experiments/STT-tensorflow
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

Move Keras related test case in graph_util_test to keras/tests.

Browse Source 
This will remove the dependency from TF to Keras.

PiperOrigin-RevId: 306082751
Change-Id: I8b87f32653c1c366992f9a3088e59889c3ef934f
This commit is contained in:
Scott Zhu 2020-04-11 20:58:20 -07:00 committed by TensorFlower Gardener
parent d8ff19311d
commit 421741965f
3 changed files with 171 additions and 102 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

102
tensorflow/python/framework/graph_util_test.py
View File

@ -18,14 +18,11 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.core.framework import attr_value_pb2
from tensorflow.core.framework import graph_pb2
from tensorflow.core.framework import node_def_pb2
from tensorflow.core.protobuf import config_pb2
from tensorflow.core.protobuf import meta_graph_pb2
from tensorflow.python import keras
from tensorflow.python.client import session
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
@ -40,7 +37,6 @@ from tensorflow.python.ops import array_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import gen_math_ops
from tensorflow.python.ops import gen_state_ops
from tensorflow.python.ops import math_ops # pylint: disable=unused-import
from tensorflow.python.ops import math_ops as math_ops_lib
from tensorflow.python.ops import variable_scope
from tensorflow.python.ops import variables
@ -322,40 +318,12 @@ class DeviceFunctionsTest(test.TestCase):
class ConvertVariablesToConstantsTest(test.TestCase):
def _get_tensors(self, sess, tensor_list):
"""Returns a list of Tensor objects from the Session."""
return [
sess.graph.get_tensor_by_name(tensor.name) for tensor in tensor_list
]
def _get_tensor_names(self, tensors):
"""Returns a list of string names for the tensors specified."""
return [tensor.name.split(":")[0] for tensor in tensors]
def _evaluate_graph_def(self, graph_def, inputs, outputs, input_data):
"""Evaluates the GraphDef using Sessions."""
with ops.Graph().as_default() as graph:
importer.import_graph_def(graph_def, name="")
sess = session.Session(graph=graph)
input_tensors = self._get_tensors(sess, inputs)
output_tensors = self._get_tensors(sess, outputs)
return sess.run(
output_tensors, feed_dict=dict(zip(input_tensors, input_data)))
def _ensure_no_variables_in_graph(self, graph_def):
"""Ensures there are no variables in the graph."""
for node in graph_def.node:
self.assertNotIn(
node.op, ["Variable", "VariableV2", "VarHandleOp", "ReadVariableOp"])
def _test_converted_keras_model(self, model, constant_graph_def, input_data):
"""Compares the converted Keras model."""
expected_value = model.predict(input_data)
actual_value = self._evaluate_graph_def(constant_graph_def, model.inputs,
model.outputs, [input_data])
np.testing.assert_almost_equal(np.array([expected_value]), actual_value, 5)
def _test_variable_to_const_conversion(self, use_resource):
with ops.Graph().as_default():
with variable_scope.variable_scope("", use_resource=use_resource):
@ -524,30 +492,6 @@ class ConvertVariablesToConstantsTest(test.TestCase):
"""Freezes a graph with functions that have been inlined using Grappler."""
self._test_convert_variables_with_functions(inline_functions=True)
def testWithEmbeddings(self):
"""Freezes a graph with embeddings."""
ops.disable_eager_execution()
state_input = keras.layers.Input(
shape=(1,), name="state_input", dtype="int32")
output = keras.layers.Embedding(
output_dim=16, input_dim=100, input_length=1, name="state")(
state_input)
model = keras.models.Model(inputs=[state_input], outputs=[output])
model.compile(
loss={"state": "sparse_categorical_crossentropy"}, optimizer="adam")
# Freeze the graph.
sess = keras.backend.get_session()
variable_graph_def = sess.graph_def
output_tensor = self._get_tensor_names(model.outputs)
constant_graph_def = graph_util.convert_variables_to_constants(
sess, variable_graph_def, output_tensor)
# Validate converted graph.
input_data = np.array(np.random.random_sample([1, 1]), dtype=np.int32)
self._ensure_no_variables_in_graph(constant_graph_def)
self._test_converted_keras_model(model, constant_graph_def, input_data)
def testGraphWithSwitch(self):
"""Freezes a graph which contains a Switch with type RESOURCE_DT."""
with ops.Graph().as_default():
@ -569,52 +513,6 @@ class ConvertVariablesToConstantsTest(test.TestCase):
self._ensure_no_variables_in_graph(constant_graph_def)
def testKerasBatchNorm(self):
"""Freezes a graph with Keras batch norm."""
ops.disable_eager_execution()
inputs = keras.layers.Input(shape=(128, 128, 1))
batch_norm = keras.layers.BatchNormalization()(inputs)
model = keras.models.Model(inputs, batch_norm, name="test")
model.compile(
optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"])
tensor_names = [tensor.name for tensor in model.inputs + model.outputs]
# Freeze the graph.
sess = keras.backend.get_session()
variable_graph_def = sess.graph_def
variable_graph_def = self._inline_functions(variable_graph_def,
tensor_names)
output_tensor = self._get_tensor_names(model.outputs)
constant_graph_def = graph_util.convert_variables_to_constants(
sess, variable_graph_def, output_tensor)
# Validate converted graph.
input_data = np.array(
np.random.random_sample([1, 128, 128, 1]), dtype=np.int32)
self._ensure_no_variables_in_graph(constant_graph_def)
self._test_converted_keras_model(model, constant_graph_def, input_data)
def testLSTM(self):
"""Freezes a Keras LSTM."""
ops.disable_eager_execution()
model = keras.models.Sequential(
[keras.layers.LSTM(units=10, input_shape=(10, 10))])
tensor_names = [tensor.name for tensor in model.inputs + model.outputs]
# Freeze the model.
sess = keras.backend.get_session()
variable_graph_def = sess.graph_def
variable_graph_def = self._inline_functions(variable_graph_def,
tensor_names)
output_tensor = self._get_tensor_names(model.outputs)
constant_graph_def = graph_util.convert_variables_to_constants(
sess, variable_graph_def, output_tensor)
# Validate converted graph.
input_data = np.array(np.random.random_sample([10, 10, 10]), dtype=np.int32)
self._ensure_no_variables_in_graph(constant_graph_def)
self._test_converted_keras_model(model, constant_graph_def, input_data)
if __name__ == "__main__":
test.main()

16
tensorflow/python/keras/tests/BUILD
View File

@ -77,6 +77,22 @@ cuda_py_test(
],
)
tf_py_test(
name = "graph_util_test",
srcs = ["graph_util_test.py"],
python_version = "PY3",
deps = [
"//tensorflow/core:protos_all_py",
"//tensorflow/python:client_testlib",
"//tensorflow/python:framework",
"//tensorflow/python:framework_ops",
"//tensorflow/python:graph_util",
"//tensorflow/python:saver",
"//tensorflow/python:session",
"//tensorflow/python:tf_optimizer",
],
)
tf_py_test(
name = "integration_test",
size = "medium",

155
tensorflow/python/keras/tests/graph_util_test.py Normal file
View File

@ -0,0 +1,155 @@
# Copyright 2015 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 tensorflow.python.client.graph_util."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.core.protobuf import config_pb2
from tensorflow.core.protobuf import meta_graph_pb2
from tensorflow.python import keras
from tensorflow.python.client import session
from tensorflow.python.framework import graph_util
from tensorflow.python.framework import importer
from tensorflow.python.framework import ops
from tensorflow.python.grappler import tf_optimizer
from tensorflow.python.platform import test
from tensorflow.python.training.saver import export_meta_graph
class ConvertVariablesToConstantsTest(test.TestCase):
def _get_tensors(self, sess, tensor_list):
"""Returns a list of Tensor objects from the Session."""
return [
sess.graph.get_tensor_by_name(tensor.name) for tensor in tensor_list
]
def _get_tensor_names(self, tensors):
"""Returns a list of string names for the tensors specified."""
return [tensor.name.split(":")[0] for tensor in tensors]
def _evaluate_graph_def(self, graph_def, inputs, outputs, input_data):
"""Evaluates the GraphDef using Sessions."""
with ops.Graph().as_default() as graph:
importer.import_graph_def(graph_def, name="")
sess = session.Session(graph=graph)
input_tensors = self._get_tensors(sess, inputs)
output_tensors = self._get_tensors(sess, outputs)
return sess.run(
output_tensors, feed_dict=dict(zip(input_tensors, input_data)))
def _ensure_no_variables_in_graph(self, graph_def):
"""Ensures there are no variables in the graph."""
for node in graph_def.node:
self.assertNotIn(
node.op, ["Variable", "VariableV2", "VarHandleOp", "ReadVariableOp"])
def _test_converted_keras_model(self, model, constant_graph_def, input_data):
"""Compares the converted Keras model."""
expected_value = model.predict(input_data)
actual_value = self._evaluate_graph_def(constant_graph_def, model.inputs,
model.outputs, [input_data])
np.testing.assert_almost_equal(np.array([expected_value]), actual_value, 5)
def _inline_functions(self, graph_def, arrays):
meta_graph = export_meta_graph(graph_def=graph_def)
fetch_collection = meta_graph_pb2.CollectionDef()
for name in arrays:
fetch_collection.node_list.value.append(name)
meta_graph.collection_def["train_op"].CopyFrom(fetch_collection)
# Initialize RewriterConfig with everything disabled except function
# inlining.
config = config_pb2.ConfigProto()
rewrite_options = config.graph_options.rewrite_options
rewrite_options.optimizers.append("function")
return tf_optimizer.OptimizeGraph(config, meta_graph)
def testWithEmbeddings(self):
"""Freezes a graph with embeddings."""
state_input = keras.layers.Input(
shape=(1,), name="state_input", dtype="int32")
output = keras.layers.Embedding(
output_dim=16, input_dim=100, input_length=1, name="state")(
state_input)
model = keras.models.Model(inputs=[state_input], outputs=[output])
model.compile(
loss={"state": "sparse_categorical_crossentropy"}, optimizer="adam")
# Freeze the graph.
sess = keras.backend.get_session()
variable_graph_def = sess.graph_def
output_tensor = self._get_tensor_names(model.outputs)
constant_graph_def = graph_util.convert_variables_to_constants(
sess, variable_graph_def, output_tensor)
# Validate converted graph.
input_data = np.array(np.random.random_sample([1, 1]), dtype=np.int32)
self._ensure_no_variables_in_graph(constant_graph_def)
self._test_converted_keras_model(model, constant_graph_def, input_data)
def testKerasBatchNorm(self):
"""Freezes a graph with Keras batch norm."""
inputs = keras.layers.Input(shape=(128, 128, 1))
batch_norm = keras.layers.BatchNormalization()(inputs)
model = keras.models.Model(inputs, batch_norm, name="test")
model.compile(
optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"])
tensor_names = [tensor.name for tensor in model.inputs + model.outputs]
# Freeze the graph.
sess = keras.backend.get_session()
variable_graph_def = sess.graph_def
variable_graph_def = self._inline_functions(variable_graph_def,
tensor_names)
output_tensor = self._get_tensor_names(model.outputs)
constant_graph_def = graph_util.convert_variables_to_constants(
sess, variable_graph_def, output_tensor)
# Validate converted graph.
input_data = np.array(
np.random.random_sample([1, 128, 128, 1]), dtype=np.int32)
self._ensure_no_variables_in_graph(constant_graph_def)
self._test_converted_keras_model(model, constant_graph_def, input_data)
def testLSTM(self):
"""Freezes a Keras LSTM."""
model = keras.models.Sequential(
[keras.layers.LSTM(units=10, input_shape=(10, 10))])
tensor_names = [tensor.name for tensor in model.inputs + model.outputs]
# Freeze the model.
sess = keras.backend.get_session()
variable_graph_def = sess.graph_def
variable_graph_def = self._inline_functions(variable_graph_def,
tensor_names)
output_tensor = self._get_tensor_names(model.outputs)
constant_graph_def = graph_util.convert_variables_to_constants(
sess, variable_graph_def, output_tensor)
# Validate converted graph.
input_data = np.array(np.random.random_sample([10, 10, 10]), dtype=np.int32)
self._ensure_no_variables_in_graph(constant_graph_def)
self._test_converted_keras_model(model, constant_graph_def, input_data)
if __name__ == "__main__":
ops.disable_eager_execution()
test.main()