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ASDA/scripts/test.sh

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+#!/bin/bash
+#SBATCH --job-name=EVAL
+#SBATCH --partition=a5000
+#SBATCH --gres=gpu:1
+#SBATCH --time=13-11:30:00
+#SBATCH --mem=28000
+#SBATCH --cpus-per-task=3
+#SBATCH --output=./eval_gref/selffltr_gref_m10_tmp007_fine_bs28_thr040.log
+
+ml purge
+ml load cuda/11.8
+eval "$(conda shell.bash hook)"
+conda activate asda
+
+cd /data2/projects/chaeyun/ASDA
+
+export MASTER_PORT=8583
+
+OPT_DIR=/data2/projects/chaeyun/ASDA/saved_models/
+SAVENAME=selffltr_gref_m10_tmp007_fine_bs28_thr040
+RESUME_PATH=${OPT_DIR}/${SAVENAME}_model_best.pth.tar
+# RESUME_OIOU=${OPT_DIR}/${SAVENAME}_best_oiou_model_best.pth.tar
+
+python test.py \
+  --dataset refcocog_u \
+  --ngpu 1 \
+  --savename $SAVENAME \
+  --resume $RESUME_PATH
+
+
+# /data2/projects/chaeyun/ASDA/exp_projection/pj_gref_m10_tmp007_fine_nofltr_bs28.log
+# /data2/projects/chaeyun/ASDA/exp_projection/pj_gref_m10_tmp007_coarse_fthr065_bs28.log
+
+
+# /data2/projects/chaeyun/ASDA/exp/gref_umd_bs28_repro.log
+# /data2/projects/chaeyun/ASDA/exp/gref_umd_bs36_repro.log
+
+# /data2/projects/chaeyun/ASDA/exp/refcoco_sanity_bs28.log
+# /data2/projects/chaeyun/ASDA/exp/refcoco_sanity_bs36.log
+
+# /data2/projects/chaeyun/ASDA/exp/refcocop_bs28_repro.log
+

ASDA/scripts/test_proj.sh

@@ -0,0 +1,42 @@
+#!/bin/bash
+#SBATCH --job-name=EVAL
+#SBATCH --partition=a5000
+#SBATCH --gres=gpu:1
+#SBATCH --time=13-11:30:00
+#SBATCH --mem=28000
+#SBATCH --cpus-per-task=3
+#SBATCH --output=./eval_gref/pj_gref_m12_tmp007_refcoarse_nofltroiou_bs28.log
+
+ml purge
+ml load cuda/11.8
+eval "$(conda shell.bash hook)"
+conda activate asda
+
+cd /data2/projects/chaeyun/ASDA
+
+export MASTER_PORT=9273
+
+OPT_DIR=/data2/projects/chaeyun/ASDA/saved_models/
+SAVENAME=pj_gref_m12_tmp007_refcoarse_nofltroiou_bs28
+RESUME_PATH=${OPT_DIR}/${SAVENAME}_model_best.pth.tar
+
+python test_proj.py \
+  --dataset refcocog_u \
+  --ngpu 1 \
+  --savename $SAVENAME \
+  --fuse_mode coarse \
+  --resume $RESUME_PATH
+
+
+# /data2/projects/chaeyun/ASDA/exp_projection/pj_gref_m10_tmp007_fine_nofltr_bs28.log
+# /data2/projects/chaeyun/ASDA/exp_projection/pj_gref_m10_tmp007_coarse_fthr065_bs28.log
+
+
+# /data2/projects/chaeyun/ASDA/exp/gref_umd_bs28_repro.log
+# /data2/projects/chaeyun/ASDA/exp/gref_umd_bs36_repro.log
+
+# /data2/projects/chaeyun/ASDA/exp/refcoco_sanity_bs28.log
+# /data2/projects/chaeyun/ASDA/exp/refcoco_sanity_bs36.log
+
+# /data2/projects/chaeyun/ASDA/exp/refcocop_bs28_repro.log
+

ASDA/scripts/test_proj_rcc.sh

@@ -0,0 +1,42 @@
+#!/bin/bash
+#SBATCH --job-name=EVAL
+#SBATCH --partition=a5000
+#SBATCH --gres=gpu:1
+#SBATCH --time=13-11:30:00
+#SBATCH --mem=28000
+#SBATCH --cpus-per-task=3
+#SBATCH --output=./eval_rccs/pj_rccp_m10_tmp007_refcoarse_nofltroiou_bs36.log
+
+ml purge
+ml load cuda/11.8
+eval "$(conda shell.bash hook)"
+conda activate asda
+
+cd /data2/projects/chaeyun/ASDA
+
+export MASTER_PORT=1085
+
+OPT_DIR=/data2/projects/chaeyun/ASDA/saved_models/
+SAVENAME=pj_rccp_m10_tmp007_refcoarse_nofltroiou_bs36
+RESUME_PATH=${OPT_DIR}/${SAVENAME}_model_best.pth.tar
+
+python test_proj.py \
+  --dataset refcoco+ \
+  --ngpu 1 \
+  --savename $SAVENAME \
+  --fuse_mode refined_coarse \
+  --resume $RESUME_PATH
+
+
+# /data2/projects/chaeyun/ASDA/exp_projection/pj_gref_m10_tmp007_fine_nofltr_bs28.log
+# /data2/projects/chaeyun/ASDA/exp_projection/pj_gref_m10_tmp007_coarse_fthr065_bs28.log
+
+
+# /data2/projects/chaeyun/ASDA/exp/gref_umd_bs28_repro.log
+# /data2/projects/chaeyun/ASDA/exp/gref_umd_bs36_repro.log
+
+# /data2/projects/chaeyun/ASDA/exp/refcoco_sanity_bs28.log
+# /data2/projects/chaeyun/ASDA/exp/refcoco_sanity_bs36.log
+
+# /data2/projects/chaeyun/ASDA/exp/refcocop_bs28_repro.log
+

ASDA/scripts/test_rcc.sh

@@ -0,0 +1,41 @@
+#!/bin/bash
+#SBATCH --job-name=EVAL
+#SBATCH --partition=a5000
+#SBATCH --gres=gpu:1
+#SBATCH --time=13-11:30:00
+#SBATCH --mem=28000
+#SBATCH --cpus-per-task=3
+#SBATCH --output=./eval_gref/refcoco_unc_oiou_bs28.log
+
+ml purge
+ml load cuda/11.8
+eval "$(conda shell.bash hook)"
+conda activate asda
+
+cd /data2/projects/chaeyun/ASDA
+
+export MASTER_PORT=9823
+
+OPT_DIR=/data2/projects/chaeyun/ASDA/saved_models/
+SAVENAME=refcoco_unc_oiou_bs28
+RESUME_PATH=${OPT_DIR}/${SAVENAME}_model_best.pth.tar
+
+python test.py \
+  --dataset refcoco \
+  --ngpu 1 \
+  --savename $SAVENAME \
+  --resume $RESUME_PATH
+
+
+# /data2/projects/chaeyun/ASDA/exp_projection/pj_gref_m10_tmp007_fine_nofltr_bs28.log
+# /data2/projects/chaeyun/ASDA/exp_projection/pj_gref_m10_tmp007_coarse_fthr065_bs28.log
+
+
+# /data2/projects/chaeyun/ASDA/exp/gref_umd_bs28_repro.log
+# /data2/projects/chaeyun/ASDA/exp/gref_umd_bs36_repro.log
+
+# /data2/projects/chaeyun/ASDA/exp/refcoco_sanity_bs28.log
+# /data2/projects/chaeyun/ASDA/exp/refcoco_sanity_bs36.log
+
+# /data2/projects/chaeyun/ASDA/exp/refcocop_bs28_repro.log
+

ASDA/scripts/train.sh

@@ -0,0 +1,43 @@
+#!/bin/bash
+#SBATCH --job-name=asda-sanity
+#SBATCH --partition=a6000
+#SBATCH --gres=gpu:1
+#SBATCH --time=13-11:30:00
+#SBATCH --mem=28000
+#SBATCH --cpus-per-task=3
+#SBATCH --output=./exp_oiou/gref_umd_oiou_bs28.log
+
+ml purge
+ml load cuda/11.8
+eval "$(conda shell.bash hook)"
+conda activate asda
+
+cd /data2/projects/chaeyun/ASDA
+
+export NCCL_P2P_DISABLE=1
+export NVIDIA_TF32_OVERRIDE=1
+export NCCL_IB_TIMEOUT=100
+export NCCL_IB_RETRY_CNT=15
+
+export MASTER_PORT=8575
+
+export CUDA_VISIBLE_DEVICES=0
+
+python train_oiou.py --dataset refcocog --splitBy umd --ngpu 1 --batch_size 28 --time 17 --savename gref_umd_oiou_bs28
+
+
+
+# python train.py --dataset refcoco+ --splitBy unc --ngpu 1 --batch_size 28 --time 17 --savename refcocop_bs28_repro 
+# python train.py --dataset refcoco+ --splitBy unc --ngpu 1 --batch_size 36 --time 17 --savename refcocop_bs36_repro 
+
+
+# python train.py --dataset refcoco --splitBy unc --ngpu 1 --batch_size 36 --time 17 --savename refcoco_bs36_repro 
+# python train.py --dataset refcocog --splitBy umd --ngpu 1 --batch_size 28 --time 17 --savename gref_umd_bs28_repro
+# python train.py --dataset refcocog --splitBy umd --ngpu 1 --batch_size 36 --time 17 --savename gref_umd_bs36_repro
+
+
+# export CUDA_VISIBLE_DEVICES=0,1; python train.py --dataset refcocog --splitBy umd --ngpu 2 --batch_size 64 --time 17 --savename gref_umd_bs64_repro
+
+# export CUDA_VISIBLE_DEVICES=0,1; python train.py --dataset refcocog --splitBy umd --ngpu 1 --batch_size 64 --time 17 --savename gref_umd_bs64_repro
+
+

ASDA/scripts/train1.sh

@@ -0,0 +1,44 @@
+#!/bin/bash
+#SBATCH --job-name=asda-sanity
+#SBATCH --partition=a6000
+#SBATCH --gres=gpu:1
+#SBATCH --time=13-11:30:00
+#SBATCH --mem=28000
+#SBATCH --cpus-per-task=3
+#SBATCH --output=./exp_oiou/refcocop_unc_oiou_bs28.log
+
+ml purge
+ml load cuda/11.8
+eval "$(conda shell.bash hook)"
+conda activate asda
+
+cd /data2/projects/chaeyun/ASDA
+
+export NCCL_P2P_DISABLE=1
+export NVIDIA_TF32_OVERRIDE=1
+export NCCL_IB_TIMEOUT=100
+export NCCL_IB_RETRY_CNT=15
+
+export MASTER_PORT=8820
+
+export CUDA_VISIBLE_DEVICES=0
+
+# python train_oiou.py --dataset refcocog --splitBy umd --ngpu 1 --batch_size 28 --time 17 --savename gref_umd_oiou_bs28
+
+python train_oiou.py --dataset refcoco+ --splitBy unc --ngpu 1 --batch_size 28 --time 17 --savename refcocop_unc_oiou_bs28
+
+
+# python train.py --dataset refcoco+ --splitBy unc --ngpu 1 --batch_size 28 --time 17 --savename refcocop_bs28_repro 
+# python train.py --dataset refcoco+ --splitBy unc --ngpu 1 --batch_size 36 --time 17 --savename refcocop_bs36_repro 
+
+
+# python train.py --dataset refcoco --splitBy unc --ngpu 1 --batch_size 36 --time 17 --savename refcoco_bs36_repro 
+# python train.py --dataset refcocog --splitBy umd --ngpu 1 --batch_size 28 --time 17 --savename gref_umd_bs28_repro
+# python train.py --dataset refcocog --splitBy umd --ngpu 1 --batch_size 36 --time 17 --savename gref_umd_bs36_repro
+
+
+# export CUDA_VISIBLE_DEVICES=0,1; python train.py --dataset refcocog --splitBy umd --ngpu 2 --batch_size 64 --time 17 --savename gref_umd_bs64_repro
+
+# export CUDA_VISIBLE_DEVICES=0,1; python train.py --dataset refcocog --splitBy umd --ngpu 1 --batch_size 64 --time 17 --savename gref_umd_bs64_repro
+
+

ASDA/scripts/train_gref_sbert.sh

@@ -0,0 +1,60 @@
+#!/bin/bash
+#SBATCH --job-name=asda-fltr
+#SBATCH --partition=a6000
+#SBATCH --gres=gpu:1
+#SBATCH --time=13-11:30:00
+#SBATCH --mem=25000
+#SBATCH --cpus-per-task=3
+#SBATCH --output=./exp/gref_m10_tmp007_fine_nofltr_bs28.log
+
+ml purge
+ml load cuda/11.8
+eval "$(conda shell.bash hook)"
+conda activate asda
+
+cd /data2/projects/chaeyun/ASDA
+
+export NCCL_P2P_DISABLE=1
+export NVIDIA_TF32_OVERRIDE=1
+export NCCL_IB_TIMEOUT=100
+export NCCL_IB_RETRY_CNT=15
+export MASTER_PORT=2721
+
+
+BS=28
+SAVENAME=gref_m10_tmp007_fine_nofltr_bs28
+MARGIN=10
+TEMP=0.07
+MODE=hardpos_only_refined
+FILTER_THRES=0.99
+FUSE_MODE=fine
+
+## options 
+# gref_m10_tmp007_coarse_nofltr_bs28
+# gref_m10_tmp007_coarse_fthr065_bs28
+# gref_m10_tmp007_coarse_fthr050_bs28
+# gref_m10_tmp007_fine_fthr065_bs28
+# gref_m10_tmp007_fine_nofltr_bs28
+
+# TRAIN
+export CUDA_VISIBLE_DEVICES=0
+python_args="--dataset refcocog \
+--splitBy umd \
+--ngpu 1 --batch_size ${BS} \
+--savename ${SAVENAME} --time 17 \
+--metric_learning \
+--margin_value ${MARGIN} \
+--filter_thres ${FILTER_THRES} \
+--temperature ${TEMP} \
+--metric_mode ${MODE} \
+--fuse_mode ${FUSE_MODE} "
+
+python train_gref_sbert.py $python_args
+
+# python train.py --dataset refcoco --splitBy unc --ngpu 1 --batch_size 36 --time 17 --savename refcoco_bs36_repro 
+# python train.py --dataset refcocog --splitBy umd --ngpu 1 --batch_size 28 --time 17 --savename gref_umd_bs28_repro
+# python train.py --dataset refcocog --splitBy umd --ngpu 1 --batch_size 36 --time 17 --savename gref_umd_bs36_repro
+# export CUDA_VISIBLE_DEVICES=0,1; python train.py --dataset refcocog --splitBy umd --ngpu 2 --batch_size 64 --time 17 --savename gref_umd_bs64_repro
+# export CUDA_VISIBLE_DEVICES=0,1; python train.py --dataset refcocog --splitBy umd --ngpu 1 --batch_size 64 --time 17 --savename gref_umd_bs64_repro
+
+

ASDA/scripts/train_gref_sbert_proj.sh

@@ -0,0 +1,64 @@
+#!/bin/bash
+#SBATCH --job-name=asda-nfltr-pj
+#SBATCH --partition=a6000
+#SBATCH --gres=gpu:1
+#SBATCH --time=13-11:30:00
+#SBATCH --mem=28000
+#SBATCH --cpus-per-task=3
+#SBATCH --output=./exp_projection/pj_gref_m10_tmp007_coarse_fthr065_bs28.log
+
+ml purge
+ml load cuda/11.8
+eval "$(conda shell.bash hook)"
+conda activate asda
+
+cd /data2/projects/chaeyun/ASDA
+
+export NCCL_P2P_DISABLE=1
+export NVIDIA_TF32_OVERRIDE=1
+export NCCL_IB_TIMEOUT=100
+export NCCL_IB_RETRY_CNT=15
+export MASTER_PORT=2969
+
+
+BS=28
+SAVENAME=pj_gref_m10_tmp007_coarse_fthr065_bs28
+MARGIN=10
+TEMP=0.07
+MODE=hardpos_only_refined
+FILTER_THRES=0.65
+FUSE_MODE=coarse
+
+## options 
+# pj_gref_m10_tmp007_coarse_nofltr_bs28
+# pj_gref_m10_tmp007_coarse_fthr065_bs28
+# pj_gref_m10_tmp007_coarse_fthr050_bs28
+
+# pj_gref_m10_tmp007_fine_nofltr_bs28
+# pj_gref_m10_tmp007_fine_fthr050_bs28
+
+
+## TODO : grid search on best setting
+
+# TRAIN
+export CUDA_VISIBLE_DEVICES=0
+python_args="--dataset refcocog \
+--splitBy umd \
+--ngpu 1 --batch_size ${BS} \
+--savename ${SAVENAME} --time 17 \
+--metric_learning --use_projections \
+--margin_value ${MARGIN} \
+--filter_thres ${FILTER_THRES} \
+--temperature ${TEMP} \
+--metric_mode ${MODE} \
+--fuse_mode ${FUSE_MODE} "
+
+python train_gref_sbert.py $python_args
+
+# python train.py --dataset refcoco --splitBy unc --ngpu 1 --batch_size 36 --time 17 --savename refcoco_bs36_repro 
+# python train.py --dataset refcocog --splitBy umd --ngpu 1 --batch_size 28 --time 17 --savename gref_umd_bs28_repro
+# python train.py --dataset refcocog --splitBy umd --ngpu 1 --batch_size 36 --time 17 --savename gref_umd_bs36_repro
+# export CUDA_VISIBLE_DEVICES=0,1; python train.py --dataset refcocog --splitBy umd --ngpu 2 --batch_size 64 --time 17 --savename gref_umd_bs64_repro
+# export CUDA_VISIBLE_DEVICES=0,1; python train.py --dataset refcocog --splitBy umd --ngpu 1 --batch_size 64 --time 17 --savename gref_umd_bs64_repro
+
+

ASDA/scripts/train_gref_sbert_proj2.sh

@@ -0,0 +1,67 @@
+#!/bin/bash
+#SBATCH --job-name=asda-rcoarse-m10
+#SBATCH --partition=a6000
+#SBATCH --gres=gpu:1
+#SBATCH --time=13-11:30:00
+#SBATCH --mem=28000
+#SBATCH --cpus-per-task=3
+#SBATCH --output=./exp_projection/pj_gref_m10_tmp007_refcoarse_nofltroiou_bs32.log
+
+ml purge
+ml load cuda/11.8
+eval "$(conda shell.bash hook)"
+conda activate asda
+
+cd /data2/projects/chaeyun/ASDA
+
+export NCCL_P2P_DISABLE=1
+export NVIDIA_TF32_OVERRIDE=1
+export NCCL_IB_TIMEOUT=100
+export NCCL_IB_RETRY_CNT=15
+export MASTER_PORT=1592
+
+
+BS=32
+SAVENAME=pj_gref_m10_tmp007_refcoarse_nofltroiou_bs32
+MARGIN=10
+TEMP=0.07
+MODE=hardpos_only_refined
+FILTER_THRES=0.99
+FUSE_MODE=refined_coarse
+
+
+# pj_gref_m10_tmp007_refcoarse_nofltroiou_bs28
+
+## options 
+# pj_gref_m10_tmp007_coarse_nofltr_bs28
+# pj_gref_m10_tmp007_coarse_fthr065_bs28
+# pj_gref_m10_tmp007_coarse_fthr050_bs28
+
+# pj_gref_m10_tmp007_fine_nofltr_bs28
+# pj_gref_m10_tmp007_fine_fthr050_bs28
+
+
+## TODO : grid search on best setting
+
+# TRAIN
+export CUDA_VISIBLE_DEVICES=0
+python_args="--dataset refcocog \
+--splitBy umd \
+--ngpu 1 --batch_size ${BS} \
+--savename ${SAVENAME} --time 17 \
+--metric_learning --use_projections \
+--margin_value ${MARGIN} \
+--filter_thres ${FILTER_THRES} \
+--temperature ${TEMP} \
+--metric_mode ${MODE} \
+--fuse_mode ${FUSE_MODE} "
+
+python train_gref_sbert_oiou.py $python_args
+
+# python train.py --dataset refcoco --splitBy unc --ngpu 1 --batch_size 36 --time 17 --savename refcoco_bs36_repro 
+# python train.py --dataset refcocog --splitBy umd --ngpu 1 --batch_size 28 --time 17 --savename gref_umd_bs28_repro
+# python train.py --dataset refcocog --splitBy umd --ngpu 1 --batch_size 36 --time 17 --savename gref_umd_bs36_repro
+# export CUDA_VISIBLE_DEVICES=0,1; python train.py --dataset refcocog --splitBy umd --ngpu 2 --batch_size 64 --time 17 --savename gref_umd_bs64_repro
+# export CUDA_VISIBLE_DEVICES=0,1; python train.py --dataset refcocog --splitBy umd --ngpu 1 --batch_size 64 --time 17 --savename gref_umd_bs64_repro
+
+

ASDA/scripts/train_gref_sbert_proj_multigpu.sh

@@ -0,0 +1,56 @@
+#!/bin/bash
+#SBATCH --job-name=asda-rcoarse-oiou
+#SBATCH --partition=a6000
+#SBATCH --gres=gpu:2
+#SBATCH --time=13-11:30:00
+#SBATCH --mem=28000
+#SBATCH --cpus-per-task=3
+#SBATCH --output=./exp_projection/pj_gref_m10_tmp007_refcoarse_nofltroiou_bs36.log
+
+ml purge
+ml load cuda/11.8
+eval "$(conda shell.bash hook)"
+conda activate asda
+
+cd /data2/projects/chaeyun/ASDA
+
+export NCCL_P2P_DISABLE=1
+export NVIDIA_TF32_OVERRIDE=1
+export NCCL_IB_TIMEOUT=100
+export NCCL_IB_RETRY_CNT=15
+export MASTER_PORT=2765
+
+
+BS=36
+SAVENAME=pj_gref_m10_tmp007_refcoarse_nofltroiou_bs36
+MARGIN=10
+TEMP=0.07
+MODE=hardpos_only_refined
+FILTER_THRES=0.99
+FUSE_MODE=refined_coarse
+
+## options 
+# pj_gref_m10_tmp007_refcoarse_nofltroiou_bs36
+
+# TRAIN
+export CUDA_VISIBLE_DEVICES=0,1; 
+python_args="--dataset refcocog \
+--splitBy umd \
+--ngpu 2 --batch_size ${BS} \
+--savename ${SAVENAME} --time 17 \
+--metric_learning --use_projections \
+--margin_value ${MARGIN} \
+--filter_thres ${FILTER_THRES} \
+--temperature ${TEMP} \
+--metric_mode ${MODE} \
+--fuse_mode ${FUSE_MODE} "
+
+python train_gref_sbert_oiou.py $python_args
+
+# python train.py --dataset refcoco --splitBy unc --ngpu 1 --batch_size 36 --time 17 --savename refcoco_bs36_repro 
+# python train.py --dataset refcocog --splitBy umd --ngpu 1 --batch_size 28 --time 17 --savename gref_umd_bs28_repro
+# python train.py --dataset refcocog --splitBy umd --ngpu 1 --batch_size 36 --time 17 --savename gref_umd_bs36_repro
+# export CUDA_VISIBLE_DEVICES=0,1; python train.py --dataset refcocog --splitBy umd --ngpu 2 --batch_size 64 --time 17 --savename gref_umd_bs64_repro
+# export CUDA_VISIBLE_DEVICES=0,1; python train.py --dataset refcocog --splitBy umd --ngpu 1 --batch_size 64 --time 17 --savename gref_umd_bs64_repro
+
+

ASDA/scripts/train_gref_selffilter.sh

@@ -0,0 +1,64 @@
+#!/bin/bash
+#SBATCH --job-name=asda-fth06
+#SBATCH --partition=a6000
+#SBATCH --gres=gpu:1
+#SBATCH --time=13-11:30:00
+#SBATCH --mem=28000
+#SBATCH --cpus-per-task=3
+#SBATCH --output=./exp_selffilter/selffltr_gref_m10_tmp007_fine_bs28_thr040.log
+
+ml purge
+ml load cuda/11.8
+eval "$(conda shell.bash hook)"
+conda activate asda
+
+cd /data2/projects/chaeyun/ASDA
+
+export NCCL_P2P_DISABLE=1
+export NVIDIA_TF32_OVERRIDE=1
+export NCCL_IB_TIMEOUT=100
+export NCCL_IB_RETRY_CNT=15
+export MASTER_PORT=3127
+
+
+## arguments to be changed
+FUSE_MODE=fine
+MTENSOR_OPTION=use_fuser
+BS=28
+SAVENAME=selffltr_gref_m10_tmp007_fine_bs28_thr040
+MARGIN=10
+TEMP=0.07
+SELF_FTHRES=0.41
+
+## Fixed arguments
+MODE=hardpos_only_refined
+FILTER_THRES=0.99
+
+
+# TRAIN
+export CUDA_VISIBLE_DEVICES=0
+python_args="--dataset refcocog \
+--splitBy umd \
+--ngpu 1 --batch_size ${BS} \
+--savename ${SAVENAME} --time 17 \
+--metric_learning \
+--margin_value ${MARGIN} \
+--filter_thres ${FILTER_THRES} \
+--metric_tensor_option ${MTENSOR_OPTION} \
+--self_filter \
+--self_fthres ${SELF_FTHRES} \
+--temperature ${TEMP} \
+--metric_mode ${MODE} \
+--fuse_mode ${FUSE_MODE} "
+
+
+
+python train_gref_selffilter.py $python_args
+
+# python train.py --dataset refcoco --splitBy unc --ngpu 1 --batch_size 36 --time 17 --savename refcoco_bs36_repro 
+# python train.py --dataset refcocog --splitBy umd --ngpu 1 --batch_size 28 --time 17 --savename gref_umd_bs28_repro
+# python train.py --dataset refcocog --splitBy umd --ngpu 1 --batch_size 36 --time 17 --savename gref_umd_bs36_repro
+# export CUDA_VISIBLE_DEVICES=0,1; python train.py --dataset refcocog --splitBy umd --ngpu 2 --batch_size 64 --time 17 --savename gref_umd_bs64_repro
+# export CUDA_VISIBLE_DEVICES=0,1; python train.py --dataset refcocog --splitBy umd --ngpu 1 --batch_size 64 --time 17 --savename gref_umd_bs64_repro
+
+

ASDA/scripts/train_rcc_sbert.sh

@@ -0,0 +1,59 @@
+#!/bin/bash
+#SBATCH --job-name=asda-rcc-pj5
+#SBATCH --partition=a6000
+#SBATCH --gres=gpu:1
+#SBATCH --time=13-11:30:00
+#SBATCH --mem=28000
+#SBATCH --cpus-per-task=3
+#SBATCH --output=./exp_rcc_projection/pj_rcc_m10_tmp007_fine_nofltroiou_bs28.log
+
+ml purge
+ml load cuda/11.8
+eval "$(conda shell.bash hook)"
+conda activate asda
+
+cd /data2/projects/chaeyun/ASDA
+
+export NCCL_P2P_DISABLE=1
+export NVIDIA_TF32_OVERRIDE=1
+export NCCL_IB_TIMEOUT=100
+export NCCL_IB_RETRY_CNT=15
+export MASTER_PORT=1989
+
+BS=28
+SAVENAME=pj_rcc_m10_tmp007_fine_nofltroiou_bs28
+MARGIN=10
+TEMP=0.07
+MODE=hardpos_only_refined
+FILTER_THRES=0.99
+FUSE_MODE=fine
+
+# Running options
+# pj_rcc_m10_tmp007_coarse_nofltroiou_bs28
+# pj_rcc_m10_tmp007_coarse_fthr070_oiou_bs28
+
+
+
+# TRAIN
+export CUDA_VISIBLE_DEVICES=0
+python_args="--dataset refcoco \
+--splitBy unc \
+--ngpu 1 --batch_size ${BS} \
+--savename ${SAVENAME} --time 17 \
+--metric_learning --use_projections \
+--margin_value ${MARGIN} \
+--filter_thres ${FILTER_THRES} \
+--temperature ${TEMP} \
+--metric_mode ${MODE} \
+--fuse_mode ${FUSE_MODE} "
+
+python train_rcc_sbert_oiou.py $python_args
+
+
+# python train.py --dataset refcoco --splitBy unc --ngpu 1 --batch_size 36 --time 17 --savename refcoco_bs36_repro 
+
+# python train.py --dataset refcoco+ --splitBy unc --ngpu 1 --batch_size 28 --time 17 --savename refcocop_bs28_repro 
+# python train.py --dataset refcoco+ --splitBy unc --ngpu 1 --batch_size 36 --time 17 --savename refcocop_bs36_repro 
+
+
+

ASDA/scripts/train_rcc_sbert2.sh

@@ -0,0 +1,59 @@
+#!/bin/bash
+#SBATCH --job-name=asda-rcc-pj7
+#SBATCH --partition=a6000
+#SBATCH --gres=gpu:1
+#SBATCH --time=13-11:30:00
+#SBATCH --mem=28000
+#SBATCH --cpus-per-task=3
+#SBATCH --output=./exp_rcc_projection/pj_rcc_m12_tmp007_refcoarse_nofltroiou_bs32.log
+
+ml purge
+ml load cuda/11.8
+eval "$(conda shell.bash hook)"
+conda activate asda
+
+cd /data2/projects/chaeyun/ASDA
+
+export NCCL_P2P_DISABLE=1
+export NVIDIA_TF32_OVERRIDE=1
+export NCCL_IB_TIMEOUT=100
+export NCCL_IB_RETRY_CNT=15
+export MASTER_PORT=2948
+
+BS=32
+SAVENAME=pj_rcc_m12_tmp007_refcoarse_nofltroiou_bs32
+MARGIN=12
+TEMP=0.07
+MODE=hardpos_only_refined
+FILTER_THRES=0.99
+FUSE_MODE=refined_coarse
+
+# Running options
+# pj_rcc_m10_tmp007_coarse_nofltroiou_bs28
+# pj_rcc_m10_tmp007_coarse_fthr070_oiou_bs28
+
+
+
+# TRAIN
+export CUDA_VISIBLE_DEVICES=0
+python_args="--dataset refcoco \
+--splitBy unc \
+--ngpu 1 --batch_size ${BS} \
+--savename ${SAVENAME} --time 17 \
+--metric_learning --use_projections \
+--margin_value ${MARGIN} \
+--filter_thres ${FILTER_THRES} \
+--temperature ${TEMP} \
+--metric_mode ${MODE} \
+--fuse_mode ${FUSE_MODE} "
+
+python train_rcc_sbert_oiou.py $python_args
+
+
+# python train.py --dataset refcoco --splitBy unc --ngpu 1 --batch_size 36 --time 17 --savename refcoco_bs36_repro 
+
+# python train.py --dataset refcoco+ --splitBy unc --ngpu 1 --batch_size 28 --time 17 --savename refcocop_bs28_repro 
+# python train.py --dataset refcoco+ --splitBy unc --ngpu 1 --batch_size 36 --time 17 --savename refcocop_bs36_repro 
+
+
+

ASDA/scripts/train_rccp_sbert.sh

@@ -0,0 +1,53 @@
+#!/bin/bash
+#SBATCH --job-name=asda-rccp-pj5
+#SBATCH --partition=a6000
+#SBATCH --gres=gpu:1
+#SBATCH --time=13-11:30:00
+#SBATCH --mem=28000
+#SBATCH --cpus-per-task=3
+#SBATCH --output=./exp_rccp_projection/pj_rccp_m12_tmp007_refcoarse_nofltroiou_bs32.log
+
+ml purge
+ml load cuda/11.8
+eval "$(conda shell.bash hook)"
+conda activate asda
+
+cd /data2/projects/chaeyun/ASDA
+
+export NCCL_P2P_DISABLE=1
+export NVIDIA_TF32_OVERRIDE=1
+export NCCL_IB_TIMEOUT=100
+export NCCL_IB_RETRY_CNT=15
+export MASTER_PORT=8128
+
+BS=32
+SAVENAME=pj_rccp_m12_tmp007_refcoarse_nofltroiou_bs32
+MARGIN=12
+TEMP=0.07
+MODE=hardpos_only_refined
+FILTER_THRES=0.99
+FUSE_MODE=refined_coarse
+
+# Running options
+# pj_rccp_m10_tmp007_coarse_nofltroiou_bs28
+
+# pending 
+# pj_rccp_m10_tmp007_coarse_fthr070_oiou_bs28
+
+
+# TRAIN
+export CUDA_VISIBLE_DEVICES=0
+python_args="--dataset refcoco+ \
+--splitBy unc \
+--ngpu 1 --batch_size ${BS} \
+--savename ${SAVENAME} --time 17 \
+--metric_learning --use_projections \
+--margin_value ${MARGIN} \
+--filter_thres ${FILTER_THRES} \
+--temperature ${TEMP} \
+--metric_mode ${MODE} \
+--fuse_mode ${FUSE_MODE} "
+
+python train_rcc_sbert_oiou.py $python_args
+
+

ASDA/scripts/train_rccp_sbert2.sh

@@ -0,0 +1,50 @@
+#!/bin/bash
+#SBATCH --job-name=asda-rccp-pj2
+#SBATCH --partition=a6000
+#SBATCH --gres=gpu:1
+#SBATCH --time=13-11:30:00
+#SBATCH --mem=28000
+#SBATCH --cpus-per-task=3
+#SBATCH --output=./exp_rccp_projection/pj_rccp_m10_tmp007_coarse_fthr070_oiou_bs28.log
+
+ml purge
+ml load cuda/11.8
+eval "$(conda shell.bash hook)"
+conda activate asda
+
+cd /data2/projects/chaeyun/ASDA
+
+export NCCL_P2P_DISABLE=1
+export NVIDIA_TF32_OVERRIDE=1
+export NCCL_IB_TIMEOUT=100
+export NCCL_IB_RETRY_CNT=15
+export MASTER_PORT=8871
+
+BS=28
+SAVENAME=pj_rccp_m10_tmp007_coarse_fthr070_oiou_bs28
+MARGIN=10
+TEMP=0.07
+MODE=hardpos_only_refined
+FILTER_THRES=0.68
+FUSE_MODE=coarse
+
+# Running options
+# pj_rccp_m10_tmp007_coarse_fthr070_oiou_bs28
+
+
+# TRAIN
+export CUDA_VISIBLE_DEVICES=0
+python_args="--dataset refcoco+ \
+--splitBy unc \
+--ngpu 1 --batch_size ${BS} \
+--savename ${SAVENAME} --time 17 \
+--metric_learning --use_projections \
+--margin_value ${MARGIN} \
+--filter_thres ${FILTER_THRES} \
+--temperature ${TEMP} \
+--metric_mode ${MODE} \
+--fuse_mode ${FUSE_MODE} "
+
+python train_rcc_sbert_oiou.py $python_args
+
+