Updated SavedModels in Python TOCO API.

PiperOrigin-RevId: 199658431

5 files changed, 15 insertions, 25 deletions

diff --git a/tensorflow/contrib/lite/python/BUILD b/tensorflow/contrib/lite/python/BUILD
index 7e6ff6c0a8..27909a9458 100644
--- a/tensorflow/contrib/lite/python/BUILD
+++ b/tensorflow/contrib/lite/python/BUILD
@@ -57,8 +57,9 @@ py_library(
         ":interpreter",
         ":lite_constants",
         ":op_hint",
-        "//tensorflow/contrib/saved_model:saved_model_py",
         "//tensorflow/python:graph_util",
+        "//tensorflow/python/saved_model:constants",
+        "//tensorflow/python/saved_model:loader",
         "//tensorflow/python/tools:freeze_graph_lib",
     ],
 )
diff --git a/tensorflow/contrib/lite/python/convert_saved_model.py b/tensorflow/contrib/lite/python/convert_saved_model.py
index 5dad49f1ed..1553464b9f 100644
--- a/tensorflow/contrib/lite/python/convert_saved_model.py
+++ b/tensorflow/contrib/lite/python/convert_saved_model.py
@@ -19,13 +19,12 @@ from __future__ import division
 from __future__ import print_function
 
 from tensorflow.contrib.lite.python.convert import tensor_name
-from tensorflow.contrib.saved_model.python.saved_model import reader
-from tensorflow.contrib.saved_model.python.saved_model import signature_def_utils
 from tensorflow.core.framework import types_pb2
 from tensorflow.python.client import session
 from tensorflow.python.framework import graph_util as tf_graph_util
 from tensorflow.python.framework import ops
 from tensorflow.python.platform import tf_logging as logging
+from tensorflow.python.saved_model import constants
 from tensorflow.python.saved_model import loader
 
 
@@ -58,21 +57,8 @@ def _get_meta_graph_def(saved_model_dir, tag_set):
   Raises:
     ValueError: No valid MetaGraphDef for given tag_set.
   """
-  saved_model = reader.read_saved_model(saved_model_dir)
-  tag_sets = []
-  result_meta_graph_def = None
-  for meta_graph_def in saved_model.meta_graphs:
-    meta_graph_tag_set = set(meta_graph_def.meta_info_def.tags)
-    tag_sets.append(meta_graph_tag_set)
-    if meta_graph_tag_set == tag_set:
-      result_meta_graph_def = meta_graph_def
-  logging.info("The given saved_model contains the following tags: %s",
-               tag_sets)
-  if result_meta_graph_def is not None:
-    return result_meta_graph_def
-  else:
-    raise ValueError("No valid MetaGraphDef for this tag_set '{}'. Possible "
-                     "values are '{}'. ".format(tag_set, tag_sets))
+  with session.Session(graph=ops.Graph()) as sess:
+    return loader.load(sess, tag_set, saved_model_dir)
 
 
 def _get_signature_def(meta_graph, signature_key):
@@ -97,9 +83,7 @@ def _get_signature_def(meta_graph, signature_key):
     raise ValueError("No '{}' in the SavedModel\'s SignatureDefs. Possible "
                      "values are '{}'.".format(signature_key,
                                                ",".join(signature_def_keys)))
-  signature_def = signature_def_utils.get_signature_def_by_key(
-      meta_graph, signature_key)
-  return signature_def
+  return signature_def_map[signature_key]
 
 
 def _get_inputs_outputs(signature_def):
@@ -247,6 +231,7 @@ def freeze_saved_model(saved_model_dir, input_arrays, input_shapes,
     ValueError:
       SavedModel doesn't contain a MetaGraphDef identified by tag_set.
       signature_key is not in the MetaGraphDef.
+      assets/ directory is in the MetaGraphDef.
       input_shapes does not match the length of input_arrays.
       input_arrays or output_arrays are not valid.
   """
@@ -255,9 +240,13 @@ def freeze_saved_model(saved_model_dir, input_arrays, input_shapes,
   signature_def = _get_signature_def(meta_graph, signature_key)
   inputs, outputs = _get_inputs_outputs(signature_def)
 
+  # Check SavedModel for assets directory.
+  collection_def = meta_graph.collection_def
+  if constants.ASSETS_KEY in collection_def:
+    raise ValueError("SavedModels with assets/ directory are not supported.")
+
   graph = ops.Graph()
   with session.Session(graph=graph) as sess:
-    # TODO(nupurgarg): Throw ValueError if SavedModel has assets/ directory.
     loader.load(sess, meta_graph.meta_info_def.tags, saved_model_dir)
 
     # Gets input and output tensors.
diff --git a/tensorflow/contrib/lite/python/lite.py b/tensorflow/contrib/lite/python/lite.py
index 253e3f72b1..e3a2d19e05 100644
--- a/tensorflow/contrib/lite/python/lite.py
+++ b/tensorflow/contrib/lite/python/lite.py
@@ -207,7 +207,7 @@ class TocoConverter(object):
 
       # Check if graph is frozen.
       if not _is_frozen_graph(sess):
-        raise ValueError("Please freeze the graph using freeze_graph.py")
+        raise ValueError("Please freeze the graph using freeze_graph.py.")
 
       # Create TocoConverter class.
       return cls(sess.graph_def, input_tensors, output_tensors)
diff --git a/tensorflow/contrib/lite/python/lite_test.py b/tensorflow/contrib/lite/python/lite_test.py
index bbb00021f9..b04caaf263 100644
--- a/tensorflow/contrib/lite/python/lite_test.py
+++ b/tensorflow/contrib/lite/python/lite_test.py
@@ -401,7 +401,7 @@ class FromFrozenGraphFile(test_util.TensorFlowTestCase):
     with self.assertRaises(ValueError) as error:
       lite.TocoConverter.from_frozen_graph(graph_def_file, ['Placeholder'],
                                            ['add'])
-    self.assertEqual('Please freeze the graph using freeze_graph.py',
+    self.assertEqual('Please freeze the graph using freeze_graph.py.',
                      str(error.exception))
 
   def testPbtxt(self):
diff --git a/tensorflow/contrib/lite/python/tflite_convert.py b/tensorflow/contrib/lite/python/tflite_convert.py
index 2b7ad29a27..4c215b62b2 100644
--- a/tensorflow/contrib/lite/python/tflite_convert.py
+++ b/tensorflow/contrib/lite/python/tflite_convert.py
@@ -114,7 +114,7 @@ def _convert_model(flags):
                        "--input_arrays must be present when specifying "
                        "--std_dev_values and --mean_values with multiple input "
                        "tensors in order to map between names and "
-                       "values".format(",".join(input_arrays)))
+                       "values.".format(",".join(input_arrays)))
     converter.quantized_input_stats = dict(zip(input_arrays, quant_stats))
   if flags.default_ranges_min and flags.default_ranges_max:
     converter.default_ranges_stats = (flags.default_ranges_min,