TimeAveragingNet

class deepinv.models.TimeAveragingNet(backbone_net)

Bases: Module, TimeMixin

Time-averaging network wrapper.

Adapts a static image reconstruction network for time-varying inputs to output static reconstructions. Average the data across the time dim before passing into network.

Note

The input physics is assumed to be a temporal physics which produced the temporal measurements y (potentially with temporal mask mask). It must either implement a to_static method to remove the time dimension, or already be a static physics (e.g. deepinv.physics.MRI).

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Example:

>>> from deepinv.models import UNet, TimeAveragingNet
>>> model = UNet(scales=2)
>>> model = TimeAveragingNet(model)
>>> y = rand(1, 1, 4, 8, 8) # B,C,T,H,W
>>> x_net = model(y, None)
>>> x_net.shape # B,C,H,W
torch.Size([1, 1, 8, 8])

Parameters:

• backbone_net (torch.nn.Module) – Base network which can only take static inputs (B,C,H,W)

• device (torch.device) – cpu or gpu.

forward(y, physics, **kwargs)

Evaluate the network

Parameters:

• y – measurements

• physics – forward operator acting on dynamic inputs