parser.add_argument('--inception_a', type=str, default='.', help="path to the pretrained inception network for domain A") parser.add_argument('--inception_b', type=str, default='.', help="path to the pretrained inception network for domain B") opts = parser.parse_args() torch.manual_seed(opts.seed) torch.cuda.manual_seed(opts.seed) # Load experiment setting config = get_config(opts.config) input_dim = config['input_dim_a'] if opts.a2b else config['input_dim_b'] # Load the inception networks if we need to compute IS or CIIS if opts.compute_IS or opts.compute_IS: inception = load_inception(opts.inception_b) if opts.a2b else load_inception(opts.inception_a) # freeze the inception models and set eval mode inception.eval() for param in inception.parameters(): param.requires_grad = False inception_up = nn.Upsample(size=(299, 299), mode='bilinear') # Setup model and data loader image_names = ImageFolder(opts.input_folder, transform=None, return_paths=True) data_loader = get_data_loader_folder(opts.input_folder, 1, False, new_size=config['new_size'], crop=False) config['vgg_model_path'] = opts.output_path if opts.trainer == 'aclgan': style_dim = config['gen']['style_dim'] trainer = aclgan_Trainer(config) else:
) opts = parser.parse_args() torch.manual_seed(opts.seed) torch.cuda.manual_seed(opts.seed) # Load experiment setting config = get_config(opts.config) input_dim = config["input_dim_a"] if opts.a2b else config["input_dim_b"] # Load the inception networks if we need to compute IS or CIIS if opts.compute_IS or opts.compute_IS: inception = ( load_inception(opts.inception_b) if opts.a2b else load_inception(opts.inception_a) ) # freeze the inception models and set eval mode inception.eval() for param in inception.parameters(): param.requires_grad = False inception_up = nn.Upsample(size=(299, 299), mode="bilinear") # Setup model and data loader image_names = ImageFolder(opts.input_folder, transform=None, return_paths=True) data_loader = get_data_loader_folder( opts.input_folder, 1, False, new_size=config["new_size_a"], crop=False )