BiRefNet / train.sh
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Move all BiRefNet github codes to the first level directory.
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#!/bin/bash
# Run script
# Settings of training & test for different tasks.
method="$1"
task=$(python3 config.py)
case "${task}" in
"DIS5K") epochs=600 && val_last=100 && step=5 ;;
"COD") epochs=150 && val_last=50 && step=5 ;;
"HRSOD") epochs=150 && val_last=50 && step=5 ;;
"DIS5K+HRSOD+HRS10K") epochs=250 && val_last=50 && step=5 ;;
"P3M-10k") epochs=150 && val_last=50 && step=5 ;;
esac
testsets=NO # Non-existing folder to skip.
# testsets=TE-COD10K # for COD
# Train
devices=$2
nproc_per_node=$(echo ${devices%%,} | grep -o "," | wc -l)
to_be_distributed=`echo ${nproc_per_node} | awk '{if($e > 0) print "True"; else print "False";}'`
echo Training started at $(date)
if [ ${to_be_distributed} == "True" ]
then
# Adapt the nproc_per_node by the number of GPUs. Give 8989 as the default value of master_port.
echo "Multi-GPU mode received..."
CUDA_VISIBLE_DEVICES=${devices} \
torchrun --nproc_per_node $((nproc_per_node+1)) --master_port=${3:-8989} \
train.py --ckpt_dir ckpt/${method} --epochs ${epochs} \
--testsets ${testsets} \
--dist ${to_be_distributed}
else
echo "Single-GPU mode received..."
CUDA_VISIBLE_DEVICES=${devices} \
python train.py --ckpt_dir ckpt/${method} --epochs ${epochs} \
--testsets ${testsets} \
--dist ${to_be_distributed} \
--resume ckpt/xx/ep100.pth
fi
echo Training finished at $(date)