1f7672ce5a
* create psenet create psenet with weight from tensorflow * delete some useless code * repalce tab with 4 blanks * fix network bug, rewrite post-processing pse algorithm * update readme * update readme * add RepVGG * fix typo * add hrnetseg w18 w32 w48 * add hrnetseg with ocr w18 w32 w48 * merge hrnet and small, add hrnet_ocr * fix warning * change project name
94 lines
3.2 KiB
Markdown
94 lines
3.2 KiB
Markdown
# HRNet-Semantic-Segmentation
|
|
|
|
This repo implemtents [HRNet-Semantic-Segmentation-v1.1](https://github.com/HRNet/HRNet-Semantic-Segmentation/tree/pytorch-v1.1) and [HRNet-Semantic-Segmentation-OCR](https://github.com/HRNet/HRNet-Semantic-Segmentation/tree/HRNet-OCR).
|
|
|
|
|
|
## How to Run
|
|
### For HRNet-Semantic-Segmentation-v1.1
|
|
1. generate .wts, use config `experiments/cityscapes/seg_hrnet_w48_train_512x1024_sgd_lr1e-2_wd5e-4_bs_12_epoch484.yaml` and pretrained weight `hrnet_w48_cityscapes_cls19_1024x2048_trainset.pth` as example. change `PRETRAINED` in `experiments/cityscapes/seg_hrnet_w48_train_512x1024_sgd_lr1e-2_wd5e-4_bs_12_epoch484.yaml` to `""`.
|
|
```
|
|
cp gen_wts.py $HRNET--Semantic-Segmentation-PROJECT-ROOT/tools
|
|
cd $HRNET--Semantic-Segmentation-PROJECT-ROOT
|
|
python tools/gen_wts.py --cfg experiments/cityscapes/seg_hrnet_w48_train_512x1024_sgd_lr1e-2_wd5e-4_bs_12_epoch484.yaml --ckpt_path hrnet_w48_cityscapes_cls19_1024x2048_trainset.pth --save_path hrnet_w48.wts
|
|
cp hrnet_w48.wts $HRNET-TENSORRT-ROOT
|
|
cd $HRNET-TENSORRT-ROOT
|
|
```
|
|
2. cmake and make
|
|
|
|
```
|
|
mkdir build
|
|
cd build
|
|
cmake ..
|
|
make
|
|
```
|
|
first serialize model to plan file
|
|
```
|
|
./hrnet -s [.wts] [.engine] [small or 18 or 32 or 48] # small for W18-Small-v2, 18 for W18, etc.
|
|
```
|
|
such as
|
|
```
|
|
./hrnet -s ../hrnet_w48.wts ./hrnet_w48.engine 48
|
|
```
|
|
then deserialize plan file and run inference
|
|
```
|
|
./hrnet -d [.engine] [image dir]
|
|
```
|
|
such as
|
|
```
|
|
./hrnet -d ./hrnet_w48.engine ../samples
|
|
```
|
|
### For HRNet-Semantic-Segmentation-OCR
|
|
|
|
1. generate .wts, use config `experiments/cityscapes/seg_hrnet_ocr_w48_train_512x1024_sgd_lr1e-2_wd5e-4_bs_12_epoch484.yaml` and pretrained weight `hrnet_ocr_cs_8162_torch11.pth` as example. change `PRETRAINED` in `experiments/cityscapes/seg_hrnet_ocr_w48_train_512x1024_sgd_lr1e-2_wd5e-4_bs_12_epoch484.yaml` to `""`.
|
|
```
|
|
cp gen_wts.py $HRNET-OCR-TRAIN-PROJECT-ROOT/tools
|
|
cd $HRNET-OCR-PROJECT-ROOT
|
|
python tools/gen_wts.py --cfg experiments/cityscapes/seg_hrnet_ocr_w48_train_512x1024_sgd_lr1e-2_wd5e-4_bs_12_epoch484.yaml --ckpt_path hrnet_ocr_cs_8162_torch11.pth --save_path hrnet_ocr_w48.wts
|
|
cp hrnet_ocr_w48.wts $HRNET-OCR-TENSORRT-ROOT
|
|
cd $HRNET-OCR-TENSORRT-ROOT
|
|
```
|
|
2. cmake and make
|
|
|
|
```
|
|
mkdir build
|
|
cd build
|
|
cmake ..
|
|
make
|
|
```
|
|
first serialize model to plan file
|
|
```
|
|
./hrnet_ocr -s [.wts] [.engine] [18 or 32 or 48]
|
|
```
|
|
such as
|
|
```
|
|
./hrnet_ocr -s ../hrnet_ocr_w48.wts ./hrnet_ocr_w48.engine 48
|
|
```
|
|
then deserialize plan file and run inference
|
|
```
|
|
./hrnet_ocr -d [.engine] [image dir]
|
|
```
|
|
such as
|
|
```
|
|
./hrnet_ocr -d ./hrnet_ocr_w48.engine ../samples
|
|
```
|
|
## Result
|
|
|
|
TRT Result:
|
|
|
|
|
|
|
|
pytorch result:
|
|
|
|
|
|
|
|
## Note
|
|
|
|
* Some source codes are changed for simplicity. But the original model can still be used.
|
|
|
|
All "upsample" op in source code are changed to `mode='bilinear', align_corners=True`
|
|
|
|
* Image preprocessing operation and postprocessing operation are put into Trt Engine.
|
|
|
|
* Zero-copy technology (CPU/GPU memory copy) is used.
|
|
|