Merge branch 'master' into bugfix/ms_ssim_pad

.github/workflows/ci-checks.yml

@@ -18,7 +18,7 @@ jobs:
  extra-typing: typing
 
  check-schema:
- uses: Lightning-AI/utilities/.github/workflows/check-schema.yml@v0.7.1
+ uses: Lightning-AI/utilities/.github/workflows/check-schema.yml@v0.8.0
 
  check-package:
  uses: Lightning-AI/utilities/.github/workflows/check-package.yml@main

.github/workflows/clear-cache.yml

@@ -13,12 +13,12 @@ jobs:
 
  cron-clear:
  if: github.event_name == 'schedule'
- uses: Lightning-AI/utilities/.github/workflows/clear-cache.yml@v0.7.1
+ uses: Lightning-AI/utilities/.github/workflows/clear-cache.yml@v0.8.0
  with:
  pattern: 'pip-latest'
 
  direct-clear:
  if: github.event_name == 'workflow_dispatch'
- uses: Lightning-AI/utilities/.github/workflows/clear-cache.yml@v0.7.1
+ uses: Lightning-AI/utilities/.github/workflows/clear-cache.yml@v0.8.0
  with:
  pattern: ${{ inputs.pattern }}

.github/workflows/publish-pypi.yml

@@ -73,7 +73,7 @@ jobs:
  # We do this, since failures on test.pypi aren't that bad
  - name: Publish to Test PyPI
  if: startsWith(github.event.ref, 'refs/tags') || github.event_name == 'release'
- uses: pypa/gh-action-pypi-publish@v1.6.4
+ uses: pypa/gh-action-pypi-publish@v1.8.4
  with:
  user: __token__
  password: ${{ secrets.test_pypi_password }}
@@ -82,7 +82,7 @@ jobs:
 
  - name: Publish distribution 📦 to PyPI
  if: startsWith(github.event.ref, 'refs/tags') || github.event_name == 'release'
- uses: pypa/gh-action-pypi-publish@v1.6.4
+ uses: pypa/gh-action-pypi-publish@v1.8.4
  with:
  user: __token__
  password: ${{ secrets.pypi_password }}

src/torchmetrics/classification/accuracy.py

@@ -480,16 +480,25 @@ def __new__(
  ) -> Metric:
  """Initialize task metric."""
  task = ClassificationTask.from_str(task)
+
  kwargs.update(
  {"multidim_average": multidim_average, "ignore_index": ignore_index, "validate_args": validate_args}
  )
+
  if task == ClassificationTask.BINARY:
  return BinaryAccuracy(threshold, **kwargs)
  if task == ClassificationTask.MULTICLASS:
- assert isinstance(num_classes, int)
- assert isinstance(top_k, int)
+ if not isinstance(num_classes, int):
+ raise ValueError(
+ f"Optional arg `num_classes` must be type `int` when task is {task}. Got {type(num_classes)}"
+ )
+ if not isinstance(top_k, int):
+ raise ValueError(f"Optional arg `top_k` must be type `int` when task is {task}. Got {type(top_k)}")
  return MulticlassAccuracy(num_classes, top_k, average, **kwargs)
  if task == ClassificationTask.MULTILABEL:
- assert isinstance(num_labels, int)
+ if not isinstance(num_labels, int):
+ raise ValueError(
+ f"Optional arg `num_labels` must be type `int` when task is {task}. Got {type(num_labels)}"
+ )
  return MultilabelAccuracy(num_labels, threshold, average, **kwargs)
- return None
+ raise ValueError(f"Not handled value: {task}") # this is for compliant of mypy

src/torchmetrics/functional/classification/accuracy.py

@@ -378,8 +378,8 @@ def accuracy(
  threshold: float = 0.5,
  num_classes: Optional[int] = None,
  num_labels: Optional[int] = None,
- average: Optional[Literal["micro", "macro", "weighted", "none"]] = "micro",
- multidim_average: Optional[Literal["global", "samplewise"]] = "global",
+ average: Literal["micro", "macro", "weighted", "none"] = "micro",
+ multidim_average: Literal["global", "samplewise"] = "global",
  top_k: Optional[int] = 1,
  ignore_index: Optional[int] = None,
  validate_args: bool = True,
@@ -409,17 +409,24 @@ def accuracy(
  tensor(0.6667)
  """
  task = ClassificationTask.from_str(task)
- assert multidim_average is not None
+
  if task == ClassificationTask.BINARY:
  return binary_accuracy(preds, target, threshold, multidim_average, ignore_index, validate_args)
  if task == ClassificationTask.MULTICLASS:
- assert isinstance(num_classes, int)
- assert isinstance(top_k, int)
+ if not isinstance(num_classes, int):
+ raise ValueError(
+ f"Optional arg `num_classes` must be type `int` when task is {task}. Got {type(num_classes)}"
+ )
+ if not isinstance(top_k, int):
+ raise ValueError(f"Optional arg `top_k` must be type `int` when task is {task}. Got {type(top_k)}")
  return multiclass_accuracy(
  preds, target, num_classes, average, top_k, multidim_average, ignore_index, validate_args
  )
  if task == ClassificationTask.MULTILABEL:
- assert isinstance(num_labels, int)
+ if not isinstance(num_labels, int):
+ raise ValueError(
+ f"Optional arg `num_labels` must be type `int` when task is {task}. Got {type(num_labels)}"
+ )
  return multilabel_accuracy(
  preds, target, num_labels, threshold, average, multidim_average, ignore_index, validate_args
  )

tests/unittests/classification/test_accuracy.py

@@ -20,10 +20,15 @@
 from sklearn.metrics import accuracy_score as sk_accuracy
 from sklearn.metrics import confusion_matrix as sk_confusion_matrix
 
-from torchmetrics.classification.accuracy import BinaryAccuracy, MulticlassAccuracy, MultilabelAccuracy
-from torchmetrics.functional.classification.accuracy import binary_accuracy, multiclass_accuracy, multilabel_accuracy
+from torchmetrics.classification.accuracy import Accuracy, BinaryAccuracy, MulticlassAccuracy, MultilabelAccuracy
+from torchmetrics.functional.classification.accuracy import (
+ accuracy,
+ binary_accuracy,
+ multiclass_accuracy,
+ multilabel_accuracy,
+)
 from unittests import NUM_CLASSES, THRESHOLD
-from unittests.classification.inputs import _binary_cases, _multiclass_cases, _multilabel_cases
+from unittests.classification.inputs import _binary_cases, _input_binary, _multiclass_cases, _multilabel_cases
 from unittests.helpers import seed_all
 from unittests.helpers.testers import MetricTester, inject_ignore_index, remove_ignore_index
 
@@ -61,6 +66,34 @@ def _sklearn_accuracy_binary(preds, target, ignore_index, multidim_average):
  return np.stack(res)
 
 
+def test_accuracy_raises_invalid_task():
+ """Tests accuracy task enum from Accuracy."""
+ task = "NotValidTask"
+ ignore_index = None
+ multidim_average = "global"
+
+ with pytest.raises(ValueError, match=r"Invalid *"):
+ Accuracy(threshold=THRESHOLD, task=task, ignore_index=ignore_index, multidim_average=multidim_average)
+
+
+def test_accuracy_functional_raises_invalid_task():
+ """Tests accuracy task enum from functional.accuracy."""
+ preds, target = _input_binary
+ task = "NotValidTask"
+ ignore_index = None
+ multidim_average = "global"
+
+ with pytest.raises(ValueError, match=r"Invalid *"):
+ accuracy(
+ preds,
+ target,
+ threshold=THRESHOLD,
+ task=task,
+ ignore_index=ignore_index,
+ multidim_average=multidim_average,
+ )
+
+
 @pytest.mark.parametrize("input", _binary_cases)
 class TestBinaryAccuracy(MetricTester):
  """Test class for `BinaryAccuracy` metric."""