ultralytics 8.3.8 replace contextlib with try for speed (#16782)

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

ultralytics/nn/tasks.py

@@ -2,6 +2,7 @@
 
 import contextlib
 import pickle
+import re
 import types
 from copy import deepcopy
 from pathlib import Path
@@ -958,8 +959,10 @@ def parse_model(d, ch, verbose=True):  # model_dict, input_channels(3)
         m = getattr(torch.nn, m[3:]) if "nn." in m else globals()[m]  # get module
         for j, a in enumerate(args):
             if isinstance(a, str):
-                with contextlib.suppress(ValueError):
+                try:
                     args[j] = locals()[a] if a in locals() else ast.literal_eval(a)
+                except ValueError:
+                    pass
 
         n = n_ = max(round(n * depth), 1) if n > 1 else n  # depth gain
         if m in {
@@ -1072,8 +1075,6 @@ def parse_model(d, ch, verbose=True):  # model_dict, input_channels(3)
 
 def yaml_model_load(path):
     """Load a YOLOv8 model from a YAML file."""
-    import re
-
     path = Path(path)
     if path.stem in (f"yolov{d}{x}6" for x in "nsmlx" for d in (5, 8)):
         new_stem = re.sub(r"(\d+)([nslmx])6(.+)?$", r"\1\2-p6\3", path.stem)
@@ -1100,11 +1101,10 @@ def guess_model_scale(model_path):
     Returns:
         (str): The size character of the model's scale, which can be n, s, m, l, or x.
     """
-    with contextlib.suppress(AttributeError):
-        import re
-
+    try:
         return re.search(r"yolo[v]?\d+([nslmx])", Path(model_path).stem).group(1)  # n, s, m, l, or x
-    return ""
+    except AttributeError:
+        return ""
 
 
 def guess_model_task(model):
@@ -1137,17 +1137,23 @@ def guess_model_task(model):
 
     # Guess from model cfg
     if isinstance(model, dict):
-        with contextlib.suppress(Exception):
+        try:
             return cfg2task(model)
+        except:  # noqa E722
+            pass
 
     # Guess from PyTorch model
     if isinstance(model, nn.Module):  # PyTorch model
         for x in "model.args", "model.model.args", "model.model.model.args":
-            with contextlib.suppress(Exception):
+            try:
                 return eval(x)["task"]
+            except:  # noqa E722
+                pass
         for x in "model.yaml", "model.model.yaml", "model.model.model.yaml":
-            with contextlib.suppress(Exception):
+            try:
                 return cfg2task(eval(x))
+            except:  # noqa E722
+                pass
 
         for m in model.modules():
             if isinstance(m, Segment):