571 lines
24 KiB
C++
571 lines
24 KiB
C++
// 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 <gtest/gtest.h>
|
|
|
|
#include <algorithm>
|
|
#include <cmath>
|
|
#include <cassert>
|
|
#include <cstddef>
|
|
#include <cstdlib>
|
|
#include <functional>
|
|
#include <random>
|
|
#include <vector>
|
|
|
|
#include <xnnpack.h>
|
|
|
|
|
|
class ResizeBilinearOperatorTester {
|
|
public:
|
|
inline ResizeBilinearOperatorTester& input_size(size_t input_height, size_t input_width) {
|
|
assert(input_height >= 1);
|
|
assert(input_width >= 1);
|
|
this->input_height_ = input_height;
|
|
this->input_width_ = input_width;
|
|
return *this;
|
|
}
|
|
|
|
inline ResizeBilinearOperatorTester& input_height(size_t input_height) {
|
|
assert(input_height >= 1);
|
|
this->input_height_ = input_height;
|
|
return *this;
|
|
}
|
|
|
|
inline size_t input_height() const {
|
|
return this->input_height_;
|
|
}
|
|
|
|
inline ResizeBilinearOperatorTester& input_width(size_t input_width) {
|
|
assert(input_width >= 1);
|
|
this->input_width_ = input_width;
|
|
return *this;
|
|
}
|
|
|
|
inline size_t input_width() const {
|
|
return this->input_width_;
|
|
}
|
|
|
|
inline ResizeBilinearOperatorTester& output_size(size_t output_height, size_t output_width) {
|
|
assert(output_height >= 1);
|
|
assert(output_width >= 1);
|
|
this->output_height_ = output_height;
|
|
this->output_width_ = output_width;
|
|
return *this;
|
|
}
|
|
|
|
inline ResizeBilinearOperatorTester& output_height(size_t output_height) {
|
|
assert(output_height >= 1);
|
|
this->output_height_ = output_height;
|
|
return *this;
|
|
}
|
|
|
|
inline size_t output_height() const {
|
|
return this->output_height_;
|
|
}
|
|
|
|
inline ResizeBilinearOperatorTester& output_width(size_t output_width) {
|
|
assert(output_width >= 1);
|
|
this->output_width_ = output_width;
|
|
return *this;
|
|
}
|
|
|
|
inline size_t output_width() const {
|
|
return this->output_width_;
|
|
}
|
|
|
|
inline float height_scale() const {
|
|
if (align_corners() && output_height() > 1) {
|
|
return float(input_height() - 1) / float(output_height() - 1);
|
|
} else {
|
|
return float(input_height()) / float(output_height());
|
|
}
|
|
}
|
|
|
|
inline float width_scale() const {
|
|
if (align_corners() && output_width() > 1) {
|
|
return float(input_width() - 1) / float(output_width() - 1);
|
|
} else {
|
|
return float(input_width()) / float(output_width());
|
|
}
|
|
}
|
|
|
|
inline ResizeBilinearOperatorTester& channels(size_t channels) {
|
|
assert(channels != 0);
|
|
this->channels_ = channels;
|
|
return *this;
|
|
}
|
|
|
|
inline size_t channels() const {
|
|
return this->channels_;
|
|
}
|
|
|
|
inline ResizeBilinearOperatorTester& batch_size(size_t batch_size) {
|
|
assert(batch_size != 0);
|
|
this->batch_size_ = batch_size;
|
|
return *this;
|
|
}
|
|
|
|
inline size_t batch_size() const {
|
|
return this->batch_size_;
|
|
}
|
|
|
|
inline ResizeBilinearOperatorTester& input_pixel_stride(size_t input_pixel_stride) {
|
|
assert(input_pixel_stride != 0);
|
|
this->input_pixel_stride_ = input_pixel_stride;
|
|
return *this;
|
|
}
|
|
|
|
inline size_t input_pixel_stride() const {
|
|
if (this->input_pixel_stride_ == 0) {
|
|
return channels();
|
|
} else {
|
|
assert(this->input_pixel_stride_ >= channels());
|
|
return this->input_pixel_stride_;
|
|
}
|
|
}
|
|
|
|
inline ResizeBilinearOperatorTester& output_pixel_stride(size_t output_pixel_stride) {
|
|
assert(output_pixel_stride != 0);
|
|
this->output_pixel_stride_ = output_pixel_stride;
|
|
return *this;
|
|
}
|
|
|
|
inline size_t output_pixel_stride() const {
|
|
if (this->output_pixel_stride_ == 0) {
|
|
return channels();
|
|
} else {
|
|
assert(this->output_pixel_stride_ >= channels());
|
|
return this->output_pixel_stride_;
|
|
}
|
|
}
|
|
|
|
inline ResizeBilinearOperatorTester& next_input_size(uint32_t next_input_height, uint32_t next_input_width) {
|
|
assert(next_input_height >= 1);
|
|
assert(next_input_width >= 1);
|
|
this->next_input_height_ = next_input_height;
|
|
this->next_input_width_ = next_input_width;
|
|
return *this;
|
|
}
|
|
|
|
inline ResizeBilinearOperatorTester& next_input_height(uint32_t next_input_height) {
|
|
assert(next_input_height >= 1);
|
|
this->next_input_height_ = next_input_height;
|
|
return *this;
|
|
}
|
|
|
|
inline uint32_t next_input_height() const {
|
|
if (this->next_input_height_ == 0) {
|
|
return input_height();
|
|
} else {
|
|
return this->next_input_height_;
|
|
}
|
|
}
|
|
|
|
inline ResizeBilinearOperatorTester& next_input_width(uint32_t next_input_width) {
|
|
assert(next_input_width >= 1);
|
|
this->next_input_width_ = next_input_width;
|
|
return *this;
|
|
}
|
|
|
|
inline uint32_t next_input_width() const {
|
|
if (this->next_input_width_ == 0) {
|
|
return input_width();
|
|
} else {
|
|
return this->next_input_width_;
|
|
}
|
|
}
|
|
|
|
inline ResizeBilinearOperatorTester& next_batch_size(size_t next_batch_size) {
|
|
assert(next_batch_size >= 1);
|
|
this->next_batch_size_ = next_batch_size;
|
|
return *this;
|
|
}
|
|
|
|
inline size_t next_batch_size() const {
|
|
if (this->next_batch_size_ == 0) {
|
|
return batch_size();
|
|
} else {
|
|
return this->next_batch_size_;
|
|
}
|
|
}
|
|
|
|
inline ResizeBilinearOperatorTester& align_corners(bool align_corners) {
|
|
this->align_corners_ = align_corners;
|
|
return *this;
|
|
}
|
|
|
|
inline bool align_corners() const {
|
|
return this->align_corners_;
|
|
}
|
|
|
|
inline ResizeBilinearOperatorTester& tf_legacy_mode(bool tf_legacy_mode) {
|
|
this->tf_legacy_mode_ = tf_legacy_mode;
|
|
return *this;
|
|
}
|
|
|
|
inline bool tf_legacy_mode() const {
|
|
return this->tf_legacy_mode_;
|
|
}
|
|
|
|
inline ResizeBilinearOperatorTester& iterations(size_t iterations) {
|
|
this->iterations_ = iterations;
|
|
return *this;
|
|
}
|
|
|
|
inline size_t iterations() const {
|
|
return this->iterations_;
|
|
}
|
|
|
|
void TestNHWCxF32() const {
|
|
if (align_corners()) {
|
|
ASSERT_FALSE(tf_legacy_mode());
|
|
}
|
|
|
|
std::random_device random_device;
|
|
auto rng = std::mt19937(random_device());
|
|
auto f32rng = std::bind(std::uniform_real_distribution<float>(), rng);
|
|
|
|
std::vector<float> input((batch_size() * input_height() * input_width() - 1) * input_pixel_stride() + channels() + XNN_EXTRA_BYTES / sizeof(float));
|
|
std::vector<float> output((batch_size() * output_height() * output_width() - 1) * output_pixel_stride() + channels());
|
|
std::vector<float> output_ref(batch_size() * output_height() * output_width() * channels());
|
|
for (size_t iteration = 0; iteration < iterations(); iteration++) {
|
|
std::generate(input.begin(), input.end(), std::ref(f32rng));
|
|
std::fill(output.begin(), output.end(), std::nanf(""));
|
|
|
|
// Compute reference results.
|
|
const float offset = (tf_legacy_mode() || align_corners()) ? 0.0f : 0.5f;
|
|
for (size_t batch_index = 0; batch_index < batch_size(); batch_index++) {
|
|
for (size_t output_y = 0; output_y < output_height(); output_y++) {
|
|
const float input_y = (float(output_y) + offset) * height_scale() - offset;
|
|
const int64_t input_y_top = std::max<int64_t>(int64_t(std::floor(input_y)), 0);
|
|
const int64_t input_y_bottom = std::min<int64_t>(int64_t(std::ceil(input_y)), input_height() - 1);
|
|
const float y_alpha = input_y - std::floor(input_y);
|
|
for (size_t output_x = 0; output_x < output_width(); output_x++) {
|
|
const float input_x = (float(output_x) + offset) * width_scale() - offset;
|
|
const int64_t input_x_left = std::max<int64_t>(int64_t(std::floor(input_x)), 0);
|
|
const int64_t input_x_right = std::min<int64_t>(int64_t(std::ceil(input_x)), input_width() - 1);
|
|
const float x_alpha = input_x - std::floor(input_x);
|
|
for (size_t c = 0; c < channels(); c++) {
|
|
output_ref[((batch_index * output_height() + output_y) * output_width() + output_x) * channels() + c] =
|
|
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) +
|
|
input[((batch_index * input_height() + input_y_top) * input_width() + input_x_right) * input_pixel_stride() + c] * (1.0f - y_alpha) * x_alpha +
|
|
input[((batch_index * input_height() + input_y_bottom) * input_width() + input_x_left) * input_pixel_stride() + c] * y_alpha * (1.0f - x_alpha) +
|
|
input[((batch_index * input_height() + input_y_bottom) * input_width() + input_x_right) * input_pixel_stride() + c] * y_alpha * x_alpha;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// Create, setup, run, and destroy Resize Bilinear operator.
|
|
ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
|
|
xnn_operator_t resize_bilinear_op = nullptr;
|
|
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_create_resize_bilinear2d_nhwc_f32(
|
|
channels(), input_pixel_stride(), output_pixel_stride(),
|
|
(align_corners() ? XNN_FLAG_ALIGN_CORNERS : 0) | (tf_legacy_mode() ? XNN_FLAG_TENSORFLOW_LEGACY_MODE : 0),
|
|
&resize_bilinear_op));
|
|
ASSERT_NE(nullptr, resize_bilinear_op);
|
|
|
|
// Smart pointer to automatically delete resize_bilinear_op.
|
|
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_resize_bilinear_op(resize_bilinear_op, xnn_delete_operator);
|
|
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_setup_resize_bilinear2d_nhwc_f32(
|
|
resize_bilinear_op,
|
|
batch_size(), input_height(), input_width(),
|
|
output_height(), output_width(),
|
|
input.data(), output.data(),
|
|
nullptr /* thread pool */));
|
|
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_run_operator(resize_bilinear_op, nullptr /* thread pool */));
|
|
|
|
// Verify results.
|
|
for (size_t i = 0; i < batch_size(); i++) {
|
|
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_NEAR(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c],
|
|
output_ref[((i * output_height() + y) * output_width() + x) * channels() + c],
|
|
std::abs(output_ref[((i * output_height() + y) * output_width() + x) * channels() + c]) * 1.0e-5f) <<
|
|
"in batch index " << i << ", pixel (" << y << ", " << x << "), channel " << c;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
void TestNHWCxS8() const {
|
|
if (align_corners()) {
|
|
ASSERT_FALSE(tf_legacy_mode());
|
|
}
|
|
|
|
std::random_device random_device;
|
|
auto rng = std::mt19937(random_device());
|
|
auto i8rng = std::bind(std::uniform_int_distribution<int32_t>(
|
|
std::numeric_limits<int8_t>::min(), std::numeric_limits<int8_t>::max()), std::ref(rng));
|
|
|
|
std::vector<int8_t> input((batch_size() * input_height() * input_width() - 1) * input_pixel_stride() + channels() + XNN_EXTRA_BYTES / sizeof(int8_t));
|
|
std::vector<int8_t> output((batch_size() * output_height() * output_width() - 1) * output_pixel_stride() + channels());
|
|
std::vector<float> output_ref(batch_size() * output_height() * output_width() * channels());
|
|
for (size_t iteration = 0; iteration < iterations(); iteration++) {
|
|
std::generate(input.begin(), input.end(), std::ref(i8rng));
|
|
std::fill(output.begin(), output.end(), INT8_C(0xA5));
|
|
|
|
// Compute reference results.
|
|
const float offset = (tf_legacy_mode() || align_corners()) ? 0.0f : 0.5f;
|
|
for (size_t batch_index = 0; batch_index < batch_size(); batch_index++) {
|
|
for (size_t output_y = 0; output_y < output_height(); output_y++) {
|
|
const float input_y = (float(output_y) + offset) * height_scale() - offset;
|
|
const int64_t input_y_top = std::max<int64_t>(int64_t(std::floor(input_y)), 0);
|
|
const int64_t input_y_bottom = std::min<int64_t>(int64_t(std::ceil(input_y)), input_height() - 1);
|
|
const float y_alpha = input_y - std::floor(input_y);
|
|
for (size_t output_x = 0; output_x < output_width(); output_x++) {
|
|
const float input_x = (float(output_x) + offset) * width_scale() - offset;
|
|
const int64_t input_x_left = std::max<int64_t>(int64_t(std::floor(input_x)), 0);
|
|
const int64_t input_x_right = std::min<int64_t>(int64_t(std::ceil(input_x)), input_width() - 1);
|
|
const float x_alpha = input_x - std::floor(input_x);
|
|
for (size_t c = 0; c < channels(); c++) {
|
|
output_ref[((batch_index * output_height() + output_y) * output_width() + output_x) * channels() + c] =
|
|
float(int32_t(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) +
|
|
float(int32_t(input[((batch_index * input_height() + input_y_top) * input_width() + input_x_right) * input_pixel_stride() + c])) * (1.0f - y_alpha) * x_alpha +
|
|
float(int32_t(input[((batch_index * input_height() + input_y_bottom) * input_width() + input_x_left) * input_pixel_stride() + c])) * y_alpha * (1.0f - x_alpha) +
|
|
float(int32_t(input[((batch_index * input_height() + input_y_bottom) * input_width() + input_x_right) * input_pixel_stride() + c])) * y_alpha * x_alpha;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// Create, setup, run, and destroy Resize Bilinear operator.
|
|
ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
|
|
xnn_operator_t resize_bilinear_op = nullptr;
|
|
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_create_resize_bilinear2d_nhwc_s8(
|
|
channels(), input_pixel_stride(), output_pixel_stride(),
|
|
(align_corners() ? XNN_FLAG_ALIGN_CORNERS : 0) | (tf_legacy_mode() ? XNN_FLAG_TENSORFLOW_LEGACY_MODE : 0),
|
|
&resize_bilinear_op));
|
|
ASSERT_NE(nullptr, resize_bilinear_op);
|
|
|
|
// Smart pointer to automatically delete resize_bilinear_op.
|
|
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_resize_bilinear_op(resize_bilinear_op, xnn_delete_operator);
|
|
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_setup_resize_bilinear2d_nhwc_s8(
|
|
resize_bilinear_op,
|
|
batch_size(), input_height(), input_width(),
|
|
output_height(), output_width(),
|
|
input.data(), output.data(),
|
|
nullptr /* thread pool */));
|
|
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_run_operator(resize_bilinear_op, nullptr /* thread pool */));
|
|
|
|
// Verify results.
|
|
for (size_t i = 0; i < batch_size(); i++) {
|
|
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_NEAR(
|
|
float(int32_t(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c])),
|
|
output_ref[((i * output_height() + y) * output_width() + x) * channels() + c],
|
|
0.6f) <<
|
|
"in batch index " << i << ", pixel (" << y << ", " << x << "), channel " << c;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
void TestNHWCxU8() const {
|
|
if (align_corners()) {
|
|
ASSERT_FALSE(tf_legacy_mode());
|
|
}
|
|
|
|
std::random_device random_device;
|
|
auto rng = std::mt19937(random_device());
|
|
auto u8rng = std::bind(
|
|
std::uniform_int_distribution<uint32_t>(0, std::numeric_limits<uint8_t>::max()), std::ref(rng));
|
|
|
|
std::vector<uint8_t> input((batch_size() * input_height() * input_width() - 1) * input_pixel_stride() + channels() + XNN_EXTRA_BYTES / sizeof(uint8_t));
|
|
std::vector<uint8_t> output((batch_size() * output_height() * output_width() - 1) * output_pixel_stride() + channels());
|
|
std::vector<float> output_ref(batch_size() * output_height() * output_width() * channels());
|
|
for (size_t iteration = 0; iteration < iterations(); iteration++) {
|
|
std::generate(input.begin(), input.end(), std::ref(u8rng));
|
|
std::fill(output.begin(), output.end(), UINT8_C(0xA5));
|
|
|
|
// Compute reference results.
|
|
const float offset = (tf_legacy_mode() || align_corners()) ? 0.0f : 0.5f;
|
|
for (size_t batch_index = 0; batch_index < batch_size(); batch_index++) {
|
|
for (size_t output_y = 0; output_y < output_height(); output_y++) {
|
|
const float input_y = (float(output_y) + offset) * height_scale() - offset;
|
|
const int64_t input_y_top = std::max<int64_t>(int64_t(std::floor(input_y)), 0);
|
|
const int64_t input_y_bottom = std::min<int64_t>(int64_t(std::ceil(input_y)), input_height() - 1);
|
|
const float y_alpha = input_y - std::floor(input_y);
|
|
for (size_t output_x = 0; output_x < output_width(); output_x++) {
|
|
const float input_x = (float(output_x) + offset) * width_scale() - offset;
|
|
const int64_t input_x_left = std::max<int64_t>(int64_t(std::floor(input_x)), 0);
|
|
const int64_t input_x_right = std::min<int64_t>(int64_t(std::ceil(input_x)), input_width() - 1);
|
|
const float x_alpha = input_x - std::floor(input_x);
|
|
for (size_t c = 0; c < channels(); c++) {
|
|
output_ref[((batch_index * output_height() + output_y) * output_width() + output_x) * channels() + c] =
|
|
float(int32_t(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) +
|
|
float(int32_t(input[((batch_index * input_height() + input_y_top) * input_width() + input_x_right) * input_pixel_stride() + c])) * (1.0f - y_alpha) * x_alpha +
|
|
float(int32_t(input[((batch_index * input_height() + input_y_bottom) * input_width() + input_x_left) * input_pixel_stride() + c])) * y_alpha * (1.0f - x_alpha) +
|
|
float(int32_t(input[((batch_index * input_height() + input_y_bottom) * input_width() + input_x_right) * input_pixel_stride() + c])) * y_alpha * x_alpha;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// Create, setup, run, and destroy Resize Bilinear operator.
|
|
ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
|
|
xnn_operator_t resize_bilinear_op = nullptr;
|
|
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_create_resize_bilinear2d_nhwc_u8(
|
|
channels(), input_pixel_stride(), output_pixel_stride(),
|
|
(align_corners() ? XNN_FLAG_ALIGN_CORNERS : 0) | (tf_legacy_mode() ? XNN_FLAG_TENSORFLOW_LEGACY_MODE : 0),
|
|
&resize_bilinear_op));
|
|
ASSERT_NE(nullptr, resize_bilinear_op);
|
|
|
|
// Smart pointer to automatically delete resize_bilinear_op.
|
|
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_resize_bilinear_op(resize_bilinear_op, xnn_delete_operator);
|
|
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_setup_resize_bilinear2d_nhwc_u8(
|
|
resize_bilinear_op,
|
|
batch_size(), input_height(), input_width(),
|
|
output_height(), output_width(),
|
|
input.data(), output.data(),
|
|
nullptr /* thread pool */));
|
|
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_run_operator(resize_bilinear_op, nullptr /* thread pool */));
|
|
|
|
// Verify results.
|
|
for (size_t i = 0; i < batch_size(); i++) {
|
|
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_NEAR(
|
|
float(int32_t(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c])),
|
|
output_ref[((i * output_height() + y) * output_width() + x) * channels() + c],
|
|
0.6f) <<
|
|
"in batch index " << i << ", pixel (" << y << ", " << x << "), channel " << c;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
void TestNCHWxF32() const {
|
|
if (align_corners()) {
|
|
ASSERT_FALSE(tf_legacy_mode());
|
|
}
|
|
|
|
std::random_device random_device;
|
|
auto rng = std::mt19937(random_device());
|
|
auto f32rng = std::bind(std::uniform_real_distribution<float>(), rng);
|
|
|
|
std::vector<float> input((batch_size() * input_height() * input_width() - 1) * input_pixel_stride() + channels() + XNN_EXTRA_BYTES / sizeof(float));
|
|
std::vector<float> output((batch_size() * output_height() * output_width() - 1) * output_pixel_stride() + channels());
|
|
std::vector<float> output_ref(batch_size() * output_height() * output_width() * channels());
|
|
for (size_t iteration = 0; iteration < iterations(); iteration++) {
|
|
std::generate(input.begin(), input.end(), std::ref(f32rng));
|
|
std::fill(output.begin(), output.end(), std::nanf(""));
|
|
|
|
// Compute reference results.
|
|
const float offset = (tf_legacy_mode() || align_corners()) ? 0.0f : 0.5f;
|
|
const int64_t input_num_pixels = input_height() * input_width();
|
|
const int64_t input_num_elements = input_num_pixels * input_pixel_stride();
|
|
const int64_t output_num_pixels = output_height() * output_width();
|
|
const int64_t output_num_elements = output_num_pixels * channels();
|
|
for (size_t batch_index = 0; batch_index < batch_size(); batch_index++) {
|
|
for (size_t output_y = 0; output_y < output_height(); output_y++) {
|
|
const float input_y = (float(output_y) + offset) * height_scale() - offset;
|
|
const int64_t input_y_top = std::max<int64_t>(int64_t(std::floor(input_y)), 0);
|
|
const int64_t input_y_bottom = std::min<int64_t>(int64_t(std::ceil(input_y)), input_height() - 1);
|
|
const float y_alpha = input_y - std::floor(input_y);
|
|
for (size_t output_x = 0; output_x < output_width(); output_x++) {
|
|
const float input_x = (float(output_x) + offset) * width_scale() - offset;
|
|
const int64_t input_x_left = std::max<int64_t>(int64_t(std::floor(input_x)), 0);
|
|
const int64_t input_x_right = std::min<int64_t>(int64_t(std::ceil(input_x)), input_width() - 1);
|
|
const float x_alpha = input_x - std::floor(input_x);
|
|
for (size_t c = 0; c < channels(); c++) {
|
|
output_ref[batch_index * output_num_elements + c * output_num_pixels + output_y * output_width() + output_x] =
|
|
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) +
|
|
input[batch_index * input_num_elements + c * input_num_pixels + input_y_top * input_width() + input_x_right] * (1.0f - y_alpha) * x_alpha +
|
|
input[batch_index * input_num_elements + c * input_num_pixels + input_y_bottom * input_width() + input_x_left] * y_alpha * (1.0f - x_alpha) +
|
|
input[batch_index * input_num_elements + c * input_num_pixels + input_y_bottom * input_width() + input_x_right] * y_alpha * x_alpha;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// Create, setup, run, and destroy Resize Bilinear operator.
|
|
ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
|
|
xnn_operator_t resize_bilinear_op = nullptr;
|
|
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_create_resize_bilinear2d_nchw_f32(
|
|
channels(), input_pixel_stride(), output_pixel_stride(),
|
|
(align_corners() ? XNN_FLAG_ALIGN_CORNERS : 0) | (tf_legacy_mode() ? XNN_FLAG_TENSORFLOW_LEGACY_MODE : 0),
|
|
&resize_bilinear_op));
|
|
ASSERT_NE(nullptr, resize_bilinear_op);
|
|
|
|
// Smart pointer to automatically delete resize_bilinear_op.
|
|
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_resize_bilinear_op(resize_bilinear_op, xnn_delete_operator);
|
|
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_setup_resize_bilinear2d_nchw_f32(
|
|
resize_bilinear_op,
|
|
batch_size(), input_height(), input_width(),
|
|
output_height(), output_width(),
|
|
input.data(), output.data(),
|
|
nullptr /* thread pool */));
|
|
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_run_operator(resize_bilinear_op, nullptr /* thread pool */));
|
|
|
|
// Verify results.
|
|
for (size_t i = 0; i < batch_size(); i++) {
|
|
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_NEAR(output[i * output_num_elements + c * output_num_pixels + y * output_width() + x],
|
|
output_ref[i * output_num_elements + c * output_num_pixels + y * output_width() + x],
|
|
1.0e-6f) <<
|
|
"in batch index " << i << ", 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};
|
|
};
|