Trainer#

class deepinv.Trainer(model, physics, optimizer, train_dataloader, ...)[source]#

Bases: object

Trainer class for training a reconstruction network.

See also

See the User Guide for more details and for how to adapt the trainer to your needs.

Training can be done by calling the deepinv.Trainer.train() method, whereas testing can be done by calling the deepinv.Trainer.test() method.

Training details are saved every ckp_interval epochs in the following format

save_path/yyyy-mm-dd_hh-mm-ss/ckp_{epoch}.pth.tar

where .pth.tar file contains a dictionary with the keys: epoch current epoch, state_dict the state dictionary of the model, loss the loss history, optimizer the state dictionary of the optimizer, and eval_metrics the evaluation metrics history.

Use online_measurements=True when measurements are simulated online from a ground truth returned by the dataloader.
Use online_measurements=False when both the ground truth and measurements are returned by the dataloader (and also optionally physics generator params). This could be from a dataset generated using deepinv.datasets.generate_dataset.

Note

The training code can synchronize with Weights & Biases for logging and visualization by setting wandb_vis=True. The user can also customize the wandb setup by providing a dictionary with the setup for wandb.

Note

The losses and evaluation metrics can be chosen from the libraries’ training losses, or can be a custom loss function, as long as it takes as input (x, x_net, y, physics, model) and returns a scalar, where x is the ground reconstruction, x_net is the network reconstruction \(\inversef{y}{A}\), y is the measurement vector, physics is the forward operator and model is the reconstruction network. Note that not all inputs need to be used by the loss, e.g., self-supervised losses will not make use of x.

Warning

If a physics generator or a noise model is used to generate random params for online measurements, the generated measurements will randomly vary each epoch. If this is not desired (i.e. you want the same online measurements each epoch), set loop_random_online_physics=True. This resets the physics generator and noise model’s random generators every epoch.

Caveat: this requires shuffle=False in your dataloaders.

An alternative, safer solution is to generate and save params offline using deepinv.datasets.generate_dataset(). The params dict will then be automatically updated every time data is loaded.

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.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 or a tuple (x, y, params) where params is a dict of physics generator parameters to be loaded into the physics each iteration.
losses (deepinv.loss.Loss, list[deepinv.loss.Loss]) – Loss or list of losses used for training the model. Optionally wrap losses using a loss scheduler for more advanced training. See the libraries’ training losses. By default, it uses the supervised mean squared error. Where relevant, the underlying metric should have reduction=None as we perform the averaging using deepinv.utils.AverageMeter to deal with uneven batch sizes.
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 or a tuple (x, y, params) where params is a dict of physics generator parameters to be loaded into the physics each iteration.
scheduler (None, torch.optim.lr_scheduler.LRScheduler) – Torch scheduler for changing the learning rate across iterations.
online_measurements (bool) – Generate the measurements in an online manner at each iteration by calling physics(x). This results in a wider range of measurements if the physics’ parameters, such as parameters of the forward operator or noise realizations, can change between each sample; the measurements are loaded from the training dataset.
physics_generator (None, deepinv.physics.generator.PhysicsGenerator) – Optional physics generator for generating the physics operators. If not None, the physics operators are randomly sampled at each iteration using the generator. Should be used in conjunction with online_measurements=True, no effect when online_measurements=False. Also see loop_random_online_physics.
loop_random_online_physics (bool) – if True, resets the physics generator and noise model back to its initial state at the beginning of each epoch, so that the same measurements are generated each epoch. Requires shuffle=False in dataloaders. If False, generates new physics every epoch. Used in conjunction with physics_generator and online_measurements=True, no effect when online_measurements=False.
metrics (Metric, list[Metric]) – Metric or list of metrics used for evaluating the model. They should have reduction=None as we perform the averaging using deepinv.utils.AverageMeter to deal with uneven batch sizes. See the libraries’ evaluation metrics.
device (str) – Device on which to run the training (e.g., ‘cuda’ or ‘cpu’).
ckpt_pretrained (str) – path of the pretrained checkpoint. If None, no pretrained checkpoint is loaded.
save_path (str) – Directory in which to save the trained model.
compare_no_learning (bool) – If True, the no learning method is compared to the network reconstruction.
no_learning_method (str) – Reconstruction method used for the no learning comparison. Options are 'A_dagger', 'A_adjoint', 'prox_l2', or 'y'. Default is 'A_dagger'. The user can also provide a custom method by overriding the no_learning_inference method.
grad_clip (float) – Gradient clipping value for the optimizer. If None, no gradient clipping is performed.
check_grad (bool) – Compute and print the gradient norm at each iteration.
wandb_vis (bool) – Logs data onto Weights & Biases, see https://wandb.ai/ for more details.
wandb_setup (dict) – Dictionary with the setup for wandb, see https://docs.wandb.ai/quickstart for more details.
ckp_interval (int) – The model is saved every ckp_interval epochs.
eval_interval (int) – Number of epochs (or train iters, if log_train_batch=True) between each evaluation of the model on the evaluation set.
plot_interval (int) – Frequency of plotting images to wandb during train evaluation (at the end of each epoch). If 1, plots at each epoch.
freq_plot (int) – deprecated. Use plot_interval
plot_images (bool) – Plots reconstructions every ckp_interval epochs.
plot_measurements (bool) – Plot the measurements y, default=`True`.
plot_convergence_metrics (bool) – Plot convergence metrics for model, default=`False`.
rescale_mode (str) – Rescale mode for plotting images. Default is 'clip'.
display_losses_eval (bool) – If True, the losses are displayed during evaluation.
log_train_batch (bool) – if True, log train batch and eval-set metrics and losses for each train batch during training. This is useful for visualising train progress inside an epoch, not just over epochs. If False (default), log average over dataset per epoch (standard training).
verbose (bool) – Output training progress information in the console.
verbose_individual_losses (bool) – If True, the value of individual losses are printed during training. Otherwise, only the total loss is printed.
show_progress_bar (bool) – Show a progress bar during training.

check_clip_grad()[source]#: Check the gradient norm and perform gradient clipping if necessary.

compute_loss(physics, x, y, train=True, epoch=None)[source]#

Compute the loss and perform the backward pass.

It evaluates the reconstruction network, computes the losses, and performs the backward pass.

Parameters:

physics (deepinv.physics.Physics) – Current physics operator.
x (torch.Tensor) – Ground truth.
y (torch.Tensor) – Measurement.
train (bool) – If True, the model is trained, otherwise it is evaluated.
epoch (int) – current epoch.

Returns:

(tuple) The network reconstruction x_net (for plotting and computing metrics) and the logs (for printing the training progress).

compute_metrics(x, x_net, y, physics, logs, train=True, epoch=None)[source]#

Compute the metrics.

It computes the metrics over the batch.

Parameters:

x (torch.Tensor) – Ground truth.
x_net (torch.Tensor) – Network reconstruction.
y (torch.Tensor) – Measurement.
physics (deepinv.physics.Physics) – Current physics operator.
logs (dict) – Dictionary containing the logs for printing the training progress.
train (bool) – If True, the model is trained, otherwise it is evaluated.
epoch (int) – current epoch.

Returns:

The logs with the metrics.

get_samples(iterators, g)[source]#

Get the samples.

This function returns a dictionary containing necessary data for the model inference. It needs to contain the measurement, the ground truth, and the current physics operator, but can also contain additional data.

Parameters:

iterators (list) – List of dataloader iterators.
g (int) – Current dataloader index.

Returns:

the tuple returned by the get_samples_online or get_samples_offline function.

get_samples_offline(iterators, g)[source]#

Get the samples for the offline measurements.

In this setting, samples have been generated offline and are loaded from the dataloader. This function returns a tuple containing necessary data for the model inference. It needs to contain the measurement, the ground truth, and the current physics operator, but can also contain additional data (you can override this function to add custom data).

If the dataloader returns 3-tuples, this is assumed to be (x, y, params) where params is a dict of physics generator params. These params are then used to update the physics.

Parameters:

iterators (list) – List of dataloader iterators.
g (int) – Current dataloader index.

Returns:

a dictionary containing at least: the ground truth, the measurement, and the current physics operator.

get_samples_online(iterators, g)[source]#

Get the samples for the online measurements.

In this setting, a new sample is generated at each iteration by calling the physics operator. This function returns a dictionary containing necessary data for the model inference. It needs to contain the measurement, the ground truth, and the current physics operator, but can also contain additional data.

Parameters:

iterators (list) – List of dataloader iterators.
g (int) – Current dataloader index.

Returns:

a tuple containing at least: the ground truth, the measurement, and the current physics operator.

load_model(ckpt_pretrained=None)[source]#

Load model from checkpoint.

Parameters:: ckpt_pretrained (str) – checkpoint filename. If None, use checkpoint passed to class init. If not None, override checkpoint passed to class.
Returns:: if checkpoint loaded, return checkpoint dict, else return None
Return type:: dict

log_metrics_wandb(logs, step, train=True)[source]#

Log the metrics to wandb.

It logs the metrics to wandb.

Parameters:

logs (dict) – Dictionary containing the metrics to log.
step (int) – Current step to log. If Trainer.log_train_batch=True, this is the batch iteration, if False (default), this is the epoch.
train (bool) – If True, the model is trained, otherwise it is evaluated.

model_inference(y, physics, x=None, train=True, **kwargs)[source]#

Perform the model inference.

It returns the network reconstruction given the samples.

Parameters:

y (torch.Tensor) – Measurement.
physics (deepinv.physics.Physics) – Current physics operator.
x (torch.Tensor) – Optional ground truth, used for computing convergence metrics.

Returns:

The network reconstruction.

no_learning_inference(y, physics)[source]#

Perform the no learning inference.

By default it returns the (linear) pseudo-inverse reconstruction given the measurement.

Parameters:

y (torch.Tensor) – Measurement.
physics (deepinv.physics.Physics) – Current physics operator.

Returns:

Reconstructed image.

plot(epoch, physics, x, y, x_net, train=True)[source]#

Plot and optinally save the reconstructions.

Parameters:

epoch (int) – Current epoch.
physics (deepinv.physics.Physics) – Current physics operator.
x (torch.Tensor) – Ground truth.
y (torch.Tensor) – Measurement.
x_net (torch.Tensor) – Network reconstruction.
train (bool) – If True, the model is trained, otherwise it is evaluated.

reset_metrics()[source]#: Reset the metrics.

save_model(epoch, eval_metrics=None, state={})[source]#

Save the model.

It saves the model every ckp_interval epochs.

Parameters:

epoch (int) – Current epoch.
eval_metrics (None, float) – Evaluation metrics across epochs.
state (dict) – custom objects to save with model

setup_train(train=True, **kwargs)[source]#

Set up the training process.

It initializes the wandb logging, the different metrics, the save path, the physics and dataloaders, and the pretrained checkpoint if given.

Parameters:: train (bool) – whether model is being trained.

step(epoch, progress_bar, train_ite=None, train=True, last_batch=False)[source]#

Train/Eval a batch.

It performs the forward pass, the backward pass, and the evaluation at each iteration.

Parameters:

epoch (int) – Current epoch.
progress_bar – tqdm progress bar.
train_ite (int) – train iteration, only needed for logging if Trainer.log_train_batch=True
train (bool) – If True, the model is trained, otherwise it is evaluated.
last_batch (bool) – If True, the last batch of the epoch is being processed.

Returns:

The current physics operator, the ground truth, the measurement, and the network reconstruction.

test(test_dataloader, save_path=None, compare_no_learning=True, log_raw_metrics=False)[source]#

Test the model, compute metrics and plot images.

Parameters:

test_dataloader (torch.utils.data.DataLoader, list[torch.utils.data.DataLoader]) – Test data loader(s) should provide a a signal x or a tuple of (x, y) signal/measurement pairs.
save_path (str) – Directory in which to save the plotted images.
compare_no_learning (bool) – If True, the linear reconstruction is compared to the network reconstruction.
log_raw_metrics (bool) – if True, also return non-aggregated metrics as a list.

Returns:

dict of metrics results with means and stds.

Return type:

dict

train()[source]#

Train the model.

It performs the training process, including the setup, the evaluation, the forward and backward passes, and the visualization.

Returns:: The trained model.

Examples using `Trainer`:#

Imaging inverse problems with adversarial networks

Remote sensing with satellite images

Tour of MRI functionality in DeepInverse

Training a reconstruction network.

Patch priors for limited-angle computed tomography

Self-supervised MRI reconstruction with Artifact2Artifact

Image transformations for Equivariant Imaging

Self-supervised learning with Equivariant Imaging for MRI.

Self-supervised learning from incomplete measurements of multiple operators.

Self-supervised denoising with the Neighbor2Neighbor loss.

Self-supervised denoising with the Generalized R2R loss.

Self-supervised learning with measurement splitting

Self-supervised denoising with the SURE loss.

Self-supervised denoising with the UNSURE loss.

Deep Equilibrium (DEQ) algorithms for image deblurring

Learned Iterative Soft-Thresholding Algorithm (LISTA) for compressed sensing

Learned iterative custom prior

Learned Primal-Dual algorithm for CT scan.

Unfolded Chambolle-Pock for constrained image inpainting

Vanilla Unfolded algorithm for super-resolution

Trainer#

Examples using Trainer:#

This Page

Examples using `Trainer`:#