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)
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 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 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:
sess.run(init_op)
expected_grad_value = sess.run(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:
sess.run(init_op)
actual_grad_value = sess.run(grad)
self.assertEqual(expected_grad_value, actual_grad_value)
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)
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)
# Delete the unbound_inputs to allow directly calling ProtoEqual.
del orig_meta_graphs[0].collection_def["unbound_inputs"]
del new_meta_graphs[0].collection_def["unbound_inputs"]
for a, b in zip(orig_meta_graphs, new_meta_graphs):
test_util.assert_meta_graph_protos_equal(self, a, b)
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)
# Delete the unbound_inputs to allow directly calling ProtoEqual.
del orig_meta_graphs[0].collection_def["unbound_inputs"]
del new_meta_graphs[0].collection_def["unbound_inputs"]
for a, b in zip(orig_meta_graphs, new_meta_graphs):
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)
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)
if use_resource:
# Bringing in collections that contain ResourceVariables will adds ops
# to the graph the first time a variable is encountered, so mimic the
# same behavior.
seen_variables = set()
for collection_key in sorted([
ops.GraphKeys.GLOBAL_VARIABLES,
ops.GraphKeys.TRAINABLE_VARIABLES,
]):
for var in expected_graph.get_collection(collection_key):
if var not in seen_variables:
var._read_variable_op()
seen_variables.add(var)
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)
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)
if use_resource:
# Bringing in a collection that contains ResourceVariables adds ops
# to the graph, so mimic the same behavior.
for collection_key in sorted([
ops.GraphKeys.GLOBAL_VARIABLES,
ops.GraphKeys.TRAINABLE_VARIABLES,
]):
for var in expected_graph.get_collection(collection_key):
var._read_variable_op()
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
# supposed to be orthogonal to scopes.
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)
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 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 assert_summaries(self,
test_case,
expected_logdir=None,
expected_graph=None,
expected_summaries=None,
expected_added_graphs=None,
expected_added_meta_graphs=None,
expected_session_logs=None):
"""Assert expected items have been added to summary writer."""
if expected_logdir is not None:
test_case.assertEqual(expected_logdir, self._logdir)
if expected_graph is not None:
test_case.assertTrue(expected_graph is self._graph)
expected_summaries = expected_summaries or {}
for step in expected_summaries:
test_case.assertTrue(step in self._summaries,
msg='Missing step %s from %s.' %
(step, self._summaries.keys()))
actual_simple_values = {}
for step_summary in self._summaries[step]:
for v in step_summary.value:
# Ignore global_step/sec since it's written by Supervisor in a
# separate thread, so it's non-deterministic how many get written.
if 'global_step/sec' != v.tag:
actual_simple_values[v.tag] = v.simple_value
test_case.assertEqual(expected_summaries[step],
actual_simple_values)
if expected_added_graphs is not None:
test_case.assertEqual(expected_added_graphs, self._added_graphs)
if expected_added_meta_graphs is not None:
test_case.assertEqual(len(expected_added_meta_graphs),
len(self._added_meta_graphs))
for expected, actual in zip(expected_added_meta_graphs,
self._added_meta_graphs):
test_util.assert_meta_graph_protos_equal(
test_case, expected, actual)
if expected_session_logs is not None:
test_case.assertEqual(expected_session_logs,
self._added_session_logs)
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)
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)
def assert_summaries(self,
test_case,
expected_logdir=None,
expected_graph=None,
expected_summaries=None,
expected_added_graphs=None,
expected_added_meta_graphs=None,
expected_session_logs=None):
"""Assert expected items have been added to summary writer."""
if expected_logdir is not None:
test_case.assertEqual(expected_logdir, self._logdir)
if expected_graph is not None:
test_case.assertTrue(expected_graph is self._graph)
expected_summaries = expected_summaries or {}
for step in expected_summaries:
test_case.assertTrue(
step in self._summaries,
msg='Missing step %s from %s.' % (step, self._summaries.keys()))
actual_simple_values = {}
for step_summary in self._summaries[step]:
for v in step_summary.value:
# Ignore global_step/sec since it's written by Supervisor in a
# separate thread, so it's non-deterministic how many get written.
if 'global_step/sec' != v.tag:
actual_simple_values[v.tag] = v.simple_value
test_case.assertEqual(expected_summaries[step], actual_simple_values)
if expected_added_graphs is not None:
test_case.assertEqual(expected_added_graphs, self._added_graphs)
if expected_added_meta_graphs is not None:
test_case.assertEqual(len(expected_added_meta_graphs),
len(self._added_meta_graphs))
for expected, actual in zip(expected_added_meta_graphs,
self._added_meta_graphs):
test_util.assert_meta_graph_protos_equal(test_case, expected, actual)
if expected_session_logs is not None:
test_case.assertEqual(expected_session_logs, self._added_session_logs)
