一直在研究如何用caffe做行人检测问题，然而参考那些经典结构比如faster-rcnn等，都是自定义的caffe层来完成的检测任务。这些都要求对caffe框架有一定程度的了解。最近看到了如何用caffe完成回归的任务，就想把检测问题当成回归问题来解决。

    我们把行人检测问题当成回归来看待，就需要限制检出目标的个数，因为我们的输出个数是固定的。所以，这里我假定每张图片最多检出的目标个数为2。即每个目标用4个值来表示其位置信息（中心位置坐标x,y。BBox的宽和高），则网络的最后输出是8个值。

制作HDF5数据

    这里我们使用HDF5格式的数据来完成我们的回归任务，那么首先我们需要的是制作h5格式的数据。这里以VOC数据集为例。下面是制作HDF5格式数据的python代码。

import h5py
import caffe
import os
import xml.etree.ElementTree as ET
import cv2
import time
import math
from os.path import join, exists
import numpy as np

def convert(size, box):
    dw = 1./size[0]
    dh = 1./size[1]
    x = (box[0] + box[1])/2.0
    y = (box[2] + box[3])/2.0
    w = box[1] - box[0]
    h = box[3] - box[2]
    x = x*dw
    w = w*dw
    y = y*dh
    h = h*dh
    return (x,y,w,h)

def shuffle_in_unison_scary(a, b):
    rng_state = np.random.get_state()
    np.random.shuffle(a)
    np.random.set_state(rng_state)
    np.random.shuffle(b)
    
def processImage(imgs):
    imgs = imgs.astype(np.float32)
    for i, img in enumerate(imgs):
        m = img.mean()
        s = img.std()
        imgs[i] = (img - m) / s
    return imgs

TrainImgDir = ‘F:/GenerateHDF5/trainImage‘
TrainLabelDir = ‘F:/GenerateHDF5/trainLabels‘
TestImgDir = ‘F:/GenerateHDF5/testImg‘
TestLabelDir = ‘F:/GenerateHDF5/testLabels‘

InImg = []
InBBox = []

for rootDir,dirs,files in os.walk(TestLabelDir):                                       #####
    for file in files:
        file_name = file.split(‘.‘)[0]
        full_file_name = ‘%s%s‘%(file_name,‘.jpg‘)
        full_file_dir = ‘%s/%s‘%(TestImgDir,full_file_name)                            #####
        Img = cv2.imread(full_file_dir,cv2.CV_LOAD_IMAGE_GRAYSCALE)
        xml_file = open("%s/%s"%(rootDir,file))
        tree = ET.parse(xml_file)
        root = tree.getroot()
        size = root.find(‘size‘)
        w = int(size.find(‘width‘).text)
        h = int(size.find(‘height‘).text)
        
        landmark = np.zeros(8)
        count = 0
        for obj in root.iter(‘object‘):
            count = count + 1
            if count == 3:
                break
            xmlbox = obj.find(‘bndbox‘)
            b = (float(xmlbox.find(‘xmin‘).text), float(xmlbox.find(‘xmax‘).text), float(xmlbox.find(‘ymin‘).text), float(xmlbox.find(‘ymax‘).text))
            bb = convert((w,h), b)
            landmark[(count-1)*4+0]=bb[0]
            landmark[(count-1)*4+1]=bb[1]
            landmark[(count-1)*4+2]=bb[2]
            landmark[(count-1)*4+3]=bb[3]
        
        InBBox.append(landmark.reshape(8))
        Img = cv2.resize(Img,(h,w))
        InImg.append(Img.reshape((1,h,w)))           
            
InImg, InBBox = np.asarray(InImg), np.asarray(InBBox)
InImg = processImage(InImg)
shuffle_in_unison_scary(InImg, InBBox)

outputDir = ‘hdf5/‘
HDF5_file_name = ‘hdf5_test.h5‘                                                  #####
if not os.path.exists(outputDir):
    os.makedirs(outputDir)
    
output = join(outputDir,HDF5_file_name)
with h5py.File(output, ‘w‘) as h5:
    h5[‘data‘] = InImg.astype(np.float32)
    h5[‘labels‘] = InBBox.astype(np.float32)
    h5.close()

制作好了HDF5数据后，注意每个H5文件大小不能超过2G（这是caffe的规定，如果一个文件超过2G，请分开制作多个）。然后建立一个TXT文件，文件里写上所有H5文件的绝对路径，比如我这里建立的文件是list_train.txt。然后我只有一个H5文件，即hdf5_train.h5。所以我的list_train.txt文件中的内容就是/home/XXX/caffe/model/hdf5/hdf5_train.h5

配置solver文件

接下来是caffe的solver文件，这个文件没有什么区别，

test_iter: 20
test_interval: 70
base_lr: 0.0000000005
display: 9
max_iter: 210000
lr_policy: "step"
gamma: 0.1
momentum: 0.9
weight_decay: 0.0001
stepsize: 700
snapshot: 500
snapshot_prefix: "snapshot"
solver_mode: GPU
net: "train_val.prototxt"
solver_type: SGD

配置train_val.prototxt文件

接下来是网络的train_val.prototxt文件。这是caffe的网络结构文件，我们这里以LeNet网络为例，我这里是这样的：

name: "LeNet"
layer {
  name: "data"
  type: "HDF5Data"
  top: "data"
  top: "labels"
  include {
    phase: TRAIN
  }
  hdf5_data_param {
    source: "list_train.txt"
    batch_size: 50
  }
}
layer {
  name: "data"
  type: "HDF5Data"
  top: "data"
  top: "labels"
  include {
    phase: TEST
  }
  hdf5_data_param {
    source: "list_test.txt"
    batch_size: 50
  }
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "scaled"
  top: "conv1"
  param {
    lr_mult: 1.0
  }
  param {
    lr_mult: 2.0
  }
  convolution_param {
    num_output: 20
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "pool1"
  top: "conv2"
  param {
    lr_mult: 1.0
  }
  param {
    lr_mult: 2.0
  }
  convolution_param {
    num_output: 50
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv2"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "ip1"
  type: "InnerProduct"
  bottom: "pool2"
  top: "ip1"
  param {
    lr_mult: 1.0
  }
  param {
    lr_mult: 2.0
  }
  inner_product_param {
    num_output: 500
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "ip1"
  top: "ip1"
}
layer {
  name: "ip2"
  type: "InnerProduct"
  bottom: "ip1"
  top: "ip2"
  param {
    lr_mult: 1.0
  }
  param {
    lr_mult: 2.0
  }
  inner_product_param {
    num_output: 8
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "error"
  type: "EuclideanLoss"
  bottom: "ip2"
  bottom: "labels"
  top: "error"
  include {
      phase: TEST
  }
}
layer {
  name: "loss"
  type: "EuclideanLoss"
  bottom: "ip2"
  bottom: "labels"
  top: "loss"
  include {
      phase: TRAIN
  }
}

开始训练

按照以上步骤配置好了，最后就是训练了。在控制台中输入以下指令来训练我们的数据：

./cafferoot/caffe/tools/caffe train --solver=solver.prototxt

使用caffe的HDF5数据完成回归任务