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June 13, 2017 11:10
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| 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 | |
| kernel_size: 7 | |
| stride: 2 | |
| pad: 3 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "conv1" | |
| top: "conv1" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv1_scale" | |
| bottom: "conv1" | |
| top: "conv1" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv1_relu" | |
| type: "ReLU" | |
| bottom: "conv1" | |
| top: "conv1" | |
| } | |
| layer { | |
| name: "pool1" | |
| type: "Pooling" | |
| bottom: "conv1" | |
| top: "pool1" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| pad: 1 | |
| ceil_mode: false | |
| } | |
| } | |
| layer { | |
| name: "resx1_conv1" | |
| type: "Convolution" | |
| bottom: "pool1" | |
| top: "resx1_conv1" | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "resx1_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "resx1_conv1" | |
| top: "resx1_conv1" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "resx1_conv1_scale" | |
| bottom: "resx1_conv1" | |
| top: "resx1_conv1" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "resx1_conv1_relu" | |
| type: "ReLU" | |
| bottom: "resx1_conv1" | |
| top: "resx1_conv1" | |
| } | |
| layer { | |
| name: "resx1_conv2" | |
| type: "Convolution" | |
| bottom: "resx1_conv1" | |
| top: "resx1_conv2" | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 3 | |
| stride: 1 | |
| group: 32 | |
| pad: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "resx1_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "resx1_conv2" | |
| top: "resx1_conv2" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "resx1_conv2_scale" | |
| bottom: "resx1_conv2" | |
| top: "resx1_conv2" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "resx1_conv2_relu" | |
| type: "ReLU" | |
| bottom: "resx1_conv2" | |
| top: "resx1_conv2" | |
| } | |
| layer { | |
| name: "resx1_conv3" | |
| type: "Convolution" | |
| bottom: "resx1_conv2" | |
| top: "resx1_conv3" | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "resx1_conv3_bn" | |
| type: "BatchNorm" | |
| bottom: "resx1_conv3" | |
| top: "resx1_conv3" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "resx1_conv3_scale" | |
| bottom: "resx1_conv3" | |
| top: "resx1_conv3" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "resx1_match_conv" | |
| type: "Convolution" | |
| bottom: "pool1" | |
| top: "resx1_match_conv" | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "resx1_match_conv_bn" | |
| type: "BatchNorm" | |
| bottom: "resx1_match_conv" | |
| top: "resx1_match_conv" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "resx1_match_conv_scale" | |
| bottom: "resx1_match_conv" | |
| top: "resx1_match_conv" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "resx1_elewise" | |
| type: "Eltwise" | |
| bottom: "resx1_match_conv" | |
| bottom: "resx1_conv3" | |
| top: "resx1_elewise" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "resx1_elewise_relu" | |
| type: "ReLU" | |
| bottom: "resx1_elewise" | |
| top: "resx1_elewise" | |
| } | |
| layer { | |
| name: "resx2_conv1" | |
| type: "Convolution" | |
| bottom: "resx1_elewise" | |
| top: "resx2_conv1" | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "resx2_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "resx2_conv1" | |
| top: "resx2_conv1" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "resx2_conv1_scale" | |
| bottom: "resx2_conv1" | |
| top: "resx2_conv1" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "resx2_conv1_relu" | |
| type: "ReLU" | |
| bottom: "resx2_conv1" | |
| top: "resx2_conv1" | |
| } | |
| layer { | |
| name: "resx2_conv2" | |
| type: "Convolution" | |
| bottom: "resx2_conv1" | |
| top: "resx2_conv2" | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 3 | |
| stride: 1 | |
| group: 32 | |
| pad: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "resx2_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "resx2_conv2" | |
| top: "resx2_conv2" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "resx2_conv2_scale" | |
| bottom: "resx2_conv2" | |
| top: "resx2_conv2" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "resx2_conv2_relu" | |
| type: "ReLU" | |
| bottom: "resx2_conv2" | |
| top: "resx2_conv2" | |
| } | |
| layer { | |
| name: "resx2_conv3" | |
| type: "Convolution" | |
| bottom: "resx2_conv2" | |
| top: "resx2_conv3" | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "resx2_conv3_bn" | |
| type: "BatchNorm" | |
| bottom: "resx2_conv3" | |
| top: "resx2_conv3" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "resx2_conv3_scale" | |
| bottom: "resx2_conv3" | |
| top: "resx2_conv3" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "resx2_elewise" | |
| type: "Eltwise" | |
| bottom: "resx1_elewise" | |
| bottom: "resx2_conv3" | |
| top: "resx2_elewise" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "resx2_elewise_relu" | |
| type: "ReLU" | |
| bottom: "resx2_elewise" | |
| top: "resx2_elewise" | |
| } | |
| layer { | |
| name: "resx3_conv1" | |
| type: "Convolution" | |
| bottom: "resx2_elewise" | |
| top: "resx3_conv1" | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "resx3_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "resx3_conv1" | |
| top: "resx3_conv1" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "resx3_conv1_scale" | |
| bottom: "resx3_conv1" | |
| top: "resx3_conv1" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "resx3_conv1_relu" | |
| type: "ReLU" | |
| bottom: "resx3_conv1" | |
| top: "resx3_conv1" | |
| } | |
| layer { | |
| name: "resx3_conv2" | |
| type: "Convolution" | |
| bottom: "resx3_conv1" | |
| top: "resx3_conv2" | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 3 | |
| stride: 1 | |
| group: 32 | |
| pad: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "resx3_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "resx3_conv2" | |
| top: "resx3_conv2" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "resx3_conv2_scale" | |
| bottom: "resx3_conv2" | |
| top: "resx3_conv2" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "resx3_conv2_relu" | |
| type: "ReLU" | |
| bottom: "resx3_conv2" | |
| top: "resx3_conv2" | |
| } | |
| layer { | |
| name: "resx3_conv3" | |
| type: "Convolution" | |
| bottom: "resx3_conv2" | |
| top: "resx3_conv3" | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "resx3_conv3_bn" | |
| type: "BatchNorm" | |
| bottom: "resx3_conv3" | |
| top: "resx3_conv3" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "resx3_conv3_scale" | |
| bottom: "resx3_conv3" | |
| top: "resx3_conv3" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "resx3_elewise" | |
| type: "Eltwise" | |
| bottom: "resx2_elewise" | |
| bottom: "resx3_conv3" | |
| top: "resx3_elewise" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "resx3_elewise_relu" | |
| type: "ReLU" | |
| bottom: "resx3_elewise" | |
| top: "resx3_elewise" | |
| } | |
| layer { | |
| name: "resx4_conv1" | |
| type: "Convolution" | |
| bottom: "resx3_elewise" | |
| top: "resx4_conv1" | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "resx4_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "resx4_conv1" | |
| top: "resx4_conv1" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "resx4_conv1_scale" | |
| bottom: "resx4_conv1" | |
| top: "resx4_conv1" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "resx4_conv1_relu" | |
| type: "ReLU" | |
| bottom: "resx4_conv1" | |
| top: "resx4_conv1" | |
| } | |
| layer { | |
| name: "resx4_conv2" | |
| type: "Convolution" | |
| bottom: "resx4_conv1" | |
| top: "resx4_conv2" | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 3 | |
| stride: 2 | |
| group: 32 | |
| pad: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "resx4_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "resx4_conv2" | |
| top: "resx4_conv2" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "resx4_conv2_scale" | |
| bottom: "resx4_conv2" | |
| top: "resx4_conv2" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "resx4_conv2_relu" | |
| type: "ReLU" | |
| bottom: "resx4_conv2" | |
| top: "resx4_conv2" | |
| } | |
| layer { | |
| name: "resx4_conv3" | |
| type: "Convolution" | |
| bottom: "resx4_conv2" | |
| top: "resx4_conv3" | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "resx4_conv3_bn" | |
| type: "BatchNorm" | |
| bottom: "resx4_conv3" | |
| top: "resx4_conv3" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "resx4_conv3_scale" | |
| bottom: "resx4_conv3" | |
| top: "resx4_conv3" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "resx4_match_conv" | |
| type: "Convolution" | |
| bottom: "resx3_elewise" | |
| top: "resx4_match_conv" | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 1 | |
| stride: 2 | |
| pad: 0 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "resx4_match_conv_bn" | |
| type: "BatchNorm" | |
| bottom: "resx4_match_conv" | |
| top: "resx4_match_conv" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "resx4_match_conv_scale" | |
| bottom: "resx4_match_conv" | |
| top: "resx4_match_conv" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "resx4_elewise" | |
| type: "Eltwise" | |
| bottom: "resx4_match_conv" | |
| bottom: "resx4_conv3" | |
| top: "resx4_elewise" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "resx4_elewise_relu" | |
| type: "ReLU" | |
| bottom: "resx4_elewise" | |
| top: "resx4_elewise" | |
| } | |
| layer { | |
| name: "resx5_conv1" | |
| type: "Convolution" | |
| bottom: "resx4_elewise" | |
| top: "resx5_conv1" | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "resx5_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "resx5_conv1" | |
| top: "resx5_conv1" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "resx5_conv1_scale" | |
| bottom: "resx5_conv1" | |
| top: "resx5_conv1" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "resx5_conv1_relu" | |
| type: "ReLU" | |
| bottom: "resx5_conv1" | |
| top: "resx5_conv1" | |
| } | |
| layer { | |
| name: "resx5_conv2" | |
| type: "Convolution" | |
| bottom: "resx5_conv1" | |
| top: "resx5_conv2" | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 3 | |
| stride: 1 | |
| group: 32 | |
| pad: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "resx5_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "resx5_conv2" | |
| top: "resx5_conv2" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "resx5_conv2_scale" | |
| bottom: "resx5_conv2" | |
| top: "resx5_conv2" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "resx5_conv2_relu" | |
| type: "ReLU" | |
| bottom: "resx5_conv2" | |
| top: "resx5_conv2" | |
| } | |
| layer { | |
| name: "resx5_conv3" | |
| type: "Convolution" | |
| bottom: "resx5_conv2" | |
| top: "resx5_conv3" | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "resx5_conv3_bn" | |
| type: "BatchNorm" | |
| bottom: "resx5_conv3" | |
| top: "resx5_conv3" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "resx5_conv3_scale" | |
| bottom: "resx5_conv3" | |
| top: "resx5_conv3" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "resx5_elewise" | |
| type: "Eltwise" | |
| bottom: "resx4_elewise" | |
| bottom: "resx5_conv3" | |
| top: "resx5_elewise" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "resx5_elewise_relu" | |
| type: "ReLU" | |
| bottom: "resx5_elewise" | |
| top: "resx5_elewise" | |
| } | |
| layer { | |
| name: "resx6_conv1" | |
| type: "Convolution" | |
| bottom: "resx5_elewise" | |
| top: "resx6_conv1" | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "resx6_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "resx6_conv1" | |
| top: "resx6_conv1" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "resx6_conv1_scale" | |
| bottom: "resx6_conv1" | |
| top: "resx6_conv1" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "resx6_conv1_relu" | |
| type: "ReLU" | |
| bottom: "resx6_conv1" | |
| top: "resx6_conv1" | |
| } | |
| layer { | |
| name: "resx6_conv2" | |
| type: "Convolution" | |
| bottom: "resx6_conv1" | |
| top: "resx6_conv2" | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 3 | |
| stride: 1 | |
| group: 32 | |
| pad: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "resx6_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "resx6_conv2" | |
| top: "resx6_conv2" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "resx6_conv2_scale" | |
| bottom: "resx6_conv2" | |
| top: "resx6_conv2" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "resx6_conv2_relu" | |
| type: "ReLU" | |
| bottom: "resx6_conv2" | |
| top: "resx6_conv2" | |
| } | |
| layer { | |
| name: "resx6_conv3" | |
| type: "Convolution" | |
| bottom: "resx6_conv2" | |
| top: "resx6_conv3" | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "resx6_conv3_bn" | |
| type: "BatchNorm" | |
| bottom: "resx6_conv3" | |
| top: "resx6_conv3" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "resx6_conv3_scale" | |
| bottom: "resx6_conv3" | |
| top: "resx6_conv3" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "resx6_elewise" | |
| type: "Eltwise" | |
| bottom: "resx5_elewise" | |
| bottom: "resx6_conv3" | |
| top: "resx6_elewise" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "resx6_elewise_relu" | |
| type: "ReLU" | |
| bottom: "resx6_elewise" | |
| top: "resx6_elewise" | |
| } | |
| layer { | |
| name: "resx7_conv1" | |
| type: "Convolution" | |
| bottom: "resx6_elewise" | |
| top: "resx7_conv1" | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "resx7_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "resx7_conv1" | |
| top: "resx7_conv1" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "resx7_conv1_scale" | |
| bottom: "resx7_conv1" | |
| top: "resx7_conv1" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "resx7_conv1_relu" | |
| type: "ReLU" | |
| bottom: "resx7_conv1" | |
| top: "resx7_conv1" | |
| } | |
| layer { | |
| name: "resx7_conv2" | |
| type: "Convolution" | |
| bottom: "resx7_conv1" | |
| top: "resx7_conv2" | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 3 | |
| stride: 1 | |
| group: 32 | |
| pad: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "resx7_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "resx7_conv2" | |
| top: "resx7_conv2" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "resx7_conv2_scale" | |
| bottom: "resx7_conv2" | |
| top: "resx7_conv2" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "resx7_conv2_relu" | |
| type: "ReLU" | |
| bottom: "resx7_conv2" | |
| top: "resx7_conv2" | |
| } | |
| layer { | |
| name: "resx7_conv3" | |
| type: "Convolution" | |
| bottom: "resx7_conv2" | |
| top: "resx7_conv3" | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "resx7_conv3_bn" | |
| type: "BatchNorm" | |
| bottom: "resx7_conv3" | |
| top: "resx7_conv3" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "resx7_conv3_scale" | |
| bottom: "resx7_conv3" | |
| top: "resx7_conv3" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "resx7_elewise" | |
| type: "Eltwise" | |
| bottom: "resx6_elewise" | |
| bottom: "resx7_conv3" | |
| top: "resx7_elewise" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "resx7_elewise_relu" | |
| type: "ReLU" | |
| bottom: "resx7_elewise" | |
| top: "resx7_elewise" | |
| } | |
| layer { | |
| name: "resx8_conv1" | |
| type: "Convolution" | |
| bottom: "resx7_elewise" | |
| top: "resx8_conv1" | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "resx8_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "resx8_conv1" | |
| top: "resx8_conv1" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "resx8_conv1_scale" | |
| bottom: "resx8_conv1" | |
| top: "resx8_conv1" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "resx8_conv1_relu" | |
| type: "ReLU" | |
| bottom: "resx8_conv1" | |
| top: "resx8_conv1" | |
| } | |
| layer { | |
| name: "resx8_conv2" | |
| type: "Convolution" | |
| bottom: "resx8_conv1" | |
| top: "resx8_conv2" | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 3 | |
| stride: 2 | |
| group: 32 | |
| pad: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "resx8_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "resx8_conv2" | |
| top: "resx8_conv2" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "resx8_conv2_scale" | |
| bottom: "resx8_conv2" | |
| top: "resx8_conv2" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "resx8_conv2_relu" | |
| type: "ReLU" | |
| bottom: "resx8_conv2" | |
| top: "resx8_conv2" | |
| } | |
| layer { | |
| name: "resx8_conv3" | |
| type: "Convolution" | |
| bottom: "resx8_conv2" | |
| top: "resx8_conv3" | |
| convolution_param { | |
| num_output: 1024 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "resx8_conv3_bn" | |
| type: "BatchNorm" | |
| bottom: "resx8_conv3" | |
| top: "resx8_conv3" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "resx8_conv3_scale" | |
| bottom: "resx8_conv3" | |
| top: "resx8_conv3" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "resx8_match_conv" | |
| type: "Convolution" | |
| bottom: "resx7_elewise" | |
| top: "resx8_match_conv" | |
| convolution_param { | |
| num_output: 1024 | |
| kernel_size: 1 | |
| stride: 2 | |
| pad: 0 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "resx8_match_conv_bn" | |
| type: "BatchNorm" | |
| bottom: "resx8_match_conv" | |
| top: "resx8_match_conv" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "resx8_match_conv_scale" | |
| bottom: "resx8_match_conv" | |
| top: "resx8_match_conv" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "resx8_elewise" | |
| type: "Eltwise" | |
| bottom: "resx8_conv3" | |
| bottom: "resx8_match_conv" | |
| top: "resx8_elewise" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "resx8_elewise_relu" | |
| type: "ReLU" | |
| bottom: "resx8_elewise" | |
| top: "resx8_elewise" | |
| } | |
| layer { | |
| name: "resx9_conv1" | |
| type: "Convolution" | |
| bottom: "resx8_elewise" | |
| top: "resx9_conv1" | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "resx9_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "resx9_conv1" | |
| top: "resx9_conv1" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "resx9_conv1_scale" | |
| bottom: "resx9_conv1" | |
| top: "resx9_conv1" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "resx9_conv1_relu" | |
| type: "ReLU" | |
| bottom: "resx9_conv1" | |
| top: "resx9_conv1" | |
| } | |
| layer { | |
| name: "resx9_conv2" | |
| type: "Convolution" | |
| bottom: "resx9_conv1" | |
| top: "resx9_conv2" | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 3 | |
| stride: 1 | |
| group: 32 | |
| pad: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "resx9_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "resx9_conv2" | |
| top: "resx9_conv2" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "resx9_conv2_scale" | |
| bottom: "resx9_conv2" | |
| top: "resx9_conv2" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "resx9_conv2_relu" | |
| type: "ReLU" | |
| bottom: "resx9_conv2" | |
| top: "resx9_conv2" | |
| } | |
| layer { | |
| name: "resx9_conv3" | |
| type: "Convolution" | |
| bottom: "resx9_conv2" | |
| top: "resx9_conv3" | |
| convolution_param { | |
| num_output: 1024 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "resx9_conv3_bn" | |
| type: "BatchNorm" | |
| bottom: "resx9_conv3" | |
| top: "resx9_conv3" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "resx9_conv3_scale" | |
| bottom: "resx9_conv3" | |
| top: "resx9_conv3" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "resx9_elewise" | |
| type: "Eltwise" | |
| bottom: "resx8_elewise" | |
| bottom: "resx9_conv3" | |
| top: "resx9_elewise" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "resx9_elewise_relu" | |
| type: "ReLU" | |
| bottom: "resx9_elewise" | |
| top: "resx9_elewise" | |
| } | |
| layer { | |
| name: "resx10_conv1" | |
| type: "Convolution" | |
| bottom: "resx9_elewise" | |
| top: "resx10_conv1" | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "resx10_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "resx10_conv1" | |
| top: "resx10_conv1" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "resx10_conv1_scale" | |
| bottom: "resx10_conv1" | |
| top: "resx10_conv1" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "resx10_conv1_relu" | |
| type: "ReLU" | |
| bottom: "resx10_conv1" | |
| top: "resx10_conv1" | |
| } | |
| layer { | |
| name: "resx10_conv2" | |
| type: "Convolution" | |
| bottom: "resx10_conv1" | |
| top: "resx10_conv2" | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 3 | |
| stride: 1 | |
| group: 32 | |
| pad: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "resx10_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "resx10_conv2" | |
| top: "resx10_conv2" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "resx10_conv2_scale" | |
| bottom: "resx10_conv2" | |
| top: "resx10_conv2" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "resx10_conv2_relu" | |
| type: "ReLU" | |
| bottom: "resx10_conv2" | |
| top: "resx10_conv2" | |
| } | |
| layer { | |
| name: "resx10_conv3" | |
| type: "Convolution" | |
| bottom: "resx10_conv2" | |
| top: "resx10_conv3" | |
| convolution_param { | |
| num_output: 1024 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "resx10_conv3_bn" | |
| type: "BatchNorm" | |
| bottom: "resx10_conv3" | |
| top: "resx10_conv3" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "resx10_conv3_scale" | |
| bottom: "resx10_conv3" | |
| top: "resx10_conv3" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "resx10_elewise" | |
| type: "Eltwise" | |
| bottom: "resx9_elewise" | |
| bottom: "resx10_conv3" | |
| top: "resx10_elewise" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "resx10_elewise_relu" | |
| type: "ReLU" | |
| bottom: "resx10_elewise" | |
| top: "resx10_elewise" | |
| } | |
| layer { | |
| name: "resx11_conv1" | |
| type: "Convolution" | |
| bottom: "resx10_elewise" | |
| top: "resx11_conv1" | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "resx11_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "resx11_conv1" | |
| top: "resx11_conv1" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "resx11_conv1_scale" | |
| bottom: "resx11_conv1" | |
| top: "resx11_conv1" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "resx11_conv1_relu" | |
| type: "ReLU" | |
| bottom: "resx11_conv1" | |
| top: "resx11_conv1" | |
| } | |
| layer { | |
| name: "resx11_conv2" | |
| type: "Convolution" | |
| bottom: "resx11_conv1" | |
| top: "resx11_conv2" | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 3 | |
| stride: 1 | |
| group: 32 | |
| pad: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "resx11_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "resx11_conv2" | |
| top: "resx11_conv2" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "resx11_conv2_scale" | |
| bottom: "resx11_conv2" | |
| top: "resx11_conv2" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "resx11_conv2_relu" | |
| type: "ReLU" | |
| bottom: "resx11_conv2" | |
| top: "resx11_conv2" | |
| } | |
| layer { | |
| name: "resx11_conv3" | |
| type: "Convolution" | |
| bottom: "resx11_conv2" | |
| top: "resx11_conv3" | |
| convolution_param { | |
| num_output: 1024 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "resx11_conv3_bn" | |
| type: "BatchNorm" | |
| bottom: "resx11_conv3" | |
| top: "resx11_conv3" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "resx11_conv3_scale" | |
| bottom: "resx11_conv3" | |
| top: "resx11_conv3" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "resx11_elewise" | |
| type: "Eltwise" | |
| bottom: "resx10_elewise" | |
| bottom: "resx11_conv3" | |
| top: "resx11_elewise" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "resx11_elewise_relu" | |
| type: "ReLU" | |
| bottom: "resx11_elewise" | |
| top: "resx11_elewise" | |
| } | |
| layer { | |
| name: "resx12_conv1" | |
| type: "Convolution" | |
| bottom: "resx11_elewise" | |
| top: "resx12_conv1" | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "resx12_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "resx12_conv1" | |
| top: "resx12_conv1" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "resx12_conv1_scale" | |
| bottom: "resx12_conv1" | |
| top: "resx12_conv1" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "resx12_conv1_relu" | |
| type: "ReLU" | |
| bottom: "resx12_conv1" | |
| top: "resx12_conv1" | |
| } | |
| layer { | |
| name: "resx12_conv2" | |
| type: "Convolution" | |
| bottom: "resx12_conv1" | |
| top: "resx12_conv2" | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 3 | |
| stride: 1 | |
| group: 32 | |
| pad: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "resx12_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "resx12_conv2" | |
| top: "resx12_conv2" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "resx12_conv2_scale" | |
| bottom: "resx12_conv2" | |
| top: "resx12_conv2" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "resx12_conv2_relu" | |
| type: "ReLU" | |
| bottom: "resx12_conv2" | |
| top: "resx12_conv2" | |
| } | |
| layer { | |
| name: "resx12_conv3" | |
| type: "Convolution" | |
| bottom: "resx12_conv2" | |
| top: "resx12_conv3" | |
| convolution_param { | |
| num_output: 1024 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "resx12_conv3_bn" | |
| type: "BatchNorm" | |
| bottom: "resx12_conv3" | |
| top: "resx12_conv3" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "resx12_conv3_scale" | |
| bottom: "resx12_conv3" | |
| top: "resx12_conv3" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "resx12_elewise" | |
| type: "Eltwise" | |
| bottom: "resx11_elewise" | |
| bottom: "resx12_conv3" | |
| top: "resx12_elewise" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "resx12_elewise_relu" | |
| type: "ReLU" | |
| bottom: "resx12_elewise" | |
| top: "resx12_elewise" | |
| } | |
| layer { | |
| name: "resx13_conv1" | |
| type: "Convolution" | |
| bottom: "resx12_elewise" | |
| top: "resx13_conv1" | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "resx13_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "resx13_conv1" | |
| top: "resx13_conv1" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "resx13_conv1_scale" | |
| bottom: "resx13_conv1" | |
| top: "resx13_conv1" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "resx13_conv1_relu" | |
| type: "ReLU" | |
| bottom: "resx13_conv1" | |
| top: "resx13_conv1" | |
| } | |
| layer { | |
| name: "resx13_conv2" | |
| type: "Convolution" | |
| bottom: "resx13_conv1" | |
| top: "resx13_conv2" | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 3 | |
| stride: 1 | |
| group: 32 | |
| pad: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "resx13_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "resx13_conv2" | |
| top: "resx13_conv2" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "resx13_conv2_scale" | |
| bottom: "resx13_conv2" | |
| top: "resx13_conv2" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "resx13_conv2_relu" | |
| type: "ReLU" | |
| bottom: "resx13_conv2" | |
| top: "resx13_conv2" | |
| } | |
| layer { | |
| name: "resx13_conv3" | |
| type: "Convolution" | |
| bottom: "resx13_conv2" | |
| top: "resx13_conv3" | |
| convolution_param { | |
| num_output: 1024 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "resx13_conv3_bn" | |
| type: "BatchNorm" | |
| bottom: "resx13_conv3" | |
| top: "resx13_conv3" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "resx13_conv3_scale" | |
| bottom: "resx13_conv3" | |
| top: "resx13_conv3" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "resx13_elewise" | |
| type: "Eltwise" | |
| bottom: "resx12_elewise" | |
| bottom: "resx13_conv3" | |
| top: "resx13_elewise" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "resx13_elewise_relu" | |
| type: "ReLU" | |
| bottom: "resx13_elewise" | |
| top: "resx13_elewise" | |
| } | |
| layer { | |
| name: "resx14_conv1" | |
| type: "Convolution" | |
| bottom: "resx13_elewise" | |
| top: "resx14_conv1" | |
| convolution_param { | |
| num_output: 1024 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "resx14_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "resx14_conv1" | |
| top: "resx14_conv1" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "resx14_conv1_scale" | |
| bottom: "resx14_conv1" | |
| top: "resx14_conv1" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "resx14_conv1_relu" | |
| type: "ReLU" | |
| bottom: "resx14_conv1" | |
| top: "resx14_conv1" | |
| } | |
| layer { | |
| name: "resx14_conv2" | |
| type: "Convolution" | |
| bottom: "resx14_conv1" | |
| top: "resx14_conv2" | |
| convolution_param { | |
| num_output: 1024 | |
| kernel_size: 3 | |
| stride: 2 | |
| group: 32 | |
| pad: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "resx14_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "resx14_conv2" | |
| top: "resx14_conv2" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "resx14_conv2_scale" | |
| bottom: "resx14_conv2" | |
| top: "resx14_conv2" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "resx14_conv2_relu" | |
| type: "ReLU" | |
| bottom: "resx14_conv2" | |
| top: "resx14_conv2" | |
| } | |
| layer { | |
| name: "resx14_conv3" | |
| type: "Convolution" | |
| bottom: "resx14_conv2" | |
| top: "resx14_conv3" | |
| convolution_param { | |
| num_output: 2048 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "resx14_conv3_bn" | |
| type: "BatchNorm" | |
| bottom: "resx14_conv3" | |
| top: "resx14_conv3" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "resx14_conv3_scale" | |
| bottom: "resx14_conv3" | |
| top: "resx14_conv3" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "resx14_match_conv" | |
| type: "Convolution" | |
| bottom: "resx13_elewise" | |
| top: "resx14_match_conv" | |
| convolution_param { | |
| num_output: 2048 | |
| kernel_size: 1 | |
| stride: 2 | |
| pad: 0 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "resx14_match_conv_bn" | |
| type: "BatchNorm" | |
| bottom: "resx14_match_conv" | |
| top: "resx14_match_conv" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "resx14_match_conv_scale" | |
| bottom: "resx14_match_conv" | |
| top: "resx14_match_conv" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "resx14_elewise" | |
| type: "Eltwise" | |
| bottom: "resx14_match_conv" | |
| bottom: "resx14_conv3" | |
| top: "resx14_elewise" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "resx14_elewise_relu" | |
| type: "ReLU" | |
| bottom: "resx14_elewise" | |
| top: "resx14_elewise" | |
| } | |
| layer { | |
| name: "resx15_conv1" | |
| type: "Convolution" | |
| bottom: "resx14_elewise" | |
| top: "resx15_conv1" | |
| convolution_param { | |
| num_output: 1024 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "resx15_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "resx15_conv1" | |
| top: "resx15_conv1" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "resx15_conv1_scale" | |
| bottom: "resx15_conv1" | |
| top: "resx15_conv1" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "resx15_conv1_relu" | |
| type: "ReLU" | |
| bottom: "resx15_conv1" | |
| top: "resx15_conv1" | |
| } | |
| layer { | |
| name: "resx15_conv2" | |
| type: "Convolution" | |
| bottom: "resx15_conv1" | |
| top: "resx15_conv2" | |
| convolution_param { | |
| num_output: 1024 | |
| kernel_size: 3 | |
| stride: 1 | |
| group: 32 | |
| pad: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "resx15_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "resx15_conv2" | |
| top: "resx15_conv2" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "resx15_conv2_scale" | |
| bottom: "resx15_conv2" | |
| top: "resx15_conv2" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "resx15_conv2_relu" | |
| type: "ReLU" | |
| bottom: "resx15_conv2" | |
| top: "resx15_conv2" | |
| } | |
| layer { | |
| name: "resx15_conv3" | |
| type: "Convolution" | |
| bottom: "resx15_conv2" | |
| top: "resx15_conv3" | |
| convolution_param { | |
| num_output: 2048 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "resx15_conv3_bn" | |
| type: "BatchNorm" | |
| bottom: "resx15_conv3" | |
| top: "resx15_conv3" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "resx15_conv3_scale" | |
| bottom: "resx15_conv3" | |
| top: "resx15_conv3" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "resx15_elewise" | |
| type: "Eltwise" | |
| bottom: "resx14_elewise" | |
| bottom: "resx15_conv3" | |
| top: "resx15_elewise" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "resx15_elewise_relu" | |
| type: "ReLU" | |
| bottom: "resx15_elewise" | |
| top: "resx15_elewise" | |
| } | |
| layer { | |
| name: "resx16_conv1" | |
| type: "Convolution" | |
| bottom: "resx15_elewise" | |
| top: "resx16_conv1" | |
| convolution_param { | |
| num_output: 1024 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "resx16_conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "resx16_conv1" | |
| top: "resx16_conv1" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "resx16_conv1_scale" | |
| bottom: "resx16_conv1" | |
| top: "resx16_conv1" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "resx16_conv1_relu" | |
| type: "ReLU" | |
| bottom: "resx16_conv1" | |
| top: "resx16_conv1" | |
| } | |
| layer { | |
| name: "resx16_conv2" | |
| type: "Convolution" | |
| bottom: "resx16_conv1" | |
| top: "resx16_conv2" | |
| convolution_param { | |
| num_output: 1024 | |
| kernel_size: 3 | |
| stride: 1 | |
| group: 32 | |
| pad: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "resx16_conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "resx16_conv2" | |
| top: "resx16_conv2" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "resx16_conv2_scale" | |
| bottom: "resx16_conv2" | |
| top: "resx16_conv2" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "resx16_conv2_relu" | |
| type: "ReLU" | |
| bottom: "resx16_conv2" | |
| top: "resx16_conv2" | |
| } | |
| layer { | |
| name: "resx16_conv3" | |
| type: "Convolution" | |
| bottom: "resx16_conv2" | |
| top: "resx16_conv3" | |
| convolution_param { | |
| num_output: 2048 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "resx16_conv3_bn" | |
| type: "BatchNorm" | |
| bottom: "resx16_conv3" | |
| top: "resx16_conv3" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "resx16_conv3_scale" | |
| bottom: "resx16_conv3" | |
| top: "resx16_conv3" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "resx16_elewise" | |
| type: "Eltwise" | |
| bottom: "resx15_elewise" | |
| bottom: "resx16_conv3" | |
| top: "resx16_elewise" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "resx16_elewise_relu" | |
| type: "ReLU" | |
| bottom: "resx16_elewise" | |
| top: "resx16_elewise" | |
| } | |
| layer { | |
| name: "pool_ave" | |
| type: "Pooling" | |
| bottom: "resx16_elewise" | |
| top: "pool_ave" | |
| pooling_param { | |
| global_pooling : true | |
| pool: AVE | |
| } | |
| } | |
| layer { | |
| name: "classifier" | |
| type: "InnerProduct" | |
| bottom: "pool_ave" | |
| top: "classifier" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 1000 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "prob" | |
| type: "Softmax" | |
| bottom: "classifier" | |
| top: "prob" | |
| } | |
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