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depth-to-space-operator-tester.h
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// Copyright 2020 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 <algorithm>
#include <cassert>
#include <cstddef>
#include <cstdint>
#include <cstdlib>
#include <functional>
#include <limits>
#include <memory>
#include <random>
#include <vector>
#include <gtest/gtest.h>
#include "xnnpack.h"
#include "replicable_random_device.h"
class DepthToSpaceOperatorTester {
public:
DepthToSpaceOperatorTester& input_size(size_t input_height, size_t input_width) {
assert(input_height >= 1);
assert(input_width >= 1);
this->input_height_ = input_height;
this->input_width_ = input_width;
return *this;
}
DepthToSpaceOperatorTester& input_height(size_t input_height) {
assert(input_height >= 1);
this->input_height_ = input_height;
return *this;
}
size_t input_height() const {
return this->input_height_;
}
DepthToSpaceOperatorTester& input_width(size_t input_width) {
assert(input_width >= 1);
this->input_width_ = input_width;
return *this;
}
size_t input_width() const {
return this->input_width_;
}
size_t output_height() const {
return input_height() * block_size();
}
size_t output_width() const {
return input_width() * block_size();
}
DepthToSpaceOperatorTester& block_size(size_t block_size) {
assert(block_size >= 2);
this->block_size_ = block_size;
return *this;
}
size_t block_size() const {
return this->block_size_;
}
size_t input_channels() const {
return output_channels() * block_size() * block_size();
}
DepthToSpaceOperatorTester& output_channels(size_t output_channels) {
assert(output_channels != 0);
this->output_channels_ = output_channels;
return *this;
}
size_t output_channels() const {
return this->output_channels_;
}
DepthToSpaceOperatorTester& batch_size(size_t batch_size) {
assert(batch_size != 0);
this->batch_size_ = batch_size;
return *this;
}
size_t batch_size() const {
return this->batch_size_;
}
size_t input_channels_stride() const {
if (this->input_channels_stride_ == 0) {
return input_channels();
} else {
assert(this->input_channels_stride_ >= input_channels());
return this->input_channels_stride_;
}
}
size_t output_channels_stride() const {
if (this->output_channels_stride_ == 0) {
return output_channels();
} else {
assert(this->output_channels_stride_ >= output_channels());
return this->output_channels_stride_;
}
}
DepthToSpaceOperatorTester& iterations(size_t iterations) {
this->iterations_ = iterations;
return *this;
}
size_t iterations() const {
return this->iterations_;
}
void TestNHWCxX8() const {
xnnpack::ReplicableRandomDevice 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));
std::vector<int8_t> input(
(batch_size() * input_height() * input_width() - 1) * input_channels_stride() + input_channels() + XNN_EXTRA_BYTES / sizeof(int8_t));
std::vector<int8_t> output(
(batch_size() * output_height() * output_width() - 1) * output_channels_stride() + output_channels());
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(input.begin(), input.end(), std::ref(i8rng));
std::fill(output.begin(), output.end(), INT8_C(0xAF));
// Create, setup, run, and destroy Depth To Space operator.
ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
xnn_operator_t depth_to_space_op = nullptr;
ASSERT_EQ(xnn_status_success,
xnn_create_depth_to_space_nhwc_x8(
block_size(), 0, &depth_to_space_op));
ASSERT_NE(nullptr, depth_to_space_op);
// Smart pointer to automatically delete depth_to_space_op.
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_depth_to_space_op(depth_to_space_op, xnn_delete_operator);
ASSERT_EQ(xnn_status_success,
xnn_reshape_depth_to_space_nhwc_x8(
depth_to_space_op,
batch_size(), input_height(), input_width(), input_channels(),
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr, /*output_channels_out=*/nullptr,
/*threadpool=*/nullptr));
ASSERT_EQ(xnn_status_success,
xnn_setup_depth_to_space_nhwc_x8(
depth_to_space_op,
input.data(), output.data()));
ASSERT_EQ(xnn_status_success,
xnn_run_operator(depth_to_space_op, /*threadpool=*/nullptr));
// Verify results.
for (size_t i = 0; i < batch_size(); i++) {
for (size_t iy = 0; iy < input_height(); iy++) {
for (size_t by = 0; by < block_size(); by++) {
for (size_t ix = 0; ix < input_width(); ix++) {
for (size_t bx = 0; bx < block_size(); bx++) {
for (size_t oc = 0; oc < output_channels(); oc++) {
const size_t input_index =
((i * input_height() + iy) * input_width() + ix) * input_channels_stride() +
(by * block_size() + bx) * output_channels() + oc;
const size_t output_index =
((i * output_height() + iy * block_size() + by) * output_width() + ix * block_size() + bx) *
output_channels_stride() + oc;
ASSERT_EQ(int32_t(output[output_index]), int32_t(input[input_index]))
<< "batch: " << i << " / " << batch_size()
<< ", input x: " << ix << " / " << input_width()
<< ", input y: " << iy << " / " << input_height()
<< ", block x: " << bx << " / " << block_size()
<< ", block y: " << by << " / " << block_size()
<< ", output channel: " << oc << " / " << output_channels()
<< ", input stride: " << input_channels_stride()
<< ", output stride: " << output_channels_stride();
}
}
}
}
}
}
}
}
void TestNHWCxX16() const {
xnnpack::ReplicableRandomDevice rng;
auto i16rng = std::bind(std::uniform_int_distribution<int16_t>(), std::ref(rng));
std::vector<int16_t> input(
(batch_size() * input_height() * input_width() - 1) * input_channels_stride() + input_channels() + XNN_EXTRA_BYTES / sizeof(int16_t));
std::vector<int16_t> output(
(batch_size() * output_height() * output_width() - 1) * output_channels_stride() + output_channels());
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(input.begin(), input.end(), std::ref(i16rng));
std::fill(output.begin(), output.end(), INT16_C(0xDEAD));
// Create, setup, run, and destroy Depth To Space operator.
ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
xnn_operator_t depth_to_space_op = nullptr;
ASSERT_EQ(xnn_status_success,
xnn_create_depth_to_space_nhwc_x16(
block_size(), 0, &depth_to_space_op));
ASSERT_NE(nullptr, depth_to_space_op);
// Smart pointer to automatically delete depth_to_space_op.
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_depth_to_space_op(depth_to_space_op, xnn_delete_operator);
ASSERT_EQ(xnn_status_success,
xnn_reshape_depth_to_space_nhwc_x16(
depth_to_space_op,
batch_size(), input_height(), input_width(), input_channels(),
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr, /*output_channels_out=*/nullptr,
/*threadpool=*/nullptr));
ASSERT_EQ(xnn_status_success,
xnn_setup_depth_to_space_nhwc_x16(
depth_to_space_op,
input.data(), output.data()));
ASSERT_EQ(xnn_status_success,
xnn_run_operator(depth_to_space_op, /*threadpool=*/nullptr));
// Verify results.
for (size_t i = 0; i < batch_size(); i++) {
for (size_t iy = 0; iy < input_height(); iy++) {
for (size_t by = 0; by < block_size(); by++) {
for (size_t ix = 0; ix < input_width(); ix++) {
for (size_t bx = 0; bx < block_size(); bx++) {
for (size_t oc = 0; oc < output_channels(); oc++) {
const size_t input_index =
((i * input_height() + iy) * input_width() + ix) * input_channels_stride() +
(by * block_size() + bx) * output_channels() + oc;
const size_t output_index =
((i * output_height() + iy * block_size() + by) * output_width() + ix * block_size() + bx) *
output_channels_stride() + oc;
ASSERT_EQ(output[output_index], input[input_index])
<< "batch: " << i << " / " << batch_size()
<< ", input x: " << ix << " / " << input_width()
<< ", input y: " << iy << " / " << input_height()
<< ", block x: " << bx << " / " << block_size()
<< ", block y: " << by << " / " << block_size()
<< ", output channel: " << oc << " / " << output_channels()
<< ", input stride: " << input_channels_stride()
<< ", output stride: " << output_channels_stride();
}
}
}
}
}
}
}
}
void TestNHWCxX32() const {
xnnpack::ReplicableRandomDevice rng;
auto i32rng = std::bind(std::uniform_int_distribution<int32_t>(), std::ref(rng));
std::vector<int32_t> input(
(batch_size() * input_height() * input_width() - 1) * input_channels_stride() + input_channels() + XNN_EXTRA_BYTES / sizeof(int32_t));
std::vector<int32_t> output(
(batch_size() * output_height() * output_width() - 1) * output_channels_stride() + output_channels());
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(input.begin(), input.end(), std::ref(i32rng));
std::fill(output.begin(), output.end(), INT32_C(0xDEADBEAF));
// Create, setup, run, and destroy Depth To Space operator.
ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
xnn_operator_t depth_to_space_op = nullptr;
ASSERT_EQ(xnn_status_success,
xnn_create_depth_to_space_nhwc_x32(
block_size(), 0, &depth_to_space_op));
ASSERT_NE(nullptr, depth_to_space_op);
// Smart pointer to automatically delete depth_to_space_op.
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_depth_to_space_op(depth_to_space_op, xnn_delete_operator);
ASSERT_EQ(xnn_status_success,
xnn_reshape_depth_to_space_nhwc_x32(
depth_to_space_op,
batch_size(), input_height(), input_width(), input_channels(),
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr, /*output_channels_out=*/nullptr,
/*threadpool=*/nullptr));
ASSERT_EQ(xnn_status_success,
xnn_setup_depth_to_space_nhwc_x32(
depth_to_space_op,
input.data(), output.data()));
ASSERT_EQ(xnn_status_success,
xnn_run_operator(depth_to_space_op, /*threadpool=*/nullptr));
// Verify results.
for (size_t i = 0; i < batch_size(); i++) {
for (size_t iy = 0; iy < input_height(); iy++) {
for (size_t by = 0; by < block_size(); by++) {
for (size_t ix = 0; ix < input_width(); ix++) {
for (size_t bx = 0; bx < block_size(); bx++) {
for (size_t oc = 0; oc < output_channels(); oc++) {
const size_t input_index =
((i * input_height() + iy) * input_width() + ix) * input_channels_stride() +
(by * block_size() + bx) * output_channels() + oc;
const size_t output_index =
((i * output_height() + iy * block_size() + by) * output_width() + ix * block_size() + bx) *
output_channels_stride() + oc;
ASSERT_EQ(output[output_index], input[input_index])
<< "batch: " << i << " / " << batch_size()
<< ", input x: " << ix << " / " << input_width()
<< ", input y: " << iy << " / " << input_height()
<< ", block x: " << bx << " / " << block_size()
<< ", block y: " << by << " / " << block_size()
<< ", output channel: " << oc << " / " << output_channels()
<< ", input stride: " << input_channels_stride()
<< ", output stride: " << output_channels_stride();
}
}
}
}
}
}
}
}
void TestNCHW2NHWCxX16() const {
xnnpack::ReplicableRandomDevice rng;
auto i16rng = std::bind(std::uniform_int_distribution<int16_t>(), std::ref(rng));
std::vector<int16_t> input(XNN_EXTRA_BYTES / sizeof(int16_t) +
((batch_size() - 1) * input_channels_stride() + input_channels()) * input_height() * input_width());
std::vector<int16_t> output(
(batch_size() * output_height() * output_width() - 1) * output_channels_stride() + output_channels());
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(input.begin(), input.end(), std::ref(i16rng));
std::fill(output.begin(), output.end(), INT16_C(0xDEAD));
// Create, setup, run, and destroy Depth To Space operator.
ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
xnn_operator_t depth_to_space_op = nullptr;
ASSERT_EQ(xnn_status_success,
xnn_create_depth_to_space_nchw2nhwc_x16(
block_size(), 0, &depth_to_space_op));
ASSERT_NE(nullptr, depth_to_space_op);
// Smart pointer to automatically delete depth_to_space_op.
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_depth_to_space_op(depth_to_space_op, xnn_delete_operator);
ASSERT_EQ(xnn_status_success,
xnn_reshape_depth_to_space_nchw2nhwc_x16(
depth_to_space_op,
batch_size(), input_height(), input_width(), input_channels(),
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr, /*output_channels_out=*/nullptr,
/*threadpool=*/nullptr));
ASSERT_EQ(xnn_status_success,
xnn_setup_depth_to_space_nchw2nhwc_x16(
depth_to_space_op,
input.data(), output.data()));
ASSERT_EQ(xnn_status_success,
xnn_run_operator(depth_to_space_op, /*threadpool=*/nullptr));
// Verify results.
for (size_t i = 0; i < batch_size(); i++) {
for (size_t iy = 0; iy < input_height(); iy++) {
for (size_t by = 0; by < block_size(); by++) {
for (size_t ix = 0; ix < input_width(); ix++) {
for (size_t bx = 0; bx < block_size(); bx++) {
for (size_t oc = 0; oc < output_channels(); oc++) {
const size_t input_index =
i * input_channels_stride() * input_height() * input_width() +
(((by * block_size() + bx) * output_channels() + oc) * input_height() + iy) * input_width() + ix;
const size_t output_index =
((i * output_height() + iy * block_size() + by) * output_width() + ix * block_size() + bx) *
output_channels_stride() + oc;
ASSERT_EQ(output[output_index], input[input_index])
<< "batch: " << i << " / " << batch_size()
<< ", input x: " << ix << " / " << input_width()
<< ", input y: " << iy << " / " << input_height()
<< ", block x: " << bx << " / " << block_size()
<< ", block y: " << by << " / " << block_size()
<< ", output channel: " << oc << " / " << output_channels()
<< ", input stride: " << input_channels_stride()
<< ", output stride: " << output_channels_stride();
}
}
}
}
}
}
}
}
void TestNCHW2NHWCxX32() const {
xnnpack::ReplicableRandomDevice rng;
auto i32rng = std::bind(std::uniform_int_distribution<int32_t>(), std::ref(rng));
std::vector<int32_t> input(XNN_EXTRA_BYTES / sizeof(int32_t) +
((batch_size() - 1) * input_channels_stride() + input_channels()) * input_height() * input_width());
std::vector<int32_t> output(
(batch_size() * output_height() * output_width() - 1) * output_channels_stride() + output_channels());
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(input.begin(), input.end(), std::ref(i32rng));
std::fill(output.begin(), output.end(), INT32_C(0xDEADBEAF));
// Create, setup, run, and destroy Depth To Space operator.
ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
xnn_operator_t depth_to_space_op = nullptr;
ASSERT_EQ(xnn_status_success,
xnn_create_depth_to_space_nchw2nhwc_x32(
block_size(), 0, &depth_to_space_op));
ASSERT_NE(nullptr, depth_to_space_op);
// Smart pointer to automatically delete depth_to_space_op.
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_depth_to_space_op(depth_to_space_op, xnn_delete_operator);
ASSERT_EQ(xnn_status_success,
xnn_reshape_depth_to_space_nchw2nhwc_x32(
depth_to_space_op,
batch_size(), input_height(), input_width(), input_channels(),
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr, /*output_channels_out=*/nullptr,
/*threadpool=*/nullptr));
ASSERT_EQ(xnn_status_success,
xnn_setup_depth_to_space_nchw2nhwc_x32(
depth_to_space_op,
input.data(), output.data()));
ASSERT_EQ(xnn_status_success,
xnn_run_operator(depth_to_space_op, /*threadpool=*/nullptr));
// Verify results.
for (size_t i = 0; i < batch_size(); i++) {
for (size_t iy = 0; iy < input_height(); iy++) {
for (size_t by = 0; by < block_size(); by++) {
for (size_t ix = 0; ix < input_width(); ix++) {
for (size_t bx = 0; bx < block_size(); bx++) {
for (size_t oc = 0; oc < output_channels(); oc++) {
const size_t input_index =
i * input_channels_stride() * input_height() * input_width() +
(((by * block_size() + bx) * output_channels() + oc) * input_height() + iy) * input_width() + ix;
const size_t output_index =
((i * output_height() + iy * block_size() + by) * output_width() + ix * block_size() + bx) *
output_channels_stride() + oc;
ASSERT_EQ(output[output_index], input[input_index])
<< "batch: " << i << " / " << batch_size()
<< ", input x: " << ix << " / " << input_width()
<< ", input y: " << iy << " / " << input_height()
<< ", block x: " << bx << " / " << block_size()
<< ", block y: " << by << " / " << block_size()
<< ", output channel: " << oc << " / " << output_channels()
<< ", input stride: " << input_channels_stride()
<< ", output stride: " << output_channels_stride();
}
}
}
}
}
}
}
}
private:
size_t input_height_{1};
size_t input_width_{1};
size_t output_channels_{1};
size_t block_size_{2};
size_t batch_size_{1};
size_t input_channels_stride_{0};
size_t output_channels_stride_{0};
size_t iterations_{1};
};