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import numpy as np
import torch
from torch_geometric.data import HeteroData
from torch_geometric.loader import HGTLoader
from torch_geometric.nn import GraphConv, to_hetero
from torch_geometric.testing import (
get_random_edge_index,
onlyOnline,
withPackage,
)
from torch_geometric.typing import SparseTensor
from torch_geometric.utils import k_hop_subgraph
def is_subset(subedge_index, edge_index, src_idx, dst_idx):
num_nodes = int(edge_index.max()) + 1
idx = num_nodes * edge_index[0] + edge_index[1]
subidx = num_nodes * src_idx[subedge_index[0]] + dst_idx[subedge_index[1]]
mask = torch.from_numpy(np.isin(subidx, idx))
return int(mask.sum()) == mask.numel()
@withPackage('torch_sparse')
def test_hgt_loader():
torch.manual_seed(12345)
data = HeteroData()
data['paper'].x = torch.arange(100)
data['author'].x = torch.arange(100, 300)
data['paper', 'paper'].edge_index = get_random_edge_index(100, 100, 500)
data['paper', 'paper'].edge_attr = torch.arange(500)
data['paper', 'author'].edge_index = get_random_edge_index(100, 200, 1000)
data['paper', 'author'].edge_attr = torch.arange(500, 1500)
data['author', 'paper'].edge_index = get_random_edge_index(200, 100, 1000)
data['author', 'paper'].edge_attr = torch.arange(1500, 2500)
r1, c1 = data['paper', 'paper'].edge_index
r2, c2 = data['paper', 'author'].edge_index + torch.tensor([[0], [100]])
r3, c3 = data['author', 'paper'].edge_index + torch.tensor([[100], [0]])
full_adj = SparseTensor(
row=torch.cat([r1, r2, r3]),
col=torch.cat([c1, c2, c3]),
value=torch.arange(2500),
)
batch_size = 20
loader = HGTLoader(data, num_samples=[5] * 4, batch_size=batch_size,
input_nodes='paper')
assert str(loader) == 'HGTLoader()'
assert len(loader) == (100 + batch_size - 1) // batch_size
for batch in loader:
assert isinstance(batch, HeteroData)
# Test node and types:
assert set(batch.node_types) == {'paper', 'author'}
assert set(batch.edge_types) == set(data.edge_types)
assert len(batch['paper']) == 4
assert batch['paper'].n_id.size() == (batch['paper'].num_nodes, )
assert batch['paper'].x.size() == (40, ) # 20 + 4 * 5
assert batch['paper'].input_id.numel() == batch_size
assert batch['paper'].batch_size == batch_size
assert batch['paper'].x.min() >= 0 and batch['paper'].x.max() < 100
assert len(batch['author']) == 2
assert batch['author'].n_id.size() == (batch['author'].num_nodes, )
assert batch['author'].x.size() == (20, ) # 4 * 5
assert batch['author'].x.min() >= 100 and batch['author'].x.max() < 300
# Test edge type selection:
assert set(batch.edge_types) == {('paper', 'to', 'paper'),
('paper', 'to', 'author'),
('author', 'to', 'paper')}
assert len(batch['paper', 'paper']) == 3
num_edges = batch['paper', 'paper'].num_edges
assert batch['paper', 'paper'].e_id.size() == (num_edges, )
row, col = batch['paper', 'paper'].edge_index
value = batch['paper', 'paper'].edge_attr
adj = full_adj[batch['paper'].x, batch['paper'].x]
assert row.min() >= 0 and row.max() < 40
assert col.min() >= 0 and col.max() < 40
assert value.min() >= 0 and value.max() < 500
assert adj.nnz() == row.size(0)
assert torch.allclose(row.unique(), adj.storage.row().unique())
assert torch.allclose(col.unique(), adj.storage.col().unique())
assert torch.allclose(value.unique(), adj.storage.value().unique())
assert is_subset(batch['paper', 'paper'].edge_index,
data['paper', 'paper'].edge_index, batch['paper'].x,
batch['paper'].x)
assert len(batch['paper', 'author']) == 3
num_edges = batch['paper', 'author'].num_edges
assert batch['paper', 'author'].e_id.size() == (num_edges, )
row, col = batch['paper', 'author'].edge_index
value = batch['paper', 'author'].edge_attr
adj = full_adj[batch['paper'].x, batch['author'].x]
assert row.min() >= 0 and row.max() < 40
assert col.min() >= 0 and col.max() < 20
assert value.min() >= 500 and value.max() < 1500
assert adj.nnz() == row.size(0)
assert torch.allclose(row.unique(), adj.storage.row().unique())
assert torch.allclose(col.unique(), adj.storage.col().unique())
assert torch.allclose(value.unique(), adj.storage.value().unique())
assert is_subset(batch['paper', 'author'].edge_index,
data['paper', 'author'].edge_index, batch['paper'].x,
batch['author'].x - 100)
assert len(batch['author', 'paper']) == 3
num_edges = batch['author', 'paper'].num_edges
assert batch['author', 'paper'].e_id.size() == (num_edges, )
row, col = batch['author', 'paper'].edge_index
value = batch['author', 'paper'].edge_attr
adj = full_adj[batch['author'].x, batch['paper'].x]
assert row.min() >= 0 and row.max() < 20
assert col.min() >= 0 and col.max() < 40
assert value.min() >= 1500 and value.max() < 2500
assert adj.nnz() == row.size(0)
assert torch.allclose(row.unique(), adj.storage.row().unique())
assert torch.allclose(col.unique(), adj.storage.col().unique())
assert torch.allclose(value.unique(), adj.storage.value().unique())
assert is_subset(batch['author', 'paper'].edge_index,
data['author', 'paper'].edge_index,
batch['author'].x - 100, batch['paper'].x)
# Test for isolated nodes (there shouldn't exist any):
n_id = torch.cat([batch['paper'].x, batch['author'].x])
row, col, _ = full_adj[n_id, n_id].coo()
assert torch.cat([row, col]).unique().numel() >= 59
@onlyOnline
@withPackage('torch_sparse')
def test_hgt_loader_on_cora(get_dataset):
dataset = get_dataset(name='Cora')
data = dataset[0]
data.edge_weight = torch.rand(data.num_edges)
hetero_data = HeteroData()
hetero_data['paper'].x = data.x
hetero_data['paper'].n_id = torch.arange(data.num_nodes)
hetero_data['paper', 'paper'].edge_index = data.edge_index
hetero_data['paper', 'paper'].edge_weight = data.edge_weight
split_idx = torch.arange(5, 8)
# Sample the complete two-hop neighborhood:
loader = HGTLoader(hetero_data, num_samples=[data.num_nodes] * 2,
batch_size=split_idx.numel(),
input_nodes=('paper', split_idx))
assert len(loader) == 1
hetero_batch = next(iter(loader))
batch_size = hetero_batch['paper'].batch_size
n_id, _, _, e_mask = k_hop_subgraph(split_idx, num_hops=2,
edge_index=data.edge_index,
num_nodes=data.num_nodes)
n_id = n_id.sort()[0]
assert n_id.tolist() == hetero_batch['paper'].n_id.sort()[0].tolist()
assert hetero_batch['paper', 'paper'].num_edges == int(e_mask.sum())
class GNN(torch.nn.Module):
def __init__(self, in_channels, hidden_channels, out_channels):
super().__init__()
self.conv1 = GraphConv(in_channels, hidden_channels)
self.conv2 = GraphConv(hidden_channels, out_channels)
def forward(self, x, edge_index, edge_weight):
x = self.conv1(x, edge_index, edge_weight).relu()
x = self.conv2(x, edge_index, edge_weight).relu()
return x
model = GNN(dataset.num_features, 16, dataset.num_classes)
hetero_model = to_hetero(model, hetero_data.metadata())
out1 = model(data.x, data.edge_index, data.edge_weight)[split_idx]
out2 = hetero_model(hetero_batch.x_dict, hetero_batch.edge_index_dict,
hetero_batch.edge_weight_dict)['paper'][:batch_size]
assert torch.allclose(out1, out2, atol=1e-6)
@withPackage('torch_sparse')
def test_hgt_loader_disconnected():
data = HeteroData()
data['paper'].x = torch.randn(10, 16)
data['author'].x = torch.randn(10, 16)
# Paper nodes are disconnected from author nodes:
data['paper', 'paper'].edge_index = get_random_edge_index(10, 10, 15)
data['paper', 'paper'].edge_attr = torch.randn(15, 8)
data['author', 'author'].edge_index = get_random_edge_index(10, 10, 15)
data['author', 'author'].edge_attr = torch.randn(15, 8)
loader = HGTLoader(data, num_samples=[2], batch_size=2,
input_nodes='paper')
for batch in loader:
assert isinstance(batch, HeteroData)
# Test node and edge types:
assert set(batch.node_types) == set(data.node_types)
assert set(batch.edge_types) == set(data.edge_types)
assert batch['author'].num_nodes == 0
assert batch['author'].x.size() == (0, 16)
assert batch['author', 'author'].num_edges == 0
assert batch['author', 'author'].edge_index.size() == (2, 0)
assert batch['author', 'author'].edge_attr.size() == (0, 8)
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