DispNetC.prototxt

layer {
  name: "img0"
  type: "Input"
  top: "img0"
  input_param { shape: { dim: 1 dim: 3 dim: 768 dim: 768} }
  include { phase: TEST }
}
layer {
  name: "img1"
  type: "Input"
  top: "img1"
  input_param { shape: { dim: 1 dim: 3 dim: 768 dim: 768 } }
  include { phase: TEST }
}
layer {
  name: "Eltwise1"
  type: "Eltwise"
  bottom: "img0"
  top: "img0_aug"
  eltwise_param {
    operation: SUM
    coeff: 0.00392156862745
  }
}
layer {
  name: "Eltwise2"
  type: "Eltwise"
  bottom: "img1"
  top: "img1_aug"
  eltwise_param {
    operation: SUM
    coeff: 0.00392156862745
  }
}

layer {
  name: "conv1"
  type: "Convolution"
  bottom: "img0_aug"
  bottom: "img1_aug"
  top: "conv1a"
  top: "conv1b"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 3
    kernel_size: 7
    stride: 2
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    engine: CUDNN
  }
}
layer {
  name: "ReLU1"
  type: "ReLU"
  bottom: "conv1a"
  top: "conv1a"
  relu_param {
    negative_slope: 0.1
  }
}
layer {
  name: "ReLU2"
  type: "ReLU"
  bottom: "conv1b"
  top: "conv1b"
  relu_param {
    negative_slope: 0.1
  }
}


layer {
  name: "conv2"
  type: "Convolution"
  bottom: "conv1a"
  bottom: "conv1b"
  top: "conv2a"
  top: "conv2b"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 2
    kernel_size: 5
    stride: 2
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    engine: CUDNN
  }
}
layer {
  name: "ReLU3"
  type: "ReLU"
  bottom: "conv2a"
  top: "conv2a"
  relu_param {
    negative_slope: 0.1
  }
}
layer {
  name: "ReLU4"
  type: "ReLU"
  bottom: "conv2b"
  top: "conv2b"
  relu_param {
    negative_slope: 0.1
  }
}


layer {
  name: "corr"
  type: "Correlation1D"
  bottom: "conv2a"
  bottom: "conv2b"
  top: "corr"
  correlation_param {
    pad: 40
    kernel_size: 1
    max_displacement: 40
    stride_1: 1
    stride_2: 1
  }
}
layer {
  name: "conv_redir"
  type: "Convolution"
  bottom: "conv2a"
  top: "conv_redir"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    engine: CUDNN
  }
}
layer {
  name: "ReLU5"
  type: "ReLU"
  bottom: "conv_redir"
  top: "conv_redir"
  relu_param {
    negative_slope: 0.1
  }
}
layer {
  name: "Concat2"
  type: "Concat"
  bottom: "corr"
  bottom: "conv_redir"
  top: "blob20"
  concat_param {
    axis: 1
  }
}
layer {
  name: "conv3"
  type: "Convolution"
  bottom: "blob20"
  top: "conv3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 2
    kernel_size: 5
    stride: 2
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    engine: CUDNN
  }
}
layer {
  name: "ReLU6"
  type: "ReLU"
  bottom: "conv3"
  top: "conv3"
  relu_param {
    negative_slope: 0.1
  }
}
layer {
  name: "conv3_1"
  type: "Convolution"
  bottom: "conv3"
  top: "conv3_1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    engine: CUDNN
  }
}
layer {
  name: "ReLU7"
  type: "ReLU"
  bottom: "conv3_1"
  top: "conv3_1"
  relu_param {
    negative_slope: 0.1
  }
}
layer {
  name: "conv4"
  type: "Convolution"
  bottom: "conv3_1"
  top: "conv4"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    stride: 2
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    engine: CUDNN
  }
}
layer {
  name: "ReLU8"
  type: "ReLU"
  bottom: "conv4"
  top: "conv4"
  relu_param {
    negative_slope: 0.1
  }
}
layer {
  name: "conv4_1"
  type: "Convolution"
  bottom: "conv4"
  top: "conv4_1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    engine: CUDNN
  }
}
layer {
  name: "ReLU9"
  type: "ReLU"
  bottom: "conv4_1"
  top: "conv4_1"
  relu_param {
    negative_slope: 0.1
  }
}
layer {
  name: "conv5"
  type: "Convolution"
  bottom: "conv4_1"
  top: "conv5"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    stride: 2
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    engine: CUDNN
  }
}
layer {
  name: "ReLU10"
  type: "ReLU"
  bottom: "conv5"
  top: "conv5"
  relu_param {
    negative_slope: 0.1
  }
}
layer {
  name: "conv5_1"
  type: "Convolution"
  bottom: "conv5"
  top: "conv5_1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    engine: CUDNN
  }
}
layer {
  name: "ReLU11"
  type: "ReLU"
  bottom: "conv5_1"
  top: "conv5_1"
  relu_param {
    negative_slope: 0.1
  }
}
layer {
  name: "conv6"
  type: "Convolution"
  bottom: "conv5_1"
  top: "conv6"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  convolution_param {
    num_output: 1024
    pad: 1
    kernel_size: 3
    stride: 2
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    engine: CUDNN
  }
}
layer {
  name: "ReLU12"
  type: "ReLU"
  bottom: "conv6"
  top: "conv6"
  relu_param {
    negative_slope: 0.1
  }
}
layer {
  name: "conv6_1"
  type: "Convolution"
  bottom: "conv6"
  top: "conv6_1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  convolution_param {
    num_output: 1024
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    engine: CUDNN
  }
}
layer {
  name: "ReLU13"
  type: "ReLU"
  bottom: "conv6_1"
  top: "conv6_1"
  relu_param {
    negative_slope: 0.1
  }
}
layer {
  name: "Convolution1"
  type: "Convolution"
  bottom: "conv6_1"
  top: "predict_flow6"
  param {
    lr_mult: 1
    decay_mult: 0
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  convolution_param {
    num_output: 1
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    engine: CUDNN
  }
}
layer {
  name: "deconv5"
  type: "Deconvolution"
  bottom: "conv6_1"
  top: "deconv5"
  param {
    lr_mult: 1
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 4
    stride: 2
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    engine: CUDNN
  }
}
layer {
  name: "ReLU14"
  type: "ReLU"
  bottom: "deconv5"
  top: "deconv5"
  relu_param {
    negative_slope: 0.1
  }
}
layer {
  name: "upsample_flow6to5"
  type: "Deconvolution"
  bottom: "predict_flow6"
  top: "upsampled_flow6_to_5"
  param {
    lr_mult: 1
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 1
    pad: 1
    kernel_size: 4
    stride: 2
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    engine: CUDNN
  }
}
layer {
  name: "Concat3"
  type: "Concat"
  bottom: "conv5_1"
  bottom: "deconv5"
  bottom: "upsampled_flow6_to_5"
  top: "blob34"
}
layer {
  name: "Convolution2"
  type: "Convolution"
  bottom: "blob34"
  top: "concat5"
  param {
    lr_mult: 1
    decay_mult: 0
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    engine: CUDNN
  }
}
layer {
  name: "Convolution3"
  type: "Convolution"
  bottom: "concat5"
  top: "predict_flow5"
  param {
    lr_mult: 1
    decay_mult: 0
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  convolution_param {
    num_output: 1
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    engine: CUDNN
  }
}
layer {
  name: "deconv4"
  type: "Deconvolution"
  bottom: "concat5"
  top: "deconv4"
  param {
    lr_mult: 1
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 4
    stride: 2
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    engine: CUDNN
  }
}
layer {
  name: "ReLU15"
  type: "ReLU"
  bottom: "deconv4"
  top: "deconv4"
  relu_param {
    negative_slope: 0.1
  }
}
layer {
  name: "upsample_flow5to4"
  type: "Deconvolution"
  bottom: "predict_flow5"
  top: "upsampled_flow5_to_4"
  param {
    lr_mult: 1
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 1
    pad: 1
    kernel_size: 4
    stride: 2
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    engine: CUDNN
  }
}
layer {
  name: "Concat4"
  type: "Concat"
  bottom: "conv4_1"
  bottom: "deconv4"
  bottom: "upsampled_flow5_to_4"
  top: "blob41"
}
layer {
  name: "Convolution4"
  type: "Convolution"
  bottom: "blob41"
  top: "concat4"
  param {
    lr_mult: 1
    decay_mult: 0
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    engine: CUDNN
  }
}
layer {
  name: "Convolution5"
  type: "Convolution"
  bottom: "concat4"
  top: "predict_flow4"
  param {
    lr_mult: 1
    decay_mult: 0
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  convolution_param {
    num_output: 1
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    engine: CUDNN
  }
}
layer {
  name: "deconv3"
  type: "Deconvolution"
  bottom: "concat4"
  top: "deconv3"
  param {
    lr_mult: 1
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 4
    stride: 2
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    engine: CUDNN
  }
}
layer {
  name: "ReLU16"
  type: "ReLU"
  bottom: "deconv3"
  top: "deconv3"
  relu_param {
    negative_slope: 0.1
  }
}
layer {
  name: "upsample_flow4to3"
  type: "Deconvolution"
  bottom: "predict_flow4"
  top: "upsampled_flow4_to_3"
  param {
    lr_mult: 1
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 1
    pad: 1
    kernel_size: 4
    stride: 2
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    engine: CUDNN
  }
}
layer {
  name: "Concat5"
  type: "Concat"
  bottom: "conv3_1"
  bottom: "deconv3"
  bottom: "upsampled_flow4_to_3"
  top: "blob48"
}
layer {
  name: "Convolution6"
  type: "Convolution"
  bottom: "blob48"
  top: "concat3"
  param {
    lr_mult: 1
    decay_mult: 0
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    engine: CUDNN
  }
}
layer {
  name: "Convolution7"
  type: "Convolution"
  bottom: "concat3"
  top: "predict_flow3"
  param {
    lr_mult: 1
    decay_mult: 0
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  convolution_param {
    num_output: 1
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    engine: CUDNN
  }
}
layer {
  name: "deconv2"
  type: "Deconvolution"
  bottom: "concat3"
  top: "deconv2"
  param {
    lr_mult: 1
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 4
    stride: 2
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    engine: CUDNN
  }
}
layer {
  name: "ReLU17"
  type: "ReLU"
  bottom: "deconv2"
  top: "deconv2"
  relu_param {
    negative_slope: 0.1
  }
}
layer {
  name: "upsample_flow3to2"
  type: "Deconvolution"
  bottom: "predict_flow3"
  top: "upsampled_flow3_to_2"
  param {
    lr_mult: 1
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 1
    pad: 1
    kernel_size: 4
    stride: 2
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    engine: CUDNN
  }
}
layer {
  name: "Concat6"
  type: "Concat"
  bottom: "conv2a"
  bottom: "deconv2"
  bottom: "upsampled_flow3_to_2"
  top: "blob55"
}
layer {
  name: "Convolution8"
  type: "Convolution"
  bottom: "blob55"
  top: "concat2"
  param {
    lr_mult: 1
    decay_mult: 0
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    engine: CUDNN
  }
}
layer {
  name: "Convolution9"
  type: "Convolution"
  bottom: "concat2"
  top: "predict_flow2"
  param {
    lr_mult: 1
    decay_mult: 0
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  convolution_param {
    num_output: 1
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    engine: CUDNN
  }
}
layer {
  name: "deconv1"
  type: "Deconvolution"
  bottom: "concat2"
  top: "deconv1"
  param {
    lr_mult: 1
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 32
    pad: 1
    kernel_size: 4
    stride: 2
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    engine: CUDNN
  }
}
layer {
  name: "ReLU18"
  type: "ReLU"
  bottom: "deconv1"
  top: "deconv1"
  relu_param {
    negative_slope: 0.1
  }
}
layer {
  name: "upsample_flow2to1"
  type: "Deconvolution"
  bottom: "predict_flow2"
  top: "upsampled_flow2_to_1"
  param {
    lr_mult: 1
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 1
    pad: 1
    kernel_size: 4
    stride: 2
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    engine: CUDNN
  }
}
layer {
  name: "Concat7"
  type: "Concat"
  bottom: "conv1a"
  bottom: "deconv1"
  bottom: "upsampled_flow2_to_1"
  top: "blob62"
}
layer {
  name: "Convolution10"
  type: "Convolution"
  bottom: "blob62"
  top: "concat1"
  param {
    lr_mult: 1
    decay_mult: 0
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  convolution_param {
    num_output: 32
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    engine: CUDNN
  }
}
layer {
  name: "Convolution11"
  type: "Convolution"
  bottom: "concat1"
  top: "Convolution11"
  param {
    lr_mult: 1
    decay_mult: 0
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  convolution_param {
    num_output: 1
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    engine: CUDNN
  }
}
layer {
  name: "NegReLU1"
  type: "NegReLU"
  bottom: "Convolution11"
  top: "predict_flow1"
  relu_param {
    negative_slope: 0
  }
}