# Copyright 2018 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 SavedModelLoader class.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import os from tensorflow.python.client import session from tensorflow.python.framework import errors from tensorflow.python.framework import ops from tensorflow.python.lib.io import file_io from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import state_ops from tensorflow.python.ops import variables from tensorflow.python.platform import test from tensorflow.python.saved_model import builder as saved_model_builder from tensorflow.python.saved_model import loader_impl from tensorflow.python.saved_model import signature_def_utils from tensorflow.python.saved_model import utils from tensorflow.python.training import saver as tf_saver def _get_export_dir(label): return os.path.join(test.get_temp_dir(), label) SIMPLE_ADD_SAVED_MODEL = _get_export_dir("simple_add_saved_model") SAVED_MODEL_WITH_MAIN_OP = _get_export_dir("saved_model_with_main_op") class SavedModelLoaderTest(test.TestCase): def setUp(self): """Write test SavedModels to a temp directory.""" with session.Session(graph=ops.Graph()) as sess: x = variables.VariableV1(5, name="x") y = variables.VariableV1(11, name="y") z = x + y sess.run(variables.global_variables_initializer()) foo_sig_def = signature_def_utils.build_signature_def( {"foo_input": utils.build_tensor_info(x)}, {"foo_output": utils.build_tensor_info(z)}) bar_sig_def = signature_def_utils.build_signature_def( {"bar_x": utils.build_tensor_info(x), "bar_y": utils.build_tensor_info(y)}, {"bar_z": utils.build_tensor_info(z)}) builder = saved_model_builder.SavedModelBuilder(SIMPLE_ADD_SAVED_MODEL) builder.add_meta_graph_and_variables( sess, ["foo_graph"], {"foo": foo_sig_def, "bar": bar_sig_def}) builder.save() # Write SavedModel with a main_op assign_op = control_flow_ops.group(state_ops.assign(y, 7)) builder = saved_model_builder.SavedModelBuilder(SAVED_MODEL_WITH_MAIN_OP) builder.add_meta_graph_and_variables( sess, ["foo_graph"], {"foo": foo_sig_def, "bar": bar_sig_def}, main_op=assign_op) builder.save() def tearDown(self): file_io.delete_recursively(test.get_temp_dir()) def test_load_function(self): loader = loader_impl.SavedModelLoader(SIMPLE_ADD_SAVED_MODEL) with self.session(graph=ops.Graph()) as sess: loader.load(sess, ["foo_graph"]) self.assertEqual(5, sess.graph.get_tensor_by_name("x:0").eval()) self.assertEqual(11, sess.graph.get_tensor_by_name("y:0").eval()) loader2 = loader_impl.SavedModelLoader(SAVED_MODEL_WITH_MAIN_OP) with self.session(graph=ops.Graph()) as sess: loader2.load(sess, ["foo_graph"]) self.assertEqual(5, sess.graph.get_tensor_by_name("x:0").eval()) self.assertEqual(7, sess.graph.get_tensor_by_name("y:0").eval()) def test_load_graph(self): loader = loader_impl.SavedModelLoader(SIMPLE_ADD_SAVED_MODEL) graph = ops.Graph() loader.load_graph(graph, ["foo_graph"]) x = graph.get_tensor_by_name("x:0") y = graph.get_tensor_by_name("y:0") with self.assertRaises(KeyError): graph.get_tensor_by_name("z:0") with self.session(graph=graph) as sess: # Check that x and y are not initialized with self.assertRaises(errors.FailedPreconditionError): sess.run(x) with self.assertRaises(errors.FailedPreconditionError): sess.run(y) def test_load_with_import_scope(self): loader = loader_impl.SavedModelLoader(SAVED_MODEL_WITH_MAIN_OP) with self.session(graph=ops.Graph()) as sess: saver, _ = loader.load_graph( sess.graph, ["foo_graph"], import_scope="baz") # The default saver should not work when the import scope is set. with self.assertRaises(errors.NotFoundError): loader.restore_variables(sess, tf_saver.Saver()) loader.restore_variables(sess, saver) loader.run_init_ops(sess, ["foo_graph"]) self.assertEqual(5, sess.graph.get_tensor_by_name("baz/x:0").eval()) self.assertEqual(7, sess.graph.get_tensor_by_name("baz/y:0").eval()) # Test combined load function. loader = loader_impl.SavedModelLoader(SAVED_MODEL_WITH_MAIN_OP) with self.session(graph=ops.Graph()) as sess: loader.load(sess, ["foo_graph"], import_scope="baa") self.assertEqual(5, sess.graph.get_tensor_by_name("baa/x:0").eval()) self.assertEqual(7, sess.graph.get_tensor_by_name("baa/y:0").eval()) def test_restore_variables(self): loader = loader_impl.SavedModelLoader(SAVED_MODEL_WITH_MAIN_OP) with self.session(graph=ops.Graph()) as sess: x = variables.VariableV1(0, name="x") y = variables.VariableV1(0, name="y") z = x * y sess.run(variables.global_variables_initializer()) # There are variables to restore, so a saver must be created. with self.assertRaises(ValueError): loader.restore_variables(sess, None) loader.restore_variables(sess, tf_saver.Saver()) self.assertEqual(55, z.eval()) def test_run_init_op(self): loader = loader_impl.SavedModelLoader(SAVED_MODEL_WITH_MAIN_OP) graph = ops.Graph() saver, _ = loader.load_graph(graph, ["foo_graph"]) with self.session(graph=graph) as sess: loader.restore_variables(sess, saver) self.assertEqual(5, sess.graph.get_tensor_by_name("x:0").eval()) self.assertEqual(11, sess.graph.get_tensor_by_name("y:0").eval()) loader.run_init_ops(sess, ["foo_graph"]) self.assertEqual(5, sess.graph.get_tensor_by_name("x:0").eval()) self.assertEqual(7, sess.graph.get_tensor_by_name("y:0").eval()) def test_parse_saved_model(self): loader = loader_impl.SavedModelLoader(SIMPLE_ADD_SAVED_MODEL) meta_graph = loader.get_meta_graph_def_from_tags(["foo_graph"]) self.assertIsNotNone(meta_graph) self.assertIn("foo", meta_graph.signature_def) self.assertIn("bar", meta_graph.signature_def) def test_load_invalid_meta_graph(self): loader = loader_impl.SavedModelLoader(SIMPLE_ADD_SAVED_MODEL) with self.assertRaises(RuntimeError): loader.get_meta_graph_def_from_tags([]) with self.assertRaises(RuntimeError): loader.get_meta_graph_def_from_tags([""]) with self.assertRaises(RuntimeError): loader.get_meta_graph_def_from_tags(["not_a_graph"]) def test_load_saved_model_with_no_variables(self): """Test that SavedModel runs saver when there appear to be no variables. When no variables are detected, this may mean that the variables were saved to different collections, or the collections weren't saved to the SavedModel. If the SavedModel MetaGraphDef contains a saver, it should still run in either of these cases. """ path = _get_export_dir("no_variable_saved_model") with session.Session(graph=ops.Graph()) as sess: x = variables.VariableV1( 5, name="x", collections=["not_global_variable"]) y = variables.VariableV1( 11, name="y", collections=["not_global_variable"]) self.assertFalse(variables._all_saveable_objects()) z = x + y sess.run(variables.variables_initializer([x, y])) foo_sig_def = signature_def_utils.build_signature_def( {"foo_input": utils.build_tensor_info(x)}, {"foo_output": utils.build_tensor_info(z)}) builder = saved_model_builder.SavedModelBuilder(path) builder.add_meta_graph_and_variables( sess, ["foo_graph"], {"foo": foo_sig_def}, saver=tf_saver.Saver([x, y])) builder.save() loader = loader_impl.SavedModelLoader(path) with self.session(graph=ops.Graph()) as sess: saver, _ = loader.load_graph(sess.graph, ["foo_graph"]) self.assertFalse(variables._all_saveable_objects()) self.assertIsNotNone(saver) with self.session(graph=ops.Graph()) as sess: loader.load(sess, ["foo_graph"]) self.assertEqual(5, sess.graph.get_tensor_by_name("x:0").eval()) self.assertEqual(11, sess.graph.get_tensor_by_name("y:0").eval()) def test_load_saved_model_graph_with_return_elements(self): """Ensure that the correct elements are returned.""" loader = loader_impl.SavedModelLoader(SIMPLE_ADD_SAVED_MODEL) graph = ops.Graph() _, ret = loader.load_graph(graph, ["foo_graph"], return_elements=["y:0", "x:0"]) self.assertEqual(graph.get_tensor_by_name("y:0"), ret[0]) self.assertEqual(graph.get_tensor_by_name("x:0"), ret[1]) with self.assertRaisesRegexp(ValueError, "not found in graph"): loader.load_graph(graph, ["foo_graph"], return_elements=["z:0"]) if __name__ == "__main__": test.main()