[Bugfix][Model] Fixing RGCN evaluation bug (#778)

examples/pytorch/rgcn/link_predict.py

@@ -182,7 +182,7 @@ def main(args):
                 model.cpu()
             model.eval()
             print("start eval")
-            mrr = utils.evaluate(test_graph, model, valid_data, num_nodes,
+            mrr = utils.evaluate(test_graph, model, valid_data,
                                  hits=[1, 3, 10], eval_bz=args.eval_batch_size)
             # save best model
             if mrr < best_mrr:
@@ -207,7 +207,7 @@ def main(args):
     model.eval()
     model.load_state_dict(checkpoint['state_dict'])
     print("Using best epoch: {}".format(checkpoint['epoch']))
-    utils.evaluate(test_graph, model, test_data, num_nodes, hits=[1, 3, 10],
+    utils.evaluate(test_graph, model, test_data, hits=[1, 3, 10],
                    eval_bz=args.eval_batch_size)
 
 

examples/pytorch/rgcn/utils.py

@@ -163,15 +163,15 @@ def sort_and_rank(score, target):
     indices = indices[:, 1].view(-1)
     return indices
 
-def perturb_and_get_rank(embedding, w, a, r, b, num_entity, batch_size=100):
+def perturb_and_get_rank(embedding, w, a, r, b, test_size, batch_size=100):
     """ Perturb one element in the triplets
     """
-    n_batch = (num_entity + batch_size - 1) // batch_size
+    n_batch = (test_size + batch_size - 1) // batch_size
     ranks = []
     for idx in range(n_batch):
         print("batch {} / {}".format(idx, n_batch))
         batch_start = idx * batch_size
-        batch_end = min(num_entity, (idx + 1) * batch_size)
+        batch_end = min(test_size, (idx + 1) * batch_size)
         batch_a = a[batch_start: batch_end]
         batch_r = r[batch_start: batch_end]
         emb_ar = embedding[batch_a] * w[batch_r]
@@ -187,17 +187,18 @@ def perturb_and_get_rank(embedding, w, a, r, b, num_entity, batch_size=100):
 
 # TODO (lingfan): implement filtered metrics
 # return MRR (raw), and Hits @ (1, 3, 10)
-def evaluate(test_graph, model, test_triplets, num_entity, hits=[], eval_bz=100):
+def evaluate(test_graph, model, test_triplets, hits=[], eval_bz=100):
     with torch.no_grad():
         embedding, w = model.evaluate(test_graph)
         s = test_triplets[:, 0]
         r = test_triplets[:, 1]
         o = test_triplets[:, 2]
+        test_size = test_triplets.shape[0]
 
         # perturb subject
-        ranks_s = perturb_and_get_rank(embedding, w, o, r, s, num_entity, eval_bz)
+        ranks_s = perturb_and_get_rank(embedding, w, o, r, s, test_size, eval_bz)
         # perturb object
-        ranks_o = perturb_and_get_rank(embedding, w, s, r, o, num_entity, eval_bz)
+        ranks_o = perturb_and_get_rank(embedding, w, s, r, o, test_size, eval_bz)
 
         ranks = torch.cat([ranks_s, ranks_o])
         ranks += 1 # change to 1-indexed