Metric Tracker

Module Interface

class torchmetrics.MetricTracker(metric, maximize=True)

A wrapper class that can help keeping track of a metric or metric collection over time and implement useful methods. The wrapper implements the standard .update(), .compute(), .reset() methods that just calls corresponding method of the currently tracked metric. However, the following additional methods are provided:

-MetricTracker.n_steps: number of metrics being tracked -MetricTracker.increment(): initialize a new metric for being tracked -MetricTracker.compute_all(): get the metric value for all steps -MetricTracker.best_metric(): returns the best value

Parameters

• metric (Union[Metric, MetricCollection]) – instance of a torchmetrics.Metric or torchmetrics.MetricCollection to keep track of at each timestep.

• maximize (Union[bool, List[bool]]) – either single bool or list of bool indicating if higher metric values are better (True) or lower is better (False).

Example (single metric):

>>> from torchmetrics import MetricTracker
>>> from torchmetrics.classification import MulticlassAccuracy
>>> _ = torch.manual_seed(42)
>>> tracker = MetricTracker(MulticlassAccuracy(num_classes=10, average='micro'))
>>> for epoch in range(5):
...     tracker.increment()
...     for batch_idx in range(5):
...         preds, target = torch.randint(10, (100,)), torch.randint(10, (100,))
...         tracker.update(preds, target)
...     print(f"current acc={tracker.compute()}")
current acc=0.1120000034570694
current acc=0.08799999952316284
current acc=0.12600000202655792
current acc=0.07999999821186066
current acc=0.10199999809265137
>>> best_acc, which_epoch = tracker.best_metric(return_step=True)
>>> best_acc  
0.1260...
>>> which_epoch
2
>>> tracker.compute_all()
tensor([0.1120, 0.0880, 0.1260, 0.0800, 0.1020])

Example (multiple metrics using MetricCollection):

>>> from torchmetrics import MetricTracker, MetricCollection, MeanSquaredError, ExplainedVariance
>>> _ = torch.manual_seed(42)
>>> tracker = MetricTracker(MetricCollection([MeanSquaredError(), ExplainedVariance()]), maximize=[False, True])
>>> for epoch in range(5):
...     tracker.increment()
...     for batch_idx in range(5):
...         preds, target = torch.randn(100), torch.randn(100)
...         tracker.update(preds, target)
...     print(f"current stats={tracker.compute()}")  
current stats={'MeanSquaredError': tensor(1.8218), 'ExplainedVariance': tensor(-0.8969)}
current stats={'MeanSquaredError': tensor(2.0268), 'ExplainedVariance': tensor(-1.0206)}
current stats={'MeanSquaredError': tensor(1.9491), 'ExplainedVariance': tensor(-0.8298)}
current stats={'MeanSquaredError': tensor(1.9800), 'ExplainedVariance': tensor(-0.9199)}
current stats={'MeanSquaredError': tensor(2.2481), 'ExplainedVariance': tensor(-1.1622)}
>>> from pprint import pprint
>>> best_res, which_epoch = tracker.best_metric(return_step=True)
>>> pprint(best_res)  
{'ExplainedVariance': -0.829...,
 'MeanSquaredError': 1.821...}
>>> which_epoch
{'MeanSquaredError': 0, 'ExplainedVariance': 2}
>>> pprint(tracker.compute_all())
{'ExplainedVariance': tensor([-0.8969, -1.0206, -0.8298, -0.9199, -1.1622]),
 'MeanSquaredError': tensor([1.8218, 2.0268, 1.9491, 1.9800, 2.2481])}

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

best_metric(return_step=False)

Returns the highest metric out of all tracked.

Parameters

return_step (bool) – If True will also return the step with the highest metric value.

Return type

Union[None, float, Tuple[float, int], Tuple[None, None], Dict[str, Optional[float]], Tuple[Dict[str, Optional[float]], Dict[str, Optional[int]]]]

Returns

Either a single value or a tuple, depends on the value of return_step and the object being tracked.

• If a single metric is being tracked and return_step=False then a single tensor will be returned

• If a single metric is being tracked and return_step=True then a 2-element tuple will be returned, where the first value is optimal value and second value is the corresponding optimal step

• If a metric collection is being tracked and return_step=False then a single dict will be returned, where keys correspond to the different values of the collection and the values are the optimal metric value

• If a metric collection is being bracked and return_step=True then a 2-element tuple will be returned where each is a dict, with keys corresponding to the different values of th collection and the values of the first dict being the optimal values and the values of the second dict being the optimal step

In addtion the value in all cases may be None if the underlying metric does have a proper defined way of being optimal.

compute_all()

Compute the metric value for all tracked metrics.

Return type

Union[Tensor, Dict[str, Tensor]]

Returns

Either a single tensor if the tracked base object is a single metric, else if a metric collection is provide a dict of tensors will be returned

Raises

ValueError – If self.increment have not been called before this method is called.

forward(*args, **kwargs)

Calls forward of the current metric being tracked.

Return type

None

increment()

Creates a new instance of the input metric that will be updated next.

Return type

None

reset()

Resets the current metric being tracked.

Return type

None

reset_all()

Resets all metrics being tracked.

Return type

None

property n_steps: int

Returns the number of times the tracker has been incremented.

Return type

int