Using NetworkX Graph as Input about overlapping-community-detection HOT 6 CLOSED

loader = nocd.data.load_dataset('data/mag_cs.npz')
A, X, Z_gt = loader['A'], loader['X'], loader['Z']
N, K = Z_gt.shape

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-8-3315ab7333dc> in <module>
     11             gnn.eval()
     12             # Compute validation loss
---> 13             Z = F.relu(gnn(x_norm, adj_norm))
     14             val_loss = decoder.loss_full(Z, A)
     15             print(f'Epoch {epoch:4d}, loss.full = {val_loss:.4f}, nmi = {get_nmi():.2f}')

~/opt/anaconda3/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
    725             result = self._slow_forward(*input, **kwargs)
    726         else:
--> 727             result = self.forward(*input, **kwargs)
    728         for hook in itertools.chain(
    729                 _global_forward_hooks.values(),

~/github/Spatial/Spectral Clustering/overlapping-community-detection-master/nocd/nn/gcn.py in forward(self, x, adj)
     85         for idx, gcn in enumerate(self.layers):
     86             if self.dropout != 0:
---> 87                 x = sparse_or_dense_dropout(x, p=self.dropout, training=self.training)
     88             x = gcn(x, adj)
     89             if idx != len(self.layers) - 1:

~/github/Spatial/Spectral Clustering/overlapping-community-detection-master/nocd/nn/gcn.py in sparse_or_dense_dropout(x, p, training)
     18         return torch.cuda.sparse.FloatTensor(x.indices(), new_values, x.size())
     19     else:
---> 20         return F.dropout(x, p=p, training=training)
     21 
     22 

~/opt/anaconda3/lib/python3.8/site-packages/torch/nn/functional.py in dropout(input, p, training, inplace)
    981     return (_VF.dropout_(input, p, training)
    982             if inplace
--> 983             else _VF.dropout(input, p, training))
    984 
    985 

TypeError: dropout(): argument 'input' (position 1) must be Tensor, not csr_matrix