ultralytics 8.0.19 seg/det dataset warning and DDP-cls/seg fixes (#595)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: 曾逸夫（Zeng Yifu） <41098760+Zengyf-CVer@users.noreply.github.com>
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>

ultralytics/nn/tasks.py

@@ -313,13 +313,39 @@ class ClassificationModel(BaseModel):
 # Functions ------------------------------------------------------------------------------------------------------------
 
 
+def torch_safe_load(weight):
+    """
+    This function attempts to load a PyTorch model with the torch.load() function. If a ModuleNotFoundError is raised, it
+    catches the error, logs a warning message, and attempts to install the missing module via the check_requirements()
+    function. After installation, the function again attempts to load the model using torch.load().
+
+    Args:
+        weight (str): The file path of the PyTorch model.
+
+    Returns:
+        The loaded PyTorch model.
+    """
+    from ultralytics.yolo.utils.downloads import attempt_download
+
+    file = attempt_download(weight)  # search online if missing locally
+    try:
+        return torch.load(file, map_location='cpu')  # load
+    except ModuleNotFoundError as e:
+        if e.name == 'omegaconf':  # e.name is missing module name
+            LOGGER.warning(f"WARNING ⚠️ {weight} requires {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 updated ultraltyics package or to "
+                           f"download updated models from https://github.com/ultralytics/assets/releases/tag/v0.0.0")
+        check_requirements(e.name)  # install missing module
+        return torch.load(file, map_location='cpu')  # load
+
+
 def attempt_load_weights(weights, device=None, inplace=True, fuse=False):
     # Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a
-    from ultralytics.yolo.utils.downloads import attempt_download
 
     model = Ensemble()
     for w in weights if isinstance(weights, list) else [weights]:
-        ckpt = torch.load(attempt_download(w), map_location='cpu')  # load
+        ckpt = torch_safe_load(w)  # load ckpt
         args = {**DEFAULT_CFG_DICT, **ckpt['train_args']}  # combine model and default args, preferring model args
         ckpt = (ckpt.get('ema') or ckpt['model']).to(device).float()  # FP32 model
 
@@ -355,18 +381,7 @@ def attempt_load_weights(weights, device=None, inplace=True, fuse=False):
 
 def attempt_load_one_weight(weight, device=None, inplace=True, fuse=False):
     # Loads a single model weights
-    from ultralytics.yolo.utils.downloads import attempt_download
-
-    weight = attempt_download(weight)
-    try:
-        ckpt = torch.load(weight, map_location='cpu')  # load
-    except ModuleNotFoundError:
-        LOGGER.warning(f"WARNING ⚠️ {weight} is deprecated as it requires omegaconf, which is now removed from "
-                       "ultralytics requirements.\nAutoInstall will occur now but this feature will be removed for "
-                       "omegaconf models in the future.\nPlease train a new model or download updated models "
-                       "from https://github.com/ultralytics/assets/releases/tag/v0.0.0")
-        check_requirements('omegaconf')
-        ckpt = torch.load(weight, map_location='cpu')  # load
+    ckpt = torch_safe_load(weight)  # load ckpt
     args = {**DEFAULT_CFG_DICT, **ckpt['train_args']}  # combine model and default args, preferring model args
     model = (ckpt.get('ema') or ckpt['model']).to(device).float()  # FP32 model