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November 30, 2017 05:33
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| input : "image" | |
| input_dim: 1 | |
| input_dim: 3 | |
| input_dim: 224 | |
| input_dim: 224 | |
| layer { | |
| name: "conv1" | |
| type: "Convolution" | |
| bottom: "image" | |
| top: "conv1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv1/bn" | |
| type: "BatchNorm" | |
| bottom: "conv1" | |
| top: "conv1" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| } | |
| layer { | |
| name: "conv1/scale" | |
| type: "Scale" | |
| bottom: "conv1" | |
| top: "conv1" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu1" | |
| type: "ReLU" | |
| bottom: "conv1" | |
| top: "conv1" | |
| } | |
| layer { | |
| name: "conv2_1/dw" | |
| type: "ConvolutionDepthwise" | |
| bottom: "conv1" | |
| top: "conv2_1/dw" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 32 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2_1/dw/bn" | |
| type: "BatchNorm" | |
| bottom: "conv2_1/dw" | |
| top: "conv2_1/dw" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| } | |
| layer { | |
| name: "conv2_1/dw/scale" | |
| type: "Scale" | |
| bottom: "conv2_1/dw" | |
| top: "conv2_1/dw" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu2_1/dw" | |
| type: "ReLU" | |
| bottom: "conv2_1/dw" | |
| top: "conv2_1/dw" | |
| } | |
| layer { | |
| name: "conv2_1/sep" | |
| type: "Convolution" | |
| bottom: "conv2_1/dw" | |
| top: "conv2_1/sep" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2_1/sep/bn" | |
| type: "BatchNorm" | |
| bottom: "conv2_1/sep" | |
| top: "conv2_1/sep" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| } | |
| layer { | |
| name: "conv2_1/sep/scale" | |
| type: "Scale" | |
| bottom: "conv2_1/sep" | |
| top: "conv2_1/sep" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu2_1/sep" | |
| type: "ReLU" | |
| bottom: "conv2_1/sep" | |
| top: "conv2_1/sep" | |
| } | |
| layer { | |
| name: "conv2_2/dw" | |
| type: "ConvolutionDepthwise" | |
| bottom: "conv2_1/sep" | |
| top: "conv2_2/dw" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 64 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2_2/dw/bn" | |
| type: "BatchNorm" | |
| bottom: "conv2_2/dw" | |
| top: "conv2_2/dw" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| } | |
| layer { | |
| name: "conv2_2/dw/scale" | |
| type: "Scale" | |
| bottom: "conv2_2/dw" | |
| top: "conv2_2/dw" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu2_2/dw" | |
| type: "ReLU" | |
| bottom: "conv2_2/dw" | |
| top: "conv2_2/dw" | |
| } | |
| layer { | |
| name: "conv2_2/sep" | |
| type: "Convolution" | |
| bottom: "conv2_2/dw" | |
| top: "conv2_2/sep" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2_2/sep/bn" | |
| type: "BatchNorm" | |
| bottom: "conv2_2/sep" | |
| top: "conv2_2/sep" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| } | |
| layer { | |
| name: "conv2_2/sep/scale" | |
| type: "Scale" | |
| bottom: "conv2_2/sep" | |
| top: "conv2_2/sep" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu2_2/sep" | |
| type: "ReLU" | |
| bottom: "conv2_2/sep" | |
| top: "conv2_2/sep" | |
| } | |
| layer { | |
| name: "conv3_1/dw" | |
| type: "ConvolutionDepthwise" | |
| bottom: "conv2_2/sep" | |
| top: "conv3_1/dw" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 128 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_1/dw/bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_1/dw" | |
| top: "conv3_1/dw" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| } | |
| layer { | |
| name: "conv3_1/dw/scale" | |
| type: "Scale" | |
| bottom: "conv3_1/dw" | |
| top: "conv3_1/dw" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu3_1/dw" | |
| type: "ReLU" | |
| bottom: "conv3_1/dw" | |
| top: "conv3_1/dw" | |
| } | |
| layer { | |
| name: "conv3_1/sep" | |
| type: "Convolution" | |
| bottom: "conv3_1/dw" | |
| top: "conv3_1/sep" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_1/sep/bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_1/sep" | |
| top: "conv3_1/sep" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| } | |
| layer { | |
| name: "conv3_1/sep/scale" | |
| type: "Scale" | |
| bottom: "conv3_1/sep" | |
| top: "conv3_1/sep" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu3_1/sep" | |
| type: "ReLU" | |
| bottom: "conv3_1/sep" | |
| top: "conv3_1/sep" | |
| } | |
| layer { | |
| name: "conv3_2/dw" | |
| type: "ConvolutionDepthwise" | |
| bottom: "conv3_1/sep" | |
| top: "conv3_2/dw" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 128 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_2/dw/bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_2/dw" | |
| top: "conv3_2/dw" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| } | |
| layer { | |
| name: "conv3_2/dw/scale" | |
| type: "Scale" | |
| bottom: "conv3_2/dw" | |
| top: "conv3_2/dw" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu3_2/dw" | |
| type: "ReLU" | |
| bottom: "conv3_2/dw" | |
| top: "conv3_2/dw" | |
| } | |
| layer { | |
| name: "conv3_2/sep" | |
| type: "Convolution" | |
| bottom: "conv3_2/dw" | |
| top: "conv3_2/sep" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_2/sep/bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_2/sep" | |
| top: "conv3_2/sep" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| } | |
| layer { | |
| name: "conv3_2/sep/scale" | |
| type: "Scale" | |
| bottom: "conv3_2/sep" | |
| top: "conv3_2/sep" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu3_2/sep" | |
| type: "ReLU" | |
| bottom: "conv3_2/sep" | |
| top: "conv3_2/sep" | |
| } | |
| layer { | |
| name: "conv4_1/dw" | |
| type: "ConvolutionDepthwise" | |
| bottom: "conv3_2/sep" | |
| top: "conv4_1/dw" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 256 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_1/dw/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_1/dw" | |
| top: "conv4_1/dw" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| } | |
| layer { | |
| name: "conv4_1/dw/scale" | |
| type: "Scale" | |
| bottom: "conv4_1/dw" | |
| top: "conv4_1/dw" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu4_1/dw" | |
| type: "ReLU" | |
| bottom: "conv4_1/dw" | |
| top: "conv4_1/dw" | |
| } | |
| layer { | |
| name: "conv4_1/sep" | |
| type: "Convolution" | |
| bottom: "conv4_1/dw" | |
| top: "conv4_1/sep" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_1/sep/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_1/sep" | |
| top: "conv4_1/sep" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| } | |
| layer { | |
| name: "conv4_1/sep/scale" | |
| type: "Scale" | |
| bottom: "conv4_1/sep" | |
| top: "conv4_1/sep" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu4_1/sep" | |
| type: "ReLU" | |
| bottom: "conv4_1/sep" | |
| top: "conv4_1/sep" | |
| } | |
| layer { | |
| name: "conv4_2/dw" | |
| type: "ConvolutionDepthwise" | |
| bottom: "conv4_1/sep" | |
| top: "conv4_2/dw" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 256 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_2/dw/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_2/dw" | |
| top: "conv4_2/dw" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| } | |
| layer { | |
| name: "conv4_2/dw/scale" | |
| type: "Scale" | |
| bottom: "conv4_2/dw" | |
| top: "conv4_2/dw" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu4_2/dw" | |
| type: "ReLU" | |
| bottom: "conv4_2/dw" | |
| top: "conv4_2/dw" | |
| } | |
| layer { | |
| name: "conv4_2/sep" | |
| type: "Convolution" | |
| bottom: "conv4_2/dw" | |
| top: "conv4_2/sep" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_2/sep/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_2/sep" | |
| top: "conv4_2/sep" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| } | |
| layer { | |
| name: "conv4_2/sep/scale" | |
| type: "Scale" | |
| bottom: "conv4_2/sep" | |
| top: "conv4_2/sep" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu4_2/sep" | |
| type: "ReLU" | |
| bottom: "conv4_2/sep" | |
| top: "conv4_2/sep" | |
| } | |
| layer { | |
| name: "conv4_3_CPM" | |
| type: "Convolution" | |
| bottom: "conv4_2/sep" | |
| 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" | |
| } | |
| } | |
| } |
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