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README.md

TensorRTx

TensorRTx aims to implement popular deep learning networks with tensorrt network definition APIs. As we know, tensorrt has builtin parsers, including caffeparser, uffparser, onnxparser, etc. But when we use these parsers, we often run into some "unsupported operations or layers" problems, especially some state-of-the-art models are using new type of layers.

So why don't we just skip all parsers? We just use TensorRT network definition APIs to build the whole network, it's not so complicated.

I wrote this project to get familiar with tensorrt API, and also to share and learn from the community.

All the models are implemented in pytorch/mxnet/tensorflown first, and export a weights file xxx.wts, and then use tensorrt to load weights, define network and do inference. Some pytorch implementations can be found in my repo Pytorchx, the remaining are from polular open-source implementations.

News

  • 5 Mar 2021. chgit0214 added the LPRNet.
  • 31 Jan 2021. RepVGG added by upczww.
  • 29 Jan 2021. U-Net added by YuzhouPeng.
  • 24 Jan 2021. IBN-Net added by TCHeish, PSENet optimized, YOLOv5 v4.0 INT8, etc.
  • 8 Jan 2021. YOLOv5 s/m/l/x updated to v4.0.
  • 27 Dec 2020. HRNet-Semantic-Segmentation added by BaofengZan.
  • 4 Dec 2020. DBNet dynamic input shape support by BaofengZan, YOLOv3 int8, PSENet(tensorflow) text detection by upczww.
  • 19 Nov 2020. YOLOv3-SPP supports dynamic input shape, including a dynamic plugin.
  • 17 Nov 2020. AlfengYuan added a Dockerfile.
  • 7 Nov 2020. All models migrated to trt7 API, and clean up the master branch.
  • 29 Oct 2020. First INT8 quantization implementation! Please check retinaface.
  • 23 Oct 2020. Add a .wts model zoo for quick evaluation.
  • 16 Sep 2020. hwh-hit added ufld(Ultra-Fast-Lane-Detection, ECCV2020).
  • 13 Sep 2020. Add crnn, and got 1000fps on GTX1080.
  • 28 Aug 2020. BaofengZan added a tutorial for compiling and running tensorrtx on windows.

Tutorials

Test Environment

  1. GTX1080 / Ubuntu16.04 / cuda10.0 / cudnn7.6.5 / tensorrt7.0.0 / nvinfer7.0.0 / opencv3.3

How to run

Each folder has a readme inside, which explains how to run the models inside.

Models

Following models are implemented.

Name Description
lenet the simplest, as a "hello world" of this project
alexnet easy to implement, all layers are supported in tensorrt
googlenet GoogLeNet (Inception v1)
inception Inception v3
mnasnet MNASNet with depth multiplier of 0.5 from the paper
mobilenetv2 MobileNet V2
mobilenetv3 V3-small, V3-large.
resnet resnet-18, resnet-50 and resnext50-32x4d are implemented
senet se-resnet50
shufflenet ShuffleNetV2 with 0.5x output channels
squeezenet SqueezeNet 1.1 model
vgg VGG 11-layer model
yolov3-tiny weights and pytorch implementation from ultralytics/yolov3
yolov3 darknet-53, weights and pytorch implementation from ultralytics/yolov3
yolov3-spp darknet-53, weights and pytorch implementation from ultralytics/yolov3
yolov4 CSPDarknet53, weights from AlexeyAB/darknet, pytorch implementation from ultralytics/yolov3
yolov5 yolov5-s/m/l/x v1.0 v2.0 v3.0 v3.1, pytorch implementation from ultralytics/yolov5
retinaface resnet50 and mobilnet0.25, weights from biubug6/Pytorch_Retinaface
arcface LResNet50E-IR, weights from deepinsight/insightface
retinafaceAntiCov mobilenet0.25, weights from deepinsight/insightface, retinaface anti-COVID-19, detect face and mask attribute
dbnet Scene Text Detection, weights from BaofengZan/DBNet.pytorch
crnn pytorch implementation from meijieru/crnn.pytorch
ufld pytorch implementation from Ultra-Fast-Lane-Detection, ECCV2020
hrnet hrnet-image-classification and hrnet-semantic-segmentation, pytorch implementation from HRNet-Image-Classification and HRNet-Semantic-Segmentation
psenet PSENet Text Detection, tensorflow implementation from liuheng92/tensorflow_PSENet
ibnnet IBN-Net, pytorch implementation from XingangPan/IBN-Net, ECCV2018
unet U-Net, pytorch implementation from milesial/Pytorch-UNet
repvgg RepVGG, pytorch implementation from DingXiaoH/RepVGG
lprnet LPRNet, pytorch implementation from xuexingyu24/License_Plate_Detection_Pytorch

Model Zoo

The .wts files can be downloaded from model zoo for quick evaluation. But it is recommended to convert .wts from pytorch/mxnet/tensorflow model, so that you can retrain your own model.

GoogleDrive | BaiduPan pwd: uvv2

Tricky Operations

Some tricky operations encountered in these models, already solved, but might have better solutions.

Name Description
BatchNorm Implement by a scale layer, used in resnet, googlenet, mobilenet, etc.
MaxPool2d(ceil_mode=True) use a padding layer before maxpool to solve ceil_mode=True, see googlenet.
average pool with padding use setAverageCountExcludesPadding() when necessary, see inception.
relu6 use Relu6(x) = Relu(x) - Relu(x-6), see mobilenet.
torch.chunk() implement the 'chunk(2, dim=C)' by tensorrt plugin, see shufflenet.
channel shuffle use two shuffle layers to implement channel_shuffle, see shufflenet.
adaptive pool use fixed input dimension, and use regular average pooling, see shufflenet.
leaky relu I wrote a leaky relu plugin, but PRelu in NvInferPlugin.h can be used, see yolov3 in branch trt4.
yolo layer v1 yolo layer is implemented as a plugin, see yolov3 in branch trt4.
yolo layer v2 three yolo layers implemented in one plugin, see yolov3-spp.
upsample replaced by a deconvolution layer, see yolov3.
hsigmoid hard sigmoid is implemented as a plugin, hsigmoid and hswish are used in mobilenetv3
retinaface output decode implement a plugin to decode bbox, confidence and landmarks, see retinaface.
mish mish activation is implemented as a plugin, mish is used in yolov4
prelu mxnet's prelu activation with trainable gamma is implemented as a plugin, used in arcface
HardSwish hard_swish = x * hard_sigmoid, used in yolov5 v3.0
LSTM Implemented pytorch nn.LSTM() with tensorrt api

Speed Benchmark

Models Device BatchSize Mode Input Shape(HxW) FPS
YOLOv3-tiny Xeon E5-2620/GTX1080 1 FP32 608x608 333
YOLOv3(darknet53) Xeon E5-2620/GTX1080 1 FP32 608x608 39.2
YOLOv3(darknet53) Xeon E5-2620/GTX1080 1 INT8 608x608 71.4
YOLOv3-spp(darknet53) Xeon E5-2620/GTX1080 1 FP32 608x608 38.5
YOLOv4(CSPDarknet53) Xeon E5-2620/GTX1080 1 FP32 608x608 35.7
YOLOv4(CSPDarknet53) Xeon E5-2620/GTX1080 4 FP32 608x608 40.9
YOLOv4(CSPDarknet53) Xeon E5-2620/GTX1080 8 FP32 608x608 41.3
YOLOv5-s v3.0 Xeon E5-2620/GTX1080 1 FP32 608x608 142
YOLOv5-s v3.0 Xeon E5-2620/GTX1080 4 FP32 608x608 173
YOLOv5-s v3.0 Xeon E5-2620/GTX1080 8 FP32 608x608 190
YOLOv5-m v3.0 Xeon E5-2620/GTX1080 1 FP32 608x608 71
YOLOv5-l v3.0 Xeon E5-2620/GTX1080 1 FP32 608x608 43
YOLOv5-x v3.0 Xeon E5-2620/GTX1080 1 FP32 608x608 29
YOLOv5-s v4.0 Xeon E5-2620/GTX1080 1 FP32 608x608 142
YOLOv5-m v4.0 Xeon E5-2620/GTX1080 1 FP32 608x608 71
YOLOv5-l v4.0 Xeon E5-2620/GTX1080 1 FP32 608x608 40
YOLOv5-x v4.0 Xeon E5-2620/GTX1080 1 FP32 608x608 27
RetinaFace(resnet50) Xeon E5-2620/GTX1080 1 FP32 480x640 90
RetinaFace(resnet50) Xeon E5-2620/GTX1080 1 INT8 480x640 204
RetinaFace(mobilenet0.25) Xeon E5-2620/GTX1080 1 FP32 480x640 417
ArcFace(LResNet50E-IR) Xeon E5-2620/GTX1080 1 FP32 112x112 333
CRNN Xeon E5-2620/GTX1080 1 FP32 32x100 1000

Help wanted, if you got speed results, please add an issue or PR.

Acknowledgments & Contact

Any contributions, questions and discussions are welcomed, contact me by following info.

E-mail: wangxinyu_es@163.com

WeChat ID: wangxinyu0375 (可加我微信进tensorrtx交流群,备注:tensorrtx)

项目简介

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源项目地址

https://github.com/wang-xinyu/tensorrtx

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开发语言

  • C++ 90.2 %
  • Cuda 5.4 %
  • Python 2.5 %
  • CMake 1.9 %
  • Dockerfile 0.0 %