Reduce complexity of FeatureAblation.attribute_future (pytorch#1368)

Summary:

Reduce complexity of FeatureAblation.attribute_future by refactoring inner function

Reviewed By: cyrjano

Differential Revision: D64361191

Author: craymichael

captum/attr/_core/feature_ablation.py

@@ -555,73 +555,9 @@ def attribute_future(
                         ]
                     )
 
-                    def eval_fut_to_ablated_out_fut(
-                        # pyre-ignore Invalid type parameters [24]
-                        eval_futs: Future[List[Future[List[object]]]],
-                        current_inputs: Tuple[Tensor, ...],
-                        current_mask: Tensor,
-                        i: int,
-                        perturbations_per_eval: int,
-                        num_examples: int,
-                        formatted_inputs: Tuple[Tensor, ...],
-                    ) -> Tuple[List[Tensor], List[Tensor]]:
-                        try:
-                            modified_eval = cast(Tensor, eval_futs.value()[1].value())
-                            initial_eval_tuple = cast(
-                                Tuple[
-                                    List[Tensor],
-                                    List[Tensor],
-                                    Tensor,
-                                    Tensor,
-                                    int,
-                                    dtype,
-                                ],
-                                eval_futs.value()[0].value(),
-                            )
-                            if len(initial_eval_tuple) != 6:
-                                raise AssertionError(
-                                    "eval_fut_to_ablated_out_fut: "
-                                    "initial_eval_tuple should have 6 elements: "
-                                    "total_attrib, weights, initial_eval, "
-                                    "flattened_initial_eval, n_outputs, attrib_type "
-                                )
-                            if not isinstance(modified_eval, Tensor):
-                                raise AssertionError(
-                                    "eval_fut_to_ablated_out_fut: "
-                                    "modified eval should be a Tensor"
-                                )
-                            (
-                                total_attrib,
-                                weights,
-                                initial_eval,
-                                flattened_initial_eval,
-                                n_outputs,
-                                attrib_type,
-                            ) = initial_eval_tuple
-                            result = self._process_ablated_out(  # type: ignore # noqa: E501 line too long
-                                modified_eval=modified_eval,
-                                current_inputs=current_inputs,
-                                current_mask=current_mask,
-                                perturbations_per_eval=perturbations_per_eval,
-                                num_examples=num_examples,
-                                initial_eval=initial_eval,
-                                flattened_initial_eval=flattened_initial_eval,
-                                inputs=formatted_inputs,
-                                n_outputs=n_outputs,
-                                total_attrib=total_attrib,
-                                weights=weights,
-                                i=i,
-                                attrib_type=attrib_type,
-                            )
-                        except FeatureAblationFutureError as e:
-                            raise FeatureAblationFutureError(
-                                "eval_fut_to_ablated_out_fut func failed)"
-                            ) from e
-                        return result
-
                     ablated_out_fut: Future[Tuple[List[Tensor], List[Tensor]]] = (
                         eval_futs.then(
-                            lambda eval_futs, current_inputs=current_inputs, current_mask=current_mask, i=i: eval_fut_to_ablated_out_fut(  # type: ignore # noqa: E501 line too long
+                            lambda eval_futs, current_inputs=current_inputs, current_mask=current_mask, i=i: self._eval_fut_to_ablated_out_fut(  # type: ignore # noqa: E501 line too long
                                 eval_futs=eval_futs,
                                 current_inputs=current_inputs,
                                 current_mask=current_mask,
@@ -660,6 +596,70 @@ def _attribute_progress_setup(
         )
         return attr_progress
 
+    def _eval_fut_to_ablated_out_fut(
+        self,
+        # pyre-ignore Invalid type parameters [24]
+        eval_futs: Future[List[Future[List[object]]]],
+        current_inputs: Tuple[Tensor, ...],
+        current_mask: Tensor,
+        i: int,
+        perturbations_per_eval: int,
+        num_examples: int,
+        formatted_inputs: Tuple[Tensor, ...],
+    ) -> Tuple[List[Tensor], List[Tensor]]:
+        try:
+            modified_eval = cast(Tensor, eval_futs.value()[1].value())
+            initial_eval_tuple = cast(
+                Tuple[
+                    List[Tensor],
+                    List[Tensor],
+                    Tensor,
+                    Tensor,
+                    int,
+                    dtype,
+                ],
+                eval_futs.value()[0].value(),
+            )
+            if len(initial_eval_tuple) != 6:
+                raise AssertionError(
+                    "eval_fut_to_ablated_out_fut: "
+                    "initial_eval_tuple should have 6 elements: "
+                    "total_attrib, weights, initial_eval, "
+                    "flattened_initial_eval, n_outputs, attrib_type "
+                )
+            if not isinstance(modified_eval, Tensor):
+                raise AssertionError(
+                    "eval_fut_to_ablated_out_fut: " "modified eval should be a Tensor"
+                )
+            (
+                total_attrib,
+                weights,
+                initial_eval,
+                flattened_initial_eval,
+                n_outputs,
+                attrib_type,
+            ) = initial_eval_tuple
+            result = self._process_ablated_out(  # type: ignore # noqa: E501 line too long
+                modified_eval=modified_eval,
+                current_inputs=current_inputs,
+                current_mask=current_mask,
+                perturbations_per_eval=perturbations_per_eval,
+                num_examples=num_examples,
+                initial_eval=initial_eval,
+                flattened_initial_eval=flattened_initial_eval,
+                inputs=formatted_inputs,
+                n_outputs=n_outputs,
+                total_attrib=total_attrib,
+                weights=weights,
+                i=i,
+                attrib_type=attrib_type,
+            )
+        except FeatureAblationFutureError as e:
+            raise FeatureAblationFutureError(
+                "eval_fut_to_ablated_out_fut func failed)"
+            ) from e
+        return result
+
     # pyre-fixme[3]: Return type must be specified as type that does not contain `Any`
     def _ith_input_ablation_generator(
         self,