275 lines
9.9 KiB
C++
275 lines
9.9 KiB
C++
// Copyright 2020 Google LLC
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//
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// This source code is licensed under the BSD-style license found in the
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// LICENSE file in the root directory of this source tree.
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#pragma once
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#include <gtest/gtest.h>
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#include <algorithm>
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#include <cmath>
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#include <cassert>
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#include <cstddef>
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#include <cstdlib>
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#include <functional>
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#include <random>
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#include <vector>
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#include <xnnpack.h>
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class DepthToSpaceOperatorTester {
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public:
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inline DepthToSpaceOperatorTester& input_size(size_t input_height, size_t input_width) {
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assert(input_height >= 1);
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assert(input_width >= 1);
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this->input_height_ = input_height;
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this->input_width_ = input_width;
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return *this;
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}
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inline DepthToSpaceOperatorTester& input_height(size_t input_height) {
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assert(input_height >= 1);
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this->input_height_ = input_height;
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return *this;
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}
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inline size_t input_height() const {
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return this->input_height_;
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}
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inline DepthToSpaceOperatorTester& input_width(size_t input_width) {
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assert(input_width >= 1);
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this->input_width_ = input_width;
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return *this;
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}
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inline size_t input_width() const {
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return this->input_width_;
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}
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inline size_t output_height() const {
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return input_height() * block_size();
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}
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inline size_t output_width() const {
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return input_width() * block_size();
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}
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inline DepthToSpaceOperatorTester& block_size(size_t block_size) {
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assert(block_size >= 2);
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this->block_size_ = block_size;
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return *this;
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}
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inline size_t block_size() const {
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return this->block_size_;
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}
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inline size_t input_channels() const {
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return output_channels() * block_size() * block_size();
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}
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inline DepthToSpaceOperatorTester& output_channels(size_t output_channels) {
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assert(output_channels != 0);
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this->output_channels_ = output_channels;
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return *this;
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}
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inline size_t output_channels() const {
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return this->output_channels_;
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}
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inline DepthToSpaceOperatorTester& batch_size(size_t batch_size) {
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assert(batch_size != 0);
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this->batch_size_ = batch_size;
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return *this;
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}
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inline size_t batch_size() const {
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return this->batch_size_;
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}
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inline DepthToSpaceOperatorTester& input_channels_stride(size_t input_channels_stride) {
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assert(input_channels_stride >= 1);
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this->input_channels_stride_ = input_channels_stride;
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return *this;
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}
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inline size_t input_channels_stride() const {
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if (this->input_channels_stride_ == 0) {
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return input_channels();
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} else {
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assert(this->input_channels_stride_ >= input_channels());
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return this->input_channels_stride_;
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}
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}
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inline DepthToSpaceOperatorTester& output_channels_stride(size_t output_channels_stride) {
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assert(output_channels_stride >= 1);
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this->output_channels_stride_ = output_channels_stride;
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return *this;
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}
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inline size_t output_channels_stride() const {
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if (this->output_channels_stride_ == 0) {
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return output_channels();
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} else {
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assert(this->output_channels_stride_ >= output_channels());
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return this->output_channels_stride_;
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}
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}
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inline DepthToSpaceOperatorTester& iterations(size_t iterations) {
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this->iterations_ = iterations;
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return *this;
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}
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inline size_t iterations() const {
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return this->iterations_;
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}
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void TestNHWCxX32() const {
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std::random_device random_device;
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auto rng = std::mt19937(random_device());
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auto i32rng = std::bind(std::uniform_int_distribution<int32_t>(), rng);
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std::vector<int32_t> input(
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(batch_size() * input_height() * input_width() - 1) * input_channels_stride() + input_channels());
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std::vector<int32_t> output(
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(batch_size() * output_height() * output_width() - 1) * output_channels_stride() + output_channels());
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for (size_t iteration = 0; iteration < iterations(); iteration++) {
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std::generate(input.begin(), input.end(), std::ref(i32rng));
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std::fill(output.begin(), output.end(), INT32_C(0xDEADBEAF));
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// Create, setup, run, and destroy Depth To Space operator.
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ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
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xnn_operator_t depth_to_space_op = nullptr;
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ASSERT_EQ(xnn_status_success,
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xnn_create_depth_to_space_nhwc_x32(
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output_channels(), input_channels_stride(), output_channels_stride(),
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block_size(), 0, &depth_to_space_op));
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ASSERT_NE(nullptr, depth_to_space_op);
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// Smart pointer to automatically delete depth_to_space_op.
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std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_depth_to_space_op(depth_to_space_op, xnn_delete_operator);
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ASSERT_EQ(xnn_status_success,
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xnn_setup_depth_to_space_nhwc_x32(
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depth_to_space_op,
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batch_size(), input_height(), input_width(),
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input.data(), output.data(), nullptr /* thread pool */));
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ASSERT_EQ(xnn_status_success,
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xnn_run_operator(depth_to_space_op, nullptr /* thread pool */));
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// Verify results.
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for (size_t i = 0; i < batch_size(); i++) {
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for (size_t iy = 0; iy < input_height(); iy++) {
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for (size_t by = 0; by < block_size(); by++) {
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for (size_t ix = 0; ix < input_width(); ix++) {
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for (size_t bx = 0; bx < block_size(); bx++) {
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for (size_t oc = 0; oc < output_channels(); oc++) {
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const size_t input_index =
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((i * input_height() + iy) * input_width() + ix) * input_channels_stride() +
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(by * block_size() + bx) * output_channels() + oc;
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const size_t output_index =
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((i * output_height() + iy * block_size() + by) * output_width() + ix * block_size() + bx) *
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output_channels_stride() + oc;
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ASSERT_EQ(output[output_index], input[input_index])
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<< "batch: " << i << " / " << batch_size()
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<< ", input x: " << ix << " / " << input_width()
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<< ", input y: " << iy << " / " << input_height()
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<< ", block x: " << bx << " / " << block_size()
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<< ", block y: " << by << " / " << block_size()
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<< ", output channel: " << oc << " / " << output_channels()
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<< ", input stride: " << input_channels_stride()
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<< ", output stride: " << output_channels_stride();
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}
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}
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}
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}
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}
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}
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}
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}
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void TestNCHW2NHWCxX32() const {
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std::random_device random_device;
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auto rng = std::mt19937(random_device());
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auto i32rng = std::bind(std::uniform_int_distribution<int32_t>(), rng);
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std::vector<int32_t> input(XNN_EXTRA_BYTES / sizeof(uint32_t) +
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((batch_size() - 1) * input_channels_stride() + input_channels()) * input_height() * input_width());
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std::vector<int32_t> output(
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(batch_size() * output_height() * output_width() - 1) * output_channels_stride() + output_channels());
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for (size_t iteration = 0; iteration < iterations(); iteration++) {
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std::generate(input.begin(), input.end(), std::ref(i32rng));
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std::fill(output.begin(), output.end(), INT32_C(0xDEADBEAF));
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// Create, setup, run, and destroy Depth To Space operator.
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ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
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xnn_operator_t depth_to_space_op = nullptr;
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ASSERT_EQ(xnn_status_success,
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xnn_create_depth_to_space_nchw2nhwc_x32(
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output_channels(), input_channels_stride(), output_channels_stride(),
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block_size(), 0, &depth_to_space_op));
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ASSERT_NE(nullptr, depth_to_space_op);
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// Smart pointer to automatically delete depth_to_space_op.
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std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_depth_to_space_op(depth_to_space_op, xnn_delete_operator);
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ASSERT_EQ(xnn_status_success,
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xnn_setup_depth_to_space_nchw2nhwc_x32(
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depth_to_space_op,
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batch_size(), input_height(), input_width(),
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input.data(), output.data(), nullptr /* thread pool */));
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ASSERT_EQ(xnn_status_success,
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xnn_run_operator(depth_to_space_op, nullptr /* thread pool */));
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// Verify results.
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for (size_t i = 0; i < batch_size(); i++) {
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for (size_t iy = 0; iy < input_height(); iy++) {
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for (size_t by = 0; by < block_size(); by++) {
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for (size_t ix = 0; ix < input_width(); ix++) {
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for (size_t bx = 0; bx < block_size(); bx++) {
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for (size_t oc = 0; oc < output_channels(); oc++) {
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const size_t input_index =
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i * input_channels_stride() * input_height() * input_width() +
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(((by * block_size() + bx) * output_channels() + oc) * input_height() + iy) * input_width() + ix;
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const size_t output_index =
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((i * output_height() + iy * block_size() + by) * output_width() + ix * block_size() + bx) *
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output_channels_stride() + oc;
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ASSERT_EQ(output[output_index], input[input_index])
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<< "batch: " << i << " / " << batch_size()
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<< ", input x: " << ix << " / " << input_width()
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<< ", input y: " << iy << " / " << input_height()
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<< ", block x: " << bx << " / " << block_size()
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<< ", block y: " << by << " / " << block_size()
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<< ", output channel: " << oc << " / " << output_channels()
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<< ", input stride: " << input_channels_stride()
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<< ", output stride: " << output_channels_stride();
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}
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}
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}
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}
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}
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}
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}
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}
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private:
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size_t input_height_{1};
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size_t input_width_{1};
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size_t output_channels_{1};
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size_t block_size_{2};
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size_t batch_size_{1};
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size_t input_channels_stride_{0};
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size_t output_channels_stride_{0};
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size_t iterations_{1};
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};
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