TensorRTx

TensorRTx aims to implement popular deep learning networks with TensorRT network definition API.

Why don't we use a parser (ONNX parser, UFF parser, caffe parser, etc), but use complex APIs to build a network from scratch? I have summarized the advantages in the following aspects.

• Flexible, easy to modify the network, add/delete a layer or input/output tensor, replace a layer, merge layers, integrate preprocessing and postprocessing into network, etc.
• Debuggable, construct the entire network in an incremental development manner, easy to get middle layer results.
• Educational, learn about the network structure during this development, rather than treating everything as a black box.

The basic workflow of TensorRTx is:

1. Get the trained models from pytorch, mxnet or tensorflow, etc. Some pytorch models can be found in my repo pytorchx, the remaining are from popular open-source repos.
2. Export the weights to a plain text file -- .wts file.
3. Load weights in TensorRT, define the network, build a TensorRT engine.
4. Load the TensorRT engine and run inference.

News

• 15 Jan 2026. zgjja Refactor multiple old CV models to support TensorRT SDK through 7~10.
• 8 Jan 2026. ydk61: YOLOv13
• 10 May 2025. pranavm-nvidia: YOLO11 writen in Tripy.
• 2 May 2025. fazligorkembal: YOLO12
• 12 Apr 2025. pranavm-nvidia: First Lenet example writen in Tripy.
• 11 Apr 2025. mpj1234: YOLO11-obb
• 22 Oct 2024. lindsayshuo: YOLOv8-obb
• 18 Oct 2024. zgjja: Refactor docker image.
• 11 Oct 2024. mpj1234: YOLO11
• 9 Oct 2024. Phoenix8215: GhostNet V1 and V2.
• 21 Aug 2024. Lemonononon: real-esrgan-general-x4v3
• 29 Jul 2024. mpj1234: Check the YOLOv5, YOLOv8 & YOLOv10 in TensorRT 10.x API, branch → trt10
• 29 Jul 2024. mpj1234: YOLOv10
• 21 Jun 2024. WuxinrongY: YOLOv9-T, YOLOv9-S, YOLOv9-M
• 28 Apr 2024. lindsayshuo: YOLOv8-pose
• 22 Apr 2024. B1SH0PP: EfficientAd: Accurate Visual Anomaly Detection at Millisecond-Level Latencies.
• 18 Apr 2024. lindsayshuo: YOLOv8-p2

Tutorials

• How to make contribution
• Install the dependencies.
• A guide for quickly getting started, taking lenet5 as a demo.
• The .wts file content format
• Frequently Asked Questions (FAQ)
• Migration Guide
• How to implement multi-GPU processing, taking YOLOv4 as example
• Check if Your GPU support FP16/INT8
• How to Compile and Run on Windows
• Deploy YOLOv4 with Triton Inference Server
• From pytorch to trt step by step, hrnet as example(Chinese)

Test Environment

1. (NOT recommended) TensorRT 7.x
2. (Recommended)TensorRT 8.x
3. (NOT recommended) TensorRT 10.x

Note

1. For history reason, some of the models are limited to specific TensorRT version, please check the README.md or code for the model you want to use.
2. Currently, TensorRT 8.x has better compatibility and the most of the features supported.

How to run

Note: this project support to build each network by the CMakeLists.txt in its subfolder, or you can build them together by the CMakeLists.txt on top of this project.

• General procedures before building and running:

# 1. generate xxx.wts from https://github.com/wang-xinyu/pytorchx/tree/master/lenet
# ...

# 2. put xxx.wts on top of this folder
# ...

• (Option 1) To build a single subproject in this project, do:

## enter the subfolder
cd tensorrtx/xxx

## configure & build
cmake -S . -B build
make -C build

• (Option 2) To build many subprojects, firstly, in the top CMakeLists.txt, uncomment the project you don't want to build or not suppoted by your TensorRT version, e.g., you cannot build subprojects in ${TensorRT_8_Targets} if your TensorRT is 7.x. Then:

## enter the top of this project
cd tensorrtx

## configure & build
# you may use "Ninja" rather than "make" to significantly boost the build speed
cmake -G Ninja -S . -B build
ninja -C build

WARNING: This part is still under development, most subprojects are not adapted yet.

• run the generated executable, e.g.:

# serialize model to plan file i.e. 'xxx.engine'
build/xxx -s

# deserialize plan file and run inference
build/xxx -d

# (Optional) check if the output is same as pytorchx/lenet
# ...

# (Optional) customize the project
# ...

For more details, each subfolder may contain a README.md inside, which explains more.

Models

Following models are implemented.

Name	Description
mlp	the very basic model for starters, properly documented
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, v4
mnasnet	MNASNet with depth multiplier of 0.5 from the paper
mobilenet	MobileNet v2, v3-small, v3-large
resnet	resnet-18, resnet-50 and resnext50-32x4d are implemented
senet	se-resnet50
shufflenet	ShuffleNet v2 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 v1.0-v7.0 of ultralytics/yolov5, detection, classification and instance segmentation
yolov7	yolov7 v0.1, pytorch implementation from WongKinYiu/yolov7
yolov8	yolov8, pytorch implementation from ultralytics
yolov9	The Pytorch implementation is WongKinYiu/yolov9.
yolov10	The Pytorch implementation is THU-MIG/yolov10.
yolo11	The Pytorch implementation is ultralytics.
yolo12	The Pytorch implementation is ultralytics.
yolop	yolop, pytorch implementation from hustvl/YOLOP
retinaface	resnet50 and mobilnet0.25, weights from biubug6/Pytorch_Retinaface
arcface	LResNet50E-IR, LResNet100E-IR and MobileFaceNet, 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
refinedet	RefineDet, pytorch implementation from luuuyi/RefineDet.PyTorch
densenet	DenseNet-121, from torchvision.models
rcnn	FasterRCNN and MaskRCNN, model from detectron2
tsm	TSM: Temporal Shift Module for Efficient Video Understanding, ICCV2019
scaled-yolov4	yolov4-csp, pytorch from WongKinYiu/ScaledYOLOv4
centernet	CenterNet DLA-34, pytorch from xingyizhou/CenterNet
efficientnet	EfficientNet b0-b8 and l2, pytorch from lukemelas/EfficientNet-PyTorch
detr	DE⫶TR, pytorch from facebookresearch/detr
swin-transformer	Swin Transformer - Semantic Segmentation, only support Swin-T. The Pytorch implementation is microsoft/Swin-Transformer
real-esrgan	Real-ESRGAN. The Pytorch implementation is real-esrgan
superpoint	SuperPoint. The Pytorch model is from magicleap/SuperPointPretrainedNetwork
csrnet	CSRNet. The Pytorch implementation is leeyeehoo/CSRNet-pytorch
EfficientAd	EfficientAd: Accurate Visual Anomaly Detection at Millisecond-Level Latencies. From anomalib

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: [email protected]

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