fix: for evaluate original REVERIE model on our dataset

map_nav_src/r2r/data_utils.py

@@ -37,7 +37,7 @@ def construct_instrs(anno_dir, dataset, splits, tokenizer, max_instr_len=512, is
         # Split multiple instructions into separate entries
         for j, instr in enumerate(item['instructions']):
             new_item = dict(item)
-            new_item['instr_id'] = '%s_%d' % (item['path_id'], j)
+            new_item['instr_id'] = '%s_%d' % (item['id'], j)
             new_item['instruction'] = instr
             new_item['instr_encoding'] = item['instr_encodings'][j][:max_instr_len]
             del new_item['instructions']

map_nav_src/r2r/main_nav.py

@@ -58,12 +58,12 @@ def build_dataset(args, rank=0, is_test=False):
     )
 
     # val_env_names = ['val_train_seen']
-    val_env_names = ['val_train_seen', 'val_seen', 'val_unseen']
+    val_env_names = ['val_seen', 'val_unseen']
     if args.dataset == 'r4r' and (not args.test):
         val_env_names[-1] == 'val_unseen_sampled'
     
-    if args.submit and args.dataset != 'r4r':
-        val_env_names.append('test')
+    # if args.submit and args.dataset != 'r4r':
+    #     val_env_names.append('test')
         
     val_envs = {}
     for split in val_env_names:

map_nav_src/reverie/main_nav_obj.py

@@ -68,8 +68,8 @@ def build_dataset(args, rank=0):
     # val_env_names = ['val_train_seen']
     val_env_names = ['val_seen', 'val_unseen']
 
-    if args.submit:
-        val_env_names.append('test')
+    # if args.submit:
+    #     val_env_names.append('test')
         
     val_envs = {}
     for split in val_env_names:

map_nav_src/scripts/run_r2r.sh

@@ -10,7 +10,7 @@ obj_ft_dim=768
 ngpus=1
 seed=0
 
-name=${train_alg}-${features}
+name=${train_alg}-${features}-reverie-glip-adversarial
 name=${name}-seed.${seed}
 name=${name}-init.aug.45k
 
@@ -57,11 +57,11 @@ flag="--root_dir ${DATA_ROOT}
 # train
 CUDA_VISIBLE_DEVICES='0' python r2r/main_nav.py $flag  \
       --tokenizer bert \
-      --bert_ckpt_file 'put the pretrained model (see pretrain_src) here' \
+      --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
-CUDA_VISIBLE_DEVICES='0' python r2r/main_nav.py $flag  \
-      --tokenizer bert \
-      --resume_file ../datasets/R2R/trained_models/best_val_unseen \
-      --test --submit
+# CUDA_VISIBLE_DEVICES='0' python r2r/main_nav.py $flag  \
+#       --tokenizer bert \
+#       --resume_file ../datasets/R2R/trained_models/best_val_unseen \
+#      --test --submit

map_nav_src/scripts/run_reverie.sh

@@ -10,7 +10,7 @@ obj_ft_dim=768
 ngpus=1
 seed=0
 
-name=${train_alg}-${features}-advanced_adversarial
+name=${train_alg}-${features}-adversarial-but-original-model-with-glip-filter
 name=${name}-seed.${seed} #-${ngpus}gpus
 
 outdir=${DATA_ROOT}/REVERIE/exprs_map/finetune/${name}
@@ -59,13 +59,14 @@ flag="--root_dir ${DATA_ROOT}
       --gamma 0."
 
 # train
-CUDA_VISIBLE_DEVICES='0' python reverie/main_nav_obj.py $flag  \
-      --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
+# CUDA_VISIBLE_DEVICES='0' python reverie/main_nav_obj.py $flag  \
+#       --tokenizer bert \
+#       --resume_file ../datasets/REVERIE/exprs_map/finetune/dagger-vitbase-adversarial-but-original-model-with-glip-filter-seed.0/ckpts/best_val_unseen \
+#       --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
 CUDA_VISIBLE_DEVICES='0' python reverie/main_nav_obj.py $flag  \
       --tokenizer bert \
-      --resume_file ../datasets/REVERIE/trained_models/best_val_unseen \
+      --resume_file ../datasets/REVERIE/exprs_map/finetune/dagger-vitbase-adversarial-but-original-model-with-glip-filter-seed.0/ckpts/best_val_unseen \
       --test --submit