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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 for tensorflow.contrib.graph_editor."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import numpy as np
import tensorflow as tf
from tensorflow.contrib import graph_editor as ge
# Precision tolerance for floating-point value tests.
ERROR_TOLERANCE = 1e-3
class TransformTest(tf.test.TestCase):
def setUp(self):
self.graph = tf.Graph()
with self.graph.as_default():
c0 = tf.constant(1.0, shape=[10], name="Const")
c1 = tf.constant(1.0, shape=[10], name="Const")
c2 = tf.constant(1.0, shape=[10], name="Const")
i = tf.constant(1.0, shape=[10], name="Input")
self.o = tf.add(c2, tf.add(c1, tf.add(c0, i)))
def test_copy(self):
graph = tf.Graph()
_, info = ge.copy(self.graph, graph)
self.assertEqual(set(op.name for op in self.graph.get_operations()),
set(op.name for op in graph.get_operations()))
src_ops = self.graph.get_operations()
dst_ops = graph.get_operations()
for op in src_ops:
op_ = info.transformed(op)
self.assertTrue(op_ in dst_ops)
self.assertEqual(op.name, op_.name)
self.assertEqual(info.original(op_), op)
src_ts = ge.util.get_tensors(self.graph)
dst_ts = ge.util.get_tensors(graph)
for t in src_ts:
t_ = info.transformed(t)
self.assertTrue(t_ in dst_ts)
self.assertEqual(t.name, t_.name)
self.assertEqual(info.original(t_), t)
def test_transform(self):
transformer = ge.Transformer()
def my_transform_op_handler(info, op):
add_noise = op.name.startswith("Add")
op_ = ge.transform.copy_op_handler(info, op)
if add_noise:
# add some noise to op
with info.graph_.as_default():
t_ = tf.add(tf.constant(1.0, shape=[10], name="Noise"),
op_.outputs[0], name="AddNoise")
# return the "noisy" op
return t_.op
else:
return op_
transformer.transform_op_handler = my_transform_op_handler
graph = tf.Graph()
transformer(self.graph, graph, "", "")
matcher0 = ge.matcher("AddNoise").input_ops(
"Noise", ge.matcher("Add").input_ops("Const", "Input"))
matcher1 = ge.matcher("AddNoise_1").input_ops(
"Noise_1", ge.matcher("Add_1").input_ops("Const_1", matcher0))
matcher2 = ge.matcher("AddNoise_2").input_ops(
"Noise_2", ge.matcher("Add_2").input_ops("Const_2", matcher1))
top = ge.select_ops("^AddNoise_2$", graph=graph)[0]
self.assertTrue(matcher2(top))
def test_transform_in_place(self):
transformer = ge.Transformer()
def my_transform_op_handler_in_place(info, op):
add_noise = op.name.startswith("Add")
op = ge.transform.transform_op_in_place(info, op,
detach_outputs=add_noise)
if add_noise:
# add some noise to op
with info.graph_.as_default():
t = tf.add(tf.constant(1.0, shape=[10], name="Noise"), op.outputs[0],
name="AddNoise")
# return the "noisy" op
return t.op
else:
return op
transformer.transform_op_handler = my_transform_op_handler_in_place
transformer(self.graph, self.graph, "", "")
matcher0 = ge.matcher("AddNoise").input_ops(
"Noise", ge.matcher("Add").input_ops("Const", "Input"))
matcher1 = ge.matcher("AddNoise_1").input_ops(
"Noise_1", ge.matcher("Add_1").input_ops("Const_1", matcher0))
matcher2 = ge.matcher("AddNoise_2").input_ops(
"Noise_2", ge.matcher("Add_2").input_ops("Const_2", matcher1))
top = ge.select_ops("^AddNoise_2$", graph=self.graph)[0]
self.assertTrue(matcher2(top))
def test_copy_with_input_replacements(self):
with self.graph.as_default():
ten = tf.constant(10.0, shape=[10], name="Input")
sgv, _ = ge.copy_with_input_replacements(self.o.op,
{self.o.op.inputs[1]: ten})
with tf.Session() as sess:
val = sess.run(sgv.outputs[0])
self.assertNear(np.linalg.norm(val - np.array([11])),
0.0, ERROR_TOLERANCE)
def test_graph_replace(self):
tf.reset_default_graph()
a = tf.constant(1.0, name="a")
b = tf.Variable(1.0, name="b")
eps = tf.constant(0.001, name="eps")
c = tf.identity(a + b + eps, name="c")
a_new = tf.constant(2.0, name="a_new")
c_new = ge.graph_replace(c, {a: a_new})
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
c_val, c_new_val = sess.run([c, c_new])
self.assertNear(c_val, 2.001, ERROR_TOLERANCE)
self.assertNear(c_new_val, 3.001, ERROR_TOLERANCE)
def test_graph_replace_dict(self):
tf.reset_default_graph()
a = tf.constant(1.0, name="a")
b = tf.Variable(1.0, name="b")
eps = tf.constant(0.001, name="eps")
c = tf.identity(a + b + eps, name="c")
a_new = tf.constant(2.0, name="a_new")
c_new = ge.graph_replace({"c": c}, {a: a_new})
self.assertTrue(isinstance(c_new, dict))
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
c_val, c_new_val = sess.run([c, c_new])
self.assertTrue(isinstance(c_new_val, dict))
self.assertNear(c_val, 2.001, ERROR_TOLERANCE)
self.assertNear(c_new_val["c"], 3.001, ERROR_TOLERANCE)
def test_graph_replace_ordered_dict(self):
tf.reset_default_graph()
a = tf.constant(1.0, name="a")
b = tf.Variable(1.0, name="b")
eps = tf.constant(0.001, name="eps")
c = tf.identity(a + b + eps, name="c")
a_new = tf.constant(2.0, name="a_new")
c_new = ge.graph_replace(collections.OrderedDict({"c": c}), {a: a_new})
self.assertTrue(isinstance(c_new, collections.OrderedDict))
def test_graph_replace_named_tuple(self):
tf.reset_default_graph()
a = tf.constant(1.0, name="a")
b = tf.Variable(1.0, name="b")
eps = tf.constant(0.001, name="eps")
c = tf.identity(a + b + eps, name="c")
a_new = tf.constant(2.0, name="a_new")
one_tensor = collections.namedtuple("OneTensor", ["t"])
c_new = ge.graph_replace(one_tensor(c), {a: a_new})
self.assertTrue(isinstance(c_new, one_tensor))
def test_graph_replace_missing(self):
tf.reset_default_graph()
a = tf.constant(1.0, name="a")
b = tf.constant(2.0, name="b")
c = a + 2 * b
d = tf.constant(2.0, name="d")
res = ge.graph_replace([b, c], {a: d})
self.assertEqual(res[0].name, "b:0")
self.assertEqual(res[1].name, "add_1:0")
if __name__ == "__main__":
tf.test.main()
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