Fix the interplay between TUDataset and pre_transform that modify node features

CHANGELOG.md

@@ -23,6 +23,7 @@ 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
+- Fixed the interplay between `TUDataset` and `pre_transform` that modify node features ([#4669](https://github.com/pyg-team/pytorch_geometric/pull/4669))
 - Make use of the `pyg_sphinx_theme` documentation template ([#4664](https://github.com/pyg-team/pyg-lib/pull/4664), [#4667](https://github.com/pyg-team/pyg-lib/pull/4667))
 - Refactored reading molecular positions from sdf file for qm9 datasets ([4654](https://github.com/pyg-team/pytorch_geometric/pull/4654))
 - Fixed `MLP.jittable()` bug in case `return_emb=True` ([#4645](https://github.com/pyg-team/pytorch_geometric/pull/4645), [#4648](https://github.com/pyg-team/pytorch_geometric/pull/4648))

torch_geometric/datasets/tu_dataset.py

@@ -121,7 +121,16 @@ def __init__(self, root: str, name: str,
         self.name = name
         self.cleaned = cleaned
         super().__init__(root, transform, pre_transform, pre_filter)
-        self.data, self.slices = torch.load(self.processed_paths[0])
+
+        out = torch.load(self.processed_paths[0])
+        if not isinstance(out, tuple) and len(out) != 3:
+            raise RuntimeError(
+                "The 'data' object was created by an older version of PyG. "
+                "If this error occurred while loading an already existing "
+                "dataset, remove the 'processed/' directory in the dataset's "
+                "root folder and try again.")
+        self.data, self.slices, self.sizes = out
+
         if self.data.x is not None and not use_node_attr:
             num_node_attributes = self.num_node_attributes
             self.data.x = self.data.x[:, num_node_attributes:]
@@ -141,34 +150,19 @@ def processed_dir(self) -> str:
 
     @property
     def num_node_labels(self) -> int:
-        if self.data.x is None:
-            return 0
-        for i in range(self.data.x.size(1)):
-            x = self.data.x[:, i:]
-            if ((x == 0) | (x == 1)).all() and (x.sum(dim=1) == 1).all():
-                return self.data.x.size(1) - i
-        return 0
+        return self.sizes['num_node_labels']
 
     @property
     def num_node_attributes(self) -> int:
-        if self.data.x is None:
-            return 0
-        return self.data.x.size(1) - self.num_node_labels
+        return self.sizes['num_node_attributes']
 
     @property
     def num_edge_labels(self) -> int:
-        if self.data.edge_attr is None:
-            return 0
-        for i in range(self.data.edge_attr.size(1)):
-            if self.data.edge_attr[:, i:].sum() == self.data.edge_attr.size(0):
-                return self.data.edge_attr.size(1) - i
-        return 0
+        return self.sizes['num_edge_labels']
 
     @property
     def num_edge_attributes(self) -> int:
-        if self.data.edge_attr is None:
-            return 0
-        return self.data.edge_attr.size(1) - self.num_edge_labels
+        return self.sizes['num_edge_attributes']
 
     @property
     def raw_file_names(self) -> List[str]:
@@ -189,7 +183,7 @@ def download(self):
         os.rename(osp.join(folder, self.name), self.raw_dir)
 
     def process(self):
-        self.data, self.slices = read_tu_data(self.raw_dir, self.name)
+        self.data, self.slices, sizes = read_tu_data(self.raw_dir, self.name)
 
         if self.pre_filter is not None:
             data_list = [self.get(idx) for idx in range(len(self))]
@@ -201,7 +195,7 @@ def process(self):
             data_list = [self.pre_transform(data) for data in data_list]
             self.data, self.slices = self.collate(data_list)
 
-        torch.save((self.data, self.slices), self.processed_paths[0])
+        torch.save((self.data, self.slices, sizes), self.processed_paths[0])
 
     def __repr__(self) -> str:
         return f'{self.name}({len(self)})'

torch_geometric/io/tu.py

@@ -24,9 +24,11 @@ def read_tu_data(folder, prefix):
     edge_index = read_file(folder, prefix, 'A', torch.long).t() - 1
     batch = read_file(folder, prefix, 'graph_indicator', torch.long) - 1
 
-    node_attributes = node_labels = None
+    node_attributes = torch.empty((batch.size(0), 0))
     if 'node_attributes' in names:
         node_attributes = read_file(folder, prefix, 'node_attributes')
+
+    node_labels = torch.empty((batch.size(0), 0))
     if 'node_labels' in names:
         node_labels = read_file(folder, prefix, 'node_labels', torch.long)
         if node_labels.dim() == 1:
@@ -35,11 +37,12 @@ def read_tu_data(folder, prefix):
         node_labels = node_labels.unbind(dim=-1)
         node_labels = [F.one_hot(x, num_classes=-1) for x in node_labels]
         node_labels = torch.cat(node_labels, dim=-1).to(torch.float)
-    x = cat([node_attributes, node_labels])
 
-    edge_attributes, edge_labels = None, None
+    edge_attributes = torch.empty((edge_index.size(1), 0))
     if 'edge_attributes' in names:
         edge_attributes = read_file(folder, prefix, 'edge_attributes')
+
+    edge_labels = torch.empty((edge_index.size(1), 0))
     if 'edge_labels' in names:
         edge_labels = read_file(folder, prefix, 'edge_labels', torch.long)
         if edge_labels.dim() == 1:
@@ -48,6 +51,8 @@ def read_tu_data(folder, prefix):
         edge_labels = edge_labels.unbind(dim=-1)
         edge_labels = [F.one_hot(e, num_classes=-1) for e in edge_labels]
         edge_labels = torch.cat(edge_labels, dim=-1).to(torch.float)
+
+    x = cat([node_attributes, node_labels])
     edge_attr = cat([edge_attributes, edge_labels])
 
     y = None
@@ -65,7 +70,14 @@ def read_tu_data(folder, prefix):
     data = Data(x=x, edge_index=edge_index, edge_attr=edge_attr, y=y)
     data, slices = split(data, batch)
 
-    return data, slices
+    sizes = {
+        'num_node_attributes': node_attributes.size(-1),
+        'num_node_labels': node_labels.size(-1),
+        'num_edge_attributes': edge_attributes.size(-1),
+        'num_edge_labels': edge_labels.size(-1),
+    }
+
+    return data, slices, sizes
 
 
 def read_file(folder, prefix, name, dtype=None):
@@ -75,6 +87,7 @@ def read_file(folder, prefix, name, dtype=None):
 
 def cat(seq):
     seq = [item for item in seq if item is not None]
+    seq = [item for item in seq if item.numel() > 0]
     seq = [item.unsqueeze(-1) if item.dim() == 1 else item for item in seq]
     return torch.cat(seq, dim=-1) if len(seq) > 0 else None