Add PyTorch-only benchmark arg
diff --git a/utils/benchmarks.py b/utils/benchmarks.py
index 1c1bb7a8f2e..c3636b9e4df 100644
--- a/utils/benchmarks.py
+++ b/utils/benchmarks.py
@@ -53,6 +53,7 @@ def run(
         device='',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
         half=False,  # use FP16 half-precision inference
         test=False,  # test exports only
+        pt_only=False,  # test PyTorch only
 ):
     y, t = [], time.time()
     formats = export.export_formats()
@@ -79,6 +80,8 @@ def run(
         except Exception as e:
             LOGGER.warning(f'WARNING: Benchmark failure for {name}: {e}')
             y.append([name, None, None])  # mAP, t_inference
+        if pt_only and i == 0:
+            break  # break after PyTorch
 
     # Print results
     LOGGER.info('\n')
@@ -98,6 +101,7 @@ def test(
         device='',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
         half=False,  # use FP16 half-precision inference
         test=False,  # test exports only
+        pt_only=False,  # test PyTorch only
 ):
     y, t = [], time.time()
     formats = export.export_formats()
@@ -130,6 +134,7 @@ def parse_opt():
     parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
     parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
     parser.add_argument('--test', action='store_true', help='test exports only')
+    parser.add_argument('--pt-only', action='store_true', help='test PyTorch only')
     opt = parser.parse_args()
     print_args(vars(opt))
     return opt

Usage:
```
python utils/benchmarks.py --weights yolov5s.pt --img 640 --pt-only
```

## 🛠️ PR Summary

<sub>Made with ❤️ by [Ultralytics Actions](https://github.com/ultralytics/actions)<sub>

### 🌟 Summary
Added option to benchmark PyTorch models exclusively.

### 📊 Key Changes
- New argument `pt_only` added to the benchmarking utility.
- The benchmarking process can be terminated early after testing PyTorch if `pt_only` is set.
- CLI now accepts `--pt-only` as an option to enable this new feature.

### 🎯 Purpose & Impact
- 🎯 **Purpose**: Simplifies benchmarking for users who only want to test the performance of PyTorch models without completing benchmarks for other export formats.
- ✅ **Impact**: Users can save time and resources during benchmarking when only PyTorch model performance is of interest, making the process more efficient and tailored to specific needs.