network_structure

name: "CAE"
layers {
  name: "data"
  type: IMAGEDATA
  top: "data"
  top: "label"
  image_data_param {
    root_folder: "/data/dywang/Database/MITOS/2012/A/data/train_patches/"
    source: "/data/dywang/Database/MITOS/2012/A/data/train_patches/train_patches_100/stage01/imagelist_wlabel_tr.txt"
    batch_size: 32
    shuffle: true
  }
  transform_param {
    scale: 0.003921
    mean_value: 104.008
    mean_value: 116.669
    mean_value: 122.675
    mirror: true
  }
  include: { phase: TRAIN }
}
layers {
  name: "data"
  type: IMAGEDATA
  top: "data"
  top: "label"
  image_data_param {
    root_folder: "/data/dywang/Database/MITOS/2012/A/data/train_patches/"
    source: "/data/dywang/Database/MITOS/2012/A/data/train_patches/train_patches_100/stage01/imagelist_wlabel_te.txt"
    batch_size: 32
    shuffle: true
  }
  transform_param {
    scale: 0.003921
    mean_value: 104.008
    mean_value: 116.669
    mean_value: 122.675
    mirror: true
  }
  include: { phase: TEST }
}

# conv1_1 : 64x64 -> 62x62
layers {  bottom: "data"  top: "conv1_1"  name: "conv1_1"  type: CONVOLUTION
  blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0
  convolution_param {    num_output: 12    pad: 0    kernel_size: 3  }}
layers { bottom: 'conv1_1' top: 'conv1_1' name: 'bn1_1' type: BN
  bn_param { scale_filler { type: 'constant' value: 1 }
             shift_filler { type: 'constant' value: 0.001 }
             bn_mode: INFERENCE } }
layers {  bottom: "conv1_1"  top: "conv1_1"  name: "relu1_1"  type: RELU}

# conv1_2 : 62x62 -> 60x60
layers {  bottom: "conv1_1"  top: "conv1_2"  name: "conv1_2"  type: CONVOLUTION
  blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0
  convolution_param {    num_output: 12    pad: 0    kernel_size: 3  }}
layers { bottom: 'conv1_2' top: 'conv1_2' name: 'bn1_2' type: BN
  bn_param { scale_filler { type: 'constant' value: 1 }
             shift_filler { type: 'constant' value: 0.001 }
             bn_mode: INFERENCE } }
layers {  bottom: "conv1_2"  top: "conv1_2"  name: "relu1_2"  type: RELU}

# pool1: : 60x60 -> 30x30
layers {
  bottom: "conv1_2"  top: "pool1" top:"pool1_mask"  name: "pool1"  type: POOLING
  pooling_param {    pool: MAX    kernel_size: 2    stride: 2  }
}

# conv2_1 : 30x30 -> 28x28
layers {  bottom: "pool1"  top: "conv2_1"  name: "conv2_1"  type: CONVOLUTION
  blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0
  convolution_param {    num_output: 24    pad: 0    kernel_size: 3  }}
layers { bottom: 'conv2_1' top: 'conv2_1' name: 'bn2_1' type: BN
  bn_param { scale_filler { type: 'constant' value: 1 }
             shift_filler { type: 'constant' value: 0.001 }
             bn_mode: INFERENCE } }
layers {  bottom: "conv2_1"  top: "conv2_1"  name: "relu2_1"  type: RELU}

# pool2: : 28x28 -> 14x14
layers {
  bottom: "conv2_1"  top: "pool2" top:"pool2_mask"  name: "pool2"  type: POOLING
  pooling_param {    pool: MAX    kernel_size: 2    stride: 2  }
}

####
# 7 x 7
# fc1
layers { bottom: 'pool2' top: 'fc1' name: 'fc1' type: CONVOLUTION
  blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0
  convolution_param { kernel_size: 14 num_output: 1024 } }
layers { bottom: 'fc1' top: 'fc1' name: 'bnfc1' type: BN
  bn_param { scale_filler { type: 'constant' value: 1 }
             shift_filler { type: 'constant' value: 0.001 }
             bn_mode: INFERENCE } }
layers {  bottom: "fc1"  top: "fc1"  name: "relu1"  type: RELU}

# 1 x 1
# fc2
layers { bottom: 'fc1' top: 'fc2' name: 'fc2' type: CONVOLUTION
  blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0
  convolution_param { kernel_size: 1 num_output: 1024 } }
layers { bottom: 'fc2' top: 'fc2' name: 'bnfc2' type: BN
  bn_param { scale_filler { type: 'constant' value: 1 }
             shift_filler { type: 'constant' value: 0.001 }
             bn_mode: INFERENCE } }
layers {  bottom: "fc2"  top: "fc2"  name: "relu2"  type: RELU}

# fc1-deconv : 1x1 -> 14x14
layers { bottom: 'fc2' top: 'fc1-deconv' name: 'fc1-deconv' type: DECONVOLUTION
  blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0
  convolution_param { num_output: 24 kernel_size: 14
    weight_filler {      type: "gaussian"      std: 0.01    }
    bias_filler {      type: "constant"      value: 0    }} }
layers { bottom: 'fc1-deconv' top: 'fc1-deconv' name: 'fc1-deconv-bn' type: BN
  bn_param { scale_filler { type: 'constant' value: 1 }
             shift_filler { type: 'constant' value: 0.001 }
             bn_mode: INFERENCE } }
layers { bottom: 'fc1-deconv' top: 'fc1-deconv' name: 'fc1-deconv-relu' type: RELU }

# 7 x 7
# unpool2 : 14x14 -> 28 x 28
layers { type: UNPOOLING  bottom: "fc1-deconv"  bottom: "pool2_mask"  top: "unpool2"  name: "unpool2"
  unpooling_param {   unpool: MAX   kernel_size: 2    stride: 2   unpool_size: 28 }
}

# 14 x 14
# deconv2_1 : 28x28 -> 30x30
layers { bottom: 'unpool2' top: 'deconv2_1' name: 'deconv2_1' type: DECONVOLUTION
  blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0
  convolution_param { num_output: 12 pad: 1  kernel_size: 3
    weight_filler {      type: "gaussian"      std: 0.01    }
    bias_filler {      type: "constant"      value: 0    }} }
layers { bottom: 'deconv2_1' top: 'deconv2_1' name: 'debn2_1' type: BN
  bn_param { scale_filler { type: 'constant' value: 1 }
             shift_filler { type: 'constant' value: 0.001 }
             bn_mode: INFERENCE } }
layers { bottom: 'deconv2_1' top: 'deconv2_1' name: 'derelu2_1' type: RELU }

# 7 x 7
# unpool1 : 30x30 -> 60 x 60
layers { type: UNPOOLING  bottom: "deconv2_1"  bottom: "pool1_mask"  top: "unpool1"  name: "unpool1"
  unpooling_param {   unpool: MAX   kernel_size: 2    stride: 2   unpool_size: 60 }
}

# 14 x 14
# deconv1_2 : 60x60 -> 62x62
layers { bottom: 'unpool1' top: 'deconv1_2' name: 'deconv1_2' type: DECONVOLUTION
  blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0
  convolution_param { num_output: 12 pad: 1  kernel_size: 3
    weight_filler {      type: "gaussian"      std: 0.01    }
    bias_filler {      type: "constant"      value: 0    }} }
layers { bottom: 'deconv1_2' top: 'deconv1_2' name: 'debn1_2' type: BN
  bn_param { scale_filler { type: 'constant' value: 1 }
             shift_filler { type: 'constant' value: 0.001 }
             bn_mode: INFERENCE } }
layers { bottom: 'deconv1_2' top: 'deconv1_2' name: 'derelu1_2' type: RELU }

# deconv1_1 : 62x62 -> 64x64
layers { name: 're-image' type: CONVOLUTION bottom: 'deconv1_2' top: 're-image'
  blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0
  convolution_param { num_output: 3 kernel_size: 1
    weight_filler {      type: "gaussian"      std: 0.01    }
    bias_filler {      type: "constant"      value: 0    }} }

##
layers {
  name: "loss"
  type: SIGMOID_CROSS_ENTROPY_LOSS
  bottom: "re-image"
  bottom: "data"
  top: "cross_entropy_error"
  loss_weight: 1
}

layers {
  name: "loss"
  type: EUCLIDEAN_LOSS
  bottom: "re-image"
  bottom: "data"
  top: "l2_error"
  loss_weight: 0
}

layers {
  name: "layers-sigmod"
  type: SIGMOID
  bottom: "label"
  top: "label-sig"
}
layers {
  name: "loss-fake"
  type: EUCLIDEAN_LOSS
  bottom: "label"
  bottom: "label-sig"
  top: "l2_error-fake"
  loss_weight: 0
}