Datasets

If you are familiar with the standard data loading workflow in PyTorch, ganslate uses similar dataset classes which are derived from torch.utils.data.Dataset to define the data fetching and preprocessing pipeline. Additionally, ganslate's datasets have an associated Python dataclass in which all the data-related settings are defined.

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The PairedImageDataset and UnpairedImageDataset classes

Two classes - PairedImageDataset and UnpairedImageDataset - are supplied by the ganslate by default which can be used out-of-the-box on your image data.

The PairedImageDataset class enables loading from the storage an A-B image pair given a common index and applying optional joint transformations on the pair. This class is to be used in paired training as well during validation and/or testing when paired data is available.

On the other hand, UnpairedImageDataset fetches randomly a domain A image and a domain B image and applies optional transformations on each independently. As the name suggects, this class is meant to be used for unpaired training.

Input and Output

Both classes expect your data directory to be structured in the following manner

<train_dataset_root_dir>
    |
    |- A
    |   |- ...
    |   |- ...
    |
    |- B
    |   |- ...
    |   |- ...

And if using validation or testing data

<val_dataset_root_dir>
    |
    |- A
    |   |- ...
    |   |- ...
    |
    |- B
    |   |- ...
    |   |- ...

where the sub-directories A and B contain the images. In situations where paired data is provided (i.e. paired training or all valdation/testing), the ordering of images in A corresponds to the ordering of images in B, meaning that the first A image the first B image are pairs and so on. Images with extensions .jpg, .jpeg, and png are supported.

Both image dataset classes implemet a __getitem__ method that outputs a sample dictionary of the following form

sample = {'A': a_tensor, 'B': b_tensor}

where the each tensor is of shape (C, H, W).

Available Settings

The configuration dataclasses associated with both default image datasets are inherited from configs.base.BaseDatasetConfig which two settings common to all dataset classes. These are num_workers and pin_memory which are the settings for the torch.utils.data.DataLoader used by ganslate internally.

The two image datasets have additional settings which are same across the two datasets. These are:

• image_channels: Refers to the number of channels in the images. Only the images with 1 and 3 channels are supported (i.e. grayscale and RGB), and the channels should be the same across A and B images (i.e. either both should be grayscale or both should be RGB).

• preprocess: Accepts a tuple of predefined strings that defines the preprocessing instructions. These predefined strings include resize, scale_width, random_zoom, random_crop, and random_flip of which resize and scale_width specify the initial resizing operations (choose either one or none), whereas the rest correspond to the random transforms used as data augmentation during training. An example value for the preprocess settings is ('resize', 'random_crop', 'random_flip')

Note: In PairedImageDataset, these transforms are applied jointly to the A and B images, whereas in UnpairedImageDataset, they are applied on each image independently.

• load_size: This parameter accepts a tuple that specifies the size (H, W) to which the images are to be loaded from the storage and resized as a result of the resize preprocessing instruction. If instead the scale_width instruction is specified, only the width component if of the load_size is considered and the image width is scaled to this value while preserving its aspect ratio.

• final_size: This parameter accepts a tuple that specifies the size (H, W) to which the images are converted as a result of the random transforms. This is the final size of the images that should be expected from the dataloader.

Note: When not using random transforms (for example, during validation/testing), specify the final_size the same as load_size.

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Custom Dataset Classes

It is also possible to define your custom dataset class for use-cases requiring specialized processing for the data, for exmaple, in case of medical images. See Your First Project for more more details on creating custom datasets.