pythonproject/wechat_qrcode/sr.prototxt

layer {
  name: "data"
  type: "Input"
  top: "data"
  input_param {
    shape {
      dim: 1
      dim: 1
      dim: 224
      dim: 224
    }
  }
}
layer {
  name: "conv0"
  type: "Convolution"
  bottom: "data"
  top: "conv0"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 32
    bias_term: true
    pad: 1
    kernel_size: 3
    group: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv0/lrelu"
  type: "ReLU"
  bottom: "conv0"
  top: "conv0"
  relu_param {
    negative_slope: 0.05000000074505806
  }
}
layer {
  name: "db1/reduce"
  type: "Convolution"
  bottom: "conv0"
  top: "db1/reduce"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 8
    bias_term: true
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "db1/reduce/lrelu"
  type: "ReLU"
  bottom: "db1/reduce"
  top: "db1/reduce"
  relu_param {
    negative_slope: 0.05000000074505806
  }
}
layer {
  name: "db1/3x3"
  type: "Convolution"
  bottom: "db1/reduce"
  top: "db1/3x3"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 8
    bias_term: true
    pad: 1
    kernel_size: 3
    group: 8
    stride: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "db1/3x3/lrelu"
  type: "ReLU"
  bottom: "db1/3x3"
  top: "db1/3x3"
  relu_param {
    negative_slope: 0.05000000074505806
  }
}
layer {
  name: "db1/1x1"
  type: "Convolution"
  bottom: "db1/3x3"
  top: "db1/1x1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 32
    bias_term: true
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "db1/1x1/lrelu"
  type: "ReLU"
  bottom: "db1/1x1"
  top: "db1/1x1"
  relu_param {
    negative_slope: 0.05000000074505806
  }
}
layer {
  name: "db1/concat"
  type: "Concat"
  bottom: "conv0"
  bottom: "db1/1x1"
  top: "db1/concat"
  concat_param {
    axis: 1
  }
}
layer {
  name: "db2/reduce"
  type: "Convolution"
  bottom: "db1/concat"
  top: "db2/reduce"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 8
    bias_term: true
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "db2/reduce/lrelu"
  type: "ReLU"
  bottom: "db2/reduce"
  top: "db2/reduce"
  relu_param {
    negative_slope: 0.05000000074505806
  }
}
layer {
  name: "db2/3x3"
  type: "Convolution"
  bottom: "db2/reduce"
  top: "db2/3x3"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 8
    bias_term: true
    pad: 1
    kernel_size: 3
    group: 8
    stride: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "db2/3x3/lrelu"
  type: "ReLU"
  bottom: "db2/3x3"
  top: "db2/3x3"
  relu_param {
    negative_slope: 0.05000000074505806
  }
}
layer {
  name: "db2/1x1"
  type: "Convolution"
  bottom: "db2/3x3"
  top: "db2/1x1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 32
    bias_term: true
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "db2/1x1/lrelu"
  type: "ReLU"
  bottom: "db2/1x1"
  top: "db2/1x1"
  relu_param {
    negative_slope: 0.05000000074505806
  }
}
layer {
  name: "db2/concat"
  type: "Concat"
  bottom: "db1/concat"
  bottom: "db2/1x1"
  top: "db2/concat"
  concat_param {
    axis: 1
  }
}
layer {
  name: "upsample/reduce"
  type: "Convolution"
  bottom: "db2/concat"
  top: "upsample/reduce"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 32
    bias_term: true
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "upsample/reduce/lrelu"
  type: "ReLU"
  bottom: "upsample/reduce"
  top: "upsample/reduce"
  relu_param {
    negative_slope: 0.05000000074505806
  }
}
layer {
  name: "upsample/deconv"
  type: "Deconvolution"
  bottom: "upsample/reduce"
  top: "upsample/deconv"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 32
    bias_term: true
    pad: 1
    kernel_size: 3
    group: 32
    stride: 2
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "upsample/lrelu"
  type: "ReLU"
  bottom: "upsample/deconv"
  top: "upsample/deconv"
  relu_param {
    negative_slope: 0.05000000074505806
  }
}
layer {
  name: "upsample/rec"
  type: "Convolution"
  bottom: "upsample/deconv"
  top: "upsample/rec"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 1
    bias_term: true
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "nearest"
  type: "Deconvolution"
  bottom: "data"
  top: "nearest"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 1
    bias_term: false
    pad: 0
    kernel_size: 2
    group: 1
    stride: 2
    weight_filler {
      type: "constant"
      value: 1.0
    }
  }
}
layer {
  name: "Crop1"
  type: "Crop"
  bottom: "nearest"
  bottom: "upsample/rec"
  top: "Crop1"
}
layer {
  name: "fc"
  type: "Eltwise"
  bottom: "Crop1"
  bottom: "upsample/rec"
  top: "fc"
  eltwise_param {
    operation: SUM
  }
}