hypergraphconv attentiion gradient = 0

[wuyiyang66]

I use pyg.nn.hypergraphConv to build a GCN model. Hypergraph attention is also used. When checking gradients, I found zero gradient of parameter gcn.att. I wonder why it happened.

Here is my code:

class HypGraph(nn.Module):
def init(self, dim, gcn_hidden, gcn_out):
super(HypGraph, self).init()
self.dim = dim
self.gcn_hidden = gcn_hidden
self.gcn_out = gcn_out
# self.embedding = nn.Embedding(29, self.dim, max_norm=True)
self.gcn1 = gnn.HypergraphConv(
self.dim,
self.gcn_hidden,
use_attention=True,
heads=4,
concat=True,
dropout=0.1,
)
self.gcn2 = gnn.HypergraphConv(
4 * self.gcn_hidden,
self.gcn_out,
use_attention=False,
heads=1,
concat=False,
dropout=0.1,
)

def forward(self, gcn_in, ve):
    ef_list = []
    for idx in range(torch.max(ve[1, :]) + 1):
        ef_list.append(torch.mean(gcn_in[ve[0, :][ve[1, :] == idx]], dim=0))
    ef = torch.stack(ef_list)

    # ef = self.embedding(torch.arange(29).to(torch.device("cuda")))

    gcn_out = self.gcn1(x=gcn_in, hyperedge_index=ve, hyperedge_attr=ef)
    gcn_out = self.gcn2(x=gcn_out, hyperedge_index=ve, hyperedge_attr=ef)
    # gcn_out = gcn_in + gcn_out
    return gcn_out