name: "RNet" layer { name: "data" type: "Input" top: "data" input_param{shape:{dim:1 dim:3 dim:24 dim:24}} } layer { name: "conv1" type: "Convolution" bottom: "data" top: "conv1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } convolution_param { num_output: 28 kernel_size: 3 stride: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "relu1_1" type: "ReLU" bottom: "conv1" top: "conv1_1" propagate_down: true } layer { name: "scale1_1" bottom: "conv1" top: "conv1_2" type: "Scale" scale_param { axis: 1 bias_term:false } } layer { name: "ReLU1_2" type: "ReLU" bottom: "conv1_2" top: "conv1_2" } layer { name: "scale1_2" bottom: "conv1_2" top: "conv1_2" type: "Scale" scale_param { axis: 1 bias_term:false } } layer { name: "eltwise-sum1" type: "Eltwise" bottom: "conv1_1" bottom: "conv1_2" top: "conv1_3" eltwise_param { operation: SUM } } layer { name: "pool1" type: "Pooling" bottom: "conv1_3" top: "pool1" pooling_param { pool: MAX kernel_size: 3 stride: 2 } } layer { name: "conv2" type: "Convolution" bottom: "pool1" top: "conv2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } convolution_param { num_output: 48 kernel_size: 3 stride: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "relu2_1" type: "ReLU" bottom: "conv2" top: "conv2_1" propagate_down: true } layer { name: "scale2_1" bottom: "conv2" top: "conv2_2" type: "Scale" scale_param { axis: 1 bias_term:false } } layer { name: "ReLU2_2" type: "ReLU" bottom: "conv2_2" top: "conv2_2" } layer { name: "scale2_2" bottom: "conv2_2" top: "conv2_2" type: "Scale" scale_param { axis: 1 bias_term:false } } layer { name: "eltwise-sum2" type: "Eltwise" bottom: "conv2_1" bottom: "conv2_2" top: "conv2_3" eltwise_param { operation: SUM } } layer { name: "pool2" type: "Pooling" bottom: "conv2_3" top: "pool2" pooling_param { pool: MAX kernel_size: 3 stride: 2 } } #################################### ################################## layer { name: "conv3" type: "Convolution" bottom: "pool2" top: "conv3" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } convolution_param { num_output: 64 kernel_size: 2 stride: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "scale3_1" bottom: "conv3" top: "conv3_2" type: "Scale" scale_param { axis: 1 bias_term:false } } layer { name: "ReLU3_2" type: "ReLU" bottom: "conv3_2" top: "conv3_2" } layer { name: "scale3_2" bottom: "conv3_2" top: "conv3_2" type: "Scale" scale_param { axis: 1 bias_term:false } } layer { name: "relu3" type: "ReLU" bottom: "conv3" top: "conv3_1" propagate_down: true } layer { name: "eltwise-sum3" type: "Eltwise" bottom: "conv3_1" bottom: "conv3_2" top: "conv3_3" eltwise_param { operation: SUM } } ############################### ############################### layer { name: "conv4" type: "InnerProduct" bottom: "conv3_3" top: "conv4" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } inner_product_param { num_output: 128 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "relu4_1" type: "ReLU" bottom: "conv4" top: "conv4_1" } layer { name: "scale4_1" bottom: "conv4" top: "conv4_2" type: "Scale" scale_param { axis: 1 bias_term:false } } layer { name: "ReLU4_2" type: "ReLU" bottom: "conv4_2" top: "conv4_2" } layer { name: "scale4_2" bottom: "conv4_2" top: "conv4_2" type: "Scale" scale_param { axis: 1 bias_term:false } } layer { name: "eltwise-sum4" type: "Eltwise" bottom: "conv4_1" bottom: "conv4_2" top: "conv4_3" eltwise_param { operation: SUM } } layer { name: "conv5-1" type: "InnerProduct" bottom: "conv4_3" top: "conv5-1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } inner_product_param { num_output: 2 #kernel_size: 1 #stride: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "conv5-2" type: "InnerProduct" bottom: "conv4_3" top: "conv5-2" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 1 } inner_product_param { num_output: 4 #kernel_size: 1 #stride: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "prob1" type: "Softmax" bottom: "conv5-1" top: "prob1" }