mirror of https://github.com/tensorflow/models.git
94 lines
3.6 KiB
Python
94 lines
3.6 KiB
Python
# 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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"""Contains a factory for building various models."""
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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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from preprocessing import cifarnet_preprocessing
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from preprocessing import inception_preprocessing
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from preprocessing import lenet_preprocessing
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from preprocessing import vgg_preprocessing
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def get_preprocessing(name, is_training=False, use_grayscale=False):
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"""Returns preprocessing_fn(image, height, width, **kwargs).
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Args:
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name: The name of the preprocessing function.
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is_training: `True` if the model is being used for training and `False`
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otherwise.
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use_grayscale: Whether to convert the image from RGB to grayscale.
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Returns:
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preprocessing_fn: A function that preprocessing a single image (pre-batch).
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It has the following signature:
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image = preprocessing_fn(image, output_height, output_width, ...).
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Raises:
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ValueError: If Preprocessing `name` is not recognized.
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"""
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preprocessing_fn_map = {
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'cifarnet': cifarnet_preprocessing,
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'inception': inception_preprocessing,
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'inception_v1': inception_preprocessing,
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'inception_v2': inception_preprocessing,
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'inception_v3': inception_preprocessing,
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'inception_v4': inception_preprocessing,
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'inception_resnet_v2': inception_preprocessing,
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'lenet': lenet_preprocessing,
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'mobilenet_v1': inception_preprocessing,
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'mobilenet_v2': inception_preprocessing,
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'mobilenet_v2_035': inception_preprocessing,
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'mobilenet_v3_small': inception_preprocessing,
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'mobilenet_v3_large': inception_preprocessing,
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'mobilenet_v3_small_minimalistic': inception_preprocessing,
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'mobilenet_v3_large_minimalistic': inception_preprocessing,
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'mobilenet_edgetpu': inception_preprocessing,
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'mobilenet_edgetpu_075': inception_preprocessing,
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'mobilenet_v2_140': inception_preprocessing,
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'nasnet_mobile': inception_preprocessing,
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'nasnet_large': inception_preprocessing,
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'pnasnet_mobile': inception_preprocessing,
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'pnasnet_large': inception_preprocessing,
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'resnet_v1_50': vgg_preprocessing,
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'resnet_v1_101': vgg_preprocessing,
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'resnet_v1_152': vgg_preprocessing,
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'resnet_v1_200': vgg_preprocessing,
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'resnet_v2_50': vgg_preprocessing,
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'resnet_v2_101': vgg_preprocessing,
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'resnet_v2_152': vgg_preprocessing,
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'resnet_v2_200': vgg_preprocessing,
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'vgg': vgg_preprocessing,
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'vgg_a': vgg_preprocessing,
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'vgg_16': vgg_preprocessing,
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'vgg_19': vgg_preprocessing,
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}
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if name not in preprocessing_fn_map:
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raise ValueError('Preprocessing name [%s] was not recognized' % name)
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def preprocessing_fn(image, output_height, output_width, **kwargs):
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return preprocessing_fn_map[name].preprocess_image(
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image,
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output_height,
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output_width,
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is_training=is_training,
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use_grayscale=use_grayscale,
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**kwargs)
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return preprocessing_fn
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