faster_rcnn_end2end.sh源码分析

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#!/bin/bash# Usage:# ./experiments/scripts/faster_rcnn_end2end.sh GPU NET DATASET [options args to {train,test}_net.py]# DATASET is either pascal_voc or coco.## Example:# ./experiments/scripts/faster_rcnn_end2end.sh 0 VGG_CNN_M_1024 pascal_voc \#   --set EXP_DIR foobar RNG_SEED 42 TRAIN.SCALES "[400, 500, 600, 700]"set -x    #将后面执行的命令输出到屏幕set -e    #如果命令的返回值不是0 则退出shellexport PYTHONUNBUFFERED="True"   #和缓存有关系的一个变量，使得按顺序输出GPU_ID=$1     #这一部分是读取命令信息，包括GPU的编号，网络类型，以及数据集类型等       NET=$2NET_lc=${NET,,}DATASET=$3array=( $@ )           len=${#array[@]}EXTRA_ARGS=${array[@]:3:$len}EXTRA_ARGS_SLUG=${EXTRA_ARGS// /_}case $DATASET in #根据输入数据类型，进行不同的处理，分为3种情况pascal_voc; coco; 错误类型  pascal_voc)    TRAIN_IMDB="voc_2007_trainval"  #定义相应的变量    TEST_IMDB="voc_2007_test"    PT_DIR="pascal_voc"    ITERS=70000              #定义迭代次数    ;;  coco)    # This is a very long and slow training schedule    # You can probably use fewer iterations and reduce the    # time to the LR drop (set in the solver to 350,000 iterations).    TRAIN_IMDB="coco_2014_train"     #定义相应的变量    TEST_IMDB="coco_2014_minival"    PT_DIR="coco"    ITERS=490000                     #定义迭代次数    ;;  *)    echo "No dataset given"    exit    ;;esacLOG="experiments/logs/faster_rcnn_end2end_${NET}_${EXTRA_ARGS_SLUG}.txt.`date +'%Y-%m-%d_%H-%M-%S'`"  #训练日志的存储路径exec &> >(tee -a "$LOG")echo Logging output to "$LOG"time ./tools/train_net.py --gpu ${GPU_ID} \     #加载网络训练的相关参数并进行训练  --solver models/${PT_DIR}/${NET}/faster_rcnn_end2end/solver.prototxt \  --weights data/imagenet_models/${NET}.v2.caffemodel \  --imdb ${TRAIN_IMDB} \  --iters ${ITERS} \  --cfg experiments/cfgs/faster_rcnn_end2end.yml \  ${EXTRA_ARGS}set +xNET_FINAL=`grep -B 1 "done solving" ${LOG} | grep "Wrote snapshot" | awk '{print $4}'`   set -xtime ./tools/test_net.py --gpu ${GPU_ID} \    #测试过程  --def models/${PT_DIR}/${NET}/faster_rcnn_end2end/test.prototxt \  --net ${NET_FINAL} \  --imdb ${TEST_IMDB} \  --cfg experiments/cfgs/faster_rcnn_end2end.yml \  ${EXTRA_ARGS}
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