I use GeForce RTX 2080 with 7G memory, loss converges to 4.4, map only reaches 60.0%, rank1 only reaches 73.3%, batchsize is set to 8,learning rate is set to 0.000075, and the step size is 100. Because there was not enough memory during the test, only a part of it was loaded. Do you think it is the parameter setting or too little test load data? I adjusted it several times, but the effect was not satisfactory. Thank you ！

Excuse me. When I tested the computer memory was not enough, I changed the loader mode of the test data set to the random mode, and the computer reported an error: ValueError: not enough values to unpack (expected 6, got 5).Looking foward for your answer.Thank you very much!

对您的这篇论文非常感兴趣，想问您一下，您在定义layers1的时候 只用到了BatchedGraphSAGEDynamicRangeMean1 这个类，其他剩下的类您注释了BatchedGraphSAGEDynamicMean1，BatchedGraphSAGEMean1，BatchedGraphSAGEMean1Temporal，BatchedGAT_cat1 请问这几个类有用吗，还有想请问您一下 ， for i in range(int(N/p)):
idx_start = max(0, i-t)
idx_end = min(i+t+1, int(N/p))
tmp_x = x[:,idx_startp:idx_endp,] dis = NearestConvolution.cos_dis(tmp_x) 这个是计算特征之间的相似性 if i==0: tk = min(dis.shape[2], self.kn) #print(tk) _, idx = torch.topk(dis, tk, dim=2) 是包含前K个近邻的邻居集 k_nearest = torch.stack([torch.stack([tmp_x[j, idx[j, i]] for i in range(p*(idx_end-idx_start))], dim=0) for j in range(b)], dim=0) #(b, xp, kn, d) #print(k_nearest) k_nearest_list.append(k_nearest[:,p(i-idx_start):p*(i-idx_start+1),]) k_nearest = torch.cat(k_nearest_list, dim=1) #(b,N, kn, d) x_neib = k_nearest[:,:,1:,].contiguous() 我们将除节点 v i 以外的超边中的所有节点特征进行平均操作,作为超边的特征. x_neib = x_neib.mean(dim=2) h_k = torch.cat((self.W_x(x), self.W_neib(x_neib)), 2)

    h_k = F.normalize(h_k, dim=2, p=2)
    h_k = F.relu(h_k)
    #print(h_k.shape)
    if self.use_bn:
        #self.bn = nn.BatchNorm1d(h_k.size(1))
        h_k = self.bn(h_k.permute(0,2,1).contiguous())
        #print(h_k.shape)
        h_k = h_k.permute(0, 2, 1)
        #print(h_k.shape)

    return h_k

请问评估每个超边的重要性的代码 在哪里啊 谢谢您的回复

Hi! As shown in table1, the mutual information module improves the performance well. But I can't find the implementation of this module in your code. Since the general mutual information estimators can only get the lower bound of the real mutual information, I have no idea how do you minimize the mutual information between different parts. Do you use the methods like CLUB to model the upper bound of the mutual information? Looking forward to your reply, thx!

Hello, I want to check the performance of this method on my dataset. However, it only provides the way to train. May I ask how to evaluate with the trained model, and how to save the results? Thank you!

Hello, I want to save the results of ReID with the function "save_results". However, it seems that Mars folder doesn't include the two txt files needed. Could you tell me how to create them? Thank you!

Hello, an error occured when I ran "sh run_hypergraphsage_part.sh". It seems to be caused by the version of cudatoolkit. I'm using python3.6.13, torch0.4.0 and cuda9.0. Could you share your environment? Thank you!