8f43927575
examples/ is based on KasperskyLab/knp at 55470ed6 with local changes to the mnist-learn altai/blifat models, cuda-backend CMake and model_loader. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
57 lines
2.2 KiB
C++
57 lines
2.2 KiB
C++
/**
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* @file dataset.cpp
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* @brief Process dataset.
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* @kaspersky_support D. Postnikov
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* @date 03.02.2026
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* @license Apache 2.0
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* @copyright © 2026 AO Kaspersky Lab
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include "dataset.h"
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#include <fstream>
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#include "global_config.h"
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Dataset process_dataset(ModelDescription const& model_desc)
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{
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// Check if required files exist.
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if (!std::filesystem::exists(model_desc.images_file_path_))
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throw std::runtime_error("Provided images file does not exists.");
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else if (!std::filesystem::exists(model_desc.labels_file_path_))
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throw std::runtime_error("Provided labels file does not exists.");
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// Create streams for reading images and labels.
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std::ifstream images_stream(model_desc.images_file_path_, std::ios::binary);
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std::ifstream labels_stream(model_desc.labels_file_path_, std::ios::in);
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Dataset dataset;
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// Process images and labels into spike representations.
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dataset.process_labels_and_images(
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images_stream, labels_stream, model_desc.train_images_amount_ + model_desc.inference_images_amount_,
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classes_amount, input_size, steps_per_image,
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dataset.make_incrementing_image_to_spikes_converter(active_steps, state_increment_factor));
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// Split dataset into training and inference portions.
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dataset.split(model_desc.train_images_amount_, model_desc.inference_images_amount_);
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// Print processing results and statistics.
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std::cout << "Processed dataset, training will last " << dataset.get_steps_amount_for_training()
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<< " steps, inference " << dataset.get_steps_amount_for_inference() << " steps\n"
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<< std::endl;
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return dataset;
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}
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