| .. | ||
| include | ||
| CMakeLists.txt | ||
| common.hpp | ||
| fillmask.cu | ||
| fillmask.h | ||
| gelu.cu | ||
| gelu.h | ||
| gen_wts.py | ||
| layerNorm.cu | ||
| layerNorm.h | ||
| logging.h | ||
| main.cpp | ||
| myhpp.h | ||
| README.md | ||
| trainsform.cpp | ||
| UpsampleKernel.cu | ||
| UpsamplePlugin.cpp | ||
| UpsamplePlugin.h | ||
| UpsmapleKernel.h | ||
| utilsn.h | ||
Swin Transform - Semantic Segmentation
The Pytorch implementation is microsoft/Swin-Transformer.
Only support Swin-T, welcome the PR for other backbones.
Authors
How to Run
- generate .wts from pytorch with .pt, or download .wts from model zoo
git clone https://github.com/microsoft/Swin-Transformer.git
git clone https://github.com/wang-xinyu/tensorrtx.git
python gen_wts.py Swin-Transform.pt
// a file 'Swin-Transform.wts' will be generated.
- build tensorrtx/swin-transform and run
cd {tensorrtx}/swin-transform/semantic-segmentation/
mkdir build
cd build
cp {microsoft}/Swin-Transformer/Swin-Transform.wts {tensorrtx}/swin-transformer/semantic-segmentation/build
cmake ..
make
sudo ./swintransformer -s [.wts] [.engine] // serialize model to plan file
sudo ./swintransformer -d [.engine] [image folder] // deserialize and run inference, the images in [image folder] will be processed.
More Information
See the readme in home page.