In standard PyTorch documentation, it says that torch.nn considers the input as mini-batch. So, for one sample it suggests to use input.unsqueeze(0) in order to add a fake batch. Is this the case for PyTorch Geometric nn modules?
More specifically, I want to feed a fully-connected graph with 35 vertices and scalar edge weights to NNConv layer. So, I represent this graph as Data object where Data.x is a 35x35 adjacency matrix, Data.edge_index is 2 x 1225 tensor since it is fully-connected and Data.edge_attr is a tensor of shape 1225 x 1 since, again, it is fully-connected and edge attributes are just scalar weights. I designed such a NNConv layer and I am inputting not mini-batches but one sample to network.
nn = Sequential(Linear(1, 1225), ReLU())
self.conv1 = NNConv(35, 35, nn, aggr='mean', root_weight=True, bias=True)
in forward function
def forward(self, data):
x, edge_index, edge_attr = data.x, data.edge_index, data.edge_attr
x = F.sigmoid(self.conv11(self.conv1(x, edge_index, edge_attr)))
What I do not understand is do I need to add fake mini-batches. Is this correct or do I need to add x.unsqueeze(0), if so which of these Data attributes (x, edge_index, edge_attr) do need a unsqueeze(0). Thanks.