Metrics¶
FROC¶
- monai.metrics.compute_froc_score(fps_per_image, total_sensitivity, eval_thresholds=(0.25, 0.5, 1, 2, 4, 8))[source]¶
This function is modified from the official evaluation code of CAMELYON 16 Challenge, and used to compute the challenge’s second evaluation metric, which is defined as the average sensitivity at the predefined false positive rates per whole slide image.
- Parameters
fps_per_image (
ndarray
) – the average number of false positives per image for different thresholds.total_sensitivity (
ndarray
) – sensitivities (true positive rates) for different thresholds.eval_thresholds (
Tuple
) – the false positive rates for calculating the average sensitivity. Defaults to (0.25, 0.5, 1, 2, 4, 8) which is the same as the CAMELYON 16 Challenge.
Metric¶
IterationMetric¶
- class monai.metrics.IterationMetric[source]¶
Base class for metrics computation at the iteration level, that is, on a min-batch of samples usually using the model outcome of one iteration.
__call__ is designed to handle y_pred and y (optional) in torch tensors or a list/tuple of tensors.
Subclasses typically implement the _compute_tensor function for the actual tensor computation logic.
Cumulative¶
- class monai.metrics.Cumulative[source]¶
Utility class for the typical cumulative computation process based on PyTorch Tensors. It provides interfaces to accumulate values in the local buffers, synchronize buffers across distributed nodes, and aggregate the buffered values.
In multi-processing, PyTorch programs usually distribute data to multiple nodes. Each node runs with a subset of the data, adds values to its local buffers. Calling get_buffer could gather all the results and aggregate can further handle the results to generate the final outcomes.
Users can implement their own aggregate method to handle the results, using get_buffer to get the buffered contents.
Note: the data list should have the same length every time calling add() in a round, it will automatically create buffers according to the length of data list.
Typically, this class is expected to execute the following steps:
from monai.metrics import Cumulative c = Cumulative() c.append(1) # adds a value c.extend([2, 3]) # adds a batch of values c.extend([4, 5, 6]) # adds a batch of values print(c.get_buffer()) # tensor([1, 2, 3, 4, 5, 6]) print(len(c)) # 6 c.reset() print(len(c)) # 0
The following is an example of maintaining two internal buffers:
from monai.metrics import Cumulative c = Cumulative() c.append(1, 2) # adds a value to two buffers respectively c.extend([3, 4], [5, 6]) # adds batches of values print(c.get_buffer()) # [tensor([1, 3, 4]), tensor([2, 5, 6])] print(len(c))
The following is an example of extending with variable length data:
import torch from monai.metrics import Cumulative c = Cumulative() c.extend(torch.zeros((8, 2)), torch.zeros((6, 2))) # adds batches c.append(torch.zeros((2, ))) # adds a value print(c.get_buffer()) # [torch.zeros((9, 2)), torch.zeros((6, 2))] print(len(c))
Initialize the internal buffers. self._buffers are local buffers, they are not usually used directly. self._sync_buffers are the buffers with all the results across all the nodes.
- __init__()[source]¶
Initialize the internal buffers. self._buffers are local buffers, they are not usually used directly. self._sync_buffers are the buffers with all the results across all the nodes.
- abstract aggregate(*args, **kwargs)[source]¶
Aggregate final results based on the gathered buffers. This method is expected to use get_buffer to gather the local buffer contents.
- append(*data)[source]¶
Add samples to the local cumulative buffers. A buffer will be allocated for each data item. Compared with self.extend, this method adds a single sample (instead of a “batch”) to the local buffers.
- Parameters
data – each item will be converted into a torch tensor. they will be stacked at the 0-th dim with a new dimension when get_buffer() is called.
- Return type
None
- extend(*data)[source]¶
Extend the local buffers with new (“batch-first”) data. A buffer will be allocated for each data item. Compared with self.append, this method adds a “batch” of data to the local buffers.
- Parameters
data – each item can be a “batch-first” tensor or a list of “channel-first” tensors. they will be concatenated at the 0-th dimension when get_buffer() is called.
- Return type
None
CumulativeIterationMetric¶
- class monai.metrics.CumulativeIterationMetric[source]¶
Base class of cumulative metric which collects metrics on each mini-batch data at the iteration level.
Typically, it computes some intermediate results for each iteration, adds them to the buffers, then the buffer contents could be gathered and aggregated for the final result when epoch completed.
For example, MeanDice inherits this class and the usage is as follows:
dice_metric = DiceMetric(include_background=True, reduction="mean") for val_data in val_loader: val_outputs = model(val_data["img"]) val_outputs = [postprocessing_transform(i) for i in decollate_batch(val_outputs)] # compute metric for current iteration dice_metric(y_pred=val_outputs, y=val_data["seg"]) # callable to add metric to the buffer # aggregate the final mean dice result metric = dice_metric.aggregate().item() # reset the status for next computation round dice_metric.reset()
And to load predictions and labels from files, then compute metrics with multi-processing, please refer to: https://github.com/Project-MONAI/tutorials/blob/master/modules/compute_metric.py.
Initialize the internal buffers. self._buffers are local buffers, they are not usually used directly. self._sync_buffers are the buffers with all the results across all the nodes.
Mean Dice¶
- monai.metrics.compute_meandice(y_pred, y, include_background=True)[source]¶
Computes Dice score metric from full size Tensor and collects average.
- Parameters
y_pred (
Tensor
) – input data to compute, typical segmentation model output. It must be one-hot format and first dim is batch, example shape: [16, 3, 32, 32]. The values should be binarized.y (
Tensor
) – ground truth to compute mean dice metric. It must be one-hot format and first dim is batch. The values should be binarized.include_background (
bool
) – whether to skip Dice computation on the first channel of the predicted output. Defaults to True.
- Return type
Tensor
- Returns
Dice scores per batch and per class, (shape [batch_size, num_classes]).
- Raises
ValueError – when y_pred and y have different shapes.
- class monai.metrics.DiceMetric(include_background=True, reduction=MetricReduction.MEAN, get_not_nans=False)[source]¶
Compute average Dice loss between two tensors. It can support both multi-classes and multi-labels tasks. Input y_pred is compared with ground truth y. y_preds is expected to have binarized predictions and y should be in one-hot format. You can use suitable transforms in
monai.transforms.post
first to achieve binarized values. The include_background parameter can be set toFalse
for an instance of DiceLoss to exclude the first category (channel index 0) which is by convention assumed to be background. If the non-background segmentations are small compared to the total image size they can get overwhelmed by the signal from the background so excluding it in such cases helps convergence. y_preds and y can be a list of channel-first Tensor (CHW[D]) or a batch-first Tensor (BCHW[D]).- Parameters
include_background (
bool
) – whether to skip Dice computation on the first channel of the predicted output. Defaults toTrue
.reduction (
Union
[MetricReduction
,str
]) – {"none"
,"mean"
,"sum"
,"mean_batch"
,"sum_batch"
,"mean_channel"
,"sum_channel"
} Define the mode to reduce computation result. Defaults to"mean"
.get_not_nans (
bool
) – whether to return the not_nans count, if True, aggregate() returns (metric, not_nans). Here not_nans count the number of not nans for the metric, thus its shape equals to the shape of the metric.
Initialize the internal buffers. self._buffers are local buffers, they are not usually used directly. self._sync_buffers are the buffers with all the results across all the nodes.
Area under the ROC curve¶
- monai.metrics.compute_roc_auc(y_pred, y, average=Average.MACRO)[source]¶
Computes Area Under the Receiver Operating Characteristic Curve (ROC AUC). Referring to: sklearn.metrics.roc_auc_score.
- Parameters
y_pred (
Tensor
) – input data to compute, typical classification model output. the first dim must be batch, if multi-classes, it must be in One-Hot format. for example: shape [16] or [16, 1] for a binary data, shape [16, 2] for 2 classes data.y (
Tensor
) – ground truth to compute ROC AUC metric, the first dim must be batch. if multi-classes, it must be in One-Hot format. for example: shape [16] or [16, 1] for a binary data, shape [16, 2] for 2 classes data.average (
Union
[Average
,str
]) –{
"macro"
,"weighted"
,"micro"
,"none"
} Type of averaging performed if not binary classification. Defaults to"macro"
."macro"
: calculate metrics for each label, and find their unweighted mean.This does not take label imbalance into account.
"weighted"
: calculate metrics for each label, and find their average,weighted by support (the number of true instances for each label).
"micro"
: calculate metrics globally by considering each element of the labelindicator matrix as a label.
"none"
: the scores for each class are returned.
- Raises
ValueError – When
y_pred
dimension is not one of [1, 2].ValueError – When
y
dimension is not one of [1, 2].ValueError – When
average
is not one of [“macro”, “weighted”, “micro”, “none”].
Note
ROCAUC expects y to be comprised of 0’s and 1’s. y_pred must be either prob. estimates or confidence values.
- class monai.metrics.ROCAUCMetric(average=Average.MACRO)[source]¶
Computes Area Under the Receiver Operating Characteristic Curve (ROC AUC). Referring to: sklearn.metrics.roc_auc_score. The input y_pred and y can be a list of channel-first Tensor or a batch-first Tensor.
- Parameters
average (
Union
[Average
,str
]) –{
"macro"
,"weighted"
,"micro"
,"none"
} Type of averaging performed if not binary classification. Defaults to"macro"
."macro"
: calculate metrics for each label, and find their unweighted mean.This does not take label imbalance into account.
"weighted"
: calculate metrics for each label, and find their average,weighted by support (the number of true instances for each label).
"micro"
: calculate metrics globally by considering each element of the labelindicator matrix as a label.
"none"
: the scores for each class are returned.
Initialize the internal buffers. self._buffers are local buffers, they are not usually used directly. self._sync_buffers are the buffers with all the results across all the nodes.
Confusion matrix¶
- monai.metrics.get_confusion_matrix(y_pred, y, include_background=True)[source]¶
Compute confusion matrix. A tensor with the shape [BC4] will be returned. Where, the third dimension represents the number of true positive, false positive, true negative and false negative values for each channel of each sample within the input batch. Where, B equals to the batch size and C equals to the number of classes that need to be computed.
- Parameters
y_pred (
Tensor
) – input data to compute. It must be one-hot format and first dim is batch. The values should be binarized.y (
Tensor
) – ground truth to compute the metric. It must be one-hot format and first dim is batch. The values should be binarized.include_background (
bool
) – whether to skip metric computation on the first channel of the predicted output. Defaults to True.
- Raises
ValueError – when y_pred and y have different shapes.
- class monai.metrics.ConfusionMatrixMetric(include_background=True, metric_name='hit_rate', compute_sample=False, reduction=MetricReduction.MEAN, get_not_nans=False)[source]¶
Compute confusion matrix related metrics. This function supports to calculate all metrics mentioned in: Confusion matrix. It can support both multi-classes and multi-labels classification and segmentation tasks. y_preds is expected to have binarized predictions and y should be in one-hot format. You can use suitable transforms in
monai.transforms.post
first to achieve binarized values. The include_background parameter can be set toFalse
for an instance to exclude the first category (channel index 0) which is by convention assumed to be background. If the non-background segmentations are small compared to the total image size they can get overwhelmed by the signal from the background so excluding it in such cases helps convergence.- Parameters
include_background (
bool
) – whether to skip metric computation on the first channel of the predicted output. Defaults to True.metric_name (
Union
[Sequence
[str
],str
]) – ["sensitivity"
,"specificity"
,"precision"
,"negative predictive value"
,"miss rate"
,"fall out"
,"false discovery rate"
,"false omission rate"
,"prevalence threshold"
,"threat score"
,"accuracy"
,"balanced accuracy"
,"f1 score"
,"matthews correlation coefficient"
,"fowlkes mallows index"
,"informedness"
,"markedness"
] Some of the metrics have multiple aliases (as shown in the wikipedia page aforementioned), and you can also input those names instead. Except for input only one metric, multiple metrics are also supported via input a sequence of metric names, such as (“sensitivity”, “precision”, “recall”), ifcompute_sample
isTrue
, multiplef
andnot_nans
will be returned with the same order as input names when calling the class.compute_sample (
bool
) – when reducing, ifTrue
, each sample’s metric will be computed based on each confusion matrix first. ifFalse
, compute reduction on the confusion matrices first, defaults toFalse
.reduction (
Union
[MetricReduction
,str
]) – {"none"
,"mean"
,"sum"
,"mean_batch"
,"sum_batch"
,"mean_channel"
,"sum_channel"
}get_not_nans (
bool
) – whether to return the not_nans count, if True, aggregate() returns [(metric, not_nans), …]. If False, aggregate() returns [metric, …]. Here not_nans count the number of not nans for True Positive, False Positive, True Negative and False Negative. Its shape depends on the shape of the metric, and it has one more dimension with size 4. For example, if the shape of the metric is [3, 3], not_nans has the shape [3, 3, 4].
Initialize the internal buffers. self._buffers are local buffers, they are not usually used directly. self._sync_buffers are the buffers with all the results across all the nodes.
Hausdorff distance¶
- monai.metrics.compute_hausdorff_distance(y_pred, y, include_background=False, distance_metric='euclidean', percentile=None, directed=False)[source]¶
Compute the Hausdorff distance.
- Parameters
y_pred (
Union
[ndarray
,Tensor
]) – input data to compute, typical segmentation model output. It must be one-hot format and first dim is batch, example shape: [16, 3, 32, 32]. The values should be binarized.y (
Union
[ndarray
,Tensor
]) – ground truth to compute mean the distance. It must be one-hot format and first dim is batch. The values should be binarized.include_background (
bool
) – whether to skip distance computation on the first channel of the predicted output. Defaults toFalse
.distance_metric (
str
) – : ["euclidean"
,"chessboard"
,"taxicab"
] the metric used to compute surface distance. Defaults to"euclidean"
.percentile (
Optional
[float
]) – an optional float number between 0 and 100. If specified, the corresponding percentile of the Hausdorff Distance rather than the maximum result will be achieved. Defaults toNone
.directed (
bool
) – whether to calculate directed Hausdorff distance. Defaults toFalse
.
- class monai.metrics.HausdorffDistanceMetric(include_background=False, distance_metric='euclidean', percentile=None, directed=False, reduction=MetricReduction.MEAN, get_not_nans=False)[source]¶
Compute Hausdorff Distance between two tensors. It can support both multi-classes and multi-labels tasks. It supports both directed and non-directed Hausdorff distance calculation. In addition, specify the percentile parameter can get the percentile of the distance. Input y_pred is compared with ground truth y. y_preds is expected to have binarized predictions and y should be in one-hot format. You can use suitable transforms in
monai.transforms.post
first to achieve binarized values. y_preds and y can be a list of channel-first Tensor (CHW[D]) or a batch-first Tensor (BCHW[D]). The implementation refers to DeepMind’s implementation.- Parameters
include_background (
bool
) – whether to include distance computation on the first channel of the predicted output. Defaults toFalse
.distance_metric (
str
) – : ["euclidean"
,"chessboard"
,"taxicab"
] the metric used to compute surface distance. Defaults to"euclidean"
.percentile (
Optional
[float
]) – an optional float number between 0 and 100. If specified, the corresponding percentile of the Hausdorff Distance rather than the maximum result will be achieved. Defaults toNone
.directed (
bool
) – whether to calculate directed Hausdorff distance. Defaults toFalse
.reduction (
Union
[MetricReduction
,str
]) – {"none"
,"mean"
,"sum"
,"mean_batch"
,"sum_batch"
,"mean_channel"
,"sum_channel"
} Define the mode to reduce computation result. Defaults to"mean"
.get_not_nans (
bool
) – whether to return the not_nans count, if True, aggregate() returns (metric, not_nans). Here not_nans count the number of not nans for the metric, thus its shape equals to the shape of the metric.
Initialize the internal buffers. self._buffers are local buffers, they are not usually used directly. self._sync_buffers are the buffers with all the results across all the nodes.
Average surface distance¶
- monai.metrics.compute_average_surface_distance(y_pred, y, include_background=False, symmetric=False, distance_metric='euclidean')[source]¶
This function is used to compute the Average Surface Distance from y_pred to y under the default setting. In addition, if sets
symmetric = True
, the average symmetric surface distance between these two inputs will be returned. The implementation refers to DeepMind’s implementation.- Parameters
y_pred (
Union
[ndarray
,Tensor
]) – input data to compute, typical segmentation model output. It must be one-hot format and first dim is batch, example shape: [16, 3, 32, 32]. The values should be binarized.y (
Union
[ndarray
,Tensor
]) – ground truth to compute mean the distance. It must be one-hot format and first dim is batch. The values should be binarized.include_background (
bool
) – whether to skip distance computation on the first channel of the predicted output. Defaults toFalse
.symmetric (
bool
) – whether to calculate the symmetric average surface distance between seg_pred and seg_gt. Defaults toFalse
.distance_metric (
str
) – : ["euclidean"
,"chessboard"
,"taxicab"
] the metric used to compute surface distance. Defaults to"euclidean"
.
- class monai.metrics.SurfaceDistanceMetric(include_background=False, symmetric=False, distance_metric='euclidean', reduction=MetricReduction.MEAN, get_not_nans=False)[source]¶
Compute Surface Distance between two tensors. It can support both multi-classes and multi-labels tasks. It supports both symmetric and asymmetric surface distance calculation. Input y_pred is compared with ground truth y. y_preds is expected to have binarized predictions and y should be in one-hot format. You can use suitable transforms in
monai.transforms.post
first to achieve binarized values. y_preds and y can be a list of channel-first Tensor (CHW[D]) or a batch-first Tensor (BCHW[D]).- Parameters
include_background (
bool
) – whether to skip distance computation on the first channel of the predicted output. Defaults toFalse
.symmetric (
bool
) – whether to calculate the symmetric average surface distance between seg_pred and seg_gt. Defaults toFalse
.distance_metric (
str
) – : ["euclidean"
,"chessboard"
,"taxicab"
] the metric used to compute surface distance. Defaults to"euclidean"
.reduction (
Union
[MetricReduction
,str
]) – {"none"
,"mean"
,"sum"
,"mean_batch"
,"sum_batch"
,"mean_channel"
,"sum_channel"
} Define the mode to reduce computation result. Defaults to"mean"
.get_not_nans (
bool
) – whether to return the not_nans count, if True, aggregate() returns (metric, not_nans). Here not_nans count the number of not nans for the metric, thus its shape equals to the shape of the metric.
Initialize the internal buffers. self._buffers are local buffers, they are not usually used directly. self._sync_buffers are the buffers with all the results across all the nodes.
Mean squared error¶
- class monai.metrics.MSEMetric(reduction=MetricReduction.MEAN, get_not_nans=False)[source]¶
Compute Mean Squared Error between two tensors using function:
\[\operatorname {MSE}\left(Y, \hat{Y}\right) =\frac {1}{n}\sum _{i=1}^{n}\left(y_i-\hat{y_i} \right)^{2}.\]More info: https://en.wikipedia.org/wiki/Mean_squared_error
Input y_pred is compared with ground truth y. Both y_pred and y are expected to be real-valued, where y_pred is output from a regression model.
- Parameters
reduction (
Union
[MetricReduction
,str
]) – {"none"
,"mean"
,"sum"
,"mean_batch"
,"sum_batch"
,"mean_channel"
,"sum_channel"
} Define the mode to reduce computation result of 1 batch data. Defaults to"mean"
.get_not_nans (
bool
) – whether to return the not_nans count, if True, aggregate() returns (metric, not_nans).
Initialize the internal buffers. self._buffers are local buffers, they are not usually used directly. self._sync_buffers are the buffers with all the results across all the nodes.
Mean absolute error¶
- class monai.metrics.MAEMetric(reduction=MetricReduction.MEAN, get_not_nans=False)[source]¶
Compute Mean Absolute Error between two tensors using function:
\[\operatorname {MAE}\left(Y, \hat{Y}\right) =\frac {1}{n}\sum _{i=1}^{n}\left|y_i-\hat{y_i}\right|.\]More info: https://en.wikipedia.org/wiki/Mean_absolute_error
Input y_pred is compared with ground truth y. Both y_pred and y are expected to be real-valued, where y_pred is output from a regression model.
- Parameters
reduction (
Union
[MetricReduction
,str
]) – {"none"
,"mean"
,"sum"
,"mean_batch"
,"sum_batch"
,"mean_channel"
,"sum_channel"
} Define the mode to reduce computation result of 1 batch data. Defaults to"mean"
.get_not_nans (
bool
) – whether to return the not_nans count, if True, aggregate() returns (metric, not_nans).
Initialize the internal buffers. self._buffers are local buffers, they are not usually used directly. self._sync_buffers are the buffers with all the results across all the nodes.
Root mean squared error¶
- class monai.metrics.RMSEMetric(reduction=MetricReduction.MEAN, get_not_nans=False)[source]¶
Compute Root Mean Squared Error between two tensors using function:
\[\operatorname {RMSE}\left(Y, \hat{Y}\right) ={ \sqrt{ \frac {1}{n}\sum _{i=1}^{n}\left(y_i-\hat{y_i}\right)^2 } } \ = \sqrt {\operatorname{MSE}\left(Y, \hat{Y}\right)}.\]More info: https://en.wikipedia.org/wiki/Root-mean-square_deviation
Input y_pred is compared with ground truth y. Both y_pred and y are expected to be real-valued, where y_pred is output from a regression model.
- Parameters
reduction (
Union
[MetricReduction
,str
]) – {"none"
,"mean"
,"sum"
,"mean_batch"
,"sum_batch"
,"mean_channel"
,"sum_channel"
} Define the mode to reduce computation result of 1 batch data. Defaults to"mean"
.get_not_nans (
bool
) – whether to return the not_nans count, if True, aggregate() returns (metric, not_nans).
Initialize the internal buffers. self._buffers are local buffers, they are not usually used directly. self._sync_buffers are the buffers with all the results across all the nodes.
Peak signal to noise ratio¶
- class monai.metrics.PSNRMetric(max_val, reduction=MetricReduction.MEAN, get_not_nans=False)[source]¶
Compute Peak Signal To Noise Ratio between two tensors using function:
\[\operatorname{PSNR}\left(Y, \hat{Y}\right) = 20 \cdot \log_{10} \left({\mathit{MAX}}_Y\right) \ -10 \cdot \log_{10}\left(\operatorname{MSE\left(Y, \hat{Y}\right)}\right)\]More info: https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio
Help taken from: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/ops/image_ops_impl.py line 4139
Input y_pred is compared with ground truth y. Both y_pred and y are expected to be real-valued, where y_pred is output from a regression model.
- Parameters
max_val (
Union
[int
,float
]) – The dynamic range of the images/volumes (i.e., the difference between the maximum and the minimum allowed values e.g. 255 for a uint8 image).reduction (
Union
[MetricReduction
,str
]) – {"none"
,"mean"
,"sum"
,"mean_batch"
,"sum_batch"
,"mean_channel"
,"sum_channel"
} Define the mode to reduce computation result of 1 batch data. Defaults to"mean"
.get_not_nans (
bool
) – whether to return the not_nans count, if True, aggregate() returns (metric, not_nans).
Initialize the internal buffers. self._buffers are local buffers, they are not usually used directly. self._sync_buffers are the buffers with all the results across all the nodes.
Cumulative average¶
- class monai.metrics.CumulativeAverage[source]¶
Cumulatively record data value and aggregate for the average value. It supports single class or multi-class data, for example, value can be 0.44 (a loss value) or [0.3, 0.4] (metrics of two classes). It also supports distributed data parallel, sync data when aggregating. For example, recording loss values and compute the overall average value in every 5 iterations:
average = CumulativeAverage() for i, d in enumerate(dataloader): loss = ... average.append(loss) if i % 5 == 0: print(f"cumulative average of loss: {average.aggregate()}") average.reset()
Initialize the internal buffers. self._buffers are local buffers, they are not usually used directly. self._sync_buffers are the buffers with all the results across all the nodes.