Merge branch 'master' into padarn/linkx-datasets

CHANGELOG.md

@@ -10,4 +10,6 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Added support for graph-level outputs in `to_hetero` ([#4582](https://github.com/pyg-team/pytorch_geometric/pull/4582))
 - Added `CHANGELOG.md` ([#4581](https://github.com/pyg-team/pytorch_geometric/pull/4581))
 ### Changed
+- The `bias` argument in `TAGConv` is now actually apllied ([#4597](https://github.com/pyg-team/pytorch_geometric/pull/4597))
+- Fixed subclass behaviour of `process` and `download` in `Datsaet` ([#4586](https://github.com/pyg-team/pytorch_geometric/pull/4586))
 ### Removed

test/data/test_dataset.py

@@ -1,6 +1,6 @@
 import torch
 
-from torch_geometric.data import Data, HeteroData, InMemoryDataset
+from torch_geometric.data import Data, Dataset, HeteroData, InMemoryDataset
 
 
 class MyTestDataset(InMemoryDataset):
@@ -99,3 +99,62 @@ def test_hetero_in_memory_dataset():
     assert dataset[1]['paper'].x.tolist() == data2['paper'].x.tolist()
     assert (dataset[1]['paper', 'paper'].edge_index.tolist() == data2[
         'paper', 'paper'].edge_index.tolist())
+
+
+def test_override_behaviour():
+    class DS(Dataset):
+        def __init__(self):
+            self.enter_download = False
+            self.enter_process = False
+            super().__init__()
+
+        def _download(self):
+            self.enter_download = True
+
+        def _process(self):
+            self.enter_process = True
+
+        def download(self):
+            pass
+
+        def process(self):
+            pass
+
+    class DS2(Dataset):
+        def __init__(self):
+            self.enter_download = False
+            self.enter_process = False
+            super().__init__()
+
+        def _download(self):
+            self.enter_download = True
+
+        def _process(self):
+            self.enter_process = True
+
+        def process(self):
+            pass
+
+    class DS3(Dataset):
+        def __init__(self):
+            self.enter_download = False
+            self.enter_process = False
+            super().__init__()
+
+        def _download(self):
+            self.enter_download = True
+
+        def _process(self):
+            self.enter_process = True
+
+    ds = DS()
+    assert ds.enter_download
+    assert ds.enter_process
+
+    ds = DS2()
+    assert not ds.enter_download
+    assert ds.enter_process
+
+    ds = DS3()
+    assert not ds.enter_download
+    assert not ds.enter_process

torch_geometric/data/dataset.py

@@ -80,10 +80,10 @@ def __init__(self, root: Optional[str] = None,
         self.pre_filter = pre_filter
         self._indices: Optional[Sequence] = None
 
-        if 'download' in self.__class__.__dict__:
+        if self.download.__qualname__.split('.')[0] != 'Dataset':
             self._download()
 
-        if 'process' in self.__class__.__dict__:
+        if self.process.__qualname__.split('.')[0] != 'Dataset':
             self._process()
 
     def indices(self) -> Sequence:

torch_geometric/data/in_memory_dataset.py

@@ -43,12 +43,6 @@ def raw_file_names(self) -> Union[str, List[str], Tuple]:
     def processed_file_names(self) -> Union[str, List[str], Tuple]:
         raise NotImplementedError
 
-    def download(self):
-        raise NotImplementedError
-
-    def process(self):
-        raise NotImplementedError
-
     def __init__(self, root: Optional[str] = None,
                  transform: Optional[Callable] = None,
                  pre_transform: Optional[Callable] = None,

torch_geometric/nn/conv/tag_conv.py

@@ -5,6 +5,7 @@
 from torch_geometric.nn.conv import MessagePassing
 from torch_geometric.nn.conv.gcn_conv import gcn_norm
 from torch_geometric.nn.dense.linear import Linear
+from torch_geometric.nn.inits import zeros
 from torch_geometric.typing import Adj, OptTensor
 
 
@@ -51,14 +52,21 @@ def __init__(self, in_channels: int, out_channels: int, K: int = 3,
         self.K = K
         self.normalize = normalize
 
-        self.lins = torch.nn.ModuleList(
-            [Linear(in_channels, out_channels) for _ in range(K + 1)])
+        self.lins = torch.nn.ModuleList([
+            Linear(in_channels, out_channels, bias=False) for _ in range(K + 1)
+        ])
+
+        if bias:
+            self.bias = torch.nn.Parameter(torch.Tensor(out_channels))
+        else:
+            self.register_parameter('bias', None)
 
         self.reset_parameters()
 
     def reset_parameters(self):
         for lin in self.lins:
             lin.reset_parameters()
+        zeros(self.bias)
 
     def forward(self, x: Tensor, edge_index: Adj,
                 edge_weight: OptTensor = None) -> Tensor:
@@ -80,6 +88,10 @@ def forward(self, x: Tensor, edge_index: Adj,
             x = self.propagate(edge_index, x=x, edge_weight=edge_weight,
                                size=None)
             out += lin.forward(x)
+
+        if self.bias is not None:
+            out += self.bias
+
         return out
 
     def message(self, x_j: Tensor, edge_weight: OptTensor) -> Tensor: