mindspore/example/yolov3_coco2017/run_distribute_train.sh

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#!/bin/bash
# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
echo "======================================================================================================================================================="
echo "Please run the scipt as: "
echo "sh run_distribute_train.sh DEVICE_NUM EPOCH_SIZE MINDRECORD_DIR IMAGE_DIR ANNO_PATH MINDSPORE_HCCL_CONFIG_PATH PRE_TRAINED PRE_TRAINED_EPOCH_SIZE"
echo "For example: sh run_distribute_train.sh 8 150 /data/Mindrecord_train /data /data/train.txt /data/hccl.json /opt/yolov3-150.ckpt(optional) 100(optional)"
echo "It is better to use absolute path."
echo "The learning rate is 0.005 as default, if you want other lr, please change the value in this script."
echo "======================================================================================================================================================="
if [ $# != 6 ] && [ $# != 8 ]
then
echo "Usage: sh run_distribute_train.sh [DEVICE_NUM] [EPOCH_SIZE] [MINDRECORD_DIR] [IMAGE_DIR] [ANNO_PATH] [MINDSPORE_HCCL_CONFIG_PATH] \
[PRE_TRAINED](optional) [PRE_TRAINED_EPOCH_SIZE](optional)"
exit 1
fi
EPOCH_SIZE=$2
MINDRECORD_DIR=$3
IMAGE_DIR=$4
ANNO_PATH=$5
PRE_TRAINED=$7
PRE_TRAINED_EPOCH_SIZE=$8
# Before start distribute train, first create mindrecord files.
python train.py --only_create_dataset=1 --mindrecord_dir=$MINDRECORD_DIR --image_dir=$IMAGE_DIR \
--anno_path=$ANNO_PATH
echo "After running the scipt, the network runs in the background. The log will be generated in LOGx/log.txt"
export MINDSPORE_HCCL_CONFIG_PATH=$6
export RANK_SIZE=$1
for((i=0;i<RANK_SIZE;i++))
do
export DEVICE_ID=$i
start=`expr $i \* 12`
end=`expr $start \+ 11`
cmdopt=$start"-"$end
rm -rf LOG$i
mkdir ./LOG$i
cp *.py ./LOG$i
cd ./LOG$i || exit
export RANK_ID=$i
echo "start training for rank $i, device $DEVICE_ID"
env > env.log
if [ $# == 6 ]
then
taskset -c $cmdopt python ../train.py \
--distribute=1 \
--lr=0.005 \
--device_num=$RANK_SIZE \
--device_id=$DEVICE_ID \
--mindrecord_dir=$MINDRECORD_DIR \
--image_dir=$IMAGE_DIR \
--epoch_size=$EPOCH_SIZE \
--anno_path=$ANNO_PATH > log.txt 2>&1 &
fi
if [ $# == 8 ]
then
taskset -c $cmdopt python ../train.py \
--distribute=1 \
--lr=0.005 \
--device_num=$RANK_SIZE \
--device_id=$DEVICE_ID \
--mindrecord_dir=$MINDRECORD_DIR \
--image_dir=$IMAGE_DIR \
--epoch_size=$EPOCH_SIZE \
--pre_trained=$PRE_TRAINED \
--pre_trained_epoch_size=$PRE_TRAINED_EPOCH_SIZE \
--anno_path=$ANNO_PATH > log.txt 2>&1 &
fi
cd ../
done