Need help with GNN Expected 'edge_index' to be two-dimensional (got 1 dimensions) train_da

[Ghadah-Al]

I had data with edge index = torch.Size([50]) and the data.x shape= torch.Size([100, 172]) where the 100 is num of node and 172 number of features when i use this

`class Net(torch.nn.Module):
def init(self, hidden_channels, in_channel, out_channel):
super(Net, self).init()
self.conv1 = GraphConv(in_channel, hidden_channels)
self.conv2 = GraphConv(hidden_channels, hidden_channels)
self.conv3 = GraphConv(hidden_channels, hidden_channels)
self.lin = torch.nn.Linear(1 * hidden_channels, out_channel)

def forward(self, x0, edge_index, edge_index_b, lam, id_new_value_old):
    #edge_index =edge_index.reshape(2, -1)
    print(edge_index.shape)
    #edge_index_b= edge_index_b.reshape(2, -1)
    print(edge_index_b.shape)
    print("x0.shape",x0.shape)
    x1 = self.conv1(x0, edge_index, x0)
    print("x1.shape",x1.shape)
    x1 = F.relu(x1)
    print("After reul x1.shape",x1.shape)
    x1 = F.dropout(x1, p=0.4, training=self.training)
   
    x2 = self.conv2(x1, edge_index, x1)
    x2 = F.relu(x2)
    x2 = F.dropout(x2, p=0.4, training=self.training)
    
    x0_b = x0[id_new_value_old]
    x1_b = x1[id_new_value_old]
    x2_b = x2[id_new_value_old]

    x0_mix = x0 * lam + x0_b * (1 - lam)

    new_x1 = self.conv1(x0, edge_index, x0_mix)
    new_x1_b = self.conv1(x0_b, edge_index_b, x0_mix)
    new_x1 = F.relu(new_x1)
    new_x1_b = F.relu(new_x1_b)

    x1_mix = new_x1 * lam + new_x1_b * (1 - lam)
    x1_mix = F.dropout(x1_mix, p=0.4, training=self.training)

    new_x2 = self.conv2(x1, edge_index, x1_mix)
    new_x2_b = self.conv2(x1_b, edge_index_b, x1_mix)
    new_x2 = F.relu(new_x2)
    new_x2_b = F.relu(new_x2_b)

    x2_mix = new_x2 * lam + new_x2_b * (1 - lam)
    x2_mix = F.dropout(x2_mix, p=0.4, training=self.training)

    new_x3 = self.conv3(x2, edge_index, x2_mix)
    new_x3_b = self.conv3(x2_b, edge_index_b, x2_mix)
    new_x3 = F.relu(new_x3)
    new_x3_b = F.relu(new_x3_b)

    x3_mix = new_x3 * lam + new_x3_b * (1 - lam)
    x3_mix = F.dropout(x3_mix, p=0.4, training=self.training)

    x = x3_mix
    x = self.lin(x)
    return x.log_softmax(dim=-1)`

I got those issues :
ValueError Traceback (most recent call last)
Cell In [1], line 216
214 labels_batch_torch = torch.from_numpy(labels_batch).float().to(device)
215 data_b, id_new_value_old = shuffleData(source_embedding)
--> 216 pred = decoder(source_embedding, source_embedding.edge_idxs, data_b.edge_idxs, lam , id_new_value_old).sigmoid()
217 #pred = torch.flatten(pred)
218 labels_batch_torch = torch.stack((1 - labels_batch_torch, labels_batch_torch), dim=-1)

File ~/opt/anaconda3/envs/M1Max/lib/python3.8/site-packages/torch/nn/modules/module.py:1190, in Module._call_impl(self, *input, **kwargs)
1186 # If we don't have any hooks, we want to skip the rest of the logic in
1187 # this function, and just call forward.
1188 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1189 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1190 return forward_call(*input, **kwargs)
1191 # Do not call functions when jit is used
1192 full_backward_hooks, non_full_backward_hooks = [], []

File ~/Documents/Codes/tgn-master 3 copy 2/utils/utils.py:21, in Net.forward(self, x0, edge_index, edge_index_b, lam, id_new_value_old)
19 print(edge_index_b.shape)
20 print("x0.shape",x0.shape)
---> 21 x1 = self.conv1(x0, edge_index, x0)
22 print("x1.shape",x1.shape)
23 x1 = F.relu(x1)

File ~/opt/anaconda3/envs/M1Max/lib/python3.8/site-packages/torch/nn/modules/module.py:1190, in Module._call_impl(self, *input, **kwargs)
1186 # If we don't have any hooks, we want to skip the rest of the logic in
1187 # this function, and just call forward.
1188 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1189 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1190 return forward_call(*input, **kwargs)
1191 # Do not call functions when jit is used
1192 full_backward_hooks, non_full_backward_hooks = [], []

File ~/Documents/Codes/tgn-master 3 copy 2/graph_conv.py:27, in GraphConv.forward(self, x, edge_index, x_cen)
25 def forward(self, x, edge_index, x_cen):
26 h = torch.matmul(x, self.weight)
---> 27 aggr_out = self.propagate(edge_index, size=None, h=h, edge_weight=None)
28 return aggr_out + self.lin(x_cen)

File ~/opt/anaconda3/envs/M1Max/lib/python3.8/site-packages/torch_geometric/nn/conv/message_passing.py:392, in MessagePassing.propagate(self, edge_index, size, **kwargs)
389 if res is not None:
390 edge_index, size, kwargs = res
--> 392 size = self.check_input(edge_index, size)
394 # Run "fused" message and aggregation (if applicable).
395 if is_sparse(edge_index) and self.fuse and not self.explain:

File ~/opt/anaconda3/envs/M1Max/lib/python3.8/site-packages/torch_geometric/nn/conv/message_passing.py:205, in MessagePassing.check_input(self, edge_index, size)
202 raise ValueError(f"Expected 'edge_index' to be of integer "
203 f"type (got '{edge_index.dtype}')")
204 if edge_index.dim() != 2:
--> 205 raise ValueError(f"Expected 'edge_index' to be two-dimensional"
206 f" (got {edge_index.dim()} dimensions)")
207 if edge_index.size(0) != 2:
208 raise ValueError(f"Expected 'edge_index' to have size '2' in "
209 f"the first dimension (got "
210 f"'{edge_index.size(0)}')")

ValueError: Expected 'edge_index' to be two-dimensional (got 1 dimensions)

[wsad1]

edge_index is expected to be of shape [2, num_edges].

[Ghadah-Al]

I work on a code that using a datasets have edge_index with shape (num_edges, )

[rusty1s]

Can you clarify what you mean? edge_index needs to decode pairs of node indices for each edge, so it shouldn't be of shape [num_edges, ]. This looks to be a error in the dataset you are using. Which one is it?

[Ghadah-Al]

The dataset I used from this paper "TGN: Temporal Graph Networks". They used the following four datasets for the experiments.
Reddit
Wikipedia
MOOC
LastFM