RoI-op-pytorch

C++ extension of RoIPool & RoIAlign (both CPU and GPU) in PyTorch，this code is converted from caffe2 operation. （need pytorch 0.4.0）

**Warning：**You may change AT_CHECK to AT_ASSERT（0.4 version using AT_ASSERT， and latest version using AT_CHECK）

**Note: **

1. roi_xxx_cpu.cpp&roi_xxx_binding.cpp：contains the cpu version of forward and backward operation.(Note: roi_xxx_binding.cpp is for pybind, you can put this code into roi_xxx_cpu.cpp as well)
2. roi_xxx_kernel.cu&roi_xxx_cuda.cpp：contains the cuda version of forward and backward operation.
3. main.py&temp.h&CMakeLists.txt：help you to debug in c++ code, rather than to run python setup.py install to debug. (Note: only support cpu version ~ I don't know how to debug .cu code :persevere:)
4. setup.py：you can run python setup.py install to install this operation as a package (You can find this package in you python site-package)
5. roi_xxx.py：wrap .cpp code to pytorch's Function & Module ，there is also a small demo testing.

Install

cd roixxx   # roipool or roialign
python setup.py install

RoI Pooling

The "strategy" of roi-pooling in this implementaion likes the follow picture:（:joy: so bad picture）

Note: (please stand on point view rather than block view)

1. scale=0.5
2. dotted line is the range of "seleted area" (int form in [left, right) and [top, bottom))

RoI Align

Note: left sample=1, right sample=2

There are several good resource to explain these two operations:

• Region of interest pooling explained
• ROI Align --- chinese
• ROI Align --- youtube

Reference

1. caffe2 operator：most of the code is coming from here.
2. extension-cpp: tutorial
3. detectorch