[Graph][Bugfix] Fix the API of map_to_subgraph_nid (dmlc#226)

* correct vid mapping API.

* fix sse.

examples/mxnet/sse/sse_batch.py

@@ -283,7 +283,7 @@ def main(args, data):
                 copy_to_gpu(subg, ctx)
 
             with mx.autograd.record():
-                logits = model_train(subg, subg_seeds.tousertensor())
+                logits = model_train(subg, subg_seeds)
                 batch_labels = mx.nd.take(labels, seeds).as_in_context(logits.context)
                 loss = mx.nd.softmax_cross_entropy(logits, batch_labels)
             loss.backward()

python/dgl/subgraph.py

@@ -128,4 +128,16 @@ def copy_from_parent(self):
                 self._parent._edge_frame[self._get_parent_eid()]))
 
     def map_to_subgraph_nid(self, parent_vids):
-        return map_to_subgraph_nid(self._graph, utils.toindex(parent_vids))
+        """Map the node Ids in the parent graph to the node Ids in the subgraph.
+
+        Parameters
+        ----------
+        parent_vids : list, tensor
+            The node ID array in the parent graph.
+
+        Returns
+        -------
+        tensor
+            The node ID array in the subgraph.
+        """
+        return map_to_subgraph_nid(self._graph, utils.toindex(parent_vids)).tousertensor()

tests/mxnet/test_sampler.py

@@ -16,36 +16,35 @@ def test_1neighbor_sampler_all():
     for subg, seed_ids in dgl.contrib.sampling.NeighborSampler(g, 1, 100, neighbor_type='in',
                                                                num_workers=4):
         assert len(seed_ids) == 1
-        src, dst, eid = g._graph.in_edges(utils.toindex(seed_ids))
+        src, dst, eid = g.in_edges(seed_ids, form='all')
         # Test if there is a self loop
-        self_loop = mx.nd.sum(src.tousertensor() == dst.tousertensor()).asnumpy() == 1
+        self_loop = mx.nd.sum(src == dst).asnumpy() == 1
         if self_loop:
             assert subg.number_of_nodes() == len(src)
         else:
             assert subg.number_of_nodes() == len(src) + 1
         assert subg.number_of_edges() >= len(src)
 
         child_ids = subg.map_to_subgraph_nid(seed_ids)
-        child_src, child_dst, child_eid = subg._graph.in_edges(child_ids)
+        child_src, child_dst, child_eid = subg.in_edges(child_ids, form='all')
 
         child_src1 = subg.map_to_subgraph_nid(src)
-        assert mx.nd.sum(child_src1.tousertensor() == child_src.tousertensor()).asnumpy() == len(src)
+        assert mx.nd.sum(child_src1 == child_src).asnumpy() == len(src)
 
 def is_sorted(arr):
     return np.sum(np.sort(arr) == arr) == len(arr)
 
 def verify_subgraph(g, subg, seed_id):
-    seed_id = utils.toindex(seed_id)
-    src, dst, eid = g._graph.in_edges(utils.toindex(seed_id))
+    src, dst, eid = g.in_edges(seed_id, form='all')
     child_id = subg.map_to_subgraph_nid(seed_id)
-    child_src, child_dst, child_eid = subg._graph.in_edges(child_id)
-    child_src = child_src.tousertensor().asnumpy()
+    child_src, child_dst, child_eid = subg.in_edges(child_id, form='all')
+    child_src = child_src.asnumpy()
     # We don't allow duplicate elements in the neighbor list.
     assert(len(np.unique(child_src)) == len(child_src))
     # The neighbor list also needs to be sorted.
     assert(is_sorted(child_src))
 
-    child_src1 = subg.map_to_subgraph_nid(src).tousertensor().asnumpy()
+    child_src1 = subg.map_to_subgraph_nid(src).asnumpy()
     child_src1 = child_src1[child_src1 >= 0]
     for i in child_src:
         assert i in child_src1
@@ -65,13 +64,13 @@ def test_10neighbor_sampler_all():
     # In this case, NeighborSampling simply gets the neighborhood of a single vertex.
     for subg, seed_ids in dgl.contrib.sampling.NeighborSampler(g, 10, 100, neighbor_type='in',
                                                                num_workers=4):
-        src, dst, eid = g._graph.in_edges(utils.toindex(seed_ids))
+        src, dst, eid = g.in_edges(seed_ids, form='all')
 
         child_ids = subg.map_to_subgraph_nid(seed_ids)
-        child_src, child_dst, child_eid = subg._graph.in_edges(child_ids)
+        child_src, child_dst, child_eid = subg.in_edges(child_ids, form='all')
 
         child_src1 = subg.map_to_subgraph_nid(src)
-        assert mx.nd.sum(child_src1.tousertensor() == child_src.tousertensor()).asnumpy() == len(src)
+        assert mx.nd.sum(child_src1 == child_src).asnumpy() == len(src)
 
 def check_10neighbor_sampler(g, seeds):
     # In this case, NeighborSampling simply gets the neighborhood of a single vertex.