def train(args):
assets = AssetManager(args.base_dir)
if not args.retrain:
model_dir = assets.recreate_model_dir(args.model_name)
tensorboard_dir = assets.recreate_tensorboard_dir(args.model_name)
else:
model_dir = assets.get_model_dir(args.model_name)
tensorboard_dir = assets.get_tensorboard_dir(args.model_name)
data = np.load(assets.get_preprocess_file_path(args.data_name))
imgs = data['imgs'].astype(np.float32) / 255.0
config = dict(
img_shape=imgs.shape[1:],
n_imgs=imgs.shape[0],
n_classes=data['n_classes'].item(),
)
config.update(base_config)
lord = Lord(config)
if args.retrain:
lord.load(model_dir, latent=True, amortized=False)
lord.config = config
lord.train_latent(imgs=imgs,
classes=data['classes'],
model_dir=model_dir,
tensorboard_dir=tensorboard_dir,
retrain=args.retrain)
lord.save(model_dir, latent=True, amortized=False)
def train(args):
assets = AssetManager(args.base_dir)
model_dir = assets.recreate_model_dir(args.model_name)
tensorboard_dir = assets.recreate_tensorboard_dir(args.model_name)
data = np.load(assets.get_preprocess_file_path(args.data_name))
imgs = data['imgs'].astype(np.float32) / 255.0
config = Config(
img_shape=imgs.shape[1:],
n_imgs=imgs.shape[0],
n_classes=data['n_classes'].item(),
content_dim=default_config['content_dim'],
class_dim=default_config['class_dim'],
content_std=default_config['content_std'],
content_decay=default_config['content_decay'],
n_adain_layers=default_config['n_adain_layers'],
adain_dim=default_config['adain_dim'],
perceptual_loss_layers=default_config['perceptual_loss']['layers'],
perceptual_loss_weights=default_config['perceptual_loss']['weights'],
perceptual_loss_scales=default_config['perceptual_loss']['scales'])
lord = Lord.build(config)
lord.train(imgs=imgs,
classes=data['classes'],
batch_size=default_config['train']['batch_size'],
n_epochs=default_config['train']['n_epochs'],
model_dir=model_dir,
tensorboard_dir=tensorboard_dir)
lord.save(model_dir)
def train(args):
wandb.config.update(default_config)
args_dict = vars(args)
args_dict.pop('func')
wandb.config.update(args_dict)
assets = AssetManager(args.base_dir)
model_dir = assets.recreate_model_dir(args.model_name)
tensorboard_dir = assets.recreate_tensorboard_dir(args.model_name)
data = np.load(assets.get_preprocess_file_path(args.data_name))
imgs, classes, contents, n_classes = data['imgs'], data['classes'], data['contents'], data['n_classes']
imgs = imgs.astype(np.float32) / 255.0
converter = Converter.build(
img_shape=imgs.shape[1:],
n_imgs=imgs.shape[0],
n_classes=n_classes,
content_dim=args.content_dim,
class_dim=args.class_dim,
content_std=args.content_std,
content_decay=args.content_decay,
n_adain_layers=default_config['n_adain_layers'],
adain_enabled=args.adain,
adain_dim=default_config['adain_dim'],
adain_normalize=args.adain_normalize,
perceptual_loss_layers=default_config['perceptual_loss']['layers'],
perceptual_loss_weights=default_config['perceptual_loss']['weights'],
perceptual_loss_scales=default_config['perceptual_loss']['scales'],
)
converter.train(
imgs=imgs,
classes=classes,
batch_size=default_config['train']['batch_size'],
n_epochs=default_config['train']['n_epochs'],
model_dir=model_dir,
tensorboard_dir=tensorboard_dir
)
converter.save(model_dir)
def train(args):
assets = AssetManager(args.base_dir)
model_dir = assets.recreate_model_dir(args.model_name)
tensorboard_dir = assets.recreate_tensorboard_dir(args.model_name)
data = np.load(assets.get_preprocess_file_path(args.data_name))
imgs = data['imgs'].astype(np.float32) / 255.0
config = dict(
img_shape=imgs.shape[1:],
n_imgs=imgs.shape[0],
n_classes=data['n_classes'].item(),
)
config.update(base_config)
lord = Lord(config)
lord.train(imgs=imgs,
classes=data['classes'],
model_dir=model_dir,
tensorboard_dir=tensorboard_dir)