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