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))
])
