY2hrEqqqmQ2GwEr+MHRdS1lUYocOgQWiwWzphHmMUWappk2F8bPYaxNGd+/siwnQDWGUY2gWCVhAEfWagyEUgpi8Lh8QVsVJVUpIDDkx1BKT3JiAc6OtpV03kBitpiTYmLoewptuHfjFo8ePaSIiRs3bvDa7XuMeapKA8agrZZ02Sxl1s6DQSSeSPiNMpDUWMszhnRBjJUAa+cn9resKqzN3wOTz4us4TZevI6SVC0yzTFESmvNrC4xpkEbTecGqrKk2Fg2mzUKmM8ayromxoRHpK+20CQLfZANhtW65eOHj3j3vQ/YtD2mKFBKUni9H0R9oCX8JgSRwZZVOfXhBh+p6hpjNKU1dH1LWRouViucC9R1w2bT0fdn2MKirOHo4BBrLDeunVCVM9zgOD0/Qw0RYx0xOLouYaxFIYFUKkG36VDKYIx8/1OKqJy8nYL0pkYi6NyNHEOW1ec1QMt3YvrypXEjKmGtxtpR9qtEERETMUQ2bcfBwZIQoet6QhC1Q4xSFeS9o2sHYWWN3oLW3M2aokj9xx5ha4us/AhZrq9ISYNRlzz3Q9/jBwcJNusN69UqA0yyjFhUK9M6ppD6JJUDi/KiMJ7Dge16NzKpirE7FlJSIsee9tJe7jV92VwFlrubhldB7cs2E182L1u3tx3Q+9nPZ2P+5i27B6r72c9+fizmUw1WbWGnwJ1xXsaAyu3p0sXOyGQaY2iahouLiyyTFT8qbC9mxsd4IbQoz2U2YLf+JmyBYH7uwfXb41NycRhCZDfwZmSFRcr6YldgVjZDunyLymDy6milUcRJXlzYQgCySlmtGFH5v9KNofJjj2Le8Ynzz5KwFylJHU6IBpU/DxAVodGaumxYLJYcHBxQ1+JLHWXC1loWiwV1VXJwsMQYM9WfVIUlhETbio90BKYX5xdYa7FlMYGr3WCr8WdjGNJqtQKEVd31K3vviV5CgsbNgKn71gtbVVoB2gkmz6YphBVWSk/Jr8YYkk10fUcyRhhbEsZYrDGolDi8fp2j5ZKL8zPazQa0ZrNZc3B0iNZRAoxUYrW54GK1IgTP9es3mB3McK4ghUTfdYQQMVpRFwVFU8tnEIXJSilRucDghvw5heyFhIS8P9rIZ6u1ygBHZyZVan8ELJcYrTKA9ZnNk00Df/0axlpiUiSl8CFC/q/rerw2+Ji4WG/oBkdR1Rwfn3D/wSOGwbFczFmvN9nPCbowGRQarCmoymYLFglcv3aNmCLPnz/lrbffom5K7t9/yGJ5yMm1a/Tecf/j+xwcHDBbznnttde4fv0GDz9+hProIW+8epPPVa+yHjqen5/z/Pk55+cXOfRJzuvoPVVZ0LeS8jw4J5sFEzuXv1sJdsN55LaUQWqSDZ60q2qQCTGQfMqbCqNtIaGtZhgcXT8wn1mCT5D8VNHkvcuBZKN3PaKVdO36toU0MqcCSn1w0zoHUT7rvAkRonjWxffe025auvVG5NuDI2QJsU6yoRGTWAxEF8G0qabRhCT+b71LIMu7scOiyp+EeGPzuyIbWzvv0S5ovCrLfcGrqtR0nl8Fp1ezCX7QY169/UWZ8x6s7uezMX/23h/m+r/ZfNKHsZ/97Gc/vyvzqQarIwDcZQ1+0G67eC7NBDzHoJDZbJZ9lCXDIB7OYRgmwLnbq7r7eFd3/Mfni7nuQi44E8aoS8cYgkjmtkwq+YL0RcnaD5ICKyUXv6TLoSAvY5mn15+yFzRfDI/HP1U2lDvBI2xBcFbZkZJcjqoEhdYolSbGV2f5rgDhkqKsmc3mNPWcup5JaJKV97csS46PTzg8PBDmJ3r6vpsArFJKWErnpp5UQMJvioKyKun6Hu/91IE6VuXsVv2MYUgAfQ6kKYqCoe9RJJpGfJojeA0hoFWiLuvsZZX31mVW1hozhViNzP3oEzZKY4sj2kGqP8b3tq5rbE5v8gutmgAAIABJREFUbhYLutDz1W98nWu3bnLnlbu4IlGoBDrx8OHHnJ6d41MkhsjzizPefvstqqpGo7GVvD6TVGZDA8pqlC4hBFLwmEJTRDN1+2492LnyxnvQ4nut6xIFDH3A5+oToxQqP38kyyhV9sXGlGuF5rR9z6aTztGUICbNegg8fvKM9brLlT+GGOHm7Vs8efqc733vA1arNdFHqqrGZxmqsgarC4wp0aYgRtDK8JNvvcV6c8Hp+RnXrx/xhZ/6Cb757d/EEdgMa5JOeDdwdO2IEBxVqejbcz547xSrDT/x9l2cD3z06BnlUHHtxjX6bqCZLRgGxz/8lX/MarUhRj+FnYUhYowlaU3KclVjdAaFWjZmQiCGACorIFLEaEjRT+A1yZcXSc1OOfla3ltlBKjN5zPu3LnDo0ePBKwXhtIWpBQYukE8tEqeM+YE36KUwKvClFhraOoqM/yKtt1M34OQbQgxBLwfRF0QAl3bTyFlfduyXq8pjMWgUDFN4VC/1Xo7rhmw3XDTShOJWZmRUMiCkZKkjqtxPVFmu6i8sAH3cj/ptM5mS8LVtW73z9V172WP+Vu9thf0yfvZz6dwjDb8xVu/SFTw353/Oh+vnjEra46qOQC/uHybv/If39on/e5nP/t5cUrDYT3nrPtsBax96sHqCJi2jOSLFxxbxnHLhgETE3d+fs7h4eEkBR1B0650VBJiJUF29+Jo7GMdAY/UWoTMWAZCEkZAJIEWY4pLEjtjJDgnxTixlikDnnndEHJysN8B5GSpnsZMxwAvYXtjFHCiVGYEd32lhhRDvqB1oGZZ5qcy4FJELWxKUlq4klEqmBNGhVWFpAu0UZhixmw+ZzFfMl8sKItqmw6s4Nq1a9y6dROtdQaS40XpGIQk/sm6aTg6OqIoCtzg8N5htIWk2Kw3VE09dayen59TlRW6EUmsNYbBice4zt6+hIQVdZ2nLCyFLSisJcWIziys954U/eRJHYFuShGjtchqM3OslCJpeSec8/gUUFZxdHhE0oqh61Ap0cxrhq4Xpt4UzA+WfPkrX+afvv8eD58+ZrF8nWbWsNms+f79Dzk5OeHzX/wCw6bjl//+32dxsOD4+JiDxRJbGkhWmKoUJfzHDaCcACMtn7kpMviYNk+ypDWfc5qRQRL23lrDbNYI0Igpp7WyDRxT8thGJUqjaXtHU5YYaxm8p/OBuG55dnbGum1JShOSoqwbbNWCLrh1+zYPHz2RQCdjGAZPUVaSRIwRj3SWl/sYOFgcYGxNv37OvKx59dW7/Po3vs7FpsfaGq2hqUu8Vbzx+qu0mxWzuqbdrFExslzOscozn9d84FseP3nCa6+9yaxZ8uDBY3yIXLt2xJ3bd4gJ3vnWO1RlI/J4YwhJvKYhxCmYjfw9dG6g8y7/3edNp0QMw8532mAs6ELOc0OkUHJ+m7JGa0UzX3Dv3mv4kFivLxgGT1VWqCTp1lCDUnTdhuC9VNtUBbbQUy/xcrmgqWtC8Jyeavq+p6xK+k7Tta1stPiIGzxnZ+es1/I/J+ddViTI+uFjQOUu1hDclH+0C/B211eV14iRHdXakJLKzHwBMaB1Yug6JsZ1BysmIlqlyYe6qxARXHoVyMZpDd8qidUEXkdFzIvS33EDbvvY+W87/5bvsWzI7cHqfj798zMnr/Mf/hdvAvDnuzv8+//59/j3+sTP/Vdf/ISPbD/72c+nfewvfIWvf7XjL3x0zN9+8uushvaTPqQfyny6wWqMl4DaLljd/fnIwO7eZ7zfCHZPT08v3QfkQky6E+MkD3bOTYDmKrOakrAKQ2YKM+G4vcALHlL2GjL2mgqQ3eVQtZhMaTcb1PhcoxRPjeBRQNgozxtfK0iYElkeqpWisJaiMNljt00qFm1ioB+csGF6TDcOgHjstLEkFCE/VooRT6K0lpRgGAIoSzMXue/h4WGWciZCFH/gyckJh4eHzOfzXAHT5ot8AUZckWdba4kpcXG+Eu+pLfA+ENyAsZLcqnWutqkb+r5ndXFBjH7qSrVKwm+staAVzstlblGU6AyOyRLF4BxVWaKUsL99P2TprKIsC6qyxBYGjXgXdZb6BhJVUTArZqjSYqoGhWLoLEMnabH1rKJqSrQ19F3H8fERP3P4M9y//xEfffA+m+Mjjg4O+Omf/iJn5+c8/uhDDk9OKKqCb73zTd5+6y0W8xlWmUxvSziOUPIZVSQl6soxvXnU2Y7eyRBIMWztgrKTwjacRn4exgAhIxU/MURiipTWEINI2AsNw5iAqxTOe56enfLs/DlgKcqS5BMBhS1ryrrm2s0bHF+/xsf3P8agsbZEKUMzW6CwpKjQpcYnT9DiJT49u+D0yTl/6Bd+DzduHvK177zL8fW7WFMQQ+Li4pzX33gNUuCVV+5yMF/w6MEDXN/z+iv3WM7mvP/BBzS1ZblsUDpwenrKen1GWTa0mxUPzx+SosrriKN3HucCGk1hK5EL51pU712W8EsitXinRdpqjCEqiy00zWxG09SUZcEwdMxinJjPbugpKUBZnp2e8Wu//jVmsxnGVpRlgS0KYvT4wWXvriJRE4LD5DC56yfHkODo6JC2bTk/e07XSTqzNQo/9JLoi3jTY1K064523dLnntZJLcJW0ZFAWOHJ7C4rjFZbhcm07o6QUSkBudZiopHQtKIgBM983rC6OKfrWlLyGKsIbsANfVZibD322+cbT1CQCp1xozGDzvG2vGYAuRc6YS4F7G2Xt5ExVepyAN+OdmS6729Dvu5nP5/4KKX4q+nW9gd1zX/7l77wyR3Qfvaznx+5Ofpzv8BfA/7dP2/446e//JnYqP1Ug9WiLCUZM8+4s74rux2rTbzzwLYmZttTuAWy4+/tXpyNMtFNrut4WXDH7q7+5Q9dvXD7C3K0kUndvW38+0skzZAvQnPQkM7y1JFlVikR82uQEJ7RO2sIIQGepOQiL/pIcA6tEoN3lEakplZnraKYalEpB5AoAdExeGLUzGczbt++zb27t5k3kvCrtKLrO2ZNw/Ub1zlYLpnN5wzDwKZdo7WmbqqcvjtI/6mRtFQB09D3XX5rBBi4zMIaY/FDoDYV1hQUtmC1WjM4h9Kag4MjCRSaoP/WP2dNObG3WhthBoeBFBzGyM/7vhNGti6JjDVH+f3P77XWudJIQTWbQWGzRtOSnCOkRIyOotAUxkKKk6evrIVVn81nvPn2Wzy+/xEXp6fMm5qirrh+7Rr3Hzzg429+k6PDQ9587XVu3LieHycRhoEUvGxmkAOwch0LU9rWBCUghzFJ8uu2bzONwGNi2UtSTKjkJk9lSgJUrRWG0Xsnj5FE6uxDpHWeR8+e8eTZqUiPDbgsGQ5JoY2hrmu0Mbzy6it8+OFHWFuhjcHakuXBASRDHAQYtd0Fs0WDToonDx9y55VDfvLzn+NXf+NraDXn/MJR15omGd798D5Pz885Olyy2qx56803ODg+4PnpE9bdOYtFQYgddWW4ef2E1WpF325YXzzn4epjYlIE37Nat6Sk2bSOGEaGTef1Quek5vxVIBGSsJB1XaJ1gXOen/v5n+f977/P2flzQoy0XTf5XgfnuH58giksjx4+xNqEVYrlcs58tsy1WY4QIpu2JQRP8MLqz+eidhgclIWl3Vxw6/YdHjx4AErTtj2bdctyucD7wNnZc4ZhkI21wRNcxA+B4CPBp6yk3dogZF15kYm8snxNm0gpbaW/ZAgZkyI4T0Ix+I4QE9Zo+t5RN/Nsc0gcHMxYX1xw+uyxgOngp3M17QDHsRd3OqKsjNh66LfHdPXvL/eg5o2bne3AcSMxxi1oVUpfqhzbz34+jfPL138fX/rLn/+kD2M/+9nPZ2B+/195m//3LwT+8JN/8Ekfyr/wfKrB6siK7npLRy/oyH6OjJ2wfVtAON7Pe2HjJiloSlP9ydhDuPv4u485shSw42VFvIy7cxXM7vpqJ6Jr9+KLHYC68/u7AFsAlLATWilC2vapjhUuEKe026mflRyORBLwmhKaxKZ32HmRLy6tsKsxUJSFSGljEElxSpSV5e7tm7xy+zYnRwfi51TivZ3PZxwfH3N0dERVVWw2G87Onk8+XpHOikzXWktT1yzmc1JKrNdrnHdopXHO0/V9Dn8JzGYzDuYzQoiZAdt+NsuDQ8pSQOPQ96QUMEbY2RQT2kiokLWWqqoyu9uhFFT12C3aT3U4IB5E2TTQ2EJj6wqQ9yFm4CIflEhyJf1UYZQwTSomlMkg0hjCZoMyEnIUuw5bldy+c5unRvP13/g6n//iF1geH3Hzxg1sYbl1+y7lbIbyHnwgDg43DFijc1rzDrs+nTRyHil2fILjeabNpTCp6bzUBpRBETFWzmWf5PUYpXGuJ/kASsv7qTXaVvh+w9PnZ7z//Y949vwcndlSFxLOBZyP+DH8ymhu3brFYrGkbx22KJnN58znS8CSoqbfDGhlWNQlyQWWM8tXft9PENLAwwdnqFTRbQI+Bc7XFywWM45OalzUfP/Bx3z44COOlw2v3L7BavOcs998gPee588f06cS7yJVobl544SjQ/HXFtePsEZzdrGWYKEo0liDIvhEUVpI0LsgmxzG0nUt9XxBConZbMnnf+qLPH72lLbrUEpTlpYEnJ+v8F6A43q9ZrlcMJ8vuHf3LgcHB3z7nW/z7OkZKUWKwjKbzajqQjzcXcf5+TlFkXudU6AsLct5xfOzc67fuMl33vk2Nq9xp6fnue5IvtPODZJijGGzOc9KBE1hS2L0Wc2R15y4G1gkZ5N0oirZwNgiVnROU0cJk5/y2qXHCqeUO3oLix96YSsJ6JyQ3tQ1w3zOZn2RV8lRnzueqDuS35fQnFc37V522+6GotxPv/A7W9B+1QO7D1jaz6d3/reTX+RL/+WbWVGzn/3sZz//gmMMX/yT51z7Wwc83Zx/0kfzLzSfarC6Cx7HupIRQO6GLskoFFsp2OjJG/s1h2GYwGpRFC/IhsfH2gXC+or0TCmwRXnp2F70X135mVKXwekVdnb3wm26LUV0TvzUKjOHGpQSCW1pt8FEIqH1k08tRYRZRWXpr1SybPqB5WKBMga0QiWF1jB0LVVp0UBhDTeu3eDN119lMWuoywJrNFVlOVwecHR0xPJgSVXXOOemNN4RqI6yamDyARujuVifZ0bIiE/VOdbthhAiddVQVAofAuu2p6rk/dVWPiNbFAzecbFe0bYthbWYnNZbVRLApJJcYIcQaNtWWOfCUFUlyYdJ2j2fN9uNAS+fSVWVFHUlMlqVUEZlH6zKcmsBo0ZplMkX+AoJ1MlVGKFvEdtjyJ5AkV/HFDk8OOBLX/oSp89PefDgAbfv3ePWjdtYWxBXa1CK6PwUkMXo0SYQo7BlQQeUNjlJdsegl0QO74InDP2UeDye26AFIEQvnlWlcq1LRMeRxS8ZGBgGx+ACQRnWvePR01Puf/yY0+crIobCFCKbzfJiP9b8GI2OIo+99+orvPvd9yiqitlsQQhQNxW2qHDugkVRUivFxeYBf/Rf+YN87q07/IN/9G3Ozh2DS8wOD+k7T7NoODy6RlXNiNGzWbWcHM+5dfM6B8sFwXXUTcnNG3c5bwdOVx5KjfOBFD11WVNVtwghcXiw5NHjp3SDI0XF+fkF5+cXaF2QfMJo8YmCoigLmpkkFr/66mssFoe89/6HPHj0kLKqWCwXeO+p6xo37zk/PyN4h1Jw/foNhmHggw8/4uLiHaLzaG0ocq1N3/fiHUdqZ2JMdF2f1xk5d/t2Tbtpeeutt0gYVpsWnQTMjmtgjIm+dwQnGxPt4BhCJDCmFyPsZdIZtcUJK8o6I73J8uHnNQw1ddGmvMGhrc1yY1mTfNdRVTWkiPcDpdGQ08a9c6zOLiBJrRG8wOG+MLvr4Gh34AogvbSuyg0vrJu74HU322D7ence4zMghdrPZ3PePLzNz731EMzbn/ShfOqm/1t/j+Hp9ru7/Ld+DpbLT/CI9rOfH50p/sQf5J9885f5I+82vHf28JM+nH/u+VSD1bFLFeTCoywFyIwhSGMa6tajdVnmpbV4F7uum7ypwARavfciIHuJ1zWOF0Hjg+ULH5HgMj33y5jZFxiCUVqndoNG1KWLL7gMaLXW4tPKEuAxHEYpA4oJkIy9lZIQG1A6TZ5XlPhUFZF+GIgJrNLEKNUzxICPHu8jN69f55VX7nH95BpNVTKrK5aLOfOmYtY0NE2dg5MkeCYEn6XIBSlKymqMOU24LLDG4AbHxg+UZUHTzFBK472AMGsLnO8ZvKeZLYjK0/YDgw8YJVLoEIIcZ5a/xgSD85JOGiPdMOBCYD6fSXBOkI2JqioxRiScWkFZlVlqLF2LIQYG1zNrZmijGLqNnGtWgJ4Z02FTRGmLKQr5gEKAEElOXntSuds2pal2wxgDVipttNKgFcvFgqIsuf/xxzx6+IjjoyMOl0uM0gxukLCiUfZrVAbOTP2pifw5KpAOIhg9hSOjPAaL2ZyIPJ7/YxCXQmS7AFpyuUCJ1N6WBeFiTXCeVdtxerHhweOn3H/0mN4F6sUCpQzJh4yRpYvWmLwRk4/99t273P/4CaYo0EUBGKm4QTFbNNSppH3ylLffuMOXf/ZNVr7nn/zaN/H+OlprVqfPqBYzlrai8ImLZ88pLFy/do27t06odMmsavjw4QO61QWnj5+yPm8hFRitSBqOD5aEmJgljdKWg+UB164fs24H1puW3/zNDTE52eDAUM8WGFOyWCy4cfMGb7z5Jvfvf8SzZ895+PgZp6fn+dyOcj4ZnXuZDScn1wghUBSWs/MVq4vzvDZptC1kg0MpfPC4jaOwlmZWEWKgbXtAWFfnBvrzjhQ9T3iG85G7d+7y8ccPaKpGEn9zgrlzA10r65nPgVBjkFzKipBxTYRISjqfM6PcdtchKgy+0XLe+BDlnDZWfMfGorSiLku6smTTbrBaGMoQc9hc9KgQcH0geEdMIW+YuGldUzlqS2WZybiuJrbA9LdL9r0kZs4bmC9bO3/QvOx39rOfT8v86cVPcvTn9kD1ZfOX/tEdvhqeTf/+s3/xMX/qryz2DPR+9vPPOCf/wb/E3/mrv8yX39nwrL34pA/nn2s+1WB1TOAd2VS5SFRT9+bo4wQksTJfi+z6VsuynGS9YwrwNiyJXA2hcc5dDhnZufjZBaExjd2IL79Y2mVKp8fIV2rCIOzcZ+chxtdkjcFog1KjXI7J4zoGN2mlc8m9SD+7rst9kRnUaJF05idDmxyukqKwJylhrFxEW6N46603ef3VV6mKkuV8zs0b16mrkqaSTk6b+0WdD4S4fZ/KsiSGMFXWlGVJkVnW4HPtjZaqFZEqImE7yuYOUE03DKyePJPwn/yZW6EDmc1mubImXJIPjsDdGM3FakXXdSwXC4ySwKSUEmdn54QQmM1rYbeAsqpwQ0/XtwJobU4YtgJUQ4w47ymqEm1EKmq1AWvBeVIIkL2hfhggs/dZd4i2Fu8chEBRV8TgUdkTWVc1b7z+Bt97912++863+cpXvgIGCm1JvcMjmzOYzOYrkXMLOtz5n3KMsimgt948pRRVVZFSwg3DJIGfztEo3lUg17M4bGVRxhAGJ+elsbT9QO8Cj58+4f6Dh1ysNjQHRxRlgxsGkbPnHRc5LcU9rI3NUu4F88USkiaQuzpTYnADy2ZOEWB2tOSP/dEv84t/5A/wl//r/57zC09RJKweIDm0MxQhUJG4dnjM6dkzjhdH/NyXfp5vfP2r/P2/948IQ8fn3niN46PreC44W3fUdSP9uoivtneOxWJBEB0rtqx59733+PCjEm1Aq0hVW8rK0jQz/o0/86e4c+8Gf+t//X/4p9979/9j781jLMvu+77P2e7ylupaepuenu7ZyOYyXERqsURLiUzLFhIHBiwkNrLYgREjiKPARpDYCCDDiQNvkQXkjwRw4sS2EDuJ4xixY0CKZUsRRVHkMLYoUhTJ5nCG5JDTs3XX+t6721nyx+/cV69rmhRtkeohXT+A7JpX9apO3ffuqfP7fTeWixXBg48RhQYNJkYuTC6gFUwmE1JKvPLKHU5OfL73dUa/NSlIRrEPEYMMj3QQbakPgb4fpMGOYuyUUiL6hLGaF198icVJg1KJFDu6tuH46CjvO0EowErRdU1+je83KFrvBcpkJH5EUBNaj81gym7QkmVdVhVN1+FjEjs4ZSATzq88co2dnW0++9nf4OT4COscXTNkXWpAk2Q4oy0Kjfdtfr+NEVyKHGSb2SKZkhvXSa9fl/q7+fmzA8EHMWA2P7e5J5+NCjuv83oz1GNbl/ivf+w7w7HzW1Fn79r/0e0T/hT8vr98E/JA+LzO67y+fu3+iR9g50/+7Hmz+q2oIlNuR9RyM8JgMx9Vay2UTfQbGs6TkxOcc2d0TqOeNBGCX2ubNh2GxQxIveGQtPnx2ASfrQc1upso7AiQsfHcwlox+Mk6SWOEMnt2XYpTRHdTryrom0albLQyfuMYRPsYNc1yyYXJlBg8tix57OYNrj96jcm0pipKLl+6xN7ONs4aJnWFWTfxgZTddjczbMffb3d3l6qqMtXR30fL01nXKRLZSAgJpYTSCp6+9/gwmtwoqrJmWjliEvpu23eimUWTEjhXUBSiTVVKcXIikR3LkwXWGba2tphOp5RFwbws6foGHyJVVXG8WNB3LbPJlLKuszmVuCP7fshIqsT4hBAo5zNwBanrT42tvAwH/DBQFAUpX4+iLEkhSEOrFL7thHJrjBzK+wFlDE/cuMnl3T0+9xuf4fLly+zs7MjvMpoehYCylpCpy2T9XVI5V3M4NRbT1qy1hknAM6JPa+QfwsZrIHRNee2AjMRBom06Bi9652XX0XQDQ4wUdYUxlq7r5f5K8h4QFG7AWoOPAaulWa3rCfOtC3TdABltHZG0oe+h77j11pu87e1P8Ou//jk+9IufpCp3CckT00DpNE88dpXJZMKFCzOC73nmHe9kfmGLjz77q3zlxS9xeHiIM4Zb77jMZOsKZaeZ4KirmpiCZO6qgcVyyWJxhDIOH6GeRuq6ZGd7TgpXsbakqqY4V1NVUz72sU/StC3PPf88Xd/hipKk5DoPw0DpKra2tgDo+57F4jX6XjJO5Z7weWYhVH1TZPTRR4ahQ+lTIzcBxTUp5fsBQwKMsYSM2i6XK0Fx+6UMQCL4IZBSQCWVhzcWrU8ZI2N01RiRpZUM8U7v1YBSkXGqN+5dIQYG79nd3WXZdvS9JykZyoQYeeXVV/nu7/luDg4PmUwnHN67SwwDCsvQNeIXEAZs3r9CsKzahbxXjCEOgzSno9Y6yb470n+lqQZBgt/YaAJr7fbZfXz9O2z8bdisB+3f53Veb6Z67/QG5n3vetjLeNPWn/0vLvOf/aXAc/4IENbb/2z3+ds/ccxPzXuu/effBSP76bzO67y+Zn3iT76Drf/y5Ye9jH+helM3q0qdIp+bRkcxxvsowiAHPZI0cCNKOaKpp9/vjJuvEsrlMGRDktxUbn792QPQ5jo2/x2f/0bHytOvU1lneF8zt4GYmowYCvpxqr+S30Vn/asihEiIQv0LPhBiJCEIF0kiZbQaJ5IpmwT1RN9j1ISd3W0ef+w6j169yGw2kQavrikLhzUKpcTEpQujezKiE0Ua4aqq1q67k0nNMAwcHOxnVrN8jdbSRPlO3DqHYcAPAesczorJkR8GVEIcUoNHaYs1grZqldYROSklqqJcGyhBXEfgbG/voFLCGb020RmGAXHplfxZ73uOj08onGO+dYHSFaANXdfkn6tIyM8rynLtAJzajti0xKTWjeP4/hpfQ2vla5UxRC8I24h6ayVOvSFGaTjoMcYyqWsuX7lC2zZ89atf5dIjV9maTfG9mOOo/P1JG/EjArELagpr5CqlU7MoiT2SpjcFwcUSieQ9Sal1TItK0vjGwaOsWSOyXSd00xADrnSoqAkprp22SQmVNM4agksMOQrHWouzJYMR3aTWjpAURgtSXqqCMPSUxvPu9z0FxvLTf/MfktIWKIvWnsJYnnnHLYLv+cpLX+be0T7veObdnDQNz37ik6zaFmMcSpXUswvc2W84WN2B5DMdFlxh2dra4bXXX6OuJ7iiYNl0DIOXxst7Ll26yPXrN3nmne9mPt/mI7/8LL/6iU/SdwGdr4U2hj5rXOfzLfb29ggxslwsODo+JmZdcYwBkmQpQ8r3bh58JTDGYazCOkvTNlmv3eYYlnEAE7NG3awHJVoJ3T7GmCnvZHRdc0qgjZAkv3mk9p4OKdIabBh3J1mbJUYvploxrlH5FIChp2lbtmZbHJ0s8EFiqWwOdHr99btcv36dz39+ySOPXme1OGL/7l2qQuKgYkxEJEbrws4O1eqI/f27DEPEKJsb5FPLsPtkF2tq+9epkca8sdeOcTUq7+X37dUp/7R0GogTMwvnvM7rzVJKKf7XP3XjYS/jzV3zOT/5Hx7yV/6q45fDXUAa1lUa+I+OFH//pTuox28+3DWe13l9G5Ta2+UPXvs+/s6dZx/2Uv65603drLatOLg+iA42opprt+AQiJH7kNWzBhzjY+uPkSzNsw3pfeYfZzSoDzLweEMTvPF9NulnWba6LrtJX0sRhRXKrdHE5Nc/yxhDzPTdkLVzPtNsR4MUlJKIl42GV3JYpdHUMbE9nfD0Ezd49OojXHvkCjtbc8rSScZokjUMw4AiEXMzOKLSIYSMXsMw9IAjhMDdu8v1AGEcEpwOF9SayqpHvejg8b1fX6+qLHClo+s70bMGT8y0S4DZbEZd13K9jFyfYejWr/NsNqMuCkYl3mq14uj4kLaNuWnWhKCoygmz2QxrNW3X052cYIyiqipcVYhZUgjiGBu1GCwFydA0KAhxjS6WRXEfsqyUIvQ9XdetdaNC85YmddVI7mxZljmWKHJxb49+6PnsZz/D3Xuvc/XqVXZ3dyVD1lpMVaFCXB+2dR66pJTxejZ3AAAgAElEQVQgBHwQOihKEccBiLUSP6REJ0hKqBizuZLGjFrmFInB40NkaHuOT5asup5+EEQypkhVlgxR0fYBiKSQSFEaM2etOE3n10AikxTHJwv8kDC2ICbRUVttMRGaxYLf+cHvppobfuHDH+eLX1pizBZNv8IUnu/7HR9Ap8RHP/Yr3Hz8CT74I7+HT9/+PJ/9xCfxSeHKCTrKe7s/XHHcfAmloC4UN65f5eruNr7vafvI9s5FlqslMYGP0PY9fddRFI6bN27y/ve/n1dfPeTjz36cz372s5wcHaO0wzfSsLqiYLo1YWu+Td97Xn/9dZqmYRgGUko5Lus0Hih4MTsyNmBtdtQlYrRFF4bpfE7b9+KebCTfVmX6w2iIpCDT2YSCm5JHYl4yQn3q25sfSxu0fr2xsYzSgxHJTGsChyC/WvJe/SivkCgjpRTNckXfDaANj924yeAD+/uHuLLkA7/zAxit+eIXX6AsLPPpVYlieumrKG0ZejGQajqPMj3WVhhbygBHJUKIuUmHMW5mzGON4z66sbeerRFZve+xNH5C5aszXivWg8Hxuen07Xpe5/WmqD927QP80WGAyeRhL+VNX+rGY/yRnY/xy3fv9yX5E8MO6uLeQ1rVeZ3Xt1nN5/y1P1oz/es/wF+/8ysPezX/XPWmblZd4dbupnDaFI7NUJ9NR7TWDMGvNatjjVrXs43q+N9anaHYbjSdm9E4D0JLH6SRehAFTc5maU35VRnx1EpiUEYdrCEDDCGSdHbkHJGPTPsTrZsXJDVTMiXiRP7blU40ib7HWEehNEPf4pzjPc+8g2fe+TauXLrE7s42s7rOpiyBofM4YyicXR+JT69V/l20YTorqXI0jNZiNNM0ghhVVYVSij5rJquqEr1xpm9bY6mKEmdFH1cVJSiJDTlZLjhanLBcrXJDYJhM5vIaRImnsdaSoieQso410rYtKUbC0NMslxweHrJcLjFGY63j+OiE3Ryzo5Tm5GS5Rl7rScV0OsM4y+B7GAacs2jr5Jpm860YIn3XEEKkLCuMdmR4POtxJedSa42xhTQvfcBYI/rJYcBoafBGCqSxhmQ0jsg7n3mGL33pS3zmc5/jne98J4898QQ6RuIg+ktdFND3hK7doETmJkRn0yRbkIZeeh1FdiUOp4khmfI+okvD0IuLrTa0TU/XdvgQGPqek5MTFAZnLSlEqsKgcHRNz+jzOvQDXdsSx3idCEPvufOS5JvqJEZgIQZUSHTdkrc8dYPved/7ePXui/zyxz5HH7fwMTCbznj3e99KVU758Ic/zCPXbvLDH/xRvnrnNW4/9yUwFSlC00XmWyVXrz5CCpHFyTHGyH3/5a+8ws7eZS5dusprr77CfFozm2+zWCzY27sEaJZNy/u/+3uYzbd47rkv8H/8nb/Hwf4Rg1eUZc3gE9Npze7eHv3QcXx8wp2TO3gv+4O1Jvt8yYDIh+zWndHYlBJW65xhLPFQxjqM1mxf2EEpxd3XXpPBAeBDyPsCOUImicZZCbNhnNb4EIlZo6rGfTD/f5IftDYv0oj7t9y/454V12seG1yltMT2EPFDT8oxRkol2nbF7u4lbly/znNfeJ5LF/dkmNT1PP30U/zQD/0QH/7wh3Cu5oM/8nvxQ8f/+Xf/Hs1yQd91dG1HNyQGP6C0Q6i9IWudR33CqRxipALzgD32vr31zOP5rXhffT2TpgdRhM/rvH67y2jDtdkun/qxq5in56hHrz3sJX1b1/e++w7Mzo2pzuu8vtFSTz/Jv9E+z9/4GiDbm7Xe1M3qKQVW3fexMUbiWrKuTx4PKDacgceG9IzT733/U6wRkk0EdkQSx8fHOmvmcRZRPYvgpiwkXMfkZIffscEmimOsMUIBViiiz2ZCVo3MS6H9hkhMkTia7Si1YbyjQCe6rqMqRL8X+o66qnjn0+/g1tNPcf2RK1za22U+r6XR61YopfEhUJUlReFw1uI2aNOjQVJV18xmc2IMNKvlmp5bFAWPPPIIfd/jnMM5d2r0MwxZVyn63yTZGkKnDYmhb1BaU1jD1rzGFQpn4fDoGD8MHBwc4JyjLiuGXhxWi8LR95LNWlUVMUYODw5QJErnuHTpEk8++SRd1+WcVU3bNNy7d08O6M5S1zWz6YyyKlitWmJKOGcwVhNCwqjRuVSRYiR6TwxR0O4QscYC4hQcMk3aWodxFqVOadmhE1dUZwsSUSjB4/vHWBLydUopbj7+OI88dp17d+/yxS88x7UrV9GZ8uv7XgxslDp1qNYabQzKWTF/cqJ5HeGjFMN95jUytBFNpQ+eoR/wvsfo0TFZ4YfAvXv7gsbnyCSVEs5qSJbkI0EnQgTvB2IIooVVikDicP+Ag/1DjJ1kpNBglMV3K+aFZm+r4uojj/BLH/0kx8sCnwqci+ztbRGD5qPP/irXH3uKve1d/tb/8ndZtT2ummDLino6ZTKt2b2wS2kK5lsTru7ucufOHe4dHNH7nttf+BLLVUvpLF9+6WVmkwo/9OwfHNJ2HdcefZTnPv95nvvC8+zvH9I1LV3Xs719mbKaYm1BAprlioPjA0KIMrRIirqe4Icek3M6jTFrlDVF0TkXhaOwdu02XU2mGOvwg2cynaKN5uDefkZie4mYys1nWscAwWSSKfLer13LRzRerz3T8t6GAX06KLNr5+wx5sYTMop7ShFW6/vaWJtdt4UKT0oU1kEKfPpTn+TwaMETTz5JVdf87M/8DO97//t55l3v5gtfeI67r7/GwcEhf+gP/hjDEPibf+On8T7hyinGKIiKskysfEAnjXFGpAhxNKMaqe2sO9HNfXtzT11//IbHZDrzBi+CB+zL49+C8zqvh1XX5nv82gd2qH/s+881lr/FmuqCW2pGsffKw17KeZ3Xt1397p98nL/wE5qfePWXCPHbQx7zpm5WN7NUNw2WNhvSTaMjlQ/443M2uWNnXSSBjDadNp9nG92vpT89+9jXaljHn2Ls6KgrP0srccREC8Kqxq40gTaGQMzawVHTBSlp0ib9OEFc67/AGYMmoFOidpZrjz3KO996iyduPEbtHHVpsVrRtw3TSY2bVCTEtVQrcfYtnBM0cZ1rO5qsBNq2yYilXR/QdUZ1qqpaa4vH1ynFSAiewUt+qVXSCFstr1HhDFobbOFISjELNWVhKZwDZWmbjuViwWq1oqpEr+q9xBdFY8SV2Rjm8xlGa8qM7sYYc45uYrlcYayj6zqi7wk+opRB65be9wy+J8ZAXdfM51NiTIR2wJVOmsEYcdZhTab1KjE0Cinhh7iOi0kJUpCMS1SmOsYk1ygpht4TVMJmp+Q4DITRDMhk2ulkTgyR1155hVdffZXlyTHXrl3DWTnUjBphrRSaPKvwARUjoR2IfQ9JdMB+GNCcvocHH4R5kFF8AVkVScf8vhPEVWvFtKpoRufjOJr1RIrCEaOi7TwWmExqlDIs2462aXnpqy+tadNaa1JU+JAorOGRR3Z4y1tv8nM/94v8s3/6JQZfMKSe3e1tJpMpn7/9ZS7sXARV8NnPPE8YLNvzbZquoznpqIsZTzx1k53dbaaTmqFrUSRsZh3YYsrh8Yr9g9vEoYPoqWuHUaJzDDFw5+VXmM6mbM3n7FzYxSjHcvk8fdfTrPp1bIsxBmssxohePkRpLid1Dai1Q/nI7rD2VKc6uoSHEOnajrLSdF3L5z73OVKUqCwZWgjyitKEMKzpuPPZjHoyYbVcyn2uxTHX5HvOkI3fkuigxRzu/gYsxTGiRvYkPdKB10irrHUYBhIGawxFKU123w90Xc/x8TF9HyjKioP9fcquBa34f3/hF/jMZ36DD37wh/nQL36Il15+lZ//xV/hyiPXufzINY4Pj1guViSfcMYis5Se5Lt1tI4M+94Aia7Xe7Zh3URW08ZjmzTfzb35Qc/9Wv99Xuf121GlLfiJSx/gD199mfoP/cDDXs63be28T/G2X9jmuXDMn9OJp//8U8BTD3tZ53Ve335lDD/+F5/gv/3xX+PVxcHDXs03VG/qZnVs8MYDoBiOjIjjqSmHNBKiQIU8b8+RIiNblmzQsYmeSpN4+j02EVi9bjrXZMp8yDTZJCStkRGlRvdg1j+fTNMcm7p1UzrK0kaKMNJ0qnhqCJKydlKkhhrxV1FrA5aEmLsYrSEFVIjoEKmN5uZj13nPM89w/ZFHqIuCwmicM1SFQ2lwzmIyikoCrKHIxkVlWTL0/brhu+9ajdFBGYFJWaMask520Boz5n2OkUHe44MX5LKQ42aIHnL+rXNy0NfrJrjCd6I7nNQ1Cui6Xq5mksakKGq6rqNplkwmNUVZybVC0Wajl5TE7GY6nzGZTCjLir7vaJYrEonee5Ztj3MFISS6fsHxYkVZOKq6pAyRuq5RCNp62hAktHPYKJrPcdDhvZd8TST3Vmk99rWEMMh7ESXNspH3bj8MGGWoylr0xsuG0hXcvHGDxfExr7xyhy+/+GW2t7fZ3t4hpAAIXTvFhIasZU6EIBm0MXh8RkqlYZSmpst6VG0NOj8/hkQILREISaJctqYVbe8Zhp7KGjSRtvfoKLEkq7ZDxYzKacvgE13bsn/3gHt3D0jJ4IzNAK/HKcWkKnjqLW/hcOH50Ed+jT5MCUSK2nLx8h4vvfQyTR9p7x6jWEBS6KKmqKd0PjGsOoY+cHBwQtt6prMJbdfQdR2rxtN6RdusePGllzFaM5/WTKcFyoFWAec0RSWDjLqe8NqrrzP0Cd8PpBA4Whwwn29TVgUhJmEJFIYUA75r2dvbo3AVrpzSdQN3791bswYiUDpHWRY0q4a2WRG8F7RadwydRDqVzrJarmQYkhIajbWGsrC0zZJl14KKhDDQLBfrmCCtDUmJGZO2VoZrSoYiMSWSJtOKlehCo6DhQhPPWbgokoprh2DJyJV389B7TGXZ3d3jytUrvPbaazz//AsoFei7gHOO1XJBSBFXFoQYeeELz3O4f8Dv/MEf5NlnP85HP/pxfuwP/AF+6If+VX72Z3+Wsk4sFycoFM46qnpK30b6MEiDTsgmUqz3UbWWNJw2mm/wHsgf6/xxXFPblTToSfZgNujFp/8/7rXnotXz+u2tn7r6u/jXpne5/meeAJ542Mv5ti73we/jAz/7Rf7jOdz4M+952Ms5r/P6tq9/OH2a7138fw97Gd9Qvamb1bIoKVyR9XenKOp6sn4/gAmw1jOONL3RxfV0qn5qxJFJZPLoxmRea406M7VPmYIX06lhydjwaq1FcEoSqt6ItGSkJo3UvbVmSw6a489IMeJzQ04UJEuibTKabB1KG0KKmVIbcUZhFMTBUxjDk48+xjtvvYXHrl2jLisKpykLg7OawjnKWhA6bTYaRGWY1LU0lmmz+T6lQcsCs8mP1mg0zliU0aK1y2jSmFNbluUpTTJGJtOaYehYrZqce+soXUkIga7v8T5gjWboeonhSWCsJQ5yqDZGMwy5eY+wWN7NSFbklVdfQ2vNha05dVWhulPad+jE8XS5WlEUxdrgqCgKoY8PDoXGh5x72TQcx5OMzCcubF/g0qU9vB8IwXNhZxtdWHHWTQlrDTFqtLEMfc9yuaTvPNPpdI00S1yHymhrHmFEQYStcTLMQDP0g7yPlIJ8zd7zXe/jU5/6FP/0E5/g6affwuXLlyjLijB4rFJoZWiWDdY6utzcxBixRl5vgKAUq0YaO1sWorHN6Go3yFAiwdqFeVqWdF2P1UoscJIhuUTvI70fSMnngYk064vFkhQTX/3KV+i7nulsR75/FGTdmMSVy9eYzC7w7Md+leUqkZSnKDRPvfUJXvrqHTrvGYK4OXedaNDT4IlKmpCirPAx8dJLr0h8j1LEFOmGHqUVIQWUUUznuwx9T9MnMAFtPZcvznns0UssFkccHhzwysuvkCKEIRGiYjqbMIQFPgVCK4ZlEsMSqErHB3/4d3Pj+jV+/hc+xLLtOTg4QGvNfD6naVuGvqddiXlW3/fEjJzGEEkh0vQDCyTKKMSAyVTjlKRtEkAxopXkn3ZdC7ARWyWDkPWQDmm8MHkIFhWxl71Rjd84keOrsgFdbu7SxrAupphpwaKJffW1u7x+925mTxi8DxSF473veS8vvvgV2mbFydEBrqio6pqvvPgV/sk//nmGEHjpzqt8+jOf5Xf/yO/lU7/+G7z45S8xnc3pVgtCJzpqY0uM86TQy7o4i5ZGRKO74RC8ubVvNrFnWCxpvZGfIsprHay6/7lvcGg6r/P6JpbRhtoWTIuK2//+Uyir0U/swIXHH/bSvmOqjpHr/9b8PFv1vM7rm1Bv+/Edyv+qoPP9w17Kb1pv6mZ1tVqxXC7vMzDaREA3H0sIDW/UU4504dGg6ewhKKUxjzSt6atrbewZau991DSVMo33wfqntR51/Fg4oaOvCOM/oj87zYwVjVqO3NGZFaezhnTUrMmPFzpoilRac/n6Yzzz9rfxxGPXmFQlBqicoypLyDTFqiqwzqFzvIvKVGljLUMILI+O1tRGVxQZBSa7nkrDXDqhsHZ9j9KGyhVr5C5pcFVF8IHeBywK5wrKssQPfW42YcxVHcqUmznwPjAMXg7o5YTkB1arFW3X0TYdrqywhRHaaoLJdLLWwm7vSYNyfHTEq6+9TgoxQzYJ54pM3RSasrgCSyamNK3yWFVVXNi+gHVGtJxhYBgEiTw6XjCZTkDB3bv3mE4n1HVNSuqUApz1hmVREgNC7V3rkhXWOvy4EWiFz8OFsWn2veg/jdGQZP0qGVbLhqeeegsXL13m2Y9/nBe+9CXe8+73sLezizWWZrmCBEPXM+ZWKkA5iw+ethP3Wu8lU7csK9quY7VaZbaBISXN4AdSioSU6HvJR7XGEhCNqrOW3ncEL79vRJNC4vjkBO8jr7zyOi+9dAdjK2EkIJpWrRLzec2ttzzJV778Cq+/ekBdTYkq8Ohj11geHaGTxneir+3aJQnF4IU+PPiAMw5rxXX6+Hifpu+Zbc2lUcsocV1PCNEzraa0pqXrpIGfTKeEmPjC8y/QtUtUjh/qhh7jHL6VSCbrbJYRWMqykvtCa65cucTnP/8Cv/SLH2bV9fhk6XphEaC1UN1TIsRAN/RYrdFGS06uTJqkIY2n+viY0c2Q72MfRL9pnJiHpST3oLWWqhLt6nIV1w2rUjIcQmdzLaXRTo+9Gil6cfeNI+L6xkqj/jkGQgyESB7+ZM5GErrydDLh5s0b3LnzMt1RwxDlvZSIbF+4wOH+ParJlDD0/IP/6+8TBs8Tjz/By3cE4bYalstjgu/FEdoWDGGAlGnAuclU61WNkoY30njP7sXj3gpvlF3cn5V9uhefU4DP61tVSil++PIz/KfDDv/KX8m01PNm6ltSP/JXnjy/tud1Xt+kUo9d5/YzT/L4r33uYS/lN603dbO6qTM9qyN94EEmJKFBZt2coBwhU8NOabqnT8lz/ax51VqLFjSlBw7hR5OktZNlkgMeSrRhSuWYmI0DksRLxPu0ZWLqpNa6txBiRm41SotpSsoHuRCzUY/WJN+jIkzKkkcuX+VtTz/N1cuXmEwqnFVUzlBXtTS4weOKgrIs8gFYomiUEROWFBOrpkErTYiRsihAKXwI+EF0eSiZFistejaVqaUxJUL+ncUZVVDYoiiE4uo9fQgcLU4oXUEYAkpLE+QKh3MFOiOzIQ4CCKnE0HW0XXf6O2fH0mGQTFljHKuuyfEwNusHPVobtvf2cMYyeE/XtoLcek9KMSOPaR15IzmxlgRMJhPm8xmT6RRnDD568pGe/cNDjo5PRDNrDG17yNZWoJ7UWFOgzTgQsczmJdo4Tk6O6YeesqoyOimQvLy2CqUCuihI3tO2rbw2RmcjrkBKgjAZ6xj8wO7eRb7/Bz7AV77yIr/2qV+nsI73vfe9FNZJBmfyrFYrrJFs16ZrSDHm950ghT5GlsslKHEPXjUd3dARM6qstOih+8GjrcUmTTSJmDwhDfK6OQjDQIqJtmtpmoa29/z6pz7F0PcU5VSGPiFgtCIGz5NPPoZWmi8+/yLWFAxDy+VHLjKtS05Olnjv6fs2uxpbQmb2a5O1wSrnhWrN9u4uHB9JhEwMzOdzZvMZZVkQomd7+wLHxzIYCsFLA609s2lBYZTotf2QafwKlSNq3AAhKlKy+CD7gA+e5577IjEMYlilFH1IFKPDtfc4K7pma+U6zyYTYgicnBzj+z7nmMpr4vMeINR9Q4xJYE2jMc6RCDRNB6SsmRVt8sji0HqMZElrTapRWStrc1YzihAgDVmXrHLKbgKI92n3x73sNINUGjwZHA3EmGhWCz70oQ/RNC1aA0MgkeiaBhKUVY1WUFeCxv/cP/pHzOZzrLU0XZszkSvaGBh8BwgLIY7XIutox31WZYdt4L69/uuirGec3s9+fvP5583qeX0r6t+89r180M/4d/7CY2DMb/6E8/qt1Xmjel7n9U0t9W1yS72pm1VrLdbev8TN7NFNVDLGmHtPidJ4kMPVegq/Yejxhs+TD0Ebrr9nUdfxMXmORhsk3/IMWnoaexM3Dk3ynBjHRjkfKFNuWhRgIBIgQWEtBNGlTqzj2uUrvO3pp3n06hUmdYFRGusMzmnKshAktbBYU2aE0d13nVQam+2shTOGRKDtetquE7pnjOsG3xjJZRyNWlISmqJdrcT91xUAlGWZkbnsPpsSxujcECu2pltMp1MA+l7iUqQxkINyHKRxskWBSn59OF81rTg/+0CTDYnaPuJPPMblaKN+wPkg+afGMJnNiFH0lEql7Mpr16/xZFKhtNAd+77n6PiExaqhnghN2FqLVpp6OoeU8DGRVEIlODpe0HUDVV2LA2zhcEWO8zGa6Wy2piYKpTii1WlG6piTmmAdAWSNO32fZYpo8EEeT4qLe5fQxlJPZnzx+Rf45Y/8Cjdu3ODalav4oV+/b0LM38+JIU/f9/goWZwghmVN27FsWroh0Hc9EYUrHCnBECLKyr8xScuulaIsS5Sx+ChI4nK5QmvN7duf55VXXmG+tSfmWUbTdZ4QErPphL3di3z+uS9wcrwApZlvT/iu73ovd+68zt3X9/EBysoJ2mkcYYig5F6ztlhTYbu+Y7Z1gWrocWXJ/MIWSimKUtD75fKY/f175LufGAJlWbO7M8UaOLz3OrvbF9BKYY3DFTXHyxWHxytcqalsyTAIJbZtO2KIoHMeqjb4MFBU1doMa5QFaGXQBkLefxIJbTXKK7m/UwINzloqV0oMTUzoTIQNo7Yga1ND36FReNOvabtaic5c3jsyWBppGgmV0VgrdN8o6K3shXmQNupCN/Y4EPQUUjZXsoQwSHZz3pdcWXBwcA9rHTEM66gjlTXC3ntmSuGKkpQi9+7d4/DwMEsvNGGQ929ZlHTBE1NAGYvKFHFSQo0TOcat+2ub2401Dv3WJnAP+JrN77GpfT3vV8/rm1WPX7jCP7l2kUv/ukE/8/jDXs55ndd5nde/UO38/kf5Y6/u8dfufORhL+Xr1pu6We37niEb+Dwowmb8eIyvGRvYkco2lvd+/fVnHX3H58YoOtGxEYZ8MNo44QjmtZl1CEYrjM6ZrGeCXoUmGEkK9EZzChsIgIgGBT3JaIhOCaPAaoXynjQM3Hz0Ot/znvdw9dIelXPZwTc77FqNK8XciOjRusAYjbOOSQ4cH7IDbQgRHwMQ0Wh8P0jzOIgZzHwyFeQmX08QuqJSiiEEmraBLlMJg2gkjRZa60hh1NkIyjlHXZQUzhFCYn//QH5/pcTEp6ox2tAPvRyYkyJ5j88OqSBobVVP6Pqe1arLRlNgnDSmwUd672m7JceLBSnJc6azCZPpJNOQxVxoa2uLsiyzhjlSVhrVaMi626btiUkxMY56MtkYlCi8Hxj6Bo3iZNWybFqJ9JnPmE5qktKYokQ7R9OsuLu/j1JQZv1qXcggoawqlLaoMKwp62NOa0pi/ONzLEzpCnwMdG3LrJ4wvXGT+WTK3bt3eemll3jhhRd461NPsru7jSvK01glpei6ThobIEToh4Gu7RhCxFiHw9J2A0kpBh8ZvDSZsRcNqTgIB6wrUDlzOMG6mVucLHnuuS9gjM1DJSMNjVI4o3nH299OTI4vvPAi1tXEGPgd3//9XLv2OB999tMoXZBih48DriiAgiF2mEIGKMZKZJLWhuPjJcMwcPnqVSaTGSi4ffs2i8UJFy/ucvHiDovlghADRVkwn8+Zb03YuTDj6qWLXLm8x0d++cOQghgs+X1WjWe57JjOLlBN5hwfrej6Fa6osnOtpy7FHVtbg3Oi6+77XqjVRhx0x3tk2ayy6VbCOHEU1sqgrQwDbGFpmo6uH4gqkVIQQyQFGE1RlLR9T4pe8qPHPcvYtcxBZYReDNYyQyNGUGGtMw+5aR4F8UqNsVgbudN6pPhH0demiM80b7JmfBg81axGZiuewtkspxe68qrtUClRTad0qwYVA4FIWdYUhWW1WqEAV5SQIu3Kk5LG2oIYkDxpoXicsljUg9HUs3vq+svVqfHSZha3lg/GJwjKnLXa53Vev5WaFBUzV/HJP/5W9K23POzlnNd5ndd5/ZZKPX6T7/Vf4q897IX8JvWmblY3tambh5hRL3Yf4pl1ZJv/vWnINH6PzTLaCJ1PazHS8eG+hvZsYyuIZD4E6jd+zVl9lFIKpbPBCRqSZBpCOEWHM2XZoIX6m0AlMR2aTWouXdjhqRs3ePrxx7kwrYmDxxnFdDqhqgXV0Faa9Lqq1gYpKUaMNrRNQ9O0KC05o2hpLrWW3FBnLXVd5TxViX4pnGOxWuIHj7GSabtYnGCsRMuoJIfDputoVz0pJmxGndZDg3z9J1UtDqneE/IBfHwVqko0guIM7IgpURYFVV2sH/NeUNrlqmGxWGCslQOw0gxBtK6TTMEcNZrDMLBYLPDeUzrR68YUOTg4yDTEjNpnI6jxsb7vcdaJXrbrhDquDcYYjNE0y2VuspkY/DYAACAASURBVCuqqmQIC/phoO8G6onE66AMk8kUYy1d28nn+x4fAnVV5cxUT2ENVVXJEMEHFELDDFGyXL33nBwd4aqStsmZuN5jjebKpUvs7eywWq04PDzg8OiAa9ceYXvrAv3QZwR91GRnsySlKKsK+oHe5wN8fg+m/Hs6Z1k1HSEl2q4jxpSzQgdWyxVN07I4PkYpzct37tAslkxnFyidpXIWtKFpem7efIIbNx7j5/7xz9N2EVu0PHHzCcp6zt/46f+d5apDGY0rDTEoQjJED0oXaCXvv3HwMZtdwBY1fS+ZyndevgOMSL7k6S5XK4ZhoKoqFosFVV1y/fpNjo72ee6FFzk4PGRn7wqvv/YKhycL/KBQ2hExrJqeZXuI96LbVSpR1gVal6DE8CzEQNM1DP2QJQJK4oHykMsYvab5Sv6ux2pLoUUv33UdvZcoKmNEsxkjGJ1ISZFMxJYFpS8hRwytB2wpZZaHXlPGfRzlBQLDR7ESF4ryiE4mdSqHQBBapRQhpkwrTvL6Gr1mhISQfYSjsBGWq5WYxKlE9IGiqqgnU/b3DymcwfcdC+/lfaVkSOWsWTNcuq7HWYNxBcYWBD/ukYmYAqSw/tqxuXwQ7fdsTNm41266t69d3DOlXW3+/TinAp/Xb7FKW/Cjl97FT223XPnT3/ewl3Ne53Ve5/VNq3ezYLees9+cPOylfM36hprVW7du/X3gCYShuAD+k9u3b//arVu33gr8NLAH3AP+8O3bt5/Lz/man/tGazywjg3hGhl4gLZpc7K+WQ/Su67NNxBNa8oU4rOmHacT/7EB3ohy2VjD5vNCkPzMdcOMYp3PGSLKGKzWpOgFOUVhszPneLCsyoIrezu85cnHubp3iVlVMp9WlFZTTKY4q5nUFbYSl1ttDDFkPVsUyp+xlq5pMMZKw4ggzL6XJkRroS6bosBmbehqecJisciHcjFPGpvGwlm0NUQSTsvbZlIW0gzFsEZau150oSk/dxj6PFwAV7g1EhWznldrwxjnU5c1VVlQFg6FmDkNgycmqKuKSxcv0rQtPiShXmYqodKCJJVliSscfd+RUqJtW7quoyyL3FCcvheiH+i9NDhlRoMXC6E3Nl2Ptk6cpTNV1xhDPZmiO3ktu95jrALl8eGYxcpQ14IqyYAElHX0rTS+RTcQg9CDZ9OaIcjaU5IGIsSBmFmb3vfZ2dfgu5Y4DNlATMvHKVE6R72zze7ONvuH+9y9d8DBwRGTSc3WfE5KMWtkNSYpYvSgFMYmCqMIQxDKs3O0/YDSSRyelWIYhIZd1TUkaFcrural77qMBA8cHBxQOMnNrZzDGUPvB+bTiqefvMnzz32exaITZNAo7h0e8Q/+75+hbSJF5cAkocUmg9IVWEPoWvBRtM8xsVo29EPEh0g/hIycJ6azGfOtLUGOo2d5skIZGTbEBPf2D/nIRz/Ou971TspK8c8+8WkKZxm6hqKQ+ydERcLSdp4QPcZYjE2CZA8DichkWmKsIg6J2AV8kGtf2II+eEbD7GGIOCeDp+iz1tvKwGboO6wT1kPM5kbOyVAnpojvO1JSglDXE5rF4r59zGedrdZy31hrcUozGoeH5AUdjWKElicUaCP6ZGLOod7Yu073ytOBnjThGmNOB4PGGNHmx7A2YVIkjFEEP2CdNLYxDJkh0a2ZFcYolDJrx/DJfE7frOi7FSiDMo4URCIBmnEM+LX277PGepv7+eZefLa53fweZzNpvxPqYf1t/pet/vaFH+D3/sUnH/Yyzuu8zuu8vun1jv/mGf6fP534weGTb1pn4G8UWf0jt2/fPgK4devW7wf+OvA+4K8C//3t27f/1q1bt/5d4H8Afld+ztf73De2uNwowRsPJpso5nj42kRfNw84Y4N09lAzart0bhrF4Hd0783KstHBNzerRm1kzpAlZxpSOtWFbv4MozVJWUhZ96iESqdVwhApjEWpBLGnNJbHbz7OE489wt7WnK35nPmkZlbXVEWBNUIp1Jqc3RlydmOgWbYUxlGUBWY0UyqcGCQpxeADRlumrs4oYotRCmcswYsLbFWVlDvb9IOnKEXHGKLEWIDGOItxBiL4YcBqg8mHQGedaCVJdP1A06wyvVmvc0bFTMrnZnl0SpbrrlCnsSfBEELEKk01meQs0EBKiq6f0vtAs2pYNS0pehQGbTUmG/VoBM1q25ZIoutarHWUlUQhOSdrvXDhAnVds1qJs25VTzFas1xJ01pkJ+mYEnEYcMZRT+cslwv6HDNSlkGuVRM4OlngnGUyqagqcRHufUBbhzKaw5MTqrKUxshojEIiieIgukREQzsacqkEfTdkk5tsqIPCOieOtClhrOHSpSts7+xydHDAwcEBbdfjjGVvb5ehHyiKkso5mqYVfeqQ0CpgMyJfFgXWwRACg/c0TYPNDVXXdYjJVU/bNDhjWC1XHNzbp8xNqjMGqxLKGm7ceIztrRnPfuxZtJGGaFJPODw4wQ+Joiix1pN0RKcKHUtisiQdqEqDMyUaeb/GEDg4OkYbiy1KadKrSl4/a2XQ4Cx1XXJv/x6rVYMtHcZafFT86q9+Wu7NKLEwF7ZmTCcTJpM5Xeex1nLnzissm4a2beX6Dw0heHzoqae72KrClYayKFgcQ9s2QGRrayauySEPM6ylWS1ZriRLWBsDSPMfUyKRB2JWzJNICWcstlJ0XSQMvUQSmdH9W9yKpaGUAYYKJut5LQojSKhSkIdUIIwPozVaSQSTMqIL9SFg8nND8PdHUzEisZYxgkv2r9EMTnJ8+74HpFGNMRK1AqWJQS5yDANdsyKS8h6l8NkBuCpLyrJmGHoBUZUMBhJ5aPOADNSz5nqbe/j4+c2/B2dZNJuN7ndwPZS/zf8y1c/s/iA/+Jcee9jLOK/zOq/z+pbV2//yu/jEn+t4xwufethLeWB9Q83q+Mcw1wUg3rp16zLyR/FH8uP/G/Df3bp16xLS4j3wc7dv3379G13cMAxrlPJBTerZiXnwcgDbPJtIE6pG41+pbB7DphulPjVfSimRYjh1/lWnOllUWiOs4zrk4CfIqdZ6rUVEgbYOHxJt2+GMQcWANdIKEwacg9Iann7yKZ55+9vZ2dpiezbl0u4OW/MZzhpUioJk2Nx0G83gPVGBsYIazqvJOqrHObem2BpjshOwY/CeVbMSF9xyhxR9btyc0FFDoKhKqrrGWEvf9/RZ8xljxJUlxllK6+i7TmI6InRtS1kaZrOpUPGcoCvLZkVCiY4uUwvHzNOUEilEMf1pGtqmWaOoKQaqqsxIehB31KQJMTGfVVhT0Exqjo6PaZqGfhhIXqI4fC8I6+7ODiB62+PFkr4fCCHSJTEdKlNisVjSZqMn5xzBe45XK7quy061fY6qiUzqiRgBWct0OqOqaxaLEzGACn7thOx9S0KhtRN6tjaIE7Cm6xuscyilBUXNSZO2LASFG1YkZbBWsoXbPhCTJgbRC08nU7ROhBQJXg73xlkSCj/0FOWER6/P6bqG/dfvcnJywuXLl7NmTzTEUYEjkJQ0oyFJY9i3knPbNM16wDO+J/qux/d9NmJMHB7s0+XGtSgspbPEbNCzNa25/Zlf5/joHrrYoppM2Kon+OVAVApXQghLht6TsgmaMor5rMAVFUTDZDLj7r17mMKBUixW0jxXVcVsPs+0bMN8Psdqw2p5Qtv22NKhrSMqg/dRqL3Bo0kMXYMPgcVyRd+9jB/EFKntesqyYtUs181TCAPb21vcettbabuG/bsHlIXFWc3hQWS1alGqQCHZxG3WBw9Dz+j+DQltNMaKyZYiyxeGbAAXApO6RimIQyRF8EHosKOkIaVT/evghaYbvBdH7gApeYgxWy2p7EQuGcqCbCqIET8M8lZTp0yQs3FeKUGMAW1UHsBtuJcjxk0pRlLfrocmSjQLhGFAGYkgCqrP953kCUu8U6JpWqzW1NWEtl2SMLiiou9W2f1brxkasoc/2BH47DBy82vH3+tB9Z3asD6sv83f6aWU4sev/SB//o9PUBf3IA/Nz+u8zuu8vlPr+r+9y+5PvTnpwN+wZvXWrVv/E/B7kD92Pwo8Brx0+/btAHD79u1w69atO/lx9XU+9w3/QdzUIsEbDzD30XtTWuuUzk7T9Wmg3+nBJ5Gb0fsPPqM3yaZx0+mhCEEQMjK4bpRHZ0oNSp2aPCkUOiVUihRWQxyonDSsW9OKtz55i/e/912849ZbuP7IFeqiQJOY1hPqsmCVG6fJpFpTCcfr0OXDtnMlzkkcTAijiy75wOuzZlbWWxRzjLm4vg7ee5TWGFk43TDIddQaZTTd4FEmQYgkIiH1cvDVkXpS4oxhNplis7FV8IGQJOojBM+Fci7EQa3FSVVnVGnTwTkmpnVJTFvSvHovrqa5kdV5/V3XorRhiBHiQF0ayr0LeD8VunAQuugwDAzeM7Qttixx1rE1m9MNA/0gbsVlXa2bB5CGvm1bnHPMZjO2trYYfE/bNvgglNz9pmE+32LZrJjNZhRlybZzHB8fcXR0RFEMuMLJAb7r8eFYaLfGoNEEa+iHQLt/RNN27GxfoCgK+q6jsI6+WYqxjlJEDSEmaV7QlFP5eUVRcLC/z8HRYY4BKhiSmHZppdCmoK4qnHXM6hmLxQknJycsl0t29y4y28qGTsFjjKYyJRFFWAqy2PZ9vgVOBzB97zk6OsaHSFUUDCHy2iuvkoLHFY5ZXVM4oaFHpfjcZz7N8fExhdaQOlx0dMeHuDSIQ9ng0SqiYsSHFlsUmMKhhpa+h6PDFaYosYVDa8t0OkW7gmXTovqexWJBjJGtrS2cczT9ir7tKIoCZTWrtmPwkcl0Rj2RXNz5bMrRwetoBlTyQmMdtJh2NQMHhwum8zmVKbHOUZYFuxd3mEymaGdpm46Tw2OsMezt7aDSAavFCUbJIKLrOqEgI8ivdSVVKf8ao1itFsToid5DUkQlQy3Jyg2kFEW3HKNo25WWTFbA5dgpBQxJhgdDvvc1Cp1ZHiMpZByeGeNw1sr9lEQ6kZS4b2skJ1rYCiN6ehr3pfT97JMkDyCmUDIQEzQ3rhFRlYzsPelUpuEKJ/ds8MQIXoHTFmsLhqzvVdpK5ms2WnoQkvqgRnMTNX2QzvXsgPM3M276dq6H8bf5O7l++MozvN/s8Wf//FMPeynndV7ndV6/baWefpLbH3yJD3xki88fvPSwl3NffcPN6u3bt/8DgFu3bv17wE8Cf+ZbtaixBBW0ZxCA08b1PpQV1rEym/Ugk6T1v5mWe19jqxQqa1rPal1JCaPG/MJTVFdriZRIGQ25X1ubSCGgYkATscC1Ry7z+370R/j+734fVy/uUFmNigOF1ThrJYqCxPaFKUW5I/EsKRJiyNpUmKHQ2SDKWCt0O1VirMvrkzVoM9JHEWTFOQiermkpqkoOxiniQ6BbNXRdS9f1BJWoykqQ4tpBP3C8OGa+NUfpxKI5ZvfCNst2wdC2lGVF4QowClvIx0oblDGgjVCqQyQNojW1VhGCEr2gTtSuBAVD15MiDP0grrlK3EircirX21q0cQQvDW/wgVXbs1iuJCdzOqFtWwYfhP+J5JuOlFcfA23bYpReI4iCnuZMSyNa5a2tOdvbFxiGAUgslytpNGKkbVtWq9WarlvXYgDUHp+AgrqWrFsBT8VRuigczjna1nNvf59m1TDfmtF1HdPpBGM0i6YhpTHyJhtvZTSqqiuWyyWDl1zQYfCsuoFJXVMUBT5GcaNNkk1qjWZ7e4fd3V1eee1VlqsV+4eHTKdT5vM5dVWR0CybThDOjSbDGEHG267n8OiQwXtyJ8TQdSxPTrBWzH6sVqIHRdxkV6slhRGKcooDqV3hU4szYkoUwoCxjkI7CgcxndAtjhm8IVFjlKNdNWy5gqqsCApMkGFQP0h8iiudoH0poZXBB9GzYmG+vcXepV0ubO9gbEUYAsvlCQkDKmGcwtrIbHfOfOuCGB1Zi1KG116/i3MF9Wwi94QPaCUu0qHrODk8wtqCy5f2OD464eToCG00dV1RVZVovJ0lpoC1lul0QigLlqsTcTUnu2xniq7JTVQIou9OURF9DptJrO/bkWEg+5AMvzJQKoZsssvI3kPOQTYKr8jN6kZudIKYYjYV2xwEjnviOAzckFaMex7CXklJ0FRheiQiikw6zr+LNPFaZdOnzKoIIdCHiDWOaD19N6CM/f/Ze/NgybK8vu9zlrvk9vZXW9fWa/UyPT1rD7MwjBhAEhEDKEASUoSxZWEDYeNAICxZNjiEtXhCMg5sg0G2CWGwAoUlI4VxWEYg9qXpWXp6uqe7qvfaX9Vbc733ns1/nHPzverp6emZgell3i+iuqrfu5l5M/PmyfP9/b4LCo81UZZwkALzatPQVwKxL/+OOLiGv5Xr9fhufqvWdxx/D7/wVzTy7YdA9bAO67C+9qr8rq/n+z7xIj/CmxSstnX+/PlfPHfu3D8BLgO3nTt3TqXurAJOAJeI+6cv9LsvqVrwN3fPFWJOkbvFbAnmXfaDzpDALdSwW004Ph/0KqVeEfTKdvJAANkCWhU3lt6nTVkERwAymZdY0yBx5EKCd7zt3D18z1/9bu69607KTKCCReNRMqDx5CI6knopKIos6s1sTZbnEYgGsE3Un+qiAKJjqFY5eBt1t1IQUHjjENKBiGYnIs/j6YVAVkZjna2dba5fu4ZznrJTMlhYoHI14/GYe++9l8FggLWWqqq4dP0yk2bKoNsnl4qFM2cwVU1ZRtrq1u5WpDf2+2Sdgn5/gO6WYCyubhC+3Uz65FYsUFonUC0QOpo4EaDwZYzJ8B4hC2DfFVUIlTbh8fXoB8GaW2U0GjMaT+h0oi50WlXM6jrRRsE6gXKSEBzWNhAi3bKNqJEqOgwDzOqKPMvxwVNVU7TOcMbNr6c8z6NhlpAsLCxS1zXD4V6irjcxjkYrIBoZjSdT8jxLE/uCqm6YXL+BEDAcjqibhqLIU46nxNkIYBYWBlgfqbDD0ZjRZIwDgiBpiwMYy2w2pUh5qSE4yjyLbr9esrq2ngykxsxmMy5fucKJEyfI8oK6bmgaS2Mso8k0TmmVomkaqnpG3TQgINMxr3fj2nXGwxG5ysh0Rp5l9LpdxuMI3stMY5omgpfgcc0EpXR0tXUNudSsr6wjZM5oOmJvNMRWBue6BBnoLx/lPe95kGMnjrM3mfDS5ct4xljfQ+mMXn8BnavIOqgqrLFYY1lbW2NxdRGVZ2zv7HH5yhVAR9AnAraZ0OvmrB9Z44H7TvPud9zHqVNL7A0VjzzyGL/3e3/A7u4ug8GAxtRc27jB0aNHWV1fQ0nN+pEjKCm4eWMTUcLS8gJZpjG1Sa9VneJfBD60lGAXP8PB45O7r0q5zFpF2rjUMR/ZWoNrDI2IdPYQ9mmtNrRgNTXkpIx5RGnhOzgwFMmqyFtPbas566RtrikpCS7M1yuZPlMtSAw+Ukta9kjipcTc6jTFF/i01ikQMq1/dq6rzvM86tNt/FmZZzHv17mIrlWMsAneYW0FUqN0RkTqCXq/AvX382JrXmFS+oV+9ladqh6sr/Z381upFsseFz50nPIdGfJt973ep/PmqPR9/uLf/TT/qlm+5VcFgh/4e6cg+YUc1mEd1pun/vpfGvGL/+wOHtt8/vU+lXl9UbB67ty5PrB8/vz5S+n/PwZsAzeAx4C/AvxS+vvTre7l3LlzX/B3r7WCi6YkSiRN6i2mIFFnGl1AgCAShY5Ee4s6q1Z5NXeIxM2nV3Og61vqWdr5CTEHp62OVbQxIDJt8GSM/LDttFMqnA8Ea1PmZEOZF9jGUshAR0s+8N738QPf+9dZWexDsAjihDFTChkCWI8MKlLjQqCuDUVZkGU5TR0pu0W3i8oFwVhCsEidRRdg4xAqaiFtzMVAlyUyz+PG1jpo4mTLeYfTiq3hHjd3tjl91x2srh9hb3ubxx57jFOnTrG5tcWjjz7Ku975LjrdDp2yjPpHKSh0xs1rG4z29hj0B1TVjK3tbW5ub2K9oxgPyfOc9fV19NY2g8EA55KGL8vwIpCVObgA3QIlBW42ReTRiEk4H41mvIpT6Uy3byKkTbbKNCBx1oJxaAIL3ZxOESmLQiqCiPTfqmqo6ghcq7ohVzl1I6gbhw+O6WwSAWKIQM2lOBLr43Wisg4ej9JhnkvpCWRF3JTXpiErcpZWVphMJlR1zDPVIsaCGOHwDqzxGFPNadnWhnnjJbpIe3TucDhMHZ1nt3Z36Xb7FEUWnYqbaIAE0YBsOJkhhYwT/9rRNFOyTIHQSQvo6HQivXVlZYUQPEeOHeWJzz1NYwN5XjKaNNTWgciQKmCcZW9vh8l0SlGU4AOZ1uzsDLn04ktoJQFFpjVF3qHsdBiNx0gRKJTAOwPBY33EJsEZgggoAusryywvlOztjmhGO2hj6HjPZHqTB95xL29/3zch85JnX3iWUTXFNNP42QcaG/CVQUwr6ukYyb5GXCC49MIlggAv4vF5p6TT6bC2tsyRtdNoGVha6nL73XdybXvIS9ducuPGNhdfuIQPguADWze3YkPECa68dIVMaJbXl7HBsnL8BE5mXL18hYVeIO9kTCYT4pRUMG0q6qbCi9j0qmYjpJDI4GOPyHs0Ai3BmRpjLKgUHxRiZnE36zIaDVFSRfO0FM/kfWgt36J+VLRNubgEKqnm66OU0QStjYU5OIV11mIO5BiTftcqJUQkCUMISRebGCRB4B1IoQh4pNZRiRvaJbNtGkpsk5pmQiR9rsU6F+UVIjpRIyIFOObtKqQSBFFFPW5o1+00QRYhsRQSaKXFtGlifAC4yiRp8ERtdxpGR2p7eGU965u1Xs/v5rdSHe0vc+F770a+422v96m8aSpcucr3//Qu02CpAjRh8/OOGf9Y4Ef/5iKsrLwOZ3hYh3VYX26pdz3I757d5j3/TcP5ncuv9+kAr22y2gP+z3PnzvWIe8Bt4GPnz58P586d+37gF86dO/fjwA7wPQdu92q/e80VdVHp3y2bVbSxMC/TOLWbMxFNQkTagYX5ZFYmc5uks0p/Wipv/Fl0/W2jbISIk0vSzzOtsc5hjcFYhwtRVwgB6yxFlmGaBq1kpAgWOSuDDt/60W/kL/6FbyfzngyP0ipOWLLowCmDoOhkSC9wwaOzFPOSHGF9CORZhku6QiElMssIMlJTpdbxuWuNkiFOdpRG+GhkFOo6GhA5y6ia8uT5Cxw/dYq77r4Haw3bN28yWFjgne94Bzu7u6yurmKNYWtrk7MLd+C8Z2mwwHA8pp5VrK6uUDU1s60KnWWMmxlbwz10phlNp5w8cRtKSqajMdPJBBs8vcGAooiaSmkFGAvGEJTCe4uZGZyzdFWGoI5vaQgRyCfDKnxLd0zvdfoTnEVK6CTDKKniZDGkOBFj43NvTNTPzWY1o8mE8XRG1cT3MvgI+CMYY07dDSG5RvvoqmqtxdQ1TboWhZTUVXQHlkKR5wXOxFxZaw3B55Ge2bQ6Y421HoEg0xkgsKLBJyManxCADx4zmTIaTxAiZcNqHSNSvMdYg7GGTOcIHfNvkYLGOkbjMUWRU+Q5Usb4k36vg/OOajrlttNnGE8rnvrcM4ynNcaDtZ6dvW2EiM+9LHKyTCNSRvD1a1dSLFFIObUZZaeMtGEh0ELgREDgksO0ilTsBCibpmFj4xrbm5uYpsGaCKy0kKwt9ejmgScee5Tt0YS818UIgVIZOhO4saVpDLZuUDLGPQmiO29jhhTTIubk5hmLKysUvQ4qy0F41laXuPfcHYyHO2zevM5nHn+cqqmpZzXdok9/sMCLz19kOp3S1IZMR33wtJ7y7DPPsT5aZ+XYGlJL1o4cxQM3rl1jodvDeU81maKUoigKrLNUpoku2VrhTEPTmPj66Cz21rxPtNoQdd5pfQpBgPFpaCpQCBBqPjmNkC026KTSqeFGdL32bk4r9t7HeKy2kZfWtZdHd83X2BDwLiBkzGMNIUYehbTuCUKcvspA+1Ek6W6ju3D8TIREQQ+BGAk2P/E4nRVp6uqcwyZ6sBBqHtkVcLhgaUEqydSpXaz3AXHU37b05lecnIpIE3+L1+v63fxWqAdXz/JbHykOgeqXUO6PH+Nv/+su193kVY/7PbfJB/6e5YM/eQhWD+uw3nS1ssKjP3gP3/5zS/zmxhOv99l8cbB6/vz5DeDrvsDvngZeMSH71X73Wutg8Du8TG9KGqq2m7E0Gb2FwRsCpOlYCNEM5ODxUshbTJkOmi+1G7pbohJEwFqTAIvDOR/daxNAJkSgiYwb1eXVFXqZ4C9925/nG97/fvq9Lso2FLkmBIeU4J1FakVWFBFIO0/wyYQonU80OTkw5ZAS2Wp5kxYujqAVaU9JoXOkUgQXCMZGx1wCN3d2eOnKJe6+9xxHjx7DGItEMBmO8Mayfuwo3gfKopgDsdF4RF3XhADrK+sRWDZ1BEvOMRnu4gkcO3GcyWSCM5a11VWKLKezuopxjudfepGt8R5l2WF5cZmOyugXHYKzmFl0ySV4FAFTTwnJWTm6q86iGYvYz74VYj4zJ3Yk9nW51llIICBCgri5z7WmyKJxz8rSEk0yY7IuMJlVjCYTGmNo6oZZU+PqCa4mOi87jzE+TgpDiEZcWpFnGVKqaN4UAjpLmmFnk9bQMp02KJVo4yEQvMUYGydiKmZotvmVOi/idDDFizgfH9MFj2kavLUxmkjJlNUZaePx5QhkWYZN0SI6OUM31uKms3mm7WRWM51VbG/vIpVmcamLdYG94YjhcEiWabJsAdIkriy7bG3tINK0TylFWZbkRYGzDgmR0i0EHtBK4RN40amJ0jRNNPQSAplL8qIk09GQzFqHlJK93U3KgcY1Y/ZsxUwoyDoYD0p4cizBNQgHaE2QkUlRFNFcrNMpGCws0Ol0WF5dwwsYjvbY2LjOUr8A13Dj2nVUfoLTp0+yuLDMzY1Nrl29FrOFStBzegAAIABJREFUnWcwGJDpgslkgvee4e6Q8WSM8ZaFxYU4UdcZyysrSARLqytsORdpzl7ECbyPkUfT6RT8fu5npNAGnGtQWUaeHK9DWoO8i82vvCjStFTgXXTjJqSJo4yU/naiGYLfp92aem4KN1//5kvhPpX24Do6l00Q0nr4CmZGtE28GKPjffqJ2HdKn1cyZYpU4gTC099CiLmxWqQgC7xv112JkLHhIrzdB6HEpk5LfJkv/LFFla7/V9alvjzm5q1Wr+d385u9cpXx/yw+zEPv3SD/2Adf79N5U9Wjv9zhQrb3xQ/8cssH/vhvXuDu2zdZ/sHPf2+e/tuPc7Pp3PKz25f3OPlj7/nTO6fDOqyvwRJ3nOVXvmObv/ivH+LfXv/M63ouX7Jm9atZr9Qxf3mwe0sRbgli7fYk+JdpVxPYk8RpTqY0WsWnP7/NgcdQKm7sW7Ml5x3GGBozixqwOJONtw0RUCqt8N4BDh9geWWBc7ef5vbbz9DtlGghUpRF2px6RwAU0ZDEOY/3+1OQ4KNuMc9zQpqsiTyP08ikXROB6EbsLUIErI8uv1JK3KyKAErI6PoKjGcVd997Hytrq+xsbdPv9eh3urDoGQ6H7G7tcPXqVVZWVuj2e3T6PXRZxkmfMZhZTV3N6C90o/4uy7DBIyTMmprtmzeZTCbs7ewgFxcouz2G4xFXrl0FJaMmsGnoZgWTvGB9eZVur4O3Hm8cSsapjhfR4db5OIkihEj7JtI8RUvpEyKCJZV2syJGj8SbxPc++IDzHp8GTW2jwnmPEipNEfsMemXKxAzMkolSUzc0dc2sblCI+XXlrMc0NTPTRDdlKcFDZWqElGipUSrSM8k0AU+TdI0xVzY5vgaH0FmMF8qiWZbUMlE+47TeGRvdjJuYiepDzGP1AoqiiGBVRECQ51kEsiK+jkIqlIzNmq2dPbxz7E3GbA/3aBqH1DpO/lTMLj19+jR1XRF1g3HKNpvVGOvoDxaoTRPzavM8Ra8EMq0oshxvm3bslj5HYq5PllJSFDErNcYIObTO42sXI0kZD7dojEGpkpWVFfasYm/aMKtqbF0TmmjqE5tPRNqylhhjIlhWEiGhLIpEma4ZDUd4V/Pp3R3Onj7Bd//lj7G0eoxHP/Upnnj8CappxeVLl6lnFWsrq2RZzs2bWzFHWCnKosB5z82rG+xu7yKUpNPvMhj043POG0bDEdV0Bgi0ihrsoBTWCarxLGp5ixKVxfxg5x34tAalmCsfnY9AyKRtFpEe7zJcmOBMa3oUnbUjtcAjhaTb7SKlZDTewzYmyR3CLVpWL5hrxl9pfZXiQGMuvAzczd/StiEYs2Pbaeqt7rsB7y1SHYwYYz7xjUCW/WgdosbaJaq0EJogW58Bj5QaKfbX8jZuTBBiA+8VwPVctiFibNlbi/x7WF9pCSE4/8BdrP3wOeDc6306h/Wy+tX//AX+t3zE8rVF7vg7z/N3/moNQvAP/o8cgM/iqLLdW27z0eka/5mPfiJfVsXMvP3/b2kmh3VYX+Ol3v8u/sXC03zo58/y5PbFOTP1q11vaLD6Sl17YK5BajNUW2BHiJuyduPVTg68j5MbpRU6xacopeJGuQW1Bx5zftuWqucT9dMaXHpsHJB8MIUALUQEHjjW1pa5//5zrKws8aEPPExdT7m2cYWTR48yKAuCNXGSJiVFWaaJlU/aK4+1Jk3iDkQupDGym83iJpH9DWAIgbzIcaZBSIFMky18NDCSmcZbwYWnP8fK+ho2wIVnn6WQmqauWV1dJYTA5tYWOsu47/63ocqCajYFrdja3WZjYyPqFaVidXmZ1eWVaDoUPFVT44n01vVjR1n3Adc0CO9x3rOyssK73/Nutnd3uXz5MqdOn+bk0eNsXr/OcDJmKc/QQlAOBrjZDCUETsr5hlkX+X42JCHRIyEkoCoTZTKyhD3W+3SppM2wjxmQLdjF+zh+dg5jI53XJ3olQqK0ZrHboV8WRAwc3XddkDgbEoh0VLMK4xzJDQdnLNNqhnetqZVAZxqlY/PDpOxWZ13a8B9wXPUehCO4AFKj8yLGDQWNUSlqJtdzwDeZTKjrClM38b23JtLC08RKK0VRlnS6HZz1zKoZu8MR1jjqpsY7gVQZSityAuPJfr4sxEZKbZoYr9M0VFXD+fPPMpnOWF1aJ9N5fNoInG3ItGTWOARECmqWYUOgbkwEfonC7L2nSaZEQlSUZZc8LzDW0ClL7jh1hBc3tti48jx1tkjlFXhBrhRGGBRxuth4R1NZVBa10UWmYzautVy9fj02UJSkyAuKvEDKwEJ/kRvXJ/zLX/mneG/J8oJM59R1TZ5neOcZVSOGwz3yLJ+DsUwremWHvCzpLQ6ovaVpYkxUoTVFp8NoNEKG6CLtXWRCZEWObQy72zuxaaQzfDAUWif5tUv01+i4G2n9bZ6zROp4LYLAFZbgHNbUETvKdoIpsdaysLCAVLC3u0twB5zARYywaum04sAGbA4e4Rag+krr8H5cGICEIPEhmpG1LBQp5s/mFn+B1it4zmpJ//U+zB8/EKX1SkqiMZaFIEH4xJpovyBblk1sJM7JMOHAfd2C0ucPeFhf41XonHsXb+NfHumy9kMPv96nc1ivUGFjg0fzKEvYcjO2mPGXfyk2+JvwhbMffzNs8Wf+1pQH/9EDX/Qx/JNP38I6AbjwS4b/mv016z8wi/zZv1Yjz911mLF7WF/zJR+4lz/4+2ep/7+ctV+58LqcwxsarB7UVs1BW1pjZNJjtVSzdqP0eVRhSMBQoGU7lQnJyIN59IMQIk4fhJgbdLQ5g63GyoXoCQRyDpqkiPTHaO/kWVlZ4uGH38lHPvL1XL16kVx5OmXO5WuXMM2U08dPkElBJiWdsgNBJBOakKJYLFLug/S5EUqiy8UMxQi0RTKJipEsJoK4IMDFaSpK0jQNTVPz1LPPkhUFZa/PeDZja2eXxW6PuokAfHNri6JTcvb2O6ic4/pLL8bXQQSevvA0165v8MC999Hv9tgbDrn43AUaZxjPZlgi+OoUBUeWljnSX+KO207R63cxPm5o2xic97z3vSytryON4bY77mDj4iWuXb3CieMnoKlQRQYuEL+e4vumEs1Rzyc3YQ5Wo8usg0R9ndOFD2jc8GJOY3Rpg+5ME6d9eYZLoDqI6HpazZpIQ0xcy326YXKXVdHQKM8EWbYPwuhkLC8P8D5Q1zVVU9E0TWyIEGI0kSrnwKFtkoQ2d5YQdYsi4EyNI4KLTCnyrIzXOlGL1+uUlLmGBOobY7CmoakrhBAURUGe4lT2RiPG4wl7wzFSKGZNRW2jiZUQgqqpqep6PoludbpVVVMbw97eiIuXr3Htxk263T4602it0nkbqtkMJaPZVdSAR223mc0g0d1DCBhj5iyHLNGxIzU4gvluJ2d3e4NBUTCaGTpdReUzhhNDVRmk1AilsEGA0mRaobSi2+0yHg8ZjycURclgYUCvv4BNul4lAzJYzp9/hmeeOU9vUNLrdbDWcvX6FTauXadbdJn5KcY4FOCtjetFp0unjEZNnX4PXRR4U2Ocxbg4RVxYXmE6mTIdjQERX78kC8jygtX1dZq6pqoqhBMIqedMAZFeD6Ukqd+GCzGGyIV4HXvvyLKcvJMxnY0jLdjFaxagaRpGoxGIRAm2DmtNApBxIuucndNnI3Dcl1a0E8h2TnlQGiGEAB8bdqQVNU7MxbxR196XT8eIAP4g6hVuvhILmZgJLtxy29iAjCBYCgEqae29x8t9oHwrtffAQ4gDhngH1s0271qwz8I5rK+t+qZjb+ddcpn7jeQ7/9Htr/fpvKkrPP8ij+fFKza1/iTqkY/v8lg2vOVnTXBf4Oj98iHg/BfvSs1++Xf4nsf6X/Q+f1Zv8bO/CD+z8Bi3/Zfv/aL3e1iH9ZavsiS75wjfeLTg32189qv+8G9osPry+jxtUtxfAQm8CnFL7Ew7XZtPWgHvok1mEPsOv1Kp+VSzPc45N58oOOfin+DxJLfZREfTQqBFQHjD2+4/x5/7c9+EzgJbV1/i+OoCC/2C61ev0+106C4NuLGzRa8sOXHkCEVZYhtD8OCtQ0uJzgqEilq0ueYr0QSFS4CrjZ2YT2MDMpNJ2xUnKT6+KOgs4+KVy+zs7rJ+/Bifeuwz2OBREhZ6fYy3XLp2lcXFJZbX17l4/TpPXThPt9/j1JkzPPbZz1LVU8498ACTuuHqpYvU1YzGNFSzCucdvX6XbqeLVoqtzW02L28wGY554L77WVlbo5lO2dnZZXlxmYXBIs1ojBSSvKNZXF3BGMv1mzc5dtttNLMKKRUqy5BaI3zMu4SAkCpOi7JI0Q7ORYAUAjKk91rqA5RE8NYjdKRORnquJDg3n7aDxFpHVVdIIclyTeY81tl4PQAkGqIxNgJa28ThrIgxSk1dR6Oc4FMUT6Bu6rSZjpNXZy3eR5qoVCJNxD1SCGQmybIcEDE/NvgIOJyL3REZJ2xaikSptGgpKPIialSdZTKN52yNjSwAEx2Qtc6jG3JVU9cWhKGxjiAlxljqpmE2a+ZTttYlW0qNVA5jLZvbu2zc3CLvdMiLHkHE9yH4QPCOpq4oyzKa2ng7n3LZNOnPkl4XwBgXJ38hEIJLGsuYt7s73KWztk6uYLmrOXrbOjdHDXfffpxL17apLVTWMa3r2KyRkoBnb2+Xpq5YGAxYWFxC5yW1sUxms0ijtgaNY3V5gfXVRXyomU3HXLp0iZ2tXeqqZhJGaJ1DEPT6AzqdTszUrRs6RYlCUE1mNJMJVXCY5NJbFgWFUhw5epwNd5XhaMRCv4+HqGMWgqLIyYsCaz3W+tRUCWilKTolgaizFi1Dwnmw0bVbyWghZp1HCItSGUrq+Bn3FpUcjWZ1hRRJI5zWwhACLrgEhhVtTuut+aoJlCba+cG1tr2NCy0rQUaWAslPIKTPhj+Y2RpZD4kIMr+mlEoyBxfvK4LwGMmzz4IQGBfjf3RyRLfOEnw70Y209HkPKm0494E18y8EIfe15SRmivjT2mEf1huuhBD81srXkUvHfX/BoN53mJv6Fdf2Nj/7v8C/Ca/dOPr/7Qi+7g8/hXr/u77ose6RT/OrnQHYL+/0fqUoePBTn0W968EveMxPPX6SJmy95vv8oZHie39kP75jECwf+Ml7vrwTPKzDepOXvO8e/q79HP/udXjsNzRYfXmu3itl6x3Ery3QfPnmRSnV9vX3Aa/wc7DaarDm0Q9Kzp0kD1JtWxMn5xy5lJEiamuKXPPhD36Ab/7oN3D69HEyHbhy5UWGox3uPP0O+p0u1zY2mMymPHvhGW47dhSlJcfW1ilkRj2r0VJh6pqy7EAAk8DD3ABFtDrXqFNr6Xzt77NCo7McEQTWmAQeBDPT8NxLL3H/gw9SmYZiNGS6s0NZFPQHfYajEWfvvJ3uwhLXNzf57Uce4eb2Fg8+9BCfevIJrm1s8MDbHqB2jifOP411DiWh1+ly8vRp1lZXyfOMuq7Jsox+t4uZ1WzduMkjn/wkZ0+e4vYzZ1lZXefFF1+g1+njnOPi5YtMJhOEENxx5izbNzfZ3twG56MxUxnQWZzotBTpPBdx2iJcNCEKHhlABAlSpcgOnzanEpRCZrJ1osFVVQTAmY55q2k6FQIEIWmMwTcGqdIE3scprpaarFAURZjTYyE2N0ajGaax+zRlk75pvZk3T7x3KX7EE6SPVF8EpmloTYi01olJANoLgtYoKfHexggTUxMS00AnnaeSccKLVPRasNiLwGV7e4fxeIRUGSCxLjooBxcppj54qrpmPJ5hTAtAxFxraJ2nKLuMJzuMJzOsg7zsIXUWMzHFPmgwpiHPNXkWs20Jgaqaxc9e0l9755KRVOoxeY+bU0sFKtM0xnB940YEsLpg8/KzDGceheD42jrXd6dIXyJUhvWxmeC8R+BZGPQ5dvw4edHBuIA2ju29PbI8ZpgKrbj73D2cPnmc3/7NX+PFF59je3ubpmrIs5zFhQW63R697oC6adA6o25qijxnd2+XyWiMLgqKfg+ro57JNBXVtGbQ69IvS9aPHmPj2lX2xhOKMhpl1XV1i9mVd566aWijVHyasMoEvp1zlJ0SQYfh3h4hBJTO8d7RNNHEq8hzlJZYWxOsZa7YD/v5qdG12RFie20uKZhPPw9MTtvj28HjKznrxu5QeqSDU85Ekw9zSnEylFIHJ6Bh3pxpjZygnX7ua5yDF4ntkq5VIZIpGUmv6/cPly0g3n/u7fM6+L0Q2Aes4S0WXXNYn19CCH5+7SN8x3dso99/76Hu8E+wwvYu/8a/dqAH8Fm7w+ypiv77v8iBPjD5zJjP2i8TqQKPuW1+6F8M+B+OX0McP/5l38/BaoLjZ/T+c15XXT5gzCE1+LC+ZkvJ+B3+1dauvqHBaltfyMlRJjCwb9QRiF43+9OCFqTG/ZBHtHE0QiGIUTYIEad0aaMnbEuNi3TfllIWMxMDRZ7h64oyU6yuLvBtf/ab+br3vJMTx9ZYWewhNZw8ssiVq1f45B8+wn0PPkRx+jSPPPood911F2dPn+HqpctkRYfR1g6DosNif8Cg34+gJkRAyss2j1JKggy3gnaR9mIOGhcNboxxTKYVjbds7+1RdMuohdOKhYUF1tfXObK2xu7ONgFB7TzPX7jAHz/2afbGU4xz7E1nPPG5p1hcWKDfH/DJT3yC2WSKyjPOnr2T5fVVtra2+Mzzz3Jja5PBwgAlFIVSrAyWOHXsGJ2B5alnn8OZwJkzZ+h3F/nEH32CB9/+IEXWodaGtbV1Nja3sI1h4+Ymx1bX8bXDuBoT6riRzzTBB6yNdJ+W6huIgM27GBGjpEoUVR0nk7N6Pome639tTZhVIAQ6y/GBGF3jHNaHJGd1KWs1uitb76jGM7I8au+8dxjTMJtVzGZVnGCnXNM4kYrA0zqfKMTRLbYxDc6r+XuJiNcrwuO8ISCRQUa6syTl5mqcPRDfFBzOpPgirZHJLbXM40Q5utVKptOY92qNiRmikQyPC7EBUFvDbBZpyiFEc6IY0SMwKedWqYxZZWisoOwP6PR6qCAotILGovWtQKTb7zAdDanqKU1To4RAJRMcH+Ik3FsX3WEBkcywokFUQOgymi4RWF3qg/ZkPcl4+xKyqhAuI8/7yEKBVFTVDNM4cpVhqhmz4ZByNcPUDZeuXEPqDIJjZWWZ204c5enz53nqc58lzzyrq6ucO3eOxcGAvb09nLHs7OxSVTOk1GS5YlZZdvdGKBFNnIz3kGm8layuH2GwsMC1q9cYDse4yqCVJC+7OA/TKjZvnAsY4agbw6DXp9PvEaYRIFrvIk05maghJJPZDJlpOkWBUordnW0Q0VwtOLCmIQSX1jA5NwyLFwfz3F7mP4+0XUHb2PJJYn2rxKJtgh3k1h70DIguvsy1/KQp7UE+YAt8o8winpNS8XpvmSrt5zA2BvfNmXxqyMVziK7IgoDQipCaKXEZTkA5tGv6rUBVtopY0boF+1smxof11q1MaX5q7cN81z88A/KQ7vsnXf/Rz8/+1O473LzBT7x0FPjKXIYvuhE/8D96fvYfvDJYvb8RDItlnrQ7X9b97/qaP/hbLxxOVw/ra7Ye/Pj9/OCPFfzUld/5qj7uGxqsvlL0QNRh7W9IDk5S05Hxd3ALkI00UomSkZIolUQItW/yIeJEdT7JDFEvKUXa6IeA8J5cgGgqernkrlMn+PZv/Rbefu/dLA869HNBmUUfV5Tm5Inb6JZ9+t0BFrj9zFlmdYWXgpVjR/jt3/t9BkWHB+46hyoL0IrWMMVYA5AodPva1IOTkIPn6kOkEvqUX6iyOAHbHg1ZXF3hs089gTGGbqfL2soqs+mMK5cvc/zkKaRSPPvC89zc2aV2jrLXpbs4SGYvir2dPTav3WBhYcBDDzzE3t4e5x97gsl0SuMsLniu7lwlSxvsy1eu8/jnnmKp2+eO4yfZm1W8cOkKmVBcv3aDpcUr3NjaxHjDtas3KIocJRXCw9GVdeqqoXJxMqd1js4iEBsPJwgR5pM9aw3OeYQISBVdVEMIWOvm76tzFgJkeTbXlgYgy3ICIur+2saGUIBDCEmWxY+GNQYpoShKwFPVs0jlDCCVpijKOMhVAusCwUOWKWSukD7SD2+dzJPiXParbZLE67ul+sb4j2jSJGg/qu30qm26QCAkyrDzEbSjFL1uGY2IjKduPNZ5rAsYG42hJtWUEGTMeU3341zU5mqtcUGwsztiPKuZ1g1Z2aHs9vCNIcs0PgiUCFhjosbWWzqdHr1+n6au6Xa74CMwdS4gfMClz7FKn8MQAnYObvY1hi44RuMhJ0+dYFbVzGYjtJH0RMlsNkbpLr2lZWzRYXNrhpmO0EpTj3e5MtrDCUkuobE13hvKlQW2t26QF4pv/ui3sLQw4OqVy2ileOmFF5hMxuSZJsvWuXz5ClU9YzQeobSmP+hTZnmk6iOoqgpVFAz39uh2+5w9fZbLly4SvKduLNbHrGPhTNSWpvUpy1pttCPLs/h+NVEP314jMlMRtALWOpaWl5iMx5EunjSlBB2psTZOTJH70ThSxoaJS3FW7Vop0wTSt804sW+YdHCtbSelt66ncTVtD5VS4aPQOp23Sy6+Yr4+z2UL6XkJsW8W117Dt2pQwy1/4vA0GQoQSRJzfHyLUVOYn+8rfV/MQewc1XJYb+H6qbUP8+99/OzrfRpv2bJ/ipMUcfQoH//2a/zEv1rhMbf9Fd2Xe5UP+rf949v52MYGv/1x+PVOnPx+KbUsi0Ogelhf2yVFO0f7qtYbGqy+0maqnaa+wtEHNvopLkHu55IiYrqIQkS9W5pCtU6/6UFu3fgkzRUh6lMVoIMll4H3PfQA3/SRr+fc2VOsLnbpFppMg3QWlWX4BASOH+mR5yVBScoHuzx36SIXnnuOy1ev4INn5ixT0yDyDJFF4NDMDHVdI4Ugz/PPi+tp9WQ+GfM455hVTdyEJvClVTQTqpqGk8uLLB5Z48qly2RSs9AfYKqa9bV1jh8/xsg0bO/sRi1rWSAzjdSakydPg7GMd0f0iw4P3fsAa50Fuk5y22CVaTVjZ3ePq5s36JBzbXuTJkTNpdSKzd0hw70xnXe8k7I3oNAZZdnlyuWrrB05gg0OnWl63S6T8YTrV68ymzZI6zHGxE1mM0v60Wj+o6Qiy2ya2IQYQ5Qcd4UQVFWD9W4e+SOCSlOWSH/VWQT/UmVRUwhkOgckpq4A0jTWo5Sc6+28d4QgCEGlaZaMUSlk84mqzlQyztGJiezmE6UY1RKzcbMsQykVnYGTYdbczGtuErafCXywpASls309YXKGtraJ17gQUesoBN1uF22jTtVXBls3VFVF3VQJQGlCEHgXjalQCqnj1NIZT1aW3NzcZndvxJH+AkEqsiJqTLWQYA3BGCAwm80oioKFxQXqakY1niT6tUcJQVAqRqeIZNCamANax0ZEpF4LjLXU9ZTGzuBSwz333EWmPHfceZq8u8Tv/NGn2BluYWQFWZflXs54bLG2oR7VIDNEkRO8YNAfcOzkCXr9LutrK8xmI25sXGU26VHXM86/+CL1dMbu7g6j0YjRaMzW1jZLiyusHznCex5+H0uLy/zq//2rkU6rFN2sJKiMPCuYjqcoJHlWsLuzE6/DPKdxFl2UkbqdGAAx/9ZijJk7Ordrjvc+GnGlbN4QAiY0+HTtkNyZQ5qaGmMQwVPkGY2382tMKIlC4kTrFByN3KTcB4iw/+/57Q7+UQIl9qefB6NpODC1bPXCB9fpkOj2c5AoRWI1xDW4dVduP2MtAHc+mj8JSWpapEYNIQF1+XmfhQisX+ZkzL6x0r7B3r6xwSEh9K1b/3T9z/CdP/EnQ/08rD/Z+rlnT/LDN24gjhx51ePUw+/gz//yeR77Chm2Hz/66tNZcfQoH/nJo3zo9z/J+LGG//BSiTuUCBzWYb3m+jvvvMY/H65ydfSlyQK+knpDg9VWeDQHqlLegugPAsvWldfTBtwfoAfjkAiEFwQBxhg8KZPUR+3qLZmDKpqHtBsflXStEk9XCT7ygYf5po98iJNH11joZhTSkSHoZjlBBJx1BKnwQVJkBbIOWGEZ9PoED5cuX+HYyRNcv3qNhaUVVo8f5cbOFvWsIlOKfl6QqahhawGp1noOUNuXpqXYAXQ6JSAoiwKpNKNqyt5wF09ge3eXumlw3pFLxaDb4dLFi3R6XapZxYVnn+Hm1ha9hQE+y+j1BzTGcuz4MXauXmexv8BdZ29npTNAV5a1coD1jsWyz/LCCnvTKVoElm87zicff5w6eLTIyHXG1mTM+RdfZKG/hKsMw+GEo2tr5CqnkyfqrJcEG7CNY29vNJ+WR/MdSZDJAAtonKPy0fW0KEvyskAQtYHOumi6IgTeBUzjyLIsTmhkIC87CCFprCWY6TxPN+oGmW+ohQgxv5TAZDKjnqUcSKmRSpFplbSuniCjrlUgUxMkYrRoiCORIpnnoMh0nhx9FaaxOBsQIuagem8wJmanarWv6YuT4ETRFERTozrMnU9lJLNHExolklmRw7nYNGgah2kM1samTOu+2+0UgKBpTAIkkkxLghRYH5BKsbO7w4svXaKuHT7EvMqF5WWEaRDW0kwj0LYhTvRnswmD3gILi4vMxmO8c+RaxYmfE9Su2W8k4RPA1/PsYecd3jh0liEUCC25+66z3HPXGbpZxpOfO8+dRwpeurHHjZ0ryM4yZblAHgxSeFSeJTp3w8rCMiLP6GSKfrdEYtna3OCma7jv3DmcszRNzc3NG4xHI4bDIU1juffeezhz5nZ8gKap+ONHH+Hm5g26nS5CabJOBxsEa6ur6Kxg4+oG1hqssdRNzfLKMidPn2Vn+ybOZAQb85mrKjYI5s7REGOlZNR+OKIDdnxfo4PtdDolyzXeOWxjoqu093EtS+BRaz0Hla27dGxu7U8u4/oYkFoRrCd+RG60p1ddAAAgAElEQVRdWw8iufa+5jmqidYukuZVxBBTCOm24YDJkvf4+fMQqRkRr+eo647XTPx51DvPI3EkHNTG7j+neM0cBMzpG4AvNi4Vc/OmW0HtYb01SgrJz61/hO/88TUoitf7dN4Y5RzMEmW33399zwX4HbfJD48FvDpWBeB9PzLgoZ9RfOZLnHgqISmF5n86NmHlP33fa7qN/uC7WXrYMPjxp5liXtUhOBeKTCh+9tubL+m8Duuw3opVftfXs/QHv85VDsEqECmRKulG46YqxRCI2DGPctMD+lQpUYkqJmBOlYyUSqKhS4opcc5iktmIFgqQcyCBlNgQYzi0BI1HhsByr+Q7v/WjvOvB+1jodxh0Mrq5JlMCqQSNseAEKsvJdYHOCryxaCXQec7NvV2uX77ESr/PzctXOH3iBPfddQ9PPvEEUgj6/QHOOY6vrLJc9NBZhrfRkdWHONlRWQRXhJglmmtNUeZY58mLArxIDr09OvWUs2dOo4qMixe3o1Yws+zt7WKNwTYmZjASKLsFU9sQZKAa7XDxDy9y99nbEdZw+fJlHrznHFkQaARNY9FljtcKguDyjetc3rrBHffew+k7zvL8pYs472lsgwoC2ziGwxFdpZnWFYOVZbbHQy5fvcqp06fIlGK0N0RKhfFuTmFsGhs3vCliBWK+qfce46Mj63Q2JSQn1U5RonOV4j8CUgaUVggddcDWtSYt4UDGpEMqRVHkaXoajXCsMXgftauBOM1CRBDXNkeEgODt3Lwl6pvT5Ip0vQpBsC7qT52fT/LtASOJpjGJLqkg+PnvnI2uxUGEtIEPye0YrPVzkCmFxFmPqyzW2zihAqxzWBMNeLQWFIUGChprqY3B+5CcllPucJZH3S6OK9c2+Le//ltsb+8xWFxhMpmi84Jut4+ZjXF4gpYILSFotNJYJ2isZ3FllaaumO7sIAFvPL6pwTpUShZ1HlBxOhdfM4e3gSIriVnDgZ3dEX/0x49y33330C0zeoXmyFKXkydPc31Y84nPPM1sNkLqko7OMKZBS03wCjMb0skEZrbHRBh2b1bgDfVsym/8xq/R6/U4evQIZSdnff0M9567j52dHS5fvsrjjz+O9YLptKLXH1B0ezQu0O928D4Cphs3N+l2+1SmwdbRkCm4wO7uLssry/R6A0ydMZtN0Uhs0k0jkn4zsQSklClaad851zmHEjGWpwVsWuc4DCIBzeBsovWXsTHhbKTntutepIXEz3cQ84lnG+F0cJoKCRj69jpu86kTNTd4kDJNKSONXiTNagu2Q9LOCxnjpEh5zzHiKDYipJLpGpbpue1PM1rIeRCQRolqyk0mtD9J/44PKJImuv2siQTUb/0iST86NNt5S1WmNP/92of57n945vC9bauuefYnnuJHnUUg+OffbMk++trA259mzf7oIt07zn7R4x7570Z85mXRNa9W9+ol1oLm4crxDf/4zi/9OsgyfuHvv43JL/0uP/P0qS942L+/vMGRH3nf4XV2WIeV6qvtBfGGBqtakHICD+T/JTdJpSK9DYj7qXRA2zyPk7k4OYvmID5lcSV9n3M4PFKC9SBDQAodzWAcIAVCeDIpKPCcPXGMb/mGD/Ku++5g0Cvp9UpyLRO9s8AFRZ53uHT5GtZ67rzzbpyL04xJU1HXY4wzvPvtb+fipYucWl/nbffdzyc/9SkWuj1OnT3DExee4frNDax3LC/dQ1Z0MKLGNA3OGIKAjurEDWDLi0ugrNA5Qkc33OCg2+1wvDjKzZs3WT16hF5eIKVkNpvR7/cZDnfp93ssDgYcPXqEqmmYesusCjgB3jieffEFTq6uU3Z67I3HdJbXCAiaUDNsZtS149lLL7Ix3iHkis29be67/wF2hzvs7u3h6hpvBUv9Lp0iZ297F+M946rmpSuXubG5SRU8/W6HajzhyPIKtYkAN1J3k+GPaOb077zI0XmMeVGy1YdmSAm5zpA6OiVLkZxtAxjbzDfK1kUdpUrgNzY+AsFZlIzX23QyQkqJsSZNUAMhURZtyo0VISCIm2itwNpoZOR9mNNag3V4F+NFgvcYW8+nanYenQNBxMzcaBRlkZEoCxCnrqT4EKHwIWp1vQsYaxO9OUOISJUUQaRrIEaWFFkR6ci1QWuBC4LKJBOeRB11yfW4qQ2bm9s8deEZnj7/DHujKQCmrpgMpxTFgL29CU09RuIoOiUojbQxE7OuasazGqEVWadDGO5i6gbhAlpC0SmoG0vlXOQza4nxFm8cMmEifKAsS5aXl5lOJ1y/ts0v/7P/i0JBVc3odvusyw4r3ZLjSzlbwzGj6R6UPbyToEqcC3Q7ObetLdI4w9nja2S55sKFZ7ixvYUChHAsLfU5d/cddLtdnn/2eZ588mk2N3cYjqfMKosQmm5/mcHSAsPRkKzsRo2tceADeVnQsR0alUDfLJBnGaPtHaTW1LUDoWlsHanVBDKtkFrgbTsdV6nxITGuntPCSZnAJPq7dzGHV0iBRGJd1LXWjYnmVWnKqpTAWYu18RrzzqE8EOJENghJEPvGSsyntFEX690BQ7l2gQ3x84EI8zxWn+jBIa2r7cLriY1BIeVca+68nX+xCSlACqwzcV0OnoAk+DD/TEqlos7WBaw3kJovgnRtt0wD4ut3SyxPq3gNYU4Tpp3KHu4z3zL1fbd9iA/XGd92qFGd1+h//V2ef3qVH1cm/STwfb/R47/6rU9z5mMC9fA7vqL7/4/rgo9n1Zd127/2+IB/Mvw9Fr//Q1/wGP/UBf6gzOGLx6oC8F61yn/xnZNXjap5TSUFve/5MD/6qgcdGnYd1mEdLPFV/kJ9Q4PVzzdYSl15tW+AEx1M4xZlHgMhIoW21bbGGIeQJkke5/z8/oTU86krwhOCRARDhiAT4OuKt7/rbbz/Pe/ibXffwdpSl0G3BOK0IE4xIAkG6fcHPPPcc4wmUx588O1YU/HUhfN0uh1O33k7gzJjcfEBtNJsbdxgvDfkbW9/iCeffJKLV65y/4MPkknJY597khNHjrK2tEwuNUWeRQBV2zgR9ZayLMmzLGnFPMJ7kMQcRmNQIU1AXKCuK0xjCAH6RwfcdttJZtMpWmvWVlfpFgXGClSuqaxFFhIZBNe2blJ1Z8ymM3bGI8q8IEjYHu5x5cZ1bu7tELSiyAqMNUg8mRAIa8iFYHVxgbOnTqGlYG93h/X1NTY3bzDc2406y+AxTYNpGjplia1rpM6QbTPCRy1ylmXgXcx4JWZU6lxTZhn9XhmvByGiG7SKm9/GNkih0Cq6phJ8bIy20/Z0jfng8T6CP+ccPniauomxKFJGDajWOBspvc47YnpJdE91zsZsTCUh6f28dXhrY7ROmvzHWFeRQGWIx7JvkhVCBK5t/mtjY8SJ1jlZJhMIcTQuEFzA+JBcizXWNPhgo+YvWPLU0BAqmn2FALPZjFnd4Dx4Ys4qQpBlBTc2t3jq6QtcuPAc23tDfAClshib41sH2YzNm9tYN6XXjc68RaeLqR3OOrIgMFXFeFbRzXI6/QVGzTYCj5YKGaBbFpSiQ+0dM2Oo6ybeVmskkSZa1xXGGPr9Pt7UjEZjKhVY6PfpdDrs7myB0py57SiLy4tcvLrBeDJDEDW4Mgh8M2O4dZ0zd97Bux+6n09++jGee+Y8VVVz9q47OHbbcbTQPPvMc1y8eJHdnV3qxtI0liPr6+zujXFe0O0UWGcQQrC1tcna+hF63T6TyYROmZPpBbY2TZQhdLpUsylT09AbDMizDO8VNVOCi00lQdSvOuGSoa5HyehInouMxjTgPVLGKacxNhpVhYAIIepJ8eCj3KAKNUop8iynLEt8sGRZxmScdKBCzcGjDPG6bxsw0NJ1wfvYBCGEWzwB2s9JSzFuabX7xkj7HgGt+VFrfNfmo8rEemknu8462h2pSMNbOW9HhlsfL1HcU85Y/DuxDG7R875M1Nqu7wfLv+yYw3rz1o+dvMHiD3xh4PO1WP/7c6f4NXVrBuqWm/E3HPzPvx448fBXdv/v/6EO/PSX59ZbBcvfuLjET/7077P0n3zw8w/Y3OS//UXNI27zNd/nN1b2Kweqh3VYh/WmqDc0WPWJ19ZOSUUykJlrGdtN0lyzuu/m64zZBwCJ2uZ9wLuY1SeFQgsNQeAFCC1SZqOhzDSFgCx4vv4bPsC9d93BQr/D4nKMl5HezHWkWaYRxGzT2WxGVubcc9+9jMYTfCbAKc6cPctnP/cEIVccPX6UTGvKhZJZVbG0uEiwjmZWIYNn0Cl5/uKLvPj8Cxy/7QQ3trdY6vTJ9AJ5pinygqZpqI2h8gaXx82vEoCIQFykaVyWZQQfuLFxnfXVVa7fuMH6+lGstSyvrTF98SV2d3YQZc6dZ87wh489hi9zyl430ul8QGnN7v/P3pvHSJre932f53jft966q4/pmZ57dndm9uLyppYUSYmyIts5ZCNGpAiwExtWEsBG5DgWYspyAEUSFYiQCMP+x5YQx1YC5ACS2AkMRJYoWpZEabna5d67M7M7d/f0Md1dXcd7PUf+eN6q7llKgsSQe1D9251dbHdXvW9Vv2/t832+VzFlbzJiY7SHQFBUBajA/8kkIkZjK4MWglhpRGVoSk2n1eLjTz5Fp9Xk7s3b2Krk5NmzGGs5efIEeVGilOLNa1dZHgzot1s0ojgwqJFGxyrIIZUkjmI63R55PiHP8/p3a8F7yiJD+Dj4TIU4kCjOpMAi1LG4qsLU8t95JYedMe0m1GfUkl9rLUKpIO31HluFgCVjbc3fiFpZWFeI+MBigUOKwIwJUcsxw0UcNjXmC+3AhNpDLJbSGu9VAMTW4QjA1frAeBnrKSsbUocBpEZQS5r9QUqwqTyTPHhrpHZUtmSS5VTGYr2kcp68rABBluW8+urzvPjSywz3x3gUOoqIdEReVHP2zHtPEjcwVYZ3UJaWojCIRAcJrJO0WimFjpiO96k09JdWsAZG93fQUmGrEuUh0gJrPRFghaTEI7yYB/Y47xnu75I2GiwP+nTaTVaPH+Pxxx9lZ3ubV157HYsixhNLxWK3A2ZE5RWlsTgPy/0WeT7ixd9/huefe5ZJliOE4uypk1RFydXXrpLnOTqOmEzzoI5wklarx2c+81leeeU1RvtjNrc2KUqD9YLzFx5i+fgKmxtb2LJgOt4njuLwunREEil270/w3tNqtYliTZlXdFotqkqRF1PwjrKoQ7W8x1qHFOE+Q9TpzPV15QFmPcPeBXbeWbw1WGuQQqJ1NO/pVUrRajTJiwyQKKkR0tc+/yA/ET5I5h8Ed8GL6upKITdjc+epvTM5MFBXg4HAu9pDPQeqtTz3YPvuQGp8KBcgBEf5ud95Jvtn9igfgOns8W7maSVsEM38zrPnmVWVwSGfax32hDjyqn4nzQ+tfoJf+mspYvX4u30q76m5/t8+z1f+iM2Yz49SfvnXf4/owxdhMPjmDqIUHRkzct+cb/O+zfibd9r8k//pN2n++SfCFxsNaDZhMODzf6Pkq7/Q458kBUNf/JGbSx9WC3zyJ5a+qfN4v47f2ADnEL1eeM+O5mjexTmSAR8aIWapkSE1dBaadLgG4WBhJBDuoJ7BORckb55amklgDeo2+bBYrIM8cEgtSXSEMyXCFHSbTf7dP/P9PHnpInt726SJ4vatm5iFAccGA+JIoYQMbJANFRRSO5zwtDttRKx4/errXLjwMAtLi5w9d45/+9u/xdPf/UlWTxxna2uLtNHgoYcuIITgez/9GV6/dpWvffWrJL0Of+4v/Ads3lljsrNH95HLCK0D2LAeJTWNOKWsKsq8ItYJQkmQKiS0uuAZM5VFqZhbN2+Qpi2kUAyHQ7a377PQH7C9vU25cY/+yjLL/QEJMBwOwTqIg9czqxNDo4Zm3+RIoUjaDSpTUZYFHhdkuzj6rQ7aedx0ymKrzcPnznPh5Cny8QRXlTx66SJaSYRIsN7S7bSYTKYsL/T5+Ic+RDttonzwL0oJcRxA00F6aUmsFbqZ1r5QP0/7lAKcqbCmlgP6kChcY0GsCyAyEM0eqaLgI3WOst7YiOM4eAa9oNFKQIT3IwQmBZkv+JAorHXo3UUgVACW4lBFjQBsveKesUQzgOtrTx/e18wtwVeIrJlbD07ghcIjQ2esdzjrqIzDuCBnD/7GEPQUxRHOOsoix3oQIshIi3HOtKhwQiJVhK9l0RvbO1x/6wZXr11lb28ICFQU4Z0ICdPO14yWxApLnueMRxNUFHp8syLHOUgTQ6QicJ5SVkRaEzdb5EVGI22ydPIkVWmo9kfEQkAN9pV1NJVGa0XuwybCTN0gFMSRBmfQkaTbbSNwRBLOnjnFNMvY3t0jbXaIG20m4ze5cOY0Tihur93DCsX67TcxUqCTlMJYFhePURQVd2/fREUJcaMZ6m6mOYKwQSClpt8fYIyh3W6G+yZRHFtcwKEZDXfZ3ryHMY5Gs8lktI9LGrTShN37O5RlSauZBrn9dEJTNBEQFAORwNfVQqYqMXUw1iwkrSpLdBTCu8JnVvgMsyaAUylC16k3BvyBrxvn0UohRGDO+/1+6CGWEoGu21+Cv91aBz5UdzG7Djk4VhRFMAN5zGpoHH8Qaxmi7uy8e1XMFAX1PTALL5slYc+sGLOZ+WZnzzer0zlgdWebPNSbk77ehHK4eWASPMDm1n8Oh00dZmqP5v09P7z6CX7pC4+/26fxnpxxFlPK6R/6/T2X85d+VXLu1zf40g+vIz/w2J/4GOLkKv/40Wv8yCvpN32eI1fyH7+UwktvAvCjdpF/7yf6sLCAOLnKJ38RPuk8//TzN8n/gH6MF8SUh0j5eG6h1/umz+P9NPb3nmf83IQLz16ntBVfOv45/vp/0/vmNx2O5mi+BXMkAz40QQoqUSoELcE37pLPE3E9iDr8w/nA0cwrFTi0BpuHigjwdYCTCGyDlhAlim63w2c++lE++6lP0JCweqzH2r01ynzKNE+4PxT0u13SJKGcThFSBmlfZSDSVFXBSy+9wO7+PhcvXWKSZ9zb3GR19STr6+u8ee0aJ1dXOXv6NApBHEVIIbj80MO0Gg1oJqzdvcO9O3c5vrjEOJ9w5+XbXHrkIt1Oh1A3IkJIiXVMxhM8jrTZQEUR1oTwKC8k7XYbayybG5u02m2ee+55iqJgdXWVZjNla3MD4ojVY8f41Ec/xtdeeIGdyZiINEhto4hRNqXRDPU7AKUzwZsmgcqBg04j5czx45jxhG7S5ImLl3ns0iXKvGBne5vBoEer1SDPQn/quTNniCLNlTeu8rEPf4iTJ05Q5QWT0Qgt6w0AYxHUlS7OAr6u6pn1ThKkxyL4VKu8DMyPCkFF3nuk0mHB76jDgAIz5MKFghSSRiNFaU2cJAglaDiH1prJdIrxnrIoMdZQlMEL1EhifNIIjJZU4X3SBwv1A1kktSz4wNc3A631xRw8s97VPam1NFSqOkTJYMvyIFSqTvIVMoTwMAPeMjCKtjIYW/tPq5KyNMSNJkkzYWdvn/s7W6ytb7C5vc327h731tcx1qFVRGWCD5Ma6M76UENFSQCw+6Mh/UEXISTOBvBjnMPaEiUkpihoxBHNejNhOJmyNOjTWVhgZzLF2IoEENYRUTPMUoDWWE8I15IeHWmwHpVoNu9tcPLYMidWjmOtY339Hmt377Czv09ZebRuYIwgUjFOKLSAssyIhEVKhfcV7TTlIx96ip3dfTY2tljf3KLMxnihyCuDjBMqG+TOeVHiESwtLvHBp55iNNxj494Wu8NtKnPg9Q1+6Ziy8LWs1dNMG+A9VZ5jnSOJw/0jmDGjlqoqqYoy9AorRVUHL+koxhqDhRpMerwxCG+DdNe64NesP8hm3aUIH7y8rRTnLTu794l0UCN472r2ccbUzqwUYp6A7ur7apa+PWNwD+pogjJlDixrFt9RS4rt7PG1gqWWDc8A6qyS6e2f3TNQOZtvAJP+4DNbzlQJhyTzglnn9EFY2jfU8FArbeonO2qneP/Of3Hyu/nif/2ni0n7dswNO+Jn/5cF/tL//BKP/vgJWHp339NfUvf5d357nfjfPyQNloK/+od4kSf//Ddp/fnH3vXzfifmX//tN8mk5JdVxW9sXJl//b+692X+0xefRH/2Y+/i2R3Nn/Y5YlYPTejngwdqaOrd+8C4HpTNz6I1ZqmU3s/yIw+lWnKwAx8WygF8pXFCFIF3OSsLHZ567BJPPXkJrRz9XhcpPO3WOaZZxp17a5TWEDUSokaMFwJdn0Po5zSUuePC2TO8fu0aN66/xdmzF7h86RLjyYRf/bX/l7Nnz7K8fAwVabAOpVXwOJYVS90ev//aS2TO8PTHPsp4f5+XXnuZpcESb63dptNus7y4hPQCWYeSuMpQuYosz0mbKY00RSlNUZZ4GRbet27f5uTpU/R7PQYLC7TbbXQSEaUp48mEpo54+sMfYbE/4Hef+3229vaQzuOkYKHTJS8KkEF2KIKSmlaSImOPzQrOHj/BsW6f9du3+cQHPshD585hipL1W7e5fPkS7Xab7a0t9ocFF594AjOdkmcTIiVZXT2BKUoEniSJUVJSVVX4/bkgw6V+j60PPaNCydpXSohs9iCiCKVrb5zQgSlCUBYl0gviKPSpSqVw4sDjrLQONUF1YrCQkuH+iNFkjFQa43JKY8jLIgAKa2uJpUJJatB5kEJ6uBf3QfBae6sBIRTzBGHv5l2tURSDCLJyY0PtSVkDVusdSZKipcJZj6+Thb0I13kI0FFkVRlqe+KUcZbz+8+/wKuvXSGKExppk0leoHREu9tjONynMg6p9DzFGALbpnVdgVJLrvN8glJ9tI6phKU0FdpFxKruj40jKhcqc1q9LpPJlP0sZ7C0iM2m7K+vhRRcamlqnVrrPEhkSDGWPiT6eo+sGedrV9+kGUfkWcatWzfZH42YZDllaZGyRMqYvKgwLvh4I60xKOI4YvXsGT716e/lzRt3uHPrJhtb98myDKkVRWWQSUpZWAaLy+iowerJUzz8yEWefeYZ3njjCjs72yQ6SNNV/SljraXKs8CkC2ikTbQSOFthKzNn+rLpFB0p4l4XQdh8iLRGunrTxbi51cG7wNaWZQgT00qRmwpTVQFcMnN0+gNZrPBAANB5Ngn3RJlRVfVPeou1FYhZ4nn9tVqBYq2ZX68P9Eu/TVL7IEtaYY2b/4/qsMplpnGZS+zr6pvZc8yfn2/sQ511ynoZkn0Py3lD2rAn0Lhufix/6PP8MKs6A64HE+7PWYXO0by/5kdXP8XP/+cpLCy826fyHTHP2h2eBf7PjW3knxD0NX/gEv9b5yo/9exxXvkT1st8K6b1Vz7zjh/znZ7X/+6L/PdC8H/tPVdv0h/N0RzNexqsAnNZ2GyXHg4DzgA4vA+ySFf3S1pn8Ui8CNJKZJ1w6T1KCxC1d1UGv2cz1iTSsrS0zEc+9Djnz56g1YwZju6TaMepEyfIx577mxtkRYER0K16TO5NSXREr9WklabhPIzBVh4tBB/5wJO8ceUao/0xj1y6zIsvfp3z587z9CefDhJeUxFJRVYUaBl6XLGG08vHePjRS4wmE17++vO0uk0+8qmPM8mmrK+vs3n9CseXjnFqaQVlXADkVQhO2d3bI5pMaDSbgX2JNcePH+fu3btIIfjAE0/iENwf7nDrxvXAsLZS7t6+w2Aw4OzKcVa+//t5+coVrt26yTAbk+UZzUYTJTVp3ADncaZCI4iU4vSZC5w+foLx7pCnP/hhjg0WGe8PuXntKpcef5STZ04ho4jd/V1a3RZb9+7S63TIsgmPPXoRrEHUNTNR3MY7T5yk5EVIRy1NDeYQCBlRGBMAs5B4qREqmjPsxpRY74iUxrjQqRvpGCmjWhZsiZBILUEFP1wjjkEKxuMxSmuG+/uUZck0z6gqQ1mVGGPxXiJqkOqFJMsLaIQNCiVFYDyNPch1ESosvK1H6LCQNlWQQ8ZxFPzSzuMJACJNG/NFfVmD1LIsqaoqAAYhMFWBtRWVKee1JDKOAIXxoYrGobDecu3KNV548WWu37oFQtHrDxA6eG+FVLTbPfK8YjweH5yymIH9cL4hdVtQ2ZyiVIxHu/R6XaIooqwKyrKAKIQESRfuz7wsQSpavR77uzts7GzT7XcppyPK/cCca1V31brgdXbOhk0fIZA+ANjpeEocR+zvjXjphZdJGzELS0scP36Sav0eJ08uYS0UhSEvLaPJFFtmTMdjKhxGhMqoq2+8znMvvsrG5g4IRSPypJ0ES0qzO+DY6mlKC7fu3uPqtau8/Mor5NMcCbTavdpnq5lOx8G/bAXTfMpkOg7e1EjjbUmZFygpWRgsMhqNKPIp2cSSJjGPPXaZjY17bG1tkcucoigCyMwLoO5+Ho/CZ5yzmPqzJARmiSBzF7ME4fC5Z41BSFt7MyXChYRlCGyvs1XwrEqJlL5WnDicNTU7eyCZFUJQmRJvHZHWc0Y0bKBo8C5I4W1YPIXH1bL38JU67EsRxzHGHdQzzaTADwLLkKQ+64idTU1ch004IQ4UMvVxZnF2UMuQBIfOxz8AWr8xoO9o3m/zQ6uf4Bc/vwqdzrt9Kt8REwnFkmzwM0tD5OVHoKrwmwehTCKJ/0jWUqyskPzFFb7wPbv82BcNN+zonTjt77jxGxswyR742sf+4RXuFyMmVU5WFX/k4y//73e49skPQhR9O0/zaI7mD5xf/InrvLF79x095nsarM4CNma79w/s/hOkZjMZmLUmVCR4cAikFLUPMLAhSmucC2EeOItSEnwVDOvW8vjly3z6uz7KUr+NMRn7u9ukzQZlt832/S1acYPz586hOk0KY5nmGbFU3FnbgJXj5HlOu9lCR5qkDjsRSJ587HFefPV1XnnhRT7wxJMcW1lh+/59XnvjdYwJgOTU6ipPXL5MnCQ04oQTJ09Q2oqFfpdPf/pT3N3cZG3jLosrK9zb3ebe3d+MkeMAACAASURBVHVGo33K8ZRzx1cRPjA11Iu08XhMWZZ0BwMiqSis4YNPPcXN27fY2dmh3e1x584dKm+4eesW58+d4+TqKrv3dxDO0ep0+J7vepqHz51nc2+X3f0he+Mx4/E4MMDOsTxYot/usDhY4PjyMhpJ8/gpXFUyvL9NWZZ84AMfoPRhkVrmGadPn6IsS4Z7Q5xz9AZ94jhCiRDWNEttRijw4ISgNJasKBiPQ3JxFMcIAToKwUPeS5xx2LJCK11vXgSmrirKWhKbE0XBs2pFANqSwKQKIdjd2yNtpnhguL/PNMuoTAjMMnUnplQSX0tm4zjBVAalFNNpRpokFPXCO9I6hOVQqwA8yEjPwUWQzhp8Ga7rsqpw3hMnCdYGqS8IiqIkzwtmfZfOB+lzlk/nnuuiKGg22+zuT8I1LwST8ZTReMyVK1d49bU3mGR57VUV7O4NGY8zkkaDVrv9AHiYna+1IXgpiiLiOK7VAg7hHWUxZTzep9VK54E+wQNb4er0beegqhylcURFQbPdZrSTs59nRO02VVlQFCWKEJI2vx9rdti74DOP4phYqsD+eU+RFfS7PRpJg92dPTqdLkrHDPf36A8WuHv1TXb39qmcp6jl+F4pNtfv8Wsbv4pQCUuDJbq9Lt/zPU9z8swJdvenvPjqFZ5/+XV29iYkrQ6DwRJCBm/vdJJTlSXCWnCWKIrC5kZDMVhcoDKGveGQ6WQMPtQoCSTD4Q7j8aR+H2Pub29z/a23WFhcYGGwwMbmBkrp4DWOorAhU5ZBEi7E3IftrKutzQEIyvo6REic9YdSfUNXrfcG70OXq3M2yI+D3neegu6MCanh/gDAzZJ9g4w99PweVq44a7C2+obUXVF/ts4yAWbs65w1rf85A5XybeBxHoRUTzieQNRssagZZus9eHsg6a3H+bexDh78TDJ86Lqen+9R0NL7an5k9bv4xz/z6FG35bdwLqouX/jZh8N/WMuNn3mJH8sP7qPvlUv8rZ/7Y7CtgwFf+qs7/MVf/jad6HfiVBVb/+BZAP76lubLGy9900+1m42ZB3IczdG8w7MnbB3q+c7NexqsKqnmC6BvWNyIUAHinJ+zqaUJYDWwbCoAVgS2qogjPe/yC72XljiSdFoRl86d5bOf/BhPXHwYJSxZMUbpsNDL8ozp/j7Lg0Xa7TanT5+kco7r197i1tYaC90exoMrCprNZlgwGUdSM60ewUPnzrE/npDGEbdu3OD1q1dAKR66+DBZnrG7c59JllF4j/KCKI5QUqAbLRbjmFa/z517a/ybf/MVhFLkRc60yOkMuoyzCa6oEFLVNSQBZBhr2dvbpdls0Wy3EVpwcvUkW/e3Mc6SxDGxSojjmN2dHfqdblgTWMt0NGJvZ4d2r8fCmfM4KSjKgt3dXbQKIKURxTQbSZBclhW2rBhubaGlot/r0j+2jJSS9fV1xuMROgodqK12i7t37oB1XLz8KKoyCE/wDktJVXeAVpXFI4gbKULHTLOSvKow9XVhfM2q17Uq1jmiaOad8yAqpFIoFUBcmob0vCwrgjyxNOR5RpqmGGuYZlOMtRRVNZfmekKwl44iPDDNhiRRHcJUWbSWKC0oZunBQiAJVTYzD6CWAaiWeY6xNiRXS4V1tg4UCt7CLC9rb25I9a2cx4kQuFTZILsMSajheaMoJo4TdnaHTLMySGBNxc0bt7hy9RqT8RipI+IEkkZah3M5Go0GRVGwuxvOOYoims0m0+m03vSxaB09IOWsjMF6idAxRTENALE3QKuYssypvEHO71WFkgLlJWVpEKJARQ1QhkazjZSK0cYmw6KgIcA7W1cDhQ0KpWTw3VYljaRBEjWQIvy3FIJ2q4ObTtgbj8hLy72tDe5t3WMyKYOH2/kgz45jdKOBl5LSWNq9Pq12i0cefojv+57PsrF1h3/5L/5vXnz1dUonWFo5zfmHH+H46hkskvv3h7z40suMxiMSHWGLgqoKrP2x5WOhY5YQ9zPc2yeOIqz1TCcTTFkiVYSOogDwnGft7hrbW9u0Wi1U3a1qfehGnQG8qioDeygkUoUNGT0PLhJzm4PSOigDDLh6c2OWCm3tLMYrbDAAtfS3ThWe+5IPbVJwAHyVVHPuUtSMt7UV3pkHgWrt+xfe47x9QGZrrQ0WDvGg9Fcc+stD/VofVMuIOgy4/kfwlTMLwuNtYPngXA68qeEzf+ZzPSzJP8I875/5myc/zRd+rHf0S/tjzuU/O+aRLw+4av7wahkpBH+rvw3UYNU5vBd8KT5YBi6evAuc/2MdUy4t8Jed5FfkH79uZjY/45q0GiFVOHp8+U/8+Pfj/Ku/e5sfuv/au30aR3M078t5T4PVWWhHYA1qKe9skTVnVB0QFllaunkAjxAO4cNipZHE2LIgVhJsRauZksQR7abm0UsXuPzQBVaWe+zubrHY69BttUgbMbu7O+yPx5R5TllWdLMO57sXwMNiv48tSvb3hggXQGKvP6ARKSKlKYqS6XQCUuKkotdp4Y3htVdfYXd/SNrtsrG9Ra/fJ2m2eP3qVaZ7Q5589FE6gx46iZmOp0gtGE8mfO13nqG7OCAvChb7C3ziYx+n32xx5aXX8MaxvDAgiuJ50ifeIzxk2RRjDcZ7lo8dQ2nNnfU1FhYW6A56bG5usrWxyZtvvMHZs2c5c+oUQgiKQjEZ7rO+ts5kOkHHCY0kIW23cJUhm0yZWBukiAL63T5LiwsIKZlmGTevv8XCwiILgwEqihjnU3Z2d9nd3aHX7nD27DkoyrqyxAevnxRIoTEuyE9DV2pI6k3TBrJSdVAR82tBKoUQEuU9WTal2WrS7bSYTqchpKgqA4gsZR1SFBbStqwQvg4xqhNqTVWFLlNnkTqAL19fiN45ms0mAkGeFzQbLYoiyHSlFCRJg0hrrHOB8RGgnEOr4HEsq7JmRDXOWsqyIE401tngLSbIZ431VJWpE3mDrzDSMc1mA2cd02xCo9XCGsd4NKJyHpRma2uTW7ducefOXcrKkKQtEILEOnQUhY7VLEd6j5aCsjLsD4coJWm1WrRbLSaTCVBXISmJMeE8AlDwaOHx1jIdTUnjJq1WB0EApd6HKhUpY7QWOKeIIkFZWkDQSFpErSZJo4lAsbO2xiTLSCRA6N50ziE9xEpTGYM3FU4IrA/Aa2trm7wqMUBmKkQNlqfZhGPHVkkbbbbv71FWDh1HdHs9UJLRZIrC4cuMzTu3+OIXfo69/T02Nre4eOECKknZG2cUoz3u3XEYJ7izvsmg1yFNG4z2RoyLYh6AZZylHUcorVjWiwyHe2TZhEiFzt9SSxAK62ZBQxYwVJVhmuXBl8ysVktgzSyoaAbILLiwETIPRBKSygZgSg1ug3da4V0N/7yrn8HhbZAHh40XOw9JOhxsNE8DroORZpVgzL/vqKoCvAVRp28fCjuaTTi/mQ91JsXV3xCiFG6lBxOA366Woc4XCNU0IdFby+DpnXm+Zz96+PGzOXy4t/tW4ciz+l6fJxbO8uWPRzQ+2UAcO/Zun877ZqLv+wQ/v/gy/8n/mjD25bz25bCa4ZePlyz+l08felDE+f/uQ9/8QQcD/sO/U/KDLzt+4ssDrhwCyvJt92VLRPzTz2XzzYfoMxe/8yWsdR7Bxhef4W/sRnxl+Oq37KkrZ/jpn7rD3/+ZC0cbOkfzp2Le02B1vio5tDDydcWF92Ie3JEkCVorsmxKQb1A8oTFmw+9mYlWRFLQ7fZoNWKOH1vmI09d5KHzJymmY27fuU4kBLgTnD97lo5K6aRNNre2uLsVEnMnZclb195keXGB0ydWaactrly5QiNNEVIgtELGEdMsx1ahxsN5TxKpmlWTfOzDH2Z7f8i93R3ysmS0vsap1ZO8deUNPnDxMksrK2zu7pJ7S6vRAjz7+/s89eiTvPjqyyAEH//Ex0mU5qWXXuXurds8fPY8xjuaUYRSeu7vjbQGISiKAud8fa4NTFkyzabcv7/N8ePH2RGSbq/LyvEVtnd3uPrGFVqdNu1mh/5ggXarRVFWdDttrDEkOiJptYmjiCiO0EoxmU6x3jEZjRlPJmTZlP5ggHeeqJGCMezt7HD69CmOLS1TTqYhqMWGRbQP1jyEEjgTFvJu5lUT0GymNGlRVGV9Nfi5X9n7sGjN87oyxhpGoyFCQKyjelHr67IQj6tBhPcOawxRHFOWZQjeUWGxHvyaat7L6pyj025TVYbRcB8lNEVRIKUkimKUCom24fccrtOyDEnFUgSWSeoQHBRCkQR5UYawHmuoqorReIp1gqwscCaADOcdrabCl+F66vQGFEXG/v6IyWTKaDzh+q01rr15nfFkHNJ7vaespZzWOrI8ZzAYcOzYMbbubVAWBd4zr1mRNSCMdATG1BJ6N+/eVFoTEoAtVga5897OkGxahi7R0oTXDUjhUNoRa8/ioBOAbbtFYWFsHMu9JeIoxRSWvbtrWG9QECqjhMPbEusCu+tMian9vQuLAxChnsUrBRI8lihRPHbhEtm0ZOf+Tt33q6nykqXBAISgzHP2du9jPWzcuY2KNFJpWnHC/tZ9rBD0FhcZ72yxvnaHtN3nu5/+FMdWV3nuhZd58esvI5UOQNVbNre3aPc6NKKEOI5YWlxgf7iPKQ2tVpO0kTKZ5kQy9J/u749q2S9oqWrJr5gD1BAYp0Iysvc4W2FMhXch8VsKOa9qSuIEqcJ7HkLBQqp52MOZyarBWVOrDmq5jgBRbwwAD1TJCEH4/BIi/KwLftT5Zy4h3MhzkHIt/gBQOPOw6loNM5uZ9/WgUuYgOfvwc4iaVn1QLuxwbsbMzn6O+T162F8dnkd+g7Q9/JuQU3A078k51upzLOnx1Z/80FGQ0jc58oNP8CsPj5n+y+f4ey+vIIBf+LE+YtbJ2Wx+y4GNWFkhWlnhix8d8uM/D6a+337hP6qQD5198If/FPmO/d01fuAf3ODl4S1KayjMN9dN+4c+v/d8cf03UT8p+IkfHxzV2BzNd/y8p8HqLPExKNfCrvqsxkOImedtlpNJnaIpa/nZrN4jVEp0mk367Sa9dpNuK+Xpj3+Ujz71CN225uatm9zNJwgBvYUe99bXSKOYdquDlpqHzj/MvZ1tdnd3aPUH9Jsdqizn+OoqzWaT0XiCjmOcc9y6u8b97W0W+3067TZaq0N+Uuj0+6TdLmm3y821O+yPR6yvr/OpT3+ahWab4f6IF159lZubGzzx6GOcWT3JubMXcJWhyAoWF5ewxvHqi69y4+ZNzp09S9pts7i4SK/dwRrDZDoNskoC89hvNGi2W3gpKKuSyln29/fZ2dtlOh7TShucO3eaZquJw9JfHCCEpDfoYkwI+Gm3OvS7Xe7f3w5VPbFm+dgycdrk+tUrGGvxOJaPLbIklsiyjDiJmY7H3FtbZz+b8Oijj9HudpgOd4mVIkoifG5wxiJqFjN486hlwJ4o0iF8ZsYaOUsUhZAj6yxlUZLlOVpHdNptyrLg3toOxpS02k2cKWk0GmgpAgCo/2caRZIkDonBeVlSljlaS9JmSmEMZVUhhEfWdUhShk7VNEkoopjRaIQQkmazRRyHYCSHRTjIqxLvbC1BFKE3VQikCUCvtIFlC1UokBcFk8mYLCuwtQRYqwhEqDRxCCZZTq/XY38yZXtrkyzPuXH9Bm/duMl4UmC9J222agAdZM9JktSbOTFlUTDcvY9UsLA4IE2bjEdjsjzHuyB3jpOI8WiKtw4nwsJfqTr8x3s8CkGEV57SVlSVI4p1kJn6UBOE9GA8hTNsb+2gtCCrHJ1+jygSTErLUm+BY6cs5XhKOdxBUlex1EjLWAPOAJI0bdFspkEh4AxS69AlKhSdfo+HHj5Hf9Dlt37rq1hb0W61KAvH0tIA5QW3b9+iqEpSrWg0m3gkw+GIKq+QkcaUI+I0ZTocUjr48Ic/wnd/7rMULuK5F17k2pVXQRg6nTaTyRRb5Bhr2N7ZodVpI4Vn+dgynXaH7e1tsmxKp93DeUGcpHQ6Xa5duwZAHMdh46yW6ed5PveF6ihGK0kcBW99NhljTLgGEQE8Kq1qj2sAjnGcUBU2pAMTQJwU4J1HBi4ZhAx+4PqDdJbbG363AiEPgormNgs3y6yuP3d9zdoeWufOZLxvl+WGjZmgRBBKHt5nfODBhy0dbw9EmvlwEdT1Q/YBoCoOgWbetvZ+ezIwMK+sOcpYeu9NJ2nyl5c+wt9/6B7tv/bpd/t03v/TbtP8kc/wpXf6uL0eX/zZPx29p3/k7Oxw+x+9xeeziK9uvv5tPZT3np9b+wo//Isf4sJPH4HVo/nOnvc0WLXGYh2UxmCp/YnGooRA1lUGFktVAWisC/5HLVUIcEkSGo2Efr9Dr91E+4rFbsrnPvk0Tz12kVYiMUXOqYUltPXEaYP1tTWmozGRUpxcPcmJ1VVQEmtKGkJw7vx57t/fZXt7i97GJqunTpEmMTKOuLu+zub2NkJI9qYTbt69y0K/x+rKCp1WlyhOsJUlz6e0pObyqfNsJW06vS4nl1bY29nh+ZdeYOwsrUGPF994jc5CHzXaJXKCRy4+wva9Ta69cYW8mPLEpcv0Bv0ARFXE+vYem9tbLC8tkSYpVVVSjKeMpxn3d3ZJ2026/T6dTotet8vZU6fYHw7Z2t4MAUBFybHFZdK4gXWO5aXl4I8dT9jb2aUqpmgJZZGRRBHT8Yg4jmk1G3gEZVUwHu0jpGI0HuP9ECUUg4UBZwbnUFpi8yyk89YeN9lIkDYwrNb6AFyQyFgxzTKsK5lMpgTgLWs2NEhVpfCBkXMGW1rG2bRmkDxps0EURWgVukIrE4J64jiumdwAhK0L0lodqdAlKwTGOaSQ5EVe+141SmqSJCGOIsqswJQmsGXOH3T9unDNlnkRui51kC0b72pWR1CZAFImWYYQiizPmWY5CKicqxmziDgO7FRIdYZQPCLZ2NrhjdevsLm5xZ27dzCVpdlqk00mGFPRbLaQUjKajHDOUZbFvOfTWcPS0hJCKqSOA2M6HuMtcwa+KC1lUcwDc8rSYkwZ+jWFwEoFSRJ+F4AxLiQGCwk2VKOEyhQY28D+6Rg8UwQRwmaI0uErB0kDH8c4U6DQQT4tRcjXsqHvUwkwddhVt9+j0+/RSBJWThznoQsX2Lq/w7/98u+wsztkebDEeJrTbDaoqpIbN65jvaPX7RPFEXmRUVQlzWaTSVEwmkwCSIs03sQ8+uSTfPozn+bW7Zv8i//n1+gtLLGytMBbb92h2+4zmUyw1tJot6gJTRqtZvBwN1KmWcZonOFGE9I0SNHTNA0bVmUV3kNryOu+3jgK/m/nHVpKup0OkVKAoxFFjMZDiiJDyrpTGUdV5qHBxQNOIINLOigBav+zkBKkCr+jmhGNlcQ5i6mrdaRUHNg5a9+pr4GrPNxnOgOmomZOwyMOHuoeBJICoE4orhPYw+NDF7KQ1CFqc8fpHKRaa+sNixocexfYYDtzrIafB4mU4MWDCcCHZcgPoGRRvwJ5xKy+V+ZHVz/FD+ew1Jpy4acfAR55t0/paI7m/9d85W9f48spfOnui+/ocX/KaP7Hu2uIk6vv6HGP5mjeyXlPg9WqslTGUFiLFyBr/5ZzDmlDjUHaTGl1OqGhRjTBgnCChm7Q7XTpdNsoYanyEYNOyl/4gc/xxENnEVVBZCSpTkiEonPmHFu7O9wZjfBAf3GBlZMngpTSGLppk2P9AeNpzo27d1ECpvfuIaRksDDAW0ur2aTRSBiORmxubaGFotPt0mp1QpCPsWTjSUitLS29ZpulM+eJ0wZRZRl0u6yeOsm1tbtMxyMWji3xO88+w8riEo+cOcdKbwHpPSePr6CkwHhHv9smqwxfe/4F1tY3yIucpaUlHjp/jnazFapmrEVKmI4zppOclRMrxIlG44mV4tSJE/g4wlQV2XiMrSrKqmJ/uEekJINeBy08zWaTJAqMbbvdxjnHeH8XBJR5TpZnGGNIGilpktAfDIiSmKTVwluLKasQOhRFYeHqwVUhdEhohZQOYcNC2hg7lyWWVYkQikhoQmqppap8nVBqoO6OtNYilCSKdC0ND7LbLC+Ioog0ToADxtDZEDwjJbXnLoQ6VVUVullFCDKKdEySNIh1RJ7lAHTanToFVc4lxcaEx2E9XnrKssIRQKi14c8kyxiNJlhnqYwLoWCE12y9xKNw1lGWJXEjoaoqVBy8Pc99/QVefvklNjY2wHmUikiSIOVttZooHV53ZQxpI63l0020JIRBScinGYUxxGkb56HRaCClxhhLWQX5bVEU84Ab50yoQBGCZtKk1UoR0mNsEdQNaFASrWRInDUVXgJC49A4C650OJdT5BV7kWYnksRYsJYKgRaSWAtwwUurZfia85Y8z6AA6yDPSzpecObUaaTw/P7v/S5Z7oloEImMe2v3yMuSRrNFb9Cn1U7RUYO02cR7z2h/SFHkqEaTKImRRcbx1ZMsrhxjbz9UNP3ar/8aL716hYXlY5RZjoo8g26HrY1NYqV44hMf4/yliygt2dvbYXdnm7KuUtK1HDwvK46f6DEejVlbu4upSsDjTYW3NvwBIhkTx9FcGr57/z54h1ICrRVaKyDGWhM2DIJdG63ioDZxjrrcJchlqT8bhUdoNfda4xxIiVQahQbn5+xk6Pq14GXdfetDSNUMAHqPmMtwD0BgkOzW4BBRs+Phczu4MCzMHifEvEJn1i08C58TdV1RbbUNfarUkLROdHccZnFF/XdgTmfVN7MNo8OA+sAPyyEgfTTvxqg6S2Htz5xH9xTRk03EmdPv8lkdzdF8C8Z5vvJ33uSHxs8w3cnf8cP/H+tf43/Y+BjqCKwezTswa194hl/Zvf2OH/c9DVad93W/pUNFmkgKfGXQUtNKUpaWltBxRJbPUjQJ0jwZUljTOEI5g7QF55YX+b7PPs3DZ06gvEHHEm8sgiAHNXhOnVghihWj6ZQnn/wA1lpeeellNjY2eOzyoygpiaWi1+0yGu9DrLm3u83OaI9TJ0/RihJODZYYb+/SbaRM8oKVlRWccwzHQ4qypN9boCorWq0WSSNIh62pmE4dhak4f/Yc/ZUV3rpzh5dffZV+r0uVFzzzta9x8fRZunETbyyddptOJ2W0P8JKSaOd8vhTT/DyKy+xPx0xnOwjJEjhaaiwIPYmJHvevHqNpaUlkkZEFMdorYhQNNop7XaXpZqNqfKM/eGQMi+Jo4SyrMiLnHa7jTEl0+mUvMixztFM2ywvLdFsNonTFFEzKtZYqvEkeGjjGKSqFYYCb234OVdLbb0PjJWzFEVOWZaYul/VOUdehuCIKIpQok4pBRAqLI6lQusoMIZS4bzAWkcUNbDGMK1y0jRFaoXWmulkEjzFcYp1nqIssd5jjUeqiEYjMMxx3KDVbJFPpxRZ6EZLkoSiCJ5KWxR4oKpK8rIKMuUatBTGMJ1Og+y3rigpyiBH9kIhhcYT+lZDEI9AaYkXIbjIOhjv7vD1r3+dN998kzLPiaOYpBGTxDGNRrPuo/XgINJBJipkSJqVUpBEmjiK0FpiS0vSbFOUBlOGhFelQ6q2lIIoUigVAHhVVXWAmUCqhE53iXa7xzSrcJVFqTh0dzqCpFRA6RzCFQjhkFqGjYc66VZ7RVmV2Fiz2G0SqQ7gEeWUssiQPrzv1HVHoTYlgJEoaeCcZ3dnj7X1LbLphE6nhRcVe7u7gOfMmVPEaYMbN2/VvcqWbq/JNMvY2dkBHIPBApeeeILffeZrrK6scP7sGZ79+vPsjSeIt96i1e1x8fKTPPHUB3nj6jWWlo/xyiuvE59YoTtY5NEPPE67P+Da9be4ffsOrWYCwnPjxg329oY00xZSKooi59ixZSaTMdPplEip4P+VMrDstZdeRsHXaoxBeuae46qq0FEIv3LOMhwOQ6ctMwZRhARt7wg3lMP52nsqZ8DN1/J0j7GOKNLEiQ4yb2eDd3ouDQYvZWDGRah+CiSnpY4PnkuNhQjMqnN+DqAJt/TBvw4hQ4Wcg8tZFZP3B9LjOld4Hp4nah2Bw1HVG22Bmz04lqs3Lq2deX7lA8m/s9cfTuVtIU5H846Nkoq/d/wz/PhProQvzPyTR3M03wlTFPzu52/wg7tffcerPA6POApZOpp3aDbvt9me/uGp49+ueU+DVU+9EBEC7YIdLpIxi70+J1ZWsNYy3B8RR5p22iZWEU6AVBIpPUp6mlpyfPkYH3vqUR5aPU43ifAUCOmJohhnQUUKW5a4ytNrtVhaWGAy3OeVV15hMp0y6PfnO/f9bjeE+VSLXL91g/3xGA0sDRbpp21klPD4w5cwUiDjGKUE6xvr7O7tkjZbTLKC5cUlKmvw+TSEiViJr6A0FaLKyYuS+/c2iQjSSqEEly9d4s5bN8haHU6fWKXV7WC95dq1azTabY6dOctoOmF7Z5tev8e1t97kxMpxHrt4KaTDKo1SkiRK8bVU1RhD3HDEcUzkBZHzICXeGmQUIYSqGTpB2myikojgYhPYMnRoUrMaQqh5uIm3Duc9UmvCuxaCU3xl8a7CmlkYT6hxEYfCUkBgTEjBLYqcytoQGlVWFFVFI02JopiiCO+TEhKpQgiN8I4oSQKwLUr0vO/UYawlVqGGxhpXd0cGaW3wt7kAlJyjkSisqxkjIdBSMdwbUmTT+WssyhzrQnKvdW5+vYKkso6iKhlNJoynIUnaWktlLMYahBQ1sAgLah1p0kaMc1OyPKOhGxhjkUpy8/p1rlx5ne3t7ZD+qgLjlsQxcRSAnSmDvLPRaMy7XkVdeVKVFWXNBjeShCRqUBpLVRkgBO3keYlUikaSUJYlaZpSFEXo1XUOpTRSJpSVZDypKEuLkBqBxDoDwiNnGw5ypv60SDzIkOrqPQglEUpiBBSA9QIjJF4odJzSThJslmGyKcaWiJpSi+IYU1lKk1EUluvXbxNFmqIM5+68DxsukaTVTOl0WpSVYWd32bl3ZwAAIABJREFUl+FoQpo2aXfaeG/JsinPPfssGkE+HvPc155ld28XGcXEkcAUBXeuv8XN69dBaK6pK1y48BCnzp0nN5Z+r8Nv/OZXuPrmWzSbKW5xEVuVGBM2KZSWpM0GJ06s0O322N7eZmtzE52mNBoNqqqiqgKb7qwNoV4ybA4IH6TpURSAbfAfQ5LEtNvtWpZdUhRVUBpEOjj564CksOEgH/CRzgCctRXGWLSa+UJnAFHOWc5wD9YgTwZG09bAekZ+ylnAEczB5ezafyB7t5Ykez9TLQTwyWGJbv292c8IKUMQmhB4Jw68rYSANGpAS/212eubbWw8GLT0YOrxEVB9Z+cTy5f4XHSCJSf5z75w7t0+naM5mm/57P7D3+b56yv84M5vv9uncjRH847NI09t8+jXT/PazjvLrr6nwWpNwKGFAONQOmLQ69NpdSimoUpi0OvTbLfRQqG8wtgKLy3eG3rNhPMnlvngIw9x6tiATqxJtKBy4FXoM02SlO2t7bnkNJIKmxcIYei1migBSwuL7O7uhgoXqVlYXqLRbNDrdBHOc/b0abqtDsOdXcq8YLC4RDttYBE4b8jyLNSTSElpHZU1PPLww0RSU+YFrqpQUUQSxwgp2dm4y42r15Ba00oSzpy/QBKHVNu1e+ucPnmKpJkiheD8+Yd44eWXePPOHS4/+Tif/OR30UhTvvbM14iSGN1IyM2ERqzRSuGKCq1ipBSUlaUyGWVckVQGkck6CKpmXbwniSOSOArZRpVFRKpGWQL8jH1RiChGeA9S4YsSYxzaWoTzCB3hjcG6ClHLBUUtUbQ2dHRKpcAFOSQ1gxPHERjJPNy5XlyXdTiR84Tk2HohqlSovXHO4zxUNSj21tJoNIh0SGp2xqK1xvlQQ+JcWAinjWYACWUZwJIHJRRFUbA/HKKlRMgAxAPgkKFDtQz9m0JoSmOY5hl5XjDNC0xVpxbXPajGBl+mdR4pLUpHCOkYjSaMp5PAECnN2toaa2t32draoixLOt0e1laUeUZZFEEmrBVaRbRarZA8LBVVEQKlAmMfUqFnzJ33nm63z+b9rbquh5pBLVFOE0fBzzsLZpqBVSk1UkUYL/DGY71Azgi9uvu1sjYwbrNUWRdk1VIElgw8xlmMs2TO4pylGSviKME6z3i8j7EFTSS2rnNJRGBmtI7QUZBI67jBqdNniJOEzY17RFHMwsKAoszwzjIZDZH1eSRJTF7kgAibQ1gEniKv0DoOdUIeIhGCgJT3uLJitLtD0mzihaaRpuzc36bdbbOxs8MLL73E7fVNhJCUZUVZVKFTVEp6vS7dbo+qskyzMUVR0GymRFEUfl9xjNYRaVOR1Qx98P3Wvbwy9MRCqMKyRVWz8mHjJSgaTFAczGqFvD9I9ZXhM83VQV7haxJVJw875zCETQoB4Z5TMnitrcfNIWDYTEGAQ9SANzxfTZvOe4hnr+HtPtGZZRUOaqbmENMfQFvB4UCk8D1XpxjL+tr1zh48bx14Rn1/i/mx3xb29A1pwP6B7x/Nt2+eWjzPv/qcIP5zF97tUzmao/mWj/mNZ/j5f73EPxttszZ6490+naM5mnd0Wn/lM/zZN67xGkdgdT5ehmRYSdhx77badNttIiFRUtLutGmkKUpLtIiwWYVyhrLI6XRiLp5d5clHLrDYbtBKI5T2FFVB1ExodpqUZcn+aBiOIcIC3FYmVJQYw9lTp/8/9t4sVrb0PM97/mkNNe/5zGP36YlsUpQ4SJasRFZiKBcJEjjwRW4dIDAQBBkcO4AM+8JGcmELCAQFRnwjJDCQBAkMA4ETAwY0WuJgWewm2ezxzOMeaq5a0z/k4l+1Tzdtg7JC8rTE/QGN7rN7n9qrqlads971ve/z4oFnx0c8O3zCaGuH3d09yrIkS1Iu7u6z1x8xHA558PgxR8dHWOt4Mh3z0ksvE3xge3uLCxcu4IKn0+9xdHQCEt5577tcOn+RQbdHYlKMMSRZynK54tzOLi9fvUGSZ2xvb7NerHDGcPXqNf7wD/4AJ8ARYq1HmvLKrVv8zte/xt2PbvP666+jkxQpJAfnz/P48BmPHz5m1B9w9cJlCIFMSby1p3ZB2wSkcEi1EYsxQJYkCUponA2UyxJpFEmeIjODMhlCuJiJC1AXFb6x0bLrfLzIJBCcR8m42dDKgGxzdG1ONPhIAhZSEuymJ5KYv0QgVNwCmsTQOM96XVA3DVmeInUSt0/RZBjTey152RO7W7VSZFkWt+FlGTfkUiCNpi5KyqqMtGAdeyHrusZZi24BQs5FeJPWOmYsa4snxB7QIGgaT1k3lHVNYy1FVVFWFdZ6rPOEoAjen1a7CKFASowSNNbim/h7nXftxrbhu9/4A6azMcF71kWBdw4lJULAoNdnLSV5mjAaDPEhUNUFeZ5FQFJVU1claZoRQtzO9ft9lJTYusJ7y/b2Fp26YjFfIuAUOiVlIE0TrLUY06Hb7TKfz/FtJlJB3NrKFpjT6hMlBC5SmqA1a4rW4ul9vBkRxPP+TiEky3XJamnpdTI6aYbuRetssA1SGmyIVmKCRzlHFEuOum648+GH9AZ9FIIkTTh3sMtwNOSju7cxaUqSZtx79JgkTdBJwrqoWK4XZFka/6zQiqauoD0/JYHUGJyNfbFJxxCahjQ3jI+eYb2jrNZUPjBdFnjrOZ7M6Q+GbA232s7TQFGUrNdrnAv0uj12RjukRjPo91ivC1arFfv7+zjrT8UmQmB9JPA2vonCUAS0lm3mWkT7fVmSZgl5nmGSDk1TU1eW2llC3ZyKtiQxNOK5AN7UxkgpT7tZRUvsFUrFG0cuAo0kHtgIZkAItEmQQYKPVTfP96icvt+xFupfnu8VjB+3qgk2/uEoXDcUYqljTtW6548RE7bEzezpoT3P1n7c/vu9HbIf36ieidUf7vyT7Z/lS//hHLnVRX7mtRd9OGdzNj/YqSr+wt/4Du8Xx9yZfftFH80n5s/9gwm/8WY461w9mz+186kWq9ZZfPCIIEiFYtjr0+t00bKFjwiBtTUIHS+mXI2rKkbdhDdfvcVrt67RTRTagEwFq6bk+OQYtGD3YJ9ESVxdoxDYuqLb6SASiQiBRCqadiOwu73NfLlgd2+HTrfD7Q9uM8i77GxtsdUfUFuHwzMtVkil6CYpX/3G17n10kuYJEKWrphrHJ0c0xlE62zd1EwmE6QQnN8/QEjJdDKNdlVt+OJPfIH5coknsLe7y9PDZ/zGb/4m169dxYvA7339a3Q7HS5euEin2+Pf/YVf5A/ffot33voWn/ns53jzs5/ja1/7Og7oDYdMnz1DZDkXD86xqByZMaRKk5qEsihYLNYxB5mmKCnpdLM2l1lQliW2cWhvWBU1Qiyfg02EwnlPWZQRJBMgMYbhaAutYL1YxItZJcnznCTPkFqxCRkLkwEeXzeRZKojSMkIxXK5BKIN1CGwbh0zknic99RlifeebrcLxItyhMB5h2t8m6f0Lawp2mDzPKeuKqyzuBBttUqrCEdqyggXQoCKdmSl1OlWpigKhNpsU6PYXxYFs8WCdVVhXaT52rZDsq4dIYh286bQRiOkxFrbVvRIfPA0tmE8PuH+/QeMJ1PKskJpwWq1otfrMRoM8N6hBCglMcbQ1DWL+Zw87zLs9yiriul0jPeeolhAiDVAprUYSyHodHLyPEUozWKxiBnGJGltwZFQm+cZ83lLee52Wa1XNNajtaDTjWJJS4MUCmctZVkSvEV62nobT0Sf6fgee0/w8av4WDcViVYKGzzj+ZqVaehmKUomYCRGRodE6avYNWsbtJAxf51mhGCpiiVZkqJVwmI2I89TXrpxk7KpCEKzf+4C33zrO9TOE5ARaNU0WAfCR0K09w4h4r/Bsz3aomqamEN2DeVqSa+TU66WlHVJZQPrxuNFipGGsqhYLFZsjQakaYb38aZGXdcU6wLftxiTxde4ruP71jQ46z9BsN1sOaWUZFkabzLUJVLGiiUpJUWxZrVe0ut16Xa6KK3o9RMaFyiLAls3NE0UtXVdnwKLiGfzJ4WjiO004uPCLoRTMSlEJBLHDt64eQ2bBwu+za/K+AM2dyzazSkfo/N+nPIbgocgP/F5iv88ByIpGTfKxqRUlcA29amron30U1eGhFNnx/eK0H9dVvXMCvzDnWu7U/TPfelFH8bZnM0PdsZj6t//Lv/J7+T8k6fffNFH86+cd2cPX/QhnM3Z/FDnUy1WQ0t9FD4w3B7S6UTyqVAC66OdDReoq4JES7AV26MeX/7857h6YY+uEYjQ0B9uExLJk2fPWNcli+MFtYOd7QGjfhctFDKDbp5jq5iVs86Bj3RZrRWXL13CA9OTCZfPnydRml6/H0m1UtIfDhmuthBK8vTpU9JOwng5I8kSposZnW6vhf2YCI1xnqJY8fjxmrosSLMOCJBKo6WmqWpcVdMfDnA+8ODuPW69/DKD0Yjf+9rXAbj1yi2CbqtVUPzZL/0Mb3/r29SrkqdPn/DwwSPe/OJPsSwLjlYLRmXB4bvvstUbcLC1zXa3jy0rbOOwAULV4EJg1B+gpMG7gEBjTEpRlMxXi7YbNJzCBJRUpxfHWhu01jRWcHQ0iVvcVIMI1KuaxXpNp9eh1+9jEoOUAuom9p+2gB8po83Y1jUBH6FAPtpIO52cum4IBKqqwvtAkhhWq2if7bTU17qypyLECaht3Exa6ynLmqaOFnIhQStF3VRRyFhPEO1zc/GitywbXIgbHdmKjcVywWy2ZLqYM50vUdpEGjGRWutCQOsEIaLQUDrmhaOYDiBjXrEsS548fcqjRw85GR/T1I6800EqSdNE26gxkc7bNDWp0dR1rDmJQKcGa2f0h0O6nYz93e3Ti3mjNdb5uEVsmtYOrFivVwgVrc1plmFMQpIEqkYTQqyiEUBZliyXS0RbE9Lppoy2ciSCRCua2mHrCkJNU9etkIhiPGbNLVIapGozk9bFjbWJACwtBIhY21LXHu/q2KErPJ1EYtIuZWlpo4s4AkYEhBakyiAJKOmigM0yppMpN3d3ee3qa/zh298mNREctlyXMeucpixXS5q6RqvoGFBK0ThLp5OhtKJpapyPZlXvHEILqqrAE4W+1orGlegsRycdlqt1zGSHQJ7mOOdYrpasi4JEK6q6RClNv98BAlVlqcoKpZ5DlYD2HJGxB/XUOxuo6orgdQQMhYBEUq5LvG1vQnQ6BCHRSiOTKA6rqjq1fwf/SbLvRqhu7LhRpLeClQjvVUKghCAE+VxMh03VTdzOWtcgQiRhB9nu0tvv/Xh2dZMjhXjjZMMgeG7J3fSleoSIVuSqsiiVn9bebGBOIr4IROUcxbHWz7fA37tF3nxtA5k6mx/ufHnvFfa++CejGkjsXYC8R7j//os+lLP5FM/kV/8ZH9zZ5Rtpyl97cmb3PZuzeZHzqRarGolCkKcZ+7t7JCaJlFrXnFo/hYhdjN419DqGz332VW5cPU8uQQSLSeKFoA0elSbMx2N8gNl8Qa/XofGBvJuRSIVtLFXdkGgdNx0h4EUEnChjqKsKpzXBWjrDEeP5lPc+/ICLl6+QdHJeu/UK9x/cJzjP6GALk6Z8+OAueZKzt+vZ2dvFec98NmMxnZHIWAPjQ9ziJZ2cvNthPl1QFhVZmmKkolyveOXlW0wWc77z/rvsHxxw9fo1yrLin7/1TS7sHXDt/EWCtbzy0ksoY+h0O5R1zbOnTzi4fIkP7t3lO+++Q55kPHQPqK7dIOwdoEW8SI6vbXvB2DQYlxJs3CY2TUVZxlymSdK4KWsszllqVz/PJopAVRWs12uCD6RJSpZppBbRligFpW1Yrit0qsi7edyUaxMvmZ2LW8mmOSV8CikRzmOMRyoVu1MrTVGW1E2Fd9E66a1jvVyws7dHWRQsV6tYV5On8YYHYG2Na2tYlJZtJYiPm1jvo+D0HkS8KK+rBiEFLgRWqzVN3TAejzkZjxlPpizXa4RSJFmOTlKsCyhtSNIcISP4ySgdN2q2ifUk0ylHx8ecnJywWq+YzqZIKeJGPYuZ5dDUKKXo9yNUZ71aYZsGLQVSgNCSprGoNGVvb6993UvOn99HSYVtGoSU5HlGWVYIKVrSMPE804Zer0e326UsS2bzOc6FVmhKur0OZdmcig8pJUZLqmIOQeOTHFtb8CBxaC0oigqTqCiCXAChPgG52biTgrPPYUshtJTYKPKFUDRSUgJeKtAZIli8b5DWkkiBaBp6QqBFAOsJyyUPHzygPxryzjvfRSpJXVXcvnOf+WyGEDpu1DsdTGo4GR+DFCR5ihKSahmfpyee3x6BMgkIQVGsKKuaJMsxRUFQKbdu3kCkXW7fe4gxQ5SMedeyLGOVEbLNagq8d2R5SmO7zBbLuBaUKjoLvEBpja/rKFaVwvoI4nLOnQpZa2M4WCkNRIdApDQ7nPNok56KP600MpOs16u2TzUgN5tJuSHtthAlEQg+ZnuVUkhl8LZpha18LigFiNACwYj/T0mFd232VUSWL7QQo7DpVY1ilDajrmT7ldCKUDYk4TYpG6KI9Qjm0wLwrRj17cOHU+vwqfimvaHZOirin6VxWys3XxOC0P7+5ynbs/lBzuvbV/h/flFi/p2vvOhD+b4j9i6gXv9ZRNrFbV+Izo+73yLMJy/60M7mUzLun7/NL//DnP97OeXu7Eykns3ZfBrmUy1WfWVJleHWzZfoZjlNHambQXiEkFhbYZSg043WuM+9+Sqjbk5RLjFZihKeNO+S5imZUVSuIUsSbLDs7+2xu7vHbD7m8ePHXDw4j6tqEm2oGks3z0mSBJTEC3BNifCeTGmk1jx4+IB3b3/Iuq5Y2YbrV6+SasPFvQN2RlvMizXv3f6Iqiy5uNfBes/tO3e4fOkS+7t7VMs1T5884cb1m9RNw/FkihWws7cbezqdp5qVjE9OQEoWxZrvvP8ub/zEm4x2d3n09AlvvfU2IQQm0xmdPGNvMIrwIDydNOWLX/g89w+f8btf/yqJkqzXC5aTCV/6yZ8iSTQf3rnDlYuXGPS6iBZw5AM8PTwkmU7pdjoEOIXBaG0ilKiJkBffApE2OdCmFZnapBACjsBsuULreBGJgFACLBESkjRhOBywvbVFfzBA6pRQFvhgo/1QxcoNiFlJGaCxDT7Y2BuqUoJ/bveFwHw6oakrjFIkiYl01LaDtWk3TsbE5wEby6BACIUPlsbFx2lqR1FV+BAo1gXPjo45evaMqonZ1ADoNCUAJkmRSsetX5rH59+CirRSrNdrHj9+zO3bt7l77x4B6Ha7pxf3zgv6/X7bcVqTZlGA2HaLrZVmNBxy4dwB08kJk8mEfn+A0TFzvLsTBetyuWS9XMXz1luqoqCqYh42QopiHc+9ew/itjDPGQ4GaK0YT6ZobViuCwKCNDX0+wNW60O0Udi6pJEBrVLKtYuUalQkRxO7eNdlQ12WKBPzs0kaN+8xI0mku3oPwkexhgClsM5HyJWMedbaudZMrHAEcBbpBRngShuPD48ioKRl7+ACzgWm0ym/9Zu/TdVYitrS6/RI0hyUoqiruC0FGmtpFov4Z4x1dLtdlFKxE9d5vLW4uAZEEnPVqTH0RyOW8xlWrBgO4o0uqTTTyTHL5Zzd3T2qymJdAN/Q63XpdHIaa5Eq1isprWL9UpIgVbRSQ2tXb4nOUkqsjdTsEFpytlRIKejkWbsB9tjGU1ZLjInwNdeuNZVSBBcBW965ljotiU2qATabRwlaK0TbZ0orZqHVfi38SAmJtdHOiwjtQ8TH2tCDNxt90VLTRZtHDQAiCk1BvPEjTv3Joa2u2YhI3xK4o+C0TTz2zeckuo2f56E/bm3+XutvhDCdidMfxbyc7mH+/Ode9GH8kSaMD7Hf+Mef/GL1o+/GPJtP2VQVrNeE5Yo3/5f73J8fvugj+jeaVVPyn//ye/zqL185q4c6mx/6dMMn6fs/ivlUi9Vht8uFrW0OdvdYL1fY8LyTTwSLEoFRr8dLV66wszdk2OvifcN4MQV69DopaScjTSIVdJBmvH7zJaqyptvpcHR8zDvvfzfWn0jD7vY2aENdFKimjpnN9Zqsk9LJEupaILzAeo9OFHknJ20zZI/uP2R8eMz169fZGW4Rgme3PyDZ2ydJMp48fkJVlpTLgkvnz7M1GLK3vcd6teLx02fUITBdL1l7x95oG2pLaBxGx7oYLSQ//eWvcPnl6/zhO+/w7kcfkQ8HXLp8iURKPnp0j7yTsj0YoRDUVU2wnvXkmFtXL3M4mXDj+nV2R9v4qubRwyfcvnefyfSEl27cpNfpUFc1ilhhsipKGutI0wwhNd5HuE2oKuqWQho7HP0pVEm0exLfbknixavEAVVjcd5FWqyUmETTNI6TkwmT8Yyt0Rb7+3t08hQjBVVZUlc1CBkv8K2P1mjbxIoaFestvIcgJUJqdGJYrlZIIcjyWLnjPlbREmtYFGVdI8Lzmg/X9sh4BN6B9Z6yanh6eMKTp4exa7ZpSBOD1IbMJG2+VpMkaeyOBVSaIYRCKQNSsViuePbsKe+++y5FUaC1wSRpBDm1r+FwuEVZlHgPTW3jf7cQnG63i1QKLzx1U/HoyWOK5QoRoKbGe42QBXo6oyjWrFeraH1dLgnOkXe7GGNYLdc01pHl+Wk3Zp6lLBeL2O8pJVLEnljfQruWyzVFWUaRJDRZkjPq9xFCIVEQBMW6pJOmFGWNrWvyJEFKKKsaYxKcdyBE3Kp5f2oBFe22K4S4VVUiUp598NGyTHQ0aKGRKiHQVvRIAcEhncCGgBFgAkxmM7q9HuvVugVONaSdHtpkOB9IUsPBuQOmixnWNRxPZggkeZrRGXbo5DlVXcescIgop6Yq0VlKpzsgyXIO9nbxUrFarJCJRJmAEYrGWupqycHeNi54QogZaluvI1xMqZgDV4pur8dquWJT85QkCqUiWEp5+Ymc58bOKluVv7mBk+ddlDIURRHtvAJc49tzOVKWtVLkSUoVoHE2BlQJhOAIUqCNOu3hTZKEqihoqgqlNlZbTh9bCoUI8dYB0sf2VRFaK7GImykRb0apf4nA69uf+3FKb3vTKoTTcyJ+s0cIT/C0G95o0d+IU98GVkX7d0DwgSCfW30/bnU+Pce+R8SeAZZ+sPOZ7au8lO7xv/6tN170ofzRx1lYL1/0UZzNp2V8oPg/f5f/7Q8u818c/gbwJxPEFkLg1x//Hv2/rfnbf7mDuHjhRR/S2fwpnr/6t67zf/03H/1I62s+1WJ1f2uHUbfParYkWIsWsacRGZACRr0Bt65d49a1K+SdlFW1QEoDOB4dPSMziu2dbarVGltV9DoZ0iSUwLpYcTI+wQVBnud4ITg8PqaXd9gaDAhSUNU1SZqwNdqirCqcc1QyokZGwyGvvnQLFzzHJ2PWIZDlOSEE1usVXZPymRsvsyhLvv3ue0xnM9IkIU9S5rM5ly9dwkjNarnCOsdwd4e18Dx8+oT1csWFnT363R79bpe6qinrGikEDx485N3330OnBpWmPD05ZtDrcv7SAe/d/4CtfMDnXn+D/d1zFGVJp/sGdx8+5OLBPp95/Q06SYpoHK9cvc7++fO88913GU8nJGlCUZYIJIvFgjRNW/iOJctypBQ0tsZ7i9ImWvRaeNDpXiR48AFlongLgPACo6Koi9ebUdBqpXGuwYdoP52Mp6yXBbs7A7aGPZI0JSAoygrrHFXdUJZVFJwy5vG8jeAWgSIEj6sbTEshxvu45WmzbBuhtKGwGqVRUrcZVYe1jsVyxcl4yrPDIw6PjlmtCyBWySid0jiLEoJOJ8MkJlJ9E4OUGkfMGzsPdd3w6PF9bt++zXQ6pa4rnPM0dkVdVWgd84Yez2KxZDFfkecp1jnqsoi1Lt0OZVkwnpxQ11WkRbfnTwSMSax11LXl0aPH5FmGEJKqrOh1u/R7/djf2u0SfCTOplnO3bv3SLO07cYVzGYzsixuQqVSpGmCc57lcslqtcLaBq0NvW6P1OStWBXgBWk/ZW9/j7ppODoeM1+sSI2iaRzWN3ipiRnDuBnzweNbKrJSkT0bxbJG6minDdGzSQiBxrdZRZkggkdphSJQ+wjekSqKpbJqGI4MnW6fra0Ri+UKpRKC1ATipvbB/fvUTUXwnjRJ6A2GJMagpKSpaiYnJzSNJe10yDs5dVPH80tarKiYTcd4qQhCkCUdKt+wszfiz//SL/CNr3+L3/md32O1btjdPYdUhsJXSBXdBlprsixjNl2c/tlmrW27YTVVFb+2gTNZa3HOn9pxIy1XxGom69ptZvxHSRVvxFgL+JY87ZFGI2XMgXvnojU7RPGpZOzHbSoX3xdno1A9bZ+JDx5CFIShrSVSUiGFIHgXb0TAKSQs8L2iMPBxofq9tTLP4UptzlSDURLbWIbDfgsKK9pu4efZ142YllK2Nwc++bjwPBe7mSj65Vl29Qc4f3P3Z/j3v/I2yS+9+aIP5WzO5o81s7/3u3z9/fP8hclH+PDBiz6cH8j86qPfJv21n+dv/JUJbG296MM5mz+tIwV/U9zkL/4I62s+1WL1/N4+y+kcITaF8lGEyBBIleTC/i4v37zK7nCAsw1SdqiaiodPHrNcz7l4sE8TPFXdMOh2Ed4ivUNLQSdPefnGdXrDAYeHRxwdn6CkpKxqev0+nU4XGSBNUoSQTMdj7ty9x87F80il6WYdRsNtptMp7z17D7Qky3OOJicR+rRe00tzVJIw6PYYDYcxV7lekaYptnEkWYpJU0yWM50vmM4jVTbRhqzXpd/to0LM7F6+cJE7jx6wcDW7W9s8nZxQWktlGyazMSfjZ3SUYjFfcPPGdfrdLkmecWl3m+2tLcqqJAVEVaGR1MWa4Bp+4ic+x/Vr16nKirsf3aaTdymLkqZpWBWrSIkNkGUpaaKBuFHWLWzI+7hlDBuYiYiVGxBtg9550jRHyUgAtt5T1ZG4G8LzvF2aRMtBOuJpAAAgAElEQVTsclmAt/R73Vag1kgV88QSQQge79sL0LZ7ViCpa9sCjuJlc2krrLUUZRW7KuvmNBuotSZLc4QoWK3WLJYryrLm+GTM0fEJZVUjlUHp1t6r4oWu1ilplrQU3ZQkTeImGYFrGlarNeuy5vHjJ9y7d5/lcnlKgTVGRaquicRlYxKU0rGSJM2o6zpaLpVuLZQe5y29bodsa+s072frBqUlZdUQvGM2m9PLO2RpRlVXDAaD6AiwTRTP1pNmHXb6A4RUzOZL5ot5zES2kKeY701obLSp1k3sg91c70spydJOpK+GKMa9i9VARVGQ5ilJEiFSSmky76mbQOXFKc1bSRX5se32T6gISvLe4UP8TAvvUSrCfbwPOBetwUoqQGJlQmIkdRFrmySKbiIZLxbsndtnb3+Pra0tTDYnSXKGox3ee+8DxsdHrKuCJE3Y293jC9eu45F88P77PH3yGN/a2nWSkiYpg8EAISVlU8cbJcUqPg9taKQk7w/Y3d3mC1/8SfYPzvHNt/8BRycndDsDFos5W9u7ZFmK9z6CsbIOQghs2ykcM6kOIVLSLKUo1oQQohW5pfIqVJu/3ECQZNyollUrvNraKWvjjZ/WOUCIlvfS2rjFJDpRvLcI2VKRq/p0w4mLglWbaEneAJLi5yzSrCOJ17c3HMTzahs+ZsENIJVEiFYQipby+4klxeYXAilpb3bF7XJiBL1ujm0s29vbGKNZLZdMp1OWy+VpBrVNu8bPMp+8EaW1PqUNR+uwaD+d8ZjUmVj9gc1//NcuEVa9F30YZ3M2f6xZ/vpv82+9u+bD6W+96EP5gc/fefxb/PXDn0OeidWz+SHOv/c/XOYf/dU/y38w/u0fyc/7VIvVTprzrHxC7SwiEdHamBgOtofsDnvcvHaFrb6hrKYQBMPegNnCMTsek/Uybrz8Ek8nxzyxlt3hgKsXLsYcGJBqja3W7PZS6nXG0dEYoRO2dvb4zoe32dna5sLBAWllWc7mzMYzpuMJg3PnUUpw98EDUpUwHAy4eOkiaMnjZ4+prWXY67NazKnKips3bvD5cwc8ePSQ46Mxl85dIM1y8m6f2WrFyXzJ9rlzfP2b/4Kj6YTB1pDDyQkf3L3Dm6++zsXdfbAO3zTcunyVk8WM8zLw6ksv8bVvfZOZdxRVw2Q8RwyG3Hz9VQoh+Wff/CbTkxO+8LnP0e/2on0ulUij8SHw4MkDdkdDrt+4weGzZ3zwwQdcuXyFS5cug4yQlvHRCfPZgqaYoV1KdzTCJAmr5QKkZHtnh7qpCUGiTbQ7Gq1pbE2apNjG4h0kWd6+7u0FcNdQVjXOxmyidbHaRkhF1unhvGU8XaGVIngoqjLagmvLcrkgIKmampPJmFVRMBlPKcr6tEIo7/UYbm+TplnMC0qFlopUJzHnGRyPnj7h+OSY8WSKsy5u8ELAh0CS5lFMtplXraPFWCmBTuJHRioNKsU2DXVtORnPOD4+oawrnjx7ymw+J4QIiTGtPbmua9IsQyqJ1ArvHWVZkKUpAotzDq0lUmb0Oh2UknQ7Obu7O6RJQnCWyWRCr9eNMB8Ra3AmJ2OQUegOt3bI8oxnh4fce/yYNM3o9vucPHrAwbkDLl+7wt27d/G2Zr1ex7x3f8BkNqcuK8qipm4a1mVN4x1Bqmgfdg6tBEmekaRp3PA5x3JdsFpHuBBEcmtqVNx+Fw5PuwUnVuMIGetGrGtiLlUpgvW4pm5hOPKU+B1k2/wpNUIEgpJYqRBZTlUGyrpmWRcYV1N8+1t0EkO/2yHtdej2huxXsV9VK8Gg28WkCWm3Q7A+Ep0nU7QySJPSG4zi+2wSinVBXVVUVYkPEbrlG4cVEqc14rxgNNzh6bMx7334T3l8OEHpjMZ6RKiolgv6g+4p5EpIQZYZsjyNAs15rBUgBcbEKqEQAt1+n7KqWC6WCCUQwUGINljrLI218X0WIJSiriqEd6cgpVjlIrDBR2xRDIy2Nt3nFtq6KtAtpdoRYV5GypaYbaPNV3icb2Fkrf02nAKOopXchxDrb0K0Dze+BSsJgQqi3Zw+t+i2fTmnGVUl43OTMgrLurIYY1guVwgBaZpx4eIllssVi8WCoioheHxwz+FQWpxaplUrloUULVQtWqOFiM4LrZIf4d9eZ3M2Z/Opm+Nj6m+8z2tffcq0PLODn83Z/LHHGH7hv7/MP/zvfp7/tPz+lU6XO7v81l+++omvvfVra35x9i/+SD/uUy1WbbAoIzFS4n2DUXDj4gE3Ll/i5pULpIlivpqzLBbMZnO2eiO2d3b4qZ/8Als7I2pXMp/PsU1Ft5Px5PiQXpaTKE1TO7IkpatzunncYvaGW7zz3ofMF0saG7Nfgzynawzd3oDPvvl5VK/L/YcPmc3mNNZS+YbL16+yXC7pdDu8/977CB945ZVXCSFQNTWqVJzf3QfnccHTeM+33v0uH9y5w9buDheMZHd3l6zT4fj4ENlm4RpnWRYrBnke7bRGcmF/j3m5xinFz//Ul3nnww94+OQJBxevcvP6NRprefrkGauy5NvvfJe7d+/yM1/6Muf397FVTd7J8MCbn3+Toqy589EHfPWrX+UrX/lprl67wvF4wt17d3n51i0uXb+M9GCrmvV8QV1bkjQh73dZr1dUTUm33z2Fraj2gle2vbdaQDASLQARN4WCgNYKZyPcpqlrprMFTWOpKstqvQJs7PAsCozUeO959OARh8+OWBVFBMwEKOuaxWrFarWmrGpQkqzXZXtvn4tXrjIYjXDOR7GDQLoIh6qqOl78e9tutKKtOdFJ3LwaQ9JaZTf9l0IKkPH9FFIRpGYynXN0dMx6XbAqS3yIz3G1WrEu1gz60c7Y2HgRbm3s162bmtl8jm2auDUMjuA9WkmEUnjvKcsSQqBcr5kcH2ObmsQY0jQhSxJMmnBycoJSiuFwSJIm1LbhybOnHBycI8076CTSimvbYL3j7r17dLo9QnBIrRmORjTOYn3AmITVuqSsaqbTOUVRQhCotvtTa02/248dpN7F5yMEo9GwhfNIjo9PEECWJJRVEwnCjW0hO3ELRmsbt86hhTq1CG+6Pl0LGxJtjtV5hww+Vpm0uUvhPda3NlVAaomTgrKuCMFhfMPheMyTZ4f0Oz1kiFTp1XKJMJrxZMzh4TFpkpAkSVvzEjeDMTdr28x0QZACkEihSNIUpw140NIggqIsKrZG2zx5/ATvLDIIqrpgSw8wScx6Z0lCkrTZcx03iZvnWNV1vEGSJLGGJkmROtpfTZLinKVpGqRUhNbm64NvM57iNL9J+++4ddT49tymrXmRSrWW+Qi7ChtLdvAQHASH87bdprZOlvb7lZQEGTZQ3o/ZheNEWy8tcxsg4Jw/zb1CK0jb/ta4tY3vZ7TuBggC20QRmqQJWktWqxXT6fTUAp9mGYGNlb9pjw/ipnbTWxvrekIApYAg2+2ri5v6szmbs/mxm3D3HvP/9wG/9EHNt07uvujD+aHP4/9jxqW//qKP4mz+1E+a8ou/cpM73Pxj/fbP/134R/+V4b9Onn7f7/1Ui9XGNeA9HaXod3Iu7O1w48plDna36SQJ62rJk2dPKOqCvNtj7Rv8fML21ojRaMDx0Zrd/pDEKFxjOTw8ZGIM2zvbdNKMPMnJTII2gZvXRjw9PubO+++xvbvPlYsXyJKU5XqF7HbJswwtFYvJjGKxOu0lnC2XLN97n6uXLzHMunzl8z9JWVesqxIvQQqBaC8wR6MRi6riw3t3eP/2bVyAtMg5PjriYH+fq+cu8G7V4PG88cotZtMJi8kJ5/Z26XW6ZHm0j+72c2rrSIqSL776Bq9cuMzB3h5Ga9796AOEbVAhcHCwz5WLF0mMod/pgHWkUlNXFfWq4A+//S3u3LvHa5/7LFduXufOw/t8+53v8uzokM7WgHPigO3hFp0sJet2aJoGZTRZmtGzfY5OjjkcH2OtjdVCWiOExOQ5WItvHFJIEqPIMrNpSURqRZZVrFdrTKWoy4Kj+YzpeBq3qCFaeIMPCC9YzuesViuybsbh8VHM6LUXnqlUiCwFHF4AzrJazFnOZriWMitEzNrJEC/oN9t1YQRpmpGlaawJ0gnaJCij2yxetGSKtlrGWodSMds7nhyxWq5oGosNsV7Euob1eo1SijzPYkYzxMxmUZRUrRV5AzXa9LA65+h2uqTG0DhLIBBc7CXt9weIEHj29AnOOo6PjzFJgja6zT1q8IK6aVqqa+Dps6c01lNVMetaLeZorXHOMZ9NMTpSgSEwXyyR6yoKQ+dYLOYsl/MoznQU6lpJtJZUVXEq7Muqoq6aFiAUydmdPG9FEdRNgzGSpiVbx32ah9DWNUUt1NahiFNRE+3O0dIphTq1eYpWAIkQIgVYa7RR0MSsdJCqrTKJG7ZUKZSirVhRWNug0pS812M8nnByfES/32ezg7O2oa491llcEDgXN3LGxEyyR+CcBwWd1NDLU2bzKanWbI8GGBnzwk0Vq2im0yndbgcpo2W81+thzARro2jagIWqKlYLmSQh62Tx/DG6rRtqkFJgjIr1SrVDiNCeQy6+orYmS1ISk0biddNQVVVb9QIIiZCb3Kjk1Eh7Cj+K9TjWWkJbjRM2HcpCPheop/MxYNK/Ioe6+frmdd1QgkFEsi/R4h3P/5hd1lrHhEd7/ABSxhsJaRrt1JtKH6UknSzDOXNa4RNrfgRKSTZ1rEpKjDGRVC4igGp398wWdzZn82M3x8f8t3+/4u89+vHp1f0zj+9wj5960YdxNmfzfefP/MrL3Pgfv//3farFqnUNOjhyoRllhqsHuxyMBgw6KQ5Lt9th2B8QFoGt0RbH8wnz8RxjwFbbnNveJT+XsFoteHL4DC01aZZz+/4Ddna3AeinOc5arAeN4N/+uZ9lMBhhneOj27djRcTeHgd5ThCBuqoo1mtkotnd3eFkMkYFwWI6Z397FwlkJkKUquDYGo3wrWgqqopArM7o9bpY76nLgmpl2OnfZGs4JH31VRpv8XXF1qjPbDZhUS45mp5w/do1tE5Iuzn97oDe8ZhqXXJpsIVSksrWvHHzJUg0b73zLfI85dKViwx7fd56+y3O7e6zv7tH3usT8Lx06xa3Xn+Nvf19CIG9g30uzGdsH+xRe8f9J4+o6wYjFVuDIRiFk4FZseTJ08e8//77HB0e8vJLL3Pj5k2Cc6TacHh0xHwyZWdrm0G/T+0apJfRAigE4DBa0e3m9Adddvb2Odif8ODePQ6PT5gWBUVVUxVFFA+AzhJ803Dt+kWqsqQpS/I8ZzGfsygKek3C+YuXcELx7HgMztIUZcxttsRiLUXcoJqEJE1RRmFMEn+dGLQ2gEQZQ5pFC3FV1e2WSCJVwnK9ZDwes1oWMa8qaO3M8SJ+Xaypmvg+p1mGd57ZbErTUomdc6cZO6UiHEelhjSLgjkjiq/UGGzTQAjUVQlStT2oguVqzfb2NkJEyE20F6dolaC1Zjaf0+/1iZUogW6ny7pYA5CmKSCoqjpuJZVGSNlSgkMUS1K2wKZ2MUcUvkmnhxDEqqFen6fPnjGdTFmtFszn83iuGINSGq0U1jUkRlAD1sc8s/e0YkW0NNfndSSitbd672NPrRQIyylUy3tHbWOvstEKqQ1C+HjjQscanbIu8etA1slQEqwt6Xb6KAym2yXJU5LEkGUJdV1GV0CIICPbNNRNjdAbkRMzlUJKlIi52cY2DHo91qs5ddWQdXrsbg24fOEC4/GEDz/4iLqumc9nlGWXXn9AnueYNOfZ4QmLxZKmqVtwVMxQZnmONpqiLCjKArfpMJVxI9k0nuBir3G0uoJSAgIMRkO2R1GEnRyf0DQeuXHcivi9EQnX2oeFhPbXccKpGJQy3pyRG2G5OQE2hODvEacbS/fHybuh3eQKH3/288/85vfRCtVwmiFXKj4O7XvvnGW9Xp/SipVSJElyulGNdnmN1hE4Zdv6HxCnr9GgP0CbCJPyIVKZu92zjOXZnM2P1fjAX/qVZ/zvj7/2oo/kbM7mbP418+s/8f2/51MtVg2BrX6HnU7Oxd0dLhzsMBxmVE2ByhJ6vR43r11nsVjwwb3bjFcTulmGVoLZZMzowgVc1dDPu5gLlziajBkvZ8xWS+blGiMMaqRQUhG8Q/rApXPnKcs1y/mCqlixKiuqpma6XLC/v49SgivXrmK94979+8zmM7RUXLt0GZMmeOsiQdUYmsqxmi8QELNoZUGWd3j56lUOdnf46PZdtJJ88fOfZ29nl9l8xt7BLkmiqb3l/oP7XLpymcOjIxbrJe98+C5vvvFZOqKLX8wwIqDyDKwD29DJM4x3OCPZ3duhM+iiE8Pvf/2rvPHyq7zy6mt4BE1VoBLD5UuXogWx30fUNYGA0YrZYsF0OkEIyWo65+TomH6nS384oHI1vbYSBQEueM6dO9dWcNS8/e57jI+PuXnjJkhJZZvT2hjbQl+klPR6PRKTtBtPz/bWiEwrRsMtno4XzJdLJtMJZVlgtKKu1igB5w72yI1mMZtQrFYcHgZGdUZvtM2tVz/DZL7k7e+8TxMEOklxPgoynZhTO2ZiIn02TTMSk7Znm4gXztogpMKFtkNSxJoU6x3T2YzpdBq3P1LS1PG5NbbBtfTSxWIRM3Qq2g+rqsLaCBHynhaipNq6GIFOJMPBAIgZwDRtgVW+tYb6wGoVszXW+ShstGa5XLaWUk0InrpuSLO4mdve2kIIwWK1oq4b8jxvxaOjqZsNt5W6aVitC7QxaJPEbSK0tt8IqpFSoqQiMxqtJM46To6PWcwXOGfZ2d0h73R59OgxWZZRFAVCeKyt8D5ufkMQ0ASCVDS+rScJHotDSn/6ed9smjfC8bQP9JTyGvDWYkNoCbee4GtUsOQqAq+CAC+iOBltjQiNo6kdToASMNrZ4cnDR6SJiQLcmCi4PlYTI5QmSInzUWhZT8yXagPB8+zxI/7cm5/l+q1bvPvBbW7f/ojHjx5x/cZLvPraK7z/3gckIf0ETKm2DpNEgRWI2Utnn1Nr67rGektRtnlSpfAejIl5Xbwkb/t3Cb7tGLbsbu8QQmCxmLNar05JvJsc5+aGxeb1FURB+lystrU0wkdws2//v/fPcUjqk+/D8/critkNZde1pOe40/VRtMrWHCw2PzGcWnZhA2iK5OMNoTpahp9XS20qp+KWW5x+hrTWdDsmZuUTE1/HqsR5T5akWNew6WRNjCHPs3+zv4DO5mz+pI1zzP7n3+e/fDDif/rChPTnbiEODl70Ub2YGY/5S3/nyY+lUPUhEJ48QZw//6IP5WzO5vtO+ktf/L7f86kWq/vbQwZmxCBNuHHtEkWx5MMHT7lw9TKdXsJ8NccWJaHy7A+3SbspFy9eYD2ZsDMaMTk+iZvOLEVpjdEarTQvXbvO06NDyqrmZLZAC0E3S9kaDKiKFUYrhr0uN69dYzyb8+5Ht3FCILQiTxIO9vbY6Q9oqppcJ+wf7IMQPJuOuf/gPgf7+xilybMsVqIUK4wxEaRjLalSnOsPcXv7XL1xlb3dHb713Xd4cvSUK1ev8vobr7Iz2GYw6vHk2VOm8wndbpeLFy+Sd7K4XahrUqmRgNACJxQiERidIoXg1dde4ejkhI9uf8QXf/rLXL1wmRDgwzt3OD4+5pVXXyUF8k6HejZDKcXDB4+4f+cuOklYzudcvXqdYrVC+ECepsj2gv/ChXNcuXSZz77+Gk+fHjIaDKmKgvsPHnBycsJXfvpnsM7x4O495vMZnU6Hvb19tFHUjaWq1pSNJdGGPM0wWsf8cLeHkAlJOmQynTLq9ZjOJsxmJ5RG4mxNVRUkKmdre8T2qM+tl68RhMYGQVFZyvWKC+cOCCpBqAQfIlQqTZLYLyliNlS22VQtTbvVi/ZVD7gQ8I0l4Fmv15xMJ6zKkvW6iH2SWuGcpWqi5RMhGI/HLFYL1sWafr9Pkmis9WhjGG3tUFWRShx8iFvC4FBCkKZpa8kF2dp/pST2sMpIaK2qBqkM3kf7897ePmkat6jj8ZgmWLQyz7N9adpajaGxFY8ePUIpRZZlJGmGC57Fco3SmjzPsS5QlRXL5RKjDVIIrI0ZWWMUqZGkiaEs18RWoMBsNkUpxWq1QqkJWkpcCCSJPt3SBd8gRYhZ3KCwPsROWhnwgUgC9m0/pghIQLbibSOcBURnggQQsc6lbfdtnCcEQeMF86rGWYEODhM8frVECMh1gneBoHKM1lTOM18tqesibhCDx5goeDbnSFU1WO8JLm7OAwLdbgN1CxtSwIO7d3jvu98hNA1GaZ49fcYXv/Qldnb3+MbXvs58NuPOnbs4Dzu7e1y9coUPP7pNZSM5enMTw1mPFAopBWmWxBsZwGi4xauvvMJiPuf+3Xs0daR0ixA35PPFlOl0wqqFd1kbbfcbMRcznA6BbC24EFtk42yswEoLVIhEYeccwcfXZbPh3ohOeF4LI0TMHovv6SQ8raqR7c+Um01uOM2XCimRYmNF9khpMNqgWtt3CPGY5MduVsRzSpJoc/q14GMUIwQRK3m0JNH6NA+sfMxb53nOoN/j3Lkf04v2s/nxmcmEX3twgRN3wl/8RsZ/9vsrfunvvuiD+tFPuHuPv/L3qx9LoQowLZf8wq98yD/9j56gfvoLL/pwzuZs/n/Pp1qsXrlyjqFWdIwmzTQn0xXLcsmd+3cYDkeMeiOCdRilGOQd9rd22NvfY6wU45MTfNOgJCxXkk6vy872FuuqYHoy5fz2Hqui5O7RfWQIvPHqKyxXK7QCKTRGS7aHI7a3d0iyDpP5nMl4TPfyJdZ1RXV8RK/TYWe0ReMdH9y5TWkb6qYhHB2xOxqRKkMgkOc5Ukg6SUawUaQ4a7l26SLn9vb46M5t7t2/A4midjXLYsm6jJvcC5fiprfb6XHx3DnA8+jJQ06Ojrlx9Tp5lpJ3um0tSGv30wZrLcvplEsH57ly7RrL+YK33n6b+WLJYGvEH7z9FjIIrly8yMHBAdY6elnG59/8HIvVChscu7t7LOdL5CVBv9tltDUkyzO629vQ1FSLFcO8i/SBqio5PjzilVdexYdAU9fMl0vuPXjI65/5DEFpJssli+UCozUWgXcF0s/I0oRhb0CeZRij2Nvu0utIXBgyX/SpmnOsy/+PvTeNtWzNz7t+77DGPZ75nJpvVd26Y3e7r2m3O3bcCHAjkCIEAQmhAMkHvgXCaCKhKEwKSqJYCIPCICTzEUUkmASQYuHE2G7Hbrfd3fd233v73prPPO55Te/Ah3edXbchUSKlSZXd5y+VSuecffZZtffap/az/s/zewqsMUgBpqzI05ROloX8mjfsHx2yf/CUw8MD3njr86R5n8oS7N1RhI40QvogVrXEe/GickMIlApvgq0Hh+B8NOZidM7RyRmLslqSa50Jb/6dNaRJSt3UzOdzTs9O8fjPiATfwl4gjiVp0sPaBkmwIgNErb0RH8A7VVkynYzpD3ohVysd8pKO21pSkZ6qqknTpO1f1dR1RaeTU1Vlu2UNIjrSEWsrq5yfXyCkpGkamqahqErm8wVRnBDHASRVlkEIBYJrgCFJEayjsv2zvrbGdDojjhMGwMXFBZFWVG03qJQaD1jTtFRYS2MMWsZoFbcVnh60DGYAG85XoULH6mUu8XIrKC43gy3VJ9iEQ4+u8B4vBMhQH1RZ8MYS40ljiBJNWdc0RU0UJVhnGKQ5s7KkKEukcyitWwiPoyrL8Pw3NU1d41polBSBcu2lwDpLlqbYpubjDz/ES0UWJQgg1hGrq6scHBxSG8edu3fZffqci9EI0xjSNCVJM5IkDnlmZ5jPC6x1rdXYs7a+xs2bN+j3ekwnY6y1nJyccHhwgDUNaZqyvr7ObDJlsVhQLAoW1izzr6L1Ukc6ajf79jOZUVqwksd70wrRsO50PuQ9FeECAoQLEpdlrpLPWIA9eBcyyLL9fn/5heAJbm3dL+BKAQQVwE5AuGioZWt3/sHN7OVzf7lBDTbtwGZSSrUZ2jbzKnUQssZQli58D2GDHEVBfOMT4ihibXWdWzfv/PD+c7qaq3kVZ32dP/2vX/Bf/eI6X6wsX/3zN1/2Eb2UufilPf67vY9e9mG81Pmdk0/4F/7Xz/FX5e+hvvwP4LO8mqt5heeVFquDQZ+VRKEJV+Bfe+0WyXHMwckxx8fHTEdTVvpDUhHT73ZQTrC4GLO5us54csHZ2SndwZDTwyOSLEE4x+Zwjbfuv0Fpar770cfU1vL63buMJlN6WUKeJC3tNAahQQjefPA6tXF843d/h71nz+n2elRFwdbaOrdv36YpS9Y21jk9P2exWDBuxvS73ZCTVJoIQaQU3lqEUjRVhdSSjY01nLfcun2D4/EZcZbQ73V5+PBjBJ7BoE+v1+fOjRukSUZTlkznM46Pj1gUBR89/oT79+9TFo5ukmKtJY1zqvkCieC1nevBKrmoEMYyG08w3lGbhsZbDnf36Pe7TOcznj59zufefZc0zxCRZjKb8v53P+Di/IJhf8g/87WvkUYaJQTUDb6scWWNrxt8JBA25CzzPCdJYpCSoq5Z395hY2ubTx8+Yv9gnyhNaJqGt998C4GgLApqY1kUNXmS0u9mdBJFJ9c4L8nzNcqqDvZRHyouqnmJs54sSZFAU01BSIara9x0giSOSOKITtoh7/RCTYx3VM2Cuq5xzgaBcJm1c45ISbSOMdZRNg1aSeJIk6UJi7Jsey0lyguMsWgdYUyD957xdIxqKa8BrpQFuJGWdDpdlJQURaiowQWia55lxHGE957JaEJ/OCDSGpkl2KbBGYeMJNPZNHSZJgmgcMB4PKbbDd+vlKQoFi2MR9Lv9zk/v2BnZ2dpF+73uuSdLmUV6moiHdHvD6ibJvSLRiEXeAnnUSoAqWxTIyK9FDuLxby1Npc45+j1umgd0e2F7G1VVRRFSa/bYTwZoyQ0TQAWKSXRUrfEWkcchbqWqng70HEAACAASURBVK7RIg7b6tYqfmn/FEIQRRqlw/1fbtcEAXglCALGWU3dQnukkMzqAqFsIC03NbWBuJeTdLoYHxBDQgjqusKahiROSJMUJRUuihAIGts2dOrQs5vkOVVdoyJJU1fsPn+ORXAymhLnXdJezPPne5ycnrO6scH9u/d5480uv/Vbv835xQXD1VXWk5R+v8+8fT7n8wUrK30ePHiD9957j+3tLebzOb/5G7/Bo0ePODs7w1vHGw8ecPvWDY4ODqnrmienj8OWPvipl+dyFMklwMh7WjDViy0rXHYft9UxSuHwWGuwDrQk2MGbQI/WUrZC8jK7Stt32m48285T1wKZ2qacsHFdWobbDKtoYXNCEsUxkZY4p5biONysFbqtql3eh/fE7Tl6CXgKjgb1gvHkwwb+Uowb0+BdoIrPZzPOTs84Pj7m3/xTf/KH/d/Uj+R8/T9/ylf+ncHLPoyr+buMfON1/tS/8QSxtgFR9LIP5x/5+KMj/rnn9cs+jFdifuXofaa/8wbDL7/sI7maq/mHm1darM6rCcV4QSwEd65fB+vpZV1WXl/l0ZOnLMqSojxidbBCp58hqoa0kyO8p9/vIwR88uknXNvZodfvI4Sgl6WkUlLMC0xZsTIY8PjRI7IkZpYm3Lx2jUxpnAgiIMtyqkVB3Rhub1/DOMP3vv8hUisWtuY7H3/Im2++yVvX3uaD99/n4NkuaZ4zGk/xUjDs9Fjv9BHOUTcGqSQqidjc3qRxDZGSpGnMFz/3DmVV8ujJE6bllPWtDc7PTnj48cfkac79B29QFiXHB8fknQ4qisj7XaZNxfTshO3+Gt0sp/Yl3joWi4IsTfHOYxY13SzlZ778k5yMznl6sA/e8d5PfIndZ894/OgxP/tP/lNU3nB6dMjx+RnPd59T1zVZmvHa66+RdDKk9dBYFifn1GUZMpuNxVbB7trPu1TzgjhO+fZ3vs3u3h5f/Zl/nI8+/j5Pnz7nx3/iS3gp+K3f/m2mZY33jsefPiTWEde3t/FDRXGyYJgnRFoSpxlZp0Oe9yjK0HnZNA6vNNYaTkYjmrJGKIuIU5LM8sYbWyAirFeoKAnW2cYFIqiHWCm8lEitQ4azqlAyCFNjGsqixFhPnmWsrq7S7Q9Q+/ucnY9xNhhTlVKBnCoJhOmzC6Io9MsSxTSyIUsyAMqiYDQaUVcVvU4niOAkwdmGYlFRlSVxFGOqUFUz6HfRWmIax8npKYvZPIDBdEQUR0gJVVlSzBdo2WE6nbR54UBQ7vf7GNNwcnJClgVKbxCxPbbSlIuLC0aTOcY50jRhPJlgTUOk1dIOq1XIq1bFYrnVXSwWKOTSGiqUpK5LoigmihK8d6wM+6yuDsnzDgcHivPxCPwCaz0Ch9ZB3tQ4kI44kVgvsa4Be2kZDeO9x5sGIzxShU30pdAKtNzwOePAItFSYvCBMuw1sgHnSnKtgrXYe2oX6pV0HFFcTIikJNKXec1QLySSmDRJkVHMdLGgqmqUcGArvDPMpyVpb8B0OmNWNdRW0FBzdLbHoqyI04zRxYTTswuuX7/Ol37iKzx99oTpbE5ldrGEc/HOa7f443/iX2NjdZ2PPvqI0+NDfumv/VXG4zGzyZTBYMCPff4L/OSXfoLT01M+eP99PvroI+azOVIEmrKUCosnTXN63R5xElNVDZPJmLqucNYTKbmsXvLehcdZhK1r04Te1uDX9a1Z1+MFxC14rq4NMnKtUAyj2vPD8wKe9Fn40vJjQbhHL9p+VkiiAN+SMmxmvZd4Z8MSl3CxIWxH/YtsaqSWGdV+vwfeM53NsNa3xG/X5l7Dz77ctgaYWbAJ1/U05Oyv5ocyv5HCVy4uYOWKsPwqjrh752Ufwkub5ncf8q3TRy/7MF6ZefCtJ+z+n4LoreuIO7f/vre/mqt5FeeVFquuqTk/O2Gl1+NsNMIbQyfvMD6fUBUVwgtUEjGrSh4/e8Ybt24SxzE6iliUc4QUbG1tMRiGHkrVbmXOW8vmg7t3IY45Oz/j9OSELO+QdjqM5jOUVGgdMZqMUSJkB3t5RlEuuL69TeMdF6MR86LAfei4trnD2soq733xixydnPDJo8cM1teoihJZG1aHg9DZqAQ716+RZjEnJ8cU5Rw1D1msTCo2BwP6vYz1rS0eff9TTGMYDFZ4+vAxUmuuXbvO8dkp6+sbnI7Oebq7ixSSnkwZdvs0xmDrYDusygolJFppitkcoRWbK2t0ul1OJ2Oe7+0xmUz52s9+jTSKKaoS09TsP3tOp5OzurLKzes3qIuSJw8fstIboBzURRCqOI/wIKWmqWqGvQFaSCbnF5Tzgjfu3Ud4z8NPPuG1+6+Tpinf+e4HSB1xfHbG6OKC1bV1RmdnHJ2eMRyuIGXEaN5gbQPMiKIxq6trbd5U0jQ1ZVmwKBZMpxOaxpLlAa7TG6zRH67SNJayaagbg2u7bZ0LXaaXoJe6DpRfZ11L3K1DFUbTIIQCb+hmGTKKQAim0zm1ado8HBhrKIqC3d1dFov5MlcHgiiK6HS6aB0yeHmWkiUJztSt7djTmBotFVkSI4VECU8cR1zf2abT6XByfMLZ6SlJHBHHQXRe5g+TNCZNE4qioGkajHXtxRnBw4cPieOY8bha1uhcCorL2+dZzLyoQoqwFRl5p8P9wbCtXOnQ1DW7z54GQI4I22pJqLdBCOIkbmtYHGVZYExDUSxw3pPlGdZa4khTaw3ehOyqDpUiQjpqY9BSk+cpRVkHu7CI255egbjcojlPY+ulvdQ5j/SeOE5wHmprcEJgvMCa0Kkb6RwfQ2UKXGXJ44hIKMra0O100UkCQmC9oywrFAK0xpga7wXzxYxOb8DacMB4MsV4gxSOplpQN54o6+Jw1JXF65TxpKCoG5SOMY3D+YanT3epKkuv12dja4ej40MiW3P37l3effcdnLU8fvyQX/mbf5PHTx7jnKffG7KxvsFXf/pn6HY7HB8f8/Wvf53f+93fpSgKnHOhFxa4RDVLpakbw3gyI0kSpJLESYpzHiOa1uoc8rZN05Ko282rdw4hVXvxxeG8D9nZWBDJAAGzKrzmLi25l8AjwrOztPACy2345dfCiEvNutzy+uVtWd6v84BQaBm2+kJ6lAyk4DgKXbiR1qytrTGbBhu0EC/yzQEi1W7XWziWc544Uu1rE+I44Wp+OPOLh79F/Bde59/6Oa4E69W8UvP2/370sg/hlZqiqVj7nz/ipzYFf+OPnKD/8FWlzdX8/ptXWqwqZ+nmGVGS8PDZY9I448G9FW7f2mSxqMKmzVkq2zCaTRFJjE5iFmWBUop+r0cv71DXJWkchUycCT2m3oN00M9yXKdCC+gPh0wWC549f05VVty6cZ08zVAtJAbvyLOcW9dvMJ7PmM4XZFFMJ81RUjIejVBa0+31WN9YpzQNT/b3qdcKVBossv1hH6Ukx4cHAaaDQ0uJq2v6/T7927epsHgl2N7c5OJijDGWo5NTpFRcjGfce/11VKR5/nwX7SSD/oDtrS2ssxwcHlIUodYlzzJ6nVA3QttDOZ9OqY0hlYqd9S1u79ygEyfgPMO8i7Ke6tZr5N0cpTTvvvs5FrM5e8+ecfhsn6YxXL+2Q6wivHFoqaBpsCZYp6UTKC946/4DVKSx3tLvdsjSmEePHzIej1nf3OTw+Jhbt2+xNlxh7/ku25tb5N0uTW1oakvZBHjLrKq4mO4SaRVIpgKsMzhvEVoQ64g4z4nilF5vgPOecl5gnQ8kVmeX1Ry+3cBc1gd5IVAInHU0ZdWedQKPRStN3ulivGfQ6xFFGjcLt7ns/nz27BlNUxNF4dyKohgArTWLeUGWpSRpRFM3SDydToYC8I47t26FTB0srbdVVXFyfEhy/TrDQY/7915jPJlycTGhrsrQmVqWKBWqZYrCo5XC2lA5kyTBYj0YDFBKLc+DlZUVzs/PSZKEuq7J8w6CivFkvNzSlUWB1uYHcoNeiJALBRpryOKETreH9z78rNpSVRUWjxDp0p6ZZRllVeKcJ9JVuEjgHd7W+EtLqAoZULwgSWKE9NjWIm3aZpXLShOJwDjXiprw2lVKI5DhF5gH4SSupQhZ2+ArQwo0tcHYCtIGJyVIRafb4+jpI2Kl6GYZjWlwVqOimCQJtOWd6zvcuHWH57v77B3sMi2mrK4OqIyAOGM0N1QmZLV12qUbdULeuQUmTcdzqmqX4eoKK6tDhisr/MxX/zDWNnzzm99gOh6ztrbK66/f49atm23Xbpfnz/Y43N/n+PiYw8Ng+6WtOcrz/IVN+rJbVKgWOgV1YxAmbFCl0kRKhteNDFnUQDxW4fHyLmCqQr9M2GpqQTfPMU2Naxo6vT7z2ZS6qX+wmoblS+UHBOpn/7Yt4bfFMi2rbrz37Ybctb2obTZVCIRUaKXbCxqtWG3FZ6Qj4igiiWNGlz3FCLw3y+zrDwhp59sctEPriDRRrZX/an5Y8x8d/C3O/uJX+SfKM7768/df9uFczdXwrX/ve0yqxcs+jFdyfuP4Q/7o3/gcP/t/PGbHwB/9i6+97EO6mqv5B55XWqy+ces2T5495pOnT+itrDLY3uLx4T77h8f0Oz2yNGU6m3J4cszdO3eQccTTgz2Ggz5bq+vgLMVsxo1r1ygWCxbFAussUoX+Ptc4itGYTEXITp+ybhhNJpyPxyRJysVsRlFUDLsdulmOlhKvFMJLNlfWqOclZd2ws7bB091dTkbnbG1v0+v1eeedd3n69AmnxydUyrN3ccqDlbusrKxQTMcoF/JmSgg6LaE1TWKKuqJpKqI04ca162xu7GCc49HTZ5yNxmxtZXjncY1ltTfgxts3iXWEc46ziws+efyYOEswI8/GxgYLb9FCksUxwgcmZxzHSGPI+hnKh57Qft7Be8/K+hbXVzZxgrBdLg0DGcPqBt8++h7fe/iQ04sL3nz9dYbdPhpFvShI4kDdBEEiBJWpQ5YuivnyP/bjnIxG5EnMFz7/OT5+9JBbt26ws7PD408fEsURvX6XebFAKsXR9IKTkxMSrennXRSC8WyG9JekUEWn2yHNM3rdASqOKYqK8XTBfDEjyzI8FuttC1EKdR++zdZJKUnihHlRUjc1WspwG2dBSpI0J8myIMK8xzQVzjZ4bxFConXE0fEB0+mUOI6pqxolJda2dRsOsiQjSxOiSLGop6R5FojKHtI0ItaKrLU0AhweHjKfTWkixXTcJYpTut0OkY5J0w57e3vMplPiOKLb64JzbK2vc3jiEKImThPW1taWgvVSNCZxjFaBtqykxBlLkiZ0fSAdL8oSaz11U2Odp9cfMJ/PKYsCpTQQBJ5zQaC+/fbbHB4ecnZ2RrfbJYo1KIGUGmuCeAVIkxRnYTEvkKLB4zGmxHrwKJROcN5jTY1UKXEcUVZhc22dw+OxViw3ZUGYtBRYAY1xSBWEjlaaJMkw2oXn3UoabxEWunGGMYbJfIbzAqljBsNVPv/5z2ObmvlkgjA2AH9MQ+kdZVnz7MkT9vcPWJQVvWGPL73zHp/7wo/xy7/6m3zy9JC6MXgUcdoh7QxwXgaB3tRIr7DeUhvDZDpjbWONd959m08/fcjxyQFvPHid1+7c5u237jAZlTx79owPvvMBjz59yMOHTxhdXCxFaZZ1aJqaOElp6oZIJzhn6eS9ECnQoXKoaWqssW1ZLcig1fDi0j5tcAKiOEb6UKkUGEphy6q0QmnN9Zs3wVmePHyMUIp+v4+SgqapXghB//e2/y5zp3ymtqaFY30WpCQ/A2AKVnO5vEiiVABOXcLFLntWL+3puiW7G+uWP+9SSIffceE2l53GQPtauNqs/rDn5/d/lf8+zvixnzvlr7xd0/3jP/OyD+lqfkTnw//wff7I/EOKpvr73/hHdH7l6H1+BYhVxP/wc4f85TTltf/4x1jS767mal7ReaXFarfbY3Njg8JYji/OuRiNKOYLaCyr91eIs4SoKrh76zYbOxscnhwwnYxZzOcoBLFWrA4H1LVhNpuFN0mXmwbvEIT+TCkDjbUcTzncP2RzY4M7d+7w9Mkzvvv9D3nj/j3yvEPjLFpJtBDYumG130fHCZOy4HwyZlQuqE+PuJNnmMLTSXM+9+67fPDp9xjNRgwHPbppgmgaqkVBpmPSboaSiqh90ymEYGNtPWTcFhVxmtLtdvn8O+/w8MlT7t69R1XV7O/vUywWOOe4fesWtXUknZz7bzxg92Cf7fU15rM588WCYj4nTzJWV1YoFgukUFy/fgPtBYmOydM01JVUNaZu8DbYZqMsRQpJWRQI57l96zaFaVBKMxyu0IkTTNOQ5BlplOBcu/k0pu31dBTFnDxNef3Oa+S9AJfZ391lfHLG6eEJ89mMe/fukXf77B4c8P4HH5D1enjvUELSvdtjMZuz+/w52xsbrK2tk2UZaZKgI8WiqvFFRVU3IDxpli0hSpedlM69sD/GSUySpDSNoSgrLODwSCGI04w0yxBKL0FLRW14+myXxaJqM6Oa09NT9vb26HQ6LBbz8GY6ikKtiJBUVUM+TMF56qKkm3XZWFsNW3QtmI5HmKrEmT5JHNPpdllbXaEuSqSCvf19pFDcuHUTqQTbm+vUdcFeuWBjY5XN1TWEhKYpEQRhF8UJxljSNKcxUyaTGVKGHtZHjx+BhzRNUUoxm81xCNbW12mOTpDKUs4CPCnLM/b39plNp0RR+PUgZbBgOu/55JNP2N/fxzrLZDohSVJq05AkCUIIsiwnS7MAVrJzmrrC1DVSSfAW2R4xXqJUROQldVPjfKhdMdaF2s825CjafLF3DmvbvKxXoVc2FiilMViMd0RpqAFqTEldSBJStBTgGlAaHQVK7vrONR6enjA+OUXUJTQlCovSkjzvMlxZY7i6zsb6Fmtrm6SdDoWt+LVf/zscHZ/jhaKsS7yMkEJCS08Om2WNEx6FIu92efOtN1hbX+Gjjz8ijjVJknJ4cMA3f+cbdPIchGA+nzObzpiOZti6tbQKETqCpUeqiKauMdaTZRGxztpMqCSOk2W/qVKu3VwalGi79rwN5//lFlUJsKAjHSBazmJcSxEXiouzC7SWNLYh8qHeKe92mc0cTVUGwFLw8QJ/782q5JLMC6GuJrwfCuLxhcANVG6W9vxlz+pnN6UeunkOwGw6DblyY/CI/29WFsA5XHtfWuvQiwxY57iaH/5Y7/i37Sbdf/UqD3c1L2nGY/5v0WNUzl72kfy+mNo2/Mbxh3xRKr77Xxi2fkYgt1eQr9972Yd2NVfzd51XWqyOZnNM49haWWPQ7TEaj2mcozPogRYcnx2TxjFRErOYzhAeBoMhcRyxe3hAv9clSmO8tXQGfXAeU1fBCuocVni89AF64mGl3+P+nVt4IRifnTMfT9ja2KCuDcZ58jQlloKmLBAeOnlKPhhw8OiMp4d7qG6GN4ZPnjxhe7jO5mCFopiRRxGrqyusrgwYTcdcnJxRzha8+/bbpINhsDmammI6J4pjvPEIL+gkWdga1A1KSN6+dw8VxwhnKYoZSSfHKTibTViUJcPhkDRNeXD3Hr1Oj9HFiL29PfIoY2WwwrNne9TWsL65yaJpqGcFnSyHKCZPUpxoqEwQdlGkcc4yLubU1lBWJePRiG4Uc+/efVIpsU1DrDVCS3QrKIqyJE5ShBCYsgQnqGYl5ayimpbEacrnX3vA/tEx55MJr127iZeSh4+e8b2PPsRYC6IgTVNu3LxJpGOaqMI6R97r0en3SJMg7BflgqqokG0VTCDVFqHegmABDDlHR6TjF39bR11XSBH6KrXWZHmG1jF10yB0TGNqDJ6TizHn4zlSxzgH8/mcvb09hBDEUUwlSrqdDqurqz9AXJ1OJ8SRJko7KCEp5wuSWIVu3iRiOBzgrUPiyLOE9fVV8iTm9Pyci3GAwXjvmY5HnB4fIZVic2PIoJsRx2HbdHR0iIpi8k6X0XjKoqjQOmI2L6gay3DYwwtF3u1jjcH50G9azkPGsq6rdkMIi7LAe8/jxw9be7JDeEe1mON7Xda3tzg6OuLk7CRsprMUHUc4QmVIWRZIKTEmAH6cc1gTakq8UThvwLe5YQRKKuIoRTpwOEzYh7WZRr+0+wZBIlpIj0J4scw/OhMuaCgVssG1raC57P6MQSWIRCOweAxl02CahlIqZrXj9OyC1VTQjxxvv/06733ly6ysrrC+dYPpvObk4IzxxYxHnz7mo6fPOJvOiDoDzsdzTGv59dZSVQVRktLv90KG1gs6nS7v/fiX8MLxt/72/0Xaifnij32eqiqYTyecn57zbLbLvQcPiJIOORFHxxfYqg5NqK291yGxzmG9IOt0UTpkMFULvoqSBOUcRVUuqbuJjkL+tn0tKyWRDoxpaFyDEhAlMQgROnydQXqFM47JaIKQPtCunUUTgHCqSZAmPH/BORwyopcU3xcW4SAew3Y1PBeitdu/qMC5FKPhdg6FRC5fP8Hi7cKm2Ifu4WKxoNPpUFfh4oc1QWQ3xiwF6+VxXOaRRStmlVJYaynbeqKr+eHOV1Yf8E//+bsv+zCu5kd1ypL/6c9d8O8f/crLPpLfd2Od5c1P3odP4Ge3v8Bf+eevam6u5tWcV1qs1o3BNIYkCuAVnxtWB0O8FJycHHNyfIIU8Pq917lx4x7DesDJ6THT6RSk4MneLhZPr98jzjOsqYh1RLVYINvtgNQK62zYA3gY9HoIpTg7PSOONMPhkNXhKmmSMplNiZQI5FStkFKz+3wXPNy5fZvCNuwdHZHoiG6cECMY9jq8fuMmvf6ASComswWzsqQ7HDBpKlRZgnVowDuBNZ6mtjQm0FqlkiAEOopQaUJd1XTyLvfvvU6cZsyKBU+ePKE7GDKbzhHe08k7zOcFq2vrIXdpLA7P+WRGP4ro9Lp89MmnJCrC1A03ru2wub7RbiQ8SRzhhaAsSsoy1JRIB4OsQ6oiUqURLZxFiABAaZoGYwLV87LP03uPEgonA8lTCUldlMQ65ta162xtbVM5xycPH/K9Dz/EOFhZXWV7c4Nep0OapgCUZUW312V7Z7u1tSrGowtmsymmaRBSonQ4lcMbV9naSC1KRcs30lpHWOvaPk+PjhLiOA6CNdIURcVsUSGkAaWYLQqOj0+pmgahFKau2N3d5fDwkE43YzqbYowhjrOlXTUIwBopQzVKlIauUHlppbQ1w/UVOnlGt9NhMpngvaEs5iyKOb1ul16/z8nxCbvPnxLHMWkaaol6vRc1OP1+j52dHQ6OjrkYL7AekiQNj7PSRJFnPg9b3/X1dYQQyy7VpqrCuS8VeR4zm8+QQlI1FYN+HylakI6xKCl/oE4mTdPlhhZgY2MD70NmNo5jtNaMx0GsJnFOpDXzmcQ0dXuONHihEFq1vbQCrcE3PmQttcIEr3Cwu9uwrQ/8HAlSIgl20gBbAuFFuC2OpjHBCmo9TkjqxtPt5dy6dZ1bt2+zujJkPrng+q1tBlHFUFtubvb56Z/+Et2NVcbTKds3N0gnNb/3re/wyYePODwZcTavcVIzHY2p6hovNN4ZtIRuJ8e5QExe39zCCwVC8vTZc57vPmU6m5NkMZPJlF4vJx4M2djYYrF4ytnJKYPVFZz3bG9t8fTRo7ZOCZQS7bZWLM+DOI7xPsCQLk22S7HmPRAqngSX3bQ+QMasxdgGXPhd4DxL+NYl3Tc4O0I2POSyLbX0JElKkoVMclUWbdcwsLwsdCkU7bK/+NJUJqRAymXbTSt0w8WGpZXY2rAFVhJwNE1F0r4uTdO052A4dxaLgqqqwuvRhrxqq0/bx81/xjoezinrWObVr+aHO7GK+B+3zMs+jKv5EZ7/5j855E9fCdV/6Pnlw2/zL/21L/CVX3rMf/Cf3QJ1lfG/mldnXmmxKjwkUcxsOmV1dZXhtSHzcoF1jsP9A6yzDFZXkZFiMhvTTRM21taIk5jnB7tEScL+yRHu6ID7d14jjyO0g1SF+oQ8ifFSMJlMPgMDCTCSfr+PtZaVlVVMbXj46adYLOvbG/jKstpbYXIxItYxgyhmdX2dw/NT9p/tsrW1QzGfcTifM+ze5f6N25RNjReKLM2R8ZTaWy7mM84mY7ppjrKOLIoDOOV8jIg0cdwhzxMa01CVBbIydDpdjHVsre8glKST94hkxJNnu6Q6Ic9zvJc8fvyEB2+8SZp3qMqarJvzVtahrKqQUzSO3aN9bt28iUoSTidjJuMxWZrRy3OkcZSLRci76ghnPDoO9y+FDNblOMYYFyAw0NrtxDJ7Zq0jihOqpqZsAUZKSVAKLwRVWTBZFGxtbnGvLDk5PceZhiSO6PW6dPIOo9GIg4MD3nzjAVGkqaqKRgiMMcuezcvwW7BiQtjV+QDgKsvQIWoCSVcphdIqZFJdANF4BPNFyWQ6pWosOgpQpE8fP+FiMgEZtrOPHj3i+fPnRFHEdDolTVOSKG43qVO01jhvKcsCvKff65JozbDXBRwCR5pE5FlCv9+j2+3i8YxGI4yxKCWx1rC5vk2aJDx58oSqqsjzfPkzk+HKUngKKSmLivPzc4TSJEmC955Op8N4PMZ7jzGBWvyCVsyS7tvMF6hYkSYpVVyxmM+pq5osy1BCknQzhsMhdVVR1zXGGPI8p9frURQFcRyzubnJ+fk5URSxvb1NmgZRU5YVvW6fqiwpC4nwmrgFUVnnMF4EgSIFWgq8DELJCpBtjtK3F5Cs8fhLtdOeX5cbc2dMgCt5HwiwxoEJG08Ra5yBm7eu8+DBm+xc32Jne4Nqcc56T5K+fZPx3iPs/Jy/89u/iZXw1a99jf7qCotmzPHFOU+P9pnOaiofYQU4pfHekWUpUZJjjKApFmxv32DRWJTQnI/HWCGZFiXWefr9AZ28y+HhEZHaoZvnbGxscHJywtHhAUpJ8rxDniZsbm6wt7ePEAqlJVma41qyrRCQJKGKqK4CYK5sN+K0ELjwuNlQ92PDRSPXblhDRapEeBtE62rItgAAIABJREFUnAV4QfgVwlPXNR6Lc2G7bp1B6yhs+hOHqWuQLsjUH7ACt8+OuPz8C3sw7YbTOQsiWO5BtD9TtBcXQhVU3F7EuPwdIgDTgsjm8zlJmlBWJbUJIhatlxbsFz83/J65/Ng5GzbAV7msH9p85195G3++DlJcbWKu5uVNVfHfLr77so/iD8z88uG3+WXg2Z/5Q/zCn30Nkquc/9W8GvNKi1XXOOqyptvpIgPmk0RHzBYLVlb6ZJ1wxV9o+N73v8f2yho3b92i2+uy5bZYlGXIr0rBeDQi29jC4mkIoJFFWWKsoaorknaL562jbhqsMWxtbNLpdDg/P6cs5qSdDs/295FKcXExZXNlDSUEsVI4Y1nrDfjJ936c0XjE6OSESCryNCFSEaYxqChiOl/Q1A0WMLMZx0cn9PIOa4MB17d2MF4wGU8YT2c8frrP9vY26+trxEmCsgKlLFmeMZssSLOMOEm5fu02OM3ZxTm7zw7o9nu89c7n8Aj2D485Pj5hfXODbrdHkmQkSc69u/eRTtDJe1yMpxweHXF2fsrm5gadJCPVEcNuDy0UCEUUaUSkqKzBGBe2YdaHN69CIGTY6F0CW6IoQSnHoqpZlDWCUM1inGMxmVCUJbU1LKqaKE24vrXF5tp6gCUtFpydnLC7eMJ0OuVz77zN9WvXmUzHVFVFXVahMkdojCtJsxwVxTSmoa4bTBOsgZd9oEIp6rJqRUYEKLyXlFVF7CUeyXxRURmH9VAtSp7u7jOZzkmyjPmi5OOPP+XJkydEUdT2l+plLlaIUOlijCFJYzY2NhAC8jgiTxOUFAz6Xaxp2p5JyXg8wlpDWVatUFXM5wsinYRjtY6V4Uprhw1WxiiKqJo6iFIhiaKE/nBI6QS1sUtxqpQiTcOWtSxL6rpGt5vnqqqYTqYtLdlgTI21lmI+A2eReJQI4mE2nZDGEb1OlyxNefDgAUVRYK2l2+0ipeTTTz9dbrLW19c5OztbCuOHDx9iTdNaR4NFNU6ikPEEagOyAaE9GoVBUBrTApsUzkmsE1jnUFEMS7ZsgGZ5b/HGo7QgkhFxosNzIQ2mKinKmoWt+fZ3vovD8Z54j7OLM65tdbl2YwczPuXLX/hnqafn9HsZ1+/copaK/YsJv/mNb/L+9x9yPJ7ivMYrjW8riwbDHouioSoWvPPOF4njDitrW3zzWx+wu3dM1u0RdzKkUqSDNGRGrWVyMQ6PZZKRpzl3bt3m/PSMo719dnZ2UFLS62RsrK0Gsq9UpGkS3AptVZQSYJqaqlygtSbWYZsvfBCkUgqEF1hnwHsipUHJ5abU8xmB6HzoIW7FnhIKax0et4QheeswxqC1biFJAi9lCyy7zIt+xvrbikwpLgUnKHG56W3dw87jCDAkaAU0AXBmrUHKKOTOpX1hJ25FcFlVeMHyfGZ5VoAT4dikEAgXRLFW4XbW2lBRdDU/lFHvvYufj172YVzNj/D4Z8/5E395xJPxVVXND3t+cf/rdP9TxZ/7k13Ezs7LPpyruZpXW6zOqwLpfbB8GYsxNlgsBaz2hzjpmc6nnJ2eICPFrCn56NH32VzfZLU/oBOnHMzm7Oxco5N3OD46QijF2to6rqkxZUmEJ9bREvwigSyOcTrCmoaqKOhkGZ97512+//gRUsfMy4LRdESsItYGwzY/5km0ZtDtMhmd4+qK1996i52dbc6PTzg5P+fajRv0Ol22VtcpmpoPP/yYoqpgzdPtDDifFcxnM7TUrG1sM5vNOB9NOT69IElS1tbW6A8a+v0+URyBV0iv8LVnZ2OHXnfAeDChqCqM8Zycn7J3cMB8seDw5GxZb+OsY9jrc+fWnZB9PD5mMp6Ejsl5gZIRxyenLIYl927eoQFklACeLE6WfZej8Zi6rlEqXtZSKKWIk5Qo1tjGYGzYCqnW3jmdTELfpzEt5Aq8scRJgpYKFcckUcR8HoT+3ddeI4oijo6OAI8xTaiJ0TrkZIVHx3Eg10oRrNRtd6rWcdiuGhOqOZRCRlGo8ClK5osCsShDf6u1OGBRlhwdnzIvS+I0w1jLs+fPOTo+otvtAp6mqUnSGO/U0g4phKAxNeW4QEpBv9dD64gsz/Gmpqpqmrrk7GTK6soKSmlmswXz+bztKw1bLZFq6romjmO63W67Va8J3klJUVYgJFVtiWtLVTekWU4qFU1TMxgOybKMk5PTZVXJou3o1DoKb+AjTbMolgImiWPyLAtf04qmqoKdWArOz8/DYxlpsiwniiNWhyt4PEVRYo1BKI1zno++9yFxEhNHEXGksTbkZJWU6DZj6XyoGVFItPA4YUkiiVaC0ngiBMIR4EK0GzgPEoVUsu3vDJAeLwAv8I6w4VHBlm6dA6VJoghrFNY2/N533ufp/h7rGyvc2B7w5mvXefzdb/NryrHeS3jjwV0++PQ5Hz18zG/93gecnM2xVgKaOEkxXqC0ol6ULOoRG+s73L//Jvfvv8k3v/UBv/3Nb1N7SdzpUzmHdB7pPJHQSAmz6Yymtlycj+nmOWmiGfT7XNva4vnTp0zOzljfWEcozfrqKqPxhMHKCsZ45vMZWkucdcxm02DF9R4lM8q6wjR1eEyEJmwSDXVdhg16Erc034aqLvGuBSH5tiqopWV757DtJlJJiVQ+WHqhBZTZJdHX4Nvs8SVUqc2qipYALNtP8WIjfClcP1PL2n6e5f147zFNcEwgHTIKxx9rTZpm9Ps9nu/tLTfBxpgX29mlqA0k4ksBHvK9oQJHX9naruZq/sDM1//Lkv/l/Bsv+zD+wM5/vfdr5L/wVf7Mv3sK6+sv+3Cu5kd8XmmxWjQVsXdchp7KRYG3Bq0EWZLSG/bo5hnj6QQiye7+LrY25GlKP88YpCmD1+6SpCn7BwdIBIuqZu/kmNW1VbpaI1vBVFcVUimcd2RZhjGuFWDh47KsGfYGPHv4CV5KOklC1TR88uQxt27eJO9kaCURWLbW11npdnn97mtU5YLCNszqioOjIzyCbpKTRSmv376Hl4JOv8/R6Sn7Rx8jdcT6YJWs0wWtGQyGCAR1bSgbx3j/CHl4QrfbYXtri/6ghzU2iDUnyLMuZdWwu3fAxXTM+cWIWUsbPTs/pyxKdnZ2WO0PEV5QlzWrgxXKMoCKqrri6OCYJInxOuLbH3/E/Xv3SLWmGydEUi0rIcrKUDcWQbnsUYyiCGMXQYC1PYdCSsqyoiwLqhZyEkUB8CJVEJFeCuI4oXEWFcesra1Q1zUX5xeURbkk+xpj6HQ6ywyl0tFS7KkoIkpkIIEiieKE6WxGVRsQMmzM5wuiOKasG6SOaBpDWYbM56xYBFBRWYZzzlnOL0acnp+h4whhQ6/m5UooiiJE+3fTNMRxRJ5nS5tuJAWLxRzhHFVVYpoGLSTj8YzhcMiiKJnNS/CC5jPb1aIo6PV7CClpGkNVN3R7PTpdKMsSYz1SQVnX4d+sJY0JlUyj8YT5oqCqa2bz5kVvqtLhfCZQYi+3fZFKWV0Zsr62ugTQnJ6cYI0hjkIlUtNUjEcjikVBp9PBNmZZDZK2NNo0SajKkl63y6ildgsBWiuUEGG7JgJkJwiMcIFHSYmOU7wXTBYVVK2N13scAikUWkpM+28VUrSbPxDiRXbVWYtwHq1BCIXUOmzyG4m0CoeiMhYvI7or17iYew7PSz7af8pKrvnVX/8GQkoqY0FnCJWidYySEUVtkHFE3skp65qf+kM/xZtvvsOv/u1f56//9f+NykpQAqVjiDQqTqi9I5UJ/cGQo4M9rHFIqZlNZ+AFaZpTLWbcuX2Lej5jNp4wG41ZW19DRYJ+r0NTluEiXR2231pHGNsgpUALhWkqtBQYAvjI2MC2Nk2zBCtZK7DGYZoa4T1KSZy3WO9wPthjfUvMFkoi2621bVzb1ypa8m+4ECWVwLsX4CZx+eXwTPy/yMCe1smNFD681qVos7YubEABvGyPweGwNN6DuuxJtbg4otfrhXPO2R+opFlamAGcXwrUSxJwnudESrd9tf9//m91NVdzNf+oxv7u+/xZNXnZh/EHfv7C/q+y+/M/yc82U/7Fq17Wq3mJ80qL1do59vZ2ub5zjUG7ZUqkZNDroZWExrA5HNJJY77z0XdRStJb6YN3xEoh275L7z29bpfdwwMWpmbWVOwe7XF/5zrXhiutdTJYCLVQOBPeAEmCRa1YLCirik6eMsy7jBcL1lZXOTkKMKfKGW7fvhlqEqqKJNL0kz40lrPRBcfjMV56xrMx3byHkJ48Trl18ybGO2pjscYync7pdHqcnJ5xeHjE57/wRVwr9JyDqmpI0gzh4WI8YzJbEClJp9MhTTI8Plif6wojPHVlEEKRZTknx6cooUjjlFvXb2I87O/tkaUp62trDAYraKXY293l1vVb6EhzdnGBE5KPnzxhfXOD9U6PYZqDb7elUUIsNNaHWhgVR6i24mQymzFflDgEtXXUVRnEUSsiVAuOon2MhZIoIdGJpraG8cWIqqoCQKmFwFgLWZ5jW7EjVej/LMuKug5VFmmWUjWGPO/ghcQ6j5DBwqmUCm/obejSzLs9GgvGh03efF4yX5SgwsWBxjS8/90PKKuKuqoCBAtPv9dvycJ6ua3x3uN50fnYNDUnpzOOXbCux7EmOB1DRUtjodvpEsU5zoGKWptspFnMZ0xnIZstpKTX67MoCpTWZHmPolhQVU1rhQwCOut0mc/nS6gSsBSUxphAL45jjDEsFotQL6R1+Hg2+4EKkH63R1WVOBdspGmcEMcJRVkus7qXtOJLUbBYLJbZXWNMeE1phRSCSCmEByE91vrlNjpOYoRUeC8wxpKnEUiHn5cgHFYpsIEMHOpMHM76FsykQi7RgRARTkBja1SUk3VSnI1o6qrNG4MTirTbY/PaDYg6zJuChY1YGI2YWTQxSRSTKklpHMW8REhLrB1xHKPiBKkUb775Bn/sj/3L/KW/9At8/5NHyChHRh0aIdBJzOrOJqV1VKWh1++3j4vHNC2sqq4oipLhoIeUkn63+/+w92ZBlmd5fd/nLP/trrlnbb13zwYzGotBhGSDwAYJgwkRMn6Q5SX8ZOlNQdiOsANbKBwhAoVsowcZ+0nYEUYvDiOkkG3ARgOMYBZgNE3P3lVd1dW15XrX/3Y2P5z//2bWDAYJGqqZyV9ERVdl37z35L3/e/N8z3fj5Rde5M0vf5nV+Tn1asnB9etkSmHalvPzMyK7HHDWRJ+2gERJgpAoLdBJ9CgTOtAePHiH0pCorh7Gx+eBEBAEtIyVI9HHLKJsN3h88CgpulAt8D4CSiljArHWKjL2wV+wot3BgZQXfmK867qLPUEIkFEK3KcY4zv2k3guFK/ZGJDUS4kRRDbYWe7eu4dKNEmSMBgMWFdrnHEbcOyt27C9/UTfvEOJXgZ9FbD0nh6lwV2FNV3N7z3h0SM+8jP3eHtx9KyX8k0xP/vwk/zvKiH5z+Av/cQtSJJnvaSr+SYc9eM//uPPeg2/22wBf+MTv/iPmM3PWa1XnJyesrU1ZWs8RguYDIeMR0OUgOGgYGdnm1RpEqlJlKJtGsp1iU4SsjzrvItxA1SWFW1dk2nNeDgidMAnBDrwITZMYe/BFMTE0elkymuvvsLJyQkyiX4wmWhOTk9w3jIejRjmOXvbOwTrUFoxHE84Oz/j7PSMyXTK2eycZVmyXK3IiwLnA9PxlGsHBwTjuHPnDq++9ipV0/Dk+JjKWBrrGE0nNM7GjZ+K0rbWWoxpKdfx/hyBNM1igrDSZGkW2TAXE4/f/773A/DZz32ON25/mVVTI1VM6w3WMRlNmI4iW9u2UVaq85zZfM7J8Wn0g3Zpp0Ipks4bKZXasHZ1VbEuy64ao2VdRtlvTAAFpSVKq81rk2xSTqFuGtadL7IfISQ6SbqeTNV5Irt6DecxHbM6GA5ojGGxWFF2ScY6TUF2bKuUCDTrsoxhPMTHmy9WzBaL2G1rDQFo2pYvfvFLnJ2fY51FeE+aJptgn9DJW/M8Z3t7u/MVp5RlSVmuMdYilSJNUvKsYFAMEUQpdKIz8qygrGKqbJ4PWK8rynWFsQYXAq216CSlqmsWizUBsNZhrAVxkX4bkHgEVV13INlsUn8Btre3aZpmU99hrWW1XDGbnTOZTABij24nl+xDaRIdvblZlnWPbWPCM4EkSTZewD70JzKwBrpDAa3jQcLGryovejOliJJMnSQbkEJXgZMkCVLEsC7vfFd4AlJ1AKa7rZISKSMQ80IQOoFwECL2m4vIyrW2ic9Xx8LvXzvEeHAhsFzMmZ+d4G2LtxbbWtq2jSFDLj5GojX5ICfJc6RS7O7u8pnf+hd88lOfIckKUJrGBdJiwGR3j1c++EG+9/v/AoLA2fEpZycnNOsKETx5lpEkivPzsy40S5IpzfX9ff7sxz7G1mQMgOvCg9JOsl6WJd45nLVYY3DW4J3DNDEZ15g2HoLI/sBDMRwUaK1iz3BwkU11HutavDMdeLOErm9aKmIPrgRr2wt/qgikWYLu33fW4Z3d0KlC0MmGZXegAGxkwv3BRCcF7uTCoauV6TtVpYgMrewOPmItlGY4GMSk8bZFSMG66zQ+vHaItYambjb303//JglYxGu2aWrqqsJay3g65q/8B38F4O8BV4bLP9hsAX9j+ZM/hdIlYjp9V+5UDCeob/+3YX2GkApM867c79V8Y4371Gf56E9/lXuLK5/qH+e44Pk/yrt826/vcGv5BvpDV53KV/PujUhzkg/8Ofg9fje/p5nV/YNDjo8fc7acszPdZlGuyaRibzSM9SrWRm+VsYzSlHT/kKqqOT45ZlHX5EXBsi2x0pPmGePpGOsMe+MR6e4uBweHtK2hXC7Z3tomS1Os80jRSQwvdTomOjYFpokkkYr97R1O5+fkg5yjk2PuvX2PxWKLva0tRuMJxrq4IQ8gjeHG/jV2tnZYrlc8Pj2iGIw4OLiGDdFDmuqMST5gmo0YFAWrpuIrn3+DvYNrJHmL81A1DYeH13AhVjFI3clercXZ6EnMs4IkS1HdJnUgcoRSDPMCHwKDwYC3bt/myekxcpBDkfD28SPsnZI//S0fJktSmqphPBjjfaA1lsGgYGEXnJ/PaeuW8XzF1nTKYDhkOp0SQqCqG5wx1HXT1UQEqnqNtR7fMaMIMM4hnSMfDCi6lNsAtMbQtE1koggRAHcsTAggld4whQgF3kY2tKzjYYJSBCGpm5aqbrA29ofqTm6cpCmy81YGIRFScD6bs1wuWaxW8Xt8TBC21nD79m2ePHlCmqYbb53qQ2dC6LyACmMMSkt0ohjrMSF4jo+jTHE4HIAXuCBpjO/CnzzgWZU1SmtSJanqltlySVs35EVGnscQKI9E6BS8Y7muaJqWPI+JzEopnLVU1RqpNFWXetyD/D5B9ejoiCzLNtLkLMsoioL57LyTcqoYyJTlHQCUEayFgAseJePrJpUgL7JNeJOUcsNUCRmvRyH7Kpr4PCkrsB2ARsTHii8m8fAoFm9uGLdAwJgGrQJaeqyI9TpCSlzoMp77WhQfcJ0n0uGiwlNKfO3wVm8OX0Qg+pq1junNacZoawzBMtmZ8I7wKPymA1b2bLFSKOnI0sB4lNMKRTYY8PkvfIFHT44ZTbZJ8hxjI2NdDAom4yGL+TlSeNJUslzMEUIwHA7Iu0Cu9brBOMtyuWQy2qdarzmcTrl1/RqFVrz64ouczc558+49lmXJZDjEtC3zxQLTtpF59B5DZOnpAGUIkQHWKtZqZWmCMU1XI9V2dU2W4C1SdAdwIQZqgUCEgPNRih6Zzngw4BGbBGfZHRBsQpaIKcRC9jU0UTocQuj6VYENQA04F/2vUkX/dTx8ELFSppOqJ0lUXRR5znQyRiB4WJY0TYOUgvV6yZe//EUQAiXkBZMLm3UIQAa5SQSOP7vDWvNH+vvqm2k+9mt3+cF/9hI/UL3Jn/+7r8AfNml5vIVIC9SHvwf3pV+H9ZXE82qeHvNLn+SHfinw1vzxs17KN+38yOnHKX4h43/79dv86x97wODf/65nvaSr+SaZ9zRYXcxnLBcLhuMxIlE8OTnh2s4ug/EI0SVSJmlCXa8JUjJIEjKlMW2DxXM2m9HYlv39fXQaA2O2xiOmRfSjGeDhkyOq9RqH4MbhNXSWIAOb4BHwGyYtehQFTVUxGQ6Ybk9QaRJZlKMjnr95k/VqzbxYUaQZeZaBDyRCsDMZY0NgZ3eL1prIxExGLOZL1os148EEOZowGIx46cUXeHj0hOl0TF2vaZxlXdY8evIYL+HGtetMJhNWy2XcFBLIBgWj4aiTuhq0EDEuqgMfuY4BRFIEdvd32TvbxeVpBOtVzVBnqCxFpSmDIkHrBJTAC7De09Y1w+EQIRV3H7yDfPSQw4MDnhfPIxFYY1ivVjjrSLQi+Lj5FUKSKNV50gJpljIcDkmzLDKIdR3TaKumq82IIT890x35GbDGxaAbH6iqBiGi37auTScxhKpuIvNITCeOicBRlmydp61K2rZlNB5zPjvnbD7DtC3GuA0zZ4xhsVjw4P47sd7DGqSSDDtg3bYt3vsNKAzec3p6itaRlVyvowx2MpmQJilt62ialrY1SCFI8yJKH5MUpTVlE1lQlSToEHBB4ImBYov1HKUV4+EY3wi0FzHhtqpJ05QkSTuvbmR4o282JU3TjSy3D2pqmoYsy6iqiqqumG5vReY3TSmyjETrDUuVppEpXVfl5t+qY9+TJNaYABuW2XQeyf5rvaRYKoFCRp+tdQyKwYYVJzh8J/ENndyZIBDeooBBniClpzEObz1SaLxX3cFBvJ6C6/o6o0kZABcswTmkj55ItekPFQjv8cZ0LCKMJiNUomKoj3ckgug7FwohBd61OJcgZCBNIruYpBnjyRZZMSTLh/g6eoWzJEUhaFdLPv5Lv8jR0RnWGcaDAe26pFytoAPbUkrOzs443N/mxrVrvPzSC8zOTnnyzjs8ePAOMkl57vp1Hh4f4RcBOxqxXCyiFF3F95YIHq0Urss2ctYRtENnaccqGoQUKCVpW49zbWdt8B057RDCdzUy8eqXKtbepJnGdsyoUhpjLYmL7+sLT2rYyPgjQAW6flaATdFQ16nKUwcNEi86n6kQBNErWCxpmqBV9ICfnJxijcFai3OO0AHjuCaDx18KOKNLIb6oINukE+uolglX1TXv6vyPD36Nn0lznoSXgD9ceFV4/DbO/D/x76dXrNnVPD3+jS/y/b9o+fTxV571Ur7ppzINf/nsV/jW33yBX939DZK/+Gef9ZKu5ptg3tNgtVlV7Ey3IFG88+ABu1tbVG0b5bcyhq9ISUwClQLnonRye2uK8Y66KhmNxwySFOUDWgkQoIsc7wNfvv0WR2cztNasqor7Dx+yvbXFwd4+tm3xzm428P0mTGuFR4N3saIFwd5kiw+99n6SJGE0GnF0csJwNGQ4GiGdJwsx8da76IPcm0wZTiYsV2vW8xlKJazWS5q2ZXnvLa5fv8ZkVPDRD38ri7Li9HwWexlPZ1hjeP3117l18yZ729tsb29j2gYhJNZZrLOdvC+ySTiLcAHnWlzwpIOCnd0dPviBD/DFN9/k4Vv32N/d5UMf+dfY2tlmOV/wuTc+z/Vr1xiPx6RZRt02ZGnGyeyck9NThJSMBkPSouD1N97g+rVrVGVJkeVxs9gF7uR5gTGmC8IJJFnGdGtKmqeUVcViucAYi3UO76PHMivyTU2Fc10QDzF9GCGx1jNfLKPktTW4EGWlUghciPU4aZZvZNwugE40bWtwrt30zB6fnuBDiL5XAb47oDDWcv/+/eiXtZbBMEegcF0dTkzq7etrUkzTUpXlphuyLEu8d13fLdS1wbSGEAJ5mpLIlKaqaZpmUzWDEJH9VArnPVVrqMoKrRWJSqjbFtNJgI21SClZlxVaW0ajIcGHjUx3MBgAbNhRKSXn5+cbv2rv3ZtOJwwHwxiiZC3GWPKOgYWYHluWZfx6npFlGWmabt6bPSjtmauv/ftG4ik1bXtxWxBYYzHdbSKrFtepZAxjQgaGuUZroGy7iiAIPnaxCqE2IVqRGPTx9XM++i6JybZCKWxrUFoQrGE1nzM/O2NrbwuhAsO8IM8y5jNLnmi0lF1ycQIi0NqWpjVUdc1kvBNDwPKcrHEEFGVZI0VKqlJSqRnnGXvXDnn9C1/C2dirW5Ulvm2RfXWM9EitsN5hjGE8HuKdZXd7h53BkFeef4Gv3n0LmWUsZzOCD2RJQpamzJ3Dhsisig6Mh64/dDwaYZ0heIcNHqkkaapIEoWxEuchmqZjB63vmFKhLphJ6yGEmPwbD5oEEo9z3do7T2v0Z4cNKOzZ1MvTqxEi09mxt/Hj96JApv8WceEvXS4W1N1hiBKy87hGabnUktYajO392ly69uLBkQ/+0hriI/Ue7ta0f+DfRVfz/z/h9BRxcPAu3M8VSL2a333q14/59PGbz3oZV3Np3ji7x2s/P+HO4RvI52/Azs6zXtLVfAPPexqsToYTwHN8fsaLL70MznDn7m22Bxlid0qiwMsElMQRQYlxFucsqU443NnFO0smIFUS633XURlZv2s3DpFZDItZL1ecnZ2xWCwiAwTg4oYu0Wkne7RUTRU7R53HO8+6rhgqxfTGTYrhiLKtCUpy7+FDzhZf5pVbz/HiwSFNE8GvBg7GE7K8YHc4hjamXzopOZnNOF7NCEeem4fXyLKcm4cTJuMpDkFRjHj7/jvs7e5QlSVyd5s0SxgMcqyJwTmmbSOjJBXemVg7ohRBaLQUGNNS1SWjNOdbX3yV7WzI3v4e2+MJR0fH3H/nPrrIeHR2wtlyjtKa4WCIyhJu3rqFcY66aRiMhty+9xZKSr70la+QZxkKwf7eHtvTLTzRdxYZwIQsy0iyFE9gtVqxKktcCLgQMM6hkxSdZhhrcTb67wJEFoYIIpvT81gBY+2G4VQqif5drUk7sJdmSdy8Qie9Yn6fAAAgAElEQVQBDJRVzdl8xmq9oqkbPAGVpARip6exnqZpYl3Q2Wmsx0l0rNNBYK0hT3S8fvqNPoI8z1FKUFYlSZIwnW51AUgtWodL8sl4/UVP6kWaKZJYT7NuOwmmpmpapFCoNOtkmJ6yqpEEnI81MkrFcKTFYklr2yhxzbKuSkgxGAxouxTZHmju7u6SZdmmG1WrBOcCddmgpKIJkZm1ztMaR54NgIaqMfjAJugKLoDABQhlk9DsnIs/MwIfPFprvGPjoxWdx1RJiRIxUVnIDuAiY01LAKElUidkrWO5jgcTLnjAQYgS0pi75AmeDkDFkB5jDUEqgvcokYAPGBcBqwjgGovwgmExYemfILwCFCLEUC6pFFoNsCGwXjck45rx1oAkSfGhjHLyEJUKOI8pK0JdM81zQlVijEcEhUChBaRpgrHQOIsIgbYx3Ll9m3GiePTWW+yPJ+yORoROxfDSc8+T/5kBv/yJT5DlGZPpiOVqjmlbtNKkWkfvb5qRJJr3ve9V7t9/m9V6iRAK6+1GvquU6hLOLaFTLxhs9AGL+LwhIEkkSRaDtMbDceygrhuEAB8szim8c0+B097jfHku+MsIUulBap8QfOka6BN/+/vrPavee1xwXcBaVD0IF0XLSZrgnY/32/lUL1+PGyDO5uHideqv4oDf7Snbmh/+7+7x8z/5hwerV3M1v+s0Dbf+37vPehVX87vMablg+lOf5t88/DA/91cfIz/yoWe9pKv5Bp33NFg9Kw2alIHKGaPZ3pniTcVkK0cV3Sm7iezNMM/RwRN8Q5LElNQ01WiihDEYi1YybnQkuLZhXBQMruWsy4qjh4/xBEY7WxwtIoM5HY4YDQboNEqDbfDoNKHtvE+pVghncT6QaUWRJOhEMVsvefToIaU11NeuYwOxg1BIiiwjTRIGxYAkS3n5+edY1RWLssTamizZJwQ4Wy4YheihHKYZSZJzbXuHPHgaa7l24zqjyRjjWlAJWsEgS2i7YBmISZtCSqx3MVzIBRQxFbStS4ok5f0vvURWFJRNTV2uqauawVAjleTxyQnL5ZIXX3yR8XiMlJqb125E+WpVMp8vcM5TDAaYtqU2BjWfMRgOGWiN9Y7BcEBe5OgkoTUt8+WK1XqNJ5DlBUpoQhuZqijxjQBLigh08IIgiRUuTYNpY8qsEDGZNEkiGBYi/sQRuKooH/We1jlmszNm8zllWWI7IJVqDQiU1gQfWJs1TVMzn53HkJo0phaHnqULoKVgd3cX24Hlpq7iIUaIQUB103aJqWkH3ASutVFqKWTHgDqCj0yQlLILzIk9s0oplNQIIE0y6rqirmsGgwHOWnzn5TUdUM/ynEFRUNWxM9Uai+zYP+99ZE2VpqpKqrJkuVjStk1MVM0L8jQ+htaaIs9xzm+6N5MkssdFUbBcr+J7p6uy6QObet+q6sJ3era1l0knOsFvEoNjvU5co0R3PblCCLyLMtIoEXaRKfWBECQKSSIDg1zjgqFubEzBVVFJ4Hz0lwsJuCgLlVzU8yilMLZGJwkheBazs/i6aUmWZWzv7XP68J3Y3xuinzp6LDVISVDgbIsxDdYZ0iJFphLvArgYJCTxYA337rzJcFSwvTWhPZ2RqxTfWtJU8epLL/LkySOOT1YkUpPmA4ypOT46Yn9nm/NqjUoVzXLF3u4e3/6nPkoxGXPn7Xu8c3LEeGvCcDGjXgqy7rWBgHWWJJU4Z6DzriqlkUIBsW9YKRUtAX3cUQfwQudd9cHHqqIiY3fvgPW6JM0y1uWapjWI4BHe4Yzprr3Ylbs5EeLiwCIqUTovayf7jWnC8f3a+76995uu1/7gRnUhbTgXv6fzudO9nlrEROLgY0BT/FgVm0OSHrBeBq8dFEd2n/1X8+7PnfqI+U9/gulf/zee9VKu5htwPv1f3o3d2Vfznp1ffvI7/Mf/8GP8L+pLyG/5wLNeztV8A857Gqx+4rc+y7e8+AI3ptuIuiIbOW69cJOd3RH3Hr7D6ekMrQbgEz74ysskoUHIgE4imHGuC+FwvTeLLmjDY6xF+cAoK8h1wo3DA4ZbW4gk4Y0vfom2bXn+5g1eGb5AEIK2bWPapVAkqaStKkxrSbVG6ASlFa2pCVKyt73N7tYWz0+3uHn9Ouu6Yn56yv7uLkiFShMcDluVFHnCcFgwLHLW83PyoqAOcL5ac3L/bT7wymsMs5xBXhCc57mDPZIsY7S1xWy95PXfeZ2DwwNu7hySKMWwyPBAaw3WWUIQiNBJma3FO0+R5oyKIT74mNobAkmi2ZlsMZ8taIxlsV5TlRXTyZSsKKhbg0oUmYp+xkE2YHdrh9liQd20DIoCkWfsHxzE4KQu0EYkChMc67KhXJUR0CXRJxkCtLYFqSnLumPkVPeLqQMuIfYzxm5OhXVNZE87H2WfJuu8IwiBkBH0Guco65rz+Zz5bEnTNFFuqHVknlWCkhFwresVbVVRr9esFnOyNGE0HHSsrGEwHLA93UIrQduUWGMISNJEgZQslzUCQZrmaK2ibLJLPAWwxnZduAobIuM1HA6BuLnXOqbiNk2DxXRMZJR0SgGmbVAySqG9j2E0uquOWa3XZGlC6xymS7I1rdnU0iRJwnKxJEkStra2aOoa21pkIaJft7utThIQUeoslUT6uOHPOhAtJZvXYcOcyouAm97Xfbnn0ge/+Xv0GgoIehN40zNqtpM2p2mKFwLRAfmIP330TrtAkQgIktZ7nDfdY/RJwOEp8NKH+1jXxlRcopR1MhlzeO06Z+dnIDzD6RSvFc4ZHAFJIFaLWqwNBCfwStDWNW1bQ5ohE8FiuQArSUWODIJimqGk4PbtNxlu7zAZDtEy5YX3PYewLdf29wjNkhcOP8jx0WOSIuM7/txf5PbdO8hEIZRk6S0ikbz86sucHB/xiX/88zx6+JBWeIpRPPSxVUUILqb6+l7WbXnr7h3qurpQFATXvTciqGvb6P1MpCBGfPXSbACJ0gnGepwLDIdjEODDOh5GyHgv3rY428brMkYxX6QChw5Xdh5VRQT9PbPZS/WTJNm89puu1O5G8XCKjbdVdved6FhrJWX8LwSkkHjBJtjna2tpNuC5Y3OdD10P7dW823N3/oTv/JLgkz/7q1eBK1fzrs6v/+hX+JH1Z5+S91/Ne3P+0aPf5If+12/hn/7YEsbjZ72cq/kGm/c0WH1wdMTDu/f4d/6t7+FwOsAIicoLqsZSlTWL+YIbN7Y4P11y/+F93vfSc0z3oofzyZMnDPKcuqzJte7YOIF1JjKNnTwREdnLnZ0d2hC4e/cu8/mM1ljq3T3u3LuLbw03Dg7JlCbP0w2L5b3Duig/Tnr/nPWkOuE7PvqnyYuC1XrFneMj3rn/Nrfv3uFD7/8Ar77yMo21JErhCTRVhZaSW9dvkA9HnCxX3Ln3GXZ2dqjairRN2NnbpsgLdsI+zjuqtqFQmo+89n7yoiDTCSAIInYySimxTncBNlEg6YRApZLRaESWZZT1GmMMOklxBJyz3Lp1i/sPHrJ8+BCdpkgkt79ym1u3bnE8X1KtO7nr1hbT7W1UolitluxuT9mabrGzHSuE8jSLG2kCVdNQV1G+K5QkTTOUSmhbA0iyNCdJM9briuVyufFayj59l86zJtXGN6mUihUqWkefLiBRNG3DqqyZzRfMF0taZ+N+WlxIVaNXT6HTlMViwWK1xAYfuxu9YzQa4YNDWhgPC5JEc3iwR1WWnJxEr6vWEehmRYFzIbKDQlDXTZdcGjfndF7YPpyoT+ntwV0vre09ot77p9J2tdYdUO8lx/G2bdtu+lNN22zY0D5FuQeCs9mM69evs7+/z2Kx2FTCeBev4Sh3vghMiiz1pZCkngkLUf4JF0Dg8t+VUk+FLF1mty5LNC+DlP656NfampbQdaqKLokbopdVCEeWSlSiqRrHomyiDFcmhBDlvlJ0stMgEPhOet0zvfH5np3PWCwWtMZigyUtBiATbGgw3iNwBATCCWzw2BAIePRqTTYakeiUwXAE52uqpsYLgWkd5boiH45Yn8/h+JTxeIub127irKGtSx4/ecT2zjaJErS2oazW3L17h+XiHJlokjxjLQO5VPz2F95geTrjNz79KUwqGexskZBFpYiW4BymqbHebOwJ3pvYfaxVZIilxHqLsW309ycxYE5IidIaGQJJGg88vIvdqkEEmtYyHGaAoEgLmrQidIdeMeU7yuajr3yTsRTTfOlDjS5LgS/8zdbap7zNX3sN9ax8D3AdfY/qxbXku2vFCx+Bqn/66/HzIoLZC+90J083Vx2ef1Tz1vwxr3y84O2rwJWrebdmueQXcsXyrHzWK7maf8n51Sef57v+Vs2v/OcfQBwePuvlXM030LynwWprHY8fP+b/+vjH+f7v+U5ENuXhyYzpJGdnZ49HDx7z5le+jE4ynrt5wL137iMe3uf9H/ogo+mU9WJBnmVxI9PfaQAVROzrDJLa1DgPSaJpmwZCYDIakSQp5+fn3D0/Z3syYTqdkk2nEWT4EDfEUsfajtj1EDdgPhCsY5hm+KphmGaxNmM0REuBSDW//cbrDLKM1155NTKHMgalDIoCbyy2Knnu8BChJN4azs5PyQc5+/uHKKlpTUtrWsbjMdcPD6OMtaqwztG0La7rCs06GWbTxh5GLWP/pbGGslyzLtdIpVivVrTGIpSiGA443NuPnlcfWK5WLKzl9PgElWhmq3lM+C0yfAg8//zz1Os1qU7Y3dlBBMiTNLJiztM6S101WOdiT2rX5VlVFaIDn7P5nKZtALEBWv2megN0upcvL4oYptTJB1tj+nIMqrZkuS45O5+xXJdxnZ3072s9cUol1HXN2fk5TVND8Js03RAcwbuYTKolVVVy//79S164LgXXWoyN4MZaSxBiE2KktY4sUPCEDni2bUtd18AF2Bbd9zjnNl+TUmKt3chre/BuraWu601CL7DpBXUuBvb0nbr94ymlyLKM1WrFYrFAdd2vPSOWZRl5nneVIeopD2q/FudcTHx2dtOTKrpDEUFELFKIGAjagRXfscs9KIlAOsHZ8uJ57FjXCMAtbdvEw6M+zEwqtE6QKsrSNVHiq5KMJNOs1w11awhE2Xgg9q72YT4QupRmojRWSo6PT/jUJz/Dhz7y4U6SDflgwGw+Q6vos42Q13dYWRA8VOuSceMYTDTjUcbhIRyLc+plE9OXq4bGrwhKYW1D0xxzenzEF6Tixe49UpclUsJoNGA4LDj54hc4OTtiZ3eb5196ETXIWZqa++/c496bdyCRNM6gXYsPFqUFOomMtTWGaNRVSJUCMSk3dCjROINKU7an49gT26U4xyRdR9u0IOJnmWmbKDO38QCuaQwKQZEVmHxIWS7jZ6jvWfP4LAV5IQW+HLgU/an93+L0hzP9AQs8fYjR32bz/y7ZS72/qIzCh65TVtJ1jG2u2acOUS7dQZ8G/bUhUFfz7s6qrXj55+5z/wqsXs27MHf+zpv89w8/+6yXcTX/ivPZk9v8u//DiJ/evsvBf/LSuxK+djVX854GqyiNU4q3njzhn/7Kr/ID3/vdpEVKCJ4iETx/83l0qpjN5ky3pzx45wEPHz2kmEyoqoqd6RSH7xhMOg+VRIkoLzTO0FoLQtHahjTRvPrSSzTGcO/ePe4/fMDe/h77hwcUoyGrpkK6QKajlE0liixNIXiaqibP0thVKBXBtuRJhlWS1157hcn2BGMMx2cnnDw5YjqZ8PyLL7JarznY2UVLzXq5QjjHjZ1dnr9xk4ePH2OCJ0jFcFhgXMu9B/dpqoad7W2K0YjKtDhrWa5XUQJqDAIo8gItVfTuSYXwkSFyLkTGxRi0SkBEhivtQHW5XNMYw7AoCAGGRUGRphydnEGqyEYFbdNyOjtFegjO8MH3vR8lJKmM/k9nbGScjKFuG3zo/GddWBAdg+eFwBpHlsbnraoafAhkRRHBXueLBL4O4PRyVGNMlLZ6h7GeqqqxLrKTOtF4LpKc+3CgXoa4WEZ5sBCwrirW6zVSSYw1TIZDRqMhZVmitWJdrtAq1sUYY2I/rA+0zm7ut/dh1l0dj7EGGS4qXnrQmabpRkrbA0sp5YYZvbxp71nU/t9ZlhErctYX6b4diGzbdiOzLIqCnZ0dnHOcn59v+lWNMXG9zm/W3IPi/vFUV2nTg9f+5wuBGNolJEppFBds2GUgsvl5O2asD3MKIa7/8kipugMMaFsXQVPHDGqdkCQaqRRt2SCUhiBRMlCkirZxHTvfHWpI2SlTo8KAILu03HhY4ENk4+aLJVVZk6qAsY7RZMrs6Jg2WASySyiOKbtBAghM07CczxlMt9DDlK3pBO8lC1XSVi1BKYIQGOuQugsi8g6ZCu7dfzu+vlLGhOSmISsyfPAxVTrRtE3F9t6UsvT4RDHYnnJ2dkpSJBSDFB8MUnqk8thg8L7pXpOAEBrrHK1zeBPfa0mWsrUzZW9/HyFlvGZTRZoN8AjK1Yq6rgnOo3zAGEfdGKQI2NaQKEVTxSRr3wUTSWT0votAcH5jW5WdN7aXYffXipRfH8LV//ti7RcsfJ9UHTqPuJTyqWszLiK+rjGg6evv/3LA0uXHiu/7K2b1aq7mT8KEBw/5SZf9/je8mvfk/NLjz/HqY/gvfmqP//pvbUNylRdwNX+4eU+DVWs9TkiCFLxzcsYvfPzX+Ms/8BfItcbXjusHO0ynAw53dlisFhyfnlKMRvz2v/gcr736CsY5BBopu0Ab4ol98FH65gkYZ5EikCiN1BqtU7ZGE7SQyCC4duM6Ozs7nJ2f8ejxY7IkZTQYMBmPmRQZwge8cbFXsPVoIZDExNbWNei0YKsYMxmPOJ+d89UvfZG8yHnplZc4OnrM6ckpOMfWaBK7HNOcRGvSLGf88kscHR/jhGAyHHL/8WPu3HmTJMuYlQtqEairOgJTLyPr4C+8kkoAIVZoKCmxPdMlBVLFjWfTNBR5AQiqssIZQ3AOOmARgGv7B5jGsDAVN2/d5PNvfB5vUna3trl54wZ5lkVQhogJni52orZ1jbEelUbmx3Se2YDEWB+ZYxfXHBm+zicaHDrRsbqGyMqFrsPRGhtrSrzHOkNr2lgDI6OPMy9ykLLrVo2SXus8gSg9jEDSUlUtq3Id1+AdVVVhrWWYDRiNBmSdzHc8GSGWgdFwgHUisnFBUNftRpYbAW8fFBQDhy58eeGpYCKIbGgMyGEDBi+zmqbrluy/p+8xVUp13umvl9j2LG3/vUnnaQ0hMB6PN/JhIQRN3ZCn6WYN/fr7++pBNzyd4NuzoP399lLljUSzAxs9wFBSb0BDrD+5AOz9fSWJRkrVge0GKTWpzslyTZ7mG6DsPfFww0WPpvOORHoyDdY7got+ZJBY42JPqFBAlGOrJHanChcolyvu3bvH7tYE4Q1CJagkA0s80OlQmOiJwxBl07OzU1SeMTm8jkxzptMxUiSsVxWqamlbi60aEhmv98jqepI8R4ioFGibmmVZsVyvUCpQ10vm81Nmi3M+erDLeDzmkbvPcDpktjrDmAatBKapcK5BKk/AIISNNT4ShPCE4AAHQpIPc7a3dymGA7I8RyiBShU6SRAogvMkOiGkgbapNoFhztsuHTtKy1fLRfd5EeJnihCXEnYlhE5JIjqw2OmCg4hf673yfUBZTFhWF3Lw7utKRWl+uCRfJ1yEMvXXVg9sEXT+YhBBICPhiuDCRhucAwS6DwLbhEFdzR/5+LDxEl/N1fxBxj864Wcf/uazXsbV/CHn7zz8Fcof/05+4r99NX7h6nPhav6A854Gq1prrI+b+co6vnr3Af/n//3L/Ic/8sOkWnFydE6iYDBI2NnZ4jn3Iq//zut8+CMfZn4+483bt3ntlZfZmW6RCEGqYpdi8K7bMNkoCw2xX1AHjasbROLZ39ph/MEB6Bj4c3x0TGsM83KNUwJ0BDnT4RCBQIZA27RI1TEyKgJCrSS0hjRJeOH5F/nB7ytYlis8ni/eu8d4OKYsS1azBS889zw6S7FNQ7tegxRkCIaTMfPFgnGWk0pJ3TQUkzG3773F8eyM528+x8vXnmN7OCFRmrqqaOoaY9ou+TWPDGMINMbQdABPCo3u6l7a1sSkTq0RSJx34DyDoiBNM1554QVCnvDGm18iEZKt0Zj3v/Iqu9s7CB83k67bUGbDAauypPUOL8A20cdpOhAlpcaFKLPUKoZTiQ0r5hmOJgCUZRlls+KSVLAD3E13nwBSSxAK72PdTAC8d2Qy7x4vAui6qlitVhhrqet2c40t5pFl0lp3DGSODJ6qqmLKKoEkGYDwmM73Zq2NHbTdGqL81lN1Mt0LvyobBjiC8hwI8flWbpOA2zTNBuj1QLX/bw8QL6ed9oDPOYfvJI69xHl7e5vRaITWegMOe09sTOnVG+B4OdW3B8KX19KD354R7mXLl/2mfbrvZQasB6nWus3znKZpt+6udzdJN0yyc5Eljd7elCzNo0LBXzBltlsLIUp1JQ6t/CZ0Kk10THcuYhJzWcZ+WiFjn6tpLVJnCDzzszOq1ZzpeEjoDsTQMf05eEHwNh6QdAnJCoFrKx4+uI+RktHOHsiUJJPkLsH7mGLcthC8IRCTn5VOoAbvL5jx1lpEsGAspm6o1kvOzk+Zr1fcuPUcWkrSTHPz1nUePn6EtU382U2DCxbja5y3KKnJkyJ+zgiNVhk6zdje3UXphDRLSfMMIUE6GeXfPlBbQ3AGW1cs5+csFwv8RiobGeFqXWKcif7hPiRrw2TG11QIEJ3nPwQfgSshpgHL7nQwfkO8JjpFRX/d9KoC37G08YDjQvoORB+uFJtr4GsZVMGFDFhcel/0k6ZpDO7yniy56Am+mj+amdUr/tMf+yL/89++qrC4mj/gOMf7/sFVp+o3yvz9h5/gZ/7ab/IPR9/Od//tW5Dnz3pJV/MncN7TYBUXUELjfEyTTJKcO/ce8Y//yS/yV3/4B9FKcPTkhL2DKToEjo6PeO19H2C5WPPOgwfkWcbR8QkhBA73D3DiImE20AdveJSIlQm2bZHI6Ge1Bk13oq8kz926yZ27dxHpgPlqiTUtSxk36FoIEiXBuwiGjUdoSZ5kBOtIBATXsHp8hCawNRyCELz8/AtUVY13luOTY7a3t7uUTQfC4Zwn0Zq6LPFtCwgOd3bRg5yj+Yzz81Mm2xOKUcFsPiM0hq3JlKLIsdZgG0PdRE9dmmUkWpFkCYXPaPKUclVF21uAIu38k96BF0itIkviI9s0zAucFrz2wkvc2D3g+uEhe7t7uNaCDzTO4YMnyTMq01LZltq0BHspgCeEjsWz6CSJXadpBGK2NSitmE4nsR7IGKbTKUVRIATUdc16Hf2Oy+USa+0GXNZNG9NqhUJrSZ5n2M73WpUVTdOwLkusi6ylsbGTVnSs6GK52LzOrbXY5QotoGnqCAITzenZGSFEaaqUkvF0SpKmSKWoqor1utwAqt4rqrrqjbIsadsWrSNb3DOc3vceVcVyue5uozahNb0MsgeKfYjR5cTd3u/aM6HD4ZA0TTeS8F5eLGWsavHeo5WKEvHuvowxTyX89j7THmD3t2nbCPC/VtYJbKTKl1nXHmT33wN0gFVtPLq9j7csywvpr86QUmOciyBKygh64ykErXGbdFdnXfTQKolpS4ypOTi4zq3nXuDo+IzTs3PWZR2TfWMvCsIbvG0IKmU+O8c1NQhBYywOj8J1YUJRfSEImz7Sqlzz5OEDvBAMJls0jaWuDUmiGRZDilSxWpUR0BO9r87ZjmyK15uSPbiLlUcIiZaSar5gWZywt7dHqlMSOeDa/j7z+YxVuUbqzhuqFMEYENC0NTrJSLKMfDBEd3J+IQVCCayNSd1FluKcpS5rFsfHzM7OOTs7jbVHwTMYDvDeU1UldCqGIKF1hkRqEh0PKmISsELgN6Fiwfno9JWxrgZ8dxjIU4ch/aFEf3ABF6D1ctdqf/DSH4AEHMHZLsndbg5CRACt5FOHMn3dkpSyO2y6SgD+4551ME/923/1Nv7xOfo7P/aMVnQ1f5Jm9j99klm9ftbLuJp3aUIIrNqKHzr7Vf7Jf/VdfPfffeWKYb2af+V5T4PV6ErykbkLYIAEyRe+eoeP//NP8UPf911kiWQ2WyLKmv3dPeaLJffu3WN7a8pwOOTBowds7+1xtlx2vkpFoXW3cRVopREhntwrIZBBEKxDKI/uah5AMEgzdqdTTuZzfNNihCAdpnz5rdvs7+5yeLhPVqTgHZlSsZrFWbQMCBfZBtElj/jusW9du875fM7s7Jwb16/Ttk1MB/YGISJjqNPYQ5plGaMsY3t7i8pGpnA+X3B9Z4+jBw/RBsrhFuPhiAAMBgMqWWPLChc8q/UapeSmzqLIcpJJ57vsOky9c6RaMRiMuuRkjzExdTbgkU5wuLVNenANAG8soQOpseoisFguqZoW20knhQfXpYAqpS8qW0TsJ/XdJlQIiRICY1qMaVFagYx1O+tyFcOFypr5YoZSmsl43FWpBPIsobUWSezfDEGgtMI4S9tGkLsuS6bbO51PtY7MronAt66iNFhKQds0SCGpnUXI0HWjNpv+UUSUdtpONqx1gnP+qU10D9K8j1UcPRNpjMEYs9mU95vrwWDAbDa7FD4TngKqmzTTjom6XBHjOs9szyD1APYyYOzBaNpJf/ugmtCFb7VtEyXXAkaj8Sa1uK+e6UNsvrYe5LJ0uF9Tv76maci7E9TezxoBCRu2Fth0tkbwHj3l3ge8g+AF3gu8sxjradoG07Yxrbdj4VItcD6qJAjx+Xjy+B3W5ZKDg+u89uqLVFXDgwePWazW8RNFBKQ3uNbhnMWZJibIakWwnsb6KJ/v3q9CxI7l4GPFU12vefLoHfa8J8lyvKtZzSuUStgaT9ndHmFdACERQmGcp65bnHWxz9XGSiJnW9JEkcqCcr1EeMf6/JzRoCDRkQ33xrCYzWhMy3h7jJ7FERUAACAASURBVE5TRKNQSYI1jjSN76O8yNnZ2aaxljzPEJ1c1jYNRkrwiuPjY86eHLE8m1GWa7x3CO8jEGxrlNIIPNa5eJAkoxUiHprF69UTILhLhxWhY1hjJkDAbWqbLkvE++ujP0S57Em/HO7VA9L+es3znKatcFLG95l3yOAj6x6iBNwT/bECMM7iqxLvPMaazfskhHDV1fgs5uyMv/4PGh45z88Vr6M+9pFnvaKreY/Pf/R2QuvM73/Dq/kTN3/p/BP8zR9T/Hv5Kc/9N1eHV1fzLz/vabBKiBsgKWTs0AwC4wMhy/j13/osh4e7fN93fwdaDJgtz8ml4qXnnmd1PqcqK+azGe/74AeY7uzwW7/9W0zHE/a3d5gUQwZZTqpjaq23LqZohignlFLgvUMlGiUUjWkR3nHrxnWm4ynniznGWZ6cHmND4Mn8FKdhkGXsb22j0shk4eNmT4q+UoFNimUQ0DjDKC/wI4NSmq2tbaxzPD455mR2xrVrh2yNpgyzAanSpPkAmWXIquYDL7/KrRs3uX3vLm5dkugBW9Np3PQJOJ3PaNqG4XAYpYwuEJzDtPGXQKx66LytUpKlCSF0YTu9Z48QPV+dV0ylKUFKrIksr/QRoFofma51WVLVNcZ5ZBLDloQPKHURJNR7UL33SBRapxu2RWtNWa6pTYNQsgNdUFeR/RkOcx4+XHJ4eIhSUdqZJglNU+NtS5rmKCVwNkoCz88WLM7PqdsWnaRoneBDTBD2ztG0ZuPXrOua4XAYA5TaNoY0JTlCd9UqzuP9xYY3+knXFz66bnoAZ4yJ/lrrN+DR+wtWtf/30dERaZpu1hFllxds6OXqjX7zf5mJ8t6DCk+B2hACaZpuQEAv8e1rbXrWzNkYAhWANEs7YApCyegBlWoDsJWQm2zXrw3I6f/0r2HPorVtu1lPv1bfgeTLFT79H63STdiScz2wEHgP9SXZd5JEJs1ZDwSyVAHJJp3ZOUe1XvHowdsslwuM8TRNRapjsJpKJAKHNXUMA/Mugi0lAY0kgBf40AOvGNLkfGRDpZS0ZcWjd+4xnmwzGk8h18xmM0y9ZJCP0DpjMJ4wGBR4D2JrjPOe87Nz1usVENCZjmFvLoK3564fkhcp8+WMdR0l8Kv5PB4kBY93Iw6vXydNEo4fPSZNIoOa5hk7O9uMJiOS1qC7oK5AoK4qnsxOWa9WnB6fUK/XaCHRSuOlwNmaREtGRYELntZE8EuIdgYRFP0r379OvQ8+BIf3lth7GpAyyp1DiJLhy77q/jq+HJjWS3t74Nr7k9u23RyM9Ne5kJF9DoSNNBzA2UC4xOTJSj71OJcl81en+X8888tnX+TTP5rzZ37iRfzpOT/78JPRK/+VVxhc7U+v5veYX/nRN/nU6koC/I06Pnj+5sN/xt8fbvGZv9ey89e+DbKrIK2r+f3nPQ1Wg4wn9jruFgmdV60Kgmpd8Uv//JMcXNvjo9/yKru7B5ydn7J0jldffom7997m4No10jTnNz7zGearJfOy4su37/C93/XnGedZBBOmjYBYyi68xZHKBOk9wXqM7cJaQmCQFGzfuM72dEzjLFVT0zqL8Zbbt+8wzDMOvu1jJEmKt5YszSJjJTsPVqQfYqWJFAgScp2yNZkglI4+Uxs3atvbOxwdndLWlsPDhFGSEiSU6xWttQxGQwQF3/7hP8Xs+RdZrxuuXbtOWVe8/jufIx8UeAQmeIosI8kSgo31E1oqlvM5QoiNfDPLMnQSOyt7+Z1WCrpNr9YJWqe01uGtw3lP1a4RWiOVpmlqmk4mmijdhZkIhJIbr1jvg9SJQsqk26AmG3+mEIE0TZCJxDlLcJayKmmbmsPDQ/I8Z7a7w/Z00nlgTTxk8I5RkUXmS0kGWcLjx48xbY1SgjRNKEZj5vN5lx4bGbjVck65XiGE2EgH+58h6oq7CzAEbGtR3ca6T9yt63oDvHpQ2PtPy7LEWYcI4qm6jh64KRWf576O5mITf1GzcZlZ7UEqXMhzN0mqXHRYDgYD8jxnMBhsXseebb3MaPZr7btV+/X3gU59b2vbtlRVxSAvYijPJe8sXWmQ95Fd8z50CbBfD1Riomz80wPb6I9t8S6glb7ExDqMcQQpNsy/EAohYi+xR2C7tGXvHT7EjzHXh+pohdQBY2tmZ8c4HzYgqjUWbQyqSxnOE40P8tJz2stKFTiLJCB0TAMWUoEH6QVaCpxxLE5PyJOE8WiMErBarFjMz7DeI8+eMBwOGQ3HDEdjUp0yHmiKbIjSksFgiCce8nhj2Nqe0rqW+cmS2hpM26JsiHU6znN6esbW7g4HB9dpVw3r5ZokyRgMhgyGI6xzDIYDtE6x1nB6fMzjx49ZL/4/9t482Lbsvuv7rLX2cKY7vqnnbrXUuhqQFVs2kS1hg41swITCmBQmISlwnImQgIGiYooitimwq0ywocoVGyeVIgEKAoTgYrRdsrCtAVm2NUuvu1+/od/83p3PsIc15I/fWvvsc7slt1SW3pN8f6prv7733H3W3nvtc9d3fb+/7/cYZ1uRHQeHw8dn2KFzI1J7owltwFuJ5dJKo4LCKKI0XcX8UgVK49Pmk4qPipKeVIWWaC96va49c66T8zkxqcmgKcvzzvE6bXhY1646/CLOw1Gi0lXnPB3Bb/peeu5O68tTx/Wc9zQfJPvTH+6+9/fP/G5G3/PMgxvUaT38tbfH+waK+V71oEdyWl/iujs74NmPHbP5/Td58Y89DVqRvfVZ2N5+0EM7rYe0viCwurOz878APwi87eLFi5/c2dl5J/DTwBC4AvyJixcv3o2v/Zw/e80VsULc1yeg8dowby2Fybly+y7/9F/+a85f+BM8/cg257bOUC3mzI6nPPf615OPhlTe8vhTTzK9dImD6TFvfdObmDUNs5u32B5PMFHGRmsJ3mOMSN2cs7TRJXZ7+0zXB9i0NcPBgILAs08+idKGg+NjrtYtk/GYQVHS1C22qTBrBp1r2hT9oIlMZey5MkZYPu+xdoFXki25NVln7+CA85vbbG5v07Qtl3evURQFW9tnUVoznc3Is5zMaLbWN1DhmHv37nH52lXu7e/iFUwm6+gjw3w249yZszz12BMMs4LgPOXmloAlZ8XoKJOe0ra1Ma8yoCV3JbIglqYNWBflgJGFRinqpmFeLSAEcmNwDvBLUGIyQx776AjgQsBoE6V8nrIsCF7cQYeDgsa17O0fQ4CtzQ3qekCeGeazKRvra6TQiswYmroizwuUVuRljtGavf1d5rMpZZ7jfYmyrfQYHx10gBkQCXBVLQ2MUJguOkZjW5Ej1k1DXdWMR8OOmUyL7BS5oZSirusOfFZVhUKR6eUj1o/o6Me89JlJWJoppden/+7LfRNTbYyBICxVURSsra117GYCYH2AkKTBidnqs7xpYZ9ALCxdho1aSpxXDXBSf6LrWON0rtY2PZZLrbBn6ZrXddOBC++95BYH6S8N0RRMIkckR9Rb6RHtS6nlfth4DXNssLS1JSsKyqIkBEVVt1jrKHND8C2+afHGoLIMVAYhgiEV0NHFVhuDNpqiLGhLYeGDs2ADvpVNKBc8u/fu0lQV47U1trYkNquuW2bVgsPD+1SLYw725BnIMjEUG6+tkecTxmsbnDt/DgjotqU5OmD9zBbu6JC6bSiLnGYuc7zxjtu37vDYI4+wsbZFNatRKNbW1mRToa4wWcbu7h737txhdnxMXS1kk8pZAb0aiM+6yXIGwyGj0ZhgHdPZQu5BgCyXfnA8tFZMxhSquz+4gNKy8WV0iJmvHnSUqLNk+ftzPG24JBa+UyWE5YaLUkpkzPKDlXmW7rswpazMA/kS/4GkgFhuqwT5zPoqrS/73+bfpHzwNFEd8czGBd795C3Qr/utfIvT+iqrmz/1In/z5icf9DBO68tUzjt250ds/Z+fAOBHHj3Dn/nLOaytPeCRndbDWK8ZrO7s7Hwd8E7gavxvDfx94E9evHjxV3Z2dv4K8KPA936+n30hgwsxZkYr2e1Xii5L0SqPawMvXHuZf/qz/5o/9T3fxSMbY0aDIRrF9OiQwlmKtTG2tRwfHXPu/Hkm6xt86jOfQbnAo9tnOL+9ydpkHaM0eVmQGXHCDUFks0Zr1tZGaG2o6gUEQ1GUHO8fkmuNUnBufQPz+BNsbm5QTedceukS1lueePIJzp7ZxiiNRlGqDB0iw+S9sBPBRxfLZV/VpBzQZCVlPqStau7cv8+sWlB7x950ytbWFtubW2RaMZvNaOsW2zqyTPPEY49RDEpx/CSwt7ePVpr5bMbR0SFbaxsE7yjzAqWQjNMIdrxzOCsSWKU1Ssv1UNqQmQyUobU1Lkp7lTGg5XoVWSbSYSUxIbkR1pBMo/OMPBoA1U0T158iMU4mScE5prMZd+/ep25q1tfXKAcDWdQaLb2sTS3jib3MJkZYeNeiMbg2cDSfc3BwAASc8xGIBaazYzHWin1z0+m0i3ix1krvsjI46zsjITEkAoWmLAd4H5ZAtAfsQPpSF4vFCutptOncSr335HnevV9/0Z1l0ssrBkxtZ4yUelDTexhjGI/Hncy4bRq5TyzzW/tAVgCjOAonljUB5XS8PM87xrUfK5NARMp+FROd5feXmZgeSBLPVfb4ZE9rOt/E9KZrbHQWo0zELAklDLO14r68qOrOkAnEnKgP4mXMqeexxTob2T+Fs4YQNGClTzReW4IieI9rHUG1SIqogGrixoDSKeZKgF2JwrYtLlhUkM0Vo6TPea/d5fDwIPZZDinLHJOPsbalrWtsWxO8oa4CShsW1ZzZbMZobcJkfYON9XU21iYMtyas25az8xnz4xnN8Ry7qKmqmllT4azn4OCISTHEmIygZONqvljgCBweHbG/v89iNiczBhEeeIL3KGPQeY4ZDgjAZDSmLEs0ilkzpW5aYa+9I9OGtfGa9DvPrPRsEyDeJ4UA1cwojPKROZcnM6jkG7xk19O8SsZKQKdSMD35uph5NXHjQn4v18uMvn5+atqoSHO5L0FPKHXJp6qv2uSaB/G3+Qupbxq/ju0/e+oOfFqndVqfu37g1i9y9UffzXcu7vC7/9YbHvRwTushq9cEVnd2dkrgJ4E/DrwvfvsdQHXx4sVfif/9U8gu7ff+Jj97zdXfKUfFpVCwZFGym2UDKtvwkU9f5ukPfIQ/+p53ob1jMBzig+Jw/wh/OGUzH7Hz5LOM1sa4usFGMHHjzh0O9vZ497veTVNVNNbTtk6YKttQFgWDssA3DfNqTlvVuNCwmM5FtmAUeZ6R5YaiOAMB7ty7x/70CBvA3rnDrf0DTAi87qmnGQ1AqYza1vjGkilFlkemSoG3lrqu0EGxNhnhgLqt2d7YYLI+4cadW9y7fZumqtlc3+ToeMZwMMQFRZHnjCcj2NJsrE04mk55/vnnMdbyute9jqIoWVQVtRsyHg4JMRcVPG2TnHYl5sOYDBUCtqlRPlAMBwzLgqAMWius9aL7U0qAqinZGA7F6Mb7TkbonKN2rWwwxN6zMsti766iLESK6XzLweE+N2/ewDnH1vYmg9jHoJFc0EW16NgTb10XqJhpTWVb8kxjbcvRdIpSGdZbvLXkRUnrAkUxYDpbEIJjOj3m+Pi4A8PVYgGAdU00d4kRMq4RhpjQGRV1QNQY6WGN+aEpFkY2HVR0TNUrTr5ANERSaJ0JM55nGG3IcxP7VaVnOMXfaK0xcaGf5xnKi8kMzlPmOcPxqFu499nNkzEfryYhTsxW6gvsy4YTC5qApdKGEFzXvxmiHFxFtYBSGm0UBOn39l6k4taJzJyQ2F1x23ZBjHxC8NhevEw0k8W7aPzV1CKnVsuYnAR4JBtUXJwDOmYmK4pyQJKhOu9kM0NryVrFdcy49GUa6bFUGqWzCGLB+giylCF4OX+lIStyykGBs455tcC7gFYZAYULUDUtrfOYOiPLDHleMFgvY4+txToHaHyA6XTKdDZjb3eXPC8YjkdsntlmvL7GpBixdWED86j0jDvnmVc1s/mMuq5o6oZyY12uUevwvqWq511fb1ZkAlCVIhuWGD2kLAeYokAXA8pBwaAo8M5xuL/H3v4+ENBG8nFdK2oEmQfimp6AoSbNTdlAlH5ShbOSf5tYUa3iZwkBF6+h6Zyg5fvBW/J8IHM6Ht2YIm6qiAGW0QoV2xGIGz/OCZOrWfazquA752ai+qLP1Jqvwp7VB/W3+Qupn73/Mb7vz2u+4ceeg/g5eFqn1S//qc/ye2/fedDDOK0HXD9141f4v4oBr/8L1/i5t+ZMvucdMBo96GGd1kNQr5VZ/WHg71+8ePHKzs5O+t5TxJ1cgIsXL97f2dnROzs725/vZxcvXtx7rYMTlqW/wBAnSg2YKNnUxnAwb/jF93+Ep85t8s6v/1paJxmY57KMw6Mjjg4Pecdb3sqLVy5z8/YNmrphY3MLXTje+qa3YIGD2Yy2aSmUwWhFbhSlUpR5LtETNoJLY7BezETyPCdoha0WIiHOcs6c2aYJjtt7++wfHHNw8DKjsuCJJ55C6ZzZosI1jWS+FsKMeAREVE0jkSsxb1TnGcOsJCgYZkNGo2dxQVG3LVcuX+H27bu84Q1v5Pz5cwzKgrqpRepnW55+/DGeevQx9g8OsM4ym88Zb25SlDkuMidaSU9gmReoSXKW1QzHIzG1CgjLpDO00bRW5JhZtmSCkyFQ8OCCEglw6DEqJscrcC50slUfGZRqMaduW+7v7rK/vw/A1tYW586elV7VqmHmZjQ9ealzjqaupbc4z1EajEYYrFYWyvPZnLZ1bJ89R9W0zBcVtbW0rfTA7h/sd+6+fSAHRAC3jNmQexE6Y6C+tDb9bv8Y6XU6KgD6x+6bvgwGA4aDAVprqqrCWstwOMAYjY1gs2+ypBCQoNLcLySj1KDwkYHqS5wTW5oYzMTu9hnUfmyIUqsL+X4OphzAd/nEfcmuMUacovXSQEd8iBSlKamqKhprmY5Jq9tG2M8UH5Xua9NG06Sl7FMrTZ4pTJ4tczm9787H5BlZ7CHOYAVgC2MuPdG+lwsblJZsXogmYoktbmWjQclGhA8BG8SACqPRRoC2JkdlmmI4wDuHdb7btJDrKAx2U0u2q4mS17zIGQ3GXfZsOuembrCtpWlbjo6n5HnG2voGZ8+eZTgaCxjLc9Y21tk6s4V1kgG8iH3RbdOIy7C3NK0YUTnnIlhFXImTg3JWkOcFIQRmsynHR0fMp0fYtsUo1VNRBIkNYsmiqyTF9skl22GDk+iuwkRzOt3dH7kPbbfpknrky7IU1YJzKC8mUIFAUEvn3o4hjXdJiUl2l6WaTNqWz1eMJgrSV5s2XVZ6rL86+1YfyN/mL6SmzYJv2/8gt//xXUb/2Td/Kd7itL7C6/U/+Snuzw8f9DBO6yGoeVPxid0rPPbLikd+fZfP/MlnT13ET+s3B6s7OzvfCHw98D9/6YezWra1K8YaWmuJRwgptkOYlsxk3L5/wC9+6Dd4/KlnefTcFllowVvWJiVr45zj3dtsjEuKwRMM9w5ofeCRx85RjodUtuHqjescHhzwuqefYXN9nXI0RGWaxjvcYoFhyVYN8lx2+X3ARkOkTBuMUuTlgCcfeQznAscH19iYTHj6yScpi4LZdMbdWzd55Nw5Njc2ca6RRaUN1PWCuhX31CIvIgiIvVkKMq0ZDgfsHx5zuLfP7t4BW+trVIsp167NWN/YYDQasbmxyZmNNULsFzJaU80byiwHH9AuUOYCGoIHE+XAmROQUxSS+1kUBSYXpqmN0TYpo9ZkS0ObxKSEEGKkCJJTq3R0C/Y4JLqn65GM/ZRVVTGrKqbTKcPhkPMXLjCZTKgWcxaLitaKvFmkxSLxWywWKCRCRIyNFIPhiOlCWKXZbMpstmA0WWc4HHJvdw/rHHXdcHh0zN3d+8IwVxKhMRwMVqIzuoWyExfYBH6ShLZpmpV+05NxLn3gF+LCOS2qZd6Gbh4ldjNJd4GVRXafxTVaE6KsOfWuGmOifHbJoCZzpDSO9JoE3k72sKbnqv8FSwOmpaRZgEI6Zl+Cm8bdP39jxEU2NxlZKcyjtcL84gODvBB2TEEbzaqsdV2vYwLeXR5neOUGQQJGVVV1Y0iGV0ninaKa+rE/vncsYYFDh2O0zgg4UHSus9LDvlR62MxijDiF53lBZjyt8z0wLRmgUiJNrpsWXdXked6ZeWkt4Df1SIcQIqMp8TZNXQHCQqtoHDQYDimKUgzP8ozM5Fhjwau4IVDEGKK0+aC7/tAQ5DN1Np0yn8+ZT4+oFnOcsyKvjy0Kxki8Vurn1vFYstHhCcEKsFUyT9rgAYc2wpxnmV7pyz577hx5nrO3tydMd5yDk/GYpqo5OjqSGCp890z15b39/9+PR+rP1b70vC8578uGT27IfKXXg/zb/IVWCIG/94mn+O+rCqKR2mmdFsCtH/0ws/bUVOm0ViuEwK3pHt/2j7b50X94EYB3/sgzp+7Bv03rtTCr3wK8Gbgcd26fAP4d8HeAp9OLdnZ2zgL+4sWLezs7O9c+18++kMHJwlJ6R7tdcoiL4awDPVmW4ULGb3zmMvb/+Rf8oW//PTz31HmGGopMFsxhYZktFmiV8ci5c9zbk77GRb3g7t17hEyjypwrN6+zfjjhDc8+w2C8ifIWGxnP3GSUZSHmO94Kk+BlYZ4bYX4yMQzl7MYm4zcO8Vrz6KOP0tYNH//4xzk+ELndxuYWWsGiriILUyEyPMkXNcbE9jlZxNqmwTvHuCh45NxZNjY2pFdtPkVpw5Vr13hu543UbcvZM2cYFSWLaiZ9qXlOZqQ3dDgcMp3OcHWLzrPOMEUpouQvAi0kA9UH4uJeYbSKMThLR1mR5knfooKYS5m+pFcuge60wVDVNYuqYlEtqJuW0WjExuYm4/GY6XQqTEsQia24NDtcBIlaa+k99I6gxACqqipm8zltY2maVvJEBwP2Dw5YzOU9dnf3uHf/PvPFQhi8yBol4CWZqVkn903L2gTwAJrGdoxjv/oMTt9dt23kOP1ImXTd2rZlNpv18ibVK47XN0DqV78XNbm09sFln9lKoOHVgOpJsHqyv7QPXvHxHisdmXNh5YNSy55DUm+5RitNWzfSy2ji+US5b2ZE0umcF/WEtwJgiwKls5W+WhevlfPuFcZQWus4lCWr2wfmWZbhvH/FeTdtGw2ZouFVcChE7irgUvJgldYSYxMCKRdYFAIObxxayaYMSkcwKbnFPtCB4P5nmVxXRdseR6CqgQSmJBbHRJa3mi+Yz+dRnqxw3uN8BGhKkxlDXpZkWUnM+KIoCspS4n/yvJC+ciV9uNY65vMZi9mUxWzKbD4nuFYUIYQYEaY66awn4FpLHgIej7UNzlqUimNFOgFknlp8I9L+LDNkmenmTzrvTr6d5zjruk25phL3aROMPM/x+ehft9SvnABrf472Je99MNo3depe99UnQX1gf5u/mPpLt97L/Id/D3/hb5waLZ2W1Ms//BG+7e5NFm39oIdyWg9p/dr9F3kPEmf0X/8Q5NHs8Uf+WIv+mtNe+N8u9ZuC1YsXL/4oYsAAwM7OzhXgDwKfBv6bnZ2dd8f+l/8O+CfxZb8GDD/Hz15zJVdaWDI3KrIpKvhuxz+EQNAZs9bx0c+8hNGG3/uur2XnmUc4tz7Ch4bJeIQ2GYeLipt37/DI+Ucwecav/eqvcng85Zve/W6UUnzoQx9ifXODO3v3mS/mjMqSUV5A2xIKjda2AxdeBZEMZxlFVkBAZH8BtscT/PoGymhUgEuXLzM9PmI8GVM3DbsHexgNbdMyGY0oR4NOcpoZQ14UeC8SPxVgkOcQJbTFZMJmZqjaDe7eu8+Vl1/m3IVz7B8d8tmLF5kMR7z9bW9jVA5JbrtlXqK1pq4qbNMwmkwIWnJSA46iyMnzrAOmWlaiHZDNcoMyBtW0LKp6yayp2LvqfFy4y52THjdH6ySDNQQBHk3TiCmUE3nkOBcH28FwKGzrYhFBf07la1DS56k6WWw0HIoxKVVTs6gFyEq/niIvSqz3HB5Nqa3leDbn3t4e+4eH6GgqVBYF8/l8JX80RdF478EvJcIJ6CUpcwKQ/QVyH4ym33POEjjJPi5fE3q/lzJv+86/fTbpJJNZ13VnftQZgsW+0+RI3J1L/5k6Mc40rnTcZC71ysW+Tre2Y3JNdLZOoCwBKwHjDTZm+obQrPTFtq7FB48xWc/gSvoZ6akoEvMpETW96JIeAKIHRqAnH9ZaziW0PdY0Alatuj5YeZ9kEpVMqFz69EFp0AmIewFkKIPzgdY3spmEjj2tpnsGslxMgboNhE4qvNzU0F6im+ScJLfUaY0xGmMyBkUufaZ51m3QKa1xccMoAMoUkc00rK1NGI+HAEynx1R1RdO00vO9WFBVNc42eFuThUCIBmVEsAoqAlMrPddx0ywzmVyTkKKKlLh3Ewjd90UCb21iZ0238bO/v99tNAggFmO4pq6xTXRDb6y4hffmXrp+fZOxjrXvAdSTjOnJ/+7P+a+mepB/m7/Y+mu3f4n8B+B/+pFTwPrbunyAg33eNzvLrenHH/RoTusrpH7m5vu7f//sz5xlYK7xPwzexPf9pXX5pjGwsfGARndaX8r6onNWL1686Hd2dv4L4Kd3dnYGRAv83+xnX0gJk+Gie2fAaI3v9TMldsIrWUCGLGPhGj7yiU8TfE1RvJuz596E8hnz+ZRyMORsOWJQDNk/OKZcm/A1b34ruwf73L5xnXI04PEnHmU4HnD1+ssYFBe2z/DEo48yyApUntF6jw2WLPZMpYgWRUADtZMeLaU13rVk+QDbtmytrzE/s82FCxd44uknmc6OuXTpJRTw2PkLjIcDciOgyXpHu5hR5gXGiINwEfvytFLYtqGpHflwwPraiEfOn2WwscH7P/AfMFqzMZngg2cwHBCc73rGXGScJhsTMFr6Nq3rQKB1tgNIOi1UJFGDJwAAIABJREFUQ9o0AOuCMMzZMlNIwJy4qQpADd2ivLWOurXRSEd1vYkJQCUZrIsGRU10t7WNJdjoTBo8AekVFufaJgISAfp1U9O0NsqFDcNRgfNwcHjE/b195lXLlavXOJ7NyPNcWDClaeqqW+jO5/NOsphlGfjVPtalu27WgVVYXQT3QWwyuQneU2R5d64qSicT45lAVbrmiTnuA8W+k6qOC/W2FRCY+vZCCF3ETF8Gm8aY3v8ke9pnVk/KJ/vnlGTIqX8xAc9lb25YuaeJOdYsJc0hEJlPcSYujLhQz+dzkWMPhyilqZqlM7EyGuWJkT50+bYuhM4pNkmfk7FUGkfq9xXDp6V0OJVIWC1EB2AfLFW9nP90+wpR7u2sMMqRgUxROvISMQpy3kFkMqUbdjVTd+lO7bvMVu/bJROOl+MmpnihmeljdAT1orZYssgiu2/lsybXTH3L/HifxWLB8fGxyHuzDOcDs+kUax0aj1Yeo4yoQ0Kazx4fPN4FbEjXQdFqTZkVvU2LBPoVWoNthTEX3XPAe2hbv8KG9iXBPn4eNU1D8B7NUlYeCK/YkDj5DMKqND+Zgp3czElzOh07AeXfDvXl+Nv8xZbzjg9xxJ954RL6udd/ud72tB6y2v/JD/DUr1180MM4ra/gun58H4Dv5ybf/+fle2878wy/8LVLWJNfKMjf884HMbzT+i0u9ZDuOD8DXH7717yV6y+/HJd+PROZyKgkGSdACBqjczQOHRoGyvIfv+N38Ae+/VtQtmJ7MuTc9hbBtpRFjm0di7qh9ZCXBYezYxrXUo5G3Ll3V4ByYymynLXRiM21dR49f5714YC2rbFeGDetFJkG5YN8YcSVNM/Jy5K6aRHDlUDVNgxGQxpvufTSJQ4O9inynLOb22RKoVE89uijFGVBCB7byILfaI3RsugLOjpbFjm1azFZhlOKo3nDpctXKfKcN++8CYB6UVHXNWfOnKFtLaPRiLqpGY/GtNbirPQPlmUpTE0QIEGA4XCIyUxkgsQEp6pbZvNKJMOR0XNOgAlaeuJ8iC6udUNdtzQRuBojZljWWlq3zHJVSoybEnvivMfapcyztU2UepsOPDWtMKk+eFrb0lpH00rMSevE+GXv4Ihbd+5x++499g+PCAqyaCyTZTkEYaeckyzdPuvT7/mUySX/R5t8pY8TVns7+6WUgiC9xv3FdZJKJpCap/gOozuArHu/k/5tjMFE4FRVFVprRqORxOhkhtFIXIFnsxnT6bQDQH0Dpe44etnHSHy2AidklbFfU2uNyYRd64PV9NWXSZ9kxCQKRhi71Jvbvz4u+M5cSkAFtN7TNDFHVS3NfTrzphDk+eqZ7DRNs5LxSvxckB5refbSPUs94GKgJJ8lxmSyudI2hCCZw0qtGgUFn8C8jgBV+kOt99F3VuNRhKiOIG20xN5blCLFuyRXZAHXkVXFxz7XBPqiFNvoaCyUNsCkzzeLLKuwjEbivZQiBCd91c4SvBNQHKBpJCvVGIVWARWgjdm0On62EMefTMIgRc1I1q1WijyP8UHOQpCNKttW8l5quQGS5rPWmiJtHCA50t19CKHb0PDBE9Ryo+Tk36Vlz/fqRk56Tlaeu1jp2UnHevzxx3nv+34R4HUISDutL7yeAS4/+4bfydWr17/og/znj72T/+17B6g3PPtbNrDT+sqo63/tI3zDyy8wbRYPeiin9VVeb9h8jB81bwTgO/7KFmxtPeARndarlRpvMvzDfxE+z9/mL5pZ/fJU7AcDYuuYLMzSbrlzXawIsXdSJMEGqw3/4WOf5db9A97y3NN83e/YYe9gyqNnNxkUGYMiI0PjMRzPZ+Qe1jc2Ga+t8czTT/HpT3+aq7vXeMub30xbW67duMHVq1f53e/6RtCKshwwO9gXxsnk5JkYCoWgMDpHGY11LQRZMBttGA6kj2zRtDRVxdbGBpPJhOODQ+bHUzbW1wWgtS1NU1NkOTrLpDcQlgyRkhunjcIoRZZnnN9eY3NjG+cc4+GIvYN99g8PODo6ohiWHM/mFNWMqq6Z1As219ajiZOjbRvKMsd7JU7LmcG2Dc5r8rwQJ+DWduY9xmicbfGEJbsXnZuDFWGh857WCigVMxiJo3Cxh7UzMwo+xsXIYrRpW1CRObJLAyvvXQSvLY2VKBLnPXXd4lG4oPHK4HHcuX+fl166yu7+AV7JgrWxbQQS4pSr1ZKJSefgrVsBNV3vJnL/Grvs1YVVhhReRbKuViW8fQljZ5LUsZ1LFrUsSzHA6h3PaE2mTWfw1B9HWeQ9qbJbWaD3maVkWqS1psjylR5QQug2fjQK9JLJ7fp6e8C3f336Ga79MafexPSe6ct6Jz3LUfKdXq+yDJQWhs3INUrnFUKgtTaaHbnuejdN071HCLLx0u9z9B4BVqwyyFohLtfp+qtAHs+ju5cRzGptwCzdnZ33cVPDimt2xEcqSK+nR4C2JPlolIrgL4TI7Aszm74nz3f8d1hueiilUKEnXzaaeObo6IIdvMO5ltbbTrYvINnKfI+MsJGdBpSTqB9pc43z1ofOLTjEsbkYK9T1p8Ze8RDHaNsW79vIBi8r3X/nZTNJzKLarje7GIoE3xhDiPMuBIm8Si7hfZY09SH3TZT6jP9JRjXd3/5mzcmxndaDr39w80P87U+9kfIUrP62Kfvvf5U/8+/GvPfo+ilQPa0vS714cJM/yk0Avu/H3sWP/41TsPqVWg81WE3GMcJkxGw/lguaFD+CFpCllcNkGhsUVme0XvHCy3fZOzxGmZy3v/kN6HLCnd0DttcnDPIhwSk219c4nimO9g4ojGY2O6KaHnNmfY1nnnyCSy++RF0v0Ch+/aMfpSxznnjqSbK8oLUNwzzHtan7SxbasmiUf6toUoT32MYxyAyvf93TDAYDjNJcW1QM85xHLlygyDPu794HpSiLQSd7bqKT6mAw6JjQTBsKk2NdIGDJlKEocoyGxx45z2Q05tr1l7lx8yZH0yl3d+8zWptw4cIFiiLHzms219eX7EQInRmSxLYEvPZoDUVZCli2bgUs1dajTQZa+mlFgupElq00ZVGQlwXBK9q2pchzbPAdyFDReGU6ndK0LXlRUJYD6rqSvscoCQXpp9RGd1ElrXVYH2isA51hnefO3fs8/+JLXL9xk8Y6zpw9B1qjvcM76YsL3mMTixUEnAmjtFyYB+c7ljUET1BqZbHczdEeEH1FD13UT6fFdh77GNN7ZL2+1L5Mt7/4hhino5TILYGyLLvrkud5xywm0DYcDlksFp1cuGNUo5Q664HUkwt7rfvusUt2NY0x3Yck60w/60uPOzluEMYxoGidxVXyjDRNQ1VVtNE0KYsRVNKDKuMvigKtNfO5uDxHK69O5twH4Okck0Ny27Yd0DVaQ5atgGLnxSgIlaS6ifWki9dxzlHXdbc5k2mDD2A1GC/nZJ1srBBkU02rGAcF3aaMREB5QGN0AqUO4bOVyJnV0lnXeQHJYq4GPkjus1ZZBHciFfbO46xHeREcJ6VJCA68g54yIAHqoOJxkd7/TCn57Og2KlIfKBij4ueZw+hsyQD38F73bPglqE1yYPm4S/dn2f+tkOcvz3P5PH8Vae7JzZ/0jKTjpTnZf1bShshyXMtNkzRXThqjndaDrbf83F1efNsV1LPPPOihnNaXuPxnnue5f3ztNJ7mtB5Y/R+3PsC//h8/y/9avo0/+NcfP3UV/gqrhxqsercERl1MQQhd71fwnqA1WEumJefTYzBZTmM9ShmKvGD3aMF7f+XDbGxusr29xfHuAbd3d3nqkUeZDCaUWc7W5iZra2Ou37yByjQ7z76e0dqE44MDzp3ZZmtjg7ZtuHblJUaTs9zf3QWtOH/+PJ60CHZdv6NSqtdXJ3mHRVw0ewIbo7EwXUrzzFNPdREn0m82JS8KrN3He8/G5hYBGK2vk8X+tXY+w7lAU7e0zjNcH1A3LSrL8EpTR9b5iccfo25bPvGpT7Gxvs7m9hZbm5t87OMfJw+aR86f46knnoSgo9xYmN0k2WxakRprnXc9p23boLWJi3JHrjXeQWsdbetkER0lhHkhxlOdg6sxYGWxKtdHQFHbClNblgU2ynP7jGv6qpoGZS3WelwIeBSt9czmx1y+coXPvvACB4dHtNZjncPevUuRMkm1JrgeIxoZMmCl79HF/Me0SE6sjw+rC+m+w+yrLZBFcbr8mYBBkQL3F9Fpfiewm4ASCDAVkxsDRq5VP+5GXm87k6g0tqIolsxwr49ProORjZNeBE8618SAZXGu9g2X6rruImZSvm4yjOr3v8p/i9OtiuCtrhtaK5EsPnjQMj+0MZ1BTwBclARXVRUVBo2oCnrgtC8J7fo3wzJHNoFVOf/l+PrnmiTg8j3JCU7XbvlajY9yf69c5yabnJu9B+utXE8CeCefBVpjokOwUgprAyG4TvocghcnYS1xM8l6Wo4rUuc0huX5ikxXWFXZXMFFp+34ealUiH2nrvtm8vZWgShX7pmmZTlKi2xegLWKrs90G27ymeu7a6mURhuFCjngaJsWwivZ1ZWe0x5bXVcVTV0v5ehxk0GcnU/GPMmzkPp1031O86wPVvvO1SfBrtyrU7D6sNXd2QG/4yc+yce++x7Zt3zDgx7OaX2Jqv35D/HWf3H7FKie1gOtEAI3j3f548fv4//9gW/hm/6jG4z/y9Pc56+UerjBamQnuziFjtFaLrgS02KDk+iHPGc40Ogo8bOtBw8Hx3P++b/6t1y/8TJvf+sbWRsVXL1zj5J7PPvsM+Qqw2jFU088wWIxx9UV+4s5w9GI8WTM0fGMKy+9zJvf9GaOjo+4dPmlTo48KErObmySK9MtbiXOphAX20zYnuAdwdsYZyIsjLMtw0HZ9bBVVc2ZM9ssFhVV01LVNeUouuV68CrQVDXegbeOylWyqGaKKQp0jLVobUuRZQQFg7LgHV/7tRzPZhRlyeUrl7l/5y7nts9ivaduGorxOLJTOcGKKVJrBVRUVYVuLU3VoFBkOhfXYGPQJsO5gAtOcjK1IcskbibLxEXUOjFuaa3Fevl+URbCPGvpLdSZwS88s9mMWbUQN97I2KSFq43/to2ltcJ2zuYLbty6w/Vbd9jfP8BkJRubW8zmC5rINh4dHUmsR+ydyzq552q0S7/HLSXJdIvuCFbT758El/2FcX+hrtUSTPVZzH4/KSxdaVNfNtCBUmGwXcy2XDqj5nkuAKrVnZNqqpPmMn0wmaJv+n2eCaT2QVJa4MuGkTDjKU6oA/IrMktkDvSAYSDETYy2F7cjslPrHIaTpk3LPFvnHMroLte3L79OYz0pCU4/L8uSwWBA01jaGFW07EFdHiOBIO89tpVNgnQcEAmwUoHGNhgvpkMiG9ZkucE3sikEvtvUyFQmgE7LZ5XWIuFXKDQRYAZQXsB+6hEN+pUu0yqyriEs7yEodCBuksXe2OA7GTJLUn4JQpUS2W/sP3dxs0+pKHNWqvu8lXfo938GiPcsxPie4N1yTD3Ze8yvWu1j7n1+a6VQIW7UKGFZpS86Iz1BfSl2Muvqz82+euFkT/OrMaqr8/i0Hqa6dnSX7/iXm/yb6fspvvNdD3o4p/VbWP6FS/zzv6v5iVBxa/olT0Y6rdN6zfVH9v49a+8f8YFLv84zP/R1D3o4p/Ua6qEGq53MMCzzVlfcUokxGj0zkhACx9MpSmkGgwGZlh182wbu7R3y/o98lMvXr/NN7/wGnjy7zplRQTkZ4dqWumkY5BlrkwnOWarFgtnhIaG1eOd47tnXsbWxJXmKaK7fuM5LL13hTc+9kSzLsXVDW9eycMp1lPY5kakqMR0xSuI+ACFjlOSgOi+MyGhYMp6Mmc0WHM8WVG3L7sE+izt3ODyasrW5yebaOrk2jIdD1sdr0kuoIOBFuug9mTGEIA6fKMVkMGQyGImc8vEnKXTGaDTm0UceAYStK/Mc2zZRlqhiDIpjNp2jM0OelRiTAY5M57jgIeZPhthTKrdCZI8gvWtEObeNjE+SmMpY6WJn6pi/GlTyaI2XqceaiYGTIyhN2zhefPElLr/8MjZosqJgWEhfazkcYYxhNpuxt7cn0tP5XGKG8oy8HEhWb1hGvqS5puPGQV9aG2ckXV4krEiU++AijZkTwAOWGauJ6T3JyvaPtwoKJfM2AawOYHu5xyoaAPVZpj4I7EuBFUvn4QSG+yC5v6hPwLVpapSSftok4039tX0Wtm9MFVSITPzS4TcoMAlItG0HNr2XbNMU4dONNfa4puMmibLWsiGSAEqSMDdNg1KqkwR736zcg7ZtJY5GI6ZfiQlGo3SI9klLZjWNTStNwHfXz3kHLsRzsXHeKzKjkKxiiWMJQcyW0gzQWkUiUthW+S3VgcWOzYz3onuNUpFdlV79gCIEJWwqxGdGgRJALQp3kbl79Mo9STJdyU016PQ7qPh+gWXEDt31IJovofzyXLQw6HKNI5Pd2yiR52C5gePcUkmg4mucSy0Uq4xpH5Cm6m8opd9dMc/qve4VG0QdHD6th6k+fO95vvW9r+OXnvz0aW7iV0n90F++xIfdfX7p3qce9FBO67RetY7rOd92/wrv+2HPk3/16x/0cE7rN6mHHqyqtIhLvassFzRpcaelCQvHMsdQG421LUF7atswGBToImdWW56/eguyj/MHvuUbeezsOruHU27deJlz21uc29oieEtmNOPJhMFoyPHREXleMCgGVFXF+to6KMXVq1d54xue4/HHHwcXqO2C2bxiPB6SQWT2pOcsywzeiqnJcDDs+uK0zrG2FZYxuu/61mJMxubmJhjD4fGM+/uHlGXJ8WxGXQugXB9OYl9ohnfSk2aMYrFYdIY5RVlSlgVt02KtI8tzNidrrL1hLE6mzmG0ZjGfE0oBLnmWUQ6GoEQSrDOD814W6BHYtt6R5RllOeRwOgWvIghqaBqRTIoMNsT/ObJMUZQDQoB5JT2VTd0KGEkMS8xRTYtXuc9gfcAFRdN6PIaD/SN+42Mf5cbN2wzHa2AU7aIWU6A8j7mxOWVZsr6+jm1aDg/2aZoGHzciRN68dN211mLbVsyHsqVJkUSUKEwEL20EWf0ezf5CeWX+pjlrdJQchw6oJ1fevuw0Mcl9l+EEFPLYa5rYwD5ATlEt/Z7OPnOYMln7faipr3IJjFYX81mWURYFbWtxbmmEpZRiOBx275l6Ztt2GcPSeid9jn4VfAclBl4pLzeB7tRzGsQSFues4J5oLJRl2UpvbpaJG24aZ3L/Tcy3956DgwOaRmS6aYOhtU10Fu+ZQnmR2GZGE3T0QVJL2an0oQqTWBYlKFgsKpqmZjgcynV0tmNsvRMQqKRZHaV97OF08VxyYSedlw2fgPREe0VQ6blRBLUa5SLjjREvyB5dJ2WP+a4JXIoR89K8KYjnk8wnAkpnAnq96wyijMkE/HpRo8j7L6XdIfU54zEpZkdHcB+SIHn1OUgxUx1Tq+hAY3J2DsgfIhM3i9J9Tc9FavuQ60BUoSzfoy87fiXY77laa8NpPZz1sd3LvP6n9njpz586BH/FVl3jr1zjr/89zd+69cuyUX5ap/UQ193ZAe94ecFnfqJh+w8/jnrm6Qc9pNP6HPVQg1UiSKXXw9X1rvZ22318rYpB9qlPzgPOtdLH1bZkLhmhaF64dINfsB+g+bq38eyzT7GYWx55bMyvffKTvOH1z/DYhfM01ZzcGM6e2aKaVyxmU9RgQFBw5/ZNHjl/lrNntjk8OsI5z8vXb3B4eMjWxgYXzp9lYzKmMAaNIdMa5530iRmNR2JorIagtOQbWlk0z6Yzceg0GcNygMIwm1VM1tdpmpaXLl9hd3+XRx99nGJ9wlq+hraBUSFuqnXbSP9pnmGMLPJdMqVR0ajGe4lVyTOyPAPvcb4lADaAdjV5XpIXOXmRRxmvY1ZV4srqA8EGvF8IiLGO4+kMUCidoXRyZFUEF8gyTd22VPWCumk4Pp7GnjmRQmqtycsS8OggLFvdOtAaZz1N21C3nnv3Drh87WVu3rrFfFFjyglBGXQIqJjzqpUw1k0856IoGIyGTEZDQJjD49mMw8PDnhR2mVVqnRP5OICiA0FJutqX8oopkHolZ6MkjkOp6FQb/6eCABHvPVVdU0RQ6SMo0r380NTLCwLIbEgbAsvoFpn6gaqqVsyBQPpdgQ6o9nuAVSYS1nm1EEDrAm1kJRPIJYihlrMWF4F5Arz92JqqqrDedT2aSXrZNLaTlfYjeJzzOJcyeuVeS86vSFgVYKIbcggC6k66LjvnyCJiSc6/qfcR6AymjBE3XuuayAQXuMaCEyCtNWiTsTQIAhsk81TeU9hFHRTOg4tzUitDlkEdlRRZr4c7RHVBittShM5dOcTeVqMgyzVeJbfb6McVf0/pJNkWma9LIFVHuW4EoElVstwVWcp+A4GgXMfyxx8LYxw8Wdd7G+e0CiiWEnhCwNuAyoi65YDWHhVctwmnohy437eq40aiRCLF5yDeFxfdg2X88aSVQzlxfddxg0f1ZL1ap3iy0EnNZS4uWdiTfxPS6/ty+JObMaf1cNX9+SFf/5Mv8KHfd4f8O77xQQ/ntL6Q8oEP/cAV3rP3/gc9ktM6rS+oFm3NMx/9LI9duscnvv06xX9y2o7wMNbDDVZZyrlSfc4Fh1qC1LQ7L4vHyOs5HxVqIl0LIfCpiy9Rz2b8/tGE7c0Rz794Gde2mMxw+85tisywsbZG8FCORgwma8zblqOjI8bDEZO1NebzGXk5ZDFbMFtUbJ45x97efRb1gjc99waGozVUcHgfsN6RZzlBaxZVTesseV7gXGCxmGNMLmxDayVGI57Lxvo6JiuorWU+3yXLDOPxhHPnzpIVBbdu32J2fMwzzzwjDJ5SlIMx2miqppYM1dif1lobe0bLbqFXzReYTEXTo2WUSnLyFBfgGEUTF4Q+eLwNGB0ZkAieJJM1ghYVF+kYPIEsk8V8XdXC6hiDQeMVqODJ84xBPsG1DVVVU5QZ86phPq85PJ5x7foNnn/pCodHx5TDIVlZCngMS+feZA4UvJcYHOI6Ppq0JKbFaM14PO5MrUTCuuyPfbWFbz+LtM9qJiYvgcRl9q8s1/smTCoscUWSBCdAqqN89SQDmnpXlVJkWjJQ+wySD8vjJHZTay1ZuZFx7I+t37/Z5bAGMTRLcTZiDCTybGcFYqRxpesFwpK5OLeIrG4n7e1tMoXez/pSz5T1mq5xFqXZJ92Fk2S5b7jW74tMbPKSjXNLNrcnBZVrkEUzoxClx4kl1LHdIMSNhiSNXfbiWidqhP64YJkDilKYZD4Vjy04ToCbgEiJk1GaLudV2MlM8lADaLPsRSVuvSXS1Kjk1AuSZROWfKYHrwNRPdy7BvGHLOdfvx9bqyDgE9lgCSHJjsHaNl7XBGQDKm4kCmO6MoKV+9M3QErP50kWNCDXydnUpx66PuUENjvVgHMdU5yMvvpztj+XTusrr57fv8Hv+4Ux/8Z/gOL3f9ODHs5pvcZ671+8xHftf/BBD+O0TuuLrpvHu3zr+9b53vdf4U/+Vw793Osf9JBOq1cPNVjtx3d8vurvrMsSdFmdZDCKMJevDfis4PLt+/yDf/YveefXv4W3velZ3v6WtzKbHrK1sYZWcOP2bba3zlCWA7RSZFnO9uYmBwf77O3uMplMyI1hfX0dozXzRUVeFJw5dxYHfPaFF9hcX2djY4Miz5nVNfaooSwKtMqoFjXWeqqqQWtZHBZlEZ1aRXa7tzvneLbAA7apWZuM2draYDwYcOPaFfb29zF5xpXr1xiNRpw5c4ambWnalrIsSAJqI8hSomagW+QdHBwwGBZsnzmzlI4aA0FhrbBfhGU+pcpSH6UsEHNjxDBKS++bdaHLo2wi0BF5to4mU9JTi/fU7UIYI6VxbUtrLXXTglJMZzMOj6ccTuc8/+Ilbty6S9CacjCIi1aAmKXYA5iJIZUxLnsp04K3rmuI0tllb66lrqUXcyX7U6lXLHz787IzWorXsw+QlNIRuPSOI5bCKwvrJBfWWtO2Dcm4ZqXPtDeOPlhOubt9SW363aqqVgB6AhBJTrsSWYPqepWTJLpOx2zEETrFwqT3SD2IIfZmB7ca7ZPnxQpQ7PcipnPqA1ijVRdpdPL3Uq9sAqHW2s4ltm9wtZKlGu9Dv/dY5MP9a5/uZ8CFIJtbIYHQZaRRILoDh+SkuwTV/XOD2IMavxdCAk6B1MedZlMXBRO/sbG2zjNPP0OeZVy9do2bd24L+6sMibuXTRP5HQGvaS6mpyHg3Ml5ksYSweMJUKeVGL7JM5pIWPm3j1Jir5zMjygvli9W3udz9V2f3DTobxws71n0Joi96SEEtPfRgTl091qZpepAIn2Wm0Yne79PPrfL3vPTepjrw/ee53f9/FN88NkX0DvPPejhnNbnqYOffD9/6uWSD0yfP5X9ntZXfH1s9zJ/lsv8o595Ez/3g4/CYAgnzCtP68HUQw1W6S0A4dUXICuLeC3RK/3FkvSzyQpM01vYAHPvKbKcUDf8/L//IOtrEx49f45HzqzxyKNP8LGP/wb3d3e5fvsujz/xJOfOnmec57imYXtjk/XxmN2DA/aPDtFZyXA04qmnn8balqPDfe7v7nLzzm3K4ZBbL7zI2toak9GQ7Y1NjMlijIeCoMh1TvCKummkrxKH0YYwn1PVDc5D6xyZ0myvT0BrvG1xbc14NMApze7BPi/fuM6Fows8/eRTTCYTCq1YW9sgRBYvMXX45cJwPB4zGJbRcEVRjkaoPBfXlqrBuYoQzX2SiZVWYLWmdRIjg15KPJUO+FYYT6NNx+pUVcXR0RGLxUJ6Wb0A4SwzXe/jorZYNLOqYlE1HE5nPP/CJa69fENieZzHB1BmGVWRgF5/PiRA5JxjsVisxLQA6J5Rz2AwoCgK6rrpMkRTvueq2+1S+tvNy5B6pM3KgjltjJz2CFP3AAAgAElEQVRkYbtIEbX6AZgMxFBgnUNpRV7knQFS0zQCqr1HK43pyW3bKOPss4npvDtjnRPgrc8cp57Y9Np0DVbY1yAMapozK7J8vdwoSOA4Obj2Gd3+des7HifDqLpuJA80Ast0Pb33HB8fd2ZOaQ73wU86Vge+I5vXj0HpvlCxlzZ0QKnt5Lt98KXwwUX5bb93dClFXr3HvmNDRaKb7nvCdpKvKr24AsJUXOA557l7+zqDImM4GrJYTMmNqGt9iJnH3vfGGKNxMjrAG4g9qkGY7pOfl+k+o+JmSRCDqM4hOrTCpqZe8yCyYKORLtngJX9YEeNU4/FV6J6F7pkwq9m8/eeyP3fkPSK4j4dwVuaz9h4TZPPARQM3E5/b7lzC0u36ZHRN/7kFOinyaT389em9a7z+bx9y6c9yClgfwvIvXGLxwRu8/tev0rj2QQ/ntE7rt7Q+ePezXPhzV/hvz/1OfvA/raQtTyvUuW3U44896OF96apt8Z+6iHrsPOr8+Qc9mpV6uMFqAgKREXo1aVdYrt4i09BRAyuSQK1Uj9EIYmiiMpzJqKwlC4b/71/9PLeu3+SP/KHvwGT3uLd7TFVZ5osjts9foHUt946O2JyM0RpG4xF5kXPr7j3mdcPW5ibz6TFlWbA2HjMZDahmM+7v7nPmzDaTyYTLLzxPfaHi7PYZlIciL7C2wQYx8smKAo8lN1l8QHTsHS2jG65HmzxKCDM210cczxfcOzyg9ZatrS3KogAVmE6P0Rom4xFKSU5mZjK00pL5GqWNZTRhMnERGKwVJtpIP2vwBbZ1smBUYsxjjBbg2kAI4s7rg/RTts5SNw2tja69zmHblvlsynw+g0Dse3VoZfBB09aWKmbGVsFzNK+4eu06116+zp179/FB4Vvp+c2LfAWc6tgb2M8ITeBtNRpmOS9sBGzJEkYrRWYM+WgkoF5pijzvokycddAZIC0lohBOMDYJ0CZWie49QYiwtAHddzFN7GqyQfbR/CgQXgE6EniUufxKqWUCaH3josSiJwmwd0sJbcr4Tb2wCeRmWSagMzMrx03/Nlkm96LHKAMrQDcdZwXg957DFcdiraWPu+dG3IH/+Bz3I2tWrp1azSVNAL/PUKdrbpRkm4YIwJOUuZsfaOkv7hhgoqQ3ZZz67hxNZjrAhOrdp5OfVb1jeO/RwaNivFb0Nacc5Ny8eS1eCxONj+K8CSKVV924YlyREtOlxNoq7Qm2E6yTpM6rm35L6X4HtJWK/bEqSv1F0tyxsCH2yHbnFSKoVStz/CRj2r/Hr5jvaSMFiYVK4DMQxDAu3Q2TdW7iOh5HG9P13K5c5hObIel7Jxnl03r46/78kG/86av88nvunUqCH5Lyn3meK/9wxh+f7fLpvWsPejindVpfspo3FT9+45f48Z9Yfu+7H/0G/mp253P+zlPf2pL9nt/5ZRjdb30d/d1f5tOfPc979j7M9z/+zfzQn7YPFTB/uMEqq5Ky/gLn5Gs6yZm8uPsdiEYlSouD5krvmsZaYf+cNczbwPs/8gmqxvLt3/q7GBRjWlezvrHN2mRCU9dcu36VxZkznD93BlvJ4v78+XMcT2fM5hVV1RLaho2NCevDEco7Mq05d/YcWik2NjZoW8ull17i8UefQJkMryAflDRNi8oMRikGQ5FQBu/BB/I8I4tsBUoW9N61aAXDPOPCubOUgwGDwYC18ZjpdApAbjT6zNmuL9FojbcW3+u1S0CnyHN0nsua0clqUZlMMhB9ik/xOCd5kjqOR1xeNbZ1VHWFdY66FRMmQottG9rWspjNwFmCEsmhdYDyZFr+3TpoguLm3V0+c/Ei12/cYr5YgBKDorwoxTQqy9AkdkZ1pizJxCctmhP4OilP1FqTZ8vYmvT7zloyk5Fpw7AsGRQFbSkO0E3TCIvsltmO8vsa52wHkgU0p/cMKwxXAiZKL6WyK319SsxoAvLz+XwugFMtXYMTC9lJZ0/IhPs9tyfBYMcyChom9QYqbbBe+gHrul66BUdAmCTNPkbIpHM1Rd6xncnYqaoqAmCyjLZdZqD259lJti9tRvkeW9mXWifA2z+3dC1Sv2+ay+k16XqnsfXlwEHLz3yQOa386udKlkdAbMUpOCRCXGmMUTirOgbS+158EatS1MTAdp9TpGsZP6KCxbuoEIg7GMNhTvDSt661bLA5F02XkGgdBaggz/PSCDgZQclnYHuCUe4o/aje9QFxx1VpQ8SjjBHjKQJB9L5Lf9/gl5+tIZreEeS94zFD+syNpOfJfuGTn999CX3q5VbpGQghAmSPtW23kSbRVS20LUVmyCJjn56F9Aymed80zfJZe5XNztN6uOvTe9f4/e99I9/5y5f5/j83fuh2+387Vbh7l+/7vxv+yc3feNBDOa3TeiD1z279Kv/s8/z8m//tW/m2n78MwLc1C97+Nx9wFFdd83d+8Cb2NezT/u/TA14+egGAH7/xSxz/5Lv45vYy3/Vjr/sSD/K11cMNVl9lcbP80SogDSF0ofZp8ZK+LyUruxWw6yyGgPcGggaV06qCj3ziEvf2jvnuP/ydvPlNX8N4oDDKcfvWTVrnOFrMWNxYcHZ7izNbWzS1ZTQoGZYDqkVNvZhTH885rC1nN7cYb25SZobpbCqAxEkv6vrmOtpofB2o6xYKTVU1ZJmhOp52C0XvPIN8aQCUYjyUiiyW0lit2VpfZzAcEpxHh8QiaQ7399FKAJoK0DYNdVWTev+Gw8FS1qg18/mcoizFDCqEyEIGsjzHOYutZSzKCIPjvaN1jtm8ipEeliSeNTi0d9i6wrmWPMvxaOrWEbShqi3YltY69vYPOZrN+Y1PfpKbt+6A0mT5gLptKYqC8WQSM1CRPjrvUGEJUNN86INDoANfKwyjtWgti9tMG7xztI2l8XXnnuu8l77WeC55EECWZdlSru09trWSr6s0WZ6TZQajDTrPujnYgcpANLtSHZACsNGV2GiDi7Le9JVneSfBTaCw37uZGND0jCTWLz0H6TgJ4GmlxL02bTx4Yc0kA1N6M7U2cWw9kBflnVlRiHOxMd21qutaZPhZBr0e0cSodU7LPdYUlvLgeKE659f+85v6ZBNgTVmrSa4My8zbfkRPMqXquyCn6J90HROQ0R0hGmjrirIsCU76Z/Msp7XyDBgjuaJGpzkYOkCb5lwfmCdwLecaO+dV6mWVz6WAR2v+f/beNNiy7CoT+/Zwzrn3viFHZWWVRoSExKBmcoM6gHaDwUC4O0wb29F0/3BE/7ChadsYS21wY6CZItoW3SawHQbjsOkwDnB344kOMxgxipaEAiGjsUollVSqysrKzDfcd4cz7ME/1l77rHPezaySKCqzirMist57955hn332ObW//X3rWwjeJXdkDSiS8upk1OSdz6xqBG1LoNFDaYMYqSyVLUo0TYcQPbS2VGoHwMnpKakqjM33O6YxgmT+VJoCs8UCbdtivdlkNtjEQLxtL1rpWdXYs6WR37eRt+0XT8asq1xI5P43ivrHB4/oPYFLZnR97zbNR3HJpdoYg6qq8n0tigIPPfQQqqrCxz72MSyXyylf9SUc7731KN6LR/HbP/kl+L9+7BKQ3plTvIjRdfg3f/IJ/NbND97vlkwxxQMbv3vzQ/hdUG3hfzzbxxe8/QgA8IbiMn7mh5JhU1G+OHmwXYfv/AeP4xduvPtz2v3nnn4X/okpsP17Cn/zRx9+8dp9l1C7pLUPQLwOwCf/wlu+GJ/+NElNzuX+peilZClGK/g0WSI2KVuRJHYjl8XRBlAWSlliE32H6La4cmGG7/j2fwPf9q3fiI995P3Yrs9w/VWvxJOf+TTmZYGrly4idC0uHF7AhcNL0MrCaI2qKMjRdr1G6x1gDIqKXF7X6zWeeuozeNOb3gTy3oz40Ic/jGdv3cblq1fx8PVHcHpyiitXrlDtwhjQ1TUKY7GoygQqejYnS0JtifliH9VsBmOSbDoElGmymtln0V8+T1aBoiS5KNeuJVfaEj5EBB9QFhVsYdG5DtttDRcj1WXUFq0LWK7WWG82OFtvAEV1WYMPMLHDZrOG9xFBKXQhovMKARpdiLhzcobVtsbZaoP1psYTT34an3jicRhNDqk+AYDMmiidWFWQwUpiSaUUVQGp/IxD17WZlbHJYbluGhTGYjabZckwsafdQF7KfQakRYEEhqqqyt8557DdbrND7kB+qPsaoQzWTAKkckxzm5VScN5BaVH3NU3GC1vk7bhsDQMjZhgl47tLdssyaabWJOvqnEPXdZjNaOEi9ogDnevgPdUKNuk6MmuWAIUPgeqypr4hhpZKu3Rdl9nN56xNa82ghq0E3HyvGJhL8Mnb8DgBeimoZJo5uO+cc4nZo++sMXjt616LC4f7ODk5xcnxEZZnS7QNlXW6cHiIbV2nHEpSCHhP+SxejBe+3rH0Fem5oxdWD/CojIsTTLnM2Y0IPubt2QEaMIDWWQpsjYGxJZx3JJlVFvPZHLYosFye5RI5UDYTrUCvSpnNZ0ktEbMJF2KEjh4mekQEgCXCMQKRytbwoQJ6gMr/U5OAdfy3lL9rkFGbgoKPSTYcYwKoCWgqeqbICEwjqaBz+9k12yTDu7Is8cwzz+Tx8vDDD+Nd//IPAODzADxxbvBN8XzidQA++fo3fBU+9anPvOgn/6brX4p//p1XMdVDfPEiPvEpfPt/fxu/8cwH7ndTppjiJRuloUW2P/n8N+HaXwL0q678mTkOxxs38L0/vcTPPf2nLyWllEKhLT78BV+Ah97+VX8mgFXtXcT8294G3OP/zQ82s5pizJRxjFfrMZoU9pNhIcUT1IDVAJhlgEJUSWSnATszWG5q/C+/+Mt46skn8S3f9K/idQ+/GrdOj+BUgdXWQWGFwij4uMLe/iVYo8lRtmkxKyxsNUOhFFykSbEPEfABr3n1a6jWZAzovMfh4SGazkEbg+V6hU3bYfmZpxCdw8HeApcvXIAPEeVsDuccZnMCpHXTpPINBhEa81mJqipRllRCxnUdtDYwtkBEz1DJiSAHlz1hBq8sSxRFleSfEa7z0IbyVX0IuVpi8B1a59G5Dk1LDKnWGm3n0LYt2mYN1zaANghQaF2Eh0bjIk5XGxydnmG1qbHZtnAh4tadI2hlcxkRaywKWxIb5IGY6j2SVNbDR8B3rgeEhuR+3jkE72GNxayaASDAqbTG4f4BAGTAV9d1ZiMBnHMDBogBtAkksvSV2Zqzs7NshMR9Wad6tC4Boq7rqI3GIJqYZb0ZrDLjrnqAJt2FlSbGiWu0cpuU6h10x6ZFfM3MOmY5sPPZbEyWXsnOrUbDhb4UT1QK2hqoxEozk+WcI7m5APRsWEXtQc/maj0A8tw+bjuAVIKol2ozg8zXJvdnsMz/+DsGxJJN5r7Oua8hoGvbxORqxBBS/rRC27TYrJYwcJiXBsWVC5iXBsuzJYyxuH79IWw2GyxXK2zrFk3TgsqnaKi08CUZVmaVxwZggcvlIHWfUtC6ELnXFt6HxGqHpLUlx9wYPO2nU15z+ppr4rK7dowdmiagaTRicLDGwCPlOUcFFzxC2pYBakjMNZRBgOvbx/phxa/TmKW/mVEFKM9WLELkHN2RKma4SEqLdk6amQ0WG/lDUM1dRWV0oPtngBck+N4vl8t8HpbOTwZLL/34jWc+gO/62bfiv/v3JqfgFyPCxx7D3/n5bgKqU0zxpww2InvTox8EHgW++fqX4Yf9B2FNwJt/4i0vGAiMN27gh39684IAVYD+H9z6Dl/w0Y/gd95+gDf9xduY/42//IIc+7OJBxusjpgXmY82/hyJNaR51PB7pVQ2N+nzy/ocK7I38clltoBWJh2zhDcR73zX+3Dj2Tv41m/5Rrz2816DV+gZzk6PsNqssKgKzM0Mmy6iPjvF0dERFvM5rly6hMoazGcFFuUCwZP01Kfcz816iy44HF68hNe97vPx8COvxtPP3MR8sUDrT3F06xZiCDg+XeLk5BTXrl5FOauwP5+TSW/nYIsSyoRkmFTBWnIq3jRtyiMFirKCTUYtAQw8LHTqM57MG6uy2+Z6vSaA6hwAkix6lybGKkJpCygNHwOcD6ibDuvNFqvNFk3n4FwDFyKapkbTNMgmQLZAMZ+j2TY4Xh7j5GyD09Ua622NCIOT5RJN06KqZgjBo0isERBRFon1poRaKukRVTJ6MtklVDJszEDyuGGnWpYEM7BhNpUlwlkaKia3WYKamLuiKIDELB4eHGBvscjjkHPl2q7Dpt5itVrlSbSHAmIvheU2AcnkyVqqtevcQCWQr0FreE9MMF+HBLx8zEG+NucGqt5BWIFkvPL8vVSyN2ziYzA7pdL1SRMm5z0tdAhjIwCYzeaoUj1flujKPFMJMvkZle7LcrGAr4ENo1h6TOMr5u8YJHKZG2ZZB3JjIDtV26Kgmr8x0OKGVlieHOHkzk0c7O/BKAVbWOzPK2htoKLH5YuH2FvM0TqPW7fv4OR0hag1rC3IyRlkiobYUq1haMr5DlRqRSsD6L6UDF9rCB7OBWgdycGc+4Xfc+jzPznHmfJLAWhKF2C1hAaxmwrpuoyCMQoaIZfnUQjkBqwIAPuOSvT4GICU458ev8Sg97mq3KL8/k0v1TGDuitf/FwkybATZZ2UGH8DJU2SL4cIOPg8BvnZl4ua0lQLINZ8ipd+/MLT78b2f/6L+Pm/dwxcunS/m/OyjrN33sQvPP3o/W7GFFO87OLXnvlj/BoArTS+9wf2svLzB757/rkbG3XdC8aojiPEgK+78258yXtfi9+yv4fZv/11L/g57hUPNlhNcS/57/h3xDhY6aeJvFC98WYAGkdsCpUdjUBiEmiyDjgXcHy2xWJW4gOPfhqffPoX8bVv/Uq85YvfBASPrgm4ePECmqDw6BOfphIuiDhabXFrucSVy5fw8CuuwoNKZRTGICoNY0u02xpHRye4c/sEOuWS1U2DrvX42Mceg5mRwU/TOcT1FmerJ3ByehFv+LzPg03yUymPdK2DVprMRwBoa1DOZvAxok1S0phkdEpTbhiX+WmaBqqLWCTARRN+R+YzLEdE6FkU5+HhEKKGDxFN1+L0bIVN3QJKY5PyNzvnsW0DXAwoygKFKlA3HW4fn+D20SnW2y3WNeUdGgusz84wn8+gYoAxsyTt7U1aXOsyQFQAyrLEfD6nvN90X8esYy+lJIDH+Zd9bdUGAJJ8FSmf1cN5l2WyEmxVVYWubeGdw3w+R2F6SW8GyVWFg/19hBixqWscHBzk8jnRk2ySTZtkPuveYgEVFaKUsUswhyQhVRgws0VRDExsJCBgkCbBamEtTNS5vEjTNAPQDoXcn8zIcjAY5H7TAuDzeKzrOvUZLQAwoGTgyDF2bDXWZGficX4jt4HPzWWFuP9y7q/IT5U5o9xX3hObmFUYQDYOKq3BQ1evoCg0gu+wvzdD17YoSitk6R5GR8yqEnt7BRbzBfb3jnGyXGFV10AyUWrqJoH3COV71jfq/l3G+blSjk3X4BHjkC3XWiP4vj9UAqqIyHVjlQK0orJS1qbcWaWhrU45ugFBeRitCHeC3n9QioBi0DDaILiO3oEpv5kNkyIDVcG0xvH7VtzbXTLvcWRAG/vjcbqC2aGUUSGSY3FKAYjomXRWI8iFDfk8SDOuKV7a8cs3/hAf+dFn8O/M3oC3/9AjQFXd7yZNMcUUU3zWEWLAO57+nfz3//GOV+KbFxv8xPdfpw8We8+PdW0a/Af/4HH8r09/bjmqzzc+ePQpfPnvXMUj7/l9/OYPfilwcPCnP+h2+5ybPNhg9S5M6t0mQXkVX0zcgd5N0wxYCpCDrSbGgl02EYiV9Ck1a7Z/gLrrYHSBbuXw67/5LpwcneKR69dw/doVPHt7iXJW4PbRETb1FsZanJ6cwFiF69ev4cazt/DIlau4dHgBpTUILmC73UArjSuXr+H05BQnJ6dYLPawqOY4WS6xv3+I06bG2dEJXveqV+Jwb47YtXjzG9+Irm1wdHSMxXyOsrAoCk1yU9fAKgMojaIskqyQrpfklRpFUWY5qXcebd0AIWJb14AKlC+pdJ7gFUUBpQ2CTyYnilhWqq3q4ALQdh7btkGTmCyVDFs6H1B3HuvWoQsAuhb10RlW61UyYepQtx1iJNC5Xq+x3ZyhsBZWk7GPUhqIKa8vRFijYUoDrSogMWMEbiKxQRiy5xyc66iUQlmWxBQ2DZWjCcRM21S3MuqIaCxCKIgt1D3raguLtm2wXq8RY8Rms8bBwQH29vZgbc9QhiQrZVA9qyrM53Maj4k9apoG2+02g1bnHM7OzpLcNrn8aqobzEwoFyQpigLW2MFEnuWjMi/UCOaUc1qVUihsgdL0LGer2vyceU8lijj/ULr/MlvJxw2xr6cqmUsGXpvNZgCUZSkZlmxKB19jDExZDK5DMol8f2XeqyzXw/1RluVAlt2DQEdOxdzWSLnNCGQgtFgscP3h61DRAcHB6AhXFujahoyENOVKRudI0t616DwZj2ljEEOEsQauI/muZPO5vTJ/tl9MUVCgEjnMBnrn0+JJXwoJimoYk9JWAFmkHHVP7LyKARoRXXAANGIETGJeNSK1VbFbEml7dQwpRz59lsZqBKC0AKRs/ZsYXWV0Bpr0rsEAdMoxOmZbx5/Th/TPB4+QFQ/Iea0SGPO4l2OXF4x4EY8XMXa9F6Z4acdHjp7Ej+BJzH/46/F3334BuHz5fjfp5RVNg4889goAE7M6xRQvVjx6/BQePX4K/+1/SHOeT3/VG7D/FXswX/3lO7dv/8W7EF3E33/vtT9zoMrxmbPb+MzZbXz9j0T86tfS/8erb/5XgNnsczre3/rHH8Ivf8e9t3mwwaqYzPQf3X2FISZwcH47Yg/4W1IcqpTLCQBJGpnyo1QkGVpARNs6aFug8xEurea/5/0fwsULT+LShUNcv3YVi705jNXkYplYkqI02DxxC0Y9i0/sPYuHrl4DXIerly5hf1ZhXhUorcfB/mVcv/ZKQCncObqNa6+4huuveQ1ON2t453F4uIfjoztwPuCZ27fRtS3WqxVON2s0dY2Dg0NYY1Bqg6ogkNq45GJaFqiqkoxftIK1BWxJICV4j9IYaKsxXxwAoFqHMcl+ASrtAU+SQB8COueT5JPcf53zaLsOddehLAoYW8BFhbk1aDqPUDdwUFhuG2y2G3RtBxc8mrpG3bRwzmM+m6GazXB0dAxjLAprURY2TT4D2oZMi2yS85ZlCZsmo9F5NEl+qrWGLYa5mgTONJDMZy5euABAoU3lWSSYI0DHOcAKxtg02eUaoRqt79C0DTrXwXUOdUNS3MWC6tiqxHIZY0myGSNiiGhaD2M0rLaICaTN5jOUVYk9t5dzM7u2Rec6ykXuOrgYkzsqAWptyEyqsMSkKq1hEoBSMSZZqIJ3nnIwqxm0JSY9hggXuyylJtacAGQQrGXdNAQUIlJepUJZKsRIea8hAtF7qnOZ3KKZQYyRy/mEtOgBAMxwkWs0wK7d5KDLxlj8mDLgkPVx+Xkel6aJMeZFgKZpMniVYJAZNumGDGjotGgTvAc0tTWAwObBwSG6psZ6dYYOGvODC+Si7T1mRYnGOZwtyRhsu22xqVvESIsIAb30WjL7Yzns+J0mc3AlUy2VAVyrRiWqMzsLk76fmNW0j3MOKpLEKCL0dYUVbae0hjEJZ8YIrQK9F2OAgWD1lSIQGxxi7Bl2IJ67Bn7/Uh6syioVBsSZCR69wzOgVenquM9CSHmsKuNnyq9FfsZ5kYavWaYADNQERUE1cad42cX33/gtqP/qG/AN4Sl84T98y/1uzssm4rO38E1HL7yccIoppnjuCImAedV7HsWXfvzz8F/+0vlFowiFv3b6ODrvcD8Wld536zFc/d/p93/2u4/gm3/y9Z/TcQKeeyH5wQarGE7qZMhV+fz3XVbPVZrgZNMO/jzGZDSj06QtIEaVv+PcPWJeiDJwkepHrm+d4uadM9w8XuPhh65hf2+B+bxCcAEqAk3t0TQbFGWBm3dq3LizhXIe1y+vsV9VeP2rXwmjAjRWKAsDaxVMYYHg4eol2rpB5x183cKWJaJV+NRnbuLW7Vu4cuUqbt16FnvzGbZeodlucfvGM/jCN38Brl65DISI2ayiUjUAEB2MIuCaWTEFzKsS88Uc2hhs6xabbY22c4hRo/MOraPc0ACFpunQMNPlY+7DEEMyqAGUMigKC4SIZrvFuq5xdLLEtvXwIcDFiM22TjmxAUVRwhRFdtMtigI+eLRt72wbEqPFNT7pFhJIiopklQoahSEQl++5AWD6nFBmNUMyp/E+ALqf3PdjitxXteb6pyYZN2moQNJZlaSws6rC3mIPQCotEiMMDLU35SirGCnvMQFLbam0jUvXo7VCjKnm63yGrutQJYlt13ZomhbNaoMYiWXqDDnzllWR80HbpkH0AbZLeXs+0H1zHl1se/mrDwMAxYZbxlBtX+89oBX2FgdUxseRO3Avh+Z6oCo9J/wMIj0rZgAatKZnCkgLJZYkwQRUqWYmy3+ZMe3VED7nIXIb+R5x+8fAdPxvHAwAqWSLJYkrS6Z9wLZu8MlPP4lqVmG7WSGGgP39fewdXkS7beGh0HUBp2cr3Dk6QecDoCyUtQgpp1srDej+WqQke9xOyUTL2rYMKgf5vOiBJT99LCOOiANgy2GMyYAxhJBSJCKc53ZwGagAbYSMN4aUo8oNBxA9tBouFIzPR3+TSVYPqPv3LxesHadpmESdOu8HedrKJFfxwFL8XvLM95/bwhJgPi4vALFcfT+VvZri5Rnfd+OdWJQz/PL3zvA1/2gyXppiiilePvGBO5/EN+OT97sZ94zvOPl9/O2/7/AflWd4zQ995fPe7wNv+zCeuHz8nNs98GAV2M2mDmV0Q5C6S2YmJ1b5eEnOphQxqTHysfi8Aiynn11ihzQoz+v49Ax102hvbK4AACAASURBVOCha9dw9fJFKlKvFILzqDuPbdsBWmG92eJwvsCTT93Eqx56CJ966iYuHCxQGoPOtdjWWyijEYJH1yYJqdIIKqLpiNHb1BsCnNYhxAU6P8PxscONG89COYflWYvZzKEoLIGZxQUAEaFtUJRzzOb7cM6h7RwAi9op2GARo0LrDBxKNK5G3XTYNl1yhCWZcAgADDn0An3amvfEtroItL6BshZn6y2euXkLq+0WnVPkfOw9Nus1lmdn8MGjLKkuoms7nJye5HxOk/pgzKzxPTzHuGkFawoUpq9pyveYy8UwAGBgI2WlEtiUZYnZbDYYN3kSrBV8S6zNfD5PRjk6g38YqpXatG2uo6q0IuOnssgTa267iuEc+yYlr2QARYBV6+Oc9+m9w3qzxrZWuUxHadMiRJrUc7TeQQU/6BMVyVzHalICmMRGhxBgCourhwfQxmK92iDWvbyYXYW5/RJcsmyX71FRFLm9LMvd29vr+1qT6J5zdselau7Gjsn7xf0ua6VKR+NdDrQMpK0pSV4t5MPUB8Dt4yMYbegeAvAeWJ4+Ctf59Oy0xHhrTTVLc0VhndlBvg7OmRzn1nKbZaqCNPYaL8Tx7/JfZjKVMElL/SHdrLl/ZQ5vVP3CDL/nKDecpb55GSKNy1RaR/SjXODZJe2VUuB7yW8Hx4o41156VvrtpbSb7zOPnTHrLt8VsibvFC/P2LQ1/q3VH+Lh7/k43v11+5h9+4trAPJyi3h6dr+bMMUUU7xEovMOP/PU7+OXZvu48j3P4lp5Ab/+fW8GFot7yoN/vZxj2Tx3GbQHHqzeC6jyBBDYXW5E7i8nUz0A3X2e8bbyb3JGbREQUVhiAk/PznC2WmG9vobr168nF02FologeJdYMWCzbVAog5t3TnD76ASLqkIMDlCUd9o4KqdhTQGjLLShCWTnO/hAOWhFqXBydgdlWeLCwQynp8fwzuKR69dwuvbwWGJ/fw+n6w43bp6iLC0Wizn29gyW9TLlsSZAUETcPL6Nuu3Qdh3atsN6U2O93aBpqX5rDEDrPZnfJFDDfccyPecdnAvwEehCQNN5tM7BBaJRCmPgUjmXtmmoLI61KIsCm/UG2802y/+6toPz3WACzvck51QK0yMGhSFGBAFcoHqDGKU1bOpL55yor0vSWmNskqj2MkPOT+QJvo9UaiiGSGVKQCCk6zpUFQFvbQyVDwkBRus+zzQOa36S/DRkRowZIZY1ZpCsqe7kYjHHbDZLJVAU6nqLzXaNuq6x3W5RFRWK1Ke2KFAkIyMuUcRsVc7V1CqXGNFKpXJEHiaBzjaxn0op1OzmjOFCwbg0jJS38nfWaszncywWiwyI5/M5lLGIAPb29uCcw/HxMUIImM1mg3xbBpfjZ5CfVQlouS13K2kjXZGdd9BOIaSSUor7QmtoTf2nFBnybOsWbVOTQzUUYCxsKrVC5acI7FlrUKUFmE45+B3sqASm43fNrveQHPfyGLKvdypMdhwn76+pTM2utshjy37dVSt2vMAybn9mcgWg1im1Qo/e0bKPeIGBjxtCSM/B7j6R55f9LZ3A2c16s9mc65MpXl6xaWs83t7AQ7+i8eQdYO9L9++a6zXFveMLf2bKVZ1iiik+uzipVzipV3gcN3DwvR/FDz7y9Xjb9+xBXbt2btv4xKfwOJ7bXAl4CYDVcYwnRuPJ07g0wjhfbAxEVbxLOQVxrryfUuh8MonRGl30UCHlCirg2dt3cLZeoyxKHOzv4+LFi6iKgkq9xAhjC8yqGbYdSVGd71L9P0OgRpcoywpRKXQuQgdqowMp6KIyaJ1C0wUsVyvcubMEQEZhzaduYG8xQ1WU2N/fw97+AjpGaKMxq0qUc5JzRpb/aY26bdG6LjEnyG6/2hrKfQwBzqc8xaZD5zYk1VPsg0LSa2Ko6NjUTg1VFDAqIkaP5ekp7hwdoes67C0WmM3nqVbnDMdHx6i3NWwCE+v1GpvtGvP5nKR7bDCklMhDVQOTnRioj+S4QPIvliUwQnKC9Sm3UoWYpL0RKjF1ffmWNBEGySxjJDfboHxmlJnJW6/XZHpkLcmEo6Y2OY+YAB6zvBlYUUsHDBCzkN57lGUJRKBpWigFVFWRmdTZrMLB4X42aTo9WdJCQGJknajrKR1SlabSRFZreOfQeao9SYwg3cu261DXDUmxU6JgBBIjGwegmkOykwzg6Tp78CLrvbq2y7mqnGvKhkgSGMnc1V3ATDKp40UrmYvM+/Zt9vDBCUaVcmzLsgSAnNOc3gIwRYnWkSy5qko47+HbFtAaVhFbWhQFVARccoqmnN8hIxpCyIZUEpTxvZeLY+N3kgSPDOQlYJNmVZJF7Z+HfkyP2d++j86/O3l/Y3qwL9sjt5N9zcZgYzA8fmfzz13vdak84PeN3E6qLu4F1jPzLtoyxcs7Qgx45R88hq/5+Bfi/16+G8U3vfV+N2mKKaaY4s9d/MjTv4XwX/8V/Gc/sE8sq4jHfuYUv3DjfXjta1/1nMd5sMGqmPwA/Sq6nKyNSyTIlfxdE9wBKwE2FCK3XJUm5uxIyQnOAIFFIBL7FAICOK81ATxt0IUAt95CqRrHp0scnZzi4oULuHjhEAd7++jaFm2qcTgrKngAukyOu5HMXTiXLygHFzwnfREijT7n92mj0bZNakuEbyM610CpBs/eWQIxoqwKlGUBYxSKskJRlSjKEsZYVIs5ZZZlIENMrjWW2A+tEYOHdp5qMDoHWA1jKI80+kAANUQCtpHSGbUxZFgTAWMVjo9vYb1aQ0Nhb7EHW1CuYGkLdE2LzWpFwD0xnmyWIif4DEytYAwZ+ADkgGq0yZJVHitSHjpmiSRj65zL+/FxJdjLgC8YBN2fWzJ5DKSzlFeySelzlr3K2q88frKjrtIIgQyI6rrObeN9GCgWtoC1BRaLPcSAQZ1TzglmSe48LQ4wyGeXaO98rrXKtVI757DZblEUFcqypBzTEFAYm8vKcL9x33AfMEjt6506dF2Xt+H71vlALHTKKeR7zrVjWTorgY7MWeWffP/5u67rsuOwZHtlOxnM8nkzKNQa2hQI3qFLudrErlNONBCpBqoBbFEiJtMxPj5CRNu22CYLdmMtpRaAapgyi661gUkOujGN3ciuuxD5naP3WT6PuB5eMJGglb8fM47j9yEbbenkshTS82a0MHRKDsHBe3oHgUyPSC2QzJLQv19ZxhtDD5Yl6GagLEszSVAtJczyO14opM0ScBX3X77j5e/83OaSTCMWe4qXf7zr2Y/gX//1N+JLfuuj+OkffSMw1dp9XvF/vu2TuL1Z3u9mTDHFFC+D+LGnfxvrH//L+JEff0P+LDz2OL7LPf93zIMNVrGbPeXfJSAZsxFyBZ7ZEylbG4LYPn8LYk4TdkyGeMKlkxMng8kuBprMC5bseLnEcrXCs7du4/DgEA9fvw4bFBCA1gfYZN5khdyUpKMeUQVAKyqvxOyCLjJY8a5DWdr+umPKjQsplys6uDqg9h0WiwWC10DQmJUL2KKEtiVsNcsT/aIoYUpLJW1cR9LQSJLJznnEqBCh4RGhlQGMgYZHoBkqNDSMJUOUtmkpz7ZpETsPHYG9+QLlrKIamXWN4APqLf0MzJBpjVJrHBwcDBgpyUQBGEzSmRUcuIgmtpIn9XzvzrHqCRTxBJnH0lh+LHNdGTzL/eV4k5Nved6mabBarWCtxXw+HzJswnSmTYDPe4/NZpPLxsh2KaVyaZT5bI4ilXvRWsN5h816A+cdYuhNlADACKmqNirVDsWgfI6LgRZkQOBLgu1S5ADztUlwLx2W6f6EDGClPFfKexmEc0kcCVwkyzp+J8ix0Z8vJqm0HrC9fAzvfb5OpRRmsxlmsxnquhaOzF12OebtutTWqqqgtCF5fLrXMZBsWifQxiCZ2p7+NlqwnpQHPgBailj/GPtFF8kqM/jmcSCvXy7OjfNeJZgfKwbydkjvF7nwF5N0PG/QR17sCyNZNr+jg0LA0PRKPgcsj5bPdH424/C93Rt7pWMomWe7ezGSn0H53PLYmuLPZ7zv1mN4Hx7Dr/3HH8V/uv9l+Pe/29KC6isfud9NeyDj3d/7Mfzt0/ei9VNd4immmOKFiZ96+vfxi3/3g/gfyi/BX/5PZohHS7z31vNPNXjgwaqckAE9u8CTLZmrdrf9eWIvjzdelR+fT54rs3LpO1uS66zPkzuqh6hBJk1k7qJgDbGt3bbGetvibLXB1StXcenCIebVDDqSI2905KDJuZyRkl7BNRYRY/5dA0D0sFYjRjIkKooSERpN06ILHYFHU8BoKueiTYmimkEXFl0A1ei0JcmTNU1klUn1IZ2Dy0xk5Hlr6u+QHUGpTIqFUiFPfustTfrX67MknaVyJSxf7doORVFgtphjvVrjzp07OD4+zoDCaHJ+taUdAEZ57yTYyPdSAAWeIBcFlehpmiYzLHwsntiP7/F4UUK6tEp2f7xoItlF+U8bgy6xi11LoI3bKEHU+PqYgZXGRQCxvk3TiAUGqgXrEkj0McLHiHI+Q5m25zZ3XQdlDJQ1gKbr325WvfNsqpuJiFSbsjcGosWQHkCydLRt2yxvloZGvKDCslrZl9RpmkyWEpAoigKLxSLXceVzMJDlv6UEVj7TkmXnYw4UFAkcM/Bh0Nc0TQaE3nus2i6bddEYM1BKYzav4EjTDB/oWffJUddqDaUiDg4OYK3F2dlZkjYTrJLSXL6WsUS7f3/pvGYmxxMvWBRFkceHHHd8XVJKzf3KP6uqQl3X+XgcUnXA92oMNCUA533l4h+Ac+N5/F7O7/G0yCefX4AN2yKMSmy0eNbOqWMSw6ugBvmvfN28UCX3kWNkij+fcWN1hLet3om3/RdAZUs8+pbX48Jb51NOq4h44wb+31mB9mgCqlNMMcULFyEG3Fgd4a/id6F+UOE3L/2lz2r/BxusjlgVOcEZSMRw3mgDGNZoBHabmYwBg4yx8YcGAVRiFVKSJ4gtiBFQJs32FU23fCoBodOkd7le42yzwu07e7h88SIO9/fhwxzzqkJhDGJ0CAEJFAOATpI7liljKI1WkepvKg0fIpTRMLDEroIkfrYoqUapVvAhktzTOWjTAVqjSH3UdcSmRgQ4R3LKtmtTrUU6L1RfRxORwHTXuYH8siwpL7dzLUKIKCwJ98qqwsxoQCkUVYnlcpkdY1nCWlUVioKkyzJ4Us4gTt7voihQFgXVkxwtQnD+JNBLiGWOo5xQ81hiYMDMWnaXTU7FVVmibpoBWJJy4MyAKZpUs9RVAiXpEpvZu2RAdbYi4yRrLfb392ELMn9yHdVh1VpjsbcHo01aGFjDdR2ip/vEzqks3TWWTJ80VC5l0tYNnOtIts3PCA1lWJ2Kb44UDToBA9m/kgmV+YU9oz1UMQDkuBxAxk0MaCUTzs+pXKRipnGct8njdsyqyfMxgOFjWWszO96l2qP8WfAeQWtEaEQVAU1yXmMtXNciIqLr+sWSsiiofq5NubrOkVGTNXnBh0OCOaDPrR2YF6X3S4ghOU33Y9UYjRCJraaCvnTDlOZ7o6ENM5xUPEYbBaqX7NG2DZzvwHkOsn970MntjUB63ygFhID8DqDzA1A988rvBM7v1lohRG5K/56MkPm5fJ70PMeYT08qCeRSY3JMyPsM8fdYNSFDvjOmmAIAGtfite//KL7h6bfgb/3zJ/Dvft8hcPny/W7WfY/tbz6Gf/j0x+93M6aYYoqXccQY8Q1Hf/BZ7fNgg9URCzpmT3mSN2Zf5SSM/9613zjGE50xa8C/p6YhHyJNvLi8Q+IFUk6bzm61ytDxzzYrrNZn2JvPcPniBVy9dBmH+/sIcNCthjEFjCmR61qmJsVARkY0gVeIMUl24xYwxI4ppaDiEOArpVAqBVWSIy6DJG0LkFSzgbEWsxnJjNerFm2zzZN5gOqT+uAABXK6VRoxJLbNOxSWZH1lUSSek0xrnAlwJqKoSiij4bxHXdc4PV0O2I5sApRYGQZ9xPJhwMzJ+84/O9fliT+zl5IFZbDIwLBtWypBk5gpydhJZqlnSTWqooSP/TH7/unB8+HhIfb39+FjQNM0mQnmBZa263B2dpbbx8Bpu93S9h0ZXlFbWpLrpr4pCpvGYcRqtUKMkYCtkHmyFNxw7mPoJZ0KgGs7bDcbhBjIdZrHc5J1FkVBEEIwZAw+pRkZs9fyOZFjjsrdtBkI8WdlWcLHiDoBeJ3YdKCXOUuzIL6msizPMdqy72XuugSs0ghIja6J7wkvTniV6vhqIPoIHzxU6IAAaK1SbrVHRIBRBgoR2+025fw6eB6PwLn25D5M55c1Qel9QufLi26aHZwttCHnYkQipa3q30cZZCoCfgwQjSGwy59FkCyZFtuG74bcL1GwjypmaXpk6a1mkMk3OmeQDp4BKJXOna4tgdJexCvfx33eeVTiXc2b7JAh53EQqG3yfX63VBDJAk8xBcc7b/4J3gng19/x1fi5n5jA6hRTTDHFgxgPNljFeZnuWG75XPuMQew47gVkx8eRIIYnpYhxwAgxO5klb4gIPjEVmhgRmvhqLLcrYlqPj3H10mVcuXQJ+4s9WBthOmLyjKbyK0hz0uAjlOK8zIC26+ACSWeVitDaJjCZXHIdldHoug7FrMICc9hCA6qANgred5TfuAlwrs25lVAhA7mua8hAKAaUZYnFfEETaAUYY6GUyfeGysAEGG1RlCXKEmgds1vErC1XZ1ieLVF3LVwqmWKMQVBA4zrY0Et5AWLPmsRmju+XzMkE0mKCAtJd6PNBkylM9MQuhxiwqbfEkgFY7O1lNpXzHrnmqo8BVpncjrOzM3hQDqNP4DuPTVC9qbZtsdlssvQSICni2q+xUSqDSZao1nWdTG2AsqxgrUEEEtNcZgDPYC74mECtQYx+kOMoQdEY3BF49rCFOVfLlBlMpXRysdZwiZkOifGTz5Q0LCIwXfQLIQmczWYzeO+x3W4RI4E7l+ruynOHEHI/cnvlIhEz0fK8UrK6S6otFxt2MfLSBIjlzJzfSbV+I7zvEKMnU6XgESPlYCv0cucQAxCSMVoc5sWPnX/lIsqud5uU2Uqp7YCBHbCh55UjkkEe90EIvZmc7DN6X5xPiZBtG48ruYh4txqv8rwMZHcdWymdDbwGZchSmSAZMt+WHYLH0mU5LnicFUmWPsUU4/ilp9+DX/vODwEA/uCR1+NVf/My1Btef59b9SLH8THe8HtP3+9WTDHFFFOciwcbrI4mZ/TRUEY3ZkPHk8Dx72O2ldkDvWv7JDUjdgFZdhYFa5IJACFrU+JzrSJU9FAqIAaFmMCPA6jMSYhYNQ22N5/F0fIMV69cxYX9fcyKArOqQmULGG1gFNVILAyBUqUUTNCwFUmMY/QkF1b8d5pIGgNlaDJeFSWVlPAEWnyMaBoy11ktl9hut7AF1UStqhJVNQOSlJUnzMH73GcAT3hBvxsNQMGYAkp3cM4DSIypSWVTigLHx8dYrdeU5yvcf9mp1rmhbI8lm1JqyhNRydzxRLoTOa2SWYuRzsefZZCrFM7OzjLjxnmhXddl46JWKSyKKu+njM55lnyeznVYni2x3qxHw/g8oyfHcF7YiESdUU6kh429HJnBHQP2vb09GENgfrVaYrVaZYZYgkh5fgb2/TMSzwFaAJnV1ooMwLqO8qB5P24LM5JKUb6ktTZLnul+US3VnFvL5WvU0EFYnluaYsk8YwnYzoGvBM4kiJXPO1+zZIXle0QaQ+nEHvL1cf80bZ3q8yIx2TqDLOdcFrRmAzbdu0hLgCcZ113B+0k2XrLIY/OlzAqL9yGAnP87XnCT139u0U9hcAypXpD35V5tz0oEPTQ56tnb4SJjPkf2Qx6eVyk1kPvm70EeAfK6zi0mjt71U0xxrzipVwCAL/rE/4fLP3mAf/nq23joG0uYr/qy+9yyFydu/MxjOGumWsRTTDHFgxcPNljFcAIynliPV9LHk5a7TWI4gpjERHE+IK32A4PJVZ4EeSrfoIwByX9TzlbsJZdaMGeKp7IMepUi9i8oxEiSXh8D2vUKW+dxtlri4sE+LhwcoisrFNqgTLmZPvjkGEqyOaUUrKGSGIgk8QsB8DGV+BHATLU1OtcibonhYLffzWYD51oyxPHIEkJOCxsbTXXeQccIa4o0KQUxfABcCCiMgbUFuraDDx2QSufwxPrO0RF852DLEiHl9Zp8H2nKyqZAcvIrJ8sDl1B5H0csC+/TmyUZACoDGP5OgoPFYgFjDNbr9aCGauxoO2OpjMvq7Izyf7VGiBHdSL4qJ908pgBm9/pJfNu2aNomgyXq54iubbMBFQMYlkaXZYm27ageqsi9ZeAiQY28TmYVbUGLGuPalTFSqZJmWw/uOY/ZMdMXY+/W3LZtBp+0EBCwWq0GQF0phQCVczolMGJprGQOxyZEXCJIfiYZSfn8yjZKllM6DXN/0b1XiJFMwcbssYqkIrDWQkdeaEh9gV6tqrWmMlM+DMbsGDRKxo/bw2yv3F72mwR/Ulosgbt8RviYfL/G+bzyeWG5sLwffU7pEADKRSNu/9gVe9cCI1I/7QSUoz7cBTTz8bQiH4AYs3x9PJbkewD5nJhiiucVR9szvOnRD+Jbll+GX5p9GPovfNH9btKfaTz2n38AX3/0yfvdjCmmmGKKnfFAg1VNgrWUmwQYRZNJzlMCzk9SgN1S4XMTcsFUjCc3KgHVPDnqdwQUsaO0sh/FvkwA5ClXAqqAj1SnUGnVs66RJ25pD0US4rrZ4lazwcnpKS4enuHqlSu4eHAIbS3gHDGsMcLA0JyYS4SoCJUn7hpa0WS1aSkPUmkFXStYazBfLGBKC2uoHM3+/gIhzHKfxBjgfETsksGQttAqoCosfOfg2w6m0gjOwWqSTCodgZhK7YSIQltEE+BjB4WI6B10adE5h1c+fB3ttkbdNFAgF2AySArg8hXeewSV7pHRKIsyl3zZ1luapKr+vuR8upiY78TmqqjQNm3Pmm7J1CnEHuAZbWFKi6KwmM3mKApL8t0kvXadh3MedWxQJClu1Bqdc2ibFj74fgIPnWTfSICUAHI/TkImiqy1aNsGdd3AJsfWGGOW6sZ0XTEA1cxSPdXWwXUe9bahMkBNmw1+5LgOKV86RgXnfD7mbDZHVZVo29QnRp+TjIZINTYD50srheD758MYk82wJJOXyyolgFwUVWLXaZuuIyZbmSFDJ5nC0hioVAYq5z3GiKqsCAgrAoPGGMCkslSJ7QeAAHo+jdIIGIK07I7sPKADrLFZvYH0pJMDdqD6xQn0V2VFzLdWKMoCwQe4pkagpE2qfQxARy4bFQBtoKERHLnWKigYZXL/FrqANsSUk4t4BBIZrpSC1RbapveYBxB1KovV12tVCvDe5XsylgtboSKQ9837kA3CaGFodyoEjdN+gYXvkaxzzc/QLoZejkf5LubzRsG60itx2I68jz4PsHWW96f3qU65wum/UBFRBYRIC4dUYWvKWZ3is4tffeaP8Vf+yefjy8uP4qd+9I35/fOyihDxs2qO03r93NtOMcUUU9yHeKDBKiNAntwA5xnQXavpd5N/jeVn8ueAfYWYKI2OwTK/ca6U1ubc5CydIGmEVcK6vaxtyDAgSxCVsuhCxPFyiW3T4E51hKuXr+DalauIvoOGQmksrDHQKk0UExDWAGKgHMj1dgPvHGxRYG9vnmWjrusArWDLxOQFRS6kKS8yRmK+XJKV5klvYmqUInfcwloyAVIaEZ7qr0aFEDy0UljM5vBrh029RVEEdK6F1gave/WrceHgEE9+5ik8feNGchwWucLQ8AkwAEmODVE/0bP0s0AMvdtr9D3jRE7FDbZhm52Ag+fcVZJHz+ezXB6Fy3s0TYOTk9M8OY9pksuyUG1SLdlkruSy7JQYcmbbzo0ZDCfsACh/MzG1VVVhPp9nQJDNmwIQVEBTN3CdGxyz/+fPjX06n8+SYgaQznl4v4Vz3bm8TVmPWOaE9t/1zJ6sNcvAiFniGLnsDuB933fMaAdxv/n4AJns2ASeqeZpm/NJ27YFQDnP3GZmSrlOLPcLMbl9Tuw5x2HhHB3RM8WSCSVAlJy2HfUdYdBUZsnYvLDACoZcggmGDHujZBcJFEYdoJGMskhPnxZY6Dvv+v4ylgzPAiKs0fm6Yuxl+LK8ErPuSqmcKy0lzrLPYuSx049XWcdVMrnjd5rMWZXjR457CV752eWQ7+q7PSdyW6WHbtD8udY6L2iN9+O28QJR4EWYKab4LOP9tx/HH6tP4Be/64/wPx6+Fd/6N1ZQ8xL6i998v5v2gsQfvf2j+Nmj99zvZkwxxRRT3DUeaLA6nrQwUNwpDROgczwJGoeUhwLDEjX8992OwxNzoJdVSnnx7lyp83lj47+H1xERo4f3CtvtFpvVGpvtFk3b4PKFQ8wKMgpxwaOwRZIvKpydnqHeEBBx3sFoKg1SVhVCIGbLpNxRrTWaukFMMmafQARP52KaHBbG9GxiiDBaY7lcAgAuX64o71QraGXzdTAjpa3FhYsXcXZjDd+2dC3bLQ4vXMLBwSG+6Isu4OFHXomPf/zjWC6X/SSZ+0Hc0845rNZrqlmagBKDaaUAbYclZ1zo82yR2lOWBsbY1A9qUMfUGEO5u6sV2gSS+Nzy3rI8meWuctyckzbGXp4pt2OQw7mxs9lsACzY2IkBGI9J/p6BFx9TtkWex7kwyHftc1njQBI7NjviY0jGjIE7B+egMhACqG4pl90JIQyMr6RzMgHGkduy7sEY1wbd398ndUAqcwMg58PGSAZXEiDzwor3Hq5ziFrlRYgQArbbLS3ICGClNI0z7ge5SFWYPg9WKZLqc7u5/xnwjxfJjOLFi/6ek6yer5sZZzJMU4rK4tDCkIH3TgBoBWikdrMEOakHRFvKssy1bbmfxmZTvMgg77VcqJAhTZ7G70wO+f4b9wOPOcnCjp+Huex5CwAAIABJREFUYb+dO3xKlxi2i48hZfX8edoij3H5PE4xxecaMUZs2hrfcfu3gf8GePXhK/ArF7Z47V+3MF/xlvvdvD9VtFFPCzlTTDHFAx0PNFgFhmABSEASQ5OU8URxIC0bgcnxcc+zUXEwub8bAyD3HU+mxqzteJLGnzH4OQduFBB5kqY0ogLO1itsNxvc3t/DKy5fxcHBPuZlCa8AeOD0bImz1QrBOeiU02qNgRL1H40xKGARmxZRAW3XgKRxtK3WmiSSifw1RqOwxODGxP503sHaIk3SDbQm+WzXOYRARkFlkeqlViVm+wvYqsTNZ5/Frdt3qEapuwNAYbG3QF3X2ZTHFgWVTMH5MkUSTPAEnftT5jryJN05n/OGeRue+M/nM8TYO9KWZQljDOq6zvLI8SSdQSF/N84THbLswh16ZHzD44fzT8uyzGyYvKZd+4zZqzGbOlYXMKsKYLBvUZQwZpjHyuNRlvgZyzuBvk6sZF4ZvLMMmN2LGQTJXNOiKEgGLMa/zCHVcSjR5+emaXpAyuCYgdcYbBdFAW0MOk9guWmawT1z6XOZH8oLUBloqZSCgF72qiN94gTA5z5ikyXuw8IYKFucu4/yuZd/c24pX9dg3Kcaptz3DDolI8oLLzxG5eKENNW6G3sp47nA3XiBcPw+HINV3uZu778QQkKqfS6sPM+4PM34fX1+8TImJnvYx+PrnGKKzzWeXN7Cly5v4a/+06/A1/7TJ/DdP/ZqIL2XXkoRPvgRvKPc3u9mTDHFFFPcMx54sArskIWJf2OgOt5vvG8cTYbld7v2AXAOuAxKK4zaMG7feJLI29zNWTMDB62hQHmsSikoY9B5j5OzM4QArJsaFw8PsLe/B60Utm1Dk1pFeXTee7jgoYyGR0SpNYrk2EoSS0dutl0Lnxgy/o4n3DFGuM4heJ70aUQo7O0fYD6f02cgmW5RavjOwxQFZmUFWxSw1sB5j6uveAVCjDhdnqHYEji9c+cOHv/EJ3Dz5s3E0FnABAQfUNo+/26XEY28HwxYQuxrpIZAxwkI56St7CrMwGpvby/VBHV9TqMYH3x/uV/kRPz5jB0pXWRgxszhYrHIMmQp6+XzSBZ1bNy0awGE/+6PR+1hgyhmIIGYckl7oCbNd2R7+JpI5jssXTIAG+jBFLePpcEZQAoZtzY9oM//ApVr4vvKtWSJIfTQ2qAsy8xEs6GTBHGScYMu8vMqZaRykSmmxHFZxsckNQFC7yQ8vucsux08+3RBCN7DRap1u2vBa8zEy+ONS/IQywuEOJTVynvNfS7Np/j4sl92vWvG7527jV95DVIeziHHrbxG+fu4D+SzAUWyaNknPL7G++9aqBwaU1FdaN5X9scUU7yQ8Ss3/gj/Qin8s+97AzQU/rdXalz5rq/sNyjKBzPHteuA1Qpf+z/dwJ/ceeJ+t2aKKaaY4p7xQIPVneZHSmfp6pjRAoZAA7g7ayAnVudX5oc/x2BUfi6Bw/hYu47HMc7B4s8GrKxWgNLwgSx+2Qxn2zXYHNU4XZ3hYP8Ae4s5Qoi0LRyc61BVFQ4PDlAWJbXRkjFEVEBRkQuvDxFlOQNKAhplVZEhVPDo2o5MY4zJ562qGYwtKJcvOdciguSRPkCXGkobNL7Dpm2gtUK5mKM5PYMyBS5euoymvYkIhc889RmcnJwgXSw8gGZDtThb04P8QhcIMaBzSeqqABiqydhPWlWqO9oDpzHrKp2Fm6YZ3Mu6rjMDtSsYMI4n/rsYGznpHy+kMLAGMDAnkiwmj1tmy5xz2G63g+OfGyejsdaD+ghp7sTyXJYC83mYGZSOuuPxytJTBs3McI6fP+7fPk/1fNvIDGcoQyVQFnNfSzDcti2sTdL3BGIlC825mrKPffBA6ktmLVkWyxBLaw1lh3VNcxsjyW4lYOI2mWTipCMAM1zQ4PYwUN01Fvh+j8Ehs+25li4v1hgDFXxuJ18jjw+5kMOxa4FM3tsxAN/1PpVjezzm5TiUx5PXyws8RjBO/PnYmVhBZedyGUqp/L6X5+f9ZYmmXe2UY2987CmmeCEixoj33XoMAPD5dwz03/l4/u5XL3w1vuKvnea/7Ze9Ebh06fwxnn0W/kNPDD4zX/RaqIceeuEaGiLc770PCBFv/40L+Pmb78nqkymmmGKKBzkeaLA6nvjzqjl/Jz8fAwM5gQJ2S3PlOfh3AIPJlWRBdwHf8YRtzE7tOscuJk6CbOnsydv54KFiSGYr5D7qmwZdCFjXW1RliSrJWZXWgNbwYMCr4COVlQkAoGiSbsoKs1mVS7Fk0OA9WpfMaLRGABmVrtsWJpDxEcsyuQRL23aIMWC1WmOzWWO1WlObjUHTkNnLerPBZrNBCBHbuk6MkIbSCiGZBxGw9FCKjHWQjKwYaDADRveFjK2gACXuvZzAtm2b8/hYhipddxm4SrmiHFNy0j9mN/mnZLkYdI6ZTymnZbaPmc6iKHbmkDLj2XXdIJ/y/EKJBM5kvNM7vEZ47xCjQVFYGKPhvcv9KtlRyaJJGSldR+/qawwxnEoN2WwGJvyPmVVpxlRVFTrvctkoybBJNtj5Ltc2DolZtNbCFIZYcxcADehUMqlNebtkUqahI5knRR/g+blOYw15IUPDFmkxpAu5dirJmlvEzOoHaL3bfCgiDvot12tVyQNYjCP5fhowwPyu0WR0BkVqBaM1gIgQqRyWioB3Dt7R/TOFBkZsbD7WCGSOx6yM8XuM2yuB7PgY43xReaxdz8r4OZGgmzbqj9HL1WlBh/PPdwFWZqN3LbCMn+ld7PIUU7yQ4YOHXD75147eBfx8//c7fu0a3upunNvv/yln+PGnPzj47G2PXMG3tXd2nufNX3OE6q9/7b0bEyI+8v0fROtpPuOjwjccf5gW8qaYYoopXkLxQIPVMXCIMfalCkZxr4mY/DneZ8xMjZnOux37bvvdTfJ2t/bJz6y1WCwWeYLvvYcPVMYDPNmLES3noMHANQ3W2xpGR5Qpl1QpBa00qpNTaKOgQky1CWnyZrRGURZQWlNe6WyGsqygTcrJcw7r9RrGGDRti6ammputc/CRzJjarsvzS+c9Cmuhjcbp6RJGK8qTjedrJmaQZQyoqAeAMM6BA9jJtq6bzNAQEOyBVFEU8J6knBrD/Es+H4Mxay3m8zmqqgKAbLZzN5kh3/9dk2AZY8Ma+VPuL68B6OvEym0kmJXnH4/T84Y+ks0KyfGX6u5EBnlGJSCnoHWR5a3jnEKZEypBgFIa1haDdsi8XQnG5IICgxK+Z1pr+IYk6jLXMt+3VIrGB5HDHSMQPIwyqfwT3XClFLj+k4+8sEPFgaMC4GN+jqqqQlmWdE26r3XrXJcAJzlxO0eLLgT6YzJJ0oPr4vvedR2iAspUxmdwPYFG93iRAsA5c66cbysk6/TIk2pARSrjFWMEQoROIBhhbE51HrDK+8L3a7xYJvv/Xs8Ej2O5mDFcNBk+P2P2VI7f8fihRPn+PJz7rLWGr7eDtvDYlDHs493P7L2e4ymmeDHibc+883lv+46nfwfvuMt3X/6uz8fX/+Hjz3mMn7r1RxM4nWKKKV7y8UCDVTkJ4t8DhhN73o7KpxDuIVBLE9kQkCaceWtwsVO27wDOg6pd4HI8GePPZFvG4HjMVoyBlJzYVVWVwZTWOjNvAYBSBv0kLCJGIOp0Xg3K2fQBLjQIMdWITOVUdDYuIRCooQBD8uIYATZZovayAY9GUZTYbms4RzJMFzwCIlQEApDqueok0SVQ5BEQggJXvnSeGbcEaALJj5UPgBpKoXlyy264XdehrusMoqQRj2S/e1AxuMtUo1SRa7IKDjNFNW+btsV2u0EMfa7beNK9S1Z5t4UQ+b0EupLR4Qm43I+BHF+PdE8NgVxw2bxoDMIlqNC6l/IyM8v3khnObBI0yjNksC1BqgTXs9ksSWh74yXnXJLm2oGLLJ+Dj8FuxvwZAzznXc7FllJi6d4q20b7x3xMqTggYDk8B58zCFmuBFZkDJZAZ/B9Xrh411hrUZjeTIpKJQ+dl3lxIBqSiAcINj2E/K4KYjFGqf59xICYj0M5wTSeQ+iNsejaCFzz80+MKykQxkZPu8bmLsZ0XJ5IjneWi6u0UIYRGOZWDKS7PPbToiK3gyC32IefI7GdEp9Luf3d3q/jhZ7h931OsnyP7wLOU0zxUoz3334c78dzg9UppphiipdDPPBgVf4MSSJ3jz3y9v2kOSTA2tdB7IFESJPHoYnIOJeUjnN+Vf5u0jv+fcyY7TqePNcudiLm8wzz+/J2INkjFDPPEVAKJtW5DDEiIEBH0XEMrGxJtUz5OnhBQGkq8RIiSYeVoTquypC8MkaoxLZBpXqn8NDGoKpmCLEHTogKRvf5jTQB3s0ASbm1BPES5Ekwx2BIa5UlnONr5Kl127Y4XZ5mcEVAfmgqtKtN8l7sYs3H93AsCx8vVMjrkqCY28KglXMsGaiOF1MYyGltYG1vIiQn6AQmhyxtL+sNGdxIV2LvfZZGc19xu8ZSddkOyTpKCagc75K9DQJMsOEVg2xpHMT9zu7Fkomk42LgNCy/4zbJtvay3tQvUdRfFcdHqmFqrUVZlKhdg+AcgvcDCfNiPs+A1nkHOMrzRgxZbiz7AFD5niilxLmHRkGDhTPRLq2JPc7P/2gMS9C9i/XcJd/NC4FCZr9r0W38npTs5lhiu0tCLM+VNsqfIQ5f7LxYQ4D4/OLgvYHnbvffCaxOMcUUU0wxxUsvHmiwKsEjTzQ8IiCAANCDWK5hKCdWEsCMJ2DA7nwqeezxvruOI/eX7Jic2O0CtfInT/6YSeTJdZZiYii1jBGUSJpoC6UAHyNikBJCkhArTeAgShlgQGZvwIA3HbcsiN0lFknDGg0FTemvZixHDSDnTSB4wKuIiL4fjOpZLSmrHk845YS5aZq8DUsBuU9yHptw7yVGygwYNplzyWNEGigp3Rvr7BpvEiTvkuruijHwHY8p/m5Xjii3g5lWea57meBoLRdfhqBYLsxIMCmPWSaHaO7TruvOyYD5e26DHO9An+Mt2yENcOS1EPjSg/bI/pEyU7m4pFT/fLEBEbXhfG671hrWWKjYl3gZs94571mpc++I/F6JbMzVf17YAj72CyWybE8G9mUBq/vyMXIcSMAv7yOpEvr7x30eI7kLI8S8aMB9AgBesLkSPO4CZvK+jftkvN34/TUGxfI+jwGwXHiSY1L2/c7nKeLctvS3PvcM7Grzrns4uJ9TzuoUU0wxxRRTvOTigQarY1aNJh27QWQA1fjkSehYUrYLONC2u6W8Y0A1BIrnQfR40jeeFI7bO56QATTx7cuLjHNhQUgyT+hAUufcXpxrh0rSaMSIgEigViuAWb1Ui1QpNWAvgkssXEffW21zbmASWKf+AKJPUshIE+UuRhhNzK7WmhgTRcyR0oqOBWK8CeiGQc1IgDIEY2o/6boJdHNuoGTiQghU1qO/6syy8jggg6Gh0yxUf4/HOZd9/6lz40JOyMf/5D3bZfoyNokZM7vyPLsm2XKsSYDFYEm6rxIYHDJR4/2lQ7LMYeXt2IyKFhC6c22Q8lopzVVK5f0G9zUtOnjvoY0eSLvv1d8EttXOz4EhKMzXqhVC588ZQbEpD78XtNYwoo/7Z5tyKHlRpDBlDxBDD6il7FpKrbXWMHpYq1beezIPw0AqzXdU5g475+A7l1an+utPO+WyUuN3xi7wNv43zh2921gMMWZFxXhsSmA7fh/Kd+i5xUUxVpRSiIHk0uOFRilP3vWM7gp6p59/jp7PvlNMMcUUU0wxxYMVDzRY9WICTnlfvcxVATlHqp988UfJQVNRLqt0Mu0nPLE3QGHGRhvEKMAIRuAPu8so7Jpgj8HrmEVkNopBwjkmY8ROME6Pqd0UDASUnMuKSSf1RWpZyumNiMEj+mQqk75DTGAWGq5zue+UmBBTbqw0MUqgXymYohwAAACp3EtBoBPJ6VW4OXP/ysmu0SY7+/JEVWuN2ayCtUWewBbJyKbrOjRdi845sPmSMTw2VHLANQAUnOtIIm0MnA9AJIfYgAid5/+C2YkBMZyfoEeIHDt1flIMIBvtyBxKTtzbBVTHrE8+poDhY+aKgZ/3vZyYQQ6Va3Ek09ZUZkgu0kjgxuVrYoyoqmpQR3iz2aRtTb4+m8y0gh+WsuG+M8agqiq0bbsTkBujYSznx/ayaZbpch9L8yqSOxd5camXqvaqAG6btRaIfakeyXrSMQO0TswsaAGnZ3D73FffuVTvGKjbBoUtyMxLaWhN90yyztqY/F6KnFeex8xwkUprWkgaLKBoNcjLzmZNaYFI9glAEmDve5Z4zHjLGL+n+oWc/nxjwJ/HmhiTY6OwMTgdA9TxYp4cW9wPWmsEFeG8o2uNw0Ugfj/LsTt+TuR18XbAbjfkKaaYYoopppjipRMPNFiFMgB0AqpsFtRP7okkJFASkUg81csRFRSiFsBjBAIRCaREKHLZ1GooD1VAdH4ArIIa1vkbM20yt2/MVtxt4jbOC/Qh5IlsnmApApNa8bSV2h8j8mS9B7w+n5OPYSzTDWl7nQxoogJUhOfcUq3gI9WZlFI/QCN6D6U5l5DNbagvmMEaM2kxegQGbIOb20+qh46wVG2HJqABVVVkqWrMl91PhrPcN+WwFkVBvdP5PLmez+coZzPoziZWTEEZ3YOVtLhhrIExPfjwzmXWNuaFktTPikykDEusE9jPQ1elfMgk7YzJ0UsLqfJ4Uh+8R8jnyt1EhxULJJmhzQyjPK/KdUddMOhcR2pxbbIZTkj7lkVBCz9pHwZfUCobD/nUms6RsdFiscD+/j7atsVmU9MzGeWijUaMCtttja5rEYLLJk9d52BtkljzgkACTJH0vFnurrVGFIsY3nuq5ZvySUNUUImlN4IZ758Xhabr6HNRr5ifuej7/jQwqXs1lNL0uzIwpTD60RE+kn7DWgOV6jl1waOs+hqwMUZY6GQyFGFTXnfwZObEbQwNOWwro6E0ba+VyX3oXIBzNfWVsZl19CFACcVJgsf5WWLGlmvdSoAm/5YpBnL/8SIZP595mzhUIoxBLqcaSNDM20jp93lwC9iSr5/+jgjwyVeA32l0Hfyeu/vCj84KDp+Op/JnU0wxxRRTTDHFSyce6P97n1+dPy/XJeOhmAHFQHInmCx5nLudh4/Zn2C4za7f5WfSJKdv33l5HoOo8bHuVi9wV1/07C41lPshbf3/t/d1sbYtWVlfVc251t739KUVUKRp6AaRKSEEBTEooPHJF038IQoJiiEmgkafTEiIicZE01EejAKBxJgYIDwQ4s8bb0b7wWhQYgjJBJVuGkWgaX7uOWevteacVT6MGlVf1Zr73HPv7XvOPveOL9nn7D3X/KlZVXOu8Y1vjFFQA02PTYXNoxiErTolx8S0wbuhIZLDMGBdN8TUrkGr7eiVYQ7z4320LZy3x4qPkLoaBqnLgVTVq80FBtrKo0AN2+zDqw+HA47HI8ZRwjnX7XK11M2YEnxZ3zW3ldrGY8H336tRur3PI5WRaclFP56tltqiv/d6/bbqr46rc22+q/ZNihsG53IhoJoLqoR1WS4457BZHidd/kXDjtXJ4Ik0rOuGZbnAOeTiQa1jhnO6gRo6K+2qhb68y+HqMYo6rAW6KBIBrs3XLcWbYi0c9ejRI8QY8eTJkyZfkivOqlqo/dsTK82jXrYVhyCOHo92PeSeLMq9Xo91/x5qUx3qPtpO7re9+aD/c0iyroPL1ZR79bQn9zomtXCXb36qKl6rRvf33r8LdB++pvZ7/4zcN9/bed4XLbs/j5XvTT/nnHWDwWAwGAyvBh40WWUDxXcVIQFVFB0k/xGIri0cosYKb1MDyYnrvohhbAj1YLVGCeFe+BkbfmzgNaF7XRt6FUPVidSd94rU0PV7wlfDginXKy+f4XNILqvFfJ2UEra4NUZ0ySl1uOofPUZDPpm8KXnoFeUefc7c8XhslnKpP0NDAAFRP+/u7nBZak4lG9B67LquOJ1OxZCHi83SHc45bF7UJs4b1nZt25ZDa2MzLxl87/rTFzZCuh7DMn7ONbmJsrssFcTXKPM61RDZEh6dc3pjjFi2TdbXzURG5+E4DBhDDUPV867rivP5jHVb89JElQBr/uT5fC598dprj3A5n7FelnKv9VHJCiRFOvREleePKqZrkoJCa6rh5Eqoe+KfYsS2rvDjWNqncw5AUfs51NU5V5R6rXzMhYmUqKqaqJ8dDgepTksEV+cVP5tKFrUvmEwXNZTWU2XCF2NLXrmPOHSbHWNKzLkvmXwzCeUxuCZ+7TPaj5Wea4+c6u/NM3DPc8H3xJ/12/r9+Fz1d31kWoeR/s7vDqB1JBgMBoPBYHg18LDJKhmFLhfp2TNaY8yGu6tKECsn90G5Z69y6e+90ZRSKvlte4SlVxQVTHL68/b3wm2vahtyyC8ZldQuOCHsqjzqdr31lJQD6bHZ6EQldOVC6Vq9vFIHO3VI1LilyZ3Tc/T3otfbM2A1PFRJhm4rYa1rzZdTUvnkyROclwtc3o/zXNkxUELDnZOiNq5dA9U5hy1VQns8Hgthcc41Y8hj1//w+HM/NNu8A7Z4tb8QVtyL3qhHOW+r1Gv/pJQQ6Tqc7+s1GqEbg3Ec4bzDlrfpebRStY5HSgnLcsHlcsa2tEvOSN53KOGa3C7sXLNUAA4ePlKRJHVIZALIZKYWXgpNxej6vhAi98Ybb+B0kpBbJagaGq7qPTuP9IfXrBXiXYtJKTnUok1KTntSrAXE2nVW5bkUwVgcSzEXF0pZWeXoBZ2DfLz2NfcFE0tWeZWsK/p8+p7gMlG+j0iW9VepTVfk07UKMZPznpTuEVfGfU4u5LzgZsvOu+q+8xoMBoPBYHj4eNBkFcihvSkCW4LL1U1LaGbZR9CrCQo2wHi7li0h+oESqEkil3OuhGfGbSu5fww1wnqCx8ZZb9jp59pGPWaPaN2n5mk/eD+QqiVbQ7heAqUSxbYwDuCkdFM2zJdlKTmMlbQBjkXC+0hHVvj4PHuhkExo9b7HcSj9m1JCyudctw3O+Uw4Ik7nM54+fVqK8vhuDV0e995BsG0bEnhcsoqUZDmfdd0Q4wmHw6Eoo1u+fh0fZHIU8++tYa/79GOo9x3ctYrPRn5jyO8Y2k2foq4XuuQ8TSEoqQmJZaUcLlfKBq76y/uAGLcmp7G0O/cH5zxqv+s15V4ikPNPWe3t11IFXz9XcuZQ5r39eD5r6PLlcinnZsVciSXPMe2DEhadqgLOSiw7uk6nU7l/DmXldvI9NoW1aHzZoVPfU7H0IYce781dbpM+I0pKdZu2j5VXJsG81m7fNu5f3cbt4rnA5HOPLLtuLnN/7DmruD29Y6I/Xz8v+7btteN+0mswGAwGg+Gh4kGTVTc4uJAJSAJ88vDBw6kB49QQ8UDnUe8NoT2yl6BKaacmQAoZqVKrxDWhkglW/nqFrg9B3WsHcO313zPq9hTNPTUPziGl/tqtiinbNARRilWVc2rB39CGGWq7RPH1gOvygjPUQFbVZU817UlqIZpEzp3zSA6lQI1zkmfrIEvinJcFp9MJd6c7bCkijEND8vp+7cep9nOrQoeSv1lzHi+XBd7XsM5xVDJSQ4j53jncs+baruWa2q9KAJx3UjeL5qQWWGrUJycOFG5/zKHi3nvAeQwdARrHEZd1Kb8D1aHinYZ7d1Vn85ho9ADPZeckZ5XnZ4yxyPbCqTm0/XotZA2vPhwO5Xp9uK2iJ8q9E4IVex4LPUZJbIwR4ziWPmAiq8rpfeGtStDYCaOqvy4505Op3rHUFCeie+N+7OcQz6P7SCU/Mwom0pfLBcuy1IJS7PyAOlq20pf8fPTEk+9L+4WdCHtt5P/1unvv5j2y2b/f9px9GjYNtPu2DrlrxdxgMBgMBsOrhQdNVntiCNTiO6xasrHZG2V6jG7rlY771ErdvyeFlVTdTyJ71UGv1StsvD8bqHuhy9wGNlSrQahGmZAGraBMd5PVv2qUy7IyoqpqF3D7RHnL66GmJOSWzskqkLZDxyYlWVZG1jxVcbCSmpSAYajh3fXYNrRR1SBdfubx48e4u7sr/a7G+UpqGI8Pj5uSJDXS2cBVsqr3peGhSmiYbOl4cIio9oMay5fLJWvxqiw6BOcRwoAQBmzbipBzTVOU5ZOkd2NZx1Ov4xJULm/uyef7j+l6Hurfqrpx4ZwwDAgaKUDh0XUuScRBb/jvKVYhBHjUZ1EJkiq52n+qxKrKqmOgJFLJs5w712imOZXSBudSDvkNWNclz5P6PPC7gcOB+RlSNZWf3T66Ye9H+4JVVyZtfb/35K0niZq32hNFnrtMfHVfJum85BDfw95z0Dg/CPoZ34s6W4CqQPPzzufp+7fst+Ns23fmVNVW58595PSa/O7nwfbKMc8Pg8FgMBgMrxYeNlm9x7ASwxAl/pdVLd2Pqdqe0scG5J4ScJ86wNfrw3V5HzZoFTFdG1f8ue6DTp3ZUx+ANsx1XWXJG+o8xMhGnyzJkfJSEMNwQKlZRf2obQ9BliBxDqVID6tYe0pJSm04ZSUecpG+/4TsyDVC8BgGyXPcy20UtXMpiharlHAurznZFpZhUtQ7JHhfPT9o/rDBzI6GXqnhucD3VhwpQfstAQlYVxkH51xe8zbCkWNBya+Sh8vlgkj30MyLrJQvl6VxchQSMNS1fAE0BCSmmNX4Ssb4vlTJLX2c+4wJeulruKKS6zX4mdS+GHMhJG2n5sEquYxRlsqJ0eHm5kbCjZcFQIJ3DuPhgDAMhWx6X8Ov9XiuIK3kWMm6XpdVV50rWlSpH2vej98xrEj2zwA/m9quYZDwdsR4NRe5r4rq7rS6ciXXTFx71VDRt5H/12P6SuS9U04jC/Q4XktWz6PoVdZyPv5951229y7lNvH5+76tbfDwvn0W+Vo6p/l/g8FgMBgMrxYeNll+se38AAAgAElEQVTFdbGaYuREAPBAysZMBAr76gw4oPXqF2OqU0QY/fY9g3BPadprf+LP6P9IBieHh+p2PZ4Nub5dxWgOrALutYWJrJDCjfLq2KiTazo4365nyoY8q0F933D/qTGp25RAcvv1HpQQqIrE97ksC54+PTWhw0AlnQnXxVV6dYuNeF4SpBj+dC+9KrNtWxkz0JjElLAtS2l/8LJupqc+YxLAbVJy1xQPirH0WT/3e4OdyZcWD1K1MkZZo1Lnl14nJam26yJkfc8dBdA5V4o3MakX50JdumTbNiHSeQ1kUIGzlFDyZ5koMOHlqrgSdi0VpR89eoTXX3skS+ScLwjB4/b2NYzjKNdcVnG+IOGSVrh8z3qfej6eJz2hKk9CJqn8/PVOFw5t577aI4s6RhuN9TAMCLldmxIz58p80pzj4/GI29tbLMuCx48fl7Hn8y656nU/R/sKw9q/fI4+RLl/n/D/PentyTK3Qfdj8r7mucjt5HP398bztFdFdV9+luQe2u+F8pyiJeL9GBkMBoPBYHh18KDJakrX4bvVUPGd4eKLsdwbJmwY6zn4fP2+e556RU/G+FysYPTt2DOUeZ9eNeFQunIO/b9rQ0wJ4zggBI9tC1iWWjSmKkLcnqqm9QaqqIGuyNL8uRqTTFZ1e0+savtqEZ5evdN9OAcPQFOlVav+Pn78GJdLVQj1vlw2+Ps+32sbG+n9erH9sTqeeh1AyB3/zeGMlZz7Zq7p/RSVrmsDV3vVNqj6pffv0RYX0u0pRiAEHG+OpV90XJyrechcJCrGCJ+LK6HrGz0uRskH5/ncOwGa8NcYARdKf4zjKERzadcvLe2maypBDCGUCr3OObzxxhuFmGlYtVbfXde1EG/va3Et7fuU0lX1Zm0Hq7xMSnvwnO3vX8FOFSaCcj1+fBO2qGueIhcpo3dKikg57HpZlrJEDs8fvQ47ANiRBbR5vvw88bzunVL9Z/3zo/e5977sHUAlIiAl+HscR9pP6lhpHWS+GSM9L88TJqQptf3Yj5/+z9c2GAwGg8HwauFBk1UgF5tBS/p6T7pzQq7YkOoJkf6uBnshK50xy/vxuViJ3QtNe17P/d5+PbFKqRbVqf2ApsiOIiapmDuMHmEI8L7mjCoRulwuWNfYGINx2zDkPrgiLN7lq1UiySGFe/ewZ9DKPZEKScYikwsAjSKihXx0bVSt/Av4pvJpGTO6Jp+nN7x5rFTB6cOB9XOeZ4WwAE2eoKqVqvgpyejnqM5TzjuMlAfbKHDhutBNWq+r7mre4ziOOB6PpT3OuULqhiFgWderQjuJhi8SYSsqc5KCZr0KxkRP+wEpNQWZVNkdxxHb2q5lC1wXEuJ5pWul9k4brvKrbS2OmASk3L9XfUxzUu8jhICbmxuEEEq/9HOAHTf9O4UdLtpuvraO4xBCIfD1PKJAF2Kbl17ROXN3d1cdCvSOUacGh7Szaqrt4pDlvXchRyv0+zwLTGr750Q/535b1zVX775WnO87x97vvXOk32+PQO9tY0ePwWAwGAyGVwsPmqwml7Ja6pslNpQ0ypotmh91rRD2BJSN5j3jR48HxIj3TvIC9ZocztsrE3zs8xpFanxyOwoJwD7Jure9Wyh94nwqa9QqIZOc0VqcxUHyXJ1Dk/cF6N9tW/t+ZQNfjeuG8BT1sRrWbHSqAd5XEQ4h4LxcsC4LTudzJXPeidPCOWyUd8cFZ/aUIx4LJuvbFrOfQvJ5AVecIkKCIuIGVOWmVriVazh4H5AScDpd8j2oWt06PfSa2o/btgFZIWQSp/sDlUDGGKuiTg6NbdtkfjpXwmd5XpU+y33K4zSGgLStVyqvc7kQUxS1j8mXjt04jqUibqmSi+oY0XDe4/FYFFFWxu5TOvVYJqraLzc3N6WoVqM4ew/40CxbpLmp3vum6jDnLeoc1aJPPVHV9rHTgJ873s4Kc79fCLV6MOfM6nE8H3h8WIlXpVo/137W++jXR+U50keS8DPC4Htl58TeO1TPx/etc0FDr9lB0c8/Jd9v5tzj+aF/P0sZ7e/rWUTWYDAYDAbDq4MHTVaRnOSkgkPq1OiqBKkaRm1+1p4RCrQ5gMWA0c+dKzmJjWlDiqeG4aI/lrfx3x153TOCr0mpg4Tr1gJF9TQ57DlfWolSSpusS7ttUh13uzQKkOqz2kSff7/PhEuQdWV1iRN3T5vVEFdFq1W2OiW4y1fT3/WYw+GA0/mM0+mc186UxVyCDwh+eKZRzjmgup3XAmUCuG2xrLKbUoR3gYZOVa8kfQ2Xl4lRwz0THwes64Z1W5v7kCGP4J4t/aZtB7rxBlVPFsO8KKKRxy2vh+s9tgjcnc9wbsHNzbEseeODz/mQEYdhhPNCJpbLWfpxHOHCoSFQOpbVoZAQMuF77bVHWJYLTqcTvA9NrvPxcEDcVqyLruUqYejbJlWNY0oS9QBIlWM4jMOIMAQsl4sQ7RAwhgPgarimjKMUU7o7nWTm+oBS6VqGBh7VqaTkXecAkHB3d4JzwM3NLYYhYNsinj59mqML1vxcOw0kKA6LJA8WAOlP7112cKhyKWvyblvEMAQcj4fS9pQAp06a5PJSR9VJo+QOUOdElLaHqtKqgq1FqdSZwesWV/W5PtM9oevn2B5x7ZXTPjJAUxJ0Nj/rGkX1pmgET5W2de7oWOnbweX3pEaQ6HszxSgOw1Rze+v1+vbouz/VZyXx+8aUVYPBYDAYXjU8aLKakqhU8rv8eN+G4cpnqVGkADTefGDfqy6Eo6tWCZQiOqqkFkMuHxeVbHbKEO+TyPDqc0P5f25L3a5nUWKE5pimTSI2ZlKmqmaCg4f3rREpvxedTupRJTH8E+3nPYqxqWbgMAwIvl3eYs0hpqyWsMpUiWQNQQZqyCeTR72/dV2xXFbcPT3VHFoKfVQS7Gm78760n8lz37dlXFJCSegs/S0ksFf2iloNIGg4czaio3MAIo6HUVRBACG4QvbE4ZFV5b6KcqdaVXK9NMqf9Gdo9pNtAxIczsuKGFfAp6xm5vk/jhgBBB9EnV5XqUTrPWJkxV2gFWedkyJR2oYxDPDIjo0EWeN4y8vUrCtSJmGXywWHg/TZkgtOJS8ELKWENW7YUq5I7D3WLSLCIbmAmDy2BHgXAJ9V/5iwJVE+4X2+rwgHjyFU8h+c/HjvRGVVVT2K0pe2NVfh3YDoMHgHhwiXNgz5HpdlkX3KOLTFi3whpmuTPyrP3AZV3eXY3KcpYF02rFDlNs+dLSEED6R8rS0Xh9sJ/+aq0KoUa//WMGpkAr/lcd2aOQPUJZWYKD9LcWSH0JYdX/32fGAhjyn/LU4Fj0DnL+8BJccxIulzwPecx2PTd6S2jd97qS2Sp8+a3LNGF+RloFy9P3l2LGfVYDAYDIZXDQ+arPYGOhPUPg9JlUMtvqIG5bMIK//FoXCKPTVWQyd7UsNqRH8OribKJOq+wi5saO31yd7vaiTvqcZNtVky9twm+X58XPldfinnKgZuaguWKJnk4j5cbEaM6ErYe8VI1U/9/O7uDqfTpQlv5GJIvbLbkz2FtrdXWgsJIaKo5+1DQJkoK1lmggegKbCkfdKEhnZEWY/vCyuxw8X7/eWQ9P9t2+Ag+arjcMC2AefzJYdMy708evQBvHb7GhyQ1yQVtdIHh3VdRFnunAocKsvz5cmTJ02YJzsOFFqwiOdbpHmjxygJ16iIYRiARKHbkDVaeb1WJVj8vNTxkXNyuLH2kRYjCiHg7u6OQs/z2raFc7UFwjTcW8+1bisiYrNN99fcZ3Xa6DnitmUHW2reRzo3dN7zORJqODg/p+fzGeu65jVmK8HOrW/eMZXgSjs4tJrnj0ZC8BrBew4eOfcqan/SZxjiRPNZrUyZMMZNQwdK3+o5+N3N/dc78vRZ4ueMj+PncYvtHO7PoffB3yEGg8FgMBheLTxostoonmRQ9v+rQaphXn2OFJ+vN/z3rrmnxBWySgZda+C2++t2NvrUQFWjUvdRY6sa9lVd5eP32rx3rb2/eXs5Vn9SakiV5knqNbVtnn5XsGLTOxPU4L6vT5jIaN9qHiGH9DKh4jEtYeApNcqlHsukmY9p1LHOmC+KXQilqJaC8yF1P606673HmkM4+b5VlernoiqZ2if9PN2bW9pHMSZ4l8msA7wf4Dzgnc99uBVydnM8QlVeWY5IwlIPYWiu2xNB/Tmfz2W7EiklDOM4Nseoo6hU4t3W3XvjnGLvHHweDyG4wOuvv47z+YzHjx8367Zyoa4yz1KE6+akziMmcRxGCydh3jG2DqdahKrtFwcH72r/6DzgedsT/5iE0BXCS8o1E1Wu1IyOUGlIsBJvJajVURexbe246LnkNNd5pgoeA22XFnHid02dCwCgfSURBYi9Yy5CBFRRpxl9/qw+h/xcsyPgKroBuGqT9h/v3+dG8/3tVQA3GAwGg8HwsPHgyeqztu8ZMVxEh5UpNsDVeOnzBnsSfF87+Hr6f0+oeV81oPR3NdSYsPToCXB/zb0+2SNyABr1sq61GUsoou5TrpnKP1f90xP+XrnsSbL3riEMXEhIx8t7WbLjdDrhcrkAuFb4eoWk+btzIPQqChMDbW8hvqiE3ckJmnkEVUzz51ygR/NKOWT0ykHREU89vleDC3nzpOLH6rTg+3EOgIvY4oJljUAehyHkMQkD1jXi7umdnH9bMR5GyR1NsnQNr0fKfa1joX2kv7MDqLaVlsnpnA6XywUI4jQQ4oh8L6motEp4h6Eq/4OrSjWrfjyfm3mYqsLJqhw/m1zgS8ZkQ4pCVnvnQu5hmr+ynuuyLVfPql5LlWIm1hJav7+kkl5Ln8dhGHA8HqWo2OlU9r1cLqU/dd6ez+fyt3ODhICjOgvU2ZNSGxXA98f92JPovq0yRyXUWefAfc5A7mMJjW77it816kzQZ6B/drj/eydh/67un/f738/XkSwGg8FgMBgeNh40WRWuUAvdtHYR5yilHNYof3EY5n1IKdUKHf32cv02VFQJ7p5xzspPT6LvI5HX6kU2bLUijbajZsK2W1KJuqOKsa70ldh3YpSnhPKjoYNdh9Y2Oikgg0y20BmGqgYpoWAyzqj9UNfg1HsX4xlYlksuyLMSqUkNUVUoKdgbHx9CKcbCecdADXXWPNeSexpjQzz7eXNFvIlg6o+SHe6TMj8A+Kwy8jm1v/ZCwoUMeJpXdWz0PBwqCgDOB8QIrGkreX9IwPlywbqtCB4IhyGz8ut71nHRtp3PZzjncDgcSuizhuTyPOX1bpnESmGsLRemUgXz+nksynisS9w458rxfJ/8nDV954BAfdo7M1jZLE4I75Accoh6F7ad0wlYCdziBj8EhDBkJ5Pkhsr9iEOhfZzyNVJqltvROVfnf20rr/vak0Btl0YLqIItKmp9d7TLNlVCyu+cnshzG5ZlaRxaut8YBhxv6lwox0Nu2jmXl0OSfOOovbLjYOPx65eZ0nPvEVCeB3pMirHmzKZ01afsPHvWd4HBYDAYDIaHiwdNVoFnec3l82LExogwDFJ1lPIdn1Vo583Ml17J0d/5fBzCy8f0++pneySsyaXMYYfFCGyUmb0QZslvTagFSuSnPa8eX0gDqVI9sW/CkjtDnlU2NpL5vtq1ImvorRb/EUVHCNA5L08jfRMQo+SZ6k9PglnZZMN6k3jIqvZ1BmtRAKMURuK1MdkA7kNduf97UqDEAsjLu5DR7L1UMGXVaG/e8Fyp+aDVAcDt79uhx12WrFTmuXe5XDD4AOczSQuk6OZldVSB60lhjBHH47GQF17ahRVpJa+AhNb2joRxHLHESlS1T/vnQfuTySE7dvaWZmFFzjkHN9Q8zm2L0OWSuN+475ErZrOqqvcnBLxWpnbO4XQ64en5hGGQZ6iS9RFKVHW+lmVbcB3yztc4HA44n89FmT+dTs26xzHKMkFMws/nc1nKR/dTcLi8tDuUZYR6crqXC81huPxcDENA2oBDzjPXNgO5wJzLlXeT9q84D9Z4PVb8LuZ5z33UE2gmvEw+kzrS6Bw1BLqu96rjIue0AksGg8FgMLxqeNhkNbUhnhr216hXTqqXRtR1S0fOcyOv/V64GRtGzXU6sGHHBrQa+5wPxSSj3koqRlNfPISvL39veSmV6/DlXqEppHxnWYY9hWkvVJe3q7LGhrASHSarrF5o3/J6nHr9bdtKMSXdX41oJkw9YeAQU73P4/F4NT6sanMBn95Jwcrhuq445xDLnqiyMd86CvYVey6UxERacxC5/1kdrPmR12OrhYHSzlzX/tbtMdZKzBw2u64rXjvcIKWIEFxR4w7HEYfxgMtdLUhUciZR1wDl66aUEMYDzsuKLUpEQ/LA0/OlEN+UEhAr+UOo52bVTEmVXlPVPF0TlYs46TPTz1smVcMwIiVtg8MwjDTXa2GnEHIebZLldBKtZavPri6Xo+Tzcz7nc3A8HvHkyRPc/er/K3mj+h6p1ZpFGb25ucHlIkv8FHUflaR774uCerlcyrmWZZHtsa5Dyg6Efs7x3AfatYa1Xd63KqrOHe1HnYeqjvf9rsfoOft5x89F71iJkrDb5r86V5ez4XednEgq+3bvRhAh1dxmTmlgZxQ/39oXvPavKasGg8FgMLyaeNBktSdVPXkDrkPDgB0iR+SDqwXvEQLdr1eBahs8hsGpKFnUQ4b3oiL2KpEqTCGIt1+qt2rb6H4p9JMNR24vK3Mxxiuy2itKe31Ywn2dqLPFEAytIcy5ar0RDaApZsTjAGSDPRvzcA53jx9L0R42RL2owmzEsqHbG6Ic7skhjtwubhvfs96XFgfi8/UqXp9PipQap4caw4UkZ+LN9+S7ebWn9Ck578k2kwVW2PKd5uNR+q5Xn8Iw4ObmKMvuBAfvHeKWsKS6Lq7ml6pxz/mqTDBj3LDF9v6BqhjyHOjDjNlx0FfGVsVL1y9lp4iOARMm7dsyRikC27USxyod96UsNVO6rxkHIc1jUSV/67d+S4hlJpWck8pEVVXPsi4uUFRuvQf96Z0DuhyNOt3a9VcryWRnRdk/P49clEzbsyy6RnEtOMehxNzXPId7pARsKeJ8WWqxsKQpGh5hyOfYIlKS5ZvWGK9SLNgJ0j8L/TuuXrt+vlHb+udo75nqIxc47NhgMBgMBsOrg1eKrOq2ZxkqQJsPxmRxj/jqOXol9L5zx6hEtq7t1xM171vlVu6jGmsS5hkwjofGkC3qBBxCd5/3KQOF7AKQyrDt0i57/Vj6xrlaS8bJOoSRDE02iPUcTGA0h03b0OfaAZUsrvm4pqKuc3CkbrmsvvSVfe8zcnvFs+8vba/+zsqcB6gYTbvMzziORfFt5hERYM5RZRLV5032869do7Mt5sTt123aX1xASH507gIpryupba7XAYYwYhhqtey7uzu4wRViqvfc30OvtEa4mjGdz68qHpODvX4H2pzRcRxpLHSZJn5+qtKqBYW4T7SoUUoJoXNk9M8G96uMTc119c43farKaQhjafOyLNiirPW6N7/42uu61mds25BwvTapjjkTSCb/ek59F/TvMb6vEK5Dyeu+tW1MRPk9wE4FPb53/OnIbDFiY2eBqwXIUp4biY5hMs/n7J/j3qHAedBM2HU+6d/9WPB9XLW/a4fBYDAYDIZXBw+arO553hV7RhCHVnLYGhvSrJr1BmSv0O2jr3Z7vTQFk5WeOPP1lSyw4SvXbffldvW5e7UtqSGZveHZk7zecFvXNStwAQmV8DjnkCJVqe2UGDWq2SDmax2PR8SU8PTp0ytVkwkfj0noCKxuZ6LLhZ36pXR4nHtjXsdJl7tBShg09DiTtvPp1FSs5b6S6rX7hYW22C5JEmPElsMr+zzfR48eYVmWEnJdxybAwSNXrLky5pHXYeX+8mFETBHbtmBZ1jJHU0o4Xy44nyIOxxEpxbJeZ4pyXg3R1kqyIYRckbneQ0qphHBymLb2h4Z1Xz2TCLU4WI4c0DxM2QelmJT+6N88T733Mo9yeLMSoXEc4eFK+CxXGNY5pspxdbzIOqvOu9I2VtZlPqUyn4sSirbatjprWMlmorllssrvgTJPdtIAYozwrobBhxCKUts+5/U9KPm5VZ1tc4zRXO++nHpWfHle62dybSG/vP9exEJVhSP2Cmr1ZJU/1zb0ijorwf0xe+fUbXuOvvuiZQwGg8FgMDxcvBJktf97zwBsyZ6AVQX2vPfqQU/k7ieqNUS1J1j9Pr03n4u5qGHGxlfzd3cPfJ99+GU5f7ouUKP30yu/TOaU5Lf9Foo6LAWJElySgkFDJmuac6pqCF9LSYVzroTGCjF3JW9XlbR62dpnmpvKilWfm6bkj9UXHQ++dyatTIaVmPO8WXIVWlY9uc94X25TMe6pD0u7aF5wG4/HY1EOm5DnMvwpK6ZVyZVzRsTIOX855DMlCYlFnSfn81mUVDjcHA9IKWKLohYeBxlHVTl1Tmq+LOdXD8MAZMWbnykdhz1FK4SAuFX12oeAcWwdDCltzRjI2Apx1iVcdLzO53PpcyV0KSUs61oKWXGxKCW4T58+rfm6hwNC8Ni2FWmTyrW986p/Zzinlaa3TMKUOMk4SQGpTASdAzIJ9j7k8WgJFUdQlCVrlIDjGv17qXd46ftBx66+F2p6Ao+Pno+V6P592D87MWrRolwsyyW4rVZSluNcUZK9j1IXuHtu+PrcBn7W1GHAin2Z6/T+537V+Xvftv4aBoPBYDAYXh08aLKq2POU9yom0CqkbHj1HvleddRt912zNeKqkdgbSXysnrM3KAEU47lvk/7vXZuH2Ru7fK9vhr376pWrsrRGEoM7DJV0Dt5j8Plz1+aNqjHZFzBhI/ZyUVKn1UpzrhtVLxVK5wpJYuLOShYbnRpyuac67SldbKRz7iGrRdq32oa+WiwrTv0c0XPrT2l3F+Kr1/z0pz9d+oqNdvk7q7BylaaIU6/Kee+xRVHxwjAgBIdt3Ur7uViTd0BMG9ZlRTweGhV6I/LBxcK4wJHOWT0n36dWwtXcyXVdkWJ1xATfPq8ajqthwdIWj2VZG7Ki59XldJRgK9FHSvB57hwOh0K+VZU7Ho84HA64vb0FADx9+qRxqiQAPuSc6ewwWTdRp3WJo2VdZf5mMhpjRIqQPPHUOpu2JTuyVFCmdxKrt71CHWMspFuJ9154MId5y3nq89jmlEuINZNuJvTqNOC29+/Eci4A21qLa3nvkXJl8RhlLWXvfVlGa93S1fuN31v9c8pkWn/vly3qHYt8bj0fP4t8Tj3OclYNBoPBYHj18KDJquZKZYZTw2JdrixJ+4gxfk1ggWrU74XQsrGunwH3k9jgfTWQ4RCTKA+OQj35uqmoKzWcDkiIUaqX9gaWGJme7iMiJsmvkxVtch5brsO0R6hr+J6TAj9RzuPytV0JI12LYe3g8vYIgIik82KPew8gYr2cs9Eb4ToC6ZwQ2piQDVuUYk29gqYEZdu2EqbJqnE/RkwcmST1+/E+vUrKBItV0d5AVjBR4L97Y1nvzwchNIe89EvUgjRkqCuZ0/NxOLWoiDLOIXiE0FZXlnvySClmEhfLfPI61pmUOieh294D27YiOuSqOELOdL4/efKkLO8hBEYVQyUA2bmgcy+Hdzp4YNtkbiQHlxIQ83JQMUHWcw1wTotn1f5blqUokxK27DEMQEobloXWQ83jLv3kSmEyIGHbVgAyp2MUAnc8jvm+NGc35m0Rl8sZKQlZO9wcgOSxXBasm4TfOjhsUQqCeeQ+AhA3VQkTfHDAlvMzEZFcVecTEtImqmNM0ok634K+e3QeIquwKZWQ8xQlpFefSwn3BsYxAHBlWR6ed5qXrHn0w3AAIMrxtsmaw5o7Wxws3iPmNoRhQNTc2hDk2VVHk/clR1Wx5/ji8HYO5e2V4D3HXOvsq0pwJZ7t0lsSvbCVOYy8HnXN4a2Fwfh9oOMgSw0ZDAaDwWB4lfBcZHWapk8AOOUfAPieeZ5/apqmbwDwwwBuAXwCwLfP8/xr+Zh7P3teFI9650WPKQG9lzylpqKsGipMDnbPvQP2wvcq3bZGIEmQpl5XDPiWHFdjqTZfDDcN0UvobL9yvZgS0qaFYOR3Df2sihug8g0bZarYlNBe3e4qEVODOWVCIecANBRX+6yoQTEiYYVLCSm2/a7GaIwx54FmIut8Q/6EUOhyIsgKjy/3wMRNiEjtxxpGXHNG1RDu1RIO5+wrwurnClY02aDuwwZ7x8deSDLnGjqIEqXX6h0hvZLF28UIl/6KsY6DnodV4ZubG1E5Y4R3Hilm0gshjt45HHM+ox4zDAOGEBCc5BM3ealO8j05jzGEIY/BUtTsEGQN15hU/RbSoCqybPNwQ2jyKffUrnWNcK4db+cczuczbm5uMAwDTqdTvv/1am4I6aoh4bpUkswvYF1TEx4bEfHbv/M7OVwXADw89T/gJKSaxsx7V3J2lSgVRXkcoEr/FrkIkJd+Drp2ayac69qov7UiuKqWKH3OhEufFVWQ9X6rI4QdbLl/UiqhudoGAG0hKHommvB3Un95vvdgB9Dec9SH1PNxvF3ysDk0WRx3/bzQfG3vxWES47VjUtvB15G5/N7LWX1Z380Gg8FgMLwovBVl9Vvmef5Z/WOaJg/gRwH8tXmePz5N098D8DEA3/msz95JY3sDX4y4XIQH7VqhSqD2QsP2wsp0u27ja9a8LA2vozDPrrDQ3vF8ft7HuVbJk2sIkWTS7XxLoJwqy6n2SV/opRCDHNrLimI5F+WJ7p1H21lCD73DGALggyhpaI3DdWtDaaWQzwoHIjFxw7ZucEgIfszqbwLSlonu1pDRXlnk8MC98dZ29+vq6vhzsRYeKyazTNb7fffy/zQsldvL52NlVe9B1MU6L1gN7q+rYbG8r+Y68v7aR+qMUGjfHI9HjOOIy/mM8XDA4SChwE+ePMHd3V1WV4dChnQ+COHYV9mULLCiXfobdX5yqHGd6/VcfWdWwQ4AABeNSURBVA6w/qiTQvuBnTIyn+p85dxrDtlOKdXc3Hw+JXDeiTqpfSTFm1onFSDktR+blBLWfL4UY4nscESUqqpX1/zs55d8Vp0sfIy2Sas5c75rTxS5b8t6yFR0SvuWSeSeg0YjH1j175177GDhdyC3h6/Xj3sfFpw0T5venXo5vq5D7Yf8hrx6b+85ITUa5D2Kl/7dbDAYDAbDu4V3Egb8dQBO8zx/PP/9QxAv7Xe+yWdvGa0Hvq0e2nj7k4S5ISUphpKyehmVkAFq3FSid00yW4PpegkYJsJqrG2aA0cqwJ56K+cSdVXPydeSFsq9yH8p/57KxiTxwEjpeh1KzuUEVO26LlKi981GZN+3/TGAxJX6EADKq6skoxq2dSmatuIvX4uXP+kVEb62GvR9eCG3UclOn9PLpGCvDWy4871wf7DToyfHAAr54HNw5WKuaqxtYNWZz6mkS6G5m1x9dhxHvP766/jMZz6Tqzdfz1VPpECPf/ToEQ6HA96IUun48ePHpR0hBNzd3eFwuCmKGiBOh9vbW6zrghi3hhAy8dhTz4IPTd8xYe2VQ1XPlSgBosbquJ/Pl3LNpj81sh7VcaTH9M4OmQ9SwCvlsOSkRYPABEyr6abynuA+5nD/PuS0hITTnFdltyfQ/K7YNmmbnNs3fabX2bbUzFd11vEY8Jj7EDCgOkz42nxunpusngO4qlbdvxv1PHvj32OPRPbvGnacAb55PzdOp03V6DbPdi9FoJL5903O6gv7bjYYDAaD4d3GWyGrPzZNkwPwcQDfC+BLAHxSP5zn+dPTNPlpmj73WZ/N8/yZt9JANtbU6GJDnz3zQsokjHaLapDpEhSh5IxB10aNsYSV9UbYHolUq5j3Lcaf63JFS17iNVmV87ehoM11yMBCSkguIaUalpxilFzS1BIgNn5rO/dDnVmRuO++e+ItFrzvDMCq9AQvVVCZoOm1tD9KMafuGkose1W1zzXmYwA14q+XruFzsjHO5L4fF0a/X39OJiOsQqqxz3OVl9npyZP+35OQm5sbHA4HnM9nnE6nhlSGEPDGG2/UcM4dx0Kie9BKwo8fP8bhcMDlssCl2g6pkhsKwXvy5AmOxyNub2/L+UTlc007eWxZsa6q2lY+Y7LG5JzbPQwDbm5uyrYYYwmTHQZ/NbdTkmJkGmqs81CrAGu7ASGMxaGRJN80xgik6iBQpwgTH/3s7unT55ozOuY8n3W91v5zvYaQ+JXGT/5X9V3neIx1rLSCMF9b5yLP3ZS39Urq3nOl8wtoSSr/KHgseD7r9TnUndXo+95XKbVkVj6rkR3cXuSCbO3x7Vi0jrkdx+Z7Cy/lu9lgMBgMhheB5yWr3zzP86emaToC+GcAvh/Av3n3miXYU7T4byUHYuTUQitAS1r2cuVSkqVGXEJjTPWGTe/VT2iNuxKe2YViVqWtzWeVpkRIwaVrVQAA0iZ5hyKItkWjnHPYYoRDLmDk9d7bMML6g6s+lPsQQ783ELnvuS+YQCpUZdL9y3EISE76d08N1TbwNXbP0xm6fN09I7q9b7mvw+HQKEus5LHhzHOlz9llwgrUojq8dqWuy1pIFCnHHNqp/cyGc+tcaImahkXzmp66bJDmJscdMqG/cYXYy+WCy+UiBZhSVeOfPn3a9LVzDq+//jo++MEP4jd/8zfx5MkTjGOrFraOg7Z/9JnYQPnM3Xj2JEP7Se9NQ5SXZcHhcChktR9vVpC1Peu64nyWQmC3t7cSjp6qquhzoZ1t2xCGAcfjTROeC6AUndq2Dac8BtpGac/QrJPL+Z08f/k50eNZlVcl2fsaIgzUe9H9pD2HxvHCIfFK9nVuSg5tLRzG81Lbx/M10r7aN3oN7jsFP9f6HGi7mqWYKJxZq0hzm/n39n0tjjbdzpEF/btp7xyM3tn4HsNL+W42GAwGg+FF4blczfM8fyr/fwbwgwC+EcAvAfiI7jNN0+cDiNk7+6zPnhspVeWMFa36OYemXRM1NqT4mGLsYj80DWhD59igdtg3hER4vCZXKUVIXl+b33fVFqd/R2xxKwabNo/VKDHcfFPUp++bYrRpOHFnzDnaj/uG1b895UgNah4TribLbeHqnKoEKUlkpbxpr7QE2nDnkMNPVyLn6siIRQ0bR/k5Hg9ZHVMnhIxBPWeivm4JtLbhcDiU/E4mBNxOVmxVtQsh4Pb2FsfjkcapJesK7Q/ti75fdLkWPbcqjMuy4HK53FN5ur0XdjIcj0fc3EiI7zge4MOAdYuAcxjGsVR/3eIG5x3Gw4ib2xs4L5VumYQBTIBiU4yMyc8eUdXPNOxXCR/PJyWXSsC4z7lgkV7vfD7jcrkUovrkyZOiYHI/OichwMMwQisV12fL5WdNyZEH8hq2qmzzD88Xbpc6LvRHyXcfPs3vJ/kMJVe4V6B1jr/22m25XwCloja3RXPFD4dDWXqHwQ4bVXxZ0dffee5656RYVBdO72jMGycW6vuTn4P+9/tC9HUpnL7Pmfz3TqA+0qF3rOh3xHsNL+u72WAwGAyGF4U3JavTND2apumD+XcH4FsB/AyAnwZwO03TN+VdvwvAT+Tfn/XZc0MJoILJY09yeiKoRlSvmrKB0+cN8rX472cROD5/a3xqW0T51BBlNtp7YyrGtYQDtu2pSoooMQPCcK0otH2X296ppXXfVtnqFUnFfeSax0Ar9PZqB49XfxxfV8kLoIWZRhyPB9zcHDCOA4YhYBxlyZIYN0hIdMzzI+UflB8pVhOR0obz+YR1XSD5gL6QElbKdO3O3oC+T63m9UTVWaDElgkDL8/DZJ1VKyV0nNfI/cHt0b7TMNDL5XI15iEEDEQMehU75CVKXAgI4yhrC+Wf5DySS9jShtPlhOQS4IHLesGWYqPu1qrE9dpX99Y5b/bCunty24dMV7V2K+Re72dZFqxLJYKa8+q9x+3tbTlPXxV628SBpBEJl8sJy3LBtq1Y1wUpiXPkcjlj21Ycj2MZZw3rVQLMc4XvXYnyKS9Nw89Dr/TLuLf3xw4f56Q68uPHT5rQ3z4P9nw+43w+X+WdqoLKfc5znd8hvdNDKj/TcwGUHwmjzkuGAeV/7it1anCBsJ7c8vuHf2cnYf9u7QnpXtt5rnFxr/cKXuZ3s8FgMBgMLwrPEwb8BQB+cpqmACAA+DkAf3Oe5zhN018B8MPTNN0gl8AHgGd99lbRk8/ekFEwsdzz1utn7f4eLoebObD3XZbsSJClWFSh1KUUtF36E0KQUMx4TTSZ7PQktRpYG6TKcD7Op1xt95o8O+ckPzapGrth2+KV4lxC5faOB+AyadtTNvcUO0AKOzk4xESh1dnA5pxHLpzkof2EImw458rSP07DkXN70rZiS60qyGocG7B71Uq5IFENq2xDoHMrwL6afm4xWez7A2gL6+h+fB4e5z2FvifFe23dm7+sfhXik8/bqH5Ac85+HvXzVElRCD4XU4q4u7sriqUqoUxUK/ms68HyWDjvyxJTvQLWE3JtBxMwvsa6Xsr9aTsOh4Ms8URErFc8tf1MZhDq8j9A0iY2zwjPq+ADvNPiUFJF23uPocwnHeft6nli8roXUaD3J/e+Qoss6fzXeX46nXA6nUrf8xzgcVzXtfZx57zjJa0SzRnti7K8Dzk25N2XSvpD72Dr5zv/rvehCjo/J/15+Hnmd7ueU4q3AYA4nHif3hHC/avoI0/eI3ip380Gg8FgMLwIuJ70PRB8FMAvfuVXTvjkJz95FVoLXBc30UIv21aVH1ZmRGmrRWGcg6aGNedjI5PVNf1M/1fjrhjn3hXS0IegsRHXG2I1RJXycsnglf1jDo3LRixSXtMUZPRf5xNq2LIuqcH3l6AkuSXPPUls+sYHhNAayw61IMwHPvCBYizqeYQJdHmGXQifXkOIV8SWjVcmDM1I7yh2ahwzieP2s/IrJC41x/M84OuxAczbWV3VsEueD2zEa5u1z7SIlF5PVVUu7sPga/PyKYUE05wr/ztXzquKpR7PY8r3KW1cyrqmWpX35uYGLonjJqVUChrJmqYHhDBcqV4AEA5jc61eqVbFmUOL2QGh7ZJrLQ35OR6PePToEVKMWC9LM/an06nMByVKrAgn50Vdzs+fc22eeU/KvPMILjSqpiqHvRpe3wce6ttidVsdO706KO8BDZeuBFLVeVFo2/WDtY80v5ef45LXnJes0ftTMqt9v5f32uevelfXOtb27/UVvzNBZJfHmYk1P3feyxrDKe7k16eEmkrBjh+poKzbuEo0O5z09w9/+Isxzz8PAF8KIWmGt46PAvjFL/vyP4pPfvKXX3ZbDAaDwfCK4yMf+TD+9//8L8AzvpvfydI17zoSVYhkQxe4LkoDJKS4ASkCcEByRbUT40YUGDGWMhEU0aAhZv15e+LSK1W6b9T8vxzG2CvCPQF0mS3L6dtQZyaXWmhEjpGfLSoZq6peSvcpzvp/R/jymdXAU2LDBXnqOWqfsPrC51diU+4zRrgUsW41/G/vZ1eRc2iIjxIPvTaHBer9s2qyR/76vErtVz0fG86szusx4zg2hZd69Z7zTrl/tD+UdKiKyp9xTud982VvPuq+vTIpc9uV8Ob+HvfOWe8n4dGjR6UPb25u8Nprr2G9LLhbnl6FYWp/904dnVe9Q6BX3vrQ+vucZ/pc8fiv61oUZO1DDifXuTGOY+u8kAvlbeIs4irU8pzVObYm4BAOlex246bhtyGEWplY30No19plUszzJzcppwG0Ybt6X95XJ0cfcs45ohxy7uh6exWBea5yPnCfg6rKZz8H+bnjd7Onisc6dpfLpaji1zmoDjGu0NVlruf09ZJWGomyF7Gi812fjwfqlDUYDAaDwfAmeNBkVRUPNdZ6Q53JTq9i8X7XBE4JQc25U7Sk6Tofas/ATylhi9WLz6RAzuGhqolCDKj71/3ba7O2j5XXalheV1jNR+4YxdeGG5O/HoWs5n/bPpAfJXMpJQzewztRmnvVpdxD6kIzdR/vgViNalZ+mDQzWdC2M/Hl0EtFuwath3OV6PG+/fhzvpu2Q8lQT1a1bZfL5YqYaAEdXZKEr6ft650K/Zhxf/WfNSQRLZHoC/zwGDChTKmq4tqvy7LAoSqfXJX1dLqDc+Gq3XoN/V/7nnOFdS7wHOHj2/GvYddKCNd1BaL0teYMs5qn8zmEgOPxWOeIq6Gi2pVKamQ+1PuQv31e17MlQqxO3t7etuPjJFyf3xOscPeqpObQOteuLdpHIPDcZHLJ48Vr/Op+vRrP+cx17FNx8mhu7rZt4njqxuhZ71qeo73Thvuw3xZ8ANIGfWddO/zqmMj2tg3svOrbJ06LB/11ZzAYDAaDYQcP9ds7AMCHPvRFVCRElTHk/zXsV/LInHOyJEc25HyQdVW9D4gpIm5tPpnkQOlx1+sNsnLGhJjJR0Mg3DUJrMrCoSGmaixuW7uUQzFs27Nk0h7gHCSfMOn919zBPeINoIYBK3EpOyWgGHs+F5fZchXU2sftyXxRnGp75fzjOEhopSxcI2tZqupHd8SVZfWzEDycF0KwxhV3pxO2LeLm5ojj8ZgNeSAEX36v910J1xY3bPkeJDR1zfm8WhipqsESqZj7W0NNqRCNhCUGyWbO9+Jyew+HsfSZKEYLtk3J2IiUuGLrkMeuKvrbFqWgjypavs3l1OvzfBFCkYlAzh6WglG+OAWa+UrPBT8zPdkQcueRUszES4nzVub6OI547eZWig/FljRLQSKXw4GzgqcEJc+PlFLJWazFuDykqNDWPDeFvKUI7zyGQYmwOiIShqGqdsF7rJcVwyiq4/F4BAA8fSLrrIYhIOR3gOwfsKSEdVmknUmrEAuhFSK+ZYVT2ntzc4v1vJTwYjhZ63hlldnVXMoth7PHQoBDGfswDBiHId8351VK8TBRMOVeYooYM7l98uRpCbuW/keZ12ln/KX4U4APoiynJO+OSnxduX7r9Ik5rH3E6XTC+XyWNnmf559EkHjnZf6pM6FxTGwyfiWcuRaBWtcNw1DTCVLeLwRX3tPqTOK2rSu9573m8+o8VGfbWhxJNXc1let/+MNfrJ3dho4Y3goCAHzRF33hy26HwWAwGN4DoO+Te7+bH2rO6jcB+E8vuxEGg8FgeM/hmwF8/GU34hWFfTcbDAaD4d3Avd/ND5WsHgF8PYBfAXB/rKzBYDAYDM+HAOALAfxXAOeX3JZXFfbdbDAYDIbPJt70u/mhklWDwWAwGAwGg8FgMLyP8Z5beM5gMBgMBoPBYDAYDK8+jKwaDAaDwWAwGAwGg+HBwciqwWAwGAwGg8FgMBgeHIysGgwGg8FgMBgMBoPhwcHIqsFgMBgMBoPBYDAYHhyMrBoMBoPBYDAYDAaD4cHByKrBYDAYDAaDwWAwGB4cjKwaDAaDwWAwGAwGg+HBYXjZDegxTdNXAPjXAD4PwG8A+KvzPP/Cy23Vw8E0Td8H4C8C+CiAr57n+Wfz9nv7zfpUME3T5wH4EQC/H8AFwC8A+BvzPP/6NE3fAOCHAdwC+ASAb5/n+dfycfd+9n7CNE3/FsCXAogAHgP42/M8/4zNvefHNE1/H8A/QH52bd49H6Zp+gSAU/4BgO+Z5/mnrP9eHOxZfjbsu/ntw76b3xnsu/mdw76b3x5e1HfzQ1RWfwjAD8zz/BUAfgByQ4aKfwvgTwD4ZLf9Wf1mfSpIAP7JPM/TPM9fDeB/AfjYNE0ewI8C+Fu5j/4jgI8BwLM+ex/iO+Z5/pp5nv8wgO8D8K/ydpt7z4Fpmr4WwDcgP7s2794yvmWe5z+Uf37K+u+Fw57lZ8O+m98+7Lv5ncG+m98B7Lv5HeNd/25+UGR1mqbfC+BrAfx43vTjAL52mqbf8/Ja9bAwz/PH53n+FG97Vr9Zn1bM8/yZeZ7/A236zwA+AuDrAJzmef543v5DAP5S/v1Zn72vMM/zb9OfHwQQbe49H6ZpOkIMgu+mzTbv3hms/14Q7Fl+c9h389uHfTe/M9h389uHfTe/K/is99+DIqsAvhjA/5nneQOA/P//zdsN9+NZ/WZ9uoPs3fluAP8ewJeAvOHzPH8agJ+m6XPf5LP3HaZp+pfTNP0SgH8E4Dtgc+958Q8B/Og8z5+gbTbv3hp+bJqm/zFN0w9O0/S7YP33ImHP8tuDvR/fIuy7+e3BvpvfNuy7+Z3jXf9ufmhk1WB4UfgXkNyO73/ZDXmVMM/zX5/n+UsAfC+Af/qy2/MqYJqmPwbgjwD4wZfdllcY3zzP89cA+HoADvbcGgzvVdh389uAfTe/ddh382cFL+S7+aGR1U8B+KJpmgIA5P8/lLcb7sez+s36tEMuhPEHAPzleZ4jgF+ChBzp558PIM7z/Jk3+ex9i3mefwTAnwLwy7C592b4kwC+EsAv5mIEHwbwUwC+HDbvngsaXjnP8xliWHwj7Ll9kbBn+e3BvpvfAuy7+Z3DvpvfEuy7+R3iRX03PyiymqtB/QyAb8ubvg3Af5/n+ddfXqsePp7Vb9anLaZp+seQmPk/lx8uAPhpALfTNH1T/vu7APzEc3z2vsE0TR+YpumL6e8/C+AzAGzuvQnmef7YPM8fmuf5o/M8fxRiRPxpiPfb5t2bYJqmR9M0fTD/7gB8K2Re2XP7gmDP8tuDfTc/P+y7+e3BvpvfPuy7+Z3hRX43u5TSZ7Pt7xjTNP1BSDnt3w3gNyHltOeX26qHg2ma/jmAvwDg9wH4NIDfmOf5q57Vb9angmmavgrAzwL4eQB3efMvzvP856dp+uOQang3qKW0fzUfd+9n7xdM0/QFAP4dgEcANsiX4d+d5/m/2dx7a8ge3D8zS3l8m3dvgmmavgzATwII+efnAPydeZ5/xfrvxcGe5WfDvpvfPuy7+e3Dvps/e7Dv5reGF/nd/ODIqsFgMBgMBoPBYDAYDA8qDNhgMBgMBoPBYDAYDAbAyKrBYDAYDAaDwWAwGB4gjKwaDAaDwWAwGAwGg+HBwciqwWAwGAwGg8FgMBgeHIysGgwGg8FgMBgMBoPhwcHIqsFgMBgMBoPBYDAYHhyMrBoMBoPBYDAYDAaD4cHh/wPBJrgPGWhKMgAAAABJRU5ErkJggg==\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"evYjR2F3-NwS"},"source":["## submission을 위한 test 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:27.469285Z","start_time":"2021-04-16T19:44:27.456021Z"},"id":"nhMLnV5d-NwS"},"source":["def test(model, data_loader, device):\n"," size = 256\n"," transform = A.Compose([A.Resize(256, 256)])\n"," print('Start prediction.')\n"," model.eval()\n"," \n"," file_name_list = []\n"," preds_array = np.empty((0, size*size), dtype=np.long)\n"," \n"," with torch.no_grad():\n"," for step, (imgs, image_infos) in enumerate(test_loader):\n","\n"," # inference (512 x 512)\n"," outs = model(torch.stack(imgs).to(device))\n"," oms = torch.argmax(outs.squeeze(), dim=1).detach().cpu().numpy()\n"," \n"," # resize (256 x 256)\n"," temp_mask = []\n"," for img, mask in zip(np.stack(imgs), oms):\n"," transformed = transform(image=img, mask=mask)\n"," mask = transformed['mask']\n"," temp_mask.append(mask)\n","\n"," oms = np.array(temp_mask)\n"," \n"," oms = oms.reshape([oms.shape[0], size*size]).astype(int)\n"," preds_array = np.vstack((preds_array, oms))\n"," \n"," file_name_list.append([i['file_name'] for i in image_infos])\n"," print(\"End prediction.\")\n"," file_names = [y for x in file_name_list for y in x]\n"," \n"," return file_names, preds_array"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"r1YKHBf4-NwT"},"source":["## submission.csv 생성"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:45:42.235310Z","start_time":"2021-04-16T19:44:30.499016Z"},"scrolled":true,"id":"Bz79_g8K-NwT","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620103458747,"user_tz":-540,"elapsed":512460,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"36e4f410-35c7-4b9c-9e4a-e66269f73292"},"source":["# sample_submisson.csv 열기\n","submission = pd.read_csv('./submission/sample_submission.csv', index_col=None)\n","\n","# test set에 대한 prediction\n","file_names, preds = test(model, test_loader, device)\n","\n","# PredictionString 대입\n","for file_name, string in zip(file_names, preds):\n"," submission = submission.append({\"image_id\" : file_name, \"PredictionString\" : ' '.join(str(e) for e in string.tolist())}, \n"," ignore_index=True)\n","\n","# submission.csv로 저장\n","submission.to_csv(\"./submission/re_pan_effb3_noisy_focal_madgrad_cosLR.csv\", index=False)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Start prediction.\n","End prediction.\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"Zgl7Ye7F-NwT"},"source":["## 제출까지\n","\n"]},{"cell_type":"code","metadata":{"id":"022E8AC7-NwT","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620103464861,"user_tz":-540,"elapsed":5218,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"1b00da55-62cc-4fa6-c823-83dcbaa871b0"},"source":["import json\n","import requests\n","import os\n","from urllib.parse import urlparse, parse_qsl, urlencode, urlunparse\n","\n","def submit(user_key='', file_path = '', desc=\"\"):\n"," if not user_key:\n"," raise Exception(\"No UserKey\" )\n"," url = urlparse('http://ec2-13-124-161-225.ap-northeast-2.compute.amazonaws.com:8000/api/v1/competition/28/presigned_url/?description=&hyperparameters={%22training%22:{},%22inference%22:{}}')\n"," qs = dict(parse_qsl(url.query))\n"," qs['description'] = desc\n"," parts = url._replace(query=urlencode(qs))\n"," url = urlunparse(parts)\n","\n"," print(url)\n"," headers = {\n"," 'Authorization': user_key\n"," }\n"," res = requests.get(url, headers=headers)\n"," print(res.text)\n"," data = json.loads(res.text)\n"," \n"," submit_url = data['url']\n"," body = {\n"," 'key':'app/Competitions/000028/Users/{}/Submissions/{}/output.csv'.format(str(data['submission']['user']).zfill(8),str(data['submission']['local_id']).zfill(4)),\n"," 'x-amz-algorithm':data['fields']['x-amz-algorithm'],\n"," 'x-amz-credential':data['fields']['x-amz-credential'],\n"," 'x-amz-date':data['fields']['x-amz-date'],\n"," 'policy':data['fields']['policy'],\n"," 'x-amz-signature':data['fields']['x-amz-signature']\n"," }\n"," requests.post(url=submit_url, data=body, files={'file': open(file_path, 'rb')})\n","\n","\n","####################################################################################\n","test_dir = \"/content/drive/MyDrive/Trash/code/submission\" # 수정 필요 : output 파일 폴더 \n","desc = 're_pan_effb3_noisy_focal_madgrad_cosLR' # 수정 필요 : 파일에 대한 설명\n","output_file = \"re_pan_effb3_noisy_focal_madgrad_cosLR.csv\" #수정 필요 : output 파일 \n","user_key = \"Bearer 7bb5f96452751a238ffaf91a93c4242bf9b72abe\" # 수정 필요 : Authorization \n","\n","\n","submit(user_key, os.path.join(test_dir, output_file),desc)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["http://ec2-13-124-161-225.ap-northeast-2.compute.amazonaws.com:8000/api/v1/competition/28/presigned_url/?hyperparameters=%7B%22training%22%3A%7B%7D%2C%22inference%22%3A%7B%7D%7D&description=re_pan_effb3_noisy_focal_madgrad_cosLR\n","{\"url\":\"https://prod-aistages-private.s3.amazonaws.com/\",\"fields\":{\"key\":\"app/Competitions/000028/Users/00000099/Submissions/0021/output.csv\",\"x-amz-algorithm\":\"AWS4-HMAC-SHA256\",\"x-amz-credential\":\"AKIA45LU4MHUJ7WLDQVO/20210504/ap-northeast-2/s3/aws4_request\",\"x-amz-date\":\"20210504T044420Z\",\"policy\":\"eyJleHBpcmF0aW9uIjogIjIwMjEtMDUtMDRUMDU6NDQ6MjBaIiwgImNvbmRpdGlvbnMiOiBbeyJidWNrZXQiOiAicHJvZC1haXN0YWdlcy1wcml2YXRlIn0sIHsia2V5IjogImFwcC9Db21wZXRpdGlvbnMvMDAwMDI4L1VzZXJzLzAwMDAwMDk5L1N1Ym1pc3Npb25zLzAwMjEvb3V0cHV0LmNzdiJ9LCB7IngtYW16LWFsZ29yaXRobSI6ICJBV1M0LUhNQUMtU0hBMjU2In0sIHsieC1hbXotY3JlZGVudGlhbCI6ICJBS0lBNDVMVTRNSFVKN1dMRFFWTy8yMDIxMDUwNC9hcC1ub3J0aGVhc3QtMi9zMy9hd3M0X3JlcXVlc3QifSwgeyJ4LWFtei1kYXRlIjogIjIwMjEwNTA0VDA0NDQyMFoifV19\",\"x-amz-signature\":\"f10073e85a92d49dacd44915305fdc789f99edede496d9b4f5029a16e422a52f\"},\"submission\":{\"id\":14867,\"phase\":\"Created\",\"type\":\"File\",\"local_id\":21,\"hyperparameters\":\"{\\\"training\\\": {}, \\\"inference\\\": {}}\",\"description\":\"re_pan_effb3_noisy_focal_madgrad_cosLR\",\"final\":false,\"created_at\":\"2021-05-04T13:44:20.427630+09:00\",\"updated_at\":\"2021-05-04T13:44:20.427663+09:00\",\"user\":99,\"competition\":28,\"image\":null}}\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"wPYl39uVqxL8"},"source":[""],"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/chanyub_seg/code/adamp.ipynb b/chanyub_seg/code/adamp.ipynb deleted file mode 100644 index 1e4180e..0000000 --- a/chanyub_seg/code/adamp.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"nbformat":4,"nbformat_minor":0,"metadata":{"hide_input":false,"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.1"},"toc":{"base_numbering":1,"nav_menu":{},"number_sections":true,"sideBar":true,"skip_h1_title":false,"title_cell":"Table of Contents","title_sidebar":"Contents","toc_cell":true,"toc_position":{"height":"calc(100% - 180px)","left":"10px","top":"150px","width":"297.278px"},"toc_section_display":true,"toc_window_display":true},"colab":{"name":"adamp.ipynb","provenance":[],"machine_shape":"hm"},"accelerator":"GPU"},"cells":[{"cell_type":"markdown","metadata":{"toc":true,"id":"cb_9XOTk8sQC"},"source":["

Table of Contents

\n","
"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QBSL7_LP9ONj","executionInfo":{"status":"ok","timestamp":1619944808745,"user_tz":-540,"elapsed":766,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"72482471-7b23-460b-9025-be10625b85c3"},"source":["ls"],"execution_count":35,"outputs":[{"output_type":"stream","text":[" FCN32s.ipynb sample_submission.csv 'UNet++ baseline.ipynb'\n"," mybaseline.ipynb \u001b[0m\u001b[01;34msaved\u001b[0m/ utils.py\n"," \u001b[01;34m__pycache__\u001b[0m/ \u001b[01;34msubmission\u001b[0m/ \u001b[01;34mwandb\u001b[0m/\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"40-m-lE19ewI","executionInfo":{"status":"ok","timestamp":1620026000385,"user_tz":-540,"elapsed":983,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"531c610d-7daa-4d6c-9658-5ea215d13d4f"},"source":["cd drive/MyDrive/Trash/code"],"execution_count":2,"outputs":[{"output_type":"stream","text":["/content/drive/MyDrive/Trash/code\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"MJojTRE39ULc","executionInfo":{"status":"ok","timestamp":1620025995161,"user_tz":-540,"elapsed":1894,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"d9d3f540-29c5-43d7-a0e3-8e005f2c753c"},"source":["from google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Ys0WTRaJ91VZ","executionInfo":{"status":"ok","timestamp":1620026007889,"user_tz":-540,"elapsed":3650,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"afd9d272-e64f-4936-c4d4-8a2f7cfa95f0"},"source":["!pip install albumentations==0.5.2"],"execution_count":3,"outputs":[{"output_type":"stream","text":["Requirement already satisfied: albumentations==0.5.2 in /usr/local/lib/python3.7/dist-packages (0.5.2)\n","Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (1.4.1)\n","Requirement already satisfied: opencv-python-headless>=4.1.1 in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (4.5.1.48)\n","Requirement already satisfied: scikit-image>=0.16.1 in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (0.16.2)\n","Requirement already satisfied: PyYAML in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (3.13)\n","Requirement already satisfied: numpy>=1.11.1 in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (1.19.5)\n","Requirement already satisfied: imgaug>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (0.4.0)\n","Requirement already satisfied: pillow>=4.3.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations==0.5.2) (7.1.2)\n","Requirement already satisfied: imageio>=2.3.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations==0.5.2) (2.4.1)\n","Requirement already satisfied: networkx>=2.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations==0.5.2) (2.5.1)\n","Requirement already satisfied: matplotlib!=3.0.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations==0.5.2) (3.2.2)\n","Requirement already satisfied: PyWavelets>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations==0.5.2) (1.1.1)\n","Requirement already satisfied: Shapely in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (1.7.1)\n","Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (1.15.0)\n","Requirement already satisfied: opencv-python in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (4.1.2.30)\n","Requirement already satisfied: decorator<5,>=4.3 in /usr/local/lib/python3.7/dist-packages (from networkx>=2.0->scikit-image>=0.16.1->albumentations==0.5.2) (4.4.2)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.16.1->albumentations==0.5.2) (0.10.0)\n","Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.16.1->albumentations==0.5.2) (2.8.1)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.16.1->albumentations==0.5.2) (1.3.1)\n","Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.16.1->albumentations==0.5.2) (2.4.7)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.826930Z","start_time":"2021-04-18T10:34:45.406686Z"},"scrolled":false,"colab":{"base_uri":"https://localhost:8080/"},"id":"k5pVFOkJ8sQX","executionInfo":{"status":"ok","timestamp":1620026012019,"user_tz":-540,"elapsed":3418,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"56b8bde2-3ad4-40c1-9808-69495f5bf76c"},"source":["import os\n","import random\n","import time\n","import json\n","import warnings \n","warnings.filterwarnings('ignore')\n","\n","import torch\n","import torch.nn as nn\n","from torch.utils.data import Dataset, DataLoader\n","from utils import label_accuracy_score\n","import cv2\n","\n","import numpy as np\n","import pandas as pd\n","\n","# 전처리를 위한 라이브러리\n","from pycocotools.coco import COCO\n","import torchvision\n","import torchvision.transforms as transforms\n","\n","import albumentations as A\n","from albumentations.pytorch import ToTensorV2\n","\n","# 시각화를 위한 라이브러리\n","import matplotlib.pyplot as plt\n","import seaborn as sns; sns.set()\n","\n","plt.rcParams['axes.grid'] = False\n","\n","print('pytorch version: {}'.format(torch.__version__))\n","print('GPU 사용 가능 여부: {}'.format(torch.cuda.is_available()))\n","\n","print(torch.cuda.get_device_name(0))\n","print(torch.cuda.device_count())\n","\n","device = \"cuda\" if torch.cuda.is_available() else \"cpu\" # GPU 사용 가능 여부에 따라 device 정보 저장"],"execution_count":4,"outputs":[{"output_type":"stream","text":["pytorch version: 1.8.1+cu101\n","GPU 사용 가능 여부: True\n","Tesla P100-PCIE-16GB\n","1\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"bGTnZqwO8sQa"},"source":["## 하이퍼파라미터 세팅 및 seed 고정"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.841930Z","start_time":"2021-04-18T10:34:47.827931Z"},"id":"dV2e6X4l8sQb","executionInfo":{"status":"ok","timestamp":1620026014401,"user_tz":-540,"elapsed":1869,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["batch_size = 8 # Mini-batch size\n","num_epochs = 20\n","learning_rate = 0.0001"],"execution_count":5,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.856930Z","start_time":"2021-04-18T10:34:47.842931Z"},"id":"lFBFwi8T8sQe","executionInfo":{"status":"ok","timestamp":1620026024069,"user_tz":-540,"elapsed":1151,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# seed 고정\n","random_seed = 42\n","torch.manual_seed(random_seed)\n","torch.cuda.manual_seed(random_seed)\n","# torch.cuda.manual_seed_all(random_seed) # if use multi-GPU\n","torch.backends.cudnn.deterministic = True\n","torch.backends.cudnn.benchmark = False\n","np.random.seed(random_seed)\n","random.seed(random_seed)"],"execution_count":6,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"d_aMBo_P8sQg"},"source":["## 학습 데이터 EDA"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.381961Z","start_time":"2021-04-18T10:34:47.857930Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"IZ8czxZE8sQg","executionInfo":{"status":"ok","timestamp":1620026032284,"user_tz":-540,"elapsed":4531,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"dd0d83fc-509d-4e5b-9923-61d5752d6469"},"source":["%matplotlib inline\n","\n","dataset_path = '../input/data'\n","anns_file_path = dataset_path + '/' + 'train.json'\n","\n","# Read annotations\n","with open(anns_file_path, 'r') as f:\n"," dataset = json.loads(f.read())\n","\n","categories = dataset['categories']\n","anns = dataset['annotations']\n","imgs = dataset['images']\n","nr_cats = len(categories)\n","nr_annotations = len(anns)\n","nr_images = len(imgs)\n","\n","# Load categories and super categories\n","cat_names = []\n","super_cat_names = []\n","super_cat_ids = {}\n","super_cat_last_name = ''\n","nr_super_cats = 0\n","for cat_it in categories:\n"," cat_names.append(cat_it['name'])\n"," super_cat_name = cat_it['supercategory']\n"," # Adding new supercat\n"," if super_cat_name != super_cat_last_name:\n"," super_cat_names.append(super_cat_name)\n"," super_cat_ids[super_cat_name] = nr_super_cats\n"," super_cat_last_name = super_cat_name\n"," nr_super_cats += 1\n","\n","print('Number of super categories:', nr_super_cats)\n","print('Number of categories:', nr_cats)\n","print('Number of annotations:', nr_annotations)\n","print('Number of images:', nr_images)"],"execution_count":7,"outputs":[{"output_type":"stream","text":["Number of super categories: 11\n","Number of categories: 11\n","Number of annotations: 21116\n","Number of images: 2617\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.546964Z","start_time":"2021-04-18T10:34:51.382969Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/","height":355},"id":"Xjp6yDNe8sQi","executionInfo":{"status":"ok","timestamp":1620026033096,"user_tz":-540,"elapsed":2276,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"efb95d92-4df6-4530-d866-cba37ba32fc3"},"source":["# Count annotations\n","cat_histogram = np.zeros(nr_cats,dtype=int)\n","for ann in anns:\n"," cat_histogram[ann['category_id']] += 1\n","\n","# Initialize the matplotlib figure\n","f, ax = plt.subplots(figsize=(5,5))\n","\n","# Convert to DataFrame\n","df = pd.DataFrame({'Categories': cat_names, 'Number of annotations': cat_histogram})\n","df = df.sort_values('Number of annotations', 0, False)\n","\n","# Plot the histogram\n","plt.title(\"category distribution of train set \")\n","plot_1 = sns.barplot(x=\"Number of annotations\", y=\"Categories\", data=df, label=\"Total\", color=\"b\")"],"execution_count":8,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.561965Z","start_time":"2021-04-18T10:34:51.547969Z"},"id":"F5imLAv78sQj","executionInfo":{"status":"ok","timestamp":1620026034354,"user_tz":-540,"elapsed":1692,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# category labeling \n","sorted_temp_df = df.sort_index()\n","\n","# background = 0 에 해당되는 label 추가 후 기존들을 모두 label + 1 로 설정\n","sorted_df = pd.DataFrame([\"Backgroud\"], columns = [\"Categories\"])\n","sorted_df = sorted_df.append(sorted_temp_df, ignore_index=True)"],"execution_count":9,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.576961Z","start_time":"2021-04-18T10:34:51.562964Z"},"scrolled":false,"colab":{"base_uri":"https://localhost:8080/","height":421},"id":"t7VfbZUe8sQj","executionInfo":{"status":"ok","timestamp":1620026034970,"user_tz":-540,"elapsed":1616,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"8f2d4f65-045e-4cfb-f274-9da743cace35"},"source":["# class (Categories) 에 따른 index 확인 (0~11 : 총 12개)\n","sorted_df"],"execution_count":10,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
CategoriesNumber of annotations
0BackgroudNaN
1UNKNOWN128.0
2General trash2225.0
3Paper7448.0
4Paper pack527.0
5Metal449.0
6Glass488.0
7Plastic2472.0
8Styrofoam1074.0
9Plastic bag6114.0
10Battery50.0
11Clothing141.0
\n","
"],"text/plain":[" Categories Number of annotations\n","0 Backgroud NaN\n","1 UNKNOWN 128.0\n","2 General trash 2225.0\n","3 Paper 7448.0\n","4 Paper pack 527.0\n","5 Metal 449.0\n","6 Glass 488.0\n","7 Plastic 2472.0\n","8 Styrofoam 1074.0\n","9 Plastic bag 6114.0\n","10 Battery 50.0\n","11 Clothing 141.0"]},"metadata":{"tags":[]},"execution_count":10}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":396},"id":"74VbmLZI7HYs","executionInfo":{"status":"ok","timestamp":1620024976623,"user_tz":-540,"elapsed":3928,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"94bbf3a6-fce8-4b7c-adfa-ebb5acae3516"},"source":["# train_loader의 output 결과(image 및 mask) 확인\n","for imgs, masks, image_infos in train_loader:\n"," image_infos = image_infos[0]\n"," temp_images = imgs\n"," temp_masks = masks\n"," \n"," break\n","\n","fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(12, 12))\n","\n","print('image shape:', list(temp_images[0].shape))\n","print('mask shape: ', list(temp_masks[0].shape))\n","print('Unique values, category of transformed mask : \\n', [{int(i),category_names[int(i)]} for i in list(np.unique(temp_masks[0]))])\n","\n","ax1.imshow(temp_images[0].permute([1,2,0]))\n","ax1.grid(False)\n","ax1.set_title(\"input image : {}\".format(image_infos['file_name']), fontsize = 15)\n","\n","ax2.imshow(temp_masks[0])\n","ax2.grid(False)\n","ax2.set_title(\"masks : {}\".format(image_infos['file_name']), fontsize = 15)\n","\n","plt.show()"],"execution_count":50,"outputs":[{"output_type":"stream","text":["image shape: [3, 512, 512]\n","mask shape: [512, 512]\n","Unique values, category of transformed mask : \n"," [{0, 'Backgroud'}, {'Plastic', 7}]\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"dL-azWBK8sQk"},"source":["## 데이터 전처리 함수 정의 (Dataset)"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:52.693328Z","start_time":"2021-04-18T10:34:52.681328Z"},"id":"rKnBddei8sQk","executionInfo":{"status":"ok","timestamp":1620026049737,"user_tz":-540,"elapsed":1205,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["category_names = list(sorted_df.Categories)\n","\n","def get_classname(classID, cats):\n"," for i in range(len(cats)):\n"," if cats[i]['id']==classID:\n"," return cats[i]['name']\n"," return \"None\"\n","\n","class CustomDataLoader(Dataset):\n"," \"\"\"COCO format\"\"\"\n"," def __init__(self, data_dir, mode = 'train', transform = None):\n"," super().__init__()\n"," self.mode = mode\n"," self.transform = transform\n"," self.coco = COCO(data_dir)\n"," \n"," def __getitem__(self, index: int):\n"," # dataset이 index되어 list처럼 동작\n"," image_id = self.coco.getImgIds(imgIds=index)\n"," image_infos = self.coco.loadImgs(image_id)[0]\n"," \n"," # cv2 를 활용하여 image 불러오기\n"," images = cv2.imread(os.path.join(dataset_path, image_infos['file_name']))\n"," images = cv2.cvtColor(images, cv2.COLOR_BGR2RGB).astype(np.float32)\n"," images /= 255.0\n"," \n"," if (self.mode in ('train', 'val')):\n"," ann_ids = self.coco.getAnnIds(imgIds=image_infos['id'])\n"," anns = self.coco.loadAnns(ann_ids)\n","\n"," # Load the categories in a variable\n"," cat_ids = self.coco.getCatIds()\n"," cats = self.coco.loadCats(cat_ids)\n","\n"," # masks : size가 (height x width)인 2D\n"," # 각각의 pixel 값에는 \"category id + 1\" 할당\n"," # Background = 0\n"," masks = np.zeros((image_infos[\"height\"], image_infos[\"width\"]))\n"," # Unknown = 1, General trash = 2, ... , Cigarette = 11\n"," for i in range(len(anns)):\n"," className = get_classname(anns[i]['category_id'], cats)\n"," pixel_value = category_names.index(className)\n"," masks = np.maximum(self.coco.annToMask(anns[i])*pixel_value, masks)\n"," masks = masks.astype(np.float32)\n","\n"," # transform -> albumentations 라이브러리 활용\n"," if self.transform is not None:\n"," transformed = self.transform(image=images, mask=masks)\n"," images = transformed[\"image\"]\n"," masks = transformed[\"mask\"]\n"," \n"," return images, masks, image_infos\n"," \n"," if self.mode == 'test':\n"," # transform -> albumentations 라이브러리 활용\n"," if self.transform is not None:\n"," transformed = self.transform(image=images)\n"," images = transformed[\"image\"]\n"," \n"," return images, image_infos\n"," \n"," \n"," def __len__(self) -> int:\n"," # 전체 dataset의 size를 return\n"," return len(self.coco.getImgIds())"],"execution_count":11,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Lui--J9t8sQm"},"source":["## Dataset 정의 및 DataLoader 할당"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:58.823175Z","start_time":"2021-04-18T10:34:54.106233Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"_1PmdNvf8sQm","executionInfo":{"status":"ok","timestamp":1620026286582,"user_tz":-540,"elapsed":5490,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"9b4aa996-341c-438b-9862-a8f09b165c16"},"source":["# train.json / validation.json / test.json 디렉토리 설정\n","train_path = dataset_path + '/train.json'\n","val_path = dataset_path + '/val.json'\n","test_path = dataset_path + '/test.json'\n","\n","# collate_fn needs for batch\n","def collate_fn(batch):\n"," return tuple(zip(*batch))\n","\n","train_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","val_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","test_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","# create own Dataset 1 (skip)\n","# validation set을 직접 나누고 싶은 경우\n","# random_split 사용하여 data set을 8:2 로 분할\n","# train_size = int(0.8*len(dataset))\n","# val_size = int(len(dataset)-train_size)\n","# dataset = CustomDataLoader(data_dir=train_path, mode='train', transform=train_transform)\n","# train_dataset, val_dataset = torch.utils.data.random_split(dataset, [train_size, val_size])\n","\n","# create own Dataset 2\n","# train dataset\n","train_dataset = CustomDataLoader(data_dir=train_path, mode='train', transform=train_transform)\n","\n","# validation dataset\n","val_dataset = CustomDataLoader(data_dir=val_path, mode='val', transform=val_transform)\n","\n","# test dataset\n","test_dataset = CustomDataLoader(data_dir=test_path, mode='test', transform=test_transform)\n","\n","\n","# DataLoader\n","train_loader = torch.utils.data.DataLoader(dataset=train_dataset, \n"," batch_size=batch_size,\n"," shuffle=True,\n"," num_workers=4,\n"," collate_fn=collate_fn,\n"," drop_last=True)\n","\n","val_loader = torch.utils.data.DataLoader(dataset=val_dataset, \n"," batch_size=batch_size,\n"," shuffle=False,\n"," num_workers=4,\n"," collate_fn=collate_fn,\n"," drop_last=True) \n","\n","test_loader = torch.utils.data.DataLoader(dataset=test_dataset,\n"," batch_size=batch_size,\n"," num_workers=4,\n"," collate_fn=collate_fn)"],"execution_count":13,"outputs":[{"output_type":"stream","text":["loading annotations into memory...\n","Done (t=3.81s)\n","creating index...\n","index created!\n","loading annotations into memory...\n","Done (t=0.80s)\n","creating index...\n","index created!\n","loading annotations into memory...\n","Done (t=0.02s)\n","creating index...\n","index created!\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"4-uzBwkH8sQ2"},"source":["## baseline model\n","\n","### [TODO] 코드 구현 UNet++ \n","\n","- 출처 : https://jinglescode.github.io/2019/12/02/biomedical-image-segmentation-u-net-nested/"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"gGgEjCSsAo7q","executionInfo":{"status":"ok","timestamp":1620026315522,"user_tz":-540,"elapsed":3607,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"701039b5-e874-4dcc-82da-dc6174aa5dc6"},"source":["!pip install wandb"],"execution_count":14,"outputs":[{"output_type":"stream","text":["Requirement already satisfied: wandb in /usr/local/lib/python3.7/dist-packages (0.10.28)\n","Requirement already satisfied: PyYAML in /usr/local/lib/python3.7/dist-packages (from wandb) (3.13)\n","Requirement already satisfied: sentry-sdk>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (1.0.0)\n","Requirement already satisfied: subprocess32>=3.5.3 in /usr/local/lib/python3.7/dist-packages (from wandb) (3.5.4)\n","Requirement already satisfied: promise<3,>=2.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (2.3)\n","Requirement already satisfied: configparser>=3.8.1 in /usr/local/lib/python3.7/dist-packages (from wandb) (5.0.2)\n","Requirement already satisfied: Click>=7.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (7.1.2)\n","Requirement already satisfied: docker-pycreds>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (0.4.0)\n","Requirement already satisfied: protobuf>=3.12.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (3.12.4)\n","Requirement already satisfied: pathtools in /usr/local/lib/python3.7/dist-packages (from wandb) (0.1.2)\n","Requirement already satisfied: psutil>=5.0.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (5.4.8)\n","Requirement already satisfied: GitPython>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (3.1.14)\n","Requirement already satisfied: six>=1.13.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (1.15.0)\n","Requirement already satisfied: shortuuid>=0.5.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (1.0.1)\n","Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.7/dist-packages (from wandb) (2.8.1)\n","Requirement already satisfied: requests<3,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (2.23.0)\n","Requirement already satisfied: urllib3>=1.10.0 in /usr/local/lib/python3.7/dist-packages (from sentry-sdk>=0.4.0->wandb) (1.24.3)\n","Requirement already satisfied: certifi in /usr/local/lib/python3.7/dist-packages (from sentry-sdk>=0.4.0->wandb) (2020.12.5)\n","Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from protobuf>=3.12.0->wandb) (56.0.0)\n","Requirement already satisfied: gitdb<5,>=4.0.1 in /usr/local/lib/python3.7/dist-packages (from GitPython>=1.0.0->wandb) (4.0.7)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.0.0->wandb) (2.10)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.0.0->wandb) (3.0.4)\n","Requirement already satisfied: smmap<5,>=3.0.1 in /usr/local/lib/python3.7/dist-packages (from gitdb<5,>=4.0.1->GitPython>=1.0.0->wandb) (4.0.0)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":173},"id":"F89FhXcJ_QpU","executionInfo":{"status":"ok","timestamp":1620026340489,"user_tz":-540,"elapsed":9599,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"3746ea9f-1436-4040-b742-537da9efcb28"},"source":["import wandb\n","\n","proj_name = 'v3+_focal_coslr_mIoU_adamp'\n","\n","wandb.init(project='chanyub',name=proj_name)\n","\n","config = wandb.config\n","config.learning_rate = 0.01"],"execution_count":15,"outputs":[{"output_type":"stream","text":["\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mpstage12\u001b[0m (use `wandb login --relogin` to force relogin)\n"],"name":"stderr"},{"output_type":"display_data","data":{"text/html":["\n"," Tracking run with wandb version 0.10.28
\n"," Syncing run v3+_focal_coslr_mIoU_adamp to Weights & Biases (Documentation).
\n"," Project page: https://wandb.ai/pstage12/chanyub
\n"," Run page: https://wandb.ai/pstage12/chanyub/runs/1aenditb
\n"," Run data is saved locally in /content/drive/My Drive/Trash/code/wandb/run-20210503_071857-1aenditb

\n"," "],"text/plain":[""]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"mVz3IiRHAuov","executionInfo":{"status":"ok","timestamp":1620026373565,"user_tz":-540,"elapsed":3715,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"a8730b5d-e359-4fb2-9c71-c3348b64d8fe"},"source":["!pip install segmentation_models_pytorch"],"execution_count":16,"outputs":[{"output_type":"stream","text":["Requirement already satisfied: segmentation_models_pytorch in /usr/local/lib/python3.7/dist-packages (0.1.3)\n","Requirement already satisfied: timm==0.3.2 in /usr/local/lib/python3.7/dist-packages (from segmentation_models_pytorch) (0.3.2)\n","Requirement already satisfied: torchvision>=0.3.0 in /usr/local/lib/python3.7/dist-packages (from segmentation_models_pytorch) (0.9.1+cu101)\n","Requirement already satisfied: efficientnet-pytorch==0.6.3 in /usr/local/lib/python3.7/dist-packages (from segmentation_models_pytorch) (0.6.3)\n","Requirement already satisfied: pretrainedmodels==0.7.4 in /usr/local/lib/python3.7/dist-packages (from segmentation_models_pytorch) (0.7.4)\n","Requirement already satisfied: torch>=1.0 in /usr/local/lib/python3.7/dist-packages (from timm==0.3.2->segmentation_models_pytorch) (1.8.1+cu101)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torchvision>=0.3.0->segmentation_models_pytorch) (1.19.5)\n","Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.7/dist-packages (from torchvision>=0.3.0->segmentation_models_pytorch) (7.1.2)\n","Requirement already satisfied: munch in /usr/local/lib/python3.7/dist-packages (from pretrainedmodels==0.7.4->segmentation_models_pytorch) (2.5.0)\n","Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from pretrainedmodels==0.7.4->segmentation_models_pytorch) (4.41.1)\n","Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch>=1.0->timm==0.3.2->segmentation_models_pytorch) (3.7.4.3)\n","Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from munch->pretrainedmodels==0.7.4->segmentation_models_pytorch) (1.15.0)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:11.634792Z","start_time":"2021-04-18T16:16:05.875817Z"},"colab":{"base_uri":"https://localhost:8080/"},"id":"E5-leCCF8sQ5","executionInfo":{"status":"ok","timestamp":1620026381204,"user_tz":-540,"elapsed":6731,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"e2e2dcd8-ee64-4979-88c1-69e78ef472b1"},"source":["# 구현된 model에 임의의 input을 넣어 output이 잘 나오는지 test\n","import segmentation_models_pytorch as smp\n","\n","# model = smp.DeepLabV3Plus('timm-efficientnet-b3', encoder_weights = 'noisy-student',classes=12)\n","model = smp.DeepLabV3Plus(classes=12)\n","x = torch.randn([2, 3, 512, 512])\n","print(\"input shape : \", x.shape)\n","out = model(x).to(device)\n","print(\"output shape : \", out.size())\n","\n","model = model.to(device)"],"execution_count":17,"outputs":[{"output_type":"stream","text":["input shape : torch.Size([2, 3, 512, 512])\n","output shape : torch.Size([2, 12, 512, 512])\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"qLpYHkTE8sQ6"},"source":["## train, validation, test 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.104200Z","start_time":"2021-04-18T16:16:18.093174Z"},"id":"l3qdtKiO8sQ6","executionInfo":{"status":"ok","timestamp":1620026383340,"user_tz":-540,"elapsed":2109,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["def train(num_epochs, model, data_loader, val_loader, criterion, optimizer, saved_dir, val_every, device, lr_scheduler):\n"," print('Start training..')\n"," best_loss = 9999999\n"," for epoch in range(num_epochs):\n"," model.train()\n"," for step, (images, masks, _) in enumerate(data_loader):\n"," images = torch.stack(images) # (batch, channel, height, width)\n"," masks = torch.stack(masks).long() # (batch, channel, height, width)\n"," \n"," # gpu 연산을 위해 device 할당\n"," images, masks = images.to(device), masks.to(device)\n"," \n"," # inference\n"," outputs = model(images)\n"," \n"," # loss 계산 (cross entropy loss)\n"," loss = criterion(outputs, masks)\n"," optimizer.zero_grad()\n"," loss.backward()\n"," optimizer.step()\n"," \n"," lr_scheduler.step()\n"," \n"," # step 주기에 따른 loss 출력\n"," if (step + 1) % 25 == 0:\n"," print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(\n"," epoch+1, num_epochs, step+1, len(train_loader), loss.item()))\n"," \n"," # validation 주기에 따른 loss 출력 및 best model 저장\n"," if (epoch + 1) % val_every == 0:\n"," avrg_loss = validation(epoch + 1, model, val_loader, criterion, device)\n"," if avrg_loss < best_loss:\n"," print('Best performance at epoch: {}'.format(epoch + 1))\n"," print('Save model in', saved_dir)\n"," best_loss = avrg_loss\n"," wandb.log({'best_loss': best_loss})\n"," save_model(model, saved_dir)"],"execution_count":18,"outputs":[]},{"cell_type":"code","metadata":{"id":"j2aKiPqjOdYt","executionInfo":{"status":"ok","timestamp":1620017597856,"user_tz":-540,"elapsed":1950,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["classes_dict = {0: 'Background',\n"," 1: 'UNKNOWN',\n"," 2: 'General trash',\n"," 3: 'Paper',\n"," 4: 'Paper pack',\n"," 5: 'Metal',\n"," 6: 'Glass',\n"," 7: 'Plastic',\n"," 8: 'Styrofoam',\n"," 9: 'Plastic bag',\n"," 10: 'Battery',\n"," 11: 'Clothing'}"],"execution_count":28,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.285795Z","start_time":"2021-04-18T16:16:18.267686Z"},"id":"lDbL-1wq8sQ7","executionInfo":{"status":"ok","timestamp":1620026390201,"user_tz":-540,"elapsed":1098,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["def validation(epoch, model, data_loader, criterion, device):\n"," print('Start validation #{}'.format(epoch))\n"," model.eval()\n"," with torch.no_grad():\n"," total_loss = 0\n"," cnt = 0\n"," mIoU_list = []\n"," for step, (images, masks, _) in enumerate(data_loader):\n"," \n"," images = torch.stack(images) # (batch, channel, height, width)\n"," masks = torch.stack(masks).long() # (batch, channel, height, width)\n","\n"," images, masks = images.to(device), masks.to(device) \n","\n"," outputs = model(images)\n"," loss = criterion(outputs, masks)\n"," total_loss += loss\n"," cnt += 1\n","\n"," # print(outputs.shape) # (8, 12, 512, 512)\n"," # print(masks.shape) # (8, 512, 512)\n","\n"," # n_o = outputs.detach().cpu().numpy()\n"," # n_o_s = np.squeeze(n_o, axis=0)\n"," # n_o_s_2 = np.squeeze(n_o_s, axis=0)\n"," \n"," # n_m = masks.detach().cpu().numpy()\n"," # n_m_s = np.squeeze(n_m, axis=0)\n","\n"," # wandb.log(wandb.Image(images, masks={\n"," # \"predictions\" : {\n"," # \"mask_data\" : n_o_s_2,\n"," # \"class_labels\" : classes_dict\n"," # },\n"," # \"ground_truth\" : {\n"," # \"mask_data\" : n_m_s,\n"," # \"class_labels\" : classes_dict\n"," # }\n"," # }))\n","\n"," outputs = torch.argmax(outputs.squeeze(), dim=1).detach().cpu().numpy()\n","\n"," mIoU = label_accuracy_score(masks.detach().cpu().numpy(), outputs, n_class=12)[2]\n"," mIoU_list.append(mIoU)\n"," \n"," avrg_loss = total_loss / cnt\n"," avrg_mIoU = np.mean(mIoU_list)\n"," print('Validation #{} Average Loss: {:.4f}, mIoU: {:.4f}'.format(epoch, avrg_loss, np.mean(mIoU_list)))\n"," wandb.log({'Train Loss':loss.item(),\n"," 'Val Loss':avrg_loss ,\n"," 'Val mIoU':np.mean(mIoU_list)})\n"," # return avrg_loss\n"," return avrg_mIoU"],"execution_count":19,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"SgQs2p6n8sQ9"},"source":["## 모델 저장 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.909918Z","start_time":"2021-04-18T16:16:18.898918Z"},"id":"h5vfGkK58sQ-","executionInfo":{"status":"ok","timestamp":1620026409584,"user_tz":-540,"elapsed":1221,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# 모델 저장 함수 정의\n","val_every = 1 \n","\n","saved_dir = './saved'\n","if not os.path.isdir(saved_dir): \n"," os.mkdir(saved_dir)\n"," \n","def save_model(model, saved_dir, file_name='v3+_focal_coslr_mIoU_adamp.pt'):\n"," check_point = {'net': model.state_dict()}\n"," output_path = os.path.join(saved_dir, file_name)\n"," torch.save(model.state_dict(), output_path)"],"execution_count":20,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"f8cT79Ad8sQ_"},"source":["## 모델 생성 및 Loss function, Optimizer 정의"]},{"cell_type":"code","metadata":{"id":"neKn53b4_-2d","executionInfo":{"status":"ok","timestamp":1620026413086,"user_tz":-540,"elapsed":1658,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["from torch.autograd import Variable\n","import torch.nn.functional as F\n","# ref : https://github.com/clcarwin/focal_loss_pytorch\n","class FocalLoss(nn.Module):\n"," def __init__(self, gamma=0, alpha=None, size_average=True):\n"," super(FocalLoss, self).__init__()\n"," self.gamma = gamma\n"," self.alpha = alpha\n"," if isinstance(alpha,(float,int)): self.alpha = torch.Tensor([alpha,1-alpha])\n"," if isinstance(alpha,list): self.alpha = torch.Tensor(alpha)\n"," self.size_average = size_average\n","\n"," def forward(self, input, target):\n"," if input.dim()>2:\n"," input = input.view(input.size(0),input.size(1),-1) # N,C,H,W => N,C,H*W\n"," input = input.transpose(1,2) # N,C,H*W => N,H*W,C\n"," input = input.contiguous().view(-1,input.size(2)) # N,H*W,C => N*H*W,C\n"," target = target.view(-1,1)\n","\n"," logpt = F.log_softmax(input)\n"," logpt = logpt.gather(1,target)\n"," logpt = logpt.view(-1)\n"," pt = Variable(logpt.data.exp())\n","\n"," if self.alpha is not None:\n"," if self.alpha.type()!=input.data.type():\n"," self.alpha = self.alpha.type_as(input.data)\n"," at = self.alpha.gather(0,target.data.view(-1))\n"," logpt = logpt * Variable(at)\n","\n"," loss = -1 * (1-pt)**self.gamma * logpt\n"," if self.size_average: return loss.mean()\n"," else: return loss.sum()"],"execution_count":21,"outputs":[]},{"cell_type":"code","metadata":{"id":"2jTZmhhz8wFC","executionInfo":{"status":"ok","timestamp":1620026413655,"user_tz":-540,"elapsed":1409,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["import math\n","from torch.optim.lr_scheduler import _LRScheduler\n","\n","class CosineAnnealingWarmUpRestarts(_LRScheduler):\n"," def __init__(self, optimizer, T_0, T_mult=1, eta_max=0.1, T_up=0, gamma=1., last_epoch=-1):\n"," if T_0 <= 0 or not isinstance(T_0, int):\n"," raise ValueError(\"Expected positive integer T_0, but got {}\".format(T_0))\n"," if T_mult < 1 or not isinstance(T_mult, int):\n"," raise ValueError(\"Expected integer T_mult >= 1, but got {}\".format(T_mult))\n"," if T_up < 0 or not isinstance(T_up, int):\n"," raise ValueError(\"Expected positive integer T_up, but got {}\".format(T_up))\n"," self.T_0 = T_0\n"," self.T_mult = T_mult\n"," self.base_eta_max = eta_max\n"," self.eta_max = eta_max\n"," self.T_up = T_up\n"," self.T_i = T_0\n"," self.gamma = gamma\n"," self.cycle = 0\n"," super(CosineAnnealingWarmUpRestarts, self).__init__(optimizer, last_epoch)\n"," self.T_cur = last_epoch\n"," \n"," def get_lr(self):\n"," if self.T_cur == -1:\n"," return self.base_lrs\n"," elif self.T_cur < self.T_up:\n"," return [(self.eta_max - base_lr)*self.T_cur / self.T_up + base_lr for base_lr in self.base_lrs]\n"," else:\n"," return [base_lr + (self.eta_max - base_lr) * (1 + math.cos(math.pi * (self.T_cur-self.T_up) / (self.T_i - self.T_up))) / 2\n"," for base_lr in self.base_lrs]\n","\n"," def step(self, epoch=None):\n"," if epoch is None:\n"," epoch = self.last_epoch + 1\n"," self.T_cur = self.T_cur + 1\n"," if self.T_cur >= self.T_i:\n"," self.cycle += 1\n"," self.T_cur = self.T_cur - self.T_i\n"," self.T_i = (self.T_i - self.T_up) * self.T_mult + self.T_up\n"," else:\n"," if epoch >= self.T_0:\n"," if self.T_mult == 1:\n"," self.T_cur = epoch % self.T_0\n"," self.cycle = epoch // self.T_0\n"," else:\n"," n = int(math.log((epoch / self.T_0 * (self.T_mult - 1) + 1), self.T_mult))\n"," self.cycle = n\n"," self.T_cur = epoch - self.T_0 * (self.T_mult ** n - 1) / (self.T_mult - 1)\n"," self.T_i = self.T_0 * self.T_mult ** (n)\n"," else:\n"," self.T_i = self.T_0\n"," self.T_cur = epoch\n"," \n"," self.eta_max = self.base_eta_max * (self.gamma**self.cycle)\n"," self.last_epoch = math.floor(epoch)\n"," for param_group, lr in zip(self.optimizer.param_groups, self.get_lr()):\n"," param_group['lr'] = lr"],"execution_count":22,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"zKnYU6_OunR1","executionInfo":{"status":"ok","timestamp":1620006885526,"user_tz":-540,"elapsed":4556,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"35e4beea-da90-44af-bfe1-e680689d5947"},"source":["!pip install madgrad"],"execution_count":51,"outputs":[{"output_type":"stream","text":["Requirement already satisfied: madgrad in /usr/local/lib/python3.7/dist-packages (1.1)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:19.698902Z","start_time":"2021-04-18T16:16:19.694902Z"},"id":"cLuQNMO08sRA","executionInfo":{"status":"ok","timestamp":1620026428237,"user_tz":-540,"elapsed":1582,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# from madgrad import MADGRAD\n","from adamp import AdamP\n","\n","# Loss function 정의\n","criterion = FocalLoss()\n","\n","# Optimizer 정의\n","# optimizer = torch.optim.Adam(params = model.parameters(), lr = learning_rate, weight_decay=1e-6)\n","optimizer = AdamP(params = model.parameters())\n","# optimizer = MADGRAD(params = model.parameters(), lr = learning_rate, momentum = 0.9, weight_decay = 0, eps = 1e-06)\n","\n","# lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min')\n","# lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size = 237, gamma = 0.65)\n","# lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.65)\n","lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=50, eta_min=0)\n","# lr_scheduler = CosineAnnealingWarmUpRestarts(optimizer, T_0=100, T_mult=2, eta_max=0.1, T_up=10, gamma=0.5)"],"execution_count":23,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"start_time":"2021-04-18T16:16:20.331Z"},"scrolled":false,"colab":{"base_uri":"https://localhost:8080/"},"id":"7fEF_a3L8sRC","executionInfo":{"status":"ok","timestamp":1620029538856,"user_tz":-540,"elapsed":3107674,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"9f81b629-b2ce-4420-fd12-4260e8630a49"},"source":["train(num_epochs, model, train_loader, val_loader, criterion, optimizer, saved_dir, val_every, device, lr_scheduler=lr_scheduler)"],"execution_count":24,"outputs":[{"output_type":"stream","text":["Start training..\n","Epoch [1/20], Step [25/327], Loss: 0.7639\n","Epoch [1/20], Step [50/327], Loss: 0.6680\n","Epoch [1/20], Step [75/327], Loss: 0.5259\n","Epoch [1/20], Step [100/327], Loss: 0.4327\n","Epoch [1/20], Step [125/327], Loss: 1.0899\n","Epoch [1/20], Step [150/327], Loss: 0.6887\n","Epoch [1/20], Step [175/327], Loss: 0.4543\n","Epoch [1/20], Step [200/327], Loss: 0.8750\n","Epoch [1/20], Step [225/327], Loss: 0.7913\n","Epoch [1/20], Step [250/327], Loss: 0.4809\n","Epoch [1/20], Step [275/327], Loss: 0.6013\n","Epoch [1/20], Step [300/327], Loss: 0.5749\n","Epoch [1/20], Step [325/327], Loss: 0.7963\n","Start validation #1\n","Validation #1 Average Loss: 0.5898, mIoU: 0.2368\n","Best performance at epoch: 1\n","Save model in ./saved\n","Epoch [2/20], Step [25/327], Loss: 0.4778\n","Epoch [2/20], Step [50/327], Loss: 0.4025\n","Epoch [2/20], Step [75/327], Loss: 0.3815\n","Epoch [2/20], Step [100/327], Loss: 0.9799\n","Epoch [2/20], Step [125/327], Loss: 0.3724\n","Epoch [2/20], Step [150/327], Loss: 0.9643\n","Epoch [2/20], Step [175/327], Loss: 0.9008\n","Epoch [2/20], Step [200/327], Loss: 0.3991\n","Epoch [2/20], Step [225/327], Loss: 0.4410\n","Epoch [2/20], Step [250/327], Loss: 0.3901\n","Epoch [2/20], Step [275/327], Loss: 0.7374\n","Epoch [2/20], Step [300/327], Loss: 0.7882\n","Epoch [2/20], Step [325/327], Loss: 0.4481\n","Start validation #2\n","Validation #2 Average Loss: 0.5277, mIoU: 0.2454\n","Epoch [3/20], Step [25/327], Loss: 0.5137\n","Epoch [3/20], Step [50/327], Loss: 0.3941\n","Epoch [3/20], Step [75/327], Loss: 0.5887\n","Epoch [3/20], Step [100/327], Loss: 0.4137\n","Epoch [3/20], Step [125/327], Loss: 0.5931\n","Epoch [3/20], Step [150/327], Loss: 0.4516\n","Epoch [3/20], Step [175/327], Loss: 0.5358\n","Epoch [3/20], Step [200/327], Loss: 0.4577\n","Epoch [3/20], Step [225/327], Loss: 0.8005\n","Epoch [3/20], Step [250/327], Loss: 0.5466\n","Epoch [3/20], Step [275/327], Loss: 0.5029\n","Epoch [3/20], Step [300/327], Loss: 0.3531\n","Epoch [3/20], Step [325/327], Loss: 0.7181\n","Start validation #3\n","Validation #3 Average Loss: 0.5289, mIoU: 0.2408\n","Epoch [4/20], Step [25/327], Loss: 0.5155\n","Epoch [4/20], Step [50/327], Loss: 0.6475\n","Epoch [4/20], Step [75/327], Loss: 0.6449\n","Epoch [4/20], Step [100/327], Loss: 0.4141\n","Epoch [4/20], Step [125/327], Loss: 0.6550\n","Epoch [4/20], Step [150/327], Loss: 0.6604\n","Epoch [4/20], Step [175/327], Loss: 0.5781\n","Epoch [4/20], Step [200/327], Loss: 0.4335\n","Epoch [4/20], Step [225/327], Loss: 0.5060\n","Epoch [4/20], Step [250/327], Loss: 0.3999\n","Epoch [4/20], Step [275/327], Loss: 0.3985\n","Epoch [4/20], Step [300/327], Loss: 0.4880\n","Epoch [4/20], Step [325/327], Loss: 0.6309\n","Start validation #4\n","Validation #4 Average Loss: 0.6884, mIoU: 0.2281\n","Best performance at epoch: 4\n","Save model in ./saved\n","Epoch [5/20], Step [25/327], Loss: 0.3688\n","Epoch [5/20], Step [50/327], Loss: 0.3387\n","Epoch [5/20], Step [75/327], Loss: 0.3636\n","Epoch [5/20], Step [100/327], Loss: 0.7522\n","Epoch [5/20], Step [125/327], Loss: 0.4143\n","Epoch [5/20], Step [150/327], Loss: 0.5508\n","Epoch [5/20], Step [175/327], Loss: 0.4926\n","Epoch [5/20], Step [200/327], Loss: 0.6415\n","Epoch [5/20], Step [225/327], Loss: 0.6818\n","Epoch [5/20], Step [250/327], Loss: 0.4284\n","Epoch [5/20], Step [275/327], Loss: 0.4238\n","Epoch [5/20], Step [300/327], Loss: 0.5869\n","Epoch [5/20], Step [325/327], Loss: 0.5150\n","Start validation #5\n","Validation #5 Average Loss: 0.4775, mIoU: 0.2658\n","Epoch [6/20], Step [25/327], Loss: 0.2528\n","Epoch [6/20], Step [50/327], Loss: 0.4997\n","Epoch [6/20], Step [75/327], Loss: 0.2972\n","Epoch [6/20], Step [100/327], Loss: 0.4961\n","Epoch [6/20], Step [125/327], Loss: 0.7799\n","Epoch [6/20], Step [150/327], Loss: 0.4765\n","Epoch [6/20], Step [175/327], Loss: 0.6837\n","Epoch [6/20], Step [200/327], Loss: 0.8474\n","Epoch [6/20], Step [225/327], Loss: 0.5080\n","Epoch [6/20], Step [250/327], Loss: 0.6074\n","Epoch [6/20], Step [275/327], Loss: 0.3584\n","Epoch [6/20], Step [300/327], Loss: 0.3762\n","Epoch [6/20], Step [325/327], Loss: 0.4425\n","Start validation #6\n","Validation #6 Average Loss: 0.4559, mIoU: 0.2702\n","Epoch [7/20], Step [25/327], Loss: 0.5039\n","Epoch [7/20], Step [50/327], Loss: 0.3425\n","Epoch [7/20], Step [75/327], Loss: 0.3806\n","Epoch [7/20], Step [100/327], Loss: 0.3574\n","Epoch [7/20], Step [125/327], Loss: 0.3338\n","Epoch [7/20], Step [150/327], Loss: 0.5464\n","Epoch [7/20], Step [175/327], Loss: 0.5057\n","Epoch [7/20], Step [200/327], Loss: 0.8272\n","Epoch [7/20], Step [225/327], Loss: 0.6203\n","Epoch [7/20], Step [250/327], Loss: 0.3860\n","Epoch [7/20], Step [275/327], Loss: 0.4441\n","Epoch [7/20], Step [300/327], Loss: 0.6750\n","Epoch [7/20], Step [325/327], Loss: 0.3088\n","Start validation #7\n","Validation #7 Average Loss: 0.4699, mIoU: 0.2629\n","Epoch [8/20], Step [25/327], Loss: 0.3260\n","Epoch [8/20], Step [50/327], Loss: 0.3320\n","Epoch [8/20], Step [75/327], Loss: 0.3827\n","Epoch [8/20], Step [100/327], Loss: 0.4708\n","Epoch [8/20], Step [125/327], Loss: 0.6071\n","Epoch [8/20], Step [150/327], Loss: 0.5390\n","Epoch [8/20], Step [175/327], Loss: 0.3377\n","Epoch [8/20], Step [200/327], Loss: 0.5833\n","Epoch [8/20], Step [225/327], Loss: 0.3013\n","Epoch [8/20], Step [250/327], Loss: 0.7120\n","Epoch [8/20], Step [275/327], Loss: 0.4843\n","Epoch [8/20], Step [300/327], Loss: 0.7913\n","Epoch [8/20], Step [325/327], Loss: 0.7860\n","Start validation #8\n","Validation #8 Average Loss: 0.5087, mIoU: 0.2527\n","Epoch [9/20], Step [25/327], Loss: 0.3495\n","Epoch [9/20], Step [50/327], Loss: 0.3072\n","Epoch [9/20], Step [75/327], Loss: 0.4489\n","Epoch [9/20], Step [100/327], Loss: 0.6627\n","Epoch [9/20], Step [125/327], Loss: 0.3209\n","Epoch [9/20], Step [150/327], Loss: 0.5315\n","Epoch [9/20], Step [175/327], Loss: 0.4128\n","Epoch [9/20], Step [200/327], Loss: 0.3631\n","Epoch [9/20], Step [225/327], Loss: 0.2650\n","Epoch [9/20], Step [250/327], Loss: 0.2654\n","Epoch [9/20], Step [275/327], Loss: 0.4518\n","Epoch [9/20], Step [300/327], Loss: 0.6922\n","Epoch [9/20], Step [325/327], Loss: 0.2673\n","Start validation #9\n","Validation #9 Average Loss: 0.4690, mIoU: 0.2594\n","Epoch [10/20], Step [25/327], Loss: 0.7933\n","Epoch [10/20], Step [50/327], Loss: 0.3641\n","Epoch [10/20], Step [75/327], Loss: 0.4833\n","Epoch [10/20], Step [100/327], Loss: 0.3486\n","Epoch [10/20], Step [125/327], Loss: 0.3135\n","Epoch [10/20], Step [150/327], Loss: 0.3829\n","Epoch [10/20], Step [175/327], Loss: 0.5549\n","Epoch [10/20], Step [200/327], Loss: 0.3623\n","Epoch [10/20], Step [225/327], Loss: 0.4588\n","Epoch [10/20], Step [250/327], Loss: 0.4059\n","Epoch [10/20], Step [275/327], Loss: 0.7401\n","Epoch [10/20], Step [300/327], Loss: 0.5981\n","Epoch [10/20], Step [325/327], Loss: 0.3626\n","Start validation #10\n","Validation #10 Average Loss: 0.4410, mIoU: 0.2748\n","Epoch [11/20], Step [25/327], Loss: 0.3016\n","Epoch [11/20], Step [50/327], Loss: 0.3621\n","Epoch [11/20], Step [75/327], Loss: 0.4547\n","Epoch [11/20], Step [100/327], Loss: 0.2987\n","Epoch [11/20], Step [125/327], Loss: 0.4554\n","Epoch [11/20], Step [150/327], Loss: 0.4926\n","Epoch [11/20], Step [175/327], Loss: 0.4273\n","Epoch [11/20], Step [200/327], Loss: 0.4569\n","Epoch [11/20], Step [225/327], Loss: 0.3650\n","Epoch [11/20], Step [250/327], Loss: 0.4806\n","Epoch [11/20], Step [275/327], Loss: 0.7160\n","Epoch [11/20], Step [300/327], Loss: 0.2781\n","Epoch [11/20], Step [325/327], Loss: 0.3472\n","Start validation #11\n","Validation #11 Average Loss: 0.4938, mIoU: 0.2431\n","Epoch [12/20], Step [25/327], Loss: 0.7706\n","Epoch [12/20], Step [50/327], Loss: 0.6121\n","Epoch [12/20], Step [75/327], Loss: 0.2373\n","Epoch [12/20], Step [100/327], Loss: 0.4340\n","Epoch [12/20], Step [125/327], Loss: 0.4705\n","Epoch [12/20], Step [150/327], Loss: 0.5743\n","Epoch [12/20], Step [175/327], Loss: 0.3666\n","Epoch [12/20], Step [200/327], Loss: 0.6075\n","Epoch [12/20], Step [225/327], Loss: 0.3873\n","Epoch [12/20], Step [250/327], Loss: 0.1769\n","Epoch [12/20], Step [275/327], Loss: 0.2752\n","Epoch [12/20], Step [300/327], Loss: 0.2719\n","Epoch [12/20], Step [325/327], Loss: 0.3158\n","Start validation #12\n","Validation #12 Average Loss: 0.5490, mIoU: 0.2322\n","Epoch [13/20], Step [25/327], Loss: 0.4252\n","Epoch [13/20], Step [50/327], Loss: 0.2494\n","Epoch [13/20], Step [75/327], Loss: 0.7046\n","Epoch [13/20], Step [100/327], Loss: 0.2290\n","Epoch [13/20], Step [125/327], Loss: 0.1911\n","Epoch [13/20], Step [150/327], Loss: 0.7501\n","Epoch [13/20], Step [175/327], Loss: 0.7265\n","Epoch [13/20], Step [200/327], Loss: 0.5486\n","Epoch [13/20], Step [225/327], Loss: 0.4010\n","Epoch [13/20], Step [250/327], Loss: 0.3238\n","Epoch [13/20], Step [275/327], Loss: 0.3114\n","Epoch [13/20], Step [300/327], Loss: 0.4821\n","Epoch [13/20], Step [325/327], Loss: 0.3864\n","Start validation #13\n","Validation #13 Average Loss: 0.4457, mIoU: 0.2813\n","Epoch [14/20], Step [25/327], Loss: 0.2354\n","Epoch [14/20], Step [50/327], Loss: 0.3953\n","Epoch [14/20], Step [75/327], Loss: 0.2632\n","Epoch [14/20], Step [100/327], Loss: 0.4140\n","Epoch [14/20], Step [125/327], Loss: 0.3761\n","Epoch [14/20], Step [150/327], Loss: 0.4838\n","Epoch [14/20], Step [175/327], Loss: 0.6523\n","Epoch [14/20], Step [200/327], Loss: 0.5583\n","Epoch [14/20], Step [225/327], Loss: 0.2606\n","Epoch [14/20], Step [250/327], Loss: 0.4337\n","Epoch [14/20], Step [275/327], Loss: 0.3573\n","Epoch [14/20], Step [300/327], Loss: 0.3338\n","Epoch [14/20], Step [325/327], Loss: 0.4374\n","Start validation #14\n","Validation #14 Average Loss: 0.4318, mIoU: 0.2837\n","Epoch [15/20], Step [25/327], Loss: 0.3336\n","Epoch [15/20], Step [50/327], Loss: 0.7130\n","Epoch [15/20], Step [75/327], Loss: 0.3811\n","Epoch [15/20], Step [100/327], Loss: 0.4524\n","Epoch [15/20], Step [125/327], Loss: 0.3739\n","Epoch [15/20], Step [150/327], Loss: 0.3570\n","Epoch [15/20], Step [175/327], Loss: 0.5210\n","Epoch [15/20], Step [200/327], Loss: 0.3092\n","Epoch [15/20], Step [225/327], Loss: 0.6212\n","Epoch [15/20], Step [250/327], Loss: 0.7562\n","Epoch [15/20], Step [275/327], Loss: 0.5089\n","Epoch [15/20], Step [300/327], Loss: 0.3915\n","Epoch [15/20], Step [325/327], Loss: 0.3492\n","Start validation #15\n","Validation #15 Average Loss: 0.5091, mIoU: 0.2437\n","Epoch [16/20], Step [25/327], Loss: 0.4474\n","Epoch [16/20], Step [50/327], Loss: 0.3285\n","Epoch [16/20], Step [75/327], Loss: 0.2743\n","Epoch [16/20], Step [100/327], Loss: 0.3066\n","Epoch [16/20], Step [125/327], Loss: 0.3113\n","Epoch [16/20], Step [150/327], Loss: 0.5706\n","Epoch [16/20], Step [175/327], Loss: 0.3249\n","Epoch [16/20], Step [200/327], Loss: 0.2797\n","Epoch [16/20], Step [225/327], Loss: 0.4950\n","Epoch [16/20], Step [250/327], Loss: 0.3682\n","Epoch [16/20], Step [275/327], Loss: 0.2808\n","Epoch [16/20], Step [300/327], Loss: 0.4243\n","Epoch [16/20], Step [325/327], Loss: 0.2930\n","Start validation #16\n","Validation #16 Average Loss: 0.4509, mIoU: 0.2793\n","Epoch [17/20], Step [25/327], Loss: 0.2603\n","Epoch [17/20], Step [50/327], Loss: 0.2697\n","Epoch [17/20], Step [75/327], Loss: 0.4416\n","Epoch [17/20], Step [100/327], Loss: 0.6580\n","Epoch [17/20], Step [125/327], Loss: 0.5260\n","Epoch [17/20], Step [150/327], Loss: 0.2570\n","Epoch [17/20], Step [175/327], Loss: 0.4689\n","Epoch [17/20], Step [200/327], Loss: 0.3089\n","Epoch [17/20], Step [225/327], Loss: 0.3347\n","Epoch [17/20], Step [250/327], Loss: 0.3203\n","Epoch [17/20], Step [275/327], Loss: 0.4102\n","Epoch [17/20], Step [300/327], Loss: 0.3369\n","Epoch [17/20], Step [325/327], Loss: 0.2738\n","Start validation #17\n","Validation #17 Average Loss: 0.4261, mIoU: 0.2926\n","Epoch [18/20], Step [25/327], Loss: 0.2742\n","Epoch [18/20], Step [50/327], Loss: 0.4885\n","Epoch [18/20], Step [75/327], Loss: 0.7080\n","Epoch [18/20], Step [100/327], Loss: 0.2589\n","Epoch [18/20], Step [125/327], Loss: 0.7841\n","Epoch [18/20], Step [150/327], Loss: 0.3615\n","Epoch [18/20], Step [175/327], Loss: 0.1761\n","Epoch [18/20], Step [200/327], Loss: 0.3789\n","Epoch [18/20], Step [225/327], Loss: 0.2860\n","Epoch [18/20], Step [250/327], Loss: 0.2793\n","Epoch [18/20], Step [275/327], Loss: 0.3785\n","Epoch [18/20], Step [300/327], Loss: 0.5261\n","Epoch [18/20], Step [325/327], Loss: 0.2213\n","Start validation #18\n","Validation #18 Average Loss: 0.4475, mIoU: 0.2808\n","Epoch [19/20], Step [25/327], Loss: 0.2461\n","Epoch [19/20], Step [50/327], Loss: 0.2625\n","Epoch [19/20], Step [75/327], Loss: 0.3279\n","Epoch [19/20], Step [100/327], Loss: 0.4016\n","Epoch [19/20], Step [125/327], Loss: 0.2914\n","Epoch [19/20], Step [150/327], Loss: 0.4892\n","Epoch [19/20], Step [175/327], Loss: 0.2751\n","Epoch [19/20], Step [200/327], Loss: 0.3434\n","Epoch [19/20], Step [225/327], Loss: 0.5609\n","Epoch [19/20], Step [250/327], Loss: 0.3614\n","Epoch [19/20], Step [275/327], Loss: 0.3941\n","Epoch [19/20], Step [300/327], Loss: 0.2286\n","Epoch [19/20], Step [325/327], Loss: 0.3245\n","Start validation #19\n","Validation #19 Average Loss: 0.5254, mIoU: 0.2471\n","Epoch [20/20], Step [25/327], Loss: 0.2115\n","Epoch [20/20], Step [50/327], Loss: 0.3620\n","Epoch [20/20], Step [75/327], Loss: 0.1260\n","Epoch [20/20], Step [100/327], Loss: 0.5803\n","Epoch [20/20], Step [125/327], Loss: 0.3596\n","Epoch [20/20], Step [150/327], Loss: 0.2776\n","Epoch [20/20], Step [175/327], Loss: 0.4044\n","Epoch [20/20], Step [200/327], Loss: 0.2554\n","Epoch [20/20], Step [225/327], Loss: 0.4845\n","Epoch [20/20], Step [250/327], Loss: 0.2284\n","Epoch [20/20], Step [275/327], Loss: 0.2468\n","Epoch [20/20], Step [300/327], Loss: 0.2489\n","Epoch [20/20], Step [325/327], Loss: 0.4520\n","Start validation #20\n","Validation #20 Average Loss: 0.4465, mIoU: 0.2866\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"8Ul-l-Ur8sRD"},"source":["## 저장된 model 불러오기 (학습된 이후) "]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:21.050200Z","start_time":"2021-04-16T19:44:20.802200Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"nz2gHKip8sRD","executionInfo":{"status":"ok","timestamp":1620006039805,"user_tz":-540,"elapsed":4886,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"57d18e7f-b26c-41a3-cb7e-5896b79d8c16"},"source":["# best model 저장된 경로\n","model_path = './saved/deeplabv3+resnet34+focalloss+madgrad+CycleLR.pt'\n","\n","# best model 불러오기\n","checkpoint = torch.load(model_path, map_location=device)\n","model.load_state_dict(checkpoint)\n","\n","# 추론을 실행하기 전에는 반드시 설정 (batch normalization, dropout 를 평가 모드로 설정)\n","# model.eval()"],"execution_count":20,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{"tags":[]},"execution_count":20}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:24.939227Z","start_time":"2021-04-16T19:44:24.518228Z"},"colab":{"base_uri":"https://localhost:8080/","height":391},"id":"HMs2G0AQ8sRD","executionInfo":{"status":"ok","timestamp":1620006057924,"user_tz":-540,"elapsed":15713,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"b3019e19-b456-422e-c29d-df3b9dfc4c61"},"source":["# 첫번째 batch의 추론 결과 확인\n","for imgs, image_infos in test_loader:\n"," image_infos = image_infos\n"," temp_images = imgs\n"," \n"," model.eval()\n"," # inference\n"," outs = model(torch.stack(temp_images).to(device))\n"," oms = torch.argmax(outs.squeeze(), dim=1).detach().cpu().numpy()\n"," \n"," break\n","\n","i = 2\n","fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(16, 16))\n","\n","print('Shape of Original Image :', list(temp_images[i].shape))\n","print('Shape of Predicted : ', list(oms[i].shape))\n","print('Unique values, category of transformed mask : \\n', [{int(i),category_names[int(i)]} for i in list(np.unique(oms[i]))])\n","\n","# Original image\n","ax1.imshow(temp_images[i].permute([1,2,0]))\n","ax1.grid(False)\n","ax1.set_title(\"Original image : {}\".format(image_infos[i]['file_name']), fontsize = 15)\n","\n","# Predicted\n","ax2.imshow(oms[i])\n","ax2.grid(False)\n","ax2.set_title(\"Predicted : {}\".format(image_infos[i]['file_name']), fontsize = 15)\n","\n","plt.show()"],"execution_count":21,"outputs":[{"output_type":"stream","text":["Shape of Original Image : [3, 512, 512]\n","Shape of Predicted : [512, 512]\n","Unique values, category of transformed mask : \n"," [{0, 'Backgroud'}, {2, 'General trash'}]\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA6sAAAHJCAYAAABjQa/9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOy9d5htWVnn/1lhh3NO1b19O5CUAQxsBh1HHVBRGREjIrRgaLMwKiZERxyEUSSqCGKeATNiIqiAAURARBgTKv6MLCNIpqH7Vjjn7LTW+v3xrr3rdN1UN3Xfhv19nnqq6pwd1o7rDd/3+6oYIxMmTJgwYcKECRMmTJgwYcKVBH1bD2DChAkTJkyYMGHChAkTJkw4jMlZnTBhwoQJEyZMmDBhwoQJVxwmZ3XChAkTJkyYMGHChAkTJlxxmJzVCRMmTJgwYcKECRMmTJhwxWFyVidMmDBhwoQJEyZMmDBhwhWHyVmdMGHChAkTJkyYMGHChAlXHOxtPYAJgqqqFPA1wDcBHwUE4E3As51zv3XEbTwZeLRz7trz3PfzgI92zt3nfNa7VGO60HHf3lFVVQS+1Tn3kxe5nTsA3ww8zzn3lttoDNcDTwc+Evg34CnOuRdufJ8D3wd8EnAfoHTOqYvZ5xnG8dnAvZ1zP3qG778E+HHgzs65WFXVvYGfAO4HnAR+No3db6yjgCcgz+a1wBuBxzjn/npjmS8CvgOogAXwVuCXgGc659qN5b4ZeDByHq4GPt0594dHOK63AL/unPvOI52ICRMmXJFI892TNj56F/AnwOOcc/96mfb5+cBvA/dwzr2lqqq7A/8OPMQ59ztH3MaXAHPn3PMu0Zi2gD3gkRe7zaqqHgH8ArDtnNu/yG2ddQ653GOoqqoAvh/4KmQu+UPgWzbn9qqq7gM8Gpm3PhJ4vnPuERe6z7OM5XHAn59pjqqq6uXA3znnHpf+/3rgccBdgb9H7unXHFrnQ4CfBD4TaIAXpOVWG8s8F3gA8KFAB/wt8FTn3Ks3lrkOeCIyl34s8E7n3N2PcEwPAF4L/Bfn3N+da/kJVwamzOqVg/+LGMp/BjwMuAF4C/Cyqqq+64jb+Fngcy5g308DHnEB610qXOi4JwjugBg/d78tdl5V1acCv4FMAA8Cfhf4tTTpD5gDXwesgD++jMP5bODbz/L9g4GXJ0f1BPBqIALXA08FHgs85dA6j0cmxR8EHgLsA6+uqupOG8tcA/wBcowPAn4e+G7ghw9t66sRJ/WV53lcD0Oc7AkTJtz+sYM4GvcDvhMxtl9TVdXiVtr/u9K+33Ae63wJt62dcGvhXHPI5caPI+f5O4EvQgKkr6qqqtxY5lOAT0UCp+++jGN5HOI0noKqqubApwO/k/7/MuC5wPOROfDvgd+pquqjN9bJkLnvbsCXAt8GfDHw04c2P0Mc2ocBXwm8D3hFVVWftLHMhyB28ruBv+bo+Cvk3r8sgaEJlwdTZvUKQFVVXwB8I/BNzrnnbnz1iqqq3g18f1VVr3LO/dUZ1s+A4Jx7O/D2893/5Yrmnsf+L2jcE64YPBH4I+fcY9L/r62q6qOA7wV+H8A5d7KqqquTk/ho4IG39iCrqtLIJPpN6aNvRCbFhzvndhGD4Bjw5Kqqnumc200GwuOBHxiyz1VV/QkSSHo08D0AzrmfOrS716ZtfUtVVd/qnIvp8092zoU0gX/ZUcfunHvTBRzyhAkTrkz0zrk/TX//aVVV/wG8Hvg84MWHF66qauacW1+qnTvnGuBPz7nghFsVVVV9KPC1wP9wzj0/ffY3SBb8K5HAPsBPOOd+LH3/F7fFWJE5vOYg+Pxk4Bedc09L43od8HHI/PmVaZkvAv4z8BHOuX9Py3XAC6qqeopz7p8BnHNfs7mjqqp+DzkHX87Bffs3zrk7pu9/KG37nEhz/XTv384wOatXBr4N+BfgZ07z3fcD34AYxv8DoKqqP0QiTb8PfBeSUbt7VVVfyyE6bVVVH4NEuz4eiST9LySD9HcDbeQwDXiDzvIxSGbok4G3Af/bOfebG9t+MBKB/K9ACfwD8L3Oud8/n4M/TAPeoGl8JvCY9PudwLcArwGeATwSoZA82zn3wxvbuh9C2bwvcAz4Z+BZzrlfObTPByARzHsiFJNvAV4O/KRz7skby12POGMfjdBEnw98t3OuO59jPAvyqqp+DKH8aIQ6+tiBOlpV1Z0R+uwDgDsj1+FFCCWmTXSuv03bem1VVQAMFNuqqq5B7qGHAicQeupzDlGcTFVV3w98PZJlfDHwHcmgOSsSZenTkeu0iRcAv1BV1XHn3E4aUzy8/lFRVdU9EHrx5zvnfnfjcwO8A5nEeyQzOtCbQSbPR6S/7wtcBbwq/f8g4JVp8toc9w8Cn4bQ5j4ZuY9eNCzgnFtWVfXbaf3vOcuw3w/kmx8458LRjviWOEwDHp5ZhHr9DOQd8BfAo5xz/7Cx3gngOUhGeAf4MeA64IuOQpmaMGHCrYK/TL/vDuPz/hvInPMNwB2BLAXcHocwOO6KvM+/zzn3i8OGUtnCk5DSkBnwEuD3Nnd2JhpwonE+BqGW7iAO9Nci740vTMsM79anDHPlUebJqqq+EPiBNO43ImUTlxr/uaqqH0ZKTd4GfJdz7iUbYzirzZJskTPOIVVV/XeEeXNfwCOlWv/zUDDxHmkMp7WbzoGBjTQu75x7R1VVb0Dmm59Nn13QPDIgHec3AnfZ3FY6P7+DXP9XI4yhJ1VVNdDWN8tWHozMn31VVR+G2FLftjHuUFXVizc/S8fwxsFRTXgp0AKfi9hrp8A556uqOsnGfHoRc+kDOEQDTtf6sUjG97S22Ma657QbJ1x6TDTg2xhVVVmEkvDbm3VyA5Kh/1rgvx/66lOQDNF3cWCIHt72HKFczJAsztOBHwH+0xGH96vAbyFUjH9Gol8fuvH9PRCD/quQieyPkWzwpxxx++fCTyE0pYchk/KvI9SQbSTC9uvAs6uq+sSNde4G/D9kgn0IMuH/QqKoAGPNxMuB9yLRuJ8CfgU5T2ws9yXIpPHniLP3FOBRyIR7RlRV9YiqqmIyCM6FxyJ1GV+BXJ9HIc7pgGuBm5CJ/XOBZyGO+k+k79+V1gV5cQ7UMqqqmiH1Ll+AUL0/D3g2cJfTjOEuSPTzWYhx9G0cDR8OZMCbD33+j8j75Z5H3M5ZkSa3P0eoaJv4NMSQewEykf8qQgsazsPTNpZ9MPD6Def0XofH7Zz7D4SqfK+NZTynTqL/uLHMiKqqTFVV80SNfgwSGLhgJ/0cuBsSTHoa8jwcB155iC72POCzkOv5KMQYuuEyjWfChAkXhrun35uUzi9H3m/fzMEz+xNIgOynkffZS4CfTzWpAx6DsFp+Gpnf1sAzzzWAqqq+B5kLX4fMGd+E2BVbyDvmtYhzNrxbfzatd855sqqqjwdeCPx/wMMRu2EMAJ5jXH+YAvRHwQuBl6V9/C3w4qqq/uvG9+eyWc44hyRH5TVIDeXXINfk9QgddRPnspvOhnsBbz9Nzetp55uLwAuRefPTDn1+A/CXzrl/Qca/A/wcB+dik933YKTkZxg3nN4OuDrVlw7LHZ5zWySRcovjq6pKVVVlq6q6pqqq/4k40D9/Pgd5njirLXZUu3HC5cGUWb3tcS1QIM7YmfBWxFHZxFXAxzrn3jN8MGTVNvBIJDJ2H+fcO9Iy/4rUxR4FP+Kc+/m03l8C7wE+H8nUsinKkyK+r0XEob4WcRgvFr/knHtW2v7bkRqIyjn3wPTZq5GX68OHY3LOvWBjTAr4I+QF9PXAr6Wvvh1xSB4yUKuqqtpFXuCb6z4LES745o3PG+D/VFX1A865959h3AFxcI7ipOwBX5yihK9ImcrvTtu/yTn3t0jtyrD//wcsEQPlW51zTaIJAfzDBrUMpD7yo4CP3xAD+oPTjOEtG9nHV6aJ++EcwcBBsrUg0fRN3Hzo+0uBFyBR3mIj63sD8PcbEdJ3Ac2h8zDgwcjkMuAEp44bZOwnNpbZP00g6WZgXlVVvhl5Ra5Nkf5+PsJkuFy4FrjeOffHMD6j/4rUOz03UY0fCnyJc+7FaZnXINH+ixIhmTBhwsUhBaoBPgzRrNhDslmb+HznXJ2W/wjEgXzkRib11Yl98ySkPtAgAeyfcs4NrI9XVlX1Kk51qjbHchXwv4Efdc5tZjw3mVQ3AXrz3Xoe8+TjgX9C3kURmetyxCk4F04J4p8FP+uc+6E0hlcimdMnIPWR57RZnHNvP8sc8gOIs/05GwHI3+NUnNVuOgeOMiddNJxz/5jshhuQczCwpK4nOefOuTdVVdUjzvMtzkVVVf8FuZ+G4z+KHXAj53d8N3Bgsy2BG5xzf37UY7wAnNUW4wh244TLhymzevvFX246qmfAfdNy7xg+SA/7udYbMNJ504TzXsTxA6S+oqqqX6yq6h0IBbNDMjeXJJuGRDEH/Ev6PTpb6aXyb2xMwlVVnaiq6serqnprGk+HRMg2x3Rf4FWHaoAOKy7fE8lAvyhF92wyLv4AoQ99NGeAc+75zjnrnDtbAGLAyw7RWX4TidR9dDoeVVXVt1dV9Q9VVa3T8fwK4hCdK0P+QOBNG47qmXCYtv0PbFznKwgvQii5nwujsfdwjjBZJIPu4ziIBF8ufDJwfyRKez3CBLhceO/gqAKk++0vgU9IHw3q3r+9scyaUw3iCRMm3Lq4hoP5ySEO6w3OuXdtLPOawVFN+AwkEPqSQ3PSa4CPTY7qXZFykZcd2t+5aKj3Q+adXzjP4zjqPPkJwG8dYpkciRrrnPsM59xnHHE8I+U3zasv4+B9eME2SyXCV5+IUILPFYQ+q910BeGFwBduBE0ehLDWjpLxfjCiEnzj5Rocwgq8bxrXS5AM9QMu4/7OaotxNLtxwmXC5Kze9ngfUnt5t7MsczekLm8TR3E474REsw7jqC+YwxGwFpmAhqjkbyHG+fcidYv3BV4xLHMJMO5/I3t1xjElPA+JyD0LmYTui1BHNpc55bwko2Az2zTU/b6cA6OiQ+p8QIyCS4H3nuH/O6ff3w78EPKyvh6ZeL8lfXeu83wNQhM+F851Ts+GIXJ6/NDnJw59f9FIQZc3cECJ+wzkOr3gjCsd4POAf3POuY3PbubUcYOM/eaNZbaSIXh4mdWhrCrOub9yzr3BSR31Y4Bvqqrqw48wvgvB4Xtn+Gy4d+4E7B0yeOHoz/+ECRMuD3aQuek+iCNzd+fcKw4tc3iOvxYwad3NOel5CEvuzsgzD2eeV86Ea9Lvo8wXh8cE554n73QBY7oQnG4fd4aLtllOAIpbZz4915x0qfBC5PoNYoc3AH+SSmHOhU0KMBzdDjjy8TnnbnbO/YVz7vecc1+FtHd66hHGdqE4ly12FLtxwmXCRAO+jZGK0/8EeHBVVd95uGi8EkXRB7ARMUw4CsX03Ujfx8O47jSfnS8+AslUPcg5N1JhUp3kbYJUq/f5SE+y5258fjgo824OnYO07tbGRzel349C6nQO499P89mF4A5n+H+YFL8YEdb57mGBSnqDHgXvR67T5cS/IsbJvZBapwH3QrIA/3SJ9/dC4BnpPrsByRyfVpThEA5PriC1M4frZO6KtNl588YyBjmPm47uKbU3p8FQ33MPLo9M/uF7Z/js79Pf7wa2q6oqDzmsl+L5nzBhwoWjd86dS8X18Bx/E5IN/BTk3XoY7+XApjvTvHImDCUtd0YC6EfFUefJd1/AmC4Ed+DgWIb/h7n0YmyWm5FzfudzLXiReDNw16qqFs655cbnR5lvzgvOuX+tREn4hkoEnB6CUMHPikpE++7HLUUVh7Hdi1uWtN0LuGkjA3u6OTdHmAXnokm/iUTnvkw4ly12FLtxwmXClFm9MvBjCA3l607z3eMR6uOF0AnfCPy3VBgOQFVVn4AU1l8shhf8qBhbVdXdkIn0tkKB3NObY9pG6vY28Ubgsw5NUoeXcUg2++4punf450z1queL6w850w9HBDGGZtUzNo4n4SsO/T9k9w5Hb18DfFwlitCXBal29LWIU72JIUp7ivDXReLFyDl5WPo5nFU9JYqdJsPP4lRn9RXA56R7ZMANyPkfHO8/BnbZOL5KhMsektY/G4Zn4VIFNg7jDlVVffLGuP4Tovo91PUMxvBDN5aZIediwoQJty/8ARI4O36GOalF6tHfjbBwNvHwc2z7T5D33tecZZnTZQiPOk++EXhoqnE96pguBA8b/kjz6vUcvA+ParOccpzJcfwz4KsPHcOlxkAh3jyOuyClJeeaby4EL+BgLp1xatuk013zzwXe4zYUkJ1z/4YEpjfnSZ3+3xz3K4D7pvM+4KGI7Xa6+t9hWwpxkC/XXArntsWOYjdOuEyYMqtXAJxzL62q6rmIIMG9EelwixjOjwCe4M7QY/Uc+AVEOfB3qqp6CvIyegpCZbgo6XMkQvZ2RI33iUitw1M4la58q8E5t1NV1RuB702F7wFx9ncQh3/AjyJU2t+uqupHEHrH45Hi+ZC2FaqqeizwSym7/Qrkxf1hiFLiFznnVqcbR1VVX41Qjz/8CHWr24hi4c8gQg9PBP5PKugHabPymKqq/gzJzn0Fp2ZL/4NkaFRVtQN0KWr//HScv1+JVL1Dsnz3dM49/hzjOh88DfjDqqp+FJGh/7z0cwtRsKqqHgQsgI9N/w990d54xPpenHPvrUQZ8ocQkbHD9TVvBu5YSfulv0OyBB+BULhed2jZ5yLR4d+squoHkWv7ZOCHXVIMds7VVVU9A3hiVVU3p+1/BxIUGRSZhz5wr0aymh4xgB4LvNBt9DGuquo+iPLnQI/7tKqqrkVErv4iLXPU++d9wC9XouK5Rp6/9yK0QJxzf1dJi53nJIf83Wns430+YcKE2weccy7ZCS+oquqZSDCqROaNezrnvs5Ji49nAj9UVdX7ELXaL0R6W55t2yerqnoa8H0puPdyxIF4MNKi5h3Iu+/6SvrCvx14p3PunUecJ38QcfZeVFXVzyF1gF97lOOuRBSOI9atfl1VVS3y7v865N0/dAI4qs1yyhzinHsLYiO8GhHf+WlE9Od+wF+4jfY/F4Mk8PRzwI8mB+1GZE56K/DLw3KVqOsOSr4ngLsN86lz7tfPY5cvQkqmnoX0Sj9Mc34zwvr7PYTu6pB74uWn2daTkfnoLYjA5tcgCr5fvrHMrwPfjcy5T0QowT8C/OrAkKqq6v7IPPUSxLa5Jm3rk5Ag8YgNG+KeiODh8P/rhmzuedw/57LFzmk3Trh8mDKrVw6+GVGsvR8iCvBi5IV/vXPuGReywTRJfC5iyL4QeZk8Dqmp2D3zmkfadoNEnnrkBfQ0RC3vsENwa+PLEdGl5yMZ699If49IE++DEZrHbwLfivSwNWycF+fcC5HI7Mci1+M3kev0VxxkM08HnbZ1lAjssxGaya8hdTQ/xy2pOE9N3z09/W451NM0UTy/HvhvyPl/48bnD0QEdp6KGBKPQ3rWXjI4596ASLl/JiKK8FDgy92p/Xafg5zHwUh5cfr59PPc5QsQOtafJiNiEy9CnLVnIufhyci1frU71DfWOXczUvdqkHP0FGTifBK3xDMQCfsnIIGkY8BnHRI4eyMSWHpxGsND0vJfdWhbj07LDL2Bn5z+f/TGMme6fw7TAt+KKEU/GTkne4hS5Sbl9xGIgfXjiAP8OiSCfVHP/4QJE24TfAsy13414jA8D3m//dHGMj+K9Nb+RmT+20Le+2eFc+4HELXhz0RskJ9CAoJ7aZH/i2T+fh553z0qrXfOeTIF4r4UoeG+FHFkj9pCy6Sfo+BLkSzhS5FeqjcMGcDzsFlON4fgnPsjhJUyRxzHFyIO49uPOLaj4jGIzfLDyPW7CfjsQ+/1j+Jg/vwwpFRs+P/IcM69DWEP3ZnTaz/8L8Qp/13kXNwXsSlPESp0zv0acs89ApljPgZRs/67jWW6tP7QL/4n0zE+amNTb0MCvt+P2BM/hsyJn+qcO+wkD8f8EISiO/z/URvLnOn+OTyfntUWO6rdOOHyQMV4uVoATrgSUVXVPRC6xqOcc+er/PcBi0r6Yr4eeKBz7rW39XgmXDpUVfVPwLOccz9zW4/lQlFV1fuBH0oGJVVVPQ/4aOfcfc664qnbsUi24M+cc2ej/E2YMGHChAkjUtnJa4Fr3Km9YG8XqKrqIYjQ1l2GTHJVVRH4VrfR2uiI25rsxlsJEw34AxxVVT0ByaS9FZGYfwJCLfmN23JctzUS7fNNHIhQPRH4G277zPCESwzn3KVqpXSro6qquyOZ6qs5qEE9n/W/GLgL8LdIRvjrEWrWV1+6UU6YMGHChA90OGmVVpxzwSsUydn+BiQbftQWjpvrT3bjbYTJWf3AR0RojXdBhAVeD3znUJP3QYwCqdO4I0Jz+n3gOw6rMX+wI7VsOROdOTrnzqdh+9n2ozg71cu7c/e3+0DEtyF1ys92zr3qAtZfAo9EarcM4rQ+xF3e5uoTJkyYMGEDR5jjwqWyP84xb+Oc6y/Ffm6H+C0kWfPICzzXk914G+Gy0oCrqron8ItIgfT7ga8+YpuJCRMmXAFIYgln6gH8Vufc3S/Rfh7B2RvSP9I597xLsa8JEz7YMc3NEybcuqiq6gEIhfZMeIpz7smXaF9/yIEA0ylwzl1OReMJEy45Lndm9bmImtYvV1X1lUjB/gPPsQ5I9OK+SLHzJcncTJgw4fzxBV/wBd+wXq/z0303m81aRNn2ovH0pz/9r1//+tefUQb+/ve//9su1b4mfNDCIEIib+TUdlAfbJjm5gkTbkU85znPufGlL33pGee4e9/73u/hEs1xX/ZlX/bUm266aXGWRS7JfiZMuEQ459x82TKrVVXdARHyuSbJqRskgvuRGw2Cz4ShaHnChAkTJky4lLg/8IbbehC3Faa5ecKECRMmXIE449x8OTOrdwXeMdS0pUnxnenzc02I7wJ46pMey803vx+loPM9dV0TfMSYDK0NdVMTQ8SHiNGGq44dY2uxoG1bOt/jfcD3gRAjEYgBiBEfAr2XoLDRGmsNSmvyXBJI9XoNQO+F1p8VBUWeoxTE2ON7T4wRraXzj7EGrTQhBow2+BCIPtL3sg/vA1I6GpnNS6697lpmZYkPnqZuaLuWrhPKewiRECLGWpqmIcaIMQZjDH3fEwOEGOW7IOsYY1nM52ij6LuO+daC0HuatsEYg0qED600Rit88HR9oOl6FIqizJnPS0Bx880naeqGkM4rMYCKxOiJQRNjJMaI0pHgOxSRWVly3XXXsb3YRmmF7zw7O7uslmvWfUdRlizmc3zw9H2P1pprTpwgeM/+cp+2acmLHGs02hqstYBib3+f/f01MabTpxQ2swzxlQgURc721oJMK6wxLFcrTu7uorWm6zokGKMoipKiKGjaFu97to/NyfOMtm1pm5bMZsxmC4wxECLNugEU8/kMlU5gCJ7d3V2WqyXGWkxmUVqjlaIoco4dOw5E2rYlxkDTtHRNR98H1us1u3t7FEXBtddeS5bn3HjjjbRti9aa3vdYa7E2kyOLEaUVfd/T1GuUUigFZTnj2LEtyrIkBE/XefreE0JAa7k+1loWixkxRhaLLdZraQertUJrje8DvgvYPGe1WrGzt0Pf93jvUTrSdx3GGrYWC665+mr6tmdvf4UPkRgDMQZQsFjMuer4MWazGTEE9vf2qOuG4CO993StJ/golTNKnoOmacbnKsZIURRkWc5ia4s8l3tVKbmnlVJ0bUfXBXyQbWVZjrUG7wN9H+i6hhB7rLFoo4gxUBQZi8VCzvnuDiFEyrIkxkjbdYQIbduhtaLIC5RShBCIyDlUEYzRGGPw3su9URSECH3fEwDvPXl6J3jfo42iKArywlKWBfNiDjGyu7vHzu4udV0TQ0Abw7HjV1EUst/3ve99tF2L1kae7RDx3uNDwGhNUeZkeZaeXY01hq5tCd6TZRnLdU3btSilMMYQQsD7kM5DSZael773xBgwJsNoi1LyWYgBrVU6XvmRZ6en66Q7U25y8iwnzzJQir7vWK3X1HWN0prMWrq+x8fAbDajnM1o2o6d3V26rsNohVEKqzR5nmGUwRqDzXKsMfSdp/EdvfcokOPwHt/LfWKNpiilL/2qXlM3NZ3vx75rIcg7yWhDnudoo4nBA4EsyzBG07QN29vbPOkpzx7nlw9iXPTcfP9Pu553vOOD/TROmDBhwoSLxYd8yJ15/eteBmeZm69UgSUPcPLkzbz3ve+h7VpC9LRti1ImdUfStG1LCFEcIKVZXXUVV199Ncvlkrqu6XuPUhptsmTEeUbLOcFai1Li/Wxvb2OMYWdnJxmwfXISxMi3mcFaw/7+PjFGMczKMhmtiq7rUErRNh113VDXNVprMYST03ntdVezu2PZ3RGDN4RA23XUTQtovA+EECjLktVqxXq9xhhDjGLEKqVQiDEJjNs9vr1ga2uLEALB93RdR13XFMnRFiNYzlMk0jQtTecx1hDjnBg61uuam266mfWqJsZkBBLQKoqzECIxglYKpSGEjllRkGeGpllDDAQfiBHe/Z73cNNNNxPQbG1vs1cUNG2N9z3z+RyjFHu7u8mZ8Mznc8qyxBhDFzx1XVPXDV0XaGpxHFGKY8ePoZLBPDgS6+0tijzDaM3+/j4n93ax1hJCoO97lDIUZYnWmqZpyPOMLFOsVvvUdUPbNGilmc+3yLOMrmlZ7i3J84LZbAZACEG2vXuSqCJZlpHlOSoFLE6cOEGIkeVyn/VqSd/3su22o206+t7jvefqq68GruHG976Hm266ieVyiVKKLMuYzWfJGZDrl2WWpqnZ399PQRXN8ePHsVaxXu+nAIan7yOr1Yosy1DJcV6t9ogxsr+/N96XWmtms5LgI23dUbcdOzs7rBtxZruuAxUwRoz+5f4ey/09QtfTB/DJkYrRJ4fiGEbD/v4e6/Wavb09QhfSPR9Z1y29D2il0MbQdR2r1Yq2bZNjbpOzmokjZTV5boGYrl2gbTt8H+i6LjmIBVobuq4nxkAInhDSc6ElgLS1taDvO2688UZCCMznc1arJTs7O+wvl4SgMJklyyyZzcRxslac/76FEMnzHGMMWivmsxmh7/BRglwxKmKMNE3Nai/+tU8AACAASURBVLUkyy2zeUnTrmEftre2aMsWFaKMu29ZLfcJIVAUBev1EqUiJ0+e5KabTuK9T4EVNd6zITnyi60Z29vbbG1toTSs1ivadT0GF+qmBq3FwfUerTVZJo6wBMkYA0wxSiAseAkIhCDvGmPF0bVWHPThXuh7CaDMihkGjVLDcTesm5oQDravjSYrCtbrFeu6Zlk38r6NEaMVmTFoFApYLBYc29rG9j1t17O3t0vdtqAUKkWi+r6HGFMQAnRyxLuuw4dAIBKCOOAyCNBorLWj853nFqUVXdOwXO1z7bXX3WJ+mXBB8ADveMe7eOtbL3V7yQkTJkyY8EGMM87Nl9NZfRvwIVVVmQ2q0V3S50dC3bSs64auazFGQ9TJ4JJsgCQrI0ZpiiJHa4WPgaZt2V/uEwLMZnO0LCbGb9cTESc1yyx9CJAyr3XTkZegraFPWUltIPSevuuIwdC2kknJ82J0VAGCFyNQa9Apwg+Mzmrf95RlyfbWcUJUkv3te8kSBcVq3RKjwloDxNGBWS6XeC8ZyeHY8zwnxLiREVHkeS4ZrhghgooQ80hmMjKTowx4bckzMcKbvGXVtmhrmM1yUGHMzpVliVLDuCVQoKNCDUkypVBaYW3JbCYZyxgjxhpCjNSrhqYR4zOz4kA1TUPTNHJuspIA9MHTdB1FUQCaGBVt29P6ntW6EedLw2q1IobIsWPH2N7aQnHg6Ec8EOnajjZC03YQ9Rhk8N5jraJrG4zRFLlla2uRstNt+t5ClOvaIftbNzXKGLJ03r337CbHbzZfiPEcFaHzaCv33XK55OTJk6zXNcH3EhBoGvq+H7NuMXpuPvl+mqYhyw1ZZ/HeU5QZ1mqICrQh6I4YJNOUWcmqzYqSxawgMwpjLGVZynlVAZ+cPrlP5PmJEdlPlrG1tTVmF3d3dpMT1WMzSxZyvPeUpUEbjQIigbZtWcYV0UuwRXLUgFaABH/aVs5hk4ItOrMYo/FtR+/FqVBKE5UcmskshVIsFouUHQ5Ya1kulywWc2JmkwPep2dK7vmmaZJzpFJgqE+MATnYEHtUlOehaWrqej066cO94n2gyEtQBq01xhqKPCPPc3m2opbsbGJC9H1LUZTCFogebSw2ZWFBnmFj9Hh/a20kGBBgf3+FisKQiCjQkrEs53Nya1kvV+zevIsKEas1UWk8EW01Wmdoo9na3uL48eNsb21jbcbe7h7L/RUEeca7zsuzpAB6jLHkeTEGLYZ3T57nFEVB10lAqm06OWfJWUUNpSCaGCHGMAbGskyCCm3d0HYdRMbgwMBeAEbmx5ihB6w2GK3RCmFmIAG+ra05xaykrht29/dYrtdpLHEMSigFVmuMEYc5xo4QA2h5/8QQ03VIDBelQGkCcm0MwnTxrWddr4nAfGvrqFPPBzouem6eMGHChAkTbi1cNmfVOffeqqr+Gvgy4JfT7zcdoSZmRF23NE2bqJxRqHIxMCtnGJux7/eFNpZZcQ6VomlqIpGu67FZTlnO2NsTx7X3XgwuJEMQlRhmxIhCjNp5KNFapX0GFGakYA6G0Xw+x9pMaIVti0Ilx0DoeLPZTDJtKZtrUlZpyEzUqxqfDMK6bqibltWqRmuDUgUx+jErq1PWZDAstZaxWCuGsWR1NbP5HJtlY4ZFackyZNZijcVoA5lkBLXSoDQ9ihADXd9hjZxbBRRFmeiiQm9UKpBnhrIo0cok2mIcjeI4GuXQtZ71usGHgA8BDSOduet6skwyhze+90Z2dndYrZYcQ2G1RZterlEyeGezGd5HOd/Gct111wmVt2mEOtt37O8vaagxWjKpbdsKDbsNeN+PFE+5DgXWmpRBjOP5VZGUYZax+uRAFXku9NVeDN7lcok2FmNtcthIx+Ul2+4jy+VSMv7pmmljKK0lLwoyaylncm5JwYatxVwcJ2MkqJKCBNba0aHM85zMWhbzBWUpAYmyLDHakNmMrJB7fwhwDE6gZJ3SNU/3UdM0eC/PUt/X+F7YBkI1H+63PmU0Peu+JqZroVPWV6GwNhfKpdbUdY33AaU1fRfoe89qvaYP4qCEGAhdoE9sgKIsKGclWZbRNi0Q6fqOEIQuLVnonhgOMo3AmOkMQc6dOJRe/k70YWvleZUx+dHpKoqS7e1ttDa0nWQgrbUMMa/ge0L0KBTa6JHmu729wBhL27Ypm38Q1On7XqjCvaftWmwmFOAhqNT3Hc3eXmJ5yHXsu56T6zW7u/tSOpAcX7kXldwnKWtflAXGGFbrNevVzezt7kqGXQu7JMsks9824uzNypm8A/qeZsOR9t6zWq3G6xSDhB2krCHRrq1OjIV4i2x1SCyHtmklC2sMNsvGe398BwB124zvudyKw22NYb1a0rYNJ05cxdUnrsZoLXT9nV329uV5ESh836O0IjNCsR+yqcZatLFEQqKJG0L0BB/GjLIE0bSsFyPL5YoQ5P4oy5J5Ykl8sONSzM0TJkyYMGHCrYXLTQP+RuAXq6r6XuBmzrMRfV3XI70xy2YsFguMUcmwN/Rtx/7+PiF05HlO13egJGvqfSSEjtVqzboRBxVIDm8kBHFCOp/ohMmo7X1LnmVE3+NDoscNhq3WFGUhxm2iROpkoLdtS9d1WGvHLGeeS8bKaC1U37pOdD/Eee4DddPStj29D+CDZCyNUIrLshQnITkakrXoEmXPcuLECYwxYuAHz6peHzi1KEIUd3uo342Jxmi0wceINUaonZ04iaHr0URUDJKl0hprcrJcas1mZYk1ls73wEEWxCbnrfeBdduwamqiFvo1MDpXs9ks1ZAJ1XpvV+jUdd1SFDN8Lc6+GJmBphEq4mw2oyykZq3vO6LviCEQfY9RQusesp+990PCfaw1HcYgNZ4tIXhMpum7ntD7MQPlUxav9z25tRiradM9trOzw2pds9jakprFrqf3vVDUQ6DreszofAjyohjprkLfluBI29aoGNiazSgKcUgGqqw2lhvf/368lzrkvu0k4wqSnc1zvO8pslzukaKgj3qsV9QbBv6Q5cpzTwjyXKzXNU3dUK9q2q6T+x/Isiw9N+KQ9V2Q2kYYnfoYJeO/vbXF8auOkRcFIT1LvRenq07BpbbtCLEXTygKrTR4z6ycMZuXoCLL5d7oTM5mM5SWnBhR9u9TXfmQHRxqtzeDN957jNUURZHGKed+yDKLgys/QyZYLlGi9htD13bJsZVsrWxDaLiz+XzMHtd1e4vzACm4MYzHB3ZP7tHWHTazoOQZGRw4pTRt19OsahRqIwBlmJULykWJTrWyWZ6TFwW+D+zu7rK7uyvvKBR9er6NVnKePVibo5TUf7ZtR9t2yYGPY9ZzqI8engeQdLdSChvsSD8HxvdsXdcQdHJwBcM9PQZ7lGK9Xo/nZahp1wrqek3fdxw7ts329jbKaOq2Y2+5z97+Puv1WupPlZascCElFUZphCJy4IDKVRme9yCBBX3Aohjo43LOWxSR+WzGfD4jzyWIM2HERc3NEyZMmDBhwq2Fy+qsOufeDHziha5f1w2rVc3x4zlbW9tsbS3GWqv1qsaHQNf3tCmjmZd5qrfLkjEszhDGopTGaDs6WCH2hDbS+x5jklMTI7WSWrQ8s9CJiInKk8iJVkIf7cSg11qEldq2H2mKsWnxMbJ9/BjXLeaS+SRgrBEPStxBfN2g0MQgwjOb1Na8nFHmGbNZyfHtrTGLM9TJNU0rjl8y3NumoWl7qeUKPmW+LEZbcU6VHpl+xpjkrKZ6M4TqG6PsW4zNQIwHmbrZLBsdIWuN7CONKYRAUZZyHDFQ1zXruk61cIbgD2iIQ4ZvED4Sel+UGtq2GTM0g5MhlOYg1Ggl9bMhGc0DDTFPdaMhKpQx9CEQQ2A+E8O0bRtihC60QmfVgahEiKrrpC5O6vlIoj1CZaSAveWS5XJJ17SS6TZCkxVHUOo9oxLHKIZAnuVj3Z81hizPyPOMIi8SHbjDe0WMmnkSpRoyqDo5P9pkLOZboCJWi9CM1mxkj7iFo2OMxXeBMs9pmpa+6+mTc+V9oCwLoVMqTfSe5d4efefFQZrP0W2LD57FYkGWZezt7dH3a6zNmJVS+6hHZoMEbcqyZL1uUMbKvasGaq4f9ytZuZgy9uIwZTZje3ub2WwmztOGgJg44gUxiPjRQPcdhMyEtj9k8ZKjGnqsVWzN5xRFSYyBul4Ltb7rCL1nPp+xmM0ATZnn1G3LetWgtLApjNGQWyIibOV9T99L5layvH26j/pRtGgISAyO0eCkhRBZr2u6rqcoc4w1+CQKJawJTfCBLMvJc8n+5nnBYr5gvlgAAWXNeE/XTc3Jm3c5efKk1FrbHBU1Nhe2RO97urYjK4pEPw6sVquDTLTWoOL4XhHhMoBATFR5oh6PpUklF1or8jxLmflBIyC9IkPABI9GyhWEMhyom4bQe4osp5hJvX3btdTrFSeOH+eOd7wjSpFYJMs0zo6I1KMOAbghGCDsEIU1FpSwYiTuEcc6Va0GZ1WPTrg8vx1aQZEXLBZziiInxjA64hMufm6eMGHChAkTbi1cqQJLAGhlKPKSxULEg5RiNA5X6xX7yyVd36OtQZmDCLtkakq6pPg5m8+SMq+m6yTD1vceHzyegDGp3rX3yaDxhGDIkwBMmSic2mpsITVi/apORrXUjnXJQAwhsFytuPF976MoS665+gRWGayVzIoPYqi1TUNASw1YEkLJsoxZKRnkRSkO3qAaOhhadZZR5K04cyHiU31f51va5NCCOHOzYp4yWon6Fw6UhQdHoOsHwx1QkNksZaHCKPgCUhc70O+MMXR9z3q9pm1bUWhVmrpej/WlSutRqGbIpmaJPniQaRIacoyDMarGbJqIzjTokYrYATEdRxgdBGMMxmagDijTVmuOHz9G17Us5nOAJGQUx8xc3wnd0Cgzqok2TcNqtTqgHhojlGMUxigRRkYl8aSaphXqZwiBLmWqtRbBHqMNeZHJ994nteBtMmtpu47CZszn86R4K0JEi8WCoMRx671k5Pq2Q2lFSMJU1lhxyho5N8ZYQiOZOq1EERlSQEJ7oZr6QJOoqENgB4wISxmNzQqA0TmLEcpSapH7vpOMv9Ip63hAS+97objP53NCXNF3fqz/BIhIpnBgHeh0jYaMqNZ6vMeH6+L7ODrwQ0Z1yMgrpUal5YgsV+byzIQQ8H1PDIG28ayWwsjIs1xElKzce/W6xlrLne50J7IsY3+5l1RyDd5rYtSJ6j9DazVmRgc6f0z35uBQDU6gCDQN35HeL6kePkRiFEq60Ybc5sn5lmfcGitMAa0Iqa50Xa85uXOS/T0Rv7JWAk95Ui/u+56u78bnqk/PY9McUHGLohB15416d4iUZUFZziVA04uDu7+/L+9co5JIkyXPM7n+MDqDQ33oECgcsrHBexGqMibdL3I/Hj9+jBNXXQUxsre3z7ptWK/Xohqd3kdZclSNFnGnoewDwIdBb0HUfuV9kAJqiPq61oZBxC7GSGYtCgk4ZFkmgcAQKIrTtgueMGHChAkTJlzBuKKd1VlZcPWJE2wvtslsRp/orCDGYN95FGakMCrUSIEbqIVlOaMschrEOMpyg/ctkZCoZPGgfQ0Q8RhtR9rYYnubIpPTZK2lKGeAKP52najUei9tNYRZJ0bsyZMnpU2DhsJmYmSikjiK1PyFECBRhQeq7GIx5/j2grLMRoXTGAM+dCO9bjafje1zupTlbfuedV2L4FTKZvV9j9E2selEOEYMUckSixEbsJkIVAlVd85qVVN3a4iRpmswuWE2K+h8wHhP3bbJsZMWEnnbU2pL3weszTH2IFtDao2RF5aizJKS6Iq2F3Eno+yYcR2cGGvUWDNqjEbEfDq6Oo7qvoNBfvz4CakZTvuTmluhy7ZJJCtLtbzDNVyv11JPqzS9P8i4jEqsG+rNsj9FH8VprxsRa2maBpMoiISQWOaRzKZgQJQsVN82xODZvuoq5rMtoVwmAa66biSb62OioAfWSQV5oG0ONXcxRo5ZcfrbtqWuW9lGc7Bs0zTjMQ4ZKnHu1Fi7m+fSoqjvehHoSfThoaZxOHatdcr8K8KYPTUoZQhAMZ+N9bHee7pWss2ZFeozCrpeKNJ9K85MMctHOvRA0B4yaqJCq7BWJ9VvRFBre3usQe26jqZpaNt+vI+NGdgS3Xjt+l4c+2uuuWas3e26juVyRdf1XH31VVgNfVvTde1YexnTvSrjsemehK4LaCNOWtdJ3XvXSZ13WZbj+Ps+jFlgqa0VNfKoGbN/JokOwQFNvU2OuU+iVuu11EfXqTa7LKW+1yg7OvJd04JWGGtHMSmh4gZpo1OWKSMvxzQwGwZGgtYGhWan3pMAUwowaG3H95AxmrqOhKDGsQLju+XgHg1jAGz8TAkjYrFYEPHc+L73sFwuE5MkolJwB62xJrFWvMgUW63B6pTl7kdHWSnJFiuEem6UTiJMCmUztBqCcAGbSbBRrq1la2subJkJEyZMmDBhwu0KV/TsvbW1ILcGm9tR/GfIau0vl/QhoLSh84F102FFZ0UcEa1EjGikjUmvUGIgRo9WAZUpYlRojbQzSfux9iCrGkOQ9htaS/a0bfCpFnCgAieNpg0hKHG8dnZ2IEbueO11klVNfSYVJKM19Rs0qYdillGWBXkuxq6Iuch+BudMnHKNzoT+1veSbYna0NRNqknVBO/p2lb2YbNR1Ccm8SGU1CQaK8bebFYmx8BQ1yJ4MpxrpVXKLGmapuXkyZOphtajtLRPaRqpBTTa0naJipeObRDrFOEeqfHLsoz5XJyvpF5FCFInuDUvyVNvyaYRh2IUVQkBlMaHlO3TGpXEoYzRZHk2ZuCHbNhwrpumJfjA3t4+ngNa5EA7Hpwd0j0kta8x5XjEyG/S+YBIlmdYK4GBoT+kMaJ+Grwnei/ZeGtTVtSMtaSSWdqlroX+nOc5RGh6qR8+cK4NyYYXpWMlddh5nmOyyHK1wpoUBEhtjoZgxUCRHI5zyBYPgQ5rjTitflDWZYPWKw5ClmWYLActLXG874kpDV8U0mpof3+ful6LonBeyvGHgIrSs9V7T55lzGazkRYsx2Zv4VgPDtEQaCqKgrquhYqdztsQPBjqwZvgaRupiyxSjfCwvmQ/w1jX3TQN8/kiZVGFuk+QbHAYWuxkeaqjbsb3kGSVY7o/PN4fjH/Ijgv1NqZ3h/T2jMQxUERSsPXB4yNYnaGNofdhVKVWWtG0IirXp2y2MQctfqyRwFP0JOEzOW9dEijKcjn2+XyGiE/1FEWWzou5RY1pCJH9/eVGrakeqdpFPpxHmM/nxChBnUGwSlSBB1V2PTrB+IM6Y200x7a3WSzm7Nx8M/t7e0L5xWCMQkcRpQsRYu8JSThODZLjIwbV8+SIpoCUnGOFNTrdR7mcn1S3ajMJPvZdR5aE5jbrySdMmDBhwoQJtw9c0c6q1QadlE7rderrF4Vqt66lx2qmLSEqurbHRz/WtA5GWQv4eMssZGY1ZZGTFYWI5Xg/Gn/eQwxxVJz13m/0goz0wY/0P1BJaCmM9Z4Knf4m9cjUXHf1tcCQyYzJ6YoHmcBMDEpUxGhRJu0h1Weq0cgaaLJd22BtRpYVZFnO9tYWZaK/rteSZQnRk1tpyzEY8IMjM9TmyXY9WVZSlgVZZtHaJAdP41Ohq+/FoM4yO4rE+CSooyPjOehaUZENIdGOVSBqMVH7vqNtG9brenSUsmxwFj3KGIqyILeWY1tzya4AMXjatqZvG1ZdElBSmrppICry/SVlWYAaej4KZbhLwkdt20qf064fFZZ970EzCmINTvCmMTvQJge6cpZnlPmMosixmUmq0kKLlOylOKwh9ciMITnPNqNM9bOr9ZL1SB/XKWPYjAJNPkSaTtSMhx6XWZYRkzKtnPKIjxEJvwAKmrYZ7/k8zw9asYSITdnYA+puCkCgUUn1dnA4Bkf1QLRGhLXK5Kxqozm5c5LBIdje3h4dGGsNRZ4zS2q4XRfIbYZC2hfNU/324HRuZvmG+9IYqbPezNLtbajpDsGEzWvmu5a2XY/3+ND/eGtri7quU7seUKpje/sY8/mM48e3aZr6IHBiLcFLBi+SFMVTTbXcgxI4kowuKWiU3eJcD8+n0Qad6P1a6VFZuE1CTDFGbJFTlqIo3rYtddOMIk7DMpnNyFKbKal51QzK5daKcrQInanxXGRJFT3LMpqmZhCJms/nY+ZZKTvSZtfrdQro2LT+gQhR14mja4yhbvpbBAu89+hUzz4EG1KYYQw8RB1FOyAGmqamLKScQylN13nqdSPBF2JSJx+ov0qCikgv5yy3LBaLsW9yvVqP+5f66YIyL9J6ABKobJsW33ciCqdFjC/6yVmdMGHChAkTbm+4op3Vvm1ZrZeSYVmuWDV1yqoJlWy9rpnNJXMYk05k7A9ok2OD+9RrUsVInttUJ1eSFyURxe7urqheAiGIgmnvpRZWa00fPLYTA9n0KrVLkMzHLR0cRudmwECxlM/EwCuKg9osY7T0YzQ6UZMjfRSHTxtR4ez7lqbpUia1p206ynJG26Y6WQ9WWbZmC4zSrNcrcmPJC2l5IrtWFHlOXkgd2v5qRd+HsQdkQBxxFSOr9ZrOe/rgMVlGVhRoAzYr0NpQFLPx2HwfRIF0w6AdnJ3IkH3hwNANPUrHMWPU99I/0SrJ8BqdaLVakVlDiH0SfJIWKFLnmloNBWlP0bYNPjSUpdQpq3hAexyN5xhu0SJDK2hSzZ9QcmvJSienZ9N5HSmnVmFsPjpYg/BV8J1Qx40eBZuyLGOxmLO1JcJFbdeyv7vHum6SmBN0rfRiNXlG0/WiXF0LdVlpxdbWAmMCbddh84yQsmgxKFar1ajEbJLDMji3w7ikF6ka6cBSExoIPqJ0QCPHlZeSSRuWEfXtDJPu/6EH5mp/zXK1Issshc5p2paYqNu5zcg3WuSIwrGo7ZokyuS9l2ZQqc3U4OSZtIw4/vXYcqSphdoKjKJBQ60stOneF/Xu+Xw+9pE9cfVVozNsbcbu7t4GvTvQtGt2d0+KovZVJ9CpVtLHSN2kLGWWSzskJOjCRo1tCJJZHFq7iEBUBiEbW2D53hODKPe2bTsGNIyxZDYH5BouV6t0LXthTKR6UZVE30RdXNgCPrUNijHgY5/uTcnaaqPGOlMRghocXSPvNMBm8g5rm5bd3X05j6nlz9CGCKQeVXnoe0Vdd+ztrfAhvV/TE5Ql0bQhix9jHLo5ybtNB/b39/C9BLCuOXE121sL2rZnvVrTrGs5HqUxmZZyh1SaEZUcS5bPyPIUyCPSrNdowujElkXOrCjHmt22bSW7GwIhvYeG945NPaAnTJgwYcKECbcvXNnOau/Z312ijlsimq6Tth/aWDJtaLTUuM3nJSp6QtuijcXmWTLC1NhyIctytJWsQlHmWJsRQkQlx7brupSZyrBZxu7uLn2XBIvU0DokJ8+y8fMY9WgAK4WIBaEOHERI9XI16zXJQctQyowUSKPN2L9RBI8kM2czg9UZykjvQ6X65CRZjInEqOg6aXkjojEGUlZ5cMCyLCPPpW6LCHnKlDRNI3WEUSXjtWNvd5/1qqVpO9arNX1/kC2OoScqGWfftdI6Jamitm1Nn0RrhmxcHI1CUfMc6Nt931HmGYutBTbPRT11tRbD2qb+r4mSqJShbnv299e0bY+1pWS+k5CO1obedzRNTYgGELp05ICeubW1xc7ODk0jQlCr9Qoz1CTmORmaq7a3mW8v2NnZQYGc/xAIiT6ulGSZ88JiE53TWMOs3GKxWLBcLtndE6cu28hKz8qSre0tssyyXq3ZW+5TNw3ruhbV3F76zkalsEpqYPf2pOdkWebM5wsyO7RZCokuLUq5xmhijxjfEWyeJWElbuGYDtlWUTkeap7BZuJI5mWBzSxZEu0p8kwyZiljaFOGs+17ur7n5O7uWBMNitVyUJ4VwSMffNqH1EBbm0nWPAnyNG2D772MN90vo5CO71mvO1bLFb4fakyXKYgkqr0HlOGDOuP5rKAsslFQaPjZ2dlBaztmwMuypO1aItJWx2ipcVzX7ejoD8isOHmjA2Yz2rYbn6m67iGp4N4iG40IAoUo5z0G6R/r/fAekTYsQyZzoG4P7Y90Cjpoo9FBYbPBUVWpdjkFr7wHpZnNS0IKCOW51MUPfZiH95nQgTuGR7JtO7q2H+9zYzRBSUCsKAq6rkl0Yk1TNzSp5lqTVK5TTXaepxrY9P70vU/Op8Jag80s+3t76CR2ttjeEvq4UejcYrIkQJZnZCZLCs8ieqe0Ji8K8kJUlJtUw12vV/guUBQZeVFQzmbS3zWJSrVNK+fSB1HiVpbOR9Z1hzGeYu6ZMGHChAkTJty+cEU7q94Hlqs1SmfS0qRL/RfrNShFWRQirROl1YUe1DrDQdZjcLiatkNZK+1KtCZykHEYDHsxjOfMZguIiqZu8T6Q56KW2saeWTGjDR0hRHrfbSifSq2V0mqsVQsh0rYNq/V+6t8qKpx936M7Q1nMsDZHa4tRkb6XzG4I0EWhK5vMolSLMZIR9n0keHGq2i6MYjzGarLcisOswGSGcl6MLTh810lGwuYoNLNiznJZEyJoq2nrwLLfp27a5DxLhm1oqWKMxvepRVCeURQ5+/vLDWp22HBSBTGk2jwt9FxtLVuLOXe47lpsnrO3v0fbJEcbRJglGf/WGNZ1TdsH6taTBXEgmqan99JDUhs10omNzohRAgImsxSl0A7f+c53pqyWx2biRA5OvA+WY8cWFEVJvdqnLyxaR4KXGkVpm6OZzQpmZUGZW7IiYzZfJCplhLVkeaT3rzhMW4sFRZ6NdO8+Sm21j9CHiLZWWhmZbsxUxXSOi5QxKnILQyY11SF3XTe28ZD6VU2eFSJYlCjim6I38tlA9x5q/zTGpL65sxnaWmyiuHu/oG1aAohidlLc9cGzTjWpIXhokshOno3OPxqiUpSLpgrQywAAIABJREFUksV8QWYy6qaj6VZyDPgxuzizhiI5wj717PW9OKr1uhlrtAca88BAGBxcn9qYzGYlszInS0I+8/mcLMvEGeuE6h1CwEehP6NAW0PwBmVAGYvSGSaTYxxVixEBIGM1WmnqpksZVMvu7g5931OWkh1VaqD0ewZ6fYwRkafSqXZeo1RqS1MKbbhpWzrv8VE4IUrLviKeGHuMyckzUbXtk+M50MOzVDYQY4TQj9T+EHq0tvS9HwMWA/1fJ/ZEiIqIRmsLSu4X0rEaO2TotdSReo9BYxINedhvNt4vntAFoSMrRUB6zYbo2d9fo7XmmmuuYTabjRTnPnjavgX9/7P3Zr1xJWma5mPrWdydpBRbVnUB9f9/V1eiMyMyJJF0P5ttc/GZmVPTc9ODwZQC8A8QIhRBkcfPJnvt3WA8DTjvySnV6i15Hzsj11n6bze2bev9uM45ijKEWFg2AfvhOOrGQeyJ04SEVhbnHTHDHiLDcvCYxzzmMY95zGP+WvNDg9WYUt01/yYL6JLJRSoTQtjJZFK2aC2VK04LWG0yTak/EWZlPwQs+dpHaYywn7fbrXq3IiEcvL+nXrVyHAet27Klg7YFtyQAJ1rnn1JaZJNKfJamMjUpR/Z968EjOUPrXIwhMQxjZVvVd59b1foLkX4mchLpc6wyuVjrd2IMIjM2iiEOWKuh5PoZdPf7tR5Xikh1j1ppY5RBFSXAMARZRAfxkhkj4VbCmLagGUkMzbkQYyaG6vXrOFXYams1xlrxMg4DQsYVLpczfhq6b3Wep5pMm4WdSYnj2Nly5jgCMYh01TvVwVbGACLDHkbxSVKkCzNVf6e1EvyzLEtNQxW2UC6W/MO7gcGP1acrHsGciiy6izDLYwW93lumwWKdwRnNEZNsbpTMWFkmYwzn00kCbmq4D6Qq09QM41jlr2CMw+47OUlvbylFGC2sBBFZ2zdRGpPZ/IJHv0a1SiSJbLel5TY2tsszewK0KAS8N4zjgHHC4glUFq8nSIXNXsFiSqkm8B70HtwQMFoxDKJCUEjf6zzPXC7iLzz2wNfXb7x+e68+U00ucj6btPjujZULIuyqeC3HUUK21nWlVdhIz3FGqYIfHOfLCaPuITsgDPXtJvVQNHlv99XaKgeuz6up3ZxBgFKry4k5yrGasaoupJ6pKTC894zDiLXmw3Vo9S4CTCUZXK57OoSpV3VDLFqRdrcKF6UU1lvI9BAk731/B32sc7rX5DRAH/vXNjDf6mTan2u+dUohxI1tF8m788JutnRmpUztJC3EHNHGYowk8A7j0KunlFLs+862b6Qs8nygnp8oygcl76C3t7ceYCZ/ZucI98qdEELfXAF69/LH9+y+7/cwu/ozci7se+4bNL1b1t89uUrrnnouFoQHs/qYxzzmMY95zF9tfmiw+vZ+43q7iVeyJr6WIjK4EEKXHmqtsUpTlOZ8OUtyb4i9IzFX8NgWNkrB9XpFKTiOvSbOhur5GziOo7MRja2RYBDpqWygSSnYt4A24re0WgJVpKKievKy4qgL3Jwzy35DKcMRIvN0+q5aZBgGkSnW9NuCFN/HlCS8p0qaM4VUA1FQituySIBIyUzTiKL09GBJGm1gdUNXJkgWfJkc5Hutq7DVTZp578L0HeSA+HIl/VfkwnvzA9fFv8gANdM8Mc8zk5ckUqVETe2r/DfnupFQYBgmYlw60PTeQq0hiimKL68mraZsBExWCbcxAsQosG1Hv+a328K2rjSZaQNHzVNrjIDCaZpQSvN0eWIcxrv0VEu0tDG6+iQD2SkUlpILutAlp+GQztbm0WyJtz19ddvQSu4l730HTCVncALqxe+rqxT3LnltQOl0mvtGQvsMMUau1yu5SkUbmLrX1PjeU9qYuOblNkaRKhBLqfRAoXEcWZaFEA5iFL9l+3kijZWE4rH6XKdJ0n+tc8zThDGa6/XG6+srf/75jZJVZ2eB78KTWhCZ97JRMI4j+yZdqQJaRN7aqolkA0XOkavPU84ZZ8cOcm63G+/v73zs3mwMY0/tLboztC3lWte6pLYh0DpVcypVRXH00DWtdfc/ruta1Qa2+zeVKrKhU2Xb+7FLXUv1rWvTQtpSfc4LOSZKjjw9PfVjaxtubaOgnbu795rv5MutvujjNft4z8cYWdaVmGL1VJ/QWrMtIpOXsChRSUi1k8IZYd5RqgPg1km8H1KfIz87fPiZCqPvNTvNs38cB9txdFDdNgfaZlfLGpB3Iv25/fgstXtcZM73r2lhT/cu2ZEWticy7e8ihh/zmMc85jGPecxfZH5osLoeB8u2Uyr70BaD915G8WYdbFzmmfN5Zj5NNYwnobSEozhvOZ3P7LW30jn/HfvQqiEamJAFjnizWo+iLHolBKUtfp/PFw7viSl0QKeUrN9STf7VWpFqUNHpfOZ6XVluqwDfI3a2T2vNnAQYC4NcCJuEy3R57b5VEC2Lstv63kGbztKj2ro/hbXZ+uIShCFxRphRCSXJ33kijWm3QyHFiFG6+m9bGI6qgLlIJyJ0v56cqwFTZbnTNDKOnsEP93NB9belfGdmoyR/Nq9j66mU+pGhX6dx8igNuQibLQFAugPlGBMpCdN0HFv95yH+ziKskXOeYRAQ3e6hI4jsshSFc55pqt97veGs1OJ4L6mj5IizFloAjII9BLSpUssClEyOSZKQ62Xz1kLOxJrYOgwD67r3GhatPadpIqRYOzgzkOW+sZr5NIEqbNvCvq+9x1OYM2HFQwUB7d5s91QLGmrVMOM44gdPqVUsjbls17CB4VRVA8aomjptBb8rCQCSxOcoSoIKIJYKDl5fX/ny5SslIv3CSpGzeH1bQnPbDNJa7udSEgqF1Qq0kVTlHLH6zrxa6+p1q9mzJTOM8tzu+871ev2+J9cP/d7+CNxANrh0vX/bfdfAnrxrXAfr27Zz1M2dcRzZto1SMtsqbKBzlpS+D+UaBkPKmffr0jcQ2jFIP7O8K5wz/X3nx5lhGO4BZaX0d1QD2w18t/dgU3+AgNXr9drfGY0Jbe/JXCu9Jj+IJ9p5+fP1Pae1rSxmqptVmegKyhpyonfAtkRmZWz32YeY5DmriU2l3DeI2nlJKVEqI1xK6Zsp8g4WgC/nKnULR2Nl5fzBx16b9nnaO6NtNviaatzebe18fVSvPOYxj3nMYx7zmL/G/NBgNeZMUVIO3xds0JkMCY+RBf88zVJAXxcowvpMnbGc5plt33udhVKK6+3Kuq6c5hNPT0+dzUopVZ8UXY4GtXOzy+8ynz69MI6OZVn6otFZW71lEmrivMX7AT8MEizjI6wbxloBpCmSYiKmyB4OxnHCGIXS6ruFVmOTh2HEOc+yrBzHPURFKYWxdwb0OGJlboRV8H7oXZ4x3M+n1J+oKhM1dfEufl9rbO/SFACFVHJoDQjz47xlmkdh2Gr4TavQaIvHcASKyjgMDo8zjqwzwzCy71LdET+wWdu2Yox8j9NJWJzT6SS9mNW/KD5R+XnHEXp36H3TQIKutFJQFNM4dSCgtWHbRFq474HWw9sWzsKoT8CEqgthZw1Ge5x10rerCgphUslZFumUvqmRs4QnKWXqgruCwZgoOVWVQEawTb12StQD/TooxfksQU5blbd+ZNzuzKIh5ghZamxslayHFHDZsYedPdT/bjU6aZSW50KSamM/b7fb7e55pW5wOIuvvZW2gt8Q0nfnugDLsnK73ljWBVV9mA1cNUBBUcTYEnSHDkT2+mzmLAmxTV7f7u/muZXAoJrMO3qcs10W/XHjRBvpX27H19jmxi635OS26XUcB7fbjX2ToKEYMyEG9m0j1NTatmml6/ORqn/0zpKqfg+mLM9n8/ramn7dzkebj0BrnudeyQLw/v4u9zzfVym19wJIn6lUbsl5KTWRXAC66fdSzhJcNk8T4zTKfRxC9UMrQBjxvVZCtZ9JUbzFiLGmBhjJszhNE/lDmFcD4iAbSilnTL5X2cgllOwA8Yzfva/WSgrxR5/tx+vVvneTw9+l7bYrFdp1bZ5xq02XEMcodVAto+Axj3nMYx7zmMf8deaHBqsF2bWHgq6LSkqhbZA3L+I8zzw/P+OcZa/Jmk3u2FJhC6AqEG0dlrJQFFah9Qt+XHBqrXsPZinCRKka3iQgbMefZgkDipFhnrlcLhzhACUeM2M1P/38Ey8vLyzLSqEwn06S5FsXwdoY8l66b7RVUPRqlCwhJvu+E0LC++bzoh+zqtUVDcTIZzBVWndPLm0BPKUUrLISSNVZUlkQOmUB25Nt5f/n2rcaO6OTc2YYBp6eLrR6mJ6obAWk5VD9n2SUVh38xSgg/fXtrSajBlIqzPPENAiTJ95O3b17+753tlHrtqBNNZhFdRmg9yIpbb4+owyX86Wfz9tNgASldImpgCM5V8PgsGaklNTvEQDzgaEUgJCgSE9kl01LExDpCMRS0MZWhtKy78e9iqVKb2tIbJeVt4Rp7z2Xy4XT6SSfoyarNrbUey8bLtdrlefWftMYuhS9BX61lOiixF8YU2R0srlwu914e7tyPp+ZpqmDPl9ZOQFh9ZkrGepn8fPMMJ06m7ssG7dlqf5v+RppRbqDmHAEssk4Z75jD5vUtW085Xxn7ampzHJtBRQ2T+c8T+QMe31G7xJv6cHN3BnmBuwbkLPVv97Y5G3bqn9UmPht22uSb67S2Niv8TyPHFX+Lh74nePYOtBqstaitEiJc6l9w/Rj/Fjb06qCFBIgZJ3j7fWV6/XagXUDtdKhuvfPVYoEyq3bUuXC945cSVG2HRTKn4FwHKzL0lnnGGOt98mUunly/xylp2zLu8nz22+/Ya3l27dvH67TXQFinUXX8621ZpomnHW8vr5jj4PL5akmD4f+LhVQeZdgqwrah2Ho/96uV/t9A7Wtdqyd/3Ec0ShutxuhVmNJ+NXDs/qYxzzmMY95zF9tfmiwer5cJNEzZqlMsJqSMt6b3l851EV7W7z0apYauNEWQvPpJN7PumMfasBQycImCYt3+rCorSFIVYbaFnWlskIAX799E7A8DqSwMziNtYqUJXxmGM4Ya3l+fmGaZt7e3mk1HMbYHggUPjCdQGV2UwckxlhCzBjtunS0sUnzPFfJ7PS/sQwfp8kvofoGUVjrq6SxdODQgPrHOo9ShCkWWaTIEofB47x4PoWNDhUky6J1Xde6GBUm0xjDum60MKOUE3uIdylw0Rh990OeTieWZe0LUQETm/TptlTQUshZAI3Shvl8JsaANRYoOGcqm2qkQ3PdKFkYqmM/kNCiW2WT5V7x3lZ5pnyPVBOflQI/SACTJKlqSpGQqyb9bODzdl059hU/DKhU6iZJ7t2oMUZi/ijVVChlv1MFtHRbpaSPc9u2ft8Nw8Bvv/0mdR7bRghHB/TtmrX7fK+1Hg0wtvPpnGFdV97f37tk86MkWH6BBAYJoJ7nUw1h6rtF9RgEhAvTOOB9fR5RNYTsLseUn+0x+l6j1O650+nE++sb+buEW2G8Gwvsved8OVWgmtm3o29CfEzJzbmQ6/ugvQvas+WcVLA05q39okqCj7qh8xFoi/8xYq2A03mautT07jvXsnmSErlQ/b22b/gYYzifzx2UCQBtHaOJgkUbuS7bvqO01OEYe5fbohRDtSYcx0GKsYaI1VC5GNHVvzmOYwd7pRRCTc9NJdUNEtMBbwvT+r+DVWMM1mjWVdJ9f/nlZ6y1EmBV7zNjdX9GQD7zp8sZYwzLsnC5XCprnvnbb7/hh6FeE1WfwUQMEXIWKX3KUptkTX8HjjXFu10PCYmLhBj6O7Gx07lIx+u935p6391l0Y95zGMe85jHPOavMT80WJ2nmXEYOdhE8mct0yhM3jhIwIt3rrJ8SYKDUqrVF3fv2r4fjNNMXxjFiPcOYywpCcumlO4hIQLqVE3STbQESiioIl65UhLrcvDH77/z+dMz4+hrYEvg6eki9STGoo2uIDKhMJQcqg9NmM8GLtoirElplYLT6YS1AmbGXUJT3q9XYgydYTDW4J3j6ekJjXRvxpRYlvW+2C6FYuhBQQ0Yb/shnbGUKlNULY8IqPUkKRPCwb6vlCyy1mkemeep9y42MOC97wyo+IupbGFjzwI5XxHZqyGEKJ+vKBKZaZp4fnrGO5H7Nd+chNh8SJBFd29lk9JaA9YKKHDVNytyVfEo5lwoSbx7y20hpUKusm7xGSucT/14Y2UovW+MjsaaylApYbPEv6soSAWLrZsQ+yaS3T1IH63WmhATt+utn951O6pMWVf2tfRz+NFjGULger1yu91qurLt/Zxfv36lhfF8TJFtqoKPITZtod6YelRhWVaWdYUiMtGSMygqwJfjFCYT5nnm6fKEq89brKmvqQdxGQZvUNp0oGPNRymmMIrDMDCOE5fLhev1rQNpYwy5BxRpWpgY3JNhe1iUHypzmPu92pjhJicexoFSWUbxZ8ceBkYp0ldafc0pJbzzItmutTi2epMVgFJ4O/R7vUmzKaVvQMiXqXrOhKXX6t6P6pytaoGaemtMlUlLonZOpV6Hu6ezbVa0QKPm32wbUlrr7rftsmAtSo12LLIpV99hu/SmokWVIh71Jre9M6GqBoUZK77bcMhGydPTE9571nVlWW71Xkr1HBVyyvjB89OnT/zt1194fX3FGMPr62tlpGcJP1uWmpRNZa1TVYHo/s74yKDeQXXpypEQAnsIVaJN36zc953r+8Hb61dA7uUm71/X5f/476DHPOYxj3nMYx7z3zs/NFh11vJ0OXMzinFwvDw98fLpuctdBbxOhOBrBUJEaY3RFkXsJfXbujMMG7HWoTTJX1sA5Vy6B7SxQAKUmldVmAetNUWJJNlZh6mMXCmJeT7jB8/L58+M08y67+Qa2tNAMij2PVTPnbBkWuvOGtwDSVIFrC1YRnpExYfpua23HkAiYUYDzmhM6/4ExnFA0QJzWm3Oh/5FrSlFUdSHBSOKrASUKfnQFHVHrwXxl7VwH8HBqjMqDTjJMTtKEdb4o88uxoSxrqfoOuvxTtiwaZowNeimJd1K8vDR2V/rHCprjj0QU6AlrBZdiElA8zRPeOd4/fZaAdxESoF127ndlgqcWnIouM4kCkhq1T3eO/QoHuG2meCcQ2nFtgWOfWE9JJDGaI0afAdLKYOKiTWtWGcJMVUfcQUEVT6sinTKxg8e1AZOWvdvB0Sqdd6azpo2NrGB1MbafZR+NnDTAI9SsIeN63VhWdfevSu6YUmUbj5epQrjNPHy8sJpPgmTuu6UUtOvlYFBY4yjVPD+0XPYNo6UUpxOJ06nU2fcYsxY4zoz98fvf3Tw2o5dGE1hJVsgj1JaJOnH3qW7TY7c7h1TA5aa2qA9azlnVKU9Cx/SiUNG64KuDKGuwFmuuWHwviYzO0kIr5LzlkIrwK3U58HQO22VRtXE6sZ8UkOIGohsPvFS6AxmA2jDMFSgtXaw2kLhnHPEJJUs97Cuu4+4n48jdK+/MZKm7dzANM0A7PtRwaJ8Hm3uMm2R57fNPc2yLOzHRqkKgbYBFoJ4fS+nE8Pg+fr1a5f6yuaYsN/vy62y6XeZsdYa7xy2Jji3HuB2zZvEu4H4dk+nnKt1QXU/8FbDxtp7U2vNuooywQ/D/8u/iR7zmMc85jGPecx/1/zQYDUcO0YpnDGoknFWMw1ePJ5VEtnqL263G+FIaOsq01BqXUSpbFAkIzUpIJUgIMm4xjQ2du+LxzYi7VSyKKcIu+UdRism75kmz/PlxKfPL0zTyFAXgNZpShHmZF12SCJnJsPgBoyx5JTRaHSRDsocc08xFiYkkHQCV0AJGPXe107IgxgPnDUMfsDWDsKcE+SCUVpkciEQgjCQjRGMKaFyRhsrPtKiMFrSgNsitQea6Op7JFcm1OC9q8DB9bCZGFUPcxG2BsKRKOQqI20VFwflev0uibn7gbUSn3L1xhnjSHElHFIvYqxFUX16KUORRX4MCUVEaQGYp9OJlCLv13fmaaYUxbYdfaNAKfOh5qKF1NDvp1Iy4zgwTyfmcZZAqJzJwLYHYgy8v994u14JMQBK0oIVlCSL6HGSEK8UCzGI7DjXjl3p+L17NhUKVTcMSm7seuR2XbndroQQ5bMrQymqSsjlHOWcuFwuPVW6KQPa+W5MaJP/gnSRpiJARldgmXKu94CroEv6TK3VXE4nTqczucBt3VmPQ6pJtK0Q12BVqX/u7sXVRvceUqmmmTqIWpblnnyLZttW1m3HaE2pioHcwpCMYzxNuHEgK4h10+V6vZJT4nw698/VJPGgmM7nKj9fOyu3rqscdzK1qkn6i6UztLJ9qj37cv6GcSBXn/vTk/gt+eBrb++JttGglMJqg7MSuKaNlQ0PpTgI5NrBeq9V0d+xsu2/NyZZqoTCd8/MR09pLgVtDbayk5LsLe/H0Xn0MN4rcDJYbSqTrHrVjYSw+X5P3mt/IlopSs4c+0YuuXu1c5XI5xg4zzP/9m9/w3sBqrJJB8uy9D7VGMT3boyrGy5yz9geBqU/bErcN1skBEw22awzcjwlU3KpfnO6LaIlLctnEa9xk8c/Pz//H/8d9JjHPOYxj3nMY/5754cGq+9vb7y9vaJUwWjXEze9N+x7ZF1W1qLwXmR+235Qtr2GmuQPi0FJf0Xf6yOaT68VxrdAkiYzE+AlCyGtRLIH9+oOa8UP+enTEy9PT4yT732SIUZyKWQy27KzXjdS9Xs6K6m7TaYsZfe1igNVO1s1KUrC7ZEOUkzCVDpPTAkKlcWiVu5MaBTvb++9ziRWprlJ5lrYUwMUzYhoaqiSt66yxVoAc1sMq9L//XQ6cT6f8d7Xc2BFLqtVT+qUAJjAvt2QihpJEm0BVvtxsG473m/dFyvSRc2+77wdBzkExnHsLNs0zV0+2cJehPUuGBTbvuFzYRhPKCWhQcty43a7YY2lVHnlum6EEBnHqTJxUa6v1l1K2Hxx57PUiORUuF5vsjlQ/cXHsXNb99qHKyyQhFfJtfF+QGtVPbKq34ODH2jdng3UN5k21EV3OPB+AHJdgN9Dg2Jl4l5f33h7e+d8PvfjPcIhHuzaTWor46eVbtbA7mtd15Vc7mmy1OdFK5ERN1mpMYbTaeY0z8Im3zZutxsxZrCaozQlAvJzAGp9iUjIYwdXUo1yDwybpqmDmOM4+OOPP+qxlhoKpSk1vdV+V08k32+rPZ9jBbwfK2vaM/D29tYl0c1bKqFhBmssGZHJppiwzuFUO+5UmWw5FmEk5f733nf/cKqe4cZqdvm2UgxOvJ40hUJK4qHVGm8dqoLJFnoWYyBs23ebDM5J0vi3b9+6YqGxq+bDZt293uUOnkExTxNzfY5ut6UnbGut0NaQumqBblWI1UbRkq1V7YGVLur9QzCW1NBoJRs1P//8E/M8E0PAO4fS4nNt5z6n0q+drynQ4Yh3uXcF+e1zS8jVxsck+GEYsK5Wh+VUq7BUfze0jQL9QQJ9HEfvlHX24Vl9zGMe85jHPOavNj80WC05Y5TCOsv5dOL5+Zlh9Fhr+PbtK/uxMw7TXXaYMqHLwz76x2Aypgbv3Hfh20JPpGgCfuCesllKwTsBoUoL+6it/CxrDM5Ld6ofBlJOpOOgKAgxkYGiDEeQIJBjDyIlVsKEKKVqjUeVKJrW91iZgxowE46DLSWMSYz1mFII5Bir90zA0nK7se0rxxElRKhAjLkHp8jiWxaeEuzi8IMXhswaBjdIh2gWkCEpwoWChFtN06WnJgvAE3lmY7NA5IT7fkjtxyGfK8QDhywS9yOwbUcN1gkYk6U2xUiX6Fa7Mq0y7PtBivdU5xBalUkDxiKnHbwnhVwrVmQhvtwWbssiATX13hBvcAOFmXVdxGdqLUq5ujFhOrAyxrEsG8vtKn9MQS6ZIwYBSlsgJpGda2PQKeGKrQBfffCBwjSOODeQU2Ldty5n1NrIf8+ZGBKpXq+PvZQNjOZ67K2OqflUSyms+0YpqYMl4wwpRwz1z6lSa40KKSeUVngzVD92QCuNs47BD7XnVa4thSrrHChF/L8hCBDMWRhOrXRPQz6Og5KlKgmliCX2DYAmS/7op5Xk4MT1eu3p0lprtNPEkIgpdA9vKfQEba3pgT3W2gqmpP6oyV1LKYQa+CXnRVU5scU7kUynykLHGFGmycBzZcslZMsNDucso587S621Jld2uPWSAt1/brTGasgpUjK4QWqWtDag5D41Tiqu1mUnxUyIsqnUUm9bMvP7+/t38tePKcpyLwOIkkR5I9VX1Qs/+JGUCvseqj/e1Q2agiqKkloatWzUyYaNYRg9pcrMcy7stef6/hyBRtQH0zTVtF/LutxQKKZp7rJ2SU2/19G0++ujNLv7eK3F6AbiU984NMYw1UCrlAO3203+rDJk2mdQdeML8geJcpOOi/f5ri54zGMe85jHPOYxf435ocGqMjBMA6d55nyZMU58U42RFMmkpWSFNY6UrxSyLBJLwQ9DBVaKeb4naKpah5FTlF8fCuwbaBS0Ctpk/CCAcPCDhAGlSKm7/c47MLBvgRBlURmz9JqWkolH4giyGM2A1UDKDONEiCtHFuZMZylRKUF6HKXSRmOcfB9jrSwAS+GIApRSSGzrRk6FXBTb0cCieGJL1j3gReo/NIWMVpZhFBAwDiODd0yjyJdTBUo5xwpmRB54eboIK1zZDMhcb1euNwkt0crIRkFsicAQUmDf9g64SpZzTskoawkfZLi5hvuEkEg1OCenVFOLLcPgOzs8zzNGa8ZpFHAd5Fh10aiC+OxixhlhsYVNRpjlCsALhaLkF0o2C4wzWC9dpEcMvL2/8369CtNU02CPQ4BMSziWyhGRiRsjya+qtA0JAV7GGdkkMAqdDLr6pJURqavSmlgrUlCKXHJnv5UWXzFFUmH9ICFW1ltiiehErVOiSoQLx370NF5ywVmP0ZkUI954ke06hzURrYxIVbVC60KKGyFJj+w2zohRAAAgAElEQVTT04npdAKtWZeN9/eb+MKBpGvtSYi0FFwAY4Sh18agi6gErJXNgBayBCLZvd1uvTamgbBhFGa3sZfDONTrljnqxsgRk7Dp3hNzJofIse21Y/YeVNY6SAFGP/LyfIGi2NaDEBItadt5zzROHOHgertCydWbOjJNM1rLPRpTIOVYPcZBPLP72iX9H9OIbQW0IYT6bEYUETdMWOpGVT2XRkvKuWv9uplaE7VLDzBZOqdzwdWQLVSpEvsCZMZh5OXpIum/UNljw7EHjHVM3ktN0fu1bsiUXuninGWehO10VhjnkgvLuvL2+krQLfBLYY0oS87TzHya6maC7Zt+YrGQzx2rVL/5TttmRTwOilLi2VayuWGUhpKJx06Mzc+bcdYyTlPv0F6uC0dIXUYvEmLbQ7NS0hzloGhRGIzD2CX/Rv/Qf9095jGPecxjHvOY/4f5of/29oPhfJl5urzgB2FUJZhDcblc+Pr1GzFGBmuFwZoGbrcFEMB3OZ8wdaEyeH/vMrSWW07SGalUl0hqbUA32atBlSLVNKcRoxXeGoZh5O3tvXotBbA92wvKakpWhKMxY3DsgW0LVRIsUtQjt97UJAxAiuhSA37QYBQxZRQZbTQJAS2aOxuRcmatzNLgD+kv9HWRWX+lnDFVFmidsIdaK0I4sEZxGgdOpwvPz8/Y6h9btxVlNDmrvki2tfPVeYfTFrKqNSwb//rjC8u2YZ2cf1PBwcfalHGcurRU2CVFrKC+1HNirSXFREF1H2X/eiWMoPR2yqezWjNPU6/EmIaBaDIpJPZjY10XCgJARZps0bp2b5al+nc1SrdOz8Q4njhfZmFvUub9/cqfX76wrGv1GIqHOufMPM8M1mKbdLqy9lIJBPGIQMZYwxEPtqA7s7sfOzHm6oFWtUdYOiyVUlLV1MKFtIJy7waeTjNPz8+8v7+y7TslFQbv2ba1dn4Kw2ZMrh7DvYIIhbOOInW5aGXk66zj7D3W6HpNMtYbHIZhHHl+eSHlzO31jW/frsICptzZSGvdd1U5co5FSirdtLlLdD/6wEMIrOvaq3KgyvZL7hs+ueTOqB4h9ECx1stpalpviBFrlGxvKEXIiRzF46yTvCc+ffrE5Xxi8J5S4M9/vbJuBwWRbDdZbaq9usLOglTnpCo/XrtlgCo13vejg1jnBtlEqWm90n0aeq2MnA/FmJD0cWovaf1eg3P3nxciOa99w0PqkQrOWC6Xi/QaI4x0SpGX5yf+/d//ncENfPv6DUrBuoHX11coCusdf375wrZtPRCqKIgx4b3j8+dP/PrrL4w1NXffd3KC9/d38SQrU9+RmnkU28HgXJc+pyRe1FwgFWFES6r1Q9W3fByHsPrUpGdTN3JsTTXvKeuZEqsk2DoBnOPIcRws68ptXfrG4l0KXVClplkXMMqAkfRoW4ObjnR0tcdjHvOYxzzmMY/568wPDVbPpzOhygOlbsQyeM80zpRS+P2ff3C7rry8fK4L2tKL5K0TiW6TzLUOPgkoSmzHzrFtaKvRzta0TifAMmdQGaMVn15euDxdKCmhlTCmslA3lCKBLZfLmWEYiCmx50hKheMILKukDqesulStpV822WP7fVu0Qwv5AVva5REfXYwBirAuKRZiTNxuC0or5rNUyWij0ZVtRCmsMzh3r4GYppFxcEzjyDSeeHl+7kzWtkuSZju+5luMMXK73dBFQab//v39lSMknK9+3dRkyZ75NDOfZ7x1aEP1nk7dO3gcgVA9iK0LUoJbMikXStFY61Fa4bzl5eWl16+81AqV1jcrYABhmUrBWUOqgVJKFcbRMwwT1hvGyZNi5nZbEZyYq1fOYJVmXxbe39/5+vbObbmS23XLdE9d65YVsJvRGoxV/Toa77A1MTlVyXcDeOIfzoyDq/LmUCt9GptKZ6AaOJPfZ4yCbblx7BI8drqccNbiakKzJOdmFNT+1cDlcmGaTiJ9TwFjjVSIxERKEVM3MFKK3Z84n2aeX16Ypoltk9TgJptv8lupczpqX2m7j1uFSwFsP562ydLOw7IsvY7mY3WTUQajDCknhhoARFZ1I6NW2qRMjhKaZpShZDhSTX91jpQCRYk6oFXkeO9RRpNK5vp2ZQsHRw1+ErAVeb9da0KxSHGncWIaJrRRHBVkkVWtP4qdqSul4Izhcj6jte4pzvsu3uyQIgYDWdjDtonTnvsQgqSaTxPGqB5IlQtSiVQ7jq02KFWq4iHIPWcMl8uF//zP/8Rozb9+/4NjP3DG8fb2Td4NSqMPw7KuopC4XOQ67gvWWn755Sf+/W+/cTrNneUex1EY6G1j8AMvL1bep+PAPI1Ya9i2rdZUHSzr0lUYGdWBbazS5nbPtO5cWz9v66ltndcaRanAWDbZHCjF29uV7TjqBoZD6yqXT3I+Pr7rl2WhHAc6G3IqEm6HWD/QD8/qYx7zmMc85jF/tfmhwWqrfElpJ6eI1iPfvn7jdrsS49GBz9evX2s9S+keN1Mlv6UUxnH8DgwYI4slSey0aGertzN3FiumgDWWbdul77SyZsf+sT5CWI/r9cqzexEZqBH25dgPqVeJiZjo7E35AF7aAq4F2rRFfQtB+rjAKwVSbD4u8R22dFPnHTHLIrvUvswmq2xMioBCz/k011AaxThMvVLDWssRAkcMPXn0Y6/hduyQCuTWmXpUOXYkbpFpnKR/8nwSH7EW6aZWpSf+Nj/etm18e30V2XDtXwS4Xq9Vspw4zWOvoBnHkefn5yqtLkzDSPxwjM2TBlBKxA2mMpiSMKy0whipIBmHgexyDdtRKBTe216ZIx2SCznG2tWqAV2Dd/R317HkTDEK5z2n04nWazpNI5TC9XoFZKH89vbWw2RKQZhMCqHWJTlne7+vSCCp1z1jjWEcZpFCx4CzutfAhHBgTjPbtrHvW686CTEIG+4dIYhH++XlhZ8+/UzRin/+/gfbmuqxSFq0yDwlKdZWaee+7fX4HPsmlT6NJG2Jr+3cN6YrJQkecr6dv9LZ9mVZ2XcBEM1v2kB5u1db4FRj7kIotepFmDNdg8GssSij+/GhCsdRCXhKZzrlvhtYl4X3241tC4jNVJFr4q34Z+Pdl1kgHAdKi0qhFFXfD3dfcZOCT9NMSrlXA63ryrbtlBqgJUnjmyQvd+a2dtHWTTTrLflD/YxGyWdTcq/kTA04m+R5XKXL+LdffyHFwO9//sn76xuDH9jqOZb3kSgZnBMJearP3OA8P/30mc+fP3WQB1QZsWLf7vUv0zQzjAPTMEg/7+3Wq3T2Y2ddJSU4F12fNUtMgXA0a4RYMXKRdOnmI23hbzkLUI19w06hjCKmxLEeVd1QxHKhNCmmej+IJ9rWjlzx1s9iSagJ2Pu+3jcKubP7j3nMYx7zmMc85q8xPzRY3be9LpqE/dm3FSiczhPzPPLTz58AxfW6VG9iriCpUFejHXBJqM/OEQPLtvYE2Bb8IRUHgVSDbVLMlBy5XhfpJBx8B4Ai802cz08cYauJrYZxsFAMMV5ZayVEjAmU7cfSQGgLZWmM4ke2JaVMyhmbUpXo5v6rsVWhykaNsazrylC8SD0rk2Wtw3sJT2peusv5wjwOPeBmGIbObAEdOLQ6iwasc86kkkkhomt4Z6rJtOM04Lzn+fm5s509dEkrptH3vsWPITEpil84pSipu7mFKUlo0ufPnzidJJG3V5wo3RnDY9vR9dhEJizXM5OrhrMGCxkBS7nkKhNMxBDQiiqPVngrUsF2bMLgalyKVa4tXjxjdO/fbMc6TSKL7L44I97VYRgw1vHHH//ky5cv7PuO1rYDFJQwkzkXhkGCat7f379LC66HhLWaYXCYGqg12ZnBWVI4iMfBp58+E2PgOBRD7Xo1RjPPE8tyI4bEy8snfv75Z4Zh4uu3b3KNw4HRLThHJJdP53NNblYst5V13SpIg21fO2tmjGEaTx1sWCvp2Q2widQ99Hu83echhM4exxQp3PtMjU6MPVDpzrpSJGnZWUOMBWsdT5UhPGJgGEfmeWbdVvm8MXYWvtWWxBj588uf3K43Yspo7Wgp0cJ4i1qiyfFjkHooY5TIW1O758Wv3KpWnHNoZbjdrpSSOMLBum4iL7auS4dLKaRSOEJA1/us9eMaK57mVDeZnHOSYKaEXT2fz6QUcc7I/Zez2BPmmZwS//zyJ9u2Ya3hOHa2pVoEhhY6VHtdcyTkjLOOnz5/4j/+x//AGM319l4BrcM7AbTrulJyZhwGpnnuXuNwSMDR7XajVMWHMKFGQsWKXPemzOi9wtpgas1We0fck5vlca1WdXnnFDkf2177sHMi5QIpixcW2SxpuQTLKp+/pPuGSEqJXMT/LyC6ba485jGPecxjHvOYv8r80GB1WTeRshWF0oVwZEpJzKeRp6cnxnHk27f3ykoFjhAZxrHLTbWWEvsMKKuJmyxK395eZQGXq1eusg2ycK0VJEAKmV0Hrtcb+7YzOFtrQjZatUOTsMaQJFRGm+7rk4V7wjrZ0W/H1Dx8jUWJMXb5YOsfLFRQV9m6nGqNR2UpSylYLymrIQZsXcTKYvdereOsxaCwxuKtgLBc/aU510CjJJLQGNJ3wLhV3hQgxEAKB95anB1qyqtnqMFVcAdfANoYVFbcbsKAts/cFu89hVmBVgIsvRNgfbnMjKMHck0KLRVQtEVo6QE8IYTa2emF0VQCWqWmowFdkQRK6GjtNi0FlcXbTCm9/qN54RrY2ffAvu+EnDDaozTs+1YrMiQp9nw+4ZwlV6nw5XLpftovX/4kxoxSrYP2LgePUQJ2VPMuhtD9qrlENPK9z/OMdQaj77UmjeEeTidu71fevr32OhUFPejndrvh3T21VSTfO8uykuIBFIxWGDMwDE3erAkxsu8HxxE7mGs/e5om5nlGa8O6LDgv3tIYArkIqBrGmett7fcs0O+Bdm5p93h99ijSi1yypPqOXu5t7wzOGFIOWON5fn7i8+fPfP36FVY4VWZ+vS3s69bB/vW6kDNcrwvW6nsCeMroIuwzRVFqQBUFSsoop/CD68E/R4qk1HptRU3Qjj2nzBbW+rxEtu1gmmZOpzNHTH3Tp20QAbjBMw4j0ziiDdXjCoO3zNOMq9fuertxHDtdMaIhVRbcGQNK2HtVwFtXwVmW8K2UsYMDTQ1mqjsf5d5F7P1ACG1TLWBq7dO6bGzbhlKaeTpVy0PpoVjHEVHa9uoajLyvjKLKpEPtPxXAmYv0U2utpX4pRgkiqxsYMcYqs9f9GFO+d9Zqa3DW9/uobfYZY9BKsgNCiKzLzu12ZVlvYtlwvvvoZR7M6mMe85jHPOYxf7X5ocHqNE4opTn2HeuEIRsG3xfMp9OZT59XtDb88ce/OrPXS+grQ6BDqEEessDfNllEk8E7j3YWauqs8+YuVSz0epkYAmQJIFFaFrfLsvDTzz+hFayb1CJIsNLROzJDSKDuXYHNAwr0BWxnL1MLkdG1dkVIQqM1OfHd11krdRgoGLyvnaSq9q7OKKUYx5mShRE99r33HjZQvFYfW+sz3XZZpEqNxR1YOWuFUVPgrWUcxhqaZChVjtyk0b0/tgbT2AoAc879ugD9MzSwAk1yONQ+0L3LvBvoF0+hnNN1XYW9rIDVaENypQJxxTDIpkWTlXafbAiSFpwyxunOlrdjaJ5bGdXZN5MNbpjQRpKVhbmc+emnz/z2229Ya7leb+RceH5+xhjLn//6k70m897ranT//JSCqRsL1A0IbXTtw1VM48Cvv/7Mp+fnexiXEnB1vV6JITBNUw9nateyMZjt/ndW7ttlWViWhb///b/YD5GG2iplbfdiCBFXZdLNSyuy8Fj9rxOtqzjnhPMOk82dXW2VR1VB4KtEGu4qh1a/0+Tp7+/vcv2dZfByj8zzxMvLs1QfOcO2HXz9+gXvvYQBjQNvb288XS7M5zO///FHDzMax7FL7dvGg9YGiFIdYy2Z1slpoGIZW9OcRXFg0EZhtUYlg7MDKNW7P0tNEC85s2973+AZhoHzfCLlTDjuUtoWGOb9wDh4LucLMYauUAAYx5FhHCj1nScbM7FX2djqaY3VBmCQiiujFLqGEAEUW/DURHH9wUtdJdHny4nPnz/jveM4NqZxZBifydUDL+qF2L3aoVoulurnVkoJo4nUFwnYTiitsR8AaKvoakz5tm0o7vVcH1UESmkk9vp7MDqM410eDl31oZSSpGMtNoZt2/j69Wut/zFoLxuHsg+Ue/DbYx7zmMc85jGP+WvNDw1W53HicjoT/EAhVenbyOfPL5LIWuWTy7Izn85VrnmXmDU5aggH27ZKiuUwcDqd5P/VxZEACOnFPM0zOZe+IOpplJssklCFc/XCpZRrdYcEoWz7Tgh32ajIlyVIqC3aPk5jv9qCtQEnlEIbJZ7GcKCV7tJGkbWKv9A7+yGcKHCqnZjDICFQAgSuKMDaJg+Vhb8wsIbT6dT9aplS/x99sdiO2RiDMxpvLU/PF6g9ruj75kAI4vkUmd9BygX74Xs0kA6ygD+dTn0h670E6nz+/FIl2SInXZYFpQRAt2CenO/fqxQJWokhEyo4VpgKhAopHX3xm1JmXXfx/JbCMDYQ4PrivAVdpVQqyDCdCZZzTa81+eWXX/iP//gPPn/+3GXZWhvGceT12zuvb+8UNH6YMNZhq7+y9aNmEk6bGkZzoFQRoO09Grg8nbnUc2S0eGtTEhbeao2toGzb9w6I7p3Bd590+/zv7+98/fra5e4mFUr92pbY7Ixl3w++fPnGuu6d/X96eubp6akD0Y+qgN7jy13qLhshroeabdvW/ePNV+69r/JoXY9BvI7D4Pjb337FOU8I0g8q99hWu34Hfv/9n2z7wqfPP3XpukhrJz59eub19ZXWUzvPc70+dROoaFKKAmqL7mBRoTjNkggtAK3VMImf/bYsdTNHEsWpPvJ23xirOZ9ncoySpl0ErDtnu6/2fD4z+oFSErncN0jG2sUbYyIkCaFq56ttDpSSOxOvK/CyWuOniZgSt9utb3i1ayQdt77Lluf5xMvnz2itWJZbt07EGHl/f+f6fq3WC41CEYjkEjtItHWjEC1JzDoWjDXkWLrXvR1D69bNOUsNVQWoStWNuSZ317ranu8dstoanPdoo/+392RXf6RaAaWks7cx2FIh5SpYlT8nt9gDrD7mMY95zGMe81ebHxqspix9jyLvLAyD4+XlmcvlSViOfacxot559iySXdvCREoh5iTpv9vKy4ssuK21bPsGSTxY1J7P44g1DXNg2yw5pZqm64i1tuR8OnWgnHPm69dviGlLVb9r7p2b0IJ0hK37yKK0UJnmp/uYFCwALJGVLKBzSVij0RqR93rXPZqDtzjryAydOfDOkWPi7f1dKk+qNFoWvrKQyzmR6mcWqWegKCr7KvUSuvoZVZXUeStyzGk6VTnigXaW8/nMtm0StJJL93YqDXOVZDf2tgETSsFb02XNmsI0eKzSaGPRXnFwsG0HIWyVmdk7w9cCXuT7GkCjj4jyCm1MlXJLV2Nf3MZECpHBD1wuJ56eT5zmM3sIbF+/UnJl0ZN0h1r1PcNsrHRnOud4eXnht99+5W9/+41hGAQsGcO6bvz+x7/488+vhBBrymsDjMJwNyDRZNpKSQDNp0/PjOOMdZYYjiotTsRwALmynxqKxjrPEQ6+fPnC9f2Gq3LnlhgtHs4N5zzjNIPS3JaV2+1Wr3X1dqN7DYsxhnQE3t6vhCTSzM5Aaks4hN0dvEdUu6X2Dpdar5K+23gZvMdZQwg7IezM88hpPuG8eCMBwrHhrMEYjzYG7wz/9m+/iR8zZ5RqdSob2mhOp4l1u7EsV15exCf99ds7xkjK8TRNkh67CaPthwHrHcu6sG5rT/s2xqEKvdYo54ytnau5iGS8FDiy9Km+vS+sy4JSSsBaqdJXI5tc8zRwPp+x1vD+/kYuEe8khdqY1hU8dPVHToWx1uY06X6KiXXbOGpitNaqd9TmlNAUYjiYpokUAykmfvr5ZwD+8Y9/dMlxCEFYTmv789s2u+Z5xlrD2/Wd49jxXgDlsi7c3t/Zt4OSpfpGnlXpm5YO3JFhGClKcYRAKQptLEeIHHvgCLsEOGWpo2psqIBFXaudNCHE/h5s/csxJRR3JradF3mHxg8bTqlvKGmjKCXVjuIkidzK9M2dUtrXqg5yH/OYxzzmMY95zF9rfmiwKqApEVEYIyyBSPzgtizk2lkYjtQTahvYOKIkl34MJRKfpXyPogrHKuypVF6IX3VZbtJl+PyERipNtAY/WJwd+fTpU2UsbAW4RwWkYL2j5MIWjpqyKuX1kveU+udqC6dt25jn+buFWwvqSUn6HJvs9DRP+GGQzlMnly0cB4MfmKaRWBdz3nvmaYJcMNowjcL0zfOMcx5rHadpJhcJcWpA7tu3jZRT9TkuFVwLiD6dT1jnRC1ZMtu69c8+ecfnz5/59u2bMFIhVvZTWKHL5dKvS/uMAOM4ME+SRtz8q7alJaNw1ot8tRSW28IRDpSSFNyXl5eeKAv0dFaRGBdiCih178uVEBzdwfM0zfzyy89A4giR27KwrCta2yp/PTrbpAudNY1R+jG1ltCbX3/9hZeX5ypljqzbyj/++Qf//Mfvwmg31ogWnKU6++O95/nyJBsLtXtXkmElVXhZbqBSZQ3XDx49w3GEyvhBjKWG+VhSyl3+rbXlNJ+Yxgk/ThQU1+uN27JI2K6CUmW6rfrmOA5yFIlpidL36p3I7nO6J7cGFTuD1qTaSkmIFYj3sDGo4gUtPdV5HHxlyuQYnHOcTjPTOEIu/Pzzp1qZs2KtwxjbmdpSCvtxsG87v/72Ky8vnyX4KEvKbju+9/d35sqQOuc5ao1M7BU7inEc8G5gHCdKoTK/sO07vspO39/f+fb6lRgSIUUBs9b1blRjDEprxnHgcjrX416gFAZnGU73YDbvB7wf5H5NSVQdyvTztO+71EetG5m72qPkIuFLKUOSEChrLVopzqcT27ry7fWVZZHQuHavFgrDOGC1qanhwgi3+1QhEt5y5N57e9R6GKWaj1hY8pKTBHANHmpfcfOjSjVNU52IrULeqbaGUUlfqjKSth1D6oxv27ySzbogvvLGcqu7z3/fRVnRwrDaJl+zHsRwkLL4YK2RzR+R/rbNNl3/+f/d302PecxjHvOYxzzm/5/5ocFqqsEm4sOcOJ9PDONAyomj+g73PXC9Xgl1EaSMFhBWq2xUTS/13ks/6HLr3sx4yAK0AagGFEM4+PT5hfM8I0BDEaMspkWGe5ehtq7AlAvWOQ4dKUUY32maUUr3ipYm8/0oYW1sgVRgTDUkJ5CSLK5arcs8Tgzei5zQC9tyWF1lfoawyffXBY5947a8k3LAu6EDdO88Rtu+aF5rt2qrlFnWpae5TpMs9MUbdlAApw05JnJau/e2AVdrPdM00+S6IYrMkg/eNKgMTV1wfwyLilE6P7WSRbzW8jVPKKx27OFg3//VWUljLaSEqd8jpQIUipKwlZRiTa21jIMTdkibCoCq9zBHMnXDI2YgEmMmF0XYDkJIaFXIMeGs5Vg3qInDTeK6HxsxJL58+cqff/7Jly9f+PLlCynlLl8Uz2CVMWbQqtaVOGEdQSThg/doBSEcsgBPkRQi2yFAQvyiAkpzolaiSBK1Ki3VV4KStu6hzpQtcBwLr6/vxCghXLnI/TqOA6b6jsWHCd4NpFwYh7nKtJuk2AKpbuwkeYZiS6U2HdxIquyIyomjhlEN3uNN9cgqzTCKEsBpw+LkvhvHEWMc19tKKZkBw37ItQxRFBLluqCNYVkPUv7Gvh/8648vrMfe78nz+dxZ1q15P2NGobHWME9j9b4PGKN5f39nWfeqCjDkLOnc1+uVbZPnwVvHWCX3wsxqnBef7zgMDIP4P2+3m6hB5gE/OIbRMXgBjaVkcsqYCianeeI0nQXEHQGjdb2+kkwO4L14Mo8Q8EZjtOH69s5vv/1KSom///1/kaoU9kgRYy3KKKnZOiLXY0FJ/DJvb2+EGDlfzlWCr9n3TfzPMeJqKJmtadg5ZVIpOGuxVtdEcLn++xG7LzqEgFaSWD04j6nv0+ZZtcZIJVbdMNDadNn5nSXVaN1Yb4XRULKwtN13rBQ5Ibs0JZFiSxsG7yxaCcCXnRA57hqpLO/3pup4zGMe85jHPOYxf5n5ocFqSzV9fn7idDpjbU12LC1AJt1BQN2RBz5I0Ir4E5WkUYYYoXqvWhE9GY4q7XTO8fR8EXZyrr2WSbo6QYKKGgPWfl4LnAlRUnOVksW9UhrvRpx1pHKvmmlBI5K2qfr3AwGv0zTyb3/7T+Kx81//9V/M08Q4Dqj6/43SUL10CmFplmVhO0Se2WpwYpRglnYuxmHAWicsUT0WEHZ2Wdbqy5RQJPH+jfz668/EGPnXv/4lUjwS1NoIAZiJIy58/fpN2D1jaqdtQTd5bus4/ADMS5VdNyl0TbIiBDkWYwcUCT80ECrsSEt5Vkrx+dMnYbNreNH1diUcB8ZoEnKfDOPA8/Mz53kmhdTWrOzbztdvX6Qto8ARItu2Y2zz2EnFiDoOkWGnQHCWk1IMo8dqQwgS+GOs4XZb+PLnF/7+v/7Bn39+IxyR5lf+CMpjDJWtF1bp2A9SDOLrdJI+267fcex1s8ZIHUiQrk2UdPuWDDnXYKiSOZ1PwmSiuV5v4nWunr28C/u1bZtcEyVgptUixXpfGyM9tDElvBPvc5NeSzfmUFlIkRO3ehljRK7cNh0aw1dqhYlzrodWtXukAdx1XVnXtT8D1+u1SpI11+vaA7m2PWCMZt0OYe84KFnz5ctXYQFrCFpuYNl71nXl/f299nveZbiDt6gCOcuz8/r6ynHcA33WdeX19Y1cMn6QZ2j0Q5fomxqCJWoAja8qi3VdyDkJazt4rJONEl+Z6ebv1crI5tvpTIqlhxa1NOl7PVbuYM4YTVay8SnoStkAACAASURBVHG5XCCL4mAcJkJNJ45V8t6qgSQMqoac1UTcfdvIyP1trSFXD7RSCjtaCrAfBxRRJSgg5SSbNylSiqIA23b0bABh6B3eCrAuDYDWz7HuO/txcOwH2tj+7vmohpHu2crEOltVNZIE3tLNm0oGCjmK1LlVWqnqR9XKYHr9lPz8koSdbvVUj3nMYx7zmMc85q8zPzRY9d7z9PRUWT7TF4ugqmR3rf5LAUKxMm2tPzTGQIzSU2mNIadEisKq7dvWJXJHyjw9OazVPI1nlJHdeaU1VgvoJWv8NKKULHiaFG0YRllUFukGNKZ0YOLs0NnFxjaknNmHnau5yoLy2Hpibiki3T2dZqIzWGOwWqMVPTUzRJHZliwV99ZZ8cpmajCT+EWFpRIP5b7vHPtOionbskrqZl1ItgVtk9HKYvjMPAvLK1UVB0NlQQbna2et9N8KMKkdtTXhVzo3hf3UFZQ0qWwIB7bLP091kb/ia9pzzoVS2cKSW8WJQqE5nc60Ghsqo6W1sENWK/w88fR8QStFOHa0NTyfL8K4OWF2wxHZ2TmiJLpK4m3TxRYhbWh+TIBCSIlUMvM4kWJCa8X7+43/+T//DmTWbeNff3zhy7dvpJiRSiD5vr4ypm3hPdQKmVIKxy49p8MwgIKYUgeVSgugCkmYq9uyoXUgpkyIEedGrHEVaAaGOGKMBNm8vn6rQVAeEC/1trVqJNvv3xCllsdW5YFzjv+LvffqcS7L0jOf7Y6hiYjPZBkNBpCAHjT0//+J7jQXjQEkdVWayjAkj9tuLtbah5GauRp0azIBLuBDZmWFIQ8P+e13vc5Zy5YSORcBbKXQ94MGlUWWZdkBhoDCe5hXk7GHEDDawfnZp9wWPCklqjGkaeLjcmGLkVKhVglDy7ns8vAm/YTCnBIheNYl8vT0xPX6KmBUK5usNYyHwy49v16vklirLP+3b1+pRSTFpWS2LfL29sYyrxLKo7LSnLOk8mo9lVV/b45R7mV3T48mJ+LO7sv3dV0vbJ8XJjmnqGy/gLhh6DgeDnLdkgBp6RXddt/v3k9q7vLX0+GE14XK7TZTCngf8F3P9XZVVYCkLTsvvdJNAp/0vW2soZYi6odt3VUnIQRSzuQsj9VUUSXU0iqpsnxm1Mq2xX2p5Zx8Tg3DwDjIMiMWWQTWWohJuqZrrfjgsc5TMfs9E6N45fvO0zlhT9vndzV39UlTvJSSsYpsjdmDnDVETgO9BkkqXheRjFcjmvnCw7P6mMc85jGPecwfbX7XYHWLkVIiGKl/MS6wbsL0fLzfmKaJEOSAtG4rxhlKKnhnOQw9loo3RqW0IuftfaAMAyZnrPVUbxitY+w7+kGTO00lrhvBHhjHE+/v7wQvklg0fKUlmtYsbOoyzTtj23xVLlTGsddQloMyFQL05tPM+/s7p+OZuC0YAzFH4jrz66//IATH4XzgMl0Zq3g7o/opBRgWrIYOO+cZDgcKhlQr5IJxgaQ9mrlEPq4XhuFApkrSrrIaIBK8NUWwwm6s00w5jNSUOQw9rxTytuCcMILLsgJymP3hT39iHEd++cc/sMZgjcgbY9o4Hk9YoOaN49AxzZmhc/zlr3/a5c3ztDCOIzEmbreJdRHvaANDqWROZwGcXeh/U59xOGnasLH0wfPy8szpdKTkwjQJg+mMgwJ96FnXiKkOg8drnYu1GQkv0gAY41jmhc7L9TbW4l3AByfNGtaQMfzyyytvbx/ChC4b19tNbtoKpRYsBhcclSIdltrBG7qOeVnk8E/hdDqxpkzcNpZ5ZlXfZQvW2bbEPK+sW0VkygljLc4btpRY1hnvDFveIMnXZ5XjxrQoO3cP9DJahST9soXL5UNSpMce6wwxZa7TTZNbhaU0WpE0zzcFjtLV2fciIcYanHanile43sPCbKWYwvc/f9eFRWXLwgD+8ss/+OWXXxiGgeennmlZd0AMwvxap17UrL2oRgDvZbpRSuLp6Xn3JA6DeMr7PvCv//qvOGcwpXJ6eeLbt2/0fSdp18aRahXmNhWcLmCc2gBEuir3YNGgo1xk0WKzZbDD7rVMtZJvErzUdR3GWVKp9N4h7HoD3Hn3K5dqqMji6XabscawbqsszlK898GmiDOyWJIgLvlei3r5s9yLl/cLbx/v1Fo5HkZqhlwLne/IJVNSJDgND1sTVPkss6birMVZQ1JPeEot7Eikx9umrLt1mk7eepOFsT2MI6HvcDaQq8rdqyWVwqYSYaqRBVa1JH3tS1WferA8PZ05dD2kJF3VzuM1wbiUyuvHO/NtkaonYyk1i3/f+z1IKnQ9IXSkUli2lXlZWLf7tbTWcqj33IDHPOYxj3nMYx7zx5jfNVj9+Pjgen3jcBzwXpignCV9M8Ys5fRGElDXdcV4pwEbVvxpLmB9h8URgsMHOfwe+5Gx61lXSZuttYIRf2vOTRIph9F1XenUK9okqMZpASriR02pMM8L2ya+N4xKdPXAHpzHGX1seExn6XxP1w2ktJJzZJon6pxJKXK7Xuj6xsa1bkGRQza5bpOYDmHABwmiKbUQU1SG1u51LZICOoIxXN4uvL2974zFMAwcj0dCcICkDJ8Owv5lBQjSfzoSgkhB56ml/hqMhVzkMYcu8O30ldPTEanXcay3hePhgLUQOr/3w97TkIMynIkYk/aS1j1wZlMZdt/3BGW6xCOcuV6vkphqwBuVI4YOQlU5YUfwYWd+dwa4CGsqXkyP92YPs+q6DpQZL15kkCH0PD2dsY6djd3WFiQlz8NZp9UeHlMq3SBgbt0irbfWWkOthVwLKKCcponb7XZPKjVWWHINA4rqoTVGWL+CwVtHynfgCiLdTDnvITWfu06b57Ql34Lc5yCJtikn5mlisYZ12ZiXBe8E0O8BSlqX5LyllirXNnTUWqDCPE8YIx5LayzVCEidp5m//OUvnM9n1nXldrtwud4Aw/vHB/OyYJ1nXgVYr/OduYXWUSqMq7EKtltf6OmJcTywbXGvf8k58/4+79VTrRu2lMT7+6xgynC7TaScd192kyG31+FzBUtKGQx7um7rEU0p7RUyIjutmpKcKFlkyc7Lf99l+8XgfeLjcqEUmDWcKCVZHi3reg/TsnZP1m3SamcdxRhyyqSSuUyzhoM5lZFLUFzoxNtcawGs3BvqybVIABEFVZ8IE2oag1wqKRVqYVeSrCmSsoC/bdOk5aMkO4vSYt0rsKpBAq+SeI1lgWP1sWiYnJFQLOccaG/x8fAk9461LOvG5XIh5czrr69y3dXXfj6fGYae4DzDKAnFpVZu88LH9cI0z/J3RUETr+/drY95zGMe85jHPOaPNb9rsCpePdnO11qIuUBVdmbbdn/fui7iE0yFEsDZiDML1lisMdROpJC+aPBGkKqTnIrIaa2yDsntaZutYiJq8mWrQGiVMVvcqGVjXTetvFlZt+ajMmAqNjm63Gl6aMFocBA4hnHk6EfmGbZYwVqME/bu/eODlORQ2HWdhjjJIbr1CoqHUPx0KSfyXOiGHtcFAUQKWrwPOOvlAOy1v7WWHfw65+j7nlzuQTlPpyOddqeWLLLCw+GgskQQf+lKSoYYV0JnySXy/fmF//Sf/nfG44F1XZjnmWAkUdm886kn8d7TWUpS6avIVBt702TPjZG01rJowNG2bUzLvAPfzln64LhNEy9fvggoc451U6/cFlnmlWXZdgljBeKnZFFZMhQF6NKnK1VBnufnZ759+8Y83zRVNe7+TJDk1n7oqFWqjkpB+jZLIaVtl25Kz+sk11Hv8ZjkIC0duk4Da9JvfH3tOrSKH/+JAey6DlMrBqu9k2UPvbn3TjplO9s9aIixAkXv/Y1bjrsMNgSPs54QvIL8SNd5Ta42UqOi4KklOaO+ZLD4INVIH+r7/PbtG845rtcr7+/vvH9cMMaKTFcXQMsiYHWeJmWdUbCo/ZwlEkJg2+IeoBRCUBl72b3L67oyz7e9Y7WBKAkvkyXJNMtjbnVS7b3eXs8mBb5fQ6sJ3Pf+2t1Pqp8RzsmywmmXrkjiHd5nXRyw/zOpD7uUuvvL13Ujxm3/+c3n3aS3zbvbJNXzPLOsImfPer28D/RdJ4C2JAVnVT8PMqaIFLcUqfQyxkBmfw3ujxMMKhmuYl3Y0ia1NHo/dl1HPwwEVZjM87zbCFqNl8icE9beQa/XhPe+k9A8qPRdx+l4xBnxu24p8vHxwdv7B1kXk85ZfBc4jQcOh8O9j9g5pnlhXhemWYD7si7CDpt7kJsoM5qt4DGPecxjHvOYx/xR5ncNVlNMbCo5M8bsjF7WQ3nrKE0piVxwy9QMHsdmvVSflEwthkrBOEnh7IYOg2FdVkoWP1zKQX1+R5W6LnsozMfHxw6aOj0gxZRZl8g0zSK9TEk8ek4O69ZaMA5vN9AUUAFdTuTHFMbjERsc83XZPVzLsmKdsKKpVEzO1HXTtM1epaGrMKelENeFnBL9IN2LSxEmw1mLtxaD+GCt9fvhuB3C+166IcdxZN3mHdzklFiUPapV2NfWK5tzBlMpVTzCH5c3ctkYx57j6cDpfNQDuyNnx8frilRY/E9JnBVSKiyLsNspyTIA2L20tVbC0IvkMSUWTZY1zQNcCqkWgutwwTOvK2/v73L9UpZArVpZVgl3KUU9qrlqUFHawUWtFVsskPbgIGMMp9OBr1+/EIKjlFHvvYoxkWm+kmJiGEdh/LyjLUNOpwP/+PWVlKN0/uZEihlrDOMw4oOXlNqc6bqe0+mI90FkqghwzwpId2+g9yrRBOPM7mnMKQK/DY/5XAHi3P3/MwasE2V9u96S6AvWdnhv6VRaX6v4BJN6RXMu8twxu8SzlqJBUUkSja1l3SKX6wfzMvHlyxecc7y+vvHjjz8LG+7utU9wB4eS5lzByj3rgoTntBTllDPGWo6nE/3QM88L8zTtidwN+KUkCeINcALKrkpXr/dyL3ed/HPVvuYW9CVhUYUQZGnlvVcVgtTudF1HjJH3tzcNVUp6ndgZUQGlkHLUECz2QKDrNJNSJqckCxMsKUVyjpL+6608EmV5s4YYlViIWZ5PjFHeD3pvSD2OxwdH0jRs8btW9WRbsJXtEyPcD4MuSexeEeRDYJoWbrcJWc45ioJxA7jg8c5pRY2oPlbW/TrXWilRPN5Vl4Ot59Q7ucbfvn3j29cXuhDUuy3Wjmwqy7ry9v7G+/uFZZWKJuc9plaG4cB4PAj7mwqlbJSyMC8CVlPSvy9y1uprkXNLyvY9hOkxj3nMYx7zmMf8ceZ3DValzkB6DE/nM1v8lW1LWAPeNyakqOzVgBcJMEDcMhRhBud5FQmhlcAae7X44PFWkl5zSRy921NaP0vGjDGalik/93K5YKwDY0XCt0Zy+hTcoTJBYe4i12mi5rtfzStonOaJ7jJQa+b9/Z1Sk7Kp634AFWY33eV7TlIuaxX5ZlYQJ/UkMymK764PHb7rpGpG+wZjinK4n2cAQvB0fccwDr8JcXHOUbyTdNWUMKbS9d0OfkqpeO84Ho+kIjLhD/U9xrhxuXwIsKuFFDdhvlL5TQJyztJvmmLier1IRYx1e1JsrSrbK5nhIGnLLVRqmm/kUrDGkLMAxWEQKXTXBdYYmd8nYhQmLqbEx/sHORecE8mxSGXZ5d67lNcJ6yh9poavX18UnHhutxvWOk6nE8/Pz2zbhrWG6/XK4XAg54i1lZwToeupFGpNdF3QJFMJZjKoVFaBSwiB5+dnzuez+AE3c2fWEKFuA6V3FriA1dTYXOg1oEcWHne2uHW8ppSEXfKWSmbbxA9prTCueuPu9yimKtMn0lSn0uFlWXcvatbeVYN2wlboh4Gbeopzjnt4z+vrK9frbX8cPnSSvvyb4K1ErQXvLcMgCdilVuIWWZakX5d5eXkhhMDHx4V5noga7AXcPdh6vYThTBwOB2438eEeDie6btjlzY1VbPd/e10a89p1nb7W8ri6ToKfWmAR3KXKcF8SOOdIO1jWvl8nfavruu1pyqWIB955K3VMRhQlzjmqtaQtEpPIskMI5Jj3KhbrLIfDcZdAG2OYbjeWZbmD5tLsC2JIz0kqipwmJrd7rSWAt+VfC1ASsJvvgWlWOlQt8pnSrA+tO7WxuAVZash1qboIDHz7+pUffviB56cTJWdu1xtznISZTZnbNPHr25v4y9t16wKH8SDLnE+Lhdt6o1D3VOG23OlCwDqr6oAOA7+RKT/mMY95zGMe85g/zvyuwephPPD+LoAt+KCMhtY0pLgffg/Hg3quvProKuu8SjpmKeKbQqW8zlK3AisEa7AGur4xKCK1bMzjPM8qRazkvClIsGzbBFimedVDsQEUIGhASAgB5wW0YRCfaUo4d69r6DphJ5ZlZYuzgqaKdZ5xPNIPImNsASFNXhhUziZ+xAIUvO0YBwliEoZ2IWlK6bJtlOttZ/JCJ/89BEetmWle91Tg9idpMIkwrZWtij+yYLG+o+aEqQWDwbuwg8yPjw9KSeq/W7h9zLvHttWYrIt4PtvjNEY6PNuBN6VE6DwBSXZu8ljvnCTr1sy6bRyPT3z//h3nRZ7svYDK2zyRUuIc5PAfc9oP685JQEuOSZkWYZ+shUrWvlX48vzE//FP/8Tr66/cbldy3DDdwPl8YhwF/DTw3feBacrUKtLZfuh2P7CziS54grM7Q3c4DGybBEg18LtLKXX5AMIMVU1cbn8asLp7qOsu8y4UrIJUAUKZVinjLBhNOK5FgKEsCJrkXd5ztYrnsdaMMWWXBLf3hdx39VMIkICbvu+Zlmln7ls/qLWW220mRgGNxhipRqHsixcBiQZrPdbBMErQ2TzP5BJx3pJyxAdLPwSut4/9fk05E69xB9GtJqhJqbtuUG94YhhGvPdcbzfmeWaapt8A+/a93ntJ8Yb9PdN6jqExgVHvV884DnsCdpOwW2epKVNLoddkbhCv8DzdqCVjNUm3AWtjwQejEvBCLYVcEylHKgUXLBjxa3ddx/lw4jCMO2he17i/h0TWnLWCRquxNLjLuV6/RiTr1kptzrpuXD6kW7bVvCSVGFfkM0jUA+JLddZRNLiuKRFkmeX3z44mQe/7juenZ769fMFWeH9927ts07qRKlznRdLKMYSu3xdI3ntyydw0+Ms5B6WSc6RUUSDwKUW4TbsuKUZy2qgl/Rv9zfSYxzzmMY95zGP+V83vGqyG7t5feL1e96TQdUmsayQEz+FwJBdNfFSwCmBq0UM1hM5Iv2At1CSHvlwyMRcpsu/7vS+1hae07sG7jFKK6n0v2/0Y10+gwexfW6okEEtyMKK7NJLO2aSp0jm4cr1dVS6MyJGN9ikWSWF9efnK+9sb0zTtQBXu3s92GBvHkdPhLLI67v2Fda+evT+PvRvTOjofoFSWaSKuKyUlsnVEL9fHWks1sKVIMAavoEU8uJWU6u55LbmQbeb1VaSRUjNRWaYVo7+/7wcJUKlo+Eul6wZaBc7n5whaD2QtVa9zLol+6Nm2dWckn5+fVf4Z99dtWVa6LnA+n5mmWatELN51CmQywYf9NROg4qhkSco1KIA0zPO0s3J9bzkMA0PfEdeNse/J6jOkVLrg+fbtKzknKoXgHOWIhshIlcy6rjtj1WSmDXgty6JLF7Mzft77vfrIKrABdpk0IGE7qXmOhc0Ueatcy67rNHBKwn7ErwrWWLoQOBwPeA3JuVwu4j8t8niLaT7YSAsQkmCp+v+4J0U2XhS4oFJcWfQAO1hPCvT6vt9/RlvU9J0AqeY/FSY+UmvWn5dEfaCdnOModUKtOqfd422R1a7lZ0n/uol8v73+KSUNEAt7zU0IQa4FMAxSRbMui7CKujSwFk6nk1ZYtTRkc1djUDgdD3tAW86ZZZ05HoShtc5yGI6kFthUROXRFkZN4dGeV/tv7T3Xrl9bJOSctJPUk1MhpyKA2Hv9PBDW0TmLdPAilS662Fjm276Q8MFpoJPBVHDWa0CYsqi6iMD/Vn7e3sdNPu2co+sc43jg6XzerRuXy5Xr5UqMmy7UIu+XG+u2EXzY76EmU16WWZcPIt2upejjLhoeJt+DNZSUf3NdUoxQK979rv+6e8xjHvOYxzzmMf8v87v+2/vLyxf+1vfEGPn111de396Zp1UTN+Vg7rylRMGEobMcDj1d6Bj6QIoSJDQtszKYM1uKkB0+eCh3VsBpRUY7GLZDrwSKyH+Lm0osgyflwvl8YF03pmnBmLt8uLFOqhwmR2GfijJbpcghMqW4g0Lr7lLPWivX65W//vWvnM9PIrlM2+7La4fB8/mkB9eOwfdYY8XftrNgv2Xl2nPq+17CRoqwROuyYo1l6IddGoiCOKlmkRoIa0QO3XpkU070fadhToacCu/v71wvV0mrjZlBpZTWWsZhUGZHw5uKoevuh3Bgf/6NlRNWRVkdI2mwOWeOhwNfv3zBWSuVPjESo4CtbRMw9PHxQa13z6csBeQ18u4OOhroo8rvF0Vn4ZdffuGnn36iFGFJc0os80zcNqbbJD7NrhP5tJXE6ePhwLJIiFLtJfG0XUtnxe93myZhLJXVnOd5f9wla3emMkRtKdFk6A1ct9CvnDOmQt93Upmj0mIBj8I4NwY/BL8HA9VaOaiEuusCn3t2S9FKIOtwFlUaiOdSZOf3ZOwmnQd2Kawx8vpJIFPdr3/zqSZNu4W6p/Z674hxUym5sMuHw4FlFoloCGEHq+1rnbNQC33f6QLkDlr9J4Y5RpH3Ju0olkVX2H3YbYEDcDweaSnN7Tn0fSddqSk2m6+GLvU7O9z+eTgcd+B+OngO4xFjLfM0Yazlzz/8QBfEw9z14qW8TRN96EjV8/bxvrO2wP7eketrf/NHXst7OJI8Z6c1Oboc+iRplvvK7feOpKuX3RsrCwNL6IxeXyeP0VrQ39cSsCU1/b4Ea2oP8c52+2MqpUiytL7+KWcJh1pWBZtV1BDTSqp1T2cG8F5k+W05Id24276Ua/df0GtkrZWlYMlU7d5uPc8l5Ydj9TGPecxjHvOYP+D8rsFq1aqSdY1S8zAvTPO6BwYty8I0TQxD4HAYxLuoVSjd0OF8wToDpuCdY9sWTfOt9F1gW7Meuu9M1Q8//EDXdfz666+aOCzsa61SG8MKf/7hT2xbAmOZpoVt+0WZDYibSP3MJw9b1bCcxogK29DhrTBdpkoICaAH7UCMGbAcjyeWZcFumgK63D4FqgTGcdRDYcJYpxU57jesYSNYP/tSnZeKlebba/48gELZAWSTRNcKi7IzRv2i1jXgxN4nefm4UaL0Mlpvf8MCjcOBnMueyttqVBpgaOxIrXVnG/fH5+0uL8waDBVCYJomfvnHP8garnK5fGCdsHYp3VnKqJ29cohN9J0sNUrJ98RdY7H6Gsy3G4uckDlqAmkFXl9f98dc2qJBfdVNXi2yZgGYIUgoT4yRmOTw7K0lZpFhC8t0YV1XvO/0UF72hYf3XgCLMdRSKZTf1KoIGFTPI5bDYdT3zLwf5uXxtloVATHeC4N4Op04nY97OFHf92zrRgjdHmTWem9LzhgKfeexpvlq656q3Py/VT2XzU/dwEuT1LaliagT0n6fWmspOVNU6p7jhgG+f/1GTBFD1bCrgDECQqdpZZpWYaStxdRKUs945xzGOnIu3Cbp6YxJfv/pdKIlGt9fwwHvvXpu82+WAs4aOifXMunz8yGANfpeHNi2jWEI3G43hn7kdBgpClyfn04Ya+m7TryTtbJM0hXd9R3zlvi4Xncgb52lV5UIqC9WgWb7nGiLinaPW5WDf753KkbC2cKwy5RbWnRLPzaYvYJHJMGSvvv0dKLvB5HSazp5tpBTohhDcIFO7+/GYrf03aThUd45TqezsruR63VimiaxKWyilInbRrEGa+R7vXM4p2nYKUkfrPeA0fs+EbqOPow4J2wqaH2QrSzWsq3b/trVWveO5Mc85jGPecxjHvPHmt81WL1eb7vXK3Qdp9OZktFAHkvrrTwcjhyPIwXxUFIrwQaokWwtYFQyfOA6iZzYO8/3v/5VQEYQryOIpHYYBq1ikENd3w+M40gXerqx43Q6CeCKkWEYSSnx66+/0kJ7pHKiKkg0e+hSUdbTCgzXOgygFlonYgN3xlhef31lPIzUIr2pGOi7e9jRMi/qGXVYZS4ENHg9ZGparB7eAQ0x6um7npK0EsKLzLmF3dyWWX2cRlk06UeMKQHyM521jEO/s3iliLwzbhGLpVbD4XDg+w9fFWxZUipcLu97t6335s4O/kZyzScQnneva2PxOpXirsvM7XplXRapDlqXnV3c1lXrKjpOxxMpJ97fLpr82jov7/Ukw9BjEeY2pcSvr684ZTSDD+JbrlE9ueVTlY+E5jRmq1YUhEvn5WEMkjy9rgKabjdSzPjQEbeNdYsKSAeu1yvLvDL0IkM19t4dervdRPIaE8u87J7nYegJXl7D5u9t4VJt+dDAQZtWpfOZXfNeZNXrshJ8p69/YV3jzopX9SiL1N6QlWFd1mVPia5a0YKRepWmTmiKAPE9eq2cWnfg2uScVINXKfO2rnz98kUB5Affvn/jhx++0XWBru+5XW/8y//13xR0teqVO9M4zwvrGjkcDnuCb1vMNHawLX3afTdNt11+2ljpuK10QRYSy7rucn2DBBf5XaIracPGyELsNi/ULJ51rMNVy+XWfL2bdJHGxLJtImWOeffKpqxyZB8oyGOuoOFJsgRa1xWLwepjTynu178tOZwX/68xlmma9qXEfdFhaUVKbQnlvOXl5Ymq0uxaK9PtJr2nSYLCjscjp+ORw+GIs551WbBGgPGyLFjjOB6O8his46bA/HYTqXGTPou/1+GspZpmp0ATi+E4DgzjIAFt84pzlvOT9OtKT6xW8KjromRJFff6/qmlyOcpVT/nHvOYxzzmMY95zB9pftdgNaZt95J2XY/3HaUY3l7fgcowjNq5J0FGuWatIjHM08Q8zeRU2eMJiAAAIABJREFUSLHc5bZYKLDOK+Of/8TLl2fWddlDjz4+PvbOR++99LBieXl+Eamug9s0CYCrFWPKDtpSan7LqtE9UKiUCgL32GWhhQJGUmeN8VQSOVeMBvGcj2dhavqevhvIJcoBlztjGrdNDojjiAva51oKWCeAwpgdBLfOQ5FKJkpxe6+jHA4jtd6lhcIkZ5VXaiLoJ7bYaJfsui4qzfSkbcNi8F5A5eFwxBpHLZVYMvO88PFxATSxOaYdJDSw2qaBLUnZNTtYdc7RKRu+zjPLLHLcVWWFX768KMguOCNM3fHpScBJLJIUaqWepb3mPjh8cJDlVZLHYXa2t+RCJbHFTdnUFpYj4VbGWF1sCBPovKWUPbFo73qVQ/+MMVaXCYbuPO5e0XVddQkggKV1VbbQosaS1k8+vWHosaZwVOCwLIsuO4z00U4io22dvV3XcTgc8P5Jk4Phpkm9jXk3xuprLyy0c4Gn0wmjSgdrHaVUUhYWs10XYwyu8ziv8th8Z/ObtLUxrC25F9glrTlnxtBxOAhA/9MPf6IfOl5f/8H5dOTblxeCd1AL2zLx888/8vNPP1Krp9NAnnbftGCnz2m/jS09HkeAPUjps0RZZO13tr2ULB2zY491lnST+7EfOl3aiM912Ra8Dzt4Sllqf/pOvm9eI9Yk9btHVXNc9f2ViFvi68uX/fOuAbmSC0Z97FV9lyklppu8pkOQvlOjdVdNuSHy5R4fAjnXHah+Zmbb+62WVjMDoQt8/frCOPbEbSWnxMflwu16oZbM8TByPEoo2DiOsoiY5euqtVLSWgs2dFjjyKkwbwvv7+/M88y8Lp9+t9krc0opWC+wOUaR+n778szT+YmUI9u6QC2cT2e+fvlCSvJ7t1Xe98L+Sjq8JL8LW971ci+WIpaFxzzmMY95zGMe88ea3zVYBTlUjeMoXkksXRBP5bZtHA7lXuVRYYuJ29uFkgvrtFKyslzFsGnNRgg9YFnXjZ9+/plK3n8PwPv7+17nEELHy5cXjoezetoKt6umwFrLPK/M80xKIhMsBjk4AY1RNUaCbKqRQ6GpIm9uPZrFgDO/9avmdD90Ni/nvYtSpgUxfQ5OAgH4FYuxDmOcAj3DtuXd55VSJof7z22BNI2FbRLTz2Ez8nXtQAjGGk0mdgyDgKDSBWGAEWlwYxoxhpTiDppylloLWUC0ipG8+xGtdbsEuJSkPZ/3BGQQ5uV6vTFPEzllQJjccTyotLPs7GN7TsJEDjvYzyXhklHmyZBLou7PW9jgseu1okX8xofDYQ/xEgAgoEuel/ibh3FQRidwvd5UmrlxmyaWdeV0PGGtE5lkZ7FGAnFC6DmNB8ZxlIWJyqHf3993eXTzBHplA40xDMPI0/MTXQj0vbDAb2/vXC5X5nnS51+0esbg/UIIno+Pd/Vu3j2G4lkWyaU8N8PxeKIPnpqTLF6UcTOmYih4Z7E24JxhPIwcxhHnPSUbbrcb1+vlt0FM+u9tUdFeU6/y+OPxjLWGL19e2OLK+Xzm+fmM2QPIEq+vb/z97/8qoUK+k+CgxiY21tEKK97AsMjRpS4K7n7Qz5Lqdt99fu99//qVzhumaSJ4x/F0UgY7cbveuE2ThrVpb+uykXShEJMEkvkQqKXw9v6O92H/2aWIUuR0OksdVExsy6oJt6i/XJjgou/1z2C/1krJGXJmVZ9p3/cMw7DLnD8zmrWCMfdkaWHBLRhdgHjH9XplWya+fn2hdoF12/Be/NXjeCAET82VuKzElJjnGdt+poYxtTC8BvpjSsSsnx3GgJEaJamUcrgg9U65JIah5/u3r5wOI9u28v76yrZtfP32hT//6S/kUgT4zjPTbSK2gC5rGcZegs2qLAyXZeF2u2Cs5XQ+/3/5K+gxj3nMYx7zmMf8/zi/a7DadQPv+Z0YC+vWpJ4b1kIpiWVdMKZyOh14fnnm7XLhepmU4ZSOv5IL1gjTYI1ljZvIhIPndr3inOF0Our2XWR8w9ALY+sDp/MZi8jYnDOs28qWIpfLlXXZJCQk1x30NawqacAqa6sAhmrsfuC21mBBZILO4K3DGgkRcurd6/oeUH9kYme0as0q3bSMh56u95gK1jhslqofZ8X3tW3CqJYsNRoNlNdSFWhnDS2Sg++yCVPhtXf2fDqxbRvvH+9aI2GwiMzR6mH6fDoxzZN0vHYiYbbGYqzVJGMUtFwFEPU93kvADcA8zWwqDYy5YNX7GILHKdBdV7PLga21yuglKpbQSSdn0KTWw2HEOcvb2xvbtgJySG01QM5JGvI8yWHaB/HuZu3TFO+d+Gd937NsG7ZW+nFQr+VGSplpmncGsoVyOeexxhJLwhjPbb4xTzPVwDJvkkhqhJ0Pe2qvSIT7buDp5YWas/gajXSteu85Ho97Mmrwnq7XrlZnOB4HDgdJ3k1r0jCyD7ZNklC70GGtMMFxiyx2IUbL5XLd04MPh8MOxFvn7TwvNHl4qVXkp8btXkdSEqbZWShFWGUFLWiFSS1V1QmyyKn6daWIfzeEjlwKfdfx/ft3jocjVX2u07yybTPGOq7XmcPBsMWJkhM//firpBpXw/F84Pn8LGnfRfy5BiR91nmcdwQfSDlzuVzkfeAs3gVWIyyn95LYLEsbURqUmvny/EzonPQ4IynRT0/PXG8zt+vK+8dVgGOT+hdZaDkf8EGWNSGEffEg7+FWRWXIeeNwOHE+PzHNs95bCec8h9MR33WkWsm1kGJiXRYFqhow5yDnbV8GDMPA+XTkMI6scWOdBNga9a6WT729jdE0VZZ1/dATgqXvO16ezqQsv895j7FOGd8sVVTrpunPVYCql/c7wLqtxFT2BOMdXFtD3/X7Yx2HUaTjCuy7TrqSv315YRgHbtcL1+sFrOHPf/kLp5PI+T8+Lry+vzPdVll86ZLQkKUfOEl4VVW2XD4XHNM0//v8RfWYxzzmMY95zGP+3eZ3DVbfPy78+NMvcriywoZSKimtGCshMk0eOc8zOeW9i7UahIW1bveoCVDy9MERguft43UPVmphM4AwNMPIvROS+0F0ujHNC/O8ULIEI8V4ZyObBBasSlEN3lsBjLXs3YrUirNOJbuBXlmee5WKhP0chgFrHe/pnZSrhBSVjZwjoQsq8V0ZxyPWOjozkPLEti0sy8I8z3jvOZ1OO1iLMRG3yHybdjbQ6yGyVKnfyblirRwkKYnj2DOOA8ZacszKXjp632GwKtEu5Cpgu1rLGiPXy01qJtRPCxXjDNZbUknqRZOuVYxh1XCqUjKmVsahlyTWuCnDGzHGkUuhYuXxlkpQ1rTrJGRmmiZKEU/i29urBkCJXzOnQlKfcAjCCqcUJYDFCEN7fnrCKti+zQtd1xF6L4sSUCl1+QRsA6fTQZcewvjfpmnvjdy2SDUW13mqkbRV4yxpynufr3OFy/Uqst+cNTnZcTgc6bueXsGqMdKHGrxlHHvGIeCdhOdcrhd++ukX3t4vWOv0+W7qHbTEbRU5r7krAELoVI4J87wqYBXvX5OlrstCFzxeQ7Puku2CL4VSNEwrdDgkmOj2cSXHROc7UBkuRhY7nXeMw0Dfi2rCe48zIt0VwDjtXmi5Lwtv75Nec4gR4mYZhwNPxxFnitayeJwNlAoUVSSUQtw2ea+nJGAcS1qTLEZqIW0JtYXv8ujOWZypHMeBabrR9cL6TvPK9TJxvc7My4YxVlK+a1bfdMbhqLWFS8G2LdSaNTX4DhQlpdezbptI8Sn0R7ku1jq2FHUxEyVBNxUNIHIYMRmIX3kYGcdB/Lmhp1LY1oVtXUlbYtXAIaepx225ZozBeqOstud4PNB1gWWTdO1lWUkp45wnZ1iWSQFr2tnq00lrvxQcrsuKMY5tk1Cmlvjc+2H/3LPW4KzBemFzQ/AErynsJTHfrkRdTvXDQOh7tpS53SYu1xsxyvs/1wyqOMilEBfxFOdc5PMRQ8qVmCLjWP7X/MX1mMc85jGPecxj/s3mdw1W52VlXuKnVMdCjnKotFbkcw2Q5ZxZlfEJwUs1iXE4rfzwXqRpxhpevn7hdDyQa+I23bDW8vz8LLJADajZPoHUlpy5bRvXaSLGhMFijNREtHCVXQGMhBLJHyOMkx4wrRVp8Hg44L3bA5GEMXQqpasa7gM5R/HMHUYu141x6Fi3Qq35zoyYBpCFPRGZYRTZXxHPaS3Q0jRbmiy57mFHgwa1GGtwqCQvalVE5/FVHps1DuMNSf1/pRSRrGI03EX9qzlzu155v3xg0MAaJ4f6nBM5J0393YgpE4LQuxIUJSzwuq2UHNVneu94zLWwblFAd0qkFKW30hplOrXqxnlyFsmgdHZ6vE/Uwg64jDG7l9d7i7EIc10S07zy8X5RSSnU2pOSeG9TrMrID8KmWquBWeLvlSXCRs5gHQqIBSillLjdblJVUyop3mtj5qmwqZzzeDyoRDvT9Z1K0YXhBCcdqYeRLnjWdePt7Y2//evf+PXXXynVEoLb7z2rsm2noU3WW15eXn5T0SSgwmHtXSYrDOssXZXGYJynVll45JKpmgadq7xmt2WGWdj0qGnbTX4qlTOSdvv8/Cyy15RlkZAzt+uNmMvOPAL3EB5rSfp6ifQfQjfw/fszhopznm9fvxF8Rwi9VFZNM9M0sW0bl8tF5KrWYjAcT6ediT2dTlhnWNaVbZmIMTP0gaenM4dx5O3tjWVZ6fuBj48PrpeJeVmZ5kWl2Ubf85VWmwPs1xXuNSsifb77RT/XZNXiCaGn63sqElC1rEv7RMEUcNZqF3ALJNPlinaY5lJlUbSszNPCsmwkTQduj8lg9+srUmlJ4AW0p1iWgPJHEsAbI9x+TvveVhHUPL/btrFuK9Zocncu+rxbUnb7PkfXiwTdOQnFKylRS+aW5DNfFlLgXdBQPXmvbGskxc+2CE1DNg7rmjS6kFOSbCvkM6M958c85jGPecxjHvPHmd81WE2pkqIED2G0HzIXcpJgIx+c9pzOsk3X1FrvPSVX1nnFYDgcTgoUtP5lXqSbdDhQauHp6YnD4cDtdqOop7R5EFvATSlF2I9cRNrXWIBdOmh2ia+wo5rK24XdwycBKXKwOhyPdKHTNFZhLoIPWGeJ60pKEkgT4woU+qEHcxR5bnckl55SRT75/PIMGOZJ+gu3bSNqt6vRypN1lUNkStqTmLL4zhQkiYT53reIpmeWGrF2RPpoO4Lv9JAostxlWSSFE6PMZ2FdN5Zl5nq5SuWIMWSV9RprqK3qxLv9gN2YkVILITgK4vUNIXA6nRh7TUlNmWmeBTCmzKbBURKs1UKbjKSnVglrkd5VASm11D2sRjyuA8ZCmmUZQS2kuJFL5nq9Mt0WCc/KThNyHX0/cBgDXTcSY9Kfz560movIY9d1w1iPw+6hTzFGbrcrIJ5XSaYWVrux/OfzeU8bFs+2pKWKKLiq7xDa8mFdK1vceHt95/X1nVzU69iJpLeFIo3jiLcwjANYkctu2yo1IhqsJDUjTjzOKm9u4UwtYTUnfS+kBMawzDMptfuo7FJrixPZM+zPv5TC6XxmHCXkSHpPN0otxJxJUSqBoIHSQsqJcRjl/U/dA5vGcWAcD5xPI8fjWTyj6lNPadql56+vr6zrShe6vR7JWEMumU6rVra0cbvdlEHucD6QclHAe6MWQ9zKDjhj3Ih543Q64FzQn2uZl1lTc9PuBZceZ7fXuvT9KJJ/LyqLFqRVay+eaf3cidsmwUWgwWVelmRVPd8uMI7ht/JtL+FOy7ISkwQ0SW2RfG5VkiyFtHJHQqQqxVS2eWbbVl221F0hAHYPQ2te3tbnW4okXHe/kbRnqjE47X1tNU/NvzoMA+NhZBwGrDPkrDU6FUy1iG9CEn1liedxLuyLyRhbknDR5GV5X1tfd7+7LLw+9+0K8/6YxzzmMY95zGP+WPO7BqtgSKnsh2YqIlU7jJSS2GIDZhHn3f6/pZrDsqW4h5m0Q2MLC7rdZo7HowYaVd7fLiJJVblpS2BtcjfrHLlU4pYxzlKT9KDuZfQpYw0E5+h6zzgOdKHndBj3Tkfp0vT78zkej3taqzCB2k2ZEiVH8YOenzgeDwRNsmzS05wztUi68OB7LtPE6+s7mzKOtRpKbkmowqZu67Y/p5wzDispxQryTDaSeGzk+47HI97LofF0PBCCgIGUMsY6tlzY1lVSabtOFgQKfMSnlkB/Vs6Z0Ilf0zhh9/q+Z7otbCmpoLFgirDAhsrpOHI8HjkejzgnnrN3BZBJ5Z2t8sRgd0/rOIq/c9sS25akb5UmXTXqSR7vaaYqmZbOT4ib+AtbSE/Xe/HxdlLpEpwAgzWKdznnsgP+GDcJalKZ8TgctHMyUvS/C1ul6ashYJ0EV9Vi+PLlC9+/f4cqFSCNgUs5a/hUkIRehPW/XWf6PrCsK9frTEyFEAaRLWsHpjGGwzjSdx1payA/7uBamFeRT8/zjZwjgCblGvo+MAwamlQKW4ys+r5rAMU5h7NO7o+cKTXjrQT3aMoYpWa6caAbelbt7VxXWSY0cOe9xwUBHKnEfeGCVd9rloVGNwS+fv8qHs3zEwZH3EThcLt98Pb2zi8//0NDugrHw2nve61WPI6Hw4HxeGTdNl5fX3eFhnWOZY1cr7cduJ0PZ0JwOwCFyvks9+bpdOZ8fqLWyv/4H/+D17cPSgVnjV4Xqbc5H+Xr2gIgxkhWSW27liKl1/TuXFUpIcyn914ZSGGax2HgcJTQKOcCKSameWaNiWla9qC2qp55rCRMNwYYMhD35PKqrH37+hB6BabyWdAYVGhBbaJkcc5pkJGEiR0OB0I37Gz9pgu5dr8bJ93Py7ZCK9UqkhFujJEEcVr3tGXbEstyY7qJQqKFNdVacfJNGNMWOCjTbfS1Ewa668Ju83jMYx7zmMc85jF/nPldg1VnPcfjiXVdBJAqyHl5eSalyG26MgyjMCq17MzFMIzkLFLaWg2+8wzjICxYFIZ1SwmzyqEml8o0T1yvV27ztB9q100kdM45YZSKeDpNLuKHNIbOdgqkC9YYDmPP+XzieDoSvN/Dmrzz3G6W223aE45b+mpKSVJbS8ZZqVExRuSFp9OBw1EYPGPAd0E9ugvzJCxIioXL7cbPP/8DY4T5MtZSctkTTxsL2sBJ3/cM3UDKIqUVj6jBeaMBUPD164tIW7V3slaj1RtJwGIq4tv1RuW2y37obhJIo5JtCUe+yzhfvnzBGsPtJqEnDawEJ/LVWgp9J9UswzCodHYSWWdMejL9bVgM1ZBiZrrN+BBZ5pW4JXK63xtSG9N/6iTdxJfLnVFuac3t9Tmfz4yHA7WC84FaYbotvL69cb3ctMJGOjdF3iw+Qu89zm47O1irpAL/8MN3vPf8/NPPwpyVSt8Fgg98+fIF77yGG7GD1RD83psqI729KWaisrLLEvG+Ux9k2Bm9FqBUVR3QkmZbL+kOEp1jWSZAem5lWSHy+aY2WNeV6/XKPEsXL6CBTOJ5beyzsICG1jYsUuBC6Lx4G6OC5Zh0uSILHOvugFBAtN3BHaAJ1GFXQ4QusG6R6faxs77v7xdl9RO5lL07WR6r3WuuJAgocZsmJu2ErZo0K0BSGPNxHAm+w4eO2+3C7Xah6zu+fv3Cy8sLx+OJlAs//fgTP//yD9Z1I3iLweKMyGxfXr7w7et3hmHg/f2dnBJJ5fg7ALQSylaLyFgNwkJ67yWIrVQoVTpD1QtvrWeaVjAby7xwvd2wVj7Wi0ppSy0SBFdan3PdQXSrowLwzsvngG2+6Erfy+cX3GXNbenWlkjLsnC5XACRVA/DCNq3eu91rfs9POxVX1El3hIw5600UMdmvVCAG/UeaZ/V6yo+4RboJABX7BbWoGFQGkDl/W4hyJpG/JjHPOYxj3nMY/4487sGqzFuypyceHv/lW1dmecb6zrQ9QJkjEEYpKEj5ghacB9j5vn5RQ/iErp0nSaWRRJZc87M08zQdxQDuVbWGFU+K766pHUtoUkZDZLQqqxYLXKAl1RWS/BW+iC/feF8PsnhPAgrNc8zlbL7vJo8TUDGwhY38bBa8fZ1QZIxz+cjXR/YtpbM6sU7e72xzImovlLjHNfLRN/3++Mvn1iUBlpbD6WAT5HdfZYwe+c4HEbWVepNQvCs28ptjpRsmCYJUGm9lbUi4UGryCgb+AHxCecqMsZc0i4Jbn2f1+uV948PBZvmDliMoR8HDodxf41//fVXPj7eJcyqVZ14jw8dXn18LX20HW6nad5liRIEI6nA7TXIOXGbrqSUdpbIWw81k3Ki73u+f//O4XAg1yoH5bhyvdx4fX3XOpC0X+fmVYSK8we6TupD5nkmZZEne+/553/+Z/7lX/6FkjND3wso9p6+70gK4qZpouRKCJ2y8d1vwKV1cqe7EMTDlyrBB56e+v3a9H2/s1ur98zTxMflnb7vd0l8A6m1Num7MNWtf7Rdv2leKEVk49frVUFjvysL+n7QRUTe65QawBRlgzDr1jnWTfs+VbpfkVAzATDCMnrv+eGHHyQ06nLZA4FE/iv9ykk9jtOU9bHJoiLnzBolMGs8SugVCsqiLhO2uPFxuYgkHHYGtikfWvDUOHR8eflKsB3zunCbFoxzPL+88PXrF06nE6Hr+PuPP/O3v/9dE5a9gFtrKDlj6Xh5etn9r5ePj/1+E4AlAu+YBEA3b3EXenywdD5QXSGuG8YarAv0Ggy2rRu3aWbRx18rkOT65ZRYt22/Z9p7g2oorkII+E/sr3hKZcnVfNbH45FhkBTseZ73DupxFGVCA9rjOO4+8Fwyt9tVbBX63mj32jgOewp4zkUXPYaiy8BVlR9N/dG+D8OuGjHWStK6vtva/YbKo52mZFsxmkuPbK3Ucq/+esxjHvOYxzzmMX+M+X2D1W1lur7z7fs3vr48c71+sG4ba14J1vP89Vm8VaZgnMUR9g5Ba/IuIVyWhWma+fj4YJm3PQgpBM+yCtiMMZJigWr1ACuHKGMspTRpmQSDtANYqUWST1UqJ36snqenk4aGWKzWYDS5b/BywP/4+NiBhHi5tD6i7xj6HmdFJhy6IMFQGk709vbOPK+8v12p1ZJLZVF5b9cNbFvcH2NLFm7ywnbobEA257J7Ddvv9qEBFatdiTOX6wWRZFfmWSSiBliXdQd+TWotlTPhDqxKoViRM7b/7+nphHdWvX0CkLquxzmRbA9jz7cvX8R/Vg2vr29cLhdKlVTPFmiUc2YYJGzGWgHEpWZKvqc3C9M+MAzjztZ574X12Zb9ejSmm5rZlkzfjfz1r3/hcBz35zZPK+sWWde4+0vbtWxeRuck2bTve6yzew9rFzrWbeZ8PvHTTz/x09//xvE4UErh5fmZGBPWGq63K9M0ywHcerbtpsA672C4Jbg6KemkJFnInE5PpCysd+g6QteR5plaMssivZTGGPq+338msPeDGr2OIP7bBlTl3yPOe5EoI+E/IXT78uF4PLIsyx2w5LK/90SqbelVsdB+bwMltbD7CmNa6fqO//yf/zMhBP7rf/0/iWlTSSc4L9Li23RR9lakwSmJVzqnoiBIGNjmqwR2+XWzFLRk7M8LlmEY2NaVcRzpukDwlnlZmMrKMs845/j2/Tt//vN3UUWUzOX1V/77f/9vvL9/7D21RnWpXTfw9CRLsx9//JGrpj23+xDEd7msC+saSUUSzZ/OT5JyiwDDoAu2nBLWCEs6zzO3SawLEjB09wfP6/Ib5cYup0ZDllTlIKykxVpHUyqI7YH9+rX7YdO+1fanWSWaUqR1q8YYmebmu2dn90U2PTLPN+Z5+RSCVlnmTZlmsXS0hZrcL1WzAAq1mB3c11qkIsuYvbrKW4fWuKrqAjpdvjyfn/7N/456zGMe85jHPOYx/77zuwar1EKMK+syczgOfPv2Fd8FrDOEIHUsLRhmW4W5oxpl++6yV2s9yyJs1bJsytJIdULzse5SUpW/UXVbT5NLCvBrnZGik0N6C1UC2nUdzltKyXr4K2wxUdaNdV0EHMaWtJn0sRllj5/puqDdiY6jhv9smjwcvCSR/vTTP6jVcJsWti1RinhKBUjLwVJkvPJ4KpUUowYzBZJKOpvHEgRMD8OAD03ODEPfaaDJJFK+nFnXRM4o+5LJKeO8Y1n1OhtJCU456TWtlJrwptuB6zAMhBAEvE8TRcOlQLo3XejpQge0Ch0Ni4lScxOCAKOShYXqOqmzSVlkgiU3EC7g7nw+q4RaZIFy+G7yVbsDLmrVHtCMRYC7+B+VLcaqt1Ikik2aDJLG2pJzW8JzC33JNisD7HHeqCTc8cMP33cGbZln8U8eDtzmRTo+S2UcBPxucWOaZ7oQGEZha4ktSdZSM3SdAM5lXQHwylJaZ+l8h9VgG+fEQ212JjrtwFVAZNmTXe+MXKJSGMdhv3bt+Z7PT3Rd0M7Z9Mm7G38TaiYyafMbz6MsSHqsae8zAf3/9E//xH/8j/+R//Jf/gu3mzDfjXWV9Fin90KQ101DvdZVlBVWE8BboFVbojTQ3FjUooE/7f3fHs+ffvgB5yzLLPU567KSUt0roM7nMz70pJxYlpUff/yRt7cPfe/pwsJ7Oi+y15Qzf//xR+K27dfdachTjJllvjItM6GX6pnT6QSlYp3j6XTAoBJXTQGOcdPANOkubT78ku9pxMY1P72km7fFg3OSlC6SeKNLObn27T3ivcNpRVHzTa8a9tRAJG3pYAy1FD4uF6KGbgmbq49XP2P7vsdYw/v7+379a2Vn8lvo1RAkfOnzAsx5T1aZtrFof3WT+9r9vso5i5w4BE3mhi54xmHAWXk/PuYxj3nMYx7zmD/W/K7Baq15T9Ydx5GnpzPWSzhHSnFnrdZ15nabyKmFiUjdx7q2lFORh87LIsnCyGGnpW42xkAObyILbNUQ//MYpKuzMUQ5ZwV/IpltgTOleDDyOOZpVbkbCrpWTXS1dF3P8Xjk6ekZa1GWzkuYTZUV1oaaAAAgAElEQVTamVIyHx8Xfvr5FwlvybCukVKUHVGPlngFq3omhVFcN5EfVwrOGWq1lIywF0PP6Xwkbhtd39F3gRwjtUiIVANjKWWVGUKtwoBVwHrRopYq3lRjwCuDmpKAnS501FLV43bAWnYZafuanBLeB03wlTqbrtu0vmPlcrmwbsKonk4njscj2yYpyc5WnPW4oZcwJZPwwTNgOZ+fds/lHTyZPchK6oeaF68IKCsF7wJ9Lyx03MQrLZU6cn+1w3UDJk0K/rmyRABuA2jyc0rNxHXl+nFRCedKipH/7T/8B3744Qf+9re/kWOSw7UXlcC6bHuY1BojeQcVTth7wNtAqZUtSuWH21nTuicud13H8XSi10Tcpg5obKswwuH/Zu9NdqVN0jyvn03v5O7nfENkZGYNCNFSxopecAlIXAFbxBWxQQIJlkhIbLkHuAQ2IZqWqrqrMiu/6Rwf3sFeG1g8ZuYnsktdXb2oigB/pE8ZeUb3dzr2t/9EzrFJbikdnko5hnHkw8f3hbG0zddtreZ2u3K93oD7/fIWlFYwkXLGFhBVwaGAJ9dqUsZp4HA48K/+1b/ir/7qr5qMvXaSKqVa8NW6SogQOZdrU453VsKcivf17qGu0vuaStu5TsJ5CsC21jL0HZBZ5lt5xqwYbRjGicN05N17qdyRNOzI589f+bu/+8S2+rJBVRjJct1t5dqov9MYAVOrX8sGVmzhb09Pz5IqrA2rX4v/cyAGYVRb6rixJKVLcvm9hilrRT8M9EPfApBqeNswDEV9IaFbbzcUeKPCyIjaIibdruVQVAo11TvESF+el1tRjNQNgVCunXovVNVC17nS0ZpIxTd79ziLjL/r5JmllGoBVEpr0i7nQTYBqz9d/K0pZ5H/lvC0BGXTSdjVmBLzukJKoH7Wf+4e85jHPOYxj3nM3zM/67/exkqC4+E40fddAxXGWlKEy+UGpTLk/Hpl33NbZEv3XmyBIihNCLKArgseUxbBVZaotW6MBNwX3FrrAspS6XSUMJwQwOfUAHWqnqsSGhJTgKxRxtC5XhZ0WwRVQF7KoDVZa/wukjnrLMoYQor4JbCsK5fzhettZll25nmXQKSY6TrpY/S7x5UOzsqWAqBqqEpg22JhbS3GQN93fPj4AYDb9dJYoBh3lmVmHI/EENi9gP2cBFzbzhJTbEEwFQykFLB930CCyJpVS6O11go4yJn5eiPEQGcd6iBdtX3XSY1HlICXPcTSlbmyl/RTYYsdWmVUTlijGfuOru+xzrIsW0kvtYSQWtpzO5cqoTTswcuxtg7pQ5XrZN+jbEZoxb4HtiKlnKaBQrKjMoz9wLIsHE4n8fRud0mtL+ySNSKFTikXRkuXsJpDYaYi0PFnv/0t//Jf/kteX1/5N3/91zhrMaWuY993UKkw/KoxnqhS+5MtGoXrVZE1i6cRhVSCpIzBYJwtmxE9GtXkmg2UqlpvI5tC1S+YUmxBUdoYjFIYZzkeJ/E95sS6LqzL0mTwwUdRJ5SE5LqZQ84Ev2N0VxJuJZRJNonk4BqteX4+crtc+PT5E72r4U5wPB05TAdu861Uuuzs3mNtJ8FKcW/+R6PlWpUAHwF42og2NKWAonRyFvBK2aSqXmq5Pjdi3Omc5c//7C9IUDaARMY/3xb++OkTnz9/Zp7Xdn0CZJVL2rKEZOkC8jrXoXLiOl8a0+mcwzrHOAx0nbxfuR8dzuhmN0hkrOuEzfUeHxOuHxhLQnGVvPf9UJ6J+xvWUTYuhnFgHKU2R0DyTkvjBVIWH22tHwqhbIZYd/d0Fwlw3QBa1xXv/RtZ850BTjnjrC0pyqmoUso/LQFUyihcCV3SugZxCZufSKicm1S7vp8QYksx9yGiTcYZi1V1I1PAtVGysQagjeZ4ejCrj3nMYx7zmMf80ubnDVa19Jiui0fruTB8G9smjNy27Xi/sywr+Q3rV1MfJelSamdM6RUEmme1Ll1kQe5aMqakTP4J8CvfaK0prCslVVe1nyGpwYqu7xi6Dr9v4oFVGq0s+y7ppGiFRhOS1FN4H4jxyjiO9ENfwlYCt+vMy8uZ223Bb4HN78QI1gxknYqftjBhKbX6hr7viowuNtAoC0rL87OE0xwOBwm7WRcg46xBK1j3wmgoxTQdUNoyLyubD1DANCVUM8fEvpcAp37g/bv3rMvC9Xpp7HPfda1TQgJefOtp7LoOkzK5p0hZO969e8c09tJxOs9sPjRQ9fT0xPff/5qhd1wvZ27XM8fDxPHpqXjYxCuZUi7exdiAl5yb3MKcQtnECCG2XtqUstStoMhJoZQRL2zMxGW9XzhkYcXCAWtFMlyTT283YY0P0xGldGNfrTVN4qlUZN8lXOl3v/sdWms+ffojyzIzTAdCVuXa9uXaskBoAVkhyoZFlcf63RfWNxJjluRhcusUlSRcJQyY3xtrtW0bSinG6W3gkn7DQNKSkw/ThHVWwrRqsJhSxH1neBOYY5RFKZrMvZ5rCcjxgEh9rekLUyoecQUYI6/xfH5t6dDGGI7HI999910DMQrpqDXG0PVdY+q0rh26QE7C+CvxuXadSORjFpm3yNIzqNw2qbpONi+UzuisOBxO/PbXv8Z1Hbd5oe87lMp8+fKVr19f+Fr6WyXsp0ReI37eFCUdvPq3a/DQHvamKKgWBK0l2fZyvYhvM0TcwbBtawmQk9e+bhu3klRcz2vK9+tbOotjsUD4AmLLM8CIDSDlRNhDA3FVTSIpwEUdYWXzQGvEH15+dlUPeO+Z57ldf6fT6Sce8Rhju1estcXTr5nG8Y39QJKb9z1Iv3TZXKi+WWOkF1kphTUOrX0LTktJUo5jTiXJXDpUY9fjorC0SkGKEXJhujvb0oUf85jHPOYxj3nML2d+1mBVF2liSpnrZeZ6nfH7zm2eSSmTYiajilTTAakwoEDOGCWAQ4rjpY6lBH3egagSb5ixulWWSOivahLRREQjoK/rHMRATJlxFI+ZtYaswDoJ1wkxEnIipFiqOSIp+QYSUpS+WFCs2yp0HYm+70ryqvhEb9eZed7YQ8LvAgyd6zHGSp1PlXqSEZWxBJGIn1A3KWjXdUXGRwFXDucMl9uVdVmFYc6xgM+9JKJC53qM7VHaEiKs61Lkk7J4dqNjmgaUlh5PZx1D39N190AgZxydk/cVU0DjMG9Cb0ypOjHGMo4Tp6cTy+3G+XJl87IQ3UNgGAd+89tfczoemW9X5mUuskbHMAzc5qWF8HjvpZJn9y1EylgBY3VRXUGtgEzx2DknGxbixcs4J9dOZTV1SRld1wXrjDDnSJ2MhHjNrOsqEmFb2WhVEqF7TscTh8OBbZ25rleefiVhRb//27/h0x//jlrNkYo6QNKYO5Qy7CHgXCfy0rCz+tIB6xxGUVJ6I6mkDucUSUnh/cq3b7F5jMMeSjCOFwmtgqOf3tTUbG82deTa+fDhA52zUqkSBBjklLDaYp/uYV3b5lFZAIV6I7utUlKgbYTcQ41yu25FDZF+IiGtycQ555YuG4p3cprGwsIHcurJKKlTepNAW+tvtNHM85W85xZeJZ2f0umplWJbFvbg6bqOcRj48O6JdRXwjK4bYZk/fvrKpVYWZV3qncTv7sOO38RP248HUgz4fccaW8KvAqoA5Nq9XAPeMrK5MnQ9MYZ2LcVS5XJbttIpLZ7UZVsJ+9beZ3izOVPTnUF6jXWxLfjNk5rMuz5oxU+ulWyqjMUXXTf9UpJncWXK271dgHatKvqJSqWoTSRY6SjVSbkGQ60sy0rk3k8cY1E1lARyqd0p4DLronyQyiptNCHuTe7brq/dE6OSTlpTAqOKfzmGhN8e1TWPecxjHvOYx/zS5mcNVlGKze+ktBUfVW41GLJoSi15M+VUFp0aNGjuPXyyCJb/lQWMCIVjFollVlqEw0o6GFUJB7HGEJOwlkabsuALGCXJlF3nOBzHxm4pJcD5dpmxxkIy7H7jelsJe5BqhlIBkmK4S4eXyHgYcE5Yi/PrhW3zJVioAquEK4xHirv8SyJrzcUX2eR2xS9ZF62VmWmptssiYTxL6Uwll/AUh9KmgDZDSAIohmHA+8juN/HvDcKKjWMPKbH6lbhH/CZyyMN4YFkWOtfTOUffSyBL2CXpU2vNWli9XM5PlRdeL2fO5yu329wCVYxRnI4T09STCSzrTEiBw+nIeJjw+86ybngv3s518y0UR8JcYuurNNYSwiZ1OjE3mXgNgzFGGJ7gA1k5rDG4XupxQgzt5x0OEoBlreV6vbYOUmNEimmMJSvxDxvjcAWwX68z67aw+oA2HX/113/Dp0+fOJ+FKVTGonOi032pEToAEELCdR05RLbdMAwd756eC1tlmecbv//9HzhfXsXvqgW43S4iua5+R60NW/FMa61QWljm622W/k5XPaUCOIAiDy/3XVLEpFDaktD4kAq7CBlFQpGVoi9sbUwCulKUxOwqZ5Bk5vvvslbkrHth21JKaGvpugFjHPseG9C11vD+wzupr9mDMP/LLr5iK0qFnDOHw6H5N2+3K4syaCMJ37bIb+vGlWyKiL/0eJxw1hKTeHpRoFJm2y68vp6Zl7XI4BPWysZQSrFsRAWcsUz9SG8d6x4hZrKSLt4UItZKvUoMwpBLqrHcrynLxhg50w89CdjWVUBmpoWRrcvK5je0lg0oFKj6PspxojCTKSTWfcM56W6tagylwFknm1EpoLVhHEaGfihhadIFq3UmpEhKAWPuTH0FpwLahdHOUYAvRtF10jn98eNHpmliXTe+fnnh9fXKMs9lk6ooUlTGGlPk0I49Rvwu0u5UnsfGmPY3QCnQOWOtgixsvspZ9v2KVxmKhaPc3+HRs/qYxzzmMY95zC9uftZgVWkl7GRIEp5TvEs1tCaX+gljDClLYilZ/KBZGTI0D2NOlOgkAaWaREoQcsZUr5sxAkp1kSZaxdD34h2MsngOYcf2PcfjxLt3T2ij2PdNuvxQrMuC3zapV0hwva1cb0tjkeIufsmwp8awRAI5d2zbVpicGykqCY0p5iytJHAqpVw8aAmlBFjnwkhp7crCD4StMmhdQ0sWkdUWJqeG/giwhS36Uv/g2ENEeY8xIiPVSuGsRWUI20a2BttLjyRKoXJGK6mVSUpxPDwxDhPWOobeiU/QaFLqm1xx8IVpLkxNSpFl8VwuN+ZFOlslFEhCmYStLWnEWipGpsMBtOF2nfE+cr0tTdIrCcYCpJyzuJRI+46OkWVZMcqWRa8ux0mY9JQTsWwCoCh+z76F7UgFUM/z8xPOOdbVt27Qfd85HZ/ou0HyW5VCFc+pyNZlA8L7DWMcn7+8sK0bt1n6cbtxQBuHKSxdTqn4Ri3j0GG0xsfA1Hc8PT/x8cMHejeQs+LbN0eOmdPxyOVy4eV8JubENE2cz68orVoYj7aanGms3eo95IQ1hl65Ios3LWCsyrFDiMSQ2byweTnD9To3X2RLZLUGbTQxSUpzleNmxCcOlMqTmhirsVak78sqPzslkYqKTDuQ84aEfe28e/+ew0F6PffNY5WmOxzY/M7L9cy27YzDROd6yIplXXl5ORfpsObp+EzXS3+veDEltXoYJNwpBJG5Wnuvfeq62iNrGYe+gH0jPkwtwWXCxCuGfqSzTtKyURymsW0ejYP4u/0ukte+qzVPIsE1SrPvnqenJ7q+Y16kbzcWBUI9dpJqHrBd8eIWZQTlHLf3lUASkhXbujVbQM5ZNtTIxChp5odJKqXiHvBlo6s+t4xKZKWbtFiC32qibySGHeSyReVMP8i9O44iEb9eb3z98sIf//iJ6/XabABS8ZPo+4HT4dBSn6NfUQiYTkXeL9dc/ol/9W0dD9AkwBWo13CvnDP3ZtbHPOYxj3nMYx7zS5mfNVgVT2HA+yDMQpGO3j9fklxTbDK8mGoxPKRSw5FybL2KGsSMRZXOBZLKqCS1JW0BVZJbD8Mzg+tYt5Wh7/A+E4On799zejrhixxTKQmuke7UHWskIfNynSUgyIj/UTtLKuBGmEOFMgqKxLECce+lhuP+rzAviXtoSAkQedsn2vcSVKJKWI4cM5Fz+gIQ68JOemHle+T1CIBWLdF3Y54lcXWepcbDGiPMSQuoQipaorC2x+NRQlyK31hpkeRVCSGAwuBsTwzSEbsVtnLbPK/nCyGk4lU88PzuiRD2UlFzT2+OMXK73fB+53aVGp/L5dLeX30/8j4lbba+Z4CYBPjXhNZ6fKtX2ZSFeQWa1+utgEzTvKB934tHNNQaIk0/dAxjhy5g2DkrbG0U5i9nCT9yXa0F0RyPIpPs+46UI8ZIYJLIW+XcWWvZga783vfv3uGsJZegLqUlWdpYQwJuy8LH52e6vuPl5Wu7PuV1d+RYJLlFihliAmPoSv1RCpltnxnHkRgj2nQFbItsuoKH6lsUML4Vn6N0bvp9bdd5lXqi73VQkmB9r17Zfe3w1YSqVNg8KSasFVmqc8LAhxBKuE/A2UFYt01+n7OWaRpZ14WUM9fblZxj83LLORWgJR2grkj4XZEbX8oTRoKbTqcnxvFQAH7HPM9NsdB1XfHWqxZCZIyRjR0dmjzWF7mu044Ydds0qOoHScy1fPv2Tc5351g2YaT3PZRgNEOKuVz/e3lWyMZX9XnWa997X/p075J7CSfa33i4NTUMzVgjmwu7bFrU8yXKk+oDlmdPiKkFicmmQgGKRfqrNEzj2OqdPn/+xO228PnzN9ZFQqeErY+tg9W5rl1DdeNH4tHF4lA/Xu9JYUxTu5//nb8NKaKNbpuPSimMfYDVxzzmMY95zGN+afOzBqt10eVLyE71ut09byV5Nt8BXRIKVXb53+6qK5He1VVXDauRlErxqQrwi+jyNal4OKdxorOSVqu6Dr/Lrr9WCmvEl1f9qPK7tHiusrCNlWGbJqldqexrKmEoYY8otbXeymE48PJyZl22EqgjbJYs5vWdoVJS05BS4rt37zidjo3ZSkUO7JxjWRaWZRYQXRizClzGcSwL+7VJ5yC3LtjdpyaTFF9ZImfx5Xrvcc4xTRPX61WY0IPIVitoDOFeFVO/voKCZV253oTtrb9DKRjGng/vP3A4TqXrUdib8/lcZMy1i1TO8bp4lLKtv7FOXdhWwPkWpOf0U29j/XjzOBbALx67ReSgpObD3LaNbdt4fT1zPp/RWjOOYwMwOSsIubFzOaUGLDvn6JzDTR1Gy7Hsu66E42xYq7leLqSUCZ3De9/O2dD1TNOE66ReR2vLfFvEM+o3Lrcr3759I8ZI3/d8/vxZmPGuYxgGPnz4UK5Vj3MCmqo8/PnpGUjtHF7Or/jNs/uAdhvLvDLPCzWVWyndmLCaOp2zYttWtk0Y8Lqx8Kcsl3grd2GUywZR3Xjxm4So1XOxritdV6qlMtyuN/F3p8Tp9MS27fzxj5+Yl4UQElkrLpeL/D4S67LS9dK96ey9U/l+/U8YKxsELy8vEpB1OBRJ8IHvv/81MWbO57PUUkWRxgujKoFsGt2kt1rrVnHlvfQ7A6X+SICldRKGZDsr8nuluVxeZbMhZ+mA1XKv90NPTpRn4S7PFCQ4qnYR139108U5V9KWDTmbsnFjC6CVkKsafFSrgZZlkTTqGn1NAYFaLBOVnawJxRVo1zR0bUSirbUqtWKSqn25XJnnhfimB7bruvYMrtdgDW2S36lRGXa/MS/b3YsLjSHVWuTDlHOgtPhVtVHi0S4p3F0550Y/wOpjHvOYxzzmMb+0+UWAVelPvYOKCmxaRtKfSMFyzmSlG/ioNTXW6CZH1FpzOIy4zrKta/lehVUKJQY8YbtcV0CpyFenqSfGgaHviUGSSUMI3G43lmVhjwmtDT7shCC9gyIPhGHoBGg4V5iJnXy9kkuHqQQxZbre0fUdfg8YZwnJs24ig8wldERCo0Q7aJ1jDx7XWVSQ171tW/OmSg9iLOzMXT5njCpSTFVeY36z4C21FFtscmvVekPvvZWn06kFxNRKi9kLe1I7PLuuY1kWYowcj0e6riPEyHW+lUXsG5ZTSx/r87unwgQJsKrdrPd+29T8pgLqh7ZZYEtdRt87QP/k2qhgc0978/feQ2Kqf06kiZRzWxmzurCvXZ8CgFNhfVMLU6qhMba3dNaRUmTsu/Jx1QKElBI5rkZ8iQL2EtoarDYEYpOh1toaay3X+Yb/w14Yxsi2hva613VlXVf6vhcfYYp8+PChMegVqGuVydkWVk/OyZ2Z17y+vrL7QNwjp6eTSMO1HMd1XctxMNR+zhDqRoHG7178qOjmodRaqnsq0xVjeONBFSA6L4t4o/d7SNBhmqTayQeOxwMh3L/ndHpi95GvX79xvlwL8+nQ+g4KnbO406F0i8q1rZBroMp+rbUs642XlxdutxtPT6fmcX56empe40+fPhXVhJNnT5KuZrJqTOTbZ1KMUdjdNz2mknosr693PaeTbAx8e32h6zuWdZHnilY8Pz9jjBPveti5Xq+NZbXWorSmwsqq6BCZry4habL5Iz5s2Yhz1jS7gJw3OQ+tpssYEhJONwyDyJdzRpcQI+9lEyukSPEmiFzXCePfdyIj934tm0q1tzc1m4UxtoTAmSZFzzFKgnOSDueq7tg2kc2n9GazqXhdrQalcnt2i5+2b/7net/r4nk9jON//B+jxzzmMY95zGMe888yP2uwWnfTaw/jn7JgbxckZFBGNaBQA2J08dNlnTGmxzrxmRWzFZ3RRC3dfUopVJZU3mEceff8juPxiF9Xdi9S1HHsJcDJGLZto+s6+l6qVoASKhOJfiOn2t/opBM151LrInUQOSSUsiglC7pt21mWDdd1dJ0lZ6l6qaDKGC1Jx+U9yyLRFbndDqTCRK58+/atgZYKFnIW72/XdTw/P7PvGyFKUq6x0h0aUyK98fBaK7JkYSg1Sif2GKR30vVkpSW1tzC+fo/M68LuPZfLRVJXS09j3/d4H9hCLOdXQGrtbM1k0Ll8zjeAdrvdms9NANkmckUnScOVVRfGnJJ27OiHrqXDUvpxp2mS41c8d5VlrwEuNQlXaVO8wTLOOZQSRq0yyfM8471vYBWESTVGYa2Thbu5Sz0VpqRWU5jmvXVCxhiZppHn5yemaeD19bUlFyuj+fT5M957fv2b33A+v/J6OQOyibCtsbG9+76J/DMZvr18wXWWw+EgwTldJ32+44gPuhwTy/v374TZjZF9C7zeXnl9ecVvW2NZoVRJEdtxkc2O+q8AeS3gqMpffzLFJ9t1XWPy933ndrsh6bU7mVqbcj8flSFPKWNtDyic6wkh8m//7d8wL+tPej5zSmgjmzl93zGOI4bMum3s3oPJTc6tNWx+4XK5sCwLwzDw/v27xvyllDifz1yvC8u8FdY8t9ecS4p0BautCzQG8fZmJEE8Q4y7yJUREH08Hun7nmVZMEZzXRaU0Ux9z3ff/QprLV++fGFZRHrt/Y7WBucMxhrZPCiXaD1W4ieVzZVxHBtITzlIBY2xrRKmPhNqZYy1DsgolelK0NIedvy240MoXnBfNm9S2/SyzjAdBoZhbFJkEy0x3jePuq4nxiS/X2us01JZo+W5kmJEK8UwHcRDv21NrdI0yOW6EGm9KW8+vQnp0lgDzii0scWmkFuYVD90/96/N495zGMe85jHPObnNz97sCoLVYX4x+6ys1ot00aJLMwWP1oqrBdaYRQSRqQzzknyZooBlEgle/cEhRVzxRM4TRPTOPL09MT1fOFyubSFs7OugVUJXykdjjESM+L/U1IF0fcCkITt9FwuZw7HE851TSIp9QvF+1m7KY3IH1PKDMMo0t4kLKgkZAZICqUcXecIe+Ll5ZWUcmM6RVq3SGqps4zjwDBIb6ZUhcjrrj6+6/XKui4YbdFavGQK6QSticTWKPpxxFrLum3ENzLrWMKulFJs3jNOEzlm/OabLDWWUCNrHcY4jA4kMjnXzYfEtq1cr1eUkt7Ny+VS0mNNAzUpZvp+aN5R8b0J4/L0/CS+zJx5+fYKSjGNE8bYIoteUVrhnG3MKvyUfVU6E2NdHAsYmaah+UdvtxvSRTs1RrX+b5VWO2tRSFXHughzam0qHaSljuQ2k1JiHEeenn7Nd999JKXIWtj+DOwlhVjkqobXIjuutU45R7bi97xLkeX1jOVcxRhYFglrIkdyEknl8SAM7Ldv34ghcn69cL5cUMAwjhyOIi3fQ2APu/Rqfv8kjLDWUsWzbUW+GUuYkmr3bwVwFUwNwyBJ1OvKvvsGau+BObp5sE+nE6GqK8rPqdLdYZj4q7/6K15ezlI9VeT4WutWm9P1d8n5Hna0UnINIs+OEAPLKtfbsixYa3l+fqbruhJitJb3kLicb01Svm1bq3DJmca8V1ANsG5yjfV9z32LLeN3T6/7kkodOJ893m+smy9gX3E6ndi959OnT6WuR5hD57rml87leaO5s5NSVZNLrc9U7nN5HlpnZPMjpsJuGryXwKVxHH7yHJB7qWcPO5fLBZRmXUXOXFntlOQ911Tww+GAMaZJ5lOI1CoiVY67BGSJj/dwOND3g2QJ5IhyjrHvsX3fNqfE812erYW1r9dJjAGt5G9DTZXWWrcEa4UEdsVyDcYY5b085jGPecxjHvOYX9T8rMHq2z7GPwUU8t+0xQtIxpKk8maMrj2LWqS9GqzO5LhzOB4Zhl5qPwpr61zXFvq5VERYrQl+/0k4kHNOwp5AGNucJQglJQkNydAp6csEzXF65niYUArpn9SGeV5RyrMsS1uIkzJ91zFfb/h9oy8LyBB2vBcpbQ3xcUiKp7AsArhOpxM5xxLaYwsoqT2OtoBDqQIRkHOvcZhnCdK5s9X33suoIWcBS1qD6/omH5SAG99AZJUB17qbw+FA2lOTABsn/ZBS1aFQaIZhEilirHLEXBbx5+Z1reClvr5hGIghlWtCF+mnxnUCxJ+eTkzTyKdPfywXBeU1S99tCEFSQ99cZ5UxQwljWBE0UloAACAASURBVCXHb/shf7IofpNG+tZ32fc9x+NR+nZ3z7rMDXDkpFiWrZD6InHs+x7nHO/fv+NXv/qOcRzYtpUPHz7IhouCr1+/snlPN/R8/foVQNjScWTdNqyTkB8FzT8dYpQAppxZbzPDMDAURtj7lZjkupZaFPGjLsvK+XyGwnAN04HzVUDaMA5MZirBNlL700KTEOCktCpsmf5JeE79GlvYRPGgdo15qwBoD4FQgMU0Tez7XipOJITqdDq1DYG/+7u/4/X1XFJl+588K5QSKf00TQzDwOVyRr3ZSEFVRtGz7+LPriCwJjx/+/ZVkr+tLWyiAP1lWdpGwt1rm9vPrvJfUU/o9pqEZU6YsslAvrOH7RlnDO8+PENClBHLSgqxPY+MFuUtIMyxVmgqo5rKcXRlU2powPntM1QrTSqhYDnXPulcjqvBuQ5jbNm4ksCqvagThL2u70lAYtdLp/C+721z0VrLHov6Q0l4WX1e1Q2Lt7J0SRaOpDebU2/95/LcMyWg7N6FnbN0MEctKpBhGJoMXykl9TchlPs+su3Vk/+YxzzmMY95zGN+KfOzBqt1MZwSGH337t2Bq3xd/f+1iqRKZrUB5zSHaaLvHcfTkcM08uHjR5Gc9SMpZZZ5xljHNB4KEyWSw/m28PLywpcvX9jWFaMNqxdJa4iywD0cJkKKbbEbs4ToZDJ9NzKOA53VZBTPzx0ow8vLK2tZAHu/iY9Na2JKaKWYb3MBUMKI5ZRkYapBFeYkpog1Guc0zmrGsWccB15fz0jlR+AWlsZqkWkAz1rDPG+s24ILlrDfmbx9D9yuM+M4oZTGb6HIEDeUBqVroIpm2/bid+2IMTQJcs6pLUazy7heEkljun9vilIPY7RlWVdiiq2CSHx5fenblIRmOc+F7VWaZV6bt9EYzTSNZFL52sS6LoSwFw+bxRjb/K8xRjDSAaqVYpoOnI5HlnVl21a8j6QCosKbYK/qU61AtjJtNYzHWlOknR23243zWcJ6BBVIl29MubCqkqSaADSEJB7e1a8oJSBiLxsC/8+//tfsYW/+whpqJUxZZCzvUSnFtsrmwdDfNxVUqeSpnsyUItpILdPlfG31Qeu64nphj6fDgXVdy/EV0GuNJWfwuy9+YfGVy3FJhYnX5BSlnzjnBh4qS1ZBZ9/3/O3f/tsWTBVCoHOOfV8Zxh5T+lCfnk+Mw8D79+8ZhpEQItfLjdt1JicBwFpXyboFEs46np+fmaaJ19dXQpBjJO+jytxpibZ1s0Jk6r5JgrvOFS+1ZpzkfdxuN7KSzS1lRMpan0EioY8lUE01uXSTCCuwNbxISWjRWsKzrJXr53q9sW+eZV5IMdHZrgU3mdING0IkhSAJuom2QSSsv/kJUK2eT601xlo0Cq0MKYrf3jr5E1DD1XLOzPOtVOMUVUX1/1p5zoYU5Hl6OND1vTyXY2TZFrwPxZurGmiUTT0J91VKApiAuwc9ZHnOkVpq959uEJYiWfG+Fon+7j0pCkAexhGUJsSMSvKavd/bdZoSLcH6MY95zGMe85jH/HLmZw9WQwjk0jX5tlfv7bTwElWrNGpKZOZ4OPGb3/6aX/3qIx8/fkA8WRB8ICXoOgF5OYNWpnRMWgkr0QIYLtcr67LQdT3vhp4QIyEIQK3y4QpctLFs3gNwOJzk52mFNq6klQb2mAghtuoHZ4uX7E1FxLJI6upU6mf2PTAUFk4Cmlacs4VRTez7xseP7zHG8OXLN2FGFllwNrBnajVOKgzRwm4txvjmv929gCJhN0IJtyp9tq3SJjR/4L5L6Ir4CU1Lbq2S6UxGG43rZJFdf1aIAkRd1zcgZq0lxYyxtiSpenKG00kAzvF4kvAq7/n69YXr5YYI/pKsZVH0vcg+13XBGMNY5L+ycK3STfGv5lwXwxbrLNvr+saLeK+taYxzYa7re6+vWSmK71QW+ufzK9frVdJVi6fY2Q6sRutIXjdSBl28yH73XK4XQgx0ncN1VnzVIJLI27XJTytbWVm5vu8Zhr6lE18vN/HLPj2171FKwebZ1nttiNaKfY8UpzBKa46nkyzqU+J6vbGuK8fjAWMs2yYptDHkN2ygqAX20hla2TdjdVES7IXFF6D68eNH1nUtbKfI6p+fn9vXzMvCOJV7XIF1msN04HQ8tVRr7/cSHJYlDMoICBNQrDDG8f7dO7777jvO57MAcOc4np6KZ/zaalBqam7dbFjXtfmQhSnVLThLaUVMkRAF3A7j8OYe2Mnl/fvyc6X3+J5ILh2rQ/tdW/Fi19AxbQyX6xlUIqdc6nocQz80BYXrekJKRBvJSs5TTvfQsyqRrgFOVbkBEKP8rzMWqy3WapyVWpoqBR6GsTxz97LxJJ5bZx3H44TWinVd6DrLVOTlqciPq+3Alns3hUiVLkgYHG1Doetcu7f2fWf3kRAl8OxteNtbNU39+SlGQgHnGsXT03Or+kEp1iJJ34r1oKpvQADrYx7zmMc85jGP+WXNzxqsysLHFq+mbqChyX7fJF8ao7Fas+9e2A1r+PDhHb/97ff82Z//luenE89PT6VOY8O7QIyZcRDP4TwvDUAKo7ALmHKmhBHtBWDJInHzO67vOJ5O1FTY2pcpXtNUGEcBR9++SWLpsmzklIg5EYqnVmkNKqGUKeE098Cfd+/eAQKMJDClw1qH68yb/tFESLLgCyEUqedegGEustMIu0h+jVHNf+Z9IOddQk5S7WusnaaR3RfPoTUtSRhqPlVgW6WiolarKFXDiFSR71FYH4vWlhgTy1ISPjPYGJsHUM6lhFjVxe04jnz//ff85je/Fo8bivP5zO06M+sZkQELmzSOAspfXr6xbRvv37/n6enEunq27cq+7yWF1Bbvr2rn+nw+8+XLF2r3bF0018U+ZOybHsu3MmABb6n997qKB1IbTUiJ1QdClLTSGHORJ8r1dfcZWl7O39qCvoL9b9++AbRKnJrWm2Mix0TwO9fyHowxvH/33DZ2vN/awj/lTDcWtjvXrs5NfJDF45xzJhbQXT2cxkpFzDzL5oacJ9O8pW9l+fsu94/eC6OWU5OH9r1ItJ2xfP36lS9fvnA4iCe8dgAbY1i2tVyj4rHueovWEMJW7isBgBX01VTZzF22/eHDO7RWzPMVYw0Z+PrttSVkS4cr7XhWea/09vomNa7XsdaatWwG1HqmGmhVPa0p3Zn3YRgYhx4QcJyKJLuyytfbjfPl0ipylNYtsRsyzliOpxOHcaJzXdkAsGQyexLWv1ZBhRzKfaOLHL1u1kjNUEqV3S59rz6w553jcWIcBlCZRKJrHbTydbfZk4Fu6GUTTGvWdWEaBjpniSkwXy/l+aZIOdOPE103EEPERy9BT+ptgFl9VhtSEkC5bZ4UhflX5Wvr5kZVarSgsXIu6j15fH5mOh7lmXdbGlMuLHcubGoESl8s/+5G52Me85jHPOYxj/l5z88arFYp6e5r+qhI2yporUyZAChD7xzWDmzbwjD0/MVf/DkfPj7RdxZtFH5fS1XFiDGefY9FcknzfuYcyUXC1nUd79+/53iUBfUf/vAHrrdb6bgEv4lkTXyNqQS/9PR91xigZRG27tvLhW0P+D2y+q0EDanCDBmcNZwOB4ZO5IZPT08NdNTFmiygDdpopmkiBE8I0l16WyW1dFs98zyz7xGybowvCNMsfaG5yB/vgEtrA2j6vkOpHWtd8ZKtBcTK4t1Y+f25/IxQvImV3XHO0pVQmXmZobCHEtrSkfPeAntqGq54ZE1jIYehk9Cowr56L0FEOQuQrEBYJKoikZRkWdUShGtnbkqJ6/XK9XolBDidnkoAlW/+t8q0bdvWjnu9rqpMtLKn9Rqs/y3fvxZPrwCGWnejsyQ+xwDJgdYCEufbgtYC5oT5zAIaUmjnu4bjpCy9rPV1WCN+ZV9YQeecAIWDVM9YY4VhvV5ZV9+uIaB5hW+3mXUTWXvUArT2EJo3EwQ8WGflNa6+SH9j8znWuh+gMYlyXKT2SDysAmSdk82P3//+93x4957r9crpdGIYunaM5bqkyUD7vudwmKTbWIPKsnGRM4QukqLU1FhrSl+owjqLK7205/MZ7z3LuhXmX1hgMlhjMVY1oFqfI9VmUK+LCphqeNQ4DYyDeLvfgqiahgxdCWc7kFNkXea2WVArmyp7C+I7RsH5cilfJwnS4zBynA4cpyNj30PZCAohkraV6/WCD3t7ZqUU27Oysri1Fqje6/Ue1UZjVJHzhiC9qFbffdsFdHvvOZ1OjOOEVhrvN8ipsKsr5FTqwAw5KzLQd/Jal8K0xhSbRFvrn3bsyqbOJj5+5Jkiz4HMsYR6iWQ9/Ykn1/Lu3TtJVY6J8/nSVAXt/iyy6fpcq5J9rX/Wf+4e85jHPOYxj3nM3zM/67/e3334yDbPLGsJI9q2JllU0IBWZ6UIHiTttx9OHA4jz+9EPrhtK9PUs657qRYR8KZQb3yIskgz2jGMI9fLrdQwCGj+y7/8T1DK8OXr19LBemDbRJZ4PB6xtmuMlcjpPK+vryyLSDNv88L5cmUv8j6lLSnuGC31K0/HiV99/IC1CmscH7/7lQC++cZ8m4U9DYFh7IunNjIMPbd553w5EyNcz1dut1nY5aRIUWp4JPBFFozCqAkgMCVFVboroe+FNZIk1K30aYondRh6ul6kduLj1HIetC7xKRJOpIzCFXZnXhJ+W9u5cs7ek1NL/2YFZl0nCcwiY5TNiH1ZCfEeQHM8bkzTxOVyaXJdpcDvO6jcjgvIwjvlzPl85vX1hXX1dE6Y6XXdCuNyD8Tx3jcvaJW5VpBnCmDSSKjL4TCRUm7fY+2dDaqs7DCIfHMPqXhUl+bx3PdQFtG5AAYtCdUpoY2R5NPC0pri3auMnsqUrkthiSsAGIdj6+99fX0t4Nk0wFXDatZVQEJNZAZYyvGQgKGObVuxShKhY0pNrVADcaTKyLafK/9y+x38hMHKTSUQY2yVPLFcQ9frrQFxH+TYyOuwgPwuRfp3lAJK3TtCd+9x1qCMyPZTiszzLJJf74klORqUhF8VmXJN6fbeN0a8+mzrdbFtG7f5xnQYixdUF6BaX6drQE/Y/ZHbbeHrl8/s69quodo3G8KO3ze0Ueyh+KdL89LpeOIwjUzjyHE6Mg1jqYsRwH29zpwLUE2FPUWJP196juV8VqVC7Tat/k+tNV3fY7X0Nb+8vNAPHU/vnnDWsS5SeTXPUnlVvcTrsqE1GFSRzueSXKzLhlBGaUPYA3sQqbwutUZ1Q0WepanZCGqAktZaZM9JqqiUyry8vPyk27hKgN8eZ+89uy+BTsYRrXRaG2tbcrk2ul3/clk+mNXHPOYxj3nMY35p87MGq1Pf8/75HadD5HK5cTmfxTNmNNZous7y/W9+RU6Jy/lMBp5OJ44nCTZyxmCNLJT95umsI3iP7npSyKSsCFE3kOn3BHi6fuR8kUX08/N7WaApzXQ4sm4bft3onOV4+EDcA9fzpckzRT77RaSllzPn85XNR5bVE3ZZzKsEVkuwT991HA4jzhiMUhhtmNeF9PUr4zix+cBtWVnmDaUz13nh9PREjDvrOpNzYltXSfncRRraOwmLMtYRijwUrfH7jl9EQpczAvAVGKuYjiMffvUMwNFMvL6c0VpklXXRKT5HLyEmyPfHIiluKZw4VIbdb/h1ad7edfXSsVpYEGMM756fmeeZ4/HI09MTHz58YN93Pn/58pMFbQzSq3q93BjHiXVdmOeZrutbAmnYI5fLtTHJ7969xxjLly+fOZ+vjOOB5+enIvl9LenNtiXy1hqWlO7+OaUt2oDSCMtnpRrj+d27BrS11uzFOzpNUseh1FdykfoaE1nSIkE1IRH3QI6xsF1yHRut6TqHVgJEe9eRQkShOEwTMUWm6YDfdm7XG0DxecrXxxQxy4xaYF4W9rADGqVFPhoL22ytJabIh4/v0crwen4hhoAiMY09794/47ededYs64YmQYo4Y3j//J2kvxqD7aXj9Hw+M88L0rda2HVEfmmURWvp8NQajsepse8mG3zYuc5zCZpKjENJXY6Kncy6Bfo+42No4CvsoUmpU/Fqyu6VIgbpNZVAnVSCtFLx59Lk3ZBZt5V3758wTrOsC6+vZ15eXkrFk8NYSyLzenlBKcXxKOc1pcwWdsiarhsaCB8GCWoT6fHK6+sr2+Yx2qKSSPV/9auPxBj59OkilSvV060ESJ+OR949PWGM5nA4YbTl5Xzmcr4xzwvnog4w1mI7kUZLMrMi7AlFxNrY5PgpRroiHc+A3zYoQU5KZUJM2L5DGYvWjnlZ+fzla0nKtiVM6ireUCX+YIVi3xOm6zEoQhLvbD/0JAWXy4Vt8+LTV6JMGadRVBohEEJmj0n82tpiy2ZHzAlT/L+X69zY2JQyMcEeErWfeN0kndhoTa7tzuX5JKoDYc51sTWkIlN/28v9mMc85jGPecxjfjnzD4LVH3744b8D/mvgPwX+8x9//PH/Kh//HfC/AB+BL8B/++OPP/7f/9Dn/jHTW4fKmd522CeDVYrrfAMSrrOcng785vvvicljDVjXlTCeI+PYi9ctysLd2cIEKgmLQWmsGfk2fyuhTFJZkxOEILUznz594Xy+YowrC12w2qD6rnk8JbTl1tis6mes8tJ120lZM00HDoejBCfFSGc0U9dzOJa6nBTxRep3Wxderzc619P3A7dZUol1CSqZlwXnTAEghrEf0MqQO5EW2rJIBVWkeEa8k6uwnGuRzFnnsE7z/v17psNQgk80Vnfw7oluXjmfL0V+Z4gl3bSm+QrbVSW+DmNUkawKUDBVIpiLdBAK+yYJocbA8Xjkw4f3rXe21lZU0FkrN2ooz+32ubBfXQsQqtUxyzoTQiiJvCPrunK9zsIwWkffDyLlvt7oOoPWQ6sfqbU7WtcQL/0meVc2P47HqcgNYwFhhbHJ1cObihdYs6wzKQu7I35NWJbayyve2hB3iLLp4qwjFdmzTpphHJtMtcoZhRG9h4yFPZJJ+F2qanJKuM7hXEfY6ybB2jyDMUaen5+xxnJ+vZCKn/p0OnE6SYqx374xDL2ETlnp67TFP6pyqfaxlhCrV9IS3oTskBPK6FKxEug6YQpjCHR9j1aKNYoEWWvLnoIwZEnUA9Y5Ou24XWe2xZNyZN+LrDQmrHElUVYX5lM6d+W6VGTuPtoaklTPr/iAY6uomueZy/nKum5vwoHEs0nIrcPXlmdDlf3W67hKfNd1wdqOGHMBquIF9ttG13e8e/dM3/f88Y9/YN897z+84y//4i85n89Nbp5L32inLX5duVxmPn3+ysvrlb0Gk+VESB60wnVy3E2RY9fk5+qdrsFR1Y9bWUmtRTLvOluY+dzYY1ETaFLKTZY9jD2n04kURSaec0bnTCALEFQKX2qnpGLoQEoBZwzaQEqB3YuCIKZIQt6rpAXLf0MSMF16aUOxCNRj/TZcr3pe5RzcGfrOuZb2q43BWFMk5pIMTM6tg/X/K/PP+bf5MY95zGMe85h/qvkPYVb/d+C/B/6PP/n4/wT8jz/++OP/+sMPP/w3wP8M/Jf/AZ/7D55pmjhOh7snqu+Z5ol5uRKyLJA+f/7E8TTx9PxUOkVlEWSt1FQoZFGbMiQUMUMI4r0KUfydNQAGBKx+/vyJ63WWoJHbTEqZ9+/fty7OuniqC1bxps44Jx8/n8/ltWi0cQUsqJLk6iBLtUg/uFatIvJNCSy5LTf2FDD6HvJCTrLYS4kYA6fTAWsNnbNlUdrhN98YJAHfij0GSQ3dd4Z+YBvEv5dzxlmL0YrD4cjz8wltaiAJUFJ7U0liFtmfKoBfJH+y0NTt885ZpmksjJMvkr8qEaV1IcYYJYBIS53FvnuWZb4zb5VxKWCqgrPYGEnTEmTfhg6JDLiyabol4R4PR7777jthqM5nAJFJv/narusaON33nXWbGxuz7zveG9bVsCxzAUoi+w0hsMzCbO97XWRnlmWl7610/BYwUhOGp2mSQ5ylvsc509JoJWBq5HA4cDqd2uv7+lWClsYCYo0xRa69ooImJ6kF6bux9d8Ow0AIgWmaWv9r3/e8fHvBWsvT81OTLwuIkRCvyhgfjyem8QDo9lpzhnVdWddN5OBak4o/UipGSheyShhtOZ1KH+yyibIhBAG/Q0cIidv1Cjm33tucIsu8k3Is0tHc2MICcVAqkVJJM84CmnLpNS5lQE1+CvdwNpGdilfzLkcWyWjd8NClBzWVNF8BvLQwJeAn0t7r9SpJ3cPY/K3GGPYCtp6f3zEMPZ8+feL19cyvf/09v/vd71BKvMM1cCzGROodMYiK5Mvnb1xvC0o7CcUqx9VYg3UGpYsv8w1ZKM+jVFhGuRfe+mMlvTgzDH07Lq3nOdf3miHfa5mGQRjVXLpeqyT/7b2pNa0HGsBoSTe3TpjnFCWULuZcGHgg63IsU/uZaNVULvU5C/V13YFms390Do+8BgmAs2R1B7d146HK5v++JPlf+Pyz/W1+zGMe85jHPOafav5BsPrjjz/+nwA//PBD+9gPP/zwPfBfAP9V+dD/BvwPP/zww68QB9bf+7kff/zx0z/mxdU0XuP60jU6cDiMfP2mmJcrzhliCJAyXe/IMYh/Vb4ZYzXOjazbek/s9B7nelIUP+NS6mS2zaOVBlRhIqQiRBa0uYGfzXu0VnR9JwmbUUKGlnVjD7Ekh3oysgAXwLdjtMaZgbF3oCQ0R1uFcQYUrN5zuc2lBkPAdIo7a9gLIyRdrJpM3D0qTzw/nTgVABKTouulS1Kp6sUFUMQg6cUpJnwJRZIFn2m+x2EY6DrLsqws89IW4X3f0w8lZRRVmE5bgJ0FNEabloistQS4aOeIrpPKjDchS+M4Finw2sBn2yjI926JGhJTU3Fr6EvrrNT6J4vPYRhwnblXgeh7unH92m/fvnI4TKUCp7CCwTd/Yk3zreFNbytqck7cbteWuquUJLjO80LYZbHv/Y61nUhTATDFvyw9ltOkmmw0F4miMLn30CKgeWHHcSqhR559D81TWzdNKviVjYn8E//t4XBoydTV7zqOIy8vL4CAl+v1XGx8qZ0HCbcR8FaBO1mqVvzu2f0uAKckGUtCb0QrCTiq7FUm8fHDOw7HibAHvN5/ojyQY79ijGYoftt5mUuoVkBpStJ16SJWYIwkylIqc0AVL3aGwtS9lXvWY/IWo9TrgpQIMaEUPD09MY5TkfomvF8xCKiS6zBAvm+Y1I2Uep2kJP3E1e8K4DfP0Pd4v7HvG9frjV//+jf8i3/xn3G9zvzhD39g2zZeX1/RWpK2w94RYuB6uREjuMJ8aqvpXI9xUmGzh10Cj0iQddvgkevyzjrnnNt5rNeWhA3pdv3ItSmJxrkgX21Uu67qBpzIr327B+/KB5oEuh5fazuIIie2xuKjAPfMHehXRa5IoYESwFTBft0AqwD47fm7J3E7OqXIqTKquvlSc84Ev5NyEnm3NvSu+4f+5Pyi5p/zb/NjHvOYxzzmMf9U8x/rWf1L4G9+/PHHCPDjjz/GH3744W/Lx9W/53P/qD+IIcjiKG0eXwJlBPD0QABFYQdFlvrxwwcJoVFKkkhLqqc1lj0ElA7se2AcNX7buVxuzPPMsix0nch8jRZ2JBObDHDbdpZ1Kf44CXiS+hPdFqiHw6EwTiK7jEmY0L4bMcaV124b6zQMDtt1HE8HYfwuF1LMhBDRRjH0AyEGYggcjgeGYUIriLss+p11TOPI6XSUhWBMbD6wlJCYbdvYQyLHzObL60e1Wh1rLOTMOAzM88K3by8cDgPrItLZl5cX+n5owE/Am8MUxjbGKLJO5Hit69oAxdj17H7ndrsREpA1tjBvpkhnhWGs6clD2TTwKK3Zw94SZGUjYSuJrOlNOmtqKauSWjri97Utpi+XC/M8E2NkGEa897x798w0HSQ4qP3spS3ka5hLTfWtwLVzHTd1Bz7y9Znb7caybGyrL92YME2mgU0XYgNPSomUucoYc9aNxc4FtB6Pxwa0tDbcbreSgiwfq8xoTS6uoP3p6al19u67gO/D4cDtdmufV0pxvV55eXnhcDgK+N/vibLGmPZ1NQBqWTbOr6+EkLjOcwv6SSm2FNraVSp1SwrjJKSp6+SeyClxuVyZ57mds3EUZnnbNqZp5Pn5fTkfO6hE34sf9FRqoST0Zy61TRaFKZsyFZjdU3zfJlPXFNp6n4YQCLvUtGQynesakLe2ayxjDSW6y1ElWfutnNp737zXNSGp+qytFZDWdV0LAosxobXh97//A58/f5aanmVFF3/oHSgHYgmRMsbQjx3jKEFQSitu85V9lwTpCvgqsJQ04CjnpwQStfddOoGFkc/Nc12v8ZQS6KoakXPZdbI50nc9sVRi1aAj+d69eITzT+wAIQSsUsSYCsCV+qKsNCmVKqisSTmRSu3W2yTmt9VN1QpQr3WgWSxEraILsx7RbwCwUgpNAd3WNSvD/w/mn+Rv82Me85jHPOYx/1Tzsw5Y2mMgEaXvr7Ol0kQCiUI4sqwL++6LLFJzmCb63pEprAHiz9wLq+r9wnVZyN8upJi4Xm/cbhLWczjEIieleQOfh2eUMeTdF5ll9R+K7HRZlgKOxOMI98UsWQJ7vvv4PaAkmdMW4KKhHwe6TpJ9q780kcnIIvH9uyecs6zrhrNdS2IV+axuMtDr9cbmN5Z1I6TUgm5u843bdSmePc26bk1ipzDk5LleM85pIGA05HRi3z3n1zPzvOKcJKimVCXImbxHUoys64Y1jmFwTaJIgm3eyCHh/UYMkSwZuqQI27qXjtWVnKtH0JBzJOfY5Lgp6SavrqCs1bQUENL3PTFGpmkSUFQ2DWqC6/V6KcmlqWxuSLUQZLZtAbSkxRYfnvSY2iZnXOatSQeDCrAJOOv77i4zXn2RTDp83NHWsGwbOcF0kMAlsiaEDXjLKuV2nVRwVQGHJNIqts1zPl/ahkk7JAAAIABJREFUua6S09vthlKKcRxbWmuIuUhWBUxXT+b5fMYYw9evX1v3a9/3zPONUEA5ZJSyDQhU0LgsV86vF85n8RDPyyYgiioDDShESq5RdFbk7E5rOqPprW2bBRXMCNizLeG29oxCYl2XwtTLJtR3333k48ePfP7ypQFwSeN1dK4vqcXi8Q5RmN3KMNfQrRjDTxjozjlh70uf5+FwKKnNIof99u0bOWfGsW/AU1QO4sGtALqyfVWVAJASP6k86jsH6i4VDyHw9eu3N+c8QjbsfqPvLTmBdY4ehdLCaE/TxPv3H0Qp4T3zfHsD0qS6RSPPjyp5DSGSUsCW9OV1Xdu1BbCtvvSbbvK0KZswrgRLWWtwnW3Jxn3fi48/R5zrAV025ZaWpF4Td+vzJew72WiWbSWXYyMM9N7OhTblXMXQQOpbdUCTFJf3UX/+tm3tHsoofBC7gYSRC2PvrEO36hpRy5DyTyTTj3nMYx7zmMc85pcx/7Fg9d8Af/7DDz+YsjtrgD8rH1f/ns/9o2Y6HvC7yFk710mNRtjZtiIfLCD1/2XvTXYkzdL0vOcM/2hmbu4RkVVZ1d0QKAqohQRIC1K6D+200VorXoIuQncgQJehe5AACpR6JbKH6qqMwd1t+KczafGdc8yiOaDZIKuzKPuARGZG+GD2T3be807GWOkH1ZqEInjxRKbcYbj5gNaWEJP0LuY+ycvlWuWyKddhtK2ufr+y+ILie9OSRJzliesmFS8hRrRSjLud+C+XGaNgGHccnvbEIP2tTdsgJS8RbUVC54NHac3T8cDr67tUuAw9MQZ2uyO7cYf3sYKasqCTYKeLhA7N0kFqGptleRKm5F3MLGKs/rwQAtao7KWFdRVv5bZtmd0MdXHovedyuUpIinMobXAhQEosy0rbSL2FQqSEjbVoY0hJvHLruqJMW8OTSgXF5haen4/fyW+BvLhe0VrCb6ZpqovtErIkKcAtnz59qoxjYe1QsbLb5/N7TgV+pu975nmR4KcYcZtnXV3tcQRq+mjXtVneO92CeYzNi+1Q/ywlBUpkqaVnVSS/UuuSULkixxOil2OUQXebu1FdZrRUDsmRbt5VXqvzxBS/Yz6BCg4KkLher/keKaFCNjPE4j+VY3EGqAv98p4lNThhmwM+BOnFzez76f3M6+u7yJxDIMREjHnTxWiapkURJRVaa7q2ozEWa2+y7UCokt8SZBVCqh2lxXs7zzPLIt2z5TUdDntSinz7+gXvNoZxl+/Plsa2lT11zqGzF7pU9/j8M+4Zc51DqoZ+R4iemIOYCut3vU6VKZVrPmZZu3TbPj09ZbAsr3ccx3r9ApL2nCt5ZEPq9rv7vqdtZHMFrSRVd/MVJApLnSBECZGyNvcN94TgWdaVaZ7Yto2ua0UK7FYBpbahdAvLeRU5fgy3buri4dy2LT/74s3v3beVgTbWohQ0rc0VUrIh5lZf70WpXnKVqS0y41uFkdQuTfkcp7t6KCBLkm9dxfV78rV9L+EuX/O3fevlmpLwLXmmaGUwWYp8vykkTLCc/5IW/J/4/EE+mx/zmMc85jGP+UPN3+vT+8///M9/+s1vfvN/Av8D8L/lf/8fxffy7/q7f58p4K94w0Je2Di3oXVH1w8MesiLocjb23vtaJyXldUJWzIvGwmFbYTBFDCy1CCapulICaxtqkS0gILr9SrMXu5R9V5Se51zrNvGurkK7hoaUq4MgYjS0jnaGEtM8QaElgmrVGZ7PSkV1uiSgXMgRllc7vcH2lZSMt/e3nIarRyHNfttXQjYpmW6q24RdiKgtSyEi9QUNLaxjEOfgYXP8l3xYZIC1+tSZZbGyEJ0noUl2Zx0tAYf0EqCZEyuB0JJV2bYVgmuCoG2u3nP5nkWf2P2xEnqb1eBR/n3ukycz+fae1nkuSWICqig8du3b7eeTpfBYV4Yj+PI4XAghFTDk2Jmmy6XqUqGy+sTcLNW1q/vez58+EDf93x7/VKZmuJR1FqRonhtC3NlrZVk5piYphmlhb0qjHAJMLr3VgbncirrxjzNhCJzbkxNqC2AHahy4sK2vr1LdVIBbCUkqGw4FP9v+TlVxuk2msaybhs+vKNzX+flcuVyvub+2tLHq3ERiOHmH81ey7EfaKxlzB25BdQknaofuRzj4v8ux7J+bZZ6QuJP//RPMEbzL//l/0sIng8fXjC2BeR9kcie6gD559wnEhfvdPm91lpUEstA0zT41eGdR6GIEa7X63dAvoCd4tXc7fa8vLygtc6/11W2W67jW3LtzW+psEbep2wAFdYzVtawBDsNg4SSxfy6C9Ne5PXOu1wpI3JjAaYBa9t6Lm+gDmQDoqnS4MLuluNjTJN9ugJIY5JOVh8kXTquoQJS7z1++96nC9Sk6ntGvhyDkDc3lDZwJ+EdhgGTU7eLoqGc93sQWsDprdc3Vqly+X3OOTZ/Y3atafL5lAC6vuspKdqAJFn//2D+UJ/Nj3nMYx7zmMf8oebvUl3zvwD/PfAj8L//5je/+frnf/7n/yXwPwH/629+85v/GXgF/se7b/t3/d3febq+o+labNPlonmpH1jXFu83LtMtQda5vIBrGvb7Ee89b+9vXK+zLISMkSAgrQjRs65LXuCpykrcB9TcS0rvZWhKW9Z1Y5lXpmlm2zJ4y6ylUiUYxrPb7en6Hu8dIUWmdeH9dOJ8uWBNQ9e1pKTweSEr78WjtM2yVAkEKf7Jddt4v1yzZzBW4Kq1pu1NBUHF52mURnicVAGV1pa26ej7Fk3C2oaub2mthRCIPjAOA/v9ka7vsxTZVPlx13bSl5pWrDEoNG6TGhrwlQFSCiLComkroCapyOpWxmakaVuURrpgYyKi0EazzBemaeN6veKc43A4sNvtqiS3sD3TJKm8p9Opgnf5GqkAkdTTPvvbFlIKdN2+ylyd2+p1VpipshAvAUNd1/H09HQX1NQAKntoA9ZYooJhHGm7Drf5LH1O+OhzSFasHs9ybopfsADKnPcj7HZmiQuDaE1LY2+p0MYKU3W9St/qPM+ZXZKNFGsNnz594ng8crmcKqiqYVXIZsuNhRMgXTZmzuczzvkKxotE3fmASQGVZevGaBpraKwAki04XPA4J5sBTdPQmIZGC7hUURGd9GMCxJQIufoIwCpNIjGMO3SC3/7lX2W/7VGAUBBWTsK5HOsi6cA2h0DJRkgkESXgqwTxoAjOM+4GjsdD3XwqVVRiBbhW8FwAlGxeUJnPr7n7t0hfy3EtHl9Rd3Q32XHu/oxBOofLNVQAnwBdLYm5VkBocCVMSOHcmq9F+foCtty61fu8bQ1JF5BPvmZbCeyCutFRrnFjDLqTAKR7+XOIsmGVuIHOCm69I7iI0TlwyQobLq+Z+l7K+67e06iqPUBhhAFOim1ev7sHymZRea0FtB4OB/b7fa2zKtL/Yr9wzqGSyqFzCrLto6SDW2uzj1wCskIIXObr3+dj6Gc7/5CfzY95zGMe85jH/KHm75IG/M+Af/Zv+PP/B/jv/i3f82/9u3+fWZaVy/Wau08DWiu2dWPLabLzMtcFo/fClBgjfZS73UhlYiCzCA06M0GtbTlfJq7XBe8kKXeZv2d5CptQUk+LNC8lJ3Ji53AlrTKBsdL3Z21HTJG27dBGs04iFV7mmdP5gneBLQWcC/T9yLKsuVIlAQmje56enjKz7JnmOXeMTtl7ONXFtW2E3bicLxkcyQLQGluBAbnvs+8abNNgjMVqaJuGfujY73cYo3NgSmK/f+Lp6YhSOh9XS9+P7PcjQ99zuUx8y/4+5zZcBtpaqxpS0/VdlnQW8GK/Y61iSlgl4Sj3QUPX68z1Kr2QHz9+5OnpqXrvis/w/f2dn376qXo8y/kXlsUSg7Ar1+uV19dXStqxyCdd3ZywtqkL5gJUS8CLhAbJ7eGcq+BG6yIzzFUtStOiKoPpg8+gWhG8J2RPXtveNh+KxPgmj7wxtvI7ije6r6x1kQGTNwAKoC2JrmVDRSp5JGBqXdc79YAAY2F61Xc/s7z/ZVkyw1i8foU1k55KYbta+q7Nmz8ScKNIOZE6MF0u0tHaNBxyONc8z9+xuvfdr/cgERXZ76Wyp2lbzpcz87LWY62NZjpfuV4XkeTnILMSfCWXlpLAnQKElCgqPn36SN9JbdK426G15u3t/aacyIx0ysnNIBsIJS28gP2/XaHiva+MdmHLS5fwPM94F2uwlDzTpG/YGC3dtn2XE3A1TWYVfWYay7UXQ6gpt+W6SSnhNoc2qfp5C0Pr3FqBca2iQlhfTLnWwl1AkfQe+xqa5DMTLNe0Ufo7OXHxSSt1k+uWa7n0nhbWtfyZdyJnvr/XizrCNrYGrw3DwDiOlQ0+nU4373gGxkUR0VgrqBxIMWJMU6/zdV1x21ZZ7xK09p/S/EN+Nj/mMY95zGMe84ean7WJ51/95V/x+fNPdYFirUWDVGFsW5WElhTNYcgySGsZxh19PyAl98IsdV3Hy8uL7NK3LSlpvEuUxpQYhe0pNQyFTXRZ+ipSU5cX91I9kkikmGsbkkJbi7EGnOJ0OaOtYppFZuhdYFkcwcf8uhTeXfHeSShJkgWkpNweUMbg5iUvquda3xKC+MIMCh1lkW8zECi9moW5aK3FDuK1k07XJi94E61tsEaSP0WGbLG2Y1tlgepCzMFMURJJh5H9OBJ94mzOXKcrzmX5Ya4VSZB9qwkfA1Zbtm0lRlmkllCadVnQY1+Bl8qy6CKFLIvvEAKXy4VlWapksgCIInMsi1mVEYuEEsn7PJ/Pd1+3sSwziZQTYMUbWySPBUyV11IW523b0rTS2xuCy4CEChoK0EuA87KQ3u32EFUOuEo0TVtfa5FLFvmySK0F5LRdj9Eaa28doUX66L3HeVEQlK7Wdd0odSzlOr1e5ZoqPuS2tRwOB0D8iF3XVmB6L60sia4pQfCREhimtdSO6Bgzc53QSbpOhfV0kPtN0Yrd4cDT0xO73D1qra0y27L5UwDcTbLusVbz8eMH9vsdr6+vnGPicj7z/PKBcRg5X66EKJ5g5zxEMFahLbQZ6BWWDwSctV3Dhw8vjOPI9XJhHAZQip8+f+br19e7lFmxEpT7vACtkkRdg9O49X7e0mvlXBV/del4XpYV7yV5uwCs8r53u5Fh7CjsNkRizJUuGYhpLQD2vuO1pPx27SBp3EZXoHyTQQeaO/n4d4xnShgT6bqGtpPNGufl+zZXpLz5Xs5e/nuf/A2QSrdpwe6VIc/+ZGsaUhKfdL3m9T2YvzG/WhuU0bS2JBpHvn37Vtngv33d3CcEFwuDpETLZqaw72s9bkrJ9b1s69/5s+cxj3nMYx7zmMf8POZnDVZ/97vf8/uffoexBq1E0Fq8bzEmiNJlWroQuw6GYU/b9Dgni9+SNjrP83fyvZAZsKbr8HnnPYaA9xEdFTEplhy4sjlHTInN3XWeGllkGWPKMh2tTQW6xlhOpxMhOHwQf2uKmugL0EmkEJnclFkh8ceNfcvT0xPLthFj4qefPjNNC9fLhDGWmMDaliir2hzkoqqErmkaDodDXtDKAq5tpS8x3TF3pRezaS3GqLzgNTRWoZVj3Rw+pOwrNXSdeMJIieiDiIsDbOtKRDoOq4+vMfiQe0pTou9bhnFAK2GCL+cL67bSth3LMmegaKssu23lGBhjqnf1vndVju+tX7QsfmUK0/N9NUxZ7EoFj+b5+ILWcvmX1GH5Pjl/ApZ3FcQKIATvhWksoGNZFpZ1y5U4Or+fDbR0/25LykA91UV713WVrRPWV9XXWySsBZyUuo2yUeGD1M0UDzCpAIVbT+vp9F7BUdu2HA6H6h3t+xZjTfWKFra6vB7gzlepMzB0BC9S0RgSLkVU24pnN8s4t81V4LDb7Tgej7T5e8u1Wc5V2bConaTbRoyett2TYqodpOfzufbFztPM+XzBuygAKoK2GpMl5vesZgo3/3NhkL99+8b1fKHtOuZl5vc//cSybIzj+B0guvcGy2ZF+o59L17Ke8mvJFrf5Kxa69pHq9ConAJeqreapmEYe2J0+RzINde2TT2XSomaJKVQmXCR1CqatmMcRrmOYsgBUUXODMPQ19qu+/dTVQ3xVllTAslCCMSk7u4rMEaeaSncZPYFjKaUCFFeX/nZJbm6PCMLq59ItUe1bP6Ixx3IzyFrDDHcNlzuA5YKw1uY2HKutNaZ+89BTDFKHU6MbHmzLmQfrnzP952tj3nMYx7zmMc85uc/P2uwaqzGaBi6RvxL64bRmn7YsRtGvM+ps/PKtCzEvNPe9x1t09C0lrZtOJ/PKKWYl5Xf//RFamuuV6Z5Q8Jtc6n8ndeNnBQ6DAattMiQlWKLGylRmTISuXBGZSZmqz/DOc80LbggDLDbNojC+oXgIQUBkCoSo2e3P/Dy8gxK8/Z25nQSRnGZFwng2SQxlcKMth1Phye0hhgC49hnYNKjFIx9j3MuS6BLOIynbRrarqVr28yURZqmw3npe21slxfXDcYc8vuRxea8SDhKSWZ13udglpQlrwKiQwz0Q8vxSVi2vuvZVpd9cbKgXJZVAEgGhCUs5fAkfrW3tzfe398zKzwQo5xv5x1aKWLydL2kGi+LsEPOy7Fum0Yk0koW3IrCEMnCet0WtLKyaaFFtmut+GYba+iHjqY1EvAVNPMiQMBtDucEtPRdLz2oqFwJM5FSFACkFKQomxIp5fPVSMhP23J6F++y9HVKOnVCkb4DFreeSedcXfy3nUgkp2kSdiskfIgE7whOump345BB0cBuHISJFN0m57NU0qQUOR6fATidTndMp6SrCtt46+RURmNzEFCRVjrnssS5QWVVQJuBbFTwerqwLBMakYk757hpdiWoanPCvCsSp/cTb+/v+RxbxmHP5Txxvc7EAN5HlnWlsQ3GKLRKWC1sb4wiEy0BVTFG+q7ly5cvXK9XYXi/fWXOMuSubXNKrMnviQzAxWcsfbCWy+VESqWnU2X1g6n3gLB4WwVqIQSmaeZ6Xejalr4fGMaRYRhk4yIlQnD5fgn5mmsIQRjsAr61LsyihDQBOS18h7VGkq2dY8np0W3Too3Oqo1E8BvG3ryexUturakspfeeGMRjamxhhaUXtmzuJaXQyn4nG1daZZ+rqtfNDfTHCpzbtsEFzbbdNoPKe28aS9u1WK3lXqnPENncgcS2bmhjKlOsUHdyf5HZh2rbkLA6o6HvGlKybJv4rQXc/kf8sHrMYx7zmMc85jH/UeZnDVaf9iP7f/SfMQ4Dp9M7rdVYYzkej/TdyPl0YTonDJq+abm4E87N7HY/0vWSvPv2JjUUl+tMiBE4yUIupBqOVBgLAJXkn5QghcS2iMQ0RQhEQmYstA634B0y4FWKEG7yYaUUYXbYJjNzSpHyYlgWepGQxGtrG8vh6Ym26zifr7y/3YDMrdohkpBAlrZt0QraRuS9SkkC6OFpj0J6Ojfv0UbqZi6TJOymlOjaFlkOKpQW6fC8SmJp03U4F3Bh43w51+Aneyez3NaVOXvvbNMQScImQpXQ7saRT59e+PDyzH43Ym3DPK28vr7JcV0dKWq2TSSWPocQtaal6xs2t/H29obWmufnF1KCbZOu0ETENA0xBprG0HUtwQfWZUajeD4+oRT8+OOPrOvC+XqpFTAxSf2Rditae9AJbRLDrpMk1OiICnxc+PY2ZYm5VKEUjywo+rZnWTYa26K1pTEtjdl4eT4SY8AYSawJBoa+4cdf/oJPHz9kae5C2FZSkI2DGkSjtbBViAQZrfAxVuZr2UTWGPNmgPiVoWk0O9Oic5VO23UMXU5e9Z7oHCFLnTcFmw+YHOIVfGRZJ/q+r4zhugh4FoAsMnBjNT5GkjaE4OX/vRemF0Mi0vUDh92eGB3LOnM+r3z79sq6rhJGlXzdUOq6FmMV0yxea6sb2qYVRYRpAI33kct5wceI84mYBOBaa4gEQtxoVIcxkFzAZjB9dRtawzCId3yaJZxsXhaGYaDPrGhJqBX5fEQ3Gogi48fgNkmvHocOfadc6PuhspUl7EdY0w5QTNPE6XxGKUPb9XS5EzcEz7atGCP9tkmBbW/hWUlF6SNeXVYAWGH/o8fYm884JE9wUr+0TEv9/SEErDZSjeNv0njvXQ35kk008dSGkFBomqavUv6bUkHSw1WukFYalLn5tUMIJJVom1tYE+Q6muSwranPA0Osm12kVEGjNZqh77DGihUibxLkiG1I0q9ttGQBaGPypoc8W53T+GCydlqC1bTSoGBzG9u6QGvoO8PmNob+Z/1x95jHPOYxj3nMY/4N87P+9B6Hkf1eQjG8F2mqSlJsv8wb07Swrrf+zuADl+uF8+UE6sD1emGaJkKIOOeLQJTz+Srs0V1NRFakVSkr3Hr6ZCEm8jwogCx+F15yS1ZN9b/l54v/Tze6ylzvPWby+zXa6CpTvZ4nLufpu7CWMkXKZ63O7OQscj2j8cHz/i7AUpJLTQ0eKjUUsshzbM4xWVu9mbcFuKqSwut1xjtJXS0BQPdevaaRy0erWw0LKTCOO3749Inn5yc0idP7G03T4V3k/e2d19dXYWwoTGcipsThcODDywshON7fXlmWhZeXF7quZV2dJBm3Lfv9jh9/9Ut++9u/yucp1mM39B1t2wl7EyNd1/P2/k5qyexqTm72kaRvqalGQ4iyZWGUrmx2DJEQY94QUBXUjb30fsaQ0NowDD1KJbxvJFgqCUvW9R37ceTjhxeCd6zLzOV8+k5yi5JQIBUlLVeYJVulmiIHvclRtc6pp72ENtm8mL+F/FB9rForpsWxbQ6bpZeJUL2i5/MZlStmrLVSOeS81EQFAYbjOLKsC9YIUNX5igzeSRqr1jw/P2fmMbG5jWXb+N3ffM3y91TBSgySMhyCZ50mmrah7zr6XgBhcoG265nf31Eqv95Y3o+v/kmdWbu2bQh+wxpD14vUuWtbvPcM4yDv6TphdoNULWUWH2CXg5YkJKm73clZ3muMpm0bjs/PnC8npmnm+fmlBvisd4FBpc+4yKtLinLxU5buXklYtjWxum2b737vfc9xYdXrvZVHql9Wqe5JOXWXRNu1tF1H8I55WbDWsG5bZjHlWinPOaUMoj7WdYOpgNXiuS3yW0gMw0jf9/U5UtjUNgeR3fejFu98uSfvX7/SSCdv/j6Tn62l5it4qdzqOkPbyj1ntEbne62qN1JCGUPb2Myya3xwbOvKsq3SPawS2sgGR9e1HJ+P//qHzGMe85jHPOYxj/lZz88arPZDj8rS3OeXT1KtcbqwbR5QbKvnOk2E4On6lk8/fGTcD7WkvkglldJ8/PgRpTXzvOB9zPUcsuhr2xbbNDnx9BY2U/5dEldTku7U+3qLAnbL3Kecyqi6wDPm5rcqvq0SdhTya23blnVbcW6rXy8yZQlyaduGpjW0bcO6LkzzBR+26gOcpumuuoEaYHQftlJAkABmnxehsUpAi2xynm61PVA6KG8APwTx2KXMOobgs+e0QSk4n0/4TZKb21bA6pcvn1md53g8VgangHFrrXS1bhvrttZzME0z67rlYydfn6Li/f3MOI5opTMDnfBRvMVKcedZE0/otMw5VdrkLlRuABwgqSoFDyHQd9n7nBIfP3xiGDuafN78Jj5po2+VG0XSedw9kbIkvOk62sZyna6c39+Zc7JzDNSE19JRK/49LUA6nydJqZV6lBJS1fUdu/1QU3Rj7rTU5uZlrd3EIQgD3DR0ucrH6FhZaunkvPlii8+xgN6S7Gq8YVlmYbh0BvM5EOx4PGKMpP421mQ2c2FZZkJMUueTAirfHyCJwI02jOOOrhuyZxnarud8OjFNE7brsbZFa83Yd5ltlFCfprF5U2ZDGUOfPbkppQw85f+39RbutawT3geapmW/3/P09HRXRyM+9uLZ9N4zZEb027dvXKYzLy8fOB6PWe6dSDHhvav+9XL/FzmwMRZtNK+vr2zbWv2a3m8Yq+m6VipdIji34pwkkrdtmyXDsQLfIskt4FopRdMY2fxqm7vu3cA0bYQYiE4CwbTWjGP+XSGgVNlI0wJ2szz+Xo4/zzOl4ud4fMJaU59jNTE7+3PLJs7995UNs+JTLZ7ioRff7n23srWWRovCQKVbP+59wjCUFOvc7aoU2upsPwhcpivX6ZJ9wgKKSxVP3/e8PD/zJ3/6p3+/D6LHPOYxj3nMYx7zDzY/a7AaSVyXGYPm/e2SAaikVfZ9x7gXiWsKnsNhz/PLE8ZqYQ2niXle2DZ3C1FRirbt+PTpI9YK+/I3v/sd1+uVhIAlnas4Yg7wSLkvNCES0s52dwD2Ptwn5gXZbREGhYUNhJBoW50XmVKvIOFFwiy4nN46DAK2m1YWmesqNRQSOnTIi10nNTJDz/nynqsmboCDpDCjrQxoWYBb22SQvqJ1Ai2S0LRskgQaAsu65roHJOE3L75Tkh5IIXoDSUFyuTJCK7CaJtfoRL/x/voVYwzzPNU6FB8ExFjb0uekX+dc9USuy8IJcNtSX/c9M15CVYahr4Dv/e0M5IqPJB7IAljKZkWMkup6vcxV1hxCIAXhpNDC7IpP0GCUpR8Gjscjb29vOOcZ+oFPHz/k6qCZy2WqtSXBm5z2LH5ln9ksSETvuGa/Xgm0McbQDcK8xZjQ2hKiyEK10Pv5ekoSlkPCuRWb/Yfj2OeNhrVugmxONnCWZakdrIVdBY1tOnxILMsqrGYOlCqkV9u2OUXY34FVRdu0pBQI3okMX4uXfBz2gGyGpOA5n2VjI7YN87JwPl+kVgYjnuqcmC2gXsDN4fkZZSzb5jNQ7Xg/n3g/ndG2RfKuc6hOTLSNxZohVwtJSFSKEZTIlK21ogh4v2bPauJ4fOJ4PGQfqQSftW3Pr371a4ZhqPUor6+vdROmhIoBfP78mev1yrjf0XV9rpC6crlc8S7kZN+9yNxzUFfMTLLUDM31Hh6GDmMUSuVNK59YF0ccdInBAAAgAElEQVQiZEYxikw5e81L5U8Jx7rvMy2bYm3b1//eto1pmpimqX59uaZLJZfWCp3/DiLBp/p1xeJQqnuOxyP7/b4GMhUFi1KqVsOU8K6UpFKpANQS3tV1XWbCC8tsiDFUZUF5f9Y2uW/a14CkdV0J3le/bA1xyk/cbZmYlivLMt89a29+5ZQiHz9+5B//438s9+kjYOkxj3nMYx7zmD+6+VmD1RAiv//dT6ikcF78XCRyWqWE2jw/P2M0jGPPMHRszmGMqXUmJXl1nmZs2xKjgKDn5yPDMNL1PX/5l39ZGcSy0CwLyrI4rOxAluUKsxJwKUpSce2cVCQRnEoQUv5aawwpJpRR9J146UrOTEqgUHjnWOY5MygilWzbplY0vLw8o7ViWWaejk8Yo/Bh4Xw+45ymazuiuiX+ArUawhhbF3ulU9L5W8rpPC9VmrxtrgLx7xlmoFSXIMAjxYjWUoeiFFhj0OpWcVFAZkITE3RdhzFWAoDalm3b+Pr1a62VcU48uuu65QW8zwFDoYLXv/5r8UJ6n9OBhbIj5cRa8Q5HTqcz67oIiPVBunDzOZaAqyJhlqRQrRVN09O2TfUYOrfRNC02g5ivX79yOp1Yc0WGyeFN27bRWMvxeECpjmm65k2JSJcDv5TSNSgGpYX1TgnQlDoirTUphuz9i7StJA4ba9j3O4ZR6pnWdamhNSnBtC24zVVZfElNlYqftkpWC0Obkqrg7Be/+IHdbsdPP/3E6fRek4j7rqfrWrxzaBJd2zH0HR8+fcQ5x+vrG32uwUGrfP3cemzlteWtngKylKLN3lhjLCQpvUkoLteJy+VKCCLxjTESYqBrO5HoWy1BR0YSard1IQbHuBtorGGZZ9wm1T7LvHA4HHh5fsZYg9sch8OBoe95fvlE07S8vb3x7ds33t/fcyCQwpi1Bhy5/CzpMmtZAJb3EozWdUVyHrhcrneJ0rpKYZWCw2FfLQH3Pa1yna/fpeS2bVsVH+WZs21bZfvve01jjFyna34WFYAnnmrxxAuoDTEyz1L9tMvHSjzJHrf5LBc39ONYA5gkDbuvwV4pfQ+U84uoao4CkEuPLup2fd0/R+d5qsdRKm4kwE4UAOud9F26XpVSaIocme+O3bxOeH8LbpJDI8/VtpF7eOgH3t/f+Zvf/lZk5Y95zGMe85jHPOaPan7WYHVdVz5//sy2BUATA3RNx24cSTGzndHT9y1t11SWbl5mkRlmX5T3XkI3NmHwDocDMQau05W+7/nhhx+4Xq+V6YuxdAymunCv3Yte2D0FoMi+L/FQxZDuqkRudTRGW/qup+97Cd/RihQS1mSGL0lPaoyK4AO2sxyPT3cy5SYD8I39fgd0eC8g6nDYsywL1jbsdvsqEbyv7hBZNP8aCE2ZWNzcxvU6Vylx+RkpB02V9w6REFOVyiogpCjHQSu6tqUxEhIUY4RcT9I0HSZLK2tNRgykGNBG0bQW2+xFXus9KcpxLoChVPPc+lhjZc6axmYAHSCJP9B5J7LOAnZRoDRN25E2hTWINDUGRPWdMEY2Nnb7ka6T6pzLRcJ/uk7Y7Msl8PnzZ15fXzOz5OhaSYX+1a9/5JeffmAYBrRW/Pa3fy0yYaN4ejrIsY+J4/NzZpxXpnnOuxV3KbpGmPhy3RmrsY3hqdvXxb9UqnR1o2OeN7YMUkVW2dTjVDZWlmWlaWLe6Ll1q+73e56fnzmdThWUSADSwMePH2maltPpJH+eEk+HPV1j+fL5J9y2Vklne5c4C9QNl5QSBvDI5pOxljGz1lrZ2/cozfv7iWlZ6bqOEKPIyRHA3XalrzcSjcG7DVJk6Af6tsNvG4rE8SD3zdj3/Pjjj4zjKJLeTnp727bjep04X37ier3y+vrK5XKp96uAaF1BmbC4DcroCka31dE0bQZmK6UHulgIlBKvsDE6e5lV7vy8eVHLFGl93w90XVPvj5vE+29f86WGRdQCLrOFtU81xSobjkmSuo2WtOEmb5iEIOyp9+LrVspgs9y83OtF0lvD4BL1mizHKoYgqpQ7lUlVc9xtlohXuSgaXH0ukRJ+c0w+sGwbl2mu701+hzyHrS5M/z1Ql2etQqHSrfs2pYA2ht040jQNbt347dsr1+vEL3/5q7//h9FjHvOYxzzmMY/5B5mfNVidp2uWJiJ1IRic3ZiuF/EqakVMgb4Xj+S2aqYccNL3fZbMeoxuhN1Rpfag4Xq9Ms1zlirqzKTJInHJ4STSq+nrAqz47W6LJqmZiDFk36LPzKzIL5uml5+pTAWBTWtQWqFTAYC3UKaXlyPDMGCNEcZBG5yXjkupb+nRStEMA9Yo/OYYh4H/4h/95yQM/Z1MUQEqic9VJ00MnuQDPjOKDmErp2mpQU9F5ncLdUl1IX6T4ZEZilQlo13X5s5MkTOP40hji3TPgLGkBOfz+92iModXGc2vf/3rCgRA4TdPipF5keqPOspIMqvWzNMii9gE2hqsNuguMQ4jtrFMl0t9LzFFDCIltdrkYxBprUIbw69+/JFv377y6dMnmlZA8dN+ZL6c6duGthHgdT5fOJ8vIgM+n3l5fuGf/tN/wjgM7MYsx1SalAJ/8usf+frtnZRgXVau14n9fl+l2O+nd86XKySRxJbOUaMkKKa12Xe9OYiBfujFI5jBTmMNfnNswbMua052FtDhcvKvNlIJ4kNEKY+xlhBjBTASZNPmAKAJiBWcHQ4Hfv3rX/Ev/sX/zfn8Tt92HA57jscjn798IUaRWBpjUcZirCGmREITxABcNxyUUtCpHEQ1MI478ahmds97qXrZnK+MbwFMtpH6qW1dWZcFBTR9T98PhGgrU940HZ8+HVFK1aqqUh+VUmIYRI76u7/5Hf/qr37L5Tplv3Kqnu6h61Ba40Nkv9+jtPjmY0qVAV2WJbOm8trLCMAKNeQKkPugsTUxGKheddnLiVjb5vunzcDwBkxvm0T58s/3jagfNgny0jrL9RVWyTPi6emJ0+kk/lEf0EoC2UoycLk3ZTPFYEyDzoC6+FHlPr8B5qJwgNszsdb/6Fs4XD3f+XXeA9lSw2OtpW9a2kY2x+Z5xnnxNN9LjZXKz13b1N99HzhllKhYtFbsh5Hdfi8BZdnjv64ryzxDgP2wYzcM/wE+lR7zmMc85jGPecwfcn7WYLV0PLrNk9BoBd4XZjBKnYKKpNSxLB0pttVr6pwwWbZpaNuOru9wIUhYyuUii8IktSRKmZrKaa0sqEWOJkE9WilCjMIoGi1yXlWYV1mEGqMYhl1O1TW5JiYvukPKqb2KFkMMkqKqVOKwH/nw4UNdnPdDL38HGQDHu3AWYX2MES9YTIGmsRz2B6ZppWladrsdb29vnE4n+q5Ha8OyrMzz/J3vbPOelJmLwpgUQH6/WBbpoq6L6FAli5ZxHBmGjv1ux+Gwk2TPFGXxnYHVukViZmlAwnjKIrzrOobdKO8lipwapdgWL5sT72/V+6aUdNuO4yjBUtNE17XsdwfaviUmYaiHvhdvcddKF2UQSbGxluPxWRbQJJ6PRz7//m/41a9+5MPLC847msaw2+0AkW3LZoRUDV2nC/O81mRXheKf/rf/hD/9k18zT1fpqjWWp6e9SF0byzQtfP7yDfd+utXCrBtfvnzl9f0VkqLr+n+NMbM1WEs8ocMgMuLD4cBut2OaJt7e3qRz1kkQ0ObuUp+dSJpJ4im2phEwGSNLDl/qOgFJMSbe3t7yOQgsy1Ll9W9v72zbxjiO9G0LKvHt9RXvPT/++CvG3cjpfJHrQ2u27NkUAOqwxtI0+R/b1STllBOZN7dxvUzMyyLdqCSRXDdN3qyRPtOYFQ9Ka8auy/7DQIhyzQ7jyNAPHA4HUpIeWUnl9bRtx26343y+8Puffs9Pn7+wrL7KyotqoW1byMx1kaHuD3sB997XwJ8YA/O8CKunDSYHK8nmVsqbVPKeYwxcr2sN+bkPOQvBVWBojK3hUc6JJL9pJKE7Ze1933eZEfV1M0Jp6QNWOan7sN+z2+2q99gaQ/TiNxU1hKGxOjOoUnmltZX6omUlcgtxKqD4JueX51xJFS5fRyrvuanPjpKo/v1zpISJaZEsZ7lvCaRCKULu0S4J01nLzC1cyUivMHL9dHak6yx919H1vSQCe8+2OdZ1RivN2A9ZiaDouwdYfcxjHvOYxzzmj21+1mBV6VzzksBoQ9O0dwu7pkrtduPAYX+QqonsL13XlXXZWFfHJV2xTUNS3HmiNnxOXVVKsS5TBlCyoAne492WgZUi+EgkiEcxS0clICdkud/A4WmPVsIeiOxPQm+EUUhsy0wKjt1+R2M1T4cPeVGvM3CIWK1yTUoJVLlJ3KRKoySZHnKI1MS6CqBIicze2SynvbGlRVJ8A6YiP9RGozX0/VjlicMwVFnq/YLzPpmz6zqGYWA3DozjSNcJ2HfOoRCASq59WeYN5332+DWZpQpoDbsskzRtg1KyAO4akek2jeX19TXLYh1tazkcRiAwDC1NY/FhQ7lEyuW4m1tJMdJ3LfvdXvy1WtO0LU9Pz1wuxUuqeXv9im0bVu/Q1hCAbhDw+PXtlXlbiUqxbBveeXykehH/9E9+zS9++AGVJeAhOkgGjfh5rVG0WfrsNmGz3t5OnM4XTqcz2+byeZXF9eEgrNA0TfV8Sy/nxm430LZtBTzOOS6XiwD6LMk2OTG4/K4CirRSlS1NCVINzJJr6xbipSqD3jQN0yTBWC8vR7lfNoe2ln7oOTw90TQd12kWT3KMhCBM1pyvMaUULgYByvoGcCSgaqnAS0J5Qn4PoigoADAE6dwUKXZzex9RQHkMUnkSIihjiCjOlwvf3k71vVjbcLpccyjWhRipDKBI1MVrDbdzWwCi23zd1Oo6CZpallmu13yPeZ8qKy5+TwGq5RyW11wSo++ZQ63ltRTQNs/iN+26PgcP9bUHdl1XlmVmmhZCECbZWFNB3NN+rIqRZRH/8nS9orVmt9tlbyj0fcc+h0XJ8d84nyWQyrZNBarLslRmtLyP3W5XX8+SNxgKAC/Hu2QEvL+/fxfuVr26MaBSTrTe7QDEA74sGC3BeUXGXFh3MsNdgpwArGnZjwNDTj733jMvM+viCDGglcVa6Zv1TmqkRMHymMc85jGPecxj/pjmZw1WjTJ0bcPTfseHDx/48OEDwUfmXEdTFlHDMLDf75jnifPlIt2OmWW6XqcsNzWkHHBircn9lLJgt0b6CNd1hSiJoRqRmYUQsY1FNbb6N0WOJ14u7xMxBabpyv6wY3/YEWOR6rnsqZLXKVI8MFZqJ56fj5zP74SwEYIAVuc9KHm9zntC9N/JItdVGNRpmrlcrjlMp2VZ1vp1MaTaTQlUcJNSqgvhzTkwhuPTk6QkZ0leykE6Wmvarq1SzqaxkhS8LvggSaKkyLouKJVIqa8L0pgSKUhAynTdxBOoSqVIqmA1RvEPlzRT5xa0ssSo2e32aCNMzfV6JgTZYFi3BUg8PR0AhEUKG23f0loLKWK1Zr/bsdv1tE3P/nCoYU7BSdXIZbpyPL5gtCWGRGNbYoTT6UIIgb/5m9+Jz9MKoIlRNi3K4n+/3zPPM9Ebvn37CimgngKt1XRdTwqBxkpX5Pl0xbsIl4VpkjoXrU29hlOEGBLTtLAsW/ZONjn0SVe5NcDr6ytvb+851Tdhra51IVKJZNnvD3jvmKZbqnJhveT6NTgXcG7jer1grKZp5J5oGpsl2XKtFLa9a6RORaGYlgWytNllpm9zns058aUa8XoqUmW2hcGXOp9pmqSeKEu/BcgK6DFapKrCnvm8kdLV97AsC8t8S0Zuu+zPjHA+X/jtb3/HPK/5/k85afiav0axLI6Ubt7fEkRVNmPWdc0JuJHz+cxut2PcDby/f+N6vWaP+FYBv3OBEFUGWj3juKNpmgz0oG1t3Xi4txQAGciZ3JmcqkVhGIZ6viUNW0DuNE05MKuhyx7hkETx4Zw869ZFFAzLfMUaeU1dY+najqenA/uDhMoppVmWTVLKG0voGmIqncU+e1pv150ELg01FMp7j87WgRLallISe8U01Xu6Msnek2KisYbn52cOhwPOOd7f34kxZuuAwTY2S+klJCkhcmpbellNU/uSYwwsfiMuC3PeBIne51RtLe/Fe2Ku5dL21mn7mMc85jGPecxj/jjmZw1WP336SAwbz89HSdQ0mug9yzIQQhRmRWmsaSi9hmQfad/3WCNsknOOrpeQnxII0zQGqy3bevNr/vTTT1l2OUqFjGm4Tle00vSDSCZDEmmsy14wkdTBuBvZ73fSMTlNbNt6WxRrK3LZw46uawnB4f2KbRTH56fqCw2zSO0uF6k9SUkBKnch+gxY4XK51gAbYVQlwXhd1/y7MyNBqAtcqZDQORnYMNqG548faAuICwHI1S05YMZoTdPbGm60nd6yt1Fl5kOkjNMkzInIPCVEal1XLpcJt5WwFJUlpjYvdrcMjppa17EsC9a0NI3U8uz3e6RfVteAHec26fc0KiemwrIt4vN8PtK1LYfdnsNhT9e1tLarvbvn04kYYvbxGXbjAWMs6zpXYPf1y9ecdPtK1/W4LTAMmpeXD7jMuh0OE0orvn79zIeXZ0KQFGeVWcACgNqu5elw4NvXN6Z5JiJ1MP0w1DAe6eeMuM3hNlcBf9c16NxDWo7d+XzmdDrlblABhsuyYjJ79/LywjAMLMvC5XJm27ZbQJBSBO+rrBWEafr27ZXj8wEQpcL1Kszzp0+fUOoGlIP3BB+zxNJxuZx5P11omhZIhETdMDGmx7nA0OW/y4C1pL2WZOJyzez3t3CtEMSnq7RCkcFgcFWaL9e343B4yr2y0iUKF17f3nj99sYwFPlsyqAmWwo2TwyRcb+rGzvldaQkGzJFSlxqpESRIBJ4Sevt6rE7HPa8vZ0IwbPbPfHy8hESXK8X1tWh1C1wCmL1xhY2spyb24ZEX/2dRepbgH3TNNmnf+twTjnUrXYp5xTj6SrJvLvdyPHpKBs340jXt2ijsTlwTescxqYVxhqSj0jNlq/Jwykl8e9mL7CEo2XAnWXMhdksNoOySVasA2XDY+haPn58ySzyXL3yh8OBvu/oGou1jaR7J/GTNxmw3ryvqXZku+DxoVyTaw4xi0QEHIcQRNqtDToEpqX44h/zmMc85jGPecwfy/ysweqf/dmfEYMjRU/wjhRFCtvkwJugpPcies+2Sh1JCatRyuB6LwydtiQSm9+yr8lBEimr1eKvMlrzdDjUovuxH0idyCghd1bqBCGhtCbE4vvboY3i48cXUop8+fKFeVpICfp+oG0sXe5IHXqRrCnd4n0j0kqtMAqGTkBj349M80Qi5mRZi9tCXpRLEE0JeTkcDry8fGC/P/D+/s7Xr9/49u0bRsuiT6R8a2VjrBWwPgwjxhqRt2bgUhbHKcWaYGqtzgAisG2O19dv2T97Y2wLk6aUyiB/JOQFqvOepQAmY7KfUZi+l5cXjs9HzqcT67pWX9k4KLQJ+BBoGs3T0xPrOhHCBjn9WSp9pIZD7UfWdWMYZLOgb1vGHFLVWEuIHj97puvE2+sbTdvStgMpaeZ1hWXl69cvXC7nLFOdaxfotnm0Nry8jDRNi/MSTLQfd5BgXhYulwuXy4Xz+Z0UjwyjvJ7ogwACJQz1ujm21WGsnIeyAI8xksL3QAYFbgu0na0L9G/fXjM7Tma7pNc2pcQ49hyPz7RtxzzPvL+94baNcehyKFcO+clp0CFI2JJIVVOuXlnzJk/Hr371K/b7Pe/v75VxjAmSUszrwtfXb8Qg5zFE2eRISbHfHeqGilaKtm3qeyz1S6VXsygUjDEoo0V27z3WWLpxh3Mbm1tyuE8gAT738Rpj2baNt7eTbFxozeksjJ6xLdo02ObmvRalgqLtesZ46xEtEtWS0JuS/F0JM+t7kcoq5F4miezY2pG+7/n27RXnVn744RccDk/Ms8iNY4ykqJFO1RtgK1LaIomGIgN2rNsmVoN4q32qIG8Y6LqugsYiHXbOoZWqLOsyzZxPV1IKdK3leDzy6cNHmlakyUVG60Mg+FunaXm+NY1h28LdcYN7kH0vk+6y/NZkwC+ydFclz+V9F0l433WM41jl3yCMadu2AqS7lsaIH3VZF+ZpIXgnvnwrG4WbE5XG9Xplc57Ve6QqueQY5GogVfp8A0prUoAUYFnLxsFjHvOYxzzmMY/5Y5mfNVjtc1n8l8+vNXW2sJDiBZUOUWJEBUNMIkE1VqS3SunMcknNjTWWUmmSYmJNS+1B9d7XnkPvpdLEKsuQw1xSpmiMFi9t1zQYndiNA/3Q0uWFuVWgEFbSIEBiHHe0XSO+2RQJPmJsRwhOklNTkoAXQgaAnn4QT5f3Ee9ui8XEjDaGcRz5xS9/xNiGL1+/8vnzF97fzpxOZySQZciLXociZbbsQwZkwlBFuJMsbyikRmaZNxKReV6zv3LBGPExklncru/FN2ckLEZr6W4USaqnySmhjTXEJN5VCGzbyg+/+IH/+r/5r/jhhx/45//8/+LLly8s68I4jMQUcX4jLZFEl6WGgc1F+m6HsZrgHCCS5XEYeD6a6tXVZJ8zcv6dW/A+cr5OnC9X+jHik2ZZ1swKz7y/v+UFuam1GVprpuvEj7/8JYf9jsvlwrxsxJjouhFUJCUlHtTLlc1FAobFJb68XTifLszLKix3TOR4VQFdmZ3S6taHWcK1lFJszuG2jZAi3jmSOO4AXZOX266VblaSSDTHgbe3N/EKktgfDrRZzj1dZ+Zl+Y6darXJgWEwTzNKJfqnPb/44QcOh32WpFq2zeWe1o1v316l89VYYhDAp5UGJTLh/eGAz5seAowCm9ty9yf88OkTwzgyTzPzMqHQKCIpRoxWmL6jbVq0Vjh/876ixFdbkrtVUszrRooFYLfEFBnGoYboaK3ZnLCb4jelXufGin+zgNTCsIpUNTLs91htWKYZaw3W9Bht2VbpD30+PvP5yxc+//SF/f5ACJGvX7+ylTCw/PubNlfFxIDKPm85xxJ8ldXAxBBx60aCfE4lsKnIa28bcAIWSRGtqMDQWMs0XXn99iodzE97DocDTWNx0UGApmtZ3ca2OlyITNcJH6J0544jxnnWdcP7IJsDudaqyR7UIl3WWnzzBaRu68qyLBVU12Cl/Ew1WtMPPeNOUrpNYyXtPHvi26bNAXbgw4bfxHsaQ8JYIzL9BOfrxOl8YrrObG4VGXsMdwBVnuMKeT7X9ODMtN4z0o95zGMe85jHPOaPZ37WYPVyuWZJowS52MYCiuBdXhyN2EbCi+KWWDdZaDWNRSkLUeSVQGahZLe9LPJiENkm3NgKlWtm5mmmbaS0vs8JpvO8oFQkqchuHIlJmLah6xhymupuHNBKcTw+Mw572qFnc45pWbhOE9aYnOrboJTFGOk9FLlj6Q5thZVtO7yX0BWRL2uejkfaVl7Psm58+fqNdV14ezsxzyvLvIk8dA3iJY2BcWhpGkPX3/oXYwwsqwRAxZhom44QAsu83bFMgdP7OYchyXGU45NQGLq2reC6LLwlXVVCUsSHGti2pX5NwtF1lra1OLeybXNOXTZyjlRiWWfc1dPNHTFE3t/PhBA57Hp0Lu2MSYKpxJs65goNIwE2SeBd8AGUVNX4mHAhoJ1ncxcuuR/y/f09B3ZZkovZEyx1KX3X8fx8ZJkmTqczStssmxS2dFk3Ygw4D223YwuKb28XlvUrr1/f2byk8tqmIYqamxhD3hgA4QsBEsPQ03YNy+KYl+wJXJVssmgjoMkaUJrj8zNNa7lcLjkVVrGsC9M8YRvLbreHJH7rECIxiQe69F1a29BYqbgJUdjWp+Oep8MBrcG7LfsMQ65R0nx+P3G5Tpn9bfDOVwnr0I08HY/CEM4zXdPQGsPiHDE4+q5lGHqen58kfXid0QrGUdjLmkCdpcSn87n6zEtfsjKWrjEkFD4DK2M0+/1AYw0hRUmLHsYKVCORrpNwqmVZ0EbT9V1lk6MvkteiEgiMw0DygcWv9H3P8fCEMYZpXjDKYpRhnTa2xXF8ekZrQ/CREG+y2bbr6PsRpUyWPHu5tklM84w1KtdZyevwzgNSY2VtqYgSz6eoMQzbutRwpiIzl3tZpNun0wmSbN4Ub+/7SdF2Lf04sLqNZRW/b0oZpKMxVmq91uvE5XohJkn/DsFn+fnG0LeQ/afW2iqTLiC2JgND3dizWd7/9PREIrDl89XqlkhidVvdfCh1Tus6E0PENg3jOLB5x/l85e31let0FY+0E4m4NvYGUNWt4icBKinIG0C3xGVNIv7H/cB6zGMe85jHPOYx/8HnZw1W//Iv/pq//qvfsm0hL5C8VC0Eh3OO8/ny3cJOQmNclrol2VXPoTJN22AaQ0qBp6cnYR6sRSubqxMMTdtDVBLks7pbTUvTy4JMGZQ1OL/R9w1aCwPS2Fx/0bXsdk9M00TTiATzdDpzulyyLzRibZMlho7WWFmc5lAZ5wT8HsY9TdOzrYFlcWwlidgomqYnRnh7e2OeZ06nE9dJPGoKnT16MQe8SHLvMHa0Q0dSwjy0bUMMidUFUk64hVt4VAFvWic+fPhQq36EiRJ2Wmst1RokmnafZXiBtm3oupa+l/qTZdm4Xq9I/QRM8xWw/MVf/BXWWr59e0NrqcHp+wHbNHz99srb+0nAVgaQbdtilKZpDQawja7po23bEVMi+Mg0rxlwii9PoVlXT4yAMmyrJ4SU5YgCUIoUdMvBMT/88Av+7M/+pPZjXmcBac7HvCjWBB9wWboq1TgNl8uEUipXB12IUdihw+GItaZ2ghawWuTT4qFuc1DRdpPOxpRrUMRPGkLI/t6hMo9ADbbx3ldf47Y6tu3WA6q1SHW7rqPvhwzUFnwI7HYCKprG5mu3zSAAYRE/f+Hb6yspKWzb4XPoV/EbD8OObdv49vWr+JDblphk06VtW7osAe17YX+tbTgej/gxEdEAACAASURBVN/JRedZ6n+u12v1kHddx/F4rCFDkOWdQRKnd+PI8fgkiceNbFYU5m/bNrq2JeWNKJ0DkNZ1JQaf33+oMliFKDlijEzLLH2whz0+Rd5eTxDlXgcJPdJKrv/NOZGbKoXNFSyFqfXesW1LZhsDwTtKknbTNBLotDiRditDQhK6ix+8eFkl+TjUc3kPzpbV1eRe25Q6mmx1QKTPktws/cDbWvpgRYp8na6s88KyrvhNwHDwIW9qSNVNkSwD31XS3LPS93/WdR0231MhBOb5yuY2tnXlcrnUVOsYE69v75BAa1s35JQLvJ8nLpcL7+/vtW7Kx4iP8f9j7812JEuy9bzPzPbsQww5VB+2WhQJkEcQ+P4vIfBKvGtKIg552F2VmTH4sAcbdbHMzCMJ3ggSiCrAF1CoqsgMD/c9hf32TxDARCUe3LzR8RG0FoZVK02SBmKICcOts/Y+97nPfe5zn/v8MeZ3DVZ//e03/vb3X9GqyWE8HUq5unBVZfGRIEVhgrxzWCchMGUBo5QwT4/Pj7WORRZYQBT/Y2NaxnHI/ZqyENdKdv1jft3j8YGXtxc8CmcdwyiL6d1+ouva7Gfr0KbJDB/M6ypSWmdRytB3KYNrg41bTiZuqzdvi5a279GmxbmV9/czm5WFp9ZNloh6zqcTLoeaxBRqTYdGI0LkhDZSabI/7Hh6fhY2c8sySKOlzqKVS2Bd18qKlBCc5+dnfvnlF5RSWd4rwFESQy37/Z7DcU9KgW1bc+DTWOWVTdMwDDp/T+5K1C3zdeV6+WdMI5UXDw+DsMUkSQPVGpJ4FL2LN9/sukmNS2tovcGYHTHCum1smyOGKADT++rvW+dVPLRRFq2lvqJvRWJc/IAxRpy1KCWgxhjDt+/fa62LyV5mrRXaKKx1ssDPoUDzvApD3XeAsIbzvOaNgJR9nCK7jRK7mjdSpG+3SF5TEnmnzn2UTf654iFWuat1Y1mveOcxRleWqwCA6/WKd9kzmzuHY4zs9/tcPaK5nC5Z6pll00pzPp1pO2FcldIcDgeU0nz79p1tsfTD8MFbaqpvcVkWTu/v2M3SD72kcceINtRaF6moEY+kSFTbCnCWZWGeZ1QFZ4H9/sDxeKjXpa7STunA7buWx+OR/W4noUfqdhxOp1OWKwsTXRg48WuPXM4novcEwGhNTIkYpGO2bIx8/vwZgJeXF9Z1pTNdVg0MaGMYxoEle8dRqvojS42L95LKm/Jrh+CIOVCpJANv2ybe5LZDd5q2afH5HhR2M9Te3ALI2rbJ4WnUftKmaXJdUUkqT1Kltd9jTMN8nTmdLvk+FHVECCLNtpslBnl+pMYQAjRao1WPNqpKzj+C0qJMKWz0x57gsnEYQuB6kWTtkCQJ+JrDugRYt/m9yHMoRcs1B0OtWVrsbH7WKz7cA3Lftm0L6meAKioZqq/71mkrr6H0Haze5z73uc997vNHm981WHUhYH0gOEkSLSBCa82Qk2RVDtMIIaCVEkCAqosnICfbCjtgMpvpvWVeHHbztG1Hn/2qY9dngNCwP+75+uUrWmteX164nE+0bSeyUu9y4u01d2COoBIxQdO2zOvK5frG+/sJ6zwJQ98NtTZiWWQx7aPUKwibJp7OEBPWeZzzvL+fWdcN7zxKG6xzeB8q4JbwpCanZArIiCFhaNDG0A9SWfH4+IBSiVmB9U5Y1pzKWZjYwnKV/y91GX/5y184n881WTilXJETA8f9AR+EXWoaqYsIEZrWZFbxiLWW9/d3nPO1Y7MxDSoZ+q5h6Cf6rmfbVuxmq2R1HEZSr3h5eREWURtCjGwu4rzUqxgjYMTlrk5ZtDbEpIghMs+LSDVDJPpIDFECspDKj6Hvazek7yXpdds2/vbr33k/vQO5WqVpMK106Cqt2Zxjs77KJV3u29w2S0yJx+cn2k6SmUtti1Y6L6LFSxcySBA2TN6jeO402ihIAqbke3Rlzc6nE8tyrRsAPgb6XupdlnmtgVUpwbbJeZR7J7NQMVTmLkbpE51nYZGlXzTQNIrgE39/+Y23t3dU7gv5WDdz6+P0AuCmkRAjdpkFWDU633MCGpZlYRiGeoyNMcyzMGghSKiW1g37vaRxl/Cq0herjZE6HBJtaxh6CecZpz2nnJQsIV6ugpimaeXck7L0P9BoU6tWAAwQsh+073sOxyNN23KdZ+ZlkV7X5DEmBwxpuQaKCqFUzrRt+1PacgmzgoRGaqugMPopVw1Jb7Kw56YC0RAC1+s1bzg0kEL2aO/r8w4SEQk4I3tEy88GsNZzej+zWYt1Iu9XKIyWDRDvnFxf+b5pjMYoaLTJjPkox0mRnzu+Ph8+ervL1wojXJ5L5Vrx3lX7BcCyLqR1QaHzZ/Y5obupm4tJ0CU+uBooppDUYq01ymipi1K3HmsQ5rdg0nIstC6p7fee1fvc5z73uc99/mjzuwarheloTYNzlnVd6Dvxkdqmpe9a2iwlDDHmnkcBciqzWSLLU9kbKp5V52Sx7HzChShBNsGx2pWt7WmbhnVduV4vJBJ//hf/gofHR9ZtYRxGjFZsdiWmwLqtnC4XtBEf43W5siwzl+sVlGa3PzCMe06nS+011Fpn5klV6WHpHZXPaSVYyUculxnrHNZKP2jpXGw7WRyjFTGJ/I6UPVuq+MsMIXrmVcJJpmmsvbQxwPX6XsFqkUqWBW/f91yvAsT/6Z/+iWEY+PHjB+sqjEwInnm5gJLF8jzPTNNA0xqazDTF6Om6Fkicz2epL1kkjOlw6EhJFsrOhdwbe2bng7CNkGWzQCIHJgnzWNib67KwbZk5VQKOu7ZnHEZOp4uEvyyWvhPvq8+dsUQJLOq6DqU7mrYFLH0vIOD9/R111ijkeGzJZpCV8mJYy4ZBiJBkZRxDIho5jvv9ns+fnxnHke/fXglBvkeCfbK3L3dLTtPINE2s68qyLLSt1IvU8KWUUCpV2afdtpzSK+BEa01S0HU9IQgr/hE8QKwst7WWZVmEtc6+76ZparWMVJAEzucrxmgul4XX11e8D3RNh0Zhg6+bAtu2oXLdUfl5PktiU4zYTbzIdrugFLLR42a5V9qOGKVaKSXouoFeKcgSVvm6yNFJiUaT02+hH3oaYxgHkZufL2f+y3/5L2zbVllya4v3WpjZYRyI3nFZZtZlodWGJvfc9n0PRnOdRcb99PQkcmpjcN5xOV3EEmAMyhhiDHWjoO86QgzsDzuGYeDt7U1CyJQEaSkkQdgYdeufVYppkvC4cRhzYrnJXnPLtq5cr9f6LDPGsBt3NKapFT6FQb3OZ06nN0BxOBxRwJK7pWVzKZIgBxpJCnMMCWdvybgpSiCcj4mh6/n09MzhuJfPieKyLDh7k0zbTVLDJfipY9tW1lU2CVLMlWJJQYR1nfFRlA5lU4gEMUWslWeJVpJFgIoYLQnTPsu0lU6gQeXjB6W/Ved7Kcr3Urqvbz3YMUqat2xQNjnE7j73uc997nOf+/yR5ncNVptGi59ud8i9pYkxB7KUVF3vxF/qctBM7lvA5PRVYcKk7F6YOAnhIS/wlSmJkpkJiR4VEiRJm/3117+hFTw9PfH49IgmkdKey/WMjxL65Lzj9e2dYRw5nS9Su+GzZzDJYqlIWQsbs64LXdcKoPQ+g9S1Ml7y92Jm6qgSSK1zVYVRkjKqhC0urCiJHEACkNis5eXlB9YtPD89cTwchYXT0mfamJa3tzfmea6S0+PxyDhKquowDPzzP/8zQE39VAWMJgGkj4+P7PdTlsCmyqT47NFbloX39/cs6Y0Mww6lhPlrmg5rJUV4Wa70/YBpJQhnXTa8j+jMHBVmLBrqMfUpEpMiJEeKia6TChAQVvGwP9L1Pd45+tDf+jxjuAXDJGr/avHvOudqmJDJvj0fXe72VbXaw+gmv6bNzGHH49MxL6ypoKL08cYk7K7Riv1+V2Xppc90GPp8ToUtcs59YKsc59NJgmq6mydQGZ2BApWtLeEy0jlarjm5vhSKtmnr/VBqXqSWUvH2/k7MYV8+BJQyFYBCeW/IOemkJqqkafuYa1cUmEZCoGJM7Hc7tA7VD+29Z5kXSbvNKbLKGNZtRSud71mq77xrOkJYOR73PD0+5QRhzfl85uXtJN7yHKRUgLPdtuohvV4urPMsSdq99JkWj2/XdbyeJGjr0+fPPD09icw1eJTWNG1Dk73tMd0UG+Xc/fLpF0L0Ulu1LNVzm1Kiyym+KPGqHg4H8cNOO7Q2pJhyUrGk9F6uF95e37CZeS7/dH1P9IE1S+qN1lznC5fLucq7UxRZb9d1tL2kmtvNYpqGZHKYk5dAp5iB8DD0DENPCJ6nh8/sph2NMVWqfD5fOF1mEjfVhXS+7qocuOs7htGzzHNVX2zrhrOWoR8wXZutAlu9forVIsYoUuSYmKYxP78jSiW6TgK8+pySnXLHqlIpe8DVrU81JWJhyoVKpW36KtNumya//n3uc5/73Oc+9/kjze8arD4c9ozdX/jy9avITLWpi3TnPO9v71xOOQV3W0lKUjaLTBZyIEuWi3ZNQ2cM09DRdB0+RjZrcVa8f20jcmBh0RRjP/Jw3KMMvJ/e2I0TX3/5SgKm607Yj+jRzrDajeu8sm1WQkusxTtJXtW6yb2IVqpklPgeU4pcLie2XFZfJIUxBt7fX+n7kabRWOfpuiYDSJWBr3Rl3nyOTU7edWhp8cwAPXK9XkCJbLAxDb/88gC94uvXBx4fnvhP/+k/VQlfSfss3awxRna7HS8vLxUQlH7NIrebpondbqRpG9ZV/Idd1+Gc58ePH/zn//xPXC4XtG5QyEK1sEOSgKpx3mVpacR7S0qR99OrhFqh68+2diNuwgIfDgeu85XoA0a3bG5jXYWZHvqRFEWm2jbCzDeNoe8Hqe1JwlAbYzIbrei6QZJugxPJaWZppI5EfMriKRRv7vW65ERdWfw3reHh4VhBgchqqQFOpTdTKU1rmgpSS8etBMyUzshYAU9Z5JMSWkPfd5VhW5aFoZ8q2/kxnKdsPjhXrqtYz7H19sasRgH8Pve9lvfUZI+l0S1aJaL3/OlPf6LrOl5eXqr03mWgrbUmhZtU1AeH0pq+a0ArXBC5u12Xm1S3EfYypUSwm4DcnJC9LCvrMtP3HbP3jOPIfndknheOhz1d3/Pj9Y2UqF5e8e0Kw+Y/hO4UUJNSQvUwDpI222QVhXOO/eHAbrcTVnKZq8+2aVta3Yq3NV8z5Xx2XSfBQfOlMt3btpFizLUskgA9jiOPxwd2ux2mbdi2DWulO1pCiBbmVZ4ftrCeURQFcNsoSll2breQ/bUiY3fOkXxi6HvMNJGSwmlfQ+Kc8zW9umz8dG2LRoD0tBvls4eADYGEsN7vpxM+QMpy36Zp6MeBfhQ5t9Qrea7Xq5zTnPjbtS3BS+p4RKwB5bzEGEmqbMBB2zXZCw4QME3xyvYM/YAyhm2zsjETUr4ujQBuH2QTRSWMvrGqKSZ02/L4+HDLBMj3433uc5/73Oc+9/njzO8arH7+9Il1nenaBq1bDjk0x1qbPWINw9Bjt00yH7X5KWCmsICX6xVjBBiVgJax6/FJEnaXJCxS1xmGQWpemqZhP40onVjXOXsMheE6HI88Pj7S9R3zssB8JeU+xRBirh6RyhkBl05AVgw0jbCPY9/ivWddJHnUZJ+mMIY3j1eR9G6blQWd1lLbgOHh8ZFxHLherzRNy9//9isqdwoK2AkMowANoxXjMHxgnTzOvUtVhda5iRZhxuxGiMLibHarTLQwPMJ2TO1I17V0XcvhcCDGwDIvzMuMcz77LxXf7YsEqjQNKQmjp1SqEkBrcyquJsuUJYAmePnHJ/GWStJxkmqiTjx1f/rTF/72t7/xfjrJ584BM975zPpGzpcT1m2ZiWvp7HZLgM0gRqotTAZv0qvrgqvgR2uNteIhjjGhsppQGB6Ncx5UYr/fCxtklLDrVny10jmaiyuRc2iUrtdFCUBKKRKCLPALa1qORZtZJpHMdnUjYb/f0/VjDa7Ztu0nUBBj+pDgGjIYvlUNfaxLKWxXX727loeHB/p+YF1njvsDX75+4cePH8I6t20FnQDOu5zCKhsopjE1VbhIqAtLXDyPcp3avIFjairyPEtNCdnrapRmfzywWsvTwwPjOPD2+gbZP126l7U2FH2FzscghMA0TbRty/F4ZDeMzNeZlP3uxUs7jqOEVy0L1lpWK5sEZSPAWVc98jevJJzPa77GxFvb56RdpRTbtlRGdRgGYkzMpwvOefGlW3/z2Zriq8/nPkYJH4Jb5yw3kNc0LU0jYV9d13E8Huq1Zq2DZSV5z+1UJ1JMxCBdrUNO7ZZ6oiaHiUnS9Txfuc4S4hWSAnMLqCp+5cLW37yhYrUwTQNRPPG2VvfI81qqggI+S6nL/SAbX9lHPvaM45Sv8ZTtE64mmYvP1Uo3bYj5udpItzYw5myDcRp5fnoG5HdC1/yuf93d5z73uc997nOf/878rn97912HsysxBBQGm32rl8uFtulzRUIjsldtSEmxrCt93zGNLV3X0TQNx+OR8/nEZleMUXRtxzgMoMAGT8qJpM5tkBLO5RqFzHJWnIHCmDee5kf2+12WiTb5Z0s4zThOLMvKKZ4rMJRO2J51W8V/aITNGMeevm/5x3/8XzkcDvz1r3/ler3StiPbJuyKUjCOA9Y67GZBpwquhr5l6FuuFwnN6dqGz58kxfS3b7/SGMPD8cg49ry8fOPx4QFrA5fzldfXE99/vJISvL6+ViZNRSWhOZnVK12a1FRb6bP8/PlTBtjC8GzbxtvbG9smKahKKbbV5koeWeQrBaZRKCWsW+nyTBnQTdOY/aCJJdeDNG1HCJG26VBKpLOH455xHASYKDmfLrM4Dw8PwjCGyDknyVqbe0t1llXqJlfedJVdBGF++l4YIx9vIKyAva7tuM5zZpg0Ssm/u27ger3IRkcnzN622bz4N/mYtex2O7ZtFXCGdHOKrBhiEoBQNlgKYJTFvBLmtjNM06EC6MJyK11SZm9eyo/vu2xe+MxgFoZst9sxTVP1UWqtM+DuuFwuOBfqMXo4HjLYEKB2OBzwIUj/cSMbLY2S4yrdyBdS9lIboysQLxU6MTNt0r8bK1O5rRvW2rrBIeen47CfSEhFTVKa9/MZFyIRYQ1FXdChdcBua7535Di0bcsvv/zCYb8npsR8uWCdrX9nt9vR9B0xBObsdfU5JEgOZML5RCLm4+jzPT3mIDJh7U0+d9NhquFTUhUk15T4w0XKuqzbh25l2aRC35j8wuY3bSPhcJk1/xhwJOFGAlrHcWIYRxSlMzZWpj1B/TwxiErj8eGh1ssopeTZ6nz1xDvnSIDJqejaGKZpqixuqXVacyLyMAxV8h5jFOCoNaZpiJTP6fOGyY09j/F2vbaNYRgknVtphbXCPs/zWp+1ISd+282iUmLserq8wVK82U9Pj3SdbAbO14sE4PU9/d2zep/73Oc+97nPH25+12B1aHtCN+CDw64r83xlXYX50M0si5pc8bCtrjJmu92OrpegpKEf5O+RWBeD0vD08EA39GzOSU+oaXBKY5eNNYqPrus6ZnPNoE3dwk50x6/fvonXbhjY73c10VLqXQLeRcZ+wnuPdStKK8ahp2slEbTtpKS+UQ2Pj0cOu4n9NPL0cGTLSaoqio9SNYrd7gAY5vlKDAnrhfH58e0H004WxufziZgCQy9M5zyf2dYVEO/Xp0+fMLphvV74/ut3ls3x8npiXbda81HAQUnt1PrWZZrEQFjPTfEdns/nKle9XpdcYWNQCtZVZIdaG56eHnNarhMvq4cYQbUSmiI/U0nysbc8Pj7y/PwJpYyEt9jcN5kZHWs33t5ilrwqFImhaznsJpZl4boukv6pEzFFXLDopFmtAN9IIiLdmU1of6rbEXlng1ZKPp+1+Vzv6c/vOU3a0Taa5+dnnHNs65W20XRNyzzPzJdZ2L0MFHe7kU+fHjlfzmybwa6bJASb0mMZ8ShC9NlvLf7jtjGAbLr0OblYKYVKSEBQBgTW2prYXD5LOX//bWKrMZp+aHn+9FhZsrbpMEYArHT0Cmss/themPbLKn+fnFicEtM0oZRmN4oUeZ5n1nlh7AciEaVuzORHUFzYwY8ATKkPwTo1zVc2jpq2px8mmrbndJklhRmNz7JjY4xUMaFQfY94pwVsFja1zV7MIrtvmkaugbap13MBayEETFY1hBAIzhEz4C9TjjXwE+OYUsI7h0aqVhrd4H3Eua3KtOfrLMm/qjD6bQ2qUkrTZrb7o0d822w9hx+7kRvdEF1kvi4oJYx4AY4gYWhFGj6OA8f9xMPxIIA6J63P8yIWgnzNKJ198FCvuZLwW0LZCpNamO3ynuT8aaKL4rEfBpy1OLdVhQmUZPZWrvcciCfsu0eRPfurxaiG4FPehNBoZZjGHV3bcMi9wtJD7PMmTptrl1TuLRb2PKZbV+197nOf+9znPvf5Y8zvGqy+vrzz/v4ii8nW4IMsPqVOQWGtx3vZab9eZ9q24XDYo/MCUufOzHVbJAwleFIKDMNI2wlLFE1DaxoBMAFs2ETWmUBVCV6RuwW02jCmqYvSIsn8GBpSwEIMga5tiUl+7sPjkb5t6fsOZRTeWqbdxNv7C5fLmXVdctgIbFte0MWIs479tCdlj22MpZszsK4bu92EM5bZzby/v/H161f+8d/+G/76H/9KCJ5lvrLf73h/O7EsC7/9+p3FCgj/CE4/vn+lVA6CmarkNOW04pQSLy8vdeFaJKTisDM1dbd4Zo/HA3/+859p24bT+cTb2xvWSpekLEwNu91UQ536fmCaJg6HI6fThcvlv1YGp82BNUpp1nUVYXeKtZZFa8VuNxGidCuGRWpdjscDbdfx48ePnAzsgQ3nBDj1fc9unCoQ6LqemBKNngnaZFZG8fz8TNMYXl9/oBT4YPHeMk0DbWtquM62WZq2y/LyFq1VruHQHA8HTjFl7+mE1ob39xMxSo/mtm2ZMW7p2q52qLZti7VWWNfCtIXA6fSCy0CiMFDGmBqAU5jAAhb3+x0Pjwf6vnQOR0gChK5XYQBLj+a2rcLqW9l0WbK/cxwG2rZlv+swpkVBTTRWZFVEdDgvUs2mbdCmwTuRuCstIKsG46SE1kaOe4ys61bTcwVsJ7bNcrlcgZv/sW1v4WI+ifS377t6HtqmoW9vYH+evciT5YIXWWr2QJf71n7wGst9Dm3XolSL9+aDhFlUE9M0VWbxI7utoKZr68aI3D5L2UlSsxURyXQMCW2oQULZqnoDzx9qY8r9FkKgMYOoQJAu5PK+iu88C95z5/HAfuppNFi7YRqR5cqzzd081VoqmuQQ6fqZymuWzYzynBAlwXYDuvlrwpZHrldJSCfF/EzIILuRxHJJ+oax75mmiZiDokJJBleG6D0kxbSb6FphXhtFPv/lGXDzEhd29nR+l8TwEPKm333uc5/73Oc+9/kjze8arIqXLxF86fCzoBWQ+xN9xNmZNacBt52kVMpiXuG84+XlhZg8l/OF/W7Hw/HANI25H9PQGEVjOro2MvSjdBJmlsDHyDhNkKWG67rJj+cms5SgFJslsxLONPQ9jdaYvqPtJja3Mk4Dj4+PPB0P7HYTb++vfJ9nvHNcrleWeeV8OjPPCwmDtQGFIm1A/MGnz58xqqHvNDGVcKOUexM1fdex309orXh9/cHz8zOPj4+k6DEKzu9nlDK8v52E9YgRrQU4F+aty2xxYXMO+wPDOFR/WgwQo8mL/jkzLCH3vMoi2QcvYGUYRFLtN5rGZJAb2O8nQth4f5NO2Eb3jOOO5+fPGN3yfn6v3sZ1XQWQ5gV6OeaXy5Vh6PFZQup84DBNBCL9kP2B45DPvUgsj8cD4zRyOr8TQyJFsjQ1Mc/CFJm8MC8S4XUR725TPZEGMFwvF66XK9po1nUhpcQ4iaxcUpC9SB2zF28YBryXEBpjDM3Q14V13/cCFklM08Aw9LRtI6nI2rDMc5VjF+mqtZZlXtBasVnpewVyF+tQAUORwRdfaZFKHg7Cqn379hsgiczCThlSpP49ndN2t21DG6kGktRh8WZu2ybdsbqpUvCS9juvc94wiBkk50RulWi7hrHKOkP12RotHm3pINaZodeM4yT3YwiS+l02R1KqFTUiV1YYbVAqEXL3bJeZu65r2LaVy7wIO59i7vwN1S9e2MISglVY074f68aAUkMGlyGDbNjv9/nzSYK5SGTl2ox5g0lrTdt10gnsio9UV/bdNHLPJQobeutSLmxqDKHKo8sm0jQM+BCx2yZsbr6ulBIZbpsrrGKKdH1P07U0BrqmxfkbiwxIv3EClUA3Ug1T/K4leKv83f+W4S1fK+dfwDS5U1auAZ/DqZrGkGLEbRvBOZ6envn0/MzheMA5z/l0IegArUbpiMod1WKzGCGzxiqz58WqIQFlhvP5wrbJ86aAaOfFc36f+9znPve5z33+WPO7BqvPX57QLRkUbNjga4Jmn2Shb/YGozRD12GMsBm7ccQ5z+vrK+/v74DCNA3Hh0eSang7X4RdC4kQIiGHgbRdR9t1rNvGvKw5hKawBJoQpIqF3PFX5LHVB6iBGLB2Yeh7np+fsjdwQhvxazaNwbSGfhy5rhvKdPgAr+9nCSMBgo9EpSU8JCWRPfIjezpVZlUj09ABkd04YhRELzUyfd/h7cbj44OArJT4/vLPuRtUmgmLV9GYhhQDCs1uHNjtd5AUj09PoOB0vkqtS4zi802KpmnZ7/doDefzCYjVq9k0PUobPn/6jFKK0/sbKiXOb2+kFPhX//pf49aFtRUWaOgnvnz5itEt37//wNuNddu4KFh2K9sm4U7D2OF8rIv1ZdlIWtEYI8FJwyBBPLqpcseua2gbkRhKIIvBlCRmDaWHMSVwfmNeQG2Kdmu5zOeaimu0Jq2ebZXzP89nFJBURGnxFD89PIsHerO0TcNugbrQMQAAIABJREFUmljWlf1+zzQNLPPCMq8Mw8jlcsU6z9S2rNvKsi74YEmqIdHy+HikaTrO5xPLtghrnCRwh6RY7MqyrFWGqZSpElStmwzwiv/XM017us4Rgq9KgHVbMKbBWoci4aJDqZCBs7B2zltO53e6rmOadlwul+qrDaGEZoHbPNbZyvqZHNhDTnwtacYFUH769My2bazrRoxFrqshBRTSTdq1Ash2055pmrhcLvjg6dsW7xyblf5lY+T+b3TuGc6hPRI3q4lJsTnP2+ma61giMSrppHUhM4pbVhQ0NI3OYUAt0zRhTFM3pLqup+vavHG1YPJnEgY95vtIoXQLypNQYBQq18aEzVeQbRoNJqGNbDQVFt7aQEzSE61yVZVWIl8OKVaGvfx7c7amSWsUjRHA3nZyHahcmaR1Qz90tf5pc57NOqyLstGUE7dFjhswymRftsZ6j8vBXyjpdfZR2OjSG1w2uEpVjABIYYO10oQobOfQSS/rskln9pcvn/jTn/7Ebr/HusB1XvDRyzM2KfpG1BplM8HarXroY66sAektXrdr/jNI6SpBX0iPbCKxu9y6Ze9zn/vc5z73uc8fY37XYLUfO/q1Q2vD9TpLKEugSv9KKvDhcKC3m4QWDSKLkwJ6l4OVpOblt2/feH17Y7fbY53Dbg4SOG8JwWLMIgv8EFkWy7osUn3SGbzbeH56ZFstWimmaWLM7J2wM01Oce0yQJn48uUzXddjrWVeLsTVE6Nny2zqujpiuKJ1Q9P0JBQ6RtARfMRlD25CMc8L22Y5HiXYCUUOPtEZDKi6iC3MWMx1MCLpnZjnDaUSj/sDz8/P/Pj+PctGDdFZ+q7l8/Nz7pNMvLy88vdfv2FyuIpWUi8habKR3W5i2vUcDnvIKcTyegKIzuczxH1mu8B7K8nNEabscdzvjnRty+vrGy8vP25VG8FjnWMcdzw8PAhb5bfMdilQmsY0uZUxsiwinT2dT1znM8fjnq5r6oLb2Q3vPEbJQjcpAI0xjSSz2o2Q/ZLFj1zlj5kBbBCvavQibw4xMO0nHh4eP6T3BrRWXOcrznm+fv3KOAxYa/GbBNfENdB2LdZuKAW//PKF79+/saxXpmHEWcvlfOZ0vuR6EpEgJxTBR+Z1ERlk12JiQ2MEhFhrKxMnUtq1SoLXdaXrBISVyp7dbo9WC86JH1LCkoSBTCkSokhDp2nCe5EwPz4+VnlsZddDqtLlIjt2zt0SYHMNj6QXT1hruVwuWOur5xFuKbd9LwqFaZIeYOfdTxU/3ksFTtKpPgtC8LXayVpLQpQYPniMNmzbRtt2WGdx3rOsK9frtUrIQYKKhmFgt9vnYw52czgrbKJ4pUvFVJGytljrqly3SLFN00JwJGVoO53DjWIFVwDD0DNNpTc6oRvxym/bRkqg9ceEZ1VDtdpW3pt0yobawZtSYlADwzhIv6xRmbEVrzyISiWEVN+rtTZ36ZY+YF3PR+33jakee7ixquX+KJsi5XtjDKgPnmSjDartMU3+nEpx2O35/Pkzf/qHr/R9x+V64fXtjPO+PsuyOpwY5JZPBFFuRGFztTZoY3IXr4B873y+Vj4EqhVbBjfP/X3uc5/73Oc+9/ljzO8arErghiyKXl/fcVnuWNjMssAqdRtNa6rkMsQo4M+0wIeFboRvP15lQamk5gKViDF76bL3cls2nHUE7/BW8fnLJx4ejtmrlTsynafrG+wWaTvp/Xx8eOTx8ZHdTmp2Xl5esFZeu+sN83Xl+v2F8+WKsx67BbRqiEmkqc557CZ+0tpJiITJOOe5XBLDMNC0muu60DVG0nLb5qcgm7ZtCSTsZrnOM95HDocDxjS0Xc9+d+ByuuZUXY3ftMiXm4br9crpdGKzVtiRzARpk/JCNjEMHceHAyGEnGos0tnasekDu2nH0PbSkeg9ShusC2jTsj+MtbLHucCyWlDSt6pNDg7KvktrLefLBZF/Fw+moW1Mldxu28o4DqzLgjaKaRqq9LXvB7RuCD7StcLAhigyzpQU3kfs5mkaKjtUQFGMEectJPBRwpJ0lqJL3c70ky+0MEy73Z6+H3h4eBJA4KQPMhbJpQIfIv/Lv/pX/Nt//Df8+3//v/Pyo/gTtxoctdvtqgS+70dUA+PQ0fVj3bBptADVdV2qdPV0On14TyId1vqQZb0r/dBVELutribqFoDSNMLmmrwZ4pxjv98zjiPOuexDlEAxkvqpR9YYU3+23Is+XxvS3TlnaXPXDR8kvbnoSd2u+3VdSWmpANxuVph2RFrdti2JhFKRYRzpetkgkU5fXcHzOI4Mo0jXQwgiM62e17b6fItHe5rk2F6vM9frkpOrb723JfiobVsul0sGjbE+j0RhYNDZ+1mCu4IPWUoMx4c9TSOMKQpMDlcq4VIheC6XM9vm6uco11rxGYvMOORe4vIc7Or9D4VVlDgx6f1VxAxWa+pvum00ffTDVr9sTNLFmzfPPgaRlWNRXqf27caAy5sn+2lEKVi3hZQi07TjeNjx+PhA3/dYu7EskuBcpN/GtPVZb6OrEugi029b2WRLKeGCk1yA/DkTHm3kvcYYUTrSKFU3Je5zn/vc5z73uc8fZ373YPXjwlW6U40EcFiH1qYuWNq2wQeIKeFz/9+y5M5JbUgoXIh4fwvXabOfjxTY7SZ2u50E4eQAlL7t8N6x2hmdEtuyYBqNc1v1b7WtYpqO/OUvf+Hp6YkYU5VnXi9XLpcz3ou0TuuRptXYzeVal0gMihi3ymbY7RaUIwFEslDumjbLNoVNblrNsmyErmVnBCx1XYe1luv1ilKaeVshweVyQSny5zNc54W///or5/MZHwQU9X3PMI4s68x1vnCdL2hjGKeep6dnnp8/5WMjYGG3m3h6emJZZkmX7XtEMi00SMgSZtO0XF/fWTdJAXXB03cDm12kdsQ6zucr67qimxacQ2lDl9k17z1vb28s65oX/8KYN0YDEVLMPbwN4zRirbCC3jseHh54fHyq51wAaUIpUwOJvPeQxB9ZFsoxgtYSnnW5ntlmqSDRQVVAAKAb9VM4FQiISikxZObYbpbXt3fWZcspsRLw02ZP4MPDA+uyMA0Tx3+557fffrC6LQOClnVZiUk2KKZxIqkC6DSnkyQ+xyZVkPkRUJVrtMmM1g1QRdZtqZJd53zt8RVGLzOkoXjFnVSWWMcyL7VSRAKMWnwI9F3HOE3EXJHSTxMJSaI1zS38yjlX2dnX1/cK8IZ+zF3GHq3J6belfxaKNDkl6fFsOwldk57YpoZ+XS6XWs1TKomapmFd1+q/DfF2vkT2XLp2NW0rYL+ERflga9+teB5vm2RrTtsu9673shEhFToiv/3ofU2NACh53xI8FGNgGHoB16ap53CeZ06nE20roUMfGU8BiOIJFQBm6Pqe4+GhysE/stAh3uSyZTOugNHCksqxCPXnFPBqjDw75TksHV5a6Qpobb7uhrIppJT4Q72rXnhr13rMxn7g8fGB48OBvm2JIVUfrKQQ56C2/POCl3ob7wLex3pt9n1PYxQpecZ+Qhtdj521jmWZiTEx9D3HhyPjOPL8fPz//DvpPve5z33uc5/7/I+d3zVYbZu2gtWua3MojvzZskgq6W63o20bASirZ57n2g1ZgGPTSnCLdiLDLQuny/UKKXHY7zgc9hwO+7oI43CraliXluV6heSJSvbuu65jGHq+fPnM4XDgeHzgcDiyritvb2+8vb1nJiDl1GJHCA7TqBzsEiGJPK9UORRWsrBUzkloCkrjUT+xyZK6K2D0sD9AihVAzPPyUxiN956vX//E8/OzdMCer1mGaVm3hcfHB8ZxYFlm1m0Rtq5t2O/3DOPI//wv/yXWWl5fX1FKEjnHccgJoyNG6ywtlLTWdVmZ5xmlDKD58fKK3SzLsjCOPfu9MHzGyOU3zzONaTkcjrTdgNbQ5cVvSolDjCitM5PnMyBypEWCifp+ZBj6/N8D49jTNIbHx0eUMnz//sL5fMW7KIxs29E0UpXTttC2kohaAJ8CdrsDwzCwrJI+q7M0cRwlBClEAZ0+OKzVPwGKZVlouwZrPZfzzPv7e2WltFEc+j0pJprG8PL9O6/ffuP56ZHr9cLL9x8cHh6YponrZUZr8WMfDkfaVip3lJLkV2FsPV071Gvno2+0SG99DigrvaxyTW71ayHEDKxUBQPlOitMX4qR5TpXua8xkpAsHaI6y+49Pl+/hbVMqSHmoBtJeZYKoPP5jLWW5+dn9vs9RgsrmqKpqbPl/ctrSady2xUmVIAqUJle7z2Hw4Gu62oy7jAMXC4XzudzZSiTjyLJzWmyJTxo2zau1yvb5qTrdbOEGKtHNn5gF8t0XVc3iooKQIBtysf0ln5rjEjXQ3Scz+/1/QnIjYToa7hYjDF7Ztt6XZXzUV6vaUTS3g9tDta6seCFRS5AVDZjPlbhxA9/VmTQtxTpCrCz+qAwrgXkl3NUjmlRVSzLgrO2vq5SihBFWr+f9uynUZ4ZOczJOY+zAR8iPoTsYRbfdIqymVA+x9APtN0t5Xo3teymnuPxkWEY6nUg3bcOlWuBgJycfGdW73Of+9znPvf5o83vGqxe54Xv318IPmF0S9Oo2hf4sQtUFnGRdROQBDBNe8bdRFRUEFfGe1+7A7uu4+nhwDC0KJ1otBZZbpAaDKMUu75lmga+/OkLqpGU4WEcIcE07XK9hhzK3377xo8fP6SWIgoQs1YYGeciplG0Xc+h63E25i7SW3+iAGZYlxmnoTESBhOCJwWRDFcQQWGZA62Bdc39h0bYM1Ukedl3OI4jSml2OzlObd/V7kdhn5YsCVQ5kbaj6zuct5yvZ+Zl5ng4Mo0T67oS/CmznbBtG3//+995fz8DIju11qF0k+XcwpRZ5zlfVrZtxTlfF8XDMIK+SoXNtGNZF9quY+yHypiUZODymbz3DG1Ha5rsifU8Pz9zPO5z7+g1S2U31mUFDKSI1nnHIwp7PnQDGs01XCEmGtPQ5JoVozTPjw/CLmYPZtu2pNwh6T8AnSKRlVAhi3Mh/+Nv7HXf0bcdRiWIkb//179BEnnwy+sLCng6Pgo733mmYSByUxgU4Bh9YD/tmEYBNB/9hjeJZmJZYv2a1ILEWj9UNjIkKbatGzVKJwl8+iDrLZ4/uYZUDl2aMoC59QwrpRiG4ScwVJjZIhV/fX3l9fWVT58+Sf+vMVzOs2zoeJul1hIS9bFKSbyqud4lUVN8S63R8/MzDw8PFdwU0GeM4evXrznUaaVtxF7Qti3awPv7O4u1sunipUrpBphz92q4PT+AekxCCLKpk33JReIcghdW2bR47zFGwsBU32c/bye+4PxeY4zEDAqLN7W8h21baZq2pieXz2QahSInDm8L16v4pAXkhcpIyzlCrn+JBvtJylw29wq4LM+XIoOPMRFjqmD2ZxAs14fI0NfqJ26ahhQjXdPT9U29/rquJWmF3Rwh+ropUWwb3iesLcdak2Kq11KRGMumVMvz0xNPz0f6fsA5S0xF4UHemGnysVmzr/jnc3if+9znPve5z31+//O7Bqtvb++8vryS0q3/szALZeHbto0wdZcT8zxzuVzxPnC5zByORxKwrluuMDBVOlyYnxgDp9M7l0tit5+kTzNJvUSjJDRp6Fv+/D/9mV/+9JUtON5O70zjDhC5oCyEIm9vJ7799g0AY1TuGwVtgJTo+paHh0ceHh9xPvLrr9/y4k4WhMYopmnEGAVJ+kkl1fgNYyQMqDFt9pmafJQU1jpM3/D4+MiyLCzrTD/0NJnpkf7MK+M40jSScrrf71nXlXHoJRBomXOHa6B0rO4Oe/aHw80b3HWgNMti+fb9JS+iO2KIOO85vb/niheRL/sQCGGl7WRhbrIsc10t5H5J70PdePAhkED6UF9+4Kxl6Idbt2OMpMzklHM4jkOVzE67iaYxtfJmnucM0FJe6Cvp0EwhSxrJCbBNZk1HnBOW/nq5iHwyX2sxFL+exhjFYHpiiujmBlqWZal1MZfrDEk8ruX9Hg4HGiNfS0nCwIzRWLsJa43iz3/+n+i6nmVe6Noe56WWSUCayUyoyOHHqcs+UwkXKgCn69oMdKSGaRiG2lc6L1d53xkQFQ/gMAyVfSzeyVIJI0FHpSNVvld8g22VmlprK7tc0n/XbctsliTlLPNMDALmPz0/czw+sswz1ooaoFTvtDkJuIDwAppu3kjZTAHq86Ak+Bbp88eeWWuF1ddaZ+Z1qL2kl+u5AqEbaympvvK5ZDNHaVUTloH6fna7XfWRiixYptRZFeksNJIqndnsbYvM81JrXWSTQKNy+viNHVRoY/AuZb9zZkeB6APKJGIM2ZPc3Ly8MeVQNqn/Ukq8vUXmrbWqx6hcH+VZ8fGc+hx4lBK10qsA5gJwr9drPeZlY6F4aI02aM1PXw8hEBP4kEObonTZ+iz5bqs8+FaPU67JlAIx3/tKaYKPXN2cw8RaqTQLsXYDxxiymqKvNTz3uc997nOf+9znjzO/a7AavSclVRdtRYFXpHKS5CkAz7oth9EoTqeTJNEi0rbruuBzt2HbdLlmJeQFvCR6agPjOPJwOBJj4nQ6k0JCY+i7vi7gTNPiXeTsLwzDyLZZ1nXjfLrw9vZOSd71XhZJ07Qj4RkGYZY+f/7E8eGBv//6LTMbTWaNVnyA69wwjQNKCctaEkqPxyPTtOPt7R2txGcoC2Hxdk3TxL/7d/8bp9OJ/+v//j9pu5bjw0MNwkkp1QX0sszM8xVhXeSYzstCY0yV0D5/esa0Ldu2cblceHl9lcoPD8tiZSPh9S0vBNu6mG3bFpKS0Juhx7otS7IBsney+Cd1lnIqVQNcnHdSbbEsrEpJn+mHRf7xeMyLYk3ftYz9gPWOtmuAxOvrG8IcyXUiTLupIUiSyppQ2Xdns2Sx+BuFrQtYt6IUGQDKn0/jxG4vEugYBVgro+vrFi+dLN4jjTFZii7XUGMkKVX6PzVd2wroCMLSjsNAP+7R2tB1qUp4+74lxpR9ktuNFXU+ewZvfsQSvhOjrxLej/5Nch1K0/SVAXt4eKjy1uotHMfKosq1s0lrUwYdxRNZwH5hB51zVYIbQiAYkzdfyLUpUfyGTcu2rtU33HelX7ZlGAQ8L8tWAWRh+gQw6QpcP3p1RcVgfwKUy7KILDuD2a7rpCIqRJEiu61205bzr7UmhnQDX1oxTtNPgLYkZJdju65rBW+i/jD5tYq8WuwIOnf2ns/nDBgb1lWk1X1vqve7vP/Cisv/S3fvrUdVYf0GmfF+evpUmeV1sYTwA+dCvh4K8EM20LQw4IVVLdfwR29sZXwzmPz5a1nW/qGyprCt5ZiYktadJOBIrme5/uScRmICYiJvE1Q5Ojlc72N4UwhSndNltvrl5ZXL5SyqmgxWh6EnBnnGALn/1+K94/Hx+f/Nr5/73Oc+97nPfe7zO5jfNVi9zFfxDCadvXLmw6KyyCLhx49vbNYSXKRrW477A10jSaExBqJzkHsBVfK4zaGzlOzp+YnjbqBpG56eHvj8+SvrahmGV+wmi87leubbjx/4GHJn43v2jWrmeUEkvqGyt2AYRwE1KQVQ0n+qtdRyOOfrAkyYDp39WoF5vrAuV3xOX316emIYesZxYhgmUoLz+YKPQepXUkKheHt/xznH0/MDT2/CsDaN4fhwpOtbYSCu5xwAJNJCY9oMorvK5kJOOw6JZb3yfj5xvV5F3hgVfT/Rtp14U1dhc7RqM/sFZmgzqInsd3uWTdfAKVmUa3a7fQ6XkdoQJU00JARceLdBErDvvUNFYYE+fRbfrcmL6bZtUQrc6Y0lA1zvZWOjHzoaY1g3BzhSyg5HpYhEWq0ya3eTlStFDovqciiQZxi6ylSOWco6TROn04lh6NCNzhLIxG63q3LYFCOb33K3pwN16+QVdtbgM/ARRKNpuwFjWmwIJGWIgGk7Ekpk4c7hcleq1kpSloPPoVARrUUKva5zZeeapiEh0vBtuwXdlA2AYRCP8Pl8pm1NlXXudjuMaZln+bkijb7JY29sZMS5ufoX2wzIyv8rRQXNUp3TZOAUaZqufk/X5XofL9LpZXln27YKzJZly8BNPKKXyyW/bvF8J7ZNsW03/qwAHGEQDfOcvdqbx/tQg8EkDEnlRG3ZFEsp0rSaph3rPVpk2AU8fpTrlv8uycBd11Vfe0p5I4CEtcL4CxAWRlXqcnY8PBwq6B7HPj8/bpsQBYinJB3RBultHoaJT58+8eXLL7Rtx/vbiZQ2bmFQMSeGd/XYFEBZPKjFh1zAaJH6FqBdrt0CSFUFnzeA+/EfYsIFm5lhcA6UusnKy8/VWpOUQceYmc8c5KQTaI13Dp/7a52zNEljmoaXtzfO7yf6vmW/P2SrQcxAWz7LfrcHNKv1rMvGOPn/P3893ec+97nPfe5zn/8B87sGq9u2ZQai+YnpECZHWKqub3l9e+Vylt5GBRwOBx6ORwlQUpFffvmaJYJWujRToOs6Dscjv/zymd0kcsw+MzoxRkkbNS3WOVbncMGxbJv0M86zdLRyWyDFmNjvDxyPh8yStLkGZmGc2hwGNQGK79+/czpdJMxlFYZKoWkakduFGJjGKfdGyoJuXTfWVSoe5mUhxpR9tzofpxP/4T/8H4yTLPqN0Thv82sMkkZLomkbRgaaRiSXwyDe3RikGid4J96xIAm8p8tFPJ+ry9LIRErXn4J3QhBPbkoRmwN4pBPR1fCoFCMK2O93tI3Ir0PwWYrt0cZUsBAy4NzWlX4YICWen5/59OmTLEYzOLhcr8QQ2KxjsyuXy7UuiLVWqL4s8l3+WsM4DblGJhFr/YcnhFuAVdsamnYkRsd+v8shXi1rDu8qQURt04KR81OOY1EBKK1JMWK0wmhZaJv9lCt1qDJphTBkxkBEkZDvA5GRex+xzvH29kZMqXrylnXDbhulmsZ7WyWwBVzUGpEkPtWPAATIIVNUVhiobKXIZqWLU5dEW0W+RjyJxLJK96+zvkps+RDE87HOpshHm8Zk/+KOZVnrn8/zjNaKdZXO1xKUtttN0uUaqZ5rZ88452toUfERF/9jAVzVaxtj3Ryx1mN0m6XhJt+/Jt9LKTPtKjOjXZVKh5iqDLYAVgl2k+t+HAfm+cq2CVNbpNXGKJyTe2Rbba0AkvtWXm+aduz3++w5LWnbO/pewpeKImIcx1vic5QNnpiTjEtyeMh/vwRwifrEZOA80rYmH7P2J3BZNjFu35cqmL0xoPzMvJIrxFC5akrXa0sYb6nwaVRhum/JyChVq4pAk8ozovpr5Z6onbqknGDs8N7hvEWhsDZwuc4SmmbazPTLRuXlKv7hbXVs1ueNq/vc5z73uc997vNHmt81WO27JvfwCRiyVrxwJstVD8cD+/1eAjQ2i0FYpFBTKjVtJ4m9X79+qdJQHyTQo23Fv9m2mpgizm0syzWzRZ7rtdRXRJwPaC81K9u2ZUBocmhIx36/p+u6+v6GQUANKmYZmq9BTD9+vPLbt+/MV4u1viZXGq3QSqF14unxqUpeS8Ls9TpnOV5ZgLZo3WSwm/jt26+0bcsvv3xlHMcKWpVSNG1hTUJe7DkGSq9h8XQGXPL4ICthrRvapiMlBampTBaURaaUY8aUfadKgnmEtdFYuxC8x4aNlGQB/un5SbyRJHbTRNs2GSBEWmPQRmG9ZehaPj09Yq1ny4zSPIsfuQDlAh5ijGzWsiwrw9AxTSLrjFkeWwCz1kj/pRLGRsCUzmxRFNmiApXPQ0liLcCrMIKVoSQv1HPgkHPyc3a7HdZ5vI80uiEEL5LlcaTrWmFbkYThECIaTdP26KbDeWGgvZfPHWLgfD5zulwYx5GYxIsoVUBRgK4iM7jCHPV9/5P30DRtDSwqXZveBULy1fvZtm31XIZc/dS2IkkNQWpaTCNAIyJg0DrHsq4Y3dAPA13f11RlYapVDhnSdUPHGAGGErB1qzchX4vi/dwQsNKwbQ7vY5Vjb+uGc4HGtNLHqxKlP7P4JEsqbwGvHytfhqHHu0jpTW1aQ99PDMONFS0eY7ke5IIXxp78WYQl9N7RNKkeO2u3zE6Tg31Eoit+bV3TmAsjXUG897y9vmMaVUHuTZ4bf2JAC3gTBn8mRukJnuclV27BsmxZZSAdxOJLHbM31VS2uxyTcr6LJP5j+rDWmpCcaJCz/bh8n/5v5L+iWkg5CE6OU/mMZYp8WYBuFnOQCCFlub2Wjt94ky6X15f7lZs/Vpf706CUyT7YRKJUly2UTmatNdr8rn/d3ec+97nPfe5zn//O/K5/ew/jyJcvX4ghcj5fhIkhMgwDnz9/4s9//hc5rGTl7fWEThK0My8z6zrjvIC1qe95enyg7UXqOc8XkaRmz6QPLrM3CWcDWjdcl5m305kQEpfrWfyFOskCzCi+fPnEP/zDPwgA8A6lNMs8E2KgaQ3OlwW3MJZd25MSnN7PvL+dWGabK20SJE3biW+27QzPD0e+fP5Eq2XBb7obQ5WSoh/Ey5skt4Zx6DmMXzGNYp5nDocDfd9hww0MlAWq+FdhmoRVlYAfi4IamlNSXadpD6rBrCuNcTi3gYLGmMoWpSSAZJpG6br1jqYR2aV1VnyuIdE0Lc/PT+z3e06n00/eN6VKP67DoDkeD3z9+oVhGPnrX/8j49BDgt9++60ySDEllNbS8+kdLkteh2HkcDjmLlBLCYtqmpZlkVoPo5sM9HX1QorE9AZMpb/zFvAjjKNF5dThbVsJ0dMNwnQWyWmtBtlyIFReaEsQj86BL7nDspGwqbbts8RbcZlnLudr7ew0xmA3R4wQk2LdbgwqiHTbKwHbhU29vR+fpfCxgkfxTLoK1GTTo8ns+FDricqx0Ur/P+y9S4ylaZ7e9Xtv3+1cIiIjM6uqu2d6ps1cerA9wzAaLIHlizC+DIiRzR1hxAoJIWQsCy+QkMAbwAu2ILFDQogVC5DYGBn3cp7dAAAgAElEQVQkxFhgZIyQbCyNPdPT01mVmRFxzvnu33th8X+/L6K8sLHcM65unb/U6qrKqoyIc8v3/z7P83vQxpAydXaF66xLocm9x2Ibfs50ppRyDjlhzHNuc13iZJGS71WWOJffg3FbOgH6fshqqpf8pQ/bzxlDkMupAEXhcq67Adg6VWOMuV6HLc/rF8k3GqNpdvXWTxxj3EBVIM9z30tudq2BWi8B6rrectRd1/H09LSp1qKCVvlrKFAQgqieK8zreGyYppmhH1FKk5B6rrXveVV0L5cLAuR6BhuBgN1O5zMxhc0NEiOkDA7r+35zfaxW9HkW5Xp9fl4Sm9fPl5cgu+fXGV9aOF/Oy6zzS9VVJ/2lBVsphbYKFjApL99JOlXlsyy9WGzTpu4/X2ikTXFVeRFeYXDPz+2zzXdVqOX9oLL9+Fpdc53rXOc617nOD9p8pZfVzz77GoUrNmXxi8/fs/iZ+/t7jscDrnCstFUUoMXK2+wb5lnygtZZ6qpgXhYeT48MQ0/XdSTYqg3KyrEsM64omCdPSppxGDBKMS8ThEjMkKTkFLt9w4//+Ld4+/YtDw8PDI8CcEmk7SC+EjaV0hht8UEO1V3X0/dTzlgljDagTabaKpqm4ubmCCoxzQOH45GyqrItr8xqjRGbW9szTTPWGn78x36E3b7h137tb4mdb5xEoayqZ1ulKygKIcx2bYcxxXYAX3OgcuA2WfGV319pTVlWFKVlJdkejhWH/ZHdTjozP//883zQVSgt9FSxx1YUhSx+ZVkyjsP23MyzWCi1EaVKFNDA3d2n7JodMSWapiaESN91XNoOa6yAjZSWap5cb2Gt5nA8oJT0va4LuNaa/X6Pc45pei91OlZR72qstUzTCix6tkOuB2ZZGATgM00TlbPbgVzUVbCFy5nKkTUrty4+0zQTfMRaJyRlFDE+U1NRGqUDShtCVFxOZx4/PqKULLOrJbPve3T+fdfvLaW4qVTkxWNdFFaFDAWFcxl8JAtLWZYsywwqQlKkFLi//wRIuQdXLOPTLBlxrQ3Bx5zFThmqpKnrXCnkJPvow8ylPeG9p2maXAGzLgnPFUpd1+NDoHBCTV67fOdJ7LHLsmzdpeuCMU8zSsnPIW+cxOLldZ+IuMLxta99yt3dPcuy5PxtmRVZQ0ohW5AtyyKAK2MtWsllR/KBFCJ1IXlkU4htXzLq8lwqnoFEq0V2WRY+//xzAKq6pO87tEq8uj3S7A+4rI7O00TXS01TnS3C4zhtlt4QAs2uya8JoXu3XS+k5EV6hddFWClZPvt+IEXQ2jIOM/PkZRHPBGOJIpSyKM9rf+u0XYKsS/XqDlmrvLYl1QdiyLU1PohiugW/xX2wXewI5+15oU2JED0kCFEqfLTRoDJBeLt4y0uml7zqS/KzVD8taK02N4kAl8SCHVPYssar+rpehojyqreLLckMm2cF+DrXuc51rnOd6/zAzFd6WdXaUFaVWG3LkqfTiYKCN29fo7Ti6fS09fvFACmN9ENHUZYUhaNwjiV4bLA8PJ344osveHp6Yp6l49Plw5xzlqoqefXqFbpwhLBgjebcP0FSlNZRZ3VGO42zhsIaxr5H5YVKaiIm/BJxtsTZUkixs+d0adntduxQPDxdCFGWo5ubA4UriD6gNRhrqMsSRcrLm9RVpJBE7e06FJJRjSlulsuh93z3u9/jm9/8EcqiQvpdO8ZxpDMd1hVM40x7GTItVJSatj1jjCaEyLAEVvLymttbraWrCis9kGI//oVf+AU++eQTAP7yX/4/aLsKUFwuF8qihqTwi6haqzJ5Pp/y0jjiClHVtEk4J+TQuq6z2iO1J+M4st8fOZ1OAOx3+60v9nLpGHNuEwJNU9JUJW3bMqSBGP1WM7PfiYpltUE5WJaZcdbsC4dxFpuETPqycgMS1hiMEoWyLhzHw5EQI+fLmbqu5fXiLB8+PLD2PyoUwUeiz6qiLdApobRhmCaMEjWpahrO54uoS8rQ9ZL/1Jmg2vdyqeJ9lMx2jCSCvC6SqHDy3AkhNSVNUpqYEhpRyY1ptqX383ef09QVhXVE77FaUZWO1/f37JudLNguEn3CzxGcKF7ee5y1GGuIabWBpy0HWxQCY9JasSwTTbPPlwAzwYtN3LiCEBOLX4gpZnVUczjestsfxSJfGKqyZsnwoxAC/dASfcAoiH6mrir2+z2QaM9nQgxUdc3929fsDzumaWAYJs7nlqGXuiqUQmtRXkOKJJ2wppBLopSYhgFipCwLVFIs2T48e+kFdmWJKwTW5axFo7ZLjmmaRC0k0bZn5nHk7uaG466mqBwh9yWfz2dCkJz8lLPDK0U8kahqISFrYxmGmTaMOV875Vqs5wuOYeg2u7aAigxax43aCwrnSpzLlu0oqmaIC3HxaKVZK5CqqqJwLgPEhDytAJUvBFRSWGVQVuODx79QLlVSuXJm7f5Nm7V5jRvI7ykRjsI6tNJYbeSvtcXHQAwegyIAMV/giaLrMxiK7QIppbRFPNYLkFXhX/8dUb7Jj0egyNAwrSVmcZ3rXOc617nOdX6w5iu9rI7jxPksdr61JzLGQD8MHPYH1oqHqqow2jGOC23b0nb9lul8tuRZ2kvP+dRCzkstfsQ6zfFw5NWrOw6HPfM4EvxCWVQoYF5mCluh0CgNzhmOxwMAi/eM0yTZwhAx2nI41NS19E3200jXSX+ptY6ulU7J9bBV1zUKqPcCkVkVgUTicjmhtYBo5mmmH0ZiTEzjwDgOkutLiaaumcaJp6enDNpZ5DA9j4TgiXOiyMqMdMrmSp/8dcTuaui7IcOTZEkehn4j9QK4QhaQeRYC7dPTE86ZDD3x+XAqS1JZiHIktRkdkInH/pkKa0yZ1RHN3d0dh8Muq8AThS2yyjmTUqCua47HI3d3r/Li66nrhqfHRz4+PNA09QuwVYHSimkKGGPR2mzfd0wRn/OtPsiBuKoqUX0gq4Ymq1hZHTaOJWdBx2kiZJWyqncUVcXT6cQ0jRRFmVVLUfMjUqvkCivZPhIxeFK2hA7TQPARYxzj2GeAz5JBPAPD+AwfAqE0b3AfYzYl2lpDivJ8isV1keXSmJxXtJyeRky2HnddJ7lLo6ibmpubI30vC9VKdH62gKZN0YtzIJEoivKFnVlx2O+5v7/n48ePVGXB4XCkbVucNVgt9m6j5ScA0Jh8wVNijKLvLxhtMFYz9N2WXYxByNDjOFA4ya7f37/i/vU9YZEM8DSO7I8HrLE8PTzSdyP9MNJe+i2/WZQF2jnmUSzs2miUFTVP1F9koTGKfuiZT1JrY13J4XhDUVayOKkEUYi6KUbqosDcHOXSaBoJpeew3/P2/p590zCHwDxNmx1XPr/M1uG8XoqsdUdF7jAWUvi4EbjX/zYlcW2M47i9TlW++BAL7nP1zgopSikQ0zPtV2stj7VxWyZ28TPeP3fCpvy/mJ5fb6LsymvweZ4/K17mVkMgg7yiPNbZeWu0yb3Ya5Y5fMlirIGAfH5N4yQE7fxr4lABrTTkHPlqp7b5tV5mUre4HAbO5/PmchGbuaYo3N/nn0jXuc51rnOd61znt3u+0suqzwpE17WbNfByuTDPC9WP1xvcRmtNWVQYU+B92LJqAhrp6fuS3a7JBNDnHGJKgbKq+PSzT/jk7RumYWDo2rz8WlxRME5TzqstVLUciA77A0ppTuczbdsKUKmsqMo63+6L3bEdxEZbFAXWWGJM+SDHZiOU5cKwFt6/pN3udqJ4eRWIORPZ9/0GDinLEmcdqgK/JM7nE0o/V3Dc3NxgjGYYRsIsZNvLuWUYR+7v7ygy/VgpwzIHUXIjL2Axlt2uoayKDS7k/cI4ev7m3/xVTqfHnOvr84EadrsKZ0tCkPzfuoSvB+5VNYsxYOxzJcZ6cG2ahsLKoVOee6HCvnr1ihgjl8sZpQxv375Fa1kwbm9v+PTTt5mC2udFNKCU5DP7vpcFLH/dED0xQRoizlmsLShKx9CNW/WMc9IfG4LHLyFnfkXpBkBpPnx84OHhYetwLauKsiqJMW0kZ5LUFTlnmaaW4BdCXLsvRU1SGFxR0rYdy7xk5XvtgpUrBZV7VleQ1bPjMuHWntOcaZQLhYCzRv7nBBg0jgKG2u8FtnNzc0Pbtnx4/3GzX8oFwwqkko+HNcNsjMGagM9AKmddVrnlQun+1T0pwf0rseM2uZv08fExvwYKpmmiLCoKZ1nmcbPByteTGpZxnLKq7DdrZ1VWOFfkvPBEjInD8YZpmjlfeuZpklznIpZenStf5vzad8bKku4Uy9yBzgRbawkqcbk80XdSwVPakltXwxLxfiKmgFfyGqhdSVGUGK1y1jfQNDX2zRupgXJyuZFCFGJvhm/J+1o9W/8z7XYFd83zzOLFLbE6JoDtki6ltD1WL+m9L//+Jd13JVy/JDNbK8A0sRIHlmUixIDOyui6/MplAVKLpRRLkNesPEd6y0M/51ifFcuX2dFViTfGoHNd05pT9z7m95XGak0gX/5l6/u6kK7/rxQCoHvxMzonr7/9fp9BfDMPD/KebNt269V92bt7netc5zrXuc51frDmK72sTtOc7XaiUM7zwjQtnE6iDsrhI9/uY3Gu3BZRyFTTYWaZAz6DWXa7w7PNjIXXd3e8ujmiUhJ4Uy3qkDWFVNVMI1o5qSJBMc+e86WlH0eens6kmGiahmjl++n7nq7vmWZR7fb7G5RWlG6tjpCO0vXA55eFpyeBiIgS7NG25P7+E+5fvebhwwNDPxCWwNQPhGXBGYPLtkCVEioldLbANdUuKwyyJEFit6tzv2pCHXbsdzXH21vqpiLkmpTjzYFpmnl8eCLGwOFwIGV4S1FazueTUG1Loe2mFHl6esrUWMd+vyeElC8VZPGWw3yxKRzrsgRQVTU3NzfEJI9TCInLpZNDtX5WesTSPLDbjTw9PQFwc3PDMHQMQ4dzhl3TcNwdeVwecNYxTTPkCwEhNy+oF4fcDfiiFd4vmd5c42epahmGhbK8BTR9P265PJ0gJc3iPT72LH7JNFM2mrLkoBe5JOh7jAKvFnb1Dcd9SYoF8zJnKvQDKsmhvm0H5lmWHGuswIeUEHFTYntNpwwMUoYNSFRXBaSEsyWkfFkTNPvdHcuyME0D09jn6qA9TV1zvL0RxT7b1yULDdO0bIuNPD5he7yATOYW1dhayzIL5fprn319W9AF/CWU3o8PHyhKh3ONOCSiWO7X5yERiSkvQmi6rs2dykJvBmj2+6ywOebZ07Y9MSYen85IBcyCUpaycJRFxa5uZBmbJvp+lN5fNARY/EzCk0xEW4eyQqadUyAYJdlYZRhmsTE75TBWsaQ5W4Vh3zQMfcc8ToQUKasSo404QS5tJtVaUTBdxbzMUpeUl7+27cUK/WJ5stYS0vPfr0vWupSulTwvc9Vr5nSlLb8kV693KjEqUM8ws0TMu2XK8C1PUgrR/uW5CyGSArnaSQkFGLb88cvO1Zc5ULnseM54+7DgnOVwOOTXtNp+PcZntRetScGzLPP2c67K6ZcyqeqZ6mytpalqqqIkzAuff3zgdDpteXGJbtTCEkgJjaK40oCvc53rXOc61/mBm6/0n96X84XT6bzZxtauSJAFRiA9S77NF6ruepha4RwxH3QWP0tudLfbbKtFobm7u8VZqZsw5rmovmlq3r59w+IDT49nuqFniRFja54eT4SshjTNTjKfg1gQT09PXLqOlOD29lbyhCBLSO6yXG1y0gWpidPMfr/j1atbbm6OlHXJ/ngQa1tVcj5f8s8bXii70t0J4JcZozV1tdvqUWKM+EXgSNL3aBmGkXlect5LiR3We1KCtu0zJCjlPJuAeMZpICbJtcVgMmTGURQl/SCKal3Vz9bUEOjaC/M8ZequkFVfEl6LQtSQr3/969IVGSNte9lUovbc8vj4RNPU238b41pfIgf0tr3w/v3nNM2Om+ORZVloL63UYkSxqxaF5JxVJgVrrQWilSt9Cuey4iOHfIE5BZZlJISQrZiDgGoSOCeL6vr4zVMPSK9vzDZNUIyj1IgYrWhqS11XfPrpG5qqwvuFrusZBqk56bqBoR/RClGYsvKeEIuwcyYr3SOw9lQmplG6NJu6Zpmlz9YvnrZtiTFyOOxJKdK1He3lQl1V3N/fc3t7S1VXaGPohwFjREEWa+qXqcGrE2CtDFmXUJAmk/W9J/RpoSIvyyILItB1F5yz3N7cigNBaz795O1m5dZKowBrrGScgxe1L7/Hq7Ji1zSUVUld1rRtJ5nkYchwHU2KYExBWWbQDwLhWV0VWtkNHha8KOtJBVQCY4EEKUDhSqwpSUBcYn6vK4yWKIArDa4oiClxupx5fHxkGLrtdYXWmEKUPLRUA1kjdN86NXRdx+l0Yhh6WQARa2zwIVteFUVZUJTFptRvFxQpbUvYSwVVXodmu3hZc9NrVnT979fFFPKlQ7YJx/hMdl6/5vr5J9lnDahcWZS2rx1i3BwGLwnIyyKXiyEEuaixhnK3o6lrBODVs9qWldK5E9UIhX3xX/oZX8KWjMluE2U3268xBu89p2yLXpXol/2xKxlbKOEVhxzfuM51rnOd61znOj8485VeVodxpG27L9nH1ioSOew4rCUf0qQiJXjJ0ontc/nS8tr3/ZYBNEbz5s0bqYjJXZ9Wa8qyJATPhw8fcGVNVdekpwvdMND2A/M0CmwoH/JiTJLP9IHT+cL5fM69qXLg6vue3eHAnBW+vu+leiWGfIgqKFxBVRXcvbrl/v4VymiU1rx//57z+UI3SLm9LGvy8x8PB7ruwuVyEVU1sRGIi6LISp+nKB1FXiRkKWyFyJoCRVkyDAPG2K0qoql37Pc7qXgxhmkYN/gUCaqy3JS3sii2w6mzjkIJkKcsxJ4sB/mCc7aJlqUoamXpNmquEG8nFp87HpXhcunouy4DfApub2+2nyulQNuepaLnuOeTt5/Q7BrmcX7OAhpHWVe03SXbjocNKFNVJeezLGGqsGgFpEjftThbYK0sr13Xcrm0gCxlzhbMPuIy4VlsxlKfI+CXBaP1Zr8ehhHiwq4phJzrPWGZpWJHK3Z1RfA7gWcphVsCwUfmbKE0xlC4khAD0zigVcKVFq3EEnk8HLg5HrO9VNTPxS6bPbIoLDEECmf5+te+xps3b+TyBCR36z1GG/b7PcMwAULcLgq7WbeLTDr23mO0zUq3VA35XMMiauecbbuBvh+2/74uC9786I/w8PCA955vfOMbkKALPRiFVRarrCyps4DE1ixjVZYcs71zff8+PZ24XC5bhYu1FqMsSi3MkyFEUfKKwgqcKSax+QYBR6mkURj8vAARFQNq8Shr2Nc1tnIsMTKrmcUHlhCISuqkIoA2LDHw4cMD0ziIGyMva5JtjrRtjy0cx8MNuwyuats2U6fB2pXAvSqSayzAbPTe1Qb8XKPE9p5bO1ef6cDPmdTnLOiyLZbrvyuZcqkTCiGgNFRViWskTjFnZX3NuxpjIFcOhSiQJK0NqEQIy+aSKIpCcqn5gkVUcgGAOSeQu+ADwa/As0xIN2xL9TgI8XoJfltG4bkLNvhntX+9/IoxMvuwWZVXq+/6uKyP1ZoHTimxzMv3+U+o61znOte5znWu81s9X+lldRyfuzJFAUw5jymEx/v7e7TWdF33XABflUBkt2syPESWVxB14nI5UZYFxspiUdcVbdtyuZzZ78VC23U9Hz4+sttJNjXGiDKa7tLh55m6Lrm5uaNpdrRt/tohME4TwzAAbIfsEALzPOOsy9m+cSNlSjw1UpW1qF1aCLyFq+iHgfPlTN9LLY4tLSkmxlFAQYfjEefMptb2Q7dZ65xz7Pb7LRcq2cvIfi850NnLYXNVA8tyVXo01hn2+wYfPOfzhbqWuo663qFVYtc0GGM5nU65n9IzDD2Hww3OuayOyM8fY+Th4UzXddvhu6oqnJMamw8fPvDw8CB5vWXOqp7Gz9IhunZyjuPINFXEGHIGuefV/R1v3rzZaLftucMYyzTPKKuE2ntuUUnjQ8AYUWSHfiCGCCmyLHo7yK6KZtPscK6kbfv8uotM44w3AVsU+GUheINWQt1t6oq6LLC7SmiqRotaFBwaQ1VYVEycHh/plMk1HqJU+sVjjcWagNGGVGr2Wiygu90OpS2XnL2LQepjjHHsdnvqZpf7Umesea4ckUsUsfCK21Ns6kXxXFM0jhPdIACnqiqztVfywmL51dR1k8nN03ZRJHb7lOtEZCkYx5G2vWzgnxXQFELAac3/+9f/BlVV8/r1PcELPMloB8ngYybd6oJoPOP5zJTBUuVhJxUlude0687bZ4EocYGwRLRaBAhEpCyd5L5JUtcSEqfzQPSJXXWAlOgvHXPXUzlLMDOUFfevX7MvGsIQGZYFjaK0BUElvJKft3AFScHj5YJPAVtX6KRwGfwTY2KaF3kMgK7tGLp+s8BL3dJa5SNqbN/3+Biod02++IpbN+9Ku12z3ttiqL7ct+peWFvFKgvWlLnOSzH0Pf0wSF+u0vjFoxQolOR4tSG8UGFF7bR4H7fncZ5HqqIkZku42LRFCdZKkXJljl8WUogoLbik1Ra8Olbm+fmyUf5+Ye22Dvny72W+NAQBaekXDo2+Fzt7WTipHeIZCCbfv9mU1PX1vlY5hWtm9TrXuc51vlKjlMJqw1//9k/w5k//nr/rv//rf+7/5Od+/f9heUGnv84P/3yll1WpbsgHjRC2pdV7gYOs4J0V5DNNE133QFE4Xr9+zeF4R0qSfauqir7vBM5UVzR1hXWWd+8+5+n0hLOW3W7H09MTj49PnNsOlGa/O2w3+nUT6U5npmlkWWQJHaeJvusIMW4H0lXFE8VF4EnLLDAlWVIVMSliitJ3mcmubdsCkWKa6MeRy/nyDKZZZsjWuLIsuX11y8cPC0d9xDrL+VwyTUMmfZaZfptIc8SHjrqu0dqwPxzQ1vL+w3sAXO5erSqz1cJITYksPd6zZeUKKwr0NLW0bcuYc7uQlcD8dVOKm13z0ooldj2Irlm8eZ45nc4Mw5CVM3leU0wYLcuW02ITXJaF0+m0KTrarEvXyDTNXE4XxmGmLKuski8sy7OFkgTTODK4rNBn6vKyLNkubrbLEKXJikzB8Xhk6J8JrCElWXKdoaksTVNxd3cDKQpp2E+kBbyxxNwNOY6ewjlSTLSjqNrrIrL4gNaGpAQupI3FGMXdqzv2+z39MLIsUrOyWiV17uSVx3+SyhiV8uNfbIfzui5x1hJD3IA1xpit9mhd0lcrr6jxjmHocy9tIQCwNa+qpAtXoWRBUVoU8WVGIYpdfgOQolCJz+cLxlqqqqYfJpTqKUtZ6iWbCCkphmHOlziJpm4wRuNckZ/fiXEYUUqjs/VzHqdtOYwx0DTS+TqGhTBb2uXC+fGE1Y7Sllgs8zRR2pIfuf2MH/uJb/D11295+/o1N/sDTVkyLxNP7YnL3PEwnPn89JGH/kwqDPWhIWlNvwiFPGlFVIG0BPBSCbMsC8EHrDZobbf3u/deunBfQI5SErv0CgdaH2cQa+7akbou6yHTvFeb7MtlVii9Kn9GuWzhDRsoLMZIWRQbnG3xc7bx54uI/O94758XZhRz/p6k21YRQtwypdM0UVUVMUSmMG0W4mVevgSGkrzo8+tPLkeqzcI75/7XFKUveK2bAelRXu3b64XcMs9iWy+cOBy0loodpbeOWp0vM4ZxFAqwNvgg+eB5nn7L/8y6znWuc53r/P+bX3zzk/yL5hv8W//rn0FVOzZ8/N9hvvWH/iTneeA/+b1/nv/we3/xS9yE6/zwzld6WQ0+5GVCb0qCUrAsUnC/Km/7/R5jNFVV0PctbXtBa8WbN2826+l+v6Nuyi9lm7xf+PjxCaUUbz/5hHq34zd+8zcZx5Gm2VFXjZA/jdkUj5DVj8vlwjhNW5m9fG+iaq4HsiV4Yqa/+lxZY62VBFmS/sxds+Pm5kbU3mxZFmusHDaXxaPUiPeBum6Y5olAYFom3r3/gphBLdpYEoZ58TydWvq+ZfELSiuMtXzySUGMC+dLKwtP1eBj2PKj60FVa517HJVYJpeZXVOyzDmHpjXD0HO5XJCnRNO2LSk92xCdc3nRGEiIUrPb7WiahuPxSEqJy+XCskg9ypo5tlYqRIxRmxoIcXueV4UmpUTXSSVO3/c8PZ4JAQ6HxE15SwpyMI1RlqEYItYa5lFAQsY+5/zWSwXJP890bUsIEbSmrEtCjIyzPP5+HjnsdxwPO17dHahKy7KMTMOQrbUL1hUUVS2WXj8xBk/hirxIpk1tUkpyd2jyX+cDeQbQzPPMOPSk6FEpscwzSwg4W+QLldMzbMcgXyMvF2uGb11qnJO8JQioyVqHzX8oDDm3aq0DFPv9cbOWrhcJMUaIURTMGFmLaMR+nsnF6x8YWd0rrENZR900aFewhMAcAnGacyXNmGnHKl9IJap6lxs3E8ucic4orHEE7+m6i7w3xlkUQq2xVvPw8SMKxe3NrTxP40IRHWYxNJT86Kc/ys/9zM/x7Z/8aX7sa9/kk+aOMmmUj6RlIS4Li5+Z/MwQRz70T3x+eeDjcOZpuPAwnnkYW0pVkLRiVoGiLAjWM/eBFDIEKAa5ePLPIKCXVS0xRIIOG0BIa73BreSz4bneZVVU18+99a+foUomP/8pL5/F9jmp4rr4xQ1ktcYPhAjsUGjGYYUgyWfW+tqMUaB0z4Avn8FLCaUMzpUYI4qnRB2kakfeoyZ/5gqVPKW4RQBeqp1prRHaQFsIkRghXSslF2krXKksClxdUeW+6xgjS0gkpVFGQ4IYFmK2VLuiRBuTKepRKPFz+L7++XSd61znOtf5e59/4u3P8K/zKf/Kf/8vYb7x7b+n/1YZC/WBP/uX/gO+9fPwP7qB//o3f+W36Du9zldlvtLLKrAduOTw9nzYeJnVWu27Hz9+oKoalsVzOp1pmh273THqjdEAACAASURBVAFItG3PvEzMWVky2lBWBdM0c3t7S1FUkjm9tNze3nFze0fhqu3w3/c9Qz8yTxPGWlkgXljP1sPjdojUipR7S4vCQYo0TUNVVTw+xu3guC5wdV3lHJ4mRJgX6URdFo9zJdM8SXekUnzv3TvGaeKLzz9nmichxBa7nE8TJWaaB1lWVeL27o6yOnM6nTidTjhbUDfSL+r9sll0Y0xYozZi8Wo3vFwE8DT0LdaKRXS3O1BVNdM446yoYMPQbwdzUWMtxjqcKzYq5zRNOX/49GU150X+zznL8bgnxpRpvdL3WhQlzlnOl0tWFXWuMppBGcZpxvY9MaRN9UsxoXTKNsXAq1d3m3XYWlnS1u8pxkQ/dAQfKaqKlJ77IKdppKkL3rx5xd3tkdJpjIqoqFGlY/ELwSei98zTyDJ7vJ9xRqHLMsNspDNWaK2iiKHyepbygp0il/OZaZxEZVJKsoDRExbPMs156ZWlriwrQhLa9TJ7ElKNMk0jPnhiCFRVKXZ4JcTp0jgUq103otTaW1ls6us8z9vi9GzxXCFniBUYUbZWXOwzoVbUrt3hZrOuyq8F+kHUuXmZCF6szVJBUmGMxvsZ1nx6UhkUtsjzPIxiuV/89nv6RR6L/e5I4yqUV4wenNf8jq9/iz/6+/8wv/tnf5FvHO9xIaGWRDovqHlBhYiOEeMTVllKNI1yFNaw21V82txzGXu+133g186fc1p6JhOYXaKdRaVclkVoU+r5dlellKtxMkyIfGmSco8psniuC6Rzzxbt9edalmVTUv92YND6mbTWuazvofWiI6UkDoTsZJgzSK1pGsqiwGhNyDVUJSXNrqEsi63Xep5mliXKpQ4RrfQGPAK2Rfp8PufPhj4TtSt2ux23NzcYLRcZKzdgA1Hl7z+hUPr5QjKGbGt2z5nVVY3dZUhTypVU62fu7f5AUvIZNi8LIVcdCchN5fdcIilI3ufs7XWuc53rXOcfxDhj+c7v+zF2/+y3cf/8n/r7+r2UsfwL/9d/xB//lf+O+G8n/pvf/Evfp+/yOl/F+Uovq8fjcct+ygFJbYcVgLquef369ZYz9T4yzx6tHdO08Ph44vb2Lh8MHxmGIWdbRe28u73BFZbbW83pdObDwwd8iDQ7sUH64GkvLV3XMeaDMuRDfowUWQ2RpbPGWpcP4jMq6EwdlmyqgECgriv6vtyUj2Hoc4G9EFS1lkW1z+TeeV4IOYuagJArYB4fH1m85+l0IoaApkNri7U6H2YX2YU0zMvC+/cPPDw+oJVimjzntkUBrhDb7G63E/hJKUqpgKJ87ij1GGMxGo7HPXd3d9ze3uF9IIYzNze3hPDAPC8ZAJPQNqtmS6Ao1szavC3Aa1XKSpEFMoQmEILZLKByCJZlb1mynTB3jq6WxBAjKM2QbcHrgV0APBqjNNZpnHG8vn9NCJ5lnokhbhTSNdu82szjCDH2TKPUHFmr+NrXPkWpSN9diIWmLguOxz0gmdfT6cLT6cTYL+z2B5rqiMvLfYyRmNVxY+Tne//hIyAH9GEctuXkHM7SoVsUoNYuVZMzy2IvFYuoPG5amU2BAyUZXSvk5S4rmDBsJNdhXFXnl1UoEWvTtkCt2cIVgpVC2JaBw3FPXVdCi84woNWxsOYLq6pitz+gtM4Wby/vu0zCTklgPBq9KYMxBqxzEKOQe31kGifOpxPTMDJPs1w2hYjTRrK/KmGUQUdFaSt0UFht+X2/7/fyz/3Sn+Brb36U+PHC8usnbDKM/QD5ZyEmFKAyRdw4g3GWg9njjGNMM8diR2Mqdvs9v3H+gu88vuMyXrj0Z5RF1FQfWHuIg49oqzMdG6nIypcwRpnsqpBlcnUhGKM34NKa0V7dGsaYbYF9hiXF7detsdt75WXNy8uKmbIsqetavg8UzhiiIlfeSI5WFsaBeV4EGLf4TfkurKOs3HYpB+JoeHx8pO976qrm088+5fb2VhTUsiQss4Czgt1eG2VZ0jQN1lrm3Ie9fr7EEPNjYbafWd4bbqsaKzJcarfbUe8arK3ohoH5Iu+bmARWlgDvly0vvH7elNXzZ811rnOd61znt2eMNtS24Lv/zs9R/un/9Pv6e9vf88v8l//tj/LXfvk9f+XDr35ff+/rfHXmK72sVpVQciXzJB2XogCJvWy3a1AqcTqd+PjxgfO5ZRzGrMQWLLPn4eMTr+5fESNcLl3OxkWMsTydL9y9umEJgYenE30/UriKy6UVJXaaOef6nCUftOuylGU1rd2FCus0x5sDTdNwaS+cTidi8Fmx0lRVyd3tDblbQ6yZ1uIK+yJ7OWZlaWGYZqZpERWsqqjKmpgSixcqbVlXuKJAzxM+yCKgk0alhDcaZRKo517E4BMxLBjtRGVIolimII/D0E9M47JdBFwulwwdkpycZMYWnJV6l76fGIcvUEpvC2pdNwDb0iSE32VbilY796q8rofRdXndqKoZhiV1LZK99H7J9lW7KThaa4w23BxvWUJkmjz9MBLjgsqWZOcKSuewVtNUJdbA2A8UheNwOPLw8JF5GLO6J5bjlETpGccJn5XHqqy5ubmTJXfxTKNHNSU3x4a7W1HuU+51HaeJMAwcD3vu7+8geemwTBB9kNxlzjgqlUThmkW5jMSccX5W3iX3rDHOUhQVu53icmkZ+hFjLd4vKO3EDil9JMj/RXk8kiIhmUPJCZ8IIWKtQK6kk1c9L6WZOl3X0lF5uVwEjJWJxsYIgEssp+FLqp4xZquHapodkcTsPd3QbtlzUbwSIXmMyzAqAuMkBOGUbfbRB4ZM314Vx+gDJimMNpAi1ji0NZItr3cs3cLXbj7jn/ojf5g/8Uf+OPFpYv61D3CasGNg6XsKrenjiFeibCsltSj4SBhGEmAKgysdB7enNh6VYEoTU3ngC/857Ycngl6wtcNojUcql1wlFUCRAMpsy1dKkaIo86XZkHP0CmOKTT1U+UJrXeZXtXvNaz7X0MisKmXw4dmK/aLyZc0v60w411pIyAJgyl3DUX70tm95eHjg/fsPXC5tztmKdX+/P1BnxXS1GX/48IG+GzHa8fbtp9y/us/E7ueMqkrSgW21pj4cuHv1itu7G6xx9H3Puy8+p20vDDmPXDcVLquvzyAqydHvKqkUWh+P3W4HSvHu83c8Pp0Yx3H7tfVxmed5U5TXz5ebm9vv9x9R17nOda5znb/DNEXF//zqd/I7//c/Lxbe34Kx3/p5frn4Jn+F67L6wzpf6WX1ueYlYTO9d78/UNd1tkEq3r17T993eblIWPeCjgk8PD6hjMFaQ900+dY95F/VSOZSlthpksWsbftNDRIisRc7aQY8WWux+VDlrKZwBqMVu6ZC64QiEmLAakddl7x+fc/9/T3n85m+69nVJU1VcLw5YlaroAKIzLPCx8jxeMPbt28Z+pG+70XB8oVksxbPGHyum1CAoqwKwpLtkzFR1SVaiRUxeE9RirWx7TpUAqsUcxKLZbss0i+ZF03vA0sG62ht8mPmUVgeHs/0/bL1bxpjOByOlGWB0pqYlaR5WfBBgEAxyPcrWTmVlepB6nWyKkUmlAYfqCuHc1KCuT+IHZh8gNXGUNbllgVUqNwXOkMSqi55OTRGUVWOsihQJLHFjgPWWQon38s4TrgyQ4zQOGcwZiZ4T+UMtnbcHQ9SPxMDiw8Ev3Bz+5r7Vzcv8qCKYZyFpmoKpskz9hMpiRV5mpZsr/QsfpFFM8hiHGJi8XLxoYxi8T73uUr1RlmUmYAtqtYK23GFRWnDOM+AFmJwhs2sVSRNnYE2YWEYRZ3UxqAyQTWltIFp1mVzzT+utURN01AVBcEvrB2ZMcYMATLEGDCmyYpqvan0PkZSzO6A3BEsap/Yoa2xBB8FRoSHkJfGGFj8wjBMTNOMXyJhTjhbyvIdJ8pK88mnt9TVke5xptE7/tGf/nn+6B/4Jb79zW8TfuOR8WOLGgKxn1A+QYxMPrLoiFdJPgNU/qyIKb8HFX5Z5DlyRmzvuuQu7hjixNdvP6NPC190H5iiqPpWa0xZbDU5IQhB2GiFtVW2tlqJEgw9KiWMlQur3V4Abl3X0XUXlFZoLbUvRruNVB1yrQ+s9lhRdaWbWEBuMQZxDETpfb69ueF4cwPAMi8oJbTqaZlzN6/krC9ty9PTI6dzyzgMaGO4Pey4f/2a25sbqrIkZXVynkZi8AIgOxw43uy5Od6itWRGlQKjFEobSicq6+H2BmMM77/4QNf3hCgVP+Mkr31r1EYv3n4eTb7ocygl+WjnpCqp7zoen068f3ikH0YhCVuXIUvrh39Ws7N6LGrz3x3ecZ3rXOc61/n+zH/82R/kj7lHfvJX/rPf8q/17/3Ff5c/99P/CyFe2QQ/jPOVXlbHacwHZlGuds1e+h5DEhroNDHNcqBdFg9aY3PmSW7TZcm6tC2Hw4GqboiZxOm9BxW5ZJuv98/1B1ppfEjEbCeV5UdUUVFlxZpa1yV3d7fc3B6QtSliVOLV7Y1YN52TjKTRlM6wq0v8NFOVYnt88+Y11hq6rtsO8VVVYQuHK0puj0eaqkYR6S4XdIKirEhKiSK3iGpX1w1vX7+hvbS0lxbnLPtGlDEtkMycM92zLDPTNPPu3ffQ/rnLUGm92RKLQuqBZi9LrEJUSq0N8+RZ5o6YwnO+znv2+/1mexZlRqOTwRq99dimGJinUdSWugaE/LsCflZVxrq0AVqquqbrOowVqnJZlOwOe+q6lixx16FINLXjeHjN4XDEL56+H+j7HqsVWiWM1aQk+dTHp6cNCKSUIviIdQ6FqJJlWVAXjvu7I01dcLPf4YOnqne0fYtSkcO+ISbPPEfqXc37Lz7y4cNHrC2oG0ffjpTW4Ax5KQ2kpMTevcy40tF2Hc46XLHWFgWmac422cTiA7t9weFwkzPBYhXuhx7rNLe3e8Zxph8XliXgbInV0mtpreVY7InRb9bRmCI2Q2tiSmIrz6pTXVUcDoeN4ro+j6tiNS8LKiWUSvgloY2iKEpSstR1la2aAWPcltuex4n20kr3sdHb7yn1KI4UZElc+1UJCe0j4zTSjwPj4uUyIyRUlHxsVZY4Y/jpb/84v+t3/QzjY+Dh13v+kZ/6ef7gP/5Pcqv38N0T/qnHDDNpCQQ/M/tAIBG1IilFJHdy5hx8kn8ir+kkCKkYAklZTNLc6VtGEznZkde7O4a5xZFIRuF2FZQObSzOlThMpvrGTY0UQrXN+WHPbrfneDxSVRXzvJBiQGt5XLVWNM2Osqho257zWfqCV1uvUJTX5TXlx+e5fskVJbu64fb2Nv/+swC/5plx8lnVNfK+DF7ynjHhfaCqG5qm4e7VHWVVkpR8RoyjVEYt00ThLMfDPqvoDbtdkym88hhKZl8uVuZ55uPHB/q+FxAb8pnR9wMhK8hkUJefPYq05Ze1RiqtqmqrG5vnmfPpxNPTiXPbE3ledFXuOVaK7XJxzUqTJN97netc5zrX+a2f//ztH+Rf+x/+JObT3/Hb8vVUfeD//rF/mJ/51b/62/L1rvPbO1/pZVUhnZOrqjDPE32vt+oDsebmPFwIqJzBe5mtijEwjkOG2zRbRnLNnY7Z/vpMGxblSCE5svXAs/5aWRTs93tubo7Udcl+32zZPql1mZkmgf98+umn7Hd7zqcTaQd11TBVku+rswK1KoRio9RS21EWXNqWd+9+E7L61+waDocDKSqmxTPkzBspodFYrWnqiq47U5QlZVVwc3OkaWqMkZ+pKAqK4i5XZ8y8f/9hW+qtMYTFE5ZIWbnNGrr2ddZVmRcLyebZDH0JITCPIyfvRfkwmuClYkIrWQLWxX7tVyxcSd3UOaP5rFj33YCxirqW5ygl+XrEDEgKnr2WPGFYPMF75mkWmFWUnN7NzTETc09UVS2Pq9bSERmWLRNYVWI7TCnRtRequqbeNSjEoumUZA6PhwP3tzcUdcE4CaQlRbFQF1byhtM48qu/+qt0/cSrV29hDnl5E5+lVHbUeB8p3Eyza7aDetM03N6+ou0GvvjiA7ImyT93TjLQHz58gdKKoetZvEcr6UTVJNrzE/O0QNIkkyAFNBFrRJke/cISA0YrmqomRFG9jVH5IkNRuIq6rlFabyr6M3Qq0nUdGqjKSi4kEpS2QCm92VhTCmhtc1erAJ/mcSKESFmUpBQYxwmlRa1cloWwSN1N8AG/5F5On2nB7UicoyhqWmGdwhYaZxRvXr3hZ/6hn2X8OFGMJf/qH/tn+Imf/lnUh5b58Yw/TYRuQsckUKro8UoRcp4xSnJUXmMk8cKqlBlJz3+toiZ6jzMGoy21q7nlyFs3YhpFahRuV/A4tEwxgjbs6obSOtrLma5rN1iV97mLNKvZdV3RNDXDILlN5xz7o4DiQpDHYxynzV2y5rBXq3wIz0Cn9bNrtUvLIioW7qenp3wBVeTPUk0ibvnlNW/sfeB4POKc43A4UO+a7dLv6fKUa7cSaIPO1ToCjKvFop0Vept7WmMSB8X5fGacRnEK2HyxET1xheUpxTiMspAvC2VRbH3T8suGoqiwEfzsOZ9bnh6emGYBU61L6gqoivlx0dmxIqqq5LqN/kr/cXed61znOj/w84+9+Sn+wn/1L6O//lPo+2/8tn1dZSw/8md/F/yb12X1h3G+0n96v3p1l6FGCu+lJ7DtWqyR3KrWimWRQ09ROPpBlNiiKLYD27rYjqPYIquqyra63OuZghy0UBtsZLWRrhUKcuBZuyalbzLGQNe1skAPoowKvEkUppubG6yx9INk8dpLh3MWpTR3d3eybCidFYc+H65chv+I8pu8WJ2LomS3K7DW0ncjp9MTCcXNzS2Hw5EQIo+PD1inqeuS43HHbldRVY6itJtqp3Ti1f0dbduhNRwPN9vhFqV4enxiGMZNYS4KUdhMPgz6sACRorDElDBao40GIsHPQEDrIvdkyiKXkqeqHPv9nsPhiFJgbZEhMyZ31kp+s6wcqIA1lqIsuLu9paxKLpcz3/nOd6jKkv1+L8vUONL1HdM4ZUVYFqXLpcVokynKYktNMTL2PSGY3C8qS5qxVrK/WU1UShGDdGXWVYExlpubG+7fvKEoHF98eI9S8PHhgR/75jfz4d/z7nvv6LuOTz75jFf3b/n83XvavGTs7+R1EJMijRP1bk9ZFixh4Xi84fXre7S2PDw8UjibyceK4/GGcRwySGvizevX3BwPUrfTVKQQmKeJXS3f/zDM6OgxzqGNETqtSjhncVjYlK5caWJWt0DMSqhiniecKxjyQr94T4pRMq5aE0NAKTgc99siLTneJWdZDzjnmOeFYRgZ+4Eqg8qGccZog8m5Y+ccUUex+XrPMIwMy8Qwj/hxQc0RFzSlNZSFQelAWTlKW1GpHX/1f/tr/OLv/EV++Q//Ep/sXjN/55H+aSB0AbUkVJSu0BACHogKIuJISCnmBQex1ZNeuEflrxMJFWOuplJo5DLmpmxYmjuccpxix8f2xG+8e0dRlxyPt5yeTgxdT9+1TFO/XVSs3arSh1oyTRPf+c53UEqz3x8wxnDK+ctEonDVBiCSbPzarWqyXVlDpgqvnbnD0GcFd4GUcNZSVRVFWeAXzzxPAnzKi3rIefk1p7zmZNcLvWEQd0IMEZXAaI2xmpASfT/I824tZeGEfIzCWIM1Fu9j7ideqKqK/X6PttIjvHb3LsuCX0TptUahESryNC1btVHT7PA+0IaeZfacLxeGadkgTCGJA2Ydky9PQkiZCpxJykpqcq5znetc5zrf//nZ+x/nd5ef8l/8hT+Dvnn7D/rbuc4P2Xyll9W7u1uGoSeEuOVJNzvkIpZV52TRaJod0+KzpddvqtAKqYG42SjXzN96aFoP61lAfV5S7fPDs2ZBYe1BnVAKWZ7CvCkUh8OB/W7P7c0rjHFM48QwDLRtlxc4x83tLdPseXz6gsfHR9r2grGGOvcIulJIrlGBs1byfdbR9wPv3r3j6XzhcDzS7A5orRmGka49Y13B/f0db96+piyL3DXq6bqBruv47LPPaJqK7373u0zjhEqKXSOW2qQUy+wlX+gFGHM87Ddqb4oeY9SmWnu/YJ3Oh3CfgU5CP729veXt27c5q6cpSoH5GGOFkOoDDw+P22M2TVPulnRC5J1nDrsdhXXsdnv6fqCua+q6Zt/s+OKLL2jbVmp4nOT7bu9ecTzc5CoN6Ym1Nmf/lFxAVFVNrWrSPpLIiwhQ5+XJe48rSurqlqYueHV3lEUj20MPhwPvv3ifv68m2yMl03x3d8e3vvUtmt2RYZho2zYrXZYQFV0npFUfPJe2IxF4+/Y1Xdfx7t0X29K2+IhxjrbtaNsLyzLLMpASzkpfcJHBXDSizj2eOlQ6o5WhrksUiqKQ90ZSkcUHxnEmJCgKNuUqBC9LjB6y0iok6PXSZbW+pwi6YKsR2e12zPP0oh6l3JS7vh8Yh4mu7TcbvfeSR9VGo5Lg643SeB1YUIzjyOVyZgmRwS/okCiVwpmEVgGNoapqClvgUkETd/zS7/+n+QO/5w/RBMXymx8ZnwaUV+gFwhIJiyeqRACi0iSlCUpisZDQpFwLJLNqrepv+2cqKVQ2w+uoUCESw0I7X/gb7/8WXwwPPI0dh/0+21hlSZbsKBgjlUArHGwYxq33c1kCxgjQrG1blmXh5vaWw34PsH1WAVt9U0ohA5p07saVEcu2WHsl5yxf0xVS+TXNQ+5PZrMTS79xhkM5y9r9671n6NoNimaMEfhTjIy9KO5Gre/rYlNkYwyEeYaUMEre+ysgarUuT9PCsszEEDBKU+/33JkD0+zpun6zwa+PFyjmxWdnjcf7hDKGsL42888C4sRZ1eW1LsvmTuXtSb3Oda5znet8X+cX3vwE/9Of+h0U/8a//w/6W7nOD+l8pZfVpq65vT0yjjNGa6qyIu73XC4Xur5DK0Wza4Q+upPOvWGQxaxt2xcLaT6KKlh8IGYLsM39f8/LatrU1bWvTynNsswCx8mZvRg9ShnqpuKTT95QlIXkZ6eJwgkM53g8EmPi4eMjfd+yLFL7UdUNwzDSDyNte9lsfraw7A97DvvAQe+FwFpVAtexFmU02lhsUdLsEs6VXM4X5nlhmie0iht5uK5rylKqQC6XC+fzaYP4fHx45Hvfe0dMCFm4KNFKg4K6qvD7PV17IYSFGEVhkZyhxlmD1mLjVarYaLTeCYipcA6tNZ998pY3b99ijN6UG5UQNXDxArPKi47VlmSlG1Ql0CjmkHJv7kfev39PiAFnHJWrCEvYLiystdze3nL36g5XFHTDkKEtAlea54XT6UKKEa0V/x977xqr67redf3uw3N6T2OMeVprr+4DPU5aaji0FIRA2u4WapGmhZYGUojBUCngBxM1MQhRSQyiMSaKgoJ+0KjRRBMS/IYWJVgCVFtLy9ztbvdhrTUP4/Qen9N98sN1P8+YC9y0xe7O1d1xJTt7rjHmGPMZ7/u877j/1/+03qxZLZfUZYn3A9pKeFRd1ywWC4ZhkIVBWeCdMFz7/Q6rxb94dX3FdntL09ScTm1elJSkCKvlhuVyzTiMtMcDwyDgYHTCKo6DQ2sjNSUpYiy8ePECrUXmPfQSlmXLGpc7TQU4qCzJHgihmqXWtiwwdvL1RYxWDL3DkKibmqIspDLJWFI/AJFFvaAsG+qyoOtb+q7LqdQFwY+cTgNdlu5qJUysUqIoWCwWrNcr1us1ZSFLmMKWWfKZ8D6yvd1xOB5xo4CL0hZEH2dPqh8GirKgC/Lai1Gx3+7YXt8SQ6CsGkBjbKJIEeVHSTEOmjIsqYozvu7jv5bf9+3fw9d//GvR2wF/s4PeYQYYR08MmQm1Fh9GYVQ1pCn9VyusKmRZke7qfqZJSd4H5D8kvEycmEkAd1TEztGdTtze3rL3J0xd4H1kvz2ItFwLaynfT7yg8j4iy42pe7YoCsbB4cNIVdY8fvIWdV3T931mVKV+6fW6LhTovBhSeTkkFS0hA9VaUnFR+b/LrBYRFt05CWWalhFFMS2STAaSsjyKr11/DHdfM9XBNE2DLUrx0KPoB4f30peaSFQ2oaNGK43VZk7DjsGjUTTLhjIDyhg847jDuUklIeC5rEpsKVL96APD2OPDnf9ZkpBFxq1yF3eZQfoHl5BTD/A9s3o/93M/9/NLOW+tLvjf/u1voPjuP/mmL+V+voTnQw1W265lv5NKGa0NCjlsnJ1t2GzW+OBmb9LxuGd0EZPrC5qmyUxPy+h6jDFUlUhAu67DAstmKSmwIRDTVBkhAruyKLJv0EpdydDj8oZ/8moqpdicbXj8+DFt1/H8/fc5HbvZXzaMjqHvOB72uS7EkpBwkePxRNd1aC1hTcv1irPzDdYaqrqmLO0cdlMWFd4nTu2teHNReOfZ7/bSD2o0Ibm5W9GNLodANbz33vvc3Gx58PAh3gdubrY5QRPO1iuUNhyPJ0KUn62uKsahw7XjzA7GEHj06AHWGMrS8vDBQ6q6yoFAw3ygnuSE5+dn7HfbzPYIW9p3Any6riX4SGnlgCzdiNmD7Dw+JxqHKN/r5cuXImXMVRze+Tvva1myWW9Yrlbc3N5wPLZUVU0IuU+yMNxcC0tYFAXL1TJLgDWr1YZ6UdHnfkogX8OIGyWxdb9tWa8W0mf7/Dnvvvt5tFFUVZlrXhqsgYTCmILLy0uev/+CV68uceNI7xzHrse7hDGWkO/XpqkZB0nWPTtbczq12MKyWZ9xbHuUMlitZva6aeqc/Orpu5ZQiB/YGA0Jzs9WQn+GSEoK53qCH6jqEqsKwtjj3UBTL7BWSUpyVWBySmrM96pClgVl06CVmROfY4xszjYiy64q3Dhmqa+wxXVd5dCcVh57pWiqGmsMQ99nqbYwnd2pBaVYrVb03cj15SXBBeqylv7YqDAGrFUkpaUCRtdsygd882/8Fr73O/8AT4pz0vs3+N2R2A8MQ08ICpSRoKAoyyj0KJim2QAAIABJREFUJP+VDk6lNNZYqrIkZM97Imaomn2XEwU3M3EZsKaI0onCFpAUbnTyOSMJwP3YUyiNt4akk6T65vqo0+lE23YYY7i4uMA5l3uhPVoZykJsCzEk9ntZYE3+VLjrWpX3JwUqg/9xyGncorCYmFitFXVZzWyn93LfLZfLXIvk5/evO2msoq4jbdvK0k0hNUwh4N0dSAVmNYPUzGjpJc5S5TixtilBTJR1gTEWN44YTU70FQVAjJExL55OxwPeDRhrePToEWdnZyitGfJ9FqKkefswYijm65LaqQJrpHcX7kLwUoqEoLA25xhY88X6VXU/93M/9/OratbVgs/9wFdR/uAPYT729W/6cu7nS3w+1GB1uZBApK7rpUqkli7PxUJqMrq+pe/FV7U/HDkcO2JKrNdrlBa57sRgyOEPOfinfNARXR2KlNNhEzaDWmstKXpSVKxXCwprOLUdddMQSRyPR6qm5tXVFYfTCaUUN9st19e3pJTYHvbSQWo0wUug0ttvf4TVasPV1VX2skk4U1lXbM7WnJ2d5UOWx/uItYqmWRJT4uXz93n/+XOGfpTwpxDz4VF+hsIUDN3IUDm22z2Lccwdqj0xij91uVzz6tUlu+2RGMANU8epJIVqpVgtl6zX6xn4TvLCB+cXbDYrmpwyPEmoJwngxMY0TUMIgevra5EdaqntWC5XmZ1xpAh1YyBXoWhtOeW0Ue9Fvv385UuePHlCVJrRB7SOeN/x6NEjFn6BQa5PKyOJuG2fZbkdXSveyynhuSjEM1sUBV3fkUicP7xgt9uy3d4w9TqulwtOh4GuPzF0LetFQ/SO4/HAOA4kIm+99U72/1q6fsS5lqurGx49esLV5S1XV9doLb7k3jna3qF1lHCqLFd0zs1dmiaDncePn1AUJW3v6McRm7sxtZYFy8WDC0prGMYR7xUpeLRRAgT8iRigroXVnx/HzhPbyPF0IkTF8XjgeDgBCWPBmkRKiqpuWKMYvadre0JMDM5R5muoyprVcolzjtvbW7bbLV3XCdtd1YBhGEbAUGVZflEUdMcT7bFjHHvQKte5yM+somLsOgwKY0t8lkFrpVit15SlJfmENSUfe/Axvv/3/EG+5Ru/BXYO9/4lYXeCoRfPrPe4qEgqZn9qImqReqMz7kyJoihZLJagNKNWkg7ss4dTJaasJTKbDBNmFWWGURBIjMnTBUfIf5WQ0CkznihMVVIWItGVoCrPcjmFJ0VORwH5SimUVfOyZ7vbAeIVn7pRBaiC9w7nh/l1VlXVvMSZuovbtpXk3/znsiwBAcyLxQJrZfFmraR2T2zkxER23UBdC7N/OB2l6mgcGfoeco/wpDqZEp9fn6kj1VpLcAJsJwBdVxWLpqKuK0lKJsqSTUtPbFVVbM6lj/XJk7dICV68fMnhsJvDykIQjtv7ca4aKoyhzEFpweXnRGuaRZMXV4EYw1xfcz/3cz/3cz///+ad9UN+7Jsf0Py5v/imL+V+fpXMhxqsrteb7JFbC6iKak751VqDklARCehRs2TtsN9jrCKGSN1Igmnwjj56SmsxWgnjEhxlUaKLghhV7ovUKJ3QBqqyoMlVDkpJn2ffddQ5xfdwODEM48ws7feSAGpy8XEIEWNKmqbm0aNHlGXJbrcTpmqzQWnxo0p6b0XwkcNhz+h61psVi+WS0Xl2ux0vXrxke7vF2lK8ahlgpZjQKOkiRdG1Hd6HXAUjHq+yqOkHz+WrGw77E103QoK+69FamCOpbKkIMaKjZrmWMB9iYrFoePz4EXVTSZ3QOM5BLFVV0bYtp7aTQ6yVFNKYwBYVPsDtzY7Dvs2SUUmA9T5hrclBSwO73Y5xdMQYJCW17bi8vMQ5CVtxmcG7ud2yP+wh1630fY+2Iq91bnrMDTEnCNdNzWq5mgG2PC8SjrXbbdnt9nM4U1VVxBAYx47CGpTW9MNI6gNlaXjrrccslwuGYeR46tC64HhsGUbPdrfj4uIB5xcXaK159OgRzgfavme/bznsT9LXm0ONtC44P7sgxsj5xQOaZsH+cCRm5leWAcy+OxIMgyOlgDWyoDBJ5+9lwGYZ7Dji3Ih3I5BAQ1lYYlT0fUuMmuWyZrVcUFjNOHpsWYqmtAO7WuJipBhdZsWFOXv16tWcpCwLgJKyqEgJed5CwpqCRdOgtZY+2WFEK01V1UASdjL36bp+JPlIXZQEHzMTF6Vnth8w3rLQa37r138T3/PPfS+/7u2vxD/fM17t0KNnOHWQIhHpTPVa2LxAImkFFpSWqCSxEDTUVYMxIr0tjEWV0t85dTmrHLylcsCaoFdIGjAKrwOjSRziwLY/4XJ9U1FYbFICWLVU7AzDwOgc4zDkQKkyp/uK8qKu6xxklP/9LMmt62oGoPI+p2Y/fQieRbOgzt528YNKEnY/9HmR5efqI++nLmRLjMLa9v0wS2RNBnkxxlktMtkZ7lhaTVPX80InZ1LNvudhSK+xrHLNIQZ57JXcVsZKMBNKvkYyASQISWlRgKzP1mzO14Biu70lRhj6YQ6l8t5jtHhRJy+0wuTU9gAK8admUBq8Yxju0qqFGXa/HL+27ud+7ud+vmRnVTb8+Lc9ZPUX/+qbvpQPTBo7/oc/8/xNX8b9fJHmQw1Wb25v8qHdUJYVWpl5O75YLHhQXlDXApiG3mHtjtPpJF2dWmPLis1mg891GNLzKGynbSoJCSkLmqbGWvE3Tem2xtjMagh7UFUl6/WGU9vRth3euxz6QZYX35XQCxPiqaqKt956wrJpiDHSdieOh5ayrDg/P8dH8UsWhcjaTn0rQExFmsWS06nl+vqW6+trjofTHNbSNFIXYY1l7IecsKulxgVF3QigOhwOoOR6ri5vGEfpo3U+CABSzDUaE5vqvVSTGGOxhUGlxDCM3N7uKFrLdnfL2dkZb731Flpr+mHg8uqa3W7PYrGg6+VwfnZ+wW63pzsc6fuRspTKlL6T9N+6Ps6e4GEYZxa8qgrquubUHjm1p/maxrLEx8B2v6fve6zRuHGkHAYW6yVTMupisUShOZ1aeT6Kgjgxx4ZZTiz3Q6IsqzkIxlpLs15yfr7i+vIV3o/YworCNh+QnR+zZw66vuNwOBECnE4DWh9w3rFcLkTKDdSLGptZtvbUMY4B5waR8CoJp6qqmleXV4DKdT4RtOL8/IzlcsGYPYkpBEL0eK1JMWC0YSwSRSHAJISIG0ZOpxNlYdhs1lR1hQ+RYXDZJ2pYNBWb1SIH4WgBecFRWJEzF5X4EY/HE8+fP6cfnagPkMevaRayMAngnSclcuWIgFRJ9+0I4yjgJlfDTIsmNzjaw1FYe20YxwHXD6JySIboE02z4A9+1x/iOz/5XTyIFePnr4j7IwwDw+jxJJJSjCGQVIJcu5OSIuqEMYqqlgonawylqSApxt7Pib8qAVHkqiovCSbANb02Eil//4QziZ1veXG84brb04cRU8rCKHiPD5HQR5JJ+CgAXJK2NW3Xz+FNq/Wa1WqVg94cIbOlpTV0XcfhcMAYkxcoxVx7I1VBAsamOhs3CsB8PTzOGJPlvQrn/J3kOLOiMFV0KXT27UsfLPk9QkBdXQsoTsFL8reWJcqYAaS1RVYHTDLlNHemqgRWywJwHAfImQFalRlUSmiT1hFbSK+z3DuB3W4n3dpRvOyThWJikK01rJYrHj18TAiR3W4/B+slf1fv47M6QBhlxzjeg9X7uZ/7uZ9/2vm7b38jT//lRx/KIKU09vzg1d9805dxP1+k+VCDVfHFSWhRYSEq6aCcZHJNZnGMFhnq+fkDjkcBM845RjfgxkGSUddrjqcDMUbeeusj88a9ris2ZxvOztbUVUkiMY494zgF4hSQtDAi8QalDNfb2/lQDnc9h0VRsFwuZzBkbcPZ2RmF0bPsb7VaUjcL6uWC46GdmY2+7xmGfpbn9d3I6SgH17aX+p6qWWSfWa6JMMJYCFAQyaExmqYsSClSVQWLRcPl1TVdJ4DHh5CZFp8lmYrN+Zr1ei1yaxdpqloYjBjph1HqddqOorT0fcv5+QOUMrx6dcl2u2W32+F9pG0lTOnJkydobSVIqu9QRmGLgpgStixwwcvHhz4/D+BziEzVlBxOx1nOKOCoISWRNp5OJ5FArlaQEqeux6dASB6tNDEKeEpEVqsFU19sykBImCrohh4f5d80WmOU4vb2Fu8EIITg8+MUiDFRVSX1QoKLyqJmdHBz+5yYNNrWDOPI6dW1HMSN5XTsqJuCGBxGT0nJIQdXRbqh59XlFWVZcsqVI02zwBqDT3FO2XXOE2KkLGsG35OSQSnD6APOtaQs465yFVLftYQYWK7PWK1XWKsJPqLRpAjOJVIMjN0Jm5l5EhilKaoCU1SUVcUwjLy6fMluv+X8/EH2WyeqosZaYSfHfqQoSvHYGsM4DPT9kAHNAFF6Ur13md2saE9HTscjwUnQTjeMnI4tVhtWzYaSgq/9xNfxQ3/4T/DPfPU3Yi5PdM9fYtxI6Hq6ocWlSNRWPK1W+pBJopZAy+KmqCyLVSMduyEwOEeKAg6H3s2vO+/9B/pJ/9ERUAdJB5xxPN9f8X57y2Aj2hiaXAtjKgm06t2Iix5TlPgQM/CLHwj3iTFlEHmkyyC2yAnQw9DnGqAlITi6zs91WFVVkZKi2x1nD6kAWTeHJRVFCYi3O4bXuqMzC0lCuoJfA7ViBUj0/Sivwaqi0vX8mAQ3io899/MubJ2fd4Xznhh99qzK12olQWwT8H/9se6IaCe1UUopgg/yPfqeU9dJ2FvSWKMx2bcv1yU+XVMbHlycc362wRrDbrfDkNislxRlOdf9lGVJVTXENNX7WJbL5pf8d9T93M/93M+X+lS25G+c/ya+/kf+HVT54XwfjT/7o2/6Eu7nizgfarD6zjvvSBl9zD2hSlMUIvHsuu4DqZXWWJbncjjfbbeUZcmZWRPiVJ/haOqK9WbFkydPaJqG4+HAMPZYoyisZr1esFgusdZw2B+4vd0xDCPBi2cs+iDBOhFCSLnaQTyIkzfy4uKcqqp58eIFzjn2ux3LRcNiseB0kqoI513uVz2w2++wxs51OCknkXZdn0OaBkKMWTYn1ROTH6y0BU3TcH52hlLC3Bqt2WzWkm7rxb86jCKBi0mkmFlFKkC3qtBGsz/sOeyPgKKp6wzaR46nUwZEcugsSpEzfv7dd3n/vfc5HA4orTC6oMveNumRHNG5qsYWlqqphSWJIbPMrfwsMaJyx23IqaPOBXzwlEXJRz/2Ueq65uryFV3b8uVf8RWklNhtt9R1TbNcoLTIXYe+5/rmGq0MdSVvqFOSqs4eu7IshGnxfg6OIQYUieNhz1WhOT/bsKglCTmlxPnFBRcXG1arghADPgacc3TDSF2vUD7SDSPjGO4qhl6+4iPvPGS5LNG5smS/O8z3rDLSj6S0MMubszMKW9CejrMkc7fb5T87ttstJGb/bULjg8L7yGrdYPO/gVKcX5xjjJb7KRnKoqKq4LA/Mg6O0tQEH1ms1ijniEmhjJUQm1G8vze3W7rjkUcPH3J2ds7heJQQpjRK7coYiF4kyClF2mGk78RDCqAxAoq03GN1KYE6U6VUVZYYFAWaypTUZc2XPfo43/KN38Z3/I5v5yObJ4TPvmDcdejO0Z06okp4NEGD0okQRjQmWwQSpiwoqlKktI1F6YTz0uUp4VmRoR3wo0O/Fi5kjPlHelanyKWcDG4STjtujlve312xix2pttTasCwrkQAbTdJQ+ZHeDYSQ6Ie7SquJ2TRGPMVTf2pViexXUnh7YpRU32EYOB4Ps0KjLMsZWE/fU9j+SS6ss69TFl5Wm9c8r3dpx0rdBTZZa9GvedMnr7G1lqSmxyLhCaRgMVpT5LTdyUNqcz/xxIrWdUVKEedGsSooJeFHWjG6ka7zxLyM0VkKPL2HT9dU14sMzJN8vUaClbRls1pT6oLLl5f44IGI0vIeo0gZ2Hu811grCye0xnlH13e/pL+f7ud+7ud+vtTnT3zZ7+AP9IFv/PF/701fyhcc99f+M77h3/ibhBh+/r98P78i50MNVlMuvB8Hn2Vm3Mk7jUGknxKiE3ygrh1lUVAWBcYYyqokxSBsXFGw2axpmoqqKEghkGKkO53Yu5HudOB42HF+fs5qvaJrO9rc+9d1I0M/0g9SpRGjBPtYW2T2t+cuiqVhsWhYLpdS25LlqV0nXZLtqScdjtiyZL1es73dcfJB0oa9A6XwPlBVo7AaMad2KpEuVqWAqIkNmfxjwpQoSRZeLYgJ9vs9r169xI3j3DUofzdgrCGRCDEIEBkGvPMsmoaub/PBTmV5c/Z+gVSP9NIjejieGEaXAa+dD8W7/YFhlI5GHwIqRQENShgiH+XAarSZD6vKKLSSQ2XMPYtf9rGP8jVPn/LpT3+aw+GEMZonT57Q9z0vnj+fPXWm0BTWEssSTh2nU0vfjTNTZo30T4q3GIah59R2jLnTcURksKfjEWOFiQtnUnOzWCxpmiU+RG5ut1Ir0jmur3ecTiNFuWDoB2IQKWo/DPS91NYo7Xj8+IxFs+b8bEPfOcZRAHJRlSitGb0wqlVZ0/W9yH3zcz55RJXSGcSPkvCaBNzHJOFE1laSlOo9VSGhR1pJinM0itEN8m+7keDFT7herVitlnMH6+C9gOekORyOwjJ7AXXb7Za260jAiCyPrC5IITGO4pFOCVKQWidSwmrDer1Ba1gtV1RlyfbmhsJYdN1gtWWzWHKxPsckwztvvc23/pbfzTd8xW+lGpLIfrdHUudIPoJR9D4QlMHHiNUJJE4HhUEbJa/9spTOUNS88PE+0vcjY+8IPmGmIuV8/wjzmF/C2cPNpGAwmqgdL/av+H/e/Sk+tX+Xy7AlFpFlXWJ8YFFV+AiDd9LtGgPOx3lRIqyjeKmtLWZJeVEUOOfmYKSytBSFvK9NKotpcTGO4xx6dmc5sLkWR83sq8mdwRNbO3399DXT11lbZCn69N5hAGFZh2EAnS0NqJxIbtHKoq0EPukMhsfRMY4j23FH2x4JQaS2tjCsciL7tIRSCZQxJDf1rbr8GKn5uhMa+hHvXqsWilOlmOK4O3BMe1KKaAvaiF9/SlCO+TmVkKnTB4C3NffVNfdzP/dzP7+Q+f53fgt/bnPiy/77P4Z58uVv+nK+4Lj/8T/i9/y7/5BP3b73pi/lfr6I86EGq8PocnIqeC8dhd3QZ2Aq9S6n9sTpdEIrzXK1Zr1cYKsKk2sPhr5nfzhgjMLFgD1I2q8bRkY/cnNzLb5EpbGlx1/fcmh7+q7n9mZHP4ycjkM+BBlQhuVyjbWGq6tXuX7GEYKjLLO8TmtC9MQUqKbwpcOO3eHAOEqCr+pHtLbEBG3fE0OgH4UVKAuRFvoM+IqqRCtJCV0uG9zoGAbP6AecH7nZRc43AgiqqiImuLnd8bnPPWe72xGjyGkViiIDOO89HvAxMYweowGVcGHEnUZiiOJLBKqyxhiNNpqqrDDWEnZ+DoqxOahlOiB3XY/WLh/Sg1TGxJTrf4ShNnOSaE5iVaCyj5OYiN6hSbz/3ru8+7nPMQwDdd3wmc98ViSFIXJqe7p+oCgtb7/9mKZasOeISorgIn4MoCKqLIlhZLWo6E57uq5ldNLT2CwadPaO2rIiRo/zkZvbA9Y2lKXl5eWWcWxJOJSC3e6YvaeRZhjn7t66rBjdgPcRpTyXVzfUlaGqFpRFxcX5Jh+qI2VRooyh3/Xowoh81klwUd+1cr86kXdaa1AY3BgZ+sDJ9KASKd1VhhTWYEwpzHHuxE3Bo5J4ZZ33jK4jKYULAz6tcAFCMkTEL5hQ7HY7Lq9v2R9OGGvp+oBCpPbWasbeUWi5h05dhzYFCXDBywIoRDSIn9QHrDG4fuR4c8tptyeODjc6iqZkUz9goZZ8/Vd+Hb/7d36Sjz74BPrlwHB7ZGxP4HIKbAz4kAgqkZDezJgiYAkoMBKUpLVCK1k8uCB/18dI2464bsBEjcWQtCIZjUqJGCJagdWZXVUQrMKriFkU9NHxqZef5m//9I/wor/mEAd0CYWymKgYgqftJHQKpdDlXaBRDDFbNXMKcvYaq+w57buO4D1VXbFc1JRVOfeZ6pn1tbMvtCyr/PmYWUiDMeLHBkVZCgNblkX2oEZCEv+z4s6jKv5Tizb6Az7dKYBIKUNZCZiNQX6OGCMogzby8RASp1YWcFMNV4weYwzL5RKtoLCFvJfhshok9y0neaC1tsQotuEQE8kFlAmg/LwhV7mwLGkFGMj9quJJDeig5oqvEOR1SH4v0kZJwJORx69u6i/a76r7uZ/7uZ8vlflnn/xa/upf+d3Y3/Dtb/pSvuCkseNf/G1/lp8cX/F/X/3sm76c+/kiz4carB4PR47HI0bLQWR04j0sqxJtDf0gaZEYTcrhNNsghyLxYPW0bcuibkio3AF6Ltv8JEzIIgPPsqzRuuDUdVzfSn/pOHjatud2d5xTZrWWZNbVaklZVDT1khgdxhSEcCedhYS1lsN+z2a9QhstclwXefTwCavVhu1uR9t2uEF8ieITldPcOPRYa1ksapHZGqkFiSEwDBIOpJBgoPOLc5Z1SVWWHI5HLi+veXV5xXa3z/LhyDh6YeNiJIVIvWioF1IFFKPDGssw9ozjIHgxRmIKkFRmqiuK0uZwIel2bZpmTiQ9ndqZ0ZmYlKIo2GwesFwuOBwOHLN3tCgKLFmKmytwZtZHaxTib3v//fdJ773H8XicazguL69eY5ci3gfWZskwOLQKFEVJVYocsB9a6rJks1rgvePJ4wfc3l7jxg43SnXQ2WaD9xJCU9d2ZqjatiPFS46nVtKLg2OxFBBQVivG8URZarqux+UFRN93QKRparRWVKVivdpgreXq8opx9FRVAWpFVTZoY3Iy8QlrDY8ePaQ9HhgH6RUeBvEznp894ObmFmNKQhhp24EYHVVVUtUFXTvAoqa0SvpD3YhRBUZZSFBVC9puS90sCdHjfOLlq1usPbFYLPAhsdseaYeB47GVn8kFqmZF8ImikCVF24q3VKMxyvDwwWMOxyPOC5vqcwKwShFvDf04QojcOI/ve7xzkg5cVMQ+cbo88slPfgu/99u+i7NQMn7mFrftiS4SfZAwsODxKYfz5FCeFBNEjdKapCJFKdVIVVNCktod7z3DONK1A8FFCmWxWpEioGBwIistypqUEMZZSQUNKuALx+Vwyz9499P8nZ/+++z1Aa8DysjCR6OIPhIm1tRabCm+7GEYZzmxye8XU7Jw8MJypuzntXVBVRUoRQadzEsfkcdqylJ6WIdhIISp8og5aGlaFE1efpDly8RMTqF0d+FKGqs1RVHNqowYA5ByerAhpUDXDQzDSAxgbUFZKuKYH9vsmQ1B6qoWiwUpCcC0VnpVj4cjx8MBnSty5B+f1B1TeJ2affpzUBMp9+KKjFgbAdMxRYgRY00OxqtQ+s4TG2PEZ0nx9G9JRvpUq/NL+dvpfu7nfu7nS2++4dFX8Td++M+hz99+05fyBSd89sf5vu/5K/wvL/6vN30p9/PLNB9qsLrb7zkcTlR13ojnQ1xRFESEFXRBgIkwd0669oIcPBeLBY8ePaIoDHXuJQRhEBarhTCWWuO9o67rHKYTM3MQhTFycvhWQN/3lEaSMw+7nTCnRYFzUTokjcYWmvP1Bq012+0t1hhOXTeDt6YuJaE1X7MbRwkFipHlYkVpDW17pLRSlbJar3BBKnVSEq/u6McMnBXOD+JN9CM75zh1Hf0YaDvxt07yw6l2gpTo/UgZLFVZkGKk7wfQMbMhdpYFnm/OWC5XIrM79bi2ox97YkiEEHNvpBxyl7mHs+9Fvjhm6fF+vxPZ7elE27Z31UPTNeVKjCnwKkYBALYo6MeBFOIsZ0wpzunA0wG9qkqqqsweRAlWSgRsoamSfHwcR4xVPHz4EGsVu92WmCIpKbyLeB8JPqKshaRJMeZE4Y5hcNJRWWjxwtY1Tb3E6IK6btjvjiLtJpJC4MGDB6zXS3b7bf45NafjkVeXL1ku1pydnROTIkbYbnd47ygKy9nZmhA84+hYLJYMQ09d14zjmOuQDFVlZ5BgrcgmjRFQGH3EoyBGyqKgsFUGCQWH/YGqXLDerPExoLXlxfNLtrsdZntiGBxd35MAHyIxSTiVUYnVsqSoKk5dR1KSGAsJ5wfCsc/A0ElSsLVUWu7L0loUELzn8nig7SQFu7E1RlV81TtfyQ981x/im37db6bYO8bLW+JpJLiAG0eGXOdCZkwT4PPrc/pYiIFFU7FcNZiqYPCOzvXCXkYY2o7Qe4wyKKvwCbAKHQKLoiAkxegdGIspLNYoonL0aeBnX32Wv/NzP8ZP3XyWAx3WyrLLWguZqZsqpKZrm3z0r4Oz6X6Pr3lp5L6tsie9y8FoUiXz+iilcwVVmkPjpo9PgUVaa4qi4A5o6rws8vn1leZ/W5Y8U/lMko5aF1+T4zLXSc3+4pBm9lXkyMO8YLLWfiCUynt5Lx7HEZtBL0i4mDFGvLsxkLiTJBeFLLvmqptsV5geJwmACq/9fQHki9z3jAqzzDmEgMkLsJASIfcsT+9nPi8D7ud+7ud+7ueD8xsefQU/YH8Nf/y//V0fWqAab96n/dN/hj/9957cA9VfZfOhBqvDOHBqWwkq8SJTLGyBsVK3MB2o+n7IiboSJHRxfs7DRw/ZrFfYwlKXJafTid1+Jx2UObSkKEoeXDwAyF2twiA4J4dilT+ekrCeRWEptIA5nQ+s4ziwXCxYLgUMtKcjdVVjtOYySx5NWeB9YNEsWC3WKKU5HltOxz0xiC+wKAsMiQAs6pJ33n6Lj37sYxgr7NvoBQhCwgWXZXfCcHZ9i64qLq+vCCHio8pMSJ0lucIqBO+z9E9+ruN+i0IRokMpYaXqskRpxWaz4fGjJ5J+fH0jiwJbkIj0Q4sPQbxh3mOLgtVqwfEY6Ds5yLpxJHhf4ToZAAAgAElEQVSPAo5a44Of5YaT39ZYM4cuAbN08fXOxqppJA04h0wppRh6YYOqqsqgzQrrqzVNU1Hagq7r8E5RlBalFWfnG4wxfPzjH+f29paXL6UWabfbzUzt7ANOKYfIiFewKIrMwJcM/UhVSnXLzc0tQz/m0CjPermiaWoOxz3b7S1NY3nx8sUcqnR+fk6zWNJ2Ha9eXXM4HFkul/mg7RlzPUnfDRgrgOeu1sfOYTri0xYmKQSV+1Bt7syNBA9uiHR+4HS6IqF48tbbKF2yamoOx5bRgfOawQWCj9TNkqqqOByPVJV4BMuqZLVeElMitWBsgU2asR847g8MnQRqGWsx1lCVFU1VURUlNoP0m92OGALWlqSgWDZrPvlNv5Pv/fbv5qsefYLx+R6/G4iHgeADXRhJitn7GfN9Mb3mlNFAoihKlssFTVUSU6AdBhwxy5wTrh8IPlLaAlCEGEla5+5VjZdCWwptiASUgaEIvGgv+fH3foqfeO8neXF8xaAHXHKEVArq0hqNWAKmcCIJ2+rnDtoJxP2jNTHTgmUCVtN/T/c9+TU1fR0wqxQmf/brIFVYVOEOp2UZSML5er2cq2a6rpV7MAWx+eZ76O6el4/JPa+IUfpQQ5DPxSi1SNPCaApEev1ajdFoXYrvNCUJxpsSh1UOiguBMIUpMYFcNXuGE/OHsmyZ/Fzf9bgarfPjnn+OkOT9OgRZQEUB5CqBSgqNJqqEUlkVcz/3cz/3cz8fmF9z9hb/+5/6csof/Lfe9KV8wfm53/En+eunR/yrLz4FfOpNX879/DLPzwtWnz59+hD4r4GvBEbgp4F/6dmzZ5dPnz79rcBfBhrgM8APPHv27FX+ui/4uV/olIXI38bsV5XKh/2cjjkM/Sydm4Br3VSsz9asN2tUTqBsj0dubm9yAFFNiCIpfvvtj7BZb7i6cpxOwvq5ID7ZplmgGLIcj/lQWVo5OK1WKxaLBdfX1/R9m0OePNYWbG93ODdQFqXIUuuKs7NzkS8GGIYR70aCdxR2SqmtKKyhsoaHjz7CkyeP59TaGAN9J75DYzTLRcOYD39FaSnKgma15ElO5bRlQ0xwOJwE/PR9rl+p0BpirGiaisII66iVoi7LuQ+0LEveevIWRVGy3e6EJdUqy3whBqnmmdJJg/f0XYdK4lMD8MlnH6HOTLcwU3OgklKokL1yo0MZkTtOh9LpeZUeWotWIjsGGEr5nPfikbMZNBijOTvbcDq03N7eChg2hsVywWZzTt/3fOQjb7HebHj/+RWK+AG2Sqk7MAF3QGEYBoJfEkJCKcvV1RWnU8fhcJRaGCP+wc1mxXqzxIee9XrFetWg0IyDp1ksUUpxeXXFdrvlcDjJc15YjscjznliTHgXObUtVVXMoAA0KTi0Fpl1jClfq4AEtKEoCworvbg+Jo6nHlJif+w4OztnuzsQbnYEkvidXSJEWVyUVUFRSm1T0yylX9ZqlqtFrp6RhN/FYkkYHOOwJ3gv/kB5sonRo0gUxmCUMNan4xGlRfa7tAWP14/4/n/++/iO3/672AyW8N6OdNvhOwdjwAVH0IHBjaA1zXJB3dRoaxjHkX4YUFqzWCxycI+nG3t88PRuJCbpNx36gXEYqUxB0kq8o0qAbohJ/mw1hIBSHltrdn7Hz7x6l7/3+X/AP7z5DJ3u6E1HGjvxmualig8eFQX8+iBLLecdLgPK14GqyTLv6X6CO0nu1AWqjbxeZFmmUK+l904pvcAdWDOGorgDwCFfg1L6NXAn1zAtX7TR2MIQwvR1d2yr3EN2vu6pz3SS+k7ff7pu6ZFWc6rz5FcXBjP/rK/1wEqFTCH2htdCn1KU+zhGYXnjzKbm12KGr9LTGubHNBmDtTHL50dC8B8Az/LASf+vyIi1BMmF8CXXs/omfzffz/3cz6/8Mdrw6vu+mup7vwP723//m76c/+9Jkc9885/im979NIfhx9/01dzPG5pfCLOagL/w7NmzHwZ4+vTpvw/8+adPn/4x4L8B/oVnz579radPn/6bwJ8H/ujTp0/1F/rcL+rqjMGUBbVVuTT+zi81Se4muZsxGqXlcDN9rutaDoc9SiXxVTUNdVNjhp7VcsXZ+Tm77Y7tbkvXt5C7SxeLxZy8uVwuefhwMYd46FxnMc2jRw/YbjXH45Hn77+kbkqapubhw4es12tG7xidY+h6Tscjp+yBNMZycb6hLCvqumLRNKzWaxZ1w3qzxvmRq6tr9vs9XT/MP3sgZQ9rgVaWFFVOJrY8fvyYlKCsFihtePH8FYfDgRSkasVqjdGQCotVirosaCphQzZn55ydn9EPA6dTm0HakX4Y8MHTth1Dr7BaUxYFTVnhcnek954xy441cnAsrRxqk1IzyJoP79qgtJo7EHXu+xwyC6zNHRD13tN2HVVRssge2UEJi9X3PWfrNWVVUxSG0hakhKTXtic2m42kuyrFMIxcXV9jCsN2u8f7iCnunkeV5OBstPifvfc4H6iqmqQ1g/M0EQY3SDqysqSYKCvxuU6pp0op3n77bZwbWS4atEo4l7i92XF9s8c5T1lWnJ1d0PcDN9e3IncFTqcT3iessZlFh8ViQV0tOJ0kmElknxZjRDY5DD1VVaNURGuLkuhTAfx+xJqS06klIiE2Xd+jVEHv3OzDbpoGF3rI398Yw6KpiT6y3+4YxhEn5bX0bUdKnsIqKC1FVeB8EFa073DDKN7oIFLq5ODJ6glf97Gv4Y/8vj/M137sKfH5lvF6Rzw50hjz4iUSrSIgoWTrsw2Lsw0hBrq2JapIWRcorfHB0R9aUVtoRUK6ZF03EAePQtGYkoTGhYlB1RAiOimi1kQFuoSx8LzXvuD//Jkf5SdefIoDPd5G2rbFO0dRlHilKYz0zIYkVUvBJ0lBVsy+1en5n7zbd/UyiYk3nGT5ZSk9paPr76T6r0njp/9ZywwYp9Rcre28SDFGzcoJAbki0UYVM9CtqhJjxO4gz6+AU4UihLt04bKshSXu5LX1etfxHfjNVWEZ1E4+U2FZ75KP7eBBQVEYTKE/ANKn91bvQmZvxaOaMuhMMZCUpL9L6JWa/42p29V5jx8HWbS9FvLmZhuImhdjMUoytv8SA6u8yd/N93M/9/Mreh4tzvipb33C8j/8y/BaD/iHadwP/3dc/4X/la/7mU/lUMX7+dU6Py9Yffbs2Q3ww6996EeAHwK+AeifPXv2t/LH/xKypf2jP8/nfsEz+fNIIm+bvFLjOM6eyNcPJVVdkFLk5uaaEDzL5YKzszPKsqAoRDasjaaua3wIvPfee+y2B25urmnbE4lEVZU8fvyYuq7RxmQWp6KqKlarlaSrdm32kHmMNpydnc3BTYtlTUqRrmvZbm8JIXKz20q4zCxzFcnkxcUFy+WCBw8esFos2azXuZoE9sc9q9WGtu3xrkVhiCkwOvGEaaN48OAhzo9UVSO9ovnF3HUnmsWK1XrJqT1lyWCJ0fDo4QPW6xVagzWKpqlYrzeEICdDfyNMxel4JCEH7uPxyDg6CqMl7CY1QKIsS5bLZe6EPM5SxQnwSBopue4nzmEwcujUqBTnvkvnPN45lNbY7BsNSHdqigmNmu+HrpNqmKIoKKsqS7pF7usGN/vpYoyUZYUtSkY3MhxFVr7f7dHZe/y6/zW9Bp7Da0xZCIF+GDke27tuWu3mQ3dVycLh1B6wW0VdC0N8cXFOYQpCeMXh2IJSLBcr3n77HUhwOvZopZk6KMuyoiyUgEgr4TcXF+ecbS549933CUGWLtIjOTB5D5USj3YIktyLkIdobVBGDvdKG7q+J/jE4FpSDixKKeWuWWHEjNGsVyvao1QpdV2LcyNlU3MIR0pjMVmmu1w3EowTAn0nNTs+5bRnNJVZ8om3fg3f+ds/ybf95m/mSXVB+sw18eYEvSgRBucJKpJKGIKjqgpRNVjN6XhgcMKozhLYnPiqEN+zj4rBOUI/YkPCIuFHKSgiCrT4hlNKqCSLgGggVInrcOInn3+Kv/dzP8Z7x/cZ1cgQBrpjTwgJayrQBSFFbAIfI34csdZmcASvp/bo7NOMSYLWpOM5s4UwBwdFL2m3ZVlR5OCwlH+2uf9Ua8hy4Un+OkuAVV5Y6dyxavSsYphYUefk/lwsZNE2jgPGSJKy1hNY1jNoHIaBtpX3tWEY5XWgNEVd5nvsbkmn9Z1/d/LMim9WvOrDMFBXjagAcmJ1ImQJtkjMJ/bUmOxTD5GocgBcjLmSS8+Ph0j+5dfVJNVvmoUEsmVgG2MiE7uZtY2vAX3QH9ID2T/tvMnfzfdzP/fzK3f+07e+le/+6s+z/s//yzd9Kf/Y+J/8P9j92f8KgK/70ffZ9ac3fEX382GYX5RnNW9lfwj4a8DHgc9On3v27NnV06dP9dOnTx/8kz6Xf8H+gmYcB4ZhYBwc4+hwzue+STeDiungFKIT3+B6TQiBi4tz3n77rTkBdwK6sp0Rz9nQCnPQdT3j6OaU4BgSwcdc3zJ5AcVX6J3DuVGSWtGUdUNVVXNaJypyfX1D18kLTClFXVWU6zWLxQKtNXVVsWgWnJ+fY4xhs9lQ2WKWyRplefTwCQ8uHtPUS7R6l2PbYmyJNsKWdd2Jumr46Ec/ykc+8jZEx+c/91n2+wP74wmtDdaWuFyHUzcVi6ZmsWh4cHGOtRqjoCwt5+fn3Oz23N5uhZlwgb4diAqOp45xHCVMyTnGoSclOVw+evSY1WpF0zSsVivW6/XMhk6LhK6XdOSpkmMcR5wTqTXprhZDKn9MTgW9Y2JiEFZkHEdObTsf4KcZxpFlDIQIY05LPTs7I8a7YCatNS4EnPe0XYt3Pj+nzPcSMLMzcCfXlMP7IOxSDpIi3ck067ri7bffZhhaUhRwcDodGIYeawqCi1xe3RKToioqjCnY7w445/NPoOg7Sf0ti5oQAtrE/Bh7SVklsljUOBcywNT4oHCuZ7lcsVrWWFtyOsrSYBgGlnXDarUQKS1yeNfGcDz1EpSkEjFJMBgpkbLfr7CWvuvxo2MYekKQ8K++3dIsalQtrKkxwnD6wTM6T3tsCS5iseiosFh+/df8en7/d/w+vulrfj2LY4DP38K2h8Hj/EgfHU4ngoqY0rKu1xhliClyOJ3m6pUJxM0hXHm5EJxnDAkfIhp5L9ARVJI/qwyMQoygE7ayKAPBeH766rP8/fd+ip/ZvctBt7iFJvQRnaAxJdEagnwHrDFEpIYmiJY2g0w1hymllMQXm5PIYxLAZQs7ez2dcznYK6G0lbTavCAxxmBgDkGbTJmvM60TcxijFz+mljRdYR+lAkobjdYFi8Vi9o92XUubl1ZTYNP0GpXvKWqVyas/S24TkNS8dJdFk5rlwkW2DUwLjxBcfn1LBZPIlCeZbsQWJkuQmcOgSEG6pOEuDfg1FnayD8QcMjVd37So0txJkgXESs3Z60sope5SkL9U55f7d/P93M/9/MqcH3742/gtP/Kvo6rlm76Uf2zi9gX/yh/56/wX7//0m76U+/mQzS82YOk/Bo7AfwJ8zy/95XxwYg6ucW7keDzg3B1TYW2BVpCCJ3qo6xJbKM43a548ecxHP/oxjNEcsvR2qmSY/JLWWgpdYnTBMIyZBfXC6ig9V5ikDHT3ux3GWnTe/gMsl2vKsuJ4bHPi7RFUmNMrV6slZ+cXc7BHWZW5H7ZktVpBTPPBrBtHDm3H8XgkhsTZ2QVlXaO0pW6W7PYnvIs0Zc2ibiTA6dUljx8/4sHFObfXl7x69Yr9/sCp6zHGUhTV7EVTKdEejyQv7FVhDFpFFqsl7A+4MTAODqU0Dx485OVLkRDHBOv1mvV6zW57i9ZyzYumYX2+xlaWZbmkWSyx1tK2LYwiLS2KgseFZXTj7EH1PtC2LeMwcDocORxEGuuDx40On4IQxHM4i4Cp4D1D3+J9yIduA0RSChitsMYwdD0pQllWLJcrvA/ZE6cprGagJ8SItgVVUTEMAhgnhmgCDtNBV67Xz/8fIyKHzl6+pmlYrZdzaNeTxw8lmVhLDcmLl69wYyQGKGxFVYnn9vLyUhJTrchFm6Zhs9nQ9/K8KRB/JODdSPSBuiqBQV4YSaOVZr0648mTh6zWS66vbtiFI6fTia4dKGxJAKzW1HWJsSXm2NIPHucdkCgLk5cTibIoqZuG6AJDJ0siktQjjeMIKmFyIJELkbZraftOfIBJo5Oh1jVF0NS64jd+7W/gj//An+ATjz9BerUjXB2J+4E4enyKDAR6FXEEFsuGs7MNhSkYek/Xdoyjl8WS1iJ5n6tHNBqDyF1dlvZKlQ5KERAW3mgNyUPw6FKhF5ZRB3bjnh/99E/wUy8+zW23JzayjPIOXNLEqGEUcKWshiKhCyOvVXIXsFZEskLA6Fy9Iv7Ivu+zPPcOeE3/m2Zi7ruuoywF9DnnhCXMy5WZXc3jvafve1JKlKWwus6NoAxKmWwHuANm+/1+fs2Jx7/COQnrShlkS0CcYxxGtDazb5S8EIgxEhHp9PT+ae2dr/UODAooHXKVmIBXkYPL60dAvA8Oa3VeyOkPPD7aWFFVKE1RmJwMLOzypNqYGFYz+Wqz+mF048yghul9G3LHrUj8U0r44PkSnl/W3833cz/38ytrVmXD/7z6TXzT3/7XPnRANY0daWj5gU/+Bf6n53/3TV/O/XwI5xcMVp8+ffofAF8N/N5nz57Fp0+ffg74xGuffwTEZ8+e3fyTPveLubhp40+WvalcrbJcrSQoyY2kGHDeURaGr/rqL+djH/u4yNJyanDfC4MRQ0ArldNnI8fjiaEbGQcJttHazsDBGCsHwRQ55QTeyUMYozB7m82GxWLNbr/jxYuXtKcT2ghoXq+XvPPO21w8uCCmRPCKGAN1U7NYLHBOGIj21BKD+La6rud0atnt9rjRU1QVq+U6sxdTMuyQk2GhLCwpBX720z/N/8vem8fYtuV3fZ817OkMNdyqO7y52wE/2xi3B7AxNhhsHNy2ROIQh0SCjCSAFEWRCAGSKEhJQEkkB8EfiVCkSAkKigQZkDJAoiAFAthgOzS22+l29+s337GGc84+Zw9ryh+/tXfdB4lkCHBvN/WTnnT7VlfdffZU67u+kx87tE7is8tppFqLP9Ray7d+5jOU1vCTP/ETuLGjsJqyLqQXVKncTVpjjKWqapz3hBTlsz4X0nJ650QsoFrx4P4DQOR3o3NcXV0xDCOH7oDRBevVClsU1HVBkxN967qeeyCD87S7HbvdTqSDfc/+cJBz4xz90OMziz7JECfvX0oR7yf2BVlY5/5J8WT6XCFipZYmRJQyGFOwaAQQlEXFOG5YLBacnp7OycBTcu+0OJ9qRiYZc9fJ8RijqeqStt0xDB3n5+esViv6vqMoRNo5DCNVuaTvJWm6qkp88NRNTVWVjLl3tq4l1VXYL8N6vaLSBWa9kMV9roshX5Nh6ChKw927D2gWda68GSGBNSXWJpyPbLc7ikITWaC1Y7tr6bo+hwxFxjFgjeX4dIVRhsO+Y79r8d5TWIsxcs8VtqCqLYXWEl61uZSKneWKO8dHWFXQ6JqzxR0WoeS7v+PX8Fs++6OsfYN79xlhe4DOkVxgjI4+OJxJhEKzWCw5PlqjU2K72eDGRD+6XI5544fMnKPcByBhZQls5iMj8vdRi9wWI5J/VShG49mw493HH/LTv/g53tt/iKsSZlEQ1Ujfj/QHCWWKo0OFKGxi0midwABZFjuBSAlECrM/FW68pdNG15zyy9+avpvmHtWuE+WCMYYAsxpg8lqmLCn23mGMnZUiIL73EMU7q5Qi5XeTBH6Jh325XDKOI5vNJvveA84Loy45AD6/JyZwOBG7NwyxUjd1NpIUHOZwp+maTIqX6WtKkUPDQk6vFutCWci7VfzepQQwlRXayHWsm2aW10/nAPQnNgBCfpfvDwdG5wi5Bmzq1055R1PnShyd5LMZ81KH3/9dz4v43Xw7t3M7Xz3zL7/6Pfzxn/wPUFq/VP7UFDz+z/5n/Pk/9IR/6vIv3vpSb+f/c35Jv73ffvvtP4J4XX7kC1/4QqZ3+Gmgefvtt783+19+N/Cnfwlf+yXPYlFhrabrRGJWGMNy2WA0RD+iEIbVGs0rr9zjjddf42i9ZLPZ0HcHlDYcDh3d/gCAVorgI4f9ge2+ZbPZcdj3MNUpKEXbtvR9T9M0xODZbrfUtdTWyI69wmpD14989PHDXJnTU1QlZWk5vXPMpz71JsfHK5ROkAxDJz/nEPbzok49x9AOg2PX7tlstlxeXuUFqKcua4qixFrpFuz7DpD0WeUTMTj6buSddw7cu3eP+/fvsVgsqeoF210rdSEpcXrnlLOTE9577ytcXV7k1OKGZrHk4uIS51qsLTg7O0cpy2EYGJ2wiyEIE+p8L4FFJM7Pz7jebjgcDsSY2G53aGWxVoJYen9gs92iFCybhuOTI6mfSeJzlboQSR0+OzsTRiR7cX2QxNW2bTkcDmw2G7q+IykJQFK5c9N5R1JIgmwMpGQJPrLft6zXJ1hbcth3FEXBft+xPlpn7+qQmXNZCC+XS9brNdvtdpYXjuP4Ce9tVUkvrs4BN5BQOs0hOsL6GHa73dyZKWBFqmTqusxVR4rzu/eAxG635enTpyhlWCwrDocOraFpxP/q3DiH4qSk2LUt4+Cy5DGwWJ6wP+zpBwnDcj4QgtxLzkWMCyQig/MMTvzd3WFkGKbeSfFuiw9bsd1saLe7G4A09KQQWCwWnJ0JYxyCBw4sF0ccH1vZTDEVC9Pw6vo+b529zvd92/fyDZ/+RtR1j3/6GL8bSTEy9D0hRYJKjCYxJo+loC4rxt4xtAdh2JNCabkOyt6k6d4w3YHosrR0jpBl9msrIwAlmkQoEs44Hh2e8bn3P88XP36HbdgTS3BEtv2OQz8wdCPBRVSIIomeXkAxoceEifETntDpHE19p5Mc9nlJ+VQv87cCWWAGqgLIbiSrepK85pTdGx+13EvW6ptwIi+KE5WrtJ6XE1fVgqIUYNf3PYfDIftRx8yqmnxMcb7PgU/8nEnq/vzxy7sr90k/p0aYGNab9OpJiRDmQCVjKlbrJYXJ7HOU+rDJi6qUorAltpSNoWkjJ6VEs6gYBzcpoxmGgf1+T0w34Vb6+c2MBCFInoDRWhQGVqG/BsHqi/rdfDu3czsv//zjr/wq/rlhyW/+3/811Ev4/nv2T/xu3vjrX3jRh3E7XwXzS6mu+RXAH0SKjf7K22+/DfCVL3zhCz/69ttv/w7gT7z99ts1OQIfIO/u/r9+7e9k7t27CzFwvdlRGmELV8sV+3bPod9nJsnQLBrOzs6wWtF3e54+fSyBHQk2Vzt2+z1Ns2C5XOFGz/X1hv1hj8usW1WW6Lww9D6w27XEGDk+WnF2dpbBakHf92x3e7SVP+93O/HPAXVTszxasj5ec3LnhLqpaNsNw+HA0EV2u60s6nZ6XlSenJxQ1TVF2WCKCm1KnE90fUfsDvgYiG6QDsehI8VI0zTcvXvG/XtnODfQ9wfatuXs7A5aa+7cOeetT30dT54+48MPP6LvOt595x3as7M56fjBg3ssVisOhw5bVMQs21PasG1brjfbmSGR+giPMQ2LRQMqcnp6zHvvvZ+Dk9LMdsQoQFCkiLJo7PoBmz2rbhhJIbJerW5YmUTuZM3y3nztl4slq+WS1WrFdrelGzr2+eckkmwOKvFhTmCh6ztCEPDgfWJ/OAiT7T1VXROjZxhHisIyjh0xBZSG3X7H1eZKQJzVlKrAFoV0NxJJCBNVFSX7vSyiC2uyJzBSFDXj6Hjy5FmWqBtihKIohUEqC3wYGcae5bKhKCzO9Zyf32G1Wue0VUlOvX//FXa7He1+YHQ91hqG7NmW8K3A8WrN6ekJPjhS0oQQGQfP9fWO/WHA6ooUFc4nEtLRm1IiBkVM4EdPNJpFrdltW7p2zziMjKOwi4JPAuvVirv37lBXVT4GTz+MaG05OTnj9PgUEzVHxRHf+ss+w2/4ju/jlfou4XFL3HS47QGDYt8fGGIgmoRPAZ+DzkyC9no3M3ohCZASlu0G4H0i1dVLwFKKAu5S3rxQKWHIYM7AoB371PGFD77M597/eT7aP6E3I854CIbNbkt72MszFkOW9WYZrVJojFQrxUQKAVsajLnxU8JN8i0wg7Ub1lM+wwxE9U1tywTy5Bm4SdSOM0s6zkyieENLSbvOCcLP+0p9ICdRR8axn+XF3lu6bsCNI8MoYVzGGORtdeO5lvnb5cpaK4yVz6CVpHVPacBSCRs+cV20VkwdrMIMS7BXYa1IvAtLItLUFcvlAp9T3KfPBFDYMvdm9yIDj5GY5LOObpSaqOe6a9Pk/5Uo5LzRIJsNNx5WnSXOiRC+tnbtX+Tv5tu5ndt5uef77n8zf/LHv5PiN/wzL/pQ/rYZ/pN/kx//UzV/9OKDF30ot/NVMr+UNOCfZ1rd/O1f+yvAr/w7/dovdVSSns07yrJaLCnLCpKisAXpOrHf73ApUqdcXaA0Dx895mqzkcW68+x2e3Zb+c8W0hkqC3KFC56yqlisGkmiTbLwKwrL0dGa115/DVLi8vKZVMh0HfuuJ2Y/5WrZgBb53Wq15OzOHZq6Yrvd0bY7UvS4MXJ5tWG320k6a2ZPyrKmOwzSeekjKUFZVdw5uwMpsd+3XDy7FBCnFN45qqqksJqUF9ivv/461mje/+B9Dvs9XT9wOPQslitAQl20UnRdxy/+4i+SUmC5WFJWDUM/So2O1hmgHxjdVhJ3XRAvqJFe1bqWxOCqqmkWJbaw1E1NisLOLBdLRhfQymSSWqFy/YvRSkB5VdG2LZfXV1JRoYWhDiHQtq0EUdUNJndEkj1qdZOrfILj8vJSpLrjgA/CoBXWkiJsdzu2my3WlvR9z/VmS8x9ulVV0e52HLo9hbW4Qrx6WkPX92y2G7bbLWVREtMUzKSeC5zRGKXpR0kgltAjacUBhWhzQ4oAACAASURBVFLyObxzQJS6DmMlJClGul4SUpfLBU+fPuXoSMK2JmYpxiCsZQrsNldcXF2xWjSEGEBBCI71epl9rorVapHDlxpJKu4c3WFkt20JQVOWImFGR4pSApr6Xnp6Q2D2YV8+u4Ak/loBAwGVElUtnt/loqHv9lw+fSpg1UX6YeDOnTPurM9YF2uKYPjs9/wg3/lN38myN4wfXeKvsuzXOzo/4FUimMQYvYAKLccQYsgpsXK+9CT/zIArJTAZ4I2jI/gAKYpCWEvErlaKpPIdpxWpTPTK8dHuKX/183+dLz59h1Z1pAUMcSBpxXDo8C4SfcQNAxjQpckS4oDGYLSSoKYkvskYA0qXM7MZQpwTdyc/JWSQBFKBlIFfUdyk2U4gddqsWSyWlGXJfr9n6HKFTxRWXd5FRfad2/xMixBEa0tVWeosU98fDgyD/EznBoZxkMAyyMFlZIlvmpN2b+poPtmHPHlmp4AmIiSdMkCVHyRByDdMMDB7oZPSIr/VJnttxTs7DB19d6AorHjSc8d0VVWkJGFm+/0+M8CJmBTBuxws9px0Lb9zBbALoJZk40lyLaFs07WQmqzI6Mf/P7+OXrp5kb+bb+d2buflnHW14NXFHf7n/+H3YN76lhd9OPPE3QXp+iG/5of/Ix731zzZX7/oQ7qdr6J5+XQBz82Th0/48KMPIekMEpaYosCFSIiJulnmPszIvhsodi37bmBwidKWjMHjoyYkQ985VO9AycLTKKSrkpBTVZccHR1zvF7LYs1oyqrk8vKS9rDn6uqS7tBJVUNZ0FQ19+/d4/TOnXkRuFguISWGw8Bms6GwwtCFBAnD9bZlGJxU11TlXMMyMRR1XXF8dERpSwyG9WIla3utGd2I0Zo7xycYrfn4g4dcPr1kvV7iY+DQDZSVZbVa0R32KKU5v3MinYldlxe9K5TW7A9ewPDFM9AKHyKb7Y4QxJuaoiST2BQoi4K6XjL2I5fugldeuU+376nLGhA2JKWU+1Qt3gXa9kAIkbIqWSwq6oX4VItKFutPLy9wLrBarVgtVxRVI32qvSzSF03Dcl1m39tICFAXJXfP7nC8XkqI0CAe4qqq2G23tK0sckPo2O12eC9BWsFLH2gIEasL9u1BfBEKjNHs2y7XhGi8E4+vHwMaRWEsPnlh85BOS2VL6V3tPd3hQIqOo/WSQ9sRYk9TV7z64FUOhwPb7Y6zsxOc81xcXNPuO/rhMavNXjpjteL4SBOTZ7fZMA4DRsEbr91HK81ut8N5x3rZyMaDTlir8X6kadYURcUwHgjBstl2oBV1UWBMAiKlLWQjYRzxKuIl5gafAkRh7vpB0ppRioTCqLzJs93TH3qMSrhhmAOhCmXRDjYfX/Pmp1/jt372R/nm17+e9GSHu9gR9j1pkE5gFyNOKyJxTspFZakpkajIdT3i8fXe565NqTWpq5qiLDkcOsIojLpU/QAqolTCJA1J4QoYy8h1avnZ9/5v/vLP/TWeHp4RqoQqAS/PvRu9MM5KS6Kv1iRi/vxyT2hlSDGRENlySoFKF/LuQIJ6JsY0uCzZzYzpDLgn1lJJN2tIck8lEs5JFc9y2QCa7XaHHx1hkjKj0GiUtoQIw+g5dKJaKAo7vzOOj1Y4d6DdeVarNV/3qTfoul6qnYZxls2Kz15SlUPwaKPQusrAdFKUeIjMEuTgAtHH2Sc+JxIjflBjNFX24McovbSBJFKziSnVGlJkGAb67oC08USKwuZkdMuYPerixR7nULTgRbWRFCSlCfmzFKUlqQQhSiVTyn2samJ0Y/a5i5w8JnA5WKkfJ5Xs7dzO7dzO19584503+Mnf/y2Uv+MPvOhD+cT4L/8U//Y//d/zxz76iy/6UG7nq3RearBaVhW7XcvQy8KrqmqKSha2UokQWK9XAHg/cnXl2G53BOcpFkeYwRFipKgEUDk3ApGyKFguGmxZkEgcHR1xfn7O+fk5TWYA9/s9jx8/4enTp3jvOT464fzsnKquc+KrZrlcsWgahnHk4uKSZ8+eYYuCpq5p245294QIVE0ji2Q3hZBkz1UOWFFKGN26qRjGnkO7J4U0eyqrSmSAR0dr3nzrTZFBpkjXd3jnODs95dVFhbWGshSWeb/fczh0XF5csd3uiDFR18LI7PcHrq6ucG6QIKWUAIUxIhNVWhNDACN1PbvdjrIsKEtD2+4za6LnhE2RAUcJ80kxhxKVM4u0b/cURYlWRhb6eaG5a1uG0c3evClJdVlLj2vT1JRlIYtSyJUzhrqusVbCYVJMNJXUulxcXBFCyIwQc40OKKwt8BkkpJAEJFVFDkgaWC6XBC8gpGkalssFw9DP3jlrLd57vI8Mg2Po5f7TJDZpB8mzWte8/vpr3L13jydPHtP1PbYwnJ3fxbmQGdYRrXr8GKjrkhAjhojRJrNvRkCrMZRFgfeOuq5RWlN4T4iRxXJBVdXCRnUDbdvnMJtEIABynxeFFXCXmW7SJAJVlEVJAmHRY8rdu5ldS5Fh6CEZ6qX8PVpDUFSq4vXjV/jB7/qN/Obv+c28snqA/2iD3xwY2wPBjVLxFAMuksEgcl9oAVvKmgwMDYWtMDnoJ7mAjyLpLaww01IDJPeptjarPZNUtyhNtMKkjibw/sUjfvoXP8cvPPwSF+MGakUyScBfBKKiLCqaRSUdvsMAfZ8luDFvUJRzgJL4US22LBF18CSLzyAuB74Za3PXqf6Er1WYvSghYDHgPZJelGuDJhYxhkBhS5FA/y0JwM/3m4IAsalW5tB11IXi/N7dmaEtiyKnZwcJH8pAte8HuY8LizU3NVnjOM5hUBJQdCMJlpl8rGGW/pZVOT+bMQoYlRAoP6tHYgy5Emnq77VUVYHRoowxhRFvqZdNjBDiXBcWY+5JTXLtCluIDzwrSkLwEpoXhfWdzo8P0i8bc73QDMbjJNm+kXDfzu3czu18Lc3Xn77GT/zzr710QDV8+Av8h7/tz/LHPr4Fqrfzdz8vNVi9e/c+b735KT788CO6TvouyanAVVXymc98B3Vd8ujR45yW2+HGkeVyxfnZGSFEdruW0lbUZUmIHj8OlEXBvXvi2xzdyNHREcvlkmEYGbuB7VbCb3Y7CZyZEmPP7pxSVhX90HPx7ILrzUNU7vnbbrfzQrKuF4Ashp132K4jBvGjAdm3ZjKYcqQUOb1zwmuvPmB0juvLa0Y/oDTUi5K6Evnhm2+9wauvvUrbSoJuWUvlRVVVKK1mT1YIgUcPn/Ds2bPcHysLZ1mUCjOz37cUhRGmph/w4aaTcGJTpgXsMPQI+Glmn9i00BU/nvSjxhjy94k/b+pSdL6n74c5kMi5Sa7oaNv97E3sJ+CQz9NyuaCua/GL1iWFkZ7GTNzIJsLxMcYWfPzoMc5JL2ld13OvK4AbHcHfyDYBFouGu/fu8sUvbuekZYVsGjSNVMxM4Pkm6MZnxtagMOKzjIFxDCwWFa+++hq2KLnebjKw1lxdXTMMnsNhn4OXpNfSGMMwOoZhoK4LyrpBAU0t8uAphGccHU3TEGIioSi15vjohJQQqact6LrrHJpk0OpGFTj1cnrvIYkclASRgHNx/nz3797j7t3zm17VFLm4eEZd17z6+qvEkNhctSxsw6fvvM6P/dBv5dt/+Wdo+oL9lz6CPjD24nf10RPIPtCkMIq5e9TagrKqMUUxd4wqkLoRF4gBfFAE71HKoVIiRofSUGhDiA6lLTqn4AarcY3nym35/Ltf5HO/+PN8cPWQlp5QQW5AwmBIEVRSGFugbcFh37Lb7WZv6OQBna61tTZ7iQ3KIP7o/O6Z7iGlPulD1crM/ss5tCiDVWNk00CClOQZG8OItRZb1Vgj1Tsqf58xN2FKkw90uqZT93MMjrKsiFE2ejYbsTm07Z5xyBUuWrYn5s9UlGhjZjA+BUE1TYPWmq7r5tRgsYIK4AtReneVNpRlQfCO/X5Pei452GhNiIHgHea5ahsJLhNZcVUIoI7qxusaUiRFL9LpzGhDIuVAtZgZ4RDl7yXoK6cUZ7ZaVBRy72pzc65Aze++5/3Gt3M7t3M7XyvTFBU/9Qe/jfK3//4XfSjzPPvRf4kf//ABz5LjT378V1/04dzOV/m81GBVYaiqhjt3znj48KHIc7USpsmIpLFZHLFcNRwOLTF4opdAjuAdbhyz/FU6WeuyIgSHGwfKUliazWYzLzofPXrEYdcC0lM4jbVWGNRhZNcKY3l1dT2zo0VREIL0eU51KbIw0hRFNUtOxd+YE2UVjMOIMQpbSAJsItE0FYtX72ewo3jwygOOjo4YB0dRWq63l4zjOHcuBh/Y7VtZYLo497huty0hJKpK/rfSFh+CVJwMA0VVslovaZqGy8srtrsdKaksjc6L7SSsR4xRvIxlyfHJ8Vy/s1wuc5hR4NB17HYtznmsKVGqmoFmCJ5Df/jEAnliOpxzuXIjzWB1WkCP3mHaFmsshbWZORZAoEjCepmSlBSLxRKtJYRLodCVEc/qKNJCY5irc7pO/JtXV9ecnJzkQBqfQZLKHsl+BrfPp55OKb9aGYL3VKXFWs3R8RGr1ZrdbkNRWNp2LzU9WrO53rDdtRhTUpSlAK3MDCoF/egZPayWC8q6YdHUdF3H06fPuL6+5k6MkvBbWJbLJVoX2fcIh31P27Y4F+bNBmEsLd452t1ONlEAmyW00RhGFymLagYPl88u2FxfcnJ6QlWVPLh3D2tKiAoV4I2z1/nuX/ld/Miv/U28srhL/HiD3wyYQ8ANfmZTPcKIhSQye1OaDEr17NnsDwc5z8YSQ2IcHMPg8mZODk3SkFSSDl0SahhpjCEkRygTrCsuXcc7T9/hp7/0U3x8+ZiRwNa0jMmjTIEyJif7SgBPjDCOnuspGE1rTGbgjLXiO80S3incSCZQVTXGZi+29oQwsZ0JDRilZ0nt815Jo02uzFKfYFwnv2dVybWKIVIUpfjySXlTx0ESUfSUfCxgePKZ5s2fYZBU7l3uVs2ftSormrLO7wCR4ne5a3XaWLPT50a8rqObmGZy+rAlJi/PT1NSFMKqaqNIKTznFc1AMCXZZAg5WMoYqqZkuVhQFgUqJ2rrpEkWkQwHT1Qxe2jFB0+S8zoBfEiY3K8djJZzECUdOgWP9+J1Fj+zEU+096QcvqRSghj+Xv56up3buZ3beaGzKGu+8utfZ/WHfy/mjW9+0YcDgPtz/wXf9K//j1z1Le34xRd9OLfzNTIvNVh13s8dgsfHx1nGm+j6Pc2iYnN9Td93PHr0UBZMGSCOw8jjx4+4vLzEuUDTLIS9856qlF7Ebi9VMf0w4L2fa0cmIOW9pyxLTk9POT4+BgTY7g89KEVV1dRNM+/YS7eiZ7k8oiiK7FncZmZxRCthGU1mVGPyKBUoy4K7985588030FoxDiLXK0ub/aV7YWubmnEc6XoJTfIh0ncDIXiqqkargqokM3chA8lyDlIp62YGXWdnZ+K5Q86t90H8bD5k9gLK0lJXFVor2t2Ovjuw3Sruh3vCrGRGaqqkOXTiU7XWUk0L466bayiGYczXT8Br38sCO8YgUsB0kzBqrJXUkCQC1pgSXd/LAjR4jNVoBU1dUZU1VV1TljX7fUfX9dRVw+npESDsq6QIR5Fa19KjWzc1trBcXV3MbFhd1yilGPI1aJpmBhZaa/b7cZZqKhRFWXJ2foe+27NYLDIjm+i6jq4bOTo65u7duxy6HqUtV1fbHCClcd4zeodWYLTK1SuKFANbq9nn+7MoCupqyfFxhTIW5zzb7Y5x8Oy7PY8ePRbWzNjMOOuZUQUJIOr7XoKIQpBr7cTrZ60lRM/V5orDvsUoRV1XHK2O6PcdJkK/33N+es4Pfef38yO/5gdZuAL/wRXuco/qE24MuBAYCfgUiGqChyIRL01FYW3esJGO46lexI2OMacMx9ybqpTI0D0JlGz+mBCpihKtQdnAUAY+2H3AT73zc/zsBz9Hb3psY9kPHSw1RpUC2BI56CuQQiKFhA+RmJnfCaRqpWXzaByJOZQsJgmAKoqCqiqx1syM/uTfnMaYiZG9kZpOAbUK8t9/EqzO0tX8vjFKgs5i6CWYKwSe+yfkzyqHemXGsTAGPw4Mw0DbSoduWZUslkuWixXr9ZqmWeSwL3lGY9/PTOPNz1b44Om6Q67J0aQ0pW5HrFIUtoEs94/RoZS8a2O6qa5JcZL9xlkyrZGwKWstRk8J2il3H5O/X/zuJIUqNNYYuWZJ+q/J6d8xRZE3jwNK5U2RHFYVs9x4ZraNllTuzPjqQhjh27md27mdr4U5WxzxM2+/yvF/9Z+/6EOZx/03P84rf+DP047diz6U2/kam5carA79MLNb6/WKQ7cnxUilpAbh0O3ZH3ZUVYFWirEfiQn2bYuPkRQj0XvGvqOua9brFevVEgWMw8BhGEDfSO3KsiT58JxXtGK1WuGcY7fdcnV1xegii9VaJHo+SGIrArTGcSTRYkxeFCnx69Wl9HQOQ49Dqk1SitRVySsPHnDn7JRlU9P3Hbvthn3XobWhaRb0/YBzH7JYLOeKGPn3xOMFSZJbmyYzJJOfVOFC5Gi5eq4Co8gLQOk67PL5lUApg3cCniZ5prWGqijo2hZj5Xu7rhM5oEoUhXSODsNAVVU8eLBEa027O4jXeBgziL9hUyd2MsaENQUxJbx3z3n8pL9SEktV7kP1khyqJGG0LEpQiapZYG3B9fWWq+vN7L/zToD0JOmc5NcSNBOoqoq6qXny5AmbzfV8XAoBVH3fY62ksQ7DMFeFFEU5M5rSoYqksPYHnj1LfPrTn2K5WvPld77COI5UdZVTfxvOzu9yebVlGEasLXH5M2ujKYzIrfXgKbQijCKtPjs7z/9mFM9r27HbtjPLGxNoZSAnFk+fN4TIGAIqCVj1oyPFiJt8hSjWiyVVU6FMzf6wJ0TParmkqZdcPLqgjJa6XPANb34DP/QDn+VX//Jvp7oY6Tdbhl1HHAJEhY8RlwKBKAFBmWVLREnZtRq0ypVDZKAU8T4wDj6DkgwAtQRDpQRGGUgGFTUpaaJReOvZ0vGz73+Rv/TFv877+8ekRmEqS7CBIIZrDNAPA4WtMdoweOlRndJ0tdEkgoQEVTe+y+nZSloLOIoCMkNQjGNiso5Oz9DETN4E+/h5E6fOioZp42sCdBOYillNMG0qWGMwPmGNEcY9SWKyfB95k0O+d2JDlYIY3LxptFyuqeua4+NjqcQqCpQyWRrc0rYto/fiWV2Vsyx2HHtGNzL178pzlzeOjOX4+BhrNH0/cOj6fL0AhM2caqqCD7OtQSN9snVdyqZSUQjjmb3pU8JvSomQ5NxKj7FFWUW0+X7K58t7j8psvVbgnJfwOQMqJwJP8uzpeTWmnK0A06bD7dzO7dzOV/v8mbPfwPf8yg85/VMvB1B1/+ef5qd/z0/xu+L7t0D1dv6+zEsNVh8+fsTFxYV0/hlFUVhCcBhTYK1huWhoFg1lWaASdPuefnBcXF6SFJyenqIy07Rer7lz5w6LRmpq+u7AsTbs+45hHGcpcLBuZj6KoqDrOjabDbvtFh8CCUNSiuVqSdPUGKNnsFvXDcEn3n//fZTSebHreeX+XY6Pj3n33XfZ71tiMoDi1dcesFoviTFwOOy5vt6w2WxwPhKBrncolIDk8REqM3pTKqdW4hWtii2aRFmWsng2hqquWa/XxBjZbrfCFFcS6NO2LcFLGIl0daY5RdMYzcmRsJLEwDgIcG8WDdooiqLk+no7pzNfXFxK9UwG19YWjFlqKKEuzL7JaZGfosqVLDehLRN7GWKQhavSpOhZLde0rXTqKq2xVgvQtQaF5uNHj2jbPcYUlGXFOEr9x2azmUGdMNUlIXicE2n4tt1yeXlJCAIWZGGvngMWdga/UwBNVVUCtFycpd5t2xKjg0Nis5EKnM1GvM7j8Ih9e+Dk5ASUyWDtJilWKQUJnI8oFfHeUZZLjteLOSQqJbi63nBxccngPM5FYgwsl2usLXAuzJ9RKSVGzRAxE4M9ipxUgE0geI9WmtJoVouGtmvZ7TYoLX2uH7/3Ea/feZU79Qm/9lu+i89+/w9x3txBP2oJz1pc3+O8sKkhJdkUAuQCRbSSoBulFc1yKUA1RXwMBDcyjh43SPqr3Ms3FS/CNkZMUmgUOmmiltqbtk68d/0Rf/nzP8Hn3vtZfONZnq1IZcFhHNm3e5GAZt/uelGiEIaORE7AzcyuTlhVzdd8UlRM4PN5/+rEgMp9EOf3xPT/e75P1Rgz1xGtViuqsqTd7znsxa8sVUgB7z+5eWOMwRYFpSo/seEwVbFMx1EUFqXU/G+nlDg6OsG5MdsRRGZubTmHMA1Dz+FwEJVDvs+tLdFGM44jh0OL9+ILtkZk0CQ4Wq9ZLhesVkuM1bhxwFrL/fv3cD5weXGVZcppZld9GPOzqSmNZb1aUFYVhbHEGOiGfk78Tfkai0KhELlx9LOSQSlFaS2JRFEUOKdvfPcL8CEIC64VddNQ5o2kvu/la7lvuVgX7HY7USnfWlZv53Zu56t4/tXXfh1/5HfV2B/73ejjey/6cAD4d3/Vv8NPhgv+j4ufe9GHcjtfw/NSg9XtdsuzywsUkcWi4vz8jOVyTVVJcu7x8RHLZSM+raRZL0/Y7lp8kKqIpllibcHQS82JVCh0jOPIernAliVV7gy01nJ0dMSw6NluN4B4n8ZxYBhyiqs22ELT9QeGscPaQnx5uRe0KGvpMS1LDocDID2gxydrqqpA6chy1aCUomlqhqHn4cOPaZom7/xXnJ2f8+ziGlsIMBqGgRCh64cbCeq8iI6QZLFdZLnn0dERhTG5vsXTti0XFxcMw4AtBMh672UhVxTSnzn2cvyF5fzsjHvn52itubi4YBxHVquVLJBzL+J+L2nJFxcXPHz4UMKUrHiDy6JCKUl39d7jnGccxxtZqtYoNErdyCCn8BdrLQaVAWmkrlcsFs38s27yUeQPbbun3e8gCZM8DMP8cyY54/R3kwfQe482OrO7N6ErEkJ0AyKf985OzJmMnhfWQJYfKlarFR9++BH7/R5bSEUOCXa7lqpu2O8PHA4HjC0IQ/pEqI8kW5N9uwN17kktipLtdsdms6XrR1LudLW2IIbEftjjvcfaYmbdow+o54J8qlJAmXfCrpZlSakghZHh0LLbbCiUpSxrdNC89eBNvuXrfgU/8r3/KN/49rdRXPeMH25gcyC2PaPrGYiMJDxR2F0SOkfdOBKqNDSrFc1iQT+Ms6w6RmG5Q0zCHJLmwKIJoFltsBncUARCCVex5Wfe+Tw/897P8fHuIfZuzbLRKBUZh5GxH0kejDWUusg/L8t6Y0IrhbIaYw0mqTnQa2JTpZZHulCnY5lGZOwTc53y9YpoLRtaU3r03bt3Z0k8iHJj8kHLcUiKtBsdKt9/xpjZY26MgahJPuC8k+uV4k2XaH7mp/9uVAM+b96JDLnvemLqZ0YxBEkgj5kOnXpJY4gU+Z03gWJIOdhJNupCDLIRpBWnd47zcySfaQ6ZIjCMDpdrhwpbUDcVi7KYQ5tijLjB0w193uRBNhGRzZo4yEZZSgFt1Lwh1zQ1VSkgVCFMs/hSFVVZ0CwX8wbdtPHQ9z2Hg2yUjeNI3/UEHxgGkUvfzu3czu18Nc5ve/W7+I//wu9Fr89e9KEAEJ6+z7/1w3+CP/7wL92G193O3/d5qcGqgJIg4SeFpapK7t49F3BXmrl+wTlhpYZ+L6xhiKA0/TCiM2AjJUn/zP7U9XrNYrlicA6UYr1eCxAoSo6PT6Xj0jm8jxhjRY7ppYezUAX90NG1W4rSzpUfsMMPPYu6kR5XbXF+4NGjhzkN0/LgwX1OT0/pe+n3fPLkKZvNhvv35e+9D6AsMRmePHnCOPq8wLzxvIkX1cwhTIW16BQJQYBnUpExA7O6aXJXIQzjOPvXUEHqQpKkano/UlcVi3pB14mMo+86zHNMU4iBZ0+fMbqBshSJXQyJMTpCPxKDSC21FqbE5NTRCRxMDC5MQTGIT03dLMITkZQ8RivKQmGN5vzslP1+z37fZUbKZ6bLQ1LCrOegF5EUh/nfmFJ15VyUhOgYXRCQpc0njmUCzSml7FO0hBBm5m25XGJMQfAps0Nx/p4YE8+uLklRUm9NUaGNYhg9+/1B0pvL7KWM4odVShGDx7uQJcg5OCZJb6VSPZeXVyIvF82vgPC8GeB8wNqSsR8wSmO0BjXVz0joT0xSyVKXFYtKvMQ6BQoSTVHwDZ/+etlcGDw2FHz713+GH/6+z/J19z+FfrhleLbDbTtiN+D9wJA8TiV87hxVQheTlEh+lTU0R2tWJ8fsN62cJyAlRQxRgsiSyJyjIneLRoy1FFh01GA0sU5s2PPO5ft87sNf4J3rDxmrSLNakqJnHAZwEeeFiTW6zH5OCFGYXJTCGgXYfA/6XOUS5nsiBHmmm6bJac1hlgPfANdEnCtSJChteh6ncKJpY6Rt2xwU5kgx0OXgMZGv1zOArpta5PvLxQzogosEo4g+4tOkODBEJb5tF2RTIOmIzim8RhlClFquKaCM7PtN3PjAJ5+1isLiT2nH2vDcQkMA/H5/mKtqtNE0VQFJOqGDj1RVLZ2qSoGSTYqqsOiqxJYldVWzrKsbljhGIil7llPub52Sy+N8PWIMGKvmIDSfAq73jP3hZoMuS3+N0QTvscuGxaLJYWWTLFsAvveevu/z7wlomsXf219Pt3M7t3M7f5/nNz34Fr5Xn/H7/9LvQzXrF304pGFP/+/9Pv7TP3fOH3t4W0dzO/9g5qUGq1onitJwdnbK+fkd7t29x6JpcG7E5VCWGEWWOQwjV9c73OjEMZcSh377iaRXawtMlv3t2r3IGHMv5+npKefn51RW6mD6vme73c4ey7IsGdPIMPRZjpcoSptZR/FH3r9/n6P1MX3vePToMcMw0jQVn/r0G8KepcAP/MD3c3V1jcPhygAAIABJREFUxfvvvU/b7ohRQpb6vue9997D+0g/SEXKrm1RShbBU9IlKaGKhCLmxE1NYcXzaIzUxzjv5j8PQ5+ldToHvUivavI+MxiT5FY8wpeXl2glXrqiKCScakppVTFLSjUk6S49OTmhHwac9wQltSxSA+MwVsJRpsWyHH6aQavWemZrUSmH3iRIjqIsKQxEP2JMQWENMfoMSD25EAWUwofAetVg7ZLr6+vMWmXQpoUlXTQL7t475+rqil1OPlaFniWHMUpA18SODVPXafatOufouo6qguDT7KW2+TNutzusFRDT9yN1vSAEkZS3bctqtSZi6PoOow1lYXHjiBsGNNAsGpq6oizFK9t1PWOutrFWPNrizdMobPY8RulJTSl3VYZc0SPgNmb/tTWGqiypmkb6Ma3BxMjYDYSDY1lW3Fue86u/6Tv4jb/2BzivjukebkjXHakdcYcBFxyeiFeKkBIhxRycY9AqUVQlxlq8AV1YNrstyUv9SIwJN3q8k85bbYrZn6yzXzcmj9dQNpZOO566S/7Gh5/ncx/8PE/7C0KVqGxNion+0DP2Dt85EkjKrDH4IF2uKJGJ2hymJu+Dm/CjGYxaSVdeLMVrPfQ9YQo/uklTko5aJEAoJNkICFnCPfQiFb++vAaNMKJAme/Xsqh46803WR8d41zgnS+/Q4yJ1WrNcrGYPZxy/+W6F60wVjZSbJElvyTpGw4QgkZnNUcMMQsNEtpYmrJC5xTk0Y2EOMimSv4M8l7VM8gexxsGVxh+2WzRusrPo4Rl9d0Ac+q2Bh/kGAjUjTwn1ghQfN47KkcmG2XjKInPMUZiyP7lKOFXUwCXMTYz1i7LggNVWcygV+fnVTzmCT86nBkzs5oI3otvOoqyoDC5Qqux0kF7O7dzO7fzVTI/+OAz/Hd/+FspPvs7X/ShAHD92/8F/teffYN/9unPvuhDuZ1/yOalBqvLVcObb77OG2+8ztnZWWaj4Orygu4wAIpxdOIp3e1vukK1lgV1iPT9MEvifBCWiZTwfsRl35NzTuppFgtSWc2SsUm+OI4jVVmyWq0ISTr+TlfHnJ6eslzWc5iPSAGXkjLcdzx9ekFVFdy9e8a9++d88Ytf5Mtf/hLHx8eE6PMCTZIwQwg8e3ZB14nvFuzMDJKkNmYCUKREihFrZYE5jgMKT2EtKUllidJSX+GcY3QelRk38mJPPLUJYsInn+WOgcPhQIrCWi4WCw77g1Rf1BCTSCalGqUkPtddOI6eFBVSf2NJGaAYe7O4fN7/N1X4zEEtORTFWk1wgbM7RyhtefTxU6YSC60UurD0fSfBVooZZJaZQZ1rZnKCqFKSxuqD9EK27Q6lEuv1MUVZcFNvI3LO1WrF4XCg7z8pA5Z6opDZwRuvoTaI5y8n3IZcQUIScFMWZa7kEIAr8suCw2HPOIxE71ksalaLBWVhhE22lqOjNSAbAl0/YpwHZTC6ZBjGfJ8MUhUElAthssqF9MTu93vxWTs/J7LWVcXx0TF1WeL7ARtLussDrz54wD/5/b+Fz3zTd1AeIu7hBrXr6Td7fO8gRvrkcEok8zED1UXdUNUVyipWx0cMbuR6u8V1g1TDRMXQDZkZlmRera1cFK3ROTk4KZHhUxqu0473rx/y1778M3zx2bvsTY+zXsC96/GD57DrGcaAMhZNYvA9OhqSApU0ShsikeDGnCYd5/tUKUVZVfP9WDcNZSmBV1MStjYmAynxTxqtSUE2ebTWYHJfbfawTn5JqVKS7mRr5Rl4cP8+Z2fnvPfhhzx5/BRjLEdHS5HUjg4fbxj6udoqK0nmmiuYN3hSzKAtxInfnT+XyQBU2OzEOLocRJQ3pTIIH8dxTuqWehk5/1I1ZfI9r/AeYgzYwmJthbU36d99LxU55GAmPcuSnSR2Zym9pF4H+kE2GFPeA4j+huE2yqC0kq7oKBuI1simTVmK1Hd6d6gsfRZbQaLvunkTbxxd7g6WpHQi87UpbcFz4cq3czu3czsv7Wiluf5DvxH9rd+O/c7f8qIPB4Ddv/Iv8pm/dsHj9ksv+lBu5x/CeanB6iuvPEBrODpas1otsNbSHQ4z0xVCoOsGxn7q0izwzzFKSmkWyyWHwyEnySoUstBMMaCDxwc/+wfHcUQnpBf0cJilckoplosFq+UKW2jquuT4+JjVagm53N458cMGH/jwgw94+PFHjKMjhpK/+bnPcXZ2yjB07Hdb7t27R1VYmuqUs9M7bDct221Lu21xY6Cupez+cDjgg/jpUNJzKHJl+ZrRsrBer5cs6pqTkyNSSjx5epFlwFMC6026iDCbIqNbr5eMw8Bud83Q9WiVWNQVq+WaEAS4HvY9dV1TVDWLVSOyxChpnCLdg8JWLBcG72OuqBFwLH7e5/2eMpPsdpby5tFaEwt45e4Jn3rjNbbtnnffeRcwRDSnJ2doLWm9wjRK6Ex36NjttpnVyUmu4aYe5Pj4CGM0ZWl59dVXWK1WlNVilmtOi/fgfa47chSFEUlk2xJCEDl1mthfxXK5ZLlc4P3I/iCgvSgMKTlZXCtLWRYUFsjs7hTMM6XEWqOJwVOXFdZo6dJMABFrpUbn+PgYW/S0+wNX11vGsWXI9/t0bW1ZYjOogYQbR4auJwWpRFEpCZtblizqmqqs8MmSWvi2b/8MP/bDP8YbZ2/AxR52DtWOGCfsuw8jPkZ6Al4JWDAgPZkp4Un4kLh+8kTOgbb4wdF3PT5K6rPO/cMKIESSVpjCgkqE6NFW4wk82j/l/3r4c/z8e1/gatwxGE8fBHC63knA1OggGZarBSFB8KOcM5MwVp6bkCbp8VSlkhnIPKaYAoESzjt27Xa+R59nBacNFJW7TidvZFEU8/8P0gz0pvvah4Gh73HjyFe+8hU+//lfICU4OjmVmiltZoUDCtncyMz49Hw8v7EzA9U0fU2k5EqJ83M6dp/l71N10ewTN5LInBTzeZkk0HLMIsnetx3GaGxhKAor0u0ILqdrh5DouqlaqxSPb6dnSbD3snGWUkJF6IeRrh8IKaK1QRkzb3CFlHLit0jVVVJYJbVOsmm04OTkiLqpaXKSe1VVxCCqhyGnkstGpLwPN5sN7e4we3olU8DMjK01t2nAt3M7t/NyjtGGbzv7OgD+wr/x9ZS/4w+84COS8X/jf2P8r/8Mr/0v7zLmQMrbuZ1/0PNSg9WzO2ecnh6DShz2e/p+oG139L3LYDVKZ2QOBRnHIS8C48warJcL7p7dITgJ2xhGYQJSVNiocEFxcnLKnTsnnJyczGBzGDrqWupglsslq+WK5XJFXVtWK0nw3W63TPLb3a7NybDw8ccPOew7lDK5IsawXC548603pQ9TK5bLBePghWk5thRlzfrohHZ34OHjxwxO2Lpx7ElJFqHn53c5P79Hu9tzOIi8WQFVWfNN3/gNvPnmG+zbPV9658s4H7i6vqbrBrp+YBhcDmqRigmtJu+ny4s5zcnxEQ/u3+Pk5JRxHHn8+DGb3ZbDMMIwEogcH60AOOwl3Of0zh2Oj48YXaDd7bny1zmM6sbzpzID6v3UsSmVF26SAeaFtyIRTeDrvu2Xc7quqKzm/M4RTy+2aGtp+71UmuiEybUdKkJhFSmCNcLOlKUsSo01HB8dc+/+3VwhUzJkYNruD2gNJyfHaKXZ7nZcX14RXKC0JYuFyHhHFLY2FFWJ9yNVZRlHT1kqwOcE3ERSiUXTZMAZKIuCelETk8OHQFlJzyQoqrqmKkqpk3FOejOn85KgHwL7Q4vzAWMsKENIhq4XiXVRFBwtG0xR0B0OxN6zvd7gncsSeTmnKSqaZkFdCTNVWs161bCya5p6zbd+5zfz6779u6m8wX98iekTYT9ik2bwXvpuUyQ6J4AtSodmROSWQ+uJ/QGsyFKlZifhhpE0p7UabO7QJKQb4EQg6oivIp0a+NLH7/Iz7/5N3tt9yIhjVOIv1SqLcKPCmpJ6vSAB+76XqiWjUNmjWBQFIUXCOJJ5SnKGzyd8yTFEYfKUknoYmEGNyE3DDG61klqnwooPe5L8h5h92DEyjg6d63kE0BrxeusFPgRMDsACpPNWT7JZYRTluscMYsNc7TP1rU4bb5PsfJKuq3xuppTguXc0Tt9zo6KYAGxQ4l1NgHTxpFkmXdiClJUmwYfs/U5EldgfWqwtODo6Qinody3mcADAFkVO+RbZtw9eEnljBFH4i0zdO2BiixM+ufx55R2xLBccHa9YrZY0TUXdVCwXC87P7lDVtXjIfaBpGvxqydD3XF9LAvchH8tytSQlUTtkDbNU5ky2gdu5ndu5nZdsfuer38MPDgX/2Of+/Rd9KPP4v/zf0v2X/xPf8Rc3vL998qIP53b+IZ+XGqwG74h50XPxTCpSfO5rTMBqvaIsS66urvDBc7ResV6tSEi663azFVmm0fhksMuG1197QFVVjOPI1WbDw0ePeO3VB7z66itYa9lsrmkWNe1+N+/c1/VC8k4TtLs919fX9H1HVQm7UJYFXTew3e7Zblratss9gnB8vGKxKqnqBmMrhrHHuZEQA2VZk0LEeT/LA8uyoR897X4PJHY7AZW2KLj/4B6nJ+dsNy1GW2wldSztbs+HH3yAG3rGzDqs1mviWgJcNpstz8ZrAb1JFqg+ODbXV1ir+WW/7NO02510qxYFV9dXInksSoqqyUm5Cu8C1hS4scePuRs118GkCF23n3tbp9AYCSAyWXbsyTpSCdlRCpRGF+L1iyFSlprz4wWViXRxZL1quNxsUYWmrmtc8CyLhrIw+HGgMJr16pyiqGbGZ7Vaz7LIxUJqYIrSMgw9jx89pN3LffTWm28RIzx+/IjttiW5SJU9f8t6yWrVZJm2oh86BtdTWEvX9fnzBVaLCqLn9PSEvutxVpGSgFlrQdmCha7nKhprBaREH1CqoK7zPZA7On1I9Du5f0DT9TvKskahWC6PqBtHWRY4N6B14OhowfHdYw67Pc+ePsb1vbCXEZS20mXaO1ZVTaULVkXJP3L3Tb776389bz14C9t7hmfX6METx4iJwh7/P+y9e6xlWX7X91lr7ed53GfVraru6Rl7pj3GzODxEAszxGMw2A5OSIBABFJEEhKEUKSERJEIEhJS/kAIJJQ/ojyEImIkJ/BH/koiISUkJEHCCYLYeOzxPPvdXV237uO89nO98sdv7X2royhKYOwqRfcnXam76tS5+5y9zz7ru76vqCJeKRyKYDK094BUKgUCQSsCkeAjMXgyA9YOjCmxNUACVgKUpCPToDREAzGHzoy8v33K1z78Ft95/h5bt8MZJ4xsSgpWMQXypPPp4lQlBPWiStJXNW9SOeshRhaLmkVd03dpo6YwL7B/Fm+nHtHksUw9oUopYfmMMOB1kglPyb/ee2KqUPHei0Q4BMZRFAJy7ZdkJgciJt2/xnHEWT+z6rPaIHk3tdZELb7aGVj6qR84YMyLqcl+BqqRiM4zAlP/K+TKJB92nCXxE6NqvRNQmTaRtBbg6kMgDAMo2XxTiNUghIALokCJaJq2m2txlJKfPMS5b3axWKLS58WHQG4dw5QGHiJlWSL2jZZhECa3rCqWq5pHjy94+OBcNgYXC8oE6DWKoe/pufPBdn1Pe2jZp+8EtKaoKmKUc6yMMNNZJp87SJ2/93M/93M/r9D86dd/gr/wcz9D9oXf+bIPZR73q/8Lf+JP/wJ/86Nvv+xDuZ/7AV5xsDpay1tvvTX7RsdRJImTnLJ8wXv26c98mscXjwgh0DYtWZ5jR8vhcOD29hal4PrmhvMH5zx8+JAPPviAYRhYLpdcX18RU2dfjJGmOQhLo0JKg+25vb3m5uYa50aUiiIDXS8xeUZEUS+WWBdwHobRErHkeYnJ85lx2m13WDfMHsYYhKGRVF3FMDRsNjturq/nKoeH5w8oSlkIvvHa6wy9Q6NYLZbEEOeU3g8++IinT59SFAVnZ2coSKxu8jVmmTBAKAFLKeDG2pGb62tZxKdqH0lGzWY/XvABT+D8/Iwf/PwP8Ku/+it47zg7O6XreurFktvbW7bbHTFKPYYE65A2DpK/Tk0hS04WykZzenTMb/7Nvwk3WD766EMyZVkfn/Hs2ce8+/5TdvuG09NzisWKqlrKa8kzjtdL8kyjCGQ6w1q5LoqiJMuLT0gpszxjs9vSNAeskxCZ87Nzuq7jgw8+omla6mpFVJpukGRdqb6JLFcL6VsNnuOjI4qi4OKiYhwH+n5gvz9IyJA2HPb72e9XlgXWOfHNLSsW9ZI8y/EucHNzg1KKqqrF+6sUIThG6/HBYscx9YMqqcCheUGCajjsthwOB46OVvzWH/kSx8tjPnrvI7QCbQo2t5vUv+ro+5bHZw85qdY8LB/yo5/77fyOL36FU32KvWloDg16GFEuYgSJEiIJlAJGIQm4KRXXS2ASCRhpSIFdHhs/KenOYuo51Sr1rTqR6laKK7vjH373V/nld77Oxm4hi/hoIVV9Tv7LYGVTxHuP9n72ty4WC5RS2BT+5X1IHbuOelHKplWMs19TrruYnsuljQNhY6c076lPdwKwEtpj8C4whvETFUxTQrb4Vl9It0Z84iboOSncWiedyErClOS+FWa5/HytajV7rl9MzZ5Y0gnkTozp5JV3/o4x1EajjabM7h4/PZ9C4fGEJKGXOpg4+3ljCqCSUOWAd47gHWiRKotcWc3BdpPfHPhE6rb4u8PMPButUVlONAKvQwgUuSHPV5RlztnpKcenJ5yennByfEJZFLNX3wfxiE9BbFOn68SAT5Lf6f2Z1BvTd4QAf/9P9kV0P/dzP/fz6zCFyfmjY/9qANUYiGNH7Bt++l/+eX7h8hsv+4ju537meaXBap7laTGik7TLodPCSyoh9njvefz4Ma89eY2h7yUUSYmXr+sHur6nqmvyosSHyLNnz3n77XexdmR0Uu8yDMMsIbbWMo6Wqqp4/OSJSER3B7bbPcPQSReoUfMitO97MpNBSu0tioLVes1isaQqK9quYb1ecHx8zNS1mGeB3W5PVU4pnIH9rmWz2XJ9fZuqIwIhWEJYsFg84OHDh1RVRXPYiEeurOYAoFxlrNcVRS5pnEfHR6yPjmm7nq4Xz+t6vUp+Ui8Sw5CkkGiurq5YrVZSCZTSOEcrfjMQD1lZi2/s5uaaEAIXFxcsljV5P7LZbGjbDu8DCk2IXnzBUaUFa5JgCv+DBpbLJWfnx3z/938/X/nKj7Gsaz744H2uLj/mg4+u+ZVf+Sbb/YGT80c8fPwaLoA2GaenC/I8IwRLnmXkxmC0ofSkagyRTFZ1DREuLy/Z7LZYN5JlhrJccHHxCIh84xvfpO9HiqKaF9nKSxgRBIaxx21HIoGLi4c8ePCAowRYnfMc9gd2e2GkrZONgBC8hDR1LWBxMYCP5KZgt92l3tStdKOyJaYE2MlXXJZFSnEWL2YIAdsPdN5Lmq4KtM2B9dGKi9Mz6rLisN/hoyMrMparJdZa2qaV66Eq+fT5I7742S/w07/jZ/jy57+MGRT28hbb9uQoTFB454hIQFZEVLsCwBRKRZRJFUPcSUeZwY3Ir+fEZwClCR5yk4MRNtUZz5aW7z59n196+1f5tY/foqNDF4pcaVQmATsQ57qgCUhJOq6k5GZZJuyZUhhtUtJtOhKlJBn8dpPAlL7zks7BPFBV5VxpMnWeTgm5031AHisdxHLtModtTUwiIc5geqoxKooCk03p2zF9nmzqRDWzZ1Qnz7X0zwobqLVKmy7CCE7hX3DnwZ185zE6BKqTUovFw67NXRerVhp0nAG7VhAxqUIpEKKfE8EDU7KwbCxJ2JIiqjhLjaXXVzYKdapSmpjg6X2YAPK0WWC0Efk+Cp9Y4qoSn+4Euossk89dFGbey4ua62qm8ycBSn6+T4/jSNM0KT17IEaxAUwbD3J/t8QY50qu+7mf+7mflz2//8k/xd/8e38BsvJlHwr2b/91vv1nf5Hf/uyXAe69qffzys0rDVan2hEBnh2HphGWK1XJxCh+TWcD7777nkgM04IxBiWJqf04LzyNyYhorPNYJ0xKWZZpwZSR5zoxCJbz83POzx6QZTmLxYoQJHjJ2hGXqkuaQ0w+1TAvQLOsSOFDEpajtaaqahaLBTc3N+y2wsQBKLVLizpF2/QcDg37fZPqYSDPzZy++9prr82L1ynBdkrmdN4SAqzWx6zX67SYD2RZwXKxYnQShqSVwdsGQiQzGS5VwVRVxdnZGevlEu/EC7zb73AhUFeLOdRlGAc2t5dkWcbrn3qNfdNQotnudjjnZcEbkidwkn8S5wUwKfXUFDkPHpzzm37oB3njjdfYbm+5fv6MzeaW51dXvP3t73B1s6esFxwdneKcSKVREuBSFDnGaMxqSVZLx6IxWroZnScqzf7Q8vz5czabjaTRElLnovgHr6+v6ftx7mNMtY+JsQEdFQUFhkhVlSwWC4Zh5OnTj9FKJ5+imdmrcRzTuRdW1I7TRoLUZxwODYeDXL9KGezoRQ7u/exH1FqjM6ld6vsBN1hUVPRtJ/7W0QKJrVKaPMu5vb6laRsun13RdwPOeryzrOqaZV5xVCz5XT/6VX76Kz/N9128CTcWv2swo8ME8WwG51Cpx1U8npKYHdWd1NMg14DONF6D90hITkqxJQD6zkdJBJWbBDQ83kSetlf8/be/xtc++CYbd8DnIjd2OKyPxADGZDNYXCwWrFYrQggcuvYTIGiSmY/pszaBJS1ojODvOnMnyevEmNZ1PTOYLn02pjCe6RqYQLJWiFRdq/nfz0AVuSdMstyqqlgspDd1+pw6G2ZgPflNJxAHzABPKc2iLjk+lpRxUBwOBzabzbx5NT1epMBRfKXJzz5J71WM6CD/bXQCqyqmxOG7flOdXpM3skFBSlFPNyYUIrvWWqF0RGs1g9RlqvqZnk82++Q+VVVVAtvFvDlgrU+d1Q6dAtdUvKvT6tqGXa4pq4xFVZJnZt7EEVlvCpBKncdM/lwX6PuRpuno+14C8vRdoNuYFArWCtgfhuF7+O10P/dzP/fzjzd/6vUf56/8t38CVS5f9qHQ/vt/ikf/1TexL4Rd3s/9vGrzSoNVax0hClDd7/fiT7Ke9XrN2dmD2VfmvefDDz9M7JkEoRR5mfxRErABnsNB/KZxXijJIu/09PQTi9DVakWel6mixLJcLjk/PyXPDU3ykhZFkXowR5TSIrntWooiBYH4QNu2LJcLHoQThmFku91yeXlJnuesVivG0XLYH6SuwYaZedV68szlrJc1J0fH5FnG06fP2O92UiOYJHdTZ6L3ng+fPkN//BytFdaNM7uQGelq1EgNx8R/qbTo985xvF7xpS99iY8+/JAPP/wIrRSZlmocpTWH3Z7t7TVllfHpT3+aerHk0HafZJlUmMHNnfRSUkyN0ShliATy3ICKDEMvgHJ7m+SentvbDbe7jqAKfNRcb7aEEFgtF8QITd+yWi1ZrVe0nTB6MUDwYNqcw/7AoWnZbG7Z7fYptXfJ4bCfA61urje0bU9mCpx3aVMB8QZnWZIXGqwdIRpOV2dEBc+fX9G1HUVRzXVH3gtTnecZZVEQQuRwaNKGRJxB5mgtdVWT6YzoD3R+nMNvIKXPpp5QJTpMxsEy9j1jP3C8WqOyyGAH8rzg+PiE0Tq+9Z3vMljP5voGo6BQhsdnF9S64rQ+4ff++O/lJ77845zqNep5i70dMN6hgidGT/RBAJnRhAheRfGiKmHBJyZcRqFVhjLCoEtITkgBWvK4We6tIh4HhaJl4Nsfv8svfPMf8tb1+4Qa8uMCBXT9yGDtXc+mknRYk+UslivKakHTHGb2c5J8goCRGIVxn8CTsKtSddR1EkYFEecEqAhYlNTqKbgoS6DsxdRqYRY1Wkk4j7S+3AHM6bl06pqNUSd5f5jlwllmOD4+5uLigqIouLm5YbPZzGnmky9Va01Z5pyenHD24JT1ek3X9jRNTHLkDGvdDNIl4VgY7dzkc0qwUncgmxiIQct5TGDVKEWW53ey2SictHM+AWo508aY6USijZauV/1ivY2ebRnT+yLe/WL293of0DqidSBPfcaiIpnqrhKri6KuK+qqhChSbtmoy5mY8hAV3nlGa+VaQaTW/dAnVrX7RPqxbAjdhVB5H+fNoPu5n/u5n5c5/9brP8Ff/Gs/hXn8uZd9KOz/5L/OD//P1/dA9X5e+Xmlwer+cAClODk9ZX10xNXVFft9w/HxCTFENvsNxggztt8f6IeOGEQKVxYVq9UqsSuKpmnYbrd0XSt1NWmBWJXV7E+bFpXDMKJTku+zZ8/YbsWL+MYbb/DGG2/QNA0x+iQ9k/7Ovu/TwjefF9WyKIvsdnueXz3n8vIZXSfBTMMwUJYV/TDgnXitJpYqMxnLRUW9qDg+XqO15vrqmqvnzxlHNyektk1DWRYoo1FomqZjHEcmmWYiyljUNcfHx1KboTQuLfSUkdCkxWJB33XYcaQsS6pKPHXE1FHpPFoJiHvzzR/i0aMLbm83iFfQUZYVZ2cFm80WZ32SKEpQizGKEN3cB2mdpJV+/PHH3N7esF6vCd6moCbLoe1wVrxqhdbs93u0Bjt2KRkXnB24umqJSnN+fo5WBmcDh71salgr0u4sy2ZA0PdD8hhGgok8OH/I8+fP8S6Q5SJ3lERXmxh3AZv1YsHZ+Rl91wtz6QLDcEhVR3ZenA/DSFVUqa7HEKOw/8u6pm1aOuvY9T3DMGJMxnKxYJyljHdhOq5p8c5hx5G+7fDWcnx8zGe/7/u5vLzk6uY5ppDU2/c//BAfwLqIMRXLPOOzr72O6jxvnL/Bv/gzf4gvvPllsr3H3XTQWTLniM5Jzy8i941KSVorkZBI0qAgqHjnx5xAkNIYVEr0jRAUNvg5eTUqEth1hFrx0f5j/uF3vsYvvv11tqHBLAx5bcA4SYJVgdHLNVMEBUFSsIuiIKI4NA2bzRalP+mN7Pv6yeRiAAAgAElEQVQeYwyL5RJjssRkCoiczvl0b5hktBNYcW6c/z9LXsg7ECXXqXzG9Mx8ykaMmgHq9GOUMPrGiOS06zpiFE/7m2++yZe+9CXeeOMNrLW88847vP/++1xfX3N5ecl+v5/ZXOtGDs2BqDxNc0hgtWUY7CyHDi/4S5kBJ0nBAC9CsRhkw0F6pwVoFiZLHdQp4Gnyv2qFQcu9RYtKYXp+pXXqPM2TmiVKF3P6fCt1J40uktdU/KN2lhIrFWf58+S5zXONyQyKmOwICmcdbdsQgieEMkm0I9bJhlAIAZ+Y4b7vGbuRw+Ewd8ZOQVV351uA/vSaps3L+7mf+7mf3+h5tDrlu3/9j6M+9QOYN774Uo8l7K8Z/vKf54f+p0uu2u1LPZb7uZ//N/NKg9VxHNntNhytT8QPmldcPFzS9z2bzYbdbo9zUhcx2gHrHSFAoTR+6HFRuquGYeBwODAMAyF6FouaPM+oipwsF2nbo0ePyUzOMAzstnukNmZMPqfAgwcPWK0W85p9HF36/Y4YlUg7k9QNhH2Zuhetdex3UmuzXC7T4tZA1CzqZZKQRqqqkv7W5RKlovSnLmpCcClwKQrrm5U8ffossRtWqipiZBztLCHWWhOUIjNaPIxtz4MHF7z22ut8+OGHvPfeewQVZlZ0tJZf/KVfpCxzmqaZF5jVomaz3VItK4pyhXPS36q1YbFY0LZS8VNVFUZnbLc7tNYURUlV5RgTEmMix2ZHN1dzDO2AGxzOW6ISqeEwWNCGk9MTTk+O6duWrj0QgqMoC/LcoHQkzzKKogQ1SbJ7trttkkoPM+AxxrDb7V4AIzLDcDNLxlXquIxxkmU6iqKgaRpef/11go94H5JfTqSq4uWtAFk4D4PF1QICpYsV8izD2gFUoCxz1sUS7wJHJ8c0rbBCMcDl1RUhSDfs0A80h5apH9QYw/n5Od3Qcbu/ZbAjusjYHhqWqxU+sa/LvGJtljRPG37Pj36VP/jP/CGeHL2Ge95j9wPGOZQbCWEUUGkSYxWFeUMpfBSwOVqHdU42UVJqrk8gTSE+TZMKUIwCk2mGMBKMIhiFV4E2WL7+/jf5u7/897gd94SFAKB8kQkDSCBYx9CNeBsgagbnCVlMmz12Pm/eewyGQKQZm1nuaYzBWXcHZLy/S+ydQoOSpPfFIKUQ4tyVqhR4b9E6m+W5kqDLnPqLQlJpEyib/m2e5xR5gdFSmzUxe0optDEcn56wWC3phl4qV5YL6uWCbL9jfXzEydkpAF3X0XUt19fX3G6Y7xt3qok79lOO8S6ESSWmVZQNak4bZgKw6X0SdYUkOGt1FyI1yaclvVmn4yfJfk16vXf9sqTnFPm0/8R7BrJRMAwDfpzqd+Lsn52eu65KyjKnLAsi0LUNOkbcaFNAkgE0xuQobchzfSfnDpq2bTkcDvTdgLVSfROTRx40y+WC9XrNarXi6OgIY0RSXZbVr8fX1P3cz/3cz//jfPnB5/i7f+OPvxJBSvbv/Je88W/8PNuh+cSa6H7u51WeVxqs+hDph5EsawHxTLrRsd/vads2LUxTUmYMJKsdQ+qtdKOfF2POWbSKHB0dsV6vOD4+pqpynjx+LIE4TcezZ5fc3t7ivaeqKrpOmMqzszPW6zXD0LPbtwKKQqQfpMPUOy/VJMCQkmPzPKesSo5PjtEKzs/PefDgnHpRMw4j/dBDVCwWi8T+QlEUyUObKhCRxMtIxNuRMgFCpQ0hRtquY+h7WfijGK1LDKFOdRd6luM2zYG33nqLhw8fAkh9jx/RSpNnGUPf493IOGSyoKxrnPeMw0huBGAURUGW5TRNx+3tJrFLGXW1IMRAURYcHa/JTcZ6vWaxKFkuS25vr2majhAkDbXvRowJKCWL+1xrrBc/qrBlNefnpxwdrdgERwgFVVmwWq1EJquh6ToJsPGOzWZHc+jncBcS+MzznOVSNjfuanY8JnmhRco94L2nruV9d26UzQfg0cUFZ2dnaQEugEYpKIqS4ANd6rpFiXdxv9/jvAUijx8/5smTC2IQtq2ua7KsYLvdcdjt6YZBgrkyxXq5wjqHHS3tpkm1KvI6TGboh16qlMYRbTIIUGYlKmhwgZUpKKzm0elD/uBP/j5+6is/zcKXhMsGuhFtR+lLDSMKR9CaGMwMWAPy2QkahmGkHwaRyVqpg1FKE4whREUWNTqAiaAjoOXzl5WaTnsaLE83z/naO7/GP3r6dZrQkC8Mo+sI2jO6QMrbYhwtcfRor1Cpfzgiv9OniioF84bKMAz4YGfgNAX+2BSSBpPkf0rTvUsCBmFlq6oiBEdE5Ol56kDVOqVlJ3CnlIao0uaPmpnVKZV5YhElOTrMoGz6GceBr33ta7z77ruUZckwDDPIijFyeno6b1yt1yt2uy1Ki//zRS/9OIwvSIYndlfuDVqndN0YEHCnZ4Y+JsA6vW6R6WazPn/yBfsQ0CGQKZW8syLZN+m5Y7qvTmDTWjvfF2MMIn8vy3mjbnre4OXYX5RaZ5khyzN88ho779PviBA10QZ0JsnOWZZTlFUKHiOdS4e1HU3TcDgcGPtRkoInv7fWFEXB6ekpjx49miXfxug5lOt+7ud+7uc3cn7y0Rf5b/6DH34lgOr483+Jn/jLX2fTH172odzP/fx/mlcarA7jKAmlaaGU5flcndH3w8w2TimeU9BJ1EGCOZSbQ1eKPGe5rDk7P+Ps7ITzszPWR+vUfxmpavFiHQ7ijxvHcQbEx8fHtG1D0x7Y7BqpokiS0rKq5yCRRVGkoI8BHz3lWOLj0ZxVul4fCXO727NYLBKgW8wLqRACzlucHTGZntND89ygdUZRVGidcdjLoleOz8riPvlvJZAkslzW5EU296EaY2jbhvfe6+bKn8nHqlJSpvcOreDhw4fiBe47OT6TMTjLkydPODk55aOPPua9995juVyyWq5npqosS+q6YlFWXFw8RBvYbm/puo6+78Q7pjPKskZrhzEi6fTBghaGa71e8uTJY7SG6C1PnlxwcvJ5Sc9VU+jMltXqGJ1pNpsNSkW0kdCZYRhSYFHG0dERy+UiLeBTZmwIjMNApg2LqiJ6S/AWoyEET2YMVbni7PR0BqpdN1XUFLPcdEggYrFY4LyELfV9DyqyXErIzs3NLWWRpcAc6PshXYdL1usjrHVsd3tiCDSHA13T0hwOEBIgSVLM3X4rrwso8oJC57jOUsScpaqocsNv/cKP8Id/9l/ih9/4Amrrsbd7tHXgBxQubWi4JBBXeBWFUdUapRXWO7qup+naBDx88kYLk2ejwxHJAylBWDYZlDaMeEYVuHYNv/zuN/mVd77F+7tndEtL0J5aRZSK5EpY2WjECesjhKiIQYK4fPREFciLLEmTZRQkECfselVV8+d9AmfTNX1XqyIs4QRiJoCbZQZjSki1VMGLIgFERloUOXW9ELl8kuaH5Lt+0Y86DOKHNckTrrWkZqsE+qxzbDZbnj59Ot8fRDouEuQmhcUtl0tiDLRtQ1lKkJcxhiLdSw77du4uBpN86iRWdHwBpCtslqf7nUiLi6KgyiuqSnz0UiUVpOo4BHza1JvugdOmnnUjHvlzjEanTYFhGGZ/qAB3M3tRZSMrS+wvHPqOwVrpvYXZe5u7HB8CZVlQFBlKkwLOApkx1EWJ0gYfxDc8+V29D3gvyb+73Y6uaclSWvx0fqf3/vb2dr6/HQ77mQE+OTn9nn9H3c/93M/9/N+N0Ybnf+Tz5L/7y+T/3J98qcfiP/4uf/H3/Rz/g33KL1299VKP5X7u5x9nXmmwOiZG4fZ2yzD0iEfSM6SaGZGgGUKY5G46eZfuJIAio5Mk4PV6TVUVnJ+f8eTJY0BCc4ZhkBCccWS5XOGc5+joCKWu8d6zWFZ47xJAlONIdtTkWTR4L1UbUypmlgl4ub294Xi1pKpLnB/ZH3Y8eHjG6empyHhtzzDeBYAUhXRpipT0LjQlhMjtzTV9b9nvDlxfXwNClIj0NGLynOCEAT1er1mvVzRdy9X1NV03kJmc4B0ByPNManiUmmV+fd8SvKTU5kXBD/zAD1DXUhXT9gNt2/Ht73yXZ8+uZNGseuwonlVhUxxFbrBaMdoO5RSbmw3NviNGxTg4tIroLE+1GAbnLBHF2fEx9eIxFxfnnBwtUx2Qoq6W5EXB8+tbrp5dcnV9zdj3fPrTn8aNjt1mR9dOnjVHnku3pyLgbY8dFMGNEDzRO1SM2GHEFBVD0+FGS6Y1i7oWIKM1kchms8GYjKqsaduOsRvwPiZGtQfg9PSUoig4NGmXUktYTF7INZfVpQQhjY7tZjsnUq/XR+R5zvPn17RNB0GhgyLYKak14qPIwhfrGpRI4uuqYlEsiIOnUiV1yDhdHvPP/o6f4l/46d9P5UvCVYM6eDLrUd7jo5MgJRUJKPkJzFUnIQRGN9J2wlgNTjY3oiJ5liMxSlpv0MjnjUiMHhUANKMJfLR5zi+/8w2+efkOHSNqDZlWOK9wWpEXGSp6MjIMYIGYZ+hFRNmAcdKEI+5mDVHPctZIAqZJluu9n8N+yrJGJxDYtq14k1W6to2ELik9beTExK4ZrJME28wUibmVapW6XlCWJSFEvJfNsBgEsL7ou5x+shf86f/XXlMQyfjUOeq9p21b8jzn2bNntG3LarVguVyxXq84Ol7z4MH5HBp3c3MjKpKuwXtHZgpI78Uw9Hhv57qqrmsZB/HxZkXJol5QlgVZbnDB4Ya7rtEQkhZDKZHaKgkCC8GhMATvRBqtIsEohl6lvmT3iTqhSWo//UxMdwgRYzIWdQ41qQN3xFpHVdUvPM6n98qnROYMHxTWe5yLOBeoUnLzoWnZ3N5ydfmc/XaHjuCR0LYQI33yIeeFnKPtdpsUJj4x0Hee5fu5n/u5n1+vKUzO1z//eR7/x3+M7Df90y/1WEK75T/86l/hr3Xf4Lubpy/1WO7nfv5J5pUGq2dnZwx9x35/SD2ewnjmec56vSbPy9l39WIq7pROOUnmxnGg71tWq5oHD864uHiISumu3/jGN9jtdjgrITxVtUjPnXNycpqAoMjzhKFU9L1lHETCejg0s0ROOjjPGMeB1XrJarUUltU52rZls9nQdR0nJ8fzMU8L3WmxPfV0Vgn0pKhPQoDnV1fc3GyIQRZ7USJBgUCeibcMoCgy9vsdXSe+xrquRQrrpc4mz4skhT5ivVqQGU3Es9/tGAdJ+Xx0esrZ6SlFkjBeXl7y7Nlz9ocmSWk1jQ8pkKkmBM9uv6XIMnwYUTqyWCwIQWFHhIG0Hfv9Dus8eZFTVQLMi6rk/MEJJ6fHPDw7RauAd5716pjdoeXrX/8m270E7TgXKLOMt777LhAo85zHFxeozPDee++zWtYcHZ/gradrOz768ENA5KGLurrzA6I5HPYoIqvlktWiRmnNmGSYOl07N9c3kEJ0hkHSnye5oXOO3W7HaAfW6zXL5YLlasH5+RllmUOE7XbH8+fPcdZRljXWHri5EYB9fX1D0zTUZT0nWMfSp40AWJ+sWa4WtG1Dkees6wWLrKYocvQY+exr38cf+tk/wI//lq/CpmfcHYjtSBY1YbQCRqfgpKmHBubgnEik7zoO7Z7ejuJTjQEfvNTSoKRdVIEBTJQYJmcUVJoBx2bc8u333+Y7H73FgQ5XOwbbEVRAW0WOJoZINAqVlcKWRkkOrvJCKpSsIwwWN4qf1DnxYGdZ8lZqjVEKa8dU2+JTiE9gHB0+SU9FimvQSj4H42iJhBT0FWeGve1Gue6KQt5zUlesEtm8yFwnf7WXRNwsmwHvJBU2xhBDxEXHJD2ek4L1nfR0ukc1TTP7ac/Ozjg+PpoB9X6/Z7QDMUaR6CcP7PHxMcvlkqZpaJuOiNTOnJ4uODs/IbgRZx03txuapiHLc7I8Z7lY3ilOQkg1NyGB8JAC2BQ6RU/HGPDOJuDoybMsVYAFUbjEu45XmLzdU1hSPtcElWVJlhWAxqeKr74X9ne6f49jYobdgLVjApOTpDmyXK04P5eU52mjYrfbsdlsaZsGFZM0PHnH5ZzcpURPIU4TA3wPVu/nfu7nN2L+2Gtf4c/kHZ/6H/+jl3occeywP/eX+Pn/NPLnnv2dl3os93M/34t5pcFqUZR0vYQITTvvk2dsvVhzdnZGXdcYY1iv1xwdHTEMPU+fPk0Mp7Ct+/2ezcanCgrNdnvDbr/n2ceXPPv4ktGKd1MpqWTo+562bUQGF5kTPrMsQ41SbzL04xzK82IBvXQ4Wtq2FTngMIgX1I4MQ0+RF9zc3BJC5Pj4iKqq0oKup+978VjlGUWSJU+ve+xHRmdBS+UORhOD1IaYLKeuKtbr9Ryw5FPYlPdWfJg+QKrWyDLDkydPeHhxDsFzc33FzfVVCoiJ1HVNXdc0hwNN23J1dcXTp0/xPqYFfcCYDKPNvJBcLBcYk+G8sMv9ID5R6wLj6Mlzn+osMkmBdQ7vk8RZKUJwODcwjgNPHj5g6EeGceTq6ppD00jXpSmIY08/jOTeoBWcHh3z5Mlr2GAxWrOoFywWS25vNozDgDFSbVPkOcuVSK6HYWCwI85ZyrLAjgP73U7AQ6bIipKyqFGJya/qOoEV+beTNLptO0CAeJ5nLzBGgZubG+xo2Wx23N7eChjtRV672+4Ye2GavAscbEtZlATnKYqKsoy4KNdU07XYcaDMc86PTnnjweuszYIffONNvvpjP85nzt+Ayx5/GIhdh44+Be8ofICIAVVAFGZSaQHh1o4MfU/TNgx2xHqH9Q6voqTFJhluJOUvxYDREafBZnAII999/iHf+ugtLndXjMYSi0BnWwbfE30g9xUqhTZFNF2wBBXTZw3yBCSUUTQm4swdEAIJgYK7kKQsy0DFO19kCDgfklRZupJjjFg3JegG8iJP/aZ3zzslAIcQGMZentsUKKXn8C/ptBWJaVkUaCNg6q4XN5uTkqcwLICQ2FutNTpVtUwy/LKUOq2joyMWC9l0mCTFwzDgbzxXz69ZLBaUVUFVlTPoyvOc07My3RcLFosFRV7Qt4G+6xJLKUxvVZQUeS5dpN4TomxauBRANVUfW++lmoqQgGuUWqjVirqqcM6y3+/pBukjljom8YHKy40pBV0lJlvUEpNkWoLJ5HfMIVAws7PeRWKQDRGflCQAfTew3zc4F1LCskihh64nukCZvPMRLTaRF0JCZs9uvOvineTc09/dz/3cz/18r+c/u/jd/Kt/78+iivplHwrf+Z1/ht/y7i+97MO4n/v5ns0rDVZvrzdcX93gk98rN9nsTVJE+ralyDLqqqSuCpaLgtWyQqvI8+eX7HZbOu+wdqAoMvLM0PctXbdnt9ux27TkpiDPy8SYKeq65uzslKLIiSoy9lI1sljUrI6OWXU92+0e7x3OyiJ06IQ5GPuBm2GU+pO0+NIKtJE6mszkaJ3THFqc9ShjOD46pu0G+sGCMiK3s47dZkueF4TgsaOjH0ac9VRlRZ4X+BDSojHS952k4+YZBM9oHX03cHR0xMX5I7z/mF3YkuU5xigenJ1SZIbNzQ1GK+pFxZPqNcahnxeMWZEzJCZLa8352RmD9VxeXqe01AiaJBmVdNe8rOn7QDeMuLAX8DrIYv1wODBaizGKopTjcM7ivWZ0lmxn0BjOjx+yXJ3x8bO3eP+DD7m52dI0LS5EurZDKy3px97Jc8TAoW1YrpY8fvKEw6Hh/Q8+4Or5NWNiQR8+upCKn+SvI9V9PH7tEUWRYzJDWRQsFzWHphVZaWKETJ7ho6TWgqKqF2hjUEazXC9R6b1QCpq2IVDx0dOn9H2LHRzttsE6T7Uw9MHS9h3tKNfaYrXEjw4doCoqCEnuimO5qLB+JETHxYMHnCxWPD664HMPv48f+8Ef5Quf+xEWo0FdWuyhBetQKVQoInLlGGVjQc09nFJbMgwdbdsyDgOjdYQYcDFiQyAajU8lKAqNjqARf6vNAkMZ+ai55Vfe+zbfevo2O9tgqozMGJy1+KBQqiCogFMRrQI6M5jcJAAjkuTcZGR5QSAyOi9s61R/A7PaYJL8GpXYt0hiCKWXNqJAaZTKZrYVoCxz8qIgzzNifJF5VZRlRYyS1l0UJc5aumFI8tdIiKAzTWZy8UUaBTFI7y7C0ocgKeAuofkwSX2nYCOdS23UcklMXtiu7fDB0vct3tuZ5e+6HhDA7Z1l6DuGvuWgNWVVorWhKiuqssZoCSkKPnK9vaFt99xcX2NH6fFdL1eUVSVA1VqG0Yo3VWt8UmnE6FFAnkFd5OSZIc9yFouSxXIp11CMdJ0jK0rO6kUKjZs6VB19P9A0B/G4WisvWRuyrBBfdJyShZWwucHjXmDEnXdyTRqTqqsCRmdAZBwsN1c3AjbTRoBWYr0wWYEyBucRr7vSiR2WjY/MFBijpTs4yj2KKOfFjfcBS/dzP/fzvZ0/8tqP8Vf/qCL7V/61lw5Uuz/3b/Ln//tT/vPLX3upx3E/9/O9nlcarO73B8Z+kIWKithxZBwGYf6qCm0Uw9iiDhBjoOsOqedUUZY5y+UiMbEZIbrZC9a2BzSG4CEENSfojuNAnmd0/YGb246yqtBGE30kxJLcFKmzs0tsqvijyqIAZFGWGc1yUYuUMqVPKq1SuqssuKyVRfHzy+ezrNSHgMkyyqzCaAHjCgg+ctgf2B1axtFS18J8APgEVuuqxo8Di7KiynO2uwMH5+m7nsvLS2w/8ujikSQRW4u1I++++w5vvvlZnjx5gnUj3/nOt9nttjx8+JCqqmZPbJblbLfbFHACVVWyXq/pui6xiI48ZtR1TZ4npqi3NK0A+EIX8/lUWt6LTGm0VnOdTZZlrJbHFFnJ06eXNE3P5eVzxsEyjp6yrHl4dMRut+Pm5poiz6iqUs5tkXF9c8P17Y1UgLSSOmxHYaw+9frrfOYzn+bk5ISbmxuaQ0OR5biwpKxKqrJEa02f5ObWOfKiYLPdopQWkNwPEqZVVGRZjpuYorRpYlI9UFEIuIkxsNns8KNDOU2eFfT9wOgdJjOsj9aoCIRIlRfkSoKwvLMSFgUcrdZUdcFyUfL44WMWquBzF9/H7/6tP8FrR5/G7MA92xNHDwiolaeMc5qxwD6RfIYILgT2XUPTHbDjgPMenx7lI4RUs5Tl0scZk5wyagilpjMj3/joHf7Bd3+FD3fPGbOA1x7jLTSJ/SwKYtAM40giI8l0hvHxjqVFQHk/yGZAlmXkNsMHh7VhZizlTeIT1SchJUCnZ0sMp7B53kkNT5ZlZAmkirfTzyE8UuMiLOFqtQTAjiNd1xIjZEZSaI3JpJwnqrQxJedG2DkBmjFG+nGYGUOtJVkbwJgcowXET8m9kgwt94RxHHHOzfUq2mi6Zj8HF81yWx/Is4KqqimLcq7h6rueNjGqWuecHK+pFxVFXuBGy3a/Y7QOpU16PUY+E/1ADI66KlitltRlQW5EZt21B57tNrTdKEm8EaxzGAVlWRBKP6f+eu9puzYlZEt6snOO0QbpVE6M8wRWp42H6TzKeyT9xqSank/IjGOQ6h2lZ/k66TrGp82yORlZzdeM1krSf/UEluOcjF7d96zez/3cz/dwfvLRF/lrf+vfxjz89Es9Dv/hN3H/9c/xfX/jLbZ981KP5X7u59djXmmwaoeOGJwsqKOnKBZz3UOMkaEfsKN4oybZr3Oe0QpDuF6vqesFxmi6rkVhOD874/TkjBg+5Pp6zzAMjM1AjJKEaYxitL0wmt5SlAI0dvst4zDSNIME+CiFMaCikmTZxSIFvpTS75lpgpMF+SSNtdYyjj3DMKawlxHvPI9fe5Lkw+KFC97JgjxGjNaAYrQWlHS/3tzcSCiUtWgjUsVFUUjXoDKgFGdnZ1RVRZ7n1HXF0ckRFxcX3N7e0jQNDx+es1yuubq64Z133ub6+oqizLh89pyHFxdsNzuOjo+pqyV73bDfbWm6gTx1l0rqqDBawFwLMQwDTUpRzrKM0YnEuigKUAKkpq7IR48e4b1nv99y2Dfsd8LUXF5e0vd9WhRH3vzcm1xcPOa9997jcDiwWFTpPZaAl27X0fbiM6zKinF0ZJmiOl6gVYZLPZ7GGLx3DP2AyQ37w566KimKnLZt2G63FGU5+w1PTk65ud2wG3cURTXLG50L+FFSmCc/3FTtMw6Woe9RURMC2HFI/aYi0VzkefIxB1zqMxUHrUr+34IizymzjJPqiGVWsxorvvTmF/nql7/CaXWKfXbLsLNkTpKcCeJxDQg0lfwkzRRD7YKnHUcOfUeTgniIAQdSIzOBgQi1EZ+0D55oYCxg1J7nu0ve+vC7fPvj97ke9nTREpQiL3PcOJKhJJwqhlTfwyyHlUTd+AJ4CTNwyUOYAWuRFwQvybwiV9ZpI0kSg3XaxIkpJ7goCrK8mDeNpp7TKQhpegzIeZsSqzOjcc6y3e6xbsR5uXalc1dhhxGnJOF5rooJAWUUR+s1KtXotO1dUq9SKvlFs9RHi6gqtEJrg9EKo+V37Pdb2eRIn3etpXNrqrhpmoYQInW94OhoxXq9pixL+r5nt9vJ3/uINoaqKuffPzpPv9slC0CkLCvyogAt98UJtGd5SZYZQlIrNN5KGq/WZKZgucjx6d6T5TllkaOIc11P3/dorVmtj+hSx7Ck7+ZkxmAyCUebJNBTKJZzbgaUAvCnDQcDRup2Evmb7Aay2zHdb7yPyYEdZ5A6MdLaSN2XyRRaSwCYfDAjSktF0XJ137N6P/dzP//k89sefp6vFI/5i3/7T6OPL17acbiv/R383/rv+JH/4j3e3n780o7jfu7n13teabA6JTkqYH205jOf+Qzr9Vo8kV2PNioFnWiatk3df+K0E7Amss7mcGAcB2FaU01Dk/yGZVlivUg867rm5PQYnySmp+fnHB2vub295enTp7RdwzhKp2EIATt6irwg04Y8z4Uh0XruWTVFOTMLk+RvGHoOhz193+N9oO96uq7j7BA757IAACAASURBVOw0hZQEjDaQaUKISVqXyWI53vn1pgqVSfYYhpFLI17U1z/1Ot45yrJkuVpS16X0eNox9SIW9MPA22+/zWazZbfbSs1FueTo+AijRa68ud0xDg6xeukEGAzBC9ACQAWsFV9vlmX0qft1CmchkqqH7nooJ7Ba1wtCCLRtx37f0Pe9BM7g5kCbuippmobnzy9Zr1c8eiQ9seujJcvlMjFwimqxEJl0JfU+RVZgtKE9NFxf3/Dxxx/PvbMa2Gxucd5xtFqJhzY4tNHUC/GnLlcryrLEZBnro3ViSsGOLvklRbqqFPMmQ3NoRBbeS1rwoWkk1ErLBkRnHWPbMPSS2hqJDM6DUpi8wCE9k+cnJ5TktM92fOpTF/zsj/4MX/zsb6Z2mvHDHfbQkQVF8AFcwEQlvlCVwKoSBjsEcN7R9h37tuXQd9jgpavWQ1Dy2KjExKgJ+OAwuSYasIXi2h34xjtv8faHb9P5liaOHFRPKCBmkX7syV4AFj44URREyItiPucTaHkxKVdphXUOF7zUn5SFhDqlrtwYwwtMnEhZVbjzKCoVMVqJ+iF5KMVPamZgPHkt81w6jPM8mzcJQkzRSkqnkKBcgJUVeXEMkoYsflBPrnJh75OqAITJ1ukajCFgRwFuRR7IFhlVWVKUIgkOrmZcLlnUJc7La9YmS7VRAVIAUpbl5HnB8fExp6enKKXZ7w80h2buZ9ZGi2c8iASf5MmcFBzGyKZVkB0WOU6tJfFaiax5tCMZYJSWjuT1muVqTVGWdP0w9zlrIiE4rBVGe7leJTl+QO8Nw7DBOQ/o5IeN6ZzElI4c5gC8SYo/BSJNieeCUicmXUC+HLqX69hZ8dcmFhcmNlthMjP3uJqUzhymuiqtBXRnGca80l9393M/9/OKz3G15H99+Hk+++9+hvyP/nsv7TiiG/kHX/5z/I0q5z/58O+/tOO4n/v5jZpX+ttbEckzQ1VVPHz4gIuLh3MgkaRKjjNj6bxFG0NRCDtV1TVdP6Revk2SjJbs9nu6ruPQNGRZzvFxTb2o8Kn25PTsmOWy5vzBKUcnJ+RFRte1rI8WvP3Wu9xcH2jbIS2GBQws67uu1KnrzzmLRpjQpmkxmWG1XrFYLFP1xECMimEcefbsGVqr5JUtpI4idQve3NzS9z2ZMZRlhdJyytq2TenBslCtlgsACWMpMrLccHJ6zHq9FFavEPDX9S37ww5nJfhovz/gXODi4oQnT56wWi25uZFAoK7r2Wx2jKOVcCSlsaME8+hUWxGiBKjsd7sZlFRVxXK5xDlH3/Qio/SaXOezZFIpxfX1DUVRUJYVTdOglKHrOqraJGCRi3Q7OJyzrFZLHj58SD90krybOirP9RnDKD2hy+WSo6MToo/c3tzinOdwOHA47AFhb47WK2wKV4ox0nYteZ7x4OIBi8WC7XZLGIOEZKVwHGdHCIZxGBJrbNAJFHnnWNQ1Dx48oGn2qf7HJqZsBAW5ztAhElKnpAuesqoo6pLRB5pOAqmO6orYWaKD3/5DP8rv/z3/PJ85eR12DrttUIMlDxD8SMCTmwysrPW9TmFGSuGJDHbk0LbsmobRWXwMuBiIATR3PaZM/6XB64jLHIPxvHv7jH/0zjd59/Ij+jAQc4iZQi8ytApYL7/YZAoVVdqciAIetJlB48SsKaXm8z/5O0OMGKQXVKS62QxqJxYekGs6ywhBHivPJ+xtsG5mDSWNNps3EPI8lwqgxUKu1xAk7deO6XMqoPHoRFJ3rbVsb3fs9wf6sZ/rpEyWzRLYoihmKf70nH1KkQ4hhRWl1yldqB0QydMxvf7kNUZnubq+5vbmGqWEpTw9OZkD5KZNqaZpUkK3vEaFnsOifPQYnaGUJiJBbNZaeZxOm0SJkTZZhjaJtYwRhcYoyI10UNdVydHxiag3rBXpexSQ6MYRY5SEQEVPUZaiMLCOMVV+SVDUSJZllGWZ2HqVmFKVwpmy+RqYyfw4bUoIYI3RJ6+1mRlYY7SwqlE2MUIwGCPfEAJ+dUqOTptRWpHnIvkVqT7z+3k/93M/9/OPO+/9qS9Q/pm/8rIPg7/62/4C/87N//6JcLn7uZ//P88rDVafvPaIopBFb1VV7HY7QgipEgX6YaQdesZxZLAj6+Wa8/PTJLXbpQAQl3bcNUpLUmfTNDjnWC6XGJ2DXlJVJXlh0BoePXrAar3ABcft7TVHR8f88A//Fupqwf/2C//HDMpkEagoq4KiFJlriOKbc84Lg5MWcjFKv2duMhb1MgWYtHjvsP3A5lq8mEfHx2gloKrrep49e8Z2u6Wul0nSLKdMxUimtSxCE6tjjOHm5oauL3njjTc4PhaWxDmLSnLET33qdZbLBd/4xre4vt6kxXzBxcUFr732mtS5JP/Xer1mGCTBtq4WomtUd32LIQQIEZuCpibAMAEUrTVVVSTG2xOjmRerMUb2+z1aCfjoExtZ1TVg53OW5zmLxUKSdv3I7eaGcRxZrRZzmqrUVyjG0RFCw+3Nntvra9q2hZQUW9dLrB0Y+p79fo+LlqkypKpLVqsV3guwHYYhbYRYCXfyHqLCpFTdLNPkmZnZRLIc70fc2FPmOcXxWhJPj48wOqOqK7x1HPZbhn5AAScnJ6xWK7Qx7JuWq+dXuN6xcIqq1fzhn/0D/NRXf4alzQhXDbQe1VnwTkKTtCJqSa8udJGChoRhHZylHUa6TmS/wzimRF5Z8HsUXmtUqpDRACbiTaCJA03s+bV3vsuvvvstdr7DF2Cjx2tJlF4eLTlbLzns99xcXjJ6T5GLV1dh5NgITOGrcWb1JDHWeSdhO4lhnR4TY0y+UgF6k3RUgIb/RIfp9GdZpudqodVqRVVVTPpnSWmWDRJnA7vugPOWru8SQNKUZcXR0ZrlconWiqHrZSOoaci0pq6PqOoKRWR5tuTBgwdyDWeG9dERMUZ2ux1XV1e0rVwrkg5M8iGPBO/FZ71cUpQZu/2G7XbLbrcjL+tUeVXMSgHnHG3b0TbdDIBfrGWJiRGOkBhiYTTzPG2gwMxU50WRvKcpJTeFkxUJFGfJR+5j5PLqKoFPCZzSetqQCfyf7L1rzG1bmtf1G2PM67q+l32vU+ecOn2qqu/VV7qbphHQhkZRg0ZURD94SUiEKBIUjfhB1KgkGA02rQGj+EWMJkiiYDCKIGkQ6AS6sZvqqnPbZ++z93td13kbc4zhh2fMud7dmiAq5+yuWk/ypqp2vXvNueZcc+3xH/8bwY33p+v2SA+0HuX6gy0gSeTHx8jhIan5oKhgZFuHUTqgEAZbWGY3blZJZ3Uy2iuGey+MvZFU8uhbJnqMQ1AMlTZd70bLhEmPMuDjHOc4f/tzPlnw9X/2y+S/5w98ZucQuhr3N/8if+yf+rP8ros/ewSqx/mmmtcarD54cJ80Mez3FbvdjqvrW7quO0i6FHh8rITImEwKrO24ubkU1jH4McRkNpswnc5IkoO/1HaO/bZCGUVRpmSZsDJFUVBVFdv9VronnaMoHvPW22/zwfvPef78YpQCLuYzJpOSsszjwthE1jTge0elFGmaRembgAulNbPpbFy4VdWO7XZHADpryWPi8X6/53ZgVpMMY1omk3T8khredwgwnUwhBLIs4d79c4oiZ7Vek2YJZ2dnZFnGzc3NoUajyFkuFzjXM5sJWN/vN9yubvHBkaYJi8WCEFSs1IF9JUm5Wim2ux3r1YokNUwnE+q6xjvpX+2tpVVK/n6st+n7Pl4X7kg0NV3b0bbt6EsryxPyQoCoEfpkrLq4ubnm9nbFbDYdZaVlWeKDoqpbbm5uWa/X9NbRd1b8c/ogR1QqsFgsUAQm8xJrO1arFaf6hOVyidaa1WqFjp5E2NPbnByi50+RpiKHJogv0CRGGGfb8vLFJ5IiHUIEo3OWc3ldtCLNJYShtxaNZr/dsbq+pdAJ7zx8k0lS8C3Lz/Ebf+WP8+W3voTbNLjdDr/t0F1AuUDwAR8cPQ6MQmmDDYGgoA+e2lo21Z5d3Ug6r3Pi20PqQUYPIBJkFFTAJYFeO2rd8XT1kq9+8h4fXj9nTwOlJslTjM5wwY1VJF3boAMUeTEym2VZMpnOsN6z3+8JXkWptBrvgfMS+DSAkCGs5y6gucu+gnxenBeJ7MACD89T7xxaM4LVEAJVVY+bUoPsNEuLUYYqHbmWru/x3rHf7yPY3EsiNpr5fCYhV0UZN8vk+XaRNUdBE/2l1toYsJWRK0XvHN55tDKYREOQfOXOtrSdgDFjDCcnJyiTjM/H9fX1eJ3quqVpWmGrAR0/yyiFVnrsH9WJRgcV64CGTlFhGYX9FMWJis+RRgLhhgRghUj1vZPe1IEphSG11xOCXOMQDaWJMZH51BRFQZL4McRIlCW9aHmjn1TmELY0yJVHljWAD+JTnc/nTCYTTk9PWS6X5Hk+Xp+Bad5ut1HFojHmoNYY1m5972jbfvx7g2XC2gNAPs5xjnOcv9VkJuVfe/ir+J0/+pzi9/7Bz+w8up/8fVz9iZe883PHlN/jfHPOaw1W79+/R28tu92eum6pqmaU/gLSZKECs9mMe+dnpJkheEc5KciLdAx4OTs75/T0BK01u91ulITttrc0bR3TUnu0hsePH8YFb0XX9aBgt6v4+OPnzGdLFoslL19ej8Eti+WSe/fOJUxoOC9iBUn0tQ2soyywGMFmkhmyLKNpaqyt2W33LOZLyvmSvMjE1+pdDPXpo6c10LYtdV1HQFdSlgWTYkrT1JGVqaibCh8cRZEfwphiIuq+qiknE6alyGHPzk5IM8Nmu2azWQk49VDXFXleju8hjTLDum3BS9/hILsrsly8nM6TpxkEaKp6POc0TeOCswVkUSs+wWRcTA4M6nQyI8vTuEmQ0/c9t7fCqE4mJY8fP+b0VDp2F4sltpdUZe8CwcdzLRNc70YWqqoqsiyhNYYsMSQm4eRkSdM0vPPOF/iWb/kWqqoiTVNubm4oyzJ+wDQPHzzg7bff5sWLSz766COa6I8WVjqnaVp6619JB+5tx36zptps0UaTFDnTxZz5YoHJc3zbYzCcTU/IesM0K/jh7/4BfvRbf4R72Rn9RYXd7tAuEHoXK1GAIWJGdJ6EoOgREFi1DZv9nm1dR5AhjJVWkq46BBeB1Nl45XEaXBq4qla8f/ERH9w8Z+tr6rRHJQmT+RSVCEDvO0tV7WnqFt/3ZCahSDOqzkqSdVHgVSBJDafnZ9jGRglrN4LSIcF68CuO0tk7abB35cOAbALFaCXn7Pj3BwZzeK26riMrbl8JAXPOYbuhY1M8ntZ2UTacSc9xXdPUNWmacH5yzmKxiDJikamDbI4M76VuGurYJXzXjzl4dI02EpxlQCsjjaDSsjPaAIYO1LuVOy4+68578X1qFcG6w0VvL7F/dgj3cs6BDmOyuYo1LkMnrfcDa21IjSHRWlJyYwiR/G9hUJM0Q8dOYUBUBfixLmiofRq8oRKCFeL9FZntcG/G+xyBr9Yi7ZW+5rsJwHK+eV5wenoySrmrqmK1ku+jYXNwuNdpmpKmOUqZEcjKRspBnmytqGrSPCdJE7n4xznOcY7z/3D+8pPv5Mt/6d/8zI5v/9Qf4X/6PR/wu9wv8MH65Wd2Hsc5zmc9rzVYVZiYnCveMG0y6romTbPY7ycgp8hTlsuFSMtizYJM4NGjR8zn4i9r25YkERbMWkeWpWRZCgru3Tvn4cMHZFnKbrent540yWm7hpvrFdfXX8OYjN22GRfeeZ6TpSnTaUlRlOz3FVVV03UtVVXT1M2h/iMmyQ7pnW3bQgtZluB9kLoV23N5eUVd15RlISxh7wlBwnKcCzTNFtt1aALz6ZSTxYI8l0XbdrthvV3z4uUnZHnG/QfnzGYzmqah7SQ5uEShTcL5+T10kFzVyaSMISgqgq+Guqmp6watN4QAi8WCxWI5yvMGdgj8K4vtpmnIsmz09/V9R5ZlUd5ooywwRA+aQ+sEbQS02L6jaQzeTwmBmN5akGVhZFWUUkwm07EqZ73a8OzZ87Fq5/z8XEKzWsvzZ88kgCempWZZxnKx4I3PPQEduLi8iMnS8N7X3ycQ2GzWY7Ks1oppWdI1LXmWsdusuXj5giLPWcznpIkA8OAFoGdZRlmWJInB9Y62aWmqhvV2C0aT5BKsM5/MmaQFpU+ZqpyHZ4/5TX/XT/Cd73w7k7bAX+zod3t0CDhvYzcpkl/jIcRQINA4H9jbmn1Tsa9qmq6jD4d7opXCR6CntMKrgAud5LAWKY3u+YVP3uPnn36NVbfBR5ZVJwpn4Ha/prEdiddkSkuokw/0zmJDIxJ379EhsN5uaG1HXhTiG4bD5pKSVOJhA2kAKsQwKKnd8fFZsBHEgDHi88yS6OOEGPSDANXoAx187OKPTO4wenHjyA3ha5AlKUWWk2YJUqHUkWUJZXkWa7GmlJMJZew0RinaroWg8L2ns47OOgFdaAga5yFYh9IDeE2jCkFSdSXxV3pm26bDuph8q+U9KJ2MrKrWmtlMviesFUVCHzdehkReSRPvxyRjqdfpAHmOtdHkWSa1NUkaa15i0BnEQCYBfz2GznZY2wtzmkollImSYvlusuPmjwBgF8XWSsLGEM9x4BCWJJt0PgbWJdEioWKVV8BFKbDIeeXzul6vxw05CaFz489gCZhOp5IEnWRjyrFzHu8Cne1p247eWrquR1tN5sFoQ9scPavHOc5x/tajleavvfndvPs//9uf2Tn0f/On+TX/yl/gr9587TM7h+Mc53WZ1xqsVk2DtS6Gj9xHJyl9b7m5uWG1uqHrWrI0Yb6c0bUtFsbFapql3L9/n5OTE9q24+LlC25ub9nt9lxdXTKdzrh3fo/5fM50PuHx48cYo6mqXay5yLldr/nwo4+4vLyM6beKJMnusIKGm9tb8I6Tk1Oc87RtO1bTJElCYmRB3PeWJEnH4KExKdMrspgaPAS+XFxcjhJYrQxZdvB4dl1LojWz2Yy33n6LJ08e4n3g6vKG6xsJWNrt96S2ZbGcjQmoPoadLE/O8D4IC9u18dz6kYExJsF2FqNNTEcVllm8s1vKsmQ2nY2pudZKZVBRFNze3kaApMnSlH1VYYy8/nw+57u+67t59uw5X//618nznOVyOcqC81y6Xatqx7NnktxclHn0F1tWqxV93zOZTOi6jquray4vLthsdlR1gwcSk2H7GErTtLLBEf23WSYf9e1uw8uXmourC+aLOcvlkpubW0LwtF1LCI6maSiLkvPzc4qsoK4qfv5v/Dwff/wxbdPgeodGjfLPIi8oylKY566ja6XexnYtXduhgKZuRHIeNM31nqkqOE3nfM93/wp+69//W/jCk7cJNzua6ytU7TFK0fUdzltUanBeWH4bK0K0Eo/uvt5zW69obSueXWI3JdGLGIEERkUmNaBShTOOl7sX/Mx7P89XX35IhSXkiiRAMRXJrO0a2rah8z3eC6BKtRmrYvo+foaBxspnXuSqht62VPuOrmlHQGnuMHJ3mVN1R8Y5eqEhglo1KguEjUtI00NAD1qN3sZB4q61GdN6B4B6CPVRZFkaK4xqTCJdqOlsFpPFBVQ1TYNGgn2s7Wg6+0oSt/jQQZshCVnhvOhZk0SPG1oaFQm9GK4VPJOyZJal+BAkxbztCMQOUgWJET8pHCTwwOjnHcCbMJ2yQeeD/Pc8Sygnk5HtHTcFiOfgnPhq458M3zvyfiRZFyWbG73ro79U2NFsrK0KI0ANIdDbdtyU6J18TtM0k1Tg6O8f5NpKxWCv6LMfXiMgnwkJc3PxuyhFKmwMSons11rHfh9VL4C8hKRAK6Q6yXt5j86LrLmuG0yScHJy9v/636LjHOc43/jzlfMv8A/mb/N7/5d/ATVZoj6DBHH39OcIH/1NvvRP/5c8315/6sc/znFex3mtwartHLt9LSBrGbs1tWE2mzEpcrbbjaT4moTddjdKM+/fv0+ZlxRZwepmxX5fsVnv2Ky2VFVFmU84WZzKopo+MhUCMLRO6LqWly+f88mLl9zcrmiajrKUtF3nPFkWQ1t6S5pKAFTbDhLlnr53wqzEqohHDx5QTkv6vme321HXntlsRlVV0aMnDETTdDHZUuS1zslC0ZiYhOocqTF4L12y1jbUdcV2u+f5i5fc3N5GVmaGcwd/V9M0JKnUsKxWawHTPmDbhhB8lNQlUW4nGwR5Xsbz6kmTnK7r2KzW7DZbwj1h7ADwHpMkwqj4gFGa87Mz3nrrLX7xF3+RT148H33GVbXHOSsBRalBxSCqsizJsiQCW4+1Pbe3K9QKBulq27YjY3V7e8t2s+Py8ortdk+SpRIk5TpJ8O1lUb6czzk9OY3poMLgagXb9ZqH9x+yWCwYvHnO9exjb2aR5ZydnDGbzNAozGROU7XMpwuKrKSua5qmjZ69KUZrnPU01U4CZ1wf61FayskEkyQ0q5pMZ+TaMNE5b997k5/40b+b3/ijP85ZvsR9vKLftQTrcMERXMD2VvymPtA7qeFAaaxzNLZmv6/Y13vqvsKHgAsBHxTgUFFaqYzG4XH0kGmchnW35umL57z/8mOeba+oEkuHxXnP+fKEIi/orSW4QKZTQJhQhcZ6TxgqaEIADD54klQSc5VS8lwNHmUCSawpGYJyhtTapmvvbJTIT3AHcClAS5i9PtZBSaqsjIBjLQDZGNJUj8B2AKtDarCwrQLcmqYGL8qI+WSCSYwEUbUdWaZIjGbfNGy31fgaAvgEDDkvkl7x/somEG64HiKvDcHh47GVkmyyIZ04aPG1dl1H03QjINVGfqfHY/fCdB6ODV3XY60anwMB3QrvezJjKPNJvD6B/W4r1zxN0NHXObLNQSTGZTmRKqroI9bGRFmv/NkQnqRid3MI4GzAu0Et4mnbhuDlfU4mJXmRU5Ylddux2+/wLsQgtENQ1HAed5PBJdk3id9/smkWgiQ3W9uP52Sti5uBFTr21oodYri/khgs90c2NwJRXXH46BznOMc5zivzjz35If7If/1bSd75vs/sHPpf+Av8G7/tT/EHn/+vn9k5HOc4r+O81mDVBS8/VnxILpWE2el0Kmm4qaGuK7quiWxHz3y+oCwnOOdZrzd4H0a/Y9dZQHHv3gNmszkhyIK2LAvSGGrU9z2XF5d89OFTrm9XaJOSJgXBx16/yESALLZOT09ZzuZsNpvxz0UC68jTnLPTUz735Aln986oqor3P/iA1WrNfl+JhzRKZPMsx/tAmiQ4L+xemuRMypiOqqTLFAXaKPI8x3vH6vaWy8srrq9uY8JxiXMKa2MAU1Ewm8/prOPDDz+irlvyrMB7T55KB+WQiuq9omnaGCqT0jQt28324DEzCd713NzcsIlVNcZIavAggR08fUPiMrGHtK5rfvZnf/aV6o+hY3G/33F1VY++XGMMdS2SRmMMWQzjGRa7aZoTPBRFSZJk6MSw2+/Z7/cC8D2Uec7y5ITptERrTVmWaAO97ZgUOYvFknIy5fmLT2JFzy3WWRbzOffu3ZOkXqW5vrrm5vqGvusJKBKTobVlMS+xto0soKauG/Z7AbtKKQk3Ch7aBqMS5tMZ83TG+WTJt7/1Jf6+X/UTfOVbvoOi0fiLLW7dEGzA0tNjJTAnMcKC2YAyKc5LD+quqdjsd1RNhXWW3ncjaAqIX1IrI4msKsQ6GrD0vLy95r2rpzzbvWDVbLGJhAF570jzBBJFY1vaJno/lSb1SkCqFqbMpKn4IJ10YCqt0SYhy3IIHhUB4xgIpPX4vAx+1Lu+xyEsy/Uu9pbq2EGqDyzxnSqUIRFXQKJHaUdiktjRe+hxFcAqwGeQz2qlCX0njLvWdF2LbzxKa/JMKlfaNjKnCKPonI9gzQkbqAYZ9sAQSwp4iCyzUnJckOfZxFAy2Zzp2W77CHqlE1UpHau39AjmghKPcZC7KD248foNwNNHkFgUBXmWUmSpsMdBKr+aVrqcVYh+Zy0hTRqFyTLyIidN0pHpBOKGVUXTiMRb0nYBJRsHwQf5Lo49ps71KDynp6ecnS1J0gSPJ6kaOivhaVqpUeY7JDUP73P0+BpDliTxGF4k116hlCFLzSFkyzm51iFgjPwbkKUpWifRE6wxiSF4aJpWQrki0B4rmo5znOMc5878E09+mP/kJ3/sMwWq7vIj/uV/8r/nJ5//+c/sHI5znNd1Xmuw+vGzZ1xdXxO8YrurSIadeA3B9eR5htKw3+0iozilKArW6w3WWpIkYTKZ0LYd6/Ua2/eUZcny5ITlYkHfW9ab1Vhvsd9XvHjxkg8/fMp6vUUFjUGTptkY4JEkCgLMZlPyImM6KVguFzRNE5kSCQMBhZ4q0izDup7rq6uxEkUpWCznmOhTa5qWyWTK/fsPKIqCtmu5ubmm73uR08XFmdaQaMNkWvLgwX3u3bvHanUT/XSglEh28zxlPp+jlOL29haUYr2WvtShJ1QFhQoF3ge07gRoAGlaRHmhInhJ1qzrdvTA2ShB7GKP7MCgnZycjPLouq75mZ/5GfI85+TkjCSRYKskSTk7O419jEJz7Pd7VqsVeZ5TlsUIZob03iRJyIsSbSVJuW1byqKMXj7xLOuY6FrXNVprJpMpb7zxBvPplOQO0JXan5TFbM50Mhf/6GxB00hCKwGyVPzOVVVRVw3r1RrvobUWZz37fU1RFqC1AEjX0ncNXdfiXEeWpUwmU4o0I5sUzJYzCp2xzKa8df45vvjoHX7Ft34vD4pTwtbS3u6h7kmcJniPU46Qarq+l1Yck9A7YR3brmOz27Deb6j7jt539MGBVsLqBYXRAlC9cmhjcNph08C62/G15+/z9OoFVdJRpz1dJsy2d5JWiwvUVSOe3a6nbzuUDyTKgIY+gA6aoI14Hr2jd+IfTZJE2GAr4VtDP2aA2Jcp91tqjKT7M0tF5umDp+8sVst7NjqRIKg7PatGa5Q6SGBhCCYK6CDssbCTB4+ygGE5t+QuqhZRdgAAIABJREFUaNQZWoXxuR+Z3aDwhBFY2QjGUQeADIw9qrKBY8A5nHUjsyo1KmEEWNorVJT1j5G1So8BScZodGJIk6EvNBmP1e4kQbssJMFcpLId1oo/V6tA03e4PkFRSugZSjYrnEj7sxjklhc5WZrJcYfnLJ7jIGm3VpQh4i3Xsfc1w/aOrhP/9CG4DIhS365ruby8Eu+sl/+/H69rH79bEqRW5gBWB6lyorUkdwPKSOha27SjV5UYFKaMItExrEpJx68xRrp9TUJWyAaH1gn7fU3d1PS9A6ViaNpxjnOc48jMspJP/vBvQb3zHSRf/pFP/wSCJzR7/qsf/gP8p+oTfvriFz79czjOcX4ZzGsNVo2SGpi6amm7uEseF0nBWbI0YTqdMMkl/Xc+n5EXGZ217NZbkS32fZSCaSazKV98912S1FC1FUrB7GRBUJrrmxVPP37Gxx89ZbveodCkJiHPcglJCgHle7RSPH78kO/5yvcxnU756OlHBN/TNBWb7Yqq6cbApKRPqNuG1XpDmkoIjDYwmRZMp1Nm0wW73U4AJQHbtZRFznI+p8yH9FyLcz0uBJJkHgN8ErQxVE3D1fUtl9e3aETimpiEk+VSPKSrFbvNjulswnw6Yeu3dMaRGKm66PuWtg2SoNk7NusNWsvCtq5rkdS6nkBPkuRMZxN2Fdi2JRmYJRT7uqF3N6SxKzJJEk7P7jEpCy6vL2nbljSVECzvxfcrqaCyEJ1OJ+RFxvn5OUqpmNjsIisrjJb3Pi6c0+jf3bDfb1FKR+m1l7AbYDIpOD0/wfW9pK+GXlikNGVRLmmbjs1mS1kWdE3L5nZNvdsTVGCbZWituY1VHrvtHu8C1XZHt68xOoY+FSWpDvh9wGvLYjJhVp6B95ydnZLlE5587vPgDLoJvL18xA9/+ft4+/xNkq0jvKjwtUV1PThP5zqc8nilcJ6YvONxOFyw7Os9q/WGXV1jfU/rAzYMvx97TCNzJnycJ2hHrTqeXr7gay8/4Pn2kkp1wrA5qTNxTlg3Y1JCgLbqSLMUFfsxVaLRaYoxCuft6G+UDQ1NEj2iWZKOUlKdxC7gO0xWCAIYfASaPkRmEH3wTHphWwmOvnMxYMmMvkrv1eitHsCvienGBEgT6eIcAKgxZuwwtdbSNA1NU2OMgEevNEobTJKifaDvPS52IqdphlLy/Pk7vlcBeL101CrpODapwegDEFcqcMByIQI/qQoaAOpBFl3GxGKLwpEXKbPpHOccF7aT5Oo0YzKdoFXCZrORTmKt8d4RfPTGaklDLooC1/e0TYPWmuVywXK5FCUDSkKsQuQYlUisne3pmlhzFBSJTimykiyTkCdhNBv60EfG/27ar4DNznp8G7twkU09FWRzTZKGQ9wk0/H+OEySMi0n0Z/v0IpYBSSy37xICSEZE5373o+bKyLz1eIP1468IDLeEBRY20o6cfDCtIaA7Q9p7cc5znG+eefh7JTvmL7Bf/cf/Bjpr/nHP5NzsH/uj7P7qT/NW3/+Q6zv72wAHuc4x/ml81qD1fl8Tp4XtI10BfauI0SWKTWKJBEW69GDB8wXUybTCbbvuV0JW0qQpM/9fsV0NuONN99gcbLk5uZmZDiVVWjdc319w+XlFfudyHOlAkXYIZNotPKENOPho3vcv3/Ofrvm6dOPeP7JcxSa3X4fext99H1JXcpHH3/EerogSWShm+XJKHus6xoV2cn1eit+yUKqaIpCmEbvHQHPZFIym83GRfdut2O3r9nt9zSdxQRF8I7ZbE5Td1T7Gut6ZrM5J8tT5os5FxcvSNNUQFpn2W3rUUrYWxelrPvIJA+BOTrKozs626KAcjIhz/PRk+hjmEnoWpEj5hlaw3q9lv5VH9A64fb2dkxsnUxKtFYkiWGxXDKbTXjw4AFd12FMQllMqOqKtm0ARpm20Qbbd1xdX4lfLqjI3hlJu/U91nZcX12IpzTPsF3P+fk9iqKk66TCJ0szqn3D7e2KqqpRSjOdlJR5jlaa7X7H6nbNZrMTOWHvybTi5ORUNkiyjLZrybVmcXLKYj7hZLkgeMfpySmL+Qm+g9DCo5N7/Nh3/RCfnz/BX1XYTUtoerzt8SFImI06hN7gpGAl4LF9x+36lvVuS2M7GmvxCvqgcIBCGCmtBIh0KqByTRN6rve3fP3FBzy9+pg9LTbxeAPedfQOYbmCH6WjSoFRCqMUIcpMnetxODKVxP8tRzUxSEk6PPXoETUm9rMQJPwHxnqSAcEdamfEl9vf8TMOKdMidz3IRK2VJNdBSipBOkTm1ETfdUqe5xRFEdNihVEdKmeGGiPnAoE+BqmlUVUgmyLD+Q4bRUOwU+Dg+bwbDjWcn7xOOjKfQ1XLqz2jjO8/y7IoTc4iGDMkBpJUs91tubq8ZLfbU5YT0jSV+qluT9fF6p4QSIzBOjsyqc45qqoSqX2acu/ePe6dnzObyQZSXVXUMfBKPPd9lDwPst6hq1VHZjZIYJjtsPH+/tIO3MNIHY5sLhHvJySJPLdtK0BxuJ4D0z3cx+CdgGGtyPMMY9I7vyPXVDYshl7eIbwpjK9HADt4oN3Q5xroe0vbdSLzPs5xjvNNPfcmS/76rzxn+Z//5Gd2Du1/+K/yxT/0c1zsV5/ZORznOL+c5rUGq1oplrMFk2JC23Xs9/uYtttydrLkwYP7LBYLlosZaSq+tLbryLJ6XLwYY0iSBYvlkpOTE7SWOpzdbo+1HV3XEoLi+vpG/qzvmU6mnJ2cMZlIKuqkyOm6lq5ryPKUi4tLPvjgQ7oo/dNaS8+k1qCkRsEYw2QyYblYkpsM58THNiw8t9vtyAAFLxK1+Xwe03El8EeYWFkc7vf7WGUjns/tdsv19W3szwQJwUnoup62XWOMZjqfYm1PVYnMODEZ0+mM6WQaPbY7lILPfe4J3sPt6hZjDE1TU5Q5BHDesVwuybI0Mp8ii57Npjx9+nGsjPGE4NEqwfU969XqsEvoZbE4eNUGlmS32wGBJDHkeUqWJdS1yHG7VpJWieFSPvZLOufY7XdstxvAU04K6qqRBTyAkoXucrlkNp1ijGaz2dD3jvV6jXNBZLW2p97XXLy8kHTl1JCmCXleYHTCbrPl6uIqysmFtU2TFJWnpGWB7Sz7dgXBMS1Slosl8xPxSgfvMeTkquR8vuTtNz/Pu0/e4cxMsVdb+lWD7gLeiv/Rh4BTgzMxCLuoNdY7NvsNN5tbtnVF53qsd/TE5NQB+OiAcsJE9Ql0KWzslvcvPuaDi495sb4gJB6VKFwfmBUTdKrZVTs6K32kSius7aJH0dC0+xGQyWZFIM8PlUuDcqC3DtfaV8AXiOdQGwnMcW7o981fAXJyuwaQoUYmdJhBHjp0nQ5gdQCR8lkyJIn4kSeTyVh5st1uXzkGHMBVCD5KheXPpfZJ4R2j39r7MPYiDwFoJqZCDl2jgyzYey8S2zwfAfawsXKQLQ8Jx4dgpuH9yZ/J9SP01FUdP6ue09Mz8ZIHT900tO2r1TxaK7wWVrcoijuVPT6yqsuxL9Y5R2stTdsgfad6TPAVBjvBmHSURivMuKmWJImEqEU29XBd9Kh2GEClKEGseG2DH489pBfL7xE3EFq6ro33Uljy4XMk6gv9ygaGyPkTmqaKQDS25MTNNu89tvfj+Y19r17cx0lMWD7OcY7zzTlKKb76D7/B5N/7qc/k+P3//if5qd/+V/mj3YdHoHqc4/xtzGsNVlXQFHnBdCKhQ/O5yGa7ruXtt9/m4YP77LYbbm5vCT6QJimttbRtzW63Z78XMPb40RMmU/FiXlxc8OzZJ9ze3tI0NWlqmE4X1PuarmkhBE6WC37wB7+fhw8fUtV71qsVFxcvsTcdu92OtutI0pz5ckJZSsdqmuUSsBPrVcrpLKYXa5pdxVX0rA7BQGVZ4vowSiq9JwYBTchzSecd5HJt17BarbiN3k7x1+5lAawUvu/jolszmUyYzyVpWPpoHc+ePePi4iWz2Zy+t2w3e0lEjX6z21jp07Y1eZExm085OzujLAuUgocPH8ZgqxLvvbCNdUVvG4osBWVib21CWebYtpOOWaWwtmcymTKZTNnvq7GXVCo6NME72sZx63qaqmY6nbLdVhht8CEye70wLs575rMp89kUQqCpaxazOevtFud6FosFp6dnPHz0gAf37xNC4L333ufi5SUfffghrg9j9Ubopdc2z1NQEtBSVdIt23UCeAZgkBiFV4rZYs58OcPuararLdOy4PT0IcvFknI6pbeO3e2O1jXcN+f84Je+h7eXj1GVo7m5Jex7lAXXOTziMxXeMkQs71E6ULc1q+2G1X7HrqlpXD+C2qHHUsVKHgV4E+iVp1WeF+sb/sazr/He5TP2vqVTlkJn4APawySAih23Aelv9c7T9x3C5yJM/rSkKEtMkkiHZewBFsw1sGdE1ly8o0Ndi9KKSVbS9xalpAJp8A0nycGPqZDkWVUcPKBt28YalXAHcPYjEBsShXX0sGqtmE4nMQFbvMsCimVT6+BdlYAmrTVZlrzCxKZpSlN3o3f17saKUiqmXYM03AbwB8/lAXiqKPPV4/GEsewjSJPqnEEm7LzDtcLOSspxEpUcKYvF6ajQcF6kyQozXrsBhLVtR1PVJMYwyYVNloRekfReXF6y2WxI0lQk3xF8BnWorBEgmKAIZIkmSZMDyA49IcqXvfMRcMpmnKgiNHkuG1dZluGcP9gWIvM8gP7EZHRxc9C7Pm7wqeiLjSFJSo/3Rd/pnTXmkC5dVXuqah/vjxn/vjED+D/ItQ8/HqMVefpa/3N3nOMc5+/QGG34386/n+/6o7+W5Pv/3k/9+KHe8tt/7Pfz0/VHfPX22ad+/OMc55f7vNb/eltr6a1FOvQURZZh8xwXK2CMVtzeSt9m11qCB50YJpOCLnb/mSgfruuKzXbL7e2G6+sb2kaqOvre07Y3dE0r/juluF2t+Mt/5S8zm02p60rYk1T8aXmeM53NSLOMxWLBYrEUb+O+oixLnpyeMp3NadqW65sbbm9uaKuaru1Eztj3pKkZpYoBcH2PSUTquNttqWtNWRYkacJuu2W7E59a07Y0TcNmsxlB4LCg6xrLdDobvaAhsjjOOYro1xxkcSFAnhekaUKa5mO9x9nZGfP5nOXyBKL0eDKZsFgsxqAaYcYc4JhOS4pC+lJPT04EMGvN9cUlL168YLvd4rShLEoW8wVN3dA5N4bvCOklzJWOQKVtLbYT6XOWpSSJZrpY8PDRfZ49+5j5fBorbjquri55990vcnZ+ivOePC9IEkNdVXzwwQdsNxuU0jx+/Jj79+7z7ONPuLq6IgRIdAJeSfhKlGJnWUqeiyf1i+++S0BxdXlN23ZorXj7rTfRAS5WW0xqSHK5fwNTvygWpJXmW9/4Ar/h+3+cN6f3CRc7ms0eZQOhcfTWCQD0ll5J3mtQEJSn6zv2zZ7tbst6u6GxDusDffAM/HkIAaUDidJoHehVT59p1l3F1z94n1949nVu7IY+A5s4nPI0oQMPiTLc7rb0tsX5liRNsL0E62SZMMTT2Uw6RpWm6To6a0nTlL53NI1IRpVSEPQInAbQMwADrTVd25EXAgZBUmbvAi2RaJcURTEqDORzqEbfqYCPQ8jSAFal6mhgW8P42bbxXE9OslFmPwBfY8zIgBZFhg9+PO/9bj8CvLugdjhu8H7I5H0lxVjONT3ImuPc7S0eUnMHFtB7h+0dCmE0D5UsljRJhAWM1TTO+TG0zcXnZgBvPniUhWl5SLuezxcxIb1mu92w3W7Z7nYUA3s8yLLjtRyqdKT+JSPLM7SSZ2JgmQGGaifn/OgVzbKcPBePsFJq7J4dWE0T05mH0VrjncMUBSYxMVCtPDDoWsdNk6HbNcTrJynLsjGhx/sv13xIFD4kJGvCWF10+Nzwyv05znGO880zeZLxPyx+kO/9a//uZ3L8/r2f4ff+o/8N/8Xzn/5Mjn+c43wjzGsNVtsIzgZPle176rqmrmuurq8i2yYLEx00vvf44KmqhCTVd/xswupIh6iVUBkURstCSYWAznX0ysnie7vb0DvLpCyZTstxAT0k3gJY27PZbmmaju12y3Q6Faby5ppPPnnJarNmt9tjlOLk5ISiKGTh6QNtZ7Gxj1VFVsB5T9u3uOCgBV971usVTVOPoHRY6H/+829QFBPatmU+n3N1eU21j8A6SynKlKquSBLNbDYhy/Lx3AfGSuR7CX0vUt++75nNpkynAtKF0bDUdUVRZKxWG9qmYT6fs5jP8A8fkGUZjx89ZjGbYUzCdruTFFkllTbbXUVVN5LGbO0rIMA5J/7IIPUvs9mMzWZ3SExezHn48D6PHz/i9GzJanXDy5cvcK6PbLbBeUuqDGlqaNuavpeF92675fbmViSrLjCNTLf3gSRJhblFFuJ9L/5RYzTLxT1OlkuS2El7fnY2LsLXN7dooPeWcjYhGE3VdrQvryhMxhv5A37kV/wqfuDdr7DMTumeXqHqHtU5gvMoHwQMhB6vAl5Dj6MPjqapWG82rOoNddfS2R7nQ6wN0iidREY6eh9NwBGosLx3dcH7Lz/i2eVzGt+iSo0xHu17DAEFaJMQlKKyHd715DGkCwJlWXJycsLDhw/Y1TXXt7fYbki/FqDqBg+g5xBkE5mrQ5XKUMUyBCIdgnXuMm3WStWMwrzCfAr4O/gKDwyljR5Q+ewM3wEiCRYv5OC1LMtyZF4HaewhDEgYU+fdeE5d14n8vA+vsKLDed0FOcMxDj7UAQSFESwPf/ZLf3cIHhqe4+EZzOOzLz55FYOdFL3raTtL3/sou/avgH3vHc732M4ymUhS8OlySVEW7OsarWUDZl9X1G0YWcdhhnO7e54DoA24KIvPhVUN7pV7GEJAITJiCWE7pDMP73eQ8Yqc/LBhkKb5aNnIs4wkGaqMXq02kudWvKfeh/EaShWRAHzx6iZSbxQ3A1wMRhvu+d3z+qXX4DjHOc439vz+x7+WnwhbvutnPn2gGlzPs1//O/hj6wf8oWMdzXGO8/9pXmuwut1tWa1Wo8SwiRJB8dmBdVLTobWCKKMNPoZ7oDk5OeHevTNOTpb4AG3nCEHSXbXWpEnCbDod5XsiVXT0fUeeZ0wnJdPp5M5CHKx1IyNiTIp3njbWwVRVxUcffcx+v5OqGGtRwGw5Z7FYjIvSYUE2sHp3F/3AnWNZkiTl/HwyMhdKKe7du8ebb77JixcXEtqUZUynJW1TYRJFUWaE4CgQ9hfkdyaTycgm1VXDer1hsVjGTlQbQUYfw5UcafQgdl3DzW30WN5Z4IcQKIqCfbWPUuSCm5sbmqZhOhUp8WZXcX19w2q1Hs9/GGMM5+enfOlLX6IoMkIIPH36jGcff0Jd12w2G4xR7Ksd7qsd2+2Gosi5f/+cspxyfn7G06cfozXM5nO8c1jb0rUN280uMqKGm+tbLl5c0tueNBGA5p2XHlEjnwOFJNt2neXy8oq6qtnv99L5mRiqqqJpa6kEMoa660i0IVUZJgS+8u1f4jf80K/ji2dvYTYO//IWXTuc7fG9i4FJ0nnqdKDX0OOpuobtbsNuv2W73bJ3HT2ySA9RcquRHk+lNV55ei1M7Ga/4f2rT/j524+5qdegPUlhSIuEoHpMyLDOo0xKUJreB5wSVixLEwgSVDOdzcjzkrbt2G52BK/wnlgP1EfAp1BRehvFyDAATR3lyDaMnk3xKcuG0wjGAhF8eKy37Pd7mqYZ2dSiiJVAENNbYx9plKCmaULvHF3bjXJoondz8KcHoHe9XCvvY0CQnEuaDonFrwbt6HhdYQDgBz/q+DuJeQWMxbc/gtvh9weGWf5/FeXasb4nBrYd+j6lf1TpWMMzvme5wkPQ1BBENIDUtmmxNjKu3cB4Kja73Viz42Ka9rCB5r2nH9hRDmBVGMiGvpdwpoG5LcsJRSrdpZoErcE5PX4X970dQfcQqHT3ffe9H48RIqOrjMa5PtZWebzr6YN4W/veywaNO4Bz9wpQl7Cxu15h7zyJBqIMu3c9Q3ezRuHi9+oQ3pUkr/U/d8c5znH+f5x///Gv45//E/8I5vPf+akf++o3/zP84adP+Hc++T8I4W986sc/znG+0ea1/te7ruoRrI6LwRjekqUZvevprMUHhwmGxIiUbXky5wtfeJOz8xM2Gwklarse72XBK2EssVYBRZoeAkuyLGc2E4DrekvbNRB3/du2xbmA95qq2krnZ0zGnU6nWGtZr9fs9/sx9TbPM05OTjg5WQJq7GOVepYDMJVwmgi8EY+WMYYnTx6jteLly5cR3J1zcnLCarViu92OgSyz6YTHTx7FvsMXVNWek7MT8vyck5NTzs7O6LqO9XpDVVURCCZcXFxQVRVJYphMSvI8w3lZiCZBWKnnL14A4vvobcd8vqAoijuLXQm2ef78E6qqHgFxWZYUk+kYbFUUBdYKSNFa8847b/Nt3/5lHj16yHvvfZ2vfvWr7HZVXFQKaFitbrm8askygzGaR48+z2K5oLdSezOZiJTQRtmi/KfIOROTgBJ2Rys99mx67yP47wle/HplUZImKduNpDI7299JdZV7b1JDlpcoNMoHzsoFT07O+TXf/yP88Je+wsP8FHe5x1cB1Si6tgWD9KAqT++d/KdRNL5ns9+y2q3ZbNYCqrqWNkp+FRqNhhAIWqShzgR6AxUNF6trPr58wYvtNbu0h3nG2ckCFRxVtaXICqQOqUelGW0rYTreOrxJcMrHDRpNCIr9bs96tcbGxF95HgR0dq0jSRO884TgpLony8bfG1nRPIvMZkZV7cfALBgSgGWTJ8tE1j+w2ndVAyaGE90dkT4rbN+PXbvD5tLwm0orAoGmbaibejyngZX9v+vvHc7rLus2BEsNoHMAi8NrHADrwRN5l4kFRsksSHDQKMF13LnmAWs7nPPRc5mRGNkcGr4TBmby7vnJNTtIls/P7zGZTiiLnKDVyO4mWRqDngRcHvptpS95eE/yOma8T8NxxyR0ownBo1Qe35uPjHZ/CE5yIYLMA6M5pG4Pt1Ip4ibI8LpK5L2IbNr5gO2lnmZ4De8OFUCSGhwVMUa8yjrRGG3oekdnD+nOB080mBgAlec5p6enf9v/Bh3nOMf55TVKKX7341/N7/gf/zn0+Ruf6rHtn/7P+IHf/Wd4Ud+ybr76qR77OMf5Rp7XGqz23gJ+9CCNC0LvCc6jx6oN8aMVWcnyZMkX3nmbR48f0HVtrE9pBeRqTVGUeCfBHsF56qpiOptQlsXIAEynkizae0cSZbPWWvKyxHaO7XbHdlfJ4s+IVG9ghyTRVxbFeZ6zXC7I8xzvpRpms9lKV6A/hIcYI8xdXVfjOXjvKcsyergc8/mchw8fslgs2G42rFcrEm2YTiYkScLjJw85OZEAKmHLCt58600ePX5EFpNYvXckkSGS8CVP23Zx4dyzWq1Zr9d0thllwdZa8rzgjc99Hh/Ew5amKcF7yqJkNltgreW9jz5iu9mRZTl9J7LoNE0xaUpRFJTllAcPHgDw4sULmkZA9s/93M/xsz/713n+/Bnb7TaGQAWc70EJAHnjjc/x5pufo5yUPHnymDRNqauarrPcv3+fLh7PWrmutzcr+naPUQatE5FD7vcURUFRFOx2O5zvcd6SGI3W0LQV3VWDVgoVNxW8c6jIGmVFzsP7D5lMpmQk5D7he9/9dn7t9/wQ3/bkC6Q7S/PiErvv0SGFYHAq+hUNON/jE0S66zouVzdc3lyyrbbYrkOhUH6QkBq0EUDk8ASlCCZQ0bHpGr764gNebq+pXIdNPKbIOJnOMElCVbc4o2i9SI+dcxgsbd1gK6n60YU5BPi4QNdb8R4mBjoXg2sMIRiCV1jnpKImPn/L5ZI0zUYAJMC/w7lAmiYYo0Yg5GPisY/+QhSjTFNrLTU3IdB7j2saQl2P4Ozuz8Eb20e/sxr9k3flnb/093+pl1Q2kQ7pxl3X0bRdDIqKf9doCf8apMNKYbQen1UJU1KvANuhVuWuxHf03DKwmfIzBE0NryHPOKMUGYggz8dnNZGfZGCoT6LX05DnxSue0TTLCfgx6beua7quI0kSptNy9KgOf0dAqh83eOT/l97XqhrqiAxlWcSaHR9lvC6yrLEux8nvDtfkEGoFQ81MYkSun5gEg4kbDWH8O4OkWhJ8h5ArdedaFZJ+raRmyNkeQiCPz7lzElamlIlMajYGaKl4D49znON8487f8+i7+WF9xr/+V37/p35s+9/+Rzz4l/4ktW0/9WMf5zjf6PNag9VBOpcXGUlvRvmaVHz0BBXQKpCkKfPFjMcPH/Ho8SMWywVVtY/JoESfKiRZwmSSkyQJbWvx1pEtl3z+85+n73s2mxXWtvjg8d5Jj6RWBC1pmE3dsNnsWa83hBCYzaZobaKc9w7761wErjOm0ylt27Lb7dhut3RdH2sw1BhYZK34QoUByIQhyDKU1mw2WyaTgvv3pabn+vqa58+exd8VT15RFPS94+nTj2mamul0xv0Hb5KkKbe3t+LdMxm73X78ESA6SCzl/YoczzKdlRgjISjBB/JcAF4IgbIscFoW5HXTsdtfcHN9zXqzxVmRWTe19JZ6D6Ux2K7D9Z62aUhMQts0NFXNZr1Cm9ihSmA2nZIaQ13tYnconJ2e8G3f+iXu3TuVpNAkoWu76GcTVihNM/K8oMg1rvfcXN5iu54h1bXrOrxztE0jC2zb01tLmugYqCRhPbaz5Fk2MrTeSWWNVpr5ZMYXnryJ9gq3bfm+b/0u/oFf/et5XJ5gb7a06z3driE4hdMRhCAppAGPU+BVYN/WXG9ueX51wXq3pQ8uAmQwWqOCgOXe9TgF5BqrHVu348PL57x/8YyX+1vy5QSXQN01ZFhsV2OdVPiUZUHnHSoofFC4fmBLQccYXxWEBbS9AzNs0OhRDi7ydIUznmyslAlj7dDoG4zJsQLrTdVuAAAgAElEQVQ0PG1bE3AjsyhK3YPUHQ5s5kFyG593pVHx+b4rwb2b7DqkxR782wfP5eH5E+A0bPTIawzg0rPb7WiaZnwP8jrJ2OtJZH1frdiR5NmBKbwrLx37aF3/ynsERomvXDvZeBsUHAdvb0ApjzaHbmdUZG1Rd0CfGm0EQ7BRG32jdZTwJmlClqejrHo+n+N6uQYDozt4fd0QQqRk8y5NUrI0QyGqgCFgSWmR8hbF4DtNx/Tfu/5Q2x3YaO8ZN96Gc06SRD7j8fqqIN/xRmu8idJyNAqPCwefat/3I8iW6yLy4DTKlHvbk6QJrpfKJ6XSWCWUHOTn8Xv5OMc5zjfefOfZW/yUfsz3/Me/kuRHfvOnemz75/44v/Av/kV+W/viCFSPc5y/Q/Nag9Uky8iLguA8VlnSO4meaZbQ2Y62a0izlPPzU9544xFpnnN5eSEMYWfZbncoNHlRkiDAZb/fU1U1xOCdtm3YbNasVrdRJlzSBCcAK6b4eu+pqprb2/UYBtL3niwbFrduBJ5aS4VMlmV0XTsylBLU48afJEligJRlMil59OgheZ5T1TWt7UiUJk0TZrPZyAh+8sknrNYS/DMkqWZZxqNHj/ja134RUExjjcp2JxU1AhwDbdNhraOqaprGSk+m68cuTWO0pAGfzKWyJUnYrLcReAgLLEysLJw3e2GJTZIxmczYbjaSemykeiegpP+wdzjXkCTyXmaTKUYr9vuAtS3OWu7fvzd2OopL07FYzHjnnbc4Pztlv9sxn8/IUlnw9tYSfEyfNQknJ6fMp3Nurm7J0pwsy2nbjs1mQ13VKERW/ujRI+azKev1LXmexaqfBUppsiTl9PSMTz55weWLl/F+Ooo854tfeJc3Tu8zCRnf8X1f5oe+8iOctgp7saNe7XBNi+17UCLf9UpYVGJSqbUd2+2Om/Utl+sbts2e1vUoI9dJEJtGI0yeSyBkmtb0PL1+wXsvn/Ls5gWVt1jjsW1FjwDdoKFuBahmRT4GiYEk2RZFgU5TcpOIKgEV03Bj+I0y1HWFhNfoCNgMgej3vAMKveeVZFrvB/ZNo5Shdz2hPfgXh6TWsVP4jhdUGNxD5UsIxHvvXwGFA6MrGyoHyfAQzDMwrGPwkDskyt5lXqV6R6Smg8z+AHTdCHiVOlSf3AXC0iesR6XFwEwaI89pGZN5755fzHsefZfCwgbS9CD5HRKDkyQFwsgeSrBQBG3O4d2rwH3YMGqaRr4v0xSTHJjlw/WDNEmZTCYkiYksuBtly4PUfUji7bqOPqY+Z1lGXuTkRS6Bdk6uoVLCGg8pvcYkdKEfXyNJsniPY91Qlgng1Eo2ZwbptNaQJBgf6F3AxWsL/1dwKe/l8LkyBk6Wy/HzoZRiV1Xs9/vxczXc77sBWMc5znG+cebyN32R4ie+l/Qf+p2f+rH/rR/4ffwlf8ufefHXPvVjH+c430zzWoPVx48fcrKY4fqezXqDUYYkMZRlSVkW2N7SdA1aize0aSuubi6pqoa6bmmalt46SaaMQOv6+prtdkvwjsRk9Nay223oe0uSaIriBJDE0f2upm1bus4SAhRFwXQ6I00ykjRhOpWe1dVqxW5XkySGxWJBmorn0vU9LniS1JDn0whaZKHXth113eB6x3Q65d133+Hs7Izr62s6241MZqI1bSuL0b7vaZpmrO4YmJmu63j//Q9ompbdfs9ut+P+/XN0YtjtJK3T/J/svXusZdue1/UZY8zXeu69q3bVqTrnnsdt+vaF7nu7m+6+yKXpC9x0hybS0KAhMTFG+AcDwUQ0MSoRBaIEI1GIUYQoxohGEwzxP6Oh/1EIaUQ6Qdru+zqnzqnnfqznXHPO8fKP3xhz7WoID+17ui6uX1I5dar22nPuueZcNb7j+9IFoNN1GcaO12EYMIXmrbcecHl5ycXFGcZouUYRqkrkhA8fPmQym9H3A+2hxQ6WpplRx8jV1RU317ep/qWiaSaJzYlJQltSlua1hXbTNNR1xW63oesOTCYT+r4fPYVN3XB2dsZ8Puf6+oq+77CuZ+nOaJop5+fnGFMy9I7dbk90kRfPXvLhhx9yfX0jGwpKU+oKqx2bzYaz8wWL5YK+67h/eZ/Pfe5XMZvNKIuS8/MLbO/YbXe4wbGYzrm5uaapJjx88JC3Ly55d3HJj/3Ar+O987dRqwG7tXTbFmc9nfdEoymqEmuDhBeZgiH5ntebDbebFav9hr3rcADKEJQiemHQ0EpYrELjDKyGHU9unvKLT7/Jy/01rgioKkkZo6eIEW8th6Gnqkq0NpS6TGE6cs+WVc10Mk8dlx1eRbQyqChsWd3UFIWi6w4Yo6mbGmUSkHAm1QRlgKnHZ82YYmQWxdMoDHmlpbIkhMih7cns9l057rHftBoZTGFnSfdpZtnCayzskaH0430k3Z53mMwEsKoUDpalyFmyLEFNR19sBs4ZSBZFQVlWr4Xx3GWG8/nmzSe5BmWSyk7G4+aNK5SoFkIQD3BZllxeXjKbzbFWPOTr9Zqu63BeUqeN0yOQzP2rWh1Tjfu+HxnSqDTohOy1QmmDwhyve2JzSQoTpaAoyrE2xjnPYIdUD3O8hpllzv24bbsjRjWeU1XVSZ77ehBUBo1aMzLm3FGcqKhQ6WfLgW1yqgrQ43t6l9W+m0JM8lqDqF32+40kkDvZQMlMbzOdoVRFDrBzKUn+NKc5zT8Zs6infOu3v8v0P/rPUObTW8rGw5a/9qU/wb9gf4FPdtev/dt2mtOc5tszbzRYnU0mTBOTur/YM3QisSjqUmSyCvphkG7SvqcfOlxKkxwGix0cRSEL46Hv2Se5nEodpDE4vHP0Q5fYAEPXdazXijalwQ6DSyxjmWokLDEGymqOUoq2bWnbVsJ6YiTGjsNBGJEiMTddNyCLbKkJATV6VM8vznjrrYc0Tc1mc0uMkeVySdf3tO2eIbEu9WaLdZb2cGC5XDJfnBNDoBsGdm3Loe3YbDcUheH8bCEuuRBp6gkAIQgb0dQT2rrj5uY2yQwjs+mEH/j+L/KZz7zDvt1zc31DVdVcX1+z2WyIQfH0k+fs9nvmywWPHz9mNikIURac8/mc4GURba1l6BPDrMSxZwyklpSR0cqAfjKZ8Orlc54/f0FIYGE6m3B+ccb5+ZJ+OPDq1QuquqQoDRt2hKi5dzGn7yzPnr7k44+fjPLmobeSVhtSKE0Q1q2sCh48uKTrDkznUz77XZ9nOmuIPlKVNYMdqCcTpjHinj6n3bU0qmJiNWeu5Fdfvs9Pfuk3caYnsLYMtwfcviOmHkhTVng8fer2NSisH9gc9lzd3rDabmj7jiE4gtK44AWQRUm1VVp8qb5SbEPL9eaWj64+4cnVx+zDATMzKK0ZvKUpSwgB14ukNBqDjznLSYn/00mfp4+RojgwuIF+6JMvUWNSsNjZ2RkgQVay0FcYIx7VEEj38FEaKzU2h6QuyJ2oBqM1dS3eQgllkmCy4J0AqJGdlFCu4ANDIWnZuefTGCNp3jEmSe1R2quUokisemYqnXOoJKfPYCgHL2VPatd1Y8drZnjl3lRoI+etEjOYpaYx9aHKqJHNy5Jdedti8q5KUm8GkLkux5gcWCabB13fjSxs1/V47+m6fqx+0Slp2ccjENbZp5uCwbIHNgexVVVFINCYkhCjJCKn87E2pO5SP4ZYZf+qdKCasQ5M2FyRoRutqZtGfMTOjsng1juCjwnIlrjKJxmuwVlHnxj4pqqlEinJo2PI1ylJq1EEpca05hDk+fFeEq992lgIIcnFkQ2c4D0RP35+FIWw+KtVPyZKgwDx2WxGUVXpnj0y86c04NOc5p+MuZye8XM/9JDZn/0Ln+px+//kj/Dzf/7AV2/+xqd63NOc5v/v80b/662DLGD22y37ds/gktQxVhReFi2bzYYnn3zC0PdM6prlcplSa7NvztG2e6y19H0PwdOMnama6XQ2snrZR3pzs2a73aYApAJFwNk+SWYhxpqqqtjvpZqjbdvX0jbLMqeC5vAVYQlcquJomoayLDk/l0qbqirZbFbUTUXTVHSblrbdYa2wY9t9y66VqphhGAho5osWIqxWa/phwFtP23UUhaEoS3yUfk3Jh5Ge1+XyjKuXV+x3W8rCEOuSXnmMgd1uy4cffpNXr16xXq9xLnI4dBxa8XlaawnIn+02u5Hxquv6tXCU4P3IkGZmWMCqR6FHv5tJskOjNG89fMR+Lz7CxWLGZ959TFUXnJ0t8NFhncVUFftDh/MKVM311Td59skLPvn4GdYOhJwAXJb4CN55FIHoHU1dc//iAucszazhM+9/hvn5nGE4oLXBR8d8vqDQFR8/eUp36GGITFzJd12+zU986at8+Qs/TDUY+us9oR2gt6ggskUpeRRvX06d3Xctq/2Gl+sbNm3L4D1DlERgcqJsCOADSitQHqccL9o1X7t+wvNXz2ntgXJacD5dEPDoomC7240JsmTwFxUuRJRRlHVN2TRY7/Fdh7UDq41N4CTJcH3yyQ6KQ+rwjTEkVrEGNMEzgkTpwk0dLHisdUl+XlOYVEMSHPvdkO43UsqrPBNlkujGMZVX4ZMveAxTOgjgUOYYgpPvXZBzMYUhJmknCrRRBB9GIJxZ1L7vKcty7FzNXlfvPYfDge5wIFdXCRvncal/1iZm8C67F4J/LbjpWD0l/t7ZbDbK/jPo1loRg2e72Yqs1smmV91U9H0/fm7kcxsDo7QmAFE0vwRn6e0wekGbphl7VYuiQKVU3eBz16tP1gKPDXb07RaFSb5lnTYP5Dr7IM9NVZcix07stUreUBWlsqo0JlUXKUxRolEMncX7fkzpLYyhKktMUeB8xEVJwc5gHwIBLSFIMRAhPauyOaHSc6RTpY/cAyr1PUvCsrcDha6oymrsWJ3PF+Nnb9PUqR9W0/U9fZLyAyMje5rTnOY7d/7Kva/w63/kKWd/8b/41I45/Of/Dj/7Zw78zv3/ybZvP7XjnuY0p5F5o8Hqw4ePePLkQ77xzY+4ub2WHkJr0UYL+5KqbJxzFMbgeot3wvRl9iJXqIh/dEDrgroWb9l8PmOxWI6eUgGRFYeDgBghvWQRqLRIJMtSFrPWDgyDHReiMcaRwamqkhgzQ3AMhikKw2JxzgcfvJ8WYHZkY7y3HNqOm8Mt6/WWoZOFfExVFdbacbHY7VuMVuy2O9p2L0yyddjBEpxnqAbKUupcJD214PLeAzarDS9evGKz2SQPotS7zOdz2rblyZMnvHjxnBgTi+RJoTJaQqQUWIRpkZ8reXethQjHgBoZYwxNVcliMkgYy2QySUnEsvjUStFMGqqqputa6rrmweVb3K5uuL5a4aPF2YBWhr6z3FxvqKstVy9vuL66FVCVwJfWGh/kHvDOQQJH07Nz7t+/h640Pnhs1+O6gaZuULqgqhqublZ8+PWPuHl+hd8NLJnw5R/6Ej/1W34r7917C9MG9qsNsXdE64nOS7eoBhcjUSuCVgxOZMm3t1fc7DZsbJek3wgrlySNOkkm+zjgC0UXLU9ePuPrLz/kqr2hdwOTxYTJfEI5LUWaq6FuJMXZDY7D/kDXD2R5bk6uHcNo0uQFf/ZTaq1QxDExNjObWZGgtf8lC3uFD4KNj1LP5DNFmDFrMyDTiW3VTCaT9HyIXDb7zZUyqec2p9EePYxwBBU5sCgD2pDAqPfy3Dgr//XWjYFgwQu4IooMP+RkWkj3aRy9pXcTg0MQ8KRSXUzfd/R9ZqLF71mWZTovP97fkvh9lhQTInGWlOOIswEfA8poZpM5dVVJD6y1FKZgUjeAPDchSu+vVpJEjH793PL1zOfedcLUVnVFWZSJDSb991hXk2X1OQCqKCpCTH2mUZKajdE4X1FXBaaQntvCaIxWTGYzqRPSIh0XWa30TYe+J/pw7MZVYL1YH0IQZjXEgArCwsOdz9MU4gWgtCEJewGokhQ7M6taK3wKgCt7UdQoreQ5JyZvf5nu+zJ9bofkr84S9ngCq6c5zXf4/Mz938Cv+9//MHpx/1M97i/+uTVfvfnZT/WYpznNaY7zRoPVsm4wRUVV18SoaPctg7vjFTOGEAOFKWgWDbPZFGsdz549G31KeYE+mUwSaKxGtqVtD6zXG0IITKfTJOvdMww9dV2NvlDn/J1FsxoZGmuPLGJmYYpCKhRyF6LWKi3iDbPZks997nNcXJzz4YffYrvdUlUVZ2dnKKV59uwpNze3dF2PURofAsGF1AVbjiEoZ2dLHj96xN+9/bt4Z7FWkn2V0hDFL1d2BSFKXc5yueRwOPDJJ09ZrzcYY7DWjWE63keeP3/Oy5evZCFJljlmWacb/WcigzR3qj/kvSAB3KqqKArFMNjxegz9MMrwRCrsRrmody4tmIXN6vuBp09f4NzAy5cv6F3H+cUZShU4Z1mtNgS/YXWzpT8Msvj2MTFuYZSY6ii1RovFHDd4ukNHrcRrjAuYUBKt5psffYvnL67YbnboAcK24535A37PT/40P/1bfpImFvjVnu1qB15Jf6kO7PtB5KgGgtJYAvtDx816xe3qlv1uSx8dA7KpEoKck0YSYr232BhwteKq3/ELn3yLZ6uX9P7A8t4ZZzoyRIsudeKj4Pz8HoUpefXqCm+lzqisBtrOojKju98niWk33rMZjMr7J5sffZJOTibTVDcjzNt+L7vGuWszg4oQjgA7/3lIUl1iSJs0FefnS6bTKSAbHa9eXdG2sgmxWCyoqgYQoFGWZXpGzchIKqNHQJM3c2TTJuK8gM8YBSxOE3DLxy7LcvSK9n0/hpfd9b0KWD6ydRlEg8iP8zXKFT337t3j4uKCoig4HA68fCnhbTks7OLiAmMM2+12lKPKuRQUpRlVFMaIxWC73eKG/rVU3bIoiIikvh+G8bxDCKCgLApmszmz+RyXupwlIbdC6wKtZOPIOo+zlq4fRkCYgXPe9HLeJ3l18uIqxgCiQ5t7cyPTyYTFfEZZVjTTKdY52rQ54qzD2uNn4vgreZd1EP+pUlJXk1njzJQfJeG8fq8ZJQoFpTC6GL8mRkl811pRVhUoqQLKtVI55GuUhud0LriT+KxOYPU0p/kOne+99x5/ig/4p37230TVs0/lmP7DnyN87W/zwe//72hPKb+nOc2v6LzRYPUb3/qQDz/8CGsts8WS6XyRwj7a5CcV5qAqKmIK6ri8vM/t7YrdbjcumgWYSZdhCIGu61JwUk/fi191uVxibZ/YJukWbZp6lPvlBE1glLh6H1Mtjk/y35KqKlMQ0xRjpK/zcDhQlEVK8K14/vwZNzc3eO9ZLpeJAQjcv/+Aoqi4fnU1yhgz8MjnXJcVhTa8evFSPLwxQAyEVNehigKFGgOOqkK+/tmz5wyDFRbHOuE0xiAc8bINvX1toXd3ISo1QibVYsbk6UvSyxgp7vgSM7AOIdDZAXeQGpncl1hqMzKwdV3ho8d5ARiFNpSFpPR6r+g7TwgaoqbvHXYItG2XJNKJCVQGFwTQCXuJhM2g6A8HmromIgvr6XRG3zu+9ovf5MWrV1zf3GA7y6xomLiCH/01P8I/+xM/xY988Ycx24H++gY7OEzUxBAJzgmQwoNReCJdcKzaLa9ub1hv13TdAefFLzqEKMeOkaAgqoiPDl2XbPqWJ09f8I2rT3i2u8WZyGIxoZqV9H1H27boGubTJXUjCctGix/T9l5CjlC0nVy7DL5y0mvTCHMXEos6BhmFADGluaLxTlhKrTVayeLeDg7LMY0XJTVSOSXaGINWEVIiszDHxxCbrDSwdkg1IiYBU5dClIQFdC4klj8QIvjkqx3DfbyXjZAYZAPHW5yzTKZT6rISxi2BsvzzA0nWfwSed+/pY5fp3QodCXXKm1yZ1dztdoAwppvNhtVqBcBkMpHUbSfhXV3Xjde3KKQ+pmnqdB/78XNnv99DiFRVHH8+n87F2gHbd7hhAKWoGwHgdSVhVO2hpet6rJP3Hm3oB4fzRzbVWkvXDaMs3zmLD07AcyGpR1opMIklDXJtnXPE4JLnN3WkRhisZdjs6AYJZgs+jmoRENlw9pDmz4YYYyJJRaae309j7lYY6WO4VYrTQuzb2MHiXMsw2PRaPW4AlGUhPa3GUChhdEM8VgZlBYGPEVxIMn1NSJLo05zmNN9Z84fe+Qp/6q/9UVRKGP92j//k/6b/0/8hv/dnpvyVZ3/zUznmaU5zmn/wvNFg9eWrVwzWjn6wLIe7uLjAO8/NzQ1939E0E7SOFIWmrhvu37/PZDLh/v37aK25vV2nIKQDfX+gbQ+JLXSp01DSUENw+BQi4pxlGGTBKzJGSRG9uLhgv5cOV+cGgHEBv1wuefDgPhcXFyyXC8pSqmluV9eEEFnMF0QCN7c348Ips0tt245gavrejLqqKIoSpWIKfVrz8ccf44Nju9swPOllIU8kBJc8ZgL+los5ZWWoyprl2YKL8wu2mw1XV9fc3NzSE5lOZ9STCd67EaAbU+B9SDUgxy7JPDF6tNKpNiJXkjjKwoyMyIMHD6jrmpcvXgKRzuaqG0nvLMsql09KuE1irn3yHqIV11c3vApXRAJ13bCcL/HWs1ltWa3WdO2BGECrQgCQKiSQJS3EdZSApaAVnbPcq0tMVTI4x4uXL7i6vmGzbolBUZuKc9PwoFnyu37st/Jbf+zHeWd+n/hsg90epKM0SURDShSNKhK0AM9df+Bmu+bV5obb9Tql8EYBUQEKZXBELAEMeBPpg+V2c83Tmxd8cvOcrT9QTAvQARctL69f4rxFFxplNPPFjGbakBNrc0BXCBFtSunp7brRF5kn+yxzaM+YoksGbRKclEmuLCGV/5eU31xPkns/Q8j9tgiA1bnGSWTARmvq5FvMgDNv6mSW3gbZvMjyeRAWNiaZf5Z0Gq1wNlWhGEMMAR0j9WKe1AIt/aEjKmHZRt9iXY/WAGOK8Zr4FPiT03Mz+5j95ne7OM0dploAeJTU3BiZzWY0TYXzFt97+qEjEtBaZKfOW8pYgNLJ+yqbXV0n6d/EiB3AW0+3b8k9rhIMVUmieFWBUtgEnAc7pOqdKOFEMYovdQjAQE5Kzp8rfS8p6UrnjlhG1YHRWj5bcrhUkvcqU47Xvq5qYoRD8r8OzhH8kV0HlWTQyaNrNFI3k72lx7qYXF8j4VXHyqERuEYJzToGZ0m115A8zU3TpARml8KjFKYwGKBI31sYZGHHf2lic4wRo02qBjrNaU7znTL/7tu/hX/tL/+eTw2oxn7Pn/kd/w3/xrO/+6kc7zSnOc0/2rzRYHUynWKTFNA6J6mpSom/tKp5/OjRHWlfQCUCJS8ojTGcn58To8JaR1XVwDBKxSRsRImnyqSFnTIjMD4c2jEsZblc8ujR4xRCI4serYuRzZG+zkXqRK2xduBwaAnBMZk0NE3DZDJhv28pjGEYhlGOPHZipoV7XdWvBbXkRf92u2GzXSepYUnbWi4vL6nriidPPiZGOD9fcHFxTlOXPHzrEZNJw3q9piwMs2nNpHmL2WzGgweP2Oz2tG3L8+fP2e/b0ZMoPj25NUb2uirRRt/p4UwMVAIFWfKcF4nZN1iVFUOSNEr1hUOnAJTMOPkEfoSBFIl1BAbb86Uv/RBVU/LzP//zbNu9SKdNKQvbGJhMa4iG7tDjnCQQaxSF1pRlRTVp2PV7QgFaR25vbtjudqhQY3xBHQq+8N538/t+1z/HD33+i0wOAV5u8bsO2w9QJY9olMAmT0ipvI7Vbsurmytutyt2fcfghjHRVCXArFQglBpXRHrj2fmOm3bN06tn7Ic9rlGUqsYFn95nRVSa+WRBM61oUpVMfg6GQ8d2s5We4Khx/sCQJLUjOE11RvnZ0IUZ5ZAZjOX+0wzYcqLuXelw/v0IRlSWaKqxdzXLRochYrQepbmb9ZpuGEYPYf4+wzDQdQNa6REcgwQloe92sOo7rF0xGjLPFksWi5nco8bI/eXk+ck/S13VeFOMycD5ns6/XGISsyJi7IGNRx9vfq6PXlqfvOhRKmc2x87Ou+FOkgI8paprQOF8GJ+L4EW+mn2/hKN/UxQRpA0cCQiLI+eokjdfEqSPTLFGU+JdQKWvFZ++G/tl66bEGJ1UJDYx1SLjN4UA6hACyh8ra/J775wba2W8Dwy9u3NN5TMwd7yK8kI2LzL4ztcl/+r74TVvslw8iFEJyEzvi1zT4+dhBq3H4CcJm6oKQ12WgAREGSOMapYDyybYUfYcw6li4jSn+U4YrTR/6O3fyL/6P/xuzLtf+PYfMEr2wh/70T/Jv//sZ779xzvNaU7zjzVvNFhVOqKNsC7Oi8zv0O05tAcKbe6kcEoX4c3qhtVqRa5JuLq65tmzF2mxo1O3Yzl6wrQuMAWjfyzLVCXZshwDTXIvZN/3PH36dFzMNk1NUczTArdntboBAk+ffoJ1A1rL957Npjx+/JimaRIoC6DEK+d9GFlVCT6ShMu6KJhM6pGdmE6n/Orv+R7aVljduq755JMnFAr6Q0tVaCbNhMooDrsNmhlXVy8pCklMLooSXWrqouTsYomLVnyT1nJ2djb2EGaA0rbt6HWs61pYbQMupXZKwM2AMVDXJYoGrRRduxvTWI2pGazUY6jEpIYQJCBqNsUn2aH4DAfmsyldd6AsSpbLBbP5jO12w+75lvVmjTGah5f3mU6mya9niD6y3u7p+46i0NIXKaphhmhRGFzvGMLAcrGgqivmfoY6FMyLGT/+Iz/KP/9P/24+ePguPLumW++JNkgFkooE30s6KxFPIOhA1x+43q54dXPNtt1zGDqst7LYjn7cfNC6QOvIgKJVjuebK57vrmlDDzPFxVsP2WxWDN2B0ouMfLKY0fYtptCcnZ1RlIZ+GIhpId+3lt1+Tz8MqUoJSMBzZBATAMrJuNgjsKzrGq00wYf0DOjxa7M89q5sNgM5OHoOcziXSGhF3hqsBJkFD/vhQNf1qCTXLMuKsqwT4FyRRbcAACAASURBVOlFVj0y83Le2qgxsMs7T7vbU1UlZVFQaI0ujh7GQ9sSE9tdFIX4kNXdLtYjM5oBegamVVXhU9hOBpdZppzD2u6C1fF7uBRWlF439Mdwqny9SlNIUu+kOQLBVB9krRvDn5TSFNpgSkl0zqnfZVkym82pm8kIaF2IHLqO2B5wLmBQaFOQ+0ijD7KJkoHZnYCpEBxDF0BF7DBIYBRQTafjZ6H3ogLI1yMEj7UB75TU0Gg1qh6qukg+1HJMMM51VfnzoaokVbjQZXo/Mosq92q+l7IUWCFJ2DEee1lz+NrdbtuYgqByCJNzjmAlqTq/dyr9vfRHu+PrE1h1d1QipznNad7M+eL9D/jr//O/jb739qfiTw3tmp//yh/lK1f/F3vbfduPd5rTnOYff95osDqdTWnb3cjuiadLFlzT6YTZbApKsV6vOHQd1zfXAnpSlcF2u8X7NVprFoulgF5nKQrDxcUZZ2dL6kbY0b7vx2oHrTWz6ZzpdMput+P29nZclAlAEKC5XJ7TNA3DMLBe37LZbLi6ejUC0qIwnJ+fMZ/NKEyBdx6jDfcuLjDaUFUC8AKRvuvY7/YATJoJ0Ru6/iCVDlJYigsis6zqivZwkN7YTgKUvuu7PmA6nTJJPt2bmxtevHiO1pq2bbm4uADgQMdk2nDo1mxWIo3OYD2zSpKUPB8ldDkcpe87TKFTSA40TcN8Pk/A1RO8Z+h7ptMp8/lcfMX7NlVreIILIm9EOh+NUbgEMBaLOZOm5vz8jEldE4JjuZjx0SdP2Lc7Li7OUs1PPYY2ZZ+mtQMBkUQnhTEBj/eRYe9YLpZMJ1MaU/Hg/jl+ajlTS37bl3+CH//R38RFrDh89BS7bSVROkRUJaE3WV6MUnRuYL1fc7NZcbNes+l2OOcJBCKBwQ1Y7zGlIQBDdHgV2NmOrz/5iI9XLzhgWV6e8fDiIYPtuF1f46yjLEoG71BVAVoTlaLvrdxLQRg/O1iCj+lnlxTgECLxTpdkBn/SGyyPdyCODGVVVRSm4NAexvd3/LoE3tQd4JcDjERunv+sQCtDRJ6F0pQUVYPWiv2+FYZbS3hTXcv7RVT4JJu/y3RWVTXK/L13KIskzCKg22iNJ7FrKCxQFiL1NUaqbLKcFSIhRpx1OO9eY9cy86m1SKszaMos4uFwSEyiGhlGOLKmZaFHsAyMyo28mXTcoNAUpkgbbGFMMA4JpOrCpLofxhCnrJSQUKYiBU7VTCZTrPfExGzmrtsQEKbdezRaPMMEgrcMw2H01+cNBZ+6VsuyZJq8ttOmoZlMQWkJdUo9sDk12McoKgWtcUGAZVkWlEUlPa1FIWw4aRMjZRfFGAQYRunATYpysdaPChlSx3ACqfj0fcRHne/jfLmzhzbLh/MxbQjs25Z+6EHpJEXO4Vx+ZKXlM/2N/qfuNKc5DfBTj3+Y//bP/Tjm8ec+leN1f/xf5hf/R8OXnp+8qac5zZs8b/S/4F134OrqKoWlRIqipChLKAJoRTd0HA4SRNP3PYdDR9eJ3C0vJLXWyWNWY60A1el0ysXFOZNJgzYKY+xxMRSg7wdevHghnYwpVTWEkBiYclxk53Tb7XabwIMAuvl8xsW9+5RlyXw25Wx5RvCe1e3tKNEkxsQ0+pTOaylTwuhkMsG6HJIiDIULDneQBeduJx2s5/fus1wu+fznv4f5rEYp0InpaaaTJPGL1LUwuv0wUFYl6+2Ww6GDKGE9q9VqDOVZLBZjqqkwL46+P1A3NWfTRaq/kNdNp1OWyyWr1QrbbZhOJpSFYTqdU9c1+/2eQhvQEaNyrypU0wYFCXwJI1cnr9zbjx8Ro2e1umW9Ecnz2dkZ7773GYqiSEFTVlKbe0lC7vsORUQXCnxEEwkRylTHUqqC82bBB299hrNywqPFJT/2xV/P937mc5QOti9eMOxbbD9w6AeCBqwR8KAN0UnI1YvVNVfbW1bbDd3Q42Ig6mPCrgseT6DzlqAVh+h5fnPDs5uXXG2v2Q4ti/tL7t1fspg3PPnoFbvdFrThMEg1kTeasirG1OnB1pRFgXfSpRmj+PK8jwjLJMFHFxcXlGUp70UCTln6K/bSo08zhPAae5g3X8qyHL2lmfXMYA2OQC17FmOUEKsj2BVJrzDKirKsJNU1JK+qTSFgCQRVVTXWSmU5fLzTb6qUyEzDHR9pVVbUpqJqGgFiRExZjMnd1lra2BKdorfH4CnyNfCg3OvXYmSP1ZGBzsym1hoz1tlIwnBOAdZap8+dw/i6fM2i93jrXgt9MsZQFgVNVY5qDX0HHBdlQVVXKBSHrme12Y2fQdZaQjxKmWOiIo0R36bz4petS8OkmY0AHXlH0NqMz+0kBTdVZQk6ddcm5t4YRQiGGDxKi7g4S3qz7zYDb2MKxNacgpKSrDtvFuX6rxAC1jmcFUnuXbY+T2bss4dafkbS982bMTr9vYTplaaS65ck6Vn+e9cHfaxFOj4DpznNad68+clHP8hf+lM/TPEjv/1TOd729/8+vv+vXvNsd/OpHO80pznN//t5o8Hqfndgt9vLQj16tLZUZYUPjl3bE4PHDsIwGWOYTGZoVdD1EgoiPj5hmfb7PdZa6rqkaapxt74sKpp6gm/86LdUSqSph0PHdDofg2nyAlAYVjeC5K7rR9B5N010sZgzm83lZ0npxV0n4LZtW6bTKe+++x7z+YLgA4vFPHlVNcNa0n/n8/lYrQEcJXeJ/bx//z7n5+dUpWG72+D6A+cXF5ydXfD8xSuGQ8d8uWA6maGNEiYU8cQZypRKbIlRwOliIYnLm82GuhZptPMDxmgePLhkvV5ze3tLjB7nBrkGnQRRZYBrjMIYAZnz6YLNeiPXtWlYnp3R1A3OWfa73fga5waGvuP5s6esb28kzVPB8nzJ+++/z+XDB3TdAdB0hzX9YRhTgY+eNoVWiqoo8MFTqhJTFTyY3+dBfc7b1T1+8Lt+DT/0vT/Io8kZ9npD23W4rqM97CW5OAZ8hGAVRdToCO1uz/XNNTf7DdtDy8EOeKTj0TthlKMCR8DrSKwMt7s1T66f83x9zX44oEvFZDnh8sEFy8UMbwfadofzAaUMg/XoAirrsb10Cc8XE3Q0KAyFhqBg8FaCkYzBpf5W0kbKbDaj7/txcyXXKCV+a2TY7nZ3ZrCaNyvuBjTlwKLcm/qah1VrDBq0GUGf3AN6/Htnhe3LAVvGFPIepR5XYYA91opsvGlqui6O5zhKjZWSjlStqScNi7OFbPAMA4NzFMQRrGagkoFZflazzzHfp8e+WPXaz5uvS37Wx5CxePQBZ1Ccq2iGYRjlxMcuW4NJ556PVZUVVV1RlRUx+lGtURQFs9mMs/NzmtkU7wKH21v2+0O6tw1VJRsPIftjvUepCDFgioKzs3Mu799nNpsRYuDm5kZS0bdyjyFfSvAeaz2oQTzk6WcVBlhqq6qyJEaDBCZ5gpfPxKosiEWUpGjnxhocMzLVScsbU9g0Mf2dEb9o1CPQvQu6lTI584ooFCwx3HlflIRjkdOo03UsTJXe3+G1e1sl369SmZFVhBBH8H6a05zmzRijDU+/+lmmv/fHUQ8/Q/F9v+lTOe7h3/oDfOGvXvFid/upHO80pznN/7d5o8HqZr1ltz32PlrbEeNu9LaptDgqyhKFwphi9MZFjqEzshA/BqlYa9m3+9HblxN51+s1q9WKvu+pqppHjx4xnc5Gb992u+XmRnbhcjVH7lmVrkCD1obJZJIWrw1FYbi+upbKCtT4Gq015+fnXF7e59Wra9abNY8ePUp9ji3eWyaThrOzM6bJY2aM4cmTJyNQFmYj8uzZc0KwbDYbmmbCarXh61//JlfX11xc3EfrguZiRt1UKBXZ7ltevXyJ761UYVgngDlIVYfIpmc8eHBJVUsi8W63G32tzjmqqqLve5ERp3TgYRjG8CmApqmggu5w4MGDS2azOdvtluvrG2FCtU4MoqUqS8qqYLVaMZvNubh3gY+Bx2+/zeJsgQ+e4GHSTNkVe0JiwAVAIGjIC1tdFxVDN0AfqGrDeTHj1372+/jJL/1mPv/We6i9o/v4FTjPfr/lYAfpPCWgSkOhIjYENrsNu/2W1WbDerPBpyoeGz1egw1WwKZWKSFY0YaBl69ueXL1nHW/o48WVSksjrceXHBx/5yuO7Dfy/X0IeC6HlNPUMbQdZZSKxpTYgfP7e0qyXdLfAx0XY93aUE/yiI9z549O4LTHCSUGEUffGLNzMho/b0MokrVMvk5KkdwJ6FaxWt+Q6XELwhxZC9z3VKukemGY8dprh7J55GBitQyHVJwWTWC2LshPTHGMV0YJdDbWks3DLjgsc5KOnKMKBhZ//z8Z3CZu0wzswyMSbIZCAKvJWCTb610LScT2Wh59uwZ1g5jT2xRmOQ5ncmzqgze+de8r0pJkFTnO5Q6BlmFGNnstmAKJoPFaGEExxR0H3DeSaexszhrcdahVaCZ17zzzgMuzi8wRYFS4BwsFzMKo5nPZgyDww6WYfBoDWVVpq89pg3n97kwhul0QlVJ1Y3WmsNB2FSjDaAZrKXvhrRRZBP4BO54ge8GcMkGQg5Winc2NtT4GZC7nVW64NpodAK1EelhruuauqkojKEoDcZUOOfFHmIlvd05l6THcm+rJJX33h9Nsac5zWl+Rcdow1cffoH/8oMDZ//Vnz/6CD6F6f69f4X7f/HvEOLJw36a03ynzBsNVvt+GKWDcFwk300MjYB1nr63EGKSH0papTY6gaeQZLuyELbOMmw79rsdL1+GcTEp3jOXAKx0G9Z1zWQyoes6hmEY2ZZ8Pjrt4ldVxcXFPS4uzpjOmrEbcLff432gSaEpRVFw7969xKq+S4yRzWZFUQhz0rZ7IDKZTJjP5zx8eMliscRax9XVNev1Bmc9ddWg0Dx//hL3yVPadktVlZydneNDZLXeAgZrHft9x5OPn2K9BPW0+xZrB5QPDNZJ4E4Q9qUoCi4uzrl3cSHppyFIN6j3bLdbjDacL8+ZzqZYa7l5dYUqhL1ASUhM7sIdhgFnA5Npw9n5ku1mx3q1Ei+cKYBIURYURtPUwnYTFtR1zcOHb1HWJbP5XNi9siZ4AfubzY7dPlW3qIKqLDFlQYEi9A7loAiG8/kZ3/e57+Wrv/4rfOUHfh0PiiXhao+9XWOHltYe6L2nsx29dxS1gHnvHdvNmqcvnnO739BFhwsR7QIuCKh1IWJjIOqILuBgB7bdgavdio9vXrGzHapSRCes6/J8yfJiSdkYbq7X3K5W9EMvLHcEFaJ47qIihkh/6Dh0LoXfQFVX1E2Ds5KqmiXuciuKRJPkTRWQqka2NHBkWsUb6l/zhTpnx+dKKYVLsl+t6zvg9ijfzCCjSGCiqSej17msitFDq7QE32T2Nj8vdxOgc+q0nIsjRjMeIzO2mRXNVTLOWcpKNkWkA1muUwbf2WRdVRXT2QxSYFjf9wwpwXrs+EzfW7ymZmTlchptBlhaBbQWC8Aw9Hhv8V780hLCVlCWhrKUPlGNRpfmNXlqZv+y5FWpCKk6iwir9YrNdi/nk/4sd6f+0tCnGMEUmvPzM87Plkwm9cgCD31H33XEEGjqmvl0Tk5Ed94R4pGhzXYEbQw+Osrsay40Skmfsk/stFeasmyYVDWzZiJhaWnzJMYISq6lj5Go4nh/WuvJ+P9ucjLckfsqhSbeYV1Jf39k86vCYFC4wTIMHT7sUerY2TyGjKlUbaNzenOqLDrJgE9zmjdi/tLFj/E7/48//qkft/3X/yUe/aVfOAHV05zmO2zeaLBqDCyXc6bTGVqbtOBzyae6lx33wiRprEcDTTPhV333dxNCGP17u92WpqlTfUyDdbLQVGj2+47Dob0DQMVrV5iSdrfHO1lUb7db2n2bFp8CaELwxOCZzaZ88Nn3efjgISEGttsN+90eWw5oI+Evfd+BFSB9di7BTO3hwPPnz+n7ntlsniSRwk7O5zNmM0nC2+8PfPLxJ/zCL3yNtm1pmoaLiwuapsFaR1koFoslbdvy8sUVXd8zDNJXSYw0VU0/DGy3W5HSRsQXGyxGCbO03Wxo6oa3Hlwym00xCnabDQrwUZioi+V9VFRst2uUU5SUTOsZ/SA/59APrIeNJAan62SMpqgKrq6v6LoeUxkMhdTapBTn6WSC95bOdhSlgejY79bcay4xRkl4jtZUTcWzF8/Gft0YIoUuKeuCqmpoVEHwA5NY8vkPvouv/Mhv5Dd86cs8Xt5HtQOHl9e4fYfrWjp/YMCNQNWUJQFouwO361uevXzJq9UNPY6QJeOJnIlG4YgMBHwR6X3Py9UVL1dXHPyAN9DUJdFAiWK2mPH48SOqSS2bFsVG3iMr4VkFOhn+PNoUWOeI3oMWAOC8w3pHPwwoBACWRUGM0gc8nVW8/c7bLBdnPH/+go8+fJL83dXIgGqlKAoJyTFaE1Wg64VVDzFVtBiNRgAsw4DznqIoaJoGHUSWraJKzJeECIXg0cYwW4jcnSAAXimN0XLOIWYZqh+9ljkddvQlJlauKLPkPeKDBCZldi6EwGAt1jkmoaGqa3wgyVElBMr7iHOerutT3ZIiRI/SUk9lCtkQyEAqM4L5lzFQFqVc88FJ2JaSTRUi9N2AS4Fv0UdmkxkX9y5o6oaiKkXtkMBy8AGfgWEEmwKXUIqyKDCFxiDATKptIsPQ4pwffcYhRoI/BjRV9XEDoSiUeNSrimoyYb/bs1pt2G52EjoUpU+1rAIKhfMOoiKkjtsupZiHGCjLgrqRhOAYYvL/HyQh3AdJKK8bqoqRdfcpxCrGkKTK1QgWXXAStOS8dLsqRVQh7yMcPdRBAqSMlmRgneTD0gecen5RBO/xBzfeC1FFAcApndikkC1JOc4JzcJQu8JhC8tsMv22/Vt1mtOc5h88X374q/mfviwgcfof/OFP9dj2L/9Z/tyfuOJP758zePsPf8FpTnOaN2reaLA6nc0wheb+vfvUTcPQW25vb3n58iWHgxLAGIMs+rQs9k2h2e02DINlu93inOPy8pJ33/0M0+kEpYWd2e12OBuEKZhM2O127PcHAMqiYrfdAWH01e3bXWIDJOWzKMSjVdaGR48e8vbjR3gfePHsGVdXV8QYU43FhJBSMLXWKK/ohx4fPDe3N1xfXyUZrRZmkRogMVUHnItcvbrmyZNP2Gy2kr5a1lI54cMoY3z/g3f5+MlHPH/+EiICZkJgu9lQGEkeLrShqZoxWTaiMEWJHQaauubx44fUdYUdBqm6GHqqVHFhdEkIsF6tub2+ZjadikyxaIgOzs/OefLJE9bbNWVVocuC2XxOk6pS+l5CpRSgUk1HkeSn1orPcjadYvuOGD1d3zLYHucbdIT9dsvXf/EbvHjxgmHfM9EVRDBK01BxMT3j3cu3+dyjD/i1v+oLfP/7n+fi7C2KzR73bEO/bxmGnsH2WG9pfU9rparHVAXBKG7XK569fMH1+pbNocXFAKUhKoWKEJVi8FayS5sCHx037Zpnty/ZdFsGBkxjmM8m1JOaoiowVUEkA3PDdrtlvdlgihJTRIyNuOCFuYtSNyILeI8mUXx3gnuUgqaucS7QdQOFge/9vs/z3nvvslptefbs6dhBWxYFRVEKk6oCVVlSVyVDP2DvMJ0qSbAiUu+R2UkfI34YCDFieiv1R3dkxDoBjtlsgjZNksIXiTn0FKXBeUffe1zq3s19psIQHkOO0MKEZob0bqVOjFEkvTHiY0yA1aL37R0g60dmOHeFhuCBMMqgc1BU/p4ggUAhqDsBPgbv7Gue1Ril3qXIibIxopVmOplKum4zlWtnpXKlqEqaShMJOO+xVoDv3fAfSJswXoqRrB2IHFnFnNQscmVFXTc0k4a6ls8Hax1R4qV4eXXLze2GfrB0hy75lkky5Z1UxihFWUmSbwgCgH3w44aSMQV1VbNcLtLmoEoe+wND1ydWV76+bVsyuHdOEn1lI6QcbQDBHv3c3rnERB+Ds+5KyqV/WSVGO32NjtKBrdVrHcJEuefkP+leVMUIVou7vusgmzRoTRhl66c5zWk+jTlrZLP97/zAO5z/kX8G5hcUn//yp3oOYXfL7/jKH+Frhxd8a/3iUz32aU5zml++eaPBalkWXF/v6Lpu9J72fc92tybiKQtDjAGdWJn5csG9e5fJ8ybe1CyFdM6z2Wywrk9s64627dHq2Ck5nU7JfaCSXCmeuohPoMoTvKOqarQS8HJ+dsZiMUcpqdC5urrCh8DFvQseP37MYrGk63pub1ajBJCoGXrHarWBmKtgNN4f60HE4yfdrjmcCaL4tmrpX+26buyavXp1zWq1eU1umBd4u92O+/drmqai6wJQYKqSwQkwuHfvPg8vL1nOZxza1FlqCupiIrUWdgAM7WrF9c0tKkYqNcEGD1EAwYvnT7FdR1NUaKQrM/QDVT2l0hVO68RGRwiRQil0CKhISjTVTOuCz3zu19B5y8ubKyKK26s1u+2ejz/8mHZ7QFnPzJc0Rc2imfHg3iXvPX6HH/rCF/n+z3+Bx2cPmVARdwP9t57RdxbvBrxzRByEvHBO3ZIa9n3HzWbNs1cveHV7w+AcymjpyiSl8BJpvUc3JU55Wrfjerdi1W0JJSzm50xmE5ppQ1EWTCYNaLi5lfTg/ctXlGXF7c2aYeil/sMUKOOJgxMPIpqgpMtWkn5lQ8QYjcJQFhKAVdc1V1c3OOeYzWq0NlxfX/O1r32DZ8+fkb2DAs5kga6NsE5NU2GHXLWSFv4x91HGO9JX+dvcg0u0KNW/5kM0yacdgkhVcwVSXVYjeKgqCcHJYUK5n/OYNCxJyjGCt24MK8r1O/l+zr7xI8hhvMezL3Y87/T7YRjwwaX77ghQs7/2rqc9jmxveK2jVepvHMaa5IM1VIX4ck1K295utyI9LQvxzytFNwb66PGc8rnm6ftBQJkSRrFKzGy2HMhnYClyd63H8811TX1/wA4t88VcariqBmNKrD1we7tKXmJPDCTQV6U+aTP6gmWDoRRvbIjsdi37/eG162WHDmddAv/HAKvsPc7JxrIBVYIC7RwuZvmvTmFHMW1w5P7UeGTatfhUVQKt2igShn2tBzuGtMGBSpVYURQI6abI90uWnocQ8IN8Hk5OzOppTvNtnx++/G6+WD3kP/3rfxxlik/Vj/pL5w/85j/J//L8537Fjn+a05zml2feaLBa1w3eB3bbW/pO2EhApIda0imNUpjks3z/vfdYLM5oDwcJEHICVJum4dDuORxakagqOLQHht6iVPY7mZEVUFp6XIdhABWQ+kXNdDqhqYVBqquKoiqZTicsz5asN2teXb1isVjw9jvvcH5xjikKukPP6nbNbrdPi7oKayWoKPhI00yo6yZJmfu0MBOv6er2Bbe3t6zXW4ZBFutN00hCr3M4F9OCUbNer9jvdwmEGabT6vUUVw2LxRxUJPiSqKAuC956/IjLB5cordjvd1g8sdAMwRN9xA6WwhRUZYXVjuViQVPXFKZgu9vjh4HSGAZn6Z3lrbfe4vzigtV6PTI2IYLzAe8DKspmQEzdjXUpEj5i4P233+MHv+8H+Bt/62/y/uXbHPYHvvWNb3HYdpid5e3mgocP7vOZB2/z7lvv8MHb7/HeZ97jrYv73Ksn+M4Rrjr6w4Zh3xOsz0WpgCeoQNACkLUyeOfY7LY8v3rF8+tXrNsdvbMCVBWSZOuk+iUaxVB6bBwwdU0znXK+NFRuDqVCN8lraTSRyH6/Y7drefnyJTe3t3jvJKk11btImvLRu5dDf6QrUti2zBTKBsbRq5hfJ4myA9/4xjeJMbDd7DC6oJ5PEmiRzR1tFDoo1utbdjuDHSw6CuMbgsdL/whiHVSjf1uhqMoqdZiq0W+YPYL5/HJKbvYMukoSbnNQUF3Xoi6wFudFNjz6EKtqTO219sg83gVLv7T39a5v/RiGFO/8PgNwLyz1HQDuvaSEV9Xx+XDOJT96oChKptPpGCYGKVm4Or6mKit06jwdBkuXAtPKoqQwxfhaYRzLxCCHFAAVkyTXo9PGmLnTI5pBfT5nYQkF4K3XK5HSG5NqoxZcXt4bE8O7rqNb79i3Hfu2k+dNtAx4F0Z2s6mr10KuJAVdqoVCOKS+ZQcxUJQlZWFSwvcxPVk8oYakV8Z5T+h7iAMRaA8H8RKnjQAJ6DL4EMdjCqud8gdUquIpDEozbhxEjinNwQe8l/vIxyg1R94ffau/5L4YvdJBEorzvx+nOc1pfnnnP370VT43iLz2x/7YO5Q//Qd/hc8Ihv/6T/K3hxObeprT/JMwbzRYHXqLs16YoOQTm80mnJ3dHyWIMQYmkwmf/ewHvPfe+7T7A9dX1wz9wHw2G1Nqey9BTTlMqDAGXwhIzUxMTgxumopJXWMKk+S+MJtPaZqGSpcpHdNjg6dt9/T9gbIqef+D96jrCUpp9vuW/eHA7c2K2+sVZ2dnTCZTvPe0rXQnGmPQqavw0Hbjot97x77d0XeyaHVOAqLOzs7S4jeOrNLhIPUZElijx7oZSVcVBuX+5T3W6xWHrmW5XIikz1kePXiLy4cPONiOV6+uabsOZTRNXbNb7+j2B/zgqIua3nfs2xYXPb7tccHTD70kuJYFQWt0WbA8P+f7v/hFvvWNb9LuW4KPeOsZXCAODmMKpvWE+XTC2XxBoTWlNnhr0X3g5/63n2USIh+8/xjm8D3ztzmfn/Hw4gFvXTxkOb9gOTtjVjRULuJ6R9h02PYK2w3Y3qETwJMm0kjQEac8QXmCUQSj6FvL9WrF8+sXvLi5YrXfYmPAqwBRY0JEKwEWGnAxsAk99WLK/P4CryOFhYt6gSoUt9sVq90WFxzdoePm5oZDe2C73dF3A9oolOqZTqcjoygATfzWRPDRg4rJt3fsrIwxjAt26VdlZN6cs1xfXVOWFdPpLAV5lez3LX0nmxfyCk3XecqyQKHxwRFJDGeIAMFa2wAAIABJREFUeDEAktOFxMqpRLrpGTc9cnowSLKuyDb1yM7mqhilFIXSmATeQwjoO6/NjOVdVm6wNp0Hf48MNwPj/Pq/39xlLeW1PgGU43kbYzBppz8/Qxlo5+/99wtSk+shgEpFSdTNtT5lJWFuRidgqSVcKgR5TiUQ69jnmvtvo5b6nxzm5L0bPwMy85nZ1HzPKKVHltWYgk2SlUsyd8fQZ9VAQYw+nUPyjRqksksfk9HlPXNJzpsCjlCYVPklqdsObTKbqnIeFF4fwWbMrTXp/ep7O4LFMXH6Thq13N8u3dsiSfYEqpiBtByvSJ20McjPL0y8hJaVxqDS5zdK48LxOcnnIYoB+TmsO4HV05zmH2d+4tEP8N//1D/866o/+C9iHn72239C/wjjX33En/htf57/1T7lb119/Vf6dE5zmtP8MswbDVZX6xU3Nzc426NVWmghfsxHDy6lIqIomM9n1HXDRx894erVNev1mul0ytnZOc7JolfSNAM+WFkcpTTSvKDJi8MYZdHWTBouLs4hsU/3L++htaZvheHdbA4iPZ5NmcymIjVO0twcDHN7u+bq6prgBHAeDv3oe51O56mjdaCPA9ut+GgPB/GD5UWdMQXL5ZLFYjEmCv8/7L15rGxbft/1WcPeu8Zzzh373X79utvtdp+221PaI57dUWKTxEEoiMjCZEARk4nFH/AP4CQEiIyS/0KkgCBKFEsIBUVGIIKMgkBCSYSAhEjBvrgd9+vu1913OqfqVNWe1sQfv7V21fX4DO6+N6/P7+no3XvPtGvX3lXru75T6XgswMA5R1NVaKXRaOZ1kwGpx6dIu9vR7vY4N2AQ2WJlK8wbmu5wIKZEoyta15JcpO08YQxUKtdbjJ4UJFAm+cCQPC56wTa1wrvAarnmfL6mfb7l0//nP2JsO/CRR3fucX53zfqtFRfn59y/e49753c4W624e3FBpYSl1imhYqKpG4ypUD6iY0I1c7T3GCxqCIztSP/kil03EAZP9AFcoM7VRSEBWuETRANRI2BVRyKJMTh27Y7nz1/wdPOC7W7HYWhxwRGNQltNUhC1dKaC+Af3fcfeDpytDIs6MbiR1nfsD9c8e/6Cd774hbyAtiLz1Za+H9jvOppGqkDGwaP1SGUtw+gYRi9JpRKH+uveA4VNFbAWSUlNMu/S7TlbzqfQG2NqYozC3GfwK/5Fl72CCmty1UhKmMqgI8RRQIfJHlaj7ZHBnLytWWqbStrv0Xd4ymT96mqSkjZbVXbyWBtjpsqbohiom0ZYOu+n6/u0+/SUUS3AeQrcSS+zqgWspiTVQoWl1FoLI5cBTZGDy/nT+FyDcwpgvXOMw4BRGlUrmqZmNptP92jjJSgrBnkebSUS+3Ec6TmCYq3LBluQYKMYGQYzbS6dnjNgOq6SsF2eQ9m08IxjwncCbsUvW3pNFd5HSvep0aIksNZirLzGld+llJL+VcnAIhapfh6tNdpoSXV2DhWyPPxk4+GUBSdlJJt0DtVKjDHI/axDTpVW04Zbef1VpHwezJH5bmZUdZ2Z6n6ydYQoz6mpaqw+bl4oL+dfn2wUuLwpEkKgG9p387ZzO7fzVTNGG+4vzgD49M/8HtQnvv2lz6vlBeYDX/8qDu3/08SrL/Ajv+c/4n99+n+/6kO5ndu5nd/Bea3B6r07d7m+c0V72OGdgySyxu1mw72L8wzgZjRVxfXVNV/44hO6vvjqNM558TllGV5KIrut60r8Xi4QYsJa6UcslQ2r1Yr5rM4LKsdme43zPffu3mO1Wk8L6aqyxBCIB+mBFFmbMBXX2xs2m21mMWC73bLb7SbpIRxllOTF4Ww2m3y5oFivV1xc3GGxkKCCIqsrnavb7Za+lw5E1w/Udc39+/f5yEe+hrZrefr0SyhF7oYVBrrrO5SGRx94xNmdC1KEdt/S7VrGXY9G0fdD7iqUMJyERtuKShnq0Eiiavab1rbCJqic5sH6godnFzxYn/PRD32Y9917wJsP3sfds7WEZSmFH0ZcP5BiIo6OODrSdkd0gRQC/eDAp2kx6530aPrs6yzMUW5uRJNTbLWVc13VYDVJW6p5Q9DI3yOMwXF9s+XZ86dsbrZc724k2CanvQYiPgWSBp8SQwj4lOj8wCF0OB1RY0fTHbjeXPPk+XM+94UvcH29wVSWs7MLDrsbiNA0sNvupUJJa1RmH7u2w1mLyz7K6RrIU8DpBMKA/GBRSU0VMcUbGaL4qgvbv1isftXPOwLHuq6oKotWCm1kQV/ZikDKl6DKAUkam2WbKR+fAAwm/2hKEZ2DwcBkcHiU5ZZaKQEVFqMNKYlPs4C2AsDEPysJ1SqDoALQQlFEZOBVOjyrytI0M44VN2E6f4UVjTGhtM5y9GMljsrhUDElUq7RCVOAkCTxFomyNgaipNkuFgvW6zXn5+cYY+hzz68EDUXc6ITRbzsG7yZ2v4BqAacD+72EtYn6oZqA3mq5ZL5YUFmLL8xyPr7C7hYLgLDa4m0HYeDjMIqSIfdMTywjElZUN5bKao7kdGZSjZbE4fK9k2xaPmKMoCREqYTBGXtkoo/hVZKmHnP3sEoq/79c1xHnSsWQPoJTa7FGUVfy94nlVvpEURCmzQuxNcgmQlRhej2V14Vjp2uREJeNh9MO6Nu5na+m+a4Hl3yiuvdr/v1TY8M/9w//7Cs4ot/58f/wb/MTf/y/uwWqt3M778F5V2D18vLy54CvQcx0e+BPPn78+B9cXl5+DPhrwD3gBfBHHj9+/Ev5e37Dz73bqaqKO+fnzOqKvm9RSA3LcrHgzp073Lm4wBrpTXXeYaylrlMOcdHsdjtKN6NzI9YaVuslTVORUsBW0HX9xLxKtc0crRWbzYb9fsdut8P5gYcP73N+fs5uv5sWmwUEd8MepUTmZm3F9uaG6+uNLJx9RGOmQJIifwQmcOlHl4H0bAJp4rVdTH7W0zAZ58bsvZNakb7vMVkOt9lsePvttycvobXi2ZW+QtCmpm7WLFZrnu92PPniE7qbA/2+pTE1VilICmsMUSn60RNNwqJQ3mN8xDjF2WzJo7v3+cijN/nQgzf40Btv8vDiLvcWK9a2plLCAPuhpb7pUGPk6ulTnj95IizQ6AjOE4ZRHr+PJB8ICXyU8KpEElAbQ5byifwvJklnjUk8bV4pvNZgLGQ5sq0bdF3TzBdUdY3Siv3hwIsXV7RtS+t7hjCQVJqkiGjpinUpMqRAi6MnUF8suLe8w+ZmgwOevrji07/8yzx78YIQI9bWNHUj1tiocKMjjIEYknRuZl+kMbl3NLNIIsPsUMpIQm4CVdjdlKY+UCUFJxloKZKWcyNJp4oQUn6ubVYMHFN05c9+Yu7G0TFrGpqmZhjkurJNTVPXOB8y+MmT0stAIJUEYQElSkmvsTHZ633igQSIiD/VWEtl7ATOyzGWIKMSeDaOI9oKWyybTBVdV6pRxP9agKd8rwC2AkiA6ecXNrauLcYeA5O01tS2klKTzCyKhLZlGHpiEk/ufD6nyufMp4TNbB9IjVUCxqxw0Eo2S0q37TA6BjcSTmSp5UMSdnuUUsxmM5bL5TFwCvDOiY9Va2LelNFK5NkKYcjJSpC6rhDLvUiIrdVEnSRUyjtknyJ7XytNVWmapqau7BQsJV7QkDfC4sQ0i4w4b1RktrY8B0Xue8qMTky0NkSlAY1XAZWOG3OSrm4y+JW/pwhKWelbzt7dohwIMeHzn0EdMwWUkntIW3Hznjz/ZY5+XybZ9akE+b0yr+q9+XZe7/kT7/9e/th4lL1/y596i+oP/dQrPKIv38R2yy98/5/hL6uGn/vi//6qD+d2bud2vgzzbpnVP/r48eMtwOXl5T8D/BXgk8BfBv7S48ePf/by8vIngP8U+FT+nt/sc+9qijxwNqshXTBrGu7duyPhIqslWmvaw4GnT5+JDBPDfL5kPl9MSZ4FhMYoC/HFYp5/tsHnQICuO9C2e/q+pS6ys3Y/dQienQvrWlivcXR4JxKzfuilMsFaTGZz3OiY1Q3KWIZhIOQ3jZKKWliVGKPIhp2ffH5aaxaLxXRcMQqTqpTC+zQd3zg66rrh7t0Fznlurq+nlNDr62vm84bRjez3I8vlgvV6iTGa+WLOxcUFs+WST//y27z99uewpuJsuaKqZ+ikGLoOH8RHqX0gHDowhovFgg+//y1+18c/wTd86Gv50INH3J2vWYZE5RKu62m/tOFqfxC2b+hwqScZYZ53uxu6tkWr0isKY9fj+lGCjFLCJ0UfY06mDSIrVNJ1KT4/QCHgVQlr5LRmUJqY62ViZgib2YxmtsjAo6Y7dLS7HaNzjGkg4gUgJA9ao6zO9TSBWGvm6wuMClRnC5JStC+ecbg+cL3Z8PTZM0m7bWqMNpKCGgXoG2TRnqpETAEfHLaSxX9MAas1Dx8+ICbYHQ4M/ZC90wZtyAyUk05MlV5ajGul82YMkwT8FDAcDocJpJYPrauXAPNsNme1WFNXI6NzYPQEVMZxmPyQKjOpwARcCsA8JslKqnEBU1LzIvdeCtJLGkIgzWYiaVU1zudAJuTnxSQMog8OcgVNYe1OU36Lf3OSr2bAJEAwTBtBBSwvFnnzyehJgVAqdwr3XGpxlssly+WC0Q0MwzCxu9YKE13lzSaXe16HcWQYR7puQBvDfDZnNhNmWKmjj7cECZXjruv6GFJkzMSigzCSyabsY44T81zY5cJIFjm1MZqQ/PR6WdhL2UBQJF1YRpO9qgjjKlwrMTEx4igJIHLegVJYE3MgkkaZlJPKy8/WxHTsRAWVgWaWL1M2FhQplcolUbuIjzYHI+Vqpr7v8G5gyGxrYcVzcHj2r8rPj1E2TSRPQJh/72TTSlsDJ5sDpCj2kbzxVnIO3mPzSt6bb+f1nOs/+o3oh2eYH/7d2G/9Pa/6cL6s0/30T/Lv/g/n9CnwV25B6u3cznt63hVYLW+Gec6BeHl5+RB5UyyviP8l8J9cXl4+QPRjv+7nHj9+/OzdHlyKIVdZKOqqwlYW7wPbmy3BycL76dMnvPOFL7Dd7bCmQptKgGhO5ByGIYPVyDAOPH/+Ah/8JK8bxnECtOIh7WUhrZjA7Yc+/CEePXof6/Wa/fZA13bc3Nxg6wqlFZqckpoletJzWuGC9EsGf2SUimSupH52XTeBggIChEGQRZxIJBPz+ZyYAn3fYm3FfD6j70e2262wY0oWegJsk7DNJ57WzWaDsYaLOxdcXNxhc7XhS29/npmpsXXN6AI+9hgU86ZhphR0I01lefTwA3zbN34T3/aJb+Yjb7zJ+WyJOvS4zZ7xyTtstnuGQyuMZd/RuZE+OFz0OOMZo0Nbg9biddMFHPnA6Eacd6QgYDUo6HLgSgwx+yNL7k+EqGTRrRIhyeLbJc1Q0my1IgJWV1gsLvbUydKYKgNBh3M9XjmG5PJKWCpshjiydx2jirQucH31RUY8fZTNgdg7gg+0fQcotNJ4H9DW0LWtALsIpEjSFm1zHYcCEKDaNDV37tzhwYMH3Oz33Ox3jG5kvT5jMV8QU6RtD9mfKEzYODrGUQCttRprK+AooU0pcXFxQakpKszqKdArKcQhJNpDT20azs7PcM5xs99LEm88ejnJgT6nwUan3ZjACdPvBPTCtLEinzd47xiGkuIdTzymItG1lUHpKvtXHagj2zql72ZGszBlRQlxDEI6hlCdHmuMkcOhRZtjpU2MUYKs0hEUVlXFfD7HWE3ja/nzCVg1xpCyVzKmKJtU44hzHm0MCRicJ9Gjlc0/N28YoDFaZe/w8WMYBBQvF57lasVi3uQuXv0SO1yej6KQWC6XNE0znfsJYHKT+1UTdVVR5wAokmzsFCl3CUeKJ17oElyUyn8xEZTPFoBEzN2mRitsJd3IKSZUPkdyzi0pSp+00qIoKc+JJPsmUk65Timi1bHGRzIEEimG/BoZCD5Iz29M0yZF6Xa1laXWDZEkx2RtDrXS0yZljBJcVuqDJDTqvcesvqr35tt5PUYpxYfP3sd/vfgAH/9v/mXMm5evtCrmyzVp7Aif+0f87B/6b/mz3f8FwG7s2N360G/ndr4q5l17Vi8vL/9z4Pcib3Y/CrwFvPP48eMA8Pjx43B5efmF/O/qN/ncu35DlMVVmpib3X7P1Ysr+r7PnZEzdrsdm+0usymeF1eSjFokt13XcbW5Zj6fMZ/PGMYSTjRMwLDs5g+D+D6bukZlsHp2tkYrw5MvPePtz3yeL33pCV/60hO0VpydrSeGVCnFfB5wLrC92U3yYmMs1lYTUC1yuSJ301oLS5t7KkESXidfXwoMY4c2aZLiOT/Q9QcO+26SfzbzJdZa5vNmYlibpmKxmGUGJ3B2fsadO3foupZf+eVfYaZrSRfO8tKmrmm0ZaEtH3rwPr7+zQ/zLV/7MT72/g9wUc1g9IxPNjx99mkOVxvGtiOMjug9bd/iY8ARGVPERS+8pZGfLYyHwgdHSFGqbFyQjwzyUlJEIgNelrVaQpckqSV7DFF5AZ7wSXgir8CDMEKFTVSSAGx1IhJAJ5SVZGCHIyJsHNbgCbR+pE+RQxy5Omx5tr1i2+7BaElsTokU4fz8girI87hYLGjmMxSwu9mJj9mKNDHGIFLOJB7Iqq5YVDMWiwXL5YLnL56yP7QcDntiFBln17fTBgMgXl0vXakltboAttLFeeqHBCYgU3pJm6aZGEfpEDY0zZyz9TkhRDbbHfvDQUCbrWQDBnDjOAFha61UtYTIGPwk4y1ALsZcFZJBbJFdhizTBpUTZ30GE3Jsy/mC2byZHkPIzOdp6rD4yO0EYMvmS9d1+TEVppVJWk0+/855lGbyQRbfo01q8oqmlCbgm8o1gdz7y5M0cTcMOO8JMeaPJCx+Zv9EaRHRyuVHzJSqHFPCjUPefBrz8zRjtVqzXp2fqDYkhK2w2UU6e3ou6rrOrxNZeqsMVoHVNSplf62Vnt+6lsc4uuHEG60zC35kySV52ZHSMdxJzqnce9FnkJk3jBQJnWvDSsCRBnwMRO/k8eYO50meqxA2NQGpKCdOOqWtRXHqmTVS86NOXzflPjJWfLMpRZI2KCubMekk6Kn4XiV0To6wtu9Nz+qreG++nVc3j1Z3+RfOvgmAh8nwU//Hv/+Kj+jLPCny9J/9KT7093/xVR/J7dzO7byieddg9fHjx38C4PLy8l8E/jzw01+ugypTFjNN0zCfi9z12l3hQ8S3Hc4Hun6YWJ2kgJAm2V1lK87PL6jrKvvEJMRlNmvo+446e/X6HMokVRo1wQdSDNNC+NmzZ1xdXbHf7+kHl4ObDF0/YrTLgUgSHjOfL2jqmvbQZoZVgPapX+rouUuMwyAsQgazR7AhwS3NrKZpaklxzamhUn3TUdWW84tz6qpmVsv3LZeLyYc3jv0xKEbDcrXEO8/1ZoPznvlqgU4Kk2CmLG+s7/LRR2/xLV93ycff+iBvnt1B947hxYYX15+hfbGl2+7p+p7ejbRDxxgcyhqShkDCR5G9hgxeVBDPYAyJQGLwXsKFyAvMkAOF0DmVNOCRjk+V2dKUfXIoQ9QKn32lnkhIioQmZblw0gqrNREYgscSCToQbSJWEKokfw4BpRWeQBc8zipiXeGGkefPbni+28pi23tmjSx6m8UM50aUFjl1WTQbY1it5/jgiMFTV4ZZPcNUmhADy+Uyg6OIMYq+b9ne7BhGl72GFd4fg4aO4FKCjsp6f/LsoSYAd7op0zTNJDMtUvO6rrGmwhuPNhprpCqm73t2hz1t2750PU4fiOQzxIgbB8Lop3usVJCUaxY0JoP0AixAZKUFNKjsqyxKAblGe4ZRgqG0lscaYprAWkl/LSzpMAzT30/PR0rklNljJY0A7YCxOrPy6fg9eYOkbFat1+sM6l0+njidS7mvB7pDK35hJU2paOkuddlPqZRBaQF0xhhRCRRpfwaGTfYK13XNnTt3xY8+hul5D6ObNh2OVTt2YiDL41eqVPtEUpLwLpEKa6Q3WjFmi4PWsrmjdWZEkyIm9ZJcuoDgYwpwknTgzL6mKbDK55ThRFUJWBxGee2LhRHNjLCZ/KXk/2egnBJaVS9tLBRpdgx+kkWXIzkqAwIxgjGa1WrJarXC2hqTr8O+l2TpGI4pzhphkyUUynLn7t3f9P3mn9R5Fe/Nt/OVn5959Cl+f33Fo0+2rP7Sf/iqD+crNm//8E/y9b/0/7zqw7id27mdVzi/7TTgx48f//XLy8v/DPg88Obl5aXJu7MGeD/wOWT39jf63LsehaL0BBYGwNqK2Xw+1Rt0fc/oPHVd5RAiWaA3TcNyueT8/JxxHDNDY/LXOdZnK5bLRfaAVaxWK66vN4yDY7/b8/TJE5x3WGvYbK65vr4GSgVHNbE8s9Uq+1hHbm521HXDfD5n6Me8GFQM+WtTSlOnZIyRm5sb8VHllVkBANKlmaYOx6QS88Wc+/fvZ2D7jBgji8WS87MLzs7Oqa2wLSanuIq8z2EzmKmrive/+X5utls+c/0ZalthkkaPkfffucf3f9O38R0f+wRv3X/EeYR4s6P99Ge5ubqi2+/Y77Z0u1aku8HjdGJInoFATOK3DJk1S8GhAbGcRvBGGDmisKFKHrCPUocjUbcakiLFkL1lOQm3BC0pncGwwqvEGBMuQUgJdJoWvFoptLUoa/EkHAFlNXZm0cHnSpoocl0FnR9p8dj1CqciL65v6KLDNDXReRSKoe2oqppU14yxePpqqtoCiWHomc9nrGPgcDiITLGpqJuKKl938vyL3/nmZkvMPsWqkmtVrqc4hSR575nN5sznc5wT77VzjsVinjcvjsE1i8WC1WrFbCZhXBNTmMQ/rZRhsVhmQBLRwOBGCfmZzyf5pFJKNhu8gGitNXVVoStIUTF6jx9GojqqAFKSeqXCipZwpBByGFaWZeoMGEVVUFjQY6fpcikA5HA4MAzZ06o0yYgHseu6l2S+JYysfP+xnqr01hYlgoRuSYJtBkhGWO+qrjk7O2O1WuGdoxs6kZXn+3Wz2Uw/32R/biShtEUbM6k+ymbCkUTMkttSR6MNtbJYu6JpKup6lpUcHW4MuVpHT8FOdV1Pr2+2hG1l64DIgo+1O94FSh+vVpqUn0vnRmL00/NkrMly3wSI/7NsAhzTcs0JWD7+3pgSqIiKEmolt6Ucs9Jyr3p/Ul8jZyMrHNS0aaKVnnp8y8+YEotTwqoKYxIkGKMXZpUi65ZvLRsfEKkqQ12LxcNYkV+7wU/nyQdPdBGV77OyCfJena/ke/PtfOXmux5c8j/+m1+D+dSPYT70za/6cL6i8/YP/Wt852d/hXhSTXY7t3M7X33zW4LVy8vLFXDn8ePHn8t//zHgCngK/APgx4Gfzf//+8X3cnl5+Rt+7t1OXTUsFytiiGyuttMiXJgLxWF3oG97SDD0A1UlwPNsteStt95itVrx7NkzusNe2Igsg1ys57z11ge4/+Ae4zjStj1DP3DvzgXOeUgBa9/AWst2u81rKwEH1mhms4ZxNCgifXfI/lOPMXryvyqdGPouy32F7T07O2PWzBiGUfphl+vJw6pTRGlhKKwyVFWNrQXwXNw95+69u1ijudncsFwtuXPngpBgs93y4vqK9bwRYGEqWYhXNffu3me5WHDYH1g0M2os7jBgIlwslnzk3vv5jo9/M99++Q188Ow+bFu6z3yBFy+u6Xc79rsb9kNHO3S0ocelgCsSPgzRakLUjD4QnM9SvITOHYvikYyE6CXWpVQwIj66kINUxHma34zSMVSnBM/I0lTArYsi4vUZb0YNIYLVljp79LSp0LUGIx5WrTTawwxLoyoOUTEQ2YeRXRzxM8OgHc93W/Z9SwyBCkVAgakIKmLqimZRs6ol/CrGSIoKN4ygFH07MPYOlTRRNMwsl2t8Eu+dC8f6jZt9y2q14u7d++x2e7wX1kh+MFhlSTrhB0cb9gLGXO7THA2j1oxuJEXx750tz2jqBo1mcANjN1LpakqIrqxFA6EoEDJQW5+f45xjt9tloCdMIFmi3nUdVVUxm82oqhoNxHHEBZHDih9Xo1JCFzb1BBCUECbxTga8T5MkV9g7ScA+O1txcXEhsmk3kLzDR48bPLYWkOEGlyt7LClGxn5AKQEi0UswlFIJUkARWSwaaltRZ2AfvZzDqmmYzRf5Ukvst1tePHuGHweiYvKvJtIEglNKsrGi49Rnq5SmrmuGcaSydX7sJfDJ58ocCR4iJ04rJexyu7+ZbAAKhYpyneu6mXznxV5wOBQ2VRFjYBxdZpFNlsWmDFgDtjLMbHXiBQ25csacgM9EDEx1NCZbEgqILH5yUQnI9Vo2IqrKUtcNdV3R5E2YqpJk5djMxT+bg6dCihgFdd4glOf9pMLHeXwIOJ9lvjFhTVYq1A1NTnAGUCkBFq0VzaxmVluIkcNhx83NDV3X0/dDvr/kHJW1bd91EoRWVXDSyfpemFf53nw7X965mK34wOIef/cvfArz3b8PffHGqz6kr+iMP/sf8+E//b+wH3vG4F714dzO7dzOK553w6wugb9xeXm5BALyZvhjjx8/TpeXl/8q8NcuLy//FHAN/JGT7/vNPveuR3oej39XiimcqPQiGq3R2pAIKK05Pz/PctkWpRSr9ZLlck7XtVhrePPNN5nNG25upIZmv9sBmsViyTD07Pd7rLV0XS/ArR8wpmI2a/iar/kg+/2e7XbD4XCgqioePXrE4XDIMt76pSClmKTHdbFYslgs6LqWvh+oa5EELhYLtFH0fUfbHmRBaGsM0ot5cXHOxZ07kALbjQRLLWYN1tYMo2PeNHT9wKHtuPE7UgBSYhg9Rv9j6rpm3sywwMV8wYPVmu/4wEf5vm/+dj75tZfcqRbQDVz/4i+x/eJTxpsd3f5AP3Z03tEnT5dGRiKhYFAUKPHu+SAfCSUy3pRQ8Vgp4oOfvIwFrMYJ8DJ513KGSw7LgRLXmqYPYf2bTy5fAAAgAElEQVRCSvgUiShhJ5OSn6sltEV6PxOolM/xjMpYLAKsGi3MswsDYwo8u7lmaMEfNNGInFIrUFqzXK2JKLpxpFnMePi+B8QY6Iee/a6lH3JfbBTp6TD0xBAym6VYh0RIkbbtJmmvSFsTbdtjzJZhOKb5Cut5DDEqabCF+XqplkUJGNdac319PbGOxQ8NTCDHO/+Sr7WqKmxdsz8cJkBWQKZSipjClMRdGNzBjTgnbOkUOnQiGY4x5aCcE5luXUkfLOSKH2Gi5XfFfP9qhqHixYvntIc90UlCc2UqkXbHUoWTpnOhUEQVxJ+ZwaWtNMMwYK2mslrqeTKwBLBVMz3G2grjXWVP5zAOokKoKprZDGsth/ZA2G6JOXW3BBKlGFH58UNiPmvEP+3CxE465yX1WVmqSj5i9Dg/TtJg8biLlHwYBmKIU3VLSTHv+356rTut5ikSYWDy+Mpzfwy9ilFkvhJqFDGmZjZrUJgp2VcY5DBdNzEGrLUoZU7S1E22VUilUrEiCBMsPbEluEiRiPP59Non13AQj2q5r5TI1733jM6Jd108AQJY7TyD63oC6H4cMjC2Wfqt6LuOQ9szuJFx9AQfcm9ukYUL2J/NZjn7WMKo3mPzSt+bb+d3fpRS/OlHP8Sf+OA73P+b/8WrPpxXMsNf/Hd44y/8b3RueNWHcju3czuvyfyWYPXx48dPgO/+DT73i8B3/XY/925HujXjkX3UR69eCVUpEkhbGSCyXq1ZLpdTAEtVVazPlogsDVmwKcXuZsd2u2O/P0xhMdfXWzabLYf9ITMbiaH3KIRBqKuGcRw5TF4/kbc9evSIcRz57Gc/yzD0GKOnIJSUEv0wEKOn6/Z0XS8JmjphrPRAShiO4t69u8xmDSlEKiUM8Hw+Q9yAmvV8SU/HODhcL8E7y2aBSprNjSNFjYpJKgxz92YMkegcZ7bmI29+mN/7nd/DN33463j/fMX4YsvmyefZXF3RHXa0h5a2a3Ex0CfHaBIDgSEzqiYpbBL5nyR1BvEYZpYs5JoOFfPCHvGxRqSTVrpE5fuKYLAwruUfIhylvwkJZkLCl3wMkk6qJPE3qYxpc8opWuSLRimMksCo+WxGZQUwGaOxtUEZxTgGvvj8KV+6fk6YGe6++TAHPSXmzQxVJYw26KpirpeSRB0Cfd/R9wNdP0zSVGMqkel6l6WPTPUtIYk8tQBNkBAkuY5atDZTem1KCQJoo5nP59Omx1TFAZP3sXR2ljoaYJJtCmg6+gKLr1USd6VySQ3D5PkswFRAiMqhT3KsRV4bU0kUZvrZ5TGlHKSkUKgTSXuKiRQCPvos0c+pvAjrp7WS6z0luq6FlKgqS1LCyI+jE/Y9QV3NWC4bmqY5yn+jVEhJfU5J79VoldO5c29y0zTMquM5gyPwb5qKZrYm5MTcqqpyNYzBaE3btuK9zedYvOAjVSXBUyTJmiVJom7MQUkaRQoeN0DwTq70FFFAM5+xWCw5W59RVRU3NzdSYRXk/IzjKD7Zrpt8u6dy3XIdWGun5xBe9gsbk2W+GTCW60TrmCtrIiH47KO2NE3FbLaeroFx9Bn8m5c2MrSWZN1jMJUkEJOEGT1W9BQ/sRV/uMrHrsqGlNg7nPdSg6OkTTjl60zne7m8MIQQGQapxPJeZOjKGNkoyy820Qe8l07epJk2DxVyDtJ7DKy+yvfm2/nyzM/f+R6+7+/9eyhb/9Zf/B6dP/5X+1ugeju3czsvzW/bs/qVnNlMJGfD0DEMY/bShZfqNM7P16xWqxz20VPZOvvehgnUPn8h3tN79+6yWCzo+z6DA5HuiVcyctj3dN2AUpbt9jAt/kT6NuPs7IzdbkeMgQ9/+MNTmM04jmy3G0py6uHQklJiuZxPskBZWIvP6t7dC1arFVXVsFzOJ7ZksZgBMHQ9VhvqWsKZxuzB0rUm+cRhe0XTLKhsjXOBoR0IgzCYVkmYiFGBxhred3GXt+494FPf+d384Dd/kgfVDG5anv/CL7F/+oybrUhfWzfQ+ZGBSNSKUXk84Al4ovgPA4xRWEsfAj6k7CxVhChSyZgraCQ+XxFUTu1NUvtRAOyx8fHI1kpNRiKG8u+JFI+JoqLGViSVAW4BqFrntH5ZEFtrsMZQacO8qqmNwWhF0hZbVwQdaf0gIUq15usuv47FnTM+/Suf5vx8jR9G3DBytj7D1DVj8CQFoxs4HA4cDi3DMGYZ64K6rjP7dQSTi8VCNlaCVG8IGDkGDllbT0moJQ0XckdlOno5TxOki/fvtOtUFurCGknSb519yzr7GE3+SCg05cRVVc16vcrXt3g3jTEcDocp8AeEdRSgKZ7glDIAzSxjyCBaQWbb8p8RkCYpswLgUiiybLBaU1cWqzWaBBncz5dLZosFVVURfBK/d4g09ZzVao1Siq7rGMeBse9x+dhQEmZmcoK4ykxs00ga9ny1pDKG4AOHbmB7syUqeHDxgOVqydXVFYfDAa0VldFURjNvKmKocEFxdnZOVdW0ndRWhRBIwWMrUXVEqzPD5yeZrrC2fQaFAsRn8zlNPSPFxHa7ReWqHm1qUHHa9HFeZLLCjuawo3wPnDLsUnGkMnA103ViCwtamVzBtaBpKpTSOOfpe5HOQprYUmuq6Wd2XQGGfgoq0qZsfshjBqZNDeltlaoe2ZQoHb/Fi+9xPlAYcudCrqs5+l9TZALI4yjhUDb/LgkMqzg/n02bHoPzHA5tvreipD8rue7btitnC6014yjg/3Zu53Ubow3/+qPv5c/99AcwP/iHvnqBaoq8/cM/yd++/pVXfSS3czu385rNaw1Wvffsdjd0XTcxCmXBLwnBc/G6GZNBZMypmND1HW50GQ5FHj58QJ0lukqLt3SzucG7OC3extHR92P2Ph0DkU6ZqeVqzke/7iM8fPBwquM45OqPmJNMx3HLODqcG0iJqRsxJfGt3rt7j/1hj1aKs7MV9+8/YNbUXF9f471jNZ9ByqmwzokPbBixxjB0A7t9y6EbUcnQd72wS7ZCRfG94jwrXfGNH/ko3/+7Psnv/vbv4o2Le/Rf+CI3n3/K1Re/yP6FBCft2oN0omoYdGRMiagVLgQJk8ksik6Qks6sTJAO2RCn9M7iKRUGCAlkQeNiwOfe1CRfDEoRSSfVJghbmtmjiLBpMoqXp8hchc1RymAyYLVaPG/WGipjqbR4+Ga2pjaWpDSqtjgiX7h6jreKuw8esGv3fOZL7zCb1VglScKzpgFge7Nl1wm7ljJAKNeM1gIEiry3/L10jfZ9nytyTuS7OnezaulLLcDztIO0VBmVrwcmQLJYLKaUWpDEXBIYXTo6ISU/bcaAzwxVYVDnVHXFbDEHpALH5n7grut45513jnVIjRyfNTYHXAhcCsHDiTc1wfScHHs15feRw39UfirVyfMmwFvj/cjZ2Rl379zBGjOBtVQpbFUTAa3sS+xwXddYrQkxMpvPsVbj3cg49FIhVAkz2nUdXdfRtl1OjhXZekwRmyxPnj7BXtnsrVVT8FHf97TtYQqa2u93oHROP05TyjdIH3Lb98d7IciDtVWFtpYYKlIOTCtM8jiOdF1HjBLApPWxszSlmMPgapwWGXeM4RjcpITt9FPwUwGOx7Ci4j/WRk2MsWygVMSYaNuW+cydqFditjKESbKutTD8Kt+21loJqTKGlI5dvgkgs/puGFEKKjNOwFlY6ZzUHAJD7gcWxlZUETpvccQAUaf8GBLUlkrr7BGupnCxlBJVHad7LkX52SkCSuGdw1aGKitxDu1eNv9u53Zek3nr7AEfnN3n5//Wv42++35Us3zVh/RKp/szP8U3fPoX3oty/du5ndv5/zmv9bv30MvCs3QtwlH+VmSIhXkyRlOpYyfjSi+nMvlxlBCc7c0NbdexWq3Y7/e0XUtTzXPwxxEMO3esdSiLv7quSUQePnzIw4cP0FrhO8/19XX2l4nseLGYs1jMRf7b98yahvc9fDiB1VJNk6LHGsusrvBjz7PtNU+ePGG9XlNZi3ejVNkrkVN2bcfoPZubHbuuI/hAZWpZ5GlZkC6aBjU67i3O+IFv/SQ/+j3fz9e/+RZqv+f6H/0C2+fPuHr2lMNhj3cjbXfAEeiTZwgBnzRYIx684iFDpMUxREnzRepMCviKZElulvmGFEhJk0Ik4glAQMCBfF3K0t7ipmNijmJMOXlUn0arZtaugFYB0OR/s0pjlcZoRVVbYWKUAFerDUZpAa62wivpWe2TY3a25MHdFVEnOtfzwbfeFFAWPIe9Yb87sNlu2B5u8CmhjaGxjfC/JqCUw7kxp0QzAajjNSkVPtoKkCjptTEmnOsIwUsQVwbppWpGSMZjpUgBsFJLtMwqgjSxscMw0LUDhclKOdhKQH2pfzLT1xe/ox9GDgdhz9qqYrfZ4oNnzKoDYTwlnTr6SEgltVZlKWecZKEyOj8vBXhnOaxSpCA1OIucdFsSexV5U6CpOT8/530P30fdWK6vN7y4umYcPG3X40LA6Cy1dn4C15U2mCpLgI2mtgvms1lOwo1oo4jZ/7nZbHJ9lWK5WmKrClsdq36cc/LchkBK4uGtKsusqdHGEPL1Xc2a/LoijHU/DijVTuBuYr6jdKwWWa0xhnomv+/QdbRdT9v1aGVzFYxCqaLCOFbASEhSxFbHnt1yvJLyXU+hT/J6leX/MUybJNLVm/2u2uRAr6xuiCmzrD37/WFi7MWXLB91ZdAnoWfS3XrszS3VUgWACqOfaOpKnt8QMpBO+VqHumlYrCqsqaYNmnEQUO5GYWCNFe93VdWUBORhGPP5FJC+mM1YNIvpdX8cHcMwMo7i9237A6OT95Fx+dUNBm7n9Zmvv/sWf++PvUnzb/35V30or8XE7VN+/m/euQWqt3M7t/PrzmsNVrVRLFeyEHHjyOgcOgHKoI0wF+LTc4QotTYXF+eTf20cM5hQidl8lqtqzJQqbK0hJlnUNY2wVTqzMYdDyziOWcamMKbi/HzNxcVZlpT14nnd7cXDGQL379/j7t17NI2AltEN1FXFcr7Ee89+v2ez2UzgpPyuPoPywtBV1jKfz5k3M1KCZjZnu9txtd0yOE+zWNK1nfRdJkUYRgBsSnzyYx/nR77ze/jub/wW7ijD/vPvcPXki1w/f86h3dEPLc472q7FRxhDwEVPUEqAZzhKEVNCUnqjMB0hpmnRHnOwUDgNS8pgM6WQgWs8+ktTlvlmqW9IIuWdUoJT+d40BWqpUnORF8hKZThUZLFG5L5G5S7Lknybv66xltpYCeAyBucH9mPH1eGG5nwJRtG7gTce3OH84pzr5y9ISWSKbdceWbu6QVeWdtsKANOaum6EpcsgpYCFMYfBaC0gqq5qVqsVUlW0oxt7/DiitWExkzCZrhvE1xsSfvQTq1/ZSpjQShituqqyl7NUu0ioVdMcK2vKuRFv5jFwp7BRwgzCOPaTzLME+qQMNMsmz6lPNoU4ST4FqGX/YYwvPSda5cTVlEhSjIkxmqapmc1mXFxI73EMfgql8t7x7Nkzrq+vqZtK5NTZb5syW+syG6eUzv7xiionC6coKbClU1jArJt8p7vdju12y36/l69XaQqP0hQJbYVRmpSkX3k2a+T1IXi6rmV0nqSE2SypzvL8B1JME2As1TPWVCJL/lUVMCV4yFjDfDbHmOqkSzWJXeCkA7UAx/LnsrlRwKz4bpuXApeMyf2ixkj6cNsKcDu01M2MupaAthgD4+A4tB3t4cA4yvnVRhNTxIScHuwD1h4BbIwCTAvLfaqwUPkcocST2qaBEOT1ROXjq7Mqps7d0CEEejXgnShjplRqEkMKqKLYIE5VY2XDQucwKJEPSzhUVVmMmefzLj+raRpWy9WX4V3qdm7ntze//Ilv4P4fvEfzJ//cqz6U12biO4/5wy/+51d9GLdzO7fzms5rDVbncwkYCiHI4s7aKTEz5UCTEgDjnEdpWais12v2+z1XV1eEEFifLTk/P5uCl46L9JCrLiTxsrC01lrW6yUpLdBafHtKR4xhWtBPab+xBJUoxtFxfX09AWZrDQmm7siSIFyCb45hKLLQXK1WPHz4kPVqxTgMEFNmNxyj87T9gKlrEsK6hW4kOE+d4MFyye/9nu/nx37gh/jovUek6xuevP02L549YbPbsOv3+OKd9Z4hOcak8DHii+Q2ZDCZ2ZKIwicIick7qgo7kn2oIWSQmaW7ITGBUsjgtoQlIZGVE1ilpANnT+rECsqUFM/00r9m3jCzVQJYRSJolIAPnSTsx2RQNasbTFXx5LDhnWdP+fzzp+x0ol4vaWYNfddzvbkm+kBT19SzhvXZGf04MDgBj67rpdYophOJrZpCvwroK2E2q9WCVWZytFIc2pab7Y2AkCSy5RQjKWpiCPT9kMEmNLV4XqUyppKOzX7gsD9MIUFFDi/X6vkkLe37frqmZNFeTQDnNFH4tOOyAFiA2bx5KW12OudKNhwEjJjJs3jqnRWW9QiOtdas12vW6+Ukjb537x5vvO8hKUWurq549uwp2+2G+XzO3bt3pUpnv8N5kXRWVUXdzPAu5P7RWoBOVQkTmgREl82esnmQkngvQ94kckU6nOtWiqd4zH2zi8WCWd1kcAPzeYMxipubLYNzJ6xxmF4rvPe40eeQLP0S46mVnqTZs1mxEji6vpfrPUmAnB8GlHKZOQyEePTklzAjSr9o9scXhngxX1BXTZaW+2nTzdojeA1BamFccByipITbymG0YXQjXSsy6WEY0NrQNDNmVZ3T1ZNUy4wD1pa+U5t9r256rCGmXxMUJuytI8Yhd7vKBsRsNsvgXE/nsO973OhJUYKUpjTrvGHmOIaHoeR6Lcoa5xyFjJFrvPRsS0r0YjGnpH49ePDg3b3x3M7t/A7PvGr462f/FD/yF78B+31fxb7U32D++Z/4G6/6EG7ndm7nNZ7XGqyCmvyBJYwGmLo4ZXd/nBbvaI9SZ9R1LSFKRrFaSwBTXdd0XcfnPvcOu5vd1GWoFGgN+8OevhumhF9rZzRNgw8jzo1UlUVrOBx2eB/oup6hL74rkUL2vbBZh0OLy4vgcRg57PYChElT/UJ5LCI3LLJC8dRdXV+zubrGasPFxR32h47d7kBKiu7Q48aBmdI0UbGyDd/44Y/w4//07+e7v/VbqULg6u1f4cnbn+X6xTNGP9KNPZ0fCXhcrikZYsRHNclwdWRK6vQhkZQiZVbUZTY0hSiSvin1NE3MV4iQtMrprYHA0csaTqW/kwRYFuwFJ08W1TyldjRbXFG5Q7Us4o3WGIXIf63JDKrIBq2taaoGjaGyNVXVEBXsu5YvbV5wiI4BqIzCJ0+3bekOLcYYOq1JSom3bnA4HxiGkcG5KaE4xRwuVK7DzJoZa5nPFgJolHh4h26gbXtubm44HA6TRzWEwM3NDmurKdW3qioW86X4BKfre8h+6nGSgBaAWMDBfn/zkvxY7pUGY2w+k8JM9f043UfeR+bzOavVatp0KR2jh1bkwZKMW03hPacMbQGnxc+dkvzcuq4nafD65N5LKTAMA7vdbgIpm+sr+r5juVxx//59lss5bdvS9T3DOGJNzWK1wlY1wR99vTYzljaD0lLxIkA8ZWZRwGOICaU1zazJj1N8nrOmYtYsMMZm5lJztdmy292gYqSuLdoUACQvEjorNlIJAYsBU1WT97iALIUi+EA3dJK8nM+5rfR0UVsjku8xerx3OO8hxfy4zHQOUZmxRAmDayxmvpg2DcjXaqkfGt1IN3THaqEYp65VHwN+GEi9dPT64BkH2egjS3kLeCwbFtZKUFoM0gEdCdOGXYwiMa/sUV5ezoP3JrPDEZSk/Rav6zBIwJKcJ8/oevFxK5vvdzX5bY3RNFVmqCmyZic1UVESo+U5MgQfs5RaNnSapmY2b6hsndOFX/O3u9t5T84ffv938Vf/zn8ApkLdXoO/Zvzf+zn+zuaXXvVh3M7t3M5rPK/1K+eYgWhZJJ8mo56yOeInFbmoc57NZsN+v0NpqQkJIbDdbtlsNmw2m9znV6EQSZ41NvtURcJqbWIYBCigIovFjPl8Rp0rQ7quY7vdMQ4uSwaXknbpRuq6ymmakgK6ud7gR0mKnc0a5rM5Td0AwpKQQM01lZUkzt3NXqpxuhYTNVcvtry42vDi+bUsYEOkSYpF1DxanfND3/6d/IEf+hSfeOMRhyfP+cefe5vt5pr2sGM3HvAxMPiBMXhc9AzOC5saExFhT0gJFRU6CZMRQiIpKZ0Rz2kOQwqRFAIh10UUIlTkwpkxFUHrsZdS6by8PUn/zSC36H1/NVCVmbTAKK3lI6VJpigfx6oMAK30MeSobjBawGpC4VNgP/Q82VyxdwNm3pBCkP7QcRQZrfcM45gX+ophHElJ4UYnabWZUU5ZFl2O8bT7cmLQuo6hH3CDm/yqpWakhFANw5BThSN1LXLhWTPLNR3DtAlTwOmpNLSE8UhfZfZoKoWtbM6ektTmxXLJbDaja1ucA5s9nhKes5i6T08rcZZLAUMm15bIfRezF/HY63msORlBaRbzBcvVkspWExvovGd/OEzPctf1PH/+AvEeC3N3fb3h5mZHVVfUlaWqa1bLNaBBadyY62msmTYvYgpSbRPCpJQQ365Ul5TaEwmKakiEDHAkwXboB8bB450nhIi1NaDoB5FpG6tYzGc0swZlRCEhQU0toCb/JXljQIKvGklBTtK7OnQ9qW0l+Cdfu6eXtlQMiVqkijXBjfk8qckjWm4Q6ZeVc6ptlRUJx/Cu8hopr0NuSnQustyS0Fs2LkgKYxRNTlzXRhN8yeAW9ljwsqFqGulI5Vg/Jdd8mq6FSelgrQTBZXmvD3GqspJwK5FNy/0q3xdT8dSG6bxoU8KkyilI5eVgkk0Lk9zka2BEVQqtqvw1Atbd6EXJYI6y9tu5na/U/Mk3f4Cf+e//FVQ9f9WH8trOT/0bf5dNv3/Vh3E7t3M7r/G81mDVZlanMDkl7Ah4SaronPQtDm6k6w7Zv6q5uLgApAakbVt2u50U2GuR7AYliyOvIzGQF5Mqg4iBlKRqxuZFsrAMia7rOewP9P2A1pZxEHYnpngMDPEOSU6NaF1ltlZPizaQhbuzUrdQ1w1t29O2HcMgATyuG+m6gbEfST4SVcLExLpq+NoHb/DjP/oH+MFv+w4ulGH39jt86Z23eXZ9xU23o/MDQwz4GHBeWBefYEyQlCHmShnI8tsYUDH/OcliPKRIyD7UkBlUYUXjtHhMSdhTtEiKA0LRxtw1mhTS0ZnkMZcSmgL1ft3QX5UBqFIonSTYRysB9lqj7RGwGmsmObBCZ6BiqCphV1UGsD54Nvs92/ZA0LCYzzBaMfSSytr1HT7mlF9rQRmCT7kvU64NrY4L6nLAwi7ayVcZos9S0kRlqsnLeBpOI3JcAQbFT7dcLifJb9f1E9go13uRjk+JyZllLGBEriFJIgamz8cY2G433NzcYIzhLHuzixRzGEYgslwuRRab/bTWWpqZfK33HqMFWAATgE5J/KvnZxeTb7LrOva7Xb7uPG3XTV7aqqpIITCOAzGGqYpHay1+6BDRSjNfyD3Utr1cu9kTaq2Zgoa8HxnabtpkOqYrZyYwJkkENhXz+Yy5b9jv9/Rdi0oJNwziF83+d6WPID40DdZq5nMJwBrGnnHsJ/npfLbAWJs3Xo7PTQhR5Np9J5UsSRETWFtJ/VVViXQ+SYjbULzmVkB6UkyscwjpyNSeBGOdXgcuBNKJbFaOX6H0SSdqvuaEzZeu1wLiZcPjaEcI9lhDpI3U3xhTYbU9ee2VTQaRPcu/GSOSe5uZdq01yszwPqCyjN7nKp7yu3TZdcivBMWePvXJGqaNsyEElD6CVWHwq0lqHkLA2lyxpGw+V9XJvQHeB/rutrvxdr4y850PPsbf+gFF85O/D/Pgg6/6cG7ndm7ndv6JntcarIpE92XZYWESpgVWYX6AxWIBRA6HPYvFgsVicZTHjaP0Xfphku6GnFJZGC6tDLPZHB88w+ByUI7B2gpyCm7f98QQc9VEjVJGElm7Hq0Vu/2e/SHi3MhqtaSqa1LULNdnrNdLjM2BIH2PC5HRDySlWSlL2w9stjv2u62AZSe+PYNmMZ8zbxrurs/42Jtv8RO//w/yrY8+SLre8eyzv8znP/cZbvYbuqHjMHaMKeJ14hA8bfCgDEoZqTbxssg1Wmf/nCT/CmtClv9KKIo/ke6mkvybfaRTim9KoISBjaQjk6pflv6ePq9Sd1L+TU2pvyXVFnWSPprlxEoplNH5Q5JgdfYxG2Mw5G5JU1Pbhvl8SWVrUIqu62mHntXFOaqRFNroPDFEKluh5ooxRGwdGXPIFCrixygeWKXQRgPhJRBRAn26rqOk/Wotnj6tNHVVT9fvJM1MEtpVVRVN00wJ0d57+q6f2Nby+Ev6NeV8Z/9p2chpZquXJJhFElw2aaakYciVJXPqyuAm7yuZ5Zfno7C0Ksn91/YDzo+kGHGuhAv56bkqPa1KaTYbAcY+eFSWhZdNJZHvGsahxxpJwW3bnqZpWK/X1LX0ro6DY3SOrhukazQluq6T+9EUv7onjI6U/ZK2qqkqPT1O8VO6nDzsiSmgtWK1WtNYjRsH+mGkrhtmzRyUJcaEtTUzq0tNMNoYlssl67MV1lZTb2thL29uduwPB663N8IcOpf7UcHqCltZEhEXA2uzZD6bURExSpFK8rhzJJVDhVRE+lZjrvhRVLaibgTYz+fzHB7nOHQ9M21PEpmztzMGvB+n5/LUXw8xd8MqtBHvtID7iNbk5OCEMUy9rSLXRSp0Uga8p1L0BOZkM0Y2xmRDsB8GhizVLRtHWivqSiqRUpRXDJEai/6ibOIIm5xAp2kj4jTMDA0hBdCa2WKeMwgUxoqtorzkxBQYnacbHbdzO1/u+cD6Pv/Tf/UvYT/+va/6UG7ndm7ndt4T81qD1ZDDUkRGCXJ1T2AAACAASURBVKW/cRx79vuWfuhQSA2CsYakFPfu3uNjX/9xZk3F9dUVN7sbhmFAKcWdu3fZXm8ZnaOpZxz2Ld7JorowUrbWpJHsV3PEZFitlpydr1mvF6xX5xICs9uLXDbBYdfS5i7Hm5sNMTpsZTlbr1mtz9jvevaHPcM4UNWWuhLGar9rUSSGbmS/3f+/7L15rG1Zft/1WcNee+9z7vDq1fBeV5fb7R7sdscxtnEcnASFIBKDQFYgQpGSgBSS/0DwR4IgTBIiIvCnI0WKEkD8RVBAyAgRSFCMI7DlOKZtt4fudnfX4K6q9+rN955hD2vij99a+5xbHSXtuDuvqnN+raequu/cc/bZe5/T67u+E1fXG+YQ0GiscWijICes0tx96Taf+Ohr/Mhnfge///t/kI/25zz56le5/+ZbPH34kOthI3LfeSJm8Cky5cREwpPQzh7SU1UkZk9MpVKmgLOa6htjTfmtEt8SCENhO47kdNV/WqXAEQrzCiglQFXlZXNBIWAYDqxqlfqxLHhZZJBZ5eXf9SJpFL+i0XpJ+wWFU4Z109I3TtJinSSykpFFc0o0q57odzx+doVBkbJCm4Yck/TyaknYDXMgR/Emai2s19KVWtC2NmbpY01V6pkFdQefIEsA1mq1om1buq4tUvNc/KASZDOOI1dXVxIyU8CdMdXzxxIkAyzsXq1cqd2jfdeDOdTeVHZZfIj+8PNS09S2Dq0MSitSCvh5Ztjv6XuRq+2HgYcPHyzSe+nWrR5Hj7GW9WpF27WEITKM0yEsx88Iuy5Sb1fkzzUUSgCVJyMdxE3rULqADKXKOYLGOVQIzMW7m3ODioqc4/KYen6yD6BntDUolPSelvfso2e32TD7icZaLi/O6LuOrDTDNDOHhNEFiGpNBqxpFobSWLN0ziqlGYeJoSR4xyiflxDiknpbNQMxJygbacaIbBnEmmCswzaZeQ6MfiKFIuVVot5A5SITlvqXvu/pu07eXwmA6vqOELOoAGIkxSibRjEyq1IlM3uC92VzSOSwjbE4K7VOtrynkCIpQtYJEDArG4OiHhHprmxwqQJuURI7Vful6yZKzplxTozFbx1CQBW7hjVKnkNpUhJJsJxnOTZVgHcFxLpIor9OglHPla4bRxpr3ZLQvPyzgP4Qw43vrdOc5lsxP/Lyd/NTf/1PY7/nR5/3oZzmNKc5zbfNfKDB6muvvUbf9+z3I0+ePGG/HwqrWhNRM9poZh/wQ8Anz37c43PENZbN5lpSdQv744MA0xQjw7AnRI8vDJCxIpkLcSYRWK0dtjG88MItLm6ds1p1tF3HNE08fPiI6+stRhvatqdtWowyONNglPheIQlrECOb7RXTJAtGPWvWvfhxh+1IiqFUXnS8dPs2Tevo+zXGWsI8k+NMZzR3zi74F/7pH+b3fPdnWY+Re7/8eb72xhs8ub5iH2eRXRbZbwhJ6iSUMFtaK3SQ0KMMhBxIurJokHSplkmQYuk/LVLgXBnVfGBRY4xkhZxLICoATSyVNSXslFyChuqiFg4y4MoaHo86Aqvv/7mpFTVKUwS/WDSNkroUrTS9dayNo7UNbeckzKrIk/fjwJPrK663W3ZxAvTCFnsfmXwUP2+I4gcs3ZTaWEKKRC/u3erdyzmx7jsaa9hutyKNVCKHTClhtbCEUnUSimxRYa1e5JfzHLi6umLYj+U9H0s2D6mwMcYbElBg2YBRqjBSWQDWceXMelUl6eHr6k6ePnu6hHx57xfv4RJ4lTNKG1CFSStMlbYZV3ygVfpbWTCjGzqjMLMpn085jvmo3qf2syqlMI1GNxo0DPMIKvPiC7ex1kpoko90vVSQDMPANM9M87Swf3LGoPow/bAn5cR6fYbrOnbbHbvdXu5h5VBaM4wT43xF2w50rq1vlq7vUEbjtINsmL1fvMhaa1IUaf84TGw22xL6pjGNxdoWpcX3bowV12dRdGQl5zWmxDT75bM0z+JrXuTeKDSy6SL3jaRcd86xXq1Yn4mywjRWFA8hECKgtXx2cyan0nuaovTFhig9w10vGzutwxjxrRrT0DSWjADBrshpa89hKmFGADkG6YxdPrOyaSRBUwllbVGfZLRR+Fk+R/M0E0NcaoxSDGQjx9E4efzCxMaI0sWvquRcVJ+uXGeNyhqyIoQs55XC8CIBWBnwPi41QSEkfJDeWjKchxNYPc23Zj5+eYe/bL6b3/tff+oEVE9zmtOc5ps8H2iwOk4jDx4+4OGjJ1wXmd3C0ClF01jaThac4zyz3ex48uQp2+2Wy8sLIBNLyqrRlmGz4dmzK6apeugctYah1leIbzBwcSGVG2fnq+KLyjx7ds08zjx8+Ij9fkBhMOaaVX8mktrSEeicVEC0bbtIJrXRErqUEo1xS+1ODXi5vLzgpZdfEkGc1uz3e/ywx3jPay+9wr/0Q7+L3/e93098/Iw3vvI6D969x9PNFUOYRSarIaaMT4k5SIhSzoqskf7U0geZ9DHzCVSZrc6LT1VAKmVhLRU1GS2LziMkWVlXkfoe+laBQ5jS3+/CHrKJlqnS34xaAJDWGoHYR4/LkrZqlF7CfnLKaGdxrqPr+6XD0RpJB56C5+l2w6PrK653O0YlLFgNrqky8eAPr1UDlGpVSYyhLPSFueq6Ducc8zzfYJQOtSOHx9auzWmaaFvHNM9sN1tCSRoOPqGUSM6VOkgg61R/YdeJh3IYBkBqXVJKrFarJZW3Jvp2XVcSgMclbbgC1drnK2zlwQNeE4grMD7us1RKo4ylbTvxjlpT+miHRaK/gI8k95cwfSUkJwuYt0X63K96nBMZM+UaXJdzuVqt5fmUyEJTTjgnqcnRh8LQqpKEK+FPxsh5m71nRaYr6b/7YU/wkjQrCckG7ydSzEzzIYQoT1L50xxtCDRNw9nZGUoZQkhLmNM4CmA2xhJiWoK+JDHcEVMUcJYPdViVBa8dovK9cAhGMkY2XET2rWmsobGGtpHe4BwLk1nCozIQU2TYD7IRluW7zvtZGMqcMVrTWIs1ukhoTVExHDaFJGBNQQKtjAR0KWEiNYW1TghLrnPxvN70wxqjF1+/kq8JmpCX8C6tdfGcSqiT0izSfWDxNKsCVBeJ8dFn6uDXln/3IYoZvtgWKMcSQizpyAcQLKnFImM+zWm+2fPmD3yG23/kY7g/9Z8+70P50M323/5T/K3Nk+d9GKc5zWk+4POBBquvv/EGb731m2y3O1nQpEMnqVKKlFuUloXfbr9nnMTjNo2BtIa269Aq0pUuzQf3HxTvm0OhmKY9IXiapuHWrVucn18sgLUGc1jTslp3pBTZ7XZsr7cH/2xMhACb7QZdkkgvL89pW8f5+Rl3P3KHt99+B+8Dwzix3e4EGI0zOit2W2FnM4l7Dzx7P0niqVbonGEY+OxHv5N//Z/7g/yeT30v8eFj3vnyV3nvwXs83m/YJ4+ymugjkSSMS/2TJfETxJcak0gLyWrxpNago5xZ6i9CSpBkUQwHz2lNDU5kkpKnzsWfWr2uqTzjws5VGrL+99/nGqtyLZU+hCrVRTsKdH2CnNFKaj0aY2mMMJem9Kk606CtRVmLNlJl02gLKTPFwL0nj3j7vXs8HK+ZlSzAK1g9HOBNkFiJX6VAG/ExVrZN6l/CstCust3a0yv3aUbrXO4nXbzNwwIWKYFQwkqxSIRrp2X1tFbPagXVFxcXaK1Lv7Bfnq/Ki733bLdb8UZP08LeVRBWQ50qiD3+uZzqfEge1gXYcPCzjqMvgTh5Yd/meS7nIi0pwQvozqCNWoC0c46u7TBWLYnHMUSGceDp06cLUK6PlQ0Jli5auTfq+VUUTFzOVbOckyq7TSkyzfOSMuuOEpFraBDANE+ixCjnonbdzrOEUNW6Fun4lPteF2BlG4NrmsWTXtOy6/s73ig47nOu577WMVWwT0golXGNBa3IWoM2i8IhhMRuGHj27JpxFJadAo6ds7RNQ2Nlg6NtHUar5b3GJIzjOE7M3pe0XFskz/K5F0+y9MrWoCNRtUioVmNrH69e6rhQNU8gEZNH64xrG3n9pfdVvmd2ux011doYI97+nItsP2OUWAHqJss0yQZN/T5SWhf29WArOMiRa5iUAHZdNuX0+7qDT3Oa38601vGl7/sEd37yL57Sfv8R54v/z23e3vzG8z6M05zmNB/w+UCD1evdnmfXG+Z5lkVHCaGpKZD73Z7N9UZ6PYskztkWa6z4SRMYbZkHYV29jyhlReqa4lIhkhPsdwPBR67sNbYxtK0jpcitWxfAbcZpYJ59CTeZlzAdpQrTYBQXFxe88MKthT2ptSIxRlKInK/PABiHkXHyBXAnMNLJebV5hlKKvrGsreWzr32MP/Xjf5gf+fRn2X75Te5/9XXuPbjP1TwwpJkxBVRUJB8IMR2Sf5GeVIxeWFeR7Row4qfLGUjiCU0kstIkEktmrzr4VGOJVKrgtPaNigQ4lSZPtfw9WbyKBzOqjJA676urUUdhSktQTFoeJ3BbFVCqaZ2jb1oaLcDVNRZnO7qmp2l7TNNim5ZGN5is8ClxPQy88/gRj7dbZp3wOUnKb00erT65lN8HVg8hT0IO5uVPnXqtj2XNNyXOaulX7bpuYdOck4qaWoEigUl68atqrYWBLB7SeZ7ZbiXeXwBIu3gaN5vyGSmvobXm+vp6AalwCCqrwUyNs2ijSKm5EYwjDBeFwi6JtBqiD/gCtI6reg4p3akAWEo6q6Vt7EHCXdhaUsJPE9F7rD18pmOMqJxLYnYkhIz3dvm9RMK1FmMPicrWWFAwzoP0gKaI9wJsQhDQIs8d8EHAqsmGnDTdUVBPTZ+tLKcujGTfybkfhmEBmrKhs9zN6HJ8xmiKGlXOS0kkNsbcAKl1E6Iy8PV+qlJhAFWSs0OSIDFtG4xtyEhqcgiBofT3SkeqpEtrBc6VROSqGkieGDVa2ZIUbFA5E0JmmoYbAVy20cvGSq3ACWHGmMNmSj3u4MPR/aaIyZfvPF+SnjXGdOXjrRb5d2Vkjy0AS/1NOfcpJQHnSlQmTWvRUZ6bmET+mxJRWVKS7zNVZMDaNBiraJbnZtko0O/7PjrNaX478xO3fx93/8Z//rwP40M78e0v8HN29bwP4zSnOc2HYD7QYLVKZVWRv1V5apCG+oMXqhbUZ9DWkmNm2O2ZxhGF1FxUgAmyqBWAcmARpjEvbOo4Djx5PJJzYrO54unTJyiVubi4ZJ5nNputJAIrW2ST4luzjWMYBtrW0TQNjx8/xs+eV199FWManG0Y9gP3773HE/9E5IDBo7Ki6xyXl+ecr8947cXbfPa17+AP/dA/w2fvvsbV62/x3ltv8e6D+2z8wNZPzEiC7zxN5Cy+wFAYToBsNGgtFTKweEyV0hKsFJP41ZQCY1EqSY9prkxphpQXIFo500SWsKHCtqbMEdMqfjZSWkKZoIJUDgC0/qD6Wd8P9PJxMjBYLaEw1lipyFCHP40Wj2rfSIdt1/d0bYczFpUFANx/8ph3Hj9kCJ7ktIBSSvhtxdZH991xuirwdQA258w8z6zX66U64xjI3kzyNfT9agGJoHBOklJtSS2tILnWz1SAAyw1SsLu1cTrAyDUWrNarZbXrr3DlTk9VNikA9AInphc8Ur3i3e1vnepzzkAtJxl86OC6Pq4lBLzPBWvpl7AeN1QOjtb0xU5fPXe1vcVY2Ce/CKXJkv4U61FWq1WnK3PF/m0ooRGmXa5qaw2oCDnyJQ98yQye3XUg5oBoyXk6ZAwywKMlp7emsALi+S6cZIknWMWX7z3ck5zrYkxaB2Z/Sz+UbJ0pjaNgML6+arsa6lYSuU6mXIuF2l28GRySRqX8K7GOWzTgBLpaw2x2m63bHcD0yg+WLn28vqy4SCSXR8mxmFYqn+yEgBstaVpHWaeCfMk9gR1CPUKPpBixDUtfZGZg1oY9BhEmpByJMQZ5Q8Kgq6zOOS7plY6yX0EkgB+CMdavL1HFgKKUqGp0mIyl5eXCxDXWrHfD1xd7xhGv9xTx59RYZoPXzM55eV8n+Y0v51RSvF/3/5RfuBf/dbJV/3//lfAWJp/8d/6lr3G857wP/53/Nn7P/e8D+M0pznNh2A+0GA1xXSjp9L7UCo2jh5UJJrGKHJWJaJEqi38XEJqtDoCvQllBASpkji5Xq95+eWXubg4JyXY7bY8ffaEeRpprCanSOMacpIAkr5fYbQk9t7oODSyyKxe1WmeUFrqcKxpePbkKZurHVppLi4uGMcRYzTr8xUf+46P8rGPfpR12/KZu6/yYz/8u7hjV1y9c59H997lwfVTNiaxzzAnxTh48FmkjkoTYiIW/6nSGowiayV+1SLfVQWhxeorjCwARCuFikcMayphSahlMZwLUJW+1cqosrym1hIRk2JcWMtFTipL94NnroQbLTLg8kcXgKqKr84oVdJLrVR46EZAqrE409A2Lc44WuNwxtEYhzMNTksP5hg87zx4j/tPHuGTVIqodJC3HlTGBy90ZXqOma+cc5FIsvg45fodZJ/17w5hSKr4Kc3yOzlLoFFKibZ19P1qAZNamQU8TZNULE3TtLBf6/V6AbPHScHAUtNUpcH1nzWB9yB5rYxbXD5ble1rCqCr56H+fowRYxts+ft5npmmqbCsAj4qEK6/2zSNMH2NpXWNpBYXlUF9vwqFKdUr8zwzBw9GOnV1CWOSz5Ymzl66WIkLo9w2Tu7zUoEUjS6gXM5x23blfFmmcV7YzbZtcc4t4AogxHm5zvXa1hAr5xxt1+Paji7G5bzkAloVEqIlktlUmHIBd8f31eJPLf9ujFmA/DRN7MedsOGq9veaxfct0t1UGPYd19dbxmFCwm6Lx9Oqcg+qpe8YlQ/BSZOEO2Hk8zPPM5Of0cawWq/oXLPcv9MwME2TbBqcn9F2HSDd0OM4fh0TKv3DVRIt4BoqGK0y3XSkTNA3qpZS8fjmo82VEGQzoykSevFKS4qwMrJ9Vtn+g/Sc5bVUVofPZpaQptOc5rc7Wml+5O/+R+jV5bfsNdQnf8fyGfp2nPjgDf7o/zA+78M4zWlO8yGZDzZYzUlsl7AsiKpXTlUAUT2sgGsc5+fngKSHSi2LL0me4sGSBaZCKcN63XH79m0+9rGP8dprr5FS4u233wUVUPoWTSNBMpnMSy/dJoaMj5GcFPv9nnkOJfE3LQu3kCL7aUQp2O/2DNPE9XZHmDxXz66Y9rIoXq/PuLy8RGvFR+68zKe/6+O88uJtvvPOXf757/8BzoeZ+6+/zsN377PZbtmZxOg0XhtisqAtIXuiEhllUiWxVyEe1ZCK100Ry3k0iB7Yx0QIiRQzThtaY4k+MPkgfjCrIXLoXi30YyQRc/E2GjkvddGK0uJ7KxsDFLlo9jeTf0VSq4oHtdRYIBx3ZT6sEj+a0prWOmELjaWzrrCpFmcdnXV0TUff9Kzcis719G1fgKtl8BNPNte8ef8dnm43GNtw3F5xzKCm4784mgW0lvcKLHLOCiyrL/E4GKqy/TmnI59iZcBMAYr2hjRX7mN1gw2tjGqVju73e4DFn1pltBVY10ClCjLrMdfnrLU3q3W/sLS1/kb83HYB08cS3gpWa43OdrslxsPr5pzZbDbL74QQyKkhxJnd3hPDQe4McH5+zmp1tnx+h2HgerchZAHRjbFMk2eeJ5GVFtZZa0VjDWdnK1brNUopWt+y3+8lMXiagLSwy7UHt22d1O94X1KTzXJuagLusWe3Vp4orTEp0VrxKccY0cO0AKwqb82FyTXGFQCql42JY+l4vT+Ofb0VmHYFXJc7Eh8Dcdiz3e+KlSAXlt1LanXOKHV4HzXkyRhD7yQNu/qsK3MbU8bPgX3eyz0WI0plpmlk1R1A/Pn5OTllxmHg6uqafHW9bJ7EGBYv9XHVUs6UTQpNgmXTRBh+oTnreTjeQKmfAWkx5gZTmnMiGk1Uif00YJANLx894xiZ53jjOY+TxnPKpXKn/v/HKWDpNP/os3Idv/7Zj/PyX/2z3xSgmh6/TXp2H/uJH/o6YPrt3tH6+//gf8EvPPzy8z6M05zmNB+S+UCDVR8C2+1WWExrIUu6aUoRowymdGzKAtxy6+KS8/NzUk5M04pYQpGuN9dYo7C2oe1abt++xSt37nLnlVe4vLgg58zV1TXvvfcejx8/RmvFxcUZL7xwi7t379I4wzgObDY7MobtZscwjEvATlsW0dM8oTXoKKmc+91OAkyUwSAyyUZLkEjb9bStw1lNoxTT1TXf8YlP8gd+8Ie5FTNvf/l1Xn/jdeYUGYNniJ7cWBprSXlinD3DOOGJ4tdKlMoZSfQNiKw0o8mFqaJIn3MJVCLCPE0E70khloRTg7WNdLGGQIpJwG4uwUpaLdFM2hhsZeS0WXyPqfjDsipVLjkvUltNBbdl4a5kkaqVLuyr1FY0JXRIK03XOLqmLYyqoWtaWlsYVNOy6tacr85wTcuq7em0JpMZ/MxvPrzHW++9iydJp6quoVMHNrTO17Gp74uEquDvICdPC3NznI56YPGPQXBJX00ZZRXKqAVEVBApIFdk6bvdlnmeaZxj3a4IJTm6Mm8VGAM3mK7aa1kBEHADHPV9z8XFOf2qXxjN43Tg98uGl/dWrmMFfNULWlk9qOoCjXMN67MVl5fnvHDrkrOzM7p2tZyjq6trxnEkRvndtm05Ozvn1osvMM6jeGoLENrutlxfXRG8B6oU+bBBhVYYY0sXc6lNygfFQD1PNaTKOcc0+oVNlu8Zj/flHj9i1SnXc5pm5vmZgKtUr68k4EoqdEMMFh9m+U4yhpzV4vusHt9j1r1KyYdhWAB8yrFsilSZdVwA+DzP5CghY1pLQnAqOnZtDKYEWEmYkXzGRXVRQTJYbYEkfv5c5cnI92LbyneEKkoKxC9qraXregnqSr5cQ1u+hw8bPMbYxWOdUmYqKejeV8ZZrlg9b8fp2fUc3WD/oaSPg48JVSppDPI1llLCGkPbVsl8cdWnQ8KyUhpd1BAqnvyqp/lHnzPX8/OvfZq7f+MvfVOeL3z1F/gLf/R/5b+893fY/dpfR9+6+w393vSX/hPU5TnuT/wH35TjeF7zYLp63odwmtOc5kM0H2iwCrLoNBqUStjGonBScp+hsZLsKSxKw3rVcvuFC7quJ+TEMAw8fPgQWzpULy/PuXv3Lnfv3mF1tsbZhqurK9555x3eeefd8ljLa6+9xuXl5VIJ4gtIuH37Ba6vRzabB1wVpsEYQ9s5rLOkGDGmBJyQhfkZR6bJY61l1a9wpiXEiA9BpK0qk4eZZAc+9fJHOA+ZN3/1C7z5hS/x3uYZrByhBNeYZMgxCYtqLaEx+KglOVgJGEq5uv6kPxUFpvQgKqVRaOm/zDMxBGJZnB9LWSnApLKqCUCrUl8B1lh0SQCtf0JIizTUF79bSgmjLQaKDI8l0VcVua/RRiTIShayunj7xONnWXUrYVqVxdmG1gij6myD1Q2t7Yr8t6FvOzrnMDkzhYlrP/DFr73Bu08fgtHommirlHh20wFowc2AnUy6sRhX6tD/WkFpDRuqP4NDkq5IdasMWi/yUPmZsExte+hOrQBQwnOGkurqUApCYTCVhhhiSUsWcFOPoYLKA1A+yJkrMKvy2fqaoWwGbTabojzghv+2ng8JFZqWhFX5I+/7uNdVRu4YpTJ93+JcU97DTPLyu/thz7Onz9hsJHhqvV6zWq2xjZUO4BChSfR9zwsXt7DKsNvtlu8DSFxdX/Ps6grTSPiQc9JP3Lb9ch8e5KRhuTZyfx7qe3LO+OBJOS7vR5lDrcoBlKujc1Ped24KUCzX0Uj1kCTnHjyux8zfIikvv1c3C1RRGVQfaN0IqMFVxhiyylLZVCTyqvSkHo7BLMqTw+fNHO4HJV5WauVTToUlFTY4A9Ps2e3HJcE4BZFr5xzJOZZNpoMSoHpZ6+ZACIngB0I+9Poe5Oep5ALIua33TWXzD75V8b7nci6kokq87ImERt5rQmMspGRL4nkkhgM4r0+ltcZYgys1Z6c5zW91Pv/dH+fVv/XNAaoAPH6XP9Y+5Y9/1+9E9eff8K9t/vY9uo/cw/2Jb96h/OOeq3/zT/J02j7vwzjNaU7zIZoPNFjt2pa2bYS5VNIjaJzFGkFvXdux7te4tqXrWl64dbns7O83W8ZpotG1luacy8sLLi7OMcby9PETHj16zNtvv82jR4/K7rzmhRcuWa/P2W53BTSITC4j0tl5FonxPM84Z1mv+0WiefBoZrS2OKeZJo9zilW/lkVmkkoM2zSkEEjDHhMj3/3qd/DpVz7K/t2H3H/zazx68pQrvyergHYOEmQ/YY0lkTGupekSMSpCnGi0JeUozKQxKKMJKsnCu3HFA0ips1GQ8iHEqC6+o0iDQ5qLnDiVOpragqMxroTPtBI+k1KSjtvdnjALQ0suUUtZocvr6QWMghTOSDVFo50kwBYAZ6x0QhptsMbSNI7WNFgKo2ocnW2wJbBq3a3pup6uX8l5NQaVEmPwvPXwHr/+zhts0kwyGp2ki1YOSYsb94hwOQ5Hql7WYwBb/Z8VcBz3ZFYm8rgS5eB9lU5YSQM6AEtjLFoZ/OzZ7XdM0yCprvoAliqwrK9d//uY/ayPg4O0uTJVzrkinz1Ilp8+fYaS/C3GcWIY9svz1+RWeS5DSgKcUq5M3vuqbRaZcSQjrFsN1dlsNxgr0vgYI8N+ZJ49m81WemZjZtWvQGmGcUKVELByNej7nrbtyrEZtFaLrFf6TgfmKZBnAZ+1duo46KkCxroREELAGkeKaZFL5yw+9rZtadt28e6GGOSSkdFWLwFixeq+vM+c0rLhUkEo6qZMu56zep7rtTpmgI0SqB/CjA+eubDd9T4zRkm1S6mRIZV+XjS2eDvl/Cmsk80k17YYY5ewoZxSkT0LoK6sfPXNeh8YBtlgo0j+jda4pqQ5K/nMONceamfq+fIJ78OyGRdjQgBn4QAAIABJREFUvY9ko0dCtgIx2ht+4cN9m5ZqHgq7KwYBLT+PWTzxtYs4RXlbRZ2RlSarArKzWmS/AubtYik5zWl+K6OV5u5f+8++qc9pf+TH+eTP/Phv+fde+l/+22/qcTyP+a++8Cqb6SvP+zBOc5rTfIjmAw1Wz8/WvPTibawxzPNIDL4sFxVd13Lr4hZn6zVt25EzxCBVHcMw8N7999jtR6wx9Gcr9KWAg+12x9e+9jXefPNNHj58TNs67tx5hdVqjTF2qQKp/juA9XqNMZqr/TW7vaSzVjBRGTGtNa5pyuJX07YNIAEqjXM0usHPM9MUUMYQU6ItErtPvfwKf/D3/rO4OfDw3YfMu4EpeKm+QBbJ7apnvV5jtSHFxDTOWLPD5GtGFGHyBeRotLMoZ4lKKiCMbWSxHBPRRwGlBaxW6aOM+F5TTiijhbUyWkKVlAKthFEtvrrqc5xnT4w1TRh0LgCvSHs15vDfWmpoKsvqTIPVhojI+tZnZ9x99Q6uaXj29Blh9KzaFQZNoxusthjtcI2jcx19v2K9XnN2cY5rDJoEGp5snvH5L3+BNx+9y2wlEMqUgKmslUhJ8zEjeEjyrfUt6giALJ7SI6nvMftYp8pND4xeAcSFRVYcWNndTryIPtSuVlWYarfUwhwn6B6DnmPp8dJFmvPiQz0OTjqWXIYQ2A87vJ9LKE4BJObrWT8BfCVMCEWIhx7ZY0AOUJO06zkLwfPs2TPxm9pmYYznqfR7zgGlRO6t9eH85hQxVippdrs9wzCWcKkeMMSYMCYf6l/Kvexnz+PhCdLzKrJWY/QN2TL5Jrt5fPz1XFemOhZQtzzmaGPi/V7nMQRIuSTVymfQNob1WnpavfdLKFO9BvX6Hm9ANKb4j4tn9OzivCTf7nn8+DFPHz/GzzNGaQ40v8A6Xc89B5WCKpsoMc5lE0T84SlH6ltQWryd1RtdAX2VOssmQU1OlyowY0XqfdwT630geAkwM8ZgkmMpMy6lVlqbBeRWEF83PYwpae9LfVR9f7p8t2cU4sOPMZPSocIsF7ZXKdGASye0Wt5bTFH6t6dToMtpfmtz5+wFvvznfjf6xdf+gY/Lw4b4pZ9Df/x3fsOS3n8SJz54g6f55B0/zWlO81ubDzRYdc5xeXFBigFFpD1bc3l5warvuTg7p+s6pmlmu9ngZ08Mie32CdebDfMc6NquLGga9vuRaXqPq6srnjx5xDAMnJ2t+chHXuWTn/wkTeOWxNDNZoNCgJXWlhRhu73G+8DTZ9c8ffoMoMjzDgu6rmuxxhT5pgDdpikSu5BJMS8JlsMwEKeJj15c8pmPf4LXXnyZ6b3H7J9eEUMkK0WjRfY6awNti728wBqDnhOKPeP1HlVApzBoLdoakoaoQRGJqiavipQ2Uv2jRt5jTqQilUNJiqjVGtNYqQHSilTAVkRYjWmemb0Xv2sBUI1tMEqXek6RIeoCVA3mED5UZIxaa7QxuOrjS4l1v+LFl17mI3deZRxHhu2ICprGStqvyRqjtADVtuOsW7Pu1/SrjqZtMAqi91xdP+PLX3uTX/7Kl9jGGa8lfVhlYVmilmPL6AI488L8LOAkKyqArXMMFOX6H+TTplz3ymJKx2mWIKsUi+S4sp6ZEKYD+6qL/FQJm/l+gFpZ0vqzJU23nE/lNLdu3eJ7vud7+PSnP829e/d44403FjBUn2cYBjabzQ0VwDHoFebrANCqx1MkqHqpkkopLYxf/V1dEralckQADSqz2+0L4C1sYJZU75yl43icJiqNbRb2X4A9SCJ4yondTupXvJcAM2EGI97HJdyoAi0YSqiSyD6lc1mxNMmkY19xublVXkDaAmhr8nUB9dYWgJxLmJiigOBcpKoHGbXWihDiwrrXEKualNx13eIjlnsiMUcB1MZq+r7j7p1XeOmll8g58c47b/P6l7/C9fWGFCWcrHEOU55j2UBIkrJty3147J0VwBdRSpjgTCbkdOP6a23QpkEbVUKdAqYqIbTCWoVWpnirY3nPqfzJh5oe26C0tC+nlNBZPh/n52e49pAqXVUqEnglsFbuS7Vg3ayKtF4jvbOmAPTCwqty04jHVheGXhNTObbC7Kd4WiSf5rc2P/Xyx3B/8j/+hz4ufvFn+Yk//TP8u3/ml9B/7N//x3BkH76Jb32ev/iv/c/89/d+9nkfymlOc5oP2XygwapGkeZE0zY06zXnZ2e8+uoduq4riydN1zWcn69QGKax+CWPPFIxRpxr0Ebj/czl+TmrrmWzuebi1i1WqzWzF9nddrsjhghlISjpqjP7vTCIKNhsduyHaQlBUVrCRVzTwDgtATDTXBkpePJsg80is52mGRr5vTYnPnb7Nv/Ud3wcuxu5fvaMwc/4LPK+nBMxZYy15Jy4unpK9AGbNHnyTGWR51xXul7FNxdSJM6z1DpUFlVLTUgMiRwyWlmyluTfxhRZaQlc0dYKK6Ok5oZaMZMSYfbMfiZGkewZGprG4lyL1RaVsniKoyyIhc1REoiljfjNlF06J2uljXUaZxuSD7z51TfIgLOOdbdipVucsmgFtpG00q7v6Ncd/aqhbxtap1Ekxmnma0/f43Nf/RJvPnkPbxQxZ2FVAUjo4rFDsfgwBbDmg9ezLIRzWfBqLX/nvV/Y1uMqjhqeA4c0U2MMGul6ldeRn6fi3auVQMqIVzelyFyeP1MqgLL0Q1ZJ7zETWH1+SsE0Dux3W3bbLTF4ASNaZKPOOVAsPZXd1C6+WGFYDwxz0xwCifq+5+zsTDZftCGWmpoQAtfX1+y2Wwm6sVbugdbRd510iabEOA/MfmYYPX6WEJ2cKCyfxVamr4TshBBobEOIiRCTyMxzJkY5JzFlOq1xTSve0JRJyROCJ0VBNrVeKJOY5rFIYYtMtbzfym7Ho4Rk8aDWcytVTLkGiyW1AL8KrPNSKnNU5VTuJVdApLFW+lZ1XNhaECBVJcYxBXyY5XmzBJIpn5jGR2w3W+5dvIttLJvra7bbHSFEmsbhjpKOtRYAbEtgVAyx3FsQQpG1L57bSM4BUVHEZaNFGGGL1pbGyntRZbMmpyTBajnjQySmjDY1KKmcywIYE6LgoHizRZBRVBrWShpxiAsTb0ytm5HNnEZBzge/dw1aUkbuFasVnWtwrikyZGF9RbovjHOIkZhZZNTzPBNAJNSnOc03OH/gzvfxsf/w+76hx9of/DH+zP/3Y9/iI/rwTtpf8Rf+yP/En7/308/7UE5zmtN8COcD/f/ecU5oNOuuR+vMC7cuuby4ADL7/Z6YEn3XsVp1zHMkpoQ2hs45nLOEINUXWjRyaN1w984rvHDrkhAjprE8ffqUq+tnpJR48uQJw35itVpzfn6xeK6maWaz2TIOI5OfcKUbcZ5nwhToOgtovI/Ms4Q/6UF6GiOQErhs0Vkz+olkMs5ZLlzHD3zqe/iOs0v8k2dsnz5jiDM+CRPko4SbrLsOXIMvzJRCQlKUEYYnRQEAcwioGIu/NZPnRA5pYUWUBR0VJlthX5XClKCkXBZ5sh4XNjWWBafWSryrMaNCRkVhfZ3W2MbR9x19t4IEcfbMaiR5Af0S6KKwGIwyqCRy3s42IgE1BttYQF5bK0PXt6SYMSiccjgsDoM1Gte2aGdo+wbXaYxNNA6syYzTxKPdFb/29uv80htfYkck5EO/LAUESoxNrbXICyipcyynFJmlEsbLVEmkzDHDCofAmOol1UqSakGSfFNK5RiyVABpuYZ1o0Dqf8zCXuoiP+7blq7r6Pt+SaCu7KbWmrPVGq01jx49WuTrIQQa16CNwzYiSzbW0K96ur7j4uJiqZjZ7Xal8uXwHmoYk3Qbe2Yf8KXPcglrKsE4Wilc4zhbnbFedYASH2zK+JAJEWJSpFx8wke1MTWcpwZShZRRqQTzlA0DYUERn7TWBfzLY7quBVzxW/ojtpQb/bF93y9BPtN+EiBTwo/qhkrJk2Xp6FyqrooKAS1VS1otIFsphTIHn3BMkf2wJ6ZYNpN0uWYiZ5Zz6nHO0jhhV+d5Ln7eypZnNLI59e47by9MbNM4+tUaaxu0bQQ3p4gYwRWNs+X+EZvAIj8OmRDiwvbLe4w3WHtrDU1zAN9KUQKbxNPeFC855buADD4ktA7oAnYTiRwjKSfmspFCFml8V5KGx2kkDbJxk0sv7bGcXikw5f2QtbC5jZZQt7JJ1PcdXdctmzgxyfurf9rGlpqqROgDs5+WjcrTnOYbmdfOX+J/+6v/MvaH/5XnfSjfHjNs+PPv/vTzPorTnOY0H9L5QIPVye9JeNbnK/q+4/xiTS71Hu3qjOAD2jhituz2A+McCqMz4VqHMYqQIlbXpF6DbTuyFtmpJJEGlLZsNhuG/USMGedavPdcX28Xz9tut2OeZ6xzCxhJKdFYYYjikZ8wpZGcM/M0orTCdSti8qSoyMGTQiYEz63LF/jMJz6BQS2BTnMIZGPQbUdjDaZp0dZh2w6rFTpl/DiTcyDYQCzJq3DoRc2i18XQgKohJAaVFConGi0+QZ+hsQ6jxUNbmb7GNiQFSSWCikuFRMgZrRq61klljXN0XU/f9yLvnDyJjKUha3vEnCgkBsaIVE/ZwgZL9Y1uNJnIq699lM/+js/Sdh1vfeV13nvzHVrjRLKrDDiLbR2rznHmOpwxaCt/QgxcDTt+/Te/ys9+/nO8/fQhsVHoQhrqrGrIKFlTgo7k744BTp1DONIhWVfYoJvpwZVlBW5IS1NKwjYZFvbOx7D0PlZZtCQpSyiNkk4OnJUqltVqtUjKa+eqAA6WcBtgSa0OIbDf75djzIjsGWCeZ3a7HTlnuq7j7OyMpmmYpumGx1bUCgKihmHg6uqKYRgYxwk/y2tba5YAKq01bduyXq/FR5pLcM80sd/t8SEulStGH2p/cs5E8vIFJKE4cu+m0l1KFHWEkjw1pnFiGidCEFDa9T1932JLB2qtoqne0wraayXPxcWFsMxZowvzKe/HorRs1hwCmeLBl6xlk0TaV0QlcCwBr8FJufrAiQy7wLgfUPogt7bWgpW6LT97/DTTGEvbOOhgnnf4eSbnhFFZJLe6dodm1msJk4spM4coKpAUwUtNzTzNaCOy42mcGMeJcZzJWYC2AG9hiCVjSYLMpJvVFiZffm6NKwx/kg7ksqGgtC4gPi/fgSZLfc7yvZgFjMYQiCHivSGGsFw3AK2qvPoAVBeGX2tUqptHsnljF/ZXpOLeB+byGY0xFh+0J2UJCEupeK+tLZ578fCe5jTfyDTanoDqN3Hiz/+fz/sQTnOa03yI5wMNVrtVi9KR1VrChUBYzmnyzJPn6npDTpkXXnhJmJ2zlnmemOaJYRjRGhonlS1d3+IaRy6euRAi+/3AfhgZx5ntds80CeC8vt7gvWe/H5bgFe+lm5EsMte6GO7aFmvt4t3UYrwip7QkcIYwo5JCJyVBKECnNK+99Arnbc+02+PnmTkEfMpEtIBVHLbrcN2Kpu/JGXKIhJykaiNpQKpfrDVSeRGk7kFlBaZU1aQEWRbXZLCmwSpFUzoqRcKYMbmklGaDykkAb5TgEm0sXdsLK2YNxhqMkVReozXBewHDSaGzhMw0xtJYScHVyhS/qsY2La5psI2lX69xfcP64ozPfPYzfOqTn+Thew9QqYS5KC2gwhia1uHaht45GqMwWmFdgzKa7X7HWw/v83e/8Ct88Z23yM7i/ViuGYD0xMrFqcJmmePamTrv//cYY5HBHqYGvBx7/o6fM6VEiKFINCXFOSKJzc45GiuMmQ+eYT+Qciq9lsKiKqUYx0MoTAVS1UtaQ4Zq6m09pliYyTAEZj+X+iW/JL9Wb6fc69dIrdPlAmJdCdAC2G6l77Vu9qQkrJnWajnOruuW19hupRd0miaGcSj+aF3YN7m/KqgHSdwdpxFdenpDATwSuCTQr/ZyVtmzMZq+71n1Pbak1LpSyWNs2Tgo52UcR/b7PcM4koHWyXdAKoxqzokQfBGwZlIMhBKGlLMAaI2BIuGOIZHVweeZCzifpomcEq5xaCM1MSlHiIfaGulBbZcwq+OkX/lnou0cjTXYcn8bows7LO8tVXAcAtM8k45qeUQJkIkh4f1cmMaEwlB3aprGLvdsrTGqcuLlOY7uN+89JJHtam0kZK3Kg0sdU4yBEEuFUpJ6HK0o3z8H328NpmuKmkOphpo8ffz50UpBysu1VrqoEIoCoXqL646JdATPsjFSrm2KsWQOGLQ9fEZPc5pvZP58873P+xC+Lcb/9F9j99/8LX7o7z193odymtOc5kM8H2iw+l3f9Z1MkwDGzXbDPMuC+/pqyzBMbDZ7/By4uHjA+mxN14mnr+872rah0Y0EoWgjwSz762UHfhgGnj59xvX19cK81KCkYRiZ53kBCscJsABt26KLf3Eui1QJMWJhzkDCcowt9R85EXypLEFx1ve89sodnNL4UcB1zJmsDcla2lVP3za4sxVnL95CNw3DMLC/3qGVIWPRuqGxCc3Bl6ZUJilF1MKI6KwxSiSmUZCDsCwoKB2JIQYsCmWbg18yZzQZazKUypB2tcJ2jupwNNqiUKRYkjmNRltIucFqSfo1taZDSRCMUQZrG9lE0IoYE03TcufOR2jbnjffeIuv/NoXuXr8BNc4VJLjtdayWvU4q2XxaTXdqqM/W+Nj4NHmir/3xV/lc1/+AldxYtbCiCnBpqTyR5EhlU2FMsds3zFwPWZXoVSYlPCdel8ASyjP+1OBlxqZo+fqum5hLld9jzUiAyXJIrwmuO52uxsBSPW5aohTHXPEaFXw0zSNyCiBUO7ZGtpUWdT9fn+QsRawVJ+3glprLZeXl0ityUxOuYC/3fIeBXwZxnHE+5lh2C/JskYilSU8pwR4HZ+7+rmavEdrOc6lb7ME/wjIqF3KbfFHO87OzlivVtjinazMn9aaVIKPnj17xmazkc93ATmjtTRNuzw2pihdqEkUCiKNDVhrsNYVsFT8kKgiW40L2FfFJ2mNLkFk1at5kDcfg0IJ4OJQ93Ik64YKxLvCGNsCFAvb7RwpJ/a7gVBCkygMYg1ryjmDieVzZVDq4BOuUyuWjmXtSwr2kYe3VuqkGJf04awOKcr1mJe6p1yZUo1zdgG4qoRP5VRUBku6dhZweXQfal2k1lmqlWxjD6xu+bwtSgclLKuKCZTUBcWYlns+B7mmuiQUe38KWDrNNzZ/+P/448/7ED70E37xb/Jv/Jmf5yfvnWpqTnOa0/z25gMNVru+Z7O55v79+zx69EgW0Wi8D4zjLGFKEXa7QdgKDW0rCcLrsx5bq1cyTOPIMI7stvuSxirsbPThsEhrrACkAo6EMQzCJpQFvbVWeifr4tbP+Kku7ptFWiphJVLXEuIo/YYx4hCm98w5vvPuq7TaME8Tcwwo12BU5my9on/hkv78jGQVtmtRxqK1YdyNhQ0NaDSNcdgqyQNSqUgxOkMS0ApIBQSA0eQEoYSXiFVSwKgyAigqS7eA9CxpnjlJinBbApislv7GmD1eBWE6rVRGmOI/Fa+fABdZiFps44QR1eW1VcOw2fFrn/9Vnj54xJqGVjls44hxBgWrxtHbBqNBO4Nb96wvzoHMg8cP+cXf+HV+9lc+x73rJ3ir8CHgjCXHJJ2x5dwIF30EQN/vVa3szhETs9TTKL0Au7pxcVyBcrzoBgHJ1VuplIQd1dCixjasVj277Q4/TqQQ5e9L9YyEy4jPtr5e9V7WOpT6uGkaF7DqQ5DrpQ8dnhIEpskp4UOohD8xxsXLGWPk+vq6eCObRVYsvtCOtpFk3a7rcK1Da7X0mlama549kYwu7J3Katm8qfdSON4A0IpcErJDCkvya934aGyzgNN139OW8CZjLZ1zrNoObZRskhSQPwwD22EvGzv7PdvtlmmeF7AjEt9UgLa8z7ZtsaZ6JjOtc5ytz2icnJfJe8Zpxvt5YfTELipeSQFddgF8jbElHEpqdJxrCtMe8X7CFStBvU9q3VFvNLk8JkaPbQSkdV0n/cZ9T/CBXR6JPpKjBLEZ04g0G3mNlCh1QZGUArW2pmka1iup+joGyuM4LiFXFcjWY7LWYpR4/uWagaZ2x9ae30QNOZKwJ4st1TambIjFmKSzuvxPjqsys3H5HFawamuNk2pu2C7q4+XzoSWtPWeCL4m/ScKeckpHfnOR19eAq9Oc5jTf+km/9kv85L1feN6HcZrTnObbYD7QYPXp42tef/03efL4MVfXVwz7PUprurYVfyWS2hqTeJXQ0v+32W55dv30xiIoRvG+jaMwRNZakfdR6w4UMUSGMMjjU0ArfZOxyMUXuvB0pTJBiVwxpViYFsSj5T0hKrKJuLbh/OKcW+tLmpT5zpfu8pGXX0bFyDxNmMZiG0OjYX3rkvbyjGwMY5gIKZKLFHnddeyngM/iw7TWoaxinmZSCYVJSlhVjKIpPakiYVUobcCCDpGkzHLsVPCQoevKwl3LOZlDYbswAlbbFUYrVNLEEIkpiGw3SwiSsRqDLjUWkvgr3rimBLk0C6OjDeQ58uz+YyY/sVKOFQ3aWYLKuNUKpxWX6xVtY1Em05+vaFcd2SoeP3jIr37lS/ydz/08v/H2W0wm4aME2YTZS4WFysRCpKpS2iMJrjfBKhykve//udSzGCjBTFXOq5Sc83qPHGpmRAJcF8i1R1Tk5XvO12d85O5HuDi/wFnLgwcPuH//vkiFK0taAoS6vsc5V4CWyM8rsJgmCQva73YSdmQN8zzTlM5fY4yAPCuL+nme8eMo1SfWst/vF2C72+1YrVb0q5V8xrLULHnvMUpCboQZFC17ZXXreRO2S1jvnJJUnJAO3s4jhnkJ7SIXj2P1MCqUqb7DSM4i660BULl4eGPx52qjSqpzPADUYb8AMF1Tp482H+pmE6TD+ykS2a5ruX3rFrdu3cI58fTudjt248h2t8X7ICyvc5AVwzDgZ8/sJfE458Sq7+n6nqwOvaVyjtQSoFXlv8fMpjC6h3MsibuWnOH6+prN9ZZ5FnvCMAykdOi8rVNTlStbW0GxyHBj8T77xe986Dk1izR5v98vHtu2bVn1vTDExmCdXRhp72fmaSRGCWRSWmOsoXEN6NIvDIdrWVKx69zw++aEVlrAttLipy33V+NckeEnsUvMM7OP+BAxpniPsyom7SIXLp/1WsdjrZHE9tOc5jTf0gmf/9vkX/8lXvpzf/N5H8ppTnOab5P5QIPV/TgzTpGQFTErktJoBU0ntQ1SSyALRG1kUVeZgYOvibKwFy8XUFiFWHoDLUpVuaEmRlnIOe3QR4vNEMKSmqtKLYTRipjT0t+oVMbICxSGVTxnNJr1+Tmf/uSneGF9zu7RU+68+BKtsRIYkxMeoLGYtiE3mqgTc/Bs9jsSGdc4WXQbg85SI+OMyHBV1mAgpMKqVaBVqjYkaAfxmxa/HMrQdCv69RpjDPv9nv2wl+NWiPdPKWKGtmmEebUG3TZYLT7QGANhDhDBGUfnGgyaMHkJRVoSbi1GG7q2lQRPJcBAKcgpFsmfpW8NOkGvpN+1dQbdKNrG0jiLawyr855br9xmCBOPr5/xpbde52c//zl+5c0vM+QA2qCTIteOSwSoBpUxUpwplUhaLZ2Qx9LUY4a0MkyVlRuGYQEZOQFGrrNWmta1y8I7Lgvweq8JWOlK9UzOmd1uxztvv83qk5/iR3/37+ajH/0ov/S5X+Rnfu5neXp1dZCdZxj3A82RNLOm2sYYD8FfjcU0h42VCmrneWYoqcEVrHnvBczXgBzAl1AiH6NsxZTj3Gw25JxxtmHKRa6qBHgdPKTmAEBDlnCcwtgfJyXX5wSpPaphTPXcC+hSi5w+ZHkPzjm0MfQFtKeY2O/2jGYCJIF3HMfl+tQAoArAtNZFZXEToC3MXzmueZ7FfzoMPHjwHqvVitVqBUqx222ZRglOm0o1i9aaHIPcwynTGEPOwjqOwyA6VsUNQDofsby1O7de65TCssHQ9z2r9ap8djTz7Lm+fso0zaSUb/g/c46kpJeArfko5bi+Vr2vx2Fk2I8Hye1RYrLWejnHVVIu8m65X5xzOG/KvQhE+eySEjHHUtVklutprCgrpmmWcK7qm+emIkFlkRk71yy1P/1qtWwUppQYiw86JUkhzkiNFWjpdy3JzRxJ51U597Xep4aNneY0p/nWjP/pv1akvydG9TSnOc03bz7QYPXdt+9x/917ZSd9WhYhffGukSLn675UIszkZErgzLyAjcoQCKi9CUrqzn/MwtMKeJWaEEkNTSUJdUQrqd2YCwjOOWKUdASqnEuwCGgjoFfYJLCN4eKFc1565Q7aGr7y5pvEqw2v9mfkLIvX3bhnN81MGkLoMTpi8sQUBGjYwuRYpZlThJQFaGWRtaosVTLKGjThyINZEksB00jdRa4Lea3pzs64/eKLJbgk4WPpqFWKuSR3aiuSvvVqjS4hQQpFKCFVWmnOzy9x2hDnQPKRrBO2LAy1sTTOYZTUm7TOoTNIv05ZYJIwuS4ulQBaq9HWoIyiaTS6UawuV7xy52VSzgzDwNv37/P//vLn+NxXvshTP6BbkQyqSooqyEYXOS1U3XPKh1qUej8cS35BmKYaYlQX8hVchBAKs5VuPM45V0KGtstzxByXc6GU4qUXX+TunbtcXV2x3WzZ7XaEELg4v+AHf/AHeefdd/Bf/SqjNkzzTEyxSHQ3S3DSwkbB0g1aU3nbtl08qbHIfn3xZNdjqpLZugFTA8IAhmFYGDatNdM0yWZN4274HOsprsBYa81qtZIAsQKSU1bFAz6wHw6KhZRvyjGVEpl9rYDRWdG1jn61YrWScDWtdElHbgleOpEfP37EfhgWEFwBqrGmKByE0bfWYo2VBOvC7qOkd1PqWQqo0VqOL0ZMkRZX//ocBAA2TYNG4edJOpORMDXFYWOjMqM+lgTcXGS0ufiby+5WzkfhXbGEXgEhxOKrHzm/uKAHhJBZAAAgAElEQVTruvL+KgMrn/8UItM0EKOlaarf1JBzcwDpZZOjXifvAykEYTiVoqGhcVYYauq9r1CaUnsTpL6UhA8TMSi8kUoZqfCRz1aKmZzlPlBGY5Ql+AhZapuC96TiWTXGYJRCTotYA5RStKWiyVj5zjjOChDrxkECLCDciFIEyv0cl+yA2vGsVa3D0Yu64TSnOc0/fNLjt/mJP/RXlv/+937q30FfvvIP/J3tX/6/+Ml7X/5WH9ppTnOaf8LmA/3/3g8fPmS72cgi3DmUAueaRY57drZmvV4RQsBowziJ3AwlwUKV0RDwNpDitMgQc6k/qMX1YMRrVpiB6jG11nJ+fk6Okd12h9aJQy+ngNXGGvqup1/1ssAvflqtFOv/n713jb0tve/6Ps9trbVv//85Zzxn7InJhbSiFUjQNkLlBUpVeINEoQhVSAjaSilVWygCiYq8ABX6oqWqKhAUQaUiBBXiIjUUNQVaqlycpLZjJ04cO/FtPJ54Zs7MnHP+t31Zl+fWF7/nWXv/bWI7ieOZKvs3Gp2Zc/5777XXXmuf5/t8b5s13/Gd30mzWPDGkyc8u3rOC81SAFJWxJSYfKAfB3ZC0yH5OBZlNav1irZtUQn2ux3D7R7fD+isJLQm5hIOYzDaoOxJPUiCbAR8aiOdpiFJn6ltRBJXwVKMkQcPHtAtF4SUuL6+JiM1J9oaqbMhS0dpyOSYWC+XrBdrrNZsr28hCwBwxmK1pBSjFdqIn/fRg4csFx2H3Y5hfyiMuHjgxKdmQEMioUzGKnDO0C47FpsFl48foZzh5uqK1954nQ997KP82Kc+wZPdDdEcGTxXQoSSLp2NgCGDSiL5vK/wvQdUK+NUgUdl3IB7YK2y9hVIOOdYLpczc5ZS4jD0EDK5/NxyuYQMNzc3hBBo25YQAk+fPuWLr36R1jW0bVd6SwVEHgZJ193txXtZwbIt1SsZ5pCjCjxrn2ecpmN6bJGeVobVez+/jwq063kYhoH9fj+//yo3NqYmxxbwXQBR13VyjapjUqxW0sFbJc0pJeYo7JPKIK2OGyq2+BWdsVxcbNhsNjRNO0uUvZ/mDZhhGNgWPyoFAOkS4lSTb7U287Gdsojdojn5PKU/1Fq5ZoOXZODLywuWyyX7/YHnV89xNLz88su8+L73sbvb8uTJW2y3W3a7HSlLmFo9j9YYMgmlLc6a+VoxjcXa+/U6McYCrsSjG2IklnO+3x/Y7vdzuFSOaWbMjdakFBiGEaU01o4lOMrOn2UNS6pJv1PtfTX6pKJHuqgryK6fi/Tp2sLcngSPxVikvAZdJNsKZhZUKbEGGOOYCqNaAXYNnlKqOFe1fHfWNOK62aKUIuY8S93rZki9X2Z1Q1FvVCAu/mGKekMUHErloi3JJ/nf5znPeb7e5H7Lf3/9MQBe+f0fRK0f/ZI/m27fIf74/86/9Ymrb9Xhnec85/l1NO9psNq1DYtFx3K1ZLValsVMQ9fVBXNmGAe2dzv2hwPDGOYd+raVhWOtnbFWoQ3EwAw0taZI6HKRiSVhTcLEOPSyQGpanDagDZObCDmhiwzUGIVrWi4u1nzwgy/z8svfRoqRX3ztdZ4/f44xhs16w+Fuz+dfeZVd3/Pw4oLWtBhtGIeBC9vSdi1tTowh4DnWXFSZab8fCEMg9CNp8GQfZamokQChzJzQKwCgJJeqfG+ZloqH1FqLa1swmhSlD9FZS9dKUi3VT2qN+MVSJMRIozVGaaZpomsbNqsNrXX0uwPr9YpGW9qmIYyBaRjxfsI5S9u1LJcLFl2D9yMhekwjksHKTGvKItNooiqSSO1ZLTsevO+C1eUGTObp7XNe+fKr/NgnPsaPffKneP3umtHkWS6oyjkI5OJly9giSwWRZcZq8ctHgFpB6inLehqYpJTUyFTwVoN5Li8vaVsJH6oSzs1mA0htkvfTDOiM0nOQUdu2LFdrnj9/zoc//GFCCHzgAx8gRJFyVpln9MJwO+vE05eFybLWwvy8avbKVqB5GIa5l/U0TbgCV5D7oEp5K4NcK2iqzLg+JmVJVx79JGmt5TptmoblcknbFIk34hNdLpfc7fYM04SPEWVOvaJH5tpqCRWq0te2eGXFcxu4uTnQ98PM8NaAo1p1k0RvjFZyvaLvy7qt1rQnzLiz9sjsx0zdnNIGqTtRCtt0oC0+lj7TGAl+Ynd7h9OG7XbL4SBpzW2zKN8TDW3TobVhmkZ8nO55n+u5stYSQ77nMz1VQqgIKkUyulxrmXHwxJAxcvnK/a2lj9aVTadqQ5B/1WxtqBssp6+VcyRnTc4VSAuYHIZwAnD1/BxiZyjnW7po5u9QYwxt00qlVwzFw544hEOxZijGsV5jbblPgSwBUeJLPt5TVbYcC6MqgWZSndVYi01uvoZihODjfG2klOZNMl1C45rW0hhDzomzCPg85/nGxv/I38f+jt/HW6/8n/Ib6pfuKA4f/0H+ne/7AX7q2Re+KuvhPOc5z3m+GfOeBqvriyUvhIc4J7K62tUnC5NYglEyk49Mg2ecJnKWIKW2dTOAkcfEeRceapBO3ZkH56wsusvCtt8fSkCLnhd7zgm7aI2haTRt61gsO973vkd88Ns+wMsvv8yxdkNqGVbLNVfPr5gOI48fP+bR5QO42+NHTwoR5Qqrl4U56FZLzKJFO0vIiUM/4McJg6G1LZNE1ggoy6pILVWRGuYZgEkyaCoy0VK9oTWNa3Bti7aGlMVrppUEQo1Dj7GabrVkc7HGuYasFT6UvsUMXdNyeXGJUQWwJIVbrzmgIJaFslZ0i46LizWLRVfkziPb3R0xehTQtC1t42hbkQgHL4EtKQWM0mQ0dtmweuGC9cMNTeO4vnrOq69/mR//qZ/kh3/qo3zp6h1GK8nGJimsNqTC5kqYlIYQsUmkpQnwWsCqRmHyfaB6ysR/ZZVHZeUquKs1JBVkHQ6HmaUGqZhZr1ZYe1GAYCPA+cSHNw4DKURub2/nx6QYZTOkSHHr5ku9BpUu7wvB365p5q7NKrmdpkmSgU+6VysQTVlYSYqk9NQ3WdOGgXnxDwIsVc5zd2lN9LVGgME0TXg3oZqGtpXuWKU1+2EUGXMSNi4mkYdXuedquaBrW9xJb2zjnICd/Z7rmxtJSy6y1SqBrsnWOYstW5WgNWNtOSZh6bu2ndlmrYocPEunsFJ5rgkSpjnMn3uaRBmxKn5u13QE77m+vp5BvPQ9j5DUnKgsgE5qYmpSbj3H9TOfpokYTvyaJ17dGMHaBudElj/6keQ9UFhGTZEYa1xjMUljG40x8tmqAsdizOUYjnVbdQOmaRxaH6uMKgtfE4rr79dAq6MEXgBwVvKdOzO3xknYFKCmCUUgkZmmSCShlKbrFjO4Ldrn8jnOYnJSqRCKMVbkDfBV0t1TH24MaU6brgF4pxtMzhlWiwWLriXnxHLZ/TL+9jnPeX79zn/9Zz7Nf/OPfhP2N/6bX/Pn/If+If/Rn/4oH396lv6e5zzn+bWb9zRYfeHRQ1KU9NS+n2aP3WloTQ0VCVHkv6l0QeacaVsJTJFk0Ko9TKRUWTQNKtMYw3LRsVkvhR3ShmW3KOxZw2ErSattJ6zuerPm4cNLLi7XdJ3j8vKCzXpFCJ6bmxv2+z0xJZxt6NoFi27Fi+/TfPt3fRfPnz3j+vkVV8px6A9E16ER1qW7WOEePcBsFpKGG2MJROkJ/UQ6eOIYyT5jooak0PWfIserYDVTWMNM6bgU1qntOlzjCCmiAT+VgJjCwRqjJShGK4wzaGtYrhYMw8DYS+1G6xpCSYg1WtOHkXEa8cMk4UvAou3olgvaTupnBh/xccJohdEW12jazuEK+6nJON2glKNtNdnB4mLJoxffhzWK29sbXnv9dT7yMz/ND3/8I7x69Q69liqUCqZq4qzRCq3k/DmlMEphciYCXmUJDs1SmZLTkUWtYLX66uB+YukpG6a1BNrsdjupRxnHWS5c6166rpUFdEwkHe893zRNkuAcI5vNhpQS77zzDtfX1zjXsLaWvpf068yxf5QMvoTfaGMYxpF0IpOcJacldfb0+MVvqSUgjGM/Z9u2M1iswLgyyTVV1liLNoYYwxF4lEkp0Zf6HKOBcn8O4yh+6RIEZLE4K5tC6/WaTfGjrlarOThpmibx20ZJWzbWEArDVj+brKROKSe5tiubLr2e5R0qRU5J/s1ybccQiL5Us7jyWc+fx5FVzEDfD/NGgfha5c/W681cx5JSQqPpOpEZD2Mv9T0x4IPIia1xBZwJ4JbE2/tg9QjINDlldJE0+9jRDwM+eKZxQGvFohVJ8HK5KDd7lhoopQghEXwk51Cul/hV36ligZBNCOfczGjmLNJ+ubbF3lA7ZeX86QJazUmvqybFPCf8in80Fulvfb3T8CyK3z6Ry+dVZco1EGneJNH63gaSANqMQoOWsDzZGCg1NsXHGwoAt9Yy9hJ61dZruzuD1fOc5xuZv/hn34d+/3d/zZ8JX/xp/tCf+gn+6Vuf+BYd1XnOc55fr/OeBqtN0zJNgWGQugZhOYyE3GrpQJxCQsI5VenUy0xjJMahLEApni6NUtKJ6LpSDQHkpHBWeknDNEgljNZcXj5guVjSDweSSpjGsO6WtI1hs17x4osvstoIQE0psz8M3N1t+exnP8eTJ28TYmLRLbm57Qk+se333Pzsp3BOM4491/2BfQxgLNY0rFqDeXiBv+jIjUVZR6MtOltMNOz3O0nx9BqVQMWEzkdWJqFwZcEHlFWiozq2YhZZbGOl55UYpMJHWVk4WsNitaRpOvHAavFdNrYVT6BVrNdLjDJoldEF8Pkw0Q8HWZBaWYhrpfExcHfY4U3L+mLF40cf4O76Bj8MqATWaBpnJUm0yJC1cyiT2axb1g82NF0DWnP1/JpXX3+NH/vpj/F//eRP8MWrt/GNZsypsCkCULOSZORY5cA5k5ViysJWCggAl0tpSk6VmiNnVby/ch1FH7DG4kqw0JGtcfeYV5GmBsZxlBqWmDDWFJAu4TKS3iuAUoDYkW1TxpCV4mc/9SlCCNxcXxNTRBuR/KbgiSkVuesRJNcAJB8kBKdtG6ZpwJgjs2nLz8YYUTmRakiQURhjubi4mHtdFbUnOOPajq5p6Q992XiRapEQJukPdiVUqFQ7xZyF3c4Jn6Tg6DCM5BgxKNrG0roVKQogXDSOpigWcobtdk9KW1JM7Hd7Ju+Z/MQ0eqYpEGNmioFcQLOtlTlGobWA6Jxg6ie8nkRZ0TSESdE0jpTinEQtPZwJqywoUzYsBEyGELm42PC+R48kRXk4sB9HYgoEH5lGzzg8KYFUojZYr9c8fulFlqsV27s7nl89Z78f5LwX5rexVq63pMhaulylpocSPJRKEq7BWsdms2G1WmGdo+9Hqa3Z3TEMPY21NNaQyzWitcWoInCNkeB92VCIhSUVK4AAQjkOpTRKGcixlFHJRpfs3ckmRrdoJRm4a7HWEUt4kfQqAyljjCJrMMaRYiJOEz4mUoIUPTkIey22BPHOV6+tdfJXjwB1YcUpAXUpZIIPaG2IcSSXaxOUVHDFTIzHwKi68RST3OdKy2ZJnzM+bunHicWyY/Pgl/bcnec8p/N7f+9f4Z994m+824fxazrxnVfBT8ff0BrzgX8VAPcf/Kmv+dg/9z1/jn+4+3m+fPf01/IQz3Oe85wHeI+D1ckHCRnZbgkhsF6vhTEqoGL24VVWLeZZuphiPtYpWOGBrDE4KyzPOIygBORYq4lh4vawK4v4S7wfufXjLO1cLlfYxvHohUsaZ1Fawonu7u7QWrNardluD+z7iX70BB8JAYbel6RghVOZ9eaSx99xwaPVhpHMqDJN69BGkVTGp0AOEKaAHzyxn4hDYNr1+D6U1FwBl6awgjlndBaJnkLAlzBQEkKitKEpgTzGisxSKwG4uvxZ0zi6doHRhpCkjsZojVUajMZ7VRJGlaS+as00TPhxYr1ZkaIE5KSYijyS4gNWhORJvWcYe3KMLNqWxXrJZnMhn09B2H3fs9ksWa2beTH71tvv8LnXXuVDn/gYP/ozP8kvXj8lOqmkSTFhjCaTiEgCaJYTQrkwUFYRS6enVgJMjBJmMSnp+VTqyBJWIAowJV8qjjQ1mbQymKvVCuekh3O3284Jt6qwRXOPbPVKNg2Nc0zek4ZBPKCFCd7t9+K5HgY59xwrN05lyqdsXO3SBOi6Zj5G5yzWrtkoJd7VwlKK/PMYHtU0DauVhHdJ2I6f31/XtbSNvMeLi4101QbPbnvHdr+Hwspq6UOaWd+UEoeSJhxCQCG+83a5Qqk816HU9yKPA1B4X7yhMTEWNteHwOEgwE/Zo5d49nSjkERYXbzbmRQSGalCWRSWuGmcsJ/qpP82JlI6+nkrm973A8+fP8c52Qwb+h4fAgpd/JfynZAKgN+rPbv9TnzZq6V0PqfIMHrGITCOAWMHrDW0rsFZi/fT3C1apeFN03BxuWK9XnNxecGiW8pmktkzTQNDb6BpxNOdgqg9gqdtFzSNsL8pJdLcY5rLexTAWK8b8bRD2zZYo4neSx/0GGRDT0ui8HLRsd6scG0DWTPk6hk+dqXWa9yU2rBYao+0NeQc59erGztaKQk5M0dZeg2/km5W2VhMKeGnAGgJnIrMAU1VLTB5LyF4hVmdXyuX6iTDrDIYR0WMieur21/NX0fn+XU0n9m/if9n/wvu9/wn7/ahfFMn3byF/1//KgC//W++ymevX5//7NFiw5uv/NOv+xzhw/+Yj4SnZ6B6nvOc51s272mwent9w+3tLcYYHj16xIMHD0gpsdvt5u7TYRgYhkGAlbaycCLP8rOcEtFnKOEdOUVCFt+p0dKDWv2Bbdvy+PFjNpsL9vtDCWYSwNB1LU3j2B0OkBLD6GmahsMgfth+lMqS2knYNh2LxQqDIkyetnE8evERDx5csFx0GO956+Yp33b5gIeuI8RAGnqSyyQfGHrP9mbHuOthSuAzTOLNVCg0qnh2q4T12CmJ1hI0AzNYda5BaXMCxgSo1oWi60qSbAatROaoskgnNZZGO1zraAvTOI4jwzRgjGb94IJF27FaLBmHkbvbOxSKRdsyjXt2u1sOQ19SQx1DnOhUpNl00k8apI5HW83qYoW1mRA8z6+v+cyrr/BDH/8wP/apn+bLV0/xBgyWNHmcEhloSPcBZ51jRdHxPNXJqobwnCZGH38V3l1CbOpz1AClrsgJ505WreZu37poFm9ixBjLovhCh+JhNNbOC2ldFth+mpi8Fx91ics97QWt/sPT/uDqaTVGnUjj9ZwG2zTNfJ/IZy1MVpXe6sI0hpDmpNba3brb7chZ0qAvNuI93u83PHt2hfd+fr993zOFwH6/n4FwPQ/OGIwWVk2pI9CAI9Cp1VLWNnOw01f23CqlSCrNx1cfI/d3QCS5sTxGJPHOHd/ncrkURjyB8kHqp0o/c5VBW9ucBBEJMEoxY4wTtUE+dtQOQ08IIGFljmmSKh1jdKlXSYXtS3jvUUHNYVLWmKJ+yHOgU9u2rNdrFssV2lhub7c8e3Yt99gg8uqswJQNnCy3L9579vuetu1KrU9TNgI0xoBShXVMqQRtBUk+NhrnBMQr50jlutIamrZhtV6xXK9xjSRLV4/taW1SvZfknB8DzoR9PUn9VccqqLZtsa5uHnTHBGfkPo65SuXlvsyIV7aGauUsCoP6TypsrTJyXk8rmBJZNqvK68cY582d85zn682T3RW/+/s/wr94/pzmj/zZd/twvmkTf+aHufzvfuxX/Hj/oX/If/ynP8qPv/Pz38SjOs95znOerz3vabC63d4xDQPrzRpy4vr6qrBAozAyZbEqjRiyKOkWXVmgyGLwcOjROhN9mAvvKb17L774PpyzbLd35AyPHz/m8ePHM0M1Tb7s/JvZn5izgBOfhKXbbu+o/rW7uy0xJFarNV3bkRMcdjssisvNhpdfeol2ISE6t9dXfHE/8IGHD0mrS3a3tyQF7mJNCHDYj6QpsWg6Ns2ShDCWevYKCmjNNRxIyhBREosq50aBsQ7rXEnmBB+DSD6NRZVE1rYkA8ecUDnRGIsymqzA9x7lEouuo7EOyByGA7vdFpTi8uIhq9WSxlhSViSTWV2uJOwoBKaDMNhd16JKdyVa062XJA3KiiQZnVlfrLBas9/f8fTmip//0hf45z/+o3zilc/w9v6WUYusN6YgFT/GCsiKGayaF9J1kXyaCvtVgLX0L35192752Xx/sV1ZzdlvWha+IQS0UXOFTU3glc5PiymVJvXxlZm958HMx47QTCqbI93c21oBnPdeujhLwFNldms9yzAMc63NNE2zKkAAWUPTuFJJ4vA+olScPYhwlBhLQNOAMKgK70eUgnGcZma0AvbahzmNI3dFAaFKwFnjJHhnHMdy7qS/9Ah2KkPsCUGu78PhcI89rp+PM8JWpxgJpXsWIMaisshgnWPRNazXKy4vL1mv1iyXK5xrCgg/0I9DeawEq4UQUcoDCuca2pJAvtlspDrocGC73c2vI4zlhhA8IXicsxjtSh/rOPcyA3NiOWSs6OaLbSCiFDStE2l+61DmyNre3e4Yhr4EDiUWi47FohO5c86yF6UtShlyDgxDP/u1tTbElDn2jsr5SzGKH1ZZkoKUNNmI2sQ6g8sWhaJpG7Q2+OBntUDwsQBwYSjrhkm9nnNmDpiSaymjSuf1fF2XeyNjyzmM82bEYrEo95yoZLwP7Pu+dLfWTTVTZPY1wE7P37vmJADKWldY7xJgqqQeLANT8L+av47O8+tsPvr0s3z5b3R89x95t4/kvTF/8Xv+PB+Nz/mht3/u3T6U85znPL/O5j0NVmUy0zgSQyCkukA9LlZTilgjAGDRLWlaWSBbayHDouuZpqH4whqMqTJZGIaevk+0bcu3fdsHefHFF5mmidvbW+luNRpJso30vfj3TOmxvNvu8SW90llHzom27Vg/2qC1LszjSPBBgn6sdEXe3t4wTgNvv/UmS2V48eED7G/4jfhxpL+7w7/5NimCMS2X6wc4o1ElUKk1TvyVCZHZKjUnZ+pcwFbxbqaU0aXD0DWtMBcxEVJClyTUputou1bATQwQRapntRavprP4ELHWsFldoAxMYYSQUdawWK94+PgFusUCawz77ZaxF8Yq5kgII27V0l7IBsJQQojapqXpGvr+ACmybBZoxC+33d7x1rO3+Phnf44f+vhH+OjnP80uTniViUbNjIpSCpWigBStifkYWnM6pywdHMEphZ2CrwiAoQSWnrCw9XqrALMu1ufEYH3splwul+LjjHHufZy7Mcsx1NCi0wCZ+t/WmTnxtPaJHg6HmSGtz9U0DYvFYmaz6nuoj6k9msc/P4bd1MCcmgRbH3daQFu93jln+r5nHCe22x3X19czw7lYSJpv0zRMTcM0TQzjiGsc1hmMMqR0eq+mGUgI6B1m4C+gSs3pvVVKXNN3GxxZzwVNM4Mn5w6cdrStZbnouLy44IVHj1gsFhhjiTExTX6+hyuAEhClmErfsgAvAc39MLJarfDec3e35XDoT7pAHVoblErs9wNaj2itGKeJvt8RfEIZCR8zjSNn6fvNOeF9ZPIT5IR1lqU1KF3ToUWdcXe3LeckzqA9hMhytZCeaTTOSYiX1gKo+2EgpFQkwW7e4Mgn9Uw1/VzOvYRmWyeBW6ep1yElhv2hyPPVLCeemVOO11ENRJJQo5IcfLIBU197HEf5Thx7bPleqt7ctm2J5AKOxXOq0HN40jQFjBV/88y866P6Qe5ZURichqTNbG8+gvfznOeXM1d3C37juEe1q3f7UL4po9qO77p8/7/0zz75P/2+X/Jxf/F7/jx/6cmPftXfr+c5z3nO862Y9zRYzaUqozJJtgBFbSxNIwxR13WSJuo6jLazTNPaYxBN9UtJMmikaRqGoWf0I92i4/Hjx1xeXuL9kY2qi9ma8iosisOHiE8J7wNkVSSCx3qY3W5XFm3CqNnNGlLk7nDg7edPS/BK4LDfcRUTn3rlFR5uHvLy+iFMkXy9xdqWrluzMA158Aw5YJXDaVtSUBRZF4lbYVB1FmZVG01SCqUSrulo2w5jJRk3hECO0iXbNA1t185sDVEW/8ZYjLKQFCoJCNZZE0dPMomkE8v1iov3PWJ5sWZ9eQElPXTVaLLNDNselRK2s5jO0o8HiAlFoNENjdb4oSf7SOoH3Caz6Dpub2547fUv85Gf+2k+/As/wye//Ap3eHypp9Gl9zEVoOgBU3YepArj2JUK96W99yTAWcKKyEf/Y12og5rrME4fWxnV+msFeKpsDNSfMcawXq+PjKEP8wK7JvRWqWINbqryWa01kz9KFoeTrtTTRYLWepaon9bpLJdLvA9cX9/cS8uW93iUSDrnUEAoLGllbeXnp7KwF1loCAZjNOMoDKOkbLeklGbgV+W3IGnAzmhSiEw5zOdXaz0DU0mrDfP5rfd4BVm+dMtWYL5arVgsTpjjEw+xUhljJSSt7RxNa6UzNYtEue+FrZwmCRTLCXyKTCf3uDDasN0f0L1cY+HqZgZ24zhiTzYVjix9nAFl9eFW1UfXNWXj7EQ+W5Nrc7kXObL6NSxoGCZCqEymbBpMk58/E9fYWf6vlMa4Bm090UsYVUrQdUfQn9JRRl7P91eqBYS5lM0UpXVJ1oZQPptpGO9V4JxK7o8qBgngksTqjCo1MnPNTLln5bM+dqo2TTN/P+WUiUnYVGsduXhWQ6ws8ZHVPWY2HQHrqaxY6orUVx3vec7zy5nf+fwj3P2jv0bzR7//3T6UX9b4H/n76N/0PXNgUh37O/4Av/ALf+CXfFx8/Rfwf/urg6U+GgNnoHqe85zn3Zr3NFg1xkjvqTGzhE5p6TV8+PAB682a5WJBt1jQuJa2WRS2QROjLFZjjKxrGM7QzxLf58+f8/z6GZMXUFCBiixw+9lLZoxhtVqXwJfIbt/TFH04h9gAACAASURBVL+eztB1i/m1QJEK+5WzLMaVUoSYuH7naZHOFtaKjAa+9OQtHj98jYf/+iUXywuWyWGMQ5tGfHSYAho1KmkJepGuDhEYKjBKelaVFsmchKQ4mq6lKcBiDnQhz7JQZ10BerIQddbRLTqcdbJI76UCSKXMbhwZ0kizaXn88P08fPwii80KrMbnCDmhnca1j0gPI2nyDMPAbb/lEEdsFlZ02bVcdEuUj5hW48eJp1dPQWtef/MNfuKnPspHP/spXr95yj56Jp3wWbpTc86oCDYJ45ONwqcEOYo8+kRqC/fBapWv1oVrrSipmxsCqErqL+ornqOyQ8PcX1rTeGsty+ZCWKIKyETCK3/B158/TRGui/j7XZtIi+6J5Pc0YEkYpQKKgjD2TdNweXkh6bHW0vfDLAOur1UDfLTWhantsdZhrSsgkwK643FDSAsT17Yti0WHtQ3DMM7PWaWuws5anJN7zyg5J9o1wiBy7Masz31arzMHoqUSzMV9CXfbtjx69IjFop0Do1KsbLlGqSSycEQO2rUtbemrPQwHtrsDu+2OmEq3r2sxyszMcz02UDSNmb3fdVOhXk/1fR8l3maWwFbWToB3mv25MYa5lmb2KM/XjJvBGsDhMLDb7un78URyzPyY6oMW+bl4NhUKZWvQVN10iGjtMSXJur726UJTKY3WYoXQpvh8Zza/1r9IevZqtcBqU+rD+vlauSeZ5xS0Fh/3yQZOBataa1544YX59ao8PoZQZPlm3sgIIeAnScK+PyKrJhUdQJZNKpCu6CgHU/8COZEqZ2IM/7K/Zs5znq85f/Ov9PyJP3iLXl6+24fyDY3/4b/HH/4zH+N782f44z/xZ1CLzTf0uHS45a/9/n/A9z85V9Gc5zzneW/NexuslsVy27Y454gIi7FYLIqEbMlyuWC9XtN1C0LIPH/+bA57McawXC5l8Zoi0yRF902zYblcchiWxMKMjONI34vk0ns/d0+KJFGqPrQ2pAxoQ4oZamrsJPU1OSdZYMWj/HMcJ2KK9P1AOFlkW2PF89qPfP5Lr/Hygxf5zS99O8vlhuAlUMRwlNRp1CxbTCfMnwJhHItMThaNBmUNTduKbLkwVSL1M3RdK55DJR2e6YRhbKybg6sm72mcww8jo58wC8PFZsODhw9ZLpegFKOfiCrTdQKyc2pIk2caBpJJMIi8rz8MqCmyWTrak57KMQdef/42n/78Z/nUZ3+Bn3/tizwPPROJoOS9GQW+nIOcwUTxrmatSFqRs0IljinAHBfPVQpYZbhzgqk1c6WHKX7I2lFbgUR9LFSwZWa/afVWTtOEtWYGvZVBlPCbfUkslkXzOI7UDshT4FqPN+d8DJA5Ado1LdkU9jWEMHdKNmVTRSlVAG5kvV7TNA1935dgnXovLFgsFgzDyHa7w9pA3byQFGHHer2eNzUqmJKqk2kGTZXhqu8rpTiHLonUvngX1bEftspMtdb3WLZTGamcm2YGN8aYIuU1c8dy27ZEX0KmcsZYw2JR03AzQ9+XehRJFc4p4hqHznIuh2m8l6x8eq+epgK3bUfblotJdOH3mL36mQHzscv71GgtLHFOR5lsva7m77IChqvUuT8MDIPH+zgzxnWTQjYUjtdLZaNjTGCm+f0cu0oj0yQ+XKnskmONIRbwp0SZ0ji6xs3Xj9KaYRgJKRFCwmlD03Ysu+XMpvcl7Rk49rOW83h6z53K6E9/Xu43fU9+Pozj/F0UiqrAT7EkVMu3nHhay71Q32+RKeecq1BCrscYySmRSghXldhrbTjPeX6582ff+mH++P774P8nYDV+6CP8H09+hh9Uiv9i/M+/YbBKv+X7n/zQr+3Bnec85znPr2De02D1pccvYY1hs1nRdR3TNHHo9wVAJGrQRkqRu+0dz55dMwwjRmtiECZxnHrefutNxmHAOTeHKOXiUbNWwlFCCGy3W3JWhXWRfscKAmJJTF2uFlKPoDSgi5+21HGg6DoJccrFf+m9Z5wSIWWyFpldAmLM6JzxZF578hafaH6BF7pLPrh5hCoS35AiOmfxo5KlElQpjC6LtCIk1FpjS7CKMrK4NY3FNRa0FLskMmhF13YsF4tSgVHDhhLOSkIoWUBADIFF12GtZbfbYbTm4uKSBw8e0bkWP4wc/IjPAds6usaiTFEpO40OcnyrpsU2S3a9+M4abfAhEHPiZnvHq0/e4Kc+/Uk++nOf4PWnbzMZGIykn6YsYS0mZ3IsCc8ZYjkXKSdB66mehyIJRhavNRFZmSIbFWSPNRbrZPNBK4msyqVq58gaaZFZzgtsQ9PYeeFdw7qmaeL6+oq7uztub2/nx0/TRCopL6eJpABk8QnWpGE4BoTlk9+7LzlNM8CtTFldiEuVSirAiNlbuVwu7/lcq89U0nEtwzDQdc0MHCpIB1nUW1vrczQwkRPEIP7XChaatqVx9pjOWxisyQ8SHtQ4mqadE4pBwGrf99zd3Zywl7LJcspcNk3Do4cPWXQLxnFAGzWzfFWav7ELNqs1L7/8El3XcX19zdXNDfvDDh/EL+t9LP2fwpJb08yvUTclQvDU7l1jbEkoFnCdU0IrAWanPbun6bhVyu2ck02fxqBNngOrxGfc0bUdkDkcevw0cogjoUiNlTIkleY6JaUQh3au4V4iz5WkYU1CkXxCqSMAzTkTivxarhv5blAgqo8YyYi/s24GLJcLMuBDxISIKgD6MIz0/Ujr5HN0rmEcp/nzr95puW7vbxTpIs893ZDIOdMPx9TopmnmoDDjHChDiEU2HylsNZhifVBK7l10IuVIaf4hyQHIHZSlA1rY2+P1KJlaX8nSnuc8X39yznzv7/4L/Mg/+ZPY7/xt7/bhfN1Rjza8b3nJo3YNxn1Djwlf+hnyF3721/jIznOe85znVzbvabD6nd/5XSy6TjxexTPhrKPvD4QoYCoEz+3twDBMHPqBrlsI6+QlzfL29hZnJPim1iU8e/aMm+sbDsNhDvUQP6GEeQgjFkuXY+kPzJBTZrFsC0vksKaZvalHFo7C0vYSjOMDsXQCorUAzJhQsVQvGFl0vnH1lJ//xS/SfrfjUbfCJWFQMwmlpVLGKFk0C3i4n3Y7y1+VwjSObrVEWwGGGWESF8ujVDOmKIs5wGIw9ijTVAqRlq7XxWOYadqGtuuIw8TNO8+ZciCpjHIGnVoGo1A6M3kBEeRMCgEXYegnlI+QYQyeIXqeXl/xmVdf4WOf+ll+7ouf4+nulslAUtLTGHOtOgGdwanSpakV0SiKElhY1pqCrO5LSOucMmkgvl5KiNJUpIGV8TqysRml0r1gplNvYl2Ad514nvtBwnvuBxtlQgr3vHun18mp/HeWTxYF4+l7OGUAq7+xvq8qGZ5K2Jcxdt5gqWxe0zTEeGQwxQO6EFl6qQSp76kCW5AFv7XCzB4OB3woLKnWJeW4Edlt62bAm7OEKMUUxf+oVJHWUmTRxzCqrutmVjZnee/1Gqze4JubG8IqME0SYlQZSWsMjbM8uHzAt/+GD/LCCw85HHrZROgPXL31tmxUKPE9TkWtYK1jHAcJHEsRE/Us1T1eO142fIxsUmgl3aQVwNbPEpivg/r4GbBahWvMzLSHGLHGlQTniXGaOPQ9oQROie/5KJuW54/l9+t1XXpmlSEbKAUt4sWnspugzTFISZFAy8aC1PoYYrm0/CTydGtNYbwTKSdSzAyTZ78/FF+3YrUQkC3HZwr7TrlOzMl9UqS/6ahuqNeGAPdBXqN8vvXxKmVQkRTle6IqHhRqVhaQOfH5mpIMXP+VP0vpCPZFHaDJqYY3nT135/mVzceffp7/8A/+Xf7uX/8S9t/+99/tw/ma0/6X/y2f+cz30f3Rfw+9eeFr/mx847MMf+l/4I/9vxf8wJOPfYuO8DznOc95fnnzngarfhpZLZd4P5L6yIMHD7BWs93eMYwDzhrCNOGjVGIsVwuMNuy2O3aHnSxom4aLiwsePnhIKoFL19fXDMNAzEnK5QtQuL29IydIMZMSjONRPqu1RlnpHg3es9/uSCnjXEvTdtjq8yyexgpYVJZqla5rSEYzeQ8pCmunDTFHTNtw1e/45KufxVnNb/n2f4WX1g8xWZND8a8lUKWiQSs9y+PIRf2qZCGalMhF20VHypkcA65tWBdZc86ZHCM6HwN3lLFoVxk0CeBpSxXLOI5Hb11KjPte2B6tUE6Th0zoDwy7HYt1BxpyioXxTAy7Pdurm5kJvd5vefPZO3zqC5/l0698js+/8Yvsw4Q3EEXphw6yJA05ocsaM0ep5khAEKyJzRmbJAk0quNCFu6Dy1N5YgUTKaayOTGdLHjz7CdU6ugprf5RuJ8sPAwDtad2vV6zXq9F0llSZ/eHA7v94StkokefX5XnnjJ1p2C1JgbD/Yqb06mbFBUwONfO4Osr5ZkpZbbbLfv9bg7UkWomURgopefkY3lOqTu6u9tye7tj6CVV2ymLcRajdWFOm5k5Nar4Oa2h7Tqm+Xz0jOMwqxXq8cmvqfx6/L2maei6jmEYyCmzWi3m97koXnLnLF3rCD4wDuMMlJqm4aWXHrMrwUoSRFbSsINHKQOkAnwzPmhCqOymml9HFVbcaI2a5dLunkz8+LNHv2aMEr5WveDijT5KuA+Hnn4Yme6l09ZrQpV/KyjMhFCTpYXtLy04gHT0Vtm4hM/p+RittXTOzteQNlJlVdn+pvxM3fiSUK+RQ6nfCSHOQUUxhllKm3OtDGL+fpBzUj7XlO4BydMNH3tyPMDMXqty/wo4rX20x3tNica3XJsiyVfle7DeVzln4gyoj5tAMUb53j3nLJ3nVzE/8ORjLP6E4X/+Ow+xv/l73+3D+Zqz/ut/6xv6ufSx/4cX/rfP/hofzXnOc57z/OrmPQ1WD4ee25trnj9/SoyRl19+P86uZGE9jSd+PI0yBj953nn2jP7Q0zUNTduyWCxKyqpI8XQBXt57xt6z3/XzAr3rllxsLmZP4jjK4s37Ce8jkOiapfi8rCUmmHzg5uq6AA3pKj0FJSYrVsuO5eWGoBX9MJK9J08V0Cl0a7GNY/H4ki0Tb16/w1JbNssNpizK8Kr4S0XmJh4uyRIRebABo3HW0nZS0VMXr03bsFguIEPwvlRDQMryHMZYdAE41lrabsE4jtzd3aG1ZrO5QFlNigGdMkqZkkiqmMJE8AGVEqZraZYtaNgNe26urtg9vWboD0SVeXZ3yyuvv8bPfeFzfOGN17je7zjEiUgmJqQTMSYC4JTCJVBJJL9BJaKGrAtAy+AwtMYQSQyEwqTdB0Kn/10lvU3TlGqMNEt56waDeE+XLBZHoHtMMY3z81U/M0DfH5imo6S0Sm0vxonr29u5Cuk+KFX3PIinHtVTn90psD2twKmvdZQoN8UT2c2pupUplZAlSfWtKcMpBy42m1k9IBLYePL/hZUMgb4f6Q/9nBgs95z82jQSwOScwxpT5OQOZYp0c2a1KZU+eQb0UokzYkr1lPQZy7ldrUT67ye5vtq2yEWNluqjAsicVTTOMk2BcZLNg8uLC2JKrDcbnl/fcHe3xWrpEEUZyHrehEhRGNeQPEQ5Xmcl1C3lSAwRlSXhOEaP99McSiWfF+VacDNoS0kCx07Z4rbriDEV+fOWwzAI+4wu/agi+61+ziPbGQFPziLxJs/if+A06bdc49rgrLDd1hi6tqSoZ0khzkjvaPVvV1BXpdmHw8BYgrSUUvM9VVnlozLgvn9WxszP5Qc/P0eVCsv9orHOziC0SoIpr0UJzSo0KqSMNQZt6qZTxmhRgogsXVj8nMXHXkKCpTbI2TlsKefM5cXF1/sr5zzn+Zrz9978CF/+I1v++Yd+y9dlLd+Lk8N0/O/9Db/nL3zyXTya85znPOf5xua9DVaHA8+unjFOwni89c7b4vksUsDFcsGDy0tc4zgMA+M4sFi0LBYN64WAWusci1Z6Ptu2xY8TOQljGENEK8VytaLtOt7//vfTdi2Hw37uZdxud+z3fZEqihw5xYg1Vvx6bUPbtvSHnuBLL6CfIEmHZeMsi66laxpU43j04CE5RHZ3W4a+x+ZENorN5SXv/47vYNge+PRrX6TfHfjOD34Hj1YXrPQCo1JhjjKJIq1UwrbKui6hlWKxbFkuFyTBoTRdQ9O1ZK3IIWKsQyUBrVoZcmHCkPpZrLP4aWC7u0MpePmD38bDR4/YbnccDnupsjEaTEYZaLPDNhrXOZq2YRgGrm6ec3N7y9XNNf1+YL8/8KU3X+czr3yBL7z+GleHLSORPnmyUShjIARINUBFKnOqfzYrSEqBEamviQmdM0YrbGNRGoyHmILIIAsjBUcJsDBkBlMSf02Rby8WC6ZpYrvdCtseI96LNDJn6WhUCNiqjFoFtsC80J6mSXzSHL2QxlouLi5YLpfEGIXV3O2K51AYqJASuaT8aiU+38o0VZb1VGY6b4IUMCjJvCPr9QpXQNMpyK3gQmTCAlzbtsXZJavVuiRdxxkI5yxgG6UIPnI4SHqwsGJuPlbrLE0n19rF5UZk9EEScIfdnn7oGUNEWS3S9ZxLwI18FjEl7NAUMNNijCalTNsaLjeXPHzhIVZJCJb3E+M40jQFmLuGxknarS2VOeJnF++5LhUsfoqQSrgTluVqxWZ9gXEN0+jph34OKhoG6ZKt4F7VKpcS3ZO1ET+0Eu9nyuJvVYBPQYLAtMVYAymTksdZw6okkYtcO9D3co2p4rEMYSJl6NoW4yzLVcd6tZZNNmOIk6fvB/phZJwC0xgI4dh7OkvEM0SVMKay+JTvomPlRMqJTCLERIxVpSHP048Th0PPbndgGLxwnMqgCpsracOZqi5oS/qUn8RuoYs1IcVE8Klcm+IenyYBvxVAH0Fynq/x+rzOacDKfYqw+13bziy31scQpdFPjF42M3yUaiJ5PgmSc6mZ66zknjjLgM/zq58Pvf1pftfv/H7+77/y76L/td+Oef93v9uH9HUnvPJx8quf5jf8p/+AMcqmY8qZ8QS8nuc85znPe3Xe02BVN5rFuuPBC5ciDbOOjEjPYows2hZrDNPoUUqzWi5mOdvkR5q2Yb1aopUpQTg3XD2/5tmzZ2y3WzSahw8ecrm5ZLVe0jaWHAPRj5AS1mouLtdcXEiaXs4Qk/i8UoiyjM2ZZZd5eLlhGkbG2o05Ckvn2pbVasNiuUAZLQt+a9EIWzGMI91ywQuXL3D97I7t7S15GDm8/WWmRvMdj1/mxUVijUPnhMuGlBBpXg1dMoYcM85KsBJWFZ+qxZYeQ7IWtisKY2FscwwiUWA0pBgZ+j3b3Y6YE8v1hslPXN3eSEKxscWra0AnYppQwGq5IETP/uaWp1fPeOfpW+wPB653dzy5u+EX336Lz3z+czx5/g5jikxEphSIJGFUcwVjGltCjkiJbBUpMQNGnSSwyGhNIuFzxCiRZ9qsiZMwTKfs6lfKYGvCaNPk2aPqnOPhw4dzNUgqnZHCQAuApIbdoLBOKldC9PcqcZTS5BRnIAvMC2VrDMuuleurbLbUvlRMZXCjMPNkSWguvrv6GvU9CeOVaFyDNhAj7A4Do490zTizYHJO5TpLMRF9RCtN13QsFh05J/b7Q/FyWyCjtXgFfUz044Htfs8UErEmshqIOaGtoVl0hBSoyPrm7hbvpZdzd9jjU6JdSKiQVkq6Mks4VMqZ1XJVpPptYegmrNVs1gtefOEhzjh2uy3BGR5crDGmVvhUeal8NhIsZlHGSr/rKMnE0+jRGC5WF3RtJxVXTUNIMCZPmKTP1FrLermhddLxGmMkhUj0R29wCIFsLBXvqPKZGl2OISaySnJ7FSZbKn0kmIisyLGkemth+HWxCaSUaJ1hsWlZdC3LRYuzhSPU0Drxbho1kYLU40h4l8KZBk0gFXZRrvGqIlEMpWLJOYvWEsumQEBfDHgv6pB+GOmHqQScy6aF0QatZIPBT2FO8q2bZgqFK4FcOWeSF4+tRq6RVMA8aJzVwgWnRA5+3iQT50EStYgWb7DIoIVxNdoiQXolcKqc3xBloydRv9K0kM1KNnpihjT5sgki52e5rPHO5znPr24+/M5n2Pzhz/BXX/oMf+i3/iKXf/tvwUnn73tpwsd/kD/+n32Iv/Pmh9/tQznPec5znl/RvLfBqjJoYzFFYhdjIpVuSa2MpP6mhA+JMYyE4PHBMwy9SFfR3N3tubm6Zrvdcnu75e7ujmEYsNbx8PIBINUzdhxFamYUMcMUvACjUrNCqU25WK2PUrgoLMbQH+gPPcZa1uuN+KnESErKoKxFW3mecRxLEqiAprZpuFiv0Qmubp6z3W1JMRA3K55Oe5bTHtM27MeejW5Z6oaWhM0Rqx3EAuYah7lYoFcNXmViTljnME4CYnKs8tIa1gJZS+esyqBVYpgiu9st0zixWC4wCbZXNyIvdh2usUwpop0DFYlhQqnE7mbidn/H9d0Vb18/52p3zfO7G770xuu89s7bXB12XN1csQ8TgUTImaxLbU5hmUA8azkxyyyFxxGgqFH3QKj4EMHHQMiREL2k+35FmJKAGrnMK0NZZai1FqlKaFerFcvVEu+nmWkUCbBshtRalvpcOWdyOvouT8Ndqty3SiSrVPfy8pJpmtjtdgAzqKzHppVUjDTWQVZM3hePcj5hiY+SZuccgWPI0jiOtKVTlcqiFmYPJdUgwyjJ2FklwjgWGasp1U4J7w9s9we2293crZlzTUk+9timFDiMkemdt7HGcNgfxJOKSMuzydgS+BRiZJxGopfzWt/T5AMpI/L1tmXZtVhjGfsR1cn7FC8paG0ltCr4OTU4IdVLArgdOU+EIEFT6/WGtm2L/Fg8qdvtlnGMDOVc1c+psuH184D74UkpZfGaW5HaayO1MKvlAte2c6L05AMpRJw1M/tLzgLivSeUoKwaOhSjI8Yg69yU0JqS1CsKkmGQ/tVhmAhRwpYkwCpV46rcH9aUjlt9rLLBYG3dLLFonUEJKxljIudRNmeAcZzIpVZJKc00iVLDOYuRs1zOhTxHIfZJKRagjADyLGFGy+WSthU5+ma5muXRs2JASy+0/Kvne+n0M04xYa1InuVjyMSY59cbvZdk8XhaAXUi/48R7yFnOUap8znPeb558yff/iH+1L/Q/MRv+6/4N372f3y3D+dfOsPf/sf8nTc/924fxnnOc57z/IrnPQ1WbfHggWK73TGOIzklqcJwwsbs93sOh4Ms0BTEGHCuIyfNG2884enTZwyH8d5ixbmWy8tLXnj0Ao2zGCuAOGWYBvEvCnsTSClzfXNLzpmXXnoJhSFG8WMtmnZOL91tJVAnhEDrGhZdJx2RSpEQpjOEif3+wO3trUibs+Hy8pIUE/v9nuurK/b9nkhkcbGAVcuzac/2aY/qIxem5YXFhheWl6xNy8opTIwobcBpVGsJOTJOHussOYh/zGphZpyy1OAgUGjrhM3QCpUSKVHAhkajUTGjdKYpHt0E5DASiaQc6Ps9kx+4Pdzy7PaGZ7tr3rh+h9efvcOTq3d47ckbbPuRpCGmRLa6JJGKpFTHRBpHak+iKonLWeeZraw+T+BkQXpMShFJZf3/kqBcKo3EU9lgjZu9dadVI0NhwavX0FqRcZ72QI7jWDDBMZH3Xl9rY0rK6TgDka9MJK7HD8yAN+csadGFYa3pwRIa1NC2HZMPKNTcS3r0GR5fX3yp92XCIUb8MJAL2DHGzO8bQE+K4AO2sXRdyziOPHt2xzSNSHVT4DCMJXyqhuBYElH6bGHuka2fjzNGpKjxKPPVzs4scmUs577g4kmszHfXtnTdgrZtyDlxfX1dAneYvY9mDvsRoIoqEvISECR+0Dv6vqfrOh49eoQxhmEY2O/3syfTh0RW6t71NYcQac1yueTy4oKu+N1zSsQQ8H4UybnWoFLZwHA0bSvJ0mMgeNkw02SSMcSUmIL0qPajBFRZa8UXqzXWaoxZoI2iaQzr1UIAY0lNrudqmkYmX3zxCVJMs068/pwt13G9xpyzNMX7KkBcjls2UPJ8zVEkzSF4sngLaJpSYZRj0RPk+R+F1Mm4xgJWJOZermNjLcYo8RwvO8Lk2axWxOjnMCYJLVcFsGrQRT4cRCot92kmxcJY55oofEyKTikyBU9MkXSaU1W+J0Rqnwo4Fk+w1BOd5zzf3Ek58btuP8lv/W1/DIBL3fEDP/oXhOVfbFDmW7vMSrtrAP7y9/5lfjA+4em0+5a+/nnOc57zfLPnPQ1WQ0pMZdd8e+gFfLgWlGb0kux7e3tHCIHNZkPjmhmo7HY9t7e3eJ9IZREdY2GvymK/XXR0XSf+RKW42+7Y7baMfipSPlkcXV1dYYzl4cNHPB+ez2DHLyNGa/a7AyFlXNuB9lzf3vHk7XdoGvGzrtZLXCPscLvouNSKvu/x44TSmdu76+KP3eJDQFnF3X7H29fP0WgWTcdlu+K273nW73i4u+NRu+bx+iFL29AaRxc1+7tbASZkYgnekaRQAX7ONjStyDKta9HWkNEoa5mGkT4kbNOhrchtpxBxjcI4jTIJciCnyDgM7Mc919trbvdbnt1d8eT6GW9vr/jy0zf50jtP2I49vZ+YklTvYEQ6qSjySSM9nzNYAAGd6ggeTj2adU5Dj2aGtfjrToOUKgMqYNXOac2zRLJUihxrZhK++HckqKidn6dtW+mdPBxm2XDt2xQAWIHgMd23MkVSgSN+xhgSClEGPLhs6doF+/1+Dj1SKrPZbHj48AExRg6HHmcdu93uXulGTAkVQqkAyQIwyut57+djDzmz73tJf67g2hi6rkNr2O+2DIMwzH3fH72tOclrzH4/BVq6bmvy7GHoCXGi9rPWsDPxE8r71nEiBPmKibHItc2xj1VpMBQmEOnVvPN7alhsDYnSJXXYhEjtHZ2PLQlVN04Dt7d3s/d4u9szToH2/2PvTX4ly/I8r8+Z7mDDe8+fe0ROnVJ1oAUElgAAIABJREFUNVVRtYAFCKlXbGENSAiJBWqxYcEOsUINEkKwYYkEe/4A/gTUaqnoHRJdVIXUtLIyK8Mjwof3ng13OhOL3znXzLOArkJFlkfJfpJnRri/MLt2B/PzPd+pbQklBfzCuCsBg1x+mQL4JNFYiVy51Fc1fU9OnhibAvpEGluPUeVMiBGtkgQxmdqRKmB8mmeGaVzvvcqSV6WD3G+OtpMqoJwzSzleox3b7Y6M4nQciTGhVcZpjdJWukjrjVH93lqjtDDf3qe1/iU14jV2zvLJZhDgmoammZkmz+LjlfRWzoUClM7kqMg5MgdIOdI1DfvtlsZatJKNFGU0MXn8Is/38fSCX8S64ZzDNUUGrYxUXPmED3Ks3geWxRNCxC+xAFh5trS6HHMmyz0JiPRfr8zuOgWk1u+C+Bug9ja3+euaYZn4o+//dP33/R/+OwD8ye//IT/5z/513L/1D/5/P4bwv/7PxD/6Ix7/u38CQPzNXZzb3OY2t/mBzmcNVl+9ekQbwzhOKG0kAdRayDBNs0gkjaE1IrfLBZTO8wJZsd3saFy7pr02BZj0fc92u2GeZ2EZFAzngafnJ4bxXGSWIksUmbEEEz09P0tQEsL2jOMoC6EkNSghJqZ5YVoWlhhIHiY/cxpPZVHfiDSx1OmYNW11XJmzFTiNE/M40fU9gcTH8cR0Gmiy5h3PPPZ3fJwG7rqeu3bDG5No5pGuyD4nkpwrBT4mJIWzYZO26EbRuBarlUgHlWKJEZUSrWuK5DaRciQnzzKdSU6TcuA8DTyfXjiMZ74/fuRX37/lm6d3DGnmaTjy8fjCIcwElVGNpW00ShsW7/FltRhzxl5JdVX9nzUIpi7+Sxqp1iW9uPRK1uCjIgVeA5SupJxKlU7O0u1Z2bfr7scKXpSu/akXBrTKd6uMtKbHrvdSU4OM5Ofr9dVa5LS1v7ceS33NyrJut1seHx/Z7/ccj0een5+Zl1mui5FFfFeSfQEW76W6J8XiwTMC7HMiRXn9GCqoUFdBNqpcfxkJ11ogSR1KKABiu91yOp3k/OR6PijXIxbJe00VvoTk1HNek4cpl9FYU4BR7SoW0HRJr70GiLB48QpbrSVNVxXWuDB2KKkqUurCJALMy8I4TpzOZw6HwxoAZTB8+PhE2zRY50TOWoK3rtnqCnork17TolNKHI9HhmGgbZ0895q1muYSglUl3yJTbttiGSj3bYxpBd11E6Z24Joir5fqH7dKcCGjjZUgN6fptQYlUthxFD89IIqK8rzUXtFVOaA0RY9w8XAmhQ8BY8XDWjdWUEp6oAHXmBKuVdOvFUYrlC7dp3XfKGdU9YRn6TO1xq1gPmZTNn+UpIi3JexIyb1RNyhy8YcvSyAjMmZrDSlljJVzqlRc/c6phJNprUhefMI1JVgS0hXVt361x1WkxYHb3Oa3MRUo/v6f/lP2//E/53//wz/i9X/0r+D+7f/kr/V90vDC9//ufwrAv/frzD95d6uhuc1tbvO3bz5rsHo8nXj79i0xZrq25fnphePxSNu2pJQ4HA7iN1Ua74PIbbNUuxilCqMyFQmhxjXSw/pwf89ms2XxnnleGIaB4/HAMA4ra5pzZAnCpHVdx2a7o+83tM5ijVlrN1R5Hx8CIQqwDTGBEtYypVgWuZm0ePzzM0ZrHu7vpTZlFrZlX2pEzuczMXhUSByfXyRt1Aeenp8Ji8cpx67tmYj84t1bVMw8bvf8ePfAT1+9Efli09C1DU2RH2ktybRZJZZg8LEjxAUVxA/mY2I+D+TkJcSmdpamwOJnTtOzBCgtIy/DkW+f3vOr92/55btveXv4SHBgNy3DMjEmAaqJIi8kSedkDMQQqB2XOV4qXCg9lknVRbgm5STBOQVMUH22xRuqSyJtldBWoHHdewni/ww+sMyLVIiUXtEKeLquK/VAFx/q9Wut6bBK0W02dH1PirEknEZ2u+0KuKxtiux1QfJeNKp4bStDqJRa5ZBt27Lb7dhutwW0HvDe8+77d+VnBeyk4k+U85AhSTiNdVJJMizTGrpUZZay0ZDQRqGTvvgytZYAp5RoGicbAyGsVTwxRjn/XIA9CNikhlmVpGTKZsJ1FU9lvqxtVlBbn8mL51Culyte4cqu55wIxQMcinKiymaV9+LTRJhv6wrDniPnUZjUEFM5L46u64o8eyGhaLt+VUpQAN01S/6b3akVvFZQaayAMOk8zRhTvMPaXoCndeLnNI4leJYgdgKtzcp2r59HXXpu27ZZk4PrvRJLD3BMoVTbCMBc/EIKVb4c0UYAWvUB6ytpsEY2LepmiyKDERnzdWCXPFhJfLNRFBQgmwtkRQKsMjhXqnCMxl1tWDircUaY0jWt2mo2vQQaGWPKpkpY067rcwxgtUYxs0TxorZGZPAx5gJSKYxrYFqWssGosE6SwOtxaK1RlMTpq9+vG1fmtyzHvM1tAI7zwO/8b3/Kv/FfGf7N//of/rW+9kll/ptv/vRf/IO3uc1tbvMDns/6b+8/+ZOv+bM/+8UKImKM+HkpnixbFiCGtmlwV15Aav1Hzmij2G53vH79iv1eqjpCWHh+OnAaTjwfjxxeXiRghBqmkqi5fm3b8urxnv1+L6zI7Dkdj8SrVNOUYQmBZZ6Zllk295UmozDOYawmxwsQMtYyzjPn85nz+Uzt5mzbli+//BK/zMI2TBNh7jEpEeeFlDNDnFii5zgPzNMsYPd9ZoPjod9yt9vykzdf8OWrR3ZNR+9aNm1L4xrapqGZTsxx5ttv39K5Ho2+MFhG2OuYIonIaTjwcnrh48sHnl+e+Ob998w58nQ+8PbpHbPOqL5hd3dPNJppjpyXSeqBij9RmEsNPmISoESWnVVhZRCmRdg0vYKF6q+8DlW6Dk36zYTcypo458q1YV24XzOuXQEt5/OJmAJ939P3/SoVre+ptcZos8oSc85krei6Futc8RpqNn0vvsXGMk6e2c8YZ2hUQwoJq+167HNJZjXG8OrVK7744guWZeH5+Vm8ktstMXhewsL5PK5+z1zY4wxYZ3CNJcaAsYb9dodWdq1zqgCgMo9t27LtemEmTZF/lkKWmAVc1uqetVIk1zRbJwxWimtditaKtu3QWkB38J5Q+lerdL5pGmq9yHKVllwDqIyVCqEETOU16jEn1CVgSyly2cxIaHLK7PdbrLWEGAmL53QeVgly1wlL2ZfU37rZUL8n6r+nwgLXe+I6PbneU1U2LN81LbMPLEHCoChJs23bMM2ThHz5gFaKtu8kpdmCiolIlP5Pa2muNlack/eblxljFF27wxlL27WkmDhyko2rGIlJlAZZKQlNs3L/LyF+Im1VQsCiDXL9zKUqRlhRRSRirKbtWpRSjKOcP+ccaU6EXEO/aqdw8VznVBLSDbvthtY1a+Kx0YZcUohDeVasNoBeQ8wyUc5fkrCmumEgz7FcH1M6qmOI62ZJTQH2UWS/zhmsE7lxTVuu166+HlnUBRU4y+d39N32/8tfQ7e5zV/L/KPv/ph/xB//TR/GbW5zm9v84OazBqvjMBQvX9Fz5SwMYc6lM7IAl5TAS9iGNXplHZrGse22vHnzmr5v0Ubx8vKRj09PHF4OzEvAh4RrGrQxpYsyikcPsYDlnAjeE0PAOEnurBKfurCti/3631fJYwUmIusTVqvu7o/jhCmLqSp7bXJmmEfGccAY8ZjGlNA1ACYmQgyM3jNpTQiRJQdC8HxYTnwzPNEdGr6bDzw8fUeaFloUrXV0rqXvOu52e1nEaaknyaUjcbvZ0PUdoTBCk58Yl5HTdOZwOvD08szz+URAKoU2+x0Prx5otx1zDDw/P+OXBZVEEpikdYMck/SnZqQGJovkMKvC6BR2ThfGTiHdjb8ZUlRnDdtZfWhxBVm1mkZrTde2bEoK6TAMEgLk3JXsOq4y1erV9P4SwKKVMLeqeCpr6Mt5GNBKse1buq4VBn6J9P09+33DEhaGMUhQkIUYEstU+ztLoA1y/6aUGKeR9+/fSZcpSbpgreXh1Su5FsvCNM1M81TOh6FtWlKOKzDLWTEvc/EBqvV4tdb0fcem7WRxby26BPHUc59y5nA48PT0xLIspS5EE3PEe4+1FxbMGruCexAmb1kWzsMgz0WMdF23JizXTtq68bAysKWreFlYq2yUUqgQinT2kmgr/aQerRVNI8+DKinLh+OR4+FU0n8Nm82mpNC2KziuMu567U+nE9MwrOxmlehWlUSdKhOubLxtXUl+Fo+sSHQdflmkDzVG/CJed+ecMJ7Id5OihF+VADH5fsmFoZZrNU0jkJkXqb86nc9M8wxIYFYqz0I9bgG9kWkOIn2meG8r29k0NM5h6gZQqdUw2uGcbBZQOoljnEqoXPWpXryhRltCDFij6TphgI0x0jHdOAleKkm9OYvEP6fEMnhcI1U2MYG2NUFaVCa5XHfQ5CyAtOsakesaCVWKkfJ8eciiElAlZIkr5n/dNKxBZkUlIZ/HYK28r7/JgG9zm9vc5ja3+cHNZw1WpcfSUxNsdQ0KqTUfhYGr/iRtEHmYNaufcfITv/zlL7DWstn0KC3gMiMg1diL/HMc9cqoqMLQqpyYhhGjYLZWALPWWNuU9GHxEC7LzFJA1irJK+ycrFJrR2BlCWHyHucaOmfXRf4wDhynkbZv0Kbl7CdsMOtnjtEzj7NwwMXLl0gsSuSbWSc+LAOnuBDnCacMhEj0nrbteHz1imEYily51kAk+q6l7RpCDJjG0m07NrstEwtDEzhuYNSWrBSvHh64299hjWaaZp6fn5jGSbpdlXS9qlbYnK1ryClwPA5MU6nuKGvKtQIlZxpjaPtWAEG+eEaBvwBar+WpFwlphiwg1llJka6MqfeheAH1CgCFDW3We+hSUVIXvIoYkjA51q5+0dqHejqdSMlzt9+hFEzTyOObN3xh3/Dd9++EYU/Cps4FxFQAFmLgV3/+K75/9/1aN1MX18AKrqy13N3fs9snXl5eWJaFvm/ZF3VASpFlHiEnjJZwnRylH9RoTeMsbd0EUAqtwRpF09TwLSX+blhVCVprtNGEAl6ca2malk2n6bueu7s7drs9KWWenp5KCFTDNI9MRXJfmeT18xYpdb128v8lEKec61DCr679sPV6SMXN5bWrR3ZZFlLMhWVzq5T5+p6pYLQew7W/uW5S1C7a2tF8Ldk2xuBjIJ1Za4mqqqNKzQVAXgCSj2F9ruQ+Nxh1Cd5qXVES0NDT4my9xyLzPHI6nzidB0KI0h1bJNzOtbIZoaqyIJCVwQQPhRFtiqRZaY3SwkBylSxsrEEbUzZ2JuZ5QSlhpeu58l5+TxpmMn3bFMlyw2azoe/7IkHPRc4vgVohZUJJKtbKkLJiCYmQZvkOFHMBOSeiD6tFojGWpulJhLJJJ0B18cLo60oZI2FmUu1jJZSreO3LNwXUpOGkRPKeM/Mi13/x81/lr5/b3OY2t7nNbW7zGcxnDVYFeNg17GX1V0FZtMnC12pN2zjQsjgVRsgCpa4kRvb7HSklzqeBcRhYlgVtHRld2EVD8OKrbJxjt9thrUFrqcqIMZJiwLoGbRQxBcZpZBwnxnEieI8iowpLoEpgkPgGK/MXCwgrPIiS6g1tLRnFOM/Mi6frO+4e9rRtxzSMwpoZKyEswaHmmbhI96cxFp0yDoVzLW3jmP1SvLoZ8iIS0RxxKTEtms1+g58y00kWkVolgh8Y0ki/aWk2HXbj0J0lq4BSjnCGXBi/u/2OZV44n2eWYSQsAZOlCgddumW1wThL32+I3jOMM1ktZK3QpnSsrgFLBQQUP2UuwAEuYOX6ngAuDFYJsYkhkBEZcF8qR06nU2FVM841azdn9SdWELN6Z+t7aINS+orV02sKrtYa4yw5BHLKbLqOftNzHgZySrRtgzEFAMdYGMfqXdRrwuqyTIzjQC5SXOl1FUYMpZiXhaH4orfbLbvdjhzFV5iCpFDvtxvarsWHxOl8Ylk8sQATrRSNcyL9LiFHOWf8vEgPaNtAYY3v7++x1nI6nfDeY6yh6UTmudn0bIrss2ta2kZY2uE8YpSm61r2+zvmZWYYBpZlgpwJy0yKgcUveC/3fGUEayqsMSLnrRLSa5l0vc6SqZvp+r14vJdlBZ7OWZpNKynX5V4I3pMKQ1vfK4ZALP9cAaqxlpzSKv2uEuKEAKBcwKgOAZsS2l76e40RmXntYJZ+Wgpjqq78mFfS9VLVI6z65XM1ztC2ArQbZ0g5Ejrxa56HQQBhpgQ4iaReK42y4iX1MUO2hQXVq4c4BNmwMEpk34XeJcREmBe8XxiGEyH6cg33Us2lFKfTiZcX6aPuuo7GOfH3NhfJ7TiOeC9sutiXdUldl+upcsSpWrGkSKWndRhHvJ/Fp6oUMQa6pmOzbTHGAap4VReGYeR8GmXjKIpJQ1hfjdYWbXT5LpXzmXJaNwjku9sWpYsv3yN/1b+BbnOb29zmNre5zd/0fNZgVSu1BpNU/14oC/W6aw6sHrcq94RPvY5SUxLw84FhGEkpkLPCx2WNos1pwRnH68dH7u/vSx3HwrJMpOjZbnr6viMmmOeJYRg4HF4knbMsxAtntHrEnJMAmbZpaNoWpYTZqwApq4vMcRhGYV3ahi9ff8F+v8GnyHA6MQcPRkOKRDIY6ZTNMaERL6jCYCLomPExEqIXX2iOImfWYJ0GZ9g+3jMcBs7DjErQdC1kT0oBTMY6VQJsMsTMMs74yQsTEjPTeWIaBnIMzOMk8sMiLw0545eZ2YtcemhbtILzMDDPQXyIsXRk1jCWAmAlzVQY1etu0no967W8SPzkPJMDGLsGJlXf6jiOgCTvGmNLb2gurGJiLvLc6rOU95ENgLrgr0xcLp2QtfpEK4Wzmr7vefP6NX3XcRoGDqcjp+OBEBPRi/zVWltklpFcOmGr5HgNkAK6rqVpe5F9pguIHcdRgnKMhHvllMTf6BpUkpqT4MWLWcF417Zst1uRzoYg6cpao0qAkS33WijPlUh1xTvd9R27ux193xapcYvTUk0yjTMfPnzg+emFGKOkWxe/dd/1eD+zLDPeL5ynsdzrEm5Tg27meV5ZVAnQqaBOrm/1OTZNU2pWAKX48ssv0VozjqMAzWVCZYU1jhgT0zQxT5N8d5TU3QpYp2lce5ZV6SOV+6CkNI/jyoDXsdZKCFRjME5Cl4wWUDxNCyFcfJ1y74rs1eiLjPb6Xtao1WKQc8L7VNKGW4zVPD4+yGsvnsPxxPffv2OYZrQWdjalxOQDGtZgJVLtEi2hZEsklHsqpURuRFqutcYH8V9P08w8z8ToMUaj1LxKkbfbbXmuLhU9zknlEFpxPp/XDcGqWkgRcmFxrXGibHAO5TMZuSfnsOAXzzSPjOMZpy1932GswuiIDwmLJifwPrLMkRikdmsaJ2LKoBVGG6wTdliX79qqrEk5klKVBSeilzTiNWDqakPqNre5zW1uc5vb/DDmswarspC6LDAUYABjJBlW2ChVAF8gxizdfQiQqy18rmkIPpFTwFpHShKck5KnacXXKuE7HXd393Rtx+l44OXpCWM0j69f8ZOf/BjnHL9++w3npxOHw5FxnEkxFiZOuj5F8qdpnUjmuq7HuYam665qTCQlN8RLwM1pODEvM23TcL/bo2LgfDqTvTC9rnECLFTEdA2NUizjRCIVabItEtBMTuK71cagitTXWcuu6dh3Gw7vP/LyfCQmCbPJRkuSZ4pkndFOulW10aScefrwzDwL6/z96c8x1tJ1rfh8C4PoWkdMihQzwSfiEggxcFwWbOvwOZOKH06LGfgqbMbgtEElSQmGy2JfqU+DUq6ngskUE841K6O6LLUvtaHr+tXDCFwktzkQwoJS5pPeVPHVilQ8xCJfzaCMMKpt0wiIL6+fgcPhhaXU2gBsdzvMsqB7zbbbME0TT09PTJMwcCkldrvd6pnMOZe+38IWFU+00kb6bpeFMQT6rsOaDX3XCnvrA9MycRpGdAFgwAr2Nl2Ps5bTvDCPAuKM1mhjioRcczgc+fjx48p+9n2LsYbz+cw4nqkVLQaFzjAOI8fjiWWJtH1P123Q88K8zMUDKRsIIYjPuwLhnBXWVvm2pDTHmFZ5fK08aZ2l6xr6XmTcNaF4HEfG04ndbsumldRlTWaefZHgCzNbz2dlZ+W+MaTkVtbNB2FUr8N56ubACvCv0oKb1rDfb8rPN6UjeGYcJ+ZJvM4pRyqTevFQQs4X24JIYOvPyGZQTGn1wG+3O7q+YV48xjimaSYkCX9bJcber6xvhjWIyBi9ekavQ6LqBs3q7YyJFOpmj1R0KWRTpN4LQPE796t82Lq6SSSgdppmvA/4cm3JmowuIW0NrbEC4osdQ9K9IyFKn+qSZpYQ6dsGjcNoD40FrYkx433G+/odadZnLmUIMaNCwtrL/WaMdMtKVU5k8Z4YElXFopReN6Ruc5vb3OY2t7nND2c+a7BqtYWUSbEkTColfX2IhLTvtnRdh1IGH/wqKwNQRhIhu67l4dUDDw+vMFqTYubl5cg4DmgrgTG2sBZd12OMLlU2R0II7HYP/ORHP+Hh7p7D8UjwGT9HwpLQmJLCVFJnrS0MEit4EACRIUfIiq7vaMtn0EZjnSOnxHkceX5+IseM04p5ETnjtu9xbUs2EsRiCk0665kUEmEJQMYg0uS0RGKWfkaMkkoKoG0a9nuRUr5//6F0xDqUshJQZTLGNWhnReaqMiF5lJbOOGMtVhumYWBeJlKCdtNjbcscJ8ZxxpjCGodcGETpdUxKySI5JppGZN0xRGq06grgsyre0k89qXWxrUvwizJVkilhLcZYuraja6WuRBiyhDG2MEgCBiqYiTFgrUIrgylsZeNEvltloihJLa3eQ6MMjW3oWgc5M6fEeZj45Z//mq5t2Gylwub+/oGYEqfTif1uy49+9AXH5xe6znJ4PhTWVBhV6xzztDDPI23jiClLwnOQ0JyqWzRaFtqqeKSVEZZPKdg1lqZpC8lWwH0JWJqX4kfcbMlKM5ewJh9G7Lig9GFlymqdyDAMzPNEViJhl/OcqHlYqTCw2liSAl6eUEqz+EXu++IB9t4zzdMaOBZ0ItmiekCvzB+IfFbpWmUjIVAZ6ZY9j8MaiDWMA+dxS+NEDjwvM8vspYM2JqyTVF+VJThKF6l9jIEQM7lsRFACkqoXVAKjhEnd7bds+g0xReZpFg+3scRIsQNMEhoULzU6a4haYd59ENuAsNbxsvEiggJiks0qrRUxwXmaeT6c+PD0wn3aCUs8z4Xlr/5cL1aIJNeDrIs/k8KCyveMVOpcPqewn36tQMql9shoSVqOOWGUo2kc0SesdRgt4Hez2Qrb3zSFEY4Y43CuYfEBYsYIzwsoYpINqHmZCUq+l2LKUqP0iZRfowyr2iDEwDCYwqS7tepGmeKvzakw7PI+GWGTc4IcMyEHglconeXeMorGdiQdkQCnTLaZTb/5a/l76Ta3uc1tbnOb2/z25rMGq5KXkYWxKcFJlQF99eoVj4+PWGs5vBw5HI74UINyEjlH+k3Hmzdv+Lu/+zt0Xcf5LB6oruuYppmM4nA88fa7bzHG8OMf/1gYjXFkHMeV2RnOI8u88Hw48P79E+MowS45FaBaFpVaXypS2q4T/yEQU0ApK8eVPGSpXRDZp7A4RkNcpMZj8Qs+BDbbDdv9jr7vmb1nXhbmaeF8GplCLP2KerXAiqxRQEYxzV7YSCVMqwSNSGdl6zT7fkNKnmUJ9BthCnONFtUK60R2F4ME+JjOESZYYsIl8bEZU6W7skClgiplJGjFSwiLKr2PWmsBPat0TxNjDU7Sl1qgdOliFbbMfuJZrX2YXdsVL6WE9rRNFgYq5RWoVtArLIwhp4AxtaqlnkLpm3TW4Kwswq1V62LbaLl+ufS9ykJcWLLNVq5/DJHFL/Rdy5dfPPKw3/Dm1R1/8Ae/z7vvP/Dn37zlfBZ2MvhI17bc3+/Y7/c8Pb8wjDNNalFaUpOXeQYk1bZ1Da6RehODIgSRcTpni8Q84qMA3cqUDXai7TtSlHti9pKIa0xNQs70/Ya+lzqaGrDj/UJcwZEArFCeL600OosPdCly0HzF6F3CjWapeYmZRCIX5UMsFTYVLK6+1K4VaWi5xj7Kvbp4CVqLNsIZmsavbOuy+MIKw65xtF2H1rYcdy6g3TPNEkhVQeZ1/2YIgbZt6bqGrmvZbPvyM4rn52eOh4mTFpl7TOKPltAutbKztZdXa1EpYBQhZXK+Cpcq9oVU2NSkpI7HH88s4RvOw8Cr+z2gigLgEvgksvFyfpMwlSqD0aokTCsBnKW+JcS0Ho8kkCeUkiodlCgFFLLpt5QAsq4t519rcpZqH1XSiFVShBCZ5yJ/VhZr1dqbW3thpZ4IluwvG05X1oxLwJUumxKZ2S94X5QyWlK40Vq+P6zGKLsGb5HrppbYLlLKUJ7z6m+v73st73fOsSmbEre5zW1uc5vb3OaHM581WNUlpEhpSTKVBW3Dz3/+M37+85/TdT0xBrbbDXd3O/E4AaiMUpmHB+lHbV3L08cn6WRcIsMw8vx84Hg8cjweyTlzd3fH8Xjk6YNnmiaMcXjvOR6PK2A6HA58fHpGjgSpXymLppwTMQr7a53DGV2qajTGuuKltGgtoLvvOzabDVobTqczyxwKStQ0TV20Wza7Dfu7O6Z55v379xzmE8fzmdP5TPSyIK0SVWuMeOGCImdZlNYk2OtAoVAqKpqm4e5uz+HwhJ89bXI0TQuosoCVRXDf93x4/7Ie+6xE2jkMw9pNK3Udjru7PVpZvv32O6ZJgF0scudaIyIJz7nIufMnAUdaqzWZdg00KpJOZ5vLwr9Ig/u+F8YkqzXZtSYre+8JKa6AtYIppRQ5scoHKxsYQi7MvPgOdZGDZsTzpo0RkBFzYW7NCvrkWOW4m6bhpz/9CX/npz9Gq8TDwytiiOx3dzRdzz/7Z/8nuoQBDfeRAAAgAElEQVQ4dW3Pbrej6zp2u4+cx5nFR7xfmKeJpTCoXdsWObgkvlYpaJWxAuQ8cZ7GFexN84haJsZ5LOnBmaUw8TlHtHZruqvI1S0piS88BPElTpP4s2cfSApiCbJRKaPJhJzWzQWV6rNw9VyUB7n6C03Z0ElJ2NbKZlcA0zmHMVpCjkKisYZmt70kNmdIPki/67xAyrjiybTaoLNGlyyjxXuWeSF6j1WapBU5LKIWCJ6ApH6HZSH6GZW3WKNJJThIa01jDNEH5mlmKEDw0ucsn7VW/lSZvzFG0niRDaTgQwlaA2cMyjlSNtIxW6wMMUVaZyDFtXoohEgoAEye31TChTQUljTWjRRjSs2QAPSazKwwK7gzxn5yz4NCZWGJvU8YFdhudJHNN1RZ8zhKbVKMsfi8I1krrHU0jeQJiBohrNLnWDYLKMdWz9WqlMhIZ6wpwXO+pGJnhWsabOMwRsKuoIBShEmt/uZsIsZwZQX5dONE5OhhfSan4me+zW1uc5vb3OY2P5z5rMHqdtuz3fakJPLR/d2eH335JT/96U/o+w3TPFF9djkmgk/YxrLZbGgaS9d3TNPE+/cf+PWvf800L5A152Hk+fmF4Tys6afzNIvsLwQoi51cuxCBGCIvx5cVtKh8Sfok1V7XFucsFCDW9x1d39A0DV0Jq6neS/k5hQ+R07kczzDRNA0PD3vu73cCxkmM48jbb9/y3fffsxQvqDEGssJZAZhWK5wTb5fSG3JOjPNEyhdWw3sv/jcpMMR7z8vhwDSJJ26ZF4y5h6xW5tOaht1ux4f3z8SYpFtRQczi+1NaPMQhgFJZQHjfrv2NIk0UAL7dboHMPEtHaA3AqV2W1Vs6z7U2xa3sUNM0KPQaTFRBWgUG0ccVNNXFsTYalS9syzVYNVauVQ3nEukxJdRoIcaMayxVklwDoOS/rwFeGa1Eap5TLJ2bisf7R+72e/E7v3rFq4dHfvnLX5Kzpu835f5MNK7FuQbrxKv68OoV3Ub6Q5+fxf/aNA1NY3HWSeBY6VYtVZ1yvDGyzB7XZOw8lxTfDSlFxnFCk0g54oNn8TNt061Jv3d3O4xxhBAZx0XCdKyhaVwJ/GrlGkwTpqa/XrHe1/LOnCWtdt14KGm5AlrKpkPxd8cUyDEXGasE+sSwEFOLNiJFpch627aj67r1PSrjOI4j5/NQgpMyWl2kxbU7tcrAjTZYbXHWEqL/JOio3hfee86nM0/LE9M0orWcB2FPdfHgenJOWCN9uOVVxIMdw7oxY50V9lLJBkxKEGNgnhJN67i7u6NpG4ZhLOdKMU8TXeNkg61tBRQqCRWa5oWXl6NUDeVLVUtNHha/dFpBZfBBqnYw1NRnqfix5bm68vbnQO0pPp1EFr7Z9Fh7OZ+51NMorTEWOS5jsFa+05qmwRd/soRqKWHQryqh6rN++Xclic6NhbaBmJgXX2pqWOuMJAE4iookXTarQgyFyS7PppJnPqeED555nrDOyfeKVsVXfJvb3OY2t7nNbX5I81mD1b7teHX/QNs23D/c8eWXX/DwcI9zhhgTXWOJUeSFzkkS5WazYbPphWFcFg6HA+/ff2CeF3KCZV5IIdE1HSSYtQDUcRgLa6LXkJ5+04PSwsh6TyF2iClRIY6uqAGNcxrrBDRutz13d1ua1tEUuW9lTDKZkCLH54HgEx8+PvPhw0dijPz85z/n8fENSsPz4cTL4YXD8YWXl4MwK9qiVFq9iapKD3WiaQxt23H3cIfSiu+//57zMBBTRKHwPjDPizCNMTOkkcWLv9M1pjCDwmJYa0mw9s4K63ZhLFLKV3UgwkhmpDdxmQNKUQDpXDoihfnx3q/s03VysykeX2FFZFFZA3Iq2Iwhrv7KCmQBYgirX7Mu1lHlqpTjrf9fGVlhnqveuabyqtW7qZSiazvO5zMhRGymAHMDKhFSkvtAKfwylwRW+XV/d8f7d+959923PL4Sdj8l8Cnz/t0H6YjMkWmaWBbPZrctSa0L52HkdB44n89M41jY+U3xqCq0tlgrDCspE0PkeJa+TK0VjWtXwKCLIkGY/A3eL5xOit1ux2azY7+/Y7vdEHzkcDgyTZLkWzcIqmR6s9mQlcZYvwYm1Y2G30xtrucSIBegn1OilKoQk1+BvrOiPNhuBcArLc+xLkFcTevoup7tZrcmugroi/L72x0PD6EkN6dVMivnVcB+/QxKlRtCKVCy+VWTgmuQWwgiN64JxjGKukB88ZrsDDkVVjXLZ3LOrp9ZArpSkWQvJC8bJ1pprDPkKAArBujahrv7Oxa/cDqfxTu8LAzDUM65BD3lLJDTOVFnhBCYxstnQwmAl+sR1k2ZKjeWeihRfVwnLjtXGWCDUtWbLF55X2THxhTvb6kOqz5rtcq9Kf9/8VaTM6l8zykR1q8bHHW0VmhEHiznX0m6ddvSb4QZHpeL1zznysqG4vst91dJK78OYVNKoa3GKoMnEVNgXjLGiq/6Nre5zW1uc5vb/LDmswarbdvw6tUDX375Ja8eH+g6YcNiDEXGaC79e1pLtYc1LMtc0kxHhlGSK5umE48nnq7boJRimRZenl84DydQspNfvWJN25SkzlTAj8JYiy/gzRiDMxqtQFuD0hLwobWibVzJNgqARTrt1eoVSzEzjBNPH1+Y5oX37z5wOg50fU/KMIwzh+Mzb99+yziPKCPeMKVERpzlhXCuyEB1JhLAtBin6TcdtUKnMi9GmxVkkMvxkFBKAnpso3GNI2cJinJt7bQUieNutyspsEsJQRJ2JIZIygIY9ncb7u7u8EtcvXPi67OfAM8qsaxTPXnONavMcU3nvVqEVhBWX6uCT+9DSZeV61TBTpVhrrLTIn9WSksIT6rhTQAJXTKNtDbc3d3Ttu0ncuQKtAQcicTZGFUAnloTfQHev//A+Xzk12+/wVnLl1/+iM1mJ/U/Cs7ns4B/Y8lnOByk13KaF+bFcz6fWeZZpL+lkzRbw+wXGMDOtrDhMy+H08pEJxLTMjFNIzlH2k6k3pttRwgLXd/y+vUb9rv7AqrEo+qcpt+0KxCqCdvyjCXxSaZC6mW1hiRd+1SvgWtNeq7gSSnxdutS8aKUpCk3ztL3Lfd3+3JtRELsXCM+0sKqzktgmReWaRbfZJUeK0XbtGz3bQkU8hwOR1KQZ1kbKx5IIFUfZ9+SiOLjLQFfVWaccibFeGEES09r03Yr81/vh6oGqDLy2tkKIn03WuFKV7Q1BpUp4V5y7RSw7TvIiRz9Wms1DANK6VXKfj4PTONMRK2eWxmR05IzOUR8CU+q51z8xaybMjUAap5nchTWXHzL4nGelqVY1eVndFEU5AxoAcxtJyoOYTu5qhQL5TmS8yeduZ/Kf3WRRlttRNGg8voaOSZUUri2oes6slZM87J6YeEi/ZfXLBuF6mIluAbEcAn7qsf3m39+m9vc5ja3uc1tPv/5rMHq/f0D93d3vH79KAm6SuS42kj34jhJKq33keF8xpmmsCMSBLIsUlOy2ewwxogvFFlAjSVEqWs73rx5JObE22+/lZRca/E+iCSyLHRzjJKaS2EYrKEpHj+tFE3xEu63O/quI+fMNE+02w5jDQlJlDXGME0jHz585HA4MY7CrOgSHvTNN9/y7vvvyUpSNWPKReZWwlCmBVWYFsoiMMaA0cU/5xfOw5mcsgD1UtXSNiIRTimtIUc5SsrmbrcjJI8PUhURQiKmUXprI7StsHpLSdm1tkgyEZZZKdjtttzf39F1HS/P7zifRwEdpeam6zaFoVKrlLcunq8ZurrIrpUlVWJYpy7EK/MnNSGxeIaj+JuNXV9XlQ7KmCI+RmFZc8YoAVx1UkprvynkIoUUxu/h4YEYI0/PHyQQpwQbGaNWaTcIk7zdbnHOcTweORwOWKd5/fpRQn9yput6QhB547J4fBDQdTgcMMYIKCv1K23X0aydsZPI3XNmmiZSeY15nplmL92WbUO/lbokSGijePXqgR//+AseXz8AmXfv3qG1pW06vA8Mg9SbqCK1bly3vsc0LeTsV/9kihKUlUrokkh8019ktkrdTWW7BYSWUJ0Mxji0Vmw3G2HT+o67u51UxqSAVpqu64v0V5VNES+fdZpYlrBuhsQYmexUpNUN1lo2fU/Y7+UeLaFaFVRba2hb6U6t91j1MlbpcGX/a3hS0zQobVcv9LWcv/5cZXRrB+k0TbKBUOqIrNFo1MomLpO8j7WaqbCI9fyllNbXqveJSII1fdfT3vcrmzqPZ2K6dLkmJcCzHntNzZZNt7xuNI1+XL3CF3ArUudY5O3KSqqwKmFI2sjGRQgXUF49srIBVT28/hMv+urjVQpTNq9sYdCrlFmXftTz+YyxBlU2maqMvHrXdY1Cy4mYxc4g+yAKlS8dq7Xz2lrxlVdgfpvb3OY2t7nNbX5Y81mDVaMlxdJ7WQg+Pz+JT9IISyPMhlTXNE2HRpcFmcgeXdsyL4uU1mfIWToCh2HCL4EUIg/399zd3fHh40fmeS6yPUnObdsW66T+RNbmaq1xsIVJSymgyOz3Ox4e7tmWBGGtNQZJEB2mkWlZcM4xnEe+++4d4zjjbMs4T2SlSAomv2BSJGVLDJKCapz0Fa5BK1YSZ3PO5FhqVQw0zgqza6RqZppmUk5oZQgxMQyyIK+sZYxScaOtJpHxPhKj52jUyl6HGNDKMo7jCgRTVlKtoqRCJsRA2zlM41hCYHp+xqeITxGFRimRJr56dQ/U+o/N2v+4SgiNKYvcZQV/NSBlmqQCZZm9gJGNAN8aGiVFIAm0+NZCuIBhrQ0+enypNKq+voxZU4tTko7MZZEUXEogi7wGK5CwpiFkjyn3RtM4+v7SnyuMrNQDDcNACIE3b37Ez//O77Db7dYFu4Q2wfPLi6RS58ocZ3xhgeprhhgxtYpm1rStLNTHaVzfw+hGGEEoDKthv9+DugCJrutRCjabnnGYkc7RvPo6azJw8GENEKoyTKWkgkiOVcCfcw4VM8vym7UkpqQKR7qu4e7uTgBN8VQ65+i7jk3fr95MpRSbrmOz3aJLF6uzDq0t4zhxPB55eTmI/zRI6BewMuPLItL2CiIrk9aVTaMasCOSYPEYd337CRNegW/952mSUKHNZsN+vyemS69zvfdqIrFIrjW73W7970staGETxc/6F/y0JXWZlGlds4aLWWvRBWhqLSFcIsOtCbmyedQ4hzOq3Lde7m0EmFYFQg1SsgX05bzaPgkhcDweca6hAsB6JZUx2KahsUbCjgobfP3c1k2m+nx4/+mmk4wAZK1Emly02CWQyhBjIJaO7Prmy+LJyhf58MWHLpLyqoSQ10al4pO+pImnJPcASL6AdRpjFU37Wf91d5vb3OY2t7nNbf5v5rP+2/vb777DLxNNI0zVhyfxnhor3sztbs/D/T27/R5bpJ667MiHEBhHWYgvITIOI8fTiXGQuoucMtYYJj/xqz8/8vH5aV3gALjiBwO4v79fWQ5f5G5GC5vqnEgYHx9fs9/v0EpqJ8IiVRMZVfogF07HE09PLxwOpxJ0dKSmyBqjSpJmlERQBdpqasqwKh2WWmmWMLNMEylFrDU429JtemE/fcC/HASkhFSYkLj6TetiGCWer6wU4zwxDfJ6Eg6l6bpmXcSGENcOxDBPK1updQZMYYMyc+nVBIr0NqMVWGdQmkv1SQm3qV42Afd2BQzVq1cX4dV/uOk3xWe5XVmjCsZ+0z9ZWa+8HuunLK7Pnsa54uX1eL+Ipy1nGudKTZIhxcj5dFqZKqXBJE3bOh4fX/H4+hXb7Yb7+zvOpxFrBfQ1jWyk/N7v/QG/93v/UkmePohskYvs9NIXK5Jqn+LKrkGRUBamOa1S21z8pdKfGdRC27WEGAqjZ1b2ap4n3r59S4geaw0vLy9M40zb9sSYOZ8L4DVyjoNP5EyRAGs2m01hF1ue0gun84lUZNyZjDWWfFUxlEIgK3l+Gmu43+/o+o7T8ciyLGz6jt1uS9d2bHdbnHV4H0jlyXOuJWXpsJ2miXGcOJ8GhnESNjerAibn1b9siww/5UTw0klaa3FqP6cxVsLGkmIJC2pRRRVwkcfmVMLTysaDD4HzMLIUOXSVq1+DtQqUq9y8emQb5+i7XkCyMZRbvvx3cWVfz9Ow+otN6c8VYCYlqhKQJiqAGC/1QcKUXpJv6/Mm303yWSVxu1ZqFYVIliTs9Z6L4s1Nn4BAPvE8EzUxpZXZru95vUlhr1jPWJQJ1Tsr6uCL0iHnTApR7BsKjLEoIilEctls8jEWJrcC4WX9XrbGgYJGWwGrReJM2aggZYzSpCyJybl0PJNvMuDb3OY2t7nNbX5o81mD1dPpyLvv3xJjYvIz1XflQ2AYFw7HM09Pz/S9LKhVWRDVgKRxFKnb7MOltiVI2FDXSa/k+w/vOZ/PV7v3ek28TUn6Le/u7tb/fhonjNU4q2nbht1uw5s3r9nv9xhjJCglBHwINLZFG4s/h7JIVYzjVICGML6yuLVrh6zWGlMkk6HUMQDFOxiZw4yfL0xK4+zKKE3zhPdxPU+X7sP8SYorIKmsRpHJZXGfsdahlOF8HgDKObJYm9E6CBMyxCJLVmtHpjAaAgoliFc2AqKShbmk0g4sS1h9fVpf/KvWunXxW9mg1Rfs3MqK9d2G3W6Hc25d7EsYTlqvO1ySR2Nhnq/Z2/XPcxK2NV6CeTKJrm15/fhI33fM8yT9vkYY8s12i3WG5+eP5Jz4e3/vd/nyR1+w32/Z9Bu+efstKULTtPz0pz/jcDjQNM16f07TyPPLgcPhKHLzEl40z55pmsvGgkcVKXRlk2o1CoC98oiKb09LeZCSMJyYAjlffIt3d/fkHPn44RmlMylKfc35/Iz3fk2vXX9hsNZdgVTZsNhubAFLsnmQK9BTmoQETcn5L8yaUsU7fqRpLG3bsNm03BU2VbyTAkgrYy7hVYphHHj37j2n0xmFbISEcOny9MvFg9g0jbCcKRX2d1oBpbWWxZdkaK3RWhK6XbECVMlu3RwQZp+V/f7NYKD1GGH9PhBv5mUzRMLIHEabkty8oWkc275fE41TiusmS/VIS3+09LzW11JK46OEosmx1qTdi0c4Rr8+71pbhG3MkNT6XvWzy+sqYpDvhFpNU5/fVUpcgPCyzMzLpT+2fu7r/uPKwqeUcCXp2WqLNWXjKIvvF33xn9dKG4G2iqwVOWlSXjDGsdtteTkeS2jUp2nTKQZSjLRdR9c163OxbkTFSKQEXkVhXnP5/eADt7nNbW5zm9vc5oc1nzVYdY1miQtaG3a7Da5pQCmmcWIYJ+YQmU+eD08vstAq4K6yatcAJgRhb4wx0s2539I4x9PHj2SVsaUm424nMk4BsAk/T4xnw/39PTm32FKTYKxa/YwAH4uM+Pn5hfN5IKNoXYvrGk7DuTAvDjAY7WgbQ9f1WGtWaW7TWnKCGBZZ5KUk1StKsyxzkcMmclmA5iQMRfAe7zVKicwx+FikfrKALevrT/2hNQylLDqb0gMrUukJYzTz3KF1LMyMpus7mmlimdMqiRRmxKxARV5PY6yVwKJ0qZmR8JyEXwRQV7lvPba6+JWfFcn1ZrO5gFhlmKaJ0+lE7VW8ThS+ZntCSiQFmsvvVTBRJZLSDzkTr+uKci5BNwYfFkLKpVplz7bv2Wx64jJzPp/QQFwW5kGzjDPPHz7y8nJkt92zaTs+zO/5P/74T/n22295fHxEac3Ly4G3b99yPB4RwG9JZGLORKQnOJVQmLUS5ApULVdsYD1/8llKP6xxJAVLDIRRANV+v+P+/qHU9Aj4G4aB0+lE8BeAItfArJsftT9TKUMIskmx6bcoJYy/XmWdprBzcWWxY4zc7TfIO2b2u16qnLpu3UyKUTZKxnkmT5lpWSArpmnmdDoVH6T4FkOoPvTq2W7WhORlWUhTKuBq/uS5X72SpnpQay2PEG2xdOYKOPPlPLCC1npvhZQIKWCM3IsZTULTdBuAi6dSa7xfxIu8eEI4YYzB+8imVOyEsplVO0RRapXupuRWNrQCu+wcWhlyrqzupTbIGEdKFVSr9ZleNw/K92DdJKpeY+ccVpv1dSo4T1k2cnLO4rH1GaVr6rlcbl3uEa3lmWQNN9JFmh2xV92q189ePX6VxXYgDH1EIeFTaLjf3rHd7/EhcDqfSiK3dNWmrMQzHjQ5OtpePPH1M0Y/k4IkIKss96w1Suqbij3gNre5zW1uc5vb/HDmrwRWv/rqq/8C+C+Bf/nrr7/+p1999dXfB/5HoAd+AfwHX3/99fflZ/8f/+wvO2++fI0yIhtdvEjVfAighDiIUeSKdbf9mj3Uq4yupFAWxrIGbSx+JsWIda5UiwiTZYqPqmkksEYpRd/3dG3L8XhgGgcgi5SxcRitOZ1OPD09MQwDx+NJjhGDUiJDpKRnWusgSXqpdIe2BUDIgrBwIeWXJAtro4mFGZUOWGGtdHmt6tHTume72xF84HQa1pATAZq5MJmVrUNCiUrljvRfinRwWWZSFJZJmMsoKaBtV3xmtshyM6aEQrVXtTwhRKytvagKrcT7OQxS/7PMCymxJoWK/9GuHtG6QA+lygVYQUlMidPptDKNayUJl+TPyqgmskhDr1JCVx+dgmme1ntGIbUbSsmC+eXlhcPhWQCsUsQY2O12ayXKPM8M45lf/OKfE8LP+OKLNzjXEkLg6eNH3n7zLcMwMQwjKY88Pz/x619/w2a7IaXE+XxGKSVS8DSzFNawsmlK5TXIR0CBXtnCCqhrovIqdyahjV2lkDGCUeCsALN59jSNMH4igXc0TYcpNUGyiSHsepXRV9lnjElUAOV8O+sQkCrPmrFaelGVW5+VjGy0NI2lax13u53UN11ds5zFe47SLIsX4DrO63mWjQhJo40hlgCsTOMstlQXhdJ9WtnxdSOmyKeVludNNmIkgMwutvgcNaE8Vz6INWC9QUQqUOpX8pUHk0/AXd1Iqfds/XPFZXNIFWtAjhGt5PVSvtRP1an3vEibL0nKtlQdicJB0zRqfe/aKRqCVNegFKZI89uuW0ORRE7vCxCX499ut+z3+xLSdZDPuj4raq3x0uYSkiVJ0JeQM3LZEKg1RZnCel/8ztdAVQEpBeY54v2lPcpojSs+7ePxgGs72k66meumlMiKbam1TUzzIHVdjVs9sRfZcRFPi+6YFBMh/u1lVn/bfzff5ja3uc1tbvPbmr80WP3qq6/+VeDvA39W/l0D/xPwH3799df/+KuvvvrPgf8W+Af/b3/2Vzm4v/u7v8MXX7xiWTx/9stf8fHjR3yURaukWkogiLYGp69CeWIkRUktTTmtbEhdOAkLCyl6+q67dBKWhV/bNrzaP9B1HZvNhm3f8/LyzHA+kXNku93wox99Qds2nI8nno8vUjWyLIQUAVkICwDxxBgKaJSE2b7rS8iRLKzneWJZJkiXxW4FsTrCUli2vuskrbMygYXlDT4wDBPKOPHo+lD6DTMXxlGhtSssoSFGhXa6yIQhplAAcSSX4JiLxE/eIxR5IES0UcV/lkrtjFt9c9dySF1CfpbS7xqCXDvnGvq+BwSM1r5K+fwXVquCTXkNCTeq7Fz98+tfK4gQqmY9lnpeJYApFBl28deW6w+A0iRVIEqKULzQMcaVbY9xIQTPhw/v+clPfyR4SwljtNluWBbP4XAgpkQuCbDTLH5nkbRGau9lCCJPNNaVjRT7Sd1OZVSvwWllr/u+Z7/f0W9E0l43EoZhgASmKV20Wdj2eZxXQJfXcy0bFuXD471nHMc1eKlKhWOQDaCmaSThdt0ISKvUtOtadpstrnEYozmfDuSUyFHk1f2mJeZLkjDK4FLGuUCKZ8ZxJuZMSJmEImYI3q8AqAboZKWIOV3JY/2VXL4CTMDoNXwr1zAtn/HHI9Oy0DQNtbYlJannue7dVRVJXSkQ1m7feJHy1o2S+s/WWozWxPrPWZKmQ0lsXj2ppc5KvK4GZzXOmXL99ScS2GtPdsHR8v0ShUWVZzXK75FwVjzWrmlkQy9lnJPjlmsKw3lAqZlcNnau612uU3ytuwLqKRewKN8LAj5BNq+EWTbWlO/fy/O4Bi+pLKCy3uf20gvbtZKcHlIqEvITMWeWWTYSXAllSikWH7Ai+mLNKN9V1lga12B0xBVfO5nS9Xtlyv1bNH8Tfzff5ja3uc1tbvPbmr8UWP3qq69a4L8H/n3gfym//a8B09dff/2Py7//D8gu7T/4F/zZX3qyT5yOZ+YlMI0L4yj9ijEJTJ1mSY5VpoANowvhI2EaKl9YhhqaYq3DuZIMqjRt8UtW2XDOme12gzGuePd2LH7m5Xgi5MTr1w+8enxgu+mZ5omXwwvH06l48BbU6gmT0JVQOke1STzc3fOzn/2MpmkKazUzzSMhLGWRaErIScZa6ViMQbxgTSsM6DgOeAV+8fgkYEtnzfk0romaSgv8CFnAoaTLlvqXrFnmiHWWxjomPzANA7r0JuqcUcaSUoSsSrBMIuTI7Ge63mGskgVkrlUm15LrCqYyIQapiFEGMAVQyGumlOn7Hq1VCYoRoC4eSic/ExNt060bDSlJxUfXt+VnIz4sq/T0GtimnP8v9t5kx5Is3ff6rc7MdudNNJVVp7KKMwtdwUUMmCAh8QK8AlPEIzBmxJgZc2Y8AS/AjClSiCvdw7nUqcyMCA933401q2PwrbVsx4FzTyNRlVnsT0plZriHbzPbZtvXf/07rK3MU2gMGMjC1SjE25dFpiqSUMg6S3BUkVVqBT5GpmXmbu/Q2onc2sL9/Z79fofrO56evvJ6OhJzIuYkPZ45kpWR4J+Q0UmAI9qBjthuoBsMB60b4991HcviC6tlCni8NPm6UorX11dc53jz+MibN4/cHw7UWqLnlxeIspnR9z1Ga6aSWBt9CRAzGm30unmTFTEJoFhmAafTNBJDonMdXedINrNMM0tY0EYXT6SAVaOkLvV+3KQAACAASURBVMgqTfQLOQYUsN/uMIVl77qOYdiyhFC8uQveR/HPnkZeXk+cLxdiKPdViPggvadV9quNpqYALZVN9YEUV0+2c9Jp6uzqsyQmUgiorFBJlV7TSKwVK1edwEjNq2wyUJhCf7WJZMWnHWIQ+XaUJGmpPpJNhC7nAqokmEgbsyY6h4XtdttYSq0NaIVRGuukUicjG1TV25mpnuIqp62bBKHc62WjzYeShJ2ZZs/kPX0/tGejbpBYK9LqZZnF456vJPmpXOIMOWZMp7HaledUsgP84luvtSgBiny/bDJZZzFGs/iZKYayIZYISVKSBZBryBqjbAs0U1rqufquY+g3xBCZfEClC1EHkRuXmi5V4paX4NFeY3KpqyoJwClJX/Q1o5vzXx5Y/XP9br7NbW5zm9vc5k81/1Rm9b8D/qePHz/+zYcPH+qf/Z6ykwvw8ePHzx8+fNAfPnx48+/72sePH5/+qQf300+f+OPf/cASIufThZwUMcLixe92uVxk8eV6WcgCMctqq1YtxCCAzlpXpKkCGKxxWL16JK9lhNMkibfOOX788SfGIv19eLhHKTifT1wul8JinQGRtcrPSM1nN89zqyHZbbcMQ9/qdy7j2OS0SgtLVuWXILLZlFKRCYoc1fulLY6N1Zi8hu1ItctC67MszFdN+8w5473CaNsCXVJhWRS133T1vlnbY62k8p7PZ7qu43DYs7/biBzbR5bFczlPeB9aCvC1RFJrDVFCqmoia2zAALwvi1Yk7CamAFnTdZYU8ze+zLpQn5eZEFdJrFKV+SrsKWtnqnjccmHNVp+ttaYEJ9my8KfcI/VxUO1aGFsDvUYe7u7RGobNhu9+/Z7HxweUMjw9feWHH37k06cv5Kw4Xy6EsnlirUM8l4F58ngfqP7cYRha96zUhsh7VRlEZ6Ufcr/b8ubNG5yzzbvXD47Hxwe2uy1KZcZx5HQ6MU8T+9L1WidG6W7SJZDJdA5jtVzLAuwb25yE6euHDVy/B4WdSllk+UabwrgG+s2GzdAXQCCvVeW11vXF4wrkVdkQQuB8vnB8PXE8Xng5npnGCUXCGN1kus7UgB9DZq32qcxm8NLLWqt+jJHws+12Q993xCJpH7oeax0hSNhPZUNzFqavc67du1XO2kKISCxzJISl3GOpgNXUXlfufQVKvpZzJqpS0VI3y4wCrSXpVmtUVgVgBqBD5Uim+ya0SBhJAbZVylwToXM27RqsDGap84kBH2Szypi1Z1YC0Sp7Snn+dJEAQ8yl77V+d87tHFPyxUuf22ZYzqGB4ZR02SQydNago27vdVU1yPMpyeBaK0wyErSkNUrLMYzjSAy5qEE0u82OnKN4yeMiz2aKJKXJ2TPmNWhLrrOBsPp6659dS7n/gubP8rv5Nre5zW1uc5s/1fyjYPXDhw//GfCfAv/t//eH8/dGSRBIAmHTNBhv8EGktUbXRa1Co8TpmUpdQfkRwnBkYgj4lAujCil4phg5FXCoFIUBcq3Xswb5pJRa5+d+24Fapa7TPJNRONcTY11g5sZWbDabVvtSfZuu65r0LsaELeeptS7SUQGDSim0FRBMivhSk3HtldNKC/uirqWCtgQrSR2O0gACoo0VhiZET5oVtZfWGNtAW9c5Hh8fWZaF0+nMssxstwPfffcrdoctx+NJKjRixugzp9OZpqJVa0KoUgr0WtNSj339Omy3ElAzzzM5GzbbHTFSQFGpm8nC+J0vJ8ZxbH9eF9FKS0UQlGsga/sSWCRsS+0OyUj4Ul+ktmBwrqPvhyZXphz7w8MDm23Psizc3d1xONxxPp85Hc9oa7l/1ISkOB6PxACbzV6YKrWgtSuMmC6Lf1UA4SzyycKUQZVUSm2QzsKy2mHAGo0xUlkiAWAGqxX3d3s2mw377RajxDP9/PwsHcRlo6YmKbd05e1WwHoGZYRJDTG0zQ/xQnZobYkhEoMnpVW+Wd8Hkaw6dtstxiimcYScsc7h/ULwscnDX09HlrAwLyInHjYD2goIHS8TX76+8PJ8ZJoWpkVqYTQigTfWoq0hx0yMCvCknPHB/78EazVoVTy0HY+Pb/jrv/49Whu+fP7C8fVYWPuAscIsV8lrrSWqgVLex3J/rudOrjVSVUadm2e2yoONtpKobbRIc7X5f9gPVjC8+mDFTzqx2/TSlfz3JO4K2fTo+75VNtVaJ+kVXaXM9Z+ckoSMpYQxoX2+yKQr0LtueGltqOnZufSxSk3Q2KTL1tXPifWcVgm+ApVROhNjYVmtxS/lPlIKpeUzgasNiVyvT/n8vJxHzmdRimhtSqJ2T9dZtOkJhX2Xz5HVG1wl2NYo6ZduictrEvFf0vxZfzff5ja3uc1tbvMnmn8Ks/pfAP8K+Ldl5/Z74H8B/gfgP6jf9OHDh3dA+vjx49OHDx/+9h/62j/n4HyI+Cg+LOc6XNdRSAWOSjotc5YS+eA9mYTRimG3LRLcsIauZLDOstvt0FozjyPjOBFjYLfbsd8fWrVEZW2uPYM5g/eReZ6xzogEzmpcEhYpxkDf99TgHKUN+92O/X4vwC4ntpstqlRIeO95ev4qx7sZ6IauyIUVIcriP115vRa/FKlm9YjJMWVEsap1ke6WyVlCm7rO4jphessXCkshSasi810BQIyR/f6Rt2/f8sMPP7Rqjpgi87LAWVhfrQ2KGvAjoUDfdqyuwUbV77dWyEjK7d3dHe/fv8d78XiGEOj7gXn2JC00TMoJYub19VUYl7j2kFbGRvgvqCm1zX9KU40CrCEx5b+32y3DMJSgKIv3HqM18zQD0jG62YrkuO8GUtYsPvLyeuLL8wun05nHN49stzv2h0fgxOXyTPAZrTupx6m9nCm3oCgJL1oYLxeWecaUIC2lwGpLioZlniCnEpCU0UYTFpFJaiUS3BQDRiuWJbTwHIBpmrHF21vrZ1QJiopB/MwqamyIRC0ptxIAJmzpUrptU7m+lSWv6czbzYbddsdm05Pu7sRDTWKaDJeiZpBgHl0AiyOkxJenrxJkBEzTwvPzC+NlFkZSW5Gh10CelJDbSe4dvyzCymUBYjlntJJgH3mfhVVVORNDLEFFljdv3pBS5nQ+4+elAaa+71qYVAWA0zRJomz89jmKUe4n6WxVqAgqlbqmwsgKcygbM1RJsVvrkup1BNqzVr3JdcMg+pmYfAOVLe1ZW2K8AmPleGNMGJO+USDUn6mzbQzvWuOUyzOwpgJfn2fdnJBnKhXmNxfPffm8yRQmu2/nFmNo5+QXYT6Vy5hy78WQWbwvKcOpsNZawHBOLCESUg3HE89yLteyMs/yXHflWRHlB3r9TKmf2/V+kw5s1yqG2ubZX9b82X433+Y2t7nNbW7zp5p/FKx+/Pjxv0cCGAD48OHD3wD/JfC/A//1hw8f/vPif/lvgP+5fNv/Bmz+ga/9k+d8OXM8HsmFeXTOoaiS3kjOCzEk+TNj0EbA6Pfff892u+X56Zkvn78gXZ62MRPBe1JhVJ1z3N/f0XU90n8Y6ft+9bAhi8btdstut6N3ia53AtRK3Y2EJa1s6zjNbSFcWUDnHMuyMJUKiVhK7zebga5zxBS5jBdijHRdj+s6Ya0QeerlcinJtaYF7WitJeUypBLyo666IAXVb7YDw9ATgtS0zPNcvGamJfUej+EKlGe+fv1KSonj8bUxgMsy8/LyQjd1hdUAv8wl/dcylwAlOWdhbEMIdMY2cKmUdLceDgLi7+7uUEoxTVPrx/Q+Fp+dyLhJqaTEjt+E3FyD4TrVy1fZ3VqtYq0s7CvoGoaBzprG8NbgJ+99SVaWyp/L5UzK4r88Hk+czzOXy4V5nhjHM8/PL9w/3PH27Xu01jw9PfH6ekYr+fuJyFJUAHL/DfR93yTnZAmNqaE6KQk41UrJPRojqatMtG5sXNd15JRKd634Wvt+aN3C0i+6bkCklEqAVS5BtwqdhU3f7RxbrUqPaeRyuTBN8zfMXwVAu92O+/t7NsNQvKy2KBnkXqvPSPBBGOEUiDlKt+pmw+n8ypcvT5zPFy5lsygEkbmqokSIaQ2S0rV32NRqnBKSoxU5SnemBK3VYCX572WZ+fLlCx8/fuTt27fMs1ThTJMwz33qsFZk6XLf5JZ+LNL91O4f2RTxrYLJWotNBu8LKNOGmIoEN0ZSSXWmPIvyPHfUrtMUIzGtdUHzPGGMZX/Y8Xh/YLcbGsPbmMHShyrqg9wk3qqEhf199lA83GCSaV+T18sNsF4HScmmUP4mCAmVygZdwlpdNg4kjCtrgypVSbokBlflSUyB5BM5RLFilK0kZy1Z0Ty1PoSme6Cw4yF4YiybA0o2cCpjWj25WWsshqSKlkJ9myBePxfkc9DR9aJqSSm1xPG/lPlz/m6+zW1uc5vb3OZPNf/intWPHz+mDx8+/FfA//jhw4eBEoH/j33tn3VwncP1ssAQz6UEDsUCguwg0rS6yDRGcb8/cL8/4JzDbzfkNw8NUKosctMpJ+7u9k2mW3sfq0+xLpBBFqhamxaAsz303N3tC7uxltxX9iLEjDbCoJ1OJ0IIbDYb5nnm69evRDJd5zidjgXcRsZRkbhK0LUSVtIPA957Xl5e8EvxkmlAG9CamLMU3ShQKbWqCVMCdIah5+HhjmHTMc8S2lNZ4vEys8yZefZF/hcKQytevqenL5IqWthAqbepLBRUH2bOtJ9bQYaARqmwcSXAShJIM8PQcTgIi51S4uXlhefnZ15ejizLwjAM7HbhG7bocjmRksgt+75nu902YFlTfbWuoKqj7/vGKs7z3Bb+dUFvrQCHsQQPVYbZzxJ0VVN5QwiMY6J3Ha/PzywhMc2zSGhDwAVf2DQIMfF6PJJSrTJRaF2yaJO8d0EJQA3LgilBNF1Xr6+m7x2d64svUJhj770ENRXZY29FDlkX5efzmXGq76G6Ai2GUD3JSphESsdnzqDKe9kPA6n4qyvo6LqOoitHKdVksnXTIcZIUAsphSaR7yvrqBS5L8+Q1SRyURzAfndH8Il5CeTLKLLZQZeNhJIsm7O8l3atTHHO0PeudLOmArYMyorv2BnXQJaxFqXkY+3HH3/k6empnJMw6BJEFQtrN7dNqXmeGcexXK81lK1uMkjlUmp/bowV7/a8bghcb6AoMjl6wgKaIlmtILwyfEmCn6xWbLqOw37Pb/7qOw6HAyEEnp+f+fHHH7mcJZ25JerqNTzK+8Q8+wKwbfmxCa3keVWYBkJFipvb50T93LqW89aqnspY180cY4r/1rrmQc7E9Zpo6Z52WhjXHOQaxxCk77hUyhitpANaJVKGkDQmRYzSRT5dZPtJAL8pQFNSxBWmk9cOIYqkWOVv7suaDG3LRodzDmu6v0RW9R+cP8Xv5tvc5ja3uc1t/lTzzwarHz9+/Our//5fgX/9D3zfP/i1f+r0fY/SWqSLxUdVd+GV0gxDLabXhbEITNPEp0+fvvGTNZaksGfLIkFEmxLCkvPKUITgmWcJUqnhN6YwtlI7IVK1y+XC5TIVtlP6Ghe/SIALNFlhlfv1vRTXj/PI6SThIlXOl1LCOtt8dH6RIJ56DtM0EVNowLF6AlffmUxdjykNm82G+/s77u52uM6y2SSGYcA5y/l8ZhpnfAicLxfmRVKWjTXN26m09GlWtsx1JRE0eLSxOGuZtSYlCafKJfQkFynnfrdDKY0vi3xZHK+doNM0koo8FmqlRkApCf0xRrfAo3mekECinsfHNwzDwLL4VrOSYqTrHEPf0w89+92eoe8JKTJdRubi9a0L/GWZOY9jkZJKtcy8hMLUyfn3xVecYmCZZ5GapxJWVIJpKqidpolxHFm8LyFSubw3ErgDihgiOc5rqNWVt3dZZpyz7PcP7Lb7FpgzzbDMMzmsnZXeB8gzxla/sgYU87KILLjcayJ3V0zTiC8VNDmnEuqlGDZb2eiZZsbCyFbJZCoMuameYKWaxPx4fGW8GLrih22qgU7YPm0UQ9+LjJ1YQqzE2y19nnMBnNUzXIGf3L8+rl5esiT1+pyKJNrgSi1KBpzt6EqyN9RAJBq7vvq7r/3cuW0QyH0n13qelwbkpQJGSXenMfRXXbft2hQvaus7zcIgGrNudEGtJwpXidarJP7aR7ksM4tfmKaJzUaqre7u7ogx8pkvPH99afU86+uVwLnSR1yZX2GoU5HGqwY4q7w65zUYqn69Hv+1VqGGl9WIZFW6V42xRTliyzVc5e7OdRht0P3KVvuSWBxTXKOG28+vvawGpbIEuacKolO5P6oCIhX7QvVyp5YfIIB89fJXgN51HZ2L5f1b/n2/bn7x86f83Xyb29zmNre5zZ9q/sXM6p9ilDb4GJinkc51BaxpDnd7UqR49RZUWfAufmnexmEYWkKkeFalK7R60rTWBL8QU48r/qtxHJmmsfndqmy4so2vr6/it+xtWeDKwpusWP5e8EsFknWBWhm+xc8t+MYY0/yESoMPpSvSKvBwPp/bsZoaCFN6GdfFeJHGFvai6xz7vSQPa61Ihemp57AsMz/++CM//viF2SumsYQ2JfGokRR971AKNtsNh7u7K+Y5kYJnvz9Ij6MxhJwI3jMXP2mtX9kOGwE/SjVWZJUpF09ehphXT2sNeZIAFZHwrnVCO96+fcvhcGiLduccm82G3lq22w22ACxb3vPpOJJTorMWyuLcWkvse7lfhgFSxvtwBVZksXt3OGCtZZouDMNAzpnPT0+M04R1132ZIqHNOWGdAMj6ntQOUoUmJzCqyjA1tVszl5olZw1950rPqmxShOhZFgEl1VtZU3gFVEl9ijGGyzgSw9KATmVm53kWv2fO7b5USjOOkuKccsJfMW8xRiRQewVScr1X5jAakdLXztvKcC/LgtKZw+HAfi+gsB7HVKS4y7JgreOwv2MzhAZcQQDWEiQ4KKssUt8UCYXh73thy2NI5KwYhk17fqpM1F8l41ICrlLKbYNK67rZVc9tKSx9Sd0twUGCz8SPbYx4ZWNULUStspvdFZAFTd9Lf7AtaeQ1EfxarnsNFCtrvSwLT1++MM8jT09PraZonue2oTNNU7t3GuueVqlstTtIN6qT4LEClrXWJTguFd/6t93T18SjMKcrgK3pwc7Zct/bxrKK5z2uTG0UuXJnV2UKlMC0vALjlFeQKa+pKKr9VoGjim89k1epbwyooEjZoDXCAGcBuNeS51USrUSOnor//Ta3uc1tbnOb2/yi5mcNVl9eXnh5ehYgcJBDrT6weZo4ny8sS8AvgcvljI9SwyEs3MLQ96W0PmGtaYBP0oMlQXaePFNeuFwujKPI7bz3aO0LaMvEKIvRmqiplYC0GDIhRGa/XElKr5mVlT2onkylMtv9wOFwIObINE8syyJ/R4n31hrDokJjlI3WpOoftAqFePdSjnRO0oCd29B1jt1uy2Y7cD6feHp6ou9dk8QCXMYzX5++Ms0BH4rfzGms1WSkAqLvew6HPff398LsfP7ENI1ScaPAGCu9l1oTwvLNQryCnmkei/R3ZYKcMxIUVK5T13VYowrAQKpdwow2GUhYp3F2U3yuB+npLKAoxsQ4TtLBud+JpDJDCpGpBGSNhT1trFZO0q3bdUzzLKm5WtJ2V5Y2oRG2arvdsN2+Yb/fA9Lta6zhcDiw2Qy8vL7y/PyMUgGtN3RdD0pxuYzM04xPgRClx9IY26hvYwxD54rXObC5P/D+/TvudnuUscJ4n88FWOgWZtV3m+ZfrudWO4azSgy7LYe7O1x5RqQmR4ESz6dSCqttqUPS8ux4T8y1okYkqvkqkdYXCfUw9Ox2og5IMaKVbhsTwvh6AYlLpO8CeSebTSlLj2vwiWWWIKe7w32TgS9LlXMHASJLqaAKgZSKdLuoEGqqdjQCLEXu3WOMLgm+kwT0FCBXQWuMEWUKG2w1W92jzbfpsFWx0Da3FN+An+tqpmUWZlzsswZrpPwl54TVBmdko6sGCqUkz0ffd23jo96T1UsvB5E5nU68vLyQc279tM7KM1lTgCv4FTbTtPP4tiZG6lqqBaJ+vf4755Xh14UxdW4NmJOfTVGl5KZ0cJ27UoRUj2i1Amg8kTgvjKmktWcJVNNZo2LGOAny8iEwzYsEaWV5v13xkdeOYcmqks2e1JQkmhQUJmV6J2xsDVVKKRHTGipVrRvrOX/rcb/NbW5zm9vc5jY///lZg9W5hOrsdls2m01Z3C6czxfOJwGq4zgzjTPzsqCtgKFYgEoo0jQFDJuBzTAUz5VI/JZ54XySwJy6yKsLur7vG3O03e6w1jIMIhueppGnp6+NLZqXuTE5xhhhKVErC+Kvgj+MZlP6Nc/jWfy20ySMiLVruE66YitKOFEuvY8CFEzrXZQai4Wuc006LH8WSrjMGWNsC1mKMdF3g/gCtSwe64JZPKEbHh7uWZaFn376ia9fn8lZekudNdSU5JRk4V4Z1SoJlQ5XYcW8j1jrCgvaY0vqbg1kSUkY0v3e0neBxc9onVBaFuvf/erXvHnzlpQSf/zjD3z9+rWwvCKzzhmCl75bq7UwSuX9VSUdFqWIORF9YFLzKpEs91m9jgKeLc4YYhS5+MPD+3IPbPmP/5N/zdt3b1nmhc9fvvDl6Ynj8cjlPPL1+VlSoFGwkcV3ZcozIo2uALD2pxor7+MwdKWHd5BO4fOZ4/EErJ5qYZd0CZuaWLy8lnWWbBTv37/n4eEe5xzjZeT0eizXJxdwI6ybLmFOSl+F71z5Fq83WnIFekrROcfhcCDFyOl0bqzVNE0s3pf03lgAjQCsGhT0Te8uSICY60Q6XTYf5tkToiROixxcnltjDX0vydHSkwxeS21SzpngvSTWBvl/770c07KQSlBPLMoKpRXKuBahndUqxRUf7NrXG2MixZKQnWKTQTd2Odb+Zst16JeASY8dHUqvgFck7JKyXUG+vE5s0m1I+OCbf3YNLBPFxGazYRxHLpdLe02pqVr7Q+u19nH131e2tXo4q7c+54gEsa0hVbWT9Vo2Xf3jch/5VRqeE4pUvNJVGm/RSnMZz03OWxOddfmnqgScccxlU6FKlFVVXuTy/cWfHNMaXlUlvoqIKZ+xco16Mk7e2ytBcz3+qui4zW1uc5vb3OY2v5z5WYPVCn6MMS1Qp+74D8PAPB/LIthgnEMZRe+6tXOwSPmss01qmEt34OgncqydgrEtgpSSWpXdbsfhcODh4YHtdtsWfssSuFymIhuNjFP1WJq1BgMEEGUwKKwVVqfKJFNK4hudLmjg7nAQoGSlw3JaPJTXq4EhxIRWkIPHbi197zDWSvdqkPMEAXDGCAwTBsQ0iWCKGWs6OifBOl2p6qmVJBWwaw1fv37l5eWF4/FYFuSWGHyrNxnHCYAUv10UQlkwp9AqRuo1da6TYJWyEK6dlkZbwGCsYeu2WJuxzvD4+Mj79+/JGZ6+fGWaRuZ5asnDwlC5lixqtKYrjavGCPgXJlBAR/AelQKaqfXzClCw7d5yxffXD/J+ALx9+5bf/OY3/O53f8W79++4XC4s3vP582diSY6NIfD6/FrST2mbAH4OUDYurkNfTGH6ut7RW8MyzSXxVnG+TCzFs+xjQGfZVDiP4zdgxVqLAR4fH3j79k1LnE45tgRcAKUqw9YVthOIazhOfdaq1LK+h6EATQmpsRilSVlqVpTWaGtQWaNiIKbSZasV8zLz/PqCc6b1gU6T+H4FDEs37PF4bIoEOW4l9TQp4zpDZzTO2eabrhtM0Ue8T+TsmTVyTFl6O68/I5TSoEGhyz3o2O93xYNdP1N8AT+Qsyr3S24yX/n/RKqVSMU3X0G9Kfe8tpacrXTNxoVpkoqeqlTYbrfs93v2+z3b7bbde/WzyloLKuE61wLCaqiSNVVuG8rGzr70No/krL/xw1aVA3kNfaug95pJliqa1ZMao2ccA13XrXLw6sMtoWopCVi9DmqT0CpQdVONUiOkLTlFUo6oKy+vAogJ4xx2kPOS+zCDEs9r87KmRAjlOOWU5DOleoGjeM6vNxhrwJs239ba/P8pYOk2t7nNbW5zm7+k+XmD1Siy2Oq9y1kWlVobCbvJCW0tymjiAs51dJ3DusSyiCRQFk9amKScWaZJwoS0pivgQYBqIKXMbrfl8fGB3W7Hbr/FaM35fCwLx8zpNK7es8Ii1gVnXTCm4skyxmA7R1cWtNZaut6y2+8xWvP4cAdKsd1Vj6nmcrnwhz/8kWWa8fMiQCynIr81WKcxpSvx7rBnGkdi8Pgg4CNEj9L2ynOmUBis0SSj8T5grWO33YtMNYg0+fV1YZpmWWyHyLJIxURdlCsFQ2/oOul8bMxLXqWtUJgnZPHYuY6+71DKMgx9k/8J4ypM5mbYSBKvT+QUUSpjrGazGXh4uMday6dPn/n8+TPH47Exw5XZSkk2MTZmg9EGtLwfWUldhlS4hBbmlHOElFElpbbvezabjYTEzAuKUiukISXF5XJpcslPnz7z5csXYkr89Mcf+eHvfuB0PDEvni9PXzmfxyINFQlmRpW6JYPrOjZlMQ1VVirApLdShTKdJo6XkeN5pFaQhBDpXIc2hsv5zOUyEmLEWSuJvq7j7u5Azonn569MkwCc2XtCTBgtPsrtZou1jhpuk4sfOIQAf8/rVzcS4pX0vYb/TEV+v91t6fu+bSJd99+K93vCONPCi4QpTDhjWjVOBao1LExrKx2bWYJ2lBVAvt1u5bplYTynqVT/oEryde2dLRaAfihhPIGQUqm6cmy3G+7uDuy2WzLF11qus1L56pmh/dsYW66VKsnMCU1N3TYt2ddakaQao1B0+BjwIbAprPzd3R2H/V4+z7xnKfeUNYaMyK1jjs1DWz9Puq6TcDMlz1e93qFUb113tl5bEKq0e62tSd+cV/WBVo+uUjXAy7f6HpFeu/Lza9q3ANycUwkSo2w2VdAb22aSKq9hjaXvOpypvcylfgjpok05lWtncNbKsypvttyLs3qU9gAAIABJREFUIZSKm3xVFy2Zxavf3VwxyIquX/tvK/iurPltbnOb29zmNrf55czPGqzWqhptdFukCRsRCXH1mWmt2G6GwoREcoyle1W3BN0QPDEI4zVsOpzpZKs+yaJOpMZb3r595O7+Dr94Yor4IHJXHyIvzy+AMDA++CbVq1PlsQDWGfa7DbvtltPxiEJxOOx4++4NDw/3bLcbUspsNhuG7YbT8cSPP/3IH/7wd3z5/JVl9oQYZOHVObreNjmg1pr7+3t+//vf8+XLF47nM8knjFa4rsdozWWaSEkxDAMxJeZpKTJL8ZFudh7vFy7jBbIEzeScm5cvJ0nY3e5EsjyUIBkAv3jG4sHtnHgPYwgCg1RqLOLd4YBShpwUd/d7tNFM01i6FpUwRLYvXtELySByTYRhzgWcGC2skEhGPbVbs1WIZGH6bCeM5TRNhJTo0kDXD6BLOFQUDyUluVjlzDLPHDOSvqwkAfn+/h5jFSEK0/zDDz9wf3/Pu3dv+PTpicvlwqdPXxgvYwP2vki9ldYlsVoLg73pUWhc8VxWQOALa7YsAjbGy4XX4yuXccHHJOEyKCgSS4ClgKW1osiwKUBOul8veB8be9v3UoNTGd0qYTVWYbTBFPYwFh8qQPQeHyOxSLUPJWgqR+lgXZaldLZSlAK6dYoaY0hZ6n2WxTcZNMhmxmYzkGNqXZsU1YExXfFnDkxz6SLOiZQEtNaaJKPrhkhsjK8xGutKoNZUanqGvmykSPescZLo65zDoEtit/iBpVpJuoqTDzXfh1w2Y5TSIrc27sqTna/uP9O6Pil+7qF3uM6htEKXiqDdbscw9EBew9aWBeccw9C3z6gayJSRjToQ+X/IgRoUl65Sjld5bu0HLr3OxsiWUc7y/l55Nq/7SduzRBZvtKL4PGNTZBgjoWU1jblzoh7RWtQjKLEmyGZF3QTxWF0VBTUcTMm9Us4rVNl58YznlFugUsq5VS+FspGYcwWptI2L5DOu6+j6vkmjJaRM5MQpJZZ5wUdPiOv9eJvb3OY2t7nNbX4Z87MGq9M4MU0z1jl8EE9ozpL+SErEUENCZKFtkEXxbjPQ9R05ZRY/F9Aj7I82DqsglYVL3/ctuGV/2KGt4XQ+l8CdqezKJ06nE9OyEGP4hn2oYSkiq0uoLJK3vrP0ThPDglKJ+/sHfvWrdxwOe1xnSblIV40hhcgPf/zEH/6vH7hcZlQ2qOzZ9AOu0zw+PvD97/+KnBMvr894H/juN79h8p7PX1+YfSKhJMgmZcZ54nyZ6FxPSJnFB2YvTM8SPMqYsuDWxFDSTTGyuFYSbKR7zZu3D7x//078wiHhvVRHTOPEeLlgteHX373HWsvnz5+bv7imnO73ewFXWrPdbgHFK5mc+xb8IswMaJ1RBFCKeQ6k5ws5WYZhKhJFYeG6zmFt12SrxghbuNkMLbzmsN9ijGG/3wJwPstiWNlSFTOLP44U0cYSF0/wAYzGpyhduK5DW0sKntPpxOfPn7m/v6frBrpu4PHxPXd3D/yf/+7fcZl+QinpBU4poa1ht98zDD1D37Xe1MZ0pcQSPK7vSBnGaWFaAvMSQRsMq59UawHp5IzKme3QtyqP7dCxHRzOKMbzjAa2vUi7/eSxZu0hrq8/DEOTZ1tr6bsehSJ6YdlDCOSU2fQDd3cH9vudLPj9wmUMaGta2u3kF/w8t15hbQzkBEvEhwm/RLRR7HZb3rx5aP24fllENeBD2WzS9G6g63t8iM3LKt26mvEySSCTkKvYzhJzBJ1BU4BNLgAMtMpstxvxivYObTXOSDWUkuyuEnalieWZiTmRlZKbkdQSeFGi5NBaY7UqEmNJK0ZlQhKQmLKwq8ZZFh/ICg6HHZvNpkjNXfuMkMAlj1TkwDyLSmPxizDiIWC1JpcNhpr6vZQkc2stAzDnQIpBuqrIGKVKTypkEkZRviaTspyXUkpISqUkLC4U5UGMAv6VRVvdPKukRAoRyrOsFSSVMagWTpaiAHVtOna7LUNniWHtps1ALpLfWDYLKvNZ7/UlBOZF7okQIj4uxHL+8jNysVfktomgUIR5Ek96Tuz3ewxAVqRIk3fHmPHLurF4m9vc5ja3uc1tfhnz8war88z5fGbY9BKMpCU4JysJjblczrI7rx0pRnpbvHVaY5QhG3DkwnZWFkF2/5USqelmsyl+UUlQfXmRPsPT6UQIsXmoakjN5XIu/am5sRJVgqa1KgyEZbsTGetmsyFlWTzvdjtyzszzIovWnNH6zDwFfvzxJ07nS/FwZaztcM7QdYbvvvuO3/3ud0zzBWM1r8czx+OR5+dXnp6+EoqfNcRAuARZDBZ5rg/SExqKLM8UtuR8OaOVatJFq61IX3OQzs/DwLt3b7g7yDFPSyTnVCTPuXnmPn36jLWOEAJ939H3QwlqEQAfvLDNymhSTJwvF0wJ4bHOAQpbPIZzCU5KCebZF3nwwjheuFykQma327Hd7q/CsOQ92G6H5m2uYT61uqh261Zmvu87TAnrQSl8SC10afGel9dXQtzw9s0buv2eT59+5P/4N/+GH3/6CWMMb9++4/HxDbOvTN5AN/Tk2TdQaIzBaMNy1TPb9eIHnJe5VLjY5nEOKaOdw2mRWV9XnmQSKpek6KtUa9eJTFMkmWvS6zwvraLJWt02Y9aOXhFqZyCU5OT6M5TS7HZ9uc4S7uQLmKnPSd/3AjCWuQFqgKVIWKdZpPYSKGZ58+YNv/vdb+m6Trzal5G5MPNV12mNQxvx09YUV5H9x+Zprc+o0ZrtZksMVdq51kS17lByYZeF5a+p4EaLbNgYh9IBHz2LLwE/1W+sFNkIMs557Wit/1wHKtURKbQqlVkSnlU9ndJvvPozhVF26CKfjTER4kxGWNsQEiFJynkMCWO9YMbisS8SBrkeqXa9pvY+CGtcPeQCyiVoToPJhVG33/g413MT4NuCp4xIf2s1UH2GWh9zSU2ep4WUFMZIz+rgDFpJlZQ2chzVEy+vm9r1q8cZZlF/VO90itUHq0hRgq5C81ojbC4ilSYnpvGCIpM3G5aiAskpE9IaFHab29zmNre5zW1+WfOzBqs1MCirxOFgW63E5XJhvMxNyiYLMdV23GVhJItcWWjLjnrX9a0X0RSJ8HXS5XWSqHRChlZ9AEhy7zwhXs7VF1qlfMaID/bufs+bN4/c398zbDakHJtEWKS2hpQ0Fs1PP33h8+cnnl9e22vUKpu8JJwzzPPETz9+IuXANM58+vSZ4/FSElRlgZlITaabM+3cUgEN1T+mlVTdeD+LD81QAEfGWiPMyL5nv98wlITgGvIUY24/r57/8Xhaw4S8Z1k8XSd1OcvimWbxWIbCDh1PpwbSXElE9cG3QKoYM9Y65nnh5eUZrVVJOu7Y7/cMw4bttgDoaSJnhXOWh4d7tNb89NNPXC6X8p4M33j2qs8vK4VznVQE+cDivTB1WRGXVI4nirzQWS7jzOvxmc9PX0gp0/3N34q8urBcXS/ptvO81qUsyyI+4Xa9TPHPioQ6hMBUUqAxusjNZdOhsmlrDYmALKWkLqTrZJNlt9thrW7f671HFZnrer6qyY+vPYw5Z8ZpLKBW/sw6AVabzaawp4ZlmRknYbXr+2ZMYX7T2ssKIgsex5HFLyURO6FNj3MS7uTcmly79riKzDnECMVn2vyg2hXJr7xvrV+4VtOEjA++hPuuybcpS6VUiqmFWRVRdZEXC4iX0J8aFlSlsvX71JXXMbVjBoqkVTepcL3+KSmsDWz6gcN+T9c7nOsYBum+XdN0S1VUzuXvSc+xsXIvKyWJ5ilEqWeqICspCbaqYC4rkpKUY41IdEWKu6aPA+XeUe3+rzLu+nNboFEQ24FS11UvIhuHRAziF1ZKYZwrfauavofg5Z6fxgVQzG6t8DHGCtjOlRWFXG6a+pyKzL+EY1Erc+ReGceR0/nE6XIi1mcYSnWXg5wk/M1ocopM03hVr5OI1bt6lRB8m9vc5ja3uc1tfhnzswarFQCmEFlmCf+Z56X5QnMKLKMXv5vr0cqiMMQASacSxqQhyYLMWanUWKsqZJFWE1arL7b6ybyPq8+vMHQVpFWvagUNWiucM2yGnsPhwN3dXVn09+gCKC6XEec6/BJ5evrKp09feHl55Xy6EMqCPKWEdRajTWGWMl++fOXp+RnILMvMawHSIaQWDlPTNgVApwZe6mK6jlJSB+KcxtWWEmW4OxwKo2UwNrfAGK2FLbLGMU+RnBYulxOXy0iMGaMt2hjx9VlL3SiQuhwt3aTGEIIsdsmanBTTuKDVKP2RSeFcT99nvD+Rkpzn6XREKdhset69e8t2u2UubHsNARLgNhBC4PX1lZ9++okYI/v9XrywKbUNCKABttl7EopxWTiNF2p/7hIlxXicZ55fXyXRtPT3GqPpXMdlGnk9HUlJwNB2u2Wa5sZmrvUmIltWSuGXQLicG8sbyYQ5kYpHsDLCfgktQKpY/JAWT9BapKbb7cD9g9xfdVNit9sV76+wed6H0rOaG3iWvl3TgEtlufq++yaZ9nQ6MY4jm81QmFzTfsY8zw3cxCRhVVElFKaBg1yCzQQwRF5fX3j6PHB/f0cKC8EvhelO5RlUK+AuzKnSGqsFWFUpbc6ZGAILmvEizHPMmWJ1LK+nyNljjGfoehQJo3JjQr0H5WFexL/svSfH1Q8qeKY+R+JpV7lGhtHuIWstuWwIzPPc/ryld5f3tb5H15Ur1zVZK3sur2s7UVRE7/HLGl5VU4mVFmZUAp3lPVaAM7KJ0HcOrfr2d2KSFOwlRKw2ElLEdYCUafeBKx8I1yFgPgj4NEZkzGRE3q0keCrkSE6qSaVz2Zg7LgudtfiYGJKASm1MeQ25tlrB0HVYbdDG0PebEoQmlUM1vKpWR21P2/Y5LRtxoXzuuhaUhlbrhlFLhs7lHg7/sl9Et7nNbW5zm9vc5s82P2uwap3DOdtYKFXkaI3hsZahl2AQssKaXkAehQGkBqEAKhMLS+O6TvxrVepWAMyyrImnwriu31MXdJVlqQukyt4ti8e5bQFsNHZ02HT0g8UYYQ+mKfD58xN/+7d/4OnLkwT+lGOsPsLOdTjrynEj3tiYyFnkoTEUBkjVQBWoUsjK1lSgAuviM2dhYYRNAqUSu92Ox8c3fPfdd3IefsYY+VrnOpSqQMUzzZ559ozTyDjNkCnJxJrtboezDpTwF6aEVimtca4T8L9cqDLs2pU6jlPp4lw9bKfTsdWKWKubR/JyuTDPM9Z2JW1XFtTTNElY1OXSWLla3VIX41W+WIHWOE2cp5FlFv+xMqZJzZW2AgTm9RyNtSgNqbBOKPFKjvPUNk9irCBE5OIpRfqhbyyP1KTIQjwVYFiBTquMKcDFGJGyV5klCDC+uz9wd3dgGPoWtlVl2SmWoKAkzLdI3xXb7bYlH4ewNLB0nQBc/7v6sQXkS4WTUgIAKqCu91l9FmznIEdiEKm57AMpNkPH4bCn73tejy/M89ikvHUTRZ4xLWnMIQgLX45pIdKnviRJW4L3WGPZ7w+EEDm+HmWTR0ldSYqRWKTOZGEKFbqBF3m9jMZACqSSaKs0WGXED48iBjG2KjTWyWeDL3Lkeo1ySthSV1Ul1pLorVpC+DRNTWrtrC0++LFdf6g1TyWpVymIApr9Egi++Oy1yIaTvkrwTbEFDqEUiSpTjgX4SWdwTLJJRvm8MJ3DOkOIUSTqRT4uNU26SeNBQqZUls0nqUrq2udiSpkcYvGU5pIQXuXDAoJjzkzzTEqRvIFhqH2u8oxarXDl87KCV6sNqdxvS9k8rKz5UKq2FErqh6xmWea2aWSsxXa2pKqPhMtZnrmU0FG3Davb3OY2t7nNbW7zy5mfNVhNaWUinLL03YC2poT3HNDaMF6mwmiBxjT5WEqREEXOKYuoTEqemBQOWcjrsgtfmdW6a1+nAoXrxXyV/W63AkyrbLcxaGVBaowmxoCxmcQAaD5/euLz52f++HefOJ9GQOOsKRUama54EPtuwBqLc5aYAufLieADSotMVogH3VgvOa5VrlilzdW7KedSgbkqLKzi4VESbt+8ecdut+N4PNIpK/7PkuQ5TUsBlNKVeZmE3Zb+Wlfqgnq0sYyF/R6GHrvZ0PWbdq2enp6Kr7LIPkMihJWlyzm3a68U9L3IRq2VcKZahyILXScVItaA0i09dlmWIjs0HI9HXl5eJLyn778BV957luBlIR0r+57QxjB0xafpNSwLRpcqjK4jZQFTNV11KWm3MUSsMaQkDLa1hhxl0R5TpKjQibkE+WRJL673at0wMcagUVf9tHIthI0yuE5kusKQWnJOhWEvdSFB/n9ZfPGfKmrYlaTtdg2s1lCbes829q74a1vyc2Gx1sCn6+oded+WaQXcilzkqAJ9rNHsqvfVey6XCyGkxiALI2eIIRJ9JKQVnMQkx9V3DmsM3geWuaRtG8tuu0MvCzHnpkxQxuCMnLNRmt7JMxWjl/7UwtjZrkhmhTwlxESMJWE2RQncSrndn5VZbtJYrem7rr0fSik2G9m8oiTizot4MM/nc7MTTMWre10zUxntuPhWdRSW5ZvnWSpxan1PTcmN+OglJK6XzQtrNEa1ylup19obJusEPKeI1R1DPxA6SeL2YcHo6qPV7Zhy3Xjzc6sCyln6qpVKGCSoqXpojTFFPp2pqcCZTEgZHyM2BPrONTWAudoslE0i1c63Xh+llGyehAA503dd24gYuoG7w75d6xgDhKqeEFe2NrqkC6/P621uc5vb3OY2t/nlzM8brJYF0n6/4/HxUf4sS93BMAwoNHPxUFkDm2HAuZ5lmcSLOE4iVQSGzcB2sy3hMLKQPx6Pja2rbFGt+QC4rku4Xvjv93sAziU1WL5X2DxyLMRExvuOJUz4H2bGceJyXnj68sI0eozpy2JVo7Ws+Kw14uszin7TlxCjM8JGwlKAovQSKkwnFTbSbXlufjilMtbmbxjhCn6kXsXx+OaR77574OH+Aa0Vr68veL+IZzIKu1uli+fTmct5JsTK3gpYrgBeWNMgicspFhlrLgyfYRjOpZ9zbOBRpIA0gHdd5aHLJkHOisc3b/j1r3/N4ifmSQBmLrGvNRQnFXn0+XIh58xmGEhkYvnzUNgmrTTn8dL+biogxypbwH1A5YwGSWMtabpN3hmX1ct35YUlFRB0BeqUFqmt7VxhNIvXLiZCDBK803VSF1LudXLGaoMqkspu6LDWlE2byGa7Yb/fsdvtAEpdzYQwbQIS5nlmmuZvApemaaJWo3i/Bj7J/aeatL6yj9XnWZni1o1ZzxcaM1uvDdAqWqr8l5y5nM4YbXjz9g1KKU6nE5fLVMKRoAWfZSWS1bR2iDqrUcYyjjNdN7TgqRoElZVit93J++wX8XmmBMZIGJQ2bIaO7WaDUqUyxs/ChnYW4ywpK6k6igvS/yuqBL/4Vmul1Fpj1FhRaxp4rSFt9bNCK4X3Ea6k/cascurmXS9yYig1OFmRQyZlT0KY+5SyJPHiRRZbNhf8Ir5gNKWipWufH7216CKtroyvMZoQAuM88fLyIqqUzSAgWHdst5t2HEDpVZV/D0NHLkF1y7w0Sbl4oXVLZ1dKKrvkOojXXKTL8pwvy0Ln7BrAVu6/Fv4ExbdeffZglGZwHT6Ekhsln0HLOPHp9Iqxkj2AUqJSGeUcZr/g48pgk2WD4Ta3uc1tbnOb2/yy5mcNVq2z7Pd7fvX+PW/fvW3shnMObS0vz6+NPaypkEplMpF5vhDTwsPjA7/97feF6RRp3+l04tOnz7y+HglhZZjqYnu32xUvWWqL/evFZQ2SEbZTNXZqWQzWqMI6rUBuWmbO55GX5yPT5DG2o3OdLNYLyAGIKTMvMyHEcl6GUIJ36vGt8knF0Pdst1tCWPDLiLW9MG1eGBpnXQvuKe2RGKO5P+x5fLhn6HtSTszjXBbQ0tV6Or1yuYi8eJkj42Vi8RGlTAFpqgVVKaWEsTJSXVI3B8T7eG5y6MpgD8MgGwapdiuKR3iaZ4zR7Pd7Orctfs+O/eGO7XZDnxxDH1DHE9PiqV2b4lmM0s85CQM8jmNb9IaYiMmj8BhrG1tvKkjQwjZrpUjRk2JksxkgV6Y8NOZrCQFyKvfbOlp/6wEV/yNQQHcqklEFJB2wxjYvYx1VqjxSjHRO5Ldd79BGC6Pa9+wP+1bJsiySlgzi/5UU2dhCclJak3HFVynBYKn4AUUKLmFbOUslSD1PWKXHIRpcSRO+ZqfrPdsVMK6V9Jvu94fCNGaC98zzxDSNfCpJyiGE1ula7xOR8Ar7lbmSJ7OCJ+9966odR/EGmxJuhRbfbQU41mo2w0DnOjpXgKvVbLc75nkikxiGDmUNvvi+Q+Ibpl/Ob70210FLkuArXc4VWAkrLanktj4HKq+hUDG2hOcG0BQsS/HfZ+j7DcbkwlDKa8xXDOv1+ylBRBmjhHE+X070zpSqHoPTBhNN21yRTaKOEANLCcGSvtuy+ZCk9qXek0rJtdRK4/1C33cc9vtSYTS2VHBrLeTVU02Wn6g15GzIWhWAGclaMc0TKOi7rgQKy7Hpcp3k/CR5ODf/syKnLP/k6hl2LEs9j9BsAaoAeqnjkXMz2rQQvNvc5ja3uc1tbvPLmp81WH28e0DFyJv37+i6jmkaAZpcrXOusECuhXFU8Lrf7/j+8bf8h//Rv+K3v/2eP/zhDzw9fWVZPOfLCVRui8xrxvTdu3eNrZymRapzhqFJ9p6+fOVyHpsHUjxjtN7Q9d8O1zmpyXAdPoBPrySlJCBFK3wI6CyJvFoLsxRzQhEYx8s3i9RrvyXI93fWoFLETzM5Ff9fEmZQpYxSht52zMVXKVLFgceHB7bDUNKSPcvsMSXRVxUgA7KAFvGhhN3YrkcpQwiRrutpqcsqiVc4CENirchOY0kBFnlwqY9JCVekuov3LH5a62ZsD2plsrvOEcPC8fiKsQa/SJiND55c0lpzKh7dJRB8klTSucr9ytErXaSvVxJvnaRT0ihUjihEbnx/t2O325Oi6EOTj6RyPPvtDqM1vrLA2pIQEKOvAq5k8SwbG85YhDiXn9e5js3dpnjuHAoagx1CQOm1IqbKcnOU4B+rxVd7Pp95fn4u162mKK/pv1pbcp6vWP8krHmRdmtjGusktSO+eIiL/LgmGFuFTxG0YrdZZe9rII8rYWJ7ckm7BlrCbl+kxzEmXl9fGosu1SkG6yQpViqhLmWTody/V89zTemOzoliIkiIkYuSJm2KHL6C2b5zUl9lDFkhzDoG2/dgDNHPuG5AaYNSArqWOeBNxCth11XOhfkTKXfM+soSUHpzi7xavOgT01Q800oCkFSRj/d9/03QWb0OAspX+fWyrInLKNXk5hUQX1sRrj2vInk1bbNns+nJZROu9tlqo9luNyij0EaV+q20bgbMMy/es91uSiKxkvutMwxDLyB3WeSzdbtjXmbxglYZuzGYJKBSZNkR53Sp5xHQ25UNImE4EyFmQqRV4VhV3/fclA+SPyBAVZ53uYed7TgcHohxKZtWa6WQ0gpbfMTyWSmBXZt+88/47XOb29zmNre5zW1+DvOzBqvv3r2TBW/vOJ6OHE+nAmIym82WYdgyz8X7plZvprEKpSSJM8bE3/zNv+X19cg4ToUpS62jsbKl2+2WN2/ecDgciDFyOp04nS7ily1sw/F4bNLf6jcMIUjfn1JtUSkS4AVtYNhspV/0NLLMsaQRwzyPgCKkjEo1YKmyHFFCfUryMNDY1pyFidLalJqOKH68UnsCNCazBp3IdSkM3Wag6xw+LPhlaoC9LrpB5M/DsGHoOxY8ZyMhSKnIp2tvZwiVIctM8ygS5ZgIcZWbSu/m3CSmNcCn6xw1Y9WVTYcYI19fXui0KjUnal1omk3pJ53wta4nChtdUz7rYvXaj3kt4b5mPoMPJZRLPKl3+z2/+c1vePP4BmMsl/PUfo7zgZgTQ9+J7DQljscj43hhnsRLZ64qXbTWOCf31N3dfTs3AVAWaw1DP0g1UM7ShbuIjNU6U2SdkmZaj7sGUdmvzxIqNc+E0k9bNxBy6fXUWhN8IEUJFQshiIf06nrW1FTvA8Gv12tNlM7FWyzH9nB3R9/1zUNoreVw2PP4eI8raoMqbwUBCM5YSLDbbEkxcjqfWZaZnME5Tdc5drtd8WBD8AG/LJKka03zgQcf2jMQQmTxoZyvF09vNuRkICeMUkwFiA/DgCrM3jiJ/DfFiM7C3Lm+Lz9frl1nHV4HxjQWJlXk3jGl4n1ffcD1c2Oz2dD3ffHjnpqKQbzNsV2X+mzVDa3KkF4DUe/X781qrZWp8uJQgalSzUvtOofRAvBOpyOvm4Ht9ldtgy8snuPrkcs0skwetCqfi7Gln0/TxOVyoVaFGSN2CbmPHaaz+FCUC6WeqdbviO1AZL8AiYQFjHWtf7p2X2ulxWteMgVy9UkvUkHkug5nbdnXWTcSRe7PGqSVEkpruqFHa5Ei18A8WEOrTOnK1VoLm9vfmNXb3OY2t7nNbX5p87MGq11ZHF/OF07HE9M8o7Wi7weU0izzQgwRrW0LYqrBGqjI5y9feD2+NmbDe8/lPHG5jFwu30p7a2Lr09MTr6+vJVnWsN3uOJ/PDaRW8Fp9aiklUq4gcmU+lmXBh5lxWjieRl5eTsKCpRoipBuzJf9PW5wNvWW33aCUEUap+DdlIQuUzlTrpCey6ztiivT9wOl4JoRYmLk1CMcYU5J7JbXWZklXldoZTYqJKcwoLYE+u92e7bDBYOn7iXEswVHWllROVQJyNJUdyilfgauxJXouV4t1oLDSGmM14Jr8VFg18e7W3lalFJ1fsN4KE2oNPqaSiKyKbHS9Z6oX8xqwVg/kZiPMyjhdpI4maTa7Db96957vv/+ev/ru11jrmKYZ0jPLvMEqw1QAR/XdbTYbNsPA68sLOWf6vmd/OLDZbFpY18PDHe9y5h0+AAAgAElEQVTf/4rtdteA+n67K6FVU1MG+AJSYwWUShVfX4drYU5r/cxlnJoEV/zOinGcmo+zJltLUFDZlMmx3DPytzKgjP6/2Xuz5kiO9Fzz8S22zARQqCKb3WQfHZlG///f6GqOZNbd3GoDcovF17n4PCJBzdyNJCPN8msrq64iKpEZG/z1d9s2Kkq+hVy9lY0KS3t7TblOb4nYTVMZ9BgreJ8qGKvsuxEgEYN4hh8eHn5TI7Qsc31dVZndhC6gq4x3GAZKEQA1+wXn2GpqUArbiLSzcXbzhMrrLszzzOl0EgDUuM2HKzVCMyoXmq6jaRsW7+smlgQrbRLSLLLslPPW0bmm4a5M8jzP7Ha7ek8UpmmEXGjbBiqwbWqf8HofrhtDbz2w4iM2G2ubZReCLf13ZRmr91KbWwiWqaFz2mpOpxMpSZDV++f3PB4OvPvwTL8sjNeJy+VCWLykjjcind7tduz3ey6X03b+V6DpnCWVQt93dF3L6XjmdDqzBs1pe5Pv/sbHnBMKhaq+V2vEVqDyegWCqnL65D1jjOhloWka2aSrimtTNwZyLjWEqYi/OxVUBNeIT3dlsK11rNVdb+u7ShHJ9H3uc5/73Oc+9/ljze8arI7jzJcvX1gWz+IXlhCAQowZZ8etBsK5dvNELssiiyMnTKJfPK+vJ2ExYmKeF2FWkwA6U+WaSqkNkIYQ6kJNGJOXlxdhHLQh57JVgaw1DaVKMU0FAPKezAao/RJwtoFOMY6TBIdYJLQl58q23So9pBoiESubIWnGtS6iJOlCrQvzFUCvC8Y1AAnYFsIpZbRRDLthC4dKMWKto207SWNN8v20Ecar5MLlciX4tPnAci487ve8e34nfYtBqlKOxyMvLy+IJ/K2qJe+SfX/CuiZl5EQl23RrhQ0jcW5jsOwY78bKNW/uwJ/qux0rRNKOZLqvoRUwiTx0Vb55JrSvB4HBRugaRpHYxVNY/nuT9/yv374K99//xda13A8nvDzxDxe5Txag7O76jmcSSkS5oXkgyyknaNtWlrX8Pz8TCmZL18+czgceHp8FHazJsumlEghkEKgaI3PeWNUFQqrNVrdvKbrsZHjVtCqgocqGV5Z/LVaSMDOGoQTts+/egblNOo3fxbZJujt3Gx1NNZgtKEfeqkgUnr772oDEJmpynfXblaRVhtKKpyzwrmmhvgojDU8Pj4CAihP5zOvry/1dYX5bXcDXddweHig7botOTfULtRUEMm1tbStAKjGKqyxKC3dqSto8jFsvsUlBvx4paQsUnmlCEmeJeum0doju95PImc26FLrsFjZe/E4l5yZpolPnz7VeiUJfjNKi2/yTUDVylivdUXrhsrbFOYVVJUi95vSersGtNYYXeW5a3WX0dWrK8FmxjhM44hZNuqmaeLy7h2H/UE2HOo9VGpQ1fo5V6/+fj9sbO+6SRFjJCsoPgGKpnU8Ph2IQdjNt4zmJiNXis40NawqEMNCyZLAXo0NW+gUWqNtI1U8b+qBKPK65e0x14oIJKT3OKWEyYacJV3cmIzWUtvUNJaUJkq5KWlSvHml73Of+9znPve5zx9jftdgdQmB6zRzvYwsYam75YmvX1+3VNm2bSFKQujiPfMyo4yhzcIIaCO9iSlFxmki+Fi9pKvPTbPrhWFRSryu1lqWeeH19cTpeCLFTNcKyzPseoZhR66LW2OU1D5oQ982dJ2jH3oeHg9orbhcRgowTwtGJ8imhmdmdGvouz1t05GSsJGSRBrxy7xVQqSYgbwxQ6uUtORMSIkYgoRBRQHiShvx9gVfF/qBx92e7777jsN+kB7TJdH3LdYIO6eUQZumMpqRr8uZeZI6lJQU12khRE8qWfyyrsE5WQQui2XoW7SGZQmkLKxQeSOZBDZfcaw9lW3j+O5Pf9o8jyJ/zbTOkUuhGyTxOdRFdYwSAlVypqSI95GSKptjDG3T4Iyw7E4b5gqw/BoImiLGWb758IF//qcf2O96usrgjePIT68/8+XLV86Xq4R3xYRzDX0/YIyW4+UGciq0XUvMkXlZsNYxTTMvL68Mw4Axlnle+PLlCyWXyoIZqUbyfvM+rqy+MhptCtpaIDMHvwXVmCphXMF/fiNJ9b52olZZq1KSwOsXXxf9pUov1cZCUwrlTfAPWrH+DxBZtNG0jaNr155UVyWYVMBL3RySNOJpHDdZsqo9o1aJv1YbLamztaJJwq0MTQNajxKsVHtid8OOYeglOGw30LQd3i9AYVomllkqiqxzOKtxtqBVRitDUbKRkyqYbtoWU0OhrHUswVfAEjFKk5WCqixomga1pTzLPScS4FSrdW5VKKZ2mEpqtZzDZVmkDscajJaNnlVRICFJAa0VKGEFtdYYa8TfuQLUWlmUUha5L2wdrqt0ePVBr5JzY3RlFaXrWVUmfA2mmpYAxzMpQdM6ci61y7bKzmsYVKg2gFKvl1K9o9pYcinoIpJdKrB1zpFiJkS511fJuGxeiSz+8Hig7RpcU7a6JKD2SucK1GOV6NY6reqVVdRKoS3Mq6Z/y6khU1BkVNa3r1FaNrgWv20YzvMsXa1BfMkP/l5dc5/73Oc+97nPH21+12C1lEIqMC0L0zy96fqrnY9GMew7UgrMiyemgLZWpK3WkgsEn7Yd/7br6XqRHGrg4WHHvu857PfoN4vA63XkdDzy+vJKrBUbxiiGYaDf9eLjm+cqrdVoBW1r2e8HvvnmPfvDDttYoFQ2UJH2PcsycTkb8WD2HU3b882Hb4kx88svnzBKE4JIQr2/BeSsntjGOaDULs5CydJlSYEUMsXkm3esiH8MoJBRqmxyzcvpSNfKgp8aTFJqnUQIER8i0xS4jhPLHAgh4xfpeb2OE8fjkcfHB7RSGANtY2gaQ0yalDTRJ0JMVMvfxrxYa4WRKglrDc9PT/zLP/9vDvsdLy9fmRdh6KKR5FbnWubJgwqkFPF+rLUdc/XHFrp22HpUQ70GrLHsuh37bk9MwpbJYcns9jv+8pc/8b//6Qf2Q0+MmcUHfvz1Z378+WdOp8uWAGytxeoCOoORQKuHhweR29oGbTXXq4R+Ld4zjpNIiCloLdeZNZrdbofWmawyoUqeV8Zwv99jnRaPXk1sTjmhqL5jJz5X5zQ+RHKV94pHUtKWM7qy9BIMlFKCWhuktaJkjdayyJfgmtqBal39jFbCdOoGTt+LtLZtRNKbSwItadrWGkpxVQK7bEqEZVnE46hFxqnXDRUEkB2Gh21T4Hg84peAKgpnxc/bdx3Pz088Pj1UKagoE4xReL/QXCwxLJAyZI/VGmcBIgVV1QFZ6pNiqaykrpLQRI4ZZxyta4EbAyjsoMWhmKelSvVrCjDCcopHXMBj17TSI1yfFc45Dvs9fddu3vZxmnh9PVKKxgdPKaEmA6tNYpuKbFisFTUURUERs1wjbyW1qzLBGIW1Iv3XWjbJUvTMVVXQtt3G4K4s8XVcGKeFpnWyIaQUu67n4eGB6+VCql7RWDe2QEkgWcosl0kAcgXatpFrBFXI2ZO2qp/V+ywd0FAY58Cw6zkcHuh66XiNMWCqh3j1UpNuz7eizbaJsj7HSik1DAxyEhC6eA+lsOulzsh7T6nsalGipBjHEe9vwWFaG6bJ//f+wLrPfe5zn/vc5z7/5fO7BqvTLKBk9e2t4SWyoLmVx5ciHX9t2wG1LiQKO5FLQWk2FmtN/T3s9zzsB4bW0Q0iO71crxyPL7y+Hsklsn8YoGjGaSGnglKFuHiWZcYHL6mfreP9uyfevXuk7RzPz+94evdEv+vRGn799RN9txOZ8XjBP0e6ruPp6Yl+t2NZPH//2z+YJgE9TdNQnKXr2q0iByQcxBizSVFjiKhBbwmtq7/XB0/MEW3AZFMltg0hBH786SeMkv7KkhsKmYeHB5E4xoyq3Yzey2Lvcp2YJ08IAp66zpFC4Hq94qzUqRijKQjbSpWlAjevZYzYmi5MyWhjAUPbtrx//x5rDMfjkev1yjRNaC1MV9d1LLPn9fW1Sk/ZrgFd5bJN1/D8/J6maTZfsUKkjquPEeS1JH0qY5xlWTz/+PEnYedcSwyRz18+sywiN7bO0fUtRputEkXAjQCiprGsnbxrL68xhqWGShljyFmYzTXZd0031kYRvEdVSacPMymLpHtN6i0ZUr757Yxxm+xWZKG6Sh8FJKTK+pUKZNcOyy1Ztso505pkBJv8XZi7hsY6nLWgCkPf0da6F+8XqWGpLCDwRrKeNrny2rm5sYCVCU4pVeDgeffu3bZ5IyqCsHkn9/sDTdu8kZ9K/dQaULTK7lMKGKM5HA445+Tzp0Ss/aveB1ISKah1zeZdXKXsVAZUV4/lrdLJbOnd62eEtWu5VJn/jelcPZ3rhtV6za4bCaYm+m4+elXIWVWwf9tUUnUTJaZEybfQpdUnHJMnl0guDqVbdFiD5ERWC+L9XZaFFMt2nHIusvHkxUIhSoxIDlKN9N2f/8Sf/vJn5vHK8fVIinG7/nKKhChJ3qGyr8Zq+irH3VJ3lcL7wDhOTONMKZL+q7XGOsvx5ZXxfBWG1VmsFfbTaIVpWqLS9XNL2m+IiUDa5PqlplxjxPecUt5sDzlnFIUmWGajMWap56uy0zGxBKmiklCxwuzvYPU+97nPfe5znz/a/L7B6jTVHfk17RWcszUpFpQujOOlLgYNFFjmhRAjzlpsI1K9lLwkauZC33U8Pj6yGwZaZ1A1jCOEwOl0rIvqJ7Q2wsyGyDhK0u08Lyx+FrmcVjhreDjseHjY03UN754f6bqOeZ7Y7Qe6tqVtO1IUYNG4jud3e4ZhwDnHl5cX/s///e/88stHQhCvqnRD2jcMww0ULXVBrpQmJfGiyvFwONdyvU63BM2tksfirIEiTEYGumZH0wijvN8f1kJLSoHj8bVWwQSC95sHVSmNs8IcrQmxWmlQ0vkpial58+VJyFWpLJD0ea4VKk3T8PDwUAN3Jk4nCSoahkFCqyicjpLeHOsi+m1gyjAMPBweKQWp1YiJqVZxGG0IMUIRkG6dpW1EhhqTBOL89PPPvHz9DMj1oOq/abuOru+RzlLpCNVBFubPz888Pz3hvdT8HM9XzucLW2emVlsXqfQ5FmLwNI3DOWHZrTWIr1fRmA4FlRVKaKXomg605nSWvlilVkB6Ze3yvclSRd6qlKbozNB1WOMq+FGUVMPDnBPQFKIwTdXfK05WNr8sOROjl+8ZPMPQMwxdTSpeas2LSF5hZcw1xrBdp+ukFEkJUk3NLqVwPB4Zx3ELTVrB6gr8AGLIGyMYQmUXc6mebVWPZc8wDOx2O6Bwvlw4HU9crhepG0KjtKGgSEXkp9MkPvGYJfV3P3S/CWRqGgGPK/Be5xbAdgP4a1ften9aa1GwAfPVk6qQLlH2+7rRIqndzsmGAFURQU1plvsq1fqo9fhS7z1VQ8fsBpJ3u4FhGIAiYHGat3Cvte5mlciGSN3QE5nzdZo5X898+vyJv37/F7797ltOr0culwsoMNYSUmCaJ7mXqNVaKZJSLynNumwMtIBXU4+hAEY/S1ibKpBzxNe+YGMkWExpvT4i6h5X2cBpShljxCObcyEnAcVvPadKaRYfCH6pB2qtvNm+grT+4c05vc997nOf+9znPn+s+V2D1WXxbyoxBABZa3HNulDUOGfqAs9xfBmrDBOssaQgqZ7WCmCyWmMUhGXm19MRrRW7oWcYeowxHA4Huq67hQNlYQwfHx8AxevrK5eLdCnu+p7np0faVpikvmuw2vLly2e0VvRdx+V84vTyyrREQoh1odkwzws//fQLf/+HdL+GELG22Rb8kv4qnrK2ben7flvEG2PourVmpmxS0pgKIXq0FhbWey+sMhIAFWMSVkTXsB5t6Ps92jQ1LVYW1DmD0ZW1yb/tdlxZH2t7Ykwc5yNADTy6dS4qJQE0GrWxTQKQZPH88LDnL3/5Mw8PD/hlpu+HKo8UUBpSqkFUIpcuBbwXEGSMETl2N3A+X/jy5SvjNHEdR/Ejao33ga5tsY0ANVf9ePN1Zl4WjDUobUg5ModAKXED4gJQ1MZka7V2ihps0xCzeKNPp2M9H8Ik6SK1HJIYvEqfV/moBGYty7TJElXOUIGhKuC6lqFr8Un6fed53liyNRXZ+4jWawIzdF1H2zYcDj3DrtaNoGs1T97uoxAEzE/TzOVyYUnSHVtyJhZJvA1qrVaxZGcxXq6VlBPKaB4fHhmGHV+/fmUcpwos0vbe1uqmVIOQhPnixoLV1z+dThuAXEH3muQtQVo3mTOA1pJWvaZnb9U8MVYAmMRjWatynGtreFFmnBdJBq7dsDEGqXoKC8Mw0NWu4XmeUcpsz5o1QRsk+KttW2GXFfjgN8/wluKrVE2XzreqGm0Yup6nxwc5pjnh/SxJySls99N6zQkLGom1N3gN0Fq9qVqr+l4cTeNo24Z2Y6INoDiHq3iY34BVkTqDIOO8fW1J8PHzZy7XM//6L//Ch28/0O96jsejpDQr6PoGtVA3jcDaBecEcAr7XyqjbtBK0sfX/uPoAzW4XMLNtKnn51bxlGoGgYzawrHk+bdeMwof062LuUqFjVGC+Vdpew2WWyt/ck7b8yhXRUG5g9b73Oc+97nPff5w87sGq9N4ZZknqVjRkjJqKksHhafHJ/761+9xzvLycmG8/FRrHzJ4YRGsswxDh9bSqVlK4Xq5sHiRzopn7oq1ZquimeeFzMoQ8hs2Q2vDbhh4fHxgaFti9HRty+PjXhbRaWFfJYrH45XFB67XmZLBusKnj1+4jlc+/vqRqYIdpcy2CMupiMS4SgtFUporiypyOmHu8tZT2nUd5+uVQsI1dvscri6oU128amUpRTHOC/pypaA4PBxAFcZxlMATYwhhTSktkAtWC7grpRB83BbT8zxTcqZp298svNd/m1IiJqn5WBnivu/48OEDz8/vaLuGEGaU1vhl5joKkLPO4b0nhkwIqbKrisNhTz907Pd7lDIsy8Lnz58Zx5FYwbKzjq7RW7+mJMca8bzGSEhR+hmdoyRFTIllnsllJIbAw+GBH77/nuf377DGMI0jp9OJ0+nMy8tR5IXzwrlKjlPOEvKlFVZZUqImOGf6vkFpTYjCkhqr6bR0ST6/e8c0zby+vBCWwK4f0EozXi8si5egLBXrwl4A9Fu2vZSySU8fHw4bw77Wn9ArFIYYI5fLKMx2kgCmG4DQlJy2nlpjFIfDY2WA2YKg2rZjGHbsdnvxCtcgmxVQrsDt1jPM9h7FI1vIMZFr0lVOmRgF5NrG4RpHARa/kGq1U6xVMisY00ZDUYQQyXncAOBSk6L7YV+9vY1IpEfZGFirn3JJ1but8GGhTc2WALyy90vtEF1Z17VnVEKAVlAkrHQIapNBS6jVrUsYwDXt9nxpnQRNNRXooSSNeE0qpkgg0zwHfIii4FjkGlrrY9q2oWulI9laQ0mR8XqmJFh82CqB3va2roBVVCgKpQQIGmOl31kXxnnm//zHv4OCH77/gW+/+xO//voLP/30y3YurXXElIgpMy8eao+wbBwYjBb/fEqSWk5RBG3IWSq9rDFVDaPrc85Wubh4n+V957ohkd70z66e1VsK9bppscqo1+7VVdmxAtRSykZe57oxtD7P73Of+9znPve5zx9nftdgtVRmUCvJKpVGiIzR0q/4l798x7/+6//F169f+NvffmZepo19W+VpbSsdh0Yb+r7DWsvoPV3T0LQdKYok0RqNscsGjNASYqS1dJ0KEyJMZ9d1hOC5RM9u17PbiTRRa83hYYcxhsv1ypcvX3l5PTNNERDmbbyOt/4/Cl3X125ASQrNa0ImtwV/jOk39TSyOEvkXLs5tSTl1qMmvtUkqZwKanrq6uEUMDVNHq1HYko451j8wjxPwvZFSZcta0JSKfI9U+F6Dfgw34KjkgCGXApWW7wX2ej6GUuKm4dREpzl351OJ6Z5ZByFDb9eL1wul3o8DON1ZllClTYn2rYh58j7988AW6puzmsDJlsH7m6/4927d/R9j7Hij9Vakci8vr5WOe0tnTW9YfJcI+DE+4XzsrBMcu7H8crlMnI5X2VRbAzONegCDbKhsdsN5Jx5fX2RUKdSWLzfmLFWOQ67Pd/96U+8f//MNE58Hgaul5G2aXl5feXjx0/MPm5hPcDmV12ZNuBNBYrmctFIWrSVBF6tsbbBuYbgI9frlfP5vNWVGK3p+qHeG8I4Kr3Kpm99pOv3TTHxyy+/Yu1XYWaXpUrS06ZEWIHN22v3N5Usb67nlKV3dT1nIUZUfc85C+Mr13epTK0kvEpNSq1G6aRbePEeU2XwBQgxEnziWit15hqEtnYTA1htqlqDzVO81v/8Zy/yeo037Vvm/XZOpklAZQpx+zuo14TW5JSYplgDt2ZQ4JxjGDq6VphxYwwxJeY5ME0z1+vIvEnH7QacTfVqr5LcMAfmyTOO1bNabn2nKzOrlCLWXlLXSKJwruepFEn0vVxH/v0//oMUE3/5/s/88NcfeHh44ueff+bjp08bcJTaI7VtYuQkIFBCqOrzWonAXNQIEgi3suU5qw1Qr9fxmiVQSmTxN6AqbHsmpkguqxR6PRZyHlNO2zlZN+T+8z3CClwLlHyvrrnPfe5zn/vc5482v2uwui4u27bFOunOW/v2csm8vh75t3/7N3755Wc+fnzhfJ6q50qkwUPf0biGmKIkBs+wH3r2Qy/plT4xzWNllgrOSeiQ/Q1LpcRDF6NUi/hJ6jRKYt8PvHv3iG2cpLHGxDdP35JzZvz0hePxzDTOjFNgWXwFbMJuWtvUEByH0aayQMsW+iISyrQFyADbAtr7QAgebVSVB3t8XITxqAvvEEQO3bc9q69yZWmtcTVNtmwsmchUQ11oSmCQx0uabEmQMtra6keTgCfeeOrQliUvwgrNYfPZqXKrSTG1k3HxMx8/zRv7E4IsukE8wss8cx2FjRavnQCg1Ru7yi1XSW1OCVNDjg67He+/+YamsaChqEJIgRwyYVkI84IPgWWZaLqWxrqNwX487Hj//pmYPD//+LVKIpctbdiHuIW0tMYRY0KpzDiOWCvsl1KKy+VcgYl4+EQ63QIwziOX6cIwCcj78OEDwzDz048/8vLywjRN+FQ24L1uvPxG1lnvixVUL4ve2GyRx5Z6/B0hJPwSajiNSFXbtmXfD+wPO6kMMSKf1EYx1bAeayzWWaxx+JS4ni+cTqct4Ga9JldWcgVJK8h9K8Fcw4rWwKjdbrf5e1fJqryeki5jMmmJlalTgCbn2iesBaiWomrfbGYJM8aECmbkM6+M6ltP5Zrw3DXNFhT0VnIryd8ZNOSiIbP5kUNYfgNigQ2gr6FeawDT+v3kXJUtLXmVwne99MOuicf1xeq9pOn7fvM9vw2rWiXVOb9Jyg2BGEP9HJlcCqYGem3vT2vxkpZMjhGtHRglFUWuw9agpL//+A/mZebbb77l/Tcf+Os//S/Qmk+fvnC9TJXpjrXoSOTEwoDnzdseY01qV2uAlLChb9/PuoHxNvxK1CW3zlY5hmCKqs+BdfMj1Wdo3o7BupHz9mdGfTJtfycM/51Zvc997nOf+9znjza/a7B62O/p2hatNY+Pj2gN03St6Z+BX3/5hc8f1ZYiOwwd3ieMURJ61LaE4OmtZdgJ42neLFCnOTJOy8baVOoWpQtKSbdprn6ylDIlePwy0jhL37c8PT/x/P6ZkhPjNEpCbtczThMvr0eOpzMpleqbNZVZyptnqwAlZ5Ygklphe4SdMLUq5JZEu4IU+SWSXGFXQ13EN3UB7CugaqzbAMON+ZAOSqWR2hotf05JfI2pygadc1VOXDB1oR+ihAtpbYghUmoYi1aK4MOWnCrBwLJgVVrL0larzV9MBcSStCveZGua+rky52kW1gaNNY6u63CNyJ/Fk+q5XsWfJ/LsHdZYYcitBEqdTieRldZKF+89KcQawwIlCRuqncM6h7U933x4z/PzM9frlePrqwDHIKDJhyC9rghzFGP8DZAU4CThUcsiwVAr89k06+aHYllmfv3lIyUV2qal5MLL1yM//fILry+veJ/ISpih1ad3W4izya0lgbipmwlqS8ZdqqRSzvdcq25EpileZS3dqzkTvIfGoLWjIH5lYb4FO1kjm0Mqli09d30/K2BbwcEK+JyzN/lmCPV7qs1v2NXaFAlaguv1wvV6ZZ4X8VF3LbkyZT6EW2BUvQ90rUJVWtM0LUUJKynvQcArRUJ8rDHENbG6qhGsMXTtLaF5ZVTXyiH52urfJsvxKwmtC7vdjr7v2O32G4BsGpETmzdJyCsIlXsvVRy6so+QYsEvEWP8dk5kA+CWdrslOaPqRo+5KTJKoWtb9rsOhSFnCTkzxmB1rUGqklgqSBV2UVKJlbKiuig3YLf6Zj9//sL5fOHTl88Mw44CdH2PD4ngA7OvPcrVlqGU1CLJ9b8C11yrkSzOmSrVlVobpfLGZq/PqTW9V15Wb5sI22coshmlNKQUBLAauY6N1pjqkV/lvqomxYsVYJX/3gLA7nOf+9znPve5zx9nftdg9fHhgafHR9ZqkHkeCT6itUj5UsiEHNHa0HQ9xiTaTgJPDvtBWJTOMbTik1zlp6fjSeSx1qJ1wIcAGqisEChZPLEm365Jn+Lva9qG/X7PP//Lv/Cw3/HjP/4mLF9TOJ7OfH155eOnz1yuU33vwvLllKGw+VN9ldquvq11Sk3FfAtUtdYbUMg51WAS8QNGL55ebS1KyWLVOUdXq3zWpOGN9TIObSBGL3LWxqGTRmlNrgvl1Q+5MmghBPzs60I+k2vrR4iJUuLGdIl0uwJuY6WXsRQa10hIz66nqaFUTeOqFFPYWElclu+fszAy1jienh5xjd1eU46RHJu+79nv9jSu4fX4QsqJL58/cTyfKgsr3aTRCwPY9z1d02Ibg7ZG6i+cpe86tNIEH2q9hnjrdFolhpl5Sx69+VZQ1b0AACAASURBVDTXX8KMj1ti7AqEYoj0vUi9RXarGaeJH3/+meAD0zgzjzOzXyhZYVyDqWDyFkxFZQLZXttaQ9e1tK1DV49lzqCNIVaAGuONcVrrV+R61CIjjbpuzBSKFhn3fhgwWnO+jMxpqkDoxj6+lfv+Riq8saMrkypRt+u9W6qU0xhL23ZbqJYcy8LlcmGa5voZdfUg3mSbq/S0McKeOufIOdMZK12pte5GeooFrKlS2A1dlZkW1oor6SRt63UmcurrdWKapk0qnVINjYqZlCNNI75npdisACklAd05oysgW4Fm3/d1AyFQylFqkVA411SAZ6FoluXWG43SVfEwstYBCfitYDul+lyIJOtoW8Nut8N7kQ+HkETSnG89sqv3XjYgcr0nFFiDclbet75thEhdTpVXm1eoHuilqhhAaqlizjTO0TXtdo7lHNXnldKbR3VLOK59z6v0dwXh4ktPku5bwfr6/tfAM1Prr+Q9ZLm2lBIQqxRmtSpwu+ZXJUfO4i1X6g5Y73Of+9znPvf5o83vGqzGGLaAE19rVFb/oSTXJpQTEKON5ulph0Kx2+847A/4ZSF4z/PzM/v9npIk6GWpicFhXEglYaxit+vZ7/ebH6/Iyl68nJPfFuPGGHIpfPPdn9jtd7yejsSS8Snip8g//vETnz5/5vV4JBXIKW7djLvdAWsarteR8/lS/VcGrYvI69YQkDd+vzXcSY5H3FJQAZrGYWzHNI/ElBnHCa1nSsrsh4G+72twUqpMcSEmRfG++tWECfTeb6Bj9cQ6Z3l4eNjYw5wzYQkkn0hFPIjjNHEex8qMxA3AaajhNAZnG4IXBurdu3d0XYtrzSbllaCeiF8CPia0szigFGHljDU0bUvft9WjK0B4BYPDsONwOKBQvFPvOJ6OnE6nClJT9fhpTNuIP9UHinX03Z6ihBF7PDzwsN/hveflyxeWeeF0ujDPnlxuQE9pTa4dnqoycEprVJUnikzSsd+LzDWlxDwvwvwbWJYGSYWVCqJlWQg+QFHSpeoMbe3LjXH1/oqXthRVNznW1F5DU8O0FIp5kY0Eox2QiCWQKpukShF59xrSpG8L/5giWUkY1PPzI09PT3z98srL11fO5+vGVFrnAHmf6733Vn4pqayJlISV77oOa2/SzJWJXCXT1GtklRGDVFWl6qFe77ec1/oaYdOsrf5N41AqUiqYb13L0Joa/OXJ9Xp01opf2976Qa27Sb9LKQzDjv0+crlcOB6PnM9XqRRa2VxdN07sGgykSSkI8+4FnO562RxbFRLX64WmdTSuq8B4IqUsGwpa1XtmlcNKyNMS1mAhqWwZx4m1A/YmP1akVAghVUWCZhgGlsVzPJ6lboqbd3gFmKsqQyGhaZSCRmGqqFchmywpyn8L0ZPyDMh1nnLBbQFfUjM0T56lkQ5crUBri3PCcIaQUDljlcLV1OKUAjF6pmUmFQmMQxmULjStwTorGyclkZJsdigFjV0l1Tf5uzEGg0GjtnuxlFxZZNmw1Nai9Mqee9a65/vc5z73uc997vPHmd81WB0G6fRb/Z77/eHWYRmlzqZpJGkzq8ywk/5SkVdKMFPfCbgdx5EUIiknGueYp4mchdUwZq3A0YxjZF5GWQwXTQxBQl5WVklJt+fh8MDXl1eWZWKaFr5+/UrTOF5eTry8vFIKPL97Zhh6DvuB/X6Pcw2vLycul2v1akYgvmFR5XO/ZVTXZNKVtRTgyeaPaxpHTIGQFkJMKKBrHB+++Ya+awjeE0NA6Rvzs7Ky1pmNWVplh13XYYzmcDjw8HCorGBd0hapvEgIsPj5l1+YfECp9BuWzXB73zlluq7l8fGRw36P0lL/MU1jTc2tvsQayKK0YVz8JotMMTNer5SyVu8Uco5vZMDy+Qy6elcNjWvQpt0k18MgnZQlFuZ5hpLp+w50oW873j0+sht68WTWkB1h265VwkiVPoof2tmbl1QpWebLcfV0Xcd3333H48MDx+OJf/z4EyEkrpeJKyMFScINMdae2tolidQk2bKG09gqHb8lUnddu20mrOye1prgPVMNAUNplNGopKHCVa0VVsv5bWwrqdgIY9xoQ9+3PD4d+O6773h+fkZry9evr7y8nPA+oJWjoDawe+uLvYXaCMj4bSrwGlwEN9/gsix8+fJlq+Z5G8R0Y+fUBlZLrTFJOaGzAJZxnG5+0BiIKROXuPlgQ0jbZkjwC85amtZWAG0ZhgGl1JagK2FDartW1n+/vhfnzJa2DDfv7TxLCJgCUojsqtVAwNHCtExoda0g9cZKZh9I+YKztxoeH/yWxm2tldRzeRpUKavIeI2V63vo+1qvpLA2M40zR0RN4JqGppFgOWFWk/hac65MpEjz5Vkjr20MNeBK3meugHlLc86ZUs8Thc2HrtT6SuusagCLj5E0ifR4GHpc41BGjt+8LHgfayhYlRNXP7MxhmmaWOtnTPUYr88UY4Rtbk27hU9RJdcxhu0zrGqZnDLeGbqu+6/60XSf+9znPve5z33+h+Z3DVZLKVyvF2JMdJ2EjvR9X5m/PW3bSMKl1sQSt6TNnDKTn8T7FpOAjgLL7GvqqKnsSmS36+m6hsenR9q2ZVlmpllVr1ci5YSxGldlhkop2q5lmkaWZeJyOTNeLsQYsM6y+AXrLA/tI4/vHnl8eKBvG8Zp5Mcff+Ll5cg8SUWFyItvwBREjqf4bfrlW5nl6hdc2TdJAk6gFG3ToTUMXcuHD+8pea0eabCuIcRAUV4WfNYKQxSl7sYaS0F8eY+PDxwOh5rEmjCmkfdUJA00Bk+IEWMcu34gVNkeCsg1wVnrKo80PD0+cnjYVS+ZVAgZY2g3mXLlAMtaByIMElC9xTNKy/9vWklyPZ8vhBAZx5EviDw058iw2/H4JKB4t9vRti1tKxUiRlavIucuEaXB1MX28fXE5XKpYUiOvu9pmishJmKS92SMpu93OOveyIAzJRfaxuGsXFslZ3xY5aSlSks1OUeM1TUkTJORxGlJOwWUll5TVaT2pKoKJBxLb6E8kqrcopRsGuRUSCGzBE8Sve3mG0UpVD3Wfddh12obkO9T7ymtNfM8c71chIXaUlfV5ntepa7C8qnfhC0JsFkDdkKVnactZXnzSzfN5udd5Z6rxH31FoaQq5w3b98bVrZQamIgY7Ta0l4FOK4dozWAa/akJMFLfRJ/sALxugLzvGzsZXzT5VmKKDhWsOScxViRyc9+ISRh0WXjQ67nnCWAyzUOrTSpFEKMpOhFNhvkGOmaqEsqxDhv31NpAYAlJjQK11ka12zgP8ZIigKMTaslAMtoSUhG07UtbdNRysyWHGwMTSsdw9M0EpO8t5ILTmtRZhhJGDbGkGr6ec4ZYhaf8+ahrxaJ2iVcshRn+SCVSKtHfUuuVuITiAkySSqjGglGslbUKT4stK1sgOXkMTXYbb0el2UmRE/JEbTd3ocxCmesBOnp6gc3cl/7cAujSiFuGyxtIz3G97nPfe5zn/vc5481v2uw6pdASQVrDI010leoRQa32++EVdUiF5bkG0Up4tlcfGSaFqka8cJYrDLXYRhIKKxzPPc979+/5+ndE/M0s0wR5xpiylwuF0nENYqu67jOI03reHzco5T47K7Xa12kScWNUtB1lsZZrA6U7DmdZv7+97/z9esrKUuSZi6KojQxS+3LGnakagSQNRYD0m8YIyF4pH+BjcEqpUp30TRG0XbSJ+us4/X1KBUttqFpZfG32x1QSm1BTsYoYSWtYjcMhBR5fn7HDz98zzTNfPz4CYpivM4VrEryznWW1F9tHM/vnvFBfJ5KK1QRQIAu5BB4enzg6elRPJJppihhRR72B3wITJMnV2CbUyEsSYKMMig0thH/4rSIJ1TPwqB6H6u3N7H4Cd027PcDDw8DbSfp0W0j6bxGgS4F6xRaO1JSOKpRr6yePoN1DQUBX1ZZdt1eNjwqoGpby25/oGkcKQiIpuQqB4YUAiknLqcT4+XMtARyjKSYSDGiVKZte4ahk4TZKIv+WOWh1mmc0bTO0XUNxoj0NlWGK4RcP3OoIHCVimpiDdnxayexc9jGUWr6b9/3AkDV2u9ZNmYvp0Tygev5Qlw8p/OF0+tRWG8qW24Npax1MoqcqR7jZQPtoX5vrS3BZ0KSDSPZvJCwG+sM2mpiqj7YLLVSt82hQI4FivhFtTLVJ9qgNGhVUCWhkohYM8j5UQmFktcNsQbxBELyoCEEAYni5ZUOXqm3GVmrTWK6hfpYZ3CtvQUd1fO1jLNcEzUMKaWCVbomJ4sPu+t7tIZQg65SUeTqHxVWXFhPSZdeWPwiygWt2A07DIWlBimVvHYks3mjnTM0zpCiPBOMNrjG8viwx1wU1IqbUmuHnLPshx25yIaDMxJu5pyp14J4oIMPTPOCX8TvW9LK6ivaTtjesER8ifWzaJHxaytp13n1oxp5ntXneI6ZiCd5T6HU1OwCOZFTwGhQiGIiZZGFu8aidIcJmhgyKgNJ0r2LKuSS8GkmK0P0y8airpsXMUpo07xMaGVw1jL3y//Iz6373Oc+97nPfe7zXze/a7Cqla5+skHCeOqCZg2b8X65SQ8RhipWJut8GrleR6ZpRmtL9qEGzChy9UJZo3l6eOT9+/dopTn7K33Xczg8cB2vLPNC0zQ1ACXTOcuf/vyB9+8fMEaRS9qAizCjpn6tJJ9qAzkFTqeRy2WqaZiGVBmLVBRKWwyZUlT1rFa5bw1UWeWM6yLOvPHOARUc93R9W1NtbWWjxOv2zTff0Pf9xn6sEstSCoZMTCJzHoaetmt4/vDM48NBgPhl3MJPjBY/WY6RhKJrO4wReWjfOqZ5wiqNsRpjwVmD1Yr/9cP35By4jBfKAiEs+AJGW6Z54XQSX2RJipwK0zyz+HmT/BUN8zLjz74GQlUvXpQwmcfHR56e9gy7jsY5nLNQMjEseD9BVjTO0XYdOTtivLIsAvSkrqh2dJaC1g5jYJ49wUfapq0sopHjM9ykt9577OVMyRFnG6w1VdotAGIFMrvdTpj/JGmzbdOIBFkVerUmz251thgloTSlCGOllK4dkbkyjqX6WIsAo65FKVuZQakTET9rW2Wx4g10zlaGUqNRrDUg8p7Bao3Vmq7rUMryqf1CKae6/jdVql1BZ5HPN8/i7wY5n43rNtZsWTxZKdZOTJQkQiuthBXMpjJgiVQlwE3bULJmzgsplppGLCnf33zzHqPB+4llFvawcS0xF5aQSFX+KeFf4m91jd08jCFGYhKAuHhJVJ4XzzzNVWpdZc7W1MAmUxPCBdznJP2wKUpKcQgSnqVRpDcqh2WRNO62bwW0VTaSN2FH+GVjIKmhWZmEypqUIzFpSEmk4kg/qFaqPv8y0yzPrrZpidETQtpsAfv9TpQKlZHNMRKLXOdD29E04p/vh5amWf3HefPDXq/SJXw6jTcJtzU87He8f/dAToHj8ZXj8cwSJKU3K72xrSjZAFvZd9mHK1XuD1BTqGtqb0kZbNm+dg0lUzWcq2072uY/SY0zFCVe7ERkCaJ+kD0H+V6p+nKXJTBPZ4w2dP3+v+cH1X3uc5/73Oc+9/lvm981WF0lh9JdqAl1kW6trjJC6fNUtQTeOCuy0Zrq6b2vXjnxaMqCKJNSpOs6hr5jd5AFzMdPn3j5+ioJnDVpVCnFbrer/x52+yf++tcfpMMzK3bdULsnY319TUlZAkOalufn91jTcDyOEvphjATWVIaHkmtgkCwUjdHkrAmLp8REqIza2kGp0beqluoHXH122la54tZpegvgKTlzvF7fyDs11mji5tftGIaeh4cDQ9eRcuZ8lnTWGCXsRCthQdrGcXh8omla/BJYvDBAskysvxfpixyGnkLCOmFbLtdQvakN07wwVt/qOM4sS+1v3IJUiizc462PMcaMSgWlC6Yy5E9PBz58eE8/iIS7lELJUudCEZlkRjFOM8lfN9klleVyrq2+XEvO0vMqwTmBvvoCxS+9B7nMbj2jStV+Ui1BR0qRs0ibvZ9xTaLrM7vdwDRfCcFXOXsnWblV4mpqPQ8oUshb9+xaC6kErZKqHLZpWppGpKlN40ixVIllX+XVDc6Jb7FpHG3b4f3C8SjX6io5Fx+qJqbM6+nEvCw8PnmsdcQs0liFQVePK9sGyup7ZkvUVRqRk9frshRLTmvFTakS6q6CtIj3SYBKll9Ka6zSKNvQHBrGcaGkQtMIY/7n7z7wzYdnYop8/OVXTqeT+EwzKBVYWKtyMr7W+xhj0MbV45lBRbLP+GWu0mO5xrZU5wQ6y/OmkDAFtJZrWhf5uhQiYfFVoiz1KVppDFk83SGypCAbJVqj1e0Ru3pAfVmqT7P2H1vpc9YoVMkoatBZjtv1VV8AyLUSSc5Lim/qbppO3pO+WQtKrXGRHuC2XhuiOnCNbGas17NkADTsdtRNEUnS1kquwb7r+PD8HeWHv/DLzx/5/PWV6+yl1ibJfaeQeyipeg+yJmbLswryb+qXckIqhawlswbMgVFG5NMlVS977WDNVX1SZLOwlEJIIhlf/32hyHHTGte2omxIIg+/z33uc5/73Oc+f6z5XYPVYddzvTqR9i1xY2Bc05AypKxgXSSSyRSmaWIcR0mDLQLyvL+FblhreXp64v37541lOJ0unE7n6g1LHF+PXMdRJHOtZU0EfXx42KpYBEykKl9NUvXStoxjIcQaHoLBGld7OmNdHEtqprDDwsioGi7UNI6wzOQ3AS9vO1ZXn2ffD1tycNd1kp5aIilF8c5ai3MCGr5++cL1emWaxt9URvRdizWKkjX7/Y79fs/T0xNN0/D15cj1LPLmtunqwlChdce7dw/sDw8SElVkUR2rLzHnTCSjsvRtams4Xc4oLaBlGAaUtSgl9SoxJaZ5YvYLOVEl0IqmEdZ4BW0ppeqdq9U7utA6qdaxWjYoxjETwoKprGSqbJTSItEcryOXy5XVg0mt11GVwUIrgk/CKI8jqRRhwoowmeea7rqyiqv0lVrRcXtvevMbN20mFy3e0vnC+XySQBzjqqcPrHUbsFRKU1yRQBhvqqc5k1PcujJzTlirca4X5jAnYgryd81A27R0XVsBbbP1go7TSC6Jr1+/Ms8eWEFti1aZ0zRxPB755ddfabuB6yhKAG1U7d6Vc7N6O9fNnA2sVtaVGuijlASc6RUsGUPfdjRNTcz1Hl0KxtUKE2Vx1tXU4UJjxTO62w0c9j2Hfc/3339H33c8Hg787W9/59dfP8m1VztOUUquqzhvFTLGOJEY5ySS71JYUhBZLaJwyAVykt91ZQG9F2mstQ6tM0aBquFBMURykk2zQpU5K41Z2cGcWSZPzKmy0nrbkKr7BMRYwRdrN6jCGo2z0gNrrNmC11ZPr09x6yRN+UpB0VqLeZN6W2o90VYxZE39HHa7NkVdkaHetyGIzSCljKlBXLvdcFN1xIkYxDvady2H/Y5dv2N3+Mwvv37hfB1JGZGzB5G7KyP9t5JcLpLkXDezKLKxVWKhLDPOi0JgtWqUVdat5CR5HzbPuDEWo80WbFaUSMZLlfSLIiWjtYSAGW3phx0lF6z7Xf+4u8997nOf+9znPv8f87v+6W2drV62sqXxliKdndpZcilcr7PI2IwiprDVzigtHXxv+0qttTw8HHj//j193xJC4HK5ME8eZx373Z4QEsfjkRgiTeveABuRQc7ThFIiFY1Lxi8eheLp8amysJ8JJ8/5fOXL51esa7hcLgQvicIos0nV1lmrGNbF4brA21ItEZbIujX4R0CTc45hGCTp03vG8YpS4q+d52kDBTnnCnDkdFtr2A29eDm1sMcSXAXTtHB8PRJDwpkGaxu0NqSiGHYt+/2wgV7puvQbQ5gpW11MzhIw4zpH01qaRhajylimaSafzozTxGUciSGj0Vv4S1TI4rvqY40xW+IugOYGDGMMHF9fiVmqf4ZhYO0SbbtOamaAZVm4XK9Y6zDabhJu60SeuXjP8fVcGbuCtY0EK8VQfbGeXe5vNRhVdmnt+rvdGMtVOqy0ASV1MyHu0VoxzxNKSfq0SGMlKGbtpFQaiinbZkYIUdivJJ9dZOkF7xdQAgxD8FXWbUg5krIFVXDNev8kuq7l6emReZ6YxoWUAkrBsgTaRt5rTIVxHBknT0Gjja2SSi+hUPU8rwm+b+tEhKELm2exbQf8LNdls4KlCuy6pkGrPbHtNqZ/DWhKpeBcw7c/fItSmVISD4eB3aHFNYphaPnzX74lxsjpdObl9YivAV0FkfnmCjiDT6jOVimvyGlzySidSZQaILRKore7ceszjdHgXEHrgjYilS4YdA0jy4Aqko6tqoxbFUXKSDVM9enmklFlDYi6BYeVkkQWbS3WKNpGvMq7oZcNuXqtSc+vsNkxyt/Ni9Qf7Xf9rdpGrYBUb+dFW7MlS5uqQFntBUpw/iYHFz+vSLBjzPU1NUELG/rly1ec0aRvvqFpWw6HA8fTyHWaUVXunYt4kguaVBI5yZnJBUq6daaWAr5uRMUkUnBXK4VAel3XTbwY3tRi1WePHDOzJWqv1olYNxO0LthikbZs6NqW3bD7//sj6T73uc997nOf+/wPz+8arEriqdrSMDGaoR8wruV6vXA+X3h9fSXngnUaH2esNRwOB1YWKCVfOxWFQRyGgXkeeX39ylq74BdZDJ3PZ+bZb1UuyyJ1DcZZ2raRIJisRJ7qIyFIIqi1jlKU+NiWSImw+IUv+hWA8/kiTEJNzL0ln4q8T6lCWBaWeSamgK7VEis4ENbF0HcdTdP8htUSmXBknidC9Bhtt0qOnERe1zQN1q4gomXX91LlkgJN6yQ1t2nwi+dyHTmfryg0xqhtcdgow9BJEvM8L3i/MM8j8+x/sxlQaq2FtZKenFWLbTpCyuQQ0Kbw9eXEl69HxnEm+FQTkRNWG4yWrsxcu1y7rqMfhpreKv7CHMOWKNtYU8OKRIq9vlbXdXRdL52Xl5GX1xOX64RSM2vtSdM0PFT29Xq58no8kWISVqtEfBQgux7r2S8Cpms36CqtVNqClnRWVRNNpU5DE6oEca3eiFE8wju9wzkrycFzqh5dCbbJddGtVKFtXT0PAnYOjw9Ij2WoVR2SeppzJhRZ4C/TzDLNTNdJ5Odakn1zzqQQMUZvQGiaRrzXjONETAljm63/N8RCqkE/fvLUwGLgVq8kx0Gj1FpTkzkcdjw/P7NMno8fP4o8UxUmP+OTF+l542j7dmMKty7bDK1r+Pab93z//XecTi8YW2idBRLzMpJzpu1a9vs9Sv3KdbwQs4Qk5QIxi9QeJBRJa4WujGfJsTLiBdn2oH7tuhmWRY6bRRabSiaoDPV8a61RxqEQL2wumpKRvmalKtuqZJNCbTpuqCyzBAFnxGUvvty2dfRtS+ssu76lbxtc2xBiJIRF0rdDIOZEqL9KKRjvmep11bgW10pSLjVI6bf9rLfZ6mly2s5nTgJQJUl5TSmnAsaG4Ec+ffrCeL3y8npkN+yJuXA6n5mXBaXtlgacUiTmUDfdfltnpLVFKV2B8RoYVj3O1Tu7MsMpFXKWBGRlNKSbJcA5B65BGwnnKiik4Vnu3WWZMVHuQ1XkwN8in+5zn/vc5z73uc8fZX7XYLXUhFxrHE3X0vU9xjjGceTXjx8Zx+lWH9FomlaA6uPjI36JteBebwE5w9BXRuYki79Q+w1jriBkld2KJ1bV8BPXNBitt7AQqy2UheC9VL4UeH19RaE4n87bgup0EqnsOM21a7GgWCmcmyQw50z0gcUvlXVYJaJ2Y6/aVvyHKUVSogKfWDsaBcR1XYtCAnkOh4P0aVYG1hqpDXl4OAjwVtA0+w34nE4C/s+XK/O0VOa3di0CaAmaSV5A4ul04ng6U1A0bVsXxwLWdrueYbcnxcj1MvH6ciLESNu2xJT49OWl+nztFixljMUoSQUuq5+0adjv9zRtiw+B3VClicvEsswsy0TbWFzjbqxRjMzLxPV63QJ3xuvEWGWtG0NjlMgOS8GHwDjPFawZWFndJNJz8TFbCboxCmsF7O+GYWOCtFJQAQJV3h1C5DKOhCAy3cv1yjz76vmM9P0ggKd6GQFc9Ydu581KfU3TNFsliakJvrdgHChFpJ1yLCHGzLKcmedJ/Hw1aEhrueUlDAeCD3iq3DOti35VJetv2FMlIU+rj1buE2HpRXbcbEB8PRdN26K0YllmYjKVLS78P+y9aY9kWXrf9zvbXWLJpap6mRlSpizBEjUiRYqLLFqSBdgQDL/wZ/Ub299ApkCJBCmRlAXZNMkZajhdS2ZGxN3O5hfPc29k0ZJsAobVDcQDdHd1ZmVmxN3y/M9/s7rx4kNgWRbmeYEqUlpbPS/Pz/z4Rz/iqy/fstt3TNOFrg/6vhYuw8z5dCLGpGy02ZJrS6nC5hmwpoi8XPKNsFY+n2JSH2jR7CORlGLA6rG3TjaRjJPuYqFO8zX9WanU9b+1QjFQikhybS2b7FZOrfbp5gzKUjtv6fqW/X7Hru/wxuhxcSsBy9qNvCwCWGNKCjaN1hTJ8w1VM4gHnr/gzXavFCKSxr0+g6qmNa9s+eZ1rUVZ/Uol4ayoBaYl89P3H3HmRDWGJRdSqThfcMYp7DfkIp7mVcK8XkdXDz7bx67svNg4Xj/3xO/sJbnaXGtpXv2SENWNXrcV2TRbv5/TDbDX0ujb3OY2t7nNbW7z3ZlvNVgd5wms+PpyzgzDQFwSz88vvJxPsqDX1NOmsewPPXd3dyorlPTfrus3717OifP5zDiOwtiWsgX4oB2Rqx9PAoqEZUoxMk8zp5cTDukLzVH6ENvQMqfEMIws81opAhjHNC/kIj4y7yWAJypLvLIWm6wyJpUfJpx1eCenRnoew5ZCu7IK1/7JirOGsOuFsfISqLPfHzZZcQiB1juVvQq7t8SIMStTkSnV8PT8wjhMCnqspNSSiNo9Os0TpSSqgVxk49FNCwAAIABJREFUcZsruFLoDzsa73HWsNsdaELD02XgdDoxjtMGasdpYppFRptyxVjHunR2bk1PTbSh4+7ujvuHRwkjGoRRW9NnrV1ZMe15VGlwyitLJ6xf3/fkLGzw+jMkeKbS9h1YwzBNzDFSJbdlW9TmJOfSN16uQ+vVNyyhTE1oNq4mFwlAKvq14+XCZRgYxpG1J3VeRLKeUmYcnzmfL6xpvavntWvC1qm7MresybGwfVyuGwWTeKyRUBnnLG3TK3jMzNOiYEEk1msn7iqxXNZrFNkwMOqVnKb5FQNXtD/WbKyyHIfA8Xhkt9vRdS3LMjOOI86tSb+Fpm0oVYC1hHGJl7TUgp0FfCzLQq1GOjNtwJjKv/t3f8bv/l7ly6/e8PBw2ECbVObMXC4XhmHAOkkwzmUixaxgWhQTdoUvteBApLY+MEe59ySUp+CdxwQNPTNyXQe1AMheQBU/rb2mG8tprhgFfM6of1X9oBVH03YYK+8xxqx+Wvk65x1d17G/E79437ZS0aUbTPkVGwno5kfcvNuyoSDqA2OtKjak32X1wstGlCSaS3q4XDO1ioR/3VRYvdDjOCpAFbZTvNlgvCFjCM5Jv6kz1ALSMOQwbq3hkhAup+B5TTy+gnbZAAM2Ke/V6nC1a8j1JoywNZZd12+y+9fvrSr4L+UKfpsQ9Fp227Fz9vo+b3Ob29zmNre5zXdrvtVgFWSBMQwDwzCoTyurL9VwOBzY7XYqWzR0XUvJMFyknsQ5r3LQjlorl4vU2bxmNNdd/aTprs47TDZbEqxIW2XReDqdMZIEhFGpZNK+xRSzLqKEiZTF+ULOAi7bThaueWP86meMglEvF6USVgCnPq7jbseh30m9hZF+VCgsyySLvuDo+wbnLbt+T9/vcM4zDKP0nyqwnOaJPCTGUQKoDvu9MHfOM1wunM5npjliqiSNUqscl3nWgBjxnPlGfKxt2zEoqMk5k4wENk3TwrIkTi8XhmlmnlRKmkRiKH2cmWWaMaDvW5JOQxPYdTt2+z3Hw5Fut9+YpcvlsoGltmnoupY3bx7x3vP09KRA32BDQ9d3+rkgC/1FKj5kAyJg1Gsqx/+akrxeF689wfv9XhgaXYADm9TaGktQJiilzPk8UEqWzt5lJuekAAb1I4v822EoMcnxzdcQrZoz1upGgNYKYQSsGGXTnfNbtc/KPK0gZfW5gtm8lytwd85jTCGnRMmRpDLXWkUKbazFbjU6r5g2BdtYg/GOru3odz27vhfmW5OQqcJspyzAZ4kZ37TcdR3TPHI6nahFGMFFz2MulZQFRDRB+k1Rj+bT0xNt53l8vJeNgCXKdXW6MM4zFdjt94S2xTrxr6aYtBNFoKQzheO+57jvkCYZA9VLpck0EVPCKPg2xoAVj68LTpljT3CGfdvSOA0os6uyV0XExtAGAYTWSvfox5eByyw9sm0ILClvDPo4STVT16+S/J62abAYapLwppSTAvm4PfcwUvvjrBMvcc4sUeqcqpHnxXp/rFaDUkS5sDLLr5976zW//t2UMpNubqyg0jqnPb9gKTQ+4EIrn41SQ7PkTNNIv3Vc5m3jZR1jrixqUXm1c+bVdWzJKWkXsTDvjVZKWQ3PWtUFTQjCYhdhuFNMLOorz1rtU3LFhkbC8JANHmrFrzVKt7nNbW5zm9vc5jsz32qwusSFT58+SYWKJsKWLB2md/f3PNxLeu3q3yulMgwXTqeT+gStgpxBPY7TFkDknMXamWmSQCZJT223pMm+65TxkhqSeRbgGWPGOAguYIywlEkX6KicdV4WpnlSHyVgMsZGleqtC0XxUcl614Cm11KllsVvQLul33X4IBUjIfiNoSiqcawVkYwGWXSLF/GFl5cT4+WCs0Y9ehCCVIjc3R3ZH48K5grj+FH8o0lqRErKmxzVKaMk4SVVmVYvAUIIkDqfz3grMkBJ3a2Mw0ipwoy3zksADpGV4JAAFyQAxjkO+x2H44G7w17CfVSKujLdwtCoZNgZ2jbQti1OfW7OCVvV9z3HuyP7w555Xjgej5KYnOQ8G2Op1mwgeL8/cHd3T4wS0FVKodUAmdeMtjd2808vrwJgrDEYY5kmkUevn1tl5OvXpLwIU+493W4n/bUpboyPbHPkDSzWWiRVWqtt9sfDVhkk7LfRcDH16qm8spTM5TJvjJnIs41KyMUv6azBUOmaBt90LDFxvlyo84KxIscWmWvBWU2qbRr2ux2Pj4/sDwftzxS2WzaULhu4XmX1Dkm/7kJL3SsA125Vi5GwIl+xes+1oaGWLAngqTBNkZeXi3iy48IwTIzDwjQljHW4AFjLfr8npkLlIux7LQKsm8BXXzzy1/7qz3J36CkpEWNhHGZO5wtPz888vZwoJdO0LaEJpCI9rZWKbwKNhc5AGzyh8ThrMeohbjRca9fv6PuevuvAWN4/j/z4zz/yzfuPzMuCd5Wo6eSHw4HQeLpOqmSMnrNlTgzni6Rgp0RMkctlkiRniWbGO4f3jZ5nOZdFgfnqcV9lt35LAnZbwNLKcq7s5Mp+CvvvsTZ+VsUF6MflGVC189daUWmECinPLPNCrcJyGgzlVbCdePVXufFVWu6sIQQ5DnKNOZXT65+tV7ZYGOygny+qJjEayua9E29xTEzLTF57rq3RzlwDpV4rgG5zm9vc5ja3uc13Zr7VYPXp6YmX82kDeNJJaDkejzy+fSMyxmUhlkgtlWEYOZ/PDMOAMW6TO64ephAC+70sKq3Ki9dwpLZtFXgIALO62Fs7CI0RUDFM4itdYhVZpQafXGWa9dVC0FHRSod5ub4HTf4FlbOq7E8CbGRx1fUNj4/3m1fRWgOmvAqPAbK8t+Nxz+F40MTWtfIkbSBoWRYqhePxwJdfvuP+4ch+v6MaT4yZcTozx0RV/1s1dpOzCmvbEYJnmhYul5HLZdRKmIDU8KjEdA3+SZerFBdLUAl0ipIOKzJoYQX3+47jYcduv+PNwwPHwwGLY5wXTqczyxzVV2fpul4ll9B2Aecsw+kkSaLW8fjmQcClhkqVlDG10niP7XpKNVdvK3wGguHK/jjn6HvpQ5U+X2FHS0x6jrMynMpMGUeMwvzOyoKtfjtZ4Fd9z9J7WTQ8Zr02Y4xUXYCvINVrxU/OmeNxz/e+/32+/zM/oGk6pmkhLldvqDGS+Cr3iVUVwAsfPnzk06ePjOMo4NY6fHCUZLC1YoyT2hN939ZIUJU3huDddg9Za3i4v+N4PNL3IrVvmobLZeT0cuJ8PvPy8rJJgNf7zRmPrYYapZvVYQihwwQJkbLGUSs4Ix5c0Z0n2q7FLoZpWXg5XwgfPtG2XtO+F5ZZNk2WOTFOE0uMIiPW844G+7Te8Xh/5Iu3D/zgyzd8/dVbrIXzy8hlGBnGifPpnp9885737z8yL5EcK9YHqWySg0vJkaVWQHo/DQmD3set+DVLdSyxME4Razy5iEfcWCg5kZP4R4VJFBWIqELkvU8aBHY+SR/y+gxZYtbqFglvKimTTd6uvYp4cGu5StydcxgNPGuaaxLwa4/oClDXFOAYswYa1c9AZqmVXddKFRUVStYuafGotq3c2+M4kHLBKCtujMqoNVxqHeeFTQ1ePm+KMJ6NKiWcsa9eYyWiGzNUSorkuMra1bOvEnaQZ1bNssmSS6JEDS5DOl9rucmAb3Ob29zmNrf5rs23Gqyu/tGVDQCR/XV9R62ZZSnKIo6Mw8RwHpimWRd2AWOs+DRdwGui71r9MU3xlTfKbwxDqUV8aNZuHrx5nomL/FOVTUtpJOUkYJdVtlm3ABTrHGu/4OvXb5Q5MAYBU8Yo47Ambwaa4Nj1wqqugMR7h/MWk+ImJ3XOsdt10hnbemotgCNoB2nX9Lw4x7xMeG/53ve+5nvf+xofBJRchpnn5zOXy0BUSaq8DjRhk83HWYzwfilmcsqUmra6iRWMrYzjmk5rMHRts/W/inR21mRUw2G/4/s/+JrHx3sJSgpefJFzEjZ7mTUxdJX3Zdq2Yd93dH2LMbJ4Timx2+14++Ytlco0jZxfnqXSQnt2U4oC/lb5dioMlwvLImFS8zyzLAtordDldJZAKN2sqOrFe51s2nUdpRQujEzjyKJe3PWcrdL01YO6XsvBN+KnVE/k+v6sLrs3wLzreXi454c//CG/8Au/wA9+8LN0u70GLYk3eduIaRq9rgQkD+cL33zzDT/+8Y/40z/9Md9881PO57Ocn2XBGUtOhZfTmY9Pz0y6mbLW/njvaZuAtQIwHh7u2fU90lssYOp0OvH88sz5dGEcR72fgia+gjNOPYSFecpYLLu2o2tbrLMCVpENn2mciWkmlUhjWg53B/oigGuaFrx3lAwpSmDW5TIyjhLOlEuhcO26bbsOSmLXBr5498j3vn7Hw8OBtrEYCse9x9nArvO8fTxy93Bg3/d8fHom5kIq6/NDwDy1SD2QcyQMJUvtjKuGYg3kypgWjElYM1FyZY6RYZpJS7z6M1URYZwVBlGfG8syCzt9kX7meVq2zSxgu8dkI0KDrkCThTMlJyBuvs41iKtpGto2bFixlkyuWb+rbNCsm2vSaXz1M8v1q4Fh8kPl+6i6JVGpWIzz9G1D13hSWojLJLLlIv2yawXT+hqsNZpKHjb5ddu2BO+hVDJ528wruQjLHhdqkc3B9V5a38NaDZRVFqxpWFjjJFEdtp//CjPf5ja3uc1tbnOb78h8q8FqLlVlYFcghxE56jRNpJS4XAZOpzPzNEMRmZqwpB4wdK16VhG54jQN7HY7gC2sSPysFwC8kx3/WgqXi7C0IAu3tZOxGqmKSSlxOAhjMc26oDJm86yui72VBVklw85ZmuA4PNzTKLM2DgO1SI+mswZnha1Lyapk2VCqLNxyTuz6nrePb+jUO2icJLvGlLDGXYOhNJCo7/cc7+7Z7XbiqZwTHz888/7DBwmaypllES9w03QYUylUqV65XDDGskyRYRz0fEBcFmrOwgobo4mywmZS1btbISYBq/MyU2vGO0fbeB4f73j37g37XSvywFqZU2JeIsM4sswzpcgxTznr8ljkw4fDnsNhh6nCqBsQP2xaNlmq957T5aI+ZwGSpYL0UDqenp4YpwnvnAQkKYMKMM8T55OEcK3ySngV0FSrBudE9YkKGA41qNcxKEuNBDmBBtZUgndyjo3Bh4C3VtJTS6YQMdby9Vdf8gu/+Ev8yq/9Gr/0S3+Xr7/3fZwGMWFkGa5LcTDqvV7lxPrnv/431EscE8NF2M95mik5EzQE6c/+7Cf8/u//Ab/3u/+SP/qjP+Lj8xPWGkJwdJ0EjLngoBbmeUKtk2AsL0+SCD1PCylJb23XCSPtrbyGtmkoteCMoZRE17Q06u+USiUJWxqHkWEaiHmhaaWWRmTywpiKBL9yucw8Pb1wuYwaACbns2q36prcnBap/em7hof7PffHPW0QT+1lGUiafmxDy3G/54sv3tHt9sRcOQ8T1UgljQte+nrW1F0qKUdMLSo/F2+w3PPIPVMSsch5cCHQN7KxYJB7oer1U5JYG+IcWaZFk5StsI9IOjLqs1xD2WpFJbJWJLf1mkQNhVITpTqads9ur1VXWTaS8isAXErcQpSuntWqdoqrVNdoB64w8+A0gGyOCyXL/W2dMKzOW5r9nsPxyNPzGbP24mBZU4qFkTY0bVAw3dKrLUM6g6P+N0tHdJbndlIfrTWiTpBZgbV8YN1oXEH79uzVz7ea3H2b29zmNre5zW2+O/OtBqspZs6nQesxOqypTJcJMiSrASRTpKaKwVMtNKGh7Tq8F1Bhg2OYB6Z5ZFkWuq6jRRZHZDaZZ86FpmkJjcgiX15eWFLEekdQj9gSF1LMDBfxkOWSOU8jTQiaiKltfwpuSq0Ea/Fa2YGFaip3h553795xf3cPiNxZajHEe+mdo2vbLZEYCsNwFiBowVvD3eHAYd/jvGMeR5IuIGPMBC9drE+nF07DQNsGsNL5OczSkfrycuH9+2c+fnqRRa8uTkPwvHkj/s1hGEV2mfLGbFpEhlxrxZRMXD2KGsRjkeoZ6y3GeoyXcJllkUWos4Zd3/D27QPv3j7Qt4E0z8pGGoJrmB3iPzQOFyykjMlZ/WmWxjta72mVRfLObymmwzDy/puPDJNIled5Yl4iK/DJyh6XajXBF1KJIrN2bImu0gAkbFaplWLA4EhFK02sIdXCMA/XhT0CQJzzWDzFVFLN5CiBOZhC4z190wKVfS/XqT/uKCUxTgO1Tnz/Z77Pf/tP/gd+47/6b/j6Z36Opj9Q60oN6eZNNdofifiR105P7JXFchVr1ae8e8vbL/SvmEKpcs5//hcrv/brv8Hv/vbv8L/8T/8zv/M7v83L+RljqnQM94FSK6kW0lIZhomqEs3T85lpkQqo9ZWF4Nn1LWIPLOz3O3IuOG8ZhoElRalBkq4aXBCg44PDNyJ7tspESlqsdHC+nE6SAv5y3jZVShFfZTUOa3l1DQf2u0DrkA0BI1JQbwzgSJM8N14uA7lYfLsjF0PBkWumPxzEt7sl8RaqWZlbR9Cgq1qLBljphkpRJO8s3rbYpqFV9YH45IUxNPq6rbXkJCnnYzOwlMy0zHSx1fciNVRo8q8Ey0lXqbDxjrCqDkrGByc2h12nGwGArTgXKAbmcSIluU6qJqG/Zv3Nmuir13yKicZYjC0ssbAoQA8+UI0hkRQwF3LNhOBp2kBwUhX2ui8VxPsMlbZt6Fp51jaNI3iDDxbvAzEGTqcTpWZyqVyGiZQ07deuQVFyfwr4FgY3I8yqNxZnHM5YGudfKVcqN8fqbW5zm9vc5jbfvflWg9WY8sasppTUxwdLnLHq5xOgZWkahw/tVnkgiZPCHs3LRC0i25RApkTOhporWas82ral63oArXCI13qHqswZyrCZiguOxgUNRxEmYrfbYZGkWGPEh9U0jaTqpkTTOO7v3/D23RseHx7IKqWsNXM8HqFCp9JOaySR2Bh4eXkBlNWzhv1ux27Xczge+PDhA6fzBesD07xoRYvIIYdxFECW65bQ6l4k1XeZF00yTpp86zbPXwh+k3quQT7WOIyroAvlNT3ZUDW0p25McmUNC6rkpH47a8EG+r7h3bs3fPXVF9w/HPHWMKt+r1ZhZ08vZ8ZxVhbSb/455wyHg8jAjdPqoRwpGXKtxEVYWakIKaS8AMK8rdLd7TUah9Fe3dWf+xe7IF+zqNYIiylETlVAeK3iWANo0K7HVBIk8c7lmDCl0DSykO+6hn3fS2BPcHRdIOfIsvR88eUb/sl//9/xa3//H3N39xUm9MKomXUzRLOyTH318wWEbT06GLTw8tXHXo+7LuJN4eHtF/zqf/n32feiOPiXv/97nM5POO+oRTyrTjc7lmXZ0mHHeWJZogQx1UpK4l10FtrG44MnZvEcFslqFbm5Khqck5qcLbHY6cZALRp8NjIviWmc+PT0zDwLg+tU/ZBLJZdM0Woea+0Gcq2p1JoZp4nT+Uy1X5GrAJy2P3AenxguMx+fz4xLIiPXZ2g6dsfD5jevtZJK1OtB04D1nhc2ctlCjaoRObYP8ljNOVPLGm6Vt6O/gidjJGzpeDyI9zZFlpS2sDgJ/IqkJOy4s07ZwqCS+1mkrgoCXbAcjsJst20rEuOUiXHZNnNKviaRXz2r14qidbaE75Tw3r1iwQ0FI0CzbTEKltd7wCDPy9B4Zf4hBHDWqnfWvrpXrrU5KWZKERZ33QzL+jrz2kOcJanZWIN1hlot1YgiQ69q7CvPq9GNv/X3x+tzcJvb3OY2t7nNbb4b860GqyWvdTDi2QRZEC3LTMlsIKZpGkLTUOra5adyyFq3TtW262nb8MpzaLFcu/dWFmAcRy4qHZXFtCykVt9i0wSchqccj0eWZeH5+Vllv8LeWANN23A4HPDeMy8zPlgeHu55+/YtbdMwT9Pmh13TZ/t+R+vDFlKz3++Z55nzWfyTa9rt3d0dbdcwzZGPH58YponQdAzjyDiOstCucLlIj2cphXGYOIXzdrzmedEaiardsGVjfl5eXpimeeu/XBe2KRX1ronc2jonrJKCufUYFiPSRGstTuhKMCIFPBz3PDzc8fBwR7/rqFrpIWm6E08vJ96//8Q0Sz+s1YWr957drufh4RHnAWNYYpTu3ZgUBMl56nY9u8N+8/XOs3iPi4LVGBMxlc0PuwKH9Z91rFaEXCXA60JYPm/M6i+VhbgwXnBNeZZqjThPBO/xzm6+v6AJrV0b6LqGUixfff2Ov/8P/iG//hv/iLvHL6is/ZDqF1RWtZqCQetKFKyaDZVeX/9/yKMnktWKxWCso5REv9vxi7/2a4S2pf8fe/7Fb/8W5+lEyVKoOcUsIUbOknNimZctHIdqxE9t4HQ6MU8Dx8OO0LUUisrkZ5YUP+u8XFUN63VTTSGXSozifV4WAVlLTCqztlvXJiDXxrKQc6QUSbBNyRANZAPJZD49n/g//vhHPD4+8L0v3xGzYVwKc4Q5wfPLhZ/89ANTLoSm43C840tj2R92GJWHL0vifBkwQN937Pd7UOm3+MQ1KM1ZQvCbSkAqZq6e65yzAN2UN++8MRLuJn26Zgu8ghUQi8JkvR+FZZS03KA9pZIsDv2+43h32NJ/l2Vh1mMnQL9oWJHdzkF9BTY3Oe1aWWTM9neSJpuv3dNtK7UylKtNQ5hnufZTEsXFyiB77wneSeK6M0jfa8GYqud3IcZFfbmGeYmM00xc4sasWqHPt+u3rN8DCemy3r16VqXtz85JFZdvwr//hrjNbW5zm9vc5jbf2vlWg9WU8wYOck4M4xm3rF2T1xqGtXc06u65c54YF0quTNNM27bs93uCb2WBWQqlwsb3lEIcB/LLSRiOZWXkVgBjt65UH6RuxRmDrYU4jdK9WivzMDCVIkzpYc/dUcJwjodeEowfH/HBM88j0yRAsQkBHDRNR9e05JSEVW079rsd1lgeHx8BWUSGEOj3O5y3vH//gefzhXGcKPVMfBVAsnrRVrwy72empcHNjiVGPj59YhgunwFRCR5Kyjyv6cZZe0qT1qBIgJNViZ0sZg0om43V6p019dM6ckziZ2scjw93HI5HmlZSjrMzGFcZx5nnlxMf3n+UBOBSqLmwlGWTUb59+5bD8cAwXpiXiHOGaZm5nC+Mo3TBdl3H4XjUDllPCEHkp8tC28r5/8lPfsrpPJBiQhpJjXj/1AmKeklDCMQlUTUIy7orewpsXZHWrp2RqzyzbpsAKSVMZas4kY/PnM+Zvm/wfo9ZIu/eveFXf/1X+PXf+AfcPX4fjMfgAWGihG9aI5gc5RUoVeih/1df/fk/PM5cAQvGYbwhWMvf+qVfojv0fPG9L/lnv/m/8vz8ibJ1G1dsY6TiKCaskQ2LFYxUCnNOxJyYYqTpW+Z5xPvmyqQmkc3WCsM4C4hdFmVW5d1IWnL6jAE0zosP2EifbUxRc39Ehlq0e3NN0XUWgrfEy8Tp/Kech5G/8df/Krtdz3geuVwmPnw68eHlwssooI5h4WUYmWLh3bs3NK3I/yXxuzCNE5dhZJomGh9k0yGsFSlmCzbKaqY1VSppyqtrYb0u1koka+22EZUX6SN22olrtaqm7Rt2/Y64SEL25TxIcJv6W9uuIXiV4PqgDLbc/xa252QpRZ99dbu3/6IMeH3OvH7+eSuVMRaESU2JOEnwFnbdIBT21Bm3bZZtGynGAZaUK75WWu1ArbUQk4RLxZSYloV5ihuozklk+kW2FcFACI2CXbnPSpYKJGcdXjdCVrC9/g5BN5W2KrHb3OY2t7nNbW7znZlvNVhdwzJkt95qxUjdAnZWliyXzGW4MMesMuDEMAyUUjd2Yw1kyvnawZdy1O5JYU8lNfaaiLkC4tf1Jss8kmKkKNO6Fs6XUohxJnjH4+MbfvZnfsDj46MuYmWR5YMwtd57+r6nbTqmcZa6BQzn04mcM00ItE1LH3ucc9zfi7c1pUSulWGU9/Lp6Vk8fTGSK8KeZPEP1rK+B8tuf6Dv9yIfjIXTeOb9+w/Mk3g5V3ZlXeiDBLmsbNDa5WmtxTpHUHYl50zKEee9+sfq5hmU+oy1k7FyPOy4uzvw9os37Pf7LZQopbIxablUnHd4X0hjIpG00qKTUCxl7j49fSKlSL/rWOLCoKzTfn/g7l5Sa7u2Vdm4Yb/fc39/z+FwYBwFbMQkgF58kSInL7mAgg5rpOriyrpKIip1vRYl6KZpAtY5ZTbNlk56DVMKhCZI2mkWX+NUFkqylBJwtrDbP/A3f/6v86u//qu8+eJrqmkxCGst9wFAwRi3eUPXnyXzl6vk0Mrd7auMWb+X9J7+tf/ib7Lf7/nqi6/47X/xW3z88IHL5cRlHJinGVMNbQgSnqUy8pTFL+3DKvWElBO2GkLQvksAb4mxEJOEf83zzLzMlFwwKrMVpnUNzamvJKGZoqBjq2fSPtgC5Gw2plb8oBbvxd/7b/7tH/OjP/tzjoc9jWuoCFgehpFUIGYJTirTwrIUhnGQJHHvRUFgLRT5OefzWTaN2pamkU2KNUto9aqXJJ3FVatbctZaJ2Xqx3HaQP4qo3dWKmeSMYCAzZwKPkhwlUEA5rIsTPMM1uCbQFDJda2FYRgUrC6yaaL/WCPhRLmySeLXe3tlT3NO2/XwOlBsWRZCcNs9X0omZ0lKzvUKDNfNIe89JYnHvFYv/ue6hjhlUsr0XatKBU8Iktqdi5xDCYkSkJ9SBCP3KPmqpHFO7zhjoPBKMfNKCeHd5j0utXIZx7/UfXKb29zmNre5zW3+08+3Gqz6EFRKKzLLthM/pVE/pwDQC6WIL1XIQPFQbqBLuy5XZsj7sC0WY1wUsGYMq1/q877NVRo7TRPjWIjziDF1864J6yVyNm8dh8ORr778gof7O5w1xLQQKSxx3pIrN2+oDdhqmCZdtM8jxjia/WGTCZaShXWYJiqQi8h7n16eOZ1OxBQZxln8p7pQF581Q/7mAAAgAElEQVSWcHFt23H/+EC720mi6jLx9PTC6eVC1N7Qrus2YCdA3irjcj0eK1C1zuMbWWimaZQ0UOtwxpDydeFbSqHmjKmZLnjujnvePN5zf9hTrSElSdJdtN9xSQlrHU27I84DJY9UFOT7IAm/08iyzFwuZ6qp7NOOUop4Eis8vGm4u78n6DlbWaPVqzrPMylGuqZl1/cb0ColbX93lTG+BurGyPsJ3gqbaIWtakKQoJgQNNF53jZE5HtZmq6nCZ6KgAFnDM479vuOpnW0XeCrr7/kF//OL/DlD77GukBF5IqyhK9QE1L/W6jK/gqzqmnAXNnez+c/wrJKjO4GVIW9VEBZDd/7wc/wj/7hf83d7sC/+lf/kv/9j/6tMJbG04RWq0KMAP8YmRZLjBFrLbvdjv2+pxpNTM6JEiM5FUoxxJiY52WTwBdEwumVQQc2GbBsmqybQfEzcHWV8AsgFQ+z9MdamwEPVthpXMPH5zMfPr3glbWkGopK1Es1YiNA/M/ny4AdzSY7bvpuk+cX9b5GPc/ikXXivcyJmMsGVnFWwZXHmIQxAkxTLjikZ7SqL9ywhiVJFcs8TUzjsklZ1w2eaZ63axdjuFzO3N/f0fftJq/NRf2ZVUOOUqJmZVZfday+3pz7i/Jfq/U3plZqTeQcWf3OpRg5p2oLkLTzqy+0FrOB8SZX3fyRz43jxLOVMLe2a2iC11ThQNtZMBFMJJc1tyBvkvsVrIfgJEhJPeK1FMor3XsphZKqsL9G7pzyl9zUuc1tbnOb29zmNv/p51sNVo+HA8s84r2l7VoOh90mLxVAUNjtRELahKj9n4D68USeacXbphUkplbG80U6PEtmiZOymS3OeV73uq4LZLNJJgXAdG3ArTv2pWpvoDCAX335JY+PD8S4cLmcmOYJ60XWtwYftW2Hd55i4HQZGC+jAmRhUR8fHmiblpQTL6cT4yiJtj4EKobzMPLp0xOny+Uq261VWQ1h+UpNWGdotefz6elJFow5ad/mrAvtZgueWgFrKWtVxrJ1p9ZaCU1LHySpdGXAvHXi0/OOYTgrSC3klMklEZyh7ZrtGLVty3k8cz5fpBKjrF2PhbgI67L2u67hKJfhotLPhXGamOaRtm0ITWCaJi6XQet2HKVWYVoVRMWYtqqPVt9jyoXgPXfHA8FZ6YVcJYKGbUFfVMvpvCelRNe1epwkGGhdzDehJUWVS6v88goAJDgmpQVXYb/r6LuWpm04Hnv6XeDn/vOf46/83F+RYJ4qDGKlgslUEpAgRUVlDqGSLNZ4qvFQG/79WadX7+x/dNT7Wq9cK9YH3n7xJX/v7/097u/uKDXz+38wUMokoT6lYq1jGkcwFe+spmIH7u/vefPmgXmR5Olxmkkq253nmWlemJdIXOI1CKfx2zW4AqWV7YertHP1Isr9WHHWCHQ3hpSVaUNSYtF0Wwlg8lQbKCURi1Fpt9TRGMD5Bg391vNWqbVsgKuUhDGVlKow6WZNl1ZFgrwikkrx1wAyZ8zWi7xuHazey5giS04SCqRVP6u0NefMvCwMF5GwY4QRz1X6Rw0aFFar9kEPen0GWpXYvzq5rxjRvAVaXVUU9bNju3qIAWWQG3KOn4HmWgX8eeM/+5rtnClIXv3k1kr4UVbLhDXiAW/GIDVjBuzGqksoWuMbbG9Z5kVBsHjjvbXiUTVQrfjjnf5euHY+q6e1ZIz6qVd/8W1uc5vb3OY2t/nuzLcarO73ez59+oC1lv1uR9d2Iu0yhrhEALxr6FpDXBJjGRBoajbGQ9J/r9UrbGzQQrWfh+sAnzE3KzC+ejnztphe/aOyUIO+3/Hu3Vu++OILDJXL5cw8z+RcCCv7lhLzLAmftcI0Tixzkt5LL4u2vu9pu07Y06dnzuez8msGsyxgrNTJLKsUUgOKrNG04ioevlIgCyPsnSPlLP2eyhg32jm42+05HA4E9WgWDZR57WcDNiCbsyw8YxQwuWs7DocD8zJhqsg71+7Rtuk57Fru7vbc3R25u7vDOisyxmlS76KVUJ0l6/uK2+I+5yJ/Xpla0ftRC8xLpL6cNY21bq/vfL4wjiMvT0+cXk4b8PHO0Xe9JDartLuzVmSkQNrqSABnsVagRanqV24Db794R9PIOR/Uu2g1aTipr9X7axLsClZzls2STCGVJGx7NJTa0vU9b96+oet3VGOhRkoeSGnk8vKR4fxMCCJlbpqWiiNj8U2LD60mocpGyGt2VUm3zz722WjNUt3SovRftQp7awwmBI5vv+Bv/lDCnC7DhX/9h/9GwnuGgSlOCmAs1sr1VO2qXoC+68Vn6jzeJ+ZpZp7j5qlOGmLWKGNpjNEUYKlpWROp11RoqJvMtGtbkaYb6cddloQxC8Z5kb4CtZqty9TUirFei0LRNFk5SKFpNum3MUavt3SVgVs2lcU6fgsZEvXGEuOWums0dGrtBxYFxjWVVvbTKtM8bSqQtd9UZPlXP+nrYyDnVcCwNY5AEKm9SnhjjFsKcNO0Wt919fJeq2rWn3kFb+sGy+vAuRWAWmvp2r349e3V11qQVPDXX7taJuZJ/LdVPwdc/cfG4MwKoAspRSm80vRq8dZKz6wBmiZQiqgIRPkg6dRrH7axZkssXpUwy7JsGQa5Smp0yvH/6VfObW5zm9vc5ja3+ZbNtxqsnl6eKSnz8OZx6yQdhoFJQ5ByKlQspjrapmNdmAujJoyoMQ7vzcYQiqdMFjhV/a/itWow2G0xfAVn18VdrVe2z9g1SKTgfWC/33M83oExWlMjoTLViHR3iYlhmLbqjhQz55czwn5puvDdkUzlw9MzP/l3P+FyuWyg2Dq/pdnmtHaOBg0yUcClMsmVXbIV5mmkU+m0qeo5VbllUJl113XUKsE10zRvgUSr7HL1ChuALCxuiZG7uzuaNkgnospevXVUW+i6jvv7I4d9R9cFHh4e2O/3jONA1GTZUipVpY/TNDEMEzlLX2ypshDepM2rTNlIr+qSFt0MyIQQ2O12GGMYh4HxMnA+nTUNuW6bFqXK9w5BFvltK0zp4XAkxsSHDx+IcdmAiQ8e7yW06e27N/yV/+xnFVBN/PSn3/Dxw0dizOQs7LBca55SZGEeGo/zdvPm1ZxIMTFMFR8cc0z4pqVpdywxMX34wDz9Oe8/fuSbb35CzjOH/Y53777AOSjF0LRHQtjhbIclqBxYqktes6j/UaC6cnzmtSd2FQxfc4VrFRnr8c1bfvGXf5lqDKHp+cM/+ANNlxU1g/ee0Mk5SBow9vLyQq3rtQhpTgyXSTZqSsVbi22cbgS0wq4ByxK39Nr1mnLO0Xj72QbUmzePHI+HjVm8XEZeXk4YI5sfKSWpbUpVU8ILzrUUJ0DGaQ2Ws4593xP0/40m2ErP6CLPCmfxzrLf9zQh4DRwLQRlNlNimuMGkKDirFc1xXo+1qobCXWTDY6ysY9b+FJK22aP916k2rqpYowheL+B/SUtWE2MtliMynqXJWGtV8B5DUkTmS6vnmdyxrekYnNlVNePT9OIM+BMT991+CZcgTOVaVmgWlJOcqwQObULHlM+/17k68+cs/hSncqkvXWbomHzydvr89ipSua1RUMk2XXbcFqffdbKPdvWKmFfy0KZy2bzuM1tbnOb29zmNt+d+VaD1aQVD6FpOJ8FfAzDuCX+ehc2Ka61nraVhUzb9ttC2VphYsdx2Px1AnA6DfMRCas1kmC5BnhY9fIty8IyR9ISsUhTg9OwoZRkt75pW4yxmiJcVRYbiHEmxcI4R86XC6XIwnG8jJLgq6mhOWfCMtN0LcMgn/v46UklkJV5ibRdL0DZiIcvhMDBSt3LNE0A5JJF9malJqZmSe5clhnvxV8p4UtlC27a7XbUWpmmhWEYuVyGz5JLrTVbcIq1To9fEoabyunlmWmctL4GGu9pm4bDfi9seN/S9y3Oe1nUL3FLG5YFcyEuieEycj4Pcg4Q+F1rIeZEH3q6brfJGEPT4IInF0mlpVSWaeb50yfAENXvuDLFXd+Jd61CzImUM95YnJGgoF3fUzuYx4l5dlitoum6jqb1tG3D4wa2R6ZpZppmUsqbZDppJ/DKHskmhN0kzUY7KKsRxs8YyxITz88n/uRP/pT9Yc8wDDw/feLl9Alq5osv3/H4uJcwGWPouwO+vcO4HooD7fDEZDnfr4pVzWdM61/wtG5/vMp+P/sb6/9YJ7Jkb+kOR/7OL/9drJMwrdPpt5iexu391lIxzuGsbMzEFJmnibj6Tquw0eM0yf2qYMPpRsjKyjVNg7NS/xSjyDebpqFrG2JcNvazaQKH/R4MpNQTQrsF+Mh9LMnaKWdyySzTJMSxsZi20e8ZCC7QNIF93yuTKM8W6zTIKUotTgjy80SRYLbNAPHfRsZR7ATFaOhUWT5jJ+vqh7dmUwoAm1JDjoPFmBmKhAP1fY9TJcJap9U2DVbrmOZ5JsdyDYFzVoKKlJEtpeC9bKi99qp+Xm/0mo3/v6tMUkqMwwWDKAWa3F5tEsimQNGbdn1Pa+0XrCoVYUlxKzvMFmi2Sq2NlzTuNf09JbluNt+2NZjqtvdJlU1HEDCPyn+lhks2HELT4J0neHkf9v+VJv42t7nNbW5zm9t8m+ZbDVaPhwO1ZM7nk3QGzjMxipTXAMUIUM0JYSDUx7VKWld/1Rqc45zduh13u91WGbHVIGiC7sri1SpsYknid8U6vFZVrHK0xojnEyPA1jmrUrQkLEmqAtCSyFenKXI6S6IwVYBvLpWyCIDLRXoHs4as1FrxRthUqgD4bSHqnMjheOXfM1KlsqUcZ5jniRQTQT1hK/Pbti1rt+w0yfEFNiYTwHu7/V1rLefzmWQkzXMYLjw/P23H2XmvfbHCVA2XQaR3BnyYWNIq8dXeyVSIMQtIPl+YxllYrKZlpblKrWRlWZKmwELBB0/jG4qX9ONplGO6ygCBjT12wZOUXfI+4K0T35vVc5+FEWobqQCR9NlIihFDwRnDeBn48P4DP/nzP+ebb75hHCYBqKluycJy7MwmnU4pUa3FJAWFRfpVjbWkXLhcRi7DnzKMI3/0f/4xIXiCN3SN4+5+T4wLS5zxjWd3POJ3HQZDNZJaLBpQEFBQFIOsRTavPawSxrTC2PoqeOlz3LqmAq+STDkH1QjD2u32/K0f/m1Jtp0X/tlv/ibn81k6kHVjyHk5rgmHt4FiBUDGRUKV4hLJpQIR7xMueexi6fuevu/x3m8bCMLsZ5pGJPIpenJOWqU0c7m8KDi7StdLEYls2zTUJuhmUGLU81JrxQZP0wXu9gea0GAM7LueENyW4uy8o6pvPedEpdB3PSE0my9SJOtRN60k4EzymoQFHodhuxZWAGiMEUVC026hQd45Sey1jr7fCQOaEs57+q6h7nY46zi9vBBjpLeOvmlx1jKxbPU3IQQMVYF+3AKvvNdzX6/pwH/xNck9bDcgepX2srHXg5HeWUkeFmntoj3Y9dV9+lnI2iYrdq9+lsXZVqXYZZPtentNXbfGaOUMtK2EPK3vbc0gSDGCvuYViL62LmTdlFvzCoK/9aze5ja3uc1tbvNdm283WD0eGYYL8zIKa4XDWaM78xLKY7Aq28salmKpW0CQgM+Ulg28VZV/tm3YPGji36rabWg3L+qyLJ8tfkIT8E3YQKL3TsKf2oa267TSxZOSgOPgW5Y0M02RZUlMo7AhKVcqVnxapb76XoGUEufLhSVl1kAm4zxLXMhj3iR96wJzWZbPAPmV2dPQGy+vd54nnp8zTdPSti1d37HEiaRgcV1wtm1LyVevnlW/XgiBJniOh93GCI/DRK+hSev0fU8bGmKMPD1/4nRJTLOwv23XbAv04BtKXohRApLmeZbkZmV2jDV456lV0kMxFrLU3DhncApILLLgrbVsQUrOOenVDYGgAVNWgazT6p3GGjyGpmmhVqkCQd7rtWs2qudvD1SeXp54//4DT09P6oOulCxhMKDBPKh8VsN4sI5qKw7hL50VCXYphXleGMaBjx8/8f79B+4f7vnyzTvePNxDDfgwcP+4SKiOWagMlDpTtGYFKtYFqD3OtTgrfZYy+l+z0qSFFbRe/yzy4TU5GhT/asiUBDrpt8GA8+zvH/jbv/gLwg5S+c1/+k+5nM+kLKFQIQSWlJVdLOz34odelkTjA0+nE9MsgUu5JEwx5JKxDto2SKiOvW461VqkQsZZUuOZxlHTmGGZZ+YlchpH4iSBTU7ZTEOlDQEfPKbx7PtOngMKRLtdy3F/oA1huweNkT2AXMsmMzXWqSd1FMWEkxThpAnd06RhUSkyL7LJtPp/SzUi9dVrw1R5xjSh43jYY40kKK++VKfPlZzFv11KoUSpjerbnhhmxmlkyqNsPCnr7JyoALqu09TdwrJMm0KiFJS5lakgYU6vAOXKaL4OsJOvzchei8XZGeutpBkr67ykyNr/u9oUQNUEoMDYYG35zD9udVNwBas1XwXtFkPbttuGEiVvz7WcMljZbinWkskSqKfnveS8bVaxLGRrt6vb35jV29zmNre5zW2+c/OtBqsGWWR1bSeBPKL2EtCpHj3rHZQqsZAUlYqKfLbq14/TSEqJeVmw1hGMJZd1AV9VbmuoNaoPtWKM3eohjMo4Q/DsevGthcbLYsoLI9d0Dc57rDHaG5qIKfPh/UfOw0TJVVN1ZSG1xJmu7WSBqMxIrZXn5xfOl2HzfyVlWLP6R+dJQJ13fmNU204Y4piSpJbq8qxtOrquRapXJsZxpJSqjKMkH8cU1c/nsbYKYGha9dUuYAQs7Pcdh92O++MdUv0zku+L9Ngq3lnigjEWb72y1I5aItMkibFzlLTiEIIycbKwjTmSSsYYYUWTspQFTUydhSVqQsAYZFOgCZDMZr30Xjym1kHTtOx2O7xvqEZel/jZJIXVO0fjdCFcYRpnLtOIRcGqAkGpPbKEJpBq4enDs8jQdXOjlitjBAJSRRIpnuiSK2kpOFelDzM4ul3Hbr+T9GFj2VuvUujK5bzwwZxYlszzZeDT6UzC4HzPZYj0/SdyqbycXricTzTBcXf/SNc/yDn1LbY2UBzWWKw3hCBySGMC1QYFTUiK8Boxu95saxDOSmm9kgmvRCvA/njkhz/8eYbTM6dPT/zzf/5bXIYL0zxhnMMom+WtgLTj8UjX9Tw+PvKjP/sx799/YEJAXeO9pC4rW7teO6tE3FqpSen2B1IyEnxUPbvdTlh0DOV82VQBTqXXMUZKLXR6vbTB41tPyQXr4Ljbs+tVWq+hReumlF0ZRpWlWmtxJZBy3TzNpQDWkjdvdd78luM0Qa00jafxnmoK1sh5aJqGivg114qZXMoWRpRylo0Cc2Xphb0VD76zwsKmKHJ/664pvCB1XwbZ2zHWiPe3JJXpr6ylnGPxsEqomLGGFOX5tCpOwJNSJeVETWBdxqZMVV9+Khqe9AoDrsFNa89zpW7IdfWP11pxxuC9Ve+9xYRX8uOUcd7SrPVgCqLlJmOr0YolgwLmqN/buzVUSs6L0eMiadH5L/kb6Da3uc1tbnOb2/ynnm89WA3ekxJb8fs1HAaMt2CvFQU5JmEg83UnvmrIRsqZ0LTc3d1Ra2GJCUvU4Br5eRLq4ZnnZYuZWeVuUJX5MJSc8MbThrX7NYhsEJiXhXGceTlfmJfE+XQiLumzpE2QQKW28VuAiLWWcRg2L1paPbdG2IKUhKmSRW6lWEMbGvb7Pf1uxzCMPJ9POCsgOue0sUDLEhWHKAPrDD54TeQtOF00llJwzrDfdeQcKUUBVtfycHfH3fFI1wionueFaV6YxolaUYAu7z/ZQlEG2wqNQ6nSuWkseMQfjIVqlY20hqq1HyhbnDaGJ2MBb7X30llyKSpBLLTB07QB30iNzm633ySl4zBRnaN6R9DanVor3hqaEMQnenpimhfapsXWTCoZnJHE1SbQ7XpZ7DuP9Z6m6QgOrQBhY6DEJ71ugVQJm0lAqZhGmMN+1xLahq7t2O/ttkCPccEZS4yZ5/OZYZ758OkTHz4+85M/+8j3vv8Vu/2eCpxOJ4ZhoA2B/XFPpdC2gZqB2BBsT9+23N017I8N9w+PHA5vCO2Btu/p+j1YlVo78TevQBUDJWamUVjIfrfHBfFpVgzGVKwzHI8H/vYPf8jp+Zlvvvkpf/iv/zcJXaqAAoZj3+K9Z7/fbyFK8/yGcRi24+XdWg0jmxSXctGNE/TaNORiCY3dGHdqxQVLCA1NqfRtzzItm4R/lYLnkliSwTh53c57jDO0jagEvNNKEz1fpRZylSRaMGQKEMlIcm/K4k0OvmGJiXGetGpJlAI+cJUHzyPFFGzbERrxxfbqea21MC+jSpf1GlIputFNgqKdqKYqgKvgfVC2GXloWTl/0zxr57TYG0II5CKvvhrZ9Kmliu+0qoy2VmpJULXSC+kiXZaJlCPB68aGsTgfdOOskmvBKqtujMGqOkUsE2vlD5iCPrShrN5Vw1Wl4hzBO6jSXxzaFqyc9zVEzaov3q6XpuwaYqxUgbW95A7EGGXzTAO50NeR1LO8eWnXeqrb3OY2t7nNbW7znZlvN1i1V5lceRX+YawsWAAoAlRlIRkp2nto1G+3VqmEELg/HjgeD9IzmhPFSJiSMXVLH/XebGDXYLQrciEET9N4+q7DWUvbeHa7jr7v6fqWJUZeTicu48hlEHngEoU9q8r4ClPnNnax9cJu5Zy0imZRprAqyyfy4FIszkLJmeqtsAvGcHd34O3btxQM0zzron8NN7nWXIjE2W0Jm875jcV0WsGR4jV90xhUWihpuQ8P93z91RcEZXO9CyzLwtPzM8MwEJfIZZwYh0l8a8bTdR37w4FUW1JawKoFEkvJVwZmPb4pJUqW47NK+VYvLFSRjCqAWeaJoiEykkDckbOhNZ6uaQlOJMIpi1TzfLlIeE+YsdZpEJJsHlwuI8/Pz7Kw7ROtyoKNSqpLkWvjcDzw7t1blnjPeBmYhlmrg8qWNJ2SsHnGGGzwWGOp8VrdcTgcuL9/kJCYKh2fWRNhCVJzY6wj1QK5Mk0jP/nmPX/8ox/zva+/4vHNG6w1nF5OnE4ncpZF+LwsulkjIOP+cODd2zsOh5amC/T9jrvjO3b9A4f9A2+++JL7xwf6XU/bdsJ89juMcbx8fOb56cQ4LRzu72nbPc6vQFXDkErGYnn37h2/8su/wof37/nmm/f80Z/8McZ7UlzwwbNEu8nxUymSygp0XUsu4vP1wdN2nW6AzJvXElCAifbaJg35WvtFr8FWtZoteXfrAFX2uxTxSNeuxddK04bNuxijPC9Wub01jlQy0zSJVD001ApTnBnGQQLMauWSR+YYuQwD8yxqja7tRYaryeBt8BRVLQQfaJuO4D27XQ8YTqcLyzKS9PpIKWO9w1mRyK73h0X2Eay1+NdJ5Ygns5i6BS4ty8LLy4s++67p0K83ykx9lQastS+rwsF7r2Fg8syqVTaI1m+02i9eV9EY9apeg5rWDUWp7RE7gcqEX3/tqkCgQnVgk7Di+loNi9TV1Erj/Na3K89PAdLeePp+x8NDs9VhCcNdNx/smmq+LJFpWv6/+LV0m9vc5ja3uc1t/n+cbzVYnf8v9t6tR5Z0ze/6vaeIyMjKqlq1Vvfq7n2Yo12WEIyQx8gX2GMbDF/AF1yAhHyF+AJIiA/AJUgI4Xu4QgiJO66MwOLCki2MPZYX9p7Zh97dq1evVafMjMN75OJ5I7J60Iz3xrj33kM+rVL36qqVlRkZEfn+3//J+7ooT2hj5EtrXN1hz6Ws3qoUU/WLnboul131nKVKpeTM/ulJgnNqcMd2u2WaRCIrU4gxIEmfnhgT1hq2216Cg5zjYtvTtQ1939G2DU3bwDBUWV2ilIRzloKibRvypiFnkRBKqJIE0iygepEabrdbjNHiKKzeLqhhKRVMl1JoXcPFxUUF2oocQg12kpRMSsEaQ1f9pIfDgXH0z8CoBDYtHbJaayhpDaaSACa3VmS8evWKj15/wjQcpbdSCBORVs8zfvYCWkNis9mQonRfutYKOM1FgKgRsDOOR+Z5QhbtB3xlngWgLt244RtBLUopARdmqfsI6/uqKzvnnEPX47rfPzF7z+EwcHd3ty7upZ5D3udSpEpE5N6noJclldQ5h2tEvt11Ha8/ec08ez68v+O+3KO1xvtQQW1eAZIQlhqDJumCNoWua9ntdmw2GzkHlAU0MVQ/dgWCs4/EHEW+rR0Kw93dI/unI675HGsMUwUm4s2WehbxByZ2V5aLneHmk0/56KOP2XQ9pMLjw4F//Pu/z937gRcvbthd9lxd7thut3z8+lNeffwJF/0lIcA4eV59/Akfv/oI20p/6hJgU1KpcmqD0Zabmxv+jT//u/yzf/oDPv/ip4zBo7TGTyM5zBgFbdfRNC3jNDH7Gescm77HzBKotXSDigLgJL03RlVwZtbvN43DWlelsRPDICAlplwTvBMlF0KUADMJB5IEaq3h4mJHY63UzkANyopsOgGbSmtmL32/bdthjGHys5wjCPsZQqwbKqUC67gyedaKVcBue+ZpJKVQN4fU+vNa25PqIywgTLyqFMSQW2eR1JZyYgaX+5APgRC9pJdrUUYs10XKYoVY7i0LyFcKSq4bfpx8q0uwktZqBZeAyJGXTUJ1iuBagpSWUKulJqz+FbFoaPHlq+qfVjz3xz77Mqa+5iUtWK5LKui0m36VaRcUXSsseYyZ4/FIjAFrLZu+p6l2i2Uj63kiczwzq+c5z3nOc57z/MrNLzVYHUdhMEwFqotcVnbxNT4EYl3c5pJPSkZtanrwRMpSf+PnmTBP6wKp6zoaZ6ufM6/AzVrLPE8cjweWGhtjVGU5a+pm09BshDlsnCMmYX+X9N6FQdhW5jVGqcvZ7/ckH9CKdYF1cXGxJhJLLQbELGyEKhIik2OiVEalq37Mtm3JdfGlnBNZcYykmg5sjeHiYotWivFckhQAACAASURBVON+D2iaRn7O1vAabdQz4MoKGlLOjOOE9xNt2zLPM8fDkcN+T6qAPMXE7IN4GY0Aba0N2+0FhyeRqaZJAmdizsSUSWlEaUlN3u/3AiTiSWJdisJovSbyLt2QwApWF8AugTTyfmz7nheXF9jaQZlKYhgGATjzTK5MuUgQ0+pJFgaG9b+9j6SYK8jJbDYtrrnAWsem67i8vGS/fwJVwUBlr1Zpo3M4Le9hkRhVnNM0reX6xTW73W5NXXXKUVBElynFE4KELfkYagewSFSN7TAmMoeZ2Y+wgKWcxS9bNM5YjHGk7Pn0Ox/zV//67/G7f+F3+PjVZ1jboYri7v07/te//bf53/6Xv8vXb3/C/VdJ5N3XL3j74x9imw1dt+PFy9fcvPqETz79FGUVlFBTlyxg0apQUsZPA/MwEGbPzYsbfud3/jX+wT/8B/zTP/gB3s+EFCkpC/Ocy+rJLpW1N9Zh6nWS0nLNLFUueQ0u6zbtet5O01Q3XBpJ1h69JAwnkZ2nnCBRz48FgGkShaFe+9pYGiegylpLzJk5REI64ut5EnNhnGaOw1wl/qJGCCHgQ6jyeVD55MHMOTKOsZ5LmX7TrRJ/uecYNIp58MxRarTmeZbHqZsdEnqUJLW3AsFSQbFIY0Xyejwe2Ww2671s+Vm5NjLWaYj6G0nEz1N6VxJUKfLi09WnJGCjDRh9+v+odbNiCaJSalGOi7IFJaFizzcatJUNRQkEy/U616u6xGgJlHJOUqSXmiwhhjOqHpu2MuEpJXzKaGtRxkrqci5Mc6ApClsl00s41jiOVfEgnw3Gnvzl5znPec5znvOc51djfqnBaqr+waUOQlsDNTFWdt0FNK0LLmPIJeNDYJznlUHNKUKufrCSq4z3gk2/qYBD0XUtoGp3Zl6ltEJySHfqUg8RY5Ju0SpBPhz2PO33jONIUeItK0WCbMbhWJmCQIpSxYEqGKO4efkCZx0++MqwZAlpEmqVkpMs5Ey3Zt1suo5NtxEWr6aBziHgvAQQ5RoMU3LGT15+X/17XdfRdC1t02GrH3SR5S1AeWFTh2HA+5m+T/zoJz/h6/cfmKdRjk0F9UoptLW0rqGxjrbtAKl5KaWQYwKlsbalIMmkRknFUAiReZrBiKxPGF5zSmWt9UDLl/RF1vc8JWFubcvFtufm+ordRU+MEm6TYxT/sg/SRVl7HzebDQqY55lcFMaIUy9nYcxyyuSUa6Lsc/mj1Ps8PNxzPB6ZJtkEyTkLc2Q0Vjmssat3OUVha53TbLcdL1/ecHV1JRsoqZBVDWCqQH6YJvbHgRAD2ug1DMZaU4N1XAUkEp6llEi5jdVYFF3nuHn1mr/0l/8iv/dX/12+89lnaNWBalAlst1d89cbi1GK//Pv/X2mpwN9o9m1BufA+4Ef/OQndD/9Ka+/831cZ3Et3Lx+JT21uqOowng8sr9/4Onujnmc1zCi7372KX/uz93y9YcPfH33HlUkHdYHz8PTo3gQ6+bCoghYBaOC2GjalpxiDfySyqTNpqffCOvZNFJ/JMctEWqw0XIOi0w1V/AmLP3SiZxLlcdrzXGQrlzrLFqZyoZnfDjUJN6ZcZpJMeNcpOsatFmyamWDZ5UrF7ketFZgqv/YzxxTfBZUVFY5dEqR4zCKdLuwypclqKisQHsJayopoVDYVuT6ul63x+MR66wEF2WpV3JWU7LcG52RcCeQjTbZVBHQtrKmzxKhTxtAkpitqgKkadxaDVNKFnVLfZ5ZFVTdsFm+bN1sMlpSnmPKRDK6KEoWKTC6rL/H1A0F2YSQe4kEJMX1OTgroB+liEF8xXqp2jHy+051O/K6tDY0TVuvF5GX55j/xT+UznOe85znPOc5z7c6v9xgNVUvlEJSaysDZ61FFU0MVXabhVmNWRarh+ORcRwrSyceSFXB50W/4fr6movLHW3XEnMUWZxS1TMnQEuYVlWTcLe8fHWDcxaVhQWZpsTD4wPjOHI8CkvYdR2bXroYx2Ei51R7XT25ZFnEtU1dBMqi7PHpgXmeZWG77dFaUZSEjGA0bV3YL75IyajNbLrToj8+PVUpnlQ+OGvxs2c6DoSwBM/IcXNWAloOh6F6QE91E8fjkVCZo8UDF2NaF9o5J5pnvrmuyjubpqVrWqx17Pd7pmkCEFmgll5RrTJd13Nx0fP4+ADlTjJizDdZHRCgl/MiL1x8iKfORihYq7m62vHZZ695eXNNrlUa0t8aoSDdrcHjnOP6+prXr1/jveft27ccjiJDFu/qUoUk55NIvC+42PZrLc9+v+cwHFY5ZNd1zLN0hrZtDYwqS1+l1O4oJd/b7bb0/QXONQzDwPEwYYxUGT09HZ51CHt89OQSV1AnHkfx6gmoStXXK7UgRWf63Ybf+u1f5/f+yu/xl/7KX+aT15+hdQM0gKEgMvHXn32P3/0Lv4MKB37ygz8kTDPbjebqckvCcByP/P6bf8IPfvRDfviTP+D3/8lv8mu/8X3+zO2f4bPPfg1dLD/64Y/48vMvebp/oMTCZrPl1SsBtN///vf45J+9JoTA03GPDwFb/YbzPK8bQAvTl+r7GSpIstaCUWsgjtYaZyWdWmsBayElZi/vXSm6elbLSfpf2fgFfCklgUtGWdpG6n2UkoRbOV+WftMNzjrGceLx8YmUhLlOWTa/rBXWLz7zRCql0OgaUHQKd/JeQtJyPqklQvCrpD2EQEGsDIs/3jlHQR57QZOq6mmX15LbQt9164ZLCMLuL49htaHYEyBbmFfxGQugxWpyXiwIwhgvCeULuF4kyUs/aeOW0CpRQRROr39hQrW20mFsLV3X0Lcd4zwxjCPzLNVd0SeoG4GL5WBJp5bHMLVip5CKXJcGYcxV1ii5WaBq2F4WCcT6XEQqHld7RX72u4KXa+A85znPec5znvP8as0vNVht21Z8ZHXXfGHIlgWfj9JvWFImlsw4e8ZxQKEqi1ZqGbxhs+notxuudjucdVzudtjG8XR4wlphTfu+pxRVg5YsMXp2ux0ff/wxXdfKIrnI4vFwODDNA9M8Mc6TSHovd7x+/ZoYI4/mkXEcadotqmxXX5iPge12C8DDw4OER9XF+cI2lFzQShbQUJnAhWkpIqk0TsDiMAyV8RtkcV8Xs8/DlGTKiTUaR/ldTq+L0+PxuALUJVlTQHJmHOeV4RTWQq2Ae9N1tG3LOI4o3jMeB8ZRakSERbG0bcfkJ/pe6kukZkSeWy4QqwxZVwkzsG4YyIJTU0p9PjWUx1kJftrtthJcZTR5EkYqxkyMhabpKEpA0PXVDS9evDj5k5VFKc04jOQkAEEWufI82rbhxYsXbPqOnBM+eEwxK7CgJhovQFcpTQxpPW5a69Vv2bYttspIHx/23N8/YG1DCIn907H6KRUheqkJ0YX9cMTMI1nJJkjKkURBWalaEfm6eCBffvQZ//a/83v8tb/2b3F5/TFK9WRaFLo20wjDphx857u/xuOf/YJdB093d0Q/s+0LXd/TbH6du/2Bv/v3/xE/+uJz/sk/+7+4vf1tgp/IIQKaH//wh/zoD3/McX+ksQ0vLm/oWkfT9Xz06hWffvIJdw8PpJwxVlhtrfQKVJfz3SwVN1WaKgzaSaK7nANLwFrI0t06TV5qoXxgmhYZ7dIvW9YNj6UrVL4yXdeuGzPCDOpnmx+s8v2UCqWoujmQ63ks55S1aX3M557LRWK7+KyttZSMsPtZel3TUulSFs+nqB+Klu7VQq6bMhLEZrSAuPDMtz1NI9I7K33OjTGVgaz3j7bFaL0eY6WrHFcbcpbjopQhlBNzq2vH8/JaNpsNTdPUruFQffuebb9ZbQILaM0501iD7URx0vdb+u1W7ilK0YeezTDw9LhnKBNRG1TddGlqwrBWGuMMzkmfakwSgrQ0Ki0bD94H6eNBS3J4KSC3mfW+mVIi1w3MVPMKlmMnSof/Vx9D5znPec5znvOc5xc4v9RgdZGarsmYtY/UOQks2u/3JwawMiUUqn+1iIRNwYvr65p42tI8Y3dijAzHgRDy6knLObHb7cTbWYOV2lZ6R/3s8ZPIS4dhYJylagZVuLjYsN1uV99k2zQYLYCOyoyEGCmz+L6kiqYGiFSwvCSipphwtsW6hrh65GqwTZXulaJ4fNrz/v177u+fmH3A1WAi6S602Oqf01oTk7COOWVCjAzDAFqt/rWcTz2Ry8IapdAlQcorw6nrvwGGYeRoj6vPUCsJeMpFmGFZfBseyxMoSTe+u3tYmauF9Ym1gmbx+JXaU1pyriD5FIp0Ah3S3DGNA9MIFAnLmaaZ41HCnpZAJeeE3ToeRw6H/RqmpZSmaVqU0ifWOueV1Uw5Vv+koZTEOAz0/RajDVMUUB9CwhiH1uUZ+yuhMpIozcq2zpPncDhyOBzJeSAnmL2cc6unEAnUoVaTlJwpWc6VkhNLxI1WWmSjWvHi5obvfe977C4v0coRi0VpkXKWUntTaYFCu7nh9WffYddnhqcdj3d3eJ9wbeZVf8lv/tav8U9/+FPefv3A4Wni7sOBr97e89HLDzStgZK4utxydbGltR1t0wNFEnRR7HaXWHNKmm6aRvzXSo73wsotHk1rzKoaSDFSslsZsBCSyGWVIhdJZR6nieAlBVgCiRY5d3nmyTx1dgqwhBBOzH1jxaO83AcECE7AvKoCFrZ/eYwQ5HeC+JmfA9VFJiuMvxXZujGUfDp3Swii4liDlbxsZDRd9VJKH+my0ZTLonoQsLlsOi3XygLQWmexpl2/t4Q8SUjc6XgYY0TqXqoX2pxCnnKm9tnm1Rfcti1t24j/2E9M04hSVKVBRqNWea5rJPCtbTdYZyVQqr63OSVGO0r/Kbp244KrcnltjHQS5wRLCvizROQYo1yXBUpRFK3JVUGDUmvolKv9ssEHSU2vSpD1fChL9dl5znOe85znPOf5VZpfarC6JFg+l5yBhBN5H9dO0hBCDfJIaC2VE4sH6nJ3wUevPlq7HH0FB3PwtftTAM08+7rQOyXiTlMkhIi1B/k7fsaPwuh47ylKwEnbtOwudlUaOjMOIyUXGudom2bd5Y8hEH1gRknNgvdYY3GNW9njnDIxFWKcmWZPyQVTgcw8SyptipnD4cj9/T3v378X5hUxjU3TVKV7jXhYKzskNR01EChKd2kqpwCWxbuZcxYJHgWKpqjTAk+YavnzwloEE+tC+LSAX16veC0bVO1G3O8PNan3IJ2rCkGc9fGWACWoASvVk2utleeTT2Cha1rmaeTtl1+yhERNk9SUhOrrXJKOUyo8Pe15OhwYhiPTNBJjFs9n9d9KmrCFIinRrrErm2WMXhOnYwiUXKofVlJjjYlrnYbRzyo8ijC8i8xXHq9W3mTZcFiqmHJZHJysigCNgpwoKaOK1BmlFCvz36CApm345PV3ePXxp2jTICFIwuyVk+0WikaVDtu94uXH36Vrj1xdWbY7x4ev7xjnQMwT3aah2/QYM5Cz5elh4osfv+Nqt+PVqyu2fc+uv6RrN7S2Q2vHNAfuH54oOXN5KdfBsuHQtu266eScoyhWcLqwkNYYuR5CoOTMZrORVOxcmGbPXNmyscrTV7CCqtdGXtNw/6hKQQK6ygp8cs6QT/eUZZPAe19riNLJT1m/Qg15W5J2F9C73J/atqFtTrU6AmANWpn6vArDMPJ02K99qOLLVZScKKmysQijmJ/5cBd5/PJcn1fzKCDVY9w0zbrhE2PEh+mPSHVV3YyCojTKaFTQRF+ZzCpJXo5hjHKv6bqWthHW0jlHt2kppWEB5koprHP0/WbtMdZaE2ZP8J5pHJnniRg8lCr9VoqSEkUVtF1ShSMlxxMT/yzBt6il9kaRUTXlnXVzDWTzpuRT2jA5Q70nWW1WW8d5znOe85znPOf51ZpfarCaU5ZKmme9m7P3DNPIcJxOQHVZoFpVZbSJrmm4uXnBbrejKJi9gMhpEsnuZrOpXZqaEFKtqHGkFLi7u2eaZgkMqSmV6wI7naRp2ppVEutcg58j43TEzzONa+gaR/LCrOSQiXMkhETOnmGYCCFhi6LvWy62lyilJKSpMm1LlcoySimMtWQKx/2eD/d3jNNYaygEJE7DiCoFtSloIdPIf0RCvTCOFEnoXNjAAmR1qqUoVfKMPIx43ooiBvGUolmfZ6nSVK3VmorcOCtBL02LrgFJQ634ES9fIZaTVG85rsYICDFKQna22x5nDcYKW0iRypqUhCHWxhJi5jBMdUFvEJyXUVkRwrT2ZMboKzAWWaBzDRcXW6wzNEUAoHPiw1tAzMLu5iySaKUC0yyhWHOIqJhxueBqYAwlU4qkR+cCh8NxBTWLPLjEIti7AtyUpP5EoYU8r8A1pVM4z/NjpLVI1W9ubvjtP/tnefnyE6ADLBpFXh5i2WxQUIoGdUG7e02IX5Maw6U2TD4x3x+IU2aYR5HcpkzOM08PT7x/954vr3ZEH3l185JXL3e8vHktfbtzxAfx3R6OR6wzXF5tMW8VOYPTlsY2oNUaDmSUoXGNnEdJmDTnGmJI5CTpsdbq9R5Q6kZG8KEyqhWkKI1RCl1UDQAqK4Nvqn+0dfYEYut9IsaTz9M5x6mCRVKIm6ZbN0mUUpX1TfggvanPpbDOOvpOupaXICNYknLFu2xtIxsgznI4HjkOh1VyHmMg5YWtPUmXn9fJCCD7pux49X1as/6cJHyLZN57u77GJW1cvLIZLWXOsokkJ7owwUV84ikF5vlkobi82OGc1F5pa1i0+ksAk7DJ4k2X3zNzf/9ADIHgPU4birWSHp4KGfBIgF7KUYK2cpJ9q1zIJFJ93TFJorh06BhylkoyVfuSy/KelvoZoeRcWBhXrTW1UecsAz7Pec5znvOc51dwfqnBqjCNdafdyu64j2FlVFdPWQ3aMDXkZHdxweXFwvAUHh8fOewPzJOwDUtdDErhg197PlNKzNOMr2EobetISVfflvjRjBImSGldd/wlrTimxMPDA9M8YCtoiVExTTPjMK9gF6PXMJmUEpu+5+bmhrZt+fDhQ5UhitQ3JentXBiStm2JKRFT4umwZ5wmYSqSLPSWY+UqIyTeOVnQrUEjlRkEVvC6hhlVICWhNVSPrFoXosYYrBJgHKNClp2yIIwxkpdQlOrT3PZbtpst2jhMfb3DcKzg30kybjxtNujKkgFQQGnFzc0LPvroIzZdKxsDWku6MxJa83D/wOSlb1LYWgXKVKDNKuVEicRW2LNQF7qLDFvkqlL7YbFu8SumtS5p6YDV2hBCZBhGhnEk5RoAlTM4C9YK0DZa+ncLTNNEzg9cXV2ilKSYlpIpErODosqxK8AGavjNwghZxHM8r69nqfn49NNP+M3f+nX6/gJFSynSpfkNEkllOa4KCgbjrmm3r5kOiqwTMTti1timlc0DlbGtQRTgnsPxkcPhyKtXr7FmS06O496jVGSaJ+7vH/jw4Y67h3ti8DVQquN4OF2jRp86OHPK32DSrXOocuoa9d7XehupJlkAtzYaFTWoXDt7tfh3lwcuRfZfVnBS1hAfOa9EMrxUvKzgT2mKrnJRlHhnmwaj9brxopSma1uRp1cmMKVISc830/Tyi6v/VbyubX1tfd/jmoZ+u2EcB+kaVosvVpJ/RRGg100NAZr8kY0KkekqpWia5vR8jangLtI2jRwUderIDSFyHCYOw7TWOTnn1rqonCPzPInct2no+57d7oKucVxciB1CaSWy3QraF4l7jOIj9gubOk4EHwDouw191+N9wPvAVFn1uASpVb260YrWNesGwvIerrYEFAW1Hg+FIuVTT6z0RZ/+vNRHLV7/BcCe5zznOc95znOeX535pQari19JJGqRyc/CkE7zunALSZJftRa51/XukstLYSmltiRzd3fPcDysIM05Kym7dSEIJ2nswrbJwjPXhWitC6FK8EpBl4J1DmNarJPE03EYKCSsMTVJNzIOIzFkgg+knDBOQFuMka7rePXyJW3bst/vOR6Pq3wWVQhV/riwoWhDzML2Pe2P+JBYOl2NFrBqjaGpjBCwygDTM3BglCYrSdM05uTBM8bg2oamgRQlWTPlLGm+6PXxFmlnjAFt9YmdyhFtNReXO17e3HB5cclFv6MA0zwxTRNNY9eQI2sNxs9MlSEvpVSvq7A6bdty8/IFH330qjI/8hpA5LvTZNjtMnb2+PCIMkoCcnKCokEZOTeMlmyWIvLDZfGrEGmkBD4JoyuYR8Cdcx0vXrzAGMXT055x8szVrzwMAykmdO2AlB5eg3MWrZRUylhDyRCGiPczztkqYffkLJsAJUsybS6ppuMuIVOKklWVK8cKuGShriqy7bqO733vu3z62SfV02hQmBoCBqx9mAWUMIHgULpFm2tQE0oHMh2Tf8QnT0yZnCNd53DasWkcWie8D1jbcX31iq5t8T5yODxyOBz4cPeBt199xd3DHcoUNpuW6+srxvH9GvbjnHsmY81r8vKpH7RUHyt1o6ihaZx4VFOikOqGS8ZYYWKdtaQgCbNKfTMJeAHJpSSW/uQ16TYEbK2ZcU4StXNy63VZcqLkRKqqBKpfetlkoAJaozVRzRQWICnSXHKuPltQKjPNGa1MZUXVmgQOhRD96heNIa+p3Ut1zwIGc5WeiyKgrHLgGCKuhlM552hpgGXD46SMEIY2YqyjoDkOw8pO61rV5FxL01hc3WzabMSHv+katn23AmQo+BRWNcA8S9/tOE7Vxx1IPqEy9JueVy9esu17UVaMI0/7J8Z54ul4YPKenKLURSlJf95u+xpiJuFr0zwxzZ6ag4b3ER/Ex22NAWvrdVtEQs8poE02kuRcCzH8f/TJdJ7znOc85znPeb6t+aUGq0VrJi+M5DRLt2XKwrZKXQ0iYVMaazTbTce278kpMYxzZbQK0zjXnxXGSQrnE1oZSk25VRqaRjxZOeU1JGb2MzkKYFsWrSkXbOPomrZWTmT8JPJdZVT1H9bFc86M3hO9eK90DqA8WmsumgaU4ng88vj4KCE1SmGsI5WMD/Pa+6qUYvYiYR6mkWkeiTmsC9dYMtboGpAUUarAynyKnDkmCW4xzmCNAKoF1IrHUNM0TvoQS2GaPClFYi6UlGsAEBQy1gnzJaAv46xmt9nRbzpef/IJNy9e0DQt1jR8+HDP4WkvdSA1aMWZBuca2rZDqwPHdKhAFAHcTcPlbkfXtOIDVZmUIyEFQBFSZJg9ISNhQroGKekk4UopYZwW1q6yYVZrUoiovNRlVKltDCQDbdvjnK7/v9B1G66urshF/LBP+5H7h0emcRIv87MaH2cMjbNrqJdSkGMgZgnTAZimeWUUjXWktDA9C/uDGE2BoqR/lQSpgmcqALVWY53l5uaS3/ytX+ejV6+BhUHmG/8W0KorgA3y2kpAFUXJlqI2ZHXB4/4t948PPN4daEzDxhYa17LpxI/tGktKgRBGmkaTS+AwPPD23Tu+fPeOr99/4DgM2EYTg6dtOrb9ltF75uDxMYg0V5sTgK6zVI3oCua6tqWxmk3bYq1hv9+jikY7R9d0oBRt03Kxu6DExHF/ZA7zysyunaZZ2OumazDOUpTUoKAkoMsYg2uMsP4xE6NIRlMqDPMkkuws57uAIEUmQwZlFI3RtG5DShlyQdV7iY+RkJNck9rgKmuv1UlOviRIl6zwIYp8ufpcu02LsZrGObq+A5Bu2ZgYR7/WzgByf5k8MRUudhf0mw5n2xW8hyybeaXqIJy19F1HjokYQ71XiXy2axqaxrLtO9lIspbLXU/Xudq3Kl2ppSDnT4JpmBnGgXmSTSfvZw7DgDMN17srXr54wavrHdfbLX0jyo7DuGM/Drz98IG7/Z6Q5bXllCgpklOk3W5EKp4SF9stykg3s2xsiRpEcgVG6SQuzwK2ilQGWefW/x9CwLlzdc15znOe85znPL9q8zOB1dvb2x8CU/0C+E/evHnzP9/e3v5F4G8BG+CHwL//5s2bd/Xv/LHf+9lHcRwHCSVJcV3kLh5LrdQaYmIUbJqGtmlEajZNEuTjJWBFa0W/2fDyxRW77YZClkWSF9/mIrdbJXhVtihJn5LWmZIAJmMtV1eXtI0jxlqlESTcqXGNhJJ03QoOhyFga7rwklKptXjh3r9/zzRNeD8jybWOXApxYQCrfNNaSc2Mi/ywnKRuK4uqNX3Xcn25E/ZTGwnhcQ22cfgQmPxMAamo2DiUkoVi8PEbcmNfq0FOSajhG522wj4Ko9220gf68uYFV5eXXOy2KGAcj3wY7ri/e6p1OXYNcgEwyqCtpm1acoo0lU2xyGNuuw7nLClF/DRzOBzwIaC1pN1OsyeGvJrRltTYTFllnehTT+WSrquVRj1L7zVG+nTbxtK2NdCplHXB78O8dkaGlER2jnDtm67j6vJSvJUlYY2uYHXZsAirZ3CaZpQGbRU5xyqx1igl51iqoTmq+oeXTtdTrYqcN91GWMcXLy757ne/Q9fuUNgq910W5DW4qoBWpT5mJqWZNO85PDzw+LBnGI58uB/4/KcfePfujnHw7DY7rrc3XOyu6Cu79urlazabDU/7B+4fvmYaB75+f8dX79/z9qv33D09MU0eqxbPZKDtN2R1Ov45BOY8r9fYwuZDBY5O6ljaxrFpBCi3pUEDh+MgR6WCNKMU5CIS2Nbho/8GAJbXXkg5rfLwhWkjCXuaUsZYi9OKYhTEk63Az54YpWu1sYbWGjYbSfae5xljDX2/XWWnz9nhXJnjOUWC0sxx8Uc7nHWo+hiLbzpXz6q2Rhj2FOm6Ta3b6gFJy/VeNjRiNIBUGBVE6TFOk9w3QqBrm7UzVZXEMIpPXGlNydBYx831NceDeGeNldofTWG72XB1dUnXuVUtUPc65HimTEiZeZwYxonj4cjxeMSHmZxyTUK2aGPYXmy5vrri1fULXu22dM5SUmDwDR8eFcYUXry44mnyPD0dam92IM4z+6eyMsuXl5e0bSf3H41WEQAAIABJREFUjioB9t5zPB6Z5oZSj3+M8RRIpTXWGKxzaKXxfubq+urn+/j5FZhf3Gfzec5znvOc5zzfzvw8zOrfePPmzT9a/nB7e6uB/xb4D9+8efN3bm9v/zPgPwf+5p/0vZ/nyQ3jgJ/9KsVVqqyJwFTJqFEGjcIakdcNw8DxOHDcH8hJvJhGa/rNhtevP+I3fv37GA1Pjw+MPhLzsCbTinexE2lZ9WyeFtZLkFCmaQx9L+lFOcP17oLL60tc6wRUVFni4/7A8XgkhLAuHgUgCVgLcaaQQOUK5JaUUYVTJ7/p4lPr2rb6aisDW4HAskDrmpbdxQW7ix221j80rqFrW2zjmIOnLxcCVLuGeZ4kqXY6AHBxcUHXdYDm8fGRthWg1DRpTRNdUn8F2Mr7sdvtuLq6YttvcNYyjiOH45GHh0fuH56Y50XGCguIWhimlBLkIgyqlYqOEqOE5BhDDJIefBz2zN7X0BgDyhCieGolXEreCz8Hwtp1GohRFvopRhKQarDNEp4jwUeOfttzcXFB07j1y1rLfr8XiXYNhrJGY5qWZBIp1qReEFlp0c+knhJ2458voFWpYTGCubq2xWhLSrJZ4r2nZKRj0wjbr/RJzi0eRcfF9oLGaVzTsO17hFU1sHhfqwIACkZnIFLKTJgP7PcPHO7veLx7z8P9B6Zp5IufvuOrr+54uN+z273g+9/9jOvrl9xc37DZbOr5bCEpHu4euL/7mrdvv+Tt1++4f9rzdDgy+oCQliLzNUajrENXwKDq+80C6kpZQfzih+66lr5tsUY8mVpryJmubSUhu4btpJQYx5F5nr8RRLZIyZfzs9QQIWDtawVQFdRprUlZmFQQyakkPHtArh2jLSjpSjVOvKHdtl/fk8V7vgS9lRDIyCZBTuDTcs4qOtfgqxf1BG4jOcf12gd1Si1GNkuW4xRDrPaChFbipdZGVRlzxvuJlAJ+Fsl113WVeR3IdXNLK4uprHK/u2CeZ0pJOCfSY9s2oDW2bWlbVwOXMjEkoo9Ms2eaZobDwDTPjNPEWK+NXDKNc3KMug6lwAdPotBsNrTOMs+FTbOjiwEzzqhYMGj6TQ8b6qaOvA6FeGolQTnRdRvatqs1ThKyhmL1FT/3uS737c1mI5uPs2N7sft5Pn5+leZb/2w+z3nOc57znOfbmn8RGfCfB6Y3b978nfrn/wbZpf2b/5zv/cwzDUeRShZWT5YkborsVyP9hgoBCDEVxsO+pnaWtW/U1HCTvu/Qmio3VfR9LyzBLO3yfd+z2Wxl8Vslm0tYx9JzqFTiYtdz0feEGHBWcX11yfWLK3L1ZIUQmb1nnkZi9KQUSMngvSxOQTyOl7tLNpuNyI2n02LaNQJKUwgkhDHdbja8uL6WBW6M+BoWhVJYZ2mbluurKy62WxrrJGwJkfrGHMmhynydAw37wwE/z5WB3tfXJj2u8+x5fHzEGFtBnSx8rVV0jaVtO6w9BUXJwlRClo7DwNPTk/jYQgQkwVhx8lous/gWQcBj23W0TVODZgrGWOnZLLlKuTPGOqx1xFQoJYoEEyXJsEbCiyjy92OQ9NGSpWuzKCUbEk7kjtIl2eIaWzt1t/T9hq5ra/hU4nA41CRaj6uSX6pcdxql4NXP0/pYTeNoW2Hghmms58+zzk+t6PsNSmm22y2lwDjMLDUuKUnvrnWWokT2LueLMJDb7VZYJkQeHmKiZNBGVZC6BNFUGXiJxDiyf7rjw/u3fP3uLY939xz3Rx7uPzAOR7766iueHo4Y1fDq5lO+/73f5NPX3+H66ppNt2GaA1+9+4qv3r3l8eE9X797y49//Id8+e4tx3kmpEIsBWNakYkmSdbGCJu3hEIt4G65rp53okoqLfV8kiAfSa9lBfiLnzWEUEHWAlBO55SrYGkBxEqLDzlV2WhKCdKSBkyV1o6rv3Pxpy/XuzEG66QGa/G9Lq/lJKF/1u9apchjDd8y1hJylBTqICqPRTq+eGiNkY0la0QyrJQ8lvQyS0hUSql2Ss+V8ZVwI4vGWYN1IiGmqjJIialu7uSa+o1SOAfamXUjTnyoIluXtOrFMS33gpzBh1S92uO6GTgcJ2YvQXexBhflnBgnz6aLGKU5GAsxE+eJMA988tFHNK76lpVGaYOfA+MwkIqqMmqxIuSs0fW4uKaVkDelaiJ0xMeanq0NRunKyuZ1s6YAaEUIsQbCpXqv+P/F/Ev/bD7Pec5znvOc59uanwes/ne3t7cK+DvAfwp8H/jR8s03b968v7291be3tzd/0vfevHlz97P+wpIzjTU0dUddwKqk2jY16VZqVBa25Vh32eXvKwX9pqPrOna7LUorHh8eSCnR1G5TVSshlsVmSolxGPF1EbakwcoirqCNAOWcIjFMaFXo+45N18liOEuCbEyV0e02tK7HmMXzJ+BFG1XlbQJMnY3fCKOZpglKRivx0vabjq5tOB6PpBgolTG21tL3PVeXl1xdXbHb7TBK83B/L/5MBT56VFIoY8hxJpfC8TiQgizg51lA8uPjE4fDsf6/mbYVULVIpBtr2fYbLrYXuGYJQImkGIkhMdWwpNknQpTkXbTBiPFvBavPZZNr5YZeAl3L2on4PGRFAISr6a6aHPyaVpxTwocT6F2eV8kFtCxgu9pF2Vdfc9d1tYvX0bZuZTAXELHU28gCWNO2HcY2q/Q6R5Ggaq2lKgh5n25uXnBxseXu/QeGYTxJno3G2pZu09F27SoLFV+11IUsoCfXjtklFEuOh1n7g733GPUscKf+o1hqhgSopjgzjweenu744qc/4Uc//APeffUlh6cjx/3IYb9nGA4cDgeiz7y4fsH15Us+e/09fu17v8Hl7oq27Qgh0jUbHu4fePf2a3784x/y5dsvGeaBRCGmjE/iA1VV6RCrt9zWYKU/Ck4XyexzSXDmJHtfgaWSXs3l55fO2pUlZUlHPtW3LPeJBeBN87TKhEspkKhKDcQPP51SbU31S7vqR3aVZTeqnDo9K8AEVgb0lKydq+w540OkVSJz77qWEhPjONT3O9L3wuYDxBBQyM8qXUPlYqx+6yAbJiE8C9gCyJSiSLkqCYyEFC2vM9bgslRlydZKB29Uce2gFXDY1Ncj4DSEyDhOTFPBh4APkXGemSbPOI48PR0YjtMasCQ2C00uihQT5Ik4z0Q/E3eXhAqyH/ZPfPr6I5wzhJxpu47NJnIYasCaRGuLHLxpnvXWmjXVeZ5npnEmpkwquSaQlxqmd6r8Wq7l9ZrKma7tf9aPnl+1+dY/m89znvOc5zzn+bbmZwWrf+nNmzc/ub29bYH/AvivgP/xX97Tkrm8vKyeSJFyhZRWhi7VqoScEkYprJZ+xJwhxpHNZsPl5Y7r6yu6rquyTEWOYa3BOQ4jx+G4yvSGYSDnozCttZLklMYp8l2tJHQlpoSqVTmUwjSOkv6ZZcGnMXVxZLGmqTLPmRiDyDvrYy6shLAbspA3WhJhXa3hWaRs+6cn3r17x/F4xFm7ynZ3ux3bbkPXddJpWFk6jbAVc2VQQ05MwaONkcVhZA17MUaTlxqWCtCsNmzaTp5rjPg5ExpFah3UXsOxyrRLhjkExmkmxcI0zwzjXPsoFymrUB4ln+okFnbp1F+ZK+iRU/PUabkAgoL3I8dhIsSEQnpyF2C4AFBYNjuEkb+5uWa7vaDvN2y6VlJ4Y1o3P3LO+DARgnh1YZGOiuT64mInbCG1TiMlmrYmO88ercUHfHV1hbWGmCXABjJt21bWvqNpmzWNWBJUpfs3VBZazt9EzhOpBGKKGGcxzqKMZpo8OUcaKwnFogpIgJdqmgI5eY6HJ+7v3vP0cM/9/Qe++OlP+MMf/ID3798zjlKnlGo6NAW6rufi4oqLi0v6/oLtdsd2cylgvinoTyTo6MPXX/HFT38slTO1m1NjUEXAg7MOW73JpfoI4QSwnns7n7OqC2gNIbA/ZgnbAUlMrpL8mE7yWJG+nmptFuCbU6GYgrJqBSs5S19zTHUDQynJsdJUK4H4ltu2StG1YbPp2Gw2OOtkUyxGgk8kLddWDMtGgn4WviYPqpWoDVKemEe5F203PXpTq3NylP7S3Zarq6v6uiN+rjU41fNrjCZTTkB1CTdS0ueaq91gkTpbnci6QFHrcV7rfZQB9NpXuxzvRWZ8cXFB33cYpSvwi3Iep8RxGGrQXWSa/AoMl03BlDLeS4DREiLlnKUgnctRwRA8+6/e8jQduLjY0m96YkzEnGmbllRk4yAX2eTS2mCMrrVNsrmQYsTPsf4+T4iJuZ7DJzvEqZc4xmHdGEsp0f7pBKu/kM/m85znPOc5z3m+rfmZwOqbN29+Uv89397e/tfA/wT8l8CvLT9ze3v7Cshv3ry5u729/fEf972f58l9/PFHbLd9XcBHVJVj+nlmHEdyEo+U9P9pUikrULl5cc3LlzfsLnerpDPlRFCFFBPjNHEcB/EJVsZnqZuhnBgbOLEnpRQ2rZOF5XGm5MjV1eVaMVKQionoU2XlkAqVyt62bUu3aVYP2pJ+G2NinnxlPxzWpAqQLri+vmaz2TCOI+/fvyfGSNM0XF1dcXl5efKH1b7BQw1NUUqJpFAr0JpYwUTKGR9FpmyyWQGjBPh46WTVUl2xsFQA0zgwp8RkMm3bYEwiJwEBORdCzAzDyOE4kGIhRKnqWY5dPZKy0H6Wg7OACWOEFWucw+gTuFkAbM6lSoJL7WuMhBiFXfQniZ+wR6p2SxaM1mw2HZeXl9zcvBAZaooc9kdCEGZJwGKQ8KylXoVSQZSh6yzWOkpR+OqTXNi3/X4vck1qpdDTE/M88f79B+bZYxu39lU65ypTKKz94SCspvdz9U3K75NzKVGjotAFqbDBi7S5JJLVHA4D0zQCHlW7VGMMPNx/4IvPP+ftl1/y+PDA4WnPhw8f+PrdB+7uHiqA1BI2ZQyta+navlbvVKk90pdrba3/QGpyxuFf4eHhax6fPvB4eCT4tMo3waCNwTgHRVy0S13S8n4u4HXpET2xhNXHXAp+miqjvWwWaebqSQXqc7RSB7WCslNVy/J+2Gf9o1ophmFk9rOkS9dwMGc11jaSQN02tG2Dsw1Ka5QS+eypq1fhQ2CsnnGlFCoq8VAW8bVKz6+ArLZp8ZS6oZC5vLzg8nJXq4E6LneX9H1/8t3OicPhWNn2+n5mCN7X8C29hpQt95SYIkpLV2oqGZVKTX6u1S3aYLJdQ5+meUYVVua5AEZrfAgMY8u236CNwjWOvu/k+8agVcIAzhg2XYvTTtLZY8KXvF5nzjk2XctFJ2B/qSoq1mKUYfAB//jIcZqlCqwqPmz1N4vsXTYUchF1QePc6tedl3T4aa6VRpw2M2JabQspyb1BFBJSrXM8Dj/Px8+vxPyiPpvPc57znOc85/m25p8LVm9vb7eAffPmzWOVGv17wP8B/D1gc3t7+29W/8t/BPz39a/9Sd/7mef6xXXt5YyEEIiHI6F4ciqkKJ2Efd/TOEuIJxll329oG0vwM8e99KrOfmYO8+pPFV9pqMm8J/Y0V7CmqmfyuWxRKQk/keqEWJN2W3INaVlYgXkOHI/SByuL8UBKYa04WZi5RYa8LC5PMllZaC7SwgVE55xFytr3vH79emWipmmSWo1ntR3WWmxvcW3Dbnch/q0YMM4xzpPI9cpJjikgXZjPbE6yyIVFmYYjbatJWUJnVH2eS0LoME3s90fm2RNqzQ1IyulpagAQPAsCkv9umoaLiwsBGDWdNKVIKiLHzTmTqVLQWRasPogMNqfaT6r0mkjqKntujK6JwiK9TClKYNJ++H8E8iysrMgPxQtsbSM1GNZy2A8cjyPGGDbAPE8cDofVO/zw8MDj4+N6zJqmxbVNfY3y/HzwNcl0kA2XnKt0OdVU2Pp+lMW3qytTlsm127LkiPeZYZi4v7/D+z1aNwyHA1999Y63X37BT3/yOT/9/Kfs90eO+4HhMDAcR+KcKCjQmaLAOsem72oIlyGlgNZI6JddKnQiIU5Aoe97PvvsU37445fsxyMZeW4xFbKYQIUVq37xNSF39X6bZxsQed24WTYkCrmmT6dVEq+UVBV571cv6fJePQe7z72vsdYXLUDTaEPbNBK4ZhWNkgodaw1td9qYkY0YUzdZFJlIirl2ruYqSa+S9foaoPqRg4DrXGSTpGsbuQ9Vhr2UjDaWq0vZNFl6TLXWct34aWU8jdaknCgJtLZovQQHyXUnj6Wli6vOwpIrTtJqrTWoTI6ZmGWzqmTZuAIqmJMu4pTF32qMxnk5zm3bYI2ldRldTpYEP4V6Ls84q2haV4G+pXUNu21fK3ukXkfuP1IhFguENIjHNmuMsljnak1WTWl+BsZDkPu/VA/BWIOd5C1a7s+syeWrQsXY6msXFcXqff5TMr/Iz+bznOc85znPeb6t+VmY1dfA/3B7e1v7EvjHwH/85s2bfHt7+x8Af+v29rajRuAD/Enf+3nGWcM8zzztBx6fnsRnmSTZlVywpq3y4LgmxTqraaypIG7EGE3bNkzzXKWZIktdUkVlQbtICROlZELMmAouQli6SiWpVvxzkgDbtI6m6zDWEGKsEsaB40EkqqDJJeP9DBQpu2/cCrZZQlWcJcaE917AbplRwFwZ5AUkxxhxzkny7nbL09MT9/f3sqg3Tmplqryy7zXGNQK2WtiimeaJmMHFTG4KYQqrB3MBq8JIZmLQDIf9yjgrCv3mEmsbtLFVNit+V58yw3FinGaRWhZJbCmiWQQKqixBNAJQjNZkBMC0Tcum6+grG3N5ucV7z/39nUhWx8q01QV4qqBB/LLiW5XFfGVYi6Y1EjyjlSKnyP7pET+P5FKY58A0jquUUWklGxRa4YKjaRtMNmQUuYhnL+fC8TAw+1lYummUOp8YhGVtHN57hmEihiihV5tNBVQiZR3Hkf1RmNThONVzSaS/oE9ApILyQqaogi4GUoKSiSGQcsDW3sm7u/e8f/85NzeXfPH2Lb//D/8x796+4+Huka++fMcwzoQxME+BGKVKScKYEtZZXGNwnaPtGjabFlTGp4nRD+yHR3LOTPPI/ulQwfgd2hiuXrxAf/mFsM0hEVIhlUKsfsumsvpFQSqFVDI6w+KoLYCPshlhjMFgKN6TU1yZxBSjpAnXHtRTP+lJnr+ynpy6W9c+01KZ/7oxZK2l32ywrrLuRgvgU7Kh1XWt/K6SpaG4biqtFUno9XdQlo0asSY8964K2yf3CG0M29YBhcYZri8v2W636/NZNkJCSKsEPcYISsv9iCXw6SSXX7yqOWW00yeQjsiDVZXdFwqlbnqUIrVbKac16XzZJACFoayvW1dlg0+R3XaLJqNKkZ/JBYNi23ds6SRlOyWMlfusa5wcf9QaflWA4+HAcTgwzQNd29A0LVppWtexaXucVSgt/tR5DoyzAHfXNMQw4n0Q+XwCHwN+lj8XpVeAT30fCjXtW2vQWiLHjHi+/5TNL+yz+TznOc95znOeb2v+uWD1zZs3fwD863/M9/534F/9eb/3s85hGPj8iy94fJIalFSBpnOO3bZHacU4joQwE6MsSBtn1xoGrQubzYZhkK5WZXRlgZIs3HNa/ZBrKEkUQGC0Qet5XRg712BMJFZ5oNUaYzqsM7IoAnwIHI9HZh/RxqGQxaXs9pcq953XhaIxlsDJqyj1KyJF3W47cha/WPDCximlaNoW7z0fPnzg8fGJYRzQShFNqjU3slCbfOQ4jEyzxwdPCJFpnhincQ0fWqSAa2JtUVCEWc5FAquk1zFyfXXFpu/JBZ6eDoQYmaaZqcozvY8CVIEln1VpTS4nnzEIwyh9k5YYTiywroB8nkdK6aF69Y7HI09PT+Rc6LoNxlphdHOqgTcnufACPiQhWr5jtKYg8lipxEikKttcNiwW4KCAEIXdMcZgvRf58TwxTbOAcFiTYa1RtG3HbndB3/fsDwemOWBrArKAaFODlGaOxwOPT48chyMlqwo+xPNXclmflxyo50E+ipyqJLqAMQ7ITNPE1++/5vHxA32v2B/uePvl53z+4y94uHtiGj0pQfKJEIW51dZirIACY/XKIhtr0AZCmrm7f09Mka++bpm9ZzgeuPtwz+PTI/M8MhxE+jyHyDxHvE/4XECqTwlrOmxlVCsgLJWFX15jzpnZp2dBOhpyknAxWDde5Pww35ANL2FHC2BdgFcpBepjbbqNXJd+Zp6mZ+E9Im+2Tm5/oXphx3FEa0Nm8Q0XSpFE2XGYvsHi/tGalOW/1SJzVgpnLcYouk56cRtn2O12z8AlDMNQw5RKlYDL65cNHUtY2PRqcdBaOp9zlrC39fnU8/0b2ciFVf4riouaDFyes9E1qbkCPglbK6ufdTyOtM7SWo3Wpt6j8tqL66rSo5SEsRJOZY2hpCzXwzQTc+E4SBfrNMu9WFLaHX2XiEnRNql6naXzdhrmKtmum4QxMowTPsRnz9lU767cBxb/7nI+pLr5uEjbjf0XCb//5Ztf5Gfzec5znvOc5zzf1vxSf3rP3jOMAyFIeIwyGmvEr5ZKYfIeSpZCQ2Th2jSOvt/QtA6rNdttz7v3kjIq/YdS45JSXlM5F4/nAh6yIAhhamuyKJS1S3Czadhc9Nx8dMOLl9do4P4xik9zSRrWFh/yuhDW+lSfUIpImCETwlz9ogKgAalV6TbCHO6PtTpiSUc17A8DMT6tzGCmoIuwTmhhNUKIPD7uRd5XeyCl0xSclg7QRKjALxJjoHENbbOhcUZYIaEraazh8nKHsY7jOPO4P0o9BhLk4kNY2aAlxZS6iC8lrzUSS/LyUnmzBC0ti/eUEsejhLuA9C1Ok8f7k7eXpe82U/3A5RkQPtUYLT7KtpFgolTrgXL2KJXISMqs9/LeP2+18OH0nMQjK5sFhlMVEhaMdTQ1JRhlUMqQciEX/u/27i3Esuyu4/h37cs5u6pOXXpqZjQzJo63LCWE6GggaqL45IuCN9SAmBdBg+iTIPiiCMqgeVITIoggieRBhOjbvInOgyBqkCCsBMmYqCGd9GSq63LO2bflw3/tfU4P6aruqp6q09O/DxTdtU/VYdeqtWvt/15r/f8UWW77g5eNLbem5/j4hOO7J9Rp76W9//ARaZuOnkgxKayea5Fm/qJLD1YYk9o0bcPp/Iw7d+7Yw5qmoV3WNHWdEjctaBYdjpwupmunzMb+nKcHCTZLbSsTXDbH5Xepu5b89lfo25az+RnHx8e8/voRy8WStqlZLBbMFwuWTUPd9bQd9Kn0CD00TU29XFBVW2PgsN6ew1L1PmU7HvZX5nlO7Fp7sLSWeCnGSFms6vMOy3St72Sp/RiXx/ZulWBo+HknaTVAjPagaVJY4BexAGdRW3DUjCVOHFleAG7cL7me0XiwntF4+J0Ctgc2PTgrJyXbO1tMilXSMKvLOjwcaymywmbBm4auiynIysegcWin6XQy/uxZntP3jib2uD7iUlmsN7ZT3dbjXtAsS7PAMRv/3q0H+quHdpYlOcuc7dOfTi3BWLQyQK7pKLueKYyzscTMlpjjbNa5tb87TWfBtSWu2ral7Smh28liTt1anekiz5jNZpTlhOlWxaJecnR8l7JINaqte6X60+CyVM93WMWBzWw7hr2vgItkuc1wT6YbPdyJiIjIN7DRo3fTWnbIPi0VtBsixpu1IRmOSzMp1XTKbGfb9qymmoFFYbMZWZ5TN5Zldela6qajaxvadlXyYJwh6XuabpWpFtJNae4o84yd2TZPHd7iqcMDdmZbtE1D3zf0saEoM/KixLmCtq/p6yEwhuWyTrNyljRmuAm1Wd50s5XZzOHJySnL5YKzs7MxscyQiGV9X+pwfFpWa2VSLNGNlb1Y1YAcZqaGzMTz+ep9t7e3uXVwYAlgUvbRPM9tmV/bUpYFdd2yrDtbcp1msm2fbkc/JE2KPV1nJXMsgFsFkpnLxiWKY5mPPi1tLVZ7HBeLs3GmZz1z7HCDvsqI6sbllxZYW5DhyGzmOxsCnJw8L8mczQRFoGk77sYTlk3KQNqsfiZbypkyF7O6gS/LgklVMammNntfFJA5uhj5+tHr3D065uTsjGpakRel1YNcnpJlp5RlMQbebvw92jLgYVlm1/W43EG03+tqxemwN9L6Yptqq7Ztx+3bt7l9+zYH+zvUy5q26chcTuZsn2PmcntPZ6VNhj2wjgwXe/quZz5f0jQ9i4UtT7YluJYYaOiDQ0mjdqhzmTLD1k1P1zti2m8d+46micTekmmtL9cdH1Kk5GLr+8CHvhCHICNxqc+sB7dDYJu+Ygy4hn5mbRbHGUXnHJNUkibGaP0kpsRIztlDj5jhXJ5mWGucyynLjAhjDU+7DuPY94Y+6dwqYLW9ovb7rOuaonDkS3toMi1Lut7KPJ2dnVHXTUrG1uGis8y2qa+PQb2LY3utsv8Ol5rtFSbt07X15Ktl0kPfuXe5dEwr853t8e6HQNXeJstWbT0suV42HZGGyZAtHNsq6/qeWNsy+azIqLqJLRPOHQVDUAwuRrarirwsaDtLZNYRyXJHl0fmi5q2PqUqrRzZzmzGpJyytbXN2XxO3TZkfWd1c1PGa1vNssoCvL4cfHhQMZQdG9pjOp2eM9qIiIjIJtrUYDUHODx8hueeP+XkeG43KKlMie09qygLm/GclJZ5sywLqumEIneURYlzdnOzNduzWcroODtbcnJ6yt3jY5bzOW1bW8ZVZ7MBDsskO8wEVKlsTJHnbM+2mVYlu3szdnd32N3ZpqomLJYL9uYLsmJCXXf0aR9iNW+pqjmxt8y5eZEz29lmMplYMpO097KPkdhFW8JbTignOU29SMmUdtKeMJvRKycTy5g73qzDbHeXalKNM4A2A7xaJth3q3Ifw828BSJzlsslLss42N/n6aefpqosUc9yviDLc9q6putadmYzTs6WbC0bcKsbwkFHtH18faRpl/cE+pDqYGIBQlkTY9D/AAAG/0lEQVSUaWa1h86SxlRbVt4FevrYplnYjOn2Dnt7+7jMMqEW5SQF+t2YrCjGaFlPnSVWmhQFkzJfzSIWw/JJAAe5leQ4unvEyempJaZKe6HjWjCyfv7OOWbbu5YleVLQd11K1pP20fY9e/sHbG3P2N3b5dlnnmW5WHB8dDQuNZ9WWxwc3LJyHl2XguP1xFBWqiQvc4ois720hQVSw95CgLZtKIvM9p66gs+FVznYv0XbOLaqXQ6fysmzHepFbe+fFbjMsvvisIRUfXpoEFub/Y69BWvRsRgSWNUN4CirimkfyeqGLC+Yuox8saSsdpjOG8gKyNN79y1Fbksy+y5aduvCsr3mqT9aErJhOXyfsrZaorMiW/XRvu+tTquz+qFDEFdVFUVZjEFW31m9TdK8XrbWz/MiH/+gFEWRlnun10pLUFQva+q2AWflsc7mc1vlEO312Hf0bWMrPGBM8gT3BslDGZthCXpZZMxm21RbEyZFSbU1TQnCGibTLbtWlx19l4LsPNXQzXLyIiWeItJ3VjZrCMzyIi1z7Tqadi1IT8Fclh5MlWVhwSe2+sKC99VstGO1XNaejGRry+vTAyBWD7qKIk8PcKDIrR4xzsoHDQ908swxKXNm1RZ7u6nebNuQ5TlkFhAXZU6LlarpYqSpe9q6pki/37Is2dvd5dbhIbv7B2MpKfv7lafEbpC5OF6n6zP3Q9usJ/VyzrG3t3/P+CKXkgM8//zbbvo8RETkLWBtPLnv2OzWA44N8n7gn276JERE5C3nA8ArN30SjymNzSIi8ma479i8qcHqFHgv8GWgu+BrRURELpIDbwP+BVje8Lk8rjQ2i4jIo3Th2LypwaqIiIiIiIg8wbKLv0RERERERETkeilYFRERERERkY2jYFVEREREREQ2joJVERERERER2TgKVkVERERERGTjKFgVERERERGRjaNgVURERERERDaOglURERERERHZOMVNn8Abee/fCfwVcAjcAX45hPD5mz2rzeG9/wjws8ALwLtDCJ9Nx+/bbmpT470/BD4BfAdQA58HfjWE8FXv/fuAPwe2gFeBXwoh3E7fd9/XniTe+08D3wb0wAnwGyGEz6jvPTjv/e8Cv0e6dtXvHoz3/lVgkT4AfjuE8LLa7/roWj6fxubL09h8NRqbr05j8+Vc19i8iTOrHwc+GkJ4J/BR7AeSlU8DPwL89xuOn9dualMTgT8KIfgQwruB/wJe8t5nwCeBX09t9I/ASwDnvfYE+lAI4T0hhO8DPgL8ZTquvvcAvPcvAu8jXbvqdw/t50II35s+Xlb7XTtdy+fT2Hx5GpuvRmPzFWhsvrI3fWzeqGDVe/8s8CLwqXToU8CL3vtnbu6sNksI4ZUQwpfWj53XbmrTlRDCayGEf1g79M/AtwLfDyxCCK+k4x8Hfj79/7zXnighhKO1T/eBXn3vwXjvp9gNwYfXDqvfXY3a75roWr6YxubL09h8NRqbL09j85vikbffRgWrwNuB/w0hdADp3/9Lx+X+zms3tek3kJ7ufBj4e+AdrD0NDyF8Dci8909d8NoTx3v/F977LwJ/AHwI9b0H9fvAJ0MIr64dU797OH/tvf8P7/3HvPcHqP2uk67ly9Hfx4eksflyNDZfmsbmq3vTx+ZNC1ZFrsufYns7/uymT+RxEkL4lRDCO4DfAf74ps/nceC9/0HgB4CP3fS5PMY+EEJ4D/BewKHrVuStSmPzJWhsfngamx+JaxmbNy1Y/RLwvPc+B0j/PpeOy/2d125q0zdIiTC+C/iFEEIPfBFbcjS8/jTQhxBeu+C1J1YI4RPAjwH/g/reRX4U+B7gCykZwbcALwPfifrdAxmWV4YQltiNxQ+j6/Y66Vq+HI3ND0Fj89VpbH4oGpuv6LrG5o0KVlM2qM8AH0yHPgj8ewjhqzd3VpvvvHZTm97Le/+H2Jr5n0oXF8C/Alve+/enz38N+JsHeO2J4b2fee/fvvb5TwKvAep7FwghvBRCeC6E8EII4QXsJuLHsaff6ncX8N7veO/30/8d8ItYv9J1e010LV+OxuYHp7H5cjQ2X57G5qu5zrHZxRgf5blfmff+u7F02reAr2PptMPNntXm8N7/CfAzwDcDXwPuhBDedV67qU2N9/5dwGeBzwHzdPgLIYSf9t7/EJYNr2KVSvsr6fvu+9qTwnv/TcDfATtAhw2GvxVC+Df1vYeTnuD+RLD0+Op3F/Defzvwt0CePv4T+M0QwpfVftdH1/L5NDZfnsbmy9PY/OhobH441zk2b1ywKiIiIiIiIrJRy4BFREREREREQMGqiIiIiIiIbCAFqyIiIiIiIrJxFKyKiIiIiIjIxlGwKiIiIiIiIhtHwaqIiIiIiIhsHAWrIiIiIiIisnH+HzbygWGdVRmjAAAAAElFTkSuQmCC\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"MtGw1aLC8sRE"},"source":["## submission을 위한 test 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:27.469285Z","start_time":"2021-04-16T19:44:27.456021Z"},"id":"BkHOlbIb8sRE","executionInfo":{"status":"ok","timestamp":1620006064042,"user_tz":-540,"elapsed":1077,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["def test(model, data_loader, device):\n"," size = 256\n"," transform = A.Compose([A.Resize(256, 256)])\n"," print('Start prediction.')\n"," model.eval()\n"," \n"," file_name_list = []\n"," preds_array = np.empty((0, size*size), dtype=np.long)\n"," \n"," with torch.no_grad():\n"," for step, (imgs, image_infos) in enumerate(test_loader):\n","\n"," # inference (512 x 512)\n"," outs = model(torch.stack(imgs).to(device))\n"," oms = torch.argmax(outs, dim=1).detach().cpu().numpy()\n"," \n"," # resize (256 x 256)\n"," temp_mask = []\n"," for img, mask in zip(np.stack(imgs), oms):\n"," transformed = transform(image=img, mask=mask)\n"," mask = transformed['mask']\n"," temp_mask.append(mask)\n","\n"," oms = np.array(temp_mask)\n"," \n"," oms = oms.reshape([oms.shape[0], size*size]).astype(int)\n"," preds_array = np.vstack((preds_array, oms))\n"," \n"," file_name_list.append([i['file_name'] for i in image_infos])\n"," print(\"End prediction.\")\n"," file_names = [y for x in file_name_list for y in x]\n"," \n"," return file_names, preds_array"],"execution_count":22,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"6JUkRi2J8sRF"},"source":["## submission.csv 생성"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:45:42.235310Z","start_time":"2021-04-16T19:44:30.499016Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"BbHNVDNr8sRF","executionInfo":{"status":"ok","timestamp":1620006369459,"user_tz":-540,"elapsed":295308,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"cc724972-4dec-4cc1-df40-2cc6a76d5e35"},"source":["# sample_submisson.csv 열기\n","submission = pd.read_csv('./submission/sample_submission.csv', index_col=None)\n","\n","# test set에 대한 prediction\n","file_names, preds = test(model, test_loader, device)\n","\n","# PredictionString 대입\n","for file_name, string in zip(file_names, preds):\n"," submission = submission.append({\"image_id\" : file_name, \"PredictionString\" : ' '.join(str(e) for e in string.tolist())}, \n"," ignore_index=True)\n","\n","# submission.csv로 저장\n","submission.to_csv(\"./submission/deeplabv3+resnet34+focalloss+madgrad+CycleLR.csv\", index=False)"],"execution_count":23,"outputs":[{"output_type":"stream","text":["Start prediction.\n","End prediction.\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"tIjoCiVp8sRG"},"source":["## Reference\n","\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ykrfzleS8sRG","executionInfo":{"status":"ok","timestamp":1620006376150,"user_tz":-540,"elapsed":4641,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"abb669af-cad6-4ec9-df82-0b3e1b996d5c"},"source":["import json\n","import requests\n","import os\n","from urllib.parse import urlparse, parse_qsl, urlencode, urlunparse\n","\n","def submit(user_key='', file_path = '', desc=\"\"):\n"," if not user_key:\n"," raise Exception(\"No UserKey\" )\n"," url = urlparse('http://ec2-13-124-161-225.ap-northeast-2.compute.amazonaws.com:8000/api/v1/competition/28/presigned_url/?description=&hyperparameters={%22training%22:{},%22inference%22:{}}')\n"," qs = dict(parse_qsl(url.query))\n"," qs['description'] = desc\n"," parts = url._replace(query=urlencode(qs))\n"," url = urlunparse(parts)\n","\n"," print(url)\n"," headers = {\n"," 'Authorization': user_key\n"," }\n"," res = requests.get(url, headers=headers)\n"," print(res.text)\n"," data = json.loads(res.text)\n"," \n"," submit_url = data['url']\n"," body = {\n"," 'key':'app/Competitions/000028/Users/{}/Submissions/{}/output.csv'.format(str(data['submission']['user']).zfill(8),str(data['submission']['local_id']).zfill(4)),\n"," 'x-amz-algorithm':data['fields']['x-amz-algorithm'],\n"," 'x-amz-credential':data['fields']['x-amz-credential'],\n"," 'x-amz-date':data['fields']['x-amz-date'],\n"," 'policy':data['fields']['policy'],\n"," 'x-amz-signature':data['fields']['x-amz-signature']\n"," }\n"," requests.post(url=submit_url, data=body, files={'file': open(file_path, 'rb')})\n","\n","\n","####################################################################################\n","test_dir = \"/content/drive/MyDrive/Trash/code/submission\" # 수정 필요 : output 파일 폴더 \n","desc = \"deeplabv3+resnet34+focalloss+madgrad+CycleLR\" # 수정 필요 : 파일에 대한 설명\n","output_file = \"deeplabv3+resnet34+focalloss+madgrad+CycleLR.csv\" #수정 필요 : output 파일 \n","user_key = \"Bearer 7bb5f96452751a238ffaf91a93c4242bf9b72abe\" # 수정 필요 : Authorization \n","\n","\n","submit(user_key, os.path.join(test_dir, output_file),desc)"],"execution_count":24,"outputs":[{"output_type":"stream","text":["http://ec2-13-124-161-225.ap-northeast-2.compute.amazonaws.com:8000/api/v1/competition/28/presigned_url/?hyperparameters=%7B%22training%22%3A%7B%7D%2C%22inference%22%3A%7B%7D%7D&description=deeplabv3%2Bresnet34%2Bfocalloss%2Bmadgrad%2BCycleLR\n","{\"url\":\"https://prod-aistages-private.s3.amazonaws.com/\",\"fields\":{\"key\":\"app/Competitions/000028/Users/00000099/Submissions/0016/output.csv\",\"x-amz-algorithm\":\"AWS4-HMAC-SHA256\",\"x-amz-credential\":\"AKIA45LU4MHUJ7WLDQVO/20210503/ap-northeast-2/s3/aws4_request\",\"x-amz-date\":\"20210503T014612Z\",\"policy\":\"eyJleHBpcmF0aW9uIjogIjIwMjEtMDUtMDNUMDI6NDY6MTJaIiwgImNvbmRpdGlvbnMiOiBbeyJidWNrZXQiOiAicHJvZC1haXN0YWdlcy1wcml2YXRlIn0sIHsia2V5IjogImFwcC9Db21wZXRpdGlvbnMvMDAwMDI4L1VzZXJzLzAwMDAwMDk5L1N1Ym1pc3Npb25zLzAwMTYvb3V0cHV0LmNzdiJ9LCB7IngtYW16LWFsZ29yaXRobSI6ICJBV1M0LUhNQUMtU0hBMjU2In0sIHsieC1hbXotY3JlZGVudGlhbCI6ICJBS0lBNDVMVTRNSFVKN1dMRFFWTy8yMDIxMDUwMy9hcC1ub3J0aGVhc3QtMi9zMy9hd3M0X3JlcXVlc3QifSwgeyJ4LWFtei1kYXRlIjogIjIwMjEwNTAzVDAxNDYxMloifV19\",\"x-amz-signature\":\"acd1533eddcf9808d7541f576cd254cb8c2e63532482dfd136472c82b04a24fd\"},\"submission\":{\"id\":14396,\"phase\":\"Created\",\"type\":\"File\",\"local_id\":16,\"hyperparameters\":\"{\\\"training\\\": {}, \\\"inference\\\": {}}\",\"description\":\"deeplabv3+resnet34+focalloss+madgrad+CycleLR\",\"final\":false,\"created_at\":\"2021-05-03T10:46:12.207003+09:00\",\"updated_at\":\"2021-05-03T10:46:12.207035+09:00\",\"user\":99,\"competition\":28,\"image\":null}}\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"sVoz-PcVcvJ3"},"source":[""],"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/chanyub_seg/code/aug2_re_pan_effb3_noisy_focal_madgrad_cosLR.ipynb b/chanyub_seg/code/aug2_re_pan_effb3_noisy_focal_madgrad_cosLR.ipynb deleted file mode 100644 index 718540b..0000000 --- a/chanyub_seg/code/aug2_re_pan_effb3_noisy_focal_madgrad_cosLR.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"nbformat":4,"nbformat_minor":0,"metadata":{"hide_input":false,"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.7"},"toc":{"base_numbering":1,"nav_menu":{},"number_sections":true,"sideBar":true,"skip_h1_title":false,"title_cell":"Table of Contents","title_sidebar":"Contents","toc_cell":true,"toc_position":{"height":"calc(100% - 180px)","left":"10px","top":"150px","width":"297.278px"},"toc_section_display":true,"toc_window_display":true},"colab":{"name":"aug2_re_pan_effb3_noisy_focal_madgrad_cosLR.ipynb","provenance":[],"collapsed_sections":[],"toc_visible":true,"machine_shape":"hm"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GW8gF48g-WSK","executionInfo":{"status":"ok","timestamp":1620215057521,"user_tz":-540,"elapsed":748,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"b08072bc-40b5-40c3-bee3-69792283789c"},"source":["from google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xDwpp4Lk-gSH","executionInfo":{"status":"ok","timestamp":1620215063742,"user_tz":-540,"elapsed":843,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"d3e79805-46d4-43e1-d0e5-f56bcb6c8413"},"source":["ls"],"execution_count":2,"outputs":[{"output_type":"stream","text":["\u001b[0m\u001b[01;34mdrive\u001b[0m/ \u001b[01;34msample_data\u001b[0m/\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sItrVDeh-iYC","executionInfo":{"status":"ok","timestamp":1620215065641,"user_tz":-540,"elapsed":700,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"637b4090-3d7f-4708-9471-acf1e3edb550"},"source":["cd drive/MyDrive/Trash/code"],"execution_count":3,"outputs":[{"output_type":"stream","text":["/content/drive/MyDrive/Trash/code\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"a54v4-kN_LVO","executionInfo":{"status":"ok","timestamp":1620215069864,"user_tz":-540,"elapsed":3030,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"4c93a9d5-01fe-40a5-fab6-40f66414ab74"},"source":["!pip install albumentations==0.5.2"],"execution_count":4,"outputs":[{"output_type":"stream","text":["Requirement already satisfied: albumentations==0.5.2 in /usr/local/lib/python3.7/dist-packages (0.5.2)\n","Requirement already satisfied: scikit-image>=0.16.1 in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (0.16.2)\n","Requirement already satisfied: imgaug>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (0.4.0)\n","Requirement already satisfied: opencv-python-headless>=4.1.1 in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (4.5.1.48)\n","Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (1.4.1)\n","Requirement already satisfied: PyYAML in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (3.13)\n","Requirement already satisfied: numpy>=1.11.1 in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (1.19.5)\n","Requirement already satisfied: pillow>=4.3.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations==0.5.2) (7.1.2)\n","Requirement already satisfied: networkx>=2.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations==0.5.2) (2.5.1)\n","Requirement already satisfied: imageio>=2.3.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations==0.5.2) (2.4.1)\n","Requirement already satisfied: matplotlib!=3.0.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations==0.5.2) (3.2.2)\n","Requirement already satisfied: PyWavelets>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations==0.5.2) (1.1.1)\n","Requirement already satisfied: Shapely in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (1.7.1)\n","Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (1.15.0)\n","Requirement already satisfied: opencv-python in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (4.1.2.30)\n","Requirement already satisfied: decorator<5,>=4.3 in /usr/local/lib/python3.7/dist-packages (from networkx>=2.0->scikit-image>=0.16.1->albumentations==0.5.2) (4.4.2)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.16.1->albumentations==0.5.2) (1.3.1)\n","Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.16.1->albumentations==0.5.2) (2.4.7)\n","Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.16.1->albumentations==0.5.2) (2.8.1)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.16.1->albumentations==0.5.2) (0.10.0)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.826930Z","start_time":"2021-04-18T10:34:45.406686Z"},"colab":{"base_uri":"https://localhost:8080/"},"id":"l_LPA4XD-NwC","executionInfo":{"status":"ok","timestamp":1620215071920,"user_tz":-540,"elapsed":4295,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"e1d3aea6-a46b-47d0-f205-6baa0155a2ec"},"source":["import os\n","import random\n","import time\n","import json\n","import warnings \n","warnings.filterwarnings('ignore')\n","\n","import torch\n","import torch.nn as nn\n","from torch.utils.data import Dataset, DataLoader\n","from utils import label_accuracy_score\n","import cv2\n","\n","import numpy as np\n","import pandas as pd\n","\n","# 전처리를 위한 라이브러리\n","from pycocotools.coco import COCO\n","import torchvision\n","import torchvision.transforms as transforms\n","\n","import albumentations as A\n","from albumentations.pytorch import ToTensorV2\n","\n","# 시각화를 위한 라이브러리\n","import matplotlib.pyplot as plt\n","import seaborn as sns; sns.set()\n","\n","plt.rcParams['axes.grid'] = False\n","\n","print('pytorch version: {}'.format(torch.__version__))\n","print('GPU 사용 가능 여부: {}'.format(torch.cuda.is_available()))\n","\n","print(torch.cuda.get_device_name(0))\n","print(torch.cuda.device_count())\n","\n","device = \"cuda\" if torch.cuda.is_available() else \"cpu\" # GPU 사용 가능 여부에 따라 device 정보 저장"],"execution_count":5,"outputs":[{"output_type":"stream","text":["pytorch version: 1.8.1+cu101\n","GPU 사용 가능 여부: True\n","Tesla P100-PCIE-16GB\n","1\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"HxgRSL_M-NwF"},"source":["## 하이퍼파라미터 세팅 및 seed 고정"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.841930Z","start_time":"2021-04-18T10:34:47.827931Z"},"id":"rV3JmGP5-NwF","executionInfo":{"status":"ok","timestamp":1620215073942,"user_tz":-540,"elapsed":2002,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["batch_size = 8 # Mini-batch size\n","num_epochs = 20\n","learning_rate = 0.0001"],"execution_count":6,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.856930Z","start_time":"2021-04-18T10:34:47.842931Z"},"id":"Z6LOuJXQ-NwG","executionInfo":{"status":"ok","timestamp":1620215073943,"user_tz":-540,"elapsed":1967,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# seed 고정\n","random_seed = 42\n","torch.manual_seed(random_seed)\n","torch.cuda.manual_seed(random_seed)\n","# torch.cuda.manual_seed_all(random_seed) # if use multi-GPU\n","torch.backends.cudnn.deterministic = True\n","torch.backends.cudnn.benchmark = False\n","np.random.seed(random_seed)\n","random.seed(random_seed)"],"execution_count":7,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"iWF_EJDu-NwG"},"source":["## 학습 데이터 EDA"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.381961Z","start_time":"2021-04-18T10:34:47.857930Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"lg0x0D0a-NwG","executionInfo":{"status":"ok","timestamp":1620215080110,"user_tz":-540,"elapsed":4214,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"0afaa8e4-2e7e-4e93-91d5-1b1718d269b7"},"source":["%matplotlib inline\n","\n","dataset_path = '../input/data'\n","anns_file_path = dataset_path + '/' + 'train.json'\n","\n","# Read annotations\n","with open(anns_file_path, 'r') as f:\n"," dataset = json.loads(f.read())\n","\n","categories = dataset['categories']\n","anns = dataset['annotations']\n","imgs = dataset['images']\n","nr_cats = len(categories)\n","nr_annotations = len(anns)\n","nr_images = len(imgs)\n","\n","# Load categories and super categories\n","cat_names = []\n","super_cat_names = []\n","super_cat_ids = {}\n","super_cat_last_name = ''\n","nr_super_cats = 0\n","for cat_it in categories:\n"," cat_names.append(cat_it['name'])\n"," super_cat_name = cat_it['supercategory']\n"," # Adding new supercat\n"," if super_cat_name != super_cat_last_name:\n"," super_cat_names.append(super_cat_name)\n"," super_cat_ids[super_cat_name] = nr_super_cats\n"," super_cat_last_name = super_cat_name\n"," nr_super_cats += 1\n","\n","print('Number of super categories:', nr_super_cats)\n","print('Number of categories:', nr_cats)\n","print('Number of annotations:', nr_annotations)\n","print('Number of images:', nr_images)"],"execution_count":8,"outputs":[{"output_type":"stream","text":["Number of super categories: 11\n","Number of categories: 11\n","Number of annotations: 21116\n","Number of images: 2617\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.546964Z","start_time":"2021-04-18T10:34:51.382969Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/","height":355},"id":"PjLyVHVY-NwH","executionInfo":{"status":"ok","timestamp":1620215080858,"user_tz":-540,"elapsed":4907,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"fc8a6df6-8f34-4f95-ccb8-cf45f9b1913a"},"source":["# Count annotations\n","cat_histogram = np.zeros(nr_cats,dtype=int)\n","for ann in anns:\n"," cat_histogram[ann['category_id']] += 1\n","\n","# Initialize the matplotlib figure\n","f, ax = plt.subplots(figsize=(5,5))\n","\n","# Convert to DataFrame\n","df = pd.DataFrame({'Categories': cat_names, 'Number of annotations': cat_histogram})\n","df = df.sort_values('Number of annotations', 0, False)\n","\n","# Plot the histogram\n","plt.title(\"category distribution of train set \")\n","plot_1 = sns.barplot(x=\"Number of annotations\", y=\"Categories\", data=df, label=\"Total\", color=\"b\")"],"execution_count":9,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.561965Z","start_time":"2021-04-18T10:34:51.547969Z"},"id":"34Tw5__i-NwI","executionInfo":{"status":"ok","timestamp":1620215080859,"user_tz":-540,"elapsed":4219,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# category labeling \n","sorted_temp_df = df.sort_index()\n","\n","# background = 0 에 해당되는 label 추가 후 기존들을 모두 label + 1 로 설정\n","sorted_df = pd.DataFrame([\"Backgroud\"], columns = [\"Categories\"])\n","sorted_df = sorted_df.append(sorted_temp_df, ignore_index=True)"],"execution_count":10,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.576961Z","start_time":"2021-04-18T10:34:51.562964Z"},"colab":{"base_uri":"https://localhost:8080/","height":421},"id":"CQk4vV5N-NwI","executionInfo":{"status":"ok","timestamp":1620215080859,"user_tz":-540,"elapsed":3808,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"7a49442d-da09-4c8e-a634-b6131c676dbf"},"source":["# class (Categories) 에 따른 index 확인 (0~11 : 총 12개)\n","sorted_df"],"execution_count":11,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
CategoriesNumber of annotations
0BackgroudNaN
1UNKNOWN128.0
2General trash2225.0
3Paper7448.0
4Paper pack527.0
5Metal449.0
6Glass488.0
7Plastic2472.0
8Styrofoam1074.0
9Plastic bag6114.0
10Battery50.0
11Clothing141.0
\n","
"],"text/plain":[" Categories Number of annotations\n","0 Backgroud NaN\n","1 UNKNOWN 128.0\n","2 General trash 2225.0\n","3 Paper 7448.0\n","4 Paper pack 527.0\n","5 Metal 449.0\n","6 Glass 488.0\n","7 Plastic 2472.0\n","8 Styrofoam 1074.0\n","9 Plastic bag 6114.0\n","10 Battery 50.0\n","11 Clothing 141.0"]},"metadata":{"tags":[]},"execution_count":11}]},{"cell_type":"markdown","metadata":{"id":"KXU0zmZs-NwI"},"source":["## 데이터 전처리 함수 정의 (Dataset)"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:52.693328Z","start_time":"2021-04-18T10:34:52.681328Z"},"id":"QFnTI8_Z-NwJ","executionInfo":{"status":"ok","timestamp":1620215082951,"user_tz":-540,"elapsed":2061,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["category_names = list(sorted_df.Categories)\n","\n","def get_classname(classID, cats):\n"," for i in range(len(cats)):\n"," if cats[i]['id']==classID:\n"," return cats[i]['name']\n"," return \"None\"\n","\n","class CustomDataLoader(Dataset):\n"," \"\"\"COCO format\"\"\"\n"," def __init__(self, data_dir, mode = 'train', transform = None):\n"," super().__init__()\n"," self.mode = mode\n"," self.transform = transform\n"," self.coco = COCO(data_dir)\n"," \n"," def __getitem__(self, index: int):\n"," # dataset이 index되어 list처럼 동작\n"," image_id = self.coco.getImgIds(imgIds=index)\n"," image_infos = self.coco.loadImgs(image_id)[0]\n"," \n"," # cv2 를 활용하여 image 불러오기\n"," images = cv2.imread(os.path.join(dataset_path, image_infos['file_name']))\n"," images = cv2.cvtColor(images, cv2.COLOR_BGR2RGB).astype(np.float32)\n"," images /= 255.0\n"," \n"," if (self.mode in ('train', 'val')):\n"," ann_ids = self.coco.getAnnIds(imgIds=image_infos['id'])\n"," anns = self.coco.loadAnns(ann_ids)\n","\n"," # Load the categories in a variable\n"," cat_ids = self.coco.getCatIds()\n"," cats = self.coco.loadCats(cat_ids)\n","\n"," # masks : size가 (height x width)인 2D\n"," # 각각의 pixel 값에는 \"category id + 1\" 할당\n"," # Background = 0\n"," masks = np.zeros((image_infos[\"height\"], image_infos[\"width\"]))\n"," # Unknown = 1, General trash = 2, ... , Cigarette = 11\n"," for i in range(len(anns)):\n"," className = get_classname(anns[i]['category_id'], cats)\n"," pixel_value = category_names.index(className)\n"," masks = np.maximum(self.coco.annToMask(anns[i])*pixel_value, masks)\n"," masks = masks.astype(np.float32)\n","\n"," # transform -> albumentations 라이브러리 활용\n"," if self.transform is not None:\n"," transformed = self.transform(image=images, mask=masks)\n"," images = transformed[\"image\"]\n"," masks = transformed[\"mask\"]\n"," \n"," return images, masks, image_infos\n"," \n"," if self.mode == 'test':\n"," # transform -> albumentations 라이브러리 활용\n"," if self.transform is not None:\n"," transformed = self.transform(image=images)\n"," images = transformed[\"image\"]\n"," \n"," return images, image_infos\n"," \n"," \n"," def __len__(self) -> int:\n"," # 전체 dataset의 size를 return\n"," return len(self.coco.getImgIds())"],"execution_count":12,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"cp2aIOlP-NwK"},"source":["## Dataset 정의 및 DataLoader 할당"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:58.823175Z","start_time":"2021-04-18T10:34:54.106233Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"P7zFlRn6-NwK","executionInfo":{"status":"ok","timestamp":1620215087272,"user_tz":-540,"elapsed":5783,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"71784daf-051c-4f6d-8874-4db06daeeb84"},"source":["# train.json / validation.json / test.json 디렉토리 설정\n","train_path = dataset_path + '/train.json'\n","val_path = dataset_path + '/val.json'\n","test_path = dataset_path + '/test.json'\n","\n","# collate_fn needs for batch\n","def collate_fn(batch):\n"," return tuple(zip(*batch))\n","\n","train_transform = A.Compose([\n"," A.transforms.Rotate(limit=30),\n"," A.augmentations.HorizontalFlip(),\n"," ToTensorV2()\n"," ])\n","\n","val_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","test_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","# create own Dataset 1 (skip)\n","# validation set을 직접 나누고 싶은 경우\n","# random_split 사용하여 data set을 8:2 로 분할\n","# train_size = int(0.8*len(dataset))\n","# val_size = int(len(dataset)-train_size)\n","# dataset = CustomDataLoader(data_dir=train_path, mode='train', transform=transform)\n","# train_dataset, val_dataset = torch.utils.data.random_split(dataset, [train_size, val_size])\n","\n","# create own Dataset 2\n","# train dataset\n","train_dataset = CustomDataLoader(data_dir=train_path, mode='train', transform=train_transform)\n","\n","# validation dataset\n","val_dataset = CustomDataLoader(data_dir=val_path, mode='val', transform=val_transform)\n","\n","# test dataset\n","test_dataset = CustomDataLoader(data_dir=test_path, mode='test', transform=test_transform)\n","\n","\n","# DataLoader\n","train_loader = torch.utils.data.DataLoader(dataset=train_dataset, \n"," batch_size=batch_size,\n"," shuffle=True,\n"," num_workers=4,\n"," collate_fn=collate_fn,\n"," drop_last=True)\n","\n","val_loader = torch.utils.data.DataLoader(dataset=val_dataset, \n"," batch_size=batch_size,\n"," shuffle=False,\n"," num_workers=4,\n"," collate_fn=collate_fn,\n"," drop_last=True) \n","\n","test_loader = torch.utils.data.DataLoader(dataset=test_dataset,\n"," batch_size=batch_size,\n"," num_workers=4,\n"," collate_fn=collate_fn)"],"execution_count":13,"outputs":[{"output_type":"stream","text":["loading annotations into memory...\n","Done (t=3.76s)\n","creating index...\n","index created!\n","loading annotations into memory...\n","Done (t=0.81s)\n","creating index...\n","index created!\n","loading annotations into memory...\n","Done (t=0.02s)\n","creating index...\n","index created!\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"8rqJiHb_-NwM"},"source":["# wandb"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"H1kHXm0uAX3R","executionInfo":{"status":"ok","timestamp":1620194961402,"user_tz":-540,"elapsed":10300,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"6030458b-60b3-4228-ef56-d73a341ca5b6"},"source":["!pip install wandb"],"execution_count":14,"outputs":[{"output_type":"stream","text":["Collecting wandb\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/67/5a/b037b50f9849212863a2fed313624d8f6f33ffa4ce89dc706e2a0e98c780/wandb-0.10.29-py2.py3-none-any.whl (2.1MB)\n","\r\u001b[K |▏ | 10kB 22.4MB/s eta 0:00:01\r\u001b[K |▎ | 20kB 20.5MB/s eta 0:00:01\r\u001b[K |▌ | 30kB 16.3MB/s eta 0:00:01\r\u001b[K |▋ | 40kB 14.9MB/s eta 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pathtools, subprocess32, shortuuid, smmap, gitdb, GitPython, docker-pycreds, configparser, wandb\n","Successfully installed GitPython-3.1.14 configparser-5.0.2 docker-pycreds-0.4.0 gitdb-4.0.7 pathtools-0.1.2 sentry-sdk-1.0.0 shortuuid-1.0.1 smmap-4.0.0 subprocess32-3.5.4 wandb-0.10.29\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":153},"id":"aMW4VV9V-NwM","executionInfo":{"status":"ok","timestamp":1620194981309,"user_tz":-540,"elapsed":18576,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"4bba5034-640c-4f76-9015-01139914936d"},"source":["import wandb\n","\n","proj_name = 'aug2_re_pan_effb3_noisy_focal_madgrad_cosLR'\n","\n","wandb.init(project='chanyub',name=proj_name)\n","\n","config = wandb.config\n","config.learning_rate = 0.01"],"execution_count":15,"outputs":[{"output_type":"display_data","data":{"application/javascript":["\n"," window._wandbApiKey = new Promise((resolve, reject) => {\n"," function loadScript(url) {\n"," return new Promise(function(resolve, reject) {\n"," let newScript = document.createElement(\"script\");\n"," newScript.onerror = reject;\n"," newScript.onload = resolve;\n"," document.body.appendChild(newScript);\n"," newScript.src = url;\n"," });\n"," }\n"," loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n"," const iframe = document.createElement('iframe')\n"," iframe.style.cssText = \"width:0;height:0;border:none\"\n"," document.body.appendChild(iframe)\n"," const handshake = new Postmate({\n"," container: iframe,\n"," url: 'https://wandb.ai/authorize'\n"," });\n"," const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n"," handshake.then(function(child) {\n"," child.on('authorize', data => {\n"," clearTimeout(timeout)\n"," resolve(data)\n"," });\n"," });\n"," })\n"," });\n"," "],"text/plain":[""]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"],"name":"stderr"},{"output_type":"display_data","data":{"text/html":["\n"," Tracking run with wandb version 0.10.29
\n"," Syncing run aug2_re_pan_effb3_noisy_focal_madgrad_cosLR to Weights & Biases (Documentation).
\n"," Project page: https://wandb.ai/pstage12/chanyub
\n"," Run page: https://wandb.ai/pstage12/chanyub/runs/2y2ubwqk
\n"," Run data is saved locally in /content/drive/My Drive/Trash/code/wandb/run-20210505_060935-2y2ubwqk

\n"," "],"text/plain":[""]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"uQRiIVGX-NwM"},"source":["## My model"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"S0xCGpNeAqeD","executionInfo":{"status":"ok","timestamp":1620215091363,"user_tz":-540,"elapsed":3059,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"59875d85-e59a-4d75-9657-519836b5a226"},"source":["!pip install segmentation_models_pytorch"],"execution_count":14,"outputs":[{"output_type":"stream","text":["Requirement already satisfied: segmentation_models_pytorch in /usr/local/lib/python3.7/dist-packages (0.1.3)\n","Requirement already satisfied: pretrainedmodels==0.7.4 in /usr/local/lib/python3.7/dist-packages (from segmentation_models_pytorch) (0.7.4)\n","Requirement already satisfied: efficientnet-pytorch==0.6.3 in /usr/local/lib/python3.7/dist-packages (from segmentation_models_pytorch) (0.6.3)\n","Requirement already satisfied: timm==0.3.2 in /usr/local/lib/python3.7/dist-packages (from segmentation_models_pytorch) (0.3.2)\n","Requirement already satisfied: torchvision>=0.3.0 in /usr/local/lib/python3.7/dist-packages (from segmentation_models_pytorch) (0.9.1+cu101)\n","Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from pretrainedmodels==0.7.4->segmentation_models_pytorch) (4.41.1)\n","Requirement already satisfied: torch in /usr/local/lib/python3.7/dist-packages (from pretrainedmodels==0.7.4->segmentation_models_pytorch) (1.8.1+cu101)\n","Requirement already satisfied: munch in /usr/local/lib/python3.7/dist-packages (from pretrainedmodels==0.7.4->segmentation_models_pytorch) (2.5.0)\n","Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.7/dist-packages (from torchvision>=0.3.0->segmentation_models_pytorch) (7.1.2)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torchvision>=0.3.0->segmentation_models_pytorch) (1.19.5)\n","Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch->pretrainedmodels==0.7.4->segmentation_models_pytorch) (3.7.4.3)\n","Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from munch->pretrainedmodels==0.7.4->segmentation_models_pytorch) (1.15.0)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:11.634792Z","start_time":"2021-04-18T16:16:05.875817Z"},"colab":{"base_uri":"https://localhost:8080/"},"id":"a8IfZfiM-NwM","executionInfo":{"status":"ok","timestamp":1620215098357,"user_tz":-540,"elapsed":7708,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"80bdfab6-62bc-46ee-edfe-28358a942a66"},"source":["# 구현된 model에 임의의 input을 넣어 output이 잘 나오는지 test\n","import segmentation_models_pytorch as smp\n","\n","model = smp.PAN(encoder_name='timm-efficientnet-b3', encoder_weights='noisy-student', classes=12)\n","x = torch.randn([2, 3, 512, 512])\n","print(\"input shape : \", x.shape)\n","out = model(x).to(device)\n","print(\"output shape : \", out.size())\n","\n","model = model.to(device)"],"execution_count":15,"outputs":[{"output_type":"stream","text":["input shape : torch.Size([2, 3, 512, 512])\n","output shape : torch.Size([2, 12, 512, 512])\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"zvDnI7_T-NwN"},"source":["## train, validation, test 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.104200Z","start_time":"2021-04-18T16:16:18.093174Z"},"id":"RA3oAapJ-NwN","executionInfo":{"status":"ok","timestamp":1620194999031,"user_tz":-540,"elapsed":18467,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["def train(num_epochs, model, data_loader, val_loader, criterion, optimizer, saved_dir, val_every, device, lr_scheduler):\n"," print('Start training..')\n"," best_loss = 9999999\n"," best_miou = 0\n"," for epoch in range(num_epochs):\n"," model.train()\n"," for step, (images, masks, _) in enumerate(data_loader):\n"," images = torch.stack(images) # (batch, channel, height, width)\n"," masks = torch.stack(masks).long() # (batch, channel, height, width)\n"," \n"," # gpu 연산을 위해 device 할당\n"," images, masks = images.to(device), masks.to(device)\n"," \n"," # inference\n"," outputs = model(images)\n"," \n"," # loss 계산 (cross entropy loss)\n"," loss = criterion(outputs, masks)\n"," optimizer.zero_grad()\n"," loss.backward()\n"," optimizer.step()\n"," \n"," lr_scheduler.step()\n"," \n"," # step 주기에 따른 loss 출력\n"," if (step + 1) % 25 == 0:\n"," print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(\n"," epoch+1, num_epochs, step+1, len(train_loader), loss.item()))\n"," \n"," # validation 주기에 따른 loss 출력 및 best model 저장\n"," if (epoch + 1) % val_every == 0:\n","# avrg_loss = validation(epoch + 1, model, val_loader, criterion, device)\n","# if avrg_loss < best_loss:\n","# print('Best performance at epoch: {}'.format(epoch + 1))\n","# print('Save model in', saved_dir)\n","# best_loss = avrg_loss\n","# wandb.log({'best_loss': best_loss})\n","# save_model(model, saved_dir)\n"," avrg_miou = validation(epoch + 1, model, val_loader, criterion, device)\n"," if avrg_miou > best_miou:\n"," print('Best performance at epoch: {}'.format(epoch + 1))\n"," print('Save model in', saved_dir)\n"," best_miou = avrg_miou\n"," wandb.log({'best_miou': best_miou})\n"," save_model(model, saved_dir)"],"execution_count":18,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.285795Z","start_time":"2021-04-18T16:16:18.267686Z"},"id":"EulIikmq-NwO","executionInfo":{"status":"ok","timestamp":1620194999031,"user_tz":-540,"elapsed":18036,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["def validation(epoch, model, data_loader, criterion, device):\n"," print('Start validation #{}'.format(epoch))\n"," model.eval()\n"," with torch.no_grad():\n"," total_loss = 0\n"," cnt = 0\n"," mIoU_list = []\n"," for step, (images, masks, _) in enumerate(data_loader):\n"," \n"," images = torch.stack(images) # (batch, channel, height, width)\n"," masks = torch.stack(masks).long() # (batch, channel, height, width)\n","\n"," images, masks = images.to(device), masks.to(device) \n","\n"," outputs = model(images)\n"," loss = criterion(outputs, masks)\n"," total_loss += loss\n"," cnt += 1\n"," \n","# print(outputs.shape)\n","# print(masks.shape)\n","# wandb.log(wandb.Image(images, masks={\n","# \"predictions\" : {\n","# \"mask_data\" : torch.squeeze(torch.squeeze(outputs, 0),1),\n","# \"class_labels\" : classes_dict\n","# },\n","# \"ground_truth\" : {\n","# \"mask_data\" : torch.squeeze(masks, 0),\n","# \"class_labels\" : classes_dict\n","# }\n","# }))\n"," \n"," outputs = torch.argmax(outputs.squeeze(), dim=1).detach().cpu().numpy()\n","\n"," mIoU = label_accuracy_score(masks.detach().cpu().numpy(), outputs, n_class=12)[2]\n"," mIoU_list.append(mIoU)\n"," \n"," avrg_loss = total_loss / cnt\n"," avrg_mIoU = np.mean(mIoU_list)\n"," print('Validation #{} Average Loss: {:.4f}, mIoU: {:.4f}'.format(epoch, avrg_loss, np.mean(mIoU_list)))\n"," wandb.log({'Train Loss':loss.item(), 'Val Loss':avrg_loss , 'Val mIoU':np.mean(mIoU_list)})\n","# return avrg_loss\n"," return avrg_mIoU"],"execution_count":19,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"C_7CtFfH-NwO"},"source":["## 모델 저장 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.909918Z","start_time":"2021-04-18T16:16:18.898918Z"},"id":"gpCM5BFO-NwO","executionInfo":{"status":"ok","timestamp":1620195001269,"user_tz":-540,"elapsed":1306,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# 모델 저장 함수 정의\n","val_every = 1 \n","\n","saved_dir = './saved'\n","if not os.path.isdir(saved_dir): \n"," os.mkdir(saved_dir)\n"," \n","def save_model(model, saved_dir, file_name='re_pan_aug2_re_pan_effb3_noisy_focal_madgrad_cosLReffb7_noisy_focal_madgrad_cosLR.pt'):\n"," check_point = {'net': model.state_dict()}\n"," output_path = os.path.join(saved_dir, file_name)\n"," torch.save(model.state_dict(), output_path)"],"execution_count":20,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"FyKGeg8T-NwP"},"source":["## 모델 생성 및 Loss function, Optimizer 정의"]},{"cell_type":"code","metadata":{"id":"ORugl8s1-NwP","executionInfo":{"status":"ok","timestamp":1620195001730,"user_tz":-540,"elapsed":1195,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["from torch.autograd import Variable\n","import torch.nn.functional as F\n","# ref : https://github.com/clcarwin/focal_loss_pytorch\n","class FocalLoss(nn.Module):\n"," def __init__(self, gamma=0, alpha=None, size_average=True):\n"," super(FocalLoss, self).__init__()\n"," self.gamma = gamma\n"," self.alpha = alpha\n"," if isinstance(alpha,(float,int)): self.alpha = torch.Tensor([alpha,1-alpha])\n"," if isinstance(alpha,list): self.alpha = torch.Tensor(alpha)\n"," self.size_average = size_average\n","\n"," def forward(self, input, target):\n"," if input.dim()>2:\n"," input = input.view(input.size(0),input.size(1),-1) # N,C,H,W => N,C,H*W\n"," input = input.transpose(1,2) # N,C,H*W => N,H*W,C\n"," input = input.contiguous().view(-1,input.size(2)) # N,H*W,C => N*H*W,C\n"," target = target.view(-1,1)\n","\n"," logpt = F.log_softmax(input)\n"," logpt = logpt.gather(1,target)\n"," logpt = logpt.view(-1)\n"," pt = Variable(logpt.data.exp())\n","\n"," if self.alpha is not None:\n"," if self.alpha.type()!=input.data.type():\n"," self.alpha = self.alpha.type_as(input.data)\n"," at = self.alpha.gather(0,target.data.view(-1))\n"," logpt = logpt * Variable(at)\n","\n"," loss = -1 * (1-pt)**self.gamma * logpt\n"," if self.size_average: return loss.mean()\n"," else: return loss.sum()"],"execution_count":21,"outputs":[]},{"cell_type":"code","metadata":{"id":"yfeFQknz-NwP"},"source":["import torch.optim.lr_scheduler as lr_scheduler\n","import math\n","class CosineAnnealingWarmUpRestart(lr_scheduler._LRScheduler):\n"," def __init__(self, optimizer, T_0, T_mult=1, eta_max=0.1, T_up=0, gamma=1., last_epoch=-1):\n"," if T_0 <= 0 or not isinstance(T_0, int):\n"," raise ValueError(\"Expected positive integer T_0, but got {}\".format(T_0))\n"," if T_mult < 1 or not isinstance(T_mult, int):\n"," raise ValueError(\"Expected integer T_mult >= 1, but got {}\".format(T_mult))\n"," if T_up < 0 or not isinstance(T_up, int):\n"," raise ValueError(\"Expected positive integer T_up, but got {}\".format(T_up))\n"," self.T_0 = T_0\n"," self.T_mult = T_mult\n"," self.base_eta_max = eta_max\n"," self.eta_max = eta_max\n"," self.T_up = T_up\n"," self.T_i = T_0\n"," self.gamma = gamma\n"," self.cycle = 0\n"," self.T_cur = last_epoch\n"," super(CosineAnnealingWarmUpRestart, self).__init__(optimizer, last_epoch)\n"," # self.T_cur = last_epoch\n"," \n"," def get_lr(self):\n"," if self.T_cur == -1:\n"," return self.base_lrs\n"," elif self.T_cur < self.T_up:\n"," return [(self.eta_max - base_lr)*self.T_cur / self.T_up + base_lr for base_lr in self.base_lrs]\n"," else:\n"," return [base_lr + (self.eta_max - base_lr) * (1 + math.cos(math.pi * (self.T_cur-self.T_up) / (self.T_i - self.T_up))) / 2\n"," for base_lr in self.base_lrs]\n","\n"," def step(self, epoch=None):\n"," if epoch is None:\n"," epoch = self.last_epoch + 1\n"," self.T_cur = self.T_cur + 1\n"," if self.T_cur >= self.T_i:\n"," self.cycle += 1\n"," self.T_cur = self.T_cur - self.T_i\n"," self.T_i = (self.T_i - self.T_up) * self.T_mult + self.T_up\n"," else:\n"," if epoch >= self.T_0:\n"," if self.T_mult == 1:\n"," self.T_cur = epoch % self.T_0\n"," self.cycle = epoch // self.T_0\n"," else:\n"," n = int(math.log((epoch / self.T_0 * (self.T_mult - 1) + 1), self.T_mult))\n"," self.cycle = n\n"," self.T_cur = epoch - self.T_0 * (self.T_mult ** n - 1) / (self.T_mult - 1)\n"," self.T_i = self.T_0 * self.T_mult ** (n)\n"," else:\n"," self.T_i = self.T_0\n"," self.T_cur = epoch\n"," \n"," self.eta_max = self.base_eta_max * (self.gamma**self.cycle)\n"," self.last_epoch = math.floor(epoch)\n"," for param_group, lr in zip(self.optimizer.param_groups, self.get_lr()):\n"," param_group['lr'] = lr"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AG1oQeu7BX1M","executionInfo":{"status":"ok","timestamp":1620060019354,"user_tz":-540,"elapsed":3477,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"98eae7c4-b66e-409d-9725-0c684d747f2a"},"source":["# !pip install adamp"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Requirement already satisfied: adamp in /usr/local/lib/python3.7/dist-packages (0.3.0)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"eH2PpwF9a-Os","executionInfo":{"status":"ok","timestamp":1620195009991,"user_tz":-540,"elapsed":3648,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"81acb2c8-7675-4409-ad24-e2ac704cd098"},"source":["!pip install madgrad"],"execution_count":22,"outputs":[{"output_type":"stream","text":["Collecting madgrad\n"," Downloading https://files.pythonhosted.org/packages/65/f0/4584f18202a2fb8903d456bf907b80e7cb54ad8fcba68604084ff41b7cf8/madgrad-1.1-py3-none-any.whl\n","Installing collected packages: madgrad\n","Successfully installed madgrad-1.1\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:19.698902Z","start_time":"2021-04-18T16:16:19.694902Z"},"id":"9Dly8KZj-NwQ","executionInfo":{"status":"ok","timestamp":1620195010304,"user_tz":-540,"elapsed":1163,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# from adamp import AdamP\n","from madgrad import MADGRAD\n","# Loss function 정의\n","# criterion = nn.CrossEntropyLoss()\n","criterion = FocalLoss()\n","\n","# Optimizer 정의\n","# optimizer = torch.optim.Adam(params = model.parameters(), lr = learning_rate, weight_decay=1e-6)\n","# optimizer = AdamP(params = model.parameters())\n","optimizer = MADGRAD(params = model.parameters(), lr = learning_rate, momentum = 0.9, weight_decay = 0, eps = 1e-06)\n","\n","# lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size = 237, gamma = 0.65)\n","lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=50, eta_min=0)\n","# lr_scheduler = CosineAnnealingWarmUpRestart(optimizer, T_0=150, T_mult=1, eta_max=0.1, T_up=10, gamma=0.5)"],"execution_count":23,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"start_time":"2021-04-18T16:16:20.331Z"},"colab":{"base_uri":"https://localhost:8080/"},"id":"cMSiFA-3-NwR","executionInfo":{"status":"ok","timestamp":1620211444209,"user_tz":-540,"elapsed":4644854,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"16313882-3563-49f2-ffc7-c1b130a8adb0"},"source":["train(num_epochs, model, train_loader, val_loader, criterion, optimizer, saved_dir, val_every, device, lr_scheduler)"],"execution_count":24,"outputs":[{"output_type":"stream","text":["Start training..\n","Epoch [1/20], Step [25/327], Loss: 0.9884\n","Epoch [1/20], Step [50/327], Loss: 1.0406\n","Epoch [1/20], Step [75/327], Loss: 0.8322\n","Epoch [1/20], Step [100/327], Loss: 0.6937\n","Epoch [1/20], Step [125/327], Loss: 0.7184\n","Epoch [1/20], Step [150/327], Loss: 0.3810\n","Epoch [1/20], Step [175/327], Loss: 0.5297\n","Epoch [1/20], Step [200/327], Loss: 0.5822\n","Epoch [1/20], Step [225/327], Loss: 0.4822\n","Epoch [1/20], Step [250/327], Loss: 0.5183\n","Epoch [1/20], Step [275/327], Loss: 0.3613\n","Epoch [1/20], Step [300/327], Loss: 0.4638\n","Epoch [1/20], Step [325/327], Loss: 0.7897\n","Start validation #1\n","Validation #1 Average Loss: 0.4465, mIoU: 0.2953\n","Best performance at epoch: 1\n","Save model in ./saved\n","Epoch [2/20], Step [25/327], Loss: 0.5056\n","Epoch [2/20], Step [50/327], Loss: 0.5131\n","Epoch [2/20], Step [75/327], Loss: 0.2988\n","Epoch [2/20], Step [100/327], Loss: 0.5167\n","Epoch [2/20], Step [125/327], Loss: 0.3849\n","Epoch [2/20], Step [150/327], Loss: 0.3814\n","Epoch [2/20], Step [175/327], Loss: 0.5537\n","Epoch [2/20], Step [200/327], Loss: 0.4461\n","Epoch [2/20], Step [225/327], Loss: 0.5075\n","Epoch [2/20], Step [250/327], Loss: 0.4250\n","Epoch [2/20], Step [275/327], Loss: 0.3597\n","Epoch [2/20], Step [300/327], Loss: 0.4532\n","Epoch [2/20], Step [325/327], Loss: 0.5280\n","Start validation #2\n","Validation #2 Average Loss: 0.3698, mIoU: 0.3353\n","Best performance at epoch: 2\n","Save model in ./saved\n","Epoch [3/20], Step [25/327], Loss: 0.3017\n","Epoch [3/20], Step [50/327], Loss: 0.4542\n","Epoch [3/20], Step [75/327], Loss: 0.3634\n","Epoch [3/20], Step [100/327], Loss: 0.2671\n","Epoch [3/20], Step [125/327], Loss: 0.4017\n","Epoch [3/20], Step [150/327], Loss: 0.1767\n","Epoch [3/20], Step [175/327], Loss: 0.3145\n","Epoch [3/20], Step [200/327], Loss: 0.7125\n","Epoch [3/20], Step [225/327], Loss: 0.6376\n","Epoch [3/20], Step [250/327], Loss: 0.4179\n","Epoch [3/20], Step [275/327], Loss: 0.2280\n","Epoch [3/20], Step [300/327], Loss: 0.3247\n","Epoch [3/20], Step [325/327], Loss: 0.1941\n","Start validation #3\n","Validation #3 Average Loss: 0.3426, mIoU: 0.3651\n","Best performance at epoch: 3\n","Save model in ./saved\n","Epoch [4/20], Step [25/327], Loss: 0.2842\n","Epoch [4/20], Step [50/327], Loss: 0.2365\n","Epoch [4/20], Step [75/327], Loss: 0.8515\n","Epoch [4/20], Step [100/327], Loss: 0.7826\n","Epoch [4/20], Step [125/327], Loss: 0.2141\n","Epoch [4/20], Step [150/327], Loss: 0.1974\n","Epoch [4/20], Step [175/327], Loss: 0.3597\n","Epoch [4/20], Step [200/327], Loss: 0.4081\n","Epoch [4/20], Step [225/327], Loss: 0.2925\n","Epoch [4/20], Step [250/327], Loss: 0.3639\n","Epoch [4/20], Step [275/327], Loss: 0.4538\n","Epoch [4/20], Step [300/327], Loss: 0.2514\n","Epoch [4/20], Step [325/327], Loss: 0.2706\n","Start validation #4\n","Validation #4 Average Loss: 0.3349, mIoU: 0.3780\n","Best performance at epoch: 4\n","Save model in ./saved\n","Epoch [5/20], Step [25/327], Loss: 0.2824\n","Epoch [5/20], Step [50/327], Loss: 0.1525\n","Epoch [5/20], Step [75/327], Loss: 0.1668\n","Epoch [5/20], Step [100/327], Loss: 0.2002\n","Epoch [5/20], Step [125/327], Loss: 0.5264\n","Epoch [5/20], Step [150/327], Loss: 0.2274\n","Epoch [5/20], Step [175/327], Loss: 0.3195\n","Epoch [5/20], Step [200/327], Loss: 0.2371\n","Epoch [5/20], Step [225/327], Loss: 0.3500\n","Epoch [5/20], Step [250/327], Loss: 0.3665\n","Epoch [5/20], Step [275/327], Loss: 0.2385\n","Epoch [5/20], Step [300/327], Loss: 0.3111\n","Epoch [5/20], Step [325/327], Loss: 0.1681\n","Start validation #5\n","Validation #5 Average Loss: 0.3145, mIoU: 0.3887\n","Best performance at epoch: 5\n","Save model in ./saved\n","Epoch [6/20], Step [25/327], Loss: 0.2729\n","Epoch [6/20], Step [50/327], Loss: 0.6575\n","Epoch [6/20], Step [75/327], Loss: 0.2101\n","Epoch [6/20], Step [100/327], Loss: 0.2728\n","Epoch [6/20], Step [125/327], Loss: 0.1697\n","Epoch [6/20], Step [150/327], Loss: 0.3800\n","Epoch [6/20], Step [175/327], Loss: 0.2419\n","Epoch [6/20], Step [200/327], Loss: 0.1968\n","Epoch [6/20], Step [225/327], Loss: 0.4074\n","Epoch [6/20], Step [250/327], Loss: 0.2368\n","Epoch [6/20], Step [275/327], Loss: 0.3471\n","Epoch [6/20], Step [300/327], Loss: 0.2135\n","Epoch [6/20], Step [325/327], Loss: 0.6181\n","Start validation #6\n","Validation #6 Average Loss: 0.3112, mIoU: 0.4134\n","Best performance at epoch: 6\n","Save model in ./saved\n","Epoch [7/20], Step [25/327], Loss: 0.2223\n","Epoch [7/20], Step [50/327], Loss: 0.5132\n","Epoch [7/20], Step [75/327], Loss: 0.3082\n","Epoch [7/20], Step [100/327], Loss: 0.1783\n","Epoch [7/20], Step [125/327], Loss: 0.2255\n","Epoch [7/20], Step [150/327], Loss: 0.3305\n","Epoch [7/20], Step [175/327], Loss: 0.2819\n","Epoch [7/20], Step [200/327], Loss: 0.2329\n","Epoch [7/20], Step [225/327], Loss: 0.2011\n","Epoch [7/20], Step [250/327], Loss: 0.1430\n","Epoch [7/20], Step [275/327], Loss: 0.2756\n","Epoch [7/20], Step [300/327], Loss: 0.2742\n","Epoch [7/20], Step [325/327], Loss: 0.3622\n","Start validation #7\n","Validation #7 Average Loss: 0.2816, mIoU: 0.4126\n","Epoch [8/20], Step [25/327], Loss: 0.1460\n","Epoch [8/20], Step [50/327], Loss: 0.3597\n","Epoch [8/20], Step [75/327], Loss: 0.2855\n","Epoch [8/20], Step [100/327], Loss: 0.6070\n","Epoch [8/20], Step [125/327], Loss: 0.2893\n","Epoch [8/20], Step [150/327], Loss: 0.1528\n","Epoch [8/20], Step [175/327], Loss: 0.2249\n","Epoch [8/20], Step [200/327], Loss: 0.2652\n","Epoch [8/20], Step [225/327], Loss: 0.3125\n","Epoch [8/20], Step [250/327], Loss: 0.1972\n","Epoch [8/20], Step [275/327], Loss: 0.2471\n","Epoch [8/20], Step [300/327], Loss: 0.3421\n","Epoch [8/20], Step [325/327], Loss: 0.1989\n","Start validation #8\n","Validation #8 Average Loss: 0.2838, mIoU: 0.4234\n","Best performance at epoch: 8\n","Save model in ./saved\n","Epoch [9/20], Step [25/327], Loss: 0.3326\n","Epoch [9/20], Step [50/327], Loss: 0.1332\n","Epoch [9/20], Step [75/327], Loss: 0.1806\n","Epoch [9/20], Step [100/327], Loss: 0.1762\n","Epoch [9/20], Step [125/327], Loss: 0.2080\n","Epoch [9/20], Step [150/327], Loss: 0.4651\n","Epoch [9/20], Step [175/327], Loss: 0.2076\n","Epoch [9/20], Step [200/327], Loss: 0.1557\n","Epoch [9/20], Step [225/327], Loss: 0.2226\n","Epoch [9/20], Step [250/327], Loss: 0.6304\n","Epoch [9/20], Step [275/327], Loss: 0.2336\n","Epoch [9/20], Step [300/327], Loss: 0.2003\n","Epoch [9/20], Step [325/327], Loss: 0.3989\n","Start validation #9\n","Validation #9 Average Loss: 0.2909, mIoU: 0.4188\n","Epoch [10/20], Step [25/327], Loss: 0.2649\n","Epoch [10/20], Step [50/327], Loss: 0.2636\n","Epoch [10/20], Step [75/327], Loss: 0.2308\n","Epoch [10/20], Step [100/327], Loss: 0.1381\n","Epoch [10/20], Step [125/327], Loss: 0.2075\n","Epoch [10/20], Step [150/327], Loss: 0.2945\n","Epoch [10/20], Step [175/327], Loss: 0.2306\n","Epoch [10/20], Step [200/327], Loss: 0.2640\n","Epoch [10/20], Step [225/327], Loss: 0.2602\n","Epoch [10/20], Step [250/327], Loss: 0.2796\n","Epoch [10/20], Step [275/327], Loss: 0.1533\n","Epoch [10/20], Step [300/327], Loss: 0.2920\n","Epoch [10/20], Step [325/327], Loss: 0.2260\n","Start validation #10\n","Validation #10 Average Loss: 0.2892, mIoU: 0.4226\n","Epoch [11/20], Step [25/327], Loss: 0.1576\n","Epoch [11/20], Step [50/327], Loss: 0.1646\n","Epoch [11/20], Step [75/327], Loss: 0.2607\n","Epoch [11/20], Step [100/327], Loss: 0.1496\n","Epoch [11/20], Step [125/327], Loss: 0.1304\n","Epoch [11/20], Step [150/327], Loss: 0.2087\n","Epoch [11/20], Step [175/327], Loss: 0.1309\n","Epoch [11/20], Step [200/327], Loss: 0.1404\n","Epoch [11/20], Step [225/327], Loss: 0.1746\n","Epoch [11/20], Step [250/327], Loss: 0.2396\n","Epoch [11/20], Step [275/327], Loss: 0.2234\n","Epoch [11/20], Step [300/327], Loss: 0.1472\n","Epoch [11/20], Step [325/327], Loss: 0.1460\n","Start validation #11\n","Validation #11 Average Loss: 0.2877, mIoU: 0.4302\n","Best performance at epoch: 11\n","Save model in ./saved\n","Epoch [12/20], Step [25/327], Loss: 0.2377\n","Epoch [12/20], Step [50/327], Loss: 0.3356\n","Epoch [12/20], Step [75/327], Loss: 0.1629\n","Epoch [12/20], Step [100/327], Loss: 0.1785\n","Epoch [12/20], Step [125/327], Loss: 0.1234\n","Epoch [12/20], Step [150/327], Loss: 0.1740\n","Epoch [12/20], Step [175/327], Loss: 0.1599\n","Epoch [12/20], Step [200/327], Loss: 0.1932\n","Epoch [12/20], Step [225/327], Loss: 0.1472\n","Epoch [12/20], Step [250/327], Loss: 0.1662\n","Epoch [12/20], Step [275/327], Loss: 0.1633\n","Epoch [12/20], Step [300/327], Loss: 0.2365\n","Epoch [12/20], Step [325/327], Loss: 0.1599\n","Start validation #12\n","Validation #12 Average Loss: 0.2776, mIoU: 0.4301\n","Epoch [13/20], Step [25/327], Loss: 0.0990\n","Epoch [13/20], Step [50/327], Loss: 0.2419\n","Epoch [13/20], Step [75/327], Loss: 0.1897\n","Epoch [13/20], Step [100/327], Loss: 0.1430\n","Epoch [13/20], Step [125/327], Loss: 0.1057\n","Epoch [13/20], Step [150/327], Loss: 0.2087\n","Epoch [13/20], Step [175/327], Loss: 0.1811\n","Epoch [13/20], Step [200/327], Loss: 0.2536\n","Epoch [13/20], Step [225/327], Loss: 0.3256\n","Epoch [13/20], Step [250/327], Loss: 0.2924\n","Epoch [13/20], Step [275/327], Loss: 0.1228\n","Epoch [13/20], Step [300/327], Loss: 0.1128\n","Epoch [13/20], Step [325/327], Loss: 0.2202\n","Start validation #13\n","Validation #13 Average Loss: 0.2798, mIoU: 0.4346\n","Best performance at epoch: 13\n","Save model in ./saved\n","Epoch [14/20], Step [25/327], Loss: 0.1257\n","Epoch [14/20], Step [50/327], Loss: 0.1634\n","Epoch [14/20], Step [75/327], Loss: 0.2045\n","Epoch [14/20], Step [100/327], Loss: 0.2379\n","Epoch [14/20], Step [125/327], Loss: 0.0810\n","Epoch [14/20], Step [150/327], Loss: 0.1268\n","Epoch [14/20], Step [175/327], Loss: 0.1600\n","Epoch [14/20], Step [200/327], Loss: 0.1649\n","Epoch [14/20], Step [225/327], Loss: 0.3203\n","Epoch [14/20], Step [250/327], Loss: 0.1347\n","Epoch [14/20], Step [275/327], Loss: 0.1438\n","Epoch [14/20], Step [300/327], Loss: 0.2368\n","Epoch [14/20], Step [325/327], Loss: 0.1688\n","Start validation #14\n","Validation #14 Average Loss: 0.2968, mIoU: 0.4355\n","Best performance at epoch: 14\n","Save model in ./saved\n","Epoch [15/20], Step [25/327], Loss: 0.1755\n","Epoch [15/20], Step [50/327], Loss: 0.1473\n","Epoch [15/20], Step [75/327], Loss: 0.2125\n","Epoch [15/20], Step [100/327], Loss: 0.1156\n","Epoch [15/20], Step [125/327], Loss: 0.2871\n","Epoch [15/20], Step [150/327], Loss: 0.1510\n","Epoch [15/20], Step [175/327], Loss: 0.3202\n","Epoch [15/20], Step [200/327], Loss: 0.2232\n","Epoch [15/20], Step [225/327], Loss: 0.3176\n","Epoch [15/20], Step [250/327], Loss: 0.1694\n","Epoch [15/20], Step [275/327], Loss: 0.1926\n","Epoch [15/20], Step [300/327], Loss: 0.1935\n","Epoch [15/20], Step [325/327], Loss: 0.2274\n","Start validation #15\n","Validation #15 Average Loss: 0.2852, mIoU: 0.4294\n","Epoch [16/20], Step [25/327], Loss: 0.2368\n","Epoch [16/20], Step [50/327], Loss: 0.1384\n","Epoch [16/20], Step [75/327], Loss: 0.1422\n","Epoch [16/20], Step [100/327], Loss: 0.0886\n","Epoch [16/20], Step [125/327], Loss: 0.2122\n","Epoch [16/20], Step [150/327], Loss: 0.2739\n","Epoch [16/20], Step [175/327], Loss: 0.1628\n","Epoch [16/20], Step [200/327], Loss: 0.2556\n","Epoch [16/20], Step [225/327], Loss: 0.2521\n","Epoch [16/20], Step [250/327], Loss: 0.1476\n","Epoch [16/20], Step [275/327], Loss: 0.1483\n","Epoch [16/20], Step [300/327], Loss: 0.2254\n","Epoch [16/20], Step [325/327], Loss: 0.1046\n","Start validation #16\n","Validation #16 Average Loss: 0.2811, mIoU: 0.4346\n","Epoch [17/20], Step [25/327], Loss: 0.1624\n","Epoch [17/20], Step [50/327], Loss: 0.1344\n","Epoch [17/20], Step [75/327], Loss: 0.0881\n","Epoch [17/20], Step [100/327], Loss: 0.1553\n","Epoch [17/20], Step [125/327], Loss: 0.0957\n","Epoch [17/20], Step [150/327], Loss: 0.1883\n","Epoch [17/20], Step [175/327], Loss: 0.1039\n","Epoch [17/20], Step [200/327], Loss: 0.1496\n","Epoch [17/20], Step [225/327], Loss: 0.0940\n","Epoch [17/20], Step [250/327], Loss: 0.2844\n","Epoch [17/20], Step [275/327], Loss: 0.1138\n","Epoch [17/20], Step [300/327], Loss: 0.2449\n","Epoch [17/20], Step [325/327], Loss: 0.1232\n","Start validation #17\n","Validation #17 Average Loss: 0.2814, mIoU: 0.4337\n","Epoch [18/20], Step [25/327], Loss: 0.1481\n","Epoch [18/20], Step [50/327], Loss: 0.0738\n","Epoch [18/20], Step [75/327], Loss: 0.1618\n","Epoch [18/20], Step [100/327], Loss: 0.1127\n","Epoch [18/20], Step [125/327], Loss: 0.1448\n","Epoch [18/20], Step [150/327], Loss: 0.1687\n","Epoch [18/20], Step [175/327], Loss: 0.1070\n","Epoch [18/20], Step [200/327], Loss: 0.1745\n","Epoch [18/20], Step [225/327], Loss: 0.1047\n","Epoch [18/20], Step [250/327], Loss: 0.2027\n","Epoch [18/20], Step [275/327], Loss: 0.3570\n","Epoch [18/20], Step [300/327], Loss: 0.1088\n","Epoch [18/20], Step [325/327], Loss: 0.0967\n","Start validation #18\n","Validation #18 Average Loss: 0.2842, mIoU: 0.4318\n","Epoch [19/20], Step [25/327], Loss: 0.0673\n","Epoch [19/20], Step [50/327], Loss: 0.1120\n","Epoch [19/20], Step [75/327], Loss: 0.2147\n","Epoch [19/20], Step [100/327], Loss: 0.1805\n","Epoch [19/20], Step [125/327], Loss: 0.3373\n","Epoch [19/20], Step [150/327], Loss: 0.2229\n","Epoch [19/20], Step [175/327], Loss: 0.1198\n","Epoch [19/20], Step [200/327], Loss: 0.1143\n","Epoch [19/20], Step [225/327], Loss: 0.1217\n","Epoch [19/20], Step [250/327], Loss: 0.0658\n","Epoch [19/20], Step [275/327], Loss: 0.0583\n","Epoch [19/20], Step [300/327], Loss: 0.2108\n","Epoch [19/20], Step [325/327], Loss: 0.1887\n","Start validation #19\n","Validation #19 Average Loss: 0.2788, mIoU: 0.4473\n","Best performance at epoch: 19\n","Save model in ./saved\n","Epoch [20/20], Step [25/327], Loss: 0.3630\n","Epoch [20/20], Step [50/327], Loss: 0.1520\n","Epoch [20/20], Step [75/327], Loss: 0.1117\n","Epoch [20/20], Step [100/327], Loss: 0.1822\n","Epoch [20/20], Step [125/327], Loss: 0.1500\n","Epoch [20/20], Step [150/327], Loss: 0.1623\n","Epoch [20/20], Step [175/327], Loss: 0.1061\n","Epoch [20/20], Step [200/327], Loss: 0.2159\n","Epoch [20/20], Step [225/327], Loss: 0.2243\n","Epoch [20/20], Step [250/327], Loss: 0.1324\n","Epoch [20/20], Step [275/327], Loss: 0.1427\n","Epoch [20/20], Step [300/327], Loss: 0.0772\n","Epoch [20/20], Step [325/327], Loss: 0.2107\n","Start validation #20\n","Validation #20 Average Loss: 0.2935, mIoU: 0.4499\n","Best performance at epoch: 20\n","Save model in ./saved\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"C6ClcO0J-NwR"},"source":["## 저장된 model 불러오기 (학습된 이후) "]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:21.050200Z","start_time":"2021-04-16T19:44:20.802200Z"},"scrolled":true,"id":"KQPtUDzd-NwR","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620215105257,"user_tz":-540,"elapsed":1042,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"c0473f08-5e62-4a7d-d5ff-21fa385f558f"},"source":["# best model 저장된 경로\n","model_path = './saved/re_pan_aug2_re_pan_effb3_noisy_focal_madgrad_cosLReffb7_noisy_focal_madgrad_cosLR.pt'\n","\n","# best model 불러오기\n","checkpoint = torch.load(model_path, map_location=device)\n","model.load_state_dict(checkpoint)\n","\n","# 추론을 실행하기 전에는 반드시 설정 (batch normalization, dropout 를 평가 모드로 설정)\n","# model.eval()"],"execution_count":16,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{"tags":[]},"execution_count":16}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:24.939227Z","start_time":"2021-04-16T19:44:24.518228Z"},"id":"0LQqrDAp-NwR","colab":{"base_uri":"https://localhost:8080/","height":502},"executionInfo":{"status":"ok","timestamp":1620215111452,"user_tz":-540,"elapsed":4169,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"59a95877-e566-4b2d-f7c6-6f5d2b09b9e6"},"source":["# 첫번째 batch의 추론 결과 확인\n","for imgs, image_infos in test_loader:\n"," image_infos = image_infos\n"," temp_images = imgs\n"," \n"," model.eval()\n"," # inference\n"," outs = model(torch.stack(temp_images).to(device))\n"," oms = torch.argmax(outs.squeeze(), dim=1).detach().cpu().numpy()\n"," \n"," break\n","\n","i = 1\n","fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(16, 16))\n","\n","print('Shape of Original Image :', list(temp_images[i].shape))\n","print('Shape of Predicted : ', list(oms[i].shape))\n","print('Unique values, category of transformed mask : \\n', [{int(i),category_names[int(i)]} for i in list(np.unique(oms[i]))])\n","\n","# Original image\n","ax1.imshow(temp_images[i].permute([1,2,0]))\n","ax1.grid(False)\n","ax1.set_title(\"Original image : {}\".format(image_infos[i]['file_name']), fontsize = 15)\n","\n","# Predicted\n","ax2.imshow(oms[i])\n","ax2.grid(False)\n","ax2.set_title(\"Predicted : {}\".format(image_infos[i]['file_name']), fontsize = 15)\n","\n","plt.show()"],"execution_count":17,"outputs":[{"output_type":"stream","text":["Shape of Original Image : [3, 512, 512]\n","Shape of Predicted : [512, 512]\n","Unique values, category of transformed mask : \n"," [{0, 'Backgroud'}, {2, 'General trash'}, {3, 'Paper'}, {9, 'Plastic bag'}]\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"evYjR2F3-NwS"},"source":["## submission을 위한 test 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:27.469285Z","start_time":"2021-04-16T19:44:27.456021Z"},"id":"nhMLnV5d-NwS","executionInfo":{"status":"ok","timestamp":1620215120661,"user_tz":-540,"elapsed":611,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["def test(model, data_loader, device):\n"," size = 256\n"," transform = A.Compose([A.Resize(256, 256)])\n"," print('Start prediction.')\n"," model.eval()\n"," \n"," file_name_list = []\n"," preds_array = np.empty((0, size*size), dtype=np.long)\n"," \n"," with torch.no_grad():\n"," for step, (imgs, image_infos) in enumerate(test_loader):\n","\n"," # inference (512 x 512)\n"," outs = model(torch.stack(imgs).to(device))\n"," oms = torch.argmax(outs.squeeze(), dim=1).detach().cpu().numpy()\n"," \n"," # resize (256 x 256)\n"," temp_mask = []\n"," for img, mask in zip(np.stack(imgs), oms):\n"," transformed = transform(image=img, mask=mask)\n"," mask = transformed['mask']\n"," temp_mask.append(mask)\n","\n"," oms = np.array(temp_mask)\n"," \n"," oms = oms.reshape([oms.shape[0], size*size]).astype(int)\n"," preds_array = np.vstack((preds_array, oms))\n"," \n"," file_name_list.append([i['file_name'] for i in image_infos])\n"," print(\"End prediction.\")\n"," file_names = [y for x in file_name_list for y in x]\n"," \n"," return file_names, preds_array"],"execution_count":18,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"r1YKHBf4-NwT"},"source":["## submission.csv 생성"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:45:42.235310Z","start_time":"2021-04-16T19:44:30.499016Z"},"scrolled":true,"id":"Bz79_g8K-NwT","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620216407600,"user_tz":-540,"elapsed":436695,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"75e1c8c4-5f50-4330-83ca-ce1a6b811cec"},"source":["# sample_submisson.csv 열기\n","submission = pd.read_csv('./submission/sample_submission.csv', index_col=None)\n","\n","# test set에 대한 prediction\n","file_names, preds = test(model, test_loader, device)\n","\n","# PredictionString 대입\n","for file_name, string in zip(file_names, preds):\n"," submission = submission.append({\"image_id\" : file_name, \"PredictionString\" : ' '.join(str(e) for e in string.tolist())}, \n"," ignore_index=True)\n","\n","# submission.csv로 저장\n","submission.to_csv(\"./submission/pan_effb3_nore_pan_aug2_re_pan_effb3_noisy_focal_madgrad_cosLReffb7_noisy_focal_madgrad_cosLRisy_focal_adamp_cosLR.csv\", index=False)"],"execution_count":21,"outputs":[{"output_type":"stream","text":["Start prediction.\n","End prediction.\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"Zgl7Ye7F-NwT"},"source":["## 제출까지\n","\n"]},{"cell_type":"code","metadata":{"id":"022E8AC7-NwT","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620215701725,"user_tz":-540,"elapsed":7679,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"d89d245b-79ab-4aa2-b05e-81821d8f4bbf"},"source":["import json\n","import requests\n","import os\n","from urllib.parse import urlparse, parse_qsl, urlencode, urlunparse\n","\n","def submit(user_key='', file_path = '', desc=\"\"):\n"," if not user_key:\n"," raise Exception(\"No UserKey\" )\n"," url = urlparse('http://ec2-13-124-161-225.ap-northeast-2.compute.amazonaws.com:8000/api/v1/competition/28/presigned_url/?description=&hyperparameters={%22training%22:{},%22inference%22:{}}')\n"," qs = dict(parse_qsl(url.query))\n"," qs['description'] = desc\n"," parts = url._replace(query=urlencode(qs))\n"," url = urlunparse(parts)\n","\n"," print(url)\n"," headers = {\n"," 'Authorization': user_key\n"," }\n"," res = requests.get(url, headers=headers)\n"," print(res.text)\n"," data = json.loads(res.text)\n"," \n"," submit_url = data['url']\n"," body = {\n"," 'key':'app/Competitions/000028/Users/{}/Submissions/{}/output.csv'.format(str(data['submission']['user']).zfill(8),str(data['submission']['local_id']).zfill(4)),\n"," 'x-amz-algorithm':data['fields']['x-amz-algorithm'],\n"," 'x-amz-credential':data['fields']['x-amz-credential'],\n"," 'x-amz-date':data['fields']['x-amz-date'],\n"," 'policy':data['fields']['policy'],\n"," 'x-amz-signature':data['fields']['x-amz-signature']\n"," }\n"," requests.post(url=submit_url, data=body, files={'file': open(file_path, 'rb')})\n","\n","\n","####################################################################################\n","test_dir = \"/content/drive/MyDrive/Trash/code/submission\" # 수정 필요 : output 파일 폴더 \n","desc = 'aug2_re_pan_effb3_noisy_focal_madgrad_cosLR' # 수정 필요 : 파일에 대한 설명\n","output_file = \"pan_effb3_nore_pan_aug2_re_pan_effb3_noisy_focal_madgrad_cosLReffb7_noisy_focal_madgrad_cosLRisy_focal_adamp_cosLR.csv\" #수정 필요 : output 파일 \n","user_key = \"Bearer 7bb5f96452751a238ffaf91a93c4242bf9b72abe\" # 수정 필요 : Authorization \n","\n","\n","submit(user_key, os.path.join(test_dir, output_file),desc)"],"execution_count":20,"outputs":[{"output_type":"stream","text":["http://ec2-13-124-161-225.ap-northeast-2.compute.amazonaws.com:8000/api/v1/competition/28/presigned_url/?hyperparameters=%7B%22training%22%3A%7B%7D%2C%22inference%22%3A%7B%7D%7D&description=aug2_re_pan_effb3_noisy_focal_madgrad_cosLR\n","{\"url\":\"https://prod-aistages-private.s3.amazonaws.com/\",\"fields\":{\"key\":\"app/Competitions/000028/Users/00000099/Submissions/0025/output.csv\",\"x-amz-algorithm\":\"AWS4-HMAC-SHA256\",\"x-amz-credential\":\"AKIA45LU4MHUJ7WLDQVO/20210505/ap-northeast-2/s3/aws4_request\",\"x-amz-date\":\"20210505T115454Z\",\"policy\":\"eyJleHBpcmF0aW9uIjogIjIwMjEtMDUtMDVUMTI6NTQ6NTRaIiwgImNvbmRpdGlvbnMiOiBbeyJidWNrZXQiOiAicHJvZC1haXN0YWdlcy1wcml2YXRlIn0sIHsia2V5IjogImFwcC9Db21wZXRpdGlvbnMvMDAwMDI4L1VzZXJzLzAwMDAwMDk5L1N1Ym1pc3Npb25zLzAwMjUvb3V0cHV0LmNzdiJ9LCB7IngtYW16LWFsZ29yaXRobSI6ICJBV1M0LUhNQUMtU0hBMjU2In0sIHsieC1hbXotY3JlZGVudGlhbCI6ICJBS0lBNDVMVTRNSFVKN1dMRFFWTy8yMDIxMDUwNS9hcC1ub3J0aGVhc3QtMi9zMy9hd3M0X3JlcXVlc3QifSwgeyJ4LWFtei1kYXRlIjogIjIwMjEwNTA1VDExNTQ1NFoifV19\",\"x-amz-signature\":\"98bce7b468301ebeb0e1afb56cb56522398da727172f7bed8b68bc5e2d713fe7\"},\"submission\":{\"id\":15450,\"phase\":\"Created\",\"type\":\"File\",\"local_id\":25,\"hyperparameters\":\"{\\\"training\\\": {}, \\\"inference\\\": {}}\",\"description\":\"aug2_re_pan_effb3_noisy_focal_madgrad_cosLR\",\"final\":false,\"created_at\":\"2021-05-05T20:54:54.549354+09:00\",\"updated_at\":\"2021-05-05T20:54:54.549387+09:00\",\"user\":99,\"competition\":28,\"image\":null}}\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"iI35aLwqYYJS"},"source":[""],"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/chanyub_seg/code/aug2_re_pan_effb5_noisy_focal_CE_madgrad_kwparam_stepLR.ipynb b/chanyub_seg/code/aug2_re_pan_effb5_noisy_focal_CE_madgrad_kwparam_stepLR.ipynb deleted file mode 100644 index d76060f..0000000 --- a/chanyub_seg/code/aug2_re_pan_effb5_noisy_focal_CE_madgrad_kwparam_stepLR.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"nbformat":4,"nbformat_minor":0,"metadata":{"hide_input":false,"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.7"},"toc":{"base_numbering":1,"nav_menu":{},"number_sections":true,"sideBar":true,"skip_h1_title":false,"title_cell":"Table of Contents","title_sidebar":"Contents","toc_cell":true,"toc_position":{"height":"calc(100% - 180px)","left":"10px","top":"150px","width":"297.278px"},"toc_section_display":true,"toc_window_display":true},"colab":{"name":"aug2_re_pan_effb5_noisy_focal_CE_madgrad_kwparam_stepLR.ipynb","provenance":[],"toc_visible":true,"machine_shape":"hm"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GW8gF48g-WSK","executionInfo":{"status":"ok","timestamp":1620194284068,"user_tz":-540,"elapsed":31721,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"77cad5b3-df57-4547-a7e6-18bf07268ddf"},"source":["from google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Mounted at /content/drive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xDwpp4Lk-gSH","executionInfo":{"status":"ok","timestamp":1620194284069,"user_tz":-540,"elapsed":3504,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"7b335edb-86f1-440c-e7fd-1fd2900ca863"},"source":["ls"],"execution_count":null,"outputs":[{"output_type":"stream","text":["\u001b[0m\u001b[01;34mdrive\u001b[0m/ \u001b[01;34msample_data\u001b[0m/\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sItrVDeh-iYC","executionInfo":{"status":"ok","timestamp":1620194284633,"user_tz":-540,"elapsed":3811,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"7c08adc3-e87c-4aca-a749-16d3a2d1a3b4"},"source":["cd drive/MyDrive/Trash/code"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/content/drive/MyDrive/Trash/code\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"a54v4-kN_LVO","executionInfo":{"status":"ok","timestamp":1620194293692,"user_tz":-540,"elapsed":12779,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"c202093d-e838-4438-8e16-708df30e1df2"},"source":["!pip install albumentations==0.5.2"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Collecting albumentations==0.5.2\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/03/58/63fb1d742dc42d9ba2800ea741de1f2bc6bb05548d8724aa84794042eaf2/albumentations-0.5.2-py3-none-any.whl (72kB)\n","\u001b[K |████████████████████████████████| 81kB 7.8MB/s \n","\u001b[?25hCollecting opencv-python-headless>=4.1.1\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/6d/6d/92f377bece9b0ec9c893081dbe073a65b38d7ac12ef572b8f70554d08760/opencv_python_headless-4.5.1.48-cp37-cp37m-manylinux2014_x86_64.whl (37.6MB)\n","\u001b[K |████████████████████████████████| 37.6MB 131kB/s \n","\u001b[?25hCollecting imgaug>=0.4.0\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/66/b1/af3142c4a85cba6da9f4ebb5ff4e21e2616309552caca5e8acefe9840622/imgaug-0.4.0-py2.py3-none-any.whl (948kB)\n","\u001b[K |████████████████████████████████| 952kB 57.9MB/s \n","\u001b[?25hRequirement already satisfied: scikit-image>=0.16.1 in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (0.16.2)\n","Requirement already satisfied: numpy>=1.11.1 in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (1.19.5)\n","Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (1.4.1)\n","Requirement already satisfied: PyYAML in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (3.13)\n","Requirement already satisfied: imageio in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (2.4.1)\n","Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (1.15.0)\n","Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (7.1.2)\n","Requirement already satisfied: Shapely in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (1.7.1)\n","Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (3.2.2)\n","Requirement already satisfied: opencv-python in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (4.1.2.30)\n","Requirement already satisfied: PyWavelets>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations==0.5.2) (1.1.1)\n","Requirement already satisfied: networkx>=2.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations==0.5.2) (2.5.1)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug>=0.4.0->albumentations==0.5.2) (0.10.0)\n","Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug>=0.4.0->albumentations==0.5.2) (2.8.1)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug>=0.4.0->albumentations==0.5.2) (1.3.1)\n","Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug>=0.4.0->albumentations==0.5.2) (2.4.7)\n","Requirement already satisfied: decorator<5,>=4.3 in /usr/local/lib/python3.7/dist-packages (from networkx>=2.0->scikit-image>=0.16.1->albumentations==0.5.2) (4.4.2)\n","Installing collected packages: opencv-python-headless, imgaug, albumentations\n"," Found existing installation: imgaug 0.2.9\n"," Uninstalling imgaug-0.2.9:\n"," Successfully uninstalled imgaug-0.2.9\n"," Found existing installation: albumentations 0.1.12\n"," Uninstalling albumentations-0.1.12:\n"," Successfully uninstalled albumentations-0.1.12\n","Successfully installed albumentations-0.5.2 imgaug-0.4.0 opencv-python-headless-4.5.1.48\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.826930Z","start_time":"2021-04-18T10:34:45.406686Z"},"colab":{"base_uri":"https://localhost:8080/"},"id":"l_LPA4XD-NwC","executionInfo":{"status":"ok","timestamp":1620185962128,"user_tz":-540,"elapsed":3810,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"d830db36-1bc7-4c71-d8c0-8c7f5a68c850"},"source":["import os\n","import random\n","import time\n","import json\n","import warnings \n","warnings.filterwarnings('ignore')\n","\n","import torch\n","import torch.nn as nn\n","from torch.utils.data import Dataset, DataLoader\n","from utils import label_accuracy_score\n","import cv2\n","\n","import numpy as np\n","import pandas as pd\n","\n","# 전처리를 위한 라이브러리\n","from pycocotools.coco import COCO\n","import torchvision\n","import torchvision.transforms as transforms\n","\n","import albumentations as A\n","from albumentations.pytorch import ToTensorV2\n","\n","# 시각화를 위한 라이브러리\n","import matplotlib.pyplot as plt\n","import seaborn as sns; sns.set()\n","\n","plt.rcParams['axes.grid'] = False\n","\n","print('pytorch version: {}'.format(torch.__version__))\n","print('GPU 사용 가능 여부: {}'.format(torch.cuda.is_available()))\n","\n","print(torch.cuda.get_device_name(0))\n","print(torch.cuda.device_count())\n","\n","device = \"cuda\" if torch.cuda.is_available() else \"cpu\" # GPU 사용 가능 여부에 따라 device 정보 저장"],"execution_count":null,"outputs":[{"output_type":"stream","text":["pytorch version: 1.8.1+cu101\n","GPU 사용 가능 여부: True\n","Tesla P100-PCIE-16GB\n","1\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"HxgRSL_M-NwF"},"source":["## 하이퍼파라미터 세팅 및 seed 고정"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.841930Z","start_time":"2021-04-18T10:34:47.827931Z"},"id":"rV3JmGP5-NwF"},"source":["batch_size = 4 # Mini-batch size\n","num_epochs = 20\n","learning_rate = 0.0001"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.856930Z","start_time":"2021-04-18T10:34:47.842931Z"},"id":"Z6LOuJXQ-NwG"},"source":["# seed 고정\n","random_seed = 42\n","torch.manual_seed(random_seed)\n","torch.cuda.manual_seed(random_seed)\n","# torch.cuda.manual_seed_all(random_seed) # if use multi-GPU\n","torch.backends.cudnn.deterministic = True\n","torch.backends.cudnn.benchmark = False\n","np.random.seed(random_seed)\n","random.seed(random_seed)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"iWF_EJDu-NwG"},"source":["## 학습 데이터 EDA"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.381961Z","start_time":"2021-04-18T10:34:47.857930Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"lg0x0D0a-NwG","executionInfo":{"status":"ok","timestamp":1620185970597,"user_tz":-540,"elapsed":7111,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"40383792-4008-446d-8cfc-b489e3e978f3"},"source":["%matplotlib inline\n","\n","dataset_path = '../input/data'\n","anns_file_path = dataset_path + '/' + 'train.json'\n","\n","# Read annotations\n","with open(anns_file_path, 'r') as f:\n"," dataset = json.loads(f.read())\n","\n","categories = dataset['categories']\n","anns = dataset['annotations']\n","imgs = dataset['images']\n","nr_cats = len(categories)\n","nr_annotations = len(anns)\n","nr_images = len(imgs)\n","\n","# Load categories and super categories\n","cat_names = []\n","super_cat_names = []\n","super_cat_ids = {}\n","super_cat_last_name = ''\n","nr_super_cats = 0\n","for cat_it in categories:\n"," cat_names.append(cat_it['name'])\n"," super_cat_name = cat_it['supercategory']\n"," # Adding new supercat\n"," if super_cat_name != super_cat_last_name:\n"," super_cat_names.append(super_cat_name)\n"," super_cat_ids[super_cat_name] = nr_super_cats\n"," super_cat_last_name = super_cat_name\n"," nr_super_cats += 1\n","\n","print('Number of super categories:', nr_super_cats)\n","print('Number of categories:', nr_cats)\n","print('Number of annotations:', nr_annotations)\n","print('Number of images:', nr_images)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Number of super categories: 11\n","Number of categories: 11\n","Number of annotations: 21116\n","Number of images: 2617\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.546964Z","start_time":"2021-04-18T10:34:51.382969Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/","height":355},"id":"PjLyVHVY-NwH","executionInfo":{"status":"ok","timestamp":1620185972109,"user_tz":-540,"elapsed":5469,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"381809cd-58ce-461d-9a5e-feca3131d6ac"},"source":["# Count annotations\n","cat_histogram = np.zeros(nr_cats,dtype=int)\n","for ann in anns:\n"," cat_histogram[ann['category_id']] += 1\n","\n","# Initialize the matplotlib figure\n","f, ax = plt.subplots(figsize=(5,5))\n","\n","# Convert to DataFrame\n","df = pd.DataFrame({'Categories': cat_names, 'Number of annotations': cat_histogram})\n","df = df.sort_values('Number of annotations', 0, False)\n","\n","# Plot the histogram\n","plt.title(\"category distribution of train set \")\n","plot_1 = sns.barplot(x=\"Number of annotations\", y=\"Categories\", data=df, label=\"Total\", color=\"b\")"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.561965Z","start_time":"2021-04-18T10:34:51.547969Z"},"id":"34Tw5__i-NwI"},"source":["# category labeling \n","sorted_temp_df = df.sort_index()\n","\n","# background = 0 에 해당되는 label 추가 후 기존들을 모두 label + 1 로 설정\n","sorted_df = pd.DataFrame([\"Backgroud\"], columns = [\"Categories\"])\n","sorted_df = sorted_df.append(sorted_temp_df, ignore_index=True)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.576961Z","start_time":"2021-04-18T10:34:51.562964Z"},"colab":{"base_uri":"https://localhost:8080/","height":421},"id":"CQk4vV5N-NwI","executionInfo":{"status":"ok","timestamp":1620185972130,"user_tz":-540,"elapsed":3849,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"290259bf-8bce-400a-d706-489d30a0758a"},"source":["# class (Categories) 에 따른 index 확인 (0~11 : 총 12개)\n","sorted_df"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
CategoriesNumber of annotations
0BackgroudNaN
1UNKNOWN128.0
2General trash2225.0
3Paper7448.0
4Paper pack527.0
5Metal449.0
6Glass488.0
7Plastic2472.0
8Styrofoam1074.0
9Plastic bag6114.0
10Battery50.0
11Clothing141.0
\n","
"],"text/plain":[" Categories Number of annotations\n","0 Backgroud NaN\n","1 UNKNOWN 128.0\n","2 General trash 2225.0\n","3 Paper 7448.0\n","4 Paper pack 527.0\n","5 Metal 449.0\n","6 Glass 488.0\n","7 Plastic 2472.0\n","8 Styrofoam 1074.0\n","9 Plastic bag 6114.0\n","10 Battery 50.0\n","11 Clothing 141.0"]},"metadata":{"tags":[]},"execution_count":11}]},{"cell_type":"markdown","metadata":{"id":"KXU0zmZs-NwI"},"source":["## 데이터 전처리 함수 정의 (Dataset)"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:52.693328Z","start_time":"2021-04-18T10:34:52.681328Z"},"id":"QFnTI8_Z-NwJ"},"source":["category_names = list(sorted_df.Categories)\n","\n","def get_classname(classID, cats):\n"," for i in range(len(cats)):\n"," if cats[i]['id']==classID:\n"," return cats[i]['name']\n"," return \"None\"\n","\n","class CustomDataLoader(Dataset):\n"," \"\"\"COCO format\"\"\"\n"," def __init__(self, data_dir, mode = 'train', transform = None):\n"," super().__init__()\n"," self.mode = mode\n"," self.transform = transform\n"," self.coco = COCO(data_dir)\n"," \n"," def __getitem__(self, index: int):\n"," # dataset이 index되어 list처럼 동작\n"," image_id = self.coco.getImgIds(imgIds=index)\n"," image_infos = self.coco.loadImgs(image_id)[0]\n"," \n"," # cv2 를 활용하여 image 불러오기\n"," images = cv2.imread(os.path.join(dataset_path, image_infos['file_name']))\n"," images = cv2.cvtColor(images, cv2.COLOR_BGR2RGB).astype(np.float32)\n"," images /= 255.0\n"," \n"," if (self.mode in ('train', 'val')):\n"," ann_ids = self.coco.getAnnIds(imgIds=image_infos['id'])\n"," anns = self.coco.loadAnns(ann_ids)\n","\n"," # Load the categories in a variable\n"," cat_ids = self.coco.getCatIds()\n"," cats = self.coco.loadCats(cat_ids)\n","\n"," # masks : size가 (height x width)인 2D\n"," # 각각의 pixel 값에는 \"category id + 1\" 할당\n"," # Background = 0\n"," masks = np.zeros((image_infos[\"height\"], image_infos[\"width\"]))\n"," # Unknown = 1, General trash = 2, ... , Cigarette = 11\n"," for i in range(len(anns)):\n"," className = get_classname(anns[i]['category_id'], cats)\n"," pixel_value = category_names.index(className)\n"," masks = np.maximum(self.coco.annToMask(anns[i])*pixel_value, masks)\n"," masks = masks.astype(np.float32)\n","\n"," # transform -> albumentations 라이브러리 활용\n"," if self.transform is not None:\n"," transformed = self.transform(image=images, mask=masks)\n"," images = transformed[\"image\"]\n"," masks = transformed[\"mask\"]\n"," \n"," return images, masks, image_infos\n"," \n"," if self.mode == 'test':\n"," # transform -> albumentations 라이브러리 활용\n"," if self.transform is not None:\n"," transformed = self.transform(image=images)\n"," images = transformed[\"image\"]\n"," \n"," return images, image_infos\n"," \n"," \n"," def __len__(self) -> int:\n"," # 전체 dataset의 size를 return\n"," return len(self.coco.getImgIds())"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"cp2aIOlP-NwK"},"source":["## Dataset 정의 및 DataLoader 할당"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:58.823175Z","start_time":"2021-04-18T10:34:54.106233Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"P7zFlRn6-NwK","executionInfo":{"status":"ok","timestamp":1620185983957,"user_tz":-540,"elapsed":9759,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"f110f8c1-b16b-4bfe-a4cb-fcacb2b17f14"},"source":["# train.json / validation.json / test.json 디렉토리 설정\n","train_path = dataset_path + '/train.json'\n","val_path = dataset_path + '/val.json'\n","test_path = dataset_path + '/test.json'\n","\n","# collate_fn needs for batch\n","def collate_fn(batch):\n"," return tuple(zip(*batch))\n","\n","train_transform = A.Compose([\n"," A.transforms.Rotate(limit=30),\n"," A.augmentations.HorizontalFlip(),\n"," ToTensorV2()\n"," ])\n","\n","val_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","test_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","# create own Dataset 1 (skip)\n","# validation set을 직접 나누고 싶은 경우\n","# random_split 사용하여 data set을 8:2 로 분할\n","# train_size = int(0.8*len(dataset))\n","# val_size = int(len(dataset)-train_size)\n","# dataset = CustomDataLoader(data_dir=train_path, mode='train', transform=transform)\n","# train_dataset, val_dataset = torch.utils.data.random_split(dataset, [train_size, val_size])\n","\n","# create own Dataset 2\n","# train dataset\n","train_dataset = CustomDataLoader(data_dir=train_path, mode='train', transform=train_transform)\n","\n","# validation dataset\n","val_dataset = CustomDataLoader(data_dir=val_path, mode='val', transform=val_transform)\n","\n","# test dataset\n","test_dataset = CustomDataLoader(data_dir=test_path, mode='test', transform=test_transform)\n","\n","\n","# DataLoader\n","train_loader = torch.utils.data.DataLoader(dataset=train_dataset, \n"," batch_size=batch_size,\n"," shuffle=True,\n"," num_workers=4,\n"," collate_fn=collate_fn,\n"," drop_last=True)\n","\n","val_loader = torch.utils.data.DataLoader(dataset=val_dataset, \n"," batch_size=batch_size,\n"," shuffle=False,\n"," num_workers=4,\n"," collate_fn=collate_fn,\n"," drop_last=True) \n","\n","test_loader = torch.utils.data.DataLoader(dataset=test_dataset,\n"," batch_size=batch_size,\n"," num_workers=4,\n"," collate_fn=collate_fn)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["loading annotations into memory...\n","Done (t=3.83s)\n","creating index...\n","index created!\n","loading annotations into memory...\n","Done (t=0.84s)\n","creating index...\n","index created!\n","loading annotations into memory...\n","Done (t=0.01s)\n","creating index...\n","index created!\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"8rqJiHb_-NwM"},"source":["# wandb"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"H1kHXm0uAX3R","executionInfo":{"status":"ok","timestamp":1620143643046,"user_tz":-540,"elapsed":2966,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"ae1e96c5-ef88-44b8-fd3d-59185e8e90f7"},"source":["!pip install wandb"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Requirement already satisfied: wandb in /usr/local/lib/python3.7/dist-packages (0.10.29)\n","Requirement already satisfied: promise<3,>=2.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (2.3)\n","Requirement already satisfied: subprocess32>=3.5.3 in /usr/local/lib/python3.7/dist-packages (from wandb) (3.5.4)\n","Requirement already satisfied: requests<3,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (2.23.0)\n","Requirement already satisfied: Click>=7.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (7.1.2)\n","Requirement already satisfied: GitPython>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (3.1.14)\n","Requirement already satisfied: protobuf>=3.12.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (3.12.4)\n","Requirement already satisfied: docker-pycreds>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (0.4.0)\n","Requirement already satisfied: pathtools in /usr/local/lib/python3.7/dist-packages (from wandb) (0.1.2)\n","Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.7/dist-packages (from wandb) (2.8.1)\n","Requirement already satisfied: configparser>=3.8.1 in /usr/local/lib/python3.7/dist-packages (from wandb) (5.0.2)\n","Requirement already satisfied: shortuuid>=0.5.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (1.0.1)\n","Requirement already satisfied: sentry-sdk>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (1.0.0)\n","Requirement already satisfied: six>=1.13.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (1.15.0)\n","Requirement already satisfied: PyYAML in /usr/local/lib/python3.7/dist-packages (from wandb) (3.13)\n","Requirement already satisfied: psutil>=5.0.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (5.4.8)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.0.0->wandb) (2.10)\n","Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.0.0->wandb) (1.24.3)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.0.0->wandb) (2020.12.5)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.0.0->wandb) (3.0.4)\n","Requirement already satisfied: gitdb<5,>=4.0.1 in /usr/local/lib/python3.7/dist-packages (from GitPython>=1.0.0->wandb) (4.0.7)\n","Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from protobuf>=3.12.0->wandb) (56.0.0)\n","Requirement already satisfied: smmap<5,>=3.0.1 in /usr/local/lib/python3.7/dist-packages (from gitdb<5,>=4.0.1->GitPython>=1.0.0->wandb) (4.0.0)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":136},"id":"aMW4VV9V-NwM","executionInfo":{"status":"ok","timestamp":1620143647641,"user_tz":-540,"elapsed":6843,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"0257eb59-6b63-48aa-ed7a-3be5d8d03e7f"},"source":["import wandb\n","\n","proj_name = 'aug2_re_pan_effb5_noisy_focal_CE_madgrad_kwparam_stepLR'\n","\n","wandb.init(project='chanyub',name=proj_name)\n","\n","config = wandb.config\n","config.learning_rate = 0.01"],"execution_count":null,"outputs":[{"output_type":"stream","text":["\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mpstage12\u001b[0m (use `wandb login --relogin` to force relogin)\n"],"name":"stderr"},{"output_type":"display_data","data":{"text/html":["\n"," Tracking run with wandb version 0.10.29
\n"," Syncing run aug2_re_pan_effb5_noisy_focal_CE_madgrad_kwparam_stepLR to Weights & Biases (Documentation).
\n"," Project page: https://wandb.ai/pstage12/chanyub
\n"," Run page: https://wandb.ai/pstage12/chanyub/runs/j4nj0cel
\n"," Run data is saved locally in /content/drive/My Drive/Trash/code/wandb/run-20210504_155404-j4nj0cel

\n"," "],"text/plain":[""]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"uQRiIVGX-NwM"},"source":["## My model"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"S0xCGpNeAqeD","executionInfo":{"status":"ok","timestamp":1620185987057,"user_tz":-540,"elapsed":4404,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"3cf9e774-151e-4453-b6ed-0ab57d752dd7"},"source":["!pip install segmentation_models_pytorch"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Requirement already satisfied: segmentation_models_pytorch in /usr/local/lib/python3.7/dist-packages (0.1.3)\n","Requirement already satisfied: pretrainedmodels==0.7.4 in /usr/local/lib/python3.7/dist-packages (from segmentation_models_pytorch) (0.7.4)\n","Requirement already satisfied: timm==0.3.2 in /usr/local/lib/python3.7/dist-packages (from segmentation_models_pytorch) (0.3.2)\n","Requirement already satisfied: efficientnet-pytorch==0.6.3 in /usr/local/lib/python3.7/dist-packages (from segmentation_models_pytorch) (0.6.3)\n","Requirement already satisfied: torchvision>=0.3.0 in /usr/local/lib/python3.7/dist-packages (from segmentation_models_pytorch) (0.9.1+cu101)\n","Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from pretrainedmodels==0.7.4->segmentation_models_pytorch) (4.41.1)\n","Requirement already satisfied: munch in /usr/local/lib/python3.7/dist-packages (from pretrainedmodels==0.7.4->segmentation_models_pytorch) (2.5.0)\n","Requirement already satisfied: torch in /usr/local/lib/python3.7/dist-packages (from pretrainedmodels==0.7.4->segmentation_models_pytorch) (1.8.1+cu101)\n","Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.7/dist-packages (from torchvision>=0.3.0->segmentation_models_pytorch) (7.1.2)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torchvision>=0.3.0->segmentation_models_pytorch) (1.19.5)\n","Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from munch->pretrainedmodels==0.7.4->segmentation_models_pytorch) (1.15.0)\n","Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch->pretrainedmodels==0.7.4->segmentation_models_pytorch) (3.7.4.3)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:11.634792Z","start_time":"2021-04-18T16:16:05.875817Z"},"colab":{"base_uri":"https://localhost:8080/"},"id":"a8IfZfiM-NwM","executionInfo":{"status":"ok","timestamp":1620185997363,"user_tz":-540,"elapsed":14270,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"17e8b46d-473f-4097-b06e-a41d1b336be2"},"source":["# 구현된 model에 임의의 input을 넣어 output이 잘 나오는지 test\n","import segmentation_models_pytorch as smp\n","\n","model = smp.PAN(encoder_name='timm-efficientnet-b5', encoder_weights='noisy-student', classes=12)\n","x = torch.randn([2, 3, 512, 512])\n","print(\"input shape : \", x.shape)\n","out = model(x).to(device)\n","print(\"output shape : \", out.size())\n","\n","model = model.to(device)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["input shape : torch.Size([2, 3, 512, 512])\n","output shape : torch.Size([2, 12, 512, 512])\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"zvDnI7_T-NwN"},"source":["## train, validation, test 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.104200Z","start_time":"2021-04-18T16:16:18.093174Z"},"id":"RA3oAapJ-NwN"},"source":["def train(num_epochs, model, data_loader, val_loader, criterion1, criterion2, optimizer, saved_dir, val_every, device, lr_scheduler):\n"," print('Start training..')\n"," best_loss = 9999999\n"," best_miou = 0\n"," for epoch in range(num_epochs):\n"," model.train()\n"," total_loss = 0\n"," for step, (images, masks, _) in enumerate(data_loader):\n"," images = torch.stack(images) # (batch, channel, height, width)\n"," masks = torch.stack(masks).long() # (batch, channel, height, width)\n"," \n"," # gpu 연산을 위해 device 할당\n"," images, masks = images.to(device), masks.to(device)\n"," \n"," # inference\n"," outputs = model(images)\n"," \n"," # loss 계산 (cross entropy loss)\n"," loss = criterion1(outputs, masks) + criterion2(outputs, masks)\n"," total_loss += loss.item()\n","\n"," optimizer.zero_grad()\n"," loss.backward()\n"," optimizer.step()\n"," \n"," lr_scheduler.step()\n"," \n"," # step 주기에 따른 loss 출력\n"," if (step + 1) % 25 == 0:\n"," print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(\n"," epoch+1, num_epochs, step+1, len(train_loader), loss.item()))\n"," \n"," # validation 주기에 따른 loss 출력 및 best model 저장\n"," if (epoch + 1) % val_every == 0:\n","# avrg_loss = validation(epoch + 1, model, val_loader, criterion, device)\n","# if avrg_loss < best_loss:\n","# print('Best performance at epoch: {}'.format(epoch + 1))\n","# print('Save model in', saved_dir)\n","# best_loss = avrg_loss\n","# wandb.log({'best_loss': best_loss})\n","# save_model(model, saved_dir)\n"," avrg_miou = validation(epoch + 1, model, val_loader, criterion1, criterion2, device)\n"," if avrg_miou > best_miou:\n"," print('Best performance at epoch: {}'.format(epoch + 1))\n"," print('Save model in', saved_dir)\n"," best_miou = avrg_miou\n"," wandb.log({'best_miou': best_miou})\n"," save_model(model, saved_dir)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.285795Z","start_time":"2021-04-18T16:16:18.267686Z"},"id":"EulIikmq-NwO"},"source":["def validation(epoch, model, data_loader, criterion1, criterion2, device):\n"," print('Start validation #{}'.format(epoch))\n"," model.eval()\n"," with torch.no_grad():\n"," total_loss = 0\n"," cnt = 0\n"," mIoU_list = []\n"," for step, (images, masks, _) in enumerate(data_loader):\n"," \n"," images = torch.stack(images) # (batch, channel, height, width)\n"," masks = torch.stack(masks).long() # (batch, channel, height, width)\n","\n"," images, masks = images.to(device), masks.to(device) \n","\n"," outputs = model(images)\n"," loss = criterion1(outputs, masks) + criterion2(outputs, masks)\n"," total_loss += loss\n"," cnt += 1\n"," \n","# print(outputs.shape)\n","# print(masks.shape)\n","# wandb.log(wandb.Image(images, masks={\n","# \"predictions\" : {\n","# \"mask_data\" : torch.squeeze(torch.squeeze(outputs, 0),1),\n","# \"class_labels\" : classes_dict\n","# },\n","# \"ground_truth\" : {\n","# \"mask_data\" : torch.squeeze(masks, 0),\n","# \"class_labels\" : classes_dict\n","# }\n","# }))\n"," \n"," outputs = torch.argmax(outputs.squeeze(), dim=1).detach().cpu().numpy()\n","\n"," mIoU = label_accuracy_score(masks.detach().cpu().numpy(), outputs, n_class=12)[2]\n"," mIoU_list.append(mIoU)\n"," \n"," avrg_loss = total_loss / cnt\n"," avrg_mIoU = np.mean(mIoU_list)\n"," print('Validation #{} Average Loss: {:.4f}, mIoU: {:.4f}'.format(epoch, avrg_loss, np.mean(mIoU_list)))\n"," wandb.log({'Train Loss':loss.item(), 'Val Loss':avrg_loss , 'Val mIoU':np.mean(mIoU_list)})\n","# return avrg_loss\n"," return avrg_mIoU"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"C_7CtFfH-NwO"},"source":["## 모델 저장 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.909918Z","start_time":"2021-04-18T16:16:18.898918Z"},"id":"gpCM5BFO-NwO"},"source":["# 모델 저장 함수 정의\n","val_every = 1 \n","\n","saved_dir = './saved'\n","if not os.path.isdir(saved_dir): \n"," os.mkdir(saved_dir)\n"," \n","def save_model(model, saved_dir, file_name='aug2_re_pan_effb5_noisy_focal_CE_madgrad_kwparam_stepLR.pt'):\n"," check_point = {'net': model.state_dict()}\n"," output_path = os.path.join(saved_dir, file_name)\n"," torch.save(model.state_dict(), output_path)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"FyKGeg8T-NwP"},"source":["## 모델 생성 및 Loss function, Optimizer 정의"]},{"cell_type":"code","metadata":{"id":"ORugl8s1-NwP"},"source":["from torch.autograd import Variable\n","import torch.nn.functional as F\n","# ref : https://github.com/clcarwin/focal_loss_pytorch\n","class FocalLoss(nn.Module):\n"," def __init__(self, gamma=0, alpha=None, size_average=True):\n"," super(FocalLoss, self).__init__()\n"," self.gamma = gamma\n"," self.alpha = alpha\n"," if isinstance(alpha,(float,int)): self.alpha = torch.Tensor([alpha,1-alpha])\n"," if isinstance(alpha,list): self.alpha = torch.Tensor(alpha)\n"," self.size_average = size_average\n","\n"," def forward(self, input, target):\n"," if input.dim()>2:\n"," input = input.view(input.size(0),input.size(1),-1) # N,C,H,W => N,C,H*W\n"," input = input.transpose(1,2) # N,C,H*W => N,H*W,C\n"," input = input.contiguous().view(-1,input.size(2)) # N,H*W,C => N*H*W,C\n"," target = target.view(-1,1)\n","\n"," logpt = F.log_softmax(input)\n"," logpt = logpt.gather(1,target)\n"," logpt = logpt.view(-1)\n"," pt = Variable(logpt.data.exp())\n","\n"," if self.alpha is not None:\n"," if self.alpha.type()!=input.data.type():\n"," self.alpha = self.alpha.type_as(input.data)\n"," at = self.alpha.gather(0,target.data.view(-1))\n"," logpt = logpt * Variable(at)\n","\n"," loss = -1 * (1-pt)**self.gamma * logpt\n"," if self.size_average: return loss.mean()\n"," else: return loss.sum()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"yfeFQknz-NwP"},"source":["# import torch.optim.lr_scheduler as lr_scheduler\n","# import math\n","# class CosineAnnealingWarmUpRestart(lr_scheduler._LRScheduler):\n","# def __init__(self, optimizer, T_0, T_mult=1, eta_max=0.1, T_up=0, gamma=1., last_epoch=-1):\n","# if T_0 <= 0 or not isinstance(T_0, int):\n","# raise ValueError(\"Expected positive integer T_0, but got {}\".format(T_0))\n","# if T_mult < 1 or not isinstance(T_mult, int):\n","# raise ValueError(\"Expected integer T_mult >= 1, but got {}\".format(T_mult))\n","# if T_up < 0 or not isinstance(T_up, int):\n","# raise ValueError(\"Expected positive integer T_up, but got {}\".format(T_up))\n","# self.T_0 = T_0\n","# self.T_mult = T_mult\n","# self.base_eta_max = eta_max\n","# self.eta_max = eta_max\n","# self.T_up = T_up\n","# self.T_i = T_0\n","# self.gamma = gamma\n","# self.cycle = 0\n","# self.T_cur = last_epoch\n","# super(CosineAnnealingWarmUpRestart, self).__init__(optimizer, last_epoch)\n","# # self.T_cur = last_epoch\n"," \n","# def get_lr(self):\n","# if self.T_cur == -1:\n","# return self.base_lrs\n","# elif self.T_cur < self.T_up:\n","# return [(self.eta_max - base_lr)*self.T_cur / self.T_up + base_lr for base_lr in self.base_lrs]\n","# else:\n","# return [base_lr + (self.eta_max - base_lr) * (1 + math.cos(math.pi * (self.T_cur-self.T_up) / (self.T_i - self.T_up))) / 2\n","# for base_lr in self.base_lrs]\n","\n","# def step(self, epoch=None):\n","# if epoch is None:\n","# epoch = self.last_epoch + 1\n","# self.T_cur = self.T_cur + 1\n","# if self.T_cur >= self.T_i:\n","# self.cycle += 1\n","# self.T_cur = self.T_cur - self.T_i\n","# self.T_i = (self.T_i - self.T_up) * self.T_mult + self.T_up\n","# else:\n","# if epoch >= self.T_0:\n","# if self.T_mult == 1:\n","# self.T_cur = epoch % self.T_0\n","# self.cycle = epoch // self.T_0\n","# else:\n","# n = int(math.log((epoch / self.T_0 * (self.T_mult - 1) + 1), self.T_mult))\n","# self.cycle = n\n","# self.T_cur = epoch - self.T_0 * (self.T_mult ** n - 1) / (self.T_mult - 1)\n","# self.T_i = self.T_0 * self.T_mult ** (n)\n","# else:\n","# self.T_i = self.T_0\n","# self.T_cur = epoch\n"," \n","# self.eta_max = self.base_eta_max * (self.gamma**self.cycle)\n","# self.last_epoch = math.floor(epoch)\n","# for param_group, lr in zip(self.optimizer.param_groups, self.get_lr()):\n","# param_group['lr'] = lr"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"AG1oQeu7BX1M"},"source":["# !pip install adamp"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"eH2PpwF9a-Os","executionInfo":{"status":"ok","timestamp":1620143681948,"user_tz":-540,"elapsed":3110,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"c70d3b5c-298a-4858-fc2a-c970fb493c04"},"source":["!pip install madgrad"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Requirement already satisfied: madgrad in /usr/local/lib/python3.7/dist-packages (1.1)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:19.698902Z","start_time":"2021-04-18T16:16:19.694902Z"},"id":"9Dly8KZj-NwQ"},"source":["# from adamp import AdamP\n","from madgrad import MADGRAD\n","# Loss function 정의\n","criterion1 = FocalLoss()\n","criterion2 = nn.CrossEntropyLoss()\n","\n","# Optimizer 정의\n","# optimizer = torch.optim.Adam(params = model.parameters(), lr = learning_rate, weight_decay=1e-6)\n","# optimizer = AdamP(params = model.parameters())\n","optimizer = MADGRAD(params = model.parameters(), lr = learning_rate, momentum = 0.9, weight_decay = 0.0001, eps = 1e-06)\n","\n","lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size = 654, gamma = 0.5)\n","# lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=50, eta_min=0)\n","# lr_scheduler = CosineAnnealingWarmUpRestart(optimizer, T_0=150, T_mult=1, eta_max=0.1, T_up=10, gamma=0.5)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"start_time":"2021-04-18T16:16:20.331Z"},"colab":{"base_uri":"https://localhost:8080/"},"id":"cMSiFA-3-NwR","executionInfo":{"status":"ok","timestamp":1620175705261,"user_tz":-540,"elapsed":32021406,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"5adc331b-e6a0-4c4b-fd9a-3cfb3fac673b"},"source":["train(num_epochs, model, train_loader, val_loader, criterion1, criterion2, optimizer, saved_dir, val_every, device, lr_scheduler)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Start training..\n","Epoch [1/20], Step [25/654], Loss: 1.4418\n","Epoch [1/20], Step [50/654], Loss: 1.4963\n","Epoch [1/20], Step [75/654], Loss: 1.5590\n","Epoch [1/20], Step [100/654], Loss: 1.4451\n","Epoch [1/20], Step [125/654], Loss: 1.3004\n","Epoch [1/20], Step [150/654], Loss: 1.0942\n","Epoch [1/20], Step [175/654], Loss: 1.8155\n","Epoch [1/20], Step [200/654], Loss: 0.6952\n","Epoch [1/20], Step [225/654], Loss: 1.4911\n","Epoch [1/20], Step [250/654], Loss: 2.1759\n","Epoch [1/20], Step [275/654], Loss: 1.2497\n","Epoch [1/20], Step [300/654], Loss: 0.8737\n","Epoch [1/20], Step [325/654], Loss: 0.6110\n","Epoch [1/20], Step [350/654], Loss: 1.3640\n","Epoch [1/20], Step [375/654], Loss: 0.9726\n","Epoch [1/20], Step [400/654], Loss: 0.3358\n","Epoch [1/20], Step [425/654], Loss: 0.4164\n","Epoch [1/20], Step [450/654], Loss: 0.6489\n","Epoch [1/20], Step [475/654], Loss: 0.6775\n","Epoch [1/20], Step [500/654], Loss: 1.5509\n","Epoch [1/20], Step [525/654], Loss: 0.8537\n","Epoch [1/20], Step [550/654], Loss: 1.7165\n","Epoch [1/20], Step [575/654], Loss: 0.5141\n","Epoch [1/20], Step [600/654], Loss: 0.7961\n","Epoch [1/20], Step [625/654], Loss: 1.1160\n","Epoch [1/20], Step [650/654], Loss: 1.4540\n","Start validation #1\n","Validation #1 Average Loss: 0.9429, mIoU: 0.2894\n","Best performance at epoch: 1\n","Save model in ./saved\n","Epoch [2/20], Step [25/654], Loss: 0.6046\n","Epoch [2/20], Step [50/654], Loss: 0.4431\n","Epoch [2/20], Step [75/654], Loss: 1.0160\n","Epoch [2/20], Step [100/654], Loss: 0.7361\n","Epoch [2/20], Step [125/654], Loss: 0.5388\n","Epoch [2/20], Step [150/654], Loss: 1.2076\n","Epoch [2/20], Step [175/654], Loss: 0.9003\n","Epoch [2/20], Step [200/654], Loss: 0.7241\n","Epoch [2/20], Step [225/654], Loss: 0.9689\n","Epoch [2/20], Step [250/654], Loss: 0.7318\n","Epoch [2/20], Step [275/654], Loss: 1.0589\n","Epoch [2/20], Step [300/654], Loss: 0.3603\n","Epoch [2/20], Step [325/654], Loss: 0.6439\n","Epoch [2/20], Step [350/654], Loss: 1.3123\n","Epoch [2/20], Step [375/654], Loss: 0.5287\n","Epoch [2/20], Step [400/654], Loss: 1.4849\n","Epoch [2/20], Step [425/654], Loss: 0.6542\n","Epoch [2/20], Step [450/654], Loss: 1.4274\n","Epoch [2/20], Step [475/654], Loss: 0.5746\n","Epoch [2/20], Step [500/654], Loss: 0.6217\n","Epoch [2/20], Step [525/654], Loss: 2.0102\n","Epoch [2/20], Step [550/654], Loss: 1.0661\n","Epoch [2/20], Step [575/654], Loss: 0.5382\n","Epoch [2/20], Step [600/654], Loss: 0.7960\n","Epoch [2/20], Step [625/654], Loss: 0.8945\n","Epoch [2/20], Step [650/654], Loss: 0.2966\n","Start validation #2\n","Validation #2 Average Loss: 0.6925, mIoU: 0.3611\n","Best performance at epoch: 2\n","Save model in ./saved\n","Epoch [3/20], Step [25/654], Loss: 0.4232\n","Epoch [3/20], Step [50/654], Loss: 0.6178\n","Epoch [3/20], Step [75/654], Loss: 0.5256\n","Epoch [3/20], Step [100/654], Loss: 0.5195\n","Epoch [3/20], Step [125/654], Loss: 0.7506\n","Epoch [3/20], Step [150/654], Loss: 0.3624\n","Epoch [3/20], Step [175/654], Loss: 0.6499\n","Epoch [3/20], Step [200/654], Loss: 0.6020\n","Epoch [3/20], Step [225/654], Loss: 0.3790\n","Epoch [3/20], Step [250/654], Loss: 0.6064\n","Epoch [3/20], Step [275/654], Loss: 0.5002\n","Epoch [3/20], Step [300/654], Loss: 0.6529\n","Epoch [3/20], Step [325/654], Loss: 0.3496\n","Epoch [3/20], Step [350/654], Loss: 0.8727\n","Epoch [3/20], Step [375/654], Loss: 0.3883\n","Epoch [3/20], Step [400/654], Loss: 0.9408\n","Epoch [3/20], Step [425/654], Loss: 0.6156\n","Epoch [3/20], Step [450/654], Loss: 0.4775\n","Epoch [3/20], Step [475/654], Loss: 0.4535\n","Epoch [3/20], Step [500/654], Loss: 0.6019\n","Epoch [3/20], Step [525/654], Loss: 0.1929\n","Epoch [3/20], Step [550/654], Loss: 0.2063\n","Epoch [3/20], Step [575/654], Loss: 0.7135\n","Epoch [3/20], Step [600/654], Loss: 0.3751\n","Epoch [3/20], Step [625/654], Loss: 0.5554\n","Epoch [3/20], Step [650/654], Loss: 0.3910\n","Start validation #3\n","Validation #3 Average Loss: 0.6020, mIoU: 0.3884\n","Best performance at epoch: 3\n","Save model in ./saved\n","Epoch [4/20], Step [25/654], Loss: 0.2729\n","Epoch [4/20], Step [50/654], Loss: 0.5953\n","Epoch [4/20], Step [75/654], Loss: 0.1796\n","Epoch [4/20], Step [100/654], Loss: 1.2144\n","Epoch [4/20], Step [125/654], Loss: 0.6117\n","Epoch [4/20], Step [150/654], Loss: 0.6552\n","Epoch [4/20], Step [175/654], Loss: 0.2444\n","Epoch [4/20], Step [200/654], Loss: 0.2882\n","Epoch [4/20], Step [225/654], Loss: 0.4152\n","Epoch [4/20], Step [250/654], Loss: 0.3723\n","Epoch [4/20], Step [275/654], Loss: 0.3156\n","Epoch [4/20], Step [300/654], Loss: 0.5190\n","Epoch [4/20], Step [325/654], Loss: 0.3389\n","Epoch [4/20], Step [350/654], Loss: 0.3003\n","Epoch [4/20], Step [375/654], Loss: 0.1873\n","Epoch [4/20], Step [400/654], Loss: 0.4331\n","Epoch [4/20], Step [425/654], Loss: 0.7165\n","Epoch [4/20], Step [450/654], Loss: 0.7337\n","Epoch [4/20], Step [475/654], Loss: 0.5555\n","Epoch [4/20], Step [500/654], Loss: 0.3941\n","Epoch [4/20], Step [525/654], Loss: 0.9638\n","Epoch [4/20], Step [550/654], Loss: 0.5711\n","Epoch [4/20], Step [575/654], Loss: 0.4032\n","Epoch [4/20], Step [600/654], Loss: 0.5915\n","Epoch [4/20], Step [625/654], Loss: 0.2461\n","Epoch [4/20], Step [650/654], Loss: 0.4042\n","Start validation #4\n","Validation #4 Average Loss: 0.5501, mIoU: 0.4199\n","Best performance at epoch: 4\n","Save model in ./saved\n","Epoch [5/20], Step [25/654], Loss: 0.2627\n","Epoch [5/20], Step [50/654], Loss: 0.4895\n","Epoch [5/20], Step [75/654], Loss: 0.2866\n","Epoch [5/20], Step [100/654], Loss: 0.3312\n","Epoch [5/20], Step [125/654], Loss: 0.8928\n","Epoch [5/20], Step [150/654], Loss: 0.4318\n","Epoch [5/20], Step [175/654], Loss: 0.2173\n","Epoch [5/20], Step [200/654], Loss: 0.3286\n","Epoch [5/20], Step [225/654], Loss: 0.4291\n","Epoch [5/20], Step [250/654], Loss: 0.4943\n","Epoch [5/20], Step [275/654], Loss: 0.2938\n","Epoch [5/20], Step [300/654], Loss: 0.2702\n","Epoch [5/20], Step [325/654], Loss: 0.5579\n","Epoch [5/20], Step [350/654], Loss: 0.5311\n","Epoch [5/20], Step [375/654], Loss: 0.4553\n","Epoch [5/20], Step [400/654], Loss: 0.3895\n","Epoch [5/20], Step [425/654], Loss: 0.9001\n","Epoch [5/20], Step [450/654], Loss: 0.2931\n","Epoch [5/20], Step [475/654], Loss: 0.3877\n","Epoch [5/20], Step [500/654], Loss: 0.5174\n","Epoch [5/20], Step [525/654], Loss: 1.5183\n","Epoch [5/20], Step [550/654], Loss: 0.3562\n","Epoch [5/20], Step [575/654], Loss: 0.5312\n","Epoch [5/20], Step [600/654], Loss: 0.3282\n","Epoch [5/20], Step [625/654], Loss: 0.3794\n","Epoch [5/20], Step [650/654], Loss: 0.1764\n","Start validation #5\n","Validation #5 Average Loss: 0.5629, mIoU: 0.4252\n","Best performance at epoch: 5\n","Save model in ./saved\n","Epoch [6/20], Step [25/654], Loss: 0.3945\n","Epoch [6/20], Step [50/654], Loss: 0.1853\n","Epoch [6/20], Step [75/654], Loss: 0.5619\n","Epoch [6/20], Step [100/654], Loss: 0.2644\n","Epoch [6/20], Step [125/654], Loss: 0.4830\n","Epoch [6/20], Step [150/654], Loss: 0.3591\n","Epoch [6/20], Step [175/654], Loss: 0.6958\n","Epoch [6/20], Step [200/654], Loss: 0.2517\n","Epoch [6/20], Step [225/654], Loss: 0.7142\n","Epoch [6/20], Step [250/654], Loss: 0.4305\n","Epoch [6/20], Step [275/654], Loss: 0.2472\n","Epoch [6/20], Step [300/654], Loss: 0.5895\n","Epoch [6/20], Step [325/654], Loss: 0.5973\n","Epoch [6/20], Step [350/654], Loss: 0.2378\n","Epoch [6/20], Step [375/654], Loss: 0.6631\n","Epoch [6/20], Step [400/654], Loss: 1.1211\n","Epoch [6/20], Step [425/654], Loss: 0.1629\n","Epoch [6/20], Step [450/654], Loss: 0.2585\n","Epoch [6/20], Step [475/654], Loss: 0.6167\n","Epoch [6/20], Step [500/654], Loss: 0.2767\n","Epoch [6/20], Step [525/654], Loss: 0.4304\n","Epoch [6/20], Step [550/654], Loss: 0.2984\n","Epoch [6/20], Step [575/654], Loss: 0.5872\n","Epoch [6/20], Step [600/654], Loss: 0.3733\n","Epoch [6/20], Step [625/654], Loss: 0.4079\n","Epoch [6/20], Step [650/654], Loss: 0.2857\n","Start validation #6\n","Validation #6 Average Loss: 0.5472, mIoU: 0.4330\n","Best performance at epoch: 6\n","Save model in ./saved\n","Epoch [7/20], Step [25/654], Loss: 0.2045\n","Epoch [7/20], Step [50/654], Loss: 0.2123\n","Epoch [7/20], Step [75/654], Loss: 0.3125\n","Epoch [7/20], Step [100/654], Loss: 0.6578\n","Epoch [7/20], Step [125/654], Loss: 0.6835\n","Epoch [7/20], Step [150/654], Loss: 0.3036\n","Epoch [7/20], Step [175/654], Loss: 0.2554\n","Epoch [7/20], Step [200/654], Loss: 0.3786\n","Epoch [7/20], Step [225/654], Loss: 0.4893\n","Epoch [7/20], Step [250/654], Loss: 0.1459\n","Epoch [7/20], Step [275/654], Loss: 0.1772\n","Epoch [7/20], Step [300/654], Loss: 0.1666\n","Epoch [7/20], Step [325/654], Loss: 0.1550\n","Epoch [7/20], Step [350/654], Loss: 0.5846\n","Epoch [7/20], Step [375/654], Loss: 0.2319\n","Epoch [7/20], Step [400/654], Loss: 0.7416\n","Epoch [7/20], Step [425/654], Loss: 0.1549\n","Epoch [7/20], Step [450/654], Loss: 0.1068\n","Epoch [7/20], Step [475/654], Loss: 0.5891\n","Epoch [7/20], Step [500/654], Loss: 0.3762\n","Epoch [7/20], Step [525/654], Loss: 0.3434\n","Epoch [7/20], Step [550/654], Loss: 0.4098\n","Epoch [7/20], Step [575/654], Loss: 0.7660\n","Epoch [7/20], Step [600/654], Loss: 0.6794\n","Epoch [7/20], Step [625/654], Loss: 0.4701\n","Epoch [7/20], Step [650/654], Loss: 0.8929\n","Start validation #7\n","Validation #7 Average Loss: 0.5538, mIoU: 0.4227\n","Epoch [8/20], Step [25/654], Loss: 0.1885\n","Epoch [8/20], Step [50/654], Loss: 0.2395\n","Epoch [8/20], Step [75/654], Loss: 0.8672\n","Epoch [8/20], Step [100/654], Loss: 0.1849\n","Epoch [8/20], Step [125/654], Loss: 0.5650\n","Epoch [8/20], Step [150/654], Loss: 0.9543\n","Epoch [8/20], Step [175/654], Loss: 0.2854\n","Epoch [8/20], Step [200/654], Loss: 0.2489\n","Epoch [8/20], Step [225/654], Loss: 0.3415\n","Epoch [8/20], Step [250/654], Loss: 0.2129\n","Epoch [8/20], Step [275/654], Loss: 0.1682\n","Epoch [8/20], Step [300/654], Loss: 0.5344\n","Epoch [8/20], Step [325/654], Loss: 0.0931\n","Epoch [8/20], Step [350/654], Loss: 0.2194\n","Epoch [8/20], Step [375/654], Loss: 0.3099\n","Epoch [8/20], Step [400/654], Loss: 0.4175\n","Epoch [8/20], Step [425/654], Loss: 0.5128\n","Epoch [8/20], Step [450/654], Loss: 0.5602\n","Epoch [8/20], Step [475/654], Loss: 0.2585\n","Epoch [8/20], Step [500/654], Loss: 0.3712\n","Epoch [8/20], Step [525/654], Loss: 0.6950\n","Epoch [8/20], Step [550/654], Loss: 0.3419\n","Epoch [8/20], Step [575/654], Loss: 0.4508\n","Epoch [8/20], Step [600/654], Loss: 0.5767\n","Epoch [8/20], Step [625/654], Loss: 0.8485\n","Epoch [8/20], Step [650/654], Loss: 0.3287\n","Start validation #8\n","Validation #8 Average Loss: 0.5671, mIoU: 0.4255\n","Epoch [9/20], Step [25/654], Loss: 0.2841\n","Epoch [9/20], Step [50/654], Loss: 0.2921\n","Epoch [9/20], Step [75/654], Loss: 0.2502\n","Epoch [9/20], Step [100/654], Loss: 0.3595\n","Epoch [9/20], Step [125/654], Loss: 0.2798\n","Epoch [9/20], Step [150/654], Loss: 0.3844\n","Epoch [9/20], Step [175/654], Loss: 0.3773\n","Epoch [9/20], Step [200/654], Loss: 0.4727\n","Epoch [9/20], Step [225/654], Loss: 0.4415\n","Epoch [9/20], Step [250/654], Loss: 0.4617\n","Epoch [9/20], Step [275/654], Loss: 0.2980\n","Epoch [9/20], Step [300/654], Loss: 0.5105\n","Epoch [9/20], Step [325/654], Loss: 0.4500\n","Epoch [9/20], Step [350/654], Loss: 0.2915\n","Epoch [9/20], Step [375/654], Loss: 0.3934\n","Epoch [9/20], Step [400/654], Loss: 0.5114\n","Epoch [9/20], Step [425/654], Loss: 0.2151\n","Epoch [9/20], Step [450/654], Loss: 0.4883\n","Epoch [9/20], Step [475/654], Loss: 0.6925\n","Epoch [9/20], Step [500/654], Loss: 0.3688\n","Epoch [9/20], Step [525/654], Loss: 0.7787\n","Epoch [9/20], Step [550/654], Loss: 0.1080\n","Epoch [9/20], Step [575/654], Loss: 0.1979\n","Epoch [9/20], Step [600/654], Loss: 0.4503\n","Epoch [9/20], Step [625/654], Loss: 0.1263\n","Epoch [9/20], Step [650/654], Loss: 0.3418\n","Start validation #9\n","Validation #9 Average Loss: 0.5691, mIoU: 0.4306\n","Epoch [10/20], Step [25/654], Loss: 0.5057\n","Epoch [10/20], Step [50/654], Loss: 0.4934\n","Epoch [10/20], Step [75/654], Loss: 0.1796\n","Epoch [10/20], Step [100/654], Loss: 0.5275\n","Epoch [10/20], Step [125/654], Loss: 0.2869\n","Epoch [10/20], Step [150/654], Loss: 0.3785\n","Epoch [10/20], Step [175/654], Loss: 0.9210\n","Epoch [10/20], Step [200/654], Loss: 0.5679\n","Epoch [10/20], Step [225/654], Loss: 0.2385\n","Epoch [10/20], Step [250/654], Loss: 0.2936\n","Epoch [10/20], Step [275/654], Loss: 0.3099\n","Epoch [10/20], Step [300/654], Loss: 0.4310\n","Epoch [10/20], Step [325/654], Loss: 0.3184\n","Epoch [10/20], Step [350/654], Loss: 0.2723\n","Epoch [10/20], Step [375/654], Loss: 0.3387\n","Epoch [10/20], Step [400/654], Loss: 0.5162\n","Epoch [10/20], Step [425/654], Loss: 0.4691\n","Epoch [10/20], Step [450/654], Loss: 0.4017\n","Epoch [10/20], Step [475/654], Loss: 0.9830\n","Epoch [10/20], Step [500/654], Loss: 0.4022\n","Epoch [10/20], Step [525/654], Loss: 0.2118\n","Epoch [10/20], Step [550/654], Loss: 0.2995\n","Epoch [10/20], Step [575/654], Loss: 0.1580\n","Epoch [10/20], Step [600/654], Loss: 0.2265\n","Epoch [10/20], Step [625/654], Loss: 0.5859\n","Epoch [10/20], Step [650/654], Loss: 0.2745\n","Start validation #10\n","Validation #10 Average Loss: 0.5669, mIoU: 0.4259\n","Epoch [11/20], Step [25/654], Loss: 0.1626\n","Epoch [11/20], Step [50/654], Loss: 0.4927\n","Epoch [11/20], Step [75/654], Loss: 0.2100\n","Epoch [11/20], Step [100/654], Loss: 0.5456\n","Epoch [11/20], Step [125/654], Loss: 0.6431\n","Epoch [11/20], Step [150/654], Loss: 0.2075\n","Epoch [11/20], Step [175/654], Loss: 0.4522\n","Epoch [11/20], Step [200/654], Loss: 0.1968\n","Epoch [11/20], Step [225/654], Loss: 1.1782\n","Epoch [11/20], Step [250/654], Loss: 0.2424\n","Epoch [11/20], Step [275/654], Loss: 0.3993\n","Epoch [11/20], Step [300/654], Loss: 0.2273\n","Epoch [11/20], Step [325/654], Loss: 0.2260\n","Epoch [11/20], Step [350/654], Loss: 0.2018\n","Epoch [11/20], Step [375/654], Loss: 0.3896\n","Epoch [11/20], Step [400/654], Loss: 0.3269\n","Epoch [11/20], Step [425/654], Loss: 0.2992\n","Epoch [11/20], Step [450/654], Loss: 0.5236\n","Epoch [11/20], Step [475/654], Loss: 0.1617\n","Epoch [11/20], Step [500/654], Loss: 0.2548\n","Epoch [11/20], Step [525/654], Loss: 0.5883\n","Epoch [11/20], Step [550/654], Loss: 0.3220\n","Epoch [11/20], Step [575/654], Loss: 0.4726\n","Epoch [11/20], Step [600/654], Loss: 0.2970\n","Epoch [11/20], Step [625/654], Loss: 0.4708\n","Epoch [11/20], Step [650/654], Loss: 0.3320\n","Start validation #11\n","Validation #11 Average Loss: 0.5646, mIoU: 0.4348\n","Best performance at epoch: 11\n","Save model in ./saved\n","Epoch [12/20], Step [25/654], Loss: 0.7662\n","Epoch [12/20], Step [50/654], Loss: 0.7445\n","Epoch [12/20], Step [75/654], Loss: 0.2678\n","Epoch [12/20], Step [100/654], Loss: 0.6086\n","Epoch [12/20], Step [125/654], Loss: 0.5362\n","Epoch [12/20], Step [150/654], Loss: 0.3718\n","Epoch [12/20], Step [175/654], Loss: 0.2983\n","Epoch [12/20], Step [200/654], Loss: 0.4909\n","Epoch [12/20], Step [225/654], Loss: 0.3034\n","Epoch [12/20], Step [250/654], Loss: 0.2102\n","Epoch [12/20], Step [275/654], Loss: 0.6765\n","Epoch [12/20], Step [300/654], Loss: 0.5255\n","Epoch [12/20], Step [325/654], Loss: 0.3007\n","Epoch [12/20], Step [350/654], Loss: 0.3436\n","Epoch [12/20], Step [375/654], Loss: 0.2890\n","Epoch [12/20], Step [400/654], Loss: 0.4759\n","Epoch [12/20], Step [425/654], Loss: 0.2060\n","Epoch [12/20], Step [450/654], Loss: 0.3237\n","Epoch [12/20], Step [475/654], Loss: 0.5761\n","Epoch [12/20], Step [500/654], Loss: 0.3561\n","Epoch [12/20], Step [525/654], Loss: 0.2203\n","Epoch [12/20], Step [550/654], Loss: 0.3269\n","Epoch [12/20], Step [575/654], Loss: 0.4603\n","Epoch [12/20], Step [600/654], Loss: 0.1833\n","Epoch [12/20], Step [625/654], Loss: 0.4462\n","Epoch [12/20], Step [650/654], Loss: 0.2219\n","Start validation #12\n","Validation #12 Average Loss: 0.5625, mIoU: 0.4280\n","Epoch [13/20], Step [25/654], Loss: 0.6295\n","Epoch [13/20], Step [50/654], Loss: 0.3437\n","Epoch [13/20], Step [75/654], Loss: 0.6881\n","Epoch [13/20], Step [100/654], Loss: 0.4495\n","Epoch [13/20], Step [125/654], Loss: 0.5329\n","Epoch [13/20], Step [150/654], Loss: 0.4817\n","Epoch [13/20], Step [175/654], Loss: 0.2362\n","Epoch [13/20], Step [200/654], Loss: 0.1677\n","Epoch [13/20], Step [225/654], Loss: 0.1636\n","Epoch [13/20], Step [250/654], Loss: 0.3200\n","Epoch [13/20], Step [275/654], Loss: 0.4068\n","Epoch [13/20], Step [300/654], Loss: 0.2315\n","Epoch [13/20], Step [325/654], Loss: 0.2434\n","Epoch [13/20], Step [350/654], Loss: 0.2650\n","Epoch [13/20], Step [375/654], Loss: 0.3612\n","Epoch [13/20], Step [400/654], Loss: 0.2694\n","Epoch [13/20], Step [425/654], Loss: 0.2218\n","Epoch [13/20], Step [450/654], Loss: 0.3760\n","Epoch [13/20], Step [475/654], Loss: 0.6089\n","Epoch [13/20], Step [500/654], Loss: 0.6792\n","Epoch [13/20], Step [525/654], Loss: 0.2454\n","Epoch [13/20], Step [550/654], Loss: 0.2859\n","Epoch [13/20], Step [575/654], Loss: 0.1819\n","Epoch [13/20], Step [600/654], Loss: 0.2814\n","Epoch [13/20], Step [625/654], Loss: 0.2847\n","Epoch [13/20], Step [650/654], Loss: 0.4639\n","Start validation #13\n","Validation #13 Average Loss: 0.5767, mIoU: 0.4218\n","Epoch [14/20], Step [25/654], Loss: 0.2221\n","Epoch [14/20], Step [50/654], Loss: 0.2822\n","Epoch [14/20], Step [75/654], Loss: 0.3572\n","Epoch [14/20], Step [100/654], Loss: 0.1572\n","Epoch [14/20], Step [125/654], Loss: 0.3900\n","Epoch [14/20], Step [150/654], Loss: 0.3736\n","Epoch [14/20], Step [175/654], Loss: 0.3638\n","Epoch [14/20], Step [200/654], Loss: 0.3915\n","Epoch [14/20], Step [225/654], Loss: 0.6008\n","Epoch [14/20], Step [250/654], Loss: 0.2004\n","Epoch [14/20], Step [275/654], Loss: 0.3851\n","Epoch [14/20], Step [300/654], Loss: 0.5808\n","Epoch [14/20], Step [325/654], Loss: 0.1839\n","Epoch [14/20], Step [350/654], Loss: 0.3890\n","Epoch [14/20], Step [375/654], Loss: 0.3985\n","Epoch [14/20], Step [400/654], Loss: 0.8747\n","Epoch [14/20], Step [425/654], Loss: 0.4557\n","Epoch [14/20], Step [450/654], Loss: 0.2583\n","Epoch [14/20], Step [475/654], Loss: 0.2747\n","Epoch [14/20], Step [500/654], Loss: 0.5715\n","Epoch [14/20], Step [525/654], Loss: 0.3319\n","Epoch [14/20], Step [550/654], Loss: 0.3939\n","Epoch [14/20], Step [575/654], Loss: 0.3626\n","Epoch [14/20], Step [600/654], Loss: 0.1541\n","Epoch [14/20], Step [625/654], Loss: 1.1515\n","Epoch [14/20], Step [650/654], Loss: 0.3596\n","Start validation #14\n","Validation #14 Average Loss: 0.5581, mIoU: 0.4276\n","Epoch [15/20], Step [25/654], Loss: 0.3186\n","Epoch [15/20], Step [50/654], Loss: 0.4963\n","Epoch [15/20], Step [75/654], Loss: 0.3104\n","Epoch [15/20], Step [100/654], Loss: 0.4209\n","Epoch [15/20], Step [125/654], Loss: 0.4228\n","Epoch [15/20], Step [150/654], Loss: 0.2117\n","Epoch [15/20], Step [175/654], Loss: 0.3716\n","Epoch [15/20], Step [200/654], Loss: 0.4054\n","Epoch [15/20], Step [225/654], Loss: 0.1742\n","Epoch [15/20], Step [250/654], Loss: 0.2988\n","Epoch [15/20], Step [275/654], Loss: 0.4417\n","Epoch [15/20], Step [300/654], Loss: 1.5308\n","Epoch [15/20], Step [325/654], Loss: 0.3121\n","Epoch [15/20], Step [350/654], Loss: 0.3526\n","Epoch [15/20], Step [375/654], Loss: 0.4429\n","Epoch [15/20], Step [400/654], Loss: 0.3470\n","Epoch [15/20], Step [425/654], Loss: 0.4545\n","Epoch [15/20], Step [450/654], Loss: 0.5542\n","Epoch [15/20], Step [475/654], Loss: 0.1230\n","Epoch [15/20], Step [500/654], Loss: 0.5197\n","Epoch [15/20], Step [525/654], Loss: 0.2037\n","Epoch [15/20], Step [550/654], Loss: 0.6392\n","Epoch [15/20], Step [575/654], Loss: 0.2422\n","Epoch [15/20], Step [600/654], Loss: 0.3895\n","Epoch [15/20], Step [625/654], Loss: 0.5340\n","Epoch [15/20], Step [650/654], Loss: 0.2022\n","Start validation #15\n","Validation #15 Average Loss: 0.5601, mIoU: 0.4356\n","Best performance at epoch: 15\n","Save model in ./saved\n","Epoch [16/20], Step [25/654], Loss: 0.4875\n","Epoch [16/20], Step [50/654], Loss: 0.5776\n","Epoch [16/20], Step [75/654], Loss: 0.4949\n","Epoch [16/20], Step [100/654], Loss: 0.5558\n","Epoch [16/20], Step [125/654], Loss: 0.3848\n","Epoch [16/20], Step [150/654], Loss: 0.5701\n","Epoch [16/20], Step [175/654], Loss: 0.4775\n","Epoch [16/20], Step [200/654], Loss: 0.4356\n","Epoch [16/20], Step [225/654], Loss: 0.5194\n","Epoch [16/20], Step [250/654], Loss: 0.5899\n","Epoch [16/20], Step [275/654], Loss: 0.2989\n","Epoch [16/20], Step [300/654], Loss: 0.2782\n","Epoch [16/20], Step [325/654], Loss: 0.2563\n","Epoch [16/20], Step [350/654], Loss: 0.6428\n","Epoch [16/20], Step [375/654], Loss: 0.1875\n","Epoch [16/20], Step [400/654], Loss: 0.6736\n","Epoch [16/20], Step [425/654], Loss: 0.2344\n","Epoch [16/20], Step [450/654], Loss: 0.4751\n","Epoch [16/20], Step [475/654], Loss: 0.2965\n","Epoch [16/20], Step [500/654], Loss: 0.2626\n","Epoch [16/20], Step [525/654], Loss: 0.3973\n","Epoch [16/20], Step [550/654], Loss: 0.4449\n","Epoch [16/20], Step [575/654], Loss: 0.1995\n","Epoch [16/20], Step [600/654], Loss: 0.3080\n","Epoch [16/20], Step [625/654], Loss: 0.4492\n","Epoch [16/20], Step [650/654], Loss: 0.2103\n","Start validation #16\n","Validation #16 Average Loss: 0.5818, mIoU: 0.4272\n","Epoch [17/20], Step [25/654], Loss: 0.3594\n","Epoch [17/20], Step [50/654], Loss: 0.1056\n","Epoch [17/20], Step [75/654], Loss: 0.5455\n","Epoch [17/20], Step [100/654], Loss: 0.3893\n","Epoch [17/20], Step [125/654], Loss: 0.5094\n","Epoch [17/20], Step [150/654], Loss: 0.5060\n","Epoch [17/20], Step [175/654], Loss: 0.4084\n","Epoch [17/20], Step [200/654], Loss: 0.1997\n","Epoch [17/20], Step [225/654], Loss: 0.2506\n","Epoch [17/20], Step [250/654], Loss: 0.3450\n","Epoch [17/20], Step [275/654], Loss: 0.2553\n","Epoch [17/20], Step [300/654], Loss: 0.2913\n","Epoch [17/20], Step [325/654], Loss: 0.1881\n","Epoch [17/20], Step [350/654], Loss: 0.1560\n","Epoch [17/20], Step [375/654], Loss: 0.7472\n","Epoch [17/20], Step [400/654], Loss: 0.1549\n","Epoch [17/20], Step [425/654], Loss: 0.3113\n","Epoch [17/20], Step [450/654], Loss: 0.4554\n","Epoch [17/20], Step [475/654], Loss: 0.1605\n","Epoch [17/20], Step [500/654], Loss: 0.4134\n","Epoch [17/20], Step [525/654], Loss: 0.1979\n","Epoch [17/20], Step [550/654], Loss: 0.7878\n","Epoch [17/20], Step [575/654], Loss: 0.4759\n","Epoch [17/20], Step [600/654], Loss: 0.3633\n","Epoch [17/20], Step [625/654], Loss: 0.2715\n","Epoch [17/20], Step [650/654], Loss: 0.3249\n","Start validation #17\n","Validation #17 Average Loss: 0.5679, mIoU: 0.4245\n","Epoch [18/20], Step [25/654], Loss: 0.5004\n","Epoch [18/20], Step [50/654], Loss: 0.2374\n","Epoch [18/20], Step [75/654], Loss: 0.4663\n","Epoch [18/20], Step [100/654], Loss: 0.1249\n","Epoch [18/20], Step [125/654], Loss: 0.5095\n","Epoch [18/20], Step [150/654], Loss: 0.6071\n","Epoch [18/20], Step [175/654], Loss: 0.3770\n","Epoch [18/20], Step [200/654], Loss: 0.3525\n","Epoch [18/20], Step [225/654], Loss: 0.6368\n","Epoch [18/20], Step [250/654], Loss: 0.1375\n","Epoch [18/20], Step [275/654], Loss: 0.1276\n","Epoch [18/20], Step [300/654], Loss: 0.3610\n","Epoch [18/20], Step [325/654], Loss: 0.5078\n","Epoch [18/20], Step [350/654], Loss: 0.3634\n","Epoch [18/20], Step [375/654], Loss: 0.1688\n","Epoch [18/20], Step [400/654], Loss: 0.3702\n","Epoch [18/20], Step [425/654], Loss: 0.1997\n","Epoch [18/20], Step [450/654], Loss: 0.3988\n","Epoch [18/20], Step [475/654], Loss: 0.7295\n","Epoch [18/20], Step [500/654], Loss: 0.6494\n","Epoch [18/20], Step [525/654], Loss: 0.2713\n","Epoch [18/20], Step [550/654], Loss: 0.6339\n","Epoch [18/20], Step [575/654], Loss: 0.2635\n","Epoch [18/20], Step [600/654], Loss: 0.1547\n","Epoch [18/20], Step [625/654], Loss: 0.4407\n","Epoch [18/20], Step [650/654], Loss: 0.8191\n","Start validation #18\n","Validation #18 Average Loss: 0.5697, mIoU: 0.4290\n","Epoch [19/20], Step [25/654], Loss: 0.4309\n","Epoch [19/20], Step [50/654], Loss: 0.3901\n","Epoch [19/20], Step [75/654], Loss: 0.4485\n","Epoch [19/20], Step [100/654], Loss: 0.2524\n","Epoch [19/20], Step [125/654], Loss: 0.2547\n","Epoch [19/20], Step [150/654], Loss: 0.2312\n","Epoch [19/20], Step [175/654], Loss: 0.1677\n","Epoch [19/20], Step [200/654], Loss: 0.4289\n","Epoch [19/20], Step [225/654], Loss: 0.4050\n","Epoch [19/20], Step [250/654], Loss: 0.2688\n","Epoch [19/20], Step [275/654], Loss: 0.4619\n","Epoch [19/20], Step [300/654], Loss: 0.9270\n","Epoch [19/20], Step [325/654], Loss: 0.5893\n","Epoch [19/20], Step [350/654], Loss: 0.3249\n","Epoch [19/20], Step [375/654], Loss: 0.3810\n","Epoch [19/20], Step [400/654], Loss: 0.1018\n","Epoch [19/20], Step [425/654], Loss: 0.3974\n","Epoch [19/20], Step [450/654], Loss: 0.2350\n","Epoch [19/20], Step [475/654], Loss: 0.3137\n","Epoch [19/20], Step [500/654], Loss: 0.3801\n","Epoch [19/20], Step [525/654], Loss: 0.1303\n","Epoch [19/20], Step [550/654], Loss: 0.1702\n","Epoch [19/20], Step [575/654], Loss: 0.6272\n","Epoch [19/20], Step [600/654], Loss: 0.3285\n","Epoch [19/20], Step [625/654], Loss: 0.1771\n","Epoch [19/20], Step [650/654], Loss: 0.3190\n","Start validation #19\n","Validation #19 Average Loss: 0.5855, mIoU: 0.4343\n","Epoch [20/20], Step [25/654], Loss: 0.5026\n","Epoch [20/20], Step [50/654], Loss: 0.2641\n","Epoch [20/20], Step [75/654], Loss: 0.9840\n","Epoch [20/20], Step [100/654], Loss: 0.2554\n","Epoch [20/20], Step [125/654], Loss: 0.2767\n","Epoch [20/20], Step [150/654], Loss: 0.3167\n","Epoch [20/20], Step [175/654], Loss: 0.3502\n","Epoch [20/20], Step [200/654], Loss: 0.3280\n","Epoch [20/20], Step [225/654], Loss: 0.4604\n","Epoch [20/20], Step [250/654], Loss: 0.3482\n","Epoch [20/20], Step [275/654], Loss: 0.2587\n","Epoch [20/20], Step [300/654], Loss: 0.3810\n","Epoch [20/20], Step [325/654], Loss: 0.6325\n","Epoch [20/20], Step [350/654], Loss: 0.2576\n","Epoch [20/20], Step [375/654], Loss: 0.1228\n","Epoch [20/20], Step [400/654], Loss: 0.2681\n","Epoch [20/20], Step [425/654], Loss: 0.2972\n","Epoch [20/20], Step [450/654], Loss: 0.5690\n","Epoch [20/20], Step [475/654], Loss: 0.4336\n","Epoch [20/20], Step [500/654], Loss: 0.2935\n","Epoch [20/20], Step [525/654], Loss: 0.5076\n","Epoch [20/20], Step [550/654], Loss: 0.3036\n","Epoch [20/20], Step [575/654], Loss: 0.1421\n","Epoch [20/20], Step [600/654], Loss: 0.2344\n","Epoch [20/20], Step [625/654], Loss: 0.5185\n","Epoch [20/20], Step [650/654], Loss: 0.4697\n","Start validation #20\n","Validation #20 Average Loss: 0.5721, mIoU: 0.4422\n","Best performance at epoch: 20\n","Save model in ./saved\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"QBM5cYVks5I2"},"source":["# Sleep for a few seconds.\n","import time\n","time.sleep(2)\n","# Play an audio beep. Any audio URL will do.\n","from google.colab import output\n","output.eval_js('new Audio(\"https://upload.wikimedia.org/wikipedia/commons/0/05/Beep-09.ogg\").play()')"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"C6ClcO0J-NwR"},"source":["## 저장된 model 불러오기 (학습된 이후) "]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:21.050200Z","start_time":"2021-04-16T19:44:20.802200Z"},"scrolled":true,"id":"KQPtUDzd-NwR","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620186005431,"user_tz":-540,"elapsed":919,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"76d29b5f-6a3f-4aee-b234-10f72c44361e"},"source":["# best model 저장된 경로\n","model_path = './saved/aug2_re_pan_effb5_noisy_focal_CE_madgrad_kwparam_stepLR.pt'\n","\n","# best model 불러오기\n","checkpoint = torch.load(model_path, map_location=device)\n","model.load_state_dict(checkpoint)\n","\n","# 추론을 실행하기 전에는 반드시 설정 (batch normalization, dropout 를 평가 모드로 설정)\n","# model.eval()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{"tags":[]},"execution_count":17}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:24.939227Z","start_time":"2021-04-16T19:44:24.518228Z"},"id":"0LQqrDAp-NwR","colab":{"base_uri":"https://localhost:8080/","height":500},"executionInfo":{"status":"ok","timestamp":1620186012345,"user_tz":-540,"elapsed":5649,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"b356ada1-df04-48e3-e15e-17905758008e"},"source":["# 첫번째 batch의 추론 결과 확인\n","for imgs, image_infos in test_loader:\n"," image_infos = image_infos\n"," temp_images = imgs\n"," \n"," model.eval()\n"," # inference\n"," outs = model(torch.stack(temp_images).to(device))\n"," oms = torch.argmax(outs.squeeze(), dim=1).detach().cpu().numpy()\n"," \n"," break\n","\n","i = 1\n","fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(16, 16))\n","\n","print('Shape of Original Image :', list(temp_images[i].shape))\n","print('Shape of Predicted : ', list(oms[i].shape))\n","print('Unique values, category of transformed mask : \\n', [{int(i),category_names[int(i)]} for i in list(np.unique(oms[i]))])\n","\n","# Original image\n","ax1.imshow(temp_images[i].permute([1,2,0]))\n","ax1.grid(False)\n","ax1.set_title(\"Original image : {}\".format(image_infos[i]['file_name']), fontsize = 15)\n","\n","# Predicted\n","ax2.imshow(oms[i])\n","ax2.grid(False)\n","ax2.set_title(\"Predicted : {}\".format(image_infos[i]['file_name']), fontsize = 15)\n","\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Shape of Original Image : [3, 512, 512]\n","Shape of Predicted : [512, 512]\n","Unique values, category of transformed mask : \n"," [{0, 'Backgroud'}, {'General trash', 2}, {9, 'Plastic bag'}]\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"evYjR2F3-NwS"},"source":["## submission을 위한 test 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:27.469285Z","start_time":"2021-04-16T19:44:27.456021Z"},"id":"nhMLnV5d-NwS"},"source":["def test(model, data_loader, device):\n"," size = 256\n"," transform = A.Compose([A.Resize(256, 256)])\n"," print('Start prediction.')\n"," model.eval()\n"," \n"," file_name_list = []\n"," preds_array = np.empty((0, size*size), dtype=np.long)\n"," \n"," with torch.no_grad():\n"," for step, (imgs, image_infos) in enumerate(test_loader):\n","\n"," # inference (512 x 512)\n"," outs = model(torch.stack(imgs).to(device))\n"," oms = torch.argmax(outs.squeeze(), dim=1).detach().cpu().numpy()\n"," \n"," # resize (256 x 256)\n"," temp_mask = []\n"," for img, mask in zip(np.stack(imgs), oms):\n"," transformed = transform(image=img, mask=mask)\n"," mask = transformed['mask']\n"," temp_mask.append(mask)\n","\n"," oms = np.array(temp_mask)\n"," \n"," oms = oms.reshape([oms.shape[0], size*size]).astype(int)\n"," preds_array = np.vstack((preds_array, oms))\n"," \n"," file_name_list.append([i['file_name'] for i in image_infos])\n"," print(\"End prediction.\")\n"," file_names = [y for x in file_name_list for y in x]\n"," \n"," return file_names, preds_array"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"r1YKHBf4-NwT"},"source":["## submission.csv 생성"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:45:42.235310Z","start_time":"2021-04-16T19:44:30.499016Z"},"scrolled":true,"id":"Bz79_g8K-NwT","colab":{"base_uri":"https://localhost:8080/","height":409},"executionInfo":{"status":"error","timestamp":1620186655257,"user_tz":-540,"elapsed":640460,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"231e9b5a-42a0-4db9-87e6-67b2aa886468"},"source":["# sample_submisson.csv 열기\n","submission = pd.read_csv('./submission/sample_submission.csv', index_col=None)\n","\n","# test set에 대한 prediction\n","file_names, preds = test(model, test_loader, device)\n","\n","# PredictionString 대입\n","for file_name, string in zip(file_names, preds):\n"," submission = submission.append({\"image_id\" : file_name, \"PredictionString\" : ' '.join(str(e) for e in string.tolist())}, \n"," ignore_index=True)\n","\n","# submission.csv로 저장\n","submission.to_csv(\"./submission/aug2_re_pan_effb5_noisy_focal_CE_madgrad_kwparam_stepLR.csv\", index=False)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Start prediction.\n"],"name":"stdout"},{"output_type":"error","ename":"ValueError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)","\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m# test set에 대한 prediction\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mfile_names\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpreds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0moms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m \u001b[0mtransformed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 21\u001b[0m \u001b[0mmask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtransformed\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'mask'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 22\u001b[0m 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\u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpand_dims\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/albumentations/augmentations/functional.py\u001b[0m in \u001b[0;36mresize\u001b[0;34m(img, height, width, interpolation)\u001b[0m\n\u001b[1;32m 205\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mpreserve_channel_dim\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 206\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mresize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mINTER_LINEAR\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 207\u001b[0;31m \u001b[0mimg_height\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg_width\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 208\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mheight\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mimg_height\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mwidth\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mimg_width\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 209\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mValueError\u001b[0m: not enough values to unpack (expected 2, got 1)"]}]},{"cell_type":"markdown","metadata":{"id":"Zgl7Ye7F-NwT"},"source":["## 제출까지\n","\n"]},{"cell_type":"code","metadata":{"id":"022E8AC7-NwT"},"source":["import json\n","import requests\n","import os\n","from urllib.parse import urlparse, parse_qsl, urlencode, urlunparse\n","\n","def submit(user_key='', file_path = '', desc=\"\"):\n"," if not user_key:\n"," raise Exception(\"No UserKey\" )\n"," url = urlparse('http://ec2-13-124-161-225.ap-northeast-2.compute.amazonaws.com:8000/api/v1/competition/28/presigned_url/?description=&hyperparameters={%22training%22:{},%22inference%22:{}}')\n"," qs = dict(parse_qsl(url.query))\n"," qs['description'] = desc\n"," parts = url._replace(query=urlencode(qs))\n"," url = urlunparse(parts)\n","\n"," print(url)\n"," headers = {\n"," 'Authorization': user_key\n"," }\n"," res = requests.get(url, headers=headers)\n"," print(res.text)\n"," data = json.loads(res.text)\n"," \n"," submit_url = data['url']\n"," body = {\n"," 'key':'app/Competitions/000028/Users/{}/Submissions/{}/output.csv'.format(str(data['submission']['user']).zfill(8),str(data['submission']['local_id']).zfill(4)),\n"," 'x-amz-algorithm':data['fields']['x-amz-algorithm'],\n"," 'x-amz-credential':data['fields']['x-amz-credential'],\n"," 'x-amz-date':data['fields']['x-amz-date'],\n"," 'policy':data['fields']['policy'],\n"," 'x-amz-signature':data['fields']['x-amz-signature']\n"," }\n"," requests.post(url=submit_url, data=body, files={'file': open(file_path, 'rb')})\n","\n","\n","####################################################################################\n","test_dir = \"/content/drive/MyDrive/Trash/code/submission\" # 수정 필요 : output 파일 폴더 \n","desc = 'aug2_re_pan_effb5_noisy_focal_CE_madgrad_kwparam_stepLR' # 수정 필요 : 파일에 대한 설명\n","output_file = \"aug2_re_pan_effb5_noisy_focal_CE_madgrad_kwparam_stepLR.csv\" #수정 필요 : output 파일 \n","user_key = \"Bearer 7bb5f96452751a238ffaf91a93c4242bf9b72abe\" # 수정 필요 : Authorization \n","\n","\n","submit(user_key, os.path.join(test_dir, output_file),desc)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"q5BR_x5GkC82"},"source":[""],"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/chanyub_seg/code/aug2_re_pan_effb5_noisy_focal_madgrad_cosLR.ipynb b/chanyub_seg/code/aug2_re_pan_effb5_noisy_focal_madgrad_cosLR.ipynb deleted file mode 100644 index fbc75bd..0000000 --- a/chanyub_seg/code/aug2_re_pan_effb5_noisy_focal_madgrad_cosLR.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"nbformat":4,"nbformat_minor":0,"metadata":{"hide_input":false,"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.7"},"toc":{"base_numbering":1,"nav_menu":{},"number_sections":true,"sideBar":true,"skip_h1_title":false,"title_cell":"Table of Contents","title_sidebar":"Contents","toc_cell":true,"toc_position":{"height":"calc(100% - 180px)","left":"10px","top":"150px","width":"297.278px"},"toc_section_display":true,"toc_window_display":true},"colab":{"name":"aug2_re_pan_effb5_noisy_focal_madgrad_cosLR.ipynb","provenance":[],"collapsed_sections":[],"toc_visible":true,"machine_shape":"hm"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GW8gF48g-WSK","executionInfo":{"status":"ok","timestamp":1620262814858,"user_tz":-540,"elapsed":1138,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"b45a55fe-7960-4341-98a6-19d26f0cb432"},"source":["from google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xDwpp4Lk-gSH","executionInfo":{"status":"ok","timestamp":1620262815191,"user_tz":-540,"elapsed":1460,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"4e00e79c-c42d-4e12-c6a2-8cf109451f39"},"source":["ls"],"execution_count":2,"outputs":[{"output_type":"stream","text":["\u001b[0m\u001b[01;34mdrive\u001b[0m/ \u001b[01;34msample_data\u001b[0m/\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sItrVDeh-iYC","executionInfo":{"status":"ok","timestamp":1620262815191,"user_tz":-540,"elapsed":1451,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"bc2513f3-91a6-44fa-b4cd-47f518b55f2a"},"source":["cd drive/MyDrive/Trash/code"],"execution_count":3,"outputs":[{"output_type":"stream","text":["/content/drive/MyDrive/Trash/code\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"a54v4-kN_LVO","executionInfo":{"status":"ok","timestamp":1620262817941,"user_tz":-540,"elapsed":4190,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"941c0f9e-2857-4377-a829-e860d1f829cc"},"source":["!pip install albumentations==0.5.2"],"execution_count":4,"outputs":[{"output_type":"stream","text":["Requirement already satisfied: albumentations==0.5.2 in /usr/local/lib/python3.7/dist-packages (0.5.2)\n","Requirement already satisfied: opencv-python-headless>=4.1.1 in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (4.5.1.48)\n","Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (1.4.1)\n","Requirement already satisfied: numpy>=1.11.1 in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (1.19.5)\n","Requirement already satisfied: imgaug>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (0.4.0)\n","Requirement already satisfied: scikit-image>=0.16.1 in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (0.16.2)\n","Requirement already satisfied: PyYAML in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (3.13)\n","Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (1.15.0)\n","Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (7.1.2)\n","Requirement already satisfied: imageio in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (2.4.1)\n","Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (3.2.2)\n","Requirement already satisfied: opencv-python in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (4.1.2.30)\n","Requirement already satisfied: Shapely in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (1.7.1)\n","Requirement already satisfied: PyWavelets>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations==0.5.2) (1.1.1)\n","Requirement already satisfied: networkx>=2.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations==0.5.2) (2.5.1)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug>=0.4.0->albumentations==0.5.2) (1.3.1)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug>=0.4.0->albumentations==0.5.2) (0.10.0)\n","Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug>=0.4.0->albumentations==0.5.2) (2.4.7)\n","Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug>=0.4.0->albumentations==0.5.2) (2.8.1)\n","Requirement already satisfied: decorator<5,>=4.3 in /usr/local/lib/python3.7/dist-packages (from networkx>=2.0->scikit-image>=0.16.1->albumentations==0.5.2) (4.4.2)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.826930Z","start_time":"2021-04-18T10:34:45.406686Z"},"colab":{"base_uri":"https://localhost:8080/"},"id":"l_LPA4XD-NwC","executionInfo":{"status":"ok","timestamp":1620262822380,"user_tz":-540,"elapsed":1772,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"27344406-816a-4493-ee19-71f10247c47b"},"source":["import os\n","import random\n","import time\n","import json\n","import warnings \n","warnings.filterwarnings('ignore')\n","\n","import torch\n","import torch.nn as nn\n","from torch.utils.data import Dataset, DataLoader\n","from utils import label_accuracy_score\n","import cv2\n","\n","import numpy as np\n","import pandas as pd\n","\n","# 전처리를 위한 라이브러리\n","from pycocotools.coco import COCO\n","import torchvision\n","import torchvision.transforms as transforms\n","\n","import albumentations as A\n","from albumentations.pytorch import ToTensorV2\n","\n","# 시각화를 위한 라이브러리\n","import matplotlib.pyplot as plt\n","import seaborn as sns; sns.set()\n","\n","plt.rcParams['axes.grid'] = False\n","\n","print('pytorch version: {}'.format(torch.__version__))\n","print('GPU 사용 가능 여부: {}'.format(torch.cuda.is_available()))\n","\n","print(torch.cuda.get_device_name(0))\n","print(torch.cuda.device_count())\n","\n","device = \"cuda\" if torch.cuda.is_available() else \"cpu\" # GPU 사용 가능 여부에 따라 device 정보 저장"],"execution_count":5,"outputs":[{"output_type":"stream","text":["pytorch version: 1.8.1+cu101\n","GPU 사용 가능 여부: True\n","Tesla V100-SXM2-16GB\n","1\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"HxgRSL_M-NwF"},"source":["## 하이퍼파라미터 세팅 및 seed 고정"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.841930Z","start_time":"2021-04-18T10:34:47.827931Z"},"id":"rV3JmGP5-NwF","executionInfo":{"status":"ok","timestamp":1620262824998,"user_tz":-540,"elapsed":778,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["batch_size = 3 # Mini-batch size\n","num_epochs = 20\n","learning_rate = 0.0001"],"execution_count":6,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.856930Z","start_time":"2021-04-18T10:34:47.842931Z"},"id":"Z6LOuJXQ-NwG","executionInfo":{"status":"ok","timestamp":1620262829558,"user_tz":-540,"elapsed":775,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# seed 고정\n","random_seed = 42\n","torch.manual_seed(random_seed)\n","torch.cuda.manual_seed(random_seed)\n","# torch.cuda.manual_seed_all(random_seed) # if use multi-GPU\n","torch.backends.cudnn.deterministic = True\n","torch.backends.cudnn.benchmark = False\n","np.random.seed(random_seed)\n","random.seed(random_seed)"],"execution_count":7,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"iWF_EJDu-NwG"},"source":["## 학습 데이터 EDA"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.381961Z","start_time":"2021-04-18T10:34:47.857930Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"lg0x0D0a-NwG","executionInfo":{"status":"ok","timestamp":1620262836653,"user_tz":-540,"elapsed":4568,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"3a219a26-8fe2-4711-d50e-303a2dd6d9ec"},"source":["%matplotlib inline\n","\n","dataset_path = '../input/data'\n","anns_file_path = dataset_path + '/' + 'train.json'\n","\n","# Read annotations\n","with open(anns_file_path, 'r') as f:\n"," dataset = json.loads(f.read())\n","\n","categories = dataset['categories']\n","anns = dataset['annotations']\n","imgs = dataset['images']\n","nr_cats = len(categories)\n","nr_annotations = len(anns)\n","nr_images = len(imgs)\n","\n","# Load categories and super categories\n","cat_names = []\n","super_cat_names = []\n","super_cat_ids = {}\n","super_cat_last_name = ''\n","nr_super_cats = 0\n","for cat_it in categories:\n"," cat_names.append(cat_it['name'])\n"," super_cat_name = cat_it['supercategory']\n"," # Adding new supercat\n"," if super_cat_name != super_cat_last_name:\n"," super_cat_names.append(super_cat_name)\n"," super_cat_ids[super_cat_name] = nr_super_cats\n"," super_cat_last_name = super_cat_name\n"," nr_super_cats += 1\n","\n","print('Number of super categories:', nr_super_cats)\n","print('Number of categories:', nr_cats)\n","print('Number of annotations:', nr_annotations)\n","print('Number of images:', nr_images)"],"execution_count":8,"outputs":[{"output_type":"stream","text":["Number of super categories: 11\n","Number of categories: 11\n","Number of annotations: 21116\n","Number of images: 2617\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.546964Z","start_time":"2021-04-18T10:34:51.382969Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/","height":355},"id":"PjLyVHVY-NwH","executionInfo":{"status":"ok","timestamp":1620262837419,"user_tz":-540,"elapsed":3211,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"65f48c18-4f10-49b1-fb07-560cb53081d5"},"source":["# Count annotations\n","cat_histogram = np.zeros(nr_cats,dtype=int)\n","for ann in anns:\n"," cat_histogram[ann['category_id']] += 1\n","\n","# Initialize the matplotlib figure\n","f, ax = plt.subplots(figsize=(5,5))\n","\n","# Convert to DataFrame\n","df = pd.DataFrame({'Categories': cat_names, 'Number of annotations': cat_histogram})\n","df = df.sort_values('Number of annotations', 0, False)\n","\n","# Plot the histogram\n","plt.title(\"category distribution of train set \")\n","plot_1 = sns.barplot(x=\"Number of annotations\", y=\"Categories\", data=df, label=\"Total\", color=\"b\")"],"execution_count":9,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.561965Z","start_time":"2021-04-18T10:34:51.547969Z"},"id":"34Tw5__i-NwI","executionInfo":{"status":"ok","timestamp":1620262838989,"user_tz":-540,"elapsed":911,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# category labeling \n","sorted_temp_df = df.sort_index()\n","\n","# background = 0 에 해당되는 label 추가 후 기존들을 모두 label + 1 로 설정\n","sorted_df = pd.DataFrame([\"Backgroud\"], columns = [\"Categories\"])\n","sorted_df = sorted_df.append(sorted_temp_df, ignore_index=True)"],"execution_count":10,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.576961Z","start_time":"2021-04-18T10:34:51.562964Z"},"colab":{"base_uri":"https://localhost:8080/","height":421},"id":"CQk4vV5N-NwI","executionInfo":{"status":"ok","timestamp":1620262839746,"user_tz":-540,"elapsed":941,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"50bcfa53-43e3-4dea-b52d-69ed6fc9fac4"},"source":["# class (Categories) 에 따른 index 확인 (0~11 : 총 12개)\n","sorted_df"],"execution_count":11,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
CategoriesNumber of annotations
0BackgroudNaN
1UNKNOWN128.0
2General trash2225.0
3Paper7448.0
4Paper pack527.0
5Metal449.0
6Glass488.0
7Plastic2472.0
8Styrofoam1074.0
9Plastic bag6114.0
10Battery50.0
11Clothing141.0
\n","
"],"text/plain":[" Categories Number of annotations\n","0 Backgroud NaN\n","1 UNKNOWN 128.0\n","2 General trash 2225.0\n","3 Paper 7448.0\n","4 Paper pack 527.0\n","5 Metal 449.0\n","6 Glass 488.0\n","7 Plastic 2472.0\n","8 Styrofoam 1074.0\n","9 Plastic bag 6114.0\n","10 Battery 50.0\n","11 Clothing 141.0"]},"metadata":{"tags":[]},"execution_count":11}]},{"cell_type":"markdown","metadata":{"id":"KXU0zmZs-NwI"},"source":["## 데이터 전처리 함수 정의 (Dataset)"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:52.693328Z","start_time":"2021-04-18T10:34:52.681328Z"},"id":"QFnTI8_Z-NwJ","executionInfo":{"status":"ok","timestamp":1620262841806,"user_tz":-540,"elapsed":602,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["category_names = list(sorted_df.Categories)\n","\n","def get_classname(classID, cats):\n"," for i in range(len(cats)):\n"," if cats[i]['id']==classID:\n"," return cats[i]['name']\n"," return \"None\"\n","\n","class CustomDataLoader(Dataset):\n"," \"\"\"COCO format\"\"\"\n"," def __init__(self, data_dir, mode = 'train', transform = None):\n"," super().__init__()\n"," self.mode = mode\n"," self.transform = transform\n"," self.coco = COCO(data_dir)\n"," \n"," def __getitem__(self, index: int):\n"," # dataset이 index되어 list처럼 동작\n"," image_id = self.coco.getImgIds(imgIds=index)\n"," image_infos = self.coco.loadImgs(image_id)[0]\n"," \n"," # cv2 를 활용하여 image 불러오기\n"," images = cv2.imread(os.path.join(dataset_path, image_infos['file_name']))\n"," images = cv2.cvtColor(images, cv2.COLOR_BGR2RGB).astype(np.float32)\n"," images /= 255.0\n"," \n"," if (self.mode in ('train', 'val')):\n"," ann_ids = self.coco.getAnnIds(imgIds=image_infos['id'])\n"," anns = self.coco.loadAnns(ann_ids)\n","\n"," # Load the categories in a variable\n"," cat_ids = self.coco.getCatIds()\n"," cats = self.coco.loadCats(cat_ids)\n","\n"," # masks : size가 (height x width)인 2D\n"," # 각각의 pixel 값에는 \"category id + 1\" 할당\n"," # Background = 0\n"," masks = np.zeros((image_infos[\"height\"], image_infos[\"width\"]))\n"," # Unknown = 1, General trash = 2, ... , Cigarette = 11\n"," for i in range(len(anns)):\n"," className = get_classname(anns[i]['category_id'], cats)\n"," pixel_value = category_names.index(className)\n"," masks = np.maximum(self.coco.annToMask(anns[i])*pixel_value, masks)\n"," masks = masks.astype(np.float32)\n","\n"," # transform -> albumentations 라이브러리 활용\n"," if self.transform is not None:\n"," transformed = self.transform(image=images, mask=masks)\n"," images = transformed[\"image\"]\n"," masks = transformed[\"mask\"]\n"," \n"," return images, masks, image_infos\n"," \n"," if self.mode == 'test':\n"," # transform -> albumentations 라이브러리 활용\n"," if self.transform is not None:\n"," transformed = self.transform(image=images)\n"," images = transformed[\"image\"]\n"," \n"," return images, image_infos\n"," \n"," \n"," def __len__(self) -> int:\n"," # 전체 dataset의 size를 return\n"," return len(self.coco.getImgIds())"],"execution_count":12,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"cp2aIOlP-NwK"},"source":["## Dataset 정의 및 DataLoader 할당"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:58.823175Z","start_time":"2021-04-18T10:34:54.106233Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"P7zFlRn6-NwK","executionInfo":{"status":"ok","timestamp":1620262850388,"user_tz":-540,"elapsed":6122,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"d3bb670f-4645-4e8d-e766-8e04f1df388d"},"source":["# train.json / validation.json / test.json 디렉토리 설정\n","train_path = dataset_path + '/train.json'\n","val_path = dataset_path + '/val.json'\n","test_path = dataset_path + '/test.json'\n","\n","# collate_fn needs for batch\n","def collate_fn(batch):\n"," return tuple(zip(*batch))\n","\n","train_transform = A.Compose([\n"," A.transforms.Rotate(limit=30),\n"," A.augmentations.HorizontalFlip(),\n"," ToTensorV2()\n"," ])\n","\n","val_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","test_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","# create own Dataset 1 (skip)\n","# validation set을 직접 나누고 싶은 경우\n","# random_split 사용하여 data set을 8:2 로 분할\n","# train_size = int(0.8*len(dataset))\n","# val_size = int(len(dataset)-train_size)\n","# dataset = CustomDataLoader(data_dir=train_path, mode='train', transform=transform)\n","# train_dataset, val_dataset = torch.utils.data.random_split(dataset, [train_size, val_size])\n","\n","# create own Dataset 2\n","# train dataset\n","train_dataset = CustomDataLoader(data_dir=train_path, mode='train', transform=train_transform)\n","\n","# validation dataset\n","val_dataset = CustomDataLoader(data_dir=val_path, mode='val', transform=val_transform)\n","\n","# test dataset\n","test_dataset = CustomDataLoader(data_dir=test_path, mode='test', transform=test_transform)\n","\n","\n","# DataLoader\n","train_loader = torch.utils.data.DataLoader(dataset=train_dataset, \n"," batch_size=batch_size,\n"," shuffle=True,\n"," num_workers=4,\n"," collate_fn=collate_fn,\n"," drop_last=True)\n","\n","val_loader = torch.utils.data.DataLoader(dataset=val_dataset, \n"," batch_size=batch_size,\n"," shuffle=False,\n"," num_workers=4,\n"," collate_fn=collate_fn,\n"," drop_last=True) \n","\n","test_loader = torch.utils.data.DataLoader(dataset=test_dataset,\n"," batch_size=batch_size,\n"," num_workers=4,\n"," collate_fn=collate_fn)"],"execution_count":13,"outputs":[{"output_type":"stream","text":["loading annotations into memory...\n","Done (t=3.92s)\n","creating index...\n","index created!\n","loading annotations into memory...\n","Done (t=0.87s)\n","creating index...\n","index created!\n","loading annotations into memory...\n","Done (t=0.01s)\n","creating index...\n","index created!\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"8rqJiHb_-NwM"},"source":["# wandb"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"H1kHXm0uAX3R","executionInfo":{"status":"ok","timestamp":1620220395449,"user_tz":-540,"elapsed":12481,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"bcaf2ea9-95bd-46d1-b2f0-b2fa769ea54b"},"source":["!pip install wandb"],"execution_count":14,"outputs":[{"output_type":"stream","text":["Collecting wandb\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/67/5a/b037b50f9849212863a2fed313624d8f6f33ffa4ce89dc706e2a0e98c780/wandb-0.10.29-py2.py3-none-any.whl (2.1MB)\n","\u001b[K |████████████████████████████████| 2.1MB 4.1MB/s \n","\u001b[?25hCollecting docker-pycreds>=0.4.0\n"," Downloading 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wandb) (3.13)\n","Collecting GitPython>=1.0.0\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/a6/99/98019716955ba243657daedd1de8f3a88ca1f5b75057c38e959db22fb87b/GitPython-3.1.14-py3-none-any.whl (159kB)\n","\u001b[K |████████████████████████████████| 163kB 70.1MB/s \n","\u001b[?25hRequirement already satisfied: protobuf>=3.12.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (3.12.4)\n","Requirement already satisfied: requests<3,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (2.23.0)\n","Requirement already satisfied: urllib3>=1.10.0 in /usr/local/lib/python3.7/dist-packages (from sentry-sdk>=0.4.0->wandb) (1.24.3)\n","Requirement already satisfied: certifi in /usr/local/lib/python3.7/dist-packages (from sentry-sdk>=0.4.0->wandb) (2020.12.5)\n","Collecting gitdb<5,>=4.0.1\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/ea/e8/f414d1a4f0bbc668ed441f74f44c116d9816833a48bf81d22b697090dba8/gitdb-4.0.7-py3-none-any.whl 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/root/.cache/pip/wheels/68/39/1a/5e402bdfdf004af1786c8b853fd92f8c4a04f22aad179654d1\n"," Building wheel for pathtools (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for pathtools: filename=pathtools-0.1.2-cp37-none-any.whl size=8786 sha256=dfc1f221eecedb7470631739c39c4e37d9a8588f2b36e9ab781a84c2feeaeaaf\n"," Stored in directory: /root/.cache/pip/wheels/0b/04/79/c3b0c3a0266a3cb4376da31e5bfe8bba0c489246968a68e843\n","Successfully built subprocess32 pathtools\n","Installing collected packages: docker-pycreds, subprocess32, shortuuid, pathtools, sentry-sdk, configparser, smmap, gitdb, GitPython, wandb\n","Successfully installed GitPython-3.1.14 configparser-5.0.2 docker-pycreds-0.4.0 gitdb-4.0.7 pathtools-0.1.2 sentry-sdk-1.0.0 shortuuid-1.0.1 smmap-4.0.0 subprocess32-3.5.4 wandb-0.10.29\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":153},"id":"aMW4VV9V-NwM","executionInfo":{"status":"ok","timestamp":1620220409003,"user_tz":-540,"elapsed":11936,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"253e5a8a-a353-44ac-81c5-828c54ee03c3"},"source":["import wandb\n","\n","proj_name = 'aug2_re_pan_effb5_noisy_focal_madgrad_cosLR'\n","\n","wandb.init(project='chanyub',name=proj_name)\n","\n","config = wandb.config\n","config.learning_rate = 0.01"],"execution_count":15,"outputs":[{"output_type":"display_data","data":{"application/javascript":["\n"," window._wandbApiKey = new Promise((resolve, reject) => {\n"," function loadScript(url) {\n"," return new Promise(function(resolve, reject) {\n"," let newScript = document.createElement(\"script\");\n"," newScript.onerror = reject;\n"," newScript.onload = resolve;\n"," document.body.appendChild(newScript);\n"," newScript.src = url;\n"," });\n"," }\n"," loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n"," const iframe = document.createElement('iframe')\n"," iframe.style.cssText = \"width:0;height:0;border:none\"\n"," document.body.appendChild(iframe)\n"," const handshake = new Postmate({\n"," container: iframe,\n"," url: 'https://wandb.ai/authorize'\n"," });\n"," const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n"," handshake.then(function(child) {\n"," child.on('authorize', data => {\n"," clearTimeout(timeout)\n"," resolve(data)\n"," });\n"," });\n"," })\n"," });\n"," "],"text/plain":[""]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"],"name":"stderr"},{"output_type":"display_data","data":{"text/html":["\n"," Tracking run with wandb version 0.10.29
\n"," Syncing run aug2_re_pan_effb5_noisy_focal_madgrad_cosLR to Weights & Biases (Documentation).
\n"," Project page: https://wandb.ai/pstage12/chanyub
\n"," Run page: https://wandb.ai/pstage12/chanyub/runs/1nh9y7ay
\n"," Run data is saved locally in /content/drive/My Drive/Trash/code/wandb/run-20210505_131325-1nh9y7ay

\n"," "],"text/plain":[""]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"uQRiIVGX-NwM"},"source":["## My model"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"S0xCGpNeAqeD","executionInfo":{"status":"ok","timestamp":1620262858818,"user_tz":-540,"elapsed":3172,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"952311eb-6cc9-4463-fae4-938b2d87b745"},"source":["!pip install segmentation_models_pytorch"],"execution_count":14,"outputs":[{"output_type":"stream","text":["Requirement already satisfied: segmentation_models_pytorch in /usr/local/lib/python3.7/dist-packages (0.1.3)\n","Requirement already satisfied: torchvision>=0.3.0 in /usr/local/lib/python3.7/dist-packages (from segmentation_models_pytorch) (0.9.1+cu101)\n","Requirement already satisfied: pretrainedmodels==0.7.4 in /usr/local/lib/python3.7/dist-packages (from segmentation_models_pytorch) (0.7.4)\n","Requirement already satisfied: timm==0.3.2 in /usr/local/lib/python3.7/dist-packages (from segmentation_models_pytorch) (0.3.2)\n","Requirement already satisfied: efficientnet-pytorch==0.6.3 in /usr/local/lib/python3.7/dist-packages (from segmentation_models_pytorch) (0.6.3)\n","Requirement already satisfied: torch==1.8.1 in /usr/local/lib/python3.7/dist-packages (from torchvision>=0.3.0->segmentation_models_pytorch) (1.8.1+cu101)\n","Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.7/dist-packages (from torchvision>=0.3.0->segmentation_models_pytorch) (7.1.2)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torchvision>=0.3.0->segmentation_models_pytorch) (1.19.5)\n","Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from pretrainedmodels==0.7.4->segmentation_models_pytorch) (4.41.1)\n","Requirement already satisfied: munch in /usr/local/lib/python3.7/dist-packages (from pretrainedmodels==0.7.4->segmentation_models_pytorch) (2.5.0)\n","Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch==1.8.1->torchvision>=0.3.0->segmentation_models_pytorch) (3.7.4.3)\n","Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from munch->pretrainedmodels==0.7.4->segmentation_models_pytorch) (1.15.0)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:11.634792Z","start_time":"2021-04-18T16:16:05.875817Z"},"colab":{"base_uri":"https://localhost:8080/"},"id":"a8IfZfiM-NwM","executionInfo":{"status":"ok","timestamp":1620262872054,"user_tz":-540,"elapsed":16047,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"92b98567-1061-48d4-b2c0-55bb91a801d3"},"source":["# 구현된 model에 임의의 input을 넣어 output이 잘 나오는지 test\n","import segmentation_models_pytorch as smp\n","\n","model = smp.PAN(encoder_name='timm-efficientnet-b5', encoder_weights='noisy-student', classes=12)\n","x = torch.randn([2, 3, 512, 512])\n","print(\"input shape : \", x.shape)\n","out = model(x).to(device)\n","print(\"output shape : \", out.size())\n","\n","model = model.to(device)"],"execution_count":15,"outputs":[{"output_type":"stream","text":["input shape : torch.Size([2, 3, 512, 512])\n","output shape : torch.Size([2, 12, 512, 512])\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"zvDnI7_T-NwN"},"source":["## train, validation, test 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.104200Z","start_time":"2021-04-18T16:16:18.093174Z"},"id":"RA3oAapJ-NwN","executionInfo":{"status":"ok","timestamp":1620220437833,"user_tz":-540,"elapsed":1921,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["def train(num_epochs, model, data_loader, val_loader, criterion, optimizer, saved_dir, val_every, device, lr_scheduler):\n"," print('Start training..')\n"," best_loss = 9999999\n"," best_miou = 0\n"," for epoch in range(num_epochs):\n"," model.train()\n"," for step, (images, masks, _) in enumerate(data_loader):\n"," images = torch.stack(images) # (batch, channel, height, width)\n"," masks = torch.stack(masks).long() # (batch, channel, height, width)\n"," \n"," # gpu 연산을 위해 device 할당\n"," images, masks = images.to(device), masks.to(device)\n"," \n"," # inference\n"," outputs = model(images)\n"," \n"," # loss 계산 (cross entropy loss)\n"," loss = criterion(outputs, masks)\n"," optimizer.zero_grad()\n"," loss.backward()\n"," optimizer.step()\n"," \n"," lr_scheduler.step()\n"," \n"," # step 주기에 따른 loss 출력\n"," if (step + 1) % 25 == 0:\n"," print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(\n"," epoch+1, num_epochs, step+1, len(train_loader), loss.item()))\n"," \n"," # validation 주기에 따른 loss 출력 및 best model 저장\n"," if (epoch + 1) % val_every == 0:\n","# avrg_loss = validation(epoch + 1, model, val_loader, criterion, device)\n","# if avrg_loss < best_loss:\n","# print('Best performance at epoch: {}'.format(epoch + 1))\n","# print('Save model in', saved_dir)\n","# best_loss = avrg_loss\n","# wandb.log({'best_loss': best_loss})\n","# save_model(model, saved_dir)\n"," avrg_miou = validation(epoch + 1, model, val_loader, criterion, device)\n"," if avrg_miou > best_miou:\n"," print('Best performance at epoch: {}'.format(epoch + 1))\n"," print('Save model in', saved_dir)\n"," best_miou = avrg_miou\n"," wandb.log({'best_miou': best_miou})\n"," save_model(model, saved_dir)"],"execution_count":18,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.285795Z","start_time":"2021-04-18T16:16:18.267686Z"},"id":"EulIikmq-NwO","executionInfo":{"status":"ok","timestamp":1620220437837,"user_tz":-540,"elapsed":1922,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["def validation(epoch, model, data_loader, criterion, device):\n"," print('Start validation #{}'.format(epoch))\n"," model.eval()\n"," with torch.no_grad():\n"," total_loss = 0\n"," cnt = 0\n"," mIoU_list = []\n"," for step, (images, masks, _) in enumerate(data_loader):\n"," \n"," images = torch.stack(images) # (batch, channel, height, width)\n"," masks = torch.stack(masks).long() # (batch, channel, height, width)\n","\n"," images, masks = images.to(device), masks.to(device) \n","\n"," outputs = model(images)\n"," loss = criterion(outputs, masks)\n"," total_loss += loss\n"," cnt += 1\n"," \n","# print(outputs.shape)\n","# print(masks.shape)\n","# wandb.log(wandb.Image(images, masks={\n","# \"predictions\" : {\n","# \"mask_data\" : torch.squeeze(torch.squeeze(outputs, 0),1),\n","# \"class_labels\" : classes_dict\n","# },\n","# \"ground_truth\" : {\n","# \"mask_data\" : torch.squeeze(masks, 0),\n","# \"class_labels\" : classes_dict\n","# }\n","# }))\n"," \n"," outputs = torch.argmax(outputs.squeeze(), dim=1).detach().cpu().numpy()\n","\n"," mIoU = label_accuracy_score(masks.detach().cpu().numpy(), outputs, n_class=12)[2]\n"," mIoU_list.append(mIoU)\n"," \n"," avrg_loss = total_loss / cnt\n"," avrg_mIoU = np.mean(mIoU_list)\n"," print('Validation #{} Average Loss: {:.4f}, mIoU: {:.4f}'.format(epoch, avrg_loss, np.mean(mIoU_list)))\n"," wandb.log({'Train Loss':loss.item(), 'Val Loss':avrg_loss , 'Val mIoU':np.mean(mIoU_list)})\n","# return avrg_loss\n"," return avrg_mIoU"],"execution_count":19,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"C_7CtFfH-NwO"},"source":["## 모델 저장 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.909918Z","start_time":"2021-04-18T16:16:18.898918Z"},"id":"gpCM5BFO-NwO","executionInfo":{"status":"ok","timestamp":1620220442477,"user_tz":-540,"elapsed":1786,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# 모델 저장 함수 정의\n","val_every = 1 \n","\n","saved_dir = './saved'\n","if not os.path.isdir(saved_dir): \n"," os.mkdir(saved_dir)\n"," \n","def save_model(model, saved_dir, file_name='aug2_re_pan_effb5_noisy_focal_madgrad_cosLR.pt'):\n"," check_point = {'net': model.state_dict()}\n"," output_path = os.path.join(saved_dir, file_name)\n"," torch.save(model.state_dict(), output_path)"],"execution_count":20,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"FyKGeg8T-NwP"},"source":["## 모델 생성 및 Loss function, Optimizer 정의"]},{"cell_type":"code","metadata":{"id":"ORugl8s1-NwP","executionInfo":{"status":"ok","timestamp":1620220444268,"user_tz":-540,"elapsed":1113,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["from torch.autograd import Variable\n","import torch.nn.functional as F\n","# ref : https://github.com/clcarwin/focal_loss_pytorch\n","class FocalLoss(nn.Module):\n"," def __init__(self, gamma=0, alpha=None, size_average=True):\n"," super(FocalLoss, self).__init__()\n"," self.gamma = gamma\n"," self.alpha = alpha\n"," if isinstance(alpha,(float,int)): self.alpha = torch.Tensor([alpha,1-alpha])\n"," if isinstance(alpha,list): self.alpha = torch.Tensor(alpha)\n"," self.size_average = size_average\n","\n"," def forward(self, input, target):\n"," if input.dim()>2:\n"," input = input.view(input.size(0),input.size(1),-1) # N,C,H,W => N,C,H*W\n"," input = input.transpose(1,2) # N,C,H*W => N,H*W,C\n"," input = input.contiguous().view(-1,input.size(2)) # N,H*W,C => N*H*W,C\n"," target = target.view(-1,1)\n","\n"," logpt = F.log_softmax(input)\n"," logpt = logpt.gather(1,target)\n"," logpt = logpt.view(-1)\n"," pt = Variable(logpt.data.exp())\n","\n"," if self.alpha is not None:\n"," if self.alpha.type()!=input.data.type():\n"," self.alpha = self.alpha.type_as(input.data)\n"," at = self.alpha.gather(0,target.data.view(-1))\n"," logpt = logpt * Variable(at)\n","\n"," loss = -1 * (1-pt)**self.gamma * logpt\n"," if self.size_average: return loss.mean()\n"," else: return loss.sum()"],"execution_count":21,"outputs":[]},{"cell_type":"code","metadata":{"id":"yfeFQknz-NwP"},"source":["# import torch.optim.lr_scheduler as lr_scheduler\n","# import math\n","# class CosineAnnealingWarmUpRestart(lr_scheduler._LRScheduler):\n","# def __init__(self, optimizer, T_0, T_mult=1, eta_max=0.1, T_up=0, gamma=1., last_epoch=-1):\n","# if T_0 <= 0 or not isinstance(T_0, int):\n","# raise ValueError(\"Expected positive integer T_0, but got {}\".format(T_0))\n","# if T_mult < 1 or not isinstance(T_mult, int):\n","# raise ValueError(\"Expected integer T_mult >= 1, but got {}\".format(T_mult))\n","# if T_up < 0 or not isinstance(T_up, int):\n","# raise ValueError(\"Expected positive integer T_up, but got {}\".format(T_up))\n","# self.T_0 = T_0\n","# self.T_mult = T_mult\n","# self.base_eta_max = eta_max\n","# self.eta_max = eta_max\n","# self.T_up = T_up\n","# self.T_i = T_0\n","# self.gamma = gamma\n","# self.cycle = 0\n","# self.T_cur = last_epoch\n","# super(CosineAnnealingWarmUpRestart, self).__init__(optimizer, last_epoch)\n","# # self.T_cur = last_epoch\n"," \n","# def get_lr(self):\n","# if self.T_cur == -1:\n","# return self.base_lrs\n","# elif self.T_cur < self.T_up:\n","# return [(self.eta_max - base_lr)*self.T_cur / self.T_up + base_lr for base_lr in self.base_lrs]\n","# else:\n","# return [base_lr + (self.eta_max - base_lr) * (1 + math.cos(math.pi * (self.T_cur-self.T_up) / (self.T_i - self.T_up))) / 2\n","# for base_lr in self.base_lrs]\n","\n","# def step(self, epoch=None):\n","# if epoch is None:\n","# epoch = self.last_epoch + 1\n","# self.T_cur = self.T_cur + 1\n","# if self.T_cur >= self.T_i:\n","# self.cycle += 1\n","# self.T_cur = self.T_cur - self.T_i\n","# self.T_i = (self.T_i - self.T_up) * self.T_mult + self.T_up\n","# else:\n","# if epoch >= self.T_0:\n","# if self.T_mult == 1:\n","# self.T_cur = epoch % self.T_0\n","# self.cycle = epoch // self.T_0\n","# else:\n","# n = int(math.log((epoch / self.T_0 * (self.T_mult - 1) + 1), self.T_mult))\n","# self.cycle = n\n","# self.T_cur = epoch - self.T_0 * (self.T_mult ** n - 1) / (self.T_mult - 1)\n","# self.T_i = self.T_0 * self.T_mult ** (n)\n","# else:\n","# self.T_i = self.T_0\n","# self.T_cur = epoch\n"," \n","# self.eta_max = self.base_eta_max * (self.gamma**self.cycle)\n","# self.last_epoch = math.floor(epoch)\n","# for param_group, lr in zip(self.optimizer.param_groups, self.get_lr()):\n","# param_group['lr'] = lr"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AG1oQeu7BX1M","executionInfo":{"status":"ok","timestamp":1620060019354,"user_tz":-540,"elapsed":3477,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"98eae7c4-b66e-409d-9725-0c684d747f2a"},"source":["# !pip install adamp"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Requirement already satisfied: adamp in /usr/local/lib/python3.7/dist-packages (0.3.0)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"eH2PpwF9a-Os","executionInfo":{"status":"ok","timestamp":1620220457633,"user_tz":-540,"elapsed":3721,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"59f11643-e901-4e05-e831-0e65bda78516"},"source":["!pip install madgrad"],"execution_count":22,"outputs":[{"output_type":"stream","text":["Collecting madgrad\n"," Downloading https://files.pythonhosted.org/packages/65/f0/4584f18202a2fb8903d456bf907b80e7cb54ad8fcba68604084ff41b7cf8/madgrad-1.1-py3-none-any.whl\n","Installing collected packages: madgrad\n","Successfully installed madgrad-1.1\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:19.698902Z","start_time":"2021-04-18T16:16:19.694902Z"},"id":"9Dly8KZj-NwQ","executionInfo":{"status":"ok","timestamp":1620220472831,"user_tz":-540,"elapsed":1062,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# from adamp import AdamP\n","from madgrad import MADGRAD\n","# Loss function 정의\n","# criterion = nn.CrossEntropyLoss()\n","criterion = FocalLoss()\n","\n","# Optimizer 정의\n","# optimizer = torch.optim.Adam(params = model.parameters(), lr = learning_rate, weight_decay=1e-6)\n","# optimizer = AdamP(params = model.parameters())\n","optimizer = MADGRAD(params = model.parameters(), lr = learning_rate, momentum = 0.9, weight_decay = 0, eps = 1e-06)\n","\n","# lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size = 237, gamma = 0.65)\n","lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=50, eta_min=0)\n","# lr_scheduler = CosineAnnealingWarmUpRestart(optimizer, T_0=150, T_mult=1, eta_max=0.1, T_up=10, gamma=0.5)"],"execution_count":23,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"start_time":"2021-04-18T16:16:20.331Z"},"colab":{"base_uri":"https://localhost:8080/"},"id":"cMSiFA-3-NwR","executionInfo":{"status":"ok","timestamp":1620252022924,"user_tz":-540,"elapsed":31544106,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"4bde33d9-0cab-45b4-dd88-8138d221d96d"},"source":["train(num_epochs, model, train_loader, val_loader, criterion, optimizer, saved_dir, val_every, device, lr_scheduler)"],"execution_count":24,"outputs":[{"output_type":"stream","text":["Start training..\n","Epoch [1/20], Step [25/654], Loss: 0.9751\n","Epoch [1/20], Step [50/654], Loss: 0.5889\n","Epoch [1/20], Step [75/654], Loss: 0.8733\n","Epoch [1/20], Step [100/654], Loss: 0.7867\n","Epoch [1/20], Step [125/654], Loss: 0.8078\n","Epoch [1/20], Step [150/654], Loss: 0.5702\n","Epoch [1/20], Step [175/654], Loss: 0.9096\n","Epoch [1/20], Step [200/654], Loss: 0.3957\n","Epoch [1/20], Step [225/654], Loss: 0.7639\n","Epoch [1/20], Step [250/654], Loss: 1.3533\n","Epoch [1/20], Step [275/654], Loss: 0.5879\n","Epoch [1/20], Step [300/654], Loss: 0.4095\n","Epoch [1/20], Step [325/654], Loss: 0.2701\n","Epoch [1/20], Step [350/654], Loss: 0.5636\n","Epoch [1/20], Step [375/654], Loss: 0.5505\n","Epoch [1/20], Step [400/654], Loss: 0.1946\n","Epoch [1/20], Step [425/654], Loss: 0.2911\n","Epoch [1/20], Step [450/654], Loss: 0.3576\n","Epoch [1/20], Step [475/654], Loss: 0.3498\n","Epoch [1/20], Step [500/654], Loss: 0.7630\n","Epoch [1/20], Step [525/654], Loss: 0.2934\n","Epoch [1/20], Step [550/654], Loss: 0.9079\n","Epoch [1/20], Step [575/654], Loss: 0.1978\n","Epoch [1/20], Step [600/654], Loss: 0.3023\n","Epoch [1/20], Step [625/654], Loss: 0.6244\n","Epoch [1/20], Step [650/654], Loss: 0.9353\n","Start validation #1\n","Validation #1 Average Loss: 0.4268, mIoU: 0.3282\n","Best performance at epoch: 1\n","Save model in ./saved\n","Epoch [2/20], Step [25/654], Loss: 0.2879\n","Epoch [2/20], Step [50/654], Loss: 0.3175\n","Epoch [2/20], Step [75/654], Loss: 0.6336\n","Epoch [2/20], Step [100/654], Loss: 0.4658\n","Epoch [2/20], Step [125/654], Loss: 0.4207\n","Epoch [2/20], Step [150/654], Loss: 0.7447\n","Epoch [2/20], Step [175/654], Loss: 0.4056\n","Epoch [2/20], Step [200/654], Loss: 0.3193\n","Epoch [2/20], Step [225/654], Loss: 0.5377\n","Epoch [2/20], Step [250/654], Loss: 0.5307\n","Epoch [2/20], Step [275/654], Loss: 0.7065\n","Epoch [2/20], Step [300/654], Loss: 0.3319\n","Epoch [2/20], Step [325/654], Loss: 0.3788\n","Epoch [2/20], Step [350/654], Loss: 0.6320\n","Epoch [2/20], Step [375/654], Loss: 0.3366\n","Epoch [2/20], Step [400/654], Loss: 0.4342\n","Epoch [2/20], Step [425/654], Loss: 0.3318\n","Epoch [2/20], Step [450/654], Loss: 0.7032\n","Epoch [2/20], Step [475/654], Loss: 0.3679\n","Epoch [2/20], Step [500/654], Loss: 0.3077\n","Epoch [2/20], Step [525/654], Loss: 1.3375\n","Epoch [2/20], Step [550/654], Loss: 0.5740\n","Epoch [2/20], Step [575/654], Loss: 0.3945\n","Epoch [2/20], Step [600/654], Loss: 0.4335\n","Epoch [2/20], Step [625/654], Loss: 0.4187\n","Epoch [2/20], Step [650/654], Loss: 0.2510\n","Start validation #2\n","Validation #2 Average Loss: 0.3982, mIoU: 0.3117\n","Epoch [3/20], Step [25/654], Loss: 0.3893\n","Epoch [3/20], Step [50/654], Loss: 0.6833\n","Epoch [3/20], Step [75/654], Loss: 0.3741\n","Epoch [3/20], Step [100/654], Loss: 0.5103\n","Epoch [3/20], Step [125/654], Loss: 0.4813\n","Epoch [3/20], Step [150/654], Loss: 0.2224\n","Epoch [3/20], Step [175/654], Loss: 0.3735\n","Epoch [3/20], Step [200/654], Loss: 0.3568\n","Epoch [3/20], Step [225/654], Loss: 0.4606\n","Epoch [3/20], Step [250/654], Loss: 0.3614\n","Epoch [3/20], Step [275/654], Loss: 0.2593\n","Epoch [3/20], Step [300/654], Loss: 0.3358\n","Epoch [3/20], Step [325/654], Loss: 0.1819\n","Epoch [3/20], Step [350/654], Loss: 0.5383\n","Epoch [3/20], Step [375/654], Loss: 0.2420\n","Epoch [3/20], Step [400/654], Loss: 0.6653\n","Epoch [3/20], Step [425/654], Loss: 0.3665\n","Epoch [3/20], Step [450/654], Loss: 0.2715\n","Epoch [3/20], Step [475/654], Loss: 0.1880\n","Epoch [3/20], Step [500/654], Loss: 0.4102\n","Epoch [3/20], Step [525/654], Loss: 0.1411\n","Epoch [3/20], Step [550/654], Loss: 0.0982\n","Epoch [3/20], Step [575/654], Loss: 0.4801\n","Epoch [3/20], Step [600/654], Loss: 0.1860\n","Epoch [3/20], Step [625/654], Loss: 0.2358\n","Epoch [3/20], Step [650/654], Loss: 0.2348\n","Start validation #3\n","Validation #3 Average Loss: 0.4271, mIoU: 0.3384\n","Best performance at epoch: 3\n","Save model in ./saved\n","Epoch [4/20], Step [25/654], Loss: 0.1296\n","Epoch [4/20], Step [50/654], Loss: 0.4726\n","Epoch [4/20], Step [75/654], Loss: 0.1616\n","Epoch [4/20], Step [100/654], Loss: 0.7400\n","Epoch [4/20], Step [125/654], Loss: 0.3373\n","Epoch [4/20], Step [150/654], Loss: 0.3475\n","Epoch [4/20], Step [175/654], Loss: 0.1468\n","Epoch [4/20], Step [200/654], Loss: 0.1858\n","Epoch [4/20], Step [225/654], Loss: 0.3034\n","Epoch [4/20], Step [250/654], Loss: 0.2771\n","Epoch [4/20], Step [275/654], Loss: 0.1849\n","Epoch [4/20], Step [300/654], Loss: 0.4345\n","Epoch [4/20], Step [325/654], Loss: 0.1804\n","Epoch [4/20], Step [350/654], Loss: 0.2139\n","Epoch [4/20], Step [375/654], Loss: 0.2243\n","Epoch [4/20], Step [400/654], Loss: 0.3520\n","Epoch [4/20], Step [425/654], Loss: 0.3522\n","Epoch [4/20], Step [450/654], Loss: 0.4098\n","Epoch [4/20], Step [475/654], Loss: 0.3804\n","Epoch [4/20], Step [500/654], Loss: 0.2821\n","Epoch [4/20], Step [525/654], Loss: 0.6260\n","Epoch [4/20], Step [550/654], Loss: 0.4061\n","Epoch [4/20], Step [575/654], Loss: 0.2098\n","Epoch [4/20], Step [600/654], Loss: 0.4246\n","Epoch [4/20], Step [625/654], Loss: 0.1854\n","Epoch [4/20], Step [650/654], Loss: 0.2960\n","Start validation #4\n","Validation #4 Average Loss: 0.3352, mIoU: 0.3880\n","Best performance at epoch: 4\n","Save model in ./saved\n","Epoch [5/20], Step [25/654], Loss: 0.2229\n","Epoch [5/20], Step [50/654], Loss: 0.4077\n","Epoch [5/20], Step [75/654], Loss: 0.1563\n","Epoch [5/20], Step [100/654], Loss: 0.1742\n","Epoch [5/20], Step [125/654], Loss: 0.5181\n","Epoch [5/20], Step [150/654], Loss: 0.2472\n","Epoch [5/20], Step [175/654], Loss: 0.0972\n","Epoch [5/20], Step [200/654], Loss: 0.1988\n","Epoch [5/20], Step [225/654], Loss: 0.3158\n","Epoch [5/20], Step [250/654], Loss: 0.5722\n","Epoch [5/20], Step [275/654], Loss: 0.2265\n","Epoch [5/20], Step [300/654], Loss: 0.1944\n","Epoch [5/20], Step [325/654], Loss: 0.4754\n","Epoch [5/20], Step [350/654], Loss: 0.3697\n","Epoch [5/20], Step [375/654], Loss: 0.3991\n","Epoch [5/20], Step [400/654], Loss: 0.2272\n","Epoch [5/20], Step [425/654], Loss: 0.7447\n","Epoch [5/20], Step [450/654], Loss: 0.1780\n","Epoch [5/20], Step [475/654], Loss: 0.3200\n","Epoch [5/20], Step [500/654], Loss: 0.3662\n","Epoch [5/20], Step [525/654], Loss: 1.1952\n","Epoch [5/20], Step [550/654], Loss: 0.2612\n","Epoch [5/20], Step [575/654], Loss: 0.4588\n","Epoch [5/20], Step [600/654], Loss: 0.2489\n","Epoch [5/20], Step [625/654], Loss: 0.3170\n","Epoch [5/20], Step [650/654], Loss: 0.1067\n","Start validation #5\n","Validation #5 Average Loss: 0.3053, mIoU: 0.3797\n","Epoch [6/20], Step [25/654], Loss: 0.2551\n","Epoch [6/20], Step [50/654], Loss: 0.1303\n","Epoch [6/20], Step [75/654], Loss: 0.3038\n","Epoch [6/20], Step [100/654], Loss: 0.2170\n","Epoch [6/20], Step [125/654], Loss: 0.2545\n","Epoch [6/20], Step [150/654], Loss: 0.2293\n","Epoch [6/20], Step [175/654], Loss: 0.9653\n","Epoch [6/20], Step [200/654], Loss: 0.1518\n","Epoch [6/20], Step [225/654], Loss: 0.3216\n","Epoch [6/20], Step [250/654], Loss: 0.2618\n","Epoch [6/20], Step [275/654], Loss: 0.1834\n","Epoch [6/20], Step [300/654], Loss: 0.3607\n","Epoch [6/20], Step [325/654], Loss: 0.3408\n","Epoch [6/20], Step [350/654], Loss: 0.1708\n","Epoch [6/20], Step [375/654], Loss: 0.2825\n","Epoch [6/20], Step [400/654], Loss: 0.6203\n","Epoch [6/20], Step [425/654], Loss: 0.1264\n","Epoch [6/20], Step [450/654], Loss: 0.1112\n","Epoch [6/20], Step [475/654], Loss: 0.2913\n","Epoch [6/20], Step [500/654], Loss: 0.1983\n","Epoch [6/20], Step [525/654], Loss: 0.5112\n","Epoch [6/20], Step [550/654], Loss: 0.1465\n","Epoch [6/20], Step [575/654], Loss: 0.4173\n","Epoch [6/20], Step [600/654], Loss: 0.3292\n","Epoch [6/20], Step [625/654], Loss: 0.2149\n","Epoch [6/20], Step [650/654], Loss: 0.1935\n","Start validation #6\n","Validation #6 Average Loss: 0.3327, mIoU: 0.3781\n","Epoch [7/20], Step [25/654], Loss: 0.1734\n","Epoch [7/20], Step [50/654], Loss: 0.0865\n","Epoch [7/20], Step [75/654], Loss: 0.1705\n","Epoch [7/20], Step [100/654], Loss: 0.4508\n","Epoch [7/20], Step [125/654], Loss: 0.5776\n","Epoch [7/20], Step [150/654], Loss: 0.1331\n","Epoch [7/20], Step [175/654], Loss: 0.1485\n","Epoch [7/20], Step [200/654], Loss: 0.2381\n","Epoch [7/20], Step [225/654], Loss: 0.2474\n","Epoch [7/20], Step [250/654], Loss: 0.1191\n","Epoch [7/20], Step [275/654], Loss: 0.0925\n","Epoch [7/20], Step [300/654], Loss: 0.1330\n","Epoch [7/20], Step [325/654], Loss: 0.1853\n","Epoch [7/20], Step [350/654], Loss: 0.4469\n","Epoch [7/20], Step [375/654], Loss: 0.1679\n","Epoch [7/20], Step [400/654], Loss: 0.4110\n","Epoch [7/20], Step [425/654], Loss: 0.0873\n","Epoch [7/20], Step [450/654], Loss: 0.0632\n","Epoch [7/20], Step [475/654], Loss: 0.3033\n","Epoch [7/20], Step [500/654], Loss: 0.2078\n","Epoch [7/20], Step [525/654], Loss: 0.2306\n","Epoch [7/20], Step [550/654], Loss: 0.2545\n","Epoch [7/20], Step [575/654], Loss: 0.6169\n","Epoch [7/20], Step [600/654], Loss: 0.5175\n","Epoch [7/20], Step [625/654], Loss: 0.3550\n","Epoch [7/20], Step [650/654], Loss: 0.5576\n","Start validation #7\n","Validation #7 Average Loss: 0.2923, mIoU: 0.4172\n","Best performance at epoch: 7\n","Save model in ./saved\n","Epoch [8/20], Step [25/654], Loss: 0.2624\n","Epoch [8/20], Step [50/654], Loss: 0.1416\n","Epoch [8/20], Step [75/654], Loss: 0.4717\n","Epoch [8/20], Step [100/654], Loss: 0.1265\n","Epoch [8/20], Step [125/654], Loss: 0.2957\n","Epoch [8/20], Step [150/654], Loss: 0.8716\n","Epoch [8/20], Step [175/654], Loss: 0.2172\n","Epoch [8/20], Step [200/654], Loss: 0.2200\n","Epoch [8/20], Step [225/654], Loss: 0.2092\n","Epoch [8/20], Step [250/654], Loss: 0.0866\n","Epoch [8/20], Step [275/654], Loss: 0.0958\n","Epoch [8/20], Step [300/654], Loss: 0.2906\n","Epoch [8/20], Step [325/654], Loss: 0.0565\n","Epoch [8/20], Step [350/654], Loss: 0.1154\n","Epoch [8/20], Step [375/654], Loss: 0.4455\n","Epoch [8/20], Step [400/654], Loss: 0.1767\n","Epoch [8/20], Step [425/654], Loss: 0.4701\n","Epoch [8/20], Step [450/654], Loss: 0.4172\n","Epoch [8/20], Step [475/654], Loss: 0.1337\n","Epoch [8/20], Step [500/654], Loss: 0.2412\n","Epoch [8/20], Step [525/654], Loss: 0.4169\n","Epoch [8/20], Step [550/654], Loss: 0.1540\n","Epoch [8/20], Step [575/654], Loss: 0.2339\n","Epoch [8/20], Step [600/654], Loss: 0.3220\n","Epoch [8/20], Step [625/654], Loss: 0.4514\n","Epoch [8/20], Step [650/654], Loss: 0.3032\n","Start validation #8\n","Validation #8 Average Loss: 0.2957, mIoU: 0.4300\n","Best performance at epoch: 8\n","Save model in ./saved\n","Epoch [9/20], Step [25/654], Loss: 0.2241\n","Epoch [9/20], Step [50/654], Loss: 0.1407\n","Epoch [9/20], Step [75/654], Loss: 0.1008\n","Epoch [9/20], Step [100/654], Loss: 0.3843\n","Epoch [9/20], Step [125/654], Loss: 0.4525\n","Epoch [9/20], Step [150/654], Loss: 0.1920\n","Epoch [9/20], Step [175/654], Loss: 0.1620\n","Epoch [9/20], Step [200/654], Loss: 0.2360\n","Epoch [9/20], Step [225/654], Loss: 0.2982\n","Epoch [9/20], Step [250/654], Loss: 0.4014\n","Epoch [9/20], Step [275/654], Loss: 0.2149\n","Epoch [9/20], Step [300/654], Loss: 0.2782\n","Epoch [9/20], Step [325/654], Loss: 0.2087\n","Epoch [9/20], Step [350/654], Loss: 0.1015\n","Epoch [9/20], Step [375/654], Loss: 0.3334\n","Epoch [9/20], Step [400/654], Loss: 0.2955\n","Epoch [9/20], Step [425/654], Loss: 0.1152\n","Epoch [9/20], Step [450/654], Loss: 0.2650\n","Epoch [9/20], Step [475/654], Loss: 0.3414\n","Epoch [9/20], Step [500/654], Loss: 0.1595\n","Epoch [9/20], Step [525/654], Loss: 0.6353\n","Epoch [9/20], Step [550/654], Loss: 0.0916\n","Epoch [9/20], Step [575/654], Loss: 0.1074\n","Epoch [9/20], Step [600/654], Loss: 0.2679\n","Epoch [9/20], Step [625/654], Loss: 0.1323\n","Epoch [9/20], Step [650/654], Loss: 0.1758\n","Start validation #9\n","Validation #9 Average Loss: 0.2842, mIoU: 0.4346\n","Best performance at epoch: 9\n","Save model in ./saved\n","Epoch [10/20], Step [25/654], Loss: 0.2194\n","Epoch [10/20], Step [50/654], Loss: 0.6087\n","Epoch [10/20], Step [75/654], Loss: 0.1132\n","Epoch [10/20], Step [100/654], Loss: 0.3269\n","Epoch [10/20], Step [125/654], Loss: 0.1624\n","Epoch [10/20], Step [150/654], Loss: 0.2023\n","Epoch [10/20], Step [175/654], Loss: 0.5361\n","Epoch [10/20], Step [200/654], Loss: 0.4221\n","Epoch [10/20], Step [225/654], Loss: 0.1288\n","Epoch [10/20], Step [250/654], Loss: 0.1655\n","Epoch [10/20], Step [275/654], Loss: 0.1959\n","Epoch [10/20], Step [300/654], Loss: 0.3513\n","Epoch [10/20], Step [325/654], Loss: 0.1825\n","Epoch [10/20], Step [350/654], Loss: 0.1958\n","Epoch [10/20], Step [375/654], Loss: 0.1663\n","Epoch [10/20], Step [400/654], Loss: 0.2682\n","Epoch [10/20], Step [425/654], Loss: 0.3357\n","Epoch [10/20], Step [450/654], Loss: 0.2647\n","Epoch [10/20], Step [475/654], Loss: 0.5344\n","Epoch [10/20], Step [500/654], Loss: 0.4442\n","Epoch [10/20], Step [525/654], Loss: 0.1723\n","Epoch [10/20], Step [550/654], Loss: 0.1503\n","Epoch [10/20], Step [575/654], Loss: 0.0979\n","Epoch [10/20], Step [600/654], Loss: 0.1079\n","Epoch [10/20], Step [625/654], Loss: 0.3898\n","Epoch [10/20], Step [650/654], Loss: 0.1246\n","Start validation #10\n","Validation #10 Average Loss: 0.3544, mIoU: 0.4020\n","Epoch [11/20], Step [25/654], Loss: 0.2620\n","Epoch [11/20], Step [50/654], Loss: 0.4011\n","Epoch [11/20], Step [75/654], Loss: 0.0838\n","Epoch [11/20], Step [100/654], Loss: 0.3212\n","Epoch [11/20], Step [125/654], Loss: 0.3077\n","Epoch [11/20], Step [150/654], Loss: 0.1374\n","Epoch [11/20], Step [175/654], Loss: 0.2670\n","Epoch [11/20], Step [200/654], Loss: 0.0760\n","Epoch [11/20], Step [225/654], Loss: 0.3778\n","Epoch [11/20], Step [250/654], Loss: 0.1469\n","Epoch [11/20], Step [275/654], Loss: 0.2135\n","Epoch [11/20], Step [300/654], Loss: 0.1083\n","Epoch [11/20], Step [325/654], Loss: 0.0993\n","Epoch [11/20], Step [350/654], Loss: 0.1286\n","Epoch [11/20], Step [375/654], Loss: 0.1447\n","Epoch [11/20], Step [400/654], Loss: 0.2343\n","Epoch [11/20], Step [425/654], Loss: 0.2038\n","Epoch [11/20], Step [450/654], Loss: 0.2620\n","Epoch [11/20], Step [475/654], Loss: 0.3124\n","Epoch [11/20], Step [500/654], Loss: 0.2223\n","Epoch [11/20], Step [525/654], Loss: 0.2262\n","Epoch [11/20], Step [550/654], Loss: 0.1517\n","Epoch [11/20], Step [575/654], Loss: 0.2068\n","Epoch [11/20], Step [600/654], Loss: 0.2520\n","Epoch [11/20], Step [625/654], Loss: 0.2302\n","Epoch [11/20], Step [650/654], Loss: 0.1641\n","Start validation #11\n","Validation #11 Average Loss: 0.3176, mIoU: 0.4390\n","Best performance at epoch: 11\n","Save model in ./saved\n","Epoch [12/20], Step [25/654], Loss: 0.4086\n","Epoch [12/20], Step [50/654], Loss: 0.4286\n","Epoch [12/20], Step [75/654], Loss: 0.0919\n","Epoch [12/20], Step [100/654], Loss: 0.2675\n","Epoch [12/20], Step [125/654], Loss: 0.2294\n","Epoch [12/20], Step [150/654], Loss: 0.1603\n","Epoch [12/20], Step [175/654], Loss: 0.1671\n","Epoch [12/20], Step [200/654], Loss: 0.1877\n","Epoch [12/20], Step [225/654], Loss: 0.1334\n","Epoch [12/20], Step [250/654], Loss: 0.1586\n","Epoch [12/20], Step [275/654], Loss: 0.3962\n","Epoch [12/20], Step [300/654], Loss: 0.2865\n","Epoch [12/20], Step [325/654], Loss: 0.1927\n","Epoch [12/20], Step [350/654], Loss: 0.1771\n","Epoch [12/20], Step [375/654], Loss: 0.1252\n","Epoch [12/20], Step [400/654], Loss: 0.2910\n","Epoch [12/20], Step [425/654], Loss: 0.0720\n","Epoch [12/20], Step [450/654], Loss: 0.1747\n","Epoch [12/20], Step [475/654], Loss: 0.2520\n","Epoch [12/20], Step [500/654], Loss: 0.3272\n","Epoch [12/20], Step [525/654], Loss: 0.1561\n","Epoch [12/20], Step [550/654], Loss: 0.1991\n","Epoch [12/20], Step [575/654], Loss: 0.3103\n","Epoch [12/20], Step [600/654], Loss: 0.1070\n","Epoch [12/20], Step [625/654], Loss: 0.2172\n","Epoch [12/20], Step [650/654], Loss: 0.1488\n","Start validation #12\n","Validation #12 Average Loss: 0.3128, mIoU: 0.4341\n","Epoch [13/20], Step [25/654], Loss: 0.2595\n","Epoch [13/20], Step [50/654], Loss: 0.1137\n","Epoch [13/20], Step [75/654], Loss: 0.4418\n","Epoch [13/20], Step [100/654], Loss: 0.2937\n","Epoch [13/20], Step [125/654], Loss: 0.2059\n","Epoch [13/20], Step [150/654], Loss: 0.2388\n","Epoch [13/20], Step [175/654], Loss: 0.1108\n","Epoch [13/20], Step [200/654], Loss: 0.0819\n","Epoch [13/20], Step [225/654], Loss: 0.0760\n","Epoch [13/20], Step [250/654], Loss: 0.1357\n","Epoch [13/20], Step [275/654], Loss: 0.1753\n","Epoch [13/20], Step [300/654], Loss: 0.1226\n","Epoch [13/20], Step [325/654], Loss: 0.1108\n","Epoch [13/20], Step [350/654], Loss: 0.1542\n","Epoch [13/20], Step [375/654], Loss: 0.1688\n","Epoch [13/20], Step [400/654], Loss: 0.1663\n","Epoch [13/20], Step [425/654], Loss: 0.2196\n","Epoch [13/20], Step [450/654], Loss: 0.1365\n","Epoch [13/20], Step [475/654], Loss: 0.3227\n","Epoch [13/20], Step [500/654], Loss: 0.3409\n","Epoch [13/20], Step [525/654], Loss: 0.1110\n","Epoch [13/20], Step [550/654], Loss: 0.1605\n","Epoch [13/20], Step [575/654], Loss: 0.0552\n","Epoch [13/20], Step [600/654], Loss: 0.3179\n","Epoch [13/20], Step [625/654], Loss: 0.1514\n","Epoch [13/20], Step [650/654], Loss: 0.2405\n","Start validation #13\n","Validation #13 Average Loss: 0.3112, mIoU: 0.4417\n","Best performance at epoch: 13\n","Save model in ./saved\n","Epoch [14/20], Step [25/654], Loss: 0.1177\n","Epoch [14/20], Step [50/654], Loss: 0.1547\n","Epoch [14/20], Step [75/654], Loss: 0.1735\n","Epoch [14/20], Step [100/654], Loss: 0.0765\n","Epoch [14/20], Step [125/654], Loss: 0.1735\n","Epoch [14/20], Step [150/654], Loss: 0.1745\n","Epoch [14/20], Step [175/654], Loss: 0.2058\n","Epoch [14/20], Step [200/654], Loss: 0.1336\n","Epoch [14/20], Step [225/654], Loss: 0.2750\n","Epoch [14/20], Step [250/654], Loss: 0.1110\n","Epoch [14/20], Step [275/654], Loss: 0.1971\n","Epoch [14/20], Step [300/654], Loss: 0.4038\n","Epoch [14/20], Step [325/654], Loss: 0.1438\n","Epoch [14/20], Step [350/654], Loss: 0.1962\n","Epoch [14/20], Step [375/654], Loss: 0.2148\n","Epoch [14/20], Step [400/654], Loss: 0.3937\n","Epoch [14/20], Step [425/654], Loss: 0.3313\n","Epoch [14/20], Step [450/654], Loss: 0.1617\n","Epoch [14/20], Step [475/654], Loss: 0.1054\n","Epoch [14/20], Step [500/654], Loss: 0.2032\n","Epoch [14/20], Step [525/654], Loss: 0.1806\n","Epoch [14/20], Step [550/654], Loss: 0.2176\n","Epoch [14/20], Step [575/654], Loss: 0.1163\n","Epoch [14/20], Step [600/654], Loss: 0.0856\n","Epoch [14/20], Step [625/654], Loss: 0.5528\n","Epoch [14/20], Step [650/654], Loss: 0.1753\n","Start validation #14\n","Validation #14 Average Loss: 0.2894, mIoU: 0.4492\n","Best performance at epoch: 14\n","Save model in ./saved\n","Epoch [15/20], Step [25/654], Loss: 0.1709\n","Epoch [15/20], Step [50/654], Loss: 0.1850\n","Epoch [15/20], Step [75/654], Loss: 0.1689\n","Epoch [15/20], Step [100/654], Loss: 0.1390\n","Epoch [15/20], Step [125/654], Loss: 0.1963\n","Epoch [15/20], Step [150/654], Loss: 0.1165\n","Epoch [15/20], Step [175/654], Loss: 0.2879\n","Epoch [15/20], Step [200/654], Loss: 0.2232\n","Epoch [15/20], Step [225/654], Loss: 0.0612\n","Epoch [15/20], Step [250/654], Loss: 0.1162\n","Epoch [15/20], Step [275/654], Loss: 0.2344\n","Epoch [15/20], Step [300/654], Loss: 0.5841\n","Epoch [15/20], Step [325/654], Loss: 0.1484\n","Epoch [15/20], Step [350/654], Loss: 0.1770\n","Epoch [15/20], Step [375/654], Loss: 0.1904\n","Epoch [15/20], Step [400/654], Loss: 0.1763\n","Epoch [15/20], Step [425/654], Loss: 0.2263\n","Epoch [15/20], Step [450/654], Loss: 0.3270\n","Epoch [15/20], Step [475/654], Loss: 0.0782\n","Epoch [15/20], Step [500/654], Loss: 0.2588\n","Epoch [15/20], Step [525/654], Loss: 0.0949\n","Epoch [15/20], Step [550/654], Loss: 0.4777\n","Epoch [15/20], Step [575/654], Loss: 0.1066\n","Epoch [15/20], Step [600/654], Loss: 0.2348\n","Epoch [15/20], Step [625/654], Loss: 0.2567\n","Epoch [15/20], Step [650/654], Loss: 0.1026\n","Start validation #15\n","Validation #15 Average Loss: 0.3426, mIoU: 0.4359\n","Epoch [16/20], Step [25/654], Loss: 0.2050\n","Epoch [16/20], Step [50/654], Loss: 0.1949\n","Epoch [16/20], Step [75/654], Loss: 0.2191\n","Epoch [16/20], Step [100/654], Loss: 0.3369\n","Epoch [16/20], Step [125/654], Loss: 0.2033\n","Epoch [16/20], Step [150/654], Loss: 0.2308\n","Epoch [16/20], Step [175/654], Loss: 0.2635\n","Epoch [16/20], Step [200/654], Loss: 0.1970\n","Epoch [16/20], Step [225/654], Loss: 0.2740\n","Epoch [16/20], Step [250/654], Loss: 0.3213\n","Epoch [16/20], Step [275/654], Loss: 0.1335\n","Epoch [16/20], Step [300/654], Loss: 0.0891\n","Epoch [16/20], Step [325/654], Loss: 0.1634\n","Epoch [16/20], Step [350/654], Loss: 0.1693\n","Epoch [16/20], Step [375/654], Loss: 0.0811\n","Epoch [16/20], Step [400/654], Loss: 0.2654\n","Epoch [16/20], Step [425/654], Loss: 0.1332\n","Epoch [16/20], Step [450/654], Loss: 0.2028\n","Epoch [16/20], Step [475/654], Loss: 0.1242\n","Epoch [16/20], Step [500/654], Loss: 0.1821\n","Epoch [16/20], Step [525/654], Loss: 0.1528\n","Epoch [16/20], Step [550/654], Loss: 0.5107\n","Epoch [16/20], Step [575/654], Loss: 0.0927\n","Epoch [16/20], Step [600/654], Loss: 0.1612\n","Epoch [16/20], Step [625/654], Loss: 0.1912\n","Epoch [16/20], Step [650/654], Loss: 0.0564\n","Start validation #16\n","Validation #16 Average Loss: 0.3044, mIoU: 0.4400\n","Epoch [17/20], Step [25/654], Loss: 0.1143\n","Epoch [17/20], Step [50/654], Loss: 0.0448\n","Epoch [17/20], Step [75/654], Loss: 0.2740\n","Epoch [17/20], Step [100/654], Loss: 0.1746\n","Epoch [17/20], Step [125/654], Loss: 0.1313\n","Epoch [17/20], Step [150/654], Loss: 0.2295\n","Epoch [17/20], Step [175/654], Loss: 0.1957\n","Epoch [17/20], Step [200/654], Loss: 0.1257\n","Epoch [17/20], Step [225/654], Loss: 0.0913\n","Epoch [17/20], Step [250/654], Loss: 0.1582\n","Epoch [17/20], Step [275/654], Loss: 0.1514\n","Epoch [17/20], Step [300/654], Loss: 0.1288\n","Epoch [17/20], Step [325/654], Loss: 0.0988\n","Epoch [17/20], Step [350/654], Loss: 0.0723\n","Epoch [17/20], Step [375/654], Loss: 0.1203\n","Epoch [17/20], Step [400/654], Loss: 0.1351\n","Epoch [17/20], Step [425/654], Loss: 0.1422\n","Epoch [17/20], Step [450/654], Loss: 0.1520\n","Epoch [17/20], Step [475/654], Loss: 0.0696\n","Epoch [17/20], Step [500/654], Loss: 0.1730\n","Epoch [17/20], Step [525/654], Loss: 0.0834\n","Epoch [17/20], Step [550/654], Loss: 0.4097\n","Epoch [17/20], Step [575/654], Loss: 0.1933\n","Epoch [17/20], Step [600/654], Loss: 0.1349\n","Epoch [17/20], Step [625/654], Loss: 0.0857\n","Epoch [17/20], Step [650/654], Loss: 0.1440\n","Start validation #17\n","Validation #17 Average Loss: 0.3016, mIoU: 0.4392\n","Epoch [18/20], Step [25/654], Loss: 0.3971\n","Epoch [18/20], Step [50/654], Loss: 0.1216\n","Epoch [18/20], Step [75/654], Loss: 0.2010\n","Epoch [18/20], Step [100/654], Loss: 0.0403\n","Epoch [18/20], Step [125/654], Loss: 0.4095\n","Epoch [18/20], Step [150/654], Loss: 0.2166\n","Epoch [18/20], Step [175/654], Loss: 0.1621\n","Epoch [18/20], Step [200/654], Loss: 0.2037\n","Epoch [18/20], Step [225/654], Loss: 0.2890\n","Epoch [18/20], Step [250/654], Loss: 0.0512\n","Epoch [18/20], Step [275/654], Loss: 0.0471\n","Epoch [18/20], Step [300/654], Loss: 0.2015\n","Epoch [18/20], Step [325/654], Loss: 0.2484\n","Epoch [18/20], Step [350/654], Loss: 0.1161\n","Epoch [18/20], Step [375/654], Loss: 0.0804\n","Epoch [18/20], Step [400/654], Loss: 0.1681\n","Epoch [18/20], Step [425/654], Loss: 0.2096\n","Epoch [18/20], Step [450/654], Loss: 0.1758\n","Epoch [18/20], Step [475/654], Loss: 0.2791\n","Epoch [18/20], Step [500/654], Loss: 0.2942\n","Epoch [18/20], Step [525/654], Loss: 0.1514\n","Epoch [18/20], Step [550/654], Loss: 0.2326\n","Epoch [18/20], Step [575/654], Loss: 0.1351\n","Epoch [18/20], Step [600/654], Loss: 0.0918\n","Epoch [18/20], Step [625/654], Loss: 0.1085\n","Epoch [18/20], Step [650/654], Loss: 0.4019\n","Start validation #18\n","Validation #18 Average Loss: 0.2991, mIoU: 0.4468\n","Epoch [19/20], Step [25/654], Loss: 0.1733\n","Epoch [19/20], Step [50/654], Loss: 0.1971\n","Epoch [19/20], Step [75/654], Loss: 0.1433\n","Epoch [19/20], Step [100/654], Loss: 0.0542\n","Epoch [19/20], Step [125/654], Loss: 0.1268\n","Epoch [19/20], Step [150/654], Loss: 0.0769\n","Epoch [19/20], Step [175/654], Loss: 0.0666\n","Epoch [19/20], Step [200/654], Loss: 0.2072\n","Epoch [19/20], Step [225/654], Loss: 0.1201\n","Epoch [19/20], Step [250/654], Loss: 0.1067\n","Epoch [19/20], Step [275/654], Loss: 0.1891\n","Epoch [19/20], Step [300/654], Loss: 0.1859\n","Epoch [19/20], Step [325/654], Loss: 0.1837\n","Epoch [19/20], Step [350/654], Loss: 0.1239\n","Epoch [19/20], Step [375/654], Loss: 0.1520\n","Epoch [19/20], Step [400/654], Loss: 0.0515\n","Epoch [19/20], Step [425/654], Loss: 0.1642\n","Epoch [19/20], Step [450/654], Loss: 0.0753\n","Epoch [19/20], Step [475/654], Loss: 0.1137\n","Epoch [19/20], Step [500/654], Loss: 0.1507\n","Epoch [19/20], Step [525/654], Loss: 0.0513\n","Epoch [19/20], Step [550/654], Loss: 0.0750\n","Epoch [19/20], Step [575/654], Loss: 0.1342\n","Epoch [19/20], Step [600/654], Loss: 0.1617\n","Epoch [19/20], Step [625/654], Loss: 0.0742\n","Epoch [19/20], Step [650/654], Loss: 0.1211\n","Start validation #19\n","Validation #19 Average Loss: 0.3114, mIoU: 0.4398\n","Epoch [20/20], Step [25/654], Loss: 0.2141\n","Epoch [20/20], Step [50/654], Loss: 0.1155\n","Epoch [20/20], Step [75/654], Loss: 0.3635\n","Epoch [20/20], Step [100/654], Loss: 0.0877\n","Epoch [20/20], Step [125/654], Loss: 0.0942\n","Epoch [20/20], Step [150/654], Loss: 0.1408\n","Epoch [20/20], Step [175/654], Loss: 0.1042\n","Epoch [20/20], Step [200/654], Loss: 0.2138\n","Epoch [20/20], Step [225/654], Loss: 0.2633\n","Epoch [20/20], Step [250/654], Loss: 0.0841\n","Epoch [20/20], Step [275/654], Loss: 0.1140\n","Epoch [20/20], Step [300/654], Loss: 0.1568\n","Epoch [20/20], Step [325/654], Loss: 0.1981\n","Epoch [20/20], Step [350/654], Loss: 0.1557\n","Epoch [20/20], Step [375/654], Loss: 0.0454\n","Epoch [20/20], Step [400/654], Loss: 0.1026\n","Epoch [20/20], Step [425/654], Loss: 0.1240\n","Epoch [20/20], Step [450/654], Loss: 0.1677\n","Epoch [20/20], Step [475/654], Loss: 0.1699\n","Epoch [20/20], Step [500/654], Loss: 0.1075\n","Epoch [20/20], Step [525/654], Loss: 0.3077\n","Epoch [20/20], Step [550/654], Loss: 0.1484\n","Epoch [20/20], Step [575/654], Loss: 0.0416\n","Epoch [20/20], Step [600/654], Loss: 0.2171\n","Epoch [20/20], Step [625/654], Loss: 0.3169\n","Epoch [20/20], Step [650/654], Loss: 0.2215\n","Start validation #20\n","Validation #20 Average Loss: 0.3386, mIoU: 0.4430\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"C6ClcO0J-NwR"},"source":["## 저장된 model 불러오기 (학습된 이후) "]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:21.050200Z","start_time":"2021-04-16T19:44:20.802200Z"},"scrolled":true,"id":"KQPtUDzd-NwR","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620262923606,"user_tz":-540,"elapsed":1121,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"802f9e62-dae1-4dcb-e553-0631315e0d31"},"source":["# best model 저장된 경로\n","model_path = './saved/aug2_re_pan_effb5_noisy_focal_madgrad_cosLR.pt'\n","\n","# best model 불러오기\n","checkpoint = torch.load(model_path, map_location=device)\n","model.load_state_dict(checkpoint)\n","\n","# 추론을 실행하기 전에는 반드시 설정 (batch normalization, dropout 를 평가 모드로 설정)\n","# model.eval()"],"execution_count":16,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{"tags":[]},"execution_count":16}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:24.939227Z","start_time":"2021-04-16T19:44:24.518228Z"},"id":"0LQqrDAp-NwR","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"error","timestamp":1620262931055,"user_tz":-540,"elapsed":3898,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"55ecfc92-734f-447b-f2cb-3f581214640f"},"source":["# 첫번째 batch의 추론 결과 확인\n","for imgs, image_infos in test_loader:\n"," image_infos = image_infos\n"," temp_images = imgs\n"," \n"," model.eval()\n"," # inference\n"," outs = model(torch.stack(temp_images).to(device))\n"," oms = torch.argmax(outs.squeeze(), dim=1).detach().cpu().numpy()\n"," \n"," break\n","\n","i = 4\n","fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(16, 16))\n","\n","print('Shape of Original Image :', list(temp_images[i].shape))\n","print('Shape of Predicted : ', list(oms[i].shape))\n","print('Unique values, category of transformed mask : \\n', [{int(i),category_names[int(i)]} for i in list(np.unique(oms[i]))])\n","\n","# Original image\n","ax1.imshow(temp_images[i].permute([1,2,0]))\n","ax1.grid(False)\n","ax1.set_title(\"Original image : {}\".format(image_infos[i]['file_name']), fontsize = 15)\n","\n","# Predicted\n","ax2.imshow(oms[i])\n","ax2.grid(False)\n","ax2.set_title(\"Predicted : {}\".format(image_infos[i]['file_name']), fontsize = 15)\n","\n","plt.show()"],"execution_count":17,"outputs":[{"output_type":"error","ename":"IndexError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)","\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0max1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max2\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnrows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mncols\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m16\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Shape of Original Image :'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtemp_images\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 17\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Shape of Predicted : '\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moms\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Unique values, category of transformed mask : \\n'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcategory_names\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m}\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moms\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mIndexError\u001b[0m: tuple index out of 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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"evYjR2F3-NwS"},"source":["## submission을 위한 test 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:27.469285Z","start_time":"2021-04-16T19:44:27.456021Z"},"id":"nhMLnV5d-NwS","executionInfo":{"status":"ok","timestamp":1620261806629,"user_tz":-540,"elapsed":837,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["def test(model, data_loader, device):\n"," size = 256\n"," transform = A.Compose([A.Resize(256, 256)])\n"," print('Start prediction.')\n"," model.eval()\n"," \n"," file_name_list = []\n"," preds_array = np.empty((0, size*size), dtype=np.long)\n"," \n"," with torch.no_grad():\n"," for step, (imgs, image_infos) in enumerate(test_loader):\n","\n"," # inference (512 x 512)\n"," outs = model(torch.stack(imgs).to(device))\n"," oms = torch.argmax(outs.squeeze(), dim=1).detach().cpu().numpy()\n"," \n"," # resize (256 x 256)\n"," temp_mask = []\n"," for img, mask in zip(np.stack(imgs), oms):\n"," transformed = transform(image=img, mask=mask)\n"," mask = transformed['mask']\n"," temp_mask.append(mask)\n","\n"," oms = np.array(temp_mask)\n"," \n"," oms = oms.reshape([oms.shape[0], size*size]).astype(int)\n"," preds_array = np.vstack((preds_array, oms))\n"," \n"," file_name_list.append([i['file_name'] for i in image_infos])\n"," print(\"End prediction.\")\n"," file_names = [y for x in file_name_list for y in x]\n"," \n"," return file_names, preds_array"],"execution_count":20,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"r1YKHBf4-NwT"},"source":["## submission.csv 생성"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:45:42.235310Z","start_time":"2021-04-16T19:44:30.499016Z"},"scrolled":true,"id":"Bz79_g8K-NwT","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620262656959,"user_tz":-540,"elapsed":682299,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"aa60534f-93c7-4ce4-fd4c-d781e68cda55"},"source":["# sample_submisson.csv 열기\n","submission = pd.read_csv('./submission/sample_submission.csv', index_col=None)\n","\n","# test set에 대한 prediction\n","file_names, preds = test(model, test_loader, device)\n","np.save('/content/drive/MyDrive/Trash/ensemble/aug2_re_pan_effb5_noisy_focal_madgrad_cosLR',preds)\n","\n","# PredictionString 대입\n","for file_name, string in zip(file_names, preds):\n"," submission = submission.append({\"image_id\" : file_name, \"PredictionString\" : ' '.join(str(e) for e in string.tolist())}, \n"," ignore_index=True)\n","\n","# submission.csv로 저장\n","submission.to_csv(\"./submission/aug2_re_pan_effb5_noisy_focal_madgrad_cosLR.csv\", index=False)"],"execution_count":21,"outputs":[{"output_type":"stream","text":["Start prediction.\n","End prediction.\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"Zgl7Ye7F-NwT"},"source":["## 제출까지\n","\n"]},{"cell_type":"code","metadata":{"id":"022E8AC7-NwT","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620215701725,"user_tz":-540,"elapsed":7679,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"d89d245b-79ab-4aa2-b05e-81821d8f4bbf"},"source":["import json\n","import requests\n","import os\n","from urllib.parse import urlparse, parse_qsl, urlencode, urlunparse\n","\n","def submit(user_key='', file_path = '', desc=\"\"):\n"," if not user_key:\n"," raise Exception(\"No UserKey\" )\n"," url = urlparse('http://ec2-13-124-161-225.ap-northeast-2.compute.amazonaws.com:8000/api/v1/competition/28/presigned_url/?description=&hyperparameters={%22training%22:{},%22inference%22:{}}')\n"," qs = dict(parse_qsl(url.query))\n"," qs['description'] = desc\n"," parts = url._replace(query=urlencode(qs))\n"," url = urlunparse(parts)\n","\n"," print(url)\n"," headers = {\n"," 'Authorization': user_key\n"," }\n"," res = requests.get(url, headers=headers)\n"," print(res.text)\n"," data = json.loads(res.text)\n"," \n"," submit_url = data['url']\n"," body = {\n"," 'key':'app/Competitions/000028/Users/{}/Submissions/{}/output.csv'.format(str(data['submission']['user']).zfill(8),str(data['submission']['local_id']).zfill(4)),\n"," 'x-amz-algorithm':data['fields']['x-amz-algorithm'],\n"," 'x-amz-credential':data['fields']['x-amz-credential'],\n"," 'x-amz-date':data['fields']['x-amz-date'],\n"," 'policy':data['fields']['policy'],\n"," 'x-amz-signature':data['fields']['x-amz-signature']\n"," }\n"," requests.post(url=submit_url, data=body, files={'file': open(file_path, 'rb')})\n","\n","\n","####################################################################################\n","test_dir = \"/content/drive/MyDrive/Trash/code/submission\" # 수정 필요 : output 파일 폴더 \n","desc = 'aug2_re_pan_effb3_noisy_focal_madgrad_cosLR' # 수정 필요 : 파일에 대한 설명\n","output_file = \"pan_effb3_nore_pan_aug2_re_pan_effb3_noisy_focal_madgrad_cosLReffb7_noisy_focal_madgrad_cosLRisy_focal_adamp_cosLR.csv\" #수정 필요 : output 파일 \n","user_key = \"Bearer 7bb5f96452751a238ffaf91a93c4242bf9b72abe\" # 수정 필요 : Authorization \n","\n","\n","submit(user_key, os.path.join(test_dir, output_file),desc)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["http://ec2-13-124-161-225.ap-northeast-2.compute.amazonaws.com:8000/api/v1/competition/28/presigned_url/?hyperparameters=%7B%22training%22%3A%7B%7D%2C%22inference%22%3A%7B%7D%7D&description=aug2_re_pan_effb3_noisy_focal_madgrad_cosLR\n","{\"url\":\"https://prod-aistages-private.s3.amazonaws.com/\",\"fields\":{\"key\":\"app/Competitions/000028/Users/00000099/Submissions/0025/output.csv\",\"x-amz-algorithm\":\"AWS4-HMAC-SHA256\",\"x-amz-credential\":\"AKIA45LU4MHUJ7WLDQVO/20210505/ap-northeast-2/s3/aws4_request\",\"x-amz-date\":\"20210505T115454Z\",\"policy\":\"eyJleHBpcmF0aW9uIjogIjIwMjEtMDUtMDVUMTI6NTQ6NTRaIiwgImNvbmRpdGlvbnMiOiBbeyJidWNrZXQiOiAicHJvZC1haXN0YWdlcy1wcml2YXRlIn0sIHsia2V5IjogImFwcC9Db21wZXRpdGlvbnMvMDAwMDI4L1VzZXJzLzAwMDAwMDk5L1N1Ym1pc3Npb25zLzAwMjUvb3V0cHV0LmNzdiJ9LCB7IngtYW16LWFsZ29yaXRobSI6ICJBV1M0LUhNQUMtU0hBMjU2In0sIHsieC1hbXotY3JlZGVudGlhbCI6ICJBS0lBNDVMVTRNSFVKN1dMRFFWTy8yMDIxMDUwNS9hcC1ub3J0aGVhc3QtMi9zMy9hd3M0X3JlcXVlc3QifSwgeyJ4LWFtei1kYXRlIjogIjIwMjEwNTA1VDExNTQ1NFoifV19\",\"x-amz-signature\":\"98bce7b468301ebeb0e1afb56cb56522398da727172f7bed8b68bc5e2d713fe7\"},\"submission\":{\"id\":15450,\"phase\":\"Created\",\"type\":\"File\",\"local_id\":25,\"hyperparameters\":\"{\\\"training\\\": {}, \\\"inference\\\": {}}\",\"description\":\"aug2_re_pan_effb3_noisy_focal_madgrad_cosLR\",\"final\":false,\"created_at\":\"2021-05-05T20:54:54.549354+09:00\",\"updated_at\":\"2021-05-05T20:54:54.549387+09:00\",\"user\":99,\"competition\":28,\"image\":null}}\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"iI35aLwqYYJS"},"source":[""],"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/chanyub_seg/code/dlv3p_effb3_noisy_focal_madgrad_cosLR.ipynb b/chanyub_seg/code/dlv3p_effb3_noisy_focal_madgrad_cosLR.ipynb deleted file mode 100644 index 5b6a28e..0000000 --- a/chanyub_seg/code/dlv3p_effb3_noisy_focal_madgrad_cosLR.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"nbformat":4,"nbformat_minor":0,"metadata":{"hide_input":false,"kernelspec":{"display_name":"Python 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google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Mounted at /content/drive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xDwpp4Lk-gSH","executionInfo":{"status":"ok","timestamp":1620111673315,"user_tz":-540,"elapsed":5939,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"1ff16c87-d9fa-40c0-81bc-c254be0aa91d"},"source":["ls"],"execution_count":2,"outputs":[{"output_type":"stream","text":["\u001b[0m\u001b[01;34mdrive\u001b[0m/ \u001b[01;34msample_data\u001b[0m/\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sItrVDeh-iYC","executionInfo":{"status":"ok","timestamp":1620111673316,"user_tz":-540,"elapsed":5498,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"67a4310e-e7b2-413f-80cc-b2bf4fc50cf6"},"source":["cd drive/MyDrive/Trash/code"],"execution_count":3,"outputs":[{"output_type":"stream","text":["/content/drive/MyDrive/Trash/code\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"a54v4-kN_LVO","executionInfo":{"status":"ok","timestamp":1620111681552,"user_tz":-540,"elapsed":13366,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"70daac58-540b-4983-8a71-2af27a3ccf78"},"source":["!pip install albumentations==0.5.2"],"execution_count":4,"outputs":[{"output_type":"stream","text":["Collecting albumentations==0.5.2\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/03/58/63fb1d742dc42d9ba2800ea741de1f2bc6bb05548d8724aa84794042eaf2/albumentations-0.5.2-py3-none-any.whl (72kB)\n","\r\u001b[K |████▌ | 10kB 29.1MB/s eta 0:00:01\r\u001b[K |█████████ | 20kB 35.9MB/s eta 0:00:01\r\u001b[K |█████████████▋ | 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kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug>=0.4.0->albumentations==0.5.2) (1.3.1)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug>=0.4.0->albumentations==0.5.2) (0.10.0)\n","Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug>=0.4.0->albumentations==0.5.2) (2.8.1)\n","Requirement already satisfied: decorator<5,>=4.3 in /usr/local/lib/python3.7/dist-packages (from networkx>=2.0->scikit-image>=0.16.1->albumentations==0.5.2) (4.4.2)\n","Installing collected packages: opencv-python-headless, imgaug, albumentations\n"," Found existing installation: imgaug 0.2.9\n"," Uninstalling imgaug-0.2.9:\n"," Successfully uninstalled imgaug-0.2.9\n"," Found existing installation: albumentations 0.1.12\n"," Uninstalling albumentations-0.1.12:\n"," Successfully uninstalled albumentations-0.1.12\n","Successfully installed albumentations-0.5.2 imgaug-0.4.0 opencv-python-headless-4.5.1.48\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.826930Z","start_time":"2021-04-18T10:34:45.406686Z"},"colab":{"base_uri":"https://localhost:8080/"},"id":"l_LPA4XD-NwC","executionInfo":{"status":"ok","timestamp":1620111685239,"user_tz":-540,"elapsed":15911,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"97b7a8f7-dea5-4bbe-a226-592c273de077"},"source":["import os\n","import random\n","import time\n","import json\n","import warnings \n","warnings.filterwarnings('ignore')\n","\n","import torch\n","import torch.nn as nn\n","from torch.utils.data import Dataset, DataLoader\n","from utils import label_accuracy_score\n","import cv2\n","\n","import numpy as np\n","import pandas as pd\n","\n","# 전처리를 위한 라이브러리\n","from pycocotools.coco import COCO\n","import torchvision\n","import torchvision.transforms as transforms\n","\n","import albumentations as A\n","from albumentations.pytorch import ToTensorV2\n","\n","# 시각화를 위한 라이브러리\n","import matplotlib.pyplot as plt\n","import seaborn as sns; sns.set()\n","\n","plt.rcParams['axes.grid'] = False\n","\n","print('pytorch version: {}'.format(torch.__version__))\n","print('GPU 사용 가능 여부: {}'.format(torch.cuda.is_available()))\n","\n","print(torch.cuda.get_device_name(0))\n","print(torch.cuda.device_count())\n","\n","device = \"cuda\" if torch.cuda.is_available() else \"cpu\" # GPU 사용 가능 여부에 따라 device 정보 저장"],"execution_count":5,"outputs":[{"output_type":"stream","text":["pytorch version: 1.8.1+cu101\n","GPU 사용 가능 여부: True\n","Tesla P100-PCIE-16GB\n","1\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"HxgRSL_M-NwF"},"source":["## 하이퍼파라미터 세팅 및 seed 고정"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.841930Z","start_time":"2021-04-18T10:34:47.827931Z"},"id":"rV3JmGP5-NwF","executionInfo":{"status":"ok","timestamp":1620111685240,"user_tz":-540,"elapsed":13400,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["batch_size = 8 # Mini-batch size\n","num_epochs = 20\n","learning_rate = 0.0001"],"execution_count":6,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.856930Z","start_time":"2021-04-18T10:34:47.842931Z"},"id":"Z6LOuJXQ-NwG","executionInfo":{"status":"ok","timestamp":1620111685241,"user_tz":-540,"elapsed":12998,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# seed 고정\n","random_seed = 42\n","torch.manual_seed(random_seed)\n","torch.cuda.manual_seed(random_seed)\n","# torch.cuda.manual_seed_all(random_seed) # if use multi-GPU\n","torch.backends.cudnn.deterministic = True\n","torch.backends.cudnn.benchmark = False\n","np.random.seed(random_seed)\n","random.seed(random_seed)"],"execution_count":7,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"iWF_EJDu-NwG"},"source":["## 학습 데이터 EDA"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.381961Z","start_time":"2021-04-18T10:34:47.857930Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"lg0x0D0a-NwG","executionInfo":{"status":"ok","timestamp":1620111695300,"user_tz":-540,"elapsed":21810,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"0ad39ab8-c192-4501-96b9-6910ce5e5a7c"},"source":["%matplotlib inline\n","\n","dataset_path = '../input/data'\n","anns_file_path = dataset_path + '/' + 'train.json'\n","\n","# Read annotations\n","with open(anns_file_path, 'r') as f:\n"," dataset = json.loads(f.read())\n","\n","categories = dataset['categories']\n","anns = dataset['annotations']\n","imgs = dataset['images']\n","nr_cats = len(categories)\n","nr_annotations = len(anns)\n","nr_images = len(imgs)\n","\n","# Load categories and super categories\n","cat_names = []\n","super_cat_names = []\n","super_cat_ids = {}\n","super_cat_last_name = ''\n","nr_super_cats = 0\n","for cat_it in categories:\n"," cat_names.append(cat_it['name'])\n"," super_cat_name = cat_it['supercategory']\n"," # Adding new supercat\n"," if super_cat_name != super_cat_last_name:\n"," super_cat_names.append(super_cat_name)\n"," super_cat_ids[super_cat_name] = nr_super_cats\n"," super_cat_last_name = super_cat_name\n"," nr_super_cats += 1\n","\n","print('Number of super categories:', nr_super_cats)\n","print('Number of categories:', nr_cats)\n","print('Number of annotations:', nr_annotations)\n","print('Number of images:', nr_images)"],"execution_count":8,"outputs":[{"output_type":"stream","text":["Number of super categories: 11\n","Number of categories: 11\n","Number of annotations: 21116\n","Number of images: 2617\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.546964Z","start_time":"2021-04-18T10:34:51.382969Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/","height":355},"id":"PjLyVHVY-NwH","executionInfo":{"status":"ok","timestamp":1620111695900,"user_tz":-540,"elapsed":21633,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"af0ed264-fee4-44c9-a390-cf69551eaa42"},"source":["# Count annotations\n","cat_histogram = np.zeros(nr_cats,dtype=int)\n","for ann in anns:\n"," cat_histogram[ann['category_id']] += 1\n","\n","# Initialize the matplotlib figure\n","f, ax = plt.subplots(figsize=(5,5))\n","\n","# Convert to DataFrame\n","df = pd.DataFrame({'Categories': cat_names, 'Number of annotations': cat_histogram})\n","df = df.sort_values('Number of annotations', 0, False)\n","\n","# Plot the histogram\n","plt.title(\"category distribution of train set \")\n","plot_1 = sns.barplot(x=\"Number of annotations\", y=\"Categories\", data=df, label=\"Total\", color=\"b\")"],"execution_count":9,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.561965Z","start_time":"2021-04-18T10:34:51.547969Z"},"id":"34Tw5__i-NwI","executionInfo":{"status":"ok","timestamp":1620111695901,"user_tz":-540,"elapsed":20386,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# category labeling \n","sorted_temp_df = df.sort_index()\n","\n","# background = 0 에 해당되는 label 추가 후 기존들을 모두 label + 1 로 설정\n","sorted_df = pd.DataFrame([\"Backgroud\"], columns = [\"Categories\"])\n","sorted_df = sorted_df.append(sorted_temp_df, ignore_index=True)"],"execution_count":10,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.576961Z","start_time":"2021-04-18T10:34:51.562964Z"},"colab":{"base_uri":"https://localhost:8080/","height":421},"id":"CQk4vV5N-NwI","executionInfo":{"status":"ok","timestamp":1620111695901,"user_tz":-540,"elapsed":19988,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"b987e3b1-f450-4e6a-da9f-d5749911d518"},"source":["# class (Categories) 에 따른 index 확인 (0~11 : 총 12개)\n","sorted_df"],"execution_count":11,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
CategoriesNumber of annotations
0BackgroudNaN
1UNKNOWN128.0
2General trash2225.0
3Paper7448.0
4Paper pack527.0
5Metal449.0
6Glass488.0
7Plastic2472.0
8Styrofoam1074.0
9Plastic bag6114.0
10Battery50.0
11Clothing141.0
\n","
"],"text/plain":[" Categories Number of annotations\n","0 Backgroud NaN\n","1 UNKNOWN 128.0\n","2 General trash 2225.0\n","3 Paper 7448.0\n","4 Paper pack 527.0\n","5 Metal 449.0\n","6 Glass 488.0\n","7 Plastic 2472.0\n","8 Styrofoam 1074.0\n","9 Plastic bag 6114.0\n","10 Battery 50.0\n","11 Clothing 141.0"]},"metadata":{"tags":[]},"execution_count":11}]},{"cell_type":"markdown","metadata":{"id":"KXU0zmZs-NwI"},"source":["## 데이터 전처리 함수 정의 (Dataset)"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:52.693328Z","start_time":"2021-04-18T10:34:52.681328Z"},"id":"QFnTI8_Z-NwJ","executionInfo":{"status":"ok","timestamp":1620111695902,"user_tz":-540,"elapsed":19019,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["category_names = list(sorted_df.Categories)\n","\n","def get_classname(classID, cats):\n"," for i in range(len(cats)):\n"," if cats[i]['id']==classID:\n"," return cats[i]['name']\n"," return \"None\"\n","\n","class CustomDataLoader(Dataset):\n"," \"\"\"COCO format\"\"\"\n"," def __init__(self, data_dir, mode = 'train', transform = None):\n"," super().__init__()\n"," self.mode = mode\n"," self.transform = transform\n"," self.coco = COCO(data_dir)\n"," \n"," def __getitem__(self, index: int):\n"," # dataset이 index되어 list처럼 동작\n"," image_id = self.coco.getImgIds(imgIds=index)\n"," image_infos = self.coco.loadImgs(image_id)[0]\n"," \n"," # cv2 를 활용하여 image 불러오기\n"," images = cv2.imread(os.path.join(dataset_path, image_infos['file_name']))\n"," images = cv2.cvtColor(images, cv2.COLOR_BGR2RGB).astype(np.float32)\n"," images /= 255.0\n"," \n"," if (self.mode in ('train', 'val')):\n"," ann_ids = self.coco.getAnnIds(imgIds=image_infos['id'])\n"," anns = self.coco.loadAnns(ann_ids)\n","\n"," # Load the categories in a variable\n"," cat_ids = self.coco.getCatIds()\n"," cats = self.coco.loadCats(cat_ids)\n","\n"," # masks : size가 (height x width)인 2D\n"," # 각각의 pixel 값에는 \"category id + 1\" 할당\n"," # Background = 0\n"," masks = np.zeros((image_infos[\"height\"], image_infos[\"width\"]))\n"," # Unknown = 1, General trash = 2, ... , Cigarette = 11\n"," for i in range(len(anns)):\n"," className = get_classname(anns[i]['category_id'], cats)\n"," pixel_value = category_names.index(className)\n"," masks = np.maximum(self.coco.annToMask(anns[i])*pixel_value, masks)\n"," masks = masks.astype(np.float32)\n","\n"," # transform -> albumentations 라이브러리 활용\n"," if self.transform is not None:\n"," transformed = self.transform(image=images, mask=masks)\n"," images = transformed[\"image\"]\n"," masks = transformed[\"mask\"]\n"," \n"," return images, masks, image_infos\n"," \n"," if self.mode == 'test':\n"," # transform -> albumentations 라이브러리 활용\n"," if self.transform is not None:\n"," transformed = self.transform(image=images)\n"," images = transformed[\"image\"]\n"," \n"," return images, image_infos\n"," \n"," \n"," def __len__(self) -> int:\n"," # 전체 dataset의 size를 return\n"," return len(self.coco.getImgIds())"],"execution_count":12,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"cp2aIOlP-NwK"},"source":["## Dataset 정의 및 DataLoader 할당"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:58.823175Z","start_time":"2021-04-18T10:34:54.106233Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"P7zFlRn6-NwK","executionInfo":{"status":"ok","timestamp":1620111703690,"user_tz":-540,"elapsed":25839,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"25900256-9d7c-4354-995f-295c8110265a"},"source":["# train.json / validation.json / test.json 디렉토리 설정\n","train_path = dataset_path + '/train.json'\n","val_path = dataset_path + '/val.json'\n","test_path = dataset_path + '/test.json'\n","\n","# collate_fn needs for batch\n","def collate_fn(batch):\n"," return tuple(zip(*batch))\n","\n","train_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","val_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","test_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","# create own Dataset 1 (skip)\n","# validation set을 직접 나누고 싶은 경우\n","# random_split 사용하여 data set을 8:2 로 분할\n","# train_size = int(0.8*len(dataset))\n","# val_size = int(len(dataset)-train_size)\n","# dataset = CustomDataLoader(data_dir=train_path, mode='train', transform=transform)\n","# train_dataset, val_dataset = torch.utils.data.random_split(dataset, [train_size, val_size])\n","\n","# create own Dataset 2\n","# train dataset\n","train_dataset = CustomDataLoader(data_dir=train_path, mode='train', transform=train_transform)\n","\n","# validation dataset\n","val_dataset = CustomDataLoader(data_dir=val_path, mode='val', transform=val_transform)\n","\n","# test dataset\n","test_dataset = CustomDataLoader(data_dir=test_path, mode='test', transform=test_transform)\n","\n","\n","# DataLoader\n","train_loader = torch.utils.data.DataLoader(dataset=train_dataset, \n"," batch_size=batch_size,\n"," shuffle=True,\n"," num_workers=4,\n"," collate_fn=collate_fn,\n"," drop_last=True)\n","\n","val_loader = torch.utils.data.DataLoader(dataset=val_dataset, \n"," batch_size=batch_size,\n"," shuffle=False,\n"," num_workers=4,\n"," collate_fn=collate_fn,\n"," drop_last=True) \n","\n","test_loader = torch.utils.data.DataLoader(dataset=test_dataset,\n"," batch_size=batch_size,\n"," num_workers=4,\n"," collate_fn=collate_fn)"],"execution_count":13,"outputs":[{"output_type":"stream","text":["loading annotations into memory...\n","Done (t=3.76s)\n","creating index...\n","index created!\n","loading annotations into memory...\n","Done (t=2.53s)\n","creating index...\n","index created!\n","loading annotations into memory...\n","Done (t=1.07s)\n","creating index...\n","index created!\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"8rqJiHb_-NwM"},"source":["# wandb"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"H1kHXm0uAX3R","executionInfo":{"status":"ok","timestamp":1620111711905,"user_tz":-540,"elapsed":32370,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"ff250053-637b-4fc6-8d27-707575608484"},"source":["!pip install wandb"],"execution_count":14,"outputs":[{"output_type":"stream","text":["Collecting wandb\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/67/5a/b037b50f9849212863a2fed313624d8f6f33ffa4ce89dc706e2a0e98c780/wandb-0.10.29-py2.py3-none-any.whl (2.1MB)\n","\u001b[K |████████████████████████████████| 2.1MB 14.0MB/s \n","\u001b[?25hRequirement already satisfied: psutil>=5.0.0 in /usr/local/lib/python3.7/dist-packages (from wandb) 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sha256=624d102fe62234858fe9a31e8499f219894b3599810a444addef1f950d6b130b\n"," Stored in directory: /root/.cache/pip/wheels/0b/04/79/c3b0c3a0266a3cb4376da31e5bfe8bba0c489246968a68e843\n"," Building wheel for subprocess32 (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for subprocess32: filename=subprocess32-3.5.4-cp37-none-any.whl size=6489 sha256=296061edc0493e5882fb0d857cfc5b4f3cb65594948bc1249cd59c22610aad85\n"," Stored in directory: /root/.cache/pip/wheels/68/39/1a/5e402bdfdf004af1786c8b853fd92f8c4a04f22aad179654d1\n","Successfully built pathtools subprocess32\n","Installing collected packages: sentry-sdk, smmap, gitdb, GitPython, docker-pycreds, shortuuid, pathtools, subprocess32, configparser, wandb\n","Successfully installed GitPython-3.1.14 configparser-5.0.2 docker-pycreds-0.4.0 gitdb-4.0.7 pathtools-0.1.2 sentry-sdk-1.0.0 shortuuid-1.0.1 smmap-4.0.0 subprocess32-3.5.4 wandb-0.10.29\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":187},"id":"aMW4VV9V-NwM","executionInfo":{"status":"ok","timestamp":1620111738182,"user_tz":-540,"elapsed":57659,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"ad3ad1ca-f94d-4c47-f832-f96452490b63"},"source":["import wandb\n","\n","proj_name = 'dlv3p_effb3_noisy_focal_madgrad_cosLR'\n","\n","wandb.init(project='chanyub',name=proj_name)\n","\n","config = wandb.config\n","config.learning_rate = 0.01"],"execution_count":15,"outputs":[{"output_type":"display_data","data":{"application/javascript":["\n"," window._wandbApiKey = new Promise((resolve, reject) => {\n"," function loadScript(url) {\n"," return new Promise(function(resolve, reject) {\n"," let newScript = document.createElement(\"script\");\n"," newScript.onerror = reject;\n"," newScript.onload = resolve;\n"," document.body.appendChild(newScript);\n"," newScript.src = url;\n"," });\n"," }\n"," loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n"," const iframe = document.createElement('iframe')\n"," iframe.style.cssText = \"width:0;height:0;border:none\"\n"," document.body.appendChild(iframe)\n"," const handshake = new Postmate({\n"," container: iframe,\n"," url: 'https://wandb.ai/authorize'\n"," });\n"," const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n"," handshake.then(function(child) {\n"," child.on('authorize', data => {\n"," clearTimeout(timeout)\n"," resolve(data)\n"," });\n"," });\n"," })\n"," });\n"," "],"text/plain":[""]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n"],"name":"stderr"},{"output_type":"stream","text":["wandb: Paste an API key from your profile and hit enter: ··········\n"],"name":"stdout"},{"output_type":"stream","text":["\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"],"name":"stderr"},{"output_type":"display_data","data":{"text/html":["\n"," Tracking run with wandb version 0.10.29
\n"," Syncing run dlv3p_effb3_noisy_focal_madgrad_cosLR to Weights & Biases (Documentation).
\n"," Project page: https://wandb.ai/pstage12/chanyub
\n"," Run page: https://wandb.ai/pstage12/chanyub/runs/2rxufy9z
\n"," Run data is saved locally in /content/drive/My Drive/Trash/code/wandb/run-20210504_070215-2rxufy9z

\n"," "],"text/plain":[""]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"uQRiIVGX-NwM"},"source":["## My model"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"S0xCGpNeAqeD","executionInfo":{"status":"ok","timestamp":1620111743141,"user_tz":-540,"elapsed":59675,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"f0ab5833-9e12-4b94-cd6a-0e4c51f5d539"},"source":["!pip install segmentation_models_pytorch"],"execution_count":16,"outputs":[{"output_type":"stream","text":["Collecting segmentation_models_pytorch\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/65/54/8953f9f7ee9d451b0f3be8d635aa3a654579abf898d17502a090efe1155a/segmentation_models_pytorch-0.1.3-py3-none-any.whl (66kB)\n","\r\u001b[K |█████ | 10kB 1.1MB/s eta 0:00:01\r\u001b[K |██████████ | 20kB 2.1MB/s eta 0:00:01\r\u001b[K 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(4.41.1)\n","Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch->efficientnet-pytorch==0.6.3->segmentation_models_pytorch) (3.7.4.3)\n","Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from munch->pretrainedmodels==0.7.4->segmentation_models_pytorch) (1.15.0)\n","Building wheels for collected packages: efficientnet-pytorch, pretrainedmodels\n"," Building wheel for efficientnet-pytorch (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for efficientnet-pytorch: filename=efficientnet_pytorch-0.6.3-cp37-none-any.whl size=12420 sha256=85438995ce26f4de959a5b75a54a1f963c0f7102019509e9d034e3035f69273c\n"," Stored in directory: /root/.cache/pip/wheels/42/1e/a9/2a578ba9ad04e776e80bf0f70d8a7f4c29ec0718b92d8f6ccd\n"," Building wheel for pretrainedmodels (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for pretrainedmodels: filename=pretrainedmodels-0.7.4-cp37-none-any.whl size=60963 sha256=45bc41142679b5982252c17fbcbeab1973afde2e95b2c7dcebeb352c2a9fd935\n"," Stored in directory: /root/.cache/pip/wheels/69/df/63/62583c096289713f22db605aa2334de5b591d59861a02c2ecd\n","Successfully built efficientnet-pytorch pretrainedmodels\n","Installing collected packages: efficientnet-pytorch, timm, munch, pretrainedmodels, segmentation-models-pytorch\n","Successfully installed efficientnet-pytorch-0.6.3 munch-2.5.0 pretrainedmodels-0.7.4 segmentation-models-pytorch-0.1.3 timm-0.3.2\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:11.634792Z","start_time":"2021-04-18T16:16:05.875817Z"},"colab":{"base_uri":"https://localhost:8080/","height":137,"referenced_widgets":["2970893981e3428f9ed614d991f54030","ca9dbdf08844450babb805f22f1d80a5","b3a5365bb1f1440aa2e824aef71ce78d","5a532f0960d5414b95445f92a78fe79f","0ab751bb52b948839627179ef2420433","86f81d6b56584b71b7e0be4cf5d648b7","ddf09041623548f68a67f54ea420219c","f0fec42e29004b5983d83c3ee20d6609"]},"id":"a8IfZfiM-NwM","executionInfo":{"status":"ok","timestamp":1620111756532,"user_tz":-540,"elapsed":69439,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"3a4aa214-e3c4-42d3-d8e5-29401ac23ea9"},"source":["# 구현된 model에 임의의 input을 넣어 output이 잘 나오는지 test\n","import segmentation_models_pytorch as smp\n","\n","model = smp.DeepLabV3Plus(encoder_name='timm-efficientnet-b3', encoder_weights='noisy-student', classes=12)\n","x = torch.randn([2, 3, 512, 512])\n","print(\"input shape : \", x.shape)\n","out = model(x).to(device)\n","print(\"output shape : \", out.size())\n","\n","model = model.to(device)"],"execution_count":17,"outputs":[{"output_type":"stream","text":["Downloading: \"https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b3_ns-9d44bf68.pth\" to /root/.cache/torch/hub/checkpoints/tf_efficientnet_b3_ns-9d44bf68.pth\n"],"name":"stderr"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"2970893981e3428f9ed614d991f54030","version_minor":0,"version_major":2},"text/plain":["HBox(children=(FloatProgress(value=0.0, max=49385734.0), HTML(value='')))"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\n","input shape : torch.Size([2, 3, 512, 512])\n","output shape : torch.Size([2, 12, 512, 512])\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"zvDnI7_T-NwN"},"source":["## train, validation, test 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.104200Z","start_time":"2021-04-18T16:16:18.093174Z"},"id":"RA3oAapJ-NwN","executionInfo":{"status":"ok","timestamp":1620111756533,"user_tz":-540,"elapsed":67578,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["def train(num_epochs, model, data_loader, val_loader, criterion, optimizer, saved_dir, val_every, device, lr_scheduler):\n"," print('Start training..')\n"," best_loss = 9999999\n"," best_miou = 0\n"," for epoch in range(num_epochs):\n"," model.train()\n"," for step, (images, masks, _) in enumerate(data_loader):\n"," images = torch.stack(images) # (batch, channel, height, width)\n"," masks = torch.stack(masks).long() # (batch, channel, height, width)\n"," \n"," # gpu 연산을 위해 device 할당\n"," images, masks = images.to(device), masks.to(device)\n"," \n"," # inference\n"," outputs = model(images)\n"," \n"," # loss 계산 (cross entropy loss)\n"," loss = criterion(outputs, masks)\n"," optimizer.zero_grad()\n"," loss.backward()\n"," optimizer.step()\n"," \n"," lr_scheduler.step()\n"," \n"," # step 주기에 따른 loss 출력\n"," if (step + 1) % 25 == 0:\n"," print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(\n"," epoch+1, num_epochs, step+1, len(train_loader), loss.item()))\n"," \n"," # validation 주기에 따른 loss 출력 및 best model 저장\n"," if (epoch + 1) % val_every == 0:\n","# avrg_loss = validation(epoch + 1, model, val_loader, criterion, device)\n","# if avrg_loss < best_loss:\n","# print('Best performance at epoch: {}'.format(epoch + 1))\n","# print('Save model in', saved_dir)\n","# best_loss = avrg_loss\n","# wandb.log({'best_loss': best_loss})\n","# save_model(model, saved_dir)\n"," avrg_miou = validation(epoch + 1, model, val_loader, criterion, device)\n"," if avrg_miou > best_miou:\n"," print('Best performance at epoch: {}'.format(epoch + 1))\n"," print('Save model in', saved_dir)\n"," best_miou = avrg_miou\n"," wandb.log({'best_miou': best_miou})\n"," save_model(model, saved_dir)"],"execution_count":18,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.285795Z","start_time":"2021-04-18T16:16:18.267686Z"},"id":"EulIikmq-NwO","executionInfo":{"status":"ok","timestamp":1620111756534,"user_tz":-540,"elapsed":66782,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["def validation(epoch, model, data_loader, criterion, device):\n"," print('Start validation #{}'.format(epoch))\n"," model.eval()\n"," with torch.no_grad():\n"," total_loss = 0\n"," cnt = 0\n"," mIoU_list = []\n"," for step, (images, masks, _) in enumerate(data_loader):\n"," \n"," images = torch.stack(images) # (batch, channel, height, width)\n"," masks = torch.stack(masks).long() # (batch, channel, height, width)\n","\n"," images, masks = images.to(device), masks.to(device) \n","\n"," outputs = model(images)\n"," loss = criterion(outputs, masks)\n"," total_loss += loss\n"," cnt += 1\n"," \n","# print(outputs.shape)\n","# print(masks.shape)\n","# wandb.log(wandb.Image(images, masks={\n","# \"predictions\" : {\n","# \"mask_data\" : torch.squeeze(torch.squeeze(outputs, 0),1),\n","# \"class_labels\" : classes_dict\n","# },\n","# \"ground_truth\" : {\n","# \"mask_data\" : torch.squeeze(masks, 0),\n","# \"class_labels\" : classes_dict\n","# }\n","# }))\n"," \n"," outputs = torch.argmax(outputs.squeeze(), dim=1).detach().cpu().numpy()\n","\n"," mIoU = label_accuracy_score(masks.detach().cpu().numpy(), outputs, n_class=12)[2]\n"," mIoU_list.append(mIoU)\n"," \n"," avrg_loss = total_loss / cnt\n"," avrg_mIoU = np.mean(mIoU_list)\n"," print('Validation #{} Average Loss: {:.4f}, mIoU: {:.4f}'.format(epoch, avrg_loss, np.mean(mIoU_list)))\n"," wandb.log({'Train Loss':loss.item(), 'Val Loss':avrg_loss , 'Val mIoU':np.mean(mIoU_list)})\n","# return avrg_loss\n"," return avrg_mIoU"],"execution_count":19,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"C_7CtFfH-NwO"},"source":["## 모델 저장 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.909918Z","start_time":"2021-04-18T16:16:18.898918Z"},"id":"gpCM5BFO-NwO","executionInfo":{"status":"ok","timestamp":1620111760125,"user_tz":-540,"elapsed":1243,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# 모델 저장 함수 정의\n","val_every = 1 \n","\n","saved_dir = './saved'\n","if not os.path.isdir(saved_dir): \n"," os.mkdir(saved_dir)\n"," \n","def save_model(model, saved_dir, file_name='dlv3p_effb3_noisy_focal_madgrad_cosLR.pt'):\n"," check_point = {'net': model.state_dict()}\n"," output_path = os.path.join(saved_dir, file_name)\n"," torch.save(model.state_dict(), output_path)"],"execution_count":20,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"0D3rsEd2yJfV"},"source":[""]},{"cell_type":"markdown","metadata":{"id":"FyKGeg8T-NwP"},"source":["## 모델 생성 및 Loss function, Optimizer 정의"]},{"cell_type":"code","metadata":{"id":"ORugl8s1-NwP","executionInfo":{"status":"ok","timestamp":1620111760129,"user_tz":-540,"elapsed":1241,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["from torch.autograd import Variable\n","import torch.nn.functional as F\n","# ref : https://github.com/clcarwin/focal_loss_pytorch\n","class FocalLoss(nn.Module):\n"," def __init__(self, gamma=0, alpha=None, size_average=True):\n"," super(FocalLoss, self).__init__()\n"," self.gamma = gamma\n"," self.alpha = alpha\n"," if isinstance(alpha,(float,int)): self.alpha = torch.Tensor([alpha,1-alpha])\n"," if isinstance(alpha,list): self.alpha = torch.Tensor(alpha)\n"," self.size_average = size_average\n","\n"," def forward(self, input, target):\n"," if input.dim()>2:\n"," input = input.view(input.size(0),input.size(1),-1) # N,C,H,W => N,C,H*W\n"," input = input.transpose(1,2) # N,C,H*W => N,H*W,C\n"," input = input.contiguous().view(-1,input.size(2)) # N,H*W,C => N*H*W,C\n"," target = target.view(-1,1)\n","\n"," logpt = F.log_softmax(input)\n"," logpt = logpt.gather(1,target)\n"," logpt = logpt.view(-1)\n"," pt = Variable(logpt.data.exp())\n","\n"," if self.alpha is not None:\n"," if self.alpha.type()!=input.data.type():\n"," self.alpha = self.alpha.type_as(input.data)\n"," at = self.alpha.gather(0,target.data.view(-1))\n"," logpt = logpt * Variable(at)\n","\n"," loss = -1 * (1-pt)**self.gamma * logpt\n"," if self.size_average: return loss.mean()\n"," else: return loss.sum()"],"execution_count":21,"outputs":[]},{"cell_type":"code","metadata":{"id":"yfeFQknz-NwP","executionInfo":{"status":"ok","timestamp":1620111760129,"user_tz":-540,"elapsed":1238,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["import torch.optim.lr_scheduler as lr_scheduler\n","import math\n","class CosineAnnealingWarmUpRestart(lr_scheduler._LRScheduler):\n"," def __init__(self, optimizer, T_0, T_mult=1, eta_max=0.1, T_up=0, gamma=1., last_epoch=-1):\n"," if T_0 <= 0 or not isinstance(T_0, int):\n"," raise ValueError(\"Expected positive integer T_0, but got {}\".format(T_0))\n"," if T_mult < 1 or not isinstance(T_mult, int):\n"," raise ValueError(\"Expected integer T_mult >= 1, but got {}\".format(T_mult))\n"," if T_up < 0 or not isinstance(T_up, int):\n"," raise ValueError(\"Expected positive integer T_up, but got {}\".format(T_up))\n"," self.T_0 = T_0\n"," self.T_mult = T_mult\n"," self.base_eta_max = eta_max\n"," self.eta_max = eta_max\n"," self.T_up = T_up\n"," self.T_i = T_0\n"," self.gamma = gamma\n"," self.cycle = 0\n"," self.T_cur = last_epoch\n"," super(CosineAnnealingWarmUpRestart, self).__init__(optimizer, last_epoch)\n"," # self.T_cur = last_epoch\n"," \n"," def get_lr(self):\n"," if self.T_cur == -1:\n"," return self.base_lrs\n"," elif self.T_cur < self.T_up:\n"," return [(self.eta_max - base_lr)*self.T_cur / self.T_up + base_lr for base_lr in self.base_lrs]\n"," else:\n"," return [base_lr + (self.eta_max - base_lr) * (1 + math.cos(math.pi * (self.T_cur-self.T_up) / (self.T_i - self.T_up))) / 2\n"," for base_lr in self.base_lrs]\n","\n"," def step(self, epoch=None):\n"," if epoch is None:\n"," epoch = self.last_epoch + 1\n"," self.T_cur = self.T_cur + 1\n"," if self.T_cur >= self.T_i:\n"," self.cycle += 1\n"," self.T_cur = self.T_cur - self.T_i\n"," self.T_i = (self.T_i - self.T_up) * self.T_mult + self.T_up\n"," else:\n"," if epoch >= self.T_0:\n"," if self.T_mult == 1:\n"," self.T_cur = epoch % self.T_0\n"," self.cycle = epoch // self.T_0\n"," else:\n"," n = int(math.log((epoch / self.T_0 * (self.T_mult - 1) + 1), self.T_mult))\n"," self.cycle = n\n"," self.T_cur = epoch - self.T_0 * (self.T_mult ** n - 1) / (self.T_mult - 1)\n"," self.T_i = self.T_0 * self.T_mult ** (n)\n"," else:\n"," self.T_i = self.T_0\n"," self.T_cur = epoch\n"," \n"," self.eta_max = self.base_eta_max * (self.gamma**self.cycle)\n"," self.last_epoch = math.floor(epoch)\n"," for param_group, lr in zip(self.optimizer.param_groups, self.get_lr()):\n"," param_group['lr'] = lr"],"execution_count":22,"outputs":[]},{"cell_type":"code","metadata":{"id":"AG1oQeu7BX1M","executionInfo":{"status":"ok","timestamp":1620111760129,"user_tz":-540,"elapsed":1236,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# !pip install adamp"],"execution_count":23,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"eH2PpwF9a-Os","executionInfo":{"status":"ok","timestamp":1620111762519,"user_tz":-540,"elapsed":3617,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"ea2ca267-b8f2-46ae-ff7d-ec009b55dfcc"},"source":["!pip install madgrad"],"execution_count":24,"outputs":[{"output_type":"stream","text":["Collecting madgrad\n"," Downloading https://files.pythonhosted.org/packages/65/f0/4584f18202a2fb8903d456bf907b80e7cb54ad8fcba68604084ff41b7cf8/madgrad-1.1-py3-none-any.whl\n","Installing collected packages: madgrad\n","Successfully installed madgrad-1.1\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:19.698902Z","start_time":"2021-04-18T16:16:19.694902Z"},"id":"9Dly8KZj-NwQ","executionInfo":{"status":"ok","timestamp":1620111762519,"user_tz":-540,"elapsed":3615,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# from adamp import AdamP\n","from madgrad import MADGRAD\n","# Loss function 정의\n","# criterion = nn.CrossEntropyLoss()\n","criterion = FocalLoss()\n","\n","# Optimizer 정의\n","# optimizer = torch.optim.Adam(params = model.parameters(), lr = learning_rate, weight_decay=1e-6)\n","# optimizer = AdamP(params = model.parameters())\n","optimizer = MADGRAD(params = model.parameters(), lr = learning_rate, momentum = 0.9, weight_decay = 0, eps = 1e-06)\n","\n","lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=50, eta_min=0)\n","# lr_scheduler = CosineAnnealingWarmUpRestart(optimizer, T_0=150, T_mult=1, eta_max=0.1, T_up=10, gamma=0.5)"],"execution_count":25,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"start_time":"2021-04-18T16:16:20.331Z"},"colab":{"base_uri":"https://localhost:8080/"},"id":"cMSiFA-3-NwR","executionInfo":{"status":"ok","timestamp":1620127576883,"user_tz":-540,"elapsed":15809784,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"5ddd8118-4117-48a4-f6da-240d5d59c9a2"},"source":["train(num_epochs, model, train_loader, val_loader, criterion, optimizer, saved_dir, val_every, device, lr_scheduler)"],"execution_count":26,"outputs":[{"output_type":"stream","text":["Start training..\n","Epoch [1/20], Step [25/327], Loss: 0.9218\n","Epoch [1/20], Step [50/327], Loss: 0.6477\n","Epoch [1/20], Step [75/327], Loss: 0.9343\n","Epoch [1/20], Step [100/327], Loss: 0.6615\n","Epoch [1/20], Step [125/327], Loss: 0.4950\n","Epoch [1/20], Step [150/327], Loss: 0.4484\n","Epoch [1/20], Step [175/327], Loss: 0.4600\n","Epoch [1/20], Step [200/327], Loss: 0.4310\n","Epoch [1/20], Step [225/327], Loss: 0.3943\n","Epoch [1/20], Step [250/327], Loss: 0.5291\n","Epoch [1/20], Step [275/327], Loss: 0.3318\n","Epoch [1/20], Step [300/327], Loss: 0.5270\n","Epoch [1/20], Step [325/327], Loss: 0.4092\n","Start validation #1\n","Validation #1 Average Loss: 0.3914, mIoU: 0.3354\n","Best performance at epoch: 1\n","Save model in ./saved\n","Epoch [2/20], Step [25/327], Loss: 0.3248\n","Epoch [2/20], Step [50/327], Loss: 0.5930\n","Epoch [2/20], Step [75/327], Loss: 0.3365\n","Epoch [2/20], Step [100/327], Loss: 0.3523\n","Epoch [2/20], Step [125/327], Loss: 0.4147\n","Epoch [2/20], Step [150/327], Loss: 0.2655\n","Epoch [2/20], Step [175/327], Loss: 0.4538\n","Epoch [2/20], Step [200/327], Loss: 0.2654\n","Epoch [2/20], Step [225/327], Loss: 0.5541\n","Epoch [2/20], Step [250/327], Loss: 0.4156\n","Epoch [2/20], Step [275/327], Loss: 0.2866\n","Epoch [2/20], Step [300/327], Loss: 0.4607\n","Epoch [2/20], Step [325/327], Loss: 0.5620\n","Start validation #2\n","Validation #2 Average Loss: 0.3371, mIoU: 0.3774\n","Best performance at epoch: 2\n","Save model in ./saved\n","Epoch [3/20], Step [25/327], Loss: 0.3724\n","Epoch [3/20], Step [50/327], Loss: 0.2841\n","Epoch [3/20], Step [75/327], Loss: 0.3081\n","Epoch [3/20], Step [100/327], Loss: 0.3203\n","Epoch [3/20], Step [125/327], Loss: 0.3736\n","Epoch [3/20], Step [150/327], Loss: 0.4598\n","Epoch [3/20], Step [175/327], Loss: 0.2281\n","Epoch [3/20], Step [200/327], Loss: 0.2709\n","Epoch [3/20], Step [225/327], Loss: 0.4033\n","Epoch [3/20], Step [250/327], Loss: 0.8694\n","Epoch [3/20], Step [275/327], Loss: 0.2934\n","Epoch [3/20], Step [300/327], Loss: 0.3654\n","Epoch [3/20], Step [325/327], Loss: 0.4774\n","Start validation #3\n","Validation #3 Average Loss: 0.3087, mIoU: 0.3998\n","Best performance at epoch: 3\n","Save model in ./saved\n","Epoch [4/20], Step [25/327], Loss: 0.2533\n","Epoch [4/20], Step [50/327], Loss: 0.1888\n","Epoch [4/20], Step [75/327], Loss: 0.2215\n","Epoch [4/20], Step [100/327], Loss: 0.2205\n","Epoch [4/20], Step [125/327], Loss: 0.2759\n","Epoch [4/20], Step [150/327], Loss: 0.2246\n","Epoch [4/20], Step [175/327], Loss: 0.2020\n","Epoch [4/20], Step [200/327], Loss: 0.2234\n","Epoch [4/20], Step [225/327], Loss: 0.3120\n","Epoch [4/20], Step [250/327], Loss: 0.4925\n","Epoch [4/20], Step [275/327], Loss: 0.2265\n","Epoch [4/20], Step [300/327], Loss: 0.4532\n","Epoch [4/20], Step [325/327], Loss: 0.3335\n","Start validation #4\n","Validation #4 Average Loss: 0.3000, mIoU: 0.4203\n","Best performance at epoch: 4\n","Save model in ./saved\n","Epoch [5/20], Step [25/327], Loss: 0.3056\n","Epoch [5/20], Step [50/327], Loss: 0.3851\n","Epoch [5/20], Step [75/327], Loss: 0.3081\n","Epoch [5/20], Step [100/327], Loss: 0.2861\n","Epoch [5/20], Step [125/327], Loss: 0.2318\n","Epoch [5/20], Step [150/327], Loss: 0.2060\n","Epoch [5/20], Step [175/327], Loss: 0.1815\n","Epoch [5/20], Step [200/327], Loss: 0.3886\n","Epoch [5/20], Step [225/327], Loss: 0.2680\n","Epoch [5/20], Step [250/327], Loss: 0.2160\n","Epoch [5/20], Step [275/327], Loss: 0.1715\n","Epoch [5/20], Step [300/327], Loss: 0.2536\n","Epoch [5/20], Step [325/327], Loss: 0.2091\n","Start validation #5\n","Validation #5 Average Loss: 0.2954, mIoU: 0.4194\n","Epoch [6/20], Step [25/327], Loss: 0.2298\n","Epoch [6/20], Step [50/327], Loss: 0.1877\n","Epoch [6/20], Step [75/327], Loss: 0.1445\n","Epoch [6/20], Step [100/327], Loss: 0.3163\n","Epoch [6/20], Step [125/327], Loss: 0.2116\n","Epoch [6/20], Step [150/327], Loss: 0.1947\n","Epoch [6/20], Step [175/327], Loss: 0.2542\n","Epoch [6/20], Step [200/327], Loss: 0.2903\n","Epoch [6/20], Step [225/327], Loss: 0.1902\n","Epoch [6/20], Step [250/327], Loss: 0.2010\n","Epoch [6/20], Step [275/327], Loss: 0.2249\n","Epoch [6/20], Step [300/327], Loss: 0.3131\n","Epoch [6/20], Step [325/327], Loss: 0.1486\n","Start validation #6\n","Validation #6 Average Loss: 0.2827, mIoU: 0.4280\n","Best performance at epoch: 6\n","Save model in ./saved\n","Epoch [7/20], Step [25/327], Loss: 0.2396\n","Epoch [7/20], Step [50/327], Loss: 0.2501\n","Epoch [7/20], Step [75/327], Loss: 0.1520\n","Epoch [7/20], Step [100/327], Loss: 0.1332\n","Epoch [7/20], Step [125/327], Loss: 0.1917\n","Epoch [7/20], Step [150/327], Loss: 0.2313\n","Epoch [7/20], Step [175/327], Loss: 0.2334\n","Epoch [7/20], Step [200/327], Loss: 0.0808\n","Epoch [7/20], Step [225/327], Loss: 0.2289\n","Epoch [7/20], Step [250/327], Loss: 0.2845\n","Epoch [7/20], Step [275/327], Loss: 0.1982\n","Epoch [7/20], Step [300/327], Loss: 0.1872\n","Epoch [7/20], Step [325/327], Loss: 0.2057\n","Start validation #7\n","Validation #7 Average Loss: 0.2679, mIoU: 0.4468\n","Best performance at epoch: 7\n","Save model in ./saved\n","Epoch [8/20], Step [25/327], Loss: 0.2045\n","Epoch [8/20], Step [50/327], Loss: 0.0937\n","Epoch [8/20], Step [75/327], Loss: 0.1368\n","Epoch [8/20], Step [100/327], Loss: 0.1553\n","Epoch [8/20], Step [125/327], Loss: 0.3154\n","Epoch [8/20], Step [150/327], Loss: 0.5673\n","Epoch [8/20], Step [175/327], Loss: 0.1745\n","Epoch [8/20], Step [200/327], Loss: 0.1490\n","Epoch [8/20], Step [225/327], Loss: 0.2748\n","Epoch [8/20], Step [250/327], Loss: 0.2096\n","Epoch [8/20], Step [275/327], Loss: 0.2101\n","Epoch [8/20], Step [300/327], Loss: 0.2593\n","Epoch [8/20], Step [325/327], Loss: 0.2399\n","Start validation #8\n","Validation #8 Average Loss: 0.3139, mIoU: 0.4419\n","Epoch [9/20], Step [25/327], Loss: 0.1210\n","Epoch [9/20], Step [50/327], Loss: 0.1209\n","Epoch [9/20], Step [75/327], Loss: 0.1979\n","Epoch [9/20], Step [100/327], Loss: 0.1843\n","Epoch [9/20], Step [125/327], Loss: 0.1437\n","Epoch [9/20], Step [150/327], Loss: 0.2776\n","Epoch [9/20], Step [175/327], Loss: 0.2370\n","Epoch [9/20], Step [200/327], Loss: 0.2468\n","Epoch [9/20], Step [225/327], Loss: 0.1816\n","Epoch [9/20], Step [250/327], Loss: 0.1561\n","Epoch [9/20], Step [275/327], Loss: 0.1394\n","Epoch [9/20], Step [300/327], Loss: 0.0673\n","Epoch [9/20], Step [325/327], Loss: 0.1154\n","Start validation #9\n","Validation #9 Average Loss: 0.2711, mIoU: 0.4495\n","Best performance at epoch: 9\n","Save model in ./saved\n","Epoch [10/20], Step [25/327], Loss: 0.2542\n","Epoch [10/20], Step [50/327], Loss: 0.1583\n","Epoch [10/20], Step [75/327], Loss: 0.1991\n","Epoch [10/20], Step [100/327], Loss: 0.1740\n","Epoch [10/20], Step [125/327], Loss: 0.2054\n","Epoch [10/20], Step [150/327], Loss: 0.3160\n","Epoch [10/20], Step [175/327], Loss: 0.1586\n","Epoch [10/20], Step [200/327], Loss: 0.2681\n","Epoch [10/20], Step [225/327], Loss: 0.2215\n","Epoch [10/20], Step [250/327], Loss: 0.1129\n","Epoch [10/20], Step [275/327], Loss: 0.1230\n","Epoch [10/20], Step [300/327], Loss: 0.1888\n","Epoch [10/20], Step [325/327], Loss: 0.1909\n","Start validation #10\n","Validation #10 Average Loss: 0.2659, mIoU: 0.4590\n","Best performance at epoch: 10\n","Save model in ./saved\n","Epoch [11/20], Step [25/327], Loss: 0.1976\n","Epoch [11/20], Step [50/327], Loss: 0.2079\n","Epoch [11/20], Step [75/327], Loss: 0.1074\n","Epoch [11/20], Step [100/327], Loss: 0.1288\n","Epoch [11/20], Step [125/327], Loss: 0.1480\n","Epoch [11/20], Step [150/327], Loss: 0.2663\n","Epoch [11/20], Step [175/327], Loss: 0.2860\n","Epoch [11/20], Step [200/327], Loss: 0.1059\n","Epoch [11/20], Step [225/327], Loss: 0.1201\n","Epoch [11/20], Step [250/327], Loss: 0.2848\n","Epoch [11/20], Step [275/327], Loss: 0.2147\n","Epoch [11/20], Step [300/327], Loss: 0.1909\n","Epoch [11/20], Step [325/327], Loss: 0.1991\n","Start validation #11\n","Validation #11 Average Loss: 0.2779, mIoU: 0.4667\n","Best performance at epoch: 11\n","Save model in ./saved\n","Epoch [12/20], Step [25/327], Loss: 0.1800\n","Epoch [12/20], Step [50/327], Loss: 0.1205\n","Epoch [12/20], Step [75/327], Loss: 0.1392\n","Epoch [12/20], Step [100/327], Loss: 0.1175\n","Epoch [12/20], Step [125/327], Loss: 0.1282\n","Epoch [12/20], Step [150/327], Loss: 0.1357\n","Epoch [12/20], Step [175/327], Loss: 0.2480\n","Epoch [12/20], Step [200/327], Loss: 0.0749\n","Epoch [12/20], Step [225/327], Loss: 0.0970\n","Epoch [12/20], Step [250/327], Loss: 0.1986\n","Epoch [12/20], Step [275/327], Loss: 0.0709\n","Epoch [12/20], Step [300/327], Loss: 0.2269\n","Epoch [12/20], Step [325/327], Loss: 0.2977\n","Start validation #12\n","Validation #12 Average Loss: 0.2798, mIoU: 0.4695\n","Best performance at epoch: 12\n","Save model in ./saved\n","Epoch [13/20], Step [25/327], Loss: 0.0704\n","Epoch [13/20], Step [50/327], Loss: 0.1204\n","Epoch [13/20], Step [75/327], Loss: 0.0616\n","Epoch [13/20], Step [100/327], Loss: 0.2210\n","Epoch [13/20], Step [125/327], Loss: 0.1116\n","Epoch [13/20], Step [150/327], Loss: 0.1634\n","Epoch [13/20], Step [175/327], Loss: 0.0909\n","Epoch [13/20], Step [200/327], Loss: 0.0683\n","Epoch [13/20], Step [225/327], Loss: 0.1646\n","Epoch [13/20], Step [250/327], Loss: 0.1678\n","Epoch [13/20], Step [275/327], Loss: 0.1535\n","Epoch [13/20], Step [300/327], Loss: 0.1779\n","Epoch [13/20], Step [325/327], Loss: 0.1634\n","Start validation #13\n","Validation #13 Average Loss: 0.2997, mIoU: 0.4625\n","Epoch [14/20], Step [25/327], Loss: 0.0624\n","Epoch [14/20], Step [50/327], Loss: 0.1161\n","Epoch [14/20], Step [75/327], Loss: 0.2336\n","Epoch [14/20], Step [100/327], Loss: 0.0819\n","Epoch [14/20], Step [125/327], Loss: 0.1748\n","Epoch [14/20], Step [150/327], Loss: 0.0552\n","Epoch [14/20], Step [175/327], Loss: 0.2491\n","Epoch [14/20], Step [200/327], Loss: 0.1058\n","Epoch [14/20], Step [225/327], Loss: 0.1866\n","Epoch [14/20], Step [250/327], Loss: 0.0949\n","Epoch [14/20], Step [275/327], Loss: 0.0943\n","Epoch [14/20], Step [300/327], Loss: 0.1627\n","Epoch [14/20], Step [325/327], Loss: 0.1186\n","Start validation #14\n","Validation #14 Average Loss: 0.2798, mIoU: 0.4685\n","Epoch [15/20], Step [25/327], Loss: 0.1023\n","Epoch [15/20], Step [50/327], Loss: 0.1345\n","Epoch [15/20], Step [75/327], Loss: 0.1991\n","Epoch [15/20], Step [100/327], Loss: 0.3150\n","Epoch [15/20], Step [125/327], Loss: 0.0917\n","Epoch [15/20], Step [150/327], Loss: 0.1687\n","Epoch [15/20], Step [175/327], Loss: 0.1029\n","Epoch [15/20], Step [200/327], Loss: 0.1178\n","Epoch [15/20], Step [225/327], Loss: 0.1301\n","Epoch [15/20], Step [250/327], Loss: 0.1034\n","Epoch [15/20], Step [275/327], Loss: 0.1078\n","Epoch [15/20], Step [300/327], Loss: 0.1501\n","Epoch [15/20], Step [325/327], Loss: 0.1113\n","Start validation #15\n","Validation #15 Average Loss: 0.2760, mIoU: 0.4663\n","Epoch [16/20], Step [25/327], Loss: 0.1708\n","Epoch [16/20], Step [50/327], Loss: 0.1377\n","Epoch [16/20], Step [75/327], Loss: 0.1045\n","Epoch [16/20], Step [100/327], Loss: 0.0546\n","Epoch [16/20], Step [125/327], Loss: 0.1314\n","Epoch [16/20], Step [150/327], Loss: 0.1496\n","Epoch [16/20], Step [175/327], Loss: 0.0546\n","Epoch [16/20], Step [200/327], Loss: 0.0716\n","Epoch [16/20], Step [225/327], Loss: 0.1119\n","Epoch [16/20], Step [250/327], Loss: 0.1108\n","Epoch [16/20], Step [275/327], Loss: 0.0614\n","Epoch [16/20], Step [300/327], Loss: 0.1237\n","Epoch [16/20], Step [325/327], Loss: 0.0973\n","Start validation #16\n","Validation #16 Average Loss: 0.2761, mIoU: 0.4519\n","Epoch [17/20], Step [25/327], Loss: 0.1182\n","Epoch [17/20], Step [50/327], Loss: 0.1486\n","Epoch [17/20], Step [75/327], Loss: 0.0637\n","Epoch [17/20], Step [100/327], Loss: 0.1147\n","Epoch [17/20], Step [125/327], Loss: 0.1245\n","Epoch [17/20], Step [150/327], Loss: 0.1346\n","Epoch [17/20], Step [175/327], Loss: 0.0738\n","Epoch [17/20], Step [200/327], Loss: 0.0891\n","Epoch [17/20], Step [225/327], Loss: 0.1003\n","Epoch [17/20], Step [250/327], Loss: 0.0942\n","Epoch [17/20], Step [275/327], Loss: 0.0982\n","Epoch [17/20], Step [300/327], Loss: 0.0816\n","Epoch [17/20], Step [325/327], Loss: 0.1334\n","Start validation #17\n","Validation #17 Average Loss: 0.2702, mIoU: 0.4603\n","Epoch [18/20], Step [25/327], Loss: 0.0496\n","Epoch [18/20], Step [50/327], Loss: 0.1211\n","Epoch [18/20], Step [75/327], Loss: 0.0909\n","Epoch [18/20], Step [100/327], Loss: 0.0813\n","Epoch [18/20], Step [125/327], Loss: 0.0364\n","Epoch [18/20], Step [150/327], Loss: 0.0794\n","Epoch [18/20], Step [175/327], Loss: 0.1165\n","Epoch [18/20], Step [200/327], Loss: 0.1301\n","Epoch [18/20], Step [225/327], Loss: 0.0741\n","Epoch [18/20], Step [250/327], Loss: 0.1176\n","Epoch [18/20], Step [275/327], Loss: 0.1148\n","Epoch [18/20], Step [300/327], Loss: 0.0717\n","Epoch [18/20], Step [325/327], Loss: 0.1106\n","Start validation #18\n","Validation #18 Average Loss: 0.2664, mIoU: 0.4633\n","Epoch [19/20], Step [25/327], Loss: 0.0486\n","Epoch [19/20], Step [50/327], Loss: 0.0916\n","Epoch [19/20], Step [75/327], Loss: 0.0931\n","Epoch [19/20], Step [100/327], Loss: 0.0763\n","Epoch [19/20], Step [125/327], Loss: 0.0397\n","Epoch [19/20], Step [150/327], Loss: 0.0810\n","Epoch [19/20], Step [175/327], Loss: 0.0287\n","Epoch [19/20], Step [200/327], Loss: 0.0794\n","Epoch [19/20], Step [225/327], Loss: 0.0569\n","Epoch [19/20], Step [250/327], Loss: 0.0635\n","Epoch [19/20], Step [275/327], Loss: 0.0822\n","Epoch [19/20], Step [300/327], Loss: 0.0906\n","Epoch [19/20], Step [325/327], Loss: 0.1059\n","Start validation #19\n","Validation #19 Average Loss: 0.2675, mIoU: 0.4744\n","Best performance at epoch: 19\n","Save model in ./saved\n","Epoch [20/20], Step [25/327], Loss: 0.0788\n","Epoch [20/20], Step [50/327], Loss: 0.1409\n","Epoch [20/20], Step [75/327], Loss: 0.1096\n","Epoch [20/20], Step [100/327], Loss: 0.0990\n","Epoch [20/20], Step [125/327], Loss: 0.1027\n","Epoch [20/20], Step [150/327], Loss: 0.0484\n","Epoch [20/20], Step [175/327], Loss: 0.0678\n","Epoch [20/20], Step [200/327], Loss: 0.0793\n","Epoch [20/20], Step [225/327], Loss: 0.0669\n","Epoch [20/20], Step [250/327], Loss: 0.0730\n","Epoch [20/20], Step [275/327], Loss: 0.1037\n","Epoch [20/20], Step [300/327], Loss: 0.0440\n","Epoch [20/20], Step [325/327], Loss: 0.0817\n","Start validation #20\n","Validation #20 Average Loss: 0.2788, mIoU: 0.4599\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"pJADWpckFx57","executionInfo":{"status":"ok","timestamp":1620127581687,"user_tz":-540,"elapsed":4794,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# Sleep for a few seconds.\n","import time\n","time.sleep(2)\n","# Play an audio beep. Any audio URL will do.\n","from google.colab import output\n","output.eval_js('new Audio(\"https://upload.wikimedia.org/wikipedia/commons/0/05/Beep-09.ogg\").play()')"],"execution_count":27,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"C6ClcO0J-NwR"},"source":["## 저장된 model 불러오기 (학습된 이후) "]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:21.050200Z","start_time":"2021-04-16T19:44:20.802200Z"},"scrolled":true,"id":"KQPtUDzd-NwR","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620102899370,"user_tz":-540,"elapsed":882,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"72d54b02-20cf-4ea6-991d-52fd5331c0a6"},"source":["# best model 저장된 경로\n","model_path = './saved/pan_effb3_noisy_focal_madgrad_cosLR.pt'\n","\n","# best model 불러오기\n","checkpoint = torch.load(model_path, map_location=device)\n","model.load_state_dict(checkpoint)\n","\n","# 추론을 실행하기 전에는 반드시 설정 (batch normalization, dropout 를 평가 모드로 설정)\n","# model.eval()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{"tags":[]},"execution_count":16}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:24.939227Z","start_time":"2021-04-16T19:44:24.518228Z"},"id":"0LQqrDAp-NwR","colab":{"base_uri":"https://localhost:8080/","height":502},"executionInfo":{"status":"ok","timestamp":1620102907649,"user_tz":-540,"elapsed":6266,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"b34da4ee-1fe9-4960-f841-9627d644c50b"},"source":["# 첫번째 batch의 추론 결과 확인\n","for imgs, image_infos in test_loader:\n"," image_infos = image_infos\n"," temp_images = imgs\n"," \n"," model.eval()\n"," # inference\n"," outs = model(torch.stack(temp_images).to(device))\n"," oms = torch.argmax(outs.squeeze(), dim=1).detach().cpu().numpy()\n"," \n"," break\n","\n","i = 1\n","fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(16, 16))\n","\n","print('Shape of Original Image :', list(temp_images[i].shape))\n","print('Shape of Predicted : ', list(oms[i].shape))\n","print('Unique values, category of transformed mask : \\n', [{int(i),category_names[int(i)]} for i in list(np.unique(oms[i]))])\n","\n","# Original image\n","ax1.imshow(temp_images[i].permute([1,2,0]))\n","ax1.grid(False)\n","ax1.set_title(\"Original image : {}\".format(image_infos[i]['file_name']), fontsize = 15)\n","\n","# Predicted\n","ax2.imshow(oms[i])\n","ax2.grid(False)\n","ax2.set_title(\"Predicted : {}\".format(image_infos[i]['file_name']), fontsize = 15)\n","\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Shape of Original Image : [3, 512, 512]\n","Shape of Predicted : [512, 512]\n","Unique values, category of transformed mask : \n"," [{0, 'Backgroud'}, {'General trash', 2}, {'Paper', 3}, {9, 'Plastic bag'}, {11, 'Clothing'}]\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"evYjR2F3-NwS"},"source":["## submission을 위한 test 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:27.469285Z","start_time":"2021-04-16T19:44:27.456021Z"},"id":"nhMLnV5d-NwS"},"source":["def test(model, data_loader, device):\n"," size = 256\n"," transform = A.Compose([A.Resize(256, 256)])\n"," print('Start prediction.')\n"," model.eval()\n"," \n"," file_name_list = []\n"," preds_array = np.empty((0, size*size), dtype=np.long)\n"," \n"," with torch.no_grad():\n"," for step, (imgs, image_infos) in enumerate(test_loader):\n","\n"," # inference (512 x 512)\n"," outs = model(torch.stack(imgs).to(device))\n"," oms = torch.argmax(outs.squeeze(), dim=1).detach().cpu().numpy()\n"," \n"," # resize (256 x 256)\n"," temp_mask = []\n"," for img, mask in zip(np.stack(imgs), oms):\n"," transformed = transform(image=img, mask=mask)\n"," mask = transformed['mask']\n"," temp_mask.append(mask)\n","\n"," oms = np.array(temp_mask)\n"," \n"," oms = oms.reshape([oms.shape[0], size*size]).astype(int)\n"," preds_array = np.vstack((preds_array, oms))\n"," \n"," file_name_list.append([i['file_name'] for i in image_infos])\n"," print(\"End prediction.\")\n"," file_names = [y for x in file_name_list for y in x]\n"," \n"," return file_names, preds_array"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"r1YKHBf4-NwT"},"source":["## submission.csv 생성"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:45:42.235310Z","start_time":"2021-04-16T19:44:30.499016Z"},"scrolled":true,"id":"Bz79_g8K-NwT","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620103458747,"user_tz":-540,"elapsed":512460,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"36e4f410-35c7-4b9c-9e4a-e66269f73292"},"source":["# sample_submisson.csv 열기\n","submission = pd.read_csv('./submission/sample_submission.csv', index_col=None)\n","\n","# test set에 대한 prediction\n","file_names, preds = test(model, test_loader, device)\n","\n","# PredictionString 대입\n","for file_name, string in zip(file_names, preds):\n"," submission = submission.append({\"image_id\" : file_name, \"PredictionString\" : ' '.join(str(e) for e in string.tolist())}, \n"," ignore_index=True)\n","\n","# submission.csv로 저장\n","submission.to_csv(\"./submission/re_pan_effb3_noisy_focal_madgrad_cosLR.csv\", index=False)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Start prediction.\n","End prediction.\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"Zgl7Ye7F-NwT"},"source":["## 제출까지\n","\n"]},{"cell_type":"code","metadata":{"id":"022E8AC7-NwT","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620103464861,"user_tz":-540,"elapsed":5218,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"1b00da55-62cc-4fa6-c823-83dcbaa871b0"},"source":["import json\n","import requests\n","import os\n","from urllib.parse import urlparse, parse_qsl, urlencode, urlunparse\n","\n","def submit(user_key='', file_path = '', desc=\"\"):\n"," if not user_key:\n"," raise Exception(\"No UserKey\" )\n"," url = urlparse('http://ec2-13-124-161-225.ap-northeast-2.compute.amazonaws.com:8000/api/v1/competition/28/presigned_url/?description=&hyperparameters={%22training%22:{},%22inference%22:{}}')\n"," qs = dict(parse_qsl(url.query))\n"," qs['description'] = desc\n"," parts = url._replace(query=urlencode(qs))\n"," url = urlunparse(parts)\n","\n"," print(url)\n"," headers = {\n"," 'Authorization': user_key\n"," }\n"," res = requests.get(url, headers=headers)\n"," print(res.text)\n"," data = json.loads(res.text)\n"," \n"," submit_url = data['url']\n"," body = {\n"," 'key':'app/Competitions/000028/Users/{}/Submissions/{}/output.csv'.format(str(data['submission']['user']).zfill(8),str(data['submission']['local_id']).zfill(4)),\n"," 'x-amz-algorithm':data['fields']['x-amz-algorithm'],\n"," 'x-amz-credential':data['fields']['x-amz-credential'],\n"," 'x-amz-date':data['fields']['x-amz-date'],\n"," 'policy':data['fields']['policy'],\n"," 'x-amz-signature':data['fields']['x-amz-signature']\n"," }\n"," requests.post(url=submit_url, data=body, files={'file': open(file_path, 'rb')})\n","\n","\n","####################################################################################\n","test_dir = \"/content/drive/MyDrive/Trash/code/submission\" # 수정 필요 : output 파일 폴더 \n","desc = 're_pan_effb3_noisy_focal_madgrad_cosLR' # 수정 필요 : 파일에 대한 설명\n","output_file = \"re_pan_effb3_noisy_focal_madgrad_cosLR.csv\" #수정 필요 : output 파일 \n","user_key = \"Bearer 7bb5f96452751a238ffaf91a93c4242bf9b72abe\" # 수정 필요 : Authorization \n","\n","\n","submit(user_key, os.path.join(test_dir, output_file),desc)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["http://ec2-13-124-161-225.ap-northeast-2.compute.amazonaws.com:8000/api/v1/competition/28/presigned_url/?hyperparameters=%7B%22training%22%3A%7B%7D%2C%22inference%22%3A%7B%7D%7D&description=re_pan_effb3_noisy_focal_madgrad_cosLR\n","{\"url\":\"https://prod-aistages-private.s3.amazonaws.com/\",\"fields\":{\"key\":\"app/Competitions/000028/Users/00000099/Submissions/0021/output.csv\",\"x-amz-algorithm\":\"AWS4-HMAC-SHA256\",\"x-amz-credential\":\"AKIA45LU4MHUJ7WLDQVO/20210504/ap-northeast-2/s3/aws4_request\",\"x-amz-date\":\"20210504T044420Z\",\"policy\":\"eyJleHBpcmF0aW9uIjogIjIwMjEtMDUtMDRUMDU6NDQ6MjBaIiwgImNvbmRpdGlvbnMiOiBbeyJidWNrZXQiOiAicHJvZC1haXN0YWdlcy1wcml2YXRlIn0sIHsia2V5IjogImFwcC9Db21wZXRpdGlvbnMvMDAwMDI4L1VzZXJzLzAwMDAwMDk5L1N1Ym1pc3Npb25zLzAwMjEvb3V0cHV0LmNzdiJ9LCB7IngtYW16LWFsZ29yaXRobSI6ICJBV1M0LUhNQUMtU0hBMjU2In0sIHsieC1hbXotY3JlZGVudGlhbCI6ICJBS0lBNDVMVTRNSFVKN1dMRFFWTy8yMDIxMDUwNC9hcC1ub3J0aGVhc3QtMi9zMy9hd3M0X3JlcXVlc3QifSwgeyJ4LWFtei1kYXRlIjogIjIwMjEwNTA0VDA0NDQyMFoifV19\",\"x-amz-signature\":\"f10073e85a92d49dacd44915305fdc789f99edede496d9b4f5029a16e422a52f\"},\"submission\":{\"id\":14867,\"phase\":\"Created\",\"type\":\"File\",\"local_id\":21,\"hyperparameters\":\"{\\\"training\\\": {}, \\\"inference\\\": {}}\",\"description\":\"re_pan_effb3_noisy_focal_madgrad_cosLR\",\"final\":false,\"created_at\":\"2021-05-04T13:44:20.427630+09:00\",\"updated_at\":\"2021-05-04T13:44:20.427663+09:00\",\"user\":99,\"competition\":28,\"image\":null}}\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"wPYl39uVqxL8"},"source":[""],"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/chanyub_seg/code/madgrad.ipynb b/chanyub_seg/code/madgrad.ipynb deleted file mode 100644 index 9e6858a..0000000 --- a/chanyub_seg/code/madgrad.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"nbformat":4,"nbformat_minor":0,"metadata":{"hide_input":false,"kernelspec":{"display_name":"Python 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Table of Contents

\n","
"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QBSL7_LP9ONj","executionInfo":{"status":"ok","timestamp":1619944808745,"user_tz":-540,"elapsed":766,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"72482471-7b23-460b-9025-be10625b85c3"},"source":["ls"],"execution_count":null,"outputs":[{"output_type":"stream","text":[" FCN32s.ipynb sample_submission.csv 'UNet++ baseline.ipynb'\n"," mybaseline.ipynb \u001b[0m\u001b[01;34msaved\u001b[0m/ utils.py\n"," \u001b[01;34m__pycache__\u001b[0m/ \u001b[01;34msubmission\u001b[0m/ \u001b[01;34mwandb\u001b[0m/\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"40-m-lE19ewI","executionInfo":{"status":"ok","timestamp":1620026589548,"user_tz":-540,"elapsed":1492,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"0b7b9261-c831-45ea-9c8d-123bf8aa8c84"},"source":["cd drive/MyDrive/Trash/code"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/content/drive/MyDrive/Trash/code\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"MJojTRE39ULc","executionInfo":{"status":"ok","timestamp":1620026578823,"user_tz":-540,"elapsed":50374,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"0b7f8ec9-b3d1-468c-a523-4f14f226497d"},"source":["from google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Mounted at /content/drive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Ys0WTRaJ91VZ","executionInfo":{"status":"ok","timestamp":1620026587948,"user_tz":-540,"elapsed":11942,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"636ecec0-18c5-47c4-9e11-225b538b56d7"},"source":["!pip install albumentations==0.5.2"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Collecting albumentations==0.5.2\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/03/58/63fb1d742dc42d9ba2800ea741de1f2bc6bb05548d8724aa84794042eaf2/albumentations-0.5.2-py3-none-any.whl (72kB)\n","\r\u001b[K |████▌ | 10kB 16.6MB/s eta 0:00:01\r\u001b[K |█████████ | 20kB 20.8MB/s eta 0:00:01\r\u001b[K |█████████████▋ | 30kB 14.0MB/s eta 0:00:01\r\u001b[K |██████████████████▏ | 40kB 10.3MB/s eta 0:00:01\r\u001b[K |██████████████████████▊ | 51kB 9.1MB/s eta 0:00:01\r\u001b[K |███████████████████████████▏ | 61kB 7.9MB/s eta 0:00:01\r\u001b[K |███████████████████████████████▊| 71kB 8.8MB/s eta 0:00:01\r\u001b[K |████████████████████████████████| 81kB 5.4MB/s \n","\u001b[?25hRequirement already satisfied: numpy>=1.11.1 in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (1.19.5)\n","Requirement already satisfied: scikit-image>=0.16.1 in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (0.16.2)\n","Collecting opencv-python-headless>=4.1.1\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/6d/6d/92f377bece9b0ec9c893081dbe073a65b38d7ac12ef572b8f70554d08760/opencv_python_headless-4.5.1.48-cp37-cp37m-manylinux2014_x86_64.whl (37.6MB)\n","\u001b[K |████████████████████████████████| 37.6MB 155kB/s \n","\u001b[?25hRequirement already satisfied: PyYAML in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (3.13)\n","Collecting imgaug>=0.4.0\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/66/b1/af3142c4a85cba6da9f4ebb5ff4e21e2616309552caca5e8acefe9840622/imgaug-0.4.0-py2.py3-none-any.whl (948kB)\n","\u001b[K |████████████████████████████████| 952kB 45.8MB/s \n","\u001b[?25hRequirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (1.4.1)\n","Requirement already satisfied: matplotlib!=3.0.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations==0.5.2) (3.2.2)\n","Requirement already satisfied: imageio>=2.3.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations==0.5.2) (2.4.1)\n","Requirement already satisfied: pillow>=4.3.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations==0.5.2) (7.1.2)\n","Requirement already satisfied: PyWavelets>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations==0.5.2) (1.1.1)\n","Requirement already satisfied: networkx>=2.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations==0.5.2) (2.5.1)\n","Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (1.15.0)\n","Requirement already satisfied: opencv-python in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (4.1.2.30)\n","Requirement already satisfied: Shapely in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (1.7.1)\n","Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.16.1->albumentations==0.5.2) (2.8.1)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.16.1->albumentations==0.5.2) (1.3.1)\n","Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.16.1->albumentations==0.5.2) (2.4.7)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.16.1->albumentations==0.5.2) (0.10.0)\n","Requirement already satisfied: decorator<5,>=4.3 in /usr/local/lib/python3.7/dist-packages (from networkx>=2.0->scikit-image>=0.16.1->albumentations==0.5.2) (4.4.2)\n","Installing collected packages: opencv-python-headless, imgaug, albumentations\n"," Found existing installation: imgaug 0.2.9\n"," Uninstalling imgaug-0.2.9:\n"," Successfully uninstalled imgaug-0.2.9\n"," Found existing installation: albumentations 0.1.12\n"," Uninstalling albumentations-0.1.12:\n"," Successfully uninstalled albumentations-0.1.12\n","Successfully installed albumentations-0.5.2 imgaug-0.4.0 opencv-python-headless-4.5.1.48\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.826930Z","start_time":"2021-04-18T10:34:45.406686Z"},"scrolled":false,"colab":{"base_uri":"https://localhost:8080/"},"id":"k5pVFOkJ8sQX","executionInfo":{"status":"ok","timestamp":1620026596766,"user_tz":-540,"elapsed":4760,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"ab01ab13-8f31-4064-a6e4-9a7b2e2c2227"},"source":["import os\n","import random\n","import time\n","import json\n","import warnings \n","warnings.filterwarnings('ignore')\n","\n","import torch\n","import torch.nn as nn\n","from torch.utils.data import Dataset, DataLoader\n","from utils import label_accuracy_score\n","import cv2\n","\n","import numpy as np\n","import pandas as pd\n","\n","# 전처리를 위한 라이브러리\n","from pycocotools.coco import COCO\n","import torchvision\n","import torchvision.transforms as transforms\n","\n","import albumentations as A\n","from albumentations.pytorch import ToTensorV2\n","\n","# 시각화를 위한 라이브러리\n","import matplotlib.pyplot as plt\n","import seaborn as sns; sns.set()\n","\n","plt.rcParams['axes.grid'] = False\n","\n","print('pytorch version: {}'.format(torch.__version__))\n","print('GPU 사용 가능 여부: {}'.format(torch.cuda.is_available()))\n","\n","print(torch.cuda.get_device_name(0))\n","print(torch.cuda.device_count())\n","\n","device = \"cuda\" if torch.cuda.is_available() else \"cpu\" # GPU 사용 가능 여부에 따라 device 정보 저장"],"execution_count":null,"outputs":[{"output_type":"stream","text":["pytorch version: 1.8.1+cu101\n","GPU 사용 가능 여부: True\n","Tesla P100-PCIE-16GB\n","1\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"bGTnZqwO8sQa"},"source":["## 하이퍼파라미터 세팅 및 seed 고정"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.841930Z","start_time":"2021-04-18T10:34:47.827931Z"},"id":"dV2e6X4l8sQb"},"source":["batch_size = 8 # Mini-batch size\n","num_epochs = 20\n","learning_rate = 0.0001"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.856930Z","start_time":"2021-04-18T10:34:47.842931Z"},"id":"lFBFwi8T8sQe"},"source":["# seed 고정\n","random_seed = 42\n","torch.manual_seed(random_seed)\n","torch.cuda.manual_seed(random_seed)\n","# torch.cuda.manual_seed_all(random_seed) # if use multi-GPU\n","torch.backends.cudnn.deterministic = True\n","torch.backends.cudnn.benchmark = False\n","np.random.seed(random_seed)\n","random.seed(random_seed)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"d_aMBo_P8sQg"},"source":["## 학습 데이터 EDA"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.381961Z","start_time":"2021-04-18T10:34:47.857930Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"IZ8czxZE8sQg","executionInfo":{"status":"ok","timestamp":1620026607888,"user_tz":-540,"elapsed":9815,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"2b84023c-5117-4cfb-b3fd-22c094899d4e"},"source":["%matplotlib inline\n","\n","dataset_path = '../input/data'\n","anns_file_path = dataset_path + '/' + 'train.json'\n","\n","# Read annotations\n","with open(anns_file_path, 'r') as f:\n"," dataset = json.loads(f.read())\n","\n","categories = dataset['categories']\n","anns = dataset['annotations']\n","imgs = dataset['images']\n","nr_cats = len(categories)\n","nr_annotations = len(anns)\n","nr_images = len(imgs)\n","\n","# Load categories and super categories\n","cat_names = []\n","super_cat_names = []\n","super_cat_ids = {}\n","super_cat_last_name = ''\n","nr_super_cats = 0\n","for cat_it in categories:\n"," cat_names.append(cat_it['name'])\n"," super_cat_name = cat_it['supercategory']\n"," # Adding new supercat\n"," if super_cat_name != super_cat_last_name:\n"," super_cat_names.append(super_cat_name)\n"," super_cat_ids[super_cat_name] = nr_super_cats\n"," super_cat_last_name = super_cat_name\n"," nr_super_cats += 1\n","\n","print('Number of super categories:', nr_super_cats)\n","print('Number of categories:', nr_cats)\n","print('Number of annotations:', nr_annotations)\n","print('Number of images:', nr_images)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Number of super categories: 11\n","Number of categories: 11\n","Number of annotations: 21116\n","Number of images: 2617\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.546964Z","start_time":"2021-04-18T10:34:51.382969Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/","height":355},"id":"Xjp6yDNe8sQi","executionInfo":{"status":"ok","timestamp":1620026610906,"user_tz":-540,"elapsed":2815,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"7ba53d58-f913-4751-ee67-bc2b13c2b47b"},"source":["# Count annotations\n","cat_histogram = np.zeros(nr_cats,dtype=int)\n","for ann in anns:\n"," cat_histogram[ann['category_id']] += 1\n","\n","# Initialize the matplotlib figure\n","f, ax = plt.subplots(figsize=(5,5))\n","\n","# Convert to DataFrame\n","df = pd.DataFrame({'Categories': cat_names, 'Number of annotations': cat_histogram})\n","df = df.sort_values('Number of annotations', 0, False)\n","\n","# Plot the histogram\n","plt.title(\"category distribution of train set \")\n","plot_1 = sns.barplot(x=\"Number of annotations\", y=\"Categories\", data=df, label=\"Total\", color=\"b\")"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.561965Z","start_time":"2021-04-18T10:34:51.547969Z"},"id":"F5imLAv78sQj"},"source":["# category labeling \n","sorted_temp_df = df.sort_index()\n","\n","# background = 0 에 해당되는 label 추가 후 기존들을 모두 label + 1 로 설정\n","sorted_df = pd.DataFrame([\"Backgroud\"], columns = [\"Categories\"])\n","sorted_df = sorted_df.append(sorted_temp_df, ignore_index=True)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.576961Z","start_time":"2021-04-18T10:34:51.562964Z"},"scrolled":false,"colab":{"base_uri":"https://localhost:8080/","height":421},"id":"t7VfbZUe8sQj","executionInfo":{"status":"ok","timestamp":1620026610940,"user_tz":-540,"elapsed":2728,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"ed8e50b5-90d7-4e96-95ea-15c2feddce3d"},"source":["# class (Categories) 에 따른 index 확인 (0~11 : 총 12개)\n","sorted_df"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
CategoriesNumber of annotations
0BackgroudNaN
1UNKNOWN128.0
2General trash2225.0
3Paper7448.0
4Paper pack527.0
5Metal449.0
6Glass488.0
7Plastic2472.0
8Styrofoam1074.0
9Plastic bag6114.0
10Battery50.0
11Clothing141.0
\n","
"],"text/plain":[" Categories Number of annotations\n","0 Backgroud NaN\n","1 UNKNOWN 128.0\n","2 General trash 2225.0\n","3 Paper 7448.0\n","4 Paper pack 527.0\n","5 Metal 449.0\n","6 Glass 488.0\n","7 Plastic 2472.0\n","8 Styrofoam 1074.0\n","9 Plastic bag 6114.0\n","10 Battery 50.0\n","11 Clothing 141.0"]},"metadata":{"tags":[]},"execution_count":10}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":396},"id":"74VbmLZI7HYs","executionInfo":{"status":"ok","timestamp":1620024976623,"user_tz":-540,"elapsed":3928,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"94bbf3a6-fce8-4b7c-adfa-ebb5acae3516"},"source":["# train_loader의 output 결과(image 및 mask) 확인\n","for imgs, masks, image_infos in train_loader:\n"," image_infos = image_infos[0]\n"," temp_images = imgs\n"," temp_masks = masks\n"," \n"," break\n","\n","fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(12, 12))\n","\n","print('image shape:', list(temp_images[0].shape))\n","print('mask shape: ', list(temp_masks[0].shape))\n","print('Unique values, category of transformed mask : \\n', [{int(i),category_names[int(i)]} for i in list(np.unique(temp_masks[0]))])\n","\n","ax1.imshow(temp_images[0].permute([1,2,0]))\n","ax1.grid(False)\n","ax1.set_title(\"input image : {}\".format(image_infos['file_name']), fontsize = 15)\n","\n","ax2.imshow(temp_masks[0])\n","ax2.grid(False)\n","ax2.set_title(\"masks : {}\".format(image_infos['file_name']), fontsize = 15)\n","\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["image shape: [3, 512, 512]\n","mask shape: [512, 512]\n","Unique values, category of transformed mask : \n"," [{0, 'Backgroud'}, {'Plastic', 7}]\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"dL-azWBK8sQk"},"source":["## 데이터 전처리 함수 정의 (Dataset)"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:52.693328Z","start_time":"2021-04-18T10:34:52.681328Z"},"id":"rKnBddei8sQk"},"source":["category_names = list(sorted_df.Categories)\n","\n","def get_classname(classID, cats):\n"," for i in range(len(cats)):\n"," if cats[i]['id']==classID:\n"," return cats[i]['name']\n"," return \"None\"\n","\n","class CustomDataLoader(Dataset):\n"," \"\"\"COCO format\"\"\"\n"," def __init__(self, data_dir, mode = 'train', transform = None):\n"," super().__init__()\n"," self.mode = mode\n"," self.transform = transform\n"," self.coco = COCO(data_dir)\n"," \n"," def __getitem__(self, index: int):\n"," # dataset이 index되어 list처럼 동작\n"," image_id = self.coco.getImgIds(imgIds=index)\n"," image_infos = self.coco.loadImgs(image_id)[0]\n"," \n"," # cv2 를 활용하여 image 불러오기\n"," images = cv2.imread(os.path.join(dataset_path, image_infos['file_name']))\n"," images = cv2.cvtColor(images, cv2.COLOR_BGR2RGB).astype(np.float32)\n"," images /= 255.0\n"," \n"," if (self.mode in ('train', 'val')):\n"," ann_ids = self.coco.getAnnIds(imgIds=image_infos['id'])\n"," anns = self.coco.loadAnns(ann_ids)\n","\n"," # Load the categories in a variable\n"," cat_ids = self.coco.getCatIds()\n"," cats = self.coco.loadCats(cat_ids)\n","\n"," # masks : size가 (height x width)인 2D\n"," # 각각의 pixel 값에는 \"category id + 1\" 할당\n"," # Background = 0\n"," masks = np.zeros((image_infos[\"height\"], image_infos[\"width\"]))\n"," # Unknown = 1, General trash = 2, ... , Cigarette = 11\n"," for i in range(len(anns)):\n"," className = get_classname(anns[i]['category_id'], cats)\n"," pixel_value = category_names.index(className)\n"," masks = np.maximum(self.coco.annToMask(anns[i])*pixel_value, masks)\n"," masks = masks.astype(np.float32)\n","\n"," # transform -> albumentations 라이브러리 활용\n"," if self.transform is not None:\n"," transformed = self.transform(image=images, mask=masks)\n"," images = transformed[\"image\"]\n"," masks = transformed[\"mask\"]\n"," \n"," return images, masks, image_infos\n"," \n"," if self.mode == 'test':\n"," # transform -> albumentations 라이브러리 활용\n"," if self.transform is not None:\n"," transformed = self.transform(image=images)\n"," images = transformed[\"image\"]\n"," \n"," return images, image_infos\n"," \n"," \n"," def __len__(self) -> int:\n"," # 전체 dataset의 size를 return\n"," return len(self.coco.getImgIds())"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Lui--J9t8sQm"},"source":["## Dataset 정의 및 DataLoader 할당"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:58.823175Z","start_time":"2021-04-18T10:34:54.106233Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"_1PmdNvf8sQm","executionInfo":{"status":"ok","timestamp":1620026623853,"user_tz":-540,"elapsed":7484,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"ea044851-946e-432a-9d34-b12f5b6f89c1"},"source":["# train.json / validation.json / test.json 디렉토리 설정\n","train_path = dataset_path + '/train.json'\n","val_path = dataset_path + '/val.json'\n","test_path = dataset_path + '/test.json'\n","\n","# collate_fn needs for batch\n","def collate_fn(batch):\n"," return tuple(zip(*batch))\n","\n","train_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","val_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","test_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","# create own Dataset 1 (skip)\n","# validation set을 직접 나누고 싶은 경우\n","# random_split 사용하여 data set을 8:2 로 분할\n","# train_size = int(0.8*len(dataset))\n","# val_size = int(len(dataset)-train_size)\n","# dataset = CustomDataLoader(data_dir=train_path, mode='train', transform=train_transform)\n","# train_dataset, val_dataset = torch.utils.data.random_split(dataset, [train_size, val_size])\n","\n","# create own Dataset 2\n","# train dataset\n","train_dataset = CustomDataLoader(data_dir=train_path, mode='train', transform=train_transform)\n","\n","# validation dataset\n","val_dataset = CustomDataLoader(data_dir=val_path, mode='val', transform=val_transform)\n","\n","# test dataset\n","test_dataset = CustomDataLoader(data_dir=test_path, mode='test', transform=test_transform)\n","\n","\n","# DataLoader\n","train_loader = torch.utils.data.DataLoader(dataset=train_dataset, \n"," batch_size=batch_size,\n"," shuffle=True,\n"," num_workers=4,\n"," collate_fn=collate_fn,\n"," drop_last=True)\n","\n","val_loader = torch.utils.data.DataLoader(dataset=val_dataset, \n"," batch_size=batch_size,\n"," shuffle=False,\n"," num_workers=4,\n"," collate_fn=collate_fn,\n"," drop_last=True) \n","\n","test_loader = torch.utils.data.DataLoader(dataset=test_dataset,\n"," batch_size=batch_size,\n"," num_workers=4,\n"," collate_fn=collate_fn)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["loading annotations into memory...\n","Done (t=4.19s)\n","creating index...\n","index created!\n","loading annotations into memory...\n","Done (t=1.99s)\n","creating index...\n","index created!\n","loading annotations into memory...\n","Done (t=0.52s)\n","creating index...\n","index created!\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"4-uzBwkH8sQ2"},"source":["## baseline model\n","\n","### [TODO] 코드 구현 UNet++ \n","\n","- 출처 : https://jinglescode.github.io/2019/12/02/biomedical-image-segmentation-u-net-nested/"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"gGgEjCSsAo7q","executionInfo":{"status":"ok","timestamp":1620026636162,"user_tz":-540,"elapsed":11227,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"a6aafc29-0e5b-42c9-dd3e-67ee7fa208d3"},"source":["!pip install wandb"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Collecting wandb\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/5c/ee/d755f9e5466df64c8416a2c6a860fb3aaa43ed6ea8e8e8e81460fda5788b/wandb-0.10.28-py2.py3-none-any.whl (2.1MB)\n","\u001b[K |████████████████████████████████| 2.1MB 8.0MB/s \n","\u001b[?25hCollecting sentry-sdk>=0.4.0\n","\u001b[?25l Downloading 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requests<3,>=2.0.0->wandb) (2.10)\n","Collecting gitdb<5,>=4.0.1\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/ea/e8/f414d1a4f0bbc668ed441f74f44c116d9816833a48bf81d22b697090dba8/gitdb-4.0.7-py3-none-any.whl (63kB)\n","\u001b[K |████████████████████████████████| 71kB 7.7MB/s \n","\u001b[?25hRequirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from protobuf>=3.12.0->wandb) (56.0.0)\n","Collecting smmap<5,>=3.0.1\n"," Downloading https://files.pythonhosted.org/packages/68/ee/d540eb5e5996eb81c26ceffac6ee49041d473bc5125f2aa995cf51ec1cf1/smmap-4.0.0-py2.py3-none-any.whl\n","Building wheels for collected packages: pathtools, subprocess32\n"," Building wheel for pathtools (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for pathtools: filename=pathtools-0.1.2-cp37-none-any.whl size=8786 sha256=16139842a137769e9e970db3222b5923281859ea272b2308995d9725d1bee98b\n"," Stored in directory: /root/.cache/pip/wheels/0b/04/79/c3b0c3a0266a3cb4376da31e5bfe8bba0c489246968a68e843\n"," Building wheel for subprocess32 (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for subprocess32: filename=subprocess32-3.5.4-cp37-none-any.whl size=6489 sha256=cfa961830447632364eb1314c36793ac8454aea35a07da0f0a201534178b0308\n"," Stored in directory: /root/.cache/pip/wheels/68/39/1a/5e402bdfdf004af1786c8b853fd92f8c4a04f22aad179654d1\n","Successfully built pathtools subprocess32\n","Installing collected packages: sentry-sdk, pathtools, subprocess32, configparser, smmap, gitdb, GitPython, shortuuid, docker-pycreds, wandb\n","Successfully installed GitPython-3.1.14 configparser-5.0.2 docker-pycreds-0.4.0 gitdb-4.0.7 pathtools-0.1.2 sentry-sdk-1.0.0 shortuuid-1.0.1 smmap-4.0.0 subprocess32-3.5.4 wandb-0.10.28\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":187},"id":"F89FhXcJ_QpU","executionInfo":{"status":"ok","timestamp":1620026674896,"user_tz":-540,"elapsed":36726,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"1d6aeeb1-6d51-4505-954d-eaf2143c88d6"},"source":["import wandb\n","\n","proj_name = 'v3+_focal_coslr_mIoU_madgrad'\n","\n","wandb.init(project='chanyub',name=proj_name)\n","\n","config = wandb.config\n","config.learning_rate = 0.01"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"application/javascript":["\n"," window._wandbApiKey = new Promise((resolve, reject) => {\n"," function loadScript(url) {\n"," return new Promise(function(resolve, reject) {\n"," let newScript = document.createElement(\"script\");\n"," newScript.onerror = reject;\n"," newScript.onload = resolve;\n"," document.body.appendChild(newScript);\n"," newScript.src = url;\n"," });\n"," }\n"," loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n"," const iframe = document.createElement('iframe')\n"," iframe.style.cssText = \"width:0;height:0;border:none\"\n"," document.body.appendChild(iframe)\n"," const handshake = new Postmate({\n"," container: iframe,\n"," url: 'https://wandb.ai/authorize'\n"," });\n"," const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n"," handshake.then(function(child) {\n"," child.on('authorize', data => {\n"," clearTimeout(timeout)\n"," resolve(data)\n"," });\n"," });\n"," })\n"," });\n"," "],"text/plain":[""]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n"],"name":"stderr"},{"output_type":"stream","text":["wandb: Paste an API key from your profile and hit enter: ··········\n"],"name":"stdout"},{"output_type":"stream","text":["\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"],"name":"stderr"},{"output_type":"display_data","data":{"text/html":["\n"," Tracking run with wandb version 0.10.28
\n"," Syncing run v3+_focal_coslr_mIoU_madgrad to Weights & Biases (Documentation).
\n"," Project page: https://wandb.ai/pstage12/chanyub
\n"," Run page: https://wandb.ai/pstage12/chanyub/runs/dqetr9sl
\n"," Run data is saved locally in /content/drive/My Drive/Trash/code/wandb/run-20210503_072431-dqetr9sl

\n"," "],"text/plain":[""]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"mVz3IiRHAuov","executionInfo":{"status":"ok","timestamp":1620026681980,"user_tz":-540,"elapsed":42203,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"389bb005-9f7d-4528-b535-74541b12adee"},"source":["!pip install segmentation_models_pytorch"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Collecting segmentation_models_pytorch\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/65/54/8953f9f7ee9d451b0f3be8d635aa3a654579abf898d17502a090efe1155a/segmentation_models_pytorch-0.1.3-py3-none-any.whl (66kB)\n","\u001b[K |████████████████████████████████| 71kB 3.1MB/s \n","\u001b[?25hCollecting pretrainedmodels==0.7.4\n","\u001b[?25l Downloading 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(1.8.1+cu101)\n","Collecting munch\n"," Downloading https://files.pythonhosted.org/packages/cc/ab/85d8da5c9a45e072301beb37ad7f833cd344e04c817d97e0cc75681d248f/munch-2.5.0-py2.py3-none-any.whl\n","Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from pretrainedmodels==0.7.4->segmentation_models_pytorch) (4.41.1)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torchvision>=0.3.0->segmentation_models_pytorch) (1.19.5)\n","Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.7/dist-packages (from torchvision>=0.3.0->segmentation_models_pytorch) (7.1.2)\n","Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch->pretrainedmodels==0.7.4->segmentation_models_pytorch) (3.7.4.3)\n","Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from munch->pretrainedmodels==0.7.4->segmentation_models_pytorch) (1.15.0)\n","Building wheels for collected packages: pretrainedmodels, efficientnet-pytorch\n"," Building wheel for pretrainedmodels (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for pretrainedmodels: filename=pretrainedmodels-0.7.4-cp37-none-any.whl size=60963 sha256=a11160491371e5c433ea170819225a7323442bc90955a1c7b809df3fe08512da\n"," Stored in directory: /root/.cache/pip/wheels/69/df/63/62583c096289713f22db605aa2334de5b591d59861a02c2ecd\n"," Building wheel for efficientnet-pytorch (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for efficientnet-pytorch: filename=efficientnet_pytorch-0.6.3-cp37-none-any.whl size=12420 sha256=ccab042553550a5d3dc264f6a4df306d1acf989376774b269054078650f52ffc\n"," Stored in directory: /root/.cache/pip/wheels/42/1e/a9/2a578ba9ad04e776e80bf0f70d8a7f4c29ec0718b92d8f6ccd\n","Successfully built pretrainedmodels efficientnet-pytorch\n","Installing collected packages: munch, pretrainedmodels, efficientnet-pytorch, timm, segmentation-models-pytorch\n","Successfully installed efficientnet-pytorch-0.6.3 munch-2.5.0 pretrainedmodels-0.7.4 segmentation-models-pytorch-0.1.3 timm-0.3.2\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:11.634792Z","start_time":"2021-04-18T16:16:05.875817Z"},"colab":{"base_uri":"https://localhost:8080/","height":117,"referenced_widgets":["fbbd46ad400943fb970117f277f4591c","eadebd2ff9904c0982e0d78fdb255f85","9875845065ac4d36a2e7e80c6e15aee8","d7986d30653c45208e97de01180192b8","b50b95f933bf4559ad6b65209ac13468","1b30399407374842b22870fc3fed803b","70d89c23dbb14568b7e27f410515c05f","cb90f8f7265241278b3c872b0987e7e8"]},"id":"E5-leCCF8sQ5","executionInfo":{"status":"ok","timestamp":1620026694311,"user_tz":-540,"elapsed":12319,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"95d37fce-4cb5-4792-ed4f-9b1983caf66a"},"source":["# 구현된 model에 임의의 input을 넣어 output이 잘 나오는지 test\n","import segmentation_models_pytorch as smp\n","\n","# model = smp.DeepLabV3Plus('timm-efficientnet-b3', encoder_weights = 'noisy-student',classes=12)\n","model = smp.DeepLabV3Plus(classes=12)\n","x = torch.randn([2, 3, 512, 512])\n","print(\"input shape : \", x.shape)\n","out = model(x).to(device)\n","print(\"output shape : \", out.size())\n","\n","model = model.to(device)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Downloading: \"https://download.pytorch.org/models/resnet34-333f7ec4.pth\" to /root/.cache/torch/hub/checkpoints/resnet34-333f7ec4.pth\n"],"name":"stderr"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"fbbd46ad400943fb970117f277f4591c","version_minor":0,"version_major":2},"text/plain":["HBox(children=(FloatProgress(value=0.0, max=87306240.0), HTML(value='')))"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\n","input shape : torch.Size([2, 3, 512, 512])\n","output shape : torch.Size([2, 12, 512, 512])\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"qLpYHkTE8sQ6"},"source":["## train, validation, test 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.104200Z","start_time":"2021-04-18T16:16:18.093174Z"},"id":"l3qdtKiO8sQ6"},"source":["def train(num_epochs, model, data_loader, val_loader, criterion, optimizer, saved_dir, val_every, device, lr_scheduler):\n"," print('Start training..')\n"," best_loss = 9999999\n"," for epoch in range(num_epochs):\n"," model.train()\n"," for step, (images, masks, _) in enumerate(data_loader):\n"," images = torch.stack(images) # (batch, channel, height, width)\n"," masks = torch.stack(masks).long() # (batch, channel, height, width)\n"," \n"," # gpu 연산을 위해 device 할당\n"," images, masks = images.to(device), masks.to(device)\n"," \n"," # inference\n"," outputs = model(images)\n"," \n"," # loss 계산 (cross entropy loss)\n"," loss = criterion(outputs, masks)\n"," optimizer.zero_grad()\n"," loss.backward()\n"," optimizer.step()\n"," \n"," lr_scheduler.step()\n"," \n"," # step 주기에 따른 loss 출력\n"," if (step + 1) % 25 == 0:\n"," print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(\n"," epoch+1, num_epochs, step+1, len(train_loader), loss.item()))\n"," \n"," # validation 주기에 따른 loss 출력 및 best model 저장\n"," if (epoch + 1) % val_every == 0:\n"," avrg_loss = validation(epoch + 1, model, val_loader, criterion, device)\n"," if avrg_loss < best_loss:\n"," print('Best performance at epoch: {}'.format(epoch + 1))\n"," print('Save model in', saved_dir)\n"," best_loss = avrg_loss\n"," wandb.log({'best_loss': best_loss})\n"," save_model(model, saved_dir)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"j2aKiPqjOdYt"},"source":["classes_dict = {0: 'Background',\n"," 1: 'UNKNOWN',\n"," 2: 'General trash',\n"," 3: 'Paper',\n"," 4: 'Paper pack',\n"," 5: 'Metal',\n"," 6: 'Glass',\n"," 7: 'Plastic',\n"," 8: 'Styrofoam',\n"," 9: 'Plastic bag',\n"," 10: 'Battery',\n"," 11: 'Clothing'}"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.285795Z","start_time":"2021-04-18T16:16:18.267686Z"},"id":"lDbL-1wq8sQ7"},"source":["def validation(epoch, model, data_loader, criterion, device):\n"," print('Start validation #{}'.format(epoch))\n"," model.eval()\n"," with torch.no_grad():\n"," total_loss = 0\n"," cnt = 0\n"," mIoU_list = []\n"," for step, (images, masks, _) in enumerate(data_loader):\n"," \n"," images = torch.stack(images) # (batch, channel, height, width)\n"," masks = torch.stack(masks).long() # (batch, channel, height, width)\n","\n"," images, masks = images.to(device), masks.to(device) \n","\n"," outputs = model(images)\n"," loss = criterion(outputs, masks)\n"," total_loss += loss\n"," cnt += 1\n","\n"," # print(outputs.shape) # (8, 12, 512, 512)\n"," # print(masks.shape) # (8, 512, 512)\n","\n"," # n_o = outputs.detach().cpu().numpy()\n"," # n_o_s = np.squeeze(n_o, axis=0)\n"," # n_o_s_2 = np.squeeze(n_o_s, axis=0)\n"," \n"," # n_m = masks.detach().cpu().numpy()\n"," # n_m_s = np.squeeze(n_m, axis=0)\n","\n"," # wandb.log(wandb.Image(images, masks={\n"," # \"predictions\" : {\n"," # \"mask_data\" : n_o_s_2,\n"," # \"class_labels\" : classes_dict\n"," # },\n"," # \"ground_truth\" : {\n"," # \"mask_data\" : n_m_s,\n"," # \"class_labels\" : classes_dict\n"," # }\n"," # }))\n","\n"," outputs = torch.argmax(outputs.squeeze(), dim=1).detach().cpu().numpy()\n","\n"," mIoU = label_accuracy_score(masks.detach().cpu().numpy(), outputs, n_class=12)[2]\n"," mIoU_list.append(mIoU)\n"," \n"," avrg_loss = total_loss / cnt\n"," avrg_mIoU = np.mean(mIoU_list)\n"," print('Validation #{} Average Loss: {:.4f}, mIoU: {:.4f}'.format(epoch, avrg_loss, np.mean(mIoU_list)))\n"," wandb.log({'Train Loss':loss.item(),\n"," 'Val Loss':avrg_loss ,\n"," 'Val mIoU':np.mean(mIoU_list)})\n"," # return avrg_loss\n"," return avrg_mIoU"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"SgQs2p6n8sQ9"},"source":["## 모델 저장 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.909918Z","start_time":"2021-04-18T16:16:18.898918Z"},"id":"h5vfGkK58sQ-"},"source":["# 모델 저장 함수 정의\n","val_every = 1 \n","\n","saved_dir = './saved'\n","if not os.path.isdir(saved_dir): \n"," os.mkdir(saved_dir)\n"," \n","def save_model(model, saved_dir, file_name='v3+_focal_coslr_mIoU_madgrad.pt'):\n"," check_point = {'net': model.state_dict()}\n"," output_path = os.path.join(saved_dir, file_name)\n"," torch.save(model.state_dict(), output_path)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"f8cT79Ad8sQ_"},"source":["## 모델 생성 및 Loss function, Optimizer 정의"]},{"cell_type":"code","metadata":{"id":"neKn53b4_-2d"},"source":["from torch.autograd import Variable\n","import torch.nn.functional as F\n","# ref : https://github.com/clcarwin/focal_loss_pytorch\n","class FocalLoss(nn.Module):\n"," def __init__(self, gamma=0, alpha=None, size_average=True):\n"," super(FocalLoss, self).__init__()\n"," self.gamma = gamma\n"," self.alpha = alpha\n"," if isinstance(alpha,(float,int)): self.alpha = torch.Tensor([alpha,1-alpha])\n"," if isinstance(alpha,list): self.alpha = torch.Tensor(alpha)\n"," self.size_average = size_average\n","\n"," def forward(self, input, target):\n"," if input.dim()>2:\n"," input = input.view(input.size(0),input.size(1),-1) # N,C,H,W => N,C,H*W\n"," input = input.transpose(1,2) # N,C,H*W => N,H*W,C\n"," input = input.contiguous().view(-1,input.size(2)) # N,H*W,C => N*H*W,C\n"," target = target.view(-1,1)\n","\n"," logpt = F.log_softmax(input)\n"," logpt = logpt.gather(1,target)\n"," logpt = logpt.view(-1)\n"," pt = Variable(logpt.data.exp())\n","\n"," if self.alpha is not None:\n"," if self.alpha.type()!=input.data.type():\n"," self.alpha = self.alpha.type_as(input.data)\n"," at = self.alpha.gather(0,target.data.view(-1))\n"," logpt = logpt * Variable(at)\n","\n"," loss = -1 * (1-pt)**self.gamma * logpt\n"," if self.size_average: return loss.mean()\n"," else: return loss.sum()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"2jTZmhhz8wFC"},"source":["import math\n","from torch.optim.lr_scheduler import _LRScheduler\n","\n","class CosineAnnealingWarmUpRestarts(_LRScheduler):\n"," def __init__(self, optimizer, T_0, T_mult=1, eta_max=0.1, T_up=0, gamma=1., last_epoch=-1):\n"," if T_0 <= 0 or not isinstance(T_0, int):\n"," raise ValueError(\"Expected positive integer T_0, but got {}\".format(T_0))\n"," if T_mult < 1 or not isinstance(T_mult, int):\n"," raise ValueError(\"Expected integer T_mult >= 1, but got {}\".format(T_mult))\n"," if T_up < 0 or not isinstance(T_up, int):\n"," raise ValueError(\"Expected positive integer T_up, but got {}\".format(T_up))\n"," self.T_0 = T_0\n"," self.T_mult = T_mult\n"," self.base_eta_max = eta_max\n"," self.eta_max = eta_max\n"," self.T_up = T_up\n"," self.T_i = T_0\n"," self.gamma = gamma\n"," self.cycle = 0\n"," super(CosineAnnealingWarmUpRestarts, self).__init__(optimizer, last_epoch)\n"," self.T_cur = last_epoch\n"," \n"," def get_lr(self):\n"," if self.T_cur == -1:\n"," return self.base_lrs\n"," elif self.T_cur < self.T_up:\n"," return [(self.eta_max - base_lr)*self.T_cur / self.T_up + base_lr for base_lr in self.base_lrs]\n"," else:\n"," return [base_lr + (self.eta_max - base_lr) * (1 + math.cos(math.pi * (self.T_cur-self.T_up) / (self.T_i - self.T_up))) / 2\n"," for base_lr in self.base_lrs]\n","\n"," def step(self, epoch=None):\n"," if epoch is None:\n"," epoch = self.last_epoch + 1\n"," self.T_cur = self.T_cur + 1\n"," if self.T_cur >= self.T_i:\n"," self.cycle += 1\n"," self.T_cur = self.T_cur - self.T_i\n"," self.T_i = (self.T_i - self.T_up) * self.T_mult + self.T_up\n"," else:\n"," if epoch >= self.T_0:\n"," if self.T_mult == 1:\n"," self.T_cur = epoch % self.T_0\n"," self.cycle = epoch // self.T_0\n"," else:\n"," n = int(math.log((epoch / self.T_0 * (self.T_mult - 1) + 1), self.T_mult))\n"," self.cycle = n\n"," self.T_cur = epoch - self.T_0 * (self.T_mult ** n - 1) / (self.T_mult - 1)\n"," self.T_i = self.T_0 * self.T_mult ** (n)\n"," else:\n"," self.T_i = self.T_0\n"," self.T_cur = epoch\n"," \n"," self.eta_max = self.base_eta_max * (self.gamma**self.cycle)\n"," self.last_epoch = math.floor(epoch)\n"," for param_group, lr in zip(self.optimizer.param_groups, self.get_lr()):\n"," param_group['lr'] = lr"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"zKnYU6_OunR1","executionInfo":{"status":"ok","timestamp":1620026712324,"user_tz":-540,"elapsed":4128,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"48ccbb1e-ec6d-41e9-8340-5181e54b1c56"},"source":["!pip install madgrad"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Collecting madgrad\n"," Downloading https://files.pythonhosted.org/packages/65/f0/4584f18202a2fb8903d456bf907b80e7cb54ad8fcba68604084ff41b7cf8/madgrad-1.1-py3-none-any.whl\n","Installing collected packages: madgrad\n","Successfully installed madgrad-1.1\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:19.698902Z","start_time":"2021-04-18T16:16:19.694902Z"},"id":"cLuQNMO08sRA"},"source":["from madgrad import MADGRAD\n","# from adamp import AdamP\n","\n","# Loss function 정의\n","criterion = FocalLoss()\n","\n","# Optimizer 정의\n","# optimizer = torch.optim.Adam(params = model.parameters(), lr = learning_rate, weight_decay=1e-6)\n","# optimizer = AdamP(params = model.parameters())\n","optimizer = MADGRAD(params = model.parameters(), lr = learning_rate, momentum = 0.9, weight_decay = 0, eps = 1e-06)\n","\n","# lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min')\n","# lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size = 237, gamma = 0.65)\n","# lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.65)\n","lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=50, eta_min=0)\n","# lr_scheduler = CosineAnnealingWarmUpRestarts(optimizer, T_0=100, T_mult=2, eta_max=0.1, T_up=10, gamma=0.5)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"start_time":"2021-04-18T16:16:20.331Z"},"scrolled":false,"colab":{"base_uri":"https://localhost:8080/"},"id":"7fEF_a3L8sRC","executionInfo":{"status":"ok","timestamp":1620029694824,"user_tz":-540,"elapsed":2973055,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"27ffd698-c363-4f9d-f1cf-33010abe7a4b"},"source":["train(num_epochs, model, train_loader, val_loader, criterion, optimizer, saved_dir, val_every, device, lr_scheduler=lr_scheduler)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Start training..\n","Epoch [1/20], Step [25/327], Loss: 0.8440\n","Epoch [1/20], Step [50/327], Loss: 0.5281\n","Epoch [1/20], Step [75/327], Loss: 0.5980\n","Epoch [1/20], Step [100/327], Loss: 0.3318\n","Epoch [1/20], Step [125/327], Loss: 0.8981\n","Epoch [1/20], Step [150/327], Loss: 0.4649\n","Epoch [1/20], Step [175/327], Loss: 0.3727\n","Epoch [1/20], Step [200/327], Loss: 0.6209\n","Epoch [1/20], Step [225/327], Loss: 0.5596\n","Epoch [1/20], Step [250/327], Loss: 0.4611\n","Epoch [1/20], Step [275/327], Loss: 0.4754\n","Epoch [1/20], Step [300/327], Loss: 0.3807\n","Epoch [1/20], Step [325/327], Loss: 0.6686\n","Start validation #1\n","Validation #1 Average Loss: 0.4757, mIoU: 0.2733\n","Best performance at epoch: 1\n","Save model in ./saved\n","Epoch [2/20], Step [25/327], Loss: 0.3890\n","Epoch [2/20], Step [50/327], Loss: 0.2947\n","Epoch [2/20], Step [75/327], Loss: 0.2801\n","Epoch [2/20], Step [100/327], Loss: 0.7261\n","Epoch [2/20], Step [125/327], Loss: 0.2904\n","Epoch [2/20], Step [150/327], Loss: 0.9077\n","Epoch [2/20], Step [175/327], Loss: 0.4403\n","Epoch [2/20], Step [200/327], Loss: 0.2305\n","Epoch [2/20], Step [225/327], Loss: 0.4156\n","Epoch [2/20], Step [250/327], Loss: 0.3398\n","Epoch [2/20], Step [275/327], Loss: 0.5939\n","Epoch [2/20], Step [300/327], Loss: 0.7203\n","Epoch [2/20], Step [325/327], Loss: 0.3697\n","Start validation #2\n","Validation #2 Average Loss: 0.4087, mIoU: 0.3250\n","Epoch [3/20], Step [25/327], Loss: 0.4608\n","Epoch [3/20], Step [50/327], Loss: 0.3832\n","Epoch [3/20], Step [75/327], Loss: 0.4981\n","Epoch [3/20], Step [100/327], Loss: 0.2684\n","Epoch [3/20], Step [125/327], Loss: 0.4927\n","Epoch [3/20], Step [150/327], Loss: 0.2950\n","Epoch [3/20], Step [175/327], Loss: 0.4485\n","Epoch [3/20], Step [200/327], Loss: 0.2753\n","Epoch [3/20], Step [225/327], Loss: 0.5523\n","Epoch [3/20], Step [250/327], Loss: 0.4654\n","Epoch [3/20], Step [275/327], Loss: 0.3875\n","Epoch [3/20], Step [300/327], Loss: 0.3115\n","Epoch [3/20], Step [325/327], Loss: 0.5473\n","Start validation #3\n","Validation #3 Average Loss: 0.3658, mIoU: 0.3427\n","Epoch [4/20], Step [25/327], Loss: 0.3830\n","Epoch [4/20], Step [50/327], Loss: 0.4703\n","Epoch [4/20], Step [75/327], Loss: 0.6132\n","Epoch [4/20], Step [100/327], Loss: 0.2575\n","Epoch [4/20], Step [125/327], Loss: 0.3775\n","Epoch [4/20], Step [150/327], Loss: 0.4189\n","Epoch [4/20], Step [175/327], Loss: 0.5320\n","Epoch [4/20], Step [200/327], Loss: 0.2969\n","Epoch [4/20], Step [225/327], Loss: 0.3200\n","Epoch [4/20], Step [250/327], Loss: 0.2679\n","Epoch [4/20], Step [275/327], Loss: 0.3167\n","Epoch [4/20], Step [300/327], Loss: 0.2711\n","Epoch [4/20], Step [325/327], Loss: 0.5109\n","Start validation #4\n","Validation #4 Average Loss: 0.4410, mIoU: 0.3276\n","Epoch [5/20], Step [25/327], Loss: 0.2412\n","Epoch [5/20], Step [50/327], Loss: 0.1896\n","Epoch [5/20], Step [75/327], Loss: 0.1859\n","Epoch [5/20], Step [100/327], Loss: 0.4665\n","Epoch [5/20], Step [125/327], Loss: 0.3654\n","Epoch [5/20], Step [150/327], Loss: 0.5012\n","Epoch [5/20], Step [175/327], Loss: 0.3515\n","Epoch [5/20], Step [200/327], Loss: 0.2979\n","Epoch [5/20], Step [225/327], Loss: 0.5359\n","Epoch [5/20], Step [250/327], Loss: 0.1930\n","Epoch [5/20], Step [275/327], Loss: 0.2514\n","Epoch [5/20], Step [300/327], Loss: 0.4593\n","Epoch [5/20], Step [325/327], Loss: 0.4494\n","Start validation #5\n","Validation #5 Average Loss: 0.4293, mIoU: 0.3345\n","Epoch [6/20], Step [25/327], Loss: 0.1752\n","Epoch [6/20], Step [50/327], Loss: 0.3433\n","Epoch [6/20], Step [75/327], Loss: 0.1582\n","Epoch [6/20], Step [100/327], Loss: 0.3208\n","Epoch [6/20], Step [125/327], Loss: 0.5254\n","Epoch [6/20], Step [150/327], Loss: 0.1970\n","Epoch [6/20], Step [175/327], Loss: 0.4211\n","Epoch [6/20], Step [200/327], Loss: 0.6571\n","Epoch [6/20], Step [225/327], Loss: 0.4016\n","Epoch [6/20], Step [250/327], Loss: 0.3741\n","Epoch [6/20], Step [275/327], Loss: 0.2269\n","Epoch [6/20], Step [300/327], Loss: 0.2482\n","Epoch [6/20], Step [325/327], Loss: 0.3552\n","Start validation #6\n","Validation #6 Average Loss: 0.3590, mIoU: 0.3570\n","Epoch [7/20], Step [25/327], Loss: 0.4053\n","Epoch [7/20], Step [50/327], Loss: 0.1415\n","Epoch [7/20], Step [75/327], Loss: 0.2705\n","Epoch [7/20], Step [100/327], Loss: 0.1581\n","Epoch [7/20], Step [125/327], Loss: 0.1407\n","Epoch [7/20], Step [150/327], Loss: 0.2420\n","Epoch [7/20], Step [175/327], Loss: 0.2470\n","Epoch [7/20], Step [200/327], Loss: 0.4392\n","Epoch [7/20], Step [225/327], Loss: 0.3453\n","Epoch [7/20], Step [250/327], Loss: 0.1801\n","Epoch [7/20], Step [275/327], Loss: 0.3354\n","Epoch [7/20], Step [300/327], Loss: 0.4167\n","Epoch [7/20], Step [325/327], Loss: 0.1848\n","Start validation #7\n","Validation #7 Average Loss: 0.3483, mIoU: 0.3713\n","Epoch [8/20], Step [25/327], Loss: 0.1925\n","Epoch [8/20], Step [50/327], Loss: 0.1347\n","Epoch [8/20], Step [75/327], Loss: 0.2295\n","Epoch [8/20], Step [100/327], Loss: 0.2547\n","Epoch [8/20], Step [125/327], Loss: 0.2559\n","Epoch [8/20], Step [150/327], Loss: 0.2588\n","Epoch [8/20], Step [175/327], Loss: 0.3272\n","Epoch [8/20], Step [200/327], Loss: 0.3690\n","Epoch [8/20], Step [225/327], Loss: 0.2081\n","Epoch [8/20], Step [250/327], Loss: 0.4078\n","Epoch [8/20], Step [275/327], Loss: 0.3454\n","Epoch [8/20], Step [300/327], Loss: 0.3480\n","Epoch [8/20], Step [325/327], Loss: 0.5466\n","Start validation #8\n","Validation #8 Average Loss: 0.3740, mIoU: 0.3438\n","Epoch [9/20], Step [25/327], Loss: 0.1235\n","Epoch [9/20], Step [50/327], Loss: 0.1607\n","Epoch [9/20], Step [75/327], Loss: 0.2357\n","Epoch [9/20], Step [100/327], Loss: 0.2807\n","Epoch [9/20], Step [125/327], Loss: 0.2420\n","Epoch [9/20], Step [150/327], Loss: 0.3391\n","Epoch [9/20], Step [175/327], Loss: 0.2192\n","Epoch [9/20], Step [200/327], Loss: 0.1688\n","Epoch [9/20], Step [225/327], Loss: 0.1362\n","Epoch [9/20], Step [250/327], Loss: 0.1365\n","Epoch [9/20], Step [275/327], Loss: 0.2114\n","Epoch [9/20], Step [300/327], Loss: 0.2667\n","Epoch [9/20], Step [325/327], Loss: 0.1862\n","Start validation #9\n","Validation #9 Average Loss: 0.3884, mIoU: 0.3654\n","Epoch [10/20], Step [25/327], Loss: 0.2676\n","Epoch [10/20], Step [50/327], Loss: 0.1366\n","Epoch [10/20], Step [75/327], Loss: 0.2648\n","Epoch [10/20], Step [100/327], Loss: 0.1945\n","Epoch [10/20], Step [125/327], Loss: 0.1405\n","Epoch [10/20], Step [150/327], Loss: 0.1566\n","Epoch [10/20], Step [175/327], Loss: 0.3164\n","Epoch [10/20], Step [200/327], Loss: 0.2297\n","Epoch [10/20], Step [225/327], Loss: 0.1576\n","Epoch [10/20], Step [250/327], Loss: 0.2452\n","Epoch [10/20], Step [275/327], Loss: 0.4853\n","Epoch [10/20], Step [300/327], Loss: 0.4071\n","Epoch [10/20], Step [325/327], Loss: 0.2124\n","Start validation #10\n","Validation #10 Average Loss: 0.3277, mIoU: 0.3929\n","Epoch [11/20], Step [25/327], Loss: 0.1669\n","Epoch [11/20], Step [50/327], Loss: 0.1447\n","Epoch [11/20], Step [75/327], Loss: 0.1111\n","Epoch [11/20], Step [100/327], Loss: 0.1310\n","Epoch [11/20], Step [125/327], Loss: 0.1536\n","Epoch [11/20], Step [150/327], Loss: 0.1125\n","Epoch [11/20], Step [175/327], Loss: 0.1787\n","Epoch [11/20], Step [200/327], Loss: 0.2277\n","Epoch [11/20], Step [225/327], Loss: 0.1233\n","Epoch [11/20], Step [250/327], Loss: 0.2008\n","Epoch [11/20], Step [275/327], Loss: 0.2521\n","Epoch [11/20], Step [300/327], Loss: 0.1383\n","Epoch [11/20], Step [325/327], Loss: 0.1039\n","Start validation #11\n","Validation #11 Average Loss: 0.3963, mIoU: 0.3747\n","Epoch [12/20], Step [25/327], Loss: 0.2397\n","Epoch [12/20], Step [50/327], Loss: 0.2527\n","Epoch [12/20], Step [75/327], Loss: 0.0888\n","Epoch [12/20], Step [100/327], Loss: 0.1659\n","Epoch [12/20], Step [125/327], Loss: 0.1793\n","Epoch [12/20], Step [150/327], Loss: 0.1520\n","Epoch [12/20], Step [175/327], Loss: 0.1204\n","Epoch [12/20], Step [200/327], Loss: 0.3123\n","Epoch [12/20], Step [225/327], Loss: 0.2184\n","Epoch [12/20], Step [250/327], Loss: 0.0766\n","Epoch [12/20], Step [275/327], Loss: 0.1349\n","Epoch [12/20], Step [300/327], Loss: 0.0992\n","Epoch [12/20], Step [325/327], Loss: 0.1137\n","Start validation #12\n","Validation #12 Average Loss: 0.4032, mIoU: 0.3717\n","Epoch [13/20], Step [25/327], Loss: 0.1917\n","Epoch [13/20], Step [50/327], Loss: 0.0764\n","Epoch [13/20], Step [75/327], Loss: 0.3228\n","Epoch [13/20], Step [100/327], Loss: 0.1271\n","Epoch [13/20], Step [125/327], Loss: 0.1100\n","Epoch [13/20], Step [150/327], Loss: 0.4613\n","Epoch [13/20], Step [175/327], Loss: 0.2978\n","Epoch [13/20], Step [200/327], Loss: 0.3300\n","Epoch [13/20], Step [225/327], Loss: 0.1084\n","Epoch [13/20], Step [250/327], Loss: 0.1461\n","Epoch [13/20], Step [275/327], Loss: 0.1279\n","Epoch [13/20], Step [300/327], Loss: 0.3048\n","Epoch [13/20], Step [325/327], Loss: 0.2051\n","Start validation #13\n","Validation #13 Average Loss: 0.3568, mIoU: 0.3825\n","Epoch [14/20], Step [25/327], Loss: 0.1266\n","Epoch [14/20], Step [50/327], Loss: 0.1211\n","Epoch [14/20], Step [75/327], Loss: 0.0965\n","Epoch [14/20], Step [100/327], Loss: 0.1312\n","Epoch [14/20], Step [125/327], Loss: 0.1738\n","Epoch [14/20], Step [150/327], Loss: 0.1459\n","Epoch [14/20], Step [175/327], Loss: 0.3737\n","Epoch [14/20], Step [200/327], Loss: 0.7423\n","Epoch [14/20], Step [225/327], Loss: 0.1324\n","Epoch [14/20], Step [250/327], Loss: 0.3033\n","Epoch [14/20], Step [275/327], Loss: 0.2615\n","Epoch [14/20], Step [300/327], Loss: 0.1834\n","Epoch [14/20], Step [325/327], Loss: 0.2242\n","Start validation #14\n","Validation #14 Average Loss: 0.3554, mIoU: 0.3755\n","Epoch [15/20], Step [25/327], Loss: 0.1339\n","Epoch [15/20], Step [50/327], Loss: 0.2678\n","Epoch [15/20], Step [75/327], Loss: 0.1296\n","Epoch [15/20], Step [100/327], Loss: 0.1643\n","Epoch [15/20], Step [125/327], Loss: 0.1755\n","Epoch [15/20], Step [150/327], Loss: 0.0950\n","Epoch [15/20], Step [175/327], Loss: 0.1287\n","Epoch [15/20], Step [200/327], Loss: 0.1442\n","Epoch [15/20], Step [225/327], Loss: 0.2360\n","Epoch [15/20], Step [250/327], Loss: 0.5166\n","Epoch [15/20], Step [275/327], Loss: 0.2162\n","Epoch [15/20], Step [300/327], Loss: 0.1961\n","Epoch [15/20], Step [325/327], Loss: 0.1774\n","Start validation #15\n","Validation #15 Average Loss: 0.4837, mIoU: 0.3361\n","Epoch [16/20], Step [25/327], Loss: 0.2046\n","Epoch [16/20], Step [50/327], Loss: 0.0773\n","Epoch [16/20], Step [75/327], Loss: 0.0690\n","Epoch [16/20], Step [100/327], Loss: 0.1064\n","Epoch [16/20], Step [125/327], Loss: 0.1268\n","Epoch [16/20], Step [150/327], Loss: 0.3174\n","Epoch [16/20], Step [175/327], Loss: 0.1381\n","Epoch [16/20], Step [200/327], Loss: 0.1115\n","Epoch [16/20], Step [225/327], Loss: 0.1876\n","Epoch [16/20], Step [250/327], Loss: 0.1573\n","Epoch [16/20], Step [275/327], Loss: 0.0824\n","Epoch [16/20], Step [300/327], Loss: 0.1404\n","Epoch [16/20], Step [325/327], Loss: 0.1400\n","Start validation #16\n","Validation #16 Average Loss: 0.4140, mIoU: 0.3789\n","Epoch [17/20], Step [25/327], Loss: 0.1015\n","Epoch [17/20], Step [50/327], Loss: 0.1471\n","Epoch [17/20], Step [75/327], Loss: 0.1908\n","Epoch [17/20], Step [100/327], Loss: 0.2568\n","Epoch [17/20], Step [125/327], Loss: 0.1334\n","Epoch [17/20], Step [150/327], Loss: 0.1649\n","Epoch [17/20], Step [175/327], Loss: 0.2154\n","Epoch [17/20], Step [200/327], Loss: 0.1090\n","Epoch [17/20], Step [225/327], Loss: 0.1176\n","Epoch [17/20], Step [250/327], Loss: 0.1001\n","Epoch [17/20], Step [275/327], Loss: 0.0853\n","Epoch [17/20], Step [300/327], Loss: 0.0989\n","Epoch [17/20], Step [325/327], Loss: 0.1416\n","Start validation #17\n","Validation #17 Average Loss: 0.3488, mIoU: 0.3920\n","Epoch [18/20], Step [25/327], Loss: 0.0917\n","Epoch [18/20], Step [50/327], Loss: 0.1167\n","Epoch [18/20], Step [75/327], Loss: 0.1320\n","Epoch [18/20], Step [100/327], Loss: 0.1050\n","Epoch [18/20], Step [125/327], Loss: 0.2873\n","Epoch [18/20], Step [150/327], Loss: 0.1085\n","Epoch [18/20], Step [175/327], Loss: 0.0553\n","Epoch [18/20], Step [200/327], Loss: 0.1538\n","Epoch [18/20], Step [225/327], Loss: 0.0760\n","Epoch [18/20], Step [250/327], Loss: 0.0908\n","Epoch [18/20], Step [275/327], Loss: 0.1011\n","Epoch [18/20], Step [300/327], Loss: 0.1693\n","Epoch [18/20], Step [325/327], Loss: 0.0624\n","Start validation #18\n","Validation #18 Average Loss: 0.3418, mIoU: 0.4051\n","Epoch [19/20], Step [25/327], Loss: 0.0466\n","Epoch [19/20], Step [50/327], Loss: 0.0721\n","Epoch [19/20], Step [75/327], Loss: 0.1040\n","Epoch [19/20], Step [100/327], Loss: 0.0616\n","Epoch [19/20], Step [125/327], Loss: 0.0559\n","Epoch [19/20], Step [150/327], Loss: 0.0580\n","Epoch [19/20], Step [175/327], Loss: 0.1014\n","Epoch [19/20], Step [200/327], Loss: 0.0752\n","Epoch [19/20], Step [225/327], Loss: 0.1272\n","Epoch [19/20], Step [250/327], Loss: 0.0973\n","Epoch [19/20], Step [275/327], Loss: 0.1391\n","Epoch [19/20], Step [300/327], Loss: 0.0640\n","Epoch [19/20], Step [325/327], Loss: 0.1008\n","Start validation #19\n","Validation #19 Average Loss: 0.3979, mIoU: 0.3894\n","Epoch [20/20], Step [25/327], Loss: 0.0486\n","Epoch [20/20], Step [50/327], Loss: 0.1163\n","Epoch [20/20], Step [75/327], Loss: 0.0457\n","Epoch [20/20], Step [100/327], Loss: 0.1225\n","Epoch [20/20], Step [125/327], Loss: 0.0605\n","Epoch [20/20], Step [150/327], Loss: 0.0658\n","Epoch [20/20], Step [175/327], Loss: 0.1070\n","Epoch [20/20], Step [200/327], Loss: 0.0655\n","Epoch [20/20], Step [225/327], Loss: 0.0795\n","Epoch [20/20], Step [250/327], Loss: 0.0313\n","Epoch [20/20], Step [275/327], Loss: 0.1278\n","Epoch [20/20], Step [300/327], Loss: 0.0533\n","Epoch [20/20], Step [325/327], Loss: 0.1674\n","Start validation #20\n","Validation #20 Average Loss: 0.4051, mIoU: 0.3785\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"8Ul-l-Ur8sRD"},"source":["## 저장된 model 불러오기 (학습된 이후) "]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:21.050200Z","start_time":"2021-04-16T19:44:20.802200Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"nz2gHKip8sRD","executionInfo":{"status":"ok","timestamp":1620006039805,"user_tz":-540,"elapsed":4886,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"57d18e7f-b26c-41a3-cb7e-5896b79d8c16"},"source":["# best model 저장된 경로\n","model_path = './saved/deeplabv3+resnet34+focalloss+madgrad+CycleLR.pt'\n","\n","# best model 불러오기\n","checkpoint = torch.load(model_path, map_location=device)\n","model.load_state_dict(checkpoint)\n","\n","# 추론을 실행하기 전에는 반드시 설정 (batch normalization, dropout 를 평가 모드로 설정)\n","# model.eval()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{"tags":[]},"execution_count":20}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:24.939227Z","start_time":"2021-04-16T19:44:24.518228Z"},"colab":{"base_uri":"https://localhost:8080/","height":391},"id":"HMs2G0AQ8sRD","executionInfo":{"status":"ok","timestamp":1620006057924,"user_tz":-540,"elapsed":15713,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"b3019e19-b456-422e-c29d-df3b9dfc4c61"},"source":["# 첫번째 batch의 추론 결과 확인\n","for imgs, image_infos in test_loader:\n"," image_infos = image_infos\n"," temp_images = imgs\n"," \n"," model.eval()\n"," # inference\n"," outs = model(torch.stack(temp_images).to(device))\n"," oms = torch.argmax(outs.squeeze(), dim=1).detach().cpu().numpy()\n"," \n"," break\n","\n","i = 2\n","fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(16, 16))\n","\n","print('Shape of Original Image :', list(temp_images[i].shape))\n","print('Shape of Predicted : ', list(oms[i].shape))\n","print('Unique values, category of transformed mask : \\n', [{int(i),category_names[int(i)]} for i in list(np.unique(oms[i]))])\n","\n","# Original image\n","ax1.imshow(temp_images[i].permute([1,2,0]))\n","ax1.grid(False)\n","ax1.set_title(\"Original image : {}\".format(image_infos[i]['file_name']), fontsize = 15)\n","\n","# Predicted\n","ax2.imshow(oms[i])\n","ax2.grid(False)\n","ax2.set_title(\"Predicted : {}\".format(image_infos[i]['file_name']), fontsize = 15)\n","\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Shape of Original Image : [3, 512, 512]\n","Shape of Predicted : [512, 512]\n","Unique values, category of transformed mask : \n"," [{0, 'Backgroud'}, {2, 'General trash'}]\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"MtGw1aLC8sRE"},"source":["## submission을 위한 test 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:27.469285Z","start_time":"2021-04-16T19:44:27.456021Z"},"id":"BkHOlbIb8sRE"},"source":["def test(model, data_loader, device):\n"," size = 256\n"," transform = A.Compose([A.Resize(256, 256)])\n"," print('Start prediction.')\n"," model.eval()\n"," \n"," file_name_list = []\n"," preds_array = np.empty((0, size*size), dtype=np.long)\n"," \n"," with torch.no_grad():\n"," for step, (imgs, image_infos) in enumerate(test_loader):\n","\n"," # inference (512 x 512)\n"," outs = model(torch.stack(imgs).to(device))\n"," oms = torch.argmax(outs, dim=1).detach().cpu().numpy()\n"," \n"," # resize (256 x 256)\n"," temp_mask = []\n"," for img, mask in zip(np.stack(imgs), oms):\n"," transformed = transform(image=img, mask=mask)\n"," mask = transformed['mask']\n"," temp_mask.append(mask)\n","\n"," oms = np.array(temp_mask)\n"," \n"," oms = oms.reshape([oms.shape[0], size*size]).astype(int)\n"," preds_array = np.vstack((preds_array, oms))\n"," \n"," file_name_list.append([i['file_name'] for i in image_infos])\n"," print(\"End prediction.\")\n"," file_names = [y for x in file_name_list for y in x]\n"," \n"," return file_names, preds_array"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"6JUkRi2J8sRF"},"source":["## submission.csv 생성"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:45:42.235310Z","start_time":"2021-04-16T19:44:30.499016Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"BbHNVDNr8sRF","executionInfo":{"status":"ok","timestamp":1620006369459,"user_tz":-540,"elapsed":295308,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"cc724972-4dec-4cc1-df40-2cc6a76d5e35"},"source":["# sample_submisson.csv 열기\n","submission = pd.read_csv('./submission/sample_submission.csv', index_col=None)\n","\n","# test set에 대한 prediction\n","file_names, preds = test(model, test_loader, device)\n","\n","# PredictionString 대입\n","for file_name, string in zip(file_names, preds):\n"," submission = submission.append({\"image_id\" : file_name, \"PredictionString\" : ' '.join(str(e) for e in string.tolist())}, \n"," ignore_index=True)\n","\n","# submission.csv로 저장\n","submission.to_csv(\"./submission/deeplabv3+resnet34+focalloss+madgrad+CycleLR.csv\", index=False)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Start prediction.\n","End prediction.\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"tIjoCiVp8sRG"},"source":["## Reference\n","\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ykrfzleS8sRG","executionInfo":{"status":"ok","timestamp":1620006376150,"user_tz":-540,"elapsed":4641,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"abb669af-cad6-4ec9-df82-0b3e1b996d5c"},"source":["import json\n","import requests\n","import os\n","from urllib.parse import urlparse, parse_qsl, urlencode, urlunparse\n","\n","def submit(user_key='', file_path = '', desc=\"\"):\n"," if not user_key:\n"," raise Exception(\"No UserKey\" )\n"," url = urlparse('http://ec2-13-124-161-225.ap-northeast-2.compute.amazonaws.com:8000/api/v1/competition/28/presigned_url/?description=&hyperparameters={%22training%22:{},%22inference%22:{}}')\n"," qs = dict(parse_qsl(url.query))\n"," qs['description'] = desc\n"," parts = url._replace(query=urlencode(qs))\n"," url = urlunparse(parts)\n","\n"," print(url)\n"," headers = {\n"," 'Authorization': user_key\n"," }\n"," res = requests.get(url, headers=headers)\n"," print(res.text)\n"," data = json.loads(res.text)\n"," \n"," submit_url = data['url']\n"," body = {\n"," 'key':'app/Competitions/000028/Users/{}/Submissions/{}/output.csv'.format(str(data['submission']['user']).zfill(8),str(data['submission']['local_id']).zfill(4)),\n"," 'x-amz-algorithm':data['fields']['x-amz-algorithm'],\n"," 'x-amz-credential':data['fields']['x-amz-credential'],\n"," 'x-amz-date':data['fields']['x-amz-date'],\n"," 'policy':data['fields']['policy'],\n"," 'x-amz-signature':data['fields']['x-amz-signature']\n"," }\n"," requests.post(url=submit_url, data=body, files={'file': open(file_path, 'rb')})\n","\n","\n","####################################################################################\n","test_dir = \"/content/drive/MyDrive/Trash/code/submission\" # 수정 필요 : output 파일 폴더 \n","desc = \"deeplabv3+resnet34+focalloss+madgrad+CycleLR\" # 수정 필요 : 파일에 대한 설명\n","output_file = \"deeplabv3+resnet34+focalloss+madgrad+CycleLR.csv\" #수정 필요 : output 파일 \n","user_key = \"Bearer 7bb5f96452751a238ffaf91a93c4242bf9b72abe\" # 수정 필요 : Authorization \n","\n","\n","submit(user_key, os.path.join(test_dir, output_file),desc)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["http://ec2-13-124-161-225.ap-northeast-2.compute.amazonaws.com:8000/api/v1/competition/28/presigned_url/?hyperparameters=%7B%22training%22%3A%7B%7D%2C%22inference%22%3A%7B%7D%7D&description=deeplabv3%2Bresnet34%2Bfocalloss%2Bmadgrad%2BCycleLR\n","{\"url\":\"https://prod-aistages-private.s3.amazonaws.com/\",\"fields\":{\"key\":\"app/Competitions/000028/Users/00000099/Submissions/0016/output.csv\",\"x-amz-algorithm\":\"AWS4-HMAC-SHA256\",\"x-amz-credential\":\"AKIA45LU4MHUJ7WLDQVO/20210503/ap-northeast-2/s3/aws4_request\",\"x-amz-date\":\"20210503T014612Z\",\"policy\":\"eyJleHBpcmF0aW9uIjogIjIwMjEtMDUtMDNUMDI6NDY6MTJaIiwgImNvbmRpdGlvbnMiOiBbeyJidWNrZXQiOiAicHJvZC1haXN0YWdlcy1wcml2YXRlIn0sIHsia2V5IjogImFwcC9Db21wZXRpdGlvbnMvMDAwMDI4L1VzZXJzLzAwMDAwMDk5L1N1Ym1pc3Npb25zLzAwMTYvb3V0cHV0LmNzdiJ9LCB7IngtYW16LWFsZ29yaXRobSI6ICJBV1M0LUhNQUMtU0hBMjU2In0sIHsieC1hbXotY3JlZGVudGlhbCI6ICJBS0lBNDVMVTRNSFVKN1dMRFFWTy8yMDIxMDUwMy9hcC1ub3J0aGVhc3QtMi9zMy9hd3M0X3JlcXVlc3QifSwgeyJ4LWFtei1kYXRlIjogIjIwMjEwNTAzVDAxNDYxMloifV19\",\"x-amz-signature\":\"acd1533eddcf9808d7541f576cd254cb8c2e63532482dfd136472c82b04a24fd\"},\"submission\":{\"id\":14396,\"phase\":\"Created\",\"type\":\"File\",\"local_id\":16,\"hyperparameters\":\"{\\\"training\\\": {}, \\\"inference\\\": {}}\",\"description\":\"deeplabv3+resnet34+focalloss+madgrad+CycleLR\",\"final\":false,\"created_at\":\"2021-05-03T10:46:12.207003+09:00\",\"updated_at\":\"2021-05-03T10:46:12.207035+09:00\",\"user\":99,\"competition\":28,\"image\":null}}\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"sVoz-PcVcvJ3"},"source":[""],"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/chanyub_seg/code/alltrain_aug2_re_pan_effb7_noisy_focal_madgrad_cosLR.ipynb b/chanyub_seg/code/my_model.ipynb similarity index 100% rename from chanyub_seg/code/alltrain_aug2_re_pan_effb7_noisy_focal_madgrad_cosLR.ipynb rename to chanyub_seg/code/my_model.ipynb diff --git a/chanyub_seg/code/mybaseline.ipynb b/chanyub_seg/code/mybaseline.ipynb deleted file mode 100644 index b47fb37..0000000 --- a/chanyub_seg/code/mybaseline.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"nbformat":4,"nbformat_minor":0,"metadata":{"hide_input":false,"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.7"},"toc":{"base_numbering":1,"nav_menu":{},"number_sections":true,"sideBar":true,"skip_h1_title":false,"title_cell":"Table of Contents","title_sidebar":"Contents","toc_cell":true,"toc_position":{"height":"calc(100% - 180px)","left":"10px","top":"150px","width":"297.278px"},"toc_section_display":true,"toc_window_display":true},"colab":{"name":"mybaseline.ipynb","provenance":[]}},"cells":[{"cell_type":"markdown","metadata":{"toc":true,"id":"olmN-0tcyj1m"},"source":["

Table of Contents

\n","
"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"TsmgfKqE8Alh","executionInfo":{"status":"ok","timestamp":1619772064762,"user_tz":-540,"elapsed":716,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"17e41994-3789-4e47-b332-9d3ceb46b3c2"},"source":["!python --version"],"execution_count":15,"outputs":[{"output_type":"stream","text":["Python 3.7.10\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"LLNYfCes1DQf","executionInfo":{"status":"ok","timestamp":1619770980459,"user_tz":-540,"elapsed":759,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"5cbaa10e-4393-4649-c585-329bdb3b38e8"},"source":["ls"],"execution_count":12,"outputs":[{"output_type":"stream","text":["FCN32s.ipynb \u001b[0m\u001b[01;34m__pycache__\u001b[0m/ \u001b[01;34msaved\u001b[0m/ utils.py\n","mybaseline.ipynb sample_submission.csv \u001b[01;34msubmission\u001b[0m/\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"BHEzhpnB1Euc","executionInfo":{"status":"ok","timestamp":1619770974621,"user_tz":-540,"elapsed":663,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"80d153e2-fe09-4e58-d7e7-961c2a0e307b"},"source":["cd drive/MyDrive/Trash/code"],"execution_count":11,"outputs":[{"output_type":"stream","text":["[Errno 2] No such file or directory: 'drive/MyDrive/Trash/code'\n","/content/drive/MyDrive/Trash/code\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.826930Z","start_time":"2021-04-18T10:34:45.406686Z"},"colab":{"base_uri":"https://localhost:8080/","height":351},"id":"YjjIrqDfyj1v","executionInfo":{"status":"error","timestamp":1619772090970,"user_tz":-540,"elapsed":1972,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"7e6eb00b-a82a-4788-aa69-acc4527015e2"},"source":["import os\n","import random\n","import time\n","import json\n","import warnings \n","warnings.filterwarnings('ignore')\n","\n","import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","from torch.utils.data import Dataset, DataLoader\n","from utils import label_accuracy_score\n","import cv2\n","\n","import numpy as np\n","import pandas as pd\n","\n","# 전처리를 위한 라이브러리\n","from pycocotools.coco import COCO\n","import torchvision\n","import torchvision.transforms as transforms\n","\n","import albumentations as A\n","from albumentations.pytorch import ToTensorV2\n","\n","# 시각화를 위한 라이브러리\n","import matplotlib.pyplot as plt\n","import seaborn as sns; sns.set()\n","\n","plt.rcParams['axes.grid'] = False\n","\n","print('pytorch version: {}'.format(torch.__version__))\n","print('GPU 사용 가능 여부: {}'.format(torch.cuda.is_available()))\n","\n","print(torch.cuda.get_device_name(0))\n","print(torch.cuda.device_count())\n","\n","device = \"cuda\" if torch.cuda.is_available() else \"cpu\" # GPU 사용 가능 여부에 따라 device 정보 저장"],"execution_count":16,"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)","\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilterwarnings\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'ignore'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 9\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnn\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunctional\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/__init__.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 226\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 227\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 228\u001b[0;31m __all__ += [name for name in dir(_C)\n\u001b[0m\u001b[1;32m 229\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'_'\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 230\u001b[0m not name.endswith('Base')]\n","\u001b[0;31mNameError\u001b[0m: name '_C' is not defined"]}]},{"cell_type":"markdown","metadata":{"id":"y3jS9K30yj1y"},"source":["## 하이퍼파라미터 세팅 및 seed 고정"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"DLe2RPbz0N3E","executionInfo":{"status":"ok","timestamp":1619770063237,"user_tz":-540,"elapsed":46978,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"2c77e2a1-1995-4a0a-e25d-1610bbb2bef7"},"source":["from google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Mounted at /content/drive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.841930Z","start_time":"2021-04-18T10:34:47.827931Z"},"id":"6BGWOJ4Uyj1z"},"source":["batch_size = 8 # Mini-batch size\n","num_epochs = 20\n","learning_rate = 0.0001"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.856930Z","start_time":"2021-04-18T10:34:47.842931Z"},"id":"dbQ8LiCnyj10"},"source":["# seed 고정\n","random_seed = 21\n","torch.manual_seed(random_seed)\n","torch.cuda.manual_seed(random_seed)\n","# torch.cuda.manual_seed_all(random_seed) # if use multi-GPU\n","torch.backends.cudnn.deterministic = True\n","torch.backends.cudnn.benchmark = False\n","np.random.seed(random_seed)\n","random.seed(random_seed)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"mOtC_AXzyj11"},"source":["## 학습 데이터 EDA"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.381961Z","start_time":"2021-04-18T10:34:47.857930Z"},"scrolled":true,"id":"KC7hwAESyj11","outputId":"f2df5650-923a-4909-8282-4d4b7e37d8d4"},"source":["%matplotlib inline\n","\n","dataset_path = '../input/data'\n","anns_file_path = dataset_path + '/' + 'train.json'\n","\n","# Read annotations\n","with open(anns_file_path, 'r') as f:\n"," dataset = json.loads(f.read())\n","\n","categories = dataset['categories']\n","anns = dataset['annotations']\n","imgs = dataset['images']\n","nr_cats = len(categories)\n","nr_annotations = len(anns)\n","nr_images = len(imgs)\n","\n","# Load categories and super categories\n","cat_names = []\n","super_cat_names = []\n","super_cat_ids = {}\n","super_cat_last_name = ''\n","nr_super_cats = 0\n","for cat_it in categories:\n"," cat_names.append(cat_it['name'])\n"," super_cat_name = cat_it['supercategory']\n"," # Adding new supercat\n"," if super_cat_name != super_cat_last_name:\n"," super_cat_names.append(super_cat_name)\n"," super_cat_ids[super_cat_name] = nr_super_cats\n"," super_cat_last_name = super_cat_name\n"," nr_super_cats += 1\n","\n","print('Number of super categories:', nr_super_cats)\n","print('Number of categories:', nr_cats)\n","print('Number of annotations:', nr_annotations)\n","print('Number of images:', nr_images)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Number of super categories: 11\n","Number of categories: 11\n","Number of annotations: 21116\n","Number of images: 2617\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.546964Z","start_time":"2021-04-18T10:34:51.382969Z"},"scrolled":true,"id":"JiKk-Arjyj15","outputId":"b460dc93-2b5a-453a-826b-1b850ded0b46"},"source":["# Count annotations\n","cat_histogram = np.zeros(nr_cats,dtype=int)\n","for ann in anns:\n"," cat_histogram[ann['category_id']] += 1\n","\n","# Initialize the matplotlib figure\n","f, ax = plt.subplots(figsize=(5,5))\n","\n","# Convert to DataFrame\n","df = pd.DataFrame({'Categories': cat_names, 'Number of annotations': cat_histogram})\n","df = df.sort_values('Number of annotations', 0, False)\n","\n","# Plot the histogram\n","plt.title(\"category distribution of train set \")\n","plot_1 = sns.barplot(x=\"Number of annotations\", y=\"Categories\", data=df, label=\"Total\", color=\"b\")"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.561965Z","start_time":"2021-04-18T10:34:51.547969Z"},"id":"TP8CLpVcyj2H"},"source":["# category labeling \n","sorted_temp_df = df.sort_index()\n","\n","# background = 0 에 해당되는 label 추가 후 기존들을 모두 label + 1 로 설정\n","sorted_df = pd.DataFrame([\"Backgroud\"], columns = [\"Categories\"])\n","sorted_df = sorted_df.append(sorted_temp_df, ignore_index=True)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.576961Z","start_time":"2021-04-18T10:34:51.562964Z"},"id":"wTgO4oy2yj2J","outputId":"7060491f-a987-4f8a-b1f3-644ef2b9a48b"},"source":["# class (Categories) 에 따른 index 확인 (0~11 : 총 12개)\n","sorted_df"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
CategoriesNumber of annotations
0BackgroudNaN
1UNKNOWN128.0
2General trash2225.0
3Paper7448.0
4Paper pack527.0
5Metal449.0
6Glass488.0
7Plastic2472.0
8Styrofoam1074.0
9Plastic bag6114.0
10Battery50.0
11Clothing141.0
\n","
"],"text/plain":[" Categories Number of annotations\n","0 Backgroud NaN\n","1 UNKNOWN 128.0\n","2 General trash 2225.0\n","3 Paper 7448.0\n","4 Paper pack 527.0\n","5 Metal 449.0\n","6 Glass 488.0\n","7 Plastic 2472.0\n","8 Styrofoam 1074.0\n","9 Plastic bag 6114.0\n","10 Battery 50.0\n","11 Clothing 141.0"]},"metadata":{"tags":[]},"execution_count":7}]},{"cell_type":"markdown","metadata":{"id":"KCP2uNkmyj2O"},"source":["## 데이터 전처리 함수 정의 (Dataset)"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:52.693328Z","start_time":"2021-04-18T10:34:52.681328Z"},"id":"pPOeXDzPyj2P"},"source":["category_names = list(sorted_df.Categories)\n","\n","def get_classname(classID, cats):\n"," for i in range(len(cats)):\n"," if cats[i]['id']==classID:\n"," return cats[i]['name']\n"," return \"None\"\n","\n","class CustomDataLoader(Dataset):\n"," \"\"\"COCO format\"\"\"\n"," def __init__(self, data_dir, mode = 'train', transform = None):\n"," super().__init__()\n"," self.mode = mode\n"," self.transform = transform\n"," self.coco = COCO(data_dir)\n"," \n"," def __getitem__(self, index: int):\n"," # dataset이 index되어 list처럼 동작\n"," image_id = self.coco.getImgIds(imgIds=index)\n"," image_infos = self.coco.loadImgs(image_id)[0]\n"," \n"," # cv2 를 활용하여 image 불러오기\n"," images = cv2.imread(os.path.join(dataset_path, image_infos['file_name']))\n"," images = cv2.cvtColor(images, cv2.COLOR_BGR2RGB).astype(np.float32)\n"," images /= 255.0\n"," \n"," if (self.mode in ('train', 'val')):\n"," ann_ids = self.coco.getAnnIds(imgIds=image_infos['id'])\n"," anns = self.coco.loadAnns(ann_ids)\n","\n"," # Load the categories in a variable\n"," cat_ids = self.coco.getCatIds()\n"," cats = self.coco.loadCats(cat_ids)\n","\n"," # masks : size가 (height x width)인 2D\n"," # 각각의 pixel 값에는 \"category id + 1\" 할당\n"," # Background = 0\n"," masks = np.zeros((image_infos[\"height\"], image_infos[\"width\"]))\n"," # Unknown = 1, General trash = 2, ... , Cigarette = 11\n"," for i in range(len(anns)):\n"," className = get_classname(anns[i]['category_id'], cats)\n"," pixel_value = category_names.index(className)\n"," masks = np.maximum(self.coco.annToMask(anns[i])*pixel_value, masks)\n"," masks = masks.astype(np.float32)\n","\n"," # transform -> albumentations 라이브러리 활용\n"," if self.transform is not None:\n"," transformed = self.transform(image=images, mask=masks)\n"," images = transformed[\"image\"]\n"," masks = transformed[\"mask\"]\n"," \n"," return images, masks, image_infos\n"," \n"," if self.mode == 'test':\n"," # transform -> albumentations 라이브러리 활용\n"," if self.transform is not None:\n"," transformed = self.transform(image=images)\n"," images = transformed[\"image\"]\n"," \n"," return images, image_infos\n"," \n"," \n"," def __len__(self) -> int:\n"," # 전체 dataset의 size를 return\n"," return len(self.coco.getImgIds())"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"iFdy-Es_yj2T"},"source":["## Dataset 정의 및 DataLoader 할당"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:58.823175Z","start_time":"2021-04-18T10:34:54.106233Z"},"scrolled":true,"id":"0xfuCNtkyj2f","outputId":"11495c22-84bd-4e2a-e334-22083b144cea"},"source":["# train.json / validation.json / test.json 디렉토리 설정\n","train_path = dataset_path + '/train.json'\n","val_path = dataset_path + '/val.json'\n","test_path = dataset_path + '/test.json'\n","\n","# collate_fn needs for batch\n","def collate_fn(batch):\n"," return tuple(zip(*batch))\n","\n","train_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","val_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","test_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","# create own Dataset 1 (skip)\n","# validation set을 직접 나누고 싶은 경우\n","# random_split 사용하여 data set을 8:2 로 분할\n","# train_size = int(0.8*len(dataset))\n","# val_size = int(len(dataset)-train_size)\n","# dataset = CustomDataLoader(data_dir=train_path, mode='train', transform=transform)\n","# train_dataset, val_dataset = torch.utils.data.random_split(dataset, [train_size, val_size])\n","\n","# create own Dataset 2\n","# train dataset\n","train_dataset = CustomDataLoader(data_dir=train_path, mode='train', transform=train_transform)\n","\n","# validation dataset\n","val_dataset = CustomDataLoader(data_dir=val_path, mode='val', transform=val_transform)\n","\n","# test dataset\n","test_dataset = CustomDataLoader(data_dir=test_path, mode='test', transform=test_transform)\n","\n","\n","# DataLoader\n","train_loader = torch.utils.data.DataLoader(dataset=train_dataset, \n"," batch_size=batch_size,\n"," shuffle=True,\n"," num_workers=4,\n"," collate_fn=collate_fn,\n"," drop_last=True)\n","\n","val_loader = torch.utils.data.DataLoader(dataset=val_dataset, \n"," batch_size=batch_size,\n"," shuffle=False,\n"," num_workers=4,\n"," collate_fn=collate_fn,\n"," drop_last=True) \n","\n","test_loader = torch.utils.data.DataLoader(dataset=test_dataset,\n"," batch_size=batch_size,\n"," num_workers=4,\n"," collate_fn=collate_fn)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["loading annotations into memory...\n","Done (t=3.77s)\n","creating index...\n","index created!\n","loading annotations into memory...\n","Done (t=0.86s)\n","creating index...\n","index created!\n","loading annotations into memory...\n","Done (t=0.02s)\n","creating index...\n","index created!\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"ti6b2suAyj2l"},"source":["### 데이터 샘플 시각화 (Show example image and mask)\n","\n","- `train_loader` \n","- `val_loader` \n","- `test_loader` "]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T17:59:26.346907Z","start_time":"2021-04-16T17:59:26.002907Z"},"id":"BEv__daFyj2_","outputId":"81a35cec-ef90-4987-e022-b8140d0e552e"},"source":["# train_loader의 output 결과(image 및 mask) 확인\n","for imgs, masks, image_infos in train_loader:\n"," image_infos = image_infos[0]\n"," temp_images = imgs\n"," temp_masks = masks\n"," \n"," break\n","\n","fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(12, 12))\n","\n","print('image shape:', list(temp_images[0].shape))\n","print('mask shape: ', list(temp_masks[0].shape))\n","print('Unique values, category of transformed mask : \\n', [{int(i),category_names[int(i)]} for i in list(np.unique(temp_masks[0]))])\n","\n","ax1.imshow(temp_images[0].permute([1,2,0]))\n","ax1.grid(False)\n","ax1.set_title(\"input image : {}\".format(image_infos['file_name']), fontsize = 15)\n","\n","ax2.imshow(temp_masks[0])\n","ax2.grid(False)\n","ax2.set_title(\"masks : {}\".format(image_infos['file_name']), fontsize = 15)\n","\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["image shape: [3, 512, 512]\n","mask shape: [512, 512]\n","Unique values, category of transformed mask : \n"," [{0, 'Backgroud'}, {'General trash', 2}, {'Paper', 3}]\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T13:50:43.557278Z","start_time":"2021-04-16T13:50:43.194005Z"},"id":"a24J4gZzyj3B","outputId":"99701eba-c5f9-4dc6-a0aa-08ea555bf578"},"source":["# val_loader의 output 결과(image 및 mask) 확인\n","for imgs, masks, image_infos in val_loader:\n"," image_infos = image_infos[0]\n"," temp_images = imgs\n"," temp_masks = masks\n"," \n"," break\n","\n","fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(12, 12))\n","\n","print('image shape:', list(temp_images[0].shape))\n","print('mask shape: ', list(temp_masks[0].shape))\n","\n","print('Unique values, category of transformed mask : \\n', [{int(i),category_names[int(i)]} for i in list(np.unique(temp_masks[0]))])\n","\n","ax1.imshow(temp_images[0].permute([1,2,0]))\n","ax1.grid(False)\n","ax1.set_title(\"input image : {}\".format(image_infos['file_name']), fontsize = 15)\n","\n","ax2.imshow(temp_masks[0])\n","ax2.grid(False)\n","ax2.set_title(\"masks : {}\".format(image_infos['file_name']), fontsize = 15)\n","\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["image shape: [3, 512, 512]\n","mask shape: [512, 512]\n","Unique values, category of transformed mask : \n"," [{0, 'Backgroud'}, {'Glass', 6}, {'Plastic', 7}, {9, 'Plastic bag'}]\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAtAAAAFkCAYAAAAJ5AxiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzsnXm8JUdZ979V3X3Ouefu6+yTyWSSykpIBCQIQkReWWURiCC7r4qKviqKihu46/sqvq+7yCIiq4CAASUBQoCwZEhC9koy+0xmu3P3s/VS9f5R3eeec+65yyQzzGTSv8/nzNzurq6uru6u+tVTv+cpYa0lR44cOXLkyJEjR44ca4M80wXIkSNHjhw5cuTIkeOxhJxA58iRI0eOHDly5MhxEsgJdI4cOXLkyJEjR44cJ4GcQOfIkSNHjhw5cuTIcRLICXSOHDly5MiRI0eOHCeBnEDnyJEjR44cOXLkyHES8M90Ab4XUEq9H7hca/2kM1iGVwJlrfX715DWAr+gtf6b016wswRKqXcAb9Faj52CvNZc16ejDEqpQeCvgJfgBqn/Cfyi1vpES5rnAG8CrgHOA96ptX7Ho7lul3IUgLcD/6G1vmOZNPcCf6O1/jullAB+E/hZYAy4NS33HR3nXAr8dVr2GeCf0/InLWlWzUsp9QrgtcD3AYOABv6P1vrDq9zXs4AvA1dore9eW23kyHFmoZTaC/y71vpXz8C1bwImtdYvPwV5vQ34ttb6pjNRhjW2Pz8HvAB4KjACXHuy5V1DOS4CXg38ldZ6psvxdcBhHPe4dy39Qnrei4E/BC4Edqf39tGONCvmpZTygF8FXghcmp72HeC3tNa3rnJf7+AU9cXnOh4vFug/AN5whsvwypMowzXAx09fUc55nExdnw58DHgW8D/TcjwZ+I+ONM8FngB8EaiepnIUgN8DntjtoFLqfOASXOML8BvA7wB/BrwIWABuVEqtbzlnGLgRsMCLgd8H3gq8syP7VfMCfiXd/8vAj+JI8YeUUr+wyn3dhvtGdq2SLkeOHKceb8O1b99znET78zoccf7v01ici3Dt69Ayx58P7NVa35tur9ovKKWeDnwC1xY+D7ge+LBS6n905L1aXj24NvhWnJHiNUAEfE0p9X2r3Nc/Az+ySpocPE4s0Frrx1RHq7X+5pkuQ45HBqXUNcD/AJ6ptb453XcI+JZS6oe11jemSX9Na/3W9PiLz0xpeQFwt9Z6v1KqhGtw/ySb+VBKfQPYC7wF+O30nDfjGueXaa3ngBuUUgPAO5RSf661njuJvF6ktZ5sKc+XlFIbccT6r5crdHrd/BvJkePxh1XbnzTd07TWRil1OfCqM1TWF+AI8Mn0C78D3Ky1/sV0+8tKqcuA3wW+cBJ51YDtWuvprDBKqS8CD+Da4DcuV2it9UHg4KmogHMdjwsC3SnhUEq9AXgfzgL4l8DTgAPA27XWn2w57yZgEvfivh1YB3wJ+Gmt9aE0zbPoMp3cOl2VXv/H0v3Z0o/LTtl3SjhaynE97kMaBz6FG31eCfwNbppmJ/A6rfX+lrz+FPchn4+b7voK8Fat9ZGWNEXcdNCrgAR4L3AIeJfWWrSkGwH+FDfyH8RZAn9Za/2tbvfxSKCU+gEceboUuB83lfS1luOvA346PS6AO3BkdGd6/P2sUNdKqZfipAVX4Cy/3wJ+Vmu9r+UaVwF/j3s/NG5q7KtrvIXnAUezhg1Aa/1tpdSe9NiN6T6zxvy6Ir3Py7TWT+7Y//PA/8a9q1ln8j6l1PvSv8/XWu9N/2428LhvYABn2cjKXVFKfTYtd0Z6nwf8d0tHBfARnKX5mcBn15pXB3nOcDvp81vh3p9FxzeXPuu34uQwr8XNrv0r7l0PO879fzjr0V3AzwOfw8lY3rHSdXM8tpH1Azir4f8GtuHeo9firJXvBp4C3Ae8SWt9Z8u5bwV+HPfe1IFv49q+h1rSPB34E1ybDG76/Y+01l1nE5VSm3AzUAeBH9VaV5VSvwn8JLAZmMV9D29oba8fDZRSP80yfVl6fMX+IpWhjAK/p5T6vfS0a7XWN6WygbfhyNl5wHHgRq31GzrK8Gqc5XgC+DrwUylpWwvW0v6civZ1D05u82sd+z8ObMC1YZ9Nd+9RSgHs01pvS9MFwHOA61rKvWK/kPbD1wIZeW69v/cppQa11rNrySuVs0y3ZqK1DpVS9wAbV7n3d9Ai4Whpb38kLdu1wAngj7XW/9Bx7luAX8d9Tzfg+vIbOQ0SmrMBjxcJx3L4EPAZ4KXAg8BHlFKbO9JcA/wCzir2kzhS1Tkdvxr+APcC3p7mdw1umuRk8FTg9WlZ3oaTKfw1rtH/v7gpmu3AP3WcNwH8Ma5R/KU0zZeUUq3P/s9x00DvBH4C2IojI02kH/eNwA8Dv4bTXh1n6bT8Eiil9qad12ooAx8E/gF4Ba4B/3xH/tuAD6THX40b+HxVKbU9Pb5sXSulXgt8Ejf1/0pcQ/8AbkDSWoZ/Af4RR+QawCeVUuU1lB/gYhzx78R96bFThY8CT0plGK24Dvic1noe+KF03x+yWBeHAdL7eRaLBPpi3ODpwVXKveT+0gFbtSXdWvPqhmtwz+SR4K044vETuHv+aeCPsoMpYfkccAx4Oe4Z/xvOopXj8YGtOPL227j342m4NvMj6e/lOMPSR1Idf4bNOEPFi4GfAjzgllSLSmoF/U8caf6xNJ9/ZZnpfaXUNuBmXFv0wpQ8vw5Hbv8SR1Z+FngI6F3uZpRS25RSNjUKrYa19GWr9RcvxRH797DYptyWHvtHXB/yMZz29q249rQV34+zgL4VV/9Xs7TPWglraX9OBT6G62OaUEr14erlI7h7zrTsL8PVw0tbkj8D9x7dtFy5U7S2iRcAQZd09+G42kUnkdcSpH341Tzy9vU9wJ24+/0c8PdKqRe25P9SHCfJONWd6TnnLB4XFugV8C6t9XsBlFLfAY7iPvzWUdUEcE1m1VVK7cPpiJ6rtf6vtVxEa71LKTUFyEchz+gDXpyOQLNR4U/RPo2zEfhbpVRZa11Nr/2mLIPUQvANnMXj6cDNSqlRXEP2u1rrd6Xp/hvodM56Dc56c5nW+sE03Y04C+1bcaR6OcQ4QrUaenBODh9K8/8ysB/XkP9Gej+/33I/EjfKfUpavt9frq7TtH8KfEpr3Tql95kuZfglrfWX0vMO48j4DwJred7DOOLfiWlcZ3SqcAPOCnAd7r4ygvh03OAAnP4NYFeX9+7ZuGm+W9LtYWCh1RGnpdxlpVQhteSudH/DJ5lXG5RSz8YNzN7UeWyNmAdekVqfPp92GL+llPoTrfUU7j2q4qQjtfSac7jBSI7HB0Zw7fkuAKXUE3Bt1+u11h9I9wncwPJiHClBa/3LWQZpO3oDbiD2YtyA/iLcrNxb0sErpFPunVBK7cBZf28FXtXyLTwF+ILW+u9akn+y8/wOWFzbuhaL66p92Wr9hdb6dqVUDBzsaF8vxpHy/6W1/n8t1+z8tgaAF2TSgtQ48i6lVE/2Ta6CtbQ/pwIfAd6mlHpqy32+COdX8vFUqqbT/be3zOpleAHwRa11fQ3l3t6Shi7ppjuOP9I+5rdw7/8jDU7wea3129O//1spdQFuIJr50LwdZ7z5+XT7C0qpMdxA8JzE490C3WzgtPNePYazNLTitlZJhNb662m6p3xPSriInRl5TvEQEAJf69gHLVM0SqnnKaVuUUrN4ohsNlWWjWavAEq0EEmttWVxeirDD+O8ePcopXylVDb4+gqwYnQTrfUOrfVPrnJ/GT7Vct4CiwQ5u59LlFKfUkodxXUcEaBa7mc5KFy9vG+VdCGLVgOAzAGk8704o9Bax7jO9bqW3a8AKixalVfCC3BToWsZ2Jx2pBa5DwGf1icZPaUFn+6Yuv0kbkB0ebr9ZOCGjo66cwCV49zGXt3uE5O1mV/qsm9TtkMp9VSl1A1KqRO4drSKM2pk7c4unEPsh5RSL1ZKLedYpnCW568B13UMJO8Anq+UeqdS6ikpgV0RWut9Wms/I/+rYNW+bA39xXK4Nv3//auku1W36HJZbF83dUt8pqC1vh1nqW1tX68DvqK1PrqGLFrlcWccSqkX4Aj0r2ut9Wrpl8GnOrY/CXyfUspL+cBVLG1Pz+n29fFOoDtHcSGOTLbiWJfzjuF0UN9LdCvrfAdhyBrjEoBS6sm4F/ggTud3DU4K0kwDZPKI4x35d26PpedGHb83AltO8l6Ww0IXK0SzrpVS/bhBzxbcNOQzcKTouyx9bp0YTf8/vEq6tjpt6eBWyz/DNM4S1YlhOjRppwAfAZ6oXDglcA38Z9ZoyXk+7Q38NNDXpdMeBqot9bCW+1trXkBTW/95YB9OfvFI0fmtZtvZt7qejvc6tRAtPIpr5nhsoVs72rm/sx3dimt3BPAzwA/g2p1jWZqUFD4HNwX/MeC4Uur6FmlZhqfh3sd/TgfBrXgvzor3SpxvxlGl1B+uhUivESv2ZWvsL5bDKFDp0CZ3w3L1fza2rx8FXqGUEqlE57m4NndFpJZZhZM5ZFhru0mXdMMdx0+qDtLn+lHgH7TWf7Va+VdAt/bVx3GDMZysaTUecU7h8S7hWAsmltmXEbFsiqbQkWYY5/h3JvFS3At8XWpVRil1XkeazDllHJhq2T/ekW4K56TYbTqm8eiLCjjS1TmV11rX1+Aswc/RWjc1YJkOcRVksTZP98Dnfhyx78TFnLx2fjV8BSc7uk4p9QFcZ/cnq52UTltvol2Scj+uAdyBk+Vk6NTb3U+Hzk4ptQWndby/Jc1a8sq02P+J+35emEmPHiE6v9VsO3t/jtDxXisXMaTvUVwzx7mP5+Le7xdrrSsAqcVtpDVROtX/XKVUD27G7i9xsypPbUn2PpyM4T+Ui5jw7ZbzDfAunKRhC24w+Uc4QtvmrPUIsVpftpb+YjmcAHqVUgNrINGPBmtpf04VPoqLivF0nFOlZHVJDTjr851a6wMt+9bSL+zCGaUuxrXtrWkMi9rlNfcxqXHlepyzaqdz4smiW/sa43iOwM0Id/KGzu1zCo93C/RacHVqgQCaUSImcF7YsDjFdUlLmi0sFfN3s26fbvQAUdYYpui08N2FGwQ0Q6mlGsAXdaT7Io4Q7dda7+z43XUKy9x0xEidNp7DYl1nzl6NljRPwzkWtqJbXWtcZJHXn8KydsPngfXKeeQDoJR6Ek6b9vlTeaFUfvFxnOX5lTjrTispXs668wLgW7o9CsYtuKgdTceZlNy+qKPcnwd+JJ0NyHAdTk+dNfpryislIR/HLRjwXK11NwvZyeDFqt059mVpuTI9/63Ac1KCk+FHH+U1c5z76MERmFaL8StZxgClta5prT+Lsyhf2iXJm3GDxs8rpa5YJo8DWus/xclJuuXxSLBaX7aW/gK6t6+ZBOZ1p6isy2Et7c8pgdb6HlzbcV36u1G3L3qyUvvaKd9YtV/QWjdwDvCv6Dj3OuAbLRLONfUxSqkNuDjYu3Ba+0cr13tpl+3vaK2TdDbldlp4RIpzun3NLdCr4zhwvXIhe0q4cDm3tThdHFRK7QT+QClVxQ1K3k67NRfcqPHFSqmX4Ej3w1rrh09z2W8Afkkp9Vcshhd7TWsCrfUJpdS7gXcqpSKc08wbcVaS1ob0A7iG/yal1P/BeZuP4vRzRzIHxG5QSj2E046tpoOuAX+UEueHcV7OBVyUEXCxfxeAdyul/hxnjX4Hjhi3omtdK7eC1r8ppf4N+HB6fz8EfFinYfAeLbTW31BKfQH4gFLqV3Ed758BX9OLsT4zy04Wgq4AXKqUejluGvRkiPZHcV7tv4xbcbApj9AubNEe4JVKqbtxA6U76dLAa63ryoWw+h2l1DSuDn8F9z63xmT+B5wl45NKqT/DNdrvAP4yszydRF5/h5OS/C9gVDmH1gy3px1KFr8UrfWzV6mLfuDj6ft8Gc569LepAyG4UI0/D3xWKfUunKTjN3B61kcV9irHOY0v4WZU3qeUeg/u3fpVWuQIqcb0TTgL4H7cDM/P0K6tBpylWbmIG5/AOVo9Q2v9kFLqH3H9xjdxkS6uxQ0uf325gqXtyC5c2L3VdNAr9mWsob9IcT/wAqXUf+HaY6211kqpfwL+Qik1gdN5DwEv11r/+CrlOhms2v5Ak1BuY1Fe+MzUoW3vSbb1H8W1T4M4p/1WZLNrP6OU+giuHdmNC6f3+20J19gv4KJI3ZQ+g//AtY/Px82CrDmv1EjwedxM+FuAJygXag+gkWq8SdPGOAf8tjJ3wfOUUn+EG6i8DGfcaiXMfwJ8Qin1Nzgp0A/g+ho4R9vX3AK9Om4B/hbX+b4HNyJ9SUeaV+EazQ/iQgD9Pu1T1+DIwhdwVolbcZEvTiu01p/DNb4/hnuhn4mLMtKJt+GcP96BI5ZHcffabJBSrei1uEb2nbh7+b+4Bv7brAwf1wGthirOgvFzuM5lGHi+1vpwWoajuNH5euDTuKgKb2bR6SdD17rWLrrHj+FmB/4dNyi4mFOv07oO18i8N73Gd1g6er8WZ339OI74vSL9++9P8lpfx4Xy20B3fd6bcfq0G3F1cTluSrmbg8uf4qaMfxNnIRvAyWWaTjOp1vPZuOf5Wdy78C5cbN2Tygu3GAC49+gbHb9WqY1H9/fHdmz/BW46+sO4eOnvwQ1ms7IfwjXoE7ip2F/AkR6Plnc9R45WpDNsb8CFYPtPXPjMV+BIboaHcO/jH+Panj/HzQZ1jSiTWuxeiZsB/GI6a/kNXLSf9+H0sy/FxUheSfolcO/vWvryFfuyk+gvfo1FZ+VbgWxlu5/DtQevScv/V5ziVVZPov15C649/ct0+x3p9ltO8pIfwbWfhg55hHZrB/wqjkx+PS3PD+Pqplu0rVX7Be3WPHh5ms9/4yy4r9Zad0Z0WS2vdbh45IO4d7a1be10Buz2/nS2reDWnbgaVw8vBH5ea90afOCTuMHNS9I0T2Yx1N852b4Ka7vVUw5oXwzlTJflew3lQtQFWutnnumy5Dh1UG4Rgz/XWp9VUUVOBkqpF+E6+I3Z4Ep1LD50Enk9Hfgq8ENa6y+f8sLmyJHjcYPUCt+rtX40ztBnFEqpv8D5o6h0+1l0WSxujXn9Nmn4vDU6tz+mkEs4cqCUuhZnXbkN50V+HW6U36nFyvEYR2qF/9CZLscjRap5/xmcNGct4aQ6z/8znFbvCM5T/ndwspZTqp/MkSPH4w9a69M+s3y6kPqpPANnQT7p9SqUUuO4Gccv42YenoGb0XjPuUie4TQT6NQD9F9wWtkTuGWmO1cny3HmsYD7aH4Tp417ELd87L+f0VKdZUidK1eSoiQdDjiP5loebnq2G+wpcAh5rOIzOMnNG/UjW663yOJS5/O46fZfeYR5nXPI2+wcZwqpA/CyUpQuYf8ezbVW4j7mcdoeXISTXnwLZzU+WYQ4SeTrcNKRwzh53u+cqgKebTitEg6l1JeA92qtP6iUeg3O0eGHVjsvR46zEcotl7vSQixv1I98EZDOa+0FlgshtU9rve1UXCdHjlbkbXaOMwWl1PtZOUrS+Xrpin+P5DrbgD0rJPkXrfUbHu11cpz7OG0W6NQT92qcpyY4x56/UUqNa63P6eDaOc5ZfJbFyBndsFKjfLJ4Ec5a2g2nKu52jhxN5G12jjOMd7DyMtOnKmrVw6zcjp/p9RtyPEZwOiUcW4BD2VSz1jpRSj2c7l+tMS7iXvDDuODcOXKccWi3AupKjWt/+jsV15rHSQyWw7ZTcZ0cpwUeLorIrTy2Bjt5m53jjGEN7evG79F1IG9fH294RG322epE+GScZ3yOHDlyPFbxDOBrZ7oQ3yPkbXaOHDke6zipNvt0EugDwCallJdaMjzcCPLAKudBurTooVs/R1idJzERURRhkxhsAsYCJo1U6LT+1lo69dzZdjeZd1fptwQhnN9W9r9LvOjXYG27X5e17cYWm8UL70jXml+znGJpIbpp0rsk65LekoVubN63WJrWE7LrdRbrymIxbdurla8jweKf2La6WzEPKZbNe3F/d5+6bud17mqtCyEEgR/QaDSIE6hVG8RJzIaN63nqD1zD/OwMN335ZgYGhhefZ8u5XZ8Rsm1/t1tJWFqnXd+LFe6r9YpLjy8fCtZiAUnr29LtWs37EzI9pzPdajuWljurG2tt836t6PIdmM5v2HT/dmmtZ9s8fck9dCnnqu+vyNqT7LzF8gohkFKC9FwFZo/OWBCCJA6x1tLbP8SL3vRrsLhE8mMFj7rNfsYzX8yhQ4+1286RI8fjGZs2beCrX/k0nGSbfdoItNb6mFLqDtwiIx9M/799jVq6BKDHF0SmhkgSbNQgDOtYEyMT64g0YO0igW79v33f0gssR+JaCU3zb9tKjlrJdErkW7ezDti059UNNp3p7JaujWStmUB31EMXUipXIKFZWmMMCNO2rxsp6U5cO55DFwLdea4Qop1QpceXXrNz8LK6A+ziYKL9udaBJEkoFMqIuAFJDFGNQ7s1Dz/8MJ4JmZ86jhesca0h2/leLEU3Ar1ceVeD6ZpstcGKbBsOdHu22d9WrJzXcn93RUZCW8/JPi1r2wh0e54d39Zy99W9Mpa8Q6sP0Gh+v4sHZfNbEiL9ySBNu1huIQRxHGJMG+l/TEkZTkWbfejQYfbtO3gaS5kjR44cpw0n1WafbgnHm4F/UUr9LjCNC2+yZlTmZrCJ69BiY7BSIIyHtREy7TSz7s7abp2s+78b+VuOELZayUQasWy5jtfl0b3sq5Hnzut2pm+3gK9OqoSQbYRBCOGMZB35Nm25YpHgW1d5iOxvYxwhbN2H6FKOk+MHrSSttZxpiTrSZXW7dqtzBoG3eK0sfds5At939ZUkCVJKSkGJ6elpTkwdp1gsEscxPT39REnYLHPXazXrcPl7Xe6czvtYzdLemb59n6TdrpwR4MWB39K4TNlshOkoh2haeduu1zal0UqKW76lztGeFR3lyvJfOkBjSV20X69zMLX4jJfOPrWW/WTIvjWdZN848oxMy9NSjtbHZS3GuE9nGT7/WMGjarNz5MiR4/GC00qgtdb34xboeET49L9/gh996fMZGhjECkGtUgEvoRE18AX4QmI6Ov8Mq3WarZ3rclZnY03Hfrl6B9xB3Ja7JoDny9RitXKegvZ7aCW+2flN61jLsU4CbUzn/SwtXyupWc36LITXhaSILuSn+zWXI5LLkdW1pAGwyzCY1nPiOG5ue56HFSClhySzTBdIksUBQlfZxgplaBucNHd2kTdkFt+Tseg20y0dfCy+o+1k3NAhobACm5Jd0UGe28pgW5999++ovby2g2gvEvi2czpkGNamUgyxvLV4OUIslhlopePCtmOrGstt+zDDCpnewuK3ENtUzpGldR+au+9O3dRjDI+2zc6RI0eOxwvWOD99ZlAsBdz85ZuYnZnBl5IgCDDGEARuCjVMusdVX+vUfmuH3PzbiC6yA0mn9nmt+XeSUlgkV63kOSPDneg2Bd1Jupcjw1m6zrxbyfZKFtLlpr67laPVqrwautXJYxXd7mN5Yi2W/BYJXss+aPsZa5f8QGIE7T8kiXWq7c7fYhllSp5b78ENDhct0t3I89J77vYNWUSTRxpnZ8YgWv62KZkX6a/j3TJLCXK3QV3bviXXS68hlh7r/LvzvKxcrsyivS5wv+z6RkiMWKy3LI8cOXLkyHHu42yNwuFgDYcO7Ofd//CP/MRrX49fCAiCIvXGAtL3SBoxoqMT7vp3Fx1u2zR/xzR8N0lFZ95r2b8SHNk0zikpzcPzvKbVM7t+HMd4QraR2m5k1fM8jDFNoiylJLuFbJ8Qos1ivfT+HSmR6XZr2Va6x5WOL0fATkbistZ8F7F63q1WVZtaEJ32OyNo7tgasupantVmRTr3rXQ/3eoq6ULUVrdiS0fwRHuazhkO9zwXpRer3Vs3wttRshXSC5qOwNnMgU3TiOXvZy31tlp9rHhfzdcjlbNIg0jlHc6JUILIyH1m7T43BoU5cuTIkWN1nNUWaCkFvpT09pT44Ac+QBLFrBufIPCLNKKQ8sBiyN3lLMqt2ytazrp2pktXFu2UHTyaDjPLq1gsMjAwQBw7i3oURcRxjDEGz1vUYWcku1gsEgRB2/R/FEVLLNqZ5TkIgqbVHimxQpBYC1IiPI/E2jbLZZIYhJBI6WFMJsdot5p23nY3a/ZydePI/epOlo8Uyz3PlSzfqx1f6TqnmjwvWmdFh3W49deRl2kfBHWWsVu53cbSZ9bVyrvMN9Q6y9FtO7MydztvadqOPIyzSJtk8e9u293yXS7vbuVcS/rW+zFJaoFO3OA0toYkSZrPSnRxwMyRI0eOHOcWzu6W3roOSlpJMSjwhf/6L/bs2oUnoa+vj9npmTWRl65Zr0C4VzrnVCMjz8PDw1gRUAsNC7WESi2iUY+Iwwjn5GUwWIQnGRwYZrBvkKIsYBoJUTXEhgZhLF6L5hXAE5JS0ENPsUQxKGBDg4kMJo6xSdKm883geTL9eVgjmuRMWovTiNr2sHDSul8bIe4gx8JF9pCYJeR5+XrNRAynA8vIcqxMZyzW8GlkaVtmOBIsZplxgVzFIgpgl4mksRLBd9bapdrd7Ccxiy6FaTJhTVvwi0zKkEG05JPtX9wHiXQ/m0oYMtlDqw+hEa4+EutkUQbZrJ/2OpLNfNc2sO3+90oDo27bj3QA3G2g0HZcPrZ10Dly5MiRY3Wc1RIO4RWxwsNKR/Jmp6f4/PWf4fIrLuWqJ1xJXCw6y6kxaczh9s5MthCEJQSj2xS4WbQKLx+5I2nZ7mKJTSN3JOn0ruxy7SyUnPQkYVjDmB4++KFPs/vgJCemFzD4bBwfZvt5m1g31MO2LeuJMBR6+qg1IqpzFe65427u+OatzE3PUC6XOf/CixhaN0TfYB8jG0ZJrAEDg+Uh1o+tY/fu3dx0000URJGgFDA6PkLfQA/1qMGmLZuJrSGQHsbA1VdfydGjR9F6HzMn5vGERQqLkCH9I0MIKUH6BOltXaQupFKpMT01y4H9D2OFRFiD9KBnoBdrLQEGmc7KDw8O0Ts0yNzcHJPHjtFoNLCeB0h6Sz0I6Yi650OjHlEs99LfN0R9M6AIAAAgAElEQVS0sEC9XqPaaDRD9El/8RX2hItGkghXy+3Prl3f25yht4tSgaWh20yTHC8lWovvCUAQBCzUKiAFQRDgSQ8bJ8RxTIJ1MwCJG+SEZnGw4XtOOuJ7BayQREmCIcTDc+9b4qyjXuBTj0KCICCxMcIGaZkMnkwHmlIQRQl+UMIgiOIYISRFT0ASE9mAxPOQwlIwkRtEiRKJ72NNiMRSEJAkqXZaeG7GQgqSsEGPFYQCGj4g3FAtSNxzSIR774UJQTqSbK3EWid3sFZghKsLKwy+dTMfi4RbgjDOmdNmz6d7GDtr0/pLCXc3p9Elz2uV0BjLEmnp/pFpFI6kJQC0Oycj/07nbSUgzupmNUeOHDlynAKc1S299Ay+kxoSCRdyzPcl99x9HwvzVdTFF1Iq9xCG7aHGllipVvDsb4UlwRrZzEs0e3fZlmotVm+nIxap6kEs6pDtoqUviQwSwdTkDJddfAlz1XsJSv3c/dBejs3Octv9D/DG17ySPfsPs337duJKjZJfJIlCCj0+IxNjHNxzgPpCjYP7b+bqpz4JdblidmqGwZEhZOBRqS2we/du9AMPUKvXmZydxvM85hcWOP+CbQwM9rEwt0BPbw+xcRKSO797N7fffge7HjxEsVhGGEtvucgTrryYIwePMjI+hl8A6fkYYzh08Bif+NgnSGLLwMAInpB4nmDHhduYPD5N30A/XsHHYPCDAhRL/N3fv5coiti6cRPlYgEjYN26ddQrdQYH+5FSkiQJ27ZdQE+pl5tvvoX5EycwxtDX14cIfMrlMn7BUi6X3CAqDd3n+E3HgKk7/22zHgtrlpLoNB720vTZO+UGUvV6jULBJ6zXWb9uHcQJtVqN6XqDRFhCYwg8R8KCwOnVpSAdbkG5VGK+UsPGMYHvIoBYmdYxCZWFKmPjI8zOzlIoFNx7mkoKknSRlHojcd9DI0IiCKQFErA+SRRSKgVUsYRYQnxKgYc0EhmHzYgwjdggi0WiMMHzJHESIq2T3VRxxNU3ApE5pSap852XUl4RpBMHbsbBM07eIPCQIsGTlhiDtT5CgEkt4UK4mQ6LISGLDNKij17BirwarLVdB8xd34d0cNOcnLA2HSi7QYFoeRVgUQFjsYQ2IfCDx1bw5xw5cuTI8YhwVks4HPd0VjaZWjBB4nkBx44d464778Ha1Pkuipd0rK3T6Z0dcKtWstX5rhMuz6Tl77VN+S7J29glK60JIyGBsBYyNjLe1AYHQZHSwCCh9Nl5931EVjI1NUep0IPAUKtXmJiYIAgChoeHiRoRgShyx23fpTpfZW6mgrQCE0V4nqDYU2DDxnX09/dTqVWJkpgwjJk+MYMnAxbmq5Ba3z1PIKXHyPAYQVCg4BWIooSp6RkWKg2iKKFRj/DwEXiYBOLI0N83jJQFPAJMDGE9AmMJayFhGDorJoIoMRyfnGJyapp6IyYKDZ4oUK/UGeofImo4Ei+lJEoceX1QP8S9d93P1PFZKvNVatUG0kr6evpo1EKc7tRLw+qt0YEwfR7YBGzSfC5O2mCaEoclz1t0Tt2nMw2BJGo02LJpM2NDg8ydmGbuxDSVuXlqC1Xq9TqNKMZKMDYhjkKETVg3Mcb2889HIogbISaKScKEKHJlio2hp1xkYLCParXKUP8AwwODDPT24SGxFuLYYBAEhSJhLcT3JFiDSBKkNfhBkaDcR6VewxqDJyQWHysCDBaZWXIR4BdJ8EmEJMZZ033fJ45jIiGwSDybEm/jVuWTxQApBULgnoET6rjFhNzTaQ5GhRCI5iDEPTeDJcnCUVqJNYIkHRgkuPfGCNn8v/PXqg3v1I8v6siX+7Y7fh1SFlpkJ93kJdkvwVIsFpGex9Hjx1Z9B3PkyJEjx2MbZ7UF2vc9fN93BEUmiMBihUB4Tts5X1ng29/8FldffTV9fX3U6/UljnSwVFvZhJW0kmJjEloXBxFi0YFPCLffo70zXtkRzqQLRAhskl5XmLZY08IaSqUSC9Uq3/+kq7hX76ZhBXfo3YhCma/uvIsnXnQRR6crlIMi/eUCiWeQgceG7VtoLIQEBEwenydpGD70rx/jZ375zUxNzdDf30uhIIiShOHRUcr9A/T29TM7O4vFI4otC5WQLVs3EjYMxbJPksRYKTjvvPPZ/cABDhw4zODwKIFX5KHd+3jiVVdw/MhRhgeGSYiRHkjp0983RBJLoshS8AM84TMzOU1hqJ+wHiH7BQaITcLoyACFYon5uQqzMxVEDI2wytzMPLVaDRilEUd4vqRWbWAMlIIyhUJAIAVhPabuNxgcHOTI8SOMiOHmAMhai+gyXd8WdYSlGtxlA89ZQ2cMY8Hi1H0zVoUxXHrxJdTmF3jP372bwd4BPM9jrlbj/EsViTXMhzHDQwMECDxPsmPbdr75zW9z8OAhqpU6JjLMzMxw3Wteza59e+nv70cWnRSkUa3x5Kuu5ju37uS2+zXT0/PMVRbYtHULl115JXPz8xQKBUo9RaJGyMTYCCMD/VQrIfcePcETnvJ9DEwf54h+iEYjQgRlwkKE9WKKMfT3jRL0DRD09RPjYW3C8YcPMD95FJNElIsFpJSY2CATSbk8iCwEmJ6Aan2BaH4ekogkjlyMZM/F/Iise+7FYtlZcqUgbtTAWEyS4LXEV89kGymV76p3zr7X1tmmVotvlkt7PPRF2QVtaZd+v1ls56RlCR6RhbMTHsaKNidB05LX9Nw8J06cYKEeLvM25ciRI0eOcwVnNYHG80H6TjPp++k0vYfneQgvACxhGLJz507GxsbYsWMHSYtjnO/7RFEEUhAb4xbYs61e+GJJZ5zBkfZU39jCtlZbWS7bttY51mU67MU0juBLBImM8BA0khispex7XPOkJzDzxa9z3vp1HDsxDeV+3v2hj/G8Z11LSUpKWyaomwiEx6Yd53Nk31FGNq3n8LFZqmGCLJb52i3fQV1xAUifyDgdbFAosXXbeczOLBBbmJqaZ74acXRylosuvYyp6XlG5ABBQVBtVAj8ImPrRzl8+CjHTxxDFor0xj0s1BsE5RJR3EBIgfQk1eoC6zdtYK5SZWZ2jt5yGd+XHJ+e4ZKtW5icOk5YL+MXJJ4QWBOzcf0GJv0pPC/AWg/p+ezeu4dNWzcRJnX8wAMstbBG30A/s5UF4qhM4EuKxhKaWWq1GkmSYExMHEfIwAcJpovFUaS0d/EBd2wI0aaBB0A6p7629wLb1Lln74kAbJwg8bntjruwXpG5SkjUCJlbmOeFL7mUb3z3VsbG10FiMIAvPU5MznGf3kscxiRRzMLMDGMTo2w5fysP7X8IvyAwGMKwzsjQMDd+4YvsuX8XNk6o1av09/Zw6UUXMjo4SL1ew/ckJk4olUoMDI3wmc9ez0Lk8eO/+IsMjo/yiS/eyI9cfRUP3fcAsRBERGAb+L2jjGzaztC69cTFMlZA3Khz5ZVX8qXrP8OxI4cp4dMrAyISxjZtpmd8A8Pr1uH1lyGOKIURJQQf/9gH6ekJMEZghcArlxkdnWBwYiPlvn4KhQL1uVnq05Ps3r2bMAyJooaTpVibhqVMVSAdKx+2ap9N85l0xkkXzeftVuO0GGuQy8i4lpLx5pRV26sikC4KtBBIKQlDp0e3xq1UePjoER7ctQtrLRMbNi15/3LkyJEjx7mFs5tAB0UIAgIhSRKLlAYhnFVaBk7uIFNyPTU1xa5du9i4cSO+75MkCY3IWYLi2KbEuoU8dyzZK4QgsaaNQLlpZ0nr6mTdCHRrnOXWdBlRzmQgWfQJt6iyJBYRQvoIC551euhKZZ6rLruQKIrp9SX6wGEafpFP33AjW1/3avYdn2R4pJe4FhJ4cMlVV7Dnwd1sqNS4/6G9xEi+/I2dXPaUqzk2Pc+WvnFKxYBatcHw2DADQwOENuGBfQcRsgeD5Uu3fIsnXqkQsxXGRsuEYQMpJRdccB4H9h9i+uHjJGGDynzCbXfdw9Of+v1MTh1hfN0QURhiAxidGGFo+gR61176k9gtemMjLvELeLLo6iRx9VtfmGd8fJzJySnqSQQNiSxJGrUGm4MCNrYgXUzmRiOit1ym0giJrMBPfEytSpJU2TC1lcQawiROtajWyQa6EOjM9pwRps4FL4RwtkZaBzwJ2CWOhE6n26GExVo4NDnFd+7ZReD3EYY1JCVqtoIMfHpLPWAsvgzAWgaGxvjqt++gkpSgIGk05vFLZdRll/Pde++kZ6CP0Cb4soAfFNm0eRuf+OT1BBTABiRC8MSrr+L87du5/Y7vMDg6hsFQaTS4+oon8P5//TBhYnn2S17J8MgG7vrOTp529fdz5713UJ2fp390jMCTmNhnfPMORs+/iNhKqpGlMjdJueBz9PARjh2fpNw7gJWCsB5RKPdTGlvP0HnbiT2P+SiBhZC+MOT2e+6m0FMAkSCED4USmy+8lInNW0n8EtWFCmGUUAiKREmMXwiYW5inGDiZkOd5zdU/RRdLc+v/nd9u6zfXiYxINx90y3fbec6i3bnl/HS2IcE2nZWF5xElCVbAsSNHuV/fj/R9N7g/+cAeOXLkyJHjMYazWgPteUX8oAekjxf4SCnbfr4g1QwHFAoFKpUKtVqt3XpsbWqlXKp5XvxBkizVRXeLYZskCUlsmpZuF//VNuPTmjSf7FiUxC4SQ9JaDtd5Jymxj40lSgRJ4ghDMfDo6y1RDAQ9xQLSC+jp6+P+3bsxVhKFCVIIwjCk0FtkYGwYv7dA3cTUjEEWSuzZd5CgUGahWnEaZBIqlQqDI8MEhQJIyVy9TqURcusdd1Eq9zEzO4dNDFYKGnGI7/v4hYBKvUa1HjJfC9l36AhHJk84fbJxMXHDMMQreJT7ekmwzFSrTFcqzNYjjh6fptTb60LyxTFYSb0W0tfXh0VSqddZCEMqjZj5ao1iqYcoStL405JGo+H0tAJCI4isJAZqYUSlWiNJB1cmjc+b1e1af60zEp3b7rm2L7XdVTdroVAscXxqlkh41KVPUupjwUBpcJhGElEulwFoNBokVlDq62dytoIplYllCVHoYWBsjG0XXsDk1Amk8MG6e1u3fhNTCxVksYwt9RIWSshimfENGyn1ll0ECBO7JchLPdQTyfD6TTzpB6/lkqufzPz0FIce2kV/ocD0zAx9w4MYYQgbEeWefmLpU40SKlGCXyhgjGHz+nXsfkBT8F28cWPBK5YZW7eR4YkNVGNDI4HqQoUtGzexa9eDzExPO720cRKI/sERykMjyKBElNaTEIIoijh06BCTk5OUSiU3IJbdm6Ll/A66ORWuxbl3ubQroTN9Fp/d930efPBBdu7c2dyG1WRdOXLkyJHjXMBZbYG+4JIr+PbxY8igiIhjZ90RliDwkIGHh6Dke84ZTAp83+fYsWMMj45QKDjNZqMeOecjY5oLIFhnRFq0KKZWr8yKCc7qZJJF58HsfxeloVvn2+EgmMk4xKLzoLNAO8uqM27LVA4gU61ogokbFApw0Y5tlAf6mapFzB2boV6r8uVvfIuN68axcS+jw4OIKGTBLDC2aYzR4xNsqlQ58PAkxB7/cf0N/OAznsLlF2+ir1R0C6r4sHXrZsIoZvP27ey85wEskqBQYP/DR9g8McLCfJViX4lGGOEFBdZv2kTj7t0cnZ4iFh4irjN14np+4sdeQFiLEJ6HxVmBxzesxxQC9h2aTGMLe9Tjb/CyH30hMzPHGBUlPCloRAlDY+OUBge5/bv34hV6ECbBJoZnPLPEwsIMvt+HSQyJsNTqEXP1kLlaA+FJpIkJa3NMHDnBxokRqvUE33e6XffsupOjNotlt5UmpVwyi7D4bLtluEj6SgO9mCTgua96LaHspVzqwSPhyIN38cD+/fi42ZOgENA3NEpNeLzk9f+TqNCH7xXosXUO3ncb5eEhoget05IXAkIjqFNgaHycF77+p6nG4BWKTO+6i/HNW/jGzlvpHR7GCIHwC3h9I5jeEV7+pl9wFuI44sB93+Wy7Vu47+67GB2fIExCbJKwbuN2evuG2awuZzo0YD2mpqZ4woU72HvvnezX99Lf30uUhFi/xOjm8ymNjtHwC+AViRshW4fH0bft5MCe+yj5gsQT+IU+/EIP23ZcxuD6bVTjmPnKLL3SIsIGD913N7Mzc/SUyjTqIYViQJTEBJ7flG0Ya5p/d1vOvnUGwFt11dAsCk57RI/Fb7RVL90J6RYdTAPqZI6jhUKBG754I9Vqlb6+PrfgkTF4UtJTKnV5WXLkyJEjx7mEs5pAb91+ESQxe3c9wNFD+5BJiJQQFDw3XSoEnhV4nte0YBWCAtWFCnW/7qx+wpDEYBKcVTjrmG3WabZM4WaCZSSt8Z7bLJDNGMPdZR2LHb1BCOs0vxl5RmBN0gyVJWLprikgwWCFoVAoUg8bxMCmzRvYODnJXC1hxsaEjZjPfuEGfuJFLyRpTDM61E+ShMxFdbZs38LewyfY4hU59PBxeksj3PLVW3iCehUz0wv09ZWdg14Ycd72LTxw6Cjlvl5qjYhKPeT6z3+RF/7ItYjBHmSphOcH1OtVzjvvPDZv3ctULaZSCykFPRw8Pk0ii8xVG/T09FAs+szNzNI3NMrYxDj37XkYQwEjBd/Vu9l8x3fZsWkC6RdoNBqApLe3n4l165ms7MTWEue7mYRMzszjJ1CrRgQFQSNO8Ap1rJDsefgQwvMRJiKqzjG27ggbNqynHsYUkJg4Tp/F4rPptqhJG3GyshmKTDQdPJ3IRsgVrJRW0nQ4FYbx3l5sFFAXPnGpjF/qoce3yN4SJ47PMTbYAzamVokpjRYx1qe3dxhR6iMBwkbE0MQ49z14H73lAQSSOEoo9Y0SDIziD69jeGgjXiNiYGCATcM97Lz5RpLUSh1ZQ+B7DAxN4PePUPfLLFQq1GaPcunFO9j/0P1Uwyq+9JBWUOztx+8fZd2OS5msNZB+icQkbBof4ZYv38DBhzTD/WUSYykEAcMbN7HxkstpJBbR28f8iRl6PI/Zgwe459vfpNwjiUwDY8uMrNvMxk1bGd54PnO1hHpYZ6DgM3VgPw/vfojazLST+Bgnv7IGAr+AsUkLSV2s3+UcCDPEy0TQaYUUojkIaiXMy8k+uj7yNP3d997DoUOHKBaLLqyf77uwgUGQ1u/q5cmRI0eOHI9tnNUSjhCf89RlXPHkpyKCABn4FAqFVLJRwpMBvgzwhATjwnGZJE5tupBEEcKCbwU2idL5fWf9NTYhNonz+U/5dJRYwtgQxsb9bRZ/kYUYQWIgTgyJcc5qFklicNsmI+QiDYcl3HS2dWG5YmNIrEhXdxMkNsaYGJM0EDbCJjFhIyKx4AsPGUXs2LiRif4SfcUAAo8FI5iJYbqRUGvE+DLAhobeoIdNoyOM9Rc5f/0wY70Ftm1ax+SxE0ShJYlc2Yy1hPUaG8YGWDcyQNGTFIpl5uqx01AbV/7E4CKG2Jj+ngLrx0cAiQhKUChyZHqGqblZjHTWPQGYsMHwQB9giaIGMYI4sRw4dBikJAzr+FLgS4MJawwO9JPELlJDZAUyKDE9N4vBEpnUGVRYGo0ag4O9JEJQiwwJHl6xyPGZGUgjIsRx7PTmiSE2svlLuvziRKT/p882scQGYkPzecfGEici/dn0udvm3+754eQ3sSE2hkZkqEUJjciQRDVq87MUPUewolTOg/SIEp/BkU3EskCMJUpCKgtzzE1PMz87h5QSiyFOIgq9PZQGR1iILY0IAs9n8vBBkiQiiiIC6SET8Clg8Sn09iOCAokxeL5gYWaa6dkK+w8cxibOChvahKRUpn9iI6ZQIvYCkiTh+JGDDJd9ju7fRxTWMDIgkgGx9fGLfdSsD8UewijBxg3UBds4sG+XCyNJAEE/frGP4YmN9I2vZz5x31UBKEQNju7bw8z0CfBk87uDRckNLIaQs9bFl+78ZWHmWn/d0DmozWQlrT88Hyu9tnB4QngIKxFWujjtUiJMgofFw7IwO8OxY0fwfRen3BMSE1sCz8eTbuhVDLyuZcqRI0eOHOcOzmoLdGQk6yfG6ekts3X7hYSzxyh6EktCYl14L7dSW4xn05XMpMTgOsE4yZzpBcgC1XrF9ejSEWSHVO8qWqeKE7coR6emUgDGLXEt3DJoCGO7qQEWO3CxuFBEBpNaSCWCbG1lkTk1pvrKKHSSldGBITavXwdSMHuoQRjGfOxTn+UHr7mG/p4SiY0pFErUq3Uu2HYeg329nDgxjbXr6CkEDPT2IaXk+IlJJibGHJGLYraum2DLxBjCSvYcnaHUP8B37ryHH/z+K5lfqNJTKhAIsCLkwh3nUwkT9h2bxlqLXyjxla9+nZc9/5nMzMwxOjJAID1qlSrbt25hoK/M7EKD0BoK5V527TvIs3/wadSTCAlYmyCDkNHRUcp9/SxEAqQkSkIe2rOX3ou3ISUgCyTCENXqjIyNk9jd+MUiSRJh8ZhdqIH0aCQQWIERFrf6Xfuz6LRC246FVpwlGrBZALVsuXHnRChalpzMpvCzNJlW+uiJaR6eTxDlQYI+g1f2mDv+MMX6NNZCnHgIGyMKPn5fP5XE0IhDoqjOUH+ZY1PHOL5/L8JYKrUqhULBSY+CHmK/SCOR+MUS08cO4tmYO++53a3CKaAWGUS5QH//MFsuuJD5WNBoVBFJxHBfiZ3fvIVwYY7hngKNxJJ4AQMTm+gbHaMWxxQ8n5mpI6itW/jMxz9CrTpHuVymEsb45TIDo2Nsv+xSKjZACMnU0Ye58sLt3Hzj53h434MEwpIYD3yPDZu3cf5Fl1JJB6ElH0RkufvbOzl++GGKxYJbKIVFyUTT6mzJ3PRcfbeEn2w+uy7OhK1BCLPvrNsS9W2L5ki7JPZ7bC1e0pycIigWaDRqzeP3338/8/Pz+J7XfO6BlAQ+FHyJ7wmKvsdQf+/Sa+fIkSNHjnMKZzWBtngEQyMEvT1s2nwe1b4Sno0xYYNarUGSJCwsVGkkaRxZIQi8AhaBtBJhfIQVxLEjpbEISExCbaHm9MepgxieC5m2SHKzEFg4vXIzbrNbEMJGKcGSmV5apOqP9qgA2Tku3mx6LCP0OCv4ciH0MnIWRnUuvGAHfUPDLMQJew8ewvM8bvrKVxjrfR6b1o9Sr0V40uB5ASNjoxSLPS5mrzFMrBuhXCwyNSWoVuv0FMpEjQZBIeD8jRuchfjIMSILQ+PD3LzzOzz1iVcirEAWJZVGhfHRQXZccB737j5AtdbA93xmZubwPJ8kip0jYRzheR4jQ0NsXrcOkxxlIRb4fpmwXsUrOF11IQjApGUZGGFsZJj65CxSeFjhs2fvfr7vCZdgMERGIDyfBMHw2ChWGITvYUyE7xfcwiRewTknChcrwdVb0lafzechm8vGtZBs01x5zuVg256BS96dxLnDAuF5HDx8hKOVBK+0QKF/nrggqc/NMOibdLEQD2lABiVMT5HZuE7c8Cj5HrNHjlKZnqERRQSeJCj2UIlj/FIPA+s3MlsPEUGRIG4QVWapVReYn56iHLhV7xrC0ts/yMjWbZyYr+AHPSS1GscP7qc+eYD6wiwDvX3Uw4gkKFAo9bPtgkuoNyKQBSpTk2wdHkbfcTvVudnUETAhBibWr+e8C3bQiCMEkuNHjnPVFZfwkff9E7ZWpWgayEKBCMHo6DiXP+kpVMKYxCT0FAKO7d3Dvoc0Mwf30lfuJUrc+yJbfQ/SOhZ2sa4t1oWI69YurOAsuJwcozNix3J5JNbgpyts1ut1yuUeDhw4wOHDh51fhbAUPbdcvEkSip6kGPiUfZ9yb5GC71EKzuqJvRw5cuTIcQpwdhNoa0F4YAU9ff0sTB1xFuc0CkYcG6r1kMkTU4RhSD1OCONkcXGDdHo/WxoZoFgsMjIygjWL3vgiSUPMNUm0i+KRxZqVMnNmguaiD9YtqCFltr3ol9bZObfpLDus0Z1ps3jXGaSUJHHC8EA/pSBgfHiIyvQsAyMj7D90kJHhfgrFAN9zofs8z6NcLjcdLj3Po16vO414ShwNLhLCQH8/IwP9jI0OMTU7hzGGvfv386TLryCKIhLfT1eug8GBfob6ysRhRBiG+EJyYnKa4cFBYuOiiUjpI4RgaLCfyekZqrM1TDoNPjUzx0Bg6CXTGLs6GRoc4NCxaYyMkVhqtRpSSuI4IsAjNgYpnHTHl15zCWkhnFwjq1tjs4VNBJ0cqlPrurzz2FLS3am5bdXhZv/7foH5yjQmEZiwjqxI5qoRAQlJIggyrbUFhE9oEhJpSGwMsWF+epJGrYr0cNFHpAfC4pfKyFKJurFuSey4QdyoU5mdQVpnZTUIYuFRGhhiYHSCuhUkYY3ZE8foKfgcPnGUwPMJwxAhfSIrGR+fwEqJtR5CCgIBB/bsYc+u3e6e0lUFATZt3UL/0CBzCzVOTE4yONjPvoceIKwu4BuD53vUw5j+iXE2bN5KGMUkWWzmKOKhBzXVE5MEQeDK4DnNuTVLSa97DkstzN22lzu2HEFemra7/jkbuAohKBaLzMzMcOTIEQYHB5vLqMexyTLD8wS+FHgSCr7n5GV+sGxZc+TIkSPHuYGzmkDHxoKRNELD4NgGjh86SKMRY6KEKIoIY4NXKFEo9xNSYW5+hthYwtiteFdrOCt1FEVkIdHAsH3bVsbGxlyYNumliy5YSAmZxEtjCYt06ea0YzYSbNxccttaQRIbfE84d6fWvtguknGvIy6xkJmEY6mlqhElbSH4EJKiB54RXKUu4p77NcXREUySMDTQh8FQbzTwhI8nJFEUUSwWKRRKGBMzOztLo9FIl1oW1CKLLwXCJvT3lThv8zomZ6ZZPz5Mox5RKrjlxRu1EM9ahEyQtQoDvT1csmMLh4/0E8cxcdjg0KHDTIxOMDM9z9BgP7FJoNHggvO2IKVk8MQc87UqxhgO7NvD9k0TVAKPUrGASSLC2gI7tm1hYWGB2bmKW+yiUaUUBMzWFujvLZNEMcKzDIzpHb8AACAASURBVPT0MDHURy2KSTyBNOBJmerZLQgnjBEmW8WutcIFSbIY4zvls2lMX9GUcFgWl353C2eksxBtHCtdh1A4DWwQ+MhCkcnZWYo9fcTxPF5Ux4qY0fFxwmoFv1SikRgCI50EYn4Wr9RLUq8TNyJmjhwinJlGWovnSeYWagQDQ/SOjDtrbhAT1mOi2Slmjh5i5shhCp7T1htr8fp6Of/iS5mPImIEfpxgqvMcObSPuelJessDGCGphAnbr7iM/8/em/xakmTpfb9jZn7vu2+IISOHyhq6qrtYjFYPkkiiSbRAUSAFaU9pJVDiWisC+gf0FwggJEiAtCTAhgBtJEgLCYIgLUhKzWZRRZDVzY5mlbKGzMqszIzhxZvuve5mR4tj5m7u119EZnV3ZVS0HyAQ7/n1wczc7/PPPvvOd77x577FJ5cXrJpjnj//lBMi//Tbv4t34DVyvd3hvWN1fMr//r/+b9y0Hdt9yze//is8uHeXH/7gPVzq8I3nar9nc/9N/sJf+3dZH5+x65SL509ZB88//fY/4vyTDzlyQIo439B2HeqD6Zm1OCxrBZ7zxASYFlLpvxNMAXI1Mer9nsfJv4e1JseuOf1+Ct4L2+0Nz5494+nTpxwdHdG1O1aNAWRPSxSIAqvQcLRecXZyxLppCOvAanHhWGKJJZZ47eOVBtCoI7aRLgXWp/c4vvuAlBJXV5e9b7ImwfkGF9aEoxP2+4gSIUZcA0k6HCZncDnz/4cffMTz6x1vvfUWd05PUDVbKnIlOhF6MC3ikAKysMqCSU33XCQcXTFjkOFFXr+uOwadtFN6UW6t0qzlI6WSnslAla7dgngenG341//8r3B1dYX3jpPjY9arlclKNJHEkqr2XcvF5TU/+eDHXF1dcXR0xNHRigdvvQ1i+nEvQrvdsgoN3/jSOzy/ukbuCZujY05PNqSuZdu2rFae/XZLiMrbD+6h0ZK4gvN89d23Ta8N7Ns2j8WWe/fu8c5ux8nJCVETq9WKX/rKuzS6Z3tzTfDGhre7Le+8cYdvfe0dYnJ4B/funtIEZ5ZmMdJ4R9ftWa/X/Mavfp3rbQep48h73njjDSTu0bSH1OQJh2Q5cwWO0iDTSMmW6J24fqKSUqLNziiaholPTCa/OAxLshQXiAnaLpn/d7uHlOjaLW+++QDRaHKIaCsUYdVweXNN/PRTZPUM5wLnz57jY0KjWQImhdAE3nj7bd7+0pc5v7lhLY7Y7jj/5EPOP/kE0UhKAuLoEnz9y19h2+7Zt+YzfvnkUz7+wfe5fPaY09NT9l1EnHD3nbd49xtf46azAkNPnn7M7mbLH/7Lf8E+blm7AJrwq4Ygjna3hy5x5Bz37t1Fds95/NEFPra5X4kY1vybv/3XeN4Cly20O7aX5zx673s8+ehDvHZW4dF5oiaoJoeqVoa7z1MQyykoKwIvkmaMNNBSznl4n6QH0ml0rDi7Tv0MhBCQFHny5Ann5+ekZM8uapIgn7XPTfAE74jRcXx0xGazwa3WHJ3dJaL40zszz8wSSyyxxBKvU7zSANo5oesiMSYSntXxGavtNReZpbJEHmODSyED1yneVB8EDfaSVsESAxN4e9E+fvKELkaOvvENQgjs9x3BGc8l0Es4hMrdDug0GWOdddYeIRJ7FtNlCUH5XWTMiFoNuyINmc/WFwqbBk6VrtujGOA7Od6wXjVm59c0rIO91DvNDiA4012r8uZb77A5viKEQLPyrDcn7HLVvpiTtiBxerIxwC/C8fEp62ZFJ8aGRzV3kXa/Z9ME7pxu6PKE4+zshPV6zW5/Q9Qiq4g47zk+3iC55PHx5oiTzZrd1a4vPJOSIrpns244Oz2h6yJN0/DWGw8QEbpuT5dWZlGIorHj7skRTdiz8secnRxz79490GjjqWnCPA8luDX7Pkt+bpwLhHVgtzMtfQhhKBVvSur8LxdVkUkSaEpZupNYNStbDbnZkroWjYlVcBytvkzqshwDT0qRZrXip48/ZX95gToPCc6aFZtmvOQfNaGxY3t9ydXVno8++MAA39UlMbY0zoyJI4oLgZOTY3ZXlyRxXFxe4fY7zp8+RujYd4rzlrj35ptv4sWxb/fs9x3d9pr99TVX1xd9MZOkEVEliqIkjlb2JyIItPsdKSmhly0Jd996g7O7d3i6VXY3V7jY8sGPf8SH7/+YjU+WmJlcXyJbBQOkVEmCvbzJSpfbws9nA89zv8/FnP1dXYnQZEMdMbZc3lz39nQxtTh8f40gZnFolUUdLlgZ+vXJCVEcfrXm7oN3XtqeJZZYYoklfrHjlQbQd87ucL3ds2tbnGs4Ojmj3e5I0pDUkdRZeV0nOAl439A0Aj4i0eEkF0AIia4zOUZKtvTuxPPs4oLv/uEf8su//Mscr9fUntAh+0wDVmAjAwBHcXkQ06DmfTw5s7833ygay6K5Nb659qPVKomwxPRFHwHvVzYRyPrf0DT5d9i2nelhM8vdTyycxzXCnTsN3hsA2N9s8R4SZiOWOkWSAde7d0zX4JxweX2eJx6JLnnA7P9Q5c6dNUkFFww4dl1HlxJpv7excx6RyNHREaujtdl7lURMFwjrY272JlNpYyQE4e0Hb+JE8WHF6ekpXYy41ZpWyVp16PYt737pLfZ7s6s7Oztjs9lwcXGJ9wERR0wJFTXAY7MQuxd5XGO2QdvHjp9+8Ak3NzeklLh79y7royO8byjuHCUJExfIWaDVTXK2OtJ1JA83u2tOjzZ4J4SN4+7ZHUQ8u/3eXENSwgXPVbfl2ZNPaPfmV316ekp46026fYvz5vvsg+PoaM13/t9vc3F5DWHF6ekJx0cr7p6eIKK4pqGLihPFOfjn3/42l/s96j3Hp2esXSL4hCShi47gG2LsePzTD/nh+z8y2Yf3nG2OuXx2zipP6Lp2R+MsoVaBmBIhT9BibOnoQL1p3Qm4ledXf/VX+f3vfofdXjk+PuXp4yd88IN/xWa9otttCRLsO6Ji3w1NOJsa9t+JXqmRKg30S0D0bdtvTcqdstn592Kh55zj4uKC/X6L91akSVXxQfKkOLPjJGJUbFKUODresDk9RcOGr37rIevTU9pwPNvWJZZYYoklXp94pQH0ahWsNLc400O7FX61Yr05ZXt5YV7FqTMTZrGXYAjG7HknWDphwifPLr9YU0rGGGbAl1Livffe43i95itf/hKbzcbcAFTZ58TDJhdqERFjoCu2WSRlllIQFbwMWls30nBm27MaIKvr5bW1F+7IMcAJGhxOIVTMdpfZ8dTFXNXQGFM73kp9e+8zCB50oD52xtj6hiQm+yB1WeagpBR7Bjzh0AROnbGGqcN5l913W66u9gODFwykO7XCFs4FoiZcirQpsWtjz/7GmCBmMLvrsG56uq7j+dUVMUbENXQR2tghODQ7axwdrQBjC8/Pn1uSqXiSCDFLyqdqVzDVTJmcxBi5f/8+9+/fH4p55HNOjxHV7NAxhPMG1sU7bnZbRISvfeWreGwyouJp24j3K/ZtpAlWeGcbdzx4cJ9Vp9lt44htNC/ylITGB2LXcXV9wYN7dzndHOFc4HizJoigXQvOse0iOGFVEhnbLXc2R3RRaVDSdkuD6YEleSQJK/FcPnlCJ4pvHMfHJ1x+/CGxTXgXULUE1KO1WSPGGAlHDavVit1+z263Q8TjcHTRsV4HXPD87j/6h6yPAkfNCm2OePLpOcerYJZ9ziQxibwygrHbmo3sbMJaFSxC86APSXr9Z5ME0Dq02sdXJcH7FQiGSVQNsEMIPfvcti2bzYbVKuSciSzh0lw1FHDZ8tIkU0rbthyfnnF2/w2O777F6s4bXO33nN45nXkCl1hiiSWWeJ3ilQbQSnZ3CBlgqhUv8c3KGFYXiXGoWOacMYeiRb8spnEUwRfNZTSd6Hpt5a27XL3u8uaGq6sr0zziEEn9Em/5H4Zl397mbpqMVGWcGQCzn0Vsyb+8zO2Fnnqmt7C5BbyrqpWtdo7kPKIRScbcFnbUhwAZtPZtcoLLoCC1rS0zY0A8pogpsh2KM2lMiiZHcR5xVkzGpQSo+QwnR5DBwcNyLRNeEgmHJHOOSMUvGav0GDXlQijW7+Dy2Oe2dl3XO5gkVbwzWUfqOtouEYJpljVGXPbsdd5nAJ6nRikhztnk4QV6WbsBAzspYi4kRdPadTYmU3eO4R7Pu3GU+9A0a5qVkvYG8rvYAYJzoG1HkDWdapZ3NFZcxXlS6XsT6NqI8459l0gJgnfI0RGrpqEIfdq2RbKOH4RWwCUDgm3bsT5ak7rWAOFuS9OswEOnXd/mzdGK7X7Hfrsjdh2N9xnA2xi3sUOzZsm7gGSZUYyREJp+lUa8Q0RZHzWQEpIiH3/yU2IXrN9Y8REJLpP6tSWdG39nqsRAkXq/YVL4opiC6lmQnb+LgxuOo2sTn376Kc6Tv/f2edN4UjLI34RmfO7657Dh5N4D7t5/my2eDvCrDV0340G9xBJLLLHEaxWvNIDetZGkHbHtcCLsIuyjQ/0Kv9qQFLq2o1M1WYFzrIJaIRURhCOUNmf8Y8uyPhCkAJWEZHgiTnj85Jwuwmaz4fTkONukKepNxiBiFQV7htkZM+x6RwfThRacVf73GUyByxrgXHUtRi4uLthut+x2uz5RqZy7gGJxOvwsUoHyYRskxI21uuYoMrZvq8FhvV+JslRdfq7/r/eBFq3Yvvq8IwBaGstu9toAmqCV0Cf6FXZv6t/skul3LWI+12FJ9TZ7O0yvM7qmCKnLGtgM8Exi4MiSaXIKp50L39PQhaUnme84SUmaIDR0gHgT/GiKrL0ndh0CBLWKd3uBXT+mQtdaX3ZxD971sNE5oYuRttw/781WMDO1BvCtgIx3gubKQQqoC+xiylX/+izXLIHx7HYt6jzmSxPBm16/6/b9PUw3W/S6FBLxpukmIpLYbS9HiZk3N1mnHwTJEpcE2PBVCXyYhEikllVka8LsCF22JXGgg/7YYY44fY5BOaPN90zClFL+QUkx9jIpH1z/XB0dHfP06Tnf+9738D47a3hh1XiCBFarwNHa8gzER3Aevz4ihBU+HHG0PrF6TM2Gzd076NGG/c2eTmxSvppdA1liiSWWWOJ1ilcaQKeU6LK38cXFBbubLW23Z7/f03VdXw0sqY5AYgGfaMpVA4eM+8Ikii/exRUIVLXCLLsd7W7P8ckGEaFRRbNzRWJ4eaeUbFul1SznEzWQABBFkNau67yxebvdjmdPn/dAsWma3qu6BsbGoKfxtlmw6g4AqrkNDMVBxoCyBs2Mfu5LLOthtbZyXuvYITt4wNQe7HELsE37HiiP2EkZjpljlkdyl3Iq3ME1Dq7pDj9PjCcEIiYnkDwhq88j4sfndIfs9fT8yvQclgg4fH7YVgP4GWCWZwtIMaHZmrCWpkyvX3Tz/e+TZwwGizctGuQ8g9Bq5cX1xYLI4LTr9f72jJIdakw29bIo856ihwZ6aYcUjXSqZElEIkNC73DfDyd6/QqO2MqKqtLGspoEv/d736brOu6e3bG/CwJHqzVHq4CXQNN41sG+e85bifmwOias1oT1MYbRPa5pcMFkNs5ZLgIKSyHvJZZYYonXP15pAG3ltM2ubrfbkbo9cd9akY/KzWEEoopmUUEmNss1c1y/5PrjtdhamWe0yQasoMK6WRErAG7nMXAM5Cp4gy2WiO9LeIukHghJp2y3W7bbbQ/gCwgqusx6ElAkKGO2OfenZuKsAblDQ38VDkDV9Od6DOzD3p03SzDG2tFyHZkeB6MJyTRuY4SLJKBvc72k7w6PmS7XT7fpRFZz0D/Miu/gHOJnQfo8GJ/0cwaQH5xnwowXtn3u/NN7DeOxLZOEEds/mUyoDCsZtRQl74Tkz1IFsst3SDXhnTOgXiafsZrI5bRaVcWTJ5aqowlmOWctgSp5Bz4/Y1ElM8uKiGnFNa+alMmTauyne3HoCQBeK+9nhvEU54ipRZPiEFarI/b7PT/4wY8AONkcI0lpGkfTNJwcH6ExEYJj5YM58kiRNjlcaHA+WAKlK6tWAZOjSG57vv7BnV9iiSWWWOJ1i1caQLcxGRvctuyur7m8OOf6+TMuLy9p93uSdjNgqgK4WdYB2jtR9PslLMku1npFZ/IFsWVgK+QSafcdV3INgDJhg3MRlpHEQoay0sM+hbUe2uxXTb+P9551zZDXICrTdZ4xsBKRHkSrjJlGko727fefGecpu5uqcSrtmWP55s51AEDngGTNUPfnOwT3k/ozg9xmst3aOPzsZpjsUXEa+2nSqmGiMuw3rCKM2z9Mv8r5ymrItF0jYB+HpLbp8QVM120tUbPGwzY9uLeHk4TDmJtE1asMLwLlvtrex+SZdcGP+gX2PStSiRACqxCGVRsdnDcGW7nszlF5NxdLyXobWDETmUzw7Nk0Cz2f7el+8Ed/xOXFVb5+g0e4c3Zi5bk1EZzQNGucC3hvlQWdc+ADKg0+rHFhg2uOKXaR4oJNMMXZs6lFxjUz8EssscQSS7xW8WoD6C5lucae7c0V11eXXF0+5+bmCroOJdKpJa2lzGKpFGeMDDqDQ6KtqxaQ43PiFIzBSg2MJBdPgPFSfUlQhIHh9r6wzTWL6/ulcfED6AhhXVVFG4PxKXgZ2hX6bbUntTFthQ2fSBn8XPW1MSieY6GNUTQe0FUU/pweegZbzjKq0/5M97f/q0lGmWBU+0QOz1uuVTOoKaVsFTiPYvr2aTlmAHxp8nsNzIdrTxn2PP4yBstFTvGifivjcS1H17p1Ve3Z2nq8ilZ+bjLSH8ft4z461+GM5KB9iNBNjoPDZ6CeIPb9UqXrOvMtX6958OABbrXuv4NlnAo7DTp6juYma0Wn75z0UiMRsWqW1XWvL6/44IMP6LpcACV4NidHrELDZtUQvMc5G8/gzP7QIYSQGX7nUdeYjMd5xK3wIWQmPFv6iTdZTXHRiUsS4RJLLLHE6x6vNICGgTVt25a4b235F8mJg2Pbt9ExMugnx9vK74dgr36ROznU0cJQftuY4aKzdBlIVJpRF/AFQLsxUJgmAdr+4+X92+QWbmZ7YakP+jKDIW8D6eV3A5I6lofcErd9+nkA9ADoB5A4BcAmQZjIE0ZSmurYyTiWfaftKJMDkcERwsZ2+jxMB3FeqlFcSKYAdyyNGU9giqXhVHpRP4dTZnlg56eMOr3Gf9r/OmYnQjMTk9s+G7VhApQhj6EMkiQRwXmfkyoT+92O58+fc3b/jX5VKFWuOf1AUQHSnn0eJjRU/aj7XYPv68srnj59mp12zIryaGVJgsWasjj39Em7blgxGlaAJANlQbwzdx1ktJRQ+jtddVhiiSWWWOL1jFcaQLvQ4Js1TpSbq0uuri5IbTe8wJOQYrG4q162IgOoinHikTywrHP63eElXrHRFYgIU+krluFf9ivXnbK/IQOeuvjgbQzxIbAdg6IpC23/h4NtQO/pPBc1UCuhqrjMvmsFLufA0m1Rgzh9gSNGv5/90mszRtcqDPsLrnvAeM6Ay4N7PXOOqQa6yAnmynkfKFVGSZm36K+LvXHe5ib71DKOuRh/dnhvXjTGLz7fdCJqibFTZngE0JVe8mLb8nOI9Oxwn+QbI0117M31FVETR6UMtpj9Yp2MmxuUz2nSDaeg5dw9M57Q3r47F/wBnj17xuXz53gvnJ1sCM5kJEebFY0TwNF4Z6tHInjncBKMgRYl5Al0yhMA7z0+WGXMKOO/DaVffV7GZ0iiXGKJJZZY4hc7XmkA7cOK1apFXSK2O1JsIRmA9plB6l/66ojamaVYPl61LOtK/3N/bl/KEVfssBv0s+Wc/Wf52FoHKhMGuraRc+IrtnhgS4uOGUmzALqce/z7PCNY/J+tqVXfpizpzDnnrjOMh+tB05QFHQOy+RWA+vx6AFXH8TKAp5We+WWsan/MDICe7jvHohYXjvG1bmv/dIzH55tjcdPEtaSA5rk+zMUUQL9s/xedYzxJcQefOa1cXTCdsa/6OCXmB5mFoi4Xn1F7wl0FqHEms+naPZftnt32pv8eOz+dwJQ+Vix7f9+K93sugJNMJpIqd57T4w2qSuM8zcojongPITPO3nt8v9Ik5qSSXUg0lzF1FAba2Gr7vs+PbT9h+Aze1UssscQSS/xixysNoGv2q9jWleS83pIus0ipTy4yTbS95PXAOuy268CYgR7tbxRpbo8lU/XAuSQQagHhdkiRgAzgddCSZljRSzum7aj7XvpUSypqJroHZ1J9rmOpwOcG0FbtBBEGh4PSngp46txYHbR/Xms9bcNU8wo2KRgfeejUIXKogS4HTfd7EQi3ydK8Nnza1tvFK/MAtW99NclR1VEBmNskJ/Xv9fikug0zY3jruapjetlFNUmZnmM0waykR/1pdLwfkitk9oCaPsHVe4c4Z7Z8KaIqpC4SJa947M3ju7DLMRbKfry6JJqqtg/PRD25Dc4TZPhOh/xd9c7hveCc4Eb3u/wNGK8mgH3PXJ0o7HJ/VYCE07FuXhcCeokllljitY9XGkBHzGp4t9vR7nfI7tqS+Eh04lAveAUfleQifhvNLTZ1oEoU011alrzDi7PqhthycJKx1rEHvRNZg1Xqy4CdiHkuAyR89gMWN04CdNXPIuCydqMHvZLsPPkyY/lI9tSVAlTTiPHrE8iqJW/xw3K3RyAX7bBJh+9Z7FS5dZSjnbrs2gGg4BOJiHOCKv3yfalWB5hWVBrgJXraz3CfbbLjKlu3DIgYbMtuA7+qY4cVG5uKUc2HJB07dRRm2+QBeVwn7S36Wnpf6QS4vqpdfy4pRT60t6rLVx2x+q66PjByGalXQ4aNdT/SaHunw32cG5O5uHV7eSaqffqKm3kfEenlEXWUSYtIcZ5JvT96PbkZXdt70DAAzl4WMgBjVSXl55ykfXKiFT9icMMh4X2TAfSwWuRF++TeEAKS9w8h5AnDxCrQgTl1CybZKUbVQnJWgEnFG1ufE1UTDpXGJDliVT1VI2lJIlxiiSWWeO3jlQbQKUHbdWzbPTG2aOqIydwNzBnWXn4hBDQJq9WKLsVhKTUlNFdsq3WJhXGqwcGU3R2B4RrcpsHrufZq7kF4+Wx0nkG3XIA2hR2bAGitlsqLi0Jk/oXs3NBGn0ejl4UI9vIX1wMGp8PCf4JKPpBZfQU0F4+rNLZl8lBkM8M4ziTKQb/N+vlygHcIoCsGdOLxO405KYZO/e8YJ1+CTZ4O2nWwZTiXXccmS/XpB9DpJscNUph+Xx0z1FMAXf5PU9nIzPnD55TGTFnsOqaSA7vXh+cbl9mmP1/NAjsSzqWDz17Q0KEdKRdhyd7uqbiRJCUljxZtfl5dKjKqkuDn0P571PiSsJtwTmik6dtTy53qVYWywmQrEbl5OMR7k2T1fwdytUrK3xb7W5NSZ9aaac8SSyyxxBKvd7zSABoYWCfxaLKyy2bZJv2LFhg0jd4Pnrw9YNExRBitfs+D5+nn/WcTmcf039w5M14eb3ODxGPUNBHQ8TlqO7k6ej01RadanQN65towTIIkOC0uI5i8JSdUFXZRktoydsUOzrGjRZ5xGzPcb7tFDlGfx8ZBRtKL+rPPHy8/5jaZwty5ps/Dz1op4+CwmfMMIO6PF9OJxYvGUbh9zOckHIftrTXKrsbEt153Kq2B2k1F++fLdiryqYGpL3uahMo0zTWA7qVV5RzeDX7sLxiLaf9v+46X6ZZ9XzNrr4P+eoklllhiidc7fiEANDhWqxU71CQZ5YWVJRplWVfEWOiazetcfr+pjgqEIIrLhYTrGL8kh219otPE3m5u3+m5RKaMtCUslmXxKXAfyz8OAdsIOPf71gmPeck/lTYrqs68sksxEif4XPQiMoDkpOCTH4HmuWV4AxqH7hRDMYxBOz137BRkOHFmnzcZv3rqM7YrG2K6TeZcOCb7zt3DuWehHtf+XBPmePoMzOm5gf5eG8lZaYR10JODFQC6ra0/S7wc0I0lOLf26RYAPdqHhHP2Z6WuQDjdv5QBl+r73O+rimoklsIzTpEoPTCvtdpFKlIY6OKq4YrEQ0sCoH3XK1g+6nlZSXAu+75bRvCwo8vSDnU9E18YaNFkyYtOjIXu2tuHeoklllhiidciXnkAHRFiwooYiEdpM9BL2Y91XEmt8YHOD3rJRpR9BwOrlQGFk/wirPSzIiPwCvZSD/mlXM5Zx6CTPZR9lG01zBxe9oAMhVDq4w6KpYzA1SGgUlVWqIESI7BR8YRggDNmpriTiIuK+U1Y9TS7Xh5PTZlBiz1A6cGHlmXqVPVvRirxAj/r0fgzBtKSl8Vr8JaSlZOuj58ynvXvw/9y8NlnAaFpBjiWPo6eiUo6M712HQcTBTc/DvYU1ucYW9TNtX06IenbNn02JvOvuXa6zyD3KJO9uSjjbMelSq80XrEZ7V+qK4bQty+laIeKgAqNG8YwuYSqR2M3AtBU39nCQBsbnc96i24bCpieTIhd6IuxACRxNMUnOidBpnykVBPZGCO+Mx10XAD0EkssscRrH680gBaBpmlwweObNRHTO8ZkIBoRy+gnv/RT6ivWlcSyGCMhuFwO2faNKesqxeQK+EP96kjXmV+gBi4niYcVwJq6eAxMcQVwGNgulNFLeI69rrc7NfDWyx4yeADYa0fnjd0UFyAluqis1PWV7FQEca0BEYXUKbiibfaoKPu4z9cYV9KrS2FPAVutcy1euPXnJQoQr0tT9+fNs6ED0DsDHqdygylrOieBSDOAcwrspjrpwkAfANjKtWI6LnNSiH7MelvE8rmx/9UU46Ddt2mI05gGvxVo989a9fvBNfwkme+2yYab3652kCXbMtw/nxNntcobAEgx2TMKpgunWNLVybEJUv5+uEofnjyq3ei70eci1BKO3lSP/ti6b1k0MrsSMfpdhHoKnCXS+e8BeFGiCKKR1EWUiC4AeokllljitY9XGkADw5Js8D3U9/760wAAIABJREFUsJd9BrO2YXSMaR0dqSS/JYF8HrM5S2hiKGiRDplNYATOXrYMfgDGJmvFRWLyeZbjexZ4cukePFTbGjV9cwJiiqw1IKqsshVYQuk00vkO58wB4Wi9IqXEvtv1yVWyCtC1syz3wfVv6UsNaKc/33bcVKpSM4zT687djzEQOpRw1HIJA1iHx822acaGj5k+Tfs+1y4Yf9ZXtJuETrbPsfiSbhnDnyUse3MWXI/6Mn0QJ5/fFnNj1PdPQBgcORwp33M3OMaoVMhVAF/p/cfPY/8POVgtKpHm5Efie3mH4pAq6VgFRHz/PKoOOQBYa9DUoZqTIHVx4VhiiSWWeN3jlQbQKSkxJVCXM+09OO3Bg7F6GWhVL/LCdDbk7HxxuBjZZ8bQqxJdQnQMjqc/D+04TAyaMn5TUFe0u4VpnLKkJeaAanlBD8eM2dO+mES9TR0kiKJ0LuFdi1cDx96bU0BQZXU1MPQuRiR4dl2Heocmj3hHx+BiUto4N8HoJQgzQGXumClLOz6X/V+Y/upEs+ee09dOzzXHBpeYK4n+eTTQU+bytklWLX8pgCvmYh1lUlXO2Td8OiF0hxOCMi5z96WO28Z7bp+Xg+GXM+T2qL5MU12zwQkpq0mAiVkGmZCqTlOA7Zy9h/Pg3z29bv1/ceFx2eOm73O5v0y+i+JNHlJWlZxJnkxhYraQqkP/YowoLZDQtADoJZZYYonXPV5pAA3Qth0RpW3b7MI7gDPnZKRxFBECQscA/IzBBpEA3g3AK0U0QdRUATvtP++9bUcAiAMwmybL+CWSpkEGwiAhGMBEAVTj9ttkYDwG00qEWv3ct0NWpGSVFDe7lqMn57jzS9xPPsY/f87+5prrGFl/4+tcXV1BaHh6ccEudpy++YDN/fuEzZr7b77Fs7WQQqBpmr7f9SRiWA5/Mev5WdjJYR8Z/T4HauvPbmPIbXwn9nEvAIcvm9DMXU9ngNptx5ZwzoG6PuF1eoyio/tZX3e6vbCfL1sJgMFLvOw3J8WZcaebDXmZJR3kqp+HUpB6/HurRzW5TcrGiSLF6cOjKEni0FbBKhvKeGycuF6zXK/y3DYk9bM70/r+sxGbXd8H8VjuRBpNjLuuo/GKYD8vscTPEk/+1q997mPe+J0/+FNoyRJLLPGyeKUBtGZWyYsVUhgApkNzWV2Vodx0/8JWDgCUc0JI0LmKHZ0U6ZjqQA8q3AHey2j/eqm9P5ax5hYZgzDbT9Gk1HKD4fMpKBoXzZh79yc6YzFj5Ojyis1Hj1ldXLH+9JxN1yHR0/kV67fe5vnRBX5zwvv+E86fX+D3ysX7P6VLysWHTzn95tdI64CcHOOc0GpC/KEDRs0ozwHUKfCcY6Sn57stptea/j89/jbw9icRt2mF59je2+UcL5bA3LZ96PPna7NTe2480jtg2MoFI0nKnDTmReN30NbJszt3v8r2ERP8OSc443tQ5D6zPcdkFdn9RABxB7vePgk5XIEYdVdNr62aXUryCtISS3ye+FmAc33sAqKXWOLnH680gO66lrjbE3ct++0OHwTtBJoGOoO/VkTPKoBpZh4tiSwv7apVh1PR7HYguNAgUYgu2XlUe/3iYMMmVeLgkFDXFYcHhIAzMFLYaqOkQITUA3SFSqttL/q8nKy+r7QHybSUWlKqqupweY/yCh8BlLLqnwDfIe2Okx//iF/53k+Rbsu6jWgIbPc74vqIeP6UIxU+/Ohjvv71r/PRx5/y9a//Mjfblh/98H1+/Td+i+9+/1/w3X/5XU7efoMv//qvwp0N0QutmDpUFEQjVn9vHDapmWeJLQ6tA61vHcqhh7BWjhRzoHhOPlLA9tx1ptLhOSlOfa0CumqGeOrLPT3HbYAwouZHPJpE5AS66tpMzj+r5ZXsBfEC9hkgaLZjK7sV0EwuuiOgCGm0slJfb0jSdRzaFspE1123SVXNDY4s1egbkdl4IniXq4WmKm/AQRxPZu3fUCa7nwRLIklO5nX5uyLQJeyPA5YroVLWfMTsKydMtSK58qbZW5ZvXVKlyQmP5blWtaTiLiUkeNiD04S2S/LgEl9M1AB8AdNLLPHziVcaQGsX2W93XF9est1uQQOIWZvFYC/l1JaMfJ9f/IPDA+T8qGhgqneBQPEM1cW6rpsFXHMg6JAdfkH7dahodvu2NPpsuu228w6/5P8dbNOelVPeuIx8bXOGPnibsBLO33/MarXi46srtn/0Q563HZdt4pMffYg0DTdPnnKVlJu240ef/JCTdcNf/a2/zO9+95/xT//x7/Ib//ZvI01Ag6PLYNElJerhUvWoSt8IRBdQWKYMUznAcO9q1q8s43/WsLGx4+tVgF4L20saDuUP9d3utzsO9qtZ1Dn2+zY2dW7fuWM+C2P+omdw1KdbrOdG7QF0ssphManeOdGW39aWenxcntSKWD6COGUoUe6JnVXtK4XbC7tuDHUFxEWGCWh1nSKjqG+eqtoXVRUVS46cybmcDRGxZNp6IlVJOBLFp50Di0f7C1L+X2KJLyYWRnqJJX4+8UoDaFJH1+7Ybrd0XcqNdRmkZaAkLtOwipIsKaiAJGdlgckv36kOM1W+saZnnmpp50GTrzxgy4veVS9Zi8pBQyqQIamyvhuAHLf8VIOGKQao2+gTOG/Jko0q9964B7/yFm17w8UHT4jbPSGB14S7aWlEuGqv2Sk8efqY5vSEbRf58ff+FffvnHGRIu88eIOVSzz9+GMevPvuCDDWEH+usIhm1tbpUF2uv60qvHiSkFlPJz3YGp/h9lCld1CY00/X8gX4DAB6zgd6FryW/rjRfRnr4m8DyQPzXqRFc9KPl0kpZoH55Kmp+13zxr3BRgaec44b9TluA/BJJvrijG2dMw9l+y7kAj8pEZ30Q5dwOU2P3nRj7tpz4yvkZ0Wq7y9ke8QBjB+6nownckNSpPmcD2NaSTkm93FY8SgrTguAXmKJJZZ43eOVBtBpf8PF0yecP31C4zzJnF4zK5RfpL6h2HCZrjj17JGqrelqXprtpRD5ZVeKmQSf2cnqhTkFTL3XbKWhrgs2COCqAhtBxp7QSKmM5m9l7AyzjIu7oNoXpjgo1a22TURoorJeBXxrbDrrBjYNV+0FHuFIHU/3LbERXBBoOzbe4WPHOoHf7dkgfPz7f8DHjedqv6V5cJ/t2TFrp9x/+228BuKoXb7v+9AXejuw2VB3wMr3OtVy30e68HKc9Czpi2IqJ5huH5jICbt5y/46GfNayzuWfhSm3Fr5Mj23RZGnxB6E2ebD0ZsCRxvHer9aS/wZPJ1vaVM5h6YZRvrWPk0rZw4/W2GS4fvSb8dGKuoezcdr6ohIX0a+bldhlafbRTBPd8b3pIBZRfFF13EQh0V/zOfdihI5V7twCDoq6iPUz9AIQL+A9V9iiSWWWOL1iFcaQLPbcv38Kd32xnJ/ei9nzRX8IpoSLuQqgZ3i6Ig9K5RA3Ah4FiA9WmbOL8YQmp6RBisKUgOIvkhDpcl0lKVl+zzIYVVCJOGlOejeAUjX4jNS7UPqGd65Usp9OlQToNkhfgV3Nnz44w9orz9hu73myXs/otu3XAbHdQhE8bQKbTRHiBATbmcA5a31ihvv8c5x/+13+P2PP+D0K2+zCYE9EELoQVqvE50ANpcZaPtwwuDKILEY+l4AyeCNXIPUpGrsuk4A7xT0aXZEmWF6yyrDMOQzEo451veWfXoNbn/+UGmVh0lCz8brAKxcBshWUGZwfijjOVLo9L8UHX09rNP2ao8T56YRL2KOB3xZzuH6X0e7yXjcNA2rMLY9V+wTwftmMnZuYL41Ij4g4th1CeeyOj4mRMfFeHoHGNXRvS7/QinYotpXk1QVesG0nz5vQvF1nt5bqzJZ3d8KROe7B87sNaMqzvs+VyKR8qrSZ7Q1WWKJJZZY4hc2XmkArXEPWspHG/ASUXBgWGXMCpLLU0vvyzVmGeekEHNM81xp7jFwHiQYjlINMPsrVwlg/b4yVCysrzkFM04HoNcfPwOgR59nBjomh28Czim6Cjx7fs7HP32KpMj9k1P01JFiRB20KnQKZAs/j/W7Q4ht2wPY7XbL2dkZm80G7z3e+0EeM2E8p30bZB0TwKVjm7mDMXDSg2nv/WgSUzP/dTXD6f81sJ3+PAXQdcytDBSpztx+NSscYxyAlk4S65Lpel2f5FoYazdqXzk+VtU1bwP4t7X5tv3++CEHKwuKQ3y9GuNRP5UylZ3dwe82JiGPgfHF095M+zJi6nOMPMGrFYI5Sc5nZeSrI0fn6EF0vWJyS8GWJZZYYoklXt94pQG0Ty0r7/DOMt+TEwMjgPgMVAhIUoRkJYlFEQJozO+41L8DxRmMLUvEPWPJ8HkIYah+OAHSdalu1cw+U3x9tSrdzXCcU0T96FxDoYhA/SL2fUOrAiZqDC+UCouT82cATXRsg9AEYX/viOOvvcO/8+d/m92PPuD8p8+5uL7h+uqSPQlV6HY7bvY7YlaO71MkiqNbNSDGHD59+hTunfDNb34TtzlCvOYUQINSkmRWrlDaOwUrfYLfBNSCq9xIhn1TSqzW6x6g9sBIlaZp+v1qVrLIA2DsvV0iTljeEUDlMESGVYcp6K8nZpqy1aHaSoa1w1UTiSHZzABX6pnVwj5PZRr1Nae/1wx3f4Vq8lUz1y8LFWUkZB+d1/Useg2MrbCR5AlrAc2ul8hInmj1kHjCjJfT+2ZFE1d0XWtsNvMsc2Ggaz/ygXX3+QIpW1+SV6zs+5VQfPmei1CKpCjklSwM0FOkJa5vByLgwwCenZBiWSmKNM26n8yZ7vrlVUuXWGKJJZb4xY9XGkCL7jjdNGx3juutOT6Y9FCMgRbBiUedLb0GEWIsL99coCFhrGwFLKYAZQpSXAbS5ffyvy1L+x602nEJVHEFTLmxr61YXeYKQFtinS0PQwEowADAZWDUrKjwGEDXfSjM9koCrGDtVuzayObuXXBCOD3lyfd+zPX1lovrLftGSD5LVcIKL0Lyyv3799l2kZ88eczN7po2RbYxcN5e8dHHP+WX/tyv0HiDutYvBfWzYCFSAcER85fdMUaWCdb/4o7RL78DOOnByWq9hjSwvd7btQtDPQafJSn0EED7gpdmME7SMUC2522qNR4/N2WytTmqmPpmhW9Ww77iAdeDv67riLGlbXfc3NzQdjuur68GJjWmg2dy2o+pq0T9XIx13YcyhcM+DufoS1bbDeh/TtD3td7e+5jniW2dMDuA/MP7IOJQIt6tWK03iBfSXtHY5UTEGUCvt7Prtn0ogCS93GpqvTfPjI9XliT3q04UrkB3td/JyQmfQO/CkTShcQHQSyzxqsfF3/2bP/OxZ//Z//gn2JIlflHjlQbQaKJpAiGYdCPlJfUkJmdQVXAeTR2krFdUh/ZMnPkUG6E76HbHWtjh55plq9nmEuVF6lydzJXZaNUx8C2AeeZdOmWRh3Nnm686KQvpK61ZFcYC/grbmsEJDvWmvd3gefz+h7Q313z0gx9y+fgZCeGi62ijEn3LznlUcyXGqDz99DGtwiqsaZOg2qDa4pzn6vraxtwVj9wMoPMkRlV7tKSqBAbgHCfjQd+TejwwXKMD2FHNqwl50uKcY7ffslqtaJpmVs87BdB1Mlr5vGdCZxho+ZwA2jnHer3Ge8/Z6T3W66M8JH6YJKj5XhugtzvqQ8AFwTeBZr2ibXeIF66vr0mdTQ4O+zSv0Z5r12jfmf3q4w3gO3M8Kdu1BsjWliACrmLiVfqxrq/jDu5JzGB0LHMwaYxDUsJ7T1JPcs7Oi9zS7Nqb/XAs7LtbXGCkb8ssGH/BmAAHQJkqQbhn+tVZ25O5/ahE4LD0+BJLLPHqxB8HOE/PsQDpP9vxUgD98OHD/wL4D4FvAL/56NGj7+btfx74e8AD4DHwtx89evSvXvbZ5wkHHDWOs82a8/MLA4kEfAbNIpn5ySy0w5ugIhdQIDVojMbhplx6tyqWUpaELZLJQDCQHPwK8hKwgTkQTSQgrNZ4hJhaRJ2BBMqSNTjVvlyIuENNqJSENtJoe2QAqOgACAbbu0MwWraFpDTrFTfbc+6dCB999DHvf/wxd48a5MiRCLhwjEs7Npsj4tUlwUNYr9jtO5I6OoWuVeKmodtvuRc9a7fiq195F79Zc6M7QmZd27YlyCCjmA/Tq5eJSaE6PVRL/d4Am3o8CaeJ5KzkRYrQ+EDyylXo+Ad/9B3+0m/+G7x7vSKJJe0FJ+y8sdpOTerjxRJNXQasIqWSpPSlQMpPdalr309HKtZXhpWDcQinp6ccH58iIgRpcM408PYUZWbW+V63IIkeTBcmXcKGEBKr9R1Wqwuuri6J7RUhhF5XPZIx5LZ5DiUnBezWz9tUTuNcyCDfwK9VJhwmPCWmcqV60jPc77GGu1+RGZ8pb/ejY0Vz+50jOIdvVmjKczJNgDHRaBaBRCuIYhNKN3K6cFli4dwgvRitSpDyKgB5TCauIVVYURbLO0xERBoIAVOsOxyK0oFAjLDfd8TUobKH/P3Xz2o6/acYX+Tf7SU+f7zxO3/wx6pG+CrGqwgy/yTA8xJLlPgsDPT/BPyXwD+YbP9vgf/m0aNHf//hw4f/MfDfAX/jM3z2mcOL0Hjh5PiYEBxRPUmFmLRn6cwvNmsXYwcuQDAnBu8UcR0EA82mo3VYFbdkzHT/AvXmU+sy49mszHECA+lJO1SCtSnrb1MHohFJYprrHJrB+fByNoq1ZrcBnEsHL/CDEKEHJf2yfAXw8s97D9Iq98MJVz95zG984+tsTjdcbx+zSxvOrzpubjbIvTucP3/OBcrds2Mur66QJuAUvMLu8ob1Vtm4hpsususSx0cbnJo8JKCkrqUJYUgMm7C8Q5+y53UGPn0CovMD25sBnHNm2+0Ukqe3K/QY+9eshU/2F/zBh+/xzpv/mrF/weNSzCsCEBKAQ5PYJKsCndPqgT2A78fyUG8M4Ho2eACGIQSapuHo6LiXNXjxePHZgq7YLdqEThwmS5FBXmSylYTLTLtLntM7DZvNhiefWnERXzk8zD8X5ccBnE4lFsHb53ogZyig2SYc037PJe8dyiEO9zucaNwywUq9kSSSzL89rdfsibikiI5LjiefehcWqJw5ko7AfrHMK30p4133byrLKj8756rvVp5w51UnzeOcr46IN60749UCOCwR9AXFF/Z3e4nPH68beAYDzq8KYP3TaserOElY4ucXLwXQjx49+ocADx8+7Lc9fPjwbeAvAv9e3vTfA//1w4cP38Iwyexnjx49+uTzNM45Y8KaBoLzxGgvyJiBQimEUgCGuEDx4x2W8AXE96WSa7bM6i7UL1eqn6UCT5DU4URoQuit3GKMPVM81VYOwGx4UdfSkAFkzlVXm7gYiODSmBFUPzCLLjlS40AjLsFOHG//xYfsuh0fP7vin7/3A37w6TkfPF9z+eSMv/gX/gK/+8NH/PLZu/izhpvzczbNCi8Bd3+DXtzQ4EltQmPikw8/4msP7mftt0lYvHO0yogJLPp0pbCIVEv9Q//qCUDZhuTiHarmuYvixENUnDNf7U4inz57jL4DTs2DWxI4iRSOVAuzyRgMHi7j03823j4Bpup6WU29z8jSMCfS1f7NIt4AoNjkrgxTeVaKpneoZOcQSUjWp9cJimP5S273qK1TbfxM4t/Bc1XY9hnHjMk4zMWclnpo2cuj/s6MxoQ8DpWMowfH1al7Zj6O/aqn3+Ge6T+4/4eThvn+MBm3wzZY1BVFv3hXji/y7/YSS9Rx8Xf/5gIwl3gt42fVQH8N+ODRo0cR4NGjR/Hhw4c/ydvlBZ997j/EwZut3L07Z/z08VOiCuICXX5xqhhAFgV8MueDGAd5Rq56p2LSCVTxMZJSBi6SUGK/pBzFsU9KkxK+gA4vuJiTCJsVITQ9gE5JwSVEe2+9QRM8IAADMKUaW97mvK9e/MML2jnXM+yf5eVOHvQmORoH//Pj9/kfPv0nfPPhL5EeAn/11/h//skf8PGzB6h+iQ/eOeYPf/NL/MP2im635c7bDXrznLhr8U746mngrHO861f4vXD99BLpFH+6yh6+DomK+GhZeVocCOo2WeJcSBNAp8bG1jBLndBJHPTCJbkSj6RIE1bEtOfOG/f56ccf0h551nsgeZLX3r3Eq7k+FCeGOeBcXbVvaw8GD6QxeWx1YHkh4cXhJfT/OwloYTvFg3grBy3ZmUINDJZCHBrz/S9tc+B89rt2EecH9rxIjab3XA/sEv349yKZqLS7Uum5dVaSwQu3TS0WP8sx031H++TVICBPQswq0SGQsu6639UmxGWynLIkK5H6yZp97zKrzeCw86JJ0lxb+8mvDN/FInspHLNNoAJt2/Z6eNE0m5z6CsXP7e/2EkssscTrHq90EqGqEgSSc7xx95Qnz86J+xZcoC1ODEXPnNkroF+ddqIk10Fy9p7WDKyhBymkiObkNUmlilxinxI+Jzg5H0CKR69mzagx3hJsGbnorm97WdeMZdFgF8W1NXkAOpp1tABayVWozlWfG0B0z/HqmJvdDeHXvsXj1vHON95h7z/my/feIPgND04fsD494a//jd/i6dPvgxwjIoS1p+s62m7H5eUlH1zB9ZMLHuz3nF4KH33wfd64/lW+/PYvsd9vrR8KkivolcI1Q5+Hcsg94MI8pK3q+gB0Sr8Csa/EGL3D9LkBUotzDSE4zu6c8P5HHTuXOMZ0r9EJDlsRSM5kAeIkL/EP7ZnKIAalQQa3HMoUBma0ZjgDzoXs1LICdXSa8K5BxUCg8w2xTL5cMCu3aO4rZspigEtjHGztvEOTB3xvEVe3uQbUIoL6wSVmKuGowX8P0vO1yzNUNNrmsH4o16jdNOrky3rCUZ7FsQPKPDidAu8apOIcqgnJk2VJQtLu4PiUJ2nT6zvkcHyq603bXG93k4kIkwnLbeyznRP27XY0UavnzUss8UXFk7/1a7zxO3/wRTdjiSVe6/hZAfSPga88fPjQZ6bCA1/O2+UFn32uKAUzkiir1YrjozW7thsVTqglGYO2OIODlOwF6SB1EU0uA4OShBQN3PTL3MZykV+4UY3R9uKRYCDFq9B2nb1AnaCdJS8i0XSmue1TCUdpY0ppxIzNsaS9JCW3q4ulv6VARAEHwzGdh8vdNY3znDQnhGfwrr/Ps+0z3v/eh/zkwyd8/yfnnJ4c8/5HP+Sjj37CujEHgTtnpxwfH/P2W+/QXrZ8/PgjHJ6npw3nuieEjg8vn/Dl7qs00qACrXRIMtcPlXGCW69trYCd3RjBKcQ4rgaJgKfpIZ3zzsp5dxC8sf0lcfHevXtcb694wJqkivqAqHlxqzNgLlkXO028K1HAT38P+vtVaXx7f2AxsO8qcOoka/EhBEv7VGeWisWTeLCDMzs0JFnyaLKE1K7ofrHxSzFZf1RJOVGud/NzQxmeHgyOKumViVX1QGR9ej/OhjyHidktVOnc9rE8ZAyib08gvf1c1q4qETEn0/bXKdco3UmV9GqGkVdx+Rmy7UWyIW5IC5WJBh5K8ZahXWatV4FmqXToPeC275+qeZG3bWtNzCtWLxqPVyB+Ln+3l1hiiSX+LMTPBKAfPXr08cOHD/8Z8B8Bfz///52ilXvRZ58nFHuBBRHECW/cvwvAk8stompV85xVdkP8uCJZDsETiba03rtfKOoyoK4qFrv8clZV2tgh+87kBq6jCWt7kcZSoCW/fcUTtct4y/Sb5YQDAyqjd34N3oZteXF4poy4gcHhPDGNq/ABkPY06RgHXJ5f80f/+BE//D//kL/9n/x1Pvr0R3x99Q7NG8fs9jesnkXu7I84/+QcnOPx4+d8tH/K7+/eA6AJCd9E3J1TJCpnbz+gaRpuri6IAskLHcq6ZHm5igX12ZtbrE9dpQcteZZS719wqjqreSO2hG8MtyesrMhL5yLrzZp1e8R7H33Ar3ztXcBxI4pLCVHo1KQ6DkbFcmxsx2NdGOch3Ai09YCuOFsU/JVXDvABnLdVhMwCK6aTr5f7E5LLS3vzx67vcVb6oJYEKUkMGPsGcQ7nJqxtxd72yyy57ePnyVVEsBX9KEzzlBwtspppTMHyi2QPYzB9+EzX5xy1ceLYMdyvyfF5haAIyeurOOhXaGKMeGHk4W4geZDL1H2xiUnl9Sx5tWEyBtPx6O+ASF863HuHy6seLxqrLzJ+Xn+3l/jiY2Gfl1jiTz8+i43dfwX8B8CXgP/j4cOHjx89evTrwH8K/L2HDx/+58BT4G9Xh73os88cRRNpDRUe3D0lhMD19idcxYh3EPu3bZcri40LoBRdpJWIzgxf6nqGzvbNQCUV4BFJib5gh/m9Nj0TpVFIqUPTgJKLdVV56Q+eHJA09UvFkn+369V9HR8/WjqOY0Z6YG4r2Yf33LQ7VqvAt771Lc4v/y1oI//f+09xb5xw0mx49+27PAk7fukrv0RKiUf/8pEx97Lit/7SX+bJJ5/ye7/3e2wfP6fd7zn/yWNOdhBOtvi247K9odWEiOKjYvUiMngTAypN0+BX675fNYRKYsl/Xa0/ppzDEVQQEsl7ogiejqsuEl3HZdfy408/pPWJDy4+ZfvODieBG0k0MeGi0onSAd50OCNgV8apd284ADk6SQLM7gsuZpBZxlpZeRt7XAbcgrHP3tw3cMZEJwrTbuDZiSNJVco8Dp7FZQwTis+VDFXrBMLhGRhcPKrWJ0HToYTIcHZ50IZ+j2UbM0mKMwzzFGfPTQBv+/1w/7F9JOQqjZonP31aaH31GmCXMRNCCP047XY7W7GQQV5SGPyxxGU+xhromYTeqi8hBG6216SU6Lqud5aZyk++iPgi/24v8cXHqybhWBIJl3gd47O4cPwd4O/MbP9D4K/ccsytn32eqJddPbaMv1mblGO7b40J1MmLeYaFnovPuoRdv3RFBOdC3ieQkhIl4tTK+v4s17N+Hi6mEK39AAAgAElEQVSRjxjANOciMX65RxWONmtCcLz79juchCO+/d3v8OWvn7A6XrMTePLkmosm8mwVuLnesXKJEFZoVHR3gdMdpxtPh7DZnLH3e+LugpubG3btHryBU6dCUIguAdHAmxrgSRqR1mzYUtaNj0B0paeNfZfMAdtso4Vo/iqo93SpJdJxtb3gandF64W7YgA9pWSyjZSX/XWAXPWEZE4DOx3LMaOb2zqQjbPHFJlG78IBvfNFErK+fVh9SFUb7Fmad4gwNtzRS3YOtNmMqvXp5Lx/EjKCKes8nXS87BoHx740itylxCGon/ZtzBxLL/MpCYZz+7+0FRWAHss75OBZLg5A/b88aeu6F/8t+HnEF/l3e4nPH6+zD/QSS7yu8UonEUpmllTVNKbAer3m/v37nF/dELsOSJn5Mc2opi4nc1Us3QRkW5JSzVAOUoRebxmVJBENIbtxOLRf7g2Ij5b9r950kc6hMSHpsDx4f93JC90kuyYFMQZN+/XpNDm+lPHunRCqAi2iEDsheUe76zhJ16w/esxXY+Bbq/tsnOf7P/4h9/SYu+6EcH7Obrfn8vqa1dGa5IRn3/u/uN7t+ea6Ia7v0iXH++2O+M59zn3L++ef8A2+ykrBJSWQuIrGtKWeLYx0+5aYx9+FxoCf1nKAOMgiGCQcSRIuAXiSmE447YUQHK1XPrl5Qrh/wrPLc65iR4eibUsMgnY5YbCca8Ke1jrnEkOCnsl4RFM/YSuyDDSzxjgQMe9w8YhfISGYz2LWPScZexBT+uacyYBiyoDLEi9VTPssHkSDuUyIG4HxKfs5/hnQkqRpD3lhpye8/+g5moOylZPj8LmMJxQyPs2oTTXYPiDAJ6B1KlXq96OWzchggzjZaWw7afsXJ4z1ej3kQcgAcG9rt2maJ6W8832/rf2lD4XZ1pjouj273a5/7vZdO3vNJZa4LV438LzEEn8W4pUG0Cvn+lLQiEcCuOS4c3rGneMNl9fXXHYGvqyYCZDThgx02+99uesCbL29iEeJbxrpUmvJa2KMt6aWuFdE7hLFlud9kU2rM70vgdhtcSlaEReFERutiqSMigtAcAXsAClvqyQgU9YsSRqqsuUEOUqyVd7nBMfR+WPc1Tnnv/8d/n3puH/8Bs17HRffv+avhge4B3c4WZ2x317zkx/9BBdXeO/YccONd2w55rJTruIVzjk+2QrfeZA4fecu98+O0W7PPgOSmAxMqloFwAK9BEjSkbAy4QVAW78La+uqvtlYCgkvSowrOvbgIiexQXcraDp+5B7zpGmJpxt2TyL7rjWLvA72ahroqFZ9cNBVF3lP0TbXriDDo188mjVpD2BVStloqXBkEcw7hIam2dDuO7PyI0NVcXQp0TTGkosL7LZbK+4SlN22RULD2dkZ2+2WfbuncYJ2jpVbsSfRpYLjFBkBwDop0jMFx/UUYeinq7T4lsRo2+fZWGEAxJrGSa6jqxXgnH8eto//pGThxUETVUxu4k2FPALWTgRSGuRZ+TBVRbw9++WmSZbSDJKdZPMaJBdOMs/ypFpf/qVstIi5xpRk4tJ/TcmSYQtQJ5fxTl3fRk16+4mXWKKKPy3g/CrJN5ZY4nWNVxpANyGw7ZnkkgHvaBqr2Lbd76EzD2erk1xYrfrFO/eiPCyF3X+iw/J4yjZjqrEHvCLS+0mLmv+0Zg9Y+pe5+RenlCrtZQE106XqMTteM2aF3XNSvfgxjejAkNr/Ke04fv8TLt/7Pr/+5j3ufuldjn96yfl7P8XtIrs2kp7ecOMj++0W38L19ZX1dQWEgMRI2MOqS3gvnHRCs1W+dPdN3nnwNt2uxTUhAxMh0WVf5wLoBrbXbkk0yz/MlE5SZprpctEbzZKN2IMoJaJE0EgkoC7RaUR8Saz0tNEAelK1265mcaZAyi4Xo3FVK7rT627VoW6QTtgUK8uBvD0/omPmuh5/FzziHU+ePuXOnbsGppxwfX1NVPNobi+u8gGBq5tr2qg0znN2dpd2f81+v0eT0KxMdtC1u3zvdbA5zI4tn1WSMed7PJ4kzp9nTn9f/1z/P3fczxSFAc7fUNVs1xhCBtA68lUXrVeMhntZdOmQAW65+V5MmlRA/ky7e6lNacdEomETqUMZUh1zY/2KO3EsscQSSyzxJxCvNIDenJ5wefEsg1Y/uCavVty9e5cuJa67xD7tzQyrBkiSwfCoQtjUmWH80hM1OYIVVVGCDz1b6r3PiWRq1eJEcF1LFPDamM1VZ0AO7Wa0l2PphiWYJZi8uMduW6Vt9XmGvtTL4LJKrK4u+VrY8K2/8tvgd3Tf/QGffO9DUidohO3lJZ/gaaNyFeHGCRogdpEj5wmyYu0U572BVRV2z865+fQ58hWF4NAuQkrmVW0VQVAEr7UzyACKeh9iKVA0EaP2kwDJyXbWqYTGaAlZEtlrQBt43j3Drbxpo9Xx9PqSrVeOujw2+X6mzIYnTRn8FmBoThSgVohHJgymeEQMurs03A9NOrKGK+O/3W6JChfPr3j89Cn3799nu+84Pj5mtTqiWR1xujKbPRcCJ7s7dJpY+RWdJu6ujwkh8MEHH3B5tUWS8vz5M955+81JsutQNOQwXuyMcfgMvdxJ4zbZ0UgDPPn+fFZd8cxGJOUJ06S/QJ8cCPQa46ICV9V+YiYiaJESpVTJaIbchRjj+LozfdMyUU9i30FXyTrcYQlzoC+wkqj/rrzyVnZL/BmIVy2JcIklXsd4pQH08eaE1ebYstxjBOcIuZrf6ekpLgTOd/ucAV+BBMnML+DU924F4xe5eTcXps8YXfD57em94LK/ceoU74XgVoAVVxERNATc3qHBimLEVqBzkHz+X0GtQMjwUs3go3p51zGneS1FWJxzoyIqZRtAONrQ3nuLXbfiO//L/82Pd084///Ze7cfSZIsve93jpl7RGZWVnVVd3XPZWeWu0vxgiVAirsgBist+LKgpEc96U/QswTo36D+AlLiIwEBugCkIAErQRSgJ5ILYklpoeFyyemd6dmZ7uquqrxEuJvZ0cMx80tkZHXNaronpztOIzozPfxibu5e/tln3/nOJy/4s9cveWnGCNyUxCd9YHPxhB99+gJ9kilS6Pst4/Un5N3ANm7ZUshpoPQbbi46btMAGpAoBBHI1SNbder3Mll7rVn/Bo7bLIHZLIlwwJGw5r9rhpFQKxSMIRfy2ciPX31Eep7RGEgj7Mi8GK/4oL+E3QzAKEzT7a0yoIfWYZRWaa/7ONdeXmiAm4e1VFtDrR7DQvXVc6Y9dlzd7DBVvvvdX+X29pb3n136XaUdhEi/2Tqr2kU05om1t93ImBPXtzdszrZoFIIqMSqvXr3i4uL8jlf4fXE00W9x30wyoXvWXy6/Tyu83Oexe/NO++4TSx/utxhaBzppHKZrOA821at8As1isJhBdq36JPsQv/ek7itT7Sizudxi6ocFUwxH+63ofO/WYuJ+vVUxCTM4V0/q1BAoxYG7u3/4tuMwvFUfnOIUX1T8osHz1ymB8OQu8vWNBw2gswgaIglnmELoHIQU2G63hE3PO1fX3NzcUFKegGYDbSoCCmYzAPUs+bEyUbG+SMe6Pl52OQRiiM5ql0I217TGblN1vTJVQBtDwMaBYm55N+53YBk0YHmEErBcsGpt5eWUmxTjrsRkmdQ0vdgnnehsbdYAXgOudg3Xv/4dfnAW+af/9g/5F3/0z0nbyPaDZ7wYd+wFrjYdv/7X/ia/93v/KWc/+D7/wz/+e4gm/uKv/wW6EDH2/PTVNU91w08//BHDdaL0W373b/8OT7fndOKll8WUnItru4uDL1uAHbMC1YKwTYG36fAALMtIF9HZNqxWEQxRCASIPdfygk/3nzCUjjEnskSGTvjw1Sc8eXbO1qz6K1tl9p1hdlC/6Mvm3S1hkuI02fyskq+JosjUt2b+uyz+24+JEDvO+i2pGN1mOyWuZoSoYar6tx8zoIxp9LLfMZKzzzyE4PdfGvcQ4PzRBcN+T4xeIjq0aplHYpKpLGc2Jraf6acdyoWO7q3clb0swhl5198vmejVfhcDxGmbI/tqy0IFwCWnOrjICG3mgDuJfG1/BaDYaoDhftlN312qH3Qtd968o98wELiT6Kh+nREldhtEAjH0Pog0QL1oUik+uL29vWW32/m/HQbjxS/exu4UvxzRgO4pifCLi68TmD/FlxsPGkBbgf5sS9ntSDkjQRGr1QRDIVjg/Pyc7XbLsNuTyvHs9+VLsoHfIuXgO69aphIxU7q4IafB3S6YXT1UZjAOoBop0QhZSFrQ2GFZEE2kEvDVEpqjg0+RlX7zTS/0BvJ0sd7RMt7ihU3YdnTfes4Hv/WbDJ/9MeNGefStxzzRJ+yDopseef6c/Pgd4rN32bz3DlEzN1rI6Rpjzyu74rbsGZ5Ezi8vGXcDT995jNyOFBJYcYnEgmmWNiKYGzS1r/l426LvGqgrAgEfDMwFPTyJzMQLluzLSNI9qczHGsW4Gm5cO12TvTAnP6XaxpmVCVy2qfUlqDNdsLRTf8p6ZmDR51N/67wsxlpO3MxBYC0Okosz9KpKKkzHHoah6nS9DTlntCakdd2GNOzIlUltxzssDjQBwUX774u3lRJ8nnZ3Tkg8zl7/LMee2mvLthe/eEc2WTLdh3rjO9rte47/eXKTtq8lY98GdKYuUdJ2PwDUe279DAawuaLiKU5xinWcvKBP8VWLBw2gX93ccHn5FNNXdJla1U6dTYw9qcCz9wKvr3e8+vQzhv1Lf4G1HUibhl0D1a7rSZWZUvWS0QBWxH2RDb797e/y4Yf/ztnkENDQEWJPFPEC4BOgjuTknsWqSlL1Qi2jIOr2WqQIIQOt0EVZvbS9qTMYENXF/tvZ6ATkfN2yOK9APhPCWDjrN/z6v//X+JVX3+fF8Cnfef8ZZxdnDMALCzz/y3+N737nN7jY9PzOb/8W2A0lR15dv6LIjse7M5IVbl5H7Np4/8lz7GY3JVe63hPXF7c+RJHQKvYpoeu9TWYT2zsBDtPJEaOdb2Op3aasoChYhNDz2e0LSpe5vb32vlNhr8YPPv0pf/W97/COXpKlVP2ys8ZBQaSf7iMzqcmdbfChkwf11O+iiISJSXaZjs9YhOBFokV9vXHMWDHO+zMSwn6fODvr2e12pJLZ70ZMhb7vAeXq+rqC5UjXdWjs2GzcpaOPHRo6Ukqk4tUJrQ5Q0MqcHwxO3OmhDcbs6D20/PttY3UPHmw7fce6PXcw71skLfpqpRYgGcht4JtditWArIY5N2DpbOEFedY5Da0tzeYuV3vLVpBFqlNKG2C5JASaQ4zfc03i4/exhOCzBDEgdbZADEwKovOMVpNxeMJxrVZ6ilP8DPFV8oE+Mb6n+LrEgwbQQyr05xe8e37BJy9e0JlPuGcTpEA0oXRb3nu+A3ApR3UygPrSD67TXTK3fd9TqndsYxIdnIFoIBXlu7/6a/zwRz9GLCMaEY1o1/tLM7nG0ZdXzW3xKoXBjCZUSOLyDZEEzf/X8qQTVdbAHubkwBXrufC1PgZsVJUe5Z3YMcbE1aMd/y5fIZeRfJYp25HBEkMO9FtDeyFJQs5hN4wYSjzfTtrN/e01ooXSB66vbtBNRxqyg8IkjmAs0yo4Nn9sb09AY08IsQoiZl/jUoFzg90wa6RzBT5KITCiFhm059Xtay6fn/Gj6ysAxpLRvuPVsOcnLz7hu88uHeiYEaoXtoMwB+ZQ8V6dubBJ/rLoSw3erqXOVVxrrxJW18JUGBMMY+H7//rfkKu/c9/3xBgJ2qFdJISOYUjV7k9opatv9jvGq9fknN0CDxh2twzDwDc+eE7fO8h2wK2rKntrQHq/k8zdBNa3j0PQ+ybWtn2/XOeOF/Z9IDonBPOKkzWnQGoCpSzAMVWO0zzaW7S8hqWeeZ3cuGh/WMw8yPq5a9e27dmXecEkVR80h9Ahod4b5naJIhFTWRdSyUwylFOc4hR348RCn+KrFA8aQOdipFzYPHpE112RrLifrgkanMmMIbM5P2OzPa8FOmbKztS1q6rzS11VCSGgGiglTy/RZndVVIkaefz0WV0umASf8he3sGsWbP4SjhBqYpMpEgNqgsUOLd4Wo2DV701yIdekKKt03sTS+p/YIpkNFtrWI9G2S13P7eB+t13pOU/R7d+YHQQ6VRgTZRwgFoayJ5PQ7pxOhJyULvTEvnebtQClU25zYlsZZooQTNmYkBiZgLA1dtxZO63g0xYuFmotOc81wmZGMKWIl2o3IJDpMLBI0cg4Dpz3Eb2ujgqlQBcog/H6+gp9z8u0F7NJDhFEvZbhst9MJytEWDCn6tKNQtOWz9INT15zJnqS/Jjyk598zKvX1zx+/ITzs0cMw+DAOXbOwIcwzyJUhjKZu5eoKufn565xjplxP7A5O0dE+PFHP2EYb/irf+lX5xL2Ma4A4hx3rRCXjPTPwkIvd/vnkR98nsPHMSs+lyd5P0fUBzztObTmYFddXpqshbkf5j4pBwR9W86kbL8DaFUmCcmSvT/8mK7/RgJC8TyFhcSmlYdvh7kvIfMUp7gvvirs89cxToOCr288bABthR//9Cf86qMLNpeXsB/r9LUnYCUr9Nl4/PQZYwL94Z+S9xnNPgVueBKV+yi3xC6l35xxfbMDDeRiaADJ7hDhiVsdj5++j/SRYX/LJnRgHSAQAtQpdFNPelPrMAlEnMl0pqzKFczIKWBpRM11smq5JrXl2SlAlyDI5STtBW6tMh5hKrjSXvy5JF8nXXEbzoDC7uoVFo1oATEHk7kAYcNuvGXIA0ZC6BG2iI3kMSGlI42fgUIqGSmBpAULyplFyh5K8KRKl0P0U1tCA3QaCKFzhwJRVGMFqSwkFDN4orUPQ7OhZFK4IFhHf2Z8Zp/x7PybpI9fYkWhQKSwOyv8m/ET/uNtLWaikINRaqEbJS6kNl4xUKla1dqGyX3DfM5ANaxKZFciGhEjVpnJ7X4kaMc7j98BCYR+w6Ozc0QMjbWwTOiIMfLkyRMyxqeffgrJDc9KKQyV6c9jqg4yhpjw6OwCuTgjiDPPSzeOw1gCZP+9yglYyxqWcQjE532sbdoOQfgca4/yY/vK5hKZeV2h5HacWauccyblkVxGpFQLxKmyoruwTLyw+JNcnyr/3RaJhHWGJopBcceTNs/hz5FWHxYv8e2bteIqNrHfPkCOaPBBMhoJcYuGDtPO96iKomSkDsSVnHbkYY8SERNSPgHoU5ziocTlf/Hfn2Qlp/hC4kEDaAmB292el5+94smzd0nlus7iOssoGF1K9Ntzzs53bPozbva7Wh7bJmlByYYETxAE8cQu8aRBgntcODtZKGbklPjJTz9hc3bBWEYHSqEjhEAXN5XN9MQnVaWir1kbWjIF9zAuJaO4h7WXsc4OFHKpGMPBw1Tko4YX97M6JTwDOl93AVrqFLdYIVX/6V25Iklmo70D2HqMksWTI3Pm9vaWvDd0KoDiNmsSYBM37HZ70pgIGp1hjVsHPiKAV3nUsnC5WNCApbgLhycCApN7g0OgIkBpFSOzgyVx2YvrjjsSCuy4yW731kA21PLeQ+JV3qHnHRsJFJQQYdQMORBFp3qQs/Z6IcXgLjB1oLUE0M46LvXqfYx844MPuN3t2e0zSCDlTN+5Q0PQWW5zc3ODiNBpwMiu6ZXIO+++x4sXL5y9TAnLXhb+8ePHIGmSFDUXjs+zsjsWxxjhQ830ffu7PxHubTyOK4u8AtEHbRWhmANdB7sVQBdmAC1l5aDRttU2KzPNJM2DjZIGtC4rKU1MsD8n6za2fTQJR9CuAukKnkNENaBdrH+7645imAa0FmnJeSTVYxXLYHJioE9xigcWXxSIPjHPX+942ABaHIztdjuehOAOCF4vubKaAdUyOyKECpCOTM8u4/AFt9JwqpKD8tlnn630p0vN5MR8NTVvrUxopTp8UCvIqRfvQCIWElKqFVdxD2WrGWL32X1N2sy31LSWDIjVQiTHzx2E29s9OZVJEuGMcNWHqrpVXz2kASkbFh0QOdDNrhFe7HrSOasz5tnkoCjMwbktW2Tel9Oy6qCRylj1xZ3vG6PY3MZcCqnkyS9YaklxZxt1luZUFnrSRMvsA73sn3LsnjnoP1WlCx1jysgw613bPSXiQoOmixYRrq6u2Gw23O6uiaHn9va2lgl3RpR6X52fb0HSCjA2LfTnxc/qhvEm5437vhO5mxx4X7Kg9/vbt9XMpvvcavLfoTTjcDAhVT8dY3QQy3rw4wMgvydWy+6JtVzjUL7RTAzv6q5dxlH/PbCW7HyKU5ziFKf4KseDBtA5Q4w9N7s9Dpo7EE9MM/Wkng6h76E/O2ezOeMacJupUqfuvVCFLabmU5p9Wg9fqGPJxMrSTsljWrW3zKxVY9lEBG0v/xCqp7FR6NA6zVxMsKxYyBQxJAum5slNqTFktaDLBKgbuKYSZq2gh9vBNScGp7+L67ITiCrDMLicQIWGvUQEKYZq5Onjp9xc/5ScCymnCcyBIEEZ915WumCkVLjZD9i2DgZMwGohGRZsZv1KxGUQXsHvWMlyh0bFxgX4KAiBqAqdECpLfZ2uefz0Ed22o2ggkUkIXVTGMrBHeHFzxWXpsVK9shUy5iXDS3VOiBErNrW32DEQ9fmsrAPegCiVJb5F6CrAnftCq949je43vtls2O12RPUKj5ZGLI10Kq5Tt0IMRuygFFnt623CbBY3TGfT2vI5EpD7/j6+rBz97j4QfbjusVNq7PDh+YrIJG9aAvpDHXTXdbO/e91WVd02URVEyTlNg6g7mmdCzZOolnU6S6cIWl04umnbaaZIqYNlI2W3MRzboCedfKBP8fWMk1TiFF+neNAAWk0oBG73I2ikmFCsMsC1wEWLEAJd1zlDaqnqbf0F2oCdTS9pTw5DSk0qKz45WwFeSon9fk9zEl4ChGVVQC/8UcFBKRgRMUOlw8FGP7Gtvo9Zs1mKa19NbLK/ouo0scamztES9FSXxTUEETBTb6l6wuRYMqoQo09z5wpWczLyMDP2BCUlvCIcDgYKs3fzOI6gPbe7HemJoVNio2t2jWZTZ5OEo5i5B4kZxQqx+UDbrM1NKa18mBtrbGakIsQyYgo72fPo0ZYxD5Q4+0WHEMkysJPEi9uXnMs7WMa1p9Gvr0lAghcj6RAkdI7gTFdYs4HPIjYNXmbJBy6jkdlD2gG4UUqm6zqIXlzmLG6cGccHTeO45/x8i6oXRfF9QAw6D8LIiLisZ7vt8DIsDtBSSqsEws+LpTTjbbY5xiTP/fH28SbwfCgXOdRtT5+mdV4y0EcAusuxyiTdAGfol7NDy8TBCTSXNQvtQ0UlaKjSKwfL7ryhiIbJulJrQqhph9aSRlabKhW455wZcvJE2iKkdHLhOMUpTnGKr3o8aADdXqRdncJvUcS9oIsVxmIEjcgdn2Sc1a2s0bxIpgSklqTVXsIqXS0Wotzc3ByAEk8QbNpLoJazFrfFoiYtanQ9cU0ospK82EqwCowjkFDtKLk4m1slIEpyHTJ50t4CKIFl8tsSUPjPOakqpXGVfFZKQaNrQqdKjaUwjuNUpGOpvzYTrzJY18tS2CfXeW5C78mIiBsZTIBHJteJlsQ25ectQJHrRA2k1JLp4HpqmRIvTQVzLQqf3XzK5tEZ+/0eUAieoFkw9vs9fTzn1e6KsnlMGga3fyu1P8AlMxSyZGLo6jkZLKUc7b5o90ZLPzvAoMv+NqxKBwzpAzlDJlOqT3m7nz777DOyFXfoaP7gFG5u8qRx7rqO0uQvpQ6gqAMc3g4MH1vvTUmEh9+/HfsMTIPMGZBa7ay262Xhl+XPw+O1z3267Pvuofbc+mBZFvdwA9h5kl61Y7TKncjsttL07TVL1BN1ARGXXgVxSRjV/9nXCf5sGlOVymyF3TjUQVVLrj3FKU5xilN81eNBA2hYayNVaxGVgrtgtAIGdco7hlY8o7JpdWpYJWKVwRVxmQJFEAtVN9nAatNLVL1sY7HrSxdtLFVdz+rUMGWSb1hwLYNIFTDUZDmlJczhWunSkqUCYtmZzlyBhqmXyRYwMnPREcAae+kMb/OhzhV4uZNyJnaKVgbapNmBGUPeM44jyQrjkCfDEAqkVLWcuVDGUsFoYTfsSVbQkqZrklMmhDawKVjKmLg/b86h2spFxrxmEsV/WQEmFe+jUrd3/XXh9XjNk8stOY+MJVMQkhR6M4oYQ8h8fPuSsv0mcQJp7t8gNCu06Az7YiYAmct4r+419C0Aa5nuI2Tt+ev3iExMppkRzTARLGcsVyeYUjBzuUYIAbHiyaIyg8GlfvtnYYXvA6Sr8/wZWeafJZaWdUvG2du1bufn6ZFZbH9Xj/3m6zSx2PcMBJA6kxVmBhoNWE0elOAuHI2FtuBJomK1gmZ1ycnJZ1Syj6LAlJRPEo5TnOIUp/iqx4MG0KVO62upL8JiTPOnxaqfa9MJBwfGKLIo083kDb0OZ7JqolN7mZdcl8s0hb6UbCw/0PSq1BLj3GHMshkBo4TaAmsuDwXTgrJBJGEWKSU50C4OwE0cDGqpLLq4rtO0sqbiJYab9MOnrxWRSM6Z0HVoaNPylTlVYSgjn7x6wW7YI0EpyTwn0AQripZQWWhnUTPG9e0NY0qoUO3cvG/zmGhFYUrt41wHKBrr+YeZOZ8g60KLilX+PjjILwgqhknh9e4V7z9+xEcff+SSipqE2To6YbzcXyNiiBrJWsKdA1SX5eTqCtLAbpNxHNwTTQZzEMeAml8r8yTBbnbMmL6fCHVn5o1qvVjXtQoO/V6CEkLNAFVE1DXUB/fSm+IQnL4pvkjw3GKWg7z9NkvGeamvvyMzsbtMepvdWB77bptmdxStsg1Tl4E12YbUAVD7SIg1l2CWfE1ykOo5nqwwpkTOUxlJ0qkS4SlOcW/8InyTT9rsU3wR8fDnG2UpL7DKTlZm6uBlKoIpQaYAACAASURBVKITVC7SZBdHdnlkinkp+4DZ/aAlJE2JRcw66JX28uDvpUXWG3+XWnREoyc0SUCnl/26AuExjevh921ae1nFbglCmmvBOO5XTKH7S+sERJZOJUMeGPPs7OEWZG7dlXP2MtT1Q3YHD204V2bwupy2P/pRHJjU6zqWkb4WdTm8fk13PYyjG9wd0c9aLV5SLC0qxs2Aay6AYSsg9jYg06/ZXYlC85G+c18En8G4e4+0QZq6Rj3ntwa597G4byv7+KLjsB2fyzgf+ft+QPx2Ou+3+ayPVxNk73m2D7drDhwuT6qyp5MLxylO8WDiiwTPJ2D+9Y6HD6BbVA0ycAf0LBP8YC7zCxXctCl3reyoxKmwiut2PZbV6/I41GM10OxMc0tgaj+XYFhkocOufrISuzoN3BFi+zugISLRGS5RLwHtJYM7Z75CdEYsds6OrQC1b+OgTBDpnLVGKSqMBl1QLjaR2LVBhSfFFQrX16+5urpCRKojiRcoMXPnCCmV0RNDinE7Dowkzs57+o1LDna7G253V+x2twzDnpKySz8q0PZ+WM7Zlxk8cheM6Apc+rkNMvLO2SN2N7u5LIdBCT6zYCq8Gp0db1rYCdDk5jM8UlJmHDJ5TIw5rQB/Sokx+fKc8wpYH3s8Sk0lA5A4D3AIuqpc1+6nllwZNSBqkzvENMCJkdhv0BgZzR0dlmzq5wG4ex+Xt1jnMFnv5xGH+zx0AZnY6bJoZ3VYXu/o88H2cuByKBc5HByvn8s2eKnPcHuWY4crtaJ/JLg/NPN1tvpvSHv+S4GSMikbQ06k4sm6pzjFLzJOlQ09vgyAewLRX9940BIOwV/AQ04TQDQJjGVAo5ByJojUincLACZL1lUxKa55bC9vMhqYE8ZUyFXiYBk6gTLeErsLoCd0vecbBZ3lGgKhlslWBLSKGISaou8GWWaFQqnJg1LdK0DUkFy9a1VrWfEOSvJCK6UmJlYdp4l4FcTQTWDSSnC3XBUigVwEDRteZYgdPN8KfTSuxg4EkmTXKGsha2awkRK8yl5JCREjlWGy/Sql0EvmZbnixfUL3tueM97esNvt2O/3bHSuRCjVrSCbki2jliji2pUV4BFPNJTllLuAmBIlUbJ7SN8y8ll6wTu54/YadlIQdZ11FkW2HXk/ci2JnQ5eQc6qO0gRMu58UczAXLdu6gVgUihIroMnVwFhaogp1YjQr6rhlnssZjPUr2GprsDSWOgKsqbz1IUMg2ZKGEALajN72UAdXV8BfCaVgoRqrVZlRmbr0i9LjtOPe9fJAnBHifuerwawyxHAd2yR3K+xXgLYOZrX+fy3r1MowfXDFKngWRBpRYX8fpj229ITph8uX2oJocvZknbfZjNijGQDlRmgTyBYQ00s9FLtaHDQjNtkinZI2FBE6UItPS8tx8DIJuRUyLuBtB/dsjD78z6OJw30KU7xi44vE9ieynl/PeNBA+gWn8ekTVn1rBkoo2XP35U9HO63Je+133MFCYds8/IYy78p7rm8AhLiYMwZY3M2W42CeobbtI7LTxDzEtiloMQKsJZsZFyxbsu2LAWnTcLRhbkQzNSuMrP2AXHZZtObLkHGAog1mcaLFy8INksckiVEAmGVHBYAB7EN3FuVZGQr03m05oq4XjlIINVS19kK2RJiGRMhlYyGNRBsbSxipJJJpkQryJRYunRnqF7TspixaOfnO6u/Z8wWtnvTdzN0teKgWeq5zWznmvW8q92t95P5gOHO94t7pphNBW5EpbK14gV6Ds5/autCGrMEuG/DhR4je9+03eH53bfs8Ptp3wayfE5q0ug6jhc7OnTzaH0/r7d0qwmuqp+OwaR7NvAqnLq+jm3QzhEme1k4qRTcjaM62gzDUD3d5Y7k6BSnOMWXGydW+BRfRjx4AL1klJuF2PK7KS9NnCk0Wb/8G3i+b2oX5pdys8gynV/CMUZijBOIlqCoxQrGhvklLoAFZzrbS96qBKSCc2+/EqSjiDPW2s5LvBy2WgDLWMiIuIXdxFRW0D7bc8Wp/d4On562kuiirxdkrYFOeSCPA3kYoRhefdiw4m31qWsvZy4iWJl9sXWjk7wjhMq4llp5rVS/kgCYkhFynvXMRefEsrx0KajXIJEwyWSBXPa85jXUBMZdyhCW/tdlTqhU4eVwC9mIY8KSVAG2LQYPHdoVinipZWeBdUqstOIe1+7MUbXMxY+hJS5ODFrRj+YHvATQ0z3JWrrggxUmXe1yvZY8N7VVDJKSkg9U+q6rVTdBLLTW1cGYtib5LEUbHCzA7J9XDX1s0Hoolzj23d3tlg436/WV4tcqF9YgeP37fVKTlrzp10CBQAjeJzH2nthZ/LrVqvPusNGkVyG4bEoEqif09O9EfdangbJGqCXHm+uPirLf77m+vuaTTz5BimImnJ1f3NetpzjFKb6icWKhv37x4AE0HNdRrpmhu+s5KJ1lIPfpR9eMNbSKfxIcjIa+I3TdmoWOleWlq9PfBYIDZc3RGV01MiNaDC90EsEygc5BkiYHqo1JLcVt7EpBI5QE+92e/W5HCDN4DsgEnJfnEpRaLXEkjwPnjzZse6XrggPlAlkKKQ2M48g47MhpJI17AKK6JZ2YEGKsILmHwqQPzjmjwgTKc4ZSMlYTqBqw7LKg4mVBxECDoSYuYYDVIKgNMhRz94pi7HTP9XjNdhN4dX1FQkhpmJSy7m0thKDsysCn+xuiKNvbxqIXrxbY1QpyrUCK+SAl2JK9ZCpbruYgut0dUv8nCw29U5dai0NWqcfBPXVvLGYtmlVdKaXKiLS2waCL1RqtEKMQw3qmRCzckWc36cJdBvrw2Zn3My17Wx20tn47so97T3/tC23meQZe+U+nwaX3Yd3v4f4XMwbTtQkKVghdRLVzW7kwN0TVEA21rHtl/WEBnuNkTTldFw1+bwatVQg7Yui8nSooER+8FB/UV716O69Qk0JD/KX4Z/UUP+fY/ej/vPe77bd+90tsyS8mHgrr+4tsxwlEf73il/5f+kMtaGOlGyBe6UMP4hD0THrctp8ws1DoXeDdSjiv2LE764jb3YkAYUJrSsR0ZqABMH/dG5DzwDAMvH79ms3GwW0D0M1/eSkrUTEn8rInBoYqPUEKqWT3URYl55GUhgkQexJhIHTRp6QzxO4gEc6M29tb7IlXJyzJq/CVykaXnLFWyKZUdUqhVn6sEgTRKhUJrAu3VHYyBAxnhkeBrJkYZPL5PsZAighZYKCQMMhSKwEKNO/nCpyK6Fy50vTee2IZJjoV2GDpuDHdK3eZ5zv7uAec3id5MAlNmb9gXg8GfKwWrSQcR470xnO8b5X7+vtnjyPM8jRoDRXVevEbgWnQe9iOQxB+TEbVZjmWx5j2s2zS4loeDsZdX6I+49AGTayvl1UdfIsQAn3f02mPamSz2fw5+ukUX+V4E7gGuPmv/vMvqSWnOMUpfl7x4AF0YxsnQFe8+l/zKW7v+Smx7qg+2Kd4Q3OzwBnS0oCcUafkC6gXtwixJ/YdveAuCeqlr5cyCvApdymLaf/KUkrBnT60oBqqD7RVJtrAMlJLf2NWFa4+k2xpJPY9qoFNv0UXGk1lDRoae1osoXSklMllZBNbafAmFXBgncY9w/6Wkkcsj5T96NUcS0QJ5GzE0NN1HcNo5Oz64ZevX/Oif0EsblPnUoEqdQEQ131qPzKWSNdviNvLytxD17Uku+JJcw141ms15kIhoAnGTeHT3UveeXzBi09fIV1PGfeVZc1ECVh0V5ExFF6mHecW2Q49g0j1UfaCKhqVKJ4a5l7P3hd++aTKVtyFRKq7xzwoiUgIE1BuMiER9+XWpZ/1IpZga6lLzgcgt82MtBmIiZ3OQogdmr0KZN9fTAxzS1hdUdBVBjID6UVb7rTu7nNicgQsH1n/XtD/RgDf2jlr+Q3zZyK43MSdMQyz7EV1Zgr64Lzme0Zih4pSRJ0lFkUTNOeXEHTS3vsgqFYb1ehFjyQ6+1wlHa2SpqjPQsSgaPXt1lZspc44uEApoAu/+IuLCyxB1204Pzu/p9dPcYovLz4PtP+8Y/yHf/dLPd6xeAgs+ImF/vrEgwfQh3FMazn9XhOCirm0cgqVmkUPzWTBhJldQrFSEAv+nowR7SISOjqUrutqoZLgMgTJ8xR51S9TrM3C1xe+AyLDsKCEBpTEp4DNlGagZ2ZIyJB9P0SIWei6DUPYLyy4qBIOncC0n0BBirrjgCqWMmJ4EmFoFfLK5KEtVgg4GR5VGfeGRaZCNFObGvDDGMfRJRy5TAOGVA3dfBbcp7LH/Y4xfUbsz3gSNmAbLwRTxgWLv076bNrgLBBKYMfI1XDD+fmWT1+8ooQOxr2fu1WvZXXWcsC4KiMvd9foT16TN33t10LXb4mbLaYXbCxNMmaXxFQHlcrUmwohzwVgDNhuIlaZSFsyzyqTDOHOPXjk74kxPdAQL8ttNDkH4ExqNiQoORWGyvhrF2vf61Fg3LZfg9l1O5bR/r7PtXg1GF38XD6DLSHzXm5a2rpzcqbgFo8axHXH1hI85/sis3ah8fmbxeAjRIoGQoiIdmBG6Hrc+3se3ELw76RWzQytcIq3gebzLuKjV/HnYEpMDi738Pu0Sm+qq4dZmawQc86kIWM2S1FO8fWK7bd+90sHrb+s8fq//k/p/rP/8ue+34cA4k/x9YlfOgANjf2cJReHs/HtZSfINCV8TPt8Zxtbf7ecJm6/FwEpd1k1f7eHiQGbpqmJGJkiaSEpcQmBSNVbWk3ik4KGaus1HTdMDDSAqhBkZqBbGpk1Zl1rkp8CUibnjjY9vUzIamDcrGC5+Ky1zcBoUYX7uBShCKgz8KruEaFBnZWXpcTES6NLHb2ozLedVVYaIFtwYAKMFM62Wz4eXsERTWmTULR19zkx7Eb2FKTa8A2jEVOm2+6ArQ9STNFQE0VN6TQQQmV/NwETt1cLXayDJQfM7doZTHaIZdGWdo8t++tNf/tVO2SC2/JAMtfRqypjToQu0mlHspEouro2h9uba5h8X/cww29ilFd9fM92b1rnYC9ty2l9806kjWqtSB23xelRdoBa2fRp4LEcLEfEiif3qYNfK7k+B4BVPXOrMKlNOhWmmZMChPZsqM9o6ELWsZRITdr6SUKyeE6qf/gpTvGQ4ssE9Ntv/e6DYH9PcYovM36pAPShHrTFvQzaEfB8CKRhwf6JT+FLcE1wCG5zFfs5idBZqZoMVSJorWY3STmaS8bcZlVFrKsMdJpcE0Tw5CfzpEIAsezrdkbsulpNcAbLIjIBuBlA174pgkZPVowxEmubJsYwVnmC2MTSKUKY3BxCrcC4qVIHmxwHSp7ZTSvFNdYNrLT0S3N3ZC9KXVBxzbM0r11g2qLKaRywmJdMNmEjkSTGftzx/Nm3+ej7HyHbM3dPWFw2L2leyGKMYiTxiXon8AMxRIrBmDLDfuT8vFZ9DAEN1UoQd1KIsau6cq0JbdD3W1R9sh5byGZ0YRe3uJd08fsxkHnMSWJ5b2abnUEIihat9w4Ty7nperro3tvhgM1eHmd1by9+P7SAW7bjvriPtb7/mE06dBCr8uSQQ0fAnTOspLXsRGSypj48v/ZTQ4BSkBDqDFAhBPHCJlJnRmx+Httsz5REKKFKOJaFVXQemDfHnfp9s8QE95VWUbLNxXg86dYTE3e7/Rt69BSn+HLiyyym8hDA80Nin08yjq9HPGwAXZxtjfWlXHDfXwy6qkPOKOSy0E3qpOlUCZXhvSv58GqFVVZhFZBlqVXJIqHbgAaidlUL203Je20fqrM3rAOJUqeqDTeccC2uSiPRBLVIKRk1L97SwHQo0dlc8cQ/soO4EG6RUG3sbH65+95aDlYAi3Qq7EmkbeCy79l2hetdQuMG8kAXN8S48bZtIsKWKIVBrwh9z8Xle7z47E+xMdFpYC+ZkoyxE257oTeXh2jsyGSC1YIWVOChLkuJGpDQYaGf+sxUZssw3PfZxBOwFC8vDiNaOj4pO25C5puXT/i/8hVSLhhToUTDosszNAsSOnYl83q44f3NuyR16B6CUFJBYkfJkMeRnEf6fgsiFIvTYCjEHo2x2pb5lH4MEDZbEg0ww6qSYgVbDSirKuM40sXNBKRyGdlue66vr/24puQyrgChVvmPW7rJVC9E8JmIDOQM/dmW69tbnjx5Mmluc87zPdDAdClTouS0rM1R2AyUy9IK0kBYg+JVHAH9y/1PiXfT+ur372oXxqoqZR2Q5DqTZDL7LjP1gU5SoUMQHcJSfhMRChZjHZx5BUwjI6bIYtbFpBat0ejHm6qS+qyPoPXgnc/8SJwGnN6t6jptej8nLQzjjs1mg5VA7H2we7Pb3d+fpzjFKb4WcQLRX/142AC6hjX/1SNyDZhZvKla3LTdm9m1+/YzFVZYMFNL9ms+wIEee8VuL/yB5QiTZrbO5q/tF1kkVk2lu+ciD8toqWTOtLujgYlXGlxJCnTuG09stFUyXYwKIYL2qCpRg+uChzRJJcY6BR7kLtxybfma4ZeqE132TYtWwELbNWvAVJSgHXvLjFY47zbUuiaYeuU3pKz07dmMhJEsOTAsBmFRFMPcas/lKxnVzvW3jdWv1mMStAIsmfyA3yRvWJ+rVDLT5SpNvjIMA2dnZ+RsNek1YKT1bMix/S51y4IXoAnKWDJhAdwP49Duse1t+QNWZDCIz0J83rneF3fbIatrPa+3Xqf1vbdrXaDIz08nYH8sMVhE5sTLpjGn+UHb9PDPg4bFfdme8UX1yPlcaq7D4tmjfVpSbhvIit9T83qFgpCPVXY8xSlO8YXFQ2KfT/H1iQcPoJtDwdvGoUSjvYyX4V6/azsscAZ1KpzSxSqf0En6ML30277FUBdFO8NVAfHy5e4FL/KqPWaCSSHUrMYpEZHGZI9ojIxdrLKDWa86sdCLc3UNcnuJp5ldDYrJOCXKNaDS3EJaWW2NAcN1tWZGt+nZnp/B7d4LVcSAVXBuQd1ppPLfVgHjrBV3Bj+Gnhh7B+a4d0FzrdDYVcCzlMUEAoJsA6+vrkg28OzpUzabDS9LmvpuuqZUF4tiGMWLuQTF8VRxK7s6xBiGYZpm7zc+UJBqnRaCDxYkaHVBCWicGc4ii4qR9VNqH4apfLmh/QaJkWjKZtNRSgIVxrR3P3F1N5NW0aNdc13IOyaZR6mDInPP5JwyXdxwc73j6dOn5HGcCobM99RdicgSaH+eFOPPu/zOOqYcHRUATdphRp2NSDA9U3dLfgc9DuzXDHTAstQZH2fTBaDMZcStlCkBsRVDauB5+gR35GguNz54dhaa+qx4joMnAIMngR4mVJaS2e9PEo5TnOLzYvyHf/fnkkh40l+f4hcVDx5AA28FoJcvw8Pld74XWYGLFXsadAJVrcrZsojK3f2zqkhnU9W4xZS0CLpwt2gg2i356jLm/WRzDwKtGmyZWDFm9weZbc8wRSkIxQuhdJHYdSu20wCq/d9+uCVnd8XwhKqOzdkl3eYcJDIWd30IIVAkI2JuEBKEKJFi5nVjKA5YFn1EdTeI/YYQt1hNGNS6XESqnWDt0+gOGAZE8STC17vP0I07boS+YxxHL+5yO8zMcNOslpGRwo0NjBWEWy1OoiputFKrKTbrwxA6t+QWq1P6M7jSGOi6zhPKdAaDh/fX6m8V+u2GzeaMvtvy/Plz+k3k+fN3+Wf/7J+Rs7HfjZQ8kkeb2rG0Q1zdU6qTP3ipbGYIgWEYvE1hzZxObhplLlpyqHc+vG+X35f1F3fa40m7b57LmdeZnVzuaqHb8oxmxcy18SaZJQvtA6TZIvFOH9VKoVY8iVBISIlur9jOP9Sqk3VwORX60XlmpBVUWUpzwGc7VOfiOT5L0g4efAYFYyxlktJAHZhy/Jqe4hSn+GLiBJ5P8YuKrwSAXoKaZYZ8kwZIhZftP631vmf1RJ32DVQ2MkxMtFZWegbPax1JEQjEI7rPJWAWSk0S1AqcoTFpDeAWkOwvb+tQDIldTWTqVoAtyF02vJSElUQ295OW6HpO7SJIcsZbO4olhjQwlh2ZkUzGJPDo8hlPLp/zw67HJBE615IWjLDtKYMwBmGjASllIYat7gXqSZfFHOBp6LAQEOLU1iXz3AB3jB0Wnf3dxkiyW67Gl4S4Y9jtiTEirXBL05vPFx5FSAH2xRij0Gdf7joPn8c3vGiM5YJKrL7euLZVBJXg8iCVeRAgNnt6HwzMpsECM+g+u7jk8vIJ5+cXfPDNb9B1gUdngV/7i7/Bhz/4iFSuiSWytzrsqOyy5XKHgc559HtZfCDWjtUKymiMa1BsR3yYjyQJroDdUot9WNbw2PpvsfxuNEnVnIDqPwXT6INMnR9EkVZo58AhZNnWxUBWilciJAlUn+dmNSeoV+CU6kpzsL1b1q0dN0x8gOgSH50s79pMwXQOiwTH9iw3Fv3YIP4UpzjF1zNOOuivdjxsAF11jbpgjZ181AlswHFngBbufMHKzssZzLv602VSGNTjLKqbiTQbs8n7woEWIMVt2hqmzeYslZT2Qq+smcyayRVjqIJaILN8IUOptm6NeV5qMkUW0/9SCTdmgGHFAfBYlr7VmZQGrNrdmUC/PaffnPP8/W+yGxKXZkhwUNl1nbNsGum3G9JudG1pkcUAJMxa3cUt5YxzLTsewqTTLiqTt3aIm9qGiO4T0itJE10HP/2zj6tnM1hpoKrUioZtIFO8RLlC2Ebsyqq2egF8RTDLCwbadeD+VbUMhCmpsUUpiRA6mvvLPIhx5rSUQtdtXPKh4ux13zOMiX7bYcD5xYUX5Bl7ypjoNj2pZKx4O4t4yfEgSkqJ/X4/M9M2s/vjOK7A3nJQeQywzctmRlSEo7/bPXjv8AmZAe36OG9yAlk+c9P+8qJoDJlDidWk16+r60LK4ec/SzhkMfhBDCtuXyfm4NnILsGw2TmFyi6XdiyZC+PYpM0/eN4O+ra56yx/hqCIrvMwTnGKU5ziFF/NeNgAmrebPl4y0Pd9t4oj5XkbOF4xUk1XrPOnrVc3qmDCQboBao1Bc0cOUzsA74KpVQmGTzG7CwNehtqEElyK0ZhvXVRLmzSaDUCXMus+xaeTm84TEWxs4NqBhtulZS9+HQyKcnZ+ybe/+xcZBtiePcL0CmmJeJWJHVJioLCNwT2jfb7cNd7iFd7cs9iQUkjFt9MKnFv7QQgx0PXbSSaTMUR7uhB5XV5Twki/gYvobgdBXMogEubkSFU0+pXIQdiXTOl1svprHoYiNUku5bWMI4bpuk9sbwVYKkJRl1IUjBD9mpbKGre+7breGdDKXHexd7nGfs/Z+ZaCcH7+qLo0wF5uMTW6hfVZSq5ZLxR30Zh08z5IaRYuGp2tTsXYVM32wYNyVH4BBwz05zHTb1g2gd43ykP8Tp4fEdcOT4NNs+qMUz22TabZm5lRv8uIy3J/0wCxIKFDiiDiciWt44pSCliVcZhNDPQEohf30koH3Z7v0J6xKt9obZLsQLvmCzQP6HlAd8+/Oac4xSl+7nFKHjzFLzIePICe4phWdAKw92ugD9c/BrZNZbLMWupal4mDx5w4llPNs94ZQNBFslip08nrqHZ3raqheUpewQjmTK2GcIfNU9GJJZ0YaKhqBffEDV2k73tCGik21GQ9h9kigvbutCExkMeRMRX2Y+H61Y6+32JyVc/XSy4TlK7bkoJrXA1PvsvJQfRhv5qZA/UYCNLNbF5QRNTbVnW82kW62DMkryCXNCEbQzth2/V0IRAkIRRSKWSMbsmAAhZht8/IpiamqdZkSz+eqlIskctILqOz2g1AV2ilUmUfCzBdRyXTNQih6qMr6x/jwis8Rs7Ozri52XFzc8P3//X/y7Nnz/jmNz8gjQ7eJSiRSLfZICEw7vecP7pgf7vjb/yNv8Ef/uEf0neRcT8gtZJlKQt5hnjp6PPtGdihZOhuEuFh3JdoiN59Zt4IAQ9A+FoaUu3gYCVrkuX6Obk2XlNlhg9Y2zozsLTbg/m500USoVQ/aM0uFSo1p8DdIOcy6db02BKmZFZpQLoO7gRPQm36eg2dD5qXiZEWplaVhQZ6YqUXM1inOMXbxPZbv/ulejb/vOOUxHeKr2v88gDoxhQefaneA64XIPk+YN3Aa6mgqXwOeXQfy32o27wDKg/YqRl0353ydp1wlZAId7870oZlD0zOGcuTUYEy63eX+zGTKUGt1Kl9W3jFmRRQB2/JApYzElthE99Hnli++Zwbq9ecQ/y4eofJnEok15OtMmT/feGeMMlVGuO+6Jcs2ZnNg+V+jq4zJpeJLZwTMJcuLDLrrOs1a9d02W933FvEBxYpJcZxZBxHYui4ubnhww8/JGeIMTKM8wxBCIGsOpVIf/fdd6fzm1jxxe8u+bGp/ccM8A7lE8eSB49tcwhU74vlvf2GPS53frxdzSZyksUca+/sUNL2cdgnU1/Ievaobb9+5g7uy3s/dSxaE16Pne9yf6s+qad4AtCn+DrFCTyf4usaDx5AryQT629W6zSg5KF3vofKDsv8jYiQ694DVRNZ7rcEO9q2qu0UWQOABg58vcBS5rlMGKNqpYtaLRBYEFFiUEq10hOrjhIqaGhlwOtZlJoslyEHyGJIFDZBKaokMbJSpRaGxHn6u73xhzzw8cc/4fnT72JlzQY6BvdEyl0aCAPkMRHPIpZy9Tgu9H1P1ODtU6lOBjoxzSLNqaPZxsUZQJcExRndWxmImrnoNqTbkRKMYokQNvN1aMVC1NlAUyGVQulDPTerAMi1ymI+A9AkExmj07kojrOMgPrAwZlN32dre9Nsp5TugK4G7j/++GNS8nsw5YHYnTGOmZIXoE/cd7oUQCM5jVxePuH3f/9/B6Dve5eZVPmGFD+2iDAwuDRBBbV5RuQwF+BNmuSj6xxJIjyWuLsEuseW150tvg+eJHjQFlXFgqI5Vg00tYx2zT1ojjUyPyvLInnYrQAAIABJREFUwctyBsZt7DIlekNaBUczwWKeGGix2eVj2hd651oKc1GXpUxmCd79evszlXOubjSKTcd42yTLU5ziy60YeIpTnOLnFw+eKlmB0sr0sSjQoVodMCQRYwNr7nncdMnFMlP56rJmD9W0llCW+hJsL2xwQF4cwNb/ll12LHlxArY2e80qLfEsTG0XEVQM0wLBqg+teBEThDRmYr/h3W9+g9D3FBEkRjIgGrwCYNWHGxAkQq/sukQ8Ey5ROg1k7diLYEHYxkCJinLGhg0qGyRsSLZj3L3kot9iCdcxh1r/cTG9//Hr1zx58i6X50/4049+zG4c6M+2XF5eErrOPaK7jttSSDmTx+QuGUPyctldXysUum54AijF6NW4TcaP8qdsZM83tls+/rOfcmUDSQt7EtkSrYpKl4RcwHqBkjGBm87VBSHUxDAUa0mVViDtKcmrAWrspg9BsQgluGtJSxyN0aUmm81mYjyDtOI6EUMZDVLxQYWVjEohBmfO85jIY6Jk/1guKymPKmy2HamM9NsOjUIqIxoDse/oum469gTcBMacXF4gwSUJ9XfReOfTlrd1j32vInc+QXX10Xs+rV33/o1r4FXi9PEy2gYRl9KEgOj8sToAa+3V0K3OZblMNHqCavDS3NO2QTEJmPjxTZhzGSRM4HmejdFJriTVC9zlORFTpYhgqv6RNjMhpPGGNF7XkWYF9np38HGKU5zibmy/9bu/kG2/zDgx9F/dePAM9GHcJ6E4HjPr1JLKYKldXm+3/vNnHVusk4jmJtUXej7CSpnO2gur9lg1Z7KYJ81tN+fchlfrZMc3APclW9eYy3maWyrLpyvpQjvX7VlPziOBtWSigIOTmjQVQuDly5d859u/Qhd7LBmlJDabDVe3t2zOn5JTYtv1pP1A7Htubm54fPkOMUaKUQcVszxjzCMhCsPgxVv6WoYbIGe7I8p1Jrp972XFC9yRvBxu05jV5Xez48nsiiJhrX0/zsiu+30pKVhut7xmq9kH5kHYUlazZLUnDe+BHOPw90OJw7F75L5ZlKNx0D6BSd603s16huhNMmzzA2KlDXKPa5zva9cxWcvy4+tUS8rFLE1RAdH5+G3f4pItraC7iFfa5M5+D+6jRYXEnF3fvrwfT3GKU7xd/P8BlydgeopfdPxSAOj7kqOW8o75ZaerKeU2LSvi7JOxlnA46JIpEa8sCp7cSW6aogHzctC2tR7bgbuX5xbVBWiqL3ktU6LTdK6CT0XHwJgTH/3oR0RHsFP1uwbl7ACETlriCghbwpsE9XWJqPQE6xDpcM9dIadEEOGD975BSdU3t9q3jTkRk5fJfvzkCWPOlHHkvfee8+GHPyKPiSdPnvL48WPMAkEilkGj8v0/+j4f/fhjrEC/PeN73/sez549owtxcrTwEuLG5mzLVs/55Pt/xqPNOWdhw7AfyMWn7Lehd3cMGvj0vhZxbXZPJFFI1TQhLGznFmOnFYieC2dYNYOY+07DDIKXxTKkAqzDojpLQBwWyZ9tINNs6JbRJAiluDY7xuhFYxayl3bND4H3sgjLIfB8GxB9H6A+jPU2iz5YnPfdWaLPC50GZIfHelM7YNaPt2WHA48SamnvRV/5tk2hv9aut4Ipq33dSRr2LesZuwtH9e++vb1lt9tRLE3JiYcD81Oc4hSnOMVXLx48gG4aY+5JiDrKRLXCDHUqvlRGSlmu50BUYPbVlVZ9LKxesmuGawkemh55ydZVRlIdkIkIYgHT7OxyuX9A0PZfrEyv6xACYpUZrX7LLbFOGptmXq681DY0+7vM7H2sopM1Xh97hnHWvgrKsC8oHcF6xl3CLvoJwI3Zi8Bc3VzTv/cuN/sRq24Gcbtl2BdKhtevrikI7K/4v//V9/nt3/4PiaacXz7mG++/z+//z/8L3/nOd/jtv/U9Hj17h2xeUjxjdP2WlBOv95/y5OKMD548R4YdKRvZXNKiVpU4i/4K4hpxAtyWgdwpMeHT9yorga6ZuZyHNetLgGyJIrO+FpmB9lJ3e+imcp9efgmAdTV4mhnlQ0Y8hMDFxQVXV1erfcw+w2EF/o8ln873YT3W4SOzbGsD48cmR47opo8xrG8Dwu9uc7wK4+cB6CWIXg4ucnX0WPYVMFVzbO1c9qfv867Ljoi61GTS0M/AuUVzRymlMIw7Shrr8ZhsKU9xilOc4hRf7XjwGuhjcfRFa+vEwdVXb3hRHwPKy5f1z7Odb7PPJYMMd4vGHGtfY1mhTrNXQJBzJlmpwHHWdktsFl4GtTLiuE/sdnssK+OQXZaR14BvGAZS9VMWCXTdBpHAdrtlvx8ppfD48WM+/vhjZ6/Hkc1mw/f+1t/ixz/6iP3tjj/543/D7dX1DIYXbGkqI1lGOhMutmeMKZGzJ4TF2H9uXxdqyfEj3SziQP1YP9537Y+tewwc3Qeg2+9v8wGfMXj27NnEQC/bvvz5pva/zbm9zX3+ttstmds3XZv1z+MzO3/eNr9tO+9b/jZ9eizM8pwzUaewTuzzKU5xilN8PeJBA2jVBZA5ADXA5OGL6RtedkvGqb3swwQyfVllnGiJfrOUYwKnbwGqD1/GRZaWXbUSX02eW7ZpOoauQdcsMZC5IqJVP+XgJaRn8FLIlkhpmPbp2kwQDeRWVYQqXVCDatumGsm5QFLOzy4Z94U8FtKQEQnsdgPXt7eYuvdzycrLz15zfXOLlbp/Kzz/4Bu8fPWKYRjYbDa8evmS3/md3+FP/uRP+M3f/E2eXT7h5uVr/sHf+/u8+OnHyGTLFri5ueHl7ad89vrHnGvPX/rWb3A9XpEQMkIl+VY3bGNtx3FEArwab7BeGCpjPt9Hi2tpRkppWm4L1lJEain0Cmp1rV1u2yyBdPt9ySQ3p442GzCO41GQ2eQIDTDv93t++MMfru6Btu9l+felG8QycW95Py2B7X2fmYHXez9TQp46O7v8uPzBB67t72Of5bpNMrFMNFz2wXp5Nz2bjRFe/h1jj4gXldHQzUm77by0tUlXfeH9pKtjLmUzq+dywVwfXjspRtdcZWQhL8mc4msaP2ti2y9LItwpTnGKu/GgJRzTi+sIcHVQKlVGMRczWIUtX4aBZmvWZA/NEaMxSRkvAGJWQRFe7nkCM4BDuIMyylNN66W4YJ76XSYRCgEjVeu0gIm5BKNU/bRYtcWqW2tAaoXBlvCktTy2kSenEROm9jcQd3NzM50XCEWEEM8xAtJtsRCw6H3xztPH/B//5PfJ6RbJhbzLSPJt0pC4vdpxazfYk8DZ2SU//vG/4vL8kv1+z6/92q9xcXHGh3/6pzx68ggCvPvsKReX5/zbD39A7Hv+4A/+gB/+8CN+9du/woc//CH/4O//N/z6X/73+Nv/yd+hl8inn37KR5/9O87Olfcu3uH55bsMFCwErl5fcyszcMTmCXU1v/opJUbNjNHIOfGocwcOqBIRN6OewKhZnqoOUsFPK7Zh5mWgVbyAxhKgNm/ow8FSA7rL2YMl+DpMCJzunSNM7lL3PCWoMQ8EVHXteW02fxbH9rt1LWE5fEb8XLmz3XwXy3Q3t9oth9KRw/0dW77ep2IW50TF5frL7abndF4uS5Pwuu00SyOGlvpsyMwOCwFrjReZy22r650Rl0exfJaPtbv+W0ER1JiqSPZ9JKfltb53F6c4xSpOFnanOMUvbzxoBvpt4i5rdJeNbtOqBS/4sYyCA4hSp18beF4xj3deqms7u/lAxyzu1oyWLwm+7pEKcM1qq7HOIrMdW9tPmcDygiFdAH2Yq6StijqYYtKjcYMR6fqe2HcYiefvP+Xq+lNSvkUM8uDeuVL3l1KhFN/v7fUNwz4R1Vm/6+trXl1fITFws7vlydN3uLi8oNv2vPf8OTe7W7puwzvvvMNut+Pi4gLM+KM/+iP++T/9A16/fu1a1jKyT7c8OX/Mxfk5BS9dPZQ6mKmyjxmQ+t/tOmZLhC5yu9/dWxDHB0IzRTgxyvdM279JBtCWHdq4va2M4BgL2z5tedvucPtj8bOwz8e2OdzuTfv/vHUOl33eft62zz7v2LYczx4wzvNnsY9qN3msf+8ep6z+9gETc0VK2v11ilM8jDgx3Kc4xRcXD5qBngDhREKJk0X15+Gban65HbJuDQgv3Qk8AdAWx5lYwgNt6n1JQe5i8XbztaZrdwRv63EtJgfM5PTzQOLhLPkM5bMVUslky9zejosENk+jjP0Z7zz5Nn/hO3+ZP/x/XtOfndMPO25vXvG//m//iD69R89PsDLy6tPRqV0VAoG+79lfjfzhv/iXvH7xksvzx9zc3FDMuL695a//zb/ObtiRLLHfJfb7PU+icHF5wfMP3ueb732D//Ff/Xe8+/QpF2dn/M3vfY/f+Ct/icsP3mM3jvzjf/Q/8ZObH/Dot7c8ffQONsBuGLjd76u8pQ1aDvXsfg1Uhd3twObinD+7/gEfPAaVWerQ2N5jiXsmxb3AYfKmbldjWQ0PmEp3H0oRlpUKc870fb9ifNu1WEsVcF/hA0Z3CdyW67fvl+B6eT+s7je768SxHlQeOL8c2e4OuzyRuMf3s9z+MFbbLBIrDyUSbzs4WB1nsa+pX4pgMi4GWweJo3WfKofSDmHpM38sfF8+y9N1Hbo9YzfCbrdDDaycIPQpHkZ80Qz3+A//7he6/1Oc4iHHgwbQApDdUg3ay23xcpKCaMZKK5Fcp28Ppn1LSa6nBMSsijAcekrNnBcrWN6R9QILHSGPnNuIpYRte4ooWKHrArvdjhjjxPAa5v7JqrPmVaJX0IuRMeXJAcSsWtHlXD2Z3XUjhkga9w4MrCDa/I8Fk4IQiNYRiBX2Z4ig1oMZ3d7Yl4HrfAuh8OluJOgZt+mKUYwsyplu+LVv/Qa/9Vd+i89++mOuPvuEnV7Txw09A5FPyMMtOUb2+x1W/NzcRS8Q+oBhvPve+/z0zz7m+ftPuby8REPgxz/5EZtNx+PH73B59g7XV3uG64F/+c//BX/8x3/C6+trLr/5jGff+hV+7z/6O2wenXuxFcnsdju+93v/AT+1v8A/+ei/5a9/56+iN3Bztv//2HvTGMmy9Dzv+c45d4mIjNyz1q7u6jVn5ZAmKQ7HHIrialsmTJqGBIm2IRC0af0QIAqgDNkyDHihadOUDUiGPYAlQJb0gxAEgoZsgx6D1JDDRcOmhsOeme5q9vR0VXdVZVXusd7lLP5x7o2MzMqqrp7p6a6ayQ8IZEbc7dx7T8R9z3fe731xwwR0SqoUCt/wsB1BFNrFa1qLUBLIjIZ+yuql82gnOAU6SbDBN4AXTBBqa6m9IxMf3QclUmiiMUfTtxqwOA/WWhAuEk1sEq1RSDRIQfCuyVCGgC2PuOhtkZmt61hsFmLvliAz8NdSO3xwhOAQCYTgZzxt3+hvW2vxPBhAHwPBWt2zrDn60ffI3/t5ix+Pcb3Vw4BlNZsVOE5pOqKKBFScCQphpgTTUjHmZ3aiuk072Gm2VILWRzQJW3uCJEgIJCZE8x6jqYIHaWQc443kyHuzAeOqNdyJ7VYqKsvMaB++lSC0bQtnMx8+eCwVOulQVwUmSxFbY31Ah/vJX57FWZzFycgvfZLi1u+83804i7N4x/HYUzjerVBAmibU3lHWDT/aR1pHZY+sjcfjIVmWYYwBrbDBI0aTJAmutYpudYNFZlQK5444mTM951aSTHRDYW0AibSgobE9nqN6SMtNmPssNA/2yke3Pu89Fk3pAj5EfqdIU8aoIEXIkxyFkBhDqg2JDihVoYJv2uxnxXAoobQlykTOdpIk9Ppdev0e5y6cY3llicHggOFwyGg0aM4VdnZ22N3e4WBvn6WlJS49eYVnP/AC6WIv8ptN7H79xQWUUZShhmDpmhxvHWWwDU1FUPMmJ6qVB4wUjoDMKC6VeNJeBxVoNLNDXC7RmY65DHS8xnNFnPeJ9rgnVTPm37eGGnVdY62lqirKsqQsy9n/R/zr48WBp81wWGtnxY4PojScbONp7X7Y7R9Emzj5+f2O+aDrd/JYD0PhmN/mNEUaraMrp9ZxYNvtdvHWRYWYJnt/NACKzoLGGIyOqi5a64aCIYA/KjBtjleW5entUDLjVbeFxyKKcELX/SzO4iweHF+LIcoZNeUsHoV42wz05ubmGvCPgGeBCvhT4OeuXbu2vbm5+XHgU0AHeAP4969du3a32e6+yx4+5nnIb5/9mj1877e3eUrGiWnqEAKhLtFJH0RResOgBJNGExJC1H4NITAej7HB0+31CB6q1iRDCZOqjGA6NMVfOmYQTZJEUIQnM2m0NTGG2np8iIBVmdaAQ1ChLRgz8X8lOAGtm3onVATHzcslMJ5OKXzFyBWkweKrkol1BGLm2NmKL7/yObbv3GB75w74ElGWPFNo43G1pSwmkCqoHbauCSqgsizKzeWa/kaHc/1VTCKcu3iO2tXc2b5Dv9/DK98MKizjYsLFJy6x3LtAHRTf84nv5bkPbpLkGaNpQUDI8pyqqvDek/dyDg8OSHRCp5Oxe2eLsrZ4NC6Aac1JmoI/CXE2IshRcR0KKluTKcXe8JA0z+n1F0hM0qxzdK/b+66Uws+4CfHaH033H+83x4DjCVB9ktYw36/a8MEyox81y+a1puf75+rqKs45Dg4Gs6x3CxjnZe7mKQrzYPFoFub49+Pkd0GaDOxp53i/osP59R4MFtWx/1s6TGTkmLlt54oj59rqRWgNUGLSXjCNesbsWnlPt5NSVQU6MRRFQZpneB9wKvLmMYJrbNa10hijUUqQYEDazH8coDlv8UHhXEkgITHpPS6ULf2qHSjHos5Gvcd71PsMoN/f3+yzOIuzOItvjXgYCkcA/odr1679C4DNzc1fBn5pc3PzPwL+MfBXrl279tnNzc2/DfwS8DObm5vqfsvecQtlVvrfgIgTU9WcklU75QF2T+Yw7vwIfBAzSYmvkfE2X/zi73LrxlcpioI37x5yMJliq5ppbXENLbu7uMgHP/JRXnzxRZxzZFlClhh6vR5L/UWWVpbJ85yNjQ3Onz9Pr9fj0oXzdLtdOp0OWaLpdhcQpwlKNTbWTdGgAK0El0QKCyrglUW12gpeCA5wnqkqOSwHjFzJXjGhlIJMK6ZBo7RBvMb5iu2919nZvo7gORxsYV2Jq6f4eoKzNZPxkGrPI8rQzXtMnSM4oXQFasnw3NPPsb6wxGKnx2vXv0p/YYkXNj+IdQWLi4sMh0M8IWakBwXTNc3Nm7ejO6EyTCclv/2Zz/BDP/wjTIZjRqMR62trjG3BrZ23yHsJWsH2zlbMGkdh63j6xKl7S8CFWFopwaO0QIiKJONqwvrGOUZbU7yHqrLkaRbvu9zbD7TWkSpDPfsMYvZfJA6aOKWPiTo+eTPPqz7JPT7ZD4/137l155fNm6m0KhVte0/qRM/HSeB7kt98EgADiLq3vSfbdj9AffK9l/n9HDv4rAAUIDiDqHtdDOf/9z62X8tcgWYIM/Adr3jF/v4+nU6GMilZJ2E8GcaZFFvinMNb1/CjIUs79LpdkiRjaXGBTqdDkkQt83nHR21y9g8OmZbunvvZ/n9yJgNaW/lTb817Ge/vb/b7EN9IW+f+z//aN2zfZ/G1xZmN9zd/fC20nvd6ZuJtAfS1a9f2gH8x99EfAH8V+E6guHbt2mebz/83YtbiZ95m2TcsHnZK+bTwgHOerivZ3XqTay/+Dk9eWGXkRyyFAaWPlAK0wolGGcO/9u3fzl/4S3+Jr7x6je3tbUbTMTf3d8nzPE7lN5bY1lrS1JAkCWsrKzzzzFUuXbrEJz7+vVx64gpGpyyvrUVFjOCIqiGRboDSSFAE8TNdaWge4j46KPrgKULB2E0pfUWNp5ZIfbBE+TsBYsGjA2Wpq5rReIAWT1WUceraeUIdqMYlIjWhcrhU0MaQZRmiDfuHh6RBkec53gYmowkShETnVEUNXhgPJ6wsrpAmfV783OdwQfPaa6/xse/4DhKVcPvGTYrxhOA9RgzltEB1wPqSXBSZMRTFhBBZzw1vuC3sEmiEUeJg6QjEhBCw3hFM5Dkv9fuMJmOWe/0ZEG1WbP6eUGS4p0BRUKeAy/m+dk/x2gP74ulg+V5welQ06NzxoscZkNRHlvPMeNXHZ1fm933/NsXjvV3cb9sHZaBDk9GPR5CmD863JdKVjkBzmGXnRcCo2HcjScNDiGBagid4jwgoAsvLi6RpigtQFBOSJEETWFrskaYp3YUeq8sriAqkJiFNU7ROqOsSI7Eioq5r6rqi9pFuZZLGtVIaFH/KNWppWfPX51GgbzxOv9nvRnyjwdTJ/Z8B6hj5pU+eAdmz+Lrjcea/v6MiwiZL8VeB/xN4ErjeLrt27drO5uam2tzcXH3QsubH/aFj9uAO7lhBV1tgBadPPyulsM4SfLhnPe89KIXI/ANPIUmXaryPn4740NOX2Ogp7JImKfZIyhGHVcVhbRiVAavg//31f8av/eqvsri0wGQyAe9YSgRczfLSEkopVtY22NraigoZ1nL7zTe59eYbiA/8k//jH6LTDt1ul5/8qb/Az/7HP0vUqrZzxVwNd1Mk6j4T0Gh88Hjn8MpjxXJrdIvDep9RPWDqakRBIoISHc9NBYK3FOUALUJZFPiyxuExaMTHIjWFxhQGZ2EcCjCQLILqQLXkuXGwxYu//0f4/QlKMiTAja/cpCyn5HlOcDWdvE9/YRmje/TSRZQkvPbSy3w6/7/p9RcY3N3l9z79GdbX1/nKV77CM889zXT1kCljFrwieMtkMsKkXSo7RLRCN8YVzIBzvHeJKIxqNJPFMaoK6hxqIr9YAty9e5f1i+fJsw44jpnNxOsd+ctJagB/DMy2Ri1t3zPG3MOFnu9b7f/zgLO1ihclx4BWa7N+b2Z3nkZyJCV4P15yNKO5l5J0z3folHbGv/cCwHuULjjd4XGeFnP0bnZgIALNRBsanRMShLK0tLrs7fZKH6mkOOfBWUyjbpJl2czqfHl5mTRNyTsdjDH8qz/+Ir/yK7/Cx77zuxkMDvlb/+kvoKnR1AQBk2QEb2c27EU5RonBKLC+Vd5xKB1IE41zgqhYQFjWJSfdBdtr3lI4RGSm3GOtJf/ax/Hverwfv9nvdfR//tfeUyA3f6yvB0xPfuHn3o3mPNRxur/8qXd9v+/mNa9/9e+Q/MW/8a7t71GKb6UB1+MMhr+WeKcqHH8XGAF/D3gPh55toZWfvW/jZMZHRAhzxVlyQi5rfru2CCiEQBBF7SryhWXO9RZ4rbTcePM1yoNt1tPAE0sp53qKL90eczCdMq0dBIMRGO4dxAyZrQgarj77HGMbGI1GDHd20AF87QhVjSsrFC4CYmepQ0FlDH/yJ39CWVRkeQohql9IAKMUpYAXHxNhQeEDGJVSqAKfB8bFmLv1NgfTPSb1BK8CjgRBodCoEJWPpSmp896jojch3gW8dVA7lAjaa0xI0YDyGlda5NBiy4q93SErFy+zfNGxvfc62sZCyLquwStKW6JFMZyO2L87ZmPlIqsrC2jl6XQ6vPyFl1BK0UXzxpdf5k/rGqNT7rx5HXu1Yrw54YpOsNWEw/EBU1vOMq3QUDia++UFlDQifiFaxfgQKLE4EygbN8I8z8HBeDwmoOiZHrijYs/TKBdH/cTP5Ofal7V2NtU/b5wSONqXEiHP8xlwrus6DnoaA5emB6J1C37bPnl0vCOAfn/KxDst5juNggGRwjG/zv1CPSALPWtL8EffTom1A1lmwHt8ZYnX1JIliiTRZFlGr9fDGMPq2gppmka5P6XI5o7hggPUMd6xb/jLly9e5IMf/gjf9T2f4Nd//Z8xmhQs5WCyeH0DjYKJjwWBWZbNBjBqPn1PVOwJsZM17+eFIoE54cjZb8xcH3oUMtAn4n36zX5vYx6kPA5Z0fzSJ99TE5VvFIg+i2/++FYDxe8kHhpAb25u/o/A88CPX7t2zW9ubt4Anppbvg74a9eu7T1o2Ttr3r0FVkdx9OA7mZnzb/MQOzad337WZLRdgLTT5zu//8fQxZ/hK1/8I9KDGyylwo0bb/HaTkmaGZwoaqcwQUgTg7MVAU8nT1juLzDcPphlRo0xVFXVnE/jgheTqbH630dli3nOKz4cd2ibC9248qEUUz9iUA4Y2jFTO6X2JaLaqX01w18hxOx1hKG+AQ6ggkKpEJUtGuTjvQcfM9caTbABTbSadhLIFxcweQrjuEbw7awAVHWF1hmZibztTpbj3JGsm8NR1zW9TpcgqrGkEbIsYZpAqiLQLIoCMTE73MrDxUw8EOZVMzwqRCMVxOO8Z1JMGU3GyNI56qIkNdk9sxSa433mqI/NZ2jB+yNgHPnSR2oqs8yyyGw2o91/VVUzpZWjTOW9/fg0TvF8hnve2GeewvGgfZw2WDy53knKyMNQPo6A/v3PQWZzJ+0GR3UJWgvdXo8LG+ss9HukJuqL62ZgVIco7di6J5Z1CT6Qpo1ihghiWhAd73vZKJ8469k7GJB2uowmY84vr2GkogohyhY6i2ru42QymXHJ7wd4Z/3CHxV9noyZ2s7cNXiUAPT785t9Fg8TZw6EZ3EWj388lIzd5ubmLxI5cj9x7dq1Vtfpj4DO5ubm9zXv/xPgnz7EsncYEdXNgMoDWtw+xOYzQyenrFtAc+oUvK9pFaJ1t0+2epGNJ59nbzBicXkVJ8KoKnCiCEYREs3VFz7Id3z846S9RXSWo9OMW9vbTKcTyrqiquKrKCZUVQTZQUlTHOnxtSVLUsaDIWmaE/zcgCC0agRHmXcJChUSfC2QCHfHO9we32Z7uk3ppyAOLbEgMmrytjbHHqdasBdwvkYFhRYDvuGZhjoqJGiNJHG9REB7h52WlK5iZzJE9TNWLp0jqIDXUQlDGmOQqCTiKG2FDRbrXeSkKkErwWjFUm8BW1XkJsE0PouBGp1DL+1ysL9LEE9pXbxWIWZ+W2DlGpDrZa45euPHAAAgAElEQVTwTDyIUDpLhQejGYyGrK6uRr62g9LWR73Kx5dSiiRJoiHGnCNgdHiOSiltlrntgyedA1tQO3M1bPpe2w+NMffQi+adC9v+N8tgn7L8JHUDuGe9k39P7u9+68zv/2Qb70cbOa09bZuMaIxolJiGgtL0Y1sRbM35c2t0sgChxtmCyk4pq2nTBx0hWEKINtlJlmKbQUztagJC7R3TqmRSFlGTXWlGowmD4YjdvQH7B4ccHBxwcHDAdDolhEBdO8rK4jwkaT4zummqAvASHT5btZZ2gHR0nsevV0vXaK9HO1g6qdjxfsX7+5t9Fg+K94q6cTLOpN/O4ize3XgYGbsPA38LeBX4vc3NTYCvXrt27Sc3Nzf/A+BTm5ubOY3sEUCT7Th12dcSs4zfA5I77cNt3qpbAc6HxgzEEkIEhd4LohNEjme4JQSUqykPtvmXX/iXlIc77N56g/ruW3z5T15i92DEbgVl0kHSjLqAi09d5dWXX6FwsfhvOC1IFxYi99IY8m6PybTEpAlYAasARxBNVCmOjmZ37tzhC1/4Ah/96EcxWiESObNKCeBB0UjbRfksnSgO7Jjt6TZ3ix0G0/0IQpXGtOCHEPWiJSoWhECT/Q7UdR0NZsRgEkNiDMFHqwmVCsErnAsoEbzXKC3UtmRvuI/guXTlPPtbB5SjglyS2K7gUbqZFA/RpCSCdY82BpoBTF3H7GxVVVGD1wglJdaVrC9e4WBwQOkrFpZ6FLs7BB81tqPNW/yj5mYflFIEFwgm4KVmXE24eOk83cKQZR2KyUFsj/M4dTxzeLIPKXU0CBPRGKOo67h+LAZNGz7zcTMTGhDVAudW67t9H7PX/hgobukaJ6kA85x9az2a45nptv2+/ZyjvPnJv+33IPiWAnOKNJ2WmXKEbYo2Y0ll7KtRbPvIBfTYtiey3XN1g1G2DnDOIsGTpmnsk8EhAllyZDjilSIEFweZ1sV+4gMLC9HxMssyRqMRi0tLDEcT+v0+k8mExcVFylqxvL6GWMXezh43brzFh65eQMdvGtGYZo7u4iNlK4Q4s9Jyu9tZFO8dx8RETi2ybGTsxCMSv2eRZBYID0mp+UbFo/Cb/X7E40DdeD/jLOt9Fmfx7sbDqHB8ifuU6V+7du33gI++02UPHaox0dAx99hOwYYQZg2aZQQlAk0RARXzyOLBNE5sVV1F17PGnETFjSNoIerJlrUl14btN69z94t/wMc+8izLboGXbwW2dgsKlaEzw2JvBck6fM/Hvpt/48//ef6L/+w/Z2VpmdFujbeOybjAwmzKn+DAR66xBFDMgSYVAXS32yVJEowx+FATCCg5yrK6JpMbrCMYTa08Yztm6AYM/CHeOxKdNudvGodECCo0nE4FTVZPBRP528ahBILz+KBxPmaQlfIR7BqJA5CI4PBTi+p7qqpi0gO1kuPLCVkllGVNkqdYAsF7UpNibRy0xGl5hcfF8/O+oVCAUoLH4nOHiON8tsRhMaE0nuHwENFgfILTCiuR0qCDx1uLCYI30d5bS2gAcqBwBUvrFyhe38fbgPWB1eVltna2WVxeR8IRiPbeIzr2pghcW779UTY54lmP1oJzNUon92SbQ4iSe0rrCPL1cZmzuN6RLfd8Mez8Z83as9mS+VmUEMKRG6H3ZEnSrD0HmNv1RVrCUHRZbDz/rK8RrWazGlEmUKO9RZSiDCA4DIpaNEEigFbeHBtwHh3uBKAWZgNdkUYe0qQoCWhf4X0sBvZoQlBHnO8GkNbOk6UJo8NDLmxcYjQakCSa6XRMnqfYuiTLE7QRsjyhqir6y30W1pbZ29rhuaeeZnmpH8/RZwTvqd2EbtqhmFbkvYxpZZHgcFUNotCJwVqHMZoATK3FJA7nmkE1oVG/ifcZiU6iri7BV3GAQMAj2EfAxvt9/c1+n+JxAc/vV/b5LM7iLN79eGycCGfqAA+yjOM4hxMAH7OtzrlIn2iASfDRAryuK8qyoCwLvC3Z3r7NYPcOP/RnPsJ/+BM/zPd+2/P0ul2yTh9nNKKihfdkMuGzn/0s/81//YuUxZTJZIR1FYhiOi2YTqd4f1SopokvaY8fAlqp+HCuKzpZzvr6uQimGu6xNDlq62MJoHJRxq7SnjEl25Ndtkd3GZaHKB0z3tFVrQVqrTRYAxDFEEIEBlqlkbbQrmtSlIqubMeuu4Bv1CPEeQYHh+wd7DMoJyxe3KC3vkLZWKW7igiYjaZ20TSktTuPhXQKW9YNe0WhRYNXoBRq0RCwLHd6HAz2MXmKbyza0VHODhW5zzKXgY4zB+29hyCeaV2RZIbJpGA8HtPp9bDWstjvs727Q6J1zELOsr4nsqgn6Apv19/aKfx5esT8tvejQZw81sl9ntaGNgMdByfHs/Anj6MloCQOkoxSkVKhTXToa3pYphW5caRugvHRor518GsB/zt76bnjnzg/rWbfBQlHPz2+yRInJokSkM6ztLJKFWp0npJ1chaWFqMaS2LQxjCZTqnqmsqWvPaVr9AzGco73rh9nRe/9HlEIpj1HrCBcTFFZ5pRMcKZQC2BpNchWcgpfY3ppM0ALb6SJCFL0lg7IMfrMKJ0pMW5GggzZ8z7uUqexVk8KjH5hZ87o3J8k8RZcd/7H+9UheM9jZOcw1YG7H7FdfNT6jFLffRQEx9aU7PZcjhK0wQcIRQYsfRyDcWYz3/u9zjcG3Dp3CpDdwA2YeIMpdVRXWBhEZOkZGaNyeGQdGGBYjLFEzBNMVxr4dyeT5yyj8d2zpKkCaI1tqqpiulMauuoocJoWpNphU7AdFKGTBjYIW+Nb3M4PaQOBaqxMwbmALTMHuyza+OgquoZf9Nai2+cFJ1zaGMQqWkd/0LwhHYfQF3UWBc4zAc8ce4ia09e4a3da2TjQKoVlXVYLahG7q3bzSmLkjRJEKNISNBiKIuaNE2xtkZ6BptFELWyssTN17/I8loPBgpCHAQ4iFbcBEQDbeEijSb0XMItiCfp5NS+Zmtri2deeJ7h4T4mS1FBqKpqlsU92d/mE3fzAPY0YHQ/gHtynfnXyeztPHVj7qbPfXbv+q3N+vyxW87uce6/kMDMUjFSa4TgA6kStHf4skCqA8JkDEmXZK13VGIqDfeXOGsysx865bt3dF5zZYTNfRGJ2ypMI8V4lKUOEtsjQFkUJFkc2DnnSE3G1Fc4F/v0tK5YaDjpbTa+cpa1tTVuf+V1tm5t89Rz5/BMcdUUpTSpSQiqh07iIGwymURtb+8prSXPc4wxTCYjtCicg8lwxLSwCEljB3882sFKO4hRAZQcKac8Kjzob5U4yz6/szijcjzecQacH514pAH0fNwPwIS5ZfeAFDnSyAUiiIZj5hgxG+0bikVJLlA7h9Q11XCI1CXGezppSqED5TSQGh2L0IJHvEV5R7eTUYSaOqmRRmc6tjtmj0UFlKYBB0IIDqMzahvBw/b2Nns7Ozxx5RIQmgl38Ah7B/v0kgzT7xI6cHe6zXa1y53iDjUFIo5E6xloT5Q+BsokIhkAnLKID9i6jpTiEHCNNBsS5dempgSJXNAQGjMTpQjOE7TgrWVvb4+lpWUWV5bYeOEp9r70VbSPpuE+UfR6HUJh+eBHn2N3d5+D/QEf/sAH+fKXX8FWlrxnWFha4PDwkImUDPyIhUSzvLiAOMva8gXcjfnBk2n4pTELHzwkiSHYyEENBISo41y4EkljEeFzTz3LK6+8yuWL51lcXOTO3uGsAMy5yLWdyY9wHCw/KBN9MoM8zwWOYPb0jPbJfZy27RF1RB3Tim6X20Z54ph83AmwH98rQnScicAOh9agvCdxBX64j1RTOjJCnMdpwYkjaB3VUSRSK4RWnk+d+j2cP35AHdUqtIRoiXbpgYANzRixKZZVBKrmHPM8pa4dJokDrJEbkuYJk8mYbrfLQp6RGs14MgYX3TKNMXTWUv7m3/4bDPd26PYVw8Ee1eEONk/xTqhMSlcUOIcvitngqaoK3treodPp4Jxjd3+P7/rEJ1hdXSXNFyhKx/bOPjSDwbYYsgXJtqrjQA5PaOUg/SMpZXcW73M8KuC5ja9X1u7MROX9iTPw/GjFIw2gj+Xkvs6H0vwUbAQY6lRgI6FRNxAhuCjVYLRGEU05vHWoRDVFV57galxdoUUaK+44lW9PZKFOm971jbGEUmCDpaqKBjAdnbv30SnQeE/VMfggHFaHDIsBhZ0QsIh4lEpOnVK/J+PpBe9bEw8FJ+TR2uno1rDEEQv3gpIGbMZtbF0zLaekvQ75ygImTxAbUN7htUIninpasrqxhFee4XjAxsV15FXoJCnT6RTrpwSpyPoJXgdSoxreeMyuuhD1nsNcRlaadvjgj3Hh50MZTd7tkHVSSluztrEe+bV1TZZls3txUoVlPuP7dtSNk+vcDySfXP/t+vHJrPP9tj3NRvrUfQcNEos5VQgY7yLH2VW4aoz2NSZMkaDwoWos7aVN60flkxPnOX+s087Jz/Ggj0cE4D4IZkZzMYhvaU6amkjbMcagxJGIppd1CNajlaKuKnxZN8e1eAlMhkOUhfXcIId7nM97+NSwTw2TCXfu7HBrMMJXFd57VpdXsNYiKkBlef4DT1MUBeV0ihaFrQpM2uh/J+aeSaH2Xs9UN4JDvJrNHj1M3zmLs3ic4xsBnvNLnzwDiA+Is2vz6MUjDaCti7a6usnE6eRIU3aWaVPHs4Qn+aexoMqjFMeq8f2xp2LMUhtJ8X5KYjLKakia9iiqmlQbElEkIhgF03KKC4qqslHdwlnKJptZu4auoWZeDLSuiRG4HQEf6z1BK1zwaAkcHh4wHY3Qpj1PRVVOuHHjNZ558goHhWWyM2HbbrFT7FD5MSE4tGiMTuM1amgc89dkpnfbKpn4QKIStECnlzNyFquinbdR+ax9QQm4Rv4vxOyvipsTrGNvsINTnifWL3LuuYsMt3boonAieF8TtGXn8C5ZP2flfJ9Ruc/5y8tcuvgEO7t3o5NclvJGeZPr6Zso68i0Js8aKTwfUNpQVDXeNpzw4LC1J2m42kmS4HyFURrdAP3K1oyqCZbAQr/PZFKRJIb9/X3S3uIsi9uC0JanHUHREZ/1HkrQfaga82D8iIs8p+4yNxNych+ng63jg552+1lv9Z66stjaoZVBG93sq21XQxFCsCGw2BGUt+xuvUnwNakSjC/I3QGumlCMdsjyZbRKiSo1Aa0izzxI1FtGNCLzA9B7QXXzDkFmfaUdDqRJTlVOY9ZYCdZrUiWMiynGCJnJqBsZSVvV2Lpk6603Iz1CKS5duIgNgTfffBPnHFeuXOHW1k1CWaKKio3zF/i1T/1dBn/8xyRiKBfW+f5/78dIpwXf+RN/kVdvvMHGuXPRvdN7ivGEqiooBiM6ScrWWzdZ7C4w3D+gt7rCpCwZTsrjsxFz1x8gOEdwNWVZkOp0lqUOZxSOs5iLRy37/KjGWUb7eJwB5kc/HqsiwgdlduazrvdkGE/JNqowx8Oc0TmaKWijUWkCicakKUoHQl2ThBAl5kIA78DVYG2kNng721ebmWoNTU62p5WSc87NdJqVC+xtbzHY32F0uEcxOKAYHlCNBox2b/LG6y9TyZR//lv/nO3Dm5RuRF1OGotkPcsctoOI+DJoncz+apVQTktc7dGi6HR6jZwdpGmKSZMZZWD+usxns70L4OI8/ORwyN2tOwzGI/TqAmG5gzWBTGvERlOT8XTCzTff4snLTzAajej1evx/v/lp1s+tY6nZObxLyYS0I5ja0skMSWbwOCoUToHoKKlnrcd5MGmC9Z6iKgnCjI7hnMMFC4lgxbG6sUYQ6PV6MzvobrdLXUZt7rqu7+FCn0bZOJnVn+9Hb9cf23uhtb5nH6fRQk77rO078zMop7X9ZFuNgK8n/M///X/JFz/3WySjLTrlPnlxgB1sU48OWOxoLpzbQCtD5TwhCIk2aEBLpOSICGFuv6cVLB69NCeLD7WKg8QkSzFpzn/1S7/M9/3AD/Lsh7+NT/3vf59pWXE4GTApxtS2ZDweYm3FxbU1qCqWOh2G+3ts375F8JaFXoc00XSzlIPhIQM7IlvusL91k2evXqHX7/HX/9tfZLq7w0KmObh7k7WLFxh7i+522HjiEqbfhTRlfzJha3ePWgKFrdjd3UWF6FwZdduZydK19cvxO+1iEaEPuKrG1Zbgao6cJs/iLB5t8HxWUPhoRnHrdx4p8HzWR+4fjwWAPjndfj8gfRptod3+ftB7HkTPpMmUoBMDIugk6tN2sgSt/FEWrplqjmVnfsaPFBFQx9txP/6rFok8ZOdREihGY8rxiHo6xRYFtphiiwnFaJ+yGrI/2mdQDLAhymeJd6igor7HKeDm5HURiYVP1lqUMogPhEbOrTUFie2OTm/zlulx+8bKJER5LwkKX9VMJlN0J6e3sgQmIVGRQ4tWuArGhwX7O0PypEuwcPliBNPOOW7evEmSJIgQAZ9z6FQzrcqjYjYVi0DnDU3gSHVk/vyiRJrHBU+n1wWt0MZQN4WS7X7aQU77uh+f+EGfnexj7d/T+uv9wPGDlj1o23lDoNO2if8EytE+f/mnfoLL6wss59A3IOWYRAIm1dTWU1kXXSF1MlOZiRnXo6x5kNONVx7m1c6MtNf4/IVLjCcV3V6faVlSWzv7zpRl0dClHLt3t6nKktu3bpFl0dhbE4tAK1ujE8Pzmy9w8dITZCrl/OIK/Y1VsrVlqr09rr/6GpPRgMFgwNq5c1y+8gS1d6A0nW6P2nsuP3kFZTRr6+dYXzs3K/o9qRV+Wk75WJ/xc/ddzjLQZ/F4xFlB4VmcxdcejzSF47RsX0vhOLm8fT8//R7mtg0hmoLI3PuTYLuua4zRTKqSulPx8U9+P6PRiC++9BWCL5mODqltxrSwWAdOTAR51lG5CuuikUtb7JamaczsVdWxCv0QQgTgPipPKKXoGsNob4+d27fp5iZmAXWCnU7JE0+WCf/wV/8Bqy+cB6mZjIdoPHjBpAlKDKIj3UW3NBakUbFoM2hCYgylL9CJpq5r8jxnPB6hgswGD957pHmPNFPSIjM+bAiB4MEXDpRw9+4OnV6fbKHD1Fnc/piqcJQIb71xB3GKl+6+zIULlxgODvi2b/82/vilz5N3M55/fpMv7rxCvTplqdMjWEeaZ2xPxlglM/MRfHPflJBqQ2kL7Bwwa9UpJMQBwN7hHqudHKyws72D2Bo/8aybnLSXzzLQrS30/QY782C47S+nDcZa8N7GaTbRrXrEPPBqP5vRAk7ply0tZ36Zbzjdbm4AdKxgVgTBkYYRVHuoaszhaJ/1xVWkDmT9PqMqIJnBliXWGioNHoUxgnIOguBnBZpvz/Fm1uvisFIFmu9BQu1c1GSua9bPXeDJZ57h9u1bvPLa66SdLmU5Rpylk+exyNVaNjY2GDV8eQHOnz+PI87cVFWF0pqD0YhUGdy45Fx3kfTKGj/w4z/Op3/vD/nA1eepy6i48sZrX+Wpp6+ysXGBwe4e3rloAV9ahodDlheX6OYdiqIgNAC6toEkT09hpDMrIoySis01D0dX4CzO4lHOPp/FWZzF1x+PNIAOQWj5yb6u0bpxivPQSn0xy5YCNO5wvnFTkyOFhQhaGyA+ZyQRXbVdrKSnxqY9kgtXcIuaLdvl9tZNPvnD38fTTz/NSy+/wudffovt20OsDQzKmkNfMa5LymlFUZQMxhMcsVBqbb1Dt9vl1s0tRsMSJZ4QClA1Pli0d2RZRpblXNrIWWDA8K2XGeHJkhSTZIQQWOn2uHnnDsN6wrNrOYwPMVUJOqeTJ+Q6FiT6BrRoBE8sgAohYIJGISgfOHAVEjy2tGRZhkigLmvEBXCeYjBBqbThmceCMxHBeY+oJBqJKNVUW0atkGI04vadWyx0+5hOh+LOBKk9OkmZ7I9Z6K6QmoTh9h5IwudffIkghroIvPqHr/Knl6/TNUJHLWCsQ0nKqBxTqoBxjbm6zjBKo6wFZ1HaYwmcX7vA8OYWaS+loMTqHrmbEFCI6/LyH79Mvpjx5IXzDHcPme5P6S4uQ1Wg6yleLEmWIq4iOItXsXgxEKC2iERjGEUEpZ5Gek0JwYJRGtEKUo1KEhSKwd4uTz/3Icpqyv7+PrX1aDH4UMWB05wfvXN1pAQRaHUWHQ5pTIR8cCitqKzFJApUwFMjViG+BJegpId3DtEOGzxa93HBYhQEW2LKCmMtSZYxdlXksBcTFjqasjggFFO88oSQoSVQqwwJJTp4HBlBQIufcfofSKXCzf7zzYDV2gqjNV4UJstZWlpl++4hP/2X/wqf+c3/h+l0TJYleOOpQ414wWjDaHJAagy1CGk3ZzAacvvNt6id44Mf/TD1cEg5GrBwfoXQUaiVdcLaZc5/5DtYf2WL6qs3yZzH7RYsX12kqCwdHc1ndu9ukxrD0FmK6YR93xSb1hUERVk4VJKhdYZYx9F4RuJ3pi6pbYmWaD8uykTKV0gJSp9yZc7iGxX9n/+12f+PCo82v/TJxya7+/UqcpzFWXyrxiMNoGcxLzMWDYzvu6q6Z+nDT6cqpfBByJdW6C93+eqNLQ63dvnA5TUWezm9RPPExjLLuourPPvTkkkWGBQFrqyZjqbsHBzgCZRlSZ4IqaoJCwljIoe3ykCUiYWFIqRJxtLKKs9cvcpCpvHTIYpAXWm81pQuUJQlL335S0hXWMwNC0Zhao1L0mhKgUckcqFVhI4EEbSo6I7mAgaQEMiyjGJcIY1udFFMorKINmDj5TKi8cqjncZRNyoYx3mtoXHLi2IkjqIoAFhUKSZLcbbCmBQRPbu2KUJpPVppRGuUIxqpdKJ1eaJT8B7vmwJLIcoLNgWMwbmGKhNdGuu65uBgQGoMBjBasAiII6hohpHkGd479g/3cKXl3GpGUAEtUWXFOUfCka12kFiYqn2kqXha/rqK2XcB6wIqSGNzDuBAS+ST22jb/eZb14G2AFNw3oLYpqcdTfeHYJEmox9CSyUC76JisvceV9cNbQYILs6kKB1l0wCCQiuN57hldQgOfI0OnkRiZtSFAA2Fpa6inbs4C8pHa/fo4UmQ6FwZiFQmwtdOSzhmeQ5kec6bb93krRs3SU3C0tISe/t3yfOk2UCwtaUoCkbjgo31deq6ptPrsrCwwPnz56mqinJaQFnji4qxjEl6PbLeIuQ5g9EhXQUqKA729ulXlmL/kE6nQ6fTYTAYMBmN+PCHPsT169dZWlqaZfpjHK8DaKlere33/KxBy3OXluuuzjLQ3+rxuIDns3i04lHiPp/F28fjAaB5OBm7dhq9fbhpLUeV8SeKCmcZ7FkorBhSwI7H/P4f/T7/zvd+hH/7z/4Q//h//Xv8o1depXZCaTzGCQpN0l1g9epltJswONhBxlMuh+hSZpKA8hWpSwl9RZVDXTuKoo71h0448EIRHKYcY6qCjsswNajgqb3DiTAsaz7z+T/ksCq5/MQ5nltaZm0xZdIt2HUwMU02VBs0KmaaiW6G+IDSmpAKQQzW1uTpEgOirXkny6mLMRI8iU6iM6JSBAUSBCuNlvCc3bW0FuHSFmuCVobpdEpdVYhPOd/v4rSh31vEuXquSNOD6MiNth5nA2mvQzAKI55+3mVSFFR4xmXRGOaoxh5aY0x0NEQCqTGIF/b2D0lMTmELxEFHGUTB3nifp1cvsXaxj/EVBEea5yyuLzAuR1S6T2EDGINOU4yYqOLRzlz4CJKDzvAh4CPuRFQj7acaB8VGZ6JjNM55skSTmw7BOjyRo2y0RhmDrQOEaD0epAXMuplFiXKBgjRvG5BOdE1Mk5REG/b29mbXsfJCZcF51cgnpuAtygVSI7iqwtcTgq8JWJRyOO8xOgWJYNsYQ10pjDGMrSUJfibH1tpXt9+tr/V72nLO27/Ly8t0u11+4zd+g7/+136WyWRCt7NAwFLbKg6GVMy6Ly72GY5GLPY7cVCaR5UYYwxpmrJ07hI7N2+yeLXfgOMMlPCxP/eDvP7ib7PS6XKjKLhz5yZL5841dQ2Gj377x9jb3uGN69epqoo7d+5w9ZmnZ06e3kcJy/vJ9bWFnFrrOJOjE9I0RZmENEkf6lqdxbsbj0r2+XGMsyz04xnFrd85K/J7H+OxANDz2Z4HxTyAjg+70x/6LTdzlkEFJHgyFairmnxhke/41/8sZinltz//ZUYefvTf/DFe+uIXuLH1FgqLAsrJmJ2bU7SydLwlqXapqgqvhcoolOmhTZfpdIT1Izw1ynggw3uhJxnL3S5Z3mG9k2Jcia48BI8OHjGashhxWByS9nIuL3V4brmPVAMWgqOuLEEneNFMAiiJVA0jGtEJtQ/kWQ9CLLSrg6USodPx2OmI4AK9rE8v7RGcIs8WqMRhw6QhhHiMGLyOZjBOfMxi0vCAfbx+zjmCVwQljKsJQ4Fev4M3Du8svX4aDTRqCwEqW6LEoFPFyE9xxpOiWOx2GE3GVCJMrMVajw+KoCK4dEZReU9uEmRasuATqgDDMCExAU/JQnAsSpcEw9raMuOVXYrtgo2Nc6RpHnnfyuBtHXWPvcUWE3w9QZIOoiJYFh8Qr1CNLrYWAfH4AJ1Mx6JHo8iSFKUUyyuLrKyssLi4yNbWFlcuX8QDk8mUw8N9RqMRdZnP1DNCk1V3zhGcEILHS8y6WxoaTsOzVVoAiwueypZIgCRR2GpEXQrIBkElKAwaTXCQaE8xHuLrAluXKCySxiy6qOb7oRRFbcmSDNtouaRaUwYXySReHWVTgz8VRJ8EzPcD2vNFhNEgxuIkcO7cOYwYrC0IKoJsCYL1njvbdyl6CygXoJdR2ZoUxdbWFv2VZYzSDHf3WegtEZTmoBzh93a5/oWXeOrD3832/h0UgezONs88exWrEsqiIMtTkmZQvbq6yiuvvMITly8yGAxi3ULT1vspnLQydXUd6UwqzwgYlDJAOMtAv0/RUjneCyA9Txs5GWfc5zAtfmQAACAASURBVLM4GXs//SFW/8mX3+9mfM1xptF9ejzSAPp+6hUPWn9WMHdC9guOw2l17P+moMtbIKUOkCyuMaTgd7/wJc4v9bjy/CU+/9LvUruKVEUKg9GKXj9noZMRyhGlDJGQgFGUWBb7y3Q6PQ4PPYWtCMHgfaCcpBRTx3Aq5J2Mbq/DymKfqqpm7RWJBYZFUZBlKVp5VjopytUkAWxt6arApK6oSTEJKNOoiDQAWgS62RJ1YdEY+r0MP9XYPDCclPjaU05LBA0WjCQ41xqpCKJak4s2Y99cRB9A6ei4SCPL5yBIwHuY+ClZmuJx9Je6nL9yHh8C5WDAcFiS6oTD4YCFvI9JFSQBowydLI8ue95ROhvvjGiCdyQqiUVyWs1kCI33WLFoV8dsvy3pUWOqjGBLpuNDVCjJDDhbUeloTd41XZSyKClxfkLwOYEKUVlDTxYCsYAO8WgCpuG+OgLe1aAM3lmsWATN3r5jUkzY2dth7+CgaX/DfnA1HkeaJ+hE4XwzyEM1ABq8t/hgCSFKG+I8ISi8irMAZV2RJSljY8iSlJXVRfYO7yCSEnyFCile1OxehRCwrkL5I5pHaMxRhAbMKoM2Od7ZY0okqtVra44NkQPt3n4S6L4ge75oUkRYWFignIx5/fXX+XM/8PEIqvERhLoI1q8+8zShdqRBWFhdYVqW3Ln+JkmScP78eUaDIbJQ0cm7rFy+yKAckQ0yDrfuwkc1naUN6vEhXjRVabG6KU7MzyFGs7O/x/n1dRaXlxhNxiytLN6j131a0ei8VGX76yEqzpRIQ/M5i7N43OJRyEJ/MwO1twPR36zn/c0cjzSAbh+888oa8cF2BIyVUjOFgPmHXhvt+hJO7LdZJgFcw+/0RG1k0ZqghO2DQw6KMf/Wj343l68sc/7iImXhKUcDJNSEJGN1dZ2nr17m1levMan3cHUsFOuqwFrXkOcJXd9hUkwIylOWFVMbmFSCTxXKQPAlzpaEZopba8W0qhGt2DvYZTkT1lf7XFrO8MWQw+Eu06qku7BI3yVU1iLa4bwDkyBJitM5ShvEdXhu4wof+8BHeXbjWf6X/+sfYFSP4mDM/p1tJqMRBE2wDld6rBesBHoLfcZ7h/igSUyKpYpAsLm2zjlwkWsMAtbjXE2aZgxGU3RlWFrqcOnZ83zhlc/xgz/6I+iqz+07ezz33HP8wef+gCeeuMCOP2AkY7p6naVOj3FRMgmOkbOxEFInOOtZ6fUZ7h+gfaAKNVmqsZMaBJYPdrmSJ/zMJz5JZ5hgUzjXX6Iz2Uafz6iHQhkCTldkmaaXWKBA0h2G+7cZjCZkAaZBMxWD1wlISi/roHSgnE6agkzPtCyovItyaAiJ0gTReDH0FzvNvTDseNXIspUU0wFGfCxQ8xZjIoAWnaHagYF4nC9jhlk03sZiOusqtJiGzqKBSNd48rt/kJdf/wKLqxuc3ziH0jk2gOg8zgpox2gypByOWBKFiIrXUwkmyTEeJMlIM4kcaTRad2O9gfgZ53kGiO8Dnh+G2jE/K+S9x1rL0uIiNw8P+PSnP83P/7WfYzA+bL6LDpzHKMPO3h7PPPkUb7z6GsuXz5MRqRuXL15iOp2ilGJ7Z4dKwxPLKVevXuVCUCgL9uYez3z4IxSDEduFpwqOu3fusL6xgQ8egnD16WfZvnu7+e2AwWAwk8uLSjMNR16bmfJPex5GqVg70BQti1J4ib8lD0t3OYt3P94rGsfwf/rJB2ahv5mj/tW/8+7s58Vr93y299Mf+qbL4M/z4dv/H+ds9FkcxSMNoO+fgQ7HAPN8zLScZ5qs93/Iex8NRbTWOK8pJLCMY71ruP77v8lbX36RXFn+/n/3W/SNIzOazKyQesdgtM8kwNTlhLLm9vXXMeUuSmKhnjWaW5NtXH2H/cMDrLM0dWgoUkQSulmKyiDvJCjt6WRdnA+Mx2N8U5RUTKY8udzn0oVVrqz0Ob+xwRuTfZz36GqK8RZHSmehTynxIe4h2iUnGcEnpPQ4t3SRACid0e30KaY1dRUwJqMqSqqiZjKdkPW7JL2csqrpLq2RSBYlxuopWSdhPJ1g6wgelYLQcKJVvNRRZ1rBYDLGdle5efNNDsYH7B5sM947ZHPzQwyGuzzx1CU6vQxxQhBPrjUr3QX2b9/ioCgY1zWBlLq2uBBQQdHr9Njf3yPLUio/JZM4aPhgr8ff/Hd/iuXrN3j6qSusPP00w4PbqKJgf/eA5PIlKpORJJo8MSz3F6DyVM4yUXAwzDCiCXQYqw4bl66w9tTzHGzt4IOjmyaEuopFbdMROstRWlPWNprTFAU7W2+ycXGdNMuorMXVCUtLyywsLHDj+mvs79wkVNMoh1iMOTg85Mkrlzm3cYVpWTAeHzItBjhbUU8LxsMKnGN/Z49OpwNAmucQFNZ6/vC3/ilPbr5A2rds37zGhWeXISg8cfaiqicgjm63ix1PSJKU0lo6WYYXBUoYjgtUniLBkWQJiyvL7ExHkCw30zVH8zQSPMfnbR4+ThryeO/Z2Fjj1s3r7G5vx3NLEoqqQiQqvIgIzz7/HG5a8sILL1BVFSJCv7fAwcEBG5cvUlrLU089xe27t/DDKdXOIV/+/L8ieMXVSeC5xR8hX1rgu37gk9zZuk232+X/Z+/NYiTNzjO952z/EluulZVV1SvZzW5SpIaSaFEzGo1GBgxBwMDLlQdeLgRvsH1jXRmGrwY2vMDGGDDge8OGDPvGgjGS7RnJGnsGI2ojKTbFZu9bdS1ZucT6b2f1xYnMqiapkSFz6abyBQqdmR2RGfHHH3985zvv97xt29K2LePxmOnuDs1mhSoMT9865uTkAVVVUdd1LpK3PusUvoOHziWHOxHSNnkxQvCJENKftda41g9Q1/7nT5a+V/F8rWt90vSxLqCf1D8tlORSf2ZRnbiCa33ndvLlp11KOazj1uE+ExX55ntvcqBhVJSM9vYRwZOiRyKoSoUyO4y1ZjAG2zuqasTBjkLExGQyYbFeYAdICuo6dzIv09xCFChRYKqKVBQkBYuLc8w4UpZl9mAGT/AerTWTacHBdMzOuOLG4SEnZ/fphp520zCIiqTl1eCXhG0cNcTgcCKwbtecnJ5g7jyNcwNCZmtCcNlmEEK6KrzrSYHzknU3Z1xNca0npScCWhJItlvXQuTO29ZScdnVT4I8BBkCdV1nX6kpcrrb4Dk5OeGFl1/ifH7BxXKOOtRomYvbs2HAEwlCIIXceoNhGAbKqtoGbiRiiNsiR/HUjRvslobdyYi9owMoa1TIoTFIyd7xbbyu6bo1buhAaigjRYBiNrqKLBdywnS6z+7xTd557Q3OzuaUpUGQsiddJEbTCbdu3UbvzCBAv25ZzM95/+3XWLx5TllXeCTNOrC7u8eXv/xl7jz9FG17gY0WksbEgt4ONF3HK6++SowwHleMJgXGGEql8XYgBY+QCesy4aSqKlShCCGghSX5Btdpol5D8AhZXqEane8hBsrS4DqVh0m3tpK+z5g/jMldYSEZ1RVFXWFbSxIRSMQfQM6SEIKYPDs7U6TMPmIB2xAfRdpSc2KM3Lt7F3ygFho5Kjk6OuLk5IS9vb2r+PWTRw+odEG3atBCcnM8AmBz/33e/cbXeO7zn0Xs73F8fEQzGeG3ITExRkiB+WKBEILNZoNSCh/ClUf9iq19eV4/0YWOMW5DfgT4PFD6eNF+3YH+YepHVTz/Ze5CX+ta1/qEFNDfK9XtyS70kxaOS2TWVWCFFFfm5ysk1XbL9snf721LvDjlW29/nW55yk7sOd6f0m0ahqJmsVxTVRNG44obN3Z4/tO3WdqEGD+LkYK3vpWIqw8gOlarBSm1aCUojEZpBanAh0g9KuhdzzBs6DcbpB5Q1YQueF791usgNdPZHgfHR5iygBTZn9UcTGs+fesWX/65n6frB0pTcf/+B6w7h41DLhZ1RpxJPEYMJBJeNizFkv/n1X9C9U7N6fw+wjnqUnO+DagYegfKM57VHD21x4OTlr2bt/jlf/Zvce+d+7zx7T+haea07VnG0ensldbFlqgRAirmBUmQipA8Iglc79jMW17+1Od58P4pJ/fn7M/OeO+tDzm6cZt33/2A+/0ZHCnGZcF0NOadZsM8bWjcgBMaHwVISdu2VLsV1BLXW1J0eB8Y3IAJiimGTd/B/pTQe4TT2CFgxjPMzacpdm9i334T7EnG8iWwMVGUNT6sECERh57z5T3mq4ZVm9ibzbaYOYcIkRA9w2rBK1//w22aucL6xKgoUCpgtMaUEhclZndKVY5YLta88fo36JoLkrOkYBlPSgaXCNEwnR7gfUTpSO96hIw0yw0+RqQUWKBS20K21EQp6eKA6xtqnWjtBt+1+bwGQkyoFNhsLpCpR0qBNJIhevrgEDaSnM94PwSFKeg8HN26zRvvf0ijDqnKXaJUV4tL8f+zpfqdKY8xBJ566ilCyB7/lKBQhl5KYookmS0nh4eHnNy9x/EzT3H/IneqY4zM53P2bh0xHo8ZnOX44AZLF9jbO2C5OkVFSxSeh+tHnH7rj3n55/8GOz/5eSbTMdZ6Nm1HUZWQBGVZ03ct43HNMDTs7uxdzSI8ec3hCTtZDu3JS/IrJroQ+ZwA1DUH+oeij0PX+cnHcF1MX+taPzr9KGgkn4gCWj7xQf49o4Sj5DJw5bvSCZMkyoiIj3+eIGPCEKjkef1b3+LDd99i1K04mghKJTh++mnOzh7iYkDPpnzu5U/T9x3BNXi1ZnIw4fMvf5Hf/IffItqeiKPrV9lDnQRx0CRyGEY5nuJdRbKe8WhEkTYs1xEdesoi4umxSTCtCtrB0603fLDZYOqKiKUwkqrMiD1MyQvPPItODjusac/WuJiDVCS5Q0xMyAiIiNSe8+aUoqrxF6dsVuds5hfEvqFt1pk2oQ1KCerZCC89s8M9CjXi7/3m/0YlSuLQoUSO2taXgS1KEVMepHLeQ8pb284HhFAE5+iantdfe5t3337I3v4RKiRee+UtCqP5J//XHyILSbtnkShm9ZS9nX08iUXX0fuEFZEYBYPzTI1kaAc0io3tkTqRXEQUFWNVUQ2wU+/AkFAaGrfCy4QeVzgjKcYjQqGQWuJdTwwKj6LwEm89ehuHLgVURc1mcNTVGFLHuKpxfcfQNlhrKZLDhwTSIFNAhkhMicl0Qj0asd5YpBkzGe+w2WwYBpd50UlgTIGPkuBhb/8GhzeepesG2m7FYnlCchaiJFkIMVIITSGLbSCOJHrwQ0IqkwfuXLbSFMZggyQSkETC0KJSYLVZUiNp+w4UOGuJ3kGtKOsRSUmiUGyGDlMUGYCozF+Q+/y9yTcfGcgTAmKmbyhlMEbQ9j1GS4zSeSG41Wvf/hax71EhMDs8JAJPP/8c+Mh6vUZriRWB87Zh7RPPHB/z4XrFrgqER+9RNi3Ng7t8/d4D/uaLn4LxmKKoGJGDWoSCnZ0ps+mIMLRoXbBpVpSzvdyN9hFZ5OI4yRwDnxVy6I6/DHW6fH7uCr14rR+sPg7F83fq8jFd2xOuda2/HPrYX+m1zNuoMTjgsot8uZWau0C5C61QQl8NLMkUP1JkX95OKpU5vmSLgxw2bO6/RdE3KKkJsaVQjuPDQ7AWHQNHBzMMES0dQnrGo4rn7jzL26++i5EGFR0ydoyKBHQI6SC0aBKlUQw+2yZUSpihR7ueUgmUCni/IfoVya+pzTZJUZcYUWKtJxgJLntQQkjQORQdioaqUEzKEgOoEDBKApIoNQFBTILoPCpFkrMoYFwYtBQE7yHEbBN5wqLRWsfO7pjnnr9DVQRUWqPDhgJLqTL3WAmJjClj7GIkEUgiF5HEiPIRIw1CaCKBEAfS4ClEhfCSlEp29A6lMPTVgFaBUhr2ZnusY89F63Do/DsRCKHoXUvfOULasqptpIwa4xXIxN2ze3QMmamcBopCMwRBUU1RAegtQtcoXSN7R6ESMlnwA2MFyXUkBmJ0uGDZv3ETM9nDTI4w0yOq2RHRTInSIFCIJJEktMhpgklpimrGzsEteg9SG0bTETv7M4q62oL/IikEgvXMJhNSdLz7zp/y6ORdmvUZpVHbczqgtUSbzGeOMSIVdF0LRIRMeBGIQfLUzTuI5LIVQgikSXiZi7++74khoKWA5FAigrVMTMmq7Qha0q43FFWVd2HcQGkKYhAITA4lSjE/biGRKf97UopsW5JS5JWuJNsavgPl9mQMuZSaqpowmUxwMWP9QowYU6JQGGlwMfDFn/wsP/0zX2B3b8LBwQEAZ4szzs/mTKoRRsNkt0ZIxWS2g6pLlFBIB6lztMtzjO3ozu5C0+C6PjO1dZkXmiEiY0QET/S5Gz6ZTmmHnhBBGQNSkRCEeGlzisRkM9IwCBQCUrkFiIccRqPL7+s18Fof1cexeL7Wta71l08f6wI6Y9MeF8yIJyfcLztkl12h/FS0NPlDjczyTelxvDVwhUATMTAtDG+/+k0K17FbRW7sVUxqyf5OBaFnNNIIBqpCEJylXXfsHD6FZ8zf//v/mNdffY2diaGgJ3YLVBi2Hd0xhy/8VbzcxQeD9D1DsyDEIUdUd0uEaxmXmrFRZLP0gPcbpAooHblz65DPvvg8AY93jmEYiD6Q+h4/rDHSkZxFkxgZQ63U4632lAcQt0841zQxEF1H16xpVys2yyVSJEQMaCMJ3tK2uVv64ME9vv7VP8D2a3y3RklPSj2mkCi9TbgjZkqBzQub3InOVpLgsr86poTDY5Ol61p619P7FhcdMkX2DndwZaBtW/amBxTFiIUbWPeR3kmQipAUMmavrnQBNeRUxCAEHkVH4HyzIApP5R3Dg/sMJx9guwWTccGoUijlQXkmY0PTr9mszljP5wzrNXG+RMZEITPxoio0Q7vmzrNP4e2As57N2uIC1KMZ1WRGIhdVLiW8EiQlqcsxk2qHs/vnrM83zMqK9uKcWiaG5TnJ9mRQW8AYw507d3h0ep+mmbNan7JYnmIqzeGN/RzLLiKRgDIKqTNWMITAZrPZYgYT0Q7c/eA9uq4jRK7QahGBFhK8Q/ie5Aec7RAyEJ2naRrQkovFnGEYsj9aCorCUKjMM05bv7sgUzkuOTWZVZ2uUG05oVIRt++1lAIpRVLyJBG2/3Jhne+Xj8GNGwdICU2zRihYbZZIIVFCI1AYYzg7s7zx+imbTeTu3bsURUFRjTKX20dkMFRmj3Z+xrCZs7O/u50dsMgwsFMG5Poh9eoB/fwhf/rKV7HDmhCG3DSWZOqGFOiqwsfE0Pf4voMUMEqhpUCQMvYQj8CDMNig6NyAkzn+PV5axlJApO77eyG81rWuda1rfez0sS6gn9R3TvJ/97/0xH/Td3Wfpci4LhEDMkVqKWkXC2LbMJKCZ48PONqFvUnNjf1dFvN7RD8wqgs2zQXz+RzbK55/4Z/hyz//y0z2bvPOu28zbM6IdgGhJ9rA3vQGN28+zy/88/8mauez3DsTGDQ6NYi0JgiHLAyl1hghSM5ilMAODQLHqIrI0PDs0Q6//ItfZqpjLoZSYJuKjZSC0bjKzN8YKIxCSYGMAUUikQf4QghbbJhlGAbW6zXR51S+rl0RogWR6PsG7zNirTAKvGNxdkrwlpQCwzAghNiSEBJC5+6/THmHQKTLeiTHgyMTkQAGoook4en9gqRbMANDWlPMBM++/DRWeUpdcufwGC0mLG2g8eBF5nFfxlunkIjOZ26yMYig8mSoEMyHlk5E2tAz35zhnOPw4BYHR3fQsiauGrCWYpopJjs3bjGbTdndmeX47Ugu1BHIFFBh4L0/+UNEe0HhGozvmGjBuExsFhd0/QbrOrwfsK7DuoGDnRmVVuyORxzujHj39VfQyfJH//h3Ed2akQAZBVLUbNaWvotM610O9o853D9mMt5jddHw4P4pAXeVYqhUPoedc7nTTaTUCpMS3WbN+mLBctngUx5qSzFHlBdFRfAeGVr6bk3XdaQo8kCmd8zn81x4kxiPx5kwsi3O/0xqjUi5CM4lJQmZo8AvqTchIpJBJIVIBkmRiTNJIZLKQS+iREvDwcGY/d0x1nWkaHOhnnLnPSZPdJ6bx0/x0ue+wDOfepF6POLk9JTkSkqd2KkT40mJsAJRADLC/j5nEcJsnz5UtHNPGSpmrmR1csG0GNMuN4Qh7zyEYcNrr7/CvQ/fxdk1UgUKncD3iDCgRQ40UglU1IikUakgeLDe0YcNvVgRihYvIsQRhCkifSKccZ9IXXefr3WtT6Z+HBMTPxFX+o8Wz49//l1+5+/lgRaCeLl9zOUOs0BLiZdACpRGc/Ngj7Pz9xgVBaPCsFwv0RpcGDC6wrqOUbnHbLrLo5O3GQZHCpF2OadbXiBjIMWtFSLB66+9wWz/Nu+89QYITyEDgx1Q45KiqBDB063XpJRji7P5wuN8z8hMePjB23w9ttB3Hz0GWiON3lILQImUC2iRcHy3A/WSZxtjxA3bVLoUt/fPQ5Yx5dCSS/IAcRsCEtOWzeuRMkcoS62Q6XFiHBJSkojtwGaIkRgDSgrG4zGUVSYpUHLn2dtsNhumuwdE52lDS58sZQhMRzNA0pNwQuAhF4UpIpJ4/DxIIHX2xcdM47AhYL3Hp4gXCTd4vBrQsSeqgDRTOJ8DMDQdlQsIEUhbi0tMeSjV+4ALHh8l7XxDEAZn80IkEVBKoNQ2kVEIhM6dVwk8uLvhPWcxpqSzHdF53n37NezQUmtB37fIoia6DikE1vb0fY89P8VITbbo5/ATJbfR3ttB2bDlEUNOP0wi5h2VEFkul4Q6EOLlm1mSRKAwZQ7I2SYdAiSpsjVBqe3KOV4V59Nx9dFF5//HwUGlBDJGlBJE5JaOcrnbcxm0kz4ySBijZzouEcLh7QaSh+SRgjx0qwQxae7fvYdrc3CRDRsOjo/ZHx2RugtWm68Tl5JgNWWxoXEKgiPMVnywup8tMkLibE+zGTjszzh45pDN6iHzxcPMfC4M87NHfOalF7D9QLtpuHXrFsHGXJBLQc6hEVeHI6IRSaKvnmNOdoxEtunfpHg9RHita13rWk/qMnb8xykw5mNdQD9ZDH+vcJQnv88/uywMc+xySglS2EZPB+LlfRX0Q0tRaAolczQzHr9ZMj68jYwJ224IPlCOCm7f3me+uMsf/+Hv8/q7b/LXvvgirlmyYwruvf4mWvQYIbAhsWka7OZdVvcWfOWrDwnBcvtwzFRDbAKFjmhdUZgxzWJDBPreZoybCtmTOcx55vAAsV4wixFre5yrM3qrUOzu7hL8GiWhkIKiLOm1wqXsQxZpm4yW8jEhQQweO/TYviMmz3hS0m/WJBIpZWCZG2z2Q5PQSqALhV8PiCQZhgEU6JRT7JQA23pcjCht8ta9D1evSyDhgqVrGm4d3WSkBGebBxhdoksY749pdIszEdF0VFrRMbCInkGQUXYxQvTIKGFrUUgpgSrQSaFjYCIMKRqs09RijHEKBFt2b0SGQNgsUSl343W0OQQn2O35IUkxEUPeuVDJoaRC6EDXOoySrJs1pICLkT48LgaVzhHpQQpEWaJFJAZFdAOmNFi3ARkI0iC0I0WH3IahIBKm0EgCQuTHIhVoGQke0uXAWkx5wacU3kWiz/aZvnPcf3jGovM8d/Q0Qmdv/2X8dDEao4uCvu8RakRUhgEB24VXWWq8kLjgUYVhvV5zdnbGp56qWH+P9L3tO21rE5JbO1XAdQ3Li/tYO+TjW9QoZZBSYoy5ej9GHgephODQKvJf/J3/kPGo5E+/9nvEGHl9O8ipiyJbVGio6xqbSkLsef+dR0gXqPVvUJZ/xKaZY+Qe06KilBPeeF/yC/9WZFoesOkiAk0hK6QuOA3/Ow8f/i71+BDkhGmlCK7gzqEgdS17OzfpdlYI+RB0QpWeojL0Q0QKnXmUSYDQGHeGseeEtscFhRpBTJ4QW0JMpOS+H5e/a13rx1b7v/7qRwJGrvXD04+ygP1B/e3LwvzJAv2H0fH+WBfQ38V+/o4OdP5RuiIGPE4qvMRLRbxPuWt5GagicnCHMppgM5lAa8NyuWRspuik2KwaUhJUZYmQCTc0aG05PhqjdEtsT/HrDXLwLM/mlJVESk9MoLqWui5Rm/d4erdkvmwJXmHVCFkb+sGgpUQJgTAVYfBoo6mFodaCddMggVny1KaiGjwpRtq2zcdDGrquwxiDlgqtwNoOqQu0zsNXeZgpx15fJqg5N+Btj0ghF792wFTZL9629go9571HEHNMtc0daRESUmls34MQaK3phv4jwRhym8YmETjXoFA5SloZUJLObhBpoPQjPvzwA9qjCbODG8iRQvpIoRKn7QWtzHg5L2Doe4pQUOiafujy7aSi0BW9kASTCDHx4brlvWXLT996Gq0KvE4MKWJEJEaLkiXQIyX4kANGvM+d5xBSJogkcH2bsYd4pA+4dsF6syTGiNElSkq6ziK3p5yNecEGCa8FxagmJRiVI1JwBDdQFBVd07I73aWzA9HnYypjJNgc5BNJ5FNUoZKEJPAxolBECc72pJh3F9qmI0ZYbASNW3OyaPilF/8K1gVKEzLfWSacUAwUpNEhF4uGLin+9JXX+fLLn8YODUWp6OyQSSNInHMsl8v8esfcgb5amG5JkCpe+tIzF1uJSOcvmNUONcrBNLDMHfuUSDbhYu7kPpkqaoSGqAlA4yLjkUZgSEmglH78vtYScCiv0UqCgrJw4N9Ah1c5nEEMDygAqSt6YD22dErRbrFykpxWWpQDFeCTJIQ6z0boEoGgC4Z+UeJDYv6gYNV4tDmg6WqsM0wnh4g0QooSbUY0q3vsViV/65+7xf/06w+w/YCPAZ88LibcdZLKtX5MVd3+hWsbzQ9BP26d2h+0Lo/Vk8fsyUL6srj+futjXUA/qe/sQD/x7UesHU9yoj9yf8E2QOSJ20mZvaJ9x2YY2NWGREAA1jrCYNk5mHF6ekoMjv3dMVpYpiuuugAAIABJREFU/NDQdw1t29E1PUJWpBRwyaN7h5SSUsPMeOQ4URtFTJqgK4ZokOuGJMXWHgAhbYfCfESkhIyRvu1Q0hB9xrUFl5PPEBLn/FVKmlGOIcRMxAg+x5CntO3AX6akeWJw2ef8xDErSpOLR+u2ixNx5ZvOA5iJlHIRflkUSZ4IjEBcdXHjNqkmxGyNCCFhdMmkrrDWsl+POFuvSWXB7miGj5FV1xAVKCkZ1xXt0OK2xIcYM9QhhrC1kCiiCNl2kvIWeRTgiCyN5je++keM/krkpeNn2HOSHSXY9Bu0LpDrOShJWZa5S240WmsUYL3Fu5Q7sRj6ocdaS9cOrFcbqtGIxWJBSAaIlNWYbtMD2Xaz2XT5WAuBdwKtC7yzhG2Hulm2lKZitVihjMLFcHUuh/iYTBFCwNmQ/dhO0w89WhXYfqDvBwqtmM+XdF3HMDhO5pbx3i4fnswZzfZgMoWYqMopQXq6xQbMiNPW883X3uVkPufR+Qlf+uynUUpR1hWrdYffvpZN01yFk6TwRNT9EwX04/ehvEL+pegQcSDiUCEQRUReIvAExC2D0m93QoSQ+TXc/kYl4pbsEUlRXkGnhYykbSF/iYrLb5O82FApL7xIeZgTUWGdJcq8oDvaP6ZZbwiuy15wefmQBFLY7XPpqbTK55EHLSIliv0SvDhHSEVtRPZZb3cOvNfcvDnlxtFn+a3f+hreJxaPlph6lHcfYiDE6wr6Wj+e+n4Vz9fIv+/Wk0XzdfH8/dOTx/T7XUR/rAvoJz3N323heGKAEIGQ2ZerlCTnGDz+fzLFK06xFJcf/gGtDLvTXR48+gBD5PjGAbPdzPw9Pdswno6RFDxz5xkWiwWrekPfbHj44JzoIu2mQ1cFXigiibYHs/HgNVIJntqbkvZKrB1ofY0PhuAsa7tCRocFZNKZZLD1Y0sSPkQeLs4ZhYDVktT3OGdIAVAKhMjpfkJQmYJV15AKR0g55TCRrQUp5oE/Zy3ObSke5EEwoSDYiLUWUxaZHyxF3vIPkuAjBEjegw+5mk0CtslsyQd8zLaYQEJtPckiKgpdE4NjPl9hpCVFS3ljRl1PIBjmZw3DakXwGl6GOhUUpeFrf/oW9+ZzUtwhuIxuk0GQfCIIiRP5GFWdQ4bszRYYFrt7vC7g73ztD7jZf4PDhy36ZE1oMrv6zo0xo7rIaDitmY4n1BWUpUHKAlNWGKMp6wpTapJQmKKmHRxnF/fpnGfTebrBoooxy1WLtZ7ZbEZVFWxWa2Y7E2Y7O6TU5k5z9BhjaNZrdMrHNUgoixqtC1CSoihYrxo2qwWr1WqbCgjNpiPGuB3elHm3JASUUkynU7TW/Oq/9x/xm7/zf/LcTxzzwaMN/91//t+wOjslRc2yXzEqFEbBM8cHSEbYScmjD+7y4PQCHRqem91GVyVJaYSUnJ+fXw2eZtTc4/AQRJ4bkEYAGhe2GEkRSWGAZJFYYkxoWRBiLmJjSttzUKFSRtuJJMjjmnmRB+DDAAiUNFyW6kImQCPRaKGJwiGUA3GBSGeodBkfHzMLOyZGUWJGir63tOszYnAZhZg3CbancMo4vy2G8fJ8NkKBEoTgEVVBig6UI8U8dIu8pALla8vpowuaZkGMzyGNYhgSIeVAIh/aH+BV8VrX+vGR+dJLf+mK6P1ffxW4LpJ/HPSxLqD/LH3PAnr7tdb6Ko0wd/e2RZ9IKJlxXVpINJLSwN7OLo9QGKkwVYmuEpt1h9E1MSjee+cBp2cXzKY1i/kjNo0lBk2M4FWJdS0q5iEjpce4FtbWM4TAIpyjpKcLA4UKGAwqWnol6USNkZEiKkARIyiZU6bVToW1ik3XsE4J3Q14XzNYB0ik1MRgs41D5WhiO3R4n/Aq4EWBTglE7iCHEBAxURQFbbeh7wdQ2TqQRA7sSESQGms9hTDZNx4TxDwsGENECcGVNdcHklL5dcglde6ehoB3EYJAJIkQCi1LkousujXnj1qOp7cJs4RzgZAShVT8/u99ha9++318UkQnkNFQmAo7WKazKdX+Lu/efQNcIsVAoQ2d7wiNpR7vYvse48Zs5omb42OOP/Npyph46uiIm7Mxm8UZWimOj4852D/k/XfeRJIfw9B0dMEzmsKqWdBZh5QVq94iqpJnnnsJhsgLTz1NNBW7R7e5mM/pBsvR0SHOOXRZMJ9fUJQakRyFEgxtw2vffIV7d+/SrDqcC0jRs9m0eO9x7jHbfDIac3x8i3pc8dLLB4QkuHfvHneeforlcs3777/PjZs3OT09Zd1YOjljdvwcL7z0E9Szfe6fPGBWKbQwjMoxhTYIoWjNLn6wJAlOlnTOkjYrNpsZeucQpQuKouDi4uJqiFShMvXlO2cQTH6dlTJoIoKI9ZGRlBA1XEZcpxwlL+U26h0Q6tJetb2NENvRvIRAkkT+SgmQIhFSQkpHFIGI2DLBPVH2JDEQY4kIiWA6gkpUWtG3LXqiyGu+hEwFSTiMNsQ0bBd72ya3EEiVz32AIARJJqSIaJUXMtYHtARz2b6OgpAU48kBg9vFhR6KCTsTSe8sI1/nlFOuOdA/KE1/7TeuLQTXutZfUNdF+/dXH/8COikEiuC3PtG0LYZVka0G6ZJGkX2TUSWE0aSw3UoWnqASklw8S5lQ0uVOGSXiYB9T1owUTEdjvOsIKaFNQqqALmHTrfDekkKJkoLCGEIIdE2L1hpx6V8QEucTPkVsGqCoSEmg04jkBejEID1JaAwlOnR5bB8BGkwpmRSGElhYxxByoRplwnaWYBWIGzSscHFNaFcYWeL6gUErBi1IOiELB8kjtManRIjgo+TWndu8tbmg8wNGG8paEOmwfb/dOQ/U9ZjSCzoMTnhcdKR46TFXKLNFqqVIZyPalNjB5qJXaqKPSBcojCHiCcmj0Fw8sgwiUZUTVvYCrwWYktAn2pHgw82CU7dAOUdKTWYt94lCG1zfsHrvAuk9gkQpLZ5IXY+RyhNil7uKXeDnvvQznL72FhOpkS7w8NFDTj7s2S0MQsKbb77J/sERi3snzPZ26QvB6uEJO7M91t9+m+l4l3q2w3vrE5QqQfV8/bV/xMHxM/z+K28x2t+lnO6wMzskScE/+oOvYIxhfrGhKhTRB9bLc6ajCmIO3WmayPmipy5HRARUu2gBu+Oa5HrKsmAYBu7PzwiPHA8enCJNLjj/+A+/yt5sB1xgfnqGEIrpzi7/7d/9r/n8l77Eet0iwgY3dLhyBgpiGxECKqNQKdEGT/Ke4Ab2j/ZZpQ1RBGRyRLGNgnfnCGqSN4BGCJc7tmQHRUQSLSjpEcGiFUQs2oAPAh1BCouX4KJHKZXPfZmtMiC2owrZMhG1yDsaMs8lCBRSqeyZz28lLrmNmXriURFMsqhRhe8tqOxdj1KB0iRR0XRrZAGbrkTLAyJnfPDGhs+8UCBwaOKV/SuGEUJHEg6FQHhNXZesmxXlqERLiQsuh/lsA2WUCizmDyjKmjfeWjJfnFF0NeV4zM7hEZPJhGZ98UO4MF7rWtf6pOq6kP3x0Me6gL6c+L9MHLzEbn3kNiJ3rQL5Q/exb3Pbmd5u28aUB/cepxgmhhBpe4fUBqPZUgLAOYckklwgpAGhtwQBn72QujD4LuJjRKIQhByjLcGTt4W12v4lITGqoFQ5DCQIRfAh2zW0Qm5RZTElEpHoHUrm2/YugNT4rafUxwQU1KMJyXeoZEhRk0RJtCpHQEdIQ0B78D6gVEkcwGAoKbm5f0xoPavFkkIYdND4oCm0zvYWr/HWU4iSwfrMFU65aDZSoYVGEZmMdqiEoixrqnJCPZ5RljlJ7qtf+Qpd0+KKGpIlRAdBEYUkJIsUHmSR2dwBBhd41DjmvcMOCVVpnE+opHEhB7TkAbXcpfQEvJREAbpW+KYnBYcIlruP7jGbjRiHiqPjA8q65HhvB9k2zPZ2aJqGi/mKfrqHi4GVjhzv7kEA6aEqJ/QxYMZjjg9ucvfhfWJMvPfBXXRZcff0BD2a8PDBOS4kpjuaZr1EixqiJ0aPTAHX5W382WSEMYaqKOmaC/YPbjLZ3eXi4gLvPaNRzbiq2NmdoswtjCkJbQ8KrLW88MKnsF1PjJHNpmXebrLHvtRMZjMePnzIZ7oN3jqCT0idrQlqmxgpYsq+3OAzEzxFJns7eO8ptt1h5xxCQvAZ2WhEQYjtdgchZt9yUiDy90pJUnJ450A4hIxXO0EpRapqRLAupxv6YYs8lFcoSYVARIkUguS371cZEEkgks88bimvwlqkFCg5oBDsjmvun64opKfSZfYua01ZllRVQlc59Obg+PNUxQ0enf8Jo3qT/d0ioeS2OAfK8ohmOKUoIfoB5yLee+pxTT9EQGO9wxQRQULkeUjKssCYCX0Hfd+CFFSpphrVjKYTvLu2cFzr46dL6wB8t5d5+mu/8edSMa4HCK91rY/q411AfwT6nItIIdMVbUNKiVDbEA+ZI561LtC6IEqPCBmBJoVAR022P2yjkKOiqCuqQuP2jhDdGp9AhYjdNGC3McgqYoqC6COT8Q7T3T3uPjxjMwTqvacZ2nNGRqBwyJSQOnfQYqcIvsgEACHQ2NzNkyVuGxwRY0QKhRQSrSMieVKINMNAtXPMjix468MTkh0YW0NUEaSktzWaQ7y4hZnt87MvPk05OUDoiiAk513HJiaikOiiZjIaURUl56dnjF78MqvVKts6tlvuxhi8t/iYOc+5Uy+Jg6M0huByUeP7Di2zTaMuK8pyJ3edkdgUUTp3Uod/9VcZ2pahbzPaa+iRA8xXDU3fEW1PW/d0led00iBl5FvvNbR2ilYlm2Wb42B8pBsGbAo4LSi3kdMbNEFlnrDyOaEwuoAZlbxzccpNUSAfnPKafZNhGNgtCibOklRiPJ0ym+1SSs14PCbabNOIIXBw84jzoeOi6ThbN+zvHzKdzahmRxwcPwdaUuyMMdWUrk+MRiPKihxMs2nQAkpt6LvNNpY7oYRkOp0SY6TrGvYPbvLgwQmTyQQpEq5raTdrFss5vffbKHfDB/c+JATH2WJOoTXW2uxRFpIoHP/yv/5vcOdTL6OqgnI6I/lA8J5aCLwGpRJSC9oh86aNURRFxcV8xf7EYIOlihKZFAJJXY14dLYiiUQIHik1yEvjb0JJmaPhhSRiCMHjbI8ODTpYIiOsKKl1hXeOFEFpiKljsTgjMYAIpBRyMEkIEBPebxnXckvo0CVG5yFPqRNJgjKSosiUkiA2jKWnlBrpFAyKWML5/D5a1SjpaTeancOf5Gz1Jpul4bVvw/HtRJRsd7Hyc9LyCCktQjVY21ONEiEKQuxw3Q1mk1vY/j6iOkNEgZSg05SqfpqyfpF1+wq9HUgiUs8MVS0oK0lZ1j/My+RfOn3SbRzmSy/9SP7u+p/yd6+G+v6Xv/vn3uZ7afprv/Hn3ubP0l8WH/STC5gfha673t9/fawL6Kz4BNM5baf4HxfXKXH1vZSSQuktWiwikiQkgRIqFybbgiAKULrCJ4kWBUmVqMKhlGCzbGg2PUNSBCHRCoQLPPP0Uzz3qZd46/U3mO3scef5Y249+wKv/NE/RLT3KYQlxcC690QXERqGlD+QByPwMqG1JvRbj2XyuYMtRA5DkRIEeRhPlYjxHqN6j3/xb/wLzG7MONyp2T+sefsbr/JwmRMSbz/3s4RiB19W+D7h2kASkZEZUxsNUjC4gDvb0PkF3lpOuwZrLcvlEmMM6/WSajyi77ttDHT2RffWQoxURjMymV6xWS7wtifG3EFXCFwMdEPPdG8fHwLlpGK1XjAMHUJkK4zRmomZ4GIiKShGEKUjuZ5ZMyBS5Ff+2i+xaDe8/fBDdD3CucComrC7c0hZjehc4NGmo3WO1z58i6++9idII8EHpNd45+hjRCqB1ZrnX/40e6MZTd/whWeeJdy7R+s6qtmE20c3efjwIW3bUCbD+fk5wkYumhX/wX/1X7J/fMzkznP8x//2v883v/E1lJ7x5ocnTPf2WHRr9o+f4qVPf54Y4B/8g98hBMfy9BRSYDweM7QtduiuEH8xQlFlNnLbW55++hmWyyUakNGhEahCUVdjmq6lLA2iUBSmYDQriD5Q1jXdYCnHIx6dnhFNzaP5miRAL1pWiyXjeoQYlWgD6EQQFm8TMXoSeeGjqxrrHUobQkz5MUiNLkY07Sn3Tx6yDBolAypJfCy39AubEyaVx8WOGkfo5hzqgAqGKCHIhPcto8JgbeLdd9/ipZc/zc7sNoiQo7C3lI0YHweswHYnSSmk1I9xlLZHCgjSomSPRlAWc5rQEfDE4HEJhA8goG02lAl0Ebl38X9wNv+Aw9ln+Ff+9i/w8OybIDqEGoAESWNGFwybFaNRhSgg2PwcZAFC7NB1Cq0dIoFMCoTPMeUootT0A0hVEqJEyJqq2KMwE8qq/+FfJq/1sdGPqkD+UeqTvKD5JOvHMd3vk6RPQAH9Hdoi68R2Gh4gbelXl0W0lJKUcsdMRJW3odne6fK+QhCSQGtDECC0JLgBax0eTZ8S9c4sp6SJnhc/9zLPf+ozvP36t0kxsVmtePfdd+n7nt1CMpKGFLOZwwlLCB5cIFASqx2Keox3PXLoUToTDqLfWkzYEgG8I0mBkpJ6MuXmZ36Cw5/4KQKavalksgPnqwbEmKbpoAs0zQo5iUip8SEQU6JdL+ht9imjNNbnKG5jDOtuTdM0eOeQRqAKzeA6IgFVCEptsg1BCGzvSW7rg04JU2xpDFIjhUIhSFKwI0ZMdvdYrOZcLB/RdBucGxhPakLwlLogSUM5qiFJNssFQecBspHQaJm4URb4TSC2a1btBoTCrjrcWc9kNGNn94iDTnNcz/jUT95EtY6L9Zz75/exPgBym6IoaO2ALTytHWiGPg9KhoBRms9+9mV+9os/zW//37/LuCqRKUdb0wfe+OA9JvszHl48Ymfd85/+Z/8J//3/+D/w3gcXDFGze3hIlxzSjKiLHebzJT/1Mz+DGzpWF+e4IVstgvdonTvQs9kMIfPXujCUZc2LL32WkwcPkDEyqTW2bRAicfPWbZxzJJlw3jP0HWdnZ9z74C4pRnrr2GwaLpYLDo9ucrpome3uEhGURYFWIg//JZ9jtlPEh0yd0DInT56cnHDnaJf5as2NO0+RRI6Kr8cTRuMp1agmUqHFJWpvTCKgU0cQEpQnxBLjGwbf5PFRGUhJbyktPcv1nLOzMz77uRfphp5ks5NasE0mRBLk4+RDpVS2f/jHpJ2UErWYIlMiyA5S9mRb69DlhEI5ohOYStHZlrKCneopfIqs2jm9PePOzc/z6OQBi8V7lGWZaSJbIgdJcHbxDgLo+oCRGjeAriY03QKpT9CFxV3GewoPCKIYODm7S213cAGkyeeWQCPVCESNlpMfyqXwWtf6pOmTvnvwF9WPuvt8rR+MPhEF9CUrFwkpPo4KFiLPGSWyT7IsyysSB2zJATqihULESLgsnCUZwSYTsWsQKjGbjAjrOW3nGNKIz/3iL/HCF7/I7/z2bzGVS178qS8wPdhjb08zXz6iaRasHr5PSY8sOqLINoeJrpBVRWscxgbmTnH7536Ff+lf+9t85bd/izf+3v+KW84ZTXfZrFeUQmAA4XtMBaaQlPWU6eFNPveFL+KPnkYVU0RqaOw5e4e3qMe36TZr7t89IwaL6yMu9QzDQNM0tF1OcJNSEt1AIKC0ZrnecD5f0rYtRgmW6/k2IttjlMjeWSVRCYLzWGuRWtBFnz3ZQ0uMHuct3lsGu8oEisIgtKLvWyaTEUIm6lHBpE6EQYGX9HiUswghqaZ7+fjbgWHdEbTk7IFjvoiM9S0qbdBlxUhMOJzeJHnJtN7n1sgiZOSDBx/yKz/11/ng9B7/8+9+yEDCETBIUghIY/AiMESLTxFVGToRkFry/nvv8Na3X8UUBfrGPnbdMjvY4+JszmK9pJ6NeP72McN5wxAtv/rv/jt89euv8cff+DbPfvoF3r77LlLXuA7+6k98nrff/hbT6ZjFxZKyMhkruFqwWCw4ODjg5OSEv/6LfxNnAy4ETDHi2eefR6TEq9/4KpNacv7oAW+++To3yjtMd3Z57623qeqacjLitd97nb3JjA8//BAhBDdu3WIInt/7g99n9/AOEfjUp5/jp77wOW7cuIE2gunOBJ88Ugu0NCSgqguKn/0Mn/vUEecPP2ToVtSTEdY5UvTs7d9gNNugC8NIlciQQCe8ryE5dGxA7mBM4O3XXuHZ412qcoLRHkKklBpUREYHccLh0R4eRalmxJhnCy5HGWJKqP+XvTeLtew8z/Sef1rDHs98To0cRIoSRZNqDZZtOVJiy/AAt412I7lI3wQOOkCAnhAgyIUB90WCBEFuEyTORQ9uGDaShjuGHTtwdytqS7ItmxZnssgqsoo11xn3tPYa/ikX/64j2VF7kC2bNM8LFFhVrDqn9jlrrf393/d+z2s8ArlCTyaM42nc9+r+diuSjA8iBQ+ReOkudkhnESi62oJQ2Nrj4j5RQJ4JesWYtltQlGUK9BFLlPIoBdEVCBVxWLSBLK/pFqC0wMkZImj2dr8LTc7+0avUNRgD3kd6PcvFrc+QlZ9gOvkyaInRAZNbTF6jMokuzpIIz3Smb6UPYvEMcPx3nv62i+i/iFCVM/vGd0bv8QI6nP7s4ag3xpiWBUms2od/QggSnk7r5DV++OdX7NYYwMaAlyHhr2yHMBmF8IwHBee2hnz4Y+f5P//lr+Oi4rM/9rc4so69J56h19xk+NzT0CzZP7iFjh0DJen1c0RwGG8RBIQX9HKByTKUXidvl5TlZX7gx/8zzn3ku9m7dsCb/CYb45JsvU/s3mAoQcUAQpLncdW5dNy/fZtf/7Vf5ad/9n+iFRIfDHl/HWKf+WTKaDCg98QWdecRMiPKkFjPwaV0uxDQWUoBfOgxbbqOxqXYaG8di9kcbRTroyGlSVaBpq5wNjG1817GdD7h7RvvpKadjPiYFsZ8DEidY6RKXm4pyTKDcw4pkl0lzzQDXSKlxpqEs9MqQ5U9Nje3E6lBKNroGYiMql6iMo23LUZp2mWNjRC04vrhfV68cY0Hh/c5PNznyE44WJywKARV55FCki07BkGylhWUox718ZLhqM87N9/l0uY6RWb40PlzHBwccOfBfY6vTVgfjXn3jQN6KuPipUf5vd/6Gp//qb9FMdDcuXufnX6P5z79STYvPkbrLJefvMjvfu0l5pMJR4f3OX9ulzt373L+8kUA7t+9Q9XUbO5s0zQN5y5e4I0rVxBC0dnA5vYur77+elpU9S3rA4VWAustRyeH2MEYKSV1s+TuvTv8B5/9Pg7u3WcxnXAyndG1DU215KlHH+XOwQmvvXid117+A3xbI73lU5/+JGWZr7rdGcHbdMiMnq2NPpd2RryKY3JyuLLYSEwmMXnJcH2DKNIhJJM5ta+S71dKxPI2wV8j2o6+eJet0TP4CEEM8F5hCISw4Nq1BzzyyGM0ncMhcT5NR+AhWSNFbEtKhIhpcRWJXHXM0+Ztunel0KvwHtDCoQEjSxQa1UV8l8gcZEOQHsQSbcB7yZuvH7A2/BTr6+vcP/pNzu2kDxssEFaBJzGj7TzRCbR04J5GtXus7dbU3ZSqOiJwQlmMsW5BdBcYjdd48+pb7J7bJc8gqkhdLyjzkswM0bKkLN/jj9UzfUdln3+TjV94/QNbLD7Un7bb/EHwP/959O0Wv2dF83de76MnfVh5oDntUP1RPfRFP7RyBA8hCmLwSDTSaKSMCOEp8xyVFwx0R3M/p1fk7F3YJdOSUHsePHjAflVTVRV2cgh37jKbHiRvr/eIAEokHy96lSYXPdF3EA1q9eYvIkQboQ187atfxXvL3t4e5caQ+ugt+kYiVoQELT0iBmL0CBzONggcg+GYxjriipmbZ32UDOAsg0wj0CglEHnGQ87urFpQTWcJDSYlxhhGJmdvYwOA+XzO7jDRGLztoHNoItuDMSov8N7TdDXl1g7n9/Yo+7309VMKoRVtZxHkK7qBoOs6lFJkmU4FdYQsyxAupRx22uBbh4ySykekXBXbKKTK0KYg9wJvHddeeQstBQTPfj3hpbfe4F///m9zzBKL4/zFc0zsgjo2BBOQApSL5EGwkfcZZyX7+/sMZI+6avDOUnjH1njMu+/cTD7vIMmLPhQloVrSRlA28u71W9BFZFZw/uJFll1D9Eu2tjc4ODpiY2cbEV9gb2+H0WDMtSuv8/aNt9nZ2SHPc87t7TExmv6gpK4Nk8mEcxcu0TQdSho2NkbsbKwzGg8Q3nLz6us0yxlba+tsrq2zXDaMB0MOT47Y2tjkyuuvMyz79Pt9rl9/l929Pfq9Htevvcloc4+N0RAXLOfP7TI52uer/+5LfP7zn6fIM7p6Sb/M8Xic9wzLHicnJ6xvbuO9pygKrHfYpkYZTX8wwsdAbgyuSUE2keRdnp68gw+3iN4xOZpxqzA8/qGniFrS1AGNJviczY09pCooewLrBawWINO94IBE5JAq/0P3KyIilTq9l2OMEBJhXJHQejJGogchJXmhiTLgg2AZO4iRGCF48A6KokeRlwx6W4yG68DJKi7dIGQkCoUWBZcuP8XtO9cx+ZwYNnn82b/JCy/8L0i5QOBQylEtWnTex8hLdK3jwsWS4TDDOoihRSkDTqckQq/QKvuOPQXP9P7Q8d95Gvv8m6d+6G/++QdFZ8XzX5zez9He7+d/+5+k93QBnd4+E3/1m5eNUjx1TFHVq7TfFNyQ+FRKqbQwlRn6+QBlDJkqUXkBKsUHB+dBKLSbIPIMqUD1M/o9Q9M5/tn//D+yfv4ihQo8tQb3X3+dO4f3kLpPjCfk0ZMRiCZQ+YiQGqkhxCXBRkTMMEbg/ZIv/9I/5ef++/+Knu7ouwlHxy19mzMoAn0FAokQGS42CCFYNg1ZLxXRwtbk5Q75+hCQQA4+cO/aGywnN9hb30bEjMyZt/BuAAAgAElEQVQr6uWSqqlZNjUuWNyyxcW0xIYPOBdoXYsLns651EWWkrquAE79u0JmqbCOLi1W1gvyvKTturTgpRW2abHW4pyjKArkw3hsk51GgqfMOYkyGXJQImwkdoGYF2Smx+Z4Hdt5Du2SanZECIFCGTIkSkSatsL3I/P2kIPFLSoJRb/HOw9uMNgbE2IkC5AvA2ORcbm3TtkExPGS2mpm8zmhczx+/gK/+8prXN7e5ULU3Ds5ZPfCZfprJXceHJOhCJ3nsd0dXnrhGi/9u6/z3A//ELPbd7lz/yaPPPYUg17Jl770Et/9vZ9mcrRPng3oyZxzm+sY+SiuXpBFS2g7ekpi53MKI+kpyFxHcA2DHrzz6u8Rg2I8HjM7PmJ+fMDxwQGf+tSnaI7mVNWC2WzGzu4u88WUrY1NmkXFqNfnk889i43wyeeew9cLaOd01Yxy2Gdva43HL5/n/v37/Jtf/w26pqXXL3j22acpeiWPXL7Eo+cvkheSqprzpS/+G3xIXd4s14hWsLG9Q56VBBdQ0YBcpVaGmvnhi5Tx6zS2Ry6eZnfnMr/4C7/I9jnJx5/7NLm5SIw5WvVYX9skComNkdZ6+sNRKqBJHg6FwEe5wka60/td6XT/PlwuNCGsdgRASIeOipx1pguDEwaZq7SN6BpkBBULXMiAjNGaJh/e5o237/Hxj3+CycnLEFuk1CiZE0LJ2vqTvH2lI+t/BM+bKD3g7tu3UHnH5Z1PUs1r7h9+ldFog+Vii0cf+W4Oli8Sw4I8F/yDv/eTXLla0LYtD/ZP0CKnLPuIOP/LeDye6X2gD2qB+Cd1nz+IX5eHmMBvx8rx17UAfb/rPV1Aw6o79XC3yAdQqyBgKZHym00eSYPBkEcffRwpE98WJbHW42ykcy0xBEL0hKbBti33717DPbhPEfq8ea1BK08uHRd6Crk8INqG+dKTuSf5yKXHeC1/kXoyTR0zX4NKPk4bHVpIvHNYHwi+hgKUXKKmVzknDugWJ9BOKMfbaD/DGA8hoesyU+C8x8VALx8xsR0bO31EsKtACQOUaakRR10dUxSO0OzTywYINMOepF8a5o3l6GROUTqqukYEsUotbjHCImWgCy0hBrrOEUWkCzHFfBuIziJkxHuPdR6Jp2vm6d8awS39Kga7weiIig4dc0SQuLoiKo0xGVJGiBIbLG5ZkwdFdB5Jg3MNB9VxWvZUkZ5tQAqW8xnCFMybBqFgXjeMTMny4ISsyFGdRw8N3WyOj4FSCi7JPpfHW8TDGU/sXqaqlwQXCYUnR9IvCnZ3zuFj4M7dfdSg5P7BMXnTcmiXmJjQg69ffReT9/mN//s3+We//Cs897Fn+NwPfh/DtTH/28/9r3zhCz9APZ/x+suvUJger7Uv0tRTrG159PJj3Lt3h7qq6JfFykoU2N7e5q2XX6Kq5sxPDnnq6Y8iMFSHGbOTKa5pGPZKXnvhJbK8wMXAdDnn5OiQEALz6Ql22Zz6g3WeYUcDjNC8/Pu/TWcdjz39UQKRej5jUOSYvR2wkStXrvB71e+RF5JXX+jxxd/8DT7z/Z9hNBrwsY88y/RwiswNb8+vc7SYMakiN+68mZIkW4EwS1pbIPycTzxywKA+oXJL3rz7FldvDNjbe5qnHy+oJwuO3A3qJpCpwO077xAQlIM1VF4ihCLEyMO5kSLxmCEd2tJ9/o17OK7wiiE2K9uHQEqLQdM3jrodIf2Stna41iUEZFBINJPFDJAMx5dp25LNzT5Xr+7TVhpCitn2bY+uUyzrCWVxDlU0iKxEixsU/bugKl68+xWiF4homBzNkDLj4PgXqG2krw1vvjpjffRJtjb7CCHY2NpEmg4flmQPX9yZznSmM/0R/Um87T+Lmrtf/lOTOP6qOsF/nYv/93wBHYinnWWUghCRSuKTowNiisC2HiazOZvbu8wWCwDy3jfwWJPJhPn9fZSAanJCfTyhnk6Z7V9HVA9Y720TmsQxjlhUYylVitau65rr794gBJfQZMbgnKfzAWMUwjq0VETrUyS2SsxpWhDC4sQUazsUkvVhwUC7xIEWEhc7kGBMRuUEup9jW5OSE71NY++4hKBXOLEIocYGS7Ce3bUhsVmiilFKehMp/S2TGnxEofEi4oNHKI3vGnzwBDzO+dRRdhEFRK2IxkAQ6fM7SwgBKxtkiNhuCcGmg4vr6OxqJC8FnfVEH2BFewgBtDFEzGoBLGJ9snD4zqPzSBQC6yzBR1IjcpU82DYQLc57hBHMq3nC/SlD13UEL4mdJQhwSjA2BaOo6W9s4xZLpicThr0+oyyjpzX9TOPmnmAdx82S7/rIkxzMTyCD0gnq5RKEYrA+ZnN3j861qGrB13/nt3n1yhv8zH/3j/nMZ78Hi2N7bZN7t27zoUc/zHw6wYcOJSJvvPwCy+WSfr9PNZvQ6/Xouoarb14hhMDm5iZbm5u8e+0GWZE4wVJo7t65w8bGBhvjNYSS3Lp1i50Lu5wcHzMaDIk+HQRt13Hu3C7zasH06D5mMaeeHtEbreHbBkSy1hS5ocgNMQryQcF4Y5PnnnkMGQKf/NRnuPzMU1TVjH/7f/0Szz37NJ1QbG5usmEblBryPd/9YTrRoSII2xH0kOAblu/+H5j5LdqoGFx+hktP/AiZ0tx7+8tsbpZcPr+HMcn6czyZMByvkRcDHMlWIY0+nSIppQjW/WGMnfwGgQP4xhRDpD2H4BdoIqUc8uDBeba2xoyHI3zb0HW3qVuPlltstwapcu7e22fQ2+X2rTs8+liffGcnoReFI7gC/BpRFnRYYoQs/yjBZvh4QqQEF1FC471nVjUI6YlSsJtvU+gWqda5d+L47Gc/y+//wfNsDdaImWK2XBCC+c4+FM90pjOd6Ux/5XpvF9DyIes5pjjpsFoyignpHCMsq4rj4+MU8oCgaTps2wJQZDnOdVRNQ1koDu/dYnpyzJ2334bFEuEtmV+SseDwAD70Q5/irddfxd65C8IS4xwhNJ1omXdL7t+9RTerEE6wqAOj4RqCjhi6hBjQBccddF3D1mhMFjq0W7A4eoee83jbMOjndNLThUjuPd5bFAqjoFeU1KFCZwZJoCg0NniKVW0RRUSQjJ69wZD9O0fMVM16IYmxQ5g+goC2KSgihED031i8tC4S1ICOgDcObeSKopFT6JyoNLHMUcqgVgHMNniaZkmwDlkvcM2c6B1NvcTZOiU3dimhMUqBVBojCnAKIQ1RnnID6boUmhFsx2w2O/Vmex9xXbKDLKs5LniEiOxcPM9rb7zKV/7g99AqW/loJTQWq32aLigoYsR3FZt7Fzg5usdmXtJTipERlFqwPe6zf+2EZVXz6KXz1McTouuIrWPNRR4frTGfzzlZVty89hYf+/R38/f/0c/yYP+Qjz5xGWmn9EVFrBf04wZPnt+hCB35sGCxdDz+oUdZLBuuX3+bw8NDtjY2mE9n5HnO45cvJWygc9x+9yaD/gjvI4vFgtFohNaSk8MDmmqBloqizLi4t807V65wdO8eZVkma0tRcHJyQq9XcLD/AIGkP1pjkBnCYkGIiT7TWceirdFFj57JofFMj47oFguWH32aabXAecuDg31UeAalNU3TpXAhaxGuoTEOEwLaR9rgIHpM1mexmOBQ+F4gCE2Qis3NLWKseemll1DC0Ov1mFZLrL3GeGMbpEKbHJXlFEWRiBtCoPiGJUtr/Y1DMqmYFkKcUjuQAaEcXYhoKzFqRNfd4+79m0Q3J/p2NQlSPP/7dxDkPP7kE8yO5+Qyo553+FBDbCiVQHc5zazjaP8uOxd2iMpSlvDO4WuMxiWZVPiwJAiP9xEpFIElMXi6riVXBSGUDIYZNgTu3LnDs888wps3rtC6FuHe24/VM33n9Mcl+n2QEG4flNf57eqvsy/4g6T3/JNeiARuFYhk4TCGerlk2bR4n5LNjFFY60lD3LS4t1xUnCwPUhy499QyMLn/gGa5oKumqLYjVxIVA13nOT445tb1G3zi2Y/xpaP7VG2LdQEfdGLiOscj5y7w2/s3+eGf/gd85OOf5Lee/zp333gJrn6dwlYc3blGlmvW+z2MqNHO07URliBspG8Uqu2QymNWy30pgjziu3rV3Y2ILKB1oLdWEJUAJ8DElDUtAsiCS089y6WLl/mDL/4Kb80OkFoxGm+SZRlKKerW4kLEh0AIqYsnpCaQgfDgPDEIhMzxpN8PURKcQYnstDtoncSFgoDFKWhiwGNpQ0BLQRTxFCsYXMDbiPEOKQOqDUgVVx3pQNd14JOfXSqILrKs54QQsXWLcw7nHK1taYPjN778RU58Q2VbGm/phVTWRwexTpg27z2KQL2c89HPPsn/+/Ib9PKc9a11tsocFaE7PuKpSxfoDfp4H4k+cry/T7OsWB9u8eBwwsb2Oe7sP+A/+em/yxd+4se5u5ix+9g5gooslx3aDHAh8PKVm1y/dci5cxe4efMmUin2Z6/wQ1/4HE1XM1ob463DdZa2bTk+OKbIDb0sR/RHRKkI3lOWJW2bYq5NUSRLQ/QsZlOuvfkGP/kTP8b+vfu88eZVxuPxKVLw/PnzhBCYTiY0rYVmSYgR23ZYQAyGGJMTukAPgekc9dGE9bUBk8P7nLeXENEli4mQyCgwSuMJyBCI0uNdQCuFIxCkAg/BpiRElCbP+xAFXZcSC9s6cPGRR8mEPl14nc5m7J47j1AZEUnTufT3IUW7h7RY6KynXR14nVvFta8sHLkEISJeR6SMKKFwAbw1VG2LEmmqYOOQpp0jdU2v2GRtvANOYO0UYyTRKWYTRYw9pk3O0Vtj+nKXc8NP0FxdcLi4j7k4Qe0I6vqIINdxoQZhk0dMmnStR5BywrItGQwv4m3G/skB0+mMosh48esvEEVgPNz4y31IfgD1fixG/+hi4Zk+uPqLtHGc6a9O7+kC+uF4N0li6waDplku8T6CTElv3kfyPCe4SDVf0C1rvLVEH+gVJU29ZP/uPYyQNM5ilCTKiJICfCrSW2vZv3/Ax5/5KKP+iMnhEUXeo+ksucmYncxZH4/or29x8SPfhX7sI5yrDVKWXL1zEzcLeB/YGpQgAkpIYgxY61i2gjzrIxQ4u6SIIGMkxPS5Ban7JmJEkGwqRWkwhV6RCiQgTz2kKSMZKNfo9cdMF4cIIajriuVyQQjgo0AoTRCa2iafqQ8CR0gsjxjSaB2BDw5vwQtJ21mgwvuIDT4dIrxPLF0i3XKZ0ti8xzl72uEGTmPBvXeEILC2WwVQrAI0fCrafbBkWuNjireOIdDWzWmBVTV1oo5IQbNsaX2HNuaUBy4iSC9SCl8QyBCRPrKczsmUJpMCEyPzkwkyRObTE1CwLbbZ2tyhMAV7m2OWVcM7N+7x7Pd+H4eLms3+gB/+iZ9k4Tt2tzfJ84zlbIoqhlw6f5kQBYcPZvyj//pnaOuOGzevc+/BXY6P9jk8PEQZTa834Pj4eHVYaTBGUzcNbeco+j2sd3ib4uSFksggMVlGmeXkmSaGDqMED+7epSxLtjbW6A/HKKXQmWK5XHLu3DmOHuyTK4O1llBXZAiC98RKIArASHQIaBkplaIQMVk+RKB2DiVBaJWCPleLuULKFONtVKobVwmfUQoill6/YGk1s2nF5V6ftl5graXsDxmurSNcChWxNll/utYS1WrZV5kUZgKEqBBREYMg5nF1EF4Ft3if9gKdQ/p6FXdvkFoSfcD6FqMUWpYoYD5vyftrRJGh9Rq7uz36vTHHx7cZDBQieqpqgReBLO8xO5EM8g8xynZoF4KyKLi4tcVrt77C+mCA6XX4GIGUZhrTBZ5SQ1EppTBK2sYSouHw8PB0kjKdTulcixb5d+JxeKb3uB7GWZ/pTGf6YOg9XUCn7rM9tXAcHx8zHAa6pkXpDO8DSmQoGbCN5ejgiHpRo4VkbW2bar7g+HCfm+++w9bGAFxL8B3BtYTYYglECbLoY23NS2/eBtNn55EPc/PeEQsPea/P/YMJsnBMlnM2Bnv8N3//H0J/SJ31MbZl004p3ZKtvUeYsliNqAtqu6AmI79wgagM08WE4D1GKggprjgoBUrihKAjRWkLEVlbzxkO0oLiN7B9gUhAyAx8JHYdxWjM/v3IKNOUZXrj7lxKoAtojM6p2orOWbwPNIsFUkq2N7aJSoJUHM8qbt+7T+MibeAUO5bnJdKkaHQlDUFGpkcHROcJ0eJil/y51iamc4xkWiKlwGiNFuBtWOHtstVCaCRGj3MtDofrlikaXEQkEeccITqiDhzOj6mjJxiFNDolMIeAEAoZHaENEANGGIpccuX1N8lNhrcdZVkyPVhQZAahFFs7m0SpUEYTRMBlOS4zGKX52//DzxKXlrn3PJhNKDIDdcdi0jH3sGgt/a0LTCZT7h2ccPvOPYosZ3t7k7pdMByUxLpCipxa1gxHkfWNbabTKYf7Dxjt7FIWPabTKeP1LS5dukRd1/R6PR65dJFBv8+tGzcI3rKYHEE7ZdDPqaqKCxfOM5nPGY3HHBwcMJ1OGfX6FFKBD9i2QTiHEQLbdphyCF4ickkWIstmghkLJB1ro5yTF59PgUB5ThBpouNjQApFJHByco+4toMQhlxJnAfhPSYLLGwLZIzXd7Eu4EIkL3LGa0NskOlACrTdMnWmXbInuZAWTa1NASNKKSICViQOIRVydVBUZjXNCAEZc7TWtM7QuJqyr8nDEb5tEZ2gqcC7Hvf2D+iVAyIVo1GP4A7Z2hRkmWVRTdje6hG7CcG12HiejfUnWEym3Lt+i92tPjo3PPP4j3J9/9/S2z1E9I/RWULRNe2S6Cu00AgMvu6ouwqdW2aTQFVVXL54iaZepqTExuLtWZDKX4a+VRf6WxWx326374zlfKYznemP03u6gAaFFBqpUiesLIo05vWBIDxaZ0QnuPXuu9R1zcnRMaPhAN9ZmsWMMi/ZWB/x2GPfy9U3X0UoSdnrsXVum2IVp91WLUdHU4KCK/snvPGbX6GtZvR7RSoOXY2WhoOb+8kuIPeRBPx0Tl8JjDRYmeFEwTKMCGoL7y2ha/FIVN/Q+oBdThCuZUcZpq2iLIdkXUUmNU0UdAhaodMSXhB8aGeE9RXNbEI+atBlSSQiUeAjSI3Qgf5oA1RJ0zl6g7S8pGUgOJBRovOMwUiu7BGeQksIEaNF6rSJRNTY2N6ibhyNjzR1TbAdpkg0CYTCGIPWknoxJ5hA5w3CaZQMKO0wqw6xVhIVHSJ6ovcgAzJ4VAzIlICDEKmIlqFDxgZiwAWFTBE42GDxIlK1S3yvJMSI0RpvLUJJolil2kWRCuoARhpab9FG0XaBhY1YqbHWYaWkt7HN0ckxndbIEOgGG3zuR36UC49cpp4c0jQNeZ6ThQq7CPTGYwKei7sbPNg/oj64A8saTu7Q3n+DqA1ZrvjwZj+F3Iye4He/9vt87j/8AlleMF9W3Htwj8GgR69fMD2ZsLW+Rb1sT7uty+WStutg6SiH67iuTf73usQFz7kL52nqDpVlTKdT+lmB7HmaRVqoq+uGAkWwDf2ypKFF2hahMpTO8UDZ1wykRC3mhNsLTqb73Aqe8489hg0WkYH3GoFAKUkMTbLaRDClSVHfOtI2xyglaBqLETm2i6n7bDQuSLxIQSgi+NPlPylTiE9E4lxIS7BCEF1AnDKfJaus7BWRQ6wivjWdM3ReULeOsldgbQ1+iVIdulhSqBZUQ2kERikEhhjuUMc5QgmcF/RLsG6Brjwlm8hqD721jtcVn/j8DxKtTamatWfEJ2mOXmPUT0uZWZbTtQeEOFst6Ebiquivl5ZBbxdjpshemSY6LiJC/EN+7jP9+fQnFb9/FIf2Fzkaf2i5+ON0Zsc40/tBf1pSx5n+bHpvF9DftKUf4+oNVoRkFYgQnGcxrTjcPyJGT2Y0i9mE3GSYIiOGhratmFf3uX7zOsu2RmtFlpcs6zld23JyPAXUqitmiDhMPqSzjhA0Js+RUSBUZJBpptZD8Cg8Gckv6okIocmiSW/IFpwXKNWjRTLcuUCMkfu33sHq1JFrbYCoaQJIH1iqgNA5IQo2R6PkUw4dbbXAdg0akr0DUs2x2qY0eQFCp1HzKho5hoCUAe9WiW5SopQmCp280DEiTY51HiE1TnaIrEThMUES0DjjyPIcqZONxBiTwmlUnljaMiUgeh/QZkXciBEjJSJ0RGdxsUHK5Gf1CPSqsBDIU3yZjBCFSN5uIlKs0GZKYL0DGfEuMDQZVded2jjCqkjDp+shCEljO3pSEZXCC4EXGoTEC08+HBKmUzoX2N3exI42uHDhItW8wsfAaDDg3RvXOX/+PJPZCXVX0R/1Ody/S5n1iM6SK/DdggvnNmiqJcNBj6PjI2ZLi5hEPvWpTyGF4tXXr7CxtUnZ67NsGjpn6Q/6HB4eUhZ9lm2bFuqyDO996tTqxNFeVpa66Sj6PZTJqacLZExfJ+89SohEOwkRSSCGiJEKESNaaqSQOO/A+4SAQ6IQKB/J2i5NYPBof4lAQBFxMWBWaYERh2T1PSF9HiEDBIsQ30gITPfkHyZnPFQIIR28VtdeCBBFeHhL45HJ2bP6GFLqFcYu+YwhrvjuyaZTDvoo0SXeeRgSXIHUOSiPMKebhkjAhhopO0QUp111gcfkOzTVmM7nadJT5Bx2DhGgLHtUswMQObHrE7seXnZsbm0wmz1YXbOeVYpL+rWQGF0gxQwhJU2dQpcWyyVSfuugpzP98Xo/+kIfFthnhfSZzvTB03u6gA4RrE1vkEIEogtIDUrIhKWbVdx85xZroz7LuibPJOfOb3L39i2u3r7C0dEBdVvjQ02vNyBTmuADyhSMyoyu63j0yY9wfDRHusR+fuetK9iuQUXP5z73eW7dusXVt6/yIz/0AywXM772/NdYTucQkk0jZIJOeqTskCyQTUOMAlmsMa0aLl5+hL/7M/8ty9ryL/7J/8706vMQa7yPnESDKHugINIRRcbOufP8+E/9KM9/9RcRuce1NT40oCXExBYWIuBsi5Yw6I/p9zaoZ/v4IFfJgxrhHVIn/6lSGhcsUUqiLpERAivcnJBYCV10tEIhdYEqC6LvkKsxNkLghEAKSSdM8uOaxA4MAYTSKJOQX0RHsAovFUGAUBLvOlwQaJGKbClkSoPzioBO0d8igBRE5wjW4ZEEJRAxeWR7Rcl0OkkHBC2RUaKQKFLhWWY9mm5JC8i85KSqWBsO6JoW7zy1dezunWdne5PFZEpe1vj5CYPeAK8MTdPx2KNP8MorL5EXGYVd8tLLz/OhJ58miIJMlzgbUUXJS1feYjwc8eo7NxiNd8iLEVvb67x98x2Chzfffpu9vT3atkXJSHAdo2Gfk+MpZZ5zdDKl7PfY3d5jfX2dqqroFzkxOBaLiqpqGW/tMV80ZHmfgwf3UUKiRCqYRfRIAkal60EaiQtpPiFlIlpImXZPJQpsAOvRuaX0qWyWOkvhnp2jN+qhiQQXaZZTVO8cSicPP8FB7Ah2lrjegJAGFwM+BqJIdqGoJawi3ENwZEV/Nb2QKfgI+YcK7sS1Vqtl1XT4Cyt7zuqiIyBwIVCEyLBXUpohiwcTRMhQqo9WGlFI2maGjGpVnQe0kKtF1bS4qFWfd+9sQfcoF89/Bq0HaG2IxZiDgwo9GHC8eJuPPX6Z2UTyzusHfOz7dzjav0fbdRQCVFqXQEjSa3QKXJEOVqbHcNjn53/+52mtZVbN/tKeke93vR+L5m+lP0swyPtxAfJMZzrT/1/v6QL6YdKdEAItFdPpFKkzJpMZh0cntG3LsF9wdHif9XGfS+d3qKoT1tdzIn3q9gih0tJSDC7RCYIiRo8PCd/2YP+AH/7Rn8RWM774yz8P2tPLMwpl+NJXv0TXNRRFxq//618h+BZdlJx75BGkF9x6+12a5QyVe7wIxCCJbbIyNM0RuTbs377GP/6H/yW6N6JrFqyVJTUGadLiWCsUXgZUHtH5mGzvHL/2W19lKwtkCiQO62pwliCTf5To0TKAjBiTszbeYn68T9d6pFDIFYs5pZ8nrykk64aWEhlABQjO4YUgdIljnQmB8zF9DqFPiyGIiJh+XwRPlOCDTyxBCUJLMAqpBcFHfBBYHwkiRWb7qPBB41uIwZPp1B0M0eCFQimZCugQaZcNzjnqEJBK4WPieHvrVoURidGLQBKxdcfGcB3d2VXKXCrYfYyU/SFKGqztkFESgqdZ1nRNjQgBWRT4LPLmjdd56qmnCLbm6Y89iTIZqIzx9iWkTPSSugmovOBkuWTt/HmCh0f2HuPSY0+zNhrwy//yX+B9ZDqd8n3f+33cvXWXUkvaakFwjrBYUArwXcPFvW3qtsG2FQcPlqmItDmClAw53NjmweEJSmqidyhlyI2imbUoAdGFtHDoA0V/SKEK2qpisNknyzKWzrK0ARmAoAleE9PYgBg7lEkQOSMNRkqaJnG+tRRUixnjbYFaTQVEDCgcxIZAahtnWbYiyASQ6dchy8F1iGhXHejUqQ0hIJQmRk6ju4HUUsevzBsSiGj0qqAGiGTZgM3BiEfPP8qLX/sih80RA3UXU97A2jsQJSozqGwKoUmcZ5P45iIoYkwLxoQ1+vK72F5/hjsv3CA2Nf3RGmJseOqxZ6iqik1X8u7z7+IUfNcz/zmT/a8g+kukVOB7+LBM1z0KIUVKPlQ5iPRaiyLj3r37eAF5ccaB/tPor0vx/GfVB/V1n+nPpzP03Z9P3wkby3varPcwBEWtkG+28zRVw9HhCW1tkWi6zjLq52xvrXN4eBvl58gwJdcdw55ga80wyBVGOKJviL6GaInOISKUZcmFc7vcuPEORZkl9ICUND75bZXRKQBESrTW6KJPMRgiix4dUPR6CKmIQoHOIS+JeR/T7+FEJErPuKcpZcf6IAMViMYgyxKyjP5wwHh9jazXp3GOF155nVeuXMG6hH2z1qZWPBF1ameJKZ1QCKJQKFPgAq8vQVYAACAASURBVKdWCU9cxZunLp6UcuVJ1SkaOcbTkToxJhvFyi6TQk8CQqRlRolAnY6tkz/1mzMvggAfAz56XPCEGElxMulHRIIwIEz6twmNTbX96W6kEGJlGXj4GiLWp66kiCClSrZvtTocxFV3NEai86zvrmNjQznIiUIiMIggaG2HEz7ZUKLi+PAIKTXb2+c4ODjABo8yBrOyn7jo8THQti2z2QKBomkamrpGK4F1LWXZ5yNPP4MpSu7f2+fmjRv80i/9Enme07Y1bdvyld/6EtPJIcF1zGcT+r0eIkQyk7qrdV0z7A+YHB+la0oqvLdMJhOKoiDLctrOrsJYOvTKRuOtS15fIXA2kOclMQrWtrbxWhGkpCxLhv3BKc5QSoEWEoLAr1DqSmgynQ4bBEFwPoUEAdFb5MqHnLQiwIiOGBJmEKmIIhCCQ8q0EKhIkw5iosUoIU857kR5ypBJ11A8vZbgocVIEXlI40j/30jBfDohxsjOxibCO+7eeouuOkCqFqEaRGwRWKJYEuISIS0xuhVLWqbJle/RN2NcDe28xjeWw9t3iMuW+w/ucLj/gPmDE2g9whvmJ56T/Tmucav7JcOtHPopktykr0voiD51zY1UKBnJpExe/zP9sTorIv/66YxCcqYPmt7THeiwKuSCC/R7JTffvc/JdMFguElbV2hjyHPB57/3WczOGDe7hT+5imsMLow5uaCpjw9wdo35oqPpLD4EFo1lMveY3DCZN/ziP/85ZAyYsofKC1zbrXBcDq0N3jUYmdEsa2IruXbzNkZp1i/vpRFxprh37x5rG+sEkbrEUmhE9IyGfYiOfpFjipyNrU2cj8wWFQ8ePKCaHNDahlnX0bXQeUM+clhfJKuDUPw//+rXsM2v8p/+F38PvbMHQoOIeJ86yKPNPTrvqW0HnVoFU2j8qigWq48jBKlLLiKtiKcFVQyaIAReCYS3qXjRqYv9MAhFymQNUcok1JgHF9N0ICsMwT30uAqcg+A1PmTpEwD+4ceKid4QQ0iowUDqJIeAjtBaixOSSbVEKIP0EhfBS0M5GjI5mpDJAU4ekXUCYRRm2/Hk+fNUB4G3Xz9C2siwL7h5vM9ofYMgFL4WbK0N2T1/jjdfu4rOIQuWw9t32RhsoWUJ0hNVYovnIuBiS2EyYhTEYFEhMJ9M+fJv/Tbj4Rq4yPVXX6QnBM9+9DmuXr3KRx9/giuvv0Ev1yzmE7QW+OiYVHOKomDY7zOvapRSDAajNGXpLFmWsbO9x71798i0pl/26GxLplWyV3iLlJLO+dQBzXrgYLy2wcb5i1y5fROtBGs+UsgCaQI+tIgYiTKSIyFItMywPkBn8T4dQp3rGPUzZseHlKYkN6nzH4JDhD4+zNF6xrJbIuwApCTSEn2LVhnIDOkVUimiDGgBWme00ZDpiEVjgseLla0ngRRXxahMkxJWdpNkwF6xoJdkmeKtN17k+NoVLl/e4nzvCR7s/yqqtyCKgPQZEpVCgzR4HxFapORAV5IVHbYaMh7tcHJzidYZe09/jL4KODXmeNyx9egu+XgTk0u80Fx79yq9CzGx4stIGwQu5ijRpt0CP8R3Bbhj6Bw6gLAVPe1AK6wt/+oemmc601+Rzmwp31n9+7rP3yrO+6xT/SfrzxKD/u/Te7qAVtqgfOLQvvLym+TlgNFog0XdMhwPaNoWMg1rY1oRcBt9isE2uhmgdMGYHn4yYf7gLgd3b9O26Y12rdFsjkoimscfuYyPGlP2iGWB0oIYBJlRGCWoqwrXVuzfvU1RFMyns7TMpRRKKEDS7/fprGc4HrFYLMkyTdu2bG9u8OJLX2c8HNJ1HXfv3eb20XW8j2RZxsnxYkXrANA41xKioq0dMmTgDVVV80//+T/hscsf5gd/6j/mws42iLiKFU8s7N6gpGka2kKd4uTyPI3DffCIaIkupaoJqRJNIEQiITGjEWmdLMZk4dCGQCqwvYs8bEbqTOOjxIXUW/Z+tQi4stlA6tB5l5b8AqmAl6SlqxgjRAhR0jQdTV2hsKyvj0EqfGepqhYnAnXdwsOuZYgoKSl0n0pUxGBBZDgcNkSWTNja3ODz/9GP8eS5v8Errz3PtHqLo+Mx717f59bx73J3/hqbW2u8dec6lx7f4erhMbUQbF2+xP7hEW1Xrw4eIn0vQiRaT0oRAWcdMhNUTcVsPuX2zTuc297j5GjCzu4uv/M7v4MErr/9NrZtMFrT7/cYjNe5fuNtRqMRi9mc4CLaZHRty927d3nqqacwgzGTyTF37txha2uLxWyG1ppFNSeXEtt1BG9TrL0AUAgfyIocT6Q/GlO1HUUR6ApFriUqaPxyCdKkCPcYiM5DkQ5Ewdm0DJsZdjc2eefaG7TVgrK3lYrP1XKmiA0uVCA8ctVBd84hnEuoOZm+r3G1rqdXEyOtNU4oBD4xpsXDHyRP9MrS8c3WjnTYS6YOIQTYQHCe6KaMhi3B7mOUhtBnUELrlojQptfFw7hOCQSc8xgRsQ501jKZZpj1HTYfOeGtO3cZDg0femobKk/RH/Dy9Zd46m98glwGvNB0LlDIdL3bUCOVB5Ex6u2xXGqqNhKtoy8iRdfg9+9Q+5Ys06ydf/Q7+Vg805nOdKYz/QXo9KAhv71S+D1dQEcXcF3k8PCI1gayIgVJ9AdjOmdRmaIYluhegccRMQijcF1AKQH5GKV6jPsbNGaMWszQ0jCbLnjykQ/zyqtv8Inv/wIq72OG41XSoUxvwt4mMoSAu1ffYFpBlueILuCWM8Yb63SN5f79fT7+6e/hhRdeZLyxy/qW5sGDe+hMkPWGtE4yayxbW9voyRSLxLklIsRUvPpkV/BAZgokGu8WOBtPCROPXH6MCxcuMF/McL5DK5VGyyvsm1IK59LClPd+5V1Of9eHiEQSo08LhQhCJBksYiSEhOYKYmW9CKSCZOU9jvEbWLJUGEPwAOmgEQXpvw+/aSIVzZ546tEIPCyOIiKm0A7nPPb/Y+9NYyxLz/u+37uc5W5V1dV7T0/3TM9SQ3KoIUVSomTJjpzFNhJFkgHHEYLYSeAEAYLAST4GsT4lQAAlnxIBCRAFUBzbSBwkQWLZiRNYskWKpmyRnOGQnJrhzPT0Nt1d693O8m5PPrynqodGJI8gizMy6w8Uarl1q07dc++p533e//P7+xM/NYSY+d69C0hl8UNH+0RaoLQVhTb4FBHJ3W7RimQDqk5U05q7771DPSp4b2+fn/uz/yaL48hrX7pM+/BVxNQkLvPeoz1kNOKwcTD2dC5wcTQmuh5OrQUGZUArlQtGk7vwZVly/vx5tjcvsPutN/B9IMTIp195mdu3b3Pz6Rt85zvfYTqdEkLg+PiYUTUmuMj1q9c5ODrG+8gYw3PPPs+7777H5cuXcS4wnk5Yr9eDHaRDC3kQUdJpyEzuzuZB0qTg4OiQqBReFK2LtFHApBzHbgx9FMLgNAYIklC2zBSTfGZ4/OgBi+N9jNKk1DAxBhcEUGgJkPqMHdS5ODUDCeVJwcvp4gj4QLGsQL7385MCOhM3ZCB7PPFGf9DCITGhrSamjnb9GKJmWiasnqBw1KOSbtUMC0KeWItEA3HAJYK2nmXfcmE64c5r3+LmrWeZTSrefec7qNF55sqgJSIxkICyqkEroiSSGCIRrSKKirKY0aZAdIKKQilCER1+dYQOHY0kpuOzIJUz/e46GyQ805n+8OtjXUAnD++8dRulNJvT8yhtKMY1pjBYHanKgpc+9QKIYFXESge9o+w9EKBSSDVBFZbp9kYOk5jNwDX89pf/Hz7z2S/wN//6/8D5q8/w+GCfUFk+94Uv8O1vf5tXPvvD3Lt3h4sXL3B4eMhBWPP8jSvcffQun/nCj/L3fu3XeebGLUw95Vf+6v9MVZZ88413iTFSliUxtRTlwK12ASla5m1EU1DWE5rVgt777DC1BUSh9x0uOH7iJ3+MIj3G944QAr/4i/8lW5vbyGwLZSy5JM0FpyQwpiQp6HuPMXmYLqWOKBCSUJSZcJA9nBYUpKHYDiERoyEkjfOJ6HLn2BhFWWpS0jn+WoRGOrrOnRZOKWW27wmS7kQiuahWfO/gWMqhbiiB3gveK6JSHC9afAyEpscFIZTZ4nLyw5SA+Egxq1DWoLyDYAkp4F1CdI9Pa46PD1l84zGf/5HPcOPH/xm+8tX/g0986oewG0uuXTzH9qWXeO4TfxETBco+P45Kc+P8s4R+ja1G+XmjTA7OSAofOiS1YATXO5IYUhQ2NzbZ2XmB/ccHLNcr+r5nuVzy5S9/hU9/6mVe+/prWGtZr9d86hMvMZ/PSS5SljXOB+68e4ek4NpTT+NdjvZerVZD4RjpmhzMkYNjTpwwmiA5WMhqQxOFo94RyhG9rvDVhDDdZB16EuCTJYmhDYlJWVGVgMq/Q4fM6g7iMFa4fPESySeq8UXWrkOkRJJCy5okc7TyiESS5GTJlGIeF1C5o42cePOf+JwZFmky7ETIsOTLlbY6RRJCOu08nxTmIkJlNDF4ihJW4ZjjwzXjq0+jRxu0/R51kZngKv/aPHwo2X+sdQIJWAsix6zvvcazt26QzCbjaxvIcUd355Af+onPcefBfexkSlsqrCmZnNukqwOqimhToUP+mUbXNE2HSiWhB50UJQHjluj5IWVcI97hRickkTOd6f9fZx7wM53pD78+VAG9s7PzvwPPkiu3FfDv7+7ufmNnZ+dF4FeA88AB8Od2d3ffGu7zO972YXX/9gN8FynLAmstLgTWbYPxmlvP3SQRSW7Oei+i44oyPET6RyQjmNpjzTk6qbHS07sWSRn/VZpEgaM9fsz2WOGO7rFZlKgCbr/2KmOtePDd7xJS4v2796mnM1yvmG1cZLX2PH5wQAyaw4MFrvNYY3LhXBh0PaJpVkRJlGND3/ZYBRiNiwEVHIXKhYWtS5J3nMxyJpW4ev0KL760w72/fzdjymJgY5LDYYyGlHp0BEIg9R50QdM2CBaXBOMDRSWEFOjaHmULtKnwKXePdSEkSaDUEJssCAVt73E+EbzkwSsxGJOL5BAyfSEERwhu6HDH3KEXyV1xa08La6XAnPAVhuGwEyb1CcPZ9YGkNG27RhlNUgrnEkU55rhf0HtHspBiQidL6D0pKWxR03RLlJSgMv94Y7yJlZKXnnqet3/zAbd/6+tc/6FLHN3/BrtyB7ErqDV7632esQGjC1Aj1k3DZLwBArbcBGlA5RQ5rQwKi7GB0UiGIT6H8HV8VHzrO2/w9NUr3Hr+WVarhq985as5rMZ5vv6N17jx9NMcHR1QFjWPHx3SdmtWasWVa9fZPzgkpcTla1c5PDzEWkvbauaLI86fP8963RCcR4KnsIYQsi899Pk5IAJJFHo85dzmBVoKGikx0bC0FRvTLaT36DohIbDwjs3xGCWOIHnAT1w4HewzymJMRWWzPcf7iKhEbcbEcAjxCBVbnOuwdpujowPOTa8TXU8IiqpSw46AgE4UpXliHyI/3+QDDOX8ZB+QeCefi6AYOsnDc7NxLeNJjQ+ayWjK1RvXWafIbLrNyn0H4zSiJ6S0yudMTOava01MkZAcKIgy5/Iz7/LqW7/Msy/8O7z2N/8um1XNOCTufvnLSKmxasHDLy9ZX3+a7RsTFu0eY73Gdz2WCkhYXdG7YyxX6dYndKBIch1V6ChDhJT4OPSfP6pr9pl+MHXWUf/46Mz//P3Thx0X//O7u7uv7O7ufhb4L4D/fvj6fwP80u7u7ovALwH/7Qfu87vd9qEUI9T1mLIs6bqO5WpOs55z/amr9G2DVhGL0K3nHO+9j0odLvSs24a2a+iXR6RmSeg6uqalbVtCCIgIs9lssDkErNUYnWiO52yM6twxdnkCvzDlSRQFvg9osfR9HOwX2S5RVRVlWSIieN9TVCXW2hxxXdjTrW8lEWNyKMloNGI0GqOtoe07Hu8d4L1nvpoTfML1EUlDN23AyAXnTlgAEBMy2DSUMhiTSRI+RkII+CGgw4cwWB6yPSMlIUYhhKHrp590/U5sHylly4BzDga0GnwgOEMllB6OA7DanJIUjNEYq/KbUZn3/I/wf4cfMhTT+eMMGsmWkBjysZwU5NkekMkjpylvKX9PVY5YH3sm9Tke3n2IXjVc3dxi7727aNfyyidfYLE8YLGaU5WWw4NdjvZ3QVom45owWEWCA6QCKYACGNBvqsxfp8LoHODxws5LXLl2nYOjOd/85jdZrlZcu3YNay0XL16kqireffddUoLlcsl6vc6F9P4hzjlSSqzahkePHuG9Z7lccnx8zLVr19jeOkfbtqeWh8JYui7fJ5MqhsfQFiRj0EWFLksEjQ952LJXmqBNjkTRiqg0YUj604NH3rfd6Tk/SQxMkdNzD/ljSR0qrSl0ft2c2IROwnXUSXHME5LL9+w6DOfxg2+o9D0Ujn9UJ8+RmHqiBPreszxesr+3x2KxIBHRWg2LvzLjn3Ptenr8kk6sJTkcZrS9xJd3ufHMRc5vXSL0juR7UrtCN3Nss2asLJICR8fvM5laercYjjHHeCul0DaiRKPSybHn3aDetVitkSjD6+Yj10dyzT7TD6bOiuczfZz1B5XE+KEK6N3d3fkHPt0E0s7OziXgh4G/Nnz9rwE/vLOzc/F3u+33cnB9Gzg6OObhg/fpmiXXrpzjj/zY55nUiq/9gy9z7523SV1DMz/g4b23MEXLZOcmWzevUW+MKfojivlj5HgP6xw2KvrW0fWJo3nHqk+sguBtQRsj1ajEuZay0jjfQPKk6HDdCgkd7XpO7wNFNcIFsPUEbWsWq44ohjZEXBLWrSdgeGnnk5zf3mLnxVtcvrjN+e1NKluwXq/pXGDZOA7nK+brliC5g3zp6hWiGO7dP2K5yt1QfI/GI24FOkLoCc2C2Pc0qzU+JsYbm4QI67Zlvl6zbluatmfd9bSdo3eBpu3oek/b5UWGDIg7ESE6j/c+c5xdz3oxZ354gFFCYQSjIpr8piSgiZRWU1pFXRrqsqQsNNYIhRbKQlEYgRjwvidJOC2MchE8FO0DyUOJPkUFNn0DkKPGhy15H9JgQxmY1ioXcd51PH3x09y6/kU2yovUqyV3v/Eq14zmmelVdv/hW3R+WPx0Dd3+/01wv8brr/0vvPfel7C2wXuwBYABhiJaDGDRqkTpEokGUSU/8zN/hr/xq3+bN996h5gUL3ziZearNe++d4cf/vwXmG1u8vnPf4EbN58hxEQ1mrJadzSt4/zFS9y+f5/zly8TYmJz6xyHh0copdna2uLdt29z9+59wjCgF7zn4cNHdF3HfL6k6RxJaZI2dFFBPcaOJ7SdG/zkilgWrDF0UtAnS4iCV+AGg7BYjZOIhIgWcMFnr2/MXeSQEsoqUuwJ3hP7R0T3kEK1wznMXeW+7UjxA4XzgKA7GbA9YbjH5Acec8wR95L/tiThe54TJz8npSdfK60heg9ScHX7EoTAfHmEp8fogOs7VLLZPpIghYz6C87nIdY4FNbiWOt3eP4zmr/9//7XvPSTX2TrhRt0dcG6iLQERhtTLr3yEs9/+jqr7ptIeozIGtd3SFBYsagUUeKIURGcIX4wTTEK3XINKWGLj74H/VFds890pjOd6QdFH9oDvbOz898B/wJ51/VPAk8D93d3dyPA7u5u3NnZeTB8Xf0ut+192N+5Om5yyINJXLy4yc2bV6gqTWoSWxsTlBZCVPSLBSyO8PuJYlSiJ1OUX5FWj1k+bokYlk2Hj4b1fIUuZ/zxn/lX+c7uW/zLf/5f4o3dN3np5Zd5/RuvYouKg4MDnnvxBa489RT7jx5x4fIl5qsFP/TZV3j06CFH6yWf+uwrvPvubZ69eYvnX36FoqgYTye0fY/WmscP7jOdlixWc5bzY/abJV2zJiaP84L3jof7x7i+J0VhunWJ525dYTyrOH/hKrPZZR4+OmI2m4Ffgi4IbU8RJ+A9+AYCxKgRVbOxucVicUjvEqZI+LbH+8jIlPQuZCpCEmLb04eeujREiRlvp590n1VSeOdYrxZsb2/B4HktC00ymsm4HGK3T5QDTUgOJRGlEloNkesqEaJHI0gMaDMwgJHhZyTKsqAsC3zbgcophev1Og8hokh5Ci1jBXuHirnYRnqSKEQJW+d2sPomZb3Fo7d3ee6Tz/Pgq69y/pWXOf/cp/j6V5bs/Ng2dA39+m08BZONi7TN+7z93SOee/5PkdJoCIPWp3/fk/m2xGlAnjYkNDdv3OT+nbs8evhNzm9vce78Nn/n13+NzdkGV65cYbFYcO3aNd577y5N77BVzeNHj/iZn/tZvva1r3Hx8iWOjhe8/OlXePj+fdbrNdZaDg8PuXn9KY6PDpjUE5rVmu3tcxwfH+O9z8xqpUiK/NooagqrSV2DTmOabk1RTjNKEEtC0UdP0DVWWcQElNJc2r5EK5wGFZV1CUk4PNjD1FeQqDEScWGPiToghGO0zgOMXdcwI1EUBs0TD3xSCedz3LrzHVGNMOZJRzl75hVCzHi9E655ejJkmHdU8m1WUuaQS2R9vE/fPqbc3iC5PnfEXct6nRl4RqtTrniOD0/D5xYhoKJBTMMzn5vzm7/2S3zi8z/B05/9k7QHayiEMLEcPrrN26+/zoWn9tC2I4WACkKUDp8EaAjRE1eJdWOwZd7VMUlIPlBri9GK5PsPe4n7A9VHcc0+05nO9P3Vh+muntk6/mD0oQvo3d3dvwCws7PzrwO/CPylP6iDOpVKbG5NePrpa1y7cZ62XaF0TQwdRif6vqV1nlk1YrRxjvbREWa0SXu8YlKWKNdSpoauj5gu0DkIuqaRnt/+7d8mmZJvvbHLrRd3CKrkpc/9FEECzyvL/sFj3rv3mPfu3KZ97Vs8enTEL//yX6ZpM2KsLEsuXr2GKkdcuXETa0rqyZT1qsV1DRtbK+7fyci6vUePOdjbo3ctxwcL5osGpQxNt6Ys1JB053j0cI8Xzj3P/QePOH/lOu8/3GdSjlkfPaYajUlqDN0C+oDvGrpe8GaM0ZrxbJoHDLUDZVg3S0BTo3HOE0TwMRF9wEfHxnSL0DuMykEpOgkqQHC5U6iUcOHi+Ww/KO2AzVMUpRm40grf9dkbnDzqxG6RBFI6tQckFLas6LoOdVocpYFBnLClpTIapxRhCN+IMVJoQ9InGL3cJXddQ/R93j5XCq0sMWluPfNJ+lXHSlbUZcX87gO2bcm9r7/Nq99+ny/+6Z/m1df/J25enLJ/2FFuj5mOD/FO2NqqOTx6k81zzyCyNTzxToroQBqix1OM6FwNYouSw/kxl69c4fjoiIPDBSF2PP30Tdp1x+vffoPjg0NW65a6qmjbliiJq9eu8+t/70sURcF4ukG3XPP6669TliWFtWhluHbtGrdv32Y6GbG/OqAqa9qmY3NzkxQi8+VxthXUmo2qQmnBu5bRuMQYiNEzLjVu1ZHw+NDilIAYjIU+CD56ZqMSNapIVFTliNjl8947x5REIKd1Sv8+5eQYpKe0Ba4QnFtgJKcIIhaSICovebxzuXOsso0nnTCdB9CdBEGSG87rEO5jcnLfid3j5GOGnZEUPaZQzKot1OYWIpF21YJAUWgESwqZ/6wBiTkhUSmFSpakQELAmiO68it85k98hsP7/yev/o3/i3J6HoBCaT7x6RHnX4jcfvgmY3HZmhQDIXpEK1RK4AualdC1kVGKhFYoBSyJGByFLlEf6Kp/lPpIrtlnOtOZzvQx1z+pBcXvOTJrd3f3LwM/BdwDntrZ2TEAw/trwN3h7Xe67cPLCE/duMzl6+dZNdn76LzHh9xdstZCTBRVxYWnb1FfuEoMHapb0R3vk3qBCD5pnGhUUZO0BmsQpagnY+rxlKg0Wg90i5Q7o7PJCOcc0+mUoiioJ2OmmxvMZpuMqjGlrUgJ6rqmaxpc37L/+AFvvvE6r33tH/D1v/+bfPMffo03v7XLw/cPOTxqODrqOTzuWPeRRdMRxIEGXSqKKrB9+TwKzf7RAcla1n1Pv1zzrVe/Qdd1lNbm7rN4QuhIKZB8xPlMcVCSsNqQ5CQxLmPeusFaEhL0MW+RF0VFTND7gBZQMVMFlC5pO8dsc4MUh8hsIXcvY0QrC2hCFDB6iIeO37MVn1CI0ojSaFMhpkYoMyJPFCSh1IrKGAqVsCoxMgYliYDQtn0O1fA5etwqnQcoxRNT7nAmKVFRU6kR7333TWo63OIh43MXOF70FL1hEi3d4oinbl7hj/+xn+VTn/giLihSI8yPlxwvFyxWe7z11tdZrR4jeJKoTNpIxUASMZAMmgKiBgz1ZMSt559l7/Axk8mIc+c2uXblKXwf6NYdo3JEXY7wfWA5X1GXI1aLNXU5ojQldVEzHU1xrcsYPBfpWkfbO77zxlsEgcV8xXrdMp/PWS2XPH74mLZt8X1OpyyqkrIsGY9HYGEVO4IZTokkknJgHOUQCKhDj1JtLtQTvPvm61hpSCIoZTG6QukKlzTLeUPwiqqK1OYYFQ6xWpFiy6o7oF/fodSeQmmSVGglBAk4n/332pbYokKjEDRKC33weXjWt8S2wa2XhK5Fn2APY54ryHaeiNXgU8QWBToqNq/cZOPyc4zLGahIOd5GzOjU+y9JoSShBKxWlBoIgoqQeoOxlqaLWFNxuPwu5dYjnv9cz+zyHueurbj1aUWoHpCKA6raQCjwnSL2DnEJFTRaQIum7Vz2W7tAGSaYoEnicLElEhH5eCURfl+v2Wc605k+Mp11mr+/+sd2oHd2dqbAud3d3bvD5z8NHAKPgW8APw/8j8P7r+/u7u4N3/c73vZh9cLODa5cO0ffrzFlHngzdYmKCR+FWVmQpAULfYqUtmJ0/SKFEliugCVx4TEmYfGk0GOSorKCjS2FM5R+gTt6RFofMx5NIQSavufx44ccPt5juZyzv7/P3sOHNO2K9ao/tTvkYSGdvcMDrSKEgPceSY6+WRNCoG1bMsGDfgAAIABJREFUbJE9vrNZwcXRiKocsT0bMxqNMnZOFHfu3efgwftsvPwKyvU4ifytX/1VnO4xsykvX9zKNl2/JvkVqRPQAXTLeDwmRAWqoCgmFEU/5GwrNDlYIw8feibjGQmLKsf0IeJ0gZpW0EdWnaecztjYvIA2BUUlGJ2RZ4UVjGozHznGnBQ4pMBlf3JEG43VFonQes+4DIBHFdl/jVgCiVAWhJTDXbJVYQ9nImpcYcc1TXBYW+YwlwQ6JVwvpFBglCFqj1Hg4jF21vCdO1/m2qhnY7xPYY4o9Ra3H/f8/H/8C/zWI+HlZ/8Yxw/f5Md+YoNH8110eorCjkBd4srVH8o0jqFPCoAKQGZnK+yT3HFRvHDreX70R36UvfsHjG3Nkdtj3S6ZzMa40HM4P+TClQv07ZqmaYjes31pi8Vqjot5gO7u3feYTEacP38+DxUul8PCxhBDysONQVBa07q8I9B1h6DBaMv88RFSz5hhWDUdo8kUZQztuuPSOctx6GldxOoZtWrZKKF2x6hGU/hECJa66yhMhdEFpp7TxjVN53j02kOmM8E+e8hUv42Su3RdwDdjxI259/49PvEpT1lVOaJduowURJF8i1VT+hBBZYuVJJexciliVeRwvk/CMBpPUSmiTcYZphRyFHiKubA2Dp8Cwa94/c6rFEQubFfYjYcUxZqiSijlCUFAlfgISeWi3DDBx0A9UQTX0bea6egaR4dLxhtLXDeG2RtMrGMy3sCHCucrlC4oZsLi8Dh7tqPDWEVZlIgp6KXj4KihaQqKUV6MOu9RQK9zamXFR4ux+yiv2R9W23/l2z/wKLczcsWZzvSHWx/GwjEB/vrOzs4EiOQL8U/v7u7Kzs7Ovwv8ys7Ozi8AR8Cf+8D9frfbPpSeurJFiA1GS+64jaaU5ZiiDDx960VijIyn54lhQV1uUeiL7L27h4+BlDRuYUnpAvP1khBKfADvPV0jNPuHdGGPef8dWhdPaQQnA1zOOZqmIaVE3/f0fT9QLuRJqAUnU//ptFA+IQ1sTjfYunKNuirZmE6YjApQufMKCYmJtoscHh6zXK958HgPL0JVVTxoFFfOP4VcfIb6+ie5MB2z+6DjRX2eSm3iQsAFT9v0eCJRORpraatNtEAUQxyfI0Xw5ZSiqEAbSInRxhazjU2KuqY2Fhc8SQwb5z0hQucCthQmkxGr+TGKmm7dEEIiOM90fB5jsv1kFV3mABtDSgrhCb4spkBPwqgRISS6qDhe9WhxFMbSd5BSjv3ef7wExhyueu6+fYdVU+CiIhlF8CknKCqYm2OSBAyKmBxGEoXxvPrVr/Gp529wfuMCu3u/xfb4En9n74gf+bf+Au7ijOWDO9y+3fPizWfZWyimWjOeXsBWUzZmT6HMBYIvsbY+Pa+KAjAkElGpIfwjd0i/9NWv8fruO/RNT6UtGxsbdH1ksXJ4qZCi5P2DNWVZErQmWY9TGYUmRrPoe8bjmps3n+Htt9/GWktRFLRdiylqNIqoHKJN3kWIBkHoA1SjmtY5VFVxtGiYNz1XnnkWW1YYY1FK2Nt7REoOtOBTpA9retGEMmBUDidJCVzfouopSVu6mKOw7927x/bmNcTPmS9eZ+PyQ5r1fUqt6Hyij4HOzQlyhM1uZkhC7xPoOnuvhwHIgEYSlGWJd44+BdpmRVlWjGYbGF3gYuAk6jIl0BqEiAuRFMdEDFVRsbPzOVy3oF2+R4gFMUQKC0ZHVBCgzd3nwV7v/BptoWvh7uvP87VvP8ur397jlVd+jBdf3uftV2fcv/ct/sgfvYAxS178xIjAHcq0yd/6X+/whS9uEuKayiqsbIBqEXEE73FrhVVTNBFMR+/WgMYWI6KtwH7kQ4Qf2TX7TB9eP+gLiB9kbf+Vb9P94kd9FGf6/eofW0Dv7u4+Ar74O9z2BvCjv9fbPqxS6EnJ4SWiNRS1AqXZ3LrA5y9fwwVPSJGRvoSyCqxwMd4idi0AseuwWqGLkty6KzJ1SmmS1gSBLkQa50/RXCEElOQUMucceiAKtG17Wjh/0K7QdR0i6pQgUdc1xhjKwlCXVU5ekyHJLAkxeFy/xjmHc9A5n/FkJseJO9exMZtwcTrjn/1Tj7i0uYX4RBc9s6vPgkmU1ze5uN1wMSjQNUkMMh1x/dZzWGuHRLmIQVOOaqwtB69yQlSiKApAZx7yoBNSHIDolAvH5LG6oO88hS6Gv19jjKLzjr3FHlrrJ2g5OMUEnuLwgiGFSAiRZggHKQuL9z2aNCxcwEfFYrVkf3HM0nW0fUfbOSI5gEUpg3eLTIFQiiQOFRNtN+ezL73AretXoV/x+X/u5ymURlzJMz/+k3B+SnrwDtVkwoODBTdufQa6Y1onVPUUXWwiMkZpQ/zAbKQahhxPIj7V8EpJwPalK2A0yQsBzaLrOVqu82McwfWO0WiEU4aghCSRZt4QgoMYGI/HNN2SpO7Qx0S7mjOqarquo6oqgndE55GYsAhaa3zoWa5W6ELTdR1d1Iw2ZiQRLt64ztvv3EZEePHFF6kv552F5XLOct5weVOzvy44HFcoScSk6MVil56gsnWnDwbXaZaLFssBo9GI8WhC146QcA4Jii4kmk7RdhHvIrOtKidBJkHpEmMLghhizPQUpTPWMEZBa4vVesA/JvxyTV2P0UWNVuY0yTBbfHLKZRSLMSW+D3hdotUGva8xfhsRR987VMrXCFTEMEKrAqUMUTzoFudKvvwbHa/edtj6k/zGV47YXyeMu8Zbt2+zaI55//Fb/Nv/3ud49tZlXL/BnXvvsLM8hzYWSYa+n0K9Johiseo5bmqiKwnKM29bmnWL7YSFJJTxXEkfbT7VR3nNPtOZznSmHxR9rJMIFdkKoZWmWbe4sGQ0NuwdNtTjMZ1zpLKgUmAGHJstRyQ9yklmtcH3jm4ZaZ0/DQSpqgrnemKS3K0e0tMkJIqiQkTofSQOaLV1C1pXpCRsb01QA/e2LGroOsbTycDSzd1nrUFsoh/YyGVZkoa0NU2iEqFWOVa7LEu892Aso3GFMBjTu47nrr8IzlEUJaKEXnqMzlTqMBphTUUgF342wZXt60NwRB7Wy4VEImkhiWBLg07ZloAx+JOCGaFQoElI8hhKomgKPcJFqGpLFNClRQCXoCwtN8Y3+eC8lMjQQZQhnG54/8GPAULOcSFGaHuHMYau67BlwaptcCnztXsXBmZ1JPhEiUVCxBlNVLn49jZQdwEUNM4zvjhmbXriWnh9MSItHbPyOq0b8XDVs9JQ9ZayFLR1KI6wZbbltH1H5nUzWDhAdIEaOutJQdKW/+Q//c+xRcnf/dKXefTgERvTCceH+xithsE4zWg0ous6NqYT+r4nJbBKo0QRgmcym2Z+uC3QWlNaS9+3KGuARKENRmuqKnczQ3BUoxpbFaiB7+x9RA+88T/xs38WEcGo/PwYj2pKY1GdgBFic8Q2HUZXCIqitKy0sO8iTdszGT2HVZ5/5c+8lHnXugQ1p9afQfoVygcu3Yj4WPJToxu4VNN5h2qgqg11UdF2DmWmNJ0lSo4QV8YgohDRiFZUk0sZB2cLEEUIufgWUUhKBJcQsRRGoYo8SIgtcGELrSouXHuKZG9RFYm+69AEkhmGVoMgkhneSQX6eMR4Y8K/8R/8OCszI6KxbBHDmiQdKv1rmDgj+J6k36WeLoml4i/+pf8IUQ1qQPNJ0pSVUBQFm1cil17YIKQJ3k1IskakZSQ1KuXrTrn5ZCfjTGf6nbT9V77N8vM7H/Vh/L50ZkM50w+yPtYFtHMdrnc5CCIq6rrE+wiikQC1LZC2y5HUaOrRFPHZitG5HqcTIXl6AVuWVFajFHRNi9aaQinqofMXY2A22xj8zIrxpCZJLlKqcaZIaGvxnTCdbpwGkBzPD9k/ak4DJrz3mYZR1LgYkEQuPq1BlMGHxKptCTEOnDQGDFw6DSMZlwWhaUjO8fTFSzy1VSFaGI9HTGZjFquWg8MFi2XD1vkLxAQjW1MMaYDOe6JOuJioxhXUBWhhe3uDuszFS9u0VNUILVAphQ0eJHGuLoloAoICgoHb91fMF2vq6SZNH9DaEmPk3rt3Wa1WnDt3jsViwWKxYGtri/l8nhcz1uLEs7ExpVt3SAqZN911+L7FaFDJo7Wmmk5zZxlwzg2PZY6+lhCZz5esUsCOptw/XNI1ARAOm8f8i595me1pTbKOIgY2ZxOmxSaFKamsIXULHq0eEoCjd+5x/dIUUxRESXjvKcuKw8NDmqZhPB5jjGF/f4/nnnuOIAFtcms6Jk0wM37hP/uvCFHTJ8tkMkNECD539o0xlFU+D8YYjMoeee8943F92p23Nr/0zp3bRIb4942NDXrvQat8HENyn7a5gNdFQ1kWGGMRSWxvbSDSUxQFq/sHTKdTYvKUtmA0SlTG0K3miCmpqoL15mVSF7FWU0dFOz+iA1brZaZXiEZ0B3aF6xOFHaPTOWKaoiSR9JKUFMY4tEpoA3WlKXqw1uUAoM7QubzY8CEiEtBaD4E4eeGpFUhwA6VFY206XXh4758ErhgPCLiAFY3CEkKPlJuopHLnOnmEmGkgKgwFtEZriOoySQyo97BpMhTykRSq4flZIuo9UIKKF1itrpH0ClFLtNrISD0d8nAjliCeGAtiTCRZo4yAcYBgoqCVYKuS8Qd2ds50pn9aNfsP/7eP+hDOdKaPVB/rAjrG3I/NdoDcyYrOY6sKiZ6YhKQUSWt8zANsIhpT2IEfHDAYxjonujnnQCUKq0+5s0rlf3zKKFIIaHLicHI92hQoEqUxUJUYYynGM4qiQllDVUGUAh8CoJ/EWWuDFw3a5t9hc9c2AusmcXQ8x6dI0BqjNIXJx1cXZe6Sx4jezPzkC7MpV2aKui4wBRQVjMoNSr3JpO4o6hqlFItW03tD07V0TqAw+BDBaShBlOIgOkaXDSooem8YDX7RmYGtUUEheUYxAFEpPLBs4dW7x8Sk6ffmvHvn/WHwb4TxibZVfPf9xyilMGbMsRdEZhn1R0nUgRgKOq9w6xXRwcSOafs+48yahhR7xtURGoW1J8WWYJShEkEloe0ats9NkVLz/I1nWO33JOe5Mp3yyY3LFG5F61o2ZyO2N7fQ1Rg9rnKHcmnwMRL7mAfLvEWXPZ5E71ZUo3NM68CkKgBPVWlir6lMB73HqkwrCQGULZmaEdtXrrN/1OEHz6+1JX3XEwDx2cpjdUFVlZA6rALXZYuC9/0pq/jg8TEARWGoqhbve8ppfgwYvPVJ5dTLzvWUpUUpAxKYzWbUdU1dl4OtJTAdjZnNJpw/f56y0PiwxljNdFxS2UjtAq3r6EaaSiJFUWB1Qbt2hJTQRcB1uVMd0pLCRqJEiqJk1Xq0KhkXApwM0ipcl5CQEzFTCiQVEJUfFx9S3oFJuaOvVHaTK8l/U04uzMl9+fvS6WI0KYdSgkkh75ZIIKqG5E8SD8EIxDbvtqB6UhqBWKI0iHFIrEAsBS2oNBTQQsITh3TGRAR5H6TAlhFJLSLZBiWqQasq00RMg2ZCcCDJEKKmmhZ58DN6kI4iFpjirAN9pjOd6Uz/tOtjXUAbk7tMKhmqyZiYOoy2GKXomjmiNCEYxhtjUDn5TqFyN1cJYjVJAmVZUNc1MRbZz6x40iF1jlFZYa1FWYPzHlIO8BDJLOCYQv6nT8T1LW3bE8l+4BACYfBP+5iLCqNApY5Ixmv5JPQhEuJgJYlpGLbThKTwSjCScMbStmsuXbxAu26YVDVeViyUYr0SJpOC2WzG4nCBToZKOwrpsNZgN2G1XhP6FeXYoI1hPJ7SJ8ela1cxhaF1PannlM/s256u61iRWISAksS5rS1oMwO3d4ll4ynjmr73bE+ntJM1GxsbGBPZ33uf2K2YbeSOngu5MCzLEqsLmqah7TvG4zF90xFDolmtWYaAc27whwMSWbUNoXdEHxhVJVpn37Mc7aPRmfhwfI/xxoyjbp/3787R2uBZs2rGjJKjUy1d22PCJl1cgTMQE2UQUtMjfUdtDcfOcW5zixQdi8ZjC0fvhKbrSAmmUwtmzINHx6AVWguSApI0yqwR77j/7m2ilFQDizmyYlLb3PmMDc3SfU/0tbU2M4+VYj6fo5QwGo3o+57o/VBQ5/j0oqrpfSDnm0M9mmRbUd+fRsYrA0cPIUTHpUuX2NjYQGLC1RUH7wu31S4oQxscsj5mczrihRtXuVwYlBIetQtGSWM3J9x66XlmmxssVyt233mTvb0lZTFjVBtmM+hDnzvLHWgllBq2NieMxjWXLm1jC9BG5R0GFN7lIBFrLUaT/47BwyMi+JByRM7wGjwJUBER0AqFxmiFDiXGKIz26CSQRnlnxZuM6VYRayFZnVMUhWHfBITZ0P3O3ePoBU0JgLI9RgqUSqjB758L95YkGlEepSoSGpiAscRo0LFGVEFigTJCSgUpQdcFJAbKUqNSIsWP9WX1TGf6feus+3ymM33MC+iNjQ183xJQeGFIL8t4OKUk/3Ml4VdzlFI07hBNQWlKrLVMtsY83t8n6JJlBAFE5/CG9XpN13uOj4/puswW7lP24woMzGIhpWEgLg6fnxZDOuOrlMHFvE0tDLG+SmF0kYurwfh7El89Gv42EUHqEqtVLhYkYazi+tUpulgy1j2+O0KrLaS6hCkNq/aYpj+mrCcUpeVctUWzXqKVRwdhulXC5oWc1IdiNBpRliXa6hxOMhlhlQYNzgm2VIQ0IwoYkwfkDODPcZoE2PXw3M1LlGUmZhxf3yT4PCS3WJ6jbQc7TFGcbr+fpNt1XQcqD7NpfS7bYYoqc52NObUziAimqDDGUFuTt/glR00bU+ROZoo8dfkS+4sFb+zvgTX42PFgfofV0WM2p+do3Zj9ecvyzdvMtEN366Eoq7l84yqmrti8eInq4tPUdc1mXXD9kzUKw3g8AaAfkvQAlCkJw/OOtCCFSJKCn1v1zI8zZaNt5lR1QWg6Qnhy32pU5+eIc2g9pAZiUMoQghs885l3HHy2Q5ze19aUdXX6GJ0U3lVVDZ1Xgx3XOOeo65rlcklRZK99HkTMj5kxhsloylZh8g6BioybDlMVtLMRuhVaHThc7LFY7oPRfOqV55BoSd6iQyKlQ4pSU1Rj2lZAItNSU1XZrhIwKJPtMIqE7zypzEX6aSKgCKLL7CUf5g3yQkNy5xYGFrUeIIIZjegJeVEsa1TyKN2izPtYqbEaEIcxPanMBbQQSNKSMEh3DnRPYkl0M4JUw+sCUsjHpJJFi82eEpWHDo1NSNzClEdE1aFVgTGb+HSIJA9phBrNaBpP3/X0IXvYJQZC5xHx9OuPnMJxpj8kOvMQn+lM31/9k2Rlf6wLaNf2ON+jjUHrAohIbk1mXyUJsUOxozUh+NzxioBK1HpCafI/9qhPQj+EEB2lFlzyWCKlzt1m0YJWEIciWICkBNGaOISFpOCJXUdSQgy5SIldR9D5mEQEn0ASNINNxBiDMbkoCDEXmQpNG30OCtG5aDSF5n6MiM4pbVVRMN9/PJA1Eq5boAWSOqRrHWVdoVUkeocoSww5xS/0gdFojEjuco7HIyJCUVkkZZ52SkJICecCSRTXb95AKcO6bRAbECWI0ty795AUwTnPrZvP8KXf+A2szWQS5zwKw9a5Deq6Pi2gDw72UScLD3GkFHIEdYTSFvgh7hygLismk8kQZqOZjWqm41x8FsbSxmVOQCwK7ty9S5cSu7vfoU0dKTnadESVDDM0r732Jk4ss6Lg4thSuAajFFGXROXZ2Jzw4sufhukGTd/hm47OJqqyRgePCzGfz5TPWYyJlDTGKpSuwGZO8f78EOc8r7/xNlcvbXL87iHS+yGG+olFQxeWpmlytHXXUFdjUsjDaGVlCSFQaEXfe6oqL0BCCFSlpR4WP2VZZsuQUtR1Td/7bIOo6xwyMqAXY4ynKMXpbIxzeTE4G5VcrienPuSnNreZljO0Eh4evE99boPlesG6dbjg6VOfQ3OCQsXE9pbBqIrl+oh1F0jRs0S4ePEi0+kGm1sXwJanCZK9XZ3+rpMCGkCUOaXYmMIOaZcKkSfFZk4QVGgUSWJOytSJ5ADtUSQwPaoSaqtJ0mGVI0hC0ETlkdQgyaBMRaQlsiKiMm4PTQgJHzOTOgUDkpe0xjq0ymQXkR6re5Lu0DqgMBjb5I65ruijoygUk4mhaR19F0E8SntQEVWX358L5JnO9DHQ2SLgTD+o+lgX0GVpUTFP9KfgMYYcpYxCAihtcRIpyPG5PvTYSYVr8n7u8d6C5Bw+9DldbqBA9DEP7FWF5uLWRqY8hABFiXOZ9dq0Pb1zaK1YLpfU9ZgYAkoJT125jK3sKfYOwHwwPMGA0kMkcfzAl4dC2nsPaAxCYfLvHI1G+Jg5waBzfLExTOqKqlR0XWQy3SaExGxji29842t86uVPYG0uwP1gAxARQopoWxKH4TRjDNoaEkI0FVpgNtvMjGsfSQxBFiJcuHqB5dINg289ly9dxRS52Otj4Id/5AuEkBjVE/YePRo6nQo1UEaIkcvnz53i5uoyd01HVUFRWLRSVLagtMXgbZdsPfB52z+lxPHxIX23oom5MBRyt3Y90qyT59zlCRtpgiJxvrrOuG+ZasWf/uf/KLhAIHf8U/CQZLDXBDrn+fpXvoouC8xkzMWrV9jY2MCW2/8fe+/WI1ua3nn9nvewDnHK0z5U1e5DtQ/ttsdumxkPFpqxBGjUFyBAAs0Vn4LvwAVXfAVuGG5BAgmExAUyDBLyMPg47na7yt1VXbUPuTMzMiLW6T08XLwrYu8C22ObbvV2dT51UanMnZEr1loR8X+f9//8/kzdgK1ahJrKWVxtMDIHUwrAPAAIuMoSY+Tbv/LLvP78cx6fXc7PJTEMA+ebD+iHA+M48vjinL7viXFFmgJXF5flHosRv34zKAj55KE3b3n0nXNYa+dFhLBo2zJs2tRkFcRZNNf0/Yj3K0QuANAUyJJpKmHpKipTkWuHni8YvKFWpb1akUyk3SxQo7RaMY0NJitBJpqFYBiRmHB5ZF0pOqcFGokII5o7vFWSGKwoi2XBFnrvmWIAySUoiIg9vdsEVEegWLRg3rlxzF5pxZIQOyE54JgQ6RAGcjxg3DVowkhGNFMbw5iVqAGXAiaCpg41ZcDQmQkvAc0VXlsqgWwSVIl4fC2KYkjkZEj2lhgmlECUhMg9YYKkhixKTD05Cd0kRFrEgmRF80ztmN8THuqh/rK6/Gd//NM+hL91Pdg3HuqhSr3TAnpUCEYQMYjztIsVMaZTp885x1ldIWohRTa2xlcV+dyjKjibWWQD0hDVzulowpgT97ueMI5040DXRVIK6OEN61mMULVlGChppq7rk5XjvuvJ+zLsZK2laRq8L8cTQiBFJaqcaBIpJciKSumylQ5dGTzSNA+gjaXTnDJkjZikGAtn6zXvffAU5xzOVXTdPc+ff8ajp+9xfxgZ52PQacCKko6+W+Ukwpj/r6pMqQxWqipVVWOcwzjPV772IRnlkz//IVaKl1cF+n5ffm+aeO/pB3z3z75PisrNdktMCYPFOiGlOPu/dRaIE3EKDMNE0iIOK2cxxrBatizqwsuu65baV9Suns+LYpuW5XJJFGWIgfv9jpiV3/pHf5//7n/47/neDz5hSpEwjDQaeOIt75+fc2ctLz99zv32AFmpfbHaiGaapqGqGhZna3xV8eTDr3G+ajlbL3AerPM0iwZjS/fwROc7rYtKJzVikJT42vvv8+rz57z47HPOLzZM00hde65fveD61QsOhx3Pnj1jsTjj+9//HpV1qJbO9t3dTbG5UKLgRQp+rvC5oWnrwiWfPdBlVyMxjiPTNFE7z9njR7TNEjVCP07zcJ4l5ICoFoqH9yyXa84vKs4uL8mVMBlYrGr6w8jhfk8SOOzv2e12JAQjpRPuvWM8HFjUZTFiEIZhJIY8s8491nqcKaQRa8o9NeqEWENG56FA5teQfoGf7rUsCpitUlgBzZh5QSEZoi2R8BLBOMHiShQ7ZYCw7EIJRssisnICOWCdKbtGuUIotpuQSnhRmgxGagyeEAecbUBsCYDRALYMIxtvsK5BTKBtCls6qzLEjMY1wwjpbuDmdk9OBovFHe+aBwH9M1f/fwTxQwf3of661Xzw2w9x3T+B+qXf+qd89P3/82/8e++0gFZTAkYQQazj0PdY4xmGkgx4dr4mxhLl29QOy0S/O6C5IsSMkQAaGSclq2WIGbGWhMU3DavVgs36nDC3iZ1h7iiXEJUjTivGGcWlMIWyBf9293m5XFJV1UmkZoGYCsbs6A/OKWGsxc/4MlTIvthECpZM8bPIqpwpoRMp0VQGr0qME5WvQJ6Rc8K0SzCGYUpYbzGqZes7Z6wvIiQGSuy2mfNjjlxm5pkuyvczgBTR+Ojia1+4BpG5ATv//+vv/+PTz/qyHsGbN48llHOUUpptBJ6QtNAZjuiyY8cfcObYXT16fhMhFypDNsIUJ5zziLUc7jp++x/+Nv/gH5QAHItgpwNnRHxOHD77nH/4G7/FfuiJmmchk0tiofcY4zC2eNbXF5eslguWixYjFldZ0tiRbZh90/aNF3pGsCUVQjb80//4P+Lm9ZZP1yvev7piHDr6YU/fH3h8cc4wDPM9A9/8+V8oPv4pMI6Bb3zjG7x48aLEuztXdOMsLI8CmlwWicfueYwR691pQWQQbnZbVlePaBYtYt/Eor948QLnDMPQ0W0HxFQcqh2OhG8NT957xNPzNe2jR5yvV0zWcHNbc3vbME6RcbbrqGbELmgboW0rFrXj9n5LSlo45GNksBOLZcZbS7tcIiIcDoZ+6LB2DtiZcY8WfXP8xpDTfG7nbSE1ghw7uFqGNsV60Ij2ytT1GM3UtkZswlflvjYEjE44U+FsjbqIJNBUCDrgEO/JfkWIFXFcoalG00gm471DxSPWYGyN8RF1EePBVoJ3EUkjxpZaS6ZrAAAgAElEQVTX8+ZshfCYcYCmHli2kRBywYbPmL7V2fnf5u3uoR7qoR7qb1TNB7/90z6En+l6pwV08S07okIOkYyQp5GL8wu2n37K7rPnfPiVD1Atw4BPH7dsvvE+UAMC457rj7/Hi08/oTsMfPL550wxsesntt2EYBmT0nUDEWGxOT+htA6HPRoT9/f3pBxOw3JhesOshYLeaprSqT4KBE2JHI4CqeDtJCtZiyWhaRoqV0PTYGtfopxdzdQPaJiYDj0aBurK82t/75f59q99i74/cH/zmp/7+Q9xVcMnn33OD17csLp6iq1rbLUkJCWEwMcff0zSzN3dPWIMbbvEWkvbtjjJJQ667xnGMA+6RaqmLRaPlMiZU5LgFCOvb16RcxGh/f5QzkNSIvk0CPa237V4oY+IMn9KGzx6dfu+2BuQIqqscURv0JRp64ralsWKFyCW86Upk1IgaSa6ii5GwjjREPlPvvPv0hrh8PwzLi4ueO/nv4GrKozLaE4wRV6+/IwQApvNhs35GYyW609uuLX+hFM7dB1N0xI14109JwumL9A0xmz4z/+L/xIxFd1dT1PXdN0e75jPXYaUWa5acs78L//T/wjoPFBq+L1/8bune+dI5xD9YoKjaD6J6uMg4ZHCcUx+VKP8H/0ERvh7v/7r7Mee3f2B9dkG70oAi3WOP/mjf8lnoeNpW/NsVfG6hj+IkXZ5hrl6zLSsWV89xlrHcNjy+9/7U4IWUW81kYYdaRyJoS/e+ZhwYvilb36Tx48f8/rli7JYmZ/PMHS4yp9eI+Uc6om0UfjeI9ZQhHhp/Z4GC4+7S8abUyhRTcKELTChskfpMC4AmRwn9ofM7b3j1S7RSE2aBGsaoinpjwtvqSpHiobDfQ25pm4MphoIIdF3ZTiwDBCPZMnEHEtUuPSsF/DkyYqmrciqfP6pYN2GfhJ2+0B3GBn3Hc5avHd84xf/bodjPNRPvt7lGO/1f/bf/pXH9xf9LPzud/+1j+v/jofG/Ljrb9NN/ov+/UNH+qdX77aAzgrZFDYzxcqxaDyoIaeZ0EAZSmqkIvQHrv/0j1m//wxX1aT+wPbVj1jansurilX9mKzClAyv7ztiVtRW7LuRhHAIpducc6ahCOl1c07WiKYzoHgdQ0jEuZsaQsAwFA4tMA4jmgKtRohFVDezXUFRrp5cEcPIzc0L4t4TRMgJxnHkyfk5XgxN3xG6PYlMfLoi3qz43h/9AVcXG678B7y4fs6rP/8+d7d7bl5+jmtannz9myxXZyzaCskj29evEYR+mNhvC2s4xkjdeIwU20ZWKYzhrG9EDnPHPGdUIObElDI5KodudxI8xZtcRKVxln3fo5q/EHdeEG3xFBrivcd7z3rZcHle0GzFLmBxjaP2FY1zrKoKh9IYw7A/YKUwsnfDnn4aGdRyvR1IRjEhcFYJZ21DdahYNILNA939lnXbQExITnzlUbl+xhgkT1RxRAVIkelw4HA4UNc115/8qNgf2nZOqLTFShGnQmSJhpXJZA341iA54uvS7czzDoRrHc4d0wvTTMCws1/cYJhZ19agCIrijp5wyXNy4xu7Q84ZRU9+8nKCM8u2JqHFrxtToYGEEdSSj574oSccbogHqAbHemEBhWHHkDriZkHvE0GF/aFj7G6KHz5nlMy3v/VNKmfQGOb7PaExsG5bKgdOymyCiIBRvFrq+hj7rgxDjxeh8hY1SsyRZMuCSlNm3Ti8NSdyS9+PWOcQFKnmYbxhRKdYPMpEqoUj54CiGLWE6Lm+UbZDS2oecfd6hzWeYAKaDV953FLXNcM0shshaWDhPZfLJUM/0HVjIY00DYaKvhvJuew4LNua842hMqkcgwWh7BARIyZkTExYVSpjqJ3FWeGh/up6lwXkT6P+MgH64xKd/zpR/HY9XJuH+jLVT7JL/04LaKTEGh/LIidLhbW2fNBKIUoANLWndYZx6tj1e+h60jRyf3ODMZZhDESFQx8Z1aEIkURKmZQVDbnkS+cMMZBCIMWRaRrK4F8uwiJOgZQLfivGWOKWtaDZvJlFWrKnobBCrJhKPPU4cnd3Q38Y6LUnGX8KyohZsZJxIuQcUU3EGAhDz/3dLU+uNjhn6HZ7xq7DG0s0gqbM6xfPGfuBpqmoTKL1RZSoL4OMagRcTRdGRCISAlFNiafWY7x2EX2Hw6FwsOcwjKO4ibHwbguNJJWFhSrOWURLXDiUJEHRYs9YrWsgU/uK9XqNanluIY4gpVNb1xWLxuGsZVlXrNsWg9Dv7llsVqXDr0oklIS7oKeBSe89mBIT3S6X1G1DjFMJzMgRSFgDxs6DahaMkTIgOHfcNScEJcVQ5GxO5BlNmDUDClqsIA7BaERTscwYIxgVcpbZFiOIKiYdrQg6R5hn/OwlLlabwkYWW8J0UkrMxnX0jQP7L6/5sXXu2h671GXnQObjYOYnl2OQlJA0d3jjhE+BKQVIsUTI50DTCMOYSDliRTnstjSX52QB78vbhTIvBIyiGgvmTwEVUpzAWSSXe8ZJPvnQSQmTA5oTzjiyKKIlOt1oxIihrYuNKVGQeUZhGgdif0BIWBtwTjG2nKMEiK05vzgndxZJLdiJkJWILZ7u7Ol6pZ+ULNVsscpMWQgJplzCXXCuoDIlMcVEFiUnc+qKixQEHnk+t0pJQUxz5HhOkB2GBwH9UD+e+ut0dt/2X/9l4vdBFD/UQ/34650W0EbfGuZSg8x+ynEcaZqG9XpdCB0mkyYl9yPt+xuqszPWmiGuWe5vuW0XXF/fkEPPOIwchsyr21fEpGwPIzEXMRXCWIb1rD3ZDcqW8/TWMUmxGFQNbdue8HTOuNn6cSAkhXrBtpvRV7aQMPqY2N/u2O1GyDBECCaRRBDnOMOhYll5SzQ7rPWod+yHEd+2PP3Kh1CtoFnSq2PX9yzPz1lszlg0FZsWjE+0H1xyt/Jl6GxMdN3AFBLDlHCmIuRUCA5AVimDUQpQ0HV5ZvhGVVQKWixrBmNO1ATrBEIRy0Zh0VQn28dmUc/i0lD7Nz5wM7Op20XNarEAyYShp/WOZxcX1I0HMtcvX9ENPZebBeIKAWQYI6Ytnfx4GBgwJJRkDL2ChkDd1tyHAbebqJuKlAXJiq08WMhz0I2zNWOY0Nmq4qxn0RYRu1ysiigXQYyFpOTZ23q0GRiUTEZy8c2rSBFyxwRFBDffFzrbWhwCs1A3UiwqCcqCzRhymkW5FbLMov14z4lS/stzuA+I6LwrA3n23ZeHS1hAxaAi2OTJUjFKJGAI1oIodQIzKTIpoS+otzx2PF0vuZV7Xr64o25buv0t7z86J1kFFHHCNIWS9hdtyXqRfHruGkaym+0aIZI1IdYS5t2a4/kRZ3EU4RzHTIwTYRjxTekUL9crUgQjhjgNeLF4axnDnpwUX5A8TCFz2Cm315GDMdQ+08ehnAvAS0PXwf39QBZDu1iQNDDFnlfXO4xZodkwDDCFe4wknNRlWDTD/eGe9VI4lwUWSGrnHbB5wRIiKUZMThhmisqJrPJQPyt185/+yk+NrPEgjh/qoX469U4LaHhLRhiZRUIRL3gBb8kxY0xFNwY+/ewTfnH1dbi4KC3VQ8f2+ob9GJhCIoaEqKFxnmXdEDM0zYrtfocC3dF7mjI5RKYYZv9rLh+ac4IfAAG66UCMkcVigUg42T8Uw9ANqApgOYxx7igrUwikuQuoYyRZC85SOYvFYJJQiYExYmtHnAJTGFgulzx6+gR8xcXj92jaH3D3wxdAhabMBx9+wLNHKxaLBevNkgxMMXF7c8/uvifGyH4KdGoJIdJNiS4kokIflWGMxc+sgnEFe4cpFItjBz1OHePQkXNCtOwIFNHZYG352leWtiq+7rZpTqEz1niYO6TeCCkGjIHVesGqqRh3ZUDt/v6eoFC3y7KoSZClAmvJdgdVRoIgdcZGiONEtmGOc5/IaSQOmY2rabIDTdhQIp+NujkgBsS1qCv2IFUt6ZWAa+fBUc1lAWEKHUI0gCo+W8RU83SlgvEouXDCZ6pEFohF7hLn4TkjoJRhupzn+0rLfXW0ZcjpHp/v/eP3Rf4/31MtjHJmH3EufLnyUsGUf6Dz4seZElQiBoOWFL9cFoxjhDx4xFjyNNKKsL644sPHTxmmsSAMp3HmfCdEilfdijklDJ68/1k5spbLLkOhzGSrpw65iGBdhRiLipLTLKyzIs6TKT9PM6VDtdxrwOyxN/MQbJzfHTLeKCkkIkqaBpyrgIzmWM6GySyXFusdyyWotYRg2d0rYRQkO1xtMa5H1JJywteW5aLCxANVLahkkoJmwRhbuuRJTx75o/8fOHncH+qhHuqhHurLW++0gM5/wU5o8YmWjnEhFQjGlQ/wm9c3/Omf9Pzie4/AeUgZjRGnSgwj3eEeVWHoJuIwkBT6kAl9R8owhTRvhwvjLBqhYOzM3GGOSU8i2ntDykI/lWGwrIYpxDmspfy+ImAceebn5pwLkzkVyoQ6wIE6UCskjVRtC17INmEdPDrfcHmxoa0qMLBZLli0DaSRbr8lDAN3K8uT8wVuWRdPaZrYrBZ4DE1VE0Ki6SfuZqG8NELAkhH6CGMowTDFF24wrqTm2TlhUFRJYWLoD6QUEaM4HJpzsY2441a3lo6pKjnGuWvtGIaBzap0ytFURLgooom72y1qEqhgmgZ7xLfFWPzAMZGjQnZF/MWMJI+olCCZ6MiAqkdMJulEECWagnZD5m66lm50QDBS/McigjiDnVPqjtQIP1/3jIEcyWEsojfrjH8ow6Ka587rTDFhvjfjTJTAFNJK1BLxrrOQVpQ049iYu7dHQsr/u05kird80eWr2UP9ZpWJzN3gQkUpHmwvESHjk+CiAXKh80kiSiiEmFR43MZamqouAS7WchiH8jxnoVhsVDKnI9qy8DD2dFDGVeicdmmcgJk75nPH/lTGYXS2gEh5jRgpiDrnXDG6mHI2rDdMXSgWDm/mx02AYF1hUo/DAaqKqAaTLc57EhMGpaqV2iu2ylg3YFzC2YzkmttJETKVc7SrFgV2uxLA07Q1JjVYXwYWwfCWTn7LMpNPAvqEjnyoh3qoh3qoL3W90wIaCoUDitd4US/ohwEnQkxKyploi3dzuL8j9oG+q7j+44/5+M8+on99w+2rl0S1xATdOKFa7BIhW1JSxikypcJlFutIGoghMIyJYQhghKiZcTwUgecKls0Yg49KSBHpi1/W2iIkinAxRCmiW+bupjWWLvSsmzOG0OG9Q4xFjCXNHW/nHCxaonWURG7Lo8fn9CEyxI4612VQEcGurtgNA41MvH695ebsBqOZs/fP8VfnEDONDbT1GmIZcHy5v2ccAv0Ih6QkESpXkZcNxnqa1YpqtWK1WnG2WbFoV4xj4HA48NmnP+KwKyET/WFLdxgKrzhlhmmO8Z5GvC1e2Muzc6qzJ/i65etf+SqHbsdht6c73MPYkXIkT4GqasDMpIuYQcLcyS/IOcSRnSWPEWMqROJJsGxWC3JMhJRYtJ6UhHa1msVuLN1LSVjrS/S1tVjrwC0piTcC1s2pk5xisxXwxs7DoYnD4RWGjMOCOpwTcsykXJBvOSW8t4xhKkIwlaHJlBPLpqXv+7KYoiRBQungHz3+6cQYFCQVNS5z6zXnsmQzRY/PuyKKuMJgDuOE1DUxBCrriDHjTEECKkqVLVaVaJWsxeuerGA1UqWaKRcbiuoc3uIFZcL5hImKt2/CXhBhiiOmMjhXEkILLcPAVO5xEUXVwjgh87CgPZJocsY4R1O5edDQYBuPMeBcNdtRDDFljHdIzsh6Qe0UYxTreqS6B50TPaXFOcPluXB7MPQxY6ziqok2ZJwLBM04k1ANSKyRnDAGmqbC++KpL/sFUhbk4rAuUzdCGmDoI5u1IacBleWJ522tJamQcgaxhJTwcBL+D/VQD/VQD/XlrXdaQIvMH04UnNcwDIWQ0TQcDgfcPLBVa8SHgFU4OzvDIBy294R9x34YGcZISMJu35E0cxhD6ZRhOIyhJKZhwNaMY+k0DtNEmLFkKSWsd6WTmYtf+O1t27e7TkkLDTklV1i3KSPGoIAYz7J2aBA0mJIMp5R458rOkdFlW1oMbBYt3hmmGBnHkeHQcXa2wrs5mW+IWOcRQwl9wBGC0u16mnUJBREv0JVEPmc9S1dhvBBTRmIRDWpn5JizVG3NYrHg6uqKVeuRDO3Kc7YqiXq73ZYpRLa7ntuX10wxlLhqLf5b7wxn6yXLdkGYUqEUeHh1ezd7skdCLAmEBsFYj3Flq96Y0tmEYmvIs1c2xTld0VekGMnCPKCZuN/vTmi3GEuHvD27JM1Dfkrh99oZmRdmkWmtI4mbg2YsGMFROstFQJtTl1VMwFUeJJcwjxRL3DUGLaiVQvTA4FxFf+io6tJ1b3xFCIGmaRj6CVGZ2cdzl3jWWqLF08xszSiO47nrPP+bgosrNiOZPdYYmYV66dweed8ZBSlpiqgp8wRGMc7P57ZwtEUNJME6Sw4Z31R4PxNlcsRawfkysGvmBeFquSmPaT1uxuwV24LBuooYS4CQWIMmqNumPOZMbzlGs6cYiHGibSqmaQAiqgbrLMY6jC9d6l0XCglkFr7GZI7tbLXCcq2cjYZoK2qpCslEI/utYLJj6gMhAljSaBExWKc4P4A0GF8xJWHcBrzxTJOl9gv294HQRZpNheAwxhKyR7BfeM0fF1ww7zA86OeHeqiHeqgvfb3TAvrtbeujF7dpW/b7PXd3d/g5aS2nTMyJQGQik6zQXpwhmllJYo3j+vqGBS0hK6bNGFuikO0UThi3fsqnocCQ0hfiscvX5XSZufskUgRP2UaeP1R13obWslWus48450zUXD6sp4nl5oxh6LDOkaX4go+0hGXbQk7sdlvG8RF13YLxLBYrcDVoKEHg8+B/mVnKYBTjBF9XGOvBuSI0XALNJzuJGAEJiDXF4mBKyppoEap93xPCiDSOo9Qbhsh+v6cbRsYQmUIEU/ygIuaUzqKYk7jCOuzshz4GpxhnkWiR7ArtwuQyDDdLkKP40NIMLIsVKUl2R8FyPJ86B6RY75CcynBkLtewIDEMOnvPjwLHWn8SP28nNSLlKDgJIykWk5m+YEyxFFgsSjkeAKR0eo/P8bjAOzKeh2EolBb4wt/Uk7/6zfPSt77+i+p4XMYYUpqtJMw0ESjiO2ecHKPAyxSuGMWoOT2f+Z9+4ViOf/9YxhhsltMwqDEGnQVwM3vbj0FBRwF99P6+iSGvEDt3z405WrSLD9qWHZtJIzmb09AuxuKMYN18zkxJtiRNpDDRkKiqN3zsrBlXQ9NW1JMrLnMxpEDZrcCSNUIUMBYjdQn1SYnAhK9b1GRyMqiWHY8j99tZg+aZY42dg1lKymI+eulNee55ts4AZWbioX6m6u9yNPdDPdSXuYbPfucnhrJ7pwX0W+bOso09f0hfX1/z5MkTLi4u2HUHkkQ2Ty/5ld/89+HMQVXx+Nu/DPueT7/7PcKu4/mPPse4imEaCWp4fbMlJGV36BiHwDCMVEE5fva1bVvQdUZIKWO9h5zphuGUCmeMATuLFeypWw0wJU7UgdJltMWikCPvfXDF8+fPWSyL1zTlTNPWrBYtrTW8fPmctm1578kli8WKH372kj/57p/yq7/6K3x7ueL7H33Mi5fXeF/TNA2LGtaPlmwer7l8dIl/fAW++DUnCroNyfRjYheFMQi9GuJMj1ARTO2xvsJVnvfff8pquURTxAloNiwax3q95tF779P1A/Vyw/7mhmmaOE/pNLiVwohoxhuLbdrS2a0rHj1+jIgwDB2vX78mTq5g4zQQw0RlLCIZTMRUNW4OFAk5ISlRG8vQF9FvvaOfRnKOeArFwnvLsqrQpqYbJ1btAqzBip/51hDjhPOKETeL6uOQ3lE8zl3gtyqRyovElG62CjTLBZINaUbjZY2Eocc6Q+0c+92O73znO9zc3PDZp59yOBzwxtIxYmb0Yrm9Z+PAUVAf7/e5S6taPMRvD6iV7jxkzYiFrImYI2aOzc653G95xsgJqSyain5EXFlcWGORpqVtW8bZfnRcPGpMqJeTONZUFmcAdV2z3W5x1tI2zenYjjsyRlLp2gtMcQJVnBFMLN3zQqyx82IjFlKHtdS+Ko9hzBywEgpk0ihtU2FHRcRS+wlNewIjWRWVisOh5/pmx2fXSvYtUSeaqmIcy7Bj205YVzz3tg6ITzivQEXXB8ZpwLqa9abGCqQkhPHAcnOB1CtaN5Byj2jENpaczWkBVPkG00T6vD9doZgeorwf6qEe6qG+7PVOC2jlTccsxEjbtqzXa8ZxpKoq9vs9PkOcBsL+wPWf77nVjssnT8ljIG47Pv/kE6Z+4HA4cH1zh4op4jaC8YVysVgsCmvY+Dn0wzBNE0kzzhWvppHScQspYrw7CYZTUhwwjtOJgVz70jGH2YoyP5d/4+//Jh999BF3T86p6gWLzVkhhMSI5MTlZs2Ljz/m0fsrvvrh1/jOP/l3uL15zrd/8x+z328ZVNlcPebq/Y7Nh1d89avPqCSx3rRcbtbl+OOG4Busr2guXeHUolRGOfvG3Np1NcUDDIgpHUxKZzIoWAGHQ+bAipiVr379PZ5+/T1ubotH+dXnnyJiOTs7Y7/fo5po6pp+t2O3vadt6xNL+vnLF6dz5nxVos+N4o0h9B2EEp3uqwbnK1KO+LompNLV7fqB6+sbApl2seDJ06dFIMaR9eYSiQPCxBQz4xi4OG9xrnhrzSzanPNU9ZKs8qbTzVtb7rN3VYXZSlG+nY5WCpQ8UyScqRhzmDu+RXxO/YAxjqZd8r/9zj8vNhNSEaDIG9vDzAc/dZznwcUiRjM5hLfYznLyQr+d7se8+AHKvescU8o4wFuH6PGnhhgmgkI2ljTvFmhOhGGicyPV5RJXVSwWC5Q3fHOxzBjHgBjPFOZh2Vk0H+PIgdlCE8lT6bgbEXQeKk0JxhRIcxhL7TyZBlVlsVrS7XcnWouIMKXIFBK28uQccRj22x0mKUPfU20GTJvJqkwhMXZrhl0mx5IiOUwTxESYFEfm4sJTV4oziXoxEuVQdpWmBeQFkoXaKo2PiCjZOHLKKCOqEyHP3HmjSHQYJxjv8F7m1EVLmtn0Ys1fj+P9UF+q+mli7B7qod7l+tskLv5dqXdaQAvyZqucsm07jiOuLizj2nlSCmUOzBYqwtlqxf3rWyQpsg/EMRYU3BSZpkzXd+z6CV81YA23t1sW7ZKYE6vNOW3bApwYvknjHGldkuvGaZrT8zxgaFdL+m4o2Dfg8vIRBuWDywuO4SMvX76kahsePXrE/vaGTdvwwdUv0MeMek/dLsg5c/38ms3mnLvlmkVbk23Ff/Vf/zf8k3/732KZMucffAASuTALzJ9/zvrigiFmlucrxpxJVKyWZ9BucM4RFLzOsQ4CSLFdcEpK00KRkFxsGOVbGApD12gJdUlaAkNihu19IoRE2zZsLi6xpgznrTbnhDiSQ8TWNcvNBs2Jbk74Wy7XJaxjtuKkOGGsoKLYqkZmX3MikTohZkViKkIxZWLMTDEXwRQCYba7rJsFMWZq5xh2OzRDXbcnMsLRapO0DLIBJbo8avF+i0FnQkpKiphiW0iApBISE3NhhSPQWktV1Yg6MsWTK6JMk+J9TUqJzWbDNIyICOMYccYxjQHr3Ulw5pxZrVYc+g6dxXKxNjjSfOwn+5KU3xGJb+5NVQQ3z0EakDK8hy27NDmUABPjHRJKIqTOXt0C/ihBP85V6HyOnHOM01hCf+rqFFh0FO5qik2haRqMLbafECN+XhAYUzzPIYwY4/DelgUluZxnLRi/GCckOurKkWOeo97LojUTULFUdUuMicqZE+ovobjZHhLCOBM7FpAqJE1IyjhnSnBOVioj5FzeN5qqJArVzqLBlJ2MqiXXS16/3LFeLvBOyv2cy1xCjooXx6r15Dwg4hFtECnpmr5yVFWkm3rMfD6t8QgPGLuH+mJd/rM/fuA1P9RD/RTqZziJ8M2gzrETPMZQwjRixFUes2oYd0KMCacTX3nva2Asu+s77vtbri6fEqeA81uW66dkMYwZdv0AGN7/Sknayyjb+z1jVERhmCZEim80z4ODxhhCAownYlCxDPc9ikFUWJ1t+OhHr6iqik9ebAmhHGvTNPzce+/zxz/4DKX4dN12xFtbyBt1RUyZSjzb/chuiFw9eZ99gG//m/+IJ8++RkR58eIVTx+dcwiJXoVPP3vJV77yFeLNPecXa/oUqWOkkULLVjHFtWmgdE8tRuxsVBDK4GQRzKemWbFiY6WIrDyj0mKG19cdQ1QOh4672y37QxngS7EEyNRNRX/YUzmDt47aey7OL6nr+hSlnHNGk5KYSEmJKaIhIDEyjoFxCLPf1JOSMoxhHsyyLJdr6mbFePOaaQyIWO6HCdSWgUVTceh3XG3OSSmRsyWEYgWpqqoQU0yFqyua5Yrr23tu7rZUc2R0TAVzl1W4uroi58zV1WYOMhkxRqhtxXa7xeAZD4G2qcrfirNAVeXu5vZ038YpoPPjj+N48kNbW8RljiUKvWkaxnEkxjzTjTl1po+2oKyFSQ2AKR54J5YYMqqJEEuKZi5uo8LcdkKKJSDHiMUZX5IDCSVim8x+3ikZw4R1xbdurUVzweJZ77De4Y0jxcQYE6uqXNMj2g6YFywy0ykKD32xrMk5U1mDpaRDppRwTYuTYiPanF+gqlRNS1IpAUIzYhDN5NCzPruAHKmaCbtMqJv5yynzta96Lh/V3O0dyVqUMuT44vN7xjET04Hd7YhBuH2dUBNQUXw1crau+YWfu2Kc9uRpKMPFQbk4MzRVT+z2YCp8BcZYxlgGXcuiJtOPZR7AWEfKikmZul78JN4NH+odr4cu9EP9TevL3J091pf5+b3TAtpYU0gWFPE6hDLd//Tp0zI8ZgraLNztybuOzES+2fO7/9fvc0q4uD8AACAASURBVNh17F7ecT+MBE2YquX69p7toWM7joitMdbSTyPeWMYY8E1LSpkcU2E4z13BQg6YsVUzh9oYg68WRAXjPcZVfHh5hraW2zBCU+PXnhwicdHwP//f/4rlsmW5arm+vSbGyGW7AM0YGQkh8N7lI3wtPH7/A17f3vLs2TP2+z2/87/+Dp8+f8F/8B/+e1Av+PSz77Hf9Zw9+YApCl/96tfJU483S5w0oB6SUlmHqC1eBCkcXjFvgoZP3tqZIiGzl+HtIDVF2I/K7d2OqJ6x66nEcne3pWkq7u7uCaGEyEjKLKqWbndPZGJxeUmMGWMSMfaFZjIvfnJUjGY0xSLgxWLtMU68dIMrW5FzYJwmosI4RGKekxWnYndAE+MYIE5crhaYZSi2hyxoFjKJZVvsAruu49Hjcyrf4JqW1ZljdXZ5GkpslisQy/r8jO12Sz8MvLq95dHjM6zxxUtuDV3XQbbEPkCsSbkslMI4kXMsISNwYmHnOd6dOcCkkDWEfir3s04TcQqncx6OFAspnf/jtTqG+pTrJiQxeMpAYdaMoeDfsqUMGQJ2DOjQk0zAaEMtJWDEaMQSaTTSidJPE13f8+TxBeeX56gqU3JkM7Far/H1Aq+GaRjACOuzzRvPdJq9/wlWmyuMK35/4zx13bKoK4auJwwHUphQEsZaxmHAZM9qtaLve9q2JSalaVpiht3da4SJLJFqaREVLp60BD2gpgdKpHwIn9OslM1jj0iL4PDVmm9+84oQHR/94J7x7h40EZPn/Ooph35f4rrjDWIy7z8r5JnrV1vOzx7x/MUPefzkEmfXmDDh3ABiGbd7jDsjZmW7O9B1A2nKmKQsFgusr3FV85N4O3yovwP1EKX9F5f/zV/6aR/Cl6beFt1/XXH6syDU/zr14z4H77SAhi+SOIDTlvKbLXpPUjAixGHC+IYhJqaceXm/Z8iZFDP9/cj17T2HaaJLQsgBnZFlTdPQLNbcD4HuMM5bxG+20FNI5FkkTbF0Pp33ZNlhfMPqbE1brfn0dc/d/aFEjbdT8YvGido7Yky8eHGNf2WYxh7vPT86ZHzdkHPZ5l4uA/Hmjm+8d8XCwW77mtZf8Kvf+mV+7du/wdlqDQg//3O/SBcdH73qsW7BH/7Bv+LDZ+fsJaBhxyM/Yi7Wc1fZgbgZP2wgVSdihpn9t0eKgwpMKXLYHpjGke12SzdG+gQhGK5vtkxdTxh6Gl/x8tVrrLXs9/vTdREyF2cbKufJ3YBbtjRtS9M0RWSLEKaRFAbQRJpGhMw0DuQc6bqOGOMp/XB/6Akpse8G7m/vuL694fuffoKpCm5Px579vqO1wvZmy9myprKGHMMcqe2K5WbuguuMqBNjWS4rbu93fPz9P2PoJ9rlgvc+eManz5/z3nvvsVgsqeoFInn2qCs2G7a3d6RJMUnYxgnv/SlpUmeBa0/hKHo6N9bXdF13Gs5jPudHi0QJohHS7K0GMG/5o/Mch13EtQHx4AaWZ5dEhJAi0zSxaFokFf8xXtFuTzIBJjCNRTTj8oRMiqsNhs0pDKVZlHh6FcGaiNiIq8twqailqipiLrHuISW8tRjnsPMAralbmsUS6z2r1YZhmBjGkeyh8RUpTJADKQeGYcI3Ne1yQcyJs4tzbrc77Py3nj17Shz33LzqsEYIcQCfgSL+j7Homg3KQOXKoJ+RmpAnVFrW60sur9Zw7jESyGRCGpAm8d3vvqKyT2Y7ViTlPf3uNVebc4SAkYCb/27lLKqWGB3GFJSdc1VZ8OWMmoKWdKqEtxZDD/VQD/VQP87624jAB/H8k6l3WkDn/HZ0cdnKrqrq9L0y0R/QVCb6g+aCu4qRGCMq0A0j0xSJWggEJlnGviNTPIsBRVTo933JZ3MVOQVCSicnY84loS9RAhPGKRKTMqZMvbRsn7+gbu75lV/9Nrrbk3PmsL2jbVv6/sB+TvFDlIni36yqisOUWW3Oin+UEmPNoma3q7j9/IdknVjXwthfYcJEPqtgdOzuXvOnf/SH3ISKYXfP5cWG7fVzlrrBxJawcfh2drsGJU2ZmDNTFlIvjGFi3/WMMwXiOFyWNDNMEQ0lGMZZT8SSk3LYD6RxoLvfkkLgEG7Y3e5IaR6A5I0QrBG0qaiN4hpPToEYLDGlNwi6VBh8YZrK9dMEqbCJFWGcO67GGHSa0BROQ2aVsyXmu+/YLBbEKZBrB9aRUqEjNO2CoEKMSts2RAxjHFnN/OYyGCecn5/zrW99i2EMHIaeaR50++EPf8jZ2RlnZ2czXq3CmoIx26xWjF3puB92E84IU0plaG6+lqIFUWeMzLOJWgBoKWOMLx1j74g5IWrLoKHMWLR8XDiW+09nP77mXPw1uSRdYhVNpaMfdMbc5TmKPhfzTZaMpEjShCVhcijIQA1kBaMBZxVNgawTHAd3jSL5zfCiiCWnfLK7HK+1mYf/NCWsCtY1WN/iqwrnG6bdAescMRauekoFIefMvANizRfoNTFG0nztQxyJYSxpglVDJpMlYBgRDSiJhJ3FbeGCJ02IhDLw5xuEspOhJhHDiNiEt4Kqw8nR58+MHWxQIwzDUGwpKReXky07NDlljHg0C9g3i3vrBGfsG2/6Wwv+h3qon/V66D4/1Je13mkBLWJwR8OBMdRSMwwBbw2qE5URdj/6jGn3mtgd+LVv/Tzf+73f4/aHPySpsNveM46BPkTGmBgyTDFQ5QkxBfsliw3LR+9T1S2Vq3h9/Yo0jWh6DaFDU+n4KZ4pCg7BmhJN3DhL7WDZNlRNw7h/TXfzAiURQ4dOBW3lrcO7Iv6sAdOsAGiq4u80xmCdYCWi2TCMO9qFp3E1rUDFgIl7rn/4kjo9o3/+GV8/s1z1keubj3j5PBJWDeFsjXOGl59c8MEHT1mv16wuNsgYqUTQcWLcRdIYiKGIj4gwJCHgSXP4RtM0ZRAqQds4jEaCFPGSXSJo4tAPNCaXEJrQF86zCPViQRrviVoji4rpsCMOPYN3DGPpKnddR7/fMg09Q79j6A7c3b4m58x2u+VwOBBC4O7u7jT4p6oMITKFUHjXvlBRdv2h0FjsmkXVkMUg1QK/WrHebPC+YOx8U3P+9MjpTZxfXvDxDz7hcOgZxkDO4OsGa8vC6vH7j4tvu++4uDoHsUxhKIsAcZA74jDiNZL6iMQIOcw8bbDezx3phJmPwTlbLDQpYAGNGTNTNlS1UF/wc/d8FnXGlyh5ys9jDBhVnHVkIyRKAmKaAjkoEhJT7so5EqHC0boaazJOMpWdij/ejsjSUpvIemEpqes16zNHu1wWdnLes6Jis9ng/IppXpg+e/YMX9c454q3PAQO+55qVfHkq79IVTXEaWC3vaPylpQCVW3QIBhX46jIU0/VeJw1uKalTpFshIury9LRT4qGQLVsyH2NSMNqvWKxellmGkp4OxMTJmZycmheoRJJTOAmhBYh8vjyDHEbXl1HYhy5ub3m+tUN7713xfZui1jLZvOUjz76AcvFmu3hhuXSs2od1gSSU5AJ71v6YY+xK4a+Z+x3hNCjsQyfNk2DlUTWhw70X1U/q3aGn8VBwgfx/FBf5nqnBTRoSU9TyKoYYzFWis90jmoedlum/Z5l7dnf3fGHv/9H3O0OjEE5HHoOYyDETBAhaqFiZLUFJ1Y3tI+e0F5ccfXoEX3XsTvs6YEJAXUnzm5MoYR6ULpked56RyeqpkFCz14SC6/cb7doHOlTKp21rG9sJymctuKnaSohISnhjKX1Bm+Fbzx9zJOzFU3luP/RD7j+7r+kbirG4Z7f+Pav8r//83+BYnn29V/g7uNPeXn9io/Ecxj6OV65xEgvVy0pJX7913+dYRjYbDY8On8C1uLaFclXKI5RAd8gYomaSeNUgkusI2XopxHNwmFXorhDCBx2e3a7HTkUtvUR2Xd7d42V0lWcpok+TkwhsDt05VyozAJpQoxiNTEOHdM0EWMsCZNvkSreJlLcdz0xB9QorvJUVYP3NWIc1resN2eEaSBgGLKhVotzhRiRVGDmHScN7LuJi6tHLJaBP/mT7807FRN121BVFYvVkrqucdYzThHjK2pnYZqYUhl4jMMA/w97b7IsS3al5327c4/mdLfJzJsNMpFAscCmiqwiRRZBowakNCijNNBcegBONdJQegNpJtMDaKyBKA4kk5mMRkoqsoxk0QSgEk1mZQKZedvTRePNbpYGa7ufuDeBKrABeQ0Zy+za7c6J8HD3OPHvtf/1/SnXjnqusd4qoKdES01YVDKGbwYV9PbOZJ7qcKqgi4TRWkq+G5Qdx1HfCdXGMY5jRSdmvVZ1fbnf9XjvdYFoPd5Yjd3OiVNnsN7qsbhGB2KNwwaH9QZ3fs5+H/HLlvX5OavlGSKRvL9hcf8eF/fvk0tDKEnJGE1TCR5qjzE42rbl7OyModuy29wwDr163Mk0XrndIoIzlkXb4ldLTs4uoBTGsefBw7fU+hJapYCsWobNht3Nlnv377PdjTSrVhNBiyeYgJhCMZlie0pUdF0x2skXcVifMWaA/Ix+P3Bxz9D1I/cfvsG3vvkAKZ7rq1twoPb3JeOQsKXl4rTB2J4x7QnOEpoahpQjrozkUej7HsmpDuQ6DSsqGXNsQP/C+roJyKmOw4XH+nddR1/zf/h6zQV0jck1IEXtBqI52vqfObEMls04EE1ms7lLNOsGHcTKSQCrUcl1gEsEsJ5iA6FZ8ei9b/Cb3/kOH//wIza3O8ZUcO2KMY6IaJS0EUW95ZyQotvNpoodUxrGfkfJI9vtVm0NeZwHrEpJ+OqRBO1I26Lb6fo6Mkay7s57C6Jb3FZ0SO72dss6tVycLgjG0lih6zpKv+d0FeiWgee7TLcfcF4FqrWeodehveurLc+ePePNNzPPH18qy7hZ4VZrcA2EFtcusNZX23SLOE0oTFlTGifR3O1U7Pb7be0UZ2Ic6PsegKHbz15n5xz9OOi58lPssyGnSM4JZyElHaAc6/ep4JywaCCSwShOLjQOiQUbrHqHa0fXuWojsA4fWpan54QQEOsQ6zA+YL2bheyYMk275PNPPuFmu+O99z/QlElrePHiBV3XcXO7pSCcnZzW6y4a3FHvrxgjJWccQin5lfgV5lQ+EeZzkePdeYFpUVgjsOvXW2uJJc12FTN7qc28CBMRmsYzpSQ652Zm9d0BKKKPEuewlknZGTQKW0R/TZ5s551aFQRM9V5rqqPVhElj1TZRCkQViyLm4LUKaewrV1ltMt5pTLsp9fwxvQc1KMZYz74f8Ulf10mz1MRA33Bycka3vcI3AdcrrUTTLy3G6jF5a3UXxBbE1RAXwJuW/RApZUfOmoKYY8LbwthvoQjOtpycKI5Qyp7zswYpiqprXCanAalWGGW939m61HZlcAasgrUxMsWs/5v/vDvWsY51rF+mjuL5P3y95gL6Lh53To0rgi2ZNHQMu2voNqy9JQ89z7sN19fXjNkwDpFcVHynmEgIuQglZ4ptce2a5fk93vjGN/nWn/tN/qO/+ddYLVtudj00C7Kx3JSCDB0SR+0KUshGQckTIcGTKeOOlAqxE8iRWLttRtuKUCIWrx2qXLCmYI2hUEgx4o3gsmHpAqumwURlV+MMbRvYxUzqB/7y7/4FHrz1AFxh11+TpMeHwtlZy2dPn2oEOJqAOBJJQ2a5XPL88RXWOj7+0Sc0NYZZQsvtrqdLCTGeLmqnN6VEsz6r/tyCDUtubjZVNHdIjqQxMgydLmzynQAzVuZt/QltNlEk0tCRK4LNGo+xQjYwDh3eMEd9tz7MHXodzqteaKdc4VQiKStubfLQGutxPtQUSR0StM0S2yzJxhN8i1hLMQZjHbhCEnjj0dus93u6fqDvO/b7Pc9fvCDnzBtvvsk777zD5nZHzAbjNF0llyp4ndNhurpIMtM50FjDg/TAu3akMboQo9QFwPQfOVFy5TfnOz/wof9/+rMOvEHKGUwdCK0DgJm7iGslfdQETzcdg4o86nF4Mgn9O9bStHrfZwQOoteTgHUtJQvGlHmhVVAMYXB3yZw2RfquI49Rg3UW6iu2Box1s097c3uLtZam9QTn8M4xTMFDLjCmSMjCanWiOx3G4ZzVOQQyuYbhRLGIPcX4gnNgTQDx9INl3Ccg48NAipnFasnNzVZ5zcbqXEKOpBQJDZAzyEDOA/t+xFn1rVvnEYQcE/rmr8xuRP3UOeOs7h45a3D2qKB/Xn2dus9fR7vGsV7POnaqf3X1egtoI3cpcaDcWArOFDbXzxg2t5yakWGvH4qff/Y54xiJ2QGWGEeGIVLQAblch6J8s4CwZHF2n8XJOfcevkUTYLU64fziPl0fOb94QHdzzVgE4x3eCKZkChZXO6RYoWCQIoipwSslYo0m95Vcqu854F1ArNDnPaVI7f5VwZUzwTta71h6Txw6ul1D27bsykgbWmIcuN6NvJkLm812tjykFLHOcX5+yu1mjzGG5bJltTqZu51fPv68dsYCw2ZfAzZarnY9LgRisRjnKw/bMuRblqsVu3EglR1D0kHB7faG08UKKAzdTocyraXvRrwxNE1DEWGoXdqURxofgKKdSl9DTZLQLgLj2GuXOqaXRKK1lsb5WXpO4Ssl57vBLedYrVZ4H/Q4XCBjSGOC/Y6LB/dZLHTgNCW1PQTrkJKxdQfh5vaWn/zkYz755BNyFt5++20++OAD1us165MTrq6u2PeJe/fvk8lIURbwcnVC3veMZjsvIFJKc3fVTAN9r5SrXWRjREEoxk4tarVk1M69HnM6QCia+dxol14QHGISGEXGueAxYpC6MKPe885AkqK0lZIVZ4joEF+OeJ/JRjvhoW2wwZNzwui4q5JSciSRMAc0kKkb7kxNDxw1hTOnAY9GeueSsUYXF+MwQi5zZDiSOVmuuN1cE1Oi18xGzsw5KUel3BRlSS/WK5xvSSZhLRSnSEBrLRbHYnGiIT6loCnaDTkZFjUeHEn4hSMNHa33OOdJUXd3kELrLU2wdCnq6zK5dpGFlDLBLwGDdy3BeZDAaHQo2Dm1Bi19QwgO33oOF03H+nrWUTwf63Wpo3j+1dXrLaB5pQuXC1ZQKkOMOFSo5WqBGIaBEFr6cSSljFij4jkLcmBMLAjeBQoGH6qfEw1rWa1WnJ+fs9/uWC6X+JLZ+UYFb9EIkmy0H26LmR938qhOAm867mnrfWLmHr4mMFhBA2MAbw0lJ3BhFioYRWXlPDL2kaEbkFxweCiGoRvAmtmDPNkURJRLO22vxxjpBk1v67ue1jaMY8SUQsyGZrGkZCimsF6esNvtyNbgQsO90zNub28p6zXj0GPMnYCqV4lSVPRNUc9CrlaFMqWFYzF1Gz8To3a7J3+4PWAnT+dtSi6cnmu9XpO3+pyH5AbvPUkKTbvAGwhNc9cRnawR9ZRPwnYYBoZhYLlc8ru/+7t0nWL7/uiP/giAP/8Xv8PFxQWLdkXJGWNV6OZS6HZ7hj4qa1kKjsluYWeL0bQ4OvydnyOqjann5MCmYTBfwTdO52bq8O/2AylnjDWYmoL5UnKhUw90qTahVx/PmDpbAEhKszXBGIPzhtRnuv2W0Kb5+7969HePOwUEOaP3sFRMovKhc6WC3NF0vHVYp88XQqBpGrqh1+CZlLFNU4N1elLpCU1BvBDC3f0xVWgcGA3NSSJIKYqYm47PloNTX3GBVvS6WShFudRNG+pOiqMUqV8LNbsREY1jv0uJrDsq1QU9HdfPWzwd61jHOtax/v3WL5NCuHjnP+aDD977N3r811pAzzaIqUrCIdzevEDGDevWsbvsGLqOm75nc7tlHAtDzKRqlR5TZLU+Zbvd0qcRrMMZneHPxbJen+JEBfH7777Hj3/yKS+ePmd/e8vY7aqnUyhiKMZiraEkxYJpo8mCqUlxpWg0dtEPXLV6WJwzsze75Olj904kArTW0gAuZ4I0jPsOiYmFD4TQEhx89pPPOAFCCdx7eI/uZsNb5w/Z9R0pvUAsFCOMOZH6Hbe7W1arFffv32fJErffc9asERGuN3tOzk7mBMQoEBaemBN93+sQ4nrFX/qdv8bq5Ixnz57xr/7lPyftA323Y7Va0sfaVcQg1CG32NN49WAD1ccsFV83sZJ1I947g0VoW8+Q5CuLEIqSJ6YO9HK9qh1ftTCUitvbbHacnanIf+/dRxjRBVUa9XWESsRAMilp17FxnqHvOV2fsDpZc35uOD8/JwRd6JysVpysVhhjSeOemPXYhs2GYRjmxYMxytGe7tdJQE+i+VC4al/4sArg5sfxFQmXqgVo/qp6TpzTjr9zjjfeeIMXmw2JCelY/dTOYb2b6SMxDQiWlIb5GG316YpkbCmYMuJdi7cV3WZG9rtrrp59wWr5FuZ06nrrUCRimUSlAUrWBETySCqJmCJktSm5oljBYB2W6l+msO93jLGfLSkiQtsuEWPxNT0xhBbTLAgZsI5RBl2kGYM1fo4l74cbXXDWBE0xgpiI8YZioJREIeO9xduW7WagaRaMY8961TCOGiyzbCo5pXhyVqEcR6Fg1XZTHAVINX681EXfoRd9uobHermOHdmv5zmIf/jRkcRxrF/beq0FtFUHIgBGBJFIiSO7q0vuLTxWIpeXl+w3O66urjS0IQkxZsYi3O72CI7b3Y4xjjo4hW43lzFxYRxPHn/BcuXpdjc8+dlj/p//959ydXlJv71hvL2m5AEjQjaWgsFIxOSERekKUnRb2ZSK6GDqROb6QSqQRXWSZLwzGKmcXgsFjTletR5XIt5YTLSKzyuZodvRW49zcLvd88XjJ7z7jfd0UHIsPL+54vZWg0wsghT1+QbfKN+3GDYbjdwuFMgdzgUuztZsnj7ToBFJBBtYrVpuNgnrlXN7eX3N9/74B3z20y/IOdF3O3K3wyIMu60m+nmvuLe+nzuQwIFotDp0lhIxamhKCIFF06p4Ksqo9s5QilR7wNRhNJSoArptg0Zhh8BYIk27UNxeFZ45Z3CG6+trco5c3L+PcSp4pk51Fk2u0236wptvvsl2u2Vzo2mKT5484erqCuctH7z/DQyFFEdsCXOXWF/bXXfbMg0EZmZX85/RgJxsSSq+M2ARa9TP7Ry++NmqkSs7O09d3LqY2I83mBBoQoOxliyiFpGp664nkZIBZxmTynfJBand5+l8lDhozLdRy0WKkdjvCCTFxaWIbSbLw2RTqdYTgFJwRpCsNhZjDFEKMiasq3aqFKFkvLXgPIuTc0L1RKeUyEYtLTnJ3IXvbrfkYU8rPeuzc5ZNy1CS+qlFanomWBJGDCUZHTmQhPVQslFMIA5jdAg5l8Lt7ZbTU8fNzS3OX1CKpe8iTWMIQbDGkdF49DEnlkGvWRa1IsUyLdD1frXO4YKfFzfllZ2Dr3t9HYXjYR0JHMc61q9nvd4CWmztdumHouSiKXY5423DdrOn7wftuKZCKro134+ZKGpbyEboc6aUjA+VAyyF/W7H9fU1uzzy5ePPMEXobvfEIvS7LdvbGxg6ZfKaMg9qlVKgejtt/RA1pW7hStaQCqOCwpq7jqQzQQkHqPgwUiBD4zxBOX3YecgsYwxIToz9QFwXnLMMKSPB0C5W3N7esh06vnz2gpQKuTQ1qkOFSEHtBo03rNZrZdR6S9d1xFwYxkEJEpJxriGmpGEXQ0fbnKqokcKzZ8948MYb5Jy5fPaYbbfTMA1Z4sTNXVHvHGJ14XCXomdh6sibyaag9oHgnG73m9q5rdf8kDQxCRSm1MkwDeoddG2NnZFxk1WmG3qyTP1RQ8wT7UIn9wTY7XZ03Z6+71muFoTB8f577/HOo7fY7/eUOJKGkbZtSUYw5S6aOw2jYutyVssNlcTAtKtwaJW4U9PGCjVVZb4PTT0Hh4OHhzaAwxTC+X0x2XQqjeJP63gehg4ZE5R+wV1Qi0gNXrF3z52LxqRPFotpz6SUrFamnHUg05g5HMZMj1Wvp4ax2Nn6IVV83z0HSsJwlrBYVtIODCnXBaiBopHsYxxZxoQ4EHv3HGoHMoxjwhCQbDFYZWZPoTNiNahGRyMxJmBNQ/ALgl9qimGBbp8R0WFAa8vBeU/zgqfUKytSA4gOrDeT1WiaGz7WsQ7r67yIOHahj/XrWq+1gDbGUKxFpFCkh/6Wq2efc29lGfotT778ktvL52SjGK8skHLBOIfkjFhHxmJdIUqhFI3gjf2AL7dcf9ETjdEt5uDoNlveffQW8fYaG3soCWuUCNEuHIlMLKl2GJXGYIx+2GZJFClVZAjF2iqGlfyRDDpoaMo8Y+RlZN22eIQwkTlyoaRMcJ6+2wPMorEbEm+//y0++v73+fzzL9j1kTE6QmgpSYckvffKDraW1WLBetXw4OwEkcz9iwuu2wWPHz9h7HvapqFYx4vbHYuTM663O6I3DNdXLJdrSJlx3LG7uWbo9lAywQolR4Y+Elrd1r+9vZ0FX9P4OW1Qu8qR0AaMsRjT0jaexjs08U6/PsaoVIXacT0UkgCxHyqqLdKGBlcywa8IeFJSYT2mgfXpfc7OL8gFxiHRdR2np6dYapd6jPjGEYeRVbtkv9nSbTc8vrkhpcR+3yMiLJdL7t27B0BGtIMpBVM7z6vzUxgTwRrMODL0e4yBMcaviN/pz0rnMKQx4oKfhwObdglZmc4xKv958vVPHc2UUqV+ZMYUtfNaE/zWiwXDMMy4OWRKMKyDdsYQAcKSFDNW9hhbSCVgs8PYSOk7/MUpg2TabIAeIzeQtxRrGY0jlEyOuohNknSBaC19jHV3QxjTgBMoKRH7DhFh0TryMGBypmBI9ZzsdzvaEBQFtzxToolrSVkI1lJkZLFusIsTZAjsZWDtGnzqocBoDCKWkgywoIhB324qvktdBGAKxvbYkjHWsu/22GC5ur2h63uWJ2tKMWx2I6cn9ymSSDIwIgwJYlzgaDUFMaLhLNHikkAsOOPx1ungqLe6zj6CoOf6OgtHgKu9ZQAAIABJREFUOHafp4p/+BGgwSqn//X/8rW/L/5t65claxwJHL/aeq0FdJYan12UAHB99Rzp99x/6z7//J99H8kqWLpuYBgT/TCy70eGXEhiyRi6flAfJNoJyzkzlo5gLMaMeOuIJTN28Jvf/jZffPEF/X5PMLDv9op8q2EfU6epcDA8JYUi6WCLXQU5RgcOjQGsJVYPaikFU3SwzgMLq51SjYA2OKes3ZgGgle6Q7fbYHJLCC3f+95H/PCHP6mhJI6YDNZNok0H+ZZrTY3bbG64OH+H7XaLt4Z9u+X68pqUImenp8SrDUPfqV/WWZbrFTYXSlCPcknUJMDCyfo+t5fPODt7QNM0/PCjH2Oj5e1Hj/j0009nz+2cuFetFaVQf0/4Vq0Y49DhpoHJUcNBxKkX1lbrASgn+bD76pwlpRER7TQ742h8YLPfcHJ+ws1G+b4XFxd479W7XgpN02IFkmj4hREYx5HbmxvWqxXf+vBDrHNcXd0wDBqoYq3l8bPnvPPOI2zwWLF3Xcmsg2o5q4dYRW+a+c6SC6/WIT0k5zwPlR4i6yaM4OHXT7aNw2HaUgre6704jj1N05DSDe6VYcLpfo0lk5HZhmCk6PuhFCRBGeNBp1h01sAXXehYo11dqbaJKXJb8rxbUFJdNAr62DEiRV9jTlEDYWLCN61e1zx18yPet+x3O1zT4ByUlGfSzvOnTzQrMxVO37ogIUq8AZKrJJyUMb4uGozT3Q5xIKG+TzPGZF0AZUNwgfNzjwhcXJwqrz1HTtaOlHswnmItm82ekh2UFVPzP+eXve3eOYpktV0Zoxaao3Y+1kEdheLLNQnpY/3b1S8rio/i+Vdbr7WAlrpVS/1Q329uWS8aXrx4hoiGe4gI2+22DvnVbVZjZ/Eq1pBL1iCIuhfrjJDGQQVGGwjaHOQHP/gBbz96E49ht719qXt4KGD+1GOeBh9tTaTgjiBy6H3V7DIN0LBS1ENtClIRaCmlioXL9Psd1hQW3vDZp5/jbKBkSGKw3mi4i3k50MJ7z9nJmRIqYk/Ohd2+J+fMerFEnCU0jnWz4s0Hb/LjT39KTPCt3/wOH3/8MRlDs1wRFkvS0NMEx7DXYb39fj8PTG232xnjtlgsyDnXYbzAfr9nHFUQrtdruu1uTmAMFYEnKRNL1ujwn3Mup26sDm3pv3vf6HmUOzvDOI4sl0t97BB02Cy4WdRP5ybnrAzgnNlut3z++ef88Ic/5PTsjPfee5+HDx9SSlFPt7kLRJkJGcawXC3Y38T5OllzJ3h/0T1xeB8Za+aFgTv890qt+LMsGTlnWueQ2p323s/HVr1E89frOTakkmfLhvp2D+0haT4+SyHXoc/5OIzM1pxXBb3aMeo8gOjCsExBQ8a8dA3d4XuoFPqUaJoFQz9iS2HhWjA13pzCyWqBR5AMiFGuu1iljJDqzwZTHSYH79HJVy5K0tAfBPpngyISU8VaGik0rcNKW0kwljIC2WKkIWUNfZlIM2JN9UJPliFbCTPlKJ5fqaN4PNaxjvXrXK+3gC4FSZngDV2352QZkKHn009+zOXzZyqUivodfViS+j1jKiQgFYjpjiucslDiQEEHtSACBhkS1luKCN/88FtcX74gx0hKI6ZkZeIaRYV9BU9HmUWJqR26ku+ICTpgpV07YwxSW1nBW8iFlfP4yRNdhw1TjXF2FWXXhMDQd1gDftHSd4VSLGPWYaVYBrwXmuUSb1w9J5nTs3tkSWy2W9584yE5R3JJ+rsVYk4UYMyJ/sULTs8uSAKfff4zvvt3/i4/+uRPWJ/d4/TigvcePeIf/oP/lTfefVdFsMBbbz/i/sU9Pv30U87P7tEP+9luoEmCRru9xjEMA+v1miFFTMka1S2QUyGmqJ527nB1ih1UsWu4Q9mZyuQOoUVSJlc23enJOacn56SUNIjGCv2w57w5w2IYhoFsou4CFCGT8d7x4Ycf8uGHH3J5eUnKmc8//5wf//jH9H3Pt7/9bYYUefPNh+TqnZ4WVCcnJ8iY2F0+x5KJMb9EqJssLPAyhnGybSxWy1n4ToI5pcRyuZyHB6fvOfxVpMxidOqSd9NCcEoTtLqDMfnKpZjqGbekmHQHhMpyrlaPnEaQiEFnBUpJirmzkLJ2kKUod1xywXqj3eb6WnMuBB/IdQBUcqw7KjoGHEIAY++QhJVY0baBIY6YhUesYywjDsHkwrjf4AvkGDHF4c8bUo7q4zYeMQZDwhgPhZmEMlmDsBEjhol7IpNv3haMLQTJKqdF32d4R8mONAj7vmA4QbJjHAbtmBvLGBMGpcmUoug/5wzBgK0TCFZ+PgP8WF/POgaqHOvrXL/uFpLXWkBb5yh5wFrPiydfsoh7nn7xUzZXl3hnGIcOcPimJQtcbbZkYNsPGmTiFhqzbMGZMjWqFEfnAqZub0sqZCl89tmfkEb1dDbeU3LC2hqnXLJ6lOug3OEWe0mT8FAfs/pdsxIa5G7YrXila5SkASOtB5MzFO2KiiQePnzI7e3tnTc41yHKmOgKbHYaPmK9Z9dtadtAlqTd3qTdwbOLc15cPtNAkHvnGGcxePZDT8JBEaKAbRZI17PZDzQnSy7u3ScMA3/0gx9x8fAhv/lbv8Pf+U/+U+6dn/LWu+/yf/6Df8Dj8acUsXR95Mmzp6xPT9RG0LaklDg9O+P6+lq7q84wjj3GGK6ePyOOI94ZYjGkcZiHBUuKiNdQDh0m02tVcsY4r7YOaspeMcoJDovZDvHixQuGYeDRw7+ENYb9fk8btEt92JW0GELb0HVqW/nyyy/55JNP2O12WOc4O7vg7bff5vT0lIuLC/qodpGJqiEijENiu90ionaZBmpnNs9dXfkFIurk5ATvPUMcaZpGw2Eqfm6yvEyd5Fe71q92t29ublhX4X34dcZOzGIV0BMG0BSNMM9isPX/Tf0+Uqq/IiVHSh41z3GKUs9Jr4XoNXB1EtMafazpvtegIhX5IoIzFrdYMOz6OmBabSzeqRBvPP04cnHvHv0wYAx4sZhUKOOAKR1n6xNubnazNajURbEpipH0GDQfsNp+jNqqJla5dp49eR5szNqhNrZaTBy5wBBhd9vTBocPZ4y9sNn1rBZrpe/gyakgNJQy1gVIxhuDE6kLYQ2p+aqB51hf1zqK52N9net1F8+/DCf6T6vXWkCnlFg2LWO/I5jC82dP2G5u64e4YyyJWDL7vqcfEiG0xE6RXNg8+0vJNShCJgqDIaFCpVmdMoyJ5SIQY8QY7TRbazBNW/myebZjFEnz8RljlKwAOrxVCjhXO6lKaLCoVSGXXD/QUeyXCN44jCRcDZVIo3a0vPcvdSJziQy9kKzGVRtriH1NTUO9l+MY1ZeLZbfTEBjnHDFGNrs9IIyp0I+R5XJJHhKjZFIWsNolvri44PmffMaekf7xY8Lix1w8eIPddsNH3/tXPL+6ZhgiIbTAhkzWwTfviJ0K+2Hs6utPGOPwBmIciZWDndGBTVO38XWRYisbOhNz7VjWbXKHDlBq4IiZkW6+irEpcfH+/fuM44gLgbEfaEMzWz+8Bde2xGGcSR3OOdbrNR9++CH7/Z7bzYbr61u+973v4b3nu9/9Lu1Ko9Gd88Q0AtpxXSwWPH1xpYK+vi5rDSJ30innPHeYXw3XmAWmc1h3FwHedd1X/M6H33cX4MEduo87TN/sr/b6fNNuiHWONJbaPc3YUP3DEw4ul9pBVduRtdqtFhENPLFqiZLMV44rpcQ4DJys1/NjWuP1fq+DjsY7xn5g3S7mqPEQAt57Tk6a+e+lGO2g5zgzoxWTGLAUUkm6MDV1kQLY2imXYg7oF0Z3DcSBeISA9Yr/0/OtvuWhTxi/II+RYRSadoVvPPtupB8Mxi4Qqzs9Dqcc6Hp+ihU0mt1gFdGigSrm8DiOdaxjHetYv671WgtoZyxp7LAlcrpq+OzqhW7togmDzgVyHtjs9uz7SDINfUqUOtQDBm8dFMNYMjSOIg4WK07uPaAItMtTTn3AN9rt7nZ7tQcMA5IilKS+3b4D5KXuYCmlBnsYEB20konCQcYZTeLT3fzaOcuJdQgsvEdyoXETHk89z5eX19XDrGl6OSVyETIRY4QUC0PSdDlj1AeekiHlOtSWLX1m3uYfVyvKabkTLn5JTJCTsO86xqxhHo8evckP//iHXNx7wOWTxxTr+Hj8iBcvXuAw7G9v2F9ds7/dsGgDp8sFO+kpSdhtlTO93+0J1szC1QBliAhZRXFS20gRfc1M1pes1oBiHNagQSK1I+ttRZMFT0xl9j1767ACm82GzWbDixcvePTwDRXRdlH91yrqz09P5+7pOPa0Xrftz87OWK1WXF5ecv/BA8ZRr/Uk7p49e8ajt96uQ38BYwrDMLDb7ebO7ySeX63JPzx5hgH2+70iALkToYc++0Mv9OF9dvj/078fCu1Df3dwOvQKdXjTeowpFAyxKPatYLWLrEeIpKzDeJKhpCrILW3bzs/nsaSf8zpNHfqcjrtYR1gs8CLaMcZgLfNjAXijjyUi+ODB6KCi8xoj7ttANpYxCfH2FsmFwAKMcqldRdKB2qSySRgfKGmBSIM4S4mFXAJSApKXSuGRiHcDIQzEnMhief7sFucX+PaEMcGuS8TxnE2n0epJEusTRUSmknVIsSh20mtfGo8lGDsPvVr3Wv9Y/fdSx87rsX5RHe+NY/261Gv9k95IIVjD02dPsP0Wk0dOlwu6bmC36xA0JCSngg8t/ZApeA0yMAbjnLKTrcetT1kuV2RjWVw84OLBm3zjgw/4m9/92zx58oQX11c8f/6c548f0+/2bC6fMey26l8uBeN85TMfcH0dGBwl67ateiNrF9JCymO1cQjegqHgvGPVtAQD5oAwYYwjS9Hhw2wYhzR7W1NlDRdTMFZxXaC70ybWITl/N+wmJRHHQo6JoevZ3GxnJBpWqsfYsd33DLlwu+/58vPHNIuWuNtDt8W7wOXzF9x8+UX1tWZyv4GYSEa7yHHck3Mmdp2yhEuhNA5JmZJzTZ9DWcUicxc/p77ydq0Ga4il1FkvH1oVY05Z1t57jNWOaT4Qld57FVxFO9TTYKH3nhAcNzdXXJyd4Wpnd+h62jbQeqWFNE3D9773PZ4+fcrZ2RntYsH7739T/c0iXF9fc3FxoR1k6+au8SRcc86kNOoA6Ctid7oO0/HotZK5e269q1xmM3unf9Fj/DwRfbiAK9x5pCfvtcZTF0QmnvbE2rb13+483cZQCRu6YJ0GI0s/sF54rPUY47DzolQHcyeetKve5iKC9U6Rf84AmeoRwVjIY8JVYkmRVBcShe3tLecP3tRFSuPJ1dpx79EjxjwqQlIK2Q0YsRij/vhidGEsZkEvC3K0SDoh55YkEHyDsQucX9C4M0JJCJGb28+Iwy39sAEa1ifv0/WJy9sRY1eMueHp00xKnuAtoWzBWUpO+r4hIyVhcuXBIziY7S6KsPx6e6CPAulYxzrW61a/CjvJay2ghUyOAyVH9jfXapNAt41TShjr7tLZsn44U7ezS0VeGB8woaUvhvPFmsViyaP33ue3/upf5W/9rb/N229d8H/944790NO0S3xoMH4E67G+oTCqgKiUgcNSC4etg1p3v/T5yzz4ZajbulIIweFsHTqyFnMQkDEjymxAisxDdV8po/SBl4TTgcArRfMbxdV47XGcPbb6IW/AOEqKlJhorQPvGPuO593nLBoPoWVlA9/81jcppbC5vuLLn96Qx44xZ/rKeRYRnICUut1fKuvAaJdTpOix5hpsYQpuGgrkDls3nz9Nu8AI2n2u/35IgGjbVl8L6gF++823ePDgQR0iVBLIYrGo3fmkX+/UU16iirf9voaoVHLH02fPuLq6IYTA+fk5b731Fj/72c94791v0AQ3D/rFGNntdsSk7GVTLCXlr16ig87yz6tf9O+H9ohfRPV4VVgf+qV5xS/96iDiV0S63H3N9Lg6LJhfvqfl7rlftSkc+q9TAeuVt45kpOT5OvhyF9vt5p2XMu8OWGvJVj3JOIf4BcZ6jEQKg2L25mFOSzYWYwIpnyPS4MI9jG9xAkPSBMlMQyZAQUkyI0hMFLGs2jX73rLvHHBKN3hut5k+LTCywIpBGDQ+pUzXRIk5VjRF1GAqPafai77m4vlYxzrWv179ug/a/TrXay2gnYGnj7/gdNnws8dfQk6aim3Uz5jLSBcTQ0mI8YrbKgnrWpIUivGcPnjAaBb89m/9Nm+8/YghjvzW7/wuf/tv/XUS8K++/zHPnj3j+vqW55dXXG+2DLstsVoi8qiiyxiZhd7UjRTU91xKTSU0dyl6mYyVqWunNg4pSlrwdVrfYgiLJaUUhqFTT/Tso5y2/nXL/ZDioWIH9d0W0EAXFc86iGdn0eOqoC4ipD7S93us9RQxhHZBqpSRkoSFbxhLIZhCHrYs2zU3n39K3/d4azD9ltaqCDbo4gVjGWH28krS4UHjA31lZ6vasgRnkdpFLBV91hivYioErEDMgm/UYjHbUkTPSQhhxueVUhASRYT79+/TNI16sIcBGo9zltOTE0opxDiwXi6IUThZrpQ7LcJv/MZvkFLi448/5v79+3z/+388Wzu++c1v8sEHH8yLNI2DNoyxnwVgGQr+59gtgFn0T4uuiVDSNE1dyMhLnuaf9/urVo1De0euVhansGOCc3pdjKXre3xSn7cRoaBDsqlQI+mrd7dS4Dz6ekJolXCRlBttbKMR39aRk+juyCsDksaKzgHUtE5rJxa1rVYki4mZxUIfZ3rdUPQ85MSTJ09YrU9YnFWWuLMM40C2XlGN1tbzb5Gsne8iBpGGKA/wzZ8n+BWUJWOyiCksFiOCMrtzUQ93N/ZsdyP3zi8Yu56nl5FhdBi35nZXuLzNiDnH2DO8WwIDqeyxDJpsKPpenmwr3licybhK+nChRXygmF+MITzWsY51rGP96uvfdkDwl6nXWkAjhdYXZOxI3UYHiWJmHCPBCbthZDMYsrHkHCmifmS3bCni8CcXnL3zHh985y/wxtvvcO/ePWKM/N53/zo//Pgxn/zJZ3z++Zf85Ec/oZTM9dMvubm6pIwRkyOuREocK+2jYNFEw2luMEuNdk5KKchZc+sEsOJ1uxlAIhYI3uBMxIp2Z40RShwBiykG6xzGOVLOiLdEKcrYFRXUAira65BjkVLnIoXTxXIOdDDGzcNrpYiKplIoRmiXC0RMjT3vCN6Rx0iMA8mN+LBit9shptCWQowjphSGMbIoiYxaVYSENx6RQmOpuD8qiaKAZJatuxOS1uB8mL3DcwR4UItK2yyw1hJwDDFRqqgrpb5eHN4HjVDOkRxhjIWhGMYx0y6tBmlUBkIbAvv9Fucc9y7OsA5sgf3QY63lZLnkD/7gD3hxecm9e/c4OTnj937v92iWC05PT7nZbui7gfN7F4S20XRLZ2mXSyRruIe3FsmCC5YcD0Bqh7xvc4e0M8bcsc2nvwNJysv/ZpgXUlZQfwAabGMwujjMwFAHAsdEjhHfBDBqOYpdpDjFGo7DjqU3DM4R0fvGlRFjhWw8Lkewhs5AMBbnPCKOPntOWFCyQRgxolSUKYIciuLqrFCczAOyU1y5mOoBN9rVNiHoblGMOAx5HFkuWprFKdla8jjggqVEkGFg3N2wPmm53j7jdLmg8X21bWRyhlIWYL+Ncw/IAmIt2SlhXWSh2EMESZGb21swkZP1GU+fXJKTpc9nZL8gZ7jqM+vTR9ze9NgmMJQBysCbqwUNHWM2EB0JTzQOYxKNTXjJOOPJbgntGcV5rA//7n8WHutYxzrWsV6req0FtH44Q18DQLSbBM4FhrglpUTOyg3ORfF0GnQA7XKNrNZ86zf+PH/1b3yXP/yjf0GzWPDmm29SBL7//e/z7OkLHj9+yvMnT8g5M2xviF1HSRkrEZFMyRFTFI2VydpxPrQbwNyFJus0vkFT9KxVi8KECwvuIPBiEuFk3RY2Uj3VkOckO6ndTzujsSb/LTALLYA2OFaLJSLCOKYaCgFj9cQqZky3y1WTeUJQIRucoZgExoK3tDZUn6oHg4p477C2wZgCaFx3jumg83pnNbmzpejvTbNg3/fkGtjhmjBd4HrKdDCrCLiDgA970O13VjFppRSiFWxoiBmS9fR9z9nFhQ5JNo12C62lcU3tGmd8yBgcoQmkVBhj5OT0lNV6zU9+8hM+/fSnhBB4+713WS6XfPDBBzMFYjrvryYHSj3Gw+CSqSbRPGEQgTl9cPYq1wXOV+wYL3W0XyZ43NkwdAEmUtMVJxsFd187YwKloGDAO5TcYVd76pdO9/OE1ftFFpLDevXY9flftiWllEDuQn5E9LpPiYzOOU0NrIstbx1d1ymWZoS032NbT04D3orGvtNQCFi3ZHoFk5VK/2Lohk7vl2EglRFjI0PfM6akw4XGM46eVGqCIQsWS89u3KlVqGno+mtdOhc95xR9388IwMNz5pxaxv6UIJxjHetYx/r3UUdryK++XmsBLSWSh4Ht7TXtInDzfKfiJwkvbnaMCUqxup1rHcV6rF/QnD9EbODN938Dmpb/+5/+C9585x3+s9//O1xeJ/6H//5/5GeffEpKif12SxxGxr4jSETGHnIh50QqqfJmc/Vzljq5x5z4JqVUry/EkpjMoSkLzmoqm6GwXi9ZLBZ4o9vw1hiyJFwVYNYbhEqvsHLgaTaUcifIgNk/6r1nsVhQSuH89IRl8HRdh3EgdSvdFCFO3WvvIRmSQCoGMSroYi6Y1oHxJAeWgDMNy/UpRQy5V/8pwwBj0tckNbzC3IVY3DGXaxnwriEKtIsTytR9tXb+KjGCdb4uhiLWOWLU195HjdVO1aebSxUq3tUJTodZnvCjH/2Itm1Ztm/RhBWL1pHLSPBBbR/BYq2nH3tiVCJI13Wcn5+Tc+b9998nZ2Gz381e5+12y9CPPHz4EKmdYmM0HMZ7D01DHPtKBTEUc8dffrWcc7N1Y9ftv5Lo96p9o55WAI3gzlVsi2g6orF6vyPkPPLkyZcsTk8pSWhq99OYu+HBlDO5kjiyWBWgk4ceHRC1RnBVQQfnydneCfDqqbeo1/0wNPJwoBGpNOYyGaZrYIu12jmeFn8+YJzHedFUQKPBLCklmuBwBtLY82i9YnP1nIcnS0zuaYO+J51rMbImlRULuyIbx5QWOO0O9TEheKwzSHDsto8Z+g3jPpNTiyGAX/Ozn3UsTx4grLnc6JCua9YgkVQSSnZ2jKNaRkhK3DBYApbgnC5wQ4MNLdZ6XGj/jJ9sxzrWsb7Odflf/kXu/8/f/5U+x1E8/+rrtRbQ/W7D7c0VzhiWyyVX5posht2YGIollkIasw4TuUCzPGVx/pBolyzPLygucLPt8WHB459+zt//+/8NwRm6zRWx70hjT7/Z1UAIIZVB0w9FSENPyZm7blodtKJq5CpoTE1ClJLm7wUwot00UwreCsvQaNgCokQDBGcdxrj6vUJJGvqiokWfR3nSd49rrZ0Fa65eXoBue8P64pxgBR8cKamFIzvB2kbFVz9iQ9A0vgLOOIqB0C4ooaUYi2tW+NCqHcBZTlYn7L/4kkUbkNtbCCOkTBx6Qggzsg6n4mkSe9OAZS4QfEuzXLJcr3jv3ff52Refs99vdagvJ1IayVk7lTElxjFVn+kkMKdzUs9BDb7BFtpW+OEf/4jdZs/f+3u/zziqEL93/4zg1dqTYqGTiBQ4WS2V7NA2vPvuuzp4uFqRBHY7Dezouo6TkxPOz9SfnUVpF6YIOUbKgWUHKTpkdtD5hUMv892uwd0g592w59RhfmmoUF62eRwOFYommCCSca7Be+3WhtWqhqgwJxGWkqEEvZ8p5OIYcyFlIZGxxenaMGVUBh4g9XDkPM738KtdcKoP/6XERVE/dCkHnOhSsMYhVnRwFbDBkcUhCBHISXc4hphYtIEcE1YKu9sbShoZ+sTq1CMkjFUqitDoPSNlPvfGWEqugl4cT1684PLqhuvNFsdAjiM2N6zWp1gLMXoIp2z3Br9whNZx//5DHj/5jNPlkm63Zegj1jeI2ev9XUadB7CORhxOlPZjw5LoHNl6FouTf82fdL8+9S//iz+HPH/yH/owjnWsYx3rV16vtYBO44BkpSi4EPSDfxIAWWry3jR453ChIRXwi4aYCnkc2N/cMI4vGLue0+WCm6tnyDDQ3VwiObNuGjY3t7TBkfJehUAxGm98UMZMiLiXI5o5GOx6SQRVUY4RvNGBSE0nnAb8BOeCEioUhqzfYxy5xAMiwjS8dDeYZv2dtzilVPnGowaUmIBktWykmNWOEhxUL66YSBYwLmC91+MJHrc6Bduwvv8GF2+9zcW9e3zjg/dp25Z/8o//EXnoef7Fl/SXL8hDj+REkvHlc/FKGWNYr9dI0fCSi4sLTeIbBvpebTm2EhVKUVLDOI6Uwhz/PAnPlzudUoNL1OIR+44vvviCy8tr7p2f1ZS/hLct3jcz8WSMkRjjS+J0sVhw//59jA/sdjs2mw1t23J5ecl7735j3qafFk8w8ZUVC2iLos3+tACN6TqJyHztDuO+/6wSqdHmVTwfkkkoRjvI1r5kJPkqccNqN1smz/VBwqHoLsmr11LkULh/tSy6vDQwW4N+HgGkHAhwMWDEzq9fu/ui1A4Gvd450/pAv+tYLJZshy2n9xZkSRg3LVBzjdNOZMmYGtUNkDHs+4EvvnzGZr8D4+iHiLOO0gvOGxZLz74fcO6Mgufs7Iyu32Csxp6XnBn7gcX5AmsLMAWn6LV29fVYDfTGhEZxi8aoD+lYxzrWsY71a12vtYDu9resT5Z859sf8n/85I8Zx0Q3JLb7SJ+EIpaSC7ZdIK4B14Jr6IZI6RPjfk8xhoVviH3H/vaKOPaMwx7b75FS6LdXBCD32tWctq0PUVuv4uQmQeAwZFHyh4huH88dR8nYGh+9bgO2ZLyruDmjBI779946s+dlAAAgAElEQVTgZH3Gk6dfst9uUM8z9ZdBxOKsf+mYsHekjxmZV73SXdcRnAeEYA2m8TTRMuRMJuGcoc+JJLaiuazaKpKjKR7nlyxPHnD/nW9zcn7B3/17/zl//IMf8OFvPOPZl19y/eyFsnCn4UmpA5RFk+xexaGRBcmQS2Hc7/nsk5/yqXzGmFQsY/Sc5qhhK4pPAww1FVL5wno+Yb4ERmi8erPHvuP26orlOPK//8P/jd/7vb/B7/zOXyb4M/p+YL1eEeNA21qWyyWr5Yl61g8EuQ0eMJycnLBerzHGsN3tGFPEe491Fi9mRhNOJBZrLUZUODvnSBUXOJXeN3kWzNN5mbzUGk1OXRDY2drxFc+zOfAOp1xFqEGcxyTRAUdRIZ/yCEVtH/NuiihWsYhjzLlaOe7ErhVIfY8betwiUEQTLne7PfeM7pKUkubAFiP6PYIwDAOhCVBJGVYgTfdCDR7yje56lIP1ZcqiUI9p6FVq2E9ONNZimsCqeciQBs7O14ymI0skkBQjZxINPbvNz8j2Ed41hOaEsXhiLvzox3/CZtcjYtl3PYvgKRnO7l0w7HcYZxhGWCyXuGRonGWTRp49fYKkgX7sOFl62tATU0cxBTEF66zOFFi1utiii1FZtLj1Cc5Yshw90Mc61rGO9ete/1oC+jvf+c5/C/x3wG9/9NFH/993vvOdvwn8T8AS+BPgv/roo4+e1q/9hf/3y1YphXcfPWJxfsbNzYZ+HOn6xBAzKUMsKkKKdSosgTQMxDxo09ZZck7cjiOkiIwDEgdKv6OMESh4jHYlraHkXLeEVbgN4zgLHyMT6/ZOeGRjIKdZ+MRyxwM2xlBixnvHIjTYSiMwgrKDEfoucX7RIHiwTsV1zhijHuEp9EI7ddNJOcCc1a546x0Zx82u52S1pLEO33jSEMkIKWWKsRRjySWRiwqtJFGHp3xkiAXcnt1+5OOffYEYxx/+k3/E5fOn7G+uSGOHiyMy7pA4QkqkHOdhyinsxdQhOABxhmG/m/3YScYqhg2lREQmnB+kNC0eymxRmdCBAN5AU+PJt5sb8jhUb7Rld3PN2O9ZeMsf/JN/zL/8w3/G7//+7/NX/spvM3QDTRtwOJBJmJtZzCmL2jLZAMqBQN5stqyXq5dsJIcLhJwSrlo4Drva2iVn9rDPwrN6qUGtOI8ePeKLL59guFug/WnDe2KodiFR362MWO/waPhMaJpqMZJZtE/HOUpkzI6YMmMyRBGaDOK1A11SxqSsgTf1+A7tGSXnGlUtFDO9Xu3Kq63ozmYzdeTVxiOaDukc3hiKVeFt6ywApSh+Ut+uAMRxpNtssa1j3/ecLS4wRshSkNizsi0x97TtknF7+f+z9z4/kmVZntfn3HvfMzP38IisyKzq7uqa7q5pIAQSP8QPTTNC4h9gwxIhJDSLEVt2LFixmh0SAomW0KxmVgjNwIoRGyQaEMw0QmLoVjD0dP3o6q6szIxIj3A3s/feveewOPe+98zcI6sKVWXGVNqRPNzD7Nn7cd81s+8593u+X45TBxa5eZ445i0f/+QVn35+hxZPaL/5jefc370mRvjt3/qI/d2O4zCx3T3hJ6/ecnP9lOPdJ+j+LRrvCdzxl379KV0IUD526ki7njZxYiDSIeKcbg0R+p7t9gn2HjkRftmf2Ze4xCUu8b7FY3zwX4TM3c/8Sf/ixYt/Gfg94Pv1/wH4W8C///Llyz948eLFfwz8DeCvfdFzP8/JHQ57Pvrd3+Xw2WdM08Q0ZXLW2m2/mBE7sI0M00RRY5pKdQXMZJ3ADK3211aqkob6vgazugwLTaWgVRpDU2DQaohirpAxVwhr5RlViiqlNItt/6KNIdCnjiQgpohFxApWrayP44Ef/OAH9H3y8ylTlbfDKR1zo93jNIkGkHLO3B8K17sd+8MIuy1SvHnM1JsQJ8sUN0wmWK1Q1uqxlIEyHFxBIB+Z3jqge/XZlnEYGO/eYmXCApgpahmqmYSpUqapahC7YUpTIVBTp76YOQebtuwNWEsQVg10ZXFmBCAaVjnmqsrhOJJLYdsJKBQDLBL7gJXA29vP2b+5pUsb/tu/83f54//7H/Lhhx/yV/7qv8Zv/uZvAm7FvdlsZk3vef4I5FpVzjnz/Plznj37ACmuX1Fq5TjnzDAM5JwJ6q8TZFbnmPeHzICy3SdVReJCX/jxj39MA+52BlzP77fOBiK1ajuvdBiduOJLydm1tKdppiC5xndm0olcOrIyc6BVExQoZHTKUApJbb4HM93FzKvawUEzsozZyYqDuaQd2po+lWkcXaJRmgINTlWqlBitJiTgTYQpCGhxk55pIEkhmN/soMa279FB0TwxFiOEK+5uf0zWSHryDX708Sv+5Ht/gaRrKCNJjL/8ne/wk59subreEEV4/sENb27vuTsWnj+DUj6j2wSeXvVMOrHpA5t4i+UDT58IIYKEjhh6V14JBeq7KUjAUiJLYizKVd9zffP04YfZVxBfxWf2JS5xiUt8XeJnAtAvXrzYAP8F8O8A/2N9+F8Bji9fvvyD+v//Eq9a/LWf8tzPHOM4Qt/z2WefndAYGlSRyp8Er1yWaWKcvLlLLaM6ztXEBqBpvEwJFFNKUfqYKK163HifAloW625T9Sar1TK0svBig2vWueMegEFXTT/aufrhlyqm0wDM7aiD77FQl8fNZk3j9rp5P6FJli0VSyUylQLRpdEyBS8Gr2TNinmlFz9PzQVDiSru9FYKOgUoE1YKeeyYDiObEBjz5EA+eMXSzLCmDtL4wSswJVaFMgy/FxhiruVQMAdFCErB5HHOqN9z5gp/jF2VBxTEXAFF6nbJCtNwYCIg28inH3/KkydX/OQnPyGXkd/7q7/H8+fPubm5YVIjrsDffM6y0GMcPEKsDaPr+9f4zMt98SbCNQ9+GXc7uZ6U/FpjrFrdIZ1Iz62pOfPrTE/mXaNHqLYxZT7mLK8GVX5x+VG1av1dKR2VLxRimP/263F3zSYh2N4zJ+e4mpONI90em69Blqq6VK67q3j41mY286ihyef5PsZxxPQIYgQthCAUxJseVVEVxAr3+7eMQyZurrm/f8tffPyasWSYRhJwtdshwEcffpsQIMaDa8XHjjzu6fuMBCVWffEkkMe3SIqIjFjtRxAJdTFEENH6HoQYAxpCbWqNTjN6Bxf+y4yv6jP7Epe4xE+PX5YCx0W67suNn5Ws958Af+vly5ffWz32W9TKBsDLly8/BcKLFy+e/5TnfuboA9BF/uyHP3SArFAECIZKqWoD4kBhHJkOB8bhDYf7W477N0zDkTzs0eMem0Z0HCh5JJpCySRRB1Lk+lvRaj8camXZ3LHBm9aqNNjMVbVAsCbwFcASRQO5GGoj203Pk00iqHK9u6KLsjQimhHKQNSRctjTARGhC9GNMjRjtqhyLAAm1Ipd8AZDXKItxQ3DqGQNjCUzlILbnWilKzgVJXZ95e4qMXk1FKljqO5AqMcjNgzs376h5CPH4Y6SR3d5NAgWKGOhqHOXCU4ncRqNL7W76gOYBNfpNQfOpSpEFBwQZcQNUOo1Ljxy5kpmw47RlF6gTMqk9XUUgih5OjKNB/b3n3N7+zHDcMv3//E/4off/1P+1z/4n/mb/9Xf5Pf/89/n7/43/x1/9qc/IB8mr4xawHJTyXAqggIaAhqEI0qp+r4A95/fImVEyhHDVzicxFHmSvNsMiLLfWuyda3CPE0TXecyezMlqCqarEGvyZoT3QBpwNS1vEUdNCfXj2PSyQGqCNIltKptZBIHCoqR1cjWcyjKWJQiRne4Z3sYyCGSgyJFSWxRdeULo8pFmic/glbqTvFVAKKvyKyr+hoIklACxETRBpwh4ysIWQUQisoJ8L65uWa73fLkyQ0lCpNlsr51JZTQcdCOT26Nz19FPnh2w/X1lk8//YRXrz6viiJv+fVvBn73ux+yi0/54GrLVQf5UNikHV1MfPD0yj8XhomSB8zuselzpCiH/cgwGqRAjEIeC1hyilIMnvQFIfcd5WqLbXqu4paNJUo1C/qK4yv5zL7EJS7x1cUFPP9s8YtyKfypFegXL17868C/CvxHv5Aj/hyR80j55DNeffoZu901JnvM3DhF61KzxYCVQgFKcYpFkFoPE0WncdZUbg1pOS8VPQc5pa1Sn1TaziuBS7WyVZnNlQ0qJSSI0SUHUZ0kdl2qgBPG494BsQimBQrk6QDWn+y/qU40oDVXAduYaJkbzRq3tpgxTCMhbojVSluD0w1ypZdAYBwzWqXHJl3AWi7euTeDtpVmr5mRtRBCcrWHR6gkrXJ7ImkGqKw7/5Zq+bqai5069bVoHO81oBQ1IoIGAREKVS+boYLXUtVHBo53bynHkbE7sA+33L++RVLk9tVn/KM/+od861sfcXV1xa/92m/w3d/9y3zwjW/w5NlTtldXDGUiqCcCmieGsdDFnuvtjv/hv/977h5ZQaSaPXr+rUmwUUL6vp+vvdl6l+KqLH3fs9/vF4vyszE2s3m+tnkcCCfbNcpE4+yvQxV/f5jiFtue2BQLoOq83jEj00RRJZs6H7rm10q7Z87Nl7OVEdUM1abbqmY0gFpB8+hqM2QkeDIVa6Li1CStNKNK8NHM8f6e6f4VZsb2+oq026JkRHSWyLt9e8f9PrLb3iAivHlzx93bDUE7yhj4zrd/k9/+zjNCiSQztIz0XaDvnvKjH/0Z3/72txleHTyBotTm2OLKOSoYiSLmKjmGN0giNKHpGCOY0D25Yeo3aIiEKlEYw1erwvFVfmZf4hKXuMTXJX4WCse/CfyzwJ++ePEC4DvA3wP+M+C320YvXrz4CNCXL1++evHixQ/e9dzPc3IBuL29dSrHmnu8clTTyuEs5jJeXYxkVa+Mrip6WpUXHgUnNUTCyeMny9HzNqfL+Y/tJxhsUiBFQUwJpjMwfsxVbr3svwZL5+fh/z9bOq8V2pAcwIwlEycl9qkCckPE3d4IGS2nQNePV89L5MHxTiqiD8btId3gPE6oJ/M1Pnz8Xa9dj9EJ5eJsuwbgu5RIElo25MlN9kYvMyUf9+xRPisjt/2W1598yp//2Q/YXd/wzV/7Fv/UP/3P0O+2PP/oQwToY6Lb9Fg2Pv/kM773J//4hO/czn9tivIYDaNt06rMTklxIN360h4Dz180pu3vBppbw+J5I6KDbkUtuISdRm+aNZsTIlWdKTH+vnrI6Z7vyTs4Cuf3pV3zDOrVCKFK6PEwYQqINzyOI50EJqn64hLnpkwzgdTeT4nd1Yb9kL1RFiFIh2lg2+8I1hEwumhMrRk1Ja6vrwnB/5aWbNfGx/UFSFUIMcueYK8Mg0L0JCBttowhUYgQEmXJLb7K+Mo+sy9xiUtc4n2OX1T1GX4GAP3y5cu/gTeTAPDixYvvAf8W8EfAX3/x4sW/UXlz/wHwX9fN/hDYveO5nzl2Xcf/8/KPsWoK4l/ytQJcpdhU1XWNxehiz5Dd6MLpHU7HaHrBrUIXY5gr2K2a26q/j8UaEKk27eLT52II5FaJBJ70PdGUkEuVhRUHIVqVGaB+WTczjUUyr6lvmDnfWUttYsS36brOz9dc0aKT6PbdvVMoUgCZihNLQmCzvarg7EApj0ulgTXK6glAdye5lWvdCXhfmabISpt4FYUGelm9blG7eFCVDjIrjcyP4bxXV/Owyqs2CAuoawAwGt6411l1t3Pu+6iupLCn0N1cc3efSCnxBvi4amTfHQdi6tlc7SimPP/oWzz/4Cmb3Y67uwN/9v3vU44HbKwNhHV8gVnWLsaq0Z0Xven2eEt2UkqU4rJ9ajKryeScH+hDL2P3ELQa3lyYkl9LqMYzMUZX7DA9aZDV6jafvcjqTZGqJOucD58novnvcXJ78KIQciGYzPOkqWi061kD53PO9KbrXbZRHLCbVma/e7b7a9q9i0IMRheUGIy02RG7DUOeiKFUnnJBxaqz4w4V5c3bgWEUDvtMDBuEBLm4Y2BQkIlpGme99Gc3T7m728/W5WZGFFcMydkqwBeiOVVDrZC1kOttaatKttmSN08ZFTRu2T59yjQWpjw8uFdfZnyVn9mXuMQlvvq4cKG/nPj/rbf08uVLffHixb8H/P6LFy+2VNmjn/bczxMffvABP/iL74MZeSz+k715ba5AU62NgWnco8UQzZhArPSKqXizYFvmzizAZ/5iN3Nu6bqK2NQkVlQKAaSCpBYiQogR0eLgLcLGu5EQhZiCg02cdiDBaRymQqxKDsSlIc47Ch3Ma60gt/PNquRpqpW9NOsM73Yb52QWZSqBvo+oGgTlMA4cDgdSSuzHATWdwVajBIgsTWiwAqV1SdpMFs3nmctLrQyuKsxew6x/L5bdDSh7xTusi+wzYFyUJlYNeuK2520FQVb3ywxCiMRa2bVa6WwNoTl7E2nAGFRIIRKs53BX6ETd5hmBkJimieMwuqulCFMu7D/9mO/lzGZ3g1mVOxwGkpk7V5pzgwE2m818fTln5CTpUjabDeM4zkD7BFwLM3huKxVtzFqVv41XqzQ3t2xVr4vmek9FhL7vORwOVdUi1tcLFnAJQTPXehZX81BVbBqxnCu/u4F7v+/qvak0isx8b1b3Tswl9opFQoiuyqKBQvF5Xw2D1PwcvUEX14dWdWdHAuTJk4/jgXHM7GKP9AnVAdHMJvYMZaTvrlC94ic/fs3heIXFDQElylu0QGBH0GtS6NCibLstY8l03YZSJq53V9zv3yC4njNSyDp5FRzvR8BCfe+lCvaFkDpCHt1KfbPlGDcczbCwIW03xM1C83jf4sv4zL7EJS7x5cW7gPIFPH858XMD6JcvX/7O6u//Bfjn37HdO5/7WWO72TBNE3kYud8fZmDZKkYmgVzc0tmrzNkBnRYkiJtO0IBKMydZ3NFm7YBWXT0zAznX5m0NgGtg5PsOi423FVJwCkEUp3E0pYO2j2AgeIPSXKnre4ZhWvZbu/xDCExlMf7wc/EfxYjJm5lCikxlIklbzg+MefTmvQrK2j5gqZyHEJAK2tu4rCkyyzGZ/27nuDb/iDE+qGyvt5//Hxe75TXVwMKisWttJb2Bu1W1uu2vYIS2arB63pUuElMp7Da1Uj9lT45oRjnKB8+fYpp5e+e8+i7AROHmyTWffPIJqdtQhgHMOLz9nCk7yJVaLbbsxxmzA+K+72cr8Gma2HTp5Lzv7+99nsjpGKqW+bFGx2hj6/dMG3arj3miVYp6clFBdykFQnQjD05NgBYKjlfwSz22xPpcrk2M00QnglrlZ3e7FV2pUB1cQEErD7rrOjBDzcd403ui4Fbyfj1BAlMupN4t5V2pTkkhoBRy9jFNITKMI9fX14z5QNju3LlyykgYiWKzE2gIHcNRmXJALaEZtrsN9/vPeP7hc1IaSdE/A4TImzdvkBTp+wREpmkkj5NXodWQJD52YnQxoJPS9xEovtoVYrW4L1DlKq+efoMhJEgBRcg1mU3vUJX5quLL/My+xCUu8eXFBSj/fPGLpG/Az67C8ZVETB3DcSJ2Gw6HoVacGwhUJBiIUvKITiNSMugERQnllJMsM11iqfSK2vwYnHJuHzS71ej7NFdiU9cTVl+Wppm+S1ztOlKAKFY5ltCUM06iVqtDBIrOy/++L/9ZrrdWhRFilxyIVvCqqry9PzBlxYK7zO2PIyax2jfXhjxOea0nP8VVFRrwWlcYp2l60My4rlAvwJv5NY82w8nD41YtlZNtz3nOIm580qrlbf+lanqvZdtijH6dwRhKZigZgrn+RLV/LmXi/u5zjoc7tAzoeGA63JFQynFPJwbTkXJ8Sznes7+9Zdi/4Xh/y9Vug04jqQtz1diAu7u7mQa0HsPz5spzHvxjnPj1321f53F+n+ZkhlViJN5Im03RUqkbUJ0GQ6UAma/OTJk8DkiZ3KY9j6fnYSuZujqPrK5cqFZZw5L5w//9f2M6Hgim/Mk/egkoP/zhDzEz7g8HAI6D01vu7u4ws7liPkxuXPSjv/gxh+PIMAxL7wADQaZK1Yl8/tnEZ68ywzGgJWIaSSHzne9c81t/acevf2vLcLjDteALV9c9V9tEIHM4vuH2zafksqfrod8Imx5unmzYbgJI9oTbMl3X156K4MY9TVVGOrpnz5DrJ4RuU1cgCiZK178/RiqXuMQlLnGJX06815/0BmQ17vYHLAhTWZoBtToAluosh1lFnQ4aGpPAaQTn3GadK6ozHUAWg5Nz4LyuxOaciTHNYElNwQqCg5pNinQhEsWXrjEHLGLmBhhEr3jVpj1xYvG8vN+W7c/lzLxe7teZm0xWCGjltqLZAX3sXFTNQKdFRcPM6vnag+tS1TkRcG3opRoaY6zqDY8nGet9rcetvR6kgndbPe7Avl3/SSV6YXDM+5wBtpwmIG1f6/83Dq6w6HhbtT/XUOk4phzHah0tgVKMYXRtZzuOhJYciJuJqIJapIuR+/s7QoQPP/yQjz/5hMPocnR5Rb1whYZTRZJ3JWQPzr3Os5lLbV/My48xzFrjDTSnlJAUCapzAtcSkKLMKxiuvCKgbrbCOCBlpOSRPIx0O59bRpltAhv3PbbxXZ3zfr/nw6dXyHTg7nbPB7str/78R3zjyTXT4Y7jlLl9/Zqbmxv248ibN2/46JvP+d73f8jv/M7vcHd3x6sf/zm9GB88u+KTz15xf8zsnl8R02uC3PtqSdzy+VtlGDqCXFNGV/hQFFLhyXZDyErsthSL3O9vefLkipxHXn1+xzAcfG6HI7urnr5P9JvIOB7Z7q64378lxYAr9Pg8iTEwASaRLm5Qichmh2mi6/wepM6TqXXSe4lLXOISl/jVjPe6An1/v8cquGquaCcUDNWThj5R7+RfxznIeyzOwcv5cyd/r9RAWtPh3OBY2pdtXKy3a5w35z/WQPcu4P5YnIPV1HdzpTYrlKwn1xRCmNUezq/vnB6xnhSPNQmuwX3b5rxy/0VgcX3+j8V63190/esxnMexOiSGFS/b1C3DUXNHvaKU7AouTbLPdbLjSVJB5c1H91+v3Gh3Iby7e+M8+7gYowD0ff+gkfKxazuvNH/R9l84BtXZcLacP6O5iCwugFWse+aSz8dgtaJQdKaEzOemi6b1+rxn7nqdE9M0oePApz/+EeSBj//ihwzHPbevPyUK9ClyvdsQQuDt27fEGPn888/n149Z+fTTT/nss8+4u7vj9vaWV59+yv39nScS4omjYuTJyJOQJ3DXUT+XMmamwVUzuq5ns9mQLXN3/5o3b18zDPd0vZA6Zbvr2GwDxshxeENMhjuArqhZZ3Pcb3aqQ5mcPhWElCKbTc92tyG8Y9XgEpe4xCUei180teASX0681wB6LMpxHJ1nGrv5cW98cqtu0cp1MLczXi8vr5fPW5wD5ebWpuVURuwxUG1mqLBwTgERmNQVP1IXSZjrBOsCfJz37KYrtXXR/11ZPDeb6FOainODGxifaQ9m5NW1eRPhjiBp0dSNzjtt+xKRSlmp6g1nYKjpEgMnZhjt9ylvt1VXHZCFkGgGL2bVZr3+nMdjFew16D4HmOvxn6kb6yZDFgMSp1PUJX/zJtLYqvmU5V6rokSyCmM2poI3mmZ1t8ap+I8ujpFWMsN4oO97Nrsd++P4IEno+57nz58/aBJcJ0rn9Jbzeda2XSvEPBatqbTRbmKX2Gw2szlLjL4SEiQRk1QZQ1dwFpHZ+KaZ2EhRGEfIE2UaaiPkOsGAOQ2sWtLeuFl558D1ZsPTJz2/9e1v8WTb8du/9W2++eEz+iD8X//nH/Jk1yNlRPORq+stMTk3fJqmWSHju9/9rl9XiNw8/aA2CRuCz0+lMBUoOSL0TCXgqoUjosZ0hJ/8+BYtHeOYORwObHcdn736C+4Pr5jKnuPwFgkZZOJ+/5op33N13TFOB16/fu1zigJSqiSiN5taNSNqzYX91W4e62fPns2rBim81x+rl7jEJS5xiV9AvNcUju9893f5+3/wP3F185RPP7ut1TA3bpDGa/ZequqSJgjeaLWAMpfOYl2RFGpvUHRGtArCqdZwCKEajJxzV+MMRCNQrCDR5Si2QXgaI5Jd5ktMieIkkkbViESkNhQ2Dq03JxoxOb/aaQtOIXBwWK8tVrUJa0DLq3FdShyPR1IU1HwpWyejqxblS0Okm43MAJi6jE9VSGABpAQ/JzW3PS4V9DlYrhzySguYpgk3NGwSfA0cAhKR2hVoBkKqCcWSDDiVYOHvAjMtx6y2ekqEqq4RmumLACjdLGfnVBo3XZd5NSIEq9SGCrCDcByPxOTVWTMl4OY87doaZaFoVUExRTDKlF29ot4zN+0xfv3XvsUnn3zC7etXiOmsqT1fCzCVfGLtPtNaWkJQx/NUB1xmZtKS77qhjOE0me12WxsUhVgpQMnEk4RsSKw3w6oWthnEOgdC9BHLmby/R/IRLCMUNjEREbIZg2WwSJRU+wuEnI2wgWiZ8e1b9LCnE+F+mAjdhpubDzATPry6Ie6uEBGePr2hSOSDD3pev37Nb3z727x58wbTDOVAHg5Eyxz292yef4gUJehIbwnYksOeUnwemQmTadWyLg62w5bjET7+9A3X167YczweGY4FCZ5kb3cdx+PIMO7Z7bb03YbjQXl7OzLujWyBFFxlRTQQ6d1hUyJdCMRglBAZ80TfXXN32JOePCP0kajG5is2UrnEJS5xiUv88uO9BtDb7ebBkvFjFcoWDYy8y+hhvd0apZw3dZ03ep29mHboE0UKMyLeyHX+9el850fO9ewaHKg7oHnsvLRSDVhdn1nlNqthlgjRiEHcMEW0gshHGvTOjn1aTX04Hu/afonKd7Z8sq/577OtdaW0cToep2YcjbsLj++rAdmH56WsF1gazcGdDzMFRUugCzorOyzXvyzZ+5w7bfQ7rzyHELi9vZ2voes6pmniPM7HsfHdf1o4K/1svtrynuj7/uTa1z34x8IAACAASURBVE2ELcmZf5jp2fO2wuI8qXmp1C+UlpZYepLSVknczn5JosZxhCDeDGhwtXuCVDOh7XZ7kpyORYn9hmkqdF1HEOGbz59z++M/48n1lm0Srp/dMGQj8baq6Cy8creMb9fUlmik0pSM4/FIW5wax5FJDTEjhcBxLMQCuUCY1A2GrHB/GEEffiSe05JEDAnLykm758Savl0q0Je4xCUu8Ssf7zWAzlWPtouny/uwBkqtsXC1zE8F2hJBGkjgDKxRJcJWPOEzHeh3RdMrDq3KaZBE6FMHTdLNyqzL275QveKqhApgHBS7UUrBG+ImLbPOdWueU+SENrFYeGulsAj9Js48VGKaq7VdTITWMCbeUNeoFlbL9wsAcXOaourVZ3XguoxEOBn79RitweTanMZ1rRdgJ9W05HzstUoKrnH58nw8vSciM5871OYxMxdpa4AQDLOFZuPzIjDmiU0MMwBtP1pHoeA0naY44RSOFV0mpNmiexiG2WRmHMeZ7nF9ff1OAL2Mg7vuPcis2rX7LXvnXMymhBg9qao8aETxNM6dKSUXrDWcAgS/lhL8XlqVxKNREwArU6XzKCEmitrcVNrmQjGd1UxENojCOB3ptxuunt1gaUcxgdgRBbRknt3ccH840G16SjZG6+g3V4zT5BbmZXJr9HLk1z/6Bjq+RTWTNju2smEYDqTo/ORSjFJW1CdV3G3T34uiRh5GjpMnc3mqCWZdqQJInRHjhrdvR+7usqv5TJ0bpFR6i9/ypnYjNBt01CD6nOpTQtRIKZGtNXYudLNLXOISl/i6xdfFyOW9BtD3d/dV5/a0mniuUwzLkn+rzj0WM4ChmnGcPb6OdwHo9balgq6E0IWA4G5qiNKF5FVPmCukJ5VAAXG7hpXu8wLmZvA8Vz+bPrBQWgnRHJACjGNmU3WPvZkwunpEtKrJK+SsFQwulbNWxRTiXK1cg2CvEi+Vbwsyc6hBkFDZAX5CMBew61qAnTr2Qa0q69KE5g14DnrXnHULcVZ5aPerXV84qQjKktAEN61pALltEyQt/PXgJjNGNc8pOnPW15xsB//1nOfzWqqgoeoYl1LoUzdXfQ9Vru08SimklOa5lVJiGh+qbKwTlMfmoScDnhjErlJCUlOQMbBA6BJhdIoQmpBYXLGlzXqJM+1GQiCJuUzblClDQbMRY+fzIABWKEQkCAQj4I6eQQtBCiE4PSNLxAkx7npYbCKFSCkTw+Ge3cb32UewPLKNCVslm9uraw55oIuJ7WbLZEKh0PcRMfVZWBYeuWKoRbRoVRjJ873rOgeyLsPIXK33exAYdKIlbW4UZHSi5KheTF7NgxA2Tr8ZG9deOO4PxD5TdKp0KVcDCelC4bjEJd4Vz//2H/Hq3/3nvurTuMQvMb4O4BnedwB9fyDGDglrDeKAWZ7/XpZMG9BegIeaznbP62hL5+sl9TXwnkHyI+AlVjfBeV9WiCL0Ii5fFwS3VVi2mcHoCtC18wwhzI2CpQKChw1mp5SSucLLml5hczMdyWbr6PX2ZbV2f07NOKGurCv1NCAaTs7rXdGqzI89vgan67+/kDJzto/2O6xsvNdhPG4Ep+KrBDSu9vm5vSPreozSseauu0vjcl+bZvYXJWVt+xgj0wOJxcevuyWIunpMMdIj4zff21B1yGVJwsAwaeMenY7Qrr8qlJg5CHe4+lA6UPBdOs+/zPuISSghevIinhiVqlozq9RIIAUolhnHTNxA0UIQV5Lpr67QMdFHJRc/V4JSykSURbPd1fnCQj1ZNWeu53Vblcm5zFXrvu/nXgOzMm9fSsaim9ekUBAWylhT19FqDmMsiiztfSz2s8/lS1zi6xpv/9N/m+kfvPyqT+MSX5P4ZYL59xpA57oMvgCPhdPpy/oOnn1J+dQ1EJid34CZ5yriNsLtS7At1a+5lOdAbw3ywqp5S81IZlyFwDZFOgpodqoEBurWJa1aOqszBJnd5Kba5IdESuWXFrzybDVBUHWzCsA1nZV6DjYrZyBGzlU/OCZy9uXukAOESFGFUKutIkuDJE5LoIHKIFgxQqyvMcNfWmkm5zKBCs6TWdM5VlzRVTVVxG2S1+ObUqo60ws9A9aW5syV55YIeFXUpQJTl8ijzxMJ4QGAaqGqTGaICZqW8yqqDqxl0b5u2zfQ+mBeVvCVUqo0jEXSsM2xdr/XydO6+mxmDMNACBGt87dd5/n8K0WrgsaiT22rY9xc7+bT9Cr7ROwCKQVveOwS4wRTLuyubujCxDAd2M3nI6DizXHDiGSjjAUhoeYSbUHKbAseRBju3xDI9HGD5onD/Vtuo9BfPyNsOkSCNyvWselSoovCpz/5caXfOLc8b5+wvb6hmDEqhM0TYu+yeVkaZcgIkgmhWn4Xp3GoKSF0tMbY5oa5Tmhmib3iQPf+MKEsDovrxCggjKbE3l0Rg7TuTX9f7XY73r65Rax40+2Uub+/n+3IpTaeyoUDfYlLXOLnjK8L7eFXKd5rAN3krc7NJNZgVGgUiaV62p57AEjCGoTZiVJCizWtYP3MbFKBEJpKgypJA50EokGcgbZD8yCLUQicgnszp0OEajRiIpiGk2s1s7mJrtE+TE8r2k25oTUwtoqcBEEzTMWIsfKfTU5AhnNf1/SE0yqpn3MkJRhLVQfJ1cK76FLYhKVqLboCkGke4zKnJzKrfIiXMWcL7ylnb/wKEQkRSpkr4E15BCoHvQLJsALNpalTOFF1SXyaZTUuWdflTOxc8aSME5Y6p39UisdjFI52/1rD3RqggSdD2+32hKaxlqt7bA6017fq9wzqMZdPw7nOVukEulpBUFVSSATEra9LRohkdfm9EAJd11FiZhodYGaDcSr0SbEYnfddpQijGNkMHSbMCofDkRA3M9c8CW65nTzRKqUglnn7+jX5eOB60zMc9ry523P9wUekfoP0V4RaMc7ZLbo90VBKHshq0IX53hiBEnoCikq9zzZ5PwEToXppCpGu67AS0SBstpGc2/t86TdYwHRwNZy2KlN5+SJLA6rUPCKoz1tVT87avmKMROmcXGU+z6ZpYjoeSRac875JdWXgUoG+xCUucYmvKr4sXe33GkC3OKc+tFjznlvrmK6MThr4mkHzajl2rbxwvs+2vN5gzvr4D+kVzF/KFnCOaK0dO8BfKpHr65kZtVbPXFb7VkG1zEYYDrAfGRdp+K5V2r1aZgohyVy1DHMzpScR4qe4ALngFVi/gIXe0irIKhCJaLEZNH/RMvX5MnZrulyPm4TT5kqtINkdD1eV/3Z9Z/QTZRnbdv5mLSkqJ6sG63G3OuZhdU9LKVBpIQ8oIe+gq7R5Ba1x8lTLOq+a92bQ/I55hLRmyFVTZR0zKzOpfBn3Skdp++r7njLWuTKFajftFe+5wgpggfvjgc0G6Ns5MZ8bAbRMGFJXHYI77p2NZUvSkhjD/sD1tqPbbVBRPr+9x8pEmSKxq9QrdT1uTwaUw+FAFMUkkmbN7FBXHBbpyMa/92ZQd8NsI9F1HSoJC2m2qPcVBB8jt3n398M0TTitqJ1/uyfL/VxPZzOb9a/nsVlHbSL07TKGq66kzT8RH6eXuMQl3tO4VKH/yYr3+hN/HEfg1LwDmLWeZQ32VkJfC5BxsOGAeeEJh1bBRk6/OVkslNdL8rACUtra45bXpSBEIFhzMVNE6tAaD5aVGzB06TnfLOfClJc67WxfbWEGso4hz0HYQjmIawe0qtZRKl2lgc4ZM4fwcFzXNBMW0LtUWb26Ok3TUkE+ixAiM0lWFr74eiWgAf/5/xWQtfOcdDFG8Wqgzuc7j2OlWzSQGCo95V3AWQExVxjJOUPsgYDEQB6ddrPmyzc6R6POrOk8LUrxpfw2XsfjcR7zc+rPoxXoVTLSqrDt8fXYmLUkY32voE+JlBJPn9wg98I4jmgdk3b8vZxXzGEaC8eYebpxV0CfvQXRwjSMaFJ3L4xV8xuXqdMSKJVOlMcjCOTjgXS1wUpmnEaeXO2Qzhs28zhS1NCSOe7fEKS4wUvXUaSrFKuOoiAVkHrTqGHm1utmSsINkyx6/TnnjBZPuFJK5JJJ0d8LWnw8o7ixDyYzX7rNZU/c9CQJaj0DMSasJb2l0BRgUkpoDpXz7Oc6TQPKCAJ3d3c82XYzV/oSl7jEJS7xqx3vNVnv9e09EiJahKKuCkB1t5ud1Poe6Xq6TY+GCDGhBIoEpE/EroduQ+w3pG5H2u4Yi/jzje8qYGGRXWtNSSlGggimWmu7VNoAJIyNFbZRCBGIroQQQiLKaV5yDrycS+0gtkmiQZjpGU2hAwtkLTN4DLHuI6zBnvNFC8vPoJks5hU9cUfCKRckRMSWxjAlVDviBHGDpA7pekoKWJ9I11fEqx0aApvra4Zp5F/4l/5FRitIivPxVHBKS/SGSEkJYqyGI8FBNS6htgbn64otuNV4NgfBCw0izxXIgmDRebkSEgYzJQWWRKu54yGG2mIkkxE0ChMOotX5Is4RFxjVyMj8IzGQUo+JO/hpEDT4/gnNRttdGAkRk0AxIMQ5ETufB66gsZjWNIpEo+K4prg3QloF/O7At4xbQLDiKwY319fsth3XVxtiAglaV1A6Ghe9gUMNMFhhQF1OsRhSqTmTmc/fcUJGQ0ukiCA2kI/3TKMnlXkYHVBGYZsC5fCW4XjP/TDyen/ks9s7xjwxjXvy/jXT3SuG/efOIb76gLS5QfprYr8jdNtK1SjkMqI2VorE0gwsVmB6Q7QDlAGlcBy27IeMSEE0U0om54mcfR9THuq8mUAKEpQYjCCKlhEtIyVP5GnEpoE8HRmHvf89vMHyiOo9lu9dQzpvCXE7N0KOMXB3HEjHjI0HQhCG6cj92z1WYNP1P98H3SUu8TWL53/7j77qU/jK4hd97Y2ucLED/+L4ZYzPe12BnkohZ+dDuhvegawL+DDEQZu4a6DEQIoJnbI3vMVESj19SIwlk3MmFghBF9tqm+t+nBBeebxybMIsrRYDdLFWw6sMG003t3JwwxnveR2uxbx6LIirIHzBmIgsr1nvb10hbY8Xzo9Xq5mPHEFaxdiUkGJtlFuao0opXN084R/+8R/NOscnFXV5WJGe7bVXdJfz+KLnTs7Nt16u27w6eQ7I2zWyquBqTYBalTebEldNgo0k4ZzgpXKcTd1+vdIrWgIlxHlp/9wq/rFK9WPX3H6v7c4fA9zAXO1fj9NsG52SJ3Qp0KfoKiBAs5tej3Gp1X9XfPGkIsoio+jzt2AWMfOkoQueBCVJiEQKwRVQYiRFr3bnnAm7Hbsn14zHPGtgi789KZORNgkTl8ErChYVsYChqJ7d/3lOGEYGGxEpBJQxZ97cHsm54zi8pdsUxqqEMWuMV11oHzr/iCvFz0k116ZcRac8031CCJQQCKaEKmuojJQyEUJXx6uQ80iOwlQykt2F1Cgcj0d2Nx9gZuz3+3fe+0tc4hIXJY5fZKwpHxcKyJcb7zWAjjHx+vYtZRqYSvFKsYDE4HYREvjwN36TzWbLmzdvOByP3B2OhI2bPeScGcZMtpFSii9Dq5LLUtVt4V/Ay7HXwG8NDmOMUMwbBOtSb6JC7xUYLBhdcMOONcBcS8nFlLyqZ14RLMV5z6o2f8k3Pu1J06A+AqBX6hHr5f+WEzRt5Ca15SoO9bUxECQ44DZvEut2O8bDkZiSUwN0STqmafJmvUYNqWC0jWnbbq1oMYNpgWCLcYeqMuVM4bSZcVbBWP0/hDArbVCr22XFQ26kmyYjF1xEhGKuIAHUJX6baRaxNZ+KYFmZ1CXqUoiUbEw6EbtESq6x3aUNkiI6ZQ6Hg+uktPFu8wi37Z759CtVBufNru5bqw6bnsyzxxKLdt0+DwUtI0+ut5An+iA8u76q0o9VPaOvdIIgFUj6ioYiZC0MGYgB0QmCJ4FOSykVmCdC12a3izP6qoabHMmuJ/QdadsRu45+s2GQgWma2GxcN/k4TYjB7vqGUhNenTIxtY8egdDmq1SFi5rYhNoUmgtqGTFlGmGYlONhZLN7yts3d9WEqM21NloZUyFGnwv7/UAphWHw34vb4qIG04dICoLFCQl4xTpnYoIuuOSgTpnDqIy5gCRI21nacNgfyDnzjZsbLnGJS1ziy4wLcP7y470G0CZC7HoOhwOKd8eH5KYLahkkEDc7jjlzezjw6SevUGRVkaq0DDtt6Oq67lT27oynu441oBHxhqSEg+0gQhTv5J9d8GKYlTeKWVXSlYWK0fYpzuXMTcLM8HJddgWJ9fFL0Qo4BM642YqD9HVT3Lpx8pRLa85tBZoTnZkRVMhBEQlsNr3TYkLgr/zeX+V7f/qn/PhHf840jATzynSXNgQg13FtHOY1V3kNkB/wv89Aop/vqYHJCSBt1WU1rO2vXkVKiaurK7bbLZ8PY214XCq27f7OlWoAC6h4tdliIMSI6DIe6wQgVLk8qzKAUeDZkydM08RxGgmaanJSnJddp0+3SQsHvQLTZhaCLHOtNRuanFag1+oc63tJS8aCsus7fu2b30CKIiU72Ksc/cKqOm6eHBU11CArs0yic3wLYoqY00O8ghzmKrS39pn/W8GtpOhJRwp0my1ZMx1btl1PNKcxHMvEfhi5fvIUSZ13DsRIl3rECoqvwoT1ioimej6G2UAMhmhB1VU/Jk3s70fGMZH1gJonK9R9zckqkVDdBHOeKIOvQE3HcTUX1scVJCoqgRQyMYBU8F2KU0zyMKDFOdA5F2xKxDySx4lolQM/jtzd3XGJS1zii+NiqHKJf9LjvQbQn9/ekvqO1Hfkyh0FBxRFC5MV/t9//Cdzs+Fms2F/PFS7CIghOH+1nC7xn8f54+dNdA2IhRAQPQWwzUCB9nprFsm+1N0AlZVK7ZAFHvt1WAUSKypCbeoiiHOWxavxUBvXZtfClSqzLSoDWHCliarG4LbZVPpG6/HzBkWdBcS8sqvOjSCEyNXNU7p+W7nSERN3d5ubvewhv3u+BpEZjIpnGw6gjFlD24CHd4P58QZA1/eAs2PFGNlst2y2W9fhXVV3C3WlQKUC5jov2nHOWBZqp+C+XYuqOJ/bfOl+fzygxQFbk+EzESguTQgsiRL4tZ+JvqwbBOuB5uPNz9XRac2VawCtqqSU2O12ThiqOsYpJY6lOEf8LHFp0nA+B4VKmoY5wXAA7dzwtNxLq9V7abrgDqtdqtsl3CwlMCMG2E8TuRSyuEJI7JujocvDObsiVvqMzNQYM3MOt0aMQqQawdTfJRduPz8AH5GzsumEaRpmKT7mVSSnc2BQdKLkTJ4mprHMCVgb89BWiNSwALlkwpSxzhMELMwJaePNW20M1lKQXNA8Mg0jaXvt1J+1ffolLnGJk7j5D//OV30Kl/iaxS+LH/5eA+hPX79BgxuDOM85kxXGPGFmTFr44MMP2N8fOBwOTOMeUcXrZobmxdxgrcm7/tuXzxtYevw8ZAVaYrXsxpz/jBZXUascaN92JR9WK4tS9XPNzJMBdfGtrKWeg9NO1MQrhCwyWyJV4zjGSrNw4GzhlM3cAGExdVpJfZ0Dbq+aheq9LeYVOtehrkCQSlsg00ni7/+D/4P7t3deKZ+mqhyiq2Y9p4jM51DpNWZaHzfnVEsgsIBTCQGd/B6mlDgejzTwX3ShMjQAWGhUlEViUOt4iAjb7ZZUFSmmYcQIWK34O2VCCVXzGpzrnKosXwEkWsVKDSwaJg6YDG+ODM3UxYw3t3fzPGo3SdEmLTJXLYET6cQ+efIxTONCSan7bRrFbZ6FECox22X95ip0HeqYEt94/ozr62tS16Pmdu5938PhMB8zhOBqGuJNjuNk5OR0jKJQgs1zpTVzTsNEt91h6nNCYsK00IWaPMVCVqfQxL5nfzjy5OkH6DA5z9qMw37PzYfP0W6DmktIem7j0nyx0kFEwNqcFHV6S6Vjh4C7DwavPpsoV1dP0CLErnPakxldNXhpGtDNfdBsxB1Kq0On1HEt/vmALXNIwBMsK0zj0e+LBkyVPGSy+meMGIxjYVJjPAw8eWIc7u/ort4SN1til5hqQn+JS1ziEuv4OjdP/irGew2gRzM+ffWKcb+vACC6bJhEFKdpvPr447lpSRFyye7UN/OFq803K8k0Xbr8AUKKFbic8k7NbJGAawBGlUgVnRC3M04EQjCSpdV+l4a+ZvcLXgFUmgHKApxyKeRsDyyqzZoDoNMVdNZK9pjBPUKcNXuDK1YYiFSnO6sVwIqyQ2BxsxMhRVcOUHXN5zxO3OeClcI4jrXiXGkFwaux1OtrIOTBtZ5L6+Fge6YtmPm+RWjgOJxxf7WCqba/xnUVESwIse98DEVIXTfLjIm68gd4ouHHljkJGKqLpetbA+IVXUrjkLsBS1NeKO2cynKtjulrUoDUCrfNiYGIq620e9Rk7tb3rRmznEejsMDSMOgKLVLHVNg9+YBiwud390xZXTHCIIbOwWpT/BCqVnZEup6sE4Tk/Os6vG0OleyNd32/rfSO6qLY3gMW5ufv7u959o2P+PyzT7m729e5ZkzqRkNps2VQQST6scXcmltlNofhEYqVmKJULntQghVCBLFI123I2ZsEVZXUVXdN80p6KS6V523ERsm5SiRGX4kIy/xd34uWr4TgStSlTKhGguHNhpoZjyM5Z8bsii6SAmXKjGVkf3fH9skNIhumaXhwPy9xiUtc4hK/WvFeA+gYE8OYGafM/nDwhkCl2vIW5ybmQikOSFWLN+AhrplgoGKsZbFm0HzW4OZg+fHhWDcBxhBI4uC5i25oEdDZ4noNlEUXIJnrOWitypoZhFTpFTaDw6ley3x+lS7hDXRpfrwdSyVUWsJiSWxmiFYwHcKyr9Yk2KqB1ugabqctEpHo19mMOrJ6QxmavSLXWvWiJybnEnKN6tLGbT2Grr3N3NA5X0dwXsu60t+q9+sSe2xUBjjhEbeK7263YxwG1/21U+71Oqxeu5kxlkxntdmuVqEb3SUgpGpp3u4f5iB2Bs8uy+K6w6yOp4rW5CLnfMIND3HR2t5ut97cuuIsLxSOZQzn8au/Y9ehRP78k9dutZ1ce1mIiESSUfsGQk3CXJrPJDCqIdEBtJqh+JwuVlCLhADd5gmRunJgEGKgmBAloWWiv3pCMeU+Z55+69ddvcRkrtq7xGEixGonLr4fBadm4JKH67mLVHdLFaIAMRJDhOxNsKpGyUrfb3nWP2PMEyoZyzID6rdv7moCUyUI63uiHDMlGKUsCV2YE2NP9MQKXewg9ahmNl1gOIwME2TuOdwfsaxkhYyQ6jG1FIb7O8b9nauaTMeHHyKXuMQlLnGJX6l4rwF0k09zbmRYqrd1qXnmTdZGscbvbISKtYnHvE9Z8ZVpoO2RYz/C613/HSpFUljAzknDnhMwTwDQCa8W5vM4AfVt6b+ByBVAPB6PJxzZ9Xm1prrz451sdwIoT69vXTVvtsZrgOrqF3ryunWD4Azwzq7pvHlwXZ1+bJx/3lgfd7PZOL0jl5/6urUL4npfLRrwaqA9zasYp6sDDUCLnI61VUCcUjo55vn96brO1VdWqh2n+zmdH60SHkNHSB1TViw5LcEpGw/HZx3Wqt40vj2rpKyCzSLsxJthgy3UoFb1RyNGJm03HI4jUiY6XBlHgvPlg8ncs+B0FL/2Bl/X6n8P+w8aNUZRzb76IdDs2buuY8pLwtpWjlrlf62g03j47qrYDGtKGwwa99usUrGCEaIQUyKlUHWghcnG1coJFBQUoipaalI4jPT9+EDa8BKXuMQlLvGrF+81gE6xJ6tTFpdl7EIuDpZzcbdB/wps6Lk2OEmlkLal9rNYy9gtIOwUJLcv4LWesT9nBPPGI0URWzUWNeBZjJwLkeYI2NQWatOeBSatS8sSUCtY5UPXoyzn4v7ETNM0N9bN/OZ2DRZnIFQAsoM3K0ZIXl3VSetSta9Xt+2b4x9UxYfoYLtMDl6Cl4d9ub0IyMroolbx5ntWDVAa/aVt16rP60q1mXEYh5mScA6+z2Mem2oD3gBpO2bsurnJzlavgaVRzoAUozeYrlYLKJ5uRQnLvtUpAJ5AJMSMjBIqKLUmn4zzzlvDaBCZefXuYFcctJ6jW+D+/h4zo+86juMwc6fX1yAVxLY5KCJ02x3b3Q33x3umUb3RtrjcWhSwmWff1DbqikIuZDHuDgeGjQ/NaFAEECWGhGSXU5xQAkYohopA6sha6LrEcRhIXc/m+gnH4UBGEYNoSi7Z5epMmLIS+/5kZQKMlDrQOM/00urWVhNmFGzys9CBYEouxjQVRIwyDZgpfSdYDCsajtN4YoyVW1+IQUAnJEzEVOhCeKDPbubUD1FfOeq6jk2MDIcjwxSYSiKPmaBQxK3bG61FraClsL+/9/fnanXlEpe4xCV+2XHRf353/DINZt5rAN31G0LsKWFCJbhhRBRKSgylYDEipjNvNqohbQm+Ao5cbHa3W3NX15VTqLA7RlJ1tmsgtVXEluV1JaqDFDEni4TYKuBKrJJ1ze0vm9ZKtFXep1aJLq9arq2iVQxUCUSy1apZLZa5bbVXxJrSA8bqGsKs7mECpShqeWmSEvFmspVcnFlryPNzjURidJqGiiLBqgFFA+3BOax482TbV2r256YoSojBgUjj3hqUas9dSqEYc2NgjNEBHuuqbq1c+sg4sI2RoVX0xRUlUkiE1JHV3HFS6z1NEa2GNHMzp5MbHIyqUMwH1kGTV/pVIXSx8sCdS971aQb82ZRUaSvBIKQwU1FScApH439ESZjAODilQdWIzSFRKxVFHGwH8wbAgHOSJwpR3KDHavNbjBGJkRQCMXRsbm44miHd1u+nBQIR0Ugxo0QoJEidJwUGXUwgBQsdkxYOx5HUJ0p0p8Ooyi4nnvQ9moQSIeeRKIblMJvMjGWc56aEnu2mn+9dsMKm1p59VWCpgptVJR01bFokA624+6aD5UxtPyVxT7B7YronB6FoYBqFX04PkwAAIABJREFUwB19526gREWLEWNwiept1XXuQk2bhJxH37YYlg2piW1rLpw/G4JBAZMJU39f7w+C6RYdB3Sc0NBV6rYgwdBcK+QlU8aBceix6aevgFziEpf4eknZ/TIbCC/g+auJ9xpAw3oJeuHXiqwMN8SVG0wrvYMKZCrwUqHKua0iyOPLrHbqLDdXQsuKbiDMTX1mgopSTObjeSUar/g2si7MzxlenTT15fSmuqbiLOpYLaErznCeaLUod5Ae5sYzTwWC83XPipsmNlf24poSUBMMJCIhuH0zXoEWnKpRcsG1xozsemyYCMVyVQvzZkchzVxgwyvlSsEkEKvtuIrDIed+V/k0Vm6K8+9T7i/13Fslds1E8OPH2aFvuWYIXQ9TQaPbVJ/e3tpGKFo531IbI5d9lNr4aEHmyr/hFKJgreLo//d7U5OyOq6iC23bQmu0ZAbQWKyrFMyugIrMXPgFbFpdKWhz3jWpQ+zoug3b7ZZgunC9tSBB6uy36pxovo14AicxVkm+ajRkNemxRAguiziZu35K7Ny+HHFDEnFjGqkUKZ9Srm6yhCw88LoC1OhXLXGV1htg1XRH2xjUpHPmoNdVAwZUR08CgE3qePo0sSnqcpEh05GoYju0fgepmt5WlCm4Ys9kmWJjVdtoyU7N16qiTraa80lENXMcMyFEsnUUAwlSk8Pax9AkItUbDfNwnJPiS3y94+sEDi9xia9jvN8A2gqhAbEGS2ShEDRAKJJqw16AkJyKUatK0ZzS0ZbGG8JtIKdRCoClMhwgSXCdVxEHazQ76IU2UmZAcUpZUKMqfZzzn5VJG//TnO4cA1jVa1aYsrugmaQVh9Rd5NTyrO/bJNN8Azk5vh8zPuDQNipCqBVrEUFDJIZuBi5alRqKuZ1x6DeUaSB2CZsWYwkRQVvluY2huNkN4nQFM0NLcc5oMUwjVF1rkXZeTrvRSm1R0xmQ+1g671izzo5+DViHEDxzqAY1iLC92jHdHSq1pVEtss8PaVzxipJqFbitUASEEur9rKlYEEGDEFIkikApraiOitDtvAKcc55VPEqlg2jjDoeApIBalbvDKRNmxlRto2fwHJg1uQ2rFXqhWCCkDf3umq7ruLq6OnNrXO59CIFYDDAShWh5Bo1FAoVEkcCknki4RrM4r7dkYuqwmBiHjGZ3Eux3PcEqEF+v3GjTaq73qyZ/2JIQBQvzWLRVE7XsKzIpukwgbqACVTtdlE57xDaUNj4qbDrhow8zg+49TVAIRebeCC2uh64lMRyhZLAcSTJR9EDSQqw8f22fGTjlJasDdMOdD7MaZZqwVMg5Ugz6kAjBk6hgnmI4uDeCFsqwJx/vucQlLnGJLzPeFxrHV30ev0zKxnm81wD6ePBudokVeAW3W6bSGTDlw288IwSnNtztj0zT5F+4jbeMg568qka2xsTGyZ0rzW1plkXHF5yDSgU1UYyozoM2ytzE1LjADajECjTXjVQOJrzyDDUXMDeoEHVQ8vTZB+yHo8vEFVcaiRUnYkYQW5IJDHFXClJcbmWrqfrxIg4SKvBXvHos3uyVYufL9yHNih0iPak1YDWAZYUUe8ScHzpNwwyiUkpzY9a6Ilzq+WtttGqJhg/8XD+HECgrKkurDrarXCcGrkYixNTTpY1fkyyV2s3umjwWgoIy1X1FJFlNsqrro9UxMHy5vgJqV3EphFJl7qIrdGjdRiS4VXZr8tRl1cKTCNweuzbMnXDpRV2K0QxTq5rJdcVDZF5GiKHOPwmE/6+9d4uRZcnO874VEVlVvbv35cw+c+OI5FC8lGVR5JiyJMqiRPNJL7bhi2BLgGwZggFLNiz4wYBgwYANAzYIWw++SAQJGAYMUdCDIJjSGwE/GDYfDBmUCYGgWKREcjh3cs5tn727qyozYvlhRURGVvc+c/bwzOzeveMHGt2dlZWVGZmV+a8V//qXD+ADDocPKyYcwQ9IWJtEKVmGVLPDRqlu1SSImF7bu4HgTMQyjZE0JPAgKaIRgk7IlImwWB7ZpYTEBGLyluM0WTPvIgfKRLqVCAE2W1KugZy9tyz2fN2KJLL1s41fyaJDFvNkmVO6n2db1vUadj6Cf58VRxKBlDYkl63vXCIxWvDqBzSEbOMX0DhgLiuR42EijsncPtyaUtR7PB6Jl8JqtYYUiGPCHQNOPS4JbnIM6zVHP1lQIwHwVY4TUyI4x35/ddPtrKOj4zXFt8P/+TaQZ7g9+wG2L6+tBnqK1pCgahRxqMSckU1ItAfyOI4cDgficTTpRNs0BTHtZtI5C8YyKzsvNI9iESE08oC22Egk1elp0WQP+6bJBjHPCftizUUl16UgMdUMqpFfp7mhRybdx+MR5wd7zbklQSlk/2Tf2+n/kp2NjRylrONcY8nnPSGYhZ3LcoVZJjM31qjdFlFIUyaUJZdaOguetOxONuanDgs3FQiWBOZNx3h6roqumaJ1zsGClOPOLaZxgsb5/eIkZ70/eF/qfjTrteuXY2zdNeycTdX7uXhbl2JEKDMds8Tgps+s+2Mm3UbunWX1xRuBTjl4wJl1nUStMhGcrwGViOZssKuzEQ5PnCbUaebZinfO2n9LXpZnHnSKaJxgMJ1+iqm2SLdjW+77/B2dZ38W51AsUJkz12kxnha4mv0iIpjJhclPIorTyWZu0hF1E0ljdsBZ5xkpxfoXTiZd0skKH4vV4mEFoyftDxyeTcSjEmPCO/u+j1E5HCYkJoYAUQJJPWMaWA/ByLqk3JRGzIbPl4DTpDV4k0TFLuHo6LgRvZFIx13CrSbQh+OR/XFkmiLqA2mcwAWMyNlD+emT900eoNb84jiORhowAuUcOPHgTPcJVrg1NXpbzVlE8a4WvSXMC9nII3OxkZozgag1ySgOG4WQ4k3HHGM0T+WcZZxJoJE9lzPPzpl/r042pb3fX7FarRiniUkTLgxZI6pZlmwFY7NhSM5+6+xK4URQ8QyDeRDHmHBOGIZ1znqav64fVrgw4J3jGCfrDucdYbUmhMBqtWKaIntn3ekQG+OUnQhWwdegYB3WxDhWOUPraWyEN1Lm9iUpPmvJxRlBG1zIGeuEl+xi4Ze2b0kFP9h4nT+4z2azQTNpFBHwgfP1GcENvPvW1y0gGSfC4EAjSdSarqREQKqbRnW+0PzDHISUQreW4NbCxDzL4ZxjGFYMw4qUEqvVuhLEGMs6vgYe0zRVT+5hGGrDmZSlRck5nPfmwewCUSBkqcPm3j0evPGIR2885up4QKe5ZXccJ8ZkUp6YM9JRAt6tWK+F87MzUtqwcRMiR5CBCKTkwJmcxDnr9Dl4z/Gw59nlEb8aIDlWYY2TgGrCeTu3IctfyvEOw7A4Z0C1mywwKZGloFMUNNnskBfNsY+g6knhkkmfklIk6kBKgpOAOHLdQQlOJkwG5BA2iDqIa979+oGn74/EtMG/r0z7RBoFmc4QdYQQOEarJUiYzGafJq5iwA0r1HvC+h7DxSOmMdoswPkFDyQQEc7OzsAJ6/Was7MzVCwgvXhw8VHdAjteUXSieB3PG5O7rhXv18K3Hs/LMn+r5Ry3mkDHKdXis+M4IT5YFk2P9rB0YnZXae5+F/yqTqmmlIilAk3FOpGR9ckutz7JHrHiHVETPphThWQSnZJpYdFmyhoxBiweRPFhbR7VSZFo20/OVQ9nVal66hACIRPDKSrHyQj/sFrhVivOLs754pe/wrA+MxmFE5IY+VJ1OG/kdpom9k2m0/s1ijcnEG8k5upwYNisGSQQUY6qbDZnc3ZwNRCdY7VasXEbwDLwKQRkvWZ1fo6+94TxeEQH0267sCbGyGpzxjRZxzVNiauUiOpRjWgOciwjnJtZYOsKVOeKIgMgKmEY8Dp3JqyGZ824j5j8xQXP2fk967iHElbrSmQvLi64uLjgnXffxa/BDQGORzwD03TE2jlLzdyCtcW2/cmFp2raZ+sKKVlikuo1UH87c+ZAzWUkCEjwBAk1iBjHEc2+1MMwWKvy1UDIJDyEQEoWLIq3IEJwDMHOoXjHZrVBnbA+W/HojTd44/HH8CvPveEcJDEejhyPRzzW7S94QVMkknDBM8URiGg6klLkwMSTMXGxCqhfo25FkhFQnDiejeD3EZ4emCJM6RlX6UgcZ519+X12tma9tqJGk3NMrNdrSkdNl4tU59brdh1JiQBVQCxQmUywn33IE0dd4eUx3v0gTidc8CgBFxL4A04d6jZMkmdr1BMRvAZcHPjuRw9wYYOTNWmcSDHi1CFx1maTg2YfVnhvga+K3Q/UCWFYM8aJqMKU1K5d9Xg/sFqZjEhEWJ1tLNiJkc3Z5qO4/XV0dHS80vh26pFfBm41gdb8YFNVDodD1pFa1zFXXBDiaFPdlktDo2atowAeNCFuwIk3KQeKyjQXOEnjBxwnUlSCy22jtXR/c0i2IBAfsgtDnqZ2ltX0YiRZc0MN8SAls6jCMbdxVh9yNlWJYpkvxFwukgjvvv+U4eweo9lfsDm7R1IIYUV0pr0NqxU6jqzDYK4BzhGGtRUjSqrT+Bfn56gTxuNkWUPnYDVrnYfNJgcbc6tyGTzqHftpZP/euxxUGTZnHA4Hy+QnhRiRMCAhF7tNR9NnN63MS0FmHVvfFJ7lMbf6PyM0PstZYqJqx733mcjmTPQ0EmPk4uJ89uVu9j2EFRcXF5yf3+PLX/wS0/4KVK3YMxvZpZS1757aPbJo4ltv8OAE70qbdCPGQM7ox0rYrXmJ4zBF1JkN3dXVVW67novqxIKFq+PBfJQZqnZ4c3ZuwU32AAeHHwJ+CEuf65zpPL+4x9nZmolE8AERh19Zp0ibYbHsvnqHEJm8sFo5XFKGIbC6OGNwa1Z+g9wbiKsB2Zwj7ogIBPGsVxs4e0gaLlgFx8bDg01AMLlEGQMg7591yRTnGYaBYXVGKSx0wWcrllwwKorLTWkg13IyWOY5y0os866gD3EOvH4ClyIyWLCBS4w64pIjygpx93IGPDIMWX+eW34rQkwQ3IohKTIlJkYj02Fl9oUx8f7VE1JKHA8jh9Gapkya8GGV6wDITWxMix0nI9N+WBFj5N0nTxnW9vebH/8EP/7jf+KjvyF2vBJoM453Pbv6UaGP04fDXSekrxpuNYE+v3jA48ePjTi/mQv/klXcJ4wsTjoBuekFpj9FLKs0DANuCByeHVi0007JskrjlJ0Qptrpj2SyAmIijja1LApJIyF3ZwuYdnQlQiLig0Mw144g5jgQa9GiZTXd5WX+3xxBJk1szjYMk/lDH6fEcZoImxX3fGBzcY5zgaeXzwirDSKec++ZciYbYLVaEbM3ctTEsF6zWq148OABm3tnDMPA449/kocPH2bHCmEVhloYt9lsZvI5mFfwW2+9lVs+wyqsefLkCe+99z77qwOHw4H9/ljPz7AOxHHi6urK9KveMR3HmnVNaTKNLRYATdNUfbbH0TLSToyUOk3VEq9IAiZNtbNcIWyqyv2HD6obybC2bJ/3nnVY8/jxY5IkfljATabbDSgaJ8Qp3hshLbKMYRhsbJLWBi0l411kFQ77v0gRQpmlEGG1WuGC53icuLi4QJssuh3OXGhXdcNFh07b/KYpNp3mzoYWNNryMeYiRGzWICbL5PvskV6KV2dTjMTmbIWbIgFl7cFJwnth5SHkYk2fYJKR4JSVKntZcVytiKtzLtZnluVmIk5LDbNp36eFvGWKVtiamHXj++Me7z3H41jtA8fxUOUs0zRVZxVRC4TJ8h4v4HSP0z3KCIPg3BkpDmjKTjk+2mcm+J2vvcXV1YGL80ccp8j777/PW2+9w+Nwjk/U5kCiMAyeHLeRnBBWgxnzuWA2gcFD2LBarVivz0Ad0zRxOR3xPnB2cYYbBoZh4DOf+TTizc3mwYNH3+Qd79XH537u1/n857/4snfjVqFP4X849HEydJL86uBWE+g/9sf+OFfPnkHWKI+13bFjqllH0+pGTUxTtlIbAofxSErmEOETiDrGaFpJmzZPWc+b8vTxhAYapwYjyU4xX9dUMmbCyge8FKuxudsgmOWcc7mzW4GfO/2VLKfkVuSl8C34VdatYm4JatPvpvkcMnFbMazPGNYrI6OrtVlrrVYkOXL/3LKT3iTfRkMSBAfHSRlCzqaejLPkdcvf2YXMirESHPew34+8/+RZLoRT9ocjyZnLxnTcV4I7HQ/5+IxIeu8hGlFSjeaXG0fiODEMwezDcotkywTH+rdZhM2Fhd6tGZNJHKY0Z4Grzjo7PcQEP/gHfjAfT6KY4klS6wrotFh71+YuqlSinlICdThRJEZCCIxTZEwRTdYREknoOBKcNc5JXhkxffWkWQeviRQT8Rjr/sUYa8BWtcv5M2yMlPFoNmpOfb3m3WABw8M3HmUpyCWrpsnLMQcol5dPM0EdmaaReLzk6TvvMO6v2Dg4C3C8fMajexvG/TMe3r/g4UpwErOuXjmGwOU08bVnz3DrCyZRZL3Gu8AqBIbB5AuSg7DSQj04hxssYBgGk1VsNhvGOBH8KnecBHFKCLlmAI9IWHSXJKk1AdLEehgQFxE91u/p4RA5How0T6NHvc0erFYbLs4f2gyMOmb3GUX3SpqUq6srdIr5XI+V/Ec0NysKOcBSphgZx4l3333G+K51GXRDwA8bggsEv0GcJ6ljjIp3Jk2Zqn9MR0dHR8ddxa0m0JdPn/HeO+/UTJU94I1wjOOYs1zCcRptKt0PqBMu91dsNptc5BOsg1hK9tDPLgntNHxxoNjHA8FZtrAQN8nShzA4vOaspCZg9tZdrVYEzf7MSVGnTClmsu+RqKSUM67Fzs4FNJROgxE4MmWHtyH7MJve1iHDKr/vCpX3TbM5rBhjxHvLoLogePcWIQQ2m4H79++zCo7Bmy92ybiGlW+yox4V4XA41Kzvfr+vgYeI8Lu/8zaXl3umKXHYHwlhxZTdTw7xCqcwTaORYM2dC3MXxOPxaLZkubBQ42SuJSkyjQcr4rram9tKUmK098cYmabRsoM5eLLzFYjJpB2H0QhVyRQPPsxa3LDi7bff5r333mOaJsZjtjccJ8aDtYC2NtfK8WAZc6eOw+EAuQ18sRx0ya6d5AYmTeyvbHrfixKc4HTiOI643I0vjcp+v2e9XhOZrRO99+yvjpadz4FXCaZStCColZGUzHtKif14rIVqbzz+GBf3zhk2Z6zXa5IkVt7I9ziOs32jWnvqaX/J8TCa57FV/nHv7ILVyvGJNx4yeMcbZ8I6eCvO88LoHecPH/HPn52xefTYZEfrtY1HsdrL0pU2CAAIwRHTWLP34j0xZj9rFby375+qBV82ixItE30cLeD1nvOz+3zmk5/ijTce8slPfZyvfvVLfO2rX2UcR9555x2ePnmbcTpydXXgEM0B5eHDgc3mHs77LFui+RHUga4GdAioC3i5l8eZSvpLMFNsK6MKn/nee3Wmx0ngOGoOlLMUJddclKLXe/fufetuih0dHR0dtwK3mkD/6q/8Y9596606LZ5UFrZXuMBlNGJsOtSRJ0+ecvHgPleHA48//ibjlHj36TNSzsqB1mxfSsrl5aXpexXGQ6pdBwHGgxWdleyzkW/zpsYJXmYvL9/qiBXSmHWiSXG5IGxqbOVEPP5snQsVE4dxqtlozUWD1mZbWW1MA5uScv/BI8Tbg/7+wwdc7Y1I3r/3mPPzc9abgTceXfD48cd482OPePjwfiWYJWNvbaFne7ySibf9GBm8WOe2Y+Ryf+DzX/oSh/3ICKCOw3jEucB4eZh9nqdIShbYuNz6XGNiszGJRUwj4+Fo0g61Bh1h8Fw9fYaIkkaThhTil1IiDJ51GPLySJIDMY216BMgHRMXFxccVDmMiqwH/OGApokH98/tWHNCMDiHc+ZWsT9c1uBpGlMNqLwPC/u5Q5xYn20Yzi549MYb/NAPfY7f+I3f4O23fpevf+2r7K+eWtOW/VglC4dDMy55hsM5x9OrS9M9bzbV3aPISYqrh/eew/HKgrHsFOGHwH5/yX6/t2DkeMXmbLBu6imyJ9svUoi4NUYJ4jm795g3v+/7OD8/59OfeIPv/57v4ge+/3v59V/b8dbvfoXj8cjaKyEYWT/EA4jn7Pw+q4sLjmqdMaeU8OlQrxW7hhv/c/tSwOjQOJiTiHPWRCVOiA4IENxA0iOTTgxeSJMx3OAgrKj2gHGEX/v8e/CFJ0z/6J8xrCzDPU3CMHyKT33PZ7OMR3CDQ1O5N8yWks4Fm8EpLiEyy0qkca2xuoEc7LbJY2eEWXMxqdPyfmu6U7cnvs4m4bS2Nu/o6OjouLu41QT6X/6Jn2D/7Jk92A8jb737jjVMefqUw34keeGNT32Gi4sLxqh87nOf42NvPibljKfZpAUOY+K995/w5MkTjscjT5484dmzZ3VbV1dXxKhcPr3icDiQcnbz2bNntSFJteGKE04UF3zO7moliSWL5RBC0e2qYxgGwnrV2J7ZQ31M2fbLe1arddbjBo7jyPn5Offu3WPYrCHAer3m4sEj1us1x8myd/vjVLXM9zYrLi42hMFztvFmLoBlOiVLUkRhI0M9Fk2N3ZgvLacT3ltnv2NuS/3DP/i9vP3euzx5dsnb772LqvL06ZHxsGY62li9//Q9jld79vs9U9Y3W1a6SCIiwcEoE+N+T0wT40G5vHwKSdmsfc7gp0xsQKeRZ4d9lXkko/CE9SpLCKyI742Hj7h37x4XFxesXcCLcHj0gPfffz9nvhNTHElT5HAY2e/3Ju1ouur5EHJmOmJezd66DzprxjGOI5eXl3z5y1/mnXfe4erqKhf1ndlMRM6oOxdqkKIaa6a2uG2UrHmREpXMbJEShOAYp4ONAVmg691C2uKceXcjPh/HLHMREUL1F5/lOkbmJ966HPk//uEvstmsSeeP0HvK04Y1+ly8eSWeQ3RM2Qf8eJzYDCtKHZ0V187jt9qY000isHIzifXDijPvF5nqBw+H7NY8e0jDdc9zCWc5wJxAZq21cw7v1vk9AS1tu9Oszy4SkrIfU+4aWN7fWhHWQtdUZohKpj1/75tCYyfC0Ix3mclom/2E4VbfVjs6Ojo6PgLc6jv9QYW9mv9vCoFHH/8EqvBmIbTieZaLue5tVvzyP/lV1Flh1+F45N69exwOR8QPjOPs5bvf7wluwK8Dq7Am3s8ZQGwqOWXbMevAN0sIAHycrBkFRk5KC+ciUyg6VgBPaYbiiWkiaSKqINn3dhgG4hTRCQ5X78+6bIFnz0xbOoQ1sjLd6f2L94zU+YHL/RWC5+p4sMLBi3sYKfN813d+mk99+hNm3Yf5CQ/ecyRxJZiGHGuNXbTSU9aY7/d7ZFxZ9i4EjlPkN37rS1xeXvJsv+fqaOT4eJhwaY/GRBgc5ythQ+B8tSHFobowBJmn+QuR11h8f4u2GYgzQbKW3WY312qFVYpln0lPpPk/5S5whXydnd/j7PxeJZTjOCLVSk1YeVcJEJCL0NoZAmv/qGozCCnClCLPnj7h0cP7fPrjbxKnY+MaYe+zTuTZvYW0tH0runk1lfl6vc7E1sbKyLFY0JOLDMs1ZQ4VNh5OTP6jKQdJ+Xor12FpDW4uM1lrXMZLlIf3L3KglF1IvCdlK0gngqRYrRA3fgAcm/N8DDoTXz0hpOTsdJndKJn84mDhvcfnsXDiasYd5uLMSlSdY5xsf1brDWC6aNsHh+SxLNZzuY3hLItJbZvzObPdBsPeOyRnn30m+SBMYybVkma5T+MFPk7zjEHbhbJ83riayXtHR0dHx93ErSbQ548/zXDfso4ueErnvvrQAh4zOyfEPC1O1jHjJDfHaArNmBtnFNiD01oBL5eZ1VnrMuA1XivCqxnd5rfzVghYEtc1g50dGDQJgWTT/2RylArhkuY9cybSEmwud3Vrutc5ny3aTHv8/tORy3/6Fdtu1n/a9iI4nQleI0EpEDxOL0kaa1DwxtnAo82j7PZgNn5JJ6JOFL/nkqknRdNBQw0+jGzMmnGNc6MVyXKPtvFKOR91/UKgCYDUcwtUAgagMVWHlvkYgaZNuMu/E1Sv8ILmz7yuje86DKgKEWtrXRqtBMmWe2JFo7YvRePuaoa7/F/OnW1/lj+AW6w3jiZTKRKhdmzqtetmLbuq5n2NC9Jo34mptqI3qb+R8BJ0DN6znyI+2wxKtoQUEfNZLvpeHDFRr2fLFFOJpX2uELKdZGtZKCJ4nWVQ5GAqFK9rKWWsSorRzkOKpOOEiCcej9W1ZXbumIltK4Ep117bRj7GmLuU5kZKObtfvz9iRD74lckzmtoICWW92ce9baBTvqPtdZuGTqA7Ojo67jpuNYE+u/+GWW1hZMd7j8PXVtjmbBAbkuVrt8GSVXKlmClroKtOWU5pcNPcgUIQfPN3JgTErInUE01lrva3DRHUNdvKHfuaz1IV8BMtpiaLWkheplDVZq3sV9S54ExEiBIrKUNS1XiSxBq/YBlf1xy/qtGW06HYo8RCtsZEnJLJAbTNJtO06k7E3DhE1dZVjMAUq0DTg8/nqhLBlN8nc3BUgo1CwMs5qFPtZQyFaik3BzwwRm3Ob6INEwpZm5KRWQ+VxPqTc12m/FPOqJpnxBwMlYBNRBiy1hbmwtJTaUWbpbzp+pu9uNf587KNnpou3MZsQtSa/mhbYJkz0W3rcEStM6ftRA0Gh2Hg6un7hGDdAEuAU4PMHHjipNrRAaRxD9q0HG+s9kyznQMUBZUyRwOrYVPHIUl2upGIiKszAiJzgWKRUJydnZHysUzHvbWHF8ElJSRrJe8k4V1CNBE1EiVmS8AJEZNtJEkkb/tjev2RlI7EVNqJJ46qqM7aaTtHJtVqz6FdZKEGaAW+laQ8uH/t3HZ0dHR03C3cagKNSM32akrENDGpNdmAnB3WkVJwparWgjcTjlHVsqwtARZw4nLjj/Ix+bXYEihrQNJORQNMojgtjU/sYe99yISDnJ2EULJ1+TO6xy58AAAgAElEQVSSk0zQ5sOLbrDeEvk5LKkmVilJVlUI3pObIdr7NOelPdZZTWBwgQnNZDlRabOfdbACOG0IolL3qZJSNS9ho8Ugg2MVTBeaRqwjN7bPMZYAAlR9adRNJKJqhWeqjpRiLgBtZgKcI6WJhCch+fjmdYy8Wqa9ZGhNcpCQHLgg1na6NEQp8gXnghF+EUzVG0+Iqwc0U2aqHCDGaC2m8/YTypRGJEptHlIL0tT061XbO17V81oy9+Xv9ueUOJvzx6ynVVWm3P5b1NVsp2rkcDiYx7Sa60q7XZfdacLgm8w2+MHVmYPBCd/xHd/BMATeevY+To18n202FI21eLvWV+t1Jqh23QnKan09s1okS1VLzpyFL24m02E0+U0JvCDLVubscRn/tvDy8uqqBql2PCkXAs8NbEpGnUV4Oks2bLydfXbOINfvQpGeNGRY2+DLCeKlfpbP/vJTknq8JZgYnK9e1ufn3YWjo6Oj467jVhPo3979E45FG0t2kMgPsVKIJ5Ky5Zo9AFerFcOwxntztEhqjg37y2PN1l1eXi6mfQuOcSLGZUatlWWcTo+XhHV5+BbyAVjm96b1dS5ISpKqzKBkD0uWcH64J5z3iHj8ypqgFK1o8S0WPGE1MAzeWiuvSvc7mAUORhqqr3JZgFEPn//VPI2/Yg4ADgc4HCaOU2Ia52zv4XhVLcikkr8jmp1MJPv51nHOWl+zERybafdYs5nFtq6MeYwRT7FOK4rtufFIGY8gRYereGd9x6dpMhKPuV145vOhuiSy8znWxf8uF4QVT+v7Dx7w5MkTDvs9h3JeVavWu6BeE806k6ZFMAYgWQaQVJGsw02NtABNTNmhxAlswuwqYUl7qYHVRESKj3XBMUsdVDkAv/q1L7PZbJimIxFlOhzxYSaDMZ8TEd9cv5bF9zqBzgWCyElGXR1+ZZppW+jrWIiItc22JXUcSiFl2UYrnzi7OLeMdV7ucYtrv0zyhGGA1EioSnyoCtWdQ2ugOk1TlUDZB8/f+TilOhYi1kq9BDFgxbVuIVuZZSlO86xUaL91HR0dHR13EbeaQKfDFccr6+Dn/cA6+6vGGNmPY9WktoSrZCuBLLUAj8O7ofq21iKj0hzFOdQpB44UR4S2rTMlayvCyg1VMlDs1Ir0Q9KcPtZghVKnD9oizQBXs6SqVgQ1BJeLxKRmolVBo2WZ3SGS0kjyg9FH53ImTXHRcXj/gK6OHAcYgnD//gWDhyITVk6yy5TlME7WkfFwODAdjIRdXR2YknK1z13j8Nk6LcsspmzdlqYmkxjRaK+P44ioEeepSDzIBC8WbXTWrWbNdXkvUHWuhfjGWAKPk+ukWMXl8Zw0mSODzPKPZm0bfV1mfVWzVzGzPl7weHHEIoNgqfm1c2naYnwjI8nXTiFhpUCtaGZb7S65kBPMaSVhXS9tH6XxWp6Pw+FwoZE/5GNaiRBTJMUjg/M4F3IBXoZTJJgsZLW6IOaivPI9qceVQxaRpVVjCQBb7XAlx5iri0kl2jHPhYRNcFFanstCg59JrBNc/j6Z28icuRcgljFsvlejZg1+HkmlFDlqDVBwcyfL5EJ1EKmfXoJaYp2ZsAA3IINYgJNdTYIPdTxUy6wPFrQC4juB7ujo6LjruNUE2q/XbNzcWtnlB5MbAsNmbUQwkYv1lhpQoOolWm1zcX4AakYU7GHo0rJQsc0oFrikOcEmqJrXsbIsIFQBneZMXdX0tlPFtbDPCHSdcr/p4St+QdqcCzUzV5eVdXJTCBHl618f8N40r+20fnE/KMuM3Fpmbr/f16zbOMalfrwpiIwxVrcSjSavKcsLOZ2mCYnW1nqKxzlw0dzdMe9Lat6z1EDntuv5fSVjKa71IhZrzQzmKuIcXmAcR7x3lczOY1Xs8pSUlGGztmMWRwge71d1u+W36s1FYWWfjEjPTV0KIS3HUZqi1Ox5DjZizN3/Um7Gc+JCUTO5UCUWYLKdkalKC8pnlWO1wMX2fTOcVRIeG1cQEZm//O6U9FJlMQvkAFXrv7GKhUqGOaXRvndark/HFLWOCVhck7zMqeJysnOQmnJquYyPBa/l66qIk7puOxNUMskJNZmWmPQLOTm24q7SHmNeJ7ghy1CMQKtK1VWV9VN20SnLa7FrDphXYX197Do6Ojo67hRuNYH+2Cc/VbWkbSa3JaWqWFFheaq7WXYRc+aq0BB7uM5wNAVeeYmI1My0iCyynU7JBXrLZz9QP78QaCezbVb7u32QT7rUbbaayhaLKWl11zKw5bjr+oWEeU9S5dAU8pVxDLmgyvSj2bUiDFw8WC80qcAic1rQkuG8pC4Hk2+M48g0HhDRmcSqEuNUHTNK1hmNEFPVwdZzGGfXEx9W2RM5ewEzd1hsSWs5f4XM1q6TOu+7qlZJwTiOSO5wGII1zqhjqHO779QQsbK8aH0LmfPO1Qxo0eqX8+qYZzZS1mUbmZZKvssxmJxnJv+JVIk3Cbz6SiwriU9qHTPDup47dc5+VC1z3QaYZbyYv1+tR/Ipyjbn1z1y4t08hE1dZ/4Mqu1geU1K3UDZnhTCLNURxIpLZbH9hba5ONqoXv/+lcApnyefpTg185/m7+XCBWZSJj1a23bV+nrZ7qljTNWtT1Odvbp/cQ780LXx6+jo6Oi4O7jVBNo8BwqBzB61xk5MJiGNlvWE1FaqJzJnjJDFOoUjaPkMtalm52fJRksj2kzz82EfMBXCnN/jvGmdy1S2w7LG7faKhvcUZUmdwM8P85lYZMlAXs+VzJhzaCotiAUfqLKGskWfu7XBHExoIz8oZOGUeLWZzzTFSq6q04QzzXpM1nI9aiZ6GhE3kFzMzEoRn0ijlT4W/W8lnH72GnY+2BjGiDpHVJBcyDfbw9k1Usho0kJy8/nMf4dhQLwzbfNqVbXn4j3i5n0o0xziHC4TMztWa5Zz7bMz0UuA07m4zo4hZFmEjasLPhfFMdumtSRTbRs20AktBFZhSFoJtctuFurma2cI5XvRSFKa/WvPn+p1d5DT892S6vbv2UWjIcwnAV7m/M3/iozx2vepENSiw17a081Z+8TyO1LGsJwzbca97q9fHg9p9gQvx+FFiDoff50xcmKBAiz02u0xl+8hwMW9Mzo6Ojo67jZuNYF2gyc0mcWU0iLTauQ0PzgBPZl2PtVCn5LfNpNr6zaV9SfkGajewe22b9ouQDjx6S37W/4vpLbNUD8v87fcR3cSBChghLBF2V55X3tc5fWSzUuNLbbIvK3WEaHdTntMJfsnMuuIa4YuWWGcb7atxW5NZ+/clADvECdIMpuy+RjIPTKMlJcxK9lXn315T4+z2BcSqc1W6n4nWV5HIjWTerrcTppJEUqmtnVvyCxwztzK3Aymnmfn8ixJKYCzToSoWMFj3q4v14yURh4g2Yva6ayrTmgd01PSeGrTeHo51Yz2om5gObtwun5Zb/Y9nrPvp9dr2b9231LSakdXP3PSBYEWkTrbVL6XN113hdAW8uuc4+LiYnleYOHSYeNPzUyX4KT1gy+BzZSac59/x5OZotaOcCHdyhloOQk+Ojo6OjruHm41gb64uCAlJTKThKKVLVC9LtmopOKE7C7fd5L5/bBI1993SiJu2t5ND91W4/oNPnTR9KQ+tFlmLE9xSkBMtjC/VnyugeqRPQwryIWM7RT26bG1xDqFRIyukuli+UbwrNymbt8cLgIaY/YJNj1pjLHqSIWEilZ5jXMnXsPO4TJpDpmohBCyn7XU93lx6InrRbEmVrHizykl/DAssuxGAGfCSCGtOTMKdnylyK4lkW0A5JyrmVQjbTbW02FitVox6dTIAVyzXuv5PNu0aS6ytHNnMqNSlFm04aXAs2ZOJS1cTWCpsa8ZbHc9cDsNvsoxteNU0I5xJagipkPOLeIXGXbnqs64Bh7u+vVbs+TSBGp+1nBrJr03BbNtAAlN8Jtm2UV7vZSZItXl97rNdp9+19rvlf1+/nexo6Ojo+Nu4VYT6C994ctM06zbLdnNYp0GVNs5MHnE6dSzZbPabFtDjk5wSoZuIqdDQ0A+6EF5Wol/07riw+IhDI392clDejFlXIsK5yI2N4RFxk1kLh5sSY0RtCWRt853vk6RT02r4jImra9xIX7TeJyn1XMRYZpmL98YY3XbKNpmNC62WzSporPmtNWzLs/N9fNxSubmjPjSJ/n0XLb+wi1hel5Q1C5rr5GCVe6qV6Q17XlMDpw3Miuk6i8OyuB9nhRoJRx+8Vki14sY29db0nxtliAtr6FyjG1GXuT69X56jNez1ixmG9r3nV7rLoTFOkDN9M6zQHMWv2yxyJ1qzCq5bkHKRIplsUMbXObfMaWFttvml6htxu06uf5991lWtTj+01mJk3Mw/52LCFcrOjo6OjruNm41gX7zjUdMzTTr0k3BHmRjPJFlnMg8ABbWW+QMZdsCupmWbnETyW41pc8jWSpULeUHrXuq5bxpvesPbEeq3tjzOrrfX9/XG7YzO+3NJKtk3epPk2Vvx6DOAuQCrRB8boqRaoa0ZqazdZ0ktSY4MVbvZ81BUSHQiLmZtAVawKLIDJoCxYagtmNWttm6jFw3epAFGWzf1x7j6fJ2P2oxH9yYeYWTc+skOzgsre44yfwuLOFyIFClEw3hJ80a+DIubSDQjovDXbvuWzJt27xZJ/28ALENWtp9Lvtyuh0vrsonymspxdrtsB5z/rtkhVPMnRNPnEYKqS3vG4bZWrJkjf0Q6nV4PRAz8lya2JTj2e/3c/FluScAV1dXJ9fE9SDreDQLTFXl/sU5f+qP9iLCjo6OjruMW02gky6JwSmRAvOKbTWXZYq37axXba8ohEU4TONim3DdQm5qyEBdp9VT30BYyNmu4MPiAXsjTrTZz81uxSZLKu661V1jtdW+9yZLPNVZrjCTqPn1lNKiGK3sV9FLt1ndaZrbRKsuXQuMYAfSdETV4ZMjJY/GxOgOjXNEqi27Z1eQsjOt20rbOfBmR5ayPeekWh4WezJ7f3sdNeNbflQtp1mGUlgGKeWzcVVcXPWuIvPPyT42VH3WvYtJEKS5BjS/f7627Ld5Q9v7E4Jz1Xk4f5arhaPzec6Z4EaS0JLmm4oETzPVpyjbmb2pl/IJgCGEOk7zObFMceviMYRAMfabP2se8JTyTMRJYFNmMqbmHmAzGFqlQ2WbKVkDmJQSY4wn1xKLAk5V5bg/LAPrG+IHcwmZZwTKNu37MTdz6ujo6Oi427jVBFqcR2XO5d1ENC2jmklIWdgQjPzIp2hQi9YxuOvk8lrC+Qa/OBWtWtrFvuq8bdvH63Zh146vTBcXuzPmphvtexJlyn/p+pB3iNbKbLn969lElVbSsNTwwmwTVj7/puzu/DlN5j5voiVJSSOueBunZHriEBncKmeehRAG00c7Z23Gw7JQTWIrG7h+PlzTAVBErP21/TOPVZE3aCHJEPyJNZ8qqDm9fKPzRut9XNbxbkGgVc3ezmX3hpRnD3xuI69Oqk7aQfYrNheS4gLitMmyS7FSc6SUifhUWlhLDmJOg4qEptkG8qbjKkHHqfTipuNu23a3nuLt9qfWEaOMQyrXcRMQxaUcRGUpCRERVoPp50tLcKf5+vS5iYx3BOdrm+7Nar0gwG1AMDaFgL45R1UHnr97rW3mIthoA520DHxLZr9koDeb7gPd0dHRcdfxoQj0drv9LWCffwD+6m63+/ntdvujwM8AZ8BvAX9+t9v9Tn7Pc1/7sPBhYO1m39d2Wnx+8PprD/9T3PTawgKvrFckA21xX9uxTKXJzqbFA1TkVLeas3gnGoJKbnSZ6asFj5kMJeYMn2io8oNEsWJLlF7GDus2mHSqxXRQnvm6kDI47xvv5pMsWyYGU1yS55QSXgIxRcQFUppqYxCbDpfqCyHeitLSFG0H1GctasKLkpLHhwGRYyUug5/JudnOmSREMGcO6ypoLcvL+NYhRZBMsFCsi2PeZyFneTFtq+icXVZVbGIiFwmqaWb1pEg0ThOujHkTMBR/YSO1qRZF3uQR3o5j2YEkzbJT4ltJfWM113RIRHMGPJ1YupV9b09wRunmWM7bqWxFRGpreRHBM0s9krTtrWf3CxGFSkytSc06mPtF8cw2cpmdRkqE6gTc3O0Qsu3faf1CKwfxcwBULBLVSXaQmT2+28Clng9dymAmjfX8laAnSSLqWH2nY8xjkom7lox4zpur5ABfbflYPswu+ZeOl3XP7ujo6Hhd8CIZ6D+z2+1+ufyz3W4d8LPAv7/b7X5hu93+F8BPAn/xg157kZ07OztjzE1AVBVpCEz78F/IHXRJgNp1Fg0QZtfk+p7iBdwyIHHNtlTq/233NiO5lg2Fwm+Kp/Jyv6p+V5fT39oQjjLFrE7wFGuyTFxULVUuJpHw3uPEm19ybs4xFxOW7DP1d2mkKGI8bUmg52NOCcYRxtE6FYp44licI8oGPZKUKS0z1a0eVcTPdni5oHA6HJnisY55KSQsGbxSkCjJiKMmsRbgDYeEOR9dsp0FpW34qdSjvUa0uRYWmeh2+yLEcVosa9eds/qJkoK/KVib1/vg1yvqrjRtw2llFQnvHBKWX995O/OxVGs1sWtJpLlunSLqcCET5XYbbXHlYhaj1XrPY2jLjDl6lg2IaqBatu2y3V8dtfKRc6fEa9rqfI6L5CXZxEv+3NbTmtzefp6tKZMXzbc6z9o0BYN5jBFrX26HKkhqggct+1QC0kZfnr3CbVxvAYM2fNvv2R0dHR2vC34vEo4/DOx3u90v5P9/Gsta/MVv8NqHxpe+8HnGccrZo5unl8UvK/zbbPT8uxC8OcPVqKgbAj1n/uYHayNZ0GwNdoJr5Ex1UUT4wdPjc0YQJyx5nBFnI7CtptTof2sNlnSq5Hm1WuUs2iynaH+3+uMy/VxIZ4yRaUzEZj9VtRL5cnyW8Z0t3upxx7FOz7c+0iklI8RaigrHOi1fMqzXgqIT14dTeQXaSF6qdYfkzHfJ9sr192by1J6bmfAvCwFlc13P2p7L+rdb/n861oXonb73pkY1i2CkGY/Fda/L1207S+mPFbNimff2PW4OFqW8S9IiI3x6zVowePMsT7tURIjNWOaFi6I/W2f+uyw/1exrM/sTZPbYFjGLPBHBDaXL4iybarPZ7THNY7mMxGz8IpqvRVGsjbfoPOOjCZIFedYhMV/zeUxijLVLJtMy6LpF+Jbfszs6OjpeF7wIgf7b2+1WgF8A/hrwXcDny4u73e7r2+3Wbbfbj33Qa7vd7u0P+4EPHtxnHOfmCkviaSjTqDehzSzDMtvaEurCWR1LcnxjtrDRpT4PJcPd7teNjh4n+6kyu0ssyeRJFvUGz9y2sKlOzw/PP452uaqyznpTMAuwlrSZ3GFpg1bdIE4ItGrM2lwj0Mc4lY0aSY/RsqBxJtZg8hPV2be4DWBOnRRu0vMurgFZFhuevrclru143RQInRZ6FjkAnGZJl+vdZEd4GnopZvF2miWXonXPy6Ro+pt9NNeSvL4DF/w8bq3USayJS2lEkzSC5tkNsTenFBvZktRf2uyTFSOmmoW17O6JBWCRFEmWOkmRUTTFiXnz6+CgBmVG/n09D/kjUyaxKZGyXeU4jjXQVLUM/WF/hWv84JMox2Oe4cgf2AYYtk9zF0VcvuZSYhiGWeOMyYbm7+51S8kqjZLZktIN120HXxK+7ffsjo6OjtcFH5ZA/8ndbveF7Xa7Bv4H4G8A//u3brcMUa29c8mUlWd8S3SMBz0nO12mduV69tlIRktWE+mk8O8GlzkrdLuBwC0heL987UabMZmdIsqvlDJpK2QCrKudAHqD1tuVDNgsWTCJyEkWsI7JMqt4Sh5LRjo2fR2lWNI168aUjChhJKLVDifJMhus26GqoqUVti+FhtZmvHyKdSYUUp0FMFW1qgOfCdoNx1GIkTQBVpUCi2Qy1s5M2DJpijXzm6EZA/I11w7f6blcntNl5vmm7ogq16f2RRzz5VP2c5kBvpaZZ5YvlN1fjkvj46zJrhvI+vq8XwqgaFJCaEf25FpeHH/KmXyl6NRp9rEQT1czuDkQiM0x5G3FNF6b6SmzIJrmsU0Ud5d5R7z3RAUl4RGGwRvRLcTWKffu5+6EWcsdQqizSaWg1Jr7CFq031rkP+RzOFGa3BjmxjbtaNm1mUilMLK407xcvJR7dkdHR8frgg9FoHe73Rfy78N2u/0p4B8A/yPw3WWd7Xb7JpB2u93b2+32t5/32ovsnLjB9ISSGgmHWxApf5LpKwSwJTetb60RGbO/q/9nAv08zeyChKZlk4nTDGddLS2dM1rNaDkOoBY2tWj/mzOep5+jTctjCKvrjhuLrnTMpK7Nht/UiCTGlMmr1uDFhfU8XkS8Kpptw7xzqDcCmhLme9xkr0t2TzwgRnRIls10xT96EjQZffZZXjHFY7VE02ZgTLJSCJn9vTwn8yhdl/PMmdBTuULxt26z1M5KGe09jmrPV7LikvWwpZvfqfyiniCWMxzl8+N0rFe0SQNaCUuTjS9BSZEY5ELG4qpiBLZIXmY5jJ1ze18bHkpTaFgkN+01cPq3EgnSWh8uvwPt9bQ4fnWLosJZkrGcaQFYO2ek21lwa5/jzJqu7UgZE675lhyvDtUK0WZ/mms9r1P087VOgLl1uI15U1gYTco0e6bnront9Zcj+vY4799/wBe//CUuLi7gX/vTvEy8rHt2R0dHx+uCb0igt9vtORB2u917eTrwzwK/BPwicLbdbn8s6+b+EvB389s+6LUPjUcPHtYCtbZwsLT2BhiaQqBChKZpwom1N56mKVfiz9IKlEXziDlzebOEo+VCMc4d+QohuKkpS1Zf1u3Ytk4ykK3dXkvGT0lJoQHuxLlDFbzLuucBEasv9N5+F59dmNXARi5my778cfO0uZasmkk5YwSNNnVePHhVPaSJg45mxRZj3cCixXcyoiNZP0pSs7KLEY3TQh8dj7GSwLll9nE+1phQrhO9cRwXn1fPS8raZtfY751kyZfn67oURKrzws1Z4Hb5DcnlazDJgM+f36jwdRlkzQFOS0ZTM7YOkiJuDvzK8pY8O004sWwxarMEzpegsmzXEeNJxlSvF1XW2ZGT4y42cOWn7rskUizfk3LMRlTLMUmjqU6NHMQ1MhERxblQW32npnGSBSGeCaG1LlcnTTAxH1P5rdil6F25bubXJMtJnDtpwiMy2yAq+e+YAxkj/sdnV2z8YDY2LxEv857d0dHR8brgw2SgPwn8ve126zEG+CvAf7Tb7dJ2u/13gZ/Zbrcbsu0RwAe99iL4na9+hX3rK6ulQG/uWJem2JAHexB67/FeuH//Pt573nnnvaagLZKqpngmV4W4lOnieaq21cr6xid5qaFdykFAwpy1KsSircFyps1oFmTykLWqCwItvnZtA2vUUDKVRYcbQtZzescwDDYGjXVZEiOERVJRMqyqmruozYHKPpPStrCQOPsAFzI3jcc6TuV8lCJCy2RH4nior8cYq75C41g/vy5PS6JYCHMhR+U8tYRxDk5m3XQpGGwznous9Q0zBvU8nhBo51zdZxHhVNehWnSwy2n+02skb6Duf8mg2jUHbbCldZxzkJAzpQv3mdJcR+bjNru/tvAyoSl/P/I5cTIHaLOuPXffa2ZDBj9rs3GFPF6X/BSf63n8Yv5dLm9vJJsyd9QGinPQ65EqAzLLvnn/0AgxX+eZoEdV04ontVmoNrBRnZvz6HMy6loCkJNlmq5dHzYDMivLazBe7ikp4VywzoZJSfHlEmhe4j27o6Oj43WBPI9MvGR8FvjNr3zhqxynaTndrHJS3DT7KheEECr5smnyhsiImpVbLdabyU4p/CvFUG21fdFTVpw8fNviQqXVwDZWWScSjkIkIm02vC1Qmr2e6z4xZypLVs6pq/7U5XDKVH/Va9Z20rMnsKrW4kxVrUR1UpZFkKk4bEQG503jPE5z2+M4dxBMOlXCnMbJsshNptlafB8tC91MyWu2tZtnG2Il4nWcm+xlO/bz2OXuc2r70S47Xe9UtlDQapdnGVDeT2nPd7O9hVyjyRq3ji06E+tZAmDX6lIaMI9lcc8wzW+T5SU7sNBKUcyPeykAyl368r54ZotD+zFtdLxhfNpxntHaxbUBwLxvNhsz2y6qKlOe2WkbkBRZUoFIc61SJDTlGmm+ryyz88CJZd7N97P2Glge3zLgcCfr1PXa73d9bd6XMZr0SVXZ3Dvjr/zn/ynA92BE9HXAZ4Hf/P3f90f5/Oe/+LL3paOjo+ND47u/+/fxG//0H8IL3rNvaydCD/DVr36Jw9UVSRPj0TLRhdiVbOJpki/4YF3tnDAMqyq5gJPp9yZrXCB+fugX0myZUK2d5041tqpzQrLVFk/jvGNJU9Vqt6getXl3ija3kpyqjS3rzBllsKnqEIJl6oJnCIEQAsHPTTA2Zw8425zZPk0jx/1ITBHvQ7WASzEy5mzzNE1GqFSZ4kRKmp0QIrmNCy7BFA8wtYTS3BRc9nsWVZx3BA0kIkkTUQFRhtXKpBxZK6IpVvJRxlR19oGuGcsT/+VyTk8h6TrR1hOSeJ0cXt+eXWdxbgDCfA2dEixtifNzkE4KDeG63zFclxe112QlrovPVk6J83zsz9GBN3Kl6+40s6yh5F0tY3yTVGl5PLnZeL6Wbfm6rjfr/oVlAyQR+8zy/aqNegCHx3lPzIGaWeDlZjIkaKVX7Req2cdCjU+zzqcE2p9spuyjZM1zew40zX7QcVKc88Q4sT47Lx97awyhvw3wAJ/5zKdf9n50dHR0vBCa+9YL3bNvawb6x4D/+2XvREdHR8fvAX8Ss5B7HdDv2R0dHa86XuiefVsJ9Br4I8BXgJcuKOzo6Oh4AXjg08D/Cxxe8r58u9Dv2R0dHa8qvql79m0l0B0dHR0dHR0dHR23Et+4rV5HR0dHR0dHR0dHR0Un0D5utrYAAAUFSURBVB0dHR0dHR0dHR0vgE6gOzo6Ojo6Ojo6Ol4AnUB3dHR0dHR0dHR0vAA6ge7o6Ojo6Ojo6Oh4AXQC3dHR0dHR0dHR0fEC6AS6o6Ojo6Ojo6Oj4wVw61p5b7fbHwD+N+Ax8Bbw7+12u19/uXv1zWO73f514N8CPgv8od1u98t5+XOP81Udg+12+xj4W8D3Akfg14H/cLfb/e52u/1R4GeAM6zX/J/f7Xa/k9/33NduO7bb7c8B34P1hH4K/Ce73e6X7uL5Ldhut/8l8F+Rr+c7fG5/C9jnH4C/utvtfv6uHu83i1f9ej7F63TPhtfvvt3v2f2ezUd0vLcxA/3TwN/c7XY/APxN7MBeZfwc8KeAz58s/6DjfFXHQIH/brfbbXe73R8C/hnwk9vt1gE/C/zH+Zj+L+AnAT7otVcEf2G32/3wbrf7F4C/DvyvefldPL9st9sfAX6UfD3f8XML8Gd2u93n8s/PvwbH+83glb2en4PX6Z4Nr999u9+z7+Z5Lfi23bNvFYHebrefAH4E+Dt50d8BfmS73X785e3V7w273e4XdrvdF9plH3Scr/IY7Ha7t3e73f/ZLPp/gO8G/jCw3+12pcf8TwP/dv77g1679djtdu81/z4E0l09v9vtdo09PP5ys/jOntvn4HU73g/Eq3w9Pw+v0z0bXr/7dr9n383z+gH4lh3vrSLQwHcCX9rtdhEg//5yXn6X8EHHeSfGIEd2fxn4B8B30WRzdrvd1wG33W4/9g1eeyWw3W7/l+12+9vAfwP8Be7u+f2vgZ/d7Xa/1Sy70+cW+Nvb7fYfb7fbn9put4+4+8f7oniVr+cXwV39Ti/wuty3+z37bp7XjG/bPfu2EeiOu4P/GdOX/Y2XvSPfaux2u/9gt9t9F/DXgP/+Ze/PtwLb7faPA/8i8FMve1++jfiTu93uh4E/AgivwbXc8drjtbhv93v2ncW39Z592wj0F4DPbLdbD5B/f0defpfwQcf5yo9BLsL5fuDf2e12CfhtbEqwvP4mkHa73dvf4LVXCrvd7m8BPwF8kbt3fn8c+APAb+ZCjd8H/DzwfdzRc1um8Xe73QF7CP0JXpNr+QXwql7PL4o7fc+G1/O+3e/Zd+u8frvv2beKQOfKx18C/lxe9OeA/2+32/3uy9urjx4fdJyv+hhst9v/FtMV/ev5Igb4ReBsu93+WP7/LwF/90O8dqux3W4vttvtdzb//6vA28CdO7+73e4nd7vdd+x2u8/udrvPYg+cP41lb+7iuT3fbrcP898C/FnsvN3Ja/mbxat6Pb8o7vI9G16f+3a/Z/d7Nh/h8YqqfhT7/pFhu93+c5hdzBvAO5hdzO7l7tU3j+12+z8B/ybwKeDrwFu73e4PftBxvqpjsN1u/yDwy8CvAVd58W/udrt/Y7vd/ktY5fKG2Srma/l9z33tNmO73X4S+PvAORCxG/F/ttvt/tFdPL8tckbjX9mZJdJdPLe/H/h7gM8/vwL8ld1u95W7eLy/F9yF67nF63TPhtfrvt3v2f2ezUd4vLeOQHd0dHR0dHR0dHTcZtwqCUdHR0dHR0dHR0fHbUcn0B0dHR0dHR0dHR0vgE6gOzo6Ojo6Ojo6Ol4AnUB3dHR0dHR0dHR0vAA6ge7o6Ojo6Ojo6Oh4AXQC3dHR0dHR0dHR0fEC6AS6o6Ojo6Ojo6Oj4wXQCXRHR0dHR0dHR0fHC+D/BxcD/fRtwEWaAAAAAElFTkSuQmCC\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T13:51:11.569325Z","start_time":"2021-04-16T13:51:11.377327Z"},"id":"omdLqIo1yj3D","outputId":"65a1b7e5-037d-43eb-9d99-264f1196f366"},"source":["# test_loader의 output 결과(image 및 mask) 확인\n","for imgs, image_infos in test_loader:\n"," image_infos = image_infos[0]\n"," temp_images = imgs\n"," # temp_masks = masks\n"," \n"," break\n","\n","fig, ax1 = plt.subplots(nrows=1, ncols=1, figsize=(6, 6))\n","\n","print('image shape:', list(temp_images[0].shape))\n","\n","ax1.imshow(temp_images[0].permute([1,2,0]))\n","ax1.grid(False)\n","ax1.set_title(\"input image : {}\".format(image_infos['file_name']), fontsize = 15)\n","\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["image shape: [3, 512, 512]\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"mpFX_KOsyj3E"},"source":["## baseline model\n","\n","### [TODO] 코드 구현 UNet++ \n","\n","- 출처 : https://jinglescode.github.io/2019/12/02/biomedical-image-segmentation-u-net-nested/"]},{"cell_type":"code","metadata":{"id":"ztnXOov8yj3F","outputId":"5fd5239a-a9ff-4957-a7ad-8ae330f64468"},"source":["import wandb\n","\n","wandb.init(project='chanyub',name='deeplabv3+resnet34+focalloss+stepLR')\n","\n","config = wandb.config\n","config.learning_rate = 0.01"],"execution_count":null,"outputs":[{"output_type":"stream","text":["\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mpstage12\u001b[0m (use `wandb login --relogin` to force relogin)\n"],"name":"stderr"},{"output_type":"display_data","data":{"text/html":["\n"," Tracking run with wandb version 0.10.28
\n"," Syncing run deeplabv3+resnet34+focalloss to Weights & Biases (Documentation).
\n"," Project page: https://wandb.ai/pstage12/chanyub
\n"," Run page: https://wandb.ai/pstage12/chanyub/runs/2ak8y04f
\n"," Run data is saved locally in /opt/ml/code/wandb/run-20210430_065553-2ak8y04f

\n"," "],"text/plain":[""]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:11.634792Z","start_time":"2021-04-18T16:16:05.875817Z"},"id":"1voUupchyj3F","outputId":"100ac5f7-5338-4bb8-ba3c-ed3ab4c9f1f8"},"source":["# 구현된 model에 임의의 input을 넣어 output이 잘 나오는지 test\n","import segmentation_models_pytorch as smp\n","\n","model = smp.DeepLabV3Plus(classes=12)\n","x = torch.randn([2, 3, 512, 512])\n","print(\"input shape : \", x.shape)\n","out = model(x).to(device)\n","print(\"output shape : \", out.size())\n","\n","model = model.to(device)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["input shape : torch.Size([2, 3, 512, 512])\n","output shape : torch.Size([2, 12, 512, 512])\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"YgH_porNyj3N"},"source":["## train, validation, test 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.104200Z","start_time":"2021-04-18T16:16:18.093174Z"},"id":"XivjyvdGyj3O"},"source":["def train(num_epochs, model, data_loader, val_loader, criterion, optimizer, saved_dir, val_every, device):\n"," print('Start training..')\n"," best_loss = 9999999\n"," for epoch in range(num_epochs):\n"," model.train()\n"," for step, (images, masks, _) in enumerate(data_loader):\n"," images = torch.stack(images) # (batch, channel, height, width)\n"," masks = torch.stack(masks).long() # (batch, channel, height, width)\n"," \n"," # gpu 연산을 위해 device 할당\n"," images, masks = images.to(device), masks.to(device)\n"," \n"," # inference\n"," outputs = model(images)\n"," \n"," # loss 계산 (cross entropy loss)\n"," loss = criterion(outputs, masks)\n"," optimizer.zero_grad()\n"," loss.backward()\n"," optimizer.step()\n"," \n"," lr_scheduler.step()\n"," \n"," # step 주기에 따른 loss 출력\n"," if (step + 1) % 25 == 0:\n"," print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(\n"," epoch+1, num_epochs, step+1, len(train_loader), loss.item()))\n"," \n"," # validation 주기에 따른 loss 출력 및 best model 저장\n"," if (epoch + 1) % val_every == 0:\n"," avrg_loss = validation(epoch + 1, model, val_loader, criterion, device)\n"," if avrg_loss < best_loss:\n"," print('Best performance at epoch: {}'.format(epoch + 1))\n"," print('Save model in', saved_dir)\n"," best_loss = avrg_loss\n"," wandb.log({'best_loss': best_loss})\n"," save_model(model, saved_dir)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.285795Z","start_time":"2021-04-18T16:16:18.267686Z"},"id":"_lKW7Exzyj3S"},"source":["def validation(epoch, model, data_loader, criterion, device):\n"," print('Start validation #{}'.format(epoch))\n"," model.eval()\n"," with torch.no_grad():\n"," total_loss = 0\n"," cnt = 0\n"," mIoU_list = []\n"," for step, (images, masks, _) in enumerate(data_loader):\n"," \n"," images = torch.stack(images) # (batch, channel, height, width)\n"," masks = torch.stack(masks).long() # (batch, channel, height, width)\n","\n"," images, masks = images.to(device), masks.to(device) \n","\n"," outputs = model(images)\n"," loss = criterion(outputs, masks)\n"," total_loss += loss\n"," cnt += 1\n"," \n"," outputs = torch.argmax(outputs.squeeze(), dim=1).detach().cpu().numpy()\n","\n"," mIoU = label_accuracy_score(masks.detach().cpu().numpy(), outputs, n_class=12)[2]\n"," mIoU_list.append(mIoU)\n"," \n"," avrg_loss = total_loss / cnt\n"," print('Validation #{} Average Loss: {:.4f}, mIoU: {:.4f}'.format(epoch, avrg_loss, np.mean(mIoU_list)))\n"," wandb.log({'Train Loss':loss.item(), 'Val Loss':avrg_loss , 'Val mIoU':np.mean(mIoU_list)})\n"," return avrg_loss"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"L-0J4_Nzyj3V"},"source":["## 모델 저장 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.909918Z","start_time":"2021-04-18T16:16:18.898918Z"},"id":"-b8Z3cC3yj3V"},"source":["# 모델 저장 함수 정의\n","val_every = 1 \n","\n","saved_dir = './saved'\n","if not os.path.isdir(saved_dir): \n"," os.mkdir(saved_dir)\n"," \n","def save_model(model, saved_dir, file_name='deepv3_focal.pt'):\n"," check_point = {'net': model.state_dict()}\n"," output_path = os.path.join(saved_dir, file_name)\n"," torch.save(model.state_dict(), output_path)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"wJvnUQAoyj3W"},"source":["## 모델 생성 및 Loss function, Optimizer 정의"]},{"cell_type":"code","metadata":{"id":"NofK_Fzfyj3W"},"source":["from torch.autograd import Variable\n","import torch.nn.functional as F\n","# ref : https://github.com/clcarwin/focal_loss_pytorch\n","class FocalLoss(nn.Module):\n"," def __init__(self, gamma=0, alpha=None, size_average=True):\n"," super(FocalLoss, self).__init__()\n"," self.gamma = gamma\n"," self.alpha = alpha\n"," if isinstance(alpha,(float,int)): self.alpha = torch.Tensor([alpha,1-alpha])\n"," if isinstance(alpha,list): self.alpha = torch.Tensor(alpha)\n"," self.size_average = size_average\n","\n"," def forward(self, input, target):\n"," if input.dim()>2:\n"," input = input.view(input.size(0),input.size(1),-1) # N,C,H,W => N,C,H*W\n"," input = input.transpose(1,2) # N,C,H*W => N,H*W,C\n"," input = input.contiguous().view(-1,input.size(2)) # N,H*W,C => N*H*W,C\n"," target = target.view(-1,1)\n","\n"," logpt = F.log_softmax(input)\n"," logpt = logpt.gather(1,target)\n"," logpt = logpt.view(-1)\n"," pt = Variable(logpt.data.exp())\n","\n"," if self.alpha is not None:\n"," if self.alpha.type()!=input.data.type():\n"," self.alpha = self.alpha.type_as(input.data)\n"," at = self.alpha.gather(0,target.data.view(-1))\n"," logpt = logpt * Variable(at)\n","\n"," loss = -1 * (1-pt)**self.gamma * logpt\n"," if self.size_average: return loss.mean()\n"," else: return loss.sum()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:19.698902Z","start_time":"2021-04-18T16:16:19.694902Z"},"id":"QI8Fi75Xyj3Y"},"source":["# Loss function 정의\n","criterion = FocalLoss()\n","\n","# Optimizer 정의\n","optimizer = torch.optim.Adam(params = model.parameters(), lr = learning_rate, weight_decay=1e-6)\n","\n","lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size = 237, gamma = 0.65)\n","# scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=50, eta_min=0)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"start_time":"2021-04-18T16:16:20.331Z"},"id":"3Zl2Qqusyj3Z","outputId":"b2b6c012-47b9-4a02-898e-7b1715a906c5"},"source":["train(num_epochs, model, train_loader, val_loader, criterion, optimizer, saved_dir, val_every, device)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Start training..\n","Epoch [1/20], Step [25/327], Loss: 1.6343\n","Epoch [1/20], Step [50/327], Loss: 0.9595\n","Epoch [1/20], Step [75/327], Loss: 1.2087\n","Epoch [1/20], Step [100/327], Loss: 0.9462\n","Epoch [1/20], Step [125/327], Loss: 0.7297\n","Epoch [1/20], Step [150/327], Loss: 0.7986\n","Epoch [1/20], Step [175/327], Loss: 0.7771\n","Epoch [1/20], Step [200/327], Loss: 0.6682\n","Epoch [1/20], Step [225/327], Loss: 0.8059\n","Epoch [1/20], Step [250/327], Loss: 0.8289\n","Epoch [1/20], Step [275/327], Loss: 0.7207\n","Epoch [1/20], Step [300/327], Loss: 0.7145\n","Epoch [1/20], Step [325/327], Loss: 0.3928\n","Start validation #1\n","Validation #1 Average Loss: 0.4501, mIoU: 0.3229\n","Best performance at epoch: 1\n","Save model in ./saved\n","Epoch [2/20], Step [25/327], Loss: 0.4868\n","Epoch [2/20], Step [50/327], Loss: 0.7230\n","Epoch [2/20], Step [75/327], Loss: 0.9658\n","Epoch [2/20], Step [100/327], Loss: 0.3730\n","Epoch [2/20], Step [125/327], Loss: 0.4759\n","Epoch [2/20], Step [150/327], Loss: 0.8234\n","Epoch [2/20], Step [175/327], Loss: 0.3041\n","Epoch [2/20], Step [200/327], Loss: 0.2599\n","Epoch [2/20], Step [225/327], Loss: 0.2667\n","Epoch [2/20], Step [250/327], Loss: 0.2750\n","Epoch [2/20], Step [275/327], Loss: 0.3345\n","Epoch [2/20], Step [300/327], Loss: 0.2455\n","Epoch [2/20], Step [325/327], Loss: 0.1968\n","Start validation #2\n","Validation #2 Average Loss: 0.3723, mIoU: 0.3575\n","Best performance at epoch: 2\n","Save model in ./saved\n","Epoch [3/20], Step [25/327], Loss: 0.2446\n","Epoch [3/20], Step [50/327], Loss: 0.5166\n","Epoch [3/20], Step [75/327], Loss: 0.3381\n","Epoch [3/20], Step [100/327], Loss: 0.5187\n","Epoch [3/20], Step [125/327], Loss: 0.4144\n","Epoch [3/20], Step [150/327], Loss: 0.2159\n","Epoch [3/20], Step [175/327], Loss: 0.1617\n","Epoch [3/20], Step [200/327], Loss: 0.2148\n","Epoch [3/20], Step [225/327], Loss: 0.3663\n","Epoch [3/20], Step [250/327], Loss: 0.2323\n","Epoch [3/20], Step [275/327], Loss: 0.3110\n","Epoch [3/20], Step [300/327], Loss: 0.2016\n","Epoch [3/20], Step [325/327], Loss: 0.1956\n","Start validation #3\n","Validation #3 Average Loss: 0.3526, mIoU: 0.3715\n","Best performance at epoch: 3\n","Save model in ./saved\n","Epoch [4/20], Step [25/327], Loss: 0.2644\n","Epoch [4/20], Step [50/327], Loss: 0.2171\n","Epoch [4/20], Step [75/327], Loss: 0.2712\n","Epoch [4/20], Step [100/327], Loss: 0.3039\n","Epoch [4/20], Step [125/327], Loss: 0.2359\n","Epoch [4/20], Step [150/327], Loss: 0.2302\n","Epoch [4/20], Step [175/327], Loss: 0.1082\n","Epoch [4/20], Step [200/327], Loss: 0.2047\n","Epoch [4/20], Step [225/327], Loss: 0.1375\n","Epoch [4/20], Step [250/327], Loss: 0.1863\n","Epoch [4/20], Step [275/327], Loss: 0.5203\n","Epoch [4/20], Step [300/327], Loss: 0.1535\n","Epoch [4/20], Step [325/327], Loss: 0.3194\n","Start validation #4\n","Validation #4 Average Loss: 0.3824, mIoU: 0.3471\n","Epoch [5/20], Step [25/327], Loss: 0.2444\n","Epoch [5/20], Step [50/327], Loss: 0.1906\n","Epoch [5/20], Step [75/327], Loss: 0.1882\n","Epoch [5/20], Step [100/327], Loss: 0.3537\n","Epoch [5/20], Step [125/327], Loss: 0.1636\n","Epoch [5/20], Step [150/327], Loss: 0.1792\n","Epoch [5/20], Step [175/327], Loss: 0.2287\n","Epoch [5/20], Step [200/327], Loss: 0.3495\n","Epoch [5/20], Step [225/327], Loss: 0.2932\n","Epoch [5/20], Step [250/327], Loss: 0.2243\n","Epoch [5/20], Step [275/327], Loss: 0.2652\n","Epoch [5/20], Step [300/327], Loss: 0.2194\n","Epoch [5/20], Step [325/327], Loss: 0.1801\n","Start validation #5\n","Validation #5 Average Loss: 0.3458, mIoU: 0.3909\n","Best performance at epoch: 5\n","Save model in ./saved\n","Epoch [6/20], Step [25/327], Loss: 0.2016\n","Epoch [6/20], Step [50/327], Loss: 0.2891\n","Epoch [6/20], Step [75/327], Loss: 0.1781\n","Epoch [6/20], Step [100/327], Loss: 0.2395\n","Epoch [6/20], Step [125/327], Loss: 0.1781\n","Epoch [6/20], Step [150/327], Loss: 0.1550\n","Epoch [6/20], Step [175/327], Loss: 0.1989\n","Epoch [6/20], Step [200/327], Loss: 0.1467\n","Epoch [6/20], Step [225/327], Loss: 0.1928\n","Epoch [6/20], Step [250/327], Loss: 0.2315\n","Epoch [6/20], Step [275/327], Loss: 0.1571\n","Epoch [6/20], Step [300/327], Loss: 0.1732\n","Epoch [6/20], Step [325/327], Loss: 0.1736\n","Start validation #6\n","Validation #6 Average Loss: 0.3400, mIoU: 0.4035\n","Best performance at epoch: 6\n","Save model in ./saved\n","Epoch [7/20], Step [25/327], Loss: 0.1382\n","Epoch [7/20], Step [50/327], Loss: 0.1088\n","Epoch [7/20], Step [75/327], Loss: 0.2667\n","Epoch [7/20], Step [100/327], Loss: 0.1408\n","Epoch [7/20], Step [125/327], Loss: 0.1077\n","Epoch [7/20], Step [150/327], Loss: 0.1829\n","Epoch [7/20], Step [175/327], Loss: 0.1023\n","Epoch [7/20], Step [200/327], Loss: 0.0986\n","Epoch [7/20], Step [225/327], Loss: 0.2324\n","Epoch [7/20], Step [250/327], Loss: 0.0855\n","Epoch [7/20], Step [275/327], Loss: 0.1070\n","Epoch [7/20], Step [300/327], Loss: 0.1989\n","Epoch [7/20], Step [325/327], Loss: 0.2529\n","Start validation #7\n","Validation #7 Average Loss: 0.3585, mIoU: 0.4032\n","Epoch [8/20], Step [25/327], Loss: 0.0845\n","Epoch [8/20], Step [50/327], Loss: 0.2076\n","Epoch [8/20], Step [75/327], Loss: 0.1672\n","Epoch [8/20], Step [100/327], Loss: 0.0842\n","Epoch [8/20], Step [125/327], Loss: 0.0706\n","Epoch [8/20], Step [150/327], Loss: 0.1223\n","Epoch [8/20], Step [175/327], Loss: 0.0779\n","Epoch [8/20], Step [200/327], Loss: 0.1207\n","Epoch [8/20], Step [225/327], Loss: 0.1322\n","Epoch [8/20], Step [250/327], Loss: 0.1470\n","Epoch [8/20], Step [275/327], Loss: 0.1371\n","Epoch [8/20], Step [300/327], Loss: 0.2432\n","Epoch [8/20], Step [325/327], Loss: 0.0788\n","Start validation #8\n","Validation #8 Average Loss: 0.3494, mIoU: 0.4060\n","Epoch [9/20], Step [25/327], Loss: 0.0735\n","Epoch [9/20], Step [50/327], Loss: 0.1683\n","Epoch [9/20], Step [75/327], Loss: 0.0755\n","Epoch [9/20], Step [100/327], Loss: 0.1281\n","Epoch [9/20], Step [125/327], Loss: 0.1275\n","Epoch [9/20], Step [150/327], Loss: 0.1107\n","Epoch [9/20], Step [175/327], Loss: 0.1166\n","Epoch [9/20], Step [200/327], Loss: 0.1266\n","Epoch [9/20], Step [225/327], Loss: 0.1265\n","Epoch [9/20], Step [250/327], Loss: 0.1155\n","Epoch [9/20], Step [275/327], Loss: 0.0687\n","Epoch [9/20], Step [300/327], Loss: 0.2874\n","Epoch [9/20], Step [325/327], Loss: 0.0957\n","Start validation #9\n","Validation #9 Average Loss: 0.3247, mIoU: 0.4181\n","Best performance at epoch: 9\n","Save model in ./saved\n","Epoch [10/20], Step [25/327], Loss: 0.0867\n","Epoch [10/20], Step [50/327], Loss: 0.1320\n","Epoch [10/20], Step [75/327], Loss: 0.0562\n","Epoch [10/20], Step [100/327], Loss: 0.0988\n","Epoch [10/20], Step [125/327], Loss: 0.0989\n","Epoch [10/20], Step [150/327], Loss: 0.1408\n","Epoch [10/20], Step [175/327], Loss: 0.1498\n","Epoch [10/20], Step [200/327], Loss: 0.1783\n","Epoch [10/20], Step [225/327], Loss: 0.0374\n","Epoch [10/20], Step [250/327], Loss: 0.1117\n","Epoch [10/20], Step [275/327], Loss: 0.0804\n","Epoch [10/20], Step [300/327], Loss: 0.1218\n","Epoch [10/20], Step [325/327], Loss: 0.0593\n","Start validation #10\n","Validation #10 Average Loss: 0.3415, mIoU: 0.4207\n","Epoch [11/20], Step [25/327], Loss: 0.0745\n","Epoch [11/20], Step [50/327], Loss: 0.1355\n","Epoch [11/20], Step [75/327], Loss: 0.0779\n","Epoch [11/20], Step [100/327], Loss: 0.1329\n","Epoch [11/20], Step [125/327], Loss: 0.0725\n","Epoch [11/20], Step [150/327], Loss: 0.0907\n","Epoch [11/20], Step [175/327], Loss: 0.0884\n","Epoch [11/20], Step [200/327], Loss: 0.0852\n","Epoch [11/20], Step [225/327], Loss: 0.0822\n","Epoch [11/20], Step [250/327], Loss: 0.0864\n","Epoch [11/20], Step [275/327], Loss: 0.0743\n","Epoch [11/20], Step [300/327], Loss: 0.0831\n","Epoch [11/20], Step [325/327], Loss: 0.1903\n","Start validation #11\n","Validation #11 Average Loss: 0.4945, mIoU: 0.3391\n","Epoch [12/20], Step [25/327], Loss: 0.0802\n","Epoch [12/20], Step [50/327], Loss: 0.0798\n","Epoch [12/20], Step [75/327], Loss: 0.0898\n","Epoch [12/20], Step [100/327], Loss: 0.0879\n","Epoch [12/20], Step [125/327], Loss: 0.0470\n","Epoch [12/20], Step [150/327], Loss: 0.0820\n","Epoch [12/20], Step [175/327], Loss: 0.0414\n","Epoch [12/20], Step [200/327], Loss: 0.0732\n","Epoch [12/20], Step [225/327], Loss: 0.0784\n","Epoch [12/20], Step [250/327], Loss: 0.0559\n","Epoch [12/20], Step [275/327], Loss: 0.0765\n","Epoch [12/20], Step [300/327], Loss: 0.0511\n","Epoch [12/20], Step [325/327], Loss: 0.1644\n","Start validation #12\n","Validation #12 Average Loss: 0.3715, mIoU: 0.3974\n","Epoch [13/20], Step [25/327], Loss: 0.0750\n","Epoch [13/20], Step [50/327], Loss: 0.1047\n","Epoch [13/20], Step [75/327], Loss: 0.2114\n","Epoch [13/20], Step [100/327], Loss: 0.0942\n","Epoch [13/20], Step [125/327], Loss: 0.1281\n","Epoch [13/20], Step [150/327], Loss: 0.0969\n","Epoch [13/20], Step [175/327], Loss: 0.1436\n","Epoch [13/20], Step [200/327], Loss: 0.0560\n","Epoch [13/20], Step [225/327], Loss: 0.0780\n","Epoch [13/20], Step [250/327], Loss: 0.1473\n","Epoch [13/20], Step [275/327], Loss: 0.0786\n","Epoch [13/20], Step [300/327], Loss: 0.0469\n","Epoch [13/20], Step [325/327], Loss: 0.1055\n","Start validation #13\n","Validation #13 Average Loss: 0.3451, mIoU: 0.4026\n","Epoch [14/20], Step [25/327], Loss: 0.0597\n","Epoch [14/20], Step [50/327], Loss: 0.0501\n","Epoch [14/20], Step [75/327], Loss: 0.0891\n","Epoch [14/20], Step [100/327], Loss: 0.1296\n","Epoch [14/20], Step [125/327], Loss: 0.0521\n","Epoch [14/20], Step [150/327], Loss: 0.0594\n","Epoch [14/20], Step [175/327], Loss: 0.0785\n","Epoch [14/20], Step [200/327], Loss: 0.0599\n","Epoch [14/20], Step [225/327], Loss: 0.0726\n","Epoch [14/20], Step [250/327], Loss: 0.0949\n","Epoch [14/20], Step [275/327], Loss: 0.1369\n","Epoch [14/20], Step [300/327], Loss: 0.0790\n","Epoch [14/20], Step [325/327], Loss: 0.3124\n","Start validation #14\n","Validation #14 Average Loss: 0.3481, mIoU: 0.4151\n","Epoch [15/20], Step [25/327], Loss: 0.0309\n","Epoch [15/20], Step [50/327], Loss: 0.0513\n","Epoch [15/20], Step [75/327], Loss: 0.0433\n","Epoch [15/20], Step [100/327], Loss: 0.0549\n","Epoch [15/20], Step [125/327], Loss: 0.0454\n","Epoch [15/20], Step [150/327], Loss: 0.0356\n","Epoch [15/20], Step [175/327], Loss: 0.1140\n","Epoch [15/20], Step [200/327], Loss: 0.0338\n","Epoch [15/20], Step [225/327], Loss: 0.0647\n","Epoch [15/20], Step [250/327], Loss: 0.0789\n","Epoch [15/20], Step [275/327], Loss: 0.1068\n","Epoch [15/20], Step [300/327], Loss: 0.0496\n","Epoch [15/20], Step [325/327], Loss: 0.0585\n","Start validation #15\n","Validation #15 Average Loss: 0.3667, mIoU: 0.4089\n","Epoch [16/20], Step [25/327], Loss: 0.0731\n","Epoch [16/20], Step [50/327], Loss: 0.1075\n","Epoch [16/20], Step [75/327], Loss: 0.1096\n","Epoch [16/20], Step [100/327], Loss: 0.0655\n","Epoch [16/20], Step [125/327], Loss: 0.0832\n","Epoch [16/20], Step [150/327], Loss: 0.0861\n","Epoch [16/20], Step [175/327], Loss: 0.0575\n","Epoch [16/20], Step [200/327], Loss: 0.1043\n","Epoch [16/20], Step [225/327], Loss: 0.1118\n","Epoch [16/20], Step [250/327], Loss: 0.0809\n","Epoch [16/20], Step [275/327], Loss: 0.1300\n","Epoch [16/20], Step [300/327], Loss: 0.0849\n","Epoch [16/20], Step [325/327], Loss: 0.0706\n","Start validation #16\n","Validation #16 Average Loss: 0.4375, mIoU: 0.3523\n","Epoch [17/20], Step [25/327], Loss: 0.0691\n","Epoch [17/20], Step [50/327], Loss: 0.1020\n","Epoch [17/20], Step [75/327], Loss: 0.0599\n","Epoch [17/20], Step [100/327], Loss: 0.0790\n","Epoch [17/20], Step [125/327], Loss: 0.0541\n","Epoch [17/20], Step [150/327], Loss: 0.0885\n","Epoch [17/20], Step [175/327], Loss: 0.0571\n","Epoch [17/20], Step [200/327], Loss: 0.0590\n","Epoch [17/20], Step [225/327], Loss: 0.0734\n","Epoch [17/20], Step [250/327], Loss: 0.0567\n","Epoch [17/20], Step [275/327], Loss: 0.0532\n","Epoch [17/20], Step [300/327], Loss: 0.0803\n","Epoch [17/20], Step [325/327], Loss: 0.0556\n","Start validation #17\n","Validation #17 Average Loss: 0.3738, mIoU: 0.3848\n","Epoch [18/20], Step [25/327], Loss: 0.0884\n","Epoch [18/20], Step [50/327], Loss: 0.0510\n","Epoch [18/20], Step [75/327], Loss: 0.1147\n","Epoch [18/20], Step [100/327], Loss: 0.0577\n","Epoch [18/20], Step [125/327], Loss: 0.0718\n","Epoch [18/20], Step [150/327], Loss: 0.0673\n","Epoch [18/20], Step [175/327], Loss: 0.1015\n","Epoch [18/20], Step [200/327], Loss: 0.0771\n","Epoch [18/20], Step [225/327], Loss: 0.0652\n","Epoch [18/20], Step [250/327], Loss: 0.0589\n","Epoch [18/20], Step [275/327], Loss: 0.0365\n","Epoch [18/20], Step [300/327], Loss: 0.0555\n","Epoch [18/20], Step [325/327], Loss: 0.1390\n","Start validation #18\n","Validation #18 Average Loss: 0.3758, mIoU: 0.3942\n","Epoch [19/20], Step [25/327], Loss: 0.0714\n","Epoch [19/20], Step [50/327], Loss: 0.0580\n","Epoch [19/20], Step [75/327], Loss: 0.0774\n","Epoch [19/20], Step [100/327], Loss: 0.0312\n","Epoch [19/20], Step [125/327], Loss: 0.0727\n","Epoch [19/20], Step [150/327], Loss: 0.1005\n","Epoch [19/20], Step [175/327], Loss: 0.0398\n","Epoch [19/20], Step [200/327], Loss: 0.0620\n","Epoch [19/20], Step [225/327], Loss: 0.0609\n","Epoch [19/20], Step [250/327], Loss: 0.0649\n","Epoch [19/20], Step [275/327], Loss: 0.0862\n","Epoch [19/20], Step [300/327], Loss: 0.0502\n","Epoch [19/20], Step [325/327], Loss: 0.1048\n","Start validation #19\n","Validation #19 Average Loss: 0.4323, mIoU: 0.3958\n","Epoch [20/20], Step [25/327], Loss: 0.0446\n","Epoch [20/20], Step [50/327], Loss: 0.0337\n","Epoch [20/20], Step [75/327], Loss: 0.0907\n","Epoch [20/20], Step [100/327], Loss: 0.0747\n","Epoch [20/20], Step [125/327], Loss: 0.1185\n","Epoch [20/20], Step [150/327], Loss: 0.1069\n","Epoch [20/20], Step [175/327], Loss: 0.0367\n","Epoch [20/20], Step [200/327], Loss: 0.0804\n","Epoch [20/20], Step [225/327], Loss: 0.0658\n","Epoch [20/20], Step [250/327], Loss: 0.0483\n","Epoch [20/20], Step [275/327], Loss: 0.0380\n","Epoch [20/20], Step [300/327], Loss: 0.0487\n","Epoch [20/20], Step [325/327], Loss: 0.0613\n","Start validation #20\n","Validation #20 Average Loss: 0.3721, mIoU: 0.4082\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"DEj86kCfyj3a"},"source":["## 저장된 model 불러오기 (학습된 이후) "]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:21.050200Z","start_time":"2021-04-16T19:44:20.802200Z"},"scrolled":true,"id":"wHTjtQmUyj3b","outputId":"3b353783-7aaa-4bac-c37a-f23c8c571e0b"},"source":["# best model 저장된 경로\n","model_path = './saved/deepv3_focal.pt'\n","\n","# best model 불러오기\n","checkpoint = torch.load(model_path, map_location=device)\n","model.load_state_dict(checkpoint)\n","\n","# 추론을 실행하기 전에는 반드시 설정 (batch normalization, dropout 를 평가 모드로 설정)\n","# model.eval()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{"tags":[]},"execution_count":21}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:24.939227Z","start_time":"2021-04-16T19:44:24.518228Z"},"id":"AyGTZGBFyj3c","outputId":"fccc6b27-7001-43e5-edc9-53968fee0405"},"source":["# 첫번째 batch의 추론 결과 확인\n","for imgs, image_infos in test_loader:\n"," image_infos = image_infos\n"," temp_images = imgs\n"," \n"," model.eval()\n"," # inference\n"," outs = model(torch.stack(temp_images).to(device))\n"," oms = torch.argmax(outs.squeeze(), dim=1).detach().cpu().numpy()\n"," \n"," break\n","\n","i = 3\n","fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(16, 16))\n","\n","print('Shape of Original Image :', list(temp_images[i].shape))\n","print('Shape of Predicted : ', list(oms[i].shape))\n","print('Unique values, category of transformed mask : \\n', [{int(i),category_names[int(i)]} for i in list(np.unique(oms[i]))])\n","\n","# Original image\n","ax1.imshow(temp_images[i].permute([1,2,0]))\n","ax1.grid(False)\n","ax1.set_title(\"Original image : {}\".format(image_infos[i]['file_name']), fontsize = 15)\n","\n","# Predicted\n","ax2.imshow(oms[i])\n","ax2.grid(False)\n","ax2.set_title(\"Predicted : {}\".format(image_infos[i]['file_name']), fontsize = 15)\n","\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Shape of Original Image : [3, 512, 512]\n","Shape of Predicted : [512, 512]\n","Unique values, category of transformed mask : \n"," [{0, 'Backgroud'}, {9, 'Plastic bag'}]\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"ciI0Wt67yj3e"},"source":["## submission을 위한 test 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:27.469285Z","start_time":"2021-04-16T19:44:27.456021Z"},"id":"svU8bNRsyj3e"},"source":["def test(model, data_loader, device):\n"," size = 256\n"," transform = A.Compose([A.Resize(256, 256)])\n"," print('Start prediction.')\n"," model.eval()\n"," \n"," file_name_list = []\n"," preds_array = np.empty((0, size*size), dtype=np.long)\n"," \n"," with torch.no_grad():\n"," for step, (imgs, image_infos) in enumerate(test_loader):\n","\n"," # inference (512 x 512)\n"," outs = model(torch.stack(imgs).to(device))\n"," oms = torch.argmax(outs, dim=1).detach().cpu().numpy()\n"," \n"," # resize (256 x 256)\n"," temp_mask = []\n"," for img, mask in zip(np.stack(imgs), oms):\n"," transformed = transform(image=img, mask=mask)\n"," mask = transformed['mask']\n"," temp_mask.append(mask)\n","\n"," oms = np.array(temp_mask)\n"," \n"," oms = oms.reshape([oms.shape[0], size*size]).astype(int)\n"," preds_array = np.vstack((preds_array, oms))\n"," \n"," file_name_list.append([i['file_name'] for i in image_infos])\n"," print(\"End prediction.\")\n"," file_names = [y for x in file_name_list for y in x]\n"," \n"," return file_names, preds_array"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"hx6LblNUyj3r"},"source":["## submission.csv 생성"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:45:42.235310Z","start_time":"2021-04-16T19:44:30.499016Z"},"scrolled":true,"id":"5axg9rzmyj3r","outputId":"c38dc59e-3562-473f-85cc-d938e3fd4728"},"source":["# sample_submisson.csv 열기\n","submission = pd.read_csv('./submission/sample_submission.csv', index_col=None)\n","\n","# test set에 대한 prediction\n","file_names, preds = test(model, test_loader, device)\n","\n","# PredictionString 대입\n","for file_name, string in zip(file_names, preds):\n"," submission = submission.append({\"image_id\" : file_name, \"PredictionString\" : ' '.join(str(e) for e in string.tolist())}, \n"," ignore_index=True)\n","\n","# submission.csv로 저장\n","submission.to_csv(\"./submission/deepv3_focal.csv\", index=False)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Start prediction.\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"h74az6Etyj3t"},"source":["## Reference\n","\n"]},{"cell_type":"code","metadata":{"id":"t3PK9lGayj3u"},"source":["import json\n","import requests\n","import os\n","from urllib.parse import urlparse, parse_qsl, urlencode, urlunparse\n","\n","def submit(user_key='', file_path = '', desc=\"\"):\n"," if not user_key:\n"," raise Exception(\"No UserKey\" )\n"," url = urlparse('http://ec2-13-124-161-225.ap-northeast-2.compute.amazonaws.com:8000/api/v1/competition/28/presigned_url/?description=&hyperparameters={%22training%22:{},%22inference%22:{}}')\n"," qs = dict(parse_qsl(url.query))\n"," qs['description'] = desc\n"," parts = url._replace(query=urlencode(qs))\n"," url = urlunparse(parts)\n","\n"," print(url)\n"," headers = {\n"," 'Authorization': user_key\n"," }\n"," res = requests.get(url, headers=headers)\n"," print(res.text)\n"," data = json.loads(res.text)\n"," \n"," submit_url = data['url']\n"," body = {\n"," 'key':'app/Competitions/000028/Users/{}/Submissions/{}/output.csv'.format(str(data['submission']['user']).zfill(8),str(data['submission']['local_id']).zfill(4)),\n"," 'x-amz-algorithm':data['fields']['x-amz-algorithm'],\n"," 'x-amz-credential':data['fields']['x-amz-credential'],\n"," 'x-amz-date':data['fields']['x-amz-date'],\n"," 'policy':data['fields']['policy'],\n"," 'x-amz-signature':data['fields']['x-amz-signature']\n"," }\n"," requests.post(url=submit_url, data=body, files={'file': open(file_path, 'rb')})\n","\n","\n","####################################################################################\n","test_dir = \"/opt/ml/code/submission\" # 수정 필요 : output 파일 폴더 \n","desc = \"deepv3_focal\" # 수정 필요 : 파일에 대한 설명\n","output_file = \"deepv3_focal.csv\" #수정 필요 : output 파일 \n","user_key = \"Bearer 7bb5f96452751a238ffaf91a93c4242bf9b72abe\" # 수정 필요 : Authorization \n","\n","\n","submit(user_key, os.path.join(test_dir, output_file),desc)"],"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/chanyub_seg/code/pan_effb3_noisy_focal_adamp_coswarmLR.ipynb b/chanyub_seg/code/pan_effb3_noisy_focal_adamp_coswarmLR.ipynb deleted file mode 100644 index e721a29..0000000 --- a/chanyub_seg/code/pan_effb3_noisy_focal_adamp_coswarmLR.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"nbformat":4,"nbformat_minor":0,"metadata":{"hide_input":false,"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.7"},"toc":{"base_numbering":1,"nav_menu":{},"number_sections":true,"sideBar":true,"skip_h1_title":false,"title_cell":"Table of Contents","title_sidebar":"Contents","toc_cell":true,"toc_position":{"height":"calc(100% - 180px)","left":"10px","top":"150px","width":"297.278px"},"toc_section_display":true,"toc_window_display":true},"colab":{"name":"pan_effb3_noisy_focal_adamp_coswarmLR.ipynb","provenance":[],"toc_visible":true},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GW8gF48g-WSK","executionInfo":{"status":"ok","timestamp":1620059952057,"user_tz":-540,"elapsed":910,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"51a7d6b4-f8ce-4dff-923b-b710d7b5f1fa"},"source":["from google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xDwpp4Lk-gSH","executionInfo":{"status":"ok","timestamp":1620059960272,"user_tz":-540,"elapsed":1347,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"d1e6348d-2d5d-4aaf-f637-449cb7623bf7"},"source":["ls"],"execution_count":2,"outputs":[{"output_type":"stream","text":["\u001b[0m\u001b[01;34mdrive\u001b[0m/ \u001b[01;34msample_data\u001b[0m/\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sItrVDeh-iYC","executionInfo":{"status":"ok","timestamp":1620059960274,"user_tz":-540,"elapsed":1006,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"62308432-de35-43b0-d072-30cac6d479f6"},"source":["cd drive/MyDrive/Trash/code"],"execution_count":3,"outputs":[{"output_type":"stream","text":["/content/drive/MyDrive/Trash/code\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"a54v4-kN_LVO","executionInfo":{"status":"ok","timestamp":1620059962896,"user_tz":-540,"elapsed":3164,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"455bca20-7632-477e-db26-1a4b9e454987"},"source":["!pip install albumentations==0.5.2"],"execution_count":4,"outputs":[{"output_type":"stream","text":["Requirement already satisfied: albumentations==0.5.2 in /usr/local/lib/python3.7/dist-packages (0.5.2)\n","Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (1.4.1)\n","Requirement already satisfied: PyYAML in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (3.13)\n","Requirement already satisfied: imgaug>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (0.4.0)\n","Requirement already satisfied: numpy>=1.11.1 in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (1.19.5)\n","Requirement already satisfied: opencv-python-headless>=4.1.1 in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (4.5.1.48)\n","Requirement already satisfied: scikit-image>=0.16.1 in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (0.16.2)\n","Requirement already satisfied: opencv-python in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (4.1.2.30)\n","Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (1.15.0)\n","Requirement already satisfied: imageio in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (2.4.1)\n","Requirement already satisfied: Shapely in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (1.7.1)\n","Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (7.1.2)\n","Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (3.2.2)\n","Requirement already satisfied: networkx>=2.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations==0.5.2) (2.5.1)\n","Requirement already satisfied: PyWavelets>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations==0.5.2) (1.1.1)\n","Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug>=0.4.0->albumentations==0.5.2) (2.4.7)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug>=0.4.0->albumentations==0.5.2) (0.10.0)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug>=0.4.0->albumentations==0.5.2) (1.3.1)\n","Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug>=0.4.0->albumentations==0.5.2) (2.8.1)\n","Requirement already satisfied: decorator<5,>=4.3 in /usr/local/lib/python3.7/dist-packages (from networkx>=2.0->scikit-image>=0.16.1->albumentations==0.5.2) (4.4.2)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.826930Z","start_time":"2021-04-18T10:34:45.406686Z"},"colab":{"base_uri":"https://localhost:8080/"},"id":"l_LPA4XD-NwC","executionInfo":{"status":"ok","timestamp":1620059963701,"user_tz":-540,"elapsed":3573,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"fa5fcf66-69d1-49e4-9030-d8b03040e8ff"},"source":["import os\n","import random\n","import time\n","import json\n","import warnings \n","warnings.filterwarnings('ignore')\n","\n","import torch\n","import torch.nn as nn\n","from torch.utils.data import Dataset, DataLoader\n","from utils import label_accuracy_score\n","import cv2\n","\n","import numpy as np\n","import pandas as pd\n","\n","# 전처리를 위한 라이브러리\n","from pycocotools.coco import COCO\n","import torchvision\n","import torchvision.transforms as transforms\n","\n","import albumentations as A\n","from albumentations.pytorch import ToTensorV2\n","\n","# 시각화를 위한 라이브러리\n","import matplotlib.pyplot as plt\n","import seaborn as sns; sns.set()\n","\n","plt.rcParams['axes.grid'] = False\n","\n","print('pytorch version: {}'.format(torch.__version__))\n","print('GPU 사용 가능 여부: {}'.format(torch.cuda.is_available()))\n","\n","print(torch.cuda.get_device_name(0))\n","print(torch.cuda.device_count())\n","\n","device = \"cuda\" if torch.cuda.is_available() else \"cpu\" # GPU 사용 가능 여부에 따라 device 정보 저장"],"execution_count":5,"outputs":[{"output_type":"stream","text":["pytorch version: 1.8.1+cu101\n","GPU 사용 가능 여부: True\n","Tesla P100-PCIE-16GB\n","1\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"HxgRSL_M-NwF"},"source":["## 하이퍼파라미터 세팅 및 seed 고정"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.841930Z","start_time":"2021-04-18T10:34:47.827931Z"},"id":"rV3JmGP5-NwF","executionInfo":{"status":"ok","timestamp":1620059963703,"user_tz":-540,"elapsed":2256,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["batch_size = 8 # Mini-batch size\n","num_epochs = 20\n","learning_rate = 0.0001"],"execution_count":6,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.856930Z","start_time":"2021-04-18T10:34:47.842931Z"},"id":"Z6LOuJXQ-NwG","executionInfo":{"status":"ok","timestamp":1620059963704,"user_tz":-540,"elapsed":1826,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# seed 고정\n","random_seed = 42\n","torch.manual_seed(random_seed)\n","torch.cuda.manual_seed(random_seed)\n","# torch.cuda.manual_seed_all(random_seed) # if use multi-GPU\n","torch.backends.cudnn.deterministic = True\n","torch.backends.cudnn.benchmark = False\n","np.random.seed(random_seed)\n","random.seed(random_seed)"],"execution_count":7,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"iWF_EJDu-NwG"},"source":["## 학습 데이터 EDA"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.381961Z","start_time":"2021-04-18T10:34:47.857930Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"lg0x0D0a-NwG","executionInfo":{"status":"ok","timestamp":1620059968322,"user_tz":-540,"elapsed":5432,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"5df7666f-370e-44d2-b61a-c6a520116cd8"},"source":["%matplotlib inline\n","\n","dataset_path = '../input/data'\n","anns_file_path = dataset_path + '/' + 'train.json'\n","\n","# Read annotations\n","with open(anns_file_path, 'r') as f:\n"," dataset = json.loads(f.read())\n","\n","categories = dataset['categories']\n","anns = dataset['annotations']\n","imgs = dataset['images']\n","nr_cats = len(categories)\n","nr_annotations = len(anns)\n","nr_images = len(imgs)\n","\n","# Load categories and super categories\n","cat_names = []\n","super_cat_names = []\n","super_cat_ids = {}\n","super_cat_last_name = ''\n","nr_super_cats = 0\n","for cat_it in categories:\n"," cat_names.append(cat_it['name'])\n"," super_cat_name = cat_it['supercategory']\n"," # Adding new supercat\n"," if super_cat_name != super_cat_last_name:\n"," super_cat_names.append(super_cat_name)\n"," super_cat_ids[super_cat_name] = nr_super_cats\n"," super_cat_last_name = super_cat_name\n"," nr_super_cats += 1\n","\n","print('Number of super categories:', nr_super_cats)\n","print('Number of categories:', nr_cats)\n","print('Number of annotations:', nr_annotations)\n","print('Number of images:', nr_images)"],"execution_count":8,"outputs":[{"output_type":"stream","text":["Number of super categories: 11\n","Number of categories: 11\n","Number of annotations: 21116\n","Number of images: 2617\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.546964Z","start_time":"2021-04-18T10:34:51.382969Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/","height":355},"id":"PjLyVHVY-NwH","executionInfo":{"status":"ok","timestamp":1620059970277,"user_tz":-540,"elapsed":1936,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"dd0e8b4a-f428-4001-d256-84295f9195c6"},"source":["# Count annotations\n","cat_histogram = np.zeros(nr_cats,dtype=int)\n","for ann in anns:\n"," cat_histogram[ann['category_id']] += 1\n","\n","# Initialize the matplotlib figure\n","f, ax = plt.subplots(figsize=(5,5))\n","\n","# Convert to DataFrame\n","df = pd.DataFrame({'Categories': cat_names, 'Number of annotations': cat_histogram})\n","df = df.sort_values('Number of annotations', 0, False)\n","\n","# Plot the histogram\n","plt.title(\"category distribution of train set \")\n","plot_1 = sns.barplot(x=\"Number of annotations\", y=\"Categories\", data=df, label=\"Total\", color=\"b\")"],"execution_count":9,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.561965Z","start_time":"2021-04-18T10:34:51.547969Z"},"id":"34Tw5__i-NwI","executionInfo":{"status":"ok","timestamp":1620059970282,"user_tz":-540,"elapsed":1932,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# category labeling \n","sorted_temp_df = df.sort_index()\n","\n","# background = 0 에 해당되는 label 추가 후 기존들을 모두 label + 1 로 설정\n","sorted_df = pd.DataFrame([\"Backgroud\"], columns = [\"Categories\"])\n","sorted_df = sorted_df.append(sorted_temp_df, ignore_index=True)"],"execution_count":10,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.576961Z","start_time":"2021-04-18T10:34:51.562964Z"},"colab":{"base_uri":"https://localhost:8080/","height":421},"id":"CQk4vV5N-NwI","executionInfo":{"status":"ok","timestamp":1620059970300,"user_tz":-540,"elapsed":1937,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"aa6b44f5-7331-4a93-d06e-87e336c93376"},"source":["# class (Categories) 에 따른 index 확인 (0~11 : 총 12개)\n","sorted_df"],"execution_count":11,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
CategoriesNumber of annotations
0BackgroudNaN
1UNKNOWN128.0
2General trash2225.0
3Paper7448.0
4Paper pack527.0
5Metal449.0
6Glass488.0
7Plastic2472.0
8Styrofoam1074.0
9Plastic bag6114.0
10Battery50.0
11Clothing141.0
\n","
"],"text/plain":[" Categories Number of annotations\n","0 Backgroud NaN\n","1 UNKNOWN 128.0\n","2 General trash 2225.0\n","3 Paper 7448.0\n","4 Paper pack 527.0\n","5 Metal 449.0\n","6 Glass 488.0\n","7 Plastic 2472.0\n","8 Styrofoam 1074.0\n","9 Plastic bag 6114.0\n","10 Battery 50.0\n","11 Clothing 141.0"]},"metadata":{"tags":[]},"execution_count":11}]},{"cell_type":"markdown","metadata":{"id":"KXU0zmZs-NwI"},"source":["## 데이터 전처리 함수 정의 (Dataset)"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:52.693328Z","start_time":"2021-04-18T10:34:52.681328Z"},"id":"QFnTI8_Z-NwJ","executionInfo":{"status":"ok","timestamp":1620059970300,"user_tz":-540,"elapsed":1927,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["category_names = list(sorted_df.Categories)\n","\n","def get_classname(classID, cats):\n"," for i in range(len(cats)):\n"," if cats[i]['id']==classID:\n"," return cats[i]['name']\n"," return \"None\"\n","\n","class CustomDataLoader(Dataset):\n"," \"\"\"COCO format\"\"\"\n"," def __init__(self, data_dir, mode = 'train', transform = None):\n"," super().__init__()\n"," self.mode = mode\n"," self.transform = transform\n"," self.coco = COCO(data_dir)\n"," \n"," def __getitem__(self, index: int):\n"," # dataset이 index되어 list처럼 동작\n"," image_id = self.coco.getImgIds(imgIds=index)\n"," image_infos = self.coco.loadImgs(image_id)[0]\n"," \n"," # cv2 를 활용하여 image 불러오기\n"," images = cv2.imread(os.path.join(dataset_path, image_infos['file_name']))\n"," images = cv2.cvtColor(images, cv2.COLOR_BGR2RGB).astype(np.float32)\n"," images /= 255.0\n"," \n"," if (self.mode in ('train', 'val')):\n"," ann_ids = self.coco.getAnnIds(imgIds=image_infos['id'])\n"," anns = self.coco.loadAnns(ann_ids)\n","\n"," # Load the categories in a variable\n"," cat_ids = self.coco.getCatIds()\n"," cats = self.coco.loadCats(cat_ids)\n","\n"," # masks : size가 (height x width)인 2D\n"," # 각각의 pixel 값에는 \"category id + 1\" 할당\n"," # Background = 0\n"," masks = np.zeros((image_infos[\"height\"], image_infos[\"width\"]))\n"," # Unknown = 1, General trash = 2, ... , Cigarette = 11\n"," for i in range(len(anns)):\n"," className = get_classname(anns[i]['category_id'], cats)\n"," pixel_value = category_names.index(className)\n"," masks = np.maximum(self.coco.annToMask(anns[i])*pixel_value, masks)\n"," masks = masks.astype(np.float32)\n","\n"," # transform -> albumentations 라이브러리 활용\n"," if self.transform is not None:\n"," transformed = self.transform(image=images, mask=masks)\n"," images = transformed[\"image\"]\n"," masks = transformed[\"mask\"]\n"," \n"," return images, masks, image_infos\n"," \n"," if self.mode == 'test':\n"," # transform -> albumentations 라이브러리 활용\n"," if self.transform is not None:\n"," transformed = self.transform(image=images)\n"," images = transformed[\"image\"]\n"," \n"," return images, image_infos\n"," \n"," \n"," def __len__(self) -> int:\n"," # 전체 dataset의 size를 return\n"," return len(self.coco.getImgIds())"],"execution_count":12,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"cp2aIOlP-NwK"},"source":["## Dataset 정의 및 DataLoader 할당"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:58.823175Z","start_time":"2021-04-18T10:34:54.106233Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"P7zFlRn6-NwK","executionInfo":{"status":"ok","timestamp":1620059975043,"user_tz":-540,"elapsed":6663,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"60d48918-e5da-44d4-b191-c32b0dfc248c"},"source":["# train.json / validation.json / test.json 디렉토리 설정\n","train_path = dataset_path + '/train.json'\n","val_path = dataset_path + '/val.json'\n","test_path = dataset_path + '/test.json'\n","\n","# collate_fn needs for batch\n","def collate_fn(batch):\n"," return tuple(zip(*batch))\n","\n","train_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","val_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","test_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","# create own Dataset 1 (skip)\n","# validation set을 직접 나누고 싶은 경우\n","# random_split 사용하여 data set을 8:2 로 분할\n","# train_size = int(0.8*len(dataset))\n","# val_size = int(len(dataset)-train_size)\n","# dataset = CustomDataLoader(data_dir=train_path, mode='train', transform=transform)\n","# train_dataset, val_dataset = torch.utils.data.random_split(dataset, [train_size, val_size])\n","\n","# create own Dataset 2\n","# train dataset\n","train_dataset = CustomDataLoader(data_dir=train_path, mode='train', transform=train_transform)\n","\n","# validation dataset\n","val_dataset = CustomDataLoader(data_dir=val_path, mode='val', transform=val_transform)\n","\n","# test dataset\n","test_dataset = CustomDataLoader(data_dir=test_path, mode='test', transform=test_transform)\n","\n","\n","# DataLoader\n","train_loader = torch.utils.data.DataLoader(dataset=train_dataset, \n"," batch_size=batch_size,\n"," shuffle=True,\n"," num_workers=4,\n"," collate_fn=collate_fn,\n"," drop_last=True)\n","\n","val_loader = torch.utils.data.DataLoader(dataset=val_dataset, \n"," batch_size=batch_size,\n"," shuffle=False,\n"," num_workers=4,\n"," collate_fn=collate_fn,\n"," drop_last=True) \n","\n","test_loader = torch.utils.data.DataLoader(dataset=test_dataset,\n"," batch_size=batch_size,\n"," num_workers=4,\n"," collate_fn=collate_fn)"],"execution_count":13,"outputs":[{"output_type":"stream","text":["loading annotations into memory...\n","Done (t=3.94s)\n","creating index...\n","index created!\n","loading annotations into memory...\n","Done (t=0.83s)\n","creating index...\n","index created!\n","loading annotations into memory...\n","Done (t=0.02s)\n","creating index...\n","index created!\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"8rqJiHb_-NwM"},"source":["# wandb"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"H1kHXm0uAX3R","executionInfo":{"status":"ok","timestamp":1620059979098,"user_tz":-540,"elapsed":3215,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"a30f6240-f89a-4797-a409-c33ed3dd954a"},"source":["!pip install wandb"],"execution_count":14,"outputs":[{"output_type":"stream","text":["Requirement already satisfied: wandb in /usr/local/lib/python3.7/dist-packages (0.10.28)\n","Requirement already satisfied: docker-pycreds>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (0.4.0)\n","Requirement already satisfied: PyYAML in /usr/local/lib/python3.7/dist-packages (from wandb) (3.13)\n","Requirement already satisfied: psutil>=5.0.0 in 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satisfied: pathtools in /usr/local/lib/python3.7/dist-packages (from wandb) (0.1.2)\n","Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.7/dist-packages (from wandb) (2.8.1)\n","Requirement already satisfied: configparser>=3.8.1 in /usr/local/lib/python3.7/dist-packages (from wandb) (5.0.2)\n","Requirement already satisfied: shortuuid>=0.5.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (1.0.1)\n","Requirement already satisfied: gitdb<5,>=4.0.1 in /usr/local/lib/python3.7/dist-packages (from GitPython>=1.0.0->wandb) (4.0.7)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.0.0->wandb) (3.0.4)\n","Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.0.0->wandb) (1.24.3)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.0.0->wandb) (2020.12.5)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.0.0->wandb) (2.10)\n","Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from protobuf>=3.12.0->wandb) (56.0.0)\n","Requirement already satisfied: smmap<5,>=3.0.1 in /usr/local/lib/python3.7/dist-packages (from gitdb<5,>=4.0.1->GitPython>=1.0.0->wandb) (4.0.0)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":136},"id":"aMW4VV9V-NwM","executionInfo":{"status":"ok","timestamp":1620059985690,"user_tz":-540,"elapsed":5530,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"0ee9ecfa-7c70-46e7-88be-253b8c49e2c9"},"source":["import wandb\n","\n","proj_name = 'pan_effb3_noisy_focal_adamp_coswarmLR'\n","\n","wandb.init(project='chanyub',name=proj_name)\n","\n","config = wandb.config\n","config.learning_rate = 0.01"],"execution_count":15,"outputs":[{"output_type":"stream","text":["\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mpstage12\u001b[0m (use `wandb login --relogin` to force relogin)\n"],"name":"stderr"},{"output_type":"display_data","data":{"text/html":["\n"," Tracking run with wandb version 0.10.28
\n"," Syncing run pan_effb3_noisy_focal_adamp_coswarmLR to Weights & Biases (Documentation).
\n"," Project page: https://wandb.ai/pstage12/chanyub
\n"," Run page: https://wandb.ai/pstage12/chanyub/runs/3v9zjihs
\n"," Run data is saved locally in /content/drive/My Drive/Trash/code/wandb/run-20210503_163944-3v9zjihs

\n"," "],"text/plain":[""]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"uQRiIVGX-NwM"},"source":["## My model"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"S0xCGpNeAqeD","executionInfo":{"status":"ok","timestamp":1620059988013,"user_tz":-540,"elapsed":5034,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"2ac9289e-92b7-4ce6-a08a-17c5e7ad9f4b"},"source":["!pip install segmentation_models_pytorch"],"execution_count":16,"outputs":[{"output_type":"stream","text":["Requirement already satisfied: segmentation_models_pytorch in /usr/local/lib/python3.7/dist-packages (0.1.3)\n","Requirement already satisfied: pretrainedmodels==0.7.4 in /usr/local/lib/python3.7/dist-packages (from segmentation_models_pytorch) (0.7.4)\n","Requirement already satisfied: timm==0.3.2 in /usr/local/lib/python3.7/dist-packages (from segmentation_models_pytorch) (0.3.2)\n","Requirement already satisfied: torchvision>=0.3.0 in /usr/local/lib/python3.7/dist-packages (from segmentation_models_pytorch) (0.9.1+cu101)\n","Requirement already satisfied: efficientnet-pytorch==0.6.3 in /usr/local/lib/python3.7/dist-packages (from segmentation_models_pytorch) (0.6.3)\n","Requirement already satisfied: munch in /usr/local/lib/python3.7/dist-packages (from pretrainedmodels==0.7.4->segmentation_models_pytorch) (2.5.0)\n","Requirement already satisfied: torch in /usr/local/lib/python3.7/dist-packages (from pretrainedmodels==0.7.4->segmentation_models_pytorch) (1.8.1+cu101)\n","Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from pretrainedmodels==0.7.4->segmentation_models_pytorch) (4.41.1)\n","Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.7/dist-packages (from torchvision>=0.3.0->segmentation_models_pytorch) (7.1.2)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torchvision>=0.3.0->segmentation_models_pytorch) (1.19.5)\n","Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from munch->pretrainedmodels==0.7.4->segmentation_models_pytorch) (1.15.0)\n","Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch->pretrainedmodels==0.7.4->segmentation_models_pytorch) (3.7.4.3)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:11.634792Z","start_time":"2021-04-18T16:16:05.875817Z"},"colab":{"base_uri":"https://localhost:8080/"},"id":"a8IfZfiM-NwM","executionInfo":{"status":"ok","timestamp":1620059999058,"user_tz":-540,"elapsed":11024,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"acacd402-eb26-429c-b625-7e2dd9407a08"},"source":["# 구현된 model에 임의의 input을 넣어 output이 잘 나오는지 test\n","import segmentation_models_pytorch as smp\n","\n","model = smp.PAN(encoder_name='timm-efficientnet-b3', encoder_weights='noisy-student', classes=12)\n","x = torch.randn([2, 3, 512, 512])\n","print(\"input shape : \", x.shape)\n","out = model(x).to(device)\n","print(\"output shape : \", out.size())\n","\n","model = model.to(device)"],"execution_count":17,"outputs":[{"output_type":"stream","text":["input shape : torch.Size([2, 3, 512, 512])\n","output shape : torch.Size([2, 12, 512, 512])\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"zvDnI7_T-NwN"},"source":["## train, validation, test 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.104200Z","start_time":"2021-04-18T16:16:18.093174Z"},"id":"RA3oAapJ-NwN","executionInfo":{"status":"ok","timestamp":1620059999058,"user_tz":-540,"elapsed":11012,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["def train(num_epochs, model, data_loader, val_loader, criterion, optimizer, saved_dir, val_every, device, lr_scheduler):\n"," print('Start training..')\n"," best_loss = 9999999\n"," for epoch in range(num_epochs):\n"," model.train()\n"," for step, (images, masks, _) in enumerate(data_loader):\n"," images = torch.stack(images) # (batch, channel, height, width)\n"," masks = torch.stack(masks).long() # (batch, channel, height, width)\n"," \n"," # gpu 연산을 위해 device 할당\n"," images, masks = images.to(device), masks.to(device)\n"," \n"," # inference\n"," outputs = model(images)\n"," \n"," # loss 계산 (cross entropy loss)\n"," loss = criterion(outputs, masks)\n"," optimizer.zero_grad()\n"," loss.backward()\n"," optimizer.step()\n"," \n"," lr_scheduler.step()\n"," \n"," # step 주기에 따른 loss 출력\n"," if (step + 1) % 25 == 0:\n"," print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(\n"," epoch+1, num_epochs, step+1, len(train_loader), loss.item()))\n"," \n"," # validation 주기에 따른 loss 출력 및 best model 저장\n"," if (epoch + 1) % val_every == 0:\n"," avrg_loss = validation(epoch + 1, model, val_loader, criterion, device)\n"," if avrg_loss < best_loss:\n"," print('Best performance at epoch: {}'.format(epoch + 1))\n"," print('Save model in', saved_dir)\n"," best_loss = avrg_loss\n"," wandb.log({'best_loss': best_loss})\n"," save_model(model, saved_dir)"],"execution_count":18,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.285795Z","start_time":"2021-04-18T16:16:18.267686Z"},"id":"EulIikmq-NwO","executionInfo":{"status":"ok","timestamp":1620060002440,"user_tz":-540,"elapsed":826,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["def validation(epoch, model, data_loader, criterion, device):\n"," print('Start validation #{}'.format(epoch))\n"," model.eval()\n"," with torch.no_grad():\n"," total_loss = 0\n"," cnt = 0\n"," mIoU_list = []\n"," for step, (images, masks, _) in enumerate(data_loader):\n"," \n"," images = torch.stack(images) # (batch, channel, height, width)\n"," masks = torch.stack(masks).long() # (batch, channel, height, width)\n","\n"," images, masks = images.to(device), masks.to(device) \n","\n"," outputs = model(images)\n"," loss = criterion(outputs, masks)\n"," total_loss += loss\n"," cnt += 1\n"," \n","# print(outputs.shape)\n","# print(masks.shape)\n","# wandb.log(wandb.Image(images, masks={\n","# \"predictions\" : {\n","# \"mask_data\" : torch.squeeze(torch.squeeze(outputs, 0),1),\n","# \"class_labels\" : classes_dict\n","# },\n","# \"ground_truth\" : {\n","# \"mask_data\" : torch.squeeze(masks, 0),\n","# \"class_labels\" : classes_dict\n","# }\n","# }))\n"," \n"," outputs = torch.argmax(outputs.squeeze(), dim=1).detach().cpu().numpy()\n","\n"," mIoU = label_accuracy_score(masks.detach().cpu().numpy(), outputs, n_class=12)[2]\n"," mIoU_list.append(mIoU)\n"," \n"," avrg_loss = total_loss / cnt\n"," avrg_mIoU = np.mean(mIoU_list)\n"," print('Validation #{} Average Loss: {:.4f}, mIoU: {:.4f}'.format(epoch, avrg_loss, np.mean(mIoU_list)))\n"," wandb.log({'Train Loss':loss.item(), 'Val Loss':avrg_loss , 'Val mIoU':np.mean(mIoU_list)})\n","# return avrg_loss\n"," return avrg_mIoU"],"execution_count":19,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"C_7CtFfH-NwO"},"source":["## 모델 저장 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.909918Z","start_time":"2021-04-18T16:16:18.898918Z"},"id":"gpCM5BFO-NwO","executionInfo":{"status":"ok","timestamp":1620060008591,"user_tz":-540,"elapsed":896,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# 모델 저장 함수 정의\n","val_every = 1 \n","\n","saved_dir = './saved'\n","if not os.path.isdir(saved_dir): \n"," os.mkdir(saved_dir)\n"," \n","def save_model(model, saved_dir, file_name='pan_effb3_focal_adamp_coswarmLR.pt'):\n"," check_point = {'net': model.state_dict()}\n"," output_path = os.path.join(saved_dir, file_name)\n"," torch.save(model.state_dict(), output_path)"],"execution_count":20,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"FyKGeg8T-NwP"},"source":["## 모델 생성 및 Loss function, Optimizer 정의"]},{"cell_type":"code","metadata":{"id":"ORugl8s1-NwP","executionInfo":{"status":"ok","timestamp":1620060010742,"user_tz":-540,"elapsed":600,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["from torch.autograd import Variable\n","import torch.nn.functional as F\n","# ref : https://github.com/clcarwin/focal_loss_pytorch\n","class FocalLoss(nn.Module):\n"," def __init__(self, gamma=0, alpha=None, size_average=True):\n"," super(FocalLoss, self).__init__()\n"," self.gamma = gamma\n"," self.alpha = alpha\n"," if isinstance(alpha,(float,int)): self.alpha = torch.Tensor([alpha,1-alpha])\n"," if isinstance(alpha,list): self.alpha = torch.Tensor(alpha)\n"," self.size_average = size_average\n","\n"," def forward(self, input, target):\n"," if input.dim()>2:\n"," input = input.view(input.size(0),input.size(1),-1) # N,C,H,W => N,C,H*W\n"," input = input.transpose(1,2) # N,C,H*W => N,H*W,C\n"," input = input.contiguous().view(-1,input.size(2)) # N,H*W,C => N*H*W,C\n"," target = target.view(-1,1)\n","\n"," logpt = F.log_softmax(input)\n"," logpt = logpt.gather(1,target)\n"," logpt = logpt.view(-1)\n"," pt = Variable(logpt.data.exp())\n","\n"," if self.alpha is not None:\n"," if self.alpha.type()!=input.data.type():\n"," self.alpha = self.alpha.type_as(input.data)\n"," at = self.alpha.gather(0,target.data.view(-1))\n"," logpt = logpt * Variable(at)\n","\n"," loss = -1 * (1-pt)**self.gamma * logpt\n"," if self.size_average: return loss.mean()\n"," else: return loss.sum()"],"execution_count":21,"outputs":[]},{"cell_type":"code","metadata":{"id":"yfeFQknz-NwP","executionInfo":{"status":"ok","timestamp":1620060015023,"user_tz":-540,"elapsed":636,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["import torch.optim.lr_scheduler as lr_scheduler\n","import math\n","class CosineAnnealingWarmUpRestart(lr_scheduler._LRScheduler):\n"," def __init__(self, optimizer, T_0, T_mult=1, eta_max=0.1, T_up=0, gamma=1., last_epoch=-1):\n"," if T_0 <= 0 or not isinstance(T_0, int):\n"," raise ValueError(\"Expected positive integer T_0, but got {}\".format(T_0))\n"," if T_mult < 1 or not isinstance(T_mult, int):\n"," raise ValueError(\"Expected integer T_mult >= 1, but got {}\".format(T_mult))\n"," if T_up < 0 or not isinstance(T_up, int):\n"," raise ValueError(\"Expected positive integer T_up, but got {}\".format(T_up))\n"," self.T_0 = T_0\n"," self.T_mult = T_mult\n"," self.base_eta_max = eta_max\n"," self.eta_max = eta_max\n"," self.T_up = T_up\n"," self.T_i = T_0\n"," self.gamma = gamma\n"," self.cycle = 0\n"," self.T_cur = last_epoch\n"," super(CosineAnnealingWarmUpRestart, self).__init__(optimizer, last_epoch)\n"," # self.T_cur = last_epoch\n"," \n"," def get_lr(self):\n"," if self.T_cur == -1:\n"," return self.base_lrs\n"," elif self.T_cur < self.T_up:\n"," return [(self.eta_max - base_lr)*self.T_cur / self.T_up + base_lr for base_lr in self.base_lrs]\n"," else:\n"," return [base_lr + (self.eta_max - base_lr) * (1 + math.cos(math.pi * (self.T_cur-self.T_up) / (self.T_i - self.T_up))) / 2\n"," for base_lr in self.base_lrs]\n","\n"," def step(self, epoch=None):\n"," if epoch is None:\n"," epoch = self.last_epoch + 1\n"," self.T_cur = self.T_cur + 1\n"," if self.T_cur >= self.T_i:\n"," self.cycle += 1\n"," self.T_cur = self.T_cur - self.T_i\n"," self.T_i = (self.T_i - self.T_up) * self.T_mult + self.T_up\n"," else:\n"," if epoch >= self.T_0:\n"," if self.T_mult == 1:\n"," self.T_cur = epoch % self.T_0\n"," self.cycle = epoch // self.T_0\n"," else:\n"," n = int(math.log((epoch / self.T_0 * (self.T_mult - 1) + 1), self.T_mult))\n"," self.cycle = n\n"," self.T_cur = epoch - self.T_0 * (self.T_mult ** n - 1) / (self.T_mult - 1)\n"," self.T_i = self.T_0 * self.T_mult ** (n)\n"," else:\n"," self.T_i = self.T_0\n"," self.T_cur = epoch\n"," \n"," self.eta_max = self.base_eta_max * (self.gamma**self.cycle)\n"," self.last_epoch = math.floor(epoch)\n"," for param_group, lr in zip(self.optimizer.param_groups, self.get_lr()):\n"," param_group['lr'] = lr"],"execution_count":22,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AG1oQeu7BX1M","executionInfo":{"status":"ok","timestamp":1620060019354,"user_tz":-540,"elapsed":3477,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"98eae7c4-b66e-409d-9725-0c684d747f2a"},"source":["!pip install adamp"],"execution_count":23,"outputs":[{"output_type":"stream","text":["Requirement already satisfied: adamp in /usr/local/lib/python3.7/dist-packages (0.3.0)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:19.698902Z","start_time":"2021-04-18T16:16:19.694902Z"},"id":"9Dly8KZj-NwQ","executionInfo":{"status":"ok","timestamp":1620060020323,"user_tz":-540,"elapsed":946,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["from adamp import AdamP\n","# Loss function 정의\n","# criterion = nn.CrossEntropyLoss()\n","criterion = FocalLoss()\n","\n","# Optimizer 정의\n","# optimizer = torch.optim.Adam(params = model.parameters(), lr = learning_rate, weight_decay=1e-6)\n","optimizer = AdamP(params = model.parameters())\n","\n","# lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=50, eta_min=0)\n","lr_scheduler = CosineAnnealingWarmUpRestart(optimizer, T_0=150, T_mult=1, eta_max=0.1, T_up=10, gamma=0.5)"],"execution_count":24,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"start_time":"2021-04-18T16:16:20.331Z"},"colab":{"base_uri":"https://localhost:8080/"},"id":"cMSiFA-3-NwR","executionInfo":{"status":"ok","timestamp":1620076571110,"user_tz":-540,"elapsed":16546496,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"1e68230b-492a-4034-f1d5-6feebe5f45c4"},"source":["train(num_epochs, model, train_loader, val_loader, criterion, optimizer, saved_dir, val_every, device, lr_scheduler)"],"execution_count":25,"outputs":[{"output_type":"stream","text":["Start training..\n","Epoch [1/20], Step [25/327], Loss: 1.4878\n","Epoch [1/20], Step [50/327], Loss: 1.0338\n","Epoch [1/20], Step [75/327], Loss: 0.9771\n","Epoch [1/20], Step [100/327], Loss: 1.0408\n","Epoch [1/20], Step [125/327], Loss: 1.1446\n","Epoch [1/20], Step [150/327], Loss: 0.6848\n","Epoch [1/20], Step [175/327], Loss: 0.7905\n","Epoch [1/20], Step [200/327], Loss: 0.9091\n","Epoch [1/20], Step [225/327], Loss: 0.8830\n","Epoch [1/20], Step [250/327], Loss: 0.9353\n","Epoch [1/20], Step [275/327], Loss: 0.8128\n","Epoch [1/20], Step [300/327], Loss: 0.8310\n","Epoch [1/20], Step [325/327], Loss: 1.1233\n","Start validation #1\n","Validation #1 Average Loss: 3.1578, mIoU: 0.1203\n","Best performance at epoch: 1\n","Save model in ./saved\n","Epoch [2/20], Step [25/327], Loss: 1.0747\n","Epoch [2/20], Step [50/327], Loss: 0.8276\n","Epoch [2/20], Step [75/327], Loss: 0.7259\n","Epoch [2/20], Step [100/327], Loss: 0.8520\n","Epoch [2/20], Step [125/327], Loss: 0.9329\n","Epoch [2/20], Step [150/327], Loss: 0.7575\n","Epoch [2/20], Step [175/327], Loss: 0.9540\n","Epoch [2/20], Step [200/327], Loss: 0.7770\n","Epoch [2/20], Step [225/327], Loss: 1.0508\n","Epoch [2/20], Step [250/327], Loss: 0.7710\n","Epoch [2/20], Step [275/327], Loss: 0.6967\n","Epoch [2/20], Step [300/327], Loss: 0.7724\n","Epoch [2/20], Step [325/327], Loss: 1.0448\n","Start validation #2\n","Validation #2 Average Loss: 0.9205, mIoU: 0.1434\n","Epoch [3/20], Step [25/327], Loss: 0.8298\n","Epoch [3/20], Step [50/327], Loss: 1.0132\n","Epoch [3/20], Step [75/327], Loss: 0.7200\n","Epoch [3/20], Step [100/327], Loss: 0.6251\n","Epoch [3/20], Step [125/327], Loss: 0.9575\n","Epoch [3/20], Step [150/327], Loss: 0.5906\n","Epoch [3/20], Step [175/327], Loss: 0.7522\n","Epoch [3/20], Step [200/327], Loss: 1.2267\n","Epoch [3/20], Step [225/327], Loss: 1.0148\n","Epoch [3/20], Step [250/327], Loss: 1.1269\n","Epoch [3/20], Step [275/327], Loss: 0.6510\n","Epoch [3/20], Step [300/327], Loss: 0.7096\n","Epoch [3/20], Step [325/327], Loss: 0.6294\n","Start validation #3\n","Validation #3 Average Loss: 0.9553, mIoU: 0.1427\n","Epoch [4/20], Step [25/327], Loss: 0.8930\n","Epoch [4/20], Step [50/327], Loss: 0.7245\n","Epoch [4/20], Step [75/327], Loss: 1.1183\n","Epoch [4/20], Step [100/327], Loss: 1.2460\n","Epoch [4/20], Step [125/327], Loss: 0.6387\n","Epoch [4/20], Step [150/327], Loss: 0.6473\n","Epoch [4/20], Step [175/327], Loss: 0.8338\n","Epoch [4/20], Step [200/327], Loss: 0.9681\n","Epoch [4/20], Step [225/327], Loss: 0.7774\n","Epoch [4/20], Step [250/327], Loss: 0.7228\n","Epoch [4/20], Step [275/327], Loss: 1.0707\n","Epoch [4/20], Step [300/327], Loss: 0.8829\n","Epoch [4/20], Step [325/327], Loss: 0.7013\n","Start validation #4\n","Validation #4 Average Loss: 0.8587, mIoU: 0.1453\n","Epoch [5/20], Step [25/327], Loss: 0.6805\n","Epoch [5/20], Step [50/327], Loss: 0.6845\n","Epoch [5/20], Step [75/327], Loss: 0.5996\n","Epoch [5/20], Step [100/327], Loss: 0.9624\n","Epoch [5/20], Step [125/327], Loss: 0.9923\n","Epoch [5/20], Step [150/327], Loss: 0.6793\n","Epoch [5/20], Step [175/327], Loss: 0.8684\n","Epoch [5/20], Step [200/327], Loss: 0.7147\n","Epoch [5/20], Step [225/327], Loss: 0.8625\n","Epoch [5/20], Step [250/327], Loss: 0.9400\n","Epoch [5/20], Step [275/327], Loss: 0.6557\n","Epoch [5/20], Step [300/327], Loss: 0.8341\n","Epoch [5/20], Step [325/327], Loss: 0.5390\n","Start validation #5\n","Validation #5 Average Loss: 0.9726, mIoU: 0.1484\n","Epoch [6/20], Step [25/327], Loss: 0.6590\n","Epoch [6/20], Step [50/327], Loss: 1.4296\n","Epoch [6/20], Step [75/327], Loss: 0.8526\n","Epoch [6/20], Step [100/327], Loss: 1.0729\n","Epoch [6/20], Step [125/327], Loss: 0.7719\n","Epoch [6/20], Step [150/327], Loss: 1.0494\n","Epoch [6/20], Step [175/327], Loss: 0.6924\n","Epoch [6/20], Step [200/327], Loss: 0.6506\n","Epoch [6/20], Step [225/327], Loss: 0.9188\n","Epoch [6/20], Step [250/327], Loss: 0.7122\n","Epoch [6/20], Step [275/327], Loss: 1.0748\n","Epoch [6/20], Step [300/327], Loss: 0.7213\n","Epoch [6/20], Step [325/327], Loss: 1.0518\n","Start validation #6\n","Validation #6 Average Loss: 0.8394, mIoU: 0.1474\n","Epoch [7/20], Step [25/327], Loss: 0.7954\n","Epoch [7/20], Step [50/327], Loss: 0.9341\n","Epoch [7/20], Step [75/327], Loss: 0.5889\n","Epoch [7/20], Step [100/327], Loss: 0.5434\n","Epoch [7/20], Step [125/327], Loss: 0.7626\n","Epoch [7/20], Step [150/327], Loss: 1.0324\n","Epoch [7/20], Step [175/327], Loss: 0.8975\n","Epoch [7/20], Step [200/327], Loss: 0.6771\n","Epoch [7/20], Step [225/327], Loss: 0.8287\n","Epoch [7/20], Step [250/327], Loss: 0.7825\n","Epoch [7/20], Step [275/327], Loss: 0.8614\n","Epoch [7/20], Step [300/327], Loss: 0.6455\n","Epoch [7/20], Step [325/327], Loss: 1.1796\n","Start validation #7\n","Validation #7 Average Loss: 0.8558, mIoU: 0.1476\n","Epoch [8/20], Step [25/327], Loss: 0.6508\n","Epoch [8/20], Step [50/327], Loss: 0.7798\n","Epoch [8/20], Step [75/327], Loss: 0.6977\n","Epoch [8/20], Step [100/327], Loss: 1.3597\n","Epoch [8/20], Step [125/327], Loss: 0.6494\n","Epoch [8/20], Step [150/327], Loss: 0.5158\n","Epoch [8/20], Step [175/327], Loss: 0.8787\n","Epoch [8/20], Step [200/327], Loss: 1.3575\n","Epoch [8/20], Step [225/327], Loss: 1.0498\n","Epoch [8/20], Step [250/327], Loss: 0.5415\n","Epoch [8/20], Step [275/327], Loss: 0.6978\n","Epoch [8/20], Step [300/327], Loss: 0.9187\n","Epoch [8/20], Step [325/327], Loss: 0.6676\n","Start validation #8\n","Validation #8 Average Loss: 0.8632, mIoU: 0.1492\n","Epoch [9/20], Step [25/327], Loss: 0.9979\n","Epoch [9/20], Step [50/327], Loss: 0.5924\n","Epoch [9/20], Step [75/327], Loss: 0.7564\n","Epoch [9/20], Step [100/327], Loss: 0.6870\n","Epoch [9/20], Step [125/327], Loss: 0.7932\n","Epoch [9/20], Step [150/327], Loss: 1.1310\n","Epoch [9/20], Step [175/327], Loss: 0.6095\n","Epoch [9/20], Step [200/327], Loss: 0.5800\n","Epoch [9/20], Step [225/327], Loss: 0.7413\n","Epoch [9/20], Step [250/327], Loss: 1.1563\n","Epoch [9/20], Step [275/327], Loss: 0.5844\n","Epoch [9/20], Step [300/327], Loss: 0.6437\n","Epoch [9/20], Step [325/327], Loss: 1.1452\n","Start validation #9\n","Validation #9 Average Loss: 1.1000, mIoU: 0.1487\n","Epoch [10/20], Step [25/327], Loss: 0.6933\n","Epoch [10/20], Step [50/327], Loss: 0.8881\n","Epoch [10/20], Step [75/327], Loss: 0.9029\n","Epoch [10/20], Step [100/327], Loss: 0.8987\n","Epoch [10/20], Step [125/327], Loss: 0.7771\n","Epoch [10/20], Step [150/327], Loss: 0.6724\n","Epoch [10/20], Step [175/327], Loss: 0.8433\n","Epoch [10/20], Step [200/327], Loss: 0.6386\n","Epoch [10/20], Step [225/327], Loss: 0.8427\n","Epoch [10/20], Step [250/327], Loss: 0.7332\n","Epoch [10/20], Step [275/327], Loss: 0.5199\n","Epoch [10/20], Step [300/327], Loss: 0.5734\n","Epoch [10/20], Step [325/327], Loss: 0.7133\n","Start validation #10\n","Validation #10 Average Loss: 1.1291, mIoU: 0.1470\n","Epoch [11/20], Step [25/327], Loss: 0.9347\n","Epoch [11/20], Step [50/327], Loss: 0.8262\n","Epoch [11/20], Step [75/327], Loss: 0.8859\n","Epoch [11/20], Step [100/327], Loss: 0.6279\n","Epoch [11/20], Step [125/327], Loss: 0.6612\n","Epoch [11/20], Step [150/327], Loss: 0.8332\n","Epoch [11/20], Step [175/327], Loss: 0.5690\n","Epoch [11/20], Step [200/327], Loss: 0.5760\n","Epoch [11/20], Step [225/327], Loss: 0.6251\n","Epoch [11/20], Step [250/327], Loss: 0.9425\n","Epoch [11/20], Step [275/327], Loss: 0.9346\n","Epoch [11/20], Step [300/327], Loss: 0.5729\n","Epoch [11/20], Step [325/327], Loss: 0.8964\n","Start validation #11\n","Validation #11 Average Loss: 0.9696, mIoU: 0.1478\n","Epoch [12/20], Step [25/327], Loss: 0.6780\n","Epoch [12/20], Step [50/327], Loss: 0.9477\n","Epoch [12/20], Step [75/327], Loss: 0.6054\n","Epoch [12/20], Step [100/327], Loss: 0.5830\n","Epoch [12/20], Step [125/327], Loss: 0.5598\n","Epoch [12/20], Step [150/327], Loss: 0.7389\n","Epoch [12/20], Step [175/327], Loss: 0.7262\n","Epoch [12/20], Step [200/327], Loss: 0.7159\n","Epoch [12/20], Step [225/327], Loss: 0.6948\n","Epoch [12/20], Step [250/327], Loss: 0.6641\n","Epoch [12/20], Step [275/327], Loss: 0.7826\n","Epoch [12/20], Step [300/327], Loss: 0.7880\n","Epoch [12/20], Step [325/327], Loss: 0.7807\n","Start validation #12\n","Validation #12 Average Loss: 1.1248, mIoU: 0.1480\n","Epoch [13/20], Step [25/327], Loss: 0.7843\n","Epoch [13/20], Step [50/327], Loss: 0.7133\n","Epoch [13/20], Step [75/327], Loss: 0.6708\n","Epoch [13/20], Step [100/327], Loss: 0.7322\n","Epoch [13/20], Step [125/327], Loss: 0.5833\n","Epoch [13/20], Step [150/327], Loss: 0.8006\n","Epoch [13/20], Step [175/327], Loss: 0.9302\n","Epoch [13/20], Step [200/327], Loss: 1.0020\n","Epoch [13/20], Step [225/327], Loss: 0.7004\n","Epoch [13/20], Step [250/327], Loss: 0.7569\n","Epoch [13/20], Step [275/327], Loss: 0.7703\n","Epoch [13/20], Step [300/327], Loss: 0.7915\n","Epoch [13/20], Step [325/327], Loss: 1.3387\n","Start validation #13\n","Validation #13 Average Loss: 1.4015, mIoU: 0.1492\n","Epoch [14/20], Step [25/327], Loss: 0.5065\n","Epoch [14/20], Step [50/327], Loss: 0.8273\n","Epoch [14/20], Step [75/327], Loss: 0.6770\n","Epoch [14/20], Step [100/327], Loss: 0.7555\n","Epoch [14/20], Step [125/327], Loss: 0.6423\n","Epoch [14/20], Step [150/327], Loss: 0.7349\n","Epoch [14/20], Step [175/327], Loss: 0.6832\n","Epoch [14/20], Step [200/327], Loss: 0.8013\n","Epoch [14/20], Step [225/327], Loss: 1.0347\n","Epoch [14/20], Step [250/327], Loss: 0.9478\n","Epoch [14/20], Step [275/327], Loss: 0.6106\n","Epoch [14/20], Step [300/327], Loss: 0.8378\n","Epoch [14/20], Step [325/327], Loss: 0.5152\n","Start validation #14\n","Validation #14 Average Loss: 0.8845, mIoU: 0.1504\n","Epoch [15/20], Step [25/327], Loss: 0.9145\n","Epoch [15/20], Step [50/327], Loss: 0.5703\n","Epoch [15/20], Step [75/327], Loss: 0.7414\n","Epoch [15/20], Step [100/327], Loss: 0.8532\n","Epoch [15/20], Step [125/327], Loss: 0.7933\n","Epoch [15/20], Step [150/327], Loss: 0.6976\n","Epoch [15/20], Step [175/327], Loss: 0.8867\n","Epoch [15/20], Step [200/327], Loss: 1.1604\n","Epoch [15/20], Step [225/327], Loss: 0.8618\n","Epoch [15/20], Step [250/327], Loss: 1.0106\n","Epoch [15/20], Step [275/327], Loss: 0.7524\n","Epoch [15/20], Step [300/327], Loss: 0.7261\n","Epoch [15/20], Step [325/327], Loss: 0.5443\n","Start validation #15\n","Validation #15 Average Loss: 1.2397, mIoU: 0.1500\n","Epoch [16/20], Step [25/327], Loss: 0.9305\n","Epoch [16/20], Step [50/327], Loss: 0.9357\n","Epoch [16/20], Step [75/327], Loss: 0.7119\n","Epoch [16/20], Step [100/327], Loss: 0.4615\n","Epoch [16/20], Step [125/327], Loss: 0.8112\n","Epoch [16/20], Step [150/327], Loss: 1.0127\n","Epoch [16/20], Step [175/327], Loss: 0.7213\n","Epoch [16/20], Step [200/327], Loss: 0.9317\n","Epoch [16/20], Step [225/327], Loss: 1.0678\n","Epoch [16/20], Step [250/327], Loss: 0.6895\n","Epoch [16/20], Step [275/327], Loss: 0.7478\n","Epoch [16/20], Step [300/327], Loss: 0.8373\n","Epoch [16/20], Step [325/327], Loss: 0.6372\n","Start validation #16\n","Validation #16 Average Loss: 1.0395, mIoU: 0.1513\n","Epoch [17/20], Step [25/327], Loss: 0.7852\n","Epoch [17/20], Step [50/327], Loss: 0.6716\n","Epoch [17/20], Step [75/327], Loss: 0.6270\n","Epoch [17/20], Step [100/327], Loss: 0.6381\n","Epoch [17/20], Step [125/327], Loss: 0.5395\n","Epoch [17/20], Step [150/327], Loss: 0.6487\n","Epoch [17/20], Step [175/327], Loss: 0.5342\n","Epoch [17/20], Step [200/327], Loss: 0.6740\n","Epoch [17/20], Step [225/327], Loss: 0.6671\n","Epoch [17/20], Step [250/327], Loss: 1.0618\n","Epoch [17/20], Step [275/327], Loss: 1.0156\n","Epoch [17/20], Step [300/327], Loss: 1.0374\n","Epoch [17/20], Step [325/327], Loss: 0.8932\n","Start validation #17\n","Validation #17 Average Loss: 1.2382, mIoU: 0.1512\n","Epoch [18/20], Step [25/327], Loss: 0.6902\n","Epoch [18/20], Step [50/327], Loss: 0.7845\n","Epoch [18/20], Step [75/327], Loss: 0.9722\n","Epoch [18/20], Step [100/327], Loss: 0.4735\n","Epoch [18/20], Step [125/327], Loss: 0.7298\n","Epoch [18/20], Step [150/327], Loss: 0.6962\n","Epoch [18/20], Step [175/327], Loss: 0.7477\n","Epoch [18/20], Step [200/327], Loss: 1.1135\n","Epoch [18/20], Step [225/327], Loss: 0.8674\n","Epoch [18/20], Step [250/327], Loss: 1.1094\n","Epoch [18/20], Step [275/327], Loss: 0.9257\n","Epoch [18/20], Step [300/327], Loss: 0.7118\n","Epoch [18/20], Step [325/327], Loss: 0.5878\n","Start validation #18\n","Validation #18 Average Loss: 0.9722, mIoU: 0.1510\n","Epoch [19/20], Step [25/327], Loss: 0.5138\n","Epoch [19/20], Step [50/327], Loss: 0.7529\n","Epoch [19/20], Step [75/327], Loss: 1.1161\n","Epoch [19/20], Step [100/327], Loss: 0.8934\n","Epoch [19/20], Step [125/327], Loss: 1.5396\n","Epoch [19/20], Step [150/327], Loss: 0.6717\n","Epoch [19/20], Step [175/327], Loss: 0.7233\n","Epoch [19/20], Step [200/327], Loss: 0.5214\n","Epoch [19/20], Step [225/327], Loss: 0.8428\n","Epoch [19/20], Step [250/327], Loss: 0.5654\n","Epoch [19/20], Step [275/327], Loss: 0.9668\n","Epoch [19/20], Step [300/327], Loss: 0.7285\n","Epoch [19/20], Step [325/327], Loss: 0.6303\n","Start validation #19\n","Validation #19 Average Loss: 1.0899, mIoU: 0.1516\n","Epoch [20/20], Step [25/327], Loss: 1.6440\n","Epoch [20/20], Step [50/327], Loss: 0.7963\n","Epoch [20/20], Step [75/327], Loss: 0.7951\n","Epoch [20/20], Step [100/327], Loss: 0.7948\n","Epoch [20/20], Step [125/327], Loss: 0.7730\n","Epoch [20/20], Step [150/327], Loss: 0.6791\n","Epoch [20/20], Step [175/327], Loss: 0.7988\n","Epoch [20/20], Step [200/327], Loss: 0.8840\n","Epoch [20/20], Step [225/327], Loss: 0.6747\n","Epoch [20/20], Step [250/327], Loss: 0.9044\n","Epoch [20/20], Step [275/327], Loss: 0.6956\n","Epoch [20/20], Step [300/327], Loss: 0.6776\n","Epoch [20/20], Step [325/327], Loss: 0.8130\n","Start validation #20\n","Validation #20 Average Loss: 0.8210, mIoU: 0.1540\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"C6ClcO0J-NwR"},"source":["## 저장된 model 불러오기 (학습된 이후) "]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:21.050200Z","start_time":"2021-04-16T19:44:20.802200Z"},"scrolled":true,"id":"KQPtUDzd-NwR"},"source":["# best model 저장된 경로\n","model_path = './saved/pan_effb3_noisy_focal_adamp_cosLR.pt'\n","\n","# best model 불러오기\n","checkpoint = torch.load(model_path, map_location=device)\n","model.load_state_dict(checkpoint)\n","\n","# 추론을 실행하기 전에는 반드시 설정 (batch normalization, dropout 를 평가 모드로 설정)\n","# model.eval()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:24.939227Z","start_time":"2021-04-16T19:44:24.518228Z"},"id":"0LQqrDAp-NwR"},"source":["# 첫번째 batch의 추론 결과 확인\n","for imgs, image_infos in test_loader:\n"," image_infos = image_infos\n"," temp_images = imgs\n"," \n"," model.eval()\n"," # inference\n"," outs = model(torch.stack(temp_images).to(device))\n"," oms = torch.argmax(outs.squeeze(), dim=1).detach().cpu().numpy()\n"," \n"," break\n","\n","i = 3\n","fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(16, 16))\n","\n","print('Shape of Original Image :', list(temp_images[i].shape))\n","print('Shape of Predicted : ', list(oms[i].shape))\n","print('Unique values, category of transformed mask : \\n', [{int(i),category_names[int(i)]} for i in list(np.unique(oms[i]))])\n","\n","# Original image\n","ax1.imshow(temp_images[i].permute([1,2,0]))\n","ax1.grid(False)\n","ax1.set_title(\"Original image : {}\".format(image_infos[i]['file_name']), fontsize = 15)\n","\n","# Predicted\n","ax2.imshow(oms[i])\n","ax2.grid(False)\n","ax2.set_title(\"Predicted : {}\".format(image_infos[i]['file_name']), fontsize = 15)\n","\n","plt.show()"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"evYjR2F3-NwS"},"source":["## submission을 위한 test 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:27.469285Z","start_time":"2021-04-16T19:44:27.456021Z"},"id":"nhMLnV5d-NwS"},"source":["def test(model, data_loader, device):\n"," size = 256\n"," transform = A.Compose([A.Resize(256, 256)])\n"," print('Start prediction.')\n"," model.eval()\n"," \n"," file_name_list = []\n"," preds_array = np.empty((0, size*size), dtype=np.long)\n"," \n"," with torch.no_grad():\n"," for step, (imgs, image_infos) in enumerate(test_loader):\n","\n"," # inference (512 x 512)\n"," outs = model(torch.stack(imgs).to(device))\n"," oms = torch.argmax(outs.squeeze(), dim=1).detach().cpu().numpy()\n"," \n"," # resize (256 x 256)\n"," temp_mask = []\n"," for img, mask in zip(np.stack(imgs), oms):\n"," transformed = transform(image=img, mask=mask)\n"," mask = transformed['mask']\n"," temp_mask.append(mask)\n","\n"," oms = np.array(temp_mask)\n"," \n"," oms = oms.reshape([oms.shape[0], size*size]).astype(int)\n"," preds_array = np.vstack((preds_array, oms))\n"," \n"," file_name_list.append([i['file_name'] for i in image_infos])\n"," print(\"End prediction.\")\n"," file_names = [y for x in file_name_list for y in x]\n"," \n"," return file_names, preds_array"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"r1YKHBf4-NwT"},"source":["## submission.csv 생성"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:45:42.235310Z","start_time":"2021-04-16T19:44:30.499016Z"},"scrolled":true,"id":"Bz79_g8K-NwT"},"source":["# sample_submisson.csv 열기\n","submission = pd.read_csv('./submission/sample_submission.csv', index_col=None)\n","\n","# test set에 대한 prediction\n","file_names, preds = test(model, test_loader, device)\n","\n","# PredictionString 대입\n","for file_name, string in zip(file_names, preds):\n"," submission = submission.append({\"image_id\" : file_name, \"PredictionString\" : ' '.join(str(e) for e in string.tolist())}, \n"," ignore_index=True)\n","\n","# submission.csv로 저장\n","submission.to_csv(\"./submission/pan_effb3_noisy_focal_adamp_cosLR.csv\", index=False)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Zgl7Ye7F-NwT"},"source":["## 제출까지\n","\n"]},{"cell_type":"code","metadata":{"id":"022E8AC7-NwT"},"source":["import json\n","import requests\n","import os\n","from urllib.parse import urlparse, parse_qsl, urlencode, urlunparse\n","\n","def submit(user_key='', file_path = '', desc=\"\"):\n"," if not user_key:\n"," raise Exception(\"No UserKey\" )\n"," url = urlparse('http://ec2-13-124-161-225.ap-northeast-2.compute.amazonaws.com:8000/api/v1/competition/28/presigned_url/?description=&hyperparameters={%22training%22:{},%22inference%22:{}}')\n"," qs = dict(parse_qsl(url.query))\n"," qs['description'] = desc\n"," parts = url._replace(query=urlencode(qs))\n"," url = urlunparse(parts)\n","\n"," print(url)\n"," headers = {\n"," 'Authorization': user_key\n"," }\n"," res = requests.get(url, headers=headers)\n"," print(res.text)\n"," data = json.loads(res.text)\n"," \n"," submit_url = data['url']\n"," body = {\n"," 'key':'app/Competitions/000028/Users/{}/Submissions/{}/output.csv'.format(str(data['submission']['user']).zfill(8),str(data['submission']['local_id']).zfill(4)),\n"," 'x-amz-algorithm':data['fields']['x-amz-algorithm'],\n"," 'x-amz-credential':data['fields']['x-amz-credential'],\n"," 'x-amz-date':data['fields']['x-amz-date'],\n"," 'policy':data['fields']['policy'],\n"," 'x-amz-signature':data['fields']['x-amz-signature']\n"," }\n"," requests.post(url=submit_url, data=body, files={'file': open(file_path, 'rb')})\n","\n","\n","####################################################################################\n","test_dir = \"/opt/ml/code/submission\" # 수정 필요 : output 파일 폴더 \n","desc = 'pan_effb3_noisy_focal_adamp_cosLR' # 수정 필요 : 파일에 대한 설명\n","output_file = \"pan_effb3_noisy_focal_adamp_cosLR.csv\" #수정 필요 : output 파일 \n","user_key = \"Bearer 7bb5f96452751a238ffaf91a93c4242bf9b72abe\" # 수정 필요 : Authorization \n","\n","\n","submit(user_key, os.path.join(test_dir, output_file),desc)"],"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/chanyub_seg/code/pan_effb3_noisy_focal_madgrad_coswarmLR.ipynb b/chanyub_seg/code/pan_effb3_noisy_focal_madgrad_coswarmLR.ipynb deleted file mode 100644 index 1c39de6..0000000 --- a/chanyub_seg/code/pan_effb3_noisy_focal_madgrad_coswarmLR.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"nbformat":4,"nbformat_minor":0,"metadata":{"hide_input":false,"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.7"},"toc":{"base_numbering":1,"nav_menu":{},"number_sections":true,"sideBar":true,"skip_h1_title":false,"title_cell":"Table of 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google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Mounted at /content/drive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xDwpp4Lk-gSH","executionInfo":{"status":"ok","timestamp":1620059960272,"user_tz":-540,"elapsed":1347,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"d1e6348d-2d5d-4aaf-f637-449cb7623bf7"},"source":["ls"],"execution_count":null,"outputs":[{"output_type":"stream","text":["\u001b[0m\u001b[01;34mdrive\u001b[0m/ \u001b[01;34msample_data\u001b[0m/\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sItrVDeh-iYC","executionInfo":{"status":"ok","timestamp":1620082041109,"user_tz":-540,"elapsed":4255,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"a4a0bcb1-2f60-4edc-abfe-05ff03ce0ed1"},"source":["cd drive/MyDrive/Trash/code"],"execution_count":2,"outputs":[{"output_type":"stream","text":["/content/drive/MyDrive/Trash/code\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"a54v4-kN_LVO","executionInfo":{"status":"ok","timestamp":1620082049469,"user_tz":-540,"elapsed":11338,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"0a37df82-6247-4df1-d0b5-bf9fdd3e32db"},"source":["!pip install albumentations==0.5.2"],"execution_count":3,"outputs":[{"output_type":"stream","text":["Collecting albumentations==0.5.2\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/03/58/63fb1d742dc42d9ba2800ea741de1f2bc6bb05548d8724aa84794042eaf2/albumentations-0.5.2-py3-none-any.whl (72kB)\n","\r\u001b[K |████▌ | 10kB 17.1MB/s eta 0:00:01\r\u001b[K |█████████ | 20kB 24.0MB/s eta 0:00:01\r\u001b[K |█████████████▋ | 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albumentations-0.1.12\n","Successfully installed albumentations-0.5.2 imgaug-0.4.0 opencv-python-headless-4.5.1.48\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.826930Z","start_time":"2021-04-18T10:34:45.406686Z"},"colab":{"base_uri":"https://localhost:8080/"},"id":"l_LPA4XD-NwC","executionInfo":{"status":"ok","timestamp":1620082063536,"user_tz":-540,"elapsed":4918,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"eb055b24-f5a0-4e8f-d3d1-5586af056130"},"source":["import os\n","import random\n","import time\n","import json\n","import warnings \n","warnings.filterwarnings('ignore')\n","\n","import torch\n","import torch.nn as nn\n","from torch.utils.data import Dataset, DataLoader\n","from utils import label_accuracy_score\n","import cv2\n","\n","import numpy as np\n","import pandas as pd\n","\n","# 전처리를 위한 라이브러리\n","from pycocotools.coco import COCO\n","import torchvision\n","import torchvision.transforms as transforms\n","\n","import albumentations as A\n","from albumentations.pytorch import ToTensorV2\n","\n","# 시각화를 위한 라이브러리\n","import matplotlib.pyplot as plt\n","import seaborn as sns; sns.set()\n","\n","plt.rcParams['axes.grid'] = False\n","\n","print('pytorch version: {}'.format(torch.__version__))\n","print('GPU 사용 가능 여부: {}'.format(torch.cuda.is_available()))\n","\n","print(torch.cuda.get_device_name(0))\n","print(torch.cuda.device_count())\n","\n","device = \"cuda\" if torch.cuda.is_available() else \"cpu\" # GPU 사용 가능 여부에 따라 device 정보 저장"],"execution_count":4,"outputs":[{"output_type":"stream","text":["pytorch version: 1.8.1+cu101\n","GPU 사용 가능 여부: True\n","Tesla P100-PCIE-16GB\n","1\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"HxgRSL_M-NwF"},"source":["## 하이퍼파라미터 세팅 및 seed 고정"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.841930Z","start_time":"2021-04-18T10:34:47.827931Z"},"id":"rV3JmGP5-NwF","executionInfo":{"status":"ok","timestamp":1620082063537,"user_tz":-540,"elapsed":4047,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["batch_size = 8 # Mini-batch size\n","num_epochs = 20\n","learning_rate = 0.0001"],"execution_count":5,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.856930Z","start_time":"2021-04-18T10:34:47.842931Z"},"id":"Z6LOuJXQ-NwG","executionInfo":{"status":"ok","timestamp":1620082063538,"user_tz":-540,"elapsed":3789,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# seed 고정\n","random_seed = 42\n","torch.manual_seed(random_seed)\n","torch.cuda.manual_seed(random_seed)\n","# torch.cuda.manual_seed_all(random_seed) # if use multi-GPU\n","torch.backends.cudnn.deterministic = True\n","torch.backends.cudnn.benchmark = False\n","np.random.seed(random_seed)\n","random.seed(random_seed)"],"execution_count":6,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"iWF_EJDu-NwG"},"source":["## 학습 데이터 EDA"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.381961Z","start_time":"2021-04-18T10:34:47.857930Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"lg0x0D0a-NwG","executionInfo":{"status":"ok","timestamp":1620082073764,"user_tz":-540,"elapsed":12769,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"7b212023-7895-4c2b-bf80-2843d9c6ca9b"},"source":["%matplotlib inline\n","\n","dataset_path = '../input/data'\n","anns_file_path = dataset_path + '/' + 'train.json'\n","\n","# Read annotations\n","with open(anns_file_path, 'r') as f:\n"," dataset = json.loads(f.read())\n","\n","categories = dataset['categories']\n","anns = dataset['annotations']\n","imgs = dataset['images']\n","nr_cats = len(categories)\n","nr_annotations = len(anns)\n","nr_images = len(imgs)\n","\n","# Load categories and super categories\n","cat_names = []\n","super_cat_names = []\n","super_cat_ids = {}\n","super_cat_last_name = ''\n","nr_super_cats = 0\n","for cat_it in categories:\n"," cat_names.append(cat_it['name'])\n"," super_cat_name = cat_it['supercategory']\n"," # Adding new supercat\n"," if super_cat_name != super_cat_last_name:\n"," super_cat_names.append(super_cat_name)\n"," super_cat_ids[super_cat_name] = nr_super_cats\n"," super_cat_last_name = super_cat_name\n"," nr_super_cats += 1\n","\n","print('Number of super categories:', nr_super_cats)\n","print('Number of categories:', nr_cats)\n","print('Number of annotations:', nr_annotations)\n","print('Number of images:', nr_images)"],"execution_count":7,"outputs":[{"output_type":"stream","text":["Number of super categories: 11\n","Number of categories: 11\n","Number of annotations: 21116\n","Number of images: 2617\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.546964Z","start_time":"2021-04-18T10:34:51.382969Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/","height":355},"id":"PjLyVHVY-NwH","executionInfo":{"status":"ok","timestamp":1620082074454,"user_tz":-540,"elapsed":12476,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"8d6d1705-7f0b-4545-82ab-a460fb5d3b9a"},"source":["# Count annotations\n","cat_histogram = np.zeros(nr_cats,dtype=int)\n","for ann in anns:\n"," cat_histogram[ann['category_id']] += 1\n","\n","# Initialize the matplotlib figure\n","f, ax = plt.subplots(figsize=(5,5))\n","\n","# Convert to DataFrame\n","df = pd.DataFrame({'Categories': cat_names, 'Number of annotations': cat_histogram})\n","df = df.sort_values('Number of annotations', 0, False)\n","\n","# Plot the histogram\n","plt.title(\"category distribution of train set \")\n","plot_1 = sns.barplot(x=\"Number of annotations\", y=\"Categories\", data=df, label=\"Total\", color=\"b\")"],"execution_count":8,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.561965Z","start_time":"2021-04-18T10:34:51.547969Z"},"id":"34Tw5__i-NwI","executionInfo":{"status":"ok","timestamp":1620082074455,"user_tz":-540,"elapsed":12079,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# category labeling \n","sorted_temp_df = df.sort_index()\n","\n","# background = 0 에 해당되는 label 추가 후 기존들을 모두 label + 1 로 설정\n","sorted_df = pd.DataFrame([\"Backgroud\"], columns = [\"Categories\"])\n","sorted_df = sorted_df.append(sorted_temp_df, ignore_index=True)"],"execution_count":9,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.576961Z","start_time":"2021-04-18T10:34:51.562964Z"},"colab":{"base_uri":"https://localhost:8080/","height":421},"id":"CQk4vV5N-NwI","executionInfo":{"status":"ok","timestamp":1620082074459,"user_tz":-540,"elapsed":11592,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"e1247618-545e-4d1c-ad44-6b348b9514dd"},"source":["# class (Categories) 에 따른 index 확인 (0~11 : 총 12개)\n","sorted_df"],"execution_count":10,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
CategoriesNumber of annotations
0BackgroudNaN
1UNKNOWN128.0
2General trash2225.0
3Paper7448.0
4Paper pack527.0
5Metal449.0
6Glass488.0
7Plastic2472.0
8Styrofoam1074.0
9Plastic bag6114.0
10Battery50.0
11Clothing141.0
\n","
"],"text/plain":[" Categories Number of annotations\n","0 Backgroud NaN\n","1 UNKNOWN 128.0\n","2 General trash 2225.0\n","3 Paper 7448.0\n","4 Paper pack 527.0\n","5 Metal 449.0\n","6 Glass 488.0\n","7 Plastic 2472.0\n","8 Styrofoam 1074.0\n","9 Plastic bag 6114.0\n","10 Battery 50.0\n","11 Clothing 141.0"]},"metadata":{"tags":[]},"execution_count":10}]},{"cell_type":"markdown","metadata":{"id":"KXU0zmZs-NwI"},"source":["## 데이터 전처리 함수 정의 (Dataset)"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:52.693328Z","start_time":"2021-04-18T10:34:52.681328Z"},"id":"QFnTI8_Z-NwJ","executionInfo":{"status":"ok","timestamp":1620082074459,"user_tz":-540,"elapsed":9130,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["category_names = list(sorted_df.Categories)\n","\n","def get_classname(classID, cats):\n"," for i in range(len(cats)):\n"," if cats[i]['id']==classID:\n"," return cats[i]['name']\n"," return \"None\"\n","\n","class CustomDataLoader(Dataset):\n"," \"\"\"COCO format\"\"\"\n"," def __init__(self, data_dir, mode = 'train', transform = None):\n"," super().__init__()\n"," self.mode = mode\n"," self.transform = transform\n"," self.coco = COCO(data_dir)\n"," \n"," def __getitem__(self, index: int):\n"," # dataset이 index되어 list처럼 동작\n"," image_id = self.coco.getImgIds(imgIds=index)\n"," image_infos = self.coco.loadImgs(image_id)[0]\n"," \n"," # cv2 를 활용하여 image 불러오기\n"," images = cv2.imread(os.path.join(dataset_path, image_infos['file_name']))\n"," images = cv2.cvtColor(images, cv2.COLOR_BGR2RGB).astype(np.float32)\n"," images /= 255.0\n"," \n"," if (self.mode in ('train', 'val')):\n"," ann_ids = self.coco.getAnnIds(imgIds=image_infos['id'])\n"," anns = self.coco.loadAnns(ann_ids)\n","\n"," # Load the categories in a variable\n"," cat_ids = self.coco.getCatIds()\n"," cats = self.coco.loadCats(cat_ids)\n","\n"," # masks : size가 (height x width)인 2D\n"," # 각각의 pixel 값에는 \"category id + 1\" 할당\n"," # Background = 0\n"," masks = np.zeros((image_infos[\"height\"], image_infos[\"width\"]))\n"," # Unknown = 1, General trash = 2, ... , Cigarette = 11\n"," for i in range(len(anns)):\n"," className = get_classname(anns[i]['category_id'], cats)\n"," pixel_value = category_names.index(className)\n"," masks = np.maximum(self.coco.annToMask(anns[i])*pixel_value, masks)\n"," masks = masks.astype(np.float32)\n","\n"," # transform -> albumentations 라이브러리 활용\n"," if self.transform is not None:\n"," transformed = self.transform(image=images, mask=masks)\n"," images = transformed[\"image\"]\n"," masks = transformed[\"mask\"]\n"," \n"," return images, masks, image_infos\n"," \n"," if self.mode == 'test':\n"," # transform -> albumentations 라이브러리 활용\n"," if self.transform is not None:\n"," transformed = self.transform(image=images)\n"," images = transformed[\"image\"]\n"," \n"," return images, image_infos\n"," \n"," \n"," def __len__(self) -> int:\n"," # 전체 dataset의 size를 return\n"," return len(self.coco.getImgIds())"],"execution_count":11,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"cp2aIOlP-NwK"},"source":["## Dataset 정의 및 DataLoader 할당"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:58.823175Z","start_time":"2021-04-18T10:34:54.106233Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"P7zFlRn6-NwK","executionInfo":{"status":"ok","timestamp":1620082083540,"user_tz":-540,"elapsed":12872,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"2fc33d1c-4b70-428e-dc6f-44fa04744439"},"source":["# train.json / validation.json / test.json 디렉토리 설정\n","train_path = dataset_path + '/train.json'\n","val_path = dataset_path + '/val.json'\n","test_path = dataset_path + '/test.json'\n","\n","# collate_fn needs for batch\n","def collate_fn(batch):\n"," return tuple(zip(*batch))\n","\n","train_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","val_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","test_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","# create own Dataset 1 (skip)\n","# validation set을 직접 나누고 싶은 경우\n","# random_split 사용하여 data set을 8:2 로 분할\n","# train_size = int(0.8*len(dataset))\n","# val_size = int(len(dataset)-train_size)\n","# dataset = CustomDataLoader(data_dir=train_path, mode='train', transform=transform)\n","# train_dataset, val_dataset = torch.utils.data.random_split(dataset, [train_size, val_size])\n","\n","# create own Dataset 2\n","# train dataset\n","train_dataset = CustomDataLoader(data_dir=train_path, mode='train', transform=train_transform)\n","\n","# validation dataset\n","val_dataset = CustomDataLoader(data_dir=val_path, mode='val', transform=val_transform)\n","\n","# test dataset\n","test_dataset = CustomDataLoader(data_dir=test_path, mode='test', transform=test_transform)\n","\n","\n","# DataLoader\n","train_loader = torch.utils.data.DataLoader(dataset=train_dataset, \n"," batch_size=batch_size,\n"," shuffle=True,\n"," num_workers=4,\n"," collate_fn=collate_fn,\n"," drop_last=True)\n","\n","val_loader = torch.utils.data.DataLoader(dataset=val_dataset, \n"," batch_size=batch_size,\n"," shuffle=False,\n"," num_workers=4,\n"," collate_fn=collate_fn,\n"," drop_last=True) \n","\n","test_loader = torch.utils.data.DataLoader(dataset=test_dataset,\n"," batch_size=batch_size,\n"," num_workers=4,\n"," collate_fn=collate_fn)"],"execution_count":12,"outputs":[{"output_type":"stream","text":["loading annotations into memory...\n","Done (t=4.00s)\n","creating index...\n","index created!\n","loading annotations into memory...\n","Done (t=3.47s)\n","creating index...\n","index created!\n","loading annotations into memory...\n","Done (t=0.89s)\n","creating index...\n","index created!\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"8rqJiHb_-NwM"},"source":["# wandb"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"H1kHXm0uAX3R","executionInfo":{"status":"ok","timestamp":1620082090733,"user_tz":-540,"elapsed":13697,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"e1f5ee98-52bf-4127-8f74-98f5b56fa85d"},"source":["!pip install wandb"],"execution_count":13,"outputs":[{"output_type":"stream","text":["Collecting wandb\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/5c/ee/d755f9e5466df64c8416a2c6a860fb3aaa43ed6ea8e8e8e81460fda5788b/wandb-0.10.28-py2.py3-none-any.whl (2.1MB)\n","\u001b[K |████████████████████████████████| 2.1MB 13.8MB/s \n","\u001b[?25hCollecting configparser>=3.8.1\n"," Downloading 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/root/.cache/pip/wheels/68/39/1a/5e402bdfdf004af1786c8b853fd92f8c4a04f22aad179654d1\n"," Building wheel for pathtools (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for pathtools: filename=pathtools-0.1.2-cp37-none-any.whl size=8786 sha256=9a826aaebf676b314460905a9433b9631bff18d82a17d9357984848fd5e18b34\n"," Stored in directory: /root/.cache/pip/wheels/0b/04/79/c3b0c3a0266a3cb4376da31e5bfe8bba0c489246968a68e843\n","Successfully built subprocess32 pathtools\n","Installing collected packages: configparser, smmap, gitdb, GitPython, sentry-sdk, docker-pycreds, subprocess32, pathtools, shortuuid, wandb\n","Successfully installed GitPython-3.1.14 configparser-5.0.2 docker-pycreds-0.4.0 gitdb-4.0.7 pathtools-0.1.2 sentry-sdk-1.0.0 shortuuid-1.0.1 smmap-4.0.0 subprocess32-3.5.4 wandb-0.10.28\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":153},"id":"aMW4VV9V-NwM","executionInfo":{"status":"ok","timestamp":1620082103932,"user_tz":-540,"elapsed":10754,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"1f50d84c-f6f0-48ad-e101-0455c3c65388"},"source":["import wandb\n","\n","proj_name = 'pan_effb3_noisy_focal_madgrad_coswarmLR'\n","\n","wandb.init(project='chanyub',name=proj_name)\n","\n","config = wandb.config\n","config.learning_rate = 0.01"],"execution_count":14,"outputs":[{"output_type":"display_data","data":{"application/javascript":["\n"," window._wandbApiKey = new Promise((resolve, reject) => {\n"," function loadScript(url) {\n"," return new Promise(function(resolve, reject) {\n"," let newScript = document.createElement(\"script\");\n"," newScript.onerror = reject;\n"," newScript.onload = resolve;\n"," document.body.appendChild(newScript);\n"," newScript.src = url;\n"," });\n"," }\n"," loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n"," const iframe = document.createElement('iframe')\n"," iframe.style.cssText = \"width:0;height:0;border:none\"\n"," document.body.appendChild(iframe)\n"," const handshake = new Postmate({\n"," container: iframe,\n"," url: 'https://wandb.ai/authorize'\n"," });\n"," const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n"," handshake.then(function(child) {\n"," child.on('authorize', data => {\n"," clearTimeout(timeout)\n"," resolve(data)\n"," });\n"," });\n"," })\n"," });\n"," "],"text/plain":[""]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"],"name":"stderr"},{"output_type":"display_data","data":{"text/html":["\n"," Tracking run with wandb version 0.10.28
\n"," Syncing run pan_effb3_noisy_focal_madgrad_coswarmLR to Weights & Biases (Documentation).
\n"," Project page: https://wandb.ai/pstage12/chanyub
\n"," Run page: https://wandb.ai/pstage12/chanyub/runs/2wyh2b6u
\n"," Run data is saved locally in /content/drive/My Drive/Trash/code/wandb/run-20210503_224822-2wyh2b6u

\n"," "],"text/plain":[""]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"uQRiIVGX-NwM"},"source":["## My model"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"S0xCGpNeAqeD","executionInfo":{"status":"ok","timestamp":1620082108935,"user_tz":-540,"elapsed":15706,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"0eae6f46-6382-4fd2-dff5-484ee00d92ce"},"source":["!pip install segmentation_models_pytorch"],"execution_count":15,"outputs":[{"output_type":"stream","text":["Collecting segmentation_models_pytorch\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/65/54/8953f9f7ee9d451b0f3be8d635aa3a654579abf898d17502a090efe1155a/segmentation_models_pytorch-0.1.3-py3-none-any.whl (66kB)\n","\r\u001b[K |█████ | 10kB 1.0MB/s eta 0:00:01\r\u001b[K |██████████ | 20kB 2.0MB/s eta 0:00:01\r\u001b[K 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sha256=616fc4d95948b435a59deb1769fffe92f41eaad7c758485bbe24a9792d5af422\n"," Stored in directory: /root/.cache/pip/wheels/69/df/63/62583c096289713f22db605aa2334de5b591d59861a02c2ecd\n","Successfully built efficientnet-pytorch pretrainedmodels\n","Installing collected packages: timm, efficientnet-pytorch, munch, pretrainedmodels, segmentation-models-pytorch\n","Successfully installed efficientnet-pytorch-0.6.3 munch-2.5.0 pretrainedmodels-0.7.4 segmentation-models-pytorch-0.1.3 timm-0.3.2\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:11.634792Z","start_time":"2021-04-18T16:16:05.875817Z"},"colab":{"base_uri":"https://localhost:8080/","height":137,"referenced_widgets":["b445f7163a5344bf89e98d088a4a6f0a","44c6adb0e39245eebac897fab0a53837","549abccea23648f6b6312112783bca84","b25aa0b85fbe4ee486886370d7f20552","c2d208a1dd65478d86148adad13e0149","1464e544d9874d1eb205753363591e02","d4936f6c68644f779313deab18450816","e8ea3ee8867c49eabf7aa195552f5218"]},"id":"a8IfZfiM-NwM","executionInfo":{"status":"ok","timestamp":1620082126081,"user_tz":-540,"elapsed":17111,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"c96e5620-0f1c-433b-926c-a79329720ea2"},"source":["# 구현된 model에 임의의 input을 넣어 output이 잘 나오는지 test\n","import segmentation_models_pytorch as smp\n","\n","model = smp.PAN(encoder_name='timm-efficientnet-b3', encoder_weights='noisy-student', classes=12)\n","x = torch.randn([2, 3, 512, 512])\n","print(\"input shape : \", x.shape)\n","out = model(x).to(device)\n","print(\"output shape : \", out.size())\n","\n","model = model.to(device)"],"execution_count":16,"outputs":[{"output_type":"stream","text":["Downloading: \"https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b3_ns-9d44bf68.pth\" to /root/.cache/torch/hub/checkpoints/tf_efficientnet_b3_ns-9d44bf68.pth\n"],"name":"stderr"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"b445f7163a5344bf89e98d088a4a6f0a","version_minor":0,"version_major":2},"text/plain":["HBox(children=(FloatProgress(value=0.0, max=49385734.0), HTML(value='')))"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\n","input shape : torch.Size([2, 3, 512, 512])\n","output shape : torch.Size([2, 12, 512, 512])\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"zvDnI7_T-NwN"},"source":["## train, validation, test 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.104200Z","start_time":"2021-04-18T16:16:18.093174Z"},"id":"RA3oAapJ-NwN","executionInfo":{"status":"ok","timestamp":1620082126082,"user_tz":-540,"elapsed":17109,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["def train(num_epochs, model, data_loader, val_loader, criterion, optimizer, saved_dir, val_every, device, lr_scheduler):\n"," print('Start training..')\n"," best_loss = 9999999\n"," for epoch in range(num_epochs):\n"," model.train()\n"," for step, (images, masks, _) in enumerate(data_loader):\n"," images = torch.stack(images) # (batch, channel, height, width)\n"," masks = torch.stack(masks).long() # (batch, channel, height, width)\n"," \n"," # gpu 연산을 위해 device 할당\n"," images, masks = images.to(device), masks.to(device)\n"," \n"," # inference\n"," outputs = model(images)\n"," \n"," # loss 계산 (cross entropy loss)\n"," loss = criterion(outputs, masks)\n"," optimizer.zero_grad()\n"," loss.backward()\n"," optimizer.step()\n"," \n"," lr_scheduler.step()\n"," \n"," # step 주기에 따른 loss 출력\n"," if (step + 1) % 25 == 0:\n"," print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(\n"," epoch+1, num_epochs, step+1, len(train_loader), loss.item()))\n"," \n"," # validation 주기에 따른 loss 출력 및 best model 저장\n"," if (epoch + 1) % val_every == 0:\n"," avrg_loss = validation(epoch + 1, model, val_loader, criterion, device)\n"," if avrg_loss < best_loss:\n"," print('Best performance at epoch: {}'.format(epoch + 1))\n"," print('Save model in', saved_dir)\n"," best_loss = avrg_loss\n"," wandb.log({'best_loss': best_loss})\n"," save_model(model, saved_dir)"],"execution_count":17,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.285795Z","start_time":"2021-04-18T16:16:18.267686Z"},"id":"EulIikmq-NwO","executionInfo":{"status":"ok","timestamp":1620082126082,"user_tz":-540,"elapsed":17100,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["def validation(epoch, model, data_loader, criterion, device):\n"," print('Start validation #{}'.format(epoch))\n"," model.eval()\n"," with torch.no_grad():\n"," total_loss = 0\n"," cnt = 0\n"," mIoU_list = []\n"," for step, (images, masks, _) in enumerate(data_loader):\n"," \n"," images = torch.stack(images) # (batch, channel, height, width)\n"," masks = torch.stack(masks).long() # (batch, channel, height, width)\n","\n"," images, masks = images.to(device), masks.to(device) \n","\n"," outputs = model(images)\n"," loss = criterion(outputs, masks)\n"," total_loss += loss\n"," cnt += 1\n"," \n","# print(outputs.shape)\n","# print(masks.shape)\n","# wandb.log(wandb.Image(images, masks={\n","# \"predictions\" : {\n","# \"mask_data\" : torch.squeeze(torch.squeeze(outputs, 0),1),\n","# \"class_labels\" : classes_dict\n","# },\n","# \"ground_truth\" : {\n","# \"mask_data\" : torch.squeeze(masks, 0),\n","# \"class_labels\" : classes_dict\n","# }\n","# }))\n"," \n"," outputs = torch.argmax(outputs.squeeze(), dim=1).detach().cpu().numpy()\n","\n"," mIoU = label_accuracy_score(masks.detach().cpu().numpy(), outputs, n_class=12)[2]\n"," mIoU_list.append(mIoU)\n"," \n"," avrg_loss = total_loss / cnt\n"," avrg_mIoU = np.mean(mIoU_list)\n"," print('Validation #{} Average Loss: {:.4f}, mIoU: {:.4f}'.format(epoch, avrg_loss, np.mean(mIoU_list)))\n"," wandb.log({'Train Loss':loss.item(), 'Val Loss':avrg_loss , 'Val mIoU':np.mean(mIoU_list)})\n","# return avrg_loss\n"," return avrg_mIoU"],"execution_count":18,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"C_7CtFfH-NwO"},"source":["## 모델 저장 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.909918Z","start_time":"2021-04-18T16:16:18.898918Z"},"id":"gpCM5BFO-NwO","executionInfo":{"status":"ok","timestamp":1620082129424,"user_tz":-540,"elapsed":1870,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# 모델 저장 함수 정의\n","val_every = 1 \n","\n","saved_dir = './saved'\n","if not os.path.isdir(saved_dir): \n"," os.mkdir(saved_dir)\n"," \n","def save_model(model, saved_dir, file_name='pan_effb3_noisy_focal_madgrad_coswarmLR.pt'):\n"," check_point = {'net': model.state_dict()}\n"," output_path = os.path.join(saved_dir, file_name)\n"," torch.save(model.state_dict(), output_path)"],"execution_count":19,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"FyKGeg8T-NwP"},"source":["## 모델 생성 및 Loss function, Optimizer 정의"]},{"cell_type":"code","metadata":{"id":"ORugl8s1-NwP","executionInfo":{"status":"ok","timestamp":1620082133111,"user_tz":-540,"elapsed":1471,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["from torch.autograd import Variable\n","import torch.nn.functional as F\n","# ref : https://github.com/clcarwin/focal_loss_pytorch\n","class FocalLoss(nn.Module):\n"," def __init__(self, gamma=0, alpha=None, size_average=True):\n"," super(FocalLoss, self).__init__()\n"," self.gamma = gamma\n"," self.alpha = alpha\n"," if isinstance(alpha,(float,int)): self.alpha = torch.Tensor([alpha,1-alpha])\n"," if isinstance(alpha,list): self.alpha = torch.Tensor(alpha)\n"," self.size_average = size_average\n","\n"," def forward(self, input, target):\n"," if input.dim()>2:\n"," input = input.view(input.size(0),input.size(1),-1) # N,C,H,W => N,C,H*W\n"," input = input.transpose(1,2) # N,C,H*W => N,H*W,C\n"," input = input.contiguous().view(-1,input.size(2)) # N,H*W,C => N*H*W,C\n"," target = target.view(-1,1)\n","\n"," logpt = F.log_softmax(input)\n"," logpt = logpt.gather(1,target)\n"," logpt = logpt.view(-1)\n"," pt = Variable(logpt.data.exp())\n","\n"," if self.alpha is not None:\n"," if self.alpha.type()!=input.data.type():\n"," self.alpha = self.alpha.type_as(input.data)\n"," at = self.alpha.gather(0,target.data.view(-1))\n"," logpt = logpt * Variable(at)\n","\n"," loss = -1 * (1-pt)**self.gamma * logpt\n"," if self.size_average: return loss.mean()\n"," else: return loss.sum()"],"execution_count":20,"outputs":[]},{"cell_type":"code","metadata":{"id":"yfeFQknz-NwP","executionInfo":{"status":"ok","timestamp":1620082134528,"user_tz":-540,"elapsed":1415,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["import torch.optim.lr_scheduler as lr_scheduler\n","import math\n","class CosineAnnealingWarmUpRestart(lr_scheduler._LRScheduler):\n"," def __init__(self, optimizer, T_0, T_mult=1, eta_max=0.1, T_up=0, gamma=1., last_epoch=-1):\n"," if T_0 <= 0 or not isinstance(T_0, int):\n"," raise ValueError(\"Expected positive integer T_0, but got {}\".format(T_0))\n"," if T_mult < 1 or not isinstance(T_mult, int):\n"," raise ValueError(\"Expected integer T_mult >= 1, but got {}\".format(T_mult))\n"," if T_up < 0 or not isinstance(T_up, int):\n"," raise ValueError(\"Expected positive integer T_up, but got {}\".format(T_up))\n"," self.T_0 = T_0\n"," self.T_mult = T_mult\n"," self.base_eta_max = eta_max\n"," self.eta_max = eta_max\n"," self.T_up = T_up\n"," self.T_i = T_0\n"," self.gamma = gamma\n"," self.cycle = 0\n"," self.T_cur = last_epoch\n"," super(CosineAnnealingWarmUpRestart, self).__init__(optimizer, last_epoch)\n"," # self.T_cur = last_epoch\n"," \n"," def get_lr(self):\n"," if self.T_cur == -1:\n"," return self.base_lrs\n"," elif self.T_cur < self.T_up:\n"," return [(self.eta_max - base_lr)*self.T_cur / self.T_up + base_lr for base_lr in self.base_lrs]\n"," else:\n"," return [base_lr + (self.eta_max - base_lr) * (1 + math.cos(math.pi * (self.T_cur-self.T_up) / (self.T_i - self.T_up))) / 2\n"," for base_lr in self.base_lrs]\n","\n"," def step(self, epoch=None):\n"," if epoch is None:\n"," epoch = self.last_epoch + 1\n"," self.T_cur = self.T_cur + 1\n"," if self.T_cur >= self.T_i:\n"," self.cycle += 1\n"," self.T_cur = self.T_cur - self.T_i\n"," self.T_i = (self.T_i - self.T_up) * self.T_mult + self.T_up\n"," else:\n"," if epoch >= self.T_0:\n"," if self.T_mult == 1:\n"," self.T_cur = epoch % self.T_0\n"," self.cycle = epoch // self.T_0\n"," else:\n"," n = int(math.log((epoch / self.T_0 * (self.T_mult - 1) + 1), self.T_mult))\n"," self.cycle = n\n"," self.T_cur = epoch - self.T_0 * (self.T_mult ** n - 1) / (self.T_mult - 1)\n"," self.T_i = self.T_0 * self.T_mult ** (n)\n"," else:\n"," self.T_i = self.T_0\n"," self.T_cur = epoch\n"," \n"," self.eta_max = self.base_eta_max * (self.gamma**self.cycle)\n"," self.last_epoch = math.floor(epoch)\n"," for param_group, lr in zip(self.optimizer.param_groups, self.get_lr()):\n"," param_group['lr'] = lr"],"execution_count":21,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AG1oQeu7BX1M","executionInfo":{"status":"ok","timestamp":1620060019354,"user_tz":-540,"elapsed":3477,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"98eae7c4-b66e-409d-9725-0c684d747f2a"},"source":["# !pip install adamp"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Requirement already satisfied: adamp in /usr/local/lib/python3.7/dist-packages (0.3.0)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"eH2PpwF9a-Os","executionInfo":{"status":"ok","timestamp":1620082190374,"user_tz":-540,"elapsed":3938,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"17d3e240-d5ba-48cc-afd2-fa144426937f"},"source":["!pip install madgrad"],"execution_count":22,"outputs":[{"output_type":"stream","text":["Collecting madgrad\n"," Downloading https://files.pythonhosted.org/packages/65/f0/4584f18202a2fb8903d456bf907b80e7cb54ad8fcba68604084ff41b7cf8/madgrad-1.1-py3-none-any.whl\n","Installing collected packages: madgrad\n","Successfully installed madgrad-1.1\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:19.698902Z","start_time":"2021-04-18T16:16:19.694902Z"},"id":"9Dly8KZj-NwQ","executionInfo":{"status":"ok","timestamp":1620082190374,"user_tz":-540,"elapsed":3469,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# from adamp import AdamP\n","from madgrad import MADGRAD\n","# Loss function 정의\n","# criterion = nn.CrossEntropyLoss()\n","criterion = FocalLoss()\n","\n","# Optimizer 정의\n","# optimizer = torch.optim.Adam(params = model.parameters(), lr = learning_rate, weight_decay=1e-6)\n","# optimizer = AdamP(params = model.parameters())\n","optimizer = MADGRAD(params = model.parameters(), lr = learning_rate, momentum = 0.9, weight_decay = 0, eps = 1e-06)\n","\n","# lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=50, eta_min=0)\n","lr_scheduler = CosineAnnealingWarmUpRestart(optimizer, T_0=150, T_mult=1, eta_max=0.1, T_up=10, gamma=0.5)"],"execution_count":23,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"start_time":"2021-04-18T16:16:20.331Z"},"colab":{"base_uri":"https://localhost:8080/"},"id":"cMSiFA-3-NwR","executionInfo":{"status":"ok","timestamp":1620098366712,"user_tz":-540,"elapsed":16170530,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"3822e661-9f41-4c5c-abc1-7c041553f267"},"source":["train(num_epochs, model, train_loader, val_loader, criterion, optimizer, saved_dir, val_every, device, lr_scheduler)"],"execution_count":24,"outputs":[{"output_type":"stream","text":["Start training..\n","Epoch [1/20], Step [25/327], Loss: 1.5601\n","Epoch [1/20], Step [50/327], Loss: 1.1251\n","Epoch [1/20], Step [75/327], Loss: 0.9768\n","Epoch [1/20], Step [100/327], Loss: 0.9681\n","Epoch [1/20], Step [125/327], Loss: 1.1120\n","Epoch [1/20], Step [150/327], Loss: 0.7144\n","Epoch [1/20], Step [175/327], Loss: 0.8535\n","Epoch [1/20], Step [200/327], Loss: 0.8117\n","Epoch [1/20], Step [225/327], Loss: 0.8348\n","Epoch [1/20], Step [250/327], Loss: 0.8131\n","Epoch [1/20], Step [275/327], Loss: 0.6985\n","Epoch [1/20], Step [300/327], Loss: 0.7158\n","Epoch [1/20], Step [325/327], Loss: 0.9812\n","Start validation #1\n","Validation #1 Average Loss: 0.8076, mIoU: 0.1630\n","Best performance at epoch: 1\n","Save model in ./saved\n","Epoch [2/20], Step [25/327], Loss: 0.9983\n","Epoch [2/20], Step [50/327], Loss: 0.7525\n","Epoch [2/20], Step [75/327], Loss: 0.6491\n","Epoch [2/20], Step [100/327], Loss: 0.7976\n","Epoch [2/20], Step [125/327], Loss: 0.7884\n","Epoch [2/20], Step [150/327], Loss: 0.6069\n","Epoch [2/20], Step [175/327], Loss: 0.8097\n","Epoch [2/20], Step [200/327], Loss: 0.6993\n","Epoch [2/20], Step [225/327], Loss: 0.8382\n","Epoch [2/20], Step [250/327], Loss: 0.6649\n","Epoch [2/20], Step [275/327], Loss: 0.6117\n","Epoch [2/20], Step [300/327], Loss: 0.7483\n","Epoch [2/20], Step [325/327], Loss: 0.8093\n","Start validation #2\n","Validation #2 Average Loss: 0.7279, mIoU: 0.1925\n","Epoch [3/20], Step [25/327], Loss: 0.7402\n","Epoch [3/20], Step [50/327], Loss: 0.8580\n","Epoch [3/20], Step [75/327], Loss: 0.6111\n","Epoch [3/20], Step [100/327], Loss: 0.5262\n","Epoch [3/20], Step [125/327], Loss: 0.9544\n","Epoch [3/20], Step [150/327], Loss: 0.4647\n","Epoch [3/20], Step [175/327], Loss: 0.5403\n","Epoch [3/20], Step [200/327], Loss: 0.9728\n","Epoch [3/20], Step [225/327], Loss: 0.9351\n","Epoch [3/20], Step [250/327], Loss: 1.0509\n","Epoch [3/20], Step [275/327], Loss: 0.5214\n","Epoch [3/20], Step [300/327], Loss: 0.6134\n","Epoch [3/20], Step [325/327], Loss: 0.4597\n","Start validation #3\n","Validation #3 Average Loss: 0.6823, mIoU: 0.2017\n","Epoch [4/20], Step [25/327], Loss: 0.7123\n","Epoch [4/20], Step [50/327], Loss: 0.5452\n","Epoch [4/20], Step [75/327], Loss: 1.0142\n","Epoch [4/20], Step [100/327], Loss: 1.0954\n","Epoch [4/20], Step [125/327], Loss: 0.4631\n","Epoch [4/20], Step [150/327], Loss: 0.4816\n","Epoch [4/20], Step [175/327], Loss: 0.5933\n","Epoch [4/20], Step [200/327], Loss: 0.8212\n","Epoch [4/20], Step [225/327], Loss: 0.6756\n","Epoch [4/20], Step [250/327], Loss: 0.5951\n","Epoch [4/20], Step [275/327], Loss: 0.9777\n","Epoch [4/20], Step [300/327], Loss: 0.6676\n","Epoch [4/20], Step [325/327], Loss: 0.5688\n","Start validation #4\n","Validation #4 Average Loss: 0.6664, mIoU: 0.2085\n","Epoch [5/20], Step [25/327], Loss: 0.6424\n","Epoch [5/20], Step [50/327], Loss: 0.4709\n","Epoch [5/20], Step [75/327], Loss: 0.4627\n","Epoch [5/20], Step [100/327], Loss: 0.8755\n","Epoch [5/20], Step [125/327], Loss: 0.9177\n","Epoch [5/20], Step [150/327], Loss: 0.5459\n","Epoch [5/20], Step [175/327], Loss: 0.7176\n","Epoch [5/20], Step [200/327], Loss: 0.5781\n","Epoch [5/20], Step [225/327], Loss: 0.7329\n","Epoch [5/20], Step [250/327], Loss: 0.7807\n","Epoch [5/20], Step [275/327], Loss: 0.5123\n","Epoch [5/20], Step [300/327], Loss: 0.6692\n","Epoch [5/20], Step [325/327], Loss: 0.3747\n","Start validation #5\n","Validation #5 Average Loss: 0.6608, mIoU: 0.2101\n","Epoch [6/20], Step [25/327], Loss: 0.6173\n","Epoch [6/20], Step [50/327], Loss: 1.2598\n","Epoch [6/20], Step [75/327], Loss: 0.6947\n","Epoch [6/20], Step [100/327], Loss: 1.0414\n","Epoch [6/20], Step [125/327], Loss: 0.5890\n","Epoch [6/20], Step [150/327], Loss: 0.8851\n","Epoch [6/20], Step [175/327], Loss: 0.5268\n","Epoch [6/20], Step [200/327], Loss: 0.5514\n","Epoch [6/20], Step [225/327], Loss: 0.7662\n","Epoch [6/20], Step [250/327], Loss: 0.6266\n","Epoch [6/20], Step [275/327], Loss: 0.7798\n","Epoch [6/20], Step [300/327], Loss: 0.7093\n","Epoch [6/20], Step [325/327], Loss: 1.0222\n","Start validation #6\n","Validation #6 Average Loss: 0.6583, mIoU: 0.2108\n","Epoch [7/20], Step [25/327], Loss: 0.6446\n","Epoch [7/20], Step [50/327], Loss: 0.8468\n","Epoch [7/20], Step [75/327], Loss: 0.5475\n","Epoch [7/20], Step [100/327], Loss: 0.4579\n","Epoch [7/20], Step [125/327], Loss: 0.6907\n","Epoch [7/20], Step [150/327], Loss: 0.9251\n","Epoch [7/20], Step [175/327], Loss: 0.7323\n","Epoch [7/20], Step [200/327], Loss: 0.5763\n","Epoch [7/20], Step [225/327], Loss: 0.6437\n","Epoch [7/20], Step [250/327], Loss: 0.5849\n","Epoch [7/20], Step [275/327], Loss: 0.7501\n","Epoch [7/20], Step [300/327], Loss: 0.5080\n","Epoch [7/20], Step [325/327], Loss: 1.0062\n","Start validation #7\n","Validation #7 Average Loss: 0.6592, mIoU: 0.2100\n","Epoch [8/20], Step [25/327], Loss: 0.5654\n","Epoch [8/20], Step [50/327], Loss: 0.6796\n","Epoch [8/20], Step [75/327], Loss: 0.5558\n","Epoch [8/20], Step [100/327], Loss: 1.2597\n","Epoch [8/20], Step [125/327], Loss: 0.5373\n","Epoch [8/20], Step [150/327], Loss: 0.4405\n","Epoch [8/20], Step [175/327], Loss: 0.6805\n","Epoch [8/20], Step [200/327], Loss: 1.1938\n","Epoch [8/20], Step [225/327], Loss: 0.9617\n","Epoch [8/20], Step [250/327], Loss: 0.4541\n","Epoch [8/20], Step [275/327], Loss: 0.5603\n","Epoch [8/20], Step [300/327], Loss: 0.8228\n","Epoch [8/20], Step [325/327], Loss: 0.5261\n","Start validation #8\n","Validation #8 Average Loss: 0.6576, mIoU: 0.2106\n","Epoch [9/20], Step [25/327], Loss: 0.9631\n","Epoch [9/20], Step [50/327], Loss: 0.5018\n","Epoch [9/20], Step [75/327], Loss: 0.6420\n","Epoch [9/20], Step [100/327], Loss: 0.6324\n","Epoch [9/20], Step [125/327], Loss: 0.7130\n","Epoch [9/20], Step [150/327], Loss: 1.1188\n","Epoch [9/20], Step [175/327], Loss: 0.4484\n","Epoch [9/20], Step [200/327], Loss: 0.4533\n","Epoch [9/20], Step [225/327], Loss: 0.6537\n","Epoch [9/20], Step [250/327], Loss: 1.0822\n","Epoch [9/20], Step [275/327], Loss: 0.6135\n","Epoch [9/20], Step [300/327], Loss: 0.5096\n","Epoch [9/20], Step [325/327], Loss: 0.9847\n","Start validation #9\n","Validation #9 Average Loss: 0.6533, mIoU: 0.2131\n","Epoch [10/20], Step [25/327], Loss: 0.5398\n","Epoch [10/20], Step [50/327], Loss: 0.7911\n","Epoch [10/20], Step [75/327], Loss: 0.7398\n","Epoch [10/20], Step [100/327], Loss: 0.7225\n","Epoch [10/20], Step [125/327], Loss: 0.6409\n","Epoch [10/20], Step [150/327], Loss: 0.5193\n","Epoch [10/20], Step [175/327], Loss: 0.7324\n","Epoch [10/20], Step [200/327], Loss: 0.4883\n","Epoch [10/20], Step [225/327], Loss: 0.7844\n","Epoch [10/20], Step [250/327], Loss: 0.5393\n","Epoch [10/20], Step [275/327], Loss: 0.4728\n","Epoch [10/20], Step [300/327], Loss: 0.4781\n","Epoch [10/20], Step [325/327], Loss: 0.5630\n","Start validation #10\n","Validation #10 Average Loss: 0.6530, mIoU: 0.2121\n","Epoch [11/20], Step [25/327], Loss: 0.7136\n","Epoch [11/20], Step [50/327], Loss: 0.6365\n","Epoch [11/20], Step [75/327], Loss: 0.7217\n","Epoch [11/20], Step [100/327], Loss: 0.4405\n","Epoch [11/20], Step [125/327], Loss: 0.5015\n","Epoch [11/20], Step [150/327], Loss: 0.6392\n","Epoch [11/20], Step [175/327], Loss: 0.5536\n","Epoch [11/20], Step [200/327], Loss: 0.5687\n","Epoch [11/20], Step [225/327], Loss: 0.4709\n","Epoch [11/20], Step [250/327], Loss: 0.8913\n","Epoch [11/20], Step [275/327], Loss: 0.7273\n","Epoch [11/20], Step [300/327], Loss: 0.4066\n","Epoch [11/20], Step [325/327], Loss: 0.7438\n","Start validation #11\n","Validation #11 Average Loss: 0.6497, mIoU: 0.2144\n","Epoch [12/20], Step [25/327], Loss: 0.5996\n","Epoch [12/20], Step [50/327], Loss: 0.8941\n","Epoch [12/20], Step [75/327], Loss: 0.5494\n","Epoch [12/20], Step [100/327], Loss: 0.5454\n","Epoch [12/20], Step [125/327], Loss: 0.4460\n","Epoch [12/20], Step [150/327], Loss: 0.6440\n","Epoch [12/20], Step [175/327], Loss: 0.6041\n","Epoch [12/20], Step [200/327], Loss: 0.5955\n","Epoch [12/20], Step [225/327], Loss: 0.5536\n","Epoch [12/20], Step [250/327], Loss: 0.5489\n","Epoch [12/20], Step [275/327], Loss: 0.6663\n","Epoch [12/20], Step [300/327], Loss: 0.6564\n","Epoch [12/20], Step [325/327], Loss: 0.7617\n","Start validation #12\n","Validation #12 Average Loss: 0.6494, mIoU: 0.2108\n","Epoch [13/20], Step [25/327], Loss: 0.6891\n","Epoch [13/20], Step [50/327], Loss: 0.5945\n","Epoch [13/20], Step [75/327], Loss: 0.6816\n","Epoch [13/20], Step [100/327], Loss: 0.6209\n","Epoch [13/20], Step [125/327], Loss: 0.4973\n","Epoch [13/20], Step [150/327], Loss: 0.6762\n","Epoch [13/20], Step [175/327], Loss: 0.8353\n","Epoch [13/20], Step [200/327], Loss: 0.8859\n","Epoch [13/20], Step [225/327], Loss: 0.6785\n","Epoch [13/20], Step [250/327], Loss: 0.6696\n","Epoch [13/20], Step [275/327], Loss: 0.6766\n","Epoch [13/20], Step [300/327], Loss: 0.5851\n","Epoch [13/20], Step [325/327], Loss: 1.2845\n","Start validation #13\n","Validation #13 Average Loss: 0.6475, mIoU: 0.2141\n","Epoch [14/20], Step [25/327], Loss: 0.3937\n","Epoch [14/20], Step [50/327], Loss: 0.6587\n","Epoch [14/20], Step [75/327], Loss: 0.5350\n","Epoch [14/20], Step [100/327], Loss: 0.5851\n","Epoch [14/20], Step [125/327], Loss: 0.6151\n","Epoch [14/20], Step [150/327], Loss: 0.6191\n","Epoch [14/20], Step [175/327], Loss: 0.5235\n","Epoch [14/20], Step [200/327], Loss: 0.6959\n","Epoch [14/20], Step [225/327], Loss: 0.8132\n","Epoch [14/20], Step [250/327], Loss: 0.8304\n","Epoch [14/20], Step [275/327], Loss: 0.5100\n","Epoch [14/20], Step [300/327], Loss: 0.7278\n","Epoch [14/20], Step [325/327], Loss: 0.4773\n","Start validation #14\n","Validation #14 Average Loss: 0.6461, mIoU: 0.2165\n","Epoch [15/20], Step [25/327], Loss: 0.8065\n","Epoch [15/20], Step [50/327], Loss: 0.4461\n","Epoch [15/20], Step [75/327], Loss: 0.7160\n","Epoch [15/20], Step [100/327], Loss: 0.5918\n","Epoch [15/20], Step [125/327], Loss: 0.6975\n","Epoch [15/20], Step [150/327], Loss: 0.5312\n","Epoch [15/20], Step [175/327], Loss: 0.8085\n","Epoch [15/20], Step [200/327], Loss: 1.0749\n","Epoch [15/20], Step [225/327], Loss: 0.8193\n","Epoch [15/20], Step [250/327], Loss: 0.8962\n","Epoch [15/20], Step [275/327], Loss: 0.6127\n","Epoch [15/20], Step [300/327], Loss: 0.6207\n","Epoch [15/20], Step [325/327], Loss: 0.5151\n","Start validation #15\n","Validation #15 Average Loss: 0.6456, mIoU: 0.2148\n","Epoch [16/20], Step [25/327], Loss: 0.8942\n","Epoch [16/20], Step [50/327], Loss: 0.7175\n","Epoch [16/20], Step [75/327], Loss: 0.5978\n","Epoch [16/20], Step [100/327], Loss: 0.4091\n","Epoch [16/20], Step [125/327], Loss: 0.7093\n","Epoch [16/20], Step [150/327], Loss: 0.8684\n","Epoch [16/20], Step [175/327], Loss: 0.5503\n","Epoch [16/20], Step [200/327], Loss: 0.9502\n","Epoch [16/20], Step [225/327], Loss: 0.8907\n","Epoch [16/20], Step [250/327], Loss: 0.5109\n","Epoch [16/20], Step [275/327], Loss: 0.7772\n","Epoch [16/20], Step [300/327], Loss: 0.7164\n","Epoch [16/20], Step [325/327], Loss: 0.5193\n","Start validation #16\n","Validation #16 Average Loss: 0.6423, mIoU: 0.2139\n","Epoch [17/20], Step [25/327], Loss: 0.6049\n","Epoch [17/20], Step [50/327], Loss: 0.5246\n","Epoch [17/20], Step [75/327], Loss: 0.5850\n","Epoch [17/20], Step [100/327], Loss: 0.6897\n","Epoch [17/20], Step [125/327], Loss: 0.4351\n","Epoch [17/20], Step [150/327], Loss: 0.5314\n","Epoch [17/20], Step [175/327], Loss: 0.3878\n","Epoch [17/20], Step [200/327], Loss: 0.6360\n","Epoch [17/20], Step [225/327], Loss: 0.4555\n","Epoch [17/20], Step [250/327], Loss: 0.8941\n","Epoch [17/20], Step [275/327], Loss: 0.9779\n","Epoch [17/20], Step [300/327], Loss: 0.9522\n","Epoch [17/20], Step [325/327], Loss: 0.7528\n","Start validation #17\n","Validation #17 Average Loss: 0.6423, mIoU: 0.2157\n","Epoch [18/20], Step [25/327], Loss: 0.6815\n","Epoch [18/20], Step [50/327], Loss: 0.7462\n","Epoch [18/20], Step [75/327], Loss: 0.7787\n","Epoch [18/20], Step [100/327], Loss: 0.4528\n","Epoch [18/20], Step [125/327], Loss: 0.6509\n","Epoch [18/20], Step [150/327], Loss: 0.6000\n","Epoch [18/20], Step [175/327], Loss: 0.6451\n","Epoch [18/20], Step [200/327], Loss: 1.1558\n","Epoch [18/20], Step [225/327], Loss: 0.7756\n","Epoch [18/20], Step [250/327], Loss: 0.9924\n","Epoch [18/20], Step [275/327], Loss: 0.8076\n","Epoch [18/20], Step [300/327], Loss: 0.5887\n","Epoch [18/20], Step [325/327], Loss: 0.4342\n","Start validation #18\n","Validation #18 Average Loss: 0.6412, mIoU: 0.2156\n","Epoch [19/20], Step [25/327], Loss: 0.4379\n","Epoch [19/20], Step [50/327], Loss: 0.6351\n","Epoch [19/20], Step [75/327], Loss: 0.9661\n","Epoch [19/20], Step [100/327], Loss: 0.7074\n","Epoch [19/20], Step [125/327], Loss: 1.4312\n","Epoch [19/20], Step [150/327], Loss: 0.6579\n","Epoch [19/20], Step [175/327], Loss: 0.6929\n","Epoch [19/20], Step [200/327], Loss: 0.4824\n","Epoch [19/20], Step [225/327], Loss: 0.6210\n","Epoch [19/20], Step [250/327], Loss: 0.5412\n","Epoch [19/20], Step [275/327], Loss: 0.8933\n","Epoch [19/20], Step [300/327], Loss: 0.6910\n","Epoch [19/20], Step [325/327], Loss: 0.6051\n","Start validation #19\n","Validation #19 Average Loss: 0.6394, mIoU: 0.2160\n","Epoch [20/20], Step [25/327], Loss: 1.7844\n","Epoch [20/20], Step [50/327], Loss: 0.6888\n","Epoch [20/20], Step [75/327], Loss: 0.7047\n","Epoch [20/20], Step [100/327], Loss: 0.6667\n","Epoch [20/20], Step [125/327], Loss: 0.7663\n","Epoch [20/20], Step [150/327], Loss: 0.6705\n","Epoch [20/20], Step [175/327], Loss: 0.7229\n","Epoch [20/20], Step [200/327], Loss: 0.7450\n","Epoch [20/20], Step [225/327], Loss: 0.6676\n","Epoch [20/20], Step [250/327], Loss: 0.8530\n","Epoch [20/20], Step [275/327], Loss: 0.5260\n","Epoch [20/20], Step [300/327], Loss: 0.5503\n","Epoch [20/20], Step [325/327], Loss: 0.7639\n","Start validation #20\n","Validation #20 Average Loss: 0.6341, mIoU: 0.2183\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"C6ClcO0J-NwR"},"source":["## 저장된 model 불러오기 (학습된 이후) "]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:21.050200Z","start_time":"2021-04-16T19:44:20.802200Z"},"scrolled":true,"id":"KQPtUDzd-NwR"},"source":["# best model 저장된 경로\n","model_path = './saved/pan_effb3_noisy_focal_adamp_cosLR.pt'\n","\n","# best model 불러오기\n","checkpoint = torch.load(model_path, map_location=device)\n","model.load_state_dict(checkpoint)\n","\n","# 추론을 실행하기 전에는 반드시 설정 (batch normalization, dropout 를 평가 모드로 설정)\n","# model.eval()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:24.939227Z","start_time":"2021-04-16T19:44:24.518228Z"},"id":"0LQqrDAp-NwR"},"source":["# 첫번째 batch의 추론 결과 확인\n","for imgs, image_infos in test_loader:\n"," image_infos = image_infos\n"," temp_images = imgs\n"," \n"," model.eval()\n"," # inference\n"," outs = model(torch.stack(temp_images).to(device))\n"," oms = torch.argmax(outs.squeeze(), dim=1).detach().cpu().numpy()\n"," \n"," break\n","\n","i = 3\n","fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(16, 16))\n","\n","print('Shape of Original Image :', list(temp_images[i].shape))\n","print('Shape of Predicted : ', list(oms[i].shape))\n","print('Unique values, category of transformed mask : \\n', [{int(i),category_names[int(i)]} for i in list(np.unique(oms[i]))])\n","\n","# Original image\n","ax1.imshow(temp_images[i].permute([1,2,0]))\n","ax1.grid(False)\n","ax1.set_title(\"Original image : {}\".format(image_infos[i]['file_name']), fontsize = 15)\n","\n","# Predicted\n","ax2.imshow(oms[i])\n","ax2.grid(False)\n","ax2.set_title(\"Predicted : {}\".format(image_infos[i]['file_name']), fontsize = 15)\n","\n","plt.show()"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"evYjR2F3-NwS"},"source":["## submission을 위한 test 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:27.469285Z","start_time":"2021-04-16T19:44:27.456021Z"},"id":"nhMLnV5d-NwS"},"source":["def test(model, data_loader, device):\n"," size = 256\n"," transform = A.Compose([A.Resize(256, 256)])\n"," print('Start prediction.')\n"," model.eval()\n"," \n"," file_name_list = []\n"," preds_array = np.empty((0, size*size), dtype=np.long)\n"," \n"," with torch.no_grad():\n"," for step, (imgs, image_infos) in enumerate(test_loader):\n","\n"," # inference (512 x 512)\n"," outs = model(torch.stack(imgs).to(device))\n"," oms = torch.argmax(outs.squeeze(), dim=1).detach().cpu().numpy()\n"," \n"," # resize (256 x 256)\n"," temp_mask = []\n"," for img, mask in zip(np.stack(imgs), oms):\n"," transformed = transform(image=img, mask=mask)\n"," mask = transformed['mask']\n"," temp_mask.append(mask)\n","\n"," oms = np.array(temp_mask)\n"," \n"," oms = oms.reshape([oms.shape[0], size*size]).astype(int)\n"," preds_array = np.vstack((preds_array, oms))\n"," \n"," file_name_list.append([i['file_name'] for i in image_infos])\n"," print(\"End prediction.\")\n"," file_names = [y for x in file_name_list for y in x]\n"," \n"," return file_names, preds_array"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"r1YKHBf4-NwT"},"source":["## submission.csv 생성"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:45:42.235310Z","start_time":"2021-04-16T19:44:30.499016Z"},"scrolled":true,"id":"Bz79_g8K-NwT"},"source":["# sample_submisson.csv 열기\n","submission = pd.read_csv('./submission/sample_submission.csv', index_col=None)\n","\n","# test set에 대한 prediction\n","file_names, preds = test(model, test_loader, device)\n","\n","# PredictionString 대입\n","for file_name, string in zip(file_names, preds):\n"," submission = submission.append({\"image_id\" : file_name, \"PredictionString\" : ' '.join(str(e) for e in string.tolist())}, \n"," ignore_index=True)\n","\n","# submission.csv로 저장\n","submission.to_csv(\"./submission/pan_effb3_noisy_focal_adamp_cosLR.csv\", index=False)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Zgl7Ye7F-NwT"},"source":["## 제출까지\n","\n"]},{"cell_type":"code","metadata":{"id":"022E8AC7-NwT"},"source":["import json\n","import requests\n","import os\n","from urllib.parse import urlparse, parse_qsl, urlencode, urlunparse\n","\n","def submit(user_key='', file_path = '', desc=\"\"):\n"," if not user_key:\n"," raise Exception(\"No UserKey\" )\n"," url = urlparse('http://ec2-13-124-161-225.ap-northeast-2.compute.amazonaws.com:8000/api/v1/competition/28/presigned_url/?description=&hyperparameters={%22training%22:{},%22inference%22:{}}')\n"," qs = dict(parse_qsl(url.query))\n"," qs['description'] = desc\n"," parts = url._replace(query=urlencode(qs))\n"," url = urlunparse(parts)\n","\n"," print(url)\n"," headers = {\n"," 'Authorization': user_key\n"," }\n"," res = requests.get(url, headers=headers)\n"," print(res.text)\n"," data = json.loads(res.text)\n"," \n"," submit_url = data['url']\n"," body = {\n"," 'key':'app/Competitions/000028/Users/{}/Submissions/{}/output.csv'.format(str(data['submission']['user']).zfill(8),str(data['submission']['local_id']).zfill(4)),\n"," 'x-amz-algorithm':data['fields']['x-amz-algorithm'],\n"," 'x-amz-credential':data['fields']['x-amz-credential'],\n"," 'x-amz-date':data['fields']['x-amz-date'],\n"," 'policy':data['fields']['policy'],\n"," 'x-amz-signature':data['fields']['x-amz-signature']\n"," }\n"," requests.post(url=submit_url, data=body, files={'file': open(file_path, 'rb')})\n","\n","\n","####################################################################################\n","test_dir = \"/opt/ml/code/submission\" # 수정 필요 : output 파일 폴더 \n","desc = 'pan_effb3_noisy_focal_adamp_cosLR' # 수정 필요 : 파일에 대한 설명\n","output_file = \"pan_effb3_noisy_focal_adamp_cosLR.csv\" #수정 필요 : output 파일 \n","user_key = \"Bearer 7bb5f96452751a238ffaf91a93c4242bf9b72abe\" # 수정 필요 : Authorization \n","\n","\n","submit(user_key, os.path.join(test_dir, output_file),desc)"],"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/chanyub_seg/code/pan_resnet101_imagenet_focal_adamp_cosLR.ipynb b/chanyub_seg/code/pan_resnet101_imagenet_focal_adamp_cosLR.ipynb deleted file mode 100644 index 98d9858..0000000 --- a/chanyub_seg/code/pan_resnet101_imagenet_focal_adamp_cosLR.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"nbformat":4,"nbformat_minor":0,"metadata":{"hide_input":false,"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.7"},"toc":{"base_numbering":1,"nav_menu":{},"number_sections":true,"sideBar":true,"skip_h1_title":false,"title_cell":"Table of Contents","title_sidebar":"Contents","toc_cell":true,"toc_position":{"height":"calc(100% - 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google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Mounted at /content/drive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xDwpp4Lk-gSH","executionInfo":{"status":"ok","timestamp":1620062405816,"user_tz":-540,"elapsed":2969,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"54446845-447a-4c72-e35a-1515d34bccd1"},"source":["ls"],"execution_count":2,"outputs":[{"output_type":"stream","text":["\u001b[0m\u001b[01;34mdrive\u001b[0m/ \u001b[01;34msample_data\u001b[0m/\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sItrVDeh-iYC","executionInfo":{"status":"ok","timestamp":1620062406213,"user_tz":-540,"elapsed":2447,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"387df760-3c0c-419d-b9cf-4b9052b32fef"},"source":["cd drive/MyDrive/Trash/code"],"execution_count":3,"outputs":[{"output_type":"stream","text":["/content/drive/MyDrive/Trash/code\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"a54v4-kN_LVO","executionInfo":{"status":"ok","timestamp":1620062417008,"user_tz":-540,"elapsed":12020,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"1a74980d-5ed1-44b9-976f-590cfaefe3fa"},"source":["!pip install albumentations==0.5.2"],"execution_count":4,"outputs":[{"output_type":"stream","text":["Collecting albumentations==0.5.2\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/03/58/63fb1d742dc42d9ba2800ea741de1f2bc6bb05548d8724aa84794042eaf2/albumentations-0.5.2-py3-none-any.whl (72kB)\n","\u001b[K |████████████████████████████████| 81kB 2.7MB/s \n","\u001b[?25hRequirement already satisfied: numpy>=1.11.1 in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (1.19.5)\n","Collecting opencv-python-headless>=4.1.1\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/6d/6d/92f377bece9b0ec9c893081dbe073a65b38d7ac12ef572b8f70554d08760/opencv_python_headless-4.5.1.48-cp37-cp37m-manylinux2014_x86_64.whl (37.6MB)\n","\u001b[K |████████████████████████████████| 37.6MB 1.3MB/s \n","\u001b[?25hCollecting imgaug>=0.4.0\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/66/b1/af3142c4a85cba6da9f4ebb5ff4e21e2616309552caca5e8acefe9840622/imgaug-0.4.0-py2.py3-none-any.whl (948kB)\n","\u001b[K |████████████████████████████████| 952kB 39.6MB/s \n","\u001b[?25hRequirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (1.4.1)\n","Requirement already satisfied: PyYAML in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (3.13)\n","Requirement already satisfied: scikit-image>=0.16.1 in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (0.16.2)\n","Requirement already satisfied: opencv-python in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (4.1.2.30)\n","Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (7.1.2)\n","Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (3.2.2)\n","Requirement already satisfied: Shapely in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (1.7.1)\n","Requirement already satisfied: imageio in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (2.4.1)\n","Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (1.15.0)\n","Requirement already satisfied: networkx>=2.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations==0.5.2) (2.5.1)\n","Requirement already satisfied: PyWavelets>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations==0.5.2) (1.1.1)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug>=0.4.0->albumentations==0.5.2) (0.10.0)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug>=0.4.0->albumentations==0.5.2) (1.3.1)\n","Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug>=0.4.0->albumentations==0.5.2) (2.8.1)\n","Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug>=0.4.0->albumentations==0.5.2) (2.4.7)\n","Requirement already satisfied: decorator<5,>=4.3 in /usr/local/lib/python3.7/dist-packages (from networkx>=2.0->scikit-image>=0.16.1->albumentations==0.5.2) (4.4.2)\n","Installing collected packages: opencv-python-headless, imgaug, albumentations\n"," Found existing installation: imgaug 0.2.9\n"," Uninstalling imgaug-0.2.9:\n"," Successfully uninstalled imgaug-0.2.9\n"," Found existing installation: albumentations 0.1.12\n"," Uninstalling albumentations-0.1.12:\n"," Successfully uninstalled albumentations-0.1.12\n","Successfully installed albumentations-0.5.2 imgaug-0.4.0 opencv-python-headless-4.5.1.48\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.826930Z","start_time":"2021-04-18T10:34:45.406686Z"},"colab":{"base_uri":"https://localhost:8080/"},"id":"l_LPA4XD-NwC","executionInfo":{"status":"ok","timestamp":1620062421435,"user_tz":-540,"elapsed":10122,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"8dd94bf9-5b74-40a6-f8c0-2b848382cbf4"},"source":["import os\n","import random\n","import time\n","import json\n","import warnings \n","warnings.filterwarnings('ignore')\n","\n","import torch\n","import torch.nn as nn\n","from torch.utils.data import Dataset, DataLoader\n","from utils import label_accuracy_score\n","import cv2\n","\n","import numpy as np\n","import pandas as pd\n","\n","# 전처리를 위한 라이브러리\n","from pycocotools.coco import COCO\n","import torchvision\n","import torchvision.transforms as transforms\n","\n","import albumentations as A\n","from albumentations.pytorch import ToTensorV2\n","\n","# 시각화를 위한 라이브러리\n","import matplotlib.pyplot as plt\n","import seaborn as sns; sns.set()\n","\n","plt.rcParams['axes.grid'] = False\n","\n","print('pytorch version: {}'.format(torch.__version__))\n","print('GPU 사용 가능 여부: {}'.format(torch.cuda.is_available()))\n","\n","print(torch.cuda.get_device_name(0))\n","print(torch.cuda.device_count())\n","\n","device = \"cuda\" if torch.cuda.is_available() else \"cpu\" # GPU 사용 가능 여부에 따라 device 정보 저장"],"execution_count":5,"outputs":[{"output_type":"stream","text":["pytorch version: 1.8.1+cu101\n","GPU 사용 가능 여부: True\n","Tesla P100-PCIE-16GB\n","1\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"HxgRSL_M-NwF"},"source":["## 하이퍼파라미터 세팅 및 seed 고정"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.841930Z","start_time":"2021-04-18T10:34:47.827931Z"},"id":"rV3JmGP5-NwF","executionInfo":{"status":"ok","timestamp":1620062431996,"user_tz":-540,"elapsed":823,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["batch_size = 8 # Mini-batch size\n","num_epochs = 20\n","learning_rate = 0.0001"],"execution_count":6,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.856930Z","start_time":"2021-04-18T10:34:47.842931Z"},"id":"Z6LOuJXQ-NwG","executionInfo":{"status":"ok","timestamp":1620062433439,"user_tz":-540,"elapsed":1441,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# seed 고정\n","random_seed = 42\n","torch.manual_seed(random_seed)\n","torch.cuda.manual_seed(random_seed)\n","# torch.cuda.manual_seed_all(random_seed) # if use multi-GPU\n","torch.backends.cudnn.deterministic = True\n","torch.backends.cudnn.benchmark = False\n","np.random.seed(random_seed)\n","random.seed(random_seed)"],"execution_count":7,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"iWF_EJDu-NwG"},"source":["## 학습 데이터 EDA"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.381961Z","start_time":"2021-04-18T10:34:47.857930Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"lg0x0D0a-NwG","executionInfo":{"status":"ok","timestamp":1620062444786,"user_tz":-540,"elapsed":8059,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"7c2e69e8-8aa9-4913-e8f3-ca5c13e7f14b"},"source":["%matplotlib inline\n","\n","dataset_path = '../input/data'\n","anns_file_path = dataset_path + '/' + 'train.json'\n","\n","# Read annotations\n","with open(anns_file_path, 'r') as f:\n"," dataset = json.loads(f.read())\n","\n","categories = dataset['categories']\n","anns = dataset['annotations']\n","imgs = dataset['images']\n","nr_cats = len(categories)\n","nr_annotations = len(anns)\n","nr_images = len(imgs)\n","\n","# Load categories and super categories\n","cat_names = []\n","super_cat_names = []\n","super_cat_ids = {}\n","super_cat_last_name = ''\n","nr_super_cats = 0\n","for cat_it in categories:\n"," cat_names.append(cat_it['name'])\n"," super_cat_name = cat_it['supercategory']\n"," # Adding new supercat\n"," if super_cat_name != super_cat_last_name:\n"," super_cat_names.append(super_cat_name)\n"," super_cat_ids[super_cat_name] = nr_super_cats\n"," super_cat_last_name = super_cat_name\n"," nr_super_cats += 1\n","\n","print('Number of super categories:', nr_super_cats)\n","print('Number of categories:', nr_cats)\n","print('Number of annotations:', nr_annotations)\n","print('Number of images:', nr_images)"],"execution_count":8,"outputs":[{"output_type":"stream","text":["Number of super categories: 11\n","Number of categories: 11\n","Number of annotations: 21116\n","Number of images: 2617\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.546964Z","start_time":"2021-04-18T10:34:51.382969Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/","height":355},"id":"PjLyVHVY-NwH","executionInfo":{"status":"ok","timestamp":1620062445345,"user_tz":-540,"elapsed":8607,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"86f7b3f2-38f3-4223-d8b1-4ccaa3c5113f"},"source":["# Count annotations\n","cat_histogram = np.zeros(nr_cats,dtype=int)\n","for ann in anns:\n"," cat_histogram[ann['category_id']] += 1\n","\n","# Initialize the matplotlib figure\n","f, ax = plt.subplots(figsize=(5,5))\n","\n","# Convert to DataFrame\n","df = pd.DataFrame({'Categories': cat_names, 'Number of annotations': cat_histogram})\n","df = df.sort_values('Number of annotations', 0, False)\n","\n","# Plot the histogram\n","plt.title(\"category distribution of train set \")\n","plot_1 = sns.barplot(x=\"Number of annotations\", y=\"Categories\", data=df, label=\"Total\", color=\"b\")"],"execution_count":9,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.561965Z","start_time":"2021-04-18T10:34:51.547969Z"},"id":"34Tw5__i-NwI","executionInfo":{"status":"ok","timestamp":1620062445345,"user_tz":-540,"elapsed":8540,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# category labeling \n","sorted_temp_df = df.sort_index()\n","\n","# background = 0 에 해당되는 label 추가 후 기존들을 모두 label + 1 로 설정\n","sorted_df = pd.DataFrame([\"Backgroud\"], columns = [\"Categories\"])\n","sorted_df = sorted_df.append(sorted_temp_df, ignore_index=True)"],"execution_count":10,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.576961Z","start_time":"2021-04-18T10:34:51.562964Z"},"colab":{"base_uri":"https://localhost:8080/","height":421},"id":"CQk4vV5N-NwI","executionInfo":{"status":"ok","timestamp":1620062445861,"user_tz":-540,"elapsed":8351,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"13bf5384-03ac-40f9-bc77-15e97ac8634f"},"source":["# class (Categories) 에 따른 index 확인 (0~11 : 총 12개)\n","sorted_df"],"execution_count":11,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
CategoriesNumber of annotations
0BackgroudNaN
1UNKNOWN128.0
2General trash2225.0
3Paper7448.0
4Paper pack527.0
5Metal449.0
6Glass488.0
7Plastic2472.0
8Styrofoam1074.0
9Plastic bag6114.0
10Battery50.0
11Clothing141.0
\n","
"],"text/plain":[" Categories Number of annotations\n","0 Backgroud NaN\n","1 UNKNOWN 128.0\n","2 General trash 2225.0\n","3 Paper 7448.0\n","4 Paper pack 527.0\n","5 Metal 449.0\n","6 Glass 488.0\n","7 Plastic 2472.0\n","8 Styrofoam 1074.0\n","9 Plastic bag 6114.0\n","10 Battery 50.0\n","11 Clothing 141.0"]},"metadata":{"tags":[]},"execution_count":11}]},{"cell_type":"markdown","metadata":{"id":"KXU0zmZs-NwI"},"source":["## 데이터 전처리 함수 정의 (Dataset)"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:52.693328Z","start_time":"2021-04-18T10:34:52.681328Z"},"id":"QFnTI8_Z-NwJ","executionInfo":{"status":"ok","timestamp":1620062445871,"user_tz":-540,"elapsed":5969,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["category_names = list(sorted_df.Categories)\n","\n","def get_classname(classID, cats):\n"," for i in range(len(cats)):\n"," if cats[i]['id']==classID:\n"," return cats[i]['name']\n"," return \"None\"\n","\n","class CustomDataLoader(Dataset):\n"," \"\"\"COCO format\"\"\"\n"," def __init__(self, data_dir, mode = 'train', transform = None):\n"," super().__init__()\n"," self.mode = mode\n"," self.transform = transform\n"," self.coco = COCO(data_dir)\n"," \n"," def __getitem__(self, index: int):\n"," # dataset이 index되어 list처럼 동작\n"," image_id = self.coco.getImgIds(imgIds=index)\n"," image_infos = self.coco.loadImgs(image_id)[0]\n"," \n"," # cv2 를 활용하여 image 불러오기\n"," images = cv2.imread(os.path.join(dataset_path, image_infos['file_name']))\n"," images = cv2.cvtColor(images, cv2.COLOR_BGR2RGB).astype(np.float32)\n"," images /= 255.0\n"," \n"," if (self.mode in ('train', 'val')):\n"," ann_ids = self.coco.getAnnIds(imgIds=image_infos['id'])\n"," anns = self.coco.loadAnns(ann_ids)\n","\n"," # Load the categories in a variable\n"," cat_ids = self.coco.getCatIds()\n"," cats = self.coco.loadCats(cat_ids)\n","\n"," # masks : size가 (height x width)인 2D\n"," # 각각의 pixel 값에는 \"category id + 1\" 할당\n"," # Background = 0\n"," masks = np.zeros((image_infos[\"height\"], image_infos[\"width\"]))\n"," # Unknown = 1, General trash = 2, ... , Cigarette = 11\n"," for i in range(len(anns)):\n"," className = get_classname(anns[i]['category_id'], cats)\n"," pixel_value = category_names.index(className)\n"," masks = np.maximum(self.coco.annToMask(anns[i])*pixel_value, masks)\n"," masks = masks.astype(np.float32)\n","\n"," # transform -> albumentations 라이브러리 활용\n"," if self.transform is not None:\n"," transformed = self.transform(image=images, mask=masks)\n"," images = transformed[\"image\"]\n"," masks = transformed[\"mask\"]\n"," \n"," return images, masks, image_infos\n"," \n"," if self.mode == 'test':\n"," # transform -> albumentations 라이브러리 활용\n"," if self.transform is not None:\n"," transformed = self.transform(image=images)\n"," images = transformed[\"image\"]\n"," \n"," return images, image_infos\n"," \n"," \n"," def __len__(self) -> int:\n"," # 전체 dataset의 size를 return\n"," return len(self.coco.getImgIds())"],"execution_count":12,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"cp2aIOlP-NwK"},"source":["## Dataset 정의 및 DataLoader 할당"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:58.823175Z","start_time":"2021-04-18T10:34:54.106233Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"P7zFlRn6-NwK","executionInfo":{"status":"ok","timestamp":1620062454789,"user_tz":-540,"elapsed":8888,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"ffcf565b-1efd-4358-ccef-9c5ab9e203dd"},"source":["# train.json / validation.json / test.json 디렉토리 설정\n","train_path = dataset_path + '/train.json'\n","val_path = dataset_path + '/val.json'\n","test_path = dataset_path + '/test.json'\n","\n","# collate_fn needs for batch\n","def collate_fn(batch):\n"," return tuple(zip(*batch))\n","\n","train_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","val_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","test_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","# create own Dataset 1 (skip)\n","# validation set을 직접 나누고 싶은 경우\n","# random_split 사용하여 data set을 8:2 로 분할\n","# train_size = int(0.8*len(dataset))\n","# val_size = int(len(dataset)-train_size)\n","# dataset = CustomDataLoader(data_dir=train_path, mode='train', transform=transform)\n","# train_dataset, val_dataset = torch.utils.data.random_split(dataset, [train_size, val_size])\n","\n","# create own Dataset 2\n","# train dataset\n","train_dataset = CustomDataLoader(data_dir=train_path, mode='train', transform=train_transform)\n","\n","# validation dataset\n","val_dataset = CustomDataLoader(data_dir=val_path, mode='val', transform=val_transform)\n","\n","# test dataset\n","test_dataset = CustomDataLoader(data_dir=test_path, mode='test', transform=test_transform)\n","\n","\n","# DataLoader\n","train_loader = torch.utils.data.DataLoader(dataset=train_dataset, \n"," batch_size=batch_size,\n"," shuffle=True,\n"," num_workers=4,\n"," collate_fn=collate_fn,\n"," drop_last=True)\n","\n","val_loader = torch.utils.data.DataLoader(dataset=val_dataset, \n"," batch_size=batch_size,\n"," shuffle=False,\n"," num_workers=4,\n"," collate_fn=collate_fn,\n"," drop_last=True) \n","\n","test_loader = torch.utils.data.DataLoader(dataset=test_dataset,\n"," batch_size=batch_size,\n"," num_workers=4,\n"," collate_fn=collate_fn)"],"execution_count":13,"outputs":[{"output_type":"stream","text":["loading annotations into memory...\n","Done (t=4.60s)\n","creating index...\n","index created!\n","loading annotations into memory...\n","Done (t=3.45s)\n","creating index...\n","index created!\n","loading annotations into memory...\n","Done (t=0.14s)\n","creating index...\n","index created!\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"8rqJiHb_-NwM"},"source":["# wandb"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"H1kHXm0uAX3R","executionInfo":{"status":"ok","timestamp":1620062465955,"user_tz":-540,"elapsed":16407,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"cbbedf8a-305b-4195-c564-7eab7793c907"},"source":["!pip install wandb"],"execution_count":14,"outputs":[{"output_type":"stream","text":["Collecting wandb\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/5c/ee/d755f9e5466df64c8416a2c6a860fb3aaa43ed6ea8e8e8e81460fda5788b/wandb-0.10.28-py2.py3-none-any.whl (2.1MB)\n","\u001b[K |████████████████████████████████| 2.1MB 3.8MB/s \n","\u001b[?25hRequirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.7/dist-packages (from wandb) 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subprocess32>=3.5.3\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/32/c8/564be4d12629b912ea431f1a50eb8b3b9d00f1a0b1ceff17f266be190007/subprocess32-3.5.4.tar.gz (97kB)\n","\u001b[K |████████████████████████████████| 102kB 6.2MB/s \n","\u001b[?25hRequirement already satisfied: promise<3,>=2.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (2.3)\n","Requirement already satisfied: requests<3,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (2.23.0)\n","Requirement already satisfied: protobuf>=3.12.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (3.12.4)\n","Requirement already satisfied: six>=1.13.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (1.15.0)\n","Collecting pathtools\n"," Downloading https://files.pythonhosted.org/packages/e7/7f/470d6fcdf23f9f3518f6b0b76be9df16dcc8630ad409947f8be2eb0ed13a/pathtools-0.1.2.tar.gz\n","Requirement already satisfied: Click>=7.0 in /usr/local/lib/python3.7/dist-packages (from wandb) 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/root/.cache/pip/wheels/68/39/1a/5e402bdfdf004af1786c8b853fd92f8c4a04f22aad179654d1\n"," Building wheel for pathtools (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for pathtools: filename=pathtools-0.1.2-cp37-none-any.whl size=8786 sha256=063493532e8df45e37b2961c11408d8dedb731cecc7cca8983f40c17c44569f9\n"," Stored in directory: /root/.cache/pip/wheels/0b/04/79/c3b0c3a0266a3cb4376da31e5bfe8bba0c489246968a68e843\n","Successfully built subprocess32 pathtools\n","Installing collected packages: docker-pycreds, configparser, smmap, gitdb, GitPython, shortuuid, subprocess32, pathtools, sentry-sdk, wandb\n","Successfully installed GitPython-3.1.14 configparser-5.0.2 docker-pycreds-0.4.0 gitdb-4.0.7 pathtools-0.1.2 sentry-sdk-1.0.0 shortuuid-1.0.1 smmap-4.0.0 subprocess32-3.5.4 wandb-0.10.28\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":153},"id":"aMW4VV9V-NwM","executionInfo":{"status":"ok","timestamp":1620062476221,"user_tz":-540,"elapsed":20949,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"0dc147c6-e3a5-426f-c0f2-029129354703"},"source":["import wandb\n","\n","proj_name = 'pan_resnet101_imagenet_focal_adamp_cosLR'\n","\n","wandb.init(project='chanyub',name=proj_name)\n","\n","config = wandb.config\n","config.learning_rate = 0.01"],"execution_count":15,"outputs":[{"output_type":"display_data","data":{"application/javascript":["\n"," window._wandbApiKey = new Promise((resolve, reject) => {\n"," function loadScript(url) {\n"," return new Promise(function(resolve, reject) {\n"," let newScript = document.createElement(\"script\");\n"," newScript.onerror = reject;\n"," newScript.onload = resolve;\n"," document.body.appendChild(newScript);\n"," newScript.src = url;\n"," });\n"," }\n"," loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n"," const iframe = document.createElement('iframe')\n"," iframe.style.cssText = \"width:0;height:0;border:none\"\n"," document.body.appendChild(iframe)\n"," const handshake = new Postmate({\n"," container: iframe,\n"," url: 'https://wandb.ai/authorize'\n"," });\n"," const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n"," handshake.then(function(child) {\n"," child.on('authorize', data => {\n"," clearTimeout(timeout)\n"," resolve(data)\n"," });\n"," });\n"," })\n"," });\n"," "],"text/plain":[""]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"],"name":"stderr"},{"output_type":"display_data","data":{"text/html":["\n"," Tracking run with wandb version 0.10.28
\n"," Syncing run pan_resnet101_imagenet_focal_adamp_cosLR to Weights & Biases (Documentation).
\n"," Project page: https://wandb.ai/pstage12/chanyub
\n"," Run page: https://wandb.ai/pstage12/chanyub/runs/3714ft00
\n"," Run data is saved locally in /content/drive/My Drive/Trash/code/wandb/run-20210503_172112-3714ft00

\n"," "],"text/plain":[""]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"uQRiIVGX-NwM"},"source":["## My model"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"S0xCGpNeAqeD","executionInfo":{"status":"ok","timestamp":1620062483691,"user_tz":-540,"elapsed":26013,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"58ec6e8e-4f29-496b-a4f2-a662dc19ab60"},"source":["!pip install segmentation_models_pytorch"],"execution_count":16,"outputs":[{"output_type":"stream","text":["Collecting segmentation_models_pytorch\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/65/54/8953f9f7ee9d451b0f3be8d635aa3a654579abf898d17502a090efe1155a/segmentation_models_pytorch-0.1.3-py3-none-any.whl (66kB)\n","\u001b[K |████████████████████████████████| 71kB 1.9MB/s \n","\u001b[?25hCollecting timm==0.3.2\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/51/2d/39ecc56fbb202e1891c317e8e44667299bc3b0762ea2ed6aaaa2c2f6613c/timm-0.3.2-py3-none-any.whl (244kB)\n","\u001b[K |████████████████████████████████| 245kB 4.9MB/s \n","\u001b[?25hRequirement already satisfied: torchvision>=0.3.0 in /usr/local/lib/python3.7/dist-packages (from segmentation_models_pytorch) (0.9.1+cu101)\n","Collecting efficientnet-pytorch==0.6.3\n"," Downloading https://files.pythonhosted.org/packages/b8/cb/0309a6e3d404862ae4bc017f89645cf150ac94c14c88ef81d215c8e52925/efficientnet_pytorch-0.6.3.tar.gz\n","Collecting pretrainedmodels==0.7.4\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/84/0e/be6a0e58447ac16c938799d49bfb5fb7a80ac35e137547fc6cee2c08c4cf/pretrainedmodels-0.7.4.tar.gz (58kB)\n","\u001b[K |████████████████████████████████| 61kB 6.2MB/s \n","\u001b[?25hRequirement already satisfied: torch>=1.0 in /usr/local/lib/python3.7/dist-packages (from 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(1.15.0)\n","Building wheels for collected packages: efficientnet-pytorch, pretrainedmodels\n"," Building wheel for efficientnet-pytorch (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for efficientnet-pytorch: filename=efficientnet_pytorch-0.6.3-cp37-none-any.whl size=12420 sha256=7c901c80ab7c64664116a53671e379ebd5fb9b7bb1611e97f6abe811b4b1d7e0\n"," Stored in directory: /root/.cache/pip/wheels/42/1e/a9/2a578ba9ad04e776e80bf0f70d8a7f4c29ec0718b92d8f6ccd\n"," Building wheel for pretrainedmodels (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for pretrainedmodels: filename=pretrainedmodels-0.7.4-cp37-none-any.whl size=60963 sha256=45f5cd0c038a4fc81ed180adc0462c7a83678ce04c9a8b7fafa698e1c1907795\n"," Stored in directory: /root/.cache/pip/wheels/69/df/63/62583c096289713f22db605aa2334de5b591d59861a02c2ecd\n","Successfully built efficientnet-pytorch pretrainedmodels\n","Installing collected packages: timm, efficientnet-pytorch, munch, pretrainedmodels, segmentation-models-pytorch\n","Successfully installed efficientnet-pytorch-0.6.3 munch-2.5.0 pretrainedmodels-0.7.4 segmentation-models-pytorch-0.1.3 timm-0.3.2\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:11.634792Z","start_time":"2021-04-18T16:16:05.875817Z"},"colab":{"base_uri":"https://localhost:8080/","height":137,"referenced_widgets":["7b3c6c908b454dd6adfc61e0f544b65a","83918597cf334400890ffc806a671f09","9bf9be1c904f4cb68f95ea057a4f5f80","0fc30ceafe594405ad6a13ca1ec15dee","eb7a623b3dad4d97942e0dc144c2f0d9","31e889e6ed8d4f6a9e0d6bbbf61c8ae2","616b4abd110f41a89be2380cd51852a1","621020ed1e8943848cc235a730f6de25"]},"id":"a8IfZfiM-NwM","executionInfo":{"status":"ok","timestamp":1620062502470,"user_tz":-540,"elapsed":18768,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"3a122891-fb8e-4eb6-bb94-fa4028d4d092"},"source":["# 구현된 model에 임의의 input을 넣어 output이 잘 나오는지 test\n","import segmentation_models_pytorch as smp\n","\n","model = smp.PAN(encoder_name='resnet101', classes=12)\n","x = torch.randn([2, 3, 512, 512])\n","print(\"input shape : \", x.shape)\n","out = model(x).to(device)\n","print(\"output shape : \", out.size())\n","\n","model = model.to(device)"],"execution_count":17,"outputs":[{"output_type":"stream","text":["Downloading: \"https://download.pytorch.org/models/resnet101-5d3b4d8f.pth\" to /root/.cache/torch/hub/checkpoints/resnet101-5d3b4d8f.pth\n"],"name":"stderr"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"7b3c6c908b454dd6adfc61e0f544b65a","version_minor":0,"version_major":2},"text/plain":["HBox(children=(FloatProgress(value=0.0, max=178728960.0), HTML(value='')))"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\n","input shape : torch.Size([2, 3, 512, 512])\n","output shape : torch.Size([2, 12, 512, 512])\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"zvDnI7_T-NwN"},"source":["## train, validation, test 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.104200Z","start_time":"2021-04-18T16:16:18.093174Z"},"id":"RA3oAapJ-NwN","executionInfo":{"status":"ok","timestamp":1620062502471,"user_tz":-540,"elapsed":18763,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["def train(num_epochs, model, data_loader, val_loader, criterion, optimizer, saved_dir, val_every, device, lr_scheduler):\n"," print('Start training..')\n"," best_loss = 9999999\n"," for epoch in range(num_epochs):\n"," model.train()\n"," for step, (images, masks, _) in enumerate(data_loader):\n"," images = torch.stack(images) # (batch, channel, height, width)\n"," masks = torch.stack(masks).long() # (batch, channel, height, width)\n"," \n"," # gpu 연산을 위해 device 할당\n"," images, masks = images.to(device), masks.to(device)\n"," \n"," # inference\n"," outputs = model(images)\n"," \n"," # loss 계산 (cross entropy loss)\n"," loss = criterion(outputs, masks)\n"," optimizer.zero_grad()\n"," loss.backward()\n"," optimizer.step()\n"," \n"," lr_scheduler.step()\n"," \n"," # step 주기에 따른 loss 출력\n"," if (step + 1) % 25 == 0:\n"," print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(\n"," epoch+1, num_epochs, step+1, len(train_loader), loss.item()))\n"," \n"," # validation 주기에 따른 loss 출력 및 best model 저장\n"," if (epoch + 1) % val_every == 0:\n"," avrg_loss = validation(epoch + 1, model, val_loader, criterion, device)\n"," if avrg_loss < best_loss:\n"," print('Best performance at epoch: {}'.format(epoch + 1))\n"," print('Save model in', saved_dir)\n"," best_loss = avrg_loss\n"," wandb.log({'best_loss': best_loss})\n"," save_model(model, saved_dir)"],"execution_count":18,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.285795Z","start_time":"2021-04-18T16:16:18.267686Z"},"id":"EulIikmq-NwO","executionInfo":{"status":"ok","timestamp":1620062502471,"user_tz":-540,"elapsed":18760,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["def validation(epoch, model, data_loader, criterion, device):\n"," print('Start validation #{}'.format(epoch))\n"," model.eval()\n"," with torch.no_grad():\n"," total_loss = 0\n"," cnt = 0\n"," mIoU_list = []\n"," for step, (images, masks, _) in enumerate(data_loader):\n"," \n"," images = torch.stack(images) # (batch, channel, height, width)\n"," masks = torch.stack(masks).long() # (batch, channel, height, width)\n","\n"," images, masks = images.to(device), masks.to(device) \n","\n"," outputs = model(images)\n"," loss = criterion(outputs, masks)\n"," total_loss += loss\n"," cnt += 1\n"," \n","# print(outputs.shape)\n","# print(masks.shape)\n","# wandb.log(wandb.Image(images, masks={\n","# \"predictions\" : {\n","# \"mask_data\" : torch.squeeze(torch.squeeze(outputs, 0),1),\n","# \"class_labels\" : classes_dict\n","# },\n","# \"ground_truth\" : {\n","# \"mask_data\" : torch.squeeze(masks, 0),\n","# \"class_labels\" : classes_dict\n","# }\n","# }))\n"," \n"," outputs = torch.argmax(outputs.squeeze(), dim=1).detach().cpu().numpy()\n","\n"," mIoU = label_accuracy_score(masks.detach().cpu().numpy(), outputs, n_class=12)[2]\n"," mIoU_list.append(mIoU)\n"," \n"," avrg_loss = total_loss / cnt\n"," avrg_mIoU = np.mean(mIoU_list)\n"," print('Validation #{} Average Loss: {:.4f}, mIoU: {:.4f}'.format(epoch, avrg_loss, np.mean(mIoU_list)))\n"," wandb.log({'Train Loss':loss.item(), 'Val Loss':avrg_loss , 'Val mIoU':np.mean(mIoU_list)})\n","# return avrg_loss\n"," return avrg_mIoU"],"execution_count":19,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"C_7CtFfH-NwO"},"source":["## 모델 저장 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.909918Z","start_time":"2021-04-18T16:16:18.898918Z"},"id":"gpCM5BFO-NwO","executionInfo":{"status":"ok","timestamp":1620062502472,"user_tz":-540,"elapsed":18755,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# 모델 저장 함수 정의\n","val_every = 1 \n","\n","saved_dir = './saved'\n","if not os.path.isdir(saved_dir): \n"," os.mkdir(saved_dir)\n"," \n","def save_model(model, saved_dir, file_name='pan_resnet101_imagenet_focal_adamp_cosLR.pt'):\n"," check_point = {'net': model.state_dict()}\n"," output_path = os.path.join(saved_dir, file_name)\n"," torch.save(model.state_dict(), output_path)"],"execution_count":20,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"FyKGeg8T-NwP"},"source":["## 모델 생성 및 Loss function, Optimizer 정의"]},{"cell_type":"code","metadata":{"id":"ORugl8s1-NwP","executionInfo":{"status":"ok","timestamp":1620062502472,"user_tz":-540,"elapsed":18753,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["from torch.autograd import Variable\n","import torch.nn.functional as F\n","# ref : https://github.com/clcarwin/focal_loss_pytorch\n","class FocalLoss(nn.Module):\n"," def __init__(self, gamma=0, alpha=None, size_average=True):\n"," super(FocalLoss, self).__init__()\n"," self.gamma = gamma\n"," self.alpha = alpha\n"," if isinstance(alpha,(float,int)): self.alpha = torch.Tensor([alpha,1-alpha])\n"," if isinstance(alpha,list): self.alpha = torch.Tensor(alpha)\n"," self.size_average = size_average\n","\n"," def forward(self, input, target):\n"," if input.dim()>2:\n"," input = input.view(input.size(0),input.size(1),-1) # N,C,H,W => N,C,H*W\n"," input = input.transpose(1,2) # N,C,H*W => N,H*W,C\n"," input = input.contiguous().view(-1,input.size(2)) # N,H*W,C => N*H*W,C\n"," target = target.view(-1,1)\n","\n"," logpt = F.log_softmax(input)\n"," logpt = logpt.gather(1,target)\n"," logpt = logpt.view(-1)\n"," pt = Variable(logpt.data.exp())\n","\n"," if self.alpha is not None:\n"," if self.alpha.type()!=input.data.type():\n"," self.alpha = self.alpha.type_as(input.data)\n"," at = self.alpha.gather(0,target.data.view(-1))\n"," logpt = logpt * Variable(at)\n","\n"," loss = -1 * (1-pt)**self.gamma * logpt\n"," if self.size_average: return loss.mean()\n"," else: return loss.sum()"],"execution_count":21,"outputs":[]},{"cell_type":"code","metadata":{"id":"yfeFQknz-NwP"},"source":["import torch.optim.lr_scheduler as lr_scheduler\n","import math\n","class CosineAnnealingWarmUpRestart(lr_scheduler._LRScheduler):\n"," def __init__(self, optimizer, T_0, T_mult=1, eta_max=0.1, T_up=0, gamma=1., last_epoch=-1):\n"," if T_0 <= 0 or not isinstance(T_0, int):\n"," raise ValueError(\"Expected positive integer T_0, but got {}\".format(T_0))\n"," if T_mult < 1 or not isinstance(T_mult, int):\n"," raise ValueError(\"Expected integer T_mult >= 1, but got {}\".format(T_mult))\n"," if T_up < 0 or not isinstance(T_up, int):\n"," raise ValueError(\"Expected positive integer T_up, but got {}\".format(T_up))\n"," self.T_0 = T_0\n"," self.T_mult = T_mult\n"," self.base_eta_max = eta_max\n"," self.eta_max = eta_max\n"," self.T_up = T_up\n"," self.T_i = T_0\n"," self.gamma = gamma\n"," self.cycle = 0\n"," self.T_cur = last_epoch\n"," super(CosineAnnealingWarmUpRestart, self).__init__(optimizer, last_epoch)\n"," # self.T_cur = last_epoch\n"," \n"," def get_lr(self):\n"," if self.T_cur == -1:\n"," return self.base_lrs\n"," elif self.T_cur < self.T_up:\n"," return [(self.eta_max - base_lr)*self.T_cur / self.T_up + base_lr for base_lr in self.base_lrs]\n"," else:\n"," return [base_lr + (self.eta_max - base_lr) * (1 + math.cos(math.pi * (self.T_cur-self.T_up) / (self.T_i - self.T_up))) / 2\n"," for base_lr in self.base_lrs]\n","\n"," def step(self, epoch=None):\n"," if epoch is None:\n"," epoch = self.last_epoch + 1\n"," self.T_cur = self.T_cur + 1\n"," if self.T_cur >= self.T_i:\n"," self.cycle += 1\n"," self.T_cur = self.T_cur - self.T_i\n"," self.T_i = (self.T_i - self.T_up) * self.T_mult + self.T_up\n"," else:\n"," if epoch >= self.T_0:\n"," if self.T_mult == 1:\n"," self.T_cur = epoch % self.T_0\n"," self.cycle = epoch // self.T_0\n"," else:\n"," n = int(math.log((epoch / self.T_0 * (self.T_mult - 1) + 1), self.T_mult))\n"," self.cycle = n\n"," self.T_cur = epoch - self.T_0 * (self.T_mult ** n - 1) / (self.T_mult - 1)\n"," self.T_i = self.T_0 * self.T_mult ** (n)\n"," else:\n"," self.T_i = self.T_0\n"," self.T_cur = epoch\n"," \n"," self.eta_max = self.base_eta_max * (self.gamma**self.cycle)\n"," self.last_epoch = math.floor(epoch)\n"," for param_group, lr in zip(self.optimizer.param_groups, self.get_lr()):\n"," param_group['lr'] = lr"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AG1oQeu7BX1M","executionInfo":{"status":"ok","timestamp":1620062507798,"user_tz":-540,"elapsed":23844,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"0857f855-0e29-4038-c3e7-bdbd3a7fbb57"},"source":["!pip install adamp"],"execution_count":22,"outputs":[{"output_type":"stream","text":["Collecting adamp\n"," Downloading https://files.pythonhosted.org/packages/c8/56/182b8c93f18feb0244b83f9b2eff1c6b036c04d4c3880e8d222750b0d5e5/adamp-0.3.0.tar.gz\n","Building wheels for collected packages: adamp\n"," Building wheel for adamp (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for adamp: filename=adamp-0.3.0-cp37-none-any.whl size=5999 sha256=4fb92d5e20009f90983a59c1fef46287322cd25d6e3851f8ef15223c391e8d98\n"," Stored in directory: /root/.cache/pip/wheels/6a/89/67/879fe55977ebcbfaa5b929eb111af7fe11eb3552867850dd76\n","Successfully built adamp\n","Installing collected packages: adamp\n","Successfully installed adamp-0.3.0\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:19.698902Z","start_time":"2021-04-18T16:16:19.694902Z"},"id":"9Dly8KZj-NwQ","executionInfo":{"status":"ok","timestamp":1620062513058,"user_tz":-540,"elapsed":725,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["from adamp import AdamP\n","# Loss function 정의\n","# criterion = nn.CrossEntropyLoss()\n","criterion = FocalLoss()\n","\n","# Optimizer 정의\n","# optimizer = torch.optim.Adam(params = model.parameters(), lr = learning_rate, weight_decay=1e-6)\n","optimizer = AdamP(params = model.parameters())\n","\n","lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=50, eta_min=0)\n","# lr_scheduler = CosineAnnealingWarmUpRestart(optimizer, T_0=150, T_mult=1, eta_max=0.1, T_up=10, gamma=0.5)"],"execution_count":23,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"start_time":"2021-04-18T16:16:20.331Z"},"colab":{"base_uri":"https://localhost:8080/"},"id":"cMSiFA-3-NwR","executionInfo":{"status":"ok","timestamp":1620068513585,"user_tz":-540,"elapsed":6000016,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"5b0e699f-5651-442a-a322-57daa49728f6"},"source":["train(num_epochs, model, train_loader, val_loader, criterion, optimizer, saved_dir, val_every, device, lr_scheduler)"],"execution_count":24,"outputs":[{"output_type":"stream","text":["Start training..\n","Epoch [1/20], Step [25/327], Loss: 1.1150\n","Epoch [1/20], Step [50/327], Loss: 0.6429\n","Epoch [1/20], Step [75/327], Loss: 0.9175\n","Epoch [1/20], Step [100/327], Loss: 1.0231\n","Epoch [1/20], Step [125/327], Loss: 0.5067\n","Epoch [1/20], Step [150/327], Loss: 0.6844\n","Epoch [1/20], Step [175/327], Loss: 0.7005\n","Epoch [1/20], Step [200/327], Loss: 0.9162\n","Epoch [1/20], Step [225/327], Loss: 0.5607\n","Epoch [1/20], Step [250/327], Loss: 0.5093\n","Epoch [1/20], Step [275/327], Loss: 0.4789\n","Epoch [1/20], Step [300/327], Loss: 0.5613\n","Epoch [1/20], Step [325/327], Loss: 1.6703\n","Start validation #1\n","Validation #1 Average Loss: 0.7921, mIoU: 0.1805\n","Best performance at epoch: 1\n","Save model in ./saved\n","Epoch [2/20], Step [25/327], Loss: 0.6131\n","Epoch [2/20], Step [50/327], Loss: 0.6800\n","Epoch [2/20], Step [75/327], Loss: 0.5884\n","Epoch [2/20], Step [100/327], Loss: 0.3928\n","Epoch [2/20], Step [125/327], Loss: 0.5478\n","Epoch [2/20], Step [150/327], Loss: 0.6239\n","Epoch [2/20], Step [175/327], Loss: 0.4357\n","Epoch [2/20], Step [200/327], Loss: 0.7330\n","Epoch [2/20], Step [225/327], Loss: 1.1025\n","Epoch [2/20], Step [250/327], Loss: 0.6422\n","Epoch [2/20], Step [275/327], Loss: 0.5239\n","Epoch [2/20], Step [300/327], Loss: 0.7233\n","Epoch [2/20], Step [325/327], Loss: 0.6307\n","Start validation #2\n","Validation #2 Average Loss: 0.6242, mIoU: 0.2198\n","Epoch [3/20], Step [25/327], Loss: 0.5284\n","Epoch [3/20], Step [50/327], Loss: 0.4649\n","Epoch [3/20], Step [75/327], Loss: 0.5213\n","Epoch [3/20], Step [100/327], Loss: 0.5845\n","Epoch [3/20], Step [125/327], Loss: 1.3032\n","Epoch [3/20], Step [150/327], Loss: 0.4139\n","Epoch [3/20], Step [175/327], Loss: 0.5695\n","Epoch [3/20], Step [200/327], Loss: 0.3877\n","Epoch [3/20], Step [225/327], Loss: 0.4218\n","Epoch [3/20], Step [250/327], Loss: 0.7679\n","Epoch [3/20], Step [275/327], Loss: 0.7397\n","Epoch [3/20], Step [300/327], Loss: 0.3801\n","Epoch [3/20], Step [325/327], Loss: 0.6294\n","Start validation #3\n","Validation #3 Average Loss: 0.5431, mIoU: 0.2486\n","Epoch [4/20], Step [25/327], Loss: 0.6159\n","Epoch [4/20], Step [50/327], Loss: 0.4152\n","Epoch [4/20], Step [75/327], Loss: 0.7843\n","Epoch [4/20], Step [100/327], Loss: 0.4230\n","Epoch [4/20], Step [125/327], Loss: 0.5287\n","Epoch [4/20], Step [150/327], Loss: 0.4587\n","Epoch [4/20], Step [175/327], Loss: 0.3406\n","Epoch [4/20], Step [200/327], Loss: 0.6197\n","Epoch [4/20], Step [225/327], Loss: 0.4519\n","Epoch [4/20], Step [250/327], Loss: 0.7637\n","Epoch [4/20], Step [275/327], Loss: 0.3024\n","Epoch [4/20], Step [300/327], Loss: 0.3369\n","Epoch [4/20], Step [325/327], Loss: 0.5599\n","Start validation #4\n","Validation #4 Average Loss: 0.6315, mIoU: 0.2200\n","Epoch [5/20], Step [25/327], Loss: 0.6194\n","Epoch [5/20], Step [50/327], Loss: 0.3650\n","Epoch [5/20], Step [75/327], Loss: 0.4793\n","Epoch [5/20], Step [100/327], Loss: 0.3886\n","Epoch [5/20], Step [125/327], Loss: 0.6243\n","Epoch [5/20], Step [150/327], Loss: 0.5002\n","Epoch [5/20], Step [175/327], Loss: 0.4298\n","Epoch [5/20], Step [200/327], Loss: 0.7269\n","Epoch [5/20], Step [225/327], Loss: 0.7116\n","Epoch [5/20], Step [250/327], Loss: 0.3341\n","Epoch [5/20], Step [275/327], Loss: 0.5041\n","Epoch [5/20], Step [300/327], Loss: 0.4162\n","Epoch [5/20], Step [325/327], Loss: 0.3004\n","Start validation #5\n","Validation #5 Average Loss: 0.5916, mIoU: 0.2373\n","Epoch [6/20], Step [25/327], Loss: 0.6173\n","Epoch [6/20], Step [50/327], Loss: 0.3638\n","Epoch [6/20], Step [75/327], Loss: 0.3512\n","Epoch [6/20], Step [100/327], Loss: 0.3787\n","Epoch [6/20], Step [125/327], Loss: 0.7157\n","Epoch [6/20], Step [150/327], Loss: 0.3084\n","Epoch [6/20], Step [175/327], Loss: 0.6489\n","Epoch [6/20], Step [200/327], Loss: 0.4337\n","Epoch [6/20], Step [225/327], Loss: 0.2728\n","Epoch [6/20], Step [250/327], Loss: 0.6181\n","Epoch [6/20], Step [275/327], Loss: 0.6895\n","Epoch [6/20], Step [300/327], Loss: 0.5457\n","Epoch [6/20], Step [325/327], Loss: 0.2582\n","Start validation #6\n","Validation #6 Average Loss: 0.4859, mIoU: 0.2575\n","Epoch [7/20], Step [25/327], Loss: 0.4626\n","Epoch [7/20], Step [50/327], Loss: 0.4315\n","Epoch [7/20], Step [75/327], Loss: 0.6088\n","Epoch [7/20], Step [100/327], Loss: 0.3662\n","Epoch [7/20], Step [125/327], Loss: 0.2800\n","Epoch [7/20], Step [150/327], Loss: 0.3791\n","Epoch [7/20], Step [175/327], Loss: 0.3318\n","Epoch [7/20], Step [200/327], Loss: 0.4872\n","Epoch [7/20], Step [225/327], Loss: 0.5693\n","Epoch [7/20], Step [250/327], Loss: 0.2818\n","Epoch [7/20], Step [275/327], Loss: 0.3837\n","Epoch [7/20], Step [300/327], Loss: 0.4885\n","Epoch [7/20], Step [325/327], Loss: 0.5572\n","Start validation #7\n","Validation #7 Average Loss: 0.4885, mIoU: 0.2582\n","Epoch [8/20], Step [25/327], Loss: 0.3787\n","Epoch [8/20], Step [50/327], Loss: 0.4796\n","Epoch [8/20], Step [75/327], Loss: 0.4545\n","Epoch [8/20], Step [100/327], Loss: 0.7116\n","Epoch [8/20], Step [125/327], Loss: 0.4093\n","Epoch [8/20], Step [150/327], Loss: 0.5883\n","Epoch [8/20], Step [175/327], Loss: 0.3986\n","Epoch [8/20], Step [200/327], Loss: 0.4284\n","Epoch [8/20], Step [225/327], Loss: 0.9428\n","Epoch [8/20], Step [250/327], Loss: 0.4378\n","Epoch [8/20], Step [275/327], Loss: 0.4615\n","Epoch [8/20], Step [300/327], Loss: 0.3274\n","Epoch [8/20], Step [325/327], Loss: 0.4698\n","Start validation #8\n","Validation #8 Average Loss: 0.6794, mIoU: 0.1973\n","Epoch [9/20], Step [25/327], Loss: 0.3971\n","Epoch [9/20], Step [50/327], Loss: 0.3284\n","Epoch [9/20], Step [75/327], Loss: 0.3468\n","Epoch [9/20], Step [100/327], Loss: 0.4031\n","Epoch [9/20], Step [125/327], Loss: 0.3870\n","Epoch [9/20], Step [150/327], Loss: 0.2927\n","Epoch [9/20], Step [175/327], Loss: 0.3974\n","Epoch [9/20], Step [200/327], Loss: 0.3059\n","Epoch [9/20], Step [225/327], Loss: 0.5072\n","Epoch [9/20], Step [250/327], Loss: 0.3582\n","Epoch [9/20], Step [275/327], Loss: 0.5803\n","Epoch [9/20], Step [300/327], Loss: 0.4980\n","Epoch [9/20], Step [325/327], Loss: 0.5843\n","Start validation #9\n","Validation #9 Average Loss: 0.5233, mIoU: 0.2570\n","Epoch [10/20], Step [25/327], Loss: 1.1056\n","Epoch [10/20], Step [50/327], Loss: 0.3493\n","Epoch [10/20], Step [75/327], Loss: 0.3733\n","Epoch [10/20], Step [100/327], Loss: 0.4463\n","Epoch [10/20], Step [125/327], Loss: 0.3765\n","Epoch [10/20], Step [150/327], Loss: 0.3150\n","Epoch [10/20], Step [175/327], Loss: 0.3293\n","Epoch [10/20], Step [200/327], Loss: 0.4179\n","Epoch [10/20], Step [225/327], Loss: 0.2490\n","Epoch [10/20], Step [250/327], Loss: 0.4649\n","Epoch [10/20], Step [275/327], Loss: 0.4861\n","Epoch [10/20], Step [300/327], Loss: 0.8638\n","Epoch [10/20], Step [325/327], Loss: 0.3433\n","Start validation #10\n","Validation #10 Average Loss: 0.4553, mIoU: 0.2883\n","Epoch [11/20], Step [25/327], Loss: 0.3760\n","Epoch [11/20], Step [50/327], Loss: 0.4568\n","Epoch [11/20], Step [75/327], Loss: 0.5285\n","Epoch [11/20], Step [100/327], Loss: 0.4226\n","Epoch [11/20], Step [125/327], Loss: 0.6320\n","Epoch [11/20], Step [150/327], Loss: 0.6501\n","Epoch [11/20], Step [175/327], Loss: 0.4626\n","Epoch [11/20], Step [200/327], Loss: 0.4418\n","Epoch [11/20], Step [225/327], Loss: 0.5268\n","Epoch [11/20], Step [250/327], Loss: 0.4950\n","Epoch [11/20], Step [275/327], Loss: 0.4251\n","Epoch [11/20], Step [300/327], Loss: 0.6266\n","Epoch [11/20], Step [325/327], Loss: 0.3854\n","Start validation #11\n","Validation #11 Average Loss: 1.0007, mIoU: 0.1702\n","Best performance at epoch: 11\n","Save model in ./saved\n","Epoch [12/20], Step [25/327], Loss: 0.4684\n","Epoch [12/20], Step [50/327], Loss: 0.4414\n","Epoch [12/20], Step [75/327], Loss: 0.5766\n","Epoch [12/20], Step [100/327], Loss: 0.4117\n","Epoch [12/20], Step [125/327], Loss: 0.5735\n","Epoch [12/20], Step [150/327], Loss: 0.5408\n","Epoch [12/20], Step [175/327], Loss: 0.3535\n","Epoch [12/20], Step [200/327], Loss: 0.7128\n","Epoch [12/20], Step [225/327], Loss: 0.4693\n","Epoch [12/20], Step [250/327], Loss: 0.3589\n","Epoch [12/20], Step [275/327], Loss: 0.5159\n","Epoch [12/20], Step [300/327], Loss: 0.4620\n","Epoch [12/20], Step [325/327], Loss: 0.5230\n","Start validation #12\n","Validation #12 Average Loss: 1.7007, mIoU: 0.1297\n","Best performance at epoch: 12\n","Save model in ./saved\n","Epoch [13/20], Step [25/327], Loss: 0.4104\n","Epoch [13/20], Step [50/327], Loss: 0.3326\n","Epoch [13/20], Step [75/327], Loss: 0.2550\n","Epoch [13/20], Step [100/327], Loss: 0.4511\n","Epoch [13/20], Step [125/327], Loss: 0.3347\n","Epoch [13/20], Step [150/327], Loss: 0.3271\n","Epoch [13/20], Step [175/327], Loss: 0.3618\n","Epoch [13/20], Step [200/327], Loss: 0.3180\n","Epoch [13/20], Step [225/327], Loss: 0.5966\n","Epoch [13/20], Step [250/327], Loss: 0.4321\n","Epoch [13/20], Step [275/327], Loss: 0.3675\n","Epoch [13/20], Step [300/327], Loss: 0.4106\n","Epoch [13/20], Step [325/327], Loss: 0.5888\n","Start validation #13\n","Validation #13 Average Loss: 0.4938, mIoU: 0.2755\n","Epoch [14/20], Step [25/327], Loss: 0.5156\n","Epoch [14/20], Step [50/327], Loss: 0.4521\n","Epoch [14/20], Step [75/327], Loss: 0.5578\n","Epoch [14/20], Step [100/327], Loss: 0.5034\n","Epoch [14/20], Step [125/327], Loss: 0.4493\n","Epoch [14/20], Step [150/327], Loss: 0.3101\n","Epoch [14/20], Step [175/327], Loss: 0.3247\n","Epoch [14/20], Step [200/327], Loss: 0.7352\n","Epoch [14/20], Step [225/327], Loss: 0.6550\n","Epoch [14/20], Step [250/327], Loss: 0.3478\n","Epoch [14/20], Step [275/327], Loss: 0.3971\n","Epoch [14/20], Step [300/327], Loss: 0.5185\n","Epoch [14/20], Step [325/327], Loss: 1.1887\n","Start validation #14\n","Validation #14 Average Loss: 0.4762, mIoU: 0.2904\n","Epoch [15/20], Step [25/327], Loss: 0.3913\n","Epoch [15/20], Step [50/327], Loss: 0.7828\n","Epoch [15/20], Step [75/327], Loss: 0.2917\n","Epoch [15/20], Step [100/327], Loss: 0.4105\n","Epoch [15/20], Step [125/327], Loss: 0.4205\n","Epoch [15/20], Step [150/327], Loss: 0.3791\n","Epoch [15/20], Step [175/327], Loss: 0.3924\n","Epoch [15/20], Step [200/327], Loss: 0.4679\n","Epoch [15/20], Step [225/327], Loss: 0.5460\n","Epoch [15/20], Step [250/327], Loss: 0.2398\n","Epoch [15/20], Step [275/327], Loss: 0.6567\n","Epoch [15/20], Step [300/327], Loss: 0.2352\n","Epoch [15/20], Step [325/327], Loss: 0.2122\n","Start validation #15\n","Validation #15 Average Loss: 0.9945, mIoU: 0.1707\n","Epoch [16/20], Step [25/327], Loss: 0.2936\n","Epoch [16/20], Step [50/327], Loss: 0.4675\n","Epoch [16/20], Step [75/327], Loss: 0.3555\n","Epoch [16/20], Step [100/327], Loss: 0.5495\n","Epoch [16/20], Step [125/327], Loss: 0.5347\n","Epoch [16/20], Step [150/327], Loss: 0.4031\n","Epoch [16/20], Step [175/327], Loss: 0.3470\n","Epoch [16/20], Step [200/327], Loss: 0.6237\n","Epoch [16/20], Step [225/327], Loss: 0.3218\n","Epoch [16/20], Step [250/327], Loss: 0.4274\n","Epoch [16/20], Step [275/327], Loss: 0.3205\n","Epoch [16/20], Step [300/327], Loss: 0.5987\n","Epoch [16/20], Step [325/327], Loss: 0.3971\n","Start validation #16\n","Validation #16 Average Loss: 0.4962, mIoU: 0.2722\n","Epoch [17/20], Step [25/327], Loss: 0.3910\n","Epoch [17/20], Step [50/327], Loss: 0.7281\n","Epoch [17/20], Step [75/327], Loss: 0.3786\n","Epoch [17/20], Step [100/327], Loss: 0.3737\n","Epoch [17/20], Step [125/327], Loss: 0.4464\n","Epoch [17/20], Step [150/327], Loss: 0.6425\n","Epoch [17/20], Step [175/327], Loss: 0.2169\n","Epoch [17/20], Step [200/327], Loss: 0.3266\n","Epoch [17/20], Step [225/327], Loss: 0.4186\n","Epoch [17/20], Step [250/327], Loss: 0.4372\n","Epoch [17/20], Step [275/327], Loss: 0.5225\n","Epoch [17/20], Step [300/327], Loss: 0.4536\n","Epoch [17/20], Step [325/327], Loss: 0.5831\n","Start validation #17\n","Validation #17 Average Loss: 0.4397, mIoU: 0.2940\n","Epoch [18/20], Step [25/327], Loss: 0.4060\n","Epoch [18/20], Step [50/327], Loss: 0.4665\n","Epoch [18/20], Step [75/327], Loss: 0.4193\n","Epoch [18/20], Step [100/327], Loss: 0.2295\n","Epoch [18/20], Step [125/327], Loss: 0.6114\n","Epoch [18/20], Step [150/327], Loss: 0.4936\n","Epoch [18/20], Step [175/327], Loss: 0.4314\n","Epoch [18/20], Step [200/327], Loss: 0.5753\n","Epoch [18/20], Step [225/327], Loss: 0.3855\n","Epoch [18/20], Step [250/327], Loss: 0.3199\n","Epoch [18/20], Step [275/327], Loss: 0.4088\n","Epoch [18/20], Step [300/327], Loss: 0.6687\n","Epoch [18/20], Step [325/327], Loss: 0.4516\n","Start validation #18\n","Validation #18 Average Loss: 0.4612, mIoU: 0.2769\n","Epoch [19/20], Step [25/327], Loss: 0.3129\n","Epoch [19/20], Step [50/327], Loss: 0.3937\n","Epoch [19/20], Step [75/327], Loss: 0.3774\n","Epoch [19/20], Step [100/327], Loss: 0.4800\n","Epoch [19/20], Step [125/327], Loss: 0.3543\n","Epoch [19/20], Step [150/327], Loss: 0.3353\n","Epoch [19/20], Step [175/327], Loss: 0.3886\n","Epoch [19/20], Step [200/327], Loss: 0.1569\n","Epoch [19/20], Step [225/327], Loss: 0.3714\n","Epoch [19/20], Step [250/327], Loss: 0.4778\n","Epoch [19/20], Step [275/327], Loss: 0.3113\n","Epoch [19/20], Step [300/327], Loss: 0.4246\n","Epoch [19/20], Step [325/327], Loss: 0.4849\n","Start validation #19\n","Validation #19 Average Loss: 0.9137, mIoU: 0.1791\n","Epoch [20/20], Step [25/327], Loss: 0.3158\n","Epoch [20/20], Step [50/327], Loss: 0.2648\n","Epoch [20/20], Step [75/327], Loss: 0.3601\n","Epoch [20/20], Step [100/327], Loss: 0.8423\n","Epoch [20/20], Step [125/327], Loss: 0.3085\n","Epoch [20/20], Step [150/327], Loss: 0.3952\n","Epoch [20/20], Step [175/327], Loss: 0.3360\n","Epoch [20/20], Step [200/327], Loss: 0.2837\n","Epoch [20/20], Step [225/327], Loss: 0.5620\n","Epoch [20/20], Step [250/327], Loss: 0.2654\n","Epoch [20/20], Step [275/327], Loss: 0.3238\n","Epoch [20/20], Step [300/327], Loss: 0.4176\n","Epoch [20/20], Step [325/327], Loss: 0.4113\n","Start validation #20\n","Validation #20 Average Loss: 0.4484, mIoU: 0.2898\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"C6ClcO0J-NwR"},"source":["## 저장된 model 불러오기 (학습된 이후) "]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:21.050200Z","start_time":"2021-04-16T19:44:20.802200Z"},"scrolled":true,"id":"KQPtUDzd-NwR"},"source":["# best model 저장된 경로\n","model_path = './saved/pan_effb3_noisy_focal_adamp_cosLR.pt'\n","\n","# best model 불러오기\n","checkpoint = torch.load(model_path, map_location=device)\n","model.load_state_dict(checkpoint)\n","\n","# 추론을 실행하기 전에는 반드시 설정 (batch normalization, dropout 를 평가 모드로 설정)\n","# model.eval()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:24.939227Z","start_time":"2021-04-16T19:44:24.518228Z"},"id":"0LQqrDAp-NwR"},"source":["# 첫번째 batch의 추론 결과 확인\n","for imgs, image_infos in test_loader:\n"," image_infos = image_infos\n"," temp_images = imgs\n"," \n"," model.eval()\n"," # inference\n"," outs = model(torch.stack(temp_images).to(device))\n"," oms = torch.argmax(outs.squeeze(), dim=1).detach().cpu().numpy()\n"," \n"," break\n","\n","i = 3\n","fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(16, 16))\n","\n","print('Shape of Original Image :', list(temp_images[i].shape))\n","print('Shape of Predicted : ', list(oms[i].shape))\n","print('Unique values, category of transformed mask : \\n', [{int(i),category_names[int(i)]} for i in list(np.unique(oms[i]))])\n","\n","# Original image\n","ax1.imshow(temp_images[i].permute([1,2,0]))\n","ax1.grid(False)\n","ax1.set_title(\"Original image : {}\".format(image_infos[i]['file_name']), fontsize = 15)\n","\n","# Predicted\n","ax2.imshow(oms[i])\n","ax2.grid(False)\n","ax2.set_title(\"Predicted : {}\".format(image_infos[i]['file_name']), fontsize = 15)\n","\n","plt.show()"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"evYjR2F3-NwS"},"source":["## submission을 위한 test 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:27.469285Z","start_time":"2021-04-16T19:44:27.456021Z"},"id":"nhMLnV5d-NwS"},"source":["def test(model, data_loader, device):\n"," size = 256\n"," transform = A.Compose([A.Resize(256, 256)])\n"," print('Start prediction.')\n"," model.eval()\n"," \n"," file_name_list = []\n"," preds_array = np.empty((0, size*size), dtype=np.long)\n"," \n"," with torch.no_grad():\n"," for step, (imgs, image_infos) in enumerate(test_loader):\n","\n"," # inference (512 x 512)\n"," outs = model(torch.stack(imgs).to(device))\n"," oms = torch.argmax(outs.squeeze(), dim=1).detach().cpu().numpy()\n"," \n"," # resize (256 x 256)\n"," temp_mask = []\n"," for img, mask in zip(np.stack(imgs), oms):\n"," transformed = transform(image=img, mask=mask)\n"," mask = transformed['mask']\n"," temp_mask.append(mask)\n","\n"," oms = np.array(temp_mask)\n"," \n"," oms = oms.reshape([oms.shape[0], size*size]).astype(int)\n"," preds_array = np.vstack((preds_array, oms))\n"," \n"," file_name_list.append([i['file_name'] for i in image_infos])\n"," print(\"End prediction.\")\n"," file_names = [y for x in file_name_list for y in x]\n"," \n"," return file_names, preds_array"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"r1YKHBf4-NwT"},"source":["## submission.csv 생성"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:45:42.235310Z","start_time":"2021-04-16T19:44:30.499016Z"},"scrolled":true,"id":"Bz79_g8K-NwT"},"source":["# sample_submisson.csv 열기\n","submission = pd.read_csv('./submission/sample_submission.csv', index_col=None)\n","\n","# test set에 대한 prediction\n","file_names, preds = test(model, test_loader, device)\n","\n","# PredictionString 대입\n","for file_name, string in zip(file_names, preds):\n"," submission = submission.append({\"image_id\" : file_name, \"PredictionString\" : ' '.join(str(e) for e in string.tolist())}, \n"," ignore_index=True)\n","\n","# submission.csv로 저장\n","submission.to_csv(\"./submission/pan_effb3_noisy_focal_adamp_cosLR.csv\", index=False)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Zgl7Ye7F-NwT"},"source":["## 제출까지\n","\n"]},{"cell_type":"code","metadata":{"id":"022E8AC7-NwT"},"source":["import json\n","import requests\n","import os\n","from urllib.parse import urlparse, parse_qsl, urlencode, urlunparse\n","\n","def submit(user_key='', file_path = '', desc=\"\"):\n"," if not user_key:\n"," raise Exception(\"No UserKey\" )\n"," url = urlparse('http://ec2-13-124-161-225.ap-northeast-2.compute.amazonaws.com:8000/api/v1/competition/28/presigned_url/?description=&hyperparameters={%22training%22:{},%22inference%22:{}}')\n"," qs = dict(parse_qsl(url.query))\n"," qs['description'] = desc\n"," parts = url._replace(query=urlencode(qs))\n"," url = urlunparse(parts)\n","\n"," print(url)\n"," headers = {\n"," 'Authorization': user_key\n"," }\n"," res = requests.get(url, headers=headers)\n"," print(res.text)\n"," data = json.loads(res.text)\n"," \n"," submit_url = data['url']\n"," body = {\n"," 'key':'app/Competitions/000028/Users/{}/Submissions/{}/output.csv'.format(str(data['submission']['user']).zfill(8),str(data['submission']['local_id']).zfill(4)),\n"," 'x-amz-algorithm':data['fields']['x-amz-algorithm'],\n"," 'x-amz-credential':data['fields']['x-amz-credential'],\n"," 'x-amz-date':data['fields']['x-amz-date'],\n"," 'policy':data['fields']['policy'],\n"," 'x-amz-signature':data['fields']['x-amz-signature']\n"," }\n"," requests.post(url=submit_url, data=body, files={'file': open(file_path, 'rb')})\n","\n","\n","####################################################################################\n","test_dir = \"/opt/ml/code/submission\" # 수정 필요 : output 파일 폴더 \n","desc = 'pan_effb3_noisy_focal_adamp_cosLR' # 수정 필요 : 파일에 대한 설명\n","output_file = \"pan_effb3_noisy_focal_adamp_cosLR.csv\" #수정 필요 : output 파일 \n","user_key = \"Bearer 7bb5f96452751a238ffaf91a93c4242bf9b72abe\" # 수정 필요 : Authorization \n","\n","\n","submit(user_key, os.path.join(test_dir, output_file),desc)"],"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/chanyub_seg/code/pan_resnet101_imagenet_focal_adamp_coswarmLR.ipynb b/chanyub_seg/code/pan_resnet101_imagenet_focal_adamp_coswarmLR.ipynb deleted file mode 100644 index 2850248..0000000 --- a/chanyub_seg/code/pan_resnet101_imagenet_focal_adamp_coswarmLR.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"nbformat":4,"nbformat_minor":0,"metadata":{"hide_input":false,"kernelspec":{"display_name":"Python 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google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Mounted at /content/drive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xDwpp4Lk-gSH","executionInfo":{"status":"ok","timestamp":1620060274343,"user_tz":-540,"elapsed":1311,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"81b6b6a0-29e3-4c6f-d425-4a04d78bcd94"},"source":["ls"],"execution_count":2,"outputs":[{"output_type":"stream","text":["\u001b[0m\u001b[01;34mdrive\u001b[0m/ \u001b[01;34msample_data\u001b[0m/\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sItrVDeh-iYC","executionInfo":{"status":"ok","timestamp":1620060274344,"user_tz":-540,"elapsed":1034,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"34768317-c62c-4be0-a7b4-dd55656ac214"},"source":["cd drive/MyDrive/Trash/code"],"execution_count":3,"outputs":[{"output_type":"stream","text":["/content/drive/MyDrive/Trash/code\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"a54v4-kN_LVO","executionInfo":{"status":"ok","timestamp":1620060282842,"user_tz":-540,"elapsed":9176,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"822a8d49-c88a-457c-f488-fa70cb089d1f"},"source":["!pip install albumentations==0.5.2"],"execution_count":4,"outputs":[{"output_type":"stream","text":["Collecting albumentations==0.5.2\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/03/58/63fb1d742dc42d9ba2800ea741de1f2bc6bb05548d8724aa84794042eaf2/albumentations-0.5.2-py3-none-any.whl (72kB)\n","\u001b[K |████████████████████████████████| 81kB 8.0MB/s \n","\u001b[?25hCollecting opencv-python-headless>=4.1.1\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/6d/6d/92f377bece9b0ec9c893081dbe073a65b38d7ac12ef572b8f70554d08760/opencv_python_headless-4.5.1.48-cp37-cp37m-manylinux2014_x86_64.whl (37.6MB)\n","\u001b[K |████████████████████████████████| 37.6MB 128kB/s \n","\u001b[?25hRequirement already satisfied: scikit-image>=0.16.1 in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (0.16.2)\n","Collecting imgaug>=0.4.0\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/66/b1/af3142c4a85cba6da9f4ebb5ff4e21e2616309552caca5e8acefe9840622/imgaug-0.4.0-py2.py3-none-any.whl (948kB)\n","\u001b[K |████████████████████████████████| 952kB 45.3MB/s \n","\u001b[?25hRequirement already satisfied: PyYAML in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (3.13)\n","Requirement already satisfied: numpy>=1.11.1 in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (1.19.5)\n","Requirement 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(1.15.0)\n","Requirement already satisfied: opencv-python in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (4.1.2.30)\n","Requirement already satisfied: Shapely in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (1.7.1)\n","Requirement already satisfied: decorator<5,>=4.3 in /usr/local/lib/python3.7/dist-packages (from networkx>=2.0->scikit-image>=0.16.1->albumentations==0.5.2) (4.4.2)\n","Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.16.1->albumentations==0.5.2) (2.8.1)\n","Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.16.1->albumentations==0.5.2) (2.4.7)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.16.1->albumentations==0.5.2) (0.10.0)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.16.1->albumentations==0.5.2) (1.3.1)\n","Installing collected packages: opencv-python-headless, imgaug, albumentations\n"," Found existing installation: imgaug 0.2.9\n"," Uninstalling imgaug-0.2.9:\n"," Successfully uninstalled imgaug-0.2.9\n"," Found existing installation: albumentations 0.1.12\n"," Uninstalling albumentations-0.1.12:\n"," Successfully uninstalled albumentations-0.1.12\n","Successfully installed albumentations-0.5.2 imgaug-0.4.0 opencv-python-headless-4.5.1.48\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.826930Z","start_time":"2021-04-18T10:34:45.406686Z"},"colab":{"base_uri":"https://localhost:8080/"},"id":"l_LPA4XD-NwC","executionInfo":{"status":"ok","timestamp":1620060302884,"user_tz":-540,"elapsed":4541,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"bbae3a61-8d1f-452e-a5eb-a09d6845577d"},"source":["import os\n","import random\n","import time\n","import json\n","import warnings \n","warnings.filterwarnings('ignore')\n","\n","import torch\n","import torch.nn as nn\n","from torch.utils.data import Dataset, DataLoader\n","from utils import label_accuracy_score\n","import cv2\n","\n","import numpy as np\n","import pandas as pd\n","\n","# 전처리를 위한 라이브러리\n","from pycocotools.coco import COCO\n","import torchvision\n","import torchvision.transforms as transforms\n","\n","import albumentations as A\n","from albumentations.pytorch import ToTensorV2\n","\n","# 시각화를 위한 라이브러리\n","import matplotlib.pyplot as plt\n","import seaborn as sns; sns.set()\n","\n","plt.rcParams['axes.grid'] = False\n","\n","print('pytorch version: {}'.format(torch.__version__))\n","print('GPU 사용 가능 여부: {}'.format(torch.cuda.is_available()))\n","\n","print(torch.cuda.get_device_name(0))\n","print(torch.cuda.device_count())\n","\n","device = \"cuda\" if torch.cuda.is_available() else \"cpu\" # GPU 사용 가능 여부에 따라 device 정보 저장"],"execution_count":5,"outputs":[{"output_type":"stream","text":["pytorch version: 1.8.1+cu101\n","GPU 사용 가능 여부: True\n","Tesla P100-PCIE-16GB\n","1\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"HxgRSL_M-NwF"},"source":["## 하이퍼파라미터 세팅 및 seed 고정"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.841930Z","start_time":"2021-04-18T10:34:47.827931Z"},"id":"rV3JmGP5-NwF","executionInfo":{"status":"ok","timestamp":1620060303189,"user_tz":-540,"elapsed":760,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["batch_size = 8 # Mini-batch size\n","num_epochs = 20\n","learning_rate = 0.0001"],"execution_count":6,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.856930Z","start_time":"2021-04-18T10:34:47.842931Z"},"id":"Z6LOuJXQ-NwG","executionInfo":{"status":"ok","timestamp":1620060303922,"user_tz":-540,"elapsed":1260,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# seed 고정\n","random_seed = 42\n","torch.manual_seed(random_seed)\n","torch.cuda.manual_seed(random_seed)\n","# torch.cuda.manual_seed_all(random_seed) # if use multi-GPU\n","torch.backends.cudnn.deterministic = True\n","torch.backends.cudnn.benchmark = False\n","np.random.seed(random_seed)\n","random.seed(random_seed)"],"execution_count":7,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"iWF_EJDu-NwG"},"source":["## 학습 데이터 EDA"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.381961Z","start_time":"2021-04-18T10:34:47.857930Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"lg0x0D0a-NwG","executionInfo":{"status":"ok","timestamp":1620060317521,"user_tz":-540,"elapsed":11251,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"bc0bbe51-b90f-442f-f163-1d955705081c"},"source":["%matplotlib inline\n","\n","dataset_path = '../input/data'\n","anns_file_path = dataset_path + '/' + 'train.json'\n","\n","# Read annotations\n","with open(anns_file_path, 'r') as f:\n"," dataset = json.loads(f.read())\n","\n","categories = dataset['categories']\n","anns = dataset['annotations']\n","imgs = dataset['images']\n","nr_cats = len(categories)\n","nr_annotations = len(anns)\n","nr_images = len(imgs)\n","\n","# Load categories and super categories\n","cat_names = []\n","super_cat_names = []\n","super_cat_ids = {}\n","super_cat_last_name = ''\n","nr_super_cats = 0\n","for cat_it in categories:\n"," cat_names.append(cat_it['name'])\n"," super_cat_name = cat_it['supercategory']\n"," # Adding new supercat\n"," if super_cat_name != super_cat_last_name:\n"," super_cat_names.append(super_cat_name)\n"," super_cat_ids[super_cat_name] = nr_super_cats\n"," super_cat_last_name = super_cat_name\n"," nr_super_cats += 1\n","\n","print('Number of super categories:', nr_super_cats)\n","print('Number of categories:', nr_cats)\n","print('Number of annotations:', nr_annotations)\n","print('Number of images:', nr_images)"],"execution_count":8,"outputs":[{"output_type":"stream","text":["Number of super categories: 11\n","Number of categories: 11\n","Number of annotations: 21116\n","Number of images: 2617\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.546964Z","start_time":"2021-04-18T10:34:51.382969Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/","height":355},"id":"PjLyVHVY-NwH","executionInfo":{"status":"ok","timestamp":1620060318918,"user_tz":-540,"elapsed":1386,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"fb20ff1a-b650-43d2-eef7-17d44a9bb3be"},"source":["# Count annotations\n","cat_histogram = np.zeros(nr_cats,dtype=int)\n","for ann in anns:\n"," cat_histogram[ann['category_id']] += 1\n","\n","# Initialize the matplotlib figure\n","f, ax = plt.subplots(figsize=(5,5))\n","\n","# Convert to DataFrame\n","df = pd.DataFrame({'Categories': cat_names, 'Number of annotations': cat_histogram})\n","df = df.sort_values('Number of annotations', 0, False)\n","\n","# Plot the histogram\n","plt.title(\"category distribution of train set \")\n","plot_1 = sns.barplot(x=\"Number of annotations\", y=\"Categories\", data=df, label=\"Total\", color=\"b\")"],"execution_count":9,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.561965Z","start_time":"2021-04-18T10:34:51.547969Z"},"id":"34Tw5__i-NwI","executionInfo":{"status":"ok","timestamp":1620060318919,"user_tz":-540,"elapsed":1373,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# category labeling \n","sorted_temp_df = df.sort_index()\n","\n","# background = 0 에 해당되는 label 추가 후 기존들을 모두 label + 1 로 설정\n","sorted_df = pd.DataFrame([\"Backgroud\"], columns = [\"Categories\"])\n","sorted_df = sorted_df.append(sorted_temp_df, ignore_index=True)"],"execution_count":10,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.576961Z","start_time":"2021-04-18T10:34:51.562964Z"},"colab":{"base_uri":"https://localhost:8080/","height":421},"id":"CQk4vV5N-NwI","executionInfo":{"status":"ok","timestamp":1620060319684,"user_tz":-540,"elapsed":2112,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"68548dae-a79c-4560-dcd8-da351ef7220c"},"source":["# class (Categories) 에 따른 index 확인 (0~11 : 총 12개)\n","sorted_df"],"execution_count":11,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
CategoriesNumber of annotations
0BackgroudNaN
1UNKNOWN128.0
2General trash2225.0
3Paper7448.0
4Paper pack527.0
5Metal449.0
6Glass488.0
7Plastic2472.0
8Styrofoam1074.0
9Plastic bag6114.0
10Battery50.0
11Clothing141.0
\n","
"],"text/plain":[" Categories Number of annotations\n","0 Backgroud NaN\n","1 UNKNOWN 128.0\n","2 General trash 2225.0\n","3 Paper 7448.0\n","4 Paper pack 527.0\n","5 Metal 449.0\n","6 Glass 488.0\n","7 Plastic 2472.0\n","8 Styrofoam 1074.0\n","9 Plastic bag 6114.0\n","10 Battery 50.0\n","11 Clothing 141.0"]},"metadata":{"tags":[]},"execution_count":11}]},{"cell_type":"markdown","metadata":{"id":"KXU0zmZs-NwI"},"source":["## 데이터 전처리 함수 정의 (Dataset)"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:52.693328Z","start_time":"2021-04-18T10:34:52.681328Z"},"id":"QFnTI8_Z-NwJ","executionInfo":{"status":"ok","timestamp":1620060319686,"user_tz":-540,"elapsed":2108,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["category_names = list(sorted_df.Categories)\n","\n","def get_classname(classID, cats):\n"," for i in range(len(cats)):\n"," if cats[i]['id']==classID:\n"," return cats[i]['name']\n"," return \"None\"\n","\n","class CustomDataLoader(Dataset):\n"," \"\"\"COCO format\"\"\"\n"," def __init__(self, data_dir, mode = 'train', transform = None):\n"," super().__init__()\n"," self.mode = mode\n"," self.transform = transform\n"," self.coco = COCO(data_dir)\n"," \n"," def __getitem__(self, index: int):\n"," # dataset이 index되어 list처럼 동작\n"," image_id = self.coco.getImgIds(imgIds=index)\n"," image_infos = self.coco.loadImgs(image_id)[0]\n"," \n"," # cv2 를 활용하여 image 불러오기\n"," images = cv2.imread(os.path.join(dataset_path, image_infos['file_name']))\n"," images = cv2.cvtColor(images, cv2.COLOR_BGR2RGB).astype(np.float32)\n"," images /= 255.0\n"," \n"," if (self.mode in ('train', 'val')):\n"," ann_ids = self.coco.getAnnIds(imgIds=image_infos['id'])\n"," anns = self.coco.loadAnns(ann_ids)\n","\n"," # Load the categories in a variable\n"," cat_ids = self.coco.getCatIds()\n"," cats = self.coco.loadCats(cat_ids)\n","\n"," # masks : size가 (height x width)인 2D\n"," # 각각의 pixel 값에는 \"category id + 1\" 할당\n"," # Background = 0\n"," masks = np.zeros((image_infos[\"height\"], image_infos[\"width\"]))\n"," # Unknown = 1, General trash = 2, ... , Cigarette = 11\n"," for i in range(len(anns)):\n"," className = get_classname(anns[i]['category_id'], cats)\n"," pixel_value = category_names.index(className)\n"," masks = np.maximum(self.coco.annToMask(anns[i])*pixel_value, masks)\n"," masks = masks.astype(np.float32)\n","\n"," # transform -> albumentations 라이브러리 활용\n"," if self.transform is not None:\n"," transformed = self.transform(image=images, mask=masks)\n"," images = transformed[\"image\"]\n"," masks = transformed[\"mask\"]\n"," \n"," return images, masks, image_infos\n"," \n"," if self.mode == 'test':\n"," # transform -> albumentations 라이브러리 활용\n"," if self.transform is not None:\n"," transformed = self.transform(image=images)\n"," images = transformed[\"image\"]\n"," \n"," return images, image_infos\n"," \n"," \n"," def __len__(self) -> int:\n"," # 전체 dataset의 size를 return\n"," return len(self.coco.getImgIds())"],"execution_count":12,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"cp2aIOlP-NwK"},"source":["## Dataset 정의 및 DataLoader 할당"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:58.823175Z","start_time":"2021-04-18T10:34:54.106233Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"P7zFlRn6-NwK","executionInfo":{"status":"ok","timestamp":1620060327804,"user_tz":-540,"elapsed":10217,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"c4acf0d6-dae6-4802-8aa3-2611ba4a9a81"},"source":["# train.json / validation.json / test.json 디렉토리 설정\n","train_path = dataset_path + '/train.json'\n","val_path = dataset_path + '/val.json'\n","test_path = dataset_path + '/test.json'\n","\n","# collate_fn needs for batch\n","def collate_fn(batch):\n"," return tuple(zip(*batch))\n","\n","train_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","val_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","test_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","# create own Dataset 1 (skip)\n","# validation set을 직접 나누고 싶은 경우\n","# random_split 사용하여 data set을 8:2 로 분할\n","# train_size = int(0.8*len(dataset))\n","# val_size = int(len(dataset)-train_size)\n","# dataset = CustomDataLoader(data_dir=train_path, mode='train', transform=transform)\n","# train_dataset, val_dataset = torch.utils.data.random_split(dataset, [train_size, val_size])\n","\n","# create own Dataset 2\n","# train dataset\n","train_dataset = CustomDataLoader(data_dir=train_path, mode='train', transform=train_transform)\n","\n","# validation dataset\n","val_dataset = CustomDataLoader(data_dir=val_path, mode='val', transform=val_transform)\n","\n","# test dataset\n","test_dataset = CustomDataLoader(data_dir=test_path, mode='test', transform=test_transform)\n","\n","\n","# DataLoader\n","train_loader = torch.utils.data.DataLoader(dataset=train_dataset, \n"," batch_size=batch_size,\n"," shuffle=True,\n"," num_workers=4,\n"," collate_fn=collate_fn,\n"," drop_last=True)\n","\n","val_loader = torch.utils.data.DataLoader(dataset=val_dataset, \n"," batch_size=batch_size,\n"," shuffle=False,\n"," num_workers=4,\n"," collate_fn=collate_fn,\n"," drop_last=True) \n","\n","test_loader = torch.utils.data.DataLoader(dataset=test_dataset,\n"," batch_size=batch_size,\n"," num_workers=4,\n"," collate_fn=collate_fn)"],"execution_count":13,"outputs":[{"output_type":"stream","text":["loading annotations into memory...\n","Done (t=3.89s)\n","creating index...\n","index created!\n","loading annotations into memory...\n","Done (t=3.24s)\n","creating index...\n","index created!\n","loading annotations into memory...\n","Done (t=1.12s)\n","creating index...\n","index created!\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"8rqJiHb_-NwM"},"source":["# wandb"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"H1kHXm0uAX3R","executionInfo":{"status":"ok","timestamp":1620060335444,"user_tz":-540,"elapsed":14094,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"edbc49a8-cce7-45bb-f4c2-7a0f28d17f89"},"source":["!pip install wandb"],"execution_count":14,"outputs":[{"output_type":"stream","text":["Collecting wandb\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/5c/ee/d755f9e5466df64c8416a2c6a860fb3aaa43ed6ea8e8e8e81460fda5788b/wandb-0.10.28-py2.py3-none-any.whl (2.1MB)\n","\u001b[K |████████████████████████████████| 2.1MB 14.6MB/s \n","\u001b[?25hRequirement already satisfied: Click>=7.0 in /usr/local/lib/python3.7/dist-packages (from wandb) 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sentry-sdk>=0.4.0\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/f3/92/5a33be64990ba815364a8f2dd9e6f51de60d23dfddafb4f1fc5577d4dc64/sentry_sdk-1.0.0-py2.py3-none-any.whl (131kB)\n","\u001b[K |████████████████████████████████| 133kB 56.2MB/s \n","\u001b[?25hRequirement already satisfied: protobuf>=3.12.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (3.12.4)\n","Requirement already satisfied: promise<3,>=2.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (2.3)\n","Requirement already satisfied: requests<3,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (2.23.0)\n","Requirement already satisfied: psutil>=5.0.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (5.4.8)\n","Collecting docker-pycreds>=0.4.0\n"," Downloading https://files.pythonhosted.org/packages/f5/e8/f6bd1eee09314e7e6dee49cbe2c5e22314ccdb38db16c9fc72d2fa80d054/docker_pycreds-0.4.0-py2.py3-none-any.whl\n","Collecting configparser>=3.8.1\n"," Downloading https://files.pythonhosted.org/packages/fd/01/ff260a18caaf4457eb028c96eeb405c4a230ca06c8ec9c1379f813caa52e/configparser-5.0.2-py3-none-any.whl\n","Collecting GitPython>=1.0.0\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/a6/99/98019716955ba243657daedd1de8f3a88ca1f5b75057c38e959db22fb87b/GitPython-3.1.14-py3-none-any.whl (159kB)\n","\u001b[K |████████████████████████████████| 163kB 57.5MB/s \n","\u001b[?25hRequirement already satisfied: urllib3>=1.10.0 in /usr/local/lib/python3.7/dist-packages (from sentry-sdk>=0.4.0->wandb) (1.24.3)\n","Requirement already satisfied: certifi in /usr/local/lib/python3.7/dist-packages (from sentry-sdk>=0.4.0->wandb) (2020.12.5)\n","Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from protobuf>=3.12.0->wandb) (56.0.0)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.0.0->wandb) (3.0.4)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.0.0->wandb) (2.10)\n","Collecting gitdb<5,>=4.0.1\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/ea/e8/f414d1a4f0bbc668ed441f74f44c116d9816833a48bf81d22b697090dba8/gitdb-4.0.7-py3-none-any.whl (63kB)\n","\u001b[K |████████████████████████████████| 71kB 11.4MB/s \n","\u001b[?25hCollecting smmap<5,>=3.0.1\n"," Downloading https://files.pythonhosted.org/packages/68/ee/d540eb5e5996eb81c26ceffac6ee49041d473bc5125f2aa995cf51ec1cf1/smmap-4.0.0-py2.py3-none-any.whl\n","Building wheels for collected packages: subprocess32, pathtools\n"," Building wheel for subprocess32 (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for subprocess32: filename=subprocess32-3.5.4-cp37-none-any.whl size=6489 sha256=783e4f14fdaeea376e666cfb67b239b2e1dca6a4c46544ad564a2eb11f7cf071\n"," Stored in directory: /root/.cache/pip/wheels/68/39/1a/5e402bdfdf004af1786c8b853fd92f8c4a04f22aad179654d1\n"," Building wheel for pathtools (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for pathtools: filename=pathtools-0.1.2-cp37-none-any.whl size=8786 sha256=6d9b51ef86295a9a244e36195b4e1ffdaa6a92e01b4531b4c88d315cf7e5755f\n"," Stored in directory: /root/.cache/pip/wheels/0b/04/79/c3b0c3a0266a3cb4376da31e5bfe8bba0c489246968a68e843\n","Successfully built subprocess32 pathtools\n","Installing collected packages: subprocess32, shortuuid, pathtools, sentry-sdk, docker-pycreds, configparser, smmap, gitdb, GitPython, wandb\n","Successfully installed GitPython-3.1.14 configparser-5.0.2 docker-pycreds-0.4.0 gitdb-4.0.7 pathtools-0.1.2 sentry-sdk-1.0.0 shortuuid-1.0.1 smmap-4.0.0 subprocess32-3.5.4 wandb-0.10.28\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":153},"id":"aMW4VV9V-NwM","executionInfo":{"status":"ok","timestamp":1620060354566,"user_tz":-540,"elapsed":8931,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"9138a3ba-e879-4b83-969f-ec9d082c67f0"},"source":["import wandb\n","\n","proj_name = 'pan_resnet101_imagenet_focal_adamp_coswarmLR'\n","\n","wandb.init(project='chanyub',name=proj_name)\n","\n","config = wandb.config\n","config.learning_rate = 0.01"],"execution_count":15,"outputs":[{"output_type":"display_data","data":{"application/javascript":["\n"," window._wandbApiKey = new Promise((resolve, reject) => {\n"," function loadScript(url) {\n"," return new Promise(function(resolve, reject) {\n"," let newScript = document.createElement(\"script\");\n"," newScript.onerror = reject;\n"," newScript.onload = resolve;\n"," document.body.appendChild(newScript);\n"," newScript.src = url;\n"," });\n"," }\n"," loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n"," const iframe = document.createElement('iframe')\n"," iframe.style.cssText = \"width:0;height:0;border:none\"\n"," document.body.appendChild(iframe)\n"," const handshake = new Postmate({\n"," container: iframe,\n"," url: 'https://wandb.ai/authorize'\n"," });\n"," const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n"," handshake.then(function(child) {\n"," child.on('authorize', data => {\n"," clearTimeout(timeout)\n"," resolve(data)\n"," });\n"," });\n"," })\n"," });\n"," "],"text/plain":[""]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"],"name":"stderr"},{"output_type":"display_data","data":{"text/html":["\n"," Tracking run with wandb version 0.10.28
\n"," Syncing run pan_resnet101_imagenet_focal_adamp_coswarmLR to Weights & Biases (Documentation).
\n"," Project page: https://wandb.ai/pstage12/chanyub
\n"," Run page: https://wandb.ai/pstage12/chanyub/runs/2dzzp1p6
\n"," Run data is saved locally in /content/drive/My Drive/Trash/code/wandb/run-20210503_164553-2dzzp1p6

\n"," "],"text/plain":[""]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"uQRiIVGX-NwM"},"source":["## My model"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"S0xCGpNeAqeD","executionInfo":{"status":"ok","timestamp":1620060365067,"user_tz":-540,"elapsed":5762,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"a43fb66c-56db-4f76-ad3f-7128657de174"},"source":["!pip install segmentation_models_pytorch"],"execution_count":16,"outputs":[{"output_type":"stream","text":["Collecting segmentation_models_pytorch\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/65/54/8953f9f7ee9d451b0f3be8d635aa3a654579abf898d17502a090efe1155a/segmentation_models_pytorch-0.1.3-py3-none-any.whl (66kB)\n","\u001b[K |████████████████████████████████| 71kB 3.7MB/s \n","\u001b[?25hCollecting pretrainedmodels==0.7.4\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/84/0e/be6a0e58447ac16c938799d49bfb5fb7a80ac35e137547fc6cee2c08c4cf/pretrainedmodels-0.7.4.tar.gz (58kB)\n","\u001b[K |████████████████████████████████| 61kB 9.9MB/s \n","\u001b[?25hCollecting timm==0.3.2\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/51/2d/39ecc56fbb202e1891c317e8e44667299bc3b0762ea2ed6aaaa2c2f6613c/timm-0.3.2-py3-none-any.whl (244kB)\n","\u001b[K |████████████████████████████████| 245kB 42.0MB/s \n","\u001b[?25hRequirement already satisfied: torchvision>=0.3.0 in /usr/local/lib/python3.7/dist-packages (from segmentation_models_pytorch) (0.9.1+cu101)\n","Collecting efficientnet-pytorch==0.6.3\n"," Downloading https://files.pythonhosted.org/packages/b8/cb/0309a6e3d404862ae4bc017f89645cf150ac94c14c88ef81d215c8e52925/efficientnet_pytorch-0.6.3.tar.gz\n","Requirement already satisfied: torch in /usr/local/lib/python3.7/dist-packages (from 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munch->pretrainedmodels==0.7.4->segmentation_models_pytorch) (1.15.0)\n","Building wheels for collected packages: pretrainedmodels, efficientnet-pytorch\n"," Building wheel for pretrainedmodels (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for pretrainedmodels: filename=pretrainedmodels-0.7.4-cp37-none-any.whl size=60963 sha256=31c5f70bac60eed7d8ab29dd95baee87fc62038a266c0207029d81419951c9b4\n"," Stored in directory: /root/.cache/pip/wheels/69/df/63/62583c096289713f22db605aa2334de5b591d59861a02c2ecd\n"," Building wheel for efficientnet-pytorch (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for efficientnet-pytorch: filename=efficientnet_pytorch-0.6.3-cp37-none-any.whl size=12420 sha256=05f0131c621ce78d390bd56f65ef8e39cc327399f8e90dc4922cdaf61d21a9c7\n"," Stored in directory: /root/.cache/pip/wheels/42/1e/a9/2a578ba9ad04e776e80bf0f70d8a7f4c29ec0718b92d8f6ccd\n","Successfully built pretrainedmodels efficientnet-pytorch\n","Installing collected packages: munch, pretrainedmodels, timm, efficientnet-pytorch, segmentation-models-pytorch\n","Successfully installed efficientnet-pytorch-0.6.3 munch-2.5.0 pretrainedmodels-0.7.4 segmentation-models-pytorch-0.1.3 timm-0.3.2\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:11.634792Z","start_time":"2021-04-18T16:16:05.875817Z"},"colab":{"base_uri":"https://localhost:8080/","height":137,"referenced_widgets":["bb8800f2dad0423cbfb4d674ae59468d","4ce85596cd00453095f251fa8ddec431","495d762da2d24d30862aa61f1767a908","d73b305d8e6447f4b414712cd87781f7","dd36a79e775f49b4b17ab41630972e6b","0dce25e9a52542089a8ae81d346da02c","9f3c0cb770274c099f9d7a46c39aec3e","27b9747087034e5db5d6fc23cf1d0b8b"]},"id":"a8IfZfiM-NwM","executionInfo":{"status":"ok","timestamp":1620060411010,"user_tz":-540,"elapsed":16422,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"b9e8bc5b-5895-4e4d-d221-3aae43b8eb90"},"source":["# 구현된 model에 임의의 input을 넣어 output이 잘 나오는지 test\n","import segmentation_models_pytorch as smp\n","\n","model = smp.PAN(encoder_name='resnet101', classes=12)\n","x = torch.randn([2, 3, 512, 512])\n","print(\"input shape : \", x.shape)\n","out = model(x).to(device)\n","print(\"output shape : \", out.size())\n","\n","model = model.to(device)"],"execution_count":17,"outputs":[{"output_type":"stream","text":["Downloading: \"https://download.pytorch.org/models/resnet101-5d3b4d8f.pth\" to /root/.cache/torch/hub/checkpoints/resnet101-5d3b4d8f.pth\n"],"name":"stderr"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"bb8800f2dad0423cbfb4d674ae59468d","version_minor":0,"version_major":2},"text/plain":["HBox(children=(FloatProgress(value=0.0, max=178728960.0), HTML(value='')))"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\n","input shape : torch.Size([2, 3, 512, 512])\n","output shape : torch.Size([2, 12, 512, 512])\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"zvDnI7_T-NwN"},"source":["## train, validation, test 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.104200Z","start_time":"2021-04-18T16:16:18.093174Z"},"id":"RA3oAapJ-NwN","executionInfo":{"status":"ok","timestamp":1620060417057,"user_tz":-540,"elapsed":1089,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["def train(num_epochs, model, data_loader, val_loader, criterion, optimizer, saved_dir, val_every, device, lr_scheduler):\n"," print('Start training..')\n"," best_loss = 9999999\n"," for epoch in range(num_epochs):\n"," model.train()\n"," for step, (images, masks, _) in enumerate(data_loader):\n"," images = torch.stack(images) # (batch, channel, height, width)\n"," masks = torch.stack(masks).long() # (batch, channel, height, width)\n"," \n"," # gpu 연산을 위해 device 할당\n"," images, masks = images.to(device), masks.to(device)\n"," \n"," # inference\n"," outputs = model(images)\n"," \n"," # loss 계산 (cross entropy loss)\n"," loss = criterion(outputs, masks)\n"," optimizer.zero_grad()\n"," loss.backward()\n"," optimizer.step()\n"," \n"," lr_scheduler.step()\n"," \n"," # step 주기에 따른 loss 출력\n"," if (step + 1) % 25 == 0:\n"," print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(\n"," epoch+1, num_epochs, step+1, len(train_loader), loss.item()))\n"," \n"," # validation 주기에 따른 loss 출력 및 best model 저장\n"," if (epoch + 1) % val_every == 0:\n"," avrg_loss = validation(epoch + 1, model, val_loader, criterion, device)\n"," if avrg_loss < best_loss:\n"," print('Best performance at epoch: {}'.format(epoch + 1))\n"," print('Save model in', saved_dir)\n"," best_loss = avrg_loss\n"," wandb.log({'best_loss': best_loss})\n"," save_model(model, saved_dir)"],"execution_count":18,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.285795Z","start_time":"2021-04-18T16:16:18.267686Z"},"id":"EulIikmq-NwO","executionInfo":{"status":"ok","timestamp":1620060417571,"user_tz":-540,"elapsed":1600,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["def validation(epoch, model, data_loader, criterion, device):\n"," print('Start validation #{}'.format(epoch))\n"," model.eval()\n"," with torch.no_grad():\n"," total_loss = 0\n"," cnt = 0\n"," mIoU_list = []\n"," for step, (images, masks, _) in enumerate(data_loader):\n"," \n"," images = torch.stack(images) # (batch, channel, height, width)\n"," masks = torch.stack(masks).long() # (batch, channel, height, width)\n","\n"," images, masks = images.to(device), masks.to(device) \n","\n"," outputs = model(images)\n"," loss = criterion(outputs, masks)\n"," total_loss += loss\n"," cnt += 1\n"," \n","# print(outputs.shape)\n","# print(masks.shape)\n","# wandb.log(wandb.Image(images, masks={\n","# \"predictions\" : {\n","# \"mask_data\" : torch.squeeze(torch.squeeze(outputs, 0),1),\n","# \"class_labels\" : classes_dict\n","# },\n","# \"ground_truth\" : {\n","# \"mask_data\" : torch.squeeze(masks, 0),\n","# \"class_labels\" : classes_dict\n","# }\n","# }))\n"," \n"," outputs = torch.argmax(outputs.squeeze(), dim=1).detach().cpu().numpy()\n","\n"," mIoU = label_accuracy_score(masks.detach().cpu().numpy(), outputs, n_class=12)[2]\n"," mIoU_list.append(mIoU)\n"," \n"," avrg_loss = total_loss / cnt\n"," avrg_mIoU = np.mean(mIoU_list)\n"," print('Validation #{} Average Loss: {:.4f}, mIoU: {:.4f}'.format(epoch, avrg_loss, np.mean(mIoU_list)))\n"," wandb.log({'Train Loss':loss.item(), 'Val Loss':avrg_loss , 'Val mIoU':np.mean(mIoU_list)})\n","# return avrg_loss\n"," return avrg_mIoU"],"execution_count":19,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"C_7CtFfH-NwO"},"source":["## 모델 저장 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.909918Z","start_time":"2021-04-18T16:16:18.898918Z"},"id":"gpCM5BFO-NwO","executionInfo":{"status":"ok","timestamp":1620060426365,"user_tz":-540,"elapsed":901,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# 모델 저장 함수 정의\n","val_every = 1 \n","\n","saved_dir = './saved'\n","if not os.path.isdir(saved_dir): \n"," os.mkdir(saved_dir)\n"," \n","def save_model(model, saved_dir, file_name='pan_resnet101_imagenet_focal_adamp_coswarmLR.pt'):\n"," check_point = {'net': model.state_dict()}\n"," output_path = os.path.join(saved_dir, file_name)\n"," torch.save(model.state_dict(), output_path)"],"execution_count":20,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"FyKGeg8T-NwP"},"source":["## 모델 생성 및 Loss function, Optimizer 정의"]},{"cell_type":"code","metadata":{"id":"ORugl8s1-NwP","executionInfo":{"status":"ok","timestamp":1620060436500,"user_tz":-540,"elapsed":1021,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["from torch.autograd import Variable\n","import torch.nn.functional as F\n","# ref : https://github.com/clcarwin/focal_loss_pytorch\n","class FocalLoss(nn.Module):\n"," def __init__(self, gamma=0, alpha=None, size_average=True):\n"," super(FocalLoss, self).__init__()\n"," self.gamma = gamma\n"," self.alpha = alpha\n"," if isinstance(alpha,(float,int)): self.alpha = torch.Tensor([alpha,1-alpha])\n"," if isinstance(alpha,list): self.alpha = torch.Tensor(alpha)\n"," self.size_average = size_average\n","\n"," def forward(self, input, target):\n"," if input.dim()>2:\n"," input = input.view(input.size(0),input.size(1),-1) # N,C,H,W => N,C,H*W\n"," input = input.transpose(1,2) # N,C,H*W => N,H*W,C\n"," input = input.contiguous().view(-1,input.size(2)) # N,H*W,C => N*H*W,C\n"," target = target.view(-1,1)\n","\n"," logpt = F.log_softmax(input)\n"," logpt = logpt.gather(1,target)\n"," logpt = logpt.view(-1)\n"," pt = Variable(logpt.data.exp())\n","\n"," if self.alpha is not None:\n"," if self.alpha.type()!=input.data.type():\n"," self.alpha = self.alpha.type_as(input.data)\n"," at = self.alpha.gather(0,target.data.view(-1))\n"," logpt = logpt * Variable(at)\n","\n"," loss = -1 * (1-pt)**self.gamma * logpt\n"," if self.size_average: return loss.mean()\n"," else: return loss.sum()"],"execution_count":21,"outputs":[]},{"cell_type":"code","metadata":{"id":"yfeFQknz-NwP","executionInfo":{"status":"ok","timestamp":1620060446264,"user_tz":-540,"elapsed":967,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["import torch.optim.lr_scheduler as lr_scheduler\n","import math\n","class CosineAnnealingWarmUpRestart(lr_scheduler._LRScheduler):\n"," def __init__(self, optimizer, T_0, T_mult=1, eta_max=0.1, T_up=0, gamma=1., last_epoch=-1):\n"," if T_0 <= 0 or not isinstance(T_0, int):\n"," raise ValueError(\"Expected positive integer T_0, but got {}\".format(T_0))\n"," if T_mult < 1 or not isinstance(T_mult, int):\n"," raise ValueError(\"Expected integer T_mult >= 1, but got {}\".format(T_mult))\n"," if T_up < 0 or not isinstance(T_up, int):\n"," raise ValueError(\"Expected positive integer T_up, but got {}\".format(T_up))\n"," self.T_0 = T_0\n"," self.T_mult = T_mult\n"," self.base_eta_max = eta_max\n"," self.eta_max = eta_max\n"," self.T_up = T_up\n"," self.T_i = T_0\n"," self.gamma = gamma\n"," self.cycle = 0\n"," self.T_cur = last_epoch\n"," super(CosineAnnealingWarmUpRestart, self).__init__(optimizer, last_epoch)\n"," # self.T_cur = last_epoch\n"," \n"," def get_lr(self):\n"," if self.T_cur == -1:\n"," return self.base_lrs\n"," elif self.T_cur < self.T_up:\n"," return [(self.eta_max - base_lr)*self.T_cur / self.T_up + base_lr for base_lr in self.base_lrs]\n"," else:\n"," return [base_lr + (self.eta_max - base_lr) * (1 + math.cos(math.pi * (self.T_cur-self.T_up) / (self.T_i - self.T_up))) / 2\n"," for base_lr in self.base_lrs]\n","\n"," def step(self, epoch=None):\n"," if epoch is None:\n"," epoch = self.last_epoch + 1\n"," self.T_cur = self.T_cur + 1\n"," if self.T_cur >= self.T_i:\n"," self.cycle += 1\n"," self.T_cur = self.T_cur - self.T_i\n"," self.T_i = (self.T_i - self.T_up) * self.T_mult + self.T_up\n"," else:\n"," if epoch >= self.T_0:\n"," if self.T_mult == 1:\n"," self.T_cur = epoch % self.T_0\n"," self.cycle = epoch // self.T_0\n"," else:\n"," n = int(math.log((epoch / self.T_0 * (self.T_mult - 1) + 1), self.T_mult))\n"," self.cycle = n\n"," self.T_cur = epoch - self.T_0 * (self.T_mult ** n - 1) / (self.T_mult - 1)\n"," self.T_i = self.T_0 * self.T_mult ** (n)\n"," else:\n"," self.T_i = self.T_0\n"," self.T_cur = epoch\n"," \n"," self.eta_max = self.base_eta_max * (self.gamma**self.cycle)\n"," self.last_epoch = math.floor(epoch)\n"," for param_group, lr in zip(self.optimizer.param_groups, self.get_lr()):\n"," param_group['lr'] = lr"],"execution_count":22,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AG1oQeu7BX1M","executionInfo":{"status":"ok","timestamp":1620060451932,"user_tz":-540,"elapsed":4563,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"11b97bb1-b0d0-4c1c-e462-ea4f6bd73cd3"},"source":["!pip install adamp"],"execution_count":23,"outputs":[{"output_type":"stream","text":["Collecting adamp\n"," Downloading https://files.pythonhosted.org/packages/c8/56/182b8c93f18feb0244b83f9b2eff1c6b036c04d4c3880e8d222750b0d5e5/adamp-0.3.0.tar.gz\n","Building wheels for collected packages: adamp\n"," Building wheel for adamp (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for adamp: filename=adamp-0.3.0-cp37-none-any.whl size=5999 sha256=f539e77b51016467007048c2dc524433d3c26179d3e5b0f3c06bc9daee9a97a5\n"," Stored in directory: /root/.cache/pip/wheels/6a/89/67/879fe55977ebcbfaa5b929eb111af7fe11eb3552867850dd76\n","Successfully built adamp\n","Installing collected packages: adamp\n","Successfully installed adamp-0.3.0\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:19.698902Z","start_time":"2021-04-18T16:16:19.694902Z"},"id":"9Dly8KZj-NwQ","executionInfo":{"status":"ok","timestamp":1620060461156,"user_tz":-540,"elapsed":807,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["from adamp import AdamP\n","# Loss function 정의\n","# criterion = nn.CrossEntropyLoss()\n","criterion = FocalLoss()\n","\n","# Optimizer 정의\n","# optimizer = torch.optim.Adam(params = model.parameters(), lr = learning_rate, weight_decay=1e-6)\n","optimizer = AdamP(params = model.parameters())\n","\n","# lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=50, eta_min=0)\n","lr_scheduler = CosineAnnealingWarmUpRestart(optimizer, T_0=150, T_mult=1, eta_max=0.1, T_up=10, gamma=0.5)"],"execution_count":24,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"start_time":"2021-04-18T16:16:20.331Z"},"colab":{"base_uri":"https://localhost:8080/"},"id":"cMSiFA-3-NwR","executionInfo":{"status":"ok","timestamp":1620066079144,"user_tz":-540,"elapsed":5617437,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"9982d641-ae35-4c0f-c193-6b25c6011a64"},"source":["train(num_epochs, model, train_loader, val_loader, criterion, optimizer, saved_dir, val_every, device, lr_scheduler)"],"execution_count":25,"outputs":[{"output_type":"stream","text":["Start training..\n","Epoch [1/20], Step [25/327], Loss: 1.1327\n","Epoch [1/20], Step [50/327], Loss: 0.9397\n","Epoch [1/20], Step [75/327], Loss: 1.1335\n","Epoch [1/20], Step [100/327], Loss: 0.9795\n","Epoch [1/20], Step [125/327], Loss: 0.7304\n","Epoch [1/20], Step [150/327], Loss: 0.9349\n","Epoch [1/20], Step [175/327], Loss: 1.1541\n","Epoch [1/20], Step [200/327], Loss: 1.1935\n","Epoch [1/20], Step [225/327], Loss: 0.7842\n","Epoch [1/20], Step [250/327], Loss: 0.7588\n","Epoch [1/20], Step [275/327], Loss: 0.7812\n","Epoch [1/20], Step [300/327], Loss: 0.7703\n","Epoch [1/20], Step [325/327], Loss: 1.4975\n","Start validation #1\n","Validation #1 Average Loss: 11.6535, mIoU: 0.1272\n","Best performance at epoch: 1\n","Save model in ./saved\n","Epoch [2/20], Step [25/327], Loss: 1.0137\n","Epoch [2/20], Step [50/327], Loss: 1.0486\n","Epoch [2/20], Step [75/327], Loss: 0.8018\n","Epoch [2/20], Step [100/327], Loss: 0.6469\n","Epoch [2/20], Step [125/327], Loss: 0.8964\n","Epoch [2/20], Step [150/327], Loss: 0.9662\n","Epoch [2/20], Step [175/327], Loss: 0.5813\n","Epoch [2/20], Step [200/327], Loss: 0.9345\n","Epoch [2/20], Step [225/327], Loss: 1.3840\n","Epoch [2/20], Step [250/327], Loss: 0.8004\n","Epoch [2/20], Step [275/327], Loss: 0.7181\n","Epoch [2/20], Step [300/327], Loss: 0.8567\n","Epoch [2/20], Step [325/327], Loss: 0.8429\n","Start validation #2\n","Validation #2 Average Loss: 1.7325, mIoU: 0.1487\n","Epoch [3/20], Step [25/327], Loss: 0.7316\n","Epoch [3/20], Step [50/327], Loss: 0.7911\n","Epoch [3/20], Step [75/327], Loss: 0.7557\n","Epoch [3/20], Step [100/327], Loss: 0.9394\n","Epoch [3/20], Step [125/327], Loss: 1.3404\n","Epoch [3/20], Step [150/327], Loss: 0.6937\n","Epoch [3/20], Step [175/327], Loss: 0.8031\n","Epoch [3/20], Step [200/327], Loss: 0.6796\n","Epoch [3/20], Step [225/327], Loss: 0.8303\n","Epoch [3/20], Step [250/327], Loss: 1.0354\n","Epoch [3/20], Step [275/327], Loss: 0.8455\n","Epoch [3/20], Step [300/327], Loss: 0.6274\n","Epoch [3/20], Step [325/327], Loss: 0.9890\n","Start validation #3\n","Validation #3 Average Loss: 1.1545, mIoU: 0.1703\n","Epoch [4/20], Step [25/327], Loss: 0.7102\n","Epoch [4/20], Step [50/327], Loss: 0.5596\n","Epoch [4/20], Step [75/327], Loss: 1.0759\n","Epoch [4/20], Step [100/327], Loss: 0.6503\n","Epoch [4/20], Step [125/327], Loss: 0.8047\n","Epoch [4/20], Step [150/327], Loss: 0.6476\n","Epoch [4/20], Step [175/327], Loss: 0.6959\n","Epoch [4/20], Step [200/327], Loss: 0.8079\n","Epoch [4/20], Step [225/327], Loss: 0.7309\n","Epoch [4/20], Step [250/327], Loss: 0.8848\n","Epoch [4/20], Step [275/327], Loss: 0.5192\n","Epoch [4/20], Step [300/327], Loss: 0.6134\n","Epoch [4/20], Step [325/327], Loss: 0.8198\n","Start validation #4\n","Validation #4 Average Loss: 0.9122, mIoU: 0.1730\n","Epoch [5/20], Step [25/327], Loss: 0.8735\n","Epoch [5/20], Step [50/327], Loss: 0.5823\n","Epoch [5/20], Step [75/327], Loss: 0.7121\n","Epoch [5/20], Step [100/327], Loss: 0.5461\n","Epoch [5/20], Step [125/327], Loss: 0.9457\n","Epoch [5/20], Step [150/327], Loss: 0.7301\n","Epoch [5/20], Step [175/327], Loss: 0.6934\n","Epoch [5/20], Step [200/327], Loss: 0.9779\n","Epoch [5/20], Step [225/327], Loss: 0.9188\n","Epoch [5/20], Step [250/327], Loss: 0.6044\n","Epoch [5/20], Step [275/327], Loss: 0.8054\n","Epoch [5/20], Step [300/327], Loss: 0.5367\n","Epoch [5/20], Step [325/327], Loss: 0.6970\n","Start validation #5\n","Validation #5 Average Loss: 0.8584, mIoU: 0.1689\n","Epoch [6/20], Step [25/327], Loss: 0.7945\n","Epoch [6/20], Step [50/327], Loss: 0.6658\n","Epoch [6/20], Step [75/327], Loss: 0.6187\n","Epoch [6/20], Step [100/327], Loss: 0.5482\n","Epoch [6/20], Step [125/327], Loss: 0.9986\n","Epoch [6/20], Step [150/327], Loss: 0.6411\n","Epoch [6/20], Step [175/327], Loss: 0.7793\n","Epoch [6/20], Step [200/327], Loss: 0.6754\n","Epoch [6/20], Step [225/327], Loss: 0.5762\n","Epoch [6/20], Step [250/327], Loss: 0.9543\n","Epoch [6/20], Step [275/327], Loss: 1.0088\n","Epoch [6/20], Step [300/327], Loss: 0.8127\n","Epoch [6/20], Step [325/327], Loss: 0.7117\n","Start validation #6\n","Validation #6 Average Loss: 0.8208, mIoU: 0.1744\n","Epoch [7/20], Step [25/327], Loss: 0.6969\n","Epoch [7/20], Step [50/327], Loss: 0.7162\n","Epoch [7/20], Step [75/327], Loss: 0.9245\n","Epoch [7/20], Step [100/327], Loss: 0.6324\n","Epoch [7/20], Step [125/327], Loss: 0.4972\n","Epoch [7/20], Step [150/327], Loss: 0.7673\n","Epoch [7/20], Step [175/327], Loss: 0.5743\n","Epoch [7/20], Step [200/327], Loss: 0.7903\n","Epoch [7/20], Step [225/327], Loss: 0.6064\n","Epoch [7/20], Step [250/327], Loss: 0.5333\n","Epoch [7/20], Step [275/327], Loss: 0.6744\n","Epoch [7/20], Step [300/327], Loss: 0.7788\n","Epoch [7/20], Step [325/327], Loss: 0.7972\n","Start validation #7\n","Validation #7 Average Loss: 0.7776, mIoU: 0.1832\n","Epoch [8/20], Step [25/327], Loss: 0.5648\n","Epoch [8/20], Step [50/327], Loss: 0.6366\n","Epoch [8/20], Step [75/327], Loss: 0.6696\n","Epoch [8/20], Step [100/327], Loss: 1.0038\n","Epoch [8/20], Step [125/327], Loss: 0.6465\n","Epoch [8/20], Step [150/327], Loss: 0.9937\n","Epoch [8/20], Step [175/327], Loss: 0.5988\n","Epoch [8/20], Step [200/327], Loss: 0.6688\n","Epoch [8/20], Step [225/327], Loss: 1.2559\n","Epoch [8/20], Step [250/327], Loss: 0.6821\n","Epoch [8/20], Step [275/327], Loss: 0.7173\n","Epoch [8/20], Step [300/327], Loss: 0.5793\n","Epoch [8/20], Step [325/327], Loss: 0.7233\n","Start validation #8\n","Validation #8 Average Loss: 0.7768, mIoU: 0.1865\n","Epoch [9/20], Step [25/327], Loss: 0.7699\n","Epoch [9/20], Step [50/327], Loss: 0.5669\n","Epoch [9/20], Step [75/327], Loss: 0.8095\n","Epoch [9/20], Step [100/327], Loss: 0.5696\n","Epoch [9/20], Step [125/327], Loss: 0.7740\n","Epoch [9/20], Step [150/327], Loss: 0.6041\n","Epoch [9/20], Step [175/327], Loss: 0.7045\n","Epoch [9/20], Step [200/327], Loss: 0.5215\n","Epoch [9/20], Step [225/327], Loss: 0.7181\n","Epoch [9/20], Step [250/327], Loss: 0.6495\n","Epoch [9/20], Step [275/327], Loss: 0.8257\n","Epoch [9/20], Step [300/327], Loss: 0.6209\n","Epoch [9/20], Step [325/327], Loss: 0.8376\n","Start validation #9\n","Validation #9 Average Loss: 0.7963, mIoU: 0.1795\n","Epoch [10/20], Step [25/327], Loss: 1.7799\n","Epoch [10/20], Step [50/327], Loss: 0.6546\n","Epoch [10/20], Step [75/327], Loss: 0.5310\n","Epoch [10/20], Step [100/327], Loss: 0.7178\n","Epoch [10/20], Step [125/327], Loss: 0.8862\n","Epoch [10/20], Step [150/327], Loss: 0.6430\n","Epoch [10/20], Step [175/327], Loss: 0.5030\n","Epoch [10/20], Step [200/327], Loss: 0.6020\n","Epoch [10/20], Step [225/327], Loss: 0.5746\n","Epoch [10/20], Step [250/327], Loss: 0.7725\n","Epoch [10/20], Step [275/327], Loss: 0.6608\n","Epoch [10/20], Step [300/327], Loss: 1.2211\n","Epoch [10/20], Step [325/327], Loss: 0.5878\n","Start validation #10\n","Validation #10 Average Loss: 1.4511, mIoU: 0.1522\n","Epoch [11/20], Step [25/327], Loss: 0.7734\n","Epoch [11/20], Step [50/327], Loss: 0.7712\n","Epoch [11/20], Step [75/327], Loss: 0.8669\n","Epoch [11/20], Step [100/327], Loss: 0.7328\n","Epoch [11/20], Step [125/327], Loss: 1.2200\n","Epoch [11/20], Step [150/327], Loss: 1.0516\n","Epoch [11/20], Step [175/327], Loss: 0.6782\n","Epoch [11/20], Step [200/327], Loss: 0.9615\n","Epoch [11/20], Step [225/327], Loss: 0.5992\n","Epoch [11/20], Step [250/327], Loss: 0.7939\n","Epoch [11/20], Step [275/327], Loss: 0.6409\n","Epoch [11/20], Step [300/327], Loss: 0.9087\n","Epoch [11/20], Step [325/327], Loss: 0.5830\n","Start validation #11\n","Validation #11 Average Loss: 0.8149, mIoU: 0.1675\n","Epoch [12/20], Step [25/327], Loss: 0.7365\n","Epoch [12/20], Step [50/327], Loss: 0.6477\n","Epoch [12/20], Step [75/327], Loss: 0.7039\n","Epoch [12/20], Step [100/327], Loss: 0.6489\n","Epoch [12/20], Step [125/327], Loss: 0.7828\n","Epoch [12/20], Step [150/327], Loss: 0.9389\n","Epoch [12/20], Step [175/327], Loss: 0.5902\n","Epoch [12/20], Step [200/327], Loss: 0.6281\n","Epoch [12/20], Step [225/327], Loss: 0.6909\n","Epoch [12/20], Step [250/327], Loss: 0.6520\n","Epoch [12/20], Step [275/327], Loss: 0.7257\n","Epoch [12/20], Step [300/327], Loss: 0.6801\n","Epoch [12/20], Step [325/327], Loss: 0.7305\n","Start validation #12\n","Validation #12 Average Loss: 0.9063, mIoU: 0.1881\n","Epoch [13/20], Step [25/327], Loss: 0.6639\n","Epoch [13/20], Step [50/327], Loss: 0.5985\n","Epoch [13/20], Step [75/327], Loss: 0.5583\n","Epoch [13/20], Step [100/327], Loss: 0.8094\n","Epoch [13/20], Step [125/327], Loss: 0.6548\n","Epoch [13/20], Step [150/327], Loss: 0.6866\n","Epoch [13/20], Step [175/327], Loss: 0.7532\n","Epoch [13/20], Step [200/327], Loss: 0.5654\n","Epoch [13/20], Step [225/327], Loss: 0.7816\n","Epoch [13/20], Step [250/327], Loss: 0.8981\n","Epoch [13/20], Step [275/327], Loss: 0.6214\n","Epoch [13/20], Step [300/327], Loss: 0.5052\n","Epoch [13/20], Step [325/327], Loss: 0.9263\n","Start validation #13\n","Validation #13 Average Loss: 0.7342, mIoU: 0.1904\n","Epoch [14/20], Step [25/327], Loss: 0.9747\n","Epoch [14/20], Step [50/327], Loss: 0.7149\n","Epoch [14/20], Step [75/327], Loss: 1.0166\n","Epoch [14/20], Step [100/327], Loss: 0.8410\n","Epoch [14/20], Step [125/327], Loss: 0.9077\n","Epoch [14/20], Step [150/327], Loss: 0.5362\n","Epoch [14/20], Step [175/327], Loss: 0.7062\n","Epoch [14/20], Step [200/327], Loss: 0.9685\n","Epoch [14/20], Step [225/327], Loss: 1.0869\n","Epoch [14/20], Step [250/327], Loss: 0.4906\n","Epoch [14/20], Step [275/327], Loss: 0.5649\n","Epoch [14/20], Step [300/327], Loss: 0.6690\n","Epoch [14/20], Step [325/327], Loss: 1.2077\n","Start validation #14\n","Validation #14 Average Loss: 0.7222, mIoU: 0.1943\n","Epoch [15/20], Step [25/327], Loss: 0.6286\n","Epoch [15/20], Step [50/327], Loss: 1.3461\n","Epoch [15/20], Step [75/327], Loss: 0.7013\n","Epoch [15/20], Step [100/327], Loss: 0.7694\n","Epoch [15/20], Step [125/327], Loss: 0.6070\n","Epoch [15/20], Step [150/327], Loss: 0.5437\n","Epoch [15/20], Step [175/327], Loss: 0.6900\n","Epoch [15/20], Step [200/327], Loss: 0.7110\n","Epoch [15/20], Step [225/327], Loss: 0.8248\n","Epoch [15/20], Step [250/327], Loss: 0.4478\n","Epoch [15/20], Step [275/327], Loss: 1.1005\n","Epoch [15/20], Step [300/327], Loss: 0.4706\n","Epoch [15/20], Step [325/327], Loss: 0.5611\n","Start validation #15\n","Validation #15 Average Loss: 0.7522, mIoU: 0.1814\n","Epoch [16/20], Step [25/327], Loss: 0.7241\n","Epoch [16/20], Step [50/327], Loss: 0.7695\n","Epoch [16/20], Step [75/327], Loss: 0.6674\n","Epoch [16/20], Step [100/327], Loss: 0.9528\n","Epoch [16/20], Step [125/327], Loss: 0.7993\n","Epoch [16/20], Step [150/327], Loss: 0.4911\n","Epoch [16/20], Step [175/327], Loss: 0.6596\n","Epoch [16/20], Step [200/327], Loss: 1.0159\n","Epoch [16/20], Step [225/327], Loss: 0.5575\n","Epoch [16/20], Step [250/327], Loss: 0.5913\n","Epoch [16/20], Step [275/327], Loss: 0.5807\n","Epoch [16/20], Step [300/327], Loss: 0.8045\n","Epoch [16/20], Step [325/327], Loss: 0.6449\n","Start validation #16\n","Validation #16 Average Loss: 2.2354, mIoU: 0.1891\n","Epoch [17/20], Step [25/327], Loss: 0.6888\n","Epoch [17/20], Step [50/327], Loss: 0.9762\n","Epoch [17/20], Step [75/327], Loss: 0.7282\n","Epoch [17/20], Step [100/327], Loss: 0.6508\n","Epoch [17/20], Step [125/327], Loss: 0.7299\n","Epoch [17/20], Step [150/327], Loss: 1.1111\n","Epoch [17/20], Step [175/327], Loss: 0.4305\n","Epoch [17/20], Step [200/327], Loss: 0.7197\n","Epoch [17/20], Step [225/327], Loss: 0.7287\n","Epoch [17/20], Step [250/327], Loss: 0.7478\n","Epoch [17/20], Step [275/327], Loss: 0.8442\n","Epoch [17/20], Step [300/327], Loss: 0.6934\n","Epoch [17/20], Step [325/327], Loss: 0.7409\n","Start validation #17\n","Validation #17 Average Loss: 0.7676, mIoU: 0.1845\n","Epoch [18/20], Step [25/327], Loss: 0.6333\n","Epoch [18/20], Step [50/327], Loss: 0.7144\n","Epoch [18/20], Step [75/327], Loss: 0.5551\n","Epoch [18/20], Step [100/327], Loss: 0.3917\n","Epoch [18/20], Step [125/327], Loss: 1.0390\n","Epoch [18/20], Step [150/327], Loss: 0.7751\n","Epoch [18/20], Step [175/327], Loss: 0.5347\n","Epoch [18/20], Step [200/327], Loss: 0.6642\n","Epoch [18/20], Step [225/327], Loss: 0.6443\n","Epoch [18/20], Step [250/327], Loss: 0.4608\n","Epoch [18/20], Step [275/327], Loss: 0.8999\n","Epoch [18/20], Step [300/327], Loss: 0.9494\n","Epoch [18/20], Step [325/327], Loss: 0.8047\n","Start validation #18\n","Validation #18 Average Loss: 0.7960, mIoU: 0.2044\n","Epoch [19/20], Step [25/327], Loss: 0.5000\n","Epoch [19/20], Step [50/327], Loss: 0.7344\n","Epoch [19/20], Step [75/327], Loss: 0.6677\n","Epoch [19/20], Step [100/327], Loss: 0.7619\n","Epoch [19/20], Step [125/327], Loss: 0.4828\n","Epoch [19/20], Step [150/327], Loss: 0.5829\n","Epoch [19/20], Step [175/327], Loss: 0.6162\n","Epoch [19/20], Step [200/327], Loss: 0.4412\n","Epoch [19/20], Step [225/327], Loss: 0.4856\n","Epoch [19/20], Step [250/327], Loss: 0.7382\n","Epoch [19/20], Step [275/327], Loss: 0.6795\n","Epoch [19/20], Step [300/327], Loss: 0.7104\n","Epoch [19/20], Step [325/327], Loss: 0.7024\n","Start validation #19\n","Validation #19 Average Loss: 0.7434, mIoU: 0.1966\n","Epoch [20/20], Step [25/327], Loss: 0.5094\n","Epoch [20/20], Step [50/327], Loss: 0.4371\n","Epoch [20/20], Step [75/327], Loss: 0.7505\n","Epoch [20/20], Step [100/327], Loss: 0.9663\n","Epoch [20/20], Step [125/327], Loss: 0.4638\n","Epoch [20/20], Step [150/327], Loss: 0.7069\n","Epoch [20/20], Step [175/327], Loss: 0.4451\n","Epoch [20/20], Step [200/327], Loss: 0.6422\n","Epoch [20/20], Step [225/327], Loss: 0.9221\n","Epoch [20/20], Step [250/327], Loss: 0.6446\n","Epoch [20/20], Step [275/327], Loss: 0.6255\n","Epoch [20/20], Step [300/327], Loss: 0.6264\n","Epoch [20/20], Step [325/327], Loss: 0.6347\n","Start validation #20\n","Validation #20 Average Loss: 0.7400, mIoU: 0.2114\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"C6ClcO0J-NwR"},"source":["## 저장된 model 불러오기 (학습된 이후) "]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:21.050200Z","start_time":"2021-04-16T19:44:20.802200Z"},"scrolled":true,"id":"KQPtUDzd-NwR"},"source":["# best model 저장된 경로\n","model_path = './saved/pan_effb3_noisy_focal_adamp_cosLR.pt'\n","\n","# best model 불러오기\n","checkpoint = torch.load(model_path, map_location=device)\n","model.load_state_dict(checkpoint)\n","\n","# 추론을 실행하기 전에는 반드시 설정 (batch normalization, dropout 를 평가 모드로 설정)\n","# model.eval()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:24.939227Z","start_time":"2021-04-16T19:44:24.518228Z"},"id":"0LQqrDAp-NwR"},"source":["# 첫번째 batch의 추론 결과 확인\n","for imgs, image_infos in test_loader:\n"," image_infos = image_infos\n"," temp_images = imgs\n"," \n"," model.eval()\n"," # inference\n"," outs = model(torch.stack(temp_images).to(device))\n"," oms = torch.argmax(outs.squeeze(), dim=1).detach().cpu().numpy()\n"," \n"," break\n","\n","i = 3\n","fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(16, 16))\n","\n","print('Shape of Original Image :', list(temp_images[i].shape))\n","print('Shape of Predicted : ', list(oms[i].shape))\n","print('Unique values, category of transformed mask : \\n', [{int(i),category_names[int(i)]} for i in list(np.unique(oms[i]))])\n","\n","# Original image\n","ax1.imshow(temp_images[i].permute([1,2,0]))\n","ax1.grid(False)\n","ax1.set_title(\"Original image : {}\".format(image_infos[i]['file_name']), fontsize = 15)\n","\n","# Predicted\n","ax2.imshow(oms[i])\n","ax2.grid(False)\n","ax2.set_title(\"Predicted : {}\".format(image_infos[i]['file_name']), fontsize = 15)\n","\n","plt.show()"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"evYjR2F3-NwS"},"source":["## submission을 위한 test 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:27.469285Z","start_time":"2021-04-16T19:44:27.456021Z"},"id":"nhMLnV5d-NwS"},"source":["def test(model, data_loader, device):\n"," size = 256\n"," transform = A.Compose([A.Resize(256, 256)])\n"," print('Start prediction.')\n"," model.eval()\n"," \n"," file_name_list = []\n"," preds_array = np.empty((0, size*size), dtype=np.long)\n"," \n"," with torch.no_grad():\n"," for step, (imgs, image_infos) in enumerate(test_loader):\n","\n"," # inference (512 x 512)\n"," outs = model(torch.stack(imgs).to(device))\n"," oms = torch.argmax(outs.squeeze(), dim=1).detach().cpu().numpy()\n"," \n"," # resize (256 x 256)\n"," temp_mask = []\n"," for img, mask in zip(np.stack(imgs), oms):\n"," transformed = transform(image=img, mask=mask)\n"," mask = transformed['mask']\n"," temp_mask.append(mask)\n","\n"," oms = np.array(temp_mask)\n"," \n"," oms = oms.reshape([oms.shape[0], size*size]).astype(int)\n"," preds_array = np.vstack((preds_array, oms))\n"," \n"," file_name_list.append([i['file_name'] for i in image_infos])\n"," print(\"End prediction.\")\n"," file_names = [y for x in file_name_list for y in x]\n"," \n"," return file_names, preds_array"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"r1YKHBf4-NwT"},"source":["## submission.csv 생성"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:45:42.235310Z","start_time":"2021-04-16T19:44:30.499016Z"},"scrolled":true,"id":"Bz79_g8K-NwT"},"source":["# sample_submisson.csv 열기\n","submission = pd.read_csv('./submission/sample_submission.csv', index_col=None)\n","\n","# test set에 대한 prediction\n","file_names, preds = test(model, test_loader, device)\n","\n","# PredictionString 대입\n","for file_name, string in zip(file_names, preds):\n"," submission = submission.append({\"image_id\" : file_name, \"PredictionString\" : ' '.join(str(e) for e in string.tolist())}, \n"," ignore_index=True)\n","\n","# submission.csv로 저장\n","submission.to_csv(\"./submission/pan_effb3_noisy_focal_adamp_cosLR.csv\", index=False)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Zgl7Ye7F-NwT"},"source":["## 제출까지\n","\n"]},{"cell_type":"code","metadata":{"id":"022E8AC7-NwT"},"source":["import json\n","import requests\n","import os\n","from urllib.parse import urlparse, parse_qsl, urlencode, urlunparse\n","\n","def submit(user_key='', file_path = '', desc=\"\"):\n"," if not user_key:\n"," raise Exception(\"No UserKey\" )\n"," url = urlparse('http://ec2-13-124-161-225.ap-northeast-2.compute.amazonaws.com:8000/api/v1/competition/28/presigned_url/?description=&hyperparameters={%22training%22:{},%22inference%22:{}}')\n"," qs = dict(parse_qsl(url.query))\n"," qs['description'] = desc\n"," parts = url._replace(query=urlencode(qs))\n"," url = urlunparse(parts)\n","\n"," print(url)\n"," headers = {\n"," 'Authorization': user_key\n"," }\n"," res = requests.get(url, headers=headers)\n"," print(res.text)\n"," data = json.loads(res.text)\n"," \n"," submit_url = data['url']\n"," body = {\n"," 'key':'app/Competitions/000028/Users/{}/Submissions/{}/output.csv'.format(str(data['submission']['user']).zfill(8),str(data['submission']['local_id']).zfill(4)),\n"," 'x-amz-algorithm':data['fields']['x-amz-algorithm'],\n"," 'x-amz-credential':data['fields']['x-amz-credential'],\n"," 'x-amz-date':data['fields']['x-amz-date'],\n"," 'policy':data['fields']['policy'],\n"," 'x-amz-signature':data['fields']['x-amz-signature']\n"," }\n"," requests.post(url=submit_url, data=body, files={'file': open(file_path, 'rb')})\n","\n","\n","####################################################################################\n","test_dir = \"/opt/ml/code/submission\" # 수정 필요 : output 파일 폴더 \n","desc = 'pan_effb3_noisy_focal_adamp_cosLR' # 수정 필요 : 파일에 대한 설명\n","output_file = \"pan_effb3_noisy_focal_adamp_cosLR.csv\" #수정 필요 : output 파일 \n","user_key = \"Bearer 7bb5f96452751a238ffaf91a93c4242bf9b72abe\" # 수정 필요 : Authorization \n","\n","\n","submit(user_key, os.path.join(test_dir, output_file),desc)"],"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/chanyub_seg/code/pan_resnet101_imagenet_focal_madgrad_cosLR.ipynb b/chanyub_seg/code/pan_resnet101_imagenet_focal_madgrad_cosLR.ipynb deleted file mode 100644 index 708c645..0000000 --- a/chanyub_seg/code/pan_resnet101_imagenet_focal_madgrad_cosLR.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"nbformat":4,"nbformat_minor":0,"metadata":{"hide_input":false,"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.7"},"toc":{"base_numbering":1,"nav_menu":{},"number_sections":true,"sideBar":true,"skip_h1_title":false,"title_cell":"Table of Contents","title_sidebar":"Contents","toc_cell":true,"toc_position":{"height":"calc(100% - 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google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Mounted at /content/drive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xDwpp4Lk-gSH","executionInfo":{"status":"ok","timestamp":1620066619066,"user_tz":-540,"elapsed":1451,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"aa4f12c3-a02d-4a75-ca0e-f00ca038bf30"},"source":["ls"],"execution_count":2,"outputs":[{"output_type":"stream","text":["\u001b[0m\u001b[01;34mdrive\u001b[0m/ \u001b[01;34msample_data\u001b[0m/\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sItrVDeh-iYC","executionInfo":{"status":"ok","timestamp":1620081684805,"user_tz":-540,"elapsed":23676,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"afd50aa7-30b0-4393-e26f-00caf5108190"},"source":["cd drive/MyDrive/Trash/code"],"execution_count":2,"outputs":[{"output_type":"stream","text":["/content/drive/MyDrive/Trash/code\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"a54v4-kN_LVO","executionInfo":{"status":"ok","timestamp":1620081701648,"user_tz":-540,"elapsed":40186,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"e2ab9483-1c69-414e-96ba-ef78814821f2"},"source":["!pip install albumentations==0.5.2"],"execution_count":3,"outputs":[{"output_type":"stream","text":["Collecting albumentations==0.5.2\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/03/58/63fb1d742dc42d9ba2800ea741de1f2bc6bb05548d8724aa84794042eaf2/albumentations-0.5.2-py3-none-any.whl (72kB)\n","\r\u001b[K |████▌ | 10kB 18.2MB/s eta 0:00:01\r\u001b[K |█████████ | 20kB 20.0MB/s eta 0:00:01\r\u001b[K |█████████████▋ | 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in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations==0.5.2) (3.2.2)\n","Requirement already satisfied: imageio>=2.3.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations==0.5.2) (2.4.1)\n","Requirement already satisfied: Shapely in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (1.7.1)\n","Requirement already satisfied: opencv-python in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (4.1.2.30)\n","Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (1.15.0)\n","Requirement already satisfied: decorator<5,>=4.3 in /usr/local/lib/python3.7/dist-packages (from networkx>=2.0->scikit-image>=0.16.1->albumentations==0.5.2) (4.4.2)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from 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albumentations-0.1.12\n","Successfully installed albumentations-0.5.2 imgaug-0.4.0 opencv-python-headless-4.5.1.48\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.826930Z","start_time":"2021-04-18T10:34:45.406686Z"},"colab":{"base_uri":"https://localhost:8080/"},"id":"l_LPA4XD-NwC","executionInfo":{"status":"ok","timestamp":1620081706257,"user_tz":-540,"elapsed":4772,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"ae4c4417-43af-416c-c373-50c41975b66a"},"source":["import os\n","import random\n","import time\n","import json\n","import warnings \n","warnings.filterwarnings('ignore')\n","\n","import torch\n","import torch.nn as nn\n","from torch.utils.data import Dataset, DataLoader\n","from utils import label_accuracy_score\n","import cv2\n","\n","import numpy as np\n","import pandas as pd\n","\n","# 전처리를 위한 라이브러리\n","from pycocotools.coco import COCO\n","import torchvision\n","import torchvision.transforms as transforms\n","\n","import albumentations as A\n","from albumentations.pytorch import ToTensorV2\n","\n","# 시각화를 위한 라이브러리\n","import matplotlib.pyplot as plt\n","import seaborn as sns; sns.set()\n","\n","plt.rcParams['axes.grid'] = False\n","\n","print('pytorch version: {}'.format(torch.__version__))\n","print('GPU 사용 가능 여부: {}'.format(torch.cuda.is_available()))\n","\n","print(torch.cuda.get_device_name(0))\n","print(torch.cuda.device_count())\n","\n","device = \"cuda\" if torch.cuda.is_available() else \"cpu\" # GPU 사용 가능 여부에 따라 device 정보 저장"],"execution_count":4,"outputs":[{"output_type":"stream","text":["pytorch version: 1.8.1+cu101\n","GPU 사용 가능 여부: True\n","Tesla V100-SXM2-16GB\n","1\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"HxgRSL_M-NwF"},"source":["## 하이퍼파라미터 세팅 및 seed 고정"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.841930Z","start_time":"2021-04-18T10:34:47.827931Z"},"id":"rV3JmGP5-NwF","executionInfo":{"status":"ok","timestamp":1620081706258,"user_tz":-540,"elapsed":2924,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["batch_size = 8 # Mini-batch size\n","num_epochs = 20\n","learning_rate = 0.0001"],"execution_count":5,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.856930Z","start_time":"2021-04-18T10:34:47.842931Z"},"id":"Z6LOuJXQ-NwG","executionInfo":{"status":"ok","timestamp":1620081706258,"user_tz":-540,"elapsed":2515,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# seed 고정\n","random_seed = 42\n","torch.manual_seed(random_seed)\n","torch.cuda.manual_seed(random_seed)\n","# torch.cuda.manual_seed_all(random_seed) # if use multi-GPU\n","torch.backends.cudnn.deterministic = True\n","torch.backends.cudnn.benchmark = False\n","np.random.seed(random_seed)\n","random.seed(random_seed)"],"execution_count":6,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"iWF_EJDu-NwG"},"source":["## 학습 데이터 EDA"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.381961Z","start_time":"2021-04-18T10:34:47.857930Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"lg0x0D0a-NwG","executionInfo":{"status":"ok","timestamp":1620081718120,"user_tz":-540,"elapsed":11705,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"8f063680-6bc3-45d3-d71b-993ee7d29b4d"},"source":["%matplotlib inline\n","\n","dataset_path = '../input/data'\n","anns_file_path = dataset_path + '/' + 'train.json'\n","\n","# Read annotations\n","with open(anns_file_path, 'r') as f:\n"," dataset = json.loads(f.read())\n","\n","categories = dataset['categories']\n","anns = dataset['annotations']\n","imgs = dataset['images']\n","nr_cats = len(categories)\n","nr_annotations = len(anns)\n","nr_images = len(imgs)\n","\n","# Load categories and super categories\n","cat_names = []\n","super_cat_names = []\n","super_cat_ids = {}\n","super_cat_last_name = ''\n","nr_super_cats = 0\n","for cat_it in categories:\n"," cat_names.append(cat_it['name'])\n"," super_cat_name = cat_it['supercategory']\n"," # Adding new supercat\n"," if super_cat_name != super_cat_last_name:\n"," super_cat_names.append(super_cat_name)\n"," super_cat_ids[super_cat_name] = nr_super_cats\n"," super_cat_last_name = super_cat_name\n"," nr_super_cats += 1\n","\n","print('Number of super categories:', nr_super_cats)\n","print('Number of categories:', nr_cats)\n","print('Number of annotations:', nr_annotations)\n","print('Number of images:', nr_images)"],"execution_count":7,"outputs":[{"output_type":"stream","text":["Number of super categories: 11\n","Number of categories: 11\n","Number of annotations: 21116\n","Number of images: 2617\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.546964Z","start_time":"2021-04-18T10:34:51.382969Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/","height":355},"id":"PjLyVHVY-NwH","executionInfo":{"status":"ok","timestamp":1620081718668,"user_tz":-540,"elapsed":11852,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"558ccc36-e308-4d42-c19f-73e7120adcc6"},"source":["# Count annotations\n","cat_histogram = np.zeros(nr_cats,dtype=int)\n","for ann in anns:\n"," cat_histogram[ann['category_id']] += 1\n","\n","# Initialize the matplotlib figure\n","f, ax = plt.subplots(figsize=(5,5))\n","\n","# Convert to DataFrame\n","df = pd.DataFrame({'Categories': cat_names, 'Number of annotations': cat_histogram})\n","df = df.sort_values('Number of annotations', 0, False)\n","\n","# Plot the histogram\n","plt.title(\"category distribution of train set \")\n","plot_1 = sns.barplot(x=\"Number of annotations\", y=\"Categories\", data=df, label=\"Total\", color=\"b\")"],"execution_count":8,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.561965Z","start_time":"2021-04-18T10:34:51.547969Z"},"id":"34Tw5__i-NwI","executionInfo":{"status":"ok","timestamp":1620081718669,"user_tz":-540,"elapsed":11161,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# category labeling \n","sorted_temp_df = df.sort_index()\n","\n","# background = 0 에 해당되는 label 추가 후 기존들을 모두 label + 1 로 설정\n","sorted_df = pd.DataFrame([\"Backgroud\"], columns = [\"Categories\"])\n","sorted_df = sorted_df.append(sorted_temp_df, ignore_index=True)"],"execution_count":9,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.576961Z","start_time":"2021-04-18T10:34:51.562964Z"},"colab":{"base_uri":"https://localhost:8080/","height":421},"id":"CQk4vV5N-NwI","executionInfo":{"status":"ok","timestamp":1620081718669,"user_tz":-540,"elapsed":9650,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"943e19cd-d9e7-4d34-891c-b19b3236f82c"},"source":["# class (Categories) 에 따른 index 확인 (0~11 : 총 12개)\n","sorted_df"],"execution_count":10,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
CategoriesNumber of annotations
0BackgroudNaN
1UNKNOWN128.0
2General trash2225.0
3Paper7448.0
4Paper pack527.0
5Metal449.0
6Glass488.0
7Plastic2472.0
8Styrofoam1074.0
9Plastic bag6114.0
10Battery50.0
11Clothing141.0
\n","
"],"text/plain":[" Categories Number of annotations\n","0 Backgroud NaN\n","1 UNKNOWN 128.0\n","2 General trash 2225.0\n","3 Paper 7448.0\n","4 Paper pack 527.0\n","5 Metal 449.0\n","6 Glass 488.0\n","7 Plastic 2472.0\n","8 Styrofoam 1074.0\n","9 Plastic bag 6114.0\n","10 Battery 50.0\n","11 Clothing 141.0"]},"metadata":{"tags":[]},"execution_count":10}]},{"cell_type":"markdown","metadata":{"id":"KXU0zmZs-NwI"},"source":["## 데이터 전처리 함수 정의 (Dataset)"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:52.693328Z","start_time":"2021-04-18T10:34:52.681328Z"},"id":"QFnTI8_Z-NwJ","executionInfo":{"status":"ok","timestamp":1620081718670,"user_tz":-540,"elapsed":6790,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["category_names = list(sorted_df.Categories)\n","\n","def get_classname(classID, cats):\n"," for i in range(len(cats)):\n"," if cats[i]['id']==classID:\n"," return cats[i]['name']\n"," return \"None\"\n","\n","class CustomDataLoader(Dataset):\n"," \"\"\"COCO format\"\"\"\n"," def __init__(self, data_dir, mode = 'train', transform = None):\n"," super().__init__()\n"," self.mode = mode\n"," self.transform = transform\n"," self.coco = COCO(data_dir)\n"," \n"," def __getitem__(self, index: int):\n"," # dataset이 index되어 list처럼 동작\n"," image_id = self.coco.getImgIds(imgIds=index)\n"," image_infos = self.coco.loadImgs(image_id)[0]\n"," \n"," # cv2 를 활용하여 image 불러오기\n"," images = cv2.imread(os.path.join(dataset_path, image_infos['file_name']))\n"," images = cv2.cvtColor(images, cv2.COLOR_BGR2RGB).astype(np.float32)\n"," images /= 255.0\n"," \n"," if (self.mode in ('train', 'val')):\n"," ann_ids = self.coco.getAnnIds(imgIds=image_infos['id'])\n"," anns = self.coco.loadAnns(ann_ids)\n","\n"," # Load the categories in a variable\n"," cat_ids = self.coco.getCatIds()\n"," cats = self.coco.loadCats(cat_ids)\n","\n"," # masks : size가 (height x width)인 2D\n"," # 각각의 pixel 값에는 \"category id + 1\" 할당\n"," # Background = 0\n"," masks = np.zeros((image_infos[\"height\"], image_infos[\"width\"]))\n"," # Unknown = 1, General trash = 2, ... , Cigarette = 11\n"," for i in range(len(anns)):\n"," className = get_classname(anns[i]['category_id'], cats)\n"," pixel_value = category_names.index(className)\n"," masks = np.maximum(self.coco.annToMask(anns[i])*pixel_value, masks)\n"," masks = masks.astype(np.float32)\n","\n"," # transform -> albumentations 라이브러리 활용\n"," if self.transform is not None:\n"," transformed = self.transform(image=images, mask=masks)\n"," images = transformed[\"image\"]\n"," masks = transformed[\"mask\"]\n"," \n"," return images, masks, image_infos\n"," \n"," if self.mode == 'test':\n"," # transform -> albumentations 라이브러리 활용\n"," if self.transform is not None:\n"," transformed = self.transform(image=images)\n"," images = transformed[\"image\"]\n"," \n"," return images, image_infos\n"," \n"," \n"," def __len__(self) -> int:\n"," # 전체 dataset의 size를 return\n"," return len(self.coco.getImgIds())"],"execution_count":11,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"cp2aIOlP-NwK"},"source":["## Dataset 정의 및 DataLoader 할당"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:58.823175Z","start_time":"2021-04-18T10:34:54.106233Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"P7zFlRn6-NwK","executionInfo":{"status":"ok","timestamp":1620081737263,"user_tz":-540,"elapsed":8934,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"5ec8605d-0462-4bbe-bdf7-b93826c7f7b3"},"source":["# train.json / validation.json / test.json 디렉토리 설정\n","train_path = dataset_path + '/train.json'\n","val_path = dataset_path + '/val.json'\n","test_path = dataset_path + '/test.json'\n","\n","# collate_fn needs for batch\n","def collate_fn(batch):\n"," return tuple(zip(*batch))\n","\n","train_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","val_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","test_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","# create own Dataset 1 (skip)\n","# validation set을 직접 나누고 싶은 경우\n","# random_split 사용하여 data set을 8:2 로 분할\n","# train_size = int(0.8*len(dataset))\n","# val_size = int(len(dataset)-train_size)\n","# dataset = CustomDataLoader(data_dir=train_path, mode='train', transform=transform)\n","# train_dataset, val_dataset = torch.utils.data.random_split(dataset, [train_size, val_size])\n","\n","# create own Dataset 2\n","# train dataset\n","train_dataset = CustomDataLoader(data_dir=train_path, mode='train', transform=train_transform)\n","\n","# validation dataset\n","val_dataset = CustomDataLoader(data_dir=val_path, mode='val', transform=val_transform)\n","\n","# test dataset\n","test_dataset = CustomDataLoader(data_dir=test_path, mode='test', transform=test_transform)\n","\n","\n","# DataLoader\n","train_loader = torch.utils.data.DataLoader(dataset=train_dataset, \n"," batch_size=batch_size,\n"," shuffle=True,\n"," num_workers=4,\n"," collate_fn=collate_fn,\n"," drop_last=True)\n","\n","val_loader = torch.utils.data.DataLoader(dataset=val_dataset, \n"," batch_size=batch_size,\n"," shuffle=False,\n"," num_workers=4,\n"," collate_fn=collate_fn,\n"," drop_last=True) \n","\n","test_loader = torch.utils.data.DataLoader(dataset=test_dataset,\n"," batch_size=batch_size,\n"," num_workers=4,\n"," collate_fn=collate_fn)"],"execution_count":12,"outputs":[{"output_type":"stream","text":["loading annotations into memory...\n","Done (t=4.05s)\n","creating index...\n","index created!\n","loading annotations into memory...\n","Done (t=3.47s)\n","creating index...\n","index created!\n","loading annotations into memory...\n","Done (t=0.59s)\n","creating index...\n","index created!\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"8rqJiHb_-NwM"},"source":["# wandb"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"H1kHXm0uAX3R","executionInfo":{"status":"ok","timestamp":1620066661852,"user_tz":-540,"elapsed":22814,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"dad25f5a-00c6-4a4d-b4b3-fafe9f25e814"},"source":["!pip install wandb"],"execution_count":14,"outputs":[{"output_type":"stream","text":["Collecting wandb\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/5c/ee/d755f9e5466df64c8416a2c6a860fb3aaa43ed6ea8e8e8e81460fda5788b/wandb-0.10.28-py2.py3-none-any.whl (2.1MB)\n","\u001b[K |████████████████████████████████| 2.1MB 17.1MB/s \n","\u001b[?25hCollecting sentry-sdk>=0.4.0\n","\u001b[?25l Downloading 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filename=subprocess32-3.5.4-cp37-none-any.whl size=6489 sha256=4842c4795301656c644e044977e9c90ac88b48cdebe08fc5745b56bba742e3ce\n"," Stored in directory: /root/.cache/pip/wheels/68/39/1a/5e402bdfdf004af1786c8b853fd92f8c4a04f22aad179654d1\n"," Building wheel for pathtools (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for pathtools: filename=pathtools-0.1.2-cp37-none-any.whl size=8786 sha256=9a7fed8dc3b18e1ad0e7826eed30fdbc2d62d482d2fa3523eeea471ae5dc2f36\n"," Stored in directory: /root/.cache/pip/wheels/0b/04/79/c3b0c3a0266a3cb4376da31e5bfe8bba0c489246968a68e843\n","Successfully built subprocess32 pathtools\n","Installing collected packages: sentry-sdk, docker-pycreds, configparser, smmap, gitdb, GitPython, shortuuid, subprocess32, pathtools, wandb\n","Successfully installed GitPython-3.1.14 configparser-5.0.2 docker-pycreds-0.4.0 gitdb-4.0.7 pathtools-0.1.2 sentry-sdk-1.0.0 shortuuid-1.0.1 smmap-4.0.0 subprocess32-3.5.4 wandb-0.10.28\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":153},"id":"aMW4VV9V-NwM","executionInfo":{"status":"ok","timestamp":1620066674151,"user_tz":-540,"elapsed":10632,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"909c44af-5532-4300-9d5a-71445cf5515f"},"source":["import wandb\n","\n","proj_name = 'pan_resnet101_imagenet_focal_madgrad_cosLR'\n","\n","wandb.init(project='chanyub',name=proj_name)\n","\n","config = wandb.config\n","config.learning_rate = 0.01"],"execution_count":15,"outputs":[{"output_type":"display_data","data":{"application/javascript":["\n"," window._wandbApiKey = new Promise((resolve, reject) => {\n"," function loadScript(url) {\n"," return new Promise(function(resolve, reject) {\n"," let newScript = document.createElement(\"script\");\n"," newScript.onerror = reject;\n"," newScript.onload = resolve;\n"," document.body.appendChild(newScript);\n"," newScript.src = url;\n"," });\n"," }\n"," loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n"," const iframe = document.createElement('iframe')\n"," iframe.style.cssText = \"width:0;height:0;border:none\"\n"," document.body.appendChild(iframe)\n"," const handshake = new Postmate({\n"," container: iframe,\n"," url: 'https://wandb.ai/authorize'\n"," });\n"," const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n"," handshake.then(function(child) {\n"," child.on('authorize', data => {\n"," clearTimeout(timeout)\n"," resolve(data)\n"," });\n"," });\n"," })\n"," });\n"," "],"text/plain":[""]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"],"name":"stderr"},{"output_type":"display_data","data":{"text/html":["\n"," Tracking run with wandb version 0.10.28
\n"," Syncing run pan_resnet101_imagenet_focal_madgrad_cosLR to Weights & Biases (Documentation).
\n"," Project page: https://wandb.ai/pstage12/chanyub
\n"," Run page: https://wandb.ai/pstage12/chanyub/runs/bra86z70
\n"," Run data is saved locally in /content/drive/My Drive/Trash/code/wandb/run-20210503_183113-bra86z70

\n"," "],"text/plain":[""]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"uQRiIVGX-NwM"},"source":["## My model"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"S0xCGpNeAqeD","executionInfo":{"status":"ok","timestamp":1620081746593,"user_tz":-540,"elapsed":6903,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"6fc49131-bb56-4335-db8f-a567fa3c0e0b"},"source":["!pip install segmentation_models_pytorch"],"execution_count":13,"outputs":[{"output_type":"stream","text":["Collecting segmentation_models_pytorch\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/65/54/8953f9f7ee9d451b0f3be8d635aa3a654579abf898d17502a090efe1155a/segmentation_models_pytorch-0.1.3-py3-none-any.whl (66kB)\n","\r\u001b[K |█████ | 10kB 17.3MB/s eta 0:00:01\r\u001b[K |██████████ | 20kB 24.3MB/s eta 0:00:01\r\u001b[K 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sha256=e57f47614be4522385bbb759c2d9a3b80a2d9d82fc71feab65aae0fa6f9b1cb8\n"," Stored in directory: /root/.cache/pip/wheels/42/1e/a9/2a578ba9ad04e776e80bf0f70d8a7f4c29ec0718b92d8f6ccd\n","Successfully built pretrainedmodels efficientnet-pytorch\n","Installing collected packages: munch, pretrainedmodels, efficientnet-pytorch, timm, segmentation-models-pytorch\n","Successfully installed efficientnet-pytorch-0.6.3 munch-2.5.0 pretrainedmodels-0.7.4 segmentation-models-pytorch-0.1.3 timm-0.3.2\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:11.634792Z","start_time":"2021-04-18T16:16:05.875817Z"},"colab":{"base_uri":"https://localhost:8080/","height":137,"referenced_widgets":["47b9d119a7454972be0bfee252da6924","db4536203c224abb83fcb7f1e5a1b9a9","ae496f86f47547b6b91a6257c75fc05c","04084e96c7894bbbb4c470565a286626","227283ea04ee4773a1a257ada7414033","4b969fa6b0044a5c97197ca5c514a25e","a0cb06bee2aa455ea95e3bc121844041","71ecd03414a94f9fb7870a0033ea3708"]},"id":"a8IfZfiM-NwM","executionInfo":{"status":"ok","timestamp":1620081762672,"user_tz":-540,"elapsed":16053,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"15ed76a7-a764-4c9c-9a98-348d41721df1"},"source":["# 구현된 model에 임의의 input을 넣어 output이 잘 나오는지 test\n","import segmentation_models_pytorch as smp\n","\n","model = smp.PAN(encoder_name='resnet101', classes=12)\n","x = torch.randn([2, 3, 512, 512])\n","print(\"input shape : \", x.shape)\n","out = model(x).to(device)\n","print(\"output shape : \", out.size())\n","\n","model = model.to(device)"],"execution_count":14,"outputs":[{"output_type":"stream","text":["Downloading: \"https://download.pytorch.org/models/resnet101-5d3b4d8f.pth\" to /root/.cache/torch/hub/checkpoints/resnet101-5d3b4d8f.pth\n"],"name":"stderr"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"47b9d119a7454972be0bfee252da6924","version_minor":0,"version_major":2},"text/plain":["HBox(children=(FloatProgress(value=0.0, max=178728960.0), HTML(value='')))"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\n","input shape : torch.Size([2, 3, 512, 512])\n","output shape : torch.Size([2, 12, 512, 512])\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"zvDnI7_T-NwN"},"source":["## train, validation, test 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.104200Z","start_time":"2021-04-18T16:16:18.093174Z"},"id":"RA3oAapJ-NwN","executionInfo":{"status":"ok","timestamp":1620066700981,"user_tz":-540,"elapsed":1550,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["def train(num_epochs, model, data_loader, val_loader, criterion, optimizer, saved_dir, val_every, device, lr_scheduler):\n"," print('Start training..')\n"," best_loss = 9999999\n"," for epoch in range(num_epochs):\n"," model.train()\n"," for step, (images, masks, _) in enumerate(data_loader):\n"," images = torch.stack(images) # (batch, channel, height, width)\n"," masks = torch.stack(masks).long() # (batch, channel, height, width)\n"," \n"," # gpu 연산을 위해 device 할당\n"," images, masks = images.to(device), masks.to(device)\n"," \n"," # inference\n"," outputs = model(images)\n"," \n"," # loss 계산 (cross entropy loss)\n"," loss = criterion(outputs, masks)\n"," optimizer.zero_grad()\n"," loss.backward()\n"," optimizer.step()\n"," \n"," lr_scheduler.step()\n"," \n"," # step 주기에 따른 loss 출력\n"," if (step + 1) % 25 == 0:\n"," print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(\n"," epoch+1, num_epochs, step+1, len(train_loader), loss.item()))\n"," \n"," # validation 주기에 따른 loss 출력 및 best model 저장\n"," if (epoch + 1) % val_every == 0:\n"," avrg_loss = validation(epoch + 1, model, val_loader, criterion, device)\n"," if avrg_loss < best_loss:\n"," print('Best performance at epoch: {}'.format(epoch + 1))\n"," print('Save model in', saved_dir)\n"," best_loss = avrg_loss\n"," wandb.log({'best_loss': best_loss})\n"," save_model(model, saved_dir)"],"execution_count":18,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.285795Z","start_time":"2021-04-18T16:16:18.267686Z"},"id":"EulIikmq-NwO","executionInfo":{"status":"ok","timestamp":1620066700982,"user_tz":-540,"elapsed":1547,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["def validation(epoch, model, data_loader, criterion, device):\n"," print('Start validation #{}'.format(epoch))\n"," model.eval()\n"," with torch.no_grad():\n"," total_loss = 0\n"," cnt = 0\n"," mIoU_list = []\n"," for step, (images, masks, _) in enumerate(data_loader):\n"," \n"," images = torch.stack(images) # (batch, channel, height, width)\n"," masks = torch.stack(masks).long() # (batch, channel, height, width)\n","\n"," images, masks = images.to(device), masks.to(device) \n","\n"," outputs = model(images)\n"," loss = criterion(outputs, masks)\n"," total_loss += loss\n"," cnt += 1\n"," \n","# print(outputs.shape)\n","# print(masks.shape)\n","# wandb.log(wandb.Image(images, masks={\n","# \"predictions\" : {\n","# \"mask_data\" : torch.squeeze(torch.squeeze(outputs, 0),1),\n","# \"class_labels\" : classes_dict\n","# },\n","# \"ground_truth\" : {\n","# \"mask_data\" : torch.squeeze(masks, 0),\n","# \"class_labels\" : classes_dict\n","# }\n","# }))\n"," \n"," outputs = torch.argmax(outputs.squeeze(), dim=1).detach().cpu().numpy()\n","\n"," mIoU = label_accuracy_score(masks.detach().cpu().numpy(), outputs, n_class=12)[2]\n"," mIoU_list.append(mIoU)\n"," \n"," avrg_loss = total_loss / cnt\n"," avrg_mIoU = np.mean(mIoU_list)\n"," print('Validation #{} Average Loss: {:.4f}, mIoU: {:.4f}'.format(epoch, avrg_loss, np.mean(mIoU_list)))\n"," wandb.log({'Train Loss':loss.item(), 'Val Loss':avrg_loss , 'Val mIoU':np.mean(mIoU_list)})\n","# return avrg_loss\n"," return avrg_mIoU"],"execution_count":19,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"C_7CtFfH-NwO"},"source":["## 모델 저장 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.909918Z","start_time":"2021-04-18T16:16:18.898918Z"},"id":"gpCM5BFO-NwO","executionInfo":{"status":"ok","timestamp":1620066700982,"user_tz":-540,"elapsed":1248,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# 모델 저장 함수 정의\n","val_every = 1 \n","\n","saved_dir = './saved'\n","if not os.path.isdir(saved_dir): \n"," os.mkdir(saved_dir)\n"," \n","def save_model(model, saved_dir, file_name='pan_resnet101_imagenet_focal_madgrad_cosLR.pt'):\n"," check_point = {'net': model.state_dict()}\n"," output_path = os.path.join(saved_dir, file_name)\n"," torch.save(model.state_dict(), output_path)"],"execution_count":20,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"FyKGeg8T-NwP"},"source":["## 모델 생성 및 Loss function, Optimizer 정의"]},{"cell_type":"code","metadata":{"id":"ORugl8s1-NwP","executionInfo":{"status":"ok","timestamp":1620066707025,"user_tz":-540,"elapsed":1068,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["from torch.autograd import Variable\n","import torch.nn.functional as F\n","# ref : https://github.com/clcarwin/focal_loss_pytorch\n","class FocalLoss(nn.Module):\n"," def __init__(self, gamma=0, alpha=None, size_average=True):\n"," super(FocalLoss, self).__init__()\n"," self.gamma = gamma\n"," self.alpha = alpha\n"," if isinstance(alpha,(float,int)): self.alpha = torch.Tensor([alpha,1-alpha])\n"," if isinstance(alpha,list): self.alpha = torch.Tensor(alpha)\n"," self.size_average = size_average\n","\n"," def forward(self, input, target):\n"," if input.dim()>2:\n"," input = input.view(input.size(0),input.size(1),-1) # N,C,H,W => N,C,H*W\n"," input = input.transpose(1,2) # N,C,H*W => N,H*W,C\n"," input = input.contiguous().view(-1,input.size(2)) # N,H*W,C => N*H*W,C\n"," target = target.view(-1,1)\n","\n"," logpt = F.log_softmax(input)\n"," logpt = logpt.gather(1,target)\n"," logpt = logpt.view(-1)\n"," pt = Variable(logpt.data.exp())\n","\n"," if self.alpha is not None:\n"," if self.alpha.type()!=input.data.type():\n"," self.alpha = self.alpha.type_as(input.data)\n"," at = self.alpha.gather(0,target.data.view(-1))\n"," logpt = logpt * Variable(at)\n","\n"," loss = -1 * (1-pt)**self.gamma * logpt\n"," if self.size_average: return loss.mean()\n"," else: return loss.sum()"],"execution_count":21,"outputs":[]},{"cell_type":"code","metadata":{"id":"yfeFQknz-NwP"},"source":["# import torch.optim.lr_scheduler as lr_scheduler\n","# import math\n","# class CosineAnnealingWarmUpRestart(lr_scheduler._LRScheduler):\n","# def __init__(self, optimizer, T_0, T_mult=1, eta_max=0.1, T_up=0, gamma=1., last_epoch=-1):\n","# if T_0 <= 0 or not isinstance(T_0, int):\n","# raise ValueError(\"Expected positive integer T_0, but got {}\".format(T_0))\n","# if T_mult < 1 or not isinstance(T_mult, int):\n","# raise ValueError(\"Expected integer T_mult >= 1, but got {}\".format(T_mult))\n","# if T_up < 0 or not isinstance(T_up, int):\n","# raise ValueError(\"Expected positive integer T_up, but got {}\".format(T_up))\n","# self.T_0 = T_0\n","# self.T_mult = T_mult\n","# self.base_eta_max = eta_max\n","# self.eta_max = eta_max\n","# self.T_up = T_up\n","# self.T_i = T_0\n","# self.gamma = gamma\n","# self.cycle = 0\n","# self.T_cur = last_epoch\n","# super(CosineAnnealingWarmUpRestart, self).__init__(optimizer, last_epoch)\n","# # self.T_cur = last_epoch\n"," \n","# def get_lr(self):\n","# if self.T_cur == -1:\n","# return self.base_lrs\n","# elif self.T_cur < self.T_up:\n","# return [(self.eta_max - base_lr)*self.T_cur / self.T_up + base_lr for base_lr in self.base_lrs]\n","# else:\n","# return [base_lr + (self.eta_max - base_lr) * (1 + math.cos(math.pi * (self.T_cur-self.T_up) / (self.T_i - self.T_up))) / 2\n","# for base_lr in self.base_lrs]\n","\n","# def step(self, epoch=None):\n","# if epoch is None:\n","# epoch = self.last_epoch + 1\n","# self.T_cur = self.T_cur + 1\n","# if self.T_cur >= self.T_i:\n","# self.cycle += 1\n","# self.T_cur = self.T_cur - self.T_i\n","# self.T_i = (self.T_i - self.T_up) * self.T_mult + self.T_up\n","# else:\n","# if epoch >= self.T_0:\n","# if self.T_mult == 1:\n","# self.T_cur = epoch % self.T_0\n","# self.cycle = epoch // self.T_0\n","# else:\n","# n = int(math.log((epoch / self.T_0 * (self.T_mult - 1) + 1), self.T_mult))\n","# self.cycle = n\n","# self.T_cur = epoch - self.T_0 * (self.T_mult ** n - 1) / (self.T_mult - 1)\n","# self.T_i = self.T_0 * self.T_mult ** (n)\n","# else:\n","# self.T_i = self.T_0\n","# self.T_cur = epoch\n"," \n","# self.eta_max = self.base_eta_max * (self.gamma**self.cycle)\n","# self.last_epoch = math.floor(epoch)\n","# for param_group, lr in zip(self.optimizer.param_groups, self.get_lr()):\n","# param_group['lr'] = lr"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AG1oQeu7BX1M","executionInfo":{"status":"ok","timestamp":1620060451932,"user_tz":-540,"elapsed":4563,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"11b97bb1-b0d0-4c1c-e462-ea4f6bd73cd3"},"source":["# !pip install adamp"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Collecting adamp\n"," Downloading https://files.pythonhosted.org/packages/c8/56/182b8c93f18feb0244b83f9b2eff1c6b036c04d4c3880e8d222750b0d5e5/adamp-0.3.0.tar.gz\n","Building wheels for collected packages: adamp\n"," Building wheel for adamp (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for adamp: filename=adamp-0.3.0-cp37-none-any.whl size=5999 sha256=f539e77b51016467007048c2dc524433d3c26179d3e5b0f3c06bc9daee9a97a5\n"," Stored in directory: /root/.cache/pip/wheels/6a/89/67/879fe55977ebcbfaa5b929eb111af7fe11eb3552867850dd76\n","Successfully built adamp\n","Installing collected packages: adamp\n","Successfully installed adamp-0.3.0\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5A_iHxtWfSgx","executionInfo":{"status":"ok","timestamp":1620066731216,"user_tz":-540,"elapsed":3995,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"0ac3da5d-8944-4444-bc06-0bc3d0570432"},"source":["!pip install madgrad"],"execution_count":22,"outputs":[{"output_type":"stream","text":["Collecting madgrad\n"," Downloading https://files.pythonhosted.org/packages/65/f0/4584f18202a2fb8903d456bf907b80e7cb54ad8fcba68604084ff41b7cf8/madgrad-1.1-py3-none-any.whl\n","Installing collected packages: madgrad\n","Successfully installed madgrad-1.1\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:19.698902Z","start_time":"2021-04-18T16:16:19.694902Z"},"id":"9Dly8KZj-NwQ","executionInfo":{"status":"ok","timestamp":1620066742463,"user_tz":-540,"elapsed":1109,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# from adamp import AdamP\n","from madgrad import MADGRAD\n","# Loss function 정의\n","# criterion = nn.CrossEntropyLoss()\n","criterion = FocalLoss()\n","\n","# Optimizer 정의\n","# optimizer = torch.optim.Adam(params = model.parameters(), lr = learning_rate, weight_decay=1e-6)\n","# optimizer = AdamP(params = model.parameters())\n","optimizer = MADGRAD(params = model.parameters(), lr = learning_rate, momentum = 0.9, weight_decay = 0, eps = 1e-06)\n","\n","lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=50, eta_min=0)\n","# lr_scheduler = CosineAnnealingWarmUpRestart(optimizer, T_0=150, T_mult=1, eta_max=0.1, T_up=10, gamma=0.5)"],"execution_count":23,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"start_time":"2021-04-18T16:16:20.331Z"},"colab":{"base_uri":"https://localhost:8080/"},"id":"cMSiFA-3-NwR","executionInfo":{"status":"ok","timestamp":1620072111788,"user_tz":-540,"elapsed":5368110,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"b1b0b3d7-d758-46d7-f943-7fd8364bf077"},"source":["train(num_epochs, model, train_loader, val_loader, criterion, optimizer, saved_dir, val_every, device, lr_scheduler)"],"execution_count":24,"outputs":[{"output_type":"stream","text":["Start training..\n","Epoch [1/20], Step [25/327], Loss: 0.9623\n","Epoch [1/20], Step [50/327], Loss: 0.5143\n","Epoch [1/20], Step [75/327], Loss: 0.7130\n","Epoch [1/20], Step [100/327], Loss: 0.7286\n","Epoch [1/20], Step [125/327], Loss: 0.2750\n","Epoch [1/20], Step [150/327], Loss: 0.3631\n","Epoch [1/20], Step [175/327], Loss: 0.4651\n","Epoch [1/20], Step [200/327], Loss: 0.5811\n","Epoch [1/20], Step [225/327], Loss: 0.3772\n","Epoch [1/20], Step [250/327], Loss: 0.3893\n","Epoch [1/20], Step [275/327], Loss: 0.3007\n","Epoch [1/20], Step [300/327], Loss: 0.4464\n","Epoch [1/20], Step [325/327], Loss: 1.1720\n","Start validation #1\n","Validation #1 Average Loss: 0.5023, mIoU: 0.2694\n","Best performance at epoch: 1\n","Save model in ./saved\n","Epoch [2/20], Step [25/327], Loss: 0.4969\n","Epoch [2/20], Step [50/327], Loss: 0.4349\n","Epoch [2/20], Step [75/327], Loss: 0.2547\n","Epoch [2/20], Step [100/327], Loss: 0.2909\n","Epoch [2/20], Step [125/327], Loss: 0.6112\n","Epoch [2/20], Step [150/327], Loss: 0.4782\n","Epoch [2/20], Step [175/327], Loss: 0.3167\n","Epoch [2/20], Step [200/327], Loss: 0.4659\n","Epoch [2/20], Step [225/327], Loss: 0.6684\n","Epoch [2/20], Step [250/327], Loss: 0.3314\n","Epoch [2/20], Step [275/327], Loss: 0.3629\n","Epoch [2/20], Step [300/327], Loss: 0.4402\n","Epoch [2/20], Step [325/327], Loss: 0.3928\n","Start validation #2\n","Validation #2 Average Loss: 0.4348, mIoU: 0.3181\n","Epoch [3/20], Step [25/327], Loss: 0.3373\n","Epoch [3/20], Step [50/327], Loss: 0.2956\n","Epoch [3/20], Step [75/327], Loss: 0.3766\n","Epoch [3/20], Step [100/327], Loss: 0.3809\n","Epoch [3/20], Step [125/327], Loss: 0.6440\n","Epoch [3/20], Step [150/327], Loss: 0.3523\n","Epoch [3/20], Step [175/327], Loss: 0.3511\n","Epoch [3/20], Step [200/327], Loss: 0.3298\n","Epoch [3/20], Step [225/327], Loss: 0.2849\n","Epoch [3/20], Step [250/327], Loss: 0.4799\n","Epoch [3/20], Step [275/327], Loss: 0.6277\n","Epoch [3/20], Step [300/327], Loss: 0.2830\n","Epoch [3/20], Step [325/327], Loss: 0.4216\n","Start validation #3\n","Validation #3 Average Loss: 0.4059, mIoU: 0.3153\n","Epoch [4/20], Step [25/327], Loss: 0.3842\n","Epoch [4/20], Step [50/327], Loss: 0.3560\n","Epoch [4/20], Step [75/327], Loss: 0.6631\n","Epoch [4/20], Step [100/327], Loss: 0.2534\n","Epoch [4/20], Step [125/327], Loss: 0.3754\n","Epoch [4/20], Step [150/327], Loss: 0.3035\n","Epoch [4/20], Step [175/327], Loss: 0.1988\n","Epoch [4/20], Step [200/327], Loss: 0.3844\n","Epoch [4/20], Step [225/327], Loss: 0.2627\n","Epoch [4/20], Step [250/327], Loss: 0.5386\n","Epoch [4/20], Step [275/327], Loss: 0.2673\n","Epoch [4/20], Step [300/327], Loss: 0.1567\n","Epoch [4/20], Step [325/327], Loss: 0.3518\n","Start validation #4\n","Validation #4 Average Loss: 0.4022, mIoU: 0.3222\n","Epoch [5/20], Step [25/327], Loss: 0.3666\n","Epoch [5/20], Step [50/327], Loss: 0.2354\n","Epoch [5/20], Step [75/327], Loss: 0.3153\n","Epoch [5/20], Step [100/327], Loss: 0.2324\n","Epoch [5/20], Step [125/327], Loss: 0.3203\n","Epoch [5/20], Step [150/327], Loss: 0.2743\n","Epoch [5/20], Step [175/327], Loss: 0.2605\n","Epoch [5/20], Step [200/327], Loss: 0.5635\n","Epoch [5/20], Step [225/327], Loss: 0.4279\n","Epoch [5/20], Step [250/327], Loss: 0.1938\n","Epoch [5/20], Step [275/327], Loss: 0.3542\n","Epoch [5/20], Step [300/327], Loss: 0.2842\n","Epoch [5/20], Step [325/327], Loss: 0.1980\n","Start validation #5\n","Validation #5 Average Loss: 0.4168, mIoU: 0.3077\n","Epoch [6/20], Step [25/327], Loss: 0.3980\n","Epoch [6/20], Step [50/327], Loss: 0.1847\n","Epoch [6/20], Step [75/327], Loss: 0.1798\n","Epoch [6/20], Step [100/327], Loss: 0.2030\n","Epoch [6/20], Step [125/327], Loss: 0.4667\n","Epoch [6/20], Step [150/327], Loss: 0.1439\n","Epoch [6/20], Step [175/327], Loss: 0.4660\n","Epoch [6/20], Step [200/327], Loss: 0.2983\n","Epoch [6/20], Step [225/327], Loss: 0.1219\n","Epoch [6/20], Step [250/327], Loss: 0.1970\n","Epoch [6/20], Step [275/327], Loss: 0.4741\n","Epoch [6/20], Step [300/327], Loss: 0.3100\n","Epoch [6/20], Step [325/327], Loss: 0.2145\n","Start validation #6\n","Validation #6 Average Loss: 0.3782, mIoU: 0.3321\n","Epoch [7/20], Step [25/327], Loss: 0.1745\n","Epoch [7/20], Step [50/327], Loss: 0.2179\n","Epoch [7/20], Step [75/327], Loss: 0.2765\n","Epoch [7/20], Step [100/327], Loss: 0.1822\n","Epoch [7/20], Step [125/327], Loss: 0.1175\n","Epoch [7/20], Step [150/327], Loss: 0.1874\n","Epoch [7/20], Step [175/327], Loss: 0.2049\n","Epoch [7/20], Step [200/327], Loss: 0.3162\n","Epoch [7/20], Step [225/327], Loss: 0.3260\n","Epoch [7/20], Step [250/327], Loss: 0.1766\n","Epoch [7/20], Step [275/327], Loss: 0.1786\n","Epoch [7/20], Step [300/327], Loss: 0.2053\n","Epoch [7/20], Step [325/327], Loss: 0.3195\n","Start validation #7\n","Validation #7 Average Loss: 0.3514, mIoU: 0.3687\n","Epoch [8/20], Step [25/327], Loss: 0.1679\n","Epoch [8/20], Step [50/327], Loss: 0.2464\n","Epoch [8/20], Step [75/327], Loss: 0.1845\n","Epoch [8/20], Step [100/327], Loss: 0.4022\n","Epoch [8/20], Step [125/327], Loss: 0.1208\n","Epoch [8/20], Step [150/327], Loss: 0.2306\n","Epoch [8/20], Step [175/327], Loss: 0.2100\n","Epoch [8/20], Step [200/327], Loss: 0.1611\n","Epoch [8/20], Step [225/327], Loss: 0.7312\n","Epoch [8/20], Step [250/327], Loss: 0.2571\n","Epoch [8/20], Step [275/327], Loss: 0.2399\n","Epoch [8/20], Step [300/327], Loss: 0.1240\n","Epoch [8/20], Step [325/327], Loss: 0.2595\n","Start validation #8\n","Validation #8 Average Loss: 0.5684, mIoU: 0.2903\n","Epoch [9/20], Step [25/327], Loss: 0.2238\n","Epoch [9/20], Step [50/327], Loss: 0.1330\n","Epoch [9/20], Step [75/327], Loss: 0.1033\n","Epoch [9/20], Step [100/327], Loss: 0.2729\n","Epoch [9/20], Step [125/327], Loss: 0.2149\n","Epoch [9/20], Step [150/327], Loss: 0.1813\n","Epoch [9/20], Step [175/327], Loss: 0.1796\n","Epoch [9/20], Step [200/327], Loss: 0.1798\n","Epoch [9/20], Step [225/327], Loss: 0.2347\n","Epoch [9/20], Step [250/327], Loss: 0.1908\n","Epoch [9/20], Step [275/327], Loss: 0.2476\n","Epoch [9/20], Step [300/327], Loss: 0.3058\n","Epoch [9/20], Step [325/327], Loss: 0.2451\n","Start validation #9\n","Validation #9 Average Loss: 0.4516, mIoU: 0.3175\n","Epoch [10/20], Step [25/327], Loss: 0.4823\n","Epoch [10/20], Step [50/327], Loss: 0.1581\n","Epoch [10/20], Step [75/327], Loss: 0.1699\n","Epoch [10/20], Step [100/327], Loss: 0.2576\n","Epoch [10/20], Step [125/327], Loss: 0.1237\n","Epoch [10/20], Step [150/327], Loss: 0.1755\n","Epoch [10/20], Step [175/327], Loss: 0.1402\n","Epoch [10/20], Step [200/327], Loss: 0.2139\n","Epoch [10/20], Step [225/327], Loss: 0.1173\n","Epoch [10/20], Step [250/327], Loss: 0.1397\n","Epoch [10/20], Step [275/327], Loss: 0.2896\n","Epoch [10/20], Step [300/327], Loss: 0.4029\n","Epoch [10/20], Step [325/327], Loss: 0.1393\n","Start validation #10\n","Validation #10 Average Loss: 0.3329, mIoU: 0.3694\n","Epoch [11/20], Step [25/327], Loss: 0.0978\n","Epoch [11/20], Step [50/327], Loss: 0.1802\n","Epoch [11/20], Step [75/327], Loss: 0.2391\n","Epoch [11/20], Step [100/327], Loss: 0.1021\n","Epoch [11/20], Step [125/327], Loss: 0.2880\n","Epoch [11/20], Step [150/327], Loss: 0.2701\n","Epoch [11/20], Step [175/327], Loss: 0.1648\n","Epoch [11/20], Step [200/327], Loss: 0.2342\n","Epoch [11/20], Step [225/327], Loss: 0.2491\n","Epoch [11/20], Step [250/327], Loss: 0.1578\n","Epoch [11/20], Step [275/327], Loss: 0.1773\n","Epoch [11/20], Step [300/327], Loss: 0.2645\n","Epoch [11/20], Step [325/327], Loss: 0.1680\n","Start validation #11\n","Validation #11 Average Loss: 0.3886, mIoU: 0.3476\n","Epoch [12/20], Step [25/327], Loss: 0.2265\n","Epoch [12/20], Step [50/327], Loss: 0.1236\n","Epoch [12/20], Step [75/327], Loss: 0.1812\n","Epoch [12/20], Step [100/327], Loss: 0.1094\n","Epoch [12/20], Step [125/327], Loss: 0.1250\n","Epoch [12/20], Step [150/327], Loss: 0.1532\n","Epoch [12/20], Step [175/327], Loss: 0.1509\n","Epoch [12/20], Step [200/327], Loss: 0.2974\n","Epoch [12/20], Step [225/327], Loss: 0.2270\n","Epoch [12/20], Step [250/327], Loss: 0.1764\n","Epoch [12/20], Step [275/327], Loss: 0.2871\n","Epoch [12/20], Step [300/327], Loss: 0.2725\n","Epoch [12/20], Step [325/327], Loss: 0.2215\n","Start validation #12\n","Validation #12 Average Loss: 0.7730, mIoU: 0.2859\n","Epoch [13/20], Step [25/327], Loss: 0.3772\n","Epoch [13/20], Step [50/327], Loss: 0.0712\n","Epoch [13/20], Step [75/327], Loss: 0.0689\n","Epoch [13/20], Step [100/327], Loss: 0.2143\n","Epoch [13/20], Step [125/327], Loss: 0.1715\n","Epoch [13/20], Step [150/327], Loss: 0.1793\n","Epoch [13/20], Step [175/327], Loss: 0.1739\n","Epoch [13/20], Step [200/327], Loss: 0.1002\n","Epoch [13/20], Step [225/327], Loss: 0.1520\n","Epoch [13/20], Step [250/327], Loss: 0.1436\n","Epoch [13/20], Step [275/327], Loss: 0.1349\n","Epoch [13/20], Step [300/327], Loss: 0.1594\n","Epoch [13/20], Step [325/327], Loss: 0.2101\n","Start validation #13\n","Validation #13 Average Loss: 0.3436, mIoU: 0.3824\n","Epoch [14/20], Step [25/327], Loss: 0.1885\n","Epoch [14/20], Step [50/327], Loss: 0.1123\n","Epoch [14/20], Step [75/327], Loss: 0.1479\n","Epoch [14/20], Step [100/327], Loss: 0.2643\n","Epoch [14/20], Step [125/327], Loss: 0.1202\n","Epoch [14/20], Step [150/327], Loss: 0.0878\n","Epoch [14/20], Step [175/327], Loss: 0.2210\n","Epoch [14/20], Step [200/327], Loss: 0.1776\n","Epoch [14/20], Step [225/327], Loss: 0.1816\n","Epoch [14/20], Step [250/327], Loss: 0.1150\n","Epoch [14/20], Step [275/327], Loss: 0.1129\n","Epoch [14/20], Step [300/327], Loss: 0.1366\n","Epoch [14/20], Step [325/327], Loss: 0.4301\n","Start validation #14\n","Validation #14 Average Loss: 0.3310, mIoU: 0.3977\n","Epoch [15/20], Step [25/327], Loss: 0.0872\n","Epoch [15/20], Step [50/327], Loss: 0.1926\n","Epoch [15/20], Step [75/327], Loss: 0.0744\n","Epoch [15/20], Step [100/327], Loss: 0.1261\n","Epoch [15/20], Step [125/327], Loss: 0.1134\n","Epoch [15/20], Step [150/327], Loss: 0.0687\n","Epoch [15/20], Step [175/327], Loss: 0.1037\n","Epoch [15/20], Step [200/327], Loss: 0.1243\n","Epoch [15/20], Step [225/327], Loss: 0.2390\n","Epoch [15/20], Step [250/327], Loss: 0.0811\n","Epoch [15/20], Step [275/327], Loss: 0.1444\n","Epoch [15/20], Step [300/327], Loss: 0.0661\n","Epoch [15/20], Step [325/327], Loss: 0.0596\n","Start validation #15\n","Validation #15 Average Loss: 0.3301, mIoU: 0.3932\n","Epoch [16/20], Step [25/327], Loss: 0.1149\n","Epoch [16/20], Step [50/327], Loss: 0.0677\n","Epoch [16/20], Step [75/327], Loss: 0.1011\n","Epoch [16/20], Step [100/327], Loss: 0.2086\n","Epoch [16/20], Step [125/327], Loss: 0.0954\n","Epoch [16/20], Step [150/327], Loss: 0.0527\n","Epoch [16/20], Step [175/327], Loss: 0.1279\n","Epoch [16/20], Step [200/327], Loss: 0.0960\n","Epoch [16/20], Step [225/327], Loss: 0.1206\n","Epoch [16/20], Step [250/327], Loss: 0.1090\n","Epoch [16/20], Step [275/327], Loss: 0.0643\n","Epoch [16/20], Step [300/327], Loss: 0.1592\n","Epoch [16/20], Step [325/327], Loss: 0.1321\n","Start validation #16\n","Validation #16 Average Loss: 0.3323, mIoU: 0.4104\n","Epoch [17/20], Step [25/327], Loss: 0.1132\n","Epoch [17/20], Step [50/327], Loss: 0.1362\n","Epoch [17/20], Step [75/327], Loss: 0.1209\n","Epoch [17/20], Step [100/327], Loss: 0.0747\n","Epoch [17/20], Step [125/327], Loss: 0.1462\n","Epoch [17/20], Step [150/327], Loss: 0.1099\n","Epoch [17/20], Step [175/327], Loss: 0.0514\n","Epoch [17/20], Step [200/327], Loss: 0.0785\n","Epoch [17/20], Step [225/327], Loss: 0.0908\n","Epoch [17/20], Step [250/327], Loss: 0.1056\n","Epoch [17/20], Step [275/327], Loss: 0.1550\n","Epoch [17/20], Step [300/327], Loss: 0.1131\n","Epoch [17/20], Step [325/327], Loss: 0.1710\n","Start validation #17\n","Validation #17 Average Loss: 0.3421, mIoU: 0.3815\n","Epoch [18/20], Step [25/327], Loss: 0.0711\n","Epoch [18/20], Step [50/327], Loss: 0.1017\n","Epoch [18/20], Step [75/327], Loss: 0.1408\n","Epoch [18/20], Step [100/327], Loss: 0.1087\n","Epoch [18/20], Step [125/327], Loss: 0.1334\n","Epoch [18/20], Step [150/327], Loss: 0.1401\n","Epoch [18/20], Step [175/327], Loss: 0.1058\n","Epoch [18/20], Step [200/327], Loss: 0.2272\n","Epoch [18/20], Step [225/327], Loss: 0.0921\n","Epoch [18/20], Step [250/327], Loss: 0.0899\n","Epoch [18/20], Step [275/327], Loss: 0.2389\n","Epoch [18/20], Step [300/327], Loss: 0.3536\n","Epoch [18/20], Step [325/327], Loss: 0.2342\n","Start validation #18\n","Validation #18 Average Loss: 0.3435, mIoU: 0.3760\n","Epoch [19/20], Step [25/327], Loss: 0.0676\n","Epoch [19/20], Step [50/327], Loss: 0.1228\n","Epoch [19/20], Step [75/327], Loss: 0.1798\n","Epoch [19/20], Step [100/327], Loss: 0.0840\n","Epoch [19/20], Step [125/327], Loss: 0.1456\n","Epoch [19/20], Step [150/327], Loss: 0.0903\n","Epoch [19/20], Step [175/327], Loss: 0.0694\n","Epoch [19/20], Step [200/327], Loss: 0.0440\n","Epoch [19/20], Step [225/327], Loss: 0.0570\n","Epoch [19/20], Step [250/327], Loss: 0.0663\n","Epoch [19/20], Step [275/327], Loss: 0.0671\n","Epoch [19/20], Step [300/327], Loss: 0.1227\n","Epoch [19/20], Step [325/327], Loss: 0.1582\n","Start validation #19\n","Validation #19 Average Loss: 0.3989, mIoU: 0.3485\n","Epoch [20/20], Step [25/327], Loss: 0.1065\n","Epoch [20/20], Step [50/327], Loss: 0.0795\n","Epoch [20/20], Step [75/327], Loss: 0.0895\n","Epoch [20/20], Step [100/327], Loss: 0.1536\n","Epoch [20/20], Step [125/327], Loss: 0.0906\n","Epoch [20/20], Step [150/327], Loss: 0.0966\n","Epoch [20/20], Step [175/327], Loss: 0.0486\n","Epoch [20/20], Step [200/327], Loss: 0.0606\n","Epoch [20/20], Step [225/327], Loss: 0.1030\n","Epoch [20/20], Step [250/327], Loss: 0.0573\n","Epoch [20/20], Step [275/327], Loss: 0.1071\n","Epoch [20/20], Step [300/327], Loss: 0.0650\n","Epoch [20/20], Step [325/327], Loss: 0.1520\n","Start validation #20\n","Validation #20 Average Loss: 0.4064, mIoU: 0.3812\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"C6ClcO0J-NwR"},"source":["## 저장된 model 불러오기 (학습된 이후) "]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:21.050200Z","start_time":"2021-04-16T19:44:20.802200Z"},"scrolled":true,"id":"KQPtUDzd-NwR","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620081781768,"user_tz":-540,"elapsed":2433,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"93ba44dc-21ad-4e0b-912a-66f8fceb7695"},"source":["# best model 저장된 경로\n","model_path = './saved/pan_resnet101_imagenet_focal_madgrad_cosLR.pt'\n","\n","# best model 불러오기\n","checkpoint = torch.load(model_path, map_location=device)\n","model.load_state_dict(checkpoint)\n","\n","# 추론을 실행하기 전에는 반드시 설정 (batch normalization, dropout 를 평가 모드로 설정)\n","# model.eval()"],"execution_count":15,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{"tags":[]},"execution_count":15}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:24.939227Z","start_time":"2021-04-16T19:44:24.518228Z"},"id":"0LQqrDAp-NwR","colab":{"base_uri":"https://localhost:8080/","height":502},"executionInfo":{"status":"ok","timestamp":1620081805472,"user_tz":-540,"elapsed":18652,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"88d12115-d279-4636-f833-c440dbc0acc5"},"source":["# 첫번째 batch의 추론 결과 확인\n","for imgs, image_infos in test_loader:\n"," image_infos = image_infos\n"," temp_images = imgs\n"," \n"," model.eval()\n"," # inference\n"," outs = model(torch.stack(temp_images).to(device))\n"," oms = torch.argmax(outs.squeeze(), dim=1).detach().cpu().numpy()\n"," \n"," break\n","\n","i = 3\n","fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(16, 16))\n","\n","print('Shape of Original Image :', list(temp_images[i].shape))\n","print('Shape of Predicted : ', list(oms[i].shape))\n","print('Unique values, category of transformed mask : \\n', [{int(i),category_names[int(i)]} for i in list(np.unique(oms[i]))])\n","\n","# Original image\n","ax1.imshow(temp_images[i].permute([1,2,0]))\n","ax1.grid(False)\n","ax1.set_title(\"Original image : {}\".format(image_infos[i]['file_name']), fontsize = 15)\n","\n","# Predicted\n","ax2.imshow(oms[i])\n","ax2.grid(False)\n","ax2.set_title(\"Predicted : {}\".format(image_infos[i]['file_name']), fontsize = 15)\n","\n","plt.show()"],"execution_count":16,"outputs":[{"output_type":"stream","text":["Shape of Original Image : [3, 512, 512]\n","Shape of Predicted : [512, 512]\n","Unique values, category of transformed mask : \n"," [{0, 'Backgroud'}, {9, 'Plastic bag'}]\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"evYjR2F3-NwS"},"source":["## submission을 위한 test 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:27.469285Z","start_time":"2021-04-16T19:44:27.456021Z"},"id":"nhMLnV5d-NwS","executionInfo":{"status":"ok","timestamp":1620081814006,"user_tz":-540,"elapsed":1334,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["def test(model, data_loader, device):\n"," size = 256\n"," transform = A.Compose([A.Resize(256, 256)])\n"," print('Start prediction.')\n"," model.eval()\n"," \n"," file_name_list = []\n"," preds_array = np.empty((0, size*size), dtype=np.long)\n"," \n"," with torch.no_grad():\n"," for step, (imgs, image_infos) in enumerate(test_loader):\n","\n"," # inference (512 x 512)\n"," outs = model(torch.stack(imgs).to(device))\n"," oms = torch.argmax(outs.squeeze(), dim=1).detach().cpu().numpy()\n"," \n"," # resize (256 x 256)\n"," temp_mask = []\n"," for img, mask in zip(np.stack(imgs), oms):\n"," transformed = transform(image=img, mask=mask)\n"," mask = transformed['mask']\n"," temp_mask.append(mask)\n","\n"," oms = np.array(temp_mask)\n"," \n"," oms = oms.reshape([oms.shape[0], size*size]).astype(int)\n"," preds_array = np.vstack((preds_array, oms))\n"," \n"," file_name_list.append([i['file_name'] for i in image_infos])\n"," print(\"End prediction.\")\n"," file_names = [y for x in file_name_list for y in x]\n"," \n"," return file_names, preds_array"],"execution_count":17,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"r1YKHBf4-NwT"},"source":["## submission.csv 생성"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:45:42.235310Z","start_time":"2021-04-16T19:44:30.499016Z"},"scrolled":true,"id":"Bz79_g8K-NwT","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620082134053,"user_tz":-540,"elapsed":309692,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"0028d061-3020-439f-de44-45f1cda77d88"},"source":["# sample_submisson.csv 열기\n","submission = pd.read_csv('./submission/sample_submission.csv', index_col=None)\n","\n","# test set에 대한 prediction\n","file_names, preds = test(model, test_loader, device)\n","\n","# PredictionString 대입\n","for file_name, string in zip(file_names, preds):\n"," submission = submission.append({\"image_id\" : file_name, \"PredictionString\" : ' '.join(str(e) for e in string.tolist())}, \n"," ignore_index=True)\n","\n","# submission.csv로 저장\n","submission.to_csv(\"./submission/pan_resnet101_imagenet_focal_madgrad_cosLR.csv\", index=False)"],"execution_count":18,"outputs":[{"output_type":"stream","text":["Start prediction.\n","End prediction.\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"Zgl7Ye7F-NwT"},"source":["## 제출까지\n","\n"]},{"cell_type":"code","metadata":{"id":"022E8AC7-NwT","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620082278553,"user_tz":-540,"elapsed":13661,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"9fc6900a-94fe-4a7b-99b8-03b9b0d59d2c"},"source":["import json\n","import requests\n","import os\n","from urllib.parse import urlparse, parse_qsl, urlencode, urlunparse\n","\n","def submit(user_key='', file_path = '', desc=\"\"):\n"," if not user_key:\n"," raise Exception(\"No UserKey\" )\n"," url = urlparse('http://ec2-13-124-161-225.ap-northeast-2.compute.amazonaws.com:8000/api/v1/competition/28/presigned_url/?description=&hyperparameters={%22training%22:{},%22inference%22:{}}')\n"," qs = dict(parse_qsl(url.query))\n"," qs['description'] = desc\n"," parts = url._replace(query=urlencode(qs))\n"," url = urlunparse(parts)\n","\n"," print(url)\n"," headers = {\n"," 'Authorization': user_key\n"," }\n"," res = requests.get(url, headers=headers)\n"," print(res.text)\n"," data = json.loads(res.text)\n"," \n"," submit_url = data['url']\n"," body = {\n"," 'key':'app/Competitions/000028/Users/{}/Submissions/{}/output.csv'.format(str(data['submission']['user']).zfill(8),str(data['submission']['local_id']).zfill(4)),\n"," 'x-amz-algorithm':data['fields']['x-amz-algorithm'],\n"," 'x-amz-credential':data['fields']['x-amz-credential'],\n"," 'x-amz-date':data['fields']['x-amz-date'],\n"," 'policy':data['fields']['policy'],\n"," 'x-amz-signature':data['fields']['x-amz-signature']\n"," }\n"," requests.post(url=submit_url, data=body, files={'file': open(file_path, 'rb')})\n","\n","\n","####################################################################################\n","test_dir = \"/content/drive/MyDrive/Trash/code/submission\" # 수정 필요 : output 파일 폴더 \n","desc = 'pan_resnet101_imagenet_focal_madgrad_cosLR' # 수정 필요 : 파일에 대한 설명\n","output_file = \"pan_resnet101_imagenet_focal_madgrad_cosLR.csv\" #수정 필요 : output 파일 \n","user_key = \"Bearer 7bb5f96452751a238ffaf91a93c4242bf9b72abe\" # 수정 필요 : Authorization \n","\n","\n","submit(user_key, os.path.join(test_dir, output_file),desc)"],"execution_count":20,"outputs":[{"output_type":"stream","text":["http://ec2-13-124-161-225.ap-northeast-2.compute.amazonaws.com:8000/api/v1/competition/28/presigned_url/?hyperparameters=%7B%22training%22%3A%7B%7D%2C%22inference%22%3A%7B%7D%7D&description=pan_resnet101_imagenet_focal_madgrad_cosLR\n","{\"url\":\"https://prod-aistages-private.s3.amazonaws.com/\",\"fields\":{\"key\":\"app/Competitions/000028/Users/00000099/Submissions/0020/output.csv\",\"x-amz-algorithm\":\"AWS4-HMAC-SHA256\",\"x-amz-credential\":\"AKIA45LU4MHUJ7WLDQVO/20210503/ap-northeast-2/s3/aws4_request\",\"x-amz-date\":\"20210503T225107Z\",\"policy\":\"eyJleHBpcmF0aW9uIjogIjIwMjEtMDUtMDNUMjM6NTE6MDdaIiwgImNvbmRpdGlvbnMiOiBbeyJidWNrZXQiOiAicHJvZC1haXN0YWdlcy1wcml2YXRlIn0sIHsia2V5IjogImFwcC9Db21wZXRpdGlvbnMvMDAwMDI4L1VzZXJzLzAwMDAwMDk5L1N1Ym1pc3Npb25zLzAwMjAvb3V0cHV0LmNzdiJ9LCB7IngtYW16LWFsZ29yaXRobSI6ICJBV1M0LUhNQUMtU0hBMjU2In0sIHsieC1hbXotY3JlZGVudGlhbCI6ICJBS0lBNDVMVTRNSFVKN1dMRFFWTy8yMDIxMDUwMy9hcC1ub3J0aGVhc3QtMi9zMy9hd3M0X3JlcXVlc3QifSwgeyJ4LWFtei1kYXRlIjogIjIwMjEwNTAzVDIyNTEwN1oifV19\",\"x-amz-signature\":\"c90737408d3d60be79d7dacf16884719a977f17c84c6dde5c547ece96ab7e5a9\"},\"submission\":{\"id\":14744,\"phase\":\"Created\",\"type\":\"File\",\"local_id\":20,\"hyperparameters\":\"{\\\"training\\\": {}, \\\"inference\\\": {}}\",\"description\":\"pan_resnet101_imagenet_focal_madgrad_cosLR\",\"final\":false,\"created_at\":\"2021-05-04T07:51:07.654308+09:00\",\"updated_at\":\"2021-05-04T07:51:07.654341+09:00\",\"user\":99,\"competition\":28,\"image\":null}}\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"-uEns8CRZzxO"},"source":[""],"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/chanyub_seg/code/pan_resnet101_imagenet_focal_madgrad_coswarmLR.ipynb b/chanyub_seg/code/pan_resnet101_imagenet_focal_madgrad_coswarmLR.ipynb deleted file mode 100644 index 8ca0d56..0000000 --- a/chanyub_seg/code/pan_resnet101_imagenet_focal_madgrad_coswarmLR.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"nbformat":4,"nbformat_minor":0,"metadata":{"hide_input":false,"kernelspec":{"display_name":"Python 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google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Mounted at /content/drive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xDwpp4Lk-gSH","executionInfo":{"status":"ok","timestamp":1620066619066,"user_tz":-540,"elapsed":1451,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"aa4f12c3-a02d-4a75-ca0e-f00ca038bf30"},"source":["ls"],"execution_count":null,"outputs":[{"output_type":"stream","text":["\u001b[0m\u001b[01;34mdrive\u001b[0m/ \u001b[01;34msample_data\u001b[0m/\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sItrVDeh-iYC","executionInfo":{"status":"ok","timestamp":1620081375250,"user_tz":-540,"elapsed":1147,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"ab992b78-fbba-403a-82a9-fd0e69fc2222"},"source":["cd drive/MyDrive/Trash/code"],"execution_count":2,"outputs":[{"output_type":"stream","text":["/content/drive/MyDrive/Trash/code\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"a54v4-kN_LVO","executionInfo":{"status":"ok","timestamp":1620081384358,"user_tz":-540,"elapsed":9988,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"32fdfb3d-7b4c-4d0f-dabe-9ff0ecefda18"},"source":["!pip install albumentations==0.5.2"],"execution_count":3,"outputs":[{"output_type":"stream","text":["Collecting albumentations==0.5.2\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/03/58/63fb1d742dc42d9ba2800ea741de1f2bc6bb05548d8724aa84794042eaf2/albumentations-0.5.2-py3-none-any.whl (72kB)\n","\u001b[K |████████████████████████████████| 81kB 3.9MB/s \n","\u001b[?25hCollecting opencv-python-headless>=4.1.1\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/6d/6d/92f377bece9b0ec9c893081dbe073a65b38d7ac12ef572b8f70554d08760/opencv_python_headless-4.5.1.48-cp37-cp37m-manylinux2014_x86_64.whl (37.6MB)\n","\u001b[K |████████████████████████████████| 37.6MB 1.3MB/s \n","\u001b[?25hCollecting imgaug>=0.4.0\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/66/b1/af3142c4a85cba6da9f4ebb5ff4e21e2616309552caca5e8acefe9840622/imgaug-0.4.0-py2.py3-none-any.whl (948kB)\n","\u001b[K |████████████████████████████████| 952kB 46.0MB/s \n","\u001b[?25hRequirement already satisfied: PyYAML in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (3.13)\n","Requirement already satisfied: scikit-image>=0.16.1 in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (0.16.2)\n","Requirement already satisfied: numpy>=1.11.1 in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (1.19.5)\n","Requirement 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/usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations==0.5.2) (2.5.1)\n","Requirement already satisfied: PyWavelets>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations==0.5.2) (1.1.1)\n","Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug>=0.4.0->albumentations==0.5.2) (2.8.1)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug>=0.4.0->albumentations==0.5.2) (0.10.0)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug>=0.4.0->albumentations==0.5.2) (1.3.1)\n","Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug>=0.4.0->albumentations==0.5.2) (2.4.7)\n","Requirement already satisfied: decorator<5,>=4.3 in /usr/local/lib/python3.7/dist-packages (from networkx>=2.0->scikit-image>=0.16.1->albumentations==0.5.2) (4.4.2)\n","Installing collected packages: opencv-python-headless, imgaug, albumentations\n"," Found existing installation: imgaug 0.2.9\n"," Uninstalling imgaug-0.2.9:\n"," Successfully uninstalled imgaug-0.2.9\n"," Found existing installation: albumentations 0.1.12\n"," Uninstalling albumentations-0.1.12:\n"," Successfully uninstalled albumentations-0.1.12\n","Successfully installed albumentations-0.5.2 imgaug-0.4.0 opencv-python-headless-4.5.1.48\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.826930Z","start_time":"2021-04-18T10:34:45.406686Z"},"colab":{"base_uri":"https://localhost:8080/"},"id":"l_LPA4XD-NwC","executionInfo":{"status":"ok","timestamp":1620081392190,"user_tz":-540,"elapsed":4968,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"2fac63d2-0b57-4233-be45-97e930ff2012"},"source":["import os\n","import random\n","import time\n","import json\n","import warnings \n","warnings.filterwarnings('ignore')\n","\n","import torch\n","import torch.nn as nn\n","from torch.utils.data import Dataset, DataLoader\n","from utils import label_accuracy_score\n","import cv2\n","\n","import numpy as np\n","import pandas as pd\n","\n","# 전처리를 위한 라이브러리\n","from pycocotools.coco import COCO\n","import torchvision\n","import torchvision.transforms as transforms\n","\n","import albumentations as A\n","from albumentations.pytorch import ToTensorV2\n","\n","# 시각화를 위한 라이브러리\n","import matplotlib.pyplot as plt\n","import seaborn as sns; sns.set()\n","\n","plt.rcParams['axes.grid'] = False\n","\n","print('pytorch version: {}'.format(torch.__version__))\n","print('GPU 사용 가능 여부: {}'.format(torch.cuda.is_available()))\n","\n","print(torch.cuda.get_device_name(0))\n","print(torch.cuda.device_count())\n","\n","device = \"cuda\" if torch.cuda.is_available() else \"cpu\" # GPU 사용 가능 여부에 따라 device 정보 저장"],"execution_count":4,"outputs":[{"output_type":"stream","text":["pytorch version: 1.8.1+cu101\n","GPU 사용 가능 여부: True\n","Tesla V100-SXM2-16GB\n","1\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"HxgRSL_M-NwF"},"source":["## 하이퍼파라미터 세팅 및 seed 고정"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.841930Z","start_time":"2021-04-18T10:34:47.827931Z"},"id":"rV3JmGP5-NwF","executionInfo":{"status":"ok","timestamp":1620081393938,"user_tz":-540,"elapsed":1743,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["batch_size = 8 # Mini-batch size\n","num_epochs = 20\n","learning_rate = 0.0001"],"execution_count":5,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.856930Z","start_time":"2021-04-18T10:34:47.842931Z"},"id":"Z6LOuJXQ-NwG","executionInfo":{"status":"ok","timestamp":1620081393939,"user_tz":-540,"elapsed":1723,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# seed 고정\n","random_seed = 42\n","torch.manual_seed(random_seed)\n","torch.cuda.manual_seed(random_seed)\n","# torch.cuda.manual_seed_all(random_seed) # if use multi-GPU\n","torch.backends.cudnn.deterministic = True\n","torch.backends.cudnn.benchmark = False\n","np.random.seed(random_seed)\n","random.seed(random_seed)"],"execution_count":6,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"iWF_EJDu-NwG"},"source":["## 학습 데이터 EDA"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.381961Z","start_time":"2021-04-18T10:34:47.857930Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"lg0x0D0a-NwG","executionInfo":{"status":"ok","timestamp":1620081409963,"user_tz":-540,"elapsed":10477,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"a91ef992-d81e-46a0-f370-61ba471127c8"},"source":["%matplotlib inline\n","\n","dataset_path = '../input/data'\n","anns_file_path = dataset_path + '/' + 'train.json'\n","\n","# Read annotations\n","with open(anns_file_path, 'r') as f:\n"," dataset = json.loads(f.read())\n","\n","categories = dataset['categories']\n","anns = dataset['annotations']\n","imgs = dataset['images']\n","nr_cats = len(categories)\n","nr_annotations = len(anns)\n","nr_images = len(imgs)\n","\n","# Load categories and super categories\n","cat_names = []\n","super_cat_names = []\n","super_cat_ids = {}\n","super_cat_last_name = ''\n","nr_super_cats = 0\n","for cat_it in categories:\n"," cat_names.append(cat_it['name'])\n"," super_cat_name = cat_it['supercategory']\n"," # Adding new supercat\n"," if super_cat_name != super_cat_last_name:\n"," super_cat_names.append(super_cat_name)\n"," super_cat_ids[super_cat_name] = nr_super_cats\n"," super_cat_last_name = super_cat_name\n"," nr_super_cats += 1\n","\n","print('Number of super categories:', nr_super_cats)\n","print('Number of categories:', nr_cats)\n","print('Number of annotations:', nr_annotations)\n","print('Number of images:', nr_images)"],"execution_count":7,"outputs":[{"output_type":"stream","text":["Number of super categories: 11\n","Number of categories: 11\n","Number of annotations: 21116\n","Number of images: 2617\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.546964Z","start_time":"2021-04-18T10:34:51.382969Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/","height":355},"id":"PjLyVHVY-NwH","executionInfo":{"status":"ok","timestamp":1620081411809,"user_tz":-540,"elapsed":1813,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"e665244d-22d3-4c8b-f7bd-5d34c71a673f"},"source":["# Count annotations\n","cat_histogram = np.zeros(nr_cats,dtype=int)\n","for ann in anns:\n"," cat_histogram[ann['category_id']] += 1\n","\n","# Initialize the matplotlib figure\n","f, ax = plt.subplots(figsize=(5,5))\n","\n","# Convert to DataFrame\n","df = pd.DataFrame({'Categories': cat_names, 'Number of annotations': cat_histogram})\n","df = df.sort_values('Number of annotations', 0, False)\n","\n","# Plot the histogram\n","plt.title(\"category distribution of train set \")\n","plot_1 = sns.barplot(x=\"Number of annotations\", y=\"Categories\", data=df, label=\"Total\", color=\"b\")"],"execution_count":8,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYsAAAFSCAYAAAAD0fNsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3deZwdRb3+8U8SdgIE4oKsAYRH9rAjgoAXBZRNxY2AICIq+gNBQEU2kU1EkahcLoqshlXlImEXwiKgIJu4PERNFAW9ISxJgARI8vujashhmJkzk8ye5/16zSvndHVXV/c56e+pqu6qIXPnziUiIqIjQ/u6ABER0f8lWERERFMJFhER0VSCRURENJVgERERTSVYREREUwkWEd1E0oWSTq6vt5Pkbsz7Bkn719cHSLq7G/MeI+nm7sqvC/t9l6SJkmZI2quH9nGupON6Iu+FzSJ9XYCIRpImAwfZvrWPi7JAbN8FqNl6kk4E3m573yb57dod5ZI0CpgELGr71Zr3T4Gfdkf+XXQS8APbZ7eV2B3fBdufm99t55ekucDatv/S2/vuSalZRACS+uUPJ0lDJA3W/6erA3+Y343762c2WA3JE9zREyStCpwNbEf5UXKZ7S9KWgv4EbAxMBe4CfiC7eckXQKMAWYBs4GTbJ8haWvgu8B6wN+Bw2xPqPtZA7gI2AT4DWBguZZf6pL2AE4DVgYeBj5v+081bTLw33WfAo4Ftrb94YbjGAvMtX1YG8e4CXA+sDZwfT2ev9g+VtIOwKW2V6nrfgU4FFgWeBI4BFgUuBYYUo/5r7Y3ljQB+DWwA7ApsCHw45rfjyUdAHwGeAjYD3iqnsNfNRzXa7/IG2svkv4BrAq8UA/jvfXYD7K9bV1/m/rZrQM8Xs/3PTVtAnAX8B5gI+BeYB/bT7c+P3X9zwBfAVYA7gY+Z/tJSX8F1mDeZz3S9qyG7d7wXQCupNSKDgJOACbbfrekqyjfsyWBRyif8R9qPhcC/2z8TICzaplmA8fYvqCdsh8AHA+8GXgaOLbWwpB0IHAUsCLwW+Bg23+XdGcty4uU78OnbV/RVv4DzWD9xRJ9SNIw4DrKhX0U5UJ9eU0eQrl4rwSsS7lwnQhgez/gH8DutofXQLEyMB44mXLBORL4maQ31/zGUf6zjqz57NdQjnWAy4AvUf7DXw/8UtJiDcX9BPABYATlQrKLpBF1+0WAjwMXt3GMiwHXAJfUcl0FfLj1enVdAV8EtrC9DLAz5UJ3I3AqcEU93o0bNtsPOBhYpp7H1rYC/gq8iXLh/LmkFdrafyvvrv+OqPu8t1VZV6Cc77GUc/pdYLykkQ2r7QN8CngLsBjlM2nruN9D+aw/CrytHsflALbX4vWf9azGbdv6LjQkb0/57uxc399ACdhvAR6k4ya1FYHlKN/JTwM/lLR8G2Vfup6DXetntg3lxwaS9gSOAT5E+V7dRfmeYbvl/G5cyz0oAgWkzyJ6xpaUYHBUS7s45VcltR23pS13iqTvUi527dkXuN729fX9LZIeAN4v6XZgC+C/bL8M3C3p2oZtPwaMt30LgKQzgcMo//En1HXG2n6ivn6p/jL8CKX2swvwtO3ftVGurSk1g+/ZngtcLemIdo5hNrA4sJ6kKbYnd3C8LS5s+XVcy946/f8a9n2FpC9Tgt4lnci7Ix8AJtpuyecySYcCuwMX1mUX2H68lutKYI928hoD/MT2g3XdrwHPShrVyXPQnhNtt9SMsP2Tlte1FvWspOVsP9/Gtq9QaqyvAtdLmkGpWd3XxrpzgA0k/cP2U5QaHMDngNMaaqinAsdIWt12W4F9UEjNInrCqsDfGwLFayS9VdLlkv4laRrl1/ybOshrdeAjkp5r+QO2pfxSXQl4xvaLDes/0fB6JRp+ldueU9NXbmd9KE1aLZ3N+9L+xXcl4F/1Yt2izQtFDZBfotR8/q8e/0rt5NteuVpra9/N8uyM152zhrwbz9m/G16/CAzvTF62ZwBTW+U1P147N5KGSTpd0l/r92lyTWrvOzW11feyzfLXYPQxSmB4StJ4Se+oyasDZzd8H5+h1JgX9Lj6tQSL6AlPAKu10wF5KqUtd0Pby1IuyEMa0lt3oj0BXGJ7RMPf0rZPp/zSW0HSUg3rr9rw+knKf2ygdBbX9H91sL9rgI0kbQDsRvtNGk8BK9c8W6zWzrrYHlf7BFav+/xWO/tvr1yttbXvJ+vrF4DGc7JiF/J93TlryPtfbazbTOvzvzSlaauzeXXm3OwD7AnsRGleGlWXD2EB2b7J9nspP0z+TKltQvlOfrbVd3LJln6dwSrNUNETfku5mJ4u6QRKM8xmtn9NaYN/Hni+9kcc1Wrb/wBrNry/FLhf0s7ArZSmn60pHcl/r01SJ0o6FtiM0lzyy7rtlcBXJf0XcCelCWoW0O5/atszJV1N7Qux/Y92Vr0XeBU4VNI5db9bAre3XrH2WaxM6bSeCbwEDGs43vdKGlprPp31loZ970Vpw29pqnsY+LikGyg3EuwN3FjTplCaV9akdF63dj3wfUn7UM7fhyk3FlzXhbK1uIzSjDUO+BPlh8JvutAE1fq70JZlKJ/pVEqAPHU+yvkGkt5K+Z7dSvm8ZlDOG8C5wDclPWz7D5KWA95n+6pW5c6tsxEdsT2bcvF8O6WT8p+UKj3ANyh3+DxP6Uj9eavNTwOOrVX8I2t/QkuH4hTKr7qjmPfdHQO8k3KxOBm4gnLxwLYpNZfvU+5m2Z3SYfpyk0O4iHIHUrvt/zWPDwEHUJohPtbGsbRYHDi9luHflAv912paywVmqqQHm5Sr0W8onbpPA6cAe9ueWtOOA9YCnqWc73EN5X6xrv/reo63bnVcUyk1qi9TzunRwG7t3e3UkXo31nHAzyg/Htai3DDQWa/7LrSzzsWUpq5/AX+k7b6H+TEUOIJSO3qG0qn+eQDbv6DUDC+vTV+PAY3PwZwIXFTL/dFuKk+fy62zMahIugL4s+2OOs2b5bEapdlhRdvTuq1wEQNYmqFiQJO0BeWX3yTgfZRayOkLkF/LL8rLEygi5kmwiIFuRUrzz0hKc9fnbT80PxnVDtj/UJo1dum2EkYMAmmGioiIptLBPfAsQrk9MLXCiOhOHV5bcsEZeFan3JK3HaXZJSKiO6xCGbrk7ZShZF4nwWLgeVv9964+LUVEDFZvI8FiUHgK4NlnX2DOnPQ3RUT3GDp0CMsvvzTMGwPrdRIsBp7ZQMuHGhHRppmzXmH6tJnzs+nsthYmWAxQh552DU8/+0LzFSNioTTujDFMZ76CRZtyN1RERDSVYBEREU0lWERERFMJFhER0VSCRURENJW7odohaTJloppZlIlqTrZ9eV+WKSKir6Rm0bG9bW8M7AdcIKmjuaIXmKRhzdeKiOh9qVl0gu2HJE0HrpC0LLAYZYayA+vUnqOABygzrL2XMv/vIbbvApD0fuDrwBLAy8Dhtu+TtAMwFvgdsAlwLPM3fWVERI9KsOgESTtSLvQfa5leUtJBlKkVW6aJHAk8YvvLNQhcJmktyuBcxwE7254maX3gBmC1ut36lMnf7+21A4qI6KIEi45dLWkmMI0ycf2ukr4ADOeN5+5l4FIA2xMkvQQI2JYy9/CdklrWXaROCA8wMYEiIvq7BIuO7W37MQBJqwOXAVvYniRpG2BcJ/IYAtxo+5OtEyStC8zozgJHRPSEdHB33rKU2sO/6zzNn2uVvhiwD4Ck7YAlgT8DNwO71OYnavoWvVLiiIhukppFJ9n+vaSrgD9SOrevB97dsMpUYLSkoym1iU/YfhmYKGlf4HxJS1KCyq+B+3v1ACIiFkDm4O4GLXdD2e7RW2urUcCkjDobER0Zd8YYpkyZ3un1hw4dwsiRwwHWACa/Ib3bShYREYNWmqG6ge3JQG/UKiIi+kRqFhER0VSCRURENJUO7oFnFDCprwsREf1bV+fgbtbBnT6LAWrq1BnMmZNAHxG9I81QERHRVIJFREQ0lWARERFNpc9igKodURHRg7raSTyYJVgMUBnuI6LnjTtjDNNJsIA0Q0VERCckWERERFMJFhER0VSCRURENDUgOrglTQZmArOAYcDJti+XdACwm+295zPfA4B7bD9e3+8BbGf7qC7kcSFlLosfzE8ZIiIGggERLKq9bT8maRPgHkm3dkOeB1BmvXscwPa1wLXdkG9ExKAykIIFALYfkjSdMtjVayStCFxGmSt7CWC87aNr2p7AycBsyjF/sW6/OTBW0snAkcAqNNRUJB0IHFZ38XJN+08bxdpY0j2UOS3uAL5g+2VJ+9TtF6vrHWn7VzXv7YBzgLnA7cBewAdsP7Yg5ycioicMuD4LSTtSgsHEVknPAbvb3gwYDWwuaZeadhJwsO3RwMbAg7YvAB4ADrU92vbraiqSdgCOAXa2vTGwI/B8O8XaCngfsB6wOnBwXX4TsLXtTYCPAxfVvBenBLZDbG8ETABW6+KpiIjoNQMpWFwt6WHgG8CHbT/XKn0Y8G1JjwC/AzagBA2A24CzJB0FrGt7Wif29wHgYtv/BrA9w3Z7T+dcUdNfpQSE99TlawE3SfoDcAWwYq0BCXjJ9l01719Qgl1ERL80kILF3rUG8G7bt7SRfgSwPLBV/bV+DaUGgu3Dgc9QmpKukvSZXirzZcA5ttcHNgVebSlTRMRAMpCCRTMjgKdsz5S0MrBnS4Ik2f697bOBS4EtatI0YLl28hsPfFLSW2sewyW1d6H/iKSlJS0C7EepybSUqWWiogOBxetrA0tJelfNe8+6bkREvzTgOrg7MJZSa3gM+Cfwq4a00yWtTfll/xzw6br8POA7tXnqyMbMbE+QdBpwq6Q5lNt2d4c2B4q5H7gZeAul/+G8uvxLwDWSngVuBKbWvGfVzu9zJc2ldIr/H+33iURE9KlMq9pHJC1je3p9vSNwIbCG7TlNNh0FTMpAghE9b9wZY5gyZXpfF6NXZFrV/uvDkg6nNAXOBPbpRKCIiOgTCRZ9xPaFlNpERES/N5g6uCMioockWERERFPp4B54RjHvdtyI6EEL07Sq6eAepKZOncGcOQn0EdE70gwVERFNJVhERERTCRYREdFU+iwGqNoRFT1kYerYjOiMBIsBKsN99KxxZ4xhepvDgEUsnNIMFRERTSVYREREUwkWERHRVIJFREQ0lWARERFN5W4oQNJkypwSs4BhwMmUubJ3s733fOZ5AHCP7cfr+z2A7Wwf1Q1FjojoValZzLO37Y0pc2hfALxpAfM7AFin5Y3taxMoImKgSs2iFdsPSZoODGlZJmlF4DJgWUqNY7zto2vanpSayGzK+fwiZdTGzYGxkk6mzO+9Cg01FUkHAofVXbxc0/7T80cYEdF1qVm0UufDXgJ4pWHxc8DutjcDRgObS9qlpp0EHGx7NLAx8KDtC4AHgENtj7Z9a6t97AAcA+xcazM7As/34GFFRCyQ1CzmuVrSTGAa8GFg5Ya0YcC3JW1DqXGsSAkaNwK3AWdJ+hlwg+3HOrGvDwAX2/43gO0Z3XcYERHdLzWLefautYB3276lVdoRwPLAVrY3Aq6h1D6wfTjwGUpT0lWSPtObhY6I6A0JFp0zAnjK9kxJKwN7tiRIku3f2z4buBTYoiZNA5ZrJ7/xwCclvbXmMVzSEj1X/IiIBZNmqM4ZS6k1PAb8E/hVQ9rpktYGXqX0bXy6Lj8P+I6koygd3K+xPUHSacCtkuZQbtndHTJyXUT0T5mDe+AZBUzKqLM9a9wZY5gyZXpfFyOi1zSbgzvNUBER0VSCRURENJVgERERTSVYREREU+ngHnhGAZP6uhCDXebgjoVNsw7u3Do7QE2dOoM5cxLoI6J3pBkqIiKaSrCIiIimEiwiIqKp9FkMULUjKuZDOq8jui7BYoDKcB/zb9wZY5ieYbgiuiTNUBER0VSCRURENJVgERERTSVYREREUwkWERHRVK/cDSVpUeDrwCcoM8q9CkwEjrf9x94oQ0ckHQDsZnvvdtLusf14N+5vB+BM25t3V54RET2pt2oWFwAbAVvZXh8YXZepN3YuaUGC4gHAOh3kPWwB8o6IGBB6vGZR56f+ILCK7ecAbM8FxjessxhwCrA9sDjwKPB52zMkXUiZm3odYFXgXmB/23MlLQt8lxKIlgBuB46wPVvSBOBhYGvgGUl71H2OBJYEfgt81vbLHZT9U8DmwFhJJ1Pm0l4F2BeYDqwN7Cvpv4CPU87nzFr2hyUtBVwErA+8Ug7dH63ZLyLpf4B3AnOBj9v+U1fPb0REb+iNmsUmwETbz3awztHA87a3tL0x8CTwtYb0DYD3Uy66mwE71eXfBe6wvSWltvIW4MCG7dYEtrX9fmA2sE9t+tkAGNZq3TewfQHwAHCo7dG2b61JWwNH2t7A9sPAxba3sL0JcBxwbl1vZ2BZ2+vV4/psQ/brA+fa3gi4Eji2o7JERPSlXn+CW9J6wDhgKeAG24cBewDLSmrpM1gceKRhs2tsz6zbPwisBdxSt9tS0pfreksB/2zYbpztV+vrocCRknalBIrlgRfn8zDutv3XhvebSToGWAGYw7xmq0eAdSX9EJhAQ22KUst4qL6+D9h9PssSEdHjeiNYPASsLWmE7edqh/ZoSV+kNPEADAEOsX1bO3k0js0wm3nlHgLsZftv7Ww3o+H1PsC2wHa2p9eLe7t9EU28lm9tQrsaeLftByWtBPwLwPbfJK0P/BewK3CqpA2bHFNERL/T481QticC/wv8SNJyDUlLN7y+FjhC0pIAkpaRtG4nsr8W+GpLJ7OkN0lao511RwBP10CxHCV4dMY0YLkO0pegXOifqO8PaUmQtAow2/Y1wOHAmym1j4iIAaW37oY6APgzcL+kP0i6m9L3MLamn05psrlf0qPA3UBngsWXKL/KH5H0e+BGYOV21r0YWEbSn4FfAnd1suznAcdLeljSTq0TbU8Djq9l/x3QOLrfhsC9kh6hdKifZvvJTu43IqLfyBzcA88oYFJGnZ1/484Yw5Qp0/u6GBH9SrM5uPMEd0RENJVgERERTSVYREREUwkWERHRVDq4B55RwKS+LsRAljm4I96oWQd3HgQboKZOncGcOQn0EdE70gwVERFNJVhERERTCRYREdFU+iwGqNoR1e+k8zhicEqwGKD663Af484Yw3QSLCIGmzRDRUREUwkWERHRVIJFREQ0Nd/BQtKOkrbvzsJERET/1OkObkl3AMfY/rWkrwBHAK9K+qHtU3ushG8sx0eAYyhTqi4BPGh7H0knAqfafrmb97cXcBplGtSP23Z35h8RMRB0pWaxAXBfff0ZYEdga+Bz3V2o9kh6G3AOsIft0ZTZ9L5dk08AFpuPPJsFzM8Cx9veJIEiIhZWXbl1digwV9JawBDbfwSQtHyPlKxtKwKvAFMBbM8FHpL0w5p+j6Q5wPuB3wFr2J5Zy3ktcDlwD/AAcCHwHuA8SbcC/0OZI/tVSg3qRklnAduVzXWI7R0l7UKpaQwDpgCftf0XSSsClwHLUmo8420fXfd9IvCOmrZOLdvpwHeA1YGf2z6qB85XRES36ErN4m7gB8CZwC8AauB4ugfK1Z6Wuaz/IelqSV+SNNL2F2r6NrZH13mu7wA+Vss5CtgcuLquNxK43/amts8FfgqMs70RsC9wqaQ32z6cElgOrYHiLcAlwJi67ri6LcBzwO62NwNGA5vXwNJiM+ATgCiB43RgV2AjYH9Ja3fniYqI6E5dCRYHUC6IjwIn1mXvAM7u3iK1z/Yc23sBOwC3Ax8AHpW0QhurjwUOqa8/B/ykoT9jJnAlgKRlKBf3C+o+/gg8TGlia20r4JGWWlXdZnTNYxjwbUmPUGoOG9R8W9xk+3nbsynn8Bbbs2y/ABhYq0snIyKiF3W6Gcr2VErHcuOy8d1eos6V5THgMeCHkv5ICR6t17lH0jBJ76IEui0akl+oTVjd6QhgeWAr2zMlnUdpjmrR+Fjz7Dbe52n6iOi3Ol2zkLS4pFMk/U3S83XZ+yR9seeK94YyrCzpnQ3vV6H0M0wCpgPLtdrk+9R+CttPtJWn7emUmsT+Nc91gY2Z15nf6D5gY0nvqO/3Bx6qeYwAnqqBYmVgz/k7yoiI/qcrzVBnUZpWxgAtv8r/AHy+uwvVgUWAb0iypIeB64FjbT9E6Sy+TdLDkkbU9S+n/No/p0m+Y4B9JT1K6YPYz/aU1ivVZfsB4+q6+9Y/KM1e75L0GHA+8KsFOdCIiP6k09OqSnoKeLvtFyQ9Y3uFuvw52yOabN4nJG0LnAts2APNTn1lFDCpPw8kOGXK9L4uRkR0UXdOq/py6/UlvZl6G2t/I+l84L3AJwdRoIiI6BNdCRZXARdJOhxee0Due5Smnn7H9qf7ugwREYNFV/osjqF0JP+e0pk7EXgS+EYPlCsiIvqRrtw6+zJwOHB4bX56Os07ERELhw6DhaRRtifX12u2Sl5GEgC2/9YjpYuIiH6hWc3i98Ay9fVfKLfMDmm1zlzK08vRi8Z+ba++LkKbZs56pa+LEBE9oNO3zka/MQqYNHXqDObMyWcXEd2jW26dlTQMeBxYz/as7ixgRET0f526G6oOfjcbWLJnixMREf1RV56z+B5whaRTgX8yb8iPdHBHRAxyXQkWP6j/vrfV8nRw94HatrjAZs56henTZjZfMSIWal15zqIrD/BFD+uusaHGnTGG6SRYRETHujyHgqTVgJWBf7Y37HdERAwunQ4WdSyoy4F3UgYPHCnpPuDjdRrTiIgYpLrStPTflDmwl7f9Nso8EQ9RhgCPiIhBrCvNUNsCb7P9CkCd1+Jo4F89UrKIiOg3uhIsngXWo9QuWgh4rltL1IqkyZT5qmdR7ro62Xa/HBa9KyTtAJxpe/O+LktERDNdCRZnALfWSYX+DqwOfAo4ricK1sreth+TtAlwj6RbbT/dkzuUNKw+jBgRsdDryq2zP5L0V2AfYCPKXBb72O61uaZtPyRpOrCGpK8C2wOLAU8DB9r+u6RRwAPARZRnQoYAh9i+C0DS+4GvA0tQZv873PZ99Zf+WOB3wCbAscB1LfvuKF9JiwDjgZGUp9x/C3y2DuuOpK9Rztsc4AVKkx4NeY8Afg780vZZ3XW+IiK6S5dunbV9G3BbD5WlKUk7Ui7yE4HTbR9Zlx8EfAv4eF11JPCI7S/XIHCZpLWAVSg1oZ1tT5O0PnADsFrdbn3KRf7edorQXr4vUwLnVElDKAHlQOBcSfsDewDb2J4uaaTtOS3Du0tanRIoTrN9dXecp4iI7taVW2dPaidpFmX4jxtt/6dbSvVGV0uaCUwDPmz7OUn7SfoCMJw3HsfLwKUAtidIeonSv7ItsBZwZ8vFGlhE0lvr64kdBIqO8v0DcKSkXSn9KssDL9ZtdgP+2/b0ul3jnOVvA26nzBN+d+dPR0RE7+pKzWId4IOUJpYngFWBLYFfArsD50j6sO0bu72Utc+i5U39NX4WsIXtSZK2AcZ1Ip8hlKD2ydYJktYFZsxn+fahBKLtau3hGMr5auZZyrl8P5BgERH9VleesxhKeQBvO9v72N4O+Cgw2/bWwCHA6T1RyDYsS/mV/29JQ4HPtUpfjHIBR9J2lH6EPwM3A7vU5idq+hZd2G97+Y6gTDM7XdJyLetU1wGfl7RM3W5kQ9pMYE9gPUln1yasiIh+pyvBYmfg2lbLrgN2ra8vBVpPvdojbP8euAr4I/AbYFKrVaYCoyU9CpwDfML2y7YnAvsC50t6RNKfgM92Yddt5gtcTJlm9s+UmtZdDdtcXJfdJ+lh4H9rgGs5lpeBvYG3Auc1pkVE9BddaYb6K/B55o0+C+UX/V/r6zcxr52+29ge1c7yw4DDGhad0Cr9yHa2u5lSw2i9fALQ9JmHtvK1/TywUzvrzwVOrX+NXtuf7VeZ1zkfEdHvdCVYHAT8XNJXKE9tr0yZEOlDNV30zjMXERHRy7rynMWDktYGtgZWAp4C7m0Y/uNO4M4eKWUX2J5MqeUMiHwjIgaC+W4fr8FhMUlLd2N5IiKiH+p0sJC0IfA48CPg/Lp4e+AnPVCuiIjoR7rSZ/HfwPG2L5H0bF12ByV4RC8b+7W9uiWfmbNe6ZZ8ImJw60qwWJ/69DJl3u2WYcqX7PZSRVNTp85gzpy5fV2MiFhIdKXPYjKwWeMCSVsCf+nOAkVERP/TlZrFccB4SedSOra/RnnO4jM9UrKIiOg3Ol2zsH0dsAvwZkpfxerAh+pDbhERMYh1ZdTZj9i+ijIGVOPyvTO0du8bOXL4Aucxc9YrTJ82sxtKExGDXVeaoc6njMfU2nlAgkUvO/S0a3j62RcWKI9xZ4xhOgkWEdFc02AhqWVwwKGS1qAM891iTcjVJiJisOtMzeIvlFtlhzBv0MAW/wZO7OYyRUREP9M0WNgeCiDpDtvb93yRIiKiv+nK3VAJFBERC6mu3A21COVOqO0po6++1ndh+93dX7SIiOgvunI31FnAeyh3P50CfJ0yGdLlPVCufkPSopRj/QTwav2bCBxPmVZ2eHsTLUVEDBZdGe7jQ8Cuts8GXq3/7gXs2CMl6z8uADYCtrK9PjC6LlOflioiohd1pWaxFPBEff2SpKVs/1nSJj1Qrn6hTvb0QWAV28/Ba9Okjq/pGzesuyFlXu6lgSWA82x/r6YdDBwOzKIE6I9Shnv/AaW2NguYYftdvXNkERFd05WaxZ+ALerrB4ATJR1LmWJ1sNoEmGj72aZrloEWd7K9KbAlcLCkdWvat4H32B5NOYf/ADam1MrWs70xsFt3Fz4iort0pWZxGGXObYAjKPNbDGchGkhQ0nrAOEot6wagMYgsBfx3rW3MoUw9uzElyN4GXCTpl8B423+T9DdgUeB8SbcB1/XekUREdE3TmoWkd0n6lu37bT8IYHui7Z0oAwq+2tOF7EMPAWtLGgFg+4+1djAWWK7VuqdSHlLcpNYUfktpjoLS33MspYnqdkm72n6eMkfI5ZQ+kT9IWrGnDygiYn50phnqGODOdtJup9wpNCjZngj8L/AjSY3Boa15x0cAT9h+VdIGwHbw2i3Ha9r+re3TgZuBTSS9GVjK9k3AV4HnKcOnRET0O51phhoN3NhO2q0M/jm4D+2zzykAABWUSURBVKDM5XG/pFcoTU9PAqcDezSsdzJwiaRPUzqvWwLsMODCWjuZQ7lJ4KuUId5/VIPJIpRmrft6/GgiIuZDZ4LFssBiwEttpC0KLNOtJepnbL9MCRbHtZH8YMN6DwEbtJPNdm0sm0qrmQcjIvqrzjRD/Rl4Xztp76vpERExiHWmZnEW8D+ShgHX2J4jaSjlgbwfUu6MioiIQawzo86Oq3fpXAQsLulpythQs4ATbF/Ww2WMiIg+1qnnLGx/V9KPgXcCIynt7ffantaThYuIiP5hyNy5c/u6DNE1o4BJ3ZFR5uCOiBZDhw5h5MjhAGtQRqR4na48wR39yNSpM5gzJ4E+InpHV8aGioiIhVSCRURENJVgERERTaXPYoCqHVHzJR3bEdFVCRYD1KGnXcPTz74wX9uOO2MM00mwiIjOSzNUREQ0lWARERFNJVhERERTCRYREdFUgkVERDSVYNEOSZMlPVWHZm9ZdoCkuZK+2GTbvSRt2cn9nCjpzAUtb0RET0qw6NiTwM4N7w+gYXa8DuwFdCpYREQMBHnOomMXUgLE9ZLWBJYGfg8gaTHgFGB7YHHgUeDzwLsoc3PvJOkg4LvAzcBllClqlwDG2z66Nw8kImJBpGbRsQnAhpKWB/YHLm5IOxp43vaWtjem1EK+Zvsm4FrgdNujbV8MPAfsbnszYDSwuaRdevNAIiIWRGoWHZsLXAl8vP5tA2xW0/YAlpW0d32/OPBIO/kMA74taRtgCLAiJWjc2EPljojoVgkWzV0E/Aa40/ZUSS3LhwCH2L6tE3kcASwPbGV7pqTzKM1REREDQpqhmrD9N+DrwDdbJV0LHCFpSQBJy0hat6ZNA5ZrWHcE8FQNFCsDe/ZwsSMiulVqFp1g+7w2Fp8OnAjcL2kOpcnqG8CfgEuACyV9hNLBPRa4StJjwD+BX/VGuSMiukvm4B54RgGTFnTU2SlTpndroSJiYGs2B3eaoSIioqkEi4iIaCrBIiIimkqwiIiIptLBPfCMAiYtSAaZgzsiWmvWwZ1bZweoqVNnMGdOAn1E9I40Q0VERFMJFhER0VSCRURENJU+iwGqdkS9Jp3WEdGTEiwGqNbDfYw7YwzTSbCIiJ6RZqiIiGgqwSIiIppKsIiIiKYSLCIioqlB38EtaVHgOMoc2jOB2cBtwJ+BnW3v3cHmSNoBWMz2zfX9KOAB229qY92VgJ/a3rE7jyEioq8N+mABXAAsCWxme7qkRYADgcU7uf0OwHDg5mYr2n4SSKCIiEFnUAcLSWsDHwRWsT0dwParwHmSDmi17leA/erb+4H/RxlQ63PAUEk7AZfXPySdArwfWAr4tO27W9c6JM2lzN/9QWAkcJTtn9W0DwOnAC8BV9XXy9ie0f1nIiJiwQz2PotNgIm2n+1oJUm7UgLFNsCGwDDgONu/B84FLrY92vbpdZORwL22NwFOAr7VQfbTbG9R8x9b9/dW4Dxg95rHS/N7gBERvWGwB4vO2gm43PY023MpF/KdOlh/hu3r6uv7gLU6WPfyhvVWkrQEsBXwoO2JNe0n81/0iIieN9iDxUPA2pKW7+Z8ZzW8nk3HzXkzAWzPru8HddNfRAxOgzpY1F/u1wL/I2kZAEnDJB1E6bRucSvwMUnLSBoCHATcUtOmAct1c9F+A2wqqaVGsn835x8R0a0GdbCo9gcmAr+T9Bjwe+AdNNQObN8AXArcW9MBTq7//gLYQtLDkr7aHQWy/R9Kx/n1kh4C3gy8ArzYHflHRHS3TKvaRyQt03KHlqRPUe6o2rYTm44CJrU1kOCUKdN7pKwRMfhlWtX+61BJH6F8Bs8An+nj8kREtCvBoo/YPoXybEVERL+3MPRZRETEAkqwiIiIptLBPfCMAia1XphpVSNiQaSDe5CaOnUGc+Yk0EdE70gzVERENJVgERERTSVYREREU+mzGKBqR9Rr0sEdET0pwWKAamu4j+kkWEREz0gzVERENJVgERERTSVYREREUwkWERHRVIJFREQ0NSDuhpI0F1jG9oyGZU8Dm9ueLGkCsB6wZss6ddmZtq+TdCIw3PaRNe1g4GhgZ2BV4Hbgq7a/VdN3qNtuXt8vD5wJ7Ai8Ckyp698laSngWWC1OgMekh4AJtn+SH2/OfAL26vWspwAbG37NzX9deWLiOhvBlPN4kXgy81WknQ0cBiwve2/1sVPAYdLGtHOZldR5uJe2/Y6wDHAzyW93faLwG+BHWr+ywJLARs2bL8DMKHh/d+B0zp1VBER/cBgChanAYdIelN7K0g6BfgoJVD8qyHpSUpA+Eob27wbEHC07dkAtu8AfgJ8ra42gRosgG2BO4GJktavy3ag1F5a/AwYKWnnzh9eRETfGUzB4l/AxcDX20k/ANgTeI/tp9tIPxn4tKS3tVq+EfA726+0Wn4fsHF9fTvzgsUOwB2UgLGDpGGUADKhYdu5lNrJqZKGdHRQERH9wUAPFq3H6D4d2EfSqm2s+1tgJLBrWxnV/obzgONaJXXmYn4vsIaktwLbUwLDHZTAsQnwvO2/tdrfeOAl4COdyD8iok8NlGAxhXKhB0DSIsBydflrbE8Fvg98o408/kjp0P6epI+1s59vAx8E1mpY9giwmaRFW627NfBo3e9LwG+A3Sgd1U8BDwKb8sb+ikZfBb7JALnRICIWXgMlWNwCfLbh/cHAfbVzubWzKEFhzdYJth+taWe3FTBsPw98Bzi2YdmdwETgjNqk1NKP8Wle30k9gdLn8eu63avAX2tZG/srGvd3d817TFvpERH9xUAJFl8CRkl6VNLDlKak/dpa0fYLlIt4W01RTQMG8APe+Et/b2AE8BdJjwPfAva2PbFhnduBtSnNTy3uqMsmdHBsxwCrdZAeEdHnMgf3wDMKmNTWqLNTpkzvs0JFxMDWbA7ugVKziIiIPpRgERERTSVYREREUwkWERHRVDq4B55RwKTWCzMHd0QsiGYd3HkYbICaOnUGc+Yk0EdE70gzVERENJVgERERTSVYREREUwkWA9TIkcNZZtkl+roYEbGQSLAYoA497RqWWLz1QLgRET0jwSIiIppKsIiIiKYSLCIioqkEi4iIaCrBIiIimlrohvuQNBmYWf+WAO4CDrH9SgfbHADcY/vx+n40sI7tK3u6vBER/cHCWrPY2/ZoYP3696Em6x8ArNPwfjTw0fnZsaSFLkBHxMC3sF+4lqh/z0r6L+Dk+n4R4BTbl0v6FLA5MFbSyZT5vU8Clq3zgd9p+1BJWwGnA8vWvI+3PV7SKOAB4ELgPcB5kk4ANrX9FICkscC/bZ/aK0cdEdFFC2uwuFrSTGAt4GbbN0taHtjW9mxJbwV+J+km2xdI2h840/Z1AJKWBHazvXd9PwI4F3i/7ackvQ24X9IGdX8jgfttH1nXHwUcDHxD0nDg40DLuhER/c7C3gz1ZmAJSV+qr6+W9BhwE7ACoE7mtw1lDPgbam3jBmAu8PaaPhNo7N/4IfCp2iS1LyVg/d8CHlNERI9ZWGsWANieKek6YDdgd+Ba4EO250p6nNIk1RlDgEdtv7t1Qq1FvGD7tcknbD8h6QFgT+ALlFpGRES/tbDWLACQNBTYHngcGAFMroHivcyrFQBMA5br4P09wNqSdmzIewtJQzrY/feB7wGv2L53wY4kIqJnLazB4uraXPQY5RycBHwVOLMu/yjwaMP65wHHS3pY0k7Ar4ClJT0iaaztZ4E9gBPqsj8BJ1JqHG2yfQeleeqc7j+8iIjutdA1Q9ke1U7SLcDa7WxzHXBdq8XbtFrnfmCHNjafDLyp9UJJawBLA+M6Km9ERH+wsNYs+pSkkygPA37Z9ot9XZ6IiGYWuppFf2D7eOD4vi5HRERnpWYRERFNJVhERERTQ+bOndt8rehPRgGTAGbOeoXp02b2bWkiYlAYOnQII0cOh/KA8eTW6emzGHiGATz77AvMmTOXoUM7epQjIqJzGq4lw9pKT7AYeN4GsPzyS/d1OSJicHob8NfWC9MMNfAsDmwBPAXM7uOyRMTgMYwSKO4HZrVOTLCIiIimcjdUREQ0lWARERFNJVhERERTCRYREdFUgkVERDSVYBEREU0lWERERFN5gnuAkbQOcBEwEpgKfNL2xG7M/0zgw5QxqDa0/Viz/c5vWifLMxK4BFgLeBmYCHzW9hRJWwP/AyxJGctmX9v/V7ebr7ROlOcaytg5c4AZwP+z/XBfnZ+Gcp1AmZ1xQ9uP9cW5qdtPpswA2TJo2Vds39RHn9USwFnATrU899o+uC8+K0mjgGsaFo0AlrW9Ql9/dzorNYuB51zgh7bXAX5I+Y/Una4B3g38vQv7nd+0zpgLnGFbtjekDENwep0//VLgCzXvO4HT4bW51buc1kn7297Y9ibAmcBPFvAcLPDnKWlTYGvqZ9aH56bF3rZH17+b+rA8Z1CCxDr1u3NcXd7rn5XtyQ3nZDTl/1nLLJl99t3pigSLAUTSW4BNgcvqosuATSW9ubv2Yftu2090dr/zm9aF8jxje0LDovuA1YHNgJm2767Lz6XMnc4CpHWmPM83vF0OmNOX50fS4pQLxecbFvfJuelAr5dH0nDgk8BxtucC2P5PX35WDWVbDBgD/KQ/lKezEiwGllWBf9meDVD/fbIu76v9zm9al9Vfmp8HrgVWo6H2Y/tpYKikFRYgrbPl+LGkfwCnAPs3Oc6ePj8nAZfantywrM/OTfVTSY9KOkfSiD4qz1qUppkTJD0gaYKkbekf3+U9al4P9pPydEqCRQwk36f0E/ygLwth+yDbqwHHAN/uq3JIeiewOXBOX5WhDdvZ3pgy2OUQ+u6zGgasCTxke3PgK8DPgeF9VJ5GBzKv+XLASLAYWJ4AVpY0DKD+u1Jd3lf7nd+0Lqkd72sDH7M9B/gHpTmqJf1NwBzbzyxAWpfYvgTYEfhnB8fZk+dne2BdYFLtWF4FuAl4+3we/wKfm5YmTNuzKEHsXQuwzwUpzz+AV6nNNLZ/AzwNvEQffpclrUz53H5aF/X5/63OSrAYQOpdIA8Dn6iLPkH55TSlr/Y7v2ld2b+kUynt13vVixDA74Ala9MCwOeAqxYwrVk5hktateH97sAzQJ+cH9un217J9ijboyhBa2dKbadXzw2ApKUlLVdfDwE+Xo+v1z+r2mR1O/DeWp51gLcAj9OH32VKs+V421NrOfv0/1ZXZIjyAUbSOyi3yy0PPEu5Xc7dmP9Y4EPAipRfYlNtr9/Rfuc3rZPlWR94jPKf/KW6eJLtD0rahnIHyBLMu63yP3W7+UprUpa3Av8LLE2ZS+QZ4EjbD/bV+WlVvsnAbi63zvbquanbrgn8jNIENAz4I3Co7af6sDw/odxa+grwdds39OVnJenxek5ubFjW59+dzkiwiIiIptIMFRERTSVYREREUwkWERHRVIJFREQ0lWARERFNZdTZiAUg6ULgn7aP7YN9D6HcGroXMNH2lr1dhp4iaQxl0Mb39XVZokiwiEGlPmuwFLCG7RfqsoMo9+fv0Hcl6xHbUh46W6XlWPsDSQcAB9nettm6df1RwCRgUduvAtj+KfOeco5+IM1QMRgNAw7r60J0VcvQDV2wOjC5PwWKGLxSs4jB6NvA0ZLOsf1cY0Jbv2IlTaCM3Prj+qv4M8BvgU9RntLeF1gH+CawOHCU7Ysasn2TpFsoc0o8SHmStmVuiXdQBkDcDJhCGTL7ypp2IeWp9NUp4wXtCdzaqrwrUYbm3raW5Vu2fyTp05ShyReVNAP4ju0TWm27FvAjYGPKvCA3UeaGeK6mT6YM9PfJWoYbKU0/MyXtQJlL4izKIHyzgWNsX1C3Xa4e167Ai3U/pwKq5W0p16u2R0j6AHAyZTTY54HzbZ9Yi3pn/fc5SVBqS6KhdlKf5D67fg6PA4fZvqfh87sLeA+wEXAvsI/tp1UmQPpxLecwyuRZu3X2KfCYJzWLGIweACYAR87n9lsBj1KGiRgHXE4ZRfXtlMDxA5X5ElqMoQSSN1HG6/kplLGSgFtqHm+hjJV0jqT1GrbdhzLU+TLA3bzR5ZQxn1YC9gZOlfQe2+dTxkq61/bw1oGiGgKcVrddlzJ89Ymt1vkosAtl9r+NgAMa0lakzNmxMvBp4IeSlq9p369pa1IC3SeBT9n+U6tyjajrv1DXGQF8APi8pL1q2rvrvyPqNvc2FrAOST4eGEv5TL4LjFeZRbHFPpTg/hZgMeZ99vvXcq5at/0c84aNiS5IzSIGq+OBX0s6ez62ndTwC/oK4OvASXUQw5slvUwJHA/X9cfbvrOu/3Xg+Trg4DaUZqIL6noPSfoZ8BHgG3XZ/9r+dX3dMhUpNa9VKaO2fsD2TOBhST+mXHRva3YQtv8C/KW+nSLpu0DroDLW9pN1f78ERjekvVKP+1Xg+lpTkKT7KYFvtO3pwHRJ3wH2A85vpywTGt4+KukySpC5pq31W/kApQP/kvr+MkmHArsDF9ZlF9h+vB7HlZQ5I1qOYSTwdtuPUgYnjPmQYBGDUh1M7zrgq8Cfurh5YxPFSzW/1ssaaxavDQtte4akZyi/5lcHtpLU2BS2CGVO8Tds24aVgGfqBbnF3ylzWDRVBz48G9iOUnMZShlwrtG/G16/WPfZYmpLU11D+nBKDWpRXj/17t8pNZD2yrIVZUrUDSi//Ben8yParsQbp/ltvb/Wx9Hy+VxCqVVcrjIR06WUAQVf6eS+o0ozVAxmJ1D6HxovKi2dwUs1LFtxAffTOGz5cGAFyqxlTwB32B7R8DfcduMUqB2N5PkksIKkZRqWrQb8q5PlOrXmv6HtZSlNaEM6uW1Hnqb8Yl+9YVljudo6pnGUGQ5Xtb0cpV9jSAfrN3qy1b5a769dtl+x/Q3b61FqertRambRRQkWMWjVZpgrgEMblk2hXGT2lTRM0oGUTtcF8X5J26rMrfxN4D6XSYCuA9aRtJ+kRevfFpLW7WT5nwDuAU6TtISkjSh9B5d2slzLUGYWfL5OunNUVw+snXLNBq4ETpG0jKTVgSMayvUfYJV6PhrL8kztPN+S0sfQYgowh9L/0ZbrKedxH0mLSPoYsB7l/HZI0o6SNqx3mk2jBLk5nT7YeE2CRQx2J1Hmn2j0GcqFcyqwPuWCvCDGUWoxz1DuetoXoDYfvY/Svv8kpankW5QmmM76BDCqbv8L4ATbt3a4xTzfADal3H00njKtaHf5f5Ra2t8oHfPjmDdV6G3AH4B/S3q6LjsEOEnSdEp/0pUtGdl+kdLJ/2tJz0naunFHdaKg3YAvUz6zoyl3ND1NcysCV1MCxZ+AO3h9M2B0UuaziIiIplKziIiIphIsIiKiqQSLiIhoKsEiIiKaSrCIiIimEiwiIqKpBIuIiGgqwSIiIppKsIiIiKb+P/w9qsgP/nCeAAAAAElFTkSuQmCC\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.561965Z","start_time":"2021-04-18T10:34:51.547969Z"},"id":"34Tw5__i-NwI","executionInfo":{"status":"ok","timestamp":1620081411810,"user_tz":-540,"elapsed":1810,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# category labeling \n","sorted_temp_df = df.sort_index()\n","\n","# background = 0 에 해당되는 label 추가 후 기존들을 모두 label + 1 로 설정\n","sorted_df = pd.DataFrame([\"Backgroud\"], columns = [\"Categories\"])\n","sorted_df = sorted_df.append(sorted_temp_df, ignore_index=True)"],"execution_count":9,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.576961Z","start_time":"2021-04-18T10:34:51.562964Z"},"colab":{"base_uri":"https://localhost:8080/","height":421},"id":"CQk4vV5N-NwI","executionInfo":{"status":"ok","timestamp":1620081412448,"user_tz":-540,"elapsed":2421,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"33c67a1b-452e-4966-c55c-cb6a596bb3f3"},"source":["# class (Categories) 에 따른 index 확인 (0~11 : 총 12개)\n","sorted_df"],"execution_count":10,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
CategoriesNumber of annotations
0BackgroudNaN
1UNKNOWN128.0
2General trash2225.0
3Paper7448.0
4Paper pack527.0
5Metal449.0
6Glass488.0
7Plastic2472.0
8Styrofoam1074.0
9Plastic bag6114.0
10Battery50.0
11Clothing141.0
\n","
"],"text/plain":[" Categories Number of annotations\n","0 Backgroud NaN\n","1 UNKNOWN 128.0\n","2 General trash 2225.0\n","3 Paper 7448.0\n","4 Paper pack 527.0\n","5 Metal 449.0\n","6 Glass 488.0\n","7 Plastic 2472.0\n","8 Styrofoam 1074.0\n","9 Plastic bag 6114.0\n","10 Battery 50.0\n","11 Clothing 141.0"]},"metadata":{"tags":[]},"execution_count":10}]},{"cell_type":"markdown","metadata":{"id":"KXU0zmZs-NwI"},"source":["## 데이터 전처리 함수 정의 (Dataset)"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:52.693328Z","start_time":"2021-04-18T10:34:52.681328Z"},"id":"QFnTI8_Z-NwJ","executionInfo":{"status":"ok","timestamp":1620081412450,"user_tz":-540,"elapsed":2418,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["category_names = list(sorted_df.Categories)\n","\n","def get_classname(classID, cats):\n"," for i in range(len(cats)):\n"," if cats[i]['id']==classID:\n"," return cats[i]['name']\n"," return \"None\"\n","\n","class CustomDataLoader(Dataset):\n"," \"\"\"COCO format\"\"\"\n"," def __init__(self, data_dir, mode = 'train', transform = None):\n"," super().__init__()\n"," self.mode = mode\n"," self.transform = transform\n"," self.coco = COCO(data_dir)\n"," \n"," def __getitem__(self, index: int):\n"," # dataset이 index되어 list처럼 동작\n"," image_id = self.coco.getImgIds(imgIds=index)\n"," image_infos = self.coco.loadImgs(image_id)[0]\n"," \n"," # cv2 를 활용하여 image 불러오기\n"," images = cv2.imread(os.path.join(dataset_path, image_infos['file_name']))\n"," images = cv2.cvtColor(images, cv2.COLOR_BGR2RGB).astype(np.float32)\n"," images /= 255.0\n"," \n"," if (self.mode in ('train', 'val')):\n"," ann_ids = self.coco.getAnnIds(imgIds=image_infos['id'])\n"," anns = self.coco.loadAnns(ann_ids)\n","\n"," # Load the categories in a variable\n"," cat_ids = self.coco.getCatIds()\n"," cats = self.coco.loadCats(cat_ids)\n","\n"," # masks : size가 (height x width)인 2D\n"," # 각각의 pixel 값에는 \"category id + 1\" 할당\n"," # Background = 0\n"," masks = np.zeros((image_infos[\"height\"], image_infos[\"width\"]))\n"," # Unknown = 1, General trash = 2, ... , Cigarette = 11\n"," for i in range(len(anns)):\n"," className = get_classname(anns[i]['category_id'], cats)\n"," pixel_value = category_names.index(className)\n"," masks = np.maximum(self.coco.annToMask(anns[i])*pixel_value, masks)\n"," masks = masks.astype(np.float32)\n","\n"," # transform -> albumentations 라이브러리 활용\n"," if self.transform is not None:\n"," transformed = self.transform(image=images, mask=masks)\n"," images = transformed[\"image\"]\n"," masks = transformed[\"mask\"]\n"," \n"," return images, masks, image_infos\n"," \n"," if self.mode == 'test':\n"," # transform -> albumentations 라이브러리 활용\n"," if self.transform is not None:\n"," transformed = self.transform(image=images)\n"," images = transformed[\"image\"]\n"," \n"," return images, image_infos\n"," \n"," \n"," def __len__(self) -> int:\n"," # 전체 dataset의 size를 return\n"," return len(self.coco.getImgIds())"],"execution_count":11,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"cp2aIOlP-NwK"},"source":["## Dataset 정의 및 DataLoader 할당"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:58.823175Z","start_time":"2021-04-18T10:34:54.106233Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"P7zFlRn6-NwK","executionInfo":{"status":"ok","timestamp":1620081423844,"user_tz":-540,"elapsed":8507,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"e42fb730-d910-4b41-c300-79453e8adeb9"},"source":["# train.json / validation.json / test.json 디렉토리 설정\n","train_path = dataset_path + '/train.json'\n","val_path = dataset_path + '/val.json'\n","test_path = dataset_path + '/test.json'\n","\n","# collate_fn needs for batch\n","def collate_fn(batch):\n"," return tuple(zip(*batch))\n","\n","train_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","val_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","test_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","# create own Dataset 1 (skip)\n","# validation set을 직접 나누고 싶은 경우\n","# random_split 사용하여 data set을 8:2 로 분할\n","# train_size = int(0.8*len(dataset))\n","# val_size = int(len(dataset)-train_size)\n","# dataset = CustomDataLoader(data_dir=train_path, mode='train', transform=transform)\n","# train_dataset, val_dataset = torch.utils.data.random_split(dataset, [train_size, val_size])\n","\n","# create own Dataset 2\n","# train dataset\n","train_dataset = CustomDataLoader(data_dir=train_path, mode='train', transform=train_transform)\n","\n","# validation dataset\n","val_dataset = CustomDataLoader(data_dir=val_path, mode='val', transform=val_transform)\n","\n","# test dataset\n","test_dataset = CustomDataLoader(data_dir=test_path, mode='test', transform=test_transform)\n","\n","\n","# DataLoader\n","train_loader = torch.utils.data.DataLoader(dataset=train_dataset, \n"," batch_size=batch_size,\n"," shuffle=True,\n"," num_workers=4,\n"," collate_fn=collate_fn,\n"," drop_last=True)\n","\n","val_loader = torch.utils.data.DataLoader(dataset=val_dataset, \n"," batch_size=batch_size,\n"," shuffle=False,\n"," num_workers=4,\n"," collate_fn=collate_fn,\n"," drop_last=True) \n","\n","test_loader = torch.utils.data.DataLoader(dataset=test_dataset,\n"," batch_size=batch_size,\n"," num_workers=4,\n"," collate_fn=collate_fn)"],"execution_count":12,"outputs":[{"output_type":"stream","text":["loading annotations into memory...\n","Done (t=4.05s)\n","creating index...\n","index created!\n","loading annotations into memory...\n","Done (t=3.05s)\n","creating index...\n","index created!\n","loading annotations into memory...\n","Done (t=0.46s)\n","creating index...\n","index created!\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"8rqJiHb_-NwM"},"source":["# wandb"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"H1kHXm0uAX3R","executionInfo":{"status":"ok","timestamp":1620081432124,"user_tz":-540,"elapsed":13700,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"452d2f85-0e8e-4f27-8219-2b94741a1262"},"source":["!pip install wandb"],"execution_count":13,"outputs":[{"output_type":"stream","text":["Collecting wandb\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/5c/ee/d755f9e5466df64c8416a2c6a860fb3aaa43ed6ea8e8e8e81460fda5788b/wandb-0.10.28-py2.py3-none-any.whl (2.1MB)\n","\u001b[K |████████████████████████████████| 2.1MB 4.3MB/s \n","\u001b[?25hCollecting pathtools\n"," Downloading 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/root/.cache/pip/wheels/0b/04/79/c3b0c3a0266a3cb4376da31e5bfe8bba0c489246968a68e843\n"," Building wheel for subprocess32 (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for subprocess32: filename=subprocess32-3.5.4-cp37-none-any.whl size=6489 sha256=8b571826599af0e958c1482eee2d33a0ba7411ac9ee15ffc3a99cf99066c5da9\n"," Stored in directory: /root/.cache/pip/wheels/68/39/1a/5e402bdfdf004af1786c8b853fd92f8c4a04f22aad179654d1\n","Successfully built pathtools subprocess32\n","Installing collected packages: pathtools, docker-pycreds, subprocess32, smmap, gitdb, GitPython, shortuuid, sentry-sdk, configparser, wandb\n","Successfully installed GitPython-3.1.14 configparser-5.0.2 docker-pycreds-0.4.0 gitdb-4.0.7 pathtools-0.1.2 sentry-sdk-1.0.0 shortuuid-1.0.1 smmap-4.0.0 subprocess32-3.5.4 wandb-0.10.28\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":153},"id":"aMW4VV9V-NwM","executionInfo":{"status":"ok","timestamp":1620081444440,"user_tz":-540,"elapsed":10067,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"761aaa4f-0b04-40cd-d6db-edce7119da28"},"source":["import wandb\n","\n","proj_name = 'pan_resnet101_imagenet_focal_madgrad_coswarmLR'\n","\n","wandb.init(project='chanyub',name=proj_name)\n","\n","config = wandb.config\n","config.learning_rate = 0.01"],"execution_count":14,"outputs":[{"output_type":"display_data","data":{"application/javascript":["\n"," window._wandbApiKey = new Promise((resolve, reject) => {\n"," function loadScript(url) {\n"," return new Promise(function(resolve, reject) {\n"," let newScript = document.createElement(\"script\");\n"," newScript.onerror = reject;\n"," newScript.onload = resolve;\n"," document.body.appendChild(newScript);\n"," newScript.src = url;\n"," });\n"," }\n"," loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n"," const iframe = document.createElement('iframe')\n"," iframe.style.cssText = \"width:0;height:0;border:none\"\n"," document.body.appendChild(iframe)\n"," const handshake = new Postmate({\n"," container: iframe,\n"," url: 'https://wandb.ai/authorize'\n"," });\n"," const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n"," handshake.then(function(child) {\n"," child.on('authorize', data => {\n"," clearTimeout(timeout)\n"," resolve(data)\n"," });\n"," });\n"," })\n"," });\n"," "],"text/plain":[""]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"],"name":"stderr"},{"output_type":"display_data","data":{"text/html":["\n"," Tracking run with wandb version 0.10.28
\n"," Syncing run pan_resnet101_imagenet_focal_madgrad_coswarmLR to Weights & Biases (Documentation).
\n"," Project page: https://wandb.ai/pstage12/chanyub
\n"," Run page: https://wandb.ai/pstage12/chanyub/runs/1t5n88ha
\n"," Run data is saved locally in /content/drive/My Drive/Trash/code/wandb/run-20210503_223722-1t5n88ha

\n"," "],"text/plain":[""]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"uQRiIVGX-NwM"},"source":["## My model"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"S0xCGpNeAqeD","executionInfo":{"status":"ok","timestamp":1620081452180,"user_tz":-540,"elapsed":6471,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"f6e7b37f-42c5-4b36-c627-03aa528d57f4"},"source":["!pip install segmentation_models_pytorch"],"execution_count":15,"outputs":[{"output_type":"stream","text":["Collecting segmentation_models_pytorch\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/65/54/8953f9f7ee9d451b0f3be8d635aa3a654579abf898d17502a090efe1155a/segmentation_models_pytorch-0.1.3-py3-none-any.whl (66kB)\n","\r\u001b[K |█████ | 10kB 910kB/s eta 0:00:01\r\u001b[K |██████████ | 20kB 1.8MB/s eta 0:00:01\r\u001b[K 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sha256=f3c9150e8b8ede29b2e20bb3df3c42266d966b254d71ab62dbb6b7550efb5a4d\n"," Stored in directory: /root/.cache/pip/wheels/69/df/63/62583c096289713f22db605aa2334de5b591d59861a02c2ecd\n","Successfully built efficientnet-pytorch pretrainedmodels\n","Installing collected packages: efficientnet-pytorch, munch, pretrainedmodels, timm, segmentation-models-pytorch\n","Successfully installed efficientnet-pytorch-0.6.3 munch-2.5.0 pretrainedmodels-0.7.4 segmentation-models-pytorch-0.1.3 timm-0.3.2\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:11.634792Z","start_time":"2021-04-18T16:16:05.875817Z"},"colab":{"base_uri":"https://localhost:8080/","height":137,"referenced_widgets":["742877638622475d8e3795d8daaeafac","5f3bc533d793423b85bef90e97c7e565","c79e744fc1704d6782cf4fdafd025289","0005114c828344d68bd4af1a805619c8","1ea0be07cf7c4b80a9ca15a553a71de1","98139053f8f34294a6209219e3f8b76b","fa45332ccaef438d85d5860862ac57bb","839168d6a4594afba3dc9e0fceb212b9"]},"id":"a8IfZfiM-NwM","executionInfo":{"status":"ok","timestamp":1620081469146,"user_tz":-540,"elapsed":16951,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"572713ca-dcda-4e78-ce65-63c961e50dbd"},"source":["# 구현된 model에 임의의 input을 넣어 output이 잘 나오는지 test\n","import segmentation_models_pytorch as smp\n","\n","model = smp.PAN(encoder_name='resnet101', classes=12)\n","x = torch.randn([2, 3, 512, 512])\n","print(\"input shape : \", x.shape)\n","out = model(x).to(device)\n","print(\"output shape : \", out.size())\n","\n","model = model.to(device)"],"execution_count":16,"outputs":[{"output_type":"stream","text":["Downloading: \"https://download.pytorch.org/models/resnet101-5d3b4d8f.pth\" to /root/.cache/torch/hub/checkpoints/resnet101-5d3b4d8f.pth\n"],"name":"stderr"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"742877638622475d8e3795d8daaeafac","version_minor":0,"version_major":2},"text/plain":["HBox(children=(FloatProgress(value=0.0, max=178728960.0), HTML(value='')))"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\n","input shape : torch.Size([2, 3, 512, 512])\n","output shape : torch.Size([2, 12, 512, 512])\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"zvDnI7_T-NwN"},"source":["## train, validation, test 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.104200Z","start_time":"2021-04-18T16:16:18.093174Z"},"id":"RA3oAapJ-NwN","executionInfo":{"status":"ok","timestamp":1620081469147,"user_tz":-540,"elapsed":13436,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["def train(num_epochs, model, data_loader, val_loader, criterion, optimizer, saved_dir, val_every, device, lr_scheduler):\n"," print('Start training..')\n"," best_loss = 9999999\n"," for epoch in range(num_epochs):\n"," model.train()\n"," for step, (images, masks, _) in enumerate(data_loader):\n"," images = torch.stack(images) # (batch, channel, height, width)\n"," masks = torch.stack(masks).long() # (batch, channel, height, width)\n"," \n"," # gpu 연산을 위해 device 할당\n"," images, masks = images.to(device), masks.to(device)\n"," \n"," # inference\n"," outputs = model(images)\n"," \n"," # loss 계산 (cross entropy loss)\n"," loss = criterion(outputs, masks)\n"," optimizer.zero_grad()\n"," loss.backward()\n"," optimizer.step()\n"," \n"," lr_scheduler.step()\n"," \n"," # step 주기에 따른 loss 출력\n"," if (step + 1) % 25 == 0:\n"," print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(\n"," epoch+1, num_epochs, step+1, len(train_loader), loss.item()))\n"," \n"," # validation 주기에 따른 loss 출력 및 best model 저장\n"," if (epoch + 1) % val_every == 0:\n"," avrg_loss = validation(epoch + 1, model, val_loader, criterion, device)\n"," if avrg_loss < best_loss:\n"," print('Best performance at epoch: {}'.format(epoch + 1))\n"," print('Save model in', saved_dir)\n"," best_loss = avrg_loss\n"," wandb.log({'best_loss': best_loss})\n"," save_model(model, saved_dir)"],"execution_count":17,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.285795Z","start_time":"2021-04-18T16:16:18.267686Z"},"id":"EulIikmq-NwO","executionInfo":{"status":"ok","timestamp":1620081469147,"user_tz":-540,"elapsed":12805,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["def validation(epoch, model, data_loader, criterion, device):\n"," print('Start validation #{}'.format(epoch))\n"," model.eval()\n"," with torch.no_grad():\n"," total_loss = 0\n"," cnt = 0\n"," mIoU_list = []\n"," for step, (images, masks, _) in enumerate(data_loader):\n"," \n"," images = torch.stack(images) # (batch, channel, height, width)\n"," masks = torch.stack(masks).long() # (batch, channel, height, width)\n","\n"," images, masks = images.to(device), masks.to(device) \n","\n"," outputs = model(images)\n"," loss = criterion(outputs, masks)\n"," total_loss += loss\n"," cnt += 1\n"," \n","# print(outputs.shape)\n","# print(masks.shape)\n","# wandb.log(wandb.Image(images, masks={\n","# \"predictions\" : {\n","# \"mask_data\" : torch.squeeze(torch.squeeze(outputs, 0),1),\n","# \"class_labels\" : classes_dict\n","# },\n","# \"ground_truth\" : {\n","# \"mask_data\" : torch.squeeze(masks, 0),\n","# \"class_labels\" : classes_dict\n","# }\n","# }))\n"," \n"," outputs = torch.argmax(outputs.squeeze(), dim=1).detach().cpu().numpy()\n","\n"," mIoU = label_accuracy_score(masks.detach().cpu().numpy(), outputs, n_class=12)[2]\n"," mIoU_list.append(mIoU)\n"," \n"," avrg_loss = total_loss / cnt\n"," avrg_mIoU = np.mean(mIoU_list)\n"," print('Validation #{} Average Loss: {:.4f}, mIoU: {:.4f}'.format(epoch, avrg_loss, np.mean(mIoU_list)))\n"," wandb.log({'Train Loss':loss.item(), 'Val Loss':avrg_loss , 'Val mIoU':np.mean(mIoU_list)})\n","# return avrg_loss\n"," return avrg_mIoU"],"execution_count":18,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"C_7CtFfH-NwO"},"source":["## 모델 저장 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.909918Z","start_time":"2021-04-18T16:16:18.898918Z"},"id":"gpCM5BFO-NwO","executionInfo":{"status":"ok","timestamp":1620081470252,"user_tz":-540,"elapsed":1098,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# 모델 저장 함수 정의\n","val_every = 1 \n","\n","saved_dir = './saved'\n","if not os.path.isdir(saved_dir): \n"," os.mkdir(saved_dir)\n"," \n","def save_model(model, saved_dir, file_name='pan_resnet101_imagenet_focal_madgrad_coswarmLR.pt'):\n"," check_point = {'net': model.state_dict()}\n"," output_path = os.path.join(saved_dir, file_name)\n"," torch.save(model.state_dict(), output_path)"],"execution_count":19,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"FyKGeg8T-NwP"},"source":["## 모델 생성 및 Loss function, Optimizer 정의"]},{"cell_type":"code","metadata":{"id":"ORugl8s1-NwP","executionInfo":{"status":"ok","timestamp":1620081470784,"user_tz":-540,"elapsed":1618,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["from torch.autograd import Variable\n","import torch.nn.functional as F\n","# ref : https://github.com/clcarwin/focal_loss_pytorch\n","class FocalLoss(nn.Module):\n"," def __init__(self, gamma=0, alpha=None, size_average=True):\n"," super(FocalLoss, self).__init__()\n"," self.gamma = gamma\n"," self.alpha = alpha\n"," if isinstance(alpha,(float,int)): self.alpha = torch.Tensor([alpha,1-alpha])\n"," if isinstance(alpha,list): self.alpha = torch.Tensor(alpha)\n"," self.size_average = size_average\n","\n"," def forward(self, input, target):\n"," if input.dim()>2:\n"," input = input.view(input.size(0),input.size(1),-1) # N,C,H,W => N,C,H*W\n"," input = input.transpose(1,2) # N,C,H*W => N,H*W,C\n"," input = input.contiguous().view(-1,input.size(2)) # N,H*W,C => N*H*W,C\n"," target = target.view(-1,1)\n","\n"," logpt = F.log_softmax(input)\n"," logpt = logpt.gather(1,target)\n"," logpt = logpt.view(-1)\n"," pt = Variable(logpt.data.exp())\n","\n"," if self.alpha is not None:\n"," if self.alpha.type()!=input.data.type():\n"," self.alpha = self.alpha.type_as(input.data)\n"," at = self.alpha.gather(0,target.data.view(-1))\n"," logpt = logpt * Variable(at)\n","\n"," loss = -1 * (1-pt)**self.gamma * logpt\n"," if self.size_average: return loss.mean()\n"," else: return loss.sum()"],"execution_count":20,"outputs":[]},{"cell_type":"code","metadata":{"id":"yfeFQknz-NwP","executionInfo":{"status":"ok","timestamp":1620081478278,"user_tz":-540,"elapsed":1293,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["import torch.optim.lr_scheduler as lr_scheduler\n","import math\n","class CosineAnnealingWarmUpRestart(lr_scheduler._LRScheduler):\n"," def __init__(self, optimizer, T_0, T_mult=1, eta_max=0.1, T_up=0, gamma=1., last_epoch=-1):\n"," if T_0 <= 0 or not isinstance(T_0, int):\n"," raise ValueError(\"Expected positive integer T_0, but got {}\".format(T_0))\n"," if T_mult < 1 or not isinstance(T_mult, int):\n"," raise ValueError(\"Expected integer T_mult >= 1, but got {}\".format(T_mult))\n"," if T_up < 0 or not isinstance(T_up, int):\n"," raise ValueError(\"Expected positive integer T_up, but got {}\".format(T_up))\n"," self.T_0 = T_0\n"," self.T_mult = T_mult\n"," self.base_eta_max = eta_max\n"," self.eta_max = eta_max\n"," self.T_up = T_up\n"," self.T_i = T_0\n"," self.gamma = gamma\n"," self.cycle = 0\n"," self.T_cur = last_epoch\n"," super(CosineAnnealingWarmUpRestart, self).__init__(optimizer, last_epoch)\n"," # self.T_cur = last_epoch\n"," \n"," def get_lr(self):\n"," if self.T_cur == -1:\n"," return self.base_lrs\n"," elif self.T_cur < self.T_up:\n"," return [(self.eta_max - base_lr)*self.T_cur / self.T_up + base_lr for base_lr in self.base_lrs]\n"," else:\n"," return [base_lr + (self.eta_max - base_lr) * (1 + math.cos(math.pi * (self.T_cur-self.T_up) / (self.T_i - self.T_up))) / 2\n"," for base_lr in self.base_lrs]\n","\n"," def step(self, epoch=None):\n"," if epoch is None:\n"," epoch = self.last_epoch + 1\n"," self.T_cur = self.T_cur + 1\n"," if self.T_cur >= self.T_i:\n"," self.cycle += 1\n"," self.T_cur = self.T_cur - self.T_i\n"," self.T_i = (self.T_i - self.T_up) * self.T_mult + self.T_up\n"," else:\n"," if epoch >= self.T_0:\n"," if self.T_mult == 1:\n"," self.T_cur = epoch % self.T_0\n"," self.cycle = epoch // self.T_0\n"," else:\n"," n = int(math.log((epoch / self.T_0 * (self.T_mult - 1) + 1), self.T_mult))\n"," self.cycle = n\n"," self.T_cur = epoch - self.T_0 * (self.T_mult ** n - 1) / (self.T_mult - 1)\n"," self.T_i = self.T_0 * self.T_mult ** (n)\n"," else:\n"," self.T_i = self.T_0\n"," self.T_cur = epoch\n"," \n"," self.eta_max = self.base_eta_max * (self.gamma**self.cycle)\n"," self.last_epoch = math.floor(epoch)\n"," for param_group, lr in zip(self.optimizer.param_groups, self.get_lr()):\n"," param_group['lr'] = lr"],"execution_count":21,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AG1oQeu7BX1M","executionInfo":{"status":"ok","timestamp":1620060451932,"user_tz":-540,"elapsed":4563,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"11b97bb1-b0d0-4c1c-e462-ea4f6bd73cd3"},"source":["# !pip install adamp"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Collecting adamp\n"," Downloading https://files.pythonhosted.org/packages/c8/56/182b8c93f18feb0244b83f9b2eff1c6b036c04d4c3880e8d222750b0d5e5/adamp-0.3.0.tar.gz\n","Building wheels for collected packages: adamp\n"," Building wheel for adamp (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for adamp: filename=adamp-0.3.0-cp37-none-any.whl size=5999 sha256=f539e77b51016467007048c2dc524433d3c26179d3e5b0f3c06bc9daee9a97a5\n"," Stored in directory: /root/.cache/pip/wheels/6a/89/67/879fe55977ebcbfaa5b929eb111af7fe11eb3552867850dd76\n","Successfully built adamp\n","Installing collected packages: adamp\n","Successfully installed adamp-0.3.0\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5A_iHxtWfSgx","executionInfo":{"status":"ok","timestamp":1620081485775,"user_tz":-540,"elapsed":4264,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"99a1248a-cca8-4904-cd40-c3f9916ecf9d"},"source":["!pip install madgrad"],"execution_count":22,"outputs":[{"output_type":"stream","text":["Collecting madgrad\n"," Downloading https://files.pythonhosted.org/packages/65/f0/4584f18202a2fb8903d456bf907b80e7cb54ad8fcba68604084ff41b7cf8/madgrad-1.1-py3-none-any.whl\n","Installing collected packages: madgrad\n","Successfully installed madgrad-1.1\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:19.698902Z","start_time":"2021-04-18T16:16:19.694902Z"},"id":"9Dly8KZj-NwQ","executionInfo":{"status":"ok","timestamp":1620081490363,"user_tz":-540,"elapsed":1111,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# from adamp import AdamP\n","from madgrad import MADGRAD\n","# Loss function 정의\n","# criterion = nn.CrossEntropyLoss()\n","criterion = FocalLoss()\n","\n","# Optimizer 정의\n","# optimizer = torch.optim.Adam(params = model.parameters(), lr = learning_rate, weight_decay=1e-6)\n","# optimizer = AdamP(params = model.parameters())\n","optimizer = MADGRAD(params = model.parameters(), lr = learning_rate, momentum = 0.9, weight_decay = 0, eps = 1e-06)\n","\n","# lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=50, eta_min=0)\n","lr_scheduler = CosineAnnealingWarmUpRestart(optimizer, T_0=150, T_mult=1, eta_max=0.1, T_up=10, gamma=0.5)"],"execution_count":23,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"start_time":"2021-04-18T16:16:20.331Z"},"colab":{"base_uri":"https://localhost:8080/"},"id":"cMSiFA-3-NwR","executionInfo":{"status":"ok","timestamp":1620084955841,"user_tz":-540,"elapsed":3464122,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"4f2766f2-1028-4e29-deab-3694452fc235"},"source":["train(num_epochs, model, train_loader, val_loader, criterion, optimizer, saved_dir, val_every, device, lr_scheduler)"],"execution_count":24,"outputs":[{"output_type":"stream","text":["Start training..\n","Epoch [1/20], Step [25/327], Loss: 1.3404\n","Epoch [1/20], Step [50/327], Loss: 0.9490\n","Epoch [1/20], Step [75/327], Loss: 1.0696\n","Epoch [1/20], Step [100/327], Loss: 1.0181\n","Epoch [1/20], Step [125/327], Loss: 0.7060\n","Epoch [1/20], Step [150/327], Loss: 0.9022\n","Epoch [1/20], Step [175/327], Loss: 1.1802\n","Epoch [1/20], Step [200/327], Loss: 1.2541\n","Epoch [1/20], Step [225/327], Loss: 0.6982\n","Epoch [1/20], Step [250/327], Loss: 0.8186\n","Epoch [1/20], Step [275/327], Loss: 0.7327\n","Epoch [1/20], Step [300/327], Loss: 0.6678\n","Epoch [1/20], Step [325/327], Loss: 1.4839\n","Start validation #1\n","Validation #1 Average Loss: 0.9250, mIoU: 0.1358\n","Best performance at epoch: 1\n","Save model in ./saved\n","Epoch [2/20], Step [25/327], Loss: 1.0896\n","Epoch [2/20], Step [50/327], Loss: 1.0054\n","Epoch [2/20], Step [75/327], Loss: 0.7629\n","Epoch [2/20], Step [100/327], Loss: 0.6572\n","Epoch [2/20], Step [125/327], Loss: 0.9154\n","Epoch [2/20], Step [150/327], Loss: 0.9589\n","Epoch [2/20], Step [175/327], Loss: 0.6814\n","Epoch [2/20], Step [200/327], Loss: 0.9632\n","Epoch [2/20], Step [225/327], Loss: 1.4182\n","Epoch [2/20], Step [250/327], Loss: 0.8047\n","Epoch [2/20], Step [275/327], Loss: 0.6770\n","Epoch [2/20], Step [300/327], Loss: 0.8803\n","Epoch [2/20], Step [325/327], Loss: 0.8366\n","Start validation #2\n","Validation #2 Average Loss: 0.8271, mIoU: 0.1634\n","Epoch [3/20], Step [25/327], Loss: 0.7140\n","Epoch [3/20], Step [50/327], Loss: 0.7750\n","Epoch [3/20], Step [75/327], Loss: 0.7306\n","Epoch [3/20], Step [100/327], Loss: 0.8487\n","Epoch [3/20], Step [125/327], Loss: 1.2999\n","Epoch [3/20], Step [150/327], Loss: 0.6295\n","Epoch [3/20], Step [175/327], Loss: 0.7685\n","Epoch [3/20], Step [200/327], Loss: 0.6602\n","Epoch [3/20], Step [225/327], Loss: 0.8553\n","Epoch [3/20], Step [250/327], Loss: 1.0163\n","Epoch [3/20], Step [275/327], Loss: 0.8648\n","Epoch [3/20], Step [300/327], Loss: 0.5656\n","Epoch [3/20], Step [325/327], Loss: 1.0096\n","Start validation #3\n","Validation #3 Average Loss: 0.8136, mIoU: 0.1735\n","Epoch [4/20], Step [25/327], Loss: 0.6853\n","Epoch [4/20], Step [50/327], Loss: 0.5687\n","Epoch [4/20], Step [75/327], Loss: 1.0972\n","Epoch [4/20], Step [100/327], Loss: 0.6824\n","Epoch [4/20], Step [125/327], Loss: 0.8087\n","Epoch [4/20], Step [150/327], Loss: 0.6199\n","Epoch [4/20], Step [175/327], Loss: 0.7041\n","Epoch [4/20], Step [200/327], Loss: 0.8150\n","Epoch [4/20], Step [225/327], Loss: 0.7255\n","Epoch [4/20], Step [250/327], Loss: 0.9100\n","Epoch [4/20], Step [275/327], Loss: 0.4704\n","Epoch [4/20], Step [300/327], Loss: 0.6058\n","Epoch [4/20], Step [325/327], Loss: 0.8397\n","Start validation #4\n","Validation #4 Average Loss: 0.7954, mIoU: 0.1721\n","Epoch [5/20], Step [25/327], Loss: 0.8808\n","Epoch [5/20], Step [50/327], Loss: 0.6248\n","Epoch [5/20], Step [75/327], Loss: 0.7402\n","Epoch [5/20], Step [100/327], Loss: 0.4767\n","Epoch [5/20], Step [125/327], Loss: 0.9656\n","Epoch [5/20], Step [150/327], Loss: 0.7533\n","Epoch [5/20], Step [175/327], Loss: 0.7160\n","Epoch [5/20], Step [200/327], Loss: 1.1267\n","Epoch [5/20], Step [225/327], Loss: 0.8787\n","Epoch [5/20], Step [250/327], Loss: 0.5943\n","Epoch [5/20], Step [275/327], Loss: 0.8528\n","Epoch [5/20], Step [300/327], Loss: 0.4768\n","Epoch [5/20], Step [325/327], Loss: 0.7230\n","Start validation #5\n","Validation #5 Average Loss: 0.7932, mIoU: 0.1718\n","Epoch [6/20], Step [25/327], Loss: 0.8181\n","Epoch [6/20], Step [50/327], Loss: 0.7427\n","Epoch [6/20], Step [75/327], Loss: 0.6742\n","Epoch [6/20], Step [100/327], Loss: 0.6055\n","Epoch [6/20], Step [125/327], Loss: 1.0069\n","Epoch [6/20], Step [150/327], Loss: 0.6450\n","Epoch [6/20], Step [175/327], Loss: 0.8424\n","Epoch [6/20], Step [200/327], Loss: 0.6989\n","Epoch [6/20], Step [225/327], Loss: 0.5765\n","Epoch [6/20], Step [250/327], Loss: 0.8760\n","Epoch [6/20], Step [275/327], Loss: 1.1125\n","Epoch [6/20], Step [300/327], Loss: 0.8011\n","Epoch [6/20], Step [325/327], Loss: 0.6975\n","Start validation #6\n","Validation #6 Average Loss: 0.7922, mIoU: 0.1749\n","Epoch [7/20], Step [25/327], Loss: 0.6893\n","Epoch [7/20], Step [50/327], Loss: 0.7442\n","Epoch [7/20], Step [75/327], Loss: 0.8926\n","Epoch [7/20], Step [100/327], Loss: 0.6364\n","Epoch [7/20], Step [125/327], Loss: 0.5423\n","Epoch [7/20], Step [150/327], Loss: 0.7486\n","Epoch [7/20], Step [175/327], Loss: 0.5839\n","Epoch [7/20], Step [200/327], Loss: 0.8060\n","Epoch [7/20], Step [225/327], Loss: 0.6281\n","Epoch [7/20], Step [250/327], Loss: 0.5392\n","Epoch [7/20], Step [275/327], Loss: 0.6895\n","Epoch [7/20], Step [300/327], Loss: 0.8270\n","Epoch [7/20], Step [325/327], Loss: 0.8935\n","Start validation #7\n","Validation #7 Average Loss: 0.7925, mIoU: 0.1743\n","Epoch [8/20], Step [25/327], Loss: 0.5867\n","Epoch [8/20], Step [50/327], Loss: 0.6933\n","Epoch [8/20], Step [75/327], Loss: 0.7411\n","Epoch [8/20], Step [100/327], Loss: 1.0525\n","Epoch [8/20], Step [125/327], Loss: 0.7223\n","Epoch [8/20], Step [150/327], Loss: 1.0133\n","Epoch [8/20], Step [175/327], Loss: 0.5382\n","Epoch [8/20], Step [200/327], Loss: 0.7512\n","Epoch [8/20], Step [225/327], Loss: 1.3481\n","Epoch [8/20], Step [250/327], Loss: 0.6959\n","Epoch [8/20], Step [275/327], Loss: 0.6873\n","Epoch [8/20], Step [300/327], Loss: 0.6558\n","Epoch [8/20], Step [325/327], Loss: 0.7508\n","Start validation #8\n","Validation #8 Average Loss: 0.7879, mIoU: 0.1763\n","Epoch [9/20], Step [25/327], Loss: 0.8423\n","Epoch [9/20], Step [50/327], Loss: 0.5807\n","Epoch [9/20], Step [75/327], Loss: 0.8398\n","Epoch [9/20], Step [100/327], Loss: 0.5681\n","Epoch [9/20], Step [125/327], Loss: 0.8069\n","Epoch [9/20], Step [150/327], Loss: 0.6626\n","Epoch [9/20], Step [175/327], Loss: 0.7323\n","Epoch [9/20], Step [200/327], Loss: 0.5877\n","Epoch [9/20], Step [225/327], Loss: 0.7616\n","Epoch [9/20], Step [250/327], Loss: 0.6827\n","Epoch [9/20], Step [275/327], Loss: 0.9535\n","Epoch [9/20], Step [300/327], Loss: 0.6502\n","Epoch [9/20], Step [325/327], Loss: 0.7820\n","Start validation #9\n","Validation #9 Average Loss: 0.7872, mIoU: 0.1755\n","Epoch [10/20], Step [25/327], Loss: 1.7108\n","Epoch [10/20], Step [50/327], Loss: 0.6769\n","Epoch [10/20], Step [75/327], Loss: 0.5221\n","Epoch [10/20], Step [100/327], Loss: 0.7915\n","Epoch [10/20], Step [125/327], Loss: 0.9742\n","Epoch [10/20], Step [150/327], Loss: 0.7156\n","Epoch [10/20], Step [175/327], Loss: 0.4828\n","Epoch [10/20], Step [200/327], Loss: 0.6089\n","Epoch [10/20], Step [225/327], Loss: 0.6054\n","Epoch [10/20], Step [250/327], Loss: 0.7823\n","Epoch [10/20], Step [275/327], Loss: 0.7007\n","Epoch [10/20], Step [300/327], Loss: 1.2939\n","Epoch [10/20], Step [325/327], Loss: 0.6547\n","Start validation #10\n","Validation #10 Average Loss: 0.7868, mIoU: 0.1746\n","Epoch [11/20], Step [25/327], Loss: 0.7515\n","Epoch [11/20], Step [50/327], Loss: 0.8481\n","Epoch [11/20], Step [75/327], Loss: 0.9942\n","Epoch [11/20], Step [100/327], Loss: 0.8246\n","Epoch [11/20], Step [125/327], Loss: 1.1555\n","Epoch [11/20], Step [150/327], Loss: 1.0886\n","Epoch [11/20], Step [175/327], Loss: 0.7469\n","Epoch [11/20], Step [200/327], Loss: 0.9848\n","Epoch [11/20], Step [225/327], Loss: 0.6726\n","Epoch [11/20], Step [250/327], Loss: 0.7877\n","Epoch [11/20], Step [275/327], Loss: 0.7568\n","Epoch [11/20], Step [300/327], Loss: 0.9610\n","Epoch [11/20], Step [325/327], Loss: 0.6785\n","Start validation #11\n","Validation #11 Average Loss: 0.7922, mIoU: 0.1752\n","Epoch [12/20], Step [25/327], Loss: 0.7991\n","Epoch [12/20], Step [50/327], Loss: 0.6726\n","Epoch [12/20], Step [75/327], Loss: 0.7591\n","Epoch [12/20], Step [100/327], Loss: 0.8332\n","Epoch [12/20], Step [125/327], Loss: 0.8926\n","Epoch [12/20], Step [150/327], Loss: 1.0147\n","Epoch [12/20], Step [175/327], Loss: 0.6769\n","Epoch [12/20], Step [200/327], Loss: 0.7650\n","Epoch [12/20], Step [225/327], Loss: 0.7325\n","Epoch [12/20], Step [250/327], Loss: 0.7073\n","Epoch [12/20], Step [275/327], Loss: 0.8801\n","Epoch [12/20], Step [300/327], Loss: 0.7414\n","Epoch [12/20], Step [325/327], Loss: 0.8195\n","Start validation #12\n","Validation #12 Average Loss: 0.7863, mIoU: 0.1732\n","Epoch [13/20], Step [25/327], Loss: 0.7355\n","Epoch [13/20], Step [50/327], Loss: 0.6719\n","Epoch [13/20], Step [75/327], Loss: 0.6567\n","Epoch [13/20], Step [100/327], Loss: 0.8883\n","Epoch [13/20], Step [125/327], Loss: 0.7075\n","Epoch [13/20], Step [150/327], Loss: 0.7686\n","Epoch [13/20], Step [175/327], Loss: 0.7710\n","Epoch [13/20], Step [200/327], Loss: 0.6286\n","Epoch [13/20], Step [225/327], Loss: 0.7870\n","Epoch [13/20], Step [250/327], Loss: 0.9272\n","Epoch [13/20], Step [275/327], Loss: 0.6546\n","Epoch [13/20], Step [300/327], Loss: 0.6398\n","Epoch [13/20], Step [325/327], Loss: 1.0414\n","Start validation #13\n","Validation #13 Average Loss: 0.7828, mIoU: 0.1742\n","Epoch [14/20], Step [25/327], Loss: 1.1030\n","Epoch [14/20], Step [50/327], Loss: 0.7547\n","Epoch [14/20], Step [75/327], Loss: 1.0474\n","Epoch [14/20], Step [100/327], Loss: 0.8554\n","Epoch [14/20], Step [125/327], Loss: 0.8439\n","Epoch [14/20], Step [150/327], Loss: 0.5652\n","Epoch [14/20], Step [175/327], Loss: 0.8059\n","Epoch [14/20], Step [200/327], Loss: 1.0368\n","Epoch [14/20], Step [225/327], Loss: 1.1749\n","Epoch [14/20], Step [250/327], Loss: 0.5607\n","Epoch [14/20], Step [275/327], Loss: 0.6441\n","Epoch [14/20], Step [300/327], Loss: 0.7171\n","Epoch [14/20], Step [325/327], Loss: 1.2208\n","Start validation #14\n","Validation #14 Average Loss: 0.7840, mIoU: 0.1762\n","Epoch [15/20], Step [25/327], Loss: 0.7105\n","Epoch [15/20], Step [50/327], Loss: 1.4431\n","Epoch [15/20], Step [75/327], Loss: 0.7579\n","Epoch [15/20], Step [100/327], Loss: 0.8587\n","Epoch [15/20], Step [125/327], Loss: 0.7133\n","Epoch [15/20], Step [150/327], Loss: 0.5948\n","Epoch [15/20], Step [175/327], Loss: 0.7634\n","Epoch [15/20], Step [200/327], Loss: 0.7696\n","Epoch [15/20], Step [225/327], Loss: 0.9070\n","Epoch [15/20], Step [250/327], Loss: 0.5401\n","Epoch [15/20], Step [275/327], Loss: 1.1585\n","Epoch [15/20], Step [300/327], Loss: 0.5244\n","Epoch [15/20], Step [325/327], Loss: 0.6101\n","Start validation #15\n","Validation #15 Average Loss: 0.7818, mIoU: 0.1765\n","Epoch [16/20], Step [25/327], Loss: 0.7590\n","Epoch [16/20], Step [50/327], Loss: 0.8749\n","Epoch [16/20], Step [75/327], Loss: 0.7263\n","Epoch [16/20], Step [100/327], Loss: 1.0436\n","Epoch [16/20], Step [125/327], Loss: 0.8860\n","Epoch [16/20], Step [150/327], Loss: 0.5937\n","Epoch [16/20], Step [175/327], Loss: 0.6770\n","Epoch [16/20], Step [200/327], Loss: 1.0899\n","Epoch [16/20], Step [225/327], Loss: 0.6439\n","Epoch [16/20], Step [250/327], Loss: 0.6783\n","Epoch [16/20], Step [275/327], Loss: 0.6713\n","Epoch [16/20], Step [300/327], Loss: 0.8240\n","Epoch [16/20], Step [325/327], Loss: 0.6308\n","Start validation #16\n","Validation #16 Average Loss: 0.7849, mIoU: 0.1725\n","Epoch [17/20], Step [25/327], Loss: 0.7632\n","Epoch [17/20], Step [50/327], Loss: 0.9935\n","Epoch [17/20], Step [75/327], Loss: 0.7575\n","Epoch [17/20], Step [100/327], Loss: 0.7400\n","Epoch [17/20], Step [125/327], Loss: 0.8194\n","Epoch [17/20], Step [150/327], Loss: 1.1859\n","Epoch [17/20], Step [175/327], Loss: 0.5058\n","Epoch [17/20], Step [200/327], Loss: 0.8796\n","Epoch [17/20], Step [225/327], Loss: 0.7460\n","Epoch [17/20], Step [250/327], Loss: 0.8585\n","Epoch [17/20], Step [275/327], Loss: 0.8968\n","Epoch [17/20], Step [300/327], Loss: 0.7931\n","Epoch [17/20], Step [325/327], Loss: 0.8964\n","Start validation #17\n","Validation #17 Average Loss: 0.7871, mIoU: 0.1758\n","Epoch [18/20], Step [25/327], Loss: 0.8203\n","Epoch [18/20], Step [50/327], Loss: 0.7452\n","Epoch [18/20], Step [75/327], Loss: 0.6543\n","Epoch [18/20], Step [100/327], Loss: 0.4986\n","Epoch [18/20], Step [125/327], Loss: 1.0547\n","Epoch [18/20], Step [150/327], Loss: 0.8887\n","Epoch [18/20], Step [175/327], Loss: 0.6260\n","Epoch [18/20], Step [200/327], Loss: 0.7944\n","Epoch [18/20], Step [225/327], Loss: 0.7318\n","Epoch [18/20], Step [250/327], Loss: 0.5109\n","Epoch [18/20], Step [275/327], Loss: 1.0181\n","Epoch [18/20], Step [300/327], Loss: 1.0613\n","Epoch [18/20], Step [325/327], Loss: 0.8320\n","Start validation #18\n","Validation #18 Average Loss: 0.7767, mIoU: 0.1772\n","Epoch [19/20], Step [25/327], Loss: 0.5464\n","Epoch [19/20], Step [50/327], Loss: 0.9107\n","Epoch [19/20], Step [75/327], Loss: 0.8360\n","Epoch [19/20], Step [100/327], Loss: 0.8210\n","Epoch [19/20], Step [125/327], Loss: 0.5722\n","Epoch [19/20], Step [150/327], Loss: 0.6313\n","Epoch [19/20], Step [175/327], Loss: 0.6781\n","Epoch [19/20], Step [200/327], Loss: 0.5307\n","Epoch [19/20], Step [225/327], Loss: 0.5890\n","Epoch [19/20], Step [250/327], Loss: 0.8416\n","Epoch [19/20], Step [275/327], Loss: 0.7966\n","Epoch [19/20], Step [300/327], Loss: 0.8717\n","Epoch [19/20], Step [325/327], Loss: 0.8133\n","Start validation #19\n","Validation #19 Average Loss: 0.7760, mIoU: 0.1779\n","Epoch [20/20], Step [25/327], Loss: 0.6331\n","Epoch [20/20], Step [50/327], Loss: 0.5971\n","Epoch [20/20], Step [75/327], Loss: 0.9251\n","Epoch [20/20], Step [100/327], Loss: 1.1529\n","Epoch [20/20], Step [125/327], Loss: 0.5342\n","Epoch [20/20], Step [150/327], Loss: 0.7732\n","Epoch [20/20], Step [175/327], Loss: 0.6224\n","Epoch [20/20], Step [200/327], Loss: 0.6570\n","Epoch [20/20], Step [225/327], Loss: 0.9729\n","Epoch [20/20], Step [250/327], Loss: 0.6656\n","Epoch [20/20], Step [275/327], Loss: 0.7468\n","Epoch [20/20], Step [300/327], Loss: 0.6953\n","Epoch [20/20], Step [325/327], Loss: 0.7678\n","Start validation #20\n","Validation #20 Average Loss: 0.7748, mIoU: 0.1787\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"C6ClcO0J-NwR"},"source":["## 저장된 model 불러오기 (학습된 이후) "]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:21.050200Z","start_time":"2021-04-16T19:44:20.802200Z"},"scrolled":true,"id":"KQPtUDzd-NwR"},"source":["# best model 저장된 경로\n","model_path = './saved/pan_effb3_noisy_focal_adamp_cosLR.pt'\n","\n","# best model 불러오기\n","checkpoint = torch.load(model_path, map_location=device)\n","model.load_state_dict(checkpoint)\n","\n","# 추론을 실행하기 전에는 반드시 설정 (batch normalization, dropout 를 평가 모드로 설정)\n","# model.eval()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:24.939227Z","start_time":"2021-04-16T19:44:24.518228Z"},"id":"0LQqrDAp-NwR"},"source":["# 첫번째 batch의 추론 결과 확인\n","for imgs, image_infos in test_loader:\n"," image_infos = image_infos\n"," temp_images = imgs\n"," \n"," model.eval()\n"," # inference\n"," outs = model(torch.stack(temp_images).to(device))\n"," oms = torch.argmax(outs.squeeze(), dim=1).detach().cpu().numpy()\n"," \n"," break\n","\n","i = 3\n","fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(16, 16))\n","\n","print('Shape of Original Image :', list(temp_images[i].shape))\n","print('Shape of Predicted : ', list(oms[i].shape))\n","print('Unique values, category of transformed mask : \\n', [{int(i),category_names[int(i)]} for i in list(np.unique(oms[i]))])\n","\n","# Original image\n","ax1.imshow(temp_images[i].permute([1,2,0]))\n","ax1.grid(False)\n","ax1.set_title(\"Original image : {}\".format(image_infos[i]['file_name']), fontsize = 15)\n","\n","# Predicted\n","ax2.imshow(oms[i])\n","ax2.grid(False)\n","ax2.set_title(\"Predicted : {}\".format(image_infos[i]['file_name']), fontsize = 15)\n","\n","plt.show()"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"evYjR2F3-NwS"},"source":["## submission을 위한 test 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:27.469285Z","start_time":"2021-04-16T19:44:27.456021Z"},"id":"nhMLnV5d-NwS"},"source":["def test(model, data_loader, device):\n"," size = 256\n"," transform = A.Compose([A.Resize(256, 256)])\n"," print('Start prediction.')\n"," model.eval()\n"," \n"," file_name_list = []\n"," preds_array = np.empty((0, size*size), dtype=np.long)\n"," \n"," with torch.no_grad():\n"," for step, (imgs, image_infos) in enumerate(test_loader):\n","\n"," # inference (512 x 512)\n"," outs = model(torch.stack(imgs).to(device))\n"," oms = torch.argmax(outs.squeeze(), dim=1).detach().cpu().numpy()\n"," \n"," # resize (256 x 256)\n"," temp_mask = []\n"," for img, mask in zip(np.stack(imgs), oms):\n"," transformed = transform(image=img, mask=mask)\n"," mask = transformed['mask']\n"," temp_mask.append(mask)\n","\n"," oms = np.array(temp_mask)\n"," \n"," oms = oms.reshape([oms.shape[0], size*size]).astype(int)\n"," preds_array = np.vstack((preds_array, oms))\n"," \n"," file_name_list.append([i['file_name'] for i in image_infos])\n"," print(\"End prediction.\")\n"," file_names = [y for x in file_name_list for y in x]\n"," \n"," return file_names, preds_array"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"r1YKHBf4-NwT"},"source":["## submission.csv 생성"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:45:42.235310Z","start_time":"2021-04-16T19:44:30.499016Z"},"scrolled":true,"id":"Bz79_g8K-NwT"},"source":["# sample_submisson.csv 열기\n","submission = pd.read_csv('./submission/sample_submission.csv', index_col=None)\n","\n","# test set에 대한 prediction\n","file_names, preds = test(model, test_loader, device)\n","\n","# PredictionString 대입\n","for file_name, string in zip(file_names, preds):\n"," submission = submission.append({\"image_id\" : file_name, \"PredictionString\" : ' '.join(str(e) for e in string.tolist())}, \n"," ignore_index=True)\n","\n","# submission.csv로 저장\n","submission.to_csv(\"./submission/pan_effb3_noisy_focal_adamp_cosLR.csv\", index=False)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Zgl7Ye7F-NwT"},"source":["## 제출까지\n","\n"]},{"cell_type":"code","metadata":{"id":"022E8AC7-NwT"},"source":["import json\n","import requests\n","import os\n","from urllib.parse import urlparse, parse_qsl, urlencode, urlunparse\n","\n","def submit(user_key='', file_path = '', desc=\"\"):\n"," if not user_key:\n"," raise Exception(\"No UserKey\" )\n"," url = urlparse('http://ec2-13-124-161-225.ap-northeast-2.compute.amazonaws.com:8000/api/v1/competition/28/presigned_url/?description=&hyperparameters={%22training%22:{},%22inference%22:{}}')\n"," qs = dict(parse_qsl(url.query))\n"," qs['description'] = desc\n"," parts = url._replace(query=urlencode(qs))\n"," url = urlunparse(parts)\n","\n"," print(url)\n"," headers = {\n"," 'Authorization': user_key\n"," }\n"," res = requests.get(url, headers=headers)\n"," print(res.text)\n"," data = json.loads(res.text)\n"," \n"," submit_url = data['url']\n"," body = {\n"," 'key':'app/Competitions/000028/Users/{}/Submissions/{}/output.csv'.format(str(data['submission']['user']).zfill(8),str(data['submission']['local_id']).zfill(4)),\n"," 'x-amz-algorithm':data['fields']['x-amz-algorithm'],\n"," 'x-amz-credential':data['fields']['x-amz-credential'],\n"," 'x-amz-date':data['fields']['x-amz-date'],\n"," 'policy':data['fields']['policy'],\n"," 'x-amz-signature':data['fields']['x-amz-signature']\n"," }\n"," requests.post(url=submit_url, data=body, files={'file': open(file_path, 'rb')})\n","\n","\n","####################################################################################\n","test_dir = \"/opt/ml/code/submission\" # 수정 필요 : output 파일 폴더 \n","desc = 'pan_effb3_noisy_focal_adamp_cosLR' # 수정 필요 : 파일에 대한 설명\n","output_file = \"pan_effb3_noisy_focal_adamp_cosLR.csv\" #수정 필요 : output 파일 \n","user_key = \"Bearer 7bb5f96452751a238ffaf91a93c4242bf9b72abe\" # 수정 필요 : Authorization \n","\n","\n","submit(user_key, os.path.join(test_dir, output_file),desc)"],"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/chanyub_seg/code/re_pan_effb3_noisy_focal_CE_madgrad_kwparam_stepLR.ipynb b/chanyub_seg/code/re_pan_effb3_noisy_focal_CE_madgrad_kwparam_stepLR.ipynb deleted file mode 100644 index f448483..0000000 --- a/chanyub_seg/code/re_pan_effb3_noisy_focal_CE_madgrad_kwparam_stepLR.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"nbformat":4,"nbformat_minor":0,"metadata":{"hide_input":false,"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.7"},"toc":{"base_numbering":1,"nav_menu":{},"number_sections":true,"sideBar":true,"skip_h1_title":false,"title_cell":"Table of Contents","title_sidebar":"Contents","toc_cell":true,"toc_position":{"height":"calc(100% - 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google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Mounted at /content/drive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xDwpp4Lk-gSH","executionInfo":{"status":"ok","timestamp":1620187196119,"user_tz":-540,"elapsed":6406,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"19c3f036-2b07-42c3-c9b9-930d21c425f2"},"source":["ls"],"execution_count":2,"outputs":[{"output_type":"stream","text":["\u001b[0m\u001b[01;34mdrive\u001b[0m/ \u001b[01;34msample_data\u001b[0m/\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sItrVDeh-iYC","executionInfo":{"status":"ok","timestamp":1620187197489,"user_tz":-540,"elapsed":7508,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"626b516a-2d57-4efa-d20d-6763a9f7a99f"},"source":["cd drive/MyDrive/Trash/code"],"execution_count":3,"outputs":[{"output_type":"stream","text":["/content/drive/MyDrive/Trash/code\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"a54v4-kN_LVO","executionInfo":{"status":"ok","timestamp":1620187207009,"user_tz":-540,"elapsed":16708,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"73bdd0e0-42de-4efc-9ea3-1e2ff19c5e26"},"source":["!pip install albumentations==0.5.2"],"execution_count":4,"outputs":[{"output_type":"stream","text":["Collecting albumentations==0.5.2\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/03/58/63fb1d742dc42d9ba2800ea741de1f2bc6bb05548d8724aa84794042eaf2/albumentations-0.5.2-py3-none-any.whl (72kB)\n","\r\u001b[K |████▌ | 10kB 21.6MB/s eta 0:00:01\r\u001b[K |█████████ | 20kB 28.0MB/s eta 0:00:01\r\u001b[K |█████████████▋ | 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https://files.pythonhosted.org/packages/6d/6d/92f377bece9b0ec9c893081dbe073a65b38d7ac12ef572b8f70554d08760/opencv_python_headless-4.5.1.48-cp37-cp37m-manylinux2014_x86_64.whl (37.6MB)\n","\u001b[K |████████████████████████████████| 37.6MB 1.2MB/s \n","\u001b[?25hRequirement already satisfied: PyYAML in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (3.13)\n","Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (1.4.1)\n","Requirement already satisfied: scikit-image>=0.16.1 in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (0.16.2)\n","Requirement already satisfied: Shapely in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (1.7.1)\n","Requirement already satisfied: opencv-python in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (4.1.2.30)\n","Requirement already satisfied: imageio in 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python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug>=0.4.0->albumentations==0.5.2) (2.8.1)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug>=0.4.0->albumentations==0.5.2) (1.3.1)\n","Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug>=0.4.0->albumentations==0.5.2) (2.4.7)\n","Requirement already satisfied: decorator<5,>=4.3 in /usr/local/lib/python3.7/dist-packages (from networkx>=2.0->scikit-image>=0.16.1->albumentations==0.5.2) (4.4.2)\n","Installing collected packages: imgaug, opencv-python-headless, albumentations\n"," Found existing installation: imgaug 0.2.9\n"," Uninstalling imgaug-0.2.9:\n"," Successfully uninstalled imgaug-0.2.9\n"," Found existing installation: albumentations 0.1.12\n"," Uninstalling albumentations-0.1.12:\n"," Successfully uninstalled albumentations-0.1.12\n","Successfully installed albumentations-0.5.2 imgaug-0.4.0 opencv-python-headless-4.5.1.48\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.826930Z","start_time":"2021-04-18T10:34:45.406686Z"},"colab":{"base_uri":"https://localhost:8080/"},"id":"l_LPA4XD-NwC","executionInfo":{"status":"ok","timestamp":1620187211040,"user_tz":-540,"elapsed":17819,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"2159d8a1-086a-4177-8787-78417ac6f954"},"source":["import os\n","import random\n","import time\n","import json\n","import warnings \n","warnings.filterwarnings('ignore')\n","\n","import torch\n","import torch.nn as nn\n","from torch.utils.data import Dataset, DataLoader\n","from utils import label_accuracy_score\n","import cv2\n","\n","import numpy as np\n","import pandas as pd\n","\n","# 전처리를 위한 라이브러리\n","from pycocotools.coco import COCO\n","import torchvision\n","import torchvision.transforms as transforms\n","\n","import albumentations as A\n","from albumentations.pytorch import ToTensorV2\n","\n","# 시각화를 위한 라이브러리\n","import matplotlib.pyplot as plt\n","import seaborn as sns; sns.set()\n","\n","plt.rcParams['axes.grid'] = False\n","\n","print('pytorch version: {}'.format(torch.__version__))\n","print('GPU 사용 가능 여부: {}'.format(torch.cuda.is_available()))\n","\n","print(torch.cuda.get_device_name(0))\n","print(torch.cuda.device_count())\n","\n","device = \"cuda\" if torch.cuda.is_available() else \"cpu\" # GPU 사용 가능 여부에 따라 device 정보 저장"],"execution_count":5,"outputs":[{"output_type":"stream","text":["pytorch version: 1.8.1+cu101\n","GPU 사용 가능 여부: True\n","Tesla V100-SXM2-16GB\n","1\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"HxgRSL_M-NwF"},"source":["## 하이퍼파라미터 세팅 및 seed 고정"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.841930Z","start_time":"2021-04-18T10:34:47.827931Z"},"id":"rV3JmGP5-NwF","executionInfo":{"status":"ok","timestamp":1620187211041,"user_tz":-540,"elapsed":8401,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["batch_size = 8 # Mini-batch size\n","num_epochs = 20\n","learning_rate = 0.0001"],"execution_count":6,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.856930Z","start_time":"2021-04-18T10:34:47.842931Z"},"id":"Z6LOuJXQ-NwG","executionInfo":{"status":"ok","timestamp":1620187211042,"user_tz":-540,"elapsed":8182,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# seed 고정\n","random_seed = 42\n","torch.manual_seed(random_seed)\n","torch.cuda.manual_seed(random_seed)\n","# torch.cuda.manual_seed_all(random_seed) # if use multi-GPU\n","torch.backends.cudnn.deterministic = True\n","torch.backends.cudnn.benchmark = False\n","np.random.seed(random_seed)\n","random.seed(random_seed)"],"execution_count":7,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"iWF_EJDu-NwG"},"source":["## 학습 데이터 EDA"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.381961Z","start_time":"2021-04-18T10:34:47.857930Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"lg0x0D0a-NwG","executionInfo":{"status":"ok","timestamp":1620187221958,"user_tz":-540,"elapsed":17424,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"c05d781a-5e8f-4164-8c9e-285b897526f9"},"source":["%matplotlib inline\n","\n","dataset_path = '../input/data'\n","anns_file_path = dataset_path + '/' + 'train.json'\n","\n","# Read annotations\n","with open(anns_file_path, 'r') as f:\n"," dataset = json.loads(f.read())\n","\n","categories = dataset['categories']\n","anns = dataset['annotations']\n","imgs = dataset['images']\n","nr_cats = len(categories)\n","nr_annotations = len(anns)\n","nr_images = len(imgs)\n","\n","# Load categories and super categories\n","cat_names = []\n","super_cat_names = []\n","super_cat_ids = {}\n","super_cat_last_name = ''\n","nr_super_cats = 0\n","for cat_it in categories:\n"," cat_names.append(cat_it['name'])\n"," super_cat_name = cat_it['supercategory']\n"," # Adding new supercat\n"," if super_cat_name != super_cat_last_name:\n"," super_cat_names.append(super_cat_name)\n"," super_cat_ids[super_cat_name] = nr_super_cats\n"," super_cat_last_name = super_cat_name\n"," nr_super_cats += 1\n","\n","print('Number of super categories:', nr_super_cats)\n","print('Number of categories:', nr_cats)\n","print('Number of annotations:', nr_annotations)\n","print('Number of images:', nr_images)"],"execution_count":8,"outputs":[{"output_type":"stream","text":["Number of super categories: 11\n","Number of categories: 11\n","Number of annotations: 21116\n","Number of images: 2617\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.546964Z","start_time":"2021-04-18T10:34:51.382969Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/","height":355},"id":"PjLyVHVY-NwH","executionInfo":{"status":"ok","timestamp":1620187222647,"user_tz":-540,"elapsed":17137,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"99af754b-079c-4844-d113-2b77fc776c4c"},"source":["# Count annotations\n","cat_histogram = np.zeros(nr_cats,dtype=int)\n","for ann in anns:\n"," cat_histogram[ann['category_id']] += 1\n","\n","# Initialize the matplotlib figure\n","f, ax = plt.subplots(figsize=(5,5))\n","\n","# Convert to DataFrame\n","df = pd.DataFrame({'Categories': cat_names, 'Number of annotations': cat_histogram})\n","df = df.sort_values('Number of annotations', 0, False)\n","\n","# Plot the histogram\n","plt.title(\"category distribution of train set \")\n","plot_1 = sns.barplot(x=\"Number of annotations\", y=\"Categories\", data=df, label=\"Total\", color=\"b\")"],"execution_count":9,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.561965Z","start_time":"2021-04-18T10:34:51.547969Z"},"id":"34Tw5__i-NwI","executionInfo":{"status":"ok","timestamp":1620187222648,"user_tz":-540,"elapsed":15492,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# category labeling \n","sorted_temp_df = df.sort_index()\n","\n","# background = 0 에 해당되는 label 추가 후 기존들을 모두 label + 1 로 설정\n","sorted_df = pd.DataFrame([\"Backgroud\"], columns = [\"Categories\"])\n","sorted_df = sorted_df.append(sorted_temp_df, ignore_index=True)"],"execution_count":10,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.576961Z","start_time":"2021-04-18T10:34:51.562964Z"},"colab":{"base_uri":"https://localhost:8080/","height":421},"id":"CQk4vV5N-NwI","executionInfo":{"status":"ok","timestamp":1620187222649,"user_tz":-540,"elapsed":15358,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"44b51837-2abc-41fb-9af2-4da12b05044e"},"source":["# class (Categories) 에 따른 index 확인 (0~11 : 총 12개)\n","sorted_df"],"execution_count":11,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
CategoriesNumber of annotations
0BackgroudNaN
1UNKNOWN128.0
2General trash2225.0
3Paper7448.0
4Paper pack527.0
5Metal449.0
6Glass488.0
7Plastic2472.0
8Styrofoam1074.0
9Plastic bag6114.0
10Battery50.0
11Clothing141.0
\n","
"],"text/plain":[" Categories Number of annotations\n","0 Backgroud NaN\n","1 UNKNOWN 128.0\n","2 General trash 2225.0\n","3 Paper 7448.0\n","4 Paper pack 527.0\n","5 Metal 449.0\n","6 Glass 488.0\n","7 Plastic 2472.0\n","8 Styrofoam 1074.0\n","9 Plastic bag 6114.0\n","10 Battery 50.0\n","11 Clothing 141.0"]},"metadata":{"tags":[]},"execution_count":11}]},{"cell_type":"markdown","metadata":{"id":"KXU0zmZs-NwI"},"source":["## 데이터 전처리 함수 정의 (Dataset)"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:52.693328Z","start_time":"2021-04-18T10:34:52.681328Z"},"id":"QFnTI8_Z-NwJ","executionInfo":{"status":"ok","timestamp":1620187230669,"user_tz":-540,"elapsed":639,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["category_names = list(sorted_df.Categories)\n","\n","def get_classname(classID, cats):\n"," for i in range(len(cats)):\n"," if cats[i]['id']==classID:\n"," return cats[i]['name']\n"," return \"None\"\n","\n","class CustomDataLoader(Dataset):\n"," \"\"\"COCO format\"\"\"\n"," def __init__(self, data_dir, mode = 'train', transform = None):\n"," super().__init__()\n"," self.mode = mode\n"," self.transform = transform\n"," self.coco = COCO(data_dir)\n"," \n"," def __getitem__(self, index: int):\n"," # dataset이 index되어 list처럼 동작\n"," image_id = self.coco.getImgIds(imgIds=index)\n"," image_infos = self.coco.loadImgs(image_id)[0]\n"," \n"," # cv2 를 활용하여 image 불러오기\n"," images = cv2.imread(os.path.join(dataset_path, image_infos['file_name']))\n"," images = cv2.cvtColor(images, cv2.COLOR_BGR2RGB).astype(np.float32)\n"," images /= 255.0\n"," \n"," if (self.mode in ('train', 'val')):\n"," ann_ids = self.coco.getAnnIds(imgIds=image_infos['id'])\n"," anns = self.coco.loadAnns(ann_ids)\n","\n"," # Load the categories in a variable\n"," cat_ids = self.coco.getCatIds()\n"," cats = self.coco.loadCats(cat_ids)\n","\n"," # masks : size가 (height x width)인 2D\n"," # 각각의 pixel 값에는 \"category id + 1\" 할당\n"," # Background = 0\n"," masks = np.zeros((image_infos[\"height\"], image_infos[\"width\"]))\n"," # Unknown = 1, General trash = 2, ... , Cigarette = 11\n"," for i in range(len(anns)):\n"," className = get_classname(anns[i]['category_id'], cats)\n"," pixel_value = category_names.index(className)\n"," masks = np.maximum(self.coco.annToMask(anns[i])*pixel_value, masks)\n"," masks = masks.astype(np.float32)\n","\n"," # transform -> albumentations 라이브러리 활용\n"," if self.transform is not None:\n"," transformed = self.transform(image=images, mask=masks)\n"," images = transformed[\"image\"]\n"," masks = transformed[\"mask\"]\n"," \n"," return images, masks, image_infos\n"," \n"," if self.mode == 'test':\n"," # transform -> albumentations 라이브러리 활용\n"," if self.transform is not None:\n"," transformed = self.transform(image=images)\n"," images = transformed[\"image\"]\n"," \n"," return images, image_infos\n"," \n"," \n"," def __len__(self) -> int:\n"," # 전체 dataset의 size를 return\n"," return len(self.coco.getImgIds())"],"execution_count":12,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"cp2aIOlP-NwK"},"source":["## Dataset 정의 및 DataLoader 할당"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:58.823175Z","start_time":"2021-04-18T10:34:54.106233Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"P7zFlRn6-NwK","executionInfo":{"status":"ok","timestamp":1620187240881,"user_tz":-540,"elapsed":9259,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"655d5766-d40a-4cde-f3b2-84443e68bf00"},"source":["# train.json / validation.json / test.json 디렉토리 설정\n","train_path = dataset_path + '/train.json'\n","val_path = dataset_path + '/val.json'\n","test_path = dataset_path + '/test.json'\n","\n","# collate_fn needs for batch\n","def collate_fn(batch):\n"," return tuple(zip(*batch))\n","\n","train_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","val_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","test_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","# create own Dataset 1 (skip)\n","# validation set을 직접 나누고 싶은 경우\n","# random_split 사용하여 data set을 8:2 로 분할\n","# train_size = int(0.8*len(dataset))\n","# val_size = int(len(dataset)-train_size)\n","# dataset = CustomDataLoader(data_dir=train_path, mode='train', transform=transform)\n","# train_dataset, val_dataset = torch.utils.data.random_split(dataset, [train_size, val_size])\n","\n","# create own Dataset 2\n","# train dataset\n","train_dataset = CustomDataLoader(data_dir=train_path, mode='train', transform=train_transform)\n","\n","# validation dataset\n","val_dataset = CustomDataLoader(data_dir=val_path, mode='val', transform=val_transform)\n","\n","# test dataset\n","test_dataset = CustomDataLoader(data_dir=test_path, mode='test', transform=test_transform)\n","\n","\n","# DataLoader\n","train_loader = torch.utils.data.DataLoader(dataset=train_dataset, \n"," batch_size=batch_size,\n"," shuffle=True,\n"," num_workers=4,\n"," collate_fn=collate_fn,\n"," drop_last=True)\n","\n","val_loader = torch.utils.data.DataLoader(dataset=val_dataset, \n"," batch_size=batch_size,\n"," shuffle=False,\n"," num_workers=4,\n"," collate_fn=collate_fn,\n"," drop_last=True) \n","\n","test_loader = torch.utils.data.DataLoader(dataset=test_dataset,\n"," batch_size=batch_size,\n"," num_workers=4,\n"," collate_fn=collate_fn)"],"execution_count":13,"outputs":[{"output_type":"stream","text":["loading annotations into memory...\n","Done (t=4.12s)\n","creating index...\n","index created!\n","loading annotations into memory...\n","Done (t=3.37s)\n","creating index...\n","index created!\n","loading annotations into memory...\n","Done (t=0.95s)\n","creating index...\n","index created!\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"8rqJiHb_-NwM"},"source":["# wandb"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"H1kHXm0uAX3R","executionInfo":{"status":"ok","timestamp":1620134755267,"user_tz":-540,"elapsed":8060,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"22ef791e-8446-49b5-ebd4-443b4cf49dda"},"source":["!pip install wandb"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Collecting wandb\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/67/5a/b037b50f9849212863a2fed313624d8f6f33ffa4ce89dc706e2a0e98c780/wandb-0.10.29-py2.py3-none-any.whl (2.1MB)\n","\u001b[K |████████████████████████████████| 2.1MB 9.1MB/s \n","\u001b[?25hCollecting subprocess32>=3.5.3\n","\u001b[?25l Downloading 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directory: /root/.cache/pip/wheels/68/39/1a/5e402bdfdf004af1786c8b853fd92f8c4a04f22aad179654d1\n"," Building wheel for pathtools (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for pathtools: filename=pathtools-0.1.2-cp37-none-any.whl size=8786 sha256=ef4dc306cb5a85d14636d2caa889ac49fa7710df09cf5c6c7371de6ed3e430d4\n"," Stored in directory: /root/.cache/pip/wheels/0b/04/79/c3b0c3a0266a3cb4376da31e5bfe8bba0c489246968a68e843\n","Successfully built subprocess32 pathtools\n","Installing collected packages: subprocess32, shortuuid, sentry-sdk, smmap, gitdb, GitPython, docker-pycreds, configparser, pathtools, wandb\n","Successfully installed GitPython-3.1.14 configparser-5.0.2 docker-pycreds-0.4.0 gitdb-4.0.7 pathtools-0.1.2 sentry-sdk-1.0.0 shortuuid-1.0.1 smmap-4.0.0 subprocess32-3.5.4 wandb-0.10.29\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":153},"id":"aMW4VV9V-NwM","executionInfo":{"status":"ok","timestamp":1620134765729,"user_tz":-540,"elapsed":10439,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"70e16b67-52d6-4140-8632-74f5d9e48adc"},"source":["import wandb\n","\n","proj_name = 're_pan_effb3_noisy_focal_CE_madgrad_kwparam_stepLR'\n","\n","wandb.init(project='chanyub',name=proj_name)\n","\n","config = wandb.config\n","config.learning_rate = 0.01"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"application/javascript":["\n"," window._wandbApiKey = new Promise((resolve, reject) => {\n"," function loadScript(url) {\n"," return new Promise(function(resolve, reject) {\n"," let newScript = document.createElement(\"script\");\n"," newScript.onerror = reject;\n"," newScript.onload = resolve;\n"," document.body.appendChild(newScript);\n"," newScript.src = url;\n"," });\n"," }\n"," loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n"," const iframe = document.createElement('iframe')\n"," iframe.style.cssText = \"width:0;height:0;border:none\"\n"," document.body.appendChild(iframe)\n"," const handshake = new Postmate({\n"," container: iframe,\n"," url: 'https://wandb.ai/authorize'\n"," });\n"," const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n"," handshake.then(function(child) {\n"," child.on('authorize', data => {\n"," clearTimeout(timeout)\n"," resolve(data)\n"," });\n"," });\n"," })\n"," });\n"," "],"text/plain":[""]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"],"name":"stderr"},{"output_type":"display_data","data":{"text/html":["\n"," Tracking run with wandb version 0.10.29
\n"," Syncing run re_pan_effb3_noisy_focal_CE_madgrad_kwparam_stepLR to Weights & Biases (Documentation).
\n"," Project page: https://wandb.ai/pstage12/chanyub
\n"," Run page: https://wandb.ai/pstage12/chanyub/runs/26jh986f
\n"," Run data is saved locally in /content/drive/My Drive/Trash/code/wandb/run-20210504_132602-26jh986f

\n"," "],"text/plain":[""]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"uQRiIVGX-NwM"},"source":["## My model"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"S0xCGpNeAqeD","executionInfo":{"status":"ok","timestamp":1620187285034,"user_tz":-540,"elapsed":6210,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"b37e7ca0-48e6-4268-b04a-cbfb07fbb899"},"source":["!pip install segmentation_models_pytorch"],"execution_count":15,"outputs":[{"output_type":"stream","text":["Collecting segmentation_models_pytorch\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/65/54/8953f9f7ee9d451b0f3be8d635aa3a654579abf898d17502a090efe1155a/segmentation_models_pytorch-0.1.3-py3-none-any.whl (66kB)\n","\r\u001b[K |█████ | 10kB 19.1MB/s eta 0:00:01\r\u001b[K |██████████ | 20kB 26.3MB/s eta 0:00:01\r\u001b[K 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sha256=e628e2a9877e5ed264a6831f61ba73fde485d3db7aede9b23f7e07f4e43a9c0d\n"," Stored in directory: /root/.cache/pip/wheels/42/1e/a9/2a578ba9ad04e776e80bf0f70d8a7f4c29ec0718b92d8f6ccd\n","Successfully built pretrainedmodels efficientnet-pytorch\n","Installing collected packages: timm, munch, pretrainedmodels, efficientnet-pytorch, segmentation-models-pytorch\n","Successfully installed efficientnet-pytorch-0.6.3 munch-2.5.0 pretrainedmodels-0.7.4 segmentation-models-pytorch-0.1.3 timm-0.3.2\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:11.634792Z","start_time":"2021-04-18T16:16:05.875817Z"},"colab":{"base_uri":"https://localhost:8080/","height":137,"referenced_widgets":["b198ec0e993148148630f9e07505cd88","8d0741952e8540ae9f729d7935454316","f348cadd050b491fb67fcd06c30b2b24","a81bff0549bb43a48324de5485fa9dd4","75a3a303ff5c4ceaa809e6f864b16602","5580b80c542e4429bd12e35322e3ac77","2d33288195a84840bc19c45c9279d1e0","e53a847b6de44546acc9916708925e3f"]},"id":"a8IfZfiM-NwM","executionInfo":{"status":"ok","timestamp":1620187295761,"user_tz":-540,"elapsed":15696,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"df189db4-0086-4141-8406-9d91541f79f8"},"source":["# 구현된 model에 임의의 input을 넣어 output이 잘 나오는지 test\n","import segmentation_models_pytorch as smp\n","\n","model = smp.PAN(encoder_name='timm-efficientnet-b3', encoder_weights='noisy-student', classes=12)\n","x = torch.randn([2, 3, 512, 512])\n","print(\"input shape : \", x.shape)\n","out = model(x).to(device)\n","print(\"output shape : \", out.size())\n","\n","model = model.to(device)"],"execution_count":16,"outputs":[{"output_type":"stream","text":["Downloading: \"https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b3_ns-9d44bf68.pth\" to /root/.cache/torch/hub/checkpoints/tf_efficientnet_b3_ns-9d44bf68.pth\n"],"name":"stderr"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"b198ec0e993148148630f9e07505cd88","version_minor":0,"version_major":2},"text/plain":["HBox(children=(FloatProgress(value=0.0, max=49385734.0), HTML(value='')))"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\n","input shape : torch.Size([2, 3, 512, 512])\n","output shape : torch.Size([2, 12, 512, 512])\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"zvDnI7_T-NwN"},"source":["## train, validation, test 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.104200Z","start_time":"2021-04-18T16:16:18.093174Z"},"id":"RA3oAapJ-NwN"},"source":["def train(num_epochs, model, data_loader, val_loader, criterion1, criterion2, optimizer, saved_dir, val_every, device, lr_scheduler):\n"," print('Start training..')\n"," best_loss = 9999999\n"," best_miou = 0\n"," for epoch in range(num_epochs):\n"," model.train()\n"," total_loss = 0\n"," for step, (images, masks, _) in enumerate(data_loader):\n"," images = torch.stack(images) # (batch, channel, height, width)\n"," masks = torch.stack(masks).long() # (batch, channel, height, width)\n"," \n"," # gpu 연산을 위해 device 할당\n"," images, masks = images.to(device), masks.to(device)\n"," \n"," # inference\n"," outputs = model(images)\n"," \n"," # loss 계산 (cross entropy loss)\n"," loss = criterion1(outputs, masks) + criterion2(outputs, masks)\n"," total_loss += loss.item()\n","\n"," optimizer.zero_grad()\n"," loss.backward()\n"," optimizer.step()\n"," \n"," lr_scheduler.step()\n"," \n"," # step 주기에 따른 loss 출력\n"," if (step + 1) % 25 == 0:\n"," print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(\n"," epoch+1, num_epochs, step+1, len(train_loader), loss.item()))\n"," \n"," # validation 주기에 따른 loss 출력 및 best model 저장\n"," if (epoch + 1) % val_every == 0:\n","# avrg_loss = validation(epoch + 1, model, val_loader, criterion, device)\n","# if avrg_loss < best_loss:\n","# print('Best performance at epoch: {}'.format(epoch + 1))\n","# print('Save model in', saved_dir)\n","# best_loss = avrg_loss\n","# wandb.log({'best_loss': best_loss})\n","# save_model(model, saved_dir)\n"," avrg_miou = validation(epoch + 1, model, val_loader, criterion1, criterion2, device)\n"," if avrg_miou > best_miou:\n"," print('Best performance at epoch: {}'.format(epoch + 1))\n"," print('Save model in', saved_dir)\n"," best_miou = avrg_miou\n"," wandb.log({'best_miou': best_miou})\n"," save_model(model, saved_dir)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.285795Z","start_time":"2021-04-18T16:16:18.267686Z"},"id":"EulIikmq-NwO"},"source":["def validation(epoch, model, data_loader, criterion1, criterion2, device):\n"," print('Start validation #{}'.format(epoch))\n"," model.eval()\n"," with torch.no_grad():\n"," total_loss = 0\n"," cnt = 0\n"," mIoU_list = []\n"," for step, (images, masks, _) in enumerate(data_loader):\n"," \n"," images = torch.stack(images) # (batch, channel, height, width)\n"," masks = torch.stack(masks).long() # (batch, channel, height, width)\n","\n"," images, masks = images.to(device), masks.to(device) \n","\n"," outputs = model(images)\n"," loss = criterion1(outputs, masks) + criterion2(outputs, masks)\n"," total_loss += loss\n"," cnt += 1\n"," \n","# print(outputs.shape)\n","# print(masks.shape)\n","# wandb.log(wandb.Image(images, masks={\n","# \"predictions\" : {\n","# \"mask_data\" : torch.squeeze(torch.squeeze(outputs, 0),1),\n","# \"class_labels\" : classes_dict\n","# },\n","# \"ground_truth\" : {\n","# \"mask_data\" : torch.squeeze(masks, 0),\n","# \"class_labels\" : classes_dict\n","# }\n","# }))\n"," \n"," outputs = torch.argmax(outputs.squeeze(), dim=1).detach().cpu().numpy()\n","\n"," mIoU = label_accuracy_score(masks.detach().cpu().numpy(), outputs, n_class=12)[2]\n"," mIoU_list.append(mIoU)\n"," \n"," avrg_loss = total_loss / cnt\n"," avrg_mIoU = np.mean(mIoU_list)\n"," print('Validation #{} Average Loss: {:.4f}, mIoU: {:.4f}'.format(epoch, avrg_loss, np.mean(mIoU_list)))\n"," wandb.log({'Train Loss':loss.item(), 'Val Loss':avrg_loss , 'Val mIoU':np.mean(mIoU_list)})\n","# return avrg_loss\n"," return avrg_mIoU"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"C_7CtFfH-NwO"},"source":["## 모델 저장 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.909918Z","start_time":"2021-04-18T16:16:18.898918Z"},"id":"gpCM5BFO-NwO"},"source":["# 모델 저장 함수 정의\n","val_every = 1 \n","\n","saved_dir = './saved'\n","if not os.path.isdir(saved_dir): \n"," os.mkdir(saved_dir)\n"," \n","def save_model(model, saved_dir, file_name='re_pan_effb3_noisy_focal_CE_madgrad_kwparam_stepLR.pt'):\n"," check_point = {'net': model.state_dict()}\n"," output_path = os.path.join(saved_dir, file_name)\n"," torch.save(model.state_dict(), output_path)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"FyKGeg8T-NwP"},"source":["## 모델 생성 및 Loss function, Optimizer 정의"]},{"cell_type":"code","metadata":{"id":"ORugl8s1-NwP"},"source":["from torch.autograd import Variable\n","import torch.nn.functional as F\n","# ref : https://github.com/clcarwin/focal_loss_pytorch\n","class FocalLoss(nn.Module):\n"," def __init__(self, gamma=0, alpha=None, size_average=True):\n"," super(FocalLoss, self).__init__()\n"," self.gamma = gamma\n"," self.alpha = alpha\n"," if isinstance(alpha,(float,int)): self.alpha = torch.Tensor([alpha,1-alpha])\n"," if isinstance(alpha,list): self.alpha = torch.Tensor(alpha)\n"," self.size_average = size_average\n","\n"," def forward(self, input, target):\n"," if input.dim()>2:\n"," input = input.view(input.size(0),input.size(1),-1) # N,C,H,W => N,C,H*W\n"," input = input.transpose(1,2) # N,C,H*W => N,H*W,C\n"," input = input.contiguous().view(-1,input.size(2)) # N,H*W,C => N*H*W,C\n"," target = target.view(-1,1)\n","\n"," logpt = F.log_softmax(input)\n"," logpt = logpt.gather(1,target)\n"," logpt = logpt.view(-1)\n"," pt = Variable(logpt.data.exp())\n","\n"," if self.alpha is not None:\n"," if self.alpha.type()!=input.data.type():\n"," self.alpha = self.alpha.type_as(input.data)\n"," at = self.alpha.gather(0,target.data.view(-1))\n"," logpt = logpt * Variable(at)\n","\n"," loss = -1 * (1-pt)**self.gamma * logpt\n"," if self.size_average: return loss.mean()\n"," else: return loss.sum()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"yfeFQknz-NwP"},"source":["import torch.optim.lr_scheduler as lr_scheduler\n","import math\n","class CosineAnnealingWarmUpRestart(lr_scheduler._LRScheduler):\n"," def __init__(self, optimizer, T_0, T_mult=1, eta_max=0.1, T_up=0, gamma=1., last_epoch=-1):\n"," if T_0 <= 0 or not isinstance(T_0, int):\n"," raise ValueError(\"Expected positive integer T_0, but got {}\".format(T_0))\n"," if T_mult < 1 or not isinstance(T_mult, int):\n"," raise ValueError(\"Expected integer T_mult >= 1, but got {}\".format(T_mult))\n"," if T_up < 0 or not isinstance(T_up, int):\n"," raise ValueError(\"Expected positive integer T_up, but got {}\".format(T_up))\n"," self.T_0 = T_0\n"," self.T_mult = T_mult\n"," self.base_eta_max = eta_max\n"," self.eta_max = eta_max\n"," self.T_up = T_up\n"," self.T_i = T_0\n"," self.gamma = gamma\n"," self.cycle = 0\n"," self.T_cur = last_epoch\n"," super(CosineAnnealingWarmUpRestart, self).__init__(optimizer, last_epoch)\n"," # self.T_cur = last_epoch\n"," \n"," def get_lr(self):\n"," if self.T_cur == -1:\n"," return self.base_lrs\n"," elif self.T_cur < self.T_up:\n"," return [(self.eta_max - base_lr)*self.T_cur / self.T_up + base_lr for base_lr in self.base_lrs]\n"," else:\n"," return [base_lr + (self.eta_max - base_lr) * (1 + math.cos(math.pi * (self.T_cur-self.T_up) / (self.T_i - self.T_up))) / 2\n"," for base_lr in self.base_lrs]\n","\n"," def step(self, epoch=None):\n"," if epoch is None:\n"," epoch = self.last_epoch + 1\n"," self.T_cur = self.T_cur + 1\n"," if self.T_cur >= self.T_i:\n"," self.cycle += 1\n"," self.T_cur = self.T_cur - self.T_i\n"," self.T_i = (self.T_i - self.T_up) * self.T_mult + self.T_up\n"," else:\n"," if epoch >= self.T_0:\n"," if self.T_mult == 1:\n"," self.T_cur = epoch % self.T_0\n"," self.cycle = epoch // self.T_0\n"," else:\n"," n = int(math.log((epoch / self.T_0 * (self.T_mult - 1) + 1), self.T_mult))\n"," self.cycle = n\n"," self.T_cur = epoch - self.T_0 * (self.T_mult ** n - 1) / (self.T_mult - 1)\n"," self.T_i = self.T_0 * self.T_mult ** (n)\n"," else:\n"," self.T_i = self.T_0\n"," self.T_cur = epoch\n"," \n"," self.eta_max = self.base_eta_max * (self.gamma**self.cycle)\n"," self.last_epoch = math.floor(epoch)\n"," for param_group, lr in zip(self.optimizer.param_groups, self.get_lr()):\n"," param_group['lr'] = lr"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AG1oQeu7BX1M","executionInfo":{"status":"ok","timestamp":1620060019354,"user_tz":-540,"elapsed":3477,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"98eae7c4-b66e-409d-9725-0c684d747f2a"},"source":["# !pip install adamp"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Requirement already satisfied: adamp in /usr/local/lib/python3.7/dist-packages (0.3.0)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"eH2PpwF9a-Os","executionInfo":{"status":"ok","timestamp":1620134814207,"user_tz":-540,"elapsed":4222,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"3c61070c-cc52-43cb-9a45-24ec19ed626c"},"source":["!pip install madgrad"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Collecting madgrad\n"," Downloading https://files.pythonhosted.org/packages/65/f0/4584f18202a2fb8903d456bf907b80e7cb54ad8fcba68604084ff41b7cf8/madgrad-1.1-py3-none-any.whl\n","Installing collected packages: madgrad\n","Successfully installed madgrad-1.1\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:19.698902Z","start_time":"2021-04-18T16:16:19.694902Z"},"id":"9Dly8KZj-NwQ"},"source":["# from adamp import AdamP\n","from madgrad import MADGRAD\n","# Loss function 정의\n","criterion1 = FocalLoss()\n","criterion2 = nn.CrossEntropyLoss()\n","\n","# Optimizer 정의\n","# optimizer = torch.optim.Adam(params = model.parameters(), lr = learning_rate, weight_decay=1e-6)\n","# optimizer = AdamP(params = model.parameters())\n","optimizer = MADGRAD(params = model.parameters(), lr = learning_rate, momentum = 0.9, weight_decay = 0.0001, eps = 1e-06)\n","\n","lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size = 654, gamma = 0.5)\n","# lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=50, eta_min=0)\n","# lr_scheduler = CosineAnnealingWarmUpRestart(optimizer, T_0=150, T_mult=1, eta_max=0.1, T_up=10, gamma=0.5)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"start_time":"2021-04-18T16:16:20.331Z"},"colab":{"base_uri":"https://localhost:8080/"},"id":"cMSiFA-3-NwR","outputId":"1eefe095-f259-4147-ab14-3586ab8f4145"},"source":["train(num_epochs, model, train_loader, val_loader, criterion1, criterion2, optimizer, saved_dir, val_every, device, lr_scheduler)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Start training..\n","Epoch [1/20], Step [25/327], Loss: 1.4256\n","Epoch [1/20], Step [50/327], Loss: 1.9043\n","Epoch [1/20], Step [75/327], Loss: 1.1702\n","Epoch [1/20], Step [100/327], Loss: 1.0969\n","Epoch [1/20], Step [125/327], Loss: 1.4311\n","Epoch [1/20], Step [150/327], Loss: 0.5481\n","Epoch [1/20], Step [175/327], Loss: 0.8265\n","Epoch [1/20], Step [200/327], Loss: 0.9635\n","Epoch [1/20], Step [225/327], Loss: 0.8958\n","Epoch [1/20], Step [250/327], Loss: 0.7370\n","Epoch [1/20], Step [275/327], Loss: 0.5275\n","Epoch [1/20], Step [300/327], Loss: 0.6565\n","Epoch [1/20], Step [325/327], Loss: 1.9046\n","Start validation #1\n","Validation #1 Average Loss: 0.7932, mIoU: 0.3370\n","Best performance at epoch: 1\n","Save model in ./saved\n","Epoch [2/20], Step [25/327], Loss: 0.8467\n","Epoch [2/20], Step [50/327], Loss: 0.7077\n","Epoch [2/20], Step [75/327], Loss: 0.6686\n","Epoch [2/20], Step [100/327], Loss: 0.9098\n","Epoch [2/20], Step [125/327], Loss: 0.4804\n","Epoch [2/20], Step [150/327], Loss: 0.7299\n","Epoch [2/20], Step [175/327], Loss: 1.0011\n","Epoch [2/20], Step [200/327], Loss: 0.7135\n","Epoch [2/20], Step [225/327], Loss: 0.9825\n","Epoch [2/20], Step [250/327], Loss: 0.6534\n","Epoch [2/20], Step [275/327], Loss: 0.6482\n","Epoch [2/20], Step [300/327], Loss: 0.7734\n","Epoch [2/20], Step [325/327], Loss: 0.7575\n","Start validation #2\n","Validation #2 Average Loss: 0.6589, mIoU: 0.3842\n","Best performance at epoch: 2\n","Save model in ./saved\n","Epoch [3/20], Step [25/327], Loss: 0.5493\n","Epoch [3/20], Step [50/327], Loss: 0.6370\n","Epoch [3/20], Step [75/327], Loss: 0.4722\n","Epoch [3/20], Step [100/327], Loss: 0.3966\n","Epoch [3/20], Step [125/327], Loss: 0.6486\n","Epoch [3/20], Step [150/327], Loss: 0.3147\n","Epoch [3/20], Step [175/327], Loss: 0.5934\n","Epoch [3/20], Step [200/327], Loss: 0.8060\n","Epoch [3/20], Step [225/327], Loss: 0.9262\n","Epoch [3/20], Step [250/327], Loss: 0.5509\n","Epoch [3/20], Step [275/327], Loss: 0.3732\n","Epoch [3/20], Step [300/327], Loss: 0.4216\n","Epoch [3/20], Step [325/327], Loss: 0.2806\n","Start validation #3\n","Validation #3 Average Loss: 0.6019, mIoU: 0.4031\n","Best performance at epoch: 3\n","Save model in ./saved\n","Epoch [4/20], Step [25/327], Loss: 0.4852\n","Epoch [4/20], Step [50/327], Loss: 0.3227\n","Epoch [4/20], Step [75/327], Loss: 1.1630\n","Epoch [4/20], Step [100/327], Loss: 0.8615\n","Epoch [4/20], Step [125/327], Loss: 0.3249\n","Epoch [4/20], Step [150/327], Loss: 0.3594\n","Epoch [4/20], Step [175/327], Loss: 0.4196\n","Epoch [4/20], Step [200/327], Loss: 0.7148\n","Epoch [4/20], Step [225/327], Loss: 0.3489\n","Epoch [4/20], Step [250/327], Loss: 0.6244\n","Epoch [4/20], Step [275/327], Loss: 0.6272\n","Epoch [4/20], Step [300/327], Loss: 0.3649\n","Epoch [4/20], Step [325/327], Loss: 0.4788\n","Start validation #4\n","Validation #4 Average Loss: 0.5973, mIoU: 0.4130\n","Best performance at epoch: 4\n","Save model in ./saved\n","Epoch [5/20], Step [25/327], Loss: 0.4139\n","Epoch [5/20], Step [50/327], Loss: 0.1997\n","Epoch [5/20], Step [75/327], Loss: 0.2538\n","Epoch [5/20], Step [100/327], Loss: 0.1842\n","Epoch [5/20], Step [125/327], Loss: 0.6732\n","Epoch [5/20], Step [150/327], Loss: 0.2124\n","Epoch [5/20], Step [175/327], Loss: 0.4855\n","Epoch [5/20], Step [200/327], Loss: 0.3733\n","Epoch [5/20], Step [225/327], Loss: 0.5608\n","Epoch [5/20], Step [250/327], Loss: 0.5927\n","Epoch [5/20], Step [275/327], Loss: 0.3868\n","Epoch [5/20], Step [300/327], Loss: 0.3100\n","Epoch [5/20], Step [325/327], Loss: 0.2565\n","Start validation #5\n","Validation #5 Average Loss: 0.5888, mIoU: 0.4150\n","Best performance at epoch: 5\n","Save model in ./saved\n","Epoch [6/20], Step [25/327], Loss: 0.4167\n","Epoch [6/20], Step [50/327], Loss: 0.9249\n","Epoch [6/20], Step [75/327], Loss: 0.3910\n","Epoch [6/20], Step [100/327], Loss: 0.3584\n","Epoch [6/20], Step [125/327], Loss: 0.2019\n","Epoch [6/20], Step [150/327], Loss: 0.4902\n","Epoch [6/20], Step [175/327], Loss: 0.3250\n","Epoch [6/20], Step [200/327], Loss: 0.2266\n","Epoch [6/20], Step [225/327], Loss: 0.5233\n","Epoch [6/20], Step [250/327], Loss: 0.2808\n","Epoch [6/20], Step [275/327], Loss: 0.4664\n","Epoch [6/20], Step [300/327], Loss: 0.2393\n","Epoch [6/20], Step [325/327], Loss: 0.6294\n","Start validation #6\n","Validation #6 Average Loss: 0.5739, mIoU: 0.4309\n","Best performance at epoch: 6\n","Save model in ./saved\n","Epoch [7/20], Step [25/327], Loss: 0.2854\n","Epoch [7/20], Step [50/327], Loss: 0.6539\n","Epoch [7/20], Step [75/327], Loss: 0.3173\n","Epoch [7/20], Step [100/327], Loss: 0.2289\n","Epoch [7/20], Step [125/327], Loss: 0.3696\n","Epoch [7/20], Step [150/327], Loss: 0.4191\n","Epoch [7/20], Step [175/327], Loss: 0.3190\n","Epoch [7/20], Step [200/327], Loss: 0.2906\n","Epoch [7/20], Step [225/327], Loss: 0.2913\n","Epoch [7/20], Step [250/327], Loss: 0.1594\n","Epoch [7/20], Step [275/327], Loss: 0.3520\n","Epoch [7/20], Step [300/327], Loss: 0.2327\n","Epoch [7/20], Step [325/327], Loss: 0.4328\n","Start validation #7\n","Validation #7 Average Loss: 0.5759, mIoU: 0.4307\n","Epoch [8/20], Step [25/327], Loss: 0.1798\n","Epoch [8/20], Step [50/327], Loss: 0.3413\n","Epoch [8/20], Step [75/327], Loss: 0.4255\n","Epoch [8/20], Step [100/327], Loss: 0.7574\n","Epoch [8/20], Step [125/327], Loss: 0.2541\n","Epoch [8/20], Step [150/327], Loss: 0.1793\n","Epoch [8/20], Step [175/327], Loss: 0.3356\n","Epoch [8/20], Step [200/327], Loss: 0.4090\n","Epoch [8/20], Step [225/327], Loss: 0.4094\n","Epoch [8/20], Step [250/327], Loss: 0.2363\n","Epoch [8/20], Step [275/327], Loss: 0.3965\n","Epoch [8/20], Step [300/327], Loss: 0.4076\n","Epoch [8/20], Step [325/327], Loss: 0.2678\n","Start validation #8\n","Validation #8 Average Loss: 0.5822, mIoU: 0.4273\n","Epoch [9/20], Step [25/327], Loss: 0.3066\n","Epoch [9/20], Step [50/327], Loss: 0.1792\n","Epoch [9/20], Step [75/327], Loss: 0.2841\n","Epoch [9/20], Step [100/327], Loss: 0.2021\n","Epoch [9/20], Step [125/327], Loss: 0.2620\n","Epoch [9/20], Step [150/327], Loss: 0.4276\n","Epoch [9/20], Step [175/327], Loss: 0.2561\n","Epoch [9/20], Step [200/327], Loss: 0.2336\n","Epoch [9/20], Step [225/327], Loss: 0.3405\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"QBM5cYVks5I2"},"source":["# Sleep for a few seconds.\n","import time\n","time.sleep(2)\n","# Play an audio beep. Any audio URL will do.\n","from google.colab import output\n","output.eval_js('new Audio(\"https://upload.wikimedia.org/wikipedia/commons/0/05/Beep-09.ogg\").play()')"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"C6ClcO0J-NwR"},"source":["## 저장된 model 불러오기 (학습된 이후) "]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:21.050200Z","start_time":"2021-04-16T19:44:20.802200Z"},"scrolled":true,"id":"KQPtUDzd-NwR","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620187296917,"user_tz":-540,"elapsed":11039,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"73de8cec-adda-4807-b560-09b699b2b13e"},"source":["# best model 저장된 경로\n","model_path = './saved/re_pan_effb3_noisy_focal_CE_madgrad_kwparam_stepLR.pt'\n","\n","# best model 불러오기\n","checkpoint = torch.load(model_path, map_location=device)\n","model.load_state_dict(checkpoint)\n","\n","# 추론을 실행하기 전에는 반드시 설정 (batch normalization, dropout 를 평가 모드로 설정)\n","# model.eval()"],"execution_count":17,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{"tags":[]},"execution_count":17}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:24.939227Z","start_time":"2021-04-16T19:44:24.518228Z"},"id":"0LQqrDAp-NwR","colab":{"base_uri":"https://localhost:8080/","height":502},"executionInfo":{"status":"ok","timestamp":1620187308404,"user_tz":-540,"elapsed":11456,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"af5aba4c-4f39-49ac-8465-df25eecb00a5"},"source":["# 첫번째 batch의 추론 결과 확인\n","for imgs, image_infos in test_loader:\n"," image_infos = image_infos\n"," temp_images = imgs\n"," \n"," model.eval()\n"," # inference\n"," outs = model(torch.stack(temp_images).to(device))\n"," oms = torch.argmax(outs.squeeze(), dim=1).detach().cpu().numpy()\n"," \n"," break\n","\n","i = 3\n","fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(16, 16))\n","\n","print('Shape of Original Image :', list(temp_images[i].shape))\n","print('Shape of Predicted : ', list(oms[i].shape))\n","print('Unique values, category of transformed mask : \\n', [{int(i),category_names[int(i)]} for i in list(np.unique(oms[i]))])\n","\n","# Original image\n","ax1.imshow(temp_images[i].permute([1,2,0]))\n","ax1.grid(False)\n","ax1.set_title(\"Original image : {}\".format(image_infos[i]['file_name']), fontsize = 15)\n","\n","# Predicted\n","ax2.imshow(oms[i])\n","ax2.grid(False)\n","ax2.set_title(\"Predicted : {}\".format(image_infos[i]['file_name']), fontsize = 15)\n","\n","plt.show()"],"execution_count":18,"outputs":[{"output_type":"stream","text":["Shape of Original Image : [3, 512, 512]\n","Shape of Predicted : [512, 512]\n","Unique values, category of transformed mask : \n"," [{0, 'Backgroud'}, {'Paper', 3}, {9, 'Plastic bag'}]\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"evYjR2F3-NwS"},"source":["## submission을 위한 test 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:27.469285Z","start_time":"2021-04-16T19:44:27.456021Z"},"id":"nhMLnV5d-NwS","executionInfo":{"status":"ok","timestamp":1620187308405,"user_tz":-540,"elapsed":10542,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["def test(model, data_loader, device):\n"," size = 256\n"," transform = A.Compose([A.Resize(256, 256)])\n"," print('Start prediction.')\n"," model.eval()\n"," \n"," file_name_list = []\n"," preds_array = np.empty((0, size*size), dtype=np.long)\n"," \n"," with torch.no_grad():\n"," for step, (imgs, image_infos) in enumerate(test_loader):\n","\n"," # inference (512 x 512)\n"," outs = model(torch.stack(imgs).to(device))\n"," oms = torch.argmax(outs.squeeze(), dim=1).detach().cpu().numpy()\n"," \n"," # resize (256 x 256)\n"," temp_mask = []\n"," for img, mask in zip(np.stack(imgs), oms):\n"," transformed = transform(image=img, mask=mask)\n"," mask = transformed['mask']\n"," temp_mask.append(mask)\n","\n"," oms = np.array(temp_mask)\n"," \n"," oms = oms.reshape([oms.shape[0], size*size]).astype(int)\n"," preds_array = np.vstack((preds_array, oms))\n"," \n"," file_name_list.append([i['file_name'] for i in image_infos])\n"," print(\"End prediction.\")\n"," file_names = [y for x in file_name_list for y in x]\n"," \n"," return file_names, preds_array"],"execution_count":19,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"r1YKHBf4-NwT"},"source":["## submission.csv 생성"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:45:42.235310Z","start_time":"2021-04-16T19:44:30.499016Z"},"scrolled":true,"id":"Bz79_g8K-NwT","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620187806252,"user_tz":-540,"elapsed":497723,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"dd7ccc8a-f88f-4d35-9141-364fe6213f90"},"source":["# sample_submisson.csv 열기\n","submission = pd.read_csv('./submission/sample_submission.csv', index_col=None)\n","\n","# test set에 대한 prediction\n","file_names, preds = test(model, test_loader, device)\n","\n","# PredictionString 대입\n","for file_name, string in zip(file_names, preds):\n"," submission = submission.append({\"image_id\" : file_name, \"PredictionString\" : ' '.join(str(e) for e in string.tolist())}, \n"," ignore_index=True)\n","\n","# submission.csv로 저장\n","submission.to_csv(\"./submission/re_pan_effb3_noisy_focal_CE_madgrad_kwparam_stepLR.csv\", index=False)"],"execution_count":20,"outputs":[{"output_type":"stream","text":["Start prediction.\n","End prediction.\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"Zgl7Ye7F-NwT"},"source":["## 제출까지\n","\n"]},{"cell_type":"code","metadata":{"id":"022E8AC7-NwT","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620187822029,"user_tz":-540,"elapsed":14521,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"429d0353-b353-4375-b876-098c15b6084c"},"source":["import json\n","import requests\n","import os\n","from urllib.parse import urlparse, parse_qsl, urlencode, urlunparse\n","\n","def submit(user_key='', file_path = '', desc=\"\"):\n"," if not user_key:\n"," raise Exception(\"No UserKey\" )\n"," url = urlparse('http://ec2-13-124-161-225.ap-northeast-2.compute.amazonaws.com:8000/api/v1/competition/28/presigned_url/?description=&hyperparameters={%22training%22:{},%22inference%22:{}}')\n"," qs = dict(parse_qsl(url.query))\n"," qs['description'] = desc\n"," parts = url._replace(query=urlencode(qs))\n"," url = urlunparse(parts)\n","\n"," print(url)\n"," headers = {\n"," 'Authorization': user_key\n"," }\n"," res = requests.get(url, headers=headers)\n"," print(res.text)\n"," data = json.loads(res.text)\n"," \n"," submit_url = data['url']\n"," body = {\n"," 'key':'app/Competitions/000028/Users/{}/Submissions/{}/output.csv'.format(str(data['submission']['user']).zfill(8),str(data['submission']['local_id']).zfill(4)),\n"," 'x-amz-algorithm':data['fields']['x-amz-algorithm'],\n"," 'x-amz-credential':data['fields']['x-amz-credential'],\n"," 'x-amz-date':data['fields']['x-amz-date'],\n"," 'policy':data['fields']['policy'],\n"," 'x-amz-signature':data['fields']['x-amz-signature']\n"," }\n"," requests.post(url=submit_url, data=body, files={'file': open(file_path, 'rb')})\n","\n","\n","####################################################################################\n","test_dir = \"/content/drive/MyDrive/Trash/code/submission\" # 수정 필요 : output 파일 폴더 \n","desc = 're_pan_effb3_noisy_focal_CE_madgrad_kwparam_stepLR' # 수정 필요 : 파일에 대한 설명\n","output_file = \"re_pan_effb3_noisy_focal_CE_madgrad_kwparam_stepLR.csv\" #수정 필요 : output 파일 \n","user_key = \"Bearer 7bb5f96452751a238ffaf91a93c4242bf9b72abe\" # 수정 필요 : Authorization \n","\n","\n","submit(user_key, os.path.join(test_dir, output_file),desc)"],"execution_count":21,"outputs":[{"output_type":"stream","text":["http://ec2-13-124-161-225.ap-northeast-2.compute.amazonaws.com:8000/api/v1/competition/28/presigned_url/?hyperparameters=%7B%22training%22%3A%7B%7D%2C%22inference%22%3A%7B%7D%7D&description=re_pan_effb3_noisy_focal_CE_madgrad_kwparam_stepLR\n","{\"url\":\"https://prod-aistages-private.s3.amazonaws.com/\",\"fields\":{\"key\":\"app/Competitions/000028/Users/00000099/Submissions/0022/output.csv\",\"x-amz-algorithm\":\"AWS4-HMAC-SHA256\",\"x-amz-credential\":\"AKIA45LU4MHUJ7WLDQVO/20210505/ap-northeast-2/s3/aws4_request\",\"x-amz-date\":\"20210505T041008Z\",\"policy\":\"eyJleHBpcmF0aW9uIjogIjIwMjEtMDUtMDVUMDU6MTA6MDhaIiwgImNvbmRpdGlvbnMiOiBbeyJidWNrZXQiOiAicHJvZC1haXN0YWdlcy1wcml2YXRlIn0sIHsia2V5IjogImFwcC9Db21wZXRpdGlvbnMvMDAwMDI4L1VzZXJzLzAwMDAwMDk5L1N1Ym1pc3Npb25zLzAwMjIvb3V0cHV0LmNzdiJ9LCB7IngtYW16LWFsZ29yaXRobSI6ICJBV1M0LUhNQUMtU0hBMjU2In0sIHsieC1hbXotY3JlZGVudGlhbCI6ICJBS0lBNDVMVTRNSFVKN1dMRFFWTy8yMDIxMDUwNS9hcC1ub3J0aGVhc3QtMi9zMy9hd3M0X3JlcXVlc3QifSwgeyJ4LWFtei1kYXRlIjogIjIwMjEwNTA1VDA0MTAwOFoifV19\",\"x-amz-signature\":\"b18a542fdbe1ae8eadd23f545e5a9e2ebd201e80bb99d0fb3df5a48699a90fa4\"},\"submission\":{\"id\":15285,\"phase\":\"Created\",\"type\":\"File\",\"local_id\":22,\"hyperparameters\":\"{\\\"training\\\": {}, \\\"inference\\\": {}}\",\"description\":\"re_pan_effb3_noisy_focal_CE_madgrad_kwparam_stepLR\",\"final\":false,\"created_at\":\"2021-05-05T13:10:08.476069+09:00\",\"updated_at\":\"2021-05-05T13:10:08.476100+09:00\",\"user\":99,\"competition\":28,\"image\":null}}\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"bIweoridsTFI"},"source":[""],"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/chanyub_seg/code/re_pan_effb3_noisy_focal_madgrad_cosLR.ipynb b/chanyub_seg/code/re_pan_effb3_noisy_focal_madgrad_cosLR.ipynb deleted file mode 100644 index 5b1068f..0000000 --- a/chanyub_seg/code/re_pan_effb3_noisy_focal_madgrad_cosLR.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"nbformat":4,"nbformat_minor":0,"metadata":{"hide_input":false,"kernelspec":{"display_name":"Python 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google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Mounted at /content/drive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xDwpp4Lk-gSH","executionInfo":{"status":"ok","timestamp":1620204675841,"user_tz":-540,"elapsed":7672,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"9e7c6f65-e721-4c3d-debc-388ccd77e0cb"},"source":["ls"],"execution_count":2,"outputs":[{"output_type":"stream","text":["\u001b[0m\u001b[01;34mdrive\u001b[0m/ \u001b[01;34msample_data\u001b[0m/\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sItrVDeh-iYC","executionInfo":{"status":"ok","timestamp":1620204678380,"user_tz":-540,"elapsed":9562,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"0e80c310-6e56-41d4-d798-f263e25d316c"},"source":["cd drive/MyDrive/Trash/code"],"execution_count":3,"outputs":[{"output_type":"stream","text":["/content/drive/MyDrive/Trash/code\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"a54v4-kN_LVO","executionInfo":{"status":"ok","timestamp":1620204686893,"user_tz":-540,"elapsed":17590,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"59a75ec3-0a92-42f5-ff2b-53fc794fc72f"},"source":["!pip install albumentations==0.5.2"],"execution_count":4,"outputs":[{"output_type":"stream","text":["Collecting albumentations==0.5.2\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/03/58/63fb1d742dc42d9ba2800ea741de1f2bc6bb05548d8724aa84794042eaf2/albumentations-0.5.2-py3-none-any.whl (72kB)\n","\r\u001b[K |████▌ | 10kB 16.4MB/s eta 0:00:01\r\u001b[K |█████████ | 20kB 10.0MB/s eta 0:00:01\r\u001b[K |█████████████▋ | 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albumentations-0.1.12\n","Successfully installed albumentations-0.5.2 imgaug-0.4.0 opencv-python-headless-4.5.1.48\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.826930Z","start_time":"2021-04-18T10:34:45.406686Z"},"colab":{"base_uri":"https://localhost:8080/"},"id":"l_LPA4XD-NwC","executionInfo":{"status":"ok","timestamp":1620204727687,"user_tz":-540,"elapsed":6974,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"0e1e32a5-0820-4746-fbf7-f0fa25f2a7f2"},"source":["import os\n","import random\n","import time\n","import json\n","import warnings \n","warnings.filterwarnings('ignore')\n","\n","import torch\n","import torch.nn as nn\n","from torch.utils.data import Dataset, DataLoader\n","from utils import label_accuracy_score\n","import cv2\n","\n","import numpy as np\n","import pandas as pd\n","\n","# 전처리를 위한 라이브러리\n","from pycocotools.coco import COCO\n","import torchvision\n","import torchvision.transforms as transforms\n","\n","import albumentations as A\n","from albumentations.pytorch import ToTensorV2\n","\n","# 시각화를 위한 라이브러리\n","import matplotlib.pyplot as plt\n","import seaborn as sns; sns.set()\n","\n","plt.rcParams['axes.grid'] = False\n","\n","print('pytorch version: {}'.format(torch.__version__))\n","print('GPU 사용 가능 여부: {}'.format(torch.cuda.is_available()))\n","\n","print(torch.cuda.get_device_name(0))\n","print(torch.cuda.device_count())\n","\n","device = \"cuda\" if torch.cuda.is_available() else \"cpu\" # GPU 사용 가능 여부에 따라 device 정보 저장"],"execution_count":5,"outputs":[{"output_type":"stream","text":["pytorch version: 1.8.1+cu101\n","GPU 사용 가능 여부: True\n","Tesla P100-PCIE-16GB\n","1\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"HxgRSL_M-NwF"},"source":["## 하이퍼파라미터 세팅 및 seed 고정"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.841930Z","start_time":"2021-04-18T10:34:47.827931Z"},"id":"rV3JmGP5-NwF","executionInfo":{"status":"ok","timestamp":1620204727690,"user_tz":-540,"elapsed":2843,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["batch_size = 8 # Mini-batch size\n","num_epochs = 20\n","learning_rate = 0.0001"],"execution_count":6,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.856930Z","start_time":"2021-04-18T10:34:47.842931Z"},"id":"Z6LOuJXQ-NwG","executionInfo":{"status":"ok","timestamp":1620204727694,"user_tz":-540,"elapsed":2622,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# seed 고정\n","random_seed = 42\n","torch.manual_seed(random_seed)\n","torch.cuda.manual_seed(random_seed)\n","# torch.cuda.manual_seed_all(random_seed) # if use multi-GPU\n","torch.backends.cudnn.deterministic = True\n","torch.backends.cudnn.benchmark = False\n","np.random.seed(random_seed)\n","random.seed(random_seed)"],"execution_count":7,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"iWF_EJDu-NwG"},"source":["## 학습 데이터 EDA"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.381961Z","start_time":"2021-04-18T10:34:47.857930Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"lg0x0D0a-NwG","executionInfo":{"status":"ok","timestamp":1620204768150,"user_tz":-540,"elapsed":9406,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"ffe09570-4048-4bd0-aa90-daa68707ec44"},"source":["%matplotlib inline\n","\n","dataset_path = '../input/data'\n","anns_file_path = dataset_path + '/' + 'train.json'\n","\n","# Read annotations\n","with open(anns_file_path, 'r') as f:\n"," dataset = json.loads(f.read())\n","\n","categories = dataset['categories']\n","anns = dataset['annotations']\n","imgs = dataset['images']\n","nr_cats = len(categories)\n","nr_annotations = len(anns)\n","nr_images = len(imgs)\n","\n","# Load categories and super categories\n","cat_names = []\n","super_cat_names = []\n","super_cat_ids = {}\n","super_cat_last_name = ''\n","nr_super_cats = 0\n","for cat_it in categories:\n"," cat_names.append(cat_it['name'])\n"," super_cat_name = cat_it['supercategory']\n"," # Adding new supercat\n"," if super_cat_name != super_cat_last_name:\n"," super_cat_names.append(super_cat_name)\n"," super_cat_ids[super_cat_name] = nr_super_cats\n"," super_cat_last_name = super_cat_name\n"," nr_super_cats += 1\n","\n","print('Number of super categories:', nr_super_cats)\n","print('Number of categories:', nr_cats)\n","print('Number of annotations:', nr_annotations)\n","print('Number of images:', nr_images)"],"execution_count":8,"outputs":[{"output_type":"stream","text":["Number of super categories: 11\n","Number of categories: 11\n","Number of annotations: 21116\n","Number of images: 2617\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.546964Z","start_time":"2021-04-18T10:34:51.382969Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/","height":355},"id":"PjLyVHVY-NwH","executionInfo":{"status":"ok","timestamp":1620204768158,"user_tz":-540,"elapsed":7009,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"9330af92-45b3-4133-811c-2d95ef43a9da"},"source":["# Count annotations\n","cat_histogram = np.zeros(nr_cats,dtype=int)\n","for ann in anns:\n"," cat_histogram[ann['category_id']] += 1\n","\n","# Initialize the matplotlib figure\n","f, ax = plt.subplots(figsize=(5,5))\n","\n","# Convert to DataFrame\n","df = pd.DataFrame({'Categories': cat_names, 'Number of annotations': cat_histogram})\n","df = df.sort_values('Number of annotations', 0, False)\n","\n","# Plot the histogram\n","plt.title(\"category distribution of train set \")\n","plot_1 = sns.barplot(x=\"Number of annotations\", y=\"Categories\", data=df, label=\"Total\", color=\"b\")"],"execution_count":9,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.561965Z","start_time":"2021-04-18T10:34:51.547969Z"},"id":"34Tw5__i-NwI","executionInfo":{"status":"ok","timestamp":1620204768159,"user_tz":-540,"elapsed":4034,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# category labeling \n","sorted_temp_df = df.sort_index()\n","\n","# background = 0 에 해당되는 label 추가 후 기존들을 모두 label + 1 로 설정\n","sorted_df = pd.DataFrame([\"Backgroud\"], columns = [\"Categories\"])\n","sorted_df = sorted_df.append(sorted_temp_df, ignore_index=True)"],"execution_count":10,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.576961Z","start_time":"2021-04-18T10:34:51.562964Z"},"colab":{"base_uri":"https://localhost:8080/","height":421},"id":"CQk4vV5N-NwI","executionInfo":{"status":"ok","timestamp":1620204773023,"user_tz":-540,"elapsed":8478,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"842dbc56-55ec-4e3f-c911-a6a5c4e2b21e"},"source":["# class (Categories) 에 따른 index 확인 (0~11 : 총 12개)\n","sorted_df"],"execution_count":11,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
CategoriesNumber of annotations
0BackgroudNaN
1UNKNOWN128.0
2General trash2225.0
3Paper7448.0
4Paper pack527.0
5Metal449.0
6Glass488.0
7Plastic2472.0
8Styrofoam1074.0
9Plastic bag6114.0
10Battery50.0
11Clothing141.0
\n","
"],"text/plain":[" Categories Number of annotations\n","0 Backgroud NaN\n","1 UNKNOWN 128.0\n","2 General trash 2225.0\n","3 Paper 7448.0\n","4 Paper pack 527.0\n","5 Metal 449.0\n","6 Glass 488.0\n","7 Plastic 2472.0\n","8 Styrofoam 1074.0\n","9 Plastic bag 6114.0\n","10 Battery 50.0\n","11 Clothing 141.0"]},"metadata":{"tags":[]},"execution_count":11}]},{"cell_type":"markdown","metadata":{"id":"KXU0zmZs-NwI"},"source":["## 데이터 전처리 함수 정의 (Dataset)"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:52.693328Z","start_time":"2021-04-18T10:34:52.681328Z"},"id":"QFnTI8_Z-NwJ","executionInfo":{"status":"ok","timestamp":1620204777487,"user_tz":-540,"elapsed":2707,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["category_names = list(sorted_df.Categories)\n","\n","def get_classname(classID, cats):\n"," for i in range(len(cats)):\n"," if cats[i]['id']==classID:\n"," return cats[i]['name']\n"," return \"None\"\n","\n","class CustomDataLoader(Dataset):\n"," \"\"\"COCO format\"\"\"\n"," def __init__(self, data_dir, mode = 'train', transform = None):\n"," super().__init__()\n"," self.mode = mode\n"," self.transform = transform\n"," self.coco = COCO(data_dir)\n"," \n"," def __getitem__(self, index: int):\n"," # dataset이 index되어 list처럼 동작\n"," image_id = self.coco.getImgIds(imgIds=index)\n"," image_infos = self.coco.loadImgs(image_id)[0]\n"," \n"," # cv2 를 활용하여 image 불러오기\n"," images = cv2.imread(os.path.join(dataset_path, image_infos['file_name']))\n"," images = cv2.cvtColor(images, cv2.COLOR_BGR2RGB).astype(np.float32)\n"," images /= 255.0\n"," \n"," if (self.mode in ('train', 'val')):\n"," ann_ids = self.coco.getAnnIds(imgIds=image_infos['id'])\n"," anns = self.coco.loadAnns(ann_ids)\n","\n"," # Load the categories in a variable\n"," cat_ids = self.coco.getCatIds()\n"," cats = self.coco.loadCats(cat_ids)\n","\n"," # masks : size가 (height x width)인 2D\n"," # 각각의 pixel 값에는 \"category id + 1\" 할당\n"," # Background = 0\n"," masks = np.zeros((image_infos[\"height\"], image_infos[\"width\"]))\n"," # Unknown = 1, General trash = 2, ... , Cigarette = 11\n"," for i in range(len(anns)):\n"," className = get_classname(anns[i]['category_id'], cats)\n"," pixel_value = category_names.index(className)\n"," masks = np.maximum(self.coco.annToMask(anns[i])*pixel_value, masks)\n"," masks = masks.astype(np.float32)\n","\n"," # transform -> albumentations 라이브러리 활용\n"," if self.transform is not None:\n"," transformed = self.transform(image=images, mask=masks)\n"," images = transformed[\"image\"]\n"," masks = transformed[\"mask\"]\n"," \n"," return images, masks, image_infos\n"," \n"," if self.mode == 'test':\n"," # transform -> albumentations 라이브러리 활용\n"," if self.transform is not None:\n"," transformed = self.transform(image=images)\n"," images = transformed[\"image\"]\n"," \n"," return images, image_infos\n"," \n"," \n"," def __len__(self) -> int:\n"," # 전체 dataset의 size를 return\n"," return len(self.coco.getImgIds())"],"execution_count":12,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"cp2aIOlP-NwK"},"source":["## Dataset 정의 및 DataLoader 할당"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:58.823175Z","start_time":"2021-04-18T10:34:54.106233Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"P7zFlRn6-NwK","executionInfo":{"status":"ok","timestamp":1620204791959,"user_tz":-540,"elapsed":11468,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"19d2d9e7-5bb7-47d7-e9be-fbe178824532"},"source":["# train.json / validation.json / test.json 디렉토리 설정\n","train_path = dataset_path + '/train.json'\n","val_path = dataset_path + '/val.json'\n","test_path = dataset_path + '/test.json'\n","\n","# collate_fn needs for batch\n","def collate_fn(batch):\n"," return tuple(zip(*batch))\n","\n","train_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","val_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","test_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","# create own Dataset 1 (skip)\n","# validation set을 직접 나누고 싶은 경우\n","# random_split 사용하여 data set을 8:2 로 분할\n","# train_size = int(0.8*len(dataset))\n","# val_size = int(len(dataset)-train_size)\n","# dataset = CustomDataLoader(data_dir=train_path, mode='train', transform=transform)\n","# train_dataset, val_dataset = torch.utils.data.random_split(dataset, [train_size, val_size])\n","\n","# create own Dataset 2\n","# train dataset\n","train_dataset = CustomDataLoader(data_dir=train_path, mode='train', transform=train_transform)\n","\n","# validation dataset\n","val_dataset = CustomDataLoader(data_dir=val_path, mode='val', transform=val_transform)\n","\n","# test dataset\n","test_dataset = CustomDataLoader(data_dir=test_path, mode='test', transform=test_transform)\n","\n","\n","# DataLoader\n","train_loader = torch.utils.data.DataLoader(dataset=train_dataset, \n"," batch_size=batch_size,\n"," shuffle=True,\n"," num_workers=4,\n"," collate_fn=collate_fn,\n"," drop_last=True)\n","\n","val_loader = torch.utils.data.DataLoader(dataset=val_dataset, \n"," batch_size=batch_size,\n"," shuffle=False,\n"," num_workers=4,\n"," collate_fn=collate_fn,\n"," drop_last=True) \n","\n","test_loader = torch.utils.data.DataLoader(dataset=test_dataset,\n"," batch_size=batch_size,\n"," num_workers=4,\n"," collate_fn=collate_fn)"],"execution_count":13,"outputs":[{"output_type":"stream","text":["loading annotations into memory...\n","Done (t=3.85s)\n","creating index...\n","index created!\n","loading annotations into memory...\n","Done (t=3.25s)\n","creating index...\n","index created!\n","loading annotations into memory...\n","Done (t=0.51s)\n","creating index...\n","index created!\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"8rqJiHb_-NwM"},"source":["# wandb"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"H1kHXm0uAX3R","executionInfo":{"status":"ok","timestamp":1620085447186,"user_tz":-540,"elapsed":16341,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"1ca945cd-0b7d-4981-e118-6147e81d4af1"},"source":["!pip install wandb"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Collecting wandb\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/5c/ee/d755f9e5466df64c8416a2c6a860fb3aaa43ed6ea8e8e8e81460fda5788b/wandb-0.10.28-py2.py3-none-any.whl (2.1MB)\n","\u001b[K |████████████████████████████████| 2.1MB 4.3MB/s \n","\u001b[?25hCollecting configparser>=3.8.1\n"," Downloading 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/root/.cache/pip/wheels/0b/04/79/c3b0c3a0266a3cb4376da31e5bfe8bba0c489246968a68e843\n"," Building wheel for subprocess32 (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for subprocess32: filename=subprocess32-3.5.4-cp37-none-any.whl size=6489 sha256=b1ffeee148a0368285ddc00ea711cdd2d892b8b36008b5a313ad9fda754a6099\n"," Stored in directory: /root/.cache/pip/wheels/68/39/1a/5e402bdfdf004af1786c8b853fd92f8c4a04f22aad179654d1\n","Successfully built pathtools subprocess32\n","Installing collected packages: configparser, docker-pycreds, sentry-sdk, smmap, gitdb, GitPython, pathtools, shortuuid, subprocess32, wandb\n","Successfully installed GitPython-3.1.14 configparser-5.0.2 docker-pycreds-0.4.0 gitdb-4.0.7 pathtools-0.1.2 sentry-sdk-1.0.0 shortuuid-1.0.1 smmap-4.0.0 subprocess32-3.5.4 wandb-0.10.28\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":232},"id":"aMW4VV9V-NwM","executionInfo":{"status":"error","timestamp":1620204796451,"user_tz":-540,"elapsed":1584,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"64db9564-74fb-491c-84ea-9b0818566525"},"source":["import wandb\n","\n","proj_name = 're_pan_effb3_noisy_focal_madgrad_cosLR'\n","\n","wandb.init(project='chanyub',name=proj_name)\n","\n","config = wandb.config\n","config.learning_rate = 0.01"],"execution_count":14,"outputs":[{"output_type":"error","ename":"AttributeError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)","\u001b[0;32m\u001b[0m in 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'init'"]}]},{"cell_type":"markdown","metadata":{"id":"uQRiIVGX-NwM"},"source":["## My model"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"S0xCGpNeAqeD","executionInfo":{"status":"ok","timestamp":1620204809194,"user_tz":-540,"elapsed":6778,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"ce6050e9-7d0b-4ab8-8744-71243bce5c49"},"source":["!pip install segmentation_models_pytorch"],"execution_count":15,"outputs":[{"output_type":"stream","text":["Collecting segmentation_models_pytorch\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/65/54/8953f9f7ee9d451b0f3be8d635aa3a654579abf898d17502a090efe1155a/segmentation_models_pytorch-0.1.3-py3-none-any.whl (66kB)\n","\r\u001b[K |█████ | 10kB 21.2MB/s eta 0:00:01\r\u001b[K |██████████ | 20kB 10.8MB/s eta 0:00:01\r\u001b[K |██████████████▉ | 30kB 8.3MB/s eta 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(4.41.1)\n","Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch==1.8.1->torchvision>=0.3.0->segmentation_models_pytorch) (3.7.4.3)\n","Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from munch->pretrainedmodels==0.7.4->segmentation_models_pytorch) (1.15.0)\n","Building wheels for collected packages: efficientnet-pytorch, pretrainedmodels\n"," Building wheel for efficientnet-pytorch (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for efficientnet-pytorch: filename=efficientnet_pytorch-0.6.3-cp37-none-any.whl size=12420 sha256=4ca515854968cacdfb9b4854cb80de229ce128c68183a69c1031515364c1e168\n"," Stored in directory: /root/.cache/pip/wheels/42/1e/a9/2a578ba9ad04e776e80bf0f70d8a7f4c29ec0718b92d8f6ccd\n"," Building wheel for pretrainedmodels (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for pretrainedmodels: filename=pretrainedmodels-0.7.4-cp37-none-any.whl size=60963 sha256=f55b554c00dc838956b2f60b74cd6919fcf71e457015a8f0750b8effdce1c857\n"," Stored in directory: /root/.cache/pip/wheels/69/df/63/62583c096289713f22db605aa2334de5b591d59861a02c2ecd\n","Successfully built efficientnet-pytorch pretrainedmodels\n","Installing collected packages: efficientnet-pytorch, timm, munch, pretrainedmodels, segmentation-models-pytorch\n","Successfully installed efficientnet-pytorch-0.6.3 munch-2.5.0 pretrainedmodels-0.7.4 segmentation-models-pytorch-0.1.3 timm-0.3.2\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:11.634792Z","start_time":"2021-04-18T16:16:05.875817Z"},"colab":{"base_uri":"https://localhost:8080/","height":137,"referenced_widgets":["4402a1e740bd4888b86cc4556b27c378","b68a6f5a4f2b4a0587c6fc8af607dce9","f91fc9dc65df4130ae7ecd4d33b9cccc","1973c413c6ec4b76a50379c8b7d371c5","c63980e4d8a3408f934f352a1588097b","95df5be81ac945d29bf955076121ffc6","cc93f19734c34e318cba7fd84da9ab53","7d2ca5494fed48a08528d0a4544a70cd"]},"id":"a8IfZfiM-NwM","executionInfo":{"status":"ok","timestamp":1620204825293,"user_tz":-540,"elapsed":18561,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"4b480ea7-4cfa-4205-a360-8c8a5d4b702c"},"source":["# 구현된 model에 임의의 input을 넣어 output이 잘 나오는지 test\n","import segmentation_models_pytorch as smp\n","\n","model = smp.PAN(encoder_name='timm-efficientnet-b3', encoder_weights='noisy-student', classes=12)\n","x = torch.randn([2, 3, 512, 512])\n","print(\"input shape : \", x.shape)\n","out = model(x).to(device)\n","print(\"output shape : \", out.size())\n","\n","model = model.to(device)"],"execution_count":16,"outputs":[{"output_type":"stream","text":["Downloading: \"https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b3_ns-9d44bf68.pth\" to /root/.cache/torch/hub/checkpoints/tf_efficientnet_b3_ns-9d44bf68.pth\n"],"name":"stderr"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"4402a1e740bd4888b86cc4556b27c378","version_minor":0,"version_major":2},"text/plain":["HBox(children=(FloatProgress(value=0.0, max=49385734.0), HTML(value='')))"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\n","input shape : torch.Size([2, 3, 512, 512])\n","output shape : torch.Size([2, 12, 512, 512])\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"zvDnI7_T-NwN"},"source":["## train, validation, test 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.104200Z","start_time":"2021-04-18T16:16:18.093174Z"},"id":"RA3oAapJ-NwN","executionInfo":{"status":"ok","timestamp":1620204832246,"user_tz":-540,"elapsed":2024,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["def train(num_epochs, model, data_loader, val_loader, criterion, optimizer, saved_dir, val_every, device, lr_scheduler):\n"," print('Start training..')\n"," best_loss = 9999999\n"," best_miou = 0\n"," for epoch in range(num_epochs):\n"," model.train()\n"," for step, (images, masks, _) in enumerate(data_loader):\n"," images = torch.stack(images) # (batch, channel, height, width)\n"," masks = torch.stack(masks).long() # (batch, channel, height, width)\n"," \n"," # gpu 연산을 위해 device 할당\n"," images, masks = images.to(device), masks.to(device)\n"," \n"," # inference\n"," outputs = model(images)\n"," \n"," # loss 계산 (cross entropy loss)\n"," loss = criterion(outputs, masks)\n"," optimizer.zero_grad()\n"," loss.backward()\n"," optimizer.step()\n"," \n"," lr_scheduler.step()\n"," \n"," # step 주기에 따른 loss 출력\n"," if (step + 1) % 25 == 0:\n"," print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(\n"," epoch+1, num_epochs, step+1, len(train_loader), loss.item()))\n"," \n"," # validation 주기에 따른 loss 출력 및 best model 저장\n"," if (epoch + 1) % val_every == 0:\n","# avrg_loss = validation(epoch + 1, model, val_loader, criterion, device)\n","# if avrg_loss < best_loss:\n","# print('Best performance at epoch: {}'.format(epoch + 1))\n","# print('Save model in', saved_dir)\n","# best_loss = avrg_loss\n","# wandb.log({'best_loss': best_loss})\n","# save_model(model, saved_dir)\n"," avrg_miou = validation(epoch + 1, model, val_loader, criterion, device)\n"," if avrg_miou > best_miou:\n"," print('Best performance at epoch: {}'.format(epoch + 1))\n"," print('Save model in', saved_dir)\n"," best_miou = avrg_miou\n"," wandb.log({'best_miou': best_miou})\n"," save_model(model, saved_dir)"],"execution_count":17,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.285795Z","start_time":"2021-04-18T16:16:18.267686Z"},"id":"EulIikmq-NwO","executionInfo":{"status":"ok","timestamp":1620204833346,"user_tz":-540,"elapsed":3102,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["def validation(epoch, model, data_loader, criterion, device):\n"," print('Start validation #{}'.format(epoch))\n"," model.eval()\n"," with torch.no_grad():\n"," total_loss = 0\n"," cnt = 0\n"," mIoU_list = []\n"," for step, (images, masks, _) in enumerate(data_loader):\n"," \n"," images = torch.stack(images) # (batch, channel, height, width)\n"," masks = torch.stack(masks).long() # (batch, channel, height, width)\n","\n"," images, masks = images.to(device), masks.to(device) \n","\n"," outputs = model(images)\n"," loss = criterion(outputs, masks)\n"," total_loss += loss\n"," cnt += 1\n"," \n","# print(outputs.shape)\n","# print(masks.shape)\n","# wandb.log(wandb.Image(images, masks={\n","# \"predictions\" : {\n","# \"mask_data\" : torch.squeeze(torch.squeeze(outputs, 0),1),\n","# \"class_labels\" : classes_dict\n","# },\n","# \"ground_truth\" : {\n","# \"mask_data\" : torch.squeeze(masks, 0),\n","# \"class_labels\" : classes_dict\n","# }\n","# }))\n"," \n"," outputs = torch.argmax(outputs.squeeze(), dim=1).detach().cpu().numpy()\n","\n"," mIoU = label_accuracy_score(masks.detach().cpu().numpy(), outputs, n_class=12)[2]\n"," mIoU_list.append(mIoU)\n"," \n"," avrg_loss = total_loss / cnt\n"," avrg_mIoU = np.mean(mIoU_list)\n"," print('Validation #{} Average Loss: {:.4f}, mIoU: {:.4f}'.format(epoch, avrg_loss, np.mean(mIoU_list)))\n"," wandb.log({'Train Loss':loss.item(), 'Val Loss':avrg_loss , 'Val mIoU':np.mean(mIoU_list)})\n","# return avrg_loss\n"," return avrg_mIoU"],"execution_count":18,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"C_7CtFfH-NwO"},"source":["## 모델 저장 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.909918Z","start_time":"2021-04-18T16:16:18.898918Z"},"id":"gpCM5BFO-NwO"},"source":["# 모델 저장 함수 정의\n","val_every = 1 \n","\n","saved_dir = './saved'\n","if not os.path.isdir(saved_dir): \n"," os.mkdir(saved_dir)\n"," \n","def save_model(model, saved_dir, file_name='re_pan_effb3_noisy_focal_madgrad_cosLR.pt'):\n"," check_point = {'net': model.state_dict()}\n"," output_path = os.path.join(saved_dir, file_name)\n"," torch.save(model.state_dict(), output_path)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"FyKGeg8T-NwP"},"source":["## 모델 생성 및 Loss function, Optimizer 정의"]},{"cell_type":"code","metadata":{"id":"ORugl8s1-NwP"},"source":["from torch.autograd import Variable\n","import torch.nn.functional as F\n","# ref : https://github.com/clcarwin/focal_loss_pytorch\n","class FocalLoss(nn.Module):\n"," def __init__(self, gamma=0, alpha=None, size_average=True):\n"," super(FocalLoss, self).__init__()\n"," self.gamma = gamma\n"," self.alpha = alpha\n"," if isinstance(alpha,(float,int)): self.alpha = torch.Tensor([alpha,1-alpha])\n"," if isinstance(alpha,list): self.alpha = torch.Tensor(alpha)\n"," self.size_average = size_average\n","\n"," def forward(self, input, target):\n"," if input.dim()>2:\n"," input = input.view(input.size(0),input.size(1),-1) # N,C,H,W => N,C,H*W\n"," input = input.transpose(1,2) # N,C,H*W => N,H*W,C\n"," input = input.contiguous().view(-1,input.size(2)) # N,H*W,C => N*H*W,C\n"," target = target.view(-1,1)\n","\n"," logpt = F.log_softmax(input)\n"," logpt = logpt.gather(1,target)\n"," logpt = logpt.view(-1)\n"," pt = Variable(logpt.data.exp())\n","\n"," if self.alpha is not None:\n"," if self.alpha.type()!=input.data.type():\n"," self.alpha = self.alpha.type_as(input.data)\n"," at = self.alpha.gather(0,target.data.view(-1))\n"," logpt = logpt * Variable(at)\n","\n"," loss = -1 * (1-pt)**self.gamma * logpt\n"," if self.size_average: return loss.mean()\n"," else: return loss.sum()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"yfeFQknz-NwP"},"source":["import torch.optim.lr_scheduler as lr_scheduler\n","import math\n","class CosineAnnealingWarmUpRestart(lr_scheduler._LRScheduler):\n"," def __init__(self, optimizer, T_0, T_mult=1, eta_max=0.1, T_up=0, gamma=1., last_epoch=-1):\n"," if T_0 <= 0 or not isinstance(T_0, int):\n"," raise ValueError(\"Expected positive integer T_0, but got {}\".format(T_0))\n"," if T_mult < 1 or not isinstance(T_mult, int):\n"," raise ValueError(\"Expected integer T_mult >= 1, but got {}\".format(T_mult))\n"," if T_up < 0 or not isinstance(T_up, int):\n"," raise ValueError(\"Expected positive integer T_up, but got {}\".format(T_up))\n"," self.T_0 = T_0\n"," self.T_mult = T_mult\n"," self.base_eta_max = eta_max\n"," self.eta_max = eta_max\n"," self.T_up = T_up\n"," self.T_i = T_0\n"," self.gamma = gamma\n"," self.cycle = 0\n"," self.T_cur = last_epoch\n"," super(CosineAnnealingWarmUpRestart, self).__init__(optimizer, last_epoch)\n"," # self.T_cur = last_epoch\n"," \n"," def get_lr(self):\n"," if self.T_cur == -1:\n"," return self.base_lrs\n"," elif self.T_cur < self.T_up:\n"," return [(self.eta_max - base_lr)*self.T_cur / self.T_up + base_lr for base_lr in self.base_lrs]\n"," else:\n"," return [base_lr + (self.eta_max - base_lr) * (1 + math.cos(math.pi * (self.T_cur-self.T_up) / (self.T_i - self.T_up))) / 2\n"," for base_lr in self.base_lrs]\n","\n"," def step(self, epoch=None):\n"," if epoch is None:\n"," epoch = self.last_epoch + 1\n"," self.T_cur = self.T_cur + 1\n"," if self.T_cur >= self.T_i:\n"," self.cycle += 1\n"," self.T_cur = self.T_cur - self.T_i\n"," self.T_i = (self.T_i - self.T_up) * self.T_mult + self.T_up\n"," else:\n"," if epoch >= self.T_0:\n"," if self.T_mult == 1:\n"," self.T_cur = epoch % self.T_0\n"," self.cycle = epoch // self.T_0\n"," else:\n"," n = int(math.log((epoch / self.T_0 * (self.T_mult - 1) + 1), self.T_mult))\n"," self.cycle = n\n"," self.T_cur = epoch - self.T_0 * (self.T_mult ** n - 1) / (self.T_mult - 1)\n"," self.T_i = self.T_0 * self.T_mult ** (n)\n"," else:\n"," self.T_i = self.T_0\n"," self.T_cur = epoch\n"," \n"," self.eta_max = self.base_eta_max * (self.gamma**self.cycle)\n"," self.last_epoch = math.floor(epoch)\n"," for param_group, lr in zip(self.optimizer.param_groups, self.get_lr()):\n"," param_group['lr'] = lr"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AG1oQeu7BX1M","executionInfo":{"status":"ok","timestamp":1620060019354,"user_tz":-540,"elapsed":3477,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"98eae7c4-b66e-409d-9725-0c684d747f2a"},"source":["# !pip install adamp"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Requirement already satisfied: adamp in /usr/local/lib/python3.7/dist-packages (0.3.0)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"eH2PpwF9a-Os","executionInfo":{"status":"ok","timestamp":1620085557624,"user_tz":-540,"elapsed":3897,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"a78e14f8-d5e3-419d-a790-dda6f857e227"},"source":["!pip install madgrad"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Collecting madgrad\n"," Downloading https://files.pythonhosted.org/packages/65/f0/4584f18202a2fb8903d456bf907b80e7cb54ad8fcba68604084ff41b7cf8/madgrad-1.1-py3-none-any.whl\n","Installing collected packages: madgrad\n","Successfully installed madgrad-1.1\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:19.698902Z","start_time":"2021-04-18T16:16:19.694902Z"},"id":"9Dly8KZj-NwQ"},"source":["# from adamp import AdamP\n","from madgrad import MADGRAD\n","# Loss function 정의\n","# criterion = nn.CrossEntropyLoss()\n","criterion = FocalLoss()\n","\n","# Optimizer 정의\n","# optimizer = torch.optim.Adam(params = model.parameters(), lr = learning_rate, weight_decay=1e-6)\n","# optimizer = AdamP(params = model.parameters())\n","optimizer = MADGRAD(params = model.parameters(), lr = learning_rate, momentum = 0.9, weight_decay = 0, eps = 1e-06)\n","\n","# lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size = 237, gamma = 0.65)\n","lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=50, eta_min=0)\n","# lr_scheduler = CosineAnnealingWarmUpRestart(optimizer, T_0=150, T_mult=1, eta_max=0.1, T_up=10, gamma=0.5)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"start_time":"2021-04-18T16:16:20.331Z"},"colab":{"base_uri":"https://localhost:8080/"},"id":"cMSiFA-3-NwR","executionInfo":{"status":"ok","timestamp":1620102365361,"user_tz":-540,"elapsed":16802515,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"4a8d5ffa-5ba1-47b5-f7c3-2c6bcfa02fb6"},"source":["train(num_epochs, model, train_loader, val_loader, criterion, optimizer, saved_dir, val_every, device, lr_scheduler)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Start training..\n","Epoch [1/20], Step [25/327], Loss: 0.8220\n","Epoch [1/20], Step [50/327], Loss: 0.9693\n","Epoch [1/20], Step [75/327], Loss: 0.5957\n","Epoch [1/20], Step [100/327], Loss: 0.5607\n","Epoch [1/20], Step [125/327], Loss: 0.6950\n","Epoch [1/20], Step [150/327], Loss: 0.3106\n","Epoch [1/20], Step [175/327], Loss: 0.4186\n","Epoch [1/20], Step [200/327], Loss: 0.4975\n","Epoch [1/20], Step [225/327], Loss: 0.4637\n","Epoch [1/20], Step [250/327], Loss: 0.3797\n","Epoch [1/20], Step [275/327], Loss: 0.2911\n","Epoch [1/20], Step [300/327], Loss: 0.3394\n","Epoch [1/20], Step [325/327], Loss: 0.9269\n","Start validation #1\n","Validation #1 Average Loss: 0.4036, mIoU: 0.3302\n","Best performance at epoch: 1\n","Save model in ./saved\n","Epoch [2/20], Step [25/327], Loss: 0.4637\n","Epoch [2/20], Step [50/327], Loss: 0.3740\n","Epoch [2/20], Step [75/327], Loss: 0.2870\n","Epoch [2/20], Step [100/327], Loss: 0.4495\n","Epoch [2/20], Step [125/327], Loss: 0.2755\n","Epoch [2/20], Step [150/327], Loss: 0.3229\n","Epoch [2/20], Step [175/327], Loss: 0.4972\n","Epoch [2/20], Step [200/327], Loss: 0.4102\n","Epoch [2/20], Step [225/327], Loss: 0.5059\n","Epoch [2/20], Step [250/327], Loss: 0.3355\n","Epoch [2/20], Step [275/327], Loss: 0.2518\n","Epoch [2/20], Step [300/327], Loss: 0.3774\n","Epoch [2/20], Step [325/327], Loss: 0.3262\n","Start validation #2\n","Validation #2 Average Loss: 0.3380, mIoU: 0.3628\n","Best performance at epoch: 2\n","Save model in ./saved\n","Epoch [3/20], Step [25/327], Loss: 0.2823\n","Epoch [3/20], Step [50/327], Loss: 0.3700\n","Epoch [3/20], Step [75/327], Loss: 0.2374\n","Epoch [3/20], Step [100/327], Loss: 0.2222\n","Epoch [3/20], Step [125/327], Loss: 0.3173\n","Epoch [3/20], Step [150/327], Loss: 0.1524\n","Epoch [3/20], Step [175/327], Loss: 0.2998\n","Epoch [3/20], Step [200/327], Loss: 0.4724\n","Epoch [3/20], Step [225/327], Loss: 0.5351\n","Epoch [3/20], Step [250/327], Loss: 0.2975\n","Epoch [3/20], Step [275/327], Loss: 0.1980\n","Epoch [3/20], Step [300/327], Loss: 0.2431\n","Epoch [3/20], Step [325/327], Loss: 0.1727\n","Start validation #3\n","Validation #3 Average Loss: 0.3223, mIoU: 0.3757\n","Best performance at epoch: 3\n","Save model in ./saved\n","Epoch [4/20], Step [25/327], Loss: 0.2651\n","Epoch [4/20], Step [50/327], Loss: 0.1643\n","Epoch [4/20], Step [75/327], Loss: 0.5499\n","Epoch [4/20], Step [100/327], Loss: 0.5083\n","Epoch [4/20], Step [125/327], Loss: 0.1990\n","Epoch [4/20], Step [150/327], Loss: 0.1870\n","Epoch [4/20], Step [175/327], Loss: 0.2616\n","Epoch [4/20], Step [200/327], Loss: 0.3783\n","Epoch [4/20], Step [225/327], Loss: 0.2177\n","Epoch [4/20], Step [250/327], Loss: 0.2972\n","Epoch [4/20], Step [275/327], Loss: 0.3182\n","Epoch [4/20], Step [300/327], Loss: 0.2135\n","Epoch [4/20], Step [325/327], Loss: 0.2213\n","Start validation #4\n","Validation #4 Average Loss: 0.3261, mIoU: 0.3845\n","Best performance at epoch: 4\n","Save model in ./saved\n","Epoch [5/20], Step [25/327], Loss: 0.2593\n","Epoch [5/20], Step [50/327], Loss: 0.1275\n","Epoch [5/20], Step [75/327], Loss: 0.1957\n","Epoch [5/20], Step [100/327], Loss: 0.1422\n","Epoch [5/20], Step [125/327], Loss: 0.3824\n","Epoch [5/20], Step [150/327], Loss: 0.1738\n","Epoch [5/20], Step [175/327], Loss: 0.2868\n","Epoch [5/20], Step [200/327], Loss: 0.1969\n","Epoch [5/20], Step [225/327], Loss: 0.3230\n","Epoch [5/20], Step [250/327], Loss: 0.3353\n","Epoch [5/20], Step [275/327], Loss: 0.2106\n","Epoch [5/20], Step [300/327], Loss: 0.2099\n","Epoch [5/20], Step [325/327], Loss: 0.1516\n","Start validation #5\n","Validation #5 Average Loss: 0.3211, mIoU: 0.3903\n","Best performance at epoch: 5\n","Save model in ./saved\n","Epoch [6/20], Step [25/327], Loss: 0.2465\n","Epoch [6/20], Step [50/327], Loss: 0.5391\n","Epoch [6/20], Step [75/327], Loss: 0.2195\n","Epoch [6/20], Step [100/327], Loss: 0.2353\n","Epoch [6/20], Step [125/327], Loss: 0.1401\n","Epoch [6/20], Step [150/327], Loss: 0.3294\n","Epoch [6/20], Step [175/327], Loss: 0.2092\n","Epoch [6/20], Step [200/327], Loss: 0.1416\n","Epoch [6/20], Step [225/327], Loss: 0.2856\n","Epoch [6/20], Step [250/327], Loss: 0.1882\n","Epoch [6/20], Step [275/327], Loss: 0.2622\n","Epoch [6/20], Step [300/327], Loss: 0.2210\n","Epoch [6/20], Step [325/327], Loss: 0.4069\n","Start validation #6\n","Validation #6 Average Loss: 0.3064, mIoU: 0.4024\n","Best performance at epoch: 6\n","Save model in ./saved\n","Epoch [7/20], Step [25/327], Loss: 0.1971\n","Epoch [7/20], Step [50/327], Loss: 0.3775\n","Epoch [7/20], Step [75/327], Loss: 0.2329\n","Epoch [7/20], Step [100/327], Loss: 0.1812\n","Epoch [7/20], Step [125/327], Loss: 0.2603\n","Epoch [7/20], Step [150/327], Loss: 0.2951\n","Epoch [7/20], Step [175/327], Loss: 0.2191\n","Epoch [7/20], Step [200/327], Loss: 0.2100\n","Epoch [7/20], Step [225/327], Loss: 0.2288\n","Epoch [7/20], Step [250/327], Loss: 0.1154\n","Epoch [7/20], Step [275/327], Loss: 0.2520\n","Epoch [7/20], Step [300/327], Loss: 0.1991\n","Epoch [7/20], Step [325/327], Loss: 0.2768\n","Start validation #7\n","Validation #7 Average Loss: 0.3020, mIoU: 0.4029\n","Best performance at epoch: 7\n","Save model in ./saved\n","Epoch [8/20], Step [25/327], Loss: 0.1435\n","Epoch [8/20], Step [50/327], Loss: 0.2481\n","Epoch [8/20], Step [75/327], Loss: 0.2333\n","Epoch [8/20], Step [100/327], Loss: 0.5998\n","Epoch [8/20], Step [125/327], Loss: 0.1876\n","Epoch [8/20], Step [150/327], Loss: 0.1315\n","Epoch [8/20], Step [175/327], Loss: 0.2060\n","Epoch [8/20], Step [200/327], Loss: 0.3043\n","Epoch [8/20], Step [225/327], Loss: 0.3325\n","Epoch [8/20], Step [250/327], Loss: 0.1677\n","Epoch [8/20], Step [275/327], Loss: 0.2762\n","Epoch [8/20], Step [300/327], Loss: 0.2651\n","Epoch [8/20], Step [325/327], Loss: 0.1829\n","Start validation #8\n","Validation #8 Average Loss: 0.3218, mIoU: 0.3910\n","Epoch [9/20], Step [25/327], Loss: 0.2955\n","Epoch [9/20], Step [50/327], Loss: 0.1504\n","Epoch [9/20], Step [75/327], Loss: 0.2052\n","Epoch [9/20], Step [100/327], Loss: 0.1681\n","Epoch [9/20], Step [125/327], Loss: 0.2105\n","Epoch [9/20], Step [150/327], Loss: 0.3958\n","Epoch [9/20], Step [175/327], Loss: 0.2038\n","Epoch [9/20], Step [200/327], Loss: 0.1581\n","Epoch [9/20], Step [225/327], Loss: 0.2321\n","Epoch [9/20], Step [250/327], Loss: 0.5859\n","Epoch [9/20], Step [275/327], Loss: 0.1786\n","Epoch [9/20], Step [300/327], Loss: 0.2025\n","Epoch [9/20], Step [325/327], Loss: 0.2564\n","Start validation #9\n","Validation #9 Average Loss: 0.3119, mIoU: 0.3999\n","Epoch [10/20], Step [25/327], Loss: 0.2226\n","Epoch [10/20], Step [50/327], Loss: 0.2789\n","Epoch [10/20], Step [75/327], Loss: 0.2737\n","Epoch [10/20], Step [100/327], Loss: 0.1672\n","Epoch [10/20], Step [125/327], Loss: 0.2229\n","Epoch [10/20], Step [150/327], Loss: 0.1895\n","Epoch [10/20], Step [175/327], Loss: 0.2673\n","Epoch [10/20], Step [200/327], Loss: 0.1930\n","Epoch [10/20], Step [225/327], Loss: 0.2665\n","Epoch [10/20], Step [250/327], Loss: 0.2793\n","Epoch [10/20], Step [275/327], Loss: 0.1416\n","Epoch [10/20], Step [300/327], Loss: 0.1874\n","Epoch [10/20], Step [325/327], Loss: 0.2545\n","Start validation #10\n","Validation #10 Average Loss: 0.3110, mIoU: 0.4005\n","Epoch [11/20], Step [25/327], Loss: 0.1683\n","Epoch [11/20], Step [50/327], Loss: 0.1382\n","Epoch [11/20], Step [75/327], Loss: 0.2597\n","Epoch [11/20], Step [100/327], Loss: 0.1811\n","Epoch [11/20], Step [125/327], Loss: 0.1636\n","Epoch [11/20], Step [150/327], Loss: 0.2341\n","Epoch [11/20], Step [175/327], Loss: 0.1371\n","Epoch [11/20], Step [200/327], Loss: 0.1651\n","Epoch [11/20], Step [225/327], Loss: 0.1613\n","Epoch [11/20], Step [250/327], Loss: 0.2747\n","Epoch [11/20], Step [275/327], Loss: 0.2168\n","Epoch [11/20], Step [300/327], Loss: 0.1324\n","Epoch [11/20], Step [325/327], Loss: 0.1794\n","Start validation #11\n","Validation #11 Average Loss: 0.3163, mIoU: 0.3971\n","Epoch [12/20], Step [25/327], Loss: 0.2183\n","Epoch [12/20], Step [50/327], Loss: 0.2838\n","Epoch [12/20], Step [75/327], Loss: 0.1586\n","Epoch [12/20], Step [100/327], Loss: 0.1709\n","Epoch [12/20], Step [125/327], Loss: 0.1035\n","Epoch [12/20], Step [150/327], Loss: 0.2441\n","Epoch [12/20], Step [175/327], Loss: 0.1597\n","Epoch [12/20], Step [200/327], Loss: 0.2000\n","Epoch [12/20], Step [225/327], Loss: 0.1169\n","Epoch [12/20], Step [250/327], Loss: 0.2083\n","Epoch [12/20], Step [275/327], Loss: 0.1702\n","Epoch [12/20], Step [300/327], Loss: 0.2536\n","Epoch [12/20], Step [325/327], Loss: 0.2076\n","Start validation #12\n","Validation #12 Average Loss: 0.3084, mIoU: 0.3996\n","Epoch [13/20], Step [25/327], Loss: 0.1396\n","Epoch [13/20], Step [50/327], Loss: 0.2793\n","Epoch [13/20], Step [75/327], Loss: 0.2214\n","Epoch [13/20], Step [100/327], Loss: 0.1829\n","Epoch [13/20], Step [125/327], Loss: 0.1186\n","Epoch [13/20], Step [150/327], Loss: 0.2502\n","Epoch [13/20], Step [175/327], Loss: 0.1820\n","Epoch [13/20], Step [200/327], Loss: 0.2334\n","Epoch [13/20], Step [225/327], Loss: 0.2726\n","Epoch [13/20], Step [250/327], Loss: 0.2181\n","Epoch [13/20], Step [275/327], Loss: 0.2053\n","Epoch [13/20], Step [300/327], Loss: 0.1475\n","Epoch [13/20], Step [325/327], Loss: 0.2240\n","Start validation #13\n","Validation #13 Average Loss: 0.3037, mIoU: 0.4046\n","Best performance at epoch: 13\n","Save model in ./saved\n","Epoch [14/20], Step [25/327], Loss: 0.1344\n","Epoch [14/20], Step [50/327], Loss: 0.1763\n","Epoch [14/20], Step [75/327], Loss: 0.2505\n","Epoch [14/20], Step [100/327], Loss: 0.2819\n","Epoch [14/20], Step [125/327], Loss: 0.1049\n","Epoch [14/20], Step [150/327], Loss: 0.1711\n","Epoch [14/20], Step [175/327], Loss: 0.1770\n","Epoch [14/20], Step [200/327], Loss: 0.2304\n","Epoch [14/20], Step [225/327], Loss: 0.2550\n","Epoch [14/20], Step [250/327], Loss: 0.1755\n","Epoch [14/20], Step [275/327], Loss: 0.1470\n","Epoch [14/20], Step [300/327], Loss: 0.1680\n","Epoch [14/20], Step [325/327], Loss: 0.1469\n","Start validation #14\n","Validation #14 Average Loss: 0.3078, mIoU: 0.4008\n","Epoch [15/20], Step [25/327], Loss: 0.2224\n","Epoch [15/20], Step [50/327], Loss: 0.1466\n","Epoch [15/20], Step [75/327], Loss: 0.2352\n","Epoch [15/20], Step [100/327], Loss: 0.1803\n","Epoch [15/20], Step [125/327], Loss: 0.3149\n","Epoch [15/20], Step [150/327], Loss: 0.2038\n","Epoch [15/20], Step [175/327], Loss: 0.3633\n","Epoch [15/20], Step [200/327], Loss: 0.3436\n","Epoch [15/20], Step [225/327], Loss: 0.3966\n","Epoch [15/20], Step [250/327], Loss: 0.1886\n","Epoch [15/20], Step [275/327], Loss: 0.2162\n","Epoch [15/20], Step [300/327], Loss: 0.2388\n","Epoch [15/20], Step [325/327], Loss: 0.2111\n","Start validation #15\n","Validation #15 Average Loss: 0.3039, mIoU: 0.4039\n","Epoch [16/20], Step [25/327], Loss: 0.2404\n","Epoch [16/20], Step [50/327], Loss: 0.1919\n","Epoch [16/20], Step [75/327], Loss: 0.1601\n","Epoch [16/20], Step [100/327], Loss: 0.1066\n","Epoch [16/20], Step [125/327], Loss: 0.2508\n","Epoch [16/20], Step [150/327], Loss: 0.3109\n","Epoch [16/20], Step [175/327], Loss: 0.1376\n","Epoch [16/20], Step [200/327], Loss: 0.3027\n","Epoch [16/20], Step [225/327], Loss: 0.2755\n","Epoch [16/20], Step [250/327], Loss: 0.1969\n","Epoch [16/20], Step [275/327], Loss: 0.1959\n","Epoch [16/20], Step [300/327], Loss: 0.2527\n","Epoch [16/20], Step [325/327], Loss: 0.1785\n","Start validation #16\n","Validation #16 Average Loss: 0.3170, mIoU: 0.3962\n","Epoch [17/20], Step [25/327], Loss: 0.2603\n","Epoch [17/20], Step [50/327], Loss: 0.1757\n","Epoch [17/20], Step [75/327], Loss: 0.1134\n","Epoch [17/20], Step [100/327], Loss: 0.2251\n","Epoch [17/20], Step [125/327], Loss: 0.1246\n","Epoch [17/20], Step [150/327], Loss: 0.1843\n","Epoch [17/20], Step [175/327], Loss: 0.1178\n","Epoch [17/20], Step [200/327], Loss: 0.1842\n","Epoch [17/20], Step [225/327], Loss: 0.1353\n","Epoch [17/20], Step [250/327], Loss: 0.3383\n","Epoch [17/20], Step [275/327], Loss: 0.1871\n","Epoch [17/20], Step [300/327], Loss: 0.3401\n","Epoch [17/20], Step [325/327], Loss: 0.2371\n","Start validation #17\n","Validation #17 Average Loss: 0.3028, mIoU: 0.4015\n","Epoch [18/20], Step [25/327], Loss: 0.1990\n","Epoch [18/20], Step [50/327], Loss: 0.1347\n","Epoch [18/20], Step [75/327], Loss: 0.2431\n","Epoch [18/20], Step [100/327], Loss: 0.1710\n","Epoch [18/20], Step [125/327], Loss: 0.1884\n","Epoch [18/20], Step [150/327], Loss: 0.2045\n","Epoch [18/20], Step [175/327], Loss: 0.1383\n","Epoch [18/20], Step [200/327], Loss: 0.2550\n","Epoch [18/20], Step [225/327], Loss: 0.1336\n","Epoch [18/20], Step [250/327], Loss: 0.1860\n","Epoch [18/20], Step [275/327], Loss: 0.3575\n","Epoch [18/20], Step [300/327], Loss: 0.1588\n","Epoch [18/20], Step [325/327], Loss: 0.0986\n","Start validation #18\n","Validation #18 Average Loss: 0.3105, mIoU: 0.3947\n","Epoch [19/20], Step [25/327], Loss: 0.1259\n","Epoch [19/20], Step [50/327], Loss: 0.1682\n","Epoch [19/20], Step [75/327], Loss: 0.2996\n","Epoch [19/20], Step [100/327], Loss: 0.1441\n","Epoch [19/20], Step [125/327], Loss: 0.5350\n","Epoch [19/20], Step [150/327], Loss: 0.2107\n","Epoch [19/20], Step [175/327], Loss: 0.1675\n","Epoch [19/20], Step [200/327], Loss: 0.1417\n","Epoch [19/20], Step [225/327], Loss: 0.2037\n","Epoch [19/20], Step [250/327], Loss: 0.1059\n","Epoch [19/20], Step [275/327], Loss: 0.0922\n","Epoch [19/20], Step [300/327], Loss: 0.2640\n","Epoch [19/20], Step [325/327], Loss: 0.2383\n","Start validation #19\n","Validation #19 Average Loss: 0.3082, mIoU: 0.4029\n","Epoch [20/20], Step [25/327], Loss: 0.5513\n","Epoch [20/20], Step [50/327], Loss: 0.1728\n","Epoch [20/20], Step [75/327], Loss: 0.2459\n","Epoch [20/20], Step [100/327], Loss: 0.3029\n","Epoch [20/20], Step [125/327], Loss: 0.2330\n","Epoch [20/20], Step [150/327], Loss: 0.1795\n","Epoch [20/20], Step [175/327], Loss: 0.1673\n","Epoch [20/20], Step [200/327], Loss: 0.2743\n","Epoch [20/20], Step [225/327], Loss: 0.2923\n","Epoch [20/20], Step [250/327], Loss: 0.2168\n","Epoch [20/20], Step [275/327], Loss: 0.1909\n","Epoch [20/20], Step [300/327], Loss: 0.1407\n","Epoch [20/20], Step [325/327], Loss: 0.2757\n","Start validation #20\n","Validation #20 Average Loss: 0.3049, mIoU: 0.4011\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"C6ClcO0J-NwR"},"source":["## 저장된 model 불러오기 (학습된 이후) "]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:21.050200Z","start_time":"2021-04-16T19:44:20.802200Z"},"scrolled":true,"id":"KQPtUDzd-NwR","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620205133254,"user_tz":-540,"elapsed":3334,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"77835d9f-aeaa-45d4-e1b7-58cb87a720a0"},"source":["# best model 저장된 경로\n","model_path = './saved/re_pan_aug2_re_pan_effb3_noisy_focal_madgrad_cosLReffb7_noisy_focal_madgrad_cosLR.pt'\n","\n","# best model 불러오기\n","checkpoint = torch.load(model_path, map_location=device)\n","model.load_state_dict(checkpoint)\n","\n","# 추론을 실행하기 전에는 반드시 설정 (batch normalization, dropout 를 평가 모드로 설정)\n","# model.eval()"],"execution_count":22,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{"tags":[]},"execution_count":22}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:24.939227Z","start_time":"2021-04-16T19:44:24.518228Z"},"id":"0LQqrDAp-NwR","colab":{"base_uri":"https://localhost:8080/","height":409},"executionInfo":{"status":"error","timestamp":1620205140385,"user_tz":-540,"elapsed":3173,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"ac51f4c0-fd51-4e94-d4e6-73180806d6fc"},"source":["# 첫번째 batch의 추론 결과 확인\n","for imgs, image_infos in test_loader:\n"," image_infos = image_infos\n"," temp_images = imgs\n"," \n"," model.eval()\n"," # inference\n"," outs = model(torch.stack(temp_images).to(device))\n"," oms = torch.argmax(outs.squeeze(), dim=1).detach().cpu().numpy()\n"," \n"," break\n","\n","i = 1\n","fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(16, 16))\n","\n","print('Shape of Original Image :', list(temp_images[i].shape))\n","print('Shape of Predicted : ', list(oms[i].shape))\n","print('Unique values, category of transformed mask : \\n', [{int(i),category_names[int(i)]} for i in list(np.unique(oms[i]))])\n","\n","# Original image\n","ax1.imshow(temp_images[i].permute([1,2,0]))\n","ax1.grid(False)\n","ax1.set_title(\"Original image : {}\".format(image_infos[i]['file_name']), fontsize = 15)\n","\n","# Predicted\n","ax2.imshow(oms[i])\n","ax2.grid(False)\n","ax2.set_title(\"Predicted : {}\".format(image_infos[i]['file_name']), fontsize = 15)\n","\n","plt.show()"],"execution_count":23,"outputs":[{"output_type":"error","ename":"RuntimeError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)","\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;31m# inference\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtemp_images\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 9\u001b[0m \u001b[0moms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetach\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcpu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 887\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 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\u001b[0mConv3d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_ConvNd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py\u001b[0m in \u001b[0;36m_conv_forward\u001b[0;34m(self, input, weight, bias)\u001b[0m\n\u001b[1;32m 394\u001b[0m _pair(0), self.dilation, self.groups)\n\u001b[1;32m 395\u001b[0m return F.conv2d(input, weight, bias, self.stride,\n\u001b[0;32m--> 396\u001b[0;31m self.padding, self.dilation, self.groups)\n\u001b[0m\u001b[1;32m 397\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 398\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mRuntimeError\u001b[0m: CUDA out of memory. Tried to allocate 80.00 MiB (GPU 0; 15.90 GiB total capacity; 14.86 GiB already allocated; 81.75 MiB free; 14.94 GiB reserved in total by PyTorch)"]}]},{"cell_type":"markdown","metadata":{"id":"evYjR2F3-NwS"},"source":["## submission을 위한 test 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:27.469285Z","start_time":"2021-04-16T19:44:27.456021Z"},"id":"nhMLnV5d-NwS"},"source":["def test(model, data_loader, device):\n"," size = 256\n"," transform = A.Compose([A.Resize(256, 256)])\n"," print('Start prediction.')\n"," model.eval()\n"," \n"," file_name_list = []\n"," preds_array = np.empty((0, size*size), dtype=np.long)\n"," \n"," with torch.no_grad():\n"," for step, (imgs, image_infos) in enumerate(test_loader):\n","\n"," # inference (512 x 512)\n"," outs = model(torch.stack(imgs).to(device))\n"," oms = torch.argmax(outs.squeeze(), dim=1).detach().cpu().numpy()\n"," \n"," # resize (256 x 256)\n"," temp_mask = []\n"," for img, mask in zip(np.stack(imgs), oms):\n"," transformed = transform(image=img, mask=mask)\n"," mask = transformed['mask']\n"," temp_mask.append(mask)\n","\n"," oms = np.array(temp_mask)\n"," \n"," oms = oms.reshape([oms.shape[0], size*size]).astype(int)\n"," preds_array = np.vstack((preds_array, oms))\n"," \n"," file_name_list.append([i['file_name'] for i in image_infos])\n"," print(\"End prediction.\")\n"," file_names = [y for x in file_name_list for y in x]\n"," \n"," return file_names, preds_array"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"r1YKHBf4-NwT"},"source":["## submission.csv 생성"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:45:42.235310Z","start_time":"2021-04-16T19:44:30.499016Z"},"scrolled":true,"id":"Bz79_g8K-NwT"},"source":["# sample_submisson.csv 열기\n","submission = pd.read_csv('./submission/sample_submission.csv', index_col=None)\n","\n","# test set에 대한 prediction\n","file_names, preds = test(model, test_loader, device) # 여가\n","\n","# PredictionString 대입\n","for file_name, string in zip(file_names, preds):\n"," submission = submission.append({\"image_id\" : file_name, \"PredictionString\" : ' '.join(str(e) for e in string.tolist())}, \n"," ignore_index=True)\n","\n","# submission.csv로 저장\n","submission.to_csv(\"./submission/pan_effb3_noisy_focal_adamp_cosLR.csv\", index=False)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Zgl7Ye7F-NwT"},"source":["## 제출까지\n","\n"]},{"cell_type":"code","metadata":{"id":"022E8AC7-NwT"},"source":["import json\n","import requests\n","import os\n","from urllib.parse import urlparse, parse_qsl, urlencode, urlunparse\n","\n","def submit(user_key='', file_path = '', desc=\"\"):\n"," if not user_key:\n"," raise Exception(\"No UserKey\" )\n"," url = urlparse('http://ec2-13-124-161-225.ap-northeast-2.compute.amazonaws.com:8000/api/v1/competition/28/presigned_url/?description=&hyperparameters={%22training%22:{},%22inference%22:{}}')\n"," qs = dict(parse_qsl(url.query))\n"," qs['description'] = desc\n"," parts = url._replace(query=urlencode(qs))\n"," url = urlunparse(parts)\n","\n"," print(url)\n"," headers = {\n"," 'Authorization': user_key\n"," }\n"," res = requests.get(url, headers=headers)\n"," print(res.text)\n"," data = json.loads(res.text)\n"," \n"," submit_url = data['url']\n"," body = {\n"," 'key':'app/Competitions/000028/Users/{}/Submissions/{}/output.csv'.format(str(data['submission']['user']).zfill(8),str(data['submission']['local_id']).zfill(4)),\n"," 'x-amz-algorithm':data['fields']['x-amz-algorithm'],\n"," 'x-amz-credential':data['fields']['x-amz-credential'],\n"," 'x-amz-date':data['fields']['x-amz-date'],\n"," 'policy':data['fields']['policy'],\n"," 'x-amz-signature':data['fields']['x-amz-signature']\n"," }\n"," requests.post(url=submit_url, data=body, files={'file': open(file_path, 'rb')})\n","\n","\n","####################################################################################\n","test_dir = \"/opt/ml/code/submission\" # 수정 필요 : output 파일 폴더 \n","desc = 'pan_effb3_noisy_focal_adamp_cosLR' # 수정 필요 : 파일에 대한 설명\n","output_file = \"pan_effb3_noisy_focal_adamp_cosLR.csv\" #수정 필요 : output 파일 \n","user_key = \"Bearer 7bb5f96452751a238ffaf91a93c4242bf9b72abe\" # 수정 필요 : Authorization \n","\n","\n","submit(user_key, os.path.join(test_dir, output_file),desc)"],"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/chanyub_seg/code/re_pan_effb3_noisy_focal_madgrad_kwparam_stepLR.ipynb b/chanyub_seg/code/re_pan_effb3_noisy_focal_madgrad_kwparam_stepLR.ipynb deleted file mode 100644 index 527f9ae..0000000 --- a/chanyub_seg/code/re_pan_effb3_noisy_focal_madgrad_kwparam_stepLR.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"nbformat":4,"nbformat_minor":0,"metadata":{"hide_input":false,"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.7"},"toc":{"base_numbering":1,"nav_menu":{},"number_sections":true,"sideBar":true,"skip_h1_title":false,"title_cell":"Table of Contents","title_sidebar":"Contents","toc_cell":true,"toc_position":{"height":"calc(100% - 180px)","left":"10px","top":"150px","width":"297.278px"},"toc_section_display":true,"toc_window_display":true},"colab":{"name":"re_pan_effb3_noisy_focal_madgrad_kwparam_stepLR.ipynb","provenance":[],"toc_visible":true},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GW8gF48g-WSK","executionInfo":{"status":"ok","timestamp":1620190671900,"user_tz":-540,"elapsed":1234,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"462e635f-1619-47d6-9cdb-348dd902a9bf"},"source":["from google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xDwpp4Lk-gSH","executionInfo":{"status":"ok","timestamp":1620190672463,"user_tz":-540,"elapsed":1759,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"94f933f2-302d-46e5-8394-a662cf571dcc"},"source":["ls"],"execution_count":2,"outputs":[{"output_type":"stream","text":["\u001b[0m\u001b[01;34mdrive\u001b[0m/ \u001b[01;34msample_data\u001b[0m/\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sItrVDeh-iYC","executionInfo":{"status":"ok","timestamp":1620190672464,"user_tz":-540,"elapsed":1747,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"82114f39-a718-40a4-b910-3eb54ab4ecfa"},"source":["cd drive/MyDrive/Trash/code"],"execution_count":3,"outputs":[{"output_type":"stream","text":["/content/drive/MyDrive/Trash/code\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"a54v4-kN_LVO","executionInfo":{"status":"ok","timestamp":1620190674722,"user_tz":-540,"elapsed":3995,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"e21619a3-063e-4b3c-8a6b-13f5590549d8"},"source":["!pip install albumentations==0.5.2"],"execution_count":4,"outputs":[{"output_type":"stream","text":["Requirement already satisfied: albumentations==0.5.2 in /usr/local/lib/python3.7/dist-packages (0.5.2)\n","Requirement already satisfied: imgaug>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (0.4.0)\n","Requirement already satisfied: opencv-python-headless>=4.1.1 in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (4.5.1.48)\n","Requirement already satisfied: PyYAML in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (3.13)\n","Requirement already satisfied: numpy>=1.11.1 in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (1.19.5)\n","Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (1.4.1)\n","Requirement already satisfied: scikit-image>=0.16.1 in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (0.16.2)\n","Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (1.15.0)\n","Requirement already satisfied: opencv-python in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (4.1.2.30)\n","Requirement already satisfied: Shapely in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (1.7.1)\n","Requirement already satisfied: imageio in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (2.4.1)\n","Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (3.2.2)\n","Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (7.1.2)\n","Requirement already satisfied: networkx>=2.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations==0.5.2) (2.5.1)\n","Requirement already satisfied: PyWavelets>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations==0.5.2) (1.1.1)\n","Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug>=0.4.0->albumentations==0.5.2) (2.8.1)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug>=0.4.0->albumentations==0.5.2) (1.3.1)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug>=0.4.0->albumentations==0.5.2) (0.10.0)\n","Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug>=0.4.0->albumentations==0.5.2) (2.4.7)\n","Requirement already satisfied: decorator<5,>=4.3 in /usr/local/lib/python3.7/dist-packages (from networkx>=2.0->scikit-image>=0.16.1->albumentations==0.5.2) (4.4.2)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.826930Z","start_time":"2021-04-18T10:34:45.406686Z"},"colab":{"base_uri":"https://localhost:8080/"},"id":"l_LPA4XD-NwC","executionInfo":{"status":"ok","timestamp":1620190677106,"user_tz":-540,"elapsed":6366,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"25cb63ec-be84-4d6c-af92-330936ad2ab3"},"source":["import os\n","import random\n","import time\n","import json\n","import warnings \n","warnings.filterwarnings('ignore')\n","\n","import torch\n","import torch.nn as nn\n","from torch.utils.data import Dataset, DataLoader\n","from utils import label_accuracy_score\n","import cv2\n","\n","import numpy as np\n","import pandas as pd\n","\n","# 전처리를 위한 라이브러리\n","from pycocotools.coco import COCO\n","import torchvision\n","import torchvision.transforms as transforms\n","\n","import albumentations as A\n","from albumentations.pytorch import ToTensorV2\n","\n","# 시각화를 위한 라이브러리\n","import matplotlib.pyplot as plt\n","import seaborn as sns; sns.set()\n","\n","plt.rcParams['axes.grid'] = False\n","\n","print('pytorch version: {}'.format(torch.__version__))\n","print('GPU 사용 가능 여부: {}'.format(torch.cuda.is_available()))\n","\n","print(torch.cuda.get_device_name(0))\n","print(torch.cuda.device_count())\n","\n","device = \"cuda\" if torch.cuda.is_available() else \"cpu\" # GPU 사용 가능 여부에 따라 device 정보 저장"],"execution_count":5,"outputs":[{"output_type":"stream","text":["pytorch version: 1.8.1+cu101\n","GPU 사용 가능 여부: True\n","Tesla P100-PCIE-16GB\n","1\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"HxgRSL_M-NwF"},"source":["## 하이퍼파라미터 세팅 및 seed 고정"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.841930Z","start_time":"2021-04-18T10:34:47.827931Z"},"id":"rV3JmGP5-NwF","executionInfo":{"status":"ok","timestamp":1620190677107,"user_tz":-540,"elapsed":6358,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["batch_size = 8 # Mini-batch size\n","num_epochs = 20\n","learning_rate = 0.0001"],"execution_count":6,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.856930Z","start_time":"2021-04-18T10:34:47.842931Z"},"id":"Z6LOuJXQ-NwG","executionInfo":{"status":"ok","timestamp":1620190677108,"user_tz":-540,"elapsed":6351,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# seed 고정\n","random_seed = 42\n","torch.manual_seed(random_seed)\n","torch.cuda.manual_seed(random_seed)\n","# torch.cuda.manual_seed_all(random_seed) # if use multi-GPU\n","torch.backends.cudnn.deterministic = True\n","torch.backends.cudnn.benchmark = False\n","np.random.seed(random_seed)\n","random.seed(random_seed)"],"execution_count":7,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"iWF_EJDu-NwG"},"source":["## 학습 데이터 EDA"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.381961Z","start_time":"2021-04-18T10:34:47.857930Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"lg0x0D0a-NwG","executionInfo":{"status":"ok","timestamp":1620190681064,"user_tz":-540,"elapsed":10295,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"27309e75-aad6-4bbd-863d-755b3bad8a3a"},"source":["%matplotlib inline\n","\n","dataset_path = '../input/data'\n","anns_file_path = dataset_path + '/' + 'train.json'\n","\n","# Read annotations\n","with open(anns_file_path, 'r') as f:\n"," dataset = json.loads(f.read())\n","\n","categories = dataset['categories']\n","anns = dataset['annotations']\n","imgs = dataset['images']\n","nr_cats = len(categories)\n","nr_annotations = len(anns)\n","nr_images = len(imgs)\n","\n","# Load categories and super categories\n","cat_names = []\n","super_cat_names = []\n","super_cat_ids = {}\n","super_cat_last_name = ''\n","nr_super_cats = 0\n","for cat_it in categories:\n"," cat_names.append(cat_it['name'])\n"," super_cat_name = cat_it['supercategory']\n"," # Adding new supercat\n"," if super_cat_name != super_cat_last_name:\n"," super_cat_names.append(super_cat_name)\n"," super_cat_ids[super_cat_name] = nr_super_cats\n"," super_cat_last_name = super_cat_name\n"," nr_super_cats += 1\n","\n","print('Number of super categories:', nr_super_cats)\n","print('Number of categories:', nr_cats)\n","print('Number of annotations:', nr_annotations)\n","print('Number of images:', nr_images)"],"execution_count":8,"outputs":[{"output_type":"stream","text":["Number of super categories: 11\n","Number of categories: 11\n","Number of annotations: 21116\n","Number of images: 2617\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.546964Z","start_time":"2021-04-18T10:34:51.382969Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/","height":355},"id":"PjLyVHVY-NwH","executionInfo":{"status":"ok","timestamp":1620190682078,"user_tz":-540,"elapsed":11294,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"ae3d40a0-36de-41ab-9005-9eb557a80951"},"source":["# Count annotations\n","cat_histogram = np.zeros(nr_cats,dtype=int)\n","for ann in anns:\n"," cat_histogram[ann['category_id']] += 1\n","\n","# Initialize the matplotlib figure\n","f, ax = plt.subplots(figsize=(5,5))\n","\n","# Convert to DataFrame\n","df = pd.DataFrame({'Categories': cat_names, 'Number of annotations': cat_histogram})\n","df = df.sort_values('Number of annotations', 0, False)\n","\n","# Plot the histogram\n","plt.title(\"category distribution of train set \")\n","plot_1 = sns.barplot(x=\"Number of annotations\", y=\"Categories\", data=df, label=\"Total\", color=\"b\")"],"execution_count":9,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.561965Z","start_time":"2021-04-18T10:34:51.547969Z"},"id":"34Tw5__i-NwI","executionInfo":{"status":"ok","timestamp":1620190682080,"user_tz":-540,"elapsed":11288,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# category labeling \n","sorted_temp_df = df.sort_index()\n","\n","# background = 0 에 해당되는 label 추가 후 기존들을 모두 label + 1 로 설정\n","sorted_df = pd.DataFrame([\"Backgroud\"], columns = [\"Categories\"])\n","sorted_df = sorted_df.append(sorted_temp_df, ignore_index=True)"],"execution_count":10,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.576961Z","start_time":"2021-04-18T10:34:51.562964Z"},"colab":{"base_uri":"https://localhost:8080/","height":421},"id":"CQk4vV5N-NwI","executionInfo":{"status":"ok","timestamp":1620190682080,"user_tz":-540,"elapsed":11273,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"ef13a08a-670a-45ba-adb0-cc056e315d5a"},"source":["# class (Categories) 에 따른 index 확인 (0~11 : 총 12개)\n","sorted_df"],"execution_count":11,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
CategoriesNumber of annotations
0BackgroudNaN
1UNKNOWN128.0
2General trash2225.0
3Paper7448.0
4Paper pack527.0
5Metal449.0
6Glass488.0
7Plastic2472.0
8Styrofoam1074.0
9Plastic bag6114.0
10Battery50.0
11Clothing141.0
\n","
"],"text/plain":[" Categories Number of annotations\n","0 Backgroud NaN\n","1 UNKNOWN 128.0\n","2 General trash 2225.0\n","3 Paper 7448.0\n","4 Paper pack 527.0\n","5 Metal 449.0\n","6 Glass 488.0\n","7 Plastic 2472.0\n","8 Styrofoam 1074.0\n","9 Plastic bag 6114.0\n","10 Battery 50.0\n","11 Clothing 141.0"]},"metadata":{"tags":[]},"execution_count":11}]},{"cell_type":"markdown","metadata":{"id":"KXU0zmZs-NwI"},"source":["## 데이터 전처리 함수 정의 (Dataset)"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:52.693328Z","start_time":"2021-04-18T10:34:52.681328Z"},"id":"QFnTI8_Z-NwJ","executionInfo":{"status":"ok","timestamp":1620190682083,"user_tz":-540,"elapsed":11151,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["category_names = list(sorted_df.Categories)\n","\n","def get_classname(classID, cats):\n"," for i in range(len(cats)):\n"," if cats[i]['id']==classID:\n"," return cats[i]['name']\n"," return \"None\"\n","\n","class CustomDataLoader(Dataset):\n"," \"\"\"COCO format\"\"\"\n"," def __init__(self, data_dir, mode = 'train', transform = None):\n"," super().__init__()\n"," self.mode = mode\n"," self.transform = transform\n"," self.coco = COCO(data_dir)\n"," \n"," def __getitem__(self, index: int):\n"," # dataset이 index되어 list처럼 동작\n"," image_id = self.coco.getImgIds(imgIds=index)\n"," image_infos = self.coco.loadImgs(image_id)[0]\n"," \n"," # cv2 를 활용하여 image 불러오기\n"," images = cv2.imread(os.path.join(dataset_path, image_infos['file_name']))\n"," images = cv2.cvtColor(images, cv2.COLOR_BGR2RGB).astype(np.float32)\n"," images /= 255.0\n"," \n"," if (self.mode in ('train', 'val')):\n"," ann_ids = self.coco.getAnnIds(imgIds=image_infos['id'])\n"," anns = self.coco.loadAnns(ann_ids)\n","\n"," # Load the categories in a variable\n"," cat_ids = self.coco.getCatIds()\n"," cats = self.coco.loadCats(cat_ids)\n","\n"," # masks : size가 (height x width)인 2D\n"," # 각각의 pixel 값에는 \"category id + 1\" 할당\n"," # Background = 0\n"," masks = np.zeros((image_infos[\"height\"], image_infos[\"width\"]))\n"," # Unknown = 1, General trash = 2, ... , Cigarette = 11\n"," for i in range(len(anns)):\n"," className = get_classname(anns[i]['category_id'], cats)\n"," pixel_value = category_names.index(className)\n"," masks = np.maximum(self.coco.annToMask(anns[i])*pixel_value, masks)\n"," masks = masks.astype(np.float32)\n","\n"," # transform -> albumentations 라이브러리 활용\n"," if self.transform is not None:\n"," transformed = self.transform(image=images, mask=masks)\n"," images = transformed[\"image\"]\n"," masks = transformed[\"mask\"]\n"," \n"," return images, masks, image_infos\n"," \n"," if self.mode == 'test':\n"," # transform -> albumentations 라이브러리 활용\n"," if self.transform is not None:\n"," transformed = self.transform(image=images)\n"," images = transformed[\"image\"]\n"," \n"," return images, image_infos\n"," \n"," \n"," def __len__(self) -> int:\n"," # 전체 dataset의 size를 return\n"," return len(self.coco.getImgIds())"],"execution_count":12,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"cp2aIOlP-NwK"},"source":["## Dataset 정의 및 DataLoader 할당"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:58.823175Z","start_time":"2021-04-18T10:34:54.106233Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"P7zFlRn6-NwK","executionInfo":{"status":"ok","timestamp":1620190688999,"user_tz":-540,"elapsed":16893,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"f4cf265b-3fb4-4639-a284-168f21617bc5"},"source":["# train.json / validation.json / test.json 디렉토리 설정\n","train_path = dataset_path + '/train.json'\n","val_path = dataset_path + '/val.json'\n","test_path = dataset_path + '/test.json'\n","\n","# collate_fn needs for batch\n","def collate_fn(batch):\n"," return tuple(zip(*batch))\n","\n","train_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","val_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","test_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","# create own Dataset 1 (skip)\n","# validation set을 직접 나누고 싶은 경우\n","# random_split 사용하여 data set을 8:2 로 분할\n","# train_size = int(0.8*len(dataset))\n","# val_size = int(len(dataset)-train_size)\n","# dataset = CustomDataLoader(data_dir=train_path, mode='train', transform=transform)\n","# train_dataset, val_dataset = torch.utils.data.random_split(dataset, [train_size, val_size])\n","\n","# create own Dataset 2\n","# train dataset\n","train_dataset = CustomDataLoader(data_dir=train_path, mode='train', transform=train_transform)\n","\n","# validation dataset\n","val_dataset = CustomDataLoader(data_dir=val_path, mode='val', transform=val_transform)\n","\n","# test dataset\n","test_dataset = CustomDataLoader(data_dir=test_path, mode='test', transform=test_transform)\n","\n","\n","# DataLoader\n","train_loader = torch.utils.data.DataLoader(dataset=train_dataset, \n"," batch_size=batch_size,\n"," shuffle=True,\n"," num_workers=4,\n"," collate_fn=collate_fn,\n"," drop_last=True)\n","\n","val_loader = torch.utils.data.DataLoader(dataset=val_dataset, \n"," batch_size=batch_size,\n"," shuffle=False,\n"," num_workers=4,\n"," collate_fn=collate_fn,\n"," drop_last=True) \n","\n","test_loader = torch.utils.data.DataLoader(dataset=test_dataset,\n"," batch_size=batch_size,\n"," num_workers=4,\n"," collate_fn=collate_fn)"],"execution_count":13,"outputs":[{"output_type":"stream","text":["loading annotations into memory...\n","Done (t=4.12s)\n","creating index...\n","index created!\n","loading annotations into memory...\n","Done (t=2.55s)\n","creating index...\n","index created!\n","loading annotations into memory...\n","Done (t=0.01s)\n","creating index...\n","index created!\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"8rqJiHb_-NwM"},"source":["# wandb"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"H1kHXm0uAX3R","executionInfo":{"status":"ok","timestamp":1620119873945,"user_tz":-540,"elapsed":8777,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"584d8ef2-18e5-4e25-e100-0b84fd89228e"},"source":["!pip install wandb"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Collecting wandb\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/67/5a/b037b50f9849212863a2fed313624d8f6f33ffa4ce89dc706e2a0e98c780/wandb-0.10.29-py2.py3-none-any.whl (2.1MB)\n","\r\u001b[K |▏ | 10kB 22.7MB/s eta 0:00:01\r\u001b[K |▎ | 20kB 30.7MB/s eta 0:00:01\r\u001b[K |▌ | 30kB 24.0MB/s eta 0:00:01\r\u001b[K |▋ | 40kB 18.1MB/s eta 0:00:01\r\u001b[K |▉ | 51kB 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're_pan_effb3_noisy_focal_madgrad_kwparam_stepLR'\n","\n","wandb.init(project='chanyub',name=proj_name)\n","\n","config = wandb.config\n","config.learning_rate = 0.01"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"application/javascript":["\n"," window._wandbApiKey = new Promise((resolve, reject) => {\n"," function loadScript(url) {\n"," return new Promise(function(resolve, reject) {\n"," let newScript = document.createElement(\"script\");\n"," newScript.onerror = reject;\n"," newScript.onload = resolve;\n"," document.body.appendChild(newScript);\n"," newScript.src = url;\n"," });\n"," }\n"," loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n"," const iframe = document.createElement('iframe')\n"," iframe.style.cssText = \"width:0;height:0;border:none\"\n"," document.body.appendChild(iframe)\n"," const handshake = new Postmate({\n"," container: iframe,\n"," url: 'https://wandb.ai/authorize'\n"," });\n"," const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n"," handshake.then(function(child) {\n"," child.on('authorize', data => {\n"," clearTimeout(timeout)\n"," resolve(data)\n"," });\n"," });\n"," })\n"," });\n"," "],"text/plain":[""]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"],"name":"stderr"},{"output_type":"display_data","data":{"text/html":["\n"," Tracking run with wandb version 0.10.29
\n"," Syncing run re_pan_effb3_noisy_focal_madgrad_kwparam_stepLR to Weights & Biases (Documentation).
\n"," Project page: https://wandb.ai/pstage12/chanyub
\n"," Run page: https://wandb.ai/pstage12/chanyub/runs/1q5ov7ic
\n"," Run data is saved locally in /content/drive/My Drive/Trash/code/wandb/run-20210504_091804-1q5ov7ic

\n"," "],"text/plain":[""]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"uQRiIVGX-NwM"},"source":["## My model"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"S0xCGpNeAqeD","executionInfo":{"status":"ok","timestamp":1620190690952,"user_tz":-540,"elapsed":12571,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"1ec8c471-50b0-4543-db91-899177ffd37b"},"source":["!pip install segmentation_models_pytorch"],"execution_count":14,"outputs":[{"output_type":"stream","text":["Requirement already satisfied: segmentation_models_pytorch in /usr/local/lib/python3.7/dist-packages (0.1.3)\n","Requirement already satisfied: torchvision>=0.3.0 in /usr/local/lib/python3.7/dist-packages (from segmentation_models_pytorch) (0.9.1+cu101)\n","Requirement already satisfied: pretrainedmodels==0.7.4 in /usr/local/lib/python3.7/dist-packages (from segmentation_models_pytorch) (0.7.4)\n","Requirement already satisfied: efficientnet-pytorch==0.6.3 in /usr/local/lib/python3.7/dist-packages (from segmentation_models_pytorch) (0.6.3)\n","Requirement already satisfied: timm==0.3.2 in /usr/local/lib/python3.7/dist-packages (from segmentation_models_pytorch) (0.3.2)\n","Requirement already satisfied: torch==1.8.1 in /usr/local/lib/python3.7/dist-packages (from torchvision>=0.3.0->segmentation_models_pytorch) (1.8.1+cu101)\n","Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.7/dist-packages (from torchvision>=0.3.0->segmentation_models_pytorch) (7.1.2)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torchvision>=0.3.0->segmentation_models_pytorch) (1.19.5)\n","Requirement already satisfied: munch in /usr/local/lib/python3.7/dist-packages (from pretrainedmodels==0.7.4->segmentation_models_pytorch) (2.5.0)\n","Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from pretrainedmodels==0.7.4->segmentation_models_pytorch) (4.41.1)\n","Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch==1.8.1->torchvision>=0.3.0->segmentation_models_pytorch) (3.7.4.3)\n","Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from munch->pretrainedmodels==0.7.4->segmentation_models_pytorch) (1.15.0)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:11.634792Z","start_time":"2021-04-18T16:16:05.875817Z"},"colab":{"base_uri":"https://localhost:8080/"},"id":"a8IfZfiM-NwM","executionInfo":{"status":"ok","timestamp":1620190701963,"user_tz":-540,"elapsed":22467,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"06919fa6-a2ff-4bfd-eb5b-c739a6f6def7"},"source":["# 구현된 model에 임의의 input을 넣어 output이 잘 나오는지 test\n","import segmentation_models_pytorch as smp\n","\n","model = smp.PAN(encoder_name='timm-efficientnet-b3', encoder_weights='noisy-student', classes=12)\n","x = torch.randn([2, 3, 512, 512])\n","print(\"input shape : \", x.shape)\n","out = model(x).to(device)\n","print(\"output shape : \", out.size())\n","\n","model = model.to(device)"],"execution_count":15,"outputs":[{"output_type":"stream","text":["input shape : torch.Size([2, 3, 512, 512])\n","output shape : torch.Size([2, 12, 512, 512])\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"zvDnI7_T-NwN"},"source":["## train, validation, test 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.104200Z","start_time":"2021-04-18T16:16:18.093174Z"},"id":"RA3oAapJ-NwN"},"source":["def train(num_epochs, model, data_loader, val_loader, criterion, optimizer, saved_dir, val_every, device, lr_scheduler):\n"," print('Start training..')\n"," best_loss = 9999999\n"," best_miou = 0\n"," for epoch in range(num_epochs):\n"," model.train()\n"," for step, (images, masks, _) in enumerate(data_loader):\n"," images = torch.stack(images) # (batch, channel, height, width)\n"," masks = torch.stack(masks).long() # (batch, channel, height, width)\n"," \n"," # gpu 연산을 위해 device 할당\n"," images, masks = images.to(device), masks.to(device)\n"," \n"," # inference\n"," outputs = model(images)\n"," \n"," # loss 계산 (cross entropy loss)\n"," loss = criterion(outputs, masks)\n"," optimizer.zero_grad()\n"," loss.backward()\n"," optimizer.step()\n"," \n"," lr_scheduler.step()\n"," \n"," # step 주기에 따른 loss 출력\n"," if (step + 1) % 25 == 0:\n"," print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(\n"," epoch+1, num_epochs, step+1, len(train_loader), loss.item()))\n"," \n"," # validation 주기에 따른 loss 출력 및 best model 저장\n"," if (epoch + 1) % val_every == 0:\n","# avrg_loss = validation(epoch + 1, model, val_loader, criterion, device)\n","# if avrg_loss < best_loss:\n","# print('Best performance at epoch: {}'.format(epoch + 1))\n","# print('Save model in', saved_dir)\n","# best_loss = avrg_loss\n","# wandb.log({'best_loss': best_loss})\n","# save_model(model, saved_dir)\n"," avrg_miou = validation(epoch + 1, model, val_loader, criterion, device)\n"," if avrg_miou > best_miou:\n"," print('Best performance at epoch: {}'.format(epoch + 1))\n"," print('Save model in', saved_dir)\n"," best_miou = avrg_miou\n"," wandb.log({'best_miou': best_miou})\n"," save_model(model, saved_dir)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.285795Z","start_time":"2021-04-18T16:16:18.267686Z"},"id":"EulIikmq-NwO"},"source":["def validation(epoch, model, data_loader, criterion, device):\n"," print('Start validation #{}'.format(epoch))\n"," model.eval()\n"," with torch.no_grad():\n"," total_loss = 0\n"," cnt = 0\n"," mIoU_list = []\n"," for step, (images, masks, _) in enumerate(data_loader):\n"," \n"," images = torch.stack(images) # (batch, channel, height, width)\n"," masks = torch.stack(masks).long() # (batch, channel, height, width)\n","\n"," images, masks = images.to(device), masks.to(device) \n","\n"," outputs = model(images)\n"," loss = criterion(outputs, masks)\n"," total_loss += loss\n"," cnt += 1\n"," \n","# print(outputs.shape)\n","# print(masks.shape)\n","# wandb.log(wandb.Image(images, masks={\n","# \"predictions\" : {\n","# \"mask_data\" : torch.squeeze(torch.squeeze(outputs, 0),1),\n","# \"class_labels\" : classes_dict\n","# },\n","# \"ground_truth\" : {\n","# \"mask_data\" : torch.squeeze(masks, 0),\n","# \"class_labels\" : classes_dict\n","# }\n","# }))\n"," \n"," outputs = torch.argmax(outputs.squeeze(), dim=1).detach().cpu().numpy()\n","\n"," mIoU = label_accuracy_score(masks.detach().cpu().numpy(), outputs, n_class=12)[2]\n"," mIoU_list.append(mIoU)\n"," \n"," avrg_loss = total_loss / cnt\n"," avrg_mIoU = np.mean(mIoU_list)\n"," print('Validation #{} Average Loss: {:.4f}, mIoU: {:.4f}'.format(epoch, avrg_loss, np.mean(mIoU_list)))\n"," wandb.log({'Train Loss':loss.item(), 'Val Loss':avrg_loss , 'Val mIoU':np.mean(mIoU_list)})\n","# return avrg_loss\n"," return avrg_mIoU"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"C_7CtFfH-NwO"},"source":["## 모델 저장 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.909918Z","start_time":"2021-04-18T16:16:18.898918Z"},"id":"gpCM5BFO-NwO"},"source":["# 모델 저장 함수 정의\n","val_every = 1 \n","\n","saved_dir = './saved'\n","if not os.path.isdir(saved_dir): \n"," os.mkdir(saved_dir)\n"," \n","def save_model(model, saved_dir, file_name='re_pan_effb3_noisy_focal_madgrad_kwparam_stepLR.pt'):\n"," check_point = {'net': model.state_dict()}\n"," output_path = os.path.join(saved_dir, file_name)\n"," torch.save(model.state_dict(), output_path)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"FyKGeg8T-NwP"},"source":["## 모델 생성 및 Loss function, Optimizer 정의"]},{"cell_type":"code","metadata":{"id":"ORugl8s1-NwP"},"source":["from torch.autograd import Variable\n","import torch.nn.functional as F\n","# ref : https://github.com/clcarwin/focal_loss_pytorch\n","class FocalLoss(nn.Module):\n"," def __init__(self, gamma=0, alpha=None, size_average=True):\n"," super(FocalLoss, self).__init__()\n"," self.gamma = gamma\n"," self.alpha = alpha\n"," if isinstance(alpha,(float,int)): self.alpha = torch.Tensor([alpha,1-alpha])\n"," if isinstance(alpha,list): self.alpha = torch.Tensor(alpha)\n"," self.size_average = size_average\n","\n"," def forward(self, input, target):\n"," if input.dim()>2:\n"," input = input.view(input.size(0),input.size(1),-1) # N,C,H,W => N,C,H*W\n"," input = input.transpose(1,2) # N,C,H*W => N,H*W,C\n"," input = input.contiguous().view(-1,input.size(2)) # N,H*W,C => N*H*W,C\n"," target = target.view(-1,1)\n","\n"," logpt = F.log_softmax(input)\n"," logpt = logpt.gather(1,target)\n"," logpt = logpt.view(-1)\n"," pt = Variable(logpt.data.exp())\n","\n"," if self.alpha is not None:\n"," if self.alpha.type()!=input.data.type():\n"," self.alpha = self.alpha.type_as(input.data)\n"," at = self.alpha.gather(0,target.data.view(-1))\n"," logpt = logpt * Variable(at)\n","\n"," loss = -1 * (1-pt)**self.gamma * logpt\n"," if self.size_average: return loss.mean()\n"," else: return loss.sum()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"yfeFQknz-NwP"},"source":["import torch.optim.lr_scheduler as lr_scheduler\n","import math\n","class CosineAnnealingWarmUpRestart(lr_scheduler._LRScheduler):\n"," def __init__(self, optimizer, T_0, T_mult=1, eta_max=0.1, T_up=0, gamma=1., last_epoch=-1):\n"," if T_0 <= 0 or not isinstance(T_0, int):\n"," raise ValueError(\"Expected positive integer T_0, but got {}\".format(T_0))\n"," if T_mult < 1 or not isinstance(T_mult, int):\n"," raise ValueError(\"Expected integer T_mult >= 1, but got {}\".format(T_mult))\n"," if T_up < 0 or not isinstance(T_up, int):\n"," raise ValueError(\"Expected positive integer T_up, but got {}\".format(T_up))\n"," self.T_0 = T_0\n"," self.T_mult = T_mult\n"," self.base_eta_max = eta_max\n"," self.eta_max = eta_max\n"," self.T_up = T_up\n"," self.T_i = T_0\n"," self.gamma = gamma\n"," self.cycle = 0\n"," self.T_cur = last_epoch\n"," super(CosineAnnealingWarmUpRestart, self).__init__(optimizer, last_epoch)\n"," # self.T_cur = last_epoch\n"," \n"," def get_lr(self):\n"," if self.T_cur == -1:\n"," return self.base_lrs\n"," elif self.T_cur < self.T_up:\n"," return [(self.eta_max - base_lr)*self.T_cur / self.T_up + base_lr for base_lr in self.base_lrs]\n"," else:\n"," return [base_lr + (self.eta_max - base_lr) * (1 + math.cos(math.pi * (self.T_cur-self.T_up) / (self.T_i - self.T_up))) / 2\n"," for base_lr in self.base_lrs]\n","\n"," def step(self, epoch=None):\n"," if epoch is None:\n"," epoch = self.last_epoch + 1\n"," self.T_cur = self.T_cur + 1\n"," if self.T_cur >= self.T_i:\n"," self.cycle += 1\n"," self.T_cur = self.T_cur - self.T_i\n"," self.T_i = (self.T_i - self.T_up) * self.T_mult + self.T_up\n"," else:\n"," if epoch >= self.T_0:\n"," if self.T_mult == 1:\n"," self.T_cur = epoch % self.T_0\n"," self.cycle = epoch // self.T_0\n"," else:\n"," n = int(math.log((epoch / self.T_0 * (self.T_mult - 1) + 1), self.T_mult))\n"," self.cycle = n\n"," self.T_cur = epoch - self.T_0 * (self.T_mult ** n - 1) / (self.T_mult - 1)\n"," self.T_i = self.T_0 * self.T_mult ** (n)\n"," else:\n"," self.T_i = self.T_0\n"," self.T_cur = epoch\n"," \n"," self.eta_max = self.base_eta_max * (self.gamma**self.cycle)\n"," self.last_epoch = math.floor(epoch)\n"," for param_group, lr in zip(self.optimizer.param_groups, self.get_lr()):\n"," param_group['lr'] = lr"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AG1oQeu7BX1M","executionInfo":{"status":"ok","timestamp":1620060019354,"user_tz":-540,"elapsed":3477,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"98eae7c4-b66e-409d-9725-0c684d747f2a"},"source":["# !pip install adamp"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Requirement already satisfied: adamp in /usr/local/lib/python3.7/dist-packages (0.3.0)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"eH2PpwF9a-Os","executionInfo":{"status":"ok","timestamp":1620119929514,"user_tz":-540,"elapsed":5703,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"5a155e4b-56ab-4d5b-ff8f-93042195402d"},"source":["!pip install madgrad"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Collecting madgrad\n"," Downloading https://files.pythonhosted.org/packages/65/f0/4584f18202a2fb8903d456bf907b80e7cb54ad8fcba68604084ff41b7cf8/madgrad-1.1-py3-none-any.whl\n","Installing collected packages: madgrad\n","Successfully installed madgrad-1.1\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:19.698902Z","start_time":"2021-04-18T16:16:19.694902Z"},"id":"9Dly8KZj-NwQ"},"source":["# from adamp import AdamP\n","from madgrad import MADGRAD\n","# Loss function 정의\n","# criterion = nn.CrossEntropyLoss()\n","criterion = FocalLoss()\n","\n","# Optimizer 정의\n","# optimizer = torch.optim.Adam(params = model.parameters(), lr = learning_rate, weight_decay=1e-6)\n","# optimizer = AdamP(params = model.parameters())\n","optimizer = MADGRAD(params = model.parameters(), lr = learning_rate, momentum = 0.9, weight_decay = 0.0001, eps = 1e-06)\n","\n","lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size = 654, gamma = 0.5)\n","# lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=50, eta_min=0)\n","# lr_scheduler = CosineAnnealingWarmUpRestart(optimizer, T_0=150, T_mult=1, eta_max=0.1, T_up=10, gamma=0.5)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"start_time":"2021-04-18T16:16:20.331Z"},"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"cMSiFA-3-NwR","executionInfo":{"status":"error","timestamp":1620134631821,"user_tz":-540,"elapsed":14654014,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"21107ea6-fd63-4a7e-dc81-bf35ccc2cc10"},"source":["train(num_epochs, model, train_loader, val_loader, criterion, optimizer, saved_dir, val_every, device, lr_scheduler)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Start training..\n","Epoch [1/20], Step [25/327], Loss: 0.8198\n","Epoch [1/20], Step [50/327], Loss: 0.9713\n","Epoch [1/20], Step [75/327], Loss: 0.6090\n","Epoch [1/20], Step [100/327], Loss: 0.5336\n","Epoch [1/20], Step [125/327], Loss: 0.6895\n","Epoch [1/20], Step [150/327], Loss: 0.3069\n","Epoch [1/20], Step [175/327], Loss: 0.4218\n","Epoch [1/20], Step [200/327], Loss: 0.5319\n","Epoch [1/20], Step [225/327], Loss: 0.4869\n","Epoch [1/20], Step [250/327], Loss: 0.3474\n","Epoch [1/20], Step [275/327], Loss: 0.2858\n","Epoch [1/20], Step [300/327], Loss: 0.3579\n","Epoch [1/20], Step [325/327], Loss: 0.8720\n","Start validation #1\n","Validation #1 Average Loss: 0.3953, mIoU: 0.3284\n","Best performance at epoch: 1\n","Save model in ./saved\n","Epoch [2/20], Step [25/327], Loss: 0.4505\n","Epoch [2/20], Step [50/327], Loss: 0.3896\n","Epoch [2/20], Step [75/327], Loss: 0.2647\n","Epoch [2/20], Step [100/327], Loss: 0.5003\n","Epoch [2/20], Step [125/327], Loss: 0.2459\n","Epoch [2/20], Step [150/327], Loss: 0.3360\n","Epoch [2/20], Step [175/327], Loss: 0.5536\n","Epoch [2/20], Step [200/327], Loss: 0.3739\n","Epoch [2/20], Step [225/327], Loss: 0.4878\n","Epoch [2/20], Step [250/327], Loss: 0.3480\n","Epoch [2/20], Step [275/327], Loss: 0.3028\n","Epoch [2/20], Step [300/327], Loss: 0.3372\n","Epoch [2/20], Step [325/327], Loss: 0.2951\n","Start validation #2\n","Validation #2 Average Loss: 0.3315, mIoU: 0.3737\n","Best performance at epoch: 2\n","Save model in ./saved\n","Epoch [3/20], Step [25/327], Loss: 0.2781\n","Epoch [3/20], Step [50/327], Loss: 0.3494\n","Epoch [3/20], Step [75/327], Loss: 0.2409\n","Epoch [3/20], Step [100/327], Loss: 0.2100\n","Epoch [3/20], Step [125/327], Loss: 0.3469\n","Epoch [3/20], Step [150/327], Loss: 0.1259\n","Epoch [3/20], Step [175/327], Loss: 0.2974\n","Epoch [3/20], Step [200/327], Loss: 0.4319\n","Epoch [3/20], Step [225/327], Loss: 0.4997\n","Epoch [3/20], Step [250/327], Loss: 0.2654\n","Epoch [3/20], Step [275/327], Loss: 0.1998\n","Epoch [3/20], Step [300/327], Loss: 0.2201\n","Epoch [3/20], Step [325/327], Loss: 0.1587\n","Start validation #3\n","Validation #3 Average Loss: 0.3048, mIoU: 0.4021\n","Best performance at epoch: 3\n","Save model in ./saved\n","Epoch [4/20], Step [25/327], Loss: 0.2404\n","Epoch [4/20], Step [50/327], Loss: 0.1569\n","Epoch [4/20], Step [75/327], Loss: 0.6080\n","Epoch [4/20], Step [100/327], Loss: 0.3973\n","Epoch [4/20], Step [125/327], Loss: 0.1623\n","Epoch [4/20], Step [150/327], Loss: 0.1807\n","Epoch [4/20], Step [175/327], Loss: 0.2471\n","Epoch [4/20], Step [200/327], Loss: 0.3909\n","Epoch [4/20], Step [225/327], Loss: 0.1648\n","Epoch [4/20], Step [250/327], Loss: 0.3210\n","Epoch [4/20], Step [275/327], Loss: 0.3005\n","Epoch [4/20], Step [300/327], Loss: 0.1548\n","Epoch [4/20], Step [325/327], Loss: 0.2271\n","Start validation #4\n","Validation #4 Average Loss: 0.3049, mIoU: 0.4076\n","Best performance at epoch: 4\n","Save model in ./saved\n","Epoch [5/20], Step [25/327], Loss: 0.2311\n","Epoch [5/20], Step [50/327], Loss: 0.1008\n","Epoch [5/20], Step [75/327], Loss: 0.1504\n","Epoch [5/20], Step [100/327], Loss: 0.1174\n","Epoch [5/20], Step [125/327], Loss: 0.3463\n","Epoch [5/20], Step [150/327], Loss: 0.1242\n","Epoch [5/20], Step [175/327], Loss: 0.2663\n","Epoch [5/20], Step [200/327], Loss: 0.1899\n","Epoch [5/20], Step [225/327], Loss: 0.2668\n","Epoch [5/20], Step [250/327], Loss: 0.3070\n","Epoch [5/20], Step [275/327], Loss: 0.1905\n","Epoch [5/20], Step [300/327], Loss: 0.1619\n","Epoch [5/20], Step [325/327], Loss: 0.1328\n","Start validation #5\n","Validation #5 Average Loss: 0.3015, mIoU: 0.4155\n","Best performance at epoch: 5\n","Save model in ./saved\n","Epoch [6/20], Step [25/327], Loss: 0.2184\n","Epoch [6/20], Step [50/327], Loss: 0.4652\n","Epoch [6/20], Step [75/327], Loss: 0.1913\n","Epoch [6/20], Step [100/327], Loss: 0.2075\n","Epoch [6/20], Step [125/327], Loss: 0.1148\n","Epoch [6/20], Step [150/327], Loss: 0.2694\n","Epoch [6/20], Step [175/327], Loss: 0.1693\n","Epoch [6/20], Step [200/327], Loss: 0.1229\n","Epoch [6/20], Step [225/327], Loss: 0.2525\n","Epoch [6/20], Step [250/327], Loss: 0.1529\n","Epoch [6/20], Step [275/327], Loss: 0.2399\n","Epoch [6/20], Step [300/327], Loss: 0.1234\n","Epoch [6/20], Step [325/327], Loss: 0.3608\n","Start validation #6\n","Validation #6 Average Loss: 0.2914, mIoU: 0.4292\n","Best performance at epoch: 6\n","Save model in ./saved\n","Epoch [7/20], Step [25/327], Loss: 0.1479\n","Epoch [7/20], Step [50/327], Loss: 0.3272\n","Epoch [7/20], Step [75/327], Loss: 0.1766\n","Epoch [7/20], Step [100/327], Loss: 0.1316\n","Epoch [7/20], Step [125/327], Loss: 0.1948\n","Epoch [7/20], Step [150/327], Loss: 0.2247\n","Epoch [7/20], Step [175/327], Loss: 0.1992\n","Epoch [7/20], Step [200/327], Loss: 0.1570\n","Epoch [7/20], Step [225/327], Loss: 0.1723\n","Epoch [7/20], Step [250/327], Loss: 0.0944\n","Epoch [7/20], Step [275/327], Loss: 0.1937\n","Epoch [7/20], Step [300/327], Loss: 0.1344\n","Epoch [7/20], Step [325/327], Loss: 0.2316\n","Start validation #7\n","Validation #7 Average Loss: 0.2905, mIoU: 0.4268\n","Epoch [8/20], Step [25/327], Loss: 0.1144\n","Epoch [8/20], Step [50/327], Loss: 0.2133\n","Epoch [8/20], Step [75/327], Loss: 0.2023\n","Epoch [8/20], Step [100/327], Loss: 0.4200\n","Epoch [8/20], Step [125/327], Loss: 0.1886\n","Epoch [8/20], Step [150/327], Loss: 0.0861\n","Epoch [8/20], Step [175/327], Loss: 0.1671\n","Epoch [8/20], Step [200/327], Loss: 0.2295\n","Epoch [8/20], Step [225/327], Loss: 0.2411\n","Epoch [8/20], Step [250/327], Loss: 0.1338\n","Epoch [8/20], Step [275/327], Loss: 0.2047\n","Epoch [8/20], Step [300/327], Loss: 0.1992\n","Epoch [8/20], Step [325/327], Loss: 0.1447\n","Start validation #8\n","Validation #8 Average Loss: 0.2958, mIoU: 0.4238\n","Epoch [9/20], Step [25/327], Loss: 0.1619\n","Epoch [9/20], Step [50/327], Loss: 0.1028\n","Epoch [9/20], Step [75/327], Loss: 0.1625\n","Epoch [9/20], Step [100/327], Loss: 0.1083\n","Epoch [9/20], Step [125/327], Loss: 0.1489\n","Epoch [9/20], Step [150/327], Loss: 0.2824\n","Epoch [9/20], Step [175/327], Loss: 0.1471\n","Epoch [9/20], Step [200/327], Loss: 0.1378\n","Epoch [9/20], Step [225/327], Loss: 0.1728\n","Epoch [9/20], Step [250/327], Loss: 0.4593\n","Epoch [9/20], Step [275/327], Loss: 0.1319\n","Epoch [9/20], Step [300/327], Loss: 0.0881\n","Epoch [9/20], Step [325/327], Loss: 0.1721\n","Start validation #9\n","Validation #9 Average Loss: 0.2961, mIoU: 0.4230\n","Epoch [10/20], Step [25/327], Loss: 0.1738\n","Epoch [10/20], Step [50/327], Loss: 0.1984\n","Epoch [10/20], Step [75/327], Loss: 0.1955\n","Epoch [10/20], Step [100/327], Loss: 0.1076\n","Epoch [10/20], Step [125/327], Loss: 0.1569\n","Epoch [10/20], Step [150/327], Loss: 0.1611\n","Epoch [10/20], Step [175/327], Loss: 0.1800\n","Epoch [10/20], Step [200/327], Loss: 0.1540\n","Epoch [10/20], Step [225/327], Loss: 0.1921\n","Epoch [10/20], Step [250/327], Loss: 0.2275\n","Epoch [10/20], Step [275/327], Loss: 0.1001\n","Epoch [10/20], Step [300/327], Loss: 0.1516\n","Epoch [10/20], Step [325/327], Loss: 0.1818\n","Start validation #10\n","Validation #10 Average Loss: 0.2976, mIoU: 0.4213\n","Epoch [11/20], Step [25/327], Loss: 0.1233\n","Epoch [11/20], Step [50/327], Loss: 0.1091\n","Epoch [11/20], Step [75/327], Loss: 0.1688\n","Epoch [11/20], Step [100/327], Loss: 0.1461\n","Epoch [11/20], Step [125/327], Loss: 0.1151\n","Epoch [11/20], Step [150/327], Loss: 0.1677\n","Epoch [11/20], Step [175/327], Loss: 0.1043\n","Epoch [11/20], Step [200/327], Loss: 0.1269\n","Epoch [11/20], Step [225/327], Loss: 0.1092\n","Epoch [11/20], Step [250/327], Loss: 0.1877\n","Epoch [11/20], Step [275/327], Loss: 0.1777\n","Epoch [11/20], Step [300/327], Loss: 0.0969\n","Epoch [11/20], Step [325/327], Loss: 0.1158\n","Start validation #11\n","Validation #11 Average Loss: 0.2944, mIoU: 0.4257\n","Epoch [12/20], Step [25/327], Loss: 0.1555\n","Epoch [12/20], Step [50/327], Loss: 0.1861\n","Epoch [12/20], Step [75/327], Loss: 0.1103\n","Epoch [12/20], Step [100/327], Loss: 0.1331\n","Epoch [12/20], Step [125/327], Loss: 0.0856\n","Epoch [12/20], Step [150/327], Loss: 0.1543\n","Epoch [12/20], Step [175/327], Loss: 0.1066\n","Epoch [12/20], Step [200/327], Loss: 0.1326\n","Epoch [12/20], Step [225/327], Loss: 0.0753\n","Epoch [12/20], Step [250/327], Loss: 0.1602\n","Epoch [12/20], Step [275/327], Loss: 0.1192\n","Epoch [12/20], Step [300/327], Loss: 0.1696\n","Epoch [12/20], Step [325/327], Loss: 0.1623\n","Start validation #12\n","Validation #12 Average Loss: 0.2930, mIoU: 0.4240\n","Epoch [13/20], Step [25/327], Loss: 0.1082\n","Epoch [13/20], Step [50/327], Loss: 0.2142\n","Epoch [13/20], Step [75/327], Loss: 0.1543\n","Epoch [13/20], Step [100/327], Loss: 0.1195\n","Epoch [13/20], Step [125/327], Loss: 0.0861\n","Epoch [13/20], Step [150/327], Loss: 0.1650\n","Epoch [13/20], Step [175/327], Loss: 0.1329\n","Epoch [13/20], Step [200/327], Loss: 0.1851\n","Epoch [13/20], Step [225/327], Loss: 0.1807\n","Epoch [13/20], Step [250/327], Loss: 0.1484\n","Epoch [13/20], Step [275/327], Loss: 0.1468\n","Epoch [13/20], Step [300/327], Loss: 0.1078\n","Epoch [13/20], Step [325/327], Loss: 0.1461\n","Start validation #13\n","Validation #13 Average Loss: 0.2940, mIoU: 0.4329\n","Best performance at epoch: 13\n","Save model in ./saved\n","Epoch [14/20], Step [25/327], Loss: 0.1087\n","Epoch [14/20], Step [50/327], Loss: 0.1248\n","Epoch [14/20], Step [75/327], Loss: 0.1733\n","Epoch [14/20], Step [100/327], Loss: 0.1938\n","Epoch [14/20], Step [125/327], Loss: 0.0783\n","Epoch [14/20], Step [150/327], Loss: 0.1258\n","Epoch [14/20], Step [175/327], Loss: 0.1371\n","Epoch [14/20], Step [200/327], Loss: 0.1478\n","Epoch [14/20], Step [225/327], Loss: 0.1842\n","Epoch [14/20], Step [250/327], Loss: 0.1123\n","Epoch [14/20], Step [275/327], Loss: 0.1106\n","Epoch [14/20], Step [300/327], Loss: 0.0940\n","Epoch [14/20], Step [325/327], Loss: 0.1038\n","Start validation #14\n","Validation #14 Average Loss: 0.2988, mIoU: 0.4261\n","Epoch [15/20], Step [25/327], Loss: 0.1292\n","Epoch [15/20], Step [50/327], Loss: 0.1131\n","Epoch [15/20], Step [75/327], Loss: 0.1729\n","Epoch [15/20], Step [100/327], Loss: 0.1125\n","Epoch [15/20], Step [125/327], Loss: 0.2529\n","Epoch [15/20], Step [150/327], Loss: 0.1565\n","Epoch [15/20], Step [175/327], Loss: 0.2756\n","Epoch [15/20], Step [200/327], Loss: 0.2463\n","Epoch [15/20], Step [225/327], Loss: 0.2562\n","Epoch [15/20], Step [250/327], Loss: 0.1450\n","Epoch [15/20], Step [275/327], Loss: 0.1415\n","Epoch [15/20], Step [300/327], Loss: 0.1719\n","Epoch [15/20], Step [325/327], Loss: 0.1439\n","Start validation #15\n","Validation #15 Average Loss: 0.2921, mIoU: 0.4248\n","Epoch [16/20], Step [25/327], Loss: 0.1777\n","Epoch [16/20], Step [50/327], Loss: 0.1103\n","Epoch [16/20], Step [75/327], Loss: 0.1278\n","Epoch [16/20], Step [100/327], Loss: 0.0840\n","Epoch [16/20], Step [125/327], Loss: 0.1936\n","Epoch [16/20], Step [150/327], Loss: 0.1903\n","Epoch [16/20], Step [175/327], Loss: 0.0999\n","Epoch [16/20], Step [200/327], Loss: 0.2041\n","Epoch [16/20], Step [225/327], Loss: 0.2188\n","Epoch [16/20], Step [250/327], Loss: 0.1494\n","Epoch [16/20], Step [275/327], Loss: 0.1321\n","Epoch [16/20], Step [300/327], Loss: 0.1613\n","Epoch [16/20], Step [325/327], Loss: 0.1119\n","Start validation #16\n","Validation #16 Average Loss: 0.3023, mIoU: 0.4220\n","Epoch [17/20], Step [25/327], Loss: 0.1715\n","Epoch [17/20], Step [50/327], Loss: 0.1343\n","Epoch [17/20], Step [75/327], Loss: 0.0749\n","Epoch [17/20], Step [100/327], Loss: 0.1654\n","Epoch [17/20], Step [125/327], Loss: 0.0905\n","Epoch [17/20], Step [150/327], Loss: 0.1450\n","Epoch [17/20], Step [175/327], Loss: 0.0900\n","Epoch [17/20], Step [200/327], Loss: 0.1342\n","Epoch [17/20], Step [225/327], Loss: 0.0873\n","Epoch [17/20], Step [250/327], Loss: 0.2640\n"],"name":"stdout"},{"output_type":"error","ename":"KeyboardInterrupt","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)","\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_epochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_loader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_loader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msaved_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_every\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr_scheduler\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(num_epochs, model, data_loader, val_loader, criterion, optimizer, saved_dir, val_every, device, lr_scheduler)\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;31m# inference\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimages\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0;31m# loss 계산 (cross entropy loss)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 887\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 888\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 889\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 890\u001b[0m for hook in itertools.chain(\n\u001b[1;32m 891\u001b[0m \u001b[0m_global_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/segmentation_models_pytorch/base/model.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;34m\"\"\"Sequentially pass `x` trough model`s encoder, decoder and heads\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0mfeatures\u001b[0m \u001b[0;34m=\u001b[0m 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\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 888\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 889\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 890\u001b[0m for hook in itertools.chain(\n\u001b[1;32m 891\u001b[0m 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\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}]},{"cell_type":"markdown","metadata":{"id":"C6ClcO0J-NwR"},"source":["## 저장된 model 불러오기 (학습된 이후) "]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:21.050200Z","start_time":"2021-04-16T19:44:20.802200Z"},"scrolled":true,"id":"KQPtUDzd-NwR","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620190702629,"user_tz":-540,"elapsed":19506,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"d33c2900-0308-43ab-9714-836941631b7a"},"source":["# best model 저장된 경로\n","model_path = './saved/re_pan_effb3_noisy_focal_madgrad_kwparam_stepLR.pt'\n","\n","# best model 불러오기\n","checkpoint = torch.load(model_path, map_location=device)\n","model.load_state_dict(checkpoint)\n","\n","# 추론을 실행하기 전에는 반드시 설정 (batch normalization, dropout 를 평가 모드로 설정)\n","# model.eval()"],"execution_count":16,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{"tags":[]},"execution_count":16}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:24.939227Z","start_time":"2021-04-16T19:44:24.518228Z"},"id":"0LQqrDAp-NwR","colab":{"base_uri":"https://localhost:8080/","height":502},"executionInfo":{"status":"ok","timestamp":1620190707116,"user_tz":-540,"elapsed":23182,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"4055d89f-f3e6-4f82-adfc-64cebaa7fa8f"},"source":["# 첫번째 batch의 추론 결과 확인\n","for imgs, image_infos in test_loader:\n"," image_infos = image_infos\n"," temp_images = imgs\n"," \n"," model.eval()\n"," # inference\n"," outs = model(torch.stack(temp_images).to(device))\n"," oms = torch.argmax(outs.squeeze(), dim=1).detach().cpu().numpy()\n"," \n"," break\n","\n","i = 1\n","fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(16, 16))\n","\n","print('Shape of Original Image :', list(temp_images[i].shape))\n","print('Shape of Predicted : ', list(oms[i].shape))\n","print('Unique values, category of transformed mask : \\n', [{int(i),category_names[int(i)]} for i in list(np.unique(oms[i]))])\n","\n","# Original image\n","ax1.imshow(temp_images[i].permute([1,2,0]))\n","ax1.grid(False)\n","ax1.set_title(\"Original image : {}\".format(image_infos[i]['file_name']), fontsize = 15)\n","\n","# Predicted\n","ax2.imshow(oms[i])\n","ax2.grid(False)\n","ax2.set_title(\"Predicted : {}\".format(image_infos[i]['file_name']), fontsize = 15)\n","\n","plt.show()"],"execution_count":17,"outputs":[{"output_type":"stream","text":["Shape of Original Image : [3, 512, 512]\n","Shape of Predicted : [512, 512]\n","Unique values, category of transformed mask : \n"," [{0, 'Backgroud'}, {2, 'General trash'}, {'Plastic bag', 9}, {'Clothing', 11}]\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"evYjR2F3-NwS"},"source":["## submission을 위한 test 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:27.469285Z","start_time":"2021-04-16T19:44:27.456021Z"},"id":"nhMLnV5d-NwS","executionInfo":{"status":"ok","timestamp":1620190707117,"user_tz":-540,"elapsed":20210,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["def test(model, data_loader, device):\n"," size = 256\n"," transform = A.Compose([A.Resize(256, 256)])\n"," print('Start prediction.')\n"," model.eval()\n"," \n"," file_name_list = []\n"," preds_array = np.empty((0, size*size), dtype=np.long)\n"," \n"," with torch.no_grad():\n"," for step, (imgs, image_infos) in enumerate(test_loader):\n","\n"," # inference (512 x 512)\n"," outs = model(torch.stack(imgs).to(device))\n"," oms = torch.argmax(outs.squeeze(), dim=1).detach().cpu().numpy()\n"," \n"," # resize (256 x 256)\n"," temp_mask = []\n"," for img, mask in zip(np.stack(imgs), oms):\n"," transformed = transform(image=img, mask=mask)\n"," mask = transformed['mask']\n"," temp_mask.append(mask)\n","\n"," oms = np.array(temp_mask)\n"," \n"," oms = oms.reshape([oms.shape[0], size*size]).astype(int)\n"," preds_array = np.vstack((preds_array, oms))\n"," \n"," file_name_list.append([i['file_name'] for i in image_infos])\n"," print(\"End prediction.\")\n"," file_names = [y for x in file_name_list for y in x]\n"," \n"," return file_names, preds_array"],"execution_count":18,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"r1YKHBf4-NwT"},"source":["## submission.csv 생성"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:45:42.235310Z","start_time":"2021-04-16T19:44:30.499016Z"},"scrolled":true,"id":"Bz79_g8K-NwT","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620191202874,"user_tz":-540,"elapsed":495744,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"c4cb11c7-7032-46be-8572-742a213b8a8b"},"source":["# sample_submisson.csv 열기\n","submission = pd.read_csv('./submission/sample_submission.csv', index_col=None)\n","\n","# test set에 대한 prediction\n","file_names, preds = test(model, test_loader, device)\n","\n","# PredictionString 대입\n","for file_name, string in zip(file_names, preds):\n"," submission = submission.append({\"image_id\" : file_name, \"PredictionString\" : ' '.join(str(e) for e in string.tolist())}, \n"," ignore_index=True)\n","\n","# submission.csv로 저장\n","submission.to_csv(\"./submission/re_pan_effb3_noisy_focal_madgrad_kwparam_stepLR.csv\", index=False)"],"execution_count":19,"outputs":[{"output_type":"stream","text":["Start prediction.\n","End prediction.\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"Zgl7Ye7F-NwT"},"source":["## 제출까지\n","\n"]},{"cell_type":"code","metadata":{"id":"022E8AC7-NwT","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620191317439,"user_tz":-540,"elapsed":11115,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"417d7097-75d0-4ab8-b68a-fb8d2e95161d"},"source":["import json\n","import requests\n","import os\n","from urllib.parse import urlparse, parse_qsl, urlencode, urlunparse\n","\n","def submit(user_key='', file_path = '', desc=\"\"):\n"," if not user_key:\n"," raise Exception(\"No UserKey\" )\n"," url = urlparse('http://ec2-13-124-161-225.ap-northeast-2.compute.amazonaws.com:8000/api/v1/competition/28/presigned_url/?description=&hyperparameters={%22training%22:{},%22inference%22:{}}')\n"," qs = dict(parse_qsl(url.query))\n"," qs['description'] = desc\n"," parts = url._replace(query=urlencode(qs))\n"," url = urlunparse(parts)\n","\n"," print(url)\n"," headers = {\n"," 'Authorization': user_key\n"," }\n"," res = requests.get(url, headers=headers)\n"," print(res.text)\n"," data = json.loads(res.text)\n"," \n"," submit_url = data['url']\n"," body = {\n"," 'key':'app/Competitions/000028/Users/{}/Submissions/{}/output.csv'.format(str(data['submission']['user']).zfill(8),str(data['submission']['local_id']).zfill(4)),\n"," 'x-amz-algorithm':data['fields']['x-amz-algorithm'],\n"," 'x-amz-credential':data['fields']['x-amz-credential'],\n"," 'x-amz-date':data['fields']['x-amz-date'],\n"," 'policy':data['fields']['policy'],\n"," 'x-amz-signature':data['fields']['x-amz-signature']\n"," }\n"," requests.post(url=submit_url, data=body, files={'file': open(file_path, 'rb')})\n","\n","\n","####################################################################################\n","test_dir = \"/content/drive/MyDrive/Trash/code/submission\" # 수정 필요 : output 파일 폴더 \n","desc = 're_pan_effb3_noisy_focal_madgrad_kwparam_stepLR' # 수정 필요 : 파일에 대한 설명\n","output_file = \"re_pan_effb3_noisy_focal_madgrad_kwparam_stepLR.csv\" #수정 필요 : output 파일 \n","user_key = \"Bearer 7bb5f96452751a238ffaf91a93c4242bf9b72abe\" # 수정 필요 : Authorization \n","\n","\n","submit(user_key, os.path.join(test_dir, output_file),desc)"],"execution_count":20,"outputs":[{"output_type":"stream","text":["http://ec2-13-124-161-225.ap-northeast-2.compute.amazonaws.com:8000/api/v1/competition/28/presigned_url/?hyperparameters=%7B%22training%22%3A%7B%7D%2C%22inference%22%3A%7B%7D%7D&description=re_pan_effb3_noisy_focal_madgrad_kwparam_stepLR\n","{\"url\":\"https://prod-aistages-private.s3.amazonaws.com/\",\"fields\":{\"key\":\"app/Competitions/000028/Users/00000099/Submissions/0023/output.csv\",\"x-amz-algorithm\":\"AWS4-HMAC-SHA256\",\"x-amz-credential\":\"AKIA45LU4MHUJ7WLDQVO/20210505/ap-northeast-2/s3/aws4_request\",\"x-amz-date\":\"20210505T050827Z\",\"policy\":\"eyJleHBpcmF0aW9uIjogIjIwMjEtMDUtMDVUMDY6MDg6MjdaIiwgImNvbmRpdGlvbnMiOiBbeyJidWNrZXQiOiAicHJvZC1haXN0YWdlcy1wcml2YXRlIn0sIHsia2V5IjogImFwcC9Db21wZXRpdGlvbnMvMDAwMDI4L1VzZXJzLzAwMDAwMDk5L1N1Ym1pc3Npb25zLzAwMjMvb3V0cHV0LmNzdiJ9LCB7IngtYW16LWFsZ29yaXRobSI6ICJBV1M0LUhNQUMtU0hBMjU2In0sIHsieC1hbXotY3JlZGVudGlhbCI6ICJBS0lBNDVMVTRNSFVKN1dMRFFWTy8yMDIxMDUwNS9hcC1ub3J0aGVhc3QtMi9zMy9hd3M0X3JlcXVlc3QifSwgeyJ4LWFtei1kYXRlIjogIjIwMjEwNTA1VDA1MDgyN1oifV19\",\"x-amz-signature\":\"766842ca12120e7246cbab691df5235caf2dea758f4a1b460bb46cff15d11b87\"},\"submission\":{\"id\":15310,\"phase\":\"Created\",\"type\":\"File\",\"local_id\":23,\"hyperparameters\":\"{\\\"training\\\": {}, \\\"inference\\\": {}}\",\"description\":\"re_pan_effb3_noisy_focal_madgrad_kwparam_stepLR\",\"final\":false,\"created_at\":\"2021-05-05T14:08:27.177446+09:00\",\"updated_at\":\"2021-05-05T14:08:27.177479+09:00\",\"user\":99,\"competition\":28,\"image\":null}}\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"d9mUjGaM7V83"},"source":[""],"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/chanyub_seg/code/re_pan_effb3_noisy_focal_madgrad_stepLR.ipynb b/chanyub_seg/code/re_pan_effb3_noisy_focal_madgrad_stepLR.ipynb deleted file mode 100644 index dae4638..0000000 --- a/chanyub_seg/code/re_pan_effb3_noisy_focal_madgrad_stepLR.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"nbformat":4,"nbformat_minor":0,"metadata":{"hide_input":false,"kernelspec":{"display_name":"Python 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google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Mounted at /content/drive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xDwpp4Lk-gSH","executionInfo":{"status":"ok","timestamp":1620082392428,"user_tz":-540,"elapsed":21738,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"2fd047b9-0913-4554-ff65-785d65a242fd"},"source":["ls"],"execution_count":null,"outputs":[{"output_type":"stream","text":["\u001b[0m\u001b[01;34mdrive\u001b[0m/ \u001b[01;34msample_data\u001b[0m/\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sItrVDeh-iYC","executionInfo":{"status":"ok","timestamp":1620085406475,"user_tz":-540,"elapsed":609,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"d7a551a1-fc55-4142-febf-0ecd909aa132"},"source":["cd drive/MyDrive/Trash/code"],"execution_count":2,"outputs":[{"output_type":"stream","text":["/content/drive/MyDrive/Trash/code\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"a54v4-kN_LVO","executionInfo":{"status":"ok","timestamp":1620085417120,"user_tz":-540,"elapsed":11239,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"e04ed935-5bb1-4b09-a9a9-feda96d13e89"},"source":["!pip install albumentations==0.5.2"],"execution_count":3,"outputs":[{"output_type":"stream","text":["Collecting albumentations==0.5.2\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/03/58/63fb1d742dc42d9ba2800ea741de1f2bc6bb05548d8724aa84794042eaf2/albumentations-0.5.2-py3-none-any.whl (72kB)\n","\r\u001b[K |████▌ | 10kB 22.6MB/s eta 0:00:01\r\u001b[K |█████████ | 20kB 10.7MB/s eta 0:00:01\r\u001b[K |█████████████▋ | 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(37.6MB)\n","\u001b[K |████████████████████████████████| 37.6MB 120kB/s \n","\u001b[?25hCollecting imgaug>=0.4.0\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/66/b1/af3142c4a85cba6da9f4ebb5ff4e21e2616309552caca5e8acefe9840622/imgaug-0.4.0-py2.py3-none-any.whl (948kB)\n","\u001b[K |████████████████████████████████| 952kB 54.5MB/s \n","\u001b[?25hRequirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (1.4.1)\n","Requirement already satisfied: pillow>=4.3.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations==0.5.2) (7.1.2)\n","Requirement already satisfied: PyWavelets>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations==0.5.2) (1.1.1)\n","Requirement already satisfied: matplotlib!=3.0.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations==0.5.2) (3.2.2)\n","Requirement already satisfied: imageio>=2.3.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations==0.5.2) (2.4.1)\n","Requirement already satisfied: networkx>=2.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations==0.5.2) (2.5.1)\n","Requirement already satisfied: opencv-python in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (4.1.2.30)\n","Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (1.15.0)\n","Requirement already satisfied: Shapely in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (1.7.1)\n","Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.16.1->albumentations==0.5.2) (2.8.1)\n","Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from 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albumentations-0.5.2 imgaug-0.4.0 opencv-python-headless-4.5.1.48\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.826930Z","start_time":"2021-04-18T10:34:45.406686Z"},"colab":{"base_uri":"https://localhost:8080/"},"id":"l_LPA4XD-NwC","executionInfo":{"status":"ok","timestamp":1620085420986,"user_tz":-540,"elapsed":12993,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"840e3a5a-eaec-4025-adc1-1a9a4b7bd108"},"source":["import os\n","import random\n","import time\n","import json\n","import warnings \n","warnings.filterwarnings('ignore')\n","\n","import torch\n","import torch.nn as nn\n","from torch.utils.data import Dataset, DataLoader\n","from utils import label_accuracy_score\n","import cv2\n","\n","import numpy as np\n","import pandas as pd\n","\n","# 전처리를 위한 라이브러리\n","from pycocotools.coco import COCO\n","import torchvision\n","import torchvision.transforms as transforms\n","\n","import albumentations as A\n","from albumentations.pytorch import ToTensorV2\n","\n","# 시각화를 위한 라이브러리\n","import matplotlib.pyplot as plt\n","import seaborn as sns; sns.set()\n","\n","plt.rcParams['axes.grid'] = False\n","\n","print('pytorch version: {}'.format(torch.__version__))\n","print('GPU 사용 가능 여부: {}'.format(torch.cuda.is_available()))\n","\n","print(torch.cuda.get_device_name(0))\n","print(torch.cuda.device_count())\n","\n","device = \"cuda\" if torch.cuda.is_available() else \"cpu\" # GPU 사용 가능 여부에 따라 device 정보 저장"],"execution_count":4,"outputs":[{"output_type":"stream","text":["pytorch version: 1.8.1+cu101\n","GPU 사용 가능 여부: True\n","Tesla P100-PCIE-16GB\n","1\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"HxgRSL_M-NwF"},"source":["## 하이퍼파라미터 세팅 및 seed 고정"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.841930Z","start_time":"2021-04-18T10:34:47.827931Z"},"id":"rV3JmGP5-NwF","executionInfo":{"status":"ok","timestamp":1620085420987,"user_tz":-540,"elapsed":11388,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["batch_size = 8 # Mini-batch size\n","num_epochs = 20\n","learning_rate = 0.0001"],"execution_count":5,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.856930Z","start_time":"2021-04-18T10:34:47.842931Z"},"id":"Z6LOuJXQ-NwG","executionInfo":{"status":"ok","timestamp":1620085420987,"user_tz":-540,"elapsed":11169,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# seed 고정\n","random_seed = 42\n","torch.manual_seed(random_seed)\n","torch.cuda.manual_seed(random_seed)\n","# torch.cuda.manual_seed_all(random_seed) # if use multi-GPU\n","torch.backends.cudnn.deterministic = True\n","torch.backends.cudnn.benchmark = False\n","np.random.seed(random_seed)\n","random.seed(random_seed)"],"execution_count":6,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"iWF_EJDu-NwG"},"source":["## 학습 데이터 EDA"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.381961Z","start_time":"2021-04-18T10:34:47.857930Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"lg0x0D0a-NwG","executionInfo":{"status":"ok","timestamp":1620085430279,"user_tz":-540,"elapsed":18788,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"775cb5ff-0267-4a03-a4ba-d5b0d7ed2ceb"},"source":["%matplotlib inline\n","\n","dataset_path = '../input/data'\n","anns_file_path = dataset_path + '/' + 'train.json'\n","\n","# Read annotations\n","with open(anns_file_path, 'r') as f:\n"," dataset = json.loads(f.read())\n","\n","categories = dataset['categories']\n","anns = dataset['annotations']\n","imgs = dataset['images']\n","nr_cats = len(categories)\n","nr_annotations = len(anns)\n","nr_images = len(imgs)\n","\n","# Load categories and super categories\n","cat_names = []\n","super_cat_names = []\n","super_cat_ids = {}\n","super_cat_last_name = ''\n","nr_super_cats = 0\n","for cat_it in categories:\n"," cat_names.append(cat_it['name'])\n"," super_cat_name = cat_it['supercategory']\n"," # Adding new supercat\n"," if super_cat_name != super_cat_last_name:\n"," super_cat_names.append(super_cat_name)\n"," super_cat_ids[super_cat_name] = nr_super_cats\n"," super_cat_last_name = super_cat_name\n"," nr_super_cats += 1\n","\n","print('Number of super categories:', nr_super_cats)\n","print('Number of categories:', nr_cats)\n","print('Number of annotations:', nr_annotations)\n","print('Number of images:', nr_images)"],"execution_count":7,"outputs":[{"output_type":"stream","text":["Number of super categories: 11\n","Number of categories: 11\n","Number of annotations: 21116\n","Number of images: 2617\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.546964Z","start_time":"2021-04-18T10:34:51.382969Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/","height":355},"id":"PjLyVHVY-NwH","executionInfo":{"status":"ok","timestamp":1620085430286,"user_tz":-540,"elapsed":18314,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"362b9787-6732-4f38-ca11-70b5b9f42a6e"},"source":["# Count annotations\n","cat_histogram = np.zeros(nr_cats,dtype=int)\n","for ann in anns:\n"," cat_histogram[ann['category_id']] += 1\n","\n","# Initialize the matplotlib figure\n","f, ax = plt.subplots(figsize=(5,5))\n","\n","# Convert to DataFrame\n","df = pd.DataFrame({'Categories': cat_names, 'Number of annotations': cat_histogram})\n","df = df.sort_values('Number of annotations', 0, False)\n","\n","# Plot the histogram\n","plt.title(\"category distribution of train set \")\n","plot_1 = sns.barplot(x=\"Number of annotations\", y=\"Categories\", data=df, label=\"Total\", color=\"b\")"],"execution_count":8,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYsAAAFSCAYAAAAD0fNsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3deZwdRb3+8U8SdgIE4oKsAYRH9rAjgoAXBZRNxY2AICIq+gNBQEU2kU1EkahcLoqshlXlImEXwiKgIJu4PERNFAW9ISxJgARI8vujashhmJkzk8ye5/16zSvndHVXV/c56e+pqu6qIXPnziUiIqIjQ/u6ABER0f8lWERERFMJFhER0VSCRURENJVgERERTSVYREREUwkWEd1E0oWSTq6vt5Pkbsz7Bkn719cHSLq7G/MeI+nm7sqvC/t9l6SJkmZI2quH9nGupON6Iu+FzSJ9XYCIRpImAwfZvrWPi7JAbN8FqNl6kk4E3m573yb57dod5ZI0CpgELGr71Zr3T4Gfdkf+XXQS8APbZ7eV2B3fBdufm99t55ekucDatv/S2/vuSalZRACS+uUPJ0lDJA3W/6erA3+Y343762c2WA3JE9zREyStCpwNbEf5UXKZ7S9KWgv4EbAxMBe4CfiC7eckXQKMAWYBs4GTbJ8haWvgu8B6wN+Bw2xPqPtZA7gI2AT4DWBguZZf6pL2AE4DVgYeBj5v+081bTLw33WfAo4Ftrb94YbjGAvMtX1YG8e4CXA+sDZwfT2ev9g+VtIOwKW2V6nrfgU4FFgWeBI4BFgUuBYYUo/5r7Y3ljQB+DWwA7ApsCHw45rfjyUdAHwGeAjYD3iqnsNfNRzXa7/IG2svkv4BrAq8UA/jvfXYD7K9bV1/m/rZrQM8Xs/3PTVtAnAX8B5gI+BeYB/bT7c+P3X9zwBfAVYA7gY+Z/tJSX8F1mDeZz3S9qyG7d7wXQCupNSKDgJOACbbfrekqyjfsyWBRyif8R9qPhcC/2z8TICzaplmA8fYvqCdsh8AHA+8GXgaOLbWwpB0IHAUsCLwW+Bg23+XdGcty4uU78OnbV/RVv4DzWD9xRJ9SNIw4DrKhX0U5UJ9eU0eQrl4rwSsS7lwnQhgez/gH8DutofXQLEyMB44mXLBORL4maQ31/zGUf6zjqz57NdQjnWAy4AvUf7DXw/8UtJiDcX9BPABYATlQrKLpBF1+0WAjwMXt3GMiwHXAJfUcl0FfLj1enVdAV8EtrC9DLAz5UJ3I3AqcEU93o0bNtsPOBhYpp7H1rYC/gq8iXLh/LmkFdrafyvvrv+OqPu8t1VZV6Cc77GUc/pdYLykkQ2r7QN8CngLsBjlM2nruN9D+aw/CrytHsflALbX4vWf9azGbdv6LjQkb0/57uxc399ACdhvAR6k4ya1FYHlKN/JTwM/lLR8G2Vfup6DXetntg3lxwaS9gSOAT5E+V7dRfmeYbvl/G5cyz0oAgWkzyJ6xpaUYHBUS7s45VcltR23pS13iqTvUi527dkXuN729fX9LZIeAN4v6XZgC+C/bL8M3C3p2oZtPwaMt30LgKQzgcMo//En1HXG2n6ivn6p/jL8CKX2swvwtO3ftVGurSk1g+/ZngtcLemIdo5hNrA4sJ6kKbYnd3C8LS5s+XVcy946/f8a9n2FpC9Tgt4lnci7Ix8AJtpuyecySYcCuwMX1mUX2H68lutKYI928hoD/MT2g3XdrwHPShrVyXPQnhNtt9SMsP2Tlte1FvWspOVsP9/Gtq9QaqyvAtdLmkGpWd3XxrpzgA0k/cP2U5QaHMDngNMaaqinAsdIWt12W4F9UEjNInrCqsDfGwLFayS9VdLlkv4laRrl1/ybOshrdeAjkp5r+QO2pfxSXQl4xvaLDes/0fB6JRp+ldueU9NXbmd9KE1aLZ3N+9L+xXcl4F/1Yt2izQtFDZBfotR8/q8e/0rt5NteuVpra9/N8uyM152zhrwbz9m/G16/CAzvTF62ZwBTW+U1P147N5KGSTpd0l/r92lyTWrvOzW11feyzfLXYPQxSmB4StJ4Se+oyasDZzd8H5+h1JgX9Lj6tQSL6AlPAKu10wF5KqUtd0Pby1IuyEMa0lt3oj0BXGJ7RMPf0rZPp/zSW0HSUg3rr9rw+knKf2ygdBbX9H91sL9rgI0kbQDsRvtNGk8BK9c8W6zWzrrYHlf7BFav+/xWO/tvr1yttbXvJ+vrF4DGc7JiF/J93TlryPtfbazbTOvzvzSlaauzeXXm3OwD7AnsRGleGlWXD2EB2b7J9nspP0z+TKltQvlOfrbVd3LJln6dwSrNUNETfku5mJ4u6QRKM8xmtn9NaYN/Hni+9kcc1Wrb/wBrNry/FLhf0s7ArZSmn60pHcl/r01SJ0o6FtiM0lzyy7rtlcBXJf0XcCelCWoW0O5/atszJV1N7Qux/Y92Vr0XeBU4VNI5db9bAre3XrH2WaxM6bSeCbwEDGs43vdKGlprPp31loZ970Vpw29pqnsY+LikGyg3EuwN3FjTplCaV9akdF63dj3wfUn7UM7fhyk3FlzXhbK1uIzSjDUO+BPlh8JvutAE1fq70JZlKJ/pVEqAPHU+yvkGkt5K+Z7dSvm8ZlDOG8C5wDclPWz7D5KWA95n+6pW5c6tsxEdsT2bcvF8O6WT8p+UKj3ANyh3+DxP6Uj9eavNTwOOrVX8I2t/QkuH4hTKr7qjmPfdHQO8k3KxOBm4gnLxwLYpNZfvU+5m2Z3SYfpyk0O4iHIHUrvt/zWPDwEHUJohPtbGsbRYHDi9luHflAv912paywVmqqQHm5Sr0W8onbpPA6cAe9ueWtOOA9YCnqWc73EN5X6xrv/reo63bnVcUyk1qi9TzunRwG7t3e3UkXo31nHAzyg/Htai3DDQWa/7LrSzzsWUpq5/AX+k7b6H+TEUOIJSO3qG0qn+eQDbv6DUDC+vTV+PAY3PwZwIXFTL/dFuKk+fy62zMahIugL4s+2OOs2b5bEapdlhRdvTuq1wEQNYmqFiQJO0BeWX3yTgfZRayOkLkF/LL8rLEygi5kmwiIFuRUrzz0hKc9fnbT80PxnVDtj/UJo1dum2EkYMAmmGioiIptLBPfAsQrk9MLXCiOhOHV5bcsEZeFan3JK3HaXZJSKiO6xCGbrk7ZShZF4nwWLgeVv9964+LUVEDFZvI8FiUHgK4NlnX2DOnPQ3RUT3GDp0CMsvvzTMGwPrdRIsBp7ZQMuHGhHRppmzXmH6tJnzs+nsthYmWAxQh552DU8/+0LzFSNioTTujDFMZ76CRZtyN1RERDSVYBEREU0lWERERFMJFhER0VSCRURENJW7odohaTJloppZlIlqTrZ9eV+WKSKir6Rm0bG9bW8M7AdcIKmjuaIXmKRhzdeKiOh9qVl0gu2HJE0HrpC0LLAYZYayA+vUnqOABygzrL2XMv/vIbbvApD0fuDrwBLAy8Dhtu+TtAMwFvgdsAlwLPM3fWVERI9KsOgESTtSLvQfa5leUtJBlKkVW6aJHAk8YvvLNQhcJmktyuBcxwE7254maX3gBmC1ut36lMnf7+21A4qI6KIEi45dLWkmMI0ycf2ukr4ADOeN5+5l4FIA2xMkvQQI2JYy9/CdklrWXaROCA8wMYEiIvq7BIuO7W37MQBJqwOXAVvYniRpG2BcJ/IYAtxo+5OtEyStC8zozgJHRPSEdHB33rKU2sO/6zzNn2uVvhiwD4Ck7YAlgT8DNwO71OYnavoWvVLiiIhukppFJ9n+vaSrgD9SOrevB97dsMpUYLSkoym1iU/YfhmYKGlf4HxJS1KCyq+B+3v1ACIiFkDm4O4GLXdD2e7RW2urUcCkjDobER0Zd8YYpkyZ3un1hw4dwsiRwwHWACa/Ib3bShYREYNWmqG6ge3JQG/UKiIi+kRqFhER0VSCRURENJUO7oFnFDCprwsREf1bV+fgbtbBnT6LAWrq1BnMmZNAHxG9I81QERHRVIJFREQ0lWARERFNpc9igKodURHRg7raSTyYJVgMUBnuI6LnjTtjDNNJsIA0Q0VERCckWERERFMJFhER0VSCRURENDUgOrglTQZmArOAYcDJti+XdACwm+295zPfA4B7bD9e3+8BbGf7qC7kcSFlLosfzE8ZIiIGggERLKq9bT8maRPgHkm3dkOeB1BmvXscwPa1wLXdkG9ExKAykIIFALYfkjSdMtjVayStCFxGmSt7CWC87aNr2p7AycBsyjF/sW6/OTBW0snAkcAqNNRUJB0IHFZ38XJN+08bxdpY0j2UOS3uAL5g+2VJ+9TtF6vrHWn7VzXv7YBzgLnA7cBewAdsP7Yg5ycioicMuD4LSTtSgsHEVknPAbvb3gwYDWwuaZeadhJwsO3RwMbAg7YvAB4ADrU92vbraiqSdgCOAXa2vTGwI/B8O8XaCngfsB6wOnBwXX4TsLXtTYCPAxfVvBenBLZDbG8ETABW6+KpiIjoNQMpWFwt6WHgG8CHbT/XKn0Y8G1JjwC/AzagBA2A24CzJB0FrGt7Wif29wHgYtv/BrA9w3Z7T+dcUdNfpQSE99TlawE3SfoDcAWwYq0BCXjJ9l01719Qgl1ERL80kILF3rUG8G7bt7SRfgSwPLBV/bV+DaUGgu3Dgc9QmpKukvSZXirzZcA5ttcHNgVebSlTRMRAMpCCRTMjgKdsz5S0MrBnS4Ik2f697bOBS4EtatI0YLl28hsPfFLSW2sewyW1d6H/iKSlJS0C7EepybSUqWWiogOBxetrA0tJelfNe8+6bkREvzTgOrg7MJZSa3gM+Cfwq4a00yWtTfll/xzw6br8POA7tXnqyMbMbE+QdBpwq6Q5lNt2d4c2B4q5H7gZeAul/+G8uvxLwDWSngVuBKbWvGfVzu9zJc2ldIr/H+33iURE9KlMq9pHJC1je3p9vSNwIbCG7TlNNh0FTMpAghE9b9wZY5gyZXpfF6NXZFrV/uvDkg6nNAXOBPbpRKCIiOgTCRZ9xPaFlNpERES/N5g6uCMioockWERERFPp4B54RjHvdtyI6EEL07Sq6eAepKZOncGcOQn0EdE70gwVERFNJVhERERTCRYREdFU+iwGqNoRFT1kYerYjOiMBIsBKsN99KxxZ4xhepvDgEUsnNIMFRERTSVYREREUwkWERHRVIJFREQ0lWARERFN5W4oQNJkypwSs4BhwMmUubJ3s733fOZ5AHCP7cfr+z2A7Wwf1Q1FjojoValZzLO37Y0pc2hfALxpAfM7AFin5Y3taxMoImKgSs2iFdsPSZoODGlZJmlF4DJgWUqNY7zto2vanpSayGzK+fwiZdTGzYGxkk6mzO+9Cg01FUkHAofVXbxc0/7T80cYEdF1qVm0UufDXgJ4pWHxc8DutjcDRgObS9qlpp0EHGx7NLAx8KDtC4AHgENtj7Z9a6t97AAcA+xcazM7As/34GFFRCyQ1CzmuVrSTGAa8GFg5Ya0YcC3JW1DqXGsSAkaNwK3AWdJ+hlwg+3HOrGvDwAX2/43gO0Z3XcYERHdLzWLefautYB3276lVdoRwPLAVrY3Aq6h1D6wfTjwGUpT0lWSPtObhY6I6A0JFp0zAnjK9kxJKwN7tiRIku3f2z4buBTYoiZNA5ZrJ7/xwCclvbXmMVzSEj1X/IiIBZNmqM4ZS6k1PAb8E/hVQ9rpktYGXqX0bXy6Lj8P+I6koygd3K+xPUHSacCtkuZQbtndHTJyXUT0T5mDe+AZBUzKqLM9a9wZY5gyZXpfFyOi1zSbgzvNUBER0VSCRURENJVgERERTSVYREREU+ngHnhGAZP6uhCDXebgjoVNsw7u3Do7QE2dOoM5cxLoI6J3pBkqIiKaSrCIiIimEiwiIqKp9FkMULUjKuZDOq8jui7BYoDKcB/zb9wZY5ieYbgiuiTNUBER0VSCRURENJVgERERTSVYREREUwkWERHRVK/cDSVpUeDrwCcoM8q9CkwEjrf9x94oQ0ckHQDsZnvvdtLusf14N+5vB+BM25t3V54RET2pt2oWFwAbAVvZXh8YXZepN3YuaUGC4gHAOh3kPWwB8o6IGBB6vGZR56f+ILCK7ecAbM8FxjessxhwCrA9sDjwKPB52zMkXUiZm3odYFXgXmB/23MlLQt8lxKIlgBuB46wPVvSBOBhYGvgGUl71H2OBJYEfgt81vbLHZT9U8DmwFhJJ1Pm0l4F2BeYDqwN7Cvpv4CPU87nzFr2hyUtBVwErA+8Ug7dH63ZLyLpf4B3AnOBj9v+U1fPb0REb+iNmsUmwETbz3awztHA87a3tL0x8CTwtYb0DYD3Uy66mwE71eXfBe6wvSWltvIW4MCG7dYEtrX9fmA2sE9t+tkAGNZq3TewfQHwAHCo7dG2b61JWwNH2t7A9sPAxba3sL0JcBxwbl1vZ2BZ2+vV4/psQ/brA+fa3gi4Eji2o7JERPSlXn+CW9J6wDhgKeAG24cBewDLSmrpM1gceKRhs2tsz6zbPwisBdxSt9tS0pfreksB/2zYbpztV+vrocCRknalBIrlgRfn8zDutv3XhvebSToGWAGYw7xmq0eAdSX9EJhAQ22KUst4qL6+D9h9PssSEdHjeiNYPASsLWmE7edqh/ZoSV+kNPEADAEOsX1bO3k0js0wm3nlHgLsZftv7Ww3o+H1PsC2wHa2p9eLe7t9EU28lm9tQrsaeLftByWtBPwLwPbfJK0P/BewK3CqpA2bHFNERL/T481QticC/wv8SNJyDUlLN7y+FjhC0pIAkpaRtG4nsr8W+GpLJ7OkN0lao511RwBP10CxHCV4dMY0YLkO0pegXOifqO8PaUmQtAow2/Y1wOHAmym1j4iIAaW37oY6APgzcL+kP0i6m9L3MLamn05psrlf0qPA3UBngsWXKL/KH5H0e+BGYOV21r0YWEbSn4FfAnd1suznAcdLeljSTq0TbU8Djq9l/x3QOLrfhsC9kh6hdKifZvvJTu43IqLfyBzcA88oYFJGnZ1/484Yw5Qp0/u6GBH9SrM5uPMEd0RENJVgERERTSVYREREUwkWERHRVDq4B55RwKS+LsRAljm4I96oWQd3HgQboKZOncGcOQn0EdE70gwVERFNJVhERERTCRYREdFU+iwGqNoR1e+k8zhicEqwGKD663Af484Yw3QSLCIGmzRDRUREUwkWERHRVIJFREQ0Nd/BQtKOkrbvzsJERET/1OkObkl3AMfY/rWkrwBHAK9K+qHtU3ushG8sx0eAYyhTqi4BPGh7H0knAqfafrmb97cXcBplGtSP23Z35h8RMRB0pWaxAXBfff0ZYEdga+Bz3V2o9kh6G3AOsIft0ZTZ9L5dk08AFpuPPJsFzM8Cx9veJIEiIhZWXbl1digwV9JawBDbfwSQtHyPlKxtKwKvAFMBbM8FHpL0w5p+j6Q5wPuB3wFr2J5Zy3ktcDlwD/AAcCHwHuA8SbcC/0OZI/tVSg3qRklnAduVzXWI7R0l7UKpaQwDpgCftf0XSSsClwHLUmo8420fXfd9IvCOmrZOLdvpwHeA1YGf2z6qB85XRES36ErN4m7gB8CZwC8AauB4ugfK1Z6Wuaz/IelqSV+SNNL2F2r6NrZH13mu7wA+Vss5CtgcuLquNxK43/amts8FfgqMs70RsC9wqaQ32z6cElgOrYHiLcAlwJi67ri6LcBzwO62NwNGA5vXwNJiM+ATgCiB43RgV2AjYH9Ja3fniYqI6E5dCRYHUC6IjwIn1mXvAM7u3iK1z/Yc23sBOwC3Ax8AHpW0QhurjwUOqa8/B/ykoT9jJnAlgKRlKBf3C+o+/gg8TGlia20r4JGWWlXdZnTNYxjwbUmPUGoOG9R8W9xk+3nbsynn8Bbbs2y/ABhYq0snIyKiF3W6Gcr2VErHcuOy8d1eos6V5THgMeCHkv5ICR6t17lH0jBJ76IEui0akl+oTVjd6QhgeWAr2zMlnUdpjmrR+Fjz7Dbe52n6iOi3Ol2zkLS4pFMk/U3S83XZ+yR9seeK94YyrCzpnQ3vV6H0M0wCpgPLtdrk+9R+CttPtJWn7emUmsT+Nc91gY2Z15nf6D5gY0nvqO/3Bx6qeYwAnqqBYmVgz/k7yoiI/qcrzVBnUZpWxgAtv8r/AHy+uwvVgUWAb0iypIeB64FjbT9E6Sy+TdLDkkbU9S+n/No/p0m+Y4B9JT1K6YPYz/aU1ivVZfsB4+q6+9Y/KM1e75L0GHA+8KsFOdCIiP6k09OqSnoKeLvtFyQ9Y3uFuvw52yOabN4nJG0LnAts2APNTn1lFDCpPw8kOGXK9L4uRkR0UXdOq/py6/UlvZl6G2t/I+l84L3AJwdRoIiI6BNdCRZXARdJOhxee0Due5Smnn7H9qf7ugwREYNFV/osjqF0JP+e0pk7EXgS+EYPlCsiIvqRrtw6+zJwOHB4bX56Os07ERELhw6DhaRRtifX12u2Sl5GEgC2/9YjpYuIiH6hWc3i98Ay9fVfKLfMDmm1zlzK08vRi8Z+ba++LkKbZs56pa+LEBE9oNO3zka/MQqYNHXqDObMyWcXEd2jW26dlTQMeBxYz/as7ixgRET0f526G6oOfjcbWLJnixMREf1RV56z+B5whaRTgX8yb8iPdHBHRAxyXQkWP6j/vrfV8nRw94HatrjAZs56henTZjZfMSIWal15zqIrD/BFD+uusaHGnTGG6SRYRETHujyHgqTVgJWBf7Y37HdERAwunQ4WdSyoy4F3UgYPHCnpPuDjdRrTiIgYpLrStPTflDmwl7f9Nso8EQ9RhgCPiIhBrCvNUNsCb7P9CkCd1+Jo4F89UrKIiOg3uhIsngXWo9QuWgh4rltL1IqkyZT5qmdR7ro62Xa/HBa9KyTtAJxpe/O+LktERDNdCRZnALfWSYX+DqwOfAo4ricK1sreth+TtAlwj6RbbT/dkzuUNKw+jBgRsdDryq2zP5L0V2AfYCPKXBb72O61uaZtPyRpOrCGpK8C2wOLAU8DB9r+u6RRwAPARZRnQoYAh9i+C0DS+4GvA0tQZv873PZ99Zf+WOB3wCbAscB1LfvuKF9JiwDjgZGUp9x/C3y2DuuOpK9Rztsc4AVKkx4NeY8Afg780vZZ3XW+IiK6S5dunbV9G3BbD5WlKUk7Ui7yE4HTbR9Zlx8EfAv4eF11JPCI7S/XIHCZpLWAVSg1oZ1tT5O0PnADsFrdbn3KRf7edorQXr4vUwLnVElDKAHlQOBcSfsDewDb2J4uaaTtOS3Du0tanRIoTrN9dXecp4iI7taVW2dPaidpFmX4jxtt/6dbSvVGV0uaCUwDPmz7OUn7SfoCMJw3HsfLwKUAtidIeonSv7ItsBZwZ8vFGlhE0lvr64kdBIqO8v0DcKSkXSn9KssDL9ZtdgP+2/b0ul3jnOVvA26nzBN+d+dPR0RE7+pKzWId4IOUJpYngFWBLYFfArsD50j6sO0bu72Utc+i5U39NX4WsIXtSZK2AcZ1Ip8hlKD2ydYJktYFZsxn+fahBKLtau3hGMr5auZZyrl8P5BgERH9VleesxhKeQBvO9v72N4O+Cgw2/bWwCHA6T1RyDYsS/mV/29JQ4HPtUpfjHIBR9J2lH6EPwM3A7vU5idq+hZd2G97+Y6gTDM7XdJyLetU1wGfl7RM3W5kQ9pMYE9gPUln1yasiIh+pyvBYmfg2lbLrgN2ra8vBVpPvdojbP8euAr4I/AbYFKrVaYCoyU9CpwDfML2y7YnAvsC50t6RNKfgM92Yddt5gtcTJlm9s+UmtZdDdtcXJfdJ+lh4H9rgGs5lpeBvYG3Auc1pkVE9BddaYb6K/B55o0+C+UX/V/r6zcxr52+29ge1c7yw4DDGhad0Cr9yHa2u5lSw2i9fALQ9JmHtvK1/TywUzvrzwVOrX+NXtuf7VeZ1zkfEdHvdCVYHAT8XNJXKE9tr0yZEOlDNV30zjMXERHRy7rynMWDktYGtgZWAp4C7m0Y/uNO4M4eKWUX2J5MqeUMiHwjIgaC+W4fr8FhMUlLd2N5IiKiH+p0sJC0IfA48CPg/Lp4e+AnPVCuiIjoR7rSZ/HfwPG2L5H0bF12ByV4RC8b+7W9uiWfmbNe6ZZ8ImJw60qwWJ/69DJl3u2WYcqX7PZSRVNTp85gzpy5fV2MiFhIdKXPYjKwWeMCSVsCf+nOAkVERP/TlZrFccB4SedSOra/RnnO4jM9UrKIiOg3Ol2zsH0dsAvwZkpfxerAh+pDbhERMYh1ZdTZj9i+ijIGVOPyvTO0du8bOXL4Aucxc9YrTJ82sxtKExGDXVeaoc6njMfU2nlAgkUvO/S0a3j62RcWKI9xZ4xhOgkWEdFc02AhqWVwwKGS1qAM891iTcjVJiJisOtMzeIvlFtlhzBv0MAW/wZO7OYyRUREP9M0WNgeCiDpDtvb93yRIiKiv+nK3VAJFBERC6mu3A21COVOqO0po6++1ndh+93dX7SIiOgvunI31FnAeyh3P50CfJ0yGdLlPVCufkPSopRj/QTwav2bCBxPmVZ2eHsTLUVEDBZdGe7jQ8Cuts8GXq3/7gXs2CMl6z8uADYCtrK9PjC6LlOflioiohd1pWaxFPBEff2SpKVs/1nSJj1Qrn6hTvb0QWAV28/Ba9Okjq/pGzesuyFlXu6lgSWA82x/r6YdDBwOzKIE6I9Shnv/AaW2NguYYftdvXNkERFd05WaxZ+ALerrB4ATJR1LmWJ1sNoEmGj72aZrloEWd7K9KbAlcLCkdWvat4H32B5NOYf/ADam1MrWs70xsFt3Fz4iort0pWZxGGXObYAjKPNbDGchGkhQ0nrAOEot6wagMYgsBfx3rW3MoUw9uzElyN4GXCTpl8B423+T9DdgUeB8SbcB1/XekUREdE3TmoWkd0n6lu37bT8IYHui7Z0oAwq+2tOF7EMPAWtLGgFg+4+1djAWWK7VuqdSHlLcpNYUfktpjoLS33MspYnqdkm72n6eMkfI5ZQ+kT9IWrGnDygiYn50phnqGODOdtJup9wpNCjZngj8L/AjSY3Boa15x0cAT9h+VdIGwHbw2i3Ha9r+re3TgZuBTSS9GVjK9k3AV4HnKcOnRET0O51phhoN3NhO2q0M/jm4D+2zzykAABWUSURBVKDM5XG/pFcoTU9PAqcDezSsdzJwiaRPUzqvWwLsMODCWjuZQ7lJ4KuUId5/VIPJIpRmrft6/GgiIuZDZ4LFssBiwEttpC0KLNOtJepnbL9MCRbHtZH8YMN6DwEbtJPNdm0sm0qrmQcjIvqrzjRD/Rl4Xztp76vpERExiHWmZnEW8D+ShgHX2J4jaSjlgbwfUu6MioiIQawzo86Oq3fpXAQsLulpythQs4ATbF/Ww2WMiIg+1qnnLGx/V9KPgXcCIynt7ffantaThYuIiP5hyNy5c/u6DNE1o4BJ3ZFR5uCOiBZDhw5h5MjhAGtQRqR4na48wR39yNSpM5gzJ4E+InpHV8aGioiIhVSCRURENJVgERERTaXPYoCqHVHzJR3bEdFVCRYD1KGnXcPTz74wX9uOO2MM00mwiIjOSzNUREQ0lWARERFNJVhERERTCRYREdFUgkVERDSVYNEOSZMlPVWHZm9ZdoCkuZK+2GTbvSRt2cn9nCjpzAUtb0RET0qw6NiTwM4N7w+gYXa8DuwFdCpYREQMBHnOomMXUgLE9ZLWBJYGfg8gaTHgFGB7YHHgUeDzwLsoc3PvJOkg4LvAzcBllClqlwDG2z66Nw8kImJBpGbRsQnAhpKWB/YHLm5IOxp43vaWtjem1EK+Zvsm4FrgdNujbV8MPAfsbnszYDSwuaRdevNAIiIWRGoWHZsLXAl8vP5tA2xW0/YAlpW0d32/OPBIO/kMA74taRtgCLAiJWjc2EPljojoVgkWzV0E/Aa40/ZUSS3LhwCH2L6tE3kcASwPbGV7pqTzKM1REREDQpqhmrD9N+DrwDdbJV0LHCFpSQBJy0hat6ZNA5ZrWHcE8FQNFCsDe/ZwsSMiulVqFp1g+7w2Fp8OnAjcL2kOpcnqG8CfgEuACyV9hNLBPRa4StJjwD+BX/VGuSMiukvm4B54RgGTFnTU2SlTpndroSJiYGs2B3eaoSIioqkEi4iIaCrBIiIimkqwiIiIptLBPfCMAiYtSAaZgzsiWmvWwZ1bZweoqVNnMGdOAn1E9I40Q0VERFMJFhER0VSCRURENJU+iwGqdkS9Jp3WEdGTEiwGqNbDfYw7YwzTSbCIiJ6RZqiIiGgqwSIiIppKsIiIiKYSLCIioqlB38EtaVHgOMoc2jOB2cBtwJ+BnW3v3cHmSNoBWMz2zfX9KOAB229qY92VgJ/a3rE7jyEioq8N+mABXAAsCWxme7qkRYADgcU7uf0OwHDg5mYr2n4SSKCIiEFnUAcLSWsDHwRWsT0dwParwHmSDmi17leA/erb+4H/RxlQ63PAUEk7AZfXPySdArwfWAr4tO27W9c6JM2lzN/9QWAkcJTtn9W0DwOnAC8BV9XXy9ie0f1nIiJiwQz2PotNgIm2n+1oJUm7UgLFNsCGwDDgONu/B84FLrY92vbpdZORwL22NwFOAr7VQfbTbG9R8x9b9/dW4Dxg95rHS/N7gBERvWGwB4vO2gm43PY023MpF/KdOlh/hu3r6uv7gLU6WPfyhvVWkrQEsBXwoO2JNe0n81/0iIieN9iDxUPA2pKW7+Z8ZzW8nk3HzXkzAWzPru8HddNfRAxOgzpY1F/u1wL/I2kZAEnDJB1E6bRucSvwMUnLSBoCHATcUtOmAct1c9F+A2wqqaVGsn835x8R0a0GdbCo9gcmAr+T9Bjwe+AdNNQObN8AXArcW9MBTq7//gLYQtLDkr7aHQWy/R9Kx/n1kh4C3gy8ArzYHflHRHS3TKvaRyQt03KHlqRPUe6o2rYTm44CJrU1kOCUKdN7pKwRMfhlWtX+61BJH6F8Bs8An+nj8kREtCvBoo/YPoXybEVERL+3MPRZRETEAkqwiIiIptLBPfCMAia1XphpVSNiQaSDe5CaOnUGc+Yk0EdE70gzVERENJVgERERTSVYREREU+mzGKBqR9Rr0sEdET0pwWKAamu4j+kkWEREz0gzVERENJVgERERTSVYREREUwkWERHRVIJFREQ0NSDuhpI0F1jG9oyGZU8Dm9ueLGkCsB6wZss6ddmZtq+TdCIw3PaRNe1g4GhgZ2BV4Hbgq7a/VdN3qNtuXt8vD5wJ7Ai8Ckyp698laSngWWC1OgMekh4AJtn+SH2/OfAL26vWspwAbG37NzX9deWLiOhvBlPN4kXgy81WknQ0cBiwve2/1sVPAYdLGtHOZldR5uJe2/Y6wDHAzyW93faLwG+BHWr+ywJLARs2bL8DMKHh/d+B0zp1VBER/cBgChanAYdIelN7K0g6BfgoJVD8qyHpSUpA+Eob27wbEHC07dkAtu8AfgJ8ra42gRosgG2BO4GJktavy3ag1F5a/AwYKWnnzh9eRETfGUzB4l/AxcDX20k/ANgTeI/tp9tIPxn4tKS3tVq+EfA726+0Wn4fsHF9fTvzgsUOwB2UgLGDpGGUADKhYdu5lNrJqZKGdHRQERH9wUAPFq3H6D4d2EfSqm2s+1tgJLBrWxnV/obzgONaJXXmYn4vsIaktwLbUwLDHZTAsQnwvO2/tdrfeOAl4COdyD8iok8NlGAxhXKhB0DSIsBydflrbE8Fvg98o408/kjp0P6epI+1s59vAx8E1mpY9giwmaRFW627NfBo3e9LwG+A3Sgd1U8BDwKb8sb+ikZfBb7JALnRICIWXgMlWNwCfLbh/cHAfbVzubWzKEFhzdYJth+taWe3FTBsPw98Bzi2YdmdwETgjNqk1NKP8Wle30k9gdLn8eu63avAX2tZG/srGvd3d817TFvpERH9xUAJFl8CRkl6VNLDlKak/dpa0fYLlIt4W01RTQMG8APe+Et/b2AE8BdJjwPfAva2PbFhnduBtSnNTy3uqMsmdHBsxwCrdZAeEdHnMgf3wDMKmNTWqLNTpkzvs0JFxMDWbA7ugVKziIiIPpRgERERTSVYREREUwkWERHRVDq4B55RwKTWCzMHd0QsiGYd3HkYbICaOnUGc+Yk0EdE70gzVERENJVgERERTSVYREREUwkWA9TIkcNZZtkl+roYEbGQSLAYoA497RqWWLz1QLgRET0jwSIiIppKsIiIiKYSLCIioqkEi4iIaCrBIiIimlrohvuQNBmYWf+WAO4CDrH9SgfbHADcY/vx+n40sI7tK3u6vBER/cHCWrPY2/ZoYP3696Em6x8ArNPwfjTw0fnZsaSFLkBHxMC3sF+4lqh/z0r6L+Dk+n4R4BTbl0v6FLA5MFbSyZT5vU8Clq3zgd9p+1BJWwGnA8vWvI+3PV7SKOAB4ELgPcB5kk4ANrX9FICkscC/bZ/aK0cdEdFFC2uwuFrSTGAt4GbbN0taHtjW9mxJbwV+J+km2xdI2h840/Z1AJKWBHazvXd9PwI4F3i/7ackvQ24X9IGdX8jgfttH1nXHwUcDHxD0nDg40DLuhER/c7C3gz1ZmAJSV+qr6+W9BhwE7ACoE7mtw1lDPgbam3jBmAu8PaaPhNo7N/4IfCp2iS1LyVg/d8CHlNERI9ZWGsWANieKek6YDdgd+Ba4EO250p6nNIk1RlDgEdtv7t1Qq1FvGD7tcknbD8h6QFgT+ALlFpGRES/tbDWLACQNBTYHngcGAFMroHivcyrFQBMA5br4P09wNqSdmzIewtJQzrY/feB7wGv2L53wY4kIqJnLazB4uraXPQY5RycBHwVOLMu/yjwaMP65wHHS3pY0k7Ar4ClJT0iaaztZ4E9gBPqsj8BJ1JqHG2yfQeleeqc7j+8iIjutdA1Q9ke1U7SLcDa7WxzHXBdq8XbtFrnfmCHNjafDLyp9UJJawBLA+M6Km9ERH+wsNYs+pSkkygPA37Z9ot9XZ6IiGYWuppFf2D7eOD4vi5HRERnpWYRERFNJVhERERTQ+bOndt8rehPRgGTAGbOeoXp02b2bWkiYlAYOnQII0cOh/KA8eTW6emzGHiGATz77AvMmTOXoUM7epQjIqJzGq4lw9pKT7AYeN4GsPzyS/d1OSJicHob8NfWC9MMNfAsDmwBPAXM7uOyRMTgMYwSKO4HZrVOTLCIiIimcjdUREQ0lWARERFNJVhERERTCRYREdFUgkVERDSVYBEREU0lWERERFN5gnuAkbQOcBEwEpgKfNL2xG7M/0zgw5QxqDa0/Viz/c5vWifLMxK4BFgLeBmYCHzW9hRJWwP/AyxJGctmX9v/V7ebr7ROlOcaytg5c4AZwP+z/XBfnZ+Gcp1AmZ1xQ9uP9cW5qdtPpswA2TJo2Vds39RHn9USwFnATrU899o+uC8+K0mjgGsaFo0AlrW9Ql9/dzorNYuB51zgh7bXAX5I+Y/Una4B3g38vQv7nd+0zpgLnGFbtjekDENwep0//VLgCzXvO4HT4bW51buc1kn7297Y9ibAmcBPFvAcLPDnKWlTYGvqZ9aH56bF3rZH17+b+rA8Z1CCxDr1u3NcXd7rn5XtyQ3nZDTl/1nLLJl99t3pigSLAUTSW4BNgcvqosuATSW9ubv2Yftu2090dr/zm9aF8jxje0LDovuA1YHNgJm2767Lz6XMnc4CpHWmPM83vF0OmNOX50fS4pQLxecbFvfJuelAr5dH0nDgk8BxtucC2P5PX35WDWVbDBgD/KQ/lKezEiwGllWBf9meDVD/fbIu76v9zm9al9Vfmp8HrgVWo6H2Y/tpYKikFRYgrbPl+LGkfwCnAPs3Oc6ePj8nAZfantywrM/OTfVTSY9KOkfSiD4qz1qUppkTJD0gaYKkbekf3+U9al4P9pPydEqCRQwk36f0E/ygLwth+yDbqwHHAN/uq3JIeiewOXBOX5WhDdvZ3pgy2OUQ+u6zGgasCTxke3PgK8DPgeF9VJ5GBzKv+XLASLAYWJ4AVpY0DKD+u1Jd3lf7nd+0Lqkd72sDH7M9B/gHpTmqJf1NwBzbzyxAWpfYvgTYEfhnB8fZk+dne2BdYFLtWF4FuAl4+3we/wKfm5YmTNuzKEHsXQuwzwUpzz+AV6nNNLZ/AzwNvEQffpclrUz53H5aF/X5/63OSrAYQOpdIA8Dn6iLPkH55TSlr/Y7v2ld2b+kUynt13vVixDA74Ala9MCwOeAqxYwrVk5hktateH97sAzQJ+cH9un217J9ijboyhBa2dKbadXzw2ApKUlLVdfDwE+Xo+v1z+r2mR1O/DeWp51gLcAj9OH32VKs+V421NrOfv0/1ZXZIjyAUbSOyi3yy0PPEu5Xc7dmP9Y4EPAipRfYlNtr9/Rfuc3rZPlWR94jPKf/KW6eJLtD0rahnIHyBLMu63yP3W7+UprUpa3Av8LLE2ZS+QZ4EjbD/bV+WlVvsnAbi63zvbquanbrgn8jNIENAz4I3Co7af6sDw/odxa+grwdds39OVnJenxek5ubFjW59+dzkiwiIiIptIMFRERTSVYREREUwkWERHRVIJFREQ0lWARERFNZdTZiAUg6ULgn7aP7YN9D6HcGroXMNH2lr1dhp4iaQxl0Mb39XVZokiwiEGlPmuwFLCG7RfqsoMo9+fv0Hcl6xHbUh46W6XlWPsDSQcAB9nettm6df1RwCRgUduvAtj+KfOeco5+IM1QMRgNAw7r60J0VcvQDV2wOjC5PwWKGLxSs4jB6NvA0ZLOsf1cY0Jbv2IlTaCM3Prj+qv4M8BvgU9RntLeF1gH+CawOHCU7Ysasn2TpFsoc0o8SHmStmVuiXdQBkDcDJhCGTL7ypp2IeWp9NUp4wXtCdzaqrwrUYbm3raW5Vu2fyTp05ShyReVNAP4ju0TWm27FvAjYGPKvCA3UeaGeK6mT6YM9PfJWoYbKU0/MyXtQJlL4izKIHyzgWNsX1C3Xa4e167Ai3U/pwKq5W0p16u2R0j6AHAyZTTY54HzbZ9Yi3pn/fc5SVBqS6KhdlKf5D67fg6PA4fZvqfh87sLeA+wEXAvsI/tp1UmQPpxLecwyuRZu3X2KfCYJzWLGIweACYAR87n9lsBj1KGiRgHXE4ZRfXtlMDxA5X5ElqMoQSSN1HG6/kplLGSgFtqHm+hjJV0jqT1GrbdhzLU+TLA3bzR5ZQxn1YC9gZOlfQe2+dTxkq61/bw1oGiGgKcVrddlzJ89Ymt1vkosAtl9r+NgAMa0lakzNmxMvBp4IeSlq9p369pa1IC3SeBT9n+U6tyjajrv1DXGQF8APi8pL1q2rvrvyPqNvc2FrAOST4eGEv5TL4LjFeZRbHFPpTg/hZgMeZ99vvXcq5at/0c84aNiS5IzSIGq+OBX0s6ez62ndTwC/oK4OvASXUQw5slvUwJHA/X9cfbvrOu/3Xg+Trg4DaUZqIL6noPSfoZ8BHgG3XZ/9r+dX3dMhUpNa9VKaO2fsD2TOBhST+mXHRva3YQtv8C/KW+nSLpu0DroDLW9pN1f78ERjekvVKP+1Xg+lpTkKT7KYFvtO3pwHRJ3wH2A85vpywTGt4+KukySpC5pq31W/kApQP/kvr+MkmHArsDF9ZlF9h+vB7HlZQ5I1qOYSTwdtuPUgYnjPmQYBGDUh1M7zrgq8Cfurh5YxPFSzW/1ssaaxavDQtte4akZyi/5lcHtpLU2BS2CGVO8Tds24aVgGfqBbnF3ylzWDRVBz48G9iOUnMZShlwrtG/G16/WPfZYmpLU11D+nBKDWpRXj/17t8pNZD2yrIVZUrUDSi//Ben8yParsQbp/ltvb/Wx9Hy+VxCqVVcrjIR06WUAQVf6eS+o0ozVAxmJ1D6HxovKi2dwUs1LFtxAffTOGz5cGAFyqxlTwB32B7R8DfcduMUqB2N5PkksIKkZRqWrQb8q5PlOrXmv6HtZSlNaEM6uW1Hnqb8Yl+9YVljudo6pnGUGQ5Xtb0cpV9jSAfrN3qy1b5a769dtl+x/Q3b61FqertRambRRQkWMWjVZpgrgEMblk2hXGT2lTRM0oGUTtcF8X5J26rMrfxN4D6XSYCuA9aRtJ+kRevfFpLW7WT5nwDuAU6TtISkjSh9B5d2slzLUGYWfL5OunNUVw+snXLNBq4ETpG0jKTVgSMayvUfYJV6PhrL8kztPN+S0sfQYgowh9L/0ZbrKedxH0mLSPoYsB7l/HZI0o6SNqx3mk2jBLk5nT7YeE2CRQx2J1Hmn2j0GcqFcyqwPuWCvCDGUWoxz1DuetoXoDYfvY/Svv8kpankW5QmmM76BDCqbv8L4ATbt3a4xTzfADal3H00njKtaHf5f5Ra2t8oHfPjmDdV6G3AH4B/S3q6LjsEOEnSdEp/0pUtGdl+kdLJ/2tJz0naunFHdaKg3YAvUz6zoyl3ND1NcysCV1MCxZ+AO3h9M2B0UuaziIiIplKziIiIphIsIiKiqQSLiIhoKsEiIiKaSrCIiIimEiwiIqKpBIuIiGgqwSIiIppKsIiIiKb+P/w9qsgP/nCeAAAAAElFTkSuQmCC\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.561965Z","start_time":"2021-04-18T10:34:51.547969Z"},"id":"34Tw5__i-NwI","executionInfo":{"status":"ok","timestamp":1620085430287,"user_tz":-540,"elapsed":17390,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# category labeling \n","sorted_temp_df = df.sort_index()\n","\n","# background = 0 에 해당되는 label 추가 후 기존들을 모두 label + 1 로 설정\n","sorted_df = pd.DataFrame([\"Backgroud\"], columns = [\"Categories\"])\n","sorted_df = sorted_df.append(sorted_temp_df, ignore_index=True)"],"execution_count":9,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.576961Z","start_time":"2021-04-18T10:34:51.562964Z"},"colab":{"base_uri":"https://localhost:8080/","height":421},"id":"CQk4vV5N-NwI","executionInfo":{"status":"ok","timestamp":1620085430825,"user_tz":-540,"elapsed":17346,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"67aee407-9d1e-4fed-d9d9-6f32ece92cd5"},"source":["# class (Categories) 에 따른 index 확인 (0~11 : 총 12개)\n","sorted_df"],"execution_count":10,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
CategoriesNumber of annotations
0BackgroudNaN
1UNKNOWN128.0
2General trash2225.0
3Paper7448.0
4Paper pack527.0
5Metal449.0
6Glass488.0
7Plastic2472.0
8Styrofoam1074.0
9Plastic bag6114.0
10Battery50.0
11Clothing141.0
\n","
"],"text/plain":[" Categories Number of annotations\n","0 Backgroud NaN\n","1 UNKNOWN 128.0\n","2 General trash 2225.0\n","3 Paper 7448.0\n","4 Paper pack 527.0\n","5 Metal 449.0\n","6 Glass 488.0\n","7 Plastic 2472.0\n","8 Styrofoam 1074.0\n","9 Plastic bag 6114.0\n","10 Battery 50.0\n","11 Clothing 141.0"]},"metadata":{"tags":[]},"execution_count":10}]},{"cell_type":"markdown","metadata":{"id":"KXU0zmZs-NwI"},"source":["## 데이터 전처리 함수 정의 (Dataset)"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:52.693328Z","start_time":"2021-04-18T10:34:52.681328Z"},"id":"QFnTI8_Z-NwJ","executionInfo":{"status":"ok","timestamp":1620085430827,"user_tz":-540,"elapsed":16285,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["category_names = list(sorted_df.Categories)\n","\n","def get_classname(classID, cats):\n"," for i in range(len(cats)):\n"," if cats[i]['id']==classID:\n"," return cats[i]['name']\n"," return \"None\"\n","\n","class CustomDataLoader(Dataset):\n"," \"\"\"COCO format\"\"\"\n"," def __init__(self, data_dir, mode = 'train', transform = None):\n"," super().__init__()\n"," self.mode = mode\n"," self.transform = transform\n"," self.coco = COCO(data_dir)\n"," \n"," def __getitem__(self, index: int):\n"," # dataset이 index되어 list처럼 동작\n"," image_id = self.coco.getImgIds(imgIds=index)\n"," image_infos = self.coco.loadImgs(image_id)[0]\n"," \n"," # cv2 를 활용하여 image 불러오기\n"," images = cv2.imread(os.path.join(dataset_path, image_infos['file_name']))\n"," images = cv2.cvtColor(images, cv2.COLOR_BGR2RGB).astype(np.float32)\n"," images /= 255.0\n"," \n"," if (self.mode in ('train', 'val')):\n"," ann_ids = self.coco.getAnnIds(imgIds=image_infos['id'])\n"," anns = self.coco.loadAnns(ann_ids)\n","\n"," # Load the categories in a variable\n"," cat_ids = self.coco.getCatIds()\n"," cats = self.coco.loadCats(cat_ids)\n","\n"," # masks : size가 (height x width)인 2D\n"," # 각각의 pixel 값에는 \"category id + 1\" 할당\n"," # Background = 0\n"," masks = np.zeros((image_infos[\"height\"], image_infos[\"width\"]))\n"," # Unknown = 1, General trash = 2, ... , Cigarette = 11\n"," for i in range(len(anns)):\n"," className = get_classname(anns[i]['category_id'], cats)\n"," pixel_value = category_names.index(className)\n"," masks = np.maximum(self.coco.annToMask(anns[i])*pixel_value, masks)\n"," masks = masks.astype(np.float32)\n","\n"," # transform -> albumentations 라이브러리 활용\n"," if self.transform is not None:\n"," transformed = self.transform(image=images, mask=masks)\n"," images = transformed[\"image\"]\n"," masks = transformed[\"mask\"]\n"," \n"," return images, masks, image_infos\n"," \n"," if self.mode == 'test':\n"," # transform -> albumentations 라이브러리 활용\n"," if self.transform is not None:\n"," transformed = self.transform(image=images)\n"," images = transformed[\"image\"]\n"," \n"," return images, image_infos\n"," \n"," \n"," def __len__(self) -> int:\n"," # 전체 dataset의 size를 return\n"," return len(self.coco.getImgIds())"],"execution_count":11,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"cp2aIOlP-NwK"},"source":["## Dataset 정의 및 DataLoader 할당"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:58.823175Z","start_time":"2021-04-18T10:34:54.106233Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"P7zFlRn6-NwK","executionInfo":{"status":"ok","timestamp":1620085439720,"user_tz":-540,"elapsed":8885,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"c9c88730-b5d7-45ef-cccd-54c6a15d74f2"},"source":["# train.json / validation.json / test.json 디렉토리 설정\n","train_path = dataset_path + '/train.json'\n","val_path = dataset_path + '/val.json'\n","test_path = dataset_path + '/test.json'\n","\n","# collate_fn needs for batch\n","def collate_fn(batch):\n"," return tuple(zip(*batch))\n","\n","train_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","val_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","test_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","# create own Dataset 1 (skip)\n","# validation set을 직접 나누고 싶은 경우\n","# random_split 사용하여 data set을 8:2 로 분할\n","# train_size = int(0.8*len(dataset))\n","# val_size = int(len(dataset)-train_size)\n","# dataset = CustomDataLoader(data_dir=train_path, mode='train', transform=transform)\n","# train_dataset, val_dataset = torch.utils.data.random_split(dataset, [train_size, val_size])\n","\n","# create own Dataset 2\n","# train dataset\n","train_dataset = CustomDataLoader(data_dir=train_path, mode='train', transform=train_transform)\n","\n","# validation dataset\n","val_dataset = CustomDataLoader(data_dir=val_path, mode='val', transform=val_transform)\n","\n","# test dataset\n","test_dataset = CustomDataLoader(data_dir=test_path, mode='test', transform=test_transform)\n","\n","\n","# DataLoader\n","train_loader = torch.utils.data.DataLoader(dataset=train_dataset, \n"," batch_size=batch_size,\n"," shuffle=True,\n"," num_workers=4,\n"," collate_fn=collate_fn,\n"," drop_last=True)\n","\n","val_loader = torch.utils.data.DataLoader(dataset=val_dataset, \n"," batch_size=batch_size,\n"," shuffle=False,\n"," num_workers=4,\n"," collate_fn=collate_fn,\n"," drop_last=True) \n","\n","test_loader = torch.utils.data.DataLoader(dataset=test_dataset,\n"," batch_size=batch_size,\n"," num_workers=4,\n"," collate_fn=collate_fn)"],"execution_count":12,"outputs":[{"output_type":"stream","text":["loading annotations into memory...\n","Done (t=3.91s)\n","creating index...\n","index created!\n","loading annotations into memory...\n","Done (t=2.85s)\n","creating index...\n","index created!\n","loading annotations into memory...\n","Done (t=0.47s)\n","creating index...\n","index created!\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"8rqJiHb_-NwM"},"source":["# wandb"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"H1kHXm0uAX3R","executionInfo":{"status":"ok","timestamp":1620085447186,"user_tz":-540,"elapsed":16341,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"1ca945cd-0b7d-4981-e118-6147e81d4af1"},"source":["!pip install wandb"],"execution_count":13,"outputs":[{"output_type":"stream","text":["Collecting wandb\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/5c/ee/d755f9e5466df64c8416a2c6a860fb3aaa43ed6ea8e8e8e81460fda5788b/wandb-0.10.28-py2.py3-none-any.whl (2.1MB)\n","\u001b[K |████████████████████████████████| 2.1MB 4.3MB/s \n","\u001b[?25hCollecting configparser>=3.8.1\n"," Downloading 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https://files.pythonhosted.org/packages/25/a6/2ecc1daa6a304e7f1b216f0896b26156b78e7c38e1211e9b798b4716c53d/shortuuid-1.0.1-py3-none-any.whl\n","Requirement already satisfied: promise<3,>=2.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (2.3)\n","Collecting subprocess32>=3.5.3\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/32/c8/564be4d12629b912ea431f1a50eb8b3b9d00f1a0b1ceff17f266be190007/subprocess32-3.5.4.tar.gz (97kB)\n","\u001b[K |████████████████████████████████| 102kB 13.4MB/s \n","\u001b[?25hRequirement already satisfied: urllib3>=1.10.0 in /usr/local/lib/python3.7/dist-packages (from sentry-sdk>=0.4.0->wandb) (1.24.3)\n","Requirement already satisfied: certifi in /usr/local/lib/python3.7/dist-packages (from sentry-sdk>=0.4.0->wandb) (2020.12.5)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.0.0->wandb) (2.10)\n","Requirement already satisfied: chardet<4,>=3.0.2 in 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/root/.cache/pip/wheels/0b/04/79/c3b0c3a0266a3cb4376da31e5bfe8bba0c489246968a68e843\n"," Building wheel for subprocess32 (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for subprocess32: filename=subprocess32-3.5.4-cp37-none-any.whl size=6489 sha256=b1ffeee148a0368285ddc00ea711cdd2d892b8b36008b5a313ad9fda754a6099\n"," Stored in directory: /root/.cache/pip/wheels/68/39/1a/5e402bdfdf004af1786c8b853fd92f8c4a04f22aad179654d1\n","Successfully built pathtools subprocess32\n","Installing collected packages: configparser, docker-pycreds, sentry-sdk, smmap, gitdb, GitPython, pathtools, shortuuid, subprocess32, wandb\n","Successfully installed GitPython-3.1.14 configparser-5.0.2 docker-pycreds-0.4.0 gitdb-4.0.7 pathtools-0.1.2 sentry-sdk-1.0.0 shortuuid-1.0.1 smmap-4.0.0 subprocess32-3.5.4 wandb-0.10.28\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":153},"id":"aMW4VV9V-NwM","executionInfo":{"status":"ok","timestamp":1620085459006,"user_tz":-540,"elapsed":10704,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"8156d78c-bbb5-4732-ecd7-82b06386c678"},"source":["import wandb\n","\n","proj_name = 're_pan_effb3_noisy_focal_madgrad_stepLR'\n","\n","wandb.init(project='chanyub',name=proj_name)\n","\n","config = wandb.config\n","config.learning_rate = 0.01"],"execution_count":14,"outputs":[{"output_type":"display_data","data":{"application/javascript":["\n"," window._wandbApiKey = new Promise((resolve, reject) => {\n"," function loadScript(url) {\n"," return new Promise(function(resolve, reject) {\n"," let newScript = document.createElement(\"script\");\n"," newScript.onerror = reject;\n"," newScript.onload = resolve;\n"," document.body.appendChild(newScript);\n"," newScript.src = url;\n"," });\n"," }\n"," loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n"," const iframe = document.createElement('iframe')\n"," iframe.style.cssText = \"width:0;height:0;border:none\"\n"," document.body.appendChild(iframe)\n"," const handshake = new Postmate({\n"," container: iframe,\n"," url: 'https://wandb.ai/authorize'\n"," });\n"," const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n"," handshake.then(function(child) {\n"," child.on('authorize', data => {\n"," clearTimeout(timeout)\n"," resolve(data)\n"," });\n"," });\n"," })\n"," });\n"," "],"text/plain":[""]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"],"name":"stderr"},{"output_type":"display_data","data":{"text/html":["\n"," Tracking run with wandb version 0.10.28
\n"," Syncing run re_pan_effb3_noisy_focal_madgrad_stepLR to Weights & Biases (Documentation).
\n"," Project page: https://wandb.ai/pstage12/chanyub
\n"," Run page: https://wandb.ai/pstage12/chanyub/runs/21cokq5i
\n"," Run data is saved locally in /content/drive/My Drive/Trash/code/wandb/run-20210503_234415-21cokq5i

\n"," "],"text/plain":[""]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"uQRiIVGX-NwM"},"source":["## My model"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"S0xCGpNeAqeD","executionInfo":{"status":"ok","timestamp":1620085464359,"user_tz":-540,"elapsed":16036,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"d50ac852-3bfc-461e-9ed2-dc7dbfd92f1f"},"source":["!pip install segmentation_models_pytorch"],"execution_count":15,"outputs":[{"output_type":"stream","text":["Collecting segmentation_models_pytorch\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/65/54/8953f9f7ee9d451b0f3be8d635aa3a654579abf898d17502a090efe1155a/segmentation_models_pytorch-0.1.3-py3-none-any.whl (66kB)\n","\u001b[K |████████████████████████████████| 71kB 2.5MB/s \n","\u001b[?25hCollecting pretrainedmodels==0.7.4\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/84/0e/be6a0e58447ac16c938799d49bfb5fb7a80ac35e137547fc6cee2c08c4cf/pretrainedmodels-0.7.4.tar.gz (58kB)\n","\u001b[K |████████████████████████████████| 61kB 6.6MB/s \n","\u001b[?25hRequirement already satisfied: torchvision>=0.3.0 in /usr/local/lib/python3.7/dist-packages (from segmentation_models_pytorch) (0.9.1+cu101)\n","Collecting efficientnet-pytorch==0.6.3\n"," Downloading https://files.pythonhosted.org/packages/b8/cb/0309a6e3d404862ae4bc017f89645cf150ac94c14c88ef81d215c8e52925/efficientnet_pytorch-0.6.3.tar.gz\n","Collecting timm==0.3.2\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/51/2d/39ecc56fbb202e1891c317e8e44667299bc3b0762ea2ed6aaaa2c2f6613c/timm-0.3.2-py3-none-any.whl (244kB)\n","\u001b[K |████████████████████████████████| 245kB 20.3MB/s \n","\u001b[?25hRequirement already satisfied: torch in /usr/local/lib/python3.7/dist-packages (from 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munch->pretrainedmodels==0.7.4->segmentation_models_pytorch) (1.15.0)\n","Building wheels for collected packages: pretrainedmodels, efficientnet-pytorch\n"," Building wheel for pretrainedmodels (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for pretrainedmodels: filename=pretrainedmodels-0.7.4-cp37-none-any.whl size=60963 sha256=6db86bb918dd52db46b63dd9b4aa0c11e269b1ee803e15d42c0105943f9478c8\n"," Stored in directory: /root/.cache/pip/wheels/69/df/63/62583c096289713f22db605aa2334de5b591d59861a02c2ecd\n"," Building wheel for efficientnet-pytorch (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for efficientnet-pytorch: filename=efficientnet_pytorch-0.6.3-cp37-none-any.whl size=12420 sha256=af6aa39fefd5d49d5d1215a69d0be7f35fb4f57c3df195d08f044d9bca4ad694\n"," Stored in directory: /root/.cache/pip/wheels/42/1e/a9/2a578ba9ad04e776e80bf0f70d8a7f4c29ec0718b92d8f6ccd\n","Successfully built pretrainedmodels efficientnet-pytorch\n","Installing collected packages: munch, pretrainedmodels, efficientnet-pytorch, timm, segmentation-models-pytorch\n","Successfully installed efficientnet-pytorch-0.6.3 munch-2.5.0 pretrainedmodels-0.7.4 segmentation-models-pytorch-0.1.3 timm-0.3.2\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:11.634792Z","start_time":"2021-04-18T16:16:05.875817Z"},"colab":{"base_uri":"https://localhost:8080/","height":137,"referenced_widgets":["b7b91f4505ec4429ac835faf8a1af14c","fb758c04fb7f423eb0b515fbf6a54626","20654c90b3574127b5a4bdb7858dd7ae","dc180b6bdc5249699ea4a50be08cc971","8c9a7f82467648c3a324b9997dd1fd4b","812df0bd238846419640b77e12a50179","af0897df83b644209a699f1824a997b9","f5bd2fe17a524a51b6c3d506352ea648"]},"id":"a8IfZfiM-NwM","executionInfo":{"status":"ok","timestamp":1620085481167,"user_tz":-540,"elapsed":32788,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"c4827511-1b18-4222-f32d-987daefa6cf5"},"source":["# 구현된 model에 임의의 input을 넣어 output이 잘 나오는지 test\n","import segmentation_models_pytorch as smp\n","\n","model = smp.PAN(encoder_name='timm-efficientnet-b3', encoder_weights='noisy-student', classes=12)\n","x = torch.randn([2, 3, 512, 512])\n","print(\"input shape : \", x.shape)\n","out = model(x).to(device)\n","print(\"output shape : \", out.size())\n","\n","model = model.to(device)"],"execution_count":16,"outputs":[{"output_type":"stream","text":["Downloading: \"https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b3_ns-9d44bf68.pth\" to /root/.cache/torch/hub/checkpoints/tf_efficientnet_b3_ns-9d44bf68.pth\n"],"name":"stderr"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"b7b91f4505ec4429ac835faf8a1af14c","version_minor":0,"version_major":2},"text/plain":["HBox(children=(FloatProgress(value=0.0, max=49385734.0), HTML(value='')))"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\n","input shape : torch.Size([2, 3, 512, 512])\n","output shape : torch.Size([2, 12, 512, 512])\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"zvDnI7_T-NwN"},"source":["## train, validation, test 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.104200Z","start_time":"2021-04-18T16:16:18.093174Z"},"id":"RA3oAapJ-NwN","executionInfo":{"status":"ok","timestamp":1620085481167,"user_tz":-540,"elapsed":32786,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["def train(num_epochs, model, data_loader, val_loader, criterion, optimizer, saved_dir, val_every, device, lr_scheduler):\n"," print('Start training..')\n"," best_loss = 9999999\n"," best_miou = 0\n"," for epoch in range(num_epochs):\n"," model.train()\n"," for step, (images, masks, _) in enumerate(data_loader):\n"," images = torch.stack(images) # (batch, channel, height, width)\n"," masks = torch.stack(masks).long() # (batch, channel, height, width)\n"," \n"," # gpu 연산을 위해 device 할당\n"," images, masks = images.to(device), masks.to(device)\n"," \n"," # inference\n"," outputs = model(images)\n"," \n"," # loss 계산 (cross entropy loss)\n"," loss = criterion(outputs, masks)\n"," optimizer.zero_grad()\n"," loss.backward()\n"," optimizer.step()\n"," \n"," lr_scheduler.step()\n"," \n"," # step 주기에 따른 loss 출력\n"," if (step + 1) % 25 == 0:\n"," print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(\n"," epoch+1, num_epochs, step+1, len(train_loader), loss.item()))\n"," \n"," # validation 주기에 따른 loss 출력 및 best model 저장\n"," if (epoch + 1) % val_every == 0:\n","# avrg_loss = validation(epoch + 1, model, val_loader, criterion, device)\n","# if avrg_loss < best_loss:\n","# print('Best performance at epoch: {}'.format(epoch + 1))\n","# print('Save model in', saved_dir)\n","# best_loss = avrg_loss\n","# wandb.log({'best_loss': best_loss})\n","# save_model(model, saved_dir)\n"," avrg_miou = validation(epoch + 1, model, val_loader, criterion, device)\n"," if avrg_miou > best_miou:\n"," print('Best performance at epoch: {}'.format(epoch + 1))\n"," print('Save model in', saved_dir)\n"," best_miou = avrg_miou\n"," wandb.log({'best_miou': best_miou})\n"," save_model(model, saved_dir)"],"execution_count":17,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.285795Z","start_time":"2021-04-18T16:16:18.267686Z"},"id":"EulIikmq-NwO","executionInfo":{"status":"ok","timestamp":1620085481168,"user_tz":-540,"elapsed":32784,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["def validation(epoch, model, data_loader, criterion, device):\n"," print('Start validation #{}'.format(epoch))\n"," model.eval()\n"," with torch.no_grad():\n"," total_loss = 0\n"," cnt = 0\n"," mIoU_list = []\n"," for step, (images, masks, _) in enumerate(data_loader):\n"," \n"," images = torch.stack(images) # (batch, channel, height, width)\n"," masks = torch.stack(masks).long() # (batch, channel, height, width)\n","\n"," images, masks = images.to(device), masks.to(device) \n","\n"," outputs = model(images)\n"," loss = criterion(outputs, masks)\n"," total_loss += loss\n"," cnt += 1\n"," \n","# print(outputs.shape)\n","# print(masks.shape)\n","# wandb.log(wandb.Image(images, masks={\n","# \"predictions\" : {\n","# \"mask_data\" : torch.squeeze(torch.squeeze(outputs, 0),1),\n","# \"class_labels\" : classes_dict\n","# },\n","# \"ground_truth\" : {\n","# \"mask_data\" : torch.squeeze(masks, 0),\n","# \"class_labels\" : classes_dict\n","# }\n","# }))\n"," \n"," outputs = torch.argmax(outputs.squeeze(), dim=1).detach().cpu().numpy()\n","\n"," mIoU = label_accuracy_score(masks.detach().cpu().numpy(), outputs, n_class=12)[2]\n"," mIoU_list.append(mIoU)\n"," \n"," avrg_loss = total_loss / cnt\n"," avrg_mIoU = np.mean(mIoU_list)\n"," print('Validation #{} Average Loss: {:.4f}, mIoU: {:.4f}'.format(epoch, avrg_loss, np.mean(mIoU_list)))\n"," wandb.log({'Train Loss':loss.item(), 'Val Loss':avrg_loss , 'Val mIoU':np.mean(mIoU_list)})\n","# return avrg_loss\n"," return avrg_mIoU"],"execution_count":18,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"C_7CtFfH-NwO"},"source":["## 모델 저장 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.909918Z","start_time":"2021-04-18T16:16:18.898918Z"},"id":"gpCM5BFO-NwO","executionInfo":{"status":"ok","timestamp":1620085499283,"user_tz":-540,"elapsed":986,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# 모델 저장 함수 정의\n","val_every = 1 \n","\n","saved_dir = './saved'\n","if not os.path.isdir(saved_dir): \n"," os.mkdir(saved_dir)\n"," \n","def save_model(model, saved_dir, file_name='pan_effb3_noisy_focal_madgrad_stepLR.pt'):\n"," check_point = {'net': model.state_dict()}\n"," output_path = os.path.join(saved_dir, file_name)\n"," torch.save(model.state_dict(), output_path)"],"execution_count":19,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"FyKGeg8T-NwP"},"source":["## 모델 생성 및 Loss function, Optimizer 정의"]},{"cell_type":"code","metadata":{"id":"ORugl8s1-NwP","executionInfo":{"status":"ok","timestamp":1620085510612,"user_tz":-540,"elapsed":1029,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["from torch.autograd import Variable\n","import torch.nn.functional as F\n","# ref : https://github.com/clcarwin/focal_loss_pytorch\n","class FocalLoss(nn.Module):\n"," def __init__(self, gamma=0, alpha=None, size_average=True):\n"," super(FocalLoss, self).__init__()\n"," self.gamma = gamma\n"," self.alpha = alpha\n"," if isinstance(alpha,(float,int)): self.alpha = torch.Tensor([alpha,1-alpha])\n"," if isinstance(alpha,list): self.alpha = torch.Tensor(alpha)\n"," self.size_average = size_average\n","\n"," def forward(self, input, target):\n"," if input.dim()>2:\n"," input = input.view(input.size(0),input.size(1),-1) # N,C,H,W => N,C,H*W\n"," input = input.transpose(1,2) # N,C,H*W => N,H*W,C\n"," input = input.contiguous().view(-1,input.size(2)) # N,H*W,C => N*H*W,C\n"," target = target.view(-1,1)\n","\n"," logpt = F.log_softmax(input)\n"," logpt = logpt.gather(1,target)\n"," logpt = logpt.view(-1)\n"," pt = Variable(logpt.data.exp())\n","\n"," if self.alpha is not None:\n"," if self.alpha.type()!=input.data.type():\n"," self.alpha = self.alpha.type_as(input.data)\n"," at = self.alpha.gather(0,target.data.view(-1))\n"," logpt = logpt * Variable(at)\n","\n"," loss = -1 * (1-pt)**self.gamma * logpt\n"," if self.size_average: return loss.mean()\n"," else: return loss.sum()"],"execution_count":20,"outputs":[]},{"cell_type":"code","metadata":{"id":"yfeFQknz-NwP"},"source":["import torch.optim.lr_scheduler as lr_scheduler\n","import math\n","class CosineAnnealingWarmUpRestart(lr_scheduler._LRScheduler):\n"," def __init__(self, optimizer, T_0, T_mult=1, eta_max=0.1, T_up=0, gamma=1., last_epoch=-1):\n"," if T_0 <= 0 or not isinstance(T_0, int):\n"," raise ValueError(\"Expected positive integer T_0, but got {}\".format(T_0))\n"," if T_mult < 1 or not isinstance(T_mult, int):\n"," raise ValueError(\"Expected integer T_mult >= 1, but got {}\".format(T_mult))\n"," if T_up < 0 or not isinstance(T_up, int):\n"," raise ValueError(\"Expected positive integer T_up, but got {}\".format(T_up))\n"," self.T_0 = T_0\n"," self.T_mult = T_mult\n"," self.base_eta_max = eta_max\n"," self.eta_max = eta_max\n"," self.T_up = T_up\n"," self.T_i = T_0\n"," self.gamma = gamma\n"," self.cycle = 0\n"," self.T_cur = last_epoch\n"," super(CosineAnnealingWarmUpRestart, self).__init__(optimizer, last_epoch)\n"," # self.T_cur = last_epoch\n"," \n"," def get_lr(self):\n"," if self.T_cur == -1:\n"," return self.base_lrs\n"," elif self.T_cur < self.T_up:\n"," return [(self.eta_max - base_lr)*self.T_cur / self.T_up + base_lr for base_lr in self.base_lrs]\n"," else:\n"," return [base_lr + (self.eta_max - base_lr) * (1 + math.cos(math.pi * (self.T_cur-self.T_up) / (self.T_i - self.T_up))) / 2\n"," for base_lr in self.base_lrs]\n","\n"," def step(self, epoch=None):\n"," if epoch is None:\n"," epoch = self.last_epoch + 1\n"," self.T_cur = self.T_cur + 1\n"," if self.T_cur >= self.T_i:\n"," self.cycle += 1\n"," self.T_cur = self.T_cur - self.T_i\n"," self.T_i = (self.T_i - self.T_up) * self.T_mult + self.T_up\n"," else:\n"," if epoch >= self.T_0:\n"," if self.T_mult == 1:\n"," self.T_cur = epoch % self.T_0\n"," self.cycle = epoch // self.T_0\n"," else:\n"," n = int(math.log((epoch / self.T_0 * (self.T_mult - 1) + 1), self.T_mult))\n"," self.cycle = n\n"," self.T_cur = epoch - self.T_0 * (self.T_mult ** n - 1) / (self.T_mult - 1)\n"," self.T_i = self.T_0 * self.T_mult ** (n)\n"," else:\n"," self.T_i = self.T_0\n"," self.T_cur = epoch\n"," \n"," self.eta_max = self.base_eta_max * (self.gamma**self.cycle)\n"," self.last_epoch = math.floor(epoch)\n"," for param_group, lr in zip(self.optimizer.param_groups, self.get_lr()):\n"," param_group['lr'] = lr"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AG1oQeu7BX1M","executionInfo":{"status":"ok","timestamp":1620060019354,"user_tz":-540,"elapsed":3477,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"98eae7c4-b66e-409d-9725-0c684d747f2a"},"source":["# !pip install adamp"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Requirement already satisfied: adamp in /usr/local/lib/python3.7/dist-packages (0.3.0)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"eH2PpwF9a-Os","executionInfo":{"status":"ok","timestamp":1620085557624,"user_tz":-540,"elapsed":3897,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"a78e14f8-d5e3-419d-a790-dda6f857e227"},"source":["!pip install madgrad"],"execution_count":23,"outputs":[{"output_type":"stream","text":["Collecting madgrad\n"," Downloading https://files.pythonhosted.org/packages/65/f0/4584f18202a2fb8903d456bf907b80e7cb54ad8fcba68604084ff41b7cf8/madgrad-1.1-py3-none-any.whl\n","Installing collected packages: madgrad\n","Successfully installed madgrad-1.1\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:19.698902Z","start_time":"2021-04-18T16:16:19.694902Z"},"id":"9Dly8KZj-NwQ","executionInfo":{"status":"ok","timestamp":1620085558049,"user_tz":-540,"elapsed":792,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# from adamp import AdamP\n","from madgrad import MADGRAD\n","# Loss function 정의\n","# criterion = nn.CrossEntropyLoss()\n","criterion = FocalLoss()\n","\n","# Optimizer 정의\n","# optimizer = torch.optim.Adam(params = model.parameters(), lr = learning_rate, weight_decay=1e-6)\n","# optimizer = AdamP(params = model.parameters())\n","optimizer = MADGRAD(params = model.parameters(), lr = learning_rate, momentum = 0.9, weight_decay = 0, eps = 1e-06)\n","\n","lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size = 237, gamma = 0.65)\n","# lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=50, eta_min=0)\n","# lr_scheduler = CosineAnnealingWarmUpRestart(optimizer, T_0=150, T_mult=1, eta_max=0.1, T_up=10, gamma=0.5)"],"execution_count":24,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"start_time":"2021-04-18T16:16:20.331Z"},"colab":{"base_uri":"https://localhost:8080/"},"id":"cMSiFA-3-NwR","executionInfo":{"status":"ok","timestamp":1620102365361,"user_tz":-540,"elapsed":16802515,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"4a8d5ffa-5ba1-47b5-f7c3-2c6bcfa02fb6"},"source":["train(num_epochs, model, train_loader, val_loader, criterion, optimizer, saved_dir, val_every, device, lr_scheduler)"],"execution_count":25,"outputs":[{"output_type":"stream","text":["Start training..\n","Epoch [1/20], Step [25/327], Loss: 0.8220\n","Epoch [1/20], Step [50/327], Loss: 0.9693\n","Epoch [1/20], Step [75/327], Loss: 0.5957\n","Epoch [1/20], Step [100/327], Loss: 0.5607\n","Epoch [1/20], Step [125/327], Loss: 0.6950\n","Epoch [1/20], Step [150/327], Loss: 0.3106\n","Epoch [1/20], Step [175/327], Loss: 0.4186\n","Epoch [1/20], Step [200/327], Loss: 0.4975\n","Epoch [1/20], Step [225/327], Loss: 0.4637\n","Epoch [1/20], Step [250/327], Loss: 0.3797\n","Epoch [1/20], Step [275/327], Loss: 0.2911\n","Epoch [1/20], Step [300/327], Loss: 0.3394\n","Epoch [1/20], Step [325/327], Loss: 0.9269\n","Start validation #1\n","Validation #1 Average Loss: 0.4036, mIoU: 0.3302\n","Best performance at epoch: 1\n","Save model in ./saved\n","Epoch [2/20], Step [25/327], Loss: 0.4637\n","Epoch [2/20], Step [50/327], Loss: 0.3740\n","Epoch [2/20], Step [75/327], Loss: 0.2870\n","Epoch [2/20], Step [100/327], Loss: 0.4495\n","Epoch [2/20], Step [125/327], Loss: 0.2755\n","Epoch [2/20], Step [150/327], Loss: 0.3229\n","Epoch [2/20], Step [175/327], Loss: 0.4972\n","Epoch [2/20], Step [200/327], Loss: 0.4102\n","Epoch [2/20], Step [225/327], Loss: 0.5059\n","Epoch [2/20], Step [250/327], Loss: 0.3355\n","Epoch [2/20], Step [275/327], Loss: 0.2518\n","Epoch [2/20], Step [300/327], Loss: 0.3774\n","Epoch [2/20], Step [325/327], Loss: 0.3262\n","Start validation #2\n","Validation #2 Average Loss: 0.3380, mIoU: 0.3628\n","Best performance at epoch: 2\n","Save model in ./saved\n","Epoch [3/20], Step [25/327], Loss: 0.2823\n","Epoch [3/20], Step [50/327], Loss: 0.3700\n","Epoch [3/20], Step [75/327], Loss: 0.2374\n","Epoch [3/20], Step [100/327], Loss: 0.2222\n","Epoch [3/20], Step [125/327], Loss: 0.3173\n","Epoch [3/20], Step [150/327], Loss: 0.1524\n","Epoch [3/20], Step [175/327], Loss: 0.2998\n","Epoch [3/20], Step [200/327], Loss: 0.4724\n","Epoch [3/20], Step [225/327], Loss: 0.5351\n","Epoch [3/20], Step [250/327], Loss: 0.2975\n","Epoch [3/20], Step [275/327], Loss: 0.1980\n","Epoch [3/20], Step [300/327], Loss: 0.2431\n","Epoch [3/20], Step [325/327], Loss: 0.1727\n","Start validation #3\n","Validation #3 Average Loss: 0.3223, mIoU: 0.3757\n","Best performance at epoch: 3\n","Save model in ./saved\n","Epoch [4/20], Step [25/327], Loss: 0.2651\n","Epoch [4/20], Step [50/327], Loss: 0.1643\n","Epoch [4/20], Step [75/327], Loss: 0.5499\n","Epoch [4/20], Step [100/327], Loss: 0.5083\n","Epoch [4/20], Step [125/327], Loss: 0.1990\n","Epoch [4/20], Step [150/327], Loss: 0.1870\n","Epoch [4/20], Step [175/327], Loss: 0.2616\n","Epoch [4/20], Step [200/327], Loss: 0.3783\n","Epoch [4/20], Step [225/327], Loss: 0.2177\n","Epoch [4/20], Step [250/327], Loss: 0.2972\n","Epoch [4/20], Step [275/327], Loss: 0.3182\n","Epoch [4/20], Step [300/327], Loss: 0.2135\n","Epoch [4/20], Step [325/327], Loss: 0.2213\n","Start validation #4\n","Validation #4 Average Loss: 0.3261, mIoU: 0.3845\n","Best performance at epoch: 4\n","Save model in ./saved\n","Epoch [5/20], Step [25/327], Loss: 0.2593\n","Epoch [5/20], Step [50/327], Loss: 0.1275\n","Epoch [5/20], Step [75/327], Loss: 0.1957\n","Epoch [5/20], Step [100/327], Loss: 0.1422\n","Epoch [5/20], Step [125/327], Loss: 0.3824\n","Epoch [5/20], Step [150/327], Loss: 0.1738\n","Epoch [5/20], Step [175/327], Loss: 0.2868\n","Epoch [5/20], Step [200/327], Loss: 0.1969\n","Epoch [5/20], Step [225/327], Loss: 0.3230\n","Epoch [5/20], Step [250/327], Loss: 0.3353\n","Epoch [5/20], Step [275/327], Loss: 0.2106\n","Epoch [5/20], Step [300/327], Loss: 0.2099\n","Epoch [5/20], Step [325/327], Loss: 0.1516\n","Start validation #5\n","Validation #5 Average Loss: 0.3211, mIoU: 0.3903\n","Best performance at epoch: 5\n","Save model in ./saved\n","Epoch [6/20], Step [25/327], Loss: 0.2465\n","Epoch [6/20], Step [50/327], Loss: 0.5391\n","Epoch [6/20], Step [75/327], Loss: 0.2195\n","Epoch [6/20], Step [100/327], Loss: 0.2353\n","Epoch [6/20], Step [125/327], Loss: 0.1401\n","Epoch [6/20], Step [150/327], Loss: 0.3294\n","Epoch [6/20], Step [175/327], Loss: 0.2092\n","Epoch [6/20], Step [200/327], Loss: 0.1416\n","Epoch [6/20], Step [225/327], Loss: 0.2856\n","Epoch [6/20], Step [250/327], Loss: 0.1882\n","Epoch [6/20], Step [275/327], Loss: 0.2622\n","Epoch [6/20], Step [300/327], Loss: 0.2210\n","Epoch [6/20], Step [325/327], Loss: 0.4069\n","Start validation #6\n","Validation #6 Average Loss: 0.3064, mIoU: 0.4024\n","Best performance at epoch: 6\n","Save model in ./saved\n","Epoch [7/20], Step [25/327], Loss: 0.1971\n","Epoch [7/20], Step [50/327], Loss: 0.3775\n","Epoch [7/20], Step [75/327], Loss: 0.2329\n","Epoch [7/20], Step [100/327], Loss: 0.1812\n","Epoch [7/20], Step [125/327], Loss: 0.2603\n","Epoch [7/20], Step [150/327], Loss: 0.2951\n","Epoch [7/20], Step [175/327], Loss: 0.2191\n","Epoch [7/20], Step [200/327], Loss: 0.2100\n","Epoch [7/20], Step [225/327], Loss: 0.2288\n","Epoch [7/20], Step [250/327], Loss: 0.1154\n","Epoch [7/20], Step [275/327], Loss: 0.2520\n","Epoch [7/20], Step [300/327], Loss: 0.1991\n","Epoch [7/20], Step [325/327], Loss: 0.2768\n","Start validation #7\n","Validation #7 Average Loss: 0.3020, mIoU: 0.4029\n","Best performance at epoch: 7\n","Save model in ./saved\n","Epoch [8/20], Step [25/327], Loss: 0.1435\n","Epoch [8/20], Step [50/327], Loss: 0.2481\n","Epoch [8/20], Step [75/327], Loss: 0.2333\n","Epoch [8/20], Step [100/327], Loss: 0.5998\n","Epoch [8/20], Step [125/327], Loss: 0.1876\n","Epoch [8/20], Step [150/327], Loss: 0.1315\n","Epoch [8/20], Step [175/327], Loss: 0.2060\n","Epoch [8/20], Step [200/327], Loss: 0.3043\n","Epoch [8/20], Step [225/327], Loss: 0.3325\n","Epoch [8/20], Step [250/327], Loss: 0.1677\n","Epoch [8/20], Step [275/327], Loss: 0.2762\n","Epoch [8/20], Step [300/327], Loss: 0.2651\n","Epoch [8/20], Step [325/327], Loss: 0.1829\n","Start validation #8\n","Validation #8 Average Loss: 0.3218, mIoU: 0.3910\n","Epoch [9/20], Step [25/327], Loss: 0.2955\n","Epoch [9/20], Step [50/327], Loss: 0.1504\n","Epoch [9/20], Step [75/327], Loss: 0.2052\n","Epoch [9/20], Step [100/327], Loss: 0.1681\n","Epoch [9/20], Step [125/327], Loss: 0.2105\n","Epoch [9/20], Step [150/327], Loss: 0.3958\n","Epoch [9/20], Step [175/327], Loss: 0.2038\n","Epoch [9/20], Step [200/327], Loss: 0.1581\n","Epoch [9/20], Step [225/327], Loss: 0.2321\n","Epoch [9/20], Step [250/327], Loss: 0.5859\n","Epoch [9/20], Step [275/327], Loss: 0.1786\n","Epoch [9/20], Step [300/327], Loss: 0.2025\n","Epoch [9/20], Step [325/327], Loss: 0.2564\n","Start validation #9\n","Validation #9 Average Loss: 0.3119, mIoU: 0.3999\n","Epoch [10/20], Step [25/327], Loss: 0.2226\n","Epoch [10/20], Step [50/327], Loss: 0.2789\n","Epoch [10/20], Step [75/327], Loss: 0.2737\n","Epoch [10/20], Step [100/327], Loss: 0.1672\n","Epoch [10/20], Step [125/327], Loss: 0.2229\n","Epoch [10/20], Step [150/327], Loss: 0.1895\n","Epoch [10/20], Step [175/327], Loss: 0.2673\n","Epoch [10/20], Step [200/327], Loss: 0.1930\n","Epoch [10/20], Step [225/327], Loss: 0.2665\n","Epoch [10/20], Step [250/327], Loss: 0.2793\n","Epoch [10/20], Step [275/327], Loss: 0.1416\n","Epoch [10/20], Step [300/327], Loss: 0.1874\n","Epoch [10/20], Step [325/327], Loss: 0.2545\n","Start validation #10\n","Validation #10 Average Loss: 0.3110, mIoU: 0.4005\n","Epoch [11/20], Step [25/327], Loss: 0.1683\n","Epoch [11/20], Step [50/327], Loss: 0.1382\n","Epoch [11/20], Step [75/327], Loss: 0.2597\n","Epoch [11/20], Step [100/327], Loss: 0.1811\n","Epoch [11/20], Step [125/327], Loss: 0.1636\n","Epoch [11/20], Step [150/327], Loss: 0.2341\n","Epoch [11/20], Step [175/327], Loss: 0.1371\n","Epoch [11/20], Step [200/327], Loss: 0.1651\n","Epoch [11/20], Step [225/327], Loss: 0.1613\n","Epoch [11/20], Step [250/327], Loss: 0.2747\n","Epoch [11/20], Step [275/327], Loss: 0.2168\n","Epoch [11/20], Step [300/327], Loss: 0.1324\n","Epoch [11/20], Step [325/327], Loss: 0.1794\n","Start validation #11\n","Validation #11 Average Loss: 0.3163, mIoU: 0.3971\n","Epoch [12/20], Step [25/327], Loss: 0.2183\n","Epoch [12/20], Step [50/327], Loss: 0.2838\n","Epoch [12/20], Step [75/327], Loss: 0.1586\n","Epoch [12/20], Step [100/327], Loss: 0.1709\n","Epoch [12/20], Step [125/327], Loss: 0.1035\n","Epoch [12/20], Step [150/327], Loss: 0.2441\n","Epoch [12/20], Step [175/327], Loss: 0.1597\n","Epoch [12/20], Step [200/327], Loss: 0.2000\n","Epoch [12/20], Step [225/327], Loss: 0.1169\n","Epoch [12/20], Step [250/327], Loss: 0.2083\n","Epoch [12/20], Step [275/327], Loss: 0.1702\n","Epoch [12/20], Step [300/327], Loss: 0.2536\n","Epoch [12/20], Step [325/327], Loss: 0.2076\n","Start validation #12\n","Validation #12 Average Loss: 0.3084, mIoU: 0.3996\n","Epoch [13/20], Step [25/327], Loss: 0.1396\n","Epoch [13/20], Step [50/327], Loss: 0.2793\n","Epoch [13/20], Step [75/327], Loss: 0.2214\n","Epoch [13/20], Step [100/327], Loss: 0.1829\n","Epoch [13/20], Step [125/327], Loss: 0.1186\n","Epoch [13/20], Step [150/327], Loss: 0.2502\n","Epoch [13/20], Step [175/327], Loss: 0.1820\n","Epoch [13/20], Step [200/327], Loss: 0.2334\n","Epoch [13/20], Step [225/327], Loss: 0.2726\n","Epoch [13/20], Step [250/327], Loss: 0.2181\n","Epoch [13/20], Step [275/327], Loss: 0.2053\n","Epoch [13/20], Step [300/327], Loss: 0.1475\n","Epoch [13/20], Step [325/327], Loss: 0.2240\n","Start validation #13\n","Validation #13 Average Loss: 0.3037, mIoU: 0.4046\n","Best performance at epoch: 13\n","Save model in ./saved\n","Epoch [14/20], Step [25/327], Loss: 0.1344\n","Epoch [14/20], Step [50/327], Loss: 0.1763\n","Epoch [14/20], Step [75/327], Loss: 0.2505\n","Epoch [14/20], Step [100/327], Loss: 0.2819\n","Epoch [14/20], Step [125/327], Loss: 0.1049\n","Epoch [14/20], Step [150/327], Loss: 0.1711\n","Epoch [14/20], Step [175/327], Loss: 0.1770\n","Epoch [14/20], Step [200/327], Loss: 0.2304\n","Epoch [14/20], Step [225/327], Loss: 0.2550\n","Epoch [14/20], Step [250/327], Loss: 0.1755\n","Epoch [14/20], Step [275/327], Loss: 0.1470\n","Epoch [14/20], Step [300/327], Loss: 0.1680\n","Epoch [14/20], Step [325/327], Loss: 0.1469\n","Start validation #14\n","Validation #14 Average Loss: 0.3078, mIoU: 0.4008\n","Epoch [15/20], Step [25/327], Loss: 0.2224\n","Epoch [15/20], Step [50/327], Loss: 0.1466\n","Epoch [15/20], Step [75/327], Loss: 0.2352\n","Epoch [15/20], Step [100/327], Loss: 0.1803\n","Epoch [15/20], Step [125/327], Loss: 0.3149\n","Epoch [15/20], Step [150/327], Loss: 0.2038\n","Epoch [15/20], Step [175/327], Loss: 0.3633\n","Epoch [15/20], Step [200/327], Loss: 0.3436\n","Epoch [15/20], Step [225/327], Loss: 0.3966\n","Epoch [15/20], Step [250/327], Loss: 0.1886\n","Epoch [15/20], Step [275/327], Loss: 0.2162\n","Epoch [15/20], Step [300/327], Loss: 0.2388\n","Epoch [15/20], Step [325/327], Loss: 0.2111\n","Start validation #15\n","Validation #15 Average Loss: 0.3039, mIoU: 0.4039\n","Epoch [16/20], Step [25/327], Loss: 0.2404\n","Epoch [16/20], Step [50/327], Loss: 0.1919\n","Epoch [16/20], Step [75/327], Loss: 0.1601\n","Epoch [16/20], Step [100/327], Loss: 0.1066\n","Epoch [16/20], Step [125/327], Loss: 0.2508\n","Epoch [16/20], Step [150/327], Loss: 0.3109\n","Epoch [16/20], Step [175/327], Loss: 0.1376\n","Epoch [16/20], Step [200/327], Loss: 0.3027\n","Epoch [16/20], Step [225/327], Loss: 0.2755\n","Epoch [16/20], Step [250/327], Loss: 0.1969\n","Epoch [16/20], Step [275/327], Loss: 0.1959\n","Epoch [16/20], Step [300/327], Loss: 0.2527\n","Epoch [16/20], Step [325/327], Loss: 0.1785\n","Start validation #16\n","Validation #16 Average Loss: 0.3170, mIoU: 0.3962\n","Epoch [17/20], Step [25/327], Loss: 0.2603\n","Epoch [17/20], Step [50/327], Loss: 0.1757\n","Epoch [17/20], Step [75/327], Loss: 0.1134\n","Epoch [17/20], Step [100/327], Loss: 0.2251\n","Epoch [17/20], Step [125/327], Loss: 0.1246\n","Epoch [17/20], Step [150/327], Loss: 0.1843\n","Epoch [17/20], Step [175/327], Loss: 0.1178\n","Epoch [17/20], Step [200/327], Loss: 0.1842\n","Epoch [17/20], Step [225/327], Loss: 0.1353\n","Epoch [17/20], Step [250/327], Loss: 0.3383\n","Epoch [17/20], Step [275/327], Loss: 0.1871\n","Epoch [17/20], Step [300/327], Loss: 0.3401\n","Epoch [17/20], Step [325/327], Loss: 0.2371\n","Start validation #17\n","Validation #17 Average Loss: 0.3028, mIoU: 0.4015\n","Epoch [18/20], Step [25/327], Loss: 0.1990\n","Epoch [18/20], Step [50/327], Loss: 0.1347\n","Epoch [18/20], Step [75/327], Loss: 0.2431\n","Epoch [18/20], Step [100/327], Loss: 0.1710\n","Epoch [18/20], Step [125/327], Loss: 0.1884\n","Epoch [18/20], Step [150/327], Loss: 0.2045\n","Epoch [18/20], Step [175/327], Loss: 0.1383\n","Epoch [18/20], Step [200/327], Loss: 0.2550\n","Epoch [18/20], Step [225/327], Loss: 0.1336\n","Epoch [18/20], Step [250/327], Loss: 0.1860\n","Epoch [18/20], Step [275/327], Loss: 0.3575\n","Epoch [18/20], Step [300/327], Loss: 0.1588\n","Epoch [18/20], Step [325/327], Loss: 0.0986\n","Start validation #18\n","Validation #18 Average Loss: 0.3105, mIoU: 0.3947\n","Epoch [19/20], Step [25/327], Loss: 0.1259\n","Epoch [19/20], Step [50/327], Loss: 0.1682\n","Epoch [19/20], Step [75/327], Loss: 0.2996\n","Epoch [19/20], Step [100/327], Loss: 0.1441\n","Epoch [19/20], Step [125/327], Loss: 0.5350\n","Epoch [19/20], Step [150/327], Loss: 0.2107\n","Epoch [19/20], Step [175/327], Loss: 0.1675\n","Epoch [19/20], Step [200/327], Loss: 0.1417\n","Epoch [19/20], Step [225/327], Loss: 0.2037\n","Epoch [19/20], Step [250/327], Loss: 0.1059\n","Epoch [19/20], Step [275/327], Loss: 0.0922\n","Epoch [19/20], Step [300/327], Loss: 0.2640\n","Epoch [19/20], Step [325/327], Loss: 0.2383\n","Start validation #19\n","Validation #19 Average Loss: 0.3082, mIoU: 0.4029\n","Epoch [20/20], Step [25/327], Loss: 0.5513\n","Epoch [20/20], Step [50/327], Loss: 0.1728\n","Epoch [20/20], Step [75/327], Loss: 0.2459\n","Epoch [20/20], Step [100/327], Loss: 0.3029\n","Epoch [20/20], Step [125/327], Loss: 0.2330\n","Epoch [20/20], Step [150/327], Loss: 0.1795\n","Epoch [20/20], Step [175/327], Loss: 0.1673\n","Epoch [20/20], Step [200/327], Loss: 0.2743\n","Epoch [20/20], Step [225/327], Loss: 0.2923\n","Epoch [20/20], Step [250/327], Loss: 0.2168\n","Epoch [20/20], Step [275/327], Loss: 0.1909\n","Epoch [20/20], Step [300/327], Loss: 0.1407\n","Epoch [20/20], Step [325/327], Loss: 0.2757\n","Start validation #20\n","Validation #20 Average Loss: 0.3049, mIoU: 0.4011\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"C6ClcO0J-NwR"},"source":["## 저장된 model 불러오기 (학습된 이후) "]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:21.050200Z","start_time":"2021-04-16T19:44:20.802200Z"},"scrolled":true,"id":"KQPtUDzd-NwR","colab":{"base_uri":"https://localhost:8080/","height":232},"executionInfo":{"status":"error","timestamp":1620103401786,"user_tz":-540,"elapsed":856,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"91ea2508-84d8-4234-80da-f8139bf02f5b"},"source":["# best model 저장된 경로\n","model_path = './saved/pan_effb3_noisy_focal_madgrad_stepLR.pt'\n","\n","# best model 불러오기\n","checkpoint = torch.load(model_path, map_location=device)\n","model.load_state_dict(checkpoint)\n","\n","# 추론을 실행하기 전에는 반드시 설정 (batch normalization, dropout 를 평가 모드로 설정)\n","# model.eval()"],"execution_count":1,"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)","\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m# best model 불러오기\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mcheckpoint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmap_location\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_state_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcheckpoint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNameError\u001b[0m: name 'torch' is not defined"]}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:24.939227Z","start_time":"2021-04-16T19:44:24.518228Z"},"id":"0LQqrDAp-NwR"},"source":["# 첫번째 batch의 추론 결과 확인\n","for imgs, image_infos in test_loader:\n"," image_infos = image_infos\n"," temp_images = imgs\n"," \n"," model.eval()\n"," # inference\n"," outs = model(torch.stack(temp_images).to(device))\n"," oms = torch.argmax(outs.squeeze(), dim=1).detach().cpu().numpy()\n"," \n"," break\n","\n","i = 3\n","fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(16, 16))\n","\n","print('Shape of Original Image :', list(temp_images[i].shape))\n","print('Shape of Predicted : ', list(oms[i].shape))\n","print('Unique values, category of transformed mask : \\n', [{int(i),category_names[int(i)]} for i in list(np.unique(oms[i]))])\n","\n","# Original image\n","ax1.imshow(temp_images[i].permute([1,2,0]))\n","ax1.grid(False)\n","ax1.set_title(\"Original image : {}\".format(image_infos[i]['file_name']), fontsize = 15)\n","\n","# Predicted\n","ax2.imshow(oms[i])\n","ax2.grid(False)\n","ax2.set_title(\"Predicted : {}\".format(image_infos[i]['file_name']), fontsize = 15)\n","\n","plt.show()"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"evYjR2F3-NwS"},"source":["## submission을 위한 test 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:27.469285Z","start_time":"2021-04-16T19:44:27.456021Z"},"id":"nhMLnV5d-NwS"},"source":["def test(model, data_loader, device):\n"," size = 256\n"," transform = A.Compose([A.Resize(256, 256)])\n"," print('Start prediction.')\n"," model.eval()\n"," \n"," file_name_list = []\n"," preds_array = np.empty((0, size*size), dtype=np.long)\n"," \n"," with torch.no_grad():\n"," for step, (imgs, image_infos) in enumerate(test_loader):\n","\n"," # inference (512 x 512)\n"," outs = model(torch.stack(imgs).to(device))\n"," oms = torch.argmax(outs.squeeze(), dim=1).detach().cpu().numpy()\n"," \n"," # resize (256 x 256)\n"," temp_mask = []\n"," for img, mask in zip(np.stack(imgs), oms):\n"," transformed = transform(image=img, mask=mask)\n"," mask = transformed['mask']\n"," temp_mask.append(mask)\n","\n"," oms = np.array(temp_mask)\n"," \n"," oms = oms.reshape([oms.shape[0], size*size]).astype(int)\n"," preds_array = np.vstack((preds_array, oms))\n"," \n"," file_name_list.append([i['file_name'] for i in image_infos])\n"," print(\"End prediction.\")\n"," file_names = [y for x in file_name_list for y in x]\n"," \n"," return file_names, preds_array"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"r1YKHBf4-NwT"},"source":["## submission.csv 생성"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:45:42.235310Z","start_time":"2021-04-16T19:44:30.499016Z"},"scrolled":true,"id":"Bz79_g8K-NwT"},"source":["# sample_submisson.csv 열기\n","submission = pd.read_csv('./submission/sample_submission.csv', index_col=None)\n","\n","# test set에 대한 prediction\n","file_names, preds = test(model, test_loader, device)\n","\n","# PredictionString 대입\n","for file_name, string in zip(file_names, preds):\n"," submission = submission.append({\"image_id\" : file_name, \"PredictionString\" : ' '.join(str(e) for e in string.tolist())}, \n"," ignore_index=True)\n","\n","# submission.csv로 저장\n","submission.to_csv(\"./submission/pan_effb3_noisy_focal_adamp_cosLR.csv\", index=False)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Zgl7Ye7F-NwT"},"source":["## 제출까지\n","\n"]},{"cell_type":"code","metadata":{"id":"022E8AC7-NwT"},"source":["import json\n","import requests\n","import os\n","from urllib.parse import urlparse, parse_qsl, urlencode, urlunparse\n","\n","def submit(user_key='', file_path = '', desc=\"\"):\n"," if not user_key:\n"," raise Exception(\"No UserKey\" )\n"," url = urlparse('http://ec2-13-124-161-225.ap-northeast-2.compute.amazonaws.com:8000/api/v1/competition/28/presigned_url/?description=&hyperparameters={%22training%22:{},%22inference%22:{}}')\n"," qs = dict(parse_qsl(url.query))\n"," qs['description'] = desc\n"," parts = url._replace(query=urlencode(qs))\n"," url = urlunparse(parts)\n","\n"," print(url)\n"," headers = {\n"," 'Authorization': user_key\n"," }\n"," res = requests.get(url, headers=headers)\n"," print(res.text)\n"," data = json.loads(res.text)\n"," \n"," submit_url = data['url']\n"," body = {\n"," 'key':'app/Competitions/000028/Users/{}/Submissions/{}/output.csv'.format(str(data['submission']['user']).zfill(8),str(data['submission']['local_id']).zfill(4)),\n"," 'x-amz-algorithm':data['fields']['x-amz-algorithm'],\n"," 'x-amz-credential':data['fields']['x-amz-credential'],\n"," 'x-amz-date':data['fields']['x-amz-date'],\n"," 'policy':data['fields']['policy'],\n"," 'x-amz-signature':data['fields']['x-amz-signature']\n"," }\n"," requests.post(url=submit_url, data=body, files={'file': open(file_path, 'rb')})\n","\n","\n","####################################################################################\n","test_dir = \"/opt/ml/code/submission\" # 수정 필요 : output 파일 폴더 \n","desc = 'pan_effb3_noisy_focal_adamp_cosLR' # 수정 필요 : 파일에 대한 설명\n","output_file = \"pan_effb3_noisy_focal_adamp_cosLR.csv\" #수정 필요 : output 파일 \n","user_key = \"Bearer 7bb5f96452751a238ffaf91a93c4242bf9b72abe\" # 수정 필요 : Authorization \n","\n","\n","submit(user_key, os.path.join(test_dir, output_file),desc)"],"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/chanyub_seg/code/re_pan_effb7_noisy_focal_madgrad_cosLR.ipynb b/chanyub_seg/code/re_pan_effb7_noisy_focal_madgrad_cosLR.ipynb deleted file mode 100644 index d3bb753..0000000 --- a/chanyub_seg/code/re_pan_effb7_noisy_focal_madgrad_cosLR.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"nbformat":4,"nbformat_minor":0,"metadata":{"hide_input":false,"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.7"},"toc":{"base_numbering":1,"nav_menu":{},"number_sections":true,"sideBar":true,"skip_h1_title":false,"title_cell":"Table of 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google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":3,"outputs":[{"output_type":"stream","text":["Mounted at /content/drive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xDwpp4Lk-gSH","executionInfo":{"status":"ok","timestamp":1620192756412,"user_tz":-540,"elapsed":5986,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"406ff877-e013-4767-f726-5a15ed17609d"},"source":["ls"],"execution_count":4,"outputs":[{"output_type":"stream","text":["\u001b[0m\u001b[01;34mdrive\u001b[0m/ \u001b[01;34msample_data\u001b[0m/\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sItrVDeh-iYC","executionInfo":{"status":"ok","timestamp":1620192756414,"user_tz":-540,"elapsed":5426,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"1c099ae0-42f6-4c49-f058-446be14ed064"},"source":["cd drive/MyDrive/Trash/code"],"execution_count":5,"outputs":[{"output_type":"stream","text":["/content/drive/MyDrive/Trash/code\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"a54v4-kN_LVO","executionInfo":{"status":"ok","timestamp":1620192764525,"user_tz":-540,"elapsed":12604,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"5f627cb9-5315-48f1-b5f1-c180d24df6d2"},"source":["!pip install albumentations==0.5.2"],"execution_count":6,"outputs":[{"output_type":"stream","text":["Collecting albumentations==0.5.2\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/03/58/63fb1d742dc42d9ba2800ea741de1f2bc6bb05548d8724aa84794042eaf2/albumentations-0.5.2-py3-none-any.whl (72kB)\n","\r\u001b[K |████▌ | 10kB 26.1MB/s eta 0:00:01\r\u001b[K |█████████ | 20kB 14.0MB/s eta 0:00:01\r\u001b[K |█████████████▋ | 30kB 12.1MB/s eta 0:00:01\r\u001b[K |██████████████████▏ | 40kB 11.9MB/s eta 0:00:01\r\u001b[K |██████████████████████▊ | 51kB 6.9MB/s eta 0:00:01\r\u001b[K |███████████████████████████▏ | 61kB 7.2MB/s eta 0:00:01\r\u001b[K |███████████████████████████████▊| 71kB 8.2MB/s eta 0:00:01\r\u001b[K |████████████████████████████████| 81kB 5.6MB/s \n","\u001b[?25hRequirement already satisfied: PyYAML in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (3.13)\n","Collecting imgaug>=0.4.0\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/66/b1/af3142c4a85cba6da9f4ebb5ff4e21e2616309552caca5e8acefe9840622/imgaug-0.4.0-py2.py3-none-any.whl (948kB)\n","\u001b[K |████████████████████████████████| 952kB 12.7MB/s \n","\u001b[?25hCollecting opencv-python-headless>=4.1.1\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/6d/6d/92f377bece9b0ec9c893081dbe073a65b38d7ac12ef572b8f70554d08760/opencv_python_headless-4.5.1.48-cp37-cp37m-manylinux2014_x86_64.whl (37.6MB)\n","\u001b[K |████████████████████████████████| 37.6MB 135kB/s \n","\u001b[?25hRequirement already satisfied: scikit-image>=0.16.1 in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (0.16.2)\n","Requirement already satisfied: numpy>=1.11.1 in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (1.19.5)\n","Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from albumentations==0.5.2) (1.4.1)\n","Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (7.1.2)\n","Requirement already satisfied: imageio in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (2.4.1)\n","Requirement already satisfied: opencv-python in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (4.1.2.30)\n","Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (3.2.2)\n","Requirement already satisfied: Shapely in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (1.7.1)\n","Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from imgaug>=0.4.0->albumentations==0.5.2) (1.15.0)\n","Requirement already satisfied: networkx>=2.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations==0.5.2) (2.5.1)\n","Requirement already satisfied: PyWavelets>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations==0.5.2) (1.1.1)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug>=0.4.0->albumentations==0.5.2) (0.10.0)\n","Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug>=0.4.0->albumentations==0.5.2) (2.8.1)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug>=0.4.0->albumentations==0.5.2) (1.3.1)\n","Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug>=0.4.0->albumentations==0.5.2) (2.4.7)\n","Requirement already satisfied: decorator<5,>=4.3 in /usr/local/lib/python3.7/dist-packages (from networkx>=2.0->scikit-image>=0.16.1->albumentations==0.5.2) (4.4.2)\n","Installing collected packages: imgaug, opencv-python-headless, albumentations\n"," Found existing installation: imgaug 0.2.9\n"," Uninstalling imgaug-0.2.9:\n"," Successfully uninstalled imgaug-0.2.9\n"," Found existing installation: albumentations 0.1.12\n"," Uninstalling albumentations-0.1.12:\n"," Successfully uninstalled albumentations-0.1.12\n","Successfully installed albumentations-0.5.2 imgaug-0.4.0 opencv-python-headless-4.5.1.48\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.826930Z","start_time":"2021-04-18T10:34:45.406686Z"},"colab":{"base_uri":"https://localhost:8080/"},"id":"l_LPA4XD-NwC","executionInfo":{"status":"ok","timestamp":1620192768210,"user_tz":-540,"elapsed":14595,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"ada42d5e-d182-49a9-b82f-e58705bf9660"},"source":["import os\n","import random\n","import time\n","import json\n","import warnings \n","warnings.filterwarnings('ignore')\n","\n","import torch\n","import torch.nn as nn\n","from torch.utils.data import Dataset, DataLoader\n","from utils import label_accuracy_score\n","import cv2\n","\n","import numpy as np\n","import pandas as pd\n","\n","# 전처리를 위한 라이브러리\n","from pycocotools.coco import COCO\n","import torchvision\n","import torchvision.transforms as transforms\n","\n","import albumentations as A\n","from albumentations.pytorch import ToTensorV2\n","\n","# 시각화를 위한 라이브러리\n","import matplotlib.pyplot as plt\n","import seaborn as sns; sns.set()\n","\n","plt.rcParams['axes.grid'] = False\n","\n","print('pytorch version: {}'.format(torch.__version__))\n","print('GPU 사용 가능 여부: {}'.format(torch.cuda.is_available()))\n","\n","print(torch.cuda.get_device_name(0))\n","print(torch.cuda.device_count())\n","\n","device = \"cuda\" if torch.cuda.is_available() else \"cpu\" # GPU 사용 가능 여부에 따라 device 정보 저장"],"execution_count":7,"outputs":[{"output_type":"stream","text":["pytorch version: 1.8.1+cu101\n","GPU 사용 가능 여부: True\n","Tesla P100-PCIE-16GB\n","1\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"HxgRSL_M-NwF"},"source":["## 하이퍼파라미터 세팅 및 seed 고정"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.841930Z","start_time":"2021-04-18T10:34:47.827931Z"},"id":"rV3JmGP5-NwF","executionInfo":{"status":"ok","timestamp":1620192770139,"user_tz":-540,"elapsed":1910,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["batch_size = 4 # Mini-batch size\n","num_epochs = 20\n","learning_rate = 0.0001"],"execution_count":8,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:47.856930Z","start_time":"2021-04-18T10:34:47.842931Z"},"id":"Z6LOuJXQ-NwG","executionInfo":{"status":"ok","timestamp":1620192770140,"user_tz":-540,"elapsed":1897,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# seed 고정\n","random_seed = 42\n","torch.manual_seed(random_seed)\n","torch.cuda.manual_seed(random_seed)\n","# torch.cuda.manual_seed_all(random_seed) # if use multi-GPU\n","torch.backends.cudnn.deterministic = True\n","torch.backends.cudnn.benchmark = False\n","np.random.seed(random_seed)\n","random.seed(random_seed)"],"execution_count":9,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"iWF_EJDu-NwG"},"source":["## 학습 데이터 EDA"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.381961Z","start_time":"2021-04-18T10:34:47.857930Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"lg0x0D0a-NwG","executionInfo":{"status":"ok","timestamp":1620192777089,"user_tz":-540,"elapsed":8806,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"44341c4b-1f33-40fe-a41b-35564ca5e4a6"},"source":["%matplotlib inline\n","\n","dataset_path = '../input/data'\n","anns_file_path = dataset_path + '/' + 'train.json'\n","\n","# Read annotations\n","with open(anns_file_path, 'r') as f:\n"," dataset = json.loads(f.read())\n","\n","categories = dataset['categories']\n","anns = dataset['annotations']\n","imgs = dataset['images']\n","nr_cats = len(categories)\n","nr_annotations = len(anns)\n","nr_images = len(imgs)\n","\n","# Load categories and super categories\n","cat_names = []\n","super_cat_names = []\n","super_cat_ids = {}\n","super_cat_last_name = ''\n","nr_super_cats = 0\n","for cat_it in categories:\n"," cat_names.append(cat_it['name'])\n"," super_cat_name = cat_it['supercategory']\n"," # Adding new supercat\n"," if super_cat_name != super_cat_last_name:\n"," super_cat_names.append(super_cat_name)\n"," super_cat_ids[super_cat_name] = nr_super_cats\n"," super_cat_last_name = super_cat_name\n"," nr_super_cats += 1\n","\n","print('Number of super categories:', nr_super_cats)\n","print('Number of categories:', nr_cats)\n","print('Number of annotations:', nr_annotations)\n","print('Number of images:', nr_images)"],"execution_count":10,"outputs":[{"output_type":"stream","text":["Number of super categories: 11\n","Number of categories: 11\n","Number of annotations: 21116\n","Number of images: 2617\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.546964Z","start_time":"2021-04-18T10:34:51.382969Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/","height":355},"id":"PjLyVHVY-NwH","executionInfo":{"status":"ok","timestamp":1620192777873,"user_tz":-540,"elapsed":9548,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"191ca058-b23d-4ed3-ea69-2066b4af34f2"},"source":["# Count annotations\n","cat_histogram = np.zeros(nr_cats,dtype=int)\n","for ann in anns:\n"," cat_histogram[ann['category_id']] += 1\n","\n","# Initialize the matplotlib figure\n","f, ax = plt.subplots(figsize=(5,5))\n","\n","# Convert to DataFrame\n","df = pd.DataFrame({'Categories': cat_names, 'Number of annotations': cat_histogram})\n","df = df.sort_values('Number of annotations', 0, False)\n","\n","# Plot the histogram\n","plt.title(\"category distribution of train set \")\n","plot_1 = sns.barplot(x=\"Number of annotations\", y=\"Categories\", data=df, label=\"Total\", color=\"b\")"],"execution_count":11,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.561965Z","start_time":"2021-04-18T10:34:51.547969Z"},"id":"34Tw5__i-NwI","executionInfo":{"status":"ok","timestamp":1620192777878,"user_tz":-540,"elapsed":9515,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# category labeling \n","sorted_temp_df = df.sort_index()\n","\n","# background = 0 에 해당되는 label 추가 후 기존들을 모두 label + 1 로 설정\n","sorted_df = pd.DataFrame([\"Backgroud\"], columns = [\"Categories\"])\n","sorted_df = sorted_df.append(sorted_temp_df, ignore_index=True)"],"execution_count":12,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:51.576961Z","start_time":"2021-04-18T10:34:51.562964Z"},"colab":{"base_uri":"https://localhost:8080/","height":421},"id":"CQk4vV5N-NwI","executionInfo":{"status":"ok","timestamp":1620192777879,"user_tz":-540,"elapsed":9456,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"4eced11c-6256-40f1-cfc1-5b729c11cd1c"},"source":["# class (Categories) 에 따른 index 확인 (0~11 : 총 12개)\n","sorted_df"],"execution_count":13,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
CategoriesNumber of annotations
0BackgroudNaN
1UNKNOWN128.0
2General trash2225.0
3Paper7448.0
4Paper pack527.0
5Metal449.0
6Glass488.0
7Plastic2472.0
8Styrofoam1074.0
9Plastic bag6114.0
10Battery50.0
11Clothing141.0
\n","
"],"text/plain":[" Categories Number of annotations\n","0 Backgroud NaN\n","1 UNKNOWN 128.0\n","2 General trash 2225.0\n","3 Paper 7448.0\n","4 Paper pack 527.0\n","5 Metal 449.0\n","6 Glass 488.0\n","7 Plastic 2472.0\n","8 Styrofoam 1074.0\n","9 Plastic bag 6114.0\n","10 Battery 50.0\n","11 Clothing 141.0"]},"metadata":{"tags":[]},"execution_count":13}]},{"cell_type":"markdown","metadata":{"id":"KXU0zmZs-NwI"},"source":["## 데이터 전처리 함수 정의 (Dataset)"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:52.693328Z","start_time":"2021-04-18T10:34:52.681328Z"},"id":"QFnTI8_Z-NwJ","executionInfo":{"status":"ok","timestamp":1620192777885,"user_tz":-540,"elapsed":9011,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["category_names = list(sorted_df.Categories)\n","\n","def get_classname(classID, cats):\n"," for i in range(len(cats)):\n"," if cats[i]['id']==classID:\n"," return cats[i]['name']\n"," return \"None\"\n","\n","class CustomDataLoader(Dataset):\n"," \"\"\"COCO format\"\"\"\n"," def __init__(self, data_dir, mode = 'train', transform = None):\n"," super().__init__()\n"," self.mode = mode\n"," self.transform = transform\n"," self.coco = COCO(data_dir)\n"," \n"," def __getitem__(self, index: int):\n"," # dataset이 index되어 list처럼 동작\n"," image_id = self.coco.getImgIds(imgIds=index)\n"," image_infos = self.coco.loadImgs(image_id)[0]\n"," \n"," # cv2 를 활용하여 image 불러오기\n"," images = cv2.imread(os.path.join(dataset_path, image_infos['file_name']))\n"," images = cv2.cvtColor(images, cv2.COLOR_BGR2RGB).astype(np.float32)\n"," images /= 255.0\n"," \n"," if (self.mode in ('train', 'val')):\n"," ann_ids = self.coco.getAnnIds(imgIds=image_infos['id'])\n"," anns = self.coco.loadAnns(ann_ids)\n","\n"," # Load the categories in a variable\n"," cat_ids = self.coco.getCatIds()\n"," cats = self.coco.loadCats(cat_ids)\n","\n"," # masks : size가 (height x width)인 2D\n"," # 각각의 pixel 값에는 \"category id + 1\" 할당\n"," # Background = 0\n"," masks = np.zeros((image_infos[\"height\"], image_infos[\"width\"]))\n"," # Unknown = 1, General trash = 2, ... , Cigarette = 11\n"," for i in range(len(anns)):\n"," className = get_classname(anns[i]['category_id'], cats)\n"," pixel_value = category_names.index(className)\n"," masks = np.maximum(self.coco.annToMask(anns[i])*pixel_value, masks)\n"," masks = masks.astype(np.float32)\n","\n"," # transform -> albumentations 라이브러리 활용\n"," if self.transform is not None:\n"," transformed = self.transform(image=images, mask=masks)\n"," images = transformed[\"image\"]\n"," masks = transformed[\"mask\"]\n"," \n"," return images, masks, image_infos\n"," \n"," if self.mode == 'test':\n"," # transform -> albumentations 라이브러리 활용\n"," if self.transform is not None:\n"," transformed = self.transform(image=images)\n"," images = transformed[\"image\"]\n"," \n"," return images, image_infos\n"," \n"," \n"," def __len__(self) -> int:\n"," # 전체 dataset의 size를 return\n"," return len(self.coco.getImgIds())"],"execution_count":14,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"cp2aIOlP-NwK"},"source":["## Dataset 정의 및 DataLoader 할당"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T10:34:58.823175Z","start_time":"2021-04-18T10:34:54.106233Z"},"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"P7zFlRn6-NwK","executionInfo":{"status":"ok","timestamp":1620192786411,"user_tz":-540,"elapsed":8488,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"639beb87-3f93-4e77-87b7-bef26ef50a09"},"source":["# train.json / validation.json / test.json 디렉토리 설정\n","train_path = dataset_path + '/train.json'\n","val_path = dataset_path + '/val.json'\n","test_path = dataset_path + '/test.json'\n","\n","# collate_fn needs for batch\n","def collate_fn(batch):\n"," return tuple(zip(*batch))\n","\n","train_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","val_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","test_transform = A.Compose([\n"," ToTensorV2()\n"," ])\n","\n","# create own Dataset 1 (skip)\n","# validation set을 직접 나누고 싶은 경우\n","# random_split 사용하여 data set을 8:2 로 분할\n","# train_size = int(0.8*len(dataset))\n","# val_size = int(len(dataset)-train_size)\n","# dataset = CustomDataLoader(data_dir=train_path, mode='train', transform=transform)\n","# train_dataset, val_dataset = torch.utils.data.random_split(dataset, [train_size, val_size])\n","\n","# create own Dataset 2\n","# train dataset\n","train_dataset = CustomDataLoader(data_dir=train_path, mode='train', transform=train_transform)\n","\n","# validation dataset\n","val_dataset = CustomDataLoader(data_dir=val_path, mode='val', transform=val_transform)\n","\n","# test dataset\n","test_dataset = CustomDataLoader(data_dir=test_path, mode='test', transform=test_transform)\n","\n","\n","# DataLoader\n","train_loader = torch.utils.data.DataLoader(dataset=train_dataset, \n"," batch_size=batch_size,\n"," shuffle=True,\n"," num_workers=4,\n"," collate_fn=collate_fn,\n"," drop_last=True)\n","\n","val_loader = torch.utils.data.DataLoader(dataset=val_dataset, \n"," batch_size=batch_size,\n"," shuffle=False,\n"," num_workers=4,\n"," collate_fn=collate_fn,\n"," drop_last=True) \n","\n","test_loader = torch.utils.data.DataLoader(dataset=test_dataset,\n"," batch_size=batch_size,\n"," num_workers=4,\n"," collate_fn=collate_fn)"],"execution_count":15,"outputs":[{"output_type":"stream","text":["loading annotations into memory...\n","Done (t=3.66s)\n","creating index...\n","index created!\n","loading annotations into memory...\n","Done (t=2.44s)\n","creating index...\n","index created!\n","loading annotations into memory...\n","Done (t=0.56s)\n","creating index...\n","index created!\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"8rqJiHb_-NwM"},"source":["# wandb"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"H1kHXm0uAX3R","executionInfo":{"status":"ok","timestamp":1620192795081,"user_tz":-540,"elapsed":16597,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"a503358c-42d5-4f0b-df1b-c1cb722cd43f"},"source":["!pip install wandb"],"execution_count":16,"outputs":[{"output_type":"stream","text":["Collecting wandb\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/67/5a/b037b50f9849212863a2fed313624d8f6f33ffa4ce89dc706e2a0e98c780/wandb-0.10.29-py2.py3-none-any.whl (2.1MB)\n","\u001b[K |████████████████████████████████| 2.1MB 7.9MB/s \n","\u001b[?25hRequirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.7/dist-packages (from wandb) 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\u001b[?25l\u001b[?25hdone\n"," Created wheel for subprocess32: filename=subprocess32-3.5.4-cp37-none-any.whl size=6489 sha256=65cbfe7a6af0b895738c0dc05ee439d221ae773871c89ec7fe4394efd45ab8ff\n"," Stored in directory: /root/.cache/pip/wheels/68/39/1a/5e402bdfdf004af1786c8b853fd92f8c4a04f22aad179654d1\n","Successfully built pathtools subprocess32\n","Installing collected packages: docker-pycreds, smmap, gitdb, GitPython, sentry-sdk, configparser, pathtools, subprocess32, shortuuid, wandb\n","Successfully installed GitPython-3.1.14 configparser-5.0.2 docker-pycreds-0.4.0 gitdb-4.0.7 pathtools-0.1.2 sentry-sdk-1.0.0 shortuuid-1.0.1 smmap-4.0.0 subprocess32-3.5.4 wandb-0.10.29\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":153},"id":"aMW4VV9V-NwM","executionInfo":{"status":"ok","timestamp":1620192806957,"user_tz":-540,"elapsed":11846,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"c1a48ed2-0317-4c74-d282-c563f9020ab3"},"source":["import wandb\n","\n","proj_name = 're_pan_effb7_noisy_focal_madgrad_cosLR'\n","\n","wandb.init(project='chanyub',name=proj_name)\n","\n","config = wandb.config\n","config.learning_rate = 0.01"],"execution_count":17,"outputs":[{"output_type":"display_data","data":{"application/javascript":["\n"," window._wandbApiKey = new Promise((resolve, reject) => {\n"," function loadScript(url) {\n"," return new Promise(function(resolve, reject) {\n"," let newScript = document.createElement(\"script\");\n"," newScript.onerror = reject;\n"," newScript.onload = resolve;\n"," document.body.appendChild(newScript);\n"," newScript.src = url;\n"," });\n"," }\n"," loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n"," const iframe = document.createElement('iframe')\n"," iframe.style.cssText = \"width:0;height:0;border:none\"\n"," document.body.appendChild(iframe)\n"," const handshake = new Postmate({\n"," container: iframe,\n"," url: 'https://wandb.ai/authorize'\n"," });\n"," const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n"," handshake.then(function(child) {\n"," child.on('authorize', data => {\n"," clearTimeout(timeout)\n"," resolve(data)\n"," });\n"," });\n"," })\n"," });\n"," "],"text/plain":[""]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"],"name":"stderr"},{"output_type":"display_data","data":{"text/html":["\n"," Tracking run with wandb version 0.10.29
\n"," Syncing run re_pan_effb7_noisy_focal_madgrad_cosLR to Weights & Biases (Documentation).
\n"," Project page: https://wandb.ai/pstage12/chanyub
\n"," Run page: https://wandb.ai/pstage12/chanyub/runs/147phguo
\n"," Run data is saved locally in /content/drive/My Drive/Trash/code/wandb/run-20210505_053323-147phguo

\n"," "],"text/plain":[""]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"uQRiIVGX-NwM"},"source":["## My model"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"S0xCGpNeAqeD","executionInfo":{"status":"ok","timestamp":1620192811916,"user_tz":-540,"elapsed":16789,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"bc894365-0de7-4540-9b6d-6f03ca245485"},"source":["!pip install segmentation_models_pytorch"],"execution_count":18,"outputs":[{"output_type":"stream","text":["Collecting segmentation_models_pytorch\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/65/54/8953f9f7ee9d451b0f3be8d635aa3a654579abf898d17502a090efe1155a/segmentation_models_pytorch-0.1.3-py3-none-any.whl (66kB)\n","\u001b[K |████████████████████████████████| 71kB 3.3MB/s \n","\u001b[?25hCollecting pretrainedmodels==0.7.4\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/84/0e/be6a0e58447ac16c938799d49bfb5fb7a80ac35e137547fc6cee2c08c4cf/pretrainedmodels-0.7.4.tar.gz (58kB)\n","\u001b[K |████████████████████████████████| 61kB 9.0MB/s \n","\u001b[?25hCollecting efficientnet-pytorch==0.6.3\n"," Downloading https://files.pythonhosted.org/packages/b8/cb/0309a6e3d404862ae4bc017f89645cf150ac94c14c88ef81d215c8e52925/efficientnet_pytorch-0.6.3.tar.gz\n","Requirement already satisfied: torchvision>=0.3.0 in /usr/local/lib/python3.7/dist-packages (from segmentation_models_pytorch) (0.9.1+cu101)\n","Collecting timm==0.3.2\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/51/2d/39ecc56fbb202e1891c317e8e44667299bc3b0762ea2ed6aaaa2c2f6613c/timm-0.3.2-py3-none-any.whl (244kB)\n","\u001b[K |████████████████████████████████| 245kB 34.4MB/s \n","\u001b[?25hRequirement already satisfied: torch in /usr/local/lib/python3.7/dist-packages (from 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munch->pretrainedmodels==0.7.4->segmentation_models_pytorch) (1.15.0)\n","Building wheels for collected packages: pretrainedmodels, efficientnet-pytorch\n"," Building wheel for pretrainedmodels (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for pretrainedmodels: filename=pretrainedmodels-0.7.4-cp37-none-any.whl size=60963 sha256=af4cddcaeaf9a3c0203640cf6adb9790b7c922b53a1dc32dcbeb6793f7f01c86\n"," Stored in directory: /root/.cache/pip/wheels/69/df/63/62583c096289713f22db605aa2334de5b591d59861a02c2ecd\n"," Building wheel for efficientnet-pytorch (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for efficientnet-pytorch: filename=efficientnet_pytorch-0.6.3-cp37-none-any.whl size=12420 sha256=c17898b621d04ad1e200c051a346098a4565bed0770ef9944c618019943cedfe\n"," Stored in directory: /root/.cache/pip/wheels/42/1e/a9/2a578ba9ad04e776e80bf0f70d8a7f4c29ec0718b92d8f6ccd\n","Successfully built pretrainedmodels efficientnet-pytorch\n","Installing collected packages: munch, pretrainedmodels, efficientnet-pytorch, timm, segmentation-models-pytorch\n","Successfully installed efficientnet-pytorch-0.6.3 munch-2.5.0 pretrainedmodels-0.7.4 segmentation-models-pytorch-0.1.3 timm-0.3.2\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:11.634792Z","start_time":"2021-04-18T16:16:05.875817Z"},"colab":{"base_uri":"https://localhost:8080/","height":137,"referenced_widgets":["00473af30c154d41ae9df37c7708e557","de4e269934224c70ad9d2726d012206b","1de038bc5bc741428eda494fb3b2a599","2209356c549e4b83b01427128dbf14c9","83555793b530424cbd482119cfdccf35","1ee3009936e24a0097fcd10b213b7a83","6a56fd48194549348af212ea4fb2eb9e","2380b07f3004417888657db63874145b"]},"id":"a8IfZfiM-NwM","executionInfo":{"status":"ok","timestamp":1620192838385,"user_tz":-540,"elapsed":42590,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"319b5a3a-5a4d-4855-c18e-24563136c681"},"source":["# 구현된 model에 임의의 input을 넣어 output이 잘 나오는지 test\n","import segmentation_models_pytorch as smp\n","\n","model = smp.PAN(encoder_name='timm-efficientnet-b7', encoder_weights='noisy-student', classes=12)\n","x = torch.randn([2, 3, 512, 512])\n","print(\"input shape : \", x.shape)\n","out = model(x).to(device)\n","print(\"output shape : \", out.size())\n","\n","model = model.to(device)"],"execution_count":19,"outputs":[{"output_type":"stream","text":["Downloading: \"https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b7_ns-1dbc32de.pth\" to /root/.cache/torch/hub/checkpoints/tf_efficientnet_b7_ns-1dbc32de.pth\n"],"name":"stderr"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"00473af30c154d41ae9df37c7708e557","version_minor":0,"version_major":2},"text/plain":["HBox(children=(FloatProgress(value=0.0, max=266853140.0), HTML(value='')))"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\n","input shape : torch.Size([2, 3, 512, 512])\n","output shape : torch.Size([2, 12, 512, 512])\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"zvDnI7_T-NwN"},"source":["## train, validation, test 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.104200Z","start_time":"2021-04-18T16:16:18.093174Z"},"id":"RA3oAapJ-NwN","executionInfo":{"status":"ok","timestamp":1620192843843,"user_tz":-540,"elapsed":928,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["def train(num_epochs, model, data_loader, val_loader, criterion, optimizer, saved_dir, val_every, device, lr_scheduler):\n"," print('Start training..')\n"," best_loss = 9999999\n"," best_miou = 0\n"," for epoch in range(num_epochs):\n"," model.train()\n"," for step, (images, masks, _) in enumerate(data_loader):\n"," images = torch.stack(images) # (batch, channel, height, width)\n"," masks = torch.stack(masks).long() # (batch, channel, height, width)\n"," \n"," # gpu 연산을 위해 device 할당\n"," images, masks = images.to(device), masks.to(device)\n"," \n"," # inference\n"," outputs = model(images)\n"," \n"," # loss 계산 (cross entropy loss)\n"," loss = criterion(outputs, masks)\n"," optimizer.zero_grad()\n"," loss.backward()\n"," optimizer.step()\n"," \n"," lr_scheduler.step()\n"," \n"," # step 주기에 따른 loss 출력\n"," if (step + 1) % 25 == 0:\n"," print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(\n"," epoch+1, num_epochs, step+1, len(train_loader), loss.item()))\n"," \n"," # validation 주기에 따른 loss 출력 및 best model 저장\n"," if (epoch + 1) % val_every == 0:\n","# avrg_loss = validation(epoch + 1, model, val_loader, criterion, device)\n","# if avrg_loss < best_loss:\n","# print('Best performance at epoch: {}'.format(epoch + 1))\n","# print('Save model in', saved_dir)\n","# best_loss = avrg_loss\n","# wandb.log({'best_loss': best_loss})\n","# save_model(model, saved_dir)\n"," avrg_miou = validation(epoch + 1, model, val_loader, criterion, device)\n"," if avrg_miou > best_miou:\n"," print('Best performance at epoch: {}'.format(epoch + 1))\n"," print('Save model in', saved_dir)\n"," best_miou = avrg_miou\n"," wandb.log({'best_miou': best_miou})\n"," save_model(model, saved_dir)"],"execution_count":20,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.285795Z","start_time":"2021-04-18T16:16:18.267686Z"},"id":"EulIikmq-NwO","executionInfo":{"status":"ok","timestamp":1620192845144,"user_tz":-540,"elapsed":1015,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["def validation(epoch, model, data_loader, criterion, device):\n"," print('Start validation #{}'.format(epoch))\n"," model.eval()\n"," with torch.no_grad():\n"," total_loss = 0\n"," cnt = 0\n"," mIoU_list = []\n"," for step, (images, masks, _) in enumerate(data_loader):\n"," \n"," images = torch.stack(images) # (batch, channel, height, width)\n"," masks = torch.stack(masks).long() # (batch, channel, height, width)\n","\n"," images, masks = images.to(device), masks.to(device) \n","\n"," outputs = model(images)\n"," loss = criterion(outputs, masks)\n"," total_loss += loss\n"," cnt += 1\n"," \n","# print(outputs.shape)\n","# print(masks.shape)\n","# wandb.log(wandb.Image(images, masks={\n","# \"predictions\" : {\n","# \"mask_data\" : torch.squeeze(torch.squeeze(outputs, 0),1),\n","# \"class_labels\" : classes_dict\n","# },\n","# \"ground_truth\" : {\n","# \"mask_data\" : torch.squeeze(masks, 0),\n","# \"class_labels\" : classes_dict\n","# }\n","# }))\n"," \n"," outputs = torch.argmax(outputs.squeeze(), dim=1).detach().cpu().numpy()\n","\n"," mIoU = label_accuracy_score(masks.detach().cpu().numpy(), outputs, n_class=12)[2]\n"," mIoU_list.append(mIoU)\n"," \n"," avrg_loss = total_loss / cnt\n"," avrg_mIoU = np.mean(mIoU_list)\n"," print('Validation #{} Average Loss: {:.4f}, mIoU: {:.4f}'.format(epoch, avrg_loss, np.mean(mIoU_list)))\n"," wandb.log({'Train Loss':loss.item(), 'Val Loss':avrg_loss , 'Val mIoU':np.mean(mIoU_list)})\n","# return avrg_loss\n"," return avrg_mIoU"],"execution_count":21,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"C_7CtFfH-NwO"},"source":["## 모델 저장 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:18.909918Z","start_time":"2021-04-18T16:16:18.898918Z"},"id":"gpCM5BFO-NwO","executionInfo":{"status":"ok","timestamp":1620192851447,"user_tz":-540,"elapsed":749,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# 모델 저장 함수 정의\n","val_every = 1 \n","\n","saved_dir = './saved'\n","if not os.path.isdir(saved_dir): \n"," os.mkdir(saved_dir)\n"," \n","def save_model(model, saved_dir, file_name='re_pan_effb7_noisy_focal_madgrad_cosLR.pt'):\n"," check_point = {'net': model.state_dict()}\n"," output_path = os.path.join(saved_dir, file_name)\n"," torch.save(model.state_dict(), output_path)"],"execution_count":22,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"FyKGeg8T-NwP"},"source":["## 모델 생성 및 Loss function, Optimizer 정의"]},{"cell_type":"code","metadata":{"id":"ORugl8s1-NwP","executionInfo":{"status":"ok","timestamp":1620192860535,"user_tz":-540,"elapsed":854,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["from torch.autograd import Variable\n","import torch.nn.functional as F\n","# ref : https://github.com/clcarwin/focal_loss_pytorch\n","class FocalLoss(nn.Module):\n"," def __init__(self, gamma=0, alpha=None, size_average=True):\n"," super(FocalLoss, self).__init__()\n"," self.gamma = gamma\n"," self.alpha = alpha\n"," if isinstance(alpha,(float,int)): self.alpha = torch.Tensor([alpha,1-alpha])\n"," if isinstance(alpha,list): self.alpha = torch.Tensor(alpha)\n"," self.size_average = size_average\n","\n"," def forward(self, input, target):\n"," if input.dim()>2:\n"," input = input.view(input.size(0),input.size(1),-1) # N,C,H,W => N,C,H*W\n"," input = input.transpose(1,2) # N,C,H*W => N,H*W,C\n"," input = input.contiguous().view(-1,input.size(2)) # N,H*W,C => N*H*W,C\n"," target = target.view(-1,1)\n","\n"," logpt = F.log_softmax(input)\n"," logpt = logpt.gather(1,target)\n"," logpt = logpt.view(-1)\n"," pt = Variable(logpt.data.exp())\n","\n"," if self.alpha is not None:\n"," if self.alpha.type()!=input.data.type():\n"," self.alpha = self.alpha.type_as(input.data)\n"," at = self.alpha.gather(0,target.data.view(-1))\n"," logpt = logpt * Variable(at)\n","\n"," loss = -1 * (1-pt)**self.gamma * logpt\n"," if self.size_average: return loss.mean()\n"," else: return loss.sum()"],"execution_count":23,"outputs":[]},{"cell_type":"code","metadata":{"id":"yfeFQknz-NwP"},"source":["import torch.optim.lr_scheduler as lr_scheduler\n","import math\n","class CosineAnnealingWarmUpRestart(lr_scheduler._LRScheduler):\n"," def __init__(self, optimizer, T_0, T_mult=1, eta_max=0.1, T_up=0, gamma=1., last_epoch=-1):\n"," if T_0 <= 0 or not isinstance(T_0, int):\n"," raise ValueError(\"Expected positive integer T_0, but got {}\".format(T_0))\n"," if T_mult < 1 or not isinstance(T_mult, int):\n"," raise ValueError(\"Expected integer T_mult >= 1, but got {}\".format(T_mult))\n"," if T_up < 0 or not isinstance(T_up, int):\n"," raise ValueError(\"Expected positive integer T_up, but got {}\".format(T_up))\n"," self.T_0 = T_0\n"," self.T_mult = T_mult\n"," self.base_eta_max = eta_max\n"," self.eta_max = eta_max\n"," self.T_up = T_up\n"," self.T_i = T_0\n"," self.gamma = gamma\n"," self.cycle = 0\n"," self.T_cur = last_epoch\n"," super(CosineAnnealingWarmUpRestart, self).__init__(optimizer, last_epoch)\n"," # self.T_cur = last_epoch\n"," \n"," def get_lr(self):\n"," if self.T_cur == -1:\n"," return self.base_lrs\n"," elif self.T_cur < self.T_up:\n"," return [(self.eta_max - base_lr)*self.T_cur / self.T_up + base_lr for base_lr in self.base_lrs]\n"," else:\n"," return [base_lr + (self.eta_max - base_lr) * (1 + math.cos(math.pi * (self.T_cur-self.T_up) / (self.T_i - self.T_up))) / 2\n"," for base_lr in self.base_lrs]\n","\n"," def step(self, epoch=None):\n"," if epoch is None:\n"," epoch = self.last_epoch + 1\n"," self.T_cur = self.T_cur + 1\n"," if self.T_cur >= self.T_i:\n"," self.cycle += 1\n"," self.T_cur = self.T_cur - self.T_i\n"," self.T_i = (self.T_i - self.T_up) * self.T_mult + self.T_up\n"," else:\n"," if epoch >= self.T_0:\n"," if self.T_mult == 1:\n"," self.T_cur = epoch % self.T_0\n"," self.cycle = epoch // self.T_0\n"," else:\n"," n = int(math.log((epoch / self.T_0 * (self.T_mult - 1) + 1), self.T_mult))\n"," self.cycle = n\n"," self.T_cur = epoch - self.T_0 * (self.T_mult ** n - 1) / (self.T_mult - 1)\n"," self.T_i = self.T_0 * self.T_mult ** (n)\n"," else:\n"," self.T_i = self.T_0\n"," self.T_cur = epoch\n"," \n"," self.eta_max = self.base_eta_max * (self.gamma**self.cycle)\n"," self.last_epoch = math.floor(epoch)\n"," for param_group, lr in zip(self.optimizer.param_groups, self.get_lr()):\n"," param_group['lr'] = lr"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AG1oQeu7BX1M","executionInfo":{"status":"ok","timestamp":1620060019354,"user_tz":-540,"elapsed":3477,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"98eae7c4-b66e-409d-9725-0c684d747f2a"},"source":["# !pip install adamp"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Requirement already satisfied: adamp in /usr/local/lib/python3.7/dist-packages (0.3.0)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"eH2PpwF9a-Os","executionInfo":{"status":"ok","timestamp":1620192868472,"user_tz":-540,"elapsed":3509,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"cdfcd1b2-a905-4ce9-ad56-d6cfee888431"},"source":["!pip install madgrad"],"execution_count":24,"outputs":[{"output_type":"stream","text":["Collecting madgrad\n"," Downloading https://files.pythonhosted.org/packages/65/f0/4584f18202a2fb8903d456bf907b80e7cb54ad8fcba68604084ff41b7cf8/madgrad-1.1-py3-none-any.whl\n","Installing collected packages: madgrad\n","Successfully installed madgrad-1.1\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-18T16:16:19.698902Z","start_time":"2021-04-18T16:16:19.694902Z"},"id":"9Dly8KZj-NwQ","executionInfo":{"status":"ok","timestamp":1620192882165,"user_tz":-540,"elapsed":743,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}}},"source":["# from adamp import AdamP\n","from madgrad import MADGRAD\n","# Loss function 정의\n","# criterion = nn.CrossEntropyLoss()\n","criterion = FocalLoss()\n","\n","# Optimizer 정의\n","# optimizer = torch.optim.Adam(params = model.parameters(), lr = learning_rate, weight_decay=1e-6)\n","# optimizer = AdamP(params = model.parameters())\n","optimizer = MADGRAD(params = model.parameters(), lr = learning_rate, momentum = 0.9, weight_decay = 0, eps = 1e-06)\n","\n","# lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size = 237, gamma = 0.65)\n","lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=50, eta_min=0)\n","# lr_scheduler = CosineAnnealingWarmUpRestart(optimizer, T_0=150, T_mult=1, eta_max=0.1, T_up=10, gamma=0.5)"],"execution_count":25,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"start_time":"2021-04-18T16:16:20.331Z"},"colab":{"base_uri":"https://localhost:8080/","height":375},"id":"cMSiFA-3-NwR","executionInfo":{"status":"error","timestamp":1620192900627,"user_tz":-540,"elapsed":17700,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"84406229-d938-4538-82a5-78f83a30f46e"},"source":["train(num_epochs, model, train_loader, val_loader, criterion, optimizer, saved_dir, val_every, device, lr_scheduler)"],"execution_count":26,"outputs":[{"output_type":"stream","text":["Start training..\n"],"name":"stdout"},{"output_type":"error","ename":"RuntimeError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)","\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_epochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_loader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_loader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msaved_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_every\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr_scheduler\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(num_epochs, model, data_loader, val_loader, criterion, optimizer, saved_dir, val_every, device, lr_scheduler)\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;31m# inference\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimages\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0;31m# loss 계산 (cross entropy loss)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 887\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 888\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 889\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 889\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 890\u001b[0m for hook in itertools.chain(\n\u001b[1;32m 891\u001b[0m \u001b[0m_global_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 397\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 398\u001b[0m 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\u001b[0mConv3d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_ConvNd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py\u001b[0m in \u001b[0;36m_conv_forward\u001b[0;34m(self, input, weight, bias)\u001b[0m\n\u001b[1;32m 394\u001b[0m _pair(0), self.dilation, self.groups)\n\u001b[1;32m 395\u001b[0m return F.conv2d(input, weight, bias, self.stride,\n\u001b[0;32m--> 396\u001b[0;31m self.padding, self.dilation, self.groups)\n\u001b[0m\u001b[1;32m 397\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 398\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mRuntimeError\u001b[0m: CUDA out of memory. Tried to allocate 36.00 MiB (GPU 0; 15.90 GiB total capacity; 14.92 GiB already allocated; 43.75 MiB free; 14.98 GiB reserved in total by PyTorch)"]}]},{"cell_type":"markdown","metadata":{"id":"C6ClcO0J-NwR"},"source":["## 저장된 model 불러오기 (학습된 이후) "]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:21.050200Z","start_time":"2021-04-16T19:44:20.802200Z"},"scrolled":true,"id":"KQPtUDzd-NwR","colab":{"base_uri":"https://localhost:8080/","height":232},"executionInfo":{"status":"error","timestamp":1620103401786,"user_tz":-540,"elapsed":856,"user":{"displayName":"신찬엽","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjIFu8yE_RMQcdyWMMm8-WUcSPg1PVDbrOp0NO6Yg=s64","userId":"00918694046621707424"}},"outputId":"91ea2508-84d8-4234-80da-f8139bf02f5b"},"source":["# best model 저장된 경로\n","model_path = './saved/pan_effb3_noisy_focal_madgrad_stepLR.pt'\n","\n","# best model 불러오기\n","checkpoint = torch.load(model_path, map_location=device)\n","model.load_state_dict(checkpoint)\n","\n","# 추론을 실행하기 전에는 반드시 설정 (batch normalization, dropout 를 평가 모드로 설정)\n","# model.eval()"],"execution_count":null,"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)","\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m# best model 불러오기\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mcheckpoint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmap_location\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_state_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcheckpoint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNameError\u001b[0m: name 'torch' is not defined"]}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:24.939227Z","start_time":"2021-04-16T19:44:24.518228Z"},"id":"0LQqrDAp-NwR"},"source":["# 첫번째 batch의 추론 결과 확인\n","for imgs, image_infos in test_loader:\n"," image_infos = image_infos\n"," temp_images = imgs\n"," \n"," model.eval()\n"," # inference\n"," outs = model(torch.stack(temp_images).to(device))\n"," oms = torch.argmax(outs.squeeze(), dim=1).detach().cpu().numpy()\n"," \n"," break\n","\n","i = 3\n","fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(16, 16))\n","\n","print('Shape of Original Image :', list(temp_images[i].shape))\n","print('Shape of Predicted : ', list(oms[i].shape))\n","print('Unique values, category of transformed mask : \\n', [{int(i),category_names[int(i)]} for i in list(np.unique(oms[i]))])\n","\n","# Original image\n","ax1.imshow(temp_images[i].permute([1,2,0]))\n","ax1.grid(False)\n","ax1.set_title(\"Original image : {}\".format(image_infos[i]['file_name']), fontsize = 15)\n","\n","# Predicted\n","ax2.imshow(oms[i])\n","ax2.grid(False)\n","ax2.set_title(\"Predicted : {}\".format(image_infos[i]['file_name']), fontsize = 15)\n","\n","plt.show()"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"evYjR2F3-NwS"},"source":["## submission을 위한 test 함수 정의"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:44:27.469285Z","start_time":"2021-04-16T19:44:27.456021Z"},"id":"nhMLnV5d-NwS"},"source":["def test(model, data_loader, device):\n"," size = 256\n"," transform = A.Compose([A.Resize(256, 256)])\n"," print('Start prediction.')\n"," model.eval()\n"," \n"," file_name_list = []\n"," preds_array = np.empty((0, size*size), dtype=np.long)\n"," \n"," with torch.no_grad():\n"," for step, (imgs, image_infos) in enumerate(test_loader):\n","\n"," # inference (512 x 512)\n"," outs = model(torch.stack(imgs).to(device))\n"," oms = torch.argmax(outs.squeeze(), dim=1).detach().cpu().numpy()\n"," \n"," # resize (256 x 256)\n"," temp_mask = []\n"," for img, mask in zip(np.stack(imgs), oms):\n"," transformed = transform(image=img, mask=mask)\n"," mask = transformed['mask']\n"," temp_mask.append(mask)\n","\n"," oms = np.array(temp_mask)\n"," \n"," oms = oms.reshape([oms.shape[0], size*size]).astype(int)\n"," preds_array = np.vstack((preds_array, oms))\n"," \n"," file_name_list.append([i['file_name'] for i in image_infos])\n"," print(\"End prediction.\")\n"," file_names = [y for x in file_name_list for y in x]\n"," \n"," return file_names, preds_array"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"r1YKHBf4-NwT"},"source":["## submission.csv 생성"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2021-04-16T19:45:42.235310Z","start_time":"2021-04-16T19:44:30.499016Z"},"scrolled":true,"id":"Bz79_g8K-NwT"},"source":["# sample_submisson.csv 열기\n","submission = pd.read_csv('./submission/sample_submission.csv', index_col=None)\n","\n","# test set에 대한 prediction\n","file_names, preds = test(model, test_loader, device)\n","\n","# PredictionString 대입\n","for file_name, string in zip(file_names, preds):\n"," submission = submission.append({\"image_id\" : file_name, \"PredictionString\" : ' '.join(str(e) for e in string.tolist())}, \n"," ignore_index=True)\n","\n","# submission.csv로 저장\n","submission.to_csv(\"./submission/pan_effb3_noisy_focal_adamp_cosLR.csv\", index=False)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Zgl7Ye7F-NwT"},"source":["## 제출까지\n","\n"]},{"cell_type":"code","metadata":{"id":"022E8AC7-NwT"},"source":["import json\n","import requests\n","import os\n","from urllib.parse import urlparse, parse_qsl, urlencode, urlunparse\n","\n","def submit(user_key='', file_path = '', desc=\"\"):\n"," if not user_key:\n"," raise Exception(\"No UserKey\" )\n"," url = urlparse('http://ec2-13-124-161-225.ap-northeast-2.compute.amazonaws.com:8000/api/v1/competition/28/presigned_url/?description=&hyperparameters={%22training%22:{},%22inference%22:{}}')\n"," qs = dict(parse_qsl(url.query))\n"," qs['description'] = desc\n"," parts = url._replace(query=urlencode(qs))\n"," url = urlunparse(parts)\n","\n"," print(url)\n"," headers = {\n"," 'Authorization': user_key\n"," }\n"," res = requests.get(url, headers=headers)\n"," print(res.text)\n"," data = json.loads(res.text)\n"," \n"," submit_url = data['url']\n"," body = {\n"," 'key':'app/Competitions/000028/Users/{}/Submissions/{}/output.csv'.format(str(data['submission']['user']).zfill(8),str(data['submission']['local_id']).zfill(4)),\n"," 'x-amz-algorithm':data['fields']['x-amz-algorithm'],\n"," 'x-amz-credential':data['fields']['x-amz-credential'],\n"," 'x-amz-date':data['fields']['x-amz-date'],\n"," 'policy':data['fields']['policy'],\n"," 'x-amz-signature':data['fields']['x-amz-signature']\n"," }\n"," requests.post(url=submit_url, data=body, files={'file': open(file_path, 'rb')})\n","\n","\n","####################################################################################\n","test_dir = \"/opt/ml/code/submission\" # 수정 필요 : output 파일 폴더 \n","desc = 'pan_effb3_noisy_focal_adamp_cosLR' # 수정 필요 : 파일에 대한 설명\n","output_file = \"pan_effb3_noisy_focal_adamp_cosLR.csv\" #수정 필요 : output 파일 \n","user_key = \"Bearer 7bb5f96452751a238ffaf91a93c4242bf9b72abe\" # 수정 필요 : Authorization \n","\n","\n","submit(user_key, os.path.join(test_dir, output_file),desc)"],"execution_count":null,"outputs":[]}]} \ No newline at end of file