def from_saved_model(export_dir,
signature_def_key=None,
signature_def=None,
tags=None,
graph=None):
"""Constructs a `Predictor` from a `SavedModel` on disk.
Args:
export_dir: a path to a directory containing a `SavedModel`.
signature_def_key: Optional string specifying the signature to use. If
`None`, then `DEFAULT_SERVING_SIGNATURE_DEF_KEY` is used. Only one of
`signature_def_key` and `signature_def`
signature_def: A `SignatureDef` proto specifying the inputs and outputs
for prediction. Only one of `signature_def_key` and `signature_def`
should be specified.
tags: Optional. Tags that will be used to retrieve the correct
`SignatureDef`. Defaults to `DEFAULT_TAGS`.
graph: Optional. The Tensorflow `graph` in which prediction should be
done.
Returns:
An initialized `Predictor`.
Raises:
ValueError: More than one of `signature_def_key` and `signature_def` is
specified.
"""
return saved_model_predictor.SavedModelPredictor(
export_dir,
signature_def_key=signature_def_key,
signature_def=signature_def,
tags=tags,
graph=graph)
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 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 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 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 from_saved_model(export_dir,
signature_def_key=None,
signature_def=None,
input_names=None,
output_names=None,
tags=None,
graph=None,
config=None):
"""Constructs a `Predictor` from a `SavedModel` on disk.
Args:
export_dir: a path to a directory containing a `SavedModel`.
signature_def_key: Optional string specifying the signature to use. If
`None`, then `DEFAULT_SERVING_SIGNATURE_DEF_KEY` is used. Only one of
`signature_def_key` and `signature_def`
signature_def: A `SignatureDef` proto specifying the inputs and outputs
for prediction. Only one of `signature_def_key` and `signature_def`
should be specified.
input_names: A dictionary mapping strings to `Tensor`s in the `SavedModel`
that represent the input. The keys can be any string of the user's
choosing.
output_names: A dictionary mapping strings to `Tensor`s in the
`SavedModel` that represent the output. The keys can be any string of
the user's choosing.
tags: Optional. Tags that will be used to retrieve the correct
`SignatureDef`. Defaults to `DEFAULT_TAGS`.
graph: Optional. The Tensorflow `graph` in which prediction should be
done.
config: `ConfigProto` proto used to configure the session.
Returns:
An initialized `Predictor`.
Raises:
ValueError: More than one of `signature_def_key` and `signature_def` is
specified.
"""
return saved_model_predictor.SavedModelPredictor(
export_dir,
signature_def_key=signature_def_key,
signature_def=signature_def,
input_names=input_names,
output_names=output_names,
tags=tags,
graph=graph,
config=config)
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)
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'))