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run_voc.sh
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run_voc.sh
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#!/usr/bin/env bash
EXP_NAME=$1
SAVE_DIR=checkpoints/voc/${EXP_NAME}
IMAGENET_PRETRAIN=/data/.pretrain_weights/ImageNetPretrained/MSRA/R-101.pkl # <-- change it to you path
IMAGENET_PRETRAIN_TORCH=/data/.pretrain_weights/ImageNetPretrained/torchvision/resnet101-5d3b4d8f.pth # <-- change it to you path
SPLIT_ID=$2
# ------------------------------- Base Pre-train ---------------------------------- #
python3 main.py --num-gpus 8 --config-file configs/voc/defrcn_det_r101_base${SPLIT_ID}.yaml \
--opts MODEL.WEIGHTS ${IMAGENET_PRETRAIN} \
OUTPUT_DIR ${SAVE_DIR}/defrcn_det_r101_base${SPLIT_ID}
# ------------------------------ Model Preparation -------------------------------- #
python3 tools/model_surgery.py --dataset voc --method remove \
--src-path ${SAVE_DIR}/defrcn_det_r101_base${SPLIT_ID}/model_final.pth \
--save-dir ${SAVE_DIR}/defrcn_det_r101_base${SPLIT_ID}
BASE_WEIGHT=${SAVE_DIR}/defrcn_det_r101_base${SPLIT_ID}/model_reset_remove.pth
# ------------------------------ Novel Fine-tuning -------------------------------- #
# --> 1. FSRW-like, i.e. run seed0 10 times (the FSOD results on voc in most papers)
for repeat_id in 0 1 2 3 4 5 6 7 8 9
do
for shot in 1 2 3 5 10 # if final, 10 -> 1 2 3 5 10
do
for seed in 0
do
python3 tools/create_config.py --dataset voc --config_root configs/voc \
--shot ${shot} --seed ${seed} --setting 'fsod' --split ${SPLIT_ID}
CONFIG_PATH=configs/voc/defrcn_fsod_r101_novel${SPLIT_ID}_${shot}shot_seed${seed}.yaml
OUTPUT_DIR=${SAVE_DIR}/defrcn_fsod_r101_novel${SPLIT_ID}/fsrw-like/${shot}shot_seed${seed}_repeat${repeat_id}
python3 main.py --num-gpus 8 --config-file ${CONFIG_PATH} \
--opts MODEL.WEIGHTS ${BASE_WEIGHT} OUTPUT_DIR ${OUTPUT_DIR} \
TEST.PCB_MODELPATH ${IMAGENET_PRETRAIN_TORCH}
rm ${CONFIG_PATH}
rm ${OUTPUT_DIR}/model_final.pth
done
done
done
python3 tools/extract_results.py --res-dir ${SAVE_DIR}/defrcn_fsod_r101_novel${SPLIT_ID}/fsrw-like --shot-list 1 2 3 5 10 # surmarize all results
# ----------------------------- Model Preparation --------------------------------- #
python3 tools/model_surgery.py --dataset voc --method randinit \
--src-path ${SAVE_DIR}/defrcn_det_r101_base${SPLIT_ID}/model_final.pth \
--save-dir ${SAVE_DIR}/defrcn_det_r101_base${SPLIT_ID}
BASE_WEIGHT=${SAVE_DIR}/defrcn_det_r101_base${SPLIT_ID}/model_reset_surgery.pth
# ------------------------------ Novel Fine-tuning ------------------------------- #
# --> 2. TFA-like, i.e. run seed0~9 for robust results (G-FSOD, 80 classes)
for seed in 0 1 2 3 4 5 6 7 8 9
do
for shot in 1 2 3 5 10 # if final, 10 -> 1 2 3 5 10
do
python3 tools/create_config.py --dataset voc --config_root configs/voc \
--shot ${shot} --seed ${seed} --setting 'gfsod' --split ${SPLIT_ID}
CONFIG_PATH=configs/voc/defrcn_gfsod_r101_novel${SPLIT_ID}_${shot}shot_seed${seed}.yaml
OUTPUT_DIR=${SAVE_DIR}/defrcn_gfsod_r101_novel${SPLIT_ID}/tfa-like/${shot}shot_seed${seed}
python3 main.py --num-gpus 8 --config-file ${CONFIG_PATH} \
--opts MODEL.WEIGHTS ${BASE_WEIGHT} OUTPUT_DIR ${OUTPUT_DIR} \
TEST.PCB_MODELPATH ${IMAGENET_PRETRAIN_TORCH}
rm ${CONFIG_PATH}
rm ${OUTPUT_DIR}/model_final.pth
done
done
python3 tools/extract_results.py --res-dir ${SAVE_DIR}/defrcn_gfsod_r101_novel${SPLIT_ID}/tfa-like --shot-list 1 2 3 5 10 # surmarize all results
# ------------------------------ Novel Fine-tuning ------------------------------- # not necessary, just for the completeness of defrcn
# --> 3. TFA-like, i.e. run seed0~9 for robust results
BASE_WEIGHT=${SAVE_DIR}/defrcn_det_r101_base${SPLIT_ID}/model_reset_remove.pth
for seed in 0 1 2 3 4 5 6 7 8 9
do
for shot in 1 2 3 5 10 # if final, 10 -> 1 2 3 5 10
do
python3 tools/create_config.py --dataset voc --config_root configs/voc \
--shot ${shot} --seed ${seed} --setting 'fsod' --split ${SPLIT_ID}
CONFIG_PATH=configs/voc/defrcn_fsod_r101_novel${SPLIT_ID}_${shot}shot_seed${seed}.yaml
OUTPUT_DIR=${SAVE_DIR}/defrcn_fsod_r101_novel${SPLIT_ID}/tfa-like/${shot}shot_seed${seed}
python3 main.py --num-gpus 8 --config-file ${CONFIG_PATH} \
--opts MODEL.WEIGHTS ${BASE_WEIGHT} OUTPUT_DIR ${OUTPUT_DIR} \
TEST.PCB_MODELPATH ${IMAGENET_PRETRAIN_TORCH}
rm ${CONFIG_PATH}
rm ${OUTPUT_DIR}/model_final.pth
done
done
python3 tools/extract_results.py --res-dir ${SAVE_DIR}/defrcn_fsod_r101_novel${SPLIT_ID}/tfa-like --shot-list 1 2 3 5 10 # surmarize all results