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[Code Coverage] PNAConv and DegreeScalerAggregation #6600

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merged 9 commits into from
Feb 6, 2023
Merged

[Code Coverage] PNAConv and DegreeScalerAggregation #6600

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dac4a09
PNAConv test coverage and minor fixes
zechengz Feb 5, 2023
9480097
Update CHANGELOG.md
zechengz Feb 5, 2023
552baa0
Revert fixes and update tests
zechengz Feb 6, 2023
59ab4a5
Merge branch 'master' into zecheng_pna_conv_cov
zechengz Feb 6, 2023
e4d56a9
Update
zechengz Feb 6, 2023
0b97e00
Merge branch 'master' into zecheng_pna_conv_cov
rusty1s Feb 6, 2023
9bf0fbd
Update test/nn/aggr/test_scaler.py
rusty1s Feb 6, 2023
15de963
Update CHANGELOG.md
rusty1s Feb 6, 2023
960428a
Update test/nn/conv/test_pna_conv.py
rusty1s Feb 6, 2023
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5 changes: 3 additions & 2 deletions test/nn/aggr/test_scaler.py
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Expand Up @@ -4,7 +4,8 @@
from torch_geometric.nn import DegreeScalerAggregation


def test_degree_scaler_aggregation():
@pytest.mark.parametrize("train_norm", [True, False])
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def test_degree_scaler_aggregation(train_norm):
x = torch.randn(6, 16)
index = torch.tensor([0, 0, 1, 1, 1, 2])
ptr = torch.tensor([0, 2, 5, 6])
Expand All @@ -14,7 +15,7 @@ def test_degree_scaler_aggregation():
scaler = [
'identity', 'amplification', 'attenuation', 'linear', 'inverse_linear'
]
aggr = DegreeScalerAggregation(aggr, scaler, deg)
aggr = DegreeScalerAggregation(aggr, scaler, deg, train_norm=train_norm)
assert str(aggr) == 'DegreeScalerAggregation()'

out = aggr(x, index)
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12 changes: 7 additions & 5 deletions test/nn/conv/test_pna_conv.py
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@@ -1,3 +1,4 @@
import pytest
import torch
from torch_sparse import SparseTensor

Expand All @@ -12,16 +13,17 @@
]


def test_pna_conv():
@pytest.mark.parametrize("divide_input", [True, False])
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def test_pna_conv(divide_input):
x = torch.randn(4, 16)
edge_index = torch.tensor([[0, 0, 0, 1, 2, 3], [1, 2, 3, 0, 0, 0]])
deg = torch.tensor([0, 3, 0, 1])
row, col = edge_index
value = torch.rand(row.size(0), 3)
adj = SparseTensor(row=row, col=col, value=value, sparse_sizes=(4, 4))

conv = PNAConv(16, 32, aggregators, scalers,
deg=torch.tensor([0, 3, 0, 1]), edge_dim=3, towers=4)
assert conv.__repr__() == 'PNAConv(16, 32, towers=4, edge_dim=3)'
conv = PNAConv(16, 32, aggregators, scalers, deg=deg, edge_dim=3, towers=4,
pre_layers=2, post_layers=2, divide_input=divide_input)
assert str(conv) == 'PNAConv(16, 32, towers=4, edge_dim=3)'
out = conv(x, edge_index, value)
assert out.size() == (4, 32)
assert torch.allclose(conv(x, adj.t()), out, atol=1e-6)
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