算法部署-使用TensorRT部署Deformable-DERT目标检测算法-优质算法部署项目实战.zip

# TensorRT Deformable Detr (Python) TensorRT for Deformable Detr(use python) ## Test enviroments TensorRT 8.4.1.5 Ubuntu 18.04 mmdetection 2.24.1 polygraphy(TensorRT Tool) ## A quick demo ### 1. Convert deformable detr model form pytorch to onnx python3 onnx_deformable_detr.py --config ${mmdet_path}/configs/deformable_detr/deformable_detr_twostage_refine_r50_16x2_50e_coco.py --checkpoint ${mmdet_path}/work_dirs/deformable_detr_twostage_refine_r50_16x2_50e_coco_20210419_220613-9d28ab72.pth --output deformable_detr.onnx --h 750 --w 1333 ### 2.Genrate TensorRT engine polygraphy convert deformable_detr.onnx --workspace 8G -o deformable_detr_fp32.engine ### 3.Inference python3 demo.py --engine deformable_detr_fp32.engine --thresh 0.3 --img ${your_picture_path} --output result.jpg ## Inference performance(only inference time in GPU) GPU|Model|Mode|Inference time :--: | :--: | :--: | :--: | 3090 | deformable_detr_twostage_refine_r50_16x2_50e_coco | fp32 | 35ms 3090 | deformable_detr_twostage_refine_r50_16x2_50e_coco | fp16 | 17ms <center><img src="./data/result.jpg" width="500px"></img></center>