deploy_msra_baseline.prototxt

name: "Pose-REN-MSRA-baseline"
layer {
  name: "data"
  type: "Input"
  top: "data"
  input_param {
    shape {
      dim: 1
      dim: 1
      dim: 96
      dim: 96
    }
  }
}
layer {
  name: "prev_pose"
  type: "Input"
  top: "prev_pose"
  input_param {
    shape {
      dim: 1
      dim: 63
    }
  }
}
layer {
  name: "conv0"
  type: "Convolution"
  bottom: "data"
  top: "conv0"
  param {
    lr_mult: 1.0
  }
  param {
    lr_mult: 2.0
  }
  convolution_param {
    num_output: 16
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu0"
  type: "ReLU"
  bottom: "conv0"
  top: "conv0"
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "conv0"
  top: "conv1"
  param {
    lr_mult: 1.0
  }
  param {
    lr_mult: 2.0
  }
  convolution_param {
    num_output: 16
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "pool1"
  top: "pool1"
}
layer {
  name: "conv2_0"
  type: "Convolution"
  bottom: "pool1"
  top: "conv2_0"
  param {
    lr_mult: 1.0
  }
  param {
    lr_mult: 2.0
  }
  convolution_param {
    num_output: 32
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu2_0"
  type: "ReLU"
  bottom: "conv2_0"
  top: "conv2_0"
}
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "conv2_0"
  top: "conv2"
  param {
    lr_mult: 1.0
  }
  param {
    lr_mult: 2.0
  }
  convolution_param {
    num_output: 32
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu2"
  type: "ReLU"
  bottom: "conv2"
  top: "conv2"
}
layer {
  name: "conv3"
  type: "Convolution"
  bottom: "conv2"
  top: "conv3"
  param {
    lr_mult: 1.0
  }
  param {
    lr_mult: 2.0
  }
  convolution_param {
    num_output: 32
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "res1"
  type: "Eltwise"
  bottom: "conv2_0"
  bottom: "conv3"
  top: "res1"
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "res1"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "relu3"
  type: "ReLU"
  bottom: "pool2"
  top: "pool2"
}
layer {
  name: "conv3_0"
  type: "Convolution"
  bottom: "pool2"
  top: "conv3_0"
  param {
    lr_mult: 1.0
  }
  param {
    lr_mult: 2.0
  }
  convolution_param {
    num_output: 64
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu3_0"
  type: "ReLU"
  bottom: "conv3_0"
  top: "conv3_0"
}
layer {
  name: "conv4"
  type: "Convolution"
  bottom: "conv3_0"
  top: "conv4"
  param {
    lr_mult: 1.0
  }
  param {
    lr_mult: 2.0
  }
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu4"
  type: "ReLU"
  bottom: "conv4"
  top: "conv4"
}
layer {
  name: "conv5"
  type: "Convolution"
  bottom: "conv4"
  top: "conv5"
  param {
    lr_mult: 1.0
  }
  param {
    lr_mult: 2.0
  }
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "res2"
  type: "Eltwise"
  bottom: "conv3_0"
  bottom: "conv5"
  top: "res2"
}
layer {
  name: "pool3"
  type: "Pooling"
  bottom: "res2"
  top: "pool3"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "relu5"
  type: "ReLU"
  bottom: "pool3"
  top: "pool3"
}
layer {
  name: "fc1"
  type: "InnerProduct"
  bottom: "pool3"
  top: "fc1"
  param {
    lr_mult: 1.0
  }
  param {
    lr_mult: 2.0
  }
  inner_product_param {
    num_output: 2048
    weight_filler {
      type: "gaussian"
      std: 0.0010000000475
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu6_0"
  type: "ReLU"
  bottom: "fc1"
  top: "fc1"
}
layer {
  name: "drop1_0"
  type: "Dropout"
  bottom: "fc1"
  top: "fc1"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "fc2"
  type: "InnerProduct"
  bottom: "fc1"
  top: "fc2"
  param {
    lr_mult: 1.0
  }
  param {
    lr_mult: 2.0
  }
  inner_product_param {
    num_output: 2048
    weight_filler {
      type: "gaussian"
      std: 0.0010000000475
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu7_0"
  type: "ReLU"
  bottom: "fc2"
  top: "fc2"
}
layer {
  name: "drop2_0"
  type: "Dropout"
  bottom: "fc2"
  top: "fc2"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "fc3"
  type: "InnerProduct"
  bottom: "fc2"
  top: "fc3"
  param {
    lr_mult: 1.0
  }
  param {
    lr_mult: 2.0
  }
  inner_product_param {
    num_output: 63
    weight_filler {
      type: "gaussian"
      std: 0.0010000000475
    }
    bias_filler {
      type: "constant"
    }
  }
}