blob: 168a0bb04c24befa710a7dc5dbce91c0a43016f9 [file] [log] [blame]
// 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 <cassert>
#include <cstddef>
#include <cstdlib>
#include <functional>
#include <limits>
#include <random>
#include <vector>
#include <xnnpack.h>
class ArgmaxPoolingOperatorTester {
public:
inline ArgmaxPoolingOperatorTester& padding_tf_same(bool padding_same) {
if (padding_same) {
assert(padding_top() == 0);
assert(padding_left() == 0);
assert(padding_bottom() == 0);
assert(padding_right() == 0);
}
this->padding_tf_same_ = padding_same;
return *this;
}
inline bool padding_tf_same() const {
return this->padding_tf_same_;
}
inline ArgmaxPoolingOperatorTester& padding(uint32_t padding) {
assert(!padding_tf_same());
this->padding_top_ = padding;
this->padding_right_ = padding;
this->padding_bottom_ = padding;
this->padding_left_ = padding;
return *this;
}
inline ArgmaxPoolingOperatorTester& padding(uint32_t padding_height, uint32_t padding_width) {
assert(!padding_tf_same());
this->padding_top_ = padding_height;
this->padding_right_ = padding_width;
this->padding_bottom_ = padding_height;
this->padding_left_ = padding_width;
return *this;
}
inline ArgmaxPoolingOperatorTester& padding_height(uint32_t padding_height) {
assert(!padding_tf_same());
this->padding_top_ = padding_height;
this->padding_bottom_ = padding_height;
return *this;
}
inline ArgmaxPoolingOperatorTester& padding_width(uint32_t padding_width) {
assert(!padding_tf_same());
this->padding_right_ = padding_width;
this->padding_left_ = padding_width;
return *this;
}
inline ArgmaxPoolingOperatorTester& padding_top(uint32_t padding_top) {
assert(!padding_tf_same());
this->padding_top_ = padding_top;
return *this;
}
inline uint32_t padding_top() const {
if (padding_tf_same()) {
const uint32_t total_padding_height = output_height() * pooling_height() - input_height();
return total_padding_height / 2;
} else {
return this->padding_top_;
}
}
inline ArgmaxPoolingOperatorTester& padding_left(uint32_t padding_left) {
assert(!padding_tf_same());
this->padding_left_ = padding_left;
return *this;
}
inline uint32_t padding_left() const {
if (padding_tf_same()) {
const uint32_t total_padding_width = output_width() * pooling_width() - input_width();
return total_padding_width / 2;
} else {
return this->padding_left_;
}
}
inline ArgmaxPoolingOperatorTester& padding_bottom(uint32_t padding_bottom) {
assert(!padding_tf_same());
this->padding_bottom_ = padding_bottom;
return *this;
}
inline uint32_t padding_bottom() const {
if (padding_tf_same()) {
const uint32_t total_padding_height = output_height() * pooling_height() - input_height();
return total_padding_height - total_padding_height / 2;
} else {
return this->padding_bottom_;
}
}
inline ArgmaxPoolingOperatorTester& padding_right(uint32_t padding_right) {
assert(!padding_tf_same());
this->padding_right_ = padding_right;
return *this;
}
inline uint32_t padding_right() const {
if (padding_tf_same()) {
const uint32_t total_padding_width = output_width() * pooling_width() - input_width();
return total_padding_width - total_padding_width / 2;
} else {
return this->padding_right_;
}
}
inline ArgmaxPoolingOperatorTester& 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 ArgmaxPoolingOperatorTester& 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 ArgmaxPoolingOperatorTester& 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 ArgmaxPoolingOperatorTester& channels(size_t channels) {
assert(channels != 0);
this->channels_ = channels;
return *this;
}
inline size_t channels() const {
return this->channels_;
}
inline ArgmaxPoolingOperatorTester& 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 ArgmaxPoolingOperatorTester& pooling_size(uint32_t pooling_size) {
assert(pooling_size >= 1);
this->pooling_height_ = pooling_size;
this->pooling_width_ = pooling_size;
return *this;
}
inline ArgmaxPoolingOperatorTester& pooling_size(uint32_t pooling_height, uint32_t pooling_width) {
assert(pooling_height >= 1);
assert(pooling_width >= 1);
this->pooling_height_ = pooling_height;
this->pooling_width_ = pooling_width;
return *this;
}
inline ArgmaxPoolingOperatorTester& pooling_height(uint32_t pooling_height) {
assert(pooling_height >= 1);
this->pooling_height_ = pooling_height;
return *this;
}
inline uint32_t pooling_height() const {
return this->pooling_height_;
}
inline ArgmaxPoolingOperatorTester& pooling_width(uint32_t pooling_width) {
assert(pooling_width >= 1);
this->pooling_width_ = pooling_width;
return *this;
}
inline uint32_t pooling_width() const {
return this->pooling_width_;
}
inline size_t output_height() const {
if (padding_tf_same()) {
return (input_height() + pooling_height() - 1) / pooling_height();
} else {
const size_t padded_input_height = padding_top() + input_height() + padding_bottom();
return padded_input_height / pooling_height();
}
}
inline size_t output_width() const {
if (padding_tf_same()) {
return (input_width() + pooling_width() - 1) / pooling_width();
} else {
const size_t padded_input_width = padding_left() + input_width() + padding_right();
return padded_input_width / pooling_width();
}
}
inline ArgmaxPoolingOperatorTester& 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 ArgmaxPoolingOperatorTester& 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 ArgmaxPoolingOperatorTester& 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 ArgmaxPoolingOperatorTester& 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 ArgmaxPoolingOperatorTester& 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 size_t next_output_height() const {
const size_t padded_next_input_height = padding_top() + next_input_height() + padding_bottom();
return padded_next_input_height / pooling_height();
}
inline size_t next_output_width() const {
const size_t padded_next_input_width = padding_left() + next_input_width() + padding_right();
return padded_next_input_width / pooling_width();
}
inline ArgmaxPoolingOperatorTester& 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 ArgmaxPoolingOperatorTester& iterations(size_t iterations) {
this->iterations_ = iterations;
return *this;
}
inline size_t iterations() const {
return this->iterations_;
}
void TestF32() const {
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto f32rng = std::bind(std::uniform_real_distribution<float>(0.0f, 1.0f), 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());
std::vector<uint32_t> index(batch_size() * output_height() * output_width() * channels());
std::vector<uint32_t> index_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(), nanf(""));
// Compute reference results, without clamping.
for (size_t i = 0; i < batch_size(); i++) {
for (size_t oy = 0; oy < output_height(); oy++) {
for (size_t ox = 0; ox < output_width(); ox++) {
for (size_t c = 0; c < channels(); c++) {
const size_t iy_top_left = std::max<size_t>(oy * pooling_height(), padding_top()) - padding_top();
const size_t ix_top_left = std::max<size_t>(ox * pooling_width(), padding_left()) - padding_left();
float max_value =
input[((i * input_height() + iy_top_left) * input_width() + ix_top_left) * input_pixel_stride() + c];
uint32_t max_index = 0;
for (size_t py = 0; py < pooling_height(); py++) {
const size_t iy = oy * pooling_height() + py - padding_top();
for (size_t px = 0; px < pooling_width(); px++) {
const size_t ix = ox * pooling_width() + px - padding_left();
if (ix < input_width() && iy < input_height()) {
const float value = input[((i * input_height() + iy) * input_width() + ix) * input_pixel_stride() + c];
if (value > max_value) {
max_value = value;
max_index = uint32_t(px * pooling_height() + py);
}
}
}
}
output_ref[((i * output_height() + oy) * output_width() + ox) * channels() + c] = max_value;
index_ref[((i * output_height() + oy) * output_width() + ox) * channels() + c] = max_index;
}
}
}
}
// Create, setup, run, and destroy Argmax Pooling operator.
ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
xnn_operator_t argmax_pooling_op = nullptr;
ASSERT_EQ(xnn_status_success,
xnn_create_argmax_pooling2d_nhwc_f32(
padding_tf_same() ? 0 : padding_top(), padding_tf_same() ? 0 : padding_right(),
padding_tf_same() ? 0 : padding_bottom(), padding_tf_same() ? 0 : padding_left(),
pooling_height(), pooling_width(),
channels(), input_pixel_stride(), output_pixel_stride(),
padding_tf_same() ? XNN_FLAG_TENSORFLOW_SAME_PADDING : 0,
&argmax_pooling_op));
ASSERT_NE(nullptr, argmax_pooling_op);
// Smart pointer to automatically delete argmax_pooling_op.
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_argmax_pooling_op(argmax_pooling_op, xnn_delete_operator);
ASSERT_EQ(xnn_status_success,
xnn_setup_argmax_pooling2d_nhwc_f32(
argmax_pooling_op,
batch_size(), input_height(), input_width(),
input.data(), output.data(), index.data(),
nullptr /* thread pool */));
ASSERT_EQ(xnn_status_success,
xnn_run_operator(argmax_pooling_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_EQ(output_ref[((i * output_height() + y) * output_width() + x) * channels() + c],
output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c]) <<
"in batch index " << i << ", pixel (" << y << ", " << x << "), channel " << c;
ASSERT_EQ(index_ref[((i * output_height() + y) * output_width() + x) * channels() + c],
index[((i * output_height() + y) * output_width() + x) * channels() + c]) <<
"in batch index " << i << ", pixel (" << y << ", " << x << "), channel " << c;
}
}
}
}
}
}
void TestSetupF32() const {
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto f32rng = std::bind(std::uniform_real_distribution<float>(0.0f, 1.0f), rng);
std::vector<float> input(XNN_EXTRA_BYTES / sizeof(float) + std::max(
(batch_size() * input_height() * input_width() - 1) * input_pixel_stride() + channels(),
(next_batch_size() * next_input_height() * next_input_width() - 1) * input_pixel_stride() + channels()));
std::vector<float> output(std::max(
(batch_size() * output_height() * output_width() - 1) * output_pixel_stride() + channels(),
(next_batch_size() * next_output_height() * next_output_width() - 1) * output_pixel_stride() + channels()));
std::vector<uint32_t> index(std::max(
batch_size() * output_height() * output_width() * channels(),
next_batch_size() * next_output_height() * next_output_width() * channels()));
std::vector<float> output_ref(batch_size() * output_height() * output_width() * channels());
std::vector<float> next_output_ref(next_batch_size() * next_output_height() * next_output_width() * channels());
std::vector<uint32_t> index_ref(batch_size() * output_height() * output_width() * channels());
std::vector<uint32_t> next_index_ref(next_batch_size() * next_output_height() * next_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(), nanf(""));
// Compute reference results, without clamping.
for (size_t i = 0; i < batch_size(); i++) {
for (size_t oy = 0; oy < output_height(); oy++) {
for (size_t ox = 0; ox < output_width(); ox++) {
for (size_t c = 0; c < channels(); c++) {
const size_t iy_top_left = std::max<size_t>(oy * pooling_height(), padding_top()) - padding_top();
const size_t ix_top_left = std::max<size_t>(ox * pooling_width(), padding_left()) - padding_left();
float max_value =
input[((i * input_height() + iy_top_left) * input_width() + ix_top_left) * input_pixel_stride() + c];
uint32_t max_index = 0;
for (size_t py = 0; py < pooling_height(); py++) {
const size_t iy = oy * pooling_height() + py - padding_top();
for (size_t px = 0; px < pooling_width(); px++) {
const size_t ix = ox * pooling_width() + px - padding_left();
if (ix < input_width() && iy < input_height()) {
const float value = input[((i * input_height() + iy) * input_width() + ix) * input_pixel_stride() + c];
if (value > max_value) {
max_value = value;
max_index = uint32_t(px * pooling_height() + py);
}
}
}
}
output_ref[((i * output_height() + oy) * output_width() + ox) * channels() + c] = max_value;
index_ref[((i * output_height() + oy) * output_width() + ox) * channels() + c] = max_index;
}
}
}
}
// Create, setup, and run Argmax Pooling operator once.
ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
xnn_operator_t argmax_pooling_op = nullptr;
ASSERT_EQ(xnn_status_success,
xnn_create_argmax_pooling2d_nhwc_f32(
padding_top(), padding_right(), padding_bottom(), padding_left(),
pooling_height(), pooling_width(),
channels(), input_pixel_stride(), output_pixel_stride(),
0, &argmax_pooling_op));
ASSERT_NE(nullptr, argmax_pooling_op);
ASSERT_EQ(xnn_status_success,
xnn_setup_argmax_pooling2d_nhwc_f32(
argmax_pooling_op,
batch_size(), input_height(), input_width(),
input.data(), output.data(), index.data(),
nullptr /* thread pool */));
ASSERT_EQ(xnn_status_success,
xnn_run_operator(argmax_pooling_op, nullptr /* thread pool */));
// Verify results of the first run.
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_EQ(
output_ref[((i * output_height() + y) * output_width() + x) * channels() + c],
output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c])
<< "in batch index " << i << ", pixel (" << y << ", " << x << "), channel " << c;
ASSERT_EQ(
index_ref[((i * output_height() + y) * output_width() + x) * channels() + c],
index[((i * output_height() + y) * output_width() + x) * channels() + c])
<< "in batch index " << i << ", 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(), 0xA5);
// Compute reference results for the second run, including clamping.
for (size_t i = 0; i < next_batch_size(); i++) {
for (size_t oy = 0; oy < next_output_height(); oy++) {
for (size_t ox = 0; ox < next_output_width(); ox++) {
for (size_t c = 0; c < channels(); c++) {
const size_t iy_top_left = std::max<size_t>(oy * pooling_height(), padding_top()) - padding_top();
const size_t ix_top_left = std::max<size_t>(ox * pooling_width(), padding_left()) - padding_left();
float max_value =
input[((i * next_input_height() + iy_top_left) * next_input_width() + ix_top_left) * input_pixel_stride() + c];
uint32_t max_index = 0;
for (size_t py = 0; py < pooling_height(); py++) {
const size_t iy = oy * pooling_height() + py - padding_top();
for (size_t px = 0; px < pooling_width(); px++) {
const size_t ix = ox * pooling_width() + px - padding_left();
if (ix < next_input_width() && iy < next_input_height()) {
const float value = input[((i * next_input_height() + iy) * next_input_width() + ix) * input_pixel_stride() + c];
if (value > max_value) {
max_value = value;
max_index = uint32_t(px * pooling_height() + py);
}
}
}
}
next_output_ref[((i * next_output_height() + oy) * next_output_width() + ox) * channels() + c] = max_value;
next_index_ref[((i * next_output_height() + oy) * next_output_width() + ox) * channels() + c] = max_index;
}
}
}
}
// Setup and run Argmax Pooling operator the second time, and destroy the operator.
ASSERT_EQ(xnn_status_success,
xnn_setup_argmax_pooling2d_nhwc_f32(
argmax_pooling_op,
next_batch_size(), next_input_height(), next_input_width(),
input.data(), output.data(), index.data(),
nullptr /* thread pool */));
ASSERT_EQ(xnn_status_success,
xnn_run_operator(argmax_pooling_op, nullptr /* thread pool */));
ASSERT_EQ(xnn_status_success,
xnn_delete_operator(argmax_pooling_op));
argmax_pooling_op = nullptr;
// Verify results of the second run.
for (size_t i = 0; i < next_batch_size(); i++) {
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_EQ(
next_output_ref[((i * next_output_height() + y) * next_output_width() + x) * channels() + c],
output[((i * next_output_height() + y) * next_output_width() + x) * output_pixel_stride() + c])
<< "in batch index " << i << ", pixel (" << y << ", " << x << "), channel " << c;
ASSERT_EQ(
next_index_ref[((i * next_output_height() + y) * next_output_width() + x) * channels() + c],
index[((i * next_output_height() + y) * next_output_width() + x) * output_pixel_stride() + c])
<< "in batch index " << i << ", pixel (" << y << ", " << x << "), channel " << c;
}
}
}
}
}
}
private:
uint32_t padding_top_{0};
uint32_t padding_right_{0};
uint32_t padding_bottom_{0};
uint32_t padding_left_{0};
bool padding_tf_same_{false};
size_t input_height_{1};
size_t input_width_{1};
size_t channels_{1};
size_t batch_size_{1};
size_t input_pixel_stride_{0};
size_t output_pixel_stride_{0};
uint32_t pooling_height_{1};
uint32_t pooling_width_{1};
size_t next_input_height_{0};
size_t next_input_width_{0};
size_t next_batch_size_{0};
uint8_t qmin_{0};
uint8_t qmax_{255};
size_t iterations_{1};
};