feat: log "success" in predict file

Why:
In visualization tool, we should check whether the agent arrive the
target viewpoint. We need to calculate the distance between the GT
viewpoint & the predicted viewpoint but it's difficult to calculate the
distance without the simulator (we run the visualization tool on Jupyter
notebook which is not in the docker container so we cannot use the
simulator)

How:
After getting the result which gather from the env. We should run the
eval_metrics() to get the success rate, FOUND score..., etc. So we get
the "success" after eval_metrics() and log it in the predicted file so
that the visualization tool can get the "success" status in the
predicted file.

map_nav_src/reverie/env.py

@@ -370,7 +370,11 @@ class ReverieObjectNavBatch(object):
         assert len(goal_viewpoints) > 0, '%s_%s'%(scan, str(gt_objid))
 
         scores['success'] = float(path[-1] in goal_viewpoints)
+        if scores['success'] == 1.0:
             scores['found_success'] = float(pred_found == gt_found) 
+        else:
+            scores['found_success'] = 0.0
+
         scores['oracle_success'] = float(any(x in goal_viewpoints for x in path))
         scores['spl'] = scores['success'] * gt_lengths / max(scores['trajectory_lengths'], gt_lengths, 0.01)
         scores['sspl'] = scores['spl'] * scores['found_success']
@@ -385,6 +389,7 @@ class ReverieObjectNavBatch(object):
         print('eval %d predictions' % (len(preds)))
         print(preds[0])
 
+
         metrics = defaultdict(list)
         for item in preds:
             instr_id = item['instr_id']
@@ -393,7 +398,14 @@ class ReverieObjectNavBatch(object):
             scan, gt_traj, gt_objid = self.gt_trajs[instr_id]
             pred_found = item['found']
             gt_found = item['gt_found']
+
+
             traj_scores = self._eval_item(scan, traj, pred_objid, gt_traj, gt_objid, pred_found, gt_found)
+
+            # record "success" in the result file
+            # let the visualization tool can get the success status 
+            item['success'] = traj_scores['success']
+
             for k, v in traj_scores.items():
                 metrics[k].append(v)
             metrics['instr_id'].append(instr_id)

map_nav_src/scripts/run_reverie.sh

@@ -10,7 +10,7 @@ obj_ft_dim=768
 ngpus=1
 seed=0
 
-name=${train_alg}-${features}
+name=${train_alg}-${features}-advanced-adversarial
 name=${name}-seed.${seed} #-${ngpus}gpus
 
 outdir=${DATA_ROOT}/REVERIE/exprs_map/finetune/${name}
@@ -63,8 +63,10 @@ flag="--root_dir ${DATA_ROOT}
 #       --tokenizer bert \
 #       --bert_ckpt_file '../datasets/REVERIE/exprs_map/pretrain/cmt-vitbase-mlm.mrc.sap.og-init.lxmert-aug.speaker/ckpts/model_step_100000.pt' \
 #       --eval_first
+
 # test
+echo /root/mount/Matterport3DSimulator/VLN-DUET/datasets/REVERIE/exprs_map/finetune/${name}/ckpts/best_val_unseen
 CUDA_VISIBLE_DEVICES='0' python3 reverie/main_nav_obj.py $flag  \
       --tokenizer bert \
-      --resume_file /root/mount/Matterport3DSimulator/VLN-DUET/datasets/REVERIE/exprs_map/finetune/dagger-vitbase-seed.0/ckpts/best_val_unseen \
+      --resume_file /root/mount/Matterport3DSimulator/VLN-DUET/datasets/REVERIE/exprs_map/finetune/${name}/ckpts/best_val_unseen \
       --test --submit