Usage: dataset_tool.py [OPTIONS]

  Convert an image dataset into a dataset archive usable with StyleGAN2 ADA
  PyTorch.

  The input dataset format is guessed from the --source argument:

  --source *_lmdb/                    Load LSUN dataset
  --source cifar-10-python.tar.gz     Load CIFAR-10 dataset
  --source train-images-idx3-ubyte.gz Load MNIST dataset
  --source path/                      Recursively load all images from path/
  --source dataset.zip                Recursively load all images from dataset.zip

  Specifying the output format and path:

  --dest /path/to/dir                 Save output files under /path/to/dir
  --dest /path/to/dataset.zip         Save output files into /path/to/dataset.zip

  The output dataset format can be either an image folder or an uncompressed
  zip archive. Zip archives makes it easier to move datasets around file
  servers and clusters, and may offer better training performance on network
  file systems.

  Images within the dataset archive will be stored as uncompressed PNG.
  Uncompresed PNGs can be efficiently decoded in the training loop.

  Class labels are stored in a file called 'dataset.json' that is stored at
  the dataset root folder.  This file has the following structure:

  {
      "labels": [
          ["00000/img00000000.png",6],
          ["00000/img00000001.png",9],
          ... repeated for every image in the datase
          ["00049/img00049999.png",1]
      ]
  }

  If the 'dataset.json' file cannot be found, the dataset is interpreted as
  not containing class labels.

  Image scale/crop and resolution requirements:

  Output images must be square-shaped and they must all have the same power-
  of-two dimensions.

  To scale arbitrary input image size to a specific width and height, use
  the --resolution option.  Output resolution will be either the original
  input resolution (if resolution was not specified) or the one specified
  with --resolution option.

  Use the --transform=center-crop or --transform=center-crop-wide options to
  apply a center crop transform on the input image.  These options should be
  used with the --resolution option.  For example:

  python dataset_tool.py --source LSUN/raw/cat_lmdb --dest /tmp/lsun_cat \
      --transform=center-crop-wide --resolution=512x384

Options:
  --source PATH                   Directory or archive name for input dataset
                                  [required]

  --dest PATH                     Output directory or archive name for output
                                  dataset  [required]

  --max-images INTEGER            Output only up to `max-images` images
  --transform [center-crop|center-crop-wide]
                                  Input crop/resize mode
  --resolution WxH                Output resolution (e.g., '512x512')
  --help                          Show this message and exit.