Created
February 18, 2018 01:09
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| input: "image" | |
| input_dim: 1 | |
| input_dim: 3 | |
| input_dim: 1 # This value will be defined at runtime | |
| input_dim: 1 # This value will be defined at runtime | |
| 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 | |
| } | |
| } |
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