Caffe学习笔记(七)：使用训练好的model做预测(mnist)

# -*- coding: UTF-8 -*-
import caffe      

def creat_deploy():
    net = caffe.NetSpec()
    net.conv1 = caffe.layers.Convolution(bottom = 'data', kernel_size = 5, num_output = 20,
                                         weight_filler = dict(type = 'xavier'))
    net.pool1 = caffe.layers.Pooling(net.conv1, kernel_size = 2, stride = 2,
                                     pool = caffe.params.Pooling.MAX)
    net.conv2 = caffe.layers.Convolution(net.pool1, kernel_size = 5, num_output = 50,
                                         weight_filler = dict(type = 'xavier'))
    net.pool2 = caffe.layers.Pooling(net.conv2, kernel_size = 2, stride = 2,
                                     pool = caffe.params.Pooling.MAX)
    net.fc1 =   caffe.layers.InnerProduct(net.pool2, num_output = 500,
                                          weight_filler = dict(type = 'xavier'))
    net.relu1 = caffe.layers.ReLU(net.fc1, in_place = True)
    net.score = caffe.layers.InnerProduct(net.relu1, num_output = 10,
                                          weight_filler = dict(type = 'xavier'))
    net.prob = caffe.layers.Softmax(net.score)
    return net.to_proto()

def write_net(deploy_proto):
    #写入deploy.prototxt文件
    with open(deploy_proto, 'w') as f:
        #写入第一层数据描述
        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(creat_deploy()))

if __name__ == '__main__':
     my_project_root = "/home/Jack-Cui/caffe-master/my-caffe-project/"  
     deploy_proto = my_project_root + "mnist/deploy.prototxt"  
     write_net(deploy_proto)   

input:"data"
input_dim:1
input_dim:3
input_dim:28
input_dim:28
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  convolution_param {
    num_output: 20
    kernel_size: 5
    weight_filler {
      type: "xavier"
    }
  }
}
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"
  convolution_param {
    num_output: 50
    kernel_size: 5
    weight_filler {
      type: "xavier"
    }
  }
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv2"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "fc1"
  type: "InnerProduct"
  bottom: "pool2"
  top: "fc1"
  inner_product_param {
    num_output: 500
    weight_filler {
      type: "xavier"
    }
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "fc1"
  top: "fc1"
}
layer {
  name: "score"
  type: "InnerProduct"
  bottom: "fc1"
  top: "score"
  inner_product_param {
    num_output: 10
    weight_filler {
      type: "xavier"
    }
  }
}
layer {
  name: "prob"
  type: "Softmax"
  bottom: "score"
  top: "prob"
}

# -*- coding: UTF-8 -*-
import caffe                                                     
import numpy as np

def test(my_project_root, deploy_proto):
    caffe_model = my_project_root + 'mnist_iter_9380.caffemodel'        #caffe_model文件的位置
    img = my_project_root + 'mnist/test/6/09269.png'                    #随机找的一张待测图片
    labels_filename = my_project_root + 'mnist/test/labels.txt'            #类别名称文件,将数字标签转换回类别名称

    net = caffe.Net(deploy_proto, caffe_model, caffe.TEST)                #加载model和deploy

    #图片预处理设置
    transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})  #设定图片的shape格式(1,3,28,28)
    transformer.set_transpose('data', (2,0,1))                            #改变维度的顺序，由原始图片(28,28,3)变为(3,28,28)
    transformer.set_raw_scale('data', 255)                                # 缩放到【0，255】之间
    transformer.set_channel_swap('data', (2,1,0))                       #交换通道，将图片由RGB变为BGR

    im = caffe.io.load_image(img)                                       #加载图片
    net.blobs['data'].data[...] = transformer.preprocess('data',im)     #执行上面设置的图片预处理操作，并将图片载入到blob中

    out = net.forward()                                                    #执行测试

    labels = np.loadtxt(labels_filename, str, delimiter='\t')           #读取类别名称文件
    prob = net.blobs['prob'].data[0].flatten()                             #取出最后一层（Softmax）属于某个类别的概率值
    order = prob.argsort()[-1]                                          #将概率值排序，取出最大值所在的序号
    print '图片数字为:',labels[order]                                   #将该序号转换成对应的类别名称，并打印

if __name__ == '__main__':
    my_project_root = "/home/Jack-Cui/caffe-master/my-caffe-project/"    #my-caffe-project目录
    deploy_proto = my_project_root + "mnist/deploy.prototxt"            #保存deploy.prototxt文件的位置
    test(my_project_root, deploy_proto)