Dice¶
Module Interface¶
- class torchmetrics.Dice(zero_division=0, num_classes=None, threshold=0.5, average='micro', mdmc_average='global', ignore_index=None, top_k=None, multiclass=None, **kwargs)[source]
Compute Dice.
Where and represent the number of true positives and false positives respecitively.
It is recommend set ignore_index to index of background class.
The reduction method (how the precision scores are aggregated) is controlled by the
average
parameter, and additionally by themdmc_average
parameter in the multi-dimensional multi-class case.As input to
forward
andupdate
the metric accepts the following input:preds
(Tensor
): Predictions from model (probabilities, logits or labels)target
(Tensor
): Ground truth values
As output to
forward
andcompute
the metric returns the following output:dice
(Tensor
): A tensor containing the dice score.If
average in ['micro', 'macro', 'weighted', 'samples']
, a one-element tensor will be returnedIf
average in ['none', None]
, the shape will be(C,)
, whereC
stands for the number of classes
- Parameters
num_classes¶ – Number of classes. Necessary for
'macro'
, andNone
average methods.threshold¶ – Threshold for transforming probability or logit predictions to binary (0,1) predictions, in the case of binary or multi-label inputs. Default value of 0.5 corresponds to input being probabilities.
zero_division¶ – The value to use for the score if denominator equals zero.
average¶ –
Defines the reduction that is applied. Should be one of the following:
'micro'
[default]: Calculate the metric globally, across all samples and classes.'macro'
: Calculate the metric for each class separately, and average the metrics across classes (with equal weights for each class).'weighted'
: Calculate the metric for each class separately, and average the metrics across classes, weighting each class by its support (tp + fn
).'none'
orNone
: Calculate the metric for each class separately, and return the metric for every class.'samples'
: Calculate the metric for each sample, and average the metrics across samples (with equal weights for each sample).
Note
What is considered a sample in the multi-dimensional multi-class case depends on the value of
mdmc_average
.mdmc_average¶ –
Defines how averaging is done for multi-dimensional multi-class inputs (on top of the
average
parameter). Should be one of the following:None
[default]: Should be left unchanged if your data is not multi-dimensional multi-class.'samplewise'
: In this case, the statistics are computed separately for each sample on theN
axis, and then averaged over samples. The computation for each sample is done by treating the flattened extra axes...
as theN
dimension within the sample, and computing the metric for the sample based on that.'global'
: In this case theN
and...
dimensions of the inputs are flattened into a newN_X
sample axis, i.e. the inputs are treated as if they were(N_X, C)
. From here on theaverage
parameter applies as usual.
ignore_index¶ – Integer specifying a target class to ignore. If given, this class index does not contribute to the returned score, regardless of reduction method. If an index is ignored, and
average=None
or'none'
, the score for the ignored class will be returned asnan
.top_k¶ – Number of the highest probability or logit score predictions considered finding the correct label, relevant only for (multi-dimensional) multi-class inputs. The default value (
None
) will be interpreted as 1 for these inputs. Should be left at default (None
) for all other types of inputs.multiclass¶ – Used only in certain special cases, where you want to treat inputs as a different type than what they appear to be.
kwargs¶ – Additional keyword arguments, see Advanced metric settings for more info.
- Raises
ValueError – If
average
is none of"micro"
,"macro"
,"samples"
,"none"
,None
.ValueError – If
mdmc_average
is not one ofNone
,"samplewise"
,"global"
.ValueError – If
average
is set butnum_classes
is not provided.ValueError – If
num_classes
is set andignore_index
is not in the range[0, num_classes)
.
Example
>>> from torch import tensor >>> from torchmetrics.classification import Dice >>> preds = tensor([2, 0, 2, 1]) >>> target = tensor([1, 1, 2, 0]) >>> dice = Dice(average='micro') >>> dice(preds, target) tensor(0.2500)
Initializes internal Module state, shared by both nn.Module and ScriptModule.
- 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 object and Axes object
- Raises
ModuleNotFoundError – If matplotlib is not installed
>>> # Example plotting a single value >>> from torch import randint >>> from torchmetrics.classification import Dice >>> metric = Dice() >>> metric.update(randint(2,(10,)), randint(2,(10,))) >>> fig_, ax_ = metric.plot()
(
Source code
,png
,hires.png
,pdf
)>>> # Example plotting multiple values >>> from torch import randint >>> from torchmetrics.classification import Dice >>> metric = Dice() >>> values = [ ] >>> for _ in range(10): ... values.append(metric(randint(2,(10,)), randint(2,(10,)))) >>> fig_, ax_ = metric.plot(values)
(
Source code
,png
,hires.png
,pdf
)
Functional Interface¶
- torchmetrics.functional.dice(preds, target, zero_division=0, average='micro', mdmc_average='global', threshold=0.5, top_k=None, num_classes=None, multiclass=None, ignore_index=None)[source]
Compute Dice.
Where and represent the number of true positives and false negatives respecitively.
It is recommend set ignore_index to index of background class.
The reduction method (how the recall scores are aggregated) is controlled by the
average
parameter, and additionally by themdmc_average
parameter in the multi-dimensional multi-class case.- Parameters
preds¶ (
Tensor
) – Predictions from model (probabilities, logits or labels)zero_division¶ (
int
) – The value to use for the score if denominator equals zeroDefines the reduction that is applied. Should be one of the following:
'micro'
[default]: Calculate the metric globally, across all samples and classes.'macro'
: Calculate the metric for each class separately, and average the metrics across classes (with equal weights for each class).'weighted'
: Calculate the metric for each class separately, and average the metrics across classes, weighting each class by its support (tp + fn
).'none'
orNone
: Calculate the metric for each class separately, and return the metric for every class.'samples'
: Calculate the metric for each sample, and average the metrics across samples (with equal weights for each sample).
Note
What is considered a sample in the multi-dimensional multi-class case depends on the value of
mdmc_average
.Note
If
'none'
and a given class doesn’t occur in thepreds
ortarget
, the value for the class will benan
.mdmc_average¶ (
Optional
[str
]) –Defines how averaging is done for multi-dimensional multi-class inputs (on top of the
average
parameter). Should be one of the following:None
[default]: Should be left unchanged if your data is not multi-dimensional multi-class.'samplewise'
: In this case, the statistics are computed separately for each sample on theN
axis, and then averaged over samples. The computation for each sample is done by treating the flattened extra axes...
as theN
dimension within the sample, and computing the metric for the sample based on that.'global'
: In this case theN
and...
dimensions of the inputs are flattened into a newN_X
sample axis, i.e. the inputs are treated as if they were(N_X, C)
. From here on theaverage
parameter applies as usual.
ignore_index¶ (
Optional
[int
]) – Integer specifying a target class to ignore. If given, this class index does not contribute to the returned score, regardless of reduction method. If an index is ignored, andaverage=None
or'none'
, the score for the ignored class will be returned asnan
.num_classes¶ (
Optional
[int
]) – Number of classes. Necessary for'macro'
,'weighted'
andNone
average methods.threshold¶ (
float
) – Threshold for transforming probability or logit predictions to binary (0,1) predictions, in the case of binary or multi-label inputs. Default value of 0.5 corresponds to input being probabilities.Number of the highest probability or logit score predictions considered finding the correct label, relevant only for (multi-dimensional) multi-class inputs. The default value (
None
) will be interpreted as 1 for these inputs.Should be left at default (
None
) for all other types of inputs.multiclass¶ (
Optional
[bool
]) – Used only in certain special cases, where you want to treat inputs as a different type than what they appear to be.
- Return type
- Returns
The shape of the returned tensor depends on the
average
parameterIf
average in ['micro', 'macro', 'weighted', 'samples']
, a one-element tensor will be returnedIf
average in ['none', None]
, the shape will be(C,)
, whereC
stands for the number of classes
- Raises
ValueError – If
average
is not one of"micro"
,"macro"
,"weighted"
,"samples"
,"none"
orNone
ValueError – If
mdmc_average
is not one ofNone
,"samplewise"
,"global"
.ValueError – If
average
is set butnum_classes
is not provided.ValueError – If
num_classes
is set andignore_index
is not in the range[0, num_classes)
.
Example
>>> from torchmetrics.functional.classification import dice >>> preds = torch.tensor([2, 0, 2, 1]) >>> target = torch.tensor([1, 1, 2, 0]) >>> dice(preds, target, average='micro') tensor(0.2500)