Label problem in using different function: SoftTargetCrossEntropy()

[LouiValley]

I try to use SoftTargetCrossEntropy as loss function.

class SoftTargetCrossEntropy(nn.Module):

    def __init__(self):
        super(SoftTargetCrossEntropy, self).__init__()

    def forward(self, x: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
        loss = torch.sum(-target * F.log_softmax(x, dim=-1), dim=-1)
        return loss.mean()

It needs label(target) and model's output(x). For x, its dimension is [Batch, Class], But for the target, its shape is [Batch]. There will be dimensional problem. How can I deal with this?