make ruff happy

Hendrik-code · Nov 18, 2024 · 60523e5 · 60523e5
1 parent 57e492b
commit 60523e5
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Showing 2 changed files with 8 additions and 8 deletions.
diff --git a/spineps/architectures/pl_unet.py b/spineps/architectures/pl_unet.py
@@ -111,7 +111,7 @@ def _shared_step(self, target, gt, detach2cpu: bool = False):
 
     def _shared_metric_step(self, loss, _, gt, pred_cls):
         dice = mF.dice(pred_cls, gt, num_classes=self.n_classes)
-        diceFG = mF.dice(pred_cls, gt, num_classes=self.n_classes, ignore_index=0)  # noqa: N806, N806
+        diceFG = mF.dice(pred_cls, gt, num_classes=self.n_classes, ignore_index=0)  # noqa: N806
         dice_p_cls = mF.dice(pred_cls, gt, average=None, num_classes=self.n_classes)
         return {"loss": loss.detach().cpu(), "dice": dice, "diceFG": diceFG, "dice_p_cls": dice_p_cls}
 

diff --git a/spineps/architectures/unet3D.py b/spineps/architectures/unet3D.py
@@ -99,30 +99,30 @@ def forward(self, x, time=None, label=None, embedding=None) -> torch.Tensor:  #
         t = None
 
         h = []
-        ö = "-"
+        o = "-"
         for block1, block2, downsample in self.downs:  # type: ignore
             x = block1(x, t)
             x = block2(x, t)
             h.append(x)
             x = downsample(x)
             if self.first_forward:
-                ö += "-"
-                print(ö, x.shape, "\t")
+                o += "-"
+                print(o, x.shape, "\t")
 
         x = self.mid_block1(x, t)
         x = self.mid_block2(x, t)
         if self.first_forward:
-            print(ö, x.shape)
-            ö = ö[:-1]
+            print(o, x.shape)
+            o = o[:-1]
 
         for block1, block2, upsample in self.ups:  # type: ignore
             x = 0.5 * (x + h.pop())
             x = block1(x, t)
             x = block2(x, t)
             x = upsample(x)
             if self.first_forward:
-                print(ö, x.shape, "\t")
-                ö = ö[:-1]
+                print(o, x.shape, "\t")
+                o = o[:-1]
 
         x = torch.cat((x, r), dim=1)