Method ASPPImpl

src/architectures/DeepLabDecoder.cpp:42–68

}

ASPPImpl::ASPPImpl(int in_channels, int out_channels, std::vector<int> atrous_rates, bool separable) {
	modules->push_back(torch::nn::Sequential(torch::nn::Conv2d(conv_options(in_channels, out_channels, 1, 1, 0, 1, false)),
						torch::nn::BatchNorm2d(torch::nn::BatchNorm2dOptions(out_channels)),
						torch::nn::ReLU()));
	if (atrous_rates.size() != 3) std::cout<< "size of atrous_rates must be 3";
	if (separable) {
		modules->push_back(ASPPSeparableConv(in_channels, out_channels, atrous_rates[0]));
		modules->push_back(ASPPSeparableConv(in_channels, out_channels, atrous_rates[1]));
		modules->push_back(ASPPSeparableConv(in_channels, out_channels, atrous_rates[2]));
	}
	else {
		modules->push_back(ASPPConv(in_channels, out_channels, atrous_rates[0]));
		modules->push_back(ASPPConv(in_channels, out_channels, atrous_rates[1]));
		modules->push_back(ASPPConv(in_channels, out_channels, atrous_rates[2]));
	}
	aspppooling = ASPPPooling(in_channels, out_channels);

	project = torch::nn::Sequential(
		torch::nn::Conv2d(conv_options(5 * out_channels, out_channels, 1, 1, 0, 1, false)),
		torch::nn::BatchNorm2d(torch::nn::BatchNorm2dOptions(out_channels)),
		torch::nn::ReLU(),
		torch::nn::Dropout(torch::nn::DropoutOptions(0.5)));

	register_module("modules", modules);
	register_module("aspppooling", aspppooling);
	register_module("project", project);
}

torch::Tensor ASPPImpl::forward(torch::Tensor x) {
	std::vector<torch::Tensor> res;