TorchMetricWrapper

class pytorch_forecasting.metrics.TorchMetricWrapper(torchmetric: torchmetrics.metric.Metric, reduction: Optional[str] = None, **kwargs)

Bases: pytorch_forecasting.metrics.Metric

Wrap a torchmetric to work with PyTorch Forecasting.

Does not support weighting of errors and only supports metrics for point predictions.

Parameters

• torchmetric (LightningMetric) – Torchmetric to wrap.

• reduction (str, optional) – use reduction with torchmetric directly. Defaults to None.

Methods

compute()	Abstract method that calcualtes metric
forward(y_pred, target, **kwargs)	Automatically calls update().
persistent([mode])	Method for post-init to change if metric states should be saved to its state_dict.
reset()	This method automatically resets the metric state variables to their default value.
update(y_pred, target, **kwargs)	Override this method to update the state variables of your metric class.

compute()

Abstract method that calcualtes metric

Should be overriden in derived classes

Parameters

• y_pred – network output

• y_actual – actual values

Returns

metric value on which backpropagation can be applied

Return type

torch.Tensor

forward(y_pred, target, **kwargs)

Automatically calls update().

Returns the metric value over inputs if compute_on_step is True.

persistent(mode: bool = False) → None

Method for post-init to change if metric states should be saved to its state_dict.

reset() → None

This method automatically resets the metric state variables to their default value.

update(y_pred: torch.Tensor, target: torch.Tensor, **kwargs) → torch.Tensor

Override this method to update the state variables of your metric class.

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convert_torchmetric_to_pytorch_forecasting_metric