Janus1984/onnx_tensorrt_project

Support Yolov5(4.0)/Yolov5(5.0)/YoloR/YoloX/Yolov4/Yolov3/CenterNet/CenterFace/RetinaFace/Classify/Unet. use darknet/libtorch/pytorch/mxnet to onnx to tensorrt

ONNX-TensorRT

Yolov5(4.0)/Yolov5(5.0)/YoloR/YoloX/Yolov4/Yolov3/CenterNet/CenterFace/RetinaFace/Classify/Unet Implementation

Yolov4/Yolov3/Yolov5/yolor/YoloX

• 

centernet

Unet

CenterFace

retinaface

INTRODUCTION

you have the trained model file from the darknet/libtorch/pytorch/mxnet

• yolov5-4.0(5s/5m/5s/5x)
• yolov5-5.0(5s/5m/5s/5x)
• yolov4 , yolov4-tiny
• yolov3 , yolov3-tiny
• yolor
• YoloX
• centernet
• Unet
• CenterFace
• RetinaFace
• classify(mnist\alexnet\resnet18\resnet34\resnet50\shufflenet_v2\mobilenet_v2)

Features

• inequal net width and height

• batch inference

  ---

  onnx-tensorrt batch inference : onnx re-export(batch:2)

• support FP32(m_config.mode = 0),FP16(m_config.mode = 1),INT8(m_config.mode = 2)

• dynamic input size(tiny_tensorrt_dyn_onnx)

BENCHMARK

window x64 (detect time)

model	size	gpu	fp32	fp16	INT8	GPU(MB)(FP32/FP16/INT8)
yolov3	608x608	2080ti	28.14ms	19.79ms	18.53ms	1382/945/778
yolov4	320x320	2080ti	8.85ms	6.62ms	6.33ms	1130/1075/961
yolov4	416x416	2080ti	12.19ms	10.20ms	9.35ms	1740/1193/1066
yolov4	512x512	2080ti	15.63ms	12.66ms	12.19ms	1960/1251/1218
yolov4	608x608	2080ti	24.39ms	17.54ms	17.24ms	1448/1180/1128
yolov4	320x320	3070	9.70ms	7.30ms	6.37ms	1393/1366/1238
yolov4	416x416	3070	14.08ms	9.80ms	9.70ms	1429/1394/1266
yolov4	512x512	3070	18.87ms	13.51ms	13.51ms	1485/1436/1299
yolov4	608x608	3070	28.57ms	19.60ms	18.52ms	1508/1483/1326
yolov4	320x320	1070	18.52ms	\	12.82ms	686//442
yolov4	416x416	1070	27.03ms	\	20.83ms	1480//477
yolov4	512x512	1070	34.48ms	\	27.03ms	1546//515
yolov4	608x608	1070	50ms	\	35.71ms	1272//584
yolov4	320x320	1660TI	16.39ms	11.90ms	10.20ms	1034/863/787
yolov4	416x416	1660TI	23.25ms	17.24ms	13.70ms	1675/1227/816
yolov4	512x512	1660TI	29.41ms	24.39ms	21.27ms	1906/1322/843
yolov4	608x608	1660TI	43.48ms	34.48ms	26.32ms	1445/1100/950
yolov5 5s	640x640	2080ti	24.47ms	22.46ms	22.38ms	720/666/652
yolov5 5m	640x640	2080ti	30.61ms	24.02ms	23.73ms	851/728/679
yolov5 5l	640x640	2080ti	32.58ms	25.84ms	24.44ms	1154/834/738
yolov5 5x	640x640	2080ti	40.69ms	29.81ms	27.19ms	1530/1001/827
yolor_csp_x	512x512	2080ti	27.89ms	20.54ms	18.71ms	2373/1060/853
yolor_csp	512x512	2080ti	21.30ms	18.06ms	17.03ms	1720/856/763
YOLOX-Nano	416x416	2080ti	6.84ms	6.81ms	6.69ms	795/782/780
YOLOX-Tiny	416x416	2080ti	7.86ms	7.13ms	6.73ms	823/798/790
YOLOX-S	640x640	2080ti	19.51ms	16.62ms	16.33ms	940/836/794
YOLOX-M	640x640	2080ti	23.35ms	18.67ms	17.87ms	919/716/684
YOLOX-L	640x640	2080ti	28.25ms	20.36ms	19.24ms	1410/855/769
YOLOX-Darknet53	640x640	2080ti	29.95ms	20.38ms	18.91ms	1552/928/772
YOLOX-X	640x640	2080ti	40.40ms	22.95ms	21.99ms	1691/1187/1020
darknet53	224*224	2080ti	3.53ms	1.84ms	1.71ms	1005/769/658
darknet53	224*224	3070	4.29ms	2.16ms	1.75ms	1227/1017/951
resnet18-v2-7	224*224	2080ti	1.89ms	1.29ms	1.18ms	878/655/624
unet	512*512	2080ti	20.91ms	17.01ms	16.05ms	1334/766/744
retinaface_r50	512x512	2080ti	12.33ms	8.96ms	8.22ms	1189/745/678
mnet.25	512x512	2080ti	6.90ms	6.32ms	6.23ms	782/603/615

x64(inference / detect time)

model	size	gpu	fp32(inference/detect)	fp16(inference/detect)	INT8(inference/detect)	GPU(MB)(FP32/FP16/INT8)
centernet	512x512	2080ti	17.8ms/39.7ms	15.7ms/36.49ms	14.37ms/36.34ms	1839/1567/1563
centerface	640x640	2080ti	5.56ms/11.79ms	4.23ms/10.89ms	/	854/646/640
centerface_bnmerged	640x640	2080ti	5.67ms/11.82ms	4.22ms/10.46ms	/	850/651/645

windows10

• dependency : spdlog，onnx，onnx-tensorrt，protobuf-3.11.4，TensorRT 7.2.2.3 , cuda 11.1 , cudnn 8.0 , opencv3.4, vs2019

• build:

  open MSVC tiny_tensorrt_onnx.sln file

  tiny_tensorrt_dyn_onnx:dynamic shape

  tiny_tensorrt_onnx: normal

• build onnx-tensorrt

  step1: https://github.com/onnx/onnx-tensorrt.git

  step2: https://drive.google.com/drive/folders/1DndiqyCZ796p3-xXI3O4AMCIGcUWQ1q2?usp=sharing or https://share.weiyun.com/CJCwngAM

  step3: builtin_op_importers.cpp replace onnx-tensorrt\builtin_op_importers.cpp

  step4: tortoiseGit->apply patch serial and choose 0001-Compile-onnx-tensorrt-by-MSVC-on-Windows.patch

  ​ look https://github.com/ttanzhiqiang/onnx-tensorrt_7.2.1

  step5:build onnx.lib\onnx_proto.lib\nvonnxparser.dll\nvonnxparser_static.lib

Model and 3rdparty

model : https://drive.google.com/drive/folders/1KzBjmCOG9ghcq9L6-iqfz6QwBQq6Hl4_?usp=sharing or https://share.weiyun.com/td9CRDhW

3rdparty:https://drive.google.com/drive/folders/1SddUgQ5kGlv6dDGPqnVWZxgCoBY85rM2?usp=sharing or https://share.weiyun.com/WEZ3TGtb

API

struct Config
{
    std::string cfgFile = "configs/yolov3.cfg";

    std::string onnxModelpath = "configs/yolov3.onnx";

    std::string engineFile = "configs/yolov3.engine";

    std::string calibration_image_list_file = "configs/images/";

    std::vector<std::string> customOutput;

    int calibration_width = 0;

    int calibration_height = 0;
    
    int maxBatchSize = 1;

    int mode; //0，1，2

    //std::string calibration_image_list_file_txt = "configs/calibration_images.txt";
};

class YoloDectector
{
void init(Config config);
void detect(const std::vector<cv::Mat>& vec_image,
	std::vector<BatchResult>& vec_batch_result);
}

REFERENCE

https://github.com/onnx/onnx-tensorrt.git

https://github.com/NVIDIA/TensorRT/tree/master/samples/opensource/sampleDynamicReshape

https://github.com/NVIDIA-AI-IOT/deepstream_reference_apps

https://github.com/enazoe/yolo-tensorrt.git

https://github.com/zerollzeng/tiny-tensorrt.git

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