use_M=use_M,
binary=binary)
val_MD = DS.MD(path=gp_path + '/images/' + mask_folder + '/setup' +
str(setup) + '/val',
H=H,
W=W,
pow_n=pow_n,
aug=False,
use_M=use_M,
binary=False)
dataloaders = {
'train':
DS.DataLoader(train_MD,
batch_size=batch_size,
shuffle=True,
num_workers=8,
pin_memory=True),
'val':
DS.DataLoader(val_MD,
batch_size=batch_size,
shuffle=True,
num_workers=8,
pin_memory=True)
}
num_class = 37
n_input = 1
if Attu == True:
model = network.attention_unet.AttU_Net(n_input, num_class).to(device)
else:
if args.pretrain_gen is not None: generator = generator_pretrain.train(generator, dataset, args) generator.saveModel(generator.pretrained_path) # Discriminator pretraining if args.pretrain_dis is not None: discriminator = discriminator_pretrain.train(discriminator, dataset, args) discriminator.saveModel(discriminator.pretrained_path) # Main Training if not args.no_train: # TRAINING generator.train() discriminator.train() training_progress = tqdm(total = len(dataset) * args.epochs, desc = "Training") training_iterator = dataset.DataLoader(len(dataset), batch_size=args.batch_size) try: for epoch in range(int(args.epochs)): for real_sample in training_iterator: fake_sample = generator.generate(args.batch_size) print(dataset.decode(fake_sample)) print(dataset.decode(real_sample)) input() # update the progress bar training_progress.update(args.batch_size) # take outputs from discriminator score_real = discriminator(real_sample) score_fake = discriminator(fake_sample)
