Question about ImgWtLossSoftNLL

[Seyoung9304]

Hello! Thank you for the great work.

I have two questions at loss.py - ImgWtLossSoftNLL.

1.

As far as I understand, from line 182 to 188, we are calculating loss for each image, iterating as much as the batch size. However, when calling the custom_nll function, weights does not give in batch units, but delivers the whole as a factor. I think it's right to fix it with weights[i], can you check if I'm right?

# As-is
for i in range(0, inputs.shape[0]):
            if not self.batch_weights:
                class_weights = self.calculate_weights(target_cpu[i])
            loss = loss + self.custom_nll(inputs[i].unsqueeze(0),
                                          target[i].unsqueeze(0),
                                          class_weights=torch.Tensor(class_weights).cuda(),
                                          border_weights=weights, mask=ignore_mask[i])

# To-be
for i in range(0, inputs.shape[0]):
            if not self.batch_weights:
                class_weights = self.calculate_weights(target_cpu[i])
            loss = loss + self.custom_nll(inputs[i].unsqueeze(0),
                                          target[i].unsqueeze(0),
                                          class_weights=torch.Tensor(class_weights).cuda(),
                                          border_weights=weights[i], mask=ignore_mask[i])

2.

At line 192 of loss.py, I think loss should be devided by batch size (i.e. inputs.shape[0]). In the case of CrossEntropyLoss2d, this criterion is returning the batch average of loss.

# As-is
return loss

# To-be
return loss / inputs.shape[0]

Thank you in advance. :)