feat: haven't fix not found

adversarial_summary.py

@@ -1,42 +0,0 @@
-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)

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)

r2r_src/agent.py

@@ -61,14 +61,16 @@ 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):
-                traj, found = self.rollout(**kwargs)
-                for index, traj in enumerate(traj):
+                trajs, found = self.rollout(**kwargs)
+                print(found)
+                for index, traj in enumerate(trajs):
                     self.loss = 0
                     self.results[traj['instr_id']] = (traj['path'], found[index])
         else:   # Do a full round
             while True:
-                traj, found = self.rollout(**kwargs)
-                for index, traj in enumerate(traj):
+                trajs, found = self.rollout(**kwargs)
+                print("FOUND: ", found)
+                for index, traj in enumerate(trajs):
                     if traj['instr_id'] in self.results:
                         looped = True
                     else:
@@ -157,8 +159,9 @@ 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>
+
+            # 補上 not fount token
+            candidate_feat[i, len(ob['candidate'])+1, :] = np.ones((self.feature_size + args.angle_feat_size))
 
         return torch.from_numpy(candidate_feat).cuda(), candidate_leng
 
@@ -190,10 +193,11 @@ class Seq2SeqAgent(BaseAgent):
                         break
                 else:   # Stop here
                     assert ob['teacher'] == ob['viewpoint']         # The teacher action should be "STAY HERE"
-                    if ob['found']:
-                        a[i] = len(ob['candidate'])
-                    else:
-                        a[i] = len(ob['candidate'])+1
+                    if ob['swap']:                                 # instruction 有被換過，所以要 not found
+                        a[i] = len(ob['candidate'])-1
+                    else:                                           # STOP
+                        a[i] = len(ob['candidate'])-2
+        print("  ", a)
         return torch.from_numpy(a).cuda()
 
     def make_equiv_action(self, a_t, perm_obs, perm_idx=None, traj=None, found=None):
@@ -212,6 +216,7 @@ class Seq2SeqAgent(BaseAgent):
 
         for i, idx in enumerate(perm_idx):
             action = a_t[i]
+            # print('action: ', 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']
@@ -235,11 +240,18 @@ 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:
+            else:
                 found[i] = action
                 
 
+
+            '''
+            elif action == -1:
+                print('<STOP>')
+            elif action == -2:
+                print('<NOT_FOUND>')
+            '''
+
     def rollout(self, train_ml=None, train_rl=True, reset=True):
         """
         :param train_ml:    The weight to train with maximum likelihood
@@ -248,6 +260,7 @@ class Seq2SeqAgent(BaseAgent):
 
         :return:
         """
+        print("ROLLOUT!!!")
         if self.feedback == 'teacher' or self.feedback == 'argmax':
             train_rl = False
 
@@ -263,8 +276,10 @@ class Seq2SeqAgent(BaseAgent):
         # Language input
         sentence, language_attention_mask, token_type_ids, \
             seq_lengths, perm_idx = self._sort_batch(obs)
+
         perm_obs = obs[perm_idx]
         
+
         ''' Language BERT '''
         language_inputs = {'mode':        'language',
                         'sentence':       sentence,
@@ -282,7 +297,8 @@ class Seq2SeqAgent(BaseAgent):
             'path': [(ob['viewpoint'], ob['heading'], ob['elevation'])],
         } for ob in perm_obs]
 
-        found = [ None for _ in range(len(perm_obs)) ]
+        found = [None for _ in range(len(perm_obs))]
+
 
         # Init the reward shaping
         last_dist = np.zeros(batch_size, np.float32)
@@ -307,15 +323,6 @@ class Seq2SeqAgent(BaseAgent):
 
             input_a_t, candidate_feat, candidate_leng = self.get_input_feat(perm_obs)
 
-            '''
-            # 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
@@ -341,30 +348,37 @@ 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)
+            for i, d in enumerate(target):
+                # print(perm_obs[i]['swap'], perm_obs[i]['instructions'])
+                # print(d)
+                _, at_t = logit.max(1)
+                '''
+                if at_t[i].item() == candidate_leng[i]-1:
+                    print("-2")
+                elif at_t[i].item() == candidate_leng[i]-2:
+                    print("-1")
+                else:
+                    print(at_t[i].item())
+                print()
+                '''
             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")
-            '''
-
+            a_predict = None
             # Determine next model inputs
-            
             if self.feedback == 'teacher':
                 a_t = target                 # teacher forcing
+                _, a_predict = logit.max(1)
+                a_predict = a_predict.detach()
             elif self.feedback == 'argmax':
                 _, a_t = logit.max(1)        # student forcing - argmax
                 a_t = a_t.detach()
+                a_predict = a_t.detach()
                 log_probs = F.log_softmax(logit, 1)                              # Calculate the log_prob here
                 policy_log_probs.append(log_probs.gather(1, a_t.unsqueeze(1)))   # Gather the log_prob for each batch
             elif self.feedback == 'sample':
@@ -372,39 +386,42 @@ class Seq2SeqAgent(BaseAgent):
                 c = torch.distributions.Categorical(probs)
                 self.logs['entropy'].append(c.entropy().sum().item())            # For log
                 entropys.append(c.entropy())                                     # For optimization
-                a_t = c.sample().detach()
+                new_c = c.sample()
+                a_t = new_c.detach()
+                a_predict = new_c.detach()
                 policy_log_probs.append(c.log_prob(a_t))
             else:
-                print(self.feedback)
+                # 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 == (args.ignoreid) or ended[i]:
-                    cpu_a_t[i] = found[i]
+                if next_id == args.ignoreid or ended[i]:
+                    if found[i] == True: 
+                        cpu_a_t[i] = -1             # Change the <end> and ignore action to -1
+                    else:
+                        cpu_a_t[i] = -2
                 elif next_id == (candidate_leng[i]-2):
-                    cpu_a_t[i] = -1
+                    cpu_a_t[i] = -1             # Change the <end> and ignore action to -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, found)
+            cpu_a_predict = a_predict.cpu().numpy()
+            for i, next_id in enumerate(cpu_a_predict):
+                if next_id == (candidate_leng[i]-2):
+                    cpu_a_predict[i] = -1             # Change the <end> and ignore action to -1
+                elif next_id == (candidate_leng[i]-1):
+                    cpu_a_predict[i] = -2
 
-            '''
-            print(self.feedback, end=' ')
-            print(cpu_a_t, end=' ')
-            for i in perm_obs:
-                print(i['found'], end=' ')
-            print(found)
-            print()
-            '''
+            # Make action and get the new state
+            print(cpu_a_t)
+            self.make_equiv_action(cpu_a_t, perm_obs, perm_idx, traj, found=found)
             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)
@@ -425,22 +442,22 @@ 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:
+                                if ob['swap']:
                                     reward[i] -= 2                        
+                                else:
+                                    reward[i] += 1
                             else:                                         # Incorrect
                                 reward[i] = -2.0
-
-                        elif action_idx == -2:
+                        elif action_idx == -2:                             # NOT_FOUND reward 設定在這裏
                             if dist[i] < 3.0:
                                 reward[i] = 2.0 + ndtw_score[i] * 2.0
-                                if ob['found']:
-                                    reward[i] -= 2
+                                if ob['swap']:
+                                    reward[i] += 3                        # 偵測到錯誤 instruction,多加一分
                                 else:
-                                    reward[i] += 1
+                                    reward[i] -= 2
                             else:                                         # Incorrect
                                 reward[i] = -2.0
+                                reward[i] += 1                            # distance > 3, 確實沒找到東西，從扣二變成扣一
                         else:                                             # The action is not end
                             # Path fidelity rewards (distance & nDTW)
                             reward[i] = - (dist[i] - last_dist[i])
@@ -458,7 +475,6 @@ class Seq2SeqAgent(BaseAgent):
                 masks.append(mask)
                 last_dist[:] = dist
                 last_ndtw[:] = ndtw_score
-            '''
 
             # Update the finished actions
             # -1 means ended or ignored (already ended)
@@ -469,7 +485,8 @@ class Seq2SeqAgent(BaseAgent):
             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)
@@ -480,6 +497,7 @@ 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,
@@ -530,7 +548,6 @@ 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
@@ -540,6 +557,7 @@ class Seq2SeqAgent(BaseAgent):
             self.losses.append(0.)
         else:
             self.losses.append(self.loss.item() / self.episode_len)  # This argument is useless.
+        print("\n\n")
 
         return traj, found
 

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
@@ -127,7 +130,7 @@ 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]
+                            new_item['swap'] = item['swap'][j]
 
                             ''' BERT tokenizer '''
                             instr_tokens = tokenizer.tokenize(instr)
@@ -137,10 +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:
@@ -329,7 +334,6 @@ 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,
@@ -344,7 +348,7 @@ class R2RBatch():
                 'teacher' : self._shortest_path_action(state, item['path'][-1]),
                 'gt_path' : item['path'],
                 'path_id' : item['path_id'],
-                'found': item['found']
+                'swap': item['swap']
             })
             if 'instr_encoding' in item:
                 obs[-1]['instr_encoding'] = item['instr_encoding']

r2r_src/eval.py

@@ -55,16 +55,11 @@ class Evaluation(object):
                 near_d = d
         return near_id
 
-    def _score_item(self, instr_id, path, predict_found):
+    def _score_item(self, instr_id, path):
         ''' 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]
@@ -73,19 +68,6 @@ 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:]:
@@ -99,7 +81,6 @@ 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:
@@ -109,14 +90,12 @@ 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'], item['found'])
-                path_counter += 1
+                self._score_item(item['instr_id'], item['trajectory'])
             else:
                 print("{} not exist".format(item['instr_id']))
                 print(item)
@@ -129,8 +108,7 @@ 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']),
-            'found_score': self.scores['found_count'] / path_counter
+            'lengths': np.average(self.scores['trajectory_lengths'])
         }
         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

@@ -105,9 +105,6 @@ 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
 
@@ -115,8 +112,6 @@ 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']:
@@ -200,11 +195,12 @@ def train_val(test_only=False):
 
     if test_only:
         featurized_scans = None
-        val_env_names = ['val_unseen']
+        val_env_names = ['val_train_seen']
     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 = ['train','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