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eval_utils.hpp
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// Copyright (C) 2018-2025 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//
#pragma once
#include <random>
#include "openvino/core/node.hpp"
#include "openvino/core/shape.hpp"
namespace {
template <typename T>
void copy_data(ov::Tensor& tv, const std::vector<T>& data) {
size_t data_size = data.size() * sizeof(T);
if (data_size > 0) {
OPENVINO_ASSERT(tv.get_byte_size() >= data_size);
memcpy(tv.data(), data.data(), data_size);
}
}
template <>
inline void copy_data<bool>(ov::Tensor& tv, const std::vector<bool>& data) {
std::vector<char> data_char(data.begin(), data.end());
copy_data(tv, data_char);
}
template <typename T>
void init_int_tv(ov::Tensor& tv, std::default_random_engine& engine, T min, T max) {
size_t size = tv.get_size();
std::uniform_int_distribution<T> dist(min, max);
std::vector<T> vec(size);
for (T& element : vec) {
element = dist(engine);
}
size_t data_size = vec.size() * sizeof(T);
OPENVINO_ASSERT(tv.get_byte_size() >= data_size);
memcpy(tv.data(), vec.data(), data_size);
}
template <>
inline void init_int_tv<char>(ov::Tensor& tv, std::default_random_engine& engine, char min, char max) {
size_t size = tv.get_size();
std::uniform_int_distribution<int16_t> dist(static_cast<short>(min), static_cast<short>(max));
std::vector<char> vec(size);
for (char& element : vec) {
element = static_cast<char>(dist(engine));
}
size_t data_size = vec.size() * sizeof(char);
OPENVINO_ASSERT(tv.get_byte_size() >= data_size);
memcpy(tv.data(), vec.data(), data_size);
}
template <>
inline void init_int_tv<int8_t>(ov::Tensor& tv, std::default_random_engine& engine, int8_t min, int8_t max) {
size_t size = tv.get_size();
std::uniform_int_distribution<int16_t> dist(static_cast<short>(min), static_cast<short>(max));
std::vector<int8_t> vec(size);
for (int8_t& element : vec) {
element = static_cast<int8_t>(dist(engine));
}
size_t data_size = vec.size() * sizeof(int8_t);
OPENVINO_ASSERT(tv.get_byte_size() >= data_size);
memcpy(tv.data(), vec.data(), data_size);
}
template <>
inline void init_int_tv<uint8_t>(ov::Tensor& tv, std::default_random_engine& engine, uint8_t min, uint8_t max) {
size_t size = tv.get_size();
std::uniform_int_distribution<int16_t> dist(static_cast<short>(min), static_cast<short>(max));
std::vector<uint8_t> vec(size);
for (uint8_t& element : vec) {
element = static_cast<uint8_t>(dist(engine));
}
size_t data_size = vec.size() * sizeof(uint8_t);
OPENVINO_ASSERT(tv.get_byte_size() >= data_size);
memcpy(tv.data(), vec.data(), data_size);
}
template <typename T>
void init_real_tv(ov::Tensor& tv, std::default_random_engine& engine, T min, T max) {
size_t size = tv.get_size();
std::uniform_real_distribution<T> dist(min, max);
std::vector<T> vec(size);
for (T& element : vec) {
element = dist(engine);
}
size_t data_size = vec.size() * sizeof(T);
OPENVINO_ASSERT(tv.get_byte_size() >= data_size);
memcpy(tv.data(), vec.data(), data_size);
}
inline void random_init(ov::Tensor& tv, std::default_random_engine& engine) {
ov::element::Type et = tv.get_element_type();
if (et == ov::element::boolean) {
init_int_tv<char>(tv, engine, 0, 1);
} else if (et == ov::element::f32) {
init_real_tv<float>(tv, engine, std::numeric_limits<float>::min(), 1.0f);
} else if (et == ov::element::f64) {
init_real_tv<double>(tv, engine, std::numeric_limits<double>::min(), 1.0);
} else if (et == ov::element::i8) {
init_int_tv<int8_t>(tv, engine, -1, 1);
} else if (et == ov::element::i16) {
init_int_tv<int16_t>(tv, engine, -1, 1);
} else if (et == ov::element::i32) {
init_int_tv<int32_t>(tv, engine, 0, 1);
} else if (et == ov::element::i64) {
init_int_tv<int64_t>(tv, engine, 0, 1);
} else if (et == ov::element::u8) {
init_int_tv<uint8_t>(tv, engine, 0, 1);
} else if (et == ov::element::u16) {
init_int_tv<uint16_t>(tv, engine, 0, 1);
} else if (et == ov::element::u32) {
init_int_tv<uint32_t>(tv, engine, 0, 1);
} else if (et == ov::element::u64) {
init_int_tv<uint64_t>(tv, engine, 0, 1);
} else {
OPENVINO_THROW("unsupported type");
}
}
} // namespace
template <ov::element::Type_t ET>
ov::Tensor make_tensor(const ov::Shape& shape,
const std::vector<typename ov::element_type_traits<ET>::value_type>& data) {
OPENVINO_ASSERT(shape_size(shape) == data.size(), "Incorrect number of initialization elements");
auto tensor = ov::Tensor(ET, shape);
copy_data(tensor, data);
return tensor;
}
template <ov::element::Type_t ET>
ov::Tensor make_tensor(const ov::Shape& shape) {
auto tensor = ov::Tensor(ET, shape);
static std::default_random_engine engine(2112);
random_init(tensor, engine);
return tensor;
}