Merge branch 'master' into lime

Author: jjuncho

captum/attr/_core/feature_ablation.py

@@ -3,7 +3,18 @@
 # pyre-strict
 
 import math
-from typing import Any, Callable, cast, Generator, List, Optional, Tuple, TypeVar, Union
+from typing import (
+    Any,
+    Callable,
+    cast,
+    Dict,
+    Generator,
+    List,
+    Optional,
+    Tuple,
+    TypeVar,
+    Union,
+)
 
 import torch
 from captum._utils.common import (
@@ -465,13 +476,21 @@ def _attribute_with_cross_tensor_feature_masks(
         attrib_type: dtype,
         **kwargs: Any,
     ) -> Tuple[List[Tensor], List[Tensor]]:
+        feature_idx_to_tensor_idx: Dict[int, List[int]] = {}
+        for i, mask in enumerate(formatted_feature_mask):
+            for feature_idx in torch.unique(mask):
+                if feature_idx.item() not in feature_idx_to_tensor_idx:
+                    feature_idx_to_tensor_idx[feature_idx.item()] = []
+                feature_idx_to_tensor_idx[feature_idx.item()].append(i)
+
         for (
             current_inputs,
             current_mask,
         ) in self._ablation_generator(
             formatted_inputs,
             baselines,
             formatted_feature_mask,
+            feature_idx_to_tensor_idx,
             **kwargs,
         ):
             # modified_eval has (n_feature_perturbed * n_outputs) elements
@@ -511,27 +530,28 @@ def _ablation_generator(
         inputs: Tuple[Tensor, ...],
         baselines: BaselineType,
         input_mask: Tuple[Tensor, ...],
+        feature_idx_to_tensor_idx: Dict[int, List[int]],
         **kwargs: Any,
     ) -> Generator[
         Tuple[
             Tuple[Tensor, ...],
-            Tuple[Tensor, ...],
+            Tuple[Optional[Tensor], ...],
         ],
         None,
         None,
     ]:
-        unique_feature_ids = torch.unique(
-            torch.cat([mask.flatten() for mask in input_mask])
-        ).tolist()
-
         if isinstance(baselines, torch.Tensor):
             baselines = baselines.reshape((1,) + tuple(baselines.shape))
 
         # Process one feature per time, rather than processing every input tensor
-        for feature_idx in unique_feature_ids:
+        for feature_idx in feature_idx_to_tensor_idx.keys():
             ablated_inputs, current_masks = (
                 self._construct_ablated_input_across_tensors(
-                    inputs, input_mask, baselines, feature_idx
+                    inputs,
+                    input_mask,
+                    baselines,
+                    feature_idx,
+                    feature_idx_to_tensor_idx[feature_idx],
                 )
             )
             yield ablated_inputs, current_masks
@@ -542,18 +562,17 @@ def _construct_ablated_input_across_tensors(
         input_mask: Tuple[Tensor, ...],
         baselines: BaselineType,
         feature_idx: int,
-    ) -> Tuple[Tuple[Tensor, ...], Tuple[Tensor, ...]]:
+        tensor_idxs: List[int],
+    ) -> Tuple[Tuple[Tensor, ...], Tuple[Optional[Tensor], ...]]:
 
         ablated_inputs = []
-        current_masks = []
+        current_masks: List[Optional[Tensor]] = []
         for i, input_tensor in enumerate(inputs):
-            mask = input_mask[i]
-            tensor_mask = mask == feature_idx
-            if not tensor_mask.any():
+            if i not in tensor_idxs:
                 ablated_inputs.append(input_tensor)
-                current_masks.append(torch.zeros_like(tensor_mask))
+                current_masks.append(None)
                 continue
-            tensor_mask = tensor_mask.to(input_tensor.device).long()
+            tensor_mask = (input_mask[i] == feature_idx).to(input_tensor.device).long()
             baseline = baselines[i] if isinstance(baselines, tuple) else baselines
             if isinstance(baseline, torch.Tensor):
                 baseline = baseline.reshape(
@@ -1173,7 +1192,7 @@ def _process_ablated_out(
     def _process_ablated_out_full(
         self,
         modified_eval: Tensor,
-        current_mask: Tuple[Tensor, ...],
+        current_mask: Tuple[Optional[Tensor], ...],
         flattened_initial_eval: Tensor,
         inputs: TensorOrTupleOfTensorsGeneric,
         n_outputs: int,
@@ -1195,9 +1214,10 @@ def _process_ablated_out_full(
 
         if self.use_weights:
             for weight, mask in zip(weights, current_mask):
-                weight += mask.float().sum(dim=0)
+                if mask is not None:
+                    weight += mask.float().sum(dim=0)
         for i, mask in enumerate(current_mask):
-            if inputs[i].numel() == 0:
+            if mask is None or inputs[i].numel() == 0:
                 continue
             eval_diff = eval_diff.reshape(
                 eval_diff_shape + (inputs[i].dim() - 1) * (1,)

captum/attr/_core/feature_permutation.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python3
 
 # pyre-strict
-from typing import Any, Callable, Optional, Tuple, Union
+from typing import Any, Callable, List, Optional, Tuple, Union
 
 import torch
 from captum._utils.typing import BaselineType, TargetType, TensorOrTupleOfTensorsGeneric
@@ -26,15 +26,15 @@ def _permute_feature(x: Tensor, feature_mask: Tensor) -> Tensor:
 
 
 def _permute_features_across_tensors(
-    inputs: Tuple[Tensor, ...], feature_masks: Tuple[Tensor, ...]
+    inputs: Tuple[Tensor, ...], feature_masks: Tuple[Optional[Tensor], ...]
 ) -> Tuple[Tensor, ...]:
     """
     Permutes features across multiple input tensors using the corresponding
     feature masks.
     """
     permuted_outputs = []
     for input_tensor, feature_mask in zip(inputs, feature_masks):
-        if not feature_mask.any():
+        if feature_mask is None or not feature_mask.any():
             permuted_outputs.append(input_tensor)
             continue
         n = input_tensor.size(0)
@@ -103,7 +103,7 @@ def __init__(
         forward_func: Callable[..., Union[int, float, Tensor, Future[Tensor]]],
         perm_func: Callable[[Tensor, Tensor], Tensor] = _permute_feature,
         perm_func_cross_tensor: Callable[
-            [Tuple[Tensor, ...], Tuple[Tensor, ...]], Tuple[Tensor, ...]
+            [Tuple[Tensor, ...], Tuple[Optional[Tensor], ...]], Tuple[Tensor, ...]
         ] = _permute_features_across_tensors,
     ) -> None:
         r"""
@@ -392,9 +392,14 @@ def _construct_ablated_input_across_tensors(
         input_mask: Tuple[Tensor, ...],
         baselines: BaselineType,
         feature_idx: int,
-    ) -> Tuple[Tuple[Tensor, ...], Tuple[Tensor, ...]]:
-        feature_masks = tuple(
-            (mask == feature_idx).to(inputs[0].device) for mask in input_mask
-        )
+        tensor_idxs: List[int],
+    ) -> Tuple[Tuple[Tensor, ...], Tuple[Optional[Tensor], ...]]:
+        current_masks: List[Optional[Tensor]] = []
+        for i, mask in enumerate(input_mask):
+            if i in tensor_idxs:
+                current_masks.append((mask == feature_idx).to(inputs[0].device))
+            else:
+                current_masks.append(None)
+        feature_masks = tuple(current_masks)
         permuted_outputs = self.perm_func_cross_tensor(inputs, feature_masks)
         return permuted_outputs, feature_masks