def main():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--batch_size', type=int, default=16)
parser.add_argument('--prefix', type=str, default='default')
parser.add_argument('--checkpoint', type=str, default=None)
parser.add_argument('--dataset', type=str, default='ImageNet', choices=['ImageNet'])
parser.add_argument('--learning_rate', type=float, default=1e-4)
parser.add_argument('--lr_weight_decay', action='store_true', default=False)
config = parser.parse_args()
if config.dataset == 'ImageNet':
import datasets.ImageNet as dataset
elif config.dataset == 'SVHN':
import datasets.svhn as dataset
elif config.dataset == 'CIFAR10':
import datasets.cifar10 as dataset
else:
raise ValueError(config.dataset)
dataset_train, dataset_test = dataset.create_default_splits()
image, _, label, _ = dataset_train.get_data(dataset_train.ids[0], dataset_train.ids[0])
config.data_info = np.concatenate([np.asarray(image.shape), np.asarray(label.shape)])
trainer = Trainer(config,
dataset_train, dataset_test)
log.warning("dataset: %s, learning_rate: %f",
config.dataset, config.learning_rate)
trainer.train(dataset_train)
def main():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--batch_size', type=int, default=16)
parser.add_argument('--checkpoint_path', type=str)
parser.add_argument('--train_dir', type=str)
parser.add_argument('--dataset', type=str, default='ImageNet', choices=['ImageNet'])
parser.add_argument('--data_id', nargs='*', default=None)
config = parser.parse_args()
if config.dataset == 'ImageNet':
import datasets.ImageNet as dataset
else:
raise ValueError(config.dataset)
_, dataset = dataset.create_default_splits(ratio=0.999)
image, _, label, _ = dataset.get_data(dataset.ids[0], dataset.ids[0])
config.data_info = np.concatenate([np.asarray(image.shape), np.asarray(label.shape)])
evaler = Evaler(config, dataset)
log.warning("dataset: %s", dataset)
evaler.eval_run()