jeonghyo.k 3946ff6a25 feat(prediction): 이미지 분석 서버 Docker 패키징 + DB 코드 제거

- 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

2026-03-10 18:37:36 +09:00

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Frequently Asked Questions (FAQ)

We list some common troubles faced by many users and their corresponding solutions here. Feel free to enrich the list if you find any frequent issues and have ways to help others to solve them. If the contents here do not cover your issue, please create an issue using the provided templates and make sure you fill in all required information in the template.

Installation

The compatible MMSegmentation and MMCV versions are as below. Please install the correct version of MMCV to avoid installation issues.

MMSegmentation version	MMCV version	MMClassification version
master	mmcv-full>=1.5.0, <=1.6.0	mmcls>=0.20.1, <=1.0.0
0.25.0	mmcv-full>=1.5.0, <=1.6.0	mmcls>=0.20.1, <=1.0.0
0.24.1	mmcv-full>=1.4.4, <=1.6.0	mmcls>=0.20.1, <=1.0.0
0.23.0	mmcv-full>=1.4.4, <=1.6.0	mmcls>=0.20.1, <=1.0.0
0.22.0	mmcv-full>=1.4.4, <=1.6.0	mmcls>=0.20.1, <=1.0.0
0.21.1	mmcv-full>=1.4.4, <=1.6.0	Not required
0.20.2	mmcv-full>=1.3.13, <=1.6.0	Not required
0.19.0	mmcv-full>=1.3.13, <1.3.17	Not required
0.18.0	mmcv-full>=1.3.13, <1.3.17	Not required
0.17.0	mmcv-full>=1.3.7, <1.3.17	Not required
0.16.0	mmcv-full>=1.3.7, <1.3.17	Not required
0.15.0	mmcv-full>=1.3.7, <1.3.17	Not required
0.14.1	mmcv-full>=1.3.7, <1.3.17	Not required
0.14.0	mmcv-full>=1.3.1, <1.3.2	Not required
0.13.0	mmcv-full>=1.3.1, <1.3.2	Not required
0.12.0	mmcv-full>=1.1.4, <1.3.2	Not required
0.11.0	mmcv-full>=1.1.4, <1.3.0	Not required
0.10.0	mmcv-full>=1.1.4, <1.3.0	Not required
0.9.0	mmcv-full>=1.1.4, <1.3.0	Not required
0.8.0	mmcv-full>=1.1.4, <1.2.0	Not required
0.7.0	mmcv-full>=1.1.2, <1.2.0	Not required
0.6.0	mmcv-full>=1.1.2, <1.2.0	Not required

You need to run pip uninstall mmcv first if you have mmcv installed. If mmcv and mmcv-full are both installed, there will be ModuleNotFoundError.

"No module named 'mmcv.ops'"; "No module named 'mmcv._ext'".
1. Uninstall existing mmcv in the environment using pip uninstall mmcv.
2. Install mmcv-full following the installation instruction.

How to know the number of GPUs needed to train the model

Infer from the name of the config file of the model. You can refer to the Config Name Style part of Learn about Configs. For example, for config file with name segformer_mit-b0_8x1_1024x1024_160k_cityscapes.py, 8x1 means training the model corresponding to it needs 8 GPUs, and the batch size of each GPU is 1.
Infer from the log file. Open the log file of the model and search nGPU in the file. The number of figures following nGPU is the number of GPUs needed to train the model. For instance, searching for nGPU in the log file yields the record nGPU 0,1,2,3,4,5,6,7, which indicates that eight GPUs are needed to train the model.

What does the auxiliary head mean

Briefly, it is a deep supervision trick to improve the accuracy. In the training phase, decode_head is for decoding semantic segmentation output, auxiliary_head is just adding an auxiliary loss, the segmentation result produced by it has no impact to your model's result, it just works in training. You may read this paper for more information.

Why is the log file not created

In the train script, we call get_root_loggerat Line 167, and get_root_logger in mmseg calls get_logger in mmcv, mmcv will return the same logger which has beed initialized in 'mmsegmentation/tools/train.py' with the parameter log_file. There is only one logger (initialized with log_file) during training. Ref: https://github.com/open-mmlab/mmcv/blob/21bada32560c7ed7b15b017dc763d862789e29a8/mmcv/utils/logging.py#L9-L16

If you find the log file not been created, you might check if mmcv.utils.get_logger is called elsewhere.

How to output the image for painting the segmentation mask when running the test script

In the test script, we provide show-dir argument to control whether output the painted images. Users might run the following command:

python tools/test.py {config} {checkpoint} --show-dir {/path/to/save/image} --opacity 1

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