A simple, efficient, easy-to-use nvidia TensorRT wrapper for cnn with c++ and python api,support caffe, uff and onnx format models. you will be able to deploy your model with tiny-tensorrt in few lines of code!
Trt* net = Trt();
net->CreateEngine(onnxModel, engineFile,maxBatchSize, int mode);
net->CopyFromHostToDevice(input, inputBindIndex);
net->Forward();
net->CopyFromDeviceToHost(output, outputBindIndex)Add support for dynamic shapes, currently dynamic shape can not work with int8 - 2021-8-18
Upgrade to TensorRT 8.0 API, checkout 7.x branch for use under TensorRT 7 - 2021-7-9
Better int8 calibrator api, refer to User Guide - 2021-5-24
Remove caffe and uff support, convert to onnx with tf2onnx or keras.onnx. - 2021-4-23
Want to implement your own onnx plugin and don't know where to start? - 2021-1-29
- Add dynamic shapes support
- Add DLA support
- Custom plugin tutorial and well_commented sample
- Custom onnx model output node
- Engine serialization and deserialization
- INT8 support
- Python api support
- Set device
cuda 11.0+(recommended)
TensorRT 8
For python api, python 3.x and numpy in needed
Make sure you had install dependencies list above, if you are familiar with docker, you can use official docker
# clone project and submodule
git clone --recurse-submodules -j8 https://github.com/zerollzeng/tiny-tensorrt.git
cd tiny-tensorrt
mkdir build && cd build && cmake .. && makeThen you can intergrate it into your own project with libtinytrt.so and Trt.h, for python module, you get pytrt.so
Custom Plugin Tutorial (En-Ch)
For the 3rd-party module and TensorRT, you need to follow their license
For the part I wrote, you can do anything you want