Automated rollback of commit 20c71535c5f1ed1d918d6cc6e327ffbba49ecbd6

PiperOrigin-RevId: 214519671

6 files changed, 7 insertions, 443 deletions

diff --git a/tensorflow/contrib/lite/build_def.bzl b/tensorflow/contrib/lite/build_def.bzl
index 7f5c6bdc2f..fc4d9b4f17 100644
--- a/tensorflow/contrib/lite/build_def.bzl
+++ b/tensorflow/contrib/lite/build_def.bzl
@@ -391,41 +391,3 @@ def gen_selected_ops(name, model):
               (tool, model, out, tflite_path[2:]),
         tools = [tool],
     )
-
-def gen_full_model_test(conversion_modes, models, data, test_suite_tag):
-    """Generates Python test targets for testing TFLite models.
-
-    Args:
-      conversion_modes: List of conversion modes to test the models on.
-      models: List of models to test.
-      data: List of BUILD targets linking the data.
-      test_suite_tag: Tag identifying the model test suite.
-    """
-    options = [
-        (conversion_mode, model)
-        for model in models
-        for conversion_mode in conversion_modes
-    ]
-
-    for conversion_mode, model_name in options:
-        native.py_test(
-            name = "model_coverage_test_%s_%s" % (model_name, conversion_mode.lower()),
-            srcs = ["model_coverage_test.py"],
-            main = "model_coverage_test.py",
-            args = [
-                "--model_name=%s" % model_name,
-                "--converter_mode=%s" % conversion_mode,
-            ],
-            data = data,
-            srcs_version = "PY2AND3",
-            tags = [
-                "no_oss",
-                "no_windows",
-                "notap",
-            ] + [test_suite_tag],
-            deps = [
-                "//tensorflow/contrib/lite/testing:model_coverage_lib",
-                "//tensorflow/contrib/lite/python:lite",
-                "//tensorflow/python:client_testlib",
-            ],
-        )
diff --git a/tensorflow/contrib/lite/python/BUILD b/tensorflow/contrib/lite/python/BUILD
index 916788f215..57e1290e07 100644
--- a/tensorflow/contrib/lite/python/BUILD
+++ b/tensorflow/contrib/lite/python/BUILD
@@ -144,7 +144,7 @@ py_library(
     name = "convert_saved_model",
     srcs = ["convert_saved_model.py"],
     srcs_version = "PY2AND3",
-    visibility = ["//tensorflow/contrib/lite:__subpackages__"],
+    visibility = ["//visibility:public"],
     deps = [
         ":convert",
         "//tensorflow/contrib/saved_model:saved_model_py",
diff --git a/tensorflow/contrib/lite/python/convert_saved_model.py b/tensorflow/contrib/lite/python/convert_saved_model.py
index d18b60d0ea..1553464b9f 100644
--- a/tensorflow/contrib/lite/python/convert_saved_model.py
+++ b/tensorflow/contrib/lite/python/convert_saved_model.py
@@ -44,7 +44,7 @@ def _log_tensor_details(tensor_info):
                  dtype)
 
 
-def get_meta_graph_def(saved_model_dir, tag_set):
+def _get_meta_graph_def(saved_model_dir, tag_set):
   """Validate saved_model and extract MetaGraphDef.
 
   Args:
@@ -61,7 +61,7 @@ def get_meta_graph_def(saved_model_dir, tag_set):
     return loader.load(sess, tag_set, saved_model_dir)
 
 
-def get_signature_def(meta_graph, signature_key):
+def _get_signature_def(meta_graph, signature_key):
   """Get the signature def from meta_graph with given signature_key.
 
   Args:
@@ -86,7 +86,7 @@ def get_signature_def(meta_graph, signature_key):
   return signature_def_map[signature_key]
 
 
-def get_inputs_outputs(signature_def):
+def _get_inputs_outputs(signature_def):
   """Get inputs and outputs from SignatureDef.
 
   Args:
@@ -236,9 +236,9 @@ def freeze_saved_model(saved_model_dir, input_arrays, input_shapes,
       input_arrays or output_arrays are not valid.
   """
   # Read SignatureDef.
-  meta_graph = get_meta_graph_def(saved_model_dir, tag_set)
-  signature_def = get_signature_def(meta_graph, signature_key)
-  inputs, outputs = get_inputs_outputs(signature_def)
+  meta_graph = _get_meta_graph_def(saved_model_dir, tag_set)
+  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
diff --git a/tensorflow/contrib/lite/testing/BUILD b/tensorflow/contrib/lite/testing/BUILD
index c4a2b03444..a4736bfee9 100644
--- a/tensorflow/contrib/lite/testing/BUILD
+++ b/tensorflow/contrib/lite/testing/BUILD
@@ -13,7 +13,6 @@ load("//tensorflow/contrib/lite:special_rules.bzl", "tflite_portable_test_suite"
 load(
     "//tensorflow:tensorflow.bzl",
     "tf_cc_test",
-    "py_test",
 )
 
 [gen_zip_test(
@@ -363,30 +362,4 @@ cc_binary(
     ],
 )
 
-py_binary(
-    name = "model_coverage_lib",
-    srcs = ["//tensorflow/contrib/lite/testing:model_coverage/model_coverage_lib.py"],
-    srcs_version = "PY2AND3",
-    visibility = ["//tensorflow/contrib/lite:__subpackages__"],
-    deps = [
-        "//tensorflow/contrib/lite/python:lite",
-        "//tensorflow/python:platform",
-    ],
-)
-
-py_test(
-    name = "model_coverage_lib_test",
-    srcs = ["//tensorflow/contrib/lite/testing:model_coverage/model_coverage_lib_test.py"],
-    srcs_version = "PY2AND3",
-    tags = [
-        "no_oss",
-        "no_windows",
-        "notap",
-    ],
-    deps = [
-        ":model_coverage_lib",
-        "//tensorflow/python:client_testlib",
-    ],
-)
-
 tflite_portable_test_suite()
diff --git a/tensorflow/contrib/lite/testing/model_coverage/model_coverage_lib.py b/tensorflow/contrib/lite/testing/model_coverage/model_coverage_lib.py
deleted file mode 100644
index f8ab394c60..0000000000
--- a/tensorflow/contrib/lite/testing/model_coverage/model_coverage_lib.py
+++ /dev/null
@@ -1,241 +0,0 @@
-# Copyright 2018 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.
-# ==============================================================================
-"""Functions to test TFLite models."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import numpy as np
-
-from tensorflow.contrib.lite.python import convert_saved_model as _convert_saved_model
-from tensorflow.contrib.lite.python import lite as _lite
-from tensorflow.core.framework import graph_pb2 as _graph_pb2
-from tensorflow.python import keras as _keras
-from tensorflow.python.client import session as _session
-from tensorflow.python.framework.importer import import_graph_def as _import_graph_def
-from tensorflow.python.lib.io import file_io as _file_io
-from tensorflow.python.saved_model import signature_constants as _signature_constants
-from tensorflow.python.saved_model import tag_constants as _tag_constants
-
-
-def _convert(converter, **kwargs):
-  """Converts the model.
-
-  Args:
-    converter: TocoConverter object.
-    **kwargs: Additional arguments to be passed into the converter. Supported
-      flags are {"converter_mode", "post_training_quant"}.
-
-  Returns:
-    The converted TFLite model in serialized format.
-  """
-  if "converter_mode" in kwargs:
-    converter.converter_mode = kwargs["converter_mode"]
-  if "post_training_quantize" in kwargs:
-    converter.post_training_quantize = kwargs["post_training_quantize"]
-  return converter.convert()
-
-
-def _generate_random_input_data(tflite_model, seed=None):
-  """Generates input data based on the input tensors in the TFLite model.
-
-  Args:
-    tflite_model: Serialized TensorFlow Lite model.
-    seed: Integer seed for the random generator. (default None)
-
-  Returns:
-    List of np.ndarray.
-  """
-  interpreter = _lite.Interpreter(model_content=tflite_model)
-  interpreter.allocate_tensors()
-  input_details = interpreter.get_input_details()
-
-  if seed:
-    np.random.seed(seed=seed)
-  return [
-      np.array(
-          np.random.random_sample(input_tensor["shape"]),
-          dtype=input_tensor["dtype"]) for input_tensor in input_details
-  ]
-
-
-def _evaluate_tflite_model(tflite_model, input_data):
-  """Returns evaluation of input data on TFLite model.
-
-  Args:
-    tflite_model: Serialized TensorFlow Lite model.
-    input_data: List of np.ndarray.
-
-  Returns:
-    List of np.ndarray.
-  """
-  interpreter = _lite.Interpreter(model_content=tflite_model)
-  interpreter.allocate_tensors()
-
-  input_details = interpreter.get_input_details()
-  output_details = interpreter.get_output_details()
-
-  for input_tensor, tensor_data in zip(input_details, input_data):
-    interpreter.set_tensor(input_tensor["index"], tensor_data)
-
-  interpreter.invoke()
-  output_data = [
-      interpreter.get_tensor(output_tensor["index"])
-      for output_tensor in output_details
-  ]
-  return output_data
-
-
-def evaluate_frozen_graph(filename, input_arrays, output_arrays):
-  """Returns a function that evaluates the frozen graph on input data.
-
-  Args:
-    filename: Full filepath of file containing frozen GraphDef.
-    input_arrays: List of input tensors to freeze graph with.
-    output_arrays: List of output tensors to freeze graph with.
-
-  Returns:
-    Lambda function ([np.ndarray data] : [np.ndarray result]).
-  """
-  with _session.Session().as_default() as sess:
-    with _file_io.FileIO(filename, "rb") as f:
-      file_content = f.read()
-
-    graph_def = _graph_pb2.GraphDef()
-    graph_def.ParseFromString(file_content)
-    _import_graph_def(graph_def, name="")
-
-    inputs = _convert_saved_model.get_tensors_from_tensor_names(
-        sess.graph, input_arrays)
-    outputs = _convert_saved_model.get_tensors_from_tensor_names(
-        sess.graph, output_arrays)
-
-    return lambda input_data: sess.run(outputs, dict(zip(inputs, input_data)))
-
-
-def evaluate_saved_model(directory, tag_set, signature_key):
-  """Returns a function that evaluates the SavedModel on input data.
-
-  Args:
-    directory: SavedModel directory to convert.
-    tag_set: Set of tags identifying the MetaGraphDef within the SavedModel to
-      analyze. All tags in the tag set must be present.
-    signature_key: Key identifying SignatureDef containing inputs and outputs.
-
-  Returns:
-    Lambda function ([np.ndarray data] : [np.ndarray result]).
-  """
-  with _session.Session().as_default() as sess:
-    if tag_set is None:
-      tag_set = set([_tag_constants.SERVING])
-    if signature_key is None:
-      signature_key = _signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY
-
-    meta_graph = _convert_saved_model.get_meta_graph_def(directory, tag_set)
-    signature_def = _convert_saved_model.get_signature_def(
-        meta_graph, signature_key)
-    inputs, outputs = _convert_saved_model.get_inputs_outputs(signature_def)
-
-    return lambda input_data: sess.run(outputs, dict(zip(inputs, input_data)))
-
-
-def evaluate_keras_model(filename):
-  """Returns a function that evaluates the tf.keras model on input data.
-
-  Args:
-    filename: Full filepath of HDF5 file containing the tf.keras model.
-
-  Returns:
-    Lambda function ([np.ndarray data] : [np.ndarray result]).
-  """
-  keras_model = _keras.models.load_model(filename)
-  return lambda input_data: [keras_model.predict(input_data)]
-
-
-# TODO(nupurgarg): Make this function a parameter to test_frozen_graph (and
-# related functions) in order to make it easy to use different data generators.
-def compare_models_random_data(tflite_model, tf_eval_func, tolerance=5):
-  """Compares TensorFlow and TFLite models with random data.
-
-  Args:
-    tflite_model: Serialized TensorFlow Lite model.
-    tf_eval_func: Lambda function that takes in input data and outputs the
-      results of the TensorFlow model ([np.ndarray data] : [np.ndarray result]).
-    tolerance: Decimal place to check accuracy to.
-  """
-  input_data = _generate_random_input_data(tflite_model)
-  tf_results = tf_eval_func(input_data)
-  tflite_results = _evaluate_tflite_model(tflite_model, input_data)
-  for tf_result, tflite_result in zip(tf_results, tflite_results):
-    np.testing.assert_almost_equal(tf_result, tflite_result, tolerance)
-
-
-def test_frozen_graph(filename, input_arrays, output_arrays, **kwargs):
-  """Validates the TensorFlow frozen graph converts to a TFLite model.
-
-  Converts the TensorFlow frozen graph to TFLite and checks the accuracy of the
-  model on random data.
-
-  Args:
-    filename: Full filepath of file containing frozen GraphDef.
-    input_arrays: List of input tensors to freeze graph with.
-    output_arrays: List of output tensors to freeze graph with.
-    **kwargs: Additional arguments to be passed into the converter.
-  """
-  converter = _lite.TocoConverter.from_frozen_graph(filename, input_arrays,
-                                                    output_arrays)
-  tflite_model = _convert(converter, **kwargs)
-
-  tf_eval_func = evaluate_frozen_graph(filename, input_arrays, output_arrays)
-  compare_models_random_data(tflite_model, tf_eval_func)
-
-
-def test_saved_model(directory, tag_set=None, signature_key=None, **kwargs):
-  """Validates the TensorFlow SavedModel converts to a TFLite model.
-
-  Converts the TensorFlow SavedModel to TFLite and checks the accuracy of the
-  model on random data.
-
-  Args:
-    directory: SavedModel directory to convert.
-    tag_set: Set of tags identifying the MetaGraphDef within the SavedModel to
-      analyze. All tags in the tag set must be present.
-    signature_key: Key identifying SignatureDef containing inputs and outputs.
-    **kwargs: Additional arguments to be passed into the converter.
-  """
-  converter = _lite.TocoConverter.from_saved_model(directory, tag_set,
-                                                   signature_key)
-  tflite_model = _convert(converter, **kwargs)
-
-  tf_eval_func = evaluate_saved_model(directory, tag_set, signature_key)
-  compare_models_random_data(tflite_model, tf_eval_func)
-
-
-def test_keras_model(filename, **kwargs):
-  """Validates the tf.keras model converts to a TFLite model.
-
-  Converts the tf.keras model to TFLite and checks the accuracy of the model on
-  random data.
-
-  Args:
-    filename: Full filepath of HDF5 file containing the tf.keras model.
-    **kwargs: Additional arguments to be passed into the converter.
-  """
-  converter = _lite.TocoConverter.from_keras_model_file(filename)
-  tflite_model = _convert(converter, **kwargs)
-
-  tf_eval_func = evaluate_keras_model(filename)
-  compare_models_random_data(tflite_model, tf_eval_func)
diff --git a/tensorflow/contrib/lite/testing/model_coverage/model_coverage_lib_test.py b/tensorflow/contrib/lite/testing/model_coverage/model_coverage_lib_test.py
deleted file mode 100644
index 5f3355e734..0000000000
--- a/tensorflow/contrib/lite/testing/model_coverage/model_coverage_lib_test.py
+++ /dev/null
@@ -1,130 +0,0 @@
-# Copyright 2018 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 model_coverage_lib.py."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import os
-import tempfile
-
-from tensorflow.contrib.lite.python import lite
-from tensorflow.contrib.lite.testing.model_coverage import model_coverage_lib as model_coverage
-from tensorflow.python import keras
-from tensorflow.python.client import session
-from tensorflow.python.framework import constant_op
-from tensorflow.python.framework import dtypes
-from tensorflow.python.ops import array_ops
-from tensorflow.python.ops import math_ops
-from tensorflow.python.platform import test
-from tensorflow.python.saved_model import saved_model
-from tensorflow.python.training.training_util import write_graph
-
-
-class EvaluateFrozenGraph(test.TestCase):
-
-  def _saveFrozenGraph(self, sess):
-    graph_def_file = os.path.join(self.get_temp_dir(), 'model.pb')
-    write_graph(sess.graph_def, '', graph_def_file, False)
-    return graph_def_file
-
-  def testFloat(self):
-    with session.Session().as_default() as sess:
-      in_tensor = array_ops.placeholder(
-          shape=[1, 16, 16, 3], dtype=dtypes.float32)
-      _ = in_tensor + in_tensor
-    filename = self._saveFrozenGraph(sess)
-
-    model_coverage.test_frozen_graph(filename, ['Placeholder'], ['add'])
-
-  def testMultipleOutputs(self):
-    with session.Session().as_default() as sess:
-      in_tensor_1 = array_ops.placeholder(
-          shape=[1, 16], dtype=dtypes.float32, name='inputA')
-      in_tensor_2 = array_ops.placeholder(
-          shape=[1, 16], dtype=dtypes.float32, name='inputB')
-
-      weight = constant_op.constant(-1.0, shape=[16, 16])
-      bias = constant_op.constant(-1.0, shape=[16])
-      layer = math_ops.matmul(in_tensor_1, weight) + bias
-      _ = math_ops.reduce_mean(math_ops.square(layer - in_tensor_2))
-    filename = self._saveFrozenGraph(sess)
-
-    model_coverage.test_frozen_graph(filename, ['inputA', 'inputB'],
-                                     ['add', 'Mean'])
-
-
-class EvaluateSavedModel(test.TestCase):
-
-  def testFloat(self):
-    saved_model_dir = os.path.join(self.get_temp_dir(), 'simple_savedmodel')
-    with session.Session().as_default() as sess:
-      in_tensor_1 = array_ops.placeholder(
-          shape=[1, 16, 16, 3], dtype=dtypes.float32, name='inputB')
-      in_tensor_2 = array_ops.placeholder(
-          shape=[1, 16, 16, 3], dtype=dtypes.float32, name='inputA')
-      out_tensor = in_tensor_1 + in_tensor_2
-
-      inputs = {'x': in_tensor_1, 'y': in_tensor_2}
-      outputs = {'z': out_tensor}
-      saved_model.simple_save(sess, saved_model_dir, inputs, outputs)
-    model_coverage.test_saved_model(saved_model_dir)
-
-
-class EvaluateKerasModel(test.TestCase):
-
-  def _getSingleInputKerasModel(self):
-    """Returns single input Sequential tf.keras model."""
-    keras.backend.clear_session()
-
-    xs = [-1, 0, 1, 2, 3, 4]
-    ys = [-3, -1, 1, 3, 5, 7]
-
-    model = keras.Sequential([keras.layers.Dense(units=1, input_shape=[1])])
-    model.compile(optimizer='sgd', loss='mean_squared_error')
-    model.train_on_batch(xs, ys)
-    return model
-
-  def _saveKerasModel(self, model):
-    try:
-      fd, keras_file = tempfile.mkstemp('.h5')
-      keras.models.save_model(model, keras_file)
-    finally:
-      os.close(fd)
-    return keras_file
-
-  def testFloat(self):
-    model = self._getSingleInputKerasModel()
-    keras_file = self._saveKerasModel(model)
-
-    model_coverage.test_keras_model(keras_file)
-
-  def testPostTrainingQuantize(self):
-    model = self._getSingleInputKerasModel()
-    keras_file = self._saveKerasModel(model)
-
-    model_coverage.test_keras_model(keras_file, post_training_quantize=True)
-
-  def testConverterMode(self):
-    model = self._getSingleInputKerasModel()
-    keras_file = self._saveKerasModel(model)
-
-    model_coverage.test_keras_model(
-        keras_file, converter_mode=lite.ConverterMode.TOCO_EXTENDED)
-
-
-if __name__ == '__main__':
-  test.main()