[bugfix] Fix a bunch of examples to be compatible with dgl 0.5 (#1957)

* upd

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* fix pinsage also

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* upd
Co-authored-by: Ubuntu <ubuntu@ip-172-31-29-3.us-east-2.compute.internal>
Co-authored-by: Quan Gan <coin2028@hotmail.com>
Co-authored-by: Jinjing Zhou <VoVAllen@users.noreply.github.com>

examples/pytorch/pinsage/sampler.py

@@ -16,8 +16,8 @@ class ItemToItemBatchSampler(IterableDataset):
         self.g = g
         self.user_type = user_type
         self.item_type = item_type
-        self.user_to_item_etype = list(g.metagraph[user_type][item_type])[0]
-        self.item_to_user_etype = list(g.metagraph[item_type][user_type])[0]
+        self.user_to_item_etype = list(g.metagraph()[user_type][item_type])[0]
+        self.item_to_user_etype = list(g.metagraph()[item_type][user_type])[0]
         self.batch_size = batch_size
 
     def __iter__(self):
@@ -38,8 +38,8 @@ class NeighborSampler(object):
         self.g = g
         self.user_type = user_type
         self.item_type = item_type
-        self.user_to_item_etype = list(g.metagraph[user_type][item_type])[0]
-        self.item_to_user_etype = list(g.metagraph[item_type][user_type])[0]
+        self.user_to_item_etype = list(g.metagraph()[user_type][item_type])[0]
+        self.item_to_user_etype = list(g.metagraph()[item_type][user_type])[0]
         self.samplers = [
             dgl.sampling.PinSAGESampler(g, item_type, user_type, random_walk_length,
                 random_walk_restart_prob, num_random_walks, num_neighbors)

examples/pytorch/sgc/sgc.py

@@ -42,7 +42,7 @@ def main(args):
         cuda = False
     else:
         cuda = True
-        g = g.to(args.gpu)
+        g = g.int().to(args.gpu)
 
     features = g.ndata['feat']
     labels = g.ndata['label']

examples/pytorch/sgc/sgc_reddit.py

@@ -36,7 +36,7 @@ def main(args):
         cuda = False
     else:
         cuda = True
-        g = g.to(args.gpu)
+        g = g.int().to(args.gpu)
 
     features = g.ndata['feat']
     labels = g.ndata['label']

examples/pytorch/transformer/README.md

 # Transformer in DGL
+
+**This example is out-dated, please refer to [BP-Transformer](http://github.com/yzh119/bpt) for efficient (Sparse) Transformer implementation in DGL.**
+
 In this example we implement the [Transformer](https://arxiv.org/pdf/1706.03762.pdf) with ACT in DGL.
 
 The folder contains training module and inferencing module (beam decoder) for Transformer.

python/dgl/data/citation_graph.py

@@ -126,7 +126,7 @@ class CitationGraphDataset(DGLBuiltinDataset):
         g.ndata['test_mask'] = generate_mask_tensor(test_mask)
         g.ndata['label'] = F.tensor(labels)
         g.ndata['feat'] = F.tensor(_preprocess_features(features), dtype=F.data_type_dict['float32'])
-        self._num_labels = onehot_labels.shape[1]
+        self._num_classes = onehot_labels.shape[1]
         self._labels = labels
         self._g = g
 
@@ -135,7 +135,7 @@ class CitationGraphDataset(DGLBuiltinDataset):
             print('  NumNodes: {}'.format(self._g.number_of_nodes()))
             print('  NumEdges: {}'.format(self._g.number_of_edges()))
             print('  NumFeats: {}'.format(self._g.ndata['feat'].shape[1]))
-            print('  NumClasses: {}'.format(self.num_labels))
+            print('  NumClasses: {}'.format(self.num_classes))
             print('  NumTrainingSamples: {}'.format(
                 F.nonzero_1d(self._g.ndata['train_mask']).shape[0]))
             print('  NumValidationSamples: {}'.format(
@@ -161,7 +161,7 @@ class CitationGraphDataset(DGLBuiltinDataset):
         info_path = os.path.join(self.save_path,
                                  self.save_name + '.pkl')
         save_graphs(str(graph_path), self._g)
-        save_info(str(info_path), {'num_labels': self.num_labels})
+        save_info(str(info_path), {'num_classes': self.num_classes})
 
     def load(self):
         graph_path = os.path.join(self.save_path,
@@ -181,7 +181,7 @@ class CitationGraphDataset(DGLBuiltinDataset):
         graph = to_networkx(graph)
         self._graph = nx.DiGraph(graph)
 
-        self._num_labels = info['num_labels']
+        self._num_classes = info['num_classes']
         self._g.ndata['train_mask'] = generate_mask_tensor(self._g.ndata['train_mask'].numpy())
         self._g.ndata['val_mask'] = generate_mask_tensor(self._g.ndata['val_mask'].numpy())
         self._g.ndata['test_mask'] = generate_mask_tensor(self._g.ndata['test_mask'].numpy())
@@ -191,7 +191,7 @@ class CitationGraphDataset(DGLBuiltinDataset):
             print('  NumNodes: {}'.format(self._g.number_of_nodes()))
             print('  NumEdges: {}'.format(self._g.number_of_edges()))
             print('  NumFeats: {}'.format(self._g.ndata['feat'].shape[1]))
-            print('  NumClasses: {}'.format(self.num_labels))
+            print('  NumClasses: {}'.format(self.num_classes))
             print('  NumTrainingSamples: {}'.format(
                 F.nonzero_1d(self._g.ndata['train_mask']).shape[0]))
             print('  NumValidationSamples: {}'.format(
@@ -212,7 +212,12 @@ class CitationGraphDataset(DGLBuiltinDataset):
 
     @property
     def num_labels(self):
-        return self._num_labels
+        deprecate_property('dataset.num_labels', 'dataset.num_classes')
+        return self.num_classes
+
+    @property
+    def num_classes(self):
+        return self._num_classes
 
     """ Citation graph is used in many examples
         We preserve these properties for compatability.
@@ -339,7 +344,7 @@ class CoraGraphDataset(CitationGraphDataset):
 
     Attributes
     ----------
-    num_labels: int
+    num_classes: int
         Number of label classes
     graph: networkx.DiGraph
         Graph structure
@@ -362,7 +367,7 @@ class CoraGraphDataset(CitationGraphDataset):
     --------
     >>> dataset = CoraGraphDataset()
     >>> g = dataset[0]
-    >>> num_class = g.num_labels
+    >>> num_class = g.num_classes
     >>>
     >>> # get node feature
     >>> feat = g.ndata['feat']
@@ -479,7 +484,7 @@ class CiteseerGraphDataset(CitationGraphDataset):
 
     Attributes
     ----------
-    num_labels: int
+    num_classes: int
         Number of label classes
     graph: networkx.DiGraph
         Graph structure
@@ -505,7 +510,7 @@ class CiteseerGraphDataset(CitationGraphDataset):
     --------
     >>> dataset = CiteseerGraphDataset()
     >>> g = dataset[0]
-    >>> num_class = g.num_labels
+    >>> num_class = g.num_classes
     >>>
     >>> # get node feature
     >>> feat = g.ndata['feat']
@@ -622,7 +627,7 @@ class PubmedGraphDataset(CitationGraphDataset):
 
     Attributes
     ----------
-    num_labels: int
+    num_classes: int
         Number of label classes
     graph: networkx.DiGraph
         Graph structure