1542 lines
72 KiB
C++
1542 lines
72 KiB
C++
// 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 <limits>
|
|
#include <random>
|
|
#include <vector>
|
|
|
|
#include <xnnpack.h>
|
|
|
|
namespace {
|
|
|
|
template<class T>
|
|
inline T doz(T a, T b) {
|
|
return a > b ? a - b : T(0);
|
|
}
|
|
|
|
} // namespace
|
|
|
|
class DeconvolutionOperatorTester {
|
|
public:
|
|
inline DeconvolutionOperatorTester& padding(uint32_t padding) {
|
|
this->padding_top_ = padding;
|
|
this->padding_right_ = padding;
|
|
this->padding_bottom_ = padding;
|
|
this->padding_left_ = padding;
|
|
return *this;
|
|
}
|
|
|
|
inline DeconvolutionOperatorTester& padding_height(uint32_t padding_height) {
|
|
this->padding_top_ = padding_height;
|
|
this->padding_bottom_ = padding_height;
|
|
return *this;
|
|
}
|
|
|
|
inline uint32_t padding_height() const {
|
|
return this->padding_top_ + this->padding_bottom_;
|
|
}
|
|
|
|
inline DeconvolutionOperatorTester& padding_width(uint32_t padding_width) {
|
|
this->padding_right_ = padding_width;
|
|
this->padding_left_ = padding_width;
|
|
return *this;
|
|
}
|
|
|
|
inline uint32_t padding_width() const {
|
|
return this->padding_left_ + this->padding_right_;
|
|
}
|
|
|
|
inline DeconvolutionOperatorTester& padding_top(uint32_t padding_top) {
|
|
this->padding_top_ = padding_top;
|
|
return *this;
|
|
}
|
|
|
|
inline uint32_t padding_top() const { return this->padding_top_; }
|
|
|
|
inline DeconvolutionOperatorTester& padding_right(uint32_t padding_right) {
|
|
this->padding_right_ = padding_right;
|
|
return *this;
|
|
}
|
|
|
|
inline uint32_t padding_right() const { return this->padding_right_; }
|
|
|
|
inline DeconvolutionOperatorTester& padding_bottom(uint32_t padding_bottom) {
|
|
this->padding_bottom_ = padding_bottom;
|
|
return *this;
|
|
}
|
|
|
|
inline uint32_t padding_bottom() const { return this->padding_bottom_; }
|
|
|
|
inline DeconvolutionOperatorTester& padding_left(uint32_t padding_left) {
|
|
this->padding_left_ = padding_left;
|
|
return *this;
|
|
}
|
|
|
|
inline uint32_t padding_left() const { return this->padding_left_; }
|
|
|
|
inline DeconvolutionOperatorTester& adjustment_height(uint32_t adjustment_height) {
|
|
this->adjustment_height_ = adjustment_height;
|
|
return *this;
|
|
}
|
|
|
|
inline uint32_t adjustment_height() const {
|
|
return this->adjustment_height_;
|
|
}
|
|
|
|
inline DeconvolutionOperatorTester& adjustment_width(uint32_t adjustment_width) {
|
|
this->adjustment_width_ = adjustment_width;
|
|
return *this;
|
|
}
|
|
|
|
inline uint32_t adjustment_width() const {
|
|
return this->adjustment_width_;
|
|
}
|
|
|
|
inline DeconvolutionOperatorTester& 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 DeconvolutionOperatorTester& 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 DeconvolutionOperatorTester& 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 DeconvolutionOperatorTester& groups(uint32_t groups) {
|
|
assert(groups >= 1);
|
|
this->groups_ = groups;
|
|
return *this;
|
|
}
|
|
|
|
inline uint32_t groups() const {
|
|
return this->groups_;
|
|
}
|
|
|
|
inline DeconvolutionOperatorTester& 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 DeconvolutionOperatorTester& 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 DeconvolutionOperatorTester& 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 DeconvolutionOperatorTester& kernel_size(uint32_t kernel_size) {
|
|
assert(kernel_size >= 1);
|
|
this->kernel_height_ = kernel_size;
|
|
this->kernel_width_ = kernel_size;
|
|
return *this;
|
|
}
|
|
|
|
inline DeconvolutionOperatorTester& 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 DeconvolutionOperatorTester& 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 DeconvolutionOperatorTester& 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 DeconvolutionOperatorTester& dilation(uint32_t dilation) {
|
|
assert(dilation >= 1);
|
|
this->dilation_height_ = dilation;
|
|
this->dilation_width_ = dilation;
|
|
return *this;
|
|
}
|
|
|
|
inline DeconvolutionOperatorTester& 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 DeconvolutionOperatorTester& 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 DeconvolutionOperatorTester& 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 DeconvolutionOperatorTester& stride(uint32_t stride) {
|
|
assert(stride >= 1);
|
|
this->stride_height_ = stride;
|
|
this->stride_width_ = stride;
|
|
return *this;
|
|
}
|
|
|
|
inline DeconvolutionOperatorTester& stride(uint32_t stride_height, uint32_t stride_width) {
|
|
assert(stride_height >= 1);
|
|
assert(stride_width >= 1);
|
|
this->stride_height_ = stride_height;
|
|
this->stride_width_ = stride_width;
|
|
return *this;
|
|
}
|
|
|
|
inline DeconvolutionOperatorTester& stride_height(uint32_t stride_height) {
|
|
assert(stride_height >= 1);
|
|
this->stride_height_ = stride_height;
|
|
return *this;
|
|
}
|
|
|
|
inline uint32_t stride_height() const {
|
|
return this->stride_height_;
|
|
}
|
|
|
|
inline DeconvolutionOperatorTester& stride_width(uint32_t stride_width) {
|
|
assert(stride_width >= 1);
|
|
this->stride_width_ = stride_width;
|
|
return *this;
|
|
}
|
|
|
|
inline uint32_t stride_width() const {
|
|
return this->stride_width_;
|
|
}
|
|
|
|
inline DeconvolutionOperatorTester& 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 DeconvolutionOperatorTester& 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 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 {
|
|
return stride_height() * (input_height() - 1) + adjustment_height() + dilated_kernel_height() - padding_height();
|
|
}
|
|
|
|
inline size_t output_width() const {
|
|
return stride_width() * (input_width() - 1) + adjustment_width() + dilated_kernel_width() - padding_width();
|
|
}
|
|
|
|
inline DeconvolutionOperatorTester& 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 DeconvolutionOperatorTester& 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 DeconvolutionOperatorTester& 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 {
|
|
return stride_height() * (next_input_height() - 1) + adjustment_height() + dilated_kernel_height() - padding_height();
|
|
}
|
|
|
|
inline size_t next_output_width() const {
|
|
return stride_width() * (next_input_width() - 1) + adjustment_width() + dilated_kernel_width() - padding_width();
|
|
}
|
|
|
|
inline DeconvolutionOperatorTester& 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 DeconvolutionOperatorTester& qmin(uint8_t qmin) {
|
|
this->qmin_ = qmin;
|
|
return *this;
|
|
}
|
|
|
|
inline uint8_t qmin() const {
|
|
return this->qmin_;
|
|
}
|
|
|
|
inline DeconvolutionOperatorTester& qmax(uint8_t qmax) {
|
|
this->qmax_ = qmax;
|
|
return *this;
|
|
}
|
|
|
|
inline uint8_t qmax() const {
|
|
return this->qmax_;
|
|
}
|
|
|
|
inline DeconvolutionOperatorTester& has_bias(bool has_bias) {
|
|
this->has_bias_ = has_bias;
|
|
return *this;
|
|
}
|
|
|
|
inline bool has_bias() const {
|
|
return this->has_bias_;
|
|
}
|
|
|
|
inline DeconvolutionOperatorTester& iterations(size_t iterations) {
|
|
this->iterations_ = iterations;
|
|
return *this;
|
|
}
|
|
|
|
inline size_t iterations() const {
|
|
return this->iterations_;
|
|
}
|
|
|
|
void TestQS8() const {
|
|
std::random_device random_device;
|
|
auto rng = std::mt19937(random_device());
|
|
auto i32rng = std::bind(std::uniform_int_distribution<int32_t>(-10000, 10000), std::ref(rng));
|
|
auto i8rng = std::bind(
|
|
std::uniform_int_distribution<int32_t>(std::numeric_limits<int8_t>::min(), std::numeric_limits<int8_t>::max()),
|
|
std::ref(rng));
|
|
auto w8rng = std::bind(
|
|
std::uniform_int_distribution<int32_t>(-std::numeric_limits<int8_t>::max(), std::numeric_limits<int8_t>::max()),
|
|
std::ref(rng));
|
|
|
|
std::vector<int8_t> input(XNN_EXTRA_BYTES / sizeof(int8_t) +
|
|
(batch_size() * input_height() * input_width() - 1) * input_pixel_stride() + groups() * group_input_channels());
|
|
std::vector<int8_t> kernel(groups() * group_output_channels() * kernel_height() * kernel_width() * group_input_channels());
|
|
std::vector<int32_t> bias(groups() * group_output_channels());
|
|
std::vector<int8_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 int8_t input_zero_point = 1;
|
|
|
|
for (size_t iteration = 0; iteration < iterations(); iteration++) {
|
|
std::generate(input.begin(), input.end(), std::ref(i8rng));
|
|
std::generate(kernel.begin(), kernel.end(), std::ref(w8rng));
|
|
std::generate(bias.begin(), bias.end(), std::ref(i32rng));
|
|
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 y = oy + padding_top() - ky * dilation_height();
|
|
const size_t iy = y / stride_height();
|
|
if (iy * stride_height() == y && iy < input_height()) {
|
|
for (size_t kx = 0; kx < kernel_width(); kx++) {
|
|
const size_t x = ox + padding_left() - kx * dilation_width();
|
|
const size_t ix = x / stride_width();
|
|
if (ix * stride_width() == x && 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]);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// 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 int8_t output_zero_point = int8_t(std::max(std::min(
|
|
lrint(-0.5 - 0.5 * double(accumulated_min + accumulated_max) / output_scale),
|
|
long(std::numeric_limits<int8_t>::max())), long(std::numeric_limits<int8_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() - 0x80) - output_zero_point), double(qmin() - 0x80) - output_zero_point);
|
|
});
|
|
|
|
// Create, setup, run, and destroy Deconvolution operator.
|
|
ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
|
|
xnn_operator_t deconvolution_op = nullptr;
|
|
|
|
ASSERT_EQ(
|
|
xnn_status_success,
|
|
xnn_create_deconvolution2d_nhwc_qs8(
|
|
padding_top(), padding_right(), padding_bottom(), padding_left(),
|
|
kernel_height(), kernel_width(), stride_height(), stride_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 */, 1.0f /* kernel scale */, kernel.data(),
|
|
has_bias() ? bias.data() : nullptr, output_zero_point,
|
|
output_scale, int8_t(qmin() - 0x80), int8_t(qmax() - 0x80),
|
|
/*flags=*/0, &deconvolution_op));
|
|
|
|
// Smart pointer to automatically delete deconvolution_op.
|
|
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_deconvolution_op(deconvolution_op, xnn_delete_operator);
|
|
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_setup_deconvolution2d_nhwc_qs8(
|
|
deconvolution_op,
|
|
batch_size(), input_height(), input_width(),
|
|
adjustment_height(), adjustment_width(),
|
|
input.data(), output.data(),
|
|
nullptr /* thread pool */));
|
|
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_run_operator(deconvolution_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() - 0x80))
|
|
<< "(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() - 0x80))
|
|
<< "(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 TestQU8() const {
|
|
std::random_device random_device;
|
|
auto rng = std::mt19937(random_device());
|
|
auto i32rng = std::bind(std::uniform_int_distribution<int32_t>(-10000, 10000), std::ref(rng));
|
|
auto u8rng = std::bind(
|
|
std::uniform_int_distribution<uint32_t>(0, std::numeric_limits<uint8_t>::max()), std::ref(rng));
|
|
|
|
std::vector<uint8_t> input(XNN_EXTRA_BYTES / sizeof(uint8_t) +
|
|
(batch_size() * input_height() * input_width() - 1) * input_pixel_stride() + groups() * group_input_channels());
|
|
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(i32rng));
|
|
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 y = oy + padding_top() - ky * dilation_height();
|
|
const size_t iy = y / stride_height();
|
|
if (iy * stride_height() == y && iy < input_height()) {
|
|
for (size_t kx = 0; kx < kernel_width(); kx++) {
|
|
const size_t x = ox + padding_left() - kx * dilation_width();
|
|
const size_t ix = x / stride_width();
|
|
if (ix * stride_width() == x && 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 Deconvolution operator.
|
|
ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
|
|
xnn_operator_t deconvolution_op = nullptr;
|
|
|
|
ASSERT_EQ(
|
|
xnn_status_success,
|
|
xnn_create_deconvolution2d_nhwc_qu8(
|
|
padding_top(), padding_right(), padding_bottom(), padding_left(),
|
|
kernel_height(), kernel_width(), stride_height(), stride_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(),
|
|
/*flags=*/0, &deconvolution_op));
|
|
|
|
// Smart pointer to automatically delete deconvolution_op.
|
|
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_deconvolution_op(deconvolution_op, xnn_delete_operator);
|
|
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_setup_deconvolution2d_nhwc_qu8(
|
|
deconvolution_op,
|
|
batch_size(), input_height(), input_width(),
|
|
adjustment_height(), adjustment_width(),
|
|
input.data(), output.data(),
|
|
nullptr /* thread pool */));
|
|
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_run_operator(deconvolution_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 TestF32() 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), std::ref(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(""));
|
|
std::fill(output_ref.begin(), output_ref.end(), 0.0f);
|
|
|
|
// 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 y = oy + padding_top() - ky * dilation_height();
|
|
const size_t iy = y / stride_height();
|
|
if (iy * stride_height() == y && iy < input_height()) {
|
|
for (size_t kx = 0; kx < kernel_width(); kx++) {
|
|
const size_t x = ox + padding_left() - kx * dilation_width();
|
|
const size_t ix = x / stride_width();
|
|
if (ix * stride_width() == x && 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 = qmin() == 0 ? -std::numeric_limits<float>::infinity() :
|
|
accumulated_min + (accumulated_max - accumulated_min) / 255.0f * float(qmin());
|
|
const float output_max = qmax() == 255 ? std::numeric_limits<float>::infinity() :
|
|
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 Deconvolution operator.
|
|
ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
|
|
xnn_operator_t deconvolution_op = nullptr;
|
|
|
|
ASSERT_EQ(
|
|
xnn_status_success,
|
|
xnn_create_deconvolution2d_nhwc_f32(
|
|
padding_top(), padding_right(), padding_bottom(), padding_left(),
|
|
kernel_height(), kernel_width(), stride_height(), stride_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,
|
|
/*flags=*/0, &deconvolution_op));
|
|
|
|
// Smart pointer to automatically delete deconvolution_op.
|
|
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_deconvolution_op(deconvolution_op, xnn_delete_operator);
|
|
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_setup_deconvolution2d_nhwc_f32(
|
|
deconvolution_op,
|
|
batch_size(), input_height(), input_width(),
|
|
adjustment_height(), adjustment_width(),
|
|
input.data(), output.data(),
|
|
nullptr /* thread pool */));
|
|
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_run_operator(deconvolution_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 TestSetupQS8() const {
|
|
std::random_device random_device;
|
|
auto rng = std::mt19937(random_device());
|
|
auto i32rng = std::bind(std::uniform_int_distribution<int32_t>(-10000, 10000), std::ref(rng));
|
|
auto i8rng = std::bind(
|
|
std::uniform_int_distribution<int32_t>(std::numeric_limits<int8_t>::min(), std::numeric_limits<int8_t>::max()),
|
|
std::ref(rng));
|
|
auto w8rng = std::bind(
|
|
std::uniform_int_distribution<int32_t>(-std::numeric_limits<int8_t>::max(), std::numeric_limits<int8_t>::max()),
|
|
std::ref(rng));
|
|
|
|
std::vector<int8_t> input(XNN_EXTRA_BYTES / sizeof(int8_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()));
|
|
std::vector<int8_t> kernel(groups() * group_output_channels() * kernel_height() * kernel_width() * group_input_channels());
|
|
std::vector<int32_t> bias(groups() * group_output_channels());
|
|
std::vector<int8_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 int8_t input_zero_point = 127;
|
|
|
|
for (size_t iteration = 0; iteration < iterations(); iteration++) {
|
|
std::generate(input.begin(), input.end(), std::ref(i8rng));
|
|
std::generate(kernel.begin(), kernel.end(), std::ref(w8rng));
|
|
std::generate(bias.begin(), bias.end(), std::ref(i32rng));
|
|
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 y = oy + padding_top() - ky * dilation_height();
|
|
const size_t iy = y / stride_height();
|
|
if (iy * stride_height() == y && iy < input_height()) {
|
|
for (size_t kx = 0; kx < kernel_width(); kx++) {
|
|
const size_t x = ox + padding_left() - kx * dilation_width();
|
|
const size_t ix = x / stride_width();
|
|
if (ix * stride_width() == x && 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]);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// 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 int8_t output_zero_point = int8_t(std::max(std::min(
|
|
lrint(-0.5 - 0.5 * double(accumulated_min + accumulated_max) / output_scale),
|
|
long(std::numeric_limits<int8_t>::max())), long(std::numeric_limits<int8_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() - 0x80) - output_zero_point), double(qmin() - 0x80) - output_zero_point);
|
|
});
|
|
|
|
// Create, setup, and run Deconvolution operator once.
|
|
ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
|
|
xnn_operator_t deconvolution_op = nullptr;
|
|
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_create_deconvolution2d_nhwc_qs8(
|
|
padding_top(), padding_right(), padding_bottom(), padding_left(),
|
|
kernel_height(), kernel_width(),
|
|
stride_height(), stride_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 */,
|
|
1.0f /* kernel scale */,
|
|
kernel.data(), has_bias() ? bias.data() : nullptr,
|
|
output_zero_point, output_scale, int8_t(qmin() - 0x80), int8_t(qmax() - 0x80),
|
|
0, &deconvolution_op));
|
|
|
|
// Smart pointer to automatically delete deconvolution_op.
|
|
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_deconvolution_op(deconvolution_op, xnn_delete_operator);
|
|
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_setup_deconvolution2d_nhwc_qs8(
|
|
deconvolution_op,
|
|
batch_size(), input_height(), input_width(),
|
|
adjustment_height(), adjustment_width(),
|
|
input.data(), output.data(),
|
|
nullptr /* thread pool */));
|
|
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_run_operator(deconvolution_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() - 0x80))
|
|
<< "(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() - 0x80))
|
|
<< "(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(i8rng));
|
|
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 y = oy + padding_top() - ky * dilation_height();
|
|
const size_t iy = y / stride_height();
|
|
if (iy * stride_height() == y && iy < next_input_height()) {
|
|
for (size_t kx = 0; kx < kernel_width(); kx++) {
|
|
const size_t x = ox + padding_left() - kx * dilation_width();
|
|
const size_t ix = x / stride_width();
|
|
if (ix * stride_width() == x && 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]);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
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() - 0x80) - output_zero_point), double(qmin() - 0x80) - output_zero_point);
|
|
});
|
|
|
|
// Setup and run Deconvolution operator the second time, and destroy the operator.
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_setup_deconvolution2d_nhwc_qs8(
|
|
deconvolution_op,
|
|
next_batch_size(), next_input_height(), next_input_width(),
|
|
adjustment_height(), adjustment_width(),
|
|
input.data(), output.data(),
|
|
nullptr /* thread pool */));
|
|
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_run_operator(deconvolution_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() - 0x80))
|
|
<< "(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() - 0x80))
|
|
<< "(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 TestSetupQU8() const {
|
|
std::random_device random_device;
|
|
auto rng = std::mt19937(random_device());
|
|
auto i32rng = std::bind(std::uniform_int_distribution<int32_t>(-10000, 10000), std::ref(rng));
|
|
auto u8rng = std::bind(
|
|
std::uniform_int_distribution<uint32_t>(0, std::numeric_limits<uint8_t>::max()), std::ref(rng));
|
|
|
|
std::vector<uint8_t> input(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()));
|
|
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(i32rng));
|
|
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 y = oy + padding_top() - ky * dilation_height();
|
|
const size_t iy = y / stride_height();
|
|
if (iy * stride_height() == y && iy < input_height()) {
|
|
for (size_t kx = 0; kx < kernel_width(); kx++) {
|
|
const size_t x = ox + padding_left() - kx * dilation_width();
|
|
const size_t ix = x / stride_width();
|
|
if (ix * stride_width() == x && 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 Deconvolution operator once.
|
|
ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
|
|
xnn_operator_t deconvolution_op = nullptr;
|
|
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_create_deconvolution2d_nhwc_qu8(
|
|
padding_top(), padding_right(), padding_bottom(), padding_left(),
|
|
kernel_height(), kernel_width(),
|
|
stride_height(), stride_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, &deconvolution_op));
|
|
|
|
// Smart pointer to automatically delete deconvolution_op.
|
|
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_deconvolution_op(deconvolution_op, xnn_delete_operator);
|
|
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_setup_deconvolution2d_nhwc_qu8(
|
|
deconvolution_op,
|
|
batch_size(), input_height(), input_width(),
|
|
adjustment_height(), adjustment_width(),
|
|
input.data(), output.data(),
|
|
nullptr /* thread pool */));
|
|
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_run_operator(deconvolution_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 y = oy + padding_top() - ky * dilation_height();
|
|
const size_t iy = y / stride_height();
|
|
if (iy * stride_height() == y && iy < next_input_height()) {
|
|
for (size_t kx = 0; kx < kernel_width(); kx++) {
|
|
const size_t x = ox + padding_left() - kx * dilation_width();
|
|
const size_t ix = x / stride_width();
|
|
if (ix * stride_width() == x && 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 Deconvolution operator the second time, and destroy the operator.
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_setup_deconvolution2d_nhwc_qu8(
|
|
deconvolution_op,
|
|
next_batch_size(), next_input_height(), next_input_width(),
|
|
adjustment_height(), adjustment_width(),
|
|
input.data(), output.data(),
|
|
nullptr /* thread pool */));
|
|
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_run_operator(deconvolution_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 TestSetupF32() 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), std::ref(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 y = oy + padding_top() - ky * dilation_height();
|
|
const size_t iy = y / stride_height();
|
|
if (iy * stride_height() == y && iy < input_height()) {
|
|
for (size_t kx = 0; kx < kernel_width(); kx++) {
|
|
const size_t x = ox + padding_left() - kx * dilation_width();
|
|
const size_t ix = x / stride_width();
|
|
if (ix * stride_width() == x && 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 Deconvolution operator once.
|
|
ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
|
|
xnn_operator_t deconvolution_op = nullptr;
|
|
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_create_deconvolution2d_nhwc_f32(
|
|
padding_top(), padding_right(), padding_bottom(), padding_left(),
|
|
kernel_height(), kernel_width(),
|
|
stride_height(), stride_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, &deconvolution_op));
|
|
|
|
// Smart pointer to automatically delete deconvolution_op.
|
|
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_deconvolution_op(deconvolution_op, xnn_delete_operator);
|
|
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_setup_deconvolution2d_nhwc_f32(
|
|
deconvolution_op,
|
|
batch_size(), input_height(), input_width(),
|
|
adjustment_height(), adjustment_width(),
|
|
input.data(), output.data(),
|
|
nullptr /* thread pool */));
|
|
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_run_operator(deconvolution_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 y = oy + padding_top() - ky * dilation_height();
|
|
const size_t iy = y / stride_height();
|
|
if (iy * stride_height() == y && iy < next_input_height()) {
|
|
for (size_t kx = 0; kx < kernel_width(); kx++) {
|
|
const size_t x = ox + padding_left() - kx * dilation_width();
|
|
const size_t ix = x / stride_width();
|
|
if (ix * stride_width() == x && 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 Deconvolution operator the second time, and destroy the operator.
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_setup_deconvolution2d_nhwc_f32(
|
|
deconvolution_op,
|
|
next_batch_size(), next_input_height(), next_input_width(),
|
|
adjustment_height(), adjustment_width(),
|
|
input.data(), output.data(),
|
|
nullptr /* thread pool */));
|
|
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_run_operator(deconvolution_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};
|
|
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 group_output_channels_{1};
|
|
size_t output_pixel_stride_{0};
|
|
size_t batch_size_{1};
|
|
uint32_t kernel_height_{1};
|
|
uint32_t kernel_width_{1};
|
|
uint32_t adjustment_height_{0};
|
|
uint32_t adjustment_width_{0};
|
|
uint32_t dilation_height_{1};
|
|
uint32_t dilation_width_{1};
|
|
uint32_t stride_height_{1};
|
|
uint32_t stride_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};
|
|
bool has_bias_{true};
|
|
size_t iterations_{1};
|
|
};
|