Last active
October 2, 2018 11:11
-
-
Save ulfimlg/10696b7676b659b87aeb3f1daef6e1f0 to your computer and use it in GitHub Desktop.
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| name: "ResNet-50" | |
| layer { | |
| name: "input-data" | |
| type: "python" | |
| top: "data" | |
| top: "im_info" | |
| top: "gt_boxes" | |
| python_param { | |
| module: "roi_data_layer.layer" | |
| layer: "RoIDataLayer" | |
| param_str: "'num_classes': 21" | |
| } | |
| } | |
| layer { | |
| bottom: "data" | |
| top: "conv1" | |
| name: "conv1" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 7 | |
| pad: 3 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| bottom: "conv1" | |
| top: "conv1" | |
| name: "bn_conv1" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "conv1" | |
| top: "conv1" | |
| name: "scale_conv1" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "conv1" | |
| top: "conv1" | |
| name: "conv1_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv1" | |
| top: "pool1" | |
| name: "pool1" | |
| type: "Pooling" | |
| pooling_param { | |
| kernel_size: 3 | |
| stride: 2 | |
| pool: MAX | |
| } | |
| } | |
| layer { | |
| bottom: "pool1" | |
| top: "res2a_branch1" | |
| name: "res2a_branch1" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 1 | |
| pad: 0 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res2a_branch1" | |
| top: "res2a_branch1" | |
| name: "bn2a_branch1" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res2a_branch1" | |
| top: "res2a_branch1" | |
| name: "scale2a_branch1" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "pool1" | |
| top: "res2a_branch2a" | |
| name: "res2a_branch2a" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 1 | |
| pad: 0 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res2a_branch2a" | |
| top: "res2a_branch2a" | |
| name: "bn2a_branch2a" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res2a_branch2a" | |
| top: "res2a_branch2a" | |
| name: "scale2a_branch2a" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res2a_branch2a" | |
| top: "res2a_branch2a" | |
| name: "res2a_branch2a_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res2a_branch2a" | |
| top: "res2a_branch2b" | |
| name: "res2a_branch2b" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 3 | |
| pad: 1 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res2a_branch2b" | |
| top: "res2a_branch2b" | |
| name: "bn2a_branch2b" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res2a_branch2b" | |
| top: "res2a_branch2b" | |
| name: "scale2a_branch2b" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res2a_branch2b" | |
| top: "res2a_branch2b" | |
| name: "res2a_branch2b_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res2a_branch2b" | |
| top: "res2a_branch2c" | |
| name: "res2a_branch2c" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 1 | |
| pad: 0 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res2a_branch2c" | |
| top: "res2a_branch2c" | |
| name: "bn2a_branch2c" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res2a_branch2c" | |
| top: "res2a_branch2c" | |
| name: "scale2a_branch2c" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res2a_branch1" | |
| bottom: "res2a_branch2c" | |
| top: "res2a" | |
| name: "res2a" | |
| type: "Eltwise" | |
| } | |
| layer { | |
| bottom: "res2a" | |
| top: "res2a" | |
| name: "res2a_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res2a" | |
| top: "res2b_branch2a" | |
| name: "res2b_branch2a" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 1 | |
| pad: 0 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res2b_branch2a" | |
| top: "res2b_branch2a" | |
| name: "bn2b_branch2a" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res2b_branch2a" | |
| top: "res2b_branch2a" | |
| name: "scale2b_branch2a" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res2b_branch2a" | |
| top: "res2b_branch2a" | |
| name: "res2b_branch2a_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res2b_branch2a" | |
| top: "res2b_branch2b" | |
| name: "res2b_branch2b" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 3 | |
| pad: 1 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res2b_branch2b" | |
| top: "res2b_branch2b" | |
| name: "bn2b_branch2b" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res2b_branch2b" | |
| top: "res2b_branch2b" | |
| name: "scale2b_branch2b" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res2b_branch2b" | |
| top: "res2b_branch2b" | |
| name: "res2b_branch2b_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res2b_branch2b" | |
| top: "res2b_branch2c" | |
| name: "res2b_branch2c" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 1 | |
| pad: 0 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res2b_branch2c" | |
| top: "res2b_branch2c" | |
| name: "bn2b_branch2c" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res2b_branch2c" | |
| top: "res2b_branch2c" | |
| name: "scale2b_branch2c" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res2a" | |
| bottom: "res2b_branch2c" | |
| top: "res2b" | |
| name: "res2b" | |
| type: "Eltwise" | |
| } | |
| layer { | |
| bottom: "res2b" | |
| top: "res2b" | |
| name: "res2b_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res2b" | |
| top: "res2c_branch2a" | |
| name: "res2c_branch2a" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 1 | |
| pad: 0 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res2c_branch2a" | |
| top: "res2c_branch2a" | |
| name: "bn2c_branch2a" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res2c_branch2a" | |
| top: "res2c_branch2a" | |
| name: "scale2c_branch2a" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res2c_branch2a" | |
| top: "res2c_branch2a" | |
| name: "res2c_branch2a_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res2c_branch2a" | |
| top: "res2c_branch2b" | |
| name: "res2c_branch2b" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 3 | |
| pad: 1 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res2c_branch2b" | |
| top: "res2c_branch2b" | |
| name: "bn2c_branch2b" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res2c_branch2b" | |
| top: "res2c_branch2b" | |
| name: "scale2c_branch2b" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res2c_branch2b" | |
| top: "res2c_branch2b" | |
| name: "res2c_branch2b_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res2c_branch2b" | |
| top: "res2c_branch2c" | |
| name: "res2c_branch2c" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 1 | |
| pad: 0 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res2c_branch2c" | |
| top: "res2c_branch2c" | |
| name: "bn2c_branch2c" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res2c_branch2c" | |
| top: "res2c_branch2c" | |
| name: "scale2c_branch2c" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res2b" | |
| bottom: "res2c_branch2c" | |
| top: "res2c" | |
| name: "res2c" | |
| type: "Eltwise" | |
| } | |
| layer { | |
| bottom: "res2c" | |
| top: "res2c" | |
| name: "res2c_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res2c" | |
| top: "res3a_branch1" | |
| name: "res3a_branch1" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 1 | |
| pad: 0 | |
| stride: 2 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res3a_branch1" | |
| top: "res3a_branch1" | |
| name: "bn3a_branch1" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res3a_branch1" | |
| top: "res3a_branch1" | |
| name: "scale3a_branch1" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res2c" | |
| top: "res3a_branch2a" | |
| name: "res3a_branch2a" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 1 | |
| pad: 0 | |
| stride: 2 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res3a_branch2a" | |
| top: "res3a_branch2a" | |
| name: "bn3a_branch2a" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res3a_branch2a" | |
| top: "res3a_branch2a" | |
| name: "scale3a_branch2a" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res3a_branch2a" | |
| top: "res3a_branch2a" | |
| name: "res3a_branch2a_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res3a_branch2a" | |
| top: "res3a_branch2b" | |
| name: "res3a_branch2b" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 3 | |
| pad: 1 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res3a_branch2b" | |
| top: "res3a_branch2b" | |
| name: "bn3a_branch2b" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res3a_branch2b" | |
| top: "res3a_branch2b" | |
| name: "scale3a_branch2b" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res3a_branch2b" | |
| top: "res3a_branch2b" | |
| name: "res3a_branch2b_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res3a_branch2b" | |
| top: "res3a_branch2c" | |
| name: "res3a_branch2c" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 1 | |
| pad: 0 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res3a_branch2c" | |
| top: "res3a_branch2c" | |
| name: "bn3a_branch2c" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res3a_branch2c" | |
| top: "res3a_branch2c" | |
| name: "scale3a_branch2c" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res3a_branch1" | |
| bottom: "res3a_branch2c" | |
| top: "res3a" | |
| name: "res3a" | |
| type: "Eltwise" | |
| } | |
| layer { | |
| bottom: "res3a" | |
| top: "res3a" | |
| name: "res3a_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res3a" | |
| top: "res3b_branch2a" | |
| name: "res3b_branch2a" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 1 | |
| pad: 0 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res3b_branch2a" | |
| top: "res3b_branch2a" | |
| name: "bn3b_branch2a" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res3b_branch2a" | |
| top: "res3b_branch2a" | |
| name: "scale3b_branch2a" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res3b_branch2a" | |
| top: "res3b_branch2a" | |
| name: "res3b_branch2a_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res3b_branch2a" | |
| top: "res3b_branch2b" | |
| name: "res3b_branch2b" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 3 | |
| pad: 1 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res3b_branch2b" | |
| top: "res3b_branch2b" | |
| name: "bn3b_branch2b" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res3b_branch2b" | |
| top: "res3b_branch2b" | |
| name: "scale3b_branch2b" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res3b_branch2b" | |
| top: "res3b_branch2b" | |
| name: "res3b_branch2b_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res3b_branch2b" | |
| top: "res3b_branch2c" | |
| name: "res3b_branch2c" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 1 | |
| pad: 0 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res3b_branch2c" | |
| top: "res3b_branch2c" | |
| name: "bn3b_branch2c" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res3b_branch2c" | |
| top: "res3b_branch2c" | |
| name: "scale3b_branch2c" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res3a" | |
| bottom: "res3b_branch2c" | |
| top: "res3b" | |
| name: "res3b" | |
| type: "Eltwise" | |
| } | |
| layer { | |
| bottom: "res3b" | |
| top: "res3b" | |
| name: "res3b_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res3b" | |
| top: "res3c_branch2a" | |
| name: "res3c_branch2a" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 1 | |
| pad: 0 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res3c_branch2a" | |
| top: "res3c_branch2a" | |
| name: "bn3c_branch2a" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res3c_branch2a" | |
| top: "res3c_branch2a" | |
| name: "scale3c_branch2a" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res3c_branch2a" | |
| top: "res3c_branch2a" | |
| name: "res3c_branch2a_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res3c_branch2a" | |
| top: "res3c_branch2b" | |
| name: "res3c_branch2b" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 3 | |
| pad: 1 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res3c_branch2b" | |
| top: "res3c_branch2b" | |
| name: "bn3c_branch2b" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res3c_branch2b" | |
| top: "res3c_branch2b" | |
| name: "scale3c_branch2b" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res3c_branch2b" | |
| top: "res3c_branch2b" | |
| name: "res3c_branch2b_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res3c_branch2b" | |
| top: "res3c_branch2c" | |
| name: "res3c_branch2c" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 1 | |
| pad: 0 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res3c_branch2c" | |
| top: "res3c_branch2c" | |
| name: "bn3c_branch2c" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res3c_branch2c" | |
| top: "res3c_branch2c" | |
| name: "scale3c_branch2c" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res3b" | |
| bottom: "res3c_branch2c" | |
| top: "res3c" | |
| name: "res3c" | |
| type: "Eltwise" | |
| } | |
| layer { | |
| bottom: "res3c" | |
| top: "res3c" | |
| name: "res3c_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res3c" | |
| top: "res3d_branch2a" | |
| name: "res3d_branch2a" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 1 | |
| pad: 0 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res3d_branch2a" | |
| top: "res3d_branch2a" | |
| name: "bn3d_branch2a" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res3d_branch2a" | |
| top: "res3d_branch2a" | |
| name: "scale3d_branch2a" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res3d_branch2a" | |
| top: "res3d_branch2a" | |
| name: "res3d_branch2a_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res3d_branch2a" | |
| top: "res3d_branch2b" | |
| name: "res3d_branch2b" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 3 | |
| pad: 1 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res3d_branch2b" | |
| top: "res3d_branch2b" | |
| name: "bn3d_branch2b" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res3d_branch2b" | |
| top: "res3d_branch2b" | |
| name: "scale3d_branch2b" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res3d_branch2b" | |
| top: "res3d_branch2b" | |
| name: "res3d_branch2b_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res3d_branch2b" | |
| top: "res3d_branch2c" | |
| name: "res3d_branch2c" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 1 | |
| pad: 0 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res3d_branch2c" | |
| top: "res3d_branch2c" | |
| name: "bn3d_branch2c" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res3d_branch2c" | |
| top: "res3d_branch2c" | |
| name: "scale3d_branch2c" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res3c" | |
| bottom: "res3d_branch2c" | |
| top: "res3d" | |
| name: "res3d" | |
| type: "Eltwise" | |
| } | |
| layer { | |
| bottom: "res3d" | |
| top: "res3d" | |
| name: "res3d_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res3d" | |
| top: "res4a_branch1" | |
| name: "res4a_branch1" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 1024 | |
| kernel_size: 1 | |
| pad: 0 | |
| stride: 2 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res4a_branch1" | |
| top: "res4a_branch1" | |
| name: "bn4a_branch1" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res4a_branch1" | |
| top: "res4a_branch1" | |
| name: "scale4a_branch1" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res3d" | |
| top: "res4a_branch2a" | |
| name: "res4a_branch2a" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 1 | |
| pad: 0 | |
| stride: 2 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res4a_branch2a" | |
| top: "res4a_branch2a" | |
| name: "bn4a_branch2a" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res4a_branch2a" | |
| top: "res4a_branch2a" | |
| name: "scale4a_branch2a" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res4a_branch2a" | |
| top: "res4a_branch2a" | |
| name: "res4a_branch2a_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res4a_branch2a" | |
| top: "res4a_branch2b" | |
| name: "res4a_branch2b" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 3 | |
| pad: 1 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res4a_branch2b" | |
| top: "res4a_branch2b" | |
| name: "bn4a_branch2b" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res4a_branch2b" | |
| top: "res4a_branch2b" | |
| name: "scale4a_branch2b" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res4a_branch2b" | |
| top: "res4a_branch2b" | |
| name: "res4a_branch2b_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res4a_branch2b" | |
| top: "res4a_branch2c" | |
| name: "res4a_branch2c" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 1024 | |
| kernel_size: 1 | |
| pad: 0 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res4a_branch2c" | |
| top: "res4a_branch2c" | |
| name: "bn4a_branch2c" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res4a_branch2c" | |
| top: "res4a_branch2c" | |
| name: "scale4a_branch2c" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res4a_branch1" | |
| bottom: "res4a_branch2c" | |
| top: "res4a" | |
| name: "res4a" | |
| type: "Eltwise" | |
| } | |
| layer { | |
| bottom: "res4a" | |
| top: "res4a" | |
| name: "res4a_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res4a" | |
| top: "res4b_branch2a" | |
| name: "res4b_branch2a" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 1 | |
| pad: 0 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res4b_branch2a" | |
| top: "res4b_branch2a" | |
| name: "bn4b_branch2a" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res4b_branch2a" | |
| top: "res4b_branch2a" | |
| name: "scale4b_branch2a" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res4b_branch2a" | |
| top: "res4b_branch2a" | |
| name: "res4b_branch2a_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res4b_branch2a" | |
| top: "res4b_branch2b" | |
| name: "res4b_branch2b" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 3 | |
| pad: 1 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res4b_branch2b" | |
| top: "res4b_branch2b" | |
| name: "bn4b_branch2b" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res4b_branch2b" | |
| top: "res4b_branch2b" | |
| name: "scale4b_branch2b" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res4b_branch2b" | |
| top: "res4b_branch2b" | |
| name: "res4b_branch2b_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res4b_branch2b" | |
| top: "res4b_branch2c" | |
| name: "res4b_branch2c" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 1024 | |
| kernel_size: 1 | |
| pad: 0 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res4b_branch2c" | |
| top: "res4b_branch2c" | |
| name: "bn4b_branch2c" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res4b_branch2c" | |
| top: "res4b_branch2c" | |
| name: "scale4b_branch2c" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res4a" | |
| bottom: "res4b_branch2c" | |
| top: "res4b" | |
| name: "res4b" | |
| type: "Eltwise" | |
| } | |
| layer { | |
| bottom: "res4b" | |
| top: "res4b" | |
| name: "res4b_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res4b" | |
| top: "res4c_branch2a" | |
| name: "res4c_branch2a" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 1 | |
| pad: 0 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res4c_branch2a" | |
| top: "res4c_branch2a" | |
| name: "bn4c_branch2a" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res4c_branch2a" | |
| top: "res4c_branch2a" | |
| name: "scale4c_branch2a" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res4c_branch2a" | |
| top: "res4c_branch2a" | |
| name: "res4c_branch2a_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res4c_branch2a" | |
| top: "res4c_branch2b" | |
| name: "res4c_branch2b" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 3 | |
| pad: 1 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res4c_branch2b" | |
| top: "res4c_branch2b" | |
| name: "bn4c_branch2b" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res4c_branch2b" | |
| top: "res4c_branch2b" | |
| name: "scale4c_branch2b" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res4c_branch2b" | |
| top: "res4c_branch2b" | |
| name: "res4c_branch2b_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res4c_branch2b" | |
| top: "res4c_branch2c" | |
| name: "res4c_branch2c" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 1024 | |
| kernel_size: 1 | |
| pad: 0 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res4c_branch2c" | |
| top: "res4c_branch2c" | |
| name: "bn4c_branch2c" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res4c_branch2c" | |
| top: "res4c_branch2c" | |
| name: "scale4c_branch2c" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res4b" | |
| bottom: "res4c_branch2c" | |
| top: "res4c" | |
| name: "res4c" | |
| type: "Eltwise" | |
| } | |
| layer { | |
| bottom: "res4c" | |
| top: "res4c" | |
| name: "res4c_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res4c" | |
| top: "res4d_branch2a" | |
| name: "res4d_branch2a" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 1 | |
| pad: 0 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res4d_branch2a" | |
| top: "res4d_branch2a" | |
| name: "bn4d_branch2a" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res4d_branch2a" | |
| top: "res4d_branch2a" | |
| name: "scale4d_branch2a" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res4d_branch2a" | |
| top: "res4d_branch2a" | |
| name: "res4d_branch2a_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res4d_branch2a" | |
| top: "res4d_branch2b" | |
| name: "res4d_branch2b" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 3 | |
| pad: 1 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res4d_branch2b" | |
| top: "res4d_branch2b" | |
| name: "bn4d_branch2b" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res4d_branch2b" | |
| top: "res4d_branch2b" | |
| name: "scale4d_branch2b" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res4d_branch2b" | |
| top: "res4d_branch2b" | |
| name: "res4d_branch2b_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res4d_branch2b" | |
| top: "res4d_branch2c" | |
| name: "res4d_branch2c" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 1024 | |
| kernel_size: 1 | |
| pad: 0 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res4d_branch2c" | |
| top: "res4d_branch2c" | |
| name: "bn4d_branch2c" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res4d_branch2c" | |
| top: "res4d_branch2c" | |
| name: "scale4d_branch2c" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res4c" | |
| bottom: "res4d_branch2c" | |
| top: "res4d" | |
| name: "res4d" | |
| type: "Eltwise" | |
| } | |
| layer { | |
| bottom: "res4d" | |
| top: "res4d" | |
| name: "res4d_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res4d" | |
| top: "res4e_branch2a" | |
| name: "res4e_branch2a" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 1 | |
| pad: 0 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res4e_branch2a" | |
| top: "res4e_branch2a" | |
| name: "bn4e_branch2a" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res4e_branch2a" | |
| top: "res4e_branch2a" | |
| name: "scale4e_branch2a" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res4e_branch2a" | |
| top: "res4e_branch2a" | |
| name: "res4e_branch2a_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res4e_branch2a" | |
| top: "res4e_branch2b" | |
| name: "res4e_branch2b" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 3 | |
| pad: 1 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res4e_branch2b" | |
| top: "res4e_branch2b" | |
| name: "bn4e_branch2b" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res4e_branch2b" | |
| top: "res4e_branch2b" | |
| name: "scale4e_branch2b" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res4e_branch2b" | |
| top: "res4e_branch2b" | |
| name: "res4e_branch2b_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res4e_branch2b" | |
| top: "res4e_branch2c" | |
| name: "res4e_branch2c" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 1024 | |
| kernel_size: 1 | |
| pad: 0 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res4e_branch2c" | |
| top: "res4e_branch2c" | |
| name: "bn4e_branch2c" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res4e_branch2c" | |
| top: "res4e_branch2c" | |
| name: "scale4e_branch2c" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res4d" | |
| bottom: "res4e_branch2c" | |
| top: "res4e" | |
| name: "res4e" | |
| type: "Eltwise" | |
| } | |
| layer { | |
| bottom: "res4e" | |
| top: "res4e" | |
| name: "res4e_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res4e" | |
| top: "res4f_branch2a" | |
| name: "res4f_branch2a" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 1 | |
| pad: 0 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res4f_branch2a" | |
| top: "res4f_branch2a" | |
| name: "bn4f_branch2a" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res4f_branch2a" | |
| top: "res4f_branch2a" | |
| name: "scale4f_branch2a" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res4f_branch2a" | |
| top: "res4f_branch2a" | |
| name: "res4f_branch2a_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res4f_branch2a" | |
| top: "res4f_branch2b" | |
| name: "res4f_branch2b" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 3 | |
| pad: 1 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res4f_branch2b" | |
| top: "res4f_branch2b" | |
| name: "bn4f_branch2b" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res4f_branch2b" | |
| top: "res4f_branch2b" | |
| name: "scale4f_branch2b" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res4f_branch2b" | |
| top: "res4f_branch2b" | |
| name: "res4f_branch2b_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res4f_branch2b" | |
| top: "res4f_branch2c" | |
| name: "res4f_branch2c" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 1024 | |
| kernel_size: 1 | |
| pad: 0 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res4f_branch2c" | |
| top: "res4f_branch2c" | |
| name: "bn4f_branch2c" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res4f_branch2c" | |
| top: "res4f_branch2c" | |
| name: "scale4f_branch2c" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res4e" | |
| bottom: "res4f_branch2c" | |
| top: "res4f" | |
| name: "res4f" | |
| type: "Eltwise" | |
| } | |
| layer { | |
| bottom: "res4f" | |
| top: "res4f" | |
| name: "res4f_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res4f" | |
| top: "res5a_branch1" | |
| name: "res5a_branch1" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 2048 | |
| kernel_size: 1 | |
| pad: 0 | |
| stride: 2 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res5a_branch1" | |
| top: "res5a_branch1" | |
| name: "bn5a_branch1" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res5a_branch1" | |
| top: "res5a_branch1" | |
| name: "scale5a_branch1" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res4f" | |
| top: "res5a_branch2a" | |
| name: "res5a_branch2a" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 1 | |
| pad: 0 | |
| stride: 2 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res5a_branch2a" | |
| top: "res5a_branch2a" | |
| name: "bn5a_branch2a" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res5a_branch2a" | |
| top: "res5a_branch2a" | |
| name: "scale5a_branch2a" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res5a_branch2a" | |
| top: "res5a_branch2a" | |
| name: "res5a_branch2a_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res5a_branch2a" | |
| top: "res5a_branch2b" | |
| name: "res5a_branch2b" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 3 | |
| pad: 1 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res5a_branch2b" | |
| top: "res5a_branch2b" | |
| name: "bn5a_branch2b" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res5a_branch2b" | |
| top: "res5a_branch2b" | |
| name: "scale5a_branch2b" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res5a_branch2b" | |
| top: "res5a_branch2b" | |
| name: "res5a_branch2b_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res5a_branch2b" | |
| top: "res5a_branch2c" | |
| name: "res5a_branch2c" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 2048 | |
| kernel_size: 1 | |
| pad: 0 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res5a_branch2c" | |
| top: "res5a_branch2c" | |
| name: "bn5a_branch2c" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res5a_branch2c" | |
| top: "res5a_branch2c" | |
| name: "scale5a_branch2c" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res5a_branch1" | |
| bottom: "res5a_branch2c" | |
| top: "res5a" | |
| name: "res5a" | |
| type: "Eltwise" | |
| } | |
| layer { | |
| bottom: "res5a" | |
| top: "res5a" | |
| name: "res5a_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res5a" | |
| top: "res5b_branch2a" | |
| name: "res5b_branch2a" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 1 | |
| pad: 0 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res5b_branch2a" | |
| top: "res5b_branch2a" | |
| name: "bn5b_branch2a" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res5b_branch2a" | |
| top: "res5b_branch2a" | |
| name: "scale5b_branch2a" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res5b_branch2a" | |
| top: "res5b_branch2a" | |
| name: "res5b_branch2a_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res5b_branch2a" | |
| top: "res5b_branch2b" | |
| name: "res5b_branch2b" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 3 | |
| pad: 1 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res5b_branch2b" | |
| top: "res5b_branch2b" | |
| name: "bn5b_branch2b" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res5b_branch2b" | |
| top: "res5b_branch2b" | |
| name: "scale5b_branch2b" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res5b_branch2b" | |
| top: "res5b_branch2b" | |
| name: "res5b_branch2b_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res5b_branch2b" | |
| top: "res5b_branch2c" | |
| name: "res5b_branch2c" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 2048 | |
| kernel_size: 1 | |
| pad: 0 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res5b_branch2c" | |
| top: "res5b_branch2c" | |
| name: "bn5b_branch2c" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res5b_branch2c" | |
| top: "res5b_branch2c" | |
| name: "scale5b_branch2c" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res5a" | |
| bottom: "res5b_branch2c" | |
| top: "res5b" | |
| name: "res5b" | |
| type: "Eltwise" | |
| } | |
| layer { | |
| bottom: "res5b" | |
| top: "res5b" | |
| name: "res5b_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res5b" | |
| top: "res5c_branch2a" | |
| name: "res5c_branch2a" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 1 | |
| pad: 0 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res5c_branch2a" | |
| top: "res5c_branch2a" | |
| name: "bn5c_branch2a" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res5c_branch2a" | |
| top: "res5c_branch2a" | |
| name: "scale5c_branch2a" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res5c_branch2a" | |
| top: "res5c_branch2a" | |
| name: "res5c_branch2a_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res5c_branch2a" | |
| top: "res5c_branch2b" | |
| name: "res5c_branch2b" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 3 | |
| pad: 1 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res5c_branch2b" | |
| top: "res5c_branch2b" | |
| name: "bn5c_branch2b" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res5c_branch2b" | |
| top: "res5c_branch2b" | |
| name: "scale5c_branch2b" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res5c_branch2b" | |
| top: "res5c_branch2b" | |
| name: "res5c_branch2b_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res5c_branch2b" | |
| top: "res5c_branch2c" | |
| name: "res5c_branch2c" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 2048 | |
| kernel_size: 1 | |
| pad: 0 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res5c_branch2c" | |
| top: "res5c_branch2c" | |
| name: "bn5c_branch2c" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res5c_branch2c" | |
| top: "res5c_branch2c" | |
| name: "scale5c_branch2c" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res5b" | |
| bottom: "res5c_branch2c" | |
| top: "res5c" | |
| name: "res5c" | |
| type: "Eltwise" | |
| } | |
| layer { | |
| bottom: "res5c" | |
| top: "res5c" | |
| name: "res5c_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res5c" | |
| top: "res6" | |
| name: "pool_res6" | |
| type: "Pooling" | |
| pooling_param { | |
| kernel_size: 3 | |
| stride: 2 | |
| pool: MAX | |
| } | |
| } | |
| ####lateral | |
| layer { | |
| bottom: "res6" | |
| top: "p6" | |
| name: "p6" | |
| param { | |
| lr_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| } | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 1 | |
| weight_filler { type: "gaussian" std: 0.001 } | |
| bias_filler { type: "constant" value: 0 } | |
| } | |
| } | |
| layer { | |
| bottom: "res5c" | |
| top: "p5" | |
| name: "p5" | |
| param { | |
| lr_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| } | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 1 | |
| weight_filler { type: "gaussian" std: 0.001 } | |
| bias_filler { type: "constant" value: 0 } | |
| } | |
| } | |
| layer { | |
| name: "upP5" | |
| type: "Deconvolution" | |
| bottom: "p5" | |
| top: "upP5" | |
| convolution_param { | |
| kernel_h : 4 | |
| kernel_w : 4 | |
| stride_h: 2 | |
| stride_w: 2 | |
| pad_h: 1 | |
| pad_w: 1 | |
| num_output: 256 | |
| group: 256 | |
| bias_term: false | |
| weight_filler { | |
| type: "bilinear" | |
| } | |
| } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| } | |
| layer { | |
| bottom: "res4f" | |
| top: "c4" | |
| name: "newC4" | |
| param { | |
| lr_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| } | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 1 | |
| weight_filler { type: "gaussian" std: 0.001 } | |
| bias_filler { type: "constant" value: 0.0 } | |
| } | |
| } | |
| layer { | |
| name: "p4" | |
| type: "Eltwise" | |
| bottom: "c4" | |
| bottom: "upP5" | |
| top: "p4" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| bottom: "p4" | |
| top: "p4_lateral" | |
| name: "p4_lateral" | |
| param { | |
| lr_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| } | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 1 | |
| weight_filler { type: "gaussian" std: 0.001 } | |
| bias_filler { type: "constant" value: 0.0 } | |
| } | |
| } | |
| layer { | |
| name: "upP4" | |
| type: "Deconvolution" | |
| bottom: "p4_lateral" | |
| top: "upP4" | |
| convolution_param { | |
| kernel_h : 4 | |
| kernel_w : 4 | |
| stride_h: 2 | |
| stride_w: 2 | |
| pad_h: 1 | |
| pad_w: 1 | |
| num_output: 256 | |
| group: 256 | |
| bias_term: false | |
| weight_filler { | |
| type: "bilinear" | |
| } | |
| } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| } | |
| layer { | |
| bottom: "res3d" | |
| top: "c3" | |
| name: "newC3" | |
| param { | |
| lr_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| } | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 1 | |
| weight_filler { type: "gaussian" std: 0.001 } | |
| bias_filler { type: "constant" value: 0.0 } | |
| } | |
| } | |
| layer { | |
| name: "p3" | |
| type: "Eltwise" | |
| bottom: "c3" | |
| bottom: "upP4" | |
| top: "p3" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| bottom: "p3" | |
| top: "p3_lateral" | |
| name: "p3_lateral" | |
| param { | |
| lr_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| } | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 1 | |
| weight_filler { type: "gaussian" std: 0.001 } | |
| bias_filler { type: "constant" value: 0.0 } | |
| } | |
| } | |
| layer { | |
| bottom: "res2c" | |
| top: "c2" | |
| name: "newC2" | |
| param { | |
| lr_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| } | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 1 | |
| weight_filler { type: "gaussian" std: 0.001 } | |
| bias_filler { type: "constant" value: 0.0 } | |
| } | |
| } | |
| layer { | |
| name: "upP2" | |
| type: "Deconvolution" | |
| bottom: "p3_lateral" | |
| top: "upP2" | |
| convolution_param { | |
| kernel_h : 4 | |
| kernel_w : 4 | |
| stride_h: 2 | |
| stride_w: 2 | |
| pad_h: 1 | |
| pad_w: 1 | |
| num_output: 256 | |
| group: 256 | |
| bias_term: false | |
| weight_filler { | |
| type: "bilinear" | |
| } | |
| } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| } | |
| layer { | |
| name: "p2" | |
| type: "Eltwise" | |
| bottom: "c2" | |
| bottom: "upP2" | |
| top: "p2" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| #### | |
| #========= RPN/p2 ============ | |
| layer { | |
| name: "rpn_conv/3x3/p2" | |
| type: "Convolution" | |
| bottom: "p2" | |
| top: "rpn/output/p2" | |
| param { lr_mult: 1.0 | |
| name: "rpn_conv_3x3_w" | |
| } | |
| param { lr_mult: 2.0 | |
| name: "rpn_conv_3x3_b" | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 3 pad: 1 stride: 1 | |
| weight_filler { type: "gaussian" std: 0.01 } | |
| bias_filler { type: "constant" value: 0 } | |
| } | |
| } | |
| layer { | |
| name: "rpn_relu/3x3/p2" | |
| type: "ReLU" | |
| bottom: "rpn/output/p2" | |
| top: "rpn/output/p2" | |
| } | |
| layer { | |
| name: "rpn_cls_score/p2" | |
| type: "Convolution" | |
| bottom: "rpn/output/p2" | |
| top: "rpn_cls_score/p2" | |
| param { lr_mult: 1.0 | |
| name: "rpn_cls_score_w" } | |
| param { lr_mult: 2.0 | |
| name: "rpn_cls_score_b" | |
| } | |
| convolution_param { | |
| num_output: 12 # 2(bg/fg) * 9(anchors) | |
| kernel_size: 1 pad: 0 stride: 1 | |
| weight_filler { type: "gaussian" std: 0.01 } | |
| bias_filler { type: "constant" value: 0 } | |
| } | |
| } | |
| layer { | |
| name: "rpn_bbox_pred/p2" | |
| type: "Convolution" | |
| bottom: "rpn/output/p2" | |
| top: "rpn_bbox_pred/p2" | |
| param { lr_mult: 1.0 | |
| name: "rpn_bbox_pred_w" | |
| } | |
| param { lr_mult: 2.0 | |
| name: "rpn_bbox_pred_b" | |
| } | |
| convolution_param { | |
| num_output: 24 # 4 * 9(anchors) | |
| kernel_size: 1 pad: 0 stride: 1 | |
| weight_filler { type: "gaussian" std: 0.01 } | |
| bias_filler { type: "constant" value: 0 } | |
| } | |
| } | |
| ###### | |
| layer { | |
| bottom: "rpn_cls_score/p2" | |
| top: "rpn_cls_score_reshape_/p2" | |
| name: "rpn_cls_score_reshape_/p2" | |
| type: "Reshape" | |
| reshape_param { shape {dim: 0 dim: 2 dim: -1 dim:0} } | |
| } | |
| layer { | |
| bottom: "rpn_bbox_pred/p2" | |
| top: "rpn_bbox_pred_reshape/p2" | |
| name: "rpn_bbox_pred_reshape/p2" | |
| type: "Reshape" | |
| reshape_param { shape { dim: 0 dim: 0 dim: -1 } } | |
| } | |
| layer { | |
| bottom: "rpn_cls_score_reshape_/p2" | |
| top: "rpn_cls_score_reshape/p2" | |
| name: "rpn_cls_score_reshape/p2" | |
| type: "Reshape" | |
| reshape_param { shape {dim: 0 dim: 2 dim: -1 } } | |
| } | |
| #####CLS out | |
| layer { | |
| name: "fpn_out/p2" | |
| type: "Softmax" | |
| bottom: "rpn_cls_score_reshape_/p2" | |
| top: "fpn_out/p2" | |
| } | |
| layer { | |
| bottom: "fpn_out/p2" | |
| top: "fpn_out_reshape/p2" | |
| name: "fpn_out_reshape/p2" | |
| type: "Reshape" | |
| reshape_param { shape {dim: 0 dim: 12 dim: -1 dim: 0 } } | |
| } | |
| #========= RPN/p3 ============ | |
| layer { | |
| name: "rpn_conv/3x3/p3" | |
| type: "Convolution" | |
| bottom: "p3" | |
| top: "rpn/output/p3" | |
| param { lr_mult: 1.0 | |
| name: "rpn_conv_3x3_w" | |
| } | |
| param { lr_mult: 2.0 | |
| name: "rpn_conv_3x3_b" | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 3 pad: 1 stride: 1 | |
| weight_filler { type: "gaussian" std: 0.001 } | |
| bias_filler { type: "constant" value: 0 } | |
| } | |
| } | |
| layer { | |
| name: "rpn_relu/3x3/p3" | |
| type: "ReLU" | |
| bottom: "rpn/output/p3" | |
| top: "rpn/output/p3" | |
| } | |
| layer { | |
| name: "rpn_cls_score/p3" | |
| type: "Convolution" | |
| bottom: "rpn/output/p3" | |
| top: "rpn_cls_score/p3" | |
| param { lr_mult: 1.0 | |
| name: "rpn_cls_score_w" | |
| } | |
| param { lr_mult: 2.0 | |
| name: "rpn_cls_score_b" | |
| } | |
| convolution_param { | |
| num_output: 12 # 2(bg/fg) * 9(anchors) | |
| kernel_size: 1 pad: 0 stride: 1 | |
| weight_filler { type: "gaussian" std: 0.001 } | |
| bias_filler { type: "constant" value: 0 } | |
| } | |
| } | |
| layer { | |
| name: "rpn_bbox_pred/p3" | |
| type: "Convolution" | |
| bottom: "rpn/output/p3" | |
| top: "rpn_bbox_pred/p3" | |
| param { lr_mult: 1.0 | |
| name:"rpn_bbox_pred_w" | |
| } | |
| param { lr_mult: 2.0 | |
| name:"rpn_bbox_pred_b" | |
| } | |
| convolution_param { | |
| num_output: 24 # 4 * 9(anchors) | |
| kernel_size: 1 pad: 0 stride: 1 | |
| weight_filler { type: "gaussian" std: 0.001 } | |
| bias_filler { type: "constant" value: 0 } | |
| } | |
| } | |
| ###### | |
| layer { | |
| bottom: "rpn_cls_score/p3" | |
| top: "rpn_cls_score_reshape_/p3" | |
| name: "rpn_cls_score_reshape_/p3" | |
| type: "Reshape" | |
| reshape_param { shape {dim: 0 dim: 2 dim: -1 dim:0} } | |
| } | |
| layer { | |
| bottom: "rpn_bbox_pred/p3" | |
| top: "rpn_bbox_pred_reshape/p3" | |
| name: "rpn_bbox_pred_reshape/p3" | |
| type: "Reshape" | |
| reshape_param { shape { dim: 0 dim: 0 dim: -1 } } | |
| } | |
| layer { | |
| bottom: "rpn_cls_score_reshape_/p3" | |
| top: "rpn_cls_score_reshape/p3" | |
| name: "rpn_cls_score_reshape/p3" | |
| type: "Reshape" | |
| reshape_param { shape {dim: 0 dim: 2 dim: -1 } } | |
| } | |
| #####CLS out | |
| layer { | |
| name: "fpn_out/p3" | |
| type: "Softmax" | |
| bottom: "rpn_cls_score_reshape_/p3" | |
| top: "fpn_out/p3" | |
| } | |
| layer { | |
| bottom: "fpn_out/p3" | |
| top: "fpn_out_reshape/p3" | |
| name: "fpn_out_reshape/p3" | |
| type: "Reshape" | |
| reshape_param { shape {dim: 0 dim: 12 dim: -1 dim: 0 } } | |
| } | |
| #========= RPN/p4 ============ | |
| layer { | |
| name: "rpn_conv/3x3/p4" | |
| type: "Convolution" | |
| bottom: "p4" | |
| top: "rpn/output/p4" | |
| param { lr_mult: 1.0 | |
| name: "rpn_conv_3x3_w" | |
| } | |
| param { lr_mult: 2.0 | |
| name: "rpn_conv_3x3_b" | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 3 pad: 1 stride: 1 | |
| weight_filler { type: "gaussian" std: 0.001 } | |
| bias_filler { type: "constant" value: 0 } | |
| } | |
| } | |
| layer { | |
| name: "rpn_relu/3x3/p4" | |
| type: "ReLU" | |
| bottom: "rpn/output/p4" | |
| top: "rpn/output/p4" | |
| } | |
| layer { | |
| name: "rpn_cls_score/p4" | |
| type: "Convolution" | |
| bottom: "rpn/output/p4" | |
| top: "rpn_cls_score/p4" | |
| param { lr_mult: 1.0 | |
| name:"rpn_cls_score_w" | |
| } | |
| param { lr_mult: 2.0 | |
| name:"rpn_cls_score_b" | |
| } | |
| convolution_param { | |
| num_output: 12 # 2(bg/fg) * 9(anchors) | |
| kernel_size: 1 pad: 0 stride: 1 | |
| weight_filler { type: "gaussian" std: 0.001 } | |
| bias_filler { type: "constant" value: 0 } | |
| } | |
| } | |
| layer { | |
| name: "rpn_bbox_pred/p4" | |
| type: "Convolution" | |
| bottom: "rpn/output/p4" | |
| top: "rpn_bbox_pred/p4" | |
| param { lr_mult: 1.0 | |
| name:"rpn_bbox_pred_w" | |
| } | |
| param { lr_mult: 2.0 | |
| name:"rpn_bbox_pred_b" | |
| } | |
| convolution_param { | |
| num_output: 24 # 4 * 9(anchors) | |
| kernel_size: 1 pad: 0 stride: 1 | |
| weight_filler { type: "gaussian" std: 0.001 } | |
| bias_filler { type: "constant" value: 0 } | |
| } | |
| } | |
| ###### | |
| layer { | |
| bottom: "rpn_cls_score/p4" | |
| top: "rpn_cls_score_reshape_/p4" | |
| name: "rpn_cls_score_reshape_/p4" | |
| type: "Reshape" | |
| reshape_param { shape {dim: 0 dim: 2 dim: -1 dim:0} } | |
| } | |
| layer { | |
| bottom: "rpn_bbox_pred/p4" | |
| top: "rpn_bbox_pred_reshape/p4" | |
| name: "rpn_bbox_pred_reshape/p4" | |
| type: "Reshape" | |
| reshape_param { shape { dim: 0 dim: 0 dim: -1 } } | |
| } | |
| layer { | |
| bottom: "rpn_cls_score_reshape_/p4" | |
| top: "rpn_cls_score_reshape/p4" | |
| name: "rpn_cls_score_reshape/p4" | |
| type: "Reshape" | |
| reshape_param { shape {dim: 0 dim: 2 dim: -1 } } | |
| } | |
| #####CLS out | |
| layer { | |
| name: "fpn_out/p4" | |
| type: "Softmax" | |
| bottom: "rpn_cls_score_reshape_/p4" | |
| top: "fpn_out/p4" | |
| } | |
| layer { | |
| bottom: "fpn_out/p4" | |
| top: "fpn_out_reshape/p4" | |
| name: "fpn_out_reshape/p4" | |
| type: "Reshape" | |
| reshape_param { shape {dim: 0 dim: 12 dim: -1 dim: 0 } } | |
| } | |
| #========= RPN/p5 ============ | |
| layer { | |
| name: "rpn_conv/3x3/p5" | |
| type: "Convolution" | |
| bottom: "p5" | |
| top: "rpn/output/p5" | |
| param { lr_mult: 1.0 | |
| name:"rpn_conv_3x3_w" | |
| } | |
| param { lr_mult: 2.0 | |
| name:"rpn_conv_3x3_b" | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 3 pad: 1 stride: 1 | |
| weight_filler { type: "gaussian" std: 0.01 } | |
| bias_filler { type: "constant" value: 0 } | |
| } | |
| } | |
| layer { | |
| name: "rpn_relu/3x3/p5" | |
| type: "ReLU" | |
| bottom: "rpn/output/p5" | |
| top: "rpn/output/p5" | |
| } | |
| layer { | |
| name: "rpn_cls_score/p5" | |
| type: "Convolution" | |
| bottom: "rpn/output/p5" | |
| top: "rpn_cls_score/p5" | |
| param { lr_mult: 1.0 | |
| name:"rpn_cls_score_w" | |
| } | |
| param { lr_mult: 2.0 | |
| name:"rpn_cls_score_b" | |
| } | |
| convolution_param { | |
| num_output: 12 # 2(bg/fg) * 9(anchors) | |
| kernel_size: 1 pad: 0 stride: 1 | |
| weight_filler { type: "gaussian" std: 0.01 } | |
| bias_filler { type: "constant" value: 0 } | |
| } | |
| } | |
| layer { | |
| name: "rpn_bbox_pred/p5" | |
| type: "Convolution" | |
| bottom: "rpn/output/p5" | |
| top: "rpn_bbox_pred/p5" | |
| param { lr_mult: 1.0 | |
| name:"rpn_bbox_pred_w" | |
| } | |
| param { lr_mult: 2.0 | |
| name:"rpn_bbox_pred_b" | |
| } | |
| convolution_param { | |
| num_output: 24 # 4 * 9(anchors) | |
| kernel_size: 1 pad: 0 stride: 1 | |
| weight_filler { type: "gaussian" std: 0.01 } | |
| bias_filler { type: "constant" value: 0 } | |
| } | |
| } | |
| ###### | |
| layer { | |
| bottom: "rpn_cls_score/p5" | |
| top: "rpn_cls_score_reshape_/p5" | |
| name: "rpn_cls_score_reshape_/p5" | |
| type: "Reshape" | |
| reshape_param { shape {dim: 0 dim: 2 dim: -1 dim:0} } | |
| } | |
| layer { | |
| bottom: "rpn_bbox_pred/p5" | |
| top: "rpn_bbox_pred_reshape/p5" | |
| name: "rpn_bbox_pred_reshape/p5" | |
| type: "Reshape" | |
| reshape_param { shape { dim: 0 dim: 0 dim: -1 } } | |
| } | |
| layer { | |
| bottom: "rpn_cls_score_reshape_/p5" | |
| top: "rpn_cls_score_reshape/p5" | |
| name: "rpn_cls_score_reshape/p5" | |
| type: "Reshape" | |
| reshape_param { shape {dim: 0 dim: 2 dim: -1 } } | |
| } | |
| #####CLS out | |
| layer { | |
| name: "fpn_out/p5" | |
| type: "Softmax" | |
| bottom: "rpn_cls_score_reshape_/p5" | |
| top: "fpn_out/p5" | |
| } | |
| layer { | |
| bottom: "fpn_out/p5" | |
| top: "fpn_out_reshape/p5" | |
| name: "fpn_out_reshape/p5" | |
| type: "Reshape" | |
| reshape_param { shape {dim: 0 dim: 12 dim: -1 dim: 0 } } | |
| } | |
| #========= RPN/p6 ============ | |
| layer { | |
| name: "rpn_conv/3x3/p6" | |
| type: "Convolution" | |
| bottom: "p6" | |
| top: "rpn/output/p6" | |
| param { lr_mult: 1.0 | |
| name:"rpn_conv_3x3_w" | |
| } | |
| param { lr_mult: 2.0 | |
| name:"rpn_conv_3x3_b" | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 3 pad: 1 stride: 1 | |
| weight_filler { type: "gaussian" std: 0.01 } | |
| bias_filler { type: "constant" value: 0 } | |
| } | |
| } | |
| layer { | |
| name: "rpn_relu/3x3/p6" | |
| type: "ReLU" | |
| bottom: "rpn/output/p6" | |
| top: "rpn/output/p6" | |
| } | |
| layer { | |
| name: "rpn_cls_score/p6" | |
| type: "Convolution" | |
| bottom: "rpn/output/p6" | |
| top: "rpn_cls_score/p6" | |
| param { lr_mult: 1.0 | |
| name:"rpn_cls_score_w" | |
| } | |
| param { lr_mult: 2.0 | |
| name:"rpn_cls_score_b" | |
| } | |
| convolution_param { | |
| num_output: 12 # 2(bg/fg) * 9(anchors) | |
| kernel_size: 1 pad: 0 stride: 1 | |
| weight_filler { type: "gaussian" std: 0.01 } | |
| bias_filler { type: "constant" value: 0 } | |
| } | |
| } | |
| layer { | |
| name: "rpn_bbox_pred/p6" | |
| type: "Convolution" | |
| bottom: "rpn/output/p6" | |
| top: "rpn_bbox_pred/p6" | |
| param { lr_mult: 1.0 | |
| name:"rpn_bbox_pred_w" | |
| } | |
| param { lr_mult: 2.0 | |
| name:"rpn_bbox_pred_b" | |
| } | |
| convolution_param { | |
| num_output: 24 # 4 * 9(anchors) | |
| kernel_size: 1 pad: 0 stride: 1 | |
| weight_filler { type: "gaussian" std: 0.01 } | |
| bias_filler { type: "constant" value: 0 } | |
| } | |
| } | |
| ###### | |
| layer { | |
| bottom: "rpn_cls_score/p6" | |
| top: "rpn_cls_score_reshape_/p6" | |
| name: "rpn_cls_score_reshape_/p6" | |
| type: "Reshape" | |
| reshape_param { shape {dim: 0 dim: 2 dim: -1 dim:0} } | |
| } | |
| layer { | |
| bottom: "rpn_bbox_pred/p6" | |
| top: "rpn_bbox_pred_reshape/p6" | |
| name: "rpn_bbox_pred_reshape/p6" | |
| type: "Reshape" | |
| reshape_param { shape { dim: 0 dim: 0 dim: -1 } } | |
| } | |
| layer { | |
| bottom: "rpn_cls_score_reshape_/p6" | |
| top: "rpn_cls_score_reshape/p6" | |
| name: "rpn_cls_score_reshape/p6" | |
| type: "Reshape" | |
| reshape_param { shape {dim: 0 dim: 2 dim: -1 } } | |
| } | |
| #####CLS out | |
| layer { | |
| name: "fpn_out/p6" | |
| type: "Softmax" | |
| bottom: "rpn_cls_score_reshape_/p6" | |
| top: "fpn_out/p6" | |
| } | |
| layer { | |
| bottom: "fpn_out/p6" | |
| top: "fpn_out_reshape/p6" | |
| name: "fpn_out_reshape/p6" | |
| type: "Reshape" | |
| reshape_param { shape {dim: 0 dim: 12 dim: -1 dim: 0 } } | |
| } | |
| ########rpn loss##################### | |
| layer { | |
| name: "rpn_cls_score_reshapee" | |
| type: "Concat" | |
| bottom: "rpn_cls_score_reshape/p2" | |
| bottom: "rpn_cls_score_reshape/p3" | |
| bottom: "rpn_cls_score_reshape/p4" | |
| bottom: "rpn_cls_score_reshape/p5" | |
| bottom: "rpn_cls_score_reshape/p6" | |
| top: "rpn_cls_score_reshape" | |
| concat_param { | |
| axis: 2 | |
| } | |
| } | |
| layer { | |
| name: "rpn_bbox_pred" | |
| type: "Concat" | |
| bottom: "rpn_bbox_pred_reshape/p2" | |
| bottom: "rpn_bbox_pred_reshape/p3" | |
| bottom: "rpn_bbox_pred_reshape/p4" | |
| bottom: "rpn_bbox_pred_reshape/p5" | |
| bottom: "rpn_bbox_pred_reshape/p6" | |
| top: "rpn_bbox_pred" | |
| concat_param { | |
| axis: 2 | |
| } | |
| } | |
| layer { | |
| name: 'rpn-data' | |
| type: 'Python' | |
| bottom: 'rpn_cls_score/p2' | |
| bottom: 'rpn_cls_score/p3' | |
| bottom: 'rpn_cls_score/p4' | |
| bottom: 'rpn_cls_score/p5' | |
| bottom: 'rpn_cls_score/p6' | |
| bottom: 'gt_boxes' | |
| bottom: 'im_info' | |
| top: 'rpn_labels' | |
| top: 'rpn_bbox_targets' | |
| top: 'rpn_bbox_inside_weights' | |
| top: 'rpn_bbox_outside_weights' | |
| python_param { | |
| module: 'rpn.anchor_target_layer' | |
| layer: 'AnchorTargetLayer' | |
| param_str: "'feat_stride': 4,8,16,32,64" | |
| } | |
| } | |
| layer { | |
| name: "fpn_loss_cls" | |
| type: "SoftmaxWithLoss" | |
| bottom: "rpn_cls_score_reshape" | |
| bottom: "rpn_labels" | |
| propagate_down: 1 | |
| propagate_down: 0 | |
| top: "FPNClsLoss" | |
| loss_weight: 1 | |
| loss_param { | |
| ignore_label: -1 | |
| normalization: VALID | |
| } | |
| } | |
| layer { | |
| name: "rpn_loss_bbox" | |
| type: "SmoothL1Loss" | |
| bottom: "rpn_bbox_pred" | |
| bottom: "rpn_bbox_targets" | |
| bottom: 'rpn_bbox_inside_weights' | |
| bottom: 'rpn_bbox_outside_weights' | |
| top: "FPNLossBBox" | |
| loss_weight: 1 | |
| smooth_l1_loss_param { sigma: 3.0 } | |
| } | |
| #========= RoI Proposal ============ | |
| layer { | |
| name: 'proposal' | |
| type: 'Python' | |
| bottom: 'im_info' | |
| bottom: 'rpn_bbox_pred/p2' | |
| bottom: 'rpn_bbox_pred/p3' | |
| bottom: 'rpn_bbox_pred/p4' | |
| bottom: 'rpn_bbox_pred/p5' | |
| bottom: 'rpn_bbox_pred/p6' | |
| bottom: 'fpn_out_reshape/p2' | |
| bottom: 'fpn_out_reshape/p3' | |
| bottom: 'fpn_out_reshape/p4' | |
| bottom: 'fpn_out_reshape/p5' | |
| bottom: 'fpn_out_reshape/p6' | |
| top: 'rpn_rois' | |
| python_param { | |
| module: 'rpn.proposal_layer' | |
| layer: 'ProposalLayer' | |
| param_str: "'feat_stride': 4,8,16,32,64" | |
| } | |
| } | |
| #================rois process====================== | |
| layer { | |
| name: 'roi-data' | |
| type: 'Python' | |
| bottom: 'rpn_rois' | |
| bottom: 'gt_boxes' | |
| bottom: 'data' | |
| top: 'rois/h2' | |
| top: 'rois/h3' | |
| top: 'rois/h4' | |
| top: 'rois/h5' | |
| top: 'labels' | |
| top: 'bbox_targets' | |
| top: 'bbox_inside_weights' | |
| top: 'bbox_outside_weights' | |
| python_param { | |
| module: 'rpn.proposal_target_layer' | |
| layer: 'ProposalTargetLayer' | |
| param_str: "'num_classes': 21" | |
| } | |
| } | |
| #========= RCNN ============ | |
| ######POOLING======= | |
| layer { | |
| name: "roi_pool/h2" | |
| type: "ROIPooling" | |
| bottom: "p2" | |
| bottom: "rois/h2" | |
| top: "roi_pool/h2" | |
| roi_pooling_param { | |
| pooled_w: 7 | |
| pooled_h: 7 | |
| spatial_scale: 0.25 # 1/4 | |
| } | |
| } | |
| layer { | |
| name: "roi_pool/h3" | |
| type: "ROIPooling" | |
| bottom: "p3" | |
| bottom: "rois/h3" | |
| top: "roi_pool/h3" | |
| roi_pooling_param { | |
| pooled_w: 7 | |
| pooled_h: 7 | |
| spatial_scale: 0.125 # 1/8 | |
| } | |
| } | |
| layer { | |
| name: "roi_pool/h4" | |
| type: "ROIPooling" | |
| bottom: "p4" | |
| bottom: "rois/h4" | |
| top: "roi_pool/h4" | |
| roi_pooling_param { | |
| pooled_w: 7 | |
| pooled_h: 7 | |
| spatial_scale: 0.0625 # 1/16 | |
| } | |
| } | |
| layer { | |
| name: "roi_pool/h5" | |
| type: "ROIPooling" | |
| bottom: "p5" | |
| bottom: "rois/h5" | |
| top: "roi_pool/h5" | |
| roi_pooling_param { | |
| pooled_w: 7 | |
| pooled_h: 7 | |
| spatial_scale: 0.03125 # 1/32 | |
| } | |
| } | |
| #h2 | |
| layer { | |
| name: "rcnn_fc6/h2" | |
| type: "InnerProduct" | |
| bottom: "roi_pool/h2" | |
| top: "rcnn_fc6/h2" | |
| param { | |
| lr_mult: 1 | |
| name: "rcnn_fc6_w" | |
| } | |
| param { | |
| lr_mult: 2 | |
| name: "rcnn_fc6_b" | |
| } | |
| inner_product_param { | |
| num_output: 4096 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu6/h2" | |
| type: "ReLU" | |
| bottom: "rcnn_fc6/h2" | |
| top: "rcnn_fc6/h2" | |
| } | |
| layer { | |
| name: "drop6/h2" | |
| type: "Dropout" | |
| bottom: "rcnn_fc6/h2" | |
| top: "rcnn_fc6/h2" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| layer { | |
| name: "fc7/h2" | |
| type: "InnerProduct" | |
| bottom: "rcnn_fc6/h2" | |
| top: "fc7/h2" | |
| param { | |
| lr_mult: 1 | |
| name:"fc7_w" | |
| } | |
| param { | |
| lr_mult: 2 | |
| name: "fc7_b" | |
| } | |
| inner_product_param { | |
| num_output: 4096 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu7/h2" | |
| type: "ReLU" | |
| bottom: "fc7/h2" | |
| top: "fc7/h2" | |
| } | |
| layer { | |
| name: "drop7/h2" | |
| type: "Dropout" | |
| bottom: "fc7/h2" | |
| top: "fc7/h2" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| layer { | |
| name: "cls_score/h2" | |
| type: "InnerProduct" | |
| bottom: "fc7/h2" | |
| top: "cls_score/h2" | |
| param { | |
| lr_mult: 1 | |
| name:"cls_score_w" | |
| } | |
| param { | |
| lr_mult: 2 | |
| name:"cls_score_b" | |
| } | |
| inner_product_param { | |
| num_output: 21 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "bbox_pred/h2" | |
| type: "InnerProduct" | |
| bottom: "fc7/h2" | |
| top: "bbox_pred/h2" | |
| param { | |
| lr_mult: 1 | |
| name:"bbox_pred_w" | |
| } | |
| param { | |
| lr_mult: 2 | |
| name:"bbox_pred_b" | |
| } | |
| inner_product_param { | |
| num_output: 84 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.001 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| #h3 | |
| layer { | |
| name: "rcnn_fc6/h3" | |
| type: "InnerProduct" | |
| bottom: "roi_pool/h3" | |
| top: "rcnn_fc6/h3" | |
| param { | |
| lr_mult: 1 | |
| name: "rcnn_fc6_w" | |
| } | |
| param { | |
| lr_mult: 2 | |
| name: "rcnn_fc6_b" | |
| } | |
| inner_product_param { | |
| num_output: 4096 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu6/h3" | |
| type: "ReLU" | |
| bottom: "rcnn_fc6/h3" | |
| top: "rcnn_fc6/h3" | |
| } | |
| layer { | |
| name: "drop6/h3" | |
| type: "Dropout" | |
| bottom: "rcnn_fc6/h3" | |
| top: "rcnn_fc6/h3" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| layer { | |
| name: "fc7/h3" | |
| type: "InnerProduct" | |
| bottom: "rcnn_fc6/h3" | |
| top: "fc7/h3" | |
| param { | |
| lr_mult: 1 | |
| name:"fc7_w" | |
| } | |
| param { | |
| lr_mult: 2 | |
| name: "fc7_b" | |
| } | |
| inner_product_param { | |
| num_output: 4096 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu7/h3" | |
| type: "ReLU" | |
| bottom: "fc7/h3" | |
| top: "fc7/h3" | |
| } | |
| layer { | |
| name: "drop7/h3" | |
| type: "Dropout" | |
| bottom: "fc7/h3" | |
| top: "fc7/h3" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| layer { | |
| name: "cls_score/h3" | |
| type: "InnerProduct" | |
| bottom: "fc7/h3" | |
| top: "cls_score/h3" | |
| param { | |
| lr_mult: 1 | |
| name:"cls_score_w" | |
| } | |
| param { | |
| lr_mult: 2 | |
| name:"cls_score_b" | |
| } | |
| inner_product_param { | |
| num_output: 21 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "bbox_pred/h3" | |
| type: "InnerProduct" | |
| bottom: "fc7/h3" | |
| top: "bbox_pred/h3" | |
| param { | |
| lr_mult: 1 | |
| name:"bbox_pred_w" | |
| } | |
| param { | |
| lr_mult: 2 | |
| name:"bbox_pred_b" | |
| } | |
| inner_product_param { | |
| num_output: 84 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.001 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| #h4 | |
| layer { | |
| name: "rcnn_fc6/h4" | |
| type: "InnerProduct" | |
| bottom: "roi_pool/h4" | |
| top: "rcnn_fc6/h4" | |
| param { | |
| lr_mult: 1 | |
| name: "rcnn_fc6_w" | |
| } | |
| param { | |
| lr_mult: 2 | |
| name: "rcnn_fc6_b" | |
| } | |
| inner_product_param { | |
| num_output: 4096 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu6/h4" | |
| type: "ReLU" | |
| bottom: "rcnn_fc6/h4" | |
| top: "rcnn_fc6/h4" | |
| } | |
| layer { | |
| name: "drop6/h4" | |
| type: "Dropout" | |
| bottom: "rcnn_fc6/h4" | |
| top: "rcnn_fc6/h4" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| layer { | |
| name: "fc7/h4" | |
| type: "InnerProduct" | |
| bottom: "rcnn_fc6/h4" | |
| top: "fc7/h4" | |
| param { | |
| lr_mult: 1 | |
| name:"fc7_w" | |
| } | |
| param { | |
| lr_mult: 2 | |
| name: "fc7_b" | |
| } | |
| inner_product_param { | |
| num_output: 4096 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu7/h4" | |
| type: "ReLU" | |
| bottom: "fc7/h4" | |
| top: "fc7/h4" | |
| } | |
| layer { | |
| name: "drop7/h4" | |
| type: "Dropout" | |
| bottom: "fc7/h4" | |
| top: "fc7/h4" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| layer { | |
| name: "cls_score/h4" | |
| type: "InnerProduct" | |
| bottom: "fc7/h4" | |
| top: "cls_score/h4" | |
| param { | |
| lr_mult: 1 | |
| name:"cls_score_w" | |
| } | |
| param { | |
| lr_mult: 2 | |
| name:"cls_score_b" | |
| } | |
| inner_product_param { | |
| num_output: 21 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "bbox_pred/h4" | |
| type: "InnerProduct" | |
| bottom: "fc7/h4" | |
| top: "bbox_pred/h4" | |
| param { | |
| lr_mult: 1 | |
| name:"bbox_pred_w" | |
| } | |
| param { | |
| lr_mult: 2 | |
| name:"bbox_pred_b" | |
| } | |
| inner_product_param { | |
| num_output: 84 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.001 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| #h5 | |
| layer { | |
| name: "rcnn_fc6/h5" | |
| type: "InnerProduct" | |
| bottom: "roi_pool/h5" | |
| top: "rcnn_fc6/h5" | |
| param { | |
| lr_mult: 1 | |
| name: "rcnn_fc6_w" | |
| } | |
| param { | |
| lr_mult: 2 | |
| name: "rcnn_fc6_b" | |
| } | |
| inner_product_param { | |
| num_output: 4096 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu6/h5" | |
| type: "ReLU" | |
| bottom: "rcnn_fc6/h5" | |
| top: "rcnn_fc6/h5" | |
| } | |
| layer { | |
| name: "drop6/h5" | |
| type: "Dropout" | |
| bottom: "rcnn_fc6/h5" | |
| top: "rcnn_fc6/h5" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| layer { | |
| name: "fc7/h5" | |
| type: "InnerProduct" | |
| bottom: "rcnn_fc6/h5" | |
| top: "fc7/h5" | |
| param { | |
| lr_mult: 1 | |
| name:"fc7_w" | |
| } | |
| param { | |
| lr_mult: 2 | |
| name: "fc7_b" | |
| } | |
| inner_product_param { | |
| num_output: 4096 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu7/h5" | |
| type: "ReLU" | |
| bottom: "fc7/h5" | |
| top: "fc7/h5" | |
| } | |
| layer { | |
| name: "drop7/h5" | |
| type: "Dropout" | |
| bottom: "fc7/h5" | |
| top: "fc7/h5" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| layer { | |
| name: "cls_score/h5" | |
| type: "InnerProduct" | |
| bottom: "fc7/h5" | |
| top: "cls_score/h5" | |
| param { | |
| lr_mult: 1 | |
| name:"cls_score_w" | |
| } | |
| param { | |
| lr_mult: 2 | |
| name:"cls_score_b" | |
| } | |
| inner_product_param { | |
| num_output: 21 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "bbox_pred/h5" | |
| type: "InnerProduct" | |
| bottom: "fc7/h5" | |
| top: "bbox_pred/h5" | |
| param { | |
| lr_mult: 1 | |
| name:"bbox_pred_w" | |
| } | |
| param { | |
| lr_mult: 2 | |
| name:"bbox_pred_b" | |
| } | |
| inner_product_param { | |
| num_output: 84 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.001 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "cls_score_concat" | |
| type: "Concat" | |
| bottom: "cls_score/h2" | |
| bottom: "cls_score/h3" | |
| bottom: "cls_score/h4" | |
| bottom: "cls_score/h5" | |
| top: "cls_score" | |
| concat_param { | |
| axis: 0 | |
| } | |
| } | |
| layer { | |
| name: "bbox_pred_concat" | |
| type: "Concat" | |
| bottom: "bbox_pred/h2" | |
| bottom: "bbox_pred/h3" | |
| bottom: "bbox_pred/h4" | |
| bottom: "bbox_pred/h5" | |
| top: "bbox_pred" | |
| concat_param { | |
| axis: 0 | |
| } | |
| } | |
| layer { | |
| name: "loss_cls" | |
| type: "SoftmaxWithLoss" | |
| bottom: "cls_score" | |
| bottom: "labels" | |
| propagate_down: 1 | |
| propagate_down: 0 | |
| top: "RcnnLossCls" | |
| loss_weight: 1 | |
| loss_param{ | |
| ignore_label: -1 | |
| normalization: VALID | |
| } | |
| } | |
| layer { | |
| name: "loss_bbox" | |
| type: "SmoothL1Loss" | |
| bottom: "bbox_pred" | |
| bottom: "bbox_targets" | |
| bottom: "bbox_inside_weights" | |
| bottom: "bbox_outside_weights" | |
| top: "RcnnLossBBox" | |
| loss_weight: 1 | |
| } |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment