Add python benchmarks.py --test for export-only

[glenn-jocher]

Export tests. CPU example. @kalenmike

Colab Pro+ CPU

$ python utils/benchmarks.py --weights yolov5n.pt --test --device cpu

benchmarks: weights=yolov5n.pt, imgsz=640, batch_size=1, data=/content/yolov5/data/coco128.yaml, device=cpu, half=False, test=True
Checking setup...
YOLOv5 🚀 v6.1-117-g7e47852 torch 1.10.0+cu111 CPU
Setup complete ✅ (8 CPUs, 51.0 GB RAM, 42.5/166.8 GB disk)

Exports complete (219.55s)
                   Format  Export
0                 PyTorch    True
1             TorchScript    True
2                    ONNX    True
3                OpenVINO    True
4                TensorRT   False
5                  CoreML    True
6   TensorFlow SavedModel    True
7     TensorFlow GraphDef    True
8         TensorFlow Lite    True
9     TensorFlow Edge TPU    True
10          TensorFlow.js    True

Colab Pro+ GPU

$ python utils/benchmarks.py --weights yolov5n.pt --test --device 0

benchmarks: weights=yolov5n.pt, imgsz=640, batch_size=1, data=/content/yolov5/data/coco128.yaml, device=0, half=False, test=True
Checking setup...
YOLOv5 🚀 v6.1-117-g7e47852 torch 1.10.0+cu111 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)
Setup complete ✅ (8 CPUs, 51.0 GB RAM, 47.2/166.8 GB disk)

Exports complete (636.24s)
                   Format  Export
0                 PyTorch    True
1             TorchScript    True
2                    ONNX    True
3                OpenVINO    True
4                TensorRT    True
5                  CoreML   False
6   TensorFlow SavedModel    True
7     TensorFlow GraphDef    True
8         TensorFlow Lite    True
9     TensorFlow Edge TPU    True
10          TensorFlow.js    True

🛠️ PR Summary

_{Made with ❤️ by Ultralytics Actions}

🌟 Summary

Improvements and new features added to the benchmarking and exporting capabilities of YOLOv5 models.

📊 Key Changes

• Added a new test flag to the benchmarking script to test model exports exclusively.
• Refined assertions for better handling and support checking for Edge TPU and TF.js export formats.
• Introduced a new function test() which tests only the exports, providing faster feedback on export validity.
• Enhanced logging with clear information whether the tests were on benchmarks or just exports.
• Both benchmark and export tests now have structured output in DataFrame form for post-process analysis and logging.

🎯 Purpose & Impact

• 🎛 The test flag and its associated test() function allow developers to quickly verify the export process without running full benchmarks, increasing efficiency.
• 🔧 Better assertions and structured error messages offer improved developer experience by quickly identifying non-supported export formats for certain devices.
• ⏱ Enhanced DataFrame outputs help in better tracking and understanding the performance and success of model exports.
• 🚀 Overall, this PR aims to streamline the development workflow, reducing the time needed to test and verify model export functionality, and enabling sharper focus on model deployment readiness.