Deeplab v2 安装及调试全过程

书籍教程 Admin ⋅ 于 3个月前 ⋅ 524 阅读

原创: Edison
来源:计算机视觉战队@微信公众号


上期为大家带来的是从FCN到DeepLab V2的一些相关知识,今天我们就来和大家分享一些DeepLab V2的安装及调试全过程,希望可以为一些需要的科研小伙伴带来一丝丝帮助,请继续欣赏下去。把Deeplabv2的 run_pascal.sh与run_densecrf.sh成功运行,现将调试过程整理如下:

首先,安装Caffe、Ubuntu 16.04+cuda8.0等环境应该不需要再次详细说了吧,如果有不清楚的小伙伴,进点击下面的链接,也是计算机视觉平台之前推送的,可以简单方便的进行安装。

链接:Caffe(含GPU)安装与测试

一、安装必要的依赖库

安装 matio:

安装方法1: sudo apt-get install libmatio-dev

安装方法2: 下载matio (https://sourceforge.net/projects/matio/files/matio/1.5.2/)

tar zxf matio-1.5.2.tar.gz

cd matio-1.5.2  

./configure  

make  

make check  

make install  

sudo ldconfig

安装 wget

sudo pip install wget

如果出错,就按照下面的命令成功:

pip install –upgrade pip –user  

pip install –upgrade setuptools –user  

sudo pip install wget

二、下载Deeplabv2并编译

下载代码:

git clone https://github.com/xmojiao/deeplab_v2.git

(试过许多Deeplab代码,这个最容易编译成功,所以我用的是这个代码编译的)

对 caffe 进行编译:

修改deeplab_v2/deeplab-public-ver2/路径下的Makefile.config.example文件,重命名为Makefile.config

接着修改这个文件中的内容,将第四行的 “# USE_CUDNN := 1”的 # 去掉。如果需要,因为我用的pycaffe编译,所以不需要修改python的路径,保存退出。

编译 caffe的命令:

cd ~/Desktop/deeplab_v2/deeplab-public-ver2

make all -j16

如果出现下面的错误1:

src/caffe/net.cpp:8:18: fatal error: hdf5.h: No such file or directory compilation terminated.

解决办法: 修改两个make文件(Makefile.config,Makefile

Makefile.config:

INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include  

LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib

修改为:

INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial

LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib/x86_64-linux-gnumake

Makefile:

LIBRARIES += glog gflags protobuf boost_system boost_filesystem m hdf5_hl hdf5

修改为:

LIBRARIES += glog gflags protobuf boost_system boost_filesystem m hdf5_serial_hl hdf5_serial matio

重新编译:

make all -j16

如果出现下面的错误2:

./include/caffe/common.cuh(9): error: function “atomicAdd(double *, double)” has already been defined

解决方法:

打开./include/caffe/common.cuh文件,在atomicAdd前添加宏判断即可。 下面为修改后文件:

 // Copyright 2014 George Papandreou
    #ifndef CAFFE_COMMON_CUH_
    #define CAFFE_COMMON_CUH_
    #include <cuda.h>
    // CUDA: atomicAdd is not defined for doubles
    #if !defined(__CUDA_ARCH__) || __CUDA_ARCH__ >= 600 
    #else
    static __inline__ __device__ double atomicAdd(double *address, double val) {
      unsigned long long int* address_as_ull = (unsigned long long int*)address;
      unsigned long long int old = *address_as_ull, assumed;      if (val==0.0)        return __longlong_as_double(old);      do {
        assumed = old;
        old = atomicCAS(address_as_ull, assumed, __double_as_longlong(val +__longlong_as_double(assumed)));
      } while (assumed != old);      return __longlong_as_double(old);
    }    #endif
    #endif

继续编译:

make all -j16

如果出现下面的错误3:

:.build_release/lib/libcaffe.so:undefined reference to cudnnConvolutionBackwardFilter_v3’

解决方法:

BVLC(https://github.com/BVLC/caffe)中的下列文件copy 到相应的文件夹:

./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

然后:

make clean  

make all -j16  

make pycaffe -j16  

这个时候一般都是编译成功。

三、对 run_pascal.sh 进行调试:

首先准备好数据

(参考: http://blog.csdn.net/Xmo_jiao/article/details/77897109

cd ~/Desktop  

mkdir -p my_dataset  

# augmented PASCAL VOC  

cd my_dataset/  

wget http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/semantic_contours/benchmark.tgz 

# 1.3 GB 

tar -zxvf benchmark.tgz  

mv benchmark_RELEASE VOC_aug  

# original PASCAL VOC 2012  

wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar 

# 2 GB  

tar -xvf VOCtrainval_11-May-2012.tar  

mv VOCdevkit/VOC2012 VOC2012_orig && rm -r VOCdevkit

数据转换

因为pascal voc2012增强数据集的label是mat格式的文件,要把mat格式的label转为png格式的图片

~/Desktop/my_dataset/VOC_aug/dataset  

mkdir cls_png  

cd ~/Desktop/deeplab_v2/voc2012/  

./mat2png.py ~/Desktop/my_dataset/VOC_aug/dataset/cls/Desktop/my_dataset/VOC_aug/dataset/cls_png

因为pascal voc2012原始数据集的label为三通道RGB图像,但是caffe最后一层softmax loss层只能识别一通道的label,所以此处我们需要对原始数据集的label进行降维

cd ~/Desktop/my_dataset/VOC2012_orig  mkdir SegmentationClass_1D  

cd ~/Desktop/deeplab_v2/voc2012/  

./convert_labels.py ~/Desktop/my_dataset/VOC2012_orig/SegmentationClass/ ~/Desktop/my_dataset  /VOC2012_orig/ImageSets/Segmentation/trainval.txt ~/Desktop/my_dataset/VOC2012_orig/Segmentat  ionClass_1D/

数据融合

此时已经处理好好pascal voc2012 增强数据集和pascal voc2012的原始数据集,为了便于train.txt等文件的调用,将两个文件夹数据合并到同一个文件中.现有文件目录如下:
file

file

现分别pascal voc2012增强数据集里的images和labels复制到增强数据集中,若重复则覆盖,合将并数据集的操作如下:

cp ~/Desktop/my_dataset/VOC2012_orig/SegmentationClass_1D/* ~/Desktop/my_dataset/VOC_aug/dataset/cls_png  

cp ~/Desktop/my_dataset/VOC2012_orig/JPEGImages/* ~/Desktop/my_dataset/VOC_aug/dataset/img/

文件名修改

对应train.txt文件的数据集文件名,修改文件名。

cd ~/Desktop/my_dataset/VOC_aug/dataset  

mv ./img ./JPEGImages 

那么我们这个阶段使用的数据已经整理完成

file

四、修改并运行 run_pascal.sh

准备必要的文件 需要的文件从这里下载 deeplabv2 有两种模型(vgg,Res102),vgg ,http://liangchiehchen.com/projects/DeepLab_Models.html

总共需要的文件如图所示:

file

下载的代码中 Desktop/deeplab_v2/voc2012/list 已经有了list文件,所以不用重新下载。

file

file

/Desktop/deeplab_v2/voc2012/config/deeplab_largeFOV中也有了相应的文件,所以也无需下载。

file

Desktop/deeplab_v2/voc2012/model/deeplab_largeFOV 里没有model,需要把下载好的model放入文件,如图所示:

file

至此,所有需要的文件全部完毕。

五、运行 train 和 test

进入/Desktop/deeplab_v2/voc2012,修改 run_pascal.sh 文件,主要是修改路径,我的修改后的文件如下:

#!/bin/sh## MODIFY PATH for YOUR SETTINGROOT_DIR=/home/mmt/Desktop/my_dataset
CAFFE_DIR=/home/mmt/Desktop/deeplab_v2/deeplab-public-ver2
CAFFE_BIN=${CAFFE_DIR}/build/tools/caffe.bin
EXP=.if [ "${EXP}" = "." ]; then
    NUM_LABELS=21
    DATA_ROOT=${ROOT_DIR}/VOC_aug/dataset/else
    NUM_LABELS=0
    echo "Wrong exp name"fi## Specify which model to train########### voc12 ################NET_ID=deeplab_largeFOV## Variables used for weakly or semi-supervisedly training#TRAIN_SET_SUFFIX=TRAIN_SET_SUFFIX=_aug#TRAIN_SET_STRONG=train#TRAIN_SET_STRONG=train200#TRAIN_SET_STRONG=train500#TRAIN_SET_STRONG=train1000#TRAIN_SET_STRONG=train750#TRAIN_SET_WEAK_LEN=5000DEV_ID=0####### Create dirsCONFIG_DIR=${EXP}/config/${NET_ID}MODEL_DIR=${EXP}/model/${NET_ID}mkdir -p ${MODEL_DIR}LOG_DIR=${EXP}/log/${NET_ID}mkdir -p ${LOG_DIR}export GLOG_log_dir=${LOG_DIR}## RunRUN_TRAIN=1   #1时trainRUN_TEST=0    #1时testRUN_TRAIN2=0RUN_TEST2=0## Training #1 (on train_aug)if [ ${RUN_TRAIN} -eq 1 ]; then
    #
    LIST_DIR=${EXP}/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=${EXP}/model/${NET_ID}/init.caffemodel    #
    echo Training net ${EXP}/${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 ${EXP}/list/${TEST_SET}.txt | wc -l`
                MODEL=${EXP}/model/${NET_ID}/test.caffemodel                if [ ! -f ${MODEL} ]; then
                        MODEL=`ls -t ${EXP}/model/${NET_ID}/train_iter_*.caffemodel | head -n 1`                fi
                #
                echo Testing net ${EXP}/${NET_ID}
                FEATURE_DIR=${EXP}/features/${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 [ ${RUN_TRAIN2} -eq 1 ]; then
    #
    LIST_DIR=${EXP}/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=${EXP}/model/${NET_ID}/init2.caffemodel    if [ ! -f ${MODEL} ]; then
                MODEL=`ls -t ${EXP}/model/${NET_ID}/train_iter_*.caffemodel | head -n 1`    fi
    #
    echo Training2 net ${EXP}/${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 ${EXP}/list/${TEST_SET}.txt | wc -l`
                MODEL=${EXP}/model/${NET_ID}/test2.caffemodel                if [ ! -f ${MODEL} ]; then
                        MODEL=`ls -t ${EXP}/model/${NET_ID}/train2_iter_*.caffemodel | head -n 1`                fi
                #
                echo Testing2 net ${EXP}/${NET_ID}
                FEATURE_DIR=${EXP}/features2/${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

接下来运行代码:

Train:

~/Desktop/deeplab_v2/voc2012  

sh ./run_pascal.sh  

运行结果如下:

file

Test:

将相应变量改为1:

~/Desktop/deeplab_v2/voc2012  

sh ./run_pascal.sh  

运行结果如下:

file

因为结果保存的是mat文件,如果想转换成png的形式,运行:

cd ~/Desktop/deeplab_v2/voc2012

修改create_labels_21.py的路径,然后此目录运行:

python create_labels_21.py

file

六、修改并运行 run_densecrf.sh

首先对densecrf进行编译。

cd ~/Desktop/deeplab_v2/deeplab-public-ver2/densecrf/ make

有许多warning,但是没出错,不用管。

数据整理

因为densecrf只识别ppm格式的图像,所以要转换格式。

进入/Desktop/deeplab_v2/deeplab-public-ver2/densecrf/my_script,里面有自带的修改ppm 的MATLAB程序,修改路径,直接运行即可。

代码如下:

% save jpg images as bin file for cpp%is_server = 1;
dataset = 'voc2012';  %'coco', 'voc2012'if is_server  if strcmp(dataset, 'voc2012')
    img_folder  = '/home/mmt/Desktop/my_dataset/VOC_aug/dataset/JPEGImages'
    save_folder = '/home/mmt/Desktop/my_dataset/VOC_aug/dataset/PPMImages';  elseif strcmp(dataset, 'coco')
    img_folder  = '/rmt/data/coco/JPEGImages';
    save_folder = '/rmt/data/coco/PPMImages';  endelse
  img_folder = '../img';
  save_folder = '../img_ppm';endif ~exist(save_folder, 'dir')
    mkdir(save_folder);endimg_dir = dir(fullfile(img_folder, '*.jpg'));for i = 1 : numel(img_dir)
    fprintf(1, 'processing %d (%d)...\n', i, numel(img_dir));
    img = imread(fullfile(img_folder, img_dir(i).name));

    img_fn = img_dir(i).name(1:end-4);
    save_fn = fullfile(save_folder, [img_fn, '.ppm']);
    imwrite(img, save_fn);   
end

接下来,修改 run_densecrf.sh, 注意把 MODEL_NAME=deeplab_largeFOV修改了。

DATASET=voc2012  修改;SAVE_DIR=/home/mmt/Desktop/deeplab_v2/${DATASET}/res/${FEATURE_NAME}/${MODEL_NAME}/${TEST_SET} 
修改;CRF_DIR=/home/mmt/Desktop/deeplab_v2/deeplab-public-ver2/densecrf  修改;if [ ${DATASET} == "voc2012" ]then
    IMG_DIR_NAME=VOC_aug/dataset     修改;FEATURE_DIR=/home/mmt/Desktop/deeplab_v2/${DATASET}/${FEATURE_NAME}/${MODEL_NAME}/${TEST_SET}/${FEATURE_TYPE}  修改;
同时把一些不需要的语句都注释掉,要不然容易出错,显示找不到文件。
修改后的文件如下:#!/bin/bash ############################################ You can either use this script to generate the DenseCRF post-processed results# or use the densecrf_layer (wrapper) in Caffe###########################################DATASET=voc2012LOAD_MAT_FILE=1MODEL_NAME=deeplab_largeFOVTEST_SET=val           #val, test# the features  folder save the features computed via the model trained with the train set# the features2 folder save the features computed via the model trained with the trainval setFEATURE_NAME=features #features, features2FEATURE_TYPE=fc8# specify the parametersMAX_ITER=10Bi_W=4Bi_X_STD=49Bi_Y_STD=49Bi_R_STD=5Bi_G_STD=5 Bi_B_STD=5POS_W=3POS_X_STD=3POS_Y_STD=3######################################## MODIFY THE PATY FOR YOUR SETTING#######################################SAVE_DIR=/home/mmt/Desktop/deeplab_v2/${DATASET}/res/${FEATURE_NAME}/${MODEL_NAME}/${TEST_SET}/${FEATURE_TYPE}/post_densecrf_W${Bi_W}_XStd${Bi_X_STD}_RStd${Bi_R_STD}_PosW${POS_W}_PosXStd${POS_X_STD}

echo "SAVE TO ${SAVE_DIR}"CRF_DIR=/home/mmt/Desktop/deeplab_v2/deeplab-public-ver2/densecrf#if [ ${DATASET} == "voc2012" ]#then
    IMG_DIR_NAME=VOC_aug/dataset#elif [ ${DATASET} == "coco" ]#then
 #   IMG_DIR_NAME=coco#elif [ ${DATASET} == "voc10_part" ]#then
  #  IMG_DIR_NAME=pascal/VOCdevkit/VOC2012#fi# NOTE THAT the densecrf code only loads ppm imagesIMG_DIR=/home/mmt/Desktop/my_dataset/${IMG_DIR_NAME}/PPMImages#if [ ${LOAD_MAT_FILE} == 1 ]#then
    # the features are saved in .mat format
    CRF_BIN=${CRF_DIR}/prog_refine_pascal_v4    FEATURE_DIR=/home/mmt/Desktop/deeplab_v2/${DATASET}/${FEATURE_NAME}/${MODEL_NAME}/${TEST_SET}/${FEATURE_TYPE}#else
    # the features are saved in .bin format (has called SaveMatAsBin.m in the densecrf/my_script)
   # CRF_BIN=${CRF_DIR}/prog_refine_pascal
   # FEATURE_DIR=/home/mmt/Desktop/deeplab_v2/${DATASET}/${FEATURE_NAME}/${MODEL_NAME}/${TEST_SET}/${FEATURE_TYPE}/bin#fimkdir -p ${SAVE_DIR}# run the program${CRF_BIN} -id ${IMG_DIR} -fd ${FEATURE_DIR} -sd ${SAVE_DIR} -i ${MAX_ITER} -px ${POS_X_STD} -py ${POS_Y_STD} -pw ${POS_W} -bx ${Bi_X_STD} -by ${Bi_Y_STD} -br ${Bi_R_STD} -bg ${Bi_G_STD} -bb ${Bi_B_STD} -bw ${Bi_W}

进入文件路径,运行程序,结果如下图:

cd ~/Desktop/deeplab_v2/voc2012/  

sh sh ./run_densecrf.sh

然后运行

/home/mmt/crf/deeplab-public-ver2/densecrf/my_script/GetDenseCRFResult.m

把bin生成图片格式

注意修改文件路径(GetDenseCRFResult.m,SetupEnv在/deeplab_v2/deeplab-public-ver2/matlab/my_script中)

两个程序的代码如下:

GetDenseCRFResult.m
% compute the densecrf result (.bin) to png
%

addpath('/home/mmt/Desktop/deeplab_v2/deeplab-public-ver2/matlab/my_script');SetupEnv;%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% You do not need to change values below%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%if is_server  if learn_crf
    post_folder = sprintf('post_densecrf_W%d_XStd%d_RStd%d_PosW%d_PosXStd%d_ModelType%d_Epoch%d', bi_w, bi_x_std, bi_r_std, pos_w, pos_x_std, model_type, epoch);

    map_folder = fullfile('/home/mmt/Desktop/deeplab_v2', dataset, 'densecrf', 'res', feature_name, model_name, testset, feature_type, post_folder); 

    save_root_folder = fullfile('/home/mmt/Desktop/deeplab_v2', dataset, 'res', feature_name, model_name, testset, feature_type, post_folder); ;  else
    post_folder = sprintf('post_densecrf_W%d_XStd%d_RStd%d_PosW%d_PosXStd%d', bi_w, bi_x_std, bi_r_std, pos_w, pos_x_std);

    map_folder = fullfile('/home/mmt/Desktop/deeplab_v2', dataset, 'res', feature_name, model_name, testset, feature_type, post_folder); 

    save_root_folder = map_folder;  endelse 
  map_folder = '../result';endmap_dir = dir(fullfile(map_folder, '*.bin'));

fprintf(1,' saving to %s\n', save_root_folder);if strcmp(dataset, 'voc2012')
  seg_res_dir = [save_root_folder '/results/VOC2012/'];
elseif strcmp(dataset, 'coco')
  seg_res_dir = [save_root_folder, '/results/COCO2014/'];else
  error('Wrong dataset!');endsave_result_folder = fullfile(seg_res_dir, 'Segmentation', [id '_' testset '_cls']);if ~exist(save_result_folder, 'dir')
    mkdir(save_result_folder);endfor i = 1 : numel(map_dir)
    fprintf(1, 'processing %d (%d)...\n', i, numel(map_dir));
    map = LoadBinFile(fullfile(map_folder, map_dir(i).name), 'int16');

    img_fn = map_dir(i).name(1:end-4);
    imwrite(uint8(map), colormap, fullfile(save_result_folder, [img_fn, '.png']));end

SetupEnv.m
% set up the environment variables
%

clear all; close all;
load('./pascal_seg_colormap.mat');

is_server       = 1;

crf_load_mat    = 1;   % the densecrf code load MAT files directly (no call SaveMatAsBin.m)
                       % used ONLY by DownSampleFeature.m
learn_crf       = 0;   % NOT USED. Set to 0is_mat          = 1;   % the results to be evaluated are saved as mat (1) or png (0)
has_postprocess = 0;   % has done densecrf post processing (1) or not (0)
is_argmax       = 0;   % the output has been taken argmax already (e.g., coco dataset). 
                       % assume the argmax takes C-convention (i.e., start from 0)

debug           = 0;   % if debug, show some results

% vgg128_noup (not optimized well), aka DeepLab% bi_w = 5, bi_x_std = 50, bi_r_std = 10% vgg128_ms_pool3, aka DeepLab-MSc% bi_w = 3, bi_x_std = 95, bi_r_std = 3% vgg128_noup_pool3_cocomix, aka DeepLab-COCO% bi_w = 5, bi_x_std = 67, bi_r_std = 3%% these are used for the bounding box weak annotation experiments (i.e., to generate the Bbox-Seg)
% erode_gt (bbox)
% bi_w = 41, bi_x_std = 33, bi_r_std = 4% erode_gt/bboxErode20% bi_w = 45, bi_x_std = 37, bi_r_std = 3, pos_w = 15, pos_x_std = 3%
% initial or default values for crf%% 这几个参数要修改与run_densecrf.sh中的一致。
bi_w           = 4; 
bi_x_std       = 49;
bi_r_std       = 5;

pos_w          = 3;
pos_x_std      = 3;

%dataset    = 'voc2012';  %'voc12', 'coco'  修改
trainset   = 'train_aug';      % not used
testset    = 'val';            %'val', 'test'model_name = 'deeplab_largeFOV';  % 修改

feature_name = 'features';
feature_type = 'fc8'; % fc8 / crf

id           = 'comp6';%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% used for cross-validation
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
rng(10)

% downsampling files for cross-validation
down_sample_method = 2;      % 1: equally sample with "down_sample_rate", 2: randomly pick "num_sample" samples
down_sample_rate   = 8;
num_sample         = 100;    % number of samples used for cross-validation

% ranges for cross-validation
range_pos_w = [3];
range_pos_x_std = [3];

range_bi_w = [5];
range_bi_x_std = [49];
range_bi_r_std = [4 5];

至此,deeplabv2 程序已调试完

file

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