使用caffe训练自己的CNN

现在有这样的一个场景：给一张行人的小矩形框图片，根据该行人的特征识别出性别。

分析：

（1），行人的姿态各异，变化多端。很难提取图像的特定特征

（2），正常人肉眼判别行人的根据是身材比例，头发长度等。（如果是冬天的情况下，行人穿着厚实，性别识别更加难）

solution：

针对难以提取特定特征的图像，可以采用卷积神经网络CNN去自动提取并训练。

数据准备：

采用 PETA数据集，Pedestrain Attribute Recognition At Far Distance。该数据集一共包含了19000张标记了行人穿着及性别信息的图片。

Peta dataset source url: http://mmlab.ie.cuhk.edu.hk/projects/PETA.html

数据处理：

针对下载解压之后的数据集，采用的流程是：

（1）对每一张图片进行resize， resize到特定的大小（实验中定为50*150），

（2）对正类负类样本的不均衡情况，进行rebalance处理，实验中对少数类样本进行随机选择n张进行data augmentation之后重新加入到dataset中。

（3）划分training set和testing set，根据train/test ratio将整个数据样本随机分为两部分。

（4）对training set 进行data augmentation 处理。扩大训练数据量。（操作包括：翻转，滤波等）

#!/usr/bin/env python
#-*- encoding: utf-8 -*- #########
## The python code to preprocess the images and resize them into (50, 150)
## Date: 2016-09-19
#########import os, sys, cv2
import numpy as np
import random image_cnt = 0
MIN_HEIGHT = 120
MIN_WIDTH = 40
targetLabel = [] positive_cnt = 0
negative_cnt = 0 def readImage( filePath , targetDir ):global image_cnt, positive_cnt, negative_cnt global targetLabel if not os.path.isdir( filePath ):print('{} is not a dir'.format(filePath)) return None listFile = os.listdir( filePath ) labelDict = {} with open( filePath + 'Label.txt', 'r') as reader:for line in reader:lines = line.split() for i in range(1, len(lines)):if lines[i] == 'personalMale':label = 1 elif lines[i] == 'personalFemale':label = 0 else:continue labelDict[lines[0]] = label break for i in range(len(listFile)):if len(listFile[i]) > 4 and (listFile[i][-4:] == '.bmp' or listFile[i][-4:] == '.jpg' or \listFile[i][-4:] == '.png' or listFile[i][-5:] == '.jpeg'):imageName = filePath + listFile[i] img = cv2.imread( imageName ) if not img.data:continue height, width = img.shape[:2] if height < MIN_HEIGHT or width < MIN_WIDTH:continue fileName = str( image_cnt ) + '.jpeg' identity = listFile[i].find('_')  if  identity == -1:identity = len(listFile[i])  idd = listFile[i][:identity] if labelDict.has_key( idd ) :targetLabel.append([ fileName, labelDict[idd]]) if labelDict[idd] == 0:negative_cnt += 1 else:positive_cnt += 1 img = cv2.resize(img, (50, 150), interpolation=cv2.INTER_CUBIC) cv2.imwrite(targetDir + fileName, img) image_cnt += 1 else:print('file {} do not have label'.format(listFile[i]) )####### pyramid operator
def MinAndEnlarge(img, Minus_pixel = 3):img = img[(3*Minus_pixel):(150 - 3*Minus_pixel), Minus_pixel:(50 - Minus_pixel), :] img = cv2.resize(img, (50, 150), interpolation = cv2.INTER_CUBIC ) return img ####### rotate operator
def Flip(img, operator = 1):if operator == 1:img = cv2.flip(img, 1) else:img = cv2.flip(img, 0) return img ####### median blurring the image
def Blur(img, kernel_size=5):img = cv2.medianBlur(img, kernel_size) return img def EnlargeData( filePath , targetDir ):global image_cnt, targetLabel  total_sample = len(targetLabel) for i in range(total_sample): img = cv2.imread( filePath + targetLabel[i][0] ) fileLabel = targetLabel[i][1] if not img.data:print('no exits image file {}'.format( filePath + targetLabel[i][0]) ) # img1 = MinAndEnlarge(img, 3) fileName = str(image_cnt) + '.jpeg' cv2.imwrite( targetDir + fileName, img1 ) image_cnt += 1 targetLabel.append( [fileName, fileLabel] ) # img2 = Flip(img1) fileName = str(image_cnt) + '.jpeg' cv2.imwrite( targetDir + fileName, img2 ) image_cnt += 1 targetLabel.append( [fileName, fileLabel] ) # img3 = Blur(img, 5) fileName = str(image_cnt) + '.jpeg' cv2.imwrite( targetDir + fileName, img3 ) image_cnt += 1 targetLabel.append( [fileName, fileLabel] ) # img4 = Blur(img1, 5) fileName = str(image_cnt) + '.jpeg' cv2.imwrite( targetDir + fileName, img4 ) image_cnt += 1 targetLabel.append([fileName, fileLabel]) # img5 = Blur(img2, 5) fileName = str(image_cnt) + '.jpeg' cv2.imwrite( targetDir + fileName, img5 ) image_cnt += 1 targetLabel.append([fileName, fileLabel]) print('The total number of images is {}'.format(image_cnt)) def saveLabel( targetDir ): global targetLabel with open(targetDir + 'label.txt', 'w') as writer:for i in range(len(targetLabel)):writer.write( str( targetLabel[i][0] ) + ' ' + str(targetLabel[i][1]) + '\n' ) ##### ReBalance operator
#######  num (the number of minority class should added)
#######  n_or_p (the label of minority class)
#######  op_chose( 1--symmetrical flip; 0--rotate image; )
def ReBalance( targetDir, num, n_or_p, op_chose = 0): global targetLabel, image_cnt  total_sample = len(targetLabel)Contain = {} while 1:if num <= 0:break key_id = random.randint(0, total_sample-1) if Contain.has_key( key_id ) or targetLabel[key_id][1] != n_or_p:continue img = cv2.imread( targetDir + targetLabel[key_id][0] ) if op_chose == 0:img = cv2.flip(img, 1)  elif op_chose == 1:img = cv2.flip(img, 0)fileName = str(image_cnt) + '.jpeg'  cv2.imwrite(targetDir + fileName, img) image_cnt += 1  targetLabel.append([fileName, n_or_p]) num -= 1 print('Finish add {} images'.format(image_cnt - total_sample))  print('Now the class is balanced and total num is {}'.format(image_cnt))  print('image_cnt is {} and len(_targetLabel_) is {} '.format(image_cnt, len(targetLabel))) def divide( targetDir, trainDir, testDir, test_ratio = 0.20):global targetLabel total_sample = len(targetLabel) assert( test_ratio < 1) test_num = int(total_sample * test_ratio ) test_half_num = test_num // 2; ml_cnt = 0; fm_cnt = 0 testLabel = [] ; trainLabel = [] for i in range(total_sample):if  ml_cnt < test_half_num and targetLabel[i][1] == 1:ml_cnt += 1 img = cv2.imread( targetDir + targetLabel[i][0] ) cv2.imwrite( testDir +  targetLabel[i][0], img ) testLabel.append(targetLabel[i]) elif fm_cnt < test_half_num and targetLabel[i][1] == 0:fm_cnt += 1 img = cv2.imread( targetDir + targetLabel[i][0] ) cv2.imwrite( testDir +  targetLabel[i][0], img ) testLabel.append(targetLabel[i]) else:img = cv2.imread( targetDir + targetLabel[i][0] ) cv2.imwrite( trainDir + targetLabel[i][0], img ) trainLabel.append(targetLabel[i])  # trainwith open( trainDir + 'label.txt', 'w') as writer:for i in range(len(trainLabel)):writer.write( str( trainLabel[i][0] ) + ' ' + str(trainLabel[i][1]) + '\n' )     with open( testDir + 'label.txt', 'w') as writer:for i in range(len(testLabel)):writer.write( str(testLabel[i][0]) + ' ' + str(testLabel[i][1]) + '\n') print('has divide into train with {} samples and test with {} samples'.format(len(trainLabel), len(testLabel)) )  return trainLabel, testLabel def DivideSet( targetDir, trainDir, testDir, test_ratio = 0.20):global targetLabel total_sample = len(targetLabel) assert( test_ratio < 1 ) test_num = int(test_ratio * total_sample) test_half_num = test_num //2 ; ml_cnt = test_half_num; fm_cnt = test_half_num  testLabel = [] ; trainLabel = [] ; testDict = {} while ml_cnt > 0 or fm_cnt > 0:idd = random.randint(0, total_sample-1) if testDict.has_key( targetLabel[idd][0] ):continue if targetLabel[idd][1] == 1 and ml_cnt > 0:img = cv2.imread( targetDir + targetLabel[idd][0] ) cv2.imwrite( testDir + targetLabel[idd][0], img ) testLabel.append( targetLabel[idd] ) testDict[targetLabel[idd][0]] = idd ml_cnt -= 1 if targetLabel[idd][1] == 0 and fm_cnt > 0:img = cv2.imread( targetDir + targetLabel[idd][0] ) cv2.imwrite( testDir + targetLabel[idd][0], img ) testLabel.append( targetLabel[idd] ) testDict[targetLabel[idd][0]] = idd fm_cnt -= 1 for i in range(total_sample):if not testDict.has_key( targetLabel[i][0] ):trainLabel.append( targetLabel[i] ) img = cv2.imread( targetDir + targetLabel[i][0] ) cv2.imwrite( trainDir + targetLabel[i][0], img ) ## save the trainset and testset with open( trainDir + 'label.txt', 'w') as writer:for i in range(len(trainLabel)):writer.write( str( trainLabel[i][0] ) + ' ' + str(trainLabel[i][1]) + '\n' )    with open( testDir + 'label.txt', 'w') as writer:for i in range(len(testLabel)):writer.write( str(testLabel[i][0]) + ' ' + str(testLabel[i][1]) + '\n') print('has divide into train with {} samples and test with {} samples'.format(len(trainLabel), len(testLabel)) )  return trainLabel, testLabel def EnlargeTrain( fileDir, targetDir, trainLabel , start_cnt):total_sample = len(trainLabel) new_cnt = start_cnt     for i in range(total_sample):img = cv2.imread( fileDir + trainLabel[i][0] ) fileLabel = trainLabel[i][1] if not img.data:print('no exits image file {}'.format( fileDir + trainLabel[i][0]) ) continue # img1 = MinAndEnlarge(img, 3) fileName = str(new_cnt) + '.jpeg' cv2.imwrite( targetDir + fileName, img1 ) new_cnt += 1 trainLabel.append( [fileName, fileLabel] ) # img2 = Flip(img1) fileName = str(new_cnt) + '.jpeg' cv2.imwrite( targetDir + fileName, img2 ) new_cnt += 1 trainLabel.append( [fileName, fileLabel] ) # img3 = Blur(img, 5) fileName = str(new_cnt) + '.jpeg' cv2.imwrite( targetDir + fileName, img3 ) new_cnt += 1 trainLabel.append( [fileName, fileLabel] ) # img4 = Blur(img1, 5) fileName = str(new_cnt) + '.jpeg' cv2.imwrite( targetDir + fileName, img4 ) new_cnt += 1 trainLabel.append([fileName, fileLabel]) # img5 = Blur(img2, 5) fileName = str(new_cnt) + '.jpeg' cv2.imwrite( targetDir + fileName, img5 ) new_cnt += 1 trainLabel.append([fileName, fileLabel]) print('The total number of training images is {}'.format(new_cnt)) with open( targetDir + 'label.txt', 'w') as writer:for i in range(len(trainLabel)):writer.write( str( trainLabel[i][0] ) + ' ' + str(trainLabel[i][1]) + '\n' ) print('The trainLabel size is {}'.format(len(trainLabel)) ) if __name__ == '__main__':fileHead = '/home/zhangyd/source/PETA_dataset/' filePath = ['3DPeS', 'CAVIAR4REID', 'CUHK', 'GRID','MIT', 'PRID','SARC3D','TownCentre', 'VIPeR','i-LID']  savePath = '/home/zhangyd/source/peta/' for i in range(len(filePath)):path = fileHead + filePath[i] + '/archive/' print ('runing dataset {}'.format(filePath[i]) )  readImage( path, savePath ) print ('The cnt is {}'.format( image_cnt )) #EnlargeData( savePath, savePath ) saveLabel( savePath ) print( 'we have {} positive labels and {} negative labels '.format( positive_cnt, negative_cnt ))if positive_cnt > negative_cnt:add_num = positive_cnt - negative_cnt ReBalance( savePath, add_num, 0, 0) else:add_num = negative_cnt - positive_cnt ReBalance( savePath, add_num, 1, 0) print('The total dataset is in {}'.format(savePath)) TrainsavePath = '/home/zhangyd/source/peta_v1/petaTrain/' TestsavePath = '/home/zhangyd/source/peta_v1/petaTest/'trainLabel, testLabel =  DivideSet(savePath, TrainsavePath, TestsavePath, 0.2 ) start_cnt = len(targetLabel) EnlargeTrain( TrainsavePath, TrainsavePath, trainLabel, start_cnt ) print('the end')

实验

使用caffe的create_lmdb.sh 转换图像数据成 imbd数据集。

定义 prototxt 等信息。

CNN 的结构是：

训练的参数设置：

# The train/test net protocol buffer definition
net: "examples/peta/petanet_train_test.prototxt"
# test_iter specifies how many forward passes the test should carry out.
# In the case of MNIST, we have test batch size 100 and 100 test iterations,
# covering the full 10,000 testing images.
test_iter: 100
# Carry out testing every 500 training iterations.
test_interval: 500
# The base learning rate, momentum and the weight decay of the network.
base_lr: 0.01
momentum: 0.9
weight_decay: 0.0005
# The learning rate policy
lr_policy: "inv"
gamma: 0.0001
power: 0.75
# Display every 100 iterations
display: 100
# The maximum number of iterations
max_iter: 10000
# snapshot intermediate results
snapshot: 5000
snapshot_prefix: "examples/peta/petanet"
# solver mode: CPU or GPU
solver_mode: GPU

使用论文《Learned vs. Hand-Crafted Features for Pedestrian Gender Recognition》中的网络结构，取得了较好的训练结果：

I0922 00:07:32.204310 16398 solver.cpp:337] Iteration 10000, Testing net (#0)
I0922 00:07:34.001411 16398 solver.cpp:404]     Test net output #0: accuracy = 0.8616
I0922 00:07:34.001471 16398 solver.cpp:404]     Test net output #1: loss = 0.721973 (* 1 = 0.721973 loss)
I0922 00:07:34.001479 16398 solver.cpp:322] Optimization Done.
I0922 00:07:34.001485 16398 caffe.cpp:254] Optimization Done.

实验分析：

因为网络不大，网络也比较简单，在GPU下进行训练，消耗的显存大概是几百M，不到1G的显存。网络结构经典，也取得较好的训练结果。

我的拓展：自己设计的CNN网络

吸取了GoogleNet的网络特征，引入inception，重新设计网络。

是两个 inception 组成，后面加上一个FC层。

其中Snapshot的网络结构prototxt文件是：

name: "petaNet"
layer {name: "data" type: "Input" top: "data" input_param {shape: {dim: 1 dim: 3 dim: 50 dim: 150 }}
}### ------------layer {name: "conv1" type: "Convolution" bottom: "data" top: "conv1" param {lr_mult: 1 }param {lr_mult: 2 }convolution_param {num_output: 20 kernel_size: 3 stride: 1 weight_filler {type: "xavier" }bias_filler {type: "constant" }}
}
layer {name: "relu1" type: "ReLU" bottom: "conv1" top: "conv1"
}##-------
# Inception 3a
##-------layer {name: "inc1_conv1" bottom: "conv1" top: "inc1_conv1" type: "Convolution" param {    lr_mult: 1 } param {    lr_mult: 2 } convolution_param {num_output: 20 kernel_size: 7 stride: 1 weight_filler { type: "xavier" } bias_filler  { type: "constant" } }
}
layer {name: "inc1_conv1_relu" type: "ReLU" bottom: "inc1_conv1" top: "inc1_conv1"
}layer {name: "inc1_conv2_1" type: "Convolution" bottom: "conv1" top: "inc1_conv2_1" param { lr_mult: 1 } param { lr_mult: 2 } convolution_param {num_output: 50 kernel_size: 3 stride: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" } }
}
layer {name: "inc1_conv2_1_relu" type: "ReLU" bottom: "inc1_conv2_1" top: "inc1_conv2_1"
}layer {name: "inc1_conv2_2"type: "Convolution" bottom: "inc1_conv2_1" top:   "inc1_conv2_2" param { lr_mult: 1 } param { lr_mult: 2 } convolution_param {num_output: 50 kernel_size: 3 stride: 1 weight_filler { type: "xavier" } bias_filler    { type: "constant" } }
}
layer {name: "inc1_conv2_2_relu" type: "ReLU" bottom: "inc1_conv2_2" top: "inc1_conv2_2"
}layer {name: "inc1_conv2_3" type: "Convolution" bottom: "inc1_conv2_2" top:  "inc1_conv2_3" param { lr_mult: 1 } param { lr_mult: 2 } convolution_param {num_output: 50 kernel_size: 3 stride: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" } }
}
layer {name: "inc1_conv2_3_relu" type: "ReLU" bottom: "inc1_conv2_3" top: "inc1_conv2_3"
}layer {name: "inc1_conv3_1" type: "Convolution" bottom: "conv1" top: "inc1_conv3_1"param { lr_mult: 1 } param { lr_mult: 2 } convolution_param {num_output: 20 kernel_size: 5 stride: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" } }
}
layer {name: "inc1_conv3_1_relu" type: "ReLU" bottom: "inc1_conv3_1" top: "inc1_conv3_1"
}layer {name: "inc1_conv3_2" type: "Convolution" bottom: "inc1_conv3_1" top:  "inc1_conv3_2" param { lr_mult: 1 } param { lr_mult: 2 } convolution_param {num_output: 20 kernel_size: 3 stride: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" } }
}
layer {name: "inc1_conv3_2_relu" type: "ReLU" bottom: "inc1_conv3_2" top: "inc1_conv3_2"
}layer {name: "inc1_concat" type: "Concat" bottom: "inc1_conv1" bottom: "inc1_conv2_3" bottom: "inc1_conv3_2" top:    "inc1_concat"
}#-----end of Inception 3a layer {name: "pool1" type: "Pooling"bottom: "inc1_concat" top: "pool1" pooling_param {pool: MAX kernel_size: 2 stride: 2 }
}##------
# Inception 2B
##------layer {name: "inc2_conv1_1" type: "Convolution" bottom: "pool1" top: "inc2_conv1_1" param { lr_mult: 1 } param { lr_mult: 2 } convolution_param {num_output: 120 kernel_size: 3 stride: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" } }
}
layer {name: "inc2_conv1_1_relu" type: "ReLU" bottom: "inc2_conv1_1" top: "inc2_conv1_1"
}layer {name: "inc2_conv1_2" type: "Convolution" bottom: "inc2_conv1_1" top:  "inc2_conv1_2" param { lr_mult: 1 } param { lr_mult: 2 } convolution_param {num_output: 120 kernel_size: 3 stride: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" } }
}
layer {name: "inc2_conv1_2_relu"type: "ReLU"  bottom: "inc2_conv1_2" top: "inc2_conv1_2"
}layer {name: "inc2_conv2" type: "Convolution" bottom: "pool1" top:   "inc2_conv2" param { lr_mult: 1 } param { lr_mult: 2 } convolution_param {num_output: 120 kernel_size: 5 stride: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" } }
}
layer {name: "inc2_conv2_relu" type: "ReLU" bottom: "inc2_conv2" top: "inc2_conv2"
}layer {name: "inc2_concat" type: "Concat" bottom: "inc2_conv1_2" bottom: "inc2_conv2" top: "inc2_concat"
}##----end of Inception 2B layer {name: "pool2" type: "Pooling" bottom: "inc2_concat" top:    "pool2" pooling_param {pool: MAX kernel_size: 2 stride: 2 }
}layer {name: "fc1" type: "InnerProduct" bottom: "pool2" top: "fc1" param { lr_mult: 1 } param { lr_mult: 2 } inner_product_param {num_output: 2 weight_filler { type: "xavier" } bias_filler { type: "constant" } }
}#### ----------layer {name: "prob" type: "Softmax" bottom: "fc1" top: "prob"
}

取得的效果比论文中的网络结构差点，训练结果是：

I0927 00:11:42.485725 20295 solver.cpp:317] Iteration 10000, loss = 0.0678897
I0927 00:11:42.485771 20295 solver.cpp:337] Iteration 10000, Testing net (#0)
I0927 00:12:06.291497 20295 solver.cpp:404]     Test net output #0: accuracy = 0.8448
I0927 00:12:06.291554 20295 solver.cpp:404]     Test net output #1: loss = 0.614111 (* 1 = 0.614111 loss)
I0927 00:12:06.291563 20295 solver.cpp:322] Optimization Done.
I0927 00:12:06.291568 20295 caffe.cpp:254] Optimization Done.

实验分析：

因为该网络的组成较为复杂， inception包含着较大的子网络，因为训练的时候，需要消耗GPU显存为3G多。训练时间也较长些。

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