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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.
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System (tested on Ubuntu 14.04LTS and Win10)
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NVIDIA GPU + CUDA CuDNN (CPU mode is also supported but significantly slower)
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Jupyter Notebook and ipywidgets (required by the demo):
# enable the widgetsnbextension before you start the notebook server jupyter nbextension enable --py --sys-prefix widgetsnbextension
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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
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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)
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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
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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
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Run the demo experiment via demo/main.ipynb
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}
}