算法部署-使用TensorRT部署PIDNet算法-优质算法部署项目实战.zip

# PIDNet_TensorRT This repository provides a step-by-step guide and code for optimizing a state-of-the-art semantic segmentation model using TorchScript, ONNX, and TensorRT. ## Prerequisites ### Device: RTX 3050 * CUDA: 12.0 (driver: 525) * cuDNN: 8.9 * TensorRT: 8.6 * PyCUDA ### Device: NVIDIA Jetson Nano * Jetpack: 4.6.2 * PyCUDA ## Usage ### 0. Setup * Clone this repository and download the pretrained model from the official [PIDNet](https://github.com/XuJiacong/PIDNet/tree/main) repository. ### 1. Export the model For TorchScript: ````bash python tools/export.py --a pidnet-s --p ./pretrained_models/cityscapes/PIDNet_S_Cityscapes_test.pt --f torchscript ```` For ONNX: ````bash python tools/export.py --a pidnet-s --p ./pretrained_models/cityscapes/PIDNet_S_Cityscapes_test.pt --f onnx ```` For TensorRT (using the above ONNX model): ```bash trtexec --onnx=path/to/onnx/model --saveEngine=path/to/engine ``` ### 2. Inference ```bash python tools/inference.py --f pytorch ``` ### 3. Speed Measurement * Measure the inference speed of PIDNet-S for Cityscapes: ````bash python models/speed/pidnet_speed.py --f all ```` | | FPS | % increase | | :---------- | :---------: |:---------: | | PyTorch | 24.72 | - | | TorchScript | 27.09 | 9.59 | | ONNX (with TensorRT EP) | 33.52 | 35.60 | | TensorRT | 32.93 | 33.21 | speed test is performed on a single Nvidia GeForce RTX 3050 GPU