Skip to content

Fix flaky test_softmax_classification_batch_multi_target test case by increasing precision #1454

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Closed
wants to merge 1 commit into from
Closed

Fix flaky test_softmax_classification_batch_multi_target test case by increasing precision #1454

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 0 additions & 2 deletions captum/attr/_core/deep_lift.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -523,8 +523,6 @@ def pre_hook(
additional_args, 2, ExpansionTypes.repeat
),
)
# pyre-fixme[60]: Concatenation not yet support for multiple
# variadic tuples: `*baseline_input_tsr, *expanded_additional_args`.
return (*baseline_input_tsr, *expanded_additional_args)
return baseline_input_tsr

Expand Down
10 changes: 7 additions & 3 deletions tests/attr/test_deeplift_classification.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -65,9 +65,13 @@ def test_softmax_classification_batch_zero_baseline(self) -> None:

def test_softmax_classification_batch_multi_target(self) -> None:
num_in = 40
inputs = torch.arange(0.0, num_in * 3.0, requires_grad=True).reshape(3, num_in)
baselines = torch.arange(1.0, num_in + 1).reshape(1, num_in)
model = SoftmaxDeepLiftModel(num_in, 20, 10)
inputs = (
torch.arange(0.0, num_in * 3.0, requires_grad=True)
.reshape(3, num_in)
.double()
)
baselines = torch.arange(1.0, num_in + 1).reshape(1, num_in).double()
model = SoftmaxDeepLiftModel(num_in, 20, 10).double()
dl = DeepLift(model)

self.softmax_classification(
Expand Down
Loading