train

deepinv.train(model: ~torch.nn.modules.module.Module, physics: ~deepinv.physics.forward.Physics, optimizer: ~torch.optim.optimizer.Optimizer, train_dataloader: ~torch.utils.data.dataloader.DataLoader, epochs: int = 100, losses: ~deepinv.loss.loss.Loss | ~typing.List[~deepinv.loss.loss.Loss] = SupLoss(   (metric): MSELoss() ), eval_dataloader: ~torch.utils.data.dataloader.DataLoader | None = None, *args, **kwargs)

Alias function for training a model using deepinv.Trainer class.

This function creates a Trainer instance and returns the trained model.

Warning

This function is deprecated and will be removed in future versions. Please use deepinv.Trainer instead.

Parameters:

• model (torch.nn.Module) – Reconstruction network, which can be PnP, unrolled, artifact removal or any other custom reconstruction network.

• physics (deepinv.physics.Physics, list[deepinv.physics.Physics]) – Forward operator(s) used by the reconstruction network.

• epochs (int) – Number of training epochs. Default is 100.

• optimizer (torch.nn.optim.Optimizer) – Torch optimizer for training the network.

• train_dataloader (torch.utils.data.DataLoader, list[torch.utils.data.DataLoader]) – Train data loader(s) should provide a a signal x or a tuple of (x, y) signal/measurement pairs.

• losses (deepinv.loss.Loss, list[deepinv.loss.Loss]) – Loss or list of losses used for training the model. See the libraries’ training losses. By default, it uses the supervised mean squared error.

• eval_dataloader (None, torch.utils.data.DataLoader, list[torch.utils.data.DataLoader]) – Evaluation data loader(s) should provide a signal x or a tuple of (x, y) signal/measurement pairs.

• args – Other positional arguments to pass to Trainer constructor. See deepinv.Trainer().

• kwargs – Keyword arguments to pass to Trainer constructor. See deepinv.Trainer().

Returns:

Trained model.