flake8

docs/source/conf.py

@@ -51,7 +51,6 @@ release = torchmetrics.__version__
 github_user = 'PyTorchLightning'
 github_repo = project
 
-
 # -- Project documents -------------------------------------------------------
 # export the READme
 with open(os.path.join(PATH_ROOT, 'README.md'), 'r') as fp:
@@ -139,7 +138,6 @@ exclude_patterns = [
 # The name of the Pygments (syntax highlighting) style to use.
 pygments_style = None
 
-
 # -- Options for HTML output -------------------------------------------------
 
 # The theme to use for HTML and HTML Help pages.  See the documentation for
@@ -178,7 +176,6 @@ html_static_path = ['_images', '_templates', '_static']
 #
 # html_sidebars = {}
 
-
 # -- Options for HTMLHelp output ---------------------------------------------
 
 # Output file base name for HTML help builder.
@@ -211,9 +208,7 @@ latex_documents = [
 
 # One entry per manual page. List of tuples
 # (source start file, name, description, authors, manual section).
-man_pages = [
-    (master_doc, project, project + ' Documentation', [author], 1)
-]
+man_pages = [(master_doc, project, project + ' Documentation', [author], 1)]
 
 # -- Options for Texinfo output ----------------------------------------------
 
@@ -221,9 +216,7 @@ man_pages = [
 # (source start file, target name, title, author,
 #  dir menu entry, description, category)
 texinfo_documents = [
-    (master_doc, project, project + ' Documentation', author, project,
-     torchmetrics.__docs__,
-     'Miscellaneous'),
+    (master_doc, project, project + ' Documentation', author, project, torchmetrics.__docs__, 'Miscellaneous'),
 ]
 
 # -- Options for Epub output -------------------------------------------------
@@ -282,13 +275,10 @@ def run_apidoc(_):
         shutil.rmtree(apidoc_output_folder)
 
     for pkg in PACKAGES:
-        argv = ['-e',
-                '-o', apidoc_output_folder,
-                os.path.join(PATH_ROOT, pkg),
-                '**/test_*',
-                '--force',
-                '--private',
-                '--module-first']
+        argv = [
+            '-e', '-o', apidoc_output_folder,
+            os.path.join(PATH_ROOT, pkg), '**/test_*', '--force', '--private', '--module-first'
+        ]
 
         apidoc.main(argv)
 
@@ -338,6 +328,7 @@ autodoc_mock_imports = MOCK_PACKAGES
 # Resolve function
 # This function is used to populate the (source) links in the API
 def linkcode_resolve(domain, info):
+
     def find_source():
         # try to find the file and line number, based on code from numpy:
         # https://github.com/numpy/numpy/blob/master/doc/source/conf.py#L286

setup.cfg

@@ -39,6 +39,18 @@ description-file = README.md
 # long_description = file:README.md
 # long_description_content_type = text/markdown
 
+[yapf]
+based_on_style = pep8
+spaces_before_comment = 2
+split_before_logical_operator = true
+split_before_arithmetic_operator = true
+COLUMN_LIMIT = 120
+COALESCE_BRACKETS = true
+DEDENT_CLOSING_BRACKETS = true
+ALLOW_SPLIT_BEFORE_DICT_VALUE = false
+BLANK_LINE_BEFORE_NESTED_CLASS_OR_DEF = true
+NO_SPACES_AROUND_SELECTED_BINARY_OPERATORS = false
+
 [mypy]
 # Typing tests is low priority, but enabling type checking on the
 # untyped test functions (using `--check-untyped-defs`) is still

setup.py

@@ -52,23 +52,19 @@ setup(
     download_url='https://github.com/PyTorchLightning/torchmetrics',
     license=torchmetrics.__license__,
     packages=find_packages(exclude=['tests', 'docs']),
-
     long_description=load_long_describtion(),
     long_description_content_type='text/markdown',
     include_package_data=True,
     zip_safe=False,
-
     keywords=['deep learning', 'pytorch', 'AI'],
     python_requires='>=3.6',
     setup_requires=[],
     install_requires=load_requirements(PATH_ROOT),
-
     project_urls={
         "Bug Tracker": "https://github.com/PyTorchLightning/torchmetrics/issues",
         "Documentation": "https://torchmetrics.rtfd.io/en/latest/",
         "Source Code": "https://github.com/PyTorchLightning/torchmetrics",
     },
-
     classifiers=[
         'Environment :: Console',
         'Natural Language :: English',

tests/__init__.py

@@ -0,0 +1,2 @@
+from tests.test_metric import Dummy  # noqa: F401
+from tests.utils import NUM_BATCHES, NUM_PROCESSES, BATCH_SIZE, MetricTester  # noqa: F401

tests/boring_model.py

@@ -17,6 +17,7 @@ from torch.utils.data import Dataset
 
 
 class RandomDictStringDataset(Dataset):
+
     def __init__(self, size, length):
         self.len = length
         self.data = torch.randn(length, size)
@@ -29,6 +30,7 @@ class RandomDictStringDataset(Dataset):
 
 
 class RandomDataset(Dataset):
+
     def __init__(self, size, length):
         self.len = length
         self.data = torch.randn(length, size)

tests/classification/test_confusion_matrix.py

@@ -124,5 +124,8 @@ def test_warning_on_nan(tmpdir):
     preds = torch.randint(3, size=(20, ))
     target = torch.randint(3, size=(20, ))
 
-    with pytest.warns(UserWarning, match='.* nan values found in confusion matrix have been replaced with zeros.'):
+    with pytest.warns(
+        UserWarning,
+        match='.* nan values found in confusion matrix have been replaced with zeros.',
+    ):
         confusion_matrix(preds, target, num_classes=5, normalize='true')

tests/test_metric_lightning.py

@@ -106,7 +106,6 @@ def test_metric_lightning(tmpdir):
 #     assert torch.allclose(torch.tensor(logged["sum_step"]), model.sum)
 #     assert torch.allclose(torch.tensor(logged["sum_epoch"]), model.sum)
 
-
 # todo: need to be fixed
 # def test_scriptable(tmpdir):
 #     class TestModel(BoringModel):
@@ -145,7 +144,6 @@ def test_metric_lightning(tmpdir):
 #     script_output = script_model(rand_input)
 #     assert torch.allclose(output, script_output)
 
-
 # def test_metric_collection_lightning_log(tmpdir):
 #
 #     class TestModel(BoringModel):

torchmetrics/__init__.py

@@ -29,28 +29,12 @@ if __LIGHTNING_SETUP__:
 else:
 
     from torchmetrics.classification import (  # noqa: F401
-        Accuracy,
-        AUC,
-        AUROC,
-        AveragePrecision,
-        ConfusionMatrix,
-        F1,
-        FBeta,
-        HammingDistance,
-        IoU,
-        Precision,
-        PrecisionRecallCurve,
-        Recall,
-        ROC,
+        Accuracy, AUC, AUROC, AveragePrecision, ConfusionMatrix, F1, FBeta,
+        HammingDistance, IoU, Precision, PrecisionRecallCurve, Recall, ROC,
         StatScores,
     )
     from torchmetrics.metric import Metric, MetricCollection  # noqa: F401
     from torchmetrics.regression import (  # noqa: F401
-        ExplainedVariance,
-        MeanAbsoluteError,
-        MeanSquaredError,
-        MeanSquaredLogError,
-        PSNR,
-        R2Score,
-        SSIM,
+        ExplainedVariance, MeanAbsoluteError, MeanSquaredError,
+        MeanSquaredLogError, PSNR, R2Score, SSIM,
     )

torchmetrics/compositional.py

@@ -84,9 +84,7 @@ class CompositionalMetric(Metric):
             self.metric_b.persistent(mode=mode)
 
     def __repr__(self):
-        repr_str = (
-            self.__class__.__name__
-            + f"(\n  {self.op.__name__}(\n    {repr(self.metric_a)},\n    {repr(self.metric_b)}\n  )\n)"
-        )
+        _op_metrics = f"(\n  {self.op.__name__}(\n    {repr(self.metric_a)},\n    {repr(self.metric_b)}\n  )\n)"
+        repr_str = (self.__class__.__name__ + _op_metrics)
 
         return repr_str

torchmetrics/functional/__init__.py

@@ -16,12 +16,8 @@ from torchmetrics.functional.auc import auc  # noqa: F401
 from torchmetrics.functional.auroc import auroc  # noqa: F401
 from torchmetrics.functional.average_precision import average_precision  # noqa: F401
 from torchmetrics.functional.classification import (  # noqa: F401
-    dice_score,
-    get_num_classes,
-    multiclass_auroc,
-    stat_scores_multiple_classes,
-    to_categorical,
-    to_onehot,
+    dice_score, get_num_classes, multiclass_auroc,
+    stat_scores_multiple_classes, to_categorical, to_onehot,
 )
 from torchmetrics.functional.confusion_matrix import confusion_matrix  # noqa: F401
 from torchmetrics.functional.explained_variance import explained_variance  # noqa: F401

torchmetrics/metric.py

@@ -318,10 +318,10 @@ class Metric(nn.Module, ABC):
         # filter all parameters based on update signature except those of
         # type VAR_POSITIONAL (*args) and VAR_KEYWORD (**kwargs)
         _params = (inspect.Parameter.VAR_POSITIONAL, inspect.Parameter.VAR_KEYWORD)
+        _sign_params = self._update_signature.parameters
         filtered_kwargs = {
             k: v
-            for k, v in kwargs.items() if k in self._update_signature.parameters.keys()
-            and self._update_signature.parameters[k].kind not in _params
+            for k, v in kwargs.items() if (k in _sign_params.keys() and _sign_params[k].kind not in _params)
         }
 
         # if no kwargs filtered, return al kwargs as default