c109b0066d2a0292ac6040154898cda7b595d20a,recognition/partial_fc/pytorch/partial_classifier.py,DistSampleClassifier,sample,#DistSampleClassifier#,53

Before Change

                torch.randperm(self.num_local, out=self.perm)
                start = self.num_local - self.num_sample
                index = torch.cat((positive, self.perm[start:]))
                index = torch.unique(index, sorted=False)
                start = index.size()[0] - self.num_sample
                index = index[start:]
            else:
                index = positive

            index = torch.sort(index)[0].long()
            self.index = index
            total_label[P] = torch.searchsorted(index, total_label[P])
            self.sub_weight = Parameter(self.weight[index])

After Change

            if self.num_sample - positive.size(0) >= 0:
                perm = torch.rand(self.num_local,device=cfg.local_rank)
                perm[positive] = 2.0
                index = torch.topk(perm,k=self.num_sample)[1]
                index = index.sort()[0]
            else:
                index = positive

Instances