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.

TensorRTx has a brother project Pytorchx. All the models are implemented in pytorch first, and export a weights file xxx.wts, and then use tensorrt to load weights, define network and do inference.

Getting Started

There is a guide for quickly getting started, taking lenet5 as a demo. Getting_Started.

Test Environment

1. Jetson TX1 / Ubuntu16.04 / cuda9.0 / cudnn7.1.5 / tensorrt4.0.2 / nvinfer4.1.3

2. GTX1080 / Ubuntu16.04 / cuda10.0 / cudnn7.6.5 / tensorrt7.0.0 / nvinfer7.0.0

Currently, TX1 ans x86 GTX1080 were tested. trt4 api were using, some api are deprecated in trt7, but still can compile successfully.

Models

Following models are implemented, each one also has a readme inside.

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
mobilenet	MobileNet V2, 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	darknet-53, weights from yolov3 authors
yolov3-spp	darknet-53, weights from ultralytics/yolov3

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.
yolo layer v1	yolo layer is implemented as a plugin, see yolov3.
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

Speed Benchmark

Models	Device	BatchSize	Mode	Input Shape(HxW)	FPS
yolov3(darknet53)	Xavier	1	FP16	320x320	55
yolov3-spp(darknet53)	GTX1080	1	FP32	256x416	94

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

Thanks @Kmarconi for yolov3(darknet53) speed test.