Skip to content

Enable aggregate mode for any case where perturbations_per_eval == 1 #1525

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

Enable aggregate mode for any case where perturbations_per_eval == 1 #1525

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: 1 addition & 1 deletion captum/attr/_utils/common.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -364,7 +364,7 @@ def _find_output_mode_and_verify(
"returns a scalar."
)
else:
agg_output_mode = False
agg_output_mode = perturbations_per_eval == 1
if not allow_multi_outputs:
assert (
isinstance(initial_eval, torch.Tensor) and initial_eval[0].numel() == 1
Expand Down
33 changes: 29 additions & 4 deletions tests/attr/test_shapley.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -806,6 +806,30 @@ def func_future(*inp):
lambda *inp: func_to_use(*inp), use_future=use_future
)

@parameterized.expand([True, False])
def test_mutli_inp_shapley_batch_scalar_tensor_expanded(self, use_future) -> None:
def func(*inp):
sum_val = torch.sum(net(*inp)).item()
return torch.tensor([sum_val, sum_val + 2.0, sum_val + 3.0])

def func_future(*inp):
temp = net_fut(*inp)
temp.wait()
sum_val = torch.sum(temp.value()).item()
fut = Future()
fut.set_result(torch.tensor([sum_val, sum_val + 2.0, sum_val + 3.0]))
return fut

if use_future:
net_fut = BasicModel_MultiLayer_MultiInput_with_Future()
func_to_use = func_future
else:
net = BasicModel_MultiLayer_MultiInput()
func_to_use = func
self._multi_input_batch_scalar_shapley_assert(
lambda *inp: func_to_use(*inp), use_future=use_future, expanded_output=True
)

@unittest.mock.patch("sys.stderr", new_callable=io.StringIO)
def test_shapley_sampling_with_show_progress(self, mock_stderr) -> None:
net = BasicModel_MultiLayer()
Expand Down Expand Up @@ -947,18 +971,19 @@ def _single_int_input_multi_sample_batch_scalar_shapley_assert(
)

def _multi_input_batch_scalar_shapley_assert(
self, func: Callable, use_future: bool = False
self, func: Callable, use_future: bool = False, expanded_output: bool = False
) -> None:
inp1 = torch.tensor([[23.0, 100.0, 0.0], [20.0, 50.0, 30.0]])
inp2 = torch.tensor([[20.0, 50.0, 30.0], [0.0, 100.0, 0.0]])
inp3 = torch.tensor([[0.0, 100.0, 10.0], [20.0, 10.0, 13.0]])
mask1 = torch.tensor([[1, 1, 1]])
mask2 = torch.tensor([[0, 1, 2]])
mask3 = torch.tensor([[0, 1, 2]])
out_mult = 3 if expanded_output else 1
expected = (
[[3850.6666, 3850.6666, 3850.6666]],
[[306.6666, 3850.6666, 410.6666]],
[[306.6666, 3850.6666, 410.6666]],
[[3850.6666, 3850.6666, 3850.6666]] * out_mult,
[[306.6666, 3850.6666, 410.6666]] * out_mult,
[[306.6666, 3850.6666, 410.6666]] * out_mult,
)
if use_future:
self._shapley_test_assert_future(
Expand Down
Loading