算法部署-基于TensorRT+INT8量化加速的YOLOX目标检测算法的部署+项目源码-优质项目分享.zip

# Tensorrt-int8-quantization-pipline a simple pipline of int8 quantization based on tensorrt. ## Example for classification <a name="classification"></a> ``` cd classification ``` #### 1、Choose a model and prepare a calibration dataset，like resnet101 training from imagenet1k. ``` wget https://hanlab.mit.edu/files/OnceForAll/ofa_cvpr_tutorial/imagenet_1k.zip unzip 'imagenet_1k.zip' mkdir model ``` #### 2、eval the float32 model performance. ``` python test_torch.py ``` #### 3、convert to onnx model. ``` python torch2onnx.py ``` #### 4、 quantization int8 trt model. ``` python quantization.py ``` #### 5、eval the int8 model performance. ``` python test_int8trt.py ``` or run a pipline including the above steps. ``` python tensorrt_PTA_classification_pipline.py ``` <img src="./classification/shot.jpg" width="400px" height="380px"> | model | accuracy | time | size | | :-: |:-:| :-:|:-:| | float32(pth)|0.759 | 0.0799 |171M| | int8(trt)|0.738 | 0.0013 | 44M | #### Note You can replace resnet101 with your network. If your dataset structure is different, you need to modify some code about dataset. ``` # test_torch.py torch2onnx.py quantization.py if __name__ == "__main__": net = models.resnet101(pretrained=True).to('cpu') ``` or ``` # tensorrt_PTA_classification_pipline.py if __name__ == "__main__": net = models.resnet101(pretrained=True).to('cpu') ``` ## Example for detection <a name="detection"></a> ``` cd detection ``` #### 1、Choose a model and test inference，like YOLOX-s. ``` wget wget https://github.com/Megvii-BaseDetection/storage/releases/download/0.0.1/yolox_s.onnx python demo_onnx.py --model_path yolox_s.onnx --label_name_path coco.label --image_path dog.jpg --output_path output_onnx.jpg ``` #### 2、random sample 2k training images as calibration data, YOLOX-s training from COCO2017. ``` mkdir calibration python sample.py --traing_data_path your_path/coco/images/train2017/ --count 2000 --calibration_path ./calibration/ ``` #### 3、quantization ``` python3 -m onnxsim yolox_s.onnx yolox_s.onnx python quantization.py ``` #### 4、test int tensort model ``` python demo_trt.py --model_path modelInt8.engine --label_name_path coco.label --image_path dog.jpg --output_path output_trt.jpg ``` | model | time | size | | :-: |:-:| :-:| | float32(pth)| 0.0064 |35M| | int8(trt)| 0.0025 | 9.2M | | float32 onnx | int8 tensorrt| | :-: |:-:| |<img src="./detection/show_img/output_onnx.jpg" height="60%" width="60%">|<img src="./detection/show_img/output_trt.jpg" height="60%" width="60%">|