Multi-domain Evaluation of Semantic Segmentation (MESS) with ZSSeg

[Website] [arXiv] [GitHub]

This directory contains the code for the MESS evaluation of ZSSeg. Please see the commits for our changes of the model.

Setup

Create a conda environment zsseg and install the required packages. See mess/README.md for details.

 bash mess/setup_env.sh

Prepare the datasets by following the instructions in mess/DATASETS.md. The zsseg env can be used for the dataset preparation. If you evaluate multiple models with MESS, you can change the dataset_dir argument and the DETECTRON2_DATASETS environment variable to a common directory (see mess/DATASETS.md and mess/eval.sh, e.g., ../mess_datasets).

Download the ZSSeg weights (see https://github.com/MendelXu/zsseg.baseline)

mkdir weights
conda activate zsseg
# Python code for downloading the weights from GDrive. Link: https://drive.google.com/file/d/1pb6UeXoMPy5xdEBtFcQYLOBKZt0xufKY/view
python -c "import gdown; gdown.download(f'https://drive.google.com/uc?export=download&confirm=pbef&id=1pb6UeXoMPy5xdEBtFcQYLOBKZt0xufKY', output='weights/model_final.pth')"

Evaluation

To evaluate the ZSSeg model on the MESS datasets, run

bash mess/eval.sh

# for evaluation in the background:
nohup bash mess/eval.sh > eval.log &
tail -f eval.log 

Note that the mask threshold was changed from 0.5 to 0.4 in commit aa327d4. We used the original threshold of 0.5 in the evaluation.

For evaluating a single dataset, select the DATASET from mess/DATASETS.md, the DETECTRON2_DATASETS path, and run

conda activate zsseg
export DETECTRON2_DATASETS="datasets"
DATASET=<dataset_name>

python train_net.py --num-gpus 1 --eval-only --config-file configs/ade20k-150/cross_dataset_test_only.yaml MODEL.WEIGHTS weights/model_final.pth OUTPUT_DIR output/ZSSeg/$DATASET DATASETS.TEST \(\"$DATASET\",\) MODEL.CLIP_ADAPTER.MASK_THR 0.5

--- Original ZSSeg README.md ---

[ECCV2022] A Simple Baseline for Open Vocabulary Semantic Segmentation with Pre-trained Vision-language Model

This repo is for our ECCV2022 paper A Simple Baseline for Open Vocabulary Semantic Segmentation with Pre-trained Vision-language Model. It is based on the official repo of MaskFormer.

🎉[News] Our new work Side Adapter Network for Open-Vocabulary Semantic Segmentation was accepted as CVPR2023 hightlight paper. Code has been released.

@article{xu2021,
  title={A Simple Baseline for Open Vocabulary Semantic Segmentation with Pre-trained Vision-language Model},
  author={Mengde Xu, Zheng Zhang, Fangyun Wei, Yutong Lin,Yue Cao, Han Hu, and Xiang Bai},
  journal={Proceedings of the IEEE/CVF European Conference on Computer Vision (ECCV)},
  year={2022}
}

Guideline

• Enviroment

  torch==1.8.0
  torchvision==0.9.0
  detectron2==0.6 #Following https://detectron2.readthedocs.io/en/latest/tutorials/install.html to install it and some required packages
  mmcv==1.3.14

  FurtherMore, install the modified clip package.

  cd third_party/CLIP
  python -m pip install -Ue .

• Data Preparation

  In our experiments, five datasets are used.

  • For Cityscapes and ADE20k, follow the tutorial in MaskFormer.
  • For Pascal Context:
    • Download data from the official website and extract it like below.

      Datasets/
          pcontext/
              #http://host.robots.ox.ac.uk/pascal/VOC/voc2010/VOCtrainval_03-May-2010.tar
              JPEGImages/
              #https://codalabuser.blob.core.windows.net/public/trainval_merged.json     
              trainval_merged.json 

    • Format the data to d2 style. install detail packpage from https://github.com/zhanghang1989/detail-api and then

      python datasets/prepare_pcontext_sem_seg.py --ori_root_dir datasets/pcontext-59 --save_dir datasets/pcontext-59

  • For COCO Stuff 164k:
    • Download data from the official dataset website and extract it like below.

      Datasets/
           coco/
                #http://images.cocodataset.org/zips/train2017.zip
                train2017/ 
                #http://images.cocodataset.org/zips/val2017.zip
                val2017/   
                #http://images.cocodataset.org/annotations/annotations_trainval2017.zip
                annotations/ 
                #http://calvin.inf.ed.ac.uk/wp-content/uploads/data/cocostuffdataset/stuffthingmaps_trainval2017.zip
                stuffthingmaps/ 

    • Format the data to d2 style and split it into Seen (Base) subset and Unseen (Novel) subset.

      python datasets/prepare_coco_stuff_164k_sem_seg.py datasets/coco
      
      python tools/mask_cls_collect.py datasets/coco/stuffthingmaps_detectron2/train2017_base datasets/coco/stuffthingmaps_detectron2/train2017_base_label_count.pkl
      
      python tools/mask_cls_collect.py datasets/coco/stuffthingmaps_detectron2/val2017 datasets/coco/stuffthingmaps_detectron2/val2017_label_count.pkl

  • For Pascal VOC 11k:
    • Download data from the offical dataset website and extract it like below.

    datasets/
       VOC2012/
            #download http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar and extract it here.
            JPEGImages/
            # copy `ImageSets/Segmentation/val.txt` here.
            val.txt
            #Download auged annotations from http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/semantic_contours/benchmark.tgz and convert the original mat file to png format. Or download it from https://www.dropbox.com/s/oeu149j8qtbs1x0/SegmentationClassAug.zip?dl=0 (Provided in https://github.com/DrSleep/tensorflow-deeplab-resnet#evaluation).
            SegmentationClassAug/
            #https://gist.githubusercontent.com/sun11/2dbda6b31acc7c6292d14a872d0c90b7/raw/5f5a5270089239ef2f6b65b1cc55208355b5acca/trainaug.txt
            train.txt
          

    • Format the data to d2 style and split it into Seen (Base) subset and Unseen (Novel) subset.

    python datasets/prepare_voc_sem_seg.py datasets/VOC2012
    
    python tools/mask_cls_collect.py datasets/VOC2012/annotations_detectron2/train_base datasets/VOC2012/annotations_detectron2/train_base_label_count.json
    
    python tools/mask_cls_collect.py datasets/VOC2012/annotations_detectron2/val datasets/VOC2012/annotations_detectron2/val_label_count.json

• Training and Evaluation

  Before training and evaluation, see the tutorial in detectron2. For example, to training a zero shot semantic segmentation model on COCO Stuff:

  • Training with manually designed prompts:

    # single prompt
    python train_net.py --config-file configs/coco-stuff-164k-156/zero_shot_maskformer_R101c_single_prompt_bs32_60k.yaml
    # imagenet prompt
    python train_net.py --config-file configs/coco-stuff-164k-156/zero_shot_maskformer_R101c_imagenet_prompt_bs32_60k.yaml
    # vild prompt
    python train_net.py --config-file configs/coco-stuff-164k-156/zero_shot_maskformer_R101c_vild_prompt_bs32_60k.yaml
    

  • Training with learned prompts:

    # Training prompts
    python train_net.py --config-file configs/coco-stuff-164k-156/zero_shot_proposal_classification_learn_prompt_bs32_10k.yaml --num-gpus 8 
    # Training seg model
    python train_net.py --config-file configs/coco-stuff-164k-156/zero_shot_maskformer_R101c_bs32_60k.yaml --num-gpus 8 MODEL.CLIP_ADAPTER.PROMPT_CHECKPOINT ${TRAINED_PROMPTS}

    Note: the prompts training will be affected by the random seed. It is better to run it multiple times.

    For evaluation, add --eval-only flag to the traing command.

  • Trained Model

    • For cross-dataset setting ( Train on seen classes and test on different datasets.) Trained Model

      # TRAINED_MODEL_PATH: the path of your downlaoded model file.
      # train on coco 156 class, test on other dataset
      # Trained with 
      # python train_net.py --resume --config-file configs/coco-stuff-164k-156/zero_shot_maskformer_R101c_vild_prompt_bs32_60k.yaml --num-gpus 8 MODEL.CLIP_ADAPTER.CLIP_ENSEMBLE True SOLVER.MAX_ITER 120000
      # DATASET: the name of other datset, can be ade20k-150, ade20k-847, cityscapes-19, pcontext-59-59. Don't test on pascal voc as it overlaps with coco stuff largely.
      python train_net.py --eval-only --resume --config-file configs/${DATASET}/cross_dataset_test_only.yaml --num-gpus 8 MODEL.WEIGHTS ${TRAINED_MODEL_PATH}
       

    • For zero-shot setting ( Train on seen classes and test on unseen classes of the same dataset.) Trained Model

      # TRAINED_MODEL_PATH: the path of your downloaded model file.
      # Trained with learned prompts
      python train_net.py --eval-only --config-file configs/coco-stuff-164k-156/zero_shot_maskformer_R101c_bs32_60k.yaml --num-gpus 8 MODEL.WEIGHTST ${TRAINED_MODEL_PATH}

    Note: For both setting, the model are only trained on coco stuff 156 classes for convenient.

• For visualization

  You can use https://github.com/facebookresearch/detectron2/blob/main/tools/visualize_json_results.py to visualize the segmentation result.

• Other information:

  • COCO Stuff thing and stuff classes split ( copied from https://github.com/nightrome/cocostuff)
  • ADE20k thing and stuff classes split ( following the same definition in [1], we also provide a text file containing all stuff classes):

[1] COCO-Stuff: Thing and Stuff Classes in Context H. Caesar, J. Uijlings, V. Ferrari, In Computer Vision and Pattern Recognition (CVPR), 2018.