add system files

[GitHub/Stricted/sm-g903f-system.git] / saiv / face / fr / Proto.prototxt

name: "DL_FR"
input: "data"
input_dim:1
input_dim:3
input_dim:61
input_dim:61
#################  laye-1
layers {
  bottom: "data"
  top: "conv1/3x3_s2_1"
  name: "conv1/3x3_s2_1"
  type: CONVOLUTION
  convolution_param {
    num_output: 64
    kernel_size: 3
    stride: 2
  }
}
layers {
  bottom: "conv1/3x3_s2_1"
  top: "conv1/3x3_s2_1"
  name: "conv1/relu_3x3"
  type: PRELU
  prelu_param {
    channel_shared: false
  }
}

layers {
  name: "conv1/norm1"
  type: LRN
  bottom: "conv1/3x3_s2_1"
  top: "conv1/norm1"
  lrn_param {
    local_size: 3
    alpha: 0.0001
    beta: 0.75
  }
}

#####################layer-2

layers {
  bottom: "conv1/norm1"
  top: "conv2/3x3_s2_1"
  name: "conv2/3x3_s2_1"
  type: CONVOLUTION
  convolution_param {
    num_output: 128
    kernel_size: 3
    stride: 2
  }
}
layers {
  bottom: "conv2/3x3_s2_1"
  top: "conv2/3x3_s2_1"
  name: "conv2/relu_3x3"
  type: PRELU
  prelu_param {
    channel_shared: false
  }
}

layers {
  name: "conv2/norm1"
  type: LRN
  bottom: "conv2/3x3_s2_1"
  top: "conv2/norm1"
  lrn_param {
    local_size: 3
    alpha: 0.0001
    beta: 0.75
  }
}


##################################### layer-3

layers {
  bottom: "conv2/norm1"
  top: "conv3/2x2_s2_1"
  name: "conv3/2x2_s2_1"
  type: CONVOLUTION
  convolution_param {
    num_output: 256
    kernel_size: 2
    stride: 2
  }
}
layers {
  bottom: "conv3/2x2_s2_1"
  top: "conv3/2x2_s2_1"
  name: "conv3/relu_2x2_1"
  type: PRELU
  prelu_param {
    channel_shared: false
  }
}
layers {
  bottom: "conv3/2x2_s2_1"
  top: "conv3/2x2_s1_2"
  name: "conv3/2x2_s1_2"
  type: CONVOLUTION
  convolution_param {
    num_output: 384
    kernel_size: 2
    pad: 1
    stride: 1
  }
}
layers {
  bottom: "conv3/2x2_s1_2"
  top: "conv3/2x2_s1_2"
  name: "conv3/relu_2x2_2"
  type: PRELU
  prelu_param {
    channel_shared: false
  }
}
#####################layer-4

layers {
  bottom: "conv3/2x2_s1_2"
  top: "conv4/2x2_s1_1"
  name: "conv4/2x2_s1_1"
  type: CONVOLUTION
  convolution_param {
    num_output: 256
    kernel_size: 2
    stride: 1
  }
}
layers {
  bottom: "conv4/2x2_s1_1"
  top: "conv4/2x2_s1_1"
  name: "conv4/relu_2x2_1"
  type: PRELU
  prelu_param {
    channel_shared: false
  }
}

layers {
  bottom: "conv4/2x2_s1_1"
  top: "conv4/2x2_s1_2"
  name: "conv4/2x2_s1_2"
  type: CONVOLUTION
  convolution_param {
    num_output: 128
    kernel_size: 2
    pad: 1
    stride: 1
  }
}
layers {
  bottom: "conv4/2x2_s1_2"
  top: "conv4/2x2_s1_2"
  name: "conv4/relu_2x2_2"
  type: PRELU
  prelu_param {
    channel_shared: false
  }
}
##############4x4 bin
layers {
  name: "pool4_1"
  type: POOLING
  bottom: "conv4/2x2_s1_2"
  top: "pool4_1"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}

layers {
  name: "pool4_1_flatten"
  type: FLATTEN
  bottom: "pool4_1"
  top: "pool4_1_flatten"
}


###############2*2 bin

layers {
  name: "pool4_2"
  type: POOLING
  bottom: "conv4/2x2_s1_2"
  top: "pool4_2"
  pooling_param {
    pool: MAX
    kernel_size: 4
    stride: 4
  }
}

layers {
  name: "pool4_2_flatten"
  type: FLATTEN
  bottom: "pool4_2"
  top: "pool4_2_flatten"
}


#############1*1 bin

layers {
  name: "pool4_3"
  type: POOLING
  bottom: "conv4/2x2_s1_2"
  top: "pool4_3"
  pooling_param {
    pool: MAX
    kernel_size: 8
    stride: 8
  }
}

layers {
  name: "pool4_3_flatten"
  type: FLATTEN
  bottom: "pool4_3"
  top: "pool4_3_flatten"
}

layers {
  bottom: "pool4_1_flatten"
  bottom: "pool4_2_flatten"
  bottom: "pool4_3_flatten"
  top: "pool4_spp"
  name: "pool4_spp"
  type: CONCAT
}


##########################fc-5

layers {
  name: "fc5"
  type: INNER_PRODUCT
  bottom: "pool4_spp"
  top: "fc5"
  inner_product_param {
    num_output: 512
  }
}
layers {
  name: "relu5"
  type: PRELU
  bottom: "fc5"
  top: "fc5"
  prelu_param {
    channel_shared: false
  }
}
#layers {
#  name: "drop5"
#  type: DROPOUT
#  bottom: "fc5"
#  top: "fc5"
#  dropout_param {
#    dropout_ratio: 0.2
#  }
#}

##############fc-6
layers {
  name: "fc6"
  type: INNER_PRODUCT
  bottom: "fc5"
  top: "fc6"
  inner_product_param {
    num_output: 256
  }
}
layers {
  name: "relu6"
  type: PRELU
  bottom: "fc6"
  top: "fc6"
  prelu_param {
    channel_shared: false
  }
}
#layers {
#  name: "drop6"
#  type: DROPOUT
#  bottom: "fc6"
#  top: "fc6"
#  dropout_param {
#    dropout_ratio: 0.1
#  }
#}
#layers {
#  name: "fc7_face64"
#  type: INNER_PRODUCT
#  bottom: "fc6"
#  top: "fc7_face64"
#  inner_product_param {
#    num_output: 2193
#   }
#}
#layers {
#  name: "accuracy_top1"
#  type: ACCURACY
#  bottom: "fc7_face64"
#  bottom: "label"
#  top: "accuracy_top1"
#  accuracy_param {
#    top_k: 1
#  }    
#  include: { phase: TEST }
#}
#layers {
#  name: "loss"
#  type: SOFTMAX_LOSS
#  bottom: "fc7_face64"
#  bottom: "label"
#  top: "loss"
#}