jeonghyo.k
3946ff6a25
- prediction/image/ FastAPI 서버 Docker 환경 구성 - Dockerfile: PyTorch 2.1 + CUDA 12.1 기반 GPU 이미지 - docker-compose.yml: GPU 할당 + 데이터 볼륨 마운트 - requirements.txt: 서버 의존성 목록 - .env.example: 환경변수 템플릿 - DOCKER_USAGE.md: 빌드/실행/API 사용법 문서 - Dockerfile에 .dockerignore 제외 폴더 mkdir -p 추가 - .gitignore: prediction/image 결과물 및 모델 가중치(.pth) 제외 추가 - dbInsert_csv.py, dbInsert_shp.py 삭제 (미사용 DB 로직) - api.py: dbInsert import 및 주석 처리된 DB 호출 코드 제거 - aerialRouter.ts: req.params 타입 오류 수정
66 lines
1.6 KiB
Python
66 lines
1.6 KiB
Python
#!/usr/bin/env python
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# Copyright (c) OpenMMLab. All rights reserved.
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import functools as func
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import glob
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import os.path as osp
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import re
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import numpy as np
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url_prefix = 'https://github.com/open-mmlab/mmsegmentation/blob/master/'
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files = sorted(glob.glob('../../configs/*/README.md'))
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stats = []
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titles = []
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num_ckpts = 0
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for f in files:
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url = osp.dirname(f.replace('../../', url_prefix))
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with open(f, 'r') as content_file:
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content = content_file.read()
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title = content.split('\n')[0].replace('#', '').strip()
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ckpts = set(x.lower().strip()
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for x in re.findall(r'https?://download.*\.pth', content)
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if 'mmsegmentation' in x)
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if len(ckpts) == 0:
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continue
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_papertype = [
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x for x in re.findall(r'<!--\s*\[([A-Z]*?)\]\s*-->', content)
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]
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assert len(_papertype) > 0
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papertype = _papertype[0]
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paper = set([(papertype, title)])
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titles.append(title)
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num_ckpts += len(ckpts)
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statsmsg = f"""
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\t* [{papertype}] [{title}]({url}) ({len(ckpts)} ckpts)
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"""
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stats.append((paper, ckpts, statsmsg))
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allpapers = func.reduce(lambda a, b: a.union(b), [p for p, _, _ in stats])
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msglist = '\n'.join(x for _, _, x in stats)
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papertypes, papercounts = np.unique([t for t, _ in allpapers],
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return_counts=True)
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countstr = '\n'.join(
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[f' - {t}: {c}' for t, c in zip(papertypes, papercounts)])
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modelzoo = f"""
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# 模型库统计数据
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* 论文数量: {len(set(titles))}
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{countstr}
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* 模型数量: {num_ckpts}
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{msglist}
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"""
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with open('modelzoo_statistics.md', 'w') as f:
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f.write(modelzoo)
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