hotfix (dmlc#971)

XiaoYixin7132 · Nov 4, 2019 · fdd0fe6 · fdd0fe6
1 parent 9a0511c
commit fdd0fe6
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Showing 19 changed files with 64 additions and 46 deletions.
diff --git a/examples/mxnet/monet/README.md b/examples/mxnet/monet/README.md
@@ -11,6 +11,11 @@ Dependencies
 Results
 =======
 
-Node classification on citation networks:
+## Citation networks
+Run with following (available dataset: "cora", "citeseer", "pubmed")
+```bash
+python3 citation.py --dataset cora --gpu 0
+```
+
 - Cora: ~0.814
 - Pubmed: ~0.748
diff --git a/examples/mxnet/monet/citation.py b/examples/mxnet/monet/citation.py
@@ -18,7 +18,8 @@ def __init__(self,
                  out_feats,
                  n_layers,
                  dim,
-                 n_kernels):
+                 n_kernels,
+                 dropout):
         super(MoNet, self).__init__()
         self.g = g
         with self.name_scope():
@@ -39,9 +40,13 @@ def __init__(self,
             self.layers.add(GMMConv(n_hidden, out_feats, dim, n_kernels))
             self.pseudo_proj.add(nn.Dense(dim, in_units=2, activation='tanh'))
 
+            self.dropout = nn.Dropout(dropout)
+
     def forward(self, feat, pseudo):
         h = feat
         for i in range(len(self.layers)):
+            if i > 0:
+                h = self.dropout(h)
             h = self.layers[i](
                 self.g, h, self.pseudo_proj[i](pseudo))
         return h
@@ -109,6 +114,7 @@ def main(args):
                   args.n_layers,
                   args.pseudo_dim,
                   args.n_kernels,
+                  args.dropout
                   )
     model.initialize(ctx=ctx)
     n_train_samples = train_mask.sum().asscalar()

diff --git a/examples/pytorch/appnp/train.py b/examples/pytorch/appnp/train.py
@@ -43,9 +43,9 @@ def main(args):
       #Val samples %d
       #Test samples %d""" %
           (n_edges, n_classes,
-           train_mask.sum().item(),
-           val_mask.sum().item(),
-           test_mask.sum().item()))
+           train_mask.int().sum().item(),
+           val_mask.int().sum().item(),
+           test_mask.int().sum().item()))
 
     if args.gpu < 0:
         cuda = False

diff --git a/examples/pytorch/cluster_gcn/cluster_gcn.py b/examples/pytorch/cluster_gcn/cluster_gcn.py
@@ -59,9 +59,9 @@ def main(args):
     n_classes = data.num_labels
     n_edges = data.graph.number_of_edges()
 
-    n_train_samples = train_mask.sum().item()
-    n_val_samples = val_mask.sum().item()
-    n_test_samples = test_mask.sum().item()
+    n_train_samples = train_mask.int().sum().item()
+    n_val_samples = val_mask.int().sum().item()
+    n_test_samples = test_mask.int().sum().item()
 
     print("""----Data statistics------'
     #Edges %d

diff --git a/examples/pytorch/gat/train.py b/examples/pytorch/gat/train.py
@@ -60,9 +60,9 @@ def main(args):
       #Val samples %d
       #Test samples %d""" %
           (n_edges, n_classes,
-           train_mask.sum().item(),
-           val_mask.sum().item(),
-           test_mask.sum().item()))
+           train_mask.int().sum().item(),
+           val_mask.int().sum().item(),
+           test_mask.int().sum().item()))
 
     if args.gpu < 0:
         cuda = False

diff --git a/examples/pytorch/gcn/gcn_mp.py b/examples/pytorch/gcn/gcn_mp.py
@@ -137,9 +137,9 @@ def main(args):
       #Val samples %d
       #Test samples %d""" %
           (n_edges, n_classes,
-              train_mask.sum().item(),
-              val_mask.sum().item(),
-              test_mask.sum().item()))
+              train_mask.int().sum().item(),
+              val_mask.int().sum().item(),
+              test_mask.int().sum().item()))
 
     if args.gpu < 0:
         cuda = False

diff --git a/examples/pytorch/gcn/train.py b/examples/pytorch/gcn/train.py
@@ -44,9 +44,9 @@ def main(args):
       #Val samples %d
       #Test samples %d""" %
           (n_edges, n_classes,
-              train_mask.sum().item(),
-              val_mask.sum().item(),
-              test_mask.sum().item()))
+              train_mask.int().sum().item(),
+              val_mask.int().sum().item(),
+              test_mask.int().sum().item()))
 
     if args.gpu < 0:
         cuda = False

diff --git a/examples/pytorch/graphsage/graphsage.py b/examples/pytorch/graphsage/graphsage.py
@@ -78,9 +78,9 @@ def main(args):
       #Val samples %d
       #Test samples %d""" %
           (n_edges, n_classes,
-           train_mask.sum().item(),
-           val_mask.sum().item(),
-           test_mask.sum().item()))
+           train_mask.int().sum().item(),
+           val_mask.int().sum().item(),
+           test_mask.int().sum().item()))
 
     if args.gpu < 0:
         cuda = False

diff --git a/examples/pytorch/model_zoo/citation_network/run.py b/examples/pytorch/model_zoo/citation_network/run.py
@@ -64,9 +64,9 @@ def main(args):
       #Val samples %d
       #Test samples %d""" %
           (n_edges, n_classes,
-              train_mask.sum().item(),
-              val_mask.sum().item(),
-              test_mask.sum().item()))
+              train_mask.int().sum().item(),
+              val_mask.int().sum().item(),
+              test_mask.int().sum().item()))
 
     if args.gpu < 0:
         cuda = False

diff --git a/examples/pytorch/monet/README.md b/examples/pytorch/monet/README.md
@@ -11,9 +11,14 @@ Dependencies
 Results
 =======
 
-Node classification on citation networks:
+## Citation networks
+Run with following (available dataset: "cora", "citeseer", "pubmed")
+```bash
+python3 citation.py --dataset cora --gpu 0
+```
+
 - Cora: ~0.816
 - Pubmed: ~0.763
 
-Image classification on MNIST:
+## Image classification:
 - please refer to [model_zoo/geometric](../model_zoo/geometric).
diff --git a/examples/pytorch/sampling/dis_sampling/gcn_cv_sc_train.py b/examples/pytorch/sampling/dis_sampling/gcn_cv_sc_train.py
@@ -36,9 +36,9 @@ def main(args):
     n_classes = data.num_labels
     n_edges = data.graph.number_of_edges()
 
-    n_train_samples = train_mask.sum().item()
-    n_val_samples = val_mask.sum().item()
-    n_test_samples = test_mask.sum().item()
+    n_train_samples = train_mask.int().sum().item()
+    n_val_samples = val_mask.int().sum().item()
+    n_test_samples = test_mask.int().sum().item()
 
     print("""----Data statistics------'
       #Edges %d

diff --git a/examples/pytorch/sampling/dis_sampling/gcn_ns_sc_train.py b/examples/pytorch/sampling/dis_sampling/gcn_ns_sc_train.py
@@ -37,9 +37,9 @@ def main(args):
     n_classes = data.num_labels
     n_edges = data.graph.number_of_edges()
 
-    n_train_samples = train_mask.sum().item()
-    n_val_samples = val_mask.sum().item()
-    n_test_samples = test_mask.sum().item()
+    n_train_samples = train_mask.int().sum().item()
+    n_val_samples = val_mask.int().sum().item()
+    n_test_samples = test_mask.int().sum().item()
 
     print("""----Data statistics------'
       #Edges %d

diff --git a/examples/pytorch/sampling/gcn_cv_sc.py b/examples/pytorch/sampling/gcn_cv_sc.py
@@ -161,9 +161,9 @@ def main(args):
     n_classes = data.num_labels
     n_edges = data.graph.number_of_edges()
 
-    n_train_samples = train_mask.sum().item()
-    n_val_samples = val_mask.sum().item()
-    n_test_samples = test_mask.sum().item()
+    n_train_samples = train_mask.int().sum().item()
+    n_val_samples = val_mask.int().sum().item()
+    n_test_samples = test_mask.int().sum().item()
 
     print("""----Data statistics------'
       #Edges %d

diff --git a/examples/pytorch/sampling/gcn_ns_sc.py b/examples/pytorch/sampling/gcn_ns_sc.py
@@ -132,9 +132,9 @@ def main(args):
     n_classes = data.num_labels
     n_edges = data.graph.number_of_edges()
 
-    n_train_samples = train_mask.sum().item()
-    n_val_samples = val_mask.sum().item()
-    n_test_samples = test_mask.sum().item()
+    n_train_samples = train_mask.int().sum().item()
+    n_val_samples = val_mask.int().sum().item()
+    n_test_samples = test_mask.int().sum().item()
 
     print("""----Data statistics------'
       #Edges %d

diff --git a/examples/pytorch/sgc/sgc.py b/examples/pytorch/sgc/sgc.py
@@ -48,9 +48,9 @@ def main(args):
       #Val samples %d
       #Test samples %d""" %
           (n_edges, n_classes,
-              train_mask.sum().item(),
-              val_mask.sum().item(),
-              test_mask.sum().item()))
+              train_mask.int().sum().item(),
+              val_mask.int().sum().item(),
+              test_mask.int().sum().item()))
 
     if args.gpu < 0:
         cuda = False

diff --git a/examples/pytorch/sgc/sgc_reddit.py b/examples/pytorch/sgc/sgc_reddit.py
@@ -51,9 +51,9 @@ def main(args):
       #Val samples %d
       #Test samples %d""" %
           (n_edges, n_classes,
-           train_mask.sum().item(),
-           val_mask.sum().item(),
-           test_mask.sum().item()))
+           train_mask.int().sum().item(),
+           val_mask.int().sum().item(),
+           test_mask.int().sum().item()))
 
     if args.gpu < 0:
         cuda = False

diff --git a/examples/pytorch/tagcn/train.py b/examples/pytorch/tagcn/train.py
@@ -42,9 +42,9 @@ def main(args):
       #Val samples %d
       #Test samples %d""" %
           (n_edges, n_classes,
-              train_mask.sum().item(),
-              val_mask.sum().item(),
-              test_mask.sum().item()))
+              train_mask.int().sum().item(),
+              val_mask.int().sum().item(),
+              test_mask.int().sum().item()))
 
     if args.gpu < 0:
         cuda = False

diff --git a/python/dgl/nn/mxnet/softmax.py b/python/dgl/nn/mxnet/softmax.py
@@ -152,6 +152,7 @@ def edge_softmax(graph, logits, eids=ALL):
     <NDArray 6x1 @cpu(0)>
 
     Apply edge softmax on first 4 edges of g:
+
     >>> edge_softmax(g, edata, nd.array([0,1,2,3], dtype='int64'))
     [[1. ]
      [0.5]

diff --git a/python/dgl/nn/pytorch/softmax.py b/python/dgl/nn/pytorch/softmax.py
@@ -154,6 +154,7 @@ def edge_softmax(graph, logits, eids=ALL):
         [0.3333]])
 
     Apply edge softmax on first 4 edges of g:
+
     >>> edge_softmax(g, edata[:4], th.Tensor([0,1,2,3]))
     tensor([[1.0000],
         [0.5000],
-Original file line number
+Diff line change
@@ Expand Up / @@ -152,6 +152,7 @@ def edge_softmax(graph, logits, eids=ALL): @@
         <NDArray 6x1 @cpu(0)>
         Apply edge softmax on first 4 edges of g:
         >>> edge_softmax(g, edata, nd.array([0,1,2,3], dtype='int64'))
         [[1. ]
          [0.5]
@@ Expand Down @@