def _unl_parser(value):
"""Cifar10 parser."""
image_size = params.image_size
value = tf.io.decode_raw(value, tf.uint8)
image = tf.reshape(value[1:], [3, 32, 32]) # uint8
image = tf.transpose(image, [1, 2, 0])
if image_size != 32:
image = tf.image.resize_bicubic([image],
[image_size, image_size])[0]
image.set_shape([image_size, image_size, 3])
image = _flip_and_jitter(image, replace_value=128)
ori_image = image
aug = augment.RandAugment(cutout_const=image_size // 8,
translate_const=image_size // 8,
magnitude=params.augment_magnitude)
aug_image = aug.distort(image)
aug_image = augment.cutout(aug_image,
pad_size=image_size // 4,
replace=128)
aug_image = _flip_and_jitter(aug_image, replace_value=128)
ori_image = convert_and_normalize(params, ori_image)
aug_image = convert_and_normalize(params, aug_image)
return {'ori_images': ori_image, 'aug_images': aug_image}
def _cifar10_parser(params, value, training):
"""Cifar10 parser."""
image_size = params.image_size
value = tf.io.decode_raw(value, tf.uint8)
label = tf.cast(value[0], tf.int32)
label = tf.one_hot(label, depth=params.num_classes, dtype=tf.float32)
image = tf.reshape(value[1:], [3, 32, 32]) # uint8
image = tf.transpose(image, [1, 2, 0])
if image_size != 32:
image = tf.image.resize_bicubic([image], [image_size, image_size])[0]
image.set_shape([image_size, image_size, 3])
if training:
if params.use_augment:
aug = augment.RandAugment(cutout_const=image_size // 8,
translate_const=image_size // 8,
magnitude=params.augment_magnitude)
image = _flip_and_jitter(image, 128)
image = aug.distort(image)
image = augment.cutout(image,
pad_size=image_size // 4,
replace=128)
else:
image = _flip_and_jitter(image, 128)
image = convert_and_normalize(params, image)
return image, label