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from typing import List, Optional, Tuple
import torch
from torch import Tensor
from torch_geometric.utils import coalesce
def test_coalesce():
edge_index = torch.tensor([[2, 1, 1, 0, 2], [1, 2, 0, 1, 1]])
edge_attr = torch.tensor([[1], [2], [3], [4], [5]])
out = coalesce(edge_index)
assert out.tolist() == [[0, 1, 1, 2], [1, 0, 2, 1]]
out = coalesce(edge_index, None)
assert out[0].tolist() == [[0, 1, 1, 2], [1, 0, 2, 1]]
assert out[1] is None
out = coalesce(edge_index, edge_attr)
assert out[0].tolist() == [[0, 1, 1, 2], [1, 0, 2, 1]]
assert out[1].tolist() == [[4], [3], [2], [6]]
out = coalesce(edge_index, [edge_attr, edge_attr.view(-1)])
assert out[0].tolist() == [[0, 1, 1, 2], [1, 0, 2, 1]]
assert out[1][0].tolist() == [[4], [3], [2], [6]]
assert out[1][1].tolist() == [4, 3, 2, 6]
out = coalesce((edge_index[0], edge_index[1]))
assert isinstance(out, tuple)
assert out[0].tolist() == [0, 1, 1, 2]
assert out[1].tolist() == [1, 0, 2, 1]
def test_coalesce_without_duplicates():
edge_index = torch.tensor([[2, 1, 1, 0], [1, 2, 0, 1]])
edge_attr = torch.tensor([[1], [2], [3], [4]])
out = coalesce(edge_index)
assert out.tolist() == [[0, 1, 1, 2], [1, 0, 2, 1]]
out = coalesce(edge_index, None)
assert out[0].tolist() == [[0, 1, 1, 2], [1, 0, 2, 1]]
assert out[1] is None
out = coalesce(edge_index, edge_attr)
assert out[0].tolist() == [[0, 1, 1, 2], [1, 0, 2, 1]]
assert out[1].tolist() == [[4], [3], [2], [1]]
out = coalesce(edge_index, [edge_attr, edge_attr.view(-1)])
assert out[0].tolist() == [[0, 1, 1, 2], [1, 0, 2, 1]]
assert out[1][0].tolist() == [[4], [3], [2], [1]]
assert out[1][1].tolist() == [4, 3, 2, 1]
def test_coalesce_jit():
@torch.jit.script
def wrapper1(edge_index: Tensor) -> Tensor:
return coalesce(edge_index)
@torch.jit.script
def wrapper2(
edge_index: Tensor,
edge_attr: Optional[Tensor],
) -> Tuple[Tensor, Optional[Tensor]]:
return coalesce(edge_index, edge_attr)
@torch.jit.script
def wrapper3(
edge_index: Tensor,
edge_attr: List[Tensor],
) -> Tuple[Tensor, List[Tensor]]:
return coalesce(edge_index, edge_attr)
edge_index = torch.tensor([[2, 1, 1, 0], [1, 2, 0, 1]])
edge_attr = torch.tensor([[1], [2], [3], [4]])
out = wrapper1(edge_index)
assert out.size() == edge_index.size()
out = wrapper2(edge_index, None)
assert out[0].size() == edge_index.size()
assert out[1] is None
out = wrapper2(edge_index, edge_attr)
assert out[0].size() == edge_index.size()
assert out[1].size() == edge_attr.size()
out = wrapper3(edge_index, [edge_attr, edge_attr.view(-1)])
assert out[0].size() == edge_index.size()
assert len(out[1]) == 2
assert out[1][0].size() == edge_attr.size()
assert out[1][1].size() == edge_attr.view(-1).size()
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