LAVT-RIS/donghwa/scripts/submit_test.sh

#!/bin/bash

#SBATCH --job-name=test      # Submit a job named "example"
#SBATCH [email protected]
#SBATCH --mail-type=BEGIN,END,FAIL
#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=log_refcoco_retrieval_433_10up_testAB.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


# test
# python test.py --model lavt_one --swin_type base --dataset refcoco --split testA --resume ./checkpoints/repro_lavt_one/model_best_refcoco_lavt_one.pth --workers 4 --ddp_trained_weights --window12 --img_size 480
# python test.py --model lavt_one --swin_type base --dataset refcoco --split testB --resume ./checkpoints/repro_lavt_one/model_best_refcoco_lavt_one.pth --workers 4 --ddp_trained_weights --window12 --img_size 480

# python test.py --model lavt_one --swin_type base --dataset refcoco+ --split val --resume ./checkpoints/refcoco+.pth --workers 4 --ddp_trained_weights --window12 --img_size 480
# python test.py --model lavt_one --swin_type base --dataset refcocog --splitBy umd --split test --resume ./checkpoints/gref_umd.pth --workers 4 --ddp_trained_weights --window12 --img_size 480
# python test.py --model lavt_one --swin_type base --dataset refcocog --splitBy google --split val --resume ./checkpoints/gref_google.pth --workers 4 --ddp_trained_weights --window12 --img_size 480
# python test.py --model lavt_one --swin_type base --dataset grefcoco --splitBy unc --split val --resume ./checkpoints/gref_umd.pth --workers 4 --ddp_trained_weights --window12 --img_size 480

# test lavt_one
# python test.py --model lavt_one --swin_type base --dataset refcocog --splitBy umd --split test --resume ./checkpoints/model_best_gref_umd_lavt_one.pth --workers 4 --ddp_trained_weights --window12 --img_size 480
# python test.py --model lavt_one --swin_type base --dataset refcocog --splitBy google --split val --resume ./checkpoints/lavt_one/gref_google.pth --workers 4 --ddp_trained_weights --window12 --img_size 480

# python test.py --model lavt_one --swin_type base --dataset refcoco --split val --resume ./checkpoints/lavt_one/refcoco.pth --workers 4 --ddp_trained_weights --window12 --img_size 480
# python test_mosaic.py --model lavt_one --swin_type base --dataset refcoco --split testA --resume ./checkpoints/mosaic_lavt_one/model_best_mosaic_refcoco_lavt_one.pth --workers 4 --ddp_trained_weights --window12 --img_size 480
# python test_mosaic.py --model lavt_one --swin_type base --dataset refcoco --split testB --resume ./checkpoints/mosaic_lavt_one/model_best_mosaic_refcoco_lavt_one.pth --workers 4 --ddp_trained_weights --window12 --img_size 480

# srun python test.py --model lavt_one --swin_type base --dataset refcoco+ --split val --resume ./checkpoints/lavt_one/refcoco+.pth --workers 4 --ddp_trained_weights --window12 --img_size 480
# srun python test.py --model lavt_one --swin_type base --dataset refcoco+ --split testA --resume ./checkpoints/retrieval_lavt_one/Retrieval_433_10wm6to3/model_best_retrieval_refcoco+_OneStage_433_10wm6to3.pth --workers 4 --ddp_trained_weights --window12 --img_size 480
# srun python test.py --model lavt_one --swin_type base --dataset refcoco+ --split testB --resume ./checkpoints/retrieval_lavt_one/Retrieval_433_10wm6to3/model_best_retrieval_refcoco+_OneStage_433_10wm6to3.pth --workers 4 --ddp_trained_weights --window12 --img_size 480

# random
# srun python test_mosaic.py --model lavt_one --swin_type base --dataset refcoco --split testA --resume experiments/refcoco_unc/refcoco_random_460/model_best_refcoco_random_460.pth --workers 4 --ddp_trained_weights --window12 --img_size 480 --config config/random_460.yaml
# srun python test_mosaic.py --model lavt_one --swin_type base --dataset refcoco --split testB --resume experiments/refcoco_unc/refcoco_random_460/model_best_refcoco_random_460.pth --workers 4 --ddp_trained_weights --window12 --img_size 480 --config config/random_460.yaml

# srun python test_mosaic.py --model lavt_one --swin_type base --dataset refcoco+ --split testA --resume experiments/refcoco+_unc/refcoco+_random_460/model_best_refcoco+_random_460.pth --workers 4 --ddp_trained_weights --window12 --img_size 480 --config config/random_460.yaml
# srun python test_mosaic.py --model lavt_one --swin_type base --dataset refcoco+ --split testB --resume experiments/refcoco+_unc/refcoco+_random_460/model_best_refcoco+_random_460.pth --workers 4 --ddp_trained_weights --window12 --img_size 480 --config config/random_460.yaml

# srun python test_mosaic.py --model lavt_one --swin_type base --dataset refcocog --splitBy umd --split test --resume experiments/refcocog_umd/random_gref_umd_460_40epoch_2/model_best_random_gref_umd_460_40epoch_2.pth --workers 4 --ddp_trained_weights --window12 --img_size 480 --config config/random_460.yaml

# retrieval
# srun python test_mosaic_retrieval.py --model lavt_one --swin_type base --dataset refcocog --splitBy umd --split test --resume experiments/refcocog_umd/retrieval_filter_gref_umd_433_10up_top200/model_best_retrieval_filter_gref_umd_433_10up_top200.pth --workers 4 --ddp_trained_weights --window12 --img_size 480 --config config/retrieval_433_10up.yaml

# srun python test_mosaic_retrieval.py --model lavt_one --swin_type base --dataset refcoco+ --split testA --resume experiments/refcoco+_unc/refcoco+_retrieval_filter_433_10up/model_best_refcoco+_retrieval_filter_433_10up.pth --workers 4 --ddp_trained_weights --window12 --img_size 480 --config config/retrieval_433_10up.yaml
# srun python test_mosaic_retrieval.py --model lavt_one --swin_type base --dataset refcoco+ --split testB --resume experiments/refcoco+_unc/refcoco+_retrieval_filter_433_10up/model_best_refcoco+_retrieval_filter_433_10up.pth --workers 4 --ddp_trained_weights --window12 --img_size 480 --config config/retrieval_433_10up.yaml

srun python test_mosaic_retrieval.py --model lavt_one --swin_type base --dataset refcoco --split testA --resume experiments/refcoco_unc/refcoco_retrieval_filter_433_10up/model_best_refcoco_retrieval_filter_433_10up.pth --workers 4 --ddp_trained_weights --window12 --img_size 480 --config config/retrieval_433_10up.yaml
srun python test_mosaic_retrieval.py --model lavt_one --swin_type base --dataset refcoco --split testB --resume experiments/refcoco_unc/refcoco_retrieval_filter_433_10up/model_best_refcoco_retrieval_filter_433_10up.pth --workers 4 --ddp_trained_weights --window12 --img_size 480 --config config/retrieval_433_10up.yaml