update

Author: rusty1s

torch_geometric/metrics/link_pred.py

@@ -670,6 +670,9 @@ class LinkPredDiversity(_LinkPredMetric):
     def __init__(self, k: int, category: Tensor) -> None:
         super().__init__(k)
 
+        self.accum: Tensor
+        self.total: Tensor
+
         if WITH_TORCHMETRICS:
             self.add_state('accum', torch.tensor(0.), dist_reduce_fx='sum')
             self.add_state('total', torch.tensor(0), dist_reduce_fx='sum')
@@ -736,11 +739,14 @@ def __init__(
         self.max_src_nodes = max_src_nodes
         self.batch_size = batch_size
 
+        self.preds: List[Tensor]
+        self.total: Tensor
+
         if WITH_TORCHMETRICS:
             self.add_state('preds', default=[], dist_reduce_fx='cat')
             self.add_state('total', torch.tensor(0), dist_reduce_fx='sum')
         else:
-            self.preds: List[Tensor] = []
+            self.preds = []
             self.register_buffer('total', torch.tensor(0), persistent=False)
 
     def update(
@@ -826,6 +832,9 @@ class LinkPredAveragePopularity(_LinkPredMetric):
     def __init__(self, k: int, popularity: Tensor) -> None:
         super().__init__(k)
 
+        self.accum: Tensor
+        self.total: Tensor
+
         if WITH_TORCHMETRICS:
             self.add_state('accum', torch.tensor(0.), dist_reduce_fx='sum')
             self.add_state('total', torch.tensor(0), dist_reduce_fx='sum')

torch_geometric/transforms/rooted_subgraph.py

@@ -94,7 +94,7 @@ def map(
         arange = torch.arange(n_id.size(0), device=data.edge_index.device)
         node_map = data.edge_index.new_ones(num_nodes, num_nodes)
         node_map[n_sub_batch, n_id] = arange
-        sub_edge_index += (arange * data.num_nodes)[e_sub_batch]
+        sub_edge_index += (arange * num_nodes)[e_sub_batch]
         sub_edge_index = node_map.view(-1)[sub_edge_index]
 
         return sub_edge_index, n_id, e_id, n_sub_batch, e_sub_batch

torch_geometric/utils/_negative_sampling.py

@@ -109,10 +109,9 @@ def negative_sampling(
         idx = idx.to('cpu')
         for _ in range(3):  # Number of tries to sample negative indices.
             rnd = sample(population, sample_size, device='cpu')
-            mask = np.isin(rnd.numpy(), idx.numpy())  # type: ignore
+            mask = torch.from_numpy(np.isin(rnd.numpy(), idx.numpy())).bool()
             if neg_idx is not None:
-                mask |= np.isin(rnd, neg_idx.to('cpu'))
-            mask = torch.from_numpy(mask).to(torch.bool)
+                mask |= torch.from_numpy(np.isin(rnd, neg_idx.cpu())).bool()
             rnd = rnd[~mask].to(edge_index.device)
             neg_idx = rnd if neg_idx is None else torch.cat([neg_idx, rnd])
             if neg_idx.numel() >= num_neg_samples: