165 lines
6.8 KiB
Python
165 lines
6.8 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 node stripping 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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from tensorflow.core.framework import graph_pb2
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from tensorflow.python.client import session
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from tensorflow.python.framework import constant_op
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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 math_ops
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from tensorflow.python.platform import test
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from tensorflow.python.tools import strip_unused_lib
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class StripUnusedTest(test_util.TensorFlowTestCase):
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def testStripUnused(self):
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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 constant 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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constant_node = constant_op.constant(1.0, name="constant_node")
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wanted_input_node = math_ops.subtract(constant_node,
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3.0,
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name="wanted_input_node")
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output_node = math_ops.multiply(
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wanted_input_node, 2.0, name="output_node")
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math_ops.add(output_node, 2.0, name="later_node")
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sess = session.Session()
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output = self.evaluate(output_node)
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self.assertNear(-4.0, output, 0.00001)
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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_binary = False
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output_binary = True
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output_node_names = "output_node"
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output_graph_path = os.path.join(self.get_temp_dir(), output_graph_name)
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def strip(input_node_names):
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strip_unused_lib.strip_unused_from_files(input_graph_path, input_binary,
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output_graph_path, output_binary,
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input_node_names,
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output_node_names,
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dtypes.float32.as_datatype_enum)
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with self.assertRaises(KeyError):
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strip("does_not_exist")
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with self.assertRaises(ValueError):
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strip("wanted_input_node:0")
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input_node_names = "wanted_input_node"
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strip(input_node_names)
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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(3, len(output_graph_def.node))
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for node in output_graph_def.node:
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self.assertNotEqual("Add", node.op)
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self.assertNotEqual("Sub", node.op)
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if node.name == input_node_names:
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self.assertTrue("shape" in node.attr)
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with session.Session() as sess:
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input_node = sess.graph.get_tensor_by_name("wanted_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: [10.0]})
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self.assertNear(20.0, output, 0.00001)
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def testStripUnusedMultipleInputs(self):
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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 multiplies two input nodes.
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with ops.Graph().as_default():
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constant_node1 = constant_op.constant(1.0, name="constant_node1")
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constant_node2 = constant_op.constant(2.0, name="constant_node2")
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input_node1 = math_ops.subtract(constant_node1, 3.0, name="input_node1")
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input_node2 = math_ops.subtract(constant_node2, 5.0, name="input_node2")
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output_node = math_ops.multiply(
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input_node1, input_node2, name="output_node")
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math_ops.add(output_node, 2.0, name="later_node")
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sess = session.Session()
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output = self.evaluate(output_node)
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self.assertNear(6.0, output, 0.00001)
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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_binary = False
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input_node_names = "input_node1,input_node2"
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input_node_types = [
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dtypes.float32.as_datatype_enum, dtypes.float32.as_datatype_enum
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]
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output_binary = True
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output_node_names = "output_node"
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output_graph_path = os.path.join(self.get_temp_dir(), output_graph_name)
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strip_unused_lib.strip_unused_from_files(input_graph_path, input_binary,
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output_graph_path, output_binary,
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input_node_names,
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output_node_names,
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input_node_types)
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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(3, len(output_graph_def.node))
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for node in output_graph_def.node:
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self.assertNotEqual("Add", node.op)
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self.assertNotEqual("Sub", node.op)
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if node.name == input_node_names:
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self.assertTrue("shape" in node.attr)
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with session.Session() as sess:
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input_node1 = sess.graph.get_tensor_by_name("input_node1:0")
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input_node2 = sess.graph.get_tensor_by_name("input_node2:0")
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output_node = sess.graph.get_tensor_by_name("output_node:0")
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output = sess.run(output_node,
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feed_dict={input_node1: [10.0],
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input_node2: [-5.0]})
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self.assertNear(-50.0, output, 0.00001)
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if __name__ == "__main__":
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test.main()
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