best_val = float('inf')
best_name = args.save_folder + "/" + args.save_name.format(architecture=args.architecture, epoch="best", dataset_name=args.dataset_name)
for epoch in range(args.start_epochs - 1, args.epochs):
epoch_1 = epoch + 1
loss_train = train(epoch_1)
if args.iterations_val > 0:
loss_val = validation(epoch_1)
else:
loss_val = loss_train
if best_val > loss_val:
is_best = True
best_val = loss_val
else:
is_best = False
if has_tensorboardX:
threading.Thread(target= lambda: train_writer.add_scalar('Loss', loss_train, int(epoch_1))).start()
if args.iterations_val > 0:
threading.Thread(target= lambda: val_writer.add_scalar('Loss', loss_val, int(epoch_1))).start()
if epoch_1 % args.save_interval == 0:
save_checkpoint({
'epoch': epoch_1,
'model': model_net.save_model(),
'best_loss': best_val,
}, is_best, args.save_folder + "/" + args.save_name.format(architecture=args.architecture, epoch=epoch_1, dataset_name=args.dataset_name))
