name: "DeconvNet_inference_deploy"

input: "data"
input_dim: 1
input_dim: 3
input_dim: 224
input_dim: 224

# 224 x 224
# conv1_1
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: 64    pad: 1    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
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: 64    pad: 1    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
layers {
  bottom: "conv1_2"  top: "pool1" top:"pool1_mask"  name: "pool1"  type: POOLING
  pooling_param {    pool: MAX    kernel_size: 2    stride: 2  }
}

# 112 x 112
# conv2_1
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: 128    pad: 1    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}
# conv2_2
layers {  bottom: "conv2_1"  top: "conv2_2"  name: "conv2_2"  type: CONVOLUTION
  blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0
  convolution_param {    num_output: 128    pad: 1    kernel_size: 3  }}
layers { bottom: 'conv2_2' top: 'conv2_2' name: 'bn2_2' type: BN
  bn_param { scale_filler { type: 'constant' value: 1 }
             shift_filler { type: 'constant' value: 0.001 }
            bn_mode: INFERENCE } }
layers {  bottom: "conv2_2"  top: "conv2_2"  name: "relu2_2"  type: RELU}

# pool2
layers {
  bottom: "conv2_2"  top: "pool2" top: "pool2_mask" name: "pool2"  type: POOLING
  pooling_param {    pool: MAX    kernel_size: 2    stride: 2  }
}

# 56 x 56
# conv3_1
layers {  bottom: "pool2"  top: "conv3_1"  name: "conv3_1"  type: CONVOLUTION
  blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0
  convolution_param {    num_output: 256    pad: 1    kernel_size: 3  }}
layers { bottom: 'conv3_1' top: 'conv3_1' name: 'bn3_1' type: BN
  bn_param { scale_filler { type: 'constant' value: 1 }
             shift_filler { type: 'constant' value: 0.001 }
             bn_mode: INFERENCE } }
layers {  bottom: "conv3_1"  top: "conv3_1"  name: "relu3_1"  type: RELU}
# conv3_2
layers {  bottom: "conv3_1"  top: "conv3_2"  name: "conv3_2"  type: CONVOLUTION
  blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0
  convolution_param {    num_output: 256    pad: 1    kernel_size: 3  }}
layers { bottom: 'conv3_2' top: 'conv3_2' name: 'bn3_2' type: BN
  bn_param { scale_filler { type: 'constant' value: 1 }
             shift_filler { type: 'constant' value: 0.001 }
             bn_mode: INFERENCE } }
layers {  bottom: "conv3_2"  top: "conv3_2"  name: "relu3_2"  type: RELU}
# conv3_3
layers {  bottom: "conv3_2"  top: "conv3_3"  name: "conv3_3"  type: CONVOLUTION
  blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0
  convolution_param {    num_output: 256    pad: 1    kernel_size: 3  }}
layers { bottom: 'conv3_3' top: 'conv3_3' name: 'bn3_3' type: BN
  bn_param { scale_filler { type: 'constant' value: 1 }
             shift_filler { type: 'constant' value: 0.001 }
             bn_mode: INFERENCE } }
layers {  bottom: "conv3_3"  top: "conv3_3"  name: "relu3_3"  type: RELU}

# pool3
layers {
  bottom: "conv3_3"  top: "pool3" top: "pool3_mask"  name: "pool3"  type: POOLING
  pooling_param {    pool: MAX    kernel_size: 2    stride: 2  }
}

# 28 x 28
# conv4_1
layers {  bottom: "pool3"  top: "conv4_1"  name: "conv4_1"  type: CONVOLUTION
  blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0
  convolution_param {    num_output: 512    pad: 1    kernel_size: 3  }}
layers { bottom: 'conv4_1' top: 'conv4_1' name: 'bn4_1' type: BN
  bn_param { scale_filler { type: 'constant' value: 1 }
             shift_filler { type: 'constant' value: 0.001 }
             bn_mode: INFERENCE } }
layers {  bottom: "conv4_1"  top: "conv4_1"  name: "relu4_1"  type: RELU}
# conv4_2
layers {  bottom: "conv4_1"  top: "conv4_2"  name: "conv4_2"  type: CONVOLUTION
  blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0
  convolution_param {    num_output: 512    pad: 1    kernel_size: 3  }}
layers { bottom: 'conv4_2' top: 'conv4_2' name: 'bn4_2' type: BN
  bn_param { scale_filler { type: 'constant' value: 1 }
             shift_filler { type: 'constant' value: 0.001 }
             bn_mode: INFERENCE } }
layers {  bottom: "conv4_2"  top: "conv4_2"  name: "relu4_2"  type: RELU}
# conv4_3
layers {  bottom: "conv4_2"  top: "conv4_3"  name: "conv4_3"  type: CONVOLUTION
  blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0
  convolution_param {    num_output: 512    pad: 1    kernel_size: 3  }}
layers { bottom: 'conv4_3' top: 'conv4_3' name: 'bn4_3' type: BN
  bn_param { scale_filler { type: 'constant' value: 1 }
             shift_filler { type: 'constant' value: 0.001 }
             bn_mode: INFERENCE } }
layers {  bottom: "conv4_3"  top: "conv4_3"  name: "relu4_3"  type: RELU}

# pool4
layers {
  bottom: "conv4_3"  top: "pool4" top: "pool4_mask"  name: "pool4"  type: POOLING
  pooling_param {    pool: MAX    kernel_size: 2    stride: 2  }
}

# 14 x 14
# conv5_1
layers {  bottom: "pool4"  top: "conv5_1"  name: "conv5_1"  type: CONVOLUTION
  blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0
  convolution_param {    num_output: 512    pad: 1    kernel_size: 3  }}
layers { bottom: 'conv5_1' top: 'conv5_1' name: 'bn5_1' type: BN
  bn_param { scale_filler { type: 'constant' value: 1 }
             shift_filler { type: 'constant' value: 0.001 } 
             bn_mode: INFERENCE } }
layers {  bottom: "conv5_1"  top: "conv5_1"  name: "relu5_1"  type: RELU}
# conv5_2
layers {  bottom: "conv5_1"  top: "conv5_2"  name: "conv5_2"  type: CONVOLUTION
  blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0
  convolution_param {    num_output: 512    pad: 1    kernel_size: 3  }}
layers { bottom: 'conv5_2' top: 'conv5_2' name: 'bn5_2' type: BN
  bn_param { scale_filler { type: 'constant' value: 1 }
             shift_filler { type: 'constant' value: 0.001 }
             bn_mode: INFERENCE } }
layers {  bottom: "conv5_2"  top: "conv5_2"  name: "relu5_2"  type: RELU}
# conv5_3
layers {  bottom: "conv5_2"  top: "conv5_3"  name: "conv5_3"  type: CONVOLUTION
  blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0
  convolution_param {    num_output: 512    pad: 1    kernel_size: 3  }}
layers { bottom: 'conv5_3' top: 'conv5_3' name: 'bn5_3' type: BN
  bn_param { scale_filler { type: 'constant' value: 1 }
             shift_filler { type: 'constant' value: 0.001 }
             bn_mode: INFERENCE } }
layers {  bottom: "conv5_3"  top: "conv5_3"  name: "relu5_3"  type: RELU}

# pool5
layers {
  bottom: "conv5_3"  top: "pool5" top: "pool5_mask"  name: "pool5"  type: POOLING
  pooling_param {    pool: MAX    kernel_size: 2    stride: 2  }
}

# 7 x 7
# fc6
layers { bottom: 'pool5' top: 'fc6' name: 'fc6' type: CONVOLUTION
  blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0
  convolution_param { kernel_size: 7 num_output: 4096 } }
layers { bottom: 'fc6' top: 'fc6' name: 'bnfc6' type: BN
  bn_param { scale_filler { type: 'constant' value: 1 }
             shift_filler { type: 'constant' value: 0.001 }
             bn_mode: INFERENCE } }
layers {  bottom: "fc6"  top: "fc6"  name: "relu6"  type: RELU}

# 1 x 1
# fc7
layers { bottom: 'fc6' top: 'fc7' name: 'fc7' type: CONVOLUTION
  blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0
  convolution_param { kernel_size: 1 num_output: 4096 } }
layers { bottom: 'fc7' top: 'fc7' name: 'bnfc7' type: BN
  bn_param { scale_filler { type: 'constant' value: 1 }
             shift_filler { type: 'constant' value: 0.001 }
             bn_mode: INFERENCE } }
layers {  bottom: "fc7"  top: "fc7"  name: "relu7"  type: RELU}

# fc6-deconv
layers { bottom: 'fc7' top: 'fc6-deconv' name: 'fc6-deconv' type: DECONVOLUTION
  blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0
  convolution_param { num_output: 512 kernel_size: 7
    weight_filler {      type: "gaussian"      std: 0.01    }
    bias_filler {      type: "constant"      value: 0    }} }
layers { bottom: 'fc6-deconv' top: 'fc6-deconv' name: 'fc6-deconv-bn' type: BN
  bn_param { scale_filler { type: 'constant' value: 1 }
             shift_filler { type: 'constant' value: 0.001 }
             bn_mode: INFERENCE } }
layers { bottom: 'fc6-deconv' top: 'fc6-deconv' name: 'fc6-deconv-relu' type: RELU }

# 7 x 7
# unpool5
layers { type: UNPOOLING  bottom: "fc6-deconv"  bottom: "pool5_mask"  top: "unpool5"  name: "unpool5"
  unpooling_param {   unpool: MAX   kernel_size: 2    stride: 2   unpool_size: 14 }
}

# 14 x 14
# deconv5_1
layers { bottom: 'unpool5' top: 'deconv5_1' name: 'deconv5_1' type: DECONVOLUTION
  blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0
  convolution_param { num_output: 512	pad: 1	kernel_size: 3
    weight_filler {      type: "gaussian"      std: 0.01    }
    bias_filler {      type: "constant"      value: 0    }} }
layers { bottom: 'deconv5_1' top: 'deconv5_1' name: 'debn5_1' type: BN
  bn_param { scale_filler { type: 'constant' value: 1 }
             shift_filler { type: 'constant' value: 0.001 }
             bn_mode: INFERENCE } }
layers { bottom: 'deconv5_1' top: 'deconv5_1' name: 'derelu5_1' type: RELU }
# deconv5_2
layers { bottom: 'deconv5_1' top: 'deconv5_2' name: 'deconv5_2' type: DECONVOLUTION
  blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0
  convolution_param { num_output: 512	pad: 1	kernel_size: 3
    weight_filler {      type: "gaussian"      std: 0.01    }
    bias_filler {      type: "constant"      value: 0    }} }
layers { bottom: 'deconv5_2' top: 'deconv5_2' name: 'debn5_2' type: BN
  bn_param { scale_filler { type: 'constant' value: 1 }
             shift_filler { type: 'constant' value: 0.001 }
             bn_mode: INFERENCE } }
layers { bottom: 'deconv5_2' top: 'deconv5_2' name: 'derelu5_2' type: RELU }
# deconv5_3
layers { bottom: 'deconv5_2' top: 'deconv5_3' name: 'deconv5_3' type: DECONVOLUTION
  blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0
  convolution_param { num_output: 512	pad: 1	kernel_size: 3
    weight_filler {      type: "gaussian"      std: 0.01    }
    bias_filler {      type: "constant"      value: 0    }} }
layers { bottom: 'deconv5_3' top: 'deconv5_3' name: 'debn5_3' type: BN
  bn_param { scale_filler { type: 'constant' value: 1 }
             shift_filler { type: 'constant' value: 0.001 }
             bn_mode: INFERENCE } }
layers { bottom: 'deconv5_3' top: 'deconv5_3' name: 'derelu5_3' type: RELU }

# unpool4
layers { type: UNPOOLING  bottom: "deconv5_3"  bottom: "pool4_mask"  top: "unpool4"  name: "unpool4"
  unpooling_param {   unpool: MAX   kernel_size: 2    stride: 2   unpool_size: 28 }
}

# 28 x 28
# deconv4_1
layers { bottom: 'unpool4' top: 'deconv4_1' name: 'deconv4_1' type: DECONVOLUTION
  blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0
  convolution_param { num_output: 512	pad: 1	kernel_size: 3
    weight_filler {      type: "gaussian"      std: 0.01    }
    bias_filler {      type: "constant"      value: 0    }} }
layers { bottom: 'deconv4_1' top: 'deconv4_1' name: 'debn4_1' type: BN
  bn_param { scale_filler { type: 'constant' value: 1 }
             shift_filler { type: 'constant' value: 0.001 }
             bn_mode: INFERENCE } }
layers { bottom: 'deconv4_1' top: 'deconv4_1' name: 'derelu4_1' type: RELU }
# deconv 4_2
layers { bottom: 'deconv4_1' top: 'deconv4_2' name: 'deconv4_2' type: DECONVOLUTION
  blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0
  convolution_param { num_output: 512	pad: 1	kernel_size: 3
    weight_filler {      type: "gaussian"      std: 0.01    }
    bias_filler {      type: "constant"      value: 0    }} }
layers { bottom: 'deconv4_2' top: 'deconv4_2' name: 'debn4_2' type: BN
  bn_param { scale_filler { type: 'constant' value: 1 }
             shift_filler { type: 'constant' value: 0.001 }
             bn_mode: INFERENCE } }
layers { bottom: 'deconv4_2' top: 'deconv4_2' name: 'derelu4_2' type: RELU }
# deconv 4_3
layers { bottom: 'deconv4_2' top: 'deconv4_3' name: 'deconv4_3' type: DECONVOLUTION
  blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0
  convolution_param { num_output: 256	pad: 1	kernel_size: 3
    weight_filler {      type: "gaussian"      std: 0.01    }
    bias_filler {      type: "constant"      value: 0    }} }
layers { bottom: 'deconv4_3' top: 'deconv4_3' name: 'debn4_3' type: BN
  bn_param { scale_filler { type: 'constant' value: 1 }
             shift_filler { type: 'constant' value: 0.001 }
             bn_mode: INFERENCE } }
layers { bottom: 'deconv4_3' top: 'deconv4_3' name: 'derelu4_3' type: RELU }

# unpool3
layers { type: UNPOOLING  bottom: "deconv4_3"  bottom: "pool3_mask"  top: "unpool3"  name: "unpool3"
  unpooling_param {   unpool: MAX   kernel_size: 2    stride: 2   unpool_size: 56 }
}

# 56 x 56
# deconv3_1
layers { bottom: 'unpool3' top: 'deconv3_1' name: 'deconv3_1' type: DECONVOLUTION
  blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0
  convolution_param { num_output:256	pad:1	kernel_size: 3
    weight_filler {      type: "gaussian"      std: 0.01    }
    bias_filler {      type: "constant"      value: 0    }} }
layers { bottom: 'deconv3_1' top: 'deconv3_1' name: 'debn3_1' type: BN
  bn_param { scale_filler { type: 'constant' value: 1 }
             shift_filler { type: 'constant' value: 0.001 }
             bn_mode: INFERENCE } }
layers { bottom: 'deconv3_1' top: 'deconv3_1' name: 'derelu3_1' type: RELU }
# deconv3_2
layers { bottom: 'deconv3_1' top: 'deconv3_2' name: 'deconv3_2' type: DECONVOLUTION
  blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0
  convolution_param { num_output:256	pad:1	kernel_size: 3
    weight_filler {      type: "gaussian"      std: 0.01    }
    bias_filler {      type: "constant"      value: 0    }} }
layers { bottom: 'deconv3_2' top: 'deconv3_2' name: 'debn3_2' type: BN
  bn_param { scale_filler { type: 'constant' value: 1 }
             shift_filler { type: 'constant' value: 0.001 }
             bn_mode: INFERENCE } }
layers { bottom: 'deconv3_2' top: 'deconv3_2' name: 'derelu3_2' type: RELU }
# deconv3_3
layers { bottom: 'deconv3_2' top: 'deconv3_3' name: 'deconv3_3' type: DECONVOLUTION
  blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0
  convolution_param { num_output:128	pad:1	kernel_size: 3
    weight_filler {      type: "gaussian"      std: 0.01    }
    bias_filler {      type: "constant"      value: 0    }} }
layers { bottom: 'deconv3_3' top: 'deconv3_3' name: 'debn3_3' type: BN
  bn_param { scale_filler { type: 'constant' value: 1 }
             shift_filler { type: 'constant' value: 0.001 }
             bn_mode: INFERENCE } }
layers { bottom: 'deconv3_3' top: 'deconv3_3' name: 'derelu3_3' type: RELU }

# unpool2
layers { type: UNPOOLING  bottom: "deconv3_3"  bottom: "pool2_mask"  top: "unpool2"  name: "unpool2"
  unpooling_param {   unpool: MAX   kernel_size: 2    stride: 2   unpool_size: 112 }
}

# 112 x 112
# deconv2_1
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:128	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 }
# deconv2_2
layers { bottom: 'deconv2_1' top: 'deconv2_2' name: 'deconv2_2' type: DECONVOLUTION
  blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0
  convolution_param { num_output:64	pad:1	kernel_size: 3
    weight_filler {      type: "gaussian"      std: 0.01    }
    bias_filler {      type: "constant"      value: 0    }} }
layers { bottom: 'deconv2_2' top: 'deconv2_2' name: 'debn2_2' type: BN
  bn_param { scale_filler { type: 'constant' value: 1 }
             shift_filler { type: 'constant' value: 0.001 }
             bn_mode: INFERENCE } }
layers { bottom: 'deconv2_2' top: 'deconv2_2' name: 'derelu2_2' type: RELU }

# unpool1
layers { type: UNPOOLING  bottom: "deconv2_2"  bottom: "pool1_mask"  top: "unpool1"  name: "unpool1"
  unpooling_param {   unpool: MAX   kernel_size: 2    stride: 2   unpool_size: 224 }
}

# deconv1_1
layers { bottom: 'unpool1' top: 'deconv1_1' name: 'deconv1_1' type: DECONVOLUTION
  blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0
  convolution_param { num_output:64	pad:1	kernel_size: 3
    weight_filler {      type: "gaussian"      std: 0.01    }
    bias_filler {      type: "constant"      value: 0    }} }
layers { bottom: 'deconv1_1' top: 'deconv1_1' name: 'debn1_1' type: BN
  bn_param { scale_filler { type: 'constant' value: 1 }
             shift_filler { type: 'constant' value: 0.001 }
             bn_mode: INFERENCE } }
layers { bottom: 'deconv1_1' top: 'deconv1_1' name: 'derelu1_1' type: RELU }

# deconv1_2
layers { bottom: 'deconv1_1' top: 'deconv1_2' name: 'deconv1_2' type: DECONVOLUTION
  blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0
  convolution_param { num_output:64     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 }

# seg-score
layers { name: 'seg-score-voc' type: CONVOLUTION bottom: 'deconv1_2' top: 'seg-score'
  blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0
  convolution_param { num_output: 21 kernel_size: 1
    weight_filler {      type: "gaussian"      std: 0.01    }
    bias_filler {      type: "constant"      value: 0    }} }