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All Projects → CHONSPQX → Modulated Deform Conv

CHONSPQX / Modulated Deform Conv

Licence: mit
deformable convolution 2D 3D DeformableConvolution DeformConv Modulated Pytorch CUDA

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python
139335 projects - #7 most used programming language

Labels

pytorchcuda

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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
  • Ubuntu18.04 , gcc 7.5 , CUDA 11.1 ,Python3.6.12, Pytorch 1.7.0
  • Windows10 , Visual Studio 2017 , CUDA 10.1 ,Python3.7.6, Pytorch 1.4.0
  • Windows10 , Visual Studio 2019 , CUDA 11.1 ,Python3.6.12, Pytorch 1.7.0

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 [email protected]

License

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

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