mae-cnn/downstream_imagenet/main.sh

#!/usr/bin/env bash

# an example to do pre-training: (not that `/path/to/imagenet` should contain directories named `train` and `val`)
# > cd /path/to/SparK/downstream_imagenet
# > bash ./main.sh experiment_name --num_nodes=1 --ngpu_per_node=8 --node_rank=0 --master_address=128.0.0.0 --master_port=30000 --data_path=/path/to/imagenet --model=convnext_small

####### template begins #######
SCRIPTS_DIR=$(cd $(dirname $0); pwd)
cd "${SCRIPTS_DIR}"
FINETUNE_DIR=$(pwd)
echo "FINETUNE_DIR=${FINETUNE_DIR}"

shopt -s expand_aliases
alias python=python3
alias to_scripts_dir='cd "${SCRIPTS_DIR}"'
alias to_spark_dir='cd "${FINETUNE_DIR}"'
alias print='echo "$(date +"[%m-%d %H:%M:%S]") (exp.sh)=>"'
function mkd() {
  mkdir -p "$1" >/dev/null 2>&1
}
####### template ends #######


EXP_NAME=$1

EXP_DIR="${FINETUNE_DIR}/output_${EXP_NAME}"


print "===================== Args ====================="
print "EXP_NAME: ${EXP_NAME}"
print "[other_args sent to launch.py]: ${*:2}"
print "================================================"
print ""


print "============== ImageNet finetuning starts =============="
to_spark_dir
touch ~/wait1
python launch.py \
--main_py_relpath main.py \
--exp_name "${EXP_NAME}" \
--exp_dir "${EXP_DIR}" \
"${*:2}"
print "============== ImageNet finetuning ends =============="
rm ~/wait1