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| # Copyright 2016 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. | |
| # ============================================================================== | |
| """Contains a factory for building various models.""" | |
| from __future__ import absolute_import | |
| from __future__ import division | |
| from __future__ import print_function | |
| import tensorflow as tf | |
| from preprocessing import cifarnet_preprocessing | |
| from preprocessing import inception_preprocessing | |
| from preprocessing import lenet_preprocessing | |
| from preprocessing import vgg_preprocessing | |
| slim = tf.contrib.slim | |
| def get_preprocessing(name, is_training=False): | |
| """Returns preprocessing_fn(image, height, width, **kwargs). | |
| Args: | |
| name: The name of the preprocessing function. | |
| is_training: `True` if the model is being used for training and `False` | |
| otherwise. | |
| Returns: | |
| preprocessing_fn: A function that preprocessing a single image (pre-batch). | |
| It has the following signature: | |
| image = preprocessing_fn(image, output_height, output_width, ...). | |
| Raises: | |
| ValueError: If Preprocessing `name` is not recognized. | |
| """ | |
| preprocessing_fn_map = { | |
| 'cifarnet': cifarnet_preprocessing, | |
| 'inception': inception_preprocessing, | |
| 'inception_v1': inception_preprocessing, | |
| 'inception_v2': inception_preprocessing, | |
| 'inception_v3': inception_preprocessing, | |
| 'inception_v4': inception_preprocessing, | |
| 'inception_resnet_v2': inception_preprocessing, | |
| 'lenet': lenet_preprocessing, | |
| 'mobilenet_v1': inception_preprocessing, | |
| 'nasnet_mobile': inception_preprocessing, | |
| 'nasnet_large': inception_preprocessing, | |
| 'pnasnet_large': inception_preprocessing, | |
| 'resnet_v1_50': vgg_preprocessing, | |
| 'resnet_v1_101': vgg_preprocessing, | |
| 'resnet_v1_152': vgg_preprocessing, | |
| 'resnet_v1_200': vgg_preprocessing, | |
| 'resnet_v2_50': vgg_preprocessing, | |
| 'resnet_v2_101': vgg_preprocessing, | |
| 'resnet_v2_152': vgg_preprocessing, | |
| 'resnet_v2_200': vgg_preprocessing, | |
| 'vgg': vgg_preprocessing, | |
| 'vgg_a': vgg_preprocessing, | |
| 'vgg_16': vgg_preprocessing, | |
| 'vgg_19': vgg_preprocessing, | |
| } | |
| if name not in preprocessing_fn_map: | |
| raise ValueError('Preprocessing name [%s] was not recognized' % name) | |
| def preprocessing_fn(image, output_height, output_width, **kwargs): | |
| return preprocessing_fn_map[name].preprocess_image( | |
| image, output_height, output_width, is_training=is_training, **kwargs) | |
| return preprocessing_fn | |