根据 *_train_test.prototxt文件生成 *

根据 _train_test.prototxt文件生成 _deploy.prototxt文件

发表于2016/8/6 19:43:11 1218人阅读

本文参考博文

（1）介绍 *_train_test.prototxt文件与 *_deploy.prototxt文件的不同：http://blog.csdn.net/sunshine_in_moon/article/details/49472901

（2）生成deploy文件的Python代码：http://www.cnblogs.com/denny402/p/5685818.html

*_train_test.prototxt文件

这是训练与测试网络配置文件

*_deploy.prototxt文件

这是模型构造文件

在博文http://www.cnblogs.com/denny402/p/5685818.html 中给出了生成 deploy.prototxt文件的Python源代码，但是每个网络不同，修改起来比较麻烦，下面给出该博文中以mnist为例生成deploy文件的源代码，可根据自己网络的设置做出相应修改：（下方代码未测试）

# -*- coding: utf-8 -*-from caffe import layers as L,params as P,to_proto
root='/home/xxx/'
deploy=root+'mnist/deploy.prototxt'    #文件保存路径def create_deploy():#少了第一层，data层conv1=L.Convolution(bottom='data', kernel_size=5, stride=1,num_output=20, pad=0,weight_filler=dict(type='xavier'))pool1=L.Pooling(conv1, pool=P.Pooling.MAX, kernel_size=2, stride=2)conv2=L.Convolution(pool1, kernel_size=5, stride=1,num_output=50, pad=0,weight_filler=dict(type='xavier'))pool2=L.Pooling(conv2, pool=P.Pooling.MAX, kernel_size=2, stride=2)fc3=L.InnerProduct(pool2, num_output=500,weight_filler=dict(type='xavier'))relu3=L.ReLU(fc3, in_place=True)fc4 = L.InnerProduct(relu3, num_output=10,weight_filler=dict(type='xavier'))#最后没有accuracy层，但有一个Softmax层prob=L.Softmax(fc4)return to_proto(prob)
def write_deploy(): with open(deploy, 'w') as f:f.write('name:"Lenet"\n')f.write('input:"data"\n')f.write('input_dim:1\n')f.write('input_dim:3\n')f.write('input_dim:28\n')f.write('input_dim:28\n')f.write(str(create_deploy()))
if __name__ == '__main__':write_deploy()

用代码生成deploy文件还是比较麻烦。我们在构建深度学习网络时，肯定会先定义好训练与测试网络的配置文件——*_train_test.prototxt文件，我们可以通过修改*_train_test.prototxt文件来生成 deploy 文件。以cifar10为例先简单介绍一下两者的区别。

（1）deploy 文件中的数据层更为简单，即将*_train_test.prototxt文件中的输入训练数据lmdb与输入测试数据lmdb这两层删除，取而代之的是，

layer {name: "data"type: "Input"top: "data"input_param { shape: { dim: 1 dim: 3 dim: 32 dim: 32 } }
}

注：shape: { dim: 1 dim: 3 dim: 32 dim: 32 }代表含义：

shape {dim: 1  #num，对待识别样本进行数据增广的数量，可自行定义。一般会进行5次crop，之后分别flip。如果该值为10则表示一个样本会变成10个，之后输入到网络进行识别。如果不进行数据增广，可以设置成1dim: 3  #通道数，表示RGB三个通道dim: 32   #图像的长和宽，通过 *_train_test.prototxt文件中数据输入层的crop_size获取dim: 32

（2）卷积层和全连接层中weight_filler{}与bias_filler{}两个参数不用再填写，因为这两个参数的值，由已经训练好的模型*.caffemodel文件提供。如下所示代码，将*_train_test.prototxt文件中的weight_filler、bias_filler全部删除。

layer {                              # weight_filler、bias_filler删除
name: "ip2"
type: "InnerProduct"
bottom: "ip1"
top: "ip2"
param {
    lr_mult: 1   #权重w的学习率倍数
}
param {
    lr_mult: 2    #偏置b的学习率倍数
}
inner_product_param {
    num_output: 10
    weight_filler {
      type: "gaussian"
      std: 0.1
    }
    bias_filler {
      type: "constant"
    }
}
}

删除后变为

layer {                             name: "ip2"type: "InnerProduct"bottom: "ip1"top: "ip2"param {lr_mult: 1}param {lr_mult: 2}inner_product_param {num_output: 10}
}

（3）输出层的变化

1）没有了test模块测试精度，将该层删除

2）输出层

1）*_deploy.prototxt文件的构造和*_train_test.prototxt文件的构造最为明显的不同点是，deploy文件没有test网络中的test模块，只有训练模块，即将*_train_test.prototxt中最后部分的test模块测试精度删除，即将如下代码删除。

layer {                                  #删除该层name: "accuracy"type: "Accuracy"bottom: "ip2"bottom: "label"top: "accuracy"include {phase: TEST}
}

2）输出层

*_train_test.prototxt文件

layer{name: "loss"   #注意此处层名称与下面的不同type: "SoftmaxWithLoss"  #注意此处与下面的不同bottom: "ip2"bottom: "label"    #注意标签项在下面没有了，因为下面的预测属于哪个标签，因此不能提供标签top: "loss"
}

*_deploy.prototxt文件

layer {name: "prob"type: "Softmax"bottom: "ip2"top: "prob"
}

注意在两个文件中输出层的类型都发生了变化一个是SoftmaxWithLoss，另一个是Softmax。另外为了方便区分训练与应用输出，训练是输出时是loss，应用时是prob。

下面给出CIFAR10中的配置文件cifar10_quick_train_test.prototxt与其模型构造文件 cifar10_quick.prototxt 直观展示两者的区别。

cifar10_quick_train_test.prototxt文件代码

name: "CIFAR10_quick"
layer {               #该层去掉
name: "cifar"
type: "Data"
top: "data"
top: "label"
include {
    phase: TRAIN
}
transform_param {
    mean_file: "examples/cifar10/mean.binaryproto"
}
data_param {
    source: "examples/cifar10/cifar10_train_lmdb"
    batch_size: 100
    backend: LMDB
}
}
layer {             #该层去掉
name: "cifar"
type: "Data"
top: "data"
top: "label"
include {
    phase: TEST
}
transform_param {
    mean_file: "examples/cifar10/mean.binaryproto"
}
data_param {
    source: "examples/cifar10/cifar10_test_lmdb"
    batch_size: 100
    backend: LMDB
}
}
layer {                        #将下方的weight_filler、bias_filler全部删除
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
    lr_mult: 1
}
param {
    lr_mult: 2
}
convolution_param {
    num_output: 32
    pad: 2
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.0001
    }
    bias_filler {
      type: "constant"
    }
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "conv1"
top: "pool1"
pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "pool1"
top: "pool1"
}
layer {                         #weight_filler、bias_filler删除
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
    lr_mult: 1
}
param {
    lr_mult: 2
}
convolution_param {
    num_output: 32
    pad: 2
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "pool2"
type: "Pooling"
bottom: "conv2"
top: "pool2"
pooling_param {
    pool: AVE
    kernel_size: 3
    stride: 2
}
}
layer {                         #weight_filler、bias_filler删除
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
    lr_mult: 1
}
param {
    lr_mult: 2
}
convolution_param {
    num_output: 64
    pad: 2
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "pool3"
type: "Pooling"
bottom: "conv3"
top: "pool3"
pooling_param {
    pool: AVE
    kernel_size: 3
    stride: 2
}
}
layer {                       #weight_filler、bias_filler删除
name: "ip1"
type: "InnerProduct"
bottom: "pool3"
top: "ip1"
param {
    lr_mult: 1
}
param {
    lr_mult: 2
}
inner_product_param {
    num_output: 64
    weight_filler {
      type: "gaussian"
      std: 0.1
    }
    bias_filler {
      type: "constant"
    }
}
}
layer {                              # weight_filler、bias_filler删除
name: "ip2"
type: "InnerProduct"
bottom: "ip1"
top: "ip2"
param {
    lr_mult: 1
}
param {
    lr_mult: 2
}
inner_product_param {
    num_output: 10
    weight_filler {
      type: "gaussian"
      std: 0.1
    }
    bias_filler {
      type: "constant"
    }
}
}
layer {                                  #将该层删除
name: "accuracy"
type: "Accuracy"
bottom: "ip2"
bottom: "label"
top: "accuracy"
include {
    phase: TEST
}
}
layer {                                 #修改
name: "loss"       #---loss 修改为 prob
type: "SoftmaxWithLoss"            # SoftmaxWithLoss 修改为 softmax
bottom: "ip2"
bottom: "label"          #去掉
top: "loss"
}

以下为cifar10_quick.prototxt

layer {               #将两个输入层修改为该层
name: "data"
type: "Input"
top: "data"
input_param { shape: { dim: 1 dim: 3 dim: 32 dim: 32 } }     #注意shape中变量值的修改，CIFAR10中的 *_train_test.protxt文件中没有 crop_size
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
    lr_mult: 1   #权重W的学习率倍数
}
param {
    lr_mult: 2   #偏置b的学习率倍数
}
convolution_param {
    num_output: 32
    pad: 2   #加边为2
   kernel_size: 5
    stride: 1
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "conv1"
top: "pool1"
pooling_param {
    pool: MAX    #Max Pooling
   kernel_size: 3
    stride: 2
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "pool1"
top: "pool1"
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
    lr_mult: 1
}
param {
    lr_mult: 2
}
convolution_param {
    num_output: 32
    pad: 2
    kernel_size: 5
    stride: 1
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "pool2"
type: "Pooling"
bottom: "conv2"
top: "pool2"
pooling_param {
    pool: AVE   #均值池化
    kernel_size: 3
    stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
    lr_mult: 1
}
param {
    lr_mult: 2
}
convolution_param {
    num_output: 64
    pad: 2
    kernel_size: 5
    stride: 1
}
}
layer {
name: "relu3"
type: "ReLU" #使用ReLU激励函数，这里需要注意的是，本层的bottom和top都是conv3>
bottom: "conv3"
top: "conv3"
}
layer {
name: "pool3"
type: "Pooling"
bottom: "conv3"
top: "pool3"
pooling_param {
    pool: AVE
kernel_size: 3
    stride: 2
}
}
layer {
name: "ip1"
type: "InnerProduct"
bottom: "pool3"
top: "ip1"
param {
    lr_mult: 1
}
param {
    lr_mult: 2
}
inner_product_param {
    num_output: 64
}
}
layer {
name: "ip2"
type: "InnerProduct"
bottom: "ip1"
top: "ip2"
param {
    lr_mult: 1
}
param {
    lr_mult: 2
}
inner_product_param {
    num_output: 10
}
}
layer {
name: "prob"
type: "Softmax"
bottom: "ip2"
top: "prob"
}