763 lines
33 KiB
C++
763 lines
33 KiB
C++
// Copyright (c) Facebook, Inc. and its affiliates.
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// All rights reserved.
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//
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// Copyright 2019 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 <cmath>
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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/AlignedAllocator.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 AvgPoolMicrokernelTester {
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public:
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inline AvgPoolMicrokernelTester& output_pixels(size_t output_pixels) {
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assert(output_pixels != 0);
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this->output_pixels_ = output_pixels;
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return *this;
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}
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inline size_t output_pixels() const {
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return this->output_pixels_;
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}
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inline AvgPoolMicrokernelTester& step(size_t step) {
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assert(step != 0);
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this->step_ = step;
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return *this;
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}
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inline size_t step() const {
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return this->step_;
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}
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inline AvgPoolMicrokernelTester& input_offset(size_t input_offset) {
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assert(input_offset != 0);
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this->input_offset_ = input_offset;
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return *this;
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}
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inline size_t input_offset() const {
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return this->input_offset_;
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}
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inline AvgPoolMicrokernelTester& zero_index(size_t zero_index) {
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this->zero_index_ = zero_index;
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return *this;
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}
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inline size_t zero_index() const {
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return this->zero_index_;
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}
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inline AvgPoolMicrokernelTester& pooling_elements(size_t pooling_elements) {
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assert(pooling_elements != 0);
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this->pooling_elements_ = pooling_elements;
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return *this;
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}
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inline size_t pooling_elements() const {
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return this->pooling_elements_;
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}
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inline size_t packed_pooling_elements() const {
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if (pooling_elements() <= primary_pooling_tile()) {
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return primary_pooling_tile();
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} else {
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return (pooling_elements() - primary_pooling_tile()) % incremental_pooling_tile() == 0 ? pooling_elements() : ((pooling_elements() - primary_pooling_tile()) / incremental_pooling_tile() + 1) * incremental_pooling_tile() + primary_pooling_tile();
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}
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}
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inline AvgPoolMicrokernelTester& pooling_tile(size_t primary_tile, size_t incremental_tile = 0) {
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assert(primary_tile != 0);
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this->primary_pooling_tile_ = primary_tile;
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this->incremental_pooling_tile_ = incremental_tile;
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return *this;
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}
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inline AvgPoolMicrokernelTester& primary_pooling_tile(size_t primary_pooling_tile) {
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assert(primary_pooling_tile != 0);
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this->primary_pooling_tile_ = primary_pooling_tile;
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return *this;
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}
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inline size_t primary_pooling_tile() const {
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return this->primary_pooling_tile_;
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}
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inline AvgPoolMicrokernelTester& incremental_pooling_tile(size_t incremental_pooling_tile) {
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assert(incremental_pooling_tile != 0);
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this->incremental_pooling_tile_ = incremental_pooling_tile;
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return *this;
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}
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inline size_t incremental_pooling_tile() const {
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return this->incremental_pooling_tile_;
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}
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inline AvgPoolMicrokernelTester& channels(size_t channels) {
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assert(channels != 0);
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this->channels_ = channels;
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return *this;
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}
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inline size_t channels() const {
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return this->channels_;
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}
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inline AvgPoolMicrokernelTester& output_stride(size_t output_stride) {
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assert(output_stride != 0);
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this->output_stride_ = output_stride;
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return *this;
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}
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inline size_t output_stride() const {
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if (this->output_stride_ == 0) {
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return channels();
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} else {
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assert(this->output_stride_ >= channels());
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return this->output_stride_;
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}
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}
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inline AvgPoolMicrokernelTester& input_scale(float input_scale) {
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assert(input_scale > 0.0f);
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assert(std::isnormal(input_scale));
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this->input_scale_ = input_scale;
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return *this;
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}
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inline float input_scale() const {
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return this->input_scale_;
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}
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inline AvgPoolMicrokernelTester& input_zero_point(uint8_t input_zero_point) {
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this->input_zero_point_ = input_zero_point;
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return *this;
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}
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inline uint8_t input_zero_point() const {
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return this->input_zero_point_;
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}
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inline AvgPoolMicrokernelTester& output_scale(float output_scale) {
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assert(output_scale > 0.0f);
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assert(std::isnormal(output_scale));
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this->output_scale_ = output_scale;
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return *this;
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}
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inline float output_scale() const {
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return this->output_scale_;
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}
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inline AvgPoolMicrokernelTester& output_zero_point(uint8_t output_zero_point) {
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this->output_zero_point_ = output_zero_point;
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return *this;
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}
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inline uint8_t output_zero_point() const {
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return this->output_zero_point_;
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}
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inline AvgPoolMicrokernelTester& 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 AvgPoolMicrokernelTester& 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 AvgPoolMicrokernelTester& 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_qu8_avgpool_minmax_unipass_ukernel_function avgpool_minmax, xnn_init_qu8_avgpool_minmax_params_fn init_params) 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 u8rng = std::bind(std::uniform_int_distribution<uint32_t>(0, std::numeric_limits<uint8_t>::max()), rng);
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std::vector<const uint8_t*> indirect_input((output_pixels() - 1) * step() + packed_pooling_elements());
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std::vector<uint8_t> input(XNN_EXTRA_BYTES / sizeof(uint8_t) +
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input_offset() + indirect_input.size() * channels());
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std::vector<uint8_t> zero(channels() + XNN_EXTRA_BYTES / sizeof(uint8_t));
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std::vector<uint8_t> output((output_pixels() - 1) * output_stride() + channels());
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std::vector<uint8_t> output_ref(output_pixels() * channels());
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std::vector<float> output_real(output_pixels() * channels());
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std::vector<int32_t> accumulator(output_pixels() * channels());
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for (size_t iteration = 0; iteration < iterations(); iteration++) {
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do {
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std::generate(input.begin(), input.end(), std::ref(u8rng));
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} while (input.size() > 1 && *std::max_element(input.cbegin(), input.cend()) == *std::min_element(input.cbegin(), input.cend()));
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std::fill(input.begin(), input.begin() + input_offset(), 0xA5);
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std::fill(input.end() - XNN_EXTRA_BYTES / sizeof(uint8_t), input.end(), 0xA5);
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std::fill(output.begin(), output.end(), 0xA5);
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for (size_t i = 0; i < (output_pixels() - 1) * step() + pooling_elements(); i++) {
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indirect_input[i] = input.data() + i * channels();
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}
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std::shuffle(indirect_input.begin(),
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indirect_input.begin() + (output_pixels() - 1) * step() + pooling_elements(), rng);
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if (zero_index() != SIZE_MAX) {
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indirect_input[zero_index()] = zero.data();
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}
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// Prepare parameters.
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xnn_qu8_avgpool_minmax_params params;
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init_params(
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¶ms,
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-int32_t(input_zero_point()) * int32_t(pooling_elements()),
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input_scale() / (output_scale() * float(pooling_elements())),
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output_zero_point(), qmin(), qmax());
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// Compute reference results.
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for (size_t x = 0; x < output_pixels(); x++) {
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for (size_t c = 0; c < channels(); c++) {
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int32_t acc = 0;
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for (size_t p = 0; p < pooling_elements(); p++) {
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const uint8_t* row = indirect_input[x * step() + p];
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if (row != zero.data()) {
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acc += int32_t(row[c + input_offset()]);
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}
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acc -= int32_t(input_zero_point());
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}
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accumulator[x * channels() + c] = acc;
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output_ref[x * channels() + c] = xnn_qu8_requantize_rndna(
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acc, input_scale() / (output_scale() * float(pooling_elements())), output_zero_point(), qmin(), qmax());
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const float scaled_acc =
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float(acc) * input_scale() / (output_scale() * float(pooling_elements())) + float(output_zero_point());
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output_real[x * channels() + c] = std::min(std::max(scaled_acc, float(qmin())), float(qmax()));
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}
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}
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// Call optimized micro-kernel.
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avgpool_minmax(output_pixels(), pooling_elements(), channels(),
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indirect_input.data(), input_offset() * sizeof(uint8_t), zero.data(),
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output.data(),
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step() * sizeof(void*),
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(output_stride() - channels()) * sizeof(uint8_t),
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¶ms);
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// Verify results.
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for (size_t x = 0; x < output_pixels(); x++) {
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for (size_t c = 0; c < channels(); c++) {
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ASSERT_GE(uint32_t(output[x * output_stride() + c]), uint32_t(qmin()))
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<< "at pixel " << x << " / " << output_pixels() << ", channel " << c << " / " << channels()
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<< ", pooling elements = " << pooling_elements() << ", step = " << step()
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<< ", input offset = " << input_offset();
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ASSERT_LE(uint32_t(output[x * output_stride() + c]), uint32_t(qmax()))
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<< "at pixel " << x << " / " << output_pixels() << ", channel " << c << " / " << channels()
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<< ", pooling elements = " << pooling_elements() << ", step = " << step()
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<< ", input offset = " << input_offset();
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ASSERT_NEAR(float(int32_t(output[x * output_stride() + c])), output_real[x * channels() + c], 0.5f)
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<< "at pixel " << x << " / " << output_pixels() << ", channel " << c << " / " << channels()
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<< ", pooling elements = " << pooling_elements() << ", step = " << step()
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<< ", input offset = " << input_offset() << ", accumulator = " << accumulator[x * channels() + c];
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ASSERT_EQ(uint32_t(output_ref[x * channels() + c]), uint32_t(output[x * output_stride() + c]))
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<< "at pixel " << x << " / " << output_pixels() << ", channel " << c << " / " << channels()
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<< ", pooling elements = " << pooling_elements() << ", step = " << step()
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<< ", input offset = " << input_offset() << ", accumulator = " << accumulator[x * channels() + c];
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}
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}
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}
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}
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void Test(xnn_qu8_avgpool_minmax_multipass_ukernel_function avgpool_minmax, xnn_init_qu8_avgpool_minmax_params_fn init_params) 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 u8rng = std::bind(std::uniform_int_distribution<uint32_t>(0, std::numeric_limits<uint8_t>::max()), rng);
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std::vector<const uint8_t*> indirect_input((output_pixels() - 1) * step() + packed_pooling_elements());
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std::vector<uint8_t> input(XNN_EXTRA_BYTES / sizeof(uint8_t) +
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input_offset() + indirect_input.size() * channels());
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std::vector<uint8_t> zero(channels() + XNN_EXTRA_BYTES / sizeof(uint8_t));
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std::vector<uint8_t> output((output_pixels() - 1) * output_stride() + channels());
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std::vector<uint8_t> output_ref(output_pixels() * channels());
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std::vector<float> output_real(output_pixels() * channels());
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std::vector<int32_t> accumulator(output_pixels() * channels());
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std::vector<int32_t, AlignedAllocator<int32_t, 64>> buffer(XNN_EXTRA_BYTES / sizeof(uint8_t) + channels());
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for (size_t iteration = 0; iteration < iterations(); iteration++) {
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do {
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std::generate(input.begin(), input.end(), std::ref(u8rng));
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} while (input.size() > 1 && *std::max_element(input.cbegin(), input.cend()) == *std::min_element(input.cbegin(), input.cend()));
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std::fill(input.begin(), input.begin() + input_offset(), 0xA5);
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std::fill(input.end() - XNN_EXTRA_BYTES / sizeof(uint8_t), input.end(), 0xA5);
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std::fill(output.begin(), output.end(), 0xA5);
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for (size_t i = 0; i < (output_pixels() - 1) * step() + pooling_elements(); i++) {
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indirect_input[i] = input.data() + i * channels();
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}
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std::shuffle(indirect_input.begin(),
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indirect_input.begin() + (output_pixels() - 1) * step() + pooling_elements(), rng);
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if (zero_index() != SIZE_MAX) {
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indirect_input[zero_index()] = zero.data();
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}
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// Prepare parameters.
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xnn_qu8_avgpool_minmax_params params;
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init_params(
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¶ms,
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-int32_t(input_zero_point()) * int32_t(pooling_elements()),
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input_scale() / (output_scale() * float(pooling_elements())),
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output_zero_point(), qmin(), qmax());
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// Compute reference results.
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for (size_t x = 0; x < output_pixels(); x++) {
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for (size_t c = 0; c < channels(); c++) {
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int32_t acc = 0;
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for (size_t p = 0; p < pooling_elements(); p++) {
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const uint8_t* row = indirect_input[x * step() + p];
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if (row != zero.data()) {
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acc += int32_t(row[c + input_offset()]);
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}
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acc -= int32_t(input_zero_point());
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}
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accumulator[x * channels() + c] = acc;
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output_ref[x * channels() + c] = xnn_qu8_requantize_rndna(
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acc, input_scale() / (output_scale() * float(pooling_elements())), output_zero_point(), qmin(), qmax());
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const float scaled_acc =
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float(acc) * input_scale() / (output_scale() * float(pooling_elements())) + float(output_zero_point());
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output_real[x * channels() + c] = std::min(std::max(scaled_acc, float(qmin())), float(qmax()));
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}
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}
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// Call optimized micro-kernel.
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avgpool_minmax(output_pixels(), pooling_elements(), channels(),
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indirect_input.data(), input_offset() * sizeof(uint8_t), zero.data(),
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buffer.data(), output.data(),
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(step() - (packed_pooling_elements() - incremental_pooling_tile())) * sizeof(void*),
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(output_stride() - channels()) * sizeof(uint8_t),
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¶ms);
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// Verify results.
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for (size_t x = 0; x < output_pixels(); x++) {
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for (size_t c = 0; c < channels(); c++) {
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ASSERT_GE(uint32_t(output[x * output_stride() + c]), uint32_t(qmin()))
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<< "at pixel " << x << " / " << output_pixels() << ", channel " << c << " / " << channels()
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<< ", pooling elements = " << pooling_elements() << ", step = " << step()
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<< ", input offset = " << input_offset();
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ASSERT_LE(uint32_t(output[x * output_stride() + c]), uint32_t(qmax()))
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<< "at pixel " << x << " / " << output_pixels() << ", channel " << c << " / " << channels()
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<< ", pooling elements = " << pooling_elements() << ", step = " << step()
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<< ", input offset = " << input_offset();
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ASSERT_NEAR(float(int32_t(output[x * output_stride() + c])), output_real[x * channels() + c], 0.5f)
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<< "at pixel " << x << " / " << output_pixels() << ", channel " << c << " / " << channels()
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<< ", pooling elements = " << pooling_elements() << ", step = " << step()
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<< ", input offset = " << input_offset() << ", accumulator = " << accumulator[x * channels() + c];
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ASSERT_EQ(uint32_t(output_ref[x * channels() + c]), uint32_t(output[x * output_stride() + c]))
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<< "at pixel " << x << " / " << output_pixels() << ", channel " << c << " / " << channels()
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<< ", pooling elements = " << pooling_elements() << ", step = " << step()
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<< ", input offset = " << input_offset() << ", accumulator = " << accumulator[x * channels() + c];
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}
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}
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}
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}
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void Test(xnn_f32_avgpool_minmax_unipass_ukernel_function avgpool_minmax, xnn_init_f32_scaleminmax_params_fn init_params) 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 f32rng = std::bind(std::uniform_real_distribution<float>(), rng);
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std::vector<const float*> indirect_input((output_pixels() - 1) * step() + packed_pooling_elements());
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std::vector<float> input(XNN_EXTRA_BYTES / sizeof(float) +
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input_offset() + indirect_input.size() * channels());
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std::vector<float> zero(channels() + XNN_EXTRA_BYTES / sizeof(float));
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std::vector<float> output((output_pixels() - 1) * output_stride() + channels());
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std::vector<float> output_ref(output_pixels() * channels());
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for (size_t iteration = 0; iteration < iterations(); iteration++) {
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std::generate(input.begin(), input.end(), std::ref(f32rng));
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std::fill(input.begin(), input.begin() + input_offset(), std::nanf(""));
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std::fill(input.end() - XNN_EXTRA_BYTES / sizeof(float), input.end(), std::nanf(""));
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std::fill(output.begin(), output.end(), std::nanf(""));
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for (size_t i = 0; i < (output_pixels() - 1) * step() + pooling_elements(); i++) {
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indirect_input[i] = input.data() + i * channels();
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}
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std::shuffle(indirect_input.begin(),
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indirect_input.begin() + (output_pixels() - 1) * step() + pooling_elements(), rng);
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if (zero_index() != SIZE_MAX) {
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indirect_input[zero_index()] = zero.data();
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}
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// Compute reference results, without clamping.
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for (size_t x = 0; x < output_pixels(); x++) {
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for (size_t c = 0; c < channels(); c++) {
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float acc = 0.0f;
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for (size_t p = 0; p < pooling_elements(); p++) {
|
|
const float* row = indirect_input[x * step() + p];
|
|
if (row != zero.data()) {
|
|
acc += row[c + input_offset()];
|
|
}
|
|
}
|
|
output_ref[x * channels() + c] = acc / float(pooling_elements());
|
|
}
|
|
}
|
|
|
|
// 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_value : output_ref) {
|
|
output_value = std::max(std::min(output_value, output_max), output_min);
|
|
}
|
|
|
|
// Prepare parameters.
|
|
xnn_f32_scaleminmax_params params;
|
|
init_params(¶ms, 1.0f / float(pooling_elements()), output_min, output_max);
|
|
|
|
// Call optimized micro-kernel.
|
|
avgpool_minmax(output_pixels(), pooling_elements(), channels(),
|
|
indirect_input.data(), input_offset() * sizeof(float), zero.data(),
|
|
output.data(),
|
|
step() * sizeof(void*),
|
|
(output_stride() - channels()) * sizeof(float),
|
|
¶ms);
|
|
|
|
// Verify results.
|
|
for (size_t x = 0; x < output_pixels(); x++) {
|
|
for (size_t c = 0; c < channels(); c++) {
|
|
ASSERT_GE(output[x * output_stride() + c], output_min)
|
|
<< "at pixel " << x << " / " << output_pixels() << ", channel " << c << " / " << channels()
|
|
<< ", pooling elements = " << pooling_elements() << ", step = " << step()
|
|
<< ", input offset = " << input_offset();
|
|
ASSERT_LE(output[x * output_stride() + c], output_max)
|
|
<< "at pixel " << x << " / " << output_pixels() << ", channel " << c << " / " << channels()
|
|
<< ", pooling elements = " << pooling_elements() << ", step = " << step()
|
|
<< ", input offset = " << input_offset();
|
|
ASSERT_NEAR(
|
|
output[x * output_stride() + c],
|
|
output_ref[x * channels() + c],
|
|
std::abs(output_ref[x * channels() + c]) * 1.0e-6f)
|
|
<< "at pixel " << x << " / " << output_pixels() << ", channel " << c << " / " << channels()
|
|
<< ", pooling elements = " << pooling_elements() << ", step = " << step()
|
|
<< ", input offset = " << input_offset();
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
void Test(xnn_f32_avgpool_minmax_multipass_ukernel_function avgpool_minmax, xnn_init_f32_scaleminmax_params_fn init_params) const {
|
|
std::random_device random_device;
|
|
auto rng = std::mt19937(random_device());
|
|
auto f32rng = std::bind(std::uniform_real_distribution<float>(), rng);
|
|
|
|
std::vector<const float*> indirect_input((output_pixels() - 1) * step() + packed_pooling_elements());
|
|
std::vector<float> input(XNN_EXTRA_BYTES / sizeof(float) +
|
|
input_offset() + indirect_input.size() * channels());
|
|
std::vector<float> zero(channels() + XNN_EXTRA_BYTES / sizeof(float));
|
|
std::vector<float> output((output_pixels() - 1) * output_stride() + channels());
|
|
std::vector<float> output_ref(output_pixels() * channels());
|
|
std::vector<float, AlignedAllocator<float, 64>> buffer(XNN_EXTRA_BYTES / sizeof(float) + channels());
|
|
for (size_t iteration = 0; iteration < iterations(); iteration++) {
|
|
std::generate(input.begin(), input.end(), std::ref(f32rng));
|
|
std::fill(input.begin(), input.begin() + input_offset(), std::nanf(""));
|
|
std::fill(input.end() - XNN_EXTRA_BYTES / sizeof(float), input.end(), std::nanf(""));
|
|
std::fill(output.begin(), output.end(), std::nanf(""));
|
|
|
|
for (size_t i = 0; i < (output_pixels() - 1) * step() + pooling_elements(); i++) {
|
|
indirect_input[i] = input.data() + i * channels();
|
|
}
|
|
std::shuffle(indirect_input.begin(),
|
|
indirect_input.begin() + (output_pixels() - 1) * step() + pooling_elements(), rng);
|
|
if (zero_index() != SIZE_MAX) {
|
|
indirect_input[zero_index()] = zero.data();
|
|
}
|
|
|
|
// Compute reference results, without clamping.
|
|
for (size_t x = 0; x < output_pixels(); x++) {
|
|
for (size_t c = 0; c < channels(); c++) {
|
|
float acc = 0.0f;
|
|
for (size_t p = 0; p < pooling_elements(); p++) {
|
|
const float* row = indirect_input[x * step() + p];
|
|
if (row != zero.data()) {
|
|
acc += row[c + input_offset()];
|
|
}
|
|
}
|
|
output_ref[x * channels() + c] = acc / float(pooling_elements());
|
|
}
|
|
}
|
|
|
|
// 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_value : output_ref) {
|
|
output_value = std::max(std::min(output_value, output_max), output_min);
|
|
}
|
|
|
|
// Prepare parameters.
|
|
xnn_f32_scaleminmax_params params;
|
|
init_params(¶ms, 1.0f / float(pooling_elements()), output_min, output_max);
|
|
|
|
// Call optimized micro-kernel.
|
|
avgpool_minmax(output_pixels(), pooling_elements(), channels(),
|
|
indirect_input.data(), input_offset() * sizeof(float), zero.data(),
|
|
buffer.data(), output.data(),
|
|
(step() - (packed_pooling_elements() - incremental_pooling_tile())) * sizeof(void*),
|
|
(output_stride() - channels()) * sizeof(float),
|
|
¶ms);
|
|
|
|
// Verify results.
|
|
for (size_t x = 0; x < output_pixels(); x++) {
|
|
for (size_t c = 0; c < channels(); c++) {
|
|
ASSERT_GE(output[x * output_stride() + c], output_min)
|
|
<< "at pixel " << x << " / " << output_pixels() << ", channel " << c << " / " << channels()
|
|
<< ", pooling elements = " << pooling_elements() << ", step = " << step()
|
|
<< ", input offset = " << input_offset();
|
|
ASSERT_LE(output[x * output_stride() + c], output_max)
|
|
<< "at pixel " << x << " / " << output_pixels() << ", channel " << c << " / " << channels()
|
|
<< ", pooling elements = " << pooling_elements() << ", step = " << step()
|
|
<< ", input offset = " << input_offset();
|
|
ASSERT_NEAR(
|
|
output[x * output_stride() + c],
|
|
output_ref[x * channels() + c],
|
|
std::abs(output_ref[x * channels() + c]) * 1.0e-6f)
|
|
<< "at pixel " << x << " / " << output_pixels() << ", channel " << c << " / " << channels()
|
|
<< ", pooling elements = " << pooling_elements() << ", step = " << step()
|
|
<< ", input offset = " << input_offset();
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
void Test(xnn_f32_pavgpool_minmax_unipass_ukernel_function pavgpool_minmax, xnn_init_f32_minmax_params_fn init_params) const {
|
|
std::random_device random_device;
|
|
auto rng = std::mt19937(random_device());
|
|
auto f32irng = std::bind(std::uniform_real_distribution<float>(), rng);
|
|
auto f32mrng = std::bind(std::uniform_real_distribution<float>(0.1f, 0.5f), rng);
|
|
|
|
std::vector<const float*> indirect_input((output_pixels() - 1) * step() + packed_pooling_elements());
|
|
std::vector<float> input(XNN_EXTRA_BYTES / sizeof(float) +
|
|
input_offset() + indirect_input.size() * channels());
|
|
std::vector<float> zero(channels() + XNN_EXTRA_BYTES / sizeof(float));
|
|
std::vector<float> multiplier(output_pixels());
|
|
std::vector<float> output((output_pixels() - 1) * output_stride() + channels());
|
|
std::vector<float> output_ref(output_pixels() * channels());
|
|
for (size_t iteration = 0; iteration < iterations(); iteration++) {
|
|
std::generate(input.begin(), input.end(), std::ref(f32irng));
|
|
std::fill(input.begin(), input.begin() + input_offset(), std::nanf(""));
|
|
std::fill(input.end() - XNN_EXTRA_BYTES / sizeof(float), input.end(), std::nanf(""));
|
|
std::generate(multiplier.begin(), multiplier.end(), std::ref(f32mrng));
|
|
std::fill(output.begin(), output.end(), std::nanf(""));
|
|
|
|
for (size_t i = 0; i < (output_pixels() - 1) * step() + pooling_elements(); i++) {
|
|
indirect_input[i] = input.data() + i * channels();
|
|
}
|
|
std::shuffle(indirect_input.begin(),
|
|
indirect_input.begin() + (output_pixels() - 1) * step() + pooling_elements(), rng);
|
|
if (zero_index() != SIZE_MAX) {
|
|
indirect_input[zero_index()] = zero.data();
|
|
}
|
|
|
|
// Compute reference results, without clamping.
|
|
for (size_t x = 0; x < output_pixels(); x++) {
|
|
for (size_t c = 0; c < channels(); c++) {
|
|
float acc = 0.0f;
|
|
for (size_t p = 0; p < pooling_elements(); p++) {
|
|
const float* row = indirect_input[x * step() + p];
|
|
if (row != zero.data()) {
|
|
acc += row[c + input_offset()];
|
|
}
|
|
}
|
|
output_ref[x * channels() + c] = acc * multiplier[x];
|
|
}
|
|
}
|
|
|
|
// 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_value : output_ref) {
|
|
output_value = std::max(std::min(output_value, output_max), output_min);
|
|
}
|
|
|
|
// Prepare parameters.
|
|
xnn_f32_minmax_params params;
|
|
init_params(¶ms, output_min, output_max);
|
|
|
|
// Call optimized micro-kernel.
|
|
pavgpool_minmax(output_pixels(), pooling_elements(), channels(),
|
|
indirect_input.data(), input_offset() * sizeof(float), zero.data(),
|
|
multiplier.data(), output.data(),
|
|
step() * sizeof(void*),
|
|
(output_stride() - channels()) * sizeof(float),
|
|
¶ms);
|
|
|
|
// Verify results.
|
|
for (size_t x = 0; x < output_pixels(); x++) {
|
|
for (size_t c = 0; c < channels(); c++) {
|
|
ASSERT_GE(output[x * output_stride() + c], output_min)
|
|
<< "at pixel " << x << " / " << output_pixels() << ", channel " << c << " / " << channels()
|
|
<< ", pooling elements = " << pooling_elements() << ", step = " << step()
|
|
<< ", input offset = " << input_offset();
|
|
ASSERT_LE(output[x * output_stride() + c], output_max)
|
|
<< "at pixel " << x << " / " << output_pixels() << ", channel " << c << " / " << channels()
|
|
<< ", pooling elements = " << pooling_elements() << ", step = " << step()
|
|
<< ", input offset = " << input_offset();
|
|
ASSERT_NEAR(
|
|
output[x * output_stride() + c],
|
|
output_ref[x * channels() + c],
|
|
std::abs(output_ref[x * channels() + c]) * 1.0e-6f)
|
|
<< "at pixel " << x << " / " << output_pixels() << ", channel " << c << " / " << channels()
|
|
<< ", pooling elements = " << pooling_elements() << ", step = " << step()
|
|
<< ", input offset = " << input_offset();
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
void Test(xnn_f32_pavgpool_minmax_multipass_ukernel_function pavgpool_minmax, xnn_init_f32_minmax_params_fn init_params) const {
|
|
std::random_device random_device;
|
|
auto rng = std::mt19937(random_device());
|
|
auto f32irng = std::bind(std::uniform_real_distribution<float>(), rng);
|
|
auto f32mrng = std::bind(std::uniform_real_distribution<float>(0.1f, 0.5f), rng);
|
|
|
|
std::vector<const float*> indirect_input((output_pixels() - 1) * step() + packed_pooling_elements());
|
|
std::vector<float> input(XNN_EXTRA_BYTES / sizeof(float) +
|
|
input_offset() + indirect_input.size() * channels());
|
|
std::vector<float> zero(channels() + XNN_EXTRA_BYTES / sizeof(float));
|
|
std::vector<float> multiplier(output_pixels());
|
|
std::vector<float> output((output_pixels() - 1) * output_stride() + channels());
|
|
std::vector<float> output_ref(output_pixels() * channels());
|
|
std::vector<float, AlignedAllocator<float, 64>> buffer(XNN_EXTRA_BYTES / sizeof(float) + channels());
|
|
for (size_t iteration = 0; iteration < iterations(); iteration++) {
|
|
std::generate(input.begin(), input.end(), std::ref(f32irng));
|
|
std::fill(input.begin(), input.begin() + input_offset(), std::nanf(""));
|
|
std::fill(input.end() - XNN_EXTRA_BYTES / sizeof(float), input.end(), std::nanf(""));
|
|
std::generate(multiplier.begin(), multiplier.end(), std::ref(f32mrng));
|
|
std::fill(output.begin(), output.end(), std::nanf(""));
|
|
|
|
for (size_t i = 0; i < (output_pixels() - 1) * step() + pooling_elements(); i++) {
|
|
indirect_input[i] = input.data() + i * channels();
|
|
}
|
|
std::shuffle(indirect_input.begin(),
|
|
indirect_input.begin() + (output_pixels() - 1) * step() + pooling_elements(), rng);
|
|
if (zero_index() != SIZE_MAX) {
|
|
indirect_input[zero_index()] = zero.data();
|
|
}
|
|
|
|
// Compute reference results, without clamping.
|
|
for (size_t x = 0; x < output_pixels(); x++) {
|
|
for (size_t c = 0; c < channels(); c++) {
|
|
float acc = 0.0f;
|
|
for (size_t p = 0; p < pooling_elements(); p++) {
|
|
const float* row = indirect_input[x * step() + p];
|
|
if (row != zero.data()) {
|
|
acc += row[c + input_offset()];
|
|
}
|
|
}
|
|
output_ref[x * channels() + c] = acc * multiplier[x];
|
|
}
|
|
}
|
|
|
|
// 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_value : output_ref) {
|
|
output_value = std::max(std::min(output_value, output_max), output_min);
|
|
}
|
|
|
|
// Prepare parameters.
|
|
xnn_f32_minmax_params params;
|
|
init_params(¶ms, output_min, output_max);
|
|
|
|
// Call optimized micro-kernel.
|
|
pavgpool_minmax(output_pixels(), pooling_elements(), channels(),
|
|
indirect_input.data(), input_offset() * sizeof(float), zero.data(),
|
|
multiplier.data(), buffer.data(), output.data(),
|
|
(step() - (packed_pooling_elements() - incremental_pooling_tile())) * sizeof(void*),
|
|
(output_stride() - channels()) * sizeof(float),
|
|
¶ms);
|
|
|
|
// Verify results.
|
|
for (size_t x = 0; x < output_pixels(); x++) {
|
|
for (size_t c = 0; c < channels(); c++) {
|
|
ASSERT_GE(output[x * output_stride() + c], output_min)
|
|
<< "at pixel " << x << " / " << output_pixels() << ", channel " << c << " / " << channels()
|
|
<< ", pooling elements = " << pooling_elements() << ", step = " << step()
|
|
<< ", input offset = " << input_offset();
|
|
ASSERT_LE(output[x * output_stride() + c], output_max)
|
|
<< "at pixel " << x << " / " << output_pixels() << ", channel " << c << " / " << channels()
|
|
<< ", pooling elements = " << pooling_elements() << ", step = " << step()
|
|
<< ", input offset = " << input_offset();
|
|
ASSERT_NEAR(
|
|
output[x * output_stride() + c],
|
|
output_ref[x * channels() + c],
|
|
std::abs(output_ref[x * channels() + c]) * 1.0e-6f)
|
|
<< "at pixel " << x << " / " << output_pixels() << ", channel " << c << " / " << channels()
|
|
<< ", pooling elements = " << pooling_elements() << ", step = " << step()
|
|
<< ", input offset = " << input_offset();
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
private:
|
|
size_t output_pixels_{1};
|
|
size_t pooling_elements_{1};
|
|
size_t channels_{1};
|
|
size_t input_offset_{0};
|
|
size_t zero_index_{SIZE_MAX};
|
|
size_t step_{1};
|
|
size_t primary_pooling_tile_{1};
|
|
size_t incremental_pooling_tile_{1};
|
|
size_t output_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_{3};
|
|
};
|