Deeplab Operating Guide

The following is only a summary, referring to the many online information, only forget. main link Deeplab home: http://liangchiehchen.com/projects/DeepLab.html Official code: https://bitbucket.org/aquariusjay/ Deeplab-public-ver2 Python version Caffe implementation: HTTPS://GITHUB.COM/THELEGENDALI/DEEPLAB-CONTEXT2 model download:/HTTP liangchiehchen.com/projects/deeplab_models.html DEEPLABV2_VGG16 Pre-training model DEEPLABV2_RESNET101 pre-training model Pytorch implementation of deeplab:https://github.com/isht7/pytorch-deeplab-resnet online open source code: Martinkersner/train-deeplab Main operating steps

Below we mainly in a version of online open source explanation: Https://github.com/xmojiao/deeplab_v2.
The main steps can be consulted:
1. Image Semantic Segmentation: training from the beginning Deeplab V2 series "Source parsing"
2. Image Semantic Segmentation: Training Deeplab V2 Series II "VOC2012 datasets" from the beginning
3. Deeplab V2 Commissioning process (Ubuntu 16.04+cuda8.0)

Here are some of the problems that I have encountered that have not been mentioned above.
1. Install Matio:
Matio-1.5.2.tar.gz is used in the above data, but I can't install it, it may not be compatible with my library, so I downloaded the latest matio-1.5.11 and ran the installation as follows:

CD matio-1.5.11 
./configure--prefix=/data1/... (Fill in your own installation directory)
Make make 
check (can be slightly) make
install 

finally add ld_library_path=/your/path/to/libmatio.so.2 on bash.rc

Reference: http://blog.csdn.net/houqiqi/article/details/46469981
2. The use of Caffe version of the older, resulting in a lot of incompatible with the latest environment, I use cudnn6.0,cuda8.0
Appear:

./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);

This is due to inconsistencies in the CUDNN version used, the author configuration environment is CUDNN 4.0, but the CUDNN interface after the 5.0 version has changed.

Workaround: Replace and recompile the following files with the latest BVLC version of the caffe corresponding file

./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

Reference: http://blog.csdn.net/tianrolin/article/details/71246472
3. How to solve the problem of Deeplab v2 recognition results for All black images.
As the author says: 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 (afte R running run_pascal.sh). The name should is matched for initialization.

The author's pre-trained model is largely biased against the model you actually tested. The main problem is FC8.
In Https://github.com/xmojiao/deeplab_v2, for example, if you use this code directly, run_pascal.sh appears in the VOC12 directory and exp2= on run_pascal.sh. , which is different from the official presets. Official code, assuming that the run_pascal.sh should appear in the VOC12 level directory. This will cause the fc8_voc12_1,fc8_voc12_2,fc8_voc12_3,fc8_voc12_4 to be ignored when the final test is in progress.

In addition, because the test needs to copy Test.prototxt to Test_val.prototxt, it should be mainly modified test.prototxt.
Test.prototxt changes as follows

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 "  (change)
    batch_size:1
    label_type:none
  }
}

Run_pascal.sh changes as follows

#!/bin/sh # # MODIFY PATH for YOUR SETTING root_dir=/data1/caiyong.wang/data/deeplab_data caffe_dir=.   /deeplab-public-ver2 caffe_bin=${caffe_dir}/.build_release/tools/caffe.bin exp=voc12 #适应原始训练好的模型目录 (change) EXP2=. #当前目录下 (change) if ["${exp2}" = "."]; Then num_labels=21 data_root=${root_dir}/voc_aug/dataset/else num_labels=0 echo "wrong EXP name" FI # # Specify which model to train ########### voc12 ################ Net_id=deeplab_largefov # # Variables used for weakly or semi-supervisedly Training #TRAIN_SET_SUFFIX = Train_set_suffix=_aug #TRAIN_SET_STRONG =train #TRAIN_SET_STRONG = train200 #TRAIN_SET_STRONG =train500 #TRAIN_SET_STRONG =train1000 #TRAIN_SET_STRONG =train750 #TRAIN_SET_WEAK_LEN = Dev_id=0 ##### # # Create dirs config_dir=${exp2}/config/${net_id} model_dir=${exp2}/model/${net_id} mkdir-p ${MO 
Del_dir} log_dir=${exp2}/log/${net_id} mkdir-p ${log_dir} export Glog_log_dir=${log_dir} # # Run run_train=0 RUN_TEST=1 Run_train2=0 run_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.c Affemodel # echo Training net ${exp2}/${net_id} for pname in train solver; Do sed "$ (eval echo $ (cat sub.sed)" \ ${config_dir}/${pname}.prototxt > ${conf Ig_dir}/${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=${dev_id} \--it Erations=${test_iter} "Echo Running ${cmd} && ${cmd} done fi # Training #2 (Finetune on Trai Nval_aug) if [${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 > ${conf Ig_dir}/${pname}_${train_set}.prototxt done cmd= "${caffe_bin} TRAIN \--solver=${config_dir}/solver2_${tr
Ain_set}.prototxt \--weights=${model} \--gpu=${dev_id} "Echo Running ${cmd} && ${cmd} Fi # # Test #2 on official test set if [${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 ${feature_di  R}/${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=${dev_id} \--iterations=${t
 Est_iter} "Echo Running ${cmd} && ${cmd} done fi