Module `flowcon.datasets.uci.bsds300`

Functions

def load_bsds300()
def main()

Classes

class BSDS300Dataset (split='train', frac=None)

An abstract class representing a :class:Dataset.

All datasets that represent a map from keys to data samples should subclass it. All subclasses should overwrite :meth:__getitem__, supporting fetching a data sample for a given key. Subclasses could also optionally overwrite :meth:__len__, which is expected to return the size of the dataset by many :class:~torch.utils.data.Sampler implementations and the default options of :class:~torch.utils.data.DataLoader. Subclasses could also optionally implement :meth:__getitems__, for speedup batched samples loading. This method accepts list of indices of samples of batch and returns list of samples.

Note

:class:~torch.utils.data.DataLoader by default constructs an index sampler that yields integral indices. To make it work with a map-style dataset with non-integral indices/keys, a custom sampler must be provided.

Expand source code

class BSDS300Dataset(data.Dataset):
    def __init__(self, split='train', frac=None):
        splits = dict(zip(
            ('train', 'val', 'test'),
            load_bsds300()
        ))
        self.data = np.array(splits[split]).astype(np.float32)
        self.n, self.dim = self.data.shape
        if frac is not None:
            self.n = int(frac * self.n)

    def __getitem__(self, item):
        return self.data[item]

    def __len__(self):
        return self.n

Ancestors

torch.utils.data.dataset.Dataset
typing.Generic