why ms1mv3 datasets use arcface.gtlink360 and wf42m use magrin loss???

[Sunshine066]

class CombinedMarginLoss(torch.nn.Module): def init(self, s, m1, m2, m3, interclass_filtering_threshold=0): super().init() self.s = s self.m1 = m1 self.m2 = m2 self.m3 = m3 self.interclass_filtering_threshold = interclass_filtering_threshold

    # For ArcFace
    self.cos_m = math.cos(self.m2)
    self.sin_m = math.sin(self.m2)
    self.theta = math.cos(math.pi - self.m2)
    self.sinmm = math.sin(math.pi - self.m2) * self.m2
    self.easy_margin = False


def forward(self, logits, labels):
    index_positive = torch.where(labels != -1)[0]

    if self.interclass_filtering_threshold > 0:
        with torch.no_grad():
            dirty = logits > self.interclass_filtering_threshold
            dirty = dirty.float()
            mask = torch.ones([index_positive.size(0), logits.size(1)], device=logits.device)
            mask.scatter_(1, labels[index_positive], 0)
            dirty[index_positive] *= mask
            tensor_mul = 1 - dirty    
        logits = tensor_mul * logits

    target_logit = logits[index_positive, labels[index_positive].view(-1)]


    if self.m1 == 1.0 and self.m3 == 0.0:
        with torch.no_grad():
            target_logit.arccos_()
            logits.arccos_()
            final_target_logit = target_logit + self.m2
            logits[index_positive, labels[index_positive].view(-1)] = final_target_logit
            logits.cos_()
        logits = logits * self.s        

    elif self.m3 > 0:
        final_target_logit = target_logit - self.m3
        logits[index_positive, labels[index_positive].view(-1)] = final_target_logit
        logits = logits * self.s
    else:
        raise

    return logits

in so many configs, only ms1mv3_**** , config.margin_list = (1.0, 0.5, 0.0) is arcface, others are config.margin_list = (1.0, 0.0, 0.4) is magrin loss??? Is the arcface not suitable for large datasets??