What are the reduce_ratios and y in your VIG code?

[leoozy]

Thank you very much for your released code for VIG. I spent some time on reading your code carefully. But I am confused about some parts of code, could you please give some hints? @iamhankai

1. reduce_ratios = [4, 2, 1, 1] I am confused about the reduced_ratios. I read from the paper that the sizes of each stages is 1/2 of their precedure stages. So what the reduce ratios are used for?

2. in the code:

   class MRConv2d(nn.Module):
   """
   Max-Relative Graph Convolution (Paper: https://arxiv.org/abs/1904.03751) for dense data type
   """
   def __init__(self, in_channels, out_channels, act='relu', norm=None, bias=True):
       super(MRConv2d, self).__init__()
       self.nn = BasicConv([in_channels*2, out_channels], act, norm, bias)
   
   def forward(self, x, edge_index, y=None):
       x_i = batched_index_select(x, edge_index[1])
       if y is not None:
           x_j = batched_index_select(y, edge_index[0])
       else:
           x_j = batched_index_select(x, edge_index[0])
       x_j, _ = torch.max(x_j - x_i, -1, keepdim=True)
       b, c, n, _ = x.shape
       x = torch.cat([x.unsqueeze(2), x_j.unsqueeze(2)], dim=2).reshape(b, 2 * c, n, _)
       return self.nn(x) 

   What the y stands for? I am really confused about the y as I found y is calculated from x based on the reduce_ratios. But I can not relate y or reduce_ratios to formulars in your VIG or Deep GCN. Could you please give me some advices? Thank you!

[iamhankai]

Thanks for the attention. Here we reduce the number of nodes by reduce_ratio so as to reduce the computational cost of distance calculation.

[leoozy]

Thanks for the attention. Here we reduce the number of nodes by reduce_ratio so as to reduce the computational cost of distance calculation. Thank you for your rapid reply. I also found that your dilation is always set to 1 in your code: min(idx // 4 + 1, max_dilation) . Does this because you have reduced the nodes by pooling based on reduce_ration? Thank you very much for your precious time!

[iamhankai]

Dilation is not always 1 since idx is the layer index in min(idx // 4 + 1, max_dilation).

[PanXiebit]

hi @iamhankai I am confused with this line x_j, _ = torch.max(x_j - x_i, -1, keepdim=True) in MRConv code,

x_j=[b, c, n, k] is the selected node by computing the distance, after this line x_j=[b, c, n, 1]. It seems that the MRConv only consider one node with the largest distance?

[iamhankai]

@PanXiebit The torch.max is applied at element-wise level, not the vector-wise.

[PanXiebit]

@iamhankai Thanks, I understand!