Pytorch ROIPooling

Welcome!

This is a generic implementation of ROIpooling operation used in the context of object detection.

Feats

• Modularized

• JIT compilation with cupy

• Works well with batches of images 😉

Getting started

We need the following requirements cuda, pytorch==1.0.1, cupy=5.1.0 which we can get most of them from anaconda.org with trusted channels.

1. Install anaconda or miniconda.

   Skip this if you already have miniconda or anaconda installed in your system.

2. Create a new environment

   conda create -n pytorch-extensions python=3.7 pytorch cupy -c pytorch

   This step creates a conda environment called pytorch-extensions. In case, you change the name keep it mind to update the next lines accordingly.

3. conda activate pytorch-extensions

4. python example.py

   Hopefully everything runs like the breeze.

Can I use it in Colab?

Sure, take a look at this notebook. It provides a guide for the setup and usage of the roi_pooling Function.

LICENSE

MIT

We highly appreciate that you leave attribution notes when you copy portions of this codebase in yours.

Did you like it?

Support me, gimme a ⭐ in the github banner or invite me a ☕/🍺. If you are in academia, I would appreciate that you cite my research:

@article{EscorciaDJGS18,
  author    = {Victor Escorcia and
               Cuong Duc Dao and
               Mihir Jain and
               Bernard Ghanem and
               Cees Snoek},
  title     = {Guess Where? Actor-Supervision for Spatiotemporal Action Localization},
  journal   = {CoRR},
  volume    = {abs/1804.01824},
  year      = {2018},
  url       = {http://arxiv.org/abs/1804.01824},
  archivePrefix = {arXiv},
  eprint    = {1804.01824}
}

This implementation was built on top of the legendary Faster-RCNN which you must cite:

@article{RenHG017,
  author    = {Shaoqing Ren and
               Kaiming He and
               Ross B. Girshick and
               Jian Sun},
  title     = {Faster {R-CNN:} Towards Real-Time Object Detection with Region Proposal
               Networks},
  journal   = {{IEEE} Trans. Pattern Anal. Mach. Intell.},
  volume    = {39},
  number    = {6},
  pages     = {1137--1149},
  year      = {2017},
  url       = {https://doi.org/10.1109/TPAMI.2016.2577031},
  doi       = {10.1109/TPAMI.2016.2577031}
}

This was also possible due to Chainer, and the easy to follow pyinn.

FAQs

Do I need to buy an anaconda license?

Of course not! You do everything with virtual environments. Indeed, I would be pleased to accept a PR with a recipe for virtual environments.

Why anaconda?

In short, due to the last five letters.

Why another ROIpooling operation?

Well, I tried many C extensions mainly taken from this repo but those did not fit my purpose of ROIPooling over batches of images.

Why?

You can clearly see here that when the batch size is greater than 1, the output is zero.

Does that mean that they are useless?

Of course not! I noticed that FastRCNN uses a batch size of 1. Probably, they did not mind to make it more general implementation.

Why didn't you remove the conditional?

I tried in one of the repos but it fails. I even removed all the binaries and compiled again but it still returned zeros. Thus, I just moved on and pursue my personal reason:

I was really curious of launching cupy kernels using data from pytorch tensors. It is simply amazing. Moreover, it was a great experience to explore CUDA and pytorch.autograd.