fix linkpred models

Author: EdisonLeeeee

graphgallery/gallery/linkpred/pyg/gae.py

@@ -35,7 +35,7 @@ def model_step(self,
                    lr=0.01,
                    bias=False):
 
-        model = get_model("autoencoder.VGAE", self.backend)
+        model = get_model("autoencoder.GAE", self.backend)
         model = model(self.graph.num_node_attrs,
                       out_features=out_features,
                       hids=hids,

graphgallery/nn/models/pyg/autoencoder/autoencoder.py

@@ -20,7 +20,7 @@ def train_step_on_batch(self,
         self.train()
         optimizer = self.optimizer
         optimizer.zero_grad()
-        x = to_device(x, device=device)
+        x, _ = to_device(x, device=device)
         z = self.encode(*x)
         # here `out_index` maybe pos_edge_index
         # or (pos_edge_index, neg_edge_index)
@@ -65,7 +65,7 @@ def test_step_on_batch(self,
                            device="cpu"):
         self.eval()
         metrics = self.metrics
-        x = to_device(x, device=device)
+        x, _ = to_device(x, device=device)
         z = self.encode(*x)
         pred = self.decode(z, out_index)
 
@@ -78,7 +78,7 @@ def test_step_on_batch(self,
     @torch.no_grad()
     def predict_step_on_batch(self, x, out_index=None, device="cpu"):
         self.eval()
-        x = to_device(x, device=device)
+        x, _ = to_device(x, device=device)
         z = self.encode(*x)
         pred = self.decode(z, out_index)
         return pred.cpu().detach()

graphgallery/nn/models/pytorch/autoencoder/autoencoder.py

@@ -27,7 +27,7 @@ def test_step_on_batch(self,
         x, y = to_device(x, y, device=device)
         z = self.encode(*x)
         out = self.decode(z, out_index)
-        loss = self.compute_loss(out, y)
+        loss, out = self.compute_loss(out, y)
         self.update_metrics(out, y)
 
         if loss is not None:

graphgallery/nn/models/pytorch/autoencoder/vgae.py

@@ -63,11 +63,12 @@ def forward(self, x, adj):
         out = self.decode(z)
         return out
 
-    def compute_loss(self, out, y):
+    def compute_loss(self, out, y, out_index=None):
+        out = self.index_select(out, out_index=out_index)
         if self.training:
             mu = self.cache.pop('mu')
             logstd = self.cache.pop('logstd')
             kl_loss = -0.5 / mu.size(0) * torch.mean(torch.sum(1 + 2 * logstd - mu.pow(2) - logstd.exp().pow(2), dim=1))
         else:
             kl_loss = 0.
-        return self.loss(out, y) + kl_loss
+        return self.loss(out, y) + kl_loss, out

graphgallery/nn/models/torch_keras.py

@@ -115,7 +115,7 @@ def test_step_on_batch(self,
     @torch.no_grad()
     def predict_step_on_batch(self, x, out_index=None, device="cpu"):
         self.eval()
-        x = to_device(x, device=device)
+        x, _ = to_device(x, device=device)
         out = self.index_select(self(*x), out_index=out_index)
         return out.cpu().detach()