66 lines
1.8 KiB
Markdown
66 lines
1.8 KiB
Markdown
# STDC — Semantic Segmentation
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## 快速開始
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### 環境安裝
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```bash
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# 建立與啟動 conda 環境
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conda create -n stdc_golface python=3.8 -y
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conda activate stdc_golface
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# 安裝 PyTorch + CUDA 11.3
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conda install pytorch=1.11.0 torchvision=0.12.0 torchaudio cudatoolkit=11.3 -c pytorch -y
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# 安裝 mmcv-full
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pip install mmcv-full==1.5.0 -f https://download.openmmlab.com/mmcv/dist/cu113/torch1.11.0/index.html
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# 安裝專案
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pip install -e .
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# 安裝工具套件
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pip install opencv-python tqdm matplotlib cityscapesscripts yapf==0.31.0
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```
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### 資料準備
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1. 使用 **Roboflow** 匯出資料集，格式選擇 `Semantic Segmentation Masks`
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2. 使用 `seg2city.py` 將 Roboflow 格式轉換為 Cityscapes 格式
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3. 將轉換後的資料放至 `data/cityscapes/`
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### 訓練與測試(需自己修改config檔案)
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```bash
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# 訓練
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python tools/train.py configs/stdc/kn_stdc1_in1k-pre_512x1024_80k_cityscapes.py
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# 測試（輸出視覺化結果）
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python tools/test.py configs/stdc/kn_stdc1_in1k-pre_512x1024_80k_cityscapes.py \
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work_dirs/kn_stdc1_in1k-pre_512x1024_80k_cityscapes/latest.pth \
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--show-dir work_dirs/vis_results
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```
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### 轉換 ONNX / NEF（Kneron Toolchain）
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```bash
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# 啟動 Docker（WSL 環境）
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docker run --rm -it \
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-v $(wslpath -u 'C:\Users\rd_de\stdc_git'):/workspace/stdc_git \
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kneron/toolchain:latest
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# 轉換 ONNX
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python tools/pytorch2onnx_kneron.py \
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configs/stdc/kn_stdc1_in1k-pre_512x1024_80k_cityscapes.py \
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--checkpoint work_dirs/kn_stdc1_in1k-pre_512x1024_80k_cityscapes/latest.pth \
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--output-file work_dirs/kn_stdc1_in1k-pre_512x1024_80k_cityscapes/latest.onnx \
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--verify
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# onnx2nef
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python onnx2nefSTDC630.py
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# 將 NEF 複製到本機
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docker cp <container_id>:/data1/kneron_flow/models_630.nef \
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"C:\Users\rd_de\stdc_git\work_dirs\nef\models_630.nef"
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```
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