[pre-commit.ci] pre-commit suggestions/consolidate autofixes

.pre-commit-config.yaml

@@ -28,7 +28,7 @@ repos:
       - id: mixed-line-ending
 
   - repo: https://github.com/asottile/pyupgrade
-    rev: v2.34.0
+    rev: v2.38.2
     hooks:
       - id: pyupgrade
         args: [--py37-plus]
@@ -40,7 +40,7 @@ repos:
           )$
 
   - repo: https://github.com/asottile/yesqa
-    rev: v1.3.0
+    rev: v1.4.0
     hooks:
       - id: yesqa
         name: Unused noqa
@@ -58,7 +58,7 @@ repos:
           )$
 
   - repo: https://github.com/hadialqattan/pycln
-    rev: v1.3.5
+    rev: v2.1.1
     hooks:
       - id: pycln
         args: [--config=pyproject.toml]

monai/bundle/config_item.py

@@ -106,7 +106,7 @@ def get_component_module_name(self, name: str) -> Optional[Union[List[str], str]
             # init component and module mapping table
             self._components_table = self._find_classes_or_functions(self._find_module_names())
 
-        mods: Optional[Union[List[str], str]] = self._components_table.get(name, None)
+        mods: Optional[Union[List[str], str]] = self._components_table.get(name)
         if isinstance(mods, list) and len(mods) == 1:
             mods = mods[0]
         return mods

monai/data/dataloader.py

@@ -76,7 +76,7 @@ def __init__(self, dataset: Dataset, num_workers: int = 0, **kwargs) -> None:
             # when num_workers > 0, random states are determined by worker_init_fn
             # this is to make the behavior consistent when num_workers == 0
             # torch.int64 doesn't work well on some versions of windows
-            _g = torch.random.default_generator if kwargs.get("generator", None) is None else kwargs["generator"]
+            _g = torch.random.default_generator if kwargs.get("generator") is None else kwargs["generator"]
             init_seed = _g.initial_seed()
             _seed = torch.empty((), dtype=torch.int64).random_(generator=_g).item()
             set_rnd(dataset, int(_seed))

monai/data/wsi_reader.py

@@ -555,9 +555,6 @@ class OpenSlideWSIReader(BaseWSIReader):
     supported_suffixes = ["tif", "tiff", "svs"]
     backend = "openslide"
 
-    def __init__(self, level: int = 0, channel_dim: int = 0, **kwargs):
-        super().__init__(level, channel_dim, **kwargs)
-
     @staticmethod
     def get_level_count(wsi) -> int:
         """
@@ -702,9 +699,6 @@ class TiffFileWSIReader(BaseWSIReader):
     supported_suffixes = ["tif", "tiff", "svs"]
     backend = "tifffile"
 
-    def __init__(self, level: int = 0, channel_dim: int = 0, **kwargs):
-        super().__init__(level, channel_dim, **kwargs)
-
     @staticmethod
     def get_level_count(wsi) -> int:
         """

monai/networks/blocks/feature_pyramid_network.py

@@ -258,6 +258,6 @@ def forward(self, x: Dict[str, Tensor]) -> Dict[str, Tensor]:
             results, names = self.extra_blocks(results, x_values, names)
 
         # make it back an OrderedDict
-        out = OrderedDict([(k, v) for k, v in zip(names, results)])
+        out = OrderedDict(list(zip(names, results)))
 
         return out

monai/networks/blocks/fft_utils_t.py

@@ -139,12 +139,12 @@ def ifftn_centered_t(ksp: Tensor, spatial_dims: int, is_complex: bool = True) ->
             output2 = ifftn_centered(ksp, spatial_dims=2, is_complex=True)
     """
     # define spatial dims to perform ifftshift, fftshift, and ifft
-    shift = [i for i in range(-spatial_dims, 0)]  # noqa: C416
+    shift = list(range(-spatial_dims, 0))
     if is_complex:
         if ksp.shape[-1] != 2:
             raise ValueError(f"ksp.shape[-1] is not 2 ({ksp.shape[-1]}).")
-        shift = [i for i in range(-spatial_dims - 1, -1)]  # noqa: C416
-    dims = [i for i in range(-spatial_dims, 0)]  # noqa: C416
+        shift = list(range(-spatial_dims - 1, -1))
+    dims = list(range(-spatial_dims, 0))
 
     x = ifftshift(ksp, shift)
 
@@ -187,12 +187,12 @@ def fftn_centered_t(im: Tensor, spatial_dims: int, is_complex: bool = True) -> T
             output2 = fftn_centered(im, spatial_dims=2, is_complex=True)
     """
     # define spatial dims to perform ifftshift, fftshift, and fft
-    shift = [i for i in range(-spatial_dims, 0)]  # noqa: C416
+    shift = list(range(-spatial_dims, 0))
     if is_complex:
         if im.shape[-1] != 2:
             raise ValueError(f"img.shape[-1] is not 2 ({im.shape[-1]}).")
-        shift = [i for i in range(-spatial_dims - 1, -1)]  # noqa: C416
-    dims = [i for i in range(-spatial_dims, 0)]  # noqa: C416
+        shift = list(range(-spatial_dims - 1, -1))
+    dims = list(range(-spatial_dims, 0))
 
     x = ifftshift(im, shift)
 

monai/networks/layers/weight_init.py

@@ -55,7 +55,7 @@ def trunc_normal_(tensor, mean=0.0, std=1.0, a=-2.0, b=2.0):
        b: the maximum cutoff value
     """
 
-    if not std > 0:
+    if std <= 0:
         raise ValueError("the standard deviation should be greater than zero.")
 
     if a >= b:

monai/transforms/utility/dictionary.py

@@ -423,7 +423,7 @@ def __call__(
             output = []
             results = [self.splitter(d[key]) for key in all_keys]
             for row in zip(*results):
-                new_dict = {k: v for k, v in zip(all_keys, row)}
+                new_dict = dict(zip(all_keys, row))
                 # fill in the extra keys with unmodified data
                 for k in set(d.keys()).difference(set(all_keys)):
                     new_dict[k] = deepcopy(d[k])

monai/transforms/utils_create_transform_ims.py

@@ -427,8 +427,6 @@ def create_transform_im(
         seed = seed + 1 if isinstance(transform, MapTransform) else seed
         transform.set_random_state(seed)
 
-    from monai.utils.misc import MONAIEnvVars
-
     out_dir = MONAIEnvVars.doc_images()
     if out_dir is None:
         raise RuntimeError(

monai/utils/misc.py

@@ -397,7 +397,7 @@ class MONAIEnvVars:
 
     @staticmethod
     def data_dir() -> Optional[str]:
-        return os.environ.get("MONAI_DATA_DIRECTORY", None)
+        return os.environ.get("MONAI_DATA_DIRECTORY")
 
     @staticmethod
     def debug() -> bool:
@@ -406,7 +406,7 @@ def debug() -> bool:
 
     @staticmethod
     def doc_images() -> Optional[str]:
-        return os.environ.get("MONAI_DOC_IMAGES", None)
+        return os.environ.get("MONAI_DOC_IMAGES")
 
 
 class ImageMetaKey:

tests/test_apply_filter.py

@@ -64,7 +64,6 @@ def test_3d(self):
                 ],
             ]
         )
-        expected = expected
         # testing shapes
         k = torch.tensor([[[1, 1, 1], [1, 1, 1], [1, 1, 1]]])
         for kernel in (k, k[None], k[None][None]):

tests/test_fpn_block.py

@@ -19,7 +19,7 @@
 from monai.networks.blocks.feature_pyramid_network import FeaturePyramidNetwork
 from monai.networks.nets.resnet import resnet50
 from monai.utils import optional_import
-from tests.utils import test_script_save
+from tests.utils import SkipIfBeforePyTorchVersion, test_script_save
 
 _, has_torchvision = optional_import("torchvision")
 
@@ -53,6 +53,7 @@ def test_fpn_block(self, input_param, input_shape, expected_shape):
         self.assertEqual(result["feat1"].shape, expected_shape[1])
 
     @parameterized.expand(TEST_CASES)
+    @SkipIfBeforePyTorchVersion((1, 9, 1))
     def test_script(self, input_param, input_shape, expected_shape):
         # test whether support torchscript
         net = FeaturePyramidNetwork(**input_param)
@@ -73,6 +74,7 @@ def test_fpn(self, input_param, input_shape, expected_shape):
         self.assertEqual(result["pool"].shape, expected_shape[1])
 
     @parameterized.expand(TEST_CASES2)
+    @SkipIfBeforePyTorchVersion((1, 9, 1))
     def test_script(self, input_param, input_shape, expected_shape):
         # test whether support torchscript
         net = _resnet_fpn_extractor(backbone=resnet50(), spatial_dims=input_param["spatial_dims"], returned_layers=[1])

tests/test_gmm.py

@@ -259,7 +259,7 @@
 @skip_if_no_cuda
 class GMMTestCase(unittest.TestCase):
     def setUp(self):
-        self._var = os.environ.get("TORCH_EXTENSIONS_DIR", None)
+        self._var = os.environ.get("TORCH_EXTENSIONS_DIR")
         self.tempdir = tempfile.mkdtemp()
         os.environ["TORCH_EXTENSIONS_DIR"] = self.tempdir
 

tests/test_k_space_spike_noised.py

@@ -43,7 +43,7 @@ def get_data(im_shape, im_type):
         create_test_image = create_test_image_2d if len(im_shape) == 2 else create_test_image_3d
         ims = create_test_image(*im_shape, rad_max=20, noise_max=0.0, num_seg_classes=5)
         ims = [im_type(im[None]) for im in ims]
-        return {k: v for k, v in zip(KEYS, ims)}
+        return dict(zip(KEYS, ims))
 
     @parameterized.expand(TESTS)
     def test_same_result(self, im_shape, im_type):

tests/test_monai_env_vars.py

@@ -19,7 +19,7 @@ class TestMONAIEnvVars(unittest.TestCase):
     @classmethod
     def setUpClass(cls):
         super(__class__, cls).setUpClass()
-        cls.orig_value = os.environ.get("MONAI_DEBUG", None)
+        cls.orig_value = os.environ.get("MONAI_DEBUG")
 
     @classmethod
     def tearDownClass(cls):

tests/test_rand_k_space_spike_noised.py

@@ -40,7 +40,7 @@ def get_data(im_shape, im_type):
         create_test_image = create_test_image_2d if len(im_shape) == 2 else create_test_image_3d
         ims = create_test_image(*im_shape, rad_max=20, noise_max=0.0, num_seg_classes=5)
         ims = [im_type(im[None]) for im in ims]
-        return {k: v for k, v in zip(KEYS, ims)}
+        return dict(zip(KEYS, ims))
 
     @parameterized.expand(TESTS)
     def test_same_result(self, im_shape, im_type):

tests/test_separable_filter.py

@@ -64,7 +64,6 @@ def test_3d(self):
                 ],
             ]
         )
-        expected = expected
         # testing shapes
         k = torch.tensor([1, 1, 1])
         for kernel in (k, [k] * 3):

tests/test_transform.py

@@ -29,7 +29,7 @@ class TestTransform(unittest.TestCase):
     @classmethod
     def setUpClass(cls):
         super(__class__, cls).setUpClass()
-        cls.orig_value = os.environ.get("MONAI_DEBUG", None)
+        cls.orig_value = os.environ.get("MONAI_DEBUG")
 
     @classmethod
     def tearDownClass(cls):

tests/test_varnet.py

@@ -18,7 +18,7 @@
 from monai.apps.reconstruction.networks.nets.complex_unet import ComplexUnet
 from monai.apps.reconstruction.networks.nets.varnet import VariationalNetworkModel
 from monai.networks import eval_mode
-from tests.utils import test_script_save
+from tests.utils import SkipIfBeforePyTorchVersion, test_script_save
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 coil_sens_model = CoilSensitivityModel(spatial_dims=2, features=[8, 16, 32, 64, 128, 8])
@@ -43,6 +43,7 @@ def test_shape(self, coil_sens_model, refinement_model, num_cascades, input_shap
         self.assertEqual(result.shape, expected_shape)
 
     @parameterized.expand(TESTS)
+    @SkipIfBeforePyTorchVersion((1, 9, 1))
     def test_script(self, coil_sens_model, refinement_model, num_cascades, input_shape, expected_shape):
         net = VariationalNetworkModel(coil_sens_model, refinement_model, num_cascades)