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from typing import List, Optional
import torch
from torch import Tensor
import torch_geometric.typing
from torch_geometric.data import Data
from torch_geometric.loader import NeighborLoader
from torch_geometric.nn import GraphConv
from torch_geometric.testing import withPackage
from torch_geometric.typing import SparseTensor
from torch_geometric.utils import trim_to_layer
from torch_geometric.utils._trim_to_layer import trim_sparse_tensor
@withPackage('torch_sparse')
def test_trim_sparse_tensor():
edge_index = torch.tensor([[0, 0, 1, 2], [1, 2, 3, 4]])
adj = SparseTensor.from_edge_index(edge_index, sparse_sizes=[5, 5])
adj = trim_sparse_tensor(adj, size=(3, 3), num_seed_nodes=1)
row, col, _ = adj.coo()
assert row.tolist() == [0, 0]
assert col.tolist() == [1, 2]
def test_trim_to_layer_basic():
x0 = torch.arange(4)
edge_index0 = torch.tensor([[1, 2, 3], [0, 1, 2]])
edge_weight0 = torch.arange(3)
num_sampled_nodes_per_hop = [1, 1, 1]
num_sampled_edges_per_hop = [1, 1, 1]
x1, edge_index1, edge_weight1 = trim_to_layer(
layer=0,
num_sampled_nodes_per_hop=num_sampled_nodes_per_hop,
num_sampled_edges_per_hop=num_sampled_edges_per_hop,
x=x0,
edge_index=edge_index0,
edge_attr=edge_weight0,
)
assert torch.equal(x1, torch.arange(4))
assert edge_index1.tolist() == [[1, 2, 3], [0, 1, 2]]
assert torch.equal(edge_weight1, torch.arange(3))
if torch_geometric.typing.WITH_TORCH_SPARSE:
adj0 = SparseTensor.from_edge_index(edge_index0, edge_weight0, (4, 4))
x1, adj_t1, _ = trim_to_layer(
layer=0,
num_sampled_nodes_per_hop=num_sampled_nodes_per_hop,
num_sampled_edges_per_hop=num_sampled_edges_per_hop,
x=x0,
edge_index=adj0.t(),
edge_attr=edge_weight0,
)
adj1 = adj_t1.t()
assert adj1.sizes() == [4, 4]
row, col, value = adj1.coo()
assert torch.equal(x1, torch.arange(4))
assert row.tolist() == [1, 2, 3]
assert col.tolist() == [0, 1, 2]
assert torch.equal(value, torch.arange(3))
x2, edge_index2, edge_weight2 = trim_to_layer(
layer=1,
num_sampled_nodes_per_hop=num_sampled_nodes_per_hop,
num_sampled_edges_per_hop=num_sampled_edges_per_hop,
x=x1,
edge_index=edge_index1,
edge_attr=edge_weight1,
)
assert torch.equal(x2, torch.arange(3))
assert edge_index2.tolist() == [[1, 2], [0, 1]]
assert torch.equal(edge_weight2, torch.arange(2))
if torch_geometric.typing.WITH_TORCH_SPARSE:
adj1 = SparseTensor.from_edge_index(edge_index1, edge_weight1, (4, 4))
x2, adj_t2, _ = trim_to_layer(
layer=1,
num_sampled_nodes_per_hop=num_sampled_nodes_per_hop,
num_sampled_edges_per_hop=num_sampled_edges_per_hop,
x=x1,
edge_index=adj1.t(),
)
adj2 = adj_t2.t()
assert adj2.sizes() == [3, 3]
row, col, value = adj2.coo()
assert torch.equal(x2, torch.arange(3))
assert row.tolist() == [1, 2]
assert col.tolist() == [0, 1]
assert torch.equal(value, torch.arange(2))
x3, edge_index3, edge_weight3 = trim_to_layer(
layer=2,
num_sampled_nodes_per_hop=num_sampled_nodes_per_hop,
num_sampled_edges_per_hop=num_sampled_edges_per_hop,
x=x2,
edge_index=edge_index2,
edge_attr=edge_weight2,
)
assert torch.equal(x3, torch.arange(2))
assert edge_index3.tolist() == [[1], [0]]
assert torch.equal(edge_weight3, torch.arange(1))
if torch_geometric.typing.WITH_TORCH_SPARSE:
adj2 = SparseTensor.from_edge_index(edge_index2, edge_weight2, (3, 3))
x3, adj_t3, _ = trim_to_layer(
layer=2,
num_sampled_nodes_per_hop=num_sampled_nodes_per_hop,
num_sampled_edges_per_hop=num_sampled_edges_per_hop,
x=x2,
edge_index=adj2.t(),
)
adj3 = adj_t3.t()
assert adj3.sizes() == [2, 2]
row, col, value = adj3.coo()
assert torch.equal(x3, torch.arange(2))
assert row.tolist() == [1]
assert col.tolist() == [0]
assert torch.equal(value, torch.arange(1))
def test_trim_to_layer_hetero():
x = {'v': torch.arange(4)}
edge_index = {('v', 'to', 'v'): torch.tensor([[1, 2, 3], [0, 1, 2]])}
edge_weight = {('v', 'to', 'v'): torch.arange(3)}
num_sampled_nodes_per_hop = {'v': [1, 1, 1, 1]}
num_sampled_edges_per_hop = {('v', 'to', 'v'): [1, 1, 1]}
x, edge_index, edge_weight = trim_to_layer(
layer=1,
num_sampled_nodes_per_hop=num_sampled_nodes_per_hop,
num_sampled_edges_per_hop=num_sampled_edges_per_hop,
x=x,
edge_index=edge_index,
edge_attr=edge_weight,
)
assert torch.equal(x['v'], torch.arange(3))
assert edge_index['v', 'to', 'v'].tolist() == [[1, 2], [0, 1]]
assert torch.equal(edge_weight['v', 'to', 'v'], torch.arange(2))
class GNN(torch.nn.Module):
def __init__(self, num_layers: int):
super().__init__()
self.convs = torch.nn.ModuleList(
GraphConv(16, 16) for _ in range(num_layers))
def forward(
self,
x: Tensor,
edge_index: Tensor,
edge_weight: Tensor,
num_sampled_nodes: Optional[List[int]] = None,
num_sampled_edges: Optional[List[int]] = None,
) -> Tensor:
for i, conv in enumerate(self.convs):
if num_sampled_nodes is not None:
x, edge_index, edge_weight = trim_to_layer(
i, num_sampled_nodes, num_sampled_edges, x, edge_index,
edge_weight)
x = conv(x, edge_index, edge_weight)
return x
@withPackage('pyg_lib')
def test_trim_to_layer_with_neighbor_loader():
x = torch.randn(14, 16)
edge_index = torch.tensor([
[2, 3, 4, 5, 7, 7, 10, 11, 12, 13],
[0, 1, 2, 3, 2, 3, 7, 7, 7, 7],
])
edge_weight = torch.rand(edge_index.size(1))
data = Data(x=x, edge_index=edge_index, edge_weight=edge_weight)
loader = NeighborLoader(
data,
num_neighbors=[1, 2, 4],
batch_size=2,
shuffle=False,
)
batch = next(iter(loader))
model = GNN(num_layers=3)
out1 = model(batch.x, batch.edge_index, batch.edge_weight)[:2]
assert out1.size() == (2, 16)
out2 = model(batch.x, batch.edge_index, batch.edge_weight,
batch.num_sampled_nodes, batch.num_sampled_edges)[:2]
assert out2.size() == (2, 16)
assert torch.allclose(out1, out2, atol=1e-6)
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