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- Add golf1/2/4/7/8 dataset classes for semantic segmentation - Add kneron-specific configs (meconfig series, kn_stdc1_golf4class) - Organize scripts into tools/check/ and tools/kneron/ - Add kneron_preprocessing module - Update README with quick-start guide - Update .gitignore to exclude data dirs, onnx, nef outputs Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
1.8 KiB
1.8 KiB
STDC GolfAce — Semantic Segmentation on Kneron
快速開始
環境安裝
# 建立與啟動 conda 環境
conda create -n stdc_golface python=3.8 -y
conda activate stdc_golface
# 安裝 PyTorch + CUDA 11.3
conda install pytorch=1.11.0 torchvision=0.12.0 torchaudio cudatoolkit=11.3 -c pytorch -y
# 安裝 mmcv-full
pip install mmcv-full==1.5.0 -f https://download.openmmlab.com/mmcv/dist/cu113/torch1.11.0/index.html
# 安裝專案
pip install -e .
# 安裝工具套件
pip install opencv-python tqdm matplotlib cityscapesscripts yapf==0.31.0
資料準備
- 使用 Roboflow 匯出資料集，格式選擇
Semantic Segmentation Masks - 使用
seg2city.py將 Roboflow 格式轉換為 Cityscapes 格式 - 將轉換後的資料放至
data/cityscapes/
訓練與測試
# 訓練
python tools/train.py configs/stdc/kn_stdc1_in1k-pre_512x1024_80k_cityscapes.py
# 測試（輸出視覺化結果）
python tools/test.py configs/stdc/kn_stdc1_in1k-pre_512x1024_80k_cityscapes.py \
work_dirs/kn_stdc1_in1k-pre_512x1024_80k_cityscapes/latest.pth \
--show-dir work_dirs/vis_results
轉換 ONNX / NEF（Kneron Toolchain）
# 啟動 Docker（WSL 環境）
docker run --rm -it \
-v $(wslpath -u 'C:\Users\rd_de\stdc_git'):/workspace/stdc_git \
kneron/toolchain:latest
# 轉換 ONNX
python tools/pytorch2onnx_kneron.py \
configs/stdc/kn_stdc1_in1k-pre_512x1024_80k_cityscapes.py \
--checkpoint work_dirs/kn_stdc1_in1k-pre_512x1024_80k_cityscapes/latest.pth \
--output-file work_dirs/kn_stdc1_in1k-pre_512x1024_80k_cityscapes/latest.onnx \
--verify
# 將 NEF 複製到本機
docker cp <container_id>:/data1/kneron_flow/models_630.nef \
"C:\Users\rd_de\stdc_git\work_dirs\nef\models_630.nef"