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115 lines
4.1 KiB
115 lines
4.1 KiB
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Provides utilities to preprocess images in CIFAR-10.
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import tensorflow.compat.v1 as tf
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tf.disable_v2_behavior()
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import tf_slim as slim
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_PADDING = 4
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def preprocess_for_train(image,
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output_height,
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output_width,
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padding=_PADDING):
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"""Preprocesses the given image for training.
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Note that the actual resizing scale is sampled from
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[`resize_size_min`, `resize_size_max`].
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Args:
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image: A `Tensor` representing an image of arbitrary size.
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output_height: The height of the image after preprocessing.
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output_width: The width of the image after preprocessing.
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padding: The amound of padding before and after each dimension of the image.
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Returns:
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A preprocessed image.
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"""
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tf.image_summary('image', tf.expand_dims(image, 0))
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# Transform the image to floats.
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image = tf.to_float(image)
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if padding > 0:
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image = tf.pad(image, [[padding, padding], [padding, padding], [0, 0]])
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# Randomly crop a [height, width] section of the image.
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distorted_image = tf.random_crop(image,
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[output_height, output_width, 3])
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# Randomly flip the image horizontally.
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distorted_image = tf.image.random_flip_left_right(distorted_image)
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tf.image_summary('distorted_image', tf.expand_dims(distorted_image, 0))
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# Because these operations are not commutative, consider randomizing
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# the order their operation.
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distorted_image = tf.image.random_brightness(distorted_image,
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max_delta=63)
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distorted_image = tf.image.random_contrast(distorted_image,
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lower=0.2, upper=1.8)
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# Subtract off the mean and divide by the variance of the pixels.
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return tf.image.per_image_whitening(distorted_image)
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def preprocess_for_eval(image, output_height, output_width):
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"""Preprocesses the given image for evaluation.
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Args:
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image: A `Tensor` representing an image of arbitrary size.
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output_height: The height of the image after preprocessing.
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output_width: The width of the image after preprocessing.
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Returns:
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A preprocessed image.
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"""
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tf.image_summary('image', tf.expand_dims(image, 0))
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# Transform the image to floats.
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image = tf.to_float(image)
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# Resize and crop if needed.
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resized_image = tf.image.resize_image_with_crop_or_pad(image,
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output_width,
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output_height)
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tf.image_summary('resized_image', tf.expand_dims(resized_image, 0))
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# Subtract off the mean and divide by the variance of the pixels.
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return tf.image.per_image_whitening(resized_image)
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def preprocess_image(image, output_height, output_width, is_training=False):
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"""Preprocesses the given image.
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Args:
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image: A `Tensor` representing an image of arbitrary size.
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output_height: The height of the image after preprocessing.
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output_width: The width of the image after preprocessing.
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is_training: `True` if we're preprocessing the image for training and
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`False` otherwise.
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Returns:
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A preprocessed image.
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"""
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if is_training:
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return preprocess_for_train(image, output_height, output_width)
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else:
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return preprocess_for_eval(image, output_height, output_width)
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