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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 use tiny-tensorrt deploy your model with few lines of code!
// create engine
trt.CreateEngine(prototxt,caffemodel,engingefile,outputBlob,calibratorData,maxBatchSize,runMode);
// transfer you input data to tensorrt engine
trt.DataTransfer(input,0,True);
// inference!!!
trt.Forward();
// retrieve network output
trt.DataTransfer(output, outputIndex, False) // you can get outputIndex in CreateEngine phase- Support TensorRT 7 now --- 2019-12-25 🎄🎄🎄
- Custom plugin tutorial and well_commented sample! ---2019-12-11 🔥🔥🔥
- Custom onnx model output node ---2019.10.18
- Upgrade with TensorRT 6.0.1.5 --- 2019.9.29
- Support onnx,caffe and tensorflow model
- Support more model and layer --working on
- PReLU and up-sample plugin
- Engine serialization and deserialization
- INT8 support for caffe model
- Python api support
- Set device
cuda 10.0+
TensorRT 6 or 7
for python api, python 2.x/3.x and numpy in needed
this project is fully test in ubuntu 16.04. I tested it with 1060ti, 1050ti, 1080ti, 1660ti, 2080, 2080ti and p4.
Custom Plugin Tutorial (En-Ch)
- upsample with custom scale, under test with yolov3.
- yolo-det, last layer of yolov3 which sum three scales output and generate final result for nms. under test with yolov3.
- PRELU, under test with openpose and mtcnn.
For the 3rd-party module and TensorRT, maybe you need to follow their license
For the part I wrote, you can do anything you want