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# YOLOX-TensorRT in Python
This tutorial includes a Python demo for TensorRT.
## Install TensorRT Toolkit
Please follow the [TensorRT Installation Guide](https://docs.nvidia.com/deeplearning/tensorrt/install-guide/index.html) and [torch2trt gitrepo](https://github.com/NVIDIA-AI-IOT/torch2trt) to install TensorRT and torch2trt.
## Convert model
YOLOX models can be easily conveted to TensorRT models using torch2trt
If you want to convert our model, use the flag -n to specify a model name:
```shell
python tools/trt.py -n <YOLOX_MODEL_NAME> -c <YOLOX_CHECKPOINT>
```
For example:
```shell
python tools/trt.py -n yolox-s -c your_ckpt.pth
```
<YOLOX_MODEL_NAME> can be: yolox-nano, yolox-tiny. yolox-s, yolox-m, yolox-l, yolox-x.
If you want to convert your customized model, use the flag -f to specify you exp file:
```shell
python tools/trt.py -f <YOLOX_EXP_FILE> -c <YOLOX_CHECKPOINT>
```
For example:
```shell
python tools/trt.py -f /path/to/your/yolox/exps/yolox_s.py -c your_ckpt.pth
```
*yolox_s.py* can be any exp file modified by you.
The converted model and the serialized engine file (for C++ demo) will be saved on your experiment output dir.
## Demo
The TensorRT python demo is merged on our pytorch demo file, so you can run the pytorch demo command with ```--trt```.
```shell
python tools/demo.py image -n yolox-s --trt --save_result
```
or
```shell
python tools/demo.py image -f exps/default/yolox_s.py --trt --save_result
```
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