# Prepare Datasets for OneFormer

- A dataset can be used by accessing [DatasetCatalog](https://detectron2.readthedocs.io/modules/data.html#detectron2.data.DatasetCatalog) for its data, or [MetadataCatalog](https://detectron2.readthedocs.io/modules/data.html#detectron2.data.MetadataCatalog) for its metadata (class names, etc).
- This document explains how to setup the builtin datasets so they can be used by the above APIs. [Training OneFormer with Custom Datasets](https://github.com/SHI-Labs/OneFormer/tree/main/datasets/custom_datasets) gives a deeper dive on how to train OneFormer with custom datasets.
- Detectron2 has builtin support for a few datasets. The datasets are assumed to exist in a directory specified by the environment variable `DETECTRON2_DATASETS`. Under this directory, detectron2 will look for datasets in the structure described below, if needed.

  ```text
  $DETECTRON2_DATASETS/
    ADEChallengeData2016/
    cityscapes/
    coco/
    mapillary_vistas/
  ```

- You can set the location for builtin datasets by `export DETECTRON2_DATASETS=/path/to/datasets`. If left unset, the default is `./datasets` relative to your current working directory.


## Expected dataset structure for [ADE20K](http://sceneparsing.csail.mit.edu/)

```text
ADEChallengeData2016/
  images/
  annotations/
  objectInfo150.txt
  # download instance annotation
  annotations_instance/
  # generated by prepare_ade20k_sem_seg.py
  annotations_detectron2/
  # below are generated by prepare_ade20k_pan_seg.py
  ade20k_panoptic_{train,val}.json
  ade20k_panoptic_{train,val}/
  # below are generated by prepare_ade20k_ins_seg.py
  ade20k_instance_{train,val}.json
```

- Generate `annotations_detectron2`:

  ```bash
  python datasets/prepare_ade20k_sem_seg.py
  ```

- Install panopticapi by:

  ```bash
  pip install git+https://github.com/cocodataset/panopticapi.git
  ```

- Download the instance annotation from <http://sceneparsing.csail.mit.edu/>:

  ```bash
  wget http://sceneparsing.csail.mit.edu/data/ChallengeData2017/annotations_instance.tar
  ```

- Then, run `python datasets/prepare_ade20k_pan_seg.py`, to combine semantic and instance annotations for panoptic annotations.

- Run `python datasets/prepare_ade20k_ins_seg.py`, to extract instance annotations in COCO format.

## Expected dataset structure for [Cityscapes](https://www.cityscapes-dataset.com/downloads/)

```text
cityscapes/
  gtFine/
    train/
      aachen/
        color.png, instanceIds.png, labelIds.png, polygons.json,
        labelTrainIds.png
      ...
    val/
    test/
    # below are generated Cityscapes panoptic annotation
    cityscapes_panoptic_train.json
    cityscapes_panoptic_train/
    cityscapes_panoptic_val.json
    cityscapes_panoptic_val/
    cityscapes_panoptic_test.json
    cityscapes_panoptic_test/
  leftImg8bit/
    train/
    val/
    test/
```

- Login and download the dataset

  ```bash
  wget --keep-session-cookies --save-cookies=cookies.txt --post-data 'username=myusername&password=mypassword&submit=Login' https://www.cityscapes-dataset.com/login/
  ######## gtFine
  wget --load-cookies cookies.txt --content-disposition https://www.cityscapes-dataset.com/file-handling/?packageID=1
  ######## leftImg8bit
  wget --load-cookies cookies.txt --content-disposition https://www.cityscapes-dataset.com/file-handling/?packageID=3
  ```

- Install cityscapes scripts by:

  ```bash
  pip install git+https://github.com/mcordts/cityscapesScripts.git
  ```

- To create labelTrainIds.png, first prepare the above structure, then run cityscapesescript with:

  ```bash
  git clone https://github.com/mcordts/cityscapesScripts.git
  ```

  ```bash
  CITYSCAPES_DATASET=/path/to/abovementioned/cityscapes python cityscapesScripts/cityscapesscripts/preparation/createTrainIdLabelImgs.py
  ```

  These files are not needed for instance segmentation.

- To generate Cityscapes panoptic dataset, run cityscapesescript with:

  ```bash
  CITYSCAPES_DATASET=/path/to/abovementioned/cityscapes python cityscapesScripts/cityscapesscripts/preparation/createPanopticImgs.py
  ```

  These files are not needed for semantic and instance segmentation.

## Expected dataset structure for [COCO](https://cocodataset.org/#download)

```text
coco/
  annotations/
    instances_{train,val}2017.json
    panoptic_{train,val}2017.json
    caption_{train,val}2017.json
    # evaluate on instance labels derived from panoptic annotations
    panoptic2instances_val2017.json
  {train,val}2017/
    # image files that are mentioned in the corresponding json
  panoptic_{train,val}2017/  # png annotations
  panoptic_semseg_{train,val}2017/  # generated by the script mentioned below
```

- Install panopticapi by:

  ```bash
  pip install git+https://github.com/cocodataset/panopticapi.git
  ```

- Then, run `python datasets/prepare_coco_semantic_annos_from_panoptic_annos.py`, to extract semantic annotations from panoptic annotations (only used for evaluation).

- Then run the following command to convert the panoptic json into instance json format (used for evaluation on instance segmentation task):

  ```bash
  python datasets/panoptic2detection_coco_format.py --things_only
  ```

## Expected dataset structure for [Mapillary Vistas](https://www.mapillary.com/dataset/vistas)

```text
mapillary_vistas/
  training/
    images/
    instances/
    labels/
    panoptic/
  validation/
    images/
    instances/
    labels/
    panoptic/
  mapillary_vistas_instance_{train,val}.json  # generated by the script mentioned below
```

No preprocessing is needed for Mapillary Vistas on semantic and panoptic segmentation.

We do not evaluate for the instance segmentation task on the Mapillary Vistas dataset.