#!/bin/bash

DATA_ROOT=../datasets
train_alg=dagger

features=internvit.6b
ft_dim=3200
obj_features=vitbase
obj_ft_dim=768

ngpus=1
bs=16
seed=0

name=${train_alg}-${features}
name=${name}-seed.${seed}

name=${name}-aug.mp3d.hm3d.srdf.vertical-2k
outdir=${DATA_ROOT}/exprs_map/finetune/${name}

# outdir=${DATA_ROOT}/R2R/exprs_map/finetune/${name}-addStop-finetuned-duet_vit-b16_ft_best_val_unseen

flag="--root_dir ${DATA_ROOT}
      --dataset r2r
      --output_dir ${outdir}
      --world_size ${ngpus}
      --seed ${seed}
      --tokenizer bert      


      --enc_full_graph
      --graph_sprels
      --fusion dynamic

      --expert_policy spl
      --train_alg ${train_alg}
      
      --num_l_layers 9
      --num_x_layers 4
      --num_pano_layers 2
      
      --max_action_len 15
      --max_instr_len 200
      --train_env_names train
      --val_env_names val_seen val_unseen 

      --batch_size ${bs}
      --lr 1e-5
      --iters 50000
      --log_every 500
      --aug_times 9

      --optim adamW

      --features ${features}
      --image_feat_size ${ft_dim}
      --angle_feat_size 4

      --ml_weight 0.15

      --feat_dropout 0.4
      --dropout 0.5
      
      --gamma 0."


CUDA_VISIBLE_DEVICES=$1 python r2r/main_nav.py $flag  \
      --tokenizer bert \
      --bert_ckpt_file ../datasets/SRDF/trained_models/pretrain/model_step_170000.pt \
      --aug  ../datasets/SRDF/data/mantis.hm3d_round3_greedy_ndtw0.9_ranked_414k_rouge0.85.json \
      --iters 60000 \
      --train_env_names train original_vertical_gpt4o_train_2k \
      --val_env_names val_seen val_unseen \
      --resume_file ../datasets/exprs_map/finetune/dagger-internvit.6b-seed.0-aug.mp3d.hm3d.srdf.vertical-2k/ckpts/best_val_unseen

      # --batch_size 32

     