I am trying to use TransformerConv to train a model which takes in graphs of different sizes. This is to be used on a graph classification task. My code snippet is as follows:
self.conv1 = TransformerConv(in_channels=-1, out_channels=256) def forward(self, x_onein, x_twoin, lambd): x_one, edge_index_one, batch_one = x_onein.x, x_onein.edge_index, x_onein.batch x_one = self.conv1(x_one, edge_index_one) return x_one
My input data come in batches of various sizes, and each graph has between tens of nodes to 1200 nodes. Because of this, I require the first input layer to be able to take in graphs of different sizes.
When I run the code, I get the error:
RuntimeError: Trying to create tensor with negative dimension -1: [256, -1]
This is strange since I followed the documentation:
in_channels (int or tuple) – Size of each input sample, or
-1 to derive the size from the first input(s) to the forward method. A tuple corresponds to the sizes of source and target dimensionalities.
Any advice on this matter would be greatly appreciated.