fix: original json contains unicode characters

- Notice: comment out RL
- Notice: always 50:50, seems there exist some bugs

adversarial_summary.py

@@ -0,0 +1,42 @@
+import json
+import os
+import re
+
+def remove_non_ascii(text):
+    return re.sub(r'[^\x00-\x7F]', ' ', text)
+
+
+for file in ['train', 'val_unseen', 'val_seen', 'train_seen', 'test', 'val_train_seen']:
+    print(file)
+    if os.path.isfile('data/adversarial/reverie_{}_fnf.json'.format(file)):
+        with open('data/adversarial/reverie_{}_fnf.json'.format(file)) as fp:
+            data = json.load(fp)
+
+
+        result = {}
+        for i in data:
+            instruction_id = i['path_id']
+            if instruction_id not in result:
+                result[instruction_id] = {
+                    'distance': float(i['distance']),
+                    'scan': i['scan'],
+                    'path_id': int(i['path_id']),
+                    'path': i['path'],
+                    'heading': float(i['heading']),
+                    'instructions': [ remove_non_ascii(i['instruction'])], 
+                    'found': [ i['found'] ],
+                    'id': i['id'],
+                    'objId': i['objId']
+                }
+            else:
+                result[instruction_id]['instructions'].append(remove_non_ascii(i['instruction']))
+                result[instruction_id]['found'].append( i['found'] )
+
+        output = []
+        for k, item in result.items():
+            output.append(item)
+    else:
+        output = []
+
+    with open('data/adversarial/R2R_{}.json'.format(file), 'w') as fp:
+        json.dump(output, fp)

r2r_src/agent.py

@@ -35,12 +35,12 @@ class BaseAgent(object):
         self.losses = [] # For learning agents
 
     def write_results(self):
-        output = [{'instr_id':k, 'trajectory': v} for k,v in self.results.items()]
+        output = [{'instr_id':k, 'trajectory': v[0], 'found': v[1]} for k,v in self.results.items()]
         with open(self.results_path, 'w') as f:
             json.dump(output, f)
 
     def get_results(self):
-        output = [{'instr_id': k, 'trajectory': v} for k, v in self.results.items()]
+        output = [{'instr_id': k, 'trajectory': v[0], 'found': v[1]} for k, v in self.results.items()]
         return output
 
     def rollout(self, **args):
@@ -61,17 +61,19 @@ class BaseAgent(object):
         if iters is not None:
             # For each time, it will run the first 'iters' iterations. (It was shuffled before)
             for i in range(iters):
-                for traj in self.rollout(**kwargs):
+                traj, found = self.rollout(**kwargs)
+                for index, traj in enumerate(traj):
                     self.loss = 0
-                    self.results[traj['instr_id']] = traj['path']
+                    self.results[traj['instr_id']] = (traj['path'], found[index])
         else:   # Do a full round
             while True:
-                for traj in self.rollout(**kwargs):
+                traj, found = self.rollout(**kwargs)
+                for index, traj in enumerate(traj):
                     if traj['instr_id'] in self.results:
                         looped = True
                     else:
                         self.loss = 0
-                        self.results[traj['instr_id']] = traj['path']
+                        self.results[traj['instr_id']] = (traj['path'], found[index])
                 if looped:
                     break
 
@@ -147,7 +149,7 @@ class Seq2SeqAgent(BaseAgent):
         return Variable(torch.from_numpy(features), requires_grad=False).cuda()
 
     def _candidate_variable(self, obs):
-        candidate_leng = [len(ob['candidate']) + 1 for ob in obs]  # +1 is for the end
+        candidate_leng = [len(ob['candidate']) + 2 for ob in obs]  # +1 is for the end
         candidate_feat = np.zeros((len(obs), max(candidate_leng), self.feature_size + args.angle_feat_size), dtype=np.float32)
 
         # Note: The candidate_feat at len(ob['candidate']) is the feature for the END
@@ -155,6 +157,8 @@ class Seq2SeqAgent(BaseAgent):
         for i, ob in enumerate(obs):
             for j, cc in enumerate(ob['candidate']):
                 candidate_feat[i, j, :] = cc['feature']
+            candidate_feat[i, len(ob['candidate']), :] = np.zeros(self.feature_size+args.angle_feat_size, dtype=np.float32)     # <STOP>
+            candidate_feat[i, len(ob['candidate'])+1, :] = np.ones(self.feature_size+args.angle_feat_size, dtype=np.float32)    # <NOT_FOUND>
 
         return torch.from_numpy(candidate_feat).cuda(), candidate_leng
 
@@ -186,10 +190,13 @@ class Seq2SeqAgent(BaseAgent):
                         break
                 else:   # Stop here
                     assert ob['teacher'] == ob['viewpoint']         # The teacher action should be "STAY HERE"
-                    a[i] = len(ob['candidate'])
+                    if ob['found']:
+                        a[i] = len(ob['candidate'])
+                    else:
+                        a[i] = len(ob['candidate'])+1
         return torch.from_numpy(a).cuda()
 
-    def make_equiv_action(self, a_t, perm_obs, perm_idx=None, traj=None):
+    def make_equiv_action(self, a_t, perm_obs, perm_idx=None, traj=None, found=None):
         """
         Interface between Panoramic view and Egocentric view
         It will convert the action panoramic view action a_t to equivalent egocentric view actions for the simulator
@@ -205,7 +212,7 @@ class Seq2SeqAgent(BaseAgent):
 
         for i, idx in enumerate(perm_idx):
             action = a_t[i]
-            if action != -1:            # -1 is the <stop> action
+            if action != -1 and action != -2:            # -1 is the <stop> action
                 select_candidate = perm_obs[i]['candidate'][action]
                 src_point = perm_obs[i]['viewIndex']
                 trg_point = select_candidate['pointId']
@@ -228,6 +235,10 @@ class Seq2SeqAgent(BaseAgent):
                 # print("action: {} view_index: {}".format(action, state.viewIndex))
                 if traj is not None:
                     traj[i]['path'].append((state.location.viewpointId, state.heading, state.elevation))
+            elif action == -1 or action == -2:
+                if found is not None:
+                    found[i] = action
+
 
     def rollout(self, train_ml=None, train_rl=True, reset=True):
         """
@@ -271,6 +282,8 @@ class Seq2SeqAgent(BaseAgent):
             'path': [(ob['viewpoint'], ob['heading'], ob['elevation'])],
         } for ob in perm_obs]
         
+        found = [ None for _ in range(len(perm_obs)) ]
+
         # Init the reward shaping
         last_dist = np.zeros(batch_size, np.float32)
         last_ndtw = np.zeros(batch_size, np.float32)
@@ -294,9 +307,15 @@ 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)
+            '''
+            # show feature
+            for index, feat in enumerate(candidate_feat):
+                for ff in feat:
+                    print(ff)
+                print(candidate_leng[index])
+                print()
+            '''
+
 
             # the first [CLS] token, initialized by the language BERT, serves
             # as the agent's state passing through time steps
@@ -329,7 +348,18 @@ class Seq2SeqAgent(BaseAgent):
             target = self._teacher_action(perm_obs, ended)
             ml_loss += self.criterion(logit, target)
 
+
+            '''
+            for index, mask in enumerate(candidate_mask):
+                print(mask)
+                print(candidate_leng[index])
+                print(logit[index])
+                print(target[index])
+                print("\n\n")
+            '''
+
             # Determine next model inputs
+            
             if self.feedback == 'teacher':
                 a_t = target                 # teacher forcing
             elif self.feedback == 'argmax':
@@ -347,18 +377,34 @@ class Seq2SeqAgent(BaseAgent):
             else:
                 print(self.feedback)
                 sys.exit('Invalid feedback option')
+
             # Prepare environment action
             # NOTE: Env action is in the perm_obs space
             cpu_a_t = a_t.cpu().numpy()
             for i, next_id in enumerate(cpu_a_t):
-                if next_id == (candidate_leng[i]-1) or next_id == args.ignoreid or ended[i]:    # The last action is <end>
-                    cpu_a_t[i] = -1             # Change the <end> and ignore action to -1
+                if next_id == (args.ignoreid) or ended[i]:
+                    cpu_a_t[i] = found[i]
+                elif next_id == (candidate_leng[i]-2):
+                    cpu_a_t[i] = -1
+                elif next_id == (candidate_leng[i]-1):
+                    cpu_a_t[i] = -2
+
 
             # Make action and get the new state
-            self.make_equiv_action(cpu_a_t, perm_obs, perm_idx, traj)
+            self.make_equiv_action(cpu_a_t, perm_obs, perm_idx, traj, found)
+            
+            '''
+            print(self.feedback, end=' ')
+            print(cpu_a_t, end=' ')
+            for i in perm_obs:
+                print(i['found'], end=' ')
+            print(found)
+            print()
+            '''
             obs = np.array(self.env._get_obs())
             perm_obs = obs[perm_idx]            # Perm the obs for the resu
 
+            '''
             if train_rl:
                 # Calculate the mask and reward
                 dist = np.zeros(batch_size, np.float32)
@@ -379,6 +425,20 @@ class Seq2SeqAgent(BaseAgent):
                         if action_idx == -1:                              # If the action now is end
                             if dist[i] < 3.0:                             # Correct
                                 reward[i] = 2.0 + ndtw_score[i] * 2.0
+                                if ob['found']:
+                                    reward[i] += 1
+                                else:
+                                    reward[i] -= 2
+                            else:                                         # Incorrect
+                                reward[i] = -2.0
+
+                        elif action_idx == -2:
+                            if dist[i] < 3.0:
+                                reward[i] = 2.0 + ndtw_score[i] * 2.0
+                                if ob['found']:
+                                    reward[i] -= 2
+                                else:
+                                    reward[i] += 1
                             else:                                         # Incorrect
                                 reward[i] = -2.0
                         else:                                             # The action is not end
@@ -398,17 +458,18 @@ class Seq2SeqAgent(BaseAgent):
                 masks.append(mask)
                 last_dist[:] = dist
                 last_ndtw[:] = ndtw_score
+            '''
 
             # Update the finished actions
             # -1 means ended or ignored (already ended)
             ended[:] = np.logical_or(ended, (cpu_a_t == -1))
+            ended[:] = np.logical_or(ended, (cpu_a_t == -2))
 
             # Early exit if all ended
             if ended.all():
                 break
 
-            print()
-
+        '''
         if train_rl:
             # Last action in A2C
             input_a_t, candidate_feat, candidate_leng = self.get_input_feat(perm_obs)
@@ -419,7 +480,6 @@ class Seq2SeqAgent(BaseAgent):
             visual_attention_mask = torch.cat((language_attention_mask, visual_temp_mask), dim=-1)
 
             self.vln_bert.vln_bert.config.directions = max(candidate_leng)
-            ''' Visual BERT '''
             visual_inputs = {'mode':              'visual',
                             'sentence':           language_features,
                             'attention_mask':     visual_attention_mask,
@@ -470,6 +530,7 @@ class Seq2SeqAgent(BaseAgent):
 
             self.loss += rl_loss
             self.logs['RL_loss'].append(rl_loss.item())
+        '''
 
         if train_ml is not None:
             self.loss += ml_loss * train_ml / batch_size
@@ -480,7 +541,7 @@ class Seq2SeqAgent(BaseAgent):
         else:
             self.losses.append(self.loss.item() / self.episode_len)  # This argument is useless.
 
-        return traj
+        return traj, found 
 
     def test(self, use_dropout=False, feedback='argmax', allow_cheat=False, iters=None):
         ''' Evaluate once on each instruction in the current environment '''

r2r_src/env.py

@@ -127,6 +127,7 @@ class R2RBatch():
                             new_item = dict(item)
                             new_item['instr_id'] = '%s_%d' % (item['path_id'], j)
                             new_item['instructions'] = instr
+                            new_item['found'] = item['found'][j]
 
                             ''' BERT tokenizer '''
                             instr_tokens = tokenizer.tokenize(instr)
@@ -328,6 +329,7 @@ class R2RBatch():
             # [visual_feature, angle_feature] for views
             feature = np.concatenate((feature, self.angle_feature[base_view_id]), -1)
 
+
             obs.append({
                 'instr_id' : item['instr_id'],
                 'scan' : state.scanId,
@@ -341,7 +343,8 @@ 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'],
+                'found': item['found']
             })
             if 'instr_encoding' in item:
                 obs[-1]['instr_encoding'] = item['instr_encoding']

r2r_src/eval.py

@@ -55,11 +55,16 @@ class Evaluation(object):
                 near_d = d
         return near_id
 
-    def _score_item(self, instr_id, path):
+    def _score_item(self, instr_id, path, predict_found):
         ''' Calculate error based on the final position in trajectory, and also
             the closest position (oracle stopping rule).
             The path contains [view_id, angle, vofv] '''
         gt = self.gt[instr_id.split('_')[-2]]
+        index = int(instr_id.split('_')[-1])
+        
+        gt_instruction = gt['instructions'][index]
+        gt_found = gt['found'][index]
+
         start = gt['path'][0]
         assert start == path[0][0], 'Result trajectories should include the start position'
         goal = gt['path'][-1]
@@ -68,6 +73,19 @@ class Evaluation(object):
         self.scores['nav_errors'].append(self.distances[gt['scan']][final_position][goal])
         self.scores['oracle_errors'].append(self.distances[gt['scan']][nearest_position][goal])
         self.scores['trajectory_steps'].append(len(path)-1)
+
+        # <STOP> <NOT_FOUND> score
+        score = 0
+        if gt_found == True:
+            if predict_found == -1:
+                score = 1
+        else:
+            if predict_found == -2:
+                score = 1
+        self.scores['found_count'] += score
+
+
+
         distance = 0  # length of the path in meters
         prev = path[0]
         for curr in path[1:]:
@@ -81,6 +99,7 @@ class Evaluation(object):
     def score(self, output_file):
         ''' Evaluate each agent trajectory based on how close it got to the goal location '''
         self.scores = defaultdict(list)
+        self.scores['found_count'] = 0
         instr_ids = set(self.instr_ids)
         if type(output_file) is str:
             with open(output_file) as f:
@@ -90,12 +109,14 @@ class Evaluation(object):
 
         # print('result length', len(results))
         # print("RESULT:", results)
+        path_counter = 0
         for item in results:
             # Check against expected ids
             if item['instr_id'] in instr_ids:
                 # print("{} exist".format(item['instr_id']))
                 instr_ids.remove(item['instr_id'])
-                self._score_item(item['instr_id'], item['trajectory'])
+                self._score_item(item['instr_id'], item['trajectory'], item['found'])
+                path_counter += 1
             else:
                 print("{} not exist".format(item['instr_id']))
                 print(item)
@@ -108,7 +129,8 @@ class Evaluation(object):
             'nav_error': np.average(self.scores['nav_errors']),
             'oracle_error': np.average(self.scores['oracle_errors']),
             'steps': np.average(self.scores['trajectory_steps']),
-            'lengths': np.average(self.scores['trajectory_lengths'])
+            'lengths': np.average(self.scores['trajectory_lengths']),
+            'found_score': self.scores['found_count'] / path_counter
         }
         num_successes = len([i for i in self.scores['nav_errors'] if i < self.error_margin])
         score_summary['success_rate'] = float(num_successes)/float(len(self.scores['nav_errors']))

r2r_src/train.py

@@ -105,6 +105,9 @@ def train(train_env, tok, n_iters, log_every=2000, val_envs={}, aug_env=None):
 
         # Run validation
         loss_str = "iter {}".format(iter)
+        
+
+        save_results = []
         for env_name, (env, evaluator) in val_envs.items():
             listner.env = env
 
@@ -112,6 +115,8 @@ def train(train_env, tok, n_iters, log_every=2000, val_envs={}, aug_env=None):
             listner.test(use_dropout=False, feedback='argmax', iters=None)
             result = listner.get_results()
             score_summary, _ = evaluator.score(result)
+            
+            print(score_summary)
             loss_str += ", %s " % env_name
             for metric, val in score_summary.items():
                 if metric in ['spl']:
@@ -195,11 +200,11 @@ 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 = ['train','val_unseen']
 
     train_env = R2RBatch(feat_dict, batch_size=args.batchSize, splits=['train'], tokenizer=tok)
     from collections import OrderedDict