Apply K-fashion 3rd solution to deepfashion2 dataset

3등 솔루션에서 DetectoRS 를 쓴 이유?(추측)

instance-segmentation에서 그 당시 SOTA 였기 때문에
https://paperswithcode.com/sota/instance-segmentation-on-coco

지금은 Dual-Swin-L(HTC, multi-scale)

Trouble Shooting

• 문제상황: docker image에 mmdet이 없음
  • current version: mmdet-2.14.0
    • Got Error by AssertionError: MMCV==1.3.5 is used but incompatible. Please install mmcv>=1.3.8, <=1.4.0.
      • pip install mmdet==2.13.0 compatible-version-link
  • KeyError: “HybridTaskCascade: ‘DetectoRS_EfficientNet is not in the models registry’”
    • mmcv/utils/registry.py 에서 오류가 나는데..
    • 다른 모델로
  • detectors_resnest50.py 로 다시
    • pretrained 에 wasabisys 를 쓰신거 보니 클라우드에 model weight 저장해 놓으신건가
  • python 으로 mmdet import 하니까 incompatible 에러가 나서 (AssertionError: MMCV==1.3.5 is used but incompatible. Please install mmcv>=1.1.5, <=1.3.)
    • mmdet이랑 mmcv version을 맞춰줘야할 듯
      • 현재: mmcv-full==1.3.5, mmdet==2.13.0
      • 위에서 똑같은 에러가 나서 했는데 2.13.0 도 안되는 거였던것.
      • dockerfile에서 mmcv-latest 라고 되어있던 게 결국 문제발생.
      • 시도:
        • pip install mmcv-full==1.2.7+torch1.6.0+cu102 -f https://openmmlab.oss-accelerate.aliyuncs.com/mmcv/dist/index.html
        • dockerfile에도 이런식으로 지정해놔야 할듯.
        • mmcv releases: https://github.com/open-mmlab/mmcv/releases
      • 실패:
        • 또 incompatible 뜨네..
        • pip install mmdet==2.9.0
• 해결
  • pip3 install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio==0.9.0 -f https://download.pytorch.org/whl/torch_stable.html
  • pip install mmcv-full==1.3.9 -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html
  • pip install mmdet==2.14.0
  • 이렇게 install
• 문제:
  • run default_runtime.py
  • TypeError: __init__() missing 2 required positional arguments: 'doc' and 'pos'

Make custom dataset training in mmdetection

• preprocessing was done by deepfashion2_to_coco.py in deepfashion2.
• created deepfasion2 in mmdetection/config (this is copy of k-fashion 3rd config and renamed it as deepfashion2)
  • /mmdetection/configs/deepfashion2
    • detectors_efficientnet_b0.py
      • ./dataset.py as _base_
        • change to dataset_type = 'CocoDataset'
        • change data_root
        • change transforms later on
        • data
          • data dict has: train_all, train, val, val_mini, test_val, test
            • what are those and where those data come from?
          • change ann_file = data_root + 'train_json.json'
          • change img_prefix = data_root + 'train/image'
        • evaluation
          • metric=['proposal', 'bbox', 'segm']
          • what is proposal and the the metric comes from?
      • ./schedule.py
        • hyper parameters to change
          • optimizer, lr, lr_config
      • ./runtime.py
        • changes
          • log_config - interval
          • workflow
            • [('train', 1), ('val', 1)]
      • data
        • samples_per_gpu=4
        • workers_per_gpu=4
      • Specify work_dirs