Formatting

Signed-off-by: Eric Kerfoot <[email protected]>

Author: ericspod

tests/__init__.py

@@ -8,4 +8,3 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-

tests/apps/detection/networks/test_retinanet.py

@@ -20,7 +20,7 @@
 from monai.networks import eval_mode
 from monai.networks.nets import resnet10, resnet18, resnet34, resnet50, resnet101, resnet152, resnet200
 from monai.utils import ensure_tuple, optional_import
-from tests.test_utils import  dict_product, skip_if_quick, test_onnx_save, test_script_save
+from tests.test_utils import dict_product, skip_if_quick, test_onnx_save, test_script_save
 
 _, has_torchvision = optional_import("torchvision")
 
@@ -94,7 +94,6 @@
 TEST_CASES_TS = [[params["model"], *params["case"]] for params in dict_product(model=MODEL_LIST, case=[TEST_CASE_1])]
 
 
-
 @unittest.skipUnless(has_torchvision, "Requires torchvision")
 @skip_if_quick
 class TestRetinaNet(unittest.TestCase):

tests/apps/detection/networks/test_retinanet_detector.py

@@ -21,7 +21,7 @@
 from monai.apps.detection.utils.anchor_utils import AnchorGeneratorWithAnchorShape
 from monai.networks import eval_mode, train_mode
 from monai.utils import optional_import
-from tests.test_utils import  skip_if_quick, test_script_save
+from tests.test_utils import skip_if_quick, test_script_save
 
 _, has_torchvision = optional_import("torchvision")
 
@@ -110,7 +110,6 @@ def forward(self, images):
         return {self.cls_key: [torch.randn(out_cls_shape)], self.box_reg_key: [torch.randn(out_box_reg_shape)]}
 
 
-
 @unittest.skipUnless(has_torchvision, "Requires torchvision")
 @skip_if_quick
 class TestRetinaNetDetector(unittest.TestCase):

tests/apps/detection/utils/test_anchor_box.py

@@ -18,7 +18,7 @@
 
 from monai.apps.detection.utils.anchor_utils import AnchorGenerator, AnchorGeneratorWithAnchorShape
 from monai.utils import optional_import
-from tests.test_utils import  assert_allclose, test_script_save
+from tests.test_utils import assert_allclose, test_script_save
 
 _, has_torchvision = optional_import("torchvision")
 
@@ -39,7 +39,6 @@
 ]
 
 
-
 @unittest.skipUnless(has_torchvision, "Requires torchvision")
 class TestAnchorGenerator(unittest.TestCase):
     @parameterized.expand(TEST_CASES_2D)

tests/apps/maisi/networks/test_autoencoderkl_maisi.py

@@ -87,7 +87,6 @@ def test_shape(self, input_param, input_shape, expected_shape, expected_latent_s
             self.assertEqual(result[2].shape, expected_latent_shape)
 
     @parameterized.expand(CASES)
-    
     def test_shape_with_convtranspose_and_checkpointing(
         self, input_param, input_shape, expected_shape, expected_latent_shape
     ):
@@ -152,7 +151,6 @@ def test_shape_reconstruction(self):
             result = net.reconstruct(torch.randn(input_shape).to(device))
             self.assertEqual(result.shape, expected_shape)
 
-    
     def test_shape_reconstruction_with_convtranspose_and_checkpointing(self):
         input_param, input_shape, expected_shape, _ = CASES[0]
         input_param = input_param.copy()
@@ -170,7 +168,6 @@ def test_shape_encode(self):
             self.assertEqual(result[0].shape, expected_latent_shape)
             self.assertEqual(result[1].shape, expected_latent_shape)
 
-    
     def test_shape_encode_with_convtranspose_and_checkpointing(self):
         input_param, input_shape, _, expected_latent_shape = CASES[0]
         input_param = input_param.copy()
@@ -190,7 +187,6 @@ def test_shape_sampling(self):
             )
             self.assertEqual(result.shape, expected_latent_shape)
 
-    
     def test_shape_sampling_convtranspose_and_checkpointing(self):
         input_param, _, _, expected_latent_shape = CASES[0]
         input_param = input_param.copy()
@@ -209,7 +205,6 @@ def test_shape_decode(self):
             result = net.decode(torch.randn(latent_shape).to(device))
             self.assertEqual(result.shape, expected_input_shape)
 
-    
     def test_shape_decode_convtranspose_and_checkpointing(self):
         input_param, expected_input_shape, _, latent_shape = CASES[0]
         input_param = input_param.copy()

tests/apps/maisi/networks/test_controlnet_maisi.py

@@ -127,7 +127,6 @@
 ]
 
 
-
 class TestControlNet(unittest.TestCase):
     @parameterized.expand(TEST_CASES)
     @skipUnless(has_einops, "Requires einops")

tests/apps/test_auto3dseg_bundlegen.py

@@ -26,12 +26,7 @@
 from monai.bundle.config_parser import ConfigParser
 from monai.data import create_test_image_3d
 from monai.utils import set_determinism
-from tests.test_utils import (
-    get_testing_algo_template_path,
-    skip_if_downloading_fails,
-    skip_if_no_cuda,
-    skip_if_quick,
-)
+from tests.test_utils import get_testing_algo_template_path, skip_if_downloading_fails, skip_if_no_cuda, skip_if_quick
 
 num_images_perfold = max(torch.cuda.device_count(), 4)
 num_images_per_batch = 2

tests/apps/vista3d/test_point_based_window_inferer.py

@@ -20,7 +20,7 @@
 from monai.networks import eval_mode
 from monai.networks.nets.vista3d import vista3d132
 from monai.utils import optional_import
-from tests.test_utils import  skip_if_quick
+from tests.test_utils import skip_if_quick
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
@@ -60,7 +60,6 @@
 ]
 
 
-
 @skip_if_quick
 class TestPointBasedWindowInferer(unittest.TestCase):
     @parameterized.expand(TEST_CASES)

tests/bundle/test_bundle_download.py

@@ -28,13 +28,12 @@
 from monai.bundle.scripts import _examine_monai_version, _list_latest_versions, download
 from monai.utils import optional_import
 from tests.test_utils import (
-    
     assert_allclose,
     command_line_tests,
     skip_if_downloading_fails,
     skip_if_no_cuda,
     skip_if_quick,
-    skip_if_windows
+    skip_if_windows,
 )
 
 _, has_huggingface_hub = optional_import("huggingface_hub")
@@ -398,7 +397,6 @@ def test_load_weights_with_net_override(self, bundle_name, device, net_override)
 
     @parameterized.expand([TEST_CASE_9])
     @skip_if_quick
-    
     def test_load_ts_module(self, bundle_files, bundle_name, version, repo, device, model_file):
         with skip_if_downloading_fails():
             # load ts module

tests/data/meta_tensor/test_meta_tensor.py

@@ -32,7 +32,7 @@
 from monai.data.utils import decollate_batch, list_data_collate
 from monai.transforms import BorderPadd, Compose, DivisiblePadd, FromMetaTensord, ToMetaTensord
 from monai.utils.enums import PostFix
-from tests.test_utils import TEST_DEVICES,  assert_allclose, dict_product, skip_if_no_cuda
+from tests.test_utils import TEST_DEVICES, assert_allclose, dict_product, skip_if_no_cuda
 
 DTYPES = [[torch.float32], [torch.float64], [torch.float16], [torch.int64], [torch.int32], [None]]
 
@@ -297,7 +297,6 @@ def test_dataset(self, device, dtype):
             self.check(im, ims[i], ids=True)
 
     @parameterized.expand(DTYPES)
-    
     def test_dataloader(self, dtype):
         batch_size = 5
         ims = [self.get_im(dtype=dtype)[0] for _ in range(batch_size * 2)]
@@ -314,7 +313,6 @@ def test_dataloader(self, dtype):
             self.assertTupleEqual(tuple(batch.affine.shape), expected_affine_shape)
             self.assertEqual(len(batch.applied_operations), batch_size)
 
-    
     def test_indexing(self):
         """
         Check the metadata is returned in the expected format depending on whether
@@ -406,7 +404,6 @@ def test_slicing(self):
         self.assertEqual(x[[True, False, True]].shape, (2, 3, 4))
 
     @parameterized.expand(DTYPES)
-    
     def test_decollate(self, dtype):
         batch_size = 3
         ims = [self.get_im(dtype=dtype)[0] for _ in range(batch_size * 2)]