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Lime Type Fixes #570

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Lime Type Fixes #570

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17 changes: 10 additions & 7 deletions captum/attr/_core/lime.py
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Original file line number Diff line number Diff line change
Expand Up @@ -527,15 +527,15 @@ def default_from_interp_rep_transform(curr_sample, original_inputs, **kwargs):
), "Must provide baselines to use default interpretable representation transfrom"
feature_mask = kwargs["feature_mask"]
if isinstance(feature_mask, Tensor):
binary_mask = curr_sample[0][feature_mask]
binary_mask = curr_sample[0][feature_mask].to(original_inputs.dtype)
return binary_mask * original_inputs + (1 - binary_mask) * kwargs["baselines"]
else:
binary_mask = tuple(
curr_sample[0][feature_mask[j]] for j in range(len(feature_mask))
)
return tuple(
binary_mask[j] * original_inputs[j]
+ (1 - binary_mask[j]) * kwargs["baselines"][j]
binary_mask[j].to(original_inputs[j].dtype) * original_inputs[j]
+ (1 - binary_mask[j].to(original_inputs[j].dtype)) * kwargs["baselines"][j]
for j in range(len(feature_mask))
)

Expand Down Expand Up @@ -575,8 +575,8 @@ def get_exp_kernel_similarity_function(
"""

def default_exp_kernel(original_inp, perturbed_inp, __, **kwargs):
flattened_original_inp = _flatten_tensor_or_tuple(original_inp)
flattened_perturbed_inp = _flatten_tensor_or_tuple(perturbed_inp)
flattened_original_inp = _flatten_tensor_or_tuple(original_inp).float()
flattened_perturbed_inp = _flatten_tensor_or_tuple(perturbed_inp).float()
if distance_mode == "cosine":
cos_sim = CosineSimilarity(dim=0)
distance = 1 - cos_sim(flattened_original_inp, flattened_perturbed_inp)
Expand All @@ -599,7 +599,7 @@ def default_perturb_func(original_inp, **kwargs):
device = original_inp[0].device

probs = torch.ones(1, kwargs["num_interp_features"]) * 0.5
return torch.bernoulli(probs).to(device=device)
return torch.bernoulli(probs).to(device=device).long()


class Lime(LimeBase):
Expand Down Expand Up @@ -1130,7 +1130,10 @@ def _convert_output_shape(
is_inputs_tuple: bool,
) -> Union[Tensor, Tuple[Tensor, ...]]:
coefs = coefs.flatten()
attr = [torch.zeros_like(single_inp) for single_inp in formatted_inp]
attr = [
torch.zeros_like(single_inp, dtype=torch.float)
for single_inp in formatted_inp
]
for tensor_ind in range(len(formatted_inp)):
for single_feature in range(num_interp_features):
attr[tensor_ind] += (
Expand Down