blob: d3fa46bd9fe1a007d462e3d31615db580b468fa6 [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 <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 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;
}
}
}
}
}
}
// void TestSetupF32() const {
// 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(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<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());
// 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, without clamping.
// for (size_t batch_index = 0; batch_index < batch_size(); batch_index++) {
// for (size_t output_y = 0; output_y < output_height(); output_y++) {
// for (size_t output_x = 0; output_x < output_width(); output_x++) {
// for (size_t c = 0; c < channels(); c++) {
// float acc = 0.0f;
// size_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 < 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};
};