credit.trainers.trainer_downscaling

Attributes

logger

Classes

Trainer

Helper class that provides a standard way to create an ABC using

Module Contents

credit.trainers.trainer_downscaling.logger

class credit.trainers.trainer_downscaling.Trainer(model: torch.nn.Module, rank: int)

Bases: credit.trainers.base_trainer.BaseTrainer

Helper class that provides a standard way to create an ABC using inheritance.

setup(conf)

train_one_epoch(epoch, conf, trainloader, optimizer, criterion, scaler, scheduler, metrics)

Train the model for one epoch.

Parameters:

• epoch (int) – The current epoch number.

• conf (Dict[str, Any]) – The configuration dictionary.

• trainloader (torch.utils.data.DataLoader) – The training data loader.

• optimizer (torch.optim.Optimizer) – The optimizer.

• criterion (torch.nn.Module) – The loss function.

• scaler (torch.cuda.amp.GradScaler) – The gradient scaler for mixed precision training.

• scheduler (torch.optim.lr_scheduler.LRScheduler) – The learning rate scheduler.

• metrics (Dict[str, Any]) – The metrics to track during training.

Returns:

A dictionary containing the training results.

Return type:

Dict[str, float]

validate(epoch, conf, valid_loader, criterion, metrics)

Validates the model on the validation dataset.

Parameters:

• epoch (int) – Current epoch number.

• conf (dict) – Configuration dictionary containing validation settings.

• valid_loader (DataLoader) – DataLoader for the validation dataset.

• criterion (callable) – Loss function used for validation.

• metrics (callable) – Function to compute metrics for evaluation.

Returns:

Dictionary containing validation metrics and loss for the epoch.

Return type:

dict