Skip to content
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

Fix flakyness in tests related to torch.unique with dim=None #2650

Merged
merged 10 commits into from
Sep 2, 2024
Merged

Fix flakyness in tests related to torch.unique with dim=None #2650

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
10 commits
Select commit Hold shift + click to select a range
7d69a29
correct check
SkafteNicki Jul 23, 2024
03e5df0
Merge branch 'master' into tests/flaky_integer_test
SkafteNicki Jul 26, 2024
87b13be
Merge branch 'master' into tests/flaky_integer_test
Borda Jul 29, 2024
74a4f20
Merge branch 'master' into tests/flaky_integer_test
Borda Aug 2, 2024
7b7bf26
Merge branch 'master' into tests/flaky_integer_test
Borda Aug 3, 2024
652f490
try
Borda Aug 3, 2024
ee7224d
Merge branch 'master' into tests/flaky_integer_test
Borda Aug 8, 2024
52f71d0
found the problem
SkafteNicki Aug 27, 2024
ef372ce
Merge branch 'master' into tests/flaky_integer_test
SkafteNicki Aug 27, 2024
c5fedd2
Merge branch 'master' into tests/flaky_integer_test
SkafteNicki Aug 28, 2024
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
10 changes: 5 additions & 5 deletions src/torchmetrics/functional/classification/confusion_matrix.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -94,7 +94,7 @@ def _binary_confusion_matrix_tensor_validation(
_check_same_shape(preds, target)

# Check that target only contains {0,1} values or value in ignore_index
unique_values = torch.unique(target)
unique_values = torch.unique(target, dim=None)
if ignore_index is None:
check = torch.any((unique_values != 0) & (unique_values != 1))
else:
Expand All @@ -107,7 +107,7 @@ def _binary_confusion_matrix_tensor_validation(

# If preds is label tensor, also check that it only contains {0,1} values
if not preds.is_floating_point():
unique_values = torch.unique(preds)
unique_values = torch.unique(preds, dim=None)
if torch.any((unique_values != 0) & (unique_values != 1)):
raise RuntimeError(
f"Detected the following values in `preds`: {unique_values} but expected only"
Expand Down Expand Up @@ -287,7 +287,7 @@ def _multiclass_confusion_matrix_tensor_validation(

check_value = num_classes if ignore_index is None else num_classes + 1
for t, name in ((target, "target"),) + ((preds, "preds"),) if not preds.is_floating_point() else (): # noqa: RUF005
num_unique_values = len(torch.unique(t))
num_unique_values = len(torch.unique(t, dim=None))
if num_unique_values > check_value:
raise RuntimeError(
f"Detected more unique values in `{name}` than expected. Expected only {check_value} but found"
Expand Down Expand Up @@ -454,7 +454,7 @@ def _multilabel_confusion_matrix_tensor_validation(
)

# Check that target only contains [0,1] values or value in ignore_index
unique_values = torch.unique(target)
unique_values = torch.unique(target, dim=None)
if ignore_index is None:
check = torch.any((unique_values != 0) & (unique_values != 1))
else:
Expand All @@ -467,7 +467,7 @@ def _multilabel_confusion_matrix_tensor_validation(

# If preds is label tensor, also check that it only contains [0,1] values
if not preds.is_floating_point():
unique_values = torch.unique(preds)
unique_values = torch.unique(preds, dim=None)
if torch.any((unique_values != 0) & (unique_values != 1)):
raise RuntimeError(
f"Detected the following values in `preds`: {unique_values} but expected only"
Expand Down
4 changes: 2 additions & 2 deletions src/torchmetrics/functional/classification/precision_recall_curve.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -148,7 +148,7 @@ def _binary_precision_recall_curve_tensor_validation(
)

# Check that target only contains {0,1} values or value in ignore_index
unique_values = torch.unique(target)
unique_values = torch.unique(target, dim=None)
if ignore_index is None:
check = torch.any((unique_values != 0) & (unique_values != 1))
else:
Expand Down Expand Up @@ -417,7 +417,7 @@ def _multiclass_precision_recall_curve_tensor_validation(
f" but got {preds.shape} and {target.shape}"
)

num_unique_values = len(torch.unique(target))
num_unique_values = len(torch.unique(target, dim=None))
check = num_unique_values > num_classes if ignore_index is None else num_unique_values > num_classes + 1
if check:
raise RuntimeError(
Expand Down
12 changes: 6 additions & 6 deletions src/torchmetrics/functional/classification/stat_scores.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -67,7 +67,7 @@ def _binary_stat_scores_tensor_validation(
_check_same_shape(preds, target)

# Check that target only contains [0,1] values or value in ignore_index
unique_values = torch.unique(target)
unique_values = torch.unique(target, dim=None)
if ignore_index is None:
check = torch.any((unique_values != 0) & (unique_values != 1))
else:
Expand All @@ -80,7 +80,7 @@ def _binary_stat_scores_tensor_validation(

# If preds is label tensor, also check that it only contains [0,1] values
if not preds.is_floating_point():
unique_values = torch.unique(preds)
unique_values = torch.unique(preds, dim=None)
if torch.any((unique_values != 0) & (unique_values != 1)):
raise RuntimeError(
f"Detected the following values in `preds`: {unique_values} but expected only"
Expand Down Expand Up @@ -314,11 +314,11 @@ def _multiclass_stat_scores_tensor_validation(

check_value = num_classes if ignore_index is None else num_classes + 1
for t, name in ((target, "target"),) + ((preds, "preds"),) if not preds.is_floating_point() else (): # noqa: RUF005
num_unique_values = len(torch.unique(t))
num_unique_values = len(torch.unique(t, dim=None))
if num_unique_values > check_value:
raise RuntimeError(
f"Detected more unique values in `{name}` than expected. Expected only {check_value} but found"
f" {num_unique_values} in `target`."
f" {num_unique_values} in `{name}`. Found values: {torch.unique(t, dim=None)}."
)


Expand Down Expand Up @@ -624,7 +624,7 @@ def _multilabel_stat_scores_tensor_validation(
)

# Check that target only contains [0,1] values or value in ignore_index
unique_values = torch.unique(target)
unique_values = torch.unique(target, dim=None)
if ignore_index is None:
check = torch.any((unique_values != 0) & (unique_values != 1))
else:
Expand All @@ -637,7 +637,7 @@ def _multilabel_stat_scores_tensor_validation(

# If preds is label tensor, also check that it only contains [0,1] values
if not preds.is_floating_point():
unique_values = torch.unique(preds)
unique_values = torch.unique(preds, dim=None)
if torch.any((unique_values != 0) & (unique_values != 1)):
raise RuntimeError(
f"Detected the following values in `preds`: {unique_values} but expected only"
Expand Down
2 changes: 0 additions & 2 deletions tests/unittests/classification/test_stat_scores.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -578,8 +578,6 @@ def test_multilabel_stat_scores_dtype_gpu(self, inputs, dtype):
)


# fixme: Expected only 5 but found 7 in `target`
@pytest.mark.flaky(reruns=5, only_rerun="RuntimeError")
def test_support_for_int():
"""See issue: https://github.com/Lightning-AI/torchmetrics/issues/1970."""
seed_all(42)
Expand Down
Loading