Classwise Wrapper

Module Interface

class torchmetrics.ClasswiseWrapper(metric, labels=None)

Wrapper metric for altering the output of classification metrics.

This metric works together with classification metrics that returns multiple values (one value per class) such that label information can be automatically included in the output.

Parameters

• metric (Metric) – base metric that should be wrapped. It is assumed that the metric outputs a single tensor that is split along the first dimension.

• labels (Optional[List[str]]) – list of strings indicating the different classes.

Example

>>> import torch
>>> _ = torch.manual_seed(42)
>>> from torchmetrics.wrappers import ClasswiseWrapper
>>> from torchmetrics.classification import MulticlassAccuracy
>>> metric = ClasswiseWrapper(MulticlassAccuracy(num_classes=3, average=None))
>>> preds = torch.randn(10, 3).softmax(dim=-1)
>>> target = torch.randint(3, (10,))
>>> metric(preds, target)  
{'multiclassaccuracy_0': tensor(0.5000),
'multiclassaccuracy_1': tensor(0.7500),
'multiclassaccuracy_2': tensor(0.)}

Example (labels as list of strings):

>>> from torchmetrics.wrappers import ClasswiseWrapper
>>> from torchmetrics.classification import MulticlassAccuracy
>>> metric = ClasswiseWrapper(
...    MulticlassAccuracy(num_classes=3, average=None),
...    labels=["horse", "fish", "dog"]
... )
>>> preds = torch.randn(10, 3).softmax(dim=-1)
>>> target = torch.randint(3, (10,))
>>> metric(preds, target)  
{'multiclassaccuracy_horse': tensor(0.3333),
'multiclassaccuracy_fish': tensor(0.6667),
'multiclassaccuracy_dog': tensor(0.)}

Example (in metric collection):

>>> from torchmetrics import MetricCollection
>>> from torchmetrics.wrappers import ClasswiseWrapper
>>> from torchmetrics.classification import MulticlassAccuracy, MulticlassRecall
>>> labels = ["horse", "fish", "dog"]
>>> metric = MetricCollection(
...     {'multiclassaccuracy': ClasswiseWrapper(MulticlassAccuracy(num_classes=3, average=None), labels),
...     'multiclassrecall': ClasswiseWrapper(MulticlassRecall(num_classes=3, average=None), labels)}
... )
>>> preds = torch.randn(10, 3).softmax(dim=-1)
>>> target = torch.randint(3, (10,))
>>> metric(preds, target)  
{'multiclassaccuracy_horse': tensor(0.),
 'multiclassaccuracy_fish': tensor(0.3333),
 'multiclassaccuracy_dog': tensor(0.4000),
 'multiclassrecall_horse': tensor(0.),
 'multiclassrecall_fish': tensor(0.3333),
 'multiclassrecall_dog': tensor(0.4000)}

Initializes internal Module state, shared by both nn.Module and ScriptModule.

compute()

Compute metric.

Return type

Dict[str, Tensor]

forward(*args, **kwargs)

Calculate on batch and accumulate to global state.

Return type

Any

plot(val=None, ax=None)

Plot a single or multiple values from the metric.

Parameters

• val (Union[Tensor, Sequence[Tensor], None]) – Either a single result from calling metric.forward or metric.compute or a list of these results. If no value is provided, will automatically call metric.compute and plot that result.

• ax (Optional[Axes]) – An matplotlib axis object. If provided will add plot to that axis

Return type

Tuple[Figure, Union[Axes, ndarray]]

Returns

Figure and Axes object

Raises

ModuleNotFoundError – If matplotlib is not installed

>>> # Example plotting a single value
>>> import torch
>>> from torchmetrics.wrappers import ClasswiseWrapper
>>> from torchmetrics.classification import MulticlassAccuracy
>>> metric = ClasswiseWrapper(MulticlassAccuracy(num_classes=3, average=None))
>>> metric.update(torch.randint(3, (20,)), torch.randint(3, (20,)))
>>> fig_, ax_ = metric.plot()

(Source code, png, hires.png, pdf)

>>> # Example plotting multiple values
>>> import torch
>>> from torchmetrics.wrappers import ClasswiseWrapper
>>> from torchmetrics.classification import MulticlassAccuracy
>>> metric = ClasswiseWrapper(MulticlassAccuracy(num_classes=3, average=None))
>>> values = [ ]
>>> for _ in range(3):
...     values.append(metric(torch.randint(3, (20,)), torch.randint(3, (20,))))
>>> fig_, ax_ = metric.plot(values)

(Source code, png, hires.png, pdf)

reset()

Reset metric.

Return type

None

update(*args, **kwargs)

Update state.

Return type

None