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August 8, 2016 02:50
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Pre-ResNet Caffe prototxt
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| name: "ResNet_50" | |
| layer { | |
| name: "data" | |
| type: "Data" | |
| top: "data" | |
| top: "label" | |
| include { | |
| phase: TRAIN | |
| } | |
| transform_param { | |
| mirror: true | |
| crop_size: 224 | |
| mean_value: 104 | |
| mean_value: 117 | |
| mean_value: 123 | |
| force_color: true | |
| } | |
| data_param { | |
| source: "examples/imagenet/ilsvrc12_train_lmdb" | |
| batch_size: 32 | |
| backend: LMDB | |
| } | |
| } | |
| layer { | |
| name: "data" | |
| type: "Data" | |
| top: "data" | |
| top: "label" | |
| include { | |
| phase: TEST | |
| } | |
| transform_param { | |
| mirror: false | |
| crop_size: 224 | |
| mean_value: 104 | |
| mean_value: 117 | |
| mean_value: 123 | |
| force_color: true | |
| } | |
| data_param { | |
| source: "examples/imagenet/ilsvrc12_val_lmdb" | |
| batch_size: 25 | |
| backend: LMDB | |
| } | |
| } | |
| layer { | |
| name: "conv1" | |
| type: "Convolution" | |
| bottom: "data" | |
| top: "conv1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| pad: 3 | |
| kernel_size: 7 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "conv1" | |
| top: "conv1" | |
| } | |
| layer { | |
| name: "conv1_scale" | |
| type: "Scale" | |
| bottom: "conv1" | |
| top: "conv1" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv1_relu" | |
| type: "ReLU" | |
| bottom: "conv1" | |
| top: "conv1" | |
| } | |
| layer { | |
| name: "pool1" | |
| type: "Pooling" | |
| bottom: "conv1" | |
| top: "pool1" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| pad: 0 | |
| } | |
| } | |
| layer { | |
| name: "conv2_res1_proj" | |
| type: "Convolution" | |
| bottom: "pool1" | |
| top: "conv2_res1_proj" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2_res1_conv1" | |
| type: "Convolution" | |
| bottom: "pool1" | |
| top: "conv2_res1_conv1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2_res1_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "conv2_res1_conv1" | |
| top: "conv2_res1_conv1" | |
| } | |
| layer { | |
| name: "conv2_res1_conv1_scale" | |
| type: "Scale" | |
| bottom: "conv2_res1_conv1" | |
| top: "conv2_res1_conv1" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2_res1_conv1_relu" | |
| type: "ReLU" | |
| bottom: "conv2_res1_conv1" | |
| top: "conv2_res1_conv1" | |
| } | |
| layer { | |
| name: "conv2_res1_conv2" | |
| type: "Convolution" | |
| bottom: "conv2_res1_conv1" | |
| top: "conv2_res1_conv2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2_res1_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "conv2_res1_conv2" | |
| top: "conv2_res1_conv2" | |
| } | |
| layer { | |
| name: "conv2_res1_conv2_scale" | |
| type: "Scale" | |
| bottom: "conv2_res1_conv2" | |
| top: "conv2_res1_conv2" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2_res1_conv2_relu" | |
| type: "ReLU" | |
| bottom: "conv2_res1_conv2" | |
| top: "conv2_res1_conv2" | |
| } | |
| layer { | |
| name: "conv2_res1_conv3" | |
| type: "Convolution" | |
| bottom: "conv2_res1_conv2" | |
| top: "conv2_res1_conv3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2_res1" | |
| type: "Eltwise" | |
| bottom: "conv2_res1_proj" | |
| bottom: "conv2_res1_conv3" | |
| top: "conv2_res1" | |
| } | |
| layer { | |
| name: "conv2_res2_pre_bn" | |
| type: "BatchNorm" | |
| bottom: "conv2_res1" | |
| top: "conv2_res2_pre" | |
| } | |
| layer { | |
| name: "conv2_res2_pre_scale" | |
| type: "Scale" | |
| bottom: "conv2_res2_pre" | |
| top: "conv2_res2_pre" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2_res2_pre_relu" | |
| type: "ReLU" | |
| bottom: "conv2_res2_pre" | |
| top: "conv2_res2_pre" | |
| } | |
| layer { | |
| name: "conv2_res2_conv1" | |
| type: "Convolution" | |
| bottom: "conv2_res2_pre" | |
| top: "conv2_res2_conv1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2_res2_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "conv2_res2_conv1" | |
| top: "conv2_res2_conv1" | |
| } | |
| layer { | |
| name: "conv2_res2_conv1_scale" | |
| type: "Scale" | |
| bottom: "conv2_res2_conv1" | |
| top: "conv2_res2_conv1" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2_res2_conv1_relu" | |
| type: "ReLU" | |
| bottom: "conv2_res2_conv1" | |
| top: "conv2_res2_conv1" | |
| } | |
| layer { | |
| name: "conv2_res2_conv2" | |
| type: "Convolution" | |
| bottom: "conv2_res2_conv1" | |
| top: "conv2_res2_conv2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2_res2_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "conv2_res2_conv2" | |
| top: "conv2_res2_conv2" | |
| } | |
| layer { | |
| name: "conv2_res2_conv2_scale" | |
| type: "Scale" | |
| bottom: "conv2_res2_conv2" | |
| top: "conv2_res2_conv2" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2_res2_conv2_relu" | |
| type: "ReLU" | |
| bottom: "conv2_res2_conv2" | |
| top: "conv2_res2_conv2" | |
| } | |
| layer { | |
| name: "conv2_res2_conv3" | |
| type: "Convolution" | |
| bottom: "conv2_res2_conv2" | |
| top: "conv2_res2_conv3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2_res2" | |
| type: "Eltwise" | |
| bottom: "conv2_res1" | |
| bottom: "conv2_res2_conv3" | |
| top: "conv2_res2" | |
| } | |
| layer { | |
| name: "conv2_res3_pre_bn" | |
| type: "BatchNorm" | |
| bottom: "conv2_res2" | |
| top: "conv2_res3_pre" | |
| } | |
| layer { | |
| name: "conv2_res3_pre_scale" | |
| type: "Scale" | |
| bottom: "conv2_res3_pre" | |
| top: "conv2_res3_pre" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2_res3_pre_relu" | |
| type: "ReLU" | |
| bottom: "conv2_res3_pre" | |
| top: "conv2_res3_pre" | |
| } | |
| layer { | |
| name: "conv2_res3_conv1" | |
| type: "Convolution" | |
| bottom: "conv2_res3_pre" | |
| top: "conv2_res3_conv1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2_res3_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "conv2_res3_conv1" | |
| top: "conv2_res3_conv1" | |
| } | |
| layer { | |
| name: "conv2_res3_conv1_scale" | |
| type: "Scale" | |
| bottom: "conv2_res3_conv1" | |
| top: "conv2_res3_conv1" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2_res3_conv1_relu" | |
| type: "ReLU" | |
| bottom: "conv2_res3_conv1" | |
| top: "conv2_res3_conv1" | |
| } | |
| layer { | |
| name: "conv2_res3_conv2" | |
| type: "Convolution" | |
| bottom: "conv2_res3_conv1" | |
| top: "conv2_res3_conv2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2_res3_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "conv2_res3_conv2" | |
| top: "conv2_res3_conv2" | |
| } | |
| layer { | |
| name: "conv2_res3_conv2_scale" | |
| type: "Scale" | |
| bottom: "conv2_res3_conv2" | |
| top: "conv2_res3_conv2" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2_res3_conv2_relu" | |
| type: "ReLU" | |
| bottom: "conv2_res3_conv2" | |
| top: "conv2_res3_conv2" | |
| } | |
| layer { | |
| name: "conv2_res3_conv3" | |
| type: "Convolution" | |
| bottom: "conv2_res3_conv2" | |
| top: "conv2_res3_conv3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2_res3" | |
| type: "Eltwise" | |
| bottom: "conv2_res2" | |
| bottom: "conv2_res3_conv3" | |
| top: "conv2_res3" | |
| } | |
| layer { | |
| name: "conv3_res1_pre_bn" | |
| type: "BatchNorm" | |
| bottom: "conv2_res3" | |
| top: "conv3_res1_pre" | |
| } | |
| layer { | |
| name: "conv3_res1_pre_scale" | |
| type: "Scale" | |
| bottom: "conv3_res1_pre" | |
| top: "conv3_res1_pre" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_res1_pre_relu" | |
| type: "ReLU" | |
| bottom: "conv3_res1_pre" | |
| top: "conv3_res1_pre" | |
| } | |
| layer { | |
| name: "conv3_res1_proj" | |
| type: "Convolution" | |
| bottom: "conv3_res1_pre" | |
| top: "conv3_res1_proj" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_res1_conv1" | |
| type: "Convolution" | |
| bottom: "conv3_res1_pre" | |
| top: "conv3_res1_conv1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_res1_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_res1_conv1" | |
| top: "conv3_res1_conv1" | |
| } | |
| layer { | |
| name: "conv3_res1_conv1_scale" | |
| type: "Scale" | |
| bottom: "conv3_res1_conv1" | |
| top: "conv3_res1_conv1" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_res1_conv1_relu" | |
| type: "ReLU" | |
| bottom: "conv3_res1_conv1" | |
| top: "conv3_res1_conv1" | |
| } | |
| layer { | |
| name: "conv3_res1_conv2" | |
| type: "Convolution" | |
| bottom: "conv3_res1_conv1" | |
| top: "conv3_res1_conv2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_res1_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_res1_conv2" | |
| top: "conv3_res1_conv2" | |
| } | |
| layer { | |
| name: "conv3_res1_conv2_scale" | |
| type: "Scale" | |
| bottom: "conv3_res1_conv2" | |
| top: "conv3_res1_conv2" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_res1_conv2_relu" | |
| type: "ReLU" | |
| bottom: "conv3_res1_conv2" | |
| top: "conv3_res1_conv2" | |
| } | |
| layer { | |
| name: "conv3_res1_conv3" | |
| type: "Convolution" | |
| bottom: "conv3_res1_conv2" | |
| top: "conv3_res1_conv3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_res1" | |
| type: "Eltwise" | |
| bottom: "conv3_res1_proj" | |
| bottom: "conv3_res1_conv3" | |
| top: "conv3_res1" | |
| } | |
| layer { | |
| name: "conv3_res2_pre_bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_res1" | |
| top: "conv3_res2_pre" | |
| } | |
| layer { | |
| name: "conv3_res2_pre_scale" | |
| type: "Scale" | |
| bottom: "conv3_res2_pre" | |
| top: "conv3_res2_pre" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_res2_pre_relu" | |
| type: "ReLU" | |
| bottom: "conv3_res2_pre" | |
| top: "conv3_res2_pre" | |
| } | |
| layer { | |
| name: "conv3_res2_conv1" | |
| type: "Convolution" | |
| bottom: "conv3_res2_pre" | |
| top: "conv3_res2_conv1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_res2_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_res2_conv1" | |
| top: "conv3_res2_conv1" | |
| } | |
| layer { | |
| name: "conv3_res2_conv1_scale" | |
| type: "Scale" | |
| bottom: "conv3_res2_conv1" | |
| top: "conv3_res2_conv1" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_res2_conv1_relu" | |
| type: "ReLU" | |
| bottom: "conv3_res2_conv1" | |
| top: "conv3_res2_conv1" | |
| } | |
| layer { | |
| name: "conv3_res2_conv2" | |
| type: "Convolution" | |
| bottom: "conv3_res2_conv1" | |
| top: "conv3_res2_conv2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_res2_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_res2_conv2" | |
| top: "conv3_res2_conv2" | |
| } | |
| layer { | |
| name: "conv3_res2_conv2_scale" | |
| type: "Scale" | |
| bottom: "conv3_res2_conv2" | |
| top: "conv3_res2_conv2" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_res2_conv2_relu" | |
| type: "ReLU" | |
| bottom: "conv3_res2_conv2" | |
| top: "conv3_res2_conv2" | |
| } | |
| layer { | |
| name: "conv3_res2_conv3" | |
| type: "Convolution" | |
| bottom: "conv3_res2_conv2" | |
| top: "conv3_res2_conv3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_res2" | |
| type: "Eltwise" | |
| bottom: "conv3_res1" | |
| bottom: "conv3_res2_conv3" | |
| top: "conv3_res2" | |
| } | |
| layer { | |
| name: "conv3_res3_pre_bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_res2" | |
| top: "conv3_res3_pre" | |
| } | |
| layer { | |
| name: "conv3_res3_pre_scale" | |
| type: "Scale" | |
| bottom: "conv3_res3_pre" | |
| top: "conv3_res3_pre" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_res3_pre_relu" | |
| type: "ReLU" | |
| bottom: "conv3_res3_pre" | |
| top: "conv3_res3_pre" | |
| } | |
| layer { | |
| name: "conv3_res3_conv1" | |
| type: "Convolution" | |
| bottom: "conv3_res3_pre" | |
| top: "conv3_res3_conv1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_res3_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_res3_conv1" | |
| top: "conv3_res3_conv1" | |
| } | |
| layer { | |
| name: "conv3_res3_conv1_scale" | |
| type: "Scale" | |
| bottom: "conv3_res3_conv1" | |
| top: "conv3_res3_conv1" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_res3_conv1_relu" | |
| type: "ReLU" | |
| bottom: "conv3_res3_conv1" | |
| top: "conv3_res3_conv1" | |
| } | |
| layer { | |
| name: "conv3_res3_conv2" | |
| type: "Convolution" | |
| bottom: "conv3_res3_conv1" | |
| top: "conv3_res3_conv2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_res3_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_res3_conv2" | |
| top: "conv3_res3_conv2" | |
| } | |
| layer { | |
| name: "conv3_res3_conv2_scale" | |
| type: "Scale" | |
| bottom: "conv3_res3_conv2" | |
| top: "conv3_res3_conv2" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_res3_conv2_relu" | |
| type: "ReLU" | |
| bottom: "conv3_res3_conv2" | |
| top: "conv3_res3_conv2" | |
| } | |
| layer { | |
| name: "conv3_res3_conv3" | |
| type: "Convolution" | |
| bottom: "conv3_res3_conv2" | |
| top: "conv3_res3_conv3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_res3" | |
| type: "Eltwise" | |
| bottom: "conv3_res2" | |
| bottom: "conv3_res3_conv3" | |
| top: "conv3_res3" | |
| } | |
| layer { | |
| name: "conv3_res4_pre_bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_res3" | |
| top: "conv3_res4_pre" | |
| } | |
| layer { | |
| name: "conv3_res4_pre_scale" | |
| type: "Scale" | |
| bottom: "conv3_res4_pre" | |
| top: "conv3_res4_pre" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_res4_pre_relu" | |
| type: "ReLU" | |
| bottom: "conv3_res4_pre" | |
| top: "conv3_res4_pre" | |
| } | |
| layer { | |
| name: "conv3_res4_conv1" | |
| type: "Convolution" | |
| bottom: "conv3_res4_pre" | |
| top: "conv3_res4_conv1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_res4_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_res4_conv1" | |
| top: "conv3_res4_conv1" | |
| } | |
| layer { | |
| name: "conv3_res4_conv1_scale" | |
| type: "Scale" | |
| bottom: "conv3_res4_conv1" | |
| top: "conv3_res4_conv1" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_res4_conv1_relu" | |
| type: "ReLU" | |
| bottom: "conv3_res4_conv1" | |
| top: "conv3_res4_conv1" | |
| } | |
| layer { | |
| name: "conv3_res4_conv2" | |
| type: "Convolution" | |
| bottom: "conv3_res4_conv1" | |
| top: "conv3_res4_conv2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_res4_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_res4_conv2" | |
| top: "conv3_res4_conv2" | |
| } | |
| layer { | |
| name: "conv3_res4_conv2_scale" | |
| type: "Scale" | |
| bottom: "conv3_res4_conv2" | |
| top: "conv3_res4_conv2" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_res4_conv2_relu" | |
| type: "ReLU" | |
| bottom: "conv3_res4_conv2" | |
| top: "conv3_res4_conv2" | |
| } | |
| layer { | |
| name: "conv3_res4_conv3" | |
| type: "Convolution" | |
| bottom: "conv3_res4_conv2" | |
| top: "conv3_res4_conv3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_res4" | |
| type: "Eltwise" | |
| bottom: "conv3_res3" | |
| bottom: "conv3_res4_conv3" | |
| top: "conv3_res4" | |
| } | |
| layer { | |
| name: "conv4_res1_pre_bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_res4" | |
| top: "conv4_res1_pre" | |
| } | |
| layer { | |
| name: "conv4_res1_pre_scale" | |
| type: "Scale" | |
| bottom: "conv4_res1_pre" | |
| top: "conv4_res1_pre" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_res1_pre_relu" | |
| type: "ReLU" | |
| bottom: "conv4_res1_pre" | |
| top: "conv4_res1_pre" | |
| } | |
| layer { | |
| name: "conv4_res1_proj" | |
| type: "Convolution" | |
| bottom: "conv4_res1_pre" | |
| top: "conv4_res1_proj" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 1024 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_res1_conv1" | |
| type: "Convolution" | |
| bottom: "conv4_res1_pre" | |
| top: "conv4_res1_conv1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_res1_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_res1_conv1" | |
| top: "conv4_res1_conv1" | |
| } | |
| layer { | |
| name: "conv4_res1_conv1_scale" | |
| type: "Scale" | |
| bottom: "conv4_res1_conv1" | |
| top: "conv4_res1_conv1" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_res1_conv1_relu" | |
| type: "ReLU" | |
| bottom: "conv4_res1_conv1" | |
| top: "conv4_res1_conv1" | |
| } | |
| layer { | |
| name: "conv4_res1_conv2" | |
| type: "Convolution" | |
| bottom: "conv4_res1_conv1" | |
| top: "conv4_res1_conv2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_res1_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_res1_conv2" | |
| top: "conv4_res1_conv2" | |
| } | |
| layer { | |
| name: "conv4_res1_conv2_scale" | |
| type: "Scale" | |
| bottom: "conv4_res1_conv2" | |
| top: "conv4_res1_conv2" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_res1_conv2_relu" | |
| type: "ReLU" | |
| bottom: "conv4_res1_conv2" | |
| top: "conv4_res1_conv2" | |
| } | |
| layer { | |
| name: "conv4_res1_conv3" | |
| type: "Convolution" | |
| bottom: "conv4_res1_conv2" | |
| top: "conv4_res1_conv3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 1024 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_res1" | |
| type: "Eltwise" | |
| bottom: "conv4_res1_proj" | |
| bottom: "conv4_res1_conv3" | |
| top: "conv4_res1" | |
| } | |
| layer { | |
| name: "conv4_res2_pre_bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_res1" | |
| top: "conv4_res2_pre" | |
| } | |
| layer { | |
| name: "conv4_res2_pre_scale" | |
| type: "Scale" | |
| bottom: "conv4_res2_pre" | |
| top: "conv4_res2_pre" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_res2_pre_relu" | |
| type: "ReLU" | |
| bottom: "conv4_res2_pre" | |
| top: "conv4_res2_pre" | |
| } | |
| layer { | |
| name: "conv4_res2_conv1" | |
| type: "Convolution" | |
| bottom: "conv4_res2_pre" | |
| top: "conv4_res2_conv1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_res2_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_res2_conv1" | |
| top: "conv4_res2_conv1" | |
| } | |
| layer { | |
| name: "conv4_res2_conv1_scale" | |
| type: "Scale" | |
| bottom: "conv4_res2_conv1" | |
| top: "conv4_res2_conv1" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_res2_conv1_relu" | |
| type: "ReLU" | |
| bottom: "conv4_res2_conv1" | |
| top: "conv4_res2_conv1" | |
| } | |
| layer { | |
| name: "conv4_res2_conv2" | |
| type: "Convolution" | |
| bottom: "conv4_res2_conv1" | |
| top: "conv4_res2_conv2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_res2_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_res2_conv2" | |
| top: "conv4_res2_conv2" | |
| } | |
| layer { | |
| name: "conv4_res2_conv2_scale" | |
| type: "Scale" | |
| bottom: "conv4_res2_conv2" | |
| top: "conv4_res2_conv2" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_res2_conv2_relu" | |
| type: "ReLU" | |
| bottom: "conv4_res2_conv2" | |
| top: "conv4_res2_conv2" | |
| } | |
| layer { | |
| name: "conv4_res2_conv3" | |
| type: "Convolution" | |
| bottom: "conv4_res2_conv2" | |
| top: "conv4_res2_conv3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 1024 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_res2" | |
| type: "Eltwise" | |
| bottom: "conv4_res1" | |
| bottom: "conv4_res2_conv3" | |
| top: "conv4_res2" | |
| } | |
| layer { | |
| name: "conv4_res3_pre_bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_res2" | |
| top: "conv4_res3_pre" | |
| } | |
| layer { | |
| name: "conv4_res3_pre_scale" | |
| type: "Scale" | |
| bottom: "conv4_res3_pre" | |
| top: "conv4_res3_pre" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_res3_pre_relu" | |
| type: "ReLU" | |
| bottom: "conv4_res3_pre" | |
| top: "conv4_res3_pre" | |
| } | |
| layer { | |
| name: "conv4_res3_conv1" | |
| type: "Convolution" | |
| bottom: "conv4_res3_pre" | |
| top: "conv4_res3_conv1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_res3_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_res3_conv1" | |
| top: "conv4_res3_conv1" | |
| } | |
| layer { | |
| name: "conv4_res3_conv1_scale" | |
| type: "Scale" | |
| bottom: "conv4_res3_conv1" | |
| top: "conv4_res3_conv1" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_res3_conv1_relu" | |
| type: "ReLU" | |
| bottom: "conv4_res3_conv1" | |
| top: "conv4_res3_conv1" | |
| } | |
| layer { | |
| name: "conv4_res3_conv2" | |
| type: "Convolution" | |
| bottom: "conv4_res3_conv1" | |
| top: "conv4_res3_conv2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_res3_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_res3_conv2" | |
| top: "conv4_res3_conv2" | |
| } | |
| layer { | |
| name: "conv4_res3_conv2_scale" | |
| type: "Scale" | |
| bottom: "conv4_res3_conv2" | |
| top: "conv4_res3_conv2" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_res3_conv2_relu" | |
| type: "ReLU" | |
| bottom: "conv4_res3_conv2" | |
| top: "conv4_res3_conv2" | |
| } | |
| layer { | |
| name: "conv4_res3_conv3" | |
| type: "Convolution" | |
| bottom: "conv4_res3_conv2" | |
| top: "conv4_res3_conv3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 1024 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_res3" | |
| type: "Eltwise" | |
| bottom: "conv4_res2" | |
| bottom: "conv4_res3_conv3" | |
| top: "conv4_res3" | |
| } | |
| layer { | |
| name: "conv4_res4_pre_bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_res3" | |
| top: "conv4_res4_pre" | |
| } | |
| layer { | |
| name: "conv4_res4_pre_scale" | |
| type: "Scale" | |
| bottom: "conv4_res4_pre" | |
| top: "conv4_res4_pre" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_res4_pre_relu" | |
| type: "ReLU" | |
| bottom: "conv4_res4_pre" | |
| top: "conv4_res4_pre" | |
| } | |
| layer { | |
| name: "conv4_res4_conv1" | |
| type: "Convolution" | |
| bottom: "conv4_res4_pre" | |
| top: "conv4_res4_conv1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_res4_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_res4_conv1" | |
| top: "conv4_res4_conv1" | |
| } | |
| layer { | |
| name: "conv4_res4_conv1_scale" | |
| type: "Scale" | |
| bottom: "conv4_res4_conv1" | |
| top: "conv4_res4_conv1" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_res4_conv1_relu" | |
| type: "ReLU" | |
| bottom: "conv4_res4_conv1" | |
| top: "conv4_res4_conv1" | |
| } | |
| layer { | |
| name: "conv4_res4_conv2" | |
| type: "Convolution" | |
| bottom: "conv4_res4_conv1" | |
| top: "conv4_res4_conv2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_res4_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_res4_conv2" | |
| top: "conv4_res4_conv2" | |
| } | |
| layer { | |
| name: "conv4_res4_conv2_scale" | |
| type: "Scale" | |
| bottom: "conv4_res4_conv2" | |
| top: "conv4_res4_conv2" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_res4_conv2_relu" | |
| type: "ReLU" | |
| bottom: "conv4_res4_conv2" | |
| top: "conv4_res4_conv2" | |
| } | |
| layer { | |
| name: "conv4_res4_conv3" | |
| type: "Convolution" | |
| bottom: "conv4_res4_conv2" | |
| top: "conv4_res4_conv3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 1024 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_res4" | |
| type: "Eltwise" | |
| bottom: "conv4_res3" | |
| bottom: "conv4_res4_conv3" | |
| top: "conv4_res4" | |
| } | |
| layer { | |
| name: "conv4_res5_pre_bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_res4" | |
| top: "conv4_res5_pre" | |
| } | |
| layer { | |
| name: "conv4_res5_pre_scale" | |
| type: "Scale" | |
| bottom: "conv4_res5_pre" | |
| top: "conv4_res5_pre" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_res5_pre_relu" | |
| type: "ReLU" | |
| bottom: "conv4_res5_pre" | |
| top: "conv4_res5_pre" | |
| } | |
| layer { | |
| name: "conv4_res5_conv1" | |
| type: "Convolution" | |
| bottom: "conv4_res5_pre" | |
| top: "conv4_res5_conv1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_res5_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_res5_conv1" | |
| top: "conv4_res5_conv1" | |
| } | |
| layer { | |
| name: "conv4_res5_conv1_scale" | |
| type: "Scale" | |
| bottom: "conv4_res5_conv1" | |
| top: "conv4_res5_conv1" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_res5_conv1_relu" | |
| type: "ReLU" | |
| bottom: "conv4_res5_conv1" | |
| top: "conv4_res5_conv1" | |
| } | |
| layer { | |
| name: "conv4_res5_conv2" | |
| type: "Convolution" | |
| bottom: "conv4_res5_conv1" | |
| top: "conv4_res5_conv2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_res5_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_res5_conv2" | |
| top: "conv4_res5_conv2" | |
| } | |
| layer { | |
| name: "conv4_res5_conv2_scale" | |
| type: "Scale" | |
| bottom: "conv4_res5_conv2" | |
| top: "conv4_res5_conv2" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_res5_conv2_relu" | |
| type: "ReLU" | |
| bottom: "conv4_res5_conv2" | |
| top: "conv4_res5_conv2" | |
| } | |
| layer { | |
| name: "conv4_res5_conv3" | |
| type: "Convolution" | |
| bottom: "conv4_res5_conv2" | |
| top: "conv4_res5_conv3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 1024 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_res5" | |
| type: "Eltwise" | |
| bottom: "conv4_res4" | |
| bottom: "conv4_res5_conv3" | |
| top: "conv4_res5" | |
| } | |
| layer { | |
| name: "conv4_res6_pre_bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_res5" | |
| top: "conv4_res6_pre" | |
| } | |
| layer { | |
| name: "conv4_res6_pre_scale" | |
| type: "Scale" | |
| bottom: "conv4_res6_pre" | |
| top: "conv4_res6_pre" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_res6_pre_relu" | |
| type: "ReLU" | |
| bottom: "conv4_res6_pre" | |
| top: "conv4_res6_pre" | |
| } | |
| layer { | |
| name: "conv4_res6_conv1" | |
| type: "Convolution" | |
| bottom: "conv4_res6_pre" | |
| top: "conv4_res6_conv1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_res6_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_res6_conv1" | |
| top: "conv4_res6_conv1" | |
| } | |
| layer { | |
| name: "conv4_res6_conv1_scale" | |
| type: "Scale" | |
| bottom: "conv4_res6_conv1" | |
| top: "conv4_res6_conv1" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_res6_conv1_relu" | |
| type: "ReLU" | |
| bottom: "conv4_res6_conv1" | |
| top: "conv4_res6_conv1" | |
| } | |
| layer { | |
| name: "conv4_res6_conv2" | |
| type: "Convolution" | |
| bottom: "conv4_res6_conv1" | |
| top: "conv4_res6_conv2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_res6_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_res6_conv2" | |
| top: "conv4_res6_conv2" | |
| } | |
| layer { | |
| name: "conv4_res6_conv2_scale" | |
| type: "Scale" | |
| bottom: "conv4_res6_conv2" | |
| top: "conv4_res6_conv2" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_res6_conv2_relu" | |
| type: "ReLU" | |
| bottom: "conv4_res6_conv2" | |
| top: "conv4_res6_conv2" | |
| } | |
| layer { | |
| name: "conv4_res6_conv3" | |
| type: "Convolution" | |
| bottom: "conv4_res6_conv2" | |
| top: "conv4_res6_conv3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 1024 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_res6" | |
| type: "Eltwise" | |
| bottom: "conv4_res5" | |
| bottom: "conv4_res6_conv3" | |
| top: "conv4_res6" | |
| } | |
| layer { | |
| name: "conv5_res1_pre_bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_res6" | |
| top: "conv5_res1_pre" | |
| } | |
| layer { | |
| name: "conv5_res1_pre_scale" | |
| type: "Scale" | |
| bottom: "conv5_res1_pre" | |
| top: "conv5_res1_pre" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_res1_pre_relu" | |
| type: "ReLU" | |
| bottom: "conv5_res1_pre" | |
| top: "conv5_res1_pre" | |
| } | |
| layer { | |
| name: "conv5_res1_proj" | |
| type: "Convolution" | |
| bottom: "conv5_res1_pre" | |
| top: "conv5_res1_proj" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 2048 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_res1_conv1" | |
| type: "Convolution" | |
| bottom: "conv5_res1_pre" | |
| top: "conv5_res1_conv1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_res1_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_res1_conv1" | |
| top: "conv5_res1_conv1" | |
| } | |
| layer { | |
| name: "conv5_res1_conv1_scale" | |
| type: "Scale" | |
| bottom: "conv5_res1_conv1" | |
| top: "conv5_res1_conv1" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_res1_conv1_relu" | |
| type: "ReLU" | |
| bottom: "conv5_res1_conv1" | |
| top: "conv5_res1_conv1" | |
| } | |
| layer { | |
| name: "conv5_res1_conv2" | |
| type: "Convolution" | |
| bottom: "conv5_res1_conv1" | |
| top: "conv5_res1_conv2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_res1_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_res1_conv2" | |
| top: "conv5_res1_conv2" | |
| } | |
| layer { | |
| name: "conv5_res1_conv2_scale" | |
| type: "Scale" | |
| bottom: "conv5_res1_conv2" | |
| top: "conv5_res1_conv2" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_res1_conv2_relu" | |
| type: "ReLU" | |
| bottom: "conv5_res1_conv2" | |
| top: "conv5_res1_conv2" | |
| } | |
| layer { | |
| name: "conv5_res1_conv3" | |
| type: "Convolution" | |
| bottom: "conv5_res1_conv2" | |
| top: "conv5_res1_conv3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 2048 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_res1" | |
| type: "Eltwise" | |
| bottom: "conv5_res1_proj" | |
| bottom: "conv5_res1_conv3" | |
| top: "conv5_res1" | |
| } | |
| layer { | |
| name: "conv5_res2_pre_bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_res1" | |
| top: "conv5_res2_pre" | |
| } | |
| layer { | |
| name: "conv5_res2_pre_scale" | |
| type: "Scale" | |
| bottom: "conv5_res2_pre" | |
| top: "conv5_res2_pre" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_res2_pre_relu" | |
| type: "ReLU" | |
| bottom: "conv5_res2_pre" | |
| top: "conv5_res2_pre" | |
| } | |
| layer { | |
| name: "conv5_res2_conv1" | |
| type: "Convolution" | |
| bottom: "conv5_res2_pre" | |
| top: "conv5_res2_conv1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_res2_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_res2_conv1" | |
| top: "conv5_res2_conv1" | |
| } | |
| layer { | |
| name: "conv5_res2_conv1_scale" | |
| type: "Scale" | |
| bottom: "conv5_res2_conv1" | |
| top: "conv5_res2_conv1" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_res2_conv1_relu" | |
| type: "ReLU" | |
| bottom: "conv5_res2_conv1" | |
| top: "conv5_res2_conv1" | |
| } | |
| layer { | |
| name: "conv5_res2_conv2" | |
| type: "Convolution" | |
| bottom: "conv5_res2_conv1" | |
| top: "conv5_res2_conv2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_res2_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_res2_conv2" | |
| top: "conv5_res2_conv2" | |
| } | |
| layer { | |
| name: "conv5_res2_conv2_scale" | |
| type: "Scale" | |
| bottom: "conv5_res2_conv2" | |
| top: "conv5_res2_conv2" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_res2_conv2_relu" | |
| type: "ReLU" | |
| bottom: "conv5_res2_conv2" | |
| top: "conv5_res2_conv2" | |
| } | |
| layer { | |
| name: "conv5_res2_conv3" | |
| type: "Convolution" | |
| bottom: "conv5_res2_conv2" | |
| top: "conv5_res2_conv3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 2048 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_res2" | |
| type: "Eltwise" | |
| bottom: "conv5_res1" | |
| bottom: "conv5_res2_conv3" | |
| top: "conv5_res2" | |
| } | |
| layer { | |
| name: "conv5_res3_pre_bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_res2" | |
| top: "conv5_res3_pre" | |
| } | |
| layer { | |
| name: "conv5_res3_pre_scale" | |
| type: "Scale" | |
| bottom: "conv5_res3_pre" | |
| top: "conv5_res3_pre" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_res3_pre_relu" | |
| type: "ReLU" | |
| bottom: "conv5_res3_pre" | |
| top: "conv5_res3_pre" | |
| } | |
| layer { | |
| name: "conv5_res3_conv1" | |
| type: "Convolution" | |
| bottom: "conv5_res3_pre" | |
| top: "conv5_res3_conv1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_res3_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_res3_conv1" | |
| top: "conv5_res3_conv1" | |
| } | |
| layer { | |
| name: "conv5_res3_conv1_scale" | |
| type: "Scale" | |
| bottom: "conv5_res3_conv1" | |
| top: "conv5_res3_conv1" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_res3_conv1_relu" | |
| type: "ReLU" | |
| bottom: "conv5_res3_conv1" | |
| top: "conv5_res3_conv1" | |
| } | |
| layer { | |
| name: "conv5_res3_conv2" | |
| type: "Convolution" | |
| bottom: "conv5_res3_conv1" | |
| top: "conv5_res3_conv2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_res3_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_res3_conv2" | |
| top: "conv5_res3_conv2" | |
| } | |
| layer { | |
| name: "conv5_res3_conv2_scale" | |
| type: "Scale" | |
| bottom: "conv5_res3_conv2" | |
| top: "conv5_res3_conv2" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_res3_conv2_relu" | |
| type: "ReLU" | |
| bottom: "conv5_res3_conv2" | |
| top: "conv5_res3_conv2" | |
| } | |
| layer { | |
| name: "conv5_res3_conv3" | |
| type: "Convolution" | |
| bottom: "conv5_res3_conv2" | |
| top: "conv5_res3_conv3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 2048 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_res3" | |
| type: "Eltwise" | |
| bottom: "conv5_res2" | |
| bottom: "conv5_res3_conv3" | |
| top: "conv5_res3" | |
| } | |
| layer { | |
| name: "conv5_bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_res3" | |
| top: "conv5" | |
| } | |
| layer { | |
| name: "conv5_scale" | |
| type: "Scale" | |
| bottom: "conv5" | |
| top: "conv5" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_relu" | |
| type: "ReLU" | |
| bottom: "conv5" | |
| top: "conv5" | |
| } | |
| layer { | |
| name: "pool5" | |
| type: "Pooling" | |
| bottom: "conv5" | |
| top: "pool5" | |
| pooling_param { | |
| pool: AVE | |
| kernel_size: 7 | |
| stride: 1 | |
| pad: 0 | |
| } | |
| } | |
| layer { | |
| name: "fc" | |
| type: "InnerProduct" | |
| bottom: "pool5" | |
| top: "fc" | |
| 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 { | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "loss" | |
| type: "SoftmaxWithLoss" | |
| bottom: "fc" | |
| bottom: "label" | |
| top: "loss" | |
| } | |
| layer { | |
| name: "accuracy_top1" | |
| type: "Accuracy" | |
| bottom: "fc" | |
| bottom: "label" | |
| top: "accuracy_top1" | |
| include { | |
| phase: TEST | |
| } | |
| accuracy_param { | |
| top_k: 1 | |
| } | |
| } | |
| layer { | |
| name: "accuracy_top5" | |
| type: "Accuracy" | |
| bottom: "fc" | |
| bottom: "label" | |
| top: "accuracy_top5" | |
| include { | |
| phase: TEST | |
| } | |
| accuracy_param { | |
| top_k: 5 | |
| } | |
| } |
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