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60 lines
2.2 KiB
60 lines
2.2 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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"""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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import tensorflow as tf |
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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 vgg_preprocessing |
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from preprocessing import ssd_vgg_preprocessing |
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slim = tf.contrib.slim |
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def get_preprocessing(name, is_training=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. |
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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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'ssd_300_vgg': ssd_vgg_preprocessing, |
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'ssd_512_vgg': ssd_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, labels, bboxes, |
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out_shape, data_format='NHWC', **kwargs): |
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return preprocessing_fn_map[name].preprocess_image( |
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image, labels, bboxes, out_shape, data_format=data_format, |
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is_training=is_training, **kwargs) |
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return preprocessing_fn
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