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/* * SPDX-FileCopyrightText: Copyright (c) 2022-2023 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 <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); 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}}; 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 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_}}; 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_ = {}; }; } // namespace inference } // namespace holoscan #endif

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