more Ruff checks (#1760)

Co-authored-by: Jirka Borovec <6035284+Borda@users.noreply.github.com>

pyproject.toml

@@ -75,6 +75,8 @@ select = [
     "D",  # see: https://pypi.org/project/pydocstyle
     "N",  # see: https://pypi.org/project/pep8-naming
     "S",  # see: https://pypi.org/project/flake8-bandit
+    "T10",  # see: https://pypi.org/project/flake8-debugger/
+    "Q",  # see: https://pypi.org/project/flake8-quotes/
 ]
 extend-select = [
     "C4",  # see: https://pypi.org/project/flake8-comprehensions
@@ -114,7 +116,7 @@ unfixable = ["F401"]
 "setup.py" = ["ANN202", "ANN401"]
 "src/**" = ["ANN401"]
 "tests/**" = [
-    "S101", "ANN001", "ANN002", "ANN003", "ANN201", "ANN202", "ANN204", "ANN205", "ANN401"
+    "S101", "ANN001", "ANN201", "ANN202", "ANN401"
 ]
 
 [tool.ruff.pydocstyle]

src/torchmetrics/functional/audio/pit.py

@@ -82,11 +82,11 @@ def _find_best_perm_by_exhaustive_method(
 
     # find the metric of each permutation
     perm_num = ps.shape[-1]
-    # shape [batch_size, spk_num, perm_num]
+    # shape of [batch_size, spk_num, perm_num]
     bps = ps[None, ...].expand(batch_size, spk_num, perm_num)
-    # shape [batch_size, spk_num, perm_num]
+    # shape of [batch_size, spk_num, perm_num]
     metric_of_ps_details = torch.gather(metric_mtx, 2, bps)
-    # shape [batch_size, perm_num]
+    # shape of [batch_size, perm_num]
     metric_of_ps = metric_of_ps_details.mean(dim=1)
 
     # find the best metric and best permutation

tests/integrations/lightning/boring_model.py

@@ -29,11 +29,11 @@ def __init__(self, size, length) -> None:
         self.len = length
         self.data = torch.randn(length, size)
 
-    def __getitem__(self, index):
+    def __getitem__(self, index) -> dict:
         """Get datapoint."""
         return {"id": str(index), "x": self.data[index]}
 
-    def __len__(self):
+    def __len__(self) -> int:
         """Return length of dataset."""
         return self.len
 
@@ -45,11 +45,11 @@ def __init__(self, size, length) -> None:
         self.len = length
         self.data = torch.randn(length, size)
 
-    def __getitem__(self, index):
+    def __getitem__(self, index) -> torch.Tensor:
         """Get datapoint."""
         return self.data[index]
 
-    def __len__(self):
+    def __len__(self) -> int:
         """Get length of dataset."""
         return self.len
 
@@ -80,7 +80,7 @@ def forward(self, x):
         return self.layer(x)
 
     @staticmethod
-    def loss(_, prediction):
+    def loss(_, prediction) -> torch.Tensor:
         """Arbitrary loss."""
         return torch.nn.functional.mse_loss(prediction, torch.ones_like(prediction))
 

tests/integrations/test_lightning.py

@@ -118,13 +118,13 @@ def _step(self, stage, batch):
 
             return loss
 
-        def training_step(self, batch, batch_idx, *args, **kwargs):
+        def training_step(self, batch, batch_idx):
             return self._step("train", batch)
 
-        def validation_step(self, batch, batch_idx, *args, **kwargs):
+        def validation_step(self, batch, batch_idx):
             return self._step("val", batch)
 
-        def test_step(self, batch, batch_idx, *args, **kwargs):
+        def test_step(self, batch, batch_idx):
             return self._step("test", batch)
 
         def _assert_epoch_end(self, stage):

tests/unittests/audio/test_pit.py

@@ -79,7 +79,6 @@ def naive_implementation_pit_scipy(
         for e in range(spk_num):
             metric_mtx[:, t, e] = metric_func(preds[:, e, ...], target[:, t, ...])
 
-    # pit_r = PermutationInvariantTraining(metric_func, eval_func)(preds, target)
     metric_mtx = metric_mtx.detach().cpu().numpy()
     best_metrics = []
     best_perms = []

tests/unittests/audio/test_sdr.py

@@ -71,9 +71,7 @@ def _average_metric(preds: Tensor, target: Tensor, metric_func: Callable) -> Ten
     "preds, target, ref_metric",
     [
         (inputs_1spk.preds, inputs_1spk.target, original_impl_compute_permutation),
-        # (inputs_1spk.preds, inputs_1spk.target, original_impl_no_compute_permutation, False),
         (inputs_2spk.preds, inputs_2spk.target, original_impl_compute_permutation),
-        # (inputs_2spk.preds, inputs_2spk.target, original_impl_no_compute_permutation, False),
     ],
 )
 class TestSDR(MetricTester):

tests/unittests/bases/test_collections.py

@@ -14,6 +14,7 @@
 import pickle
 import time
 from copy import deepcopy
+from typing import Any
 
 import pytest
 import torch
@@ -285,8 +286,8 @@ class DummyMetric(Metric):
         def __init__(self) -> None:
             super().__init__()
 
-        def update(self, *args, kwarg):
-            print("Entered DummyMetric")
+        def update(self, *args: Any, kwarg: Any):
+            pass
 
         def compute(self):
             return
@@ -298,7 +299,7 @@ def __init__(self) -> None:
             super().__init__()
 
         def update(self, preds, target, kwarg2):
-            print("Entered MyAccuracy")
+            pass
 
         def compute(self):
             return

tests/unittests/bases/test_composition.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from operator import neg, pos
+from typing import Any
 
 import pytest
 import torch
@@ -30,7 +31,7 @@ def __init__(self, val_to_return) -> None:
         self.add_state("_num_updates", tensor(0), dist_reduce_fx="sum")
         self._val_to_return = val_to_return
 
-    def update(self, *args, **kwargs) -> None:
+    def update(self, *args: Any, **kwargs: Any) -> None:
         """Compute state."""
         self._num_updates += 1
 

tests/unittests/bases/test_ddp.py

@@ -143,7 +143,7 @@ def update(self, x):
         def compute(self):
             return self.x // self.c
 
-        def __repr__(self):
+        def __repr__(self) -> str:
             return f"DummyCatMetric(x={self.x}, c={self.c})"
 
     metric = DummyCatMetric()

tests/unittests/bases/test_metric.py

@@ -14,6 +14,7 @@
 import os
 import pickle
 from collections import OrderedDict
+from typing import Any
 from unittest.mock import Mock
 
 import cloudpickle
@@ -473,7 +474,7 @@ def test_no_warning_on_custom_forward(metric_class):
     class UnsetProperty(metric_class):
         full_state_update = None
 
-        def forward(self, *args, **kwargs):
+        def forward(self, *args: Any, **kwargs: Any):
             self.update(*args, **kwargs)
 
     with no_warning_call(

tests/unittests/classification/test_group_fairness.py

@@ -109,7 +109,7 @@ def run_differentiability_test(
         metric_functional: Optional[Callable] = None,
         metric_args: Optional[dict] = None,
         groups: Optional[Tensor] = None,
-    ):
+    ) -> None:
         """Test if a metric is differentiable or not.
 
         Args:
@@ -148,7 +148,7 @@ def run_precision_test_cpu(
         metric_args: Optional[dict] = None,
         dtype: torch.dtype = torch.half,
         **kwargs_update: Any,
-    ):
+    ) -> None:
         """Test if a metric can be used with half precision tensors on cpu.
 
         Args:
@@ -185,7 +185,7 @@ def run_precision_test_gpu(
         metric_args: Optional[dict] = None,
         dtype: torch.dtype = torch.half,
         **kwargs_update: Any,
-    ):
+    ) -> None:
         """Test if a metric can be used with half precision tensors on gpu.
 
         Args:

tests/unittests/classification/test_precision_recall_curve.py

@@ -168,7 +168,6 @@ def _sklearn_precision_recall_curve_multiclass(preds, target, ignore_index=None)
         precision.append(res[0])
         recall.append(res[1])
         thresholds.append(res[2])
-    # return precision, recall, thresholds
     return [np.nan_to_num(x, nan=0.0) for x in [precision, recall, thresholds]]
 
 

tests/unittests/helpers/testers.py

@@ -449,7 +449,7 @@ def run_precision_test_cpu(
         metric_args: Optional[dict] = None,
         dtype: torch.dtype = torch.half,
         **kwargs_update: Any,
-    ):
+    ) -> None:
         """Test if a metric can be used with half precision tensors on cpu.
 
         Args:
@@ -482,7 +482,7 @@ def run_precision_test_gpu(
         metric_args: Optional[dict] = None,
         dtype: torch.dtype = torch.half,
         **kwargs_update: Any,
-    ):
+    ) -> None:
         """Test if a metric can be used with half precision tensors on gpu.
 
         Args:
@@ -513,7 +513,7 @@ def run_differentiability_test(
         metric_module: Metric,
         metric_functional: Optional[Callable] = None,
         metric_args: Optional[dict] = None,
-    ):
+    ) -> None:
         """Test if a metric is differentiable or not.
 
         Args:
@@ -549,7 +549,7 @@ class DummyMetric(Metric):
     name = "Dummy"
     full_state_update: Optional[bool] = True
 
-    def __init__(self, **kwargs) -> None:
+    def __init__(self, **kwargs: Any) -> None:
         super().__init__(**kwargs)
         self.add_state("x", tensor(0.0), dist_reduce_fx="sum")
 
@@ -568,7 +568,7 @@ class DummyListMetric(Metric):
     name = "DummyList"
     full_state_update: Optional[bool] = True
 
-    def __init__(self, **kwargs) -> None:
+    def __init__(self, **kwargs: Any) -> None:
         super().__init__(**kwargs)
         self.add_state("x", [], dist_reduce_fx="cat")
 

tests/unittests/image/test_fid.py

@@ -103,10 +103,10 @@ class _ImgDataset(Dataset):
     def __init__(self, imgs) -> None:
         self.imgs = imgs
 
-    def __getitem__(self, idx):
+    def __getitem__(self, idx) -> torch.Tensor:
         return self.imgs[idx]
 
-    def __len__(self):
+    def __len__(self) -> int:
         return self.imgs.shape[0]
 
 

tests/unittests/image/test_inception.py

@@ -98,10 +98,10 @@ class _ImgDataset(Dataset):
     def __init__(self, imgs) -> None:
         self.imgs = imgs
 
-    def __getitem__(self, idx):
+    def __getitem__(self, idx) -> torch.Tensor:
         return self.imgs[idx]
 
-    def __len__(self):
+    def __len__(self) -> int:
         return self.imgs.shape[0]
 
 

tests/unittests/image/test_kid.py

@@ -125,10 +125,10 @@ class _ImgDataset(Dataset):
     def __init__(self, imgs) -> None:
         self.imgs = imgs
 
-    def __getitem__(self, idx):
+    def __getitem__(self, idx) -> torch.Tensor:
         return self.imgs[idx]
 
-    def __len__(self):
+    def __len__(self) -> int:
         return self.imgs.shape[0]
 
 

tests/unittests/image/test_tv.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 from collections import namedtuple
 from functools import partial
+from typing import Any
 
 import pytest
 import torch
@@ -30,7 +31,7 @@
 class TotalVariationTester(TotalVariation):
     """Tester class for `TotalVariation` metric overriding its update method."""
 
-    def update(self, img, *args):
+    def update(self, img, *args: Any):
         """Update metric."""
         super().update(img=img)
 

tests/unittests/retrieval/helpers.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from functools import partial
-from typing import Callable, Dict, List, Tuple, Type, Union
+from typing import Any, Callable, Dict, List, Tuple, Type, Union
 
 import numpy as np
 import pytest
@@ -78,7 +78,7 @@ def _compute_sklearn_metric(
     empty_target_action: str = "skip",
     ignore_index: int = None,
     reverse: bool = False,
-    **kwargs,
+    **kwargs: Any,
 ) -> Tensor:
     """Compute metric with multiple iterations over every query predictions set."""
     if indexes is None:
@@ -466,7 +466,7 @@ def run_functional_metric_test(
         reference_metric: Callable,
         metric_args: dict,
         reverse: bool = False,
-        **kwargs,
+        **kwargs: Any,
     ):
         """Test functional implementation of metric."""
         _ref_metric_adapted = partial(_compute_sklearn_metric, metric=reference_metric, reverse=reverse, **metric_args)
@@ -537,7 +537,7 @@ def run_metric_class_arguments_test(
         metric_args: dict = None,
         exception_type: Type[Exception] = ValueError,
         kwargs_update: dict = None,
-    ):
+    ) -> None:
         """Test that specific errors are raised for incorrect input."""
         _errors_test_class_metric(
             indexes=indexes,
@@ -558,7 +558,7 @@ def run_functional_metric_arguments_test(
         message: str = "",
         exception_type: Type[Exception] = ValueError,
         kwargs_update: dict = None,
-    ):
+    ) -> None:
         """Test that specific errors are raised for incorrect input."""
         _errors_test_functional_metric(
             preds=preds,