name: "PCN-Tracking"

input: "data"
input_dim: 1
input_dim: 3
input_dim: 72
input_dim: 72

###########################conv1##############################
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  convolution_param {
    num_output: 8
    kernel_size: 3
    stride: 1
    pad: 1
  }
}

layer {
  name: "conv1/relu"
  type: "ReLU"
  bottom: "conv1"
  top: "conv1"
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
    pad: 0
  }
}

###########################conv2##############################

layer {
  name: "conv2"
  type: "Convolution"
  bottom: "pool1"
  top: "conv2"
  convolution_param {
    num_output: 12
    kernel_size: 3
    stride: 1
    pad: 1
  }
}

layer {
  name: "conv2/relu"
  type: "ReLU"
  bottom: "conv2"
  top: "conv2"
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv2"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
    pad: 0
  }
}

###########################conv3##############################

layer {
  name: "conv3"
  type: "Convolution"
  bottom: "pool2"
  top: "conv3"
  convolution_param {
    num_output: 24
    kernel_size: 3
    stride: 1
    pad: 1
  }
}

layer {
  name: "conv3/relu"
  type: "ReLU"
  bottom: "conv3"
  top: "conv3"
}
layer {
  name: "pool3"
  type: "Pooling"
  bottom: "conv3"
  top: "pool3"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
    pad: 0
  }
}

###########################conv4##############################

layer {
  name: "conv4"
  type: "Convolution"
  bottom: "pool3"
  top: "conv4"
  convolution_param {
    num_output: 48
    kernel_size: 3
    stride: 1
    pad: 1
  }
}

layer {
  name: "conv4/relu"
  type: "ReLU"
  bottom: "conv4"
  top: "conv4"
}
layer {
  name: "pool4"
  type: "Pooling"
  bottom: "conv4"
  top: "pool4"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
    pad: 0
  }
}

###########################conv5##############################

layer {
  name: "conv5"
  type: "Convolution"
  bottom: "pool4"
  top: "conv5"
  convolution_param {
    num_output: 96
    kernel_size: 3
    stride: 1
    pad: 1
  }
}

layer {
  name: "conv5/relu"
  type: "ReLU"
  bottom: "conv5"
  top: "conv5"
}
layer {
  name: "pool5"
  type: "Pooling"
  bottom: "conv5"
  top: "pool5"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
    pad: 0
  }
}


################################fc6#############################

layer {
  name: "fc6"
  type: "InnerProduct"
  bottom: "pool5"
  top: "fc6"
  inner_product_param {
    num_output: 128
  }
}
layer {
  name: "fc6/relu"
  type: "ReLU"
  bottom: "fc6"
  top: "fc6"
}

###################################################

layer {
  name: "fc7"
  type: "InnerProduct"
  bottom: "fc6"
  top: "fc7"
  inner_product_param {
    num_output: 2
  }
}

layer {
  name: "cls_prob"
  type: "Softmax"
  bottom: "fc7"
  top: "cls_prob"
}

layer {
  name: "bbox_reg"
  type: "InnerProduct"
  bottom: "fc6"
  top: "bbox_reg"
  inner_product_param {
    num_output: 3
  }
}


layer {
  name: "rotate_reg"
  type: "InnerProduct"
  bottom: "fc6"
  top: "rotate_reg"
  inner_product_param {
    num_output: 1
  }
}