multi_cnn_net

train_val_multi_cnn_net.prototxt

name: "fit_net"
layer {
    name: "data"
	type: "WindowPartitionData"
	top: "data"
	top: "param"
	top: "label"
	top: "mask"
	param_data_param {
		source: "/home/[email protected]/exp/lane_fit/data/train_list.txt"
		batch_size: 32
		height: 300 
		width: 260
		num_group_params: 26
		poly_degree: 2
		splitflag: "|"
		root_folder: ""
		shuffle: false
		rand_skip: 0
        param_mean: -9.48654360e-07
        param_mean: -5.82578266e-03
		param_mean: 1.25206429e+02
        param_std: 1.40006305e-04
        param_std: 6.67716265e-02
		param_std: 3.63744431e+01
	}
	transform_param {
		force_color: false
		scale: 0.00390625
		mean_value: 128
	}
}

layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  param{
     lr_mult: 1
     decay_mult: 1
   }
   param {
     lr_mult: 2
     decay_mult: 1
   }
  convolution_param {
    num_output: 32
    kernel_size: 5
    stride: 2
	group : 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
	  value:0
    }
  }
}
layer {
  name: "relu1"
  type: "PReLU"
  bottom: "conv1"
  top: "conv1"
  relu_param{
    negative_slope: 0.0
  }
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}

layer {
  name: "conv2"
  type: "Convolution"
  bottom: "pool1"
  top: "conv2"
  param{
     lr_mult: 1
     decay_mult: 1
   }
   param {
     lr_mult: 2
     decay_mult: 1
   }
  convolution_param {
    num_output: 64
    kernel_size: 3
    stride: 1
	group : 1
	pad : 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
	  value:0
    }
  }
}
layer {
  name: "relu2"
  type: "PReLU"
  bottom: "conv2"
  top: "conv2"
    relu_param{
    negative_slope:0.0
  }
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv2"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}

layer {
  name: "conv3"
  type: "Convolution"
  bottom: "pool2"
  top: "conv3"
  param{
     lr_mult: 1
     decay_mult: 1
   }
   param {
     lr_mult: 2
     decay_mult: 1
   }
  convolution_param {
    num_output: 128
    kernel_size: 3
    stride: 1
	group : 1
	pad : 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
	  value:0
    }
  }
}
layer {
  name: "relu3"
  type: "PReLU"
  bottom: "conv3"
  top: "conv3"
    relu_param{
    negative_slope:0.0
  }
}
layer {
  name: "conv4"
  type: "Convolution"
  bottom: "conv3"
  top: "conv4"
  param{
     lr_mult: 1
     decay_mult: 1
   }
   param {
     lr_mult: 2
     decay_mult: 1
   }
  convolution_param {
    num_output: 128
    kernel_size: 2
    stride: 1
	group : 1
	pad : 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
	  value:0
    }
  }
}
layer {
  name: "relu4"
  type: "PReLU"
  bottom: "conv4"
  top: "conv4"
    relu_param{
    negative_slope: 0.0
  }
}
layer {
  name: "conv5"
  type: "Convolution"
  bottom: "conv4"
  top: "conv5"
    param{
     lr_mult: 1
     decay_mult: 1
   }
   param {
     lr_mult: 2
     decay_mult: 1
   }
  convolution_param {
    num_output: 128
    kernel_size: 3
    stride: 1
	group : 1
	pad : 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
	  value:0
    }
  }
}
layer {
  name: "relu5"
  type: "PReLU"
  bottom: "conv5"
  top: "conv5"
    relu_param{
    negative_slope: 0.0
  }
}
layer {
  name: "pool5"
  type: "Pooling"
  bottom: "conv5"
  top: "pool5"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}


layer {
  name: "ip1"
  type: "InnerProduct"
  bottom: "pool5"
  top: "ip1"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  inner_product_param {
    num_output: 512
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}

layer {
  name: "ip2"
  type: "InnerProduct"
  bottom: "ip1"
  top: "ip2"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  inner_product_param {
    num_output: 256
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}

layer {
  name: "ip3"
  type: "InnerProduct"
  bottom: "ip2"
  top: "ip3"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  inner_product_param {
    num_output: 52
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}

layer {
  name: "ip4"
  type: "InnerProduct"
  bottom: "ip2"
  top: "ip4"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  inner_product_param {
    num_output: 78
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}

layer {
	name: "reshape_fc3"
	type: "Reshape"
	bottom: "ip3"
	top: "reshape_ip3"
	reshape_param { shape { dim:  -1  dim: 2  dim:  26 dim: 1} }
}

layer {
  name: "loss_cls"
  type: "SoftmaxWithLoss"
  bottom: "reshape_ip3"
  bottom: "label"
  top: "loss_cls"
  loss_weight: 1.0
}

layer {
  name: "loss_reg"
  #type: "EuclideanLossWithMask"  # 震荡
  type: "SmoothL1Loss"

  bottom: "ip4"
  bottom: "param"
  bottom: "mask"
  top: "loss_reg"
  loss_weight: 1
}