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,