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# custom config
DATA=/mnt/sdb/data/datasets
TRAINER=CoOp
DATASET=$1 # 'imagenet' 'caltech101' 'dtd' 'eurosat' 'fgvc_aircraft' 'oxford_flowers' 'food101' 'oxford_pets' 'stanford_cars' 'sun397' 'ucf101'
CFG=vit_b16_c4_ep10_batch1_ctxv1
SHOTS=16
EP=10
CTX_INIT=""
for SEED in 1 2 3
do
TRAIL_NAME=${CFG}
COMMON_DIR=${DATASET}/shots_${SHOTS}/seed${SEED}
MODEL_DIR=output/base2new/${TRAINER}/${TRAIL_NAME}/train_base/${COMMON_DIR}
DIR=output/base2new/${TRAINER}/${TRAIL_NAME}/test_new/${COMMON_DIR}
if [ -d "$MODEL_DIR" ]; then
echo "Oops! The results exist at ${DIR} (so skip this job)"
else
echo "Run this job and save the output to ${DIR}"
python train.py \
--root ${DATA} \
--seed ${SEED} \
--trainer ${TRAINER} \
--dataset-config-file configs/datasets/${DATASET}.yaml \
--config-file configs/trainers/CoCoOp/${CFG}.yaml \
--output-dir ${MODEL_DIR} \
DATASET.NUM_SHOTS ${SHOTS} \
DATASET.SUBSAMPLE_CLASSES base
fi
if [ -d "$DIR" ]; then
echo "Oops! The results exist at ${DIR} (so skip this job)"
else
echo "Evaluating model"
python train.py \
--root ${DATA} \
--seed ${SEED} \
--trainer ${TRAINER} \
--dataset-config-file configs/datasets/${DATASET}.yaml \
--config-file configs/trainers/CoCoOp/${CFG}.yaml \
--output-dir ${DIR} \
--model-dir ${MODEL_DIR} \
--load-epoch ${EP} \
--eval-only \
DATASET.NUM_SHOTS ${SHOTS} \
DATASET.SUBSAMPLE_CLASSES new
fi
done
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