# Copyright 2016 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.framework.meta_graph.py."""

from __future__ import absolute_import

from __future__ import division

from __future__ import print_function

import math

import os.path

import random

import shutil

from tensorflow.core.framework import graph_pb2

from tensorflow.core.protobuf import meta_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 function

from tensorflow.python.framework import meta_graph

from tensorflow.python.framework import ops

from tensorflow.python.framework import test_util

from tensorflow.python.ops import array_ops

from tensorflow.python.ops import control_flow_ops

from tensorflow.python.ops import data_flow_ops

from tensorflow.python.ops import gradients_impl

from tensorflow.python.ops import math_ops

from tensorflow.python.ops import metrics

from tensorflow.python.ops import nn_ops

from tensorflow.python.ops import partitioned_variables

from tensorflow.python.ops import random_ops

from tensorflow.python.ops import resource_variable_ops

from tensorflow.python.ops import variable_scope

from tensorflow.python.ops import variables

from tensorflow.python.platform import gfile

from tensorflow.python.platform import test

from tensorflow.python.training import queue_runner_impl

# pylint: disable=invalid-name

def _TestDir(test_name):

test_dir = os.path.join(test.get_temp_dir(), test_name)

if os.path.exists(test_dir):

shutil.rmtree(test_dir)

gfile.MakeDirs(test_dir)

return test_dir

# pylint: enable=invalid-name

class SimpleMetaGraphTest(test.TestCase):

@test_util.run_deprecated_v1

def testNoVariables(self):

test_dir = _TestDir("no_variables")

filename = os.path.join(test_dir, "metafile")

input_feed_value = -10 # Arbitrary input value for feed_dict.

orig_graph = ops.Graph()

with self.session(graph=orig_graph) as sess:

# Create a minimal graph with zero variables.

input_tensor = array_ops.placeholder(

dtypes.float32, shape=[], name="input")

offset = constant_op.constant(42, dtype=dtypes.float32, name="offset")

output_tensor = math_ops.add(input_tensor, offset, name="add_offset")

# Add input and output tensors to graph collections.

ops.add_to_collection("input_tensor", input_tensor)

ops.add_to_collection("output_tensor", output_tensor)

output_value = sess.run(output_tensor, {input_tensor: input_feed_value})

self.assertEqual(output_value, 32)

# Generates MetaGraphDef.

meta_graph_def, var_list = meta_graph.export_scoped_meta_graph(

filename=filename,

graph_def=ops.get_default_graph().as_graph_def(add_shapes=True),

collection_list=["input_tensor", "output_tensor"],

saver_def=None)

self.assertTrue(meta_graph_def.HasField("meta_info_def"))

self.assertNotEqual(meta_graph_def.meta_info_def.tensorflow_version, "")

self.assertNotEqual(meta_graph_def.meta_info_def.tensorflow_git_version,

"")

self.assertEqual({}, var_list)

# Create a clean graph and import the MetaGraphDef nodes.

new_graph = ops.Graph()

with self.session(graph=new_graph) as sess:

# Import the previously export meta graph.

meta_graph.import_scoped_meta_graph(filename)

# Re-exports the current graph state for comparison to the original.

new_meta_graph_def, _ = meta_graph.export_scoped_meta_graph(filename +

"_new")

test_util.assert_meta_graph_protos_equal(self, meta_graph_def,

new_meta_graph_def)

# Ensures that we can still get a reference to our graph collections.

new_input_tensor = ops.get_collection("input_tensor")[0]

new_output_tensor = ops.get_collection("output_tensor")[0]

# Verifies that the new graph computes the same result as the original.

new_output_value = sess.run(new_output_tensor,

{new_input_tensor: input_feed_value})

self.assertEqual(new_output_value, output_value)

@test_util.run_deprecated_v1

def testStrippedOpListNestedFunctions(self):

with self.cached_session():

# Square two levels deep

@function.Defun(dtypes.int32)

def f0(x):

return math_ops.square(x)

@function.Defun(dtypes.int32)

def f1(x):

return f0(x)

# At this point we've defined two functions but haven't called them, so

# there should be no used ops.

op_list = meta_graph.stripped_op_list_for_graph(ops.get_default_graph()

.as_graph_def())

self.assertEqual(len(op_list.op), 0)

# If we call the function on a constant, there should be two ops

_ = f1(constant_op.constant(7))

op_list = meta_graph.stripped_op_list_for_graph(ops.get_default_graph()

.as_graph_def())

self.assertEqual(["Const", "Square"], [op.name for op in op_list.op])

def testStrippedOpListRecursiveFunctions(self):

# The function module doesn't support recursive functions, so we build a

# recursive function situation by ourselves: A calls B calls A and Const.

graph = graph_pb2.GraphDef()

a = graph.library.function.add()

b = graph.library.function.add()

a.signature.name = "A"

b.signature.name = "B"

a.node_def.add().op = "B"

b.node_def.add().op = "Const"

b.node_def.add().op = "A"

# Use A in the graph

graph.node.add().op = "A"

# The stripped op list should contain just Const.

op_list = meta_graph.stripped_op_list_for_graph(graph)

self.assertEqual(["Const"], [op.name for op in op_list.op])

@test_util.run_deprecated_v1

def testDefaultAttrStripping(self):

"""Verifies that default attributes are stripped from a graph def."""

# Complex Op has 2 attributes with defaults:

# o "T" : float32.

# o "Tout" : complex64.

# When inputs to the Complex Op are float32 instances, "T" maps to float32

# and "Tout" maps to complex64. Since these attr values map to their

# defaults, they must be stripped unless stripping of default attrs is

# disabled.

with self.cached_session():

real_num = constant_op.constant(1.0, dtype=dtypes.float32, name="real")

imag_num = constant_op.constant(2.0, dtype=dtypes.float32, name="imag")

math_ops.complex(real_num, imag_num, name="complex")

# strip_default_attrs is enabled.

meta_graph_def, _ = meta_graph.export_scoped_meta_graph(

graph_def=ops.get_default_graph().as_graph_def(),

strip_default_attrs=True)

node_def = test_util.get_node_def_from_graph("complex",

meta_graph_def.graph_def)

self.assertNotIn("T", node_def.attr)

self.assertNotIn("Tout", node_def.attr)

self.assertTrue(meta_graph_def.meta_info_def.stripped_default_attrs)

# strip_default_attrs is disabled.

meta_graph_def, _ = meta_graph.export_scoped_meta_graph(

graph_def=ops.get_default_graph().as_graph_def(),

strip_default_attrs=False)

node_def = test_util.get_node_def_from_graph("complex",

meta_graph_def.graph_def)

self.assertIn("T", node_def.attr)

self.assertIn("Tout", node_def.attr)

self.assertFalse(meta_graph_def.meta_info_def.stripped_default_attrs)

# When inputs to the Complex Op are float64 instances, "T" maps to float64

# and "Tout" maps to complex128. Since these attr values don't map to their

# defaults, they must not be stripped.

with self.session(graph=ops.Graph()):

real_num = constant_op.constant(1.0, dtype=dtypes.float64, name="real")

imag_num = constant_op.constant(2.0, dtype=dtypes.float64, name="imag")

math_ops.complex(real_num, imag_num, name="complex")

meta_graph_def, _ = meta_graph.export_scoped_meta_graph(

graph_def=ops.get_default_graph().as_graph_def(),

strip_default_attrs=True)

node_def = test_util.get_node_def_from_graph("complex",

meta_graph_def.graph_def)

self.assertEqual(node_def.attr["T"].type, dtypes.float64)

self.assertEqual(node_def.attr["Tout"].type, dtypes.complex128)

self.assertTrue(meta_graph_def.meta_info_def.stripped_default_attrs)

@test_util.run_deprecated_v1

def testDefaultAttrStrippingNestedFunctions(self):

"""Verifies that default attributes are stripped from function node defs."""

with self.cached_session():

@function.Defun(dtypes.float32, dtypes.float32)

def f0(i, j):

return math_ops.complex(i, j, name="double_nested_complex")

@function.Defun(dtypes.float32, dtypes.float32)

def f1(i, j):

return f0(i, j)

_ = f1(constant_op.constant(1.0), constant_op.constant(2.0))

meta_graph_def, _ = meta_graph.export_scoped_meta_graph(

graph_def=ops.get_default_graph().as_graph_def(),

strip_default_attrs=True)

double_nested_complex_node_def = None

for function_def in meta_graph_def.graph_def.library.function:

for node_def in function_def.node_def:

if node_def.name.startswith("double_nested_complex"):

double_nested_complex_node_def = node_def

break

if double_nested_complex_node_def:

break

self.assertIsNotNone(double_nested_complex_node_def)

self.assertNotIn("T", double_nested_complex_node_def.attr)

self.assertNotIn("Tout", double_nested_complex_node_def.attr)

self.assertTrue(meta_graph_def.meta_info_def.stripped_default_attrs)

def testDefaultAttrStrippingUnregisteredOps(self):

"""Verifies that nodes with un-registered ops are not stripped."""

graph_def = graph_pb2.GraphDef()

node = graph_def.node.add()

node.name = "node_with_unreg_op"

node.op = "unreg_op"

node.attr["attr_1"].i = 1

meta_info_def = meta_graph_pb2.MetaGraphDef.MetaInfoDef()

meta_info_def.stripped_op_list.op.add()

with self.cached_session():

meta_graph_def = meta_graph.create_meta_graph_def(

meta_info_def=meta_info_def, graph_def=graph_def,

strip_default_attrs=True)

node_def = test_util.get_node_def_from_graph("node_with_unreg_op",

meta_graph_def.graph_def)

self.assertEqual(node_def.attr["attr_1"].i, 1)

self.assertTrue(meta_graph_def.meta_info_def.stripped_default_attrs)

@test_util.run_deprecated_v1

def testVariableObjectsAreSharedAmongCollections(self):

with ops.Graph().as_default() as graph1:

v = variables.Variable(3.0)

# A single instance of Variable is shared among the collections:

global_vars = graph1.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)

trainable_vars = graph1.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)

self.assertEqual(len(global_vars), 1)

self.assertEqual(len(trainable_vars), 1)

self.assertIs(global_vars[0], trainable_vars[0])

self.assertIs(v, global_vars[0])

orig_meta_graph, _ = meta_graph.export_scoped_meta_graph(graph=graph1)

del graph1 # To avoid accidental references in code involving graph2.

with ops.Graph().as_default() as graph2:

meta_graph.import_scoped_meta_graph(orig_meta_graph)

global_vars = graph2.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)

trainable_vars = graph2.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)

self.assertEqual(len(global_vars), 1)

self.assertEqual(len(trainable_vars), 1)

# A single instance of Variable is shared among the collections:

self.assertIs(global_vars[0], trainable_vars[0])

class ScopedMetaGraphTest(test.TestCase):

def _testScopedExport(self, test_dir, exported_filenames):

graph = ops.Graph()

with graph.as_default():

# Creates an inference graph.

# Hidden 1

colocate_constraint = constant_op.constant(1.2, name="constraint")

images = constant_op.constant(

1.2, dtypes.float32, shape=[100, 28], name="images")

with ops.name_scope("hidden1"):

with graph.colocate_with(colocate_constraint.op):

weights1 = variables.Variable(

random_ops.truncated_normal(

[28, 128], stddev=1.0 / math.sqrt(float(28))),

name="weights")

# The use of control_flow_ops.cond here is purely for adding test

# coverage the save and restore of control flow context (which doesn't

# make any sense here from a machine learning perspective). The typical

# biases is a simple Variable without the conditions.

biases1 = variables.Variable(

control_flow_ops.cond(

math_ops.less(random.random(), 0.5),

lambda: array_ops.ones([128]), lambda: array_ops.zeros([128])),

name="biases")

hidden1 = nn_ops.relu(math_ops.matmul(images, weights1) + biases1)

# Hidden 2

with ops.name_scope("hidden2"):

weights2 = variables.Variable(

random_ops.truncated_normal(

[128, 32], stddev=1.0 / math.sqrt(float(128))),

name="weights")

# The use of control_flow_ops.while_loop here is purely for adding test

# coverage the save and restore of control flow context (which doesn't

# make any sense here from a machine learning perspective). The typical

# biases is a simple Variable without the conditions.

def loop_cond(it, _):

return it < 2

def loop_body(it, biases2):

biases2 += constant_op.constant(0.1, shape=[32])

return it + 1, biases2

_, biases2 = control_flow_ops.while_loop(

loop_cond,

loop_body, [

constant_op.constant(0), variables.Variable(

array_ops.zeros([32]), name="biases")

])

hidden2 = nn_ops.relu(math_ops.matmul(hidden1, weights2) + biases2)

# Linear

with ops.name_scope("softmax_linear"):

weights3 = variables.Variable(

random_ops.truncated_normal(

[32, 10], stddev=1.0 / math.sqrt(float(32))),

name="weights")

biases3 = variables.Variable(array_ops.zeros([10]), name="biases")

logits = math_ops.matmul(hidden2, weights3) + biases3

ops.add_to_collection("logits", logits)

# Exports each sub-graph.

# Exports the first one with unbound_inputs_col_name set to default.

orig_meta_graph1, var_list = meta_graph.export_scoped_meta_graph(

filename=os.path.join(test_dir, exported_filenames[0]),

graph=ops.get_default_graph(),

export_scope="hidden1")

self.assertEqual(["biases:0", "weights:0"], sorted(var_list.keys()))

var_names = [v.name for _, v in var_list.items()]

self.assertEqual(["hidden1/biases:0", "hidden1/weights:0"],

sorted(var_names))

# Exports the rest with no unbound_inputs_col_name.

orig_meta_graph2, _ = meta_graph.export_scoped_meta_graph(

filename=os.path.join(test_dir, exported_filenames[1]),

graph=ops.get_default_graph(),

export_scope="hidden2",

unbound_inputs_col_name=None)

orig_meta_graph3, _ = meta_graph.export_scoped_meta_graph(

filename=os.path.join(test_dir, exported_filenames[2]),

graph=ops.get_default_graph(),

export_scope="softmax_linear",

unbound_inputs_col_name=None)

return [orig_meta_graph1, orig_meta_graph2, orig_meta_graph3]

def _testScopedImport(self, test_dir, exported_filenames):

graph = ops.Graph()

# Create all the missing inputs.

with graph.as_default():

new_image = constant_op.constant(

1.2, dtypes.float32, shape=[100, 28], name="images")

with self.assertRaisesRegexp(ValueError, "Graph contains unbound inputs"):

meta_graph.import_scoped_meta_graph(

os.path.join(test_dir, exported_filenames[0]),

graph=graph,

import_scope="new_hidden1")

with self.assertRaisesRegexp(ValueError, "Graph contains unbound inputs"):

meta_graph.import_scoped_meta_graph(

os.path.join(test_dir, exported_filenames[0]),

graph=graph,

input_map={"image:0": new_image},

import_scope="new_hidden1")

# Verifies we can import the original "hidden1" into "new_hidden1".

var_list = meta_graph.import_scoped_meta_graph(

os.path.join(test_dir, exported_filenames[0]),

graph=graph,

input_map={"$unbound_inputs_images": new_image},

import_scope="new_hidden1")

self.assertEqual(["biases:0", "weights:0"], sorted(var_list.keys()))

new_var_names = [v.name for _, v in var_list.items()]

self.assertEqual(["new_hidden1/biases:0", "new_hidden1/weights:0"],

sorted(new_var_names))

# Verifies we can import the original "hidden2" into "new_hidden2".

hidden1 = array_ops.identity(

graph.as_graph_element("new_hidden1/Relu:0"), name="hidden1/Relu")

var_list = meta_graph.import_scoped_meta_graph(

os.path.join(test_dir, exported_filenames[1]),

graph=graph,

input_map={"$unbound_inputs_hidden1/Relu": hidden1},

import_scope="new_hidden2",

unbound_inputs_col_name=None)

self.assertEqual(["biases:0", "weights:0"], sorted(var_list.keys()))

new_var_names = [v.name for _, v in var_list.items()]

self.assertEqual(["new_hidden2/biases:0", "new_hidden2/weights:0"],

sorted(new_var_names))

# Verifies we can import the original "softmax_linear" into

# "new_softmax_linear".

hidden2 = array_ops.identity(

graph.as_graph_element("new_hidden2/Relu:0"), name="hidden2/Relu")

var_list = meta_graph.import_scoped_meta_graph(

os.path.join(test_dir, exported_filenames[2]),

graph=graph,

input_map={"$unbound_inputs_hidden2/Relu": hidden2},

import_scope="new_softmax_linear",

unbound_inputs_col_name=None)

self.assertEqual(["biases:0", "weights:0"], sorted(var_list.keys()))

new_var_names = [v.name for _, v in var_list.items()]

self.assertEqual(

["new_softmax_linear/biases:0", "new_softmax_linear/weights:0"],

sorted(new_var_names))

# Exports the scoped meta graphs again.

new_meta_graph1, var_list = meta_graph.export_scoped_meta_graph(

graph=graph, export_scope="new_hidden1")

self.assertEqual(["biases:0", "weights:0"], sorted(var_list.keys()))

new_meta_graph2, var_list = meta_graph.export_scoped_meta_graph(

graph=graph, export_scope="new_hidden2", unbound_inputs_col_name=None)

self.assertEqual(["biases:0", "weights:0"], sorted(var_list.keys()))

new_meta_graph3, var_list = meta_graph.export_scoped_meta_graph(

graph=graph,

export_scope="new_softmax_linear",

unbound_inputs_col_name=None)

self.assertEqual(["biases:0", "weights:0"], sorted(var_list.keys()))

return [new_meta_graph1, new_meta_graph2, new_meta_graph3]

# Verifies that we can export the subgraph under each layer and import

# them into new layers in a new graph.

@test_util.run_deprecated_v1

def testScopedExportAndImport(self):

test_dir = _TestDir("scoped_export_import")

filenames = [

"exported_hidden1.pbtxt", "exported_hidden2.pbtxt",

"exported_softmax_linear.pbtxt"

]

orig_meta_graphs = self._testScopedExport(test_dir, filenames)

new_meta_graphs = self._testScopedImport(test_dir, filenames)

for a, b in zip(orig_meta_graphs, new_meta_graphs):

# The unbound input strings are slightly different with the C API enabled

# ("images" vs "images:0") due to the original import_graph_def code

# vs. ImportGraphDef in C++.

# TODO(skyewm): update the pbtxts once _USE_C_API is removed.

del a.collection_def["unbound_inputs"]

del b.collection_def["unbound_inputs"]

test_util.assert_meta_graph_protos_equal(self, a, b)

def testWhileLoopGradients(self):

# Create a simple while loop.

with ops.Graph().as_default():

with ops.name_scope("export"):

var = variables.Variable(0.)

var_name = var.name

_, output = control_flow_ops.while_loop(

lambda i, x: i < 5,

lambda i, x: (i + 1, x + math_ops.cast(i, dtypes.float32)),

[0, var])

output_name = output.name

# Generate a MetaGraphDef containing the while loop with an export scope.

meta_graph_def, _ = meta_graph.export_scoped_meta_graph(

export_scope="export")

# Build and run the gradients of the while loop. We use this below to

# verify that the gradients are correct with the imported MetaGraphDef.

init_op = variables.global_variables_initializer()

grad = gradients_impl.gradients([output], [var])

with session.Session() as sess:

self.evaluate(init_op)

expected_grad_value = self.evaluate(grad)

# Restore the MetaGraphDef into a new Graph with an import scope.

with ops.Graph().as_default():

meta_graph.import_scoped_meta_graph(meta_graph_def, import_scope="import")

# Re-export and make sure we get the same MetaGraphDef.

new_meta_graph_def, _ = meta_graph.export_scoped_meta_graph(

export_scope="import")

test_util.assert_meta_graph_protos_equal(

self, meta_graph_def, new_meta_graph_def)

# Make sure we can still build gradients and get the same result.

def new_name(tensor_name):

base_tensor_name = tensor_name.replace("export/", "")

return "import/" + base_tensor_name

var = ops.get_default_graph().get_tensor_by_name(new_name(var_name))

output = ops.get_default_graph().get_tensor_by_name(new_name(output_name))

grad = gradients_impl.gradients([output], [var])

init_op = variables.global_variables_initializer()

with session.Session() as sess:

self.evaluate(init_op)

actual_grad_value = self.evaluate(grad)

self.assertEqual(expected_grad_value, actual_grad_value)

@test_util.run_v1_only("b/120545219")

def testImportWhileLoopInWhileLoop(self):

# Create a simple while loop.

with ops.Graph().as_default():

var = variables.Variable(0.0)

_, output = control_flow_ops.while_loop(lambda i, x: i < 5,

lambda i, x: (i + 1, x * 2.0),

[0, var])

output_name = output.name

# Generate a MetaGraphDef containing the while loop with an export scope.

meta_graph_def, _ = meta_graph.export_scoped_meta_graph()

# Restore the MetaGraphDef in a while loop in a new graph.

with ops.Graph().as_default():

def body(i, _):

meta_graph.import_scoped_meta_graph(meta_graph_def)

return i + 1, ops.get_default_graph().get_tensor_by_name(output_name)

_, x = control_flow_ops.while_loop(lambda i, x: i < 2, body, [0, 0.0],

name="")

with session.Session() as sess:

self.evaluate(variables.global_variables_initializer())

self.evaluate(x)

@test_util.run_deprecated_v1

def testScopedImportUnderNameScope(self):

graph = ops.Graph()

with graph.as_default():

variables.Variable(initial_value=1.0, trainable=True, name="myvar")

meta_graph_def, _ = meta_graph.export_scoped_meta_graph(graph=graph)

graph = ops.Graph()

with graph.as_default():

with ops.name_scope("foo"):

imported_variables = meta_graph.import_scoped_meta_graph(

meta_graph_def, import_scope="bar")

self.assertEqual(len(imported_variables), 1)

self.assertEqual(list(imported_variables.values())[0].name,

"foo/bar/myvar:0")

@test_util.run_deprecated_v1

def testScopedImportUnderNameScopeNoVarScope(self):

graph = ops.Graph()

with graph.as_default():

variables.Variable(initial_value=1.0, trainable=True, name="myvar")

meta_graph_def, _ = meta_graph.export_scoped_meta_graph(graph=graph)

graph = ops.Graph()

with graph.as_default():

with ops.name_scope("foo"):

imported_variables = meta_graph.import_scoped_meta_graph(

meta_graph_def)

self.assertEqual(len(imported_variables), 1)

self.assertEqual(list(imported_variables.values())[0].name,

"foo/myvar:0")

def testImportsUsingSameScopeName(self):

with ops.Graph().as_default():

variables.Variable(0, name="v")

meta_graph_def, _ = meta_graph.export_scoped_meta_graph()

with ops.Graph().as_default():

for suffix in ["", "_1"]:

imported_variables = meta_graph.import_scoped_meta_graph(

meta_graph_def, import_scope="s")

self.assertEqual(len(imported_variables), 1)

self.assertEqual(list(imported_variables.keys())[0], "v:0")

self.assertEqual(list(imported_variables.values())[0].name,

"s" + suffix + "/v:0")

@test_util.run_deprecated_v1

def testScopedImportWithSelectedCollections(self):

meta_graph_filename = os.path.join(

_TestDir("selected_collections_import"), "meta_graph.pb")

graph = ops.Graph()

# Add a variable to populate two collections. The functionality tested is

# not specific to variables, but using variables in the test is convenient.

with graph.as_default():

variables.Variable(initial_value=1.0, trainable=True)

self.assertTrue(

all(

graph.get_collection(key)

for key in

[ops.GraphKeys.GLOBAL_VARIABLES, ops.GraphKeys.TRAINABLE_VARIABLES]

))

meta_graph.export_scoped_meta_graph(

filename=meta_graph_filename, graph=graph)

def _test_import(include_collection_keys, omit_collection_keys):

assert set(include_collection_keys).isdisjoint(omit_collection_keys)

newgraph = ops.Graph()

import_scope = "some_scope_name"

def _restore_collections_predicate(collection_key):

return (collection_key in include_collection_keys and

collection_key not in omit_collection_keys)

meta_graph.import_scoped_meta_graph(

meta_graph_filename,

graph=newgraph,

import_scope=import_scope,

restore_collections_predicate=_restore_collections_predicate)

collection_values = [

newgraph.get_collection(name=key, scope=import_scope)

for key in include_collection_keys

]

self.assertTrue(all(collection_values))

collection_values = [

newgraph.get_collection(name=key, scope=import_scope)

for key in omit_collection_keys

]

self.assertFalse(any(collection_values))

_test_import(

include_collection_keys=[

ops.GraphKeys.GLOBAL_VARIABLES, ops.GraphKeys.TRAINABLE_VARIABLES

],

omit_collection_keys=[])

_test_import(

include_collection_keys=[ops.GraphKeys.GLOBAL_VARIABLES],

omit_collection_keys=[ops.GraphKeys.TRAINABLE_VARIABLES])

_test_import(

include_collection_keys=[ops.GraphKeys.TRAINABLE_VARIABLES],

omit_collection_keys=[ops.GraphKeys.GLOBAL_VARIABLES])

_test_import(

include_collection_keys=[],

omit_collection_keys=[

ops.GraphKeys.GLOBAL_VARIABLES, ops.GraphKeys.TRAINABLE_VARIABLES

])

def _testScopedExportWithQueue(self, test_dir, exported_filename):

graph = ops.Graph()

with graph.as_default():

with ops.name_scope("queue1"):

input_queue = data_flow_ops.FIFOQueue(10, dtypes.float32)

enqueue = input_queue.enqueue((9876), name="enqueue")

close = input_queue.close(name="close")

qr = queue_runner_impl.QueueRunner(input_queue, [enqueue], close)

queue_runner_impl.add_queue_runner(qr)

input_queue.dequeue(name="dequeue")

orig_meta_graph, _ = meta_graph.export_scoped_meta_graph(

filename=os.path.join(test_dir, exported_filename),

graph=ops.get_default_graph(),

export_scope="queue1")

return orig_meta_graph

def _testScopedImportWithQueue(self, test_dir, exported_filename,

new_exported_filename):

graph = ops.Graph()

meta_graph.import_scoped_meta_graph(

os.path.join(test_dir, exported_filename),

graph=graph,

import_scope="new_queue1")

graph.as_graph_element("new_queue1/dequeue:0")

graph.as_graph_element("new_queue1/close")

with graph.as_default():

new_meta_graph, _ = meta_graph.export_scoped_meta_graph(

filename=os.path.join(test_dir, new_exported_filename),

graph=graph,

export_scope="new_queue1")

return new_meta_graph

# Verifies that we can export the subgraph containing a FIFOQueue under

# "queue1" and import it into "new_queue1" in a new graph.

@test_util.run_deprecated_v1

def testScopedWithQueue(self):

test_dir = _TestDir("scoped_with_queue")

orig_meta_graph = self._testScopedExportWithQueue(test_dir,

"exported_queue1.pbtxt")

new_meta_graph = self._testScopedImportWithQueue(

test_dir, "exported_queue1.pbtxt", "exported_new_queue1.pbtxt")

test_util.assert_meta_graph_protos_equal(self, orig_meta_graph,

new_meta_graph)

def testExportDebugInfo(self):

graph1 = ops.Graph()

with graph1.as_default():

with ops.name_scope("hidden1/hidden2/hidden3"):

images = constant_op.constant(

1.0, dtypes.float32, shape=[3, 2], name="images")

weights1 = variables.Variable([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]],

name="weights")

biases1 = resource_variable_ops.ResourceVariable(

[0.1] * 3, name="biases")

nn_ops.relu(math_ops.matmul(images, weights1) + biases1, name="relu")

debug_info_def = meta_graph.create_graph_debug_info_def(

operations=graph1.get_operations())

# The unique file names in all the stack traces should be larger or equal

# than 1.

self.assertTrue(len(debug_info_def.files) >= 1)

# All the nodes from the exported graphdef are included.

self.assertEqual(len(debug_info_def.traces), len(graph1.get_operations()))

# Verifies that we can export a subgraph in a nested name scope containing a

# "hidden1/hidden2" and import it into "new_hidden1/new_hidden2" in a new

# graph.

def doTestExportNestedNames(self, use_resource=False):

graph1 = ops.Graph()

with graph1.as_default():

with ops.name_scope("hidden1/hidden2/hidden3"):

images = constant_op.constant(

1.0, dtypes.float32, shape=[3, 2], name="images")

if use_resource:

weights1 = variables.Variable(

[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], name="weights")

biases1 = resource_variable_ops.ResourceVariable(

[0.1] * 3, name="biases")

else:

biases1 = variables.Variable([0.1] * 3, name="biases")

weights1 = variables.Variable(

[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], name="weights")

nn_ops.relu(math_ops.matmul(images, weights1) + biases1, name="relu")

orig_meta_graph, var_list = meta_graph.export_scoped_meta_graph(

export_scope="hidden1/hidden2", graph=graph1)

var_names = [v.name for _, v in var_list.items()]

self.assertEqual(["hidden3/biases:0", "hidden3/weights:0"],

sorted(var_list.keys()))

self.assertEqual([

"hidden1/hidden2/hidden3/biases:0", "hidden1/hidden2/hidden3/weights:0"

], sorted(var_names))

for node in orig_meta_graph.graph_def.node:

self.assertTrue(node.name.startswith("hidden3"))

graph2 = ops.Graph()

new_var_list = meta_graph.import_scoped_meta_graph(

orig_meta_graph, import_scope="new_hidden1/new_hidden2", graph=graph2)

self.assertEqual(["hidden3/biases:0", "hidden3/weights:0"],

sorted(new_var_list.keys()))

new_var_names = [v.name for _, v in new_var_list.items()]

self.assertEqual([

"new_hidden1/new_hidden2/hidden3/biases:0",

"new_hidden1/new_hidden2/hidden3/weights:0"

], sorted(new_var_names))

nodes = [

"new_hidden1/new_hidden2/hidden3/biases/Assign",

"new_hidden1/new_hidden2/hidden3/weights/Assign"

]

expected = [

b"loc:@new_hidden1/new_hidden2/hidden3/biases",

b"loc:@new_hidden1/new_hidden2/hidden3/weights"

]

for n, e in zip(nodes, expected):

self.assertEqual([e], graph2.get_operation_by_name(n).get_attr("_class"))

@test_util.run_deprecated_v1

def testExportNestedNames(self):

self.doTestExportNestedNames(use_resource=False)

@test_util.run_deprecated_v1

def testExportNestedNamesResource(self):

self.doTestExportNestedNames(use_resource=True)

@test_util.run_deprecated_v1

def testPotentialCycle(self):

graph1 = ops.Graph()

with graph1.as_default():

a = constant_op.constant(1.0, shape=[2, 2])

b = constant_op.constant(2.0, shape=[2, 2])

matmul = math_ops.matmul(a, b)

with ops.name_scope("hidden1"):

c = nn_ops.relu(matmul)

d = constant_op.constant(3.0, shape=[2, 2])

matmul = math_ops.matmul(c, d)

orig_meta_graph, _ = meta_graph.export_scoped_meta_graph(

export_scope="hidden1", graph=graph1)

graph2 = ops.Graph()

with graph2.as_default():

with self.assertRaisesRegexp(ValueError, "Graph contains unbound inputs"):

meta_graph.import_scoped_meta_graph(

orig_meta_graph, import_scope="new_hidden1")

meta_graph.import_scoped_meta_graph(

orig_meta_graph,

import_scope="new_hidden1",

input_map={

"$unbound_inputs_MatMul": constant_op.constant(

4.0, shape=[2, 2])

})

@test_util.run_deprecated_v1

def testClearDevices(self):

graph1 = ops.Graph()

with graph1.as_default():

with ops.device("/device:CPU:0"):

a = variables.Variable(

constant_op.constant(

1.0, shape=[2, 2]), name="a")

with ops.device("/job:ps/replica:0/task:0/device:GPU:0"):

b = variables.Variable(

constant_op.constant(

2.0, shape=[2, 2]), name="b")

with ops.device("/job:localhost/replica:0/task:0/cpu:0"):

math_ops.matmul(a, b, name="matmul")

self.assertEqual("/device:CPU:0", str(graph1.as_graph_element("a").device))

self.assertEqual("/job:ps/replica:0/task:0/device:GPU:0",

str(graph1.as_graph_element("b").device))

self.assertEqual("/job:localhost/replica:0/task:0/device:CPU:0",

str(graph1.as_graph_element("matmul").device))

# Verifies that devices are cleared on export.

orig_meta_graph, _ = meta_graph.export_scoped_meta_graph(

graph=graph1, clear_devices=True)

graph2 = ops.Graph()

with graph2.as_default():

meta_graph.import_scoped_meta_graph(orig_meta_graph, clear_devices=False)

self.assertEqual("", str(graph2.as_graph_element("a").device))

self.assertEqual("", str(graph2.as_graph_element("b").device))

self.assertEqual("", str(graph2.as_graph_element("matmul").device))

# Verifies that devices are cleared on export when passing in graph_def.

orig_meta_graph, _ = meta_graph.export_scoped_meta_graph(

graph_def=graph1.as_graph_def(), clear_devices=True)

graph2 = ops.Graph()

with graph2.as_default():

meta_graph.import_scoped_meta_graph(orig_meta_graph, clear_devices=False)

self.assertEqual("", str(graph2.as_graph_element("a").device))

self.assertEqual("", str(graph2.as_graph_element("b").device))

self.assertEqual("", str(graph2.as_graph_element("matmul").device))

# Verifies that devices are cleared on import.

orig_meta_graph, _ = meta_graph.export_scoped_meta_graph(

graph=graph1, clear_devices=False)

graph2 = ops.Graph()

with graph2.as_default():

meta_graph.import_scoped_meta_graph(orig_meta_graph, clear_devices=True)

self.assertEqual("", str(graph2.as_graph_element("a").device))

self.assertEqual("", str(graph2.as_graph_element("b").device))

self.assertEqual("", str(graph2.as_graph_element("matmul").device))

class MetaGraphWithVariableScopeTest(test.TestCase):

@test_util.run_deprecated_v1

def testMetricsCollection(self):

def _enqueue_vector(sess, queue, values, shape=None):

if not shape:

shape = (1, len(values))

dtype = queue.dtypes[0]

sess.run(

queue.enqueue(constant_op.constant(

values, dtype=dtype, shape=shape)))

meta_graph_filename = os.path.join(

_TestDir("metrics_export"), "meta_graph.pb")

graph = ops.Graph()

with self.session(graph=graph) as sess:

values_queue = data_flow_ops.FIFOQueue(

4, dtypes.float32, shapes=(1, 2))

_enqueue_vector(sess, values_queue, [0, 1])

_enqueue_vector(sess, values_queue, [-4.2, 9.1])

_enqueue_vector(sess, values_queue, [6.5, 0])

_enqueue_vector(sess, values_queue, [-3.2, 4.0])

values = values_queue.dequeue()

_, update_op = metrics.mean(values)

initializer = variables.local_variables_initializer()

self.evaluate(initializer)

self.evaluate(update_op)

meta_graph.export_scoped_meta_graph(

filename=meta_graph_filename, graph=graph)

# Verifies that importing a meta_graph with LOCAL_VARIABLES collection

# works correctly.

graph = ops.Graph()

with self.session(graph=graph) as sess:

meta_graph.import_scoped_meta_graph(meta_graph_filename)

initializer = variables.local_variables_initializer()

self.evaluate(initializer)

# Verifies that importing an old meta_graph where "local_variables"

# collection is of node_list type works, but cannot build initializer

# with the collection.

graph = ops.Graph()

with self.session(graph=graph) as sess:

meta_graph.import_scoped_meta_graph(

test.test_src_dir_path(

"python/framework/testdata/metrics_export_meta_graph.pb"))

self.assertEqual(len(ops.get_collection(ops.GraphKeys.LOCAL_VARIABLES)),

2)

with self.assertRaisesRegexp(

AttributeError, "'Tensor' object has no attribute 'initializer'"):

initializer = variables.local_variables_initializer()

class ExportImportAcrossScopesTest(test.TestCase):

@test_util.run_deprecated_v1

def testPartionedVariables(self):

def make_graph_with_partitioned_variables(use_resource):

variable_scope.get_variable(

name="weights",

partitioner=partitioned_variables.fixed_size_partitioner(3, axis=0),

initializer=random_ops.truncated_normal([100, 10]),

use_resource=use_resource)

# The next variable illustrates the necessity of restoring collections

# in a deterministic fashion when using ResourceVariables.

variable_scope.get_variable(

name="another",

shape=[],

collections=["a", "b", "z", "f", "e", "d", "g"],

use_resource=use_resource)

self._testExportImportAcrossScopes(

make_graph_with_partitioned_variables, use_resource=False)

self._testExportImportAcrossScopes(

make_graph_with_partitioned_variables, use_resource=True)

def _testExportImportAcrossScopes(self, graph_fn, use_resource):

"""Tests export and importing a graph across scopes.

Args:

graph_fn: A closure that creates a graph on the current scope.

use_resource: A bool indicating whether or not to use ResourceVariables.

"""

with ops.Graph().as_default() as original_graph:

with variable_scope.variable_scope("dropA/dropB/keepA"):

graph_fn(use_resource=use_resource)

exported_meta_graph_def = meta_graph.export_scoped_meta_graph(

graph=original_graph,

export_scope="dropA/dropB")[0]

with ops.Graph().as_default() as imported_graph:

meta_graph.import_scoped_meta_graph(

exported_meta_graph_def,

import_scope="importA")

with ops.Graph().as_default() as expected_graph:

with variable_scope.variable_scope("importA/keepA"):

graph_fn(use_resource=use_resource)

result = meta_graph.export_scoped_meta_graph(graph=imported_graph)[0]

expected = meta_graph.export_scoped_meta_graph(graph=expected_graph)[0]

if use_resource:

# Clear all shared_name attributes before comparing, since they are

# orthogonal to scopes and are not updated on export/import.

for meta_graph_def in [result, expected]:

for node in meta_graph_def.graph_def.node:

shared_name_attr = "shared_name"

shared_name_value = node.attr.get(shared_name_attr, None)

if shared_name_value and shared_name_value.HasField("s"):

if shared_name_value.s:

node.attr[shared_name_attr].s = b""

test_util.assert_meta_graph_protos_equal(self, expected, result)

if __name__ == "__main__":