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# Trimmed by Vasil Zlatanov
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""ImageNet preprocessing for ResNet."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
def resize_or_crop_image(image, target_height, target_width):
image_height = tf.shape(image)[0]
image_width = tf.shape(image)[1]
# If the viewport is long but the width is right simply crop the length of the page
# Otherwise we just resize the image bilinearly
image = tf.cond(
tf.logical_and(tf.greater(image_height, target_height),tf.equal(target_width, image_width)),
lambda: tf.cast(tf.image.crop_to_bounding_box(image, 0, 0, target_height, target_width),dtype=tf.float32),
lambda: tf.image.resize_images(image, [target_height,target_width], align_corners=True)
)
return image
def preprocess_for_train(image_bytes, target_width, target_height, resize, use_bfloat16):
"""Preprocesses the given image for evaluation.
Args:
image_bytes: `Tensor` representing an image binary of arbitrary size.
use_bfloat16: `bool` for whether to use bfloat16.
Returns:
A preprocessed image `Tensor`.
"""
image = tf.image.decode_png(image_bytes, channels=3)
if resize:
image = resize_or_crop_image(image, target_height, target_width)
else:
image = tf.cast(image, tf.float32)
return image
def preprocess_for_eval(image_bytes, target_width, target_height, resize, use_bfloat16):
"""Preprocesses the given image for evaluation.
Args:
image_bytes: `Tensor` representing an image binary of arbitrary size.
use_bfloat16: `bool` for whether to use bfloat16.
Returns:
A preprocessed image `Tensor`.
"""
image = tf.image.decode_png(image_bytes, channels=3)
if resize:
image = resize_or_crop_image(image, target_height, target_width)
else:
image = tf.cast(image, tf.float32)
return image
def preprocess_image(image_bytes, width, height, resize, is_training=False, use_bfloat16=False):
"""Preprocesses the given image.
Args:
image_bytes: `Tensor` representing an image binary of arbitrary size.
is_training: `bool` for whether the preprocessing is for training.
use_bfloat16: `bool` for whether to use bfloat16.
Returns:
A preprocessed image `Tensor`.
"""
if is_training:
return preprocess_for_train(image_bytes, width, height, resize, use_bfloat16)
else:
return preprocess_for_eval(image_bytes, width, height, resize, use_bfloat16)
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