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DETR

The Pytorch implementation is facebookresearch/detr.

For details see End-to-End Object Detection with Transformers.

Test Environment

  • GTX2080Ti / Ubuntu16.04 / cuda10.2 / cudnn8.0.4 / TensorRT7.2.1 / OpenCV4.2
  • GTX2080Ti / win10 / cuda10.2 / cudnn8.0.4 / TensorRT7.2.1 / OpenCV4.2 / VS2017

How to Run

  1. generate .wts from pytorch with .pth
// git clone https://github.com/facebookresearch/detr.git
// go to facebookresearch/detr
// download https://dl.fbaipublicfiles.com/detr/detr-r50-e632da11.pth
// download https://raw.githubusercontent.com/freedenS/TestImage/main/demo.jpg
// copy tensorrtx/detr/gen_wts.py and demo.jpg into facebookresearch/detr
python gen_wts.py
// a file 'detr.wts' will be generated.
  1. build tensorrtx/detr and run
// put detr.wts into tensorrtx/detr
// go to tensorrtx/detr
// update parameters in detr.cpp if your model is trained on custom dataset.The parameters are corresponding to config in detr.
mkdir build
cd build
cmake ..
make
sudo ./detr -s [.wts] // serialize model to plan file
sudo ./detr -d [.engine] [image folder] // deserialize and run inference, the images in [image folder] will be processed
// For example
sudo ./detr -s ../detr.wts detr.engine
sudo ./detr -d detr.engine ../samples
  1. check the images generated, as follows. _demo.jpg and so on.

Backbone

R50

1.download pretrained model
  https://dl.fbaipublicfiles.com/detr/detr-r50-e632da11.pth
2.export wts
  set first parameter in Backbone in gen_wts.py(line 23) to resnet50
  set path of pretrained model(line 87 in gen_wts.py)
3.set resnet_type in BuildResNet(line 546 in detr.cpp) to R50

R101

1.download pretrained model
  https://dl.fbaipublicfiles.com/detr/detr-r101-2c7b67e5.pth
2.export wts
  set first parameter in Backbone in gen_wts.py(line 23) to resnet101
  set path of pretrained model(line 87 in gen_wts.py)
3.set resnet_type in BuildResNet(line 546 in detr.cpp) to R101

NOTE

  • tensorrt use fixed input size, if the size of your data is different from the engine, you need to adjust your data and the result.
  • image preprocessing with c++ is a little different with python(opencv vs PIL)

Quantization

  1. quantizationType:fp32,fp16,int8. see BuildDETRModel(detr.cpp line 613) for detail.

  2. the usage of int8 is same with tensorrtx/yolov5.

Latency

average cost of doInference(in detr.cpp) from second time with batch=1 under the ubuntu environment above

fp32 fp16 int8
R50 19.57ms 9.424ms 8.38ms
R101 30.82ms 12.4ms 9.59ms