[Code Coverage] PNAConv and DegreeScalerAggregation (#6600)

PNAConv test coverage

---------

Co-authored-by: Matthias Fey <matthias.fey@tu-dortmund.de>

CHANGELOG.md

@@ -78,7 +78,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 ### Changed
 
 - Fixed a bug in `PNAConv` and `DegreeScalerAggregation` to correctly incorporate degree statistics of isolated nodes ([#6609](https://github.com/pyg-team/pytorch_geometric/pull/6609))
-- Improved code coverage ([#6523](https://github.com/pyg-team/pytorch_geometric/pull/6523), [#6538](https://github.com/pyg-team/pytorch_geometric/pull/6538), [#6555](https://github.com/pyg-team/pytorch_geometric/pull/6555), [#6558](https://github.com/pyg-team/pytorch_geometric/pull/6558), [#6568](https://github.com/pyg-team/pytorch_geometric/pull/6568), [#6573](https://github.com/pyg-team/pytorch_geometric/pull/6573), [#6578](https://github.com/pyg-team/pytorch_geometric/pull/6578), [#6597](https://github.com/pyg-team/pytorch_geometric/pull/6597))
+- Improved code coverage ([#6523](https://github.com/pyg-team/pytorch_geometric/pull/6523), [#6538](https://github.com/pyg-team/pytorch_geometric/pull/6538), [#6555](https://github.com/pyg-team/pytorch_geometric/pull/6555), [#6558](https://github.com/pyg-team/pytorch_geometric/pull/6558), [#6568](https://github.com/pyg-team/pytorch_geometric/pull/6568), [#6573](https://github.com/pyg-team/pytorch_geometric/pull/6573), [#6578](https://github.com/pyg-team/pytorch_geometric/pull/6578), [#6597](https://github.com/pyg-team/pytorch_geometric/pull/6597), [#6600](https://github.com/pyg-team/pytorch_geometric/pull/6600))
 - Fixed a bug in which `data.to_heterogeneous()` filtered attributs in the wrong dimension ([#6522](https://github.com/pyg-team/pytorch_geometric/pull/6522))
 - Breaking Change: Temporal sampling will now also sample nodes with an equal timestamp to the seed time (requires `pyg-lib>0.1.0`) ([#6517](https://github.com/pyg-team/pytorch_geometric/pull/6517))
 - Changed `DataLoader` workers with affinity to start at `cpu0` ([#6512](https://github.com/pyg-team/pytorch_geometric/pull/6512))

test/nn/aggr/test_scaler.py

@@ -4,7 +4,8 @@
 from torch_geometric.nn import DegreeScalerAggregation
 
 
-def test_degree_scaler_aggregation():
+@pytest.mark.parametrize('train_norm', [True, False])
+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])
@@ -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)

test/nn/conv/test_pna_conv.py

@@ -1,3 +1,4 @@
+import pytest
 import torch
 from torch_sparse import SparseTensor
 
@@ -12,16 +13,17 @@
 ]
 
 
-def test_pna_conv():
+@pytest.mark.parametrize('divide_input', [True, False])
+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)