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223 lines
6.4 KiB
223 lines
6.4 KiB
// Copyright (c) Facebook, Inc. and its affiliates.
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// All rights reserved.
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//
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// Copyright 2020 Google LLC
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//
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// This source code is licensed under the BSD-style license found in the
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// LICENSE file in the root directory of this source tree.
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#pragma once
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#include <gtest/gtest.h>
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#include <algorithm>
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#include <cassert>
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#include <cstddef>
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#include <cstdlib>
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#include <functional>
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#include <limits>
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#include <random>
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#include <vector>
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#include <xnnpack.h>
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#include <xnnpack/params-init.h>
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#include <xnnpack/params.h>
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#include <xnnpack/requantization.h>
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class VAddCMicrokernelTester {
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public:
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enum class Variant {
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Native,
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Scalar,
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};
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inline VAddCMicrokernelTester& batch_size(size_t batch_size) {
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assert(batch_size != 0);
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this->batch_size_ = batch_size;
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return *this;
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}
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inline size_t batch_size() const {
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return this->batch_size_;
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}
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inline VAddCMicrokernelTester& inplace(bool inplace) {
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this->inplace_ = inplace;
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return *this;
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}
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inline bool inplace() const {
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return this->inplace_;
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}
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inline VAddCMicrokernelTester& a_scale(float a_scale) {
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assert(a_scale > 0.0f);
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assert(std::isnormal(a_scale));
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this->a_scale_ = a_scale;
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return *this;
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}
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inline float a_scale() const {
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return this->a_scale_;
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}
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inline VAddCMicrokernelTester& a_zero_point(uint8_t a_zero_point) {
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this->a_zero_point_ = a_zero_point;
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return *this;
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}
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inline uint8_t a_zero_point() const {
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return this->a_zero_point_;
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}
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inline VAddCMicrokernelTester& b_scale(float b_scale) {
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assert(b_scale > 0.0f);
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assert(std::isnormal(b_scale));
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this->b_scale_ = b_scale;
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return *this;
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}
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inline float b_scale() const {
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return this->b_scale_;
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}
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inline VAddCMicrokernelTester& b_zero_point(uint8_t b_zero_point) {
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this->b_zero_point_ = b_zero_point;
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return *this;
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}
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inline uint8_t b_zero_point() const {
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return this->b_zero_point_;
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}
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inline VAddCMicrokernelTester& y_scale(float y_scale) {
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assert(y_scale > 0.0f);
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assert(std::isnormal(y_scale));
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this->y_scale_ = y_scale;
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return *this;
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}
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inline float y_scale() const {
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return this->y_scale_;
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}
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inline VAddCMicrokernelTester& y_zero_point(uint8_t y_zero_point) {
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this->y_zero_point_ = y_zero_point;
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return *this;
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}
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inline uint8_t y_zero_point() const {
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return this->y_zero_point_;
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}
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inline VAddCMicrokernelTester& qmin(uint8_t qmin) {
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this->qmin_ = qmin;
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return *this;
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}
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inline uint8_t qmin() const {
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return this->qmin_;
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}
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inline VAddCMicrokernelTester& qmax(uint8_t qmax) {
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this->qmax_ = qmax;
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return *this;
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}
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inline uint8_t qmax() const {
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return this->qmax_;
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}
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inline VAddCMicrokernelTester& iterations(size_t iterations) {
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this->iterations_ = iterations;
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return *this;
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}
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inline size_t iterations() const {
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return this->iterations_;
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}
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void Test(xnn_qs8_vadd_minmax_ukernel_function vadd_minmax, Variant variant = Variant::Native) const {
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std::random_device random_device;
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auto rng = std::mt19937(random_device());
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auto i8rng = std::bind(
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std::uniform_int_distribution<int32_t>(std::numeric_limits<int8_t>::min(), std::numeric_limits<int8_t>::max()), rng);
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std::vector<int8_t> a(batch_size() + XNN_EXTRA_BYTES / sizeof(int8_t));
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std::vector<int8_t> y(batch_size() + (inplace() ? XNN_EXTRA_BYTES / sizeof(int8_t) : 0));
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std::vector<float> y_fp(batch_size());
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std::vector<int8_t> y_ref(batch_size());
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for (size_t iteration = 0; iteration < iterations(); iteration++) {
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std::generate(a.begin(), a.end(), std::ref(i8rng));
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if (inplace()) {
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std::generate(y.begin(), y.end(), std::ref(i8rng));
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} else {
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std::fill(y.begin(), y.end(), 0xA5);
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}
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const int8_t* a_data = inplace() ? y.data() : a.data();
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const int8_t b = i8rng();
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// Prepare parameters.
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xnn_qs8_add_params quantization_params = { };
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switch (variant) {
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case Variant::Native:
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quantization_params = xnn_init_qs8_add_params(
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int8_t(a_zero_point() - 0x80), int8_t(b_zero_point() - 0x80), int8_t(y_zero_point() - 0x80),
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a_scale() / y_scale(), b_scale() / y_scale(),
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int8_t(qmin() - 0x80), int8_t(qmax() - 0x80));
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break;
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case Variant::Scalar:
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quantization_params = xnn_init_scalar_qs8_add_params(
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int8_t(a_zero_point() - 0x80), int8_t(b_zero_point() - 0x80), int8_t(y_zero_point() - 0x80),
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a_scale() / y_scale(), b_scale() / y_scale(),
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int8_t(qmin() - 0x80), int8_t(qmax() - 0x80));
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break;
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}
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const xnn_qs8_add_params scalar_quantization_params =
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xnn_init_scalar_qs8_add_params(
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int8_t(a_zero_point() - 0x80), int8_t(b_zero_point() - 0x80), int8_t(y_zero_point() - 0x80),
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a_scale() / y_scale(), b_scale() / y_scale(),
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int8_t(qmin() - 0x80), int8_t(qmax() - 0x80));
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// Compute reference results.
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for (size_t i = 0; i < batch_size(); i++) {
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y_fp[i] = float(int32_t(y_zero_point() - 0x80)) +
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float(int32_t(a_data[i]) - int32_t(a_zero_point() - 0x80)) * (a_scale() / y_scale()) +
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float(int32_t(b) - int32_t(b_zero_point() - 0x80)) * (b_scale() / y_scale());
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y_fp[i] = std::min<float>(y_fp[i], float(int32_t(qmax() - 0x80)));
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y_fp[i] = std::max<float>(y_fp[i], float(int32_t(qmin() - 0x80)));
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y_ref[i] = xnn_qs8_quantize_add(a_data[i], b, scalar_quantization_params);
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}
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// Call optimized micro-kernel.
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vadd_minmax(batch_size(), a_data, &b, y.data(), &quantization_params);
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// Verify results.
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for (size_t i = 0; i < batch_size(); i++) {
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ASSERT_LE(int32_t(y[i]), int32_t(qmax() - 0x80))
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<< "at element " << i << " / " << batch_size();
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ASSERT_GE(int32_t(y[i]), int32_t(qmin() - 0x80))
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<< "at element " << i << " / " << batch_size();
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ASSERT_EQ(int32_t(y_ref[i]), int32_t(y[i]))
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<< "at element " << i << " / " << batch_size();
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ASSERT_NEAR(float(int32_t(y[i])), y_fp[i], 0.6f)
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<< "at element " << i << " / " << batch_size();
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}
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}
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}
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private:
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size_t batch_size_{1};
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bool inplace_{false};
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float a_scale_{0.75f};
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float b_scale_{1.25f};
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float y_scale_{0.96875f};
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uint8_t a_zero_point_{121};
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uint8_t b_zero_point_{127};
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uint8_t y_zero_point_{133};
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uint8_t qmin_{0};
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uint8_t qmax_{255};
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size_t iterations_{15};
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};
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