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November 30, 2018 05:55
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| name: "ResNet-101v2(no-inplace)" | |
| input: "data" | |
| input_shape { | |
| dim: 1 | |
| dim: 3 | |
| dim: 224 | |
| dim: 224 | |
| } | |
| layer { | |
| name: "conv1" | |
| type: "Convolution" | |
| bottom: "data" | |
| top: "conv1" | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| pad: 3 | |
| kernel_size: 7 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "conv1" | |
| top: "conv1/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv1_scale" | |
| type: "Scale" | |
| bottom: "conv1/bn" | |
| top: "conv1/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv1_relu" | |
| type: "ReLU" | |
| bottom: "conv1/bn" | |
| top: "conv1/bn" | |
| } | |
| layer { | |
| name: "pool1" | |
| type: "Pooling" | |
| bottom: "conv1/bn" | |
| top: "pool1" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "res1_conv1" | |
| type: "Convolution" | |
| bottom: "pool1" | |
| top: "res1_conv1" | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res1_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "res1_conv1" | |
| top: "res1_conv1/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res1_conv1_scale" | |
| type: "Scale" | |
| bottom: "res1_conv1/bn" | |
| top: "res1_conv1/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res1_conv1_relu" | |
| type: "ReLU" | |
| bottom: "res1_conv1/bn" | |
| top: "res1_conv1/bn" | |
| } | |
| layer { | |
| name: "res1_conv2" | |
| type: "Convolution" | |
| bottom: "res1_conv1/bn" | |
| top: "res1_conv2" | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res1_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "res1_conv2" | |
| top: "res1_conv2/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res1_conv2_scale" | |
| type: "Scale" | |
| bottom: "res1_conv2/bn" | |
| top: "res1_conv2/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res1_conv2_relu" | |
| type: "ReLU" | |
| bottom: "res1_conv2/bn" | |
| top: "res1_conv2/bn" | |
| } | |
| layer { | |
| name: "res1_conv3" | |
| type: "Convolution" | |
| bottom: "res1_conv2/bn" | |
| top: "res1_conv3" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res1_match_conv" | |
| type: "Convolution" | |
| bottom: "pool1" | |
| top: "res1_match_conv" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res1_eletwise" | |
| type: "Eltwise" | |
| bottom: "res1_match_conv" | |
| bottom: "res1_conv3" | |
| top: "res1_eletwise" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "res2_bn" | |
| type: "BatchNorm" | |
| bottom: "res1_eletwise" | |
| top: "res2_bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res2_scale" | |
| type: "Scale" | |
| bottom: "res2_bn" | |
| top: "res2_bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res2_relu" | |
| type: "ReLU" | |
| bottom: "res2_bn" | |
| top: "res2_bn" | |
| } | |
| layer { | |
| name: "res2_conv1" | |
| type: "Convolution" | |
| bottom: "res2_bn" | |
| top: "res2_conv1" | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res2_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "res2_conv1" | |
| top: "res2_conv1/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res2_conv1_scale" | |
| type: "Scale" | |
| bottom: "res2_conv1/bn" | |
| top: "res2_conv1/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res2_conv1_relu" | |
| type: "ReLU" | |
| bottom: "res2_conv1/bn" | |
| top: "res2_conv1/bn" | |
| } | |
| layer { | |
| name: "res2_conv2" | |
| type: "Convolution" | |
| bottom: "res2_conv1/bn" | |
| top: "res2_conv2" | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res2_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "res2_conv2" | |
| top: "res2_conv2/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res2_conv2_scale" | |
| type: "Scale" | |
| bottom: "res2_conv2/bn" | |
| top: "res2_conv2/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res2_conv2_relu" | |
| type: "ReLU" | |
| bottom: "res2_conv2/bn" | |
| top: "res2_conv2/bn" | |
| } | |
| layer { | |
| name: "res2_conv3" | |
| type: "Convolution" | |
| bottom: "res2_conv2/bn" | |
| top: "res2_conv3" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res2_eletwise" | |
| type: "Eltwise" | |
| bottom: "res1_eletwise" | |
| bottom: "res2_conv3" | |
| top: "res2_eletwise" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "res3_bn" | |
| type: "BatchNorm" | |
| bottom: "res2_eletwise" | |
| top: "res3_bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res3_scale" | |
| type: "Scale" | |
| bottom: "res3_bn" | |
| top: "res3_bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res3_relu" | |
| type: "ReLU" | |
| bottom: "res3_bn" | |
| top: "res3_bn" | |
| } | |
| layer { | |
| name: "res3_conv1" | |
| type: "Convolution" | |
| bottom: "res3_bn" | |
| top: "res3_conv1" | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res3_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "res3_conv1" | |
| top: "res3_conv1/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res3_conv1_scale" | |
| type: "Scale" | |
| bottom: "res3_conv1/bn" | |
| top: "res3_conv1/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res3_conv1_relu" | |
| type: "ReLU" | |
| bottom: "res3_conv1/bn" | |
| top: "res3_conv1/bn" | |
| } | |
| layer { | |
| name: "res3_conv2" | |
| type: "Convolution" | |
| bottom: "res3_conv1/bn" | |
| top: "res3_conv2" | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res3_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "res3_conv2" | |
| top: "res3_conv2/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res3_conv2_scale" | |
| type: "Scale" | |
| bottom: "res3_conv2/bn" | |
| top: "res3_conv2/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res3_conv2_relu" | |
| type: "ReLU" | |
| bottom: "res3_conv2/bn" | |
| top: "res3_conv2/bn" | |
| } | |
| layer { | |
| name: "res3_conv3" | |
| type: "Convolution" | |
| bottom: "res3_conv2/bn" | |
| top: "res3_conv3" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res3_eletwise" | |
| type: "Eltwise" | |
| bottom: "res2_eletwise" | |
| bottom: "res3_conv3" | |
| top: "res3_eletwise" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "res4_bn" | |
| type: "BatchNorm" | |
| bottom: "res3_eletwise" | |
| top: "res4_bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res4_scale" | |
| type: "Scale" | |
| bottom: "res4_bn" | |
| top: "res4_bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res4_relu" | |
| type: "ReLU" | |
| bottom: "res4_bn" | |
| top: "res4_bn" | |
| } | |
| layer { | |
| name: "res4_conv1" | |
| type: "Convolution" | |
| bottom: "res4_bn" | |
| top: "res4_conv1" | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res4_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "res4_conv1" | |
| top: "res4_conv1/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res4_conv1_scale" | |
| type: "Scale" | |
| bottom: "res4_conv1/bn" | |
| top: "res4_conv1/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res4_conv1_relu" | |
| type: "ReLU" | |
| bottom: "res4_conv1/bn" | |
| top: "res4_conv1/bn" | |
| } | |
| layer { | |
| name: "res4_conv2" | |
| type: "Convolution" | |
| bottom: "res4_conv1/bn" | |
| top: "res4_conv2" | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "res4_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "res4_conv2" | |
| top: "res4_conv2/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res4_conv2_scale" | |
| type: "Scale" | |
| bottom: "res4_conv2/bn" | |
| top: "res4_conv2/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res4_conv2_relu" | |
| type: "ReLU" | |
| bottom: "res4_conv2/bn" | |
| top: "res4_conv2/bn" | |
| } | |
| layer { | |
| name: "res4_conv3" | |
| type: "Convolution" | |
| bottom: "res4_conv2/bn" | |
| top: "res4_conv3" | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res4_match_conv" | |
| type: "Convolution" | |
| bottom: "res4_bn" | |
| top: "res4_match_conv" | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "res4_eletwise" | |
| type: "Eltwise" | |
| bottom: "res4_match_conv" | |
| bottom: "res4_conv3" | |
| top: "res4_eletwise" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "res5_bn" | |
| type: "BatchNorm" | |
| bottom: "res4_eletwise" | |
| top: "res5_bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res5_scale" | |
| type: "Scale" | |
| bottom: "res5_bn" | |
| top: "res5_bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res5_relu" | |
| type: "ReLU" | |
| bottom: "res5_bn" | |
| top: "res5_bn" | |
| } | |
| layer { | |
| name: "res5_conv1" | |
| type: "Convolution" | |
| bottom: "res5_bn" | |
| top: "res5_conv1" | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res5_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "res5_conv1" | |
| top: "res5_conv1/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res5_conv1_scale" | |
| type: "Scale" | |
| bottom: "res5_conv1/bn" | |
| top: "res5_conv1/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res5_conv1_relu" | |
| type: "ReLU" | |
| bottom: "res5_conv1/bn" | |
| top: "res5_conv1/bn" | |
| } | |
| layer { | |
| name: "res5_conv2" | |
| type: "Convolution" | |
| bottom: "res5_conv1/bn" | |
| top: "res5_conv2" | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res5_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "res5_conv2" | |
| top: "res5_conv2/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res5_conv2_scale" | |
| type: "Scale" | |
| bottom: "res5_conv2/bn" | |
| top: "res5_conv2/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res5_conv2_relu" | |
| type: "ReLU" | |
| bottom: "res5_conv2/bn" | |
| top: "res5_conv2/bn" | |
| } | |
| layer { | |
| name: "res5_conv3" | |
| type: "Convolution" | |
| bottom: "res5_conv2/bn" | |
| top: "res5_conv3" | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res5_eletwise" | |
| type: "Eltwise" | |
| bottom: "res4_eletwise" | |
| bottom: "res5_conv3" | |
| top: "res5_eletwise" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "res6_bn" | |
| type: "BatchNorm" | |
| bottom: "res5_eletwise" | |
| top: "res6_bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res6_scale" | |
| type: "Scale" | |
| bottom: "res6_bn" | |
| top: "res6_bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res6_relu" | |
| type: "ReLU" | |
| bottom: "res6_bn" | |
| top: "res6_bn" | |
| } | |
| layer { | |
| name: "res6_conv1" | |
| type: "Convolution" | |
| bottom: "res6_bn" | |
| top: "res6_conv1" | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res6_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "res6_conv1" | |
| top: "res6_conv1/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res6_conv1_scale" | |
| type: "Scale" | |
| bottom: "res6_conv1/bn" | |
| top: "res6_conv1/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res6_conv1_relu" | |
| type: "ReLU" | |
| bottom: "res6_conv1/bn" | |
| top: "res6_conv1/bn" | |
| } | |
| layer { | |
| name: "res6_conv2" | |
| type: "Convolution" | |
| bottom: "res6_conv1/bn" | |
| top: "res6_conv2" | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res6_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "res6_conv2" | |
| top: "res6_conv2/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res6_conv2_scale" | |
| type: "Scale" | |
| bottom: "res6_conv2/bn" | |
| top: "res6_conv2/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res6_conv2_relu" | |
| type: "ReLU" | |
| bottom: "res6_conv2/bn" | |
| top: "res6_conv2/bn" | |
| } | |
| layer { | |
| name: "res6_conv3" | |
| type: "Convolution" | |
| bottom: "res6_conv2/bn" | |
| top: "res6_conv3" | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res6_eletwise" | |
| type: "Eltwise" | |
| bottom: "res5_eletwise" | |
| bottom: "res6_conv3" | |
| top: "res6_eletwise" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "res7_bn" | |
| type: "BatchNorm" | |
| bottom: "res6_eletwise" | |
| top: "res7_bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res7_scale" | |
| type: "Scale" | |
| bottom: "res7_bn" | |
| top: "res7_bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res7_relu" | |
| type: "ReLU" | |
| bottom: "res7_bn" | |
| top: "res7_bn" | |
| } | |
| layer { | |
| name: "res7_conv1" | |
| type: "Convolution" | |
| bottom: "res7_bn" | |
| top: "res7_conv1" | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res7_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "res7_conv1" | |
| top: "res7_conv1/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res7_conv1_scale" | |
| type: "Scale" | |
| bottom: "res7_conv1/bn" | |
| top: "res7_conv1/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res7_conv1_relu" | |
| type: "ReLU" | |
| bottom: "res7_conv1/bn" | |
| top: "res7_conv1/bn" | |
| } | |
| layer { | |
| name: "res7_conv2" | |
| type: "Convolution" | |
| bottom: "res7_conv1/bn" | |
| top: "res7_conv2" | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res7_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "res7_conv2" | |
| top: "res7_conv2/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res7_conv2_scale" | |
| type: "Scale" | |
| bottom: "res7_conv2/bn" | |
| top: "res7_conv2/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res7_conv2_relu" | |
| type: "ReLU" | |
| bottom: "res7_conv2/bn" | |
| top: "res7_conv2/bn" | |
| } | |
| layer { | |
| name: "res7_conv3" | |
| type: "Convolution" | |
| bottom: "res7_conv2/bn" | |
| top: "res7_conv3" | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res7_eletwise" | |
| type: "Eltwise" | |
| bottom: "res6_eletwise" | |
| bottom: "res7_conv3" | |
| top: "res7_eletwise" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "res8_bn" | |
| type: "BatchNorm" | |
| bottom: "res7_eletwise" | |
| top: "res8_bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res8_scale" | |
| type: "Scale" | |
| bottom: "res8_bn" | |
| top: "res8_bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res8_relu" | |
| type: "ReLU" | |
| bottom: "res8_bn" | |
| top: "res8_bn" | |
| } | |
| layer { | |
| name: "res8_conv1" | |
| type: "Convolution" | |
| bottom: "res8_bn" | |
| top: "res8_conv1" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res8_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "res8_conv1" | |
| top: "res8_conv1/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res8_conv1_scale" | |
| type: "Scale" | |
| bottom: "res8_conv1/bn" | |
| top: "res8_conv1/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res8_conv1_relu" | |
| type: "ReLU" | |
| bottom: "res8_conv1/bn" | |
| top: "res8_conv1/bn" | |
| } | |
| layer { | |
| name: "res8_conv2" | |
| type: "Convolution" | |
| bottom: "res8_conv1/bn" | |
| top: "res8_conv2" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "res8_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "res8_conv2" | |
| top: "res8_conv2/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res8_conv2_scale" | |
| type: "Scale" | |
| bottom: "res8_conv2/bn" | |
| top: "res8_conv2/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res8_conv2_relu" | |
| type: "ReLU" | |
| bottom: "res8_conv2/bn" | |
| top: "res8_conv2/bn" | |
| } | |
| layer { | |
| name: "res8_conv3" | |
| type: "Convolution" | |
| bottom: "res8_conv2/bn" | |
| top: "res8_conv3" | |
| convolution_param { | |
| num_output: 1024 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res8_match_conv" | |
| type: "Convolution" | |
| bottom: "res8_bn" | |
| top: "res8_match_conv" | |
| convolution_param { | |
| num_output: 1024 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "res8_eletwise" | |
| type: "Eltwise" | |
| bottom: "res8_match_conv" | |
| bottom: "res8_conv3" | |
| top: "res8_eletwise" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "res9_bn" | |
| type: "BatchNorm" | |
| bottom: "res8_eletwise" | |
| top: "res9_bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res9_scale" | |
| type: "Scale" | |
| bottom: "res9_bn" | |
| top: "res9_bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res9_relu" | |
| type: "ReLU" | |
| bottom: "res9_bn" | |
| top: "res9_bn" | |
| } | |
| layer { | |
| name: "res9_conv1" | |
| type: "Convolution" | |
| bottom: "res9_bn" | |
| top: "res9_conv1" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res9_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "res9_conv1" | |
| top: "res9_conv1/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res9_conv1_scale" | |
| type: "Scale" | |
| bottom: "res9_conv1/bn" | |
| top: "res9_conv1/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res9_conv1_relu" | |
| type: "ReLU" | |
| bottom: "res9_conv1/bn" | |
| top: "res9_conv1/bn" | |
| } | |
| layer { | |
| name: "res9_conv2" | |
| type: "Convolution" | |
| bottom: "res9_conv1/bn" | |
| top: "res9_conv2" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res9_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "res9_conv2" | |
| top: "res9_conv2/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res9_conv2_scale" | |
| type: "Scale" | |
| bottom: "res9_conv2/bn" | |
| top: "res9_conv2/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res9_conv2_relu" | |
| type: "ReLU" | |
| bottom: "res9_conv2/bn" | |
| top: "res9_conv2/bn" | |
| } | |
| layer { | |
| name: "res9_conv3" | |
| type: "Convolution" | |
| bottom: "res9_conv2/bn" | |
| top: "res9_conv3" | |
| convolution_param { | |
| num_output: 1024 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res9_eletwise" | |
| type: "Eltwise" | |
| bottom: "res8_eletwise" | |
| bottom: "res9_conv3" | |
| top: "res9_eletwise" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "res10_bn" | |
| type: "BatchNorm" | |
| bottom: "res9_eletwise" | |
| top: "res10_bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res10_scale" | |
| type: "Scale" | |
| bottom: "res10_bn" | |
| top: "res10_bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res10_relu" | |
| type: "ReLU" | |
| bottom: "res10_bn" | |
| top: "res10_bn" | |
| } | |
| layer { | |
| name: "res10_conv1" | |
| type: "Convolution" | |
| bottom: "res10_bn" | |
| top: "res10_conv1" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res10_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "res10_conv1" | |
| top: "res10_conv1/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res10_conv1_scale" | |
| type: "Scale" | |
| bottom: "res10_conv1/bn" | |
| top: "res10_conv1/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res10_conv1_relu" | |
| type: "ReLU" | |
| bottom: "res10_conv1/bn" | |
| top: "res10_conv1/bn" | |
| } | |
| layer { | |
| name: "res10_conv2" | |
| type: "Convolution" | |
| bottom: "res10_conv1/bn" | |
| top: "res10_conv2" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res10_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "res10_conv2" | |
| top: "res10_conv2/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res10_conv2_scale" | |
| type: "Scale" | |
| bottom: "res10_conv2/bn" | |
| top: "res10_conv2/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res10_conv2_relu" | |
| type: "ReLU" | |
| bottom: "res10_conv2/bn" | |
| top: "res10_conv2/bn" | |
| } | |
| layer { | |
| name: "res10_conv3" | |
| type: "Convolution" | |
| bottom: "res10_conv2/bn" | |
| top: "res10_conv3" | |
| convolution_param { | |
| num_output: 1024 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res10_eletwise" | |
| type: "Eltwise" | |
| bottom: "res9_eletwise" | |
| bottom: "res10_conv3" | |
| top: "res10_eletwise" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "res11_bn" | |
| type: "BatchNorm" | |
| bottom: "res10_eletwise" | |
| top: "res11_bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res11_scale" | |
| type: "Scale" | |
| bottom: "res11_bn" | |
| top: "res11_bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res11_relu" | |
| type: "ReLU" | |
| bottom: "res11_bn" | |
| top: "res11_bn" | |
| } | |
| layer { | |
| name: "res11_conv1" | |
| type: "Convolution" | |
| bottom: "res11_bn" | |
| top: "res11_conv1" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res11_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "res11_conv1" | |
| top: "res11_conv1/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res11_conv1_scale" | |
| type: "Scale" | |
| bottom: "res11_conv1/bn" | |
| top: "res11_conv1/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res11_conv1_relu" | |
| type: "ReLU" | |
| bottom: "res11_conv1/bn" | |
| top: "res11_conv1/bn" | |
| } | |
| layer { | |
| name: "res11_conv2" | |
| type: "Convolution" | |
| bottom: "res11_conv1/bn" | |
| top: "res11_conv2" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res11_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "res11_conv2" | |
| top: "res11_conv2/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res11_conv2_scale" | |
| type: "Scale" | |
| bottom: "res11_conv2/bn" | |
| top: "res11_conv2/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res11_conv2_relu" | |
| type: "ReLU" | |
| bottom: "res11_conv2/bn" | |
| top: "res11_conv2/bn" | |
| } | |
| layer { | |
| name: "res11_conv3" | |
| type: "Convolution" | |
| bottom: "res11_conv2/bn" | |
| top: "res11_conv3" | |
| convolution_param { | |
| num_output: 1024 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res11_eletwise" | |
| type: "Eltwise" | |
| bottom: "res10_eletwise" | |
| bottom: "res11_conv3" | |
| top: "res11_eletwise" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "res12_bn" | |
| type: "BatchNorm" | |
| bottom: "res11_eletwise" | |
| top: "res12_bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res12_scale" | |
| type: "Scale" | |
| bottom: "res12_bn" | |
| top: "res12_bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res12_relu" | |
| type: "ReLU" | |
| bottom: "res12_bn" | |
| top: "res12_bn" | |
| } | |
| layer { | |
| name: "res12_conv1" | |
| type: "Convolution" | |
| bottom: "res12_bn" | |
| top: "res12_conv1" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res12_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "res12_conv1" | |
| top: "res12_conv1/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res12_conv1_scale" | |
| type: "Scale" | |
| bottom: "res12_conv1/bn" | |
| top: "res12_conv1/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res12_conv1_relu" | |
| type: "ReLU" | |
| bottom: "res12_conv1/bn" | |
| top: "res12_conv1/bn" | |
| } | |
| layer { | |
| name: "res12_conv2" | |
| type: "Convolution" | |
| bottom: "res12_conv1/bn" | |
| top: "res12_conv2" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res12_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "res12_conv2" | |
| top: "res12_conv2/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res12_conv2_scale" | |
| type: "Scale" | |
| bottom: "res12_conv2/bn" | |
| top: "res12_conv2/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res12_conv2_relu" | |
| type: "ReLU" | |
| bottom: "res12_conv2/bn" | |
| top: "res12_conv2/bn" | |
| } | |
| layer { | |
| name: "res12_conv3" | |
| type: "Convolution" | |
| bottom: "res12_conv2/bn" | |
| top: "res12_conv3" | |
| convolution_param { | |
| num_output: 1024 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res12_eletwise" | |
| type: "Eltwise" | |
| bottom: "res11_eletwise" | |
| bottom: "res12_conv3" | |
| top: "res12_eletwise" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "res13_bn" | |
| type: "BatchNorm" | |
| bottom: "res12_eletwise" | |
| top: "res13_bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res13_scale" | |
| type: "Scale" | |
| bottom: "res13_bn" | |
| top: "res13_bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res13_relu" | |
| type: "ReLU" | |
| bottom: "res13_bn" | |
| top: "res13_bn" | |
| } | |
| layer { | |
| name: "res13_conv1" | |
| type: "Convolution" | |
| bottom: "res13_bn" | |
| top: "res13_conv1" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res13_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "res13_conv1" | |
| top: "res13_conv1/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res13_conv1_scale" | |
| type: "Scale" | |
| bottom: "res13_conv1/bn" | |
| top: "res13_conv1/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res13_conv1_relu" | |
| type: "ReLU" | |
| bottom: "res13_conv1/bn" | |
| top: "res13_conv1/bn" | |
| } | |
| layer { | |
| name: "res13_conv2" | |
| type: "Convolution" | |
| bottom: "res13_conv1/bn" | |
| top: "res13_conv2" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res13_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "res13_conv2" | |
| top: "res13_conv2/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res13_conv2_scale" | |
| type: "Scale" | |
| bottom: "res13_conv2/bn" | |
| top: "res13_conv2/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res13_conv2_relu" | |
| type: "ReLU" | |
| bottom: "res13_conv2/bn" | |
| top: "res13_conv2/bn" | |
| } | |
| layer { | |
| name: "res13_conv3" | |
| type: "Convolution" | |
| bottom: "res13_conv2/bn" | |
| top: "res13_conv3" | |
| convolution_param { | |
| num_output: 1024 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res13_eletwise" | |
| type: "Eltwise" | |
| bottom: "res12_eletwise" | |
| bottom: "res13_conv3" | |
| top: "res13_eletwise" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "res14_bn" | |
| type: "BatchNorm" | |
| bottom: "res13_eletwise" | |
| top: "res14_bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res14_scale" | |
| type: "Scale" | |
| bottom: "res14_bn" | |
| top: "res14_bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res14_relu" | |
| type: "ReLU" | |
| bottom: "res14_bn" | |
| top: "res14_bn" | |
| } | |
| layer { | |
| name: "res14_conv1" | |
| type: "Convolution" | |
| bottom: "res14_bn" | |
| top: "res14_conv1" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res14_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "res14_conv1" | |
| top: "res14_conv1/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res14_conv1_scale" | |
| type: "Scale" | |
| bottom: "res14_conv1/bn" | |
| top: "res14_conv1/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res14_conv1_relu" | |
| type: "ReLU" | |
| bottom: "res14_conv1/bn" | |
| top: "res14_conv1/bn" | |
| } | |
| layer { | |
| name: "res14_conv2" | |
| type: "Convolution" | |
| bottom: "res14_conv1/bn" | |
| top: "res14_conv2" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res14_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "res14_conv2" | |
| top: "res14_conv2/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res14_conv2_scale" | |
| type: "Scale" | |
| bottom: "res14_conv2/bn" | |
| top: "res14_conv2/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res14_conv2_relu" | |
| type: "ReLU" | |
| bottom: "res14_conv2/bn" | |
| top: "res14_conv2/bn" | |
| } | |
| layer { | |
| name: "res14_conv3" | |
| type: "Convolution" | |
| bottom: "res14_conv2/bn" | |
| top: "res14_conv3" | |
| convolution_param { | |
| num_output: 1024 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res14_eletwise" | |
| type: "Eltwise" | |
| bottom: "res13_eletwise" | |
| bottom: "res14_conv3" | |
| top: "res14_eletwise" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "res15_bn" | |
| type: "BatchNorm" | |
| bottom: "res14_eletwise" | |
| top: "res15_bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res15_scale" | |
| type: "Scale" | |
| bottom: "res15_bn" | |
| top: "res15_bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res15_relu" | |
| type: "ReLU" | |
| bottom: "res15_bn" | |
| top: "res15_bn" | |
| } | |
| layer { | |
| name: "res15_conv1" | |
| type: "Convolution" | |
| bottom: "res15_bn" | |
| top: "res15_conv1" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res15_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "res15_conv1" | |
| top: "res15_conv1/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res15_conv1_scale" | |
| type: "Scale" | |
| bottom: "res15_conv1/bn" | |
| top: "res15_conv1/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res15_conv1_relu" | |
| type: "ReLU" | |
| bottom: "res15_conv1/bn" | |
| top: "res15_conv1/bn" | |
| } | |
| layer { | |
| name: "res15_conv2" | |
| type: "Convolution" | |
| bottom: "res15_conv1/bn" | |
| top: "res15_conv2" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res15_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "res15_conv2" | |
| top: "res15_conv2/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res15_conv2_scale" | |
| type: "Scale" | |
| bottom: "res15_conv2/bn" | |
| top: "res15_conv2/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res15_conv2_relu" | |
| type: "ReLU" | |
| bottom: "res15_conv2/bn" | |
| top: "res15_conv2/bn" | |
| } | |
| layer { | |
| name: "res15_conv3" | |
| type: "Convolution" | |
| bottom: "res15_conv2/bn" | |
| top: "res15_conv3" | |
| convolution_param { | |
| num_output: 1024 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res15_eletwise" | |
| type: "Eltwise" | |
| bottom: "res14_eletwise" | |
| bottom: "res15_conv3" | |
| top: "res15_eletwise" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "res16_bn" | |
| type: "BatchNorm" | |
| bottom: "res15_eletwise" | |
| top: "res16_bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res16_scale" | |
| type: "Scale" | |
| bottom: "res16_bn" | |
| top: "res16_bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res16_relu" | |
| type: "ReLU" | |
| bottom: "res16_bn" | |
| top: "res16_bn" | |
| } | |
| layer { | |
| name: "res16_conv1" | |
| type: "Convolution" | |
| bottom: "res16_bn" | |
| top: "res16_conv1" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res16_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "res16_conv1" | |
| top: "res16_conv1/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res16_conv1_scale" | |
| type: "Scale" | |
| bottom: "res16_conv1/bn" | |
| top: "res16_conv1/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res16_conv1_relu" | |
| type: "ReLU" | |
| bottom: "res16_conv1/bn" | |
| top: "res16_conv1/bn" | |
| } | |
| layer { | |
| name: "res16_conv2" | |
| type: "Convolution" | |
| bottom: "res16_conv1/bn" | |
| top: "res16_conv2" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res16_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "res16_conv2" | |
| top: "res16_conv2/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res16_conv2_scale" | |
| type: "Scale" | |
| bottom: "res16_conv2/bn" | |
| top: "res16_conv2/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res16_conv2_relu" | |
| type: "ReLU" | |
| bottom: "res16_conv2/bn" | |
| top: "res16_conv2/bn" | |
| } | |
| layer { | |
| name: "res16_conv3" | |
| type: "Convolution" | |
| bottom: "res16_conv2/bn" | |
| top: "res16_conv3" | |
| convolution_param { | |
| num_output: 1024 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res16_eletwise" | |
| type: "Eltwise" | |
| bottom: "res15_eletwise" | |
| bottom: "res16_conv3" | |
| top: "res16_eletwise" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "res17_bn" | |
| type: "BatchNorm" | |
| bottom: "res16_eletwise" | |
| top: "res17_bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res17_scale" | |
| type: "Scale" | |
| bottom: "res17_bn" | |
| top: "res17_bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res17_relu" | |
| type: "ReLU" | |
| bottom: "res17_bn" | |
| top: "res17_bn" | |
| } | |
| layer { | |
| name: "res17_conv1" | |
| type: "Convolution" | |
| bottom: "res17_bn" | |
| top: "res17_conv1" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res17_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "res17_conv1" | |
| top: "res17_conv1/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res17_conv1_scale" | |
| type: "Scale" | |
| bottom: "res17_conv1/bn" | |
| top: "res17_conv1/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res17_conv1_relu" | |
| type: "ReLU" | |
| bottom: "res17_conv1/bn" | |
| top: "res17_conv1/bn" | |
| } | |
| layer { | |
| name: "res17_conv2" | |
| type: "Convolution" | |
| bottom: "res17_conv1/bn" | |
| top: "res17_conv2" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res17_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "res17_conv2" | |
| top: "res17_conv2/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res17_conv2_scale" | |
| type: "Scale" | |
| bottom: "res17_conv2/bn" | |
| top: "res17_conv2/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res17_conv2_relu" | |
| type: "ReLU" | |
| bottom: "res17_conv2/bn" | |
| top: "res17_conv2/bn" | |
| } | |
| layer { | |
| name: "res17_conv3" | |
| type: "Convolution" | |
| bottom: "res17_conv2/bn" | |
| top: "res17_conv3" | |
| convolution_param { | |
| num_output: 1024 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res17_eletwise" | |
| type: "Eltwise" | |
| bottom: "res16_eletwise" | |
| bottom: "res17_conv3" | |
| top: "res17_eletwise" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "res18_bn" | |
| type: "BatchNorm" | |
| bottom: "res17_eletwise" | |
| top: "res18_bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res18_scale" | |
| type: "Scale" | |
| bottom: "res18_bn" | |
| top: "res18_bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res18_relu" | |
| type: "ReLU" | |
| bottom: "res18_bn" | |
| top: "res18_bn" | |
| } | |
| layer { | |
| name: "res18_conv1" | |
| type: "Convolution" | |
| bottom: "res18_bn" | |
| top: "res18_conv1" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res18_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "res18_conv1" | |
| top: "res18_conv1/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res18_conv1_scale" | |
| type: "Scale" | |
| bottom: "res18_conv1/bn" | |
| top: "res18_conv1/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res18_conv1_relu" | |
| type: "ReLU" | |
| bottom: "res18_conv1/bn" | |
| top: "res18_conv1/bn" | |
| } | |
| layer { | |
| name: "res18_conv2" | |
| type: "Convolution" | |
| bottom: "res18_conv1/bn" | |
| top: "res18_conv2" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res18_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "res18_conv2" | |
| top: "res18_conv2/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res18_conv2_scale" | |
| type: "Scale" | |
| bottom: "res18_conv2/bn" | |
| top: "res18_conv2/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res18_conv2_relu" | |
| type: "ReLU" | |
| bottom: "res18_conv2/bn" | |
| top: "res18_conv2/bn" | |
| } | |
| layer { | |
| name: "res18_conv3" | |
| type: "Convolution" | |
| bottom: "res18_conv2/bn" | |
| top: "res18_conv3" | |
| convolution_param { | |
| num_output: 1024 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res18_eletwise" | |
| type: "Eltwise" | |
| bottom: "res17_eletwise" | |
| bottom: "res18_conv3" | |
| top: "res18_eletwise" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "res19_bn" | |
| type: "BatchNorm" | |
| bottom: "res18_eletwise" | |
| top: "res19_bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res19_scale" | |
| type: "Scale" | |
| bottom: "res19_bn" | |
| top: "res19_bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res19_relu" | |
| type: "ReLU" | |
| bottom: "res19_bn" | |
| top: "res19_bn" | |
| } | |
| layer { | |
| name: "res19_conv1" | |
| type: "Convolution" | |
| bottom: "res19_bn" | |
| top: "res19_conv1" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res19_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "res19_conv1" | |
| top: "res19_conv1/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res19_conv1_scale" | |
| type: "Scale" | |
| bottom: "res19_conv1/bn" | |
| top: "res19_conv1/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res19_conv1_relu" | |
| type: "ReLU" | |
| bottom: "res19_conv1/bn" | |
| top: "res19_conv1/bn" | |
| } | |
| layer { | |
| name: "res19_conv2" | |
| type: "Convolution" | |
| bottom: "res19_conv1/bn" | |
| top: "res19_conv2" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res19_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "res19_conv2" | |
| top: "res19_conv2/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res19_conv2_scale" | |
| type: "Scale" | |
| bottom: "res19_conv2/bn" | |
| top: "res19_conv2/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res19_conv2_relu" | |
| type: "ReLU" | |
| bottom: "res19_conv2/bn" | |
| top: "res19_conv2/bn" | |
| } | |
| layer { | |
| name: "res19_conv3" | |
| type: "Convolution" | |
| bottom: "res19_conv2/bn" | |
| top: "res19_conv3" | |
| convolution_param { | |
| num_output: 1024 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res19_eletwise" | |
| type: "Eltwise" | |
| bottom: "res18_eletwise" | |
| bottom: "res19_conv3" | |
| top: "res19_eletwise" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "res20_bn" | |
| type: "BatchNorm" | |
| bottom: "res19_eletwise" | |
| top: "res20_bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res20_scale" | |
| type: "Scale" | |
| bottom: "res20_bn" | |
| top: "res20_bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res20_relu" | |
| type: "ReLU" | |
| bottom: "res20_bn" | |
| top: "res20_bn" | |
| } | |
| layer { | |
| name: "res20_conv1" | |
| type: "Convolution" | |
| bottom: "res20_bn" | |
| top: "res20_conv1" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res20_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "res20_conv1" | |
| top: "res20_conv1/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res20_conv1_scale" | |
| type: "Scale" | |
| bottom: "res20_conv1/bn" | |
| top: "res20_conv1/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res20_conv1_relu" | |
| type: "ReLU" | |
| bottom: "res20_conv1/bn" | |
| top: "res20_conv1/bn" | |
| } | |
| layer { | |
| name: "res20_conv2" | |
| type: "Convolution" | |
| bottom: "res20_conv1/bn" | |
| top: "res20_conv2" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res20_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "res20_conv2" | |
| top: "res20_conv2/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res20_conv2_scale" | |
| type: "Scale" | |
| bottom: "res20_conv2/bn" | |
| top: "res20_conv2/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res20_conv2_relu" | |
| type: "ReLU" | |
| bottom: "res20_conv2/bn" | |
| top: "res20_conv2/bn" | |
| } | |
| layer { | |
| name: "res20_conv3" | |
| type: "Convolution" | |
| bottom: "res20_conv2/bn" | |
| top: "res20_conv3" | |
| convolution_param { | |
| num_output: 1024 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res20_eletwise" | |
| type: "Eltwise" | |
| bottom: "res19_eletwise" | |
| bottom: "res20_conv3" | |
| top: "res20_eletwise" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "res21_bn" | |
| type: "BatchNorm" | |
| bottom: "res20_eletwise" | |
| top: "res21_bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res21_scale" | |
| type: "Scale" | |
| bottom: "res21_bn" | |
| top: "res21_bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res21_relu" | |
| type: "ReLU" | |
| bottom: "res21_bn" | |
| top: "res21_bn" | |
| } | |
| layer { | |
| name: "res21_conv1" | |
| type: "Convolution" | |
| bottom: "res21_bn" | |
| top: "res21_conv1" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res21_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "res21_conv1" | |
| top: "res21_conv1/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res21_conv1_scale" | |
| type: "Scale" | |
| bottom: "res21_conv1/bn" | |
| top: "res21_conv1/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res21_conv1_relu" | |
| type: "ReLU" | |
| bottom: "res21_conv1/bn" | |
| top: "res21_conv1/bn" | |
| } | |
| layer { | |
| name: "res21_conv2" | |
| type: "Convolution" | |
| bottom: "res21_conv1/bn" | |
| top: "res21_conv2" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res21_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "res21_conv2" | |
| top: "res21_conv2/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res21_conv2_scale" | |
| type: "Scale" | |
| bottom: "res21_conv2/bn" | |
| top: "res21_conv2/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res21_conv2_relu" | |
| type: "ReLU" | |
| bottom: "res21_conv2/bn" | |
| top: "res21_conv2/bn" | |
| } | |
| layer { | |
| name: "res21_conv3" | |
| type: "Convolution" | |
| bottom: "res21_conv2/bn" | |
| top: "res21_conv3" | |
| convolution_param { | |
| num_output: 1024 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res21_eletwise" | |
| type: "Eltwise" | |
| bottom: "res20_eletwise" | |
| bottom: "res21_conv3" | |
| top: "res21_eletwise" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "res22_bn" | |
| type: "BatchNorm" | |
| bottom: "res21_eletwise" | |
| top: "res22_bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res22_scale" | |
| type: "Scale" | |
| bottom: "res22_bn" | |
| top: "res22_bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res22_relu" | |
| type: "ReLU" | |
| bottom: "res22_bn" | |
| top: "res22_bn" | |
| } | |
| layer { | |
| name: "res22_conv1" | |
| type: "Convolution" | |
| bottom: "res22_bn" | |
| top: "res22_conv1" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res22_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "res22_conv1" | |
| top: "res22_conv1/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res22_conv1_scale" | |
| type: "Scale" | |
| bottom: "res22_conv1/bn" | |
| top: "res22_conv1/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res22_conv1_relu" | |
| type: "ReLU" | |
| bottom: "res22_conv1/bn" | |
| top: "res22_conv1/bn" | |
| } | |
| layer { | |
| name: "res22_conv2" | |
| type: "Convolution" | |
| bottom: "res22_conv1/bn" | |
| top: "res22_conv2" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res22_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "res22_conv2" | |
| top: "res22_conv2/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res22_conv2_scale" | |
| type: "Scale" | |
| bottom: "res22_conv2/bn" | |
| top: "res22_conv2/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res22_conv2_relu" | |
| type: "ReLU" | |
| bottom: "res22_conv2/bn" | |
| top: "res22_conv2/bn" | |
| } | |
| layer { | |
| name: "res22_conv3" | |
| type: "Convolution" | |
| bottom: "res22_conv2/bn" | |
| top: "res22_conv3" | |
| convolution_param { | |
| num_output: 1024 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res22_eletwise" | |
| type: "Eltwise" | |
| bottom: "res21_eletwise" | |
| bottom: "res22_conv3" | |
| top: "res22_eletwise" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "res23_bn" | |
| type: "BatchNorm" | |
| bottom: "res22_eletwise" | |
| top: "res23_bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res23_scale" | |
| type: "Scale" | |
| bottom: "res23_bn" | |
| top: "res23_bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res23_relu" | |
| type: "ReLU" | |
| bottom: "res23_bn" | |
| top: "res23_bn" | |
| } | |
| layer { | |
| name: "res23_conv1" | |
| type: "Convolution" | |
| bottom: "res23_bn" | |
| top: "res23_conv1" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res23_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "res23_conv1" | |
| top: "res23_conv1/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res23_conv1_scale" | |
| type: "Scale" | |
| bottom: "res23_conv1/bn" | |
| top: "res23_conv1/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res23_conv1_relu" | |
| type: "ReLU" | |
| bottom: "res23_conv1/bn" | |
| top: "res23_conv1/bn" | |
| } | |
| layer { | |
| name: "res23_conv2" | |
| type: "Convolution" | |
| bottom: "res23_conv1/bn" | |
| top: "res23_conv2" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res23_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "res23_conv2" | |
| top: "res23_conv2/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res23_conv2_scale" | |
| type: "Scale" | |
| bottom: "res23_conv2/bn" | |
| top: "res23_conv2/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res23_conv2_relu" | |
| type: "ReLU" | |
| bottom: "res23_conv2/bn" | |
| top: "res23_conv2/bn" | |
| } | |
| layer { | |
| name: "res23_conv3" | |
| type: "Convolution" | |
| bottom: "res23_conv2/bn" | |
| top: "res23_conv3" | |
| convolution_param { | |
| num_output: 1024 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res23_eletwise" | |
| type: "Eltwise" | |
| bottom: "res22_eletwise" | |
| bottom: "res23_conv3" | |
| top: "res23_eletwise" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "res24_bn" | |
| type: "BatchNorm" | |
| bottom: "res23_eletwise" | |
| top: "res24_bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res24_scale" | |
| type: "Scale" | |
| bottom: "res24_bn" | |
| top: "res24_bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res24_relu" | |
| type: "ReLU" | |
| bottom: "res24_bn" | |
| top: "res24_bn" | |
| } | |
| layer { | |
| name: "res24_conv1" | |
| type: "Convolution" | |
| bottom: "res24_bn" | |
| top: "res24_conv1" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res24_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "res24_conv1" | |
| top: "res24_conv1/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res24_conv1_scale" | |
| type: "Scale" | |
| bottom: "res24_conv1/bn" | |
| top: "res24_conv1/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res24_conv1_relu" | |
| type: "ReLU" | |
| bottom: "res24_conv1/bn" | |
| top: "res24_conv1/bn" | |
| } | |
| layer { | |
| name: "res24_conv2" | |
| type: "Convolution" | |
| bottom: "res24_conv1/bn" | |
| top: "res24_conv2" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res24_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "res24_conv2" | |
| top: "res24_conv2/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res24_conv2_scale" | |
| type: "Scale" | |
| bottom: "res24_conv2/bn" | |
| top: "res24_conv2/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res24_conv2_relu" | |
| type: "ReLU" | |
| bottom: "res24_conv2/bn" | |
| top: "res24_conv2/bn" | |
| } | |
| layer { | |
| name: "res24_conv3" | |
| type: "Convolution" | |
| bottom: "res24_conv2/bn" | |
| top: "res24_conv3" | |
| convolution_param { | |
| num_output: 1024 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res24_eletwise" | |
| type: "Eltwise" | |
| bottom: "res23_eletwise" | |
| bottom: "res24_conv3" | |
| top: "res24_eletwise" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "res25_bn" | |
| type: "BatchNorm" | |
| bottom: "res24_eletwise" | |
| top: "res25_bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res25_scale" | |
| type: "Scale" | |
| bottom: "res25_bn" | |
| top: "res25_bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res25_relu" | |
| type: "ReLU" | |
| bottom: "res25_bn" | |
| top: "res25_bn" | |
| } | |
| layer { | |
| name: "res25_conv1" | |
| type: "Convolution" | |
| bottom: "res25_bn" | |
| top: "res25_conv1" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res25_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "res25_conv1" | |
| top: "res25_conv1/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res25_conv1_scale" | |
| type: "Scale" | |
| bottom: "res25_conv1/bn" | |
| top: "res25_conv1/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res25_conv1_relu" | |
| type: "ReLU" | |
| bottom: "res25_conv1/bn" | |
| top: "res25_conv1/bn" | |
| } | |
| layer { | |
| name: "res25_conv2" | |
| type: "Convolution" | |
| bottom: "res25_conv1/bn" | |
| top: "res25_conv2" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res25_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "res25_conv2" | |
| top: "res25_conv2/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res25_conv2_scale" | |
| type: "Scale" | |
| bottom: "res25_conv2/bn" | |
| top: "res25_conv2/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res25_conv2_relu" | |
| type: "ReLU" | |
| bottom: "res25_conv2/bn" | |
| top: "res25_conv2/bn" | |
| } | |
| layer { | |
| name: "res25_conv3" | |
| type: "Convolution" | |
| bottom: "res25_conv2/bn" | |
| top: "res25_conv3" | |
| convolution_param { | |
| num_output: 1024 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res25_eletwise" | |
| type: "Eltwise" | |
| bottom: "res24_eletwise" | |
| bottom: "res25_conv3" | |
| top: "res25_eletwise" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "res26_bn" | |
| type: "BatchNorm" | |
| bottom: "res25_eletwise" | |
| top: "res26_bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res26_scale" | |
| type: "Scale" | |
| bottom: "res26_bn" | |
| top: "res26_bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res26_relu" | |
| type: "ReLU" | |
| bottom: "res26_bn" | |
| top: "res26_bn" | |
| } | |
| layer { | |
| name: "res26_conv1" | |
| type: "Convolution" | |
| bottom: "res26_bn" | |
| top: "res26_conv1" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res26_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "res26_conv1" | |
| top: "res26_conv1/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res26_conv1_scale" | |
| type: "Scale" | |
| bottom: "res26_conv1/bn" | |
| top: "res26_conv1/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res26_conv1_relu" | |
| type: "ReLU" | |
| bottom: "res26_conv1/bn" | |
| top: "res26_conv1/bn" | |
| } | |
| layer { | |
| name: "res26_conv2" | |
| type: "Convolution" | |
| bottom: "res26_conv1/bn" | |
| top: "res26_conv2" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res26_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "res26_conv2" | |
| top: "res26_conv2/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res26_conv2_scale" | |
| type: "Scale" | |
| bottom: "res26_conv2/bn" | |
| top: "res26_conv2/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res26_conv2_relu" | |
| type: "ReLU" | |
| bottom: "res26_conv2/bn" | |
| top: "res26_conv2/bn" | |
| } | |
| layer { | |
| name: "res26_conv3" | |
| type: "Convolution" | |
| bottom: "res26_conv2/bn" | |
| top: "res26_conv3" | |
| convolution_param { | |
| num_output: 1024 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res26_eletwise" | |
| type: "Eltwise" | |
| bottom: "res25_eletwise" | |
| bottom: "res26_conv3" | |
| top: "res26_eletwise" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "res27_bn" | |
| type: "BatchNorm" | |
| bottom: "res26_eletwise" | |
| top: "res27_bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res27_scale" | |
| type: "Scale" | |
| bottom: "res27_bn" | |
| top: "res27_bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res27_relu" | |
| type: "ReLU" | |
| bottom: "res27_bn" | |
| top: "res27_bn" | |
| } | |
| layer { | |
| name: "res27_conv1" | |
| type: "Convolution" | |
| bottom: "res27_bn" | |
| top: "res27_conv1" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res27_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "res27_conv1" | |
| top: "res27_conv1/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res27_conv1_scale" | |
| type: "Scale" | |
| bottom: "res27_conv1/bn" | |
| top: "res27_conv1/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res27_conv1_relu" | |
| type: "ReLU" | |
| bottom: "res27_conv1/bn" | |
| top: "res27_conv1/bn" | |
| } | |
| layer { | |
| name: "res27_conv2" | |
| type: "Convolution" | |
| bottom: "res27_conv1/bn" | |
| top: "res27_conv2" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res27_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "res27_conv2" | |
| top: "res27_conv2/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res27_conv2_scale" | |
| type: "Scale" | |
| bottom: "res27_conv2/bn" | |
| top: "res27_conv2/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res27_conv2_relu" | |
| type: "ReLU" | |
| bottom: "res27_conv2/bn" | |
| top: "res27_conv2/bn" | |
| } | |
| layer { | |
| name: "res27_conv3" | |
| type: "Convolution" | |
| bottom: "res27_conv2/bn" | |
| top: "res27_conv3" | |
| convolution_param { | |
| num_output: 1024 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res27_eletwise" | |
| type: "Eltwise" | |
| bottom: "res26_eletwise" | |
| bottom: "res27_conv3" | |
| top: "res27_eletwise" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "res28_bn" | |
| type: "BatchNorm" | |
| bottom: "res27_eletwise" | |
| top: "res28_bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res28_scale" | |
| type: "Scale" | |
| bottom: "res28_bn" | |
| top: "res28_bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res28_relu" | |
| type: "ReLU" | |
| bottom: "res28_bn" | |
| top: "res28_bn" | |
| } | |
| layer { | |
| name: "res28_conv1" | |
| type: "Convolution" | |
| bottom: "res28_bn" | |
| top: "res28_conv1" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res28_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "res28_conv1" | |
| top: "res28_conv1/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res28_conv1_scale" | |
| type: "Scale" | |
| bottom: "res28_conv1/bn" | |
| top: "res28_conv1/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res28_conv1_relu" | |
| type: "ReLU" | |
| bottom: "res28_conv1/bn" | |
| top: "res28_conv1/bn" | |
| } | |
| layer { | |
| name: "res28_conv2" | |
| type: "Convolution" | |
| bottom: "res28_conv1/bn" | |
| top: "res28_conv2" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res28_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "res28_conv2" | |
| top: "res28_conv2/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res28_conv2_scale" | |
| type: "Scale" | |
| bottom: "res28_conv2/bn" | |
| top: "res28_conv2/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res28_conv2_relu" | |
| type: "ReLU" | |
| bottom: "res28_conv2/bn" | |
| top: "res28_conv2/bn" | |
| } | |
| layer { | |
| name: "res28_conv3" | |
| type: "Convolution" | |
| bottom: "res28_conv2/bn" | |
| top: "res28_conv3" | |
| convolution_param { | |
| num_output: 1024 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res28_eletwise" | |
| type: "Eltwise" | |
| bottom: "res27_eletwise" | |
| bottom: "res28_conv3" | |
| top: "res28_eletwise" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "res29_bn" | |
| type: "BatchNorm" | |
| bottom: "res28_eletwise" | |
| top: "res29_bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res29_scale" | |
| type: "Scale" | |
| bottom: "res29_bn" | |
| top: "res29_bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res29_relu" | |
| type: "ReLU" | |
| bottom: "res29_bn" | |
| top: "res29_bn" | |
| } | |
| layer { | |
| name: "res29_conv1" | |
| type: "Convolution" | |
| bottom: "res29_bn" | |
| top: "res29_conv1" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res29_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "res29_conv1" | |
| top: "res29_conv1/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res29_conv1_scale" | |
| type: "Scale" | |
| bottom: "res29_conv1/bn" | |
| top: "res29_conv1/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res29_conv1_relu" | |
| type: "ReLU" | |
| bottom: "res29_conv1/bn" | |
| top: "res29_conv1/bn" | |
| } | |
| layer { | |
| name: "res29_conv2" | |
| type: "Convolution" | |
| bottom: "res29_conv1/bn" | |
| top: "res29_conv2" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res29_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "res29_conv2" | |
| top: "res29_conv2/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res29_conv2_scale" | |
| type: "Scale" | |
| bottom: "res29_conv2/bn" | |
| top: "res29_conv2/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res29_conv2_relu" | |
| type: "ReLU" | |
| bottom: "res29_conv2/bn" | |
| top: "res29_conv2/bn" | |
| } | |
| layer { | |
| name: "res29_conv3" | |
| type: "Convolution" | |
| bottom: "res29_conv2/bn" | |
| top: "res29_conv3" | |
| convolution_param { | |
| num_output: 1024 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res29_eletwise" | |
| type: "Eltwise" | |
| bottom: "res28_eletwise" | |
| bottom: "res29_conv3" | |
| top: "res29_eletwise" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "res30_bn" | |
| type: "BatchNorm" | |
| bottom: "res29_eletwise" | |
| top: "res30_bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res30_scale" | |
| type: "Scale" | |
| bottom: "res30_bn" | |
| top: "res30_bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res30_relu" | |
| type: "ReLU" | |
| bottom: "res30_bn" | |
| top: "res30_bn" | |
| } | |
| layer { | |
| name: "res30_conv1" | |
| type: "Convolution" | |
| bottom: "res30_bn" | |
| top: "res30_conv1" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res30_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "res30_conv1" | |
| top: "res30_conv1/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res30_conv1_scale" | |
| type: "Scale" | |
| bottom: "res30_conv1/bn" | |
| top: "res30_conv1/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res30_conv1_relu" | |
| type: "ReLU" | |
| bottom: "res30_conv1/bn" | |
| top: "res30_conv1/bn" | |
| } | |
| layer { | |
| name: "res30_conv2" | |
| type: "Convolution" | |
| bottom: "res30_conv1/bn" | |
| top: "res30_conv2" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res30_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "res30_conv2" | |
| top: "res30_conv2/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res30_conv2_scale" | |
| type: "Scale" | |
| bottom: "res30_conv2/bn" | |
| top: "res30_conv2/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res30_conv2_relu" | |
| type: "ReLU" | |
| bottom: "res30_conv2/bn" | |
| top: "res30_conv2/bn" | |
| } | |
| layer { | |
| name: "res30_conv3" | |
| type: "Convolution" | |
| bottom: "res30_conv2/bn" | |
| top: "res30_conv3" | |
| convolution_param { | |
| num_output: 1024 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res30_eletwise" | |
| type: "Eltwise" | |
| bottom: "res29_eletwise" | |
| bottom: "res30_conv3" | |
| top: "res30_eletwise" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "res31_bn" | |
| type: "BatchNorm" | |
| bottom: "res30_eletwise" | |
| top: "res31_bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res31_scale" | |
| type: "Scale" | |
| bottom: "res31_bn" | |
| top: "res31_bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res31_relu" | |
| type: "ReLU" | |
| bottom: "res31_bn" | |
| top: "res31_bn" | |
| } | |
| layer { | |
| name: "res31_conv1" | |
| type: "Convolution" | |
| bottom: "res31_bn" | |
| top: "res31_conv1" | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res31_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "res31_conv1" | |
| top: "res31_conv1/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res31_conv1_scale" | |
| type: "Scale" | |
| bottom: "res31_conv1/bn" | |
| top: "res31_conv1/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res31_conv1_relu" | |
| type: "ReLU" | |
| bottom: "res31_conv1/bn" | |
| top: "res31_conv1/bn" | |
| } | |
| layer { | |
| name: "res31_conv2" | |
| type: "Convolution" | |
| bottom: "res31_conv1/bn" | |
| top: "res31_conv2" | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "res31_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "res31_conv2" | |
| top: "res31_conv2/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res31_conv2_scale" | |
| type: "Scale" | |
| bottom: "res31_conv2/bn" | |
| top: "res31_conv2/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res31_conv2_relu" | |
| type: "ReLU" | |
| bottom: "res31_conv2/bn" | |
| top: "res31_conv2/bn" | |
| } | |
| layer { | |
| name: "res31_conv3" | |
| type: "Convolution" | |
| bottom: "res31_conv2/bn" | |
| top: "res31_conv3" | |
| convolution_param { | |
| num_output: 2048 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res31_match_conv" | |
| type: "Convolution" | |
| bottom: "res31_bn" | |
| top: "res31_match_conv" | |
| convolution_param { | |
| num_output: 2048 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "res31_eletwise" | |
| type: "Eltwise" | |
| bottom: "res31_match_conv" | |
| bottom: "res31_conv3" | |
| top: "res31_eletwise" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "res32_bn" | |
| type: "BatchNorm" | |
| bottom: "res31_eletwise" | |
| top: "res32_bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res32_scale" | |
| type: "Scale" | |
| bottom: "res32_bn" | |
| top: "res32_bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res32_relu" | |
| type: "ReLU" | |
| bottom: "res32_bn" | |
| top: "res32_bn" | |
| } | |
| layer { | |
| name: "res32_conv1" | |
| type: "Convolution" | |
| bottom: "res32_bn" | |
| top: "res32_conv1" | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res32_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "res32_conv1" | |
| top: "res32_conv1/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res32_conv1_scale" | |
| type: "Scale" | |
| bottom: "res32_conv1/bn" | |
| top: "res32_conv1/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res32_conv1_relu" | |
| type: "ReLU" | |
| bottom: "res32_conv1/bn" | |
| top: "res32_conv1/bn" | |
| } | |
| layer { | |
| name: "res32_conv2" | |
| type: "Convolution" | |
| bottom: "res32_conv1/bn" | |
| top: "res32_conv2" | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res32_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "res32_conv2" | |
| top: "res32_conv2/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res32_conv2_scale" | |
| type: "Scale" | |
| bottom: "res32_conv2/bn" | |
| top: "res32_conv2/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res32_conv2_relu" | |
| type: "ReLU" | |
| bottom: "res32_conv2/bn" | |
| top: "res32_conv2/bn" | |
| } | |
| layer { | |
| name: "res32_conv3" | |
| type: "Convolution" | |
| bottom: "res32_conv2/bn" | |
| top: "res32_conv3" | |
| convolution_param { | |
| num_output: 2048 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res32_eletwise" | |
| type: "Eltwise" | |
| bottom: "res31_eletwise" | |
| bottom: "res32_conv3" | |
| top: "res32_eletwise" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "res33_bn" | |
| type: "BatchNorm" | |
| bottom: "res32_eletwise" | |
| top: "res33_bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res33_scale" | |
| type: "Scale" | |
| bottom: "res33_bn" | |
| top: "res33_bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res33_relu" | |
| type: "ReLU" | |
| bottom: "res33_bn" | |
| top: "res33_bn" | |
| } | |
| layer { | |
| name: "res33_conv1" | |
| type: "Convolution" | |
| bottom: "res33_bn" | |
| top: "res33_conv1" | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res33_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "res33_conv1" | |
| top: "res33_conv1/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res33_conv1_scale" | |
| type: "Scale" | |
| bottom: "res33_conv1/bn" | |
| top: "res33_conv1/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res33_conv1_relu" | |
| type: "ReLU" | |
| bottom: "res33_conv1/bn" | |
| top: "res33_conv1/bn" | |
| } | |
| layer { | |
| name: "res33_conv2" | |
| type: "Convolution" | |
| bottom: "res33_conv1/bn" | |
| top: "res33_conv2" | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res33_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "res33_conv2" | |
| top: "res33_conv2/bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res33_conv2_scale" | |
| type: "Scale" | |
| bottom: "res33_conv2/bn" | |
| top: "res33_conv2/bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res33_conv2_relu" | |
| type: "ReLU" | |
| bottom: "res33_conv2/bn" | |
| top: "res33_conv2/bn" | |
| } | |
| layer { | |
| name: "res33_conv3" | |
| type: "Convolution" | |
| bottom: "res33_conv2/bn" | |
| top: "res33_conv3" | |
| convolution_param { | |
| num_output: 2048 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "res33_eletwise" | |
| type: "Eltwise" | |
| bottom: "res32_eletwise" | |
| bottom: "res33_conv3" | |
| top: "res33_eletwise" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "res33_eletwise_bn" | |
| type: "BatchNorm" | |
| bottom: "res33_eletwise" | |
| top: "res33_eletwise_bn" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "res33_eletwise_scale" | |
| type: "Scale" | |
| bottom: "res33_eletwise_bn" | |
| top: "res33_eletwise_bn" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "res33_eletwise_relu" | |
| type: "ReLU" | |
| bottom: "res33_eletwise_bn" | |
| top: "res33_eletwise_bn" | |
| } | |
| layer { | |
| name: "pool5" | |
| type: "Pooling" | |
| bottom: "res33_eletwise_bn" | |
| top: "pool5" | |
| pooling_param { | |
| pool: AVE | |
| global_pooling: true | |
| } | |
| } | |
| layer { | |
| name: "classifier" | |
| type: "InnerProduct" | |
| bottom: "pool5" | |
| top: "classifier" | |
| inner_product_param { | |
| num_output: 1000 | |
| } | |
| } | |
| layer { | |
| name: "prob" | |
| type: "Softmax" | |
| bottom: "classifier" | |
| top: "prob" | |
| } |
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