Fix GINDT and #2087 (#2103)

* fix gindt

* ff

* fix

* minor fix

* fix

python/dgl/data/gindt.py

@@ -15,6 +15,7 @@ from .utils import loadtxt, save_graphs, load_graphs, save_info, load_info, down
 from ..utils import retry_method_with_fix
 from ..convert import graph as dgl_graph
 
+
 class GINDataset(DGLBuiltinDataset):
     """Datasets for Graph Isomorphism Network (GIN)
     Adapted from `<https://github.com/weihua916/powerful-gnns/blob/master/dataset.zip>`_.
@@ -232,32 +233,36 @@ class GINDataset(DGLBuiltinDataset):
             if self.degree_as_nlabel:
                 if self.verbose:
                     print('generate node features by node degree...')
-                nlabel_set = set([])
                 for g in self.graphs:
                     # actually this label shouldn't be updated
                     # in case users want to keep it
                     # but usually no features means no labels, fine.
                     g.ndata['label'] = g.in_degrees()
                     # extracting unique node labels
-                    nlabel_set = nlabel_set.union(set([F.as_scalar(nl) for nl in g.ndata['label']]))
 
-                nlabel_set = list(nlabel_set)
             # in case the labels/degrees are not continuous number
-                self.ndegree_dict = {
+            nlabel_set = set([])
+            for g in self.graphs:
+                nlabel_set = nlabel_set.union(
+                    set([F.as_scalar(nl) for nl in g.ndata['label']]))
+            nlabel_set = list(nlabel_set)
+            if len(nlabel_set) == np.max(nlabel_set) + 1 and np.min(nlabel_set) == 0:
+                # Note this is different from the author's implementation. In weihua916's implementation,
+                # the labels are relabeled anyway. But here we didn't relabel it if the labels are contiguous
+                # to make it consistent with the original dataset
+                label2idx = self.nlabel_dict
+            else:
+                label2idx = {
                     nlabel_set[i]: i
                     for i in range(len(nlabel_set))
                 }
-                label2idx = self.ndegree_dict
             # generate node attr by node label
-            else:
-                if self.verbose:
-                    print('generate node features by node label...')
-                label2idx = self.nlabel_dict
-
             for g in self.graphs:
-                g.ndata['attr'] = F.tensor(np.zeros((
-                    g.number_of_nodes(), len(label2idx))))
-                g.ndata['attr'][range(g.number_of_nodes()), [label2idx[F.as_scalar(F.reshape(nl, (1,)))] for nl in g.ndata['label']]] = 1
+                attr = np.zeros((
+                    g.number_of_nodes(), len(label2idx)))
+                attr[range(g.number_of_nodes()), [label2idx[nl]
+                                                  for nl in F.asnumpy(g.ndata['label']).tolist()]] = 1
+                g.ndata['attr'] = F.tensor(attr)
 
         # after load, get the #classes and #dim
         self.gclasses = len(self.glabel_dict)
@@ -288,8 +293,10 @@ class GINDataset(DGLBuiltinDataset):
                     self.nlabel_dict, self.ndegree_dict))
 
     def save(self):
-        graph_path = os.path.join(self.save_path, 'gin_{}_{}.bin'.format(self.name, self.hash))
-        info_path = os.path.join(self.save_path, 'gin_{}_{}.pkl'.format(self.name, self.hash))
+        graph_path = os.path.join(
+            self.save_path, 'gin_{}_{}.bin'.format(self.name, self.hash))
+        info_path = os.path.join(
+            self.save_path, 'gin_{}_{}.pkl'.format(self.name, self.hash))
         label_dict = {'labels': self.labels}
         info_dict = {'N': self.N,
                      'n': self.n,
@@ -308,8 +315,10 @@ class GINDataset(DGLBuiltinDataset):
         save_info(str(info_path), info_dict)
 
     def load(self):
-        graph_path = os.path.join(self.save_path, 'gin_{}_{}.bin'.format(self.name, self.hash))
-        info_path = os.path.join(self.save_path, 'gin_{}_{}.pkl'.format(self.name, self.hash))
+        graph_path = os.path.join(
+            self.save_path, 'gin_{}_{}.bin'.format(self.name, self.hash))
+        info_path = os.path.join(
+            self.save_path, 'gin_{}_{}.pkl'.format(self.name, self.hash))
         graphs, label_dict = load_graphs(str(graph_path))
         info_dict = load_info(str(info_path))
 
@@ -331,8 +340,10 @@ class GINDataset(DGLBuiltinDataset):
         self.degree_as_nlabel = info_dict['degree_as_nlabel']
 
     def has_cache(self):
-        graph_path = os.path.join(self.save_path, 'gin_{}_{}.bin'.format(self.name, self.hash))
-        info_path = os.path.join(self.save_path, 'gin_{}_{}.pkl'.format(self.name, self.hash))
+        graph_path = os.path.join(
+            self.save_path, 'gin_{}_{}.bin'.format(self.name, self.hash))
+        info_path = os.path.join(
+            self.save_path, 'gin_{}_{}.pkl'.format(self.name, self.hash))
         if os.path.exists(graph_path) and os.path.exists(info_path):
             return True
         return False

python/dgl/data/graph_serialize.py

@@ -108,11 +108,11 @@ def save_graphs(filename, g_list, labels=None):
     load_graphs
     """
     # if it is local file, do some sanity check
-    if filename.startswith('s3://') is False:
+    if not filename.startswith('s3://'):
         if os.path.isdir(filename):
             raise DGLError("Filename {} is an existing directory.".format(filename))
-        f_path, _ = os.path.split(filename)
-        if not os.path.exists(f_path):
+        f_path = os.path.dirname(filename)
+        if f_path and not os.path.exists(f_path):
             os.makedirs(f_path)
 
     g_sample = g_list[0] if isinstance(g_list, list) else g_list