I try to implement GCN on my custom dataset, but I got error:
my code:
# Define our GCN class as a pytorch Module
class GCN(torch.nn.Module):
def __init__(self, hidden_channels):
super(GCN, self).__init__()
# We inherit from pytorch geometric's GCN class, and we initialize three layers
self.conv1 = GCNConv(data.num_features, hidden_channels)
self.conv2 = GCNConv(hidden_channels, hidden_channels)
self.conv3 = GCNConv(hidden_channels, hidden_channels)
# Our final linear layer will define our output
self.lin = Linear(hidden_channels, data.num_classes)
def forward(self, x, edge_index, batch):
# 1. Obtain node embeddings
x = self.conv1(x, edge_index)
x = x.relu()
x = self.conv2(x, edge_index)
x = x.relu()
x = self.conv3(x, edge_index)
# 2. Readout layer
x = global_mean_pool(x, batch) # [batch_size, hidden_channels]
# 3. Apply a final classifier
x = F.dropout(x, p=0.5, training=self.training)
x = self.lin(x)
return x
model = GCN(hidden_channels=16)
print(model)
The error:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/tmp/ipykernel_59/1705364933.py in <module>
32 return x
33
---> 34 model = GCN(hidden_channels=16)
35 print(model)
/tmp/ipykernel_59/1705364933.py in __init__(self, hidden_channels)
14 self.conv3 = GCNConv(hidden_channels, hidden_channels)
15 # Our final linear layer will define our output
---> 16 self.lin = Linear(hidden_channels, data.num_classes)
17
18 def forward(self, x, edge_index, batch):
/usr/local/lib/python3.8/dist-packages/torch/nn/modules/linear.py in __init__(self, in_features, out_features, bias, device, dtype)
83 self.in_features = in_features
84 self.out_features = out_features
---> 85 self.weight = Parameter(torch.empty((out_features, in_features), **factory_kwargs))
86 if bias:
87 self.bias = Parameter(torch.empty(out_features, **factory_kwargs))
TypeError: empty(): argument 'size' must be tuple of ints, but found element of type Tensor at pos 1