Program Listing for File data_processor.hpp

            

            
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* SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* SPDX-License-Identifier: Apache-2.0
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef _HOLOSCAN_DATA_PROCESSOR_H
#define _HOLOSCAN_DATA_PROCESSOR_H

#include <bits/stdc++.h>
#include <cstring>
#include <functional>
#include <iostream>
#include <map>
#include <memory>
#include <sstream>
#include <string>
#include <vector>

#include <holoinfer.hpp>
#include <holoinfer_constants.hpp>
#include <holoinfer_utils.hpp>

#include <holoscan/core/analytics/data_exporter.hpp>
#include <process/transforms/generate_boxes.hpp>

namespace holoscan {
namespace inference {
using processor_FP =
    std::function<InferStatus(const std::vector<int>&, const void*, std::vector<int64_t>&, DataMap&,
                              const std::vector<std::string>& output_tensors,
                              const std::vector<std::string>& custom_strings)>;

// Declaration of function callback for transforms that need configuration (via a yaml file).
// Transforms additionally support multiple inputs and outputs from the processing.
using transforms_FP =
    std::function<InferStatus(const std::string&, const std::map<std::string, void*>&,
                              const std::map<std::string, std::vector<int>>&, DataMap&, DimType&)>;

class DataProcessor {
 public:
  DataProcessor() {}

  InferStatus initialize(const MultiMappings& process_operations, const std::string config_path);

  InferStatus process_operation(const std::string& operation, const std::vector<int>& in_dims,
                                const void* in_data, std::vector<int64_t>& processed_dims,
                                DataMap& processed_data_map,
                                const std::vector<std::string>& output_tensors,
                                const std::vector<std::string>& custom_strings);

  InferStatus process_transform(const std::string& transform, const std::string& key,
                                const std::map<std::string, void*>& indata,
                                const std::map<std::string, std::vector<int>>& indim,
                                DataMap& processed_data, DimType& processed_dims);

  InferStatus compute_max_per_channel_cpu(const std::vector<int>& in_dims, const void* in_data,
                                          std::vector<int64_t>& out_dims, DataMap& out_data_map,
                                          const std::vector<std::string>& output_tensors);

  InferStatus scale_intensity_cpu(const std::vector<int>& in_dims, const void* in_data,
                                  std::vector<int64_t>& out_dims, DataMap& out_data_map,
                                  const std::vector<std::string>& output_tensors);

  InferStatus print_results(const std::vector<int>& in_dims, const void* in_data);

  InferStatus print_results_int32(const std::vector<int>& in_dims, const void* in_data);

  InferStatus print_custom_binary_classification(const std::vector<int>& in_dims,
                                                 const void* in_data,
                                                 const std::vector<std::string>& custom_strings);

  InferStatus export_binary_classification_to_csv(const std::vector<int>& in_dims,
                                                  const void* in_data,
                                                  const std::vector<std::string>& custom_strings);

 private:
  inline static const std::map<std::string, holoinfer_data_processor> supported_compute_operations_{
      {"max_per_channel_scaled", holoinfer_data_processor::h_HOST},
      {"scale_intensity_cpu", holoinfer_data_processor::h_HOST}};

  inline static const std::map<std::string, holoinfer_data_processor> supported_transforms_{
      {"generate_boxes", holoinfer_data_processor::h_HOST}};

  // Map with operation name as key, with pointer to its object
  std::map<std::string, std::unique_ptr<TransformBase>> transforms_;

  inline static const std::map<std::string, holoinfer_data_processor> supported_print_operations_{
      {"print", holoinfer_data_processor::h_HOST},
      {"print_int32", holoinfer_data_processor::h_HOST},
      {"print_custom_binary_classification", holoinfer_data_processor::h_HOST}};

  inline static const std::map<std::string, holoinfer_data_processor> supported_export_operations_{
      {"export_binary_classification_to_csv", holoinfer_data_processor::h_HOST}};

  processor_FP max_per_channel_scaled_fp_ =
      [this](auto& in_dims, const void* in_data, std::vector<int64_t>& out_dims, DataMap& out_data,
             auto& output_tensors, auto& custom_strings) {
        return compute_max_per_channel_cpu(in_dims, in_data, out_dims, out_data, output_tensors);
      };

  processor_FP scale_intensity_cpu_fp_ = [this](auto& in_dims, const void* in_data,
                                                std::vector<int64_t>& out_dims, DataMap& out_data,
                                                auto& output_tensors, auto& custom_strings) {
    return scale_intensity_cpu(in_dims, in_data, out_dims, out_data, output_tensors);
  };

  processor_FP print_results_fp_ = [this](auto& in_dims, const void* in_data,
                                          std::vector<int64_t>& out_dims, DataMap& out_data,
                                          auto& output_tensors, auto& custom_strings) {
    return print_results(in_dims, in_data);
  };

  processor_FP print_custom_binary_classification_fp_ =
      [this](auto& in_dims, const void* in_data, std::vector<int64_t>& out_dims, DataMap& out_data,
             auto& output_tensors, auto& custom_strings) {
        return print_custom_binary_classification(in_dims, in_data, custom_strings);
      };

  processor_FP export_binary_classification_to_csv_fp_ =
      [this](auto& in_dims, const void* in_data, std::vector<int64_t>& out_dims, DataMap& out_data,
             auto& output_tensors, auto& custom_strings) {
        return export_binary_classification_to_csv(in_dims, in_data, custom_strings);
      };

  processor_FP print_results_i32_fp_ = [this](auto& in_dims, const void* in_data,
                                              std::vector<int64_t>& out_dims, DataMap& out_data,
                                              auto& output_tensors, auto& custom_strings) {
    return print_results_int32(in_dims, in_data);
  };

  const std::map<std::string, processor_FP> oper_to_fp_{
      {"max_per_channel_scaled", max_per_channel_scaled_fp_},
      {"scale_intensity_cpu", scale_intensity_cpu_fp_},
      {"print", print_results_fp_},
      {"print_int32", print_results_i32_fp_},
      {"print_custom_binary_classification", print_custom_binary_classification_fp_},
      {"export_binary_classification_to_csv", export_binary_classification_to_csv_fp_}};

  transforms_FP generate_boxes_fp_ = [this](const std::string& key,
                                            const std::map<std::string, void*>& indata,
                                            const std::map<std::string, std::vector<int>>& indim,
                                            DataMap& processed_data, DimType& processed_dims) {
    return transforms_.at(key)->execute(indata, indim, processed_data, processed_dims);
  };

  const std::map<std::string, transforms_FP> transform_to_fp_{
      {"generate_boxes", generate_boxes_fp_}};

  std::string config_path_ = {};

  std::unique_ptr<DataExporter> data_exporter_ = nullptr;
};
}  // namespace inference
}  // namespace holoscan

#endif