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November 23, 2017 12:24
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senet-deploy
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| name: "SE-ResNet-50" | |
| # mean_value: 104, 117, 123 | |
| layer { | |
| name: "data" | |
| type: "Input" | |
| top: "data" | |
| input_param: { | |
| shape: { | |
| dim: 1 | |
| dim: 3 | |
| dim: 224 | |
| dim: 224 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv1/7x7_s2" | |
| type: "Convolution" | |
| bottom: "data" | |
| top: "conv1/7x7_s2" | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| pad: 3 | |
| kernel_size: 7 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv1/7x7_s2/bn" | |
| type: "BatchNorm" | |
| bottom: "conv1/7x7_s2" | |
| top: "conv1/7x7_s2" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv1/7x7_s2/bn/scale" | |
| type: "Scale" | |
| bottom: "conv1/7x7_s2" | |
| top: "conv1/7x7_s2" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv1/relu_7x7_s2" | |
| type: "ReLU" | |
| bottom: "conv1/7x7_s2" | |
| top: "conv1/7x7_s2" | |
| } | |
| layer { | |
| name: "pool1/3x3_s2" | |
| type: "Pooling" | |
| bottom: "conv1/7x7_s2" | |
| top: "pool1/3x3_s2" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv2_1_1x1_reduce" | |
| type: "Convolution" | |
| bottom: "pool1/3x3_s2" | |
| top: "conv2_1_1x1_reduce" | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv2_1_1x1_reduce/bn" | |
| type: "BatchNorm" | |
| bottom: "conv2_1_1x1_reduce" | |
| top: "conv2_1_1x1_reduce" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv2_1_1x1_reduce/bn/scale" | |
| type: "Scale" | |
| bottom: "conv2_1_1x1_reduce" | |
| top: "conv2_1_1x1_reduce" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv2_1_1x1_reduce/relu" | |
| type: "ReLU" | |
| bottom: "conv2_1_1x1_reduce" | |
| top: "conv2_1_1x1_reduce" | |
| } | |
| layer { | |
| name: "conv2_1_3x3" | |
| type: "Convolution" | |
| bottom: "conv2_1_1x1_reduce" | |
| top: "conv2_1_3x3" | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv2_1_3x3/bn" | |
| type: "BatchNorm" | |
| bottom: "conv2_1_3x3" | |
| top: "conv2_1_3x3" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv2_1_3x3/bn/scale" | |
| type: "Scale" | |
| bottom: "conv2_1_3x3" | |
| top: "conv2_1_3x3" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv2_1_3x3/relu" | |
| type: "ReLU" | |
| bottom: "conv2_1_3x3" | |
| top: "conv2_1_3x3" | |
| } | |
| layer { | |
| name: "conv2_1_1x1_increase" | |
| type: "Convolution" | |
| bottom: "conv2_1_3x3" | |
| top: "conv2_1_1x1_increase" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv2_1_1x1_increase/bn" | |
| type: "BatchNorm" | |
| bottom: "conv2_1_1x1_increase" | |
| top: "conv2_1_1x1_increase" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv2_1_1x1_increase/bn/scale" | |
| type: "Scale" | |
| bottom: "conv2_1_1x1_increase" | |
| top: "conv2_1_1x1_increase" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv2_1_global_pool" | |
| type: "Pooling" | |
| bottom: "conv2_1_1x1_increase" | |
| top: "conv2_1_global_pool" | |
| pooling_param { | |
| pool: AVE | |
| engine: CAFFE | |
| global_pooling: true | |
| } | |
| } | |
| layer { | |
| name: "conv2_1_1x1_down" | |
| type: "Convolution" | |
| bottom: "conv2_1_global_pool" | |
| top: "conv2_1_1x1_down" | |
| convolution_param { | |
| num_output: 16 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv2_1_1x1_down/relu" | |
| type: "ReLU" | |
| bottom: "conv2_1_1x1_down" | |
| top: "conv2_1_1x1_down" | |
| } | |
| layer { | |
| name: "conv2_1_1x1_up" | |
| type: "Convolution" | |
| bottom: "conv2_1_1x1_down" | |
| top: "conv2_1_1x1_up" | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv2_1_prob" | |
| type: "Sigmoid" | |
| bottom: "conv2_1_1x1_up" | |
| top: "conv2_1_1x1_up" | |
| } | |
| layer { | |
| name: "conv2_1_1x1_proj" | |
| type: "Convolution" | |
| bottom: "pool1/3x3_s2" | |
| top: "conv2_1_1x1_proj" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv2_1_1x1_proj/bn" | |
| type: "BatchNorm" | |
| bottom: "conv2_1_1x1_proj" | |
| top: "conv2_1_1x1_proj" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv2_1_1x1_proj/bn/scale" | |
| type: "Scale" | |
| bottom: "conv2_1_1x1_proj" | |
| top: "conv2_1_1x1_proj" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv2_1" | |
| type: "Axpy" | |
| bottom: "conv2_1_1x1_up" | |
| bottom: "conv2_1_1x1_increase" | |
| bottom: "conv2_1_1x1_proj" | |
| top: "conv2_1" | |
| } | |
| layer { | |
| name: "conv2_1/relu" | |
| type: "ReLU" | |
| bottom: "conv2_1" | |
| top: "conv2_1" | |
| } | |
| layer { | |
| name: "conv2_2_1x1_reduce" | |
| type: "Convolution" | |
| bottom: "conv2_1" | |
| top: "conv2_2_1x1_reduce" | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv2_2_1x1_reduce/bn" | |
| type: "BatchNorm" | |
| bottom: "conv2_2_1x1_reduce" | |
| top: "conv2_2_1x1_reduce" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv2_2_1x1_reduce/bn/scale" | |
| type: "Scale" | |
| bottom: "conv2_2_1x1_reduce" | |
| top: "conv2_2_1x1_reduce" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv2_2_1x1_reduce/relu" | |
| type: "ReLU" | |
| bottom: "conv2_2_1x1_reduce" | |
| top: "conv2_2_1x1_reduce" | |
| } | |
| layer { | |
| name: "conv2_2_3x3" | |
| type: "Convolution" | |
| bottom: "conv2_2_1x1_reduce" | |
| top: "conv2_2_3x3" | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv2_2_3x3/bn" | |
| type: "BatchNorm" | |
| bottom: "conv2_2_3x3" | |
| top: "conv2_2_3x3" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv2_2_3x3/bn/scale" | |
| type: "Scale" | |
| bottom: "conv2_2_3x3" | |
| top: "conv2_2_3x3" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv2_2_3x3/relu" | |
| type: "ReLU" | |
| bottom: "conv2_2_3x3" | |
| top: "conv2_2_3x3" | |
| } | |
| layer { | |
| name: "conv2_2_1x1_increase" | |
| type: "Convolution" | |
| bottom: "conv2_2_3x3" | |
| top: "conv2_2_1x1_increase" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv2_2_1x1_increase/bn" | |
| type: "BatchNorm" | |
| bottom: "conv2_2_1x1_increase" | |
| top: "conv2_2_1x1_increase" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv2_2_1x1_increase/bn/scale" | |
| type: "Scale" | |
| bottom: "conv2_2_1x1_increase" | |
| top: "conv2_2_1x1_increase" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv2_2_global_pool" | |
| type: "Pooling" | |
| bottom: "conv2_2_1x1_increase" | |
| top: "conv2_2_global_pool" | |
| pooling_param { | |
| pool: AVE | |
| engine: CAFFE | |
| global_pooling: true | |
| } | |
| } | |
| layer { | |
| name: "conv2_2_1x1_down" | |
| type: "Convolution" | |
| bottom: "conv2_2_global_pool" | |
| top: "conv2_2_1x1_down" | |
| convolution_param { | |
| num_output: 16 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv2_2_1x1_down/relu" | |
| type: "ReLU" | |
| bottom: "conv2_2_1x1_down" | |
| top: "conv2_2_1x1_down" | |
| } | |
| layer { | |
| name: "conv2_2_1x1_up" | |
| type: "Convolution" | |
| bottom: "conv2_2_1x1_down" | |
| top: "conv2_2_1x1_up" | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv2_2_prob" | |
| type: "Sigmoid" | |
| bottom: "conv2_2_1x1_up" | |
| top: "conv2_2_1x1_up" | |
| } | |
| layer { | |
| name: "conv2_2" | |
| type: "Axpy" | |
| bottom: "conv2_2_1x1_up" | |
| bottom: "conv2_2_1x1_increase" | |
| bottom: "conv2_1" | |
| top: "conv2_2" | |
| } | |
| layer { | |
| name: "conv2_2/relu" | |
| type: "ReLU" | |
| bottom: "conv2_2" | |
| top: "conv2_2" | |
| } | |
| layer { | |
| name: "conv2_3_1x1_reduce" | |
| type: "Convolution" | |
| bottom: "conv2_2" | |
| top: "conv2_3_1x1_reduce" | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv2_3_1x1_reduce/bn" | |
| type: "BatchNorm" | |
| bottom: "conv2_3_1x1_reduce" | |
| top: "conv2_3_1x1_reduce" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv2_3_1x1_reduce/bn/scale" | |
| type: "Scale" | |
| bottom: "conv2_3_1x1_reduce" | |
| top: "conv2_3_1x1_reduce" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv2_3_1x1_reduce/relu" | |
| type: "ReLU" | |
| bottom: "conv2_3_1x1_reduce" | |
| top: "conv2_3_1x1_reduce" | |
| } | |
| layer { | |
| name: "conv2_3_3x3" | |
| type: "Convolution" | |
| bottom: "conv2_3_1x1_reduce" | |
| top: "conv2_3_3x3" | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv2_3_3x3/bn" | |
| type: "BatchNorm" | |
| bottom: "conv2_3_3x3" | |
| top: "conv2_3_3x3" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv2_3_3x3/bn/scale" | |
| type: "Scale" | |
| bottom: "conv2_3_3x3" | |
| top: "conv2_3_3x3" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv2_3_3x3/relu" | |
| type: "ReLU" | |
| bottom: "conv2_3_3x3" | |
| top: "conv2_3_3x3" | |
| } | |
| layer { | |
| name: "conv2_3_1x1_increase" | |
| type: "Convolution" | |
| bottom: "conv2_3_3x3" | |
| top: "conv2_3_1x1_increase" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv2_3_1x1_increase/bn" | |
| type: "BatchNorm" | |
| bottom: "conv2_3_1x1_increase" | |
| top: "conv2_3_1x1_increase" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv2_3_1x1_increase/bn/scale" | |
| type: "Scale" | |
| bottom: "conv2_3_1x1_increase" | |
| top: "conv2_3_1x1_increase" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv2_3_global_pool" | |
| type: "Pooling" | |
| bottom: "conv2_3_1x1_increase" | |
| top: "conv2_3_global_pool" | |
| pooling_param { | |
| pool: AVE | |
| engine: CAFFE | |
| global_pooling: true | |
| } | |
| } | |
| layer { | |
| name: "conv2_3_1x1_down" | |
| type: "Convolution" | |
| bottom: "conv2_3_global_pool" | |
| top: "conv2_3_1x1_down" | |
| convolution_param { | |
| num_output: 16 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv2_3_1x1_down/relu" | |
| type: "ReLU" | |
| bottom: "conv2_3_1x1_down" | |
| top: "conv2_3_1x1_down" | |
| } | |
| layer { | |
| name: "conv2_3_1x1_up" | |
| type: "Convolution" | |
| bottom: "conv2_3_1x1_down" | |
| top: "conv2_3_1x1_up" | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv2_3_prob" | |
| type: "Sigmoid" | |
| bottom: "conv2_3_1x1_up" | |
| top: "conv2_3_1x1_up" | |
| } | |
| layer { | |
| name: "conv2_3" | |
| type: "Axpy" | |
| bottom: "conv2_3_1x1_up" | |
| bottom: "conv2_3_1x1_increase" | |
| bottom: "conv2_2" | |
| top: "conv2_3" | |
| } | |
| layer { | |
| name: "conv2_3/relu" | |
| type: "ReLU" | |
| bottom: "conv2_3" | |
| top: "conv2_3" | |
| } | |
| layer { | |
| name: "conv3_1_1x1_reduce" | |
| type: "Convolution" | |
| bottom: "conv2_3" | |
| top: "conv3_1_1x1_reduce" | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| kernel_size: 1 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv3_1_1x1_reduce/bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_1_1x1_reduce" | |
| top: "conv3_1_1x1_reduce" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_1_1x1_reduce/bn/scale" | |
| type: "Scale" | |
| bottom: "conv3_1_1x1_reduce" | |
| top: "conv3_1_1x1_reduce" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_1_1x1_reduce/relu" | |
| type: "ReLU" | |
| bottom: "conv3_1_1x1_reduce" | |
| top: "conv3_1_1x1_reduce" | |
| } | |
| layer { | |
| name: "conv3_1_3x3" | |
| type: "Convolution" | |
| bottom: "conv3_1_1x1_reduce" | |
| top: "conv3_1_3x3" | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv3_1_3x3/bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_1_3x3" | |
| top: "conv3_1_3x3" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_1_3x3/bn/scale" | |
| type: "Scale" | |
| bottom: "conv3_1_3x3" | |
| top: "conv3_1_3x3" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_1_3x3/relu" | |
| type: "ReLU" | |
| bottom: "conv3_1_3x3" | |
| top: "conv3_1_3x3" | |
| } | |
| layer { | |
| name: "conv3_1_1x1_increase" | |
| type: "Convolution" | |
| bottom: "conv3_1_3x3" | |
| top: "conv3_1_1x1_increase" | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv3_1_1x1_increase/bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_1_1x1_increase" | |
| top: "conv3_1_1x1_increase" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_1_1x1_increase/bn/scale" | |
| type: "Scale" | |
| bottom: "conv3_1_1x1_increase" | |
| top: "conv3_1_1x1_increase" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_1_global_pool" | |
| type: "Pooling" | |
| bottom: "conv3_1_1x1_increase" | |
| top: "conv3_1_global_pool" | |
| pooling_param { | |
| pool: AVE | |
| engine: CAFFE | |
| global_pooling: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_1_1x1_down" | |
| type: "Convolution" | |
| bottom: "conv3_1_global_pool" | |
| top: "conv3_1_1x1_down" | |
| convolution_param { | |
| num_output: 32 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv3_1_1x1_down/relu" | |
| type: "ReLU" | |
| bottom: "conv3_1_1x1_down" | |
| top: "conv3_1_1x1_down" | |
| } | |
| layer { | |
| name: "conv3_1_1x1_up" | |
| type: "Convolution" | |
| bottom: "conv3_1_1x1_down" | |
| top: "conv3_1_1x1_up" | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv3_1_prob" | |
| type: "Sigmoid" | |
| bottom: "conv3_1_1x1_up" | |
| top: "conv3_1_1x1_up" | |
| } | |
| layer { | |
| name: "conv3_1_1x1_proj" | |
| type: "Convolution" | |
| bottom: "conv2_3" | |
| top: "conv3_1_1x1_proj" | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| kernel_size: 1 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv3_1_1x1_proj/bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_1_1x1_proj" | |
| top: "conv3_1_1x1_proj" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_1_1x1_proj/bn/scale" | |
| type: "Scale" | |
| bottom: "conv3_1_1x1_proj" | |
| top: "conv3_1_1x1_proj" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_1" | |
| type: "Axpy" | |
| bottom: "conv3_1_1x1_up" | |
| bottom: "conv3_1_1x1_increase" | |
| bottom: "conv3_1_1x1_proj" | |
| top: "conv3_1" | |
| } | |
| layer { | |
| name: "conv3_1/relu" | |
| type: "ReLU" | |
| bottom: "conv3_1" | |
| top: "conv3_1" | |
| } | |
| layer { | |
| name: "conv3_2_1x1_reduce" | |
| type: "Convolution" | |
| bottom: "conv3_1" | |
| top: "conv3_2_1x1_reduce" | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv3_2_1x1_reduce/bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_2_1x1_reduce" | |
| top: "conv3_2_1x1_reduce" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_2_1x1_reduce/bn/scale" | |
| type: "Scale" | |
| bottom: "conv3_2_1x1_reduce" | |
| top: "conv3_2_1x1_reduce" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_2_1x1_reduce/relu" | |
| type: "ReLU" | |
| bottom: "conv3_2_1x1_reduce" | |
| top: "conv3_2_1x1_reduce" | |
| } | |
| layer { | |
| name: "conv3_2_3x3" | |
| type: "Convolution" | |
| bottom: "conv3_2_1x1_reduce" | |
| top: "conv3_2_3x3" | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv3_2_3x3/bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_2_3x3" | |
| top: "conv3_2_3x3" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_2_3x3/bn/scale" | |
| type: "Scale" | |
| bottom: "conv3_2_3x3" | |
| top: "conv3_2_3x3" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_2_3x3/relu" | |
| type: "ReLU" | |
| bottom: "conv3_2_3x3" | |
| top: "conv3_2_3x3" | |
| } | |
| layer { | |
| name: "conv3_2_1x1_increase" | |
| type: "Convolution" | |
| bottom: "conv3_2_3x3" | |
| top: "conv3_2_1x1_increase" | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv3_2_1x1_increase/bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_2_1x1_increase" | |
| top: "conv3_2_1x1_increase" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_2_1x1_increase/bn/scale" | |
| type: "Scale" | |
| bottom: "conv3_2_1x1_increase" | |
| top: "conv3_2_1x1_increase" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_2_global_pool" | |
| type: "Pooling" | |
| bottom: "conv3_2_1x1_increase" | |
| top: "conv3_2_global_pool" | |
| pooling_param { | |
| pool: AVE | |
| engine: CAFFE | |
| global_pooling: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_2_1x1_down" | |
| type: "Convolution" | |
| bottom: "conv3_2_global_pool" | |
| top: "conv3_2_1x1_down" | |
| convolution_param { | |
| num_output: 32 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv3_2_1x1_down/relu" | |
| type: "ReLU" | |
| bottom: "conv3_2_1x1_down" | |
| top: "conv3_2_1x1_down" | |
| } | |
| layer { | |
| name: "conv3_2_1x1_up" | |
| type: "Convolution" | |
| bottom: "conv3_2_1x1_down" | |
| top: "conv3_2_1x1_up" | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv3_2_prob" | |
| type: "Sigmoid" | |
| bottom: "conv3_2_1x1_up" | |
| top: "conv3_2_1x1_up" | |
| } | |
| layer { | |
| name: "conv3_2" | |
| type: "Axpy" | |
| bottom: "conv3_2_1x1_up" | |
| bottom: "conv3_2_1x1_increase" | |
| bottom: "conv3_1" | |
| top: "conv3_2" | |
| } | |
| layer { | |
| name: "conv3_2/relu" | |
| type: "ReLU" | |
| bottom: "conv3_2" | |
| top: "conv3_2" | |
| } | |
| layer { | |
| name: "conv3_3_1x1_reduce" | |
| type: "Convolution" | |
| bottom: "conv3_2" | |
| top: "conv3_3_1x1_reduce" | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv3_3_1x1_reduce/bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_3_1x1_reduce" | |
| top: "conv3_3_1x1_reduce" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_3_1x1_reduce/bn/scale" | |
| type: "Scale" | |
| bottom: "conv3_3_1x1_reduce" | |
| top: "conv3_3_1x1_reduce" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_3_1x1_reduce/relu" | |
| type: "ReLU" | |
| bottom: "conv3_3_1x1_reduce" | |
| top: "conv3_3_1x1_reduce" | |
| } | |
| layer { | |
| name: "conv3_3_3x3" | |
| type: "Convolution" | |
| bottom: "conv3_3_1x1_reduce" | |
| top: "conv3_3_3x3" | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv3_3_3x3/bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_3_3x3" | |
| top: "conv3_3_3x3" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_3_3x3/bn/scale" | |
| type: "Scale" | |
| bottom: "conv3_3_3x3" | |
| top: "conv3_3_3x3" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_3_3x3/relu" | |
| type: "ReLU" | |
| bottom: "conv3_3_3x3" | |
| top: "conv3_3_3x3" | |
| } | |
| layer { | |
| name: "conv3_3_1x1_increase" | |
| type: "Convolution" | |
| bottom: "conv3_3_3x3" | |
| top: "conv3_3_1x1_increase" | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv3_3_1x1_increase/bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_3_1x1_increase" | |
| top: "conv3_3_1x1_increase" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_3_1x1_increase/bn/scale" | |
| type: "Scale" | |
| bottom: "conv3_3_1x1_increase" | |
| top: "conv3_3_1x1_increase" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_3_global_pool" | |
| type: "Pooling" | |
| bottom: "conv3_3_1x1_increase" | |
| top: "conv3_3_global_pool" | |
| pooling_param { | |
| pool: AVE | |
| engine: CAFFE | |
| global_pooling: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_3_1x1_down" | |
| type: "Convolution" | |
| bottom: "conv3_3_global_pool" | |
| top: "conv3_3_1x1_down" | |
| convolution_param { | |
| num_output: 32 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv3_3_1x1_down/relu" | |
| type: "ReLU" | |
| bottom: "conv3_3_1x1_down" | |
| top: "conv3_3_1x1_down" | |
| } | |
| layer { | |
| name: "conv3_3_1x1_up" | |
| type: "Convolution" | |
| bottom: "conv3_3_1x1_down" | |
| top: "conv3_3_1x1_up" | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv3_3_prob" | |
| type: "Sigmoid" | |
| bottom: "conv3_3_1x1_up" | |
| top: "conv3_3_1x1_up" | |
| } | |
| layer { | |
| name: "conv3_3" | |
| type: "Axpy" | |
| bottom: "conv3_3_1x1_up" | |
| bottom: "conv3_3_1x1_increase" | |
| bottom: "conv3_2" | |
| top: "conv3_3" | |
| } | |
| layer { | |
| name: "conv3_3/relu" | |
| type: "ReLU" | |
| bottom: "conv3_3" | |
| top: "conv3_3" | |
| } | |
| layer { | |
| name: "conv3_4_1x1_reduce" | |
| type: "Convolution" | |
| bottom: "conv3_3" | |
| top: "conv3_4_1x1_reduce" | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv3_4_1x1_reduce/bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_4_1x1_reduce" | |
| top: "conv3_4_1x1_reduce" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_4_1x1_reduce/bn/scale" | |
| type: "Scale" | |
| bottom: "conv3_4_1x1_reduce" | |
| top: "conv3_4_1x1_reduce" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_4_1x1_reduce/relu" | |
| type: "ReLU" | |
| bottom: "conv3_4_1x1_reduce" | |
| top: "conv3_4_1x1_reduce" | |
| } | |
| layer { | |
| name: "conv3_4_3x3" | |
| type: "Convolution" | |
| bottom: "conv3_4_1x1_reduce" | |
| top: "conv3_4_3x3" | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv3_4_3x3/bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_4_3x3" | |
| top: "conv3_4_3x3" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_4_3x3/bn/scale" | |
| type: "Scale" | |
| bottom: "conv3_4_3x3" | |
| top: "conv3_4_3x3" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_4_3x3/relu" | |
| type: "ReLU" | |
| bottom: "conv3_4_3x3" | |
| top: "conv3_4_3x3" | |
| } | |
| layer { | |
| name: "conv3_4_1x1_increase" | |
| type: "Convolution" | |
| bottom: "conv3_4_3x3" | |
| top: "conv3_4_1x1_increase" | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv3_4_1x1_increase/bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_4_1x1_increase" | |
| top: "conv3_4_1x1_increase" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_4_1x1_increase/bn/scale" | |
| type: "Scale" | |
| bottom: "conv3_4_1x1_increase" | |
| top: "conv3_4_1x1_increase" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_4_global_pool" | |
| type: "Pooling" | |
| bottom: "conv3_4_1x1_increase" | |
| top: "conv3_4_global_pool" | |
| pooling_param { | |
| pool: AVE | |
| engine: CAFFE | |
| global_pooling: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_4_1x1_down" | |
| type: "Convolution" | |
| bottom: "conv3_4_global_pool" | |
| top: "conv3_4_1x1_down" | |
| convolution_param { | |
| num_output: 32 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv3_4_1x1_down/relu" | |
| type: "ReLU" | |
| bottom: "conv3_4_1x1_down" | |
| top: "conv3_4_1x1_down" | |
| } | |
| layer { | |
| name: "conv3_4_1x1_up" | |
| type: "Convolution" | |
| bottom: "conv3_4_1x1_down" | |
| top: "conv3_4_1x1_up" | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv3_4_prob" | |
| type: "Sigmoid" | |
| bottom: "conv3_4_1x1_up" | |
| top: "conv3_4_1x1_up" | |
| } | |
| layer { | |
| name: "conv3_4" | |
| type: "Axpy" | |
| bottom: "conv3_4_1x1_up" | |
| bottom: "conv3_4_1x1_increase" | |
| bottom: "conv3_3" | |
| top: "conv3_4" | |
| } | |
| layer { | |
| name: "conv3_4/relu" | |
| type: "ReLU" | |
| bottom: "conv3_4" | |
| top: "conv3_4" | |
| } | |
| layer { | |
| name: "conv4_1_1x1_reduce" | |
| type: "Convolution" | |
| bottom: "conv3_4" | |
| top: "conv4_1_1x1_reduce" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| kernel_size: 1 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv4_1_1x1_reduce/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_1_1x1_reduce" | |
| top: "conv4_1_1x1_reduce" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_1_1x1_reduce/bn/scale" | |
| type: "Scale" | |
| bottom: "conv4_1_1x1_reduce" | |
| top: "conv4_1_1x1_reduce" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_1_1x1_reduce/relu" | |
| type: "ReLU" | |
| bottom: "conv4_1_1x1_reduce" | |
| top: "conv4_1_1x1_reduce" | |
| } | |
| layer { | |
| name: "conv4_1_3x3" | |
| type: "Convolution" | |
| bottom: "conv4_1_1x1_reduce" | |
| top: "conv4_1_3x3" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_1_3x3/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_1_3x3" | |
| top: "conv4_1_3x3" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_1_3x3/bn/scale" | |
| type: "Scale" | |
| bottom: "conv4_1_3x3" | |
| top: "conv4_1_3x3" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_1_3x3/relu" | |
| type: "ReLU" | |
| bottom: "conv4_1_3x3" | |
| top: "conv4_1_3x3" | |
| } | |
| layer { | |
| name: "conv4_1_1x1_increase" | |
| type: "Convolution" | |
| bottom: "conv4_1_3x3" | |
| top: "conv4_1_1x1_increase" | |
| convolution_param { | |
| num_output: 1024 | |
| bias_term: false | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_1_1x1_increase/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_1_1x1_increase" | |
| top: "conv4_1_1x1_increase" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_1_1x1_increase/bn/scale" | |
| type: "Scale" | |
| bottom: "conv4_1_1x1_increase" | |
| top: "conv4_1_1x1_increase" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_1_global_pool" | |
| type: "Pooling" | |
| bottom: "conv4_1_1x1_increase" | |
| top: "conv4_1_global_pool" | |
| pooling_param { | |
| pool: AVE | |
| engine: CAFFE | |
| global_pooling: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_1_1x1_down" | |
| type: "Convolution" | |
| bottom: "conv4_1_global_pool" | |
| top: "conv4_1_1x1_down" | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_1_1x1_down/relu" | |
| type: "ReLU" | |
| bottom: "conv4_1_1x1_down" | |
| top: "conv4_1_1x1_down" | |
| } | |
| layer { | |
| name: "conv4_1_1x1_up" | |
| type: "Convolution" | |
| bottom: "conv4_1_1x1_down" | |
| top: "conv4_1_1x1_up" | |
| convolution_param { | |
| num_output: 1024 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_1_prob" | |
| type: "Sigmoid" | |
| bottom: "conv4_1_1x1_up" | |
| top: "conv4_1_1x1_up" | |
| } | |
| layer { | |
| name: "conv4_1_1x1_proj" | |
| type: "Convolution" | |
| bottom: "conv3_4" | |
| top: "conv4_1_1x1_proj" | |
| convolution_param { | |
| num_output: 1024 | |
| bias_term: false | |
| kernel_size: 1 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv4_1_1x1_proj/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_1_1x1_proj" | |
| top: "conv4_1_1x1_proj" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_1_1x1_proj/bn/scale" | |
| type: "Scale" | |
| bottom: "conv4_1_1x1_proj" | |
| top: "conv4_1_1x1_proj" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_1" | |
| type: "Axpy" | |
| bottom: "conv4_1_1x1_up" | |
| bottom: "conv4_1_1x1_increase" | |
| bottom: "conv4_1_1x1_proj" | |
| top: "conv4_1" | |
| } | |
| layer { | |
| name: "conv4_1/relu" | |
| type: "ReLU" | |
| bottom: "conv4_1" | |
| top: "conv4_1" | |
| } | |
| layer { | |
| name: "conv4_2_1x1_reduce" | |
| type: "Convolution" | |
| bottom: "conv4_1" | |
| top: "conv4_2_1x1_reduce" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_2_1x1_reduce/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_2_1x1_reduce" | |
| top: "conv4_2_1x1_reduce" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_2_1x1_reduce/bn/scale" | |
| type: "Scale" | |
| bottom: "conv4_2_1x1_reduce" | |
| top: "conv4_2_1x1_reduce" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_2_1x1_reduce/relu" | |
| type: "ReLU" | |
| bottom: "conv4_2_1x1_reduce" | |
| top: "conv4_2_1x1_reduce" | |
| } | |
| layer { | |
| name: "conv4_2_3x3" | |
| type: "Convolution" | |
| bottom: "conv4_2_1x1_reduce" | |
| top: "conv4_2_3x3" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_2_3x3/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_2_3x3" | |
| top: "conv4_2_3x3" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_2_3x3/bn/scale" | |
| type: "Scale" | |
| bottom: "conv4_2_3x3" | |
| top: "conv4_2_3x3" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_2_3x3/relu" | |
| type: "ReLU" | |
| bottom: "conv4_2_3x3" | |
| top: "conv4_2_3x3" | |
| } | |
| layer { | |
| name: "conv4_2_1x1_increase" | |
| type: "Convolution" | |
| bottom: "conv4_2_3x3" | |
| top: "conv4_2_1x1_increase" | |
| convolution_param { | |
| num_output: 1024 | |
| bias_term: false | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_2_1x1_increase/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_2_1x1_increase" | |
| top: "conv4_2_1x1_increase" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_2_1x1_increase/bn/scale" | |
| type: "Scale" | |
| bottom: "conv4_2_1x1_increase" | |
| top: "conv4_2_1x1_increase" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_2_global_pool" | |
| type: "Pooling" | |
| bottom: "conv4_2_1x1_increase" | |
| top: "conv4_2_global_pool" | |
| pooling_param { | |
| pool: AVE | |
| engine: CAFFE | |
| global_pooling: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_2_1x1_down" | |
| type: "Convolution" | |
| bottom: "conv4_2_global_pool" | |
| top: "conv4_2_1x1_down" | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_2_1x1_down/relu" | |
| type: "ReLU" | |
| bottom: "conv4_2_1x1_down" | |
| top: "conv4_2_1x1_down" | |
| } | |
| layer { | |
| name: "conv4_2_1x1_up" | |
| type: "Convolution" | |
| bottom: "conv4_2_1x1_down" | |
| top: "conv4_2_1x1_up" | |
| convolution_param { | |
| num_output: 1024 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_2_prob" | |
| type: "Sigmoid" | |
| bottom: "conv4_2_1x1_up" | |
| top: "conv4_2_1x1_up" | |
| } | |
| layer { | |
| name: "conv4_2" | |
| type: "Axpy" | |
| bottom: "conv4_2_1x1_up" | |
| bottom: "conv4_2_1x1_increase" | |
| bottom: "conv4_1" | |
| top: "conv4_2" | |
| } | |
| layer { | |
| name: "conv4_2/relu" | |
| type: "ReLU" | |
| bottom: "conv4_2" | |
| top: "conv4_2" | |
| } | |
| layer { | |
| name: "conv4_3_1x1_reduce" | |
| type: "Convolution" | |
| bottom: "conv4_2" | |
| top: "conv4_3_1x1_reduce" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_3_1x1_reduce/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_3_1x1_reduce" | |
| top: "conv4_3_1x1_reduce" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_3_1x1_reduce/bn/scale" | |
| type: "Scale" | |
| bottom: "conv4_3_1x1_reduce" | |
| top: "conv4_3_1x1_reduce" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_3_1x1_reduce/relu" | |
| type: "ReLU" | |
| bottom: "conv4_3_1x1_reduce" | |
| top: "conv4_3_1x1_reduce" | |
| } | |
| layer { | |
| name: "conv4_3_3x3" | |
| type: "Convolution" | |
| bottom: "conv4_3_1x1_reduce" | |
| top: "conv4_3_3x3" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_3_3x3/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_3_3x3" | |
| top: "conv4_3_3x3" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_3_3x3/bn/scale" | |
| type: "Scale" | |
| bottom: "conv4_3_3x3" | |
| top: "conv4_3_3x3" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_3_3x3/relu" | |
| type: "ReLU" | |
| bottom: "conv4_3_3x3" | |
| top: "conv4_3_3x3" | |
| } | |
| layer { | |
| name: "conv4_3_1x1_increase" | |
| type: "Convolution" | |
| bottom: "conv4_3_3x3" | |
| top: "conv4_3_1x1_increase" | |
| convolution_param { | |
| num_output: 1024 | |
| bias_term: false | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_3_1x1_increase/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_3_1x1_increase" | |
| top: "conv4_3_1x1_increase" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_3_1x1_increase/bn/scale" | |
| type: "Scale" | |
| bottom: "conv4_3_1x1_increase" | |
| top: "conv4_3_1x1_increase" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_3_global_pool" | |
| type: "Pooling" | |
| bottom: "conv4_3_1x1_increase" | |
| top: "conv4_3_global_pool" | |
| pooling_param { | |
| pool: AVE | |
| engine: CAFFE | |
| global_pooling: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_3_1x1_down" | |
| type: "Convolution" | |
| bottom: "conv4_3_global_pool" | |
| top: "conv4_3_1x1_down" | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_3_1x1_down/relu" | |
| type: "ReLU" | |
| bottom: "conv4_3_1x1_down" | |
| top: "conv4_3_1x1_down" | |
| } | |
| layer { | |
| name: "conv4_3_1x1_up" | |
| type: "Convolution" | |
| bottom: "conv4_3_1x1_down" | |
| top: "conv4_3_1x1_up" | |
| convolution_param { | |
| num_output: 1024 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_3_prob" | |
| type: "Sigmoid" | |
| bottom: "conv4_3_1x1_up" | |
| top: "conv4_3_1x1_up" | |
| } | |
| layer { | |
| name: "conv4_3" | |
| type: "Axpy" | |
| bottom: "conv4_3_1x1_up" | |
| bottom: "conv4_3_1x1_increase" | |
| bottom: "conv4_2" | |
| top: "conv4_3" | |
| } | |
| layer { | |
| name: "conv4_3/relu" | |
| type: "ReLU" | |
| bottom: "conv4_3" | |
| top: "conv4_3" | |
| } | |
| layer { | |
| name: "conv4_4_1x1_reduce" | |
| type: "Convolution" | |
| bottom: "conv4_3" | |
| top: "conv4_4_1x1_reduce" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_4_1x1_reduce/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_4_1x1_reduce" | |
| top: "conv4_4_1x1_reduce" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_4_1x1_reduce/bn/scale" | |
| type: "Scale" | |
| bottom: "conv4_4_1x1_reduce" | |
| top: "conv4_4_1x1_reduce" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_4_1x1_reduce/relu" | |
| type: "ReLU" | |
| bottom: "conv4_4_1x1_reduce" | |
| top: "conv4_4_1x1_reduce" | |
| } | |
| layer { | |
| name: "conv4_4_3x3" | |
| type: "Convolution" | |
| bottom: "conv4_4_1x1_reduce" | |
| top: "conv4_4_3x3" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_4_3x3/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_4_3x3" | |
| top: "conv4_4_3x3" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_4_3x3/bn/scale" | |
| type: "Scale" | |
| bottom: "conv4_4_3x3" | |
| top: "conv4_4_3x3" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_4_3x3/relu" | |
| type: "ReLU" | |
| bottom: "conv4_4_3x3" | |
| top: "conv4_4_3x3" | |
| } | |
| layer { | |
| name: "conv4_4_1x1_increase" | |
| type: "Convolution" | |
| bottom: "conv4_4_3x3" | |
| top: "conv4_4_1x1_increase" | |
| convolution_param { | |
| num_output: 1024 | |
| bias_term: false | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_4_1x1_increase/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_4_1x1_increase" | |
| top: "conv4_4_1x1_increase" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_4_1x1_increase/bn/scale" | |
| type: "Scale" | |
| bottom: "conv4_4_1x1_increase" | |
| top: "conv4_4_1x1_increase" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_4_global_pool" | |
| type: "Pooling" | |
| bottom: "conv4_4_1x1_increase" | |
| top: "conv4_4_global_pool" | |
| pooling_param { | |
| pool: AVE | |
| engine: CAFFE | |
| global_pooling: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_4_1x1_down" | |
| type: "Convolution" | |
| bottom: "conv4_4_global_pool" | |
| top: "conv4_4_1x1_down" | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_4_1x1_down/relu" | |
| type: "ReLU" | |
| bottom: "conv4_4_1x1_down" | |
| top: "conv4_4_1x1_down" | |
| } | |
| layer { | |
| name: "conv4_4_1x1_up" | |
| type: "Convolution" | |
| bottom: "conv4_4_1x1_down" | |
| top: "conv4_4_1x1_up" | |
| convolution_param { | |
| num_output: 1024 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_4_prob" | |
| type: "Sigmoid" | |
| bottom: "conv4_4_1x1_up" | |
| top: "conv4_4_1x1_up" | |
| } | |
| layer { | |
| name: "conv4_4" | |
| type: "Axpy" | |
| bottom: "conv4_4_1x1_up" | |
| bottom: "conv4_4_1x1_increase" | |
| bottom: "conv4_3" | |
| top: "conv4_4" | |
| } | |
| layer { | |
| name: "conv4_4/relu" | |
| type: "ReLU" | |
| bottom: "conv4_4" | |
| top: "conv4_4" | |
| } | |
| layer { | |
| name: "conv4_5_1x1_reduce" | |
| type: "Convolution" | |
| bottom: "conv4_4" | |
| top: "conv4_5_1x1_reduce" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_5_1x1_reduce/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_5_1x1_reduce" | |
| top: "conv4_5_1x1_reduce" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_5_1x1_reduce/bn/scale" | |
| type: "Scale" | |
| bottom: "conv4_5_1x1_reduce" | |
| top: "conv4_5_1x1_reduce" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_5_1x1_reduce/relu" | |
| type: "ReLU" | |
| bottom: "conv4_5_1x1_reduce" | |
| top: "conv4_5_1x1_reduce" | |
| } | |
| layer { | |
| name: "conv4_5_3x3" | |
| type: "Convolution" | |
| bottom: "conv4_5_1x1_reduce" | |
| top: "conv4_5_3x3" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_5_3x3/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_5_3x3" | |
| top: "conv4_5_3x3" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_5_3x3/bn/scale" | |
| type: "Scale" | |
| bottom: "conv4_5_3x3" | |
| top: "conv4_5_3x3" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_5_3x3/relu" | |
| type: "ReLU" | |
| bottom: "conv4_5_3x3" | |
| top: "conv4_5_3x3" | |
| } | |
| layer { | |
| name: "conv4_5_1x1_increase" | |
| type: "Convolution" | |
| bottom: "conv4_5_3x3" | |
| top: "conv4_5_1x1_increase" | |
| convolution_param { | |
| num_output: 1024 | |
| bias_term: false | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_5_1x1_increase/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_5_1x1_increase" | |
| top: "conv4_5_1x1_increase" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_5_1x1_increase/bn/scale" | |
| type: "Scale" | |
| bottom: "conv4_5_1x1_increase" | |
| top: "conv4_5_1x1_increase" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_5_global_pool" | |
| type: "Pooling" | |
| bottom: "conv4_5_1x1_increase" | |
| top: "conv4_5_global_pool" | |
| pooling_param { | |
| pool: AVE | |
| engine: CAFFE | |
| global_pooling: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_5_1x1_down" | |
| type: "Convolution" | |
| bottom: "conv4_5_global_pool" | |
| top: "conv4_5_1x1_down" | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_5_1x1_down/relu" | |
| type: "ReLU" | |
| bottom: "conv4_5_1x1_down" | |
| top: "conv4_5_1x1_down" | |
| } | |
| layer { | |
| name: "conv4_5_1x1_up" | |
| type: "Convolution" | |
| bottom: "conv4_5_1x1_down" | |
| top: "conv4_5_1x1_up" | |
| convolution_param { | |
| num_output: 1024 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_5_prob" | |
| type: "Sigmoid" | |
| bottom: "conv4_5_1x1_up" | |
| top: "conv4_5_1x1_up" | |
| } | |
| layer { | |
| name: "conv4_5" | |
| type: "Axpy" | |
| bottom: "conv4_5_1x1_up" | |
| bottom: "conv4_5_1x1_increase" | |
| bottom: "conv4_4" | |
| top: "conv4_5" | |
| } | |
| layer { | |
| name: "conv4_5/relu" | |
| type: "ReLU" | |
| bottom: "conv4_5" | |
| top: "conv4_5" | |
| } | |
| layer { | |
| name: "conv4_6_1x1_reduce" | |
| type: "Convolution" | |
| bottom: "conv4_5" | |
| top: "conv4_6_1x1_reduce" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_6_1x1_reduce/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_6_1x1_reduce" | |
| top: "conv4_6_1x1_reduce" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_6_1x1_reduce/bn/scale" | |
| type: "Scale" | |
| bottom: "conv4_6_1x1_reduce" | |
| top: "conv4_6_1x1_reduce" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_6_1x1_reduce/relu" | |
| type: "ReLU" | |
| bottom: "conv4_6_1x1_reduce" | |
| top: "conv4_6_1x1_reduce" | |
| } | |
| layer { | |
| name: "conv4_6_3x3" | |
| type: "Convolution" | |
| bottom: "conv4_6_1x1_reduce" | |
| top: "conv4_6_3x3" | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_6_3x3/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_6_3x3" | |
| top: "conv4_6_3x3" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_6_3x3/bn/scale" | |
| type: "Scale" | |
| bottom: "conv4_6_3x3" | |
| top: "conv4_6_3x3" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_6_3x3/relu" | |
| type: "ReLU" | |
| bottom: "conv4_6_3x3" | |
| top: "conv4_6_3x3" | |
| } | |
| layer { | |
| name: "conv4_6_1x1_increase" | |
| type: "Convolution" | |
| bottom: "conv4_6_3x3" | |
| top: "conv4_6_1x1_increase" | |
| convolution_param { | |
| num_output: 1024 | |
| bias_term: false | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_6_1x1_increase/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_6_1x1_increase" | |
| top: "conv4_6_1x1_increase" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_6_1x1_increase/bn/scale" | |
| type: "Scale" | |
| bottom: "conv4_6_1x1_increase" | |
| top: "conv4_6_1x1_increase" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_6_global_pool" | |
| type: "Pooling" | |
| bottom: "conv4_6_1x1_increase" | |
| top: "conv4_6_global_pool" | |
| pooling_param { | |
| pool: AVE | |
| engine: CAFFE | |
| global_pooling: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_6_1x1_down" | |
| type: "Convolution" | |
| bottom: "conv4_6_global_pool" | |
| top: "conv4_6_1x1_down" | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_6_1x1_down/relu" | |
| type: "ReLU" | |
| bottom: "conv4_6_1x1_down" | |
| top: "conv4_6_1x1_down" | |
| } | |
| layer { | |
| name: "conv4_6_1x1_up" | |
| type: "Convolution" | |
| bottom: "conv4_6_1x1_down" | |
| top: "conv4_6_1x1_up" | |
| convolution_param { | |
| num_output: 1024 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_6_prob" | |
| type: "Sigmoid" | |
| bottom: "conv4_6_1x1_up" | |
| top: "conv4_6_1x1_up" | |
| } | |
| layer { | |
| name: "conv4_6" | |
| type: "Axpy" | |
| bottom: "conv4_6_1x1_up" | |
| bottom: "conv4_6_1x1_increase" | |
| bottom: "conv4_5" | |
| top: "conv4_6" | |
| } | |
| layer { | |
| name: "conv4_6/relu" | |
| type: "ReLU" | |
| bottom: "conv4_6" | |
| top: "conv4_6" | |
| } | |
| layer { | |
| name: "conv5_1_1x1_reduce" | |
| type: "Convolution" | |
| bottom: "conv4_6" | |
| top: "conv5_1_1x1_reduce" | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| kernel_size: 1 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv5_1_1x1_reduce/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_1_1x1_reduce" | |
| top: "conv5_1_1x1_reduce" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_1_1x1_reduce/bn/scale" | |
| type: "Scale" | |
| bottom: "conv5_1_1x1_reduce" | |
| top: "conv5_1_1x1_reduce" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_1_1x1_reduce/relu" | |
| type: "ReLU" | |
| bottom: "conv5_1_1x1_reduce" | |
| top: "conv5_1_1x1_reduce" | |
| } | |
| layer { | |
| name: "conv5_1_3x3" | |
| type: "Convolution" | |
| bottom: "conv5_1_1x1_reduce" | |
| top: "conv5_1_3x3" | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv5_1_3x3/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_1_3x3" | |
| top: "conv5_1_3x3" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_1_3x3/bn/scale" | |
| type: "Scale" | |
| bottom: "conv5_1_3x3" | |
| top: "conv5_1_3x3" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_1_3x3/relu" | |
| type: "ReLU" | |
| bottom: "conv5_1_3x3" | |
| top: "conv5_1_3x3" | |
| } | |
| layer { | |
| name: "conv5_1_1x1_increase" | |
| type: "Convolution" | |
| bottom: "conv5_1_3x3" | |
| top: "conv5_1_1x1_increase" | |
| convolution_param { | |
| num_output: 2048 | |
| bias_term: false | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv5_1_1x1_increase/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_1_1x1_increase" | |
| top: "conv5_1_1x1_increase" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_1_1x1_increase/bn/scale" | |
| type: "Scale" | |
| bottom: "conv5_1_1x1_increase" | |
| top: "conv5_1_1x1_increase" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_1_global_pool" | |
| type: "Pooling" | |
| bottom: "conv5_1_1x1_increase" | |
| top: "conv5_1_global_pool" | |
| pooling_param { | |
| pool: AVE | |
| engine: CAFFE | |
| global_pooling: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_1_1x1_down" | |
| type: "Convolution" | |
| bottom: "conv5_1_global_pool" | |
| top: "conv5_1_1x1_down" | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv5_1_1x1_down/relu" | |
| type: "ReLU" | |
| bottom: "conv5_1_1x1_down" | |
| top: "conv5_1_1x1_down" | |
| } | |
| layer { | |
| name: "conv5_1_1x1_up" | |
| type: "Convolution" | |
| bottom: "conv5_1_1x1_down" | |
| top: "conv5_1_1x1_up" | |
| convolution_param { | |
| num_output: 2048 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv5_1_prob" | |
| type: "Sigmoid" | |
| bottom: "conv5_1_1x1_up" | |
| top: "conv5_1_1x1_up" | |
| } | |
| layer { | |
| name: "conv5_1_1x1_proj" | |
| type: "Convolution" | |
| bottom: "conv4_6" | |
| top: "conv5_1_1x1_proj" | |
| convolution_param { | |
| num_output: 2048 | |
| bias_term: false | |
| kernel_size: 1 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv5_1_1x1_proj/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_1_1x1_proj" | |
| top: "conv5_1_1x1_proj" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_1_1x1_proj/bn/scale" | |
| type: "Scale" | |
| bottom: "conv5_1_1x1_proj" | |
| top: "conv5_1_1x1_proj" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_1" | |
| type: "Axpy" | |
| bottom: "conv5_1_1x1_up" | |
| bottom: "conv5_1_1x1_increase" | |
| bottom: "conv5_1_1x1_proj" | |
| top: "conv5_1" | |
| } | |
| layer { | |
| name: "conv5_1/relu" | |
| type: "ReLU" | |
| bottom: "conv5_1" | |
| top: "conv5_1" | |
| } | |
| layer { | |
| name: "conv5_2_1x1_reduce" | |
| type: "Convolution" | |
| bottom: "conv5_1" | |
| top: "conv5_2_1x1_reduce" | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv5_2_1x1_reduce/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_2_1x1_reduce" | |
| top: "conv5_2_1x1_reduce" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_2_1x1_reduce/bn/scale" | |
| type: "Scale" | |
| bottom: "conv5_2_1x1_reduce" | |
| top: "conv5_2_1x1_reduce" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_2_1x1_reduce/relu" | |
| type: "ReLU" | |
| bottom: "conv5_2_1x1_reduce" | |
| top: "conv5_2_1x1_reduce" | |
| } | |
| layer { | |
| name: "conv5_2_3x3" | |
| type: "Convolution" | |
| bottom: "conv5_2_1x1_reduce" | |
| top: "conv5_2_3x3" | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv5_2_3x3/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_2_3x3" | |
| top: "conv5_2_3x3" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_2_3x3/bn/scale" | |
| type: "Scale" | |
| bottom: "conv5_2_3x3" | |
| top: "conv5_2_3x3" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_2_3x3/relu" | |
| type: "ReLU" | |
| bottom: "conv5_2_3x3" | |
| top: "conv5_2_3x3" | |
| } | |
| layer { | |
| name: "conv5_2_1x1_increase" | |
| type: "Convolution" | |
| bottom: "conv5_2_3x3" | |
| top: "conv5_2_1x1_increase" | |
| convolution_param { | |
| num_output: 2048 | |
| bias_term: false | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv5_2_1x1_increase/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_2_1x1_increase" | |
| top: "conv5_2_1x1_increase" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_2_1x1_increase/bn/scale" | |
| type: "Scale" | |
| bottom: "conv5_2_1x1_increase" | |
| top: "conv5_2_1x1_increase" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_2_global_pool" | |
| type: "Pooling" | |
| bottom: "conv5_2_1x1_increase" | |
| top: "conv5_2_global_pool" | |
| pooling_param { | |
| pool: AVE | |
| engine: CAFFE | |
| global_pooling: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_2_1x1_down" | |
| type: "Convolution" | |
| bottom: "conv5_2_global_pool" | |
| top: "conv5_2_1x1_down" | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv5_2_1x1_down/relu" | |
| type: "ReLU" | |
| bottom: "conv5_2_1x1_down" | |
| top: "conv5_2_1x1_down" | |
| } | |
| layer { | |
| name: "conv5_2_1x1_up" | |
| type: "Convolution" | |
| bottom: "conv5_2_1x1_down" | |
| top: "conv5_2_1x1_up" | |
| convolution_param { | |
| num_output: 2048 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv5_2_prob" | |
| type: "Sigmoid" | |
| bottom: "conv5_2_1x1_up" | |
| top: "conv5_2_1x1_up" | |
| } | |
| layer { | |
| name: "conv5_2" | |
| type: "Axpy" | |
| bottom: "conv5_2_1x1_up" | |
| bottom: "conv5_2_1x1_increase" | |
| bottom: "conv5_1" | |
| top: "conv5_2" | |
| } | |
| layer { | |
| name: "conv5_2/relu" | |
| type: "ReLU" | |
| bottom: "conv5_2" | |
| top: "conv5_2" | |
| } | |
| layer { | |
| name: "conv5_3_1x1_reduce" | |
| type: "Convolution" | |
| bottom: "conv5_2" | |
| top: "conv5_3_1x1_reduce" | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv5_3_1x1_reduce/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_3_1x1_reduce" | |
| top: "conv5_3_1x1_reduce" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_3_1x1_reduce/bn/scale" | |
| type: "Scale" | |
| bottom: "conv5_3_1x1_reduce" | |
| top: "conv5_3_1x1_reduce" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_3_1x1_reduce/relu" | |
| type: "ReLU" | |
| bottom: "conv5_3_1x1_reduce" | |
| top: "conv5_3_1x1_reduce" | |
| } | |
| layer { | |
| name: "conv5_3_3x3" | |
| type: "Convolution" | |
| bottom: "conv5_3_1x1_reduce" | |
| top: "conv5_3_3x3" | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv5_3_3x3/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_3_3x3" | |
| top: "conv5_3_3x3" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_3_3x3/bn/scale" | |
| type: "Scale" | |
| bottom: "conv5_3_3x3" | |
| top: "conv5_3_3x3" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_3_3x3/relu" | |
| type: "ReLU" | |
| bottom: "conv5_3_3x3" | |
| top: "conv5_3_3x3" | |
| } | |
| layer { | |
| name: "conv5_3_1x1_increase" | |
| type: "Convolution" | |
| bottom: "conv5_3_3x3" | |
| top: "conv5_3_1x1_increase" | |
| convolution_param { | |
| num_output: 2048 | |
| bias_term: false | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv5_3_1x1_increase/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_3_1x1_increase" | |
| top: "conv5_3_1x1_increase" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_3_1x1_increase/bn/scale" | |
| type: "Scale" | |
| bottom: "conv5_3_1x1_increase" | |
| top: "conv5_3_1x1_increase" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_3_global_pool" | |
| type: "Pooling" | |
| bottom: "conv5_3_1x1_increase" | |
| top: "conv5_3_global_pool" | |
| pooling_param { | |
| pool: AVE | |
| engine: CAFFE | |
| global_pooling: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_3_1x1_down" | |
| type: "Convolution" | |
| bottom: "conv5_3_global_pool" | |
| top: "conv5_3_1x1_down" | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv5_3_1x1_down/relu" | |
| type: "ReLU" | |
| bottom: "conv5_3_1x1_down" | |
| top: "conv5_3_1x1_down" | |
| } | |
| layer { | |
| name: "conv5_3_1x1_up" | |
| type: "Convolution" | |
| bottom: "conv5_3_1x1_down" | |
| top: "conv5_3_1x1_up" | |
| convolution_param { | |
| num_output: 2048 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv5_3_prob" | |
| type: "Sigmoid" | |
| bottom: "conv5_3_1x1_up" | |
| top: "conv5_3_1x1_up" | |
| } | |
| layer { | |
| name: "conv5_3" | |
| type: "Axpy" | |
| bottom: "conv5_3_1x1_up" | |
| bottom: "conv5_3_1x1_increase" | |
| bottom: "conv5_2" | |
| top: "conv5_3" | |
| } | |
| layer { | |
| name: "conv5_3/relu" | |
| type: "ReLU" | |
| bottom: "conv5_3" | |
| top: "conv5_3" | |
| } | |
| layer { | |
| name: "pool5/7x7_s1" | |
| type: "Pooling" | |
| bottom: "conv5_3" | |
| top: "pool5/7x7_s1" | |
| pooling_param { | |
| pool: AVE | |
| kernel_size: 7 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "classifier" | |
| type: "InnerProduct" | |
| bottom: "pool5/7x7_s1" | |
| top: "classifier" | |
| inner_product_param { | |
| num_output: 1000 | |
| } | |
| } | |
| layer { | |
| name: "prob" | |
| type: "Softmax" | |
| bottom: "classifier" | |
| top: "prob" | |
| } |
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