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 y 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, …). The logits dimension should be greater than 1. The data type must be float16 or float32.

  • labels (Tensor) - Tensor of shape (N, C, …). 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 shapes are different.

  • TypeError – If the type of inputs are not Tensor.

  • ValueError – If the dimension of logits or labels is less than 2.

  • ValueError – If the weight shape[0] is not equal to labels.shape[1].

  • ValueError – If weight is a tensor, but the 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], [0.3, 0.1], [0.9, 0.6]]), mstype.float32)
>>> labels = Tensor(np.array([[0, 1], [1, 0], [0, 1]]), mstype.float32)
>>> output = loss(logits, labels)
>>> print(output)
0.3283009