45 lines
1.4 KiB
Markdown
45 lines
1.4 KiB
Markdown
# HRNet-Semantic-Segmentation
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The Pytorch implementation is [HRNet-Semantic-Segmentation](https://github.com/HRNet/HRNet-Semantic-Segmentation). The implemented model is **HRNetV2-W18-Small-v2**
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## How to Run
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* 1. generate .wts
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Download code and model from [HRNet-Semantic-Segmentation](https://github.com/HRNet/HRNet-Semantic-Segmentation) and config your environments.
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Put `demo.py` in the `YOUR_ROOT_DIR\HRNet-Semantic-Segmentation\tools ` folder, set `savewts in main()` as `True`, and run, the .wts will be generated.
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* 2. cmake and make
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```
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mkdir build
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cd build
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cmake ..
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make
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sudo ./hrnetseg -s // serialize model to plan file i.e. 'hrnetseg.engine'
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sudo ./hrnetseg -d ../samples // deserialize plan file and run inference, the images in samples will be processed.
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```
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## Result
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TRT Result:
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pytorch result:
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## Note
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* Some source codes are changed for simplicity. But the original model can still be used.
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All "upsample" op in source code are changed to `mode='bilinear', align_corners=True`
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* Image preprocessing operation and postprocessing operation are put into Trt Engine.
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* Zero-copy technology (CPU/GPU memory copy) is used.
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