About the LDAM Loss #13
Description
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