Dimension out of range error

[hkim716]

Hi Matt,

I'm trying to make a GNN using Mnist superpixel data modified with Delaunay transform.

A part of my network is like this

class Encodr(nn.Module):
    def __init__(self):
        super(Encodr,self).__init__()
        self.conv1_out = 8
        self.conv2_out = 16
        self.fc1_hidden = 10
        self.x_lat = 1
        self.kernel_size = 5
        self.pool_reduce = 'max'
        
        self.conv1 = SplineConv(1, self.conv1_out, dim=2, kernel_size=self.kernel_size)
        self.conv2 = SplineConv(self.conv1_out, self.conv2_out, dim=2, kernel_size=self.kernel_size)
        self.fc1 = nn.Linear(self.conv2_out, self.fc1_hidden)
        self.fc2 = nn.Linear(self.fc1_hidden, self.x_lat)
        
    def forward(self,d):
        x_conv1 = F.elu(self.conv1(d.x, d.edge_index0, d.edge_attr0))
        x_pool1 = scatter(x_conv1, d.cluster0,dim=0, reduce=self.pool_reduce)
        x_conv2 = F.elu(self.conv2(x_pool1, d.edge_index1, d.edge_attr1))
        x_pool2 = scatter(x_conv2, d.cluster1, dim=0, reduce=self.pool_reduce)
        x_fc1 = F.elu(self.fc1(x_pool2))
        return self.fc2(x_fc1)

However, when I passed my data to the model, I got an error like this below.

---> 21         x_conv1 = F.elu(self.conv1(d.x, d.edge_index0, d.edge_attr0))
   1100         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1101                 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1102             return forward_call(*input, **kwargs)
   1103         # Do not call functions when jit is used
   1104         full_backward_hooks, non_full_backward_hooks = [], []

~/miniconda3/envs/pyg_172/lib/python3.7/site-packages/torch_geometric/nn/conv/spline_conv.py in forward(self, x, edge_index, edge_attr, size)
    120 
    121         # propagate_type: (x: OptPairTensor, edge_attr: OptTensor)
--> 122         out = self.propagate(edge_index, x=x, edge_attr=edge_attr, size=size)
    123 
    124         x_r = x[1]

~/miniconda3/envs/pyg_172/lib/python3.7/site-packages/torch_geometric/nn/conv/message_passing.py in propagate(self, edge_index, size, **kwargs)
    232         elif isinstance(edge_index, Tensor) or not self.fuse:
    233             coll_dict = self.__collect__(self.__user_args__, edge_index, size,
--> 234                                          kwargs)
    235 
    236             msg_kwargs = self.inspector.distribute('message', coll_dict)

~/miniconda3/envs/pyg_172/lib/python3.7/site-packages/torch_geometric/nn/conv/message_passing.py in __collect__(self, args, edge_index, size, kwargs)
    150                     assert len(data) == 2
    151                     if isinstance(data[1 - dim], Tensor):
--> 152                         self.__set_size__(size, 1 - dim, data[1 - dim])
    153                     data = data[dim]
    154 

~/miniconda3/envs/pyg_172/lib/python3.7/site-packages/torch_geometric/nn/conv/message_passing.py in __set_size__(self, size, dim, src)
    116         the_size = size[dim]
    117         if the_size is None:
--> 118             size[dim] = src.size(self.node_dim)
    119         elif the_size != src.size(self.node_dim):
    120             raise ValueError(

IndexError: Dimension out of range (expected to be in range of [-1, 0], but got -2)

I thought the error comes from the batch training, but even though I tried to use batch=1, the error does not disappear.
I checked the shape of d.x.shape, d.edge_index0.shape, and d.edge_attr0.shape, with batch=10, which are

torch.Size([750])
torch.Size([2, 4240])
torch.Size([4240, 2])

I don't know why this error happens because I think these input shapes should be the same (or similar) with the inputs in the SplineConv example code.

[rusty1s]

Can you check if reshaping your x tensor to [750, 1] helps?

x = x.view(-1, 1)