def load_dataset(config):
"""Load dataset following instruction in `config`."""
if dataset_is_mnist_family(config['dataset']):
crop_width = config.get('crop_width', None) # unused
img_width = config.get('img_width', None) # unused
scratch = config.get('scratch', get_default_scratch())
basepath = os.path.join(scratch, config['dataset'].lower())
data_path = os.path.join(basepath, 'data')
save_path = os.path.join(basepath, 'ckpts')
tf.gfile.MakeDirs(data_path)
tf.gfile.MakeDirs(save_path)
# black-on-white MNIST (harder to learn than white-on-black MNIST)
# Running locally (pre-download data locally)
mnist_train, mnist_eval, mnist_test = local_mnist.read_data_sets(
data_path, one_hot=True)
train_data = np.concatenate([mnist_train.images, mnist_eval.images],
axis=0)
attr_train = np.concatenate([mnist_train.labels, mnist_eval.labels],
axis=0)
eval_data = mnist_test.images
attr_eval = mnist_test.labels
attribute_names = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
elif config['dataset'] == 'CELEBA':
crop_width = config['crop_width']
img_width = config['img_width']
postfix = '_crop_%d_res_%d.npy' % (crop_width, img_width)
# Load Data
scratch = config.get('scratch', get_default_scratch())
basepath = os.path.join(scratch, 'celeba')
data_path = os.path.join(basepath, 'data')
save_path = os.path.join(basepath, 'ckpts')
(train_data, eval_data, _, attr_train, attr_eval, _,
attribute_names) = _load_celeba(data_path, postfix)
else:
raise NotImplementedError
return ObjectBlob(
crop_width=crop_width,
img_width=img_width,
basepath=basepath,
data_path=data_path,
save_path=save_path,
train_data=train_data,
attr_train=attr_train,
eval_data=eval_data,
attr_eval=attr_eval,
attribute_names=attribute_names,
)
def load_dataset(config):
"""Load dataset following instruction in `config`."""
if dataset_is_mnist_family(config['dataset']):
crop_width = config.get('crop_width', None) # unused
img_width = config.get('img_width', None) # unused
scratch = config.get('scratch', get_default_scratch())
basepath = os.path.join(scratch, config['dataset'].lower())
data_path = os.path.join(basepath, 'data')
save_path = os.path.join(basepath, 'ckpts')
tf.gfile.MakeDirs(data_path)
tf.gfile.MakeDirs(save_path)
# black-on-white MNIST (harder to learn than white-on-black MNIST)
# Running locally (pre-download data locally)
mnist_train, mnist_eval, mnist_test = local_mnist.read_data_sets(
data_path, one_hot=True)
train_data = np.concatenate([mnist_train.images, mnist_eval.images], axis=0)
attr_train = np.concatenate([mnist_train.labels, mnist_eval.labels], axis=0)
eval_data = mnist_test.images
attr_eval = mnist_test.labels
attribute_names = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
elif config['dataset'] == 'CELEBA':
crop_width = config['crop_width']
img_width = config['img_width']
postfix = '_crop_%d_res_%d.npy' % (crop_width, img_width)
# Load Data
scratch = config.get('scratch', get_default_scratch())
basepath = os.path.join(scratch, 'celeba')
data_path = os.path.join(basepath, 'data')
save_path = os.path.join(basepath, 'ckpts')
(train_data, eval_data, _, attr_train, attr_eval, _,
attribute_names) = _load_celeba(data_path, postfix)
else:
raise NotImplementedError
return ObjectBlob(
crop_width=crop_width,
img_width=img_width,
basepath=basepath,
data_path=data_path,
save_path=save_path,
train_data=train_data,
attr_train=attr_train,
eval_data=eval_data,
attr_eval=attr_eval,
attribute_names=attribute_names,
)