The PyTorch implementation is from magicleap/SuperPointPretrainedNetwork.
The pretrained models are from magicleap/SuperPointPretrainedNetwork.
- FP16/FP32 can be selected by the macro
USE_FP16in supernet.cpp - GPU id and batch size can be selected by the macro
DEVICE&BATCH_SIZEin supernet.cpp
1.Generate .wts file from the baseline pytorch implementation of pretrained model. The following example described how to generate superpoint_v1.wts from pytorch implementation of superpoint_v1.
git clone https://github.com/xiang-wuu/SuperPointPretrainedNetwork
cd SuperPointPretrainedNetwork
git checkout deploy
// copy tensorrtx/superpoint/gen_wts.py to here(SuperPointPretrainedNetwork)
python gen_wts.py
// a file 'superpoint_v1.wts' will be generated.
// before running gen_wts.py python script make sure you cloned private fork and checkout to deploy branch.
2.Put .wts file into tensorrtx/superpoint, build and run
cd tensorrtx/superpoint
mkdir build
cd build
cmake ..
make
./supernet -s SuperPointPretrainedNetwork/superpoint_v1.wts // serialize model to plan file i.e. 'supernet.engine'
The live demo can be run by inffering TensorRT generated engine file or by the pre-trained pytorch weight file , the demo_superpoint.py script is modified to infer automatically by either using TensorRT or PyTorch based on the provided input weight file.
cd SuperPointPretrainedNetwork
python demo_superpoint.py assets/nyu_snippet.mp4 --cuda --weights_path tensorrtx/superpoint/build/supernet.engine
// provide absolute path to supernet.engine as input weight file
python demo_superpoint.py assets/nyu_snippet.mp4 --cuda --weights_path superpoint_v1.pth
// execute above command to infer using pytorch pre-trained weight files instead of tensorrt engine file.
As from the below result there is no significant difference in the inferred output!
| PyTorch | TensorRT |
|---|---|
|
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- Optimizing post-processing using custom TensorRT layer.
- Benchmark validation for speed accuracy tradeoff with hpatches dataset