Default strip_optimizer() to use_dill=False (#14107)

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

ultralytics/utils/torch_utils.py

@@ -7,6 +7,7 @@ import random
 import time
 from contextlib import contextmanager
 from copy import deepcopy
+from datetime import datetime
 from pathlib import Path
 from typing import Union
 
@@ -456,14 +457,17 @@ def init_seeds(seed=0, deterministic=False):
 
 
 class ModelEMA:
-    """Updated Exponential Moving Average (EMA) from https://github.com/rwightman/pytorch-image-models
-    Keeps a moving average of everything in the model state_dict (parameters and buffers)
+    """
+    Updated Exponential Moving Average (EMA) from https://github.com/rwightman/pytorch-image-models. Keeps a moving
+    average of everything in the model state_dict (parameters and buffers)
+
     For EMA details see https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage
+
     To disable EMA set the `enabled` attribute to `False`.
     """
 
     def __init__(self, model, decay=0.9999, tau=2000, updates=0):
-        """Create EMA."""
+        """Initialize EMA for 'model' with given arguments."""
         self.ema = deepcopy(de_parallel(model)).eval()  # FP32 EMA
         self.updates = updates  # number of EMA updates
         self.decay = lambda x: decay * (1 - math.exp(-x / tau))  # decay exponential ramp (to help early epochs)
@@ -506,15 +510,25 @@ def strip_optimizer(f: Union[str, Path] = "best.pt", s: str = "") -> None:
         from pathlib import Path
         from ultralytics.utils.torch_utils import strip_optimizer
 
-        for f in Path('path/to/weights').rglob('*.pt'):
+        for f in Path('path/to/model/checkpoints').rglob('*.pt'):
             strip_optimizer(f)
         ```
     """
-    x = torch.load(f, map_location=torch.device("cpu"))
-    if not isinstance(x, dict) or "model" not in x:
-        LOGGER.info(f"Skipping {f}, not a valid Ultralytics model.")
+    try:
+        x = torch.load(f, map_location=torch.device("cpu"))
+        assert isinstance(x, dict), "checkpoint is not a Python dictionary"
+        assert "model" in x, "'model' missing from checkpoint"
+    except Exception as e:
+        LOGGER.warning(f"WARNING ⚠️ Skipping {f}, not a valid Ultralytics model: {e}")
         return
 
+    updates = {
+        "date": datetime.now().isoformat(),
+        "version": __version__,
+        "license": "AGPL-3.0 License (https://ultralytics.com/license)",
+        "docs": "https://docs.ultralytics.com",
+    }
+
     # Update model
     if x.get("ema"):
         x["model"] = x["ema"]  # replace model with EMA
@@ -535,7 +549,7 @@ def strip_optimizer(f: Union[str, Path] = "best.pt", s: str = "") -> None:
     # x['model'].args = x['train_args']
 
     # Save
-    torch.save(x, s or f)
+    torch.save({**updates, **x}, s or f, use_dill=False)  # combine dicts (prefer to the right)
     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")