deeplab运行指南

来源：互联网发布：epson机器人编程编辑：程序博客网时间：2024/06/06 01:19

以下仅仅为一个总结，参考了网上的众多资料，仅备忘记。

主要链接

deeplab主页：http://liangchiehchen.com/projects/DeepLab.html
官方代码：https://bitbucket.org/aquariusjay/deeplab-public-ver2
python 版caffe实现：https://github.com/TheLegendAli/DeepLab-Context2
model下载：http://liangchiehchen.com/projects/DeepLab_Models.html
DeepLabv2_VGG16 预训练模型
DeepLabv2_ResNet101预训练模型
pytorch实现的deeplab：https://github.com/isht7/pytorch-deeplab-resnet
网上开源代码：martinkersner/train-DeepLab

主要运行步骤

如下我们主要以网上开源的一个版本讲解：https://github.com/xmojiao/deeplab_v2 .
主要的步骤可以参考：
1. 图像语义分割：从头开始训练deeplab v2系列之一【源码解析】
2. 图像语义分割：从头开始训练deeplab v2系列之二【VOC2012数据集】
3. Deeplab v2 调试全过程（Ubuntu 16.04+cuda8.0）

下面说一些这上面没有的我遇到的一些问题。
1. 安装 matio:
上面资料中都使用 matio-1.5.2.tar.gz ，但是我装不上，可能与我的库不兼容，于是我下载了最新的 matio-1.5.11 ，并且按照如下命令运行安装：

cd matio-1.5.11 ./configure --prefix=/data1/... (填写自己的安装目录)make make check （可略）make install 最后在bash.rc上加上 LD_LIBRARY_PATH=/your/path/to/libmatio.so.2

参考：http://blog.csdn.net/houqiqi/article/details/46469981
2. 使用的Caffe版本比较陈旧，导致会出现很多和最新环境不兼容的情况，我使用的是cuDNN6.0,cuda8.0
出现：

./include/caffe/util/cudnn.hpp: In function ‘void caffe::cudnn::createPoolingDesc(cudnnPoolingStruct**, caffe::PoolingParameter_PoolMethod, cudnnPoolingMode_t*, int, int, int, int, int, int)’:./include/caffe/util/cudnn.hpp:127:41: error: too few arguments to function ‘cudnnStatus_t cudnnSetPooling2dDescriptor(cudnnPoolingDescriptor_t, cudnnPoolingMode_t, cudnnNanPropagation_t, int, int, int, int, int, int)’         pad_h, pad_w, stride_h, stride_w));

这是由于所使用的cuDNN版本不一致的导致的，作者配置环境是cuDNN 4.0，但是5.0版本后的cuDNN接口有所变化。

解决方法：将以下几个文件用最新BVLC版本的caffe对应文件替换并重新编译

./include/caffe/util/cudnn.hpp./include/caffe/layers/cudnn_conv_layer.hpp./include/caffe/layers/cudnn_relu_layer.hpp./include/caffe/layers/cudnn_sigmoid_layer.hpp./include/caffe/layers/cudnn_tanh_layer.hpp./src/caffe/layers/cudnn_conv_layer.cpp./src/caffe/layers/cudnn_conv_layer.cu./src/caffe/layers/cudnn_relu_layer.cpp./src/caffe/layers/cudnn_relu_layer.cu./src/caffe/layers/cudnn_sigmoid_layer.cpp./src/caffe/layers/cudnn_sigmoid_layer.cu./src/caffe/layers/cudnn_tanh_layer.cpp./src/caffe/layers/cudnn_tanh_layer.cu

参考：http://blog.csdn.net/tianrolin/article/details/71246472
3. 如何解决deeplab v2识别结果为全黑图像的问题？
正如作者而言：http://liangchiehchen.com/projects/DeepLab_FAQ.html

Q: When evaluating the DeepLab outputs (without CRF), I got all-background results (i.e., all black results). Is there anything wrong?A: Please double check if the name of your fc8 is fc8_voc12 in the generated test_val.prototxt or test_test.prototxt (after running run_pascal.sh). The name should be matched for initialization.

主要是作者预训练的模型与你实际测试的模型出现了偏差。主要是fc8 的问题。
以https://github.com/xmojiao/deeplab_v2 为例，如果直接使用此代码，则run_pascal.sh出现在voc12目录下，而run_pascal.sh上EXP2=. ，这与官方的预设不同。官方代码，假定run_pascal.sh应该出现在voc12上一级目录下。这样最后测试时会出现fc8_voc12_1,fc8_voc12_2,fc8_voc12_3,fc8_voc12_4被忽略的情况。

另外由于测试时需要将test.prototxt复制为test_val.prototxt，所以应该主要修改test.prototxt。
test.prototxt改动如下

layer {  name: "data"  type: "ImageSegData"  top: "data"  top: "label"  top: "data_dim"  include {    phase: TEST  }  transform_param {    mirror: false    crop_size: 513    mean_value: 104.008    mean_value: 116.669    mean_value: 122.675  }  image_data_param {    root_folder: "${DATA_ROOT}"    source: "../${EXP}/list/${TEST_SET}.txt"  （改变）    batch_size: 1    label_type: NONE  }}

run_pascal.sh改变如下

#!/bin/sh## MODIFY PATH for YOUR SETTINGROOT_DIR=/data1/caiyong.wang/data/deeplab_dataCAFFE_DIR=../deeplab-public-ver2CAFFE_BIN=${CAFFE_DIR}/.build_release/tools/caffe.binEXP=voc12  #适应原始训练好的模型目录  （改变）EXP2=.   #当前目录下               （改变）if [ "${EXP2}" = "." ]; then    NUM_LABELS=21    DATA_ROOT=${ROOT_DIR}/VOC_aug/dataset/else    NUM_LABELS=0    echo "Wrong EXP name"fi## Specify which model to train########### voc12 ################NET_ID=deeplab_largeFOV## Variables used for weakly or semi-supervisedly training#TRAIN_SET_SUFFIX=TRAIN_SET_SUFFIX=_aug#TRAIN_SET_STRONG=train#TRAIN_SET_STRONG=train200#TRAIN_SET_STRONG=train500#TRAIN_SET_STRONG=train1000#TRAIN_SET_STRONG=train750#TRAIN_SET_WEAK_LEN=5000DEV_ID=0####### Create dirsCONFIG_DIR=${EXP2}/config/${NET_ID}MODEL_DIR=${EXP2}/model/${NET_ID}mkdir -p ${MODEL_DIR}LOG_DIR=${EXP2}/log/${NET_ID}mkdir -p ${LOG_DIR}export  GLOG_log_dir=${LOG_DIR}## RunRUN_TRAIN=0RUN_TEST=1RUN_TRAIN2=0RUN_TEST2=0## Training #1 (on train_aug)if [ ${RUN_TRAIN} -eq 1 ]; then    #    LIST_DIR=${EXP2}/list    TRAIN_SET=train${TRAIN_SET_SUFFIX}    if [ -z ${TRAIN_SET_WEAK_LEN} ]; then                TRAIN_SET_WEAK=${TRAIN_SET}_diff_${TRAIN_SET_STRONG}                comm -3 ${LIST_DIR}/${TRAIN_SET}.txt ${LIST_DIR}/${TRAIN_SET_STRONG}.txt > ${LIST_DIR}/${TRAIN_SET_WEAK}.txt    else                TRAIN_SET_WEAK=${TRAIN_SET}_diff_${TRAIN_SET_STRONG}_head${TRAIN_SET_WEAK_LEN}                comm -3 ${LIST_DIR}/${TRAIN_SET}.txt ${LIST_DIR}/${TRAIN_SET_STRONG}.txt | head -n ${TRAIN_SET_WEAK_LEN} > ${LIST_DIR}/${TRAIN_SET_WEAK}.txt    fi    #    MODEL=${EXP2}/model/${NET_ID}/init.caffemodel    #    echo Training net ${EXP2}/${NET_ID}    for pname in train solver; do                sed "$(eval echo $(cat sub.sed))" \                        ${CONFIG_DIR}/${pname}.prototxt > ${CONFIG_DIR}/${pname}_${TRAIN_SET}.prototxt    done        CMD="${CAFFE_BIN} train \         --solver=${CONFIG_DIR}/solver_${TRAIN_SET}.prototxt \         --gpu=${DEV_ID}"        if [ -f ${MODEL} ]; then                CMD="${CMD} --weights=${MODEL}"        fi        echo Running ${CMD} && ${CMD}fi## Test #1 specification (on val or test)if [ ${RUN_TEST} -eq 1 ]; then    #    for TEST_SET in val; do                TEST_ITER=`cat ${EXP2}/list/${TEST_SET}.txt | wc -l`                MODEL=${EXP2}/model/${NET_ID}/test.caffemodel                if [ ! -f ${MODEL} ]; then                        MODEL=`ls -t ${EXP2}/model/${NET_ID}/train_iter_*.caffemodel | head -n 1`                fi                #                echo Testing net ${EXP2}/${NET_ID}                FEATURE_DIR=${EXP2}/features/${NET_ID}                mkdir -p ${FEATURE_DIR}/${TEST_SET}/fc8        mkdir -p ${FEATURE_DIR}/${TEST_SET}/fc9                mkdir -p ${FEATURE_DIR}/${TEST_SET}/seg_score                sed "$(eval echo $(cat sub.sed))" \                        ${CONFIG_DIR}/test.prototxt > ${CONFIG_DIR}/test_${TEST_SET}.prototxt                CMD="${CAFFE_BIN} test \             --model=${CONFIG_DIR}/test_${TEST_SET}.prototxt \             --weights=${MODEL} \             --gpu=${DEV_ID} \             --iterations=${TEST_ITER}"                echo Running ${CMD} && ${CMD}    donefi## Training #2 (finetune on trainval_aug)if [ ${RUN_TRAIN2} -eq 1 ]; then    #    LIST_DIR=${EXP2}/list    TRAIN_SET=trainval${TRAIN_SET_SUFFIX}    if [ -z ${TRAIN_SET_WEAK_LEN} ]; then                TRAIN_SET_WEAK=${TRAIN_SET}_diff_${TRAIN_SET_STRONG}                comm -3 ${LIST_DIR}/${TRAIN_SET}.txt ${LIST_DIR}/${TRAIN_SET_STRONG}.txt > ${LIST_DIR}/${TRAIN_SET_WEAK}.txt    else                TRAIN_SET_WEAK=${TRAIN_SET}_diff_${TRAIN_SET_STRONG}_head${TRAIN_SET_WEAK_LEN}                comm -3 ${LIST_DIR}/${TRAIN_SET}.txt ${LIST_DIR}/${TRAIN_SET_STRONG}.txt | head -n ${TRAIN_SET_WEAK_LEN} > ${LIST_DIR}/${TRAIN_SET_WEAK}.txt    fi    #    MODEL=${EXP2}/model/${NET_ID}/init2.caffemodel    if [ ! -f ${MODEL} ]; then                MODEL=`ls -t ${EXP2}/model/${NET_ID}/train_iter_*.caffemodel | head -n 1`    fi    #    echo Training2 net ${EXP2}/${NET_ID}    for pname in train solver2; do                sed "$(eval echo $(cat sub.sed))" \                        ${CONFIG_DIR}/${pname}.prototxt > ${CONFIG_DIR}/${pname}_${TRAIN_SET}.prototxt    done    CMD="${CAFFE_BIN} train \         --solver=${CONFIG_DIR}/solver2_${TRAIN_SET}.prototxt \         --weights=${MODEL} \         --gpu=${DEV_ID}"        echo Running ${CMD} && ${CMD}fi## Test #2 on official test setif [ ${RUN_TEST2} -eq 1 ]; then    #    for TEST_SET in val test; do                TEST_ITER=`cat ${EXP2}/list/${TEST_SET}.txt | wc -l`                MODEL=${EXP2}/model/${NET_ID}/test2.caffemodel                if [ ! -f ${MODEL} ]; then                        MODEL=`ls -t ${EXP2}/model/${NET_ID}/train2_iter_*.caffemodel | head -n 1`                fi                #                echo Testing2 net ${EXP2}/${NET_ID}                FEATURE_DIR=${EXP2}/features2/${NET_ID}                mkdir -p ${FEATURE_DIR}/${TEST_SET}/fc8                mkdir -p ${FEATURE_DIR}/${TEST_SET}/crf                sed "$(eval echo $(cat sub.sed))" \                        ${CONFIG_DIR}/test.prototxt > ${CONFIG_DIR}/test_${TEST_SET}.prototxt                CMD="${CAFFE_BIN} test \             --model=${CONFIG_DIR}/test_${TEST_SET}.prototxt \             --weights=${MODEL} \             --gpu=${DEV_ID} \             --iterations=${TEST_ITER}"                echo Running ${CMD} && ${CMD}    donefi

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