credit.trainers.trainer_downscaling

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

Contents