torchmetrics.aggregation¶
Torchmetrics comes with a number of metrics for aggregation of basic statistics: mean, max, min etc. of either tensors or native python floats.
CatMetric¶
- class torchmetrics.CatMetric(nan_strategy='warn', compute_on_step=None, **kwargs)[source]
Concatenate a stream of values.
- Parameters
nan_strategy¶ (
Union
[str
,float
]) – options: -'error'
: if any nan values are encounted will give a RuntimeError -'warn'
: if any nan values are encounted will give a warning and continue -'ignore'
: all nan values are silently removed - a float: if a float is provided will impude any nan values with this valuecompute_on_step¶ (
Optional
[bool
]) –Forward only calls
update()
and returns None if this is set to False.Deprecated since version v0.8: Argument has no use anymore and will be removed v0.9.
kwargs¶ (
Dict
[str
,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 torchmetrics import CatMetric >>> metric = CatMetric() >>> metric.update(1) >>> metric.update(torch.tensor([2, 3])) >>> metric.compute() tensor([1., 2., 3.])
Initializes internal Module state, shared by both nn.Module and ScriptModule.
MaxMetric¶
- class torchmetrics.MaxMetric(nan_strategy='warn', compute_on_step=None, **kwargs)[source]
Aggregate a stream of value into their maximum value.
- Parameters
nan_strategy¶ (
Union
[str
,float
]) – options: -'error'
: if any nan values are encounted will give a RuntimeError -'warn'
: if any nan values are encounted will give a warning and continue -'ignore'
: all nan values are silently removed - a float: if a float is provided will impude any nan values with this valuecompute_on_step¶ (
Optional
[bool
]) –Forward only calls
update()
and returns None if this is set to False.Deprecated since version v0.8: Argument has no use anymore and will be removed v0.9.
kwargs¶ (
Dict
[str
,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 torchmetrics import MaxMetric >>> metric = MaxMetric() >>> metric.update(1) >>> metric.update(torch.tensor([2, 3])) >>> metric.compute() tensor(3.)
Initializes internal Module state, shared by both nn.Module and ScriptModule.
MeanMetric¶
- class torchmetrics.MeanMetric(nan_strategy='warn', compute_on_step=None, **kwargs)[source]
Aggregate a stream of value into their mean value.
- Parameters
nan_strategy¶ (
Union
[str
,float
]) –- options:
'error'
: if any nan values are encounted will give a RuntimeError'warn'
: if any nan values are encounted will give a warning and continue'ignore'
: all nan values are silently removeda float: if a float is provided will impude any nan values with this value
- compute_on_step:
Forward only calls
update()
and returns None if this is set to False.Deprecated since version v0.8: Argument has no use anymore and will be removed v0.9.
kwargs: 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 torchmetrics import MeanMetric >>> metric = MeanMetric() >>> metric.update(1) >>> metric.update(torch.tensor([2, 3])) >>> metric.compute() tensor(2.)
Initializes internal Module state, shared by both nn.Module and ScriptModule.
- update(value, weight=1.0)[source]
Update state with data.
- Parameters
value¶ (
Union
[float
,Tensor
]) – Either a float or tensor containing data. Additional tensor dimensions will be flattenedweight¶ (
Union
[float
,Tensor
]) – Either a float or tensor containing weights for calculating the average. Shape of weight should be able to broadcast with the shape of value. Default to 1.0 corresponding to simple harmonic average.
- Return type
MinMetric¶
- class torchmetrics.MinMetric(nan_strategy='warn', compute_on_step=None, **kwargs)[source]
Aggregate a stream of value into their minimum value.
- Parameters
nan_strategy¶ (
Union
[str
,float
]) – options: -'error'
: if any nan values are encounted will give a RuntimeError -'warn'
: if any nan values are encounted will give a warning and continue -'ignore'
: all nan values are silently removed - a float: if a float is provided will impude any nan values with this valuecompute_on_step¶ (
Optional
[bool
]) –Forward only calls
update()
and returns None if this is set to False.Deprecated since version v0.8: Argument has no use anymore and will be removed v0.9.
kwargs¶ (
Dict
[str
,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 torchmetrics import MinMetric >>> metric = MinMetric() >>> metric.update(1) >>> metric.update(torch.tensor([2, 3])) >>> metric.compute() tensor(1.)
Initializes internal Module state, shared by both nn.Module and ScriptModule.
SumMetric¶
- class torchmetrics.SumMetric(nan_strategy='warn', compute_on_step=None, **kwargs)[source]
Aggregate a stream of value into their sum.
- Parameters
nan_strategy¶ (
Union
[str
,float
]) – options: -'error'
: if any nan values are encounted will give a RuntimeError -'warn'
: if any nan values are encounted will give a warning and continue -'ignore'
: all nan values are silently removed - a float: if a float is provided will impude any nan values with this valuecompute_on_step¶ (
Optional
[bool
]) –Forward only calls
update()
and returns None if this is set to False.Deprecated since version v0.8: Argument has no use anymore and will be removed v0.9.
kwargs¶ (
Dict
[str
,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 torchmetrics import SumMetric >>> metric = SumMetric() >>> metric.update(1) >>> metric.update(torch.tensor([2, 3])) >>> metric.compute() tensor(6.)
Initializes internal Module state, shared by both nn.Module and ScriptModule.