clinicadl.callbacks.ModelCheckpointCallback

class clinicadl.callbacks.ModelCheckpointCallback(metric: str | None = None, epochs: Sequence[int] | None = None, save_last: bool = True)

To save checkpoints of the neural network weights at different points of the training.

Checkpoints can be saved after specified epochs and/or according to a monitored metric. In the latter case, only the best model according to this metric will be saved. The neural network weights after the last epoch can also be saved.

Parameters:

• metric (Optional[str], default=None) – A metric to monitor.

• epochs (Optional[Sequence[int]], default=None) –

  The list of epochs after which the neural network weights should be saved.

  Important

  Epochs are indexed from 1.

• save_last (bool, default=True) – Whether to save the neural network weights after the last epoch.

Examples

from clinicadl.callbacks import ModelCheckpointCallback
from clinicadl.train import Trainer
from clinicadl.metrics.config import MSEMetricConfig, LossMetricConfig

trainer = Trainer(
    metrics={"loss": LossMetricConfig(), "mse": MSEMetricConfig()},
    callbacks=[
        ModelCheckpointCallback(
            metric="mse", epochs=range(1, 100, step=10), save_last=True
        )
    ],
    ...
)

See also

clinicadl.callbacks.TrainingCheckpointCallback

on_train_start(*, maps: Maps, state: TrainerState, metrics: MetricsHandler, **kwargs) → None

Called once at the beginning of Trainer.train if resume=False.

If resuming a training, on_resume() will be called instead.

Parameters:

• model (Model) – The model associated to the Trainer.

• maps (Maps) – The MAPS associated to the Trainer.

• state (TrainerState) – The current state of the Trainer.

• split (Split) – The clinicadl.split.Split on which training is performed.

• optimizers (dict[str, torch.optim.Optimizer]) – The current torch.optim.Optimizer, as returned by by Model.backward_step.

• grad_scaler (torch.amp.GradScaler) – The torch.amp.GradScaler used to scale gradients.

• optimization (OptimizationConfig) – The optimization specifications of the training phase.

• metrics (MetricsHandler) – The validation metrics to compute.

• callbacks (CallbacksHandler) – The callbacks passed to the Trainer.

• computational (ComputationalConfig) – The clinicadl.train.ComputationalConfig defining the computational specifications of the training phase.

on_resume(*, maps: Maps, state: TrainerState, metrics: MetricsHandler, **kwargs) → None

Called once when Trainer.train is resuming a training.

More precisely, this method will be called just before loading the checkpoints.

Parameters:

• model (Model) – The model associated to the Trainer.

• maps (Maps) – The MAPS associated to the Trainer.

• state (TrainerState) – The current state of the Trainer.

• split (Split) – The clinicadl.split.Split on which training is performed.

• optimizers (dict[str, torch.optim.Optimizer]) – The current torch.optim.Optimizer, as returned by by Model.backward_step.

• grad_scaler (torch.amp.GradScaler) – The torch.amp.GradScaler used to scale gradients.

• optimization (OptimizationConfig) – The optimization specifications of the training phase.

• metrics (MetricsHandler) – The validation metrics to compute.

• callbacks (CallbacksHandler) – The callbacks passed to the Trainer.

• computational (ComputationalConfig) – The clinicadl.train.ComputationalConfig defining the computational specifications of the training phase.

on_validation_end(*, model: Model, maps: Maps, state: TrainerState, metrics: MetricsHandler, **kwargs) → None

Called at the end of every validation loop in Trainer.train.

Not to be confused with on_validate_end().

Parameters:

• model (Model) – The model associated to the Trainer.

• maps (Maps) – The MAPS associated to the Trainer.

• state (TrainerState) – The current state of the Trainer.

• metrics (MetricsHandler) – The validation metrics computed.

on_epoch_end(*, model: Model, maps: Maps, state: TrainerState, **kwargs) → None

Called at the end of an epoch in Trainer.train.

Parameters:

• model (Model) – The model associated to the Trainer.

• maps (Maps) – The MAPS associated to the Trainer.

• state (TrainerState) – The current state of the Trainer.

on_train_end(*, model: Model, maps: Maps, state: TrainerState, **kwargs) → None

Called once at the end of Trainer.train.

Parameters:

• model (Model) – The model associated to the Trainer.

• maps (Maps) – The MAPS associated to the Trainer.

• state (TrainerState) – The current state of the Trainer.

state_dict() → Mapping[str, Any]

To get a checkpoint of the current state of the callback.

Returns:

Mapping[str, Any] – The current state in a dict.

load_state_dict(state_dict: Mapping[str, Any]) → None

Sets to callbacks to a given state.

Parameters:

state_dict (Mapping[str, Any]) – The desired state of the Callback, as returned by state_dict().