专栏首页计算机视觉战队Deeplab v2 安装及调试全过程

Deeplab v2 安装及调试全过程

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:

将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:

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


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:

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。

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

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:

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:

Test:

将相应变量改为1:

~/Desktop/deeplab_v2/voc2012

sh ./run_pascal.sh

运行结果如下:

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


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

本文分享自微信公众号 - 计算机视觉战队(ComputerVisionGzq)

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原始发表时间:2018-03-26

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