Sorry to reopen #174 ... but it doesn't work with X having this dimensions: (n_nodes, n_features, window). If I run your example of MPNNLSTM and switch node features with window, even if I reshape the x, it doesn't work and I get this error:
[/usr/local/lib/python3.7/dist-packages/torch_geometric_temporal/nn/recurrent/mpnn_lstm.py] in forward(self, X, edge_index, edge_weight)
80 R = list()
81
---> 82 S = X.view(-1, self.window, self.num_nodes, self.in_channels)
RuntimeError: view size is not compatible with input tensor's size and stride (at least one dimension spans across two contiguous subspaces). Use .reshape(...) instead.
I changed the model definition:
self.recurrent = MPNNLSTM(1, 32, 32, 20, node_features, 0.5)
and the input x in the snapshot:
snapshot.x = snapshot.x.reshape(20,1,4)
Sorry to reopen #174 ... but it doesn't work with X having this dimensions: (n_nodes, n_features, window). If I run your example of MPNNLSTM and switch node features with window, even if I reshape the x, it doesn't work and I get this error:
[/usr/local/lib/python3.7/dist-packages/torch_geometric_temporal/nn/recurrent/mpnn_lstm.py] in forward(self, X, edge_index, edge_weight) 80 R = list() 81 ---> 82 S = X.view(-1, self.window, self.num_nodes, self.in_channels)
RuntimeError: view size is not compatible with input tensor's size and stride (at least one dimension spans across two contiguous subspaces). Use .reshape(...) instead.
I changed the model definition: self.recurrent = MPNNLSTM(1, 32, 32, 20, node_features, 0.5) and the input x in the snapshot: snapshot.x = snapshot.x.reshape(20,1,4)