[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

monai/losses/unified_focal_loss.py

@@ -12,10 +12,8 @@
 from __future__ import annotations
 
 import warnings
-from collections.abc import Sequence
 
 import torch
-import torch.nn.functional as F
 from torch.nn.modules.loss import _Loss
 
 from monai.losses import FocalLoss

tests/losses/test_unified_focal_loss.py

@@ -33,15 +33,15 @@
     [
         {"use_softmax": True, "include_background": False},
         {
-            "y_pred": torch.tensor([[[[LOGIT_LOW]], [[LOGIT_HIGH]], [[LOGIT_LOW]]]]), 
+            "y_pred": torch.tensor([[[[LOGIT_LOW]], [[LOGIT_HIGH]], [[LOGIT_LOW]]]]),
             "y_true": torch.tensor([[[[0.0]], [[1.0]], [[0.0]]]]),
         },
     ],
     # Case 2: Softmax + Include BG
     [
         {"use_softmax": True, "include_background": True},
         {
-            "y_pred": torch.tensor([[[[LOGIT_HIGH]], [[LOGIT_LOW]], [[LOGIT_LOW]]]]), 
+            "y_pred": torch.tensor([[[[LOGIT_HIGH]], [[LOGIT_LOW]], [[LOGIT_LOW]]]]),
             "y_true": torch.tensor([[[[1.0]], [[0.0]], [[0.0]]]]),
         },
     ],
@@ -62,7 +62,7 @@ def test_result(self, input_param, input_data):
         loss_func = AsymmetricUnifiedFocalLoss(**input_param)
         result = loss_func(**input_data)
         res_val = result.detach().cpu().numpy()
-        
+
         print(f"Params: {input_param} -> Loss: {res_val}")
 
 
@@ -78,17 +78,17 @@ def test_with_cuda(self):
         loss = AsymmetricUnifiedFocalLoss()
         i = torch.tensor([[[[5.0, -5.0], [-5.0, 5.0]]], [[[5.0, -5.0], [-5.0, 5.0]]]])
         j = torch.tensor([[[[1.0, 0], [0, 1.0]]], [[[1.0, 0], [0, 1.0]]]])
-        
+
         if torch.cuda.is_available():
             i = i.cuda()
             j = j.cuda()
             loss = loss.cuda()
-            
+
         output = loss(i, j)
         res_val = output.detach().cpu().numpy()
-        
+
         self.assertFalse(np.isnan(res_val), "CUDA Loss should not be NaN")
         self.assertTrue(res_val < 1.0, f"CUDA Loss {res_val} is too high")
 
 if __name__ == "__main__":
-    unittest.main()
+    unittest.main()