def __init__(self, in_channels, hidden_channels, out_channels, num_layers,
dropout=0.0):
super().__init__()
self.dropout = dropout
self.convs = torch.nn.ModuleList()
self.convs.append(FiLMConv(in_channels, hidden_channels))
for _ in range(num_layers - 2):
self.convs.append(FiLMConv(hidden_channels, hidden_channels))
self.convs.append(FiLMConv(hidden_channels, out_channels, act=None))
self.norms = torch.nn.ModuleList()
for _ in range(num_layers - 1):
self.norms.append(BatchNorm1d(hidden_channels))
def test_film_conv():
x1 = torch.randn(4, 4)
x2 = torch.randn(2, 16)
edge_index = torch.tensor([[0, 1, 1, 2, 2, 3], [0, 0, 1, 0, 1, 1]])
edge_type = torch.tensor([0, 1, 1, 0, 0, 1])
row, col = edge_index
adj = SparseTensor(row=row, col=col, value=edge_type, sparse_sizes=(4, 4))
conv = FiLMConv(4, 32)
assert conv.__repr__() == 'FiLMConv(4, 32, num_relations=1)'
out1 = conv(x1, edge_index)
assert out1.size() == (4, 32)
assert conv(x1, adj.t().set_value(None)).tolist() == out1.tolist()
t = '(Tensor, Tensor, OptTensor) -> Tensor'
jit = torch.jit.script(conv.jittable(t))
assert jit(x1, edge_index).tolist() == out1.tolist()
t = '(Tensor, SparseTensor, OptTensor) -> Tensor'
jit = torch.jit.script(conv.jittable(t))
assert conv(x1, adj.t().set_value(None)).tolist() == out1.tolist()
conv = FiLMConv(4, 32, num_relations=2)
assert conv.__repr__() == 'FiLMConv(4, 32, num_relations=2)'
out1 = conv(x1, edge_index, edge_type)
assert out1.size() == (4, 32)
assert conv(x1, adj.t()).tolist() == out1.tolist()
t = '(Tensor, Tensor, OptTensor) -> Tensor'
jit = torch.jit.script(conv.jittable(t))
assert jit(x1, edge_index, edge_type).tolist() == out1.tolist()
t = '(Tensor, SparseTensor, OptTensor) -> Tensor'
jit = torch.jit.script(conv.jittable(t))
assert conv(x1, adj.t()).tolist() == out1.tolist()
adj = adj.sparse_resize((4, 2))
conv = FiLMConv((4, 16), 32)
assert conv.__repr__() == 'FiLMConv((4, 16), 32, num_relations=1)'
out1 = conv((x1, x2), edge_index)
assert out1.size() == (2, 32)
assert conv((x1, x2), adj.t().set_value(None)).tolist() == out1.tolist()
t = '(PairTensor, Tensor, OptTensor) -> Tensor'
jit = torch.jit.script(conv.jittable(t))
assert jit((x1, x2), edge_index).tolist() == out1.tolist()
t = '(PairTensor, SparseTensor, OptTensor) -> Tensor'
jit = torch.jit.script(conv.jittable(t))
assert conv((x1, x2), adj.t().set_value(None)).tolist() == out1.tolist()
conv = FiLMConv((4, 16), 32, num_relations=2)
assert conv.__repr__() == 'FiLMConv((4, 16), 32, num_relations=2)'
out1 = conv((x1, x2), edge_index, edge_type)
assert out1.size() == (2, 32)
assert conv((x1, x2), adj.t()).tolist() == out1.tolist()
t = '(PairTensor, Tensor, OptTensor) -> Tensor'
jit = torch.jit.script(conv.jittable(t))
assert jit((x1, x2), edge_index, edge_type).tolist() == out1.tolist()
t = '(PairTensor, SparseTensor, OptTensor) -> Tensor'
jit = torch.jit.script(conv.jittable(t))
assert conv((x1, x2), adj.t()).tolist() == out1.tolist()