FastSurferCNN.utils.metrics

class FastSurferCNN.utils.metrics.DiceScore(num_classes, device=None, output_transform=<function DiceScore.<lambda>>)

Accumulate the component of the dice coefficient i.e. the union and intersection.

Attributes

op	(callable) A callable to update accumulator. Method’s signature is (accumulator, output). For example, to compute arithmetic mean value, op = lambda a, x: a + x.
output_transform	(callable, optional) A callable that is used to transform the Engine’s process_function’s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs.
device	(str of torch.device, optional) Device specification in case of distributed computation usage. In most of the cases, it can be defined as “cuda:local_rank” or “cuda” if already set torch.cuda.set_device(local_rank). By default, if a distributed process group is initialized and available, device is set to cuda.

Methods

comput_dice_cnf()	Compute the dice cnf.
compute_dsc()	Compute the dice score.
reset()	Reset the union and intersection matrices to zero.
update(output, cnf_mat)	Update the intersection.

comput_dice_cnf()

Compute the dice cnf.

compute_dsc()

Compute the dice score.

Returns:

dscfloat

Dice score.

reset()

Reset the union and intersection matrices to zero.

update(output, cnf_mat)

Update the intersection.

Parameters:

outputTuple[Any, Any]

Network output tensor.

cnf_matbool

Confusion matrix.

FastSurferCNN.utils.metrics.dice_score(cm)

[MISSING].

FastSurferCNN.utils.metrics.iou_score(pred_cls, true_cls, nclass=79)

Compute the intersection-over-union score.

Both inputs should be categorical (as opposed to one-hot).

Parameters:

pred_clstorch.Tensor

Network prediction (categorical).

true_clstorch.Tensor

Ground truth (categorical).

nclassint

Number of classes (Default value = 79).

Returns:

np.ndarray

An array containing the intersection for each class.

np.ndarray

An array containing the union for each class.

FastSurferCNN.utils.metrics.precision_recall(pred_cls, true_cls, nclass=79)

Calculate recall (TP/(TP + FN) and precision (TP/(TP+FP) per class.

Parameters:

pred_clstorch.Tensor

Network prediction (categorical).

true_clstorch.Tensor

Ground truth (categorical).

nclassint

Number of classes (Default value = 79).

Returns:

np.ndarray

An array containing the number of true positives for each class.

np.ndarray

An array containing the sum of true positives and false negatives for each class.

np.ndarray

An array containing the sum of true positives and false positives for each class.