Merge branch 'master' into code-coverage

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), [#6600](https://github.com/pyg-team/pytorch_geometric/pull/6600), [#6623](https://github.com/pyg-team/pytorch_geometric/pull/6623))
+- 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), [#6618](https://github.com/pyg-team/pytorch_geometric/pull/6618), [#6619](https://github.com/pyg-team/pytorch_geometric/pull/6619), [#6621](https://github.com/pyg-team/pytorch_geometric/pull/6621), [#6623](https://github.com/pyg-team/pytorch_geometric/pull/6623))
 - 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/profile/test_profile_utils.py

@@ -11,6 +11,10 @@
     get_gpu_memory_from_nvidia_smi,
     get_model_size,
 )
+from torch_geometric.profile.utils import (
+    byte_to_megabyte,
+    medibyte_to_megabyte,
+)
 from torch_geometric.testing import onlyCUDA
 
 
@@ -53,3 +57,8 @@ def test_get_gpu_memory_from_nvidia_smi():
     free_mem, used_mem = get_gpu_memory_from_nvidia_smi(device=0, digits=2)
     assert free_mem >= 0
     assert used_mem >= 0
+
+
+def test_bytes_function():
+    assert byte_to_megabyte((1024 * 1024)) == 1.00
+    assert medibyte_to_megabyte(1 / 1.0485) == 1.00

test/transforms/test_to_device.py

@@ -0,0 +1,21 @@
+import torch
+
+from torch_geometric.data import Data
+from torch_geometric.testing import withCUDA
+from torch_geometric.transforms import ToDevice
+
+
+@withCUDA
+def test_to_device(device):
+    x = torch.randn(3, 4)
+    edge_index = torch.tensor([[0, 1, 1, 2], [1, 0, 2, 1]])
+    edge_weight = torch.randn(edge_index.size(1))
+
+    data = Data(x=x, edge_index=edge_index, edge_weight=edge_weight)
+
+    transform = ToDevice(device)
+    assert str(transform) == f'ToDevice({device})'
+
+    data = transform(data)
+    for key, value in data:
+        assert value.device == device

test/utils/test_negative_sampling.py

@@ -69,6 +69,10 @@ def test_negative_sampling():
     assert neg_edge_index.size(1) == edge_index.size(1)
     assert is_negative(edge_index, neg_edge_index, (4, 4), bipartite=False)
 
+    neg_edge_index = negative_sampling(edge_index, method='dense')
+    assert neg_edge_index.size(1) == edge_index.size(1)
+    assert is_negative(edge_index, neg_edge_index, (4, 4), bipartite=False)
+
     neg_edge_index = negative_sampling(edge_index, num_neg_samples=2)
     assert neg_edge_index.size(1) == 2
     assert is_negative(edge_index, neg_edge_index, (4, 4), bipartite=False)