HDF5Dataset#

class deepinv.datasets.HDF5Dataset(path, train=True, split=None, transform=None, load_physics_generator_params=False, dtype=torch.float, complex_dtype=torch.cfloat)[source]#

Bases: Dataset

DeepInverse HDF5 dataset with signal/measurement pairs (x, y).

If there is no training ground truth (i.e. x_train) in the dataset file, the dataset returns the measurement again as the signal.

Optionally also return physics generator params as a dict per sample (x, y, params), if one was used during data generation.

Note

We support all dtypes supported by h5py including complex numbers, which will be stored as complex dtype.

Parameters:

path (str) – Path to the folder containing the dataset (one or multiple HDF5 files).
train (bool) – Set to True for training and False for testing. If split argument used, then train is ignored.
split (str) – overrides train argument if not None. Custom dataset split e.g. “train”, “test” or “val”, which selects the split name used when generating the dataset.
transform (Transform, Callable) – A deepinv or torchvision transform to apply to the data.
load_physics_generator_params (bool) – load physics generator params from dataset if they exist (e.g. if dataset created with deepinv.datasets.generate_dataset())
dtype (torch.dtype, str) – cast all real-valued data to this dtype.
complex_dtype (torch.dtype, str) – cast all complex-valued data to this dtype.