About the LDAM Loss

[sakumashirayuki]

Thanks for your code a lot!
I have read your paper and code, it's really a good idea, but here I have a question about LDAM Loss. It's in the last line where we call the basic cross_entropy function in pytorch.

    def forward(self, x, target):
        index = torch.zeros_like(x, dtype=torch.uint8)
        index.scatter_(1, target.data.view(-1, 1), 1)

        index_float = index.type(torch.cuda.FloatTensor)
        # self.m_list[None, :] add one dimension to the origin m_list
        batch_m = torch.matmul(self.m_list[None, :], index_float.transpose(0, 1))
        # equivalently transpose
        batch_m = batch_m.view((-1, 1))
        x_m = x - batch_m
        # only the target labelpostion is x_m
        output = torch.where(index, x_m, x)
        return F.cross_entropy(self.s * output, target, weight=self.weight)

why the output is multiplied by s(here is 30 times), just to make the loss greater? However, we didn't do this to the Focal loss

[lipingcoding]

the same question

[jinwon-samsung]

same question here as well

[xiangly55]

same question here

[tkasarla]

I'm not sure if anyone found an explanation to this but I also have the same question.