py-faster-rcnn训练脚本faster_rcnn_end2end.sh分析

脚本路径：experiments/scripts/faster_rcnn_end2end.sh

#!/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shell

 

export 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=$2

NET_lc=${NET,,}

DATASET=$3

 

array=( $@ )          

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

    ;;

esac

 

LOG="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 +x

NET_FINAL=`grep -B 1 "done solving" ${LOG} | grep "Wrote snapshot" | awk '{print $4}'`  

set -x

 

time./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}