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
)