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
95 lines
6.0 KiB
Python
95 lines
6.0 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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from .builder import DATASETS
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from .custom import CustomDataset
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@DATASETS.register_module()
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class COCOStuffDataset(CustomDataset):
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"""COCO-Stuff dataset.
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In segmentation map annotation for COCO-Stuff, Train-IDs of the 10k version
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are from 1 to 171, where 0 is the ignore index, and Train-ID of COCO Stuff
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164k is from 0 to 170, where 255 is the ignore index. So, they are all 171
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semantic categories. ``reduce_zero_label`` is set to True and False for the
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10k and 164k versions, respectively. The ``img_suffix`` is fixed to '.jpg',
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and ``seg_map_suffix`` is fixed to '.png'.
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"""
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CLASSES = (
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'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train',
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'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign',
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'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep',
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'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella',
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'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard',
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'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard',
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'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork',
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'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange',
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'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair',
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'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv',
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'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave',
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'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase',
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'scissors', 'teddy bear', 'hair drier', 'toothbrush', 'banner',
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'blanket', 'branch', 'bridge', 'building-other', 'bush', 'cabinet',
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'cage', 'cardboard', 'carpet', 'ceiling-other', 'ceiling-tile',
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'cloth', 'clothes', 'clouds', 'counter', 'cupboard', 'curtain',
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'desk-stuff', 'dirt', 'door-stuff', 'fence', 'floor-marble',
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'floor-other', 'floor-stone', 'floor-tile', 'floor-wood',
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'flower', 'fog', 'food-other', 'fruit', 'furniture-other', 'grass',
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'gravel', 'ground-other', 'hill', 'house', 'leaves', 'light', 'mat',
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'metal', 'mirror-stuff', 'moss', 'mountain', 'mud', 'napkin', 'net',
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'paper', 'pavement', 'pillow', 'plant-other', 'plastic', 'platform',
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'playingfield', 'railing', 'railroad', 'river', 'road', 'rock', 'roof',
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'rug', 'salad', 'sand', 'sea', 'shelf', 'sky-other', 'skyscraper',
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'snow', 'solid-other', 'stairs', 'stone', 'straw', 'structural-other',
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'table', 'tent', 'textile-other', 'towel', 'tree', 'vegetable',
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'wall-brick', 'wall-concrete', 'wall-other', 'wall-panel',
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'wall-stone', 'wall-tile', 'wall-wood', 'water-other', 'waterdrops',
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'window-blind', 'window-other', 'wood')
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PALETTE = [[0, 192, 64], [0, 192, 64], [0, 64, 96], [128, 192, 192],
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[0, 64, 64], [0, 192, 224], [0, 192, 192], [128, 192, 64],
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[0, 192, 96], [128, 192, 64], [128, 32, 192], [0, 0, 224],
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[0, 0, 64], [0, 160, 192], [128, 0, 96], [128, 0, 192],
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[0, 32, 192], [128, 128, 224], [0, 0, 192], [128, 160, 192],
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[128, 128, 0], [128, 0, 32], [128, 32, 0], [128, 0, 128],
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[64, 128, 32], [0, 160, 0], [0, 0, 0], [192, 128, 160],
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[0, 32, 0], [0, 128, 128], [64, 128, 160], [128, 160, 0],
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[0, 128, 0], [192, 128, 32], [128, 96, 128], [0, 0, 128],
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[64, 0, 32], [0, 224, 128], [128, 0, 0], [192, 0, 160],
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[0, 96, 128], [128, 128, 128], [64, 0, 160], [128, 224, 128],
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[128, 128, 64], [192, 0, 32], [128, 96, 0], [128, 0, 192],
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[0, 128, 32], [64, 224, 0], [0, 0, 64], [128, 128, 160],
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[64, 96, 0], [0, 128, 192], [0, 128, 160], [192, 224, 0],
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[0, 128, 64], [128, 128, 32], [192, 32, 128], [0, 64, 192],
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[0, 0, 32], [64, 160, 128], [128, 64, 64], [128, 0, 160],
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[64, 32, 128], [128, 192, 192], [0, 0, 160], [192, 160, 128],
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[128, 192, 0], [128, 0, 96], [192, 32, 0], [128, 64, 128],
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[64, 128, 96], [64, 160, 0], [0, 64, 0], [192, 128, 224],
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[64, 32, 0], [0, 192, 128], [64, 128, 224], [192, 160, 0],
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[0, 192, 0], [192, 128, 96], [192, 96, 128], [0, 64, 128],
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[64, 0, 96], [64, 224, 128], [128, 64, 0], [192, 0, 224],
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[64, 96, 128], [128, 192, 128], [64, 0, 224], [192, 224, 128],
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[128, 192, 64], [192, 0, 96], [192, 96, 0], [128, 64, 192],
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[0, 128, 96], [0, 224, 0], [64, 64, 64], [128, 128, 224],
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[0, 96, 0], [64, 192, 192], [0, 128, 224], [128, 224, 0],
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[64, 192, 64], [128, 128, 96], [128, 32, 128], [64, 0, 192],
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[0, 64, 96], [0, 160, 128], [192, 0, 64], [128, 64, 224],
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[0, 32, 128], [192, 128, 192], [0, 64, 224], [128, 160, 128],
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[192, 128, 0], [128, 64, 32], [128, 32, 64], [192, 0, 128],
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[64, 192, 32], [0, 160, 64], [64, 0, 0], [192, 192, 160],
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[0, 32, 64], [64, 128, 128], [64, 192, 160], [128, 160, 64],
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[64, 128, 0], [192, 192, 32], [128, 96, 192], [64, 0, 128],
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[64, 64, 32], [0, 224, 192], [192, 0, 0], [192, 64, 160],
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[0, 96, 192], [192, 128, 128], [64, 64, 160], [128, 224, 192],
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[192, 128, 64], [192, 64, 32], [128, 96, 64], [192, 0, 192],
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[0, 192, 32], [64, 224, 64], [64, 0, 64], [128, 192, 160],
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[64, 96, 64], [64, 128, 192], [0, 192, 160], [192, 224, 64],
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[64, 128, 64], [128, 192, 32], [192, 32, 192], [64, 64, 192],
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[0, 64, 32], [64, 160, 192], [192, 64, 64], [128, 64, 160],
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[64, 32, 192], [192, 192, 192], [0, 64, 160], [192, 160, 192],
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[192, 192, 0], [128, 64, 96], [192, 32, 64], [192, 64, 128],
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[64, 192, 96], [64, 160, 64], [64, 64, 0]]
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def __init__(self, **kwargs):
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super(COCOStuffDataset, self).__init__(
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img_suffix='.jpg', seg_map_suffix='_labelTrainIds.png', **kwargs)
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