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# Copyright 2015 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 SignatureDef utils."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.contrib.saved_model.python.saved_model import signature_def_utils as signature_def_contrib_utils
from tensorflow.core.protobuf import meta_graph_pb2
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.ops import array_ops
from tensorflow.python.platform import test
from tensorflow.python.saved_model import signature_constants
from tensorflow.python.saved_model import signature_def_utils
from tensorflow.python.saved_model import utils
class SignatureDefUtilsTest(test.TestCase):
def _add_to_signature_def_map(self, meta_graph_def, signature_def_map=None):
if signature_def_map is not None:
for key in signature_def_map:
meta_graph_def.signature_def[key].CopyFrom(signature_def_map[key])
def _check_tensor_info(self, tensor_info_map, map_key, expected_tensor_name):
actual_tensor_info = tensor_info_map[map_key]
self.assertEqual(expected_tensor_name, actual_tensor_info.name)
def testGetSignatureDefByKey(self):
x = array_ops.placeholder(dtypes.float32, 1, name="x")
x_tensor_info = utils.build_tensor_info(x)
y = array_ops.placeholder(dtypes.float32, name="y")
y_tensor_info = utils.build_tensor_info(y)
foo_signature_def = signature_def_utils.build_signature_def({
"foo-input": x_tensor_info
}, {"foo-output": y_tensor_info}, "foo-method-name")
bar_signature_def = signature_def_utils.build_signature_def({
"bar-input": x_tensor_info
}, {"bar-output": y_tensor_info}, "bar-method-name")
meta_graph_def = meta_graph_pb2.MetaGraphDef()
self._add_to_signature_def_map(
meta_graph_def, {"foo": foo_signature_def,
"bar": bar_signature_def})
# Look up a key that does not exist in the SignatureDefMap.
missing_key = "missing-key"
with self.assertRaisesRegexp(
ValueError,
"No SignatureDef with key '%s' found in MetaGraphDef" % missing_key):
signature_def_contrib_utils.get_signature_def_by_key(
meta_graph_def, missing_key)
# Look up the key, `foo` which exists in the SignatureDefMap.
foo_signature_def = signature_def_contrib_utils.get_signature_def_by_key(
meta_graph_def, "foo")
self.assertTrue("foo-method-name", foo_signature_def.method_name)
# Check inputs in signature def.
self.assertEqual(1, len(foo_signature_def.inputs))
self._check_tensor_info(foo_signature_def.inputs, "foo-input", "x:0")
# Check outputs in signature def.
self.assertEqual(1, len(foo_signature_def.outputs))
self._check_tensor_info(foo_signature_def.outputs, "foo-output", "y:0")
# Look up the key, `bar` which exists in the SignatureDefMap.
bar_signature_def = signature_def_contrib_utils.get_signature_def_by_key(
meta_graph_def, "bar")
self.assertTrue("bar-method-name", bar_signature_def.method_name)
# Check inputs in signature def.
self.assertEqual(1, len(bar_signature_def.inputs))
self._check_tensor_info(bar_signature_def.inputs, "bar-input", "x:0")
# Check outputs in signature def.
self.assertEqual(1, len(bar_signature_def.outputs))
self._check_tensor_info(bar_signature_def.outputs, "bar-output", "y:0")
def testGetSignatureDefByKeyRegression(self):
input1 = constant_op.constant("a", name="input-1")
output1 = constant_op.constant(7.2, name="output-1")
meta_graph_def = meta_graph_pb2.MetaGraphDef()
self._add_to_signature_def_map(meta_graph_def, {
"my_regression":
signature_def_utils.regression_signature_def(input1, output1)
})
# Look up the regression signature with the key used while saving.
signature_def = signature_def_contrib_utils.get_signature_def_by_key(
meta_graph_def, "my_regression")
# Check the method name to match the constants regression method name.
self.assertEqual(signature_constants.REGRESS_METHOD_NAME,
signature_def.method_name)
# Check inputs in signature def.
self.assertEqual(1, len(signature_def.inputs))
self._check_tensor_info(signature_def.inputs,
signature_constants.REGRESS_INPUTS, "input-1:0")
# Check outputs in signature def.
self.assertEqual(1, len(signature_def.outputs))
self._check_tensor_info(signature_def.outputs,
signature_constants.REGRESS_OUTPUTS, "output-1:0")
def testGetSignatureDefByKeyClassification(self):
input1 = constant_op.constant("a", name="input-1")
output1 = constant_op.constant("b", name="output-1")
output2 = constant_op.constant(3.0, name="output-2")
meta_graph_def = meta_graph_pb2.MetaGraphDef()
self._add_to_signature_def_map(meta_graph_def, {
"my_classification":
signature_def_utils.classification_signature_def(
input1, output1, output2)
})
# Look up the classification signature def with the key used while saving.
signature_def = signature_def_contrib_utils.get_signature_def_by_key(
meta_graph_def, "my_classification")
# Check the method name to match the constants classification method name.
self.assertEqual(signature_constants.CLASSIFY_METHOD_NAME,
signature_def.method_name)
# Check inputs in signature def.
self.assertEqual(1, len(signature_def.inputs))
self._check_tensor_info(signature_def.inputs,
signature_constants.CLASSIFY_INPUTS, "input-1:0")
# Check outputs in signature def.
self.assertEqual(2, len(signature_def.outputs))
self._check_tensor_info(signature_def.outputs,
signature_constants.CLASSIFY_OUTPUT_CLASSES,
"output-1:0")
self._check_tensor_info(signature_def.outputs,
signature_constants.CLASSIFY_OUTPUT_SCORES,
"output-2:0")
def testPredictionSignatureDef(self):
input1 = constant_op.constant("a", name="input-1")
input2 = constant_op.constant("b", name="input-2")
output1 = constant_op.constant("c", name="output-1")
output2 = constant_op.constant("d", name="output-2")
meta_graph_def = meta_graph_pb2.MetaGraphDef()
self._add_to_signature_def_map(meta_graph_def, {
"my_prediction":
signature_def_utils.predict_signature_def({
"input-1": input1,
"input-2": input2
}, {"output-1": output1,
"output-2": output2})
})
# Look up the prediction signature def with the key used while saving.
signature_def = signature_def_contrib_utils.get_signature_def_by_key(
meta_graph_def, "my_prediction")
self.assertEqual(signature_constants.PREDICT_METHOD_NAME,
signature_def.method_name)
# Check inputs in signature def.
self.assertEqual(2, len(signature_def.inputs))
self._check_tensor_info(signature_def.inputs, "input-1", "input-1:0")
self._check_tensor_info(signature_def.inputs, "input-2", "input-2:0")
# Check outputs in signature def.
self.assertEqual(2, len(signature_def.outputs))
self._check_tensor_info(signature_def.outputs, "output-1", "output-1:0")
self._check_tensor_info(signature_def.outputs, "output-2", "output-2:0")
if __name__ == "__main__":
test.main()
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