dlib C++ Library - resnet.h

    #ifndef ResNet_H
    #define ResNet_H

    #include <dlib/dnn.h>

    namespace resnet
    {
        using namespace dlib;
        // BN is bn_con or affine layer
        template<template<typename> class BN>
        struct def
        {
            // the resnet basic block, where BN is bn_con or affine
            template<long num_filters, int stride, typename SUBNET>
            using basicblock = BN<con<num_filters, 3, 3, 1, 1,
                    relu<BN<con<num_filters, 3, 3, stride, stride, SUBNET>>>>>;

            // the resnet bottleneck block
            template<long num_filters, int stride, typename SUBNET>
            using bottleneck = BN<con<4 * num_filters, 1, 1, 1, 1,
                    relu<BN<con<num_filters, 3, 3, stride, stride,
                    relu<BN<con<num_filters, 1, 1, 1, 1, SUBNET>>>>>>>>;

            // the resnet residual, where BLOCK is either basicblock or bottleneck
            template<template<long, int, typename> class BLOCK, long num_filters, typename SUBNET>
            using residual = add_prev1<BLOCK<num_filters, 1, tag1<SUBNET>>>;

            // a resnet residual that does subsampling on both paths
            template<template<long, int, typename> class BLOCK, long num_filters, typename SUBNET>
            using residual_down = add_prev2<avg_pool<2, 2, 2, 2,
                                  skip1<tag2<BLOCK<num_filters, 2,
                                  tag1<SUBNET>>>>>>;

            // residual block with optional downsampling
            template<
                template<template<long, int, typename> class, long, typename> class RESIDUAL,
                template<long, int, typename> class BLOCK,
                long num_filters,
                typename SUBNET
            >
            using residual_block = relu<RESIDUAL<BLOCK, num_filters, SUBNET>>;

            template<long num_filters, typename SUBNET>
            using resbasicblock_down = residual_block<residual_down, basicblock, num_filters, SUBNET>;
            template<long num_filters, typename SUBNET>
            using resbottleneck_down = residual_block<residual_down, bottleneck, num_filters, SUBNET>;

            // some definitions to allow the use of the repeat layer
            template<typename SUBNET> using resbasicblock_512 = residual_block<residual, basicblock, 512, SUBNET>;
            template<typename SUBNET> using resbasicblock_256 = residual_block<residual, basicblock, 256, SUBNET>;
            template<typename SUBNET> using resbasicblock_128 = residual_block<residual, basicblock, 128, SUBNET>;
            template<typename SUBNET> using resbasicblock_64  = residual_block<residual, basicblock,  64, SUBNET>;
            template<typename SUBNET> using resbottleneck_512 = residual_block<residual, bottleneck, 512, SUBNET>;
            template<typename SUBNET> using resbottleneck_256 = residual_block<residual, bottleneck, 256, SUBNET>;
            template<typename SUBNET> using resbottleneck_128 = residual_block<residual, bottleneck, 128, SUBNET>;
            template<typename SUBNET> using resbottleneck_64  = residual_block<residual, bottleneck,  64, SUBNET>;

            // common processing for standard resnet inputs
            template<typename INPUT>
            using input_processing = max_pool<3, 3, 2, 2, relu<BN<con<64, 7, 7, 2, 2, INPUT>>>>;

            // the resnet backbone with basicblocks
            template<long nb_512, long nb_256, long nb_128, long nb_64, typename INPUT>
            using backbone_basicblock =
                repeat<nb_512, resbasicblock_512, resbasicblock_down<512,
                repeat<nb_256, resbasicblock_256, resbasicblock_down<256,
                repeat<nb_128, resbasicblock_128, resbasicblock_down<128,
                repeat<nb_64,  resbasicblock_64, input_processing<INPUT>>>>>>>>;

            // the resnet backbone with bottlenecks
            template<long nb_512, long nb_256, long nb_128, long nb_64, typename INPUT>
            using backbone_bottleneck =
                repeat<nb_512, resbottleneck_512, resbottleneck_down<512,
                repeat<nb_256, resbottleneck_256, resbottleneck_down<256,
                repeat<nb_128, resbottleneck_128, resbottleneck_down<128,
                repeat<nb_64,  resbottleneck_64, input_processing<INPUT>>>>>>>>;

            // the backbones for the classic architectures
            template<typename INPUT> using backbone_18  = backbone_basicblock<1, 1, 1, 2, INPUT>;
            template<typename INPUT> using backbone_34  = backbone_basicblock<2, 5, 3, 3, INPUT>;
            template<typename INPUT> using backbone_50  = backbone_bottleneck<2, 5, 3, 3, INPUT>;
            template<typename INPUT> using backbone_101 = backbone_bottleneck<2, 22, 3, 3, INPUT>;
            template<typename INPUT> using backbone_152 = backbone_bottleneck<2, 35, 7, 3, INPUT>;

            // the typical classifier models
            using n18  = loss_multiclass_log<fc<1000, avg_pool_everything<backbone_18<input_rgb_image>>>>;
            using n34  = loss_multiclass_log<fc<1000, avg_pool_everything<backbone_34<input_rgb_image>>>>;
            using n50  = loss_multiclass_log<fc<1000, avg_pool_everything<backbone_50<input_rgb_image>>>>;
            using n101 = loss_multiclass_log<fc<1000, avg_pool_everything<backbone_101<input_rgb_image>>>>;
            using n152 = loss_multiclass_log<fc<1000, avg_pool_everything<backbone_152<input_rgb_image>>>>;
        };

        using train_18 = def<bn_con>::n18;
        using train_34 = def<bn_con>::n34;
        using train_50 = def<bn_con>::n50;
        using train_101 = def<bn_con>::n101;
        using train_152 = def<bn_con>::n152;

        using infer_18 = def<affine>::n18;
        using infer_34 = def<affine>::n34;
        using infer_50 = def<affine>::n50;
        using infer_101 = def<affine>::n101;
        using infer_152 = def<affine>::n152;
    }

    #endif // ResNet_H