utils.py

# Copyright 2017 The BEGAN-tensorflow Authors(Taehoon Kim). All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
from __future__ import print_function

import os
import math
import json
import logging
import numpy as np
from PIL import Image
from datetime import datetime

def prepare_dirs_and_logger(config):
    formatter = logging.Formatter("%(asctime)s:%(levelname)s::%(message)s")
    logger = logging.getLogger()

    for hdlr in logger.handlers:
        logger.removeHandler(hdlr)

    handler = logging.StreamHandler()
    handler.setFormatter(formatter)

    logger.addHandler(handler)

    if config.load_path:
        if config.load_path.startswith(config.log_dir):
            config.model_dir = config.load_path
        else:
            if config.load_path.startswith(config.dataset):
                config.model_name = config.load_path
            else:
                config.model_name = "{}_{}".format(config.dataset, config.load_path)
    else:
        config.model_name = "{}_{}".format(config.dataset, get_time())

    if not hasattr(config, 'model_dir'):
        config.model_dir = os.path.join(config.log_dir, config.model_name)
    config.data_path = os.path.join(config.data_dir, config.dataset)

    for path in [config.log_dir, config.data_dir, config.model_dir]:
        if not os.path.exists(path):
            os.makedirs(path)

def get_time():
    return datetime.now().strftime("%m%d_%H%M%S")

def save_config(config):
    param_path = os.path.join(config.model_dir, "params.json")

    print("[*] MODEL dir: %s" % config.model_dir)
    print("[*] PARAM path: %s" % param_path)

    with open(param_path, 'w') as fp:
        json.dump(config.__dict__, fp, indent=4, sort_keys=True)

def rank(array):
    return len(array.shape)

def make_grid(tensor, nrow=8, padding=2,
              normalize=False, scale_each=False,is_train=True):
    """Code based on https://github.com/pytorch/vision/blob/master/torchvision/utils.py"""
    if is_train:
        scale = 1
    else:
        scale = 0
    nmaps = tensor.shape[0]
    xmaps = min(nrow, nmaps)
    ymaps = int(math.ceil(float(nmaps) / xmaps))
    height, width = int(tensor.shape[1] + padding), int(tensor.shape[2] + padding)

    grid = np.zeros([height * ymaps + scale + padding // 2, width * xmaps + scale + padding // 2, 3], dtype=np.uint8)
    k = 0
    for y in range(ymaps):
        for x in range(xmaps):
            if k >= nmaps:
                break
            h, h_width = y * height + scale + padding // 2, height - padding
            w, w_width = x * width + scale + padding // 2, width - padding

            grid[h:h+h_width, w:w+w_width] = tensor[k]
            k = k + 1
    return grid

def save_image(tensor, filename, nrow=8, padding=2,
               normalize=False, scale_each=False,is_train=True):
    ndarr = make_grid(tensor, nrow=nrow, padding=padding,
                            normalize=normalize, scale_each=scale_each,is_train=is_train)
    im = Image.fromarray(ndarr)
    im.save(filename)