344 lines
14 KiB
Python
344 lines
14 KiB
Python
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Tests the graph freezing tool."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os
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import re
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from absl.testing import parameterized
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from tensorflow.core.example import example_pb2
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from tensorflow.core.framework import graph_pb2
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from tensorflow.core.protobuf import saver_pb2
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from tensorflow.python.client import session
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import graph_io
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from tensorflow.python.framework import importer
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from tensorflow.python.framework import ops
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from tensorflow.python.framework import test_util
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.ops import nn
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from tensorflow.python.ops import parsing_ops
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from tensorflow.python.ops import partitioned_variables
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from tensorflow.python.ops import variable_scope
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from tensorflow.python.ops import variables
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from tensorflow.python.platform import test
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from tensorflow.python.saved_model import builder as saved_model_builder
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from tensorflow.python.saved_model import signature_constants
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from tensorflow.python.saved_model import signature_def_utils
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from tensorflow.python.saved_model import tag_constants
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from tensorflow.python.tools import freeze_graph
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from tensorflow.python.training import saver as saver_lib
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class FreezeGraphTest(test_util.TensorFlowTestCase, parameterized.TestCase):
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def _testFreezeGraph(self, saver_write_version):
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checkpoint_prefix = os.path.join(self.get_temp_dir(), "saved_checkpoint")
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checkpoint_state_name = "checkpoint_state"
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input_graph_name = "input_graph.pb"
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output_graph_name = "output_graph.pb"
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# We'll create an input graph that has a single variable containing 1.0,
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# and that then multiplies it by 2.
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with ops.Graph().as_default():
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variable_node = variables.VariableV1(1.0, name="variable_node")
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output_node = math_ops.multiply(variable_node, 2.0, name="output_node")
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sess = session.Session()
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init = variables.global_variables_initializer()
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sess.run(init)
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output = sess.run(output_node)
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self.assertNear(2.0, output, 0.00001)
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saver = saver_lib.Saver(write_version=saver_write_version)
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checkpoint_path = saver.save(
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sess,
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checkpoint_prefix,
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global_step=0,
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latest_filename=checkpoint_state_name)
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graph_io.write_graph(sess.graph, self.get_temp_dir(), input_graph_name)
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# We save out the graph to disk, and then call the const conversion
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# routine.
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input_graph_path = os.path.join(self.get_temp_dir(), input_graph_name)
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input_saver_def_path = ""
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input_binary = False
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output_node_names = "output_node"
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restore_op_name = "save/restore_all"
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filename_tensor_name = "save/Const:0"
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output_graph_path = os.path.join(self.get_temp_dir(), output_graph_name)
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clear_devices = False
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freeze_graph.freeze_graph(
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input_graph_path,
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input_saver_def_path,
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input_binary,
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checkpoint_path,
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output_node_names,
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restore_op_name,
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filename_tensor_name,
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output_graph_path,
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clear_devices,
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"",
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"",
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"",
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checkpoint_version=saver_write_version)
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# Now we make sure the variable is now a constant, and that the graph still
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# produces the expected result.
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with ops.Graph().as_default():
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output_graph_def = graph_pb2.GraphDef()
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with open(output_graph_path, "rb") as f:
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output_graph_def.ParseFromString(f.read())
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_ = importer.import_graph_def(output_graph_def, name="")
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self.assertEqual(4, len(output_graph_def.node))
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for node in output_graph_def.node:
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self.assertNotEqual("VariableV2", node.op)
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self.assertNotEqual("Variable", node.op)
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with session.Session() as sess:
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output_node = sess.graph.get_tensor_by_name("output_node:0")
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output = sess.run(output_node)
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self.assertNear(2.0, output, 0.00001)
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def _createTFExampleString(self, feature_name, feature_value):
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"""Create a serialized tensorflow example."""
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example = example_pb2.Example()
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example.features.feature[feature_name].float_list.value.extend([
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feature_value])
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return example.SerializeToString()
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def _writeDummySavedModel(self, path, feature_name, tags):
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"""Writes a classifier with two input features to the given path."""
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with ops.Graph().as_default():
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examples = array_ops.placeholder(dtypes.string, name="input_node")
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feature_configs = {
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feature_name: parsing_ops.FixedLenFeature(shape=[],
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dtype=dtypes.float32),
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}
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features = parsing_ops.parse_example(examples, feature_configs)
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feature = features[feature_name]
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variable_node = variables.VariableV1(1.0, name="variable_node")
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scores = math_ops.multiply(variable_node, feature, name="output_node")
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class_feature = array_ops.fill(array_ops.shape(feature),
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"class_%s" % feature_name)
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classes = array_ops.transpose(class_feature)
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with session.Session() as sess:
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sess.run(variables.global_variables_initializer())
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signature = (
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signature_def_utils.classification_signature_def(
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examples=examples,
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classes=classes,
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scores=scores,))
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builder = saved_model_builder.SavedModelBuilder(path)
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builder.add_meta_graph_and_variables(
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sess,
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tags,
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signature_def_map={
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signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
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signature,
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},
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)
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builder.save(as_text=True)
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@test_util.run_v1_only("b/120545219")
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def testFreezeGraphV1(self):
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self._testFreezeGraph(saver_pb2.SaverDef.V1)
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@test_util.run_v1_only("b/120545219")
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def testFreezeGraphV2(self):
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self._testFreezeGraph(saver_pb2.SaverDef.V2)
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def testFreezeMetaGraph(self):
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tmp_dir = self.get_temp_dir()
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checkpoint_prefix = os.path.join(tmp_dir, "meta_graph_checkpoint")
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checkpoint_state_name = "checkpoint_state"
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output_graph_filename = os.path.join(tmp_dir, "output_graph.pb")
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with ops.Graph().as_default():
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variable_node = variables.VariableV1(1.0, name="variable_node")
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output_node = math_ops.multiply(variable_node, 2.0, name="output_node")
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sess = session.Session()
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init = variables.global_variables_initializer()
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sess.run(init)
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output = sess.run(output_node)
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self.assertNear(2.0, output, 0.00001)
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saver = saver_lib.Saver()
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checkpoint_path = saver.save(
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sess,
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checkpoint_prefix,
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global_step=0,
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latest_filename=checkpoint_state_name)
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input_saver_def_path = ""
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input_binary = True
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output_node_names = "output_node"
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restore_op_name = "save/restore_all"
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filename_tensor_name = "save/Const:0"
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clear_devices = False
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input_meta_graph = checkpoint_path + ".meta"
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freeze_graph.freeze_graph(
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"", input_saver_def_path, input_binary, checkpoint_path,
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output_node_names, restore_op_name, filename_tensor_name,
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output_graph_filename, clear_devices, "", "", "", input_meta_graph)
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# Now we make sure the variable is now a constant, and that the graph still
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# produces the expected result.
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with ops.Graph().as_default():
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output_graph_def = graph_pb2.GraphDef()
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with open(output_graph_filename, "rb") as f:
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output_graph_def.ParseFromString(f.read())
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_ = importer.import_graph_def(output_graph_def, name="")
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self.assertEqual(4, len(output_graph_def.node))
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for node in output_graph_def.node:
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self.assertNotEqual("VariableV2", node.op)
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self.assertNotEqual("Variable", node.op)
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with session.Session() as sess:
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output_node = sess.graph.get_tensor_by_name("output_node:0")
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output = sess.run(output_node)
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self.assertNear(2.0, output, 0.00001)
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@parameterized.named_parameters(
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("empty_tags_set", "", []),
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("default_tags_set", tag_constants.SERVING, [tag_constants.SERVING]))
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def testFreezeSavedModel(self, tags_string, tags_list):
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tmp_dir = self.get_temp_dir()
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saved_model_dir = os.path.join(tmp_dir, "saved_model_dir")
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feature_name = "feature"
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self._writeDummySavedModel(saved_model_dir, feature_name, tags_list)
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output_graph_filename = os.path.join(tmp_dir, "output_graph.pb")
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input_saved_model_dir = saved_model_dir
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output_node_names = "output_node"
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input_binary = False
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input_saver_def_path = False
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restore_op_name = None
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filename_tensor_name = None
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clear_devices = False
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input_meta_graph = False
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checkpoint_path = None
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input_graph_filename = None
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saved_model_tags = tags_string
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freeze_graph.freeze_graph(input_graph_filename, input_saver_def_path,
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input_binary, checkpoint_path, output_node_names,
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restore_op_name, filename_tensor_name,
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output_graph_filename, clear_devices, "", "", "",
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input_meta_graph, input_saved_model_dir,
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saved_model_tags)
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# Now we make sure the variable is now a constant, and that the graph still
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# produces the expected result.
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with ops.Graph().as_default():
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output_graph_def = graph_pb2.GraphDef()
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with open(output_graph_filename, "rb") as f:
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output_graph_def.ParseFromString(f.read())
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_ = importer.import_graph_def(output_graph_def, name="")
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if any(u"ParseExampleV2" in node.name for node in output_graph_def.node):
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expected_node_count = 10
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else:
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expected_node_count = 8
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self.assertEqual(expected_node_count, len(output_graph_def.node))
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for node in output_graph_def.node:
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self.assertNotEqual("VariableV2", node.op)
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self.assertNotEqual("Variable", node.op)
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feature_value = 2.0
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example = self._createTFExampleString(feature_name, feature_value)
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with session.Session() as sess:
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input_node = sess.graph.get_tensor_by_name("input_node:0")
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output_node = sess.graph.get_tensor_by_name("output_node:0")
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output = sess.run(output_node, feed_dict={input_node: [example]})
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self.assertNear(feature_value, output, 0.00001)
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def testSinglePartitionedVariable(self):
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"""Ensures partitioned variables fail cleanly with freeze graph."""
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checkpoint_prefix = os.path.join(self.get_temp_dir(), "saved_checkpoint")
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checkpoint_state_name = "checkpoint_state"
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input_graph_name = "input_graph.pb"
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output_graph_name = "output_graph.pb"
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# Create a graph with partition variables. When weights are partitioned into
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# a single partition, the weights variable is followed by a identity ->
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# identity (an additional identity node).
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partitioner = partitioned_variables.fixed_size_partitioner(1)
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with ops.Graph().as_default():
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with variable_scope.variable_scope("part", partitioner=partitioner):
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batch_size, height, width, depth = 5, 128, 128, 3
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input1 = array_ops.zeros(
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(batch_size, height, width, depth), name="input1")
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input2 = array_ops.zeros(
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(batch_size, height, width, depth), name="input2")
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num_nodes = depth
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filter1 = variable_scope.get_variable("filter", [num_nodes, num_nodes])
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filter2 = array_ops.reshape(filter1, [1, 1, num_nodes, num_nodes])
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conv = nn.conv2d(
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input=input1, filter=filter2, strides=[1, 1, 1, 1], padding="SAME")
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node = math_ops.add(conv, input2, name="test/add")
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node = nn.relu6(node, name="test/relu6")
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# Save graph and checkpoints.
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sess = session.Session()
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sess.run(variables.global_variables_initializer())
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saver = saver_lib.Saver()
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checkpoint_path = saver.save(
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sess,
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checkpoint_prefix,
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global_step=0,
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latest_filename=checkpoint_state_name)
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graph_io.write_graph(sess.graph, self.get_temp_dir(), input_graph_name)
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# Ensure this graph has partition variables.
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self.assertTrue([
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tensor.name.split(":")[0]
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for op in sess.graph.get_operations()
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for tensor in op.values()
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if re.search(r"/part_\d+/", tensor.name)
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])
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# Test freezing graph doesn't make it crash.
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output_node_names = "save/restore_all"
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output_graph_path = os.path.join(self.get_temp_dir(), output_graph_name)
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with self.assertRaises(ValueError):
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freeze_graph.freeze_graph_with_def_protos(
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input_graph_def=sess.graph_def,
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input_saver_def=None,
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input_checkpoint=checkpoint_path,
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output_node_names=output_node_names,
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restore_op_name="save/restore_all", # default value
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filename_tensor_name="save/Const:0", # default value
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output_graph=output_graph_path,
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clear_devices=False,
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initializer_nodes="")
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if __name__ == "__main__":
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test.main()
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