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