face_alignment/mtcnn_pytorch/caffe_models/det2.prototxt

name: "RNet"
input: "data"
input_dim: 1
input_dim: 3
input_dim: 24
input_dim: 24


##########################
######################
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 28
    kernel_size: 3
    stride: 1
     weight_filler {
      type: "xavier"
	}
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "prelu1"
  type: "PReLU"
  bottom: "conv1"
  top: "conv1"
  propagate_down: true
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}

layer {
  name: "conv2"
  type: "Convolution"
  bottom: "pool1"
  top: "conv2"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 48
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
	}
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "prelu2"
  type: "PReLU"
  bottom: "conv2"
  top: "conv2"
  propagate_down: true
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv2"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
####################################

##################################
layer {
  name: "conv3"
  type: "Convolution"
  bottom: "pool2"
  top: "conv3"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    kernel_size: 2
    stride: 1
    weight_filler {
      type: "xavier"
	}
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "prelu3"
  type: "PReLU"
  bottom: "conv3"
  top: "conv3"
  propagate_down: true
}
###############################

###############################

layer {
  name: "conv4"
  type: "InnerProduct"
  bottom: "conv3"
  top: "conv4"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  inner_product_param {
    num_output: 128
    weight_filler {
      type: "xavier"
	}
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "prelu4"
  type: "PReLU"
  bottom: "conv4"
  top: "conv4"
}

layer {
  name: "conv5-1"
  type: "InnerProduct"
  bottom: "conv4"
  top: "conv5-1"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  inner_product_param {
    num_output: 2
    #kernel_size: 1
    #stride: 1
    weight_filler {
      type: "xavier"
	}
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "conv5-2"
  type: "InnerProduct"
  bottom: "conv4"
  top: "conv5-2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 1
  }
  inner_product_param {
    num_output: 4
    #kernel_size: 1
    #stride: 1
     weight_filler {
      type: "xavier"
	}
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "prob1"
  type: "Softmax"
  bottom: "conv5-1"
  top: "prob1"
}