Sum
Module Interface
- class torchmetrics.aggregation.SumMetric(nan_strategy='warn', **kwargs)[source]
Aggregate a stream of value into their sum.
As input to
forward
andupdate
the metric accepts the following inputAs output of forward and compute the metric returns the following output
agg
(Tensor
): scalar float tensor with aggregated sum over all inputs received
- Parameters:
nan_strategy (
Union
[str
,float
]) – options: -'error'
: if any nan values are encountered will give a RuntimeError -'warn'
: if any nan values are encountered will give a warning and continue -'ignore'
: all nan values are silently removed - a float: if a float is provided will impute any nan values with this valuekwargs (
Any
) – Additional keyword arguments, see Advanced metric settings for more info.
- Raises:
ValueError – If
nan_strategy
is not one oferror
,warn
,ignore
or a float
Example
>>> from torch import tensor >>> from torchmetrics.aggregation import SumMetric >>> metric = SumMetric() >>> metric.update(1) >>> metric.update(tensor([2, 3])) >>> metric.compute() tensor(6.)
- plot(val=None, ax=None)[source]
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:
- Returns:
Figure and Axes object
- Raises:
ModuleNotFoundError – If matplotlib is not installed
>>> # Example plotting a single value >>> from torchmetrics.aggregation import SumMetric >>> metric = SumMetric() >>> metric.update([1, 2, 3]) >>> fig_, ax_ = metric.plot()
>>> # Example plotting multiple values >>> from torch import rand, randint >>> from torchmetrics.aggregation import SumMetric >>> metric = SumMetric() >>> values = [ ] >>> for i in range(10): ... values.append(metric([i, i+1])) >>> fig_, ax_ = metric.plot(values)