modulated-deform-conv 1.0.2

cuda implementation of deformable conv2d, modulated deformable conv2d,deformable conv3d, modulated deformable conv3d

modulated-deform-conv

该项目是一个 Pytorch C++ and CUDA Extension,采用C++和Cuda实现了deformable-conv2d,modulated-deformable-conv2d,deformable-conv3d,modulated-deformable-conv3d的forward function和backward function,并在Python中对其进行了包装。
This Project is a Pytorch C++ and CUDA Extension, which implements the forward function and backward function for deformable-conv2d, modulated-deformable-conv2d, deformable-conv3d, modulated-deformable-conv3d, then encapsulates C++ and CUDA code into Python Package.

安装 Install

• run pip install modulated-deform-conv
• or git clone https://github.com/CHONSPQX/modulated-deform-conv.git,then cd modulated-deform-conv and run python setup.py install

要求 Requires

• Python 3
• Pytorch>=1.3
• Linux, gcc版本>=4.9(For Linux, gcc version>=4.9)
• Windows,CUDA版本需要VS版本兼容(For Windows, CUDA version must be compatiable with Visual Studio version)

由于资源有限，目前测试过的环境有(Because of limited resources, only the following environment are tested)

• Ubuntu18.04 , gcc 7.4 , CUDA 10.2 ,Python3.7.4, Pytorch 1.3.1
• Ubuntu18.04 , gcc 7.4 , CUDA 10.2 ,Python3.7.4, Pytorch 1.4.0
• Windows10 , Visual Studio 2017 , CUDA 10.1 ,Python3.7.6, Pytorch 1.4.0

速度优化 Speed Optimization

• pip download modulated-deform-conv 解压得到的压缩文件，进入modulated-deform-conv，打开src/config.h，用户可根据自身显卡情况，设置以下两个变量，获得更快运行速度，然后运行 python setup.py install
  Unzip the downloaded compressed file, cd modulated-deform-conv, then open src/config.h,users are recommended to set the following VARIABLES to optimize run speed according to their NVIDIA GPU condition, then run python setup.py install

  • const int CUDA_NUM_THREADS
  • const int MAX_GRID_NUM

• 运行时可以通过传递in_step参数来优化速度，该变量控制每次并行处理的batch 大小。
  Or users can set different in_step value in run time, which controls the batch size of each parallel processing .

使用 Use

直接使用C++函数，请import MDCONV_CUDA 使用封装后的python类，请import modulated_deform_conv Using C++ functions directly, please import MDCONV_CUDA Using the packaged function by Python, please import modulated_deform_conv

文档 Documents

1.C++ and CUDA Code

• 文件 Files

Filename	Content
config.h	macro&gloabl variables&inline functions
deformable_conv.cu	MDCONV_CUDA.deform_conv2d_forward_cuda MDCONV_CUDA.deform_conv2d_backward_cuda
mdeformable_conv.cu	MDCONV_CUDA.modulated_deform_conv2d_forward_cuda MDCONV_CUDA.modulated_deform_conv2d_backward_cuda
deformable_conv3d.cu	MDCONV_CUDA.deform_conv3d_forward_cuda MDCONV_CUDA.deform_conv3d_backward_cuda
mdeformable_conv3d.cu	MDCONV_CUDA.modulated_deform_conv3d_forward_cuda MDCONV_CUDA.modulated_deform_conv2d_backward_cuda
utils.cu	some code for display debug outputs
warp.cpp	glue code between C++ and Python

• 变量 Variables

Variable Name	Type	Introduction
kernel_h	const int	first dimension size of the convolution kernel
kernel_w	const int	second dimension size of the convolution kernel
kernel_l	const int	third dimension size of the convolution kernel
stride_h	const int	stride for first dimension
stride_w	const int	stride for second dimension
stride_l	const int	stride for third dimension
pad_h	const int	zero padding for first dimension
pad_w	const int	zero padding for second dimension
pad_l	const int	zero padding for third dimension
dilation_h	const int	dilation rate for first dimension
dilation_w	const int	dilation rate for second dimension
dilation_l	const int	dilation rate for third dimension
group	const int	group of convolution
deformable_group	const int	group of offset and mask
in_step	const int	batch size of each parallel processing
with_bias	const bool	if have bias
input	at::Tensor	B,I,H,W[,L],I must be divisible bygroup and deformable_group
grad_input	at::Tensor	grad_input must be size like input
weight	at::Tensor	O,I/group,H,W[,L]，Omust be divisible bygroup
grad_weight	at::Tensor	grad_weight must be size like weight
bias	at::Tensor	[O], if with_bias=true, bias must be non-null
grad_bias	at::Tensor	grad_bias must be size like bias
offset	at::Tensor	B,deformable_group*2*kernel_h*kernel_w,H,W B,deformable_group*3*kernel_h*kernel_w*kernel_l,H,W,L
grad_offset	at::Tensor	grad_offset must be size like offset
mask	at::Tensor	B,deformable_group*kernel_h*kernel_w,H,W B,deformable_group*kernel_h*kernel_w*kernel_l,H,W,L
grad_mask	at::Tensor	grad_mask must be size like mask
output	at::Tensor	B,O,OH,OW[,OL]
grad_output	at::Tensor	grad_output must be size like output

2.Python Code

Class Name	Type
class DeformConv2dFunction	torch.autograd.Function
class ModulatedDeformConv2dFunction	torch.autograd.Function
class DeformConv3dFunction	torch.autograd.Function
class ModulatedDeformConv3dFunction	torch.autograd.Function
class DeformConv2d	torch.nn.Module
class ModulatedDeformConv2d	torch.nn.Module
class DeformConv3d	torch.nn.Module
class ModulatedDeformConv3d	torch.nn.Module

Author

Xin Qiao [e-mail]

• github/chonspqx

License

Copyright (c) 2020 Xin Qiao Released under the MIT license