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>
83 lines
3.5 KiB
C++
83 lines
3.5 KiB
C++
/**
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* @file data_processing_test.cpp
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* @brief Data processing test.
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* @kaspersky_support D. Postnikov
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* @date 21.08.2025
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* @license Apache 2.0
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* @copyright © 2025 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 <knp/framework/data_processing/classification/image.h>
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#include <tests_common.h>
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TEST(DataProcessing, ImageClassification)
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{
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constexpr size_t training_amount = 2, inference_amount = 1, classes_amount = 3, image_size = 1, steps_per_image = 1;
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std::stringstream images_stream("\x01\x02\x03");
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std::stringstream labels_stream("0\n1\n2\n");
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knp::framework::data_processing::classification::images::Dataset dataset;
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dataset.process_labels_and_images(
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images_stream, labels_stream, training_amount + inference_amount, classes_amount, image_size, steps_per_image,
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[](std::vector<uint8_t> const&) -> knp::framework::data_processing::classification::Dataset::Frame
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{ return {{true}}; });
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dataset.split(training_amount, inference_amount);
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ASSERT_EQ(dataset.get_image_size(), image_size);
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ASSERT_EQ(dataset.get_amount_of_classes(), classes_amount);
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ASSERT_EQ(dataset.get_steps_per_frame(), steps_per_image);
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ASSERT_EQ(dataset.get_steps_amount_for_training(), training_amount);
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ASSERT_EQ(dataset.get_steps_amount_for_inference(), inference_amount);
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auto [training_begin, training_end] = dataset.get_data_for_training();
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ASSERT_EQ(std::distance(training_begin, training_end), training_amount);
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ASSERT_EQ(training_begin[0].first, 0);
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ASSERT_EQ(training_begin[0].second.spikes_.size(), 1);
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ASSERT_EQ(training_begin[0].second.spikes_[0], true);
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ASSERT_EQ(training_begin[1].first, 1);
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ASSERT_EQ(training_begin[1].second.spikes_.size(), 1);
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ASSERT_EQ(training_begin[1].second.spikes_[0], true);
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auto [inference_begin, inference_end] = dataset.get_data_for_inference();
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ASSERT_EQ(std::distance(inference_begin, inference_end), inference_amount);
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ASSERT_EQ(inference_begin[0].first, 2);
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ASSERT_EQ(inference_begin[0].second.spikes_.size(), 1);
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ASSERT_EQ(inference_begin[0].second.spikes_[0], true);
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auto train_images_spikes_gen = dataset.make_training_images_spikes_generator();
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for (size_t i = 0; i < dataset.get_steps_amount_for_training(); ++i)
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{
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const auto res = train_images_spikes_gen(i);
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ASSERT_EQ(res.size(), 1);
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ASSERT_EQ(res[0], 0);
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}
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auto train_labels_gen = dataset.make_training_labels_generator();
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for (size_t i = 0; i < dataset.get_steps_amount_for_training(); ++i)
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{
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const auto res = train_labels_gen(i);
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ASSERT_EQ(res.size(), 1);
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ASSERT_EQ(res[0], i % std::distance(training_begin, training_end));
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}
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auto inf_images_spikes_gen = dataset.make_inference_images_spikes_generator();
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for (size_t i = 0; i < dataset.get_steps_amount_for_inference(); ++i)
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{
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const auto res = train_images_spikes_gen(i);
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ASSERT_EQ(res.size(), 1);
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ASSERT_EQ(res[0], 0);
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}
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}
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