105 lines
4.5 KiB
Python
105 lines
4.5 KiB
Python
# Copyright 2017 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 SavedModel simple save functionality."""
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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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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 variables
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from tensorflow.python.platform import test
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from tensorflow.python.saved_model import loader
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from tensorflow.python.saved_model import signature_constants
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from tensorflow.python.saved_model import simple_save
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from tensorflow.python.saved_model import tag_constants
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class SimpleSaveTest(test.TestCase):
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def _init_and_validate_variable(self, sess, variable_name, variable_value):
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v = variables.Variable(variable_value, name=variable_name)
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self.evaluate(variables.global_variables_initializer())
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self.assertEqual(variable_value, self.evaluate(v))
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return v
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def _check_variable_info(self, actual_variable, expected_variable):
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self.assertEqual(actual_variable.name, expected_variable.name)
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self.assertEqual(actual_variable.dtype, expected_variable.dtype)
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self.assertEqual(len(actual_variable.shape), len(expected_variable.shape))
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for i in range(len(actual_variable.shape)):
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self.assertEqual(actual_variable.shape[i], expected_variable.shape[i])
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def _check_tensor_info(self, actual_tensor_info, expected_tensor):
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self.assertEqual(actual_tensor_info.name, expected_tensor.name)
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self.assertEqual(actual_tensor_info.dtype, expected_tensor.dtype)
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self.assertEqual(
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len(actual_tensor_info.tensor_shape.dim), len(expected_tensor.shape))
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for i in range(len(actual_tensor_info.tensor_shape.dim)):
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self.assertEqual(actual_tensor_info.tensor_shape.dim[i].size,
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expected_tensor.shape[i])
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@test_util.run_deprecated_v1
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def testSimpleSave(self):
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"""Test simple_save that uses the default parameters."""
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export_dir = os.path.join(test.get_temp_dir(),
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"test_simple_save")
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# Initialize input and output variables and save a prediction graph using
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# the default parameters.
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with self.session(graph=ops.Graph()) as sess:
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var_x = self._init_and_validate_variable(sess, "var_x", 1)
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var_y = self._init_and_validate_variable(sess, "var_y", 2)
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inputs = {"x": var_x}
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outputs = {"y": var_y}
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simple_save.simple_save(sess, export_dir, inputs, outputs)
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# Restore the graph with a valid tag and check the global variables and
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# signature def map.
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with self.session(graph=ops.Graph()) as sess:
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graph = loader.load(sess, [tag_constants.SERVING], export_dir)
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collection_vars = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)
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# Check value and metadata of the saved variables.
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self.assertEqual(len(collection_vars), 2)
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self.assertEqual(1, collection_vars[0].eval())
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self.assertEqual(2, collection_vars[1].eval())
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self._check_variable_info(collection_vars[0], var_x)
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self._check_variable_info(collection_vars[1], var_y)
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# Check that the appropriate signature_def_map is created with the
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# default key and method name, and the specified inputs and outputs.
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signature_def_map = graph.signature_def
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self.assertEqual(1, len(signature_def_map))
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self.assertEqual(signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY,
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list(signature_def_map.keys())[0])
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signature_def = signature_def_map[
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signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY]
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self.assertEqual(signature_constants.PREDICT_METHOD_NAME,
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signature_def.method_name)
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self.assertEqual(1, len(signature_def.inputs))
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self._check_tensor_info(signature_def.inputs["x"], var_x)
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self.assertEqual(1, len(signature_def.outputs))
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self._check_tensor_info(signature_def.outputs["y"], var_y)
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if __name__ == "__main__":
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test.main()
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