blob: f6a092d4f8905115297607359e0fe3f8f106f16d [file] [log] [blame]
// Copyright (c) Facebook, Inc. and its affiliates.
// All rights reserved.
//
// 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 <cstdlib>
#include <functional>
#include <random>
#include <vector>
#include <xnnpack.h>
class ConvolutionOperatorTester {
public:
inline ConvolutionOperatorTester& 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 ConvolutionOperatorTester& 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 ConvolutionOperatorTester& 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 ConvolutionOperatorTester& padding_height(uint32_t padding_height) {
assert(!padding_tf_same());
this->padding_top_ = padding_height;
this->padding_bottom_ = padding_height;
return *this;
}
inline ConvolutionOperatorTester& padding_width(uint32_t padding_width) {
assert(!padding_tf_same());
this->padding_right_ = padding_width;
this->padding_left_ = padding_width;
return *this;
}
inline ConvolutionOperatorTester& 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() - 1) * subsampling_height() + dilated_kernel_height() - input_height();
return total_padding_height / 2;
} else {
return this->padding_top_;
}
}
inline ConvolutionOperatorTester& 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() - 1) * subsampling_width() + dilated_kernel_width() - input_width();
return total_padding_width / 2;
} else {
return this->padding_left_;
}
}
inline ConvolutionOperatorTester& 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() - 1) * subsampling_height() + dilated_kernel_height() - input_height();
return total_padding_height - total_padding_height / 2;
} else {
return this->padding_bottom_;
}
}
inline ConvolutionOperatorTester& 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() - 1) * subsampling_width() + dilated_kernel_width() - input_width();
return total_padding_width - total_padding_width / 2;
} else {
return this->padding_right_;
}
}
inline ConvolutionOperatorTester& input_size(uint32_t input_height, uint32_t input_width) {
assert(input_height >= 1);
assert(input_width >= 1);
this->input_height_ = input_height;
this->input_width_ = input_width;
return *this;
}
inline ConvolutionOperatorTester& input_height(uint32_t input_height) {
assert(input_height >= 1);
this->input_height_ = input_height;
return *this;
}
inline uint32_t input_height() const {
return this->input_height_;
}
inline ConvolutionOperatorTester& input_width(uint32_t input_width) {
assert(input_width >= 1);
this->input_width_ = input_width;
return *this;
}
inline uint32_t input_width() const {
return this->input_width_;
}
inline ConvolutionOperatorTester& groups(uint32_t groups) {
assert(groups >= 1);
this->groups_ = groups;
return *this;
}
inline uint32_t groups() const {
return this->groups_;
}
inline ConvolutionOperatorTester& group_input_channels(size_t group_input_channels) {
assert(group_input_channels >= 1);
this->group_input_channels_ = group_input_channels;
return *this;
}
inline size_t group_input_channels() const {
return this->group_input_channels_;
}
inline ConvolutionOperatorTester& group_output_channels(size_t group_output_channels) {
assert(group_output_channels >= 1);
this->group_output_channels_ = group_output_channels;
return *this;
}
inline size_t group_output_channels() const {
return this->group_output_channels_;
}
inline ConvolutionOperatorTester& batch_size(size_t batch_size) {
assert(batch_size >= 1);
this->batch_size_ = batch_size;
return *this;
}
inline size_t batch_size() const {
return this->batch_size_;
}
inline ConvolutionOperatorTester& kernel_size(uint32_t kernel_size) {
assert(kernel_size >= 1);
this->kernel_height_ = kernel_size;
this->kernel_width_ = kernel_size;
return *this;
}
inline ConvolutionOperatorTester& kernel_size(uint32_t kernel_height, uint32_t kernel_width) {
assert(kernel_height >= 1);
assert(kernel_width >= 1);
this->kernel_height_ = kernel_height;
this->kernel_width_ = kernel_width;
return *this;
}
inline ConvolutionOperatorTester& kernel_height(uint32_t kernel_height) {
assert(kernel_height >= 1);
this->kernel_height_ = kernel_height;
return *this;
}
inline uint32_t kernel_height() const {
return this->kernel_height_;
}
inline ConvolutionOperatorTester& kernel_width(uint32_t kernel_width) {
assert(kernel_width >= 1);
this->kernel_width_ = kernel_width;
return *this;
}
inline uint32_t kernel_width() const {
return this->kernel_width_;
}
inline ConvolutionOperatorTester& dilation(uint32_t dilation) {
assert(dilation >= 1);
this->dilation_height_ = dilation;
this->dilation_width_ = dilation;
return *this;
}
inline ConvolutionOperatorTester& dilation(uint32_t dilation_height, uint32_t dilation_width) {
assert(dilation_height >= 1);
assert(dilation_width >= 1);
this->dilation_height_ = dilation_height;
this->dilation_width_ = dilation_width;
return *this;
}
inline ConvolutionOperatorTester& dilation_height(uint32_t dilation_height) {
assert(dilation_height >= 1);
this->dilation_height_ = dilation_height;
return *this;
}
inline uint32_t dilation_height() const {
return this->dilation_height_;
}
inline ConvolutionOperatorTester& dilation_width(uint32_t dilation_width) {
assert(dilation_width >= 1);
this->dilation_width_ = dilation_width;
return *this;
}
inline uint32_t dilation_width() const {
return this->dilation_width_;
}
inline ConvolutionOperatorTester& subsampling(uint32_t subsampling) {
assert(subsampling >= 1);
this->subsampling_height_ = subsampling;
this->subsampling_width_ = subsampling;
return *this;
}
inline ConvolutionOperatorTester& subsampling(uint32_t subsampling_height, uint32_t subsampling_width) {
assert(subsampling_height >= 1);
assert(subsampling_width >= 1);
this->subsampling_height_ = subsampling_height;
this->subsampling_width_ = subsampling_width;
return *this;
}
inline ConvolutionOperatorTester& subsampling_height(uint32_t subsampling_height) {
assert(subsampling_height >= 1);
this->subsampling_height_ = subsampling_height;
return *this;
}
inline uint32_t subsampling_height() const {
return this->subsampling_height_;
}
inline ConvolutionOperatorTester& subsampling_width(uint32_t subsampling_width) {
assert(subsampling_width >= 1);
this->subsampling_width_ = subsampling_width;
return *this;
}
inline uint32_t subsampling_width() const {
return this->subsampling_width_;
}
inline ConvolutionOperatorTester& input_pixel_stride(size_t input_pixel_stride) {
assert(input_pixel_stride >= 1);
this->input_pixel_stride_ = input_pixel_stride;
return *this;
}
inline size_t input_pixel_stride() const {
if (this->input_pixel_stride_ == 0) {
return group_input_channels() * groups();
} else {
assert(this->input_pixel_stride_ >= group_input_channels() * groups());
return this->input_pixel_stride_;
}
}
inline ConvolutionOperatorTester& output_pixel_stride(size_t output_pixel_stride) {
assert(output_pixel_stride >= 1);
this->output_pixel_stride_ = output_pixel_stride;
return *this;
}
inline size_t output_pixel_stride() const {
if (this->output_pixel_stride_ == 0) {
return group_output_channels() * groups();
} else {
assert(this->output_pixel_stride_ >= group_output_channels() * groups());
return this->output_pixel_stride_;
}
}
inline ConvolutionOperatorTester& input_batch_stride(size_t input_batch_stride) {
assert(input_batch_stride >= 1);
this->input_batch_stride_ = input_batch_stride;
return *this;
}
inline size_t input_batch_stride() const {
if (this->input_batch_stride_ == 0) {
return groups() * group_input_channels() * input_height() * input_width();
} else {
assert(this->input_batch_stride_ >= groups() * group_input_channels() * input_height() * input_width());
return this->input_batch_stride_;
}
}
inline ConvolutionOperatorTester& output_batch_stride(size_t output_batch_stride) {
assert(output_batch_stride >= 1);
this->output_batch_stride_ = output_batch_stride;
return *this;
}
inline size_t output_batch_stride() const {
if (this->output_batch_stride_ == 0) {
return groups() * group_output_channels() * output_height() * output_width();
} else {
assert(this->output_batch_stride_ >= groups() * group_output_channels() * output_height() * output_width());
return this->output_batch_stride_;
}
}
inline uint32_t dilated_kernel_height() const {
return (kernel_height() - 1) * dilation_height() + 1;
}
inline uint32_t dilated_kernel_width() const {
return (kernel_width() - 1) * dilation_width() + 1;
}
inline size_t output_height() const {
if (padding_tf_same()) {
return (input_height() + subsampling_height() - 1) / subsampling_height();
} else {
const size_t padded_input_height = padding_top() + input_height() + padding_bottom();
if (padded_input_height <= dilated_kernel_height()) {
return 1;
} else {
return (padded_input_height - dilated_kernel_height()) / subsampling_height() + 1;
}
}
}
inline size_t output_width() const {
if (padding_tf_same()) {
return (input_width() + subsampling_width() - 1) / subsampling_width();
} else {
const size_t padded_input_width = padding_left() + input_width() + padding_right();
if (padded_input_width <= dilated_kernel_width()) {
return 1;
} else {
return (padded_input_width - dilated_kernel_width()) / subsampling_width() + 1;
}
}
}
inline ConvolutionOperatorTester& 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 ConvolutionOperatorTester& 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 ConvolutionOperatorTester& 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_input_height = padding_top() + next_input_height() + padding_bottom();
if (padded_input_height <= dilated_kernel_height()) {
return 1;
} else {
return (padded_input_height - dilated_kernel_height()) / subsampling_height() + 1;
}
}
inline size_t next_output_width() const {
const size_t padded_input_width = padding_left() + next_input_width() + padding_right();
if (padded_input_width <= dilated_kernel_width()) {
return 1;
} else {
return (padded_input_width - dilated_kernel_width()) / subsampling_width() + 1;
}
}
inline ConvolutionOperatorTester& 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 ConvolutionOperatorTester& sparsity(float sparsity) {
this->sparsity_ = sparsity;
return *this;
}
inline float sparsity() const {
return this->sparsity_;
}
inline ConvolutionOperatorTester& qmin(uint8_t qmin) {
this->qmin_ = qmin;
return *this;
}
inline uint8_t qmin() const {
return this->qmin_;
}
inline ConvolutionOperatorTester& qmax(uint8_t qmax) {
this->qmax_ = qmax;
return *this;
}
inline uint8_t qmax() const {
return this->qmax_;
}
inline ConvolutionOperatorTester& force_nhwc_input(bool force_nhwc_input) {
this->force_nhwc_input_ = force_nhwc_input;
return *this;
}
inline bool force_nhwc_input() const {
return this->force_nhwc_input_;
}
inline ConvolutionOperatorTester& depthwise_layout(bool depthwise_layout) {
this->depthwise_layout_ = depthwise_layout;
return *this;
}
inline bool depthwise_layout() const {
return this->depthwise_layout_;
}
inline ConvolutionOperatorTester& has_bias(bool has_bias) {
this->has_bias_ = has_bias;
return *this;
}
inline bool has_bias() const {
return this->has_bias_;
}
inline ConvolutionOperatorTester& iterations(size_t iterations) {
this->iterations_ = iterations;
return *this;
}
inline size_t iterations() const {
return this->iterations_;
}
void TestNHWCxQ8() const {
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto s32rng = std::bind(std::uniform_int_distribution<int32_t>(-10000, 10000), rng);
auto u8rng = std::bind(std::uniform_int_distribution<uint8_t>(), rng);
std::vector<uint8_t> input(XNN_EXTRA_BYTES / sizeof(uint8_t) +
batch_size() * ((input_height() * input_width() - 1) * input_pixel_stride() + groups() * group_input_channels()) + 8);
std::vector<uint8_t> kernel(groups() * group_output_channels() * kernel_height() * kernel_width() * group_input_channels());
std::vector<int32_t> bias(groups() * group_output_channels());
std::vector<uint8_t> output(batch_size() * ((output_height() * output_width() - 1) * output_pixel_stride() + groups() * group_output_channels()));
std::vector<int32_t> accumulators(batch_size() * output_height() * output_width() * groups() * group_output_channels());
std::vector<double> output_ref(batch_size() * output_height() * output_width() * groups() * group_output_channels());
const uint8_t input_zero_point = 127;
const uint8_t kernel_zero_point = 127;
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(input.begin(), input.end(), std::ref(u8rng));
std::generate(kernel.begin(), kernel.end(), std::ref(u8rng));
std::generate(bias.begin(), bias.end(), std::ref(s32rng));
std::fill(output.begin(), output.end(), 0xA5);
// Compute reference results, without renormalization.
if (has_bias()) {
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 g = 0; g < groups(); g++) {
for (size_t oc = 0; oc < group_output_channels(); oc++) {
accumulators[(((i * output_height() + oy) * output_width() + ox) * groups() + g) * group_output_channels() + oc] =
bias[g * group_output_channels() + oc];
}
}
}
}
}
} else {
std::fill(accumulators.begin(), accumulators.end(), 0);
}
if (depthwise_layout()) {
ASSERT_EQ(group_input_channels(), 1);
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 ky = 0; ky < kernel_height(); ky++) {
const size_t iy = oy * subsampling_height() + ky * dilation_height() - padding_top();
if (iy < input_height()) {
for (size_t kx = 0; kx < kernel_width(); kx++) {
const size_t ix = ox * subsampling_width() + kx * dilation_width() - padding_left();
if (ix < input_width()) {
for (size_t g = 0; g < groups(); g++) {
for (size_t oc = 0; oc < group_output_channels(); oc++) {
accumulators[(((i * output_height() + oy) * output_width() + ox) * groups() + g) * group_output_channels() + oc] +=
(int32_t(input[((i * input_height() + iy) * input_width() + ix) * input_pixel_stride() + g]) - int32_t(input_zero_point)) *
(int32_t(kernel[((ky * kernel_width() + kx) * groups() + g) * group_output_channels() + oc]) - int32_t(kernel_zero_point));
}
}
}
}
}
}
}
}
}
} else {
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 ky = 0; ky < kernel_height(); ky++) {
const size_t iy = oy * subsampling_height() + ky * dilation_height() - padding_top();
if (iy < input_height()) {
for (size_t kx = 0; kx < kernel_width(); kx++) {
const size_t ix = ox * subsampling_width() + kx * dilation_width() - padding_left();
if (ix < input_width()) {
for (size_t g = 0; g < groups(); g++) {
for (size_t oc = 0; oc < group_output_channels(); oc++) {
for (size_t ic = 0; ic < group_input_channels(); ic++) {
accumulators[(((i * output_height() + oy) * output_width() + ox) * groups() + g) * group_output_channels() + oc] +=
(int32_t(input[((i * input_height() + iy) * input_width() + ix) * input_pixel_stride() + g * group_input_channels() + ic]) - int32_t(input_zero_point)) *
(int32_t(kernel[(((g * group_output_channels() + oc) * kernel_height() + ky) * kernel_width() + kx) * group_input_channels() + ic]) - int32_t(kernel_zero_point));
}
}
}
}
}
}
}
}
}
}
}
// Compute renormalization parameters.
const int32_t accumulated_min = *std::min_element(accumulators.cbegin(), accumulators.cend());
const int32_t accumulated_max = *std::max_element(accumulators.cbegin(), accumulators.cend());
const double output_scale = double(uint32_t(accumulated_max - accumulated_min)) / 255.0;
const uint8_t output_zero_point = uint8_t(std::max(std::min(
lrint(127.5 - 0.5 * double(accumulated_min + accumulated_max) / output_scale),
long(std::numeric_limits<uint8_t>::max())), long(std::numeric_limits<uint8_t>::min())));
// Renormalize reference results.
std::transform(accumulators.cbegin(), accumulators.cend(), output_ref.begin(),
[this, output_scale, output_zero_point](int32_t x) -> double {
return std::max<double>(std::min<double>(double(x) / output_scale, double(qmax()) - output_zero_point), double(qmin()) - output_zero_point);
});
// Create, setup, run, and destroy Convolution operator.
ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
xnn_operator_t convolution_op = nullptr;
ASSERT_EQ(xnn_status_success,
xnn_create_convolution2d_nhwc_q8(
padding_tf_same() ? 0 : padding_top(), padding_tf_same() ? 0 : padding_right(),
padding_tf_same() ? 0 : padding_bottom(), padding_tf_same() ? 0 : padding_left(),
kernel_height(), kernel_width(),
subsampling_height(), subsampling_width(),
dilation_height(), dilation_width(),
groups(), group_input_channels(), group_output_channels(),
input_pixel_stride(), output_pixel_stride(),
input_zero_point, 1.0f /* input scale */,
kernel_zero_point, 1.0f /* kernel scale */,
kernel.data(), has_bias() ? bias.data() : nullptr,
output_zero_point, output_scale, qmin(), qmax(),
(depthwise_layout() ? XNN_FLAG_DEPTHWISE_CONVOLUTION : 0) | (padding_tf_same() ? XNN_FLAG_TENSORFLOW_SAME_PADDING : 0),
&convolution_op));
// Smart pointer to automatically delete convolution_op.
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_convolution_op(convolution_op, xnn_delete_operator);
ASSERT_EQ(xnn_status_success,
xnn_setup_convolution2d_nhwc_q8(
convolution_op,
batch_size(), input_height(), input_width(),
input.data(), output.data(),
nullptr /* thread pool */));
ASSERT_EQ(xnn_status_success,
xnn_run_operator(convolution_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 g = 0; g < groups(); g++) {
for (size_t c = 0; c < group_output_channels(); c++) {
ASSERT_LE(int32_t(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + g * group_output_channels() + c]), int32_t(qmax()))
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
ASSERT_GE(int32_t(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + g * group_output_channels() + c]), int32_t(qmin()))
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
ASSERT_NEAR(
output_ref[(((i * output_height() + y) * output_width() + x) * groups() + g) * group_output_channels() + c],
double(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + g * group_output_channels() + c]) - double(output_zero_point),
0.9)
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
}
}
}
}
}
}
}
void TestNHWCxF32() const {
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto f32rng = std::bind(std::uniform_real_distribution<float>(0.1f, 1.0f), rng);
std::vector<float> input(XNN_EXTRA_BYTES / sizeof(float) +
batch_size() * ((input_height() * input_width() - 1) * input_pixel_stride() + groups() * group_input_channels()));
std::vector<float> kernel(groups() * group_output_channels() * kernel_height() * kernel_width() * group_input_channels());
std::vector<float> bias(groups() * group_output_channels());
std::vector<float> output(batch_size() * ((output_height() * output_width() - 1) * output_pixel_stride() + groups() * group_output_channels()));
std::vector<float> output_ref(batch_size() * output_height() * output_width() * groups() * group_output_channels());
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(input.begin(), input.end(), std::ref(f32rng));
std::generate(kernel.begin(), kernel.end(), std::ref(f32rng));
std::generate(bias.begin(), bias.end(), std::ref(f32rng));
std::fill(output.begin(), output.end(), nanf(""));
// Compute reference results, without clamping.
if (has_bias()) {
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 g = 0; g < groups(); g++) {
for (size_t oc = 0; oc < group_output_channels(); oc++) {
output_ref[(((i * output_height() + oy) * output_width() + ox) * groups() + g) * group_output_channels() + oc] =
bias[g * group_output_channels() + oc];
}
}
}
}
}
} else {
std::fill(output_ref.begin(), output_ref.end(), 0.0f);
}
if (depthwise_layout()) {
ASSERT_EQ(group_input_channels(), 1);
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 ky = 0; ky < kernel_height(); ky++) {
const size_t iy = oy * subsampling_height() + ky * dilation_height() - padding_top();
if (iy < input_height()) {
for (size_t kx = 0; kx < kernel_width(); kx++) {
const size_t ix = ox * subsampling_width() + kx * dilation_width() - padding_left();
if (ix < input_width()) {
for (size_t g = 0; g < groups(); g++) {
for (size_t oc = 0; oc < group_output_channels(); oc++) {
output_ref[(((i * output_height() + oy) * output_width() + ox) * groups() + g) * group_output_channels() + oc] +=
input[((i * input_height() + iy) * input_width() + ix) * input_pixel_stride() + g] *
kernel[((ky * kernel_width() + kx) * groups() + g) * group_output_channels() + oc];
}
}
}
}
}
}
}
}
}
} else {
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 ky = 0; ky < kernel_height(); ky++) {
const size_t iy = oy * subsampling_height() + ky * dilation_height() - padding_top();
if (iy < input_height()) {
for (size_t kx = 0; kx < kernel_width(); kx++) {
const size_t ix = ox * subsampling_width() + kx * dilation_width() - padding_left();
if (ix < input_width()) {
for (size_t g = 0; g < groups(); g++) {
for (size_t oc = 0; oc < group_output_channels(); oc++) {
for (size_t ic = 0; ic < group_input_channels(); ic++) {
output_ref[(((i * output_height() + oy) * output_width() + ox) * groups() + g) * group_output_channels() + oc] +=
input[((i * input_height() + iy) * input_width() + ix) * input_pixel_stride() + g * group_input_channels() + ic] *
kernel[(((g * group_output_channels() + oc) * kernel_height() + ky) * kernel_width() + kx) * group_input_channels() + ic];
}
}
}
}
}
}
}
}
}
}
}
// 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 output_min = accumulated_min + (accumulated_max - accumulated_min) / 255.0f * float(qmin());
const float output_max = accumulated_max - (accumulated_max - accumulated_min) / 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, run, and destroy Convolution operator.
ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
xnn_operator_t convolution_op = nullptr;
ASSERT_EQ(xnn_status_success,
xnn_create_convolution2d_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(),
kernel_height(), kernel_width(),
subsampling_height(), subsampling_width(),
dilation_height(), dilation_width(),
groups(), group_input_channels(), group_output_channels(),
input_pixel_stride(), output_pixel_stride(),
kernel.data(), has_bias() ? bias.data() : nullptr,
output_min, output_max,
(depthwise_layout() ? XNN_FLAG_DEPTHWISE_CONVOLUTION : 0) | (padding_tf_same() ? XNN_FLAG_TENSORFLOW_SAME_PADDING : 0),
&convolution_op));
// Smart pointer to automatically delete convolution_op.
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_convolution_op(convolution_op, xnn_delete_operator);
ASSERT_EQ(xnn_status_success,
xnn_setup_convolution2d_nhwc_f32(
convolution_op,
batch_size(), input_height(), input_width(),
input.data(), output.data(),
nullptr /* thread pool */));
ASSERT_EQ(xnn_status_success,
xnn_run_operator(convolution_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 g = 0; g < groups(); g++) {
for (size_t c = 0; c < group_output_channels(); c++) {
ASSERT_GE(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + g * group_output_channels() + c], output_min)
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
ASSERT_LE(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + g * group_output_channels() + c], output_max)
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
ASSERT_NEAR(
output_ref[(((i * output_height() + y) * output_width() + x) * groups() + g) * group_output_channels() + c],
output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + g * group_output_channels() + c],
1.0e-4 * std::abs(output_ref[(((i * output_height() + y) * output_width() + x) * groups() + g) * group_output_channels() + c]))
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
}
}
}
}
}
}
}
void TestNCHWxF32() const {
ASSERT_FALSE(depthwise_layout());
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto f32rng = std::bind(std::uniform_real_distribution<float>(0.1f, 1.0f), rng);
auto prng = std::bind(std::uniform_real_distribution<float>(), rng);
std::vector<float> input(XNN_EXTRA_BYTES / sizeof(float) +
batch_size() * input_batch_stride() + groups() * group_input_channels() * input_height() * input_width());
std::vector<float> kernel(
groups() * group_output_channels() * kernel_height() * kernel_width() * group_input_channels());
std::vector<float> bias(groups() * group_output_channels());
std::vector<float> output(
batch_size() * output_batch_stride() + groups() * group_output_channels() * output_height() * output_width());
std::vector<float> output_ref(batch_size() * groups() * group_output_channels() * output_height() * output_width());
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(input.begin(), input.end(), std::ref(f32rng));
std::generate(kernel.begin(), kernel.end(), std::ref(f32rng));
for (float& k : kernel) {
if (prng() <= sparsity()) {
k = 0.0f;
}
}
std::generate(bias.begin(), bias.end(), std::ref(f32rng));
std::fill(output.begin(), output.end(), nanf(""));
// Compute reference results, without clamping.
if (has_bias()) {
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 g = 0; g < groups(); g++) {
for (size_t oc = 0; oc < group_output_channels(); oc++) {
output_ref[(((i * groups() + g) * group_output_channels() + oc) * output_height() + oy) * output_width() + ox] =
bias[g * group_output_channels() + oc];
}
}
}
}
}
} else {
std::fill(output_ref.begin(), output_ref.end(), 0.0f);
}
if (force_nhwc_input()) {
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 ky = 0; ky < kernel_height(); ky++) {
const size_t iy = oy * subsampling_height() + ky * dilation_height() - padding_top();
if (iy < input_height()) {
for (size_t kx = 0; kx < kernel_width(); kx++) {
const size_t ix = ox * subsampling_width() + kx * dilation_width() - padding_left();
if (ix < input_width()) {
for (size_t g = 0; g < groups(); g++) {
for (size_t oc = 0; oc < group_output_channels(); oc++) {
for (size_t ic = 0; ic < group_input_channels(); ic++) {
output_ref[(((i * groups() + g) * group_output_channels() + oc) * output_height() + oy) * output_width() + ox] +=
input[((((i * input_height() + iy) * input_width() + ix) * groups() + g) * group_input_channels() + ic)] *
kernel[(((g * group_output_channels() + oc) * kernel_height() + ky) * kernel_width() + kx) * group_input_channels() + ic];
}
}
}
}
}
}
}
}
}
}
} else {
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 ky = 0; ky < kernel_height(); ky++) {
const size_t iy = oy * subsampling_height() + ky * dilation_height() - padding_top();
if (iy < input_height()) {
for (size_t kx = 0; kx < kernel_width(); kx++) {
const size_t ix = ox * subsampling_width() + kx * dilation_width() - padding_left();
if (ix < input_width()) {
for (size_t g = 0; g < groups(); g++) {
for (size_t oc = 0; oc < group_output_channels(); oc++) {
for (size_t ic = 0; ic < group_input_channels(); ic++) {
output_ref[(((i * groups() + g) * group_output_channels() + oc) * output_height() + oy) * output_width() + ox] +=
input[i * input_batch_stride() +
((g * group_input_channels() + ic) * input_height() + iy) * input_width() + ix] *
kernel[(((g * group_output_channels() + oc) * kernel_height() + ky) * kernel_width() + kx) * group_input_channels() + ic];
}
}
}
}
}
}
}
}
}
}
}
// 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 output_min = accumulated_min + (accumulated_max - accumulated_min) / 255.0f * float(qmin());
const float output_max = accumulated_max - (accumulated_max - accumulated_min) / 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, run, and destroy Convolution operator.
ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
xnn_operator_t convolution_op = nullptr;
xnn_status status = xnn_create_convolution2d_nchw_f32(
padding_top(), padding_right(), padding_bottom(), padding_left(),
kernel_height(), kernel_width(),
subsampling_height(), subsampling_width(),
dilation_height(), dilation_width(),
groups(), group_input_channels(), group_output_channels(),
kernel.data(), has_bias() ? bias.data() : nullptr,
output_min, output_max,
(depthwise_layout() ? XNN_FLAG_DEPTHWISE_CONVOLUTION : 0) | (force_nhwc_input() ? XNN_FLAG_INPUT_NHWC : 0),
&convolution_op);
if (status == xnn_status_unsupported_parameter) {
GTEST_SKIP();
}
ASSERT_EQ(xnn_status_success, status);
// Smart pointer to automatically delete convolution_op.
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_convolution_op(convolution_op, xnn_delete_operator);
ASSERT_EQ(xnn_status_success,
xnn_setup_convolution2d_nchw_f32(
convolution_op,
batch_size(), input_batch_stride(), output_batch_stride(), input_height(), input_width(),
input.data(), output.data(),
nullptr /* thread pool */));
ASSERT_EQ(xnn_status_success,
xnn_run_operator(convolution_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 g = 0; g < groups(); g++) {
for (size_t c = 0; c < group_output_channels(); c++) {
ASSERT_GE(output[i * output_batch_stride() + ((g * group_output_channels() + c) * output_height() + y) * output_width() + x], output_min)
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c << ", image = " << i;
ASSERT_LE(output[i * output_batch_stride() + ((g * group_output_channels() + c) * output_height() + y) * output_width() + x], output_max)
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c << ", image = " << i;
ASSERT_NEAR(
output_ref[(((i * groups() + g) * group_output_channels() + c) * output_height() + y) * output_width() + x],
output[i * output_batch_stride() + ((g * group_output_channels() + c) * output_height() + y) * output_width() + x],
1.0e-4 * std::abs(output_ref[(((i * groups() + g) * group_output_channels() + c) * output_height() + y) * output_width() + x]))
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c << ", image = " << i;
}
}
}
}
}
}
}
void TestSetupNHWCxQ8() const {
ASSERT_FALSE(depthwise_layout());
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto s32rng = std::bind(std::uniform_int_distribution<int32_t>(-10000, 10000), rng);
auto u8rng = std::bind(std::uniform_int_distribution<uint8_t>(), rng);
std::vector<uint8_t> input(XNN_EXTRA_BYTES / sizeof(uint8_t) + std::max(
batch_size() * ((input_height() * input_width() - 1) * input_pixel_stride() + groups() * group_input_channels()),
next_batch_size() * ((next_input_height() * next_input_width() - 1) * input_pixel_stride() + groups() * group_input_channels())) + 8);
std::vector<uint8_t> kernel(groups() * group_output_channels() * kernel_height() * kernel_width() * group_input_channels());
std::vector<int32_t> bias(groups() * group_output_channels());
std::vector<uint8_t> output(std::max(
batch_size() * ((output_height() * output_width() - 1) * output_pixel_stride() + groups() * group_output_channels()),
next_batch_size() * ((next_output_height() * next_output_width() - 1) * output_pixel_stride() + groups() * group_output_channels())));
std::vector<int32_t> accumulators(batch_size() * output_height() * output_width() * groups() * group_output_channels());
std::vector<double> output_ref(batch_size() * output_height() * output_width() * groups() * group_output_channels());
std::vector<int32_t> next_accumulators(next_batch_size() * next_output_height() * next_output_width() * groups() * group_output_channels());
std::vector<double> next_output_ref(next_batch_size() * next_output_height() * next_output_width() * groups() * group_output_channels());
const uint8_t input_zero_point = 127;
const uint8_t kernel_zero_point = 127;
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(input.begin(), input.end(), std::ref(u8rng));
std::generate(kernel.begin(), kernel.end(), std::ref(u8rng));
std::generate(bias.begin(), bias.end(), std::ref(s32rng));
std::fill(output.begin(), output.end(), 0xA5);
// Compute reference results, without renormalization.
if (has_bias()) {
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 g = 0; g < groups(); g++) {
for (size_t oc = 0; oc < group_output_channels(); oc++) {
accumulators[(((i * output_height() + oy) * output_width() + ox) * groups() + g) * group_output_channels() + oc] =
bias[g * group_output_channels() + oc];
}
}
}
}
}
} else {
std::fill(accumulators.begin(), accumulators.end(), 0);
}
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 ky = 0; ky < kernel_height(); ky++) {
const size_t iy = oy * subsampling_height() + ky * dilation_height() - padding_top();
if (iy < input_height()) {
for (size_t kx = 0; kx < kernel_width(); kx++) {
const size_t ix = ox * subsampling_width() + kx * dilation_width() - padding_left();
if (ix < input_width()) {
for (size_t g = 0; g < groups(); g++) {
for (size_t oc = 0; oc < group_output_channels(); oc++) {
for (size_t ic = 0; ic < group_input_channels(); ic++) {
accumulators[(((i * output_height() + oy) * output_width() + ox) * groups() + g) * group_output_channels() + oc] +=
(int32_t(input[((i * input_height() + iy) * input_width() + ix) * input_pixel_stride() + g * group_input_channels() + ic]) - int32_t(input_zero_point)) *
(int32_t(kernel[(((g * group_output_channels() + oc) * kernel_height() + ky) * kernel_width() + kx) * group_input_channels() + ic]) - int32_t(kernel_zero_point));
}
}
}
}
}
}
}
}
}
}
// Compute renormalization parameters.
const int32_t accumulated_min = *std::min_element(accumulators.cbegin(), accumulators.cend());
const int32_t accumulated_max = *std::max_element(accumulators.cbegin(), accumulators.cend());
const double output_scale = double(uint32_t(accumulated_max - accumulated_min)) / 255.0;
const uint8_t output_zero_point = uint8_t(std::max(std::min(
lrint(127.5 - 0.5 * double(accumulated_min + accumulated_max) / output_scale),
long(std::numeric_limits<uint8_t>::max())), long(std::numeric_limits<uint8_t>::min())));
// Renormalize reference results.
std::transform(accumulators.cbegin(), accumulators.cend(), output_ref.begin(),
[this, output_scale, output_zero_point](int32_t x) -> double {
return std::max<double>(std::min<double>(double(x) / output_scale, double(qmax()) - output_zero_point), double(qmin()) - output_zero_point);
});
// Create, setup, and run Convolution operator once.
ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
xnn_operator_t convolution_op = nullptr;
ASSERT_EQ(xnn_status_success,
xnn_create_convolution2d_nhwc_q8(
padding_top(), padding_right(), padding_bottom(), padding_left(),
kernel_height(), kernel_width(),
subsampling_height(), subsampling_width(),
dilation_height(), dilation_width(),
groups(), group_input_channels(), group_output_channels(),
input_pixel_stride(), output_pixel_stride(),
input_zero_point, 1.0f /* input scale */,
kernel_zero_point, 1.0f /* kernel scale */,
kernel.data(), has_bias() ? bias.data() : nullptr,
output_zero_point, output_scale, qmin(), qmax(),
0, &convolution_op));
// Smart pointer to automatically delete convolution_op.
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_convolution_op(convolution_op, xnn_delete_operator);
ASSERT_EQ(xnn_status_success,
xnn_setup_convolution2d_nhwc_q8(
convolution_op,
batch_size(), input_height(), input_width(),
input.data(), output.data(),
nullptr /* thread pool */));
ASSERT_EQ(xnn_status_success,
xnn_run_operator(convolution_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 g = 0; g < groups(); g++) {
for (size_t c = 0; c < group_output_channels(); c++) {
ASSERT_LE(int32_t(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + g * group_output_channels() + c]), int32_t(qmax()))
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
ASSERT_GE(int32_t(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + g * group_output_channels() + c]), int32_t(qmin()))
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
ASSERT_NEAR(
output_ref[(((i * output_height() + y) * output_width() + x) * groups() + g) * group_output_channels() + c],
double(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + g * group_output_channels() + c]) - double(output_zero_point),
0.9)
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
}
}
}
}
}
// Re-generate data for the second run.
std::generate(input.begin(), input.end(), std::ref(u8rng));
std::fill(output.begin(), output.end(), 0xA5);
// Compute reference results for the second run, including renormalization.
if (has_bias()) {
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 g = 0; g < groups(); g++) {
for (size_t oc = 0; oc < group_output_channels(); oc++) {
next_accumulators[(((i * next_output_height() + oy) * next_output_width() + ox) * groups() + g) * group_output_channels() + oc] =
bias[g * group_output_channels() + oc];
}
}
}
}
}
} else {
std::fill(next_accumulators.begin(), next_accumulators.end(), 0);
}
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 ky = 0; ky < kernel_height(); ky++) {
const size_t iy = oy * subsampling_height() + ky * dilation_height() - padding_top();
if (iy < next_input_height()) {
for (size_t kx = 0; kx < kernel_width(); kx++) {
const size_t ix = ox * subsampling_width() + kx * dilation_width() - padding_left();
if (ix < next_input_width()) {
for (size_t g = 0; g < groups(); g++) {
for (size_t oc = 0; oc < group_output_channels(); oc++) {
for (size_t ic = 0; ic < group_input_channels(); ic++) {
next_accumulators[(((i * next_output_height() + oy) * next_output_width() + ox) * groups() + g) * group_output_channels() + oc] +=
(int32_t(input[((i * next_input_height() + iy) * next_input_width() + ix) * input_pixel_stride() + g * group_input_channels() + ic]) - int32_t(input_zero_point)) *
(int32_t(kernel[(((g * group_output_channels() + oc) * kernel_height() + ky) * kernel_width() + kx) * group_input_channels() + ic]) - int32_t(kernel_zero_point));
}
}
}
}
}
}
}
}
}
}
std::transform(next_accumulators.cbegin(), next_accumulators.cend(), next_output_ref.begin(),
[this, output_scale, output_zero_point](int32_t x) -> double {
return std::max<double>(std::min<double>(double(x) / output_scale, double(qmax()) - output_zero_point), double(qmin()) - output_zero_point);
});
// Setup and run Convolution operator the second time, and destroy the operator.
ASSERT_EQ(xnn_status_success,
xnn_setup_convolution2d_nhwc_q8(
convolution_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(convolution_op, nullptr /* thread pool */));
// 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 g = 0; g < groups(); g++) {
for (size_t c = 0; c < group_output_channels(); c++) {
ASSERT_LE(int32_t(output[((i * next_output_height() + y) * next_output_width() + x) * output_pixel_stride() + g * group_output_channels() + c]), int32_t(qmax()))
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
ASSERT_GE(int32_t(output[((i * next_output_height() + y) * next_output_width() + x) * output_pixel_stride() + g * group_output_channels() + c]), int32_t(qmin()))
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
ASSERT_NEAR(
next_output_ref[(((i * next_output_height() + y) * next_output_width() + x) * groups() + g) * group_output_channels() + c],
double(output[((i * next_output_height() + y) * next_output_width() + x) * output_pixel_stride() + g * group_output_channels() + c]) - double(output_zero_point),
0.9)
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
}
}
}
}
}
}
}
void TestSetupNHWCxF32() const {
ASSERT_FALSE(depthwise_layout());
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto f32rng = std::bind(std::uniform_real_distribution<float>(0.1f, 1.0f), rng);
std::vector<float> input(XNN_EXTRA_BYTES / sizeof(float) + std::max(
batch_size() * ((input_height() * input_width() - 1) * input_pixel_stride() + groups() * group_input_channels()),
next_batch_size() * ((next_input_height() * next_input_width() - 1) * input_pixel_stride() + groups() * group_input_channels())));
std::vector<float> kernel(groups() * group_output_channels() * kernel_height() * kernel_width() * group_input_channels());
std::vector<float> bias(groups() * group_output_channels());
std::vector<float> output(std::max(
batch_size() * ((output_height() * output_width() - 1) * output_pixel_stride() + groups() * group_output_channels()),
next_batch_size() * ((next_output_height() * next_output_width() - 1) * output_pixel_stride() + groups() * group_output_channels())));
std::vector<float> output_ref(batch_size() * output_height() * output_width() * groups() * group_output_channels());
std::vector<float> next_output_ref(next_batch_size() * next_output_height() * next_output_width() * groups() * group_output_channels());
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(input.begin(), input.end(), std::ref(f32rng));
std::generate(kernel.begin(), kernel.end(), std::ref(f32rng));
std::generate(bias.begin(), bias.end(), std::ref(f32rng));
std::fill(output.begin(), output.end(), nanf(""));
// Compute reference results, without clamping.
if (has_bias()) {
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 g = 0; g < groups(); g++) {
for (size_t oc = 0; oc < group_output_channels(); oc++) {
output_ref[(((i * output_height() + oy) * output_width() + ox) * groups() + g) * group_output_channels() + oc] =
bias[g * group_output_channels() + oc];
}
}
}
}
}
} else {
std::fill(output_ref.begin(), output_ref.end(), 0.0f);
}
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 ky = 0; ky < kernel_height(); ky++) {
const size_t iy = oy * subsampling_height() + ky * dilation_height() - padding_top();
if (iy < input_height()) {
for (size_t kx = 0; kx < kernel_width(); kx++) {
const size_t ix = ox * subsampling_width() + kx * dilation_width() - padding_left();
if (ix < input_width()) {
for (size_t g = 0; g < groups(); g++) {
for (size_t oc = 0; oc < group_output_channels(); oc++) {
for (size_t ic = 0; ic < group_input_channels(); ic++) {
output_ref[(((i * output_height() + oy) * output_width() + ox) * groups() + g) * group_output_channels() + oc] +=
input[((i * input_height() + iy) * input_width() + ix) * input_pixel_stride() + g * group_input_channels() + ic] *
kernel[(((g * group_output_channels() + oc) * kernel_height() + ky) * kernel_width() + kx) * group_input_channels() + ic];
}
}
}
}
}
}
}
}
}
}
// 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 output_min = accumulated_min + (accumulated_max - accumulated_min) / 255.0f * float(qmin());
const float output_max = accumulated_max - (accumulated_max - accumulated_min) / 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 Convolution operator once.
ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
xnn_operator_t convolution_op = nullptr;
ASSERT_EQ(xnn_status_success,
xnn_create_convolution2d_nhwc_f32(
padding_top(), padding_right(), padding_bottom(), padding_left(),
kernel_height(), kernel_width(),
subsampling_height(), subsampling_width(),
dilation_height(), dilation_width(),
groups(), group_input_channels(), group_output_channels(),
input_pixel_stride(), output_pixel_stride(),
kernel.data(), has_bias() ? bias.data() : nullptr,
output_min, output_max,
0, &convolution_op));
// Smart pointer to automatically delete convolution_op.
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_convolution_op(convolution_op, xnn_delete_operator);
ASSERT_EQ(xnn_status_success,
xnn_setup_convolution2d_nhwc_f32(
convolution_op,
batch_size(), input_height(), input_width(),
input.data(), output.data(),
nullptr /* thread pool */));
ASSERT_EQ(xnn_status_success,
xnn_run_operator(convolution_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 g = 0; g < groups(); g++) {
for (size_t c = 0; c < group_output_channels(); c++) {
ASSERT_GE(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + g * group_output_channels() + c], output_min)
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
ASSERT_LE(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + g * group_output_channels() + c], output_max)
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
ASSERT_NEAR(
output_ref[(((i * output_height() + y) * output_width() + x) * groups() + g) * group_output_channels() + c],
output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + g * group_output_channels() + c],
1.0e-4 * std::abs(output_ref[(((i * output_height() + y) * output_width() + x) * groups() + g) * group_output_channels() + c]))
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
}
}
}
}
}
// Re-generate data for the second run.
std::generate(input.begin(), input.end(), std::ref(f32rng));
std::fill(output.begin(), output.end(), nanf(""));
// Compute reference results for the second run, including clamping.
if (has_bias()) {
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 g = 0; g < groups(); g++) {
for (size_t oc = 0; oc < group_output_channels(); oc++) {
next_output_ref[(((i * next_output_height() + oy) * next_output_width() + ox) * groups() + g) * group_output_channels() + oc] =
bias[g * group_output_channels() + oc];
}
}
}
}
}
} else {
std::fill(next_output_ref.begin(), next_output_ref.end(), 0.0f);
}
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 ky = 0; ky < kernel_height(); ky++) {
const size_t iy = oy * subsampling_height() + ky * dilation_height() - padding_top();
if (iy < next_input_height()) {
for (size_t kx = 0; kx < kernel_width(); kx++) {
const size_t ix = ox * subsampling_width() + kx * dilation_width() - padding_left();
if (ix < next_input_width()) {
for (size_t g = 0; g < groups(); g++) {
for (size_t oc = 0; oc < group_output_channels(); oc++) {
for (size_t ic = 0; ic < group_input_channels(); ic++) {
next_output_ref[(((i * next_output_height() + oy) * next_output_width() + ox) * groups() + g) * group_output_channels() + oc] +=
input[((i * next_input_height() + iy) * next_input_width() + ix) * input_pixel_stride() + g * group_input_channels() + ic] *
kernel[(((g * group_output_channels() + oc) * kernel_height() + ky) * kernel_width() + kx) * group_input_channels() + ic];
}
}
}
}
}
}
}
}
}
}
for (float& value : next_output_ref) {
value = std::max(std::min(value, output_max), output_min);
}
// Setup and run Convolution operator the second time, and destroy the operator.
ASSERT_EQ(xnn_status_success,
xnn_setup_convolution2d_nhwc_f32(
convolution_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(convolution_op, nullptr /* thread pool */));
// 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 g = 0; g < groups(); g++) {
for (size_t c = 0; c < group_output_channels(); c++) {
ASSERT_GE(output[((i * next_output_height() + y) * next_output_width() + x) * output_pixel_stride() + g * group_output_channels() + c], output_min)
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
ASSERT_LE(output[((i * next_output_height() + y) * next_output_width() + x) * output_pixel_stride() + g * group_output_channels() + c], output_max)
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
ASSERT_NEAR(
next_output_ref[(((i * next_output_height() + y) * next_output_width() + x) * groups() + g) * group_output_channels() + c],
output[((i * next_output_height() + y) * next_output_width() + x) * output_pixel_stride() + g * group_output_channels() + c],
1.0e-4 * std::abs(next_output_ref[(((i * next_output_height() + y) * next_output_width() + x) * groups() + g) * group_output_channels() + c]))
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", 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};
uint32_t groups_{1};
size_t group_input_channels_{1};
size_t input_pixel_stride_{0};
size_t input_batch_stride_{0};
size_t group_output_channels_{1};
size_t output_pixel_stride_{0};
size_t output_batch_stride_{0};
size_t batch_size_{1};
uint32_t kernel_height_{1};
uint32_t kernel_width_{1};
uint32_t dilation_height_{1};
uint32_t dilation_width_{1};
uint32_t subsampling_height_{1};
uint32_t subsampling_width_{1};
size_t next_input_height_{0};
size_t next_input_width_{0};
size_t next_batch_size_{0};
float sparsity_{0.0f};
uint8_t qmin_{0};
uint8_t qmax_{255};
bool depthwise_layout_{false};
bool force_nhwc_input_{false};
bool has_bias_{true};
size_t iterations_{1};
};