SISR_VAE.prototxt

name: "STSR"
layer {
  name: "data"
  type: "HDF5Data"
  top: "data"
  top: "label"
  hdf5_data_param {
    source: "examples/DecisionTree_CNN/extra/train.txt"
    batch_size: 16
    shuffle: true
  }
  include: { phase: TRAIN }
}

layer {
  name: "data"
  type: "HDF5Data"
  top: "data"
  top: "label"
  hdf5_data_param {
    source: "examples/DecisionTree_CNN/extra/test.txt"
    batch_size: 16
  }
  include: { phase: TEST }
}

######################### data concatenation #########################
layer {
  name: "concat_data"
  type: "Concat"
  bottom: "data"
  bottom: "label"
  top: "concat_data"
  concat_param {
    axis: 0
  }
  include: { phase: TRAIN }
}

layer {
  name: "concat_data"
  type: "Concat"
  bottom: "data"
  top: "concat_data"
  concat_param {
    axis: 0
  }
  include: { phase: TEST }
}

#########################  feature extract #########################
layer {
  name: "feat1_1"
  type: "Convolution"
  bottom: "concat_data"
  top: "feat1_1"
  param {
    lr_mult: 1
    name: "feat1_1_w"
  }
  param {
    lr_mult: 1
    name: "feat1_1_b"
  }
  convolution_param {
    num_output: 64
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.0589
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "Relu_feat1_1"
  type: "ReLU"
  bottom: "feat1_1"
  top: "feat1_1"
}

layer {
  name: "feat1_2"
  type: "Convolution"
  bottom: "feat1_1"
  top: "feat1_2"
  param {
    lr_mult: 1
    name: "feat1_2_w"
  }
  param {
    lr_mult: 1
    name: "feat1_2_b"
  }
  convolution_param {
    num_output: 64
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.0589
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "Relu_feat1_2"
  type: "ReLU"
  bottom: "feat1_2"
  top: "feat1_2"
}

layer {
  name: "feat2_1"
  type: "Convolution"
  bottom: "feat1_2"
  top: "feat2_1"
  param {
    lr_mult: 1
    name: "feat2_1_w"
  }
  param {
    lr_mult: 1
    name: "feat2_1_b"
  }
  convolution_param {
    num_output: 128
    kernel_size: 6
    stride: 2
    pad: 2
    weight_filler {
      type: "gaussian"
      std: 0.0208
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "Relu_feat2_1"
  type: "ReLU"
  bottom: "feat2_1"
  top: "feat2_1"
}

layer {
  name: "feat2_2"
  type: "Convolution"
  bottom: "feat2_1"
  top: "feat2_2"
  param {
    lr_mult: 1
    name: "feat2_2_w"
  }
  param {
    lr_mult: 1
    name: "feat2_2_b"
  }
  convolution_param {
    num_output: 128
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.0417
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "Relu_feat2_2"
  type: "ReLU"
  bottom: "feat2_2"
  top: "feat2_2"
}

layer {
  name: "feat3_1"
  type: "Convolution"
  bottom: "feat2_2"
  top: "feat3_1"
  param {
    lr_mult: 1
    name: "feat3_1_w"
  }
  param {
    lr_mult: 1
    name: "feat3_1_b"
  }
  convolution_param {
    num_output: 256
    kernel_size: 6
    stride: 2
    pad: 2
    weight_filler {
      type: "gaussian"
      std: 0.0147
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "Relu_feat3_1"
  type: "ReLU"
  bottom: "feat3_1"
  top: "feat3_1"
}

layer {
  name: "feat3_2"
  type: "Convolution"
  bottom: "feat3_1"
  top: "feat3_2"
  param {
    lr_mult: 1
    name: "feat3_2_w"
  }
  param {
    lr_mult: 1
    name: "feat3_2_b"
  }
  convolution_param {
    num_output: 256
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.0295
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "Relu_feat3_2"
  type: "ReLU"
  bottom: "feat3_2"
  top: "feat3_2"
}

layer {
  name: "feat4_1"
  type: "Convolution"
  bottom: "feat3_2"
  top: "feat4_1"
  param {
    lr_mult: 1
    name: "feat4_1_w"
  }
  param {
    lr_mult: 1
    name: "feat4_1_b"
  }
  convolution_param {
    num_output: 512
    kernel_size: 6
    stride: 2
    pad: 2
    weight_filler {
      type: "gaussian"
      std: 0.0147
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "Relu_feat4_1"
  type: "ReLU"
  bottom: "feat4_1"
  top: "feat4_1"
}

layer {
  name: "feat4_2"
  type: "Convolution"
  bottom: "feat4_1"
  top: "feat4_2"
  param {
    lr_mult: 1
    name: "feat4_2_w"
  }
  param {
    lr_mult: 1
    name: "feat4_2_b"
  }
  convolution_param {
    num_output: 512
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.0295
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

#########################  Feat slice #########################
layer {
  name: "slicer_VGG"
  type: "Slice"
  bottom: "feat4_2"
  top: "LR_feat"
  top: "HR_feat"
  slice_param {
    axis: 0
    slice_point: 16
  }
  include {
    phase: TRAIN
  }
}

layer{
  name: "encodersum"
  type: "Concat"
  bottom: "LR_feat"
  bottom: "HR_feat"
  top: "encodersum"
  include {
    phase: TRAIN
  }
}

layer {
  name: "encodersumneuron"
  type: "ReLU"
  bottom: "encodersum"
  top: "encodersumneuron"
  include {
    phase: TRAIN
  }
}

######################### Input code #########################
layer {
  name: "input1"
  type: "Convolution"
  bottom: "LR_feat"
  top: "input1"
  param {
    lr_mult: 1
    name: "input1_w"
  }
  param {
    lr_mult: 1
    name: "input1_b"
  }
  convolution_param {
    num_output: 128
    kernel_size: 1
    stride: 1
    pad: 0
    weight_filler {
      type: "gaussian"
      std: 0.125
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
  include {
    phase: TRAIN
  }
}

layer {
  name: "input1"
  type: "Convolution"
  bottom: "feat4_2"
  top: "input1"
  param {
    lr_mult: 1
    name: "input1_w"
  }
  param {
    lr_mult: 1
    name: "input1_b"
  }
  convolution_param {
    num_output: 128
    kernel_size: 1
    stride: 1
    pad: 0
    weight_filler {
      type: "gaussian"
      std: 0.125
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
  include {
    phase: TEST
  }
}

layer {
  name: "relu_input1"
  type: "ReLU"
  bottom: "input1"
  top: "input1"
}

layer {
  name: "input2"
  type: "Convolution"
  bottom: "input1"
  top: "input2"
  param {
    lr_mult: 1
    name: "input2_w"
  }
  param {
    lr_mult: 1
    name: "input2_b"
  }
  convolution_param {
    num_output: 64
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.0589
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

######################### Encoder #########################
layer {
  name: "encoder1"
  type: "Convolution"
  bottom: "encodersumneuron"
  top: "encoder1"
  param {
    lr_mult: 1
    name: "encoder1_w"
  }
  param {
    lr_mult: 1
    name: "encoder1_b"
  }
  convolution_param {
    num_output: 256
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.0208
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
  include {
    phase: TRAIN
  }
}

layer {
  name: "Relu_encoder1"
  type: "ReLU"
  bottom: "encoder1"
  top: "encoder1"
  include {
    phase: TRAIN
  }
}

layer {
  name: "encoder2"
  type: "Convolution"
  bottom: "encoder1"
  top: "encoder2"
  param {
    lr_mult: 1
    name: "encoder2_w"
  }
  param {
    lr_mult: 1
    name: "encoder2_b"
  }
  convolution_param {
    num_output: 128
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.0208
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
  include {
    phase: TRAIN
  }
}

layer {
  name: "Relu_encoder2"
  type: "ReLU"
  bottom: "encoder2"
  top: "encoder2"
  include {
    phase: TRAIN
  }
}

layer {
  name: "encoder3"
  type: "Convolution"
  bottom: "encoder2"
  top: "encoder3"
  param {
    lr_mult: 1
    name: "encoder3_w"
  }
  param {
    lr_mult: 1
    name: "encoder3_b"
  }
  convolution_param {
    num_output: 128
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.0295
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
  include {
    phase: TRAIN
  }
}

layer {
  name: "Relu_encoder3"
  type: "ReLU"
  bottom: "encoder3"
  top: "encoder3"
  include {
    phase: TRAIN
  }
}

layer {
  name: "ip1"
  type: "InnerProduct"
  bottom: "encoder3"
  top: "ip1"
  param {
    lr_mult: 1
    decay_mult: 1
    name: "ip1_w"
  }
  param {
    lr_mult: 2
    decay_mult: 0
    name: "ip1_b"
  }
  inner_product_param {
    num_output: 4096
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
  include {
    phase: TRAIN
  }
}

layer {
  name: "relu_ip1"
  type: "ReLU"
  bottom: "ip1"
  top: "ip1"
  include {
    phase: TRAIN
  }
}

layer {
  name: "ip2"
  type: "InnerProduct"
  bottom: "ip1"
  top: "ip2"
  param {
    lr_mult: 1
    decay_mult: 1
    name: "ip2_w"
  }
  param {
    lr_mult: 2
    decay_mult: 0
    name: "ip2_b"
  }
  inner_product_param {
    num_output: 2048
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
  include {
    phase: TRAIN
  }
}

layer {
  name: "relu_ip2"
  type: "ReLU"
  bottom: "ip2"
  top: "ip2"
  include {
    phase: TRAIN
  }
}

layer {
  name: "ip3"
  type: "InnerProduct"
  bottom: "ip2"
  top: "ip3"
  param {
    lr_mult: 1
    decay_mult: 1
    name: "ip3_w"
  }
  param {
    lr_mult: 2
    decay_mult: 0
    name: "ip3_b"
  }
  inner_product_param {
    num_output: 2048
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
  include {
    phase: TRAIN
  }
}

layer {
  name: "slicer_ip3"
  type: "Slice"
  bottom: "ip3"
  top: "mu"
  top: "logsd"
  slice_param {
    axis: 1
    slice_point: 1024
  }
  include {
    phase: TRAIN
  }
}

######################### VAE #########################
layer{
  name: "sd"
  type: "Exp"
  bottom: "logsd"
  top: "sd"
  include {
    phase: TRAIN
  }
}

layer{
  name: "var"
  type: "Eltwise"
  bottom: "sd"
  bottom: "sd"
  top: "var"
  eltwise_param{
    operation: PROD
  }
  include {
    phase: TRAIN
  }
}

layer {
  name: "sd_scale"
  type: "Scale"
  bottom: "sd"
  top: "sd_scale"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  scale_param {
    filler {
      value: 10
    }
    bias_term: false
  }
  include {
    phase: TRAIN
  }
}

layer{
  name: "logsd_scale"
  type: "Log"
  bottom: "sd_scale"
  top: "logsd_scale"
  include {
    phase: TRAIN
  }
}

layer{
  name: "meansq"
  type: "Eltwise"
  bottom: "mu"
  bottom: "mu"
  top: "meansq"
  eltwise_param{
    operation: PROD
  }
  include {
    phase: TRAIN
  }
}

layer{
  name: "kldiv_plus_half"
  type: "Eltwise"
  bottom: "meansq"
  bottom: "var"
  bottom: "logsd_scale"
  top: "kldiv_plus_half"
  eltwise_param{
    operation: SUM
    coeff: 50
    coeff: 50
    coeff: -1.0
  }
  include {
    phase: TRAIN
  }
}

layer {
  name: "kldiv"
  type: "Power"
  bottom: "kldiv_plus_half"
  top: "kldiv"
  power_param{
    shift: -0.5
  }
  include {
    phase: TRAIN
  }
}

layer{
  name: "klloss"
  type: "Reduction"
  bottom: "kldiv"
  top: "klloss"
  loss_weight: 1
  include {
    phase: TRAIN
  }
}

######################### decoder #########################
layer {
  name: "generator1"
  type: "InnerProduct"
  bottom: "data"
  top: "generator1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 512
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}

layer {
  name: "relu_generator1"
  type: "ReLU"
  bottom: "generator1"
  top: "generator1"
}

layer {
  name: "generator2"
  type: "InnerProduct"
  bottom: "generator1"
  top: "generator2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 1024
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}

layer {
  name: "relu_generator2"
  type: "ReLU"
  bottom: "generator2"
  top: "generator2"
}

layer {
  name: "mvn"
  type: "MVN"
  bottom: "generator2"
  top: "generator2"
  mvn_param{
    across_channels: true
    normalize_variance: true
    eps: 1e-9
  }
}

layer {
  name: "reshape_sample"
  type: "Reshape"
  bottom: "generator2"
  top: "norm_generator2"
  reshape_param {
    shape {
      dim: 16
      dim: 1024
    }
  }
}

layer{
  name: "mu_dummy" 
  type: "DummyData"
  top: "mu"
  dummy_data_param{
    shape {
      dim: 16
      dim: 1024
    }
    data_filler{
      type: "constant"
      value: 0
    }
  }
  include {
    phase: TEST
  }
}

layer{
  name: "sd_dummy"
  type: "DummyData"
  top: "sd"
  dummy_data_param{
    shape {
      dim: 16
      dim: 1024
    }
    data_filler{
      type: "gaussian"
      value: 0.1
    }
  }
  include {
    phase: TEST
  }
}

layer{
  name: "sdnoise"
  type: "Eltwise"
  bottom: "norm_generator2"
  bottom: "sd"
  top: "sdnoise"
  eltwise_param{
    operation: PROD
  }
}

layer{
  name: "sample"
  type: "Eltwise"
  bottom: "mu"
  bottom: "sdnoise"
  top: "sample"
  eltwise_param{
    operation: SUM
  }
}

layer {
  name: "ip4"
  type: "InnerProduct"
  bottom: "sample"
  top: "ip4"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 2048
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}

layer {
  name: "relu_ip4"
  type: "ReLU"
  bottom: "ip4"
  top: "ip4"
}

layer {
  name: "ip5"
  type: "InnerProduct"
  bottom: "ip4"
  top: "ip5"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 2048
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}

layer {
  name: "relu_ip5"
  type: "ReLU"
  bottom: "ip5"
  top: "ip5"
}

layer {
  name: "ip6"
  type: "InnerProduct"
  bottom: "ip5"
  top: "ip6"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 4096
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}

layer {
  name: "relu_ip6"
  type: "ReLU"
  bottom: "ip6"
  top: "ip6"
}

layer {
  name: "reshape"
  type: "Reshape"
  bottom: "ip6"
  top: "ip6_reshape"
  reshape_param {
    shape {
      dim: 16
      dim: 16
      dim: 16
      dim: 16 
    }
  }
}

layer {
  name: "decoder1"
  type: "Convolution"
  bottom: "ip6_reshape"
  top: "decoder1"
  param {
    lr_mult: 1
    name: "decoder1_w"
  }
  param {
    lr_mult: 1
    name: "decoder1_b"
  }
  convolution_param {
    num_output: 64
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.0589
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer{
  name: "decodersum"
  type: "Concat"
  bottom: "input2"
  bottom: "decoder1"
  top: "decodersum"
}

layer {
  name: "relu_decoder1"
  type: "PReLU"
  bottom: "decodersum"
  top: "decodersum"
  prelu_param {
    channel_shared: 1
  }
}

######################### SR_s1 #########################
layer {
  name: "conv1_SR_s1"
  type: "Deconvolution"
  bottom: "decodersum"
  top: "conv1_SR_s1"
  param {
    lr_mult: 1
    name: "conv1_s1_w"
  }
  param {
    lr_mult: 1
    name: "conv1_s1_b"
  }
  convolution_param {
    num_output: 32
    kernel_size: 6
    stride: 2
    pad: 2
    weight_filler {
      type: "gaussian"
      std: 0.0417
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "relu1_SR_s1"
  type: "PReLU"
  bottom: "conv1_SR_s1"
  top: "conv1_SR_s1"
  prelu_param {
    channel_shared: 1
  }
}

layer {
  name: "conv2_SR_s1"
  type: "Convolution"
  bottom: "conv1_SR_s1"
  top: "conv2_SR_s1"
  param {
    lr_mult: 1
    name: "conv2_s1_w"
  }
  param {
    lr_mult: 1
    name: "conv2_s1_b"
  }
  convolution_param {
    num_output: 32
    kernel_size: 6
    stride: 2
    pad: 2
    weight_filler {
      type: "gaussian"
      std: 0.0417
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "relu2_SR_s1"
  type: "PReLU"
  bottom: "conv2_SR_s1"
  top: "conv2_SR_s1"
  prelu_param {
    channel_shared: 1
  }
}

layer {
  name: "local_output_SR_s1"
  type: "Convolution"
  bottom: "decodersum"
  top: "local_output_SR_s1"
  param {
    lr_mult: 1
    name: "local_output_s1_w"
  }
  param {
    lr_mult: 1
    name: "local_output_s1_b"
  }
  convolution_param {
    num_output: 32
    kernel_size: 1
    stride: 1
    pad: 0
    weight_filler {
      type: "gaussian"
      std: 0.25
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
    name: "eltwise1_SR_s1"
    type: "Eltwise"
    bottom: "local_output_SR_s1"
    bottom: "conv2_SR_s1"
    top: "eltwise1_SR_s1"
    eltwise_param {
        operation: SUM
        coeff: 1
        coeff: -1
    }
}

layer {
  name: "conv3_SR_s1"
  type: "Deconvolution"
  bottom: "eltwise1_SR_s1"
  top: "conv3_SR_s1"
  param {
    lr_mult: 1
    name: "conv3_s1_w"
  }
  param {
    lr_mult: 1
    name: "conv3_s1_b"
  }
  convolution_param {
    num_output: 32
    kernel_size: 6
    stride: 2
    pad: 2
    weight_filler {
      type: "gaussian"
      std: 0.0417
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "relu3_SR_s1"
  type: "PReLU"
  bottom: "conv3_SR_s1"
  top: "conv3_SR_s1"
  prelu_param {
    channel_shared: 1
  }
}

layer {
  name: "local_conv1_SR_s1"
  type: "Convolution"
  bottom: "conv1_SR_s1"
  top: "local_conv1_SR_s1"
  param {
    lr_mult: 1
    name: "local_conv1_s1_w"
  }
  param {
    lr_mult: 1
    name: "local_conv1_s1_b"
  }
  convolution_param {
    num_output: 32
    kernel_size: 1
    stride: 1
    pad: 0
    weight_filler {
      type: "gaussian"
      std: 0.25
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
    name: "eltwise2_SR_s1"
    type: "Eltwise"
    bottom: "local_conv1_SR_s1"
    bottom: "conv3_SR_s1"
    top: "output_SR_s1"
    eltwise_param {
        operation: 1
    }
}

layer {
  name: "relu_output_SR_s1"
  type: "PReLU"
  bottom: "output_SR_s1"
  top: "output_SR_s1"
  prelu_param {
    channel_shared: 1
  }
}

######################### SR_s2 #########################
layer {
  name: "conv1_SR_s2"
  type: "Deconvolution"
  bottom: "output_SR_s1"
  top: "conv1_SR_s2"
  param {
    lr_mult: 1
    name: "conv1_s2_w"
  }
  param {
    lr_mult: 1
    name: "conv1_s2_b"
  }
  convolution_param {
    num_output: 32
    kernel_size: 6
    stride: 2
    pad: 2
    weight_filler {
      type: "gaussian"
      std: 0.0417
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "relu1_SR_s2"
  type: "PReLU"
  bottom: "conv1_SR_s2"
  top: "conv1_SR_s2"
  prelu_param {
    channel_shared: 1
  }
}

layer {
  name: "conv2_SR_s2"
  type: "Convolution"
  bottom: "conv1_SR_s2"
  top: "conv2_SR_s2"
  param {
    lr_mult: 1
    name: "conv2_s2_w"
  }
  param {
    lr_mult: 1
    name: "conv2_s2_b"
  }
  convolution_param {
    num_output: 32
    kernel_size: 6
    stride: 2
    pad: 2
    weight_filler {
      type: "gaussian"
      std: 0.0417
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "relu2_SR_s2"
  type: "PReLU"
  bottom: "conv2_SR_s2"
  top: "conv2_SR_s2"
  prelu_param {
    channel_shared: 1
  }
}

layer {
  name: "local_output_SR_s2"
  type: "Convolution"
  bottom: "output_SR_s1"
  top: "local_output_SR_s2"
  param {
    lr_mult: 1
    name: "local_output_SR_s2_w"
  }
  param {
    lr_mult: 1
    name: "local_output_SR_s2_b"
  }
  convolution_param {
    num_output: 32
    kernel_size: 1
    stride: 1
    pad: 0
    weight_filler {
      type: "gaussian"
      std: 0.25
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
    name: "eltwise1_SR_s2"
    type: "Eltwise"
    bottom: "local_output_SR_s2"
    bottom: "conv2_SR_s2"
    top: "eltwise1_SR_s2"
    eltwise_param {
        operation: SUM
        coeff: 1
        coeff: -1
    }
}

layer {
  name: "conv3_SR_s2"
  type: "Deconvolution"
  bottom: "eltwise1_SR_s2"
  top: "conv3_SR_s2"
  param {
    lr_mult: 1
    name: "conv3_s2_w"
  }
  param {
    lr_mult: 1
    name: "conv3_s2_b"
  }
  convolution_param {
    num_output: 32
    kernel_size: 6
    stride: 2
    pad: 2
    weight_filler {
      type: "gaussian"
      std: 0.0417
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "relu3_SR_s2"
  type: "PReLU"
  bottom: "conv3_SR_s2"
  top: "conv3_SR_s2"
  prelu_param {
    channel_shared: 1
  }
}

layer {
  name: "local_conv1_SR_s2"
  type: "Convolution"
  bottom: "conv1_SR_s2"
  top: "local_conv1_SR_s2"
  param {
    lr_mult: 1
    name: "local_conv1_s2_w"
  }
  param {
    lr_mult: 1
    name: "local_conv1_s2_b"
  }
  convolution_param {
    num_output: 32
    kernel_size: 1
    stride: 1
    pad: 0
    weight_filler {
      type: "gaussian"
      std: 0.25
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
    name: "eltwise2_SR_s2"
    type: "Eltwise"
    bottom: "local_conv1_SR_s2"
    bottom: "conv3_SR_s2"
    top: "output_SR_s2"
    eltwise_param {
        operation: 1
    }
}

layer {
  name: "relu_output_SR_s2"
  type: "PReLU"
  bottom: "output_SR_s2"
  top: "output_SR_s2"
  prelu_param {
    channel_shared: 1
  }
}

######################### SR_s3 #########################
layer {
  name: "conv1_SR_s3"
  type: "Deconvolution"
  bottom: "output_SR_s2"
  top: "conv1_SR_s3"
  param {
    lr_mult: 1
    name: "conv1_s3_w"
  }
  param {
    lr_mult: 1
    name: "conv1_s3_b"
  }
  convolution_param {
    num_output: 32
    kernel_size: 6
    stride: 2
    pad: 2
    weight_filler {
      type: "gaussian"
      std: 0.0417
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "relu1_SR_s3"
  type: "PReLU"
  bottom: "conv1_SR_s3"
  top: "conv1_SR_s3"
  prelu_param {
    channel_shared: 1
  }
}

layer {
  name: "conv2_SR_s3"
  type: "Convolution"
  bottom: "conv1_SR_s3"
  top: "conv2_SR_s3"
  param {
    lr_mult: 1
    name: "conv2_s3_w"
  }
  param {
    lr_mult: 1
    name: "conv2_s3_b"
  }
  convolution_param {
    num_output: 32
    kernel_size: 6
    stride: 2
    pad: 2
    weight_filler {
      type: "gaussian"
      std: 0.0417
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "relu2_SR_s3"
  type: "PReLU"
  bottom: "conv2_SR_s3"
  top: "conv2_SR_s3"
  prelu_param {
    channel_shared: 1
  }
}

layer {
  name: "local_output_SR_s3"
  type: "Convolution"
  bottom: "output_SR_s2"
  top: "local_output_SR_s3"
  param {
    lr_mult: 1
    name: "local_output_SR_s3_w"
  }
  param {
    lr_mult: 1
    name: "local_output_SR_s3_b"
  }
  convolution_param {
    num_output: 32
    kernel_size: 1
    stride: 1
    pad: 0
    weight_filler {
      type: "gaussian"
      std: 0.25
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
    name: "eltwise1_SR_s3"
    type: "Eltwise"
    bottom: "local_output_SR_s3"
    bottom: "conv2_SR_s3"
    top: "eltwise1_SR_s3"
    eltwise_param {
        operation: SUM
        coeff: 1
        coeff: -1
    }
}

layer {
  name: "conv3_SR_s3"
  type: "Deconvolution"
  bottom: "eltwise1_SR_s3"
  top: "conv3_SR_s3"
  param {
    lr_mult: 1
    name: "conv3_s3_w"
  }
  param {
    lr_mult: 1
    name: "conv3_s3_b"
  }
  convolution_param {
    num_output: 32
    kernel_size: 6
    stride: 2
    pad: 2
    weight_filler {
      type: "gaussian"
      std: 0.0417
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "relu3_SR_s3"
  type: "PReLU"
  bottom: "conv3_SR_s3"
  top: "conv3_SR_s3"
  prelu_param {
    channel_shared: 1
  }
}

layer {
  name: "local_conv1_SR_s3"
  type: "Convolution"
  bottom: "conv1_SR_s3"
  top: "local_conv1_SR_s3"
  param {
    lr_mult: 1
    name: "local_conv1_s3_w"
  }
  param {
    lr_mult: 1
    name: "local_conv1_s3_b"
  }
  convolution_param {
    num_output: 32
    kernel_size: 1
    stride: 1
    pad: 0
    weight_filler {
      type: "gaussian"
      std: 0.25
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
    name: "eltwise2_SR_s3"
    type: "Eltwise"
    bottom: "local_conv1_SR_s3"
    bottom: "conv3_SR_s3"
    top: "output_SR_s3"
    eltwise_param {
        operation: 1
    }
}

layer {
  name: "relu_output_SR_s3"
  type: "PReLU"
  bottom: "output_SR_s3"
  top: "output_SR_s3"
  prelu_param {
    channel_shared: 1
  }
}

######################### Reconstruction #########################
layer {
  name: "SR_res"
  type: "Convolution"
  bottom: "output_SR_s3"
  top: "SR_res"
  param {
    lr_mult: 1
    name: "SR_res_w"
  }
  param {
    lr_mult: 1
    name: "SR_res_b"
  }
  convolution_param {
    num_output: 3
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.2722
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "SR"
  type: "Eltwise"
  bottom: "SR_res"
  bottom: "data"
  top: "SR"
  eltwise_param {
    operation: SUM
  }
}

######################### VGG feature extract #########################
layer {
  name: "concat_result"
  type: "Concat"
  bottom: "SR"
  bottom: "label"
  top: "concat_result"
  concat_param {
    axis: 0
  }
}

layer {
  name: "scaleAndAdd"
  type: "Scale"
  bottom: "concat_result"
  top: "concat_result"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  scale_param {
    filler {
      value: 255    
    }
    bias_term: false
  }
}

layer {
  name: "slicer_scale_result"
  type: "Slice"
  bottom: "concat_result"
  top: "R"
  top: "G"
  top: "B"
  slice_param {
    axis: 1
    slice_point: 1
    slice_point: 2
  }
}

layer {
  name: "bias_R"
  type: "Bias"
  bottom: "R"
  top: "bias_R"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  bias_param {
    filler {
      value: -123.68    
    }
  }
}

layer {
  name: "bias_G"
  type: "Bias"
  bottom: "G"
  top: "bias_G"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  bias_param {
    filler {
      value: -116.779    
    }
  }
}

layer {
  name: "bias_B"
  type: "Bias"
  bottom: "B"
  top: "bias_B"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  bias_param {
    filler {
      value: -103.939  
    }
  }
}

layer{
  name: "norm_data"
  type: "Concat"
  bottom: "bias_B"
  bottom: "bias_G"
  bottom: "bias_R"
  top: "norm_data"
  concat_param {
    axis: 1
  }
}

layer {
  bottom: "norm_data"
  top: "conv1_1"
  name: "conv1_1"
  type: "Convolution"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
  }
}
layer {
  bottom: "conv1_1"
  top: "conv1_1"
  name: "relu1_1"
  type: "ReLU"
}
layer {
  bottom: "conv1_1"
  top: "conv1_2"
  name: "conv1_2"
  type: "Convolution"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
  }
}
layer {
  bottom: "conv1_2"
  top: "conv1_2"
  name: "relu1_2"
  type: "ReLU"
}
layer {
  bottom: "conv1_2"
  top: "pool1"
  name: "pool1"
  type: "Pooling"
  pooling_param {
    pool: AVE
    kernel_size: 2
    stride: 2
  }
}
layer {
  bottom: "pool1"
  top: "conv2_1"
  name: "conv2_1"
  type: "Convolution"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
  }
}
layer {
  bottom: "conv2_1"
  top: "conv2_1"
  name: "relu2_1"
  type: "ReLU"
}
layer {
  bottom: "conv2_1"
  top: "conv2_2"
  name: "conv2_2"
  type: "Convolution"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
  }
}
layer {
  bottom: "conv2_2"
  top: "conv2_2"
  name: "relu2_2"
  type: "ReLU"
}
layer {
  bottom: "conv2_2"
  top: "pool2"
  name: "pool2"
  type: "Pooling"
  pooling_param {
    pool: AVE
    kernel_size: 2
    stride: 2
  }
}
layer {
  bottom: "pool2"
  top: "conv3_1"
  name: "conv3_1"
  type: "Convolution"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
  }
}
layer {
  bottom: "conv3_1"
  top: "conv3_1"
  name: "relu3_1"
  type: "ReLU"
}
layer {
  bottom: "conv3_1"
  top: "conv3_2"
  name: "conv3_2"
  type: "Convolution"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
  }
}
layer {
  bottom: "conv3_2"
  top: "conv3_2"
  name: "relu3_2"
  type: "ReLU"
}
layer {
  bottom: "conv3_2"
  top: "conv3_3"
  name: "conv3_3"
  type: "Convolution"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
  }
}
layer {
  bottom: "conv3_3"
  top: "conv3_3"
  name: "relu3_3"
  type: "ReLU"
}
layer {
  bottom: "conv3_3"
  top: "conv3_4"
  name: "conv3_4"
  type: "Convolution"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
  }
}
layer {
  bottom: "conv3_4"
  top: "conv3_4"
  name: "relu3_4"
  type: "ReLU"
}
layer {
  bottom: "conv3_4"
  top: "pool3"
  name: "pool3"
  type: "Pooling"
  pooling_param {
    pool: AVE
    kernel_size: 2
    stride: 2
  }
}
layer {
  bottom: "pool3"
  top: "conv4_1"
  name: "conv4_1"
  type: "Convolution"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
  }
}
layer {
  bottom: "conv4_1"
  top: "conv4_1"
  name: "relu4_1"
  type: "ReLU"
}
layer {
  bottom: "conv4_1"
  top: "conv4_2"
  name: "conv4_2"
  type: "Convolution"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
  }
}
layer {
  bottom: "conv4_2"
  top: "conv4_2"
  name: "relu4_2"
  type: "ReLU"
}
layer {
  bottom: "conv4_2"
  top: "conv4_3"
  name: "conv4_3"
  type: "Convolution"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
  }
}
layer {
  bottom: "conv4_3"
  top: "conv4_3"
  name: "relu4_3"
  type: "ReLU"
}
layer {
  bottom: "conv4_3"
  top: "conv4_4"
  name: "conv4_4"
  type: "Convolution"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
  }
}
layer {
  bottom: "conv4_4"
  top: "conv4_4"
  name: "relu4_4"
  type: "ReLU"
}
layer {
  bottom: "conv4_4"
  top: "pool4"
  name: "pool4"
  type: "Pooling"
  pooling_param {
    pool: AVE
    kernel_size: 2
    stride: 2
  }
}
layer {
  bottom: "pool4"
  top: "conv5_1"
  name: "conv5_1"
  type: "Convolution"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
  }
}
layer {
  bottom: "conv5_1"
  top: "conv5_1"
  name: "relu5_1"
  type: "ReLU"
}
layer {
  bottom: "conv5_1"
  top: "conv5_2"
  name: "conv5_2"
  type: "Convolution"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
  }
}
layer {
  bottom: "conv5_2"
  top: "conv5_2"
  name: "relu5_2"
  type: "ReLU"
}
layer {
  bottom: "conv5_2"
  top: "conv5_3"
  name: "conv5_3"
  type: "Convolution"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
  }
}
layer {
  bottom: "conv5_3"
  top: "conv5_3"
  name: "relu5_3"
  type: "ReLU"
}
layer {
  bottom: "conv5_3"
  top: "conv5_4"
  name: "conv5_4"
  type: "Convolution"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
  }
}

layer {
  name: "scale_down"
  type: "Scale"
  bottom: "conv5_4"
  top: "conv5_4_scale"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  scale_param {
    filler {
      value: 0.00390625    
    }
    bias_term: false
  }
}

layer {
  name: "slicer_VGG"
  type: "Slice"
  bottom: "conv5_4_scale"
  top: "SR_VGG"
  top: "HR_VGG"
  slice_param {
    axis: 0
    slice_point: 16
  }
}

######################### MSE and VGG loss #########################
layer {
    name: "SR_diff"
    type: "Eltwise"
    bottom: "label"
    bottom: "SR"
    top: "SR_diff"
    eltwise_param {
        operation: SUM
        coeff: 1
        coeff: -1
    }
}

layer {
  name: "SR_layer"
  bottom: "SR_diff"
  top: "SR_abs"
  type: "AbsVal"
}

layer {
  name: "SR_loss"
  type: "Reduction"
  bottom: "SR_abs"
  top: "SR_loss"
  loss_weight: 1
}

layer {
    name: "VGG_diff"
    type: "Eltwise"
    bottom: "SR_VGG"
    bottom: "HR_VGG"
    top: "VGG_diff"
    eltwise_param {
        operation: SUM
        coeff: 1
        coeff: -1
    }
}

layer {
  name: "VGG_layer"
  bottom: "VGG_diff"
  top: "VGG_abs"
  type: "AbsVal"
}

layer {
  name: "VGG_loss"
  type: "Reduction"
  bottom: "VGG_abs"
  top: "VGG_loss"
  loss_weight: 10
}