our_knp/examples/knp/tests/framework/network_test.cpp
int 8f43927575 Initial import: KNP experiments (examples, viz, cats-dogs, reports)
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>
2026-10-14 18:17:15 +03:00

222 lines
7.7 KiB
C++

/**
* @file network_test.cpp
* @brief Network class testing.
* @kaspersky_support A. Vartenkov
* @date 12.02.2024
* @license Apache 2.0
* @copyright © 2024 AO Kaspersky Lab
*
* 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.
*/
#include <knp/framework/network.h>
#include <knp/framework/population/creators.h>
#include <tests_common.h>
#include <exception>
#include <tuple>
using BLIFATParams = knp::neuron_traits::neuron_parameters<knp::neuron_traits::BLIFATNeuron>;
using DeltaProjection = knp::core::Projection<knp::synapse_traits::DeltaSynapse>;
using Synapse = DeltaProjection::Synapse;
const auto neurons_count = 10;
const auto synapses_count = 10;
static auto create_entities()
{
knp::core::Population<knp::neuron_traits::BLIFATNeuron> population1(
[=](size_t index) -> BLIFATParams
{
BLIFATParams params;
params.potential_ = static_cast<double>(index / 2);
return params;
},
neurons_count);
DeltaProjection projection1(
knp::core::UID{}, knp::core::UID{},
[=](size_t index) -> std::optional<Synapse>
{
const uint32_t id_from = index;
const uint32_t id_to = index;
return Synapse{{}, id_from, id_to};
},
synapses_count);
return std::make_tuple(population1, projection1);
}
TEST(FrameworkSuite, EmptyNetwork)
{
// Test default network constructor. This should create an empty network.
knp::framework::Network network;
// Number of projections in an empty network is zero.
ASSERT_EQ(network.projections_count(), 0);
// Number of populations in an empty network is zero.
ASSERT_EQ(network.populations_count(), 0);
// Check iterator correctness.
ASSERT_EQ(network.begin_populations(), network.end_populations());
ASSERT_EQ(network.begin_projections(), network.end_projections());
}
TEST(FrameworkSuite, NetworkCreation) //!OCLINT(False positive)
{
knp::framework::Network network;
auto [population1, projection1] = create_entities();
auto pop_uid = population1.get_uid();
auto proj_uid = projection1.get_uid();
network.add_population(std::move(population1));
ASSERT_EQ(network.populations_count(), 1);
ASSERT_EQ(network.projections_count(), 0);
network.add_projection(std::move(projection1));
ASSERT_EQ(network.populations_count(), 1);
ASSERT_EQ(network.projections_count(), 1);
ASSERT_TRUE(network.is_population_exists(pop_uid));
ASSERT_EQ(network.get_population<knp::neuron_traits::BLIFATNeuron>(pop_uid).get_uid(), pop_uid);
ASSERT_TRUE(network.is_projection_exists(proj_uid));
ASSERT_EQ(network.get_projection<DeltaProjection::ProjectionSynapseType>(proj_uid).get_uid(), proj_uid);
ASSERT_NE(network.get_projection<DeltaProjection::ProjectionSynapseType>(proj_uid).get_uid(), pop_uid);
}
TEST(FrameworkSuite, NetworkRemoveEntities) //!OCLINT(False positive)
{
knp::framework::Network network;
auto [population1, projection1] = create_entities();
auto [uid_pop, uid_proj] = std::make_tuple(population1.get_uid(), projection1.get_uid());
ASSERT_EQ(network.populations_count(), 0);
ASSERT_EQ(network.projections_count(), 0);
network.add_population(std::move(population1));
network.add_projection(std::move(projection1));
ASSERT_EQ(network.populations_count(), 1);
ASSERT_EQ(network.projections_count(), 1);
// Incorrect UID.
EXPECT_THROW(network.remove_population(uid_proj), std::logic_error); //!OCLINT(False positive)
ASSERT_EQ(network.populations_count(), 1);
// Correct UID.
network.remove_population(uid_pop);
ASSERT_EQ(network.populations_count(), 0);
// Incorrect UID.
EXPECT_THROW(network.remove_projection(uid_pop), std::logic_error); //!OCLINT(False positive)
ASSERT_EQ(network.projections_count(), 1);
// Correct UID.
network.remove_projection(uid_proj);
ASSERT_EQ(network.projections_count(), 0);
}
TEST(FrameworkSuite, NetworkConnectPopulations)
{
constexpr auto src_neurons_count = 5;
constexpr auto dst_neurons_count = 3;
auto src_pop{knp::framework::population::creators::make_random<typename knp::neuron_traits::BLIFATNeuron>(
src_neurons_count)};
auto dst_pop{knp::framework::population::creators::make_random<typename knp::neuron_traits::BLIFATNeuron>(
dst_neurons_count)};
knp::framework::Network network;
knp::core::UID proj_uid;
auto connector = [&]()
{
proj_uid = network.connect_populations<
typename knp::synapse_traits::DeltaSynapse, knp::neuron_traits::BLIFATNeuron,
knp::neuron_traits::BLIFATNeuron>(src_pop, dst_pop);
SPDLOG_DEBUG("New projection UID = {}.", std::string(proj_uid));
};
EXPECT_THROW(connector(), std::logic_error); //!OCLINT(False positive)
network.add_population(src_pop);
EXPECT_THROW(connector(), std::logic_error); //!OCLINT(False positive)
SPDLOG_DEBUG("Checking projection UID = {} [False?]", std::string(proj_uid));
ASSERT_FALSE(network.is_projection_exists(proj_uid));
network.add_population(dst_pop);
EXPECT_NO_THROW(connector()); //!OCLINT(False positive)
ASSERT_EQ(network.projections_count(), 1);
ASSERT_EQ(network.populations_count(), 2);
SPDLOG_DEBUG("Checking projection UID = {} [True?]", std::string(proj_uid));
ASSERT_TRUE(network.is_projection_exists(proj_uid));
ASSERT_EQ(
network.get_projection<knp::synapse_traits::DeltaSynapse>(proj_uid).size(),
src_neurons_count * dst_neurons_count);
}
TEST(FrameworkSuite, NetworkConnectPopulationsArbitrary)
{
constexpr auto src_neurons_count = 5;
constexpr auto dst_neurons_count = 3;
auto src_pop{
knp::framework::population::creators::make_random<knp::neuron_traits::BLIFATNeuron>(src_neurons_count)};
auto dst_pop{
knp::framework::population::creators::make_random<knp::neuron_traits::BLIFATNeuron>(dst_neurons_count)};
knp::framework::Network network;
knp::core::UID proj_uid;
auto connector = [&]()
{
proj_uid = network.connect_populations<
knp::synapse_traits::DeltaSynapse, knp::neuron_traits::BLIFATNeuron, knp::neuron_traits::BLIFATNeuron>(
src_pop, dst_pop,
[dst_neurons_count](size_t index)
{
return std::make_tuple(
knp::core::Projection<knp::synapse_traits::DeltaSynapse>::SynapseParameters(), index,
index % dst_neurons_count);
},
src_neurons_count);
SPDLOG_DEBUG("New projection UID = {}.", std::string(proj_uid));
};
EXPECT_THROW(connector(), std::logic_error); //!OCLINT(False positive)
network.add_population(src_pop);
EXPECT_THROW(connector(), std::logic_error); //!OCLINT(False positive)
ASSERT_FALSE(network.is_projection_exists(proj_uid));
network.add_population(dst_pop);
EXPECT_NO_THROW(connector()); //!OCLINT(False positive)
ASSERT_EQ(network.projections_count(), 1);
ASSERT_EQ(network.populations_count(), 2);
SPDLOG_DEBUG("Checking projection UID = {} [True?]", std::string(proj_uid));
ASSERT_TRUE(network.is_projection_exists(proj_uid));
ASSERT_EQ(network.get_projection<knp::synapse_traits::DeltaSynapse>(proj_uid).size(), src_neurons_count);
}