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// 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 <cmath>
#include <cstddef>
#include <cstdint>
#include <functional>
#include <random>
#include <vector>
#include <xnnpack.h>
#include <xnnpack/AlignedAllocator.h>
#include <xnnpack/params.h>
class BilinearMicrokernelTester {
public:
inline BilinearMicrokernelTester& pixels(uint32_t pixels) {
assert(pixels >= 1);
this->pixels_ = pixels;
return *this;
}
inline uint32_t pixels() const {
return this->pixels_;
}
inline BilinearMicrokernelTester& channels(uint32_t channels) {
assert(channels >= 1);
this->channels_ = channels;
return *this;
}
inline uint32_t channels() const {
return this->channels_;
}
inline BilinearMicrokernelTester& input_offset(uint32_t input_offset) {
this->input_offset_ = input_offset;
return *this;
}
inline uint32_t input_offset() const {
return this->input_offset_;
}
inline BilinearMicrokernelTester& output_stride(uint32_t output_stride) {
assert(output_stride != 0);
this->output_stride_ = output_stride;
return *this;
}
inline uint32_t output_stride() const {
if (this->output_stride_ == 0) {
return channels();
} else {
assert(this->output_stride_ >= channels());
return this->output_stride_;
}
}
inline BilinearMicrokernelTester& iterations(size_t iterations) {
this->iterations_ = iterations;
return *this;
}
inline size_t iterations() const {
return this->iterations_;
}
void Test(xnn_f32_bilinear_ukernel_function bilinear) 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<const float*> indirection(pixels() * 4);
std::vector<float> input(XNN_EXTRA_BYTES / sizeof(float) + indirection.size() * channels());
std::vector<float, AlignedAllocator<float, 64>> packed_weights(pixels() * 2);
std::vector<float> output((pixels() - 1) * output_stride() + channels());
std::vector<float> output_ref(pixels() * channels());
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(input.begin(), input.end(), std::ref(f32rng));
std::generate(packed_weights.begin(), packed_weights.end(), std::ref(f32rng));
std::fill(output.begin(), output.end(), nanf(""));
for (size_t i = 0; i < indirection.size(); i++) {
indirection[i] = input.data() + i * channels() - input_offset();
}
std::shuffle(indirection.begin(), indirection.end(), rng);
// Compute reference results.
for (size_t i = 0; i < pixels(); i++) {
for (size_t c = 0; c < channels(); c++) {
const float alpha_h = packed_weights[i * 2 + 0];
const float alpha_v = packed_weights[i * 2 + 1];
output_ref[i * channels() + c] =
indirection[i * 4 + 0][c + input_offset()] * (1.0f - alpha_h) * (1.0f - alpha_v) +
indirection[i * 4 + 1][c + input_offset()] * alpha_h * (1.0f - alpha_v) +
indirection[i * 4 + 2][c + input_offset()] * (1.0f - alpha_h) * alpha_v +
indirection[i * 4 + 3][c + input_offset()] * alpha_h * alpha_v;
}
}
// Call optimized micro-kernel.
bilinear(
pixels(), channels() * sizeof(float),
indirection.data(), input_offset() * sizeof(float),
packed_weights.data(), output.data(),
(output_stride() - channels()) * sizeof(float));
// Verify results.
for (size_t i = 0; i < pixels(); i++) {
for (size_t c = 0; c < channels(); c++) {
ASSERT_NEAR(
output_ref[i * channels() + c],
output[i * output_stride() + c],
std::abs(output_ref[i * channels() + c]) * 1.0e-5)
<< "i = " << i << ", channel = " << c;
}
}
}
}
private:
uint32_t channels_{1};
uint32_t pixels_{1};
uint32_t output_stride_{0};
uint32_t input_offset_{0};
size_t iterations_{3};
};