CUDANet/examples/inception_v3/inception_modules.cpp

#include "inception_v3.hpp"

#include <cudanet.cuh>

// Inception Basic Convolution 2D
BasicConv2d::BasicConv2d(
    const shape2d      inputShape,
    const int          inputChannels,
    const int          outputChannels,
    const shape2d      kernelSize,
    const shape2d      stride,
    const shape2d      padding,
    const std::string &prefix
)
    : outputChannels(outputChannels) {
    // Create the convolution layer
    conv = new CUDANet::Layers::Conv2d(
        inputShape, inputChannels, kernelSize, stride, outputChannels, padding,
        CUDANet::Layers::ActivationType::NONE
    );

    shape2d batchNormSize = conv->getOutputDims();

    batchNorm = new CUDANet::Layers::BatchNorm2d(
        batchNormSize, outputChannels, 1e-3f,
        CUDANet::Layers::ActivationType::RELU
    );

    inputSize  = inputShape.first * inputShape.second * inputChannels;
    outputSize = batchNorm->getOutputDims().first *
                 batchNorm->getOutputDims().second * outputChannels;

    addLayer(prefix + "conv", conv);
    addLayer(prefix + "bn", batchNorm);
}

BasicConv2d::~BasicConv2d() {
    delete conv;
    delete batchNorm;
}

float *BasicConv2d::forward(const float *d_input) {
    float *d_output = conv->forward(d_input);
    return batchNorm->forward(d_output);
}

shape2d BasicConv2d::getOutputDims() {
    return batchNorm->getOutputDims();
}

int BasicConv2d::getOutputChannels() {
    return outputChannels;
}

// Inception Block A
InceptionA::InceptionA(
    const shape2d      inputShape,
    const int          inputChannels,
    const int          poolFeatures,
    const std::string &prefix
)
    : inputShape(inputShape),
      inputChannels(inputChannels),
      poolFeatures(poolFeatures) {
    inputSize = inputShape.first * inputShape.second * inputChannels;

    // Branch 1x1
    branch1x1 = new BasicConv2d(
        inputShape, inputChannels, 64, {1, 1}, {1, 1}, {0, 0},
        prefix + "branch1x1."
    );
    addLayer("", branch1x1);

    // Branch 5x5
    branch5x5_1 = new BasicConv2d(
        inputShape, inputChannels, 48, {1, 1}, {1, 1}, {0, 0},
        prefix + "branch5x5_1."
    );
    addLayer("", branch5x5_1);
    branch5x5_2 = new BasicConv2d(
        branch5x5_1->getOutputDims(), 48, 64, {5, 5}, {1, 1}, {2, 2},
        prefix + "branch5x5_2."
    );
    addLayer("", branch5x5_2);

    // Branch 3x3
    branch3x3dbl_1 = new BasicConv2d(
        inputShape, inputChannels, 64, {1, 1}, {1, 1}, {0, 0},
        prefix + "branch3x3dbl_1."
    );
    addLayer("", branch3x3dbl_1);
    branch3x3dbl_2 = new BasicConv2d(
        branch3x3dbl_1->getOutputDims(), 64, 96, {3, 3}, {1, 1}, {1, 1},
        prefix + "branch3x3dbl_2."
    );
    addLayer("", branch3x3dbl_2);
    branch3x3dbl_3 = new BasicConv2d(
        branch3x3dbl_2->getOutputDims(), 96, 96, {3, 3}, {1, 1}, {1, 1},
        prefix + "branch3x3dbl_3."
    );
    addLayer("", branch3x3dbl_3);

    // Branch Pool
    branchPool_1 = new CUDANet::Layers::AvgPooling2d(
        inputShape, inputChannels, {3, 3}, {1, 1}, {1, 1},
        CUDANet::Layers::ActivationType::NONE
    );
    addLayer(prefix + "branch_pool", branchPool_1);
    branchPool_2 = new BasicConv2d(
        branchPool_1->getOutputDims(), inputChannels, poolFeatures, {1, 1},
        {1, 1}, {0, 0}, prefix + "branch_pool."
    );
    addLayer("", branchPool_2);

    // Concat
    concat_1 = new CUDANet::Layers::Concat(
        branch1x1->getOutputSize(), branch5x5_2->getOutputSize()
    );
    concat_2 = new CUDANet::Layers::Concat(
        concat_1->getOutputSize(), branch3x3dbl_3->getOutputSize()
    );
    concat_3 = new CUDANet::Layers::Concat(
        concat_2->getOutputSize(), branchPool_2->getOutputSize()
    );

    outputSize = concat_3->getOutputSize();
}

InceptionA::~InceptionA() {
    delete branch1x1;
    delete branch5x5_1;
    delete branch5x5_2;
    delete branch3x3dbl_1;
    delete branch3x3dbl_2;
    delete branch3x3dbl_3;
    delete branchPool_1;
    delete branchPool_2;
    delete concat_1;
    delete concat_2;
    delete concat_3;
}

float *InceptionA::forward(const float *d_input) {
    float *d_branch1x1_out = branch1x1->forward(d_input);

    float *d_branch5x5_out = branch5x5_1->forward(d_input);
    d_branch5x5_out        = branch5x5_2->forward(d_branch5x5_out);

    float *d_branch3x3dbl_out = branch3x3dbl_1->forward(d_input);
    d_branch3x3dbl_out        = branch3x3dbl_2->forward(d_branch3x3dbl_out);
    d_branch3x3dbl_out        = branch3x3dbl_3->forward(d_branch3x3dbl_out);

    float *d_branchPool_out = branchPool_1->forward(d_input);
    d_branchPool_out        = branchPool_2->forward(d_branchPool_out);

    float *d_output = concat_1->forward(d_branch1x1_out, d_branch5x5_out);
    d_output        = concat_2->forward(d_output, d_branch3x3dbl_out);
    d_output        = concat_3->forward(d_output, d_branchPool_out);

    return d_output;
}

shape2d InceptionA::getOutputDims() {
    return branch1x1->getOutputDims();
}

int InceptionA::getOutputChannels() {
    return branch1x1->getOutputChannels() + branch5x5_2->getOutputChannels() +
           branch3x3dbl_3->getOutputChannels() +
           branchPool_2->getOutputChannels();
}

// Inception Block B
InceptionB::InceptionB(
    const shape2d      inputShape,
    const int          inputChannels,
    const std::string &prefix
)
    : inputShape(inputShape), inputChannels(inputChannels) {
    inputSize = inputShape.first * inputShape.second * inputChannels;

    // Branch 3x3
    branch3x3 = new BasicConv2d(
        inputShape, inputChannels, 384, {3, 3}, {2, 2}, {0, 0},
        prefix + "branch3x3."
    );
    addLayer("", branch3x3);

    // Branch 3x3dbl
    branch3x3dbl_1 = new BasicConv2d(
        inputShape, inputChannels, 64, {1, 1}, {1, 1}, {0, 0},
        prefix + "branch3x3dbl_1."
    );
    addLayer("", branch3x3dbl_1);
    branch3x3dbl_2 = new BasicConv2d(
        branch3x3dbl_1->getOutputDims(), 64, 96, {3, 3}, {1, 1}, {1, 1},
        prefix + "branch3x3dbl_2."
    );
    addLayer("", branch3x3dbl_2);
    branch3x3dbl_3 = new BasicConv2d(
        branch3x3dbl_2->getOutputDims(), 96, 96, {3, 3}, {2, 2}, {0, 0},
        prefix + "branch3x3dbl_3."
    );
    addLayer("", branch3x3dbl_3);

    branchPool = new CUDANet::Layers::MaxPooling2d(
        inputShape, inputChannels, {3, 3}, {2, 2}, {0, 0},
        CUDANet::Layers::ActivationType::NONE
    );
    addLayer(prefix + "branch_pool", branchPool);

    concat_1 = new CUDANet::Layers::Concat(
        branch3x3->getOutputSize(), branch3x3dbl_3->getOutputSize()
    );
    concat_2 = new CUDANet::Layers::Concat(
        concat_1->getOutputSize(), branchPool->getOutputSize()
    );

    outputSize = concat_2->getOutputSize();
}

InceptionB::~InceptionB() {
    delete branch3x3;
    delete branch3x3dbl_1;
    delete branch3x3dbl_2;
    delete branch3x3dbl_3;
    delete branchPool;
    delete concat_1;
    delete concat_2;
}

float *InceptionB::forward(const float *d_input) {
    float *d_branch3x3_out = branch3x3->forward(d_input);

    float *d_branch3x3dbl_out = branch3x3dbl_1->forward(d_input);
    d_branch3x3dbl_out        = branch3x3dbl_2->forward(d_branch3x3dbl_out);
    d_branch3x3dbl_out        = branch3x3dbl_3->forward(d_branch3x3dbl_out);

    float *d_branchPool_out = branchPool->forward(d_input);

    float *d_output = concat_1->forward(d_branch3x3_out, d_branch3x3dbl_out);
    d_output        = concat_2->forward(d_output, d_branchPool_out);

    return d_output;
}

shape2d InceptionB::getOutputDims() {
    return branch3x3->getOutputDims();
}

int InceptionB::getOutputChannels() {
    return branch3x3->getOutputChannels() +
           branch3x3dbl_3->getOutputChannels() + inputChannels;
}

// Inception Block C
InceptionC::InceptionC(
    const shape2d      inputShape,
    const int          inputChannels,
    const int          nChannels_7x7,
    const std::string &prefix
)
    : inputShape(inputShape), inputChannels(inputChannels) {
    inputSize = inputShape.first * inputShape.second * inputChannels;

    // Branch 1x1
    branch1x1 = new BasicConv2d(
        inputShape, inputChannels, 192, {1, 1}, {1, 1}, {0, 0},
        prefix + "branch1x1."
    );
    addLayer("", branch1x1);

    // Branch 7x7
    branch7x7_1 = new BasicConv2d(
        inputShape, inputChannels, nChannels_7x7, {1, 1}, {1, 1}, {0, 0},
        prefix + "branch7x7_1."
    );
    addLayer("", branch7x7_1);
    branch7x7_2 = new BasicConv2d(
        branch7x7_1->getOutputDims(), nChannels_7x7, nChannels_7x7, {1, 7},
        {1, 1}, {0, 3}, prefix + "branch7x7_2."
    );
    addLayer("", branch7x7_2);
    branch7x7_3 = new BasicConv2d(
        branch7x7_2->getOutputDims(), nChannels_7x7, 192, {7, 1}, {1, 1},
        {3, 0}, prefix + "branch7x7_3."
    );
    addLayer("", branch7x7_3);

    // Branch 7x7dbl
    branch7x7dbl_1 = new BasicConv2d(
        inputShape, inputChannels, nChannels_7x7, {1, 1}, {1, 1}, {0, 0},
        prefix + "branch7x7dbl_1."
    );
    addLayer("", branch7x7dbl_1);
    branch7x7dbl_2 = new BasicConv2d(
        branch7x7dbl_1->getOutputDims(), nChannels_7x7, nChannels_7x7, {7, 1},
        {1, 1}, {3, 0}, prefix + "branch7x7dbl_2."
    );
    addLayer("", branch7x7dbl_2);
    branch7x7dbl_3 = new BasicConv2d(
        branch7x7dbl_2->getOutputDims(), nChannels_7x7, nChannels_7x7, {1, 7},
        {1, 1}, {0, 3}, prefix + "branch7x7dbl_3."
    );
    addLayer("", branch7x7dbl_3);
    branch7x7dbl_4 = new BasicConv2d(
        branch7x7dbl_3->getOutputDims(), nChannels_7x7, nChannels_7x7, {7, 1},
        {1, 1}, {3, 0}, prefix + "branch7x7dbl_4."
    );
    addLayer("", branch7x7dbl_4);
    branch7x7dbl_5 = new BasicConv2d(
        branch7x7dbl_4->getOutputDims(), nChannels_7x7, 192, {1, 7}, {1, 1},
        {0, 3}, prefix + "branch7x7dbl_5."
    );
    addLayer("", branch7x7dbl_5);

    // Branch Pool
    branchPool_1 = new CUDANet::Layers::AvgPooling2d(
        inputShape, inputChannels, {3, 3}, {1, 1}, {1, 1},
        CUDANet::Layers::ActivationType::NONE
    );
    addLayer(prefix + "branch_pool", branchPool_1);
    branchPool_2 = new BasicConv2d(
        branchPool_1->getOutputDims(), inputChannels, 192, {1, 1}, {1, 1},
        {0, 0}, prefix + "branch_pool."
    );
    addLayer("", branchPool_2);

    // Concat
    concat_1 = new CUDANet::Layers::Concat(
        branch1x1->getOutputSize(), branch7x7_3->getOutputSize()
    );
    concat_2 = new CUDANet::Layers::Concat(
        concat_1->getOutputSize(), branch7x7dbl_5->getOutputSize()
    );
    concat_3 = new CUDANet::Layers::Concat(
        concat_2->getOutputSize(), branchPool_2->getOutputSize()
    );

    outputSize = concat_3->getOutputSize();
}

InceptionC::~InceptionC() {
    delete branch1x1;
    delete branch7x7_1;
    delete branch7x7_2;
    delete branch7x7_3;
    delete branch7x7dbl_1;
    delete branch7x7dbl_2;
    delete branch7x7dbl_3;
    delete branch7x7dbl_4;
    delete branch7x7dbl_5;
    delete branchPool_1;
    delete branchPool_2;
    delete concat_1;
    delete concat_2;
    delete concat_3;
}

float *InceptionC::forward(const float *d_input) {
    float *branch1x1_output = branch1x1->forward(d_input);

    float *branch7x7_output = branch7x7_1->forward(d_input);
    branch7x7_output        = branch7x7_2->forward(branch7x7_output);
    branch7x7_output        = branch7x7_3->forward(branch7x7_output);

    float *branch7x7dbl_output = branch7x7dbl_1->forward(d_input);
    branch7x7dbl_output        = branch7x7dbl_2->forward(branch7x7dbl_output);
    branch7x7dbl_output        = branch7x7dbl_3->forward(branch7x7dbl_output);
    branch7x7dbl_output        = branch7x7dbl_4->forward(branch7x7dbl_output);
    branch7x7dbl_output        = branch7x7dbl_5->forward(branch7x7dbl_output);

    float *branchPool_output = branchPool_1->forward(d_input);
    branchPool_output        = branchPool_2->forward(branchPool_output);

    float *d_output = concat_1->forward(branch1x1_output, branch7x7_output);
    d_output        = concat_2->forward(d_output, branch7x7dbl_output);
    d_output        = concat_3->forward(d_output, branchPool_output);

    return d_output;
}

shape2d InceptionC::getOutputDims() {
    return branch1x1->getOutputDims();
}

int InceptionC::getOutputChannels() {
    return branch1x1->getOutputChannels() + branch7x7_3->getOutputChannels() +
           branch7x7dbl_5->getOutputChannels() +
           branchPool_2->getOutputChannels();
}

// Inception Block D
InceptionD::InceptionD(
    const shape2d      inputShape,
    const int          inputChannels,
    const std::string &prefix
)
    : inputShape(inputShape), inputChannels(inputChannels) {
    inputSize = inputShape.first * inputShape.second * inputChannels;

    // Branch 3x3
    branch3x3_1 = new BasicConv2d(
        inputShape, inputChannels, 192, {1, 1}, {1, 1}, {0, 0},
        prefix + "branch3x3_1."
    );
    addLayer("", branch3x3_1);
    branch3x3_2 = new BasicConv2d(
        inputShape, 192, 320, {3, 3}, {2, 2}, {0, 0}, prefix + "branch3x3_2."
    );
    addLayer("", branch3x3_2);

    // Branch 7x7x3
    branch7x7x3_1 = new BasicConv2d(
        inputShape, inputChannels, 192, {1, 1}, {1, 1}, {0, 0},
        prefix + "branch7x7x3_1."
    );
    addLayer("", branch7x7x3_1);
    branch7x7x3_2 = new BasicConv2d(
        inputShape, 192, 192, {1, 7}, {1, 1}, {0, 3}, prefix + "branch7x7x3_2."
    );
    addLayer("", branch7x7x3_2);
    branch7x7x3_3 = new BasicConv2d(
        inputShape, 192, 192, {7, 1}, {1, 1}, {3, 0}, prefix + "branch7x7x3_3."
    );
    addLayer("", branch7x7x3_3);
    branch7x7x3_4 = new BasicConv2d(
        inputShape, 192, 192, {3, 3}, {2, 2}, {0, 0}, prefix + "branch7x7x3_4."
    );
    addLayer("", branch7x7x3_4);

    // Branch Pool
    branchPool = new CUDANet::Layers::MaxPooling2d(
        inputShape, inputChannels, {3, 3}, {2, 2}, {0, 0},
        CUDANet::Layers::ActivationType::NONE
    );
    addLayer(prefix + "branch_pool", branchPool);

    // Concat
    concat_1 = new CUDANet::Layers::Concat(
        branch3x3_2->getOutputSize(), branch7x7x3_4->getOutputSize()
    );
    concat_2 = new CUDANet::Layers::Concat(
        concat_1->getOutputSize(), branchPool->getOutputSize()
    );

    outputSize = concat_2->getOutputSize();
}

InceptionD::~InceptionD() {
    delete branch3x3_1;
    delete branch3x3_2;
    delete branch7x7x3_1;
    delete branch7x7x3_2;
    delete branch7x7x3_3;
    delete branch7x7x3_4;
    delete branchPool;
    delete concat_1;
    delete concat_2;
}

float *InceptionD::forward(const float *d_input) {
    float *branch3x3_output = branch3x3_1->forward(d_input);
    branch3x3_output        = branch3x3_2->forward(branch3x3_output);

    float *branch7x7_output = branch7x7x3_1->forward(d_input);
    branch7x7_output        = branch7x7x3_2->forward(branch7x7_output);
    branch7x7_output        = branch7x7x3_3->forward(branch7x7_output);
    branch7x7_output        = branch7x7x3_4->forward(branch7x7_output);

    float *branchPool_output = branchPool->forward(d_input);

    float *d_output = concat_1->forward(branch3x3_output, branch7x7_output);
    d_output        = concat_2->forward(d_output, branchPool_output);

    return d_output;
}

shape2d InceptionD::getOutputDims() {
    return branch3x3_2->getOutputDims();
}

int InceptionD::getOutputChannels() {
    return branch3x3_2->getOutputChannels() +
           branch7x7x3_4->getOutputChannels() + inputChannels;
}

// Inception Block E
InceptionE::InceptionE(
    const shape2d      inputShape,
    const int          inputChannels,
    const std::string &prefix
)
    : inputShape(inputShape), inputChannels(inputChannels) {
    inputSize = inputShape.first * inputShape.second * inputChannels;

    // Branch 1x1
    branch1x1 = new BasicConv2d(
        inputShape, inputChannels, 320, {1, 1}, {1, 1}, {0, 0},
        prefix + "branch1x1."
    );
    addLayer("", branch1x1);

    // Branch 3x3
    branch3x3_1 = new BasicConv2d(
        inputShape, inputChannels, 384, {1, 1}, {1, 1}, {0, 0},
        prefix + "branch3x3_1."
    );
    addLayer("", branch3x3_1);
    branch3x3_2a = new BasicConv2d(
        inputShape, 384, 384, {1, 3}, {1, 1}, {0, 1}, prefix + "branch3x3_2a."
    );
    addLayer("", branch3x3_2a);
    branch3x3_2b = new BasicConv2d(
        inputShape, 384, 384, {3, 1}, {1, 1}, {1, 0}, prefix + "branch3x3_2b."
    );
    addLayer("", branch3x3_2b);
    branch_3x3_2_concat = new CUDANet::Layers::Concat(
        branch3x3_2a->getOutputSize(), branch3x3_2b->getOutputSize()
    );

    // Branch 3x3dbl
    branch3x3dbl_1 = new BasicConv2d(
        inputShape, inputChannels, 448, {1, 1}, {1, 1}, {0, 0},
        prefix + "branch3x3dbl_1."
    );
    addLayer("", branch3x3dbl_1);
    branch3x3dbl_2 = new BasicConv2d(
        inputShape, 448, 384, {3, 3}, {1, 1}, {1, 1}, prefix + "branch3x3dbl_2."
    );
    addLayer("", branch3x3dbl_2);
    branch3x3dbl_3a = new BasicConv2d(
        inputShape, 384, 384, {1, 3}, {1, 1}, {0, 1},
        prefix + "branch3x3dbl_3a."
    );
    addLayer("", branch3x3dbl_3a);
    branch3x3dbl_3b = new BasicConv2d(
        inputShape, 384, 384, {3, 1}, {1, 1}, {1, 0},
        prefix + "branch3x3dbl_3b."
    );
    addLayer("", branch3x3dbl_3b);
    branch_3x3dbl_3_concat = new CUDANet::Layers::Concat(
        branch3x3dbl_3a->getOutputSize(), branch3x3dbl_3b->getOutputSize()
    );

    // Branch Pool
    branchPool_1 = new CUDANet::Layers::AvgPooling2d(
        inputShape, inputChannels, {3, 3}, {1, 1}, {1, 1},
        CUDANet::Layers::ActivationType::NONE
    );
    addLayer(prefix + "branch_pool", branchPool_1);
    branchPool_2 = new BasicConv2d(
        inputShape, inputChannels, 192, {1, 1}, {1, 1}, {0, 0},
        prefix + "branch_pool."
    );
    addLayer("", branchPool_2);

    // Concat
    concat_1 = new CUDANet::Layers::Concat(
        branch1x1->getOutputSize(), branch_3x3_2_concat->getOutputSize()
    );
    concat_2 = new CUDANet::Layers::Concat(
        concat_1->getOutputSize(), branch_3x3dbl_3_concat->getOutputSize()
    );
    concat_3 = new CUDANet::Layers::Concat(
        concat_2->getOutputSize(), branchPool_2->getOutputSize()
    );

    outputSize = concat_3->getOutputSize();
}

InceptionE::~InceptionE() {
    delete branch1x1;
    delete branch3x3_1;
    delete branch3x3_2a;
    delete branch3x3_2b;
    delete branch_3x3_2_concat;
    delete branch3x3dbl_1;
    delete branch3x3dbl_2;
    delete branch3x3dbl_3a;
    delete branch3x3dbl_3b;
    delete branch_3x3dbl_3_concat;
    delete branchPool_1;
    delete branchPool_2;
    delete concat_1;
    delete concat_2;
    delete concat_3;
}

float *InceptionE::forward(const float *d_input) {
    float *branch1x1_output = branch1x1->forward(d_input);

    float *branch3x3_output    = branch3x3_1->forward(d_input);
    float *branch3x3_2a_output = branch3x3_2a->forward(branch3x3_output);
    float *branch3x3_2b_output = branch3x3_2b->forward(branch3x3_output);
    branch3x3_output =
        branch_3x3_2_concat->forward(branch3x3_2a_output, branch3x3_2b_output);

    float *branch3x3dbl_output = branch3x3dbl_1->forward(d_input);
    branch3x3dbl_output        = branch3x3dbl_2->forward(branch3x3dbl_output);
    float *branch3x3dbl_3a_output =
        branch3x3dbl_3a->forward(branch3x3dbl_output);
    float *branch3x3dbl_3b_output =
        branch3x3dbl_3b->forward(branch3x3dbl_output);
    branch3x3dbl_output = branch_3x3dbl_3_concat->forward(
        branch3x3dbl_3a_output, branch3x3dbl_3b_output
    );

    float *branchPool_output = branchPool_1->forward(d_input);
    branchPool_output        = branchPool_2->forward(branchPool_output);

    float *d_output = concat_1->forward(branch1x1_output, branch3x3_output);
    d_output        = concat_2->forward(d_output, branch3x3dbl_output);
    d_output        = concat_3->forward(d_output, branchPool_output);

    return d_output;
}

shape2d InceptionE::getOutputDims() {
    return branch3x3_2a->getOutputDims();
}

int InceptionE::getOutputChannels() {
    return branch1x1->getOutputChannels() + branch3x3_2a->getOutputChannels() +
           branch3x3_2b->getOutputChannels() +
           branch3x3dbl_3a->getOutputChannels() +
           branch3x3dbl_3b->getOutputChannels() +
           branchPool_2->getOutputChannels();
}

// InceptionV3 Model
InceptionV3::InceptionV3(
    const shape2d inputShape,
    const int     inputChannels,
    const int     outputSize
)
    : CUDANet::Model(inputShape, inputChannels, outputSize) {
    conv2d_1a_3x3 = new BasicConv2d(
        inputShape, inputChannels, 32, {3, 3}, {2, 2}, {0, 0}, "Conv2d_1a_3x3."
    );
    addLayer("", conv2d_1a_3x3);
    conv2d_2a_3x3 = new BasicConv2d(
        conv2d_1a_3x3->getOutputDims(), 32, 32, {3, 3}, {1, 1}, {0, 0},
        "Conv2d_2a_3x3."
    );
    addLayer("", conv2d_2a_3x3);
    conv2d_2b_3x3 = new BasicConv2d(
        conv2d_2a_3x3->getOutputDims(), 32, 64, {3, 3}, {1, 1}, {1, 1},
        "Conv2d_2b_3x3."
    );
    addLayer("", conv2d_2b_3x3);

    maxpool1 = new CUDANet::Layers::MaxPooling2d(
        conv2d_2b_3x3->getOutputDims(), 64, {3, 3}, {2, 2}, {0, 0},
        CUDANet::Layers::ActivationType::NONE
    );
    addLayer("Maxpool1", maxpool1);

    conv2d_3b_1x1 = new BasicConv2d(
        maxpool1->getOutputDims(), 64, 80, {1, 1}, {1, 1}, {0, 0},
        "Conv2d_3b_1x1."
    );
    addLayer("", conv2d_3b_1x1);
    conv2d_4a_3x3 = new BasicConv2d(
        conv2d_3b_1x1->getOutputDims(), 80, 192, {3, 3}, {1, 1}, {0, 0},
        "Conv2d_4a_3x3."
    );
    addLayer("", conv2d_4a_3x3);

    maxpool2 = new CUDANet::Layers::MaxPooling2d(
        conv2d_4a_3x3->getOutputDims(), 192, {3, 3}, {2, 2}, {0, 0},
        CUDANet::Layers::ActivationType::NONE
    );
    addLayer("Maxpool2", maxpool2);

    Mixed_5b = new InceptionA(maxpool2->getOutputDims(), 192, 32, "Mixed_5b.");
    addLayer("", Mixed_5b);
    Mixed_5c = new InceptionA(Mixed_5b->getOutputDims(), 256, 64, "Mixed_5c.");
    addLayer("", Mixed_5c);
    Mixed_5d = new InceptionA(Mixed_5c->getOutputDims(), 288, 64, "Mixed_5d.");
    addLayer("", Mixed_5d);

    Mixed_6a = new InceptionB(Mixed_5d->getOutputDims(), 288, "Mixed_6a.");
    addLayer("", Mixed_6a);

    Mixed_6b = new InceptionC(Mixed_6a->getOutputDims(), 768, 128, "Mixed_6b.");
    addLayer("", Mixed_6b);
    Mixed_6c = new InceptionC(Mixed_6b->getOutputDims(), 768, 160, "Mixed_6c.");
    addLayer("", Mixed_6c);
    Mixed_6d = new InceptionC(Mixed_6c->getOutputDims(), 768, 160, "Mixed_6d.");
    addLayer("", Mixed_6d);
    Mixed_6e = new InceptionC(Mixed_6d->getOutputDims(), 768, 192, "Mixed_6e.");
    addLayer("", Mixed_6e);

    Mixed_7a = new InceptionD(Mixed_6e->getOutputDims(), 768, "Mixed_7a.");
    addLayer("", Mixed_7a);

    Mixed_7b = new InceptionE(Mixed_7a->getOutputDims(), 1280, "Mixed_7b.");
    addLayer("", Mixed_7b);
    Mixed_7c = new InceptionE(Mixed_7b->getOutputDims(), 2048, "Mixed_7c.");
    addLayer("", Mixed_7c);

    avgpool = new CUDANet::Layers::AdaptiveAvgPooling2d(
        Mixed_7c->getOutputDims(), Mixed_7c->getOutputChannels(), {1, 1},
        CUDANet::Layers::ActivationType::NONE
    );
    addLayer("AveragePool", avgpool);

    fc = new CUDANet::Layers::Dense(
        avgpool->getOutputSize(), 1000, CUDANet::Layers::ActivationType::SOFTMAX
    );
    addLayer("fc", fc);
}

float *InceptionV3::predict(const float *input) {
    float *d_x = inputLayer->forward(input);

    d_x = conv2d_1a_3x3->forward(d_x);
    d_x = conv2d_2a_3x3->forward(d_x);
    d_x = conv2d_2b_3x3->forward(d_x);
    d_x = maxpool1->forward(d_x);
    d_x = conv2d_3b_1x1->forward(d_x);
    d_x = conv2d_4a_3x3->forward(d_x);
    d_x = maxpool2->forward(d_x);
    d_x = Mixed_5b->forward(d_x);
    d_x = Mixed_5c->forward(d_x);
    d_x = Mixed_5d->forward(d_x);
    d_x = Mixed_6a->forward(d_x);
    d_x = Mixed_6b->forward(d_x);
    d_x = Mixed_6c->forward(d_x);
    d_x = Mixed_6d->forward(d_x);
    d_x = Mixed_6e->forward(d_x);
    d_x = Mixed_7a->forward(d_x);
    d_x = Mixed_7b->forward(d_x);
    d_x = Mixed_7c->forward(d_x);
    d_x = avgpool->forward(d_x);
    d_x = fc->forward(d_x);

    float *output = outputLayer->forward(d_x);
    return output;
}

InceptionV3::~InceptionV3() {
    delete conv2d_1a_3x3;
    delete conv2d_2a_3x3;
    delete conv2d_2b_3x3;
    delete maxpool1;
    delete conv2d_3b_1x1;
    delete conv2d_4a_3x3;
    delete maxpool2;
    delete Mixed_5b;
    delete Mixed_5c;
    delete Mixed_5d;
    delete Mixed_6a;
    delete Mixed_6b;
    delete Mixed_6c;
    delete Mixed_6d;
    delete Mixed_6e;
    delete Mixed_7a;
    delete Mixed_7b;
    delete Mixed_7c;
    delete avgpool;
    delete fc;
}