Method BlockImpl

lesson6-Segmentation/ResNet.cpp:3–34

#include "ResNet.h"

BlockImpl::BlockImpl(int64_t inplanes, int64_t planes, int64_t stride_,
    torch::nn::Sequential downsample_, int groups, int base_width, bool _is_basic)
{
    downsample = downsample_;
    stride = stride_;
	int width = int(planes * (base_width / 64.)) * groups;

    conv1 = torch::nn::Conv2d(conv_options(inplanes, width, 3, stride_, 1, groups, false));
    bn1 = torch::nn::BatchNorm2d(torch::nn::BatchNorm2dOptions(width));
    conv2 = torch::nn::Conv2d(conv_options(width, width, 3, 1, 1, groups, false));
    bn2 = torch::nn::BatchNorm2d(torch::nn::BatchNorm2dOptions(width));
    is_basic = _is_basic;
    if (!is_basic) {
        conv1 = torch::nn::Conv2d(conv_options(inplanes, width, 1, 1, 0, 1, false));
        conv2 = torch::nn::Conv2d(conv_options(width, width, 3, stride_, 1, groups, false));
        conv3 = torch::nn::Conv2d(conv_options(width, planes * 4, 1, 1, 0, 1, false));
        bn3 = torch::nn::BatchNorm2d(torch::nn::BatchNorm2dOptions(planes * 4));
    }

    register_module("conv1", conv1);
    register_module("bn1", bn1);
    register_module("conv2", conv2);
    register_module("bn2", bn2);
    if (!is_basic) {
        register_module("conv3", conv3);
        register_module("bn3", bn3);
    }

    if (!downsample->is_empty()) {
        register_module("downsample", downsample);
    }
}

torch::Tensor BlockImpl::forward(torch::Tensor x) {
    torch::Tensor residual = x.clone();