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Thanks for your contribution!

If you're sending a large PR (e.g., >50 lines), please open an issue first about the feature / bug, and indicate how you want to contribute. See more at https://detectron2.readthedocs.io/notes/contributing.html#pull-requests about how we handle PRs.

Before submitting a PR, please run dev/linter.sh to lint the code.

Hi @timy90022 ， Thanks for sharing your excellent work. When I try to understand the implementation of Eq(6), I find a bit difference between the implementation and the Eq(6) in the paper.

 def exclude_func_and_ratio(self):
        # instance-level weight
        bg_ind = self.n_c
        weight = (self.gt_classes != bg_ind)

        gt_classes    = self.gt_classes[weight]
        # exclude_ratio = \mu_{f_j}}
        exclude_ratio = torch.mean((self.freq_info[gt_classes] < self.lambda_).float())
        # weight = E(r)
        weight = weight.float().view(self.n_i, 1).expand(self.n_i, self.n_c)

        return weight, exclude_ratio


    def threshold_func(self):
        # class-level weight
        weight = self.pred_class_logits.new_zeros(self.n_c)
        # weight = T_{lambda}(f_j)
        weight[self.freq_info < self.lambda_] = 1
        weight = weight.view(1, self.n_c).expand(self.n_i, self.n_c)

        # fg = E(r)
        # ratio = \mu_{f_j}
        fg, ratio = self.exclude_func_and_ratio()
        # bg =  1 - E(r)
        bg = 1 - fg
        # random = (1-E(r)) * rand
        random = torch.rand_like(bg) * bg

        random = torch.where(random>ratio, torch.ones_like(random), torch.zeros_like(random))
        # weight = { [ (1-E(r)) * rand > \mu_{f_j} ? 1 : 0 ] + E(r) } * T_{\lambda}(f_j)
        weight = (random + fg) * weight

        return weight


    def drop_loss(self):

        self.n_i, self.n_c = self.pred_class_logits.size()

        def expand_label(pred, gt_classes):
            target = pred.new_zeros(self.n_i, self.n_c + 1)
            target[torch.arange(self.n_i), gt_classes] = 1
            return target[:, :self.n_c]

        target = expand_label(self.pred_class_logits, self.gt_classes)
        # drop_w =  1 - { [ (1-E(r)) * rand > \mu_{f_j} ? 1 : 0 ] + E(r) } * T_{\lambda}(f_j) * (1-y_j)
        # When E(r) = 1, drop_w = 1 - T_{\lambda}(f_j) * (1-y_j)
        # When E(r) = 0, drop_w = 1 - { [ (1-E(r)) * rand > \mu_{f_j} ? 1 : 0 ] } * T_{\lambda}(f_j) * (1-y_j) ????
                        # when rand > \mu_{f_j}, drop_w = 1 - T_{\lambda}(f_j) * (1-y_j)
                        # when rand <= \mu_{f_j}, drop_w = 1
        self.drop_w = 1 - self.threshold_func() * (1 - target)

        self.cls_loss = F.binary_cross_entropy_with_logits(self.pred_class_logits, target,
                                                      reduction='none')
        return torch.sum(self.cls_loss * self.drop_w) / self.n_i

# When E(r) = 0, drop_w = 1 - [ rand > \mu_{f_j} ? 1 : 0 ] *  T_{\lambda}(f_j) * (1-y_j) 
# when rand > \mu_{f_j}, drop_w = 1 - T_{\lambda}(f_j) * (1-y_j)???????
# when rand <= \mu_{f_j}, drop_w = 1

The implementation is inconsistent with the otherwise condition in Eq(6) .

drop_w = 1 - T_{\lambda}(f_j) * (1-y_j)

Could you please explain that? Is there any misunderstanding about the code?