HDF5Dataset#

class deepinv.datasets.HDF5Dataset(path: str, train: bool = True, transform: Transform | Callable = None, load_physics_generator_params: bool = False, dtype: torch.dtype = torch.float32, complex_dtype: torch.dtype = torch.complex64)[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.
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.