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# Copyright 2016 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 predictor.saved_model_predictor."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.contrib.predictor import saved_model_predictor
from tensorflow.core.framework import tensor_shape_pb2
from tensorflow.core.framework import types_pb2
from tensorflow.core.protobuf import meta_graph_pb2
from tensorflow.python.framework import ops
from tensorflow.python.platform import test
from tensorflow.python.saved_model import signature_def_utils
KEYS_AND_OPS = (('sum', lambda x, y: x + y),
('product', lambda x, y: x * y,),
('difference', lambda x, y: x - y))
MODEL_DIR_NAME = 'contrib/predictor/test_export_dir'
class SavedModelPredictorTest(test.TestCase):
@classmethod
def setUpClass(cls):
# Load a saved model exported from the arithmetic `Estimator`.
# See `testing_common.py`.
cls._export_dir = test.test_src_dir_path(MODEL_DIR_NAME)
def testDefault(self):
"""Test prediction with default signature."""
np.random.seed(1111)
x = np.random.rand()
y = np.random.rand()
predictor = saved_model_predictor.SavedModelPredictor(
export_dir=self._export_dir)
output = predictor({'x': x, 'y': y})['outputs']
self.assertAlmostEqual(output, x + y, places=3)
def testSpecifiedSignatureKey(self):
"""Test prediction with spedicified signature key."""
np.random.seed(1234)
for signature_def_key, op in KEYS_AND_OPS:
x = np.random.rand()
y = np.random.rand()
expected_output = op(x, y)
predictor = saved_model_predictor.SavedModelPredictor(
export_dir=self._export_dir,
signature_def_key=signature_def_key)
output_tensor_name = predictor.fetch_tensors['outputs'].name
self.assertRegexpMatches(
output_tensor_name,
signature_def_key,
msg='Unexpected fetch tensor.')
output = predictor({'x': x, 'y': y})['outputs']
self.assertAlmostEqual(
expected_output, output, places=3,
msg='Failed for signature "{}." '
'Got output {} for x = {} and y = {}'.format(
signature_def_key, output, x, y))
def testSpecifiedSignature(self):
"""Test prediction with spedicified signature definition."""
np.random.seed(4444)
for key, op in KEYS_AND_OPS:
x = np.random.rand()
y = np.random.rand()
expected_output = op(x, y)
inputs = {
'x': meta_graph_pb2.TensorInfo(
name='inputs/x:0',
dtype=types_pb2.DT_FLOAT,
tensor_shape=tensor_shape_pb2.TensorShapeProto()),
'y': meta_graph_pb2.TensorInfo(
name='inputs/y:0',
dtype=types_pb2.DT_FLOAT,
tensor_shape=tensor_shape_pb2.TensorShapeProto())}
outputs = {
key: meta_graph_pb2.TensorInfo(
name='outputs/{}:0'.format(key),
dtype=types_pb2.DT_FLOAT,
tensor_shape=tensor_shape_pb2.TensorShapeProto())}
signature_def = signature_def_utils.build_signature_def(
inputs=inputs,
outputs=outputs,
method_name='tensorflow/serving/regress')
predictor = saved_model_predictor.SavedModelPredictor(
export_dir=self._export_dir,
signature_def=signature_def)
output_tensor_name = predictor.fetch_tensors[key].name
self.assertRegexpMatches(
output_tensor_name,
key,
msg='Unexpected fetch tensor.')
output = predictor({'x': x, 'y': y})[key]
self.assertAlmostEqual(
expected_output, output, places=3,
msg='Failed for signature "{}". '
'Got output {} for x = {} and y = {}'.format(key, output, x, y))
def testSpecifiedTensors(self):
"""Test prediction with spedicified `Tensor`s."""
np.random.seed(987)
for key, op in KEYS_AND_OPS:
x = np.random.rand()
y = np.random.rand()
expected_output = op(x, y)
input_names = {'x': 'inputs/x:0',
'y': 'inputs/y:0'}
output_names = {key: 'outputs/{}:0'.format(key)}
predictor = saved_model_predictor.SavedModelPredictor(
export_dir=self._export_dir,
input_names=input_names,
output_names=output_names)
output_tensor_name = predictor.fetch_tensors[key].name
self.assertRegexpMatches(
output_tensor_name,
key,
msg='Unexpected fetch tensor.')
output = predictor({'x': x, 'y': y})[key]
self.assertAlmostEqual(
expected_output, output, places=3,
msg='Failed for signature "{}". '
'Got output {} for x = {} and y = {}'.format(key, output, x, y))
def testBadTagsFail(self):
"""Test that predictor construction fails for bad tags."""
bad_tags_regex = ('.* could not be found in SavedModel')
with self.assertRaisesRegexp(RuntimeError, bad_tags_regex):
_ = saved_model_predictor.SavedModelPredictor(
export_dir=self._export_dir,
tags=('zomg, bad, tags'))
def testSpecifiedGraph(self):
"""Test that the predictor remembers a specified `Graph`."""
g = ops.Graph()
predictor = saved_model_predictor.SavedModelPredictor(
export_dir=self._export_dir,
graph=g)
self.assertEqual(predictor.graph, g)
if __name__ == '__main__':
test.main()
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