deploy.prototxt

name: "jsegnet21v2_deploy"
input: "data"
input_shape {
  dim: 1
  dim: 3
  dim: 512
  dim: 1024
}
layer {
  name: "data/bias"
  type: "Bias"
  bottom: "data"
  top: "data/bias"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  bias_param {
    filler {
      type: "constant"
      value: -128
    }
  }
}
layer {
  name: "conv1a"
  type: "Convolution"
  bottom: "data/bias"
  top: "conv1a"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 32
    bias_term: true
    pad: 2
    kernel_size: 5
    group: 1
    stride: 2
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
    dilation: 1
  }
}
layer {
  name: "conv1a/bn"
  type: "BatchNorm"
  bottom: "conv1a"
  top: "conv1a"
  batch_norm_param {
    moving_average_fraction: 0.99
    eps: 0.0001
    scale_bias: true
  }
}
layer {
  name: "conv1a/relu"
  type: "ReLU"
  bottom: "conv1a"
  top: "conv1a"
}
layer {
  name: "conv1b"
  type: "Convolution"
  bottom: "conv1a"
  top: "conv1b"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 32
    bias_term: true
    pad: 1
    kernel_size: 3
    group: 4
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
    dilation: 1
  }
}
layer {
  name: "conv1b/bn"
  type: "BatchNorm"
  bottom: "conv1b"
  top: "conv1b"
  batch_norm_param {
    moving_average_fraction: 0.99
    eps: 0.0001
    scale_bias: true
  }
}
layer {
  name: "conv1b/relu"
  type: "ReLU"
  bottom: "conv1b"
  top: "conv1b"
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1b"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "res2a_branch2a"
  type: "Convolution"
  bottom: "pool1"
  top: "res2a_branch2a"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    bias_term: true
    pad: 1
    kernel_size: 3
    group: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
    dilation: 1
  }
}
layer {
  name: "res2a_branch2a/bn"
  type: "BatchNorm"
  bottom: "res2a_branch2a"
  top: "res2a_branch2a"
  batch_norm_param {
    moving_average_fraction: 0.99
    eps: 0.0001
    scale_bias: true
  }
}
layer {
  name: "res2a_branch2a/relu"
  type: "ReLU"
  bottom: "res2a_branch2a"
  top: "res2a_branch2a"
}
layer {
  name: "res2a_branch2b"
  type: "Convolution"
  bottom: "res2a_branch2a"
  top: "res2a_branch2b"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    bias_term: true
    pad: 1
    kernel_size: 3
    group: 4
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
    dilation: 1
  }
}
layer {
  name: "res2a_branch2b/bn"
  type: "BatchNorm"
  bottom: "res2a_branch2b"
  top: "res2a_branch2b"
  batch_norm_param {
    moving_average_fraction: 0.99
    eps: 0.0001
    scale_bias: true
  }
}
layer {
  name: "res2a_branch2b/relu"
  type: "ReLU"
  bottom: "res2a_branch2b"
  top: "res2a_branch2b"
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "res2a_branch2b"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "res3a_branch2a"
  type: "Convolution"
  bottom: "pool2"
  top: "res3a_branch2a"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    bias_term: true
    pad: 1
    kernel_size: 3
    group: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
    dilation: 1
  }
}
layer {
  name: "res3a_branch2a/bn"
  type: "BatchNorm"
  bottom: "res3a_branch2a"
  top: "res3a_branch2a"
  batch_norm_param {
    moving_average_fraction: 0.99
    eps: 0.0001
    scale_bias: true
  }
}
layer {
  name: "res3a_branch2a/relu"
  type: "ReLU"
  bottom: "res3a_branch2a"
  top: "res3a_branch2a"
}
layer {
  name: "res3a_branch2b"
  type: "Convolution"
  bottom: "res3a_branch2a"
  top: "res3a_branch2b"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    bias_term: true
    pad: 1
    kernel_size: 3
    group: 4
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
    dilation: 1
  }
}
layer {
  name: "res3a_branch2b/bn"
  type: "BatchNorm"
  bottom: "res3a_branch2b"
  top: "res3a_branch2b"
  batch_norm_param {
    moving_average_fraction: 0.99
    eps: 0.0001
    scale_bias: true
  }
}
layer {
  name: "res3a_branch2b/relu"
  type: "ReLU"
  bottom: "res3a_branch2b"
  top: "res3a_branch2b"
}
layer {
  name: "pool3"
  type: "Pooling"
  bottom: "res3a_branch2b"
  top: "pool3"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "res4a_branch2a"
  type: "Convolution"
  bottom: "pool3"
  top: "res4a_branch2a"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    bias_term: true
    pad: 1
    kernel_size: 3
    group: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
    dilation: 1
  }
}
layer {
  name: "res4a_branch2a/bn"
  type: "BatchNorm"
  bottom: "res4a_branch2a"
  top: "res4a_branch2a"
  batch_norm_param {
    moving_average_fraction: 0.99
    eps: 0.0001
    scale_bias: true
  }
}
layer {
  name: "res4a_branch2a/relu"
  type: "ReLU"
  bottom: "res4a_branch2a"
  top: "res4a_branch2a"
}
layer {
  name: "res4a_branch2b"
  type: "Convolution"
  bottom: "res4a_branch2a"
  top: "res4a_branch2b"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    bias_term: true
    pad: 1
    kernel_size: 3
    group: 4
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
    dilation: 1
  }
}
layer {
  name: "res4a_branch2b/bn"
  type: "BatchNorm"
  bottom: "res4a_branch2b"
  top: "res4a_branch2b"
  batch_norm_param {
    moving_average_fraction: 0.99
    eps: 0.0001
    scale_bias: true
  }
}
layer {
  name: "res4a_branch2b/relu"
  type: "ReLU"
  bottom: "res4a_branch2b"
  top: "res4a_branch2b"
}
layer {
  name: "pool4"
  type: "Pooling"
  bottom: "res4a_branch2b"
  top: "pool4"
  pooling_param {
    pool: MAX
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "res5a_branch2a"
  type: "Convolution"
  bottom: "pool4"
  top: "res5a_branch2a"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    bias_term: true
    pad: 2
    kernel_size: 3
    group: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
    dilation: 2
  }
}
layer {
  name: "res5a_branch2a/bn"
  type: "BatchNorm"
  bottom: "res5a_branch2a"
  top: "res5a_branch2a"
  batch_norm_param {
    moving_average_fraction: 0.99
    eps: 0.0001
    scale_bias: true
  }
}
layer {
  name: "res5a_branch2a/relu"
  type: "ReLU"
  bottom: "res5a_branch2a"
  top: "res5a_branch2a"
}
layer {
  name: "res5a_branch2b"
  type: "Convolution"
  bottom: "res5a_branch2a"
  top: "res5a_branch2b"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    bias_term: true
    pad: 2
    kernel_size: 3
    group: 4
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
    dilation: 2
  }
}
layer {
  name: "res5a_branch2b/bn"
  type: "BatchNorm"
  bottom: "res5a_branch2b"
  top: "res5a_branch2b"
  batch_norm_param {
    moving_average_fraction: 0.99
    eps: 0.0001
    scale_bias: true
  }
}
layer {
  name: "res5a_branch2b/relu"
  type: "ReLU"
  bottom: "res5a_branch2b"
  top: "res5a_branch2b"
}
layer {
  name: "out5a"
  type: "Convolution"
  bottom: "res5a_branch2b"
  top: "out5a"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    bias_term: true
    pad: 4
    kernel_size: 3
    group: 2
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
    dilation: 4
  }
}
layer {
  name: "out5a/bn"
  type: "BatchNorm"
  bottom: "out5a"
  top: "out5a"
  batch_norm_param {
    moving_average_fraction: 0.99
    eps: 0.0001
    scale_bias: true
  }
}
layer {
  name: "out5a/relu"
  type: "ReLU"
  bottom: "out5a"
  top: "out5a"
}
layer {
  name: "out5a_up2"
  type: "Deconvolution"
  bottom: "out5a"
  top: "out5a_up2"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    bias_term: false
    pad: 1
    kernel_size: 4
    group: 64
    stride: 2
    weight_filler {
      type: "bilinear"
    }
  }
}
layer {
  name: "out3a"
  type: "Convolution"
  bottom: "res3a_branch2b"
  top: "out3a"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    bias_term: true
    pad: 1
    kernel_size: 3
    group: 2
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
    dilation: 1
  }
}
layer {
  name: "out3a/bn"
  type: "BatchNorm"
  bottom: "out3a"
  top: "out3a"
  batch_norm_param {
    moving_average_fraction: 0.99
    eps: 0.0001
    scale_bias: true
  }
}
layer {
  name: "out3a/relu"
  type: "ReLU"
  bottom: "out3a"
  top: "out3a"
}
layer {
  name: "out3_out5_combined"
  type: "Eltwise"
  bottom: "out5a_up2"
  bottom: "out3a"
  top: "out3_out5_combined"
}
layer {
  name: "ctx_conv1"
  type: "Convolution"
  bottom: "out3_out5_combined"
  top: "ctx_conv1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    bias_term: true
    pad: 1
    kernel_size: 3
    group: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
    dilation: 1
  }
}
layer {
  name: "ctx_conv1/bn"
  type: "BatchNorm"
  bottom: "ctx_conv1"
  top: "ctx_conv1"
  batch_norm_param {
    moving_average_fraction: 0.99
    eps: 0.0001
    scale_bias: true
  }
}
layer {
  name: "ctx_conv1/relu"
  type: "ReLU"
  bottom: "ctx_conv1"
  top: "ctx_conv1"
}
layer {
  name: "ctx_conv2"
  type: "Convolution"
  bottom: "ctx_conv1"
  top: "ctx_conv2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    bias_term: true
    pad: 4
    kernel_size: 3
    group: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
    dilation: 4
  }
}
layer {
  name: "ctx_conv2/bn"
  type: "BatchNorm"
  bottom: "ctx_conv2"
  top: "ctx_conv2"
  batch_norm_param {
    moving_average_fraction: 0.99
    eps: 0.0001
    scale_bias: true
  }
}
layer {
  name: "ctx_conv2/relu"
  type: "ReLU"
  bottom: "ctx_conv2"
  top: "ctx_conv2"
}
layer {
  name: "ctx_conv3"
  type: "Convolution"
  bottom: "ctx_conv2"
  top: "ctx_conv3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    bias_term: true
    pad: 4
    kernel_size: 3
    group: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
    dilation: 4
  }
}
layer {
  name: "ctx_conv3/bn"
  type: "BatchNorm"
  bottom: "ctx_conv3"
  top: "ctx_conv3"
  batch_norm_param {
    moving_average_fraction: 0.99
    eps: 0.0001
    scale_bias: true
  }
}
layer {
  name: "ctx_conv3/relu"
  type: "ReLU"
  bottom: "ctx_conv3"
  top: "ctx_conv3"
}
layer {
  name: "ctx_conv4"
  type: "Convolution"
  bottom: "ctx_conv3"
  top: "ctx_conv4"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    bias_term: true
    pad: 4
    kernel_size: 3
    group: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
    dilation: 4
  }
}
layer {
  name: "ctx_conv4/bn"
  type: "BatchNorm"
  bottom: "ctx_conv4"
  top: "ctx_conv4"
  batch_norm_param {
    moving_average_fraction: 0.99
    eps: 0.0001
    scale_bias: true
  }
}
layer {
  name: "ctx_conv4/relu"
  type: "ReLU"
  bottom: "ctx_conv4"
  top: "ctx_conv4"
}
layer {
  name: "ctx_final"
  type: "Convolution"
  bottom: "ctx_conv4"
  top: "ctx_final"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 8
    bias_term: true
    pad: 1
    kernel_size: 3
    kernel_size: 3
    group: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
    dilation: 1
  }
}
layer {
  name: "ctx_final/relu"
  type: "ReLU"
  bottom: "ctx_final"
  top: "ctx_final"
}
layer {
  name: "out_deconv_final_up2"
  type: "Deconvolution"
  bottom: "ctx_final"
  top: "out_deconv_final_up2"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 8
    bias_term: false
    pad: 1
    kernel_size: 4
    group: 8
    stride: 2
    weight_filler {
      type: "bilinear"
    }
  }
}
layer {
  name: "out_deconv_final_up4"
  type: "Deconvolution"
  bottom: "out_deconv_final_up2"
  top: "out_deconv_final_up4"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 8
    bias_term: false
    pad: 1
    kernel_size: 4
    group: 8
    stride: 2
    weight_filler {
      type: "bilinear"
    }
  }
}
layer {
  name: "out_deconv_final_up8"
  type: "Deconvolution"
  bottom: "out_deconv_final_up4"
  top: "out_deconv_final_up8"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 8
    bias_term: false
    pad: 1
    kernel_size: 4
    group: 8
    stride: 2
    weight_filler {
      type: "bilinear"
    }
  }
}
layer {
  name: "argMaxOut"
  type: "ArgMax"
  bottom: "out_deconv_final_up8"
  top: "argMaxOut"
  argmax_param {
    axis: 1
  }
}