def distorted_inputs(rank=0):
"""Construct distorted input for CIFAR training using the Reader ops.
Returns:
images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
labels: Labels. 1D tensor of [batch_size] size.
Raises:
ValueError: If no data_dir
"""
if not FLAGS.data_dir:
raise ValueError('Please supply a data_dir')
base_dir = FLAGS.data_dir + str(rank)
if FLAGS.dataset == 'cifar10':
data_dir = os.path.join(base_dir, 'cifar-10-batches-bin')
else:
data_dir = os.path.join(base_dir, 'cifar-100-binary')
batch_size = FLAGS.batch_size
images, labels = cifar_input.distorted_inputs(data_dir=data_dir,
batch_size=batch_size)
if FLAGS.use_fp16:
images = tf.cast(images, tf.float16)
labels = tf.cast(labels, tf.float16)
return images, labels
def get_distorted_train_batch(data_dir, batch_size):
if not data_dir:
raise ValueError('Please supply a data_dir')
images, labels = cifar_input.distorted_inputs(cifar10or20or100=n_classes,
data_dir=data_dir,
batch_size=batch_size)
return images, labels
def distorted_inputs():
"""Construct distorted input for CIFAR training using the Reader OPS.
SOURCE: https://github.com/tensorflow/tensorflow/blob/r0.7/tensorflow/models/image/cifar10/cifar10.py"""
if not FLAGS.data_dir:
raise ValueError('Please supply a data_dir')
data_dir = os.path.join(FLAGS.data_dir, 'cifar-10-batches-bin')
return cifar_input.distorted_inputs(data_dir=data_dir,
batch_size=FLAGS.batch_size)
def distorted_inputs(dataset_no):
if not FLAGS.data_dir:
raise ValueError('please supply a data_dir')
if dataset_no == '10':
data_dir = os.path.join(FLAGS.data_dir, 'cifar-10-batches-bin')
else:
data_dir = os.path.join(FLAGS.data_dir, 'cifar-100-binary')
return cifar_input.distorted_inputs(dataset_no,
data_dir=data_dir,
batch_size=FLAGS.batch_size)
def distorted_inputs(dataset_no):
if not FLAGS.data_dir:
raise ValueError('please supply a data_dir')
if dataset_no == '10':
data_dir = os.path.join(FLAGS.data_dir, 'cifar-10-batches-bin')
else:
data_dir = os.path.join(FLAGS.data_dir, 'cifar-100-binary')
return cifar_input.distorted_inputs(dataset_no,
data_dir=data_dir,
batch_size=FLAGS.batch_size)
def get_distorted_train_batch(data_dir, batch_size):
"""
:param data_dir:
:param batch_size:
:return: images 4D Tensor of [batch_size, image_size,image_size,3] size
labels 1D Tensor of [batch_size] size
"""
if not data_dir:
raise ValueError('please supply a data_dir')
images, labels = cifar_input.distorted_inputs(cifar10or20or100=n_classes,
data_dir=data_dir,
batch_size=batch_size)
return images, labels # 返回batch_size=100批次的样本
def distorted_inputs(is_cifar10):
"""Construct distorted input for CIFAR training using the Reader ops.
Returns:
images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
labels: Labels. 1D tensor of [batch_size] size.
Raises:
ValueError: If no data_dir
"""
FLAGS = parser.parse_args()
if not FLAGS.data_dir:
raise ValueError('Please supply a data_dir')
data_dir = os.path.join(FLAGS.data_dir, '%s-binary' % (FLAGS.dataset))
images, labels = cifar_input.distorted_inputs(data_dir=data_dir,
batch_size=FLAGS.batch_size,
is_cifar10=is_cifar10)
return images, labels
def get_distorted_train_batch(cifar10or20or100, data_dir, batch_size):
"""Construct distorted input for CIFAR training using the Reader ops.
Returns:
images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
labels: Labels. 1D tensor of [batch_size] size.
Raises:
ValueError: If no data_dir
"""
if not data_dir:
raise ValueError('Please supply a data_dir')
if cifar10or20or100 == 10:
data_dir = os.path.join(data_dir, 'cifar-10-batches-bin')
else:
data_dir = os.path.join(data_dir, 'cifar-100-binary')
images, labels = cifar_input.distorted_inputs(
cifar10or20or100=cifar10or20or100,
data_dir=data_dir,
batch_size=batch_size)
return images, labels