caffe + win10基于CaffeNet网络框架训练自己的图片进行分类（实践篇）

  接触caffe一段时间了 ，一直没有自己完整的跑过自己的数据，现在使用在win10系统上配置好的caffe环境，使用caffeNet网络框架，对自己准备的图片数据集进行训练，并使用生成的模型对图片进行类别预测。接触时间比较短，有的地方理解不到位，现在整理下我处理的流程以及过程之中遇到过的问题，希望可以与大家互享经验，不足之处请大家多多指教。   在caffe目录examples下新建my_classify文件夹，其余文件均在该目录下；

一、制作数据集

1、准备图片   使用Corel数据集中的400图片，4类每类选取100张，其中90张作为训练集，10张作为测试集。所以训练集train中有360张图片，测试集test中有40张图片,分别存放在train与test文件夹中。

2、生成带标签的列表list.txt   分别在train与test文件中生成list.txt列表，切记标签要从0开始。我使用python脚本分别对每个类别图像生成标签 ，然后和在一起的。（方法有点笨，附上基本的生成列表的代码，可以写个循环，直接生成train的list.txt的）

import os def generate(dir,label): files = os.listdir(dir) files.sort() print '****************' print 'input :',dir print 'start...' listText = open(dir+'\\'+'list.txt','w') for file in files: fileType = os.path.split(file) if fileType[1] == '.txt': continue name = file + ' ' + str(int(label)) +'\n' listText.write(name) listText.close() print 'down!' print '****************' if __name__ == '__main__': generate('D:\\caffe\\caffe-master\\examples\\my_classify\\Test',1)

生成的list.txt

注：如果label标记不从0开始，可能会导致 label_value < num_labels 问题：

3、生成lmdb格式数据集，并生成二进制imagemean.binaryproto的均值文件(size 256 256) 采用windows批处理格式的文件.bat,文件名为：convert_and_computeMean.bat

D:\caffe\caffe-master\Build\x64\Debug\convert_imageset.exe --resize_height=256 --resize_width=256 --shuffle --backend="lmdb" train/ train/list.txt trainlmdb D:\caffe\caffe-master\Build\x64\Debug\convert_imageset.exe --resize_height=256 --resize_width=256 --shuffle --backend="lmdb" test/ test/list.txt testlmdb D:\caffe\caffe-master\Build\x64\Debug\compute_image_mean.exe trainlmdb image_mean.binaryproto pause

运行结果：

生成trainlmdb、testlmdb和image_mean.binaryproto三个文件

注：如果数据集有改动，要将原来生成的trainlmdb和testlmdb删除，否则会造成如下问题：

至此数据集制作部分完成。

二、新建网络模型train_val.prototxt和solver.prototxt两个文件

这里直接使用caffeNet的网络架构模型 需要做如下更改： 1、训练集与测试集的路径，直接改成了绝对路径； 2、batch_size改成了4，crop_size改成了与图片一般大小的256； 3、最后一层的num_output设置成4，因为只有4类。

name: "CaffeNet" layer { name: "data" type: "Data" top: "data" top: "label" include { phase: TRAIN } transform_param { mirror: true crop_size: 256 mean_file: "D:/caffe/caffe-master/examples/my_classify/image_mean.binaryproto" } data_param { source: "D:/caffe/caffe-master/examples/my_classify/trainlmdb" batch_size: 4 backend: LMDB } } layer { name: "data" type: "Data" top: "data" top: "label" include { phase: TEST } transform_param { mirror: false crop_size: 256 mean_file: "D:/caffe/caffe-master/examples/my_classify/image_mean.binaryproto" } data_param { source: "D:/caffe/caffe-master/examples/my_classify/testlmdb" batch_size: 4 backend: LMDB } } layer { name: "conv1" type: "Convolution" bottom: "data" top: "conv1" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 96 kernel_size: 11 stride: 4 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } } layer { name: "relu1" type: "ReLU" bottom: "conv1" top: "conv1" } layer { name: "pool1" type: "Pooling" bottom: "conv1" top: "pool1" pooling_param { pool: MAX kernel_size: 3 stride: 2 } } layer { name: "norm1" type: "LRN" bottom: "pool1" top: "norm1" lrn_param { local_size: 5 alpha: 0.0001 beta: 0.75 } } layer { name: "conv2" type: "Convolution" bottom: "norm1" top: "conv2" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 256 pad: 2 kernel_size: 5 group: 2 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 1 } } } layer { name: "relu2" type: "ReLU" bottom: "conv2" top: "conv2" } layer { name: "pool2" type: "Pooling" bottom: "conv2" top: "pool2" pooling_param { pool: MAX kernel_size: 3 stride: 2 } } layer { name: "norm2" type: "LRN" bottom: "pool2" top: "norm2" lrn_param { local_size: 5 alpha: 0.0001 beta: 0.75 } } layer { name: "conv3" type: "Convolution" bottom: "norm2" top: "conv3" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 384 pad: 1 kernel_size: 3 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } } layer { name: "relu3" type: "ReLU" bottom: "conv3" top: "conv3" } layer { name: "conv4" type: "Convolution" bottom: "conv3" top: "conv4" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 384 pad: 1 kernel_size: 3 group: 2 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 1 } } } layer { name: "relu4" type: "ReLU" bottom: "conv4" top: "conv4" } layer { name: "conv5" type: "Convolution" bottom: "conv4" top: "conv5" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 256 pad: 1 kernel_size: 3 group: 2 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 1 } } } layer { name: "relu5" type: "ReLU" bottom: "conv5" top: "conv5" } layer { name: "pool5" type: "Pooling" bottom: "conv5" top: "pool5" pooling_param { pool: MAX kernel_size: 3 stride: 2 } } layer { name: "fc6" type: "InnerProduct" bottom: "pool5" top: "fc6" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } inner_product_param { num_output: 4096 weight_filler { type: "gaussian" std: 0.005 } bias_filler { type: "constant" value: 1 } } } layer { name: "relu6" type: "ReLU" bottom: "fc6" top: "fc6" } layer { name: "drop6" type: "Dropout" bottom: "fc6" top: "fc6" dropout_param { dropout_ratio: 0.5 } } layer { name: "fc7" type: "InnerProduct" bottom: "fc6" top: "fc7" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } inner_product_param { num_output: 4096 weight_filler { type: "gaussian" std: 0.005 } bias_filler { type: "constant" value: 1 } } } layer { name: "relu7" type: "ReLU" bottom: "fc7" top: "fc7" } layer { name: "drop7" type: "Dropout" bottom: "fc7" top: "fc7" dropout_param { dropout_ratio: 0.5 } } layer { name: "fc8" type: "InnerProduct" bottom: "fc7" top: "fc8" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } inner_product_param { num_output: 4 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } } layer { name: "accuracy" type: "Accuracy" bottom: "fc8" bottom: "label" top: "accuracy" include { phase: TEST } } layer { name: "loss" type: "SoftmaxWithLoss" bottom: "fc8" bottom: "label" top: "loss" }

solver.prototxt文件

net: "D:/caffe/caffe-master/examples/my_classify/train_val.prototxt" test_iter: 2 test_interval: 50 base_lr: 0.001 lr_policy: "step" gamma: 0.1 stepsize: 100 display: 20 max_iter: 500 momentum: 0.9 weight_decay: 0.005 solver_mode: GPU snapshot: 200 snapshot_prefix: "D:/caffe/caffe-master/examples/my_classify/train"

至此网络搭建部分结束，接下来可以开始训练

三、训练

新建train.bat文件

caffe.exe train --solver=solver.prototxt --gpu=all pause

训练结果，其中accuracy=0.625挺低的，首先训练集不够多，其次迭代次数较少

训练结束会生成xxx.caffemodel和xxx.solverstate分别存储训练过程的参数和中段的参数信息

四、利用生成的模型使用python接口测试自己的数据

1、通过脚本将image_mean.binaryproto转换成python可以识别的mean.npy文件

import caffe import numpy as np MEAN_PROTO_PATH = 'image_mean.binaryproto' MEAN_NPY_PATH = 'mean.npy' blob = caffe.proto.caffe_pb2.BlobProto() data = open(MEAN_PROTO_PATH, 'rb' ).read() blob.ParseFromString(data) array = np.array(caffe.io.blobproto_to_array(blob)) mean_npy = array[0] np.save(MEAN_NPY_PATH ,mean_npy)

2、编写与train_val.prototxt对应的deploy.prototxt用于测试的网络模型

name: "CaffeNet" layer { name: "data" type: "Input" top: "data" input_param { shape: { dim: 10 dim: 3 dim: 256 dim: 256 } } } layer { name: "conv1" type: "Convolution" bottom: "data" top: "conv1" convolution_param { num_output: 96 kernel_size: 11 stride: 4 } } layer { name: "relu1" type: "ReLU" bottom: "conv1" top: "conv1" } layer { name: "pool1" type: "Pooling" bottom: "conv1" top: "pool1" pooling_param { pool: MAX kernel_size: 3 stride: 2 } } layer { name: "norm1" type: "LRN" bottom: "pool1" top: "norm1" lrn_param { local_size: 5 alpha: 0.0001 beta: 0.75 } } layer { name: "conv2" type: "Convolution" bottom: "norm1" top: "conv2" convolution_param { num_output: 256 pad: 2 kernel_size: 5 group: 2 } } layer { name: "relu2" type: "ReLU" bottom: "conv2" top: "conv2" } layer { name: "pool2" type: "Pooling" bottom: "conv2" top: "pool2" pooling_param { pool: MAX kernel_size: 3 stride: 2 } } layer { name: "norm2" type: "LRN" bottom: "pool2" top: "norm2" lrn_param { local_size: 5 alpha: 0.0001 beta: 0.75 } } layer { name: "conv3" type: "Convolution" bottom: "norm2" top: "conv3" convolution_param { num_output: 384 pad: 1 kernel_size: 3 } } layer { name: "relu3" type: "ReLU" bottom: "conv3" top: "conv3" } layer { name: "conv4" type: "Convolution" bottom: "conv3" top: "conv4" convolution_param { num_output: 384 pad: 1 kernel_size: 3 group: 2 } } layer { name: "relu4" type: "ReLU" bottom: "conv4" top: "conv4" } layer { name: "conv5" type: "Convolution" bottom: "conv4" top: "conv5" convolution_param { num_output: 256 pad: 1 kernel_size: 3 group: 2 } } layer { name: "relu5" type: "ReLU" bottom: "conv5" top: "conv5" } layer { name: "pool5" type: "Pooling" bottom: "conv5" top: "pool5" pooling_param { pool: MAX kernel_size: 3 stride: 2 } } layer { name: "fc6" type: "InnerProduct" bottom: "pool5" top: "fc6" inner_product_param { num_output: 4096 } } layer { name: "relu6" type: "ReLU" bottom: "fc6" top: "fc6" } layer { name: "drop6" type: "Dropout" bottom: "fc6" top: "fc6" dropout_param { dropout_ratio: 0.5 } } layer { name: "fc7" type: "InnerProduct" bottom: "fc6" top: "fc7" inner_product_param { num_output: 4096 } } layer { name: "relu7" type: "ReLU" bottom: "fc7" top: "fc7" } layer { name: "drop7" type: "Dropout" bottom: "fc7" top: "fc7" dropout_param { dropout_ratio: 0.5 } } layer { name: "fc8" type: "InnerProduct" bottom: "fc7" top: "fc8" inner_product_param { num_output: 4 } } layer { name: "prob" type: "Softmax" bottom: "fc8" top: "prob" }

3、编写python接口

import caffe import numpy as np root = 'D:/caffe/caffe-master/examples/my_classify/' #设置测试网络 deploy = root + 'deploy.prototxt' #添加训练的网络权重参数 caffe_model = root + 'train_iter_500.caffemodel' #测试图片 img = root + '4.jpg' #标签文件 label_file = root + 'label.txt' #均值文件 mean_file = root + 'mean.npy' #设置使用GPU caffe.set_model_gpu() #构造一个net net = caffe.Net(deploy,caffe_model,caffe.TEST) # 得到data的形状，这里的图片是默认matplotlib底层加载的 transformer = caffe.io.Transformer({'data':net.blobs['data'].data.shape}) # matplotlib加载的image是像素[0-1],图片的数据格式[weight,high,channels]，RGB # caffe加载的图片需要的是[0-255]像素，数据格式[channels,weight,high],BGR，那么就需要转换 transformer.set_transpose('data', (2,0,1)) transformer.set_mean('data', np.load(mean_file).mean(1).mean(1)) # 图片像素放大到[0-255] transformer.set_raw_scale('data', 255) # RGB-->BGR 转换 transformer.set_channel_swap('data', (2,1,0)) #设置输入的图片shape，1张，3通道，长宽都是256 net.blobs['data'].reshape(1,3,256,256) #加载图片 im = caffe.io.load_image(img) net.blobs['data'].data[...] = transformer.preprocess('data', im) #输出每层网络的name和shape for layer_name,blob in net.blobs.iteritems(): print layer_name + '\t' + str(blob.data.shape) #网络向前传播 out = net.forward() labels = np.loadtxt(label_file,str,delimiter = '\t') prob = net.blobs['prob'].data[0].flatten() print prob order = prob.argsort()[-1] #输出类别 print 'the class is:',labels[order]

测试图片为： 分类结果为：