Add export inference_graph (#1702)

* Add export inference_graph

* Update Readme.md to include export_inference_graph

slim/BUILD

@@ -390,3 +390,26 @@ py_binary(
         ":preprocessing_factory",
     ],
 )
+
+py_binary(
+    name = "export_inference_graph",
+    srcs = ["export_inference_graph.py"],
+    deps = [
+        ":dataset_factory",
+        ":nets_factory",
+    ],
+)
+
+py_test(
+    name = "export_inference_graph_test",
+    size = "medium",
+    srcs = ["export_inference_graph_test.py"],
+    srcs_version = "PY2AND3",
+    tags = [
+        "manual",
+    ],
+    deps = [
+        ":export_inference_graph",
+        ":nets_factory",
+    ],
+)

slim/README.md

@@ -32,6 +32,8 @@ Maintainers of TF-slim:
 <a href='#Training'>Training from scratch</a><br>
 <a href='#Tuning'>Fine tuning to a new task</a><br>
 <a href='#Eval'>Evaluating performance</a><br>
+<a href='#Export'>Exporting Inference Graph</a><br>
+<a href='#Troubleshooting'>Troubleshooting</a><br>
 
 # Installation
 <a id='Install'></a>
@@ -204,7 +206,6 @@ Model | TF-Slim File | Checkpoint | Top-1 Accuracy| Top-5 Accuracy |
 [MobileNet_v1_1.0_224](https://arxiv.org/pdf/1704.04861.pdf)|[Code](https://github.com/tensorflow/models/blob/master/slim/nets/mobilenet_v1.py)|[mobilenet_v1_1.0_224_2017_06_14.tar.gz](http://download.tensorflow.org/models/mobilenet_v1_1.0_224_2017_06_14.tar.gz)|70.7|89.5|
 [MobileNet_v1_0.50_160](https://arxiv.org/pdf/1704.04861.pdf)|[Code](https://github.com/tensorflow/models/blob/master/slim/nets/mobilenet_v1.py)|[mobilenet_v1_0.50_160_2017_06_14.tar.gz](http://download.tensorflow.org/models/mobilenet_v1_0.50_160_2017_06_14.tar.gz)|59.9|82.5|
 [MobileNet_v1_0.25_128](https://arxiv.org/pdf/1704.04861.pdf)|[Code](https://github.com/tensorflow/models/blob/master/slim/nets/mobilenet_v1.py)|[mobilenet_v1_0.25_128_2017_06_14.tar.gz](http://download.tensorflow.org/models/mobilenet_v1_0.25_128_2017_06_14.tar.gz)|41.3|66.2|
-
 ^ ResNet V2 models use Inception pre-processing and input image size of 299 (use
 `--preprocessing_name inception --eval_image_size 299` when using
 `eval_image_classifier.py`). Performance numbers for ResNet V2 models are
@@ -327,8 +328,72 @@ $ python eval_image_classifier.py \
 ```
 
 
+# Exporting the Inference Graph
+<a id='Export'></a>
+
+Saves out a GraphDef containing the architecture of the model.
+
+To use it with a model name defined by slim, run:
+
+```shell
+$ python export_inference_graph.py \
+  --alsologtostderr \
+  --model_name=inception_v3 \
+  --output_file=/tmp/inception_v3_inf_graph.pb
+
+$ python export_inference_graph.py \
+  --alsologtostderr \
+  --model_name=mobilenet_v1 \
+  --image_size=224 \
+  --output_file=/tmp/mobilenet_v1_224.pb
+```
+
+## Freezing the exported Graph
+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:
+
+```shell
+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:
+
+```shell
+bazel build tensorflow/tools/graph_transforms:summarize_graph
+
+bazel-bin/tensorflow/tools/graph_transforms/summarize_graph \
+  --in_graph=/tmp/inception_v3_inf_graph.pb
+```
+
+## Run label image in C++
+
+To run the resulting graph in C++, you can look at the label_image sample code:
+
+```shell
+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
+```
+
 
 # Troubleshooting
+<a id='Troubleshooting'></a>
 
 #### The model runs out of CPU memory.
 

slim/export_inference_graph.py

+# 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
+
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+from tensorflow.python.platform import gfile
+from datasets import dataset_factory
+from nets import nets_factory
+
+
+slim = tf.contrib.slim
+
+tf.app.flags.DEFINE_string(
+    'model_name', 'inception_v3', 'The name of the architecture to save.')
+
+tf.app.flags.DEFINE_boolean(
+    'is_training', False,
+    'Whether to save out a training-focused version of the model.')
+
+tf.app.flags.DEFINE_integer(
+    'default_image_size', 224,
+    'The image size to use if the model does not define it.')
+
+tf.app.flags.DEFINE_string('dataset_name', 'imagenet',
+                           'The name of the dataset to use with the model.')
+
+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.')
+
+tf.app.flags.DEFINE_string(
+    'output_file', '', 'Where to save the resulting file to.')
+
+tf.app.flags.DEFINE_string(
+    'dataset_dir', '', 'Directory to save intermediate dataset files to')
+
+FLAGS = tf.app.flags.FLAGS
+
+
+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())
+
+
+if __name__ == '__main__':
+  tf.app.run()

slim/export_inference_graph_test.py

+# 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.
+# ==============================================================================
+
+"""Tests for export_inference_graph."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+
+
+import tensorflow as tf
+
+from tensorflow.python.platform import gfile
+from google3.third_party.tensorflow_models.slim import export_inference_graph
+
+
+class ExportInferenceGraphTest(tf.test.TestCase):
+
+  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))
+
+if __name__ == '__main__':
+  tf.test.main()