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June 21, 2019 12:08
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| # ResNeXt50 | |
| name: "ResNeXt50" | |
| layer { | |
| name: "train-data" | |
| type: "Data" | |
| top: "data" | |
| top: "label" | |
| transform_param { | |
| mirror: true | |
| crop_size: 224 | |
| } | |
| data_param { | |
| batch_size: 32 | |
| } | |
| include { stage: "train" } | |
| } | |
| layer { | |
| name: "val-data" | |
| type: "Data" | |
| top: "data" | |
| top: "label" | |
| transform_param { | |
| mirror: false | |
| crop_size: 224 | |
| } | |
| data_param { | |
| batch_size: 16 | |
| } | |
| include { stage: "val" } | |
| } | |
| layer { | |
| name: "bn_data" | |
| type: "BatchNorm" | |
| bottom: "data" | |
| top: "bn_data" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv0" | |
| type: "Convolution" | |
| bottom: "bn_data" | |
| top: "conv0" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 7 | |
| stride: 2 | |
| pad: 3 | |
| weight_filler { | |
| type: "msra" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "bn0" | |
| type: "BatchNorm" | |
| bottom: "conv0" | |
| top: "bn0" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu0" | |
| type: "ReLU" | |
| bottom: "bn0" | |
| top: "bn0" | |
| } | |
| layer { | |
| name: "pooling0" | |
| type: "Pooling" | |
| bottom: "bn0" | |
| top: "pooling0" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "stage1_unit1_conv1" | |
| type: "Convolution" | |
| bottom: "pooling0" | |
| top: "stage1_unit1_conv1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| weight_filler { | |
| type: "msra" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage1_unit1_bn1" | |
| type: "BatchNorm" | |
| bottom: "stage1_unit1_conv1" | |
| top: "stage1_unit1_bn1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage1_unit1_relu1" | |
| type: "ReLU" | |
| bottom: "stage1_unit1_bn1" | |
| top: "stage1_unit1_bn1" | |
| } | |
| layer { | |
| name: "stage1_unit1_conv2" | |
| type: "Convolution" | |
| bottom: "stage1_unit1_bn1" | |
| top: "stage1_unit1_conv2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 3 | |
| stride: 1 | |
| group: 32 | |
| pad: 1 | |
| weight_filler { | |
| type: "msra" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage1_unit1_bn2" | |
| type: "BatchNorm" | |
| bottom: "stage1_unit1_conv2" | |
| top: "stage1_unit1_bn2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage1_unit1_relu2" | |
| type: "ReLU" | |
| bottom: "stage1_unit1_bn2" | |
| top: "stage1_unit1_bn2" | |
| } | |
| layer { | |
| name: "stage1_unit1_conv3" | |
| type: "Convolution" | |
| bottom: "stage1_unit1_bn2" | |
| top: "stage1_unit1_conv3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| weight_filler { | |
| type: "msra" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage1_unit1_bn3" | |
| type: "BatchNorm" | |
| bottom: "stage1_unit1_conv3" | |
| top: "stage1_unit1_bn3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage1_unit1_sc" | |
| type: "Convolution" | |
| bottom: "pooling0" | |
| top: "stage1_unit1_sc" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| weight_filler { | |
| type: "msra" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage1_unit1_sc_bn" | |
| type: "BatchNorm" | |
| bottom: "stage1_unit1_sc" | |
| top: "stage1_unit1_sc_bn" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage1_unit1_plus" | |
| type: "Eltwise" | |
| bottom: "stage1_unit1_sc_bn" | |
| bottom: "stage1_unit1_bn3" | |
| top: "stage1_unit1_plus" | |
| } | |
| layer { | |
| name: "stage1_unit1_relu" | |
| type: "ReLU" | |
| bottom: "stage1_unit1_plus" | |
| top: "stage1_unit1_plus" | |
| } | |
| layer { | |
| name: "stage1_unit2_conv1" | |
| type: "Convolution" | |
| bottom: "stage1_unit1_plus" | |
| top: "stage1_unit2_conv1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| weight_filler { | |
| type: "msra" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage1_unit2_bn1" | |
| type: "BatchNorm" | |
| bottom: "stage1_unit2_conv1" | |
| top: "stage1_unit2_bn1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage1_unit2_relu1" | |
| type: "ReLU" | |
| bottom: "stage1_unit2_bn1" | |
| top: "stage1_unit2_bn1" | |
| } | |
| layer { | |
| name: "stage1_unit2_conv2" | |
| type: "Convolution" | |
| bottom: "stage1_unit2_bn1" | |
| top: "stage1_unit2_conv2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 3 | |
| stride: 1 | |
| group: 32 | |
| pad: 1 | |
| weight_filler { | |
| type: "msra" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage1_unit2_bn2" | |
| type: "BatchNorm" | |
| bottom: "stage1_unit2_conv2" | |
| top: "stage1_unit2_bn2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage1_unit2_relu2" | |
| type: "ReLU" | |
| bottom: "stage1_unit2_bn2" | |
| top: "stage1_unit2_bn2" | |
| } | |
| layer { | |
| name: "stage1_unit2_conv3" | |
| type: "Convolution" | |
| bottom: "stage1_unit2_bn2" | |
| top: "stage1_unit2_conv3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| weight_filler { | |
| type: "msra" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage1_unit2_bn3" | |
| type: "BatchNorm" | |
| bottom: "stage1_unit2_conv3" | |
| top: "stage1_unit2_bn3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage1_unit2_plus" | |
| type: "Eltwise" | |
| bottom: "stage1_unit1_plus" | |
| bottom: "stage1_unit2_bn3" | |
| top: "stage1_unit2_plus" | |
| } | |
| layer { | |
| name: "stage1_unit2_relu" | |
| type: "ReLU" | |
| bottom: "stage1_unit2_plus" | |
| top: "stage1_unit2_plus" | |
| } | |
| layer { | |
| name: "stage1_unit3_conv1" | |
| type: "Convolution" | |
| bottom: "stage1_unit2_plus" | |
| top: "stage1_unit3_conv1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| weight_filler { | |
| type: "msra" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage1_unit3_bn1" | |
| type: "BatchNorm" | |
| bottom: "stage1_unit3_conv1" | |
| top: "stage1_unit3_bn1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage1_unit3_relu1" | |
| type: "ReLU" | |
| bottom: "stage1_unit3_bn1" | |
| top: "stage1_unit3_bn1" | |
| } | |
| layer { | |
| name: "stage1_unit3_conv2" | |
| type: "Convolution" | |
| bottom: "stage1_unit3_bn1" | |
| top: "stage1_unit3_conv2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 3 | |
| stride: 1 | |
| group: 32 | |
| pad: 1 | |
| weight_filler { | |
| type: "msra" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage1_unit3_bn2" | |
| type: "BatchNorm" | |
| bottom: "stage1_unit3_conv2" | |
| top: "stage1_unit3_bn2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage1_unit3_relu2" | |
| type: "ReLU" | |
| bottom: "stage1_unit3_bn2" | |
| top: "stage1_unit3_bn2" | |
| } | |
| layer { | |
| name: "stage1_unit3_conv3" | |
| type: "Convolution" | |
| bottom: "stage1_unit3_bn2" | |
| top: "stage1_unit3_conv3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| weight_filler { | |
| type: "msra" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage1_unit3_bn3" | |
| type: "BatchNorm" | |
| bottom: "stage1_unit3_conv3" | |
| top: "stage1_unit3_bn3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage1_unit3_plus" | |
| type: "Eltwise" | |
| bottom: "stage1_unit2_plus" | |
| bottom: "stage1_unit3_bn3" | |
| top: "stage1_unit3_plus" | |
| } | |
| layer { | |
| name: "stage1_unit3_relu" | |
| type: "ReLU" | |
| bottom: "stage1_unit3_plus" | |
| top: "stage1_unit3_plus" | |
| } | |
| layer { | |
| name: "stage2_unit1_conv1" | |
| type: "Convolution" | |
| bottom: "stage1_unit3_plus" | |
| top: "stage2_unit1_conv1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| weight_filler { | |
| type: "msra" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage2_unit1_bn1" | |
| type: "BatchNorm" | |
| bottom: "stage2_unit1_conv1" | |
| top: "stage2_unit1_bn1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage2_unit1_relu1" | |
| type: "ReLU" | |
| bottom: "stage2_unit1_bn1" | |
| top: "stage2_unit1_bn1" | |
| } | |
| layer { | |
| name: "stage2_unit1_conv2" | |
| type: "Convolution" | |
| bottom: "stage2_unit1_bn1" | |
| top: "stage2_unit1_conv2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 3 | |
| stride: 2 | |
| group: 32 | |
| pad: 1 | |
| weight_filler { | |
| type: "msra" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage2_unit1_bn2" | |
| type: "BatchNorm" | |
| bottom: "stage2_unit1_conv2" | |
| top: "stage2_unit1_bn2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage2_unit1_relu2" | |
| type: "ReLU" | |
| bottom: "stage2_unit1_bn2" | |
| top: "stage2_unit1_bn2" | |
| } | |
| layer { | |
| name: "stage2_unit1_conv3" | |
| type: "Convolution" | |
| bottom: "stage2_unit1_bn2" | |
| top: "stage2_unit1_conv3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| weight_filler { | |
| type: "msra" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage2_unit1_bn3" | |
| type: "BatchNorm" | |
| bottom: "stage2_unit1_conv3" | |
| top: "stage2_unit1_bn3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage2_unit1_sc" | |
| type: "Convolution" | |
| bottom: "stage1_unit3_plus" | |
| top: "stage2_unit1_sc" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 1 | |
| stride: 2 | |
| pad: 0 | |
| weight_filler { | |
| type: "msra" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage2_unit1_sc_bn" | |
| type: "BatchNorm" | |
| bottom: "stage2_unit1_sc" | |
| top: "stage2_unit1_sc_bn" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage2_unit1_plus" | |
| type: "Eltwise" | |
| bottom: "stage2_unit1_sc_bn" | |
| bottom: "stage2_unit1_bn3" | |
| top: "stage2_unit1_plus" | |
| } | |
| layer { | |
| name: "stage2_unit1_relu" | |
| type: "ReLU" | |
| bottom: "stage2_unit1_plus" | |
| top: "stage2_unit1_plus" | |
| } | |
| layer { | |
| name: "stage2_unit2_conv1" | |
| type: "Convolution" | |
| bottom: "stage2_unit1_plus" | |
| top: "stage2_unit2_conv1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| weight_filler { | |
| type: "msra" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage2_unit2_bn1" | |
| type: "BatchNorm" | |
| bottom: "stage2_unit2_conv1" | |
| top: "stage2_unit2_bn1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage2_unit2_relu1" | |
| type: "ReLU" | |
| bottom: "stage2_unit2_bn1" | |
| top: "stage2_unit2_bn1" | |
| } | |
| layer { | |
| name: "stage2_unit2_conv2" | |
| type: "Convolution" | |
| bottom: "stage2_unit2_bn1" | |
| top: "stage2_unit2_conv2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 3 | |
| stride: 1 | |
| group: 32 | |
| pad: 1 | |
| weight_filler { | |
| type: "msra" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage2_unit2_bn2" | |
| type: "BatchNorm" | |
| bottom: "stage2_unit2_conv2" | |
| top: "stage2_unit2_bn2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage2_unit2_relu2" | |
| type: "ReLU" | |
| bottom: "stage2_unit2_bn2" | |
| top: "stage2_unit2_bn2" | |
| } | |
| layer { | |
| name: "stage2_unit2_conv3" | |
| type: "Convolution" | |
| bottom: "stage2_unit2_bn2" | |
| top: "stage2_unit2_conv3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| weight_filler { | |
| type: "msra" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage2_unit2_bn3" | |
| type: "BatchNorm" | |
| bottom: "stage2_unit2_conv3" | |
| top: "stage2_unit2_bn3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage2_unit2_plus" | |
| type: "Eltwise" | |
| bottom: "stage2_unit1_plus" | |
| bottom: "stage2_unit2_bn3" | |
| top: "stage2_unit2_plus" | |
| } | |
| layer { | |
| name: "stage2_unit2_relu" | |
| type: "ReLU" | |
| bottom: "stage2_unit2_plus" | |
| top: "stage2_unit2_plus" | |
| } | |
| layer { | |
| name: "stage2_unit3_conv1" | |
| type: "Convolution" | |
| bottom: "stage2_unit2_plus" | |
| top: "stage2_unit3_conv1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| weight_filler { | |
| type: "msra" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage2_unit3_bn1" | |
| type: "BatchNorm" | |
| bottom: "stage2_unit3_conv1" | |
| top: "stage2_unit3_bn1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage2_unit3_relu1" | |
| type: "ReLU" | |
| bottom: "stage2_unit3_bn1" | |
| top: "stage2_unit3_bn1" | |
| } | |
| layer { | |
| name: "stage2_unit3_conv2" | |
| type: "Convolution" | |
| bottom: "stage2_unit3_bn1" | |
| top: "stage2_unit3_conv2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 3 | |
| stride: 1 | |
| group: 32 | |
| pad: 1 | |
| weight_filler { | |
| type: "msra" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage2_unit3_bn2" | |
| type: "BatchNorm" | |
| bottom: "stage2_unit3_conv2" | |
| top: "stage2_unit3_bn2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage2_unit3_relu2" | |
| type: "ReLU" | |
| bottom: "stage2_unit3_bn2" | |
| top: "stage2_unit3_bn2" | |
| } | |
| layer { | |
| name: "stage2_unit3_conv3" | |
| type: "Convolution" | |
| bottom: "stage2_unit3_bn2" | |
| top: "stage2_unit3_conv3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| weight_filler { | |
| type: "msra" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage2_unit3_bn3" | |
| type: "BatchNorm" | |
| bottom: "stage2_unit3_conv3" | |
| top: "stage2_unit3_bn3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage2_unit3_plus" | |
| type: "Eltwise" | |
| bottom: "stage2_unit2_plus" | |
| bottom: "stage2_unit3_bn3" | |
| top: "stage2_unit3_plus" | |
| } | |
| layer { | |
| name: "stage2_unit3_relu" | |
| type: "ReLU" | |
| bottom: "stage2_unit3_plus" | |
| top: "stage2_unit3_plus" | |
| } | |
| layer { | |
| name: "stage2_unit4_conv1" | |
| type: "Convolution" | |
| bottom: "stage2_unit3_plus" | |
| top: "stage2_unit4_conv1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| weight_filler { | |
| type: "msra" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage2_unit4_bn1" | |
| type: "BatchNorm" | |
| bottom: "stage2_unit4_conv1" | |
| top: "stage2_unit4_bn1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage2_unit4_relu1" | |
| type: "ReLU" | |
| bottom: "stage2_unit4_bn1" | |
| top: "stage2_unit4_bn1" | |
| } | |
| layer { | |
| name: "stage2_unit4_conv2" | |
| type: "Convolution" | |
| bottom: "stage2_unit4_bn1" | |
| top: "stage2_unit4_conv2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 3 | |
| stride: 1 | |
| group: 32 | |
| pad: 1 | |
| weight_filler { | |
| type: "msra" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage2_unit4_bn2" | |
| type: "BatchNorm" | |
| bottom: "stage2_unit4_conv2" | |
| top: "stage2_unit4_bn2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage2_unit4_relu2" | |
| type: "ReLU" | |
| bottom: "stage2_unit4_bn2" | |
| top: "stage2_unit4_bn2" | |
| } | |
| layer { | |
| name: "stage2_unit4_conv3" | |
| type: "Convolution" | |
| bottom: "stage2_unit4_bn2" | |
| top: "stage2_unit4_conv3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| weight_filler { | |
| type: "msra" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage2_unit4_bn3" | |
| type: "BatchNorm" | |
| bottom: "stage2_unit4_conv3" | |
| top: "stage2_unit4_bn3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage2_unit4_plus" | |
| type: "Eltwise" | |
| bottom: "stage2_unit3_plus" | |
| bottom: "stage2_unit4_bn3" | |
| top: "stage2_unit4_plus" | |
| } | |
| layer { | |
| name: "stage2_unit4_relu" | |
| type: "ReLU" | |
| bottom: "stage2_unit4_plus" | |
| top: "stage2_unit4_plus" | |
| } | |
| layer { | |
| name: "stage3_unit1_conv1" | |
| type: "Convolution" | |
| bottom: "stage2_unit4_plus" | |
| top: "stage3_unit1_conv1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| weight_filler { | |
| type: "msra" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage3_unit1_bn1" | |
| type: "BatchNorm" | |
| bottom: "stage3_unit1_conv1" | |
| top: "stage3_unit1_bn1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage3_unit1_relu1" | |
| type: "ReLU" | |
| bottom: "stage3_unit1_bn1" | |
| top: "stage3_unit1_bn1" | |
| } | |
| layer { | |
| name: "stage3_unit1_conv2" | |
| type: "Convolution" | |
| bottom: "stage3_unit1_bn1" | |
| top: "stage3_unit1_conv2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 3 | |
| stride: 2 | |
| group: 32 | |
| pad: 1 | |
| weight_filler { | |
| type: "msra" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage3_unit1_bn2" | |
| type: "BatchNorm" | |
| bottom: "stage3_unit1_conv2" | |
| top: "stage3_unit1_bn2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage3_unit1_relu2" | |
| type: "ReLU" | |
| bottom: "stage3_unit1_bn2" | |
| top: "stage3_unit1_bn2" | |
| } | |
| layer { | |
| name: "stage3_unit1_conv3" | |
| type: "Convolution" | |
| bottom: "stage3_unit1_bn2" | |
| top: "stage3_unit1_conv3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 1024 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| weight_filler { | |
| type: "msra" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage3_unit1_bn3" | |
| type: "BatchNorm" | |
| bottom: "stage3_unit1_conv3" | |
| top: "stage3_unit1_bn3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage3_unit1_sc" | |
| type: "Convolution" | |
| bottom: "stage2_unit4_plus" | |
| top: "stage3_unit1_sc" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 1024 | |
| kernel_size: 1 | |
| stride: 2 | |
| pad: 0 | |
| weight_filler { | |
| type: "msra" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage3_unit1_sc_bn" | |
| type: "BatchNorm" | |
| bottom: "stage3_unit1_sc" | |
| top: "stage3_unit1_sc_bn" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage3_unit1_plus" | |
| type: "Eltwise" | |
| bottom: "stage3_unit1_sc_bn" | |
| bottom: "stage3_unit1_bn3" | |
| top: "stage3_unit1_plus" | |
| } | |
| layer { | |
| name: "stage3_unit1_relu" | |
| type: "ReLU" | |
| bottom: "stage3_unit1_plus" | |
| top: "stage3_unit1_plus" | |
| } | |
| layer { | |
| name: "stage3_unit2_conv1" | |
| type: "Convolution" | |
| bottom: "stage3_unit1_plus" | |
| top: "stage3_unit2_conv1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| weight_filler { | |
| type: "msra" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage3_unit2_bn1" | |
| type: "BatchNorm" | |
| bottom: "stage3_unit2_conv1" | |
| top: "stage3_unit2_bn1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage3_unit2_relu1" | |
| type: "ReLU" | |
| bottom: "stage3_unit2_bn1" | |
| top: "stage3_unit2_bn1" | |
| } | |
| layer { | |
| name: "stage3_unit2_conv2" | |
| type: "Convolution" | |
| bottom: "stage3_unit2_bn1" | |
| top: "stage3_unit2_conv2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 3 | |
| stride: 1 | |
| group: 32 | |
| pad: 1 | |
| weight_filler { | |
| type: "msra" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage3_unit2_bn2" | |
| type: "BatchNorm" | |
| bottom: "stage3_unit2_conv2" | |
| top: "stage3_unit2_bn2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage3_unit2_relu2" | |
| type: "ReLU" | |
| bottom: "stage3_unit2_bn2" | |
| top: "stage3_unit2_bn2" | |
| } | |
| layer { | |
| name: "stage3_unit2_conv3" | |
| type: "Convolution" | |
| bottom: "stage3_unit2_bn2" | |
| top: "stage3_unit2_conv3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 1024 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| weight_filler { | |
| type: "msra" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage3_unit2_bn3" | |
| type: "BatchNorm" | |
| bottom: "stage3_unit2_conv3" | |
| top: "stage3_unit2_bn3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage3_unit2_plus" | |
| type: "Eltwise" | |
| bottom: "stage3_unit1_plus" | |
| bottom: "stage3_unit2_bn3" | |
| top: "stage3_unit2_plus" | |
| } | |
| layer { | |
| name: "stage3_unit2_relu" | |
| type: "ReLU" | |
| bottom: "stage3_unit2_plus" | |
| top: "stage3_unit2_plus" | |
| } | |
| layer { | |
| name: "stage3_unit3_conv1" | |
| type: "Convolution" | |
| bottom: "stage3_unit2_plus" | |
| top: "stage3_unit3_conv1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| weight_filler { | |
| type: "msra" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage3_unit3_bn1" | |
| type: "BatchNorm" | |
| bottom: "stage3_unit3_conv1" | |
| top: "stage3_unit3_bn1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage3_unit3_relu1" | |
| type: "ReLU" | |
| bottom: "stage3_unit3_bn1" | |
| top: "stage3_unit3_bn1" | |
| } | |
| layer { | |
| name: "stage3_unit3_conv2" | |
| type: "Convolution" | |
| bottom: "stage3_unit3_bn1" | |
| top: "stage3_unit3_conv2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 3 | |
| stride: 1 | |
| group: 32 | |
| pad: 1 | |
| weight_filler { | |
| type: "msra" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage3_unit3_bn2" | |
| type: "BatchNorm" | |
| bottom: "stage3_unit3_conv2" | |
| top: "stage3_unit3_bn2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage3_unit3_relu2" | |
| type: "ReLU" | |
| bottom: "stage3_unit3_bn2" | |
| top: "stage3_unit3_bn2" | |
| } | |
| layer { | |
| name: "stage3_unit3_conv3" | |
| type: "Convolution" | |
| bottom: "stage3_unit3_bn2" | |
| top: "stage3_unit3_conv3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 1024 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| weight_filler { | |
| type: "msra" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage3_unit3_bn3" | |
| type: "BatchNorm" | |
| bottom: "stage3_unit3_conv3" | |
| top: "stage3_unit3_bn3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage3_unit3_plus" | |
| type: "Eltwise" | |
| bottom: "stage3_unit2_plus" | |
| bottom: "stage3_unit3_bn3" | |
| top: "stage3_unit3_plus" | |
| } | |
| layer { | |
| name: "stage3_unit3_relu" | |
| type: "ReLU" | |
| bottom: "stage3_unit3_plus" | |
| top: "stage3_unit3_plus" | |
| } | |
| layer { | |
| name: "stage3_unit4_conv1" | |
| type: "Convolution" | |
| bottom: "stage3_unit3_plus" | |
| top: "stage3_unit4_conv1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| weight_filler { | |
| type: "msra" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage3_unit4_bn1" | |
| type: "BatchNorm" | |
| bottom: "stage3_unit4_conv1" | |
| top: "stage3_unit4_bn1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage3_unit4_relu1" | |
| type: "ReLU" | |
| bottom: "stage3_unit4_bn1" | |
| top: "stage3_unit4_bn1" | |
| } | |
| layer { | |
| name: "stage3_unit4_conv2" | |
| type: "Convolution" | |
| bottom: "stage3_unit4_bn1" | |
| top: "stage3_unit4_conv2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 3 | |
| stride: 1 | |
| group: 32 | |
| pad: 1 | |
| weight_filler { | |
| type: "msra" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage3_unit4_bn2" | |
| type: "BatchNorm" | |
| bottom: "stage3_unit4_conv2" | |
| top: "stage3_unit4_bn2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage3_unit4_relu2" | |
| type: "ReLU" | |
| bottom: "stage3_unit4_bn2" | |
| top: "stage3_unit4_bn2" | |
| } | |
| layer { | |
| name: "stage3_unit4_conv3" | |
| type: "Convolution" | |
| bottom: "stage3_unit4_bn2" | |
| top: "stage3_unit4_conv3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 1024 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| weight_filler { | |
| type: "msra" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage3_unit4_bn3" | |
| type: "BatchNorm" | |
| bottom: "stage3_unit4_conv3" | |
| top: "stage3_unit4_bn3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage3_unit4_plus" | |
| type: "Eltwise" | |
| bottom: "stage3_unit3_plus" | |
| bottom: "stage3_unit4_bn3" | |
| top: "stage3_unit4_plus" | |
| } | |
| layer { | |
| name: "stage3_unit4_relu" | |
| type: "ReLU" | |
| bottom: "stage3_unit4_plus" | |
| top: "stage3_unit4_plus" | |
| } | |
| layer { | |
| name: "stage3_unit5_conv1" | |
| type: "Convolution" | |
| bottom: "stage3_unit4_plus" | |
| top: "stage3_unit5_conv1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| weight_filler { | |
| type: "msra" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage3_unit5_bn1" | |
| type: "BatchNorm" | |
| bottom: "stage3_unit5_conv1" | |
| top: "stage3_unit5_bn1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage3_unit5_relu1" | |
| type: "ReLU" | |
| bottom: "stage3_unit5_bn1" | |
| top: "stage3_unit5_bn1" | |
| } | |
| layer { | |
| name: "stage3_unit5_conv2" | |
| type: "Convolution" | |
| bottom: "stage3_unit5_bn1" | |
| top: "stage3_unit5_conv2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 3 | |
| stride: 1 | |
| group: 32 | |
| pad: 1 | |
| weight_filler { | |
| type: "msra" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage3_unit5_bn2" | |
| type: "BatchNorm" | |
| bottom: "stage3_unit5_conv2" | |
| top: "stage3_unit5_bn2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage3_unit5_relu2" | |
| type: "ReLU" | |
| bottom: "stage3_unit5_bn2" | |
| top: "stage3_unit5_bn2" | |
| } | |
| layer { | |
| name: "stage3_unit5_conv3" | |
| type: "Convolution" | |
| bottom: "stage3_unit5_bn2" | |
| top: "stage3_unit5_conv3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 1024 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| weight_filler { | |
| type: "msra" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage3_unit5_bn3" | |
| type: "BatchNorm" | |
| bottom: "stage3_unit5_conv3" | |
| top: "stage3_unit5_bn3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage3_unit5_plus" | |
| type: "Eltwise" | |
| bottom: "stage3_unit4_plus" | |
| bottom: "stage3_unit5_bn3" | |
| top: "stage3_unit5_plus" | |
| } | |
| layer { | |
| name: "stage3_unit5_relu" | |
| type: "ReLU" | |
| bottom: "stage3_unit5_plus" | |
| top: "stage3_unit5_plus" | |
| } | |
| layer { | |
| name: "stage3_unit6_conv1" | |
| type: "Convolution" | |
| bottom: "stage3_unit5_plus" | |
| top: "stage3_unit6_conv1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| weight_filler { | |
| type: "msra" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage3_unit6_bn1" | |
| type: "BatchNorm" | |
| bottom: "stage3_unit6_conv1" | |
| top: "stage3_unit6_bn1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage3_unit6_relu1" | |
| type: "ReLU" | |
| bottom: "stage3_unit6_bn1" | |
| top: "stage3_unit6_bn1" | |
| } | |
| layer { | |
| name: "stage3_unit6_conv2" | |
| type: "Convolution" | |
| bottom: "stage3_unit6_bn1" | |
| top: "stage3_unit6_conv2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 3 | |
| stride: 1 | |
| group: 32 | |
| pad: 1 | |
| weight_filler { | |
| type: "msra" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage3_unit6_bn2" | |
| type: "BatchNorm" | |
| bottom: "stage3_unit6_conv2" | |
| top: "stage3_unit6_bn2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage3_unit6_relu2" | |
| type: "ReLU" | |
| bottom: "stage3_unit6_bn2" | |
| top: "stage3_unit6_bn2" | |
| } | |
| layer { | |
| name: "stage3_unit6_conv3" | |
| type: "Convolution" | |
| bottom: "stage3_unit6_bn2" | |
| top: "stage3_unit6_conv3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 1024 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| weight_filler { | |
| type: "msra" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage3_unit6_bn3" | |
| type: "BatchNorm" | |
| bottom: "stage3_unit6_conv3" | |
| top: "stage3_unit6_bn3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage3_unit6_plus" | |
| type: "Eltwise" | |
| bottom: "stage3_unit5_plus" | |
| bottom: "stage3_unit6_bn3" | |
| top: "stage3_unit6_plus" | |
| } | |
| layer { | |
| name: "stage3_unit6_relu" | |
| type: "ReLU" | |
| bottom: "stage3_unit6_plus" | |
| top: "stage3_unit6_plus" | |
| } | |
| layer { | |
| name: "stage4_unit1_conv1" | |
| type: "Convolution" | |
| bottom: "stage3_unit6_plus" | |
| top: "stage4_unit1_conv1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 1024 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| weight_filler { | |
| type: "msra" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage4_unit1_bn1" | |
| type: "BatchNorm" | |
| bottom: "stage4_unit1_conv1" | |
| top: "stage4_unit1_bn1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage4_unit1_relu1" | |
| type: "ReLU" | |
| bottom: "stage4_unit1_bn1" | |
| top: "stage4_unit1_bn1" | |
| } | |
| layer { | |
| name: "stage4_unit1_conv2" | |
| type: "Convolution" | |
| bottom: "stage4_unit1_bn1" | |
| top: "stage4_unit1_conv2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 1024 | |
| kernel_size: 3 | |
| stride: 2 | |
| group: 32 | |
| pad: 1 | |
| weight_filler { | |
| type: "msra" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage4_unit1_bn2" | |
| type: "BatchNorm" | |
| bottom: "stage4_unit1_conv2" | |
| top: "stage4_unit1_bn2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage4_unit1_relu2" | |
| type: "ReLU" | |
| bottom: "stage4_unit1_bn2" | |
| top: "stage4_unit1_bn2" | |
| } | |
| layer { | |
| name: "stage4_unit1_conv3" | |
| type: "Convolution" | |
| bottom: "stage4_unit1_bn2" | |
| top: "stage4_unit1_conv3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 2048 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| weight_filler { | |
| type: "msra" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage4_unit1_bn3" | |
| type: "BatchNorm" | |
| bottom: "stage4_unit1_conv3" | |
| top: "stage4_unit1_bn3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage4_unit1_sc" | |
| type: "Convolution" | |
| bottom: "stage3_unit6_plus" | |
| top: "stage4_unit1_sc" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 2048 | |
| kernel_size: 1 | |
| stride: 2 | |
| pad: 0 | |
| weight_filler { | |
| type: "msra" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage4_unit1_sc_bn" | |
| type: "BatchNorm" | |
| bottom: "stage4_unit1_sc" | |
| top: "stage4_unit1_sc_bn" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage4_unit1_plus" | |
| type: "Eltwise" | |
| bottom: "stage4_unit1_sc_bn" | |
| bottom: "stage4_unit1_bn3" | |
| top: "stage4_unit1_plus" | |
| } | |
| layer { | |
| name: "stage4_unit1_relu" | |
| type: "ReLU" | |
| bottom: "stage4_unit1_plus" | |
| top: "stage4_unit1_plus" | |
| } | |
| layer { | |
| name: "stage4_unit2_conv1" | |
| type: "Convolution" | |
| bottom: "stage4_unit1_plus" | |
| top: "stage4_unit2_conv1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 1024 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| weight_filler { | |
| type: "msra" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage4_unit2_bn1" | |
| type: "BatchNorm" | |
| bottom: "stage4_unit2_conv1" | |
| top: "stage4_unit2_bn1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage4_unit2_relu1" | |
| type: "ReLU" | |
| bottom: "stage4_unit2_bn1" | |
| top: "stage4_unit2_bn1" | |
| } | |
| layer { | |
| name: "stage4_unit2_conv2" | |
| type: "Convolution" | |
| bottom: "stage4_unit2_bn1" | |
| top: "stage4_unit2_conv2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 1024 | |
| kernel_size: 3 | |
| stride: 1 | |
| group: 32 | |
| pad: 1 | |
| weight_filler { | |
| type: "msra" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage4_unit2_bn2" | |
| type: "BatchNorm" | |
| bottom: "stage4_unit2_conv2" | |
| top: "stage4_unit2_bn2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage4_unit2_relu2" | |
| type: "ReLU" | |
| bottom: "stage4_unit2_bn2" | |
| top: "stage4_unit2_bn2" | |
| } | |
| layer { | |
| name: "stage4_unit2_conv3" | |
| type: "Convolution" | |
| bottom: "stage4_unit2_bn2" | |
| top: "stage4_unit2_conv3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 2048 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| weight_filler { | |
| type: "msra" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage4_unit2_bn3" | |
| type: "BatchNorm" | |
| bottom: "stage4_unit2_conv3" | |
| top: "stage4_unit2_bn3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage4_unit2_plus" | |
| type: "Eltwise" | |
| bottom: "stage4_unit1_plus" | |
| bottom: "stage4_unit2_bn3" | |
| top: "stage4_unit2_plus" | |
| } | |
| layer { | |
| name: "stage4_unit2_relu" | |
| type: "ReLU" | |
| bottom: "stage4_unit2_plus" | |
| top: "stage4_unit2_plus" | |
| } | |
| layer { | |
| name: "stage4_unit3_conv1" | |
| type: "Convolution" | |
| bottom: "stage4_unit2_plus" | |
| top: "stage4_unit3_conv1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 1024 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| weight_filler { | |
| type: "msra" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage4_unit3_bn1" | |
| type: "BatchNorm" | |
| bottom: "stage4_unit3_conv1" | |
| top: "stage4_unit3_bn1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage4_unit3_relu1" | |
| type: "ReLU" | |
| bottom: "stage4_unit3_bn1" | |
| top: "stage4_unit3_bn1" | |
| } | |
| layer { | |
| name: "stage4_unit3_conv2" | |
| type: "Convolution" | |
| bottom: "stage4_unit3_bn1" | |
| top: "stage4_unit3_conv2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 1024 | |
| kernel_size: 3 | |
| stride: 1 | |
| group: 32 | |
| pad: 1 | |
| weight_filler { | |
| type: "msra" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage4_unit3_bn2" | |
| type: "BatchNorm" | |
| bottom: "stage4_unit3_conv2" | |
| top: "stage4_unit3_bn2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage4_unit3_relu2" | |
| type: "ReLU" | |
| bottom: "stage4_unit3_bn2" | |
| top: "stage4_unit3_bn2" | |
| } | |
| layer { | |
| name: "stage4_unit3_conv3" | |
| type: "Convolution" | |
| bottom: "stage4_unit3_bn2" | |
| top: "stage4_unit3_conv3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 2048 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| weight_filler { | |
| type: "msra" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage4_unit3_bn3" | |
| type: "BatchNorm" | |
| bottom: "stage4_unit3_conv3" | |
| top: "stage4_unit3_bn3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "stage4_unit3_plus" | |
| type: "Eltwise" | |
| bottom: "stage4_unit2_plus" | |
| bottom: "stage4_unit3_bn3" | |
| top: "stage4_unit3_plus" | |
| } | |
| layer { | |
| name: "stage4_unit3_relu" | |
| type: "ReLU" | |
| bottom: "stage4_unit3_plus" | |
| top: "stage4_unit3_plus" | |
| } | |
| layer { | |
| name: "pool1" | |
| type: "Pooling" | |
| bottom: "stage4_unit3_plus" | |
| top: "pool1" | |
| pooling_param { | |
| global_pooling : true | |
| pool: AVE | |
| } | |
| } | |
| layer { | |
| name: "fc1" | |
| type: "InnerProduct" | |
| bottom: "pool1" | |
| top: "fc1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| #num_output: 1000 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "accuracy" | |
| type: "Accuracy" | |
| bottom: "fc1" | |
| bottom: "label" | |
| top: "accuracy" | |
| include { stage: "val" } | |
| } | |
| layer { | |
| name: "loss" | |
| type: "SoftmaxWithLoss" | |
| bottom: "fc1" | |
| bottom: "label" | |
| top: "loss" | |
| exclude { stage: "deploy" } | |
| } | |
| layer { | |
| name: "softmax" | |
| type: "Softmax" | |
| bottom: "fc1" | |
| top: "softmax" | |
| include { stage: "deploy" } | |
| } |
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