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562 lines
24 KiB
562 lines
24 KiB
// 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 <cmath>
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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 <random>
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#include <vector>
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#include <xnnpack.h>
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class ResizeBilinearOperatorTester {
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public:
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inline ResizeBilinearOperatorTester& input_size(size_t input_height, size_t input_width) {
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assert(input_height >= 1);
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assert(input_width >= 1);
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this->input_height_ = input_height;
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this->input_width_ = input_width;
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return *this;
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}
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inline ResizeBilinearOperatorTester& input_height(size_t input_height) {
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assert(input_height >= 1);
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this->input_height_ = input_height;
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return *this;
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}
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inline size_t input_height() const {
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return this->input_height_;
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}
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inline ResizeBilinearOperatorTester& input_width(size_t input_width) {
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assert(input_width >= 1);
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this->input_width_ = input_width;
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return *this;
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}
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inline size_t input_width() const {
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return this->input_width_;
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}
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inline ResizeBilinearOperatorTester& output_size(size_t output_height, size_t output_width) {
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assert(output_height >= 1);
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assert(output_width >= 1);
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this->output_height_ = output_height;
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this->output_width_ = output_width;
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return *this;
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}
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inline ResizeBilinearOperatorTester& output_height(size_t output_height) {
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assert(output_height >= 1);
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this->output_height_ = output_height;
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return *this;
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}
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inline size_t output_height() const {
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return this->output_height_;
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}
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inline ResizeBilinearOperatorTester& output_width(size_t output_width) {
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assert(output_width >= 1);
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this->output_width_ = output_width;
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return *this;
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}
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inline size_t output_width() const {
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return this->output_width_;
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}
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inline float height_scale() const {
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if (align_corners() && output_height() > 1) {
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return float(input_height() - 1) / float(output_height() - 1);
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} else {
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return float(input_height()) / float(output_height());
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}
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}
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inline float width_scale() const {
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if (align_corners() && output_width() > 1) {
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return float(input_width() - 1) / float(output_width() - 1);
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} else {
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return float(input_width()) / float(output_width());
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}
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}
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inline ResizeBilinearOperatorTester& 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 ResizeBilinearOperatorTester& 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 ResizeBilinearOperatorTester& input_pixel_stride(size_t input_pixel_stride) {
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assert(input_pixel_stride != 0);
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this->input_pixel_stride_ = input_pixel_stride;
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return *this;
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}
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inline size_t input_pixel_stride() const {
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if (this->input_pixel_stride_ == 0) {
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return channels();
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} else {
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assert(this->input_pixel_stride_ >= channels());
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return this->input_pixel_stride_;
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}
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}
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inline ResizeBilinearOperatorTester& output_pixel_stride(size_t output_pixel_stride) {
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assert(output_pixel_stride != 0);
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this->output_pixel_stride_ = output_pixel_stride;
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return *this;
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}
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inline size_t output_pixel_stride() const {
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if (this->output_pixel_stride_ == 0) {
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return channels();
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} else {
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assert(this->output_pixel_stride_ >= channels());
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return this->output_pixel_stride_;
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}
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}
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inline ResizeBilinearOperatorTester& next_input_size(uint32_t next_input_height, uint32_t next_input_width) {
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assert(next_input_height >= 1);
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assert(next_input_width >= 1);
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this->next_input_height_ = next_input_height;
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this->next_input_width_ = next_input_width;
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return *this;
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}
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inline ResizeBilinearOperatorTester& next_input_height(uint32_t next_input_height) {
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assert(next_input_height >= 1);
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this->next_input_height_ = next_input_height;
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return *this;
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}
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inline uint32_t next_input_height() const {
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if (this->next_input_height_ == 0) {
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return input_height();
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} else {
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return this->next_input_height_;
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}
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}
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inline ResizeBilinearOperatorTester& next_input_width(uint32_t next_input_width) {
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assert(next_input_width >= 1);
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this->next_input_width_ = next_input_width;
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return *this;
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}
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inline uint32_t next_input_width() const {
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if (this->next_input_width_ == 0) {
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return input_width();
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} else {
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return this->next_input_width_;
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}
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}
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inline ResizeBilinearOperatorTester& next_batch_size(size_t next_batch_size) {
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assert(next_batch_size >= 1);
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this->next_batch_size_ = next_batch_size;
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return *this;
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}
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inline size_t next_batch_size() const {
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if (this->next_batch_size_ == 0) {
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return batch_size();
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} else {
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return this->next_batch_size_;
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}
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}
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inline ResizeBilinearOperatorTester& align_corners(bool align_corners) {
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this->align_corners_ = align_corners;
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return *this;
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}
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inline bool align_corners() const {
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return this->align_corners_;
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}
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inline ResizeBilinearOperatorTester& tf_legacy_mode(bool tf_legacy_mode) {
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this->tf_legacy_mode_ = tf_legacy_mode;
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return *this;
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}
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inline bool tf_legacy_mode() const {
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return this->tf_legacy_mode_;
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}
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inline ResizeBilinearOperatorTester& 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 TestNHWCxF32() const {
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if (align_corners()) {
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ASSERT_FALSE(tf_legacy_mode());
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}
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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<float> input((batch_size() * input_height() * input_width() - 1) * input_pixel_stride() + channels() + XNN_EXTRA_BYTES / sizeof(float));
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std::vector<float> output((batch_size() * output_height() * output_width() - 1) * output_pixel_stride() + channels());
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std::vector<float> output_ref(batch_size() * output_height() * output_width() * 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(output.begin(), output.end(), std::nanf(""));
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// Compute reference results.
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const float offset = (tf_legacy_mode() || align_corners()) ? 0.0f : 0.5f;
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for (size_t batch_index = 0; batch_index < batch_size(); batch_index++) {
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for (size_t output_y = 0; output_y < output_height(); output_y++) {
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const float input_y = (float(output_y) + offset) * height_scale() - offset;
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const int64_t input_y_top = std::max<int64_t>(int64_t(std::floor(input_y)), 0);
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const int64_t input_y_bottom = std::min<int64_t>(int64_t(std::ceil(input_y)), input_height() - 1);
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const float y_alpha = input_y - std::floor(input_y);
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for (size_t output_x = 0; output_x < output_width(); output_x++) {
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const float input_x = (float(output_x) + offset) * width_scale() - offset;
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const int64_t input_x_left = std::max<int64_t>(int64_t(std::floor(input_x)), 0);
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const int64_t input_x_right = std::min<int64_t>(int64_t(std::ceil(input_x)), input_width() - 1);
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const float x_alpha = input_x - std::floor(input_x);
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for (size_t c = 0; c < channels(); c++) {
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output_ref[((batch_index * output_height() + output_y) * output_width() + output_x) * channels() + c] =
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input[((batch_index * input_height() + input_y_top) * input_width() + input_x_left) * input_pixel_stride() + c] * (1.0f - y_alpha) * (1.0f - x_alpha) +
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input[((batch_index * input_height() + input_y_top) * input_width() + input_x_right) * input_pixel_stride() + c] * (1.0f - y_alpha) * x_alpha +
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input[((batch_index * input_height() + input_y_bottom) * input_width() + input_x_left) * input_pixel_stride() + c] * y_alpha * (1.0f - x_alpha) +
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input[((batch_index * input_height() + input_y_bottom) * input_width() + input_x_right) * input_pixel_stride() + c] * y_alpha * x_alpha;
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}
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}
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}
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}
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// Create, setup, run, and destroy Resize Bilinear operator.
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ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
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xnn_operator_t resize_bilinear_op = nullptr;
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ASSERT_EQ(xnn_status_success,
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xnn_create_resize_bilinear2d_nhwc_f32(
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channels(), input_pixel_stride(), output_pixel_stride(),
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(align_corners() ? XNN_FLAG_ALIGN_CORNERS : 0) | (tf_legacy_mode() ? XNN_FLAG_TENSORFLOW_LEGACY_MODE : 0),
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&resize_bilinear_op));
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ASSERT_NE(nullptr, resize_bilinear_op);
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// Smart pointer to automatically delete resize_bilinear_op.
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std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_resize_bilinear_op(resize_bilinear_op, xnn_delete_operator);
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ASSERT_EQ(xnn_status_success,
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xnn_setup_resize_bilinear2d_nhwc_f32(
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resize_bilinear_op,
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batch_size(), input_height(), input_width(),
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output_height(), output_width(),
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input.data(), output.data(),
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nullptr /* thread pool */));
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ASSERT_EQ(xnn_status_success,
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xnn_run_operator(resize_bilinear_op, nullptr /* thread pool */));
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// Verify results.
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for (size_t i = 0; i < batch_size(); i++) {
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for (size_t y = 0; y < output_height(); y++) {
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for (size_t x = 0; x < output_width(); x++) {
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for (size_t c = 0; c < channels(); c++) {
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ASSERT_NEAR(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c],
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output_ref[((i * output_height() + y) * output_width() + x) * channels() + c],
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std::abs(output_ref[((i * output_height() + y) * output_width() + x) * channels() + c]) * 1.0e-5f) <<
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"in batch index " << i << ", pixel (" << y << ", " << x << "), channel " << c;
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}
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}
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}
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}
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}
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}
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void TestNCHWxF32() const {
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if (align_corners()) {
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ASSERT_FALSE(tf_legacy_mode());
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}
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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<float> input((batch_size() * input_height() * input_width() - 1) * input_pixel_stride() + channels() + XNN_EXTRA_BYTES / sizeof(float));
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std::vector<float> output((batch_size() * output_height() * output_width() - 1) * output_pixel_stride() + channels());
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std::vector<float> output_ref(batch_size() * output_height() * output_width() * 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(output.begin(), output.end(), std::nanf(""));
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// Compute reference results.
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const float offset = (tf_legacy_mode() || align_corners()) ? 0.0f : 0.5f;
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const int64_t input_num_pixels = input_height() * input_width();
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const int64_t input_num_elements = input_num_pixels * input_pixel_stride();
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const int64_t output_num_pixels = output_height() * output_width();
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const int64_t output_num_elements = output_num_pixels * channels();
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for (size_t batch_index = 0; batch_index < batch_size(); batch_index++) {
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for (size_t output_y = 0; output_y < output_height(); output_y++) {
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const float input_y = (float(output_y) + offset) * height_scale() - offset;
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const int64_t input_y_top = std::max<int64_t>(int64_t(std::floor(input_y)), 0);
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const int64_t input_y_bottom = std::min<int64_t>(int64_t(std::ceil(input_y)), input_height() - 1);
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const float y_alpha = input_y - std::floor(input_y);
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for (size_t output_x = 0; output_x < output_width(); output_x++) {
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const float input_x = (float(output_x) + offset) * width_scale() - offset;
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const int64_t input_x_left = std::max<int64_t>(int64_t(std::floor(input_x)), 0);
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const int64_t input_x_right = std::min<int64_t>(int64_t(std::ceil(input_x)), input_width() - 1);
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const float x_alpha = input_x - std::floor(input_x);
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for (size_t c = 0; c < channels(); c++) {
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output_ref[batch_index * output_num_elements + c * output_num_pixels + output_y * output_width() + output_x] =
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input[batch_index * input_num_elements + c * input_num_pixels + input_y_top * input_width() + input_x_left] * (1.0f - y_alpha) * (1.0f - x_alpha) +
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input[batch_index * input_num_elements + c * input_num_pixels + input_y_top * input_width() + input_x_right] * (1.0f - y_alpha) * x_alpha +
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input[batch_index * input_num_elements + c * input_num_pixels + input_y_bottom * input_width() + input_x_left] * y_alpha * (1.0f - x_alpha) +
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input[batch_index * input_num_elements + c * input_num_pixels + input_y_bottom * input_width() + input_x_right] * y_alpha * x_alpha;
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}
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}
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}
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}
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// Create, setup, run, and destroy Resize Bilinear operator.
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ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
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xnn_operator_t resize_bilinear_op = nullptr;
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ASSERT_EQ(xnn_status_success,
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xnn_create_resize_bilinear2d_nchw_f32(
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channels(), input_pixel_stride(), output_pixel_stride(),
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(align_corners() ? XNN_FLAG_ALIGN_CORNERS : 0) | (tf_legacy_mode() ? XNN_FLAG_TENSORFLOW_LEGACY_MODE : 0),
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&resize_bilinear_op));
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ASSERT_NE(nullptr, resize_bilinear_op);
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// Smart pointer to automatically delete resize_bilinear_op.
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std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_resize_bilinear_op(resize_bilinear_op, xnn_delete_operator);
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ASSERT_EQ(xnn_status_success,
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xnn_setup_resize_bilinear2d_nchw_f32(
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resize_bilinear_op,
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batch_size(), input_height(), input_width(),
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output_height(), output_width(),
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input.data(), output.data(),
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nullptr /* thread pool */));
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ASSERT_EQ(xnn_status_success,
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xnn_run_operator(resize_bilinear_op, nullptr /* thread pool */));
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// Verify results.
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for (size_t i = 0; i < batch_size(); i++) {
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for (size_t y = 0; y < output_height(); y++) {
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for (size_t x = 0; x < output_width(); x++) {
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for (size_t c = 0; c < channels(); c++) {
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ASSERT_NEAR(output[i * output_num_elements + c * output_num_pixels + y * output_width() + x],
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output_ref[i * output_num_elements + c * output_num_pixels + y * output_width() + x],
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1.0e-6f) <<
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"in batch index " << i << ", pixel (" << y << ", " << x << "), channel " << c;
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}
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}
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}
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}
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}
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}
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// void TestSetupF32() 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<float> input(XNN_EXTRA_BYTES / sizeof(float) + std::max(
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// (batch_size() * input_height() * input_width() - 1) * input_pixel_stride() + channels(),
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// (next_batch_size() * next_input_height() * next_input_width() - 1) * input_pixel_stride() + channels()));
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// std::vector<float> output(std::max(
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// (batch_size() * output_height() * output_width() - 1) * output_pixel_stride() + channels(),
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// (next_batch_size() * next_output_height() * next_output_width() - 1) * output_pixel_stride() + channels()));
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// std::vector<float> output_ref(batch_size() * output_height() * output_width() * channels());
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// std::vector<float> next_output_ref(next_batch_size() * next_output_height() * next_output_width() * 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(output.begin(), output.end(), std::nanf(""));
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// // Compute reference results, without clamping.
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// for (size_t batch_index = 0; batch_index < batch_size(); batch_index++) {
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// for (size_t output_y = 0; output_y < output_height(); output_y++) {
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// for (size_t output_x = 0; output_x < output_width(); output_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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// size_t n = 0;
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// for (size_t py = 0; py < pooling_height(); py++) {
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// const size_t iy = output_y * stride_height() + py - padding_top();
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// for (size_t px = 0; px < pooling_width(); px++) {
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// const size_t input_x = output_x * stride_width() + px - padding_left();
|
|
// if (input_x < input_width() && iy < input_height()) {
|
|
// acc += input[((batch_index * input_height() + iy) * input_width() + input_x) * input_pixel_stride() + c];
|
|
// n += 1;
|
|
// }
|
|
// }
|
|
// }
|
|
// output_ref[((batch_index * output_height() + output_y) * output_width() + output_x) * channels() + c] = acc / float(n);
|
|
// }
|
|
// }
|
|
// }
|
|
// }
|
|
|
|
// // 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_range == 0.0f ?
|
|
// -std::numeric_limits<float>::infinity() :
|
|
// accumulated_min + accumulated_range / 255.0f * float(qmin());
|
|
// const float output_max = accumulated_range == 0.0f ?
|
|
// +std::numeric_limits<float>::infinity() :
|
|
// accumulated_max - accumulated_range / 255.0f * float(255 - qmax());
|
|
|
|
// // Clamp reference results.
|
|
// for (float& value : output_ref) {
|
|
// value = std::max(std::min(value, output_max), output_min);
|
|
// }
|
|
|
|
// // Create, setup, and run Average Pooling operator once.
|
|
// ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
|
|
// xnn_operator_t resize_bilinear_op = nullptr;
|
|
|
|
// ASSERT_EQ(xnn_status_success,
|
|
// xnn_create_average_pooling2d_nhwc_f32(
|
|
// padding_top(), padding_right(), padding_bottom(), padding_left(),
|
|
// pooling_height(), pooling_width(),
|
|
// stride_height(), stride_width(),
|
|
// channels(), input_pixel_stride(), output_pixel_stride(),
|
|
// output_min, output_max,
|
|
// 0, &resize_bilinear_op));
|
|
// ASSERT_NE(nullptr, resize_bilinear_op);
|
|
|
|
// ASSERT_EQ(xnn_status_success,
|
|
// xnn_setup_average_pooling2d_nhwc_f32(
|
|
// resize_bilinear_op,
|
|
// batch_size(), input_height(), input_width(),
|
|
// input.data(), output.data(),
|
|
// nullptr /* thread pool */));
|
|
|
|
// ASSERT_EQ(xnn_status_success,
|
|
// xnn_run_operator(resize_bilinear_op, nullptr /* thread pool */));
|
|
|
|
// // Verify results of the first run.
|
|
// for (size_t batch_index = 0; batch_index < batch_size(); batch_index++) {
|
|
// for (size_t y = 0; y < output_height(); y++) {
|
|
// for (size_t x = 0; x < output_width(); x++) {
|
|
// for (size_t c = 0; c < channels(); c++) {
|
|
// ASSERT_LE(output[((batch_index * output_height() + y) * output_width() + x) * output_pixel_stride() + c], output_max);
|
|
// ASSERT_GE(output[((batch_index * output_height() + y) * output_width() + x) * output_pixel_stride() + c], output_min);
|
|
// ASSERT_NEAR(output[((batch_index * output_height() + y) * output_width() + x) * output_pixel_stride() + c],
|
|
// output_ref[((batch_index * output_height() + y) * output_width() + x) * channels() + c],
|
|
// std::abs(output_ref[((batch_index * output_height() + y) * output_width() + x) * channels() + c]) * 1.0e-6f) <<
|
|
// "in batch index " << batch_index << ", pixel (" << y << ", " << x << "), channel " << c;
|
|
// }
|
|
// }
|
|
// }
|
|
// }
|
|
|
|
// // Re-generate data for the second run.
|
|
// std::generate(input.begin(), input.end(), std::ref(f32rng));
|
|
// std::fill(output.begin(), output.end(), std::nanf(""));
|
|
|
|
// // Compute reference results for the second run.
|
|
// for (size_t batch_index = 0; batch_index < next_batch_size(); batch_index++) {
|
|
// for (size_t output_y = 0; output_y < next_output_height(); output_y++) {
|
|
// for (size_t output_x = 0; output_x < next_output_width(); output_x++) {
|
|
// for (size_t c = 0; c < channels(); c++) {
|
|
// float acc = 0.0f;
|
|
// int32_t n = 0;
|
|
// for (size_t py = 0; py < pooling_height(); py++) {
|
|
// const size_t iy = output_y * stride_height() + py - padding_top();
|
|
// for (size_t px = 0; px < pooling_width(); px++) {
|
|
// const size_t input_x = output_x * stride_width() + px - padding_left();
|
|
// if (input_x < next_input_width() && iy < next_input_height()) {
|
|
// acc += input[((batch_index * next_input_height() + iy) * next_input_width() + input_x) * input_pixel_stride() + c];
|
|
// n += 1;
|
|
// }
|
|
// }
|
|
// }
|
|
// next_output_ref[((batch_index * next_output_height() + output_y) * next_output_width() + output_x) * channels() + c] =
|
|
// std::max(std::min(acc / float(n), output_max), output_min);
|
|
// }
|
|
// }
|
|
// }
|
|
// }
|
|
|
|
// // Setup and run Average Pooling operator the second time, and destroutput_y the operator.
|
|
// ASSERT_EQ(xnn_status_success,
|
|
// xnn_setup_average_pooling2d_nhwc_f32(
|
|
// resize_bilinear_op,
|
|
// next_batch_size(), next_input_height(), next_input_width(),
|
|
// input.data(), output.data(),
|
|
// nullptr /* thread pool */));
|
|
|
|
// ASSERT_EQ(xnn_status_success,
|
|
// xnn_run_operator(resize_bilinear_op, nullptr /* thread pool */));
|
|
|
|
// ASSERT_EQ(xnn_status_success,
|
|
// xnn_delete_operator(resize_bilinear_op));
|
|
// resize_bilinear_op = nullptr;
|
|
|
|
// // Verify results of the second run.
|
|
// for (size_t batch_index = 0; batch_index < next_batch_size(); batch_index++) {
|
|
// for (size_t y = 0; y < next_output_height(); y++) {
|
|
// for (size_t x = 0; x < next_output_width(); x++) {
|
|
// for (size_t c = 0; c < channels(); c++) {
|
|
// ASSERT_LE(output[((batch_index * next_output_height() + y) * next_output_width() + x) * output_pixel_stride() + c], output_max);
|
|
// ASSERT_GE(output[((batch_index * next_output_height() + y) * next_output_width() + x) * output_pixel_stride() + c], output_min);
|
|
// ASSERT_NEAR(output[((batch_index * next_output_height() + y) * next_output_width() + x) * output_pixel_stride() + c],
|
|
// next_output_ref[((batch_index * next_output_height() + y) * next_output_width() + x) * channels() + c],
|
|
// std::abs(next_output_ref[((batch_index * next_output_height() + y) * next_output_width() + x) * channels() + c]) * 1.0e-6f) <<
|
|
// "in batch index " << batch_index << ", pixel (" << y << ", " << x << "), channel " << c;
|
|
// }
|
|
// }
|
|
// }
|
|
// }
|
|
// }
|
|
// }
|
|
|
|
private:
|
|
size_t input_height_{1};
|
|
size_t input_width_{1};
|
|
size_t output_height_{1};
|
|
size_t output_width_{1};
|
|
size_t channels_{1};
|
|
size_t batch_size_{1};
|
|
size_t input_pixel_stride_{0};
|
|
size_t output_pixel_stride_{0};
|
|
size_t next_input_height_{0};
|
|
size_t next_input_width_{0};
|
|
size_t next_batch_size_{0};
|
|
bool align_corners_{false};
|
|
bool tf_legacy_mode_{false};
|
|
size_t iterations_{1};
|
|
};
|