input: "data" input_dim: 1 input_dim: 3 input_dim: 60 input_dim: 60 ############################ Network ################################## layer { name: "bn/data" type: "BatchNorm" bottom: "data" top: "data" } layer { name: "Scale1" type: "Scale" bottom: "data" top: "data" param { lr_mult: 1.0 decay_mult: 1.0 } param { lr_mult: 2.0 decay_mult: 1.0 } scale_param { bias_term: true } } layer { name: "conv1" type: "Convolution" bottom: "data" top: "conv1" convolution_param { num_output: 32 kernel_size: 3 stride: 1 pad: 1 weight_filler { type: "xavier" } } param { lr_mult: 1.0 #learning rate of weights decay_mult: 1 } param { lr_mult: 2.0 #learning rate of bias decay_mult: 0 } } layer { name: "drop1" type: "Dropout" bottom: "conv1" top: "conv1" dropout_param { dropout_ratio: 0.2 } } layer { name: "relu_conv1" type: "ReLU" bottom: "conv1" top: "conv1" } layer { name: "bn1" type: "BatchNorm" bottom: "conv1" top: "bn1" } layer { name: "pool1" type: "Convolution" bottom: "bn1" top: "pool1" convolution_param { num_output: 16 kernel_size: 3 stride: 2 pad: 1 weight_filler { type: "xavier" } } param { lr_mult: 1.0 #learning rate of weights decay_mult: 1 } param { lr_mult: 2.0 #learning rate of bias decay_mult: 0 } } layer { name: "conv2" type: "Convolution" bottom: "pool1" top: "conv2" convolution_param { num_output: 64 kernel_size: 3 stride: 1 pad: 1 weight_filler { type: "xavier" } } param { lr_mult: 1.0 #learning rate of weights decay_mult: 1 } param { lr_mult: 2.0 #learning rate of bias decay_mult: 0 } } layer { name: "drop2" type: "Dropout" bottom: "conv2" top: "conv2" dropout_param { dropout_ratio: 0.2 } } layer { name: "relu_conv2" type: "ReLU" bottom: "conv2" top: "conv2" } layer { name: "bn2" type: "BatchNorm" bottom: "conv2" top: "bn2" } layer { name: "pool2" type: "Pooling" bottom: "bn2" top: "pool2" pooling_param { pool: MAX kernel_size: 3 stride: 2 } } layer { name: "conv3" type: "Convolution" bottom: "pool2" top: "conv3" convolution_param { num_output: 128 kernel_size: 3 stride: 1 pad: 1 weight_filler { type: "xavier" } } param { lr_mult: 1.0 #learning rate of weights decay_mult: 1 } param { lr_mult: 2.0 #learning rate of bias decay_mult: 0 } } layer { name: "drop3" type: "Dropout" bottom: "conv3" top: "conv3" dropout_param { dropout_ratio: 0.2 } } layer { name: "relu_conv3" type: "ReLU" bottom: "conv3" top: "conv3" } layer { name: "bn3" type: "BatchNorm" bottom: "conv3" top: "bn3" } layer { name: "pool3" type: "Convolution" bottom: "bn3" top: "pool3" convolution_param { num_output: 64 kernel_size: 3 stride: 2 pad: 1 weight_filler { type: "xavier" } } param { lr_mult: 1.0 #learning rate of weights decay_mult: 1 } param { lr_mult: 2.0 #learning rate of bias decay_mult: 0 } } layer { name: "conv4" type: "Convolution" bottom: "pool3" top: "conv4" convolution_param { num_output: 128 kernel_size: 3 stride: 1 pad: 1 weight_filler { type: "xavier" } } param { lr_mult: 1.0 #learning rate of weights decay_mult: 1 } param { lr_mult: 2.0 #learning rate of bias decay_mult: 0 } } layer { name: "drop4" type: "Dropout" bottom: "conv4" top: "conv4" dropout_param { dropout_ratio: 0.2 } } layer { name: "relu_conv4" type: "ReLU" bottom: "conv4" top: "conv4" } layer { name: "bn4" type: "BatchNorm" bottom: "conv4" top: "bn4" } layer { name: "pool4" type: "Pooling" bottom: "bn4" top: "pool4" pooling_param { pool: MAX kernel_size: 3 stride: 2 } } layer { name: "conv5" type: "Convolution" bottom: "pool4" top: "conv5" convolution_param { num_output: 256 kernel_size: 3 stride: 1 pad: 1 weight_filler { type: "xavier" } } param { lr_mult: 1.0 #learning rate of weights decay_mult: 1 } param { lr_mult: 2.0 #learning rate of bias decay_mult: 0 } } layer { name: "drop5" type: "Dropout" bottom: "conv5" top: "conv5" dropout_param { dropout_ratio: 0.2 } } layer { name: "relu_conv5" type: "ReLU" bottom: "conv5" top: "conv5" } layer { name: "bn5" type: "BatchNorm" bottom: "conv5" top: "bn5" } layer { name: "conv_fm" type: "Convolution" bottom: "bn5" top: "conv_fm" convolution_param { num_output: 512 kernel_size: 3 #stride: 2 #pad: 1 weight_filler { type: "xavier" } } } layer { name: "drop_fm" type: "Dropout" bottom: "conv_fm" top: "conv_fm" dropout_param { dropout_ratio: 0.3 } } ########################## angle ################################ layer { name: "angle_y" type: "Convolution" bottom: "conv_fm" top: "angle_y" param { lr_mult: 1.0 decay_mult: 1.0 } param { lr_mult: 2.0 decay_mult: 0.0 } convolution_param { num_output: 128 kernel_size: 1 weight_filler { type: "xavier" std: 0.3 } bias_filler { type: "constant" value: 0.0 } } } layer { name: "relu_y_1" type: "ReLU" bottom: "angle_y" top: "angle_y" } layer { name: "bn_y" type: "BatchNorm" bottom: "angle_y" top: "angle_y" } layer { name: "drop_angle_y" type: "Dropout" bottom: "angle_y" top: "angle_y" dropout_param { dropout_ratio: 0.5 } } layer { name: "angle_y_fc" type: "InnerProduct" bottom: "angle_y" top: "fc_y" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } inner_product_param { num_output: 1 weight_filler { type: "xavier" std: 0.08 } bias_filler { type: "constant" value: 0.1 } } } #### layer { name: "angle_p" type: "Convolution" bottom: "conv_fm" top: "angle_p" param { lr_mult: 1.0 decay_mult: 1.0 } param { lr_mult: 2.0 decay_mult: 0.0 } convolution_param { num_output: 128 kernel_size: 1 weight_filler { type: "xavier" std: 0.3 } bias_filler { type: "constant" value: 0.0 } } } layer { name: "relu_p_1" type: "ReLU" bottom: "angle_p" top: "angle_p" } layer { name: "bn_p" type: "BatchNorm" bottom: "angle_p" top: "angle_p" } layer { name: "drop_angle_p" type: "Dropout" bottom: "angle_p" top: "angle_p" dropout_param { dropout_ratio: 0.5 } } layer { name: "angle_p_fc" type: "InnerProduct" bottom: "angle_p" top: "fc_p" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } inner_product_param { num_output: 1 weight_filler { type: "xavier" std: 0.08 } bias_filler { type: "constant" value: 0.1 } } } #### layer { name: "angle_r" type: "Convolution" bottom: "conv_fm" top: "angle_r" param { lr_mult: 1.0 decay_mult: 1.0 } param { lr_mult: 2.0 decay_mult: 0.0 } convolution_param { num_output: 128 kernel_size: 1 weight_filler { type: "xavier" std: 0.3 } bias_filler { type: "constant" value: 0.0 } } } layer { name: "bn_r" type: "BatchNorm" bottom: "angle_r" top: "angle_r" } layer { name: "relu_r_1" type: "ReLU" bottom: "angle_r" top: "angle_r" } layer { name: "drop_angle_r" type: "Dropout" bottom: "angle_r" top: "angle_r" dropout_param { dropout_ratio: 0.5 } } layer { name: "angle_r_fc" type: "InnerProduct" bottom: "angle_r" top: "fc_r" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } inner_product_param { num_output: 1 weight_filler { type: "xavier" std: 0.08 } bias_filler { type: "constant" value: 0.1 } } }