TensorDataset#

class deepinv.datasets.TensorDataset(*, x=None, y=None, params=None)[source]#

Bases: ImageDataset

Dataset wrapping data explicitly passed as tensors.

This dataset can be used to return ground truth x, ground truth and measurements (x, y), or measurements only (y). All input tensors must be of shape (N, ...) and of same N where N is the number of samples and … represents the data dimensions.

Optionally, params are returned too.

Parameters:

  • x (torch.Tensor, None) – optional input ground truth tensor x

  • y (torch.Tensor, None) – optional input measurement tensor y

  • params (dict[str, torch.Tensor], None) – optional input physics parameters params of format {"str": Tensor}

Examples:

Construct a dataset from a single measurement only:

>>> import torch
>>> from deepinv.datasets import TensorDataset
>>> y = torch.rand(1, 3, 8, 8) # B,C,H,W
>>> dataset = TensorDataset(y=y)
>>> x, y = dataset[0]
>>> x
nan
>>> y.shape
torch.Size([3, 8, 8])

Construct a dataset from a ground truth batch:

>>> x = torch.rand(4, 3, 8, 8)  # 4 samples of 3-channel 8x8 images
>>> dataset = TensorDataset(x=x)
>>> dataset[0].shape
torch.Size([3, 8, 8])

Examples using TensorDataset:#

Bring your own dataset

Bring your own dataset

Inference and fine-tune a foundation model

Inference and fine-tune a foundation model