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NN architectures
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| name: "ResNet-10" | |
| input: "data" | |
| input_shape { | |
| dim: 1 | |
| dim: 3 | |
| dim: 272 | |
| dim: 480 | |
| } | |
| layer { | |
| bottom: "data" | |
| top: "conv1" | |
| name: "conv1" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 32 | |
| kernel_size: 3 | |
| pad: 1 | |
| stride: 2 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "conv1" | |
| top: "conv1" | |
| name: "conv1_bn" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "conv1" | |
| top: "conv1" | |
| name: "conv1_scale" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "conv1" | |
| top: "conv1" | |
| name: "conv1_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv1" | |
| top: "conv2" | |
| name: "conv2" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 32 | |
| kernel_size: 3 | |
| pad: 1 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "conv2" | |
| top: "conv2" | |
| name: "conv2_bn" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "conv2" | |
| top: "conv2" | |
| name: "conv2_scale" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "conv2" | |
| top: "conv2" | |
| name: "conv2_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv2" | |
| top: "conv3" | |
| name: "conv3" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 3 | |
| pad: 1 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "conv3" | |
| top: "conv3" | |
| name: "conv3_bn" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "conv3" | |
| top: "conv3" | |
| name: "conv3_scale" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "conv3" | |
| top: "conv3" | |
| name: "conv3_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv3" | |
| top: "pool1" | |
| name: "pool1" | |
| type: "Pooling" | |
| pooling_param { | |
| kernel_size: 3 | |
| stride: 2 | |
| pad:1 | |
| pool: MAX | |
| ceil_mode: false | |
| } | |
| } | |
| layer { | |
| bottom: "pool1" | |
| top: "res1_conv1" | |
| name: "res1_conv1" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 96 | |
| kernel_size: 3 | |
| pad: 1 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res1_conv1" | |
| top: "res1_conv1" | |
| name: "res1_conv1_bn" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res1_conv1" | |
| top: "res1_conv1" | |
| name: "res1_conv1_scale" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res1_conv1" | |
| top: "res1_conv1" | |
| name: "res1_conv1_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res1_conv1" | |
| top: "res1_conv2" | |
| name: "res1_conv2" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 96 | |
| kernel_size: 3 | |
| pad: 1 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res1_conv2" | |
| top: "res1_conv2" | |
| name: "res1_conv2_bn" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res1_conv2" | |
| top: "res1_conv2" | |
| name: "res1_conv2_scale" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "pool1" | |
| top: "res1_match" | |
| name: "res1_match" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 96 | |
| kernel_size: 1 | |
| pad: 0 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res1_match" | |
| top: "res1_match" | |
| name: "res1_match_bn" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res1_match" | |
| top: "res1_match" | |
| name: "res1_match_scale" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res1_match" | |
| bottom: "res1_conv2" | |
| top: "res1" | |
| name: "res1" | |
| type: "Eltwise" | |
| } | |
| layer { | |
| bottom: "res1" | |
| top: "res1" | |
| name: "res1_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res1" | |
| top: "res2_conv1" | |
| name: "res2_conv1" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 192 | |
| kernel_size: 3 | |
| pad: 1 | |
| stride: 2 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res2_conv1" | |
| top: "res2_conv1" | |
| name: "res2_conv1_bn" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res2_conv1" | |
| top: "res2_conv1" | |
| name: "res2_conv1_scale" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res2_conv1" | |
| top: "res2_conv1" | |
| name: "res2_conv1_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res2_conv1" | |
| top: "res2_conv2" | |
| name: "res2_conv2" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 192 | |
| kernel_size: 3 | |
| pad: 1 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res2_conv2" | |
| top: "res2_conv2" | |
| name: "res2_conv2_bn" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res2_conv2" | |
| top: "res2_conv2" | |
| name: "res2_conv2_scale" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res1" | |
| top: "res2_match" | |
| name: "res2_match" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 192 | |
| kernel_size: 1 | |
| pad: 0 | |
| stride: 2 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res2_match" | |
| top: "res2_match" | |
| name: "res2_match_bn" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res2_match" | |
| top: "res2_match" | |
| name: "res2_match_scale" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res2_match" | |
| bottom: "res2_conv2" | |
| top: "res2" | |
| name: "res2" | |
| type: "Eltwise" | |
| } | |
| layer { | |
| bottom: "res2" | |
| top: "res2" | |
| name: "res2_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res2" | |
| top: "res3_conv1" | |
| name: "res3_conv1" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 384 | |
| kernel_size: 3 | |
| pad: 1 | |
| stride: 2 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res3_conv1" | |
| top: "res3_conv1" | |
| name: "res3_conv1_bn" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res3_conv1" | |
| top: "res3_conv1" | |
| name: "res3_conv1_scale" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res3_conv1" | |
| top: "res3_conv1" | |
| name: "res3_conv1_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res3_conv1" | |
| top: "res3_conv2" | |
| name: "res3_conv2" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 384 | |
| kernel_size: 3 | |
| pad: 1 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res3_conv2" | |
| top: "res3_conv2" | |
| name: "res3_conv2_bn" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res3_conv2" | |
| top: "res3_conv2" | |
| name: "res3_conv2_scale" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res2" | |
| top: "res3_match" | |
| name: "res3_match" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 384 | |
| kernel_size: 1 | |
| pad: 0 | |
| stride: 2 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res3_match" | |
| top: "res3_match" | |
| name: "res3_match_bn" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res3_match" | |
| top: "res3_match" | |
| name: "res3_match_scale" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res3_match" | |
| bottom: "res3_conv2" | |
| top: "res3" | |
| name: "res3" | |
| type: "Eltwise" | |
| } | |
| layer { | |
| bottom: "res3" | |
| top: "res3" | |
| name: "res3_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res3" | |
| top: "res4_conv1" | |
| name: "res4_conv1" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 768 | |
| kernel_size: 3 | |
| pad: 1 | |
| stride: 2 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res4_conv1" | |
| top: "res4_conv1" | |
| name: "res4_conv1_bn" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res4_conv1" | |
| top: "res4_conv1" | |
| name: "res4_conv1_scale" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res4_conv1" | |
| top: "res4_conv1" | |
| name: "res4_conv1_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res4_conv1" | |
| top: "res4_conv2" | |
| name: "res4_conv2" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 768 | |
| kernel_size: 3 | |
| pad: 1 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res4_conv2" | |
| top: "res4_conv2" | |
| name: "res4_conv2_bn" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res4_conv2" | |
| top: "res4_conv2" | |
| name: "res4_conv2_scale" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res3" | |
| top: "res4_match" | |
| name: "res4_match" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 768 | |
| kernel_size: 1 | |
| pad: 0 | |
| stride: 2 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res4_match" | |
| top: "res4_match" | |
| name: "res4_match_bn" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res4_match" | |
| top: "res4_match" | |
| name: "res4_match_scale" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res4_match" | |
| bottom: "res4_conv2" | |
| top: "res4" | |
| name: "res4" | |
| type: "Eltwise" | |
| } | |
| layer { | |
| bottom: "res4" | |
| top: "res4" | |
| name: "res4_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res4" | |
| top: "pool_avg" | |
| name: "pool_avg" | |
| type: "Pooling" | |
| pooling_param { | |
| global_pooling: true | |
| pool: AVE | |
| } | |
| } | |
| layer { | |
| bottom: "pool_avg" | |
| top: "classifier" | |
| name: "classifier" | |
| type: "InnerProduct" | |
| inner_product_param { | |
| num_output: 1000 | |
| } | |
| } | |
| layer { | |
| bottom: "classifier" | |
| top: "prob" | |
| name: "prob" | |
| type: "Softmax" | |
| } |
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