minor code cleaning

.github/assistant.py

@@ -59,7 +59,7 @@ def prune_packages(req_file: str, *pkgs: str) -> None:
             lines = [ln for ln in lines if not ln.startswith(pkg)]
         logging.info(lines)
 
-        with open(req_file, "w") as fp:
+        with open(req_file, "w", encoding="utf_8") as fp:
             fp.writelines(lines)
 
     @staticmethod
@@ -71,17 +71,17 @@ def set_min_torch_by_python(fpath: str = "requirements.txt") -> None:
         with open(fpath) as fp:
             req = fp.read()
         req = re.sub(r"torch>=[\d\.]+", f"torch>={LUT_PYTHON_TORCH[py_ver]}", req)
-        with open(fpath, "w") as fp:
+        with open(fpath, "w", encoding="utf_8") as fp:
             fp.write(req)
 
     @staticmethod
     def replace_min_requirements(fpath: str) -> None:
         """Replace all `>=` by `==` in given file."""
         logging.info(f"processing: {fpath}")
-        with open(fpath) as fp:
+        with open(fpath, encoding="utf_8") as fp:
             req = fp.read()
         req = req.replace(">=", "==")
-        with open(fpath, "w") as fp:
+        with open(fpath, "w", encoding="utf_8") as fp:
             fp.write(req)
 
     @staticmethod

docs/source/conf.py

@@ -65,7 +65,7 @@ def _transform_changelog(path_in: str, path_out: str) -> None:
         elif ln.startswith("### "):
             ln = ln.replace("###", f"### {chlog_ver} -")
             chlog_lines[i] = ln
-    with open(path_out, "w") as fp:
+    with open(path_out, "w", encoding="utf_8") as fp:
         fp.writelines(chlog_lines)
 
 

tests/audio/test_sdr.py

@@ -87,7 +87,7 @@ def test_sdr(self, preds, target, sk_metric, ddp, dist_sync_on_step):
             SignalDistortionRatio,
             sk_metric=partial(average_metric, metric_func=sk_metric),
             dist_sync_on_step=dist_sync_on_step,
-            metric_args=dict(),
+            metric_args={},
         )
 
     def test_sdr_functional(self, preds, target, sk_metric):
@@ -96,7 +96,7 @@ def test_sdr_functional(self, preds, target, sk_metric):
             target,
             signal_distortion_ratio,
             sk_metric,
-            metric_args=dict(),
+            metric_args={},
         )
 
     def test_sdr_differentiability(self, preds, target, sk_metric):
@@ -105,7 +105,7 @@ def test_sdr_differentiability(self, preds, target, sk_metric):
             target=target,
             metric_module=SignalDistortionRatio,
             metric_functional=signal_distortion_ratio,
-            metric_args=dict(),
+            metric_args={},
         )
 
     @pytest.mark.skipif(
@@ -117,7 +117,7 @@ def test_sdr_half_cpu(self, preds, target, sk_metric):
             target=target,
             metric_module=SignalDistortionRatio,
             metric_functional=signal_distortion_ratio,
-            metric_args=dict(),
+            metric_args={},
         )
 
     @pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires cuda")
@@ -127,7 +127,7 @@ def test_sdr_half_gpu(self, preds, target, sk_metric):
             target=target,
             metric_module=SignalDistortionRatio,
             metric_functional=signal_distortion_ratio,
-            metric_args=dict(),
+            metric_args={},
         )
 
 

tests/detection/test_map.py

@@ -358,7 +358,7 @@ def test_error_on_wrong_input():
         metric.update([], torch.Tensor())  # type: ignore
 
     with pytest.raises(ValueError, match="Expected argument `preds` and `target` to have the same length"):
-        metric.update([dict()], [dict(), dict()])
+        metric.update([{}], [{}, {}])
 
     with pytest.raises(ValueError, match="Expected all dicts in `preds` to contain the `boxes` key"):
         metric.update(

torchmetrics/functional/classification/auroc.py

@@ -111,9 +111,8 @@ def _auroc_compute(
         # max_fpr parameter is only support for binary
         if mode != DataType.BINARY:
             raise ValueError(
-                f"Partial AUC computation not available in"
-                f" multilabel/multiclass setting, 'max_fpr' must be"
-                f" set to `None`, received `{max_fpr}`."
+                "Partial AUC computation not available in multilabel/multiclass setting,"
+                f" 'max_fpr' must be set to `None`, received `{max_fpr}`."
             )
 
     # calculate fpr, tpr
@@ -172,7 +171,7 @@ def _auroc_compute(
 
             allowed_average = (AverageMethod.NONE.value, AverageMethod.MACRO.value, AverageMethod.WEIGHTED.value)
             raise ValueError(
-                f"Argument `average` expected to be one of the following:" f" {allowed_average} but got {average}"
+                f"Argument `average` expected to be one of the following: {allowed_average} but got {average}"
             )
 
         return _auc_compute_without_check(fpr, tpr, 1.0)

torchmetrics/functional/text/rouge.py

@@ -458,7 +458,7 @@ def rouge_score(
     stemmer = nltk.stem.porter.PorterStemmer() if use_stemmer else None
 
     if not isinstance(rouge_keys, tuple):
-        rouge_keys = tuple([rouge_keys])
+        rouge_keys = (rouge_keys,)
     for key in rouge_keys:
         if key not in ALLOWED_ROUGE_KEYS.keys():
             raise ValueError(f"Got unknown rouge key {key}. Expected to be one of {list(ALLOWED_ROUGE_KEYS.keys())}")

torchmetrics/functional/text/squad.py

@@ -133,7 +133,7 @@ def _squad_input_check(
     _fn_answer = lambda tgt: dict(
         answers=[dict(text=txt) for txt in tgt["answers"]["text"]], id=tgt["id"]  # type: ignore
     )
-    targets_dict = [dict(paragraphs=[dict(qas=[_fn_answer(target) for target in targets])])]
+    targets_dict = [{"paragraphs": [dict(qas=[_fn_answer(target) for target in targets])]}]
     return preds_dict, targets_dict
 
 

torchmetrics/image/ssim.py

@@ -219,7 +219,7 @@ def __init__(
         self.add_state("preds", default=[], dist_reduce_fx="cat")
         self.add_state("target", default=[], dist_reduce_fx="cat")
 
-        if not (isinstance(kernel_size, Sequence) or isinstance(kernel_size, int)):
+        if not (isinstance(kernel_size, (Sequence, int))):
             raise ValueError(
                 f"Argument `kernel_size` expected to be an sequence or an int, or a single int. Got {kernel_size}"
             )

torchmetrics/metric.py

@@ -583,7 +583,7 @@ def _filter_kwargs(self, **kwargs: Any) -> Dict[str, Any]:
             k: v for k, v in kwargs.items() if (k in _sign_params.keys() and _sign_params[k].kind not in _params)
         }
 
-        exists_var_keyword = any([v.kind == inspect.Parameter.VAR_KEYWORD for v in _sign_params.values()])
+        exists_var_keyword = any(v.kind == inspect.Parameter.VAR_KEYWORD for v in _sign_params.values())
         # if no kwargs filtered, return all kwargs as default
         if not filtered_kwargs and not exists_var_keyword:
             # no kwargs in update signature -> don't return any kwargs

torchmetrics/text/rouge.py

@@ -109,7 +109,7 @@ def __init__(
             import nltk
 
         if not isinstance(rouge_keys, tuple):
-            rouge_keys = tuple([rouge_keys])
+            rouge_keys = (rouge_keys,)
         for key in rouge_keys:
             if key not in ALLOWED_ROUGE_KEYS:
                 raise ValueError(f"Got unknown rouge key {key}. Expected to be one of {ALLOWED_ROUGE_KEYS}")