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import torch
from torch_geometric.data import Data
from torch_geometric.nn import GCNConv
from torch_geometric.utils import total_influence
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 x2
def test_total_influence_smoke():
x = torch.randn(6, 5)
edge_index = torch.tensor([[0, 1, 2, 3, 4], [1, 2, 3, 4, 5]])
max_hops = 2
num_samples = 4
data = Data(
x=x,
edge_index=edge_index,
)
model = GNN()
I, R = total_influence(
model,
data,
max_hops=max_hops,
num_samples=num_samples,
)
assert I.shape == (max_hops + 1, )
assert 0.0 <= R <= max_hops
I, R = total_influence(
model,
data,
max_hops=max_hops,
num_samples=num_samples,
average=False,
)
assert I.shape == torch.Size([num_samples, max_hops + 1])
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