.gitignore

@@ -24,4 +24,3 @@ snap/*
 
 logs/*
 !logs/.gitkeep
-*.json

LICENSE

@@ -1,22 +0,0 @@
-The MIT License (MIT)
-
-Copyright (c) 2021 Yicong Hong, Qi Wu, Yuankai Qi,
-Cristian Rodriguez-Opazo, Stephen Gould
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.

README.md

@@ -1,19 +1,16 @@
 # Recurrent VLN-BERT
 
-Code of the **CVPR 2021 Oral** paper:<br>
+Code of the Recurrent-VLN-BERT paper:
 **A Recurrent Vision-and-Language BERT for Navigation**<br>
 [**Yicong Hong**](http://www.yiconghong.me/), [Qi Wu](http://www.qi-wu.me/), [Yuankai Qi](https://sites.google.com/site/yuankiqi/home), [Cristian Rodriguez-Opazo](https://crodriguezo.github.io/), [Stephen Gould](http://users.cecs.anu.edu.au/~sgould/)<br>
 
-[[Paper & Appendices](https://arxiv.org/abs/2011.13922)] [[GitHub](https://github.com/YicongHong/Recurrent-VLN-BERT)]
+[[Paper & Appendices](https://arxiv.org/abs/2011.13922) | [GitHub](https://github.com/YicongHong/Recurrent-VLN-BERT)]
-
-"*Neo : Are you saying I have to choose whether Trinity lives or dies? The Oracle : No, you've already made the choice. Now you have to understand it.*" --- [The Matrix Reloaded (2003)](https://www.imdb.com/title/tt0234215/).
 
 ## Prerequisites
 
 ### Installation
 
-Install the [Matterport3D Simulator](https://github.com/peteanderson80/Matterport3DSimulator). Notice that this code uses the [old version (v0.1)](https://github.com/peteanderson80/Matterport3DSimulator/tree/v0.1) of the simulator, but you can easily change to the latest version which supports batches of agents and it is much more efficient.
+Install the [Matterport3D Simulator](https://github.com/peteanderson80/Matterport3DSimulator).
-
 Please find the versions of packages in our environment [here](https://github.com/YicongHong/Recurrent-VLN-BERT/blob/main/recurrent-vln-bert.yml).
 
 Install the [Pytorch-Transformers](https://github.com/huggingface/transformers).
@@ -72,12 +69,10 @@ The trained Navigator will be saved under `snap/`.
 ## Citation
 If you use or discuss our Recurrent VLN-BERT, please cite our paper:
 ```
-@InProceedings{Hong_2021_CVPR,
+@article{hong2020recurrent,
-    author    = {Hong, Yicong and Wu, Qi and Qi, Yuankai and Rodriguez-Opazo, Cristian and Gould, Stephen},
+  title={A Recurrent Vision-and-Language BERT for Navigation},
-    title     = {A Recurrent Vision-and-Language BERT for Navigation},
+  author={Hong, Yicong and Wu, Qi and Qi, Yuankai and Rodriguez-Opazo, Cristian and Gould, Stephen},
-    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+  journal={arXiv preprint arXiv:2011.13922},
-    month     = {June},
+  year={2020}
-    year      = {2021},
-    pages     = {1643-1653}
 }
 ```

r2r_src/agent.py

@@ -81,14 +81,14 @@ class Seq2SeqAgent(BaseAgent):
 
     # For now, the agent can't pick which forward move to make - just the one in the middle
     env_actions = {
-      'left': ([0],[-1], [0]), # left
+      'left': (0,-1, 0), # left
-      'right': ([0], [1], [0]), # right
+      'right': (0, 1, 0), # right
-      'up': ([0], [0], [1]), # up
+      'up': (0, 0, 1), # up
-      'down': ([0], [0],[-1]), # down
+      'down': (0, 0,-1), # down
-      'forward': ([1], [0], [0]), # forward
+      'forward': (1, 0, 0), # forward
-      '<end>': ([0], [0], [0]), # <end>
+      '<end>': (0, 0, 0), # <end>
-      '<start>': ([0], [0], [0]), # <start>
+      '<start>': (0, 0, 0), # <start>
-      '<ignore>': ([0], [0], [0])  # <ignore>
+      '<ignore>': (0, 0, 0)  # <ignore>
     }
 
     def __init__(self, env, results_path, tok, episode_len=20):
@@ -149,7 +149,6 @@ class Seq2SeqAgent(BaseAgent):
     def _candidate_variable(self, obs):
         candidate_leng = [len(ob['candidate']) + 1 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
         # which is zero in my implementation
         for i, ob in enumerate(obs):
@@ -196,7 +195,7 @@ 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])
 
@@ -217,15 +216,13 @@ 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()[0].viewIndex != trg_point:    # Turn right until the target
+                while self.env.env.sims[idx].getState().viewIndex != trg_point:    # Turn right until the target
                     take_action(i, idx, 'right')
                 assert select_candidate['viewpointId'] == \
-                       self.env.env.sims[idx].getState()[0].navigableLocations[select_candidate['idx']].viewpointId
+                       self.env.env.sims[idx].getState().navigableLocations[select_candidate['idx']].viewpointId
                 take_action(i, idx, select_candidate['idx'])
 
-                state = self.env.env.sims[idx].getState()[0]
+                state = self.env.env.sims[idx].getState()
-                # print(state.rgb.shape)
-                # print("action: {} view_index: {}".format(action, state.viewIndex))
                 if traj is not None:
                     traj[i]['path'].append((state.location.viewpointId, state.heading, state.elevation))
 
@@ -240,8 +237,6 @@ class Seq2SeqAgent(BaseAgent):
         if self.feedback == 'teacher' or self.feedback == 'argmax':
             train_rl = False
 
-        # self.env is `R2RBatch`
-        # get obervation
         if reset:  # Reset env
             obs = np.array(self.env.reset())
         else:
@@ -294,10 +289,6 @@ 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
             if (t >= 1) or (args.vlnbert=='prevalent'):
@@ -407,8 +398,6 @@ 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)

r2r_src/env.py

@@ -16,7 +16,7 @@ import networkx as nx
 from param import args
 
 from utils import load_datasets, load_nav_graphs, pad_instr_tokens
-from IPython import embed
+
 csv.field_size_limit(sys.maxsize)
 
 
@@ -52,7 +52,7 @@ 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.initialize()
+            sim.init()
             self.sims.append(sim)
 
     def _make_id(self, scanId, viewpointId):
@@ -60,7 +60,7 @@ class EnvBatch():
 
     def newEpisodes(self, scanIds, viewpointIds, headings):
         for i, (scanId, viewpointId, heading) in enumerate(zip(scanIds, viewpointIds, headings)):
-            self.sims[i].newEpisode([scanId], [viewpointId], [heading], [0])
+            self.sims[i].newEpisode(scanId, viewpointId, heading, 0)
 
     def getStates(self):
         """
@@ -71,7 +71,7 @@ class EnvBatch():
         """
         feature_states = []
         for i, sim in enumerate(self.sims):
-            state = sim.getState()[0]
+            state = sim.getState()
 
             long_id = self._make_id(state.scanId, state.location.viewpointId)
             if self.features:
@@ -103,11 +103,9 @@ class R2RBatch():
             self.tok = tokenizer
         scans = []
         for split in splits:
-            max_len = 0
             for i_item, item in enumerate(load_datasets([split])):
-                max_len = i_item
+                if args.test_only and i_item == 64:
-                # if args.test_only and i_item == 64:
+                    break
-                #     break
                 if "/" in split:
                     try:
                         new_item = dict(item)
@@ -121,7 +119,6 @@ class R2RBatch():
                         continue
                 else:
                     # Split multiple instructions into separate entries
-                    # print("HERE")
                     for j, instr in enumerate(item['instructions']):
                         try:
                             new_item = dict(item)
@@ -138,7 +135,6 @@ class R2RBatch():
                                 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"
@@ -226,34 +222,26 @@ class R2RBatch():
     def make_candidate(self, feature, scanId, viewpointId, viewId):
         def _loc_distance(loc):
             return np.sqrt(loc.rel_heading ** 2 + loc.rel_elevation ** 2)
-
-        # viewId 就是 view index
         base_heading = (viewId % 12) * math.radians(30)
         adj_dict = {}
         long_id = "%s_%s" % (scanId, viewpointId)
-
-        
         if long_id not in self.buffered_state_dict:
-
-            # 36 view index
             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()[0]
+                state = self.sim.getState()
                 assert state.viewIndex == ix
 
                 # Heading and elevation for the viewpoint center
                 heading = state.heading - base_heading
                 elevation = state.elevation
 
-                # feature 是 np.zeros((36, 2048))
                 visual_feat = feature[ix]
-                # (2048)
 
                 # get adjacent locations
                 for j, loc in enumerate(state.navigableLocations[1:]):
@@ -279,8 +267,6 @@ class R2RBatch():
                             'feature': np.concatenate((visual_feat, angle_feat), -1)
                         }
             candidate = list(adj_dict.values())
-
-            # 放 buffer
             self.buffered_state_dict[long_id] = [
                 {key: c[key]
                  for key in
@@ -307,24 +293,15 @@ class R2RBatch():
 
     def _get_obs(self):
         obs = []
-
-        # self.env is `EnvBatch`
-        # [ ((30, 2048), sim_state) ] * batch_size 
         for i, (feature, state) in enumerate(self.env.getStates()):
-
-            # self.batch 看不懂
             item = self.batch[i]
-
-            # now viewpoint index
             base_view_id = state.viewIndex
 
             if feature is None:
                 feature = np.zeros((36, 2048))
 
             # Full features
-            # candidate 就是 navigable viewpoint
             candidate = self.make_candidate(feature, state.scanId, state.location.viewpointId, state.viewIndex)
-
             # [visual_feature, angle_feature] for views
             feature = np.concatenate((feature, self.angle_feature[base_view_id]), -1)
 
@@ -351,7 +328,6 @@ class R2RBatch():
 
     def reset(self, batch=None, inject=False, **kwargs):
         ''' Load a new minibatch / episodes. '''
-
         if batch is None:       # Allow the user to explicitly define the batch
             self._next_minibatch(**kwargs)
         else:
@@ -363,8 +339,6 @@ 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 is `EnvBatch`
         self.env.newEpisodes(scanIds, viewpointIds, headings)
         return self._get_obs()
 

r2r_src/eval.py

@@ -24,20 +24,13 @@ class Evaluation(object):
         self.gt = {}
         self.instr_ids = []
         self.scans = []
-
-        
-        print(splits)
-
         for split in splits:
             for item in load_datasets([split]):
                 if scans is not None and item['scan'] not in scans:
-                    print("ignore {}".format(item['scan']))
                     continue
                 self.gt[str(item['path_id'])] = item
                 self.scans.append(item['scan'])
                 self.instr_ids += ['%s_%d' % (item['path_id'], i) for i in range(len(item['instructions']))]
-        # for i in self.instr_ids:
-        #     print(i)
         self.scans = set(self.scans)
         self.instr_ids = set(self.instr_ids)
         self.graphs = load_nav_graphs(self.scans)
@@ -88,22 +81,17 @@ class Evaluation(object):
         else:
             results = output_file
 
-        # print('result length', len(results))
+        print('result length', len(results))
-        # print("RESULT:", results)
         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'])
-            else:
-                print("{} not exist".format(item['instr_id']))
-                print(item)
 
-        # if 'train' not in self.splits:  # Exclude the training from this. (Because training eval may be partial)
+        if 'train' not in self.splits:  # Exclude the training from this. (Because training eval may be partial)
-            # assert len(instr_ids) == 0, 'Missing %d of %d instruction ids from %s - not in %s'\
+            assert len(instr_ids) == 0, 'Missing %d of %d instruction ids from %s - not in %s'\
-            #                % (len(instr_ids), len(self.instr_ids), ",".join(self.splits), output_file)
+                           % (len(instr_ids), len(self.instr_ids), ",".join(self.splits), output_file)
-            # assert len(self.scores['nav_errors']) == len(self.instr_ids)
+            assert len(self.scores['nav_errors']) == len(self.instr_ids)
         score_summary = {
             'nav_error': np.average(self.scores['nav_errors']),
             'oracle_error': np.average(self.scores['oracle_errors']),

r2r_src/train.py

@@ -58,7 +58,7 @@ 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}, 'val_train_seen': {"spl": 0., "sr": 0., "state":"", 'update':False}}
+    best_val = {'val_unseen': {"spl": 0., "sr": 0., "state":"", 'update':False}}
 
     for idx in range(start_iter, start_iter+n_iters, log_every):
         listner.logs = defaultdict(list)
@@ -91,10 +91,6 @@ def train(train_env, tok, n_iters, log_every=2000, val_envs={}, aug_env=None):
         RL_loss = sum(listner.logs['RL_loss']) / max(len(listner.logs['RL_loss']), 1)
         IL_loss = sum(listner.logs['IL_loss']) / max(len(listner.logs['IL_loss']), 1)
         entropy = sum(listner.logs['entropy']) / total
-        
-        print("training:")
-        print("total: {}, length: {}, critic_loss: {}, RL_loss: {}, IL_loss:{}, entropy: {}".format(total, length, critic_loss, RL_loss, IL_loss, entropy
-                                                ))
         writer.add_scalar("loss/critic", critic_loss, idx)
         writer.add_scalar("policy_entropy", entropy, idx)
         writer.add_scalar("loss/RL_loss", RL_loss, idx)
@@ -140,14 +136,13 @@ def train(train_env, tok, n_iters, log_every=2000, val_envs={}, aug_env=None):
         print(('%s (%d %d%%) %s' % (timeSince(start, float(iter)/n_iters),
                                              iter, float(iter)/n_iters*100, loss_str)))
 
-        if iter % log_every == 0:
+        if iter % 1000 == 0:
             print("BEST RESULT TILL NOW")
             for env_name in best_val:
                 print(env_name, best_val[env_name]['state'])
 
                 record_file = open('./logs/' + args.name + '.txt', 'a')
                 record_file.write('BEST RESULT TILL NOW: ' + env_name + ' | ' + best_val[env_name]['state'] + '\n')
-                # print(best_val)
                 record_file.close()
 
     listner.save(idx, os.path.join("snap", args.name, "state_dict", "LAST_iter%d" % (idx)))
@@ -198,8 +193,7 @@ 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_train_seen', 'val_seen', 'val_unseen']
-        val_env_names = ['val_train_seen']
 
     train_env = R2RBatch(feat_dict, batch_size=args.batchSize, splits=['train'], tokenizer=tok)
     from collections import OrderedDict
@@ -209,7 +203,6 @@ def train_val(test_only=False):
     else:
         pass
 
-    print("only {} in train_val()".format(val_env_names))
     val_envs = OrderedDict(
         ((split,
           (R2RBatch(feat_dict, batch_size=args.batchSize, splits=[split], tokenizer=tok),
@@ -220,7 +213,7 @@ def train_val(test_only=False):
     )
 
     if args.train == 'listener':
-        train(train_env, tok, args.iters, val_envs=val_envs, log_every=1000)
+        train(train_env, tok, args.iters, val_envs=val_envs)
     elif args.train == 'validlistener':
         valid(train_env, tok, val_envs=val_envs)
     else:
@@ -261,8 +254,6 @@ def train_val_augment(test_only=False):
 
 
 if __name__ == "__main__":
-    device = torch.device('cuda')
-    print(torch.cuda.get_device_name(device))
     if args.train in ['listener', 'validlistener']:
         train_val(test_only=args.test_only)
     elif args.train == 'auglistener':

r2r_src/utils.py

@@ -64,12 +64,11 @@ def load_datasets(splits):
         number = -1
         if len(components) > 1:
             split, number = components[0], int(components[1])
-        print("number:", number)
+
         # Load Json
         # if split in ['train', 'val_seen', 'val_unseen', 'test',
         #              'val_unseen_half1', 'val_unseen_half2', 'val_seen_half1', 'val_seen_half2']:       # Add two halves for sanity check
         if "/" not in split:
-            print('here: data/R2R_%s.json' % split)
             with open('data/R2R_%s.json' % split) as f:
                 new_data = json.load(f)
         else:
@@ -349,7 +348,7 @@ def new_simulator():
     sim.setCameraResolution(WIDTH, HEIGHT)
     sim.setCameraVFOV(math.radians(VFOV))
     sim.setDiscretizedViewingAngles(True)
-    sim.initialize()
+    sim.init()
 
     return sim
 
@@ -360,13 +359,13 @@ 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()[0]
+        state = sim.getState()
         assert state.viewIndex == ix
 
         heading = state.heading - base_heading
@@ -561,7 +560,7 @@ 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 = 'LL' * filled_length + '-' * (bar_length - filled_length)
+    bar = '█' * filled_length + '-' * (bar_length - filled_length)
 
     sys.stdout.write('\r%s |%s| %s%s %s' % (prefix, bar, percents, '%', suffix)),
 

r2r_src/vlnbert/vlnbert_OSCAR.py

@@ -9,13 +9,7 @@ from torch import nn
 import torch.nn.functional as F
 from torch.nn import CrossEntropyLoss, MSELoss
 
-#from transformers.pytorch_transformers.modeling_bert import (BertEmbeddings,
+from transformers.pytorch_transformers.modeling_bert import (BertEmbeddings,
-#        BertSelfAttention, BertAttention, BertEncoder, BertLayer,
-#        BertSelfOutput, BertIntermediate, BertOutput,
-#        BertPooler, BertLayerNorm, BertPreTrainedModel,
-#		BertPredictionHeadTransform)
-
-from pytorch_transformers.modeling_bert import (BertEmbeddings,
         BertSelfAttention, BertAttention, BertEncoder, BertLayer,
         BertSelfOutput, BertIntermediate, BertOutput,
         BertPooler, BertLayerNorm, BertPreTrainedModel,
@@ -191,8 +185,7 @@ class BertImgModel(BertPreTrainedModel):
         self.img_dim = config.img_feature_dim
         logger.info('BertImgModel Image Dimension: {}'.format(self.img_dim))
 
-        # self.apply(self.init_weights)
+        self.apply(self.init_weights)
-        self.init_weights()
 
     def forward(self, mode, input_ids, token_type_ids=None, attention_mask=None,
             position_ids=None, img_feats=None):
@@ -237,7 +230,6 @@ class VLNBert(BertPreTrainedModel):
     def __init__(self, config):
         super(VLNBert, self).__init__(config)
         self.config = config
-        print("Init VLNBERT: ", self.config)
         self.bert = BertImgModel(config)
 
         self.vis_lang_LayerNorm = BertLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
@@ -245,8 +237,7 @@ class VLNBert(BertPreTrainedModel):
         self.state_LayerNorm = BertLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
 
         self.dropout = nn.Dropout(config.hidden_dropout_prob)
-        # self.apply(self.init_weights)
+        self.apply(self.init_weights)
-        self.init_weights()
 
     def forward(self, mode, input_ids, token_type_ids=None, attention_mask=None,
                 position_ids=None, img_feats=None):

r2r_src/vlnbert/vlnbert_PREVALENT.py

@@ -14,8 +14,7 @@ import torch
 from torch import nn
 from torch.nn import CrossEntropyLoss, MSELoss
 
-#from transformers.pytorch_transformers.modeling_bert import BertPreTrainedModel, BertConfig
+from transformers.pytorch_transformers.modeling_bert import BertPreTrainedModel, BertConfig
-from pytorch_transformers.modeling_bert import BertPreTrainedModel, BertConfig 
 import pdb
 
 logger = logging.getLogger(__name__)
@@ -367,12 +366,9 @@ class VisionEncoder(nn.Module):
 class VLNBert(BertPreTrainedModel):
     def __init__(self, config):
         super(VLNBert, self).__init__(config)
-        
         self.embeddings = BertEmbeddings(config)
         self.pooler = BertPooler(config)
 
-        print("Init VLNBert: ", config)
-
         self.img_dim = config.img_feature_dim            # 2176
         logger.info('VLNBert Image Dimension: {}'.format(self.img_dim))
         self.img_feature_type = config.img_feature_type  # ''
@@ -383,8 +379,7 @@ class VLNBert(BertPreTrainedModel):
         self.addlayer = nn.ModuleList(
             [LXRTXLayer(config) for _ in range(self.vl_layers)])
         self.vision_encoder = VisionEncoder(self.config.img_feature_dim, self.config)
-        # self.apply(self.init_weights)
+        self.apply(self.init_weights)
-        self.init_weights()
 
     def forward(self, mode, input_ids, token_type_ids=None,
         attention_mask=None, lang_mask=None, vis_mask=None, position_ids=None, head_mask=None, img_feats=None):

r2r_src/vlnbert/vlnbert_init.py

@@ -1,12 +1,11 @@
 # Recurrent VLN-BERT, 2020, by Yicong.Hong@anu.edu.au
 
-#from transformers.pytorch_transformers import (BertConfig, BertTokenizer)
+from transformers.pytorch_transformers import (BertConfig, BertTokenizer)
-from pytorch_transformers import (BertConfig, BertTokenizer)
 
 def get_tokenizer(args):
     if args.vlnbert == 'oscar':
         tokenizer_class = BertTokenizer
-        model_name_or_path = 'r2r_src/vlnbert/Oscar/pretrained_models/base-no-labels/ep_67_588997'
+        model_name_or_path = 'Oscar/pretrained_models/base-no-labels/ep_67_588997'
         tokenizer = tokenizer_class.from_pretrained(model_name_or_path, do_lower_case=True)
     elif args.vlnbert == 'prevalent':
         tokenizer_class = BertTokenizer
@@ -17,10 +16,9 @@ def get_vlnbert_models(args, config=None):
     config_class = BertConfig
 
     if args.vlnbert == 'oscar':
-        print('\n VLN-BERT model is Oscar!!!')
         from vlnbert.vlnbert_OSCAR import VLNBert
         model_class = VLNBert
-        model_name_or_path = 'r2r_src/vlnbert/Oscar/pretrained_models/base-no-labels/ep_67_588997'
+        model_name_or_path = 'Oscar/pretrained_models/base-no-labels/ep_67_588997'
         vis_config = config_class.from_pretrained(model_name_or_path, num_labels=2, finetuning_task='vln-r2r')
 
         vis_config.model_type = 'visual'
@@ -33,11 +31,9 @@ def get_vlnbert_models(args, config=None):
         visual_model = model_class.from_pretrained(model_name_or_path, from_tf=False, config=vis_config)
 
     elif args.vlnbert == 'prevalent':
-        print('\n VLN-BERT model is prevalent!!!')
         from vlnbert.vlnbert_PREVALENT import VLNBert
         model_class = VLNBert
-        #model_name_or_path = './Prevalent/pretrained_model/pytorch_model.bin'
+        model_name_or_path = 'Prevalent/pretrained_model/pytorch_model.bin'
-        model_name_or_path = 'r2r_src/vlnbert/Prevalent/pretrained_model/pytorch_model.bin'
         vis_config = config_class.from_pretrained('bert-base-uncased')
         vis_config.img_feature_dim = 2176
         vis_config.img_feature_type = ""

run/test_agent.bash

@@ -23,4 +23,4 @@ flag="--vlnbert prevalent
       --dropout 0.5"
 
 mkdir -p snap/$name
-CUDA_VISIBLE_DEVICES=0 python3 r2r_src/train.py $flag --name $name
+CUDA_VISIBLE_DEVICES=1 python r2r_src/train.py $flag --name $name

run/test_agent_r2r.bash

@@ -1,26 +0,0 @@
-name=VLNBERT-test-Prevalent
-
-flag="--vlnbert prevalent
-
-      --submit 0
-      --test_only 0
-
-      --train validlistener
-      --load snap/VLNBERT-PREVALENT-final/state_dict/best_val_unseen
-
-      --features places365
-      --maxAction 15
-      --batchSize 8
-      --feedback sample
-      --lr 1e-5
-      --iters 300000
-      --optim adamW
-
-      --mlWeight 0.20
-      --maxInput 80
-      --angleFeatSize 128
-      --featdropout 0.4
-      --dropout 0.5"
-
-mkdir -p snap/$name
-CUDA_VISIBLE_DEVICES=0 python3 r2r_src/train.py $flag --name $name

run/train_agent.bash

@@ -22,4 +22,4 @@ flag="--vlnbert prevalent
       --dropout 0.5"
 
 mkdir -p snap/$name
-CUDA_VISIBLE_DEVICES=0 python3 r2r_src/train.py $flag --name $name
+CUDA_VISIBLE_DEVICES=1 python r2r_src/train.py $flag --name $name

run/train_agent_no_aug.bash

@@ -1,24 +0,0 @@
-name=VLNBERT-train-Prevalent
-
-flag="--vlnbert prevalent
-
-      --test_only 0
-
-      --train listener
-
-      --features places365
-      --maxAction 15
-      --batchSize 8
-      --feedback sample
-      --lr 1e-5
-      --iters 300000
-      --optim adamW
-
-      --mlWeight 0.20
-      --maxInput 80
-      --angleFeatSize 128
-      --featdropout 0.4
-      --dropout 0.5"
-
-mkdir -p snap/$name
-CUDA_VISIBLE_DEVICES=0 python3 r2r_src/train.py $flag --name $name