def test_dense_sage_conv():
channels = 16
sparse_conv = SAGEConv(channels, channels)
dense_conv = DenseSAGEConv(channels, channels)
assert dense_conv.__repr__() == 'DenseSAGEConv(16, 16)'
# Ensure same weights (identity) and bias (ones).
index = torch.arange(0, channels, dtype=torch.long)
sparse_conv.weight.data.fill_(0)
sparse_conv.weight.data[index, index] = 1
sparse_conv.bias.data.fill_(1)
dense_conv.weight.data.fill_(0)
dense_conv.weight.data[index, index] = 1
dense_conv.bias.data.fill_(1)
x = torch.randn((5, channels))
edge_index = torch.tensor([[0, 0, 1, 1, 2, 2, 3, 4],
[1, 2, 0, 2, 0, 1, 4, 3]])
sparse_out = sparse_conv(x, edge_index)
x = torch.cat([x, x.new_zeros(1, channels)], dim=0).view(2, 3, channels)
adj = torch.Tensor([
[[0, 1, 1], [1, 0, 1], [1, 1, 0]],
[[0, 1, 0], [1, 0, 0], [0, 0, 0]],
])
mask = torch.tensor([[1, 1, 1], [1, 1, 0]], dtype=torch.uint8)
dense_out = dense_conv(x, adj, mask)
assert dense_out.size() == (2, 3, channels)
dense_out = dense_out.view(6, channels)[:-1]
assert torch.allclose(sparse_out, dense_out, atol=1e-04)
def test_dense_sage_conv():
channels = 16
sparse_conv = SAGEConv(channels, channels, normalize=True)
dense_conv = DenseSAGEConv(channels, channels, normalize=True)
assert dense_conv.__repr__() == 'DenseSAGEConv(16, 16)'
# Ensure same weights and bias.
dense_conv.lin_rel = sparse_conv.lin_l
dense_conv.lin_root = sparse_conv.lin_r
x = torch.randn((5, channels))
edge_index = torch.tensor([[0, 0, 1, 1, 2, 2, 3, 4],
[1, 2, 0, 2, 0, 1, 4, 3]])
sparse_out = sparse_conv(x, edge_index)
assert sparse_out.size() == (5, channels)
x = torch.cat([x, x.new_zeros(1, channels)], dim=0).view(2, 3, channels)
adj = torch.Tensor([
[
[0, 1, 1],
[1, 0, 1],
[1, 1, 0],
],
[
[0, 1, 0],
[1, 0, 0],
[0, 0, 0],
],
])
mask = torch.tensor([[1, 1, 1], [1, 1, 0]], dtype=torch.bool)
dense_out = dense_conv(x, adj, mask)
assert dense_out.size() == (2, 3, channels)
assert dense_out[1, 2].abs().sum().item() == 0
dense_out = dense_out.view(6, channels)[:-1]
assert torch.allclose(sparse_out, dense_out, atol=1e-04)
