def predict_injective_single_example( self, eval_saved_model: load.EvalSavedModel, raw_example_bytes: bytes) -> types.FeaturesPredictionsLabels: """Run predict for a single example for a injective model. Args: eval_saved_model: EvalSavedModel raw_example_bytes: Raw example bytes for the example Returns: The singular FPL returned by eval_saved_model.predict on the given raw_example_bytes. """ fetched_list = eval_saved_model.predict(raw_example_bytes) self.assertEqual(1, len(fetched_list)) self.assertEqual(0, fetched_list[0].input_ref) return eval_saved_model.as_features_predictions_labels(fetched_list)[0]
def predict_injective_example_list( self, eval_saved_model: load.EvalSavedModel, raw_example_bytes_list: List[bytes] ) -> List[types.FeaturesPredictionsLabels]: """Run predict_list for a list of examples for a injective model. Args: eval_saved_model: EvalSavedModel raw_example_bytes_list: List of raw example bytes Returns: The list of FPLs returned by eval_saved_model.predict on the given raw_example_bytes. """ fetched_list = eval_saved_model.predict_list(raw_example_bytes_list) # Check that each FPL corresponds to one example. self.assertSequenceEqual( range(0, len(raw_example_bytes_list)), [fetched.input_ref for fetched in fetched_list]) return eval_saved_model.as_features_predictions_labels(fetched_list)