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import torch
import torch_geometric.typing
from torch_geometric.nn import RECT_L
from torch_geometric.testing import is_full_test
from torch_geometric.typing import SparseTensor
def test_rect():
x = torch.randn(6, 8)
y = torch.tensor([1, 0, 0, 2, 1, 1])
edge_index = torch.tensor([[0, 1, 1, 2, 4, 5], [1, 0, 2, 1, 5, 4]])
mask = torch.randint(0, 2, (6, ), dtype=torch.bool)
model = RECT_L(8, 16)
assert str(model) == 'RECT_L(8, 16)'
out = model(x, edge_index)
assert out.size() == (6, 8)
if torch_geometric.typing.WITH_TORCH_SPARSE:
adj = SparseTensor.from_edge_index(edge_index, sparse_sizes=(6, 6))
assert torch.allclose(out, model(x, adj.t()), atol=1e-6)
# Test `embed`:
embed_out = model.embed(x, edge_index)
assert embed_out.size() == (6, 16)
if torch_geometric.typing.WITH_TORCH_SPARSE:
assert torch.allclose(embed_out, model.embed(x, adj.t()), atol=1e-6)
# Test `get_semantic_labels`:
labels_out = model.get_semantic_labels(x, y, mask)
assert labels_out.size() == (int(mask.sum()), 8)
if is_full_test():
jit = torch.jit.script(model)
assert torch.allclose(jit(x, edge_index), out, atol=1e-6)
assert torch.allclose(embed_out, jit.embed(x, edge_index), atol=1e-6)
assert torch.allclose(labels_out, jit.get_semantic_labels(x, y, mask))
if torch_geometric.typing.WITH_TORCH_SPARSE:
assert torch.allclose(jit(x, adj.t()), out, atol=1e-6)
assert torch.allclose(embed_out, jit.embed(x, adj.t()), atol=1e-6)
assert torch.allclose(labels_out,
jit.get_semantic_labels(x, y, mask))
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