Evaluation does not work for RelationNet

[MrigankRaman]

The evaluation function was not implemented in the code. So, I implemented it but I get a CUDA error which I traced back to transformers. I am hereby attaching the screenshot of the error. So for context I trained the relationNet using roberta as the encoder and then when I try to evaluate the trained model, I get this error.

[MrigankRaman]

To reproduce the error just add the below code in the eval(args) function of rn.py file and run the script in eval mode.

model_path = os.path.join(args.save_dir, 'model.pt')

if 'lm' in args.ent_emb:
    print('Using contextualized embeddings for concepts')
    use_contextualized, cp_emb = True, None
else:
    use_contextualized = False
# cp_emb = [np.load(path) for path in args.ent_emb_paths]
# cp_emb = torch.tensor(np.concatenate(cp_emb, 1))

# concept_num, concept_dim = cp_emb.size(0), cp_emb.size(1)

# rel_emb = np.load(args.rel_emb_path)
# rel_emb = np.concatenate((rel_emb, -rel_emb), 0)
# rel_emb = cal_2hop_rel_emb(rel_emb)
# rel_emb = torch.tensor(rel_emb)
# relation_num, relation_dim = rel_emb.size(0), rel_emb.size(1)
device = torch.device("cuda:0" if torch.cuda.is_available() and args.cuda else "cpu")
model, old_args = torch.load(model_path, map_location = device)
model.to(device)
model.eval() 
dataset = LMRelationNetDataLoader(old_args.train_statements, args.train_rel_paths,
                                  old_args.dev_statements, args.dev_rel_paths,
                                  old_args.test_statements, args.test_rel_paths,
                                  batch_size=args.batch_size, eval_batch_size=args.eval_batch_size, device=device,
                                  model_name=args.encoder,
                                  max_tuple_num=args.max_tuple_num, max_seq_length=args.max_seq_len,
                                  is_inhouse=args.inhouse, inhouse_train_qids_path=args.inhouse_train_qids,
                                  use_contextualized=use_contextualized,
                                  train_adj_path=args.train_adj, dev_adj_path=args.dev_adj, test_adj_path=args.test_adj,
                                  train_node_features_path=args.train_node_features, dev_node_features_path=args.dev_node_features,
                                  test_node_features_path=args.test_node_features, node_feature_type=args.node_feature_type,
                                  format=args.format)
print()
print("***** runing evaluation *****")
print(f'| dataset: {old_args.dataset} | num_dev: {dataset.dev_size()} | num_test: {dataset.test_size()} | save_dir: {args.save_dir} |')
dev_acc = evaluate_accuracy(dataset.dev(), model)
test_acc = evaluate_accuracy(dataset.test(), model) if dataset.test_size() else 0.0
print("***** evaluation done *****")
print()
print(f'| dev_accuracy: {dev_acc} | test_acc: {test_acc} |')