clean up

README.md

@@ -21,7 +21,8 @@ from torch_geometric_autoscale import ScalableGNN
 
 class GNN(ScalableGNN):
     def __init__(self, num_nodes, in_channels, hidden_channels, out_channels, num_layers):
-        super(GNN, self).__init__(num_nodes, hidden_channels, num_layers)
+        super(GNN, self).__init__(num_nodes, hidden_channels, num_layers,
+                                  pool_size=2, buffer_size=5000)
 
         self.convs = ModuleList()
         self.convs.append(GCNConv(in_channels, hidden_channels))
@@ -29,11 +30,20 @@ class GNN(ScalableGNN):
             self.convs.append(GCNConv(hidden_channels, hidden_channels))
         self.convs.append(GCNConv(hidden_channels, out_channels))
 
-    def forward(self, x, adj_t, batch_size, n_id):
+    def forward(self, x, adj_t, *args):
         for conv, history in zip(self.convs[:-1], self.histories):
             x = conv(x, adj_t).relu_()
-            x = self.push_and_pull(history, x, batch_size, n_id)
+            x = self.push_and_pull(history, x, *args)
         return self.convs[-1](x, adj_t)
+
+
+
+perm, ptr = metis(data.adj_t, num_parts=40, log=True)
+data = permute(data, perm, log=True)
+loader = SubgraphLoader(data, ptr, batch_size=10, shuffle=True)
+
+for batch, *args in loader:
+    out = model(batch.x, batch.adj_t, *args)
 ```
 
 ## Requirements

torch_geometric_autoscale/data.py

@@ -110,24 +110,25 @@ def get_sbm(root: str, name: str) -> Tuple[Data, int, int]:
 def get_data(root: str, name: str) -> Tuple[Data, int, int]:
     if name.lower() in ['cora', 'citeseer', 'pubmed']:
         return get_planetoid(root, name)
-    if name.lower() in ['coauthorcs', 'coauthorphysics']:
+    elif name.lower() in ['coauthorcs', 'coauthorphysics']:
         return get_coauthor(root, name[8:])
-    if name.lower() in ['amazoncomputers', 'amazonphoto']:
+    elif name.lower() in ['amazoncomputers', 'amazonphoto']:
         return get_amazon(root, name[6:])
-    if name.lower() == 'wikics':
+    elif name.lower() == 'wikics':
         return get_wikics(root)
-    if name.lower() in ['cluster', 'pattern']:
+    elif name.lower() in ['cluster', 'pattern']:
         return get_sbm(root, name)
-    if name.lower() == 'reddit':
+    elif name.lower() == 'reddit':
         return get_reddit(root)
-    if name.lower() == 'ppi':
+    elif name.lower() == 'ppi':
         return get_ppi(root)
-    if name.lower() == 'flickr':
+    elif name.lower() == 'flickr':
         return get_flickr(root)
-    if name.lower() == 'yelp':
+    elif name.lower() == 'yelp':
         return get_yelp(root)
-    if name.lower() in ['ogbn-arxiv', 'arxiv']:
+    elif name.lower() in ['ogbn-arxiv', 'arxiv']:
         return get_arxiv(root)
-    if name.lower() in ['ogbn-products', 'products']:
+    elif name.lower() in ['ogbn-products', 'products']:
         return get_products(root)
+    else:
         raise NotImplementedError

torch_geometric_autoscale/metis.py

@@ -46,9 +46,9 @@ def permute(data: Union[Data, SparseTensor], perm: Tensor,
         for key, item in data:
             if isinstance(item, Tensor) and item.size(0) == data.num_nodes:
                 data[key] = item[perm]
-            if isinstance(item, Tensor) and item.size(0) == data.num_edges:
+            elif isinstance(item, Tensor) and item.size(0) == data.num_edges:
                 raise NotImplementedError
-            if isinstance(item, SparseTensor):
+            elif isinstance(item, SparseTensor):
                 data[key] = permute(item, perm, log=False)
     else:
         data = data.permute(perm)

torch_geometric_autoscale/models/base.py

@@ -18,7 +18,7 @@ class ScalableGNN(torch.nn.Module):
         self.num_nodes = num_nodes
         self.hidden_channels = hidden_channels
         self.num_layers = num_layers
-        self.pool_size = num_layers if pool_size is None else pool_size
+        self.pool_size = num_layers - 1 if pool_size is None else pool_size
         self.buffer_size = buffer_size
 
         self.histories = torch.nn.ModuleList([
@@ -59,13 +59,17 @@ class ScalableGNN(torch.nn.Module):
         for history in self.histories:
             history.reset_parameters()
 
-    def __call__(self, x: Optional[Tensor] = None,
+    def __call__(
+        self,
+        x: Optional[Tensor] = None,
         adj_t: Optional[SparseTensor] = None,
         batch_size: Optional[int] = None,
         n_id: Optional[Tensor] = None,
         offset: Optional[Tensor] = None,
-                 count: Optional[Tensor] = None, loader=None,
-                 **kwargs) -> Tensor:
+        count: Optional[Tensor] = None,
+        loader=None,
+        **kwargs,
+    ) -> Tensor:
 
         if loader is not None:
             return self.mini_inference(loader)