name: "RealismCNN"

layers {
  name: "positive_data"
  type: IMAGE_DATA
  top: "positive_data"
  top: "positive_label"
  image_data_param {
    source: "/data/models/RealismCNN/natural_image_train.txt"
    batch_size: 25
    new_height: 256
    new_width: 256
  }
  transform_param {
    crop_size: 224
    mean_value: 103.9390
    mean_value: 116.7790
    mean_value: 123.6800
    mirror: true
  }
  include: { phase: TRAIN }
}

layers {
  name: "negative_data"
  type: IMAGE_DATA
  top: "negative_data"
  top: "negative_label"
  image_data_param {
    source: "/data/models/RealismCNN/composites_train.txt"
    batch_size: 25
    new_height: 256
    new_width: 256
  }
  transform_param {
    crop_size: 224
    mean_value: 103.9390
    mean_value: 116.7790
    mean_value: 123.6800
    mirror: true
  }
  include: { phase: TRAIN }
}

layers {
 name: "label"
 type: CONCAT
 top: "label"
 bottom: "positive_label"
 bottom: "negative_label"
 concat_param {
 concat_dim: 0
}
 include: {phase: TRAIN}
}

layers {
 name: "data"
 type: CONCAT
 top: "data"
 bottom: "positive_data"
 bottom: "negative_data"
 concat_param {
 concat_dim: 0
}
 include: {phase: TRAIN}
}

layers {
  name: "positive_data"
  type: IMAGE_DATA
  top: "positive_data"
  top: "positive_label"
  image_data_param {
    source: "/data/models/RealismCNN/natural_image_test.txt"
    batch_size: 25
    new_height: 256
    new_width: 256
  }
  transform_param {
    crop_size: 224
    mean_value: 103.9390
    mean_value: 116.7790
    mean_value: 123.6800
    mirror: true
  }
  include: { phase: TEST }
}

layers {
  name: "negative_data"
  type: IMAGE_DATA
  top: "negative_data"
  top: "negative_label"
  image_data_param {
    source: "/data/models/RealismCNN/composites_test.txt"
    batch_size: 25
    new_height: 256
    new_width: 256
  }
  transform_param {
    crop_size: 224
    mean_value: 103.9390
    mean_value: 116.7790
    mean_value: 123.6800
    mirror: true
  }
  include: { phase: TEST }
}

layers {
 name: "label"
 type: CONCAT
 top: "label"
 bottom: "positive_label"
 bottom: "negative_label"
 concat_param {
 concat_dim: 0
}
 include: {phase: TEST}
}

layers {
 name: "data"
 type: CONCAT
 top: "data"
 bottom: "positive_data"
 bottom: "negative_data"
 concat_param {
 concat_dim: 0
}
 include: {phase: TEST}
}

layers {
  bottom: "data"
  top: "conv1_1"
  name: "conv1_1"
  type: CONVOLUTION
  blobs_lr: 1
  blobs_lr: 2
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
  }
}

layers {
  bottom: "conv1_1"
  top: "conv1_1"
  name: "relu1_1"
  type: RELU
}

layers {
  bottom: "conv1_1"
  top: "conv1_2"
  name: "conv1_2"
  type: CONVOLUTION
  blobs_lr: 1
  blobs_lr: 2
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
  }
}

layers {
  bottom: "conv1_2"
  top: "conv1_2"
  name: "relu1_2"
  type: RELU
}

layers {
  bottom: "conv1_2"
  top: "pool1"
  name: "pool1"
  type: POOLING
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}

layers {
  bottom: "pool1"
  top: "conv2_1"
  name: "conv2_1"
  type: CONVOLUTION
  blobs_lr: 1
  blobs_lr: 2
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
  }
}

layers {
  bottom: "conv2_1"
  top: "conv2_1"
  name: "relu2_1"
  type: RELU
}

layers {
  bottom: "conv2_1"
  top: "conv2_2"
  name: "conv2_2"
  type: CONVOLUTION
  blobs_lr: 1
  blobs_lr: 2
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
  }
}

layers {
  bottom: "conv2_2"
  top: "conv2_2"
  name: "relu2_2"
  type: RELU
}

layers {
  bottom: "conv2_2"
  top: "pool2"
  name: "pool2"
  type: POOLING
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}

layers {
  bottom: "pool2"
  top: "conv3_1"
  name: "conv3_1"
  type: CONVOLUTION
  blobs_lr: 1
  blobs_lr: 2
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
  }
}

layers {
  bottom: "conv3_1"
  top: "conv3_1"
  name: "relu3_1"
  type: RELU
}

layers {
  bottom: "conv3_1"
  top: "conv3_2"
  name: "conv3_2"
  type: CONVOLUTION
  blobs_lr: 1
  blobs_lr: 2
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
  }
}

layers {
  bottom: "conv3_2"
  top: "conv3_2"
  name: "relu3_2"
  type: RELU
}

layers {
  bottom: "conv3_2"
  top: "conv3_3"
  name: "conv3_3"
  type: CONVOLUTION
  blobs_lr: 1
  blobs_lr: 2
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
  }
}

layers {
  bottom: "conv3_3"
  top: "conv3_3"
  name: "relu3_3"
  type: RELU
}

layers {
  bottom: "conv3_3"
  top: "pool3"
  name: "pool3"
  type: POOLING
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}

layers {
  bottom: "pool3"
  top: "conv4_1"
  name: "conv4_1"
  type: CONVOLUTION
  blobs_lr: 1
  blobs_lr: 2
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
  }
}

layers {
  bottom: "conv4_1"
  top: "conv4_1"
  name: "relu4_1"
  type: RELU
}

layers {
  bottom: "conv4_1"
  top: "conv4_2"
  name: "conv4_2"
  type: CONVOLUTION
  blobs_lr: 1
  blobs_lr: 2
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
  }
}

layers {
  bottom: "conv4_2"
  top: "conv4_2"
  name: "relu4_2"
  type: RELU
}

layers {
  bottom: "conv4_2"
  top: "conv4_3"
  name: "conv4_3"
  type: CONVOLUTION
  blobs_lr: 1
  blobs_lr: 2
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
  }
}

layers {
  bottom: "conv4_3"
  top: "conv4_3"
  name: "relu4_3"
  type: RELU
}

layers {
  bottom: "conv4_3"
  top: "pool4"
  name: "pool4"
  type: POOLING
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}

layers {
  bottom: "pool4"
  top: "conv5_1"
  name: "conv5_1"
  type: CONVOLUTION
  blobs_lr: 1
  blobs_lr: 2
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
  }
}

layers {
  bottom: "conv5_1"
  top: "conv5_1"
  name: "relu5_1"
  type: RELU
}

layers {
  bottom: "conv5_1"
  top: "conv5_2"
  name: "conv5_2"
  type: CONVOLUTION
  blobs_lr: 1
  blobs_lr: 2
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
  }
}

layers {
  bottom: "conv5_2"
  top: "conv5_2"
  name: "relu5_2"
  type: RELU
}

layers {
  bottom: "conv5_2"
  top: "conv5_3"
  name: "conv5_3"
  type: CONVOLUTION
  blobs_lr: 1
  blobs_lr: 2
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
  }
}

layers {
  bottom: "conv5_3"
  top: "conv5_3"
  name: "relu5_3"
  type: RELU
}

layers {
  bottom: "conv5_3"
  top: "pool5"
  name: "pool5"
  type: POOLING
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}

layers {
  bottom: "pool5"
  top: "fc6"
  name: "fc6"
  type: INNER_PRODUCT
  inner_product_param {
    num_output: 4096
  }
}

layers {
  bottom: "fc6"
  top: "fc6"
  name: "relu6"
  type: RELU
}

layers {
  bottom: "fc6"
  top: "fc6"
  name: "drop6"
  type: DROPOUT
  dropout_param {
    dropout_ratio: 0.5
  }
}

layers {
  bottom: "fc6"
  top: "fc7"
  name: "fc7"
  type: INNER_PRODUCT

  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  inner_product_param {
    num_output: 4096
    weight_filler {
      type: "gaussian"
      std: 0.005
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}

layers {
  bottom: "fc7"
  top: "fc7"
  name: "relu7"
  type: RELU
}

layers {
  bottom: "fc7"
  top: "fc7"
  name: "drop7"
  type: DROPOUT
  dropout_param {
    dropout_ratio: 0.5
  }
}

layers {
  bottom: "fc7"
  top: "fc8_realism"
  name: "fc8_realism"
  blobs_lr: 10
  blobs_lr: 20
  type: INNER_PRODUCT
  inner_product_param {
    num_output: 2
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layers {
  name: "loss"
  type: SOFTMAX_LOSS
  bottom: "fc8_realism"
  bottom: "label"
}

layers {
  name: "accuracy"
  type: ACCURACY
  bottom: "fc8_realism"
  bottom: "label"
  top: "accuracy"
  include: { phase: TEST }
}