class composer.datasets.SyntheticPILDataset(*, total_dataset_size, data_shape=(64, 64, 3), num_unique_samples_to_create=100, data_type=SyntheticDataType.GAUSSIAN, label_type=SyntheticDataLabelType.CLASSIFICATION_INT, num_classes=None, label_shape=None, transform=None)[source]#

Similar to SyntheticBatchPairDataset, but yields samples of type Image and supports dataset transformations.

  • total_dataset_size (int) โ€“ The total size of the dataset to emulate.

  • data_shape (List[int]) โ€“ Shape of the tensor for input samples.

  • num_unique_samples_to_create (int) โ€“ The number of unique samples to allocate memory for.

  • data_type (str or SyntheticDataType, optional) โ€“ Default: SyntheticDataType.GAUSSIAN.

  • label_type (str or SyntheticDataLabelType, optional) โ€“ create. Default: SyntheticDataLabelType.CLASSIFICATION_INT.

  • num_classes (int, optional) โ€“ Number of classes to use. Required if SyntheticDataLabelType is CLASSIFICATION_INT or CLASSIFICATION_ONE_HOT. Default: None.

  • label_shape (List[int], optional) โ€“ Shape of the tensor for each sample label. Default: None.

  • transform (Callable, optional) โ€“ Transform(s) to apply to data. Default: None.