Pyramid Scene Parsing Network

support CUDA 8.0 and cuDNN v.5

by Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, Jiaya Jia, details are in project page.

Introduction

This repository is for 'Pyramid Scene Parsing Network', which ranked 1st place in ImageNet Scene Parsing Challenge 2016. The code is modified from hszhao and DeepLab v2 which supports cuDNN v.5.

un-updated: docs/ examples/

Installation

For installation, please follow the instructions of Caffe and DeepLab v2. To enable cuDNN for GPU acceleration, cuDNN v.5 is needed. If you meet error related with 'matio', please download and install matio as required in 'DeepLab v2'.

The code has been installed and runtested successfully on Ubuntu 16.04 with CUDA 8.0 / cudnn v.5.

Here is the installation step-by-step:

1. Clone the repository:

   git clone https://github.com/BassyKuo/PSPNET-cudnn5.git

2. Build Caffe:

   You can build the repository by cmake or makefile.

   • Using Cmake:

   cd $PSPNET_DIR
   mkdir build; cd build
   cmake .. -DBLAS=Open
   make all install -j32

   • Using Makefile:

   cd $PSPNET_DIR
   cp Makefile.config.example Makefile.config
   vim Makefile.config 
   # uncomment configures which you need, then save it.
   
   make all -j32

3. Check if the installation is successful or not:

   # For python interface, install pycaffe and add the PSPNET path to `PYTHONPATH`
   make pycaffe
   export PYTHONPATH=${PSPNET_DIR}/python:${PYTHONPATH}
   
   # Test the package installed or not
   $ python -c "import caffe; print caffe.__version__"
   1.0.0-rc3

4. Build Matlab-caffe / Octave-caffe:

   vim Makefile.config
   # uncomment `MATLAB_DIR := /usr/local`
   
   make matcaffe

   if you use octave instead of matlab, only need to build with CMake since the Makefile build only supports MATLAB: (see https://github.com/BVLC/caffe/issues/4401)

   mkdir build; cd build
   cmake .. -DBLAS:STRING=Open -DBUILD_matlab=ON
   make all install -j32
   make octave

Evaluation

The author provides Matlab code to evaluate this framework. Before using evaluation, you should download dataset and caffemodel first.

• Download dataset

  In this repository, you can use ADE20K, VOC2012 and cityscapes dataset for evaluation. Please check your dataset path, and modify the data_root in eval_all.m

  For example,

  $ vim evaluation/eval_all.m
  16 -- data_root = '/data2/hszhao/dataset/ADEChallengeData2016';
  16 ++ data_root = '/data/ADEChallengeData2016';

  And copy list files from samplelist to dataset directory, for example:

  $ cd evaluation
  $ cp samplelist/ADE20K_val.txt /data/ADEChallengeData2016/list/

  ---

  [NOTICE]

  Make sure the ground truth exist in your dataset path. For example (fetch from eval_acc.m):

  ⭕ Correct

  >> data_root = '/data/ADEChallengeData2016';
  >> eval_list = 'list/ADE20K_val.txt';
  >> list = importdata(fullfile(data_root,eval_list))
  list =
     2000×1 cell array
      'images/validation/ADE_val_00000001.jpg annotations/validation/ADE_val_00000001.png'
     ...
  
  >> str = strsplit(list{1})
  str =
     1×2 cell array
      'images/validation/ADE_val_00000001.jpg'    'annotations/validation/ADE_val_00000001.png'
  
  >> fileAnno = fullfile(pathAnno, str{2})
  fileAnno =
      '/data/ADEChallengeData2016/annotations/validation/ADE_val_00000001.png'
      
  % '/data/ADEChallengeData2016/annotations/validation/ADE_val_00000001.png' is the 
  % segmentation ground truth image for '/data/ADEChallengeData2016/images/validation/ADE_val_00000001.jpg'

  ❌ Wrong

  >> data_root = '/data/VOC2012';                
  >> eval_list = 'list/VOC2012_test.txt';
  >> list = importdata(fullfile(data_root,eval_list))
  list =
     1456×1 cell array
      '/JPEGImages/2008_000006.jpg'
      '/JPEGImages/2008_000011.jpg'
     ...
  >> str = strsplit(list{1})
  str =
     cell
      '/JPEGImages/2008_000006.jpg'
  >> fileAnno = fullfile(pathAnno, str{2})
  Index exceeds matrix dimensions.
  
  % Fail to find `str{2}` because `str` only has 1 cell.
  % In this case, you should not use 'list/VOC2012_test.txt' but 'list/VOC2012_train.txt' or 'list/VOC2012_val.txt'
  % generated yourself.

• Download caffemodels

  If you have gdrive, you can download caffemodels in the evaluation/model/ folder as below:

  $ cd evaluation/model
  $ gdrive download 0BzaU285cX7TCT1M3TmNfNjlUeEU
  $ gdrive download 0BzaU285cX7TCNVhETE5vVUdMYk0
  $ gdrive download 0BzaU285cX7TCN1R3QnUwQ0hoMTA

  or download them by WEB console:

  • pspnet50_ADE20K.caffemodel
  • pspnet101_VOC2012.caffemodel
  • pspnet101_cityscapes.caffemodel

Other options for installation

1. If you do not have GPU, you can build the repository with CPU_ONLY. For example by using cmake,

   cd $PSPNET_DIR
   mkdir build; cd build
   cmake .. -DCPU_ONLY=ON
   make all install -j32

   Or using Makefile: uncomment the line CPU_ONLY and comment USE_CUDNN, then make it.

   However, it causes an error: cannot not find <cublas_v2.h> because interp.hpp dependent on cublas_v2.h file. You should add the cuda library path in your makefile, or modify the file as below:

   // in include/caffe/util/interp.hpp
   -- #include <cublas_v2.h> 
   ++ #include </usr/local/cuda/include/cublas_v2.h> 

2. If you use octave rather than matlab, like me, build the repository by cmake wih argument -DBUILD_matlab=ON, and use make octave. You will get caffe_.mex in your matlab/+caffe/private/. (more information about mex-file you can see here)

   Move to evaluation folder, and run run_octave.sh. It seems that there are some errors called from empty in octave, like this:

   error: no such method or property `empty'
   error: called from
    	Net at line 43 column 22
    	get_net at line 28 column 5
    	Net at line 31 column 14
    	eval_sub at line 26 column 5
    	eval_all at line 73 column 3

   I have not fixed the error yet. If you do, you can make a PR to help this part to be complete.

Errors & Solutions

Here are some method to solve the problems occurred during building.

1. Errors raised during the make all install step, check here to find solutions.

2. Errors raised when building matcaffe with Matlab, please check:

   • GCC/G++ version problem

3. protobuf error:

   • If the gcc/g++ version later than 5, you should upgrade libprotobuf. Please check here see how to reinstall protobuf with the new complier.

4. 'MAT' error:

   • If the error message shown as undefined reference to Mat_XXXX, for example:

   make[1]: *** [examples/CMakeFiles/convert_mnist_siamese_data.dir/all] Error 2
   make[1]: *** Waiting for unfinished jobs....
   ../lib/libcaffe.so.1.0.0-rc3: undefined reference to `Mat_VarCreate'
   ../lib/libcaffe.so.1.0.0-rc3: undefined reference to `Mat_CreateVer'
   ../lib/libcaffe.so.1.0.0-rc3: undefined reference to `Mat_VarWrite'
   ../lib/libcaffe.so.1.0.0-rc3: undefined reference to `Mat_VarFree'
   ../lib/libcaffe.so.1.0.0-rc3: undefined reference to `Mat_VarReadInfo'
   ../lib/libcaffe.so.1.0.0-rc3: undefined reference to `Mat_Close'
   ../lib/libcaffe.so.1.0.0-rc3: undefined reference to `Mat_VarReadDataLinear'
   ../lib/libcaffe.so.1.0.0-rc3: undefined reference to `Mat_Open'
   

   you can check here to solve the problem.

If you need any further of my help, you're always welcome to open an issue.

Thank you :)

Citation

If PSPNet is useful for your research, please consider citing:

@inproceedings{zhao2017pspnet,
  author = {Hengshuang Zhao and
            Jianping Shi and
            Xiaojuan Qi and
            Xiaogang Wang and
            Jiaya Jia},
  title = {Pyramid Scene Parsing Network},
  booktitle = {Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2017}
}