4890b781b6
Skip individual test cases or entire suites that are not running in v1. Also replace some @run_deprecated_v1 annotations since simply running the test in graph mode was not enough. PiperOrigin-RevId: 224604547
275 lines
11 KiB
Python
275 lines
11 KiB
Python
# Copyright 2018 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 for SavedModelLoader class."""
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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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import shutil
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from absl.testing import parameterized
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from tensorflow.python.client import session
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from tensorflow.python.framework import errors
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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 control_flow_ops
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from tensorflow.python.ops import state_ops
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from tensorflow.python.ops import variables
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from tensorflow.python.platform import test
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from tensorflow.python.saved_model import builder as saved_model_builder
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from tensorflow.python.saved_model import loader_impl
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from tensorflow.python.saved_model import signature_def_utils
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from tensorflow.python.saved_model import utils
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from tensorflow.python.training import saver as tf_saver
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def _get_export_dir(label):
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return os.path.join(test.get_temp_dir(), label)
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SIMPLE_ADD_SAVED_MODEL = _get_export_dir("simple_add_saved_model")
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SAVED_MODEL_WITH_MAIN_OP = _get_export_dir("saved_model_with_main_op")
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def build_graph_helper():
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g = ops.Graph()
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with g.as_default():
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x = variables.VariableV1(5, name="x")
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y = variables.VariableV1(11, name="y")
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z = x + y
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foo_sig_def = signature_def_utils.build_signature_def({
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"foo_input": utils.build_tensor_info(x)
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}, {"foo_output": utils.build_tensor_info(z)})
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bar_sig_def = signature_def_utils.build_signature_def({
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"bar_x": utils.build_tensor_info(x),
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"bar_y": utils.build_tensor_info(y)
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}, {"bar_z": utils.build_tensor_info(z)})
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return g, {"foo": foo_sig_def, "bar": bar_sig_def}, y
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@parameterized.parameters((saved_model_builder.SavedModelBuilder,),
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(saved_model_builder._SavedModelBuilder,))
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class SavedModelLoaderTest(test.TestCase, parameterized.TestCase):
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def export_simple_graph(self, builder_cls):
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g, sig_def_map, _ = build_graph_helper()
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with session.Session(graph=g) as sess:
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self.evaluate(variables.global_variables_initializer())
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builder = builder_cls(SIMPLE_ADD_SAVED_MODEL)
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builder.add_meta_graph_and_variables(sess, ["foo_graph"], sig_def_map)
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builder.save()
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def export_graph_with_main_op(self, builder_cls):
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g, sig_def_map, y = build_graph_helper()
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with session.Session(graph=g) as sess:
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self.evaluate(variables.global_variables_initializer())
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assign_op = control_flow_ops.group(state_ops.assign(y, 7))
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builder = builder_cls(SAVED_MODEL_WITH_MAIN_OP)
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if builder_cls == saved_model_builder._SavedModelBuilder:
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builder.add_meta_graph_and_variables(
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sess, ["foo_graph"], sig_def_map, init_op=assign_op)
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else:
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builder.add_meta_graph_and_variables(
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sess, ["foo_graph"], sig_def_map, main_op=assign_op)
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builder.save()
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def tearDown(self):
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super(SavedModelLoaderTest, self).tearDown()
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shutil.rmtree(test.get_temp_dir(), ignore_errors=True)
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@test_util.run_v1_only("b/120545219")
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def test_load_function(self, builder_cls):
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self.export_simple_graph(builder_cls)
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loader = loader_impl.SavedModelLoader(SIMPLE_ADD_SAVED_MODEL)
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with self.session(graph=ops.Graph()) as sess:
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loader.load(sess, ["foo_graph"])
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self.assertEqual(5, sess.graph.get_tensor_by_name("x:0").eval())
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self.assertEqual(11, sess.graph.get_tensor_by_name("y:0").eval())
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self.export_graph_with_main_op(builder_cls)
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loader2 = loader_impl.SavedModelLoader(SAVED_MODEL_WITH_MAIN_OP)
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with self.session(graph=ops.Graph()) as sess:
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loader2.load(sess, ["foo_graph"])
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self.assertEqual(5, sess.graph.get_tensor_by_name("x:0").eval())
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self.assertEqual(7, sess.graph.get_tensor_by_name("y:0").eval())
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@test_util.run_v1_only("b/120545219")
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def test_load_graph(self, builder_cls):
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self.export_simple_graph(builder_cls)
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loader = loader_impl.SavedModelLoader(SIMPLE_ADD_SAVED_MODEL)
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graph = ops.Graph()
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loader.load_graph(graph, ["foo_graph"])
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x = graph.get_tensor_by_name("x:0")
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y = graph.get_tensor_by_name("y:0")
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with self.assertRaises(KeyError):
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graph.get_tensor_by_name("z:0")
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with self.session(graph=graph):
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# Check that x and y are not initialized
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with self.assertRaises(errors.FailedPreconditionError):
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self.evaluate(x)
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with self.assertRaises(errors.FailedPreconditionError):
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self.evaluate(y)
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@test_util.run_v1_only("b/120545219")
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def test_load_with_import_scope(self, builder_cls):
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self.export_graph_with_main_op(builder_cls)
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loader = loader_impl.SavedModelLoader(SAVED_MODEL_WITH_MAIN_OP)
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with self.session(graph=ops.Graph()) as sess:
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saver, _ = loader.load_graph(
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sess.graph, ["foo_graph"], import_scope="baz")
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# The default saver should not work when the import scope is set.
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with self.assertRaises(errors.NotFoundError):
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loader.restore_variables(sess, tf_saver.Saver())
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loader.restore_variables(sess, saver)
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if builder_cls == saved_model_builder._SavedModelBuilder:
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with self.assertRaises(errors.NotFoundError):
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loader.run_init_ops(sess, ["foo_graph"])
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loader.run_init_ops(sess, ["foo_graph"], import_scope="baz")
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else:
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loader.run_init_ops(sess, ["foo_graph"])
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self.assertEqual(5, sess.graph.get_tensor_by_name("baz/x:0").eval())
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self.assertEqual(7, sess.graph.get_tensor_by_name("baz/y:0").eval())
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# Test combined load function.
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loader = loader_impl.SavedModelLoader(SAVED_MODEL_WITH_MAIN_OP)
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with self.session(graph=ops.Graph()) as sess:
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loader.load(sess, ["foo_graph"], import_scope="baa")
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self.assertEqual(5, sess.graph.get_tensor_by_name("baa/x:0").eval())
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self.assertEqual(7, sess.graph.get_tensor_by_name("baa/y:0").eval())
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@test_util.run_deprecated_v1
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def test_restore_variables(self, builder_cls):
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self.export_graph_with_main_op(builder_cls)
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loader = loader_impl.SavedModelLoader(SAVED_MODEL_WITH_MAIN_OP)
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with self.session(graph=ops.Graph()) as sess:
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x = variables.VariableV1(0, name="x")
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y = variables.VariableV1(0, name="y")
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z = x * y
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self.evaluate(variables.global_variables_initializer())
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# There are variables to restore, so a saver must be created.
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with self.assertRaises(ValueError):
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loader.restore_variables(sess, None)
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loader.restore_variables(sess, tf_saver.Saver())
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self.assertEqual(55, self.evaluate(z))
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@test_util.run_v1_only("b/120545219")
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def test_run_init_op(self, builder_cls):
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self.export_graph_with_main_op(builder_cls)
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loader = loader_impl.SavedModelLoader(SAVED_MODEL_WITH_MAIN_OP)
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graph = ops.Graph()
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saver, _ = loader.load_graph(graph, ["foo_graph"])
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with self.session(graph=graph) as sess:
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loader.restore_variables(sess, saver)
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self.assertEqual(5, sess.graph.get_tensor_by_name("x:0").eval())
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self.assertEqual(11, sess.graph.get_tensor_by_name("y:0").eval())
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loader.run_init_ops(sess, ["foo_graph"])
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self.assertEqual(5, sess.graph.get_tensor_by_name("x:0").eval())
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self.assertEqual(7, sess.graph.get_tensor_by_name("y:0").eval())
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def test_parse_saved_model(self, builder_cls):
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self.export_simple_graph(builder_cls)
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loader = loader_impl.SavedModelLoader(SIMPLE_ADD_SAVED_MODEL)
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meta_graph = loader.get_meta_graph_def_from_tags(["foo_graph"])
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self.assertIsNotNone(meta_graph)
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self.assertIn("foo", meta_graph.signature_def)
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self.assertIn("bar", meta_graph.signature_def)
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def test_load_invalid_meta_graph(self, builder_cls):
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self.export_simple_graph(builder_cls)
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loader = loader_impl.SavedModelLoader(SIMPLE_ADD_SAVED_MODEL)
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with self.assertRaises(RuntimeError):
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loader.get_meta_graph_def_from_tags([])
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with self.assertRaises(RuntimeError):
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loader.get_meta_graph_def_from_tags([""])
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with self.assertRaises(RuntimeError):
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loader.get_meta_graph_def_from_tags(["not_a_graph"])
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@test_util.run_v1_only("b/120545219")
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def test_load_saved_model_with_no_variables(self, builder_cls):
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"""Test that SavedModel runs saver when there appear to be no variables.
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When no variables are detected, this may mean that the variables were saved
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to different collections, or the collections weren't saved to the
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SavedModel. If the SavedModel MetaGraphDef contains a saver, it should still
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run in either of these cases.
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Args:
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builder_cls: SavedModelBuilder or _SavedModelBuilder class
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"""
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path = _get_export_dir("no_variable_saved_model")
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with session.Session(graph=ops.Graph()) as sess:
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x = variables.VariableV1(
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5, name="x", collections=["not_global_variable"])
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y = variables.VariableV1(
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11, name="y", collections=["not_global_variable"])
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self.assertFalse(variables._all_saveable_objects())
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z = x + y
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self.evaluate(variables.variables_initializer([x, y]))
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foo_sig_def = signature_def_utils.build_signature_def(
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{"foo_input": utils.build_tensor_info(x)},
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{"foo_output": utils.build_tensor_info(z)})
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builder = saved_model_builder.SavedModelBuilder(path)
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builder.add_meta_graph_and_variables(
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sess, ["foo_graph"], {"foo": foo_sig_def},
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saver=tf_saver.Saver([x, y]))
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builder.save()
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loader = loader_impl.SavedModelLoader(path)
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with self.session(graph=ops.Graph()) as sess:
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saver, _ = loader.load_graph(sess.graph, ["foo_graph"])
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self.assertFalse(variables._all_saveable_objects())
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self.assertIsNotNone(saver)
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with self.session(graph=ops.Graph()) as sess:
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loader.load(sess, ["foo_graph"])
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self.assertEqual(5, sess.graph.get_tensor_by_name("x:0").eval())
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self.assertEqual(11, sess.graph.get_tensor_by_name("y:0").eval())
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def test_load_saved_model_graph_with_return_elements(self, builder_cls):
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"""Ensure that the correct elements are returned."""
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self.export_simple_graph(builder_cls)
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loader = loader_impl.SavedModelLoader(SIMPLE_ADD_SAVED_MODEL)
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graph = ops.Graph()
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_, ret = loader.load_graph(graph, ["foo_graph"],
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return_elements=["y:0", "x:0"])
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self.assertEqual(graph.get_tensor_by_name("y:0"), ret[0])
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self.assertEqual(graph.get_tensor_by_name("x:0"), ret[1])
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with self.assertRaisesRegexp(ValueError, "not found in graph"):
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loader.load_graph(graph, ["foo_graph"], return_elements=["z:0"])
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if __name__ == "__main__":
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test.main()
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