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Image_Colorization / colorization_deploy_v2.prototxt

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	name: "LtoAB"

	layer {
	name: "data_l"
	type: "Input"
	top: "data_l"
	input_param {
	shape { dim: 1 dim: 1 dim: 224 dim: 224 }
	}
	}

	# *****************
	# *** conv1 ***
	# *****************
	layer {
	name: "bw_conv1_1"
	type: "Convolution"
	bottom: "data_l"
	top: "conv1_1"
	# param {lr_mult: 0 decay_mult: 0}
	# param {lr_mult: 0 decay_mult: 0}
	convolution_param {
	num_output: 64
	pad: 1
	kernel_size: 3
	}
	}
	layer {
	name: "relu1_1"
	type: "ReLU"
	bottom: "conv1_1"
	top: "conv1_1"
	}
	layer {
	name: "conv1_2"
	type: "Convolution"
	bottom: "conv1_1"
	top: "conv1_2"
	# param {lr_mult: 0 decay_mult: 0}
	# param {lr_mult: 0 decay_mult: 0}
	convolution_param {
	num_output: 64
	pad: 1
	kernel_size: 3
	stride: 2
	}
	}
	layer {
	name: "relu1_2"
	type: "ReLU"
	bottom: "conv1_2"
	top: "conv1_2"
	}
	layer {
	name: "conv1_2norm"
	type: "BatchNorm"
	bottom: "conv1_2"
	top: "conv1_2norm"
	batch_norm_param{ }
	param {lr_mult: 0 decay_mult: 0}
	param {lr_mult: 0 decay_mult: 0}
	param {lr_mult: 0 decay_mult: 0}
	}
	# *****************
	# *** conv2 ***
	# *****************
	layer {
	name: "conv2_1"
	type: "Convolution"
	# bottom: "conv1_2"
	bottom: "conv1_2norm"
	# bottom: "pool1"
	top: "conv2_1"
	# param {lr_mult: 0 decay_mult: 0}
	# param {lr_mult: 0 decay_mult: 0}
	convolution_param {
	num_output: 128
	pad: 1
	kernel_size: 3
	}
	}
	layer {
	name: "relu2_1"
	type: "ReLU"
	bottom: "conv2_1"
	top: "conv2_1"
	}
	layer {
	name: "conv2_2"
	type: "Convolution"
	bottom: "conv2_1"
	top: "conv2_2"
	# param {lr_mult: 0 decay_mult: 0}
	# param {lr_mult: 0 decay_mult: 0}
	convolution_param {
	num_output: 128
	pad: 1
	kernel_size: 3
	stride: 2
	}
	}
	layer {
	name: "relu2_2"
	type: "ReLU"
	bottom: "conv2_2"
	top: "conv2_2"
	}
	layer {
	name: "conv2_2norm"
	type: "BatchNorm"
	bottom: "conv2_2"
	top: "conv2_2norm"
	batch_norm_param{ }
	param {lr_mult: 0 decay_mult: 0}
	param {lr_mult: 0 decay_mult: 0}
	param {lr_mult: 0 decay_mult: 0}
	}
	# *****************
	# *** conv3 ***
	# *****************
	layer {
	name: "conv3_1"
	type: "Convolution"
	# bottom: "conv2_2"
	bottom: "conv2_2norm"
	# bottom: "pool2"
	top: "conv3_1"
	# param {lr_mult: 0 decay_mult: 0}
	# param {lr_mult: 0 decay_mult: 0}
	convolution_param {
	num_output: 256
	pad: 1
	kernel_size: 3
	}
	}
	layer {
	name: "relu3_1"
	type: "ReLU"
	bottom: "conv3_1"
	top: "conv3_1"
	}
	layer {
	name: "conv3_2"
	type: "Convolution"
	bottom: "conv3_1"
	top: "conv3_2"
	# param {lr_mult: 0 decay_mult: 0}
	# param {lr_mult: 0 decay_mult: 0}
	convolution_param {
	num_output: 256
	pad: 1
	kernel_size: 3
	}
	}
	layer {
	name: "relu3_2"
	type: "ReLU"
	bottom: "conv3_2"
	top: "conv3_2"
	}
	layer {
	name: "conv3_3"
	type: "Convolution"
	bottom: "conv3_2"
	top: "conv3_3"
	# param {lr_mult: 0 decay_mult: 0}
	# param {lr_mult: 0 decay_mult: 0}
	convolution_param {
	num_output: 256
	pad: 1
	kernel_size: 3
	stride: 2
	}
	}
	layer {
	name: "relu3_3"
	type: "ReLU"
	bottom: "conv3_3"
	top: "conv3_3"
	}
	layer {
	name: "conv3_3norm"
	type: "BatchNorm"
	bottom: "conv3_3"
	top: "conv3_3norm"
	batch_norm_param{ }
	param {lr_mult: 0 decay_mult: 0}
	param {lr_mult: 0 decay_mult: 0}
	param {lr_mult: 0 decay_mult: 0}
	}
	# *****************
	# *** conv4 ***
	# *****************
	layer {
	name: "conv4_1"
	type: "Convolution"
	# bottom: "conv3_3"
	bottom: "conv3_3norm"
	# bottom: "pool3"
	top: "conv4_1"
	# param {lr_mult: 0 decay_mult: 0}
	# param {lr_mult: 0 decay_mult: 0}
	convolution_param {
	num_output: 512
	kernel_size: 3
	stride: 1
	pad: 1
	dilation: 1
	}
	}
	layer {
	name: "relu4_1"
	type: "ReLU"
	bottom: "conv4_1"
	top: "conv4_1"
	}
	layer {
	name: "conv4_2"
	type: "Convolution"
	bottom: "conv4_1"
	top: "conv4_2"
	# param {lr_mult: 0 decay_mult: 0}
	# param {lr_mult: 0 decay_mult: 0}
	convolution_param {
	num_output: 512
	kernel_size: 3
	stride: 1
	pad: 1
	dilation: 1
	}
	}
	layer {
	name: "relu4_2"
	type: "ReLU"
	bottom: "conv4_2"
	top: "conv4_2"
	}
	layer {
	name: "conv4_3"
	type: "Convolution"
	bottom: "conv4_2"
	top: "conv4_3"
	# param {lr_mult: 0 decay_mult: 0}
	# param {lr_mult: 0 decay_mult: 0}
	convolution_param {
	num_output: 512
	kernel_size: 3
	stride: 1
	pad: 1
	dilation: 1
	}
	}
	layer {
	name: "relu4_3"
	type: "ReLU"
	bottom: "conv4_3"
	top: "conv4_3"
	}
	layer {
	name: "conv4_3norm"
	type: "BatchNorm"
	bottom: "conv4_3"
	top: "conv4_3norm"
	batch_norm_param{ }
	param {lr_mult: 0 decay_mult: 0}
	param {lr_mult: 0 decay_mult: 0}
	param {lr_mult: 0 decay_mult: 0}
	}
	# *****************
	# *** conv5 ***
	# *****************
	layer {
	name: "conv5_1"
	type: "Convolution"
	# bottom: "conv4_3"
	bottom: "conv4_3norm"
	# bottom: "pool4"
	top: "conv5_1"
	# param {lr_mult: 0 decay_mult: 0}
	# param {lr_mult: 0 decay_mult: 0}
	convolution_param {
	num_output: 512
	kernel_size: 3
	stride: 1
	pad: 2
	dilation: 2
	}
	}
	layer {
	name: "relu5_1"
	type: "ReLU"
	bottom: "conv5_1"
	top: "conv5_1"
	}
	layer {
	name: "conv5_2"
	type: "Convolution"
	bottom: "conv5_1"
	top: "conv5_2"
	# param {lr_mult: 0 decay_mult: 0}
	# param {lr_mult: 0 decay_mult: 0}
	convolution_param {
	num_output: 512
	kernel_size: 3
	stride: 1
	pad: 2
	dilation: 2
	}
	}
	layer {
	name: "relu5_2"
	type: "ReLU"
	bottom: "conv5_2"
	top: "conv5_2"
	}
	layer {
	name: "conv5_3"
	type: "Convolution"
	bottom: "conv5_2"
	top: "conv5_3"
	# param {lr_mult: 0 decay_mult: 0}
	# param {lr_mult: 0 decay_mult: 0}
	convolution_param {
	num_output: 512
	kernel_size: 3
	stride: 1
	pad: 2
	dilation: 2
	}
	}
	layer {
	name: "relu5_3"
	type: "ReLU"
	bottom: "conv5_3"
	top: "conv5_3"
	}
	layer {
	name: "conv5_3norm"
	type: "BatchNorm"
	bottom: "conv5_3"
	top: "conv5_3norm"
	batch_norm_param{ }
	param {lr_mult: 0 decay_mult: 0}
	param {lr_mult: 0 decay_mult: 0}
	param {lr_mult: 0 decay_mult: 0}
	}
	# *****************
	# *** conv6 ***
	# *****************
	layer {
	name: "conv6_1"
	type: "Convolution"
	bottom: "conv5_3norm"
	top: "conv6_1"
	convolution_param {
	num_output: 512
	kernel_size: 3
	pad: 2
	dilation: 2
	}
	}
	layer {
	name: "relu6_1"
	type: "ReLU"
	bottom: "conv6_1"
	top: "conv6_1"
	}
	layer {
	name: "conv6_2"
	type: "Convolution"
	bottom: "conv6_1"
	top: "conv6_2"
	convolution_param {
	num_output: 512
	kernel_size: 3
	pad: 2
	dilation: 2
	}
	}
	layer {
	name: "relu6_2"
	type: "ReLU"
	bottom: "conv6_2"
	top: "conv6_2"
	}
	layer {
	name: "conv6_3"
	type: "Convolution"
	bottom: "conv6_2"
	top: "conv6_3"
	convolution_param {
	num_output: 512
	kernel_size: 3
	pad: 2
	dilation: 2
	}
	}
	layer {
	name: "relu6_3"
	type: "ReLU"
	bottom: "conv6_3"
	top: "conv6_3"
	}
	layer {
	name: "conv6_3norm"
	type: "BatchNorm"
	bottom: "conv6_3"
	top: "conv6_3norm"
	batch_norm_param{ }
	param {lr_mult: 0 decay_mult: 0}
	param {lr_mult: 0 decay_mult: 0}
	param {lr_mult: 0 decay_mult: 0}
	}
	# *****************
	# *** conv7 ***
	# *****************
	layer {
	name: "conv7_1"
	type: "Convolution"
	bottom: "conv6_3norm"
	top: "conv7_1"
	convolution_param {
	num_output: 512
	kernel_size: 3
	pad: 1
	dilation: 1
	}
	}
	layer {
	name: "relu7_1"
	type: "ReLU"
	bottom: "conv7_1"
	top: "conv7_1"
	}
	layer {
	name: "conv7_2"
	type: "Convolution"
	bottom: "conv7_1"
	top: "conv7_2"
	convolution_param {
	num_output: 512
	kernel_size: 3
	pad: 1
	dilation: 1
	}
	}
	layer {
	name: "relu7_2"
	type: "ReLU"
	bottom: "conv7_2"
	top: "conv7_2"
	}
	layer {
	name: "conv7_3"
	type: "Convolution"
	bottom: "conv7_2"
	top: "conv7_3"
	convolution_param {
	num_output: 512
	kernel_size: 3
	pad: 1
	dilation: 1
	}
	}
	layer {
	name: "relu7_3"
	type: "ReLU"
	bottom: "conv7_3"
	top: "conv7_3"
	}
	layer {
	name: "conv7_3norm"
	type: "BatchNorm"
	bottom: "conv7_3"
	top: "conv7_3norm"
	batch_norm_param{ }
	param {lr_mult: 0 decay_mult: 0}
	param {lr_mult: 0 decay_mult: 0}
	param {lr_mult: 0 decay_mult: 0}
	}
	# *****************
	# *** conv8 ***
	# *****************
	layer {
	name: "conv8_1"
	type: "Deconvolution"
	bottom: "conv7_3norm"
	top: "conv8_1"
	convolution_param {
	num_output: 256
	kernel_size: 4
	pad: 1
	dilation: 1
	stride: 2
	}
	}
	layer {
	name: "relu8_1"
	type: "ReLU"
	bottom: "conv8_1"
	top: "conv8_1"
	}
	layer {
	name: "conv8_2"
	type: "Convolution"
	bottom: "conv8_1"
	top: "conv8_2"
	convolution_param {
	num_output: 256
	kernel_size: 3
	pad: 1
	dilation: 1
	}
	}
	layer {
	name: "relu8_2"
	type: "ReLU"
	bottom: "conv8_2"
	top: "conv8_2"
	}
	layer {
	name: "conv8_3"
	type: "Convolution"
	bottom: "conv8_2"
	top: "conv8_3"
	convolution_param {
	num_output: 256
	kernel_size: 3
	pad: 1
	dilation: 1
	}
	}
	layer {
	name: "relu8_3"
	type: "ReLU"
	bottom: "conv8_3"
	top: "conv8_3"
	}
	# *******************
	# *** Softmax ***
	# *******************
	layer {
	name: "conv8_313"
	type: "Convolution"
	bottom: "conv8_3"
	top: "conv8_313"
	convolution_param {
	num_output: 313
	kernel_size: 1
	stride: 1
	dilation: 1
	}
	}
	layer {
	name: "conv8_313_rh"
	type: "Scale"
	bottom: "conv8_313"
	top: "conv8_313_rh"
	scale_param {
	bias_term: false
	filler { type: 'constant' value: 2.606 }
	}
	}
	layer {
	name: "class8_313_rh"
	type: "Softmax"
	bottom: "conv8_313_rh"
	top: "class8_313_rh"
	}
	# ********************
	# *** Decoding ***
	# ********************
	layer {
	name: "class8_ab"
	type: "Convolution"
	bottom: "class8_313_rh"
	top: "class8_ab"
	convolution_param {
	num_output: 2
	kernel_size: 1
	stride: 1
	dilation: 1
	}
	}
	layer {
	name: "Silence"
	type: "Silence"
	bottom: "class8_ab"
	}