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

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merged 10 commits into from
Sep 2, 2024
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Merged

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

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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
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10 changes: 5 additions & 5 deletions src/torchmetrics/functional/classification/confusion_matrix.py
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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
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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
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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
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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
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