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import pytest
import torch
from torch_geometric.nn import GCNConv, Linear
from torch_geometric.utils import get_embeddings
from torch_geometric.utils.embedding import get_embeddings_hetero
class GNN(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv1 = GCNConv(5, 6)
self.conv2 = GCNConv(6, 7)
def forward(self, x0, edge_index):
x1 = self.conv1(x0, edge_index)
x2 = self.conv2(x1, edge_index)
return [x1, x2]
def test_get_embeddings():
x = torch.randn(6, 5)
edge_index = torch.tensor([[0, 1, 2, 3, 4], [1, 2, 3, 4, 5]])
with pytest.warns(UserWarning, match="any 'MessagePassing' layers"):
intermediate_outs = get_embeddings(Linear(5, 5), x)
assert len(intermediate_outs) == 0
model = GNN()
expected_embeddings = model(x, edge_index)
embeddings = get_embeddings(model, x, edge_index)
assert len(embeddings) == 2
for expected, out in zip(expected_embeddings, embeddings):
assert torch.allclose(expected, out)
def test_get_embeddings_hetero(hetero_data, hetero_model):
# Create model using the metadata from hetero_data
metadata = hetero_data.metadata()
model = hetero_model(metadata)
# Get heterogeneous embeddings
embeddings_dict = get_embeddings_hetero(model, None, hetero_data.x_dict,
hetero_data.edge_index_dict)
# Verify the structure of the returned embeddings
assert isinstance(embeddings_dict, dict)
assert 'paper' in embeddings_dict
assert 'author' in embeddings_dict
# Verify that we have embeddings for both node types
assert len(embeddings_dict['paper']) > 0
assert len(embeddings_dict['author']) > 0
# Check that the embeddings have the right shape
num_paper_nodes = hetero_data['paper'].num_nodes
num_author_nodes = hetero_data['author'].num_nodes
# Verify dimensions of embeddings
assert embeddings_dict['paper'][0].shape == (num_paper_nodes, 32
) # First layer
assert embeddings_dict['author'][0].shape == (num_author_nodes, 32
) # First layer
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