diff --git a/data/adversarial.py b/data/adversarial.py
new file mode 100644
index 0000000..cf55985
--- /dev/null
+++ b/data/adversarial.py
@@ -0,0 +1,21 @@
+import json
+import sys
+import random
+
+with open(sys.argv[1]) as fp:
+    data = json.load(fp)
+
+for _, d in enumerate(data):
+    swaps = []
+    for index, ins in enumerate(d['instructions']):
+        p = random.random()
+        if p > 0.5:
+            swaps.append(True)
+            d['instructions'][index] += 'This is swap.'
+        else:
+            swaps.append(False)
+        d['swap'] = swaps
+print(data)
+
+with open(sys.argv[1], 'w') as fp:
+    json.dump(data, fp)
diff --git a/r2r_src/agent.py b/r2r_src/agent.py
index 7afbaa4..53fffb6 100644
--- a/r2r_src/agent.py
+++ b/r2r_src/agent.py
@@ -252,7 +252,10 @@ class Seq2SeqAgent(BaseAgent):
         # Language input
         sentence, language_attention_mask, token_type_ids, \
             seq_lengths, perm_idx = self._sort_batch(obs)
+
+        print("perm_index:", perm_idx)
         perm_obs = obs[perm_idx]
+        
 
         ''' Language BERT '''
         language_inputs = {'mode':        'language',
@@ -294,9 +297,6 @@ 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
@@ -322,11 +322,13 @@ class Seq2SeqAgent(BaseAgent):
 
             # Mask outputs where agent can't move forward
             # Here the logit is [b, max_candidate]
+            # (8, max(candidate))
             candidate_mask = utils.length2mask(candidate_leng)
             logit.masked_fill_(candidate_mask, -float('inf'))
 
             # Supervised training
             target = self._teacher_action(perm_obs, ended)
+            print("target: ", target.shape)
             ml_loss += self.criterion(logit, target)
 
             # Determine next model inputs
diff --git a/r2r_src/env.py b/r2r_src/env.py
index 5b36e62..d35174b 100644
--- a/r2r_src/env.py
+++ b/r2r_src/env.py
@@ -1,6 +1,8 @@
 ''' Batched Room-to-Room navigation environment '''
 
 import sys
+
+from networkx.algorithms import swap
 sys.path.append('buildpy36')
 sys.path.append('Matterport_Simulator/build/')
 import MatterSim
@@ -14,6 +16,7 @@ import os
 import random
 import networkx as nx
 from param import args
+import time
 
 from utils import load_datasets, load_nav_graphs, pad_instr_tokens
 from IPython import embed
@@ -127,6 +130,7 @@ class R2RBatch():
                             new_item = dict(item)
                             new_item['instr_id'] = '%s_%d' % (item['path_id'], j)
                             new_item['instructions'] = instr
+                            new_item['swap'] = item['swap'][j]
 
                             ''' BERT tokenizer '''
                             instr_tokens = tokenizer.tokenize(instr)
@@ -136,10 +140,12 @@ class R2RBatch():
                             if new_item['instr_encoding'] is not None:  # Filter the wrong data
                                 self.data.append(new_item)
                                 scans.append(item['scan'])
+
                         except:
                             continue
             print("split {} has {} datas in the file.".format(split, max_len))
 
+
         if name is None:
             self.name = splits[0] if len(splits) > 0 else "FAKE"
         else:
@@ -341,7 +347,8 @@ class R2RBatch():
                 'instructions' : item['instructions'],
                 'teacher' : self._shortest_path_action(state, item['path'][-1]),
                 'gt_path' : item['path'],
-                'path_id' : item['path_id']
+                'path_id' : item['path_id'],
+                'swap': item['swap']
             })
             if 'instr_encoding' in item:
                 obs[-1]['instr_encoding'] = item['instr_encoding']