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Control for when output from model is a scalar or a 1D tensor #1521

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Control for when output from model is a scalar or a 1D tensor #1521

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15 changes: 12 additions & 3 deletions captum/testing/helpers/basic_models.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,7 @@

# pyre-strict

from typing import no_type_check, Optional, Tuple, Union
from typing import Dict, no_type_check, Optional, Tuple, Union

import torch
import torch.nn as nn
Expand Down Expand Up @@ -467,7 +467,9 @@ def __init__(
self.linear3.bias = nn.Parameter(torch.tensor([-1.0, 1.0]))

@no_type_check
def forward(self, x: Tensor, add_input: Optional[Tensor] = None) -> Tensor:
def forward(
self, x: Tensor, add_input: Optional[Tensor] = None
) -> Dict[str, Tensor]:
input = x if add_input is None else x + add_input
lin0_out = self.linear0(input)
lin1_out = self.linear1(lin0_out)
Expand All @@ -485,7 +487,14 @@ def forward(self, x: Tensor, add_input: Optional[Tensor] = None) -> Tensor:

lin3_out = self.linear3(lin1_out_alt).to(torch.int64)

return torch.cat((lin2_out, lin3_out), dim=1)
output_tensors = torch.cat((lin2_out, lin3_out), dim=1)

# we return a dictionary of tensors as an output to test the case
# where an output accessor is required
return {
"task {}".format(i + 1): output_tensors[:, i]
for i in range(output_tensors.shape[1])
}


class MultiRelu(nn.Module):
Expand Down