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#!/bin/bash
# $1 is the name of the scripts folder
# $2 are the tasks to run, seperated by comma
# $3 is the gpu ids that used for training, seperated by comma
# $4 is an optional argument that, if present, skips the check for finished tasks
IFS=',' read -ra arr <<< $3
CUDA_VISIBLE_DEVICES=$4
for gene in ${arr[@]}
do
echo "Begin "$gene
for subset in 1 2 4 6
do
for seed in {0..4}
do
logdir=$(cat $1/$gene.subset.$subset.5fold/$gene.subset.$subset.fold.$seed.yaml | grep log_dir | sed 's/.*: //')
num_epochs=$(cat $1/$gene.subset.$subset.5fold/$gene.subset.$subset.fold.$seed.yaml | grep num_epochs | sed 's/.*: //')
data_file_train=$(cat $1/$gene.subset.$subset.5fold/$gene.subset.$subset.fold.$seed.yaml | grep data_file_train: | sed 's/.*: //')
# check if task has finished, unless the skip argument is present
if [ -f $logdir/FOLD.3/model.epoch.$num_epochs.pt ]; then
if [ ! -f $2/$gene/testing.subset.$subset.fold.$seed.4fold.csv ]; then
python -W ignore::UserWarning:torch_geometric.data.collate:147 train.py \
--conf $1/$gene.subset.$subset.5fold/$gene.subset.$subset.fold.$seed.yaml \
--mode interpret_4_fold --interpret-by both --out-dir $2/$gene/testing.subset.$subset.fold.$seed.4fold.csv
fi
if [ ! -f $2/$gene/training.subset.$subset.fold.$seed.4fold.csv ]; then
python -W ignore::UserWarning:torch_geometric.data.collate:147 train.py \
--conf $1/$gene.subset.$subset.5fold/$gene.subset.$subset.fold.$seed.yaml \
--data-file-test $data_file_train \
--mode interpret_4_fold --interpret-by both --out-dir $2/$gene/training.subset.$subset.fold.$seed.4fold.csv
fi
fi
done
done
done
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