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# 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 the node stripping tool."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from tensorflow.core.framework import graph_pb2
from tensorflow.python.client import session
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import graph_io
from tensorflow.python.framework import importer
from tensorflow.python.framework import ops
from tensorflow.python.framework import test_util
from tensorflow.python.ops import math_ops
from tensorflow.python.platform import test
from tensorflow.python.tools import strip_unused_lib
class StripUnusedTest(test_util.TensorFlowTestCase):
def testStripUnused(self):
input_graph_name = "input_graph.pb"
output_graph_name = "output_graph.pb"
# We'll create an input graph that has a single constant containing 1.0,
# and that then multiplies it by 2.
with ops.Graph().as_default():
constant_node = constant_op.constant(1.0, name="constant_node")
wanted_input_node = math_ops.subtract(constant_node,
3.0,
name="wanted_input_node")
output_node = math_ops.multiply(
wanted_input_node, 2.0, name="output_node")
math_ops.add(output_node, 2.0, name="later_node")
sess = session.Session()
output = sess.run(output_node)
self.assertNear(-4.0, output, 0.00001)
graph_io.write_graph(sess.graph, self.get_temp_dir(), input_graph_name)
# We save out the graph to disk, and then call the const conversion
# routine.
input_graph_path = os.path.join(self.get_temp_dir(), input_graph_name)
input_binary = False
output_binary = True
output_node_names = "output_node"
output_graph_path = os.path.join(self.get_temp_dir(), output_graph_name)
def strip(input_node_names):
strip_unused_lib.strip_unused_from_files(input_graph_path, input_binary,
output_graph_path, output_binary,
input_node_names,
output_node_names,
dtypes.float32.as_datatype_enum)
with self.assertRaises(KeyError):
strip("does_not_exist")
with self.assertRaises(ValueError):
strip("wanted_input_node:0")
input_node_names = "wanted_input_node"
strip(input_node_names)
# Now we make sure the variable is now a constant, and that the graph still
# produces the expected result.
with ops.Graph().as_default():
output_graph_def = graph_pb2.GraphDef()
with open(output_graph_path, "rb") as f:
output_graph_def.ParseFromString(f.read())
_ = importer.import_graph_def(output_graph_def, name="")
self.assertEqual(3, len(output_graph_def.node))
for node in output_graph_def.node:
self.assertNotEqual("Add", node.op)
self.assertNotEqual("Sub", node.op)
if node.name == input_node_names:
self.assertTrue("shape" in node.attr)
with session.Session() as sess:
input_node = sess.graph.get_tensor_by_name("wanted_input_node:0")
output_node = sess.graph.get_tensor_by_name("output_node:0")
output = sess.run(output_node, feed_dict={input_node: [10.0]})
self.assertNear(20.0, output, 0.00001)
def testStripUnusedMultipleInputs(self):
input_graph_name = "input_graph.pb"
output_graph_name = "output_graph.pb"
# We'll create an input graph that multiplies two input nodes.
with ops.Graph().as_default():
constant_node1 = constant_op.constant(1.0, name="constant_node1")
constant_node2 = constant_op.constant(2.0, name="constant_node2")
input_node1 = math_ops.subtract(constant_node1, 3.0, name="input_node1")
input_node2 = math_ops.subtract(constant_node2, 5.0, name="input_node2")
output_node = math_ops.multiply(
input_node1, input_node2, name="output_node")
math_ops.add(output_node, 2.0, name="later_node")
sess = session.Session()
output = sess.run(output_node)
self.assertNear(6.0, output, 0.00001)
graph_io.write_graph(sess.graph, self.get_temp_dir(), input_graph_name)
# We save out the graph to disk, and then call the const conversion
# routine.
input_graph_path = os.path.join(self.get_temp_dir(), input_graph_name)
input_binary = False
input_node_names = "input_node1,input_node2"
input_node_types = [
dtypes.float32.as_datatype_enum, dtypes.float32.as_datatype_enum
]
output_binary = True
output_node_names = "output_node"
output_graph_path = os.path.join(self.get_temp_dir(), output_graph_name)
strip_unused_lib.strip_unused_from_files(input_graph_path, input_binary,
output_graph_path, output_binary,
input_node_names,
output_node_names,
input_node_types)
# Now we make sure the variable is now a constant, and that the graph still
# produces the expected result.
with ops.Graph().as_default():
output_graph_def = graph_pb2.GraphDef()
with open(output_graph_path, "rb") as f:
output_graph_def.ParseFromString(f.read())
_ = importer.import_graph_def(output_graph_def, name="")
self.assertEqual(3, len(output_graph_def.node))
for node in output_graph_def.node:
self.assertNotEqual("Add", node.op)
self.assertNotEqual("Sub", node.op)
if node.name == input_node_names:
self.assertTrue("shape" in node.attr)
with session.Session() as sess:
input_node1 = sess.graph.get_tensor_by_name("input_node1:0")
input_node2 = sess.graph.get_tensor_by_name("input_node2:0")
output_node = sess.graph.get_tensor_by_name("output_node:0")
output = sess.run(output_node,
feed_dict={input_node1: [10.0],
input_node2: [-5.0]})
self.assertNear(-50.0, output, 0.00001)
if __name__ == "__main__":
test.main()
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