mindspore.nn.MultiClassDiceLoss

class mindspore.nn.MultiClassDiceLoss(weights=None, ignore_indiex=None, activation='softmax')[source]

When there are multiple classifications, label is transformed into multiple binary classifications by one hot. For each channel section in the channel, it can be regarded as a binary classification problem, so it can be obtained through the binary loss of each category, and then the average value.

Parameters

weights (Union[Tensor, None]) – Tensor of shape \((num\_classes, dim)\). The weight shape[0] should be equal to labels shape[1].
ignore_indiex (Union[int, None]) – Class index to ignore.
activation (Union[str, Cell]) – Activate function applied to the output of the fully connected layer, eg. ‘ReLU’. Default: ‘softmax’. Choose from: [‘softmax’, ‘logsoftmax’, ‘relu’, ‘relu6’, ‘tanh’,’Sigmoid’]

Inputs:

logits (Tensor) - Tensor of shape \((N, C, *)\) where \(*\) means, any number of additional dimensions. The logits dimension should be greater than 1. The data type must be float16 or float32.
labels (Tensor) - Tensor of shape \((N, C, *)\), same shape as the logits. The labels dimension should be greater than 1. The data type must be loat16 or float32.

Outputs:

Tensor, a tensor of shape with the per-example sampled MultiClass Dice Losses.

Raises

ValueError – If the shape of logits is different from labels.
TypeError – If the type of logits or labels is not a Tensor.
ValueError – If the dimension of logits or labels is less than 2.
ValueError – If the weights.shape[0] is not equal to labels.shape[1].
ValueError – If weights is a tensor, but its dimension is not 2.

Supported Platforms:: Ascend GPU

Examples

>>> loss = nn.MultiClassDiceLoss(weights=None, ignore_indiex=None, activation="softmax")
>>> logits = Tensor(np.array([[0.2, 0.5, 0.7], [0.3, 0.1, 0.5], [0.9, 0.6, 0.3]]), mstype.float32)
>>> labels = Tensor(np.array([[0, 1, 0], [1, 0, 0], [0, 0, 1]]), mstype.float32)
>>> output = loss(logits, labels)
>>> print(output)
0.54958105