feat: vlnbert which can run with adversarial json

r2r_src/agent.py

@@ -294,9 +294,6 @@ class Seq2SeqAgent(BaseAgent):
 
             input_a_t, candidate_feat, candidate_leng = self.get_input_feat(perm_obs)
 
-            print("input_a_t: ", input_a_t.shape)
-            print("candidate_feat: ", candidate_feat.shape)
-            print("candidate_leng: ", candidate_leng)
 
             # the first [CLS] token, initialized by the language BERT, serves
             # as the agent's state passing through time steps
@@ -407,7 +404,6 @@ class Seq2SeqAgent(BaseAgent):
             if ended.all():
                 break
 
-            print()
 
         if train_rl:
             # Last action in A2C

r2r_src/train.py

@@ -195,11 +195,11 @@ def train_val(test_only=False):
 
     if test_only:
         featurized_scans = None
-        val_env_names = ['val_train_seen']
+        val_env_names = ['val_unseen']
     else:
         featurized_scans = set([key.split("_")[0] for key in list(feat_dict.keys())])
         # val_env_names = ['val_train_seen', 'val_seen', 'val_unseen']
-        val_env_names = ['val_train_seen']
+        val_env_names = ['val_unseen']
 
     train_env = R2RBatch(feat_dict, batch_size=args.batchSize, splits=['train'], tokenizer=tok)
     from collections import OrderedDict