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March 1, 2019 08:53
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mobilenet_v2.prototxt discrip
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| name: "MobileNet-v2" | |
| input: "data" | |
| input_dim: 1 | |
| input_dim: 3 | |
| input_dim: 224 | |
| input_dim: 224 | |
| layer { | |
| name: "conv_1_1" | |
| type: "Convolution" | |
| bottom: "data" | |
| top: "conv_1_1" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "bn_1_1" | |
| type: "BatchNorm" | |
| bottom: "conv_1_1" | |
| top: "conv_1_1" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "scale_1_1" | |
| type: "Scale" | |
| bottom: "conv_1_1" | |
| top: "conv_1_1" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu_1_1" | |
| type: "ReLU" | |
| bottom: "conv_1_1" | |
| top: "conv_1_1" | |
| } | |
| layer { | |
| name: "conv_2_1_pw" | |
| type: "Convolution" | |
| bottom: "conv_1_1" | |
| top: "conv_2_1_pw" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "bn_2_1_pw" | |
| type: "BatchNorm" | |
| bottom: "conv_2_1_pw" | |
| top: "conv_2_1_pw" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "scale_2_1_pw" | |
| type: "Scale" | |
| bottom: "conv_2_1_pw" | |
| top: "conv_2_1_pw" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu_2_1_pw" | |
| type: "ReLU" | |
| bottom: "conv_2_1_pw" | |
| top: "conv_2_1_pw" | |
| } | |
| layer { | |
| name: "conv_2_1_dw" | |
| type: "Convolution" | |
| bottom: "conv_2_1_pw" | |
| top: "conv_2_1_dw" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 32 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| engine: CAFFE | |
| } | |
| } | |
| layer { | |
| name: "bn_2_1_dw" | |
| type: "BatchNorm" | |
| bottom: "conv_2_1_dw" | |
| top: "conv_2_1_dw" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "scale_2_1_dw" | |
| type: "Scale" | |
| bottom: "conv_2_1_dw" | |
| top: "conv_2_1_dw" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu_2_1_dw" | |
| type: "ReLU" | |
| bottom: "conv_2_1_dw" | |
| top: "conv_2_1_dw" | |
| } | |
| layer { | |
| name: "conv_2_1_linear" | |
| type: "Convolution" | |
| bottom: "conv_2_1_dw" | |
| top: "conv_2_1_linear" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 16 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "bn_2_1_linear" | |
| type: "BatchNorm" | |
| bottom: "conv_2_1_linear" | |
| top: "conv_2_1_linear" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "scale_2_1_linear" | |
| type: "Scale" | |
| bottom: "conv_2_1_linear" | |
| top: "conv_2_1_linear" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv_3_1_pw" | |
| type: "Convolution" | |
| bottom: "conv_2_1_linear" | |
| top: "conv_3_1_pw" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 96 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "bn_3_1_pw" | |
| type: "BatchNorm" | |
| bottom: "conv_3_1_pw" | |
| top: "conv_3_1_pw" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "scale_3_1_pw" | |
| type: "Scale" | |
| bottom: "conv_3_1_pw" | |
| top: "conv_3_1_pw" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu_3_1_pw" | |
| type: "ReLU" | |
| bottom: "conv_3_1_pw" | |
| top: "conv_3_1_pw" | |
| } | |
| layer { | |
| name: "conv_3_1_dw" | |
| type: "Convolution" | |
| bottom: "conv_3_1_pw" | |
| top: "conv_3_1_dw" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 96 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 96 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| engine: CAFFE | |
| } | |
| } | |
| layer { | |
| name: "bn_3_1_dw" | |
| type: "BatchNorm" | |
| bottom: "conv_3_1_dw" | |
| top: "conv_3_1_dw" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "scale_3_1_dw" | |
| type: "Scale" | |
| bottom: "conv_3_1_dw" | |
| top: "conv_3_1_dw" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu_3_1_dw" | |
| type: "ReLU" | |
| bottom: "conv_3_1_dw" | |
| top: "conv_3_1_dw" | |
| } | |
| layer { | |
| name: "conv_3_1_linear" | |
| type: "Convolution" | |
| bottom: "conv_3_1_dw" | |
| top: "conv_3_1_linear" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 24 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "bn_3_1_linear" | |
| type: "BatchNorm" | |
| bottom: "conv_3_1_linear" | |
| top: "conv_3_1_linear" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "scale_3_1_linear" | |
| type: "Scale" | |
| bottom: "conv_3_1_linear" | |
| top: "conv_3_1_linear" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv_3_2_pw" | |
| type: "Convolution" | |
| bottom: "conv_3_1_linear" | |
| top: "conv_3_2_pw" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 144 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "bn_3_2_pw" | |
| type: "BatchNorm" | |
| bottom: "conv_3_2_pw" | |
| top: "conv_3_2_pw" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "scale_3_2_pw" | |
| type: "Scale" | |
| bottom: "conv_3_2_pw" | |
| top: "conv_3_2_pw" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu_3_2_pw" | |
| type: "ReLU" | |
| bottom: "conv_3_2_pw" | |
| top: "conv_3_2_pw" | |
| } | |
| layer { | |
| name: "conv_3_2_dw" | |
| type: "Convolution" | |
| bottom: "conv_3_2_pw" | |
| top: "conv_3_2_dw" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 144 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 144 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| engine: CAFFE | |
| } | |
| } | |
| layer { | |
| name: "bn_3_2_dw" | |
| type: "BatchNorm" | |
| bottom: "conv_3_2_dw" | |
| top: "conv_3_2_dw" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "scale_3_2_dw" | |
| type: "Scale" | |
| bottom: "conv_3_2_dw" | |
| top: "conv_3_2_dw" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu_3_2_dw" | |
| type: "ReLU" | |
| bottom: "conv_3_2_dw" | |
| top: "conv_3_2_dw" | |
| } | |
| layer { | |
| name: "conv_3_2_linear" | |
| type: "Convolution" | |
| bottom: "conv_3_2_dw" | |
| top: "conv_3_2_linear" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 24 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "bn_3_2_linear" | |
| type: "BatchNorm" | |
| bottom: "conv_3_2_linear" | |
| top: "conv_3_2_linear" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "scale_3_2_linear" | |
| type: "Scale" | |
| bottom: "conv_3_2_linear" | |
| top: "conv_3_2_linear" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "add_3_2" | |
| type: "Eltwise" | |
| bottom: "conv_3_1_linear" | |
| bottom: "conv_3_2_linear" | |
| top: "add_3_2" | |
| } | |
| layer { | |
| name: "conv_4_1_pw" | |
| type: "Convolution" | |
| bottom: "add_3_2" | |
| top: "conv_4_1_pw" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 144 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "bn_4_1_pw" | |
| type: "BatchNorm" | |
| bottom: "conv_4_1_pw" | |
| top: "conv_4_1_pw" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "scale_4_1_pw" | |
| type: "Scale" | |
| bottom: "conv_4_1_pw" | |
| top: "conv_4_1_pw" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu_4_1_pw" | |
| type: "ReLU" | |
| bottom: "conv_4_1_pw" | |
| top: "conv_4_1_pw" | |
| } | |
| layer { | |
| name: "conv_4_1_dw" | |
| type: "Convolution" | |
| bottom: "conv_4_1_pw" | |
| top: "conv_4_1_dw" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 144 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 144 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| engine: CAFFE | |
| } | |
| } | |
| layer { | |
| name: "bn_4_1_dw" | |
| type: "BatchNorm" | |
| bottom: "conv_4_1_dw" | |
| top: "conv_4_1_dw" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "scale_4_1_dw" | |
| type: "Scale" | |
| bottom: "conv_4_1_dw" | |
| top: "conv_4_1_dw" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu_4_1_dw" | |
| type: "ReLU" | |
| bottom: "conv_4_1_dw" | |
| top: "conv_4_1_dw" | |
| } | |
| layer { | |
| name: "conv_4_1_linear" | |
| type: "Convolution" | |
| bottom: "conv_4_1_dw" | |
| top: "conv_4_1_linear" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "bn_4_1_linear" | |
| type: "BatchNorm" | |
| bottom: "conv_4_1_linear" | |
| top: "conv_4_1_linear" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "scale_4_1_linear" | |
| type: "Scale" | |
| bottom: "conv_4_1_linear" | |
| top: "conv_4_1_linear" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv_4_2_pw" | |
| type: "Convolution" | |
| bottom: "conv_4_1_linear" | |
| top: "conv_4_2_pw" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 192 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "bn_4_2_pw" | |
| type: "BatchNorm" | |
| bottom: "conv_4_2_pw" | |
| top: "conv_4_2_pw" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "scale_4_2_pw" | |
| type: "Scale" | |
| bottom: "conv_4_2_pw" | |
| top: "conv_4_2_pw" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu_4_2_pw" | |
| type: "ReLU" | |
| bottom: "conv_4_2_pw" | |
| top: "conv_4_2_pw" | |
| } | |
| layer { | |
| name: "conv_4_2_dw" | |
| type: "Convolution" | |
| bottom: "conv_4_2_pw" | |
| top: "conv_4_2_dw" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 192 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 192 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| engine: CAFFE | |
| } | |
| } | |
| layer { | |
| name: "bn_4_2_dw" | |
| type: "BatchNorm" | |
| bottom: "conv_4_2_dw" | |
| top: "conv_4_2_dw" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "scale_4_2_dw" | |
| type: "Scale" | |
| bottom: "conv_4_2_dw" | |
| top: "conv_4_2_dw" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu_4_2_dw" | |
| type: "ReLU" | |
| bottom: "conv_4_2_dw" | |
| top: "conv_4_2_dw" | |
| } | |
| layer { | |
| name: "conv_4_2_linear" | |
| type: "Convolution" | |
| bottom: "conv_4_2_dw" | |
| top: "conv_4_2_linear" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "bn_4_2_linear" | |
| type: "BatchNorm" | |
| bottom: "conv_4_2_linear" | |
| top: "conv_4_2_linear" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "scale_4_2_linear" | |
| type: "Scale" | |
| bottom: "conv_4_2_linear" | |
| top: "conv_4_2_linear" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "add_4_2" | |
| type: "Eltwise" | |
| bottom: "conv_4_1_linear" | |
| bottom: "conv_4_2_linear" | |
| top: "add_4_2" | |
| } | |
| layer { | |
| name: "conv_4_3_pw" | |
| type: "Convolution" | |
| bottom: "add_4_2" | |
| top: "conv_4_3_pw" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 192 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "bn_4_3_pw" | |
| type: "BatchNorm" | |
| bottom: "conv_4_3_pw" | |
| top: "conv_4_3_pw" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "scale_4_3_pw" | |
| type: "Scale" | |
| bottom: "conv_4_3_pw" | |
| top: "conv_4_3_pw" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu_4_3_pw" | |
| type: "ReLU" | |
| bottom: "conv_4_3_pw" | |
| top: "conv_4_3_pw" | |
| } | |
| layer { | |
| name: "conv_4_3_dw" | |
| type: "Convolution" | |
| bottom: "conv_4_3_pw" | |
| top: "conv_4_3_dw" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 192 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 192 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| engine: CAFFE | |
| } | |
| } | |
| layer { | |
| name: "bn_4_3_dw" | |
| type: "BatchNorm" | |
| bottom: "conv_4_3_dw" | |
| top: "conv_4_3_dw" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "scale_4_3_dw" | |
| type: "Scale" | |
| bottom: "conv_4_3_dw" | |
| top: "conv_4_3_dw" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu_4_3_dw" | |
| type: "ReLU" | |
| bottom: "conv_4_3_dw" | |
| top: "conv_4_3_dw" | |
| } | |
| layer { | |
| name: "conv_4_3_linear" | |
| type: "Convolution" | |
| bottom: "conv_4_3_dw" | |
| top: "conv_4_3_linear" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "bn_4_3_linear" | |
| type: "BatchNorm" | |
| bottom: "conv_4_3_linear" | |
| top: "conv_4_3_linear" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "scale_4_3_linear" | |
| type: "Scale" | |
| bottom: "conv_4_3_linear" | |
| top: "conv_4_3_linear" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "add_4_3" | |
| type: "Eltwise" | |
| bottom: "add_4_2" | |
| bottom: "conv_4_3_linear" | |
| top: "add_4_3" | |
| } | |
| layer { | |
| name: "conv_5_1_pw" | |
| type: "Convolution" | |
| bottom: "add_4_3" | |
| top: "conv_5_1_pw" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 192 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "bn_5_1_pw" | |
| type: "BatchNorm" | |
| bottom: "conv_5_1_pw" | |
| top: "conv_5_1_pw" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "scale_5_1_pw" | |
| type: "Scale" | |
| bottom: "conv_5_1_pw" | |
| top: "conv_5_1_pw" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu_5_1_pw" | |
| type: "ReLU" | |
| bottom: "conv_5_1_pw" | |
| top: "conv_5_1_pw" | |
| } | |
| layer { | |
| name: "conv_5_1_dw" | |
| type: "Convolution" | |
| bottom: "conv_5_1_pw" | |
| top: "conv_5_1_dw" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 192 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 192 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| engine: CAFFE | |
| } | |
| } | |
| layer { | |
| name: "bn_5_1_dw" | |
| type: "BatchNorm" | |
| bottom: "conv_5_1_dw" | |
| top: "conv_5_1_dw" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "scale_5_1_dw" | |
| type: "Scale" | |
| bottom: "conv_5_1_dw" | |
| top: "conv_5_1_dw" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu_5_1_dw" | |
| type: "ReLU" | |
| bottom: "conv_5_1_dw" | |
| top: "conv_5_1_dw" | |
| } | |
| layer { | |
| name: "conv_5_1_linear" | |
| type: "Convolution" | |
| bottom: "conv_5_1_dw" | |
| top: "conv_5_1_linear" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "bn_5_1_linear" | |
| type: "BatchNorm" | |
| bottom: "conv_5_1_linear" | |
| top: "conv_5_1_linear" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "scale_5_1_linear" | |
| type: "Scale" | |
| bottom: "conv_5_1_linear" | |
| top: "conv_5_1_linear" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv_5_2_pw" | |
| type: "Convolution" | |
| bottom: "conv_5_1_linear" | |
| top: "conv_5_2_pw" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 384 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "bn_5_2_pw" | |
| type: "BatchNorm" | |
| bottom: "conv_5_2_pw" | |
| top: "conv_5_2_pw" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "scale_5_2_pw" | |
| type: "Scale" | |
| bottom: "conv_5_2_pw" | |
| top: "conv_5_2_pw" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu_5_2_pw" | |
| type: "ReLU" | |
| bottom: "conv_5_2_pw" | |
| top: "conv_5_2_pw" | |
| } | |
| layer { | |
| name: "conv_5_2_dw" | |
| type: "Convolution" | |
| bottom: "conv_5_2_pw" | |
| top: "conv_5_2_dw" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 384 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 384 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| engine: CAFFE | |
| } | |
| } | |
| layer { | |
| name: "bn_5_2_dw" | |
| type: "BatchNorm" | |
| bottom: "conv_5_2_dw" | |
| top: "conv_5_2_dw" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "scale_5_2_dw" | |
| type: "Scale" | |
| bottom: "conv_5_2_dw" | |
| top: "conv_5_2_dw" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu_5_2_dw" | |
| type: "ReLU" | |
| bottom: "conv_5_2_dw" | |
| top: "conv_5_2_dw" | |
| } | |
| layer { | |
| name: "conv_5_2_linear" | |
| type: "Convolution" | |
| bottom: "conv_5_2_dw" | |
| top: "conv_5_2_linear" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "bn_5_2_linear" | |
| type: "BatchNorm" | |
| bottom: "conv_5_2_linear" | |
| top: "conv_5_2_linear" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "scale_5_2_linear" | |
| type: "Scale" | |
| bottom: "conv_5_2_linear" | |
| top: "conv_5_2_linear" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "add_5_2" | |
| type: "Eltwise" | |
| bottom: "conv_5_1_linear" | |
| bottom: "conv_5_2_linear" | |
| top: "add_5_2" | |
| } | |
| layer { | |
| name: "conv_5_3_pw" | |
| type: "Convolution" | |
| bottom: "add_5_2" | |
| top: "conv_5_3_pw" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 384 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "bn_5_3_pw" | |
| type: "BatchNorm" | |
| bottom: "conv_5_3_pw" | |
| top: "conv_5_3_pw" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "scale_5_3_pw" | |
| type: "Scale" | |
| bottom: "conv_5_3_pw" | |
| top: "conv_5_3_pw" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu_5_3_pw" | |
| type: "ReLU" | |
| bottom: "conv_5_3_pw" | |
| top: "conv_5_3_pw" | |
| } | |
| layer { | |
| name: "conv_5_3_dw" | |
| type: "Convolution" | |
| bottom: "conv_5_3_pw" | |
| top: "conv_5_3_dw" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 384 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 384 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| engine: CAFFE | |
| } | |
| } | |
| layer { | |
| name: "bn_5_3_dw" | |
| type: "BatchNorm" | |
| bottom: "conv_5_3_dw" | |
| top: "conv_5_3_dw" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "scale_5_3_dw" | |
| type: "Scale" | |
| bottom: "conv_5_3_dw" | |
| top: "conv_5_3_dw" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu_5_3_dw" | |
| type: "ReLU" | |
| bottom: "conv_5_3_dw" | |
| top: "conv_5_3_dw" | |
| } | |
| layer { | |
| name: "conv_5_3_linear" | |
| type: "Convolution" | |
| bottom: "conv_5_3_dw" | |
| top: "conv_5_3_linear" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "bn_5_3_linear" | |
| type: "BatchNorm" | |
| bottom: "conv_5_3_linear" | |
| top: "conv_5_3_linear" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "scale_5_3_linear" | |
| type: "Scale" | |
| bottom: "conv_5_3_linear" | |
| top: "conv_5_3_linear" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "add_5_3" | |
| type: "Eltwise" | |
| bottom: "add_5_2" | |
| bottom: "conv_5_3_linear" | |
| top: "add_5_3" | |
| } | |
| layer { | |
| name: "conv_5_4_pw" | |
| type: "Convolution" | |
| bottom: "add_5_3" | |
| top: "conv_5_4_pw" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 384 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "bn_5_4_pw" | |
| type: "BatchNorm" | |
| bottom: "conv_5_4_pw" | |
| top: "conv_5_4_pw" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "scale_5_4_pw" | |
| type: "Scale" | |
| bottom: "conv_5_4_pw" | |
| top: "conv_5_4_pw" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu_5_4_pw" | |
| type: "ReLU" | |
| bottom: "conv_5_4_pw" | |
| top: "conv_5_4_pw" | |
| } | |
| layer { | |
| name: "conv_5_4_dw" | |
| type: "Convolution" | |
| bottom: "conv_5_4_pw" | |
| top: "conv_5_4_dw" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 384 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 384 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| engine: CAFFE | |
| } | |
| } | |
| layer { | |
| name: "bn_5_4_dw" | |
| type: "BatchNorm" | |
| bottom: "conv_5_4_dw" | |
| top: "conv_5_4_dw" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "scale_5_4_dw" | |
| type: "Scale" | |
| bottom: "conv_5_4_dw" | |
| top: "conv_5_4_dw" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu_5_4_dw" | |
| type: "ReLU" | |
| bottom: "conv_5_4_dw" | |
| top: "conv_5_4_dw" | |
| } | |
| layer { | |
| name: "conv_5_4_linear" | |
| type: "Convolution" | |
| bottom: "conv_5_4_dw" | |
| top: "conv_5_4_linear" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "bn_5_4_linear" | |
| type: "BatchNorm" | |
| bottom: "conv_5_4_linear" | |
| top: "conv_5_4_linear" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "scale_5_4_linear" | |
| type: "Scale" | |
| bottom: "conv_5_4_linear" | |
| top: "conv_5_4_linear" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "add_5_4" | |
| type: "Eltwise" | |
| bottom: "add_5_3" | |
| bottom: "conv_5_4_linear" | |
| top: "add_5_4" | |
| } | |
| layer { | |
| name: "conv_6_1_pw" | |
| type: "Convolution" | |
| bottom: "add_5_4" | |
| top: "conv_6_1_pw" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 384 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "bn_6_1_pw" | |
| type: "BatchNorm" | |
| bottom: "conv_6_1_pw" | |
| top: "conv_6_1_pw" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "scale_6_1_pw" | |
| type: "Scale" | |
| bottom: "conv_6_1_pw" | |
| top: "conv_6_1_pw" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu_6_1_pw" | |
| type: "ReLU" | |
| bottom: "conv_6_1_pw" | |
| top: "conv_6_1_pw" | |
| } | |
| layer { | |
| name: "conv_6_1_dw" | |
| type: "Convolution" | |
| bottom: "conv_6_1_pw" | |
| top: "conv_6_1_dw" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 384 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 384 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| engine: CAFFE | |
| } | |
| } | |
| layer { | |
| name: "bn_6_1_dw" | |
| type: "BatchNorm" | |
| bottom: "conv_6_1_dw" | |
| top: "conv_6_1_dw" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "scale_6_1_dw" | |
| type: "Scale" | |
| bottom: "conv_6_1_dw" | |
| top: "conv_6_1_dw" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu_6_1_dw" | |
| type: "ReLU" | |
| bottom: "conv_6_1_dw" | |
| top: "conv_6_1_dw" | |
| } | |
| layer { | |
| name: "conv_6_1_linear" | |
| type: "Convolution" | |
| bottom: "conv_6_1_dw" | |
| top: "conv_6_1_linear" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 96 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "bn_6_1_linear" | |
| type: "BatchNorm" | |
| bottom: "conv_6_1_linear" | |
| top: "conv_6_1_linear" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "scale_6_1_linear" | |
| type: "Scale" | |
| bottom: "conv_6_1_linear" | |
| top: "conv_6_1_linear" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv_6_2_pw" | |
| type: "Convolution" | |
| bottom: "conv_6_1_linear" | |
| top: "conv_6_2_pw" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 576 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "bn_6_2_pw" | |
| type: "BatchNorm" | |
| bottom: "conv_6_2_pw" | |
| top: "conv_6_2_pw" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "scale_6_2_pw" | |
| type: "Scale" | |
| bottom: "conv_6_2_pw" | |
| top: "conv_6_2_pw" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu_6_2_pw" | |
| type: "ReLU" | |
| bottom: "conv_6_2_pw" | |
| top: "conv_6_2_pw" | |
| } | |
| layer { | |
| name: "conv_6_2_dw" | |
| type: "Convolution" | |
| bottom: "conv_6_2_pw" | |
| top: "conv_6_2_dw" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 576 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 576 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| engine: CAFFE | |
| } | |
| } | |
| layer { | |
| name: "bn_6_2_dw" | |
| type: "BatchNorm" | |
| bottom: "conv_6_2_dw" | |
| top: "conv_6_2_dw" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "scale_6_2_dw" | |
| type: "Scale" | |
| bottom: "conv_6_2_dw" | |
| top: "conv_6_2_dw" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu_6_2_dw" | |
| type: "ReLU" | |
| bottom: "conv_6_2_dw" | |
| top: "conv_6_2_dw" | |
| } | |
| layer { | |
| name: "conv_6_2_linear" | |
| type: "Convolution" | |
| bottom: "conv_6_2_dw" | |
| top: "conv_6_2_linear" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 96 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "bn_6_2_linear" | |
| type: "BatchNorm" | |
| bottom: "conv_6_2_linear" | |
| top: "conv_6_2_linear" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "scale_6_2_linear" | |
| type: "Scale" | |
| bottom: "conv_6_2_linear" | |
| top: "conv_6_2_linear" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "add_6_2" | |
| type: "Eltwise" | |
| bottom: "conv_6_1_linear" | |
| bottom: "conv_6_2_linear" | |
| top: "add_6_2" | |
| } | |
| layer { | |
| name: "conv_6_3_pw" | |
| type: "Convolution" | |
| bottom: "add_6_2" | |
| top: "conv_6_3_pw" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 576 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "bn_6_3_pw" | |
| type: "BatchNorm" | |
| bottom: "conv_6_3_pw" | |
| top: "conv_6_3_pw" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "scale_6_3_pw" | |
| type: "Scale" | |
| bottom: "conv_6_3_pw" | |
| top: "conv_6_3_pw" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu_6_3_pw" | |
| type: "ReLU" | |
| bottom: "conv_6_3_pw" | |
| top: "conv_6_3_pw" | |
| } | |
| layer { | |
| name: "conv_6_3_dw" | |
| type: "Convolution" | |
| bottom: "conv_6_3_pw" | |
| top: "conv_6_3_dw" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 576 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 576 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| engine: CAFFE | |
| } | |
| } | |
| layer { | |
| name: "bn_6_3_dw" | |
| type: "BatchNorm" | |
| bottom: "conv_6_3_dw" | |
| top: "conv_6_3_dw" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "scale_6_3_dw" | |
| type: "Scale" | |
| bottom: "conv_6_3_dw" | |
| top: "conv_6_3_dw" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu_6_3_dw" | |
| type: "ReLU" | |
| bottom: "conv_6_3_dw" | |
| top: "conv_6_3_dw" | |
| } | |
| layer { | |
| name: "conv_6_3_linear" | |
| type: "Convolution" | |
| bottom: "conv_6_3_dw" | |
| top: "conv_6_3_linear" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 96 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "bn_6_3_linear" | |
| type: "BatchNorm" | |
| bottom: "conv_6_3_linear" | |
| top: "conv_6_3_linear" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "scale_6_3_linear" | |
| type: "Scale" | |
| bottom: "conv_6_3_linear" | |
| top: "conv_6_3_linear" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "add_6_3" | |
| type: "Eltwise" | |
| bottom: "add_6_2" | |
| bottom: "conv_6_3_linear" | |
| top: "add_6_3" | |
| } | |
| layer { | |
| name: "conv_7_1_pw" | |
| type: "Convolution" | |
| bottom: "add_6_3" | |
| top: "conv_7_1_pw" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 576 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "bn_7_1_pw" | |
| type: "BatchNorm" | |
| bottom: "conv_7_1_pw" | |
| top: "conv_7_1_pw" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "scale_7_1_pw" | |
| type: "Scale" | |
| bottom: "conv_7_1_pw" | |
| top: "conv_7_1_pw" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu_7_1_pw" | |
| type: "ReLU" | |
| bottom: "conv_7_1_pw" | |
| top: "conv_7_1_pw" | |
| } | |
| layer { | |
| name: "conv_7_1_dw" | |
| type: "Convolution" | |
| bottom: "conv_7_1_pw" | |
| top: "conv_7_1_dw" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 576 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 576 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| engine: CAFFE | |
| } | |
| } | |
| layer { | |
| name: "bn_7_1_dw" | |
| type: "BatchNorm" | |
| bottom: "conv_7_1_dw" | |
| top: "conv_7_1_dw" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "scale_7_1_dw" | |
| type: "Scale" | |
| bottom: "conv_7_1_dw" | |
| top: "conv_7_1_dw" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu_7_1_dw" | |
| type: "ReLU" | |
| bottom: "conv_7_1_dw" | |
| top: "conv_7_1_dw" | |
| } | |
| layer { | |
| name: "conv_7_1_linear" | |
| type: "Convolution" | |
| bottom: "conv_7_1_dw" | |
| top: "conv_7_1_linear" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 160 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "bn_7_1_linear" | |
| type: "BatchNorm" | |
| bottom: "conv_7_1_linear" | |
| top: "conv_7_1_linear" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "scale_7_1_linear" | |
| type: "Scale" | |
| bottom: "conv_7_1_linear" | |
| top: "conv_7_1_linear" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv_7_2_pw" | |
| type: "Convolution" | |
| bottom: "conv_7_1_linear" | |
| top: "conv_7_2_pw" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 960 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "bn_7_2_pw" | |
| type: "BatchNorm" | |
| bottom: "conv_7_2_pw" | |
| top: "conv_7_2_pw" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "scale_7_2_pw" | |
| type: "Scale" | |
| bottom: "conv_7_2_pw" | |
| top: "conv_7_2_pw" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu_7_2_pw" | |
| type: "ReLU" | |
| bottom: "conv_7_2_pw" | |
| top: "conv_7_2_pw" | |
| } | |
| layer { | |
| name: "conv_7_2_dw" | |
| type: "Convolution" | |
| bottom: "conv_7_2_pw" | |
| top: "conv_7_2_dw" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 960 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 960 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| engine: CAFFE | |
| } | |
| } | |
| layer { | |
| name: "bn_7_2_dw" | |
| type: "BatchNorm" | |
| bottom: "conv_7_2_dw" | |
| top: "conv_7_2_dw" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "scale_7_2_dw" | |
| type: "Scale" | |
| bottom: "conv_7_2_dw" | |
| top: "conv_7_2_dw" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu_7_2_dw" | |
| type: "ReLU" | |
| bottom: "conv_7_2_dw" | |
| top: "conv_7_2_dw" | |
| } | |
| layer { | |
| name: "conv_7_2_linear" | |
| type: "Convolution" | |
| bottom: "conv_7_2_dw" | |
| top: "conv_7_2_linear" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 160 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "bn_7_2_linear" | |
| type: "BatchNorm" | |
| bottom: "conv_7_2_linear" | |
| top: "conv_7_2_linear" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "scale_7_2_linear" | |
| type: "Scale" | |
| bottom: "conv_7_2_linear" | |
| top: "conv_7_2_linear" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "add_7_2" | |
| type: "Eltwise" | |
| bottom: "conv_7_1_linear" | |
| bottom: "conv_7_2_linear" | |
| top: "add_7_2" | |
| } | |
| layer { | |
| name: "conv_7_3_pw" | |
| type: "Convolution" | |
| bottom: "add_7_2" | |
| top: "conv_7_3_pw" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 960 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "bn_7_3_pw" | |
| type: "BatchNorm" | |
| bottom: "conv_7_3_pw" | |
| top: "conv_7_3_pw" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "scale_7_3_pw" | |
| type: "Scale" | |
| bottom: "conv_7_3_pw" | |
| top: "conv_7_3_pw" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu_7_3_pw" | |
| type: "ReLU" | |
| bottom: "conv_7_3_pw" | |
| top: "conv_7_3_pw" | |
| } | |
| layer { | |
| name: "conv_7_3_dw" | |
| type: "Convolution" | |
| bottom: "conv_7_3_pw" | |
| top: "conv_7_3_dw" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 960 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 960 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| engine: CAFFE | |
| } | |
| } | |
| layer { | |
| name: "bn_7_3_dw" | |
| type: "BatchNorm" | |
| bottom: "conv_7_3_dw" | |
| top: "conv_7_3_dw" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "scale_7_3_dw" | |
| type: "Scale" | |
| bottom: "conv_7_3_dw" | |
| top: "conv_7_3_dw" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu_7_3_dw" | |
| type: "ReLU" | |
| bottom: "conv_7_3_dw" | |
| top: "conv_7_3_dw" | |
| } | |
| layer { | |
| name: "conv_7_3_linear" | |
| type: "Convolution" | |
| bottom: "conv_7_3_dw" | |
| top: "conv_7_3_linear" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 160 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "bn_7_3_linear" | |
| type: "BatchNorm" | |
| bottom: "conv_7_3_linear" | |
| top: "conv_7_3_linear" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "scale_7_3_linear" | |
| type: "Scale" | |
| bottom: "conv_7_3_linear" | |
| top: "conv_7_3_linear" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "add_7_3" | |
| type: "Eltwise" | |
| bottom: "add_7_2" | |
| bottom: "conv_7_3_linear" | |
| top: "add_7_3" | |
| } | |
| layer { | |
| name: "conv_8_1_pw" | |
| type: "Convolution" | |
| bottom: "add_7_3" | |
| top: "conv_8_1_pw" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 960 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "bn_8_1_pw" | |
| type: "BatchNorm" | |
| bottom: "conv_8_1_pw" | |
| top: "conv_8_1_pw" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "scale_8_1_pw" | |
| type: "Scale" | |
| bottom: "conv_8_1_pw" | |
| top: "conv_8_1_pw" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu_8_1_pw" | |
| type: "ReLU" | |
| bottom: "conv_8_1_pw" | |
| top: "conv_8_1_pw" | |
| } | |
| layer { | |
| name: "conv_8_1_dw" | |
| type: "Convolution" | |
| bottom: "conv_8_1_pw" | |
| top: "conv_8_1_dw" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 960 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 960 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| engine: CAFFE | |
| } | |
| } | |
| layer { | |
| name: "bn_8_1_dw" | |
| type: "BatchNorm" | |
| bottom: "conv_8_1_dw" | |
| top: "conv_8_1_dw" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "scale_8_1_dw" | |
| type: "Scale" | |
| bottom: "conv_8_1_dw" | |
| top: "conv_8_1_dw" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu_8_1_dw" | |
| type: "ReLU" | |
| bottom: "conv_8_1_dw" | |
| top: "conv_8_1_dw" | |
| } | |
| layer { | |
| name: "conv_8_1_linear" | |
| type: "Convolution" | |
| bottom: "conv_8_1_dw" | |
| top: "conv_8_1_linear" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 320 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "bn_8_1_linear" | |
| type: "BatchNorm" | |
| bottom: "conv_8_1_linear" | |
| top: "conv_8_1_linear" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "scale_8_1_linear" | |
| type: "Scale" | |
| bottom: "conv_8_1_linear" | |
| top: "conv_8_1_linear" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv9" | |
| type: "Convolution" | |
| bottom: "conv_8_1_linear" | |
| top: "conv9" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 1280 | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "pool9" | |
| type: "Pooling" | |
| bottom: "conv9" | |
| top: "pool9" | |
| pooling_param { | |
| pool: AVE | |
| stride: 1 | |
| global_pooling: true | |
| } | |
| } | |
| layer { | |
| name: "dropout" | |
| type: "Dropout" | |
| bottom: "pool9" | |
| top: "pool9" | |
| dropout_param { | |
| dropout_ratio : 0.2 | |
| } | |
| } | |
| layer { | |
| name: "conv10" | |
| type: "Convolution" | |
| bottom: "pool9" | |
| top: "conv10" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 1000 | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "loss" | |
| type: "Softmax" | |
| bottom: "conv10" | |
| top: "loss" | |
| } |
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