def get_metadata_protobuf(self) -> explanation_metadata.ExplanationMetadata: """Returns the current metadata as a Protobuf object. Returns: ExplanationMetadata object format of the explanation metadata. """ return explanation_metadata.ExplanationMetadata( inputs=self._inputs, outputs=self._outputs, )
def test_get_metadata_protobuf_sequential(self): self._set_up_sequential() builder = saved_model_metadata_builder.SavedModelMetadataBuilder( self.saved_model_path ) generated_object = builder.get_metadata_protobuf() expected_object = explanation_metadata.ExplanationMetadata( inputs={"dense_input": {"input_tensor_name": "dense_input"}}, outputs={"dense_2": {"output_tensor_name": "dense_2"}}, ) assert expected_object == generated_object
def test_get_metadata_protobuf_correct_inputs(self): self._set_up() md_builder = saved_model_metadata_builder.SavedModelMetadataBuilder( self.model_path, tags=[tf.saved_model.tag_constants.SERVING]) expected_object = explanation_metadata.ExplanationMetadata( inputs={"x": { "input_tensor_name": "inp:0" }}, outputs={"y": { "output_tensor_name": "Relu:0" }}, ) assert md_builder.get_metadata_protobuf() == expected_object
def test_get_metadata_protobuf_double_output(self): self._set_up() md_builder = saved_model_metadata_builder.SavedModelMetadataBuilder( self.model_path, signature_name="double", outputs_to_explain=["lin"]) expected_object = explanation_metadata.ExplanationMetadata( inputs={"x": { "input_tensor_name": "inp:0" }}, outputs={"lin": { "output_tensor_name": "Add:0" }}, ) assert md_builder.get_metadata_protobuf() == expected_object