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Installation

The project is based on the pytorch 1.11.0 with python 3.8.

1. Clone the Git repo

$ git clone https://github.com/yyliu01/RPL.git
$ cd RPL

2. Install dependencies

  1. create conda env 
    $ conda env create -f rpl.yml
  2. install the torch 1.11.0 
    $ conda activate rpl
# IF cuda version < 11.0
$ pip install torch==1.11.0+cu102 torchvision==0.12.0+cu102 torchaudio==0.11.0 --extra-index-url https://download.pytorch.org/whl/cu102
# IF cuda version >= 11.0 (e.g., 30x or above)
$ pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 torchaudio==0.11.0 --extra-index-url https://download.pytorch.org/whl/cu113

3. Prepare dataset

cityscapes

  1. please download the Cityscapes dataset (gt_Fine).
  2. (optional) you might need to preprocess Cityscapes dataset in here, as we follow the common setting with 19 classes.
  3. specify the Cityscapes dataset path in code/config/config.py file, which is C.city_root_path.

fishyscapes

  1. for the time being, you can download from the official website in here.
  2. specify the coco dataset path in code/config/config.py file, which is C.fishy_root_path.

You can alternatively download both preprocessed fishyscapes & cityscapes datasets from here.

coco (for outlier exposures)

  1. please follow Meta-OoD to prepare the COCO images serving as OoD proxy for OoD training. This script generates binary segmentation masks for COCO images not containing any instances that could also be assigned to one of the Cityscapes (train-)classes. Execute via: 
    $ python preparation/prepare_coco_segmentation.py
  2. specify the coco dataset path in code/config/config.py file, which is C.coco_root_path.

4. Dataset Structure

  1. the tree structures of the training datasets (including both cityscapes, and coco) are shown below.
city_scape/
├── annotation
│   └── city_gt_fine
│       ├── train
│       └── val
└── images
    └── city_gt_fine
        ├── train
        └── val
coco/
├── annotations
│   └── ood_seg_train2017
└── train2017
  1. the tree sturcutres of all the validation datasets are shown in below.
fishyscapes/
├── LostAndFound
│   ├── entropy
│   ├── labels
│   ├── labels_with_ROI
│   ├── logit_distance
│   ├── mae_features
│   ├── original
│   ├── semantic
│   └── synthesis
└── Static
    ├── entropy
    ├── labels
    ├── labels_with_ROI
    ├── logit_distance
    ├── mae_features
    ├── original
    └── semantic
    
segment_me
├── dataset_AnomalyTrack
│   ├── images
│   └── labels_masks
└── dataset_ObstacleTrack
    ├── images
    └── labels_masks

road_anomaly/
├── entropy
├── labels
├── mae_features
├── original
├── semantic
└── synthesis

lost_and_found/
├── gtCoarse
│   ├── test
│   │   ├── 02_Hanns_Klemm_Str_44
│   │   ├── 04_Maurener_Weg_8
│   │   ├── 05_Schafgasse_1
│   │   ├── 07_Festplatz_Flugfeld
│   │   └── 15_Rechbergstr_Deckenpfronn
│   └── train
│       ├── 01_Hanns_Klemm_Str_45
│       ├── 03_Hanns_Klemm_Str_19
│       ├── 06_Galgenbergstr_40
│       ├── 10_Schlossberg_9
│       ├── 11_Parkplatz_Flugfeld
│       ├── 12_Umberto_Nobile_Str
│       ├── 13_Elly_Beinhorn_Str
│       └── 14_Otto_Lilienthal_Str_24
└── leftImg8bit
    ├── test
    │   ├── 02_Hanns_Klemm_Str_44
    │   ├── 04_Maurener_Weg_8
    │   ├── 05_Schafgasse_1
    │   ├── 07_Festplatz_Flugfeld
    │   └── 15_Rechbergstr_Deckenpfronn
    └── train
        ├── 01_Hanns_Klemm_Str_45
        ├── 03_Hanns_Klemm_Str_19
        ├── 06_Galgenbergstr_40
        ├── 10_Schlossberg_9
        ├── 11_Parkplatz_Flugfeld
        ├── 12_Umberto_Nobile_Str
        ├── 13_Elly_Beinhorn_Str
        └── 14_Otto_Lilienthal_Str_24