def distorted_inputs():
"""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')
data_dir = os.path.join(DATA_DIR, 'cifar-10-batches-bin')
images, labels = vgg_input.distorted_inputs(
data_dir=data_dir, batch_size=BATCH_SIZE)
return images, labels
def distorted_inputs():
"""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_dirs
"""
if not data_dirs:
raise ValueError('Please supply a data_dirs')
images, labels = vgg_input.distorted_inputs(data_dirs=data_dirs,
batch_size=FLAGS.batch_size)
if FLAGS.use_fp16:
images = tf.cast(images, tf.float16)
labels = tf.cast(labels, tf.float16)
return images, labels
def distorted_inputs(data, batch_size):
"""Construct distorted input for CIFAR training using the Reader ops.
Returns:
images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 1] size.
labels: Labels. 1D tensor of [batch_size, 3] size.
Raises:
ValueError: If no data_dir
"""
if (data == 'training'):
data_dir = "trainingImagesPaths.txt"
elif (data == 'test'):
data_dir = "validationImagesPaths.txt"
else:
raise Exception('Please choose training or test as first argument')
images, paths = vgg_input.distorted_inputs(data_dir=data_dir,
batch_size=batch_size)
labels = tf.py_func(getLabels, [paths], [tf.float32])
labels = tf.convert_to_tensor(labels, dtype=tf.float32)
labels = tf.reshape(labels, [batch_size, 3])
return images, labels