def _preprocess_fn(self, features, labels, mode):
"""Resize images and convert them from uint8 -> float32."""
if 'image' in features:
ndim = len(features.image.shape)
is_sequence = (ndim > 4)
input_size = self._src_img_res
target_size = self._crop_size
features.original_image = features.image
features.image = distortion.preprocess_image(
features.image, mode, is_sequence, input_size, target_size)
features.image = tf.image.convert_image_dtype(
features.image, tf.float32)
out_feature_spec = self.get_out_feature_specification(mode)
if out_feature_spec.image.shape != features.image.shape:
features.image = meta_tfdata.multi_batch_apply(
tf.image.resize_images, 2, features.image,
out_feature_spec.image.shape.as_list()[-3:-1])
if self._mixup_alpha > 0. and labels and mode == TRAIN:
lmbda = tfp.distributions.Beta(self._mixup_alpha,
self._mixup_alpha).sample()
for key, x in features.items():
if x.dtype in FLOAT_DTYPES:
features[key] = lmbda * x + (1 - lmbda) * tf.reverse(
x, axis=[0])
for key, x in labels.items():
if x.dtype in FLOAT_DTYPES:
labels[key] = lmbda * x + (1 - lmbda) * tf.reverse(
x, axis=[0])
return features, labels
def _preprocess_fn(self, features, labels, mode):
"""Resize images and convert them from uint8 -> float32."""
ndim = len(features.image.shape)
is_sequence = (ndim > 4)
input_size = self._src_img_res
target_size = self._crop_size
features.image = distortion.preprocess_image(features.image, mode,
is_sequence, input_size,
target_size)
out_feature_spec = self.get_out_feature_specification(mode)
if out_feature_spec.image.shape != features.image.shape:
features.image = meta_tfdata.multi_batch_apply(
tf.image.resize_images, 2,
tf.image.convert_image_dtype(features.image, tf.float32),
out_feature_spec.image.shape.as_list()[-3:-1])
return features, labels
