# calculating loss 
        cen = self.centers.clone()
        feat = features.clone()
        x_2 = torch.pow(feat,2)
        c_2 = torch.pow(cen,2)
        x_2_s = torch.sum(x_2,1)
        x_2_s = x_2_s.view(self.num_batch,1)
        c_2_s = torch.sum(c_2,1)
        c_2_s = c_2_s.view(self.num_class,1)
        temp_c = torch.mm(labels.cuda(),c_2_s)
        
        x_2_s_e = x_2_s.repeat(1,self.num_class)
        c_2_s_e = c_2_s.t().repeat(self.num_batch,1)
#         c_2_s_e = temp_c
        xc = 2*torch.mm(feat,cen.t())   
        
        # we want only positive values, 
        dis = x_2_s_e + c_2_s_e - xc
        di = dis.type(torch.FloatTensor)
        di2 = torch.sqrt(torch.clamp(di, min=0))
        
        # since center loss focuses on intra distances, we are not concerened about the distance that we calculated from
        #other centers, we will use the other centers to increase inter loss.
        
        bl = labels.type(torch.ByteTensor)
        dii = torch.masked_select(di2,bl)
        center_loss = dii.sum()/self.num_batch
        with torch.no_grad():
            self.centers.copy_(centers_update)
        return center_loss


This is how I am calculating my loss value