FIx downstream impact by the replacement of torch.load in ultralytics package (#13623)

* Update torch_load

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

---------

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

train.py

@@ -44,6 +44,8 @@ if str(ROOT) not in sys.path:
     sys.path.append(str(ROOT))  # add ROOT to PATH
 ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
 
+from ultralytics.utils.patches import torch_load
+
 import val as validate  # for end-of-epoch mAP
 from models.experimental import attempt_load
 from models.yolo import Model
@@ -212,7 +214,7 @@ def train(hyp, opt, device, callbacks):
     if pretrained:
         with torch_distributed_zero_first(LOCAL_RANK):
             weights = attempt_download(weights)  # download if not found locally
-        ckpt = torch.load(weights, map_location="cpu")  # load checkpoint to CPU to avoid CUDA memory leak
+        ckpt = torch_load(weights, map_location="cpu")  # load checkpoint to CPU to avoid CUDA memory leak
         model = Model(cfg or ckpt["model"].yaml, ch=3, nc=nc, anchors=hyp.get("anchors")).to(device)  # create
         exclude = ["anchor"] if (cfg or hyp.get("anchors")) and not resume else []  # exclude keys
         csd = ckpt["model"].float().state_dict()  # checkpoint state_dict as FP32
@@ -646,7 +648,7 @@ def main(opt, callbacks=Callbacks()):
             with open(opt_yaml, errors="ignore") as f:
                 d = yaml.safe_load(f)
         else:
-            d = torch.load(last, map_location="cpu")["opt"]
+            d = torch_load(last, map_location="cpu")["opt"]
         opt = argparse.Namespace(**d)  # replace
         opt.cfg, opt.weights, opt.resume = "", str(last), True  # reinstate
         if is_url(opt_data):