Add a utility function to build and write a SavedModel, setting reasonable

defaults for the required arguments.

PiperOrigin-RevId: 162420810

3 files changed, 198 insertions, 0 deletions

diff --git a/tensorflow/contrib/saved_model/BUILD b/tensorflow/contrib/saved_model/BUILD
index e86fd318bd..a82ee6ac41 100644
--- a/tensorflow/contrib/saved_model/BUILD
+++ b/tensorflow/contrib/saved_model/BUILD
@@ -77,6 +77,21 @@ py_test(
     ],
 )
 
+py_test(
+    name = "utils_test",
+    size = "small",
+    srcs = ["python/saved_model/utils_test.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":saved_model_py",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:dtypes",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python/saved_model",
+        "//tensorflow/python/saved_model:signature_constants",
+    ],
+)
+
 filegroup(
     name = "all_files",
     srcs = glob(
diff --git a/tensorflow/contrib/saved_model/python/saved_model/utils.py b/tensorflow/contrib/saved_model/python/saved_model/utils.py
new file mode 100644
index 0000000000..9f34af64a6
--- /dev/null
+++ b/tensorflow/contrib/saved_model/python/saved_model/utils.py
@@ -0,0 +1,81 @@
+# 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.
+# ==============================================================================
+"""SavedModel utility functions."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.python.framework import ops
+from tensorflow.python.saved_model import builder
+from tensorflow.python.saved_model import signature_constants
+from tensorflow.python.saved_model import signature_def_utils
+from tensorflow.python.saved_model import tag_constants
+
+
+def simple_save(session, export_dir, inputs, outputs, legacy_init_op=None):
+  """Convenience function to build a SavedModel suitable for serving.
+
+  In many common cases, saving models for serving will be as simple as:
+
+      simple_save(session,
+                  export_dir,
+                  inputs={"x": x, "y": y},
+                  outputs={"z": z})
+
+  Although in many cases it's not necessary to understand all of the many ways
+      to configure a SavedModel, this method has a few practical implications:
+    - It will be treated as a graph for inference / serving (i.e. uses the tag
+      `tag_constants.SERVING`)
+    - The saved model will load in TensorFlow Serving and supports the
+      [Predict API](https://github.com/tensorflow/serving/blob/master/tensorflow_serving/apis/predict.proto).
+      To use the Classify, Regress, or MultiInference APIs, please
+      use either
+      [tf.Estimator](https://www.tensorflow.org/api_docs/python/tf/estimator/Estimator)
+      or the lower level
+      [SavedModel APIs](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md).
+    - Some TensorFlow ops depend on information on disk or other information
+      called "assets". These are generally handled automatically by adding the
+      assets to the `GraphKeys.ASSET_FILEPATHS` collection. Only assets in that
+      collection are exported; if you need more custom behavior, you'll need to
+      use the [SavedModelBuilder](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/builder.py).
+
+  More information about SavedModel and signatures can be found here:
+  https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md.
+
+  Args:
+    session: The TensorFlow session from which to save the meta graph and
+        variables.
+    export_dir: The path to which the SavedModel will be stored.
+    inputs: dict mapping string input names to tensors. These are added
+        to the SignatureDef as the inputs.
+    outputs:  dict mapping string output names to tensors. These are added
+        to the SignatureDef as the outputs.
+    legacy_init_op: Legacy support for op or group of ops to execute after the
+        restore op upon a load.
+  """
+  signature_def_map = {
+      signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
+          signature_def_utils.predict_signature_def(inputs, outputs)
+  }
+  b = builder.SavedModelBuilder(export_dir)
+  b.add_meta_graph_and_variables(
+      session,
+      tags=[tag_constants.SERVING],
+      signature_def_map=signature_def_map,
+      assets_collection=ops.get_collection(ops.GraphKeys.ASSET_FILEPATHS),
+      legacy_init_op=legacy_init_op,
+      clear_devices=True)
+  b.save()
diff --git a/tensorflow/contrib/saved_model/python/saved_model/utils_test.py b/tensorflow/contrib/saved_model/python/saved_model/utils_test.py
new file mode 100644
index 0000000000..36dfb88871
--- /dev/null
+++ b/tensorflow/contrib/saved_model/python/saved_model/utils_test.py
@@ -0,0 +1,102 @@
+# 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 saved_model utils."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+
+from tensorflow.contrib.saved_model.python.saved_model import utils
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import variables
+from tensorflow.python.platform import test
+from tensorflow.python.saved_model import loader
+from tensorflow.python.saved_model import signature_constants
+from tensorflow.python.saved_model import tag_constants
+
+
+class UtilsTest(test.TestCase):
+
+  def _init_and_validate_variable(self, sess, variable_name, variable_value):
+    v = variables.Variable(variable_value, name=variable_name)
+    sess.run(variables.global_variables_initializer())
+    self.assertEqual(variable_value, v.eval())
+    return v
+
+  def _check_variable_info(self, actual_variable, expected_variable):
+    self.assertEqual(actual_variable.name, expected_variable.name)
+    self.assertEqual(actual_variable.dtype, expected_variable.dtype)
+    self.assertEqual(len(actual_variable.shape), len(expected_variable.shape))
+    for i in range(len(actual_variable.shape)):
+      self.assertEqual(actual_variable.shape[i], expected_variable.shape[i])
+
+  def _check_tensor_info(self, actual_tensor_info, expected_tensor):
+    self.assertEqual(actual_tensor_info.name, expected_tensor.name)
+    self.assertEqual(actual_tensor_info.dtype, expected_tensor.dtype)
+    self.assertEqual(
+        len(actual_tensor_info.tensor_shape.dim), len(expected_tensor.shape))
+    for i in range(len(actual_tensor_info.tensor_shape.dim)):
+      self.assertEqual(actual_tensor_info.tensor_shape.dim[i].size,
+                       expected_tensor.shape[i])
+
+  def testSimpleSave(self):
+    """Test simple_save that uses the default parameters."""
+    export_dir = os.path.join(test.get_temp_dir(),
+                              "test_simple_save")
+
+    # Initialize input and output variables and save a prediction graph using
+    # the default parameters.
+    with self.test_session(graph=ops.Graph()) as sess:
+      var_x = self._init_and_validate_variable(sess, "var_x", 1)
+      var_y = self._init_and_validate_variable(sess, "var_y", 2)
+      inputs = {"x": var_x}
+      outputs = {"y": var_y}
+      utils.simple_save(sess, export_dir, inputs, outputs)
+
+    # Restore the graph with a valid tag and check the global variables and
+    # signature def map.
+    with self.test_session(graph=ops.Graph()) as sess:
+      graph = loader.load(sess, [tag_constants.SERVING], export_dir)
+      collection_vars = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)
+
+      # Check value and metadata of the saved variables.
+      self.assertEqual(len(collection_vars), 2)
+      self.assertEqual(1, collection_vars[0].eval())
+      self.assertEqual(2, collection_vars[1].eval())
+      self._check_variable_info(collection_vars[0], var_x)
+      self._check_variable_info(collection_vars[1], var_y)
+
+      # Check that the appropriate signature_def_map is created with the
+      # default key and method name, and the specified inputs and outputs.
+      signature_def_map = graph.signature_def
+      self.assertEqual(1, len(signature_def_map))
+      self.assertEqual(signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY,
+                       list(signature_def_map.keys())[0])
+
+      signature_def = signature_def_map[
+          signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY]
+      self.assertEqual(signature_constants.PREDICT_METHOD_NAME,
+                       signature_def.method_name)
+
+      self.assertEqual(1, len(signature_def.inputs))
+      self._check_tensor_info(signature_def.inputs["x"], var_x)
+      self.assertEqual(1, len(signature_def.outputs))
+      self._check_tensor_info(signature_def.outputs["y"], var_y)
+
+
+if __name__ == "__main__":
+  test.main()