update comments

nnunetv2/inference/predict_from_raw_data.py

@@ -594,7 +594,7 @@ def predict_sliding_window_return_logits(self, input_image: torch.Tensor) \
 
         empty_cache(self.device)
 
-        # Autocast is a little bitch.
+        # Autocast can be annoying
         # If the device_type is 'cpu' then it's slow as heck on some CPUs (no auto bfloat16 support detection)
         # and needs to be disabled.
         # If the device_type is 'mps' then it will complain that mps is not implemented, even if enabled=False

nnunetv2/training/nnUNetTrainer/nnUNetTrainer.py

@@ -899,7 +899,7 @@ def train_step(self, batch: dict) -> dict:
             target = target.to(self.device, non_blocking=True)
 
         self.optimizer.zero_grad(set_to_none=True)
-        # Autocast is a little bitch.
+        # Autocast can be annoying
         # If the device_type is 'cpu' then it's slow as heck and needs to be disabled.
         # If the device_type is 'mps' then it will complain that mps is not implemented, even if enabled=False is set. Whyyyyyyy. (this is why we don't make use of enabled=False)
         # So autocast will only be active if we have a cuda device.
@@ -945,7 +945,7 @@ def validation_step(self, batch: dict) -> dict:
         else:
             target = target.to(self.device, non_blocking=True)
 
-        # Autocast is a little bitch.
+        # Autocast can be annoying
         # If the device_type is 'cpu' then it's slow as heck and needs to be disabled.
         # If the device_type is 'mps' then it will complain that mps is not implemented, even if enabled=False is set. Whyyyyyyy. (this is why we don't make use of enabled=False)
         # So autocast will only be active if we have a cuda device.