#!/bin/bash

#SBATCH --job-name=n_obj_ours      # Submit a job named "example"
#SBATCH --mail-user=vip.maildummy@gmail.com
#SBATCH --mail-type=BEGIN,END,FAIL
#SBATCH --partition=a5000         # a6000 or a100
#SBATCH --gres=gpu:1
#SBATCH --time=7-00:00:00        # d-hh:mm:ss, max time limit
#SBATCH --mem=48000              # cpu memory size
#SBATCH --cpus-per-task=4        # cpu num
#SBATCH --output=log_refcocog_umd_repro_n_obj.txt         # std output filename

ml cuda/11.0                # 필요한 쿠다 버전 로드
eval "$(conda shell.bash hook)"  # Initialize Conda Environment
conda activate lavt             # Activate your conda environment


# ckpt
# srun python test_n_obj.py --model lavt_one --swin_type base --dataset refcocog --splitBy umd --split test \
#     --resume checkpoints/ckpt_lavt_one/gref_umd.pth --workers 4 --ddp_trained_weights --window12 --img_size 480 \
#     --config config/n_obj/n_12.yaml

# srun python test_n_obj.py --model lavt_one --swin_type base --dataset refcocog --splitBy umd --split test \
#     --resume checkpoints/ckpt_lavt_one/gref_umd.pth --workers 4 --ddp_trained_weights --window12 --img_size 480 \
#     --config config/n_obj/n_34.yaml

# srun python test_n_obj.py --model lavt_one --swin_type base --dataset refcocog --splitBy umd --split test \
#     --resume checkpoints/ckpt_lavt_one/gref_umd.pth --workers 4 --ddp_trained_weights --window12 --img_size 480 \
#     --config config/n_obj/n_56.yaml

# srun python test_n_obj.py --model lavt_one --swin_type base --dataset refcocog --splitBy umd --split test \
#     --resume checkpoints/ckpt_lavt_one/gref_umd.pth --workers 4 --ddp_trained_weights --window12 --img_size 480 \
#     --config config/n_obj/n_78.yaml

# repro
srun python test_n_obj.py --model lavt_one --swin_type base --dataset refcocog --splitBy umd --split test \
    --resume checkpoints/repro_lavt_one/model_best_gref_umd_lavt_one.pth \
    --workers 4 --ddp_trained_weights --window12 --img_size 480 \
    --config config/n_obj/n_12.yaml

srun python test_n_obj.py --model lavt_one --swin_type base --dataset refcocog --splitBy umd --split test \
    --resume checkpoints/repro_lavt_one/model_best_gref_umd_lavt_one.pth \
    --workers 4 --ddp_trained_weights --window12 --img_size 480 \
    --config config/n_obj/n_34.yaml

srun python test_n_obj.py --model lavt_one --swin_type base --dataset refcocog --splitBy umd --split test \
    --resume checkpoints/repro_lavt_one/model_best_gref_umd_lavt_one.pth \
    --workers 4 --ddp_trained_weights --window12 --img_size 480 \
    --config config/n_obj/n_56.yaml

srun python test_n_obj.py --model lavt_one --swin_type base --dataset refcocog --splitBy umd --split test \
    --resume checkpoints/repro_lavt_one/model_best_gref_umd_lavt_one.pth \
    --workers 4 --ddp_trained_weights --window12 --img_size 480 \
    --config config/n_obj/n_78.yaml


# our best_model (retrieval)

# srun python test_n_obj.py --model lavt_one --swin_type base --dataset refcocog --splitBy umd --split test \
#     --resume experiments/refcocog_umd/retrieval_gref_umd_433_10up_40epoch/model_best_retrieval_gref_umd_433_10up_40epoch.pth \
#     --workers 4 --ddp_trained_weights --window12 --img_size 480 \
#     --config config/n_obj/n_12.yaml

# srun python test_n_obj.py --model lavt_one --swin_type base --dataset refcocog --splitBy umd --split test \
#     --resume experiments/refcocog_umd/retrieval_gref_umd_433_10up_40epoch/model_best_retrieval_gref_umd_433_10up_40epoch.pth \
#     --workers 4 --ddp_trained_weights --window12 --img_size 480 \
#     --config config/n_obj/n_34.yaml

# srun python test_n_obj.py --model lavt_one --swin_type base --dataset refcocog --splitBy umd --split test \
#     --resume experiments/refcocog_umd/retrieval_gref_umd_433_10up_40epoch/model_best_retrieval_gref_umd_433_10up_40epoch.pth \
#     --workers 4 --ddp_trained_weights --window12 --img_size 480 \
#     --config config/n_obj/n_56.yaml

# srun python test_n_obj.py --model lavt_one --swin_type base --dataset refcocog --splitBy umd --split test \
#     --resume experiments/refcocog_umd/retrieval_gref_umd_433_10up_40epoch/model_best_retrieval_gref_umd_433_10up_40epoch.pth \
#     --workers 4 --ddp_trained_weights --window12 --img_size 480 \
#     --config config/n_obj/n_78.yaml

    

# random 

# srun python test_n_obj.py --model lavt_one --swin_type base --dataset refcocog --splitBy umd --split test \
#     --resume checkpoints/random_550_lavt_one/model_best_mosaic_gref_umd_lavt_one.pth \
#     --workers 4 --ddp_trained_weights --window12 --img_size 480 \
#     --config config/n_obj/n_12.yaml

# srun python test_n_obj.py --model lavt_one --swin_type base --dataset refcocog --splitBy umd --split test \
#     --resume checkpoints/random_550_lavt_one/model_best_mosaic_gref_umd_lavt_one.pth \
#     --workers 4 --ddp_trained_weights --window12 --img_size 480 \
#     --config config/n_obj/n_34.yaml

# srun python test_n_obj.py --model lavt_one --swin_type base --dataset refcocog --splitBy umd --split test \
#     --resume checkpoints/random_550_lavt_one/model_best_mosaic_gref_umd_lavt_one.pth \
#     --workers 4 --ddp_trained_weights --window12 --img_size 480 \
#     --config config/n_obj/n_56.yaml

# srun python test_n_obj.py --model lavt_one --swin_type base --dataset refcocog --splitBy umd --split test \
#     --resume checkpoints/random_550_lavt_one/model_best_mosaic_gref_umd_lavt_one.pth \
#     --workers 4 --ddp_trained_weights --window12 --img_size 480 \
#     --config config/n_obj/n_78.yaml