parser.add_argument('--eager', action='store_true', default=False, help='Whether to run in eager mode.') return parser.parse_args() if __name__ == "__main__": os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' args = parse_args() tf.config.experimental_run_functions_eagerly(args.eager) dataset, meta_info = Dataset.create(args, batch_size=args.batch_size) Model.add_additional_args(args, meta_info) model = Model(args) trainer = model.create_trainer() trainer.add_callbacks([ callbacks.Checkpointer(args.root_dir + '/ckpt', model.gen_ckpt_objs(), save_interval=1, max_to_keep=10), callbacks.ModelArgsSaverLoader(model, True, args.root_dir), callbacks.TqdmProgressBar(args.epochs, len(dataset)) ])
parser.add_argument('--eager', action='store_true', default=False, help='Whether to run in eager mode.') return parser.parse_args() if __name__ == "__main__": os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' args = parse_args() tf.config.experimental_run_functions_eagerly(args.eager) dataset, meta_info = Dataset.create(args, batch_size=args.batch_size, train=False) Model.add_additional_args(args, meta_info) model = Model(args) evaluator = model.create_evaluator() evaluator.add_callbacks([ callbacks.Checkpointer(args.root_dir + '/ckpt', model.gen_ckpt_objs(), is_training=False), callbacks.ModelArgsSaverLoader(model, False, args.root_dir), callbacks.TqdmProgressBar(args.epochs, len(dataset)) ])