Python tensorflow.python.saved_model.signature_constants.REGRESS_INPUTS Examples

The following are 14 code examples of tensorflow.python.saved_model.signature_constants.REGRESS_INPUTS(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module tensorflow.python.saved_model.signature_constants , or try the search function .
Example #1
Source File: signature_def_utils_test.py    From auto-alt-text-lambda-api with MIT License 6 votes vote down vote up 
def testRegressionSignatureDef(self):
    input1 = constant_op.constant("a", name="input-1")
    output1 = constant_op.constant("b", name="output-1")
    signature_def = signature_def_utils.regression_signature_def(input1,
                                                                 output1)

    self.assertEqual(signature_constants.REGRESS_METHOD_NAME,
                     signature_def.method_name)

    # Check inputs in signature def.
    self.assertEqual(1, len(signature_def.inputs))
    x_tensor_info_actual = (
        signature_def.inputs[signature_constants.REGRESS_INPUTS])
    self.assertEqual("input-1:0", x_tensor_info_actual.name)
    self.assertEqual(types_pb2.DT_STRING, x_tensor_info_actual.dtype)
    self.assertEqual(0, len(x_tensor_info_actual.tensor_shape.dim))

    # Check outputs in signature def.
    self.assertEqual(1, len(signature_def.outputs))
    y_tensor_info_actual = (
        signature_def.outputs[signature_constants.REGRESS_OUTPUTS])
    self.assertEqual("output-1:0", y_tensor_info_actual.name)
    self.assertEqual(types_pb2.DT_STRING, y_tensor_info_actual.dtype)
    self.assertEqual(0, len(y_tensor_info_actual.tensor_shape.dim))
Example #2
Source File: bundle_shim_test.py    From auto-alt-text-lambda-api with MIT License 6 votes vote down vote up 
def testConvertDefaultSignatureRegressionToSignatureDef(self):
    signatures_proto = manifest_pb2.Signatures()
    regression_signature = manifest_pb2.RegressionSignature()
    regression_signature.input.CopyFrom(
        manifest_pb2.TensorBinding(
            tensor_name=signature_constants.REGRESS_INPUTS))
    regression_signature.output.CopyFrom(
        manifest_pb2.TensorBinding(
            tensor_name=signature_constants.REGRESS_OUTPUTS))
    signatures_proto.default_signature.regression_signature.CopyFrom(
        regression_signature)
    signature_def = bundle_shim._convert_default_signature_to_signature_def(
        signatures_proto)

    # Validate regression signature correctly copied over.
    self.assertEqual(signature_def.method_name,
                     signature_constants.REGRESS_METHOD_NAME)
    self.assertEqual(len(signature_def.inputs), 1)
    self.assertEqual(len(signature_def.outputs), 1)
    self.assertProtoEquals(
        signature_def.inputs[signature_constants.REGRESS_INPUTS],
        meta_graph_pb2.TensorInfo(name=signature_constants.REGRESS_INPUTS))
    self.assertProtoEquals(
        signature_def.outputs[signature_constants.REGRESS_OUTPUTS],
        meta_graph_pb2.TensorInfo(name=signature_constants.REGRESS_OUTPUTS))
Example #3
Source File: signature_def_utils_impl.py    From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License 6 votes vote down vote up 
def _is_valid_regression_signature(signature_def):
  """Determine whether the argument is a servable 'regress' SignatureDef."""
  if signature_def.method_name != signature_constants.REGRESS_METHOD_NAME:
    return False

  if (set(signature_def.inputs.keys())
      != set([signature_constants.REGRESS_INPUTS])):
    return False
  if (signature_def.inputs[signature_constants.REGRESS_INPUTS].dtype !=
      types_pb2.DT_STRING):
    return False

  if (set(signature_def.outputs.keys())
      != set([signature_constants.REGRESS_OUTPUTS])):
    return False
  if (signature_def.outputs[signature_constants.REGRESS_OUTPUTS].dtype !=
      types_pb2.DT_FLOAT):
    return False

  return True
Example #4
Source File: signature_def_utils_test.py    From keras-lambda with MIT License 6 votes vote down vote up 
def testRegressionSignatureDef(self):
    input1 = constant_op.constant("a", name="input-1")
    output1 = constant_op.constant("b", name="output-1")
    signature_def = signature_def_utils.regression_signature_def(input1,
                                                                 output1)

    self.assertEqual(signature_constants.REGRESS_METHOD_NAME,
                     signature_def.method_name)

    # Check inputs in signature def.
    self.assertEqual(1, len(signature_def.inputs))
    x_tensor_info_actual = (
        signature_def.inputs[signature_constants.REGRESS_INPUTS])
    self.assertEqual("input-1:0", x_tensor_info_actual.name)
    self.assertEqual(types_pb2.DT_STRING, x_tensor_info_actual.dtype)
    self.assertEqual(0, len(x_tensor_info_actual.tensor_shape.dim))

    # Check outputs in signature def.
    self.assertEqual(1, len(signature_def.outputs))
    y_tensor_info_actual = (
        signature_def.outputs[signature_constants.REGRESS_OUTPUTS])
    self.assertEqual("output-1:0", y_tensor_info_actual.name)
    self.assertEqual(types_pb2.DT_STRING, y_tensor_info_actual.dtype)
    self.assertEqual(0, len(y_tensor_info_actual.tensor_shape.dim))
Example #5
Source File: bundle_shim_test.py    From keras-lambda with MIT License 6 votes vote down vote up 
def testConvertDefaultSignatureRegressionToSignatureDef(self):
    signatures_proto = manifest_pb2.Signatures()
    regression_signature = manifest_pb2.RegressionSignature()
    regression_signature.input.CopyFrom(
        manifest_pb2.TensorBinding(
            tensor_name=signature_constants.REGRESS_INPUTS))
    regression_signature.output.CopyFrom(
        manifest_pb2.TensorBinding(
            tensor_name=signature_constants.REGRESS_OUTPUTS))
    signatures_proto.default_signature.regression_signature.CopyFrom(
        regression_signature)
    signature_def = bundle_shim._convert_default_signature_to_signature_def(
        signatures_proto)

    # Validate regression signature correctly copied over.
    self.assertEqual(signature_def.method_name,
                     signature_constants.REGRESS_METHOD_NAME)
    self.assertEqual(len(signature_def.inputs), 1)
    self.assertEqual(len(signature_def.outputs), 1)
    self.assertProtoEquals(
        signature_def.inputs[signature_constants.REGRESS_INPUTS],
        meta_graph_pb2.TensorInfo(name=signature_constants.REGRESS_INPUTS))
    self.assertProtoEquals(
        signature_def.outputs[signature_constants.REGRESS_OUTPUTS],
        meta_graph_pb2.TensorInfo(name=signature_constants.REGRESS_OUTPUTS))
Example #6
Source File: signature_def_utils_impl.py    From lambda-packs with MIT License 5 votes vote down vote up 
def regression_signature_def(examples, predictions):
  """Creates regression signature from given examples and predictions.

  Args:
    examples: `Tensor`.
    predictions: `Tensor`.

  Returns:
    A regression-flavored signature_def.

  Raises:
    ValueError: If examples is `None`.
  """
  if examples is None:
    raise ValueError('examples cannot be None for regression.')
  if predictions is None:
    raise ValueError('predictions cannot be None for regression.')

  input_tensor_info = utils.build_tensor_info(examples)
  signature_inputs = {signature_constants.REGRESS_INPUTS: input_tensor_info}

  output_tensor_info = utils.build_tensor_info(predictions)
  signature_outputs = {signature_constants.REGRESS_OUTPUTS: output_tensor_info}
  signature_def = build_signature_def(
      signature_inputs, signature_outputs,
      signature_constants.REGRESS_METHOD_NAME)

  return signature_def
Example #7
Source File: signature_def_utils_impl.py    From auto-alt-text-lambda-api with MIT License 5 votes vote down vote up 
def regression_signature_def(examples, predictions):
  """Creates regression signature from given examples and predictions.

  Args:
    examples: `Tensor`.
    predictions: `Tensor`.

  Returns:
    A regression-flavored signature_def.

  Raises:
    ValueError: If examples is `None`.
  """
  if examples is None:
    raise ValueError('examples cannot be None for regression.')
  if predictions is None:
    raise ValueError('predictions cannot be None for regression.')

  input_tensor_info = utils.build_tensor_info(examples)
  signature_inputs = {signature_constants.REGRESS_INPUTS: input_tensor_info}

  output_tensor_info = utils.build_tensor_info(predictions)
  signature_outputs = {signature_constants.REGRESS_OUTPUTS: output_tensor_info}
  signature_def = build_signature_def(
      signature_inputs, signature_outputs,
      signature_constants.REGRESS_METHOD_NAME)

  return signature_def
Example #8
Source File: bundle_shim.py    From auto-alt-text-lambda-api with MIT License 5 votes vote down vote up 
def _convert_default_signature_to_signature_def(signatures):
  """Convert default signature to object of type SignatureDef.

  Args:
    signatures: object of type manifest_pb2.Signatures()

  Returns:
    object of type SignatureDef which contains a converted version of default
    signature from input signatures object

  Raises:
    RuntimeError: if default signature type is not classification or regression.
  """
  default_signature = signatures.default_signature
  signature_def = meta_graph_pb2.SignatureDef()
  if default_signature.WhichOneof("type") == "regression_signature":
    regression_signature = default_signature.regression_signature
    signature_def.method_name = signature_constants.REGRESS_METHOD_NAME
    _add_input_to_signature_def(regression_signature.input.tensor_name,
                                signature_constants.REGRESS_INPUTS,
                                signature_def)
    _add_output_to_signature_def(regression_signature.output.tensor_name,
                                 signature_constants.REGRESS_OUTPUTS,
                                 signature_def)
  elif default_signature.WhichOneof("type") == "classification_signature":
    classification_signature = default_signature.classification_signature
    signature_def.method_name = signature_constants.CLASSIFY_METHOD_NAME
    _add_input_to_signature_def(classification_signature.input.tensor_name,
                                signature_constants.CLASSIFY_INPUTS,
                                signature_def)
    _add_output_to_signature_def(classification_signature.classes.tensor_name,
                                 signature_constants.CLASSIFY_OUTPUT_CLASSES,
                                 signature_def)
    _add_output_to_signature_def(classification_signature.scores.tensor_name,
                                 signature_constants.CLASSIFY_OUTPUT_SCORES,
                                 signature_def)
  else:
    raise RuntimeError("Only classification and regression default signatures "
                       "are supported for up-conversion. %s is not "
                       "supported" % default_signature.WhichOneof("type"))
  return signature_def
Example #9
Source File: signature_def_utils_impl.py    From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License 5 votes vote down vote up 
def regression_signature_def(examples, predictions):
  """Creates regression signature from given examples and predictions.

  Args:
    examples: `Tensor`.
    predictions: `Tensor`.

  Returns:
    A regression-flavored signature_def.

  Raises:
    ValueError: If examples is `None`.
  """
  if examples is None:
    raise ValueError('Regression examples cannot be None.')
  if not isinstance(examples, ops.Tensor):
    raise ValueError('Regression examples must be a string Tensor.')
  if predictions is None:
    raise ValueError('Regression predictions cannot be None.')

  input_tensor_info = utils.build_tensor_info(examples)
  if input_tensor_info.dtype != types_pb2.DT_STRING:
    raise ValueError('Regression examples must be a string Tensor.')
  signature_inputs = {signature_constants.REGRESS_INPUTS: input_tensor_info}

  output_tensor_info = utils.build_tensor_info(predictions)
  if output_tensor_info.dtype != types_pb2.DT_FLOAT:
    raise ValueError('Regression output must be a float Tensor.')
  signature_outputs = {signature_constants.REGRESS_OUTPUTS: output_tensor_info}

  signature_def = build_signature_def(
      signature_inputs, signature_outputs,
      signature_constants.REGRESS_METHOD_NAME)

  return signature_def
Example #10
Source File: signature_def_utils_impl.py    From keras-lambda with MIT License 5 votes vote down vote up 
def regression_signature_def(examples, predictions):
  """Creates regression signature from given examples and predictions.

  Args:
    examples: `Tensor`.
    predictions: `Tensor`.

  Returns:
    A regression-flavored signature_def.

  Raises:
    ValueError: If examples is `None`.
  """
  if examples is None:
    raise ValueError('examples cannot be None for regression.')
  if predictions is None:
    raise ValueError('predictions cannot be None for regression.')

  input_tensor_info = utils.build_tensor_info(examples)
  signature_inputs = {signature_constants.REGRESS_INPUTS: input_tensor_info}

  output_tensor_info = utils.build_tensor_info(predictions)
  signature_outputs = {signature_constants.REGRESS_OUTPUTS: output_tensor_info}
  signature_def = build_signature_def(
      signature_inputs, signature_outputs,
      signature_constants.REGRESS_METHOD_NAME)

  return signature_def
Example #11
Source File: bundle_shim.py    From keras-lambda with MIT License 5 votes vote down vote up 
def _convert_default_signature_to_signature_def(signatures):
  """Convert default signature to object of type SignatureDef.

  Args:
    signatures: object of type manifest_pb2.Signatures()

  Returns:
    object of type SignatureDef which contains a converted version of default
    signature from input signatures object

  Raises:
    RuntimeError: if default signature type is not classification or regression.
  """
  default_signature = signatures.default_signature
  signature_def = meta_graph_pb2.SignatureDef()
  if default_signature.WhichOneof("type") == "regression_signature":
    regression_signature = default_signature.regression_signature
    signature_def.method_name = signature_constants.REGRESS_METHOD_NAME
    _add_input_to_signature_def(regression_signature.input.tensor_name,
                                signature_constants.REGRESS_INPUTS,
                                signature_def)
    _add_output_to_signature_def(regression_signature.output.tensor_name,
                                 signature_constants.REGRESS_OUTPUTS,
                                 signature_def)
  elif default_signature.WhichOneof("type") == "classification_signature":
    classification_signature = default_signature.classification_signature
    signature_def.method_name = signature_constants.CLASSIFY_METHOD_NAME
    _add_input_to_signature_def(classification_signature.input.tensor_name,
                                signature_constants.CLASSIFY_INPUTS,
                                signature_def)
    _add_output_to_signature_def(classification_signature.classes.tensor_name,
                                 signature_constants.CLASSIFY_OUTPUT_CLASSES,
                                 signature_def)
    _add_output_to_signature_def(classification_signature.scores.tensor_name,
                                 signature_constants.CLASSIFY_OUTPUT_SCORES,
                                 signature_def)
  else:
    raise RuntimeError("Only classification and regression default signatures "
                       "are supported for up-conversion. %s is not "
                       "supported" % default_signature.WhichOneof("type"))
  return signature_def
Example #12
Source File: bundle_shim.py    From lambda-packs with MIT License 4 votes vote down vote up 
def _convert_default_signature_to_signature_def(signatures):
  """Convert default signature to object of type SignatureDef.

  Args:
    signatures: object of type manifest_pb2.Signatures()

  Returns:
    object of type SignatureDef which contains a converted version of default
    signature from input signatures object

    Returns None if signature is of generic type because it cannot be converted
    to SignatureDef.

  """
  default_signature = signatures.default_signature
  signature_def = meta_graph_pb2.SignatureDef()
  if default_signature.WhichOneof("type") == "regression_signature":
    regression_signature = default_signature.regression_signature
    signature_def.method_name = signature_constants.REGRESS_METHOD_NAME
    _add_input_to_signature_def(regression_signature.input.tensor_name,
                                signature_constants.REGRESS_INPUTS,
                                signature_def)
    _add_output_to_signature_def(regression_signature.output.tensor_name,
                                 signature_constants.REGRESS_OUTPUTS,
                                 signature_def)
  elif default_signature.WhichOneof("type") == "classification_signature":
    classification_signature = default_signature.classification_signature
    signature_def.method_name = signature_constants.CLASSIFY_METHOD_NAME
    _add_input_to_signature_def(classification_signature.input.tensor_name,
                                signature_constants.CLASSIFY_INPUTS,
                                signature_def)
    _add_output_to_signature_def(classification_signature.classes.tensor_name,
                                 signature_constants.CLASSIFY_OUTPUT_CLASSES,
                                 signature_def)
    _add_output_to_signature_def(classification_signature.scores.tensor_name,
                                 signature_constants.CLASSIFY_OUTPUT_SCORES,
                                 signature_def)
  else:
    logging.error("Only classification and regression default signatures "
                  "are supported for up-conversion. %s is not "
                  "supported" % default_signature.WhichOneof("type"))
    return None
  return signature_def
Example #13
Source File: bundle_shim_test.py    From auto-alt-text-lambda-api with MIT License 4 votes vote down vote up 
def testConvertSignaturesToSignatureDefs(self):
    base_path = test.test_src_dir_path(SESSION_BUNDLE_PATH)
    meta_graph_filename = os.path.join(base_path,
                                       constants.META_GRAPH_DEF_FILENAME)
    metagraph_def = meta_graph.read_meta_graph_file(meta_graph_filename)
    default_signature_def, named_signature_def = (
        bundle_shim._convert_signatures_to_signature_defs(metagraph_def))
    self.assertEqual(default_signature_def.method_name,
                     signature_constants.REGRESS_METHOD_NAME)
    self.assertEqual(len(default_signature_def.inputs), 1)
    self.assertEqual(len(default_signature_def.outputs), 1)
    self.assertProtoEquals(
        default_signature_def.inputs[signature_constants.REGRESS_INPUTS],
        meta_graph_pb2.TensorInfo(name="tf_example:0"))
    self.assertProtoEquals(
        default_signature_def.outputs[signature_constants.REGRESS_OUTPUTS],
        meta_graph_pb2.TensorInfo(name="Identity:0"))
    self.assertEqual(named_signature_def.method_name,
                     signature_constants.PREDICT_METHOD_NAME)
    self.assertEqual(len(named_signature_def.inputs), 1)
    self.assertEqual(len(named_signature_def.outputs), 1)
    self.assertProtoEquals(
        named_signature_def.inputs["x"], meta_graph_pb2.TensorInfo(name="x:0"))
    self.assertProtoEquals(
        named_signature_def.outputs["y"], meta_graph_pb2.TensorInfo(name="y:0"))

    # Now try default signature only
    collection_def = metagraph_def.collection_def
    signatures_proto = manifest_pb2.Signatures()
    signatures = collection_def[constants.SIGNATURES_KEY].any_list.value[0]
    signatures.Unpack(signatures_proto)
    named_only_signatures_proto = manifest_pb2.Signatures()
    named_only_signatures_proto.CopyFrom(signatures_proto)

    default_only_signatures_proto = manifest_pb2.Signatures()
    default_only_signatures_proto.CopyFrom(signatures_proto)
    default_only_signatures_proto.named_signatures.clear()
    default_only_signatures_proto.ClearField("named_signatures")
    metagraph_def.collection_def[constants.SIGNATURES_KEY].any_list.value[
        0].Pack(default_only_signatures_proto)
    default_signature_def, named_signature_def = (
        bundle_shim._convert_signatures_to_signature_defs(metagraph_def))
    self.assertEqual(default_signature_def.method_name,
                     signature_constants.REGRESS_METHOD_NAME)
    self.assertEqual(named_signature_def, None)

    named_only_signatures_proto.ClearField("default_signature")
    metagraph_def.collection_def[constants.SIGNATURES_KEY].any_list.value[
        0].Pack(named_only_signatures_proto)
    default_signature_def, named_signature_def = (
        bundle_shim._convert_signatures_to_signature_defs(metagraph_def))
    self.assertEqual(named_signature_def.method_name,
                     signature_constants.PREDICT_METHOD_NAME)
    self.assertEqual(default_signature_def, None)
Example #14
Source File: bundle_shim_test.py    From keras-lambda with MIT License 4 votes vote down vote up 
def testConvertSignaturesToSignatureDefs(self):
    base_path = test.test_src_dir_path(SESSION_BUNDLE_PATH)
    meta_graph_filename = os.path.join(base_path,
                                       constants.META_GRAPH_DEF_FILENAME)
    metagraph_def = meta_graph.read_meta_graph_file(meta_graph_filename)
    default_signature_def, named_signature_def = (
        bundle_shim._convert_signatures_to_signature_defs(metagraph_def))
    self.assertEqual(default_signature_def.method_name,
                     signature_constants.REGRESS_METHOD_NAME)
    self.assertEqual(len(default_signature_def.inputs), 1)
    self.assertEqual(len(default_signature_def.outputs), 1)
    self.assertProtoEquals(
        default_signature_def.inputs[signature_constants.REGRESS_INPUTS],
        meta_graph_pb2.TensorInfo(name="tf_example:0"))
    self.assertProtoEquals(
        default_signature_def.outputs[signature_constants.REGRESS_OUTPUTS],
        meta_graph_pb2.TensorInfo(name="Identity:0"))
    self.assertEqual(named_signature_def.method_name,
                     signature_constants.PREDICT_METHOD_NAME)
    self.assertEqual(len(named_signature_def.inputs), 1)
    self.assertEqual(len(named_signature_def.outputs), 1)
    self.assertProtoEquals(
        named_signature_def.inputs["x"], meta_graph_pb2.TensorInfo(name="x:0"))
    self.assertProtoEquals(
        named_signature_def.outputs["y"], meta_graph_pb2.TensorInfo(name="y:0"))

    # Now try default signature only
    collection_def = metagraph_def.collection_def
    signatures_proto = manifest_pb2.Signatures()
    signatures = collection_def[constants.SIGNATURES_KEY].any_list.value[0]
    signatures.Unpack(signatures_proto)
    named_only_signatures_proto = manifest_pb2.Signatures()
    named_only_signatures_proto.CopyFrom(signatures_proto)

    default_only_signatures_proto = manifest_pb2.Signatures()
    default_only_signatures_proto.CopyFrom(signatures_proto)
    default_only_signatures_proto.named_signatures.clear()
    default_only_signatures_proto.ClearField("named_signatures")
    metagraph_def.collection_def[constants.SIGNATURES_KEY].any_list.value[
        0].Pack(default_only_signatures_proto)
    default_signature_def, named_signature_def = (
        bundle_shim._convert_signatures_to_signature_defs(metagraph_def))
    self.assertEqual(default_signature_def.method_name,
                     signature_constants.REGRESS_METHOD_NAME)
    self.assertEqual(named_signature_def, None)

    named_only_signatures_proto.ClearField("default_signature")
    metagraph_def.collection_def[constants.SIGNATURES_KEY].any_list.value[
        0].Pack(named_only_signatures_proto)
    default_signature_def, named_signature_def = (
        bundle_shim._convert_signatures_to_signature_defs(metagraph_def))
    self.assertEqual(named_signature_def.method_name,
                     signature_constants.PREDICT_METHOD_NAME)
    self.assertEqual(default_signature_def, None)