File size: 1,962 Bytes
c1b9ba8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 | """scripts/download_weights.py — 从 Hugging Face Spaces 下载推理权重
用法
----
python scripts/download_weights.py
说明
----
训练权重不放在 GitHub（二进制文件不适合 git），统一存放在 HF Spaces。
本脚本将四个算法的最优权重下载到 results/ 目录，下载后可直接运行：
streamlit run app.py # 本地 Demo
python src/train.py ... # 继续训练（可选）
依赖
----
pip install huggingface_hub # requirements.txt 已包含
"""
from __future__ import annotations
from pathlib import Path
from huggingface_hub import hf_hub_download
# HF Spaces repo（存放权重的 Space）
REPO_ID = "lil58/interview"
REPO_TYPE = "space"
# 需要下载的权重文件（HF 上的路径 → 本地保存路径）
WEIGHTS = {
"results/best_model_train_vanilla.pth": "results/best_model_train_vanilla.pth",
"results/best_model_train_double.pth": "results/best_model_train_double.pth",
"results/best_model_train_dueling.pth": "results/best_model_train_dueling.pth",
"results/best_model_train_double_dueling.pth": "results/best_model_train_double_dueling.pth",
}
def main() -> None:
results_dir = Path(__file__).parent.parent / "results"
results_dir.mkdir(exist_ok=True)
print(f"Downloading weights from HF Space: {REPO_ID}\n")
for remote_path, local_rel in WEIGHTS.items():
local_path = Path(__file__).parent.parent / local_rel
if local_path.exists():
print(f" [skip] {local_rel} (already exists)")
continue
print(f" [down] {local_rel} ...", end=" ", flush=True)
hf_hub_download(
repo_id=REPO_ID,
repo_type=REPO_TYPE,
filename=remote_path,
local_dir=str(Path(__file__).parent.parent),
)
print("done")
print("\nAll weights ready. Run: streamlit run app.py")
if __name__ == "__main__":
main()
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