check.py

from __future__ import print_function
import argparse
from math import log10

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from model import Net
from data import get_training_set, get_test_set

# Training settings
parser = argparse.ArgumentParser(description='PyTorch Super Res Example')
parser.add_argument('--upscale_factor', type=int, required=True, help="super resolution upscale factor")
parser.add_argument('--batchSize', type=int, default=64, help='training batch size')
parser.add_argument('--testBatchSize', type=int, default=10, help='testing batch size')
parser.add_argument('--nEpochs', type=int, default=2, help='number of epochs to train for')
parser.add_argument('--lr', type=float, default=0.01, help='Learning Rate. Default=0.01')
parser.add_argument('--cuda', action='store_true', help='use cuda?')
parser.add_argument('--threads', type=int, default=4, help='number of threads for data loader to use')
parser.add_argument('--seed', type=int, default=123, help='random seed to use. Default=123')
opt = parser.parse_args()

print(opt)

if opt.cuda and not torch.cuda.is_available():
    raise Exception("No GPU found, please run without --cuda")

torch.manual_seed(opt.seed)

device = torch.device("cuda" if opt.cuda else "cpu")

print('===> Loading datasets')
train_set = get_training_set(opt.upscale_factor)
test_set = get_test_set(opt.upscale_factor)
training_data_loader = DataLoader(dataset=train_set, num_workers=opt.threads, batch_size=opt.batchSize, shuffle=True)
testing_data_loader = DataLoader(dataset=test_set, num_workers=opt.threads, batch_size=opt.testBatchSize, shuffle=False)
print(train_set)