sys.stdout = log_file
print('+' * 80)
print(model_name)
print('+' * 80)
print(args.__dict__)
print('+' * 80)
init_path = '{}/{}/{}_{}.init'.format(ckpt_path, 'init', args.dataset,
args.clf)
best_path = os.path.join(ckpt_path, folder, 'models', model_name + '.best')
stop_path = os.path.join(ckpt_path, folder, 'models', model_name + '.stop')
if args.batch_size == 0:
args.batch_size = args.num_train
print("Resetting batch size: {}...".format(args.batch_size))
train_loader = get_trainloader(args.dataset, args.batch_size, False)
test_loader = get_testloader(args.dataset,
args.test_batch_size,
noise=args.noise)
print('+' * 80)
# Fire the engines
# Fire the engines
if args.clf == 'fcn':
print('Initializing FCN...')
model = FCN(args.input_size, args.output_size)
elif args.clf == 'svm':
print('Initializing SVM...')
if __name__ == '__main__':
args = Arguments().parser().parse_args()
args.device = torch.device(
'cuda:' + str(args.gpu) if torch.cuda.is_available() else 'cpu')
torch.cuda.set_device(args.device)
SAVE_IMAGES = args.save_images
SAVE_PTS_FILES = args.save_pts_files
## Code for testing
if args.train == "False":
args.train = False
if args.ckpt_load is None:
raise Exception("No checkpoint path provided!")
LOAD_CHECKPOINT = os.path.join(args.ckpt_path, args.ckpt_load)
args.batch_size = 1 # for interpolation!
model = TreeGAN(args)
model.interpolation(load_ckpt=LOAD_CHECKPOINT,
save_images=SAVE_IMAGES,
save_pts_files=SAVE_PTS_FILES)
## Code for training
else:
args.train = True
if args.ckpt_load == "None":
args.ckpt_load = None
SAVE_CHECKPOINT = os.path.join(
args.ckpt_path,
args.ckpt_save) if args.ckpt_save is not None else None
LOAD_CHECKPOINT = os.path.join(
args.ckpt_path,
