`ultralytics 8.2.48` strip model `criterion` on save (#14106)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = "8.2.47"
+__version__ = "8.2.48"
 
 import os
 

ultralytics/nn/tasks.py

@@ -788,14 +788,14 @@ def torch_safe_load(weight):
                     f"with https://github.com/ultralytics/yolov5.\nThis model is NOT forwards compatible with "
                     f"YOLOv8 at https://github.com/ultralytics/ultralytics."
                     f"\nRecommend fixes are to train a new model using the latest 'ultralytics' package or to "
-                    f"run a command with an official YOLOv8 model, i.e. 'yolo predict model=yolov8n.pt'"
+                    f"run a command with an official Ultralytics model, i.e. 'yolo predict model=yolov8n.pt'"
                 )
             ) from e
         LOGGER.warning(
-            f"WARNING ⚠️ {weight} appears to require '{e.name}', which is not in ultralytics requirements."
+            f"WARNING ⚠️ {weight} appears to require '{e.name}', which is not in Ultralytics requirements."
             f"\nAutoInstall will run now for '{e.name}' but this feature will be removed in the future."
             f"\nRecommend fixes are to train a new model using the latest 'ultralytics' package or to "
-            f"run a command with an official YOLOv8 model, i.e. 'yolo predict model=yolov8n.pt'"
+            f"run a command with an official Ultralytics model, i.e. 'yolo predict model=yolov8n.pt'"
         )
         check_requirements(e.name)  # install missing module
         ckpt = torch.load(file, map_location="cpu")

ultralytics/utils/torch_utils.py

@@ -511,23 +511,30 @@ def strip_optimizer(f: Union[str, Path] = "best.pt", s: str = "") -> None:
         ```
     """
     x = torch.load(f, map_location=torch.device("cpu"))
-    if "model" not in x:
+    if not isinstance(x, dict) or "model" not in x:
         LOGGER.info(f"Skipping {f}, not a valid Ultralytics model.")
         return
 
+    # Update model
+    if x.get("ema"):
+        x["model"] = x["ema"]  # replace model with EMA
     if hasattr(x["model"], "args"):
         x["model"].args = dict(x["model"].args)  # convert from IterableSimpleNamespace to dict
-    args = {**DEFAULT_CFG_DICT, **x["train_args"]} if "train_args" in x else None  # combine args
-    if x.get("ema"):
-        x["model"] = x["ema"]  # replace model with ema
-    for k in "optimizer", "best_fitness", "ema", "updates":  # keys
-        x[k] = None
-    x["epoch"] = -1
+    if hasattr(x["model"], "criterion"):
+        x["model"].criterion = None  # strip loss criterion
     x["model"].half()  # to FP16
     for p in x["model"].parameters():
         p.requires_grad = False
+
+    # Update other keys
+    args = {**DEFAULT_CFG_DICT, **x.get("train_args", {})}  # combine args
+    for k in "optimizer", "best_fitness", "ema", "updates":  # keys
+        x[k] = None
+    x["epoch"] = -1
     x["train_args"] = {k: v for k, v in args.items() if k in DEFAULT_CFG_KEYS}  # strip non-default keys
     # x['model'].args = x['train_args']
+
+    # Save
     torch.save(x, s or f)
     mb = os.path.getsize(s or f) / 1e6  # file size
     LOGGER.info(f"Optimizer stripped from {f},{f' saved as {s},' if s else ''} {mb:.1f}MB")