Method FPNDecoderImpl

src/architectures/FPNDecoder.cpp:75–98

}

FPNDecoderImpl::FPNDecoderImpl(std::vector<int> encoder_channels, int encoder_depth, int pyramid_channels, int segmentation_channels,
                       float dropout_, std::string merge_policy)
{
    out_channels = merge_policy == "add"? segmentation_channels :segmentation_channels * 4;
    if(encoder_depth<3) std::cout<< "Encoder depth for FPN decoder cannot be less than 3";
    std::reverse(std::begin(encoder_channels),std::end(encoder_channels));
    encoder_channels = std::vector<int> (encoder_channels.begin(),encoder_channels.begin()+encoder_depth+1);
    p5 = torch::nn::Conv2d(conv_options(encoder_channels[0], pyramid_channels, 1));
    p4 = FPNBlock(pyramid_channels, encoder_channels[1]);
    p3 = FPNBlock(pyramid_channels, encoder_channels[2]);
    p2 = FPNBlock(pyramid_channels, encoder_channels[3]);
    for(int i = 3; i>=0; i--){
        seg_blocks->push_back(SegmentationBlock(pyramid_channels, segmentation_channels, i));
    }
    merge = MergeBlock(merge_policy);
    dropout = torch::nn::Dropout2d(torch::nn::Dropout2dOptions().p(dropout_).inplace(true));

    register_module("p5",p5);
    register_module("p4",p4);
    register_module("p3",p3);
    register_module("p2",p2);
    register_module("seg_blocks",seg_blocks);
    register_module("merge",merge);
}

torch::Tensor FPNDecoderImpl::forward(std::vector<torch::Tensor> features){
	int features_len = features.size();