Export, detect and validation with TensorRT engine file (#5699)

* Export and detect with TensorRT engine file

* Resolve `isort`

* Make validation works with TensorRT engine

* feat: update export docstring

* feat: change suffix from *.trt to *.engine

* feat: get rid of pycuda

* feat: make compatiable with val.py

* feat: support detect with fp16 engine

* Add Lite to Edge TPU string

* Remove *.trt comment

* Revert to standard success logger.info string

* Fix Deprecation Warning

```
export.py:310: DeprecationWarning: Use build_serialized_network instead.
  with builder.build_engine(network, config) as engine, open(f, 'wb') as t:
```

* Revert deprecation warning fix

@imyhxy it seems we can't apply the deprecation warning fix because then export fails, so I'm reverting my previous change here.

* Update export.py

* Update export.py

* Update common.py

* export onnx to file before building TensorRT engine file

* feat: triger ONNX export failed early

* feat: load ONNX model from file

Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

detect.py

@@ -77,11 +77,11 @@ def run(weights=ROOT / 'yolov5s.pt',  # model.pt path(s)
     # Load model
     device = select_device(device)
     model = DetectMultiBackend(weights, device=device, dnn=dnn)
-    stride, names, pt, jit, onnx = model.stride, model.names, model.pt, model.jit, model.onnx
+    stride, names, pt, jit, onnx, engine = model.stride, model.names, model.pt, model.jit, model.onnx, model.engine
     imgsz = check_img_size(imgsz, s=stride)  # check image size
 
     # Half
-    half &= pt and device.type != 'cpu'  # half precision only supported by PyTorch on CUDA
+    half &= (pt or engine) and device.type != 'cpu'  # half precision only supported by PyTorch on CUDA
     if pt:
         model.model.half() if half else model.model.float()