Adding your own sampler

DIRECT currently supports sub-samplers only for gridded data (data acquired on an equispaced grid). To add a new sub-sampler follow the steps below:

• Implement your custom sampler under direct/common/subsample.py following the template:

class MyNewMaskFunc(BaseMaskFunc):
    def __init__(
        self,
        accelerations: Tuple[Number, ...],
        ...
    ):
        super().__init__(
            accelerations=accelerations,
            uniform_range=False,
        )
        ...

    def mask_func(self, shape, return_acs=False, seed=None):
        """
        Main function that outputs the sampling mask and acs_mask.

        Parameters
        ----------

        shape : iterable[int]
            The shape of the mask to be created. The shape should at least 3 dimensions.
            Samples are drawn along the second last dimension.
        seed : int (optional)
            Seed for the random number generator. Setting the seed ensures the same mask is generated
             each time for the same shape.
        return_acs : bool
            Return the autocalibration signal region as a mask.

        Returns
        -------
        torch.Tensor : the sampling mask

        """
        if len(shape) < 3:
            raise ValueError("Shape should have 3 or more dimensions")

        with temp_seed(self.rng, seed):
            num_rows = shape[-3]
            num_cols = shape[-2]
            center_fraction, acceleration = self.choose_acceleration()

            # Create the mask of shape (1, nx, ny, 1)
            mask = ...

            if return_acs:
                acs_mask = ...
                return torch.from_numpy(acs_mask)
        ...

        return torch.from_numpy(mask)

Ideally, your sub-sampler should be able to initialise only with the accelerations argument. Otherwise, update direct/common/subsample_config.py accordingly with any new keys needed to initialise your sub-sampler:

@dataclass
class MaskingConfig(BaseConfig):
    ...
    <new_keys>: ... = ...

• To use your sub-sampler, you have to request it in the config.yaml file. The following shows an example for training:

training:
    datasets:
    -   name: ...
        ...
        transforms:
            ...
            masking:
                name: MyNew
                accelerations: [...]
                ...