Apply ruff==0.9.0 formatting (#13486)

* Refactor code for speed and clarity

* Auto-format by https://ultralytics.com/actions

---------

Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

utils/torch_utils.py

@@ -121,9 +121,9 @@ def select_device(device="", batch_size=0, newline=True):
         os.environ["CUDA_VISIBLE_DEVICES"] = "-1"  # force torch.cuda.is_available() = False
     elif device:  # non-cpu device requested
         os.environ["CUDA_VISIBLE_DEVICES"] = device  # set environment variable - must be before assert is_available()
-        assert torch.cuda.is_available() and torch.cuda.device_count() >= len(
-            device.replace(",", "")
-        ), f"Invalid CUDA '--device {device}' requested, use '--device cpu' or pass valid CUDA device(s)"
+        assert torch.cuda.is_available() and torch.cuda.device_count() >= len(device.replace(",", "")), (
+            f"Invalid CUDA '--device {device}' requested, use '--device cpu' or pass valid CUDA device(s)"
+        )
 
     if not cpu and not mps and torch.cuda.is_available():  # prefer GPU if available
         devices = device.split(",") if device else "0"  # range(torch.cuda.device_count())  # i.e. 0,1,6,7
@@ -380,7 +380,7 @@ def smart_optimizer(model, name="Adam", lr=0.001, momentum=0.9, decay=1e-5):
     optimizer.add_param_group({"params": g[1], "weight_decay": 0.0})  # add g1 (BatchNorm2d weights)
     LOGGER.info(
         f"{colorstr('optimizer:')} {type(optimizer).__name__}(lr={lr}) with parameter groups "
-        f'{len(g[1])} weight(decay=0.0), {len(g[0])} weight(decay={decay}), {len(g[2])} bias'
+        f"{len(g[1])} weight(decay=0.0), {len(g[0])} weight(decay={decay}), {len(g[2])} bias"
     )
     return optimizer