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| # Runs on the vast.ai instance: install deps and download HF data subsets. | |
| set -x | |
| cd /workspace | |
| pip install -q "segmentation-models-pytorch>=0.5" opencv-python-headless shapely tifffile imagecodecs pyarrow "huggingface_hub[hf_transfer]" nbconvert ipykernel aifactory 2>&1 | tail -2 | |
| export HF_HUB_ENABLE_HF_TRANSFER=1 | |
| mkdir -p data/flair data/tcd | |
| python - <<'EOF' | |
| from huggingface_hub import hf_hub_download, snapshot_download | |
| import os | |
| # FLAIR (Etalab 2.0): a few diverse departements (forest/mountain/rural/coastal) | |
| for z in ["D006_2020", "D052_2019", "D074_2020", "D035_2020", "D029_2021", "D041_2021"]: | |
| p = hf_hub_download("IGNF/FLAIR-1-2", f"data/train-val/{z}.zip", repo_type="dataset", local_dir="data/flair") | |
| print("got", p, os.path.getsize(p) // 2**20, "MB", flush=True) | |
| # OAM-TCD (CC BY 4.0) train split parquet | |
| snapshot_download("restor/tcd", repo_type="dataset", local_dir="data/tcd", allow_patterns=["data/train-*"]) | |
| print("tcd done", flush=True) | |
| EOF | |
| cd data/flair/data/train-val && for f in *.zip; do unzip -q -o "$f" -d ../../unz && rm "$f"; done | |
| echo SETUP_DONE | |