loss_name = loss.__class__.__name__
print(f"Loss: {loss_name}\n")
for noise_value in noise_values:
# RUN Experiments
name = f'CNN_{loss_name}_{tp_noise}_{noise_value}'
print(f"Training {name} with noise of type {tp_noise} and probability {noise_value}...")
# data preparation
dataset = FashionMnistHandler(data_dir, False)
dataset.load()
train_loader, val_loader, test_loader = dataset.get_noisy_loaders(p_noise=noise_value,
type_noise=tp_noise,
val_size=1 / 6,
train_batch_size=batch_size,
val_batch_size=128,
test_batch_size=128)
# model, optimizer, summary
model = CNNModel()
optimizer = torch.optim.Adam(model.parameters(), lr=lr)
summ = Summary(name, type_noise=tp_noise, noise_rate=noise_value)
solver = Solver(name, PROJECT_DIR, batch_model_dir, batch_summaries_dir, model,
optimizer, loss, summ, train_loader, val_loader, test_loader)
solver.pretrain()
solver.train(loss)
print(f"Completed training...")
args = parser.parse_args()
args, lg = parse(args)
# Tensorboard save directory
resume = args['solver']['resume']
tensorboard_path = 'Tensorboard/{}'.format(args['name'])
if resume == False:
if osp.exists(tensorboard_path):
shutil.rmtree(tensorboard_path, True)
lg.info('Remove dir: [{}]'.format(tensorboard_path))
writer = SummaryWriter(tensorboard_path)
# create dataset
train_data = DIV2K(args['datasets']['train'])
lg.info('Create train dataset successfully!')
lg.info('Training: [{}] iterations for each epoch'.format(len(train_data)))
val_data = DIV2K(args['datasets']['val'])
lg.info('Create val dataset successfully!')
lg.info('Validating: [{}] iterations for each epoch'.format(len(val_data)))
# create solver
lg.info('Preparing for experiment: [{}]'.format(args['name']))
solver = Solver(args, train_data, val_data, writer)
# train
lg.info('Start training...')
solver.train()
