Deformable ConvNets Caffe Save

Caffe implementation of Deformable Convolutional Networks

• Thanks to offical code: https://github.com/msracver/Deformable-ConvNets

results:

faster rcnn(resnet50) result is implemented without OHEM and deformable roi pooling

train with pascal voc 2007 + 2012 test on 2007

Usage

Use modified caffe

The MNIST example is in caffe/defor/

Compile:

mkdir build cd build cmake ..  make  all

Train & test:

cd caffe/defor/
./train_lenet.sh

and the model is in caffe/defor/model_protxt/

use faster rcnn

download voc07,12 dataset ResNet50.caffemodel and rename to ResNet50.v2.caffemodel

cp ResNet50.v2.caffemodel data/pretrained_model/

• OneDrive download: link

Train &test:

./experiments/scripts/faster_rcnn_end2end.sh  0 ResNet50  pascal_voc
./test.sh  0 ResNet50  pascal_voc

Use the codes in your caffe

All codes are in deformable_conv_cxx/

1. Add layer definition to caffe.proto:

optional DeformableConvolutionParameter deformable_convolution_param = 999;
  
message DeformableConvolutionParameter {
  optional uint32 num_output = 1; 
  optional bool bias_term = 2 [default = true]; 
  repeated uint32 pad = 3; // The padding size; defaults to 0
  repeated uint32 kernel_size = 4; // The kernel size
  repeated uint32 stride = 6; // The stride; defaults to 1
  repeated uint32 dilation = 18; // The dilation; defaults to 1
  optional uint32 pad_h = 9 [default = 0]; // The padding height (2D only)
  optional uint32 pad_w = 10 [default = 0]; // The padding width (2D only)
  optional uint32 kernel_h = 11; // The kernel height (2D only)
  optional uint32 kernel_w = 12; // The kernel width (2D only)
  optional uint32 stride_h = 13; // The stride height (2D only)
  optional uint32 stride_w = 14; // The stride width (2D only)
  optional uint32 group = 5 [default = 4]; 
  optional uint32 deformable_group = 25 [default = 4]; 
  optional FillerParameter weight_filler = 7; // The filler for the weight
  optional FillerParameter bias_filler = 8; // The filler for the bias
  enum Engine {
    DEFAULT = 0;
    CAFFE = 1;
    CUDNN = 2;
  }
  optional Engine engine = 15 [default = DEFAULT];
  optional int32 axis = 16 [default = 1];
  optional bool force_nd_im2col = 17 [default = false];
}

you can read the template in deformable_conv_cxx/caffe.proto

2.Move codes to your caffe

move deformable_conv_layer.cpp and deformable_conv_layer.cu to yourcaffepath/src\caffe\layers\

move deformable_conv_layer.hpp to yourcaffepath/include\caffe\layers\

move deformable_conv_layer.hpp to yourcaffepath/include\caffe\layers\

move deformable_im2col.cu to yourcaffepath\src\caffe\util\

move deformable_im2col.hpp to yourcaffepath\include\caffe\util\

3.Compile in your caffe root path

mkdir build cd build  cmake ..   make  all

About the deformable conv layer

The params in DeformableConvolution:

bottom[0](data): (batch_size, channel, height, width)
bottom[1] (offset): (batch_size, deformable_group * kernel[0] * kernel[1]*2, height, width)

Define:

f(x,k,p,s,d) = floor((x+2*p-d*(k-1)-1)/s)+1

the output of the DeformableConvolution layer:

out_height=f(height, kernel[0], pad[0], stride[0], dilate[0])
out_width=f(width, kernel[1], pad[1], stride[1], dilate[1])

Offset layer:

layer {
  name: "offset"
  type: "Convolution"
  bottom: "pool1"
  top: "offset"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 72
    kernel_size: 3
    stride: 1
    dilation: 2
    pad: 2
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}

DeformableConvolution layer:

layer {
  name: "dec"
  type: "DeformableConvolution"
  bottom: "conv1"
  bottom: "offset"
  top: "dec"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  deformable_convolution_param {
    num_output: 512
    kernel_size: 3
    stride: 1
    pad: 2
    engine: 1
    dilation: 2
    deformable_group: 4
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}

the prototxt model should like:

The following animation is generated by Felix Lau (with his tensorflow implementation):https://github.com/felixlaumon/deform-conv/

TODO List

• all tests passed
• evaluate performance on Regular MNIST
• evaluate object detection performance on voc

Deformable Convolutional Networks

Dai, Jifeng, Haozhi Qi, Yuwen Xiong, Yi Li, Guodong Zhang, Han Hu, and Yichen Wei. 2017. “Deformable Convolutional Networks.” arXiv [cs.CV]. arXiv. http://arxiv.org/abs/1703.06211