Example_Models/segnet_pascal.prototxt · GAN/SegNet-Tutorial

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SegNet-Tutorial

Example_Models

segnet_pascal.prototxt

segnet_pascal.prototxt 22.13 KB

name: "VGG_ILSVRC_16_layer"
input: "data"
input_dim: 1
input_dim: 3
input_dim: 224
input_dim: 224
layer {
 bottom: "data"
 top: "conv1_1"
 name: "conv1_1"
 type: "Convolution"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 weight_filler {
 type: "xavier"
 }
 bias_filler {
 type: "constant"
 value: 0
 }
 num_output: 64
 pad: 1
 kernel_size: 3
 }
}
layer {
 bottom: "conv1_1"
 top: "conv1_1"
 name: "conv1_1_bn"
 type: "BN"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 1
 decay_mult: 0
 }
 bn_param {
	bn_mode: INFERENCE
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0
	}
 }
}
layer {
 bottom: "conv1_1"
 top: "conv1_1"
 name: "relu1_1"
 type: "ReLU"
}
layer {
 bottom: "conv1_1"
 top: "conv1_2"
 name: "conv1_2"
 type: "Convolution"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 weight_filler {
 type: "xavier"
 }
 bias_filler {
 type: "constant"
 value: 0
 }
 num_output: 64
 pad: 1
 kernel_size: 3
 }
}
layer {
 bottom: "conv1_2"
 top: "conv1_2"
 name: "conv1_2_bn"
 type: "BN"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 1
 decay_mult: 0
 }
 bn_param {
	bn_mode: INFERENCE
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0
	}
 }
}
layer {
 bottom: "conv1_2"
 top: "conv1_2"
 name: "relu1_2"
 type: "ReLU"
}
layer {
 name: "pool1_drop"
 type: "Dropout"
 bottom: "conv1_2"
 top: "conv1_2"
 dropout_param {
 dropout_ratio: 0.5
 }
}
layer {
 bottom: "conv1_2"
 top: "pool1"
 top: "pool1_mask"
 name: "pool1"
 type: "Pooling"
 pooling_param {
 pool: MAX
 kernel_size: 2
 stride: 2
 }
}
layer {
 bottom: "pool1"
 top: "conv2_1"
 name: "conv2_1"
 type: "Convolution"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 weight_filler {
 type: "xavier"
 }
 bias_filler {
 type: "constant"
 value: 0
 }
 num_output: 128
 pad: 1
 kernel_size: 3
 }
}
layer {
 bottom: "conv2_1"
 top: "conv2_1"
 name: "conv2_1_bn"
 type: "BN"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 1
 decay_mult: 0
 }
 bn_param {
	bn_mode: INFERENCE
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0
	}
 }
}
layer {
 bottom: "conv2_1"
 top: "conv2_1"
 name: "relu2_1"
 type: "ReLU"
}
layer {
 bottom: "conv2_1"
 top: "conv2_2"
 name: "conv2_2"
 type: "Convolution"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 weight_filler {
 type: "xavier"
 }
 bias_filler {
 type: "constant"
 value: 0
 }
 num_output: 128
 pad: 1
 kernel_size: 3
 }
}
layer {
 bottom: "conv2_2"
 top: "conv2_2"
 name: "conv2_2_bn"
 type: "BN"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 1
 decay_mult: 0
 }
 bn_param {
	bn_mode: INFERENCE
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0
	}
 }
}
layer {
 bottom: "conv2_2"
 top: "conv2_2"
 name: "relu2_2"
 type: "ReLU"
}
layer {
 name: "pool2_drop"
 type: "Dropout"
 bottom: "conv2_2"
 top: "conv2_2"
 dropout_param {
 dropout_ratio: 0.5
 }
}
layer {
 bottom: "conv2_2"
 top: "pool2"
 top: "pool2_mask"
 name: "pool2"
 type: "Pooling"
 pooling_param {
 pool: MAX
 kernel_size: 2
 stride: 2
 }
}
layer {
 bottom: "pool2"
 top: "conv3_1"
 name: "conv3_1"
 type: "Convolution"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 weight_filler {
 type: "xavier"
 }
 bias_filler {
 type: "constant"
 value: 0
 }
 num_output: 256
 pad: 1
 kernel_size: 3
 }
}
layer {
 bottom: "conv3_1"
 top: "conv3_1"
 name: "conv3_1_bn"
 type: "BN"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 1
 decay_mult: 0
 }
 bn_param {
	bn_mode: INFERENCE
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0
	}
 }
}
layer {
 bottom: "conv3_1"
 top: "conv3_1"
 name: "relu3_1"
 type: "ReLU"
}
layer {
 bottom: "conv3_1"
 top: "conv3_2"
 name: "conv3_2"
 type: "Convolution"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 weight_filler {
 type: "xavier"
 }
 bias_filler {
 type: "constant"
 value: 0
 }
 num_output: 256
 pad: 1
 kernel_size: 3
 }
}
layer {
 bottom: "conv3_2"
 top: "conv3_2"
 name: "conv3_2_bn"
 type: "BN"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 1
 decay_mult: 0
 }
 bn_param {
	bn_mode: INFERENCE
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0
	}
 }
}
layer {
 bottom: "conv3_2"
 top: "conv3_2"
 name: "relu3_2"
 type: "ReLU"
}
layer {
 bottom: "conv3_2"
 top: "conv3_3"
 name: "conv3_3"
 type: "Convolution"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 weight_filler {
 type: "xavier"
 }
 bias_filler {
 type: "constant"
 value: 0
 }
 num_output: 256
 pad: 1
 kernel_size: 3
 }
}
layer {
 bottom: "conv3_3"
 top: "conv3_3"
 name: "conv3_3_bn"
 type: "BN"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 1
 decay_mult: 0
 }
 bn_param {
	bn_mode: INFERENCE
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0
	}
 }
}
layer {
 bottom: "conv3_3"
 top: "conv3_3"
 name: "relu3_3"
 type: "ReLU"
}
layer {
 name: "pool3_drop"
 type: "Dropout"
 bottom: "conv3_3"
 top: "conv3_3"
 dropout_param {
 dropout_ratio: 0.5
 }
}
layer {
 bottom: "conv3_3"
 top: "pool3"
 top: "pool3_mask"
 name: "pool3"
 type: "Pooling"
 pooling_param {
 pool: MAX
 kernel_size: 2
 stride: 2
 }
}
layer {
 bottom: "pool3"
 top: "conv4_1"
 name: "conv4_1"
 type: "Convolution"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 weight_filler {
 type: "xavier"
 }
 bias_filler {
 type: "constant"
 value: 0
 }
 num_output: 512
 pad: 1
 kernel_size: 3
 }
}
layer {
 bottom: "conv4_1"
 top: "conv4_1"
 name: "conv4_1_bn"
 type: "BN"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 1
 decay_mult: 0
 }
 bn_param {
	bn_mode: INFERENCE
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0
	}
 }
}
layer {
 bottom: "conv4_1"
 top: "conv4_1"
 name: "relu4_1"
 type: "ReLU"
}
layer {
 bottom: "conv4_1"
 top: "conv4_2"
 name: "conv4_2"
 type: "Convolution"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 weight_filler {
 type: "xavier"
 }
 bias_filler {
 type: "constant"
 value: 0
 }
 num_output: 512
 pad: 1
 kernel_size: 3
 }
}
layer {
 bottom: "conv4_2"
 top: "conv4_2"
 name: "conv4_2_bn"
 type: "BN"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 1
 decay_mult: 0
 }
 bn_param {
	bn_mode: INFERENCE
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0
	}
 }
}
layer {
 bottom: "conv4_2"
 top: "conv4_2"
 name: "relu4_2"
 type: "ReLU"
}
layer {
 bottom: "conv4_2"
 top: "conv4_3"
 name: "conv4_3"
 type: "Convolution"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 weight_filler {
 type: "xavier"
 }
 bias_filler {
 type: "constant"
 value: 0
 }
 num_output: 512
 pad: 1
 kernel_size: 3
 }
}
layer {
 bottom: "conv4_3"
 top: "conv4_3"
 name: "conv4_3_bn"
 type: "BN"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 1
 decay_mult: 0
 }
 bn_param {
	bn_mode: INFERENCE
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0
	}
 }
}
layer {
 bottom: "conv4_3"
 top: "conv4_3"
 name: "relu4_3"
 type: "ReLU"
}
layer {
 name: "pool4_drop"
 type: "Dropout"
 bottom: "conv4_3"
 top: "conv4_3"
 dropout_param {
 dropout_ratio: 0.5
 }
}
layer {
 bottom: "conv4_3"
 top: "pool4"
 top: "pool4_mask"
 name: "pool4"
 type: "Pooling"
 pooling_param {
 pool: MAX
 kernel_size: 2
 stride: 2
 }
}
layer {
 bottom: "pool4"
 top: "conv5_1"
 name: "conv5_1"
 type: "Convolution"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 weight_filler {
 type: "xavier"
 }
 bias_filler {
 type: "constant"
 value: 0
 }
 num_output: 512
 pad: 1
 kernel_size: 3
 }
}
layer {
 bottom: "conv5_1"
 top: "conv5_1"
 name: "conv5_1_bn"
 type: "BN"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 1
 decay_mult: 0
 }
 bn_param {
	bn_mode: INFERENCE
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0
	}
 }
}
layer {
 bottom: "conv5_1"
 top: "conv5_1"
 name: "relu5_1"
 type: "ReLU"
}
layer {
 bottom: "conv5_1"
 top: "conv5_2"
 name: "conv5_2"
 type: "Convolution"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 weight_filler {
 type: "xavier"
 }
 bias_filler {
 type: "constant"
 value: 0
 }
 num_output: 512
 pad: 1
 kernel_size: 3
 }
}
layer {
 bottom: "conv5_2"
 top: "conv5_2"
 name: "conv5_2_bn"
 type: "BN"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 1
 decay_mult: 0
 }
 bn_param {
	bn_mode: INFERENCE
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0
	}
 }
}
layer {
 bottom: "conv5_2"
 top: "conv5_2"
 name: "relu5_2"
 type: "ReLU"
}
layer {
 bottom: "conv5_2"
 top: "conv5_3"
 name: "conv5_3"
 type: "Convolution"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 weight_filler {
 type: "xavier"
 }
 bias_filler {
 type: "constant"
 value: 0
 }
 num_output: 512
 pad: 1
 kernel_size: 3
 }
}
layer {
 bottom: "conv5_3"
 top: "conv5_3"
 name: "conv5_3_bn"
 type: "BN"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 1
 decay_mult: 0
 }
 bn_param {
	bn_mode: INFERENCE
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0
	}
 }
}
layer {
 bottom: "conv5_3"
 top: "conv5_3"
 name: "relu5_3"
 type: "ReLU"
}
layer {
 name: "pool5_drop"
 type: "Dropout"
 bottom: "conv5_3"
 top: "conv5_3"
 dropout_param {
 dropout_ratio: 0.5
 }
}
layer {
 bottom: "conv5_3"
 top: "pool5"
 top: "pool5_mask"
 name: "pool5"
 type: "Pooling"
 pooling_param {
 pool: MAX
 kernel_size: 2
 stride: 2
 }
}
layer {
 name: "upsample5_drop"
 type: "Dropout"
 bottom: "pool5"
 top: "pool5"
 dropout_param {
 dropout_ratio: 0.5
 }
}
layer {
 name: "upsample5"
 type: "Upsample"
 bottom: "pool5"
 top: "pool5_D"
 bottom: "pool5_mask"
 upsample_param {
 scale: 2
 }
}
layer {
 bottom: "pool5_D"
 top: "conv5_3_D"
 name: "conv5_3_D"
 type: "Convolution"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 weight_filler {
 type: "xavier"
 }
 bias_filler {
 type: "constant"
 value: 0
 }
 num_output: 512
 pad: 1
 kernel_size: 3
 }
}
layer {
 bottom: "conv5_3_D"
 top: "conv5_3_D"
 name: "conv5_3_D_bn"
 type: "BN"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 1
 decay_mult: 0
 }
 bn_param {
	bn_mode: INFERENCE
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0
	}
 }
}
layer {
 bottom: "conv5_3_D"
 top: "conv5_3_D"
 name: "relu5_3_D"
 type: "ReLU"
}

layer {
 bottom: "conv5_3_D"
 top: "conv5_2_D"
 name: "conv5_2_D"
 type: "Convolution"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 weight_filler {
 type: "xavier"
 }
 bias_filler {
 type: "constant"
 value: 0
 }
 num_output: 512
 pad: 1
 kernel_size: 3
 }
}
layer {
 bottom: "conv5_2_D"
 top: "conv5_2_D"
 name: "conv5_2_D_bn"
 type: "BN"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 1
 decay_mult: 0
 }
 bn_param {
	bn_mode: INFERENCE
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0
	}
 }
}
layer {
 bottom: "conv5_2_D"
 top: "conv5_2_D"
 name: "relu5_2_D"
 type: "ReLU"
}
layer {
 bottom: "conv5_2_D"
 top: "conv5_1_D"
 name: "conv5_1_D"
 type: "Convolution"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 weight_filler {
 type: "xavier"
 }
 bias_filler {
 type: "constant"
 value: 0
 }
 num_output: 512
 pad: 1
 kernel_size: 3
 }
}
layer {
 bottom: "conv5_1_D"
 top: "conv5_1_D"
 name: "conv5_1_D_bn"
 type: "BN"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 1
 decay_mult: 0
 }
 bn_param {
	bn_mode: INFERENCE
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0
	}
 }
}
layer {
 bottom: "conv5_1_D"
 top: "conv5_1_D"
 name: "relu5_1_D"
 type: "ReLU"
}
layer {
 name: "upsample4_drop"
 type: "Dropout"
 bottom: "conv5_1_D"
 top: "conv5_1_D"
 dropout_param {
 dropout_ratio: 0.5
 }
}
layer {
 name: "upsample4"
 type: "Upsample"
 bottom: "conv5_1_D"
 top: "pool4_D"
 bottom: "pool4_mask"
 upsample_param {
 scale: 2
 }
}
layer {
 bottom: "pool4_D"
 top: "conv4_3_D"
 name: "conv4_3_D"
 type: "Convolution"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 weight_filler {
 type: "xavier"
 }
 bias_filler {
 type: "constant"
 value: 0
 }
 num_output: 512
 pad: 1
 kernel_size: 3
 }
}
layer {
 bottom: "conv4_3_D"
 top: "conv4_3_D"
 name: "conv4_3_D_bn"
 type: "BN"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 1
 decay_mult: 0
 }
 bn_param {
	bn_mode: INFERENCE
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0
	}
 }
}
layer {
 bottom: "conv4_3_D"
 top: "conv4_3_D"
 name: "relu4_3_D"
 type: "ReLU"
}
layer {
 bottom: "conv4_3_D"
 top: "conv4_2_D"
 name: "conv4_2_D"
 type: "Convolution"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 weight_filler {
 type: "xavier"
 }
 bias_filler {
 type: "constant"
 value: 0
 }
 num_output: 512
 pad: 1
 kernel_size: 3
 }
}
layer {
 bottom: "conv4_2_D"
 top: "conv4_2_D"
 name: "conv4_2_D_bn"
 type: "BN"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 1
 decay_mult: 0
 }
 bn_param {
	bn_mode: INFERENCE
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0
	}
 }
}
layer {
 bottom: "conv4_2_D"
 top: "conv4_2_D"
 name: "relu4_2_D"
 type: "ReLU"
}
layer {
 bottom: "conv4_2_D"
 top: "conv4_1_D"
 name: "conv4_1_D"
 type: "Convolution"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 weight_filler {
 type: "xavier"
 }
 bias_filler {
 type: "constant"
 value: 0
 }
 num_output: 256
 pad: 1
 kernel_size: 3
 }
}
layer {
 bottom: "conv4_1_D"
 top: "conv4_1_D"
 name: "conv4_1_D_bn"
 type: "BN"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 1
 decay_mult: 0
 }
 bn_param {
	bn_mode: INFERENCE
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0
	}
 }
}
layer {
 bottom: "conv4_1_D"
 top: "conv4_1_D"
 name: "relu4_1_D"
 type: "ReLU"
}
layer {
 name: "upsample3_drop"
 type: "Dropout"
 bottom: "conv4_1_D"
 top: "conv4_1_D"
 dropout_param {
 dropout_ratio: 0.5
 }
}
layer {
 name: "upsample3"
 type: "Upsample"
 bottom: "conv4_1_D"
 top: "pool3_D"
 bottom: "pool3_mask"
 upsample_param {
 scale: 2
 }
}
layer {
 bottom: "pool3_D"
 top: "conv3_3_D"
 name: "conv3_3_D"
 type: "Convolution"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 weight_filler {
 type: "xavier"
 }
 bias_filler {
 type: "constant"
 value: 0
 }
 num_output: 256
 pad: 1
 kernel_size: 3
 }
}
layer {
 bottom: "conv3_3_D"
 top: "conv3_3_D"
 name: "conv3_3_D_bn"
 type: "BN"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 1
 decay_mult: 0
 }
 bn_param {
	bn_mode: INFERENCE
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0
	}
 }
}
layer {
 bottom: "conv3_3_D"
 top: "conv3_3_D"
 name: "relu3_3_D"
 type: "ReLU"
}
layer {
 bottom: "conv3_3_D"
 top: "conv3_2_D"
 name: "conv3_2_D"
 type: "Convolution"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 weight_filler {
 type: "xavier"
 }
 bias_filler {
 type: "constant"
 value: 0
 }
 num_output: 256
 pad: 1
 kernel_size: 3
 }
}
layer {
 bottom: "conv3_2_D"
 top: "conv3_2_D"
 name: "conv3_2_D_bn"
 type: "BN"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 1
 decay_mult: 0
 }
 bn_param {
	bn_mode: INFERENCE
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0
	}
 }
}
layer {
 bottom: "conv3_2_D"
 top: "conv3_2_D"
 name: "relu3_2_D"
 type: "ReLU"
}
layer {
 bottom: "conv3_2_D"
 top: "conv3_1_D"
 name: "conv3_1_D"
 type: "Convolution"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 weight_filler {
 type: "xavier"
 }
 bias_filler {
 type: "constant"
 value: 0
 }
 num_output: 128
 pad: 1
 kernel_size: 3
 }
}
layer {
 bottom: "conv3_1_D"
 top: "conv3_1_D"
 name: "conv3_1_D_bn"
 type: "BN"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 1
 decay_mult: 0
 }
 bn_param {
	bn_mode: INFERENCE
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0
	}
 }
}
layer {
 bottom: "conv3_1_D"
 top: "conv3_1_D"
 name: "relu3_1_D"
 type: "ReLU"
}
layer {
 name: "upsample2_drop"
 type: "Dropout"
 bottom: "conv3_1_D"
 top: "conv3_1_D"
 dropout_param {
 dropout_ratio: 0.5
 }
}
layer {
 name: "upsample2"
 type: "Upsample"
 bottom: "conv3_1_D"
 top: "pool2_D"
 bottom: "pool2_mask"
 upsample_param {
 scale: 2
 }
}
layer {
 bottom: "pool2_D"
 top: "conv2_2_D"
 name: "conv2_2_D"
 type: "Convolution"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 weight_filler {
 type: "xavier"
 }
 bias_filler {
 type: "constant"
 value: 0
 }
 num_output: 128
 pad: 1
 kernel_size: 3
 }
}
layer {
 bottom: "conv2_2_D"
 top: "conv2_2_D"
 name: "conv2_2_D_bn"
 type: "BN"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 1
 decay_mult: 0
 }
 bn_param {
	bn_mode: INFERENCE
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0
	}
 }
}
layer {
 bottom: "conv2_2_D"
 top: "conv2_2_D"
 name: "relu2_2_D"
 type: "ReLU"
}
layer {
 bottom: "conv2_2_D"
 top: "conv2_1_D"
 name: "conv2_1_D"
 type: "Convolution"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 weight_filler {
 type: "xavier"
 }
 bias_filler {
 type: "constant"
 value: 0
 }
 num_output: 64
 pad: 1
 kernel_size: 3
 }
}
layer {
 bottom: "conv2_1_D"
 top: "conv2_1_D"
 name: "conv2_1_D_bn"
 type: "BN"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 1
 decay_mult: 0
 }
 bn_param {
	bn_mode: INFERENCE
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0
	}
 }
}
layer {
 bottom: "conv2_1_D"
 top: "conv2_1_D"
 name: "relu2_1_D"
 type: "ReLU"
}
layer {
 name: "upsample1_drop"
 type: "Dropout"
 bottom: "conv2_1_D"
 top: "conv2_1_D"
 dropout_param {
 dropout_ratio: 0.5
 }
}
layer {
 name: "upsample1"
 type: "Upsample"
 bottom: "conv2_1_D"
 top: "pool1_D"
 bottom: "pool1_mask"
 upsample_param {
 scale: 2
 }
}
layer {
 bottom: "pool1_D"
 top: "conv1_2_D"
 name: "conv1_2_D"
 type: "Convolution"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 weight_filler {
 type: "xavier"
 }
 bias_filler {
 type: "constant"
 value: 0
 }
 num_output: 64
 pad: 1
 kernel_size: 3
 }
}
layer {
 bottom: "conv1_2_D"
 top: "conv1_2_D"
 name: "conv1_2_D_bn"
 type: "BN"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 1
 decay_mult: 0
 }
 bn_param {
	bn_mode: INFERENCE
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0
	}
 }
}
layer {
 bottom: "conv1_2_D"
 top: "conv1_2_D"
 name: "relu1_2_D"
 type: "ReLU"
}
layer {
 bottom: "conv1_2_D"
 top: "conv1_1_D"
 name: "conv1_1_D"
 type: "Convolution"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 weight_filler {
 type: "xavier"
 }
 bias_filler {
 type: "constant"
 value: 0
 }
 num_output: 21
 pad: 1
 kernel_size: 3
 }
}
layer {
 name: "prob"
 type: "Softmax"
 bottom: "conv1_1_D"
 top: "prob"
 softmax_param {engine: CAFFE}
}

一键复制编辑原始数据按行查看历史

Alex Kendall 提交于 2016年02月10日 17:30 +08:00 . Added many more models to SegNet Model Zoo

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name: "VGG_ILSVRC_16_layer"
input: "data"
input_dim: 1
input_dim: 3
input_dim: 224
input_dim: 224
layer {
 bottom: "data"
 top: "conv1_1"
 name: "conv1_1"
 type: "Convolution"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 weight_filler {
 type: "xavier"
 }
 bias_filler {
 type: "constant"
 value: 0
 }
 num_output: 64
 pad: 1
 kernel_size: 3
 }
}
layer {
 bottom: "conv1_1"
 top: "conv1_1"
 name: "conv1_1_bn"
 type: "BN"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 1
 decay_mult: 0
 }
 bn_param {
	bn_mode: INFERENCE
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0
	}
 }
}
layer {
 bottom: "conv1_1"
 top: "conv1_1"
 name: "relu1_1"
 type: "ReLU"
}
layer {
 bottom: "conv1_1"
 top: "conv1_2"
 name: "conv1_2"
 type: "Convolution"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 weight_filler {
 type: "xavier"
 }
 bias_filler {
 type: "constant"
 value: 0
 }
 num_output: 64
 pad: 1
 kernel_size: 3
 }
}
layer {
 bottom: "conv1_2"
 top: "conv1_2"
 name: "conv1_2_bn"
 type: "BN"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 1
 decay_mult: 0
 }
 bn_param {
	bn_mode: INFERENCE
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0
	}
 }
}
layer {
 bottom: "conv1_2"
 top: "conv1_2"
 name: "relu1_2"
 type: "ReLU"
}
layer {
 name: "pool1_drop"
 type: "Dropout"
 bottom: "conv1_2"
 top: "conv1_2"
 dropout_param {
 dropout_ratio: 0.5
 }
}
layer {
 bottom: "conv1_2"
 top: "pool1"
 top: "pool1_mask"
 name: "pool1"
 type: "Pooling"
 pooling_param {
 pool: MAX
 kernel_size: 2
 stride: 2
 }
}
layer {
 bottom: "pool1"
 top: "conv2_1"
 name: "conv2_1"
 type: "Convolution"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 weight_filler {
 type: "xavier"
 }
 bias_filler {
 type: "constant"
 value: 0
 }
 num_output: 128
 pad: 1
 kernel_size: 3
 }
}
layer {
 bottom: "conv2_1"
 top: "conv2_1"
 name: "conv2_1_bn"
 type: "BN"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 1
 decay_mult: 0
 }
 bn_param {
	bn_mode: INFERENCE
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0
	}
 }
}
layer {
 bottom: "conv2_1"
 top: "conv2_1"
 name: "relu2_1"
 type: "ReLU"
}
layer {
 bottom: "conv2_1"
 top: "conv2_2"
 name: "conv2_2"
 type: "Convolution"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 weight_filler {
 type: "xavier"
 }
 bias_filler {
 type: "constant"
 value: 0
 }
 num_output: 128
 pad: 1
 kernel_size: 3
 }
}
layer {
 bottom: "conv2_2"
 top: "conv2_2"
 name: "conv2_2_bn"
 type: "BN"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 1
 decay_mult: 0
 }
 bn_param {
	bn_mode: INFERENCE
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0
	}
 }
}
layer {
 bottom: "conv2_2"
 top: "conv2_2"
 name: "relu2_2"
 type: "ReLU"
}
layer {
 name: "pool2_drop"
 type: "Dropout"
 bottom: "conv2_2"
 top: "conv2_2"
 dropout_param {
 dropout_ratio: 0.5
 }
}
layer {
 bottom: "conv2_2"
 top: "pool2"
 top: "pool2_mask"
 name: "pool2"
 type: "Pooling"
 pooling_param {
 pool: MAX
 kernel_size: 2
 stride: 2
 }
}
layer {
 bottom: "pool2"
 top: "conv3_1"
 name: "conv3_1"
 type: "Convolution"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 weight_filler {
 type: "xavier"
 }
 bias_filler {
 type: "constant"
 value: 0
 }
 num_output: 256
 pad: 1
 kernel_size: 3
 }
}
layer {
 bottom: "conv3_1"
 top: "conv3_1"
 name: "conv3_1_bn"
 type: "BN"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 1
 decay_mult: 0
 }
 bn_param {
	bn_mode: INFERENCE
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0
	}
 }
}
layer {
 bottom: "conv3_1"
 top: "conv3_1"
 name: "relu3_1"
 type: "ReLU"
}
layer {
 bottom: "conv3_1"
 top: "conv3_2"
 name: "conv3_2"
 type: "Convolution"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 weight_filler {
 type: "xavier"
 }
 bias_filler {
 type: "constant"
 value: 0
 }
 num_output: 256
 pad: 1
 kernel_size: 3
 }
}
layer {
 bottom: "conv3_2"
 top: "conv3_2"
 name: "conv3_2_bn"
 type: "BN"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 1
 decay_mult: 0
 }
 bn_param {
	bn_mode: INFERENCE
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0
	}
 }
}
layer {
 bottom: "conv3_2"
 top: "conv3_2"
 name: "relu3_2"
 type: "ReLU"
}
layer {
 bottom: "conv3_2"
 top: "conv3_3"
 name: "conv3_3"
 type: "Convolution"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 weight_filler {
 type: "xavier"
 }
 bias_filler {
 type: "constant"
 value: 0
 }
 num_output: 256
 pad: 1
 kernel_size: 3
 }
}
layer {
 bottom: "conv3_3"
 top: "conv3_3"
 name: "conv3_3_bn"
 type: "BN"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 1
 decay_mult: 0
 }
 bn_param {
	bn_mode: INFERENCE
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0
	}
 }
}
layer {
 bottom: "conv3_3"
 top: "conv3_3"
 name: "relu3_3"
 type: "ReLU"
}
layer {
 name: "pool3_drop"
 type: "Dropout"
 bottom: "conv3_3"
 top: "conv3_3"
 dropout_param {
 dropout_ratio: 0.5
 }
}
layer {
 bottom: "conv3_3"
 top: "pool3"
 top: "pool3_mask"
 name: "pool3"
 type: "Pooling"
 pooling_param {
 pool: MAX
 kernel_size: 2
 stride: 2
 }
}
layer {
 bottom: "pool3"
 top: "conv4_1"
 name: "conv4_1"
 type: "Convolution"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 weight_filler {
 type: "xavier"
 }
 bias_filler {
 type: "constant"
 value: 0
 }
 num_output: 512
 pad: 1
 kernel_size: 3
 }
}
layer {
 bottom: "conv4_1"
 top: "conv4_1"
 name: "conv4_1_bn"
 type: "BN"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 1
 decay_mult: 0
 }
 bn_param {
	bn_mode: INFERENCE
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0
	}
 }
}
layer {
 bottom: "conv4_1"
 top: "conv4_1"
 name: "relu4_1"
 type: "ReLU"
}
layer {
 bottom: "conv4_1"
 top: "conv4_2"
 name: "conv4_2"
 type: "Convolution"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 weight_filler {
 type: "xavier"
 }
 bias_filler {
 type: "constant"
 value: 0
 }
 num_output: 512
 pad: 1
 kernel_size: 3
 }
}
layer {
 bottom: "conv4_2"
 top: "conv4_2"
 name: "conv4_2_bn"
 type: "BN"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 1
 decay_mult: 0
 }
 bn_param {
	bn_mode: INFERENCE
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0
	}
 }
}
layer {
 bottom: "conv4_2"
 top: "conv4_2"
 name: "relu4_2"
 type: "ReLU"
}
layer {
 bottom: "conv4_2"
 top: "conv4_3"
 name: "conv4_3"
 type: "Convolution"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 weight_filler {
 type: "xavier"
 }
 bias_filler {
 type: "constant"
 value: 0
 }
 num_output: 512
 pad: 1
 kernel_size: 3
 }
}
layer {
 bottom: "conv4_3"
 top: "conv4_3"
 name: "conv4_3_bn"
 type: "BN"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 1
 decay_mult: 0
 }
 bn_param {
	bn_mode: INFERENCE
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0
	}
 }
}
layer {
 bottom: "conv4_3"
 top: "conv4_3"
 name: "relu4_3"
 type: "ReLU"
}
layer {
 name: "pool4_drop"
 type: "Dropout"
 bottom: "conv4_3"
 top: "conv4_3"
 dropout_param {
 dropout_ratio: 0.5
 }
}
layer {
 bottom: "conv4_3"
 top: "pool4"
 top: "pool4_mask"
 name: "pool4"
 type: "Pooling"
 pooling_param {
 pool: MAX
 kernel_size: 2
 stride: 2
 }
}
layer {
 bottom: "pool4"
 top: "conv5_1"
 name: "conv5_1"
 type: "Convolution"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 weight_filler {
 type: "xavier"
 }
 bias_filler {
 type: "constant"
 value: 0
 }
 num_output: 512
 pad: 1
 kernel_size: 3
 }
}
layer {
 bottom: "conv5_1"
 top: "conv5_1"
 name: "conv5_1_bn"
 type: "BN"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 1
 decay_mult: 0
 }
 bn_param {
	bn_mode: INFERENCE
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0
	}
 }
}
layer {
 bottom: "conv5_1"
 top: "conv5_1"
 name: "relu5_1"
 type: "ReLU"
}
layer {
 bottom: "conv5_1"
 top: "conv5_2"
 name: "conv5_2"
 type: "Convolution"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 weight_filler {
 type: "xavier"
 }
 bias_filler {
 type: "constant"
 value: 0
 }
 num_output: 512
 pad: 1
 kernel_size: 3
 }
}
layer {
 bottom: "conv5_2"
 top: "conv5_2"
 name: "conv5_2_bn"
 type: "BN"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 1
 decay_mult: 0
 }
 bn_param {
	bn_mode: INFERENCE
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0
	}
 }
}
layer {
 bottom: "conv5_2"
 top: "conv5_2"
 name: "relu5_2"
 type: "ReLU"
}
layer {
 bottom: "conv5_2"
 top: "conv5_3"
 name: "conv5_3"
 type: "Convolution"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 weight_filler {
 type: "xavier"
 }
 bias_filler {
 type: "constant"
 value: 0
 }
 num_output: 512
 pad: 1
 kernel_size: 3
 }
}
layer {
 bottom: "conv5_3"
 top: "conv5_3"
 name: "conv5_3_bn"
 type: "BN"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 1
 decay_mult: 0
 }
 bn_param {
	bn_mode: INFERENCE
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0
	}
 }
}
layer {
 bottom: "conv5_3"
 top: "conv5_3"
 name: "relu5_3"
 type: "ReLU"
}
layer {
 name: "pool5_drop"
 type: "Dropout"
 bottom: "conv5_3"
 top: "conv5_3"
 dropout_param {
 dropout_ratio: 0.5
 }
}
layer {
 bottom: "conv5_3"
 top: "pool5"
 top: "pool5_mask"
 name: "pool5"
 type: "Pooling"
 pooling_param {
 pool: MAX
 kernel_size: 2
 stride: 2
 }
}
layer {
 name: "upsample5_drop"
 type: "Dropout"
 bottom: "pool5"
 top: "pool5"
 dropout_param {
 dropout_ratio: 0.5
 }
}
layer {
 name: "upsample5"
 type: "Upsample"
 bottom: "pool5"
 top: "pool5_D"
 bottom: "pool5_mask"
 upsample_param {
 scale: 2
 }
}
layer {
 bottom: "pool5_D"
 top: "conv5_3_D"
 name: "conv5_3_D"
 type: "Convolution"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 weight_filler {
 type: "xavier"
 }
 bias_filler {
 type: "constant"
 value: 0
 }
 num_output: 512
 pad: 1
 kernel_size: 3
 }
}
layer {
 bottom: "conv5_3_D"
 top: "conv5_3_D"
 name: "conv5_3_D_bn"
 type: "BN"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 1
 decay_mult: 0
 }
 bn_param {
	bn_mode: INFERENCE
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0
	}
 }
}
layer {
 bottom: "conv5_3_D"
 top: "conv5_3_D"
 name: "relu5_3_D"
 type: "ReLU"
}

layer {
 bottom: "conv5_3_D"
 top: "conv5_2_D"
 name: "conv5_2_D"
 type: "Convolution"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 weight_filler {
 type: "xavier"
 }
 bias_filler {
 type: "constant"
 value: 0
 }
 num_output: 512
 pad: 1
 kernel_size: 3
 }
}
layer {
 bottom: "conv5_2_D"
 top: "conv5_2_D"
 name: "conv5_2_D_bn"
 type: "BN"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 1
 decay_mult: 0
 }
 bn_param {
	bn_mode: INFERENCE
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0
	}
 }
}
layer {
 bottom: "conv5_2_D"
 top: "conv5_2_D"
 name: "relu5_2_D"
 type: "ReLU"
}
layer {
 bottom: "conv5_2_D"
 top: "conv5_1_D"
 name: "conv5_1_D"
 type: "Convolution"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 weight_filler {
 type: "xavier"
 }
 bias_filler {
 type: "constant"
 value: 0
 }
 num_output: 512
 pad: 1
 kernel_size: 3
 }
}
layer {
 bottom: "conv5_1_D"
 top: "conv5_1_D"
 name: "conv5_1_D_bn"
 type: "BN"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 1
 decay_mult: 0
 }
 bn_param {
	bn_mode: INFERENCE
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0
	}
 }
}
layer {
 bottom: "conv5_1_D"
 top: "conv5_1_D"
 name: "relu5_1_D"
 type: "ReLU"
}
layer {
 name: "upsample4_drop"
 type: "Dropout"
 bottom: "conv5_1_D"
 top: "conv5_1_D"
 dropout_param {
 dropout_ratio: 0.5
 }
}
layer {
 name: "upsample4"
 type: "Upsample"
 bottom: "conv5_1_D"
 top: "pool4_D"
 bottom: "pool4_mask"
 upsample_param {
 scale: 2
 }
}
layer {
 bottom: "pool4_D"
 top: "conv4_3_D"
 name: "conv4_3_D"
 type: "Convolution"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 weight_filler {
 type: "xavier"
 }
 bias_filler {
 type: "constant"
 value: 0
 }
 num_output: 512
 pad: 1
 kernel_size: 3
 }
}
layer {
 bottom: "conv4_3_D"
 top: "conv4_3_D"
 name: "conv4_3_D_bn"
 type: "BN"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 1
 decay_mult: 0
 }
 bn_param {
	bn_mode: INFERENCE
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0
	}
 }
}
layer {
 bottom: "conv4_3_D"
 top: "conv4_3_D"
 name: "relu4_3_D"
 type: "ReLU"
}
layer {
 bottom: "conv4_3_D"
 top: "conv4_2_D"
 name: "conv4_2_D"
 type: "Convolution"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 weight_filler {
 type: "xavier"
 }
 bias_filler {
 type: "constant"
 value: 0
 }
 num_output: 512
 pad: 1
 kernel_size: 3
 }
}
layer {
 bottom: "conv4_2_D"
 top: "conv4_2_D"
 name: "conv4_2_D_bn"
 type: "BN"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 1
 decay_mult: 0
 }
 bn_param {
	bn_mode: INFERENCE
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0
	}
 }
}
layer {
 bottom: "conv4_2_D"
 top: "conv4_2_D"
 name: "relu4_2_D"
 type: "ReLU"
}
layer {
 bottom: "conv4_2_D"
 top: "conv4_1_D"
 name: "conv4_1_D"
 type: "Convolution"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 weight_filler {
 type: "xavier"
 }
 bias_filler {
 type: "constant"
 value: 0
 }
 num_output: 256
 pad: 1
 kernel_size: 3
 }
}
layer {
 bottom: "conv4_1_D"
 top: "conv4_1_D"
 name: "conv4_1_D_bn"
 type: "BN"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 1
 decay_mult: 0
 }
 bn_param {
	bn_mode: INFERENCE
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0
	}
 }
}
layer {
 bottom: "conv4_1_D"
 top: "conv4_1_D"
 name: "relu4_1_D"
 type: "ReLU"
}
layer {
 name: "upsample3_drop"
 type: "Dropout"
 bottom: "conv4_1_D"
 top: "conv4_1_D"
 dropout_param {
 dropout_ratio: 0.5
 }
}
layer {
 name: "upsample3"
 type: "Upsample"
 bottom: "conv4_1_D"
 top: "pool3_D"
 bottom: "pool3_mask"
 upsample_param {
 scale: 2
 }
}
layer {
 bottom: "pool3_D"
 top: "conv3_3_D"
 name: "conv3_3_D"
 type: "Convolution"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 weight_filler {
 type: "xavier"
 }
 bias_filler {
 type: "constant"
 value: 0
 }
 num_output: 256
 pad: 1
 kernel_size: 3
 }
}
layer {
 bottom: "conv3_3_D"
 top: "conv3_3_D"
 name: "conv3_3_D_bn"
 type: "BN"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 1
 decay_mult: 0
 }
 bn_param {
	bn_mode: INFERENCE
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0
	}
 }
}
layer {
 bottom: "conv3_3_D"
 top: "conv3_3_D"
 name: "relu3_3_D"
 type: "ReLU"
}
layer {
 bottom: "conv3_3_D"
 top: "conv3_2_D"
 name: "conv3_2_D"
 type: "Convolution"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 weight_filler {
 type: "xavier"
 }
 bias_filler {
 type: "constant"
 value: 0
 }
 num_output: 256
 pad: 1
 kernel_size: 3
 }
}
layer {
 bottom: "conv3_2_D"
 top: "conv3_2_D"
 name: "conv3_2_D_bn"
 type: "BN"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 1
 decay_mult: 0
 }
 bn_param {
	bn_mode: INFERENCE
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0
	}
 }
}
layer {
 bottom: "conv3_2_D"
 top: "conv3_2_D"
 name: "relu3_2_D"
 type: "ReLU"
}
layer {
 bottom: "conv3_2_D"
 top: "conv3_1_D"
 name: "conv3_1_D"
 type: "Convolution"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 weight_filler {
 type: "xavier"
 }
 bias_filler {
 type: "constant"
 value: 0
 }
 num_output: 128
 pad: 1
 kernel_size: 3
 }
}
layer {
 bottom: "conv3_1_D"
 top: "conv3_1_D"
 name: "conv3_1_D_bn"
 type: "BN"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 1
 decay_mult: 0
 }
 bn_param {
	bn_mode: INFERENCE
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0
	}
 }
}
layer {
 bottom: "conv3_1_D"
 top: "conv3_1_D"
 name: "relu3_1_D"
 type: "ReLU"
}
layer {
 name: "upsample2_drop"
 type: "Dropout"
 bottom: "conv3_1_D"
 top: "conv3_1_D"
 dropout_param {
 dropout_ratio: 0.5
 }
}
layer {
 name: "upsample2"
 type: "Upsample"
 bottom: "conv3_1_D"
 top: "pool2_D"
 bottom: "pool2_mask"
 upsample_param {
 scale: 2
 }
}
layer {
 bottom: "pool2_D"
 top: "conv2_2_D"
 name: "conv2_2_D"
 type: "Convolution"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 weight_filler {
 type: "xavier"
 }
 bias_filler {
 type: "constant"
 value: 0
 }
 num_output: 128
 pad: 1
 kernel_size: 3
 }
}
layer {
 bottom: "conv2_2_D"
 top: "conv2_2_D"
 name: "conv2_2_D_bn"
 type: "BN"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 1
 decay_mult: 0
 }
 bn_param {
	bn_mode: INFERENCE
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0
	}
 }
}
layer {
 bottom: "conv2_2_D"
 top: "conv2_2_D"
 name: "relu2_2_D"
 type: "ReLU"
}
layer {
 bottom: "conv2_2_D"
 top: "conv2_1_D"
 name: "conv2_1_D"
 type: "Convolution"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 weight_filler {
 type: "xavier"
 }
 bias_filler {
 type: "constant"
 value: 0
 }
 num_output: 64
 pad: 1
 kernel_size: 3
 }
}
layer {
 bottom: "conv2_1_D"
 top: "conv2_1_D"
 name: "conv2_1_D_bn"
 type: "BN"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 1
 decay_mult: 0
 }
 bn_param {
	bn_mode: INFERENCE
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0
	}
 }
}
layer {
 bottom: "conv2_1_D"
 top: "conv2_1_D"
 name: "relu2_1_D"
 type: "ReLU"
}
layer {
 name: "upsample1_drop"
 type: "Dropout"
 bottom: "conv2_1_D"
 top: "conv2_1_D"
 dropout_param {
 dropout_ratio: 0.5
 }
}
layer {
 name: "upsample1"
 type: "Upsample"
 bottom: "conv2_1_D"
 top: "pool1_D"
 bottom: "pool1_mask"
 upsample_param {
 scale: 2
 }
}
layer {
 bottom: "pool1_D"
 top: "conv1_2_D"
 name: "conv1_2_D"
 type: "Convolution"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 weight_filler {
 type: "xavier"
 }
 bias_filler {
 type: "constant"
 value: 0
 }
 num_output: 64
 pad: 1
 kernel_size: 3
 }
}
layer {
 bottom: "conv1_2_D"
 top: "conv1_2_D"
 name: "conv1_2_D_bn"
 type: "BN"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 1
 decay_mult: 0
 }
 bn_param {
	bn_mode: INFERENCE
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0
	}
 }
}
layer {
 bottom: "conv1_2_D"
 top: "conv1_2_D"
 name: "relu1_2_D"
 type: "ReLU"
}
layer {
 bottom: "conv1_2_D"
 top: "conv1_1_D"
 name: "conv1_1_D"
 type: "Convolution"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 weight_filler {
 type: "xavier"
 }
 bias_filler {
 type: "constant"
 value: 0
 }
 num_output: 21
 pad: 1
 kernel_size: 3
 }
}
layer {
 name: "prob"
 type: "Softmax"
 bottom: "conv1_1_D"
 top: "prob"
 softmax_param {engine: CAFFE}
}