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Fix Lime output dimension in batch forward #1513

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Fix Lime output dimension in batch forward #1513

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15 changes: 14 additions & 1 deletion captum/attr/_core/lime.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1038,7 +1038,12 @@ def attribute( # type: ignore
coefficient of the corresponding interpretale feature.
All elements with the same value in the feature mask
will contain the same coefficient in the returned
attributions. If return_input_shape is False, a 1D
attributions.
If forward_func returns a single element per batch, then the
first dimension of each tensor will be 1, and the remaining
dimensions will have the same shape as the original input
tensor.
If return_input_shape is False, a 1D
tensor is returned, containing only the coefficients
of the trained interpreatable models, with length
num_interp_features.
Expand Down Expand Up @@ -1242,6 +1247,7 @@ def _attribute_kwargs( # type: ignore
coefs,
num_interp_features,
is_inputs_tuple,
leading_dim_one=(bsz > 1),
)
else:
return coefs
Expand All @@ -1254,6 +1260,7 @@ def _convert_output_shape(
coefs: Tensor,
num_interp_features: int,
is_inputs_tuple: Literal[True],
leading_dim_one: bool = False,
) -> Tuple[Tensor, ...]: ...

@typing.overload
Expand All @@ -1264,6 +1271,7 @@ def _convert_output_shape( # type: ignore
coefs: Tensor,
num_interp_features: int,
is_inputs_tuple: Literal[False],
leading_dim_one: bool = False,
) -> Tensor: ...

@typing.overload
Expand All @@ -1274,6 +1282,7 @@ def _convert_output_shape(
coefs: Tensor,
num_interp_features: int,
is_inputs_tuple: bool,
leading_dim_one: bool = False,
) -> Union[Tensor, Tuple[Tensor, ...]]: ...

def _convert_output_shape(
Expand All @@ -1283,6 +1292,7 @@ def _convert_output_shape(
coefs: Tensor,
num_interp_features: int,
is_inputs_tuple: bool,
leading_dim_one: bool = False,
) -> Union[Tensor, Tuple[Tensor, ...]]:
coefs = coefs.flatten()
attr = [
Expand All @@ -1295,4 +1305,7 @@ def _convert_output_shape(
coefs[single_feature].item()
* (feature_mask[tensor_ind] == single_feature).float()
)
if leading_dim_one:
for i in range(len(attr)):
attr[i] = attr[i][0:1]
return _format_output(is_inputs_tuple, tuple(attr))
6 changes: 3 additions & 3 deletions tests/attr/test_kernel_shap.py
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Expand Up @@ -348,9 +348,9 @@ def _multi_input_scalar_kernel_shap_assert(self, func: Callable) -> None:
mask2 = torch.tensor([[0, 1, 2]])
mask3 = torch.tensor([[0, 1, 2]])
expected = (
[[3850.6666, 3850.6666, 3850.6666]] * 2,
[[306.6666, 3850.6666, 410.6666]] * 2,
[[306.6666, 3850.6666, 410.6666]] * 2,
[[3850.6666, 3850.6666, 3850.6666]],
[[306.6666, 3850.6666, 410.6666]],
[[306.6666, 3850.6666, 410.6666]],
)

self._kernel_shap_test_assert(
Expand Down
6 changes: 3 additions & 3 deletions tests/attr/test_lime.py
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Expand Up @@ -494,9 +494,9 @@ def _multi_input_scalar_lime_assert(self, func: Callable) -> None:
mask2 = torch.tensor([[0, 1, 2]])
mask3 = torch.tensor([[0, 1, 2]])
expected = (
[[3850.6666, 3850.6666, 3850.6666]] * 2,
[[305.5, 3850.6666, 410.1]] * 2,
[[305.5, 3850.6666, 410.1]] * 2,
[[3850.6666, 3850.6666, 3850.6666]],
[[305.5, 3850.6666, 410.1]],
[[305.5, 3850.6666, 410.1]],
)

self._lime_test_assert(
Expand Down