fix: original airbert

reverie_src/agent.py

@@ -245,9 +245,11 @@ class Seq2SeqAgent(BaseAgent):
         """
         def take_action(i, idx, name):
             if type(name) is int:       # Go to the next view
-                self.env.env.sims[idx].makeAction(name, 0, 0)
+                self.env.env.sims[idx].makeAction([name], [0], [0])
             else:                       # Adjust
-                self.env.env.sims[idx].makeAction(*self.env_actions[name])
+                action_params = self.env_actions[name]
+                
+                self.env.env.sims[idx].makeAction([action_params[0]], [action_params[1]], [action_params[2]])
 
         if perm_idx is None:
             perm_idx = range(len(perm_obs))
@@ -266,13 +268,13 @@ class Seq2SeqAgent(BaseAgent):
                 while src_level > trg_level:    # Tune down
                     take_action(i, idx, 'down')
                     src_level -= 1
-                while self.env.env.sims[idx].getState().viewIndex != trg_point:    # Turn right until the target
+                while self.env.env.sims[idx].getState()[0].viewIndex != trg_point:    # Turn right until the target
                     take_action(i, idx, 'right')
                 assert select_candidate['viewpointId'] == \
-                       self.env.env.sims[idx].getState().navigableLocations[select_candidate['idx']].viewpointId
+                       self.env.env.sims[idx].getState()[0].navigableLocations[select_candidate['idx']].viewpointId
                 take_action(i, idx, select_candidate['idx'])
 
-                state = self.env.env.sims[idx].getState()
+                state = self.env.env.sims[idx].getState()[0]
                 if traj is not None:
                     traj[i]['path'].append((state.location.viewpointId, state.heading, state.elevation))
 

reverie_src/env.py

@@ -50,15 +50,15 @@ class EnvBatch():
             sim.setDiscretizedViewingAngles(True)   # Set increment/decrement to 30 degree. (otherwise by radians)
             sim.setCameraResolution(self.image_w, self.image_h)
             sim.setCameraVFOV(math.radians(self.vfov))
-            sim.init()
+            sim.initialize()
             self.sims.append(sim)
 
     def _make_id(self, scanId, viewpointId):
         return scanId + '_' + viewpointId
 
     def newEpisodes(self, scanIds, viewpointIds, headings):
-        for i, (scanId, viewpointId, heading) in enumerate(zip(scanIds, viewpointIds, headings)):
+        for i, (scanId, viewpointId, heading) in enumerate(zip(scanIds[0], viewpointIds[0], headings[0])):
-            self.sims[i].newEpisode(scanId, viewpointId, heading, 0)
+            self.sims[i].newEpisode([scanId], [viewpointId], [heading], [0])
 
     def getStates(self):
         """
@@ -69,7 +69,7 @@ class EnvBatch():
         """
         feature_states = []
         for i, sim in enumerate(self.sims):
-            state = sim.getState()
+            state = sim.getState()[0]
 
             long_id = self._make_id(state.scanId, state.location.viewpointId)
             if self.features:
@@ -83,7 +83,7 @@ class EnvBatch():
         ''' Take an action using the full state dependent action interface (with batched input).
             Every action element should be an (index, heading, elevation) tuple. '''
         for i, (index, heading, elevation) in enumerate(actions):
-            self.sims[i].makeAction(index, heading, elevation)
+            self.sims[i].makeAction([index], [heading], [elevation])
 
 
 class R2RBatch():
@@ -231,13 +231,13 @@ class R2RBatch():
         if long_id not in self.buffered_state_dict:
             for ix in range(36):
                 if ix == 0:
-                    self.sim.newEpisode(scanId, viewpointId, 0, math.radians(-30))
+                    self.sim.newEpisode([scanId], [viewpointId], [0], [math.radians(-30)])
                 elif ix % 12 == 0:
-                    self.sim.makeAction(0, 1.0, 1.0)
+                    self.sim.makeAction([0], [1.0], [1.0])
                 else:
-                    self.sim.makeAction(0, 1.0, 0)
+                    self.sim.makeAction([0], [1.0], [0])
 
-                state = self.sim.getState()
+                state = self.sim.getState()[0]
                 assert state.viewIndex == ix
 
                 # Heading and elevation for the viewpoint center
@@ -360,7 +360,7 @@ class R2RBatch():
         scanIds = [item['scan'] for item in self.batch]
         viewpointIds = [item['path'][0] for item in self.batch]
         headings = [item['heading'] for item in self.batch]
-        self.env.newEpisodes(scanIds, viewpointIds, headings)
+        self.env.newEpisodes([scanIds], [viewpointIds], [headings])
         return self._get_obs()
 
     def step(self, actions):

reverie_src/train.py

@@ -55,7 +55,7 @@ def train(train_env, tok, n_iters, log_every=2000, val_envs={}, aug_env=None):
     start = time.time()
     print('\nListener training starts, start iteration: %s' % str(start_iter))
 
-    best_val = {'val_unseen': {"spl": 0., "sr": 0., "state":"", 'update':False}}
+    best_val = {'val_unseen': {"spl": 0., "sr": 0., "sspl": 0. ,"state":"", 'update':False}}
 
     for idx in range(start_iter, start_iter+n_iters, log_every):
         listner.logs = defaultdict(list)
@@ -214,7 +214,7 @@ def train_val(test_only=False):
         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 = ['val_seen', 'val_unseen']
 
     train_env = R2RBatch(feat_dict, obj_feats, batch_size=args.batchSize, splits=['train'], tokenizer=tok)
     from collections import OrderedDict
@@ -252,6 +252,7 @@ def train_val_augment(test_only=False):
 
     # Load the env img features
     feat_dict = read_img_features(features, test_only=test_only)
+
     # load object feature
     with open('data/REVERIE/BBoxS/REVERIE_obj_feats.pkl', 'rb') as f_obj:
         obj_feats = pkl.load(f_obj)
@@ -261,7 +262,8 @@ def train_val_augment(test_only=False):
         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 = ['val_seen', 'val_unseen']
+        # val_env_names = ['val_train_seen', 'val_seen', 'val_unseen']
 
     # Load the augmentation data
     aug_path = args.aug

reverie_src/utils.py

@@ -346,7 +346,7 @@ def new_simulator():
     sim.setCameraResolution(WIDTH, HEIGHT)
     sim.setCameraVFOV(math.radians(VFOV))
     sim.setDiscretizedViewingAngles(True)
-    sim.init()
+    sim.initialize()
 
     return sim
 
@@ -357,13 +357,13 @@ def get_point_angle_feature(baseViewId=0):
     base_heading = (baseViewId % 12) * math.radians(30)
     for ix in range(36):
         if ix == 0:
-            sim.newEpisode('ZMojNkEp431', '2f4d90acd4024c269fb0efe49a8ac540', 0, math.radians(-30))
+            sim.newEpisode(['ZMojNkEp431'], ['2f4d90acd4024c269fb0efe49a8ac540'], [0], [math.radians(-30)])
         elif ix % 12 == 0:
-            sim.makeAction(0, 1.0, 1.0)
+            sim.makeAction([0], [1.0], [1.0])
         else:
-            sim.makeAction(0, 1.0, 0)
+            sim.makeAction([0], [1.0], [0])
 
-        state = sim.getState()
+        state = sim.getState()[0]
         assert state.viewIndex == ix
 
         heading = state.heading - base_heading
@@ -567,7 +567,7 @@ def print_progress(iteration, total, prefix='', suffix='', decimals=1, bar_lengt
     str_format = "{0:." + str(decimals) + "f}"
     percents = str_format.format(100 * (iteration / float(total)))
     filled_length = int(round(bar_length * iteration / float(total)))
-    bar = '█' * filled_length + '-' * (bar_length - filled_length)
+    bar = 'L' * filled_length + '-' * (bar_length - filled_length)
 
     sys.stdout.write('\r%s |%s| %s%s %s' % (prefix, bar, percents, '%', suffix)),
 

reverie_src/vlnbert/vlnbert_init.py

@@ -1,7 +1,7 @@
 import os
 
 def get_tokenizer(args):
-    from transformers.pytorch_transformers import BertTokenizer
+    from pytorch_transformers import BertTokenizer
 
     tokenizer_class = BertTokenizer
 

scripts/train_reverie_agent.sh

@@ -1,7 +1,7 @@
 export AIRBERT_ROOT=$(pwd)
 export PYTHONPATH=${PYTHONPATH}:${AIRBERT_ROOT}/build
 
-name=REVERIE-RC-VLN-BERT/train-init.airbert
+name=REVERIE-RC-VLN-BERT-original/train-init.airbert
 
 flag="--vlnbert vilbert
 
@@ -26,4 +26,4 @@ flag="--vlnbert vilbert
       --dropout 0.5"
 
 mkdir -p snap/$name
-CUDA_VISIBLE_DEVICES=$1 python reverie_src/train.py $flag --name $name
+CUDA_VISIBLE_DEVICES=$1 python3 reverie_src/train.py $flag --name $name

scripts/valid_reverie_agent.sh

@@ -1,6 +1,8 @@
 export AIRBERT_ROOT=$(pwd)
 export PYTHONPATH=${PYTHONPATH}:${AIRBERT_ROOT}/build
 
+echo $PYTHONPATH
+
 name=REVERIE-RC-VLN-BERT/init.airbert
 
 flag="--vlnbert vilbert
@@ -25,5 +27,5 @@ flag="--vlnbert vilbert
       --featdropout 0.4
       --dropout 0.5"
 
-CUDA_VISIBLE_DEVICES=$1 python reverie_src/train.py $flag --name $name
+CUDA_VISIBLE_DEVICES=$1 python3 reverie_src/train.py $flag --name $name