Awesome

[Home] [Project] [Paper] [Supp] [Poster] [Presentation]

PyTorch Implementation

The pytorch branch contains:

• the pytorch implementation of Peak Response Mapping (Stimulation and Backprop).
• the PASCAL-VOC demo (training, inference, and visualization).

Please follow the instruction below to install it and run the experiment demo.

Prerequisites

• System (tested on Ubuntu 14.04LTS and Win10)

• NVIDIA GPU + CUDA CuDNN (CPU mode is also supported but significantly slower)

• Python>=3.5

• PyTorch>=0.4

• Jupyter Notebook and ipywidgets (required by the demo):

  # enable the widgetsnbextension before you start the notebook server
  jupyter nbextension enable --py --sys-prefix widgetsnbextension
  

Installation

1. Install Nest, a flexible tool for building and sharing deep learning modules:

   I created Nest in the process of refactoring PRM's pytorch implementation. It aims at encouraging code reuse and ships with a bunch of useful features. PRM is now implemented as a set of Nest modules; thus you can easily install and use it as demonstrated below.

   $ pip install git+https://github.com/ZhouYanzhao/Nest.git
   

2. Install PRM via Nest's CLI tool:

   # note that data will be saved under your current path
   $ nest module install github@ZhouYanzhao/PRM:pytorch prm
   # verify the installation
   $ nest module list --filter prm
   # Output:
   #
   # 3 Nest modules found.
   # [0] prm.fc_resnet50 (1.0.0)
   # [1] prm.peak_response_mapping (1.0.0)
   # [2] prm.prm_visualize (1.0.0)
   

Run demo

1. Install Nest's build-in Pytorch modules:

   To increase reusability, I abstracted some features from the original code, such as network trainer, to build Nest's built-in pytorch module set.

   $ nest module install github@ZhouYanzhao/Nest:pytorch pytorch
   

2. Download the PASCAL-VOC2012 dataset:

   mkdir ./PRM/demo/datasets
   cd ./PRM/demo/datasets
   # download and extract data
   wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
   tar xvf VOCtrainval_11-May-2012.tar
   

3. Run the demo experiment via demo/main.ipynb

   

Citation

If you find the code useful for your research, please cite:

@INPROCEEDINGS{Zhou2018PRM,
    author = {Zhou, Yanzhao and Zhu, Yi and Ye, Qixiang and Qiu, Qiang and Jiao, Jianbin},
    title = {Weakly Supervised Instance Segmentation using Class Peak Response},
    booktitle = {CVPR},
    year = {2018}
}