#!/bin/bash
#SBATCH --job-name=dmmi-test3
#SBATCH --partition=a4000         # 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=./eval_logs/test_all4.txt

ml purge
ml load cuda/11.8
eval "$(conda shell.bash hook)"
conda activate risall 

cd /data2/projects/chaeyun/RIS-DMMI/

# MODEL_ID="dmmi_gref_google_bs12_repro"
# python test.py --model dmmi_swin_hardpos_only --swin_type base --dataset refcocog --splitBy google --split val --test_parameter ./experiments/$MODEL_ID/model_best_$MODEL_ID.pth --workers 4 --ddp_trained_weights --window12 --img_size 480 &> ./eval_logs/${MODEL_ID}_val.log

# MODEL_ID2="dmmi_refzom_bs12_repro"
# python test.py --model dmmi_swin_hardpos_only --swin_type base --dataset ref-zom --splitBy final --split val --test_parameter ./experiments/$MODEL_ID2/model_best_$MODEL_ID2.pth --workers 4 --ddp_trained_weights --window12 --img_size 480 &> ./eval_logs/${MODEL_ID2}_val.log
# python test.py --model dmmi_swin_hardpos_only --swin_type base --dataset ref-zom --splitBy final --split test --test_parameter ./experiments/$MODEL_ID2/model_best_$MODEL_ID2.pth --workers 4 --ddp_trained_weights --window12 --img_size 480 &> ./eval_logs/${MODEL_ID2}_test.log


# /data2/projects/chaeyun/RIS-DMMI/experiments/dmmi_rcc+_ace_filtered/ACE_rccp_m10_tmp007_fqfuse_thr070_bs24
# # /data2/projects/chaeyun/RIS-DMMI/experiments/dmmi_rcc+_ace_filtered/ACE_rccp_m10_tmp007_thr050_bs24
MODEL_IDS=(
"ACE_rccp_m10_tmp007_fqfuse_thr070_bs24"
)

for MODEL_ID_ in "${MODEL_IDS[@]}"; do
    echo "Running test for model: $MODEL_ID_"
    # python test.py --model dmmi_swin_hardpos_only --swin_type base --dataset refcoco+ --splitBy unc --split val --test_parameter ./experiments/dmmi_rcc+_ace_filtered/$MODEL_ID_/model_best_$MODEL_ID_.pth --workers 4 --ddp_trained_weights --window12 --img_size 480 &> ./eval_logs/${MODEL_ID_}_val.log
    python test.py --model dmmi_swin_hardpos_only --swin_type base --dataset refcoco+ --splitBy unc --split testA --test_parameter ./experiments/dmmi_rcc+_ace_filtered/$MODEL_ID_/model_best_$MODEL_ID_.pth --workers 4 --ddp_trained_weights --window12 --img_size 480 &> ./eval_logs/${MODEL_ID_}_testA.log
    python test.py --model dmmi_swin_hardpos_only --swin_type base --dataset refcoco+ --splitBy unc --split testB --test_parameter ./experiments/dmmi_rcc+_ace_filtered/$MODEL_ID_/model_best_$MODEL_ID_.pth --workers 4 --ddp_trained_weights --window12 --img_size 480 &> ./eval_logs/${MODEL_ID_}_testB.log

done

# /data2/projects/chaeyun/RIS-DMMI/experiments/dmmi_rcc_ace_filtered/ACE_rcc_m10_tmp007_thr050_bs24
# MODEL_IDS=(
# "ACE_rcc_m10_tmp007_fqfuse_thr070_bs24"
# )

# for MODEL_ID_ in "${MODEL_IDS[@]}"; do
#     echo "Running test for model: $MODEL_ID_"
#     # python test.py --model dmmi_swin_hardpos_only --swin_type base --dataset refcoco --splitBy unc --split val --test_parameter ./experiments/dmmi_rcc_ace_filtered/$MODEL_ID_/model_best_$MODEL_ID_.pth --workers 4 --ddp_trained_weights --window12 --img_size 480 &> ./eval_logs/${MODEL_ID_}_val.log
#     python test.py --model dmmi_swin_hardpos_only --swin_type base --dataset refcoco --splitBy unc --split testA --test_parameter ./experiments/dmmi_rcc_ace_filtered/$MODEL_ID_/model_best_$MODEL_ID_.pth --workers 4 --ddp_trained_weights --window12 --img_size 480 &> ./eval_logs/${MODEL_ID_}_testA.log
#     python test.py --model dmmi_swin_hardpos_only --swin_type base --dataset refcoco --splitBy unc --split testB --test_parameter ./experiments/dmmi_rcc_ace_filtered/$MODEL_ID_/model_best_$MODEL_ID_.pth --workers 4 --ddp_trained_weights --window12 --img_size 480 &> ./eval_logs/${MODEL_ID_}_testB.log

# done


# /data2/projects/chaeyun/RIS-DMMI/experiments/dmmi_refcoco_unc_bs24_repro
# MODEL_IDS=(
# "dmmi_refcoco_unc_bs24_repro"
# )

# for MODEL_ID_ in "${MODEL_IDS[@]}"; do
#     echo "Running test for model: $MODEL_ID_"
#     python test.py --model dmmi_swin --swin_type base --dataset refcoco --splitBy unc --split val --test_parameter ./experiments/$MODEL_ID_/model_best_$MODEL_ID_.pth --workers 4 --ddp_trained_weights --window12 --img_size 480 &> ./eval_logs_repro/${MODEL_ID_}_val.log
#     python test.py --model dmmi_swin --swin_type base --dataset refcoco --splitBy unc --split testA --test_parameter ./experiments/$MODEL_ID_/model_best_$MODEL_ID_.pth --workers 4 --ddp_trained_weights --window12 --img_size 480 &> ./eval_logs_repro/${MODEL_ID_}_testA.log
#     python test.py --model dmmi_swin --swin_type base --dataset refcoco --splitBy unc --split testB --test_parameter ./experiments/$MODEL_ID_/model_best_$MODEL_ID_.pth --workers 4 --ddp_trained_weights --window12 --img_size 480 &> ./eval_logs_repro/${MODEL_ID_}_testB.log

# done


# for MODEL_ID_ in "${MODEL_IDS[@]}"; do
#     echo "Running test for model: $MODEL_ID_"
#     python test.py --model dmmi_swin_hardpos_only --swin_type base --dataset refcocog --splitBy umd --split val --test_parameter ./experiments/dmmi_grefu_ace_filtered/$MODEL_ID_/model_best_$MODEL_ID_.pth --workers 4 --ddp_trained_weights --window12 --img_size 480 &> ./eval_logs/${MODEL_ID_}_val.log
#     python test.py --model dmmi_swin_hardpos_only --swin_type base --dataset refcocog --splitBy umd --split test --test_parameter ./experiments/dmmi_grefu_ace_filtered/$MODEL_ID_/model_best_$MODEL_ID_.pth --workers 4 --ddp_trained_weights --window12 --img_size 480 &> ./eval_logs/${MODEL_ID_}_test.log
# done




# things to test
# /data2/projects/chaeyun/RIS-DMMI/experiments/dmmi_grefu_ace_/gref_m10_tmp007_refined_bs8
# /data2/projects/chaeyun/RIS-DMMI/experiments/dmmi_grefu_ace_/gref_m10_tmp007_refined_bs12
# /data2/projects/chaeyun/RIS-DMMI/experiments/dmmi_grefu_ace_/gref_m12_tmp007_bs12_ver2
# /data2/projects/chaeyun/RIS-DMMI/experiments/dmmi_grefu_ace_/gref_m10_tmp007_bs12_ver2
# /data2/projects/chaeyun/RIS-DMMI/experiments/dmmi_refzom_bs12_repro/model_best_dmmi_refzom_bs12_repro.pth