Metric Tracker¶
Module Interface¶
- class torchmetrics.MetricTracker(metric, maximize=True)[source]
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
- Example (single metric):
>>> from torchmetrics import Accuracy, MetricTracker >>> _ = torch.manual_seed(42) >>> tracker = MetricTracker(Accuracy(num_classes=10)) >>> 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)[source]
Returns the highest metric out of all tracked.
- Parameters
return_step¶ (
bool
) – IfTrue
will also return the step with the highest metric value.- Return type
Union
[None
,float
,Tuple
[int
,float
],Tuple
[None
,None
],Dict
[str
,Optional
[float
]],Tuple
[Dict
[str
,Optional
[int
]],Dict
[str
,Optional
[float
]]]]- Returns
The best metric value, and optionally the time-step.
- forward(*args, **kwargs)[source]
Calls forward of the current metric being tracked.
- Return type
- increment()[source]
Creates a new instance of the input metric that will be updated next.
- Return type