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import torch
from torch_geometric.testing import is_full_test
from torch_geometric.utils import to_dense_adj
def test_to_dense_adj():
edge_index = torch.tensor([
[0, 0, 1, 2, 3, 4],
[0, 1, 0, 3, 4, 2],
])
batch = torch.tensor([0, 0, 1, 1, 1])
adj = to_dense_adj(edge_index, batch)
assert adj.size() == (2, 3, 3)
assert adj[0].tolist() == [[1, 1, 0], [1, 0, 0], [0, 0, 0]]
assert adj[1].tolist() == [[0, 1, 0], [0, 0, 1], [1, 0, 0]]
if is_full_test():
jit = torch.jit.script(to_dense_adj)
adj = jit(edge_index, batch)
assert adj.size() == (2, 3, 3)
assert adj[0].tolist() == [[1, 1, 0], [1, 0, 0], [0, 0, 0]]
assert adj[1].tolist() == [[0, 1, 0], [0, 0, 1], [1, 0, 0]]
adj = to_dense_adj(edge_index, batch, max_num_nodes=2)
assert adj.size() == (2, 2, 2)
assert adj[0].tolist() == [[1, 1], [1, 0]]
assert adj[1].tolist() == [[0, 1], [0, 0]]
adj = to_dense_adj(edge_index, batch, max_num_nodes=5)
assert adj.size() == (2, 5, 5)
assert adj[0][:3, :3].tolist() == [[1, 1, 0], [1, 0, 0], [0, 0, 0]]
assert adj[1][:3, :3].tolist() == [[0, 1, 0], [0, 0, 1], [1, 0, 0]]
edge_attr = torch.tensor([1.0, 2.0, 3.0, 4.0, 5.0, 6.0])
adj = to_dense_adj(edge_index, batch, edge_attr)
assert adj.size() == (2, 3, 3)
assert adj[0].tolist() == [[1, 2, 0], [3, 0, 0], [0, 0, 0]]
assert adj[1].tolist() == [[0, 4, 0], [0, 0, 5], [6, 0, 0]]
adj = to_dense_adj(edge_index, batch, edge_attr, max_num_nodes=5)
assert adj.size() == (2, 5, 5)
assert adj[0][:3, :3].tolist() == [[1, 2, 0], [3, 0, 0], [0, 0, 0]]
assert adj[1][:3, :3].tolist() == [[0, 4, 0], [0, 0, 5], [6, 0, 0]]
edge_attr = edge_attr.view(-1, 1)
adj = to_dense_adj(edge_index, batch, edge_attr)
assert adj.size() == (2, 3, 3, 1)
edge_attr = edge_attr.view(-1, 1)
adj = to_dense_adj(edge_index, batch, edge_attr, max_num_nodes=5)
assert adj.size() == (2, 5, 5, 1)
adj = to_dense_adj(edge_index)
assert adj.size() == (1, 5, 5)
assert adj[0].nonzero(as_tuple=False).t().tolist() == edge_index.tolist()
adj = to_dense_adj(edge_index, max_num_nodes=10)
assert adj.size() == (1, 10, 10)
assert adj[0].nonzero(as_tuple=False).t().tolist() == edge_index.tolist()
adj = to_dense_adj(edge_index, batch, batch_size=4)
assert adj.size() == (4, 3, 3)
def test_to_dense_adj_with_empty_edge_index():
edge_index = torch.tensor([[], []], dtype=torch.long)
batch = torch.tensor([0, 0, 1, 1, 1])
adj = to_dense_adj(edge_index)
assert adj.size() == (1, 0, 0)
adj = to_dense_adj(edge_index, max_num_nodes=10)
assert adj.size() == (1, 10, 10) and adj.sum() == 0
adj = to_dense_adj(edge_index, batch)
assert adj.size() == (2, 3, 3) and adj.sum() == 0
adj = to_dense_adj(edge_index, batch, max_num_nodes=10)
assert adj.size() == (2, 10, 10) and adj.sum() == 0
def test_to_dense_adj_with_duplicate_entries():
edge_index = torch.tensor([
[0, 0, 0, 1, 2, 3, 3, 4],
[0, 0, 1, 0, 3, 4, 4, 2],
])
batch = torch.tensor([0, 0, 1, 1, 1])
adj = to_dense_adj(edge_index, batch)
assert adj.size() == (2, 3, 3)
assert adj[0].tolist() == [[2, 1, 0], [1, 0, 0], [0, 0, 0]]
assert adj[1].tolist() == [[0, 1, 0], [0, 0, 2], [1, 0, 0]]
edge_attr = torch.tensor([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0])
adj = to_dense_adj(edge_index, batch, edge_attr)
assert adj.size() == (2, 3, 3)
assert adj[0].tolist() == [
[3.0, 3.0, 0.0],
[4.0, 0.0, 0.0],
[0.0, 0.0, 0.0],
]
assert adj[1].tolist() == [
[0.0, 5.0, 0.0],
[0.0, 0.0, 13.0],
[8.0, 0.0, 0.0],
]
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