def train_encoders(args): assets = AssetManager(args.base_dir) 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, classes, contents, n_classes = data['imgs'], data['classes'], data['contents'], data['n_classes'] imgs = imgs.astype(np.float32) / 255.0 converter = Converter.load(model_dir, include_encoders=False) glo_backup_dir = os.path.join(model_dir, args.glo_dir) if not os.path.exists(glo_backup_dir): os.mkdir(glo_backup_dir) converter.save(glo_backup_dir) converter.train_encoders( imgs=imgs, classes=classes, batch_size=default_config['train_encoders']['batch_size'], n_epochs=default_config['train_encoders']['n_epochs'], model_dir=model_dir, tensorboard_dir=tensorboard_dir ) converter.save(model_dir)
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_encoders(args): assets = AssetManager(args.base_dir) 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 backup_dir = os.path.join(model_dir, 'latent') if not os.path.exists(backup_dir): lord = Lord.load(model_dir, include_encoders=False) os.mkdir(backup_dir) lord.save(backup_dir) else: lord = Lord.load(backup_dir, include_encoders=False) lord.train_encoders( imgs=imgs, classes=data['classes'], batch_size=default_config['train_encoders']['batch_size'], n_epochs=default_config['train_encoders']['n_epochs'], model_dir=model_dir, tensorboard_dir=tensorboard_dir ) lord.save(model_dir)
def train_encoders(args):
assets = AssetManager(args.base_dir)
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
lord = Lord()
lord.load(model_dir, latent=True, amortized=False)
lord.train_amortized(imgs=imgs,
classes=data['classes'],
model_dir=model_dir,
tensorboard_dir=tensorboard_dir)
lord.save(model_dir, latent=False, amortized=True)
def train_encoders(args): assets = AssetManager(args.base_dir) 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) lord.train_encoders( imgs=imgs, classes=data['classes'], model_dir=model_dir, tensorboard_dir=tensorboard_dir )
