# source: https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/models/pose/coco/pose_deploy_linevec.prototxt input: "image" input_dim: 1 input_dim: 3 input_dim: 368 input_dim: 368 layer { name: "conv1_1" type: "Convolution" bottom: "image" top: "conv1_1" param { lr_mult: 1.0 decay_mult: 1 } param { lr_mult: 2.0 decay_mult: 0 } convolution_param { num_output: 64 pad: 1 kernel_size: 3 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } 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: 1.0 decay_mult: 1 } param { lr_mult: 2.0 decay_mult: 0 } convolution_param { num_output: 64 pad: 1 kernel_size: 3 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "relu1_2" type: "ReLU" bottom: "conv1_2" top: "conv1_2" } layer { name: "pool1_stage1" type: "Pooling" bottom: "conv1_2" top: "pool1_stage1" pooling_param { pool: MAX kernel_size: 2 stride: 2 } } layer { name: "conv2_1" type: "Convolution" bottom: "pool1_stage1" top: "conv2_1" param { lr_mult: 1.0 decay_mult: 1 } param { lr_mult: 2.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 1 kernel_size: 3 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } 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: 1.0 decay_mult: 1 } param { lr_mult: 2.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 1 kernel_size: 3 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "relu2_2" type: "ReLU" bottom: "conv2_2" top: "conv2_2" } layer { name: "pool2_stage1" type: "Pooling" bottom: "conv2_2" top: "pool2_stage1" pooling_param { pool: MAX kernel_size: 2 stride: 2 } } layer { name: "conv3_1" type: "Convolution" bottom: "pool2_stage1" top: "conv3_1" param { lr_mult: 1.0 decay_mult: 1 } param { lr_mult: 2.0 decay_mult: 0 } convolution_param { num_output: 256 pad: 1 kernel_size: 3 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } 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: 1.0 decay_mult: 1 } param { lr_mult: 2.0 decay_mult: 0 } convolution_param { num_output: 256 pad: 1 kernel_size: 3 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } 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: 1.0 decay_mult: 1 } param { lr_mult: 2.0 decay_mult: 0 } convolution_param { num_output: 256 pad: 1 kernel_size: 3 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "relu3_3" type: "ReLU" bottom: "conv3_3" top: "conv3_3" } layer { name: "conv3_4" type: "Convolution" bottom: "conv3_3" top: "conv3_4" param { lr_mult: 1.0 decay_mult: 1 } param { lr_mult: 2.0 decay_mult: 0 } convolution_param { num_output: 256 pad: 1 kernel_size: 3 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "relu3_4" type: "ReLU" bottom: "conv3_4" top: "conv3_4" } layer { name: "pool3_stage1" type: "Pooling" bottom: "conv3_4" top: "pool3_stage1" pooling_param { pool: MAX kernel_size: 2 stride: 2 } } layer { name: "conv4_1" type: "Convolution" bottom: "pool3_stage1" top: "conv4_1" param { lr_mult: 1.0 decay_mult: 1 } param { lr_mult: 2.0 decay_mult: 0 } convolution_param { num_output: 512 pad: 1 kernel_size: 3 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } 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: 1.0 decay_mult: 1 } param { lr_mult: 2.0 decay_mult: 0 } convolution_param { num_output: 512 pad: 1 kernel_size: 3 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "relu4_2" type: "ReLU" bottom: "conv4_2" top: "conv4_2" } layer { name: "conv4_3_CPM" type: "Convolution" bottom: "conv4_2" top: "conv4_3_CPM" param { lr_mult: 1.0 decay_mult: 1 } param { lr_mult: 2.0 decay_mult: 0 } convolution_param { num_output: 256 pad: 1 kernel_size: 3 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "relu4_3_CPM" type: "ReLU" bottom: "conv4_3_CPM" top: "conv4_3_CPM" } layer { name: "conv4_4_CPM" type: "Convolution" bottom: "conv4_3_CPM" top: "conv4_4_CPM" param { lr_mult: 1.0 decay_mult: 1 } param { lr_mult: 2.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 1 kernel_size: 3 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "relu4_4_CPM" type: "ReLU" bottom: "conv4_4_CPM" top: "conv4_4_CPM" } layer { name: "conv5_1_CPM_L1" type: "Convolution" bottom: "conv4_4_CPM" top: "conv5_1_CPM_L1" param { lr_mult: 1.0 decay_mult: 1 } param { lr_mult: 2.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 1 kernel_size: 3 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "relu5_1_CPM_L1" type: "ReLU" bottom: "conv5_1_CPM_L1" top: "conv5_1_CPM_L1" } layer { name: "conv5_1_CPM_L2" type: "Convolution" bottom: "conv4_4_CPM" top: "conv5_1_CPM_L2" param { lr_mult: 1.0 decay_mult: 1 } param { lr_mult: 2.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 1 kernel_size: 3 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "relu5_1_CPM_L2" type: "ReLU" bottom: "conv5_1_CPM_L2" top: "conv5_1_CPM_L2" } layer { name: "conv5_2_CPM_L1" type: "Convolution" bottom: "conv5_1_CPM_L1" top: "conv5_2_CPM_L1" param { lr_mult: 1.0 decay_mult: 1 } param { lr_mult: 2.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 1 kernel_size: 3 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "relu5_2_CPM_L1" type: "ReLU" bottom: "conv5_2_CPM_L1" top: "conv5_2_CPM_L1" } layer { name: "conv5_2_CPM_L2" type: "Convolution" bottom: "conv5_1_CPM_L2" top: "conv5_2_CPM_L2" param { lr_mult: 1.0 decay_mult: 1 } param { lr_mult: 2.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 1 kernel_size: 3 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "relu5_2_CPM_L2" type: "ReLU" bottom: "conv5_2_CPM_L2" top: "conv5_2_CPM_L2" } layer { name: "conv5_3_CPM_L1" type: "Convolution" bottom: "conv5_2_CPM_L1" top: "conv5_3_CPM_L1" param { lr_mult: 1.0 decay_mult: 1 } param { lr_mult: 2.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 1 kernel_size: 3 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "relu5_3_CPM_L1" type: "ReLU" bottom: "conv5_3_CPM_L1" top: "conv5_3_CPM_L1" } layer { name: "conv5_3_CPM_L2" type: "Convolution" bottom: "conv5_2_CPM_L2" top: "conv5_3_CPM_L2" param { lr_mult: 1.0 decay_mult: 1 } param { lr_mult: 2.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 1 kernel_size: 3 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "relu5_3_CPM_L2" type: "ReLU" bottom: "conv5_3_CPM_L2" top: "conv5_3_CPM_L2" } layer { name: "conv5_4_CPM_L1" type: "Convolution" bottom: "conv5_3_CPM_L1" top: "conv5_4_CPM_L1" param { lr_mult: 1.0 decay_mult: 1 } param { lr_mult: 2.0 decay_mult: 0 } convolution_param { num_output: 512 pad: 0 kernel_size: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "relu5_4_CPM_L1" type: "ReLU" bottom: "conv5_4_CPM_L1" top: "conv5_4_CPM_L1" } layer { name: "conv5_4_CPM_L2" type: "Convolution" bottom: "conv5_3_CPM_L2" top: "conv5_4_CPM_L2" param { lr_mult: 1.0 decay_mult: 1 } param { lr_mult: 2.0 decay_mult: 0 } convolution_param { num_output: 512 pad: 0 kernel_size: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "relu5_4_CPM_L2" type: "ReLU" bottom: "conv5_4_CPM_L2" top: "conv5_4_CPM_L2" } layer { name: "conv5_5_CPM_L1" type: "Convolution" bottom: "conv5_4_CPM_L1" top: "conv5_5_CPM_L1" param { lr_mult: 1.0 decay_mult: 1 } param { lr_mult: 2.0 decay_mult: 0 } convolution_param { num_output: 38 pad: 0 kernel_size: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "conv5_5_CPM_L2" type: "Convolution" bottom: "conv5_4_CPM_L2" top: "conv5_5_CPM_L2" param { lr_mult: 1.0 decay_mult: 1 } param { lr_mult: 2.0 decay_mult: 0 } convolution_param { num_output: 19 pad: 0 kernel_size: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "concat_stage2" type: "Concat" bottom: "conv5_5_CPM_L1" bottom: "conv5_5_CPM_L2" bottom: "conv4_4_CPM" top: "concat_stage2" concat_param { axis: 1 } } layer { name: "Mconv1_stage2_L1" type: "Convolution" bottom: "concat_stage2" top: "Mconv1_stage2_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu1_stage2_L1" type: "ReLU" bottom: "Mconv1_stage2_L1" top: "Mconv1_stage2_L1" } layer { name: "Mconv1_stage2_L2" type: "Convolution" bottom: "concat_stage2" top: "Mconv1_stage2_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu1_stage2_L2" type: "ReLU" bottom: "Mconv1_stage2_L2" top: "Mconv1_stage2_L2" } layer { name: "Mconv2_stage2_L1" type: "Convolution" bottom: "Mconv1_stage2_L1" top: "Mconv2_stage2_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu2_stage2_L1" type: "ReLU" bottom: "Mconv2_stage2_L1" top: "Mconv2_stage2_L1" } layer { name: "Mconv2_stage2_L2" type: "Convolution" bottom: "Mconv1_stage2_L2" top: "Mconv2_stage2_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu2_stage2_L2" type: "ReLU" bottom: "Mconv2_stage2_L2" top: "Mconv2_stage2_L2" } layer { name: "Mconv3_stage2_L1" type: "Convolution" bottom: "Mconv2_stage2_L1" top: "Mconv3_stage2_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu3_stage2_L1" type: "ReLU" bottom: "Mconv3_stage2_L1" top: "Mconv3_stage2_L1" } layer { name: "Mconv3_stage2_L2" type: "Convolution" bottom: "Mconv2_stage2_L2" top: "Mconv3_stage2_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu3_stage2_L2" type: "ReLU" bottom: "Mconv3_stage2_L2" top: "Mconv3_stage2_L2" } layer { name: "Mconv4_stage2_L1" type: "Convolution" bottom: "Mconv3_stage2_L1" top: "Mconv4_stage2_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu4_stage2_L1" type: "ReLU" bottom: "Mconv4_stage2_L1" top: "Mconv4_stage2_L1" } layer { name: "Mconv4_stage2_L2" type: "Convolution" bottom: "Mconv3_stage2_L2" top: "Mconv4_stage2_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu4_stage2_L2" type: "ReLU" bottom: "Mconv4_stage2_L2" top: "Mconv4_stage2_L2" } layer { name: "Mconv5_stage2_L1" type: "Convolution" bottom: "Mconv4_stage2_L1" top: "Mconv5_stage2_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu5_stage2_L1" type: "ReLU" bottom: "Mconv5_stage2_L1" top: "Mconv5_stage2_L1" } layer { name: "Mconv5_stage2_L2" type: "Convolution" bottom: "Mconv4_stage2_L2" top: "Mconv5_stage2_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu5_stage2_L2" type: "ReLU" bottom: "Mconv5_stage2_L2" top: "Mconv5_stage2_L2" } layer { name: "Mconv6_stage2_L1" type: "Convolution" bottom: "Mconv5_stage2_L1" top: "Mconv6_stage2_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 0 kernel_size: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu6_stage2_L1" type: "ReLU" bottom: "Mconv6_stage2_L1" top: "Mconv6_stage2_L1" } layer { name: "Mconv6_stage2_L2" type: "Convolution" bottom: "Mconv5_stage2_L2" top: "Mconv6_stage2_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 0 kernel_size: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu6_stage2_L2" type: "ReLU" bottom: "Mconv6_stage2_L2" top: "Mconv6_stage2_L2" } layer { name: "Mconv7_stage2_L1" type: "Convolution" bottom: "Mconv6_stage2_L1" top: "Mconv7_stage2_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 38 pad: 0 kernel_size: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mconv7_stage2_L2" type: "Convolution" bottom: "Mconv6_stage2_L2" top: "Mconv7_stage2_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 19 pad: 0 kernel_size: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "concat_stage3" type: "Concat" bottom: "Mconv7_stage2_L1" bottom: "Mconv7_stage2_L2" bottom: "conv4_4_CPM" top: "concat_stage3" concat_param { axis: 1 } } layer { name: "Mconv1_stage3_L1" type: "Convolution" bottom: "concat_stage3" top: "Mconv1_stage3_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu1_stage3_L1" type: "ReLU" bottom: "Mconv1_stage3_L1" top: "Mconv1_stage3_L1" } layer { name: "Mconv1_stage3_L2" type: "Convolution" bottom: "concat_stage3" top: "Mconv1_stage3_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu1_stage3_L2" type: "ReLU" bottom: "Mconv1_stage3_L2" top: "Mconv1_stage3_L2" } layer { name: "Mconv2_stage3_L1" type: "Convolution" bottom: "Mconv1_stage3_L1" top: "Mconv2_stage3_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu2_stage3_L1" type: "ReLU" bottom: "Mconv2_stage3_L1" top: "Mconv2_stage3_L1" } layer { name: "Mconv2_stage3_L2" type: "Convolution" bottom: "Mconv1_stage3_L2" top: "Mconv2_stage3_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu2_stage3_L2" type: "ReLU" bottom: "Mconv2_stage3_L2" top: "Mconv2_stage3_L2" } layer { name: "Mconv3_stage3_L1" type: "Convolution" bottom: "Mconv2_stage3_L1" top: "Mconv3_stage3_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu3_stage3_L1" type: "ReLU" bottom: "Mconv3_stage3_L1" top: "Mconv3_stage3_L1" } layer { name: "Mconv3_stage3_L2" type: "Convolution" bottom: "Mconv2_stage3_L2" top: "Mconv3_stage3_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu3_stage3_L2" type: "ReLU" bottom: "Mconv3_stage3_L2" top: "Mconv3_stage3_L2" } layer { name: "Mconv4_stage3_L1" type: "Convolution" bottom: "Mconv3_stage3_L1" top: "Mconv4_stage3_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu4_stage3_L1" type: "ReLU" bottom: "Mconv4_stage3_L1" top: "Mconv4_stage3_L1" } layer { name: "Mconv4_stage3_L2" type: "Convolution" bottom: "Mconv3_stage3_L2" top: "Mconv4_stage3_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu4_stage3_L2" type: "ReLU" bottom: "Mconv4_stage3_L2" top: "Mconv4_stage3_L2" } layer { name: "Mconv5_stage3_L1" type: "Convolution" bottom: "Mconv4_stage3_L1" top: "Mconv5_stage3_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu5_stage3_L1" type: "ReLU" bottom: "Mconv5_stage3_L1" top: "Mconv5_stage3_L1" } layer { name: "Mconv5_stage3_L2" type: "Convolution" bottom: "Mconv4_stage3_L2" top: "Mconv5_stage3_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu5_stage3_L2" type: "ReLU" bottom: "Mconv5_stage3_L2" top: "Mconv5_stage3_L2" } layer { name: "Mconv6_stage3_L1" type: "Convolution" bottom: "Mconv5_stage3_L1" top: "Mconv6_stage3_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 0 kernel_size: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu6_stage3_L1" type: "ReLU" bottom: "Mconv6_stage3_L1" top: "Mconv6_stage3_L1" } layer { name: "Mconv6_stage3_L2" type: "Convolution" bottom: "Mconv5_stage3_L2" top: "Mconv6_stage3_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 0 kernel_size: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu6_stage3_L2" type: "ReLU" bottom: "Mconv6_stage3_L2" top: "Mconv6_stage3_L2" } layer { name: "Mconv7_stage3_L1" type: "Convolution" bottom: "Mconv6_stage3_L1" top: "Mconv7_stage3_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 38 pad: 0 kernel_size: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mconv7_stage3_L2" type: "Convolution" bottom: "Mconv6_stage3_L2" top: "Mconv7_stage3_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 19 pad: 0 kernel_size: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "concat_stage4" type: "Concat" bottom: "Mconv7_stage3_L1" bottom: "Mconv7_stage3_L2" bottom: "conv4_4_CPM" top: "concat_stage4" concat_param { axis: 1 } } layer { name: "Mconv1_stage4_L1" type: "Convolution" bottom: "concat_stage4" top: "Mconv1_stage4_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu1_stage4_L1" type: "ReLU" bottom: "Mconv1_stage4_L1" top: "Mconv1_stage4_L1" } layer { name: "Mconv1_stage4_L2" type: "Convolution" bottom: "concat_stage4" top: "Mconv1_stage4_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu1_stage4_L2" type: "ReLU" bottom: "Mconv1_stage4_L2" top: "Mconv1_stage4_L2" } layer { name: "Mconv2_stage4_L1" type: "Convolution" bottom: "Mconv1_stage4_L1" top: "Mconv2_stage4_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu2_stage4_L1" type: "ReLU" bottom: "Mconv2_stage4_L1" top: "Mconv2_stage4_L1" } layer { name: "Mconv2_stage4_L2" type: "Convolution" bottom: "Mconv1_stage4_L2" top: "Mconv2_stage4_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu2_stage4_L2" type: "ReLU" bottom: "Mconv2_stage4_L2" top: "Mconv2_stage4_L2" } layer { name: "Mconv3_stage4_L1" type: "Convolution" bottom: "Mconv2_stage4_L1" top: "Mconv3_stage4_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu3_stage4_L1" type: "ReLU" bottom: "Mconv3_stage4_L1" top: "Mconv3_stage4_L1" } layer { name: "Mconv3_stage4_L2" type: "Convolution" bottom: "Mconv2_stage4_L2" top: "Mconv3_stage4_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu3_stage4_L2" type: "ReLU" bottom: "Mconv3_stage4_L2" top: "Mconv3_stage4_L2" } layer { name: "Mconv4_stage4_L1" type: "Convolution" bottom: "Mconv3_stage4_L1" top: "Mconv4_stage4_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu4_stage4_L1" type: "ReLU" bottom: "Mconv4_stage4_L1" top: "Mconv4_stage4_L1" } layer { name: "Mconv4_stage4_L2" type: "Convolution" bottom: "Mconv3_stage4_L2" top: "Mconv4_stage4_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu4_stage4_L2" type: "ReLU" bottom: "Mconv4_stage4_L2" top: "Mconv4_stage4_L2" } layer { name: "Mconv5_stage4_L1" type: "Convolution" bottom: "Mconv4_stage4_L1" top: "Mconv5_stage4_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu5_stage4_L1" type: "ReLU" bottom: "Mconv5_stage4_L1" top: "Mconv5_stage4_L1" } layer { name: "Mconv5_stage4_L2" type: "Convolution" bottom: "Mconv4_stage4_L2" top: "Mconv5_stage4_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu5_stage4_L2" type: "ReLU" bottom: "Mconv5_stage4_L2" top: "Mconv5_stage4_L2" } layer { name: "Mconv6_stage4_L1" type: "Convolution" bottom: "Mconv5_stage4_L1" top: "Mconv6_stage4_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 0 kernel_size: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu6_stage4_L1" type: "ReLU" bottom: "Mconv6_stage4_L1" top: "Mconv6_stage4_L1" } layer { name: "Mconv6_stage4_L2" type: "Convolution" bottom: "Mconv5_stage4_L2" top: "Mconv6_stage4_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 0 kernel_size: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu6_stage4_L2" type: "ReLU" bottom: "Mconv6_stage4_L2" top: "Mconv6_stage4_L2" } layer { name: "Mconv7_stage4_L1" type: "Convolution" bottom: "Mconv6_stage4_L1" top: "Mconv7_stage4_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 38 pad: 0 kernel_size: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mconv7_stage4_L2" type: "Convolution" bottom: "Mconv6_stage4_L2" top: "Mconv7_stage4_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 19 pad: 0 kernel_size: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "concat_stage5" type: "Concat" bottom: "Mconv7_stage4_L1" bottom: "Mconv7_stage4_L2" bottom: "conv4_4_CPM" top: "concat_stage5" concat_param { axis: 1 } } layer { name: "Mconv1_stage5_L1" type: "Convolution" bottom: "concat_stage5" top: "Mconv1_stage5_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu1_stage5_L1" type: "ReLU" bottom: "Mconv1_stage5_L1" top: "Mconv1_stage5_L1" } layer { name: "Mconv1_stage5_L2" type: "Convolution" bottom: "concat_stage5" top: "Mconv1_stage5_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu1_stage5_L2" type: "ReLU" bottom: "Mconv1_stage5_L2" top: "Mconv1_stage5_L2" } layer { name: "Mconv2_stage5_L1" type: "Convolution" bottom: "Mconv1_stage5_L1" top: "Mconv2_stage5_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu2_stage5_L1" type: "ReLU" bottom: "Mconv2_stage5_L1" top: "Mconv2_stage5_L1" } layer { name: "Mconv2_stage5_L2" type: "Convolution" bottom: "Mconv1_stage5_L2" top: "Mconv2_stage5_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu2_stage5_L2" type: "ReLU" bottom: "Mconv2_stage5_L2" top: "Mconv2_stage5_L2" } layer { name: "Mconv3_stage5_L1" type: "Convolution" bottom: "Mconv2_stage5_L1" top: "Mconv3_stage5_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu3_stage5_L1" type: "ReLU" bottom: "Mconv3_stage5_L1" top: "Mconv3_stage5_L1" } layer { name: "Mconv3_stage5_L2" type: "Convolution" bottom: "Mconv2_stage5_L2" top: "Mconv3_stage5_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu3_stage5_L2" type: "ReLU" bottom: "Mconv3_stage5_L2" top: "Mconv3_stage5_L2" } layer { name: "Mconv4_stage5_L1" type: "Convolution" bottom: "Mconv3_stage5_L1" top: "Mconv4_stage5_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu4_stage5_L1" type: "ReLU" bottom: "Mconv4_stage5_L1" top: "Mconv4_stage5_L1" } layer { name: "Mconv4_stage5_L2" type: "Convolution" bottom: "Mconv3_stage5_L2" top: "Mconv4_stage5_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu4_stage5_L2" type: "ReLU" bottom: "Mconv4_stage5_L2" top: "Mconv4_stage5_L2" } layer { name: "Mconv5_stage5_L1" type: "Convolution" bottom: "Mconv4_stage5_L1" top: "Mconv5_stage5_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu5_stage5_L1" type: "ReLU" bottom: "Mconv5_stage5_L1" top: "Mconv5_stage5_L1" } layer { name: "Mconv5_stage5_L2" type: "Convolution" bottom: "Mconv4_stage5_L2" top: "Mconv5_stage5_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu5_stage5_L2" type: "ReLU" bottom: "Mconv5_stage5_L2" top: "Mconv5_stage5_L2" } layer { name: "Mconv6_stage5_L1" type: "Convolution" bottom: "Mconv5_stage5_L1" top: "Mconv6_stage5_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 0 kernel_size: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu6_stage5_L1" type: "ReLU" bottom: "Mconv6_stage5_L1" top: "Mconv6_stage5_L1" } layer { name: "Mconv6_stage5_L2" type: "Convolution" bottom: "Mconv5_stage5_L2" top: "Mconv6_stage5_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 0 kernel_size: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu6_stage5_L2" type: "ReLU" bottom: "Mconv6_stage5_L2" top: "Mconv6_stage5_L2" } layer { name: "Mconv7_stage5_L1" type: "Convolution" bottom: "Mconv6_stage5_L1" top: "Mconv7_stage5_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 38 pad: 0 kernel_size: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mconv7_stage5_L2" type: "Convolution" bottom: "Mconv6_stage5_L2" top: "Mconv7_stage5_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 19 pad: 0 kernel_size: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "concat_stage6" type: "Concat" bottom: "Mconv7_stage5_L1" bottom: "Mconv7_stage5_L2" bottom: "conv4_4_CPM" top: "concat_stage6" concat_param { axis: 1 } } # layer { # name: "Mconv1_stage6_L1" # type: "Convolution" # bottom: "concat_stage6" # top: "Mconv1_stage6_L1" # param { # lr_mult: 4.0 # decay_mult: 1 # } # param { # lr_mult: 8.0 # decay_mult: 0 # } # convolution_param { # num_output: 128 # pad: 3 # kernel_size: 7 # weight_filler { # type: "gaussian" # std: 0.01 # } # bias_filler { # type: "constant" # } # } # } # layer { # name: "Mrelu1_stage6_L1" # type: "ReLU" # bottom: "Mconv1_stage6_L1" # top: "Mconv1_stage6_L1" # } layer { name: "Mconv1_stage6_L2" type: "Convolution" bottom: "concat_stage6" top: "Mconv1_stage6_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu1_stage6_L2" type: "ReLU" bottom: "Mconv1_stage6_L2" top: "Mconv1_stage6_L2" } # layer { # name: "Mconv2_stage6_L1" # type: "Convolution" # bottom: "Mconv1_stage6_L1" # top: "Mconv2_stage6_L1" # param { # lr_mult: 4.0 # decay_mult: 1 # } # param { # lr_mult: 8.0 # decay_mult: 0 # } # convolution_param { # num_output: 128 # pad: 3 # kernel_size: 7 # weight_filler { # type: "gaussian" # std: 0.01 # } # bias_filler { # type: "constant" # } # } # } # layer { # name: "Mrelu2_stage6_L1" # type: "ReLU" # bottom: "Mconv2_stage6_L1" # top: "Mconv2_stage6_L1" # } layer { name: "Mconv2_stage6_L2" type: "Convolution" bottom: "Mconv1_stage6_L2" top: "Mconv2_stage6_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu2_stage6_L2" type: "ReLU" bottom: "Mconv2_stage6_L2" top: "Mconv2_stage6_L2" } # layer { # name: "Mconv3_stage6_L1" # type: "Convolution" # bottom: "Mconv2_stage6_L1" # top: "Mconv3_stage6_L1" # param { # lr_mult: 4.0 # decay_mult: 1 # } # param { # lr_mult: 8.0 # decay_mult: 0 # } # convolution_param { # num_output: 128 # pad: 3 # kernel_size: 7 # weight_filler { # type: "gaussian" # std: 0.01 # } # bias_filler { # type: "constant" # } # } # } # layer { # name: "Mrelu3_stage6_L1" # type: "ReLU" # bottom: "Mconv3_stage6_L1" # top: "Mconv3_stage6_L1" # } layer { name: "Mconv3_stage6_L2" type: "Convolution" bottom: "Mconv2_stage6_L2" top: "Mconv3_stage6_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu3_stage6_L2" type: "ReLU" bottom: "Mconv3_stage6_L2" top: "Mconv3_stage6_L2" } # layer { # name: "Mconv4_stage6_L1" # type: "Convolution" # bottom: "Mconv3_stage6_L1" # top: "Mconv4_stage6_L1" # param { # lr_mult: 4.0 # decay_mult: 1 # } # param { # lr_mult: 8.0 # decay_mult: 0 # } # convolution_param { # num_output: 128 # pad: 3 # kernel_size: 7 # weight_filler { # type: "gaussian" # std: 0.01 # } # bias_filler { # type: "constant" # } # } # } # layer { # name: "Mrelu4_stage6_L1" # type: "ReLU" # bottom: "Mconv4_stage6_L1" # top: "Mconv4_stage6_L1" # } layer { name: "Mconv4_stage6_L2" type: "Convolution" bottom: "Mconv3_stage6_L2" top: "Mconv4_stage6_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu4_stage6_L2" type: "ReLU" bottom: "Mconv4_stage6_L2" top: "Mconv4_stage6_L2" } # layer { # name: "Mconv5_stage6_L1" # type: "Convolution" # bottom: "Mconv4_stage6_L1" # top: "Mconv5_stage6_L1" # param { # lr_mult: 4.0 # decay_mult: 1 # } # param { # lr_mult: 8.0 # decay_mult: 0 # } # convolution_param { # num_output: 128 # pad: 3 # kernel_size: 7 # weight_filler { # type: "gaussian" # std: 0.01 # } # bias_filler { # type: "constant" # } # } # } # layer { # name: "Mrelu5_stage6_L1" # type: "ReLU" # bottom: "Mconv5_stage6_L1" # top: "Mconv5_stage6_L1" # } layer { name: "Mconv5_stage6_L2" type: "Convolution" bottom: "Mconv4_stage6_L2" top: "Mconv5_stage6_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu5_stage6_L2" type: "ReLU" bottom: "Mconv5_stage6_L2" top: "Mconv5_stage6_L2" } # layer { # name: "Mconv6_stage6_L1" # type: "Convolution" # bottom: "Mconv5_stage6_L1" # top: "Mconv6_stage6_L1" # param { # lr_mult: 4.0 # decay_mult: 1 # } # param { # lr_mult: 8.0 # decay_mult: 0 # } # convolution_param { # num_output: 128 # pad: 0 # kernel_size: 1 # weight_filler { # type: "gaussian" # std: 0.01 # } # bias_filler { # type: "constant" # } # } # } # layer { # name: "Mrelu6_stage6_L1" # type: "ReLU" # bottom: "Mconv6_stage6_L1" # top: "Mconv6_stage6_L1" # } layer { name: "Mconv6_stage6_L2" type: "Convolution" bottom: "Mconv5_stage6_L2" top: "Mconv6_stage6_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 0 kernel_size: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu6_stage6_L2" type: "ReLU" bottom: "Mconv6_stage6_L2" top: "Mconv6_stage6_L2" } # layer { # name: "Mconv7_stage6_L1" # type: "Convolution" # bottom: "Mconv6_stage6_L1" # top: "Mconv7_stage6_L1" # param { # lr_mult: 4.0 # decay_mult: 1 # } # param { # lr_mult: 8.0 # decay_mult: 0 # } # convolution_param { # num_output: 38 # pad: 0 # kernel_size: 1 # weight_filler { # type: "gaussian" # std: 0.01 # } # bias_filler { # type: "constant" # } # } # } layer { name: "Mconv7_stage6_L2" type: "Convolution" bottom: "Mconv6_stage6_L2" top: "Mconv7_stage6_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 19 pad: 0 kernel_size: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } # layer { # name: "concat_stage7" # type: "Concat" # bottom: "Mconv7_stage6_L2" # bottom: "Mconv7_stage6_L1" # # top: "concat_stage7" # top: "net_output" # concat_param { # axis: 1 # } # }