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@SkafteNicki
SkafteNicki committed Aug 27, 2024
1 parent ee7224d commit 52f71d0
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Showing 3 changed files with 13 additions and 13 deletions.
10 changes: 5 additions & 5 deletions src/torchmetrics/functional/classification/confusion_matrix.py
View file
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
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
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 `{name}`. Found values: {torch.unique(t)}."
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

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