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gavgpool-microkernel-tester.h
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// Copyright (c) Facebook, Inc. and its affiliates.
// All rights reserved.
//
// Copyright 2019 Google LLC
//
// This source code is licensed under the BSD-style license found in the
// LICENSE file in the root directory of this source tree.
#pragma once
#include <algorithm>
#include <cassert>
#include <cmath>
#include <cstddef>
#include <cstdint>
#include <cstdlib>
#include <limits>
#include <random>
#include <vector>
#include <gtest/gtest.h>
#include <fp16/fp16.h>
#include "xnnpack.h"
#include "xnnpack/aligned-allocator.h"
#include "xnnpack/microfnptr.h"
#include "xnnpack/microparams.h"
#include "xnnpack/requantization.h"
#include "replicable_random_device.h"
class GAvgPoolMicrokernelTester {
public:
GAvgPoolMicrokernelTester& rows(size_t rows) {
assert(rows != 0);
this->rows_ = rows;
return *this;
}
size_t rows() const {
return this->rows_;
}
GAvgPoolMicrokernelTester& channels(size_t channels) {
assert(channels != 0);
this->channels_ = channels;
return *this;
}
size_t channels() const {
return this->channels_;
}
GAvgPoolMicrokernelTester& channel_tile(size_t channel_tile) {
assert(channel_tile != 0);
this->channel_tile_ = channel_tile;
return *this;
}
size_t channel_tile() const {
return this->channel_tile_;
}
GAvgPoolMicrokernelTester& input_stride(size_t input_stride) {
assert(input_stride != 0);
this->input_stride_ = input_stride;
return *this;
}
size_t input_stride() const {
if (this->input_stride_ == 0) {
return channels();
} else {
assert(this->input_stride_ >= channels());
return this->input_stride_;
}
}
GAvgPoolMicrokernelTester& input_scale(float input_scale) {
assert(input_scale > 0.0f);
assert(std::isnormal(input_scale));
this->input_scale_ = input_scale;
return *this;
}
float input_scale() const {
return this->input_scale_;
}
GAvgPoolMicrokernelTester& input_zero_point(uint8_t input_zero_point) {
this->input_zero_point_ = input_zero_point;
return *this;
}
uint8_t input_zero_point() const {
return this->input_zero_point_;
}
GAvgPoolMicrokernelTester& output_scale(float output_scale) {
assert(output_scale > 0.0f);
assert(std::isnormal(output_scale));
this->output_scale_ = output_scale;
return *this;
}
float output_scale() const {
return this->output_scale_;
}
GAvgPoolMicrokernelTester& output_zero_point(uint8_t output_zero_point) {
this->output_zero_point_ = output_zero_point;
return *this;
}
uint8_t output_zero_point() const {
return this->output_zero_point_;
}
GAvgPoolMicrokernelTester& qmin(uint8_t qmin) {
this->qmin_ = qmin;
return *this;
}
uint8_t qmin() const {
return this->qmin_;
}
GAvgPoolMicrokernelTester& qmax(uint8_t qmax) {
this->qmax_ = qmax;
return *this;
}
uint8_t qmax() const {
return this->qmax_;
}
GAvgPoolMicrokernelTester& iterations(size_t iterations) {
this->iterations_ = iterations;
return *this;
}
size_t iterations() const {
return this->iterations_;
}
void Test(
xnn_qu8_gavgpool_minmax_unipass_ukernel_fn gavgpool_minmax,
xnn_init_qu8_avgpool_minmax_params_fn init_params,
xnn_qu8_requantize_fn requantize) const
{
xnnpack::ReplicableRandomDevice rng;
std::uniform_int_distribution<int32_t> u8dist(
std::numeric_limits<uint8_t>::min(), std::numeric_limits<uint8_t>::max());
std::vector<uint8_t> input(XNN_EXTRA_BYTES / sizeof(uint8_t) +
(rows() - 1) * input_stride() + channels());
std::vector<uint8_t> zero(channels() + XNN_EXTRA_BYTES / sizeof(uint8_t));
std::vector<uint8_t> output(channels());
std::vector<uint8_t> output_ref(channels());
std::vector<float> output_fp(channels());
std::vector<int32_t> accumulators(channels());
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(input.begin(), input.end(), [&]() { return u8dist(rng); });
std::fill(output.begin(), output.end(), UINT8_C(0xA5));
// Prepare parameters.
union xnn_qu8_avgpool_minmax_params params;
init_params(
¶ms,
-int32_t(input_zero_point()) * int32_t(rows()),
input_scale() / (output_scale() * float(rows())),
output_zero_point(), qmin(), qmax());
// Compute reference results.
for (size_t c = 0; c < channels(); c++) {
int32_t acc = 0;
for (size_t n = 0; n < rows(); n++) {
acc += int32_t(input[n * input_stride() + c]) - int32_t(input_zero_point());
}
accumulators[c] = acc;
output_ref[c] = requantize(
acc, input_scale() / (output_scale() * float(rows())), output_zero_point(), qmin(), qmax());
output_fp[c] = float(acc) * (input_scale() / (output_scale() * float(rows()))) + float(output_zero_point());
output_fp[c] = std::min<float>(output_fp[c], float(qmax()));
output_fp[c] = std::max<float>(output_fp[c], float(qmin()));
}
// Call optimized micro-kernel.
gavgpool_minmax(rows(), channels(),
input.data(), input_stride() * sizeof(uint8_t),
zero.data(),
output.data(),
¶ms);
// Verify results.
for (size_t c = 0; c < channels(); c++) {
ASSERT_LE(uint32_t(output[c]), uint32_t(qmax()))
<< "at position " << c << ", rows = " << rows() << ", channels = " << channels();
ASSERT_GE(uint32_t(output[c]), uint32_t(qmin()))
<< "at position " << c << ", rows = " << rows() << ", channels = " << channels();
ASSERT_NEAR(float(int32_t(output[c])), output_fp[c], 0.55f)
<< "at position " << c << ", rows = " << rows() << ", channels = " << channels()
<< ", acc = " << accumulators[c];
EXPECT_EQ(uint32_t(output_ref[c]), uint32_t(output[c]))
<< "at position " << c << ", rows = " << rows() << ", channels = " << channels()
<< ", acc = " << accumulators[c];
}
}
}
void Test(
xnn_qu8_gavgpool_minmax_multipass_ukernel_fn gavgpool_minmax,
xnn_init_qu8_avgpool_minmax_params_fn init_params,
xnn_qu8_requantize_fn requantize) const
{
xnnpack::ReplicableRandomDevice rng;
std::uniform_int_distribution<int32_t> u8dist(
std::numeric_limits<uint8_t>::min(), std::numeric_limits<uint8_t>::max());
std::vector<uint8_t> input(XNN_EXTRA_BYTES / sizeof(uint8_t) +
(rows() - 1) * input_stride() + channels());
std::vector<int32_t, AlignedAllocator<int32_t, 64>> buffer(channels() + XNN_EXTRA_BYTES / sizeof(uint8_t));
std::vector<uint8_t> zero(channels() + XNN_EXTRA_BYTES / sizeof(uint8_t));
std::vector<uint8_t> output(channels());
std::vector<uint8_t> output_ref(channels());
std::vector<float> output_fp(channels());
std::vector<int32_t> accumulators(channels());
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(input.begin(), input.end(), [&]() { return u8dist(rng); });
std::fill(output.begin(), output.end(), UINT8_C(0xA5));
// Prepare parameters.
union xnn_qu8_avgpool_minmax_params params;
init_params(
¶ms,
-int32_t(input_zero_point()) * int32_t(rows()),
input_scale() / (output_scale() * float(rows())),
output_zero_point(), qmin(), qmax());
// Compute reference results.
for (size_t c = 0; c < channels(); c++) {
int32_t acc = 0;
for (size_t n = 0; n < rows(); n++) {
acc += int32_t(input[n * input_stride() + c]) - int32_t(input_zero_point());
}
accumulators[c] = acc;
output_ref[c] = requantize(
acc, input_scale() / (output_scale() * float(rows())), output_zero_point(), qmin(), qmax());
output_fp[c] = float(acc) * (input_scale() / (output_scale() * float(rows()))) + float(output_zero_point());
output_fp[c] = std::min<float>(output_fp[c], float(qmax()));
output_fp[c] = std::max<float>(output_fp[c], float(qmin()));
}
// Call optimized micro-kernel.
gavgpool_minmax(rows(), channels(),
input.data(), input_stride() * sizeof(uint8_t),
zero.data(),
buffer.data(),
output.data(),
¶ms);
// Verify results.
for (size_t c = 0; c < channels(); c++) {
ASSERT_LE(uint32_t(output[c]), uint32_t(qmax()))
<< "at position " << c << ", rows = " << rows() << ", channels = " << channels();
ASSERT_GE(uint32_t(output[c]), uint32_t(qmin()))
<< "at position " << c << ", rows = " << rows() << ", channels = " << channels();
ASSERT_NEAR(float(int32_t(output[c])), output_fp[c], 0.55f)
<< "at position " << c << ", rows = " << rows() << ", channels = " << channels()
<< ", acc = " << accumulators[c];
EXPECT_EQ(uint32_t(output_ref[c]), uint32_t(output[c]))
<< "at position " << c << ", rows = " << rows() << ", channels = " << channels()
<< ", acc = " << accumulators[c];
}
}
}
void Test(
xnn_qs8_gavgpool_minmax_unipass_ukernel_fn gavgpool_minmax,
xnn_init_qs8_avgpool_minmax_params_fn init_params,
xnn_qs8_requantize_fn requantize) const
{
xnnpack::ReplicableRandomDevice rng;
std::uniform_int_distribution<int32_t> i8dist(
std::numeric_limits<int8_t>::min(), std::numeric_limits<int8_t>::max());
std::vector<int8_t> input(XNN_EXTRA_BYTES / sizeof(int8_t) +
(rows() - 1) * input_stride() + channels());
std::vector<int8_t> zero(channels() + XNN_EXTRA_BYTES / sizeof(int8_t));
std::vector<int8_t> output(channels());
std::vector<int8_t> output_ref(channels());
std::vector<float> output_fp(channels());
std::vector<int32_t> accumulators(channels());
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(input.begin(), input.end(), [&]() { return i8dist(rng); });
std::fill(output.begin(), output.end(), INT8_C(0xA5));
// Prepare parameters.
union xnn_qs8_avgpool_minmax_params params;
init_params(
¶ms,
-int32_t(input_zero_point() - 0x80) * int32_t(rows()),
input_scale() / (output_scale() * float(rows())),
int8_t(output_zero_point() - 0x80), int8_t(qmin() - 0x80), int8_t(qmax() - 0x80));
// Compute reference results.
for (size_t c = 0; c < channels(); c++) {
int32_t acc = 0;
for (size_t n = 0; n < rows(); n++) {
acc += int32_t(input[n * input_stride() + c]) - int32_t(input_zero_point() - 0x80);
}
accumulators[c] = acc;
output_ref[c] = requantize(
acc, input_scale() / (output_scale() * float(rows())), int8_t(output_zero_point() - 0x80), int8_t(qmin() - 0x80), int8_t(qmax() - 0x80));
output_fp[c] = float(acc) * (input_scale() / (output_scale() * float(rows()))) + float(output_zero_point() - 0x80);
output_fp[c] = std::min<float>(output_fp[c], float(qmax() - 0x80));
output_fp[c] = std::max<float>(output_fp[c], float(qmin() - 0x80));
}
// Call optimized micro-kernel.
gavgpool_minmax(rows(), channels(),
input.data(), input_stride() * sizeof(int8_t),
zero.data(),
output.data(),
¶ms);
// Verify results.
for (size_t c = 0; c < channels(); c++) {
ASSERT_LE(int32_t(output[c]), int32_t(qmax() - 0x80))
<< "at channel " << c << " / " << channels() << ", rows = " << rows();
ASSERT_GE(int32_t(output[c]), int32_t(qmin() - 0x80))
<< "at channel " << c << " / " << channels() << ", rows = " << rows();
ASSERT_NEAR(float(int32_t(output[c])), output_fp[c], 0.55f)
<< "at channel " << c << " / " << channels() << ", rows = " << rows()
<< ", accumulator = " << accumulators[c];
EXPECT_EQ(int32_t(output_ref[c]), int32_t(output[c]))
<< "at channel " << c << " / " << channels() << ", rows = " << rows()
<< ", accumulator = " << accumulators[c];
}
}
}
void Test(
xnn_qs8_gavgpool_minmax_multipass_ukernel_fn gavgpool_minmax,
xnn_init_qs8_avgpool_minmax_params_fn init_params,
xnn_qs8_requantize_fn requantize) const
{
xnnpack::ReplicableRandomDevice rng;
std::uniform_int_distribution<int32_t> i8dist(
std::numeric_limits<int8_t>::min(), std::numeric_limits<int8_t>::max());
std::vector<int8_t> input(XNN_EXTRA_BYTES / sizeof(int8_t) +
(rows() - 1) * input_stride() + channels());
std::vector<int32_t, AlignedAllocator<int32_t, 64>> buffer(channels() + XNN_EXTRA_BYTES / sizeof(int8_t));
std::vector<int8_t> zero(channels() + XNN_EXTRA_BYTES / sizeof(int8_t));
std::vector<int8_t> output(channels());
std::vector<int8_t> output_ref(channels());
std::vector<float> output_fp(channels());
std::vector<int32_t> accumulators(channels());
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(input.begin(), input.end(), [&]() { return i8dist(rng); });
std::fill(output.begin(), output.end(), INT8_C(0xA5));
// Prepare parameters.
union xnn_qs8_avgpool_minmax_params params;
init_params(
¶ms,
-int32_t(input_zero_point() - 0x80) * int32_t(rows()),
input_scale() / (output_scale() * float(rows())),
int8_t(output_zero_point() - 0x80), int8_t(qmin() - 0x80), int8_t(qmax() - 0x80));
// Compute reference results.
for (size_t c = 0; c < channels(); c++) {
int32_t acc = 0;
for (size_t n = 0; n < rows(); n++) {
acc += int32_t(input[n * input_stride() + c]) - int32_t(input_zero_point() - 0x80);
}
accumulators[c] = acc;
output_ref[c] = requantize(
acc, input_scale() / (output_scale() * float(rows())), int8_t(output_zero_point() - 0x80), int8_t(qmin() - 0x80), int8_t(qmax() - 0x80));
output_fp[c] = float(acc) * (input_scale() / (output_scale() * float(rows()))) + float(output_zero_point() - 0x80);
output_fp[c] = std::min<float>(output_fp[c], float(qmax() - 0x80));
output_fp[c] = std::max<float>(output_fp[c], float(qmin() - 0x80));
}
// Call optimized micro-kernel.
gavgpool_minmax(rows(), channels(),
input.data(), input_stride() * sizeof(int8_t),
zero.data(),
buffer.data(),
output.data(),
¶ms);
// Verify results.
for (size_t c = 0; c < channels(); c++) {
ASSERT_LE(int32_t(output[c]), int32_t(qmax() - 0x80))
<< "at channel " << c << " / " << channels() << ", rows = " << rows();
ASSERT_GE(int32_t(output[c]), int32_t(qmin() - 0x80))
<< "at channel " << c << " / " << channels() << ", rows = " << rows();
ASSERT_NEAR(float(int32_t(output[c])), output_fp[c], 0.55f)
<< "at channel " << c << " / " << channels() << ", rows = " << rows()
<< ", accumulator = " << accumulators[c];
EXPECT_EQ(int32_t(output_ref[c]), int32_t(output[c]))
<< "at channel " << c << " / " << channels() << ", rows = " << rows()
<< ", accumulator = " << accumulators[c];
}
}
}
void Test(xnn_f16_gavgpool_minmax_unipass_ukernel_fn gavgpool_minmax, xnn_init_f16_scaleminmax_params_fn init_params) const {
xnnpack::ReplicableRandomDevice rng;
std::uniform_real_distribution<float> f32dist;
std::vector<xnn_float16> input((rows() - 1) * input_stride() + channels() + XNN_EXTRA_BYTES / sizeof(xnn_float16));
std::vector<xnn_float16> zero(channels() + XNN_EXTRA_BYTES / sizeof(xnn_float16));
std::vector<xnn_float16> output(channels());
std::vector<float> output_ref(channels());
std::fill(zero.begin(), zero.end(), 0);
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(input.begin(), input.end(), [&]() { return f32dist(rng); });
std::fill(output.begin(), output.end(), std::nanf(""));
// Compute reference results, without clamping.
for (size_t c = 0; c < channels(); c++) {
float acc = 0.0f;
for (size_t n = 0; n < rows(); n++) {
acc += input[n * input_stride() + c];
}
output_ref[c] = acc / float(rows());
}
// Compute clamping parameters.
const float accumulated_min = *std::min_element(output_ref.cbegin(), output_ref.cend());
const float accumulated_max = *std::max_element(output_ref.cbegin(), output_ref.cend());
const float accumulated_range = accumulated_max - accumulated_min;
const float output_min = xnn_float16(accumulated_min + float(qmin()) / 255.0f * accumulated_range);
const float output_max = xnn_float16(accumulated_max - float(255 - qmax()) / 255.0f * accumulated_range);
// Clamp reference results.
for (float& output_values : output_ref) {
output_values = std::max(std::min(output_values, output_max), output_min);
}
// Prepare parameters.
xnn_f16_scaleminmax_params params;
init_params(¶ms,
1.0f / float(rows()),
output_min,
output_max);
// Call optimized micro-kernel.
gavgpool_minmax(rows(), channels(),
input.data(), input_stride() * sizeof(xnn_float16),
zero.data(),
output.data(),
¶ms);
// Verify results.
for (size_t c = 0; c < channels(); c++) {
ASSERT_LE(output[c], output_max)
<< "at position " << c << ", rows = " << rows() << ", channels = " << channels();
ASSERT_GE(output[c], output_min)
<< "at position " << c << ", rows = " << rows() << ", channels = " << channels();
EXPECT_NEAR(output[c], output_ref[c], std::max(1.0e-4f, std::abs(output_ref[c]) * 1.0e-2f))
<< "at position " << c << ", rows = " << rows() << ", channels = " << channels();
}
}
}
void Test(xnn_f16_gavgpool_minmax_multipass_ukernel_fn gavgpool_minmax, xnn_init_f16_scaleminmax_params_fn init_params) const {
xnnpack::ReplicableRandomDevice rng;
std::uniform_real_distribution<float> f32dist;
std::vector<xnn_float16> input((rows() - 1) * input_stride() + channels() + XNN_EXTRA_BYTES / sizeof(xnn_float16));
std::vector<xnn_float16, AlignedAllocator<xnn_float16, 64>> buffer(channels() + XNN_EXTRA_BYTES / sizeof(xnn_float16));
std::vector<xnn_float16> zero(channels() + XNN_EXTRA_BYTES / sizeof(xnn_float16));
std::vector<xnn_float16> output(channels());
std::vector<float> output_ref(channels());
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(input.begin(), input.end(), [&]() { return f32dist(rng); });
std::fill(output.begin(), output.end(), std::nanf(""));
// Compute reference results, without clamping.
for (size_t c = 0; c < channels(); c++) {
float acc = 0.0f;
for (size_t n = 0; n < rows(); n++) {
acc += input[n * input_stride() + c];
}
output_ref[c] = acc / float(rows());
}
// Compute clamping parameters.
const float accumulated_min = *std::min_element(output_ref.cbegin(), output_ref.cend());
const float accumulated_max = *std::max_element(output_ref.cbegin(), output_ref.cend());
const float accumulated_range = accumulated_max - accumulated_min;
const float output_min = xnn_float16(accumulated_min + float(qmin()) / 255.0f * accumulated_range);
const float output_max = xnn_float16(accumulated_max - float(255 - qmax()) / 255.0f * accumulated_range);
// Prepare parameters.
xnn_f16_scaleminmax_params params;
init_params(¶ms,
1.0f / float(rows()),
output_min,
output_max);
// Clamp reference results.
for (float& output_values : output_ref) {
output_values = std::max(std::min(output_values, output_max), output_min);
}
// Call optimized micro-kernel.
gavgpool_minmax(rows(), channels(),
input.data(), input_stride() * sizeof(xnn_float16),
zero.data(),
buffer.data(),
output.data(),
¶ms);
// Verify results.
for (size_t c = 0; c < channels(); c++) {
ASSERT_LE(output[c], output_max)
<< "at position " << c << ", rows = " << rows() << ", channels = " << channels();
ASSERT_GE(output[c], output_min)
<< "at position " << c << ", rows = " << rows() << ", channels = " << channels();
EXPECT_NEAR(output[c], output_ref[c], std::abs(output_ref[c]) * 1.0e-0f)
<< "at position " << c << ", rows = " << rows() << ", channels = " << channels();
}
}
}
void Test(xnn_f32_gavgpool_minmax_unipass_ukernel_fn gavgpool_minmax, xnn_init_f32_scaleminmax_params_fn init_params) const {
xnnpack::ReplicableRandomDevice rng;
std::uniform_real_distribution<float> f32dist;
std::vector<float> input((rows() - 1) * input_stride() + channels() + XNN_EXTRA_BYTES / sizeof(float));
std::vector<float> zero(channels() + XNN_EXTRA_BYTES / sizeof(float));
std::vector<float> output(channels());
std::vector<float> output_ref(channels());
std::fill(zero.begin(), zero.end(), 0.0f);
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(input.begin(), input.end(), [&]() { return f32dist(rng); });
std::fill(output.begin(), output.end(), std::nanf(""));
// Compute reference results, without clamping.
for (size_t c = 0; c < channels(); c++) {
float acc = 0.0f;
for (size_t n = 0; n < rows(); n++) {
acc += input[n * input_stride() + c];
}
output_ref[c] = acc / float(rows());
}
// Compute clamping parameters.
const float accumulated_min = *std::min_element(output_ref.cbegin(), output_ref.cend());
const float accumulated_max = *std::max_element(output_ref.cbegin(), output_ref.cend());
const float accumulated_range = accumulated_max - accumulated_min;
const float output_min = accumulated_min + float(qmin()) / 255.0f * accumulated_range;
const float output_max = accumulated_max - float(255 - qmax()) / 255.0f * accumulated_range;
// Clamp reference results.
for (float& output_values : output_ref) {
output_values = std::max(std::min(output_values, output_max), output_min);
}
// Prepare parameters.
struct xnn_f32_scaleminmax_params params;
init_params(¶ms, 1.0f / float(rows()), output_min, output_max);
// Call optimized micro-kernel.
gavgpool_minmax(rows(), channels(),
input.data(), input_stride() * sizeof(float),
zero.data(),
output.data(),
¶ms);
// Verify results.
for (size_t c = 0; c < channels(); c++) {
ASSERT_LE(output[c], output_max)
<< "at position " << c << ", rows = " << rows() << ", channels = " << channels();
ASSERT_GE(output[c], output_min)
<< "at position " << c << ", rows = " << rows() << ", channels = " << channels();
EXPECT_NEAR(output[c], output_ref[c], std::abs(output_ref[c]) * 1.0e-6f)
<< "at position " << c << ", rows = " << rows() << ", channels = " << channels();
}
}
}
void Test(xnn_f32_gavgpool_minmax_multipass_ukernel_fn gavgpool_minmax, xnn_init_f32_scaleminmax_params_fn init_params) const {
xnnpack::ReplicableRandomDevice rng;
std::uniform_real_distribution<float> f32dist;
std::vector<float> input((rows() - 1) * input_stride() + channels() + XNN_EXTRA_BYTES / sizeof(float));
std::vector<float, AlignedAllocator<float, 64>> buffer(channels() + XNN_EXTRA_BYTES / sizeof(float));
std::vector<float> zero(channels() + XNN_EXTRA_BYTES / sizeof(float));
std::vector<float> output(channels());
std::vector<float> output_ref(channels());
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(input.begin(), input.end(), [&]() { return f32dist(rng); });
std::fill(output.begin(), output.end(), 0.f); // kernels accumulate.
// Compute reference results, without clamping.
for (size_t c = 0; c < channels(); c++) {
float acc = 0.0f;
for (size_t n = 0; n < rows(); n++) {
acc += input[n * input_stride() + c];
}
output_ref[c] = acc / float(rows());
}
// Compute clamping parameters.
const float accumulated_min = *std::min_element(output_ref.cbegin(), output_ref.cend());
const float accumulated_max = *std::max_element(output_ref.cbegin(), output_ref.cend());
const float accumulated_range = accumulated_max - accumulated_min;
const float output_min = accumulated_min + float(qmin()) / 255.0f * accumulated_range;
const float output_max = accumulated_max - float(255 - qmax()) / 255.0f * accumulated_range;
// Prepare parameters.
struct xnn_f32_scaleminmax_params params;
init_params(¶ms, 1.0f / float(rows()), output_min, output_max);
// Clamp reference results.
for (float& output_values : output_ref) {
output_values = std::max(std::min(output_values, output_max), output_min);
}
// Call optimized micro-kernel.
gavgpool_minmax(rows(), channels(),
input.data(), input_stride() * sizeof(float),
zero.data(),
buffer.data(),
output.data(),
¶ms);
// Verify results.
for (size_t c = 0; c < channels(); c++) {
ASSERT_LE(output[c], output_max)
<< "at position " << c << ", rows = " << rows() << ", channels = " << channels();
ASSERT_GE(output[c], output_min)
<< "at position " << c << ", rows = " << rows() << ", channels = " << channels();
EXPECT_NEAR(output[c], output_ref[c], std::abs(output_ref[c]) * 1.0e-6f)
<< "at position " << c << ", rows = " << rows() << ", channels = " << channels();
}
}
}
private:
size_t rows_{1};
size_t channels_{1};
size_t channel_tile_{1};
size_t input_stride_{0};
float input_scale_{1.25f};
float output_scale_{0.75f};
uint8_t input_zero_point_{121};
uint8_t output_zero_point_{133};
uint8_t qmin_{0};
uint8_t qmax_{255};
size_t iterations_{15};
};