[顔認識]VGG Face Modelを使用して1枚の画像をテストする

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深い学習とコンピュータの視覚
前編の手順に従って訓練を終えた場合、VGGという名前が得られます.FACE.Caffemodelのモデルをテストします.

一、新しいプロファイル


  テストの前に、VGG_とともに新しいプロファイルを作成する必要があります.FACE_deploy.prototxtは似ていますが、ちょっと違います.前編のVGG_FACE_deploy.prototxtはモデルを訓練するためであり、モデルをテストするために新しいプロファイルが必要です.VGGをコピーできますFACE_deploy.prototxtはdeployと名付けられた.prototxt,deploy.prototxtの内容は以下の通りです:(直接コピーを推奨)
name: "VGG_FACE_16_Net"
input: "data"   
input_dim: 1   
input_dim: 3   
input_dim: 224   
input_dim: 224  
force_backward: true 
layer {
  name: "conv1_1"
  type: "Convolution"
  bottom: "data"
  top: "conv1_1"
  convolution_param {
    num_output: 64
    kernel_size: 3
    pad: 1
  }
}
layer {
  name: "relu1_1"
  type: "ReLU"
  bottom: "conv1_1"
  top: "conv1_1"
}
layer {
  name: "conv1_2"
  type: "Convolution"
  bottom: "conv1_1"
  top: "conv1_2"
  convolution_param {
    num_output: 64
    kernel_size: 3
    pad: 1
  } 
}
layer {
  name: "relu1_2"
  type: "ReLU"
  bottom: "conv1_2"
  top: "conv1_2"
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1_2"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "conv2_1"
  type: "Convolution"
  bottom: "pool1"
  top: "conv2_1"
  convolution_param {
    num_output: 128
    kernel_size: 3
    pad: 1
  } 
}
layer {
  name: "relu2_1"
  type: "ReLU"
  bottom: "conv2_1"
  top: "conv2_1"
}
layer { 
  name: "conv2_2"
  type: "Convolution"
  bottom: "conv2_1"
  top: "conv2_2"
  convolution_param {
    num_output: 128
    kernel_size: 3
    pad: 1
  } 
}
layer {
  name: "relu2_2"
  type: "ReLU"
  bottom: "conv2_2"
  top: "conv2_2"
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv2_2"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "conv3_1"
  type: "Convolution"
  bottom: "pool2"
  top: "conv3_1"
  convolution_param {
    num_output: 256
    kernel_size: 3
    pad: 1
  }
}
layer {
  name: "relu3_1"
  type: "ReLU"
  bottom: "conv3_1"
  top: "conv3_1"
}
layer {
  name: "conv3_2"
  type: "Convolution"
  bottom: "conv3_1"
  top: "conv3_2"
  convolution_param {
    num_output: 256
    kernel_size: 3
    pad: 1
  }
}
layer {
  name: "relu3_2"
  type: "ReLU"
  bottom: "conv3_2"
  top: "conv3_2"
}
layer {
  name: "conv3_3"
  type: "Convolution"
  bottom: "conv3_2"
  top: "conv3_3"
  convolution_param {
    num_output: 256
    kernel_size: 3
    pad: 1
  }
}
layer {
  name: "relu3_3"
  type: "ReLU"
  bottom: "conv3_3"
  top: "conv3_3"
}
layer {
  name: "pool3"
  type: "Pooling"
  bottom: "conv3_3"
  top: "pool3"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "conv4_1"
  type: "Convolution"
  bottom: "pool3"
  top: "conv4_1"
  convolution_param {
    num_output: 512
    kernel_size: 3
    pad: 1
  }
}
layer {
  name: "relu4_1"
  type: "ReLU"
  bottom: "conv4_1"
  top: "conv4_1"
}
layer {
  name: "conv4_2"
  type: "Convolution"
  bottom: "conv4_1"
  top: "conv4_2"
  convolution_param {
    num_output: 512
    kernel_size: 3
    pad: 1
  }
}
layer {
  name: "relu4_2"
  type: "ReLU"
  bottom: "conv4_2"
  top: "conv4_2"
}
layer {
  name: "conv4_3"
  type: "Convolution"
  bottom: "conv4_2"
  top: "conv4_3"
  convolution_param {
    num_output: 512
    kernel_size: 3
    pad: 1
  }
}
layer {
  name: "relu4_3"
  type: "ReLU"
  bottom: "conv4_3"
  top: "conv4_3"
}
layer {
  name: "pool4"
  type: "Pooling"
  bottom: "conv4_3"
  top: "pool4"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "conv5_1"
  type: "Convolution"
  bottom: "pool4"
  top: "conv5_1"
  convolution_param {
    num_output: 512
    kernel_size: 3
    pad: 1
  }
}
layer {
  name: "relu5_1"
  type: "ReLU"
  bottom: "conv5_1"
  top: "conv5_1"
}
layer {
  name: "conv5_2"
  type: "Convolution"
  bottom: "conv5_1"
  top: "conv5_2"
  convolution_param {
    num_output: 512
    kernel_size: 3
    pad: 1
  }
}
layer {
  name: "relu5_2"
  type: "ReLU"
  bottom: "conv5_2"
  top: "conv5_2"
}
layer {
  name: "conv5_3"
  type: "Convolution"
  bottom: "conv5_2"
  top: "conv5_3"
  convolution_param {
    num_output: 512
    kernel_size: 3
    pad: 1
  }
}
layer {
  name: "relu5_3"
  type: "ReLU"
  bottom: "conv5_3"
  top: "conv5_3"
}
layer {
  name: "pool5"
  type: "Pooling"
  bottom: "conv5_3"
  top: "pool5"
  pooling_param {
    pool: MAX
    kernel_size: 2
    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"
  # Note that lr_mult can be set to 0 to disable any fine-tuning of this, and any other, layer
  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_flickr"
  type: "InnerProduct"
  bottom: "fc7"
  top: "fc8_flickr"
  # lr_mult is set to higher than for other layers, because this layer is starting from random while the others are already trained
  propagate_down: false
  inner_product_param {
    num_output: 1072   # 
  }
}
layer {
  name: "prob"
  type: "Softmax"
  bottom: "fc8_flickr"
  top: "prob"
}

  注意すべきは342行num_output、トレーニングの種類数に変更する必要があります.ジルコニアprototxtとVGG_FACE_deploy.prototxtの違い、推奨参考:  http://blog.csdn.net/fx409494616/article/details/53008971

二、モデルテスト


テストコードは次のとおりです.
# -*-coding:utf8-*-#
import caffe

#deploy 
deployFile =    # 
#caffemodel
modelFile =     # 
# 
imgPath =       # 

def predictImg(net,imgPath):
    #  
    transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})
    transformer.set_transpose('data', (2,0,1))
    #  【0，255】  
    transformer.set_raw_scale('data', 255)
    # ， RGB BGR 
    transformer.set_channel_swap('data', (2,1,0))
    #   
    im = caffe.io.load_image(imgPath)
    # ， blob 
    net.blobs['data'].data[...] = transformer.preprocess('data',im)   
    # #  
    output = net.forward()  
    output_prob = output['prob'][0]
    print(str(output_prob.argmax()))

if __name__ == '__main__':
    # gpu
    caffe.set_mode_gpu()
    # 
    net = caffe.Net(deployFile,modelFile,caffe.TEST)
    predictImg(net,imgPath)

  上記pythonコードを実行すると、そのピクチャが属する種類が印刷されます.参照:http://www.cnblogs.com/Allen-rg/p/5834551.html
杭電oj符号化2026
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