FilterDecoder_tool.cc

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#include "DecoderToolBase.h"

#include "art/Utilities/ToolMacros.h"

#include "lardataobj/AnalysisBase/MVAOutput.h"
#include <torch/torch.h>

using anab::FeatureVector;
using anab::MVADescription;

class FilterDecoder : public DecoderToolBase {

public:
  FilterDecoder(const fhicl::ParameterSet& pset);

  virtual ~FilterDecoder() noexcept = default;

  void declareProducts(art::ProducesCollector& collector) override
  {
    collector.produces<vector<FeatureVector<1>>>(instancename);
  }

  void writeEmptyToEvent(art::Event& e, const vector<vector<size_t>>& idsmap) override;

  void writeToEvent(art::Event& e,
                    const vector<vector<size_t>>& idsmap,
                    const vector<NuGraphOutput>& infer_output) override;
};

FilterDecoder::FilterDecoder(const fhicl::ParameterSet& p) : DecoderToolBase{p} {}

void FilterDecoder::writeEmptyToEvent(art::Event& e, const vector<vector<size_t>>& idsmap)
{
  //
  size_t size = 0;
  for (auto& v : idsmap)
    size += v.size();
  auto filtcol =
    std::make_unique<vector<FeatureVector<1>>>(size, FeatureVector<1>(std::array<float, 1>({-1.})));
  e.put(std::move(filtcol), instancename);
  //
}

void FilterDecoder::writeToEvent(art::Event& e,
                                 const vector<vector<size_t>>& idsmap,
                                 const vector<NuGraphOutput>& infer_output)
{
  //
  size_t size = 0;
  for (auto& v : idsmap)
    size += v.size();
  auto filtcol =
    std::make_unique<vector<FeatureVector<1>>>(size, FeatureVector<1>(std::array<float, 1>({-1.})));
  //
  for (size_t p = 0; p < planes.size(); p++) {
    //
    const std::vector<float>* x_filter_data = 0;
    for (auto& io : infer_output) {
      if (io.output_name == outputname + planes[p]) x_filter_data = &io.output_vec;
    }
    if (debug) {
      std::cout << outputname + planes[p] << std::endl;
      printVector(*x_filter_data);
    }
    //
    torch::TensorOptions options = torch::TensorOptions().dtype(torch::kFloat32);
    int64_t num_elements = x_filter_data->size();
    const torch::Tensor f =
      torch::from_blob(const_cast<float*>(x_filter_data->data()), {num_elements}, options);
    //
    for (int i = 0; i < f.numel(); ++i) {
      size_t idx = idsmap[p][i];
      std::array<float, 1> input({f[i].item<float>()});
      (*filtcol)[idx] = FeatureVector<1>(input);
    }
  }
  e.put(std::move(filtcol), instancename);
}

DEFINE_ART_CLASS_TOOL(FilterDecoder)