[bugfix] Fix bugs in vgae (#2727)

* [Example]Variational Graph Auto-Encoders

* change dgl dataset to single directional graph

* clean code

* refresh

* fix bug

* fix bug

* fix bug

* add gpu
Co-authored-by: Tianjun Xiao <xiaotj1990327@gmail.com>
Co-authored-by: Minjie Wang <wmjlyjemaine@gmail.com>

examples/pytorch/vgae/model.py

@@ -3,6 +3,8 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
+from train import device
+
 
 class VGAEModel(nn.Module):
     def __init__(self, in_dim, hidden1_dim, hidden2_dim):
@@ -20,8 +22,8 @@ class VGAEModel(nn.Module):
         h = self.layers[0](g, features)
         self.mean = self.layers[1](g, h)
         self.log_std = self.layers[2](g, h)
-        gaussian_noise = torch.randn(features.size(0), self.hidden2_dim)
-        sampled_z = self.mean + gaussian_noise * torch.exp(self.log_std)
+        gaussian_noise = torch.randn(features.size(0), self.hidden2_dim).to(device)
+        sampled_z = self.mean + gaussian_noise * torch.exp(self.log_std).to(device)
         return sampled_z
 
     def decoder(self, z):

examples/pytorch/vgae/preprocess.py

@@ -88,7 +88,7 @@ def mask_test_edges(adj):
 def mask_test_edges_dgl(graph, adj):
     src, dst = graph.edges()
     edges_all = torch.stack([src, dst], dim=0)
-    edges_all = edges_all.t().numpy()
+    edges_all = edges_all.t().cpu().numpy()
     num_test = int(np.floor(edges_all.shape[0] / 10.))
     num_val = int(np.floor(edges_all.shape[0] / 20.))
 

examples/pytorch/vgae/train.py

@@ -23,11 +23,15 @@ parser.add_argument('--hidden1', '-h1', type=int, default=32, help='Number of un
 parser.add_argument('--hidden2', '-h2', type=int, default=16, help='Number of units in hidden layer 2.')
 parser.add_argument('--datasrc', '-s', type=str, default='dgl',
                     help='Dataset download from dgl Dataset or website.')
-parser.add_argument('--dataset', '-d', type=str, default='pubmed', help='Dataset string.')
+parser.add_argument('--dataset', '-d', type=str, default='cora', help='Dataset string.')
 parser.add_argument('--gpu_id', type=int, default=0, help='GPU id to use.')
 args = parser.parse_args()
 
 
+# check device
+device = torch.device("cuda:{}".format(args.gpu_id) if torch.cuda.is_available() else "cpu")
+# device = "cpu"
+
 # roc_means = []
 # ap_means = []
 
@@ -35,7 +39,7 @@ def compute_loss_para(adj):
     pos_weight = ((adj.shape[0] * adj.shape[0] - adj.sum()) / adj.sum())
     norm = adj.shape[0] * adj.shape[0] / float((adj.shape[0] * adj.shape[0] - adj.sum()) * 2)
     weight_mask = adj.view(-1) == 1
-    weight_tensor = torch.ones(weight_mask.size(0))
+    weight_tensor = torch.ones(weight_mask.size(0)).to(device)
     weight_tensor[weight_mask] = pos_weight
     return weight_tensor, norm
 
@@ -51,6 +55,7 @@ def get_scores(edges_pos, edges_neg, adj_rec):
     def sigmoid(x):
         return 1 / (1 + np.exp(-x))
 
+    adj_rec = adj_rec.cpu()
     # Predict on test set of edges
     preds = []
     for e in edges_pos:
@@ -80,21 +85,18 @@ def dgl_main():
         raise NotImplementedError
     graph = dataset[0]
 
-    # check device
-    device = torch.device("cuda:{}".format(args.gpu_id) if torch.cuda.is_available() else "cpu")
-
     # Extract node features
     feats = graph.ndata.pop('feat').to(device)
     in_dim = feats.shape[-1]
 
-    graph = graph.to(device)
-
     # generate input
-    adj_orig = graph.adjacency_matrix().to_dense().to(device)
+    adj_orig = graph.adjacency_matrix().to_dense()
 
     # build test set with 10% positive links
     train_edge_idx, val_edges, val_edges_false, test_edges, test_edges_false = mask_test_edges_dgl(graph, adj_orig)
 
+    graph = graph.to(device)
+
     # create train graph
     train_edge_idx = torch.tensor(train_edge_idx).to(device)
     train_graph = dgl.edge_subgraph(graph, train_edge_idx, preserve_nodes=True)
@@ -119,7 +121,7 @@ def dgl_main():
         # Training and validation using a full graph
         vgae_model.train()
 
-        logits = vgae_model.forward(train_graph, feats)
+        logits = vgae_model.forward(graph, feats)
 
         # compute loss
         loss = norm * F.binary_cross_entropy(logits.view(-1), adj.view(-1), weight=weight_tensor)