test/global-average-pooling-operator-tester.h - platform/external/XNNPACK - Gitiles

 // 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 <cstddef>
 #include <cstdlib>
 #include <algorithm>
 #include <cmath>
 #include <functional>
 #include <random>
 #include <vector>

 #include <xnnpack.h>


 class GlobalAveragePoolingOperatorTester {
  public:
   inline GlobalAveragePoolingOperatorTester& channels(size_t channels) {
     assert(channels != 0);
     this->channels_ = channels;
     return *this;
   }

   inline size_t channels() const {
     return this->channels_;
   }

   inline GlobalAveragePoolingOperatorTester& width(size_t width) {
     assert(width != 0);
     this->width_ = width;
     return *this;
   }

   inline size_t width() const {
     return this->width_;
   }

   inline GlobalAveragePoolingOperatorTester& input_stride(size_t input_stride) {
     assert(input_stride != 0);
     this->input_stride_ = input_stride;
     return *this;
   }

   inline size_t input_stride() const {
     if (this->input_stride_ == 0) {
       return channels();
     } else {
       assert(this->input_stride_ >= channels());
       return this->input_stride_;
     }
   }

   inline GlobalAveragePoolingOperatorTester& output_stride(size_t output_stride) {
     assert(output_stride != 0);
     this->output_stride_ = output_stride;
     return *this;
   }

   inline size_t output_stride() const {
     if (this->output_stride_ == 0) {
       return channels();
     } else {
       assert(this->output_stride_ >= channels());
       return this->output_stride_;
     }
   }

   inline GlobalAveragePoolingOperatorTester& batch_size(size_t batch_size) {
     assert(batch_size != 0);
     this->batch_size_ = batch_size;
     return *this;
   }

   inline size_t batch_size() const {
     return this->batch_size_;
   }

   inline GlobalAveragePoolingOperatorTester& input_scale(float input_scale) {
     assert(input_scale > 0.0f);
     assert(std::isnormal(input_scale));
     this->input_scale_ = input_scale;
     return *this;
   }

   inline float input_scale() const {
     return this->input_scale_;
   }

   inline GlobalAveragePoolingOperatorTester& input_zero_point(uint8_t input_zero_point) {
     this->input_zero_point_ = input_zero_point;
     return *this;
   }

   inline uint8_t input_zero_point() const {
     return this->input_zero_point_;
   }

   inline GlobalAveragePoolingOperatorTester& output_scale(float output_scale) {
     assert(output_scale > 0.0f);
     assert(std::isnormal(output_scale));
     this->output_scale_ = output_scale;
     return *this;
   }

   inline float output_scale() const {
     return this->output_scale_;
   }

   inline GlobalAveragePoolingOperatorTester& output_zero_point(uint8_t output_zero_point) {
     this->output_zero_point_ = output_zero_point;
     return *this;
   }

   inline uint8_t output_zero_point() const {
     return this->output_zero_point_;
   }

   inline GlobalAveragePoolingOperatorTester& qmin(uint8_t qmin) {
     this->qmin_ = qmin;
     return *this;
   }

   inline uint8_t qmin() const {
     return this->qmin_;
   }

   inline GlobalAveragePoolingOperatorTester& qmax(uint8_t qmax) {
     this->qmax_ = qmax;
     return *this;
   }

   inline uint8_t qmax() const {
     return this->qmax_;
   }

   inline GlobalAveragePoolingOperatorTester& iterations(size_t iterations) {
     this->iterations_ = iterations;
     return *this;
   }

   inline size_t iterations() const {
     return this->iterations_;
   }

   void TestNWCxQ8() const {
     std::random_device random_device;
     auto rng = std::mt19937(random_device());
     auto u8rng = std::bind(std::uniform_int_distribution<uint8_t>(), rng);

     std::vector<uint8_t> input((batch_size() * width() - 1) * input_stride() + channels() + XNN_EXTRA_BYTES / sizeof(uint8_t));
     std::vector<uint8_t> output(batch_size() * output_stride());
     std::vector<float> output_ref(batch_size() * channels());
     for (size_t iteration = 0; iteration < iterations(); iteration++) {
       std::generate(input.begin(), input.end(), std::ref(u8rng));
       std::fill(output.begin(), output.end(), 0xA5);

       // Compute reference results.
       const double scale = double(input_scale()) / (double(width()) * double(output_scale()));
       for (size_t i = 0; i < batch_size(); i++) {
         for (size_t j = 0; j < channels(); j++) {
           double acc = 0.0f;
           for (size_t k = 0; k < width(); k++) {
             acc += double(int32_t(input[(i * width() + k) * input_stride() + j]) - int32_t(input_zero_point()));
           }
           output_ref[i * channels() + j] = float(acc * scale + double(output_zero_point()));
           output_ref[i * channels() + j] = std::min<float>(output_ref[i * channels() + j], float(qmax()));
           output_ref[i * channels() + j] = std::max<float>(output_ref[i * channels() + j], float(qmin()));
         }
       }

       // Create, setup, run, and destroy Global Average Pooling operator.
       ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
       xnn_operator_t global_average_pooling_op = nullptr;

       ASSERT_EQ(xnn_status_success,
         xnn_create_global_average_pooling_nwc_q8(
           channels(), input_stride(), output_stride(),
           input_zero_point(), input_scale(),
           output_zero_point(), output_scale(),
           qmin(), qmax(),
           0, &global_average_pooling_op));
       ASSERT_NE(nullptr, global_average_pooling_op);

       // Smart pointer to automatically delete global_average_pooling_op.
       std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_global_average_pooling_op(global_average_pooling_op, xnn_delete_operator);

       ASSERT_EQ(xnn_status_success,
         xnn_setup_global_average_pooling_nwc_q8(
           global_average_pooling_op,
           batch_size(), width(),
           input.data(), output.data(),
           nullptr /* thread pool */));

       ASSERT_EQ(xnn_status_success,
         xnn_run_operator(global_average_pooling_op, nullptr /* thread pool */));

       // Verify results.
       for (size_t i = 0; i < batch_size(); i++) {
         for (size_t c = 0; c < channels(); c++) {
           ASSERT_LE(uint32_t(output[i * output_stride() + c]), uint32_t(qmax()));
           ASSERT_GE(uint32_t(output[i * output_stride() + c]), uint32_t(qmin()));
           ASSERT_NEAR(float(int32_t(output[i * output_stride() + c])), output_ref[i * channels() + c], 0.80f) <<
             "in batch index " << i << ", channel " << c;
         }
       }
     }
   }

   void TestNWCxF32() const {
     std::random_device random_device;
     auto rng = std::mt19937(random_device());
     auto f32rng = std::bind(std::uniform_real_distribution<float>(), rng);

     std::vector<float> input((batch_size() * width() - 1) * input_stride() + channels() + XNN_EXTRA_BYTES / sizeof(float));
     std::vector<float> output(batch_size() * output_stride());
     std::vector<float> output_ref(batch_size() * channels());
     for (size_t iteration = 0; iteration < iterations(); iteration++) {
       std::generate(input.begin(), input.end(), std::ref(f32rng));
       std::fill(output.begin(), output.end(), std::nanf(""));

       // Compute reference results, without clamping.
       for (size_t i = 0; i < batch_size(); i++) {
         for (size_t j = 0; j < channels(); j++) {
           float acc = 0.0f;
           for (size_t k = 0; k < width(); k++) {
             acc += input[(i * width() + k) * input_stride() + j];
           }
           output_ref[i * channels() + j] = acc / float(width());
         }
       }

       // 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 accumulated_range = accumulated_max - accumulated_min;
       const float output_min = accumulated_range == 0.0f ?
         -std::numeric_limits<float>::infinity() :
         accumulated_min + accumulated_range / 255.0f * float(qmin());
       const float output_max = accumulated_range == 0.0f ?
         +std::numeric_limits<float>::infinity() :
         accumulated_max - accumulated_range / 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 Global Average Pooling operator.
       ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
       xnn_operator_t global_average_pooling_op = nullptr;

       ASSERT_EQ(xnn_status_success,
         xnn_create_global_average_pooling_nwc_f32(
           channels(), input_stride(), output_stride(),
           output_min, output_max,
           0, &global_average_pooling_op));
       ASSERT_NE(nullptr, global_average_pooling_op);

       // Smart pointer to automatically delete global_average_pooling_op.
       std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_global_average_pooling_op(global_average_pooling_op, xnn_delete_operator);

       ASSERT_EQ(xnn_status_success,
         xnn_setup_global_average_pooling_nwc_f32(
           global_average_pooling_op,
           batch_size(), width(),
           input.data(), output.data(),
           nullptr /* thread pool */));

       ASSERT_EQ(xnn_status_success,
         xnn_run_operator(global_average_pooling_op, nullptr /* thread pool */));

       // Verify results.
       for (size_t i = 0; i < batch_size(); i++) {
         for (size_t c = 0; c < channels(); c++) {
           ASSERT_LE(output[i * output_stride() + c], output_max);
           ASSERT_GE(output[i * output_stride() + c], output_min);
           ASSERT_NEAR(output[i * output_stride() + c], output_ref[i * channels() + c], std::abs(output_ref[i * channels() + c]) * 1.0e-6f) <<
             "in batch index " << i << ", channel " << c;
         }
       }
     }
   }

   void TestNCWxF32() const {
     std::random_device random_device;
     auto rng = std::mt19937(random_device());
     auto f32rng = std::bind(std::uniform_real_distribution<float>(), rng);

     std::vector<float> input(batch_size() * channels() * width() + XNN_EXTRA_BYTES / sizeof(float));
     std::vector<float> output(batch_size() * channels());
     std::vector<float> output_ref(batch_size() * channels());
     for (size_t iteration = 0; iteration < iterations(); iteration++) {
       std::generate(input.begin(), input.end(), std::ref(f32rng));
       std::fill(output.begin(), output.end(), std::nanf(""));

       // Compute reference results, without clamping.
       for (size_t i = 0; i < batch_size(); i++) {
         for (size_t j = 0; j < channels(); j++) {
           float acc = 0.0f;
           for (size_t k = 0; k < width(); k++) {
             acc += input[(i * channels() + j) * width() + k];
           }
           output_ref[i * channels() + j] = acc / float(width());
         }
       }

       // 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 accumulated_range = accumulated_max - accumulated_min;
       const float output_min = accumulated_range == 0.0f ?
         -std::numeric_limits<float>::infinity() :
         accumulated_min + accumulated_range / 255.0f * float(qmin());
       const float output_max = accumulated_range == 0.0f ?
         +std::numeric_limits<float>::infinity() :
         accumulated_max - accumulated_range / 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 Global Average Pooling operator.
       ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
       xnn_operator_t global_average_pooling_op = nullptr;

       xnn_status status = xnn_create_global_average_pooling_ncw_f32(
         channels(), output_min, output_max,
         0, &global_average_pooling_op);
       if (status == xnn_status_unsupported_parameter) {
         GTEST_SKIP();
       }
       ASSERT_EQ(xnn_status_success, status);

       // Smart pointer to automatically delete global_average_pooling_op.
       std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_global_average_pooling_op(global_average_pooling_op, xnn_delete_operator);

       ASSERT_EQ(xnn_status_success,
         xnn_setup_global_average_pooling_ncw_f32(
           global_average_pooling_op,
           batch_size(), width(),
           input.data(), output.data(),
           nullptr /* thread pool */));

       ASSERT_EQ(xnn_status_success,
         xnn_run_operator(global_average_pooling_op, nullptr /* thread pool */));

       // Verify results.
       for (size_t i = 0; i < batch_size(); i++) {
         for (size_t c = 0; c < channels(); c++) {
           ASSERT_LE(output[i * channels() + c], output_max);
           ASSERT_GE(output[i * channels() + c], output_min);
           ASSERT_NEAR(output[i * channels() + c], output_ref[i * channels() + c], std::abs(output_ref[i * channels() + c]) * 1.0e-5f) <<
             "in batch index " << i << ", channel " << c;
         }
       }
     }
   }

  private:
   size_t batch_size_{1};
   size_t width_{1};
   size_t channels_{1};
   size_t input_stride_{0};
   size_t output_stride_{0};
   float input_scale_{1.0f};
   float output_scale_{1.0f};
   uint8_t input_zero_point_{121};
   uint8_t output_zero_point_{133};
   uint8_t qmin_{0};
   uint8_t qmax_{255};
   size_t iterations_{1};
 };
	// 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 <cstddef>
	#include <cstdlib>
	#include <algorithm>
	#include <cmath>
	#include <functional>
	#include <random>
	#include <vector>

	#include <xnnpack.h>


	class GlobalAveragePoolingOperatorTester {
	public:
	inline GlobalAveragePoolingOperatorTester& channels(size_t channels) {
	assert(channels != 0);
	this->channels_ = channels;
	return *this;
	}

	inline size_t channels() const {
	return this->channels_;
	}

	inline GlobalAveragePoolingOperatorTester& width(size_t width) {
	assert(width != 0);
	this->width_ = width;
	return *this;
	}

	inline size_t width() const {
	return this->width_;
	}

	inline GlobalAveragePoolingOperatorTester& input_stride(size_t input_stride) {
	assert(input_stride != 0);
	this->input_stride_ = input_stride;
	return *this;
	}

	inline size_t input_stride() const {
	if (this->input_stride_ == 0) {
	return channels();
	} else {
	assert(this->input_stride_ >= channels());
	return this->input_stride_;
	}
	}

	inline GlobalAveragePoolingOperatorTester& output_stride(size_t output_stride) {
	assert(output_stride != 0);
	this->output_stride_ = output_stride;
	return *this;
	}

	inline size_t output_stride() const {
	if (this->output_stride_ == 0) {
	return channels();
	} else {
	assert(this->output_stride_ >= channels());
	return this->output_stride_;
	}
	}

	inline GlobalAveragePoolingOperatorTester& batch_size(size_t batch_size) {
	assert(batch_size != 0);
	this->batch_size_ = batch_size;
	return *this;
	}

	inline size_t batch_size() const {
	return this->batch_size_;
	}

	inline GlobalAveragePoolingOperatorTester& input_scale(float input_scale) {
	assert(input_scale > 0.0f);
	assert(std::isnormal(input_scale));
	this->input_scale_ = input_scale;
	return *this;
	}

	inline float input_scale() const {
	return this->input_scale_;
	}

	inline GlobalAveragePoolingOperatorTester& input_zero_point(uint8_t input_zero_point) {
	this->input_zero_point_ = input_zero_point;
	return *this;
	}

	inline uint8_t input_zero_point() const {
	return this->input_zero_point_;
	}

	inline GlobalAveragePoolingOperatorTester& output_scale(float output_scale) {
	assert(output_scale > 0.0f);
	assert(std::isnormal(output_scale));
	this->output_scale_ = output_scale;
	return *this;
	}

	inline float output_scale() const {
	return this->output_scale_;
	}

	inline GlobalAveragePoolingOperatorTester& output_zero_point(uint8_t output_zero_point) {
	this->output_zero_point_ = output_zero_point;
	return *this;
	}

	inline uint8_t output_zero_point() const {
	return this->output_zero_point_;
	}

	inline GlobalAveragePoolingOperatorTester& qmin(uint8_t qmin) {
	this->qmin_ = qmin;
	return *this;
	}

	inline uint8_t qmin() const {
	return this->qmin_;
	}

	inline GlobalAveragePoolingOperatorTester& qmax(uint8_t qmax) {
	this->qmax_ = qmax;
	return *this;
	}

	inline uint8_t qmax() const {
	return this->qmax_;
	}

	inline GlobalAveragePoolingOperatorTester& iterations(size_t iterations) {
	this->iterations_ = iterations;
	return *this;
	}

	inline size_t iterations() const {
	return this->iterations_;
	}

	void TestNWCxQ8() const {
	std::random_device random_device;
	auto rng = std::mt19937(random_device());
	auto u8rng = std::bind(std::uniform_int_distribution<uint8_t>(), rng);

	std::vector<uint8_t> input((batch_size() * width() - 1) * input_stride() + channels() + XNN_EXTRA_BYTES / sizeof(uint8_t));
	std::vector<uint8_t> output(batch_size() * output_stride());
	std::vector<float> output_ref(batch_size() * channels());
	for (size_t iteration = 0; iteration < iterations(); iteration++) {
	std::generate(input.begin(), input.end(), std::ref(u8rng));
	std::fill(output.begin(), output.end(), 0xA5);

	// Compute reference results.
	const double scale = double(input_scale()) / (double(width()) * double(output_scale()));
	for (size_t i = 0; i < batch_size(); i++) {
	for (size_t j = 0; j < channels(); j++) {
	double acc = 0.0f;
	for (size_t k = 0; k < width(); k++) {
	acc += double(int32_t(input[(i * width() + k) * input_stride() + j]) - int32_t(input_zero_point()));
	}
	output_ref[i * channels() + j] = float(acc * scale + double(output_zero_point()));
	output_ref[i * channels() + j] = std::min<float>(output_ref[i * channels() + j], float(qmax()));
	output_ref[i * channels() + j] = std::max<float>(output_ref[i * channels() + j], float(qmin()));
	}
	}

	// Create, setup, run, and destroy Global Average Pooling operator.
	ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
	xnn_operator_t global_average_pooling_op = nullptr;

	ASSERT_EQ(xnn_status_success,
	xnn_create_global_average_pooling_nwc_q8(
	channels(), input_stride(), output_stride(),
	input_zero_point(), input_scale(),
	output_zero_point(), output_scale(),
	qmin(), qmax(),
	0, &global_average_pooling_op));
	ASSERT_NE(nullptr, global_average_pooling_op);

	// Smart pointer to automatically delete global_average_pooling_op.
	std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_global_average_pooling_op(global_average_pooling_op, xnn_delete_operator);

	ASSERT_EQ(xnn_status_success,
	xnn_setup_global_average_pooling_nwc_q8(
	global_average_pooling_op,
	batch_size(), width(),
	input.data(), output.data(),
	nullptr /* thread pool */));

	ASSERT_EQ(xnn_status_success,
	xnn_run_operator(global_average_pooling_op, nullptr /* thread pool */));

	// Verify results.
	for (size_t i = 0; i < batch_size(); i++) {
	for (size_t c = 0; c < channels(); c++) {
	ASSERT_LE(uint32_t(output[i * output_stride() + c]), uint32_t(qmax()));
	ASSERT_GE(uint32_t(output[i * output_stride() + c]), uint32_t(qmin()));
	ASSERT_NEAR(float(int32_t(output[i * output_stride() + c])), output_ref[i * channels() + c], 0.80f) <<
	"in batch index " << i << ", channel " << c;
	}
	}
	}
	}

	void TestNWCxF32() const {
	std::random_device random_device;
	auto rng = std::mt19937(random_device());
	auto f32rng = std::bind(std::uniform_real_distribution<float>(), rng);

	std::vector<float> input((batch_size() * width() - 1) * input_stride() + channels() + XNN_EXTRA_BYTES / sizeof(float));
	std::vector<float> output(batch_size() * output_stride());
	std::vector<float> output_ref(batch_size() * channels());
	for (size_t iteration = 0; iteration < iterations(); iteration++) {
	std::generate(input.begin(), input.end(), std::ref(f32rng));
	std::fill(output.begin(), output.end(), std::nanf(""));

	// Compute reference results, without clamping.
	for (size_t i = 0; i < batch_size(); i++) {
	for (size_t j = 0; j < channels(); j++) {
	float acc = 0.0f;
	for (size_t k = 0; k < width(); k++) {
	acc += input[(i * width() + k) * input_stride() + j];
	}
	output_ref[i * channels() + j] = acc / float(width());
	}
	}

	// 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 accumulated_range = accumulated_max - accumulated_min;
	const float output_min = accumulated_range == 0.0f ?
	-std::numeric_limits<float>::infinity() :
	accumulated_min + accumulated_range / 255.0f * float(qmin());
	const float output_max = accumulated_range == 0.0f ?
	+std::numeric_limits<float>::infinity() :
	accumulated_max - accumulated_range / 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 Global Average Pooling operator.
	ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
	xnn_operator_t global_average_pooling_op = nullptr;

	ASSERT_EQ(xnn_status_success,
	xnn_create_global_average_pooling_nwc_f32(
	channels(), input_stride(), output_stride(),
	output_min, output_max,
	0, &global_average_pooling_op));
	ASSERT_NE(nullptr, global_average_pooling_op);

	// Smart pointer to automatically delete global_average_pooling_op.
	std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_global_average_pooling_op(global_average_pooling_op, xnn_delete_operator);

	ASSERT_EQ(xnn_status_success,
	xnn_setup_global_average_pooling_nwc_f32(
	global_average_pooling_op,
	batch_size(), width(),
	input.data(), output.data(),
	nullptr /* thread pool */));

	ASSERT_EQ(xnn_status_success,
	xnn_run_operator(global_average_pooling_op, nullptr /* thread pool */));

	// Verify results.
	for (size_t i = 0; i < batch_size(); i++) {
	for (size_t c = 0; c < channels(); c++) {
	ASSERT_LE(output[i * output_stride() + c], output_max);
	ASSERT_GE(output[i * output_stride() + c], output_min);
	ASSERT_NEAR(output[i * output_stride() + c], output_ref[i * channels() + c], std::abs(output_ref[i * channels() + c]) * 1.0e-6f) <<
	"in batch index " << i << ", channel " << c;
	}
	}
	}
	}

	void TestNCWxF32() const {
	std::random_device random_device;
	auto rng = std::mt19937(random_device());
	auto f32rng = std::bind(std::uniform_real_distribution<float>(), rng);

	std::vector<float> input(batch_size() * channels() * width() + XNN_EXTRA_BYTES / sizeof(float));
	std::vector<float> output(batch_size() * channels());
	std::vector<float> output_ref(batch_size() * channels());
	for (size_t iteration = 0; iteration < iterations(); iteration++) {
	std::generate(input.begin(), input.end(), std::ref(f32rng));
	std::fill(output.begin(), output.end(), std::nanf(""));

	// Compute reference results, without clamping.
	for (size_t i = 0; i < batch_size(); i++) {
	for (size_t j = 0; j < channels(); j++) {
	float acc = 0.0f;
	for (size_t k = 0; k < width(); k++) {
	acc += input[(i * channels() + j) * width() + k];
	}
	output_ref[i * channels() + j] = acc / float(width());
	}
	}

	// 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 accumulated_range = accumulated_max - accumulated_min;
	const float output_min = accumulated_range == 0.0f ?
	-std::numeric_limits<float>::infinity() :
	accumulated_min + accumulated_range / 255.0f * float(qmin());
	const float output_max = accumulated_range == 0.0f ?
	+std::numeric_limits<float>::infinity() :
	accumulated_max - accumulated_range / 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 Global Average Pooling operator.
	ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
	xnn_operator_t global_average_pooling_op = nullptr;

	xnn_status status = xnn_create_global_average_pooling_ncw_f32(
	channels(), output_min, output_max,
	0, &global_average_pooling_op);
	if (status == xnn_status_unsupported_parameter) {
	GTEST_SKIP();
	}
	ASSERT_EQ(xnn_status_success, status);

	// Smart pointer to automatically delete global_average_pooling_op.
	std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_global_average_pooling_op(global_average_pooling_op, xnn_delete_operator);

	ASSERT_EQ(xnn_status_success,
	xnn_setup_global_average_pooling_ncw_f32(
	global_average_pooling_op,
	batch_size(), width(),
	input.data(), output.data(),
	nullptr /* thread pool */));

	ASSERT_EQ(xnn_status_success,
	xnn_run_operator(global_average_pooling_op, nullptr /* thread pool */));

	// Verify results.
	for (size_t i = 0; i < batch_size(); i++) {
	for (size_t c = 0; c < channels(); c++) {
	ASSERT_LE(output[i * channels() + c], output_max);
	ASSERT_GE(output[i * channels() + c], output_min);
	ASSERT_NEAR(output[i * channels() + c], output_ref[i * channels() + c], std::abs(output_ref[i * channels() + c]) * 1.0e-5f) <<
	"in batch index " << i << ", channel " << c;
	}
	}
	}
	}

	private:
	size_t batch_size_{1};
	size_t width_{1};
	size_t channels_{1};
	size_t input_stride_{0};
	size_t output_stride_{0};
	float input_scale_{1.0f};
	float output_scale_{1.0f};
	uint8_t input_zero_point_{121};
	uint8_t output_zero_point_{133};
	uint8_t qmin_{0};
	uint8_t qmax_{255};
	size_t iterations_{1};
	};