Python saved_model_export_utils.get_output_alternatives函数代码示例

  def test_get_output_alternatives_empty_provided_with_default(self):
    prediction_tensor = constant_op.constant(["bogus"])
    model_fn_ops = model_fn.ModelFnOps(
        model_fn.ModeKeys.INFER,
        predictions={"some_output": prediction_tensor},
        output_alternatives={})

    with self.assertRaises(ValueError) as e:
      saved_model_export_utils.get_output_alternatives(model_fn_ops, "WRONG")

    self.assertEqual("Requested default_output_alternative: WRONG, but "
                     "available output_alternatives are: []", str(e.exception))

  def test_build_all_signature_defs_legacy_input_fn_not_supported(self):
    """Tests that legacy input_fn returning (features, labels) raises error.

    serving_input_fn must return InputFnOps including a default input
    alternative.
    """
    input_features = constant_op.constant(["10"])
    input_ops = ({"features": input_features}, None)
    input_alternatives, _ = (
        saved_model_export_utils.get_input_alternatives(input_ops))
    output_1 = constant_op.constant(["1"])
    output_2 = constant_op.constant(["2"])
    output_3 = constant_op.constant(["3"])
    provided_output_alternatives = {
        "head-1": (constants.ProblemType.LINEAR_REGRESSION, {
            "some_output_1": output_1
        }),
        "head-2": (constants.ProblemType.CLASSIFICATION, {
            "some_output_2": output_2
        }),
        "head-3": (constants.ProblemType.UNSPECIFIED, {
            "some_output_3": output_3
        }),
    }
    model_fn_ops = model_fn.ModelFnOps(
        model_fn.ModeKeys.INFER,
        predictions={"some_output": constant_op.constant(["4"])},
        output_alternatives=provided_output_alternatives)
    output_alternatives, _ = (saved_model_export_utils.get_output_alternatives(
        model_fn_ops, "head-1"))

    with self.assertRaisesRegexp(
        ValueError, "A default input_alternative must be provided"):
      saved_model_export_utils.build_all_signature_defs(
          input_alternatives, output_alternatives, "head-1")

  def test_build_all_signature_defs(self):
    input_features = constant_op.constant(["10"])
    input_example = constant_op.constant(["11"])
    input_ops = input_fn_utils.InputFnOps({
        "features": input_features
    }, None, {"default input": input_example})
    input_alternatives, _ = (
        saved_model_export_utils.get_input_alternatives(input_ops))
    output_1 = constant_op.constant(["1"])
    output_2 = constant_op.constant(["2"])
    output_3 = constant_op.constant(["3"])
    provided_output_alternatives = {
        "head-1": (constants.ProblemType.LINEAR_REGRESSION, {
            "some_output_1": output_1
        }),
        "head-2": (constants.ProblemType.CLASSIFICATION, {
            "some_output_2": output_2
        }),
        "head-3": (constants.ProblemType.UNSPECIFIED, {
            "some_output_3": output_3
        }),
    }
    model_fn_ops = model_fn.ModelFnOps(
        model_fn.ModeKeys.INFER,
        predictions={"some_output": constant_op.constant(["4"])},
        output_alternatives=provided_output_alternatives)
    output_alternatives, _ = (saved_model_export_utils.get_output_alternatives(
        model_fn_ops, "head-1"))

    signature_defs = saved_model_export_utils.build_all_signature_defs(
        input_alternatives, output_alternatives, "head-1")

    expected_signature_defs = {
        "serving_default":
            signature_def_utils.regression_signature_def(input_example,
                                                         output_1),
        "default_input_alternative:head-1":
            signature_def_utils.regression_signature_def(input_example,
                                                         output_1),
        "default_input_alternative:head-2":
            signature_def_utils.classification_signature_def(input_example,
                                                             output_2, None),
        "default_input_alternative:head-3":
            signature_def_utils.predict_signature_def({
                "input": input_example
            }, {"output": output_3}),
        # "features_input_alternative:head-1":
        #     signature_def_utils.regression_signature_def(input_features,
        #                                                  output_1),
        # "features_input_alternative:head-2":
        #     signature_def_utils.classification_signature_def(input_features,
        #                                                      output_2, None),
        # "features_input_alternative:head-3":
        #     signature_def_utils.predict_signature_def({
        #         "input": input_features
        #     }, {"output": output_3}),
    }

    self.assertDictEqual(expected_signature_defs, signature_defs)

  def test_get_output_alternatives_multi_no_default(self):
    provided_output_alternatives = {
        "head-1": (constants.ProblemType.LINEAR_REGRESSION,
                   "bogus output dict"),
        "head-2": (constants.ProblemType.CLASSIFICATION, "bogus output dict 2"),
        "head-3": (constants.ProblemType.UNSPECIFIED, "bogus output dict 3"),
    }
    model_fn_ops = model_fn.ModelFnOps(
        model_fn.ModeKeys.INFER,
        predictions={"some_output": "bogus_tensor"},
        output_alternatives=provided_output_alternatives)

    with self.assertRaises(ValueError) as e:
      saved_model_export_utils.get_output_alternatives(model_fn_ops)

    self.assertEqual("Please specify a default_output_alternative.  Available "
                     "output_alternatives are: ['head-1', 'head-2', 'head-3']",
                     str(e.exception))

  def test_get_output_alternatives_implicit(self):
    prediction_tensor = tf.constant(["bogus"])
    model_fn_ops = model_fn.ModelFnOps(
        model_fn.ModeKeys.INFER,
        predictions={"some_output": prediction_tensor},
        output_alternatives=None)

    output_alternatives, _ = saved_model_export_utils.get_output_alternatives(
        model_fn_ops, "some_output")
    self.assertEqual(
        {"default_output_alternative": (constants.ProblemType.UNSPECIFIED,
                                        {"some_output": prediction_tensor})},
        output_alternatives)

  def test_get_output_alternatives_explicit(self):
    provided_output_alternatives = {
        "head-1": (constants.ProblemType.LINEAR_REGRESSION,
                   "bogus output dict"),
        "head-2": (constants.ProblemType.CLASSIFICATION, "bogus output dict 2"),
        "head-3": (constants.ProblemType.UNSPECIFIED, "bogus output dict 3"),
    }
    model_fn_ops = model_fn.ModelFnOps(
        model_fn.ModeKeys.INFER,
        predictions={"some_output": "bogus_tensor"},
        output_alternatives=provided_output_alternatives)
    output_alternatives, _ = saved_model_export_utils.get_output_alternatives(
        model_fn_ops, "head-1")

    self.assertEqual(provided_output_alternatives, output_alternatives)

  def __init__(self,
               estimator,
               prediction_input_fn,
               input_alternative_key=None,
               output_alternative_key=None,
               graph=None,
               config=None):
    """Initialize a `ContribEstimatorPredictor`.

    Args:
      estimator: an instance of `tf.contrib.learn.Estimator`.
      prediction_input_fn: a function that takes no arguments and returns an
        instance of `InputFnOps`.
      input_alternative_key: Optional. Specify the input alternative used for
        prediction.
      output_alternative_key: Specify the output alternative used for
        prediction. Not needed for single-headed models but required for
        multi-headed models.
      graph: Optional. The Tensorflow `graph` in which prediction should be
        done.
      config: `ConfigProto` proto used to configure the session.
    """
    self._graph = graph or ops.Graph()
    with self._graph.as_default():
      input_fn_ops = prediction_input_fn()
      # pylint: disable=protected-access
      model_fn_ops = estimator._get_predict_ops(input_fn_ops.features)
      # pylint: enable=protected-access
      checkpoint_path = checkpoint_management.latest_checkpoint(
          estimator.model_dir)
      self._session = monitored_session.MonitoredSession(
          session_creator=monitored_session.ChiefSessionCreator(
              config=config,
              checkpoint_filename_with_path=checkpoint_path))

    input_alternative_key = (
        input_alternative_key or
        saved_model_export_utils.DEFAULT_INPUT_ALTERNATIVE_KEY)
    input_alternatives, _ = saved_model_export_utils.get_input_alternatives(
        input_fn_ops)
    self._feed_tensors = input_alternatives[input_alternative_key]

    (output_alternatives,
     output_alternative_key) = saved_model_export_utils.get_output_alternatives(
         model_fn_ops, output_alternative_key)
    _, fetch_tensors = output_alternatives[output_alternative_key]
    self._fetch_tensors = fetch_tensors

  def test_get_output_alternatives_single_no_default(self):
    prediction_tensor = constant_op.constant(["bogus"])
    provided_output_alternatives = {
        "head-1": (constants.ProblemType.LINEAR_REGRESSION,
                   {"output": prediction_tensor}),
    }
    model_fn_ops = model_fn.ModelFnOps(
        model_fn.ModeKeys.INFER,
        predictions=prediction_tensor,
        output_alternatives=provided_output_alternatives)

    output_alternatives, _ = saved_model_export_utils.get_output_alternatives(
        model_fn_ops)

    self.assertEqual({"head-1":
                      (constants.ProblemType.LINEAR_REGRESSION,
                       {"output": prediction_tensor})},
                     output_alternatives)