Global settings typechecking (#148)

ultralytics/yolo/engine/model.py

@@ -48,7 +48,6 @@ class YOLO:
         self.ckpt_path = None
         self.cfg = None  # if loaded from *.yaml
         self.overrides = {}  # overrides for trainer object
-        self.init_disabled = False  # disable model initialization
 
         # Load or create new YOLO model
         {'.pt': self._load, '.yaml': self._new}[Path(model).suffix](model)

ultralytics/yolo/utils/__init__.py

@@ -365,8 +365,15 @@ def get_settings(file=USER_CONFIG_DIR / 'settings.yaml'):
             yaml_save(file, defaults)
 
         settings = yaml_load(file)
-        if settings.keys() != defaults.keys():
+
-            settings = {**defaults, **settings}  # merge **defaults with **settings (prefer **settings)
+        # Check that settings keys and types match defaults
+        correct = settings.keys() == defaults.keys() and \
+                  all(type(a) == type(b) for a, b in zip(settings.values(), defaults.values()))
+        if not correct:
+            LOGGER.warning('WARNING ⚠️ Different global settings detected, resetting to defaults. '
+                           'This may be due to an ultralytics package update. '
+                           f'View and update your global settings directly in {file}')
+            settings = defaults  # merge **defaults with **settings (prefer **settings)
             yaml_save(file, settings)  # save updated defaults
 
         return settings

ultralytics/yolo/utils/torch_utils.py

@@ -268,8 +268,23 @@ class ModelEMA:
         copy_attr(self.ema, model, include, exclude)
 
 
-def strip_optimizer(f='best.pt', s=''):  # from utils.general import *; strip_optimizer()
+def strip_optimizer(f='best.pt', s=''):
-    # Strip optimizer from 'f' to finalize training, optionally save as 's'
+    """
+    Strip optimizer from 'f' to finalize training, optionally save as 's'.
+
+    Usage:
+        from ultralytics.yolo.utils.torch_utils import strip_optimizer
+        from pathlib import Path
+        for f in Path('/Users/glennjocher/Downloads/weights').glob('*.pt'):
+            strip_optimizer(f)
+
+    Args:
+        f (str): file path to model state to strip the optimizer from. Default is 'best.pt'.
+        s (str): file path to save the model with stripped optimizer to. Default is ''. If not provided, the original file will be overwritten.
+
+    Returns:
+        None
+    """
     x = torch.load(f, map_location=torch.device('cpu'))
     args = {**DEFAULT_CONFIG_DICT, **x['train_args']}  # combine model args with default args, preferring model args
     if x.get('ema'):