resnet.h

#ifndef RESNET_H
#define RESNET_H

#include <torch/torch.h>

namespace vision {
namespace models {
template <typename Block>
struct ResNetImpl;

namespace _resnetimpl {
// 3x3 convolution with padding
torch::nn::Conv2d conv3x3(
    int64_t in,
    int64_t out,
    int64_t stride = 1,
    int64_t groups = 1);

// 1x1 convolution
torch::nn::Conv2d conv1x1(int64_t in, int64_t out, int64_t stride = 1);

struct BasicBlock : torch::nn::Module {
  template <typename Block>
  friend struct vision::models::ResNetImpl;

  int64_t stride;
  torch::nn::Sequential downsample;

  torch::nn::Conv2d conv1{nullptr}, conv2{nullptr};
  torch::nn::BatchNorm bn1{nullptr}, bn2{nullptr};

  static int expansion;

  BasicBlock(
      int64_t inplanes,
      int64_t planes,
      int64_t stride = 1,
      torch::nn::Sequential downsample = nullptr,
      int64_t groups = 1,
      int64_t base_width = 64);

  torch::Tensor forward(torch::Tensor x);
};

struct Bottleneck : torch::nn::Module {
  template <typename Block>
  friend struct vision::models::ResNetImpl;

  int64_t stride;
  torch::nn::Sequential downsample;

  torch::nn::Conv2d conv1{nullptr}, conv2{nullptr}, conv3{nullptr};
  torch::nn::BatchNorm bn1{nullptr}, bn2{nullptr}, bn3{nullptr};

  static int expansion;

  Bottleneck(
      int64_t inplanes,
      int64_t planes,
      int64_t stride = 1,
      torch::nn::Sequential downsample = nullptr,
      int64_t groups = 1,
      int64_t base_width = 64);

  torch::Tensor forward(torch::Tensor X);
};
} // namespace _resnetimpl

template <typename Block>
struct ResNetImpl : torch::nn::Module {
  int64_t groups, base_width, inplanes;
  torch::nn::Conv2d conv1;
  torch::nn::BatchNorm bn1;
  torch::nn::Linear fc;
  torch::nn::Sequential layer1, layer2, layer3, layer4;

  torch::nn::Sequential _make_layer(
      int64_t planes,
      int64_t blocks,
      int64_t stride = 1);

  ResNetImpl(
      const std::vector<int>& layers,
      int64_t num_classes = 1000,
      bool zero_init_residual = false,
      int64_t groups = 1,
      int64_t width_per_group = 64);

  torch::Tensor forward(torch::Tensor X);
};

template <typename Block>
torch::nn::Sequential ResNetImpl<Block>::_make_layer(
    int64_t planes,
    int64_t blocks,
    int64_t stride) {
  torch::nn::Sequential downsample = nullptr;
  if (stride != 1 || inplanes != planes * Block::expansion) {
    downsample = torch::nn::Sequential(
        _resnetimpl::conv1x1(inplanes, planes * Block::expansion, stride),
        torch::nn::BatchNorm(planes * Block::expansion));
  }

  torch::nn::Sequential layers;
  layers->push_back(
      Block(inplanes, planes, stride, downsample, groups, base_width));

  inplanes = planes * Block::expansion;

  for (int i = 1; i < blocks; ++i)
    layers->push_back(Block(inplanes, planes, 1, nullptr, groups, base_width));

  return layers;
}

template <typename Block>
ResNetImpl<Block>::ResNetImpl(
    const std::vector<int>& layers,
    int64_t num_classes,
    bool zero_init_residual,
    int64_t groups,
    int64_t width_per_group)
    : groups(groups),
      base_width(width_per_group),
      inplanes(64),
      conv1(torch::nn::Conv2dOptions(3, 64, 7).stride(2).padding(3).with_bias(
          false)),
      bn1(64),
      layer1(_make_layer(64, layers[0])),
      layer2(_make_layer(128, layers[1], 2)),
      layer3(_make_layer(256, layers[2], 2)),
      layer4(_make_layer(512, layers[3], 2)),
      fc(512 * Block::expansion, num_classes) {
  register_module("conv1", conv1);
  register_module("bn1", bn1);
  register_module("fc", fc);

  register_module("layer1", layer1);
  register_module("layer2", layer2);
  register_module("layer3", layer3);
  register_module("layer4", layer4);

  for (auto& module : modules(/*include_self=*/false)) {
    if (auto M = dynamic_cast<torch::nn::Conv2dImpl*>(module.get()))
      torch::nn::init::kaiming_normal_(
          M->weight,
          /*a=*/0,
          torch::nn::init::FanMode::FanOut,
          torch::nn::init::Nonlinearity::ReLU);
    else if (auto M = dynamic_cast<torch::nn::BatchNormImpl*>(module.get())) {
      torch::nn::init::constant_(M->weight, 1);
      torch::nn::init::constant_(M->bias, 0);
    }
  }

  // Zero-initialize the last BN in each residual branch, so that the residual
  // branch starts with zeros, and each residual block behaves like an
  // identity. This improves the model by 0.2~0.3% according to
  // https://arxiv.org/abs/1706.02677
  if (zero_init_residual)
    for (auto& module : modules(/*include_self=*/false)) {
      if (auto* M = dynamic_cast<_resnetimpl::Bottleneck*>(module.get()))
        torch::nn::init::constant_(M->bn3->weight, 0);
      else if (auto* M = dynamic_cast<_resnetimpl::BasicBlock*>(module.get()))
        torch::nn::init::constant_(M->bn2->weight, 0);
    }
}

template <typename Block>
torch::Tensor ResNetImpl<Block>::forward(torch::Tensor x) {
  x = conv1->forward(x);
  x = bn1->forward(x).relu_();
  x = torch::max_pool2d(x, 3, 2, 1);

  x = layer1->forward(x);
  x = layer2->forward(x);
  x = layer3->forward(x);
  x = layer4->forward(x);

  x = torch::adaptive_avg_pool2d(x, {1, 1});
  x = x.reshape({x.size(0), -1});
  x = fc->forward(x);

  return x;
}

struct ResNet18Impl : ResNetImpl<_resnetimpl::BasicBlock> {
  ResNet18Impl(int64_t num_classes = 1000, bool zero_init_residual = false);
};

struct ResNet34Impl : ResNetImpl<_resnetimpl::BasicBlock> {
  ResNet34Impl(int64_t num_classes = 1000, bool zero_init_residual = false);
};

struct ResNet50Impl : ResNetImpl<_resnetimpl::Bottleneck> {
  ResNet50Impl(int64_t num_classes = 1000, bool zero_init_residual = false);
};

struct ResNet101Impl : ResNetImpl<_resnetimpl::Bottleneck> {
  ResNet101Impl(int64_t num_classes = 1000, bool zero_init_residual = false);
};

struct ResNet152Impl : ResNetImpl<_resnetimpl::Bottleneck> {
  ResNet152Impl(int64_t num_classes = 1000, bool zero_init_residual = false);
};

struct ResNext50_32x4dImpl : ResNetImpl<_resnetimpl::Bottleneck> {
  ResNext50_32x4dImpl(
      int64_t num_classes = 1000,
      bool zero_init_residual = false);
};

struct ResNext101_32x8dImpl : ResNetImpl<_resnetimpl::Bottleneck> {
  ResNext101_32x8dImpl(
      int64_t num_classes = 1000,
      bool zero_init_residual = false);
};

template <typename Block>
struct ResNet : torch::nn::ModuleHolder<ResNetImpl<Block>> {
  using torch::nn::ModuleHolder<ResNetImpl<Block>>::ModuleHolder;
};

TORCH_MODULE(ResNet18);
TORCH_MODULE(ResNet34);
TORCH_MODULE(ResNet50);
TORCH_MODULE(ResNet101);
TORCH_MODULE(ResNet152);
TORCH_MODULE(ResNext50_32x4d);
TORCH_MODULE(ResNext101_32x8d);

} // namespace models
} // namespace vision

#endif // RESNET_H