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Add export inference_graph (tensorflow#1702)
* Add export inference_graph * Update Readme.md to include export_inference_graph
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| # Copyright 2017 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. | ||
| # ============================================================================== | ||
| r"""Saves out a GraphDef containing the architecture of the model. | ||
| To use it, run something like this, with a model name defined by slim: | ||
| bazel build tensorflow_models/slim:export_inference_graph | ||
| bazel-bin/tensorflow_models/slim/export_inference_graph \ | ||
| --model_name=inception_v3 --output_file=/tmp/inception_v3_inf_graph.pb | ||
| If you then want to use the resulting model with your own or pretrained | ||
| checkpoints as part of a mobile model, you can run freeze_graph to get a graph | ||
| def with the variables inlined as constants using: | ||
| bazel build tensorflow/python/tools:freeze_graph | ||
| bazel-bin/tensorflow/python/tools/freeze_graph \ | ||
| --input_graph=/tmp/inception_v3_inf_graph.pb \ | ||
| --input_checkpoint=/tmp/checkpoints/inception_v3.ckpt \ | ||
| --input_binary=true --output_graph=/tmp/frozen_inception_v3.pb \ | ||
| --output_node_names=InceptionV3/Predictions/Reshape_1 | ||
| The output node names will vary depending on the model, but you can inspect and | ||
| estimate them using the summarize_graph tool: | ||
| bazel build tensorflow/tools/graph_transforms:summarize_graph | ||
| bazel-bin/tensorflow/tools/graph_transforms/summarize_graph \ | ||
| --in_graph=/tmp/inception_v3_inf_graph.pb | ||
| To run the resulting graph in C++, you can look at the label_image sample code: | ||
| bazel build tensorflow/examples/label_image:label_image | ||
| bazel-bin/tensorflow/examples/label_image/label_image \ | ||
| --image=${HOME}/Pictures/flowers.jpg \ | ||
| --input_layer=input \ | ||
| --output_layer=InceptionV3/Predictions/Reshape_1 \ | ||
| --graph=/tmp/frozen_inception_v3.pb \ | ||
| --labels=/tmp/imagenet_slim_labels.txt \ | ||
| --input_mean=0 \ | ||
| --input_std=255 \ | ||
| --logtostderr | ||
| """ | ||
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| from __future__ import absolute_import | ||
| from __future__ import division | ||
| from __future__ import print_function | ||
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| import tensorflow as tf | ||
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| from tensorflow.python.platform import gfile | ||
| from datasets import dataset_factory | ||
| from nets import nets_factory | ||
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| slim = tf.contrib.slim | ||
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| tf.app.flags.DEFINE_string( | ||
| 'model_name', 'inception_v3', 'The name of the architecture to save.') | ||
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| tf.app.flags.DEFINE_boolean( | ||
| 'is_training', False, | ||
| 'Whether to save out a training-focused version of the model.') | ||
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| tf.app.flags.DEFINE_integer( | ||
| 'default_image_size', 224, | ||
| 'The image size to use if the model does not define it.') | ||
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| tf.app.flags.DEFINE_string('dataset_name', 'imagenet', | ||
| 'The name of the dataset to use with the model.') | ||
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| tf.app.flags.DEFINE_integer( | ||
| 'labels_offset', 0, | ||
| 'An offset for the labels in the dataset. This flag is primarily used to ' | ||
| 'evaluate the VGG and ResNet architectures which do not use a background ' | ||
| 'class for the ImageNet dataset.') | ||
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| tf.app.flags.DEFINE_string( | ||
| 'output_file', '', 'Where to save the resulting file to.') | ||
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| tf.app.flags.DEFINE_string( | ||
| 'dataset_dir', '', 'Directory to save intermediate dataset files to') | ||
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| FLAGS = tf.app.flags.FLAGS | ||
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| def main(_): | ||
| if not FLAGS.output_file: | ||
| raise ValueError('You must supply the path to save to with --output_file') | ||
| tf.logging.set_verbosity(tf.logging.INFO) | ||
| with tf.Graph().as_default() as graph: | ||
| dataset = dataset_factory.get_dataset(FLAGS.dataset_name, 'validation', | ||
| FLAGS.dataset_dir) | ||
| network_fn = nets_factory.get_network_fn( | ||
| FLAGS.model_name, | ||
| num_classes=(dataset.num_classes - FLAGS.labels_offset), | ||
| is_training=FLAGS.is_training) | ||
| if hasattr(network_fn, 'default_image_size'): | ||
| image_size = network_fn.default_image_size | ||
| else: | ||
| image_size = FLAGS.default_image_size | ||
| placeholder = tf.placeholder(name='input', dtype=tf.float32, | ||
| shape=[1, image_size, image_size, 3]) | ||
| network_fn(placeholder) | ||
| graph_def = graph.as_graph_def() | ||
| with gfile.GFile(FLAGS.output_file, 'wb') as f: | ||
| f.write(graph_def.SerializeToString()) | ||
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| if __name__ == '__main__': | ||
| tf.app.run() |
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| # Copyright 2017 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. | ||
| # ============================================================================== | ||
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| """Tests for export_inference_graph.""" | ||
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| from __future__ import absolute_import | ||
| from __future__ import division | ||
| from __future__ import print_function | ||
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| import os | ||
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| import tensorflow as tf | ||
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| from tensorflow.python.platform import gfile | ||
| from google3.third_party.tensorflow_models.slim import export_inference_graph | ||
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| class ExportInferenceGraphTest(tf.test.TestCase): | ||
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| def testExportInferenceGraph(self): | ||
| tmpdir = self.get_temp_dir() | ||
| output_file = os.path.join(tmpdir, 'inception_v3.pb') | ||
| flags = tf.app.flags.FLAGS | ||
| flags.output_file = output_file | ||
| flags.model_name = 'inception_v3' | ||
| flags.dataset_dir = tmpdir | ||
| export_inference_graph.main(None) | ||
| self.assertTrue(gfile.Exists(output_file)) | ||
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| if __name__ == '__main__': | ||
| tf.test.main() |