test/fully-connected-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 FullyConnectedOperatorTester {
  public:
   inline FullyConnectedOperatorTester& input_channels(size_t input_channels) {
     assert(input_channels >= 1);
     this->input_channels_ = input_channels;
     return *this;
   }

   inline size_t input_channels() const {
     return this->input_channels_;
   }

   inline FullyConnectedOperatorTester& output_channels(size_t output_channels) {
     assert(output_channels >= 1);
     this->output_channels_ = output_channels;
     return *this;
   }

   inline size_t output_channels() const {
     return this->output_channels_;
   }

   inline FullyConnectedOperatorTester& 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 FullyConnectedOperatorTester& input_stride(size_t input_stride) {
     assert(input_stride >= 1);
     this->input_stride_ = input_stride;
     return *this;
   }

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

   inline FullyConnectedOperatorTester& output_stride(size_t output_stride) {
     assert(output_stride >= 1);
     this->output_stride_ = output_stride;
     return *this;
   }

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

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

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

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

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

   inline FullyConnectedOperatorTester& transpose_weights(bool transpose_weights) {
     this->transpose_weights_ = transpose_weights;
     return *this;
   }

   inline bool transpose_weights() const {
     return this->transpose_weights_;
   }

   inline FullyConnectedOperatorTester& has_bias(bool has_bias) {
     this->has_bias_ = has_bias;
     return *this;
   }

   inline bool has_bias() const {
     return this->has_bias_;
   }

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

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

   void TestQ8() const {
     std::random_device random_device;
     auto rng = std::mt19937(random_device());
     auto s32rng = std::bind(std::uniform_int_distribution<int32_t>(-10000, 10000), rng);
     auto u8rng = std::bind(std::uniform_int_distribution<uint8_t>(), rng);

     std::vector<uint8_t> input(XNN_EXTRA_BYTES / sizeof(uint8_t) +
       (batch_size() - 1) * input_stride() + input_channels());
     std::vector<uint8_t> kernel(output_channels() * input_channels());
     std::vector<int32_t> bias(output_channels());
     std::vector<uint8_t> output((batch_size() - 1) * output_stride() + output_channels());
     std::vector<int32_t> accumulators(batch_size() * output_channels());
     std::vector<double> output_ref(batch_size() * output_channels());

     const uint8_t input_zero_point = 127;
     const uint8_t kernel_zero_point = 127;

     for (size_t iteration = 0; iteration < iterations(); iteration++) {
       std::generate(input.begin(), input.end(), std::ref(u8rng));
       std::generate(kernel.begin(), kernel.end(), std::ref(u8rng));
       std::generate(bias.begin(), bias.end(), std::ref(s32rng));
       std::fill(output.begin(), output.end(), 0xA5);

       // Compute reference results, without renormalization.
       if (has_bias()) {
         for (size_t i = 0; i < batch_size(); i++) {
           for (size_t oc = 0; oc < output_channels(); oc++) {
             accumulators[i * output_channels() + oc] = bias[oc];
           }
         }
       } else {
         std::fill(accumulators.begin(), accumulators.end(), 0);
       }
       if (transpose_weights()) {
         for (size_t i = 0; i < batch_size(); i++) {
           for (size_t oc = 0; oc < output_channels(); oc++) {
             for (size_t ic = 0; ic < input_channels(); ic++) {
               accumulators[i * output_channels() + oc] +=
                 (int32_t(input[i * input_stride() + ic]) - int32_t(input_zero_point)) *
                 (int32_t(kernel[ic * output_channels() + oc]) - int32_t(kernel_zero_point));
             }
           }
         }
       } else {
         for (size_t i = 0; i < batch_size(); i++) {
           for (size_t oc = 0; oc < output_channels(); oc++) {
             for (size_t ic = 0; ic < input_channels(); ic++) {
               accumulators[i * output_channels() + oc] +=
                 (int32_t(input[i * input_stride() + ic]) - int32_t(input_zero_point)) *
                 (int32_t(kernel[oc * 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 Fully Connected operator.
       ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
       xnn_operator_t fully_connected_op = nullptr;

       ASSERT_EQ(xnn_status_success,
         xnn_create_fully_connected_nc_q8(
           input_channels(), output_channels(),
           input_stride(), output_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(),
           transpose_weights() ? XNN_FLAG_TRANSPOSE_WEIGHTS : 0,
           &fully_connected_op));

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

       ASSERT_EQ(xnn_status_success,
         xnn_setup_fully_connected_nc_q8(
           fully_connected_op,
           batch_size(),
           input.data(), output.data(),
           nullptr /* thread pool */));

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

       // Verify results.
       for (size_t i = 0; i < batch_size(); i++) {
         for (size_t c = 0; c < output_channels(); c++) {
           ASSERT_LE(int32_t(output[i * output_stride() + c]), int32_t(qmax()))
             << "batch index = " << i << ", channel = " << c;
           ASSERT_GE(int32_t(output[i * output_stride() + c]), int32_t(qmin()))
             << "batch index = " << i << ", channel = " << c;
           ASSERT_NEAR(
               output_ref[i * output_channels() + c],
               double(output[i * output_stride() + c]) - double(output_zero_point),
               0.9)
             << "batch index = " << i << ", 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), rng);

     std::vector<float> input(XNN_EXTRA_BYTES / sizeof(float) +
       (batch_size() - 1) * input_stride() + input_channels());
     std::vector<float> kernel(output_channels() * input_channels());
     std::vector<float> bias(output_channels());
     std::vector<float> output((batch_size() - 1) * output_stride() + output_channels());
     std::vector<float> output_ref(batch_size() * 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 renormalization.
       if (has_bias()) {
         for (size_t i = 0; i < batch_size(); i++) {
           for (size_t oc = 0; oc < output_channels(); oc++) {
             output_ref[i * output_channels() + oc] = bias[oc];
           }
         }
       } else {
         std::fill(output_ref.begin(), output_ref.end(), 0.0f);
       }
       if (transpose_weights()) {
         for (size_t i = 0; i < batch_size(); i++) {
           for (size_t oc = 0; oc < output_channels(); oc++) {
             for (size_t ic = 0; ic < input_channels(); ic++) {
               output_ref[i * output_channels() + oc] +=
                 input[i * input_stride() + ic] * kernel[ic * output_channels() + oc];
             }
           }
         }
       } else {
         for (size_t i = 0; i < batch_size(); i++) {
           for (size_t oc = 0; oc < output_channels(); oc++) {
             for (size_t ic = 0; ic < input_channels(); ic++) {
               output_ref[i * output_channels() + oc] +=
                 input[i * input_stride() + ic] * kernel[oc * input_channels() + ic];
             }
           }
         }
       }

       // Compute clamping parameters.
       const float accumulated_min = *std::min_element(output_ref.cbegin(), output_ref.cend());
       const float accumulated_max = *std::max_element(output_ref.cbegin(), output_ref.cend());

       const float output_min = accumulated_min + (accumulated_max - accumulated_min) / 255.0f * float(qmin());
       const float output_max = accumulated_max - (accumulated_max - accumulated_min) / 255.0f * float(255 - qmax());

       // Clamp reference results.
       for (float& value : output_ref) {
         value = std::max(std::min(value, output_max), output_min);
       }

       // Create, setup, run, and destroy Fully Connected operator.
       ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
       xnn_operator_t fully_connected_op = nullptr;

       ASSERT_EQ(xnn_status_success,
         xnn_create_fully_connected_nc_f32(
           input_channels(), output_channels(),
           input_stride(), output_stride(),
           kernel.data(), has_bias() ? bias.data() : nullptr,
           output_min, output_max,
           transpose_weights() ? XNN_FLAG_TRANSPOSE_WEIGHTS : 0,
           &fully_connected_op));

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

       ASSERT_EQ(xnn_status_success,
         xnn_setup_fully_connected_nc_f32(
           fully_connected_op,
           batch_size(),
           input.data(), output.data(),
           nullptr /* thread pool */));

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

       // Verify results.
       for (size_t i = 0; i < batch_size(); i++) {
         for (size_t c = 0; c < output_channels(); c++) {
           ASSERT_LE(output[i * output_stride() + c], output_max)
             << "batch index = " << i << ", channel = " << c;
           ASSERT_GE(output[i * output_stride() + c], output_min)
             << "batch index = " << i << ", channel = " << c;
           ASSERT_NEAR(
               output_ref[i * output_channels() + c],
               output[i * output_stride() + c],
               1.0e-4 * std::abs(output_ref[i * output_channels() + c]))
             << "batch index = " << i << ", channel = " << c;
         }
       }
     }
   }

  private:
   size_t input_channels_{1};
   size_t input_stride_{0};
   size_t output_channels_{1};
   size_t output_stride_{0};
   size_t batch_size_{1};
   uint8_t qmin_{0};
   uint8_t qmax_{255};
   bool transpose_weights_{false};
   bool has_bias_{true};
   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 FullyConnectedOperatorTester {
	public:
	inline FullyConnectedOperatorTester& input_channels(size_t input_channels) {
	assert(input_channels >= 1);
	this->input_channels_ = input_channels;
	return *this;
	}

	inline size_t input_channels() const {
	return this->input_channels_;
	}

	inline FullyConnectedOperatorTester& output_channels(size_t output_channels) {
	assert(output_channels >= 1);
	this->output_channels_ = output_channels;
	return *this;
	}

	inline size_t output_channels() const {
	return this->output_channels_;
	}

	inline FullyConnectedOperatorTester& 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 FullyConnectedOperatorTester& input_stride(size_t input_stride) {
	assert(input_stride >= 1);
	this->input_stride_ = input_stride;
	return *this;
	}

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

	inline FullyConnectedOperatorTester& output_stride(size_t output_stride) {
	assert(output_stride >= 1);
	this->output_stride_ = output_stride;
	return *this;
	}

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

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

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

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

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

	inline FullyConnectedOperatorTester& transpose_weights(bool transpose_weights) {
	this->transpose_weights_ = transpose_weights;
	return *this;
	}

	inline bool transpose_weights() const {
	return this->transpose_weights_;
	}

	inline FullyConnectedOperatorTester& has_bias(bool has_bias) {
	this->has_bias_ = has_bias;
	return *this;
	}

	inline bool has_bias() const {
	return this->has_bias_;
	}

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

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

	void TestQ8() const {
	std::random_device random_device;
	auto rng = std::mt19937(random_device());
	auto s32rng = std::bind(std::uniform_int_distribution<int32_t>(-10000, 10000), rng);
	auto u8rng = std::bind(std::uniform_int_distribution<uint8_t>(), rng);

	std::vector<uint8_t> input(XNN_EXTRA_BYTES / sizeof(uint8_t) +
	(batch_size() - 1) * input_stride() + input_channels());
	std::vector<uint8_t> kernel(output_channels() * input_channels());
	std::vector<int32_t> bias(output_channels());
	std::vector<uint8_t> output((batch_size() - 1) * output_stride() + output_channels());
	std::vector<int32_t> accumulators(batch_size() * output_channels());
	std::vector<double> output_ref(batch_size() * output_channels());

	const uint8_t input_zero_point = 127;
	const uint8_t kernel_zero_point = 127;

	for (size_t iteration = 0; iteration < iterations(); iteration++) {
	std::generate(input.begin(), input.end(), std::ref(u8rng));
	std::generate(kernel.begin(), kernel.end(), std::ref(u8rng));
	std::generate(bias.begin(), bias.end(), std::ref(s32rng));
	std::fill(output.begin(), output.end(), 0xA5);

	// Compute reference results, without renormalization.
	if (has_bias()) {
	for (size_t i = 0; i < batch_size(); i++) {
	for (size_t oc = 0; oc < output_channels(); oc++) {
	accumulators[i * output_channels() + oc] = bias[oc];
	}
	}
	} else {
	std::fill(accumulators.begin(), accumulators.end(), 0);
	}
	if (transpose_weights()) {
	for (size_t i = 0; i < batch_size(); i++) {
	for (size_t oc = 0; oc < output_channels(); oc++) {
	for (size_t ic = 0; ic < input_channels(); ic++) {
	accumulators[i * output_channels() + oc] +=
	(int32_t(input[i * input_stride() + ic]) - int32_t(input_zero_point)) *
	(int32_t(kernel[ic * output_channels() + oc]) - int32_t(kernel_zero_point));
	}
	}
	}
	} else {
	for (size_t i = 0; i < batch_size(); i++) {
	for (size_t oc = 0; oc < output_channels(); oc++) {
	for (size_t ic = 0; ic < input_channels(); ic++) {
	accumulators[i * output_channels() + oc] +=
	(int32_t(input[i * input_stride() + ic]) - int32_t(input_zero_point)) *
	(int32_t(kernel[oc * 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 Fully Connected operator.
	ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
	xnn_operator_t fully_connected_op = nullptr;

	ASSERT_EQ(xnn_status_success,
	xnn_create_fully_connected_nc_q8(
	input_channels(), output_channels(),
	input_stride(), output_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(),
	transpose_weights() ? XNN_FLAG_TRANSPOSE_WEIGHTS : 0,
	&fully_connected_op));

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

	ASSERT_EQ(xnn_status_success,
	xnn_setup_fully_connected_nc_q8(
	fully_connected_op,
	batch_size(),
	input.data(), output.data(),
	nullptr /* thread pool */));

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

	// Verify results.
	for (size_t i = 0; i < batch_size(); i++) {
	for (size_t c = 0; c < output_channels(); c++) {
	ASSERT_LE(int32_t(output[i * output_stride() + c]), int32_t(qmax()))
	<< "batch index = " << i << ", channel = " << c;
	ASSERT_GE(int32_t(output[i * output_stride() + c]), int32_t(qmin()))
	<< "batch index = " << i << ", channel = " << c;
	ASSERT_NEAR(
	output_ref[i * output_channels() + c],
	double(output[i * output_stride() + c]) - double(output_zero_point),
	0.9)
	<< "batch index = " << i << ", 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), rng);

	std::vector<float> input(XNN_EXTRA_BYTES / sizeof(float) +
	(batch_size() - 1) * input_stride() + input_channels());
	std::vector<float> kernel(output_channels() * input_channels());
	std::vector<float> bias(output_channels());
	std::vector<float> output((batch_size() - 1) * output_stride() + output_channels());
	std::vector<float> output_ref(batch_size() * 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 renormalization.
	if (has_bias()) {
	for (size_t i = 0; i < batch_size(); i++) {
	for (size_t oc = 0; oc < output_channels(); oc++) {
	output_ref[i * output_channels() + oc] = bias[oc];
	}
	}
	} else {
	std::fill(output_ref.begin(), output_ref.end(), 0.0f);
	}
	if (transpose_weights()) {
	for (size_t i = 0; i < batch_size(); i++) {
	for (size_t oc = 0; oc < output_channels(); oc++) {
	for (size_t ic = 0; ic < input_channels(); ic++) {
	output_ref[i * output_channels() + oc] +=
	input[i * input_stride() + ic] * kernel[ic * output_channels() + oc];
	}
	}
	}
	} else {
	for (size_t i = 0; i < batch_size(); i++) {
	for (size_t oc = 0; oc < output_channels(); oc++) {
	for (size_t ic = 0; ic < input_channels(); ic++) {
	output_ref[i * output_channels() + oc] +=
	input[i * input_stride() + ic] * kernel[oc * input_channels() + ic];
	}
	}
	}
	}

	// Compute clamping parameters.
	const float accumulated_min = *std::min_element(output_ref.cbegin(), output_ref.cend());
	const float accumulated_max = *std::max_element(output_ref.cbegin(), output_ref.cend());

	const float output_min = accumulated_min + (accumulated_max - accumulated_min) / 255.0f * float(qmin());
	const float output_max = accumulated_max - (accumulated_max - accumulated_min) / 255.0f * float(255 - qmax());

	// Clamp reference results.
	for (float& value : output_ref) {
	value = std::max(std::min(value, output_max), output_min);
	}

	// Create, setup, run, and destroy Fully Connected operator.
	ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
	xnn_operator_t fully_connected_op = nullptr;

	ASSERT_EQ(xnn_status_success,
	xnn_create_fully_connected_nc_f32(
	input_channels(), output_channels(),
	input_stride(), output_stride(),
	kernel.data(), has_bias() ? bias.data() : nullptr,
	output_min, output_max,
	transpose_weights() ? XNN_FLAG_TRANSPOSE_WEIGHTS : 0,
	&fully_connected_op));

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

	ASSERT_EQ(xnn_status_success,
	xnn_setup_fully_connected_nc_f32(
	fully_connected_op,
	batch_size(),
	input.data(), output.data(),
	nullptr /* thread pool */));

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

	// Verify results.
	for (size_t i = 0; i < batch_size(); i++) {
	for (size_t c = 0; c < output_channels(); c++) {
	ASSERT_LE(output[i * output_stride() + c], output_max)
	<< "batch index = " << i << ", channel = " << c;
	ASSERT_GE(output[i * output_stride() + c], output_min)
	<< "batch index = " << i << ", channel = " << c;
	ASSERT_NEAR(
	output_ref[i * output_channels() + c],
	output[i * output_stride() + c],
	1.0e-4 * std::abs(output_ref[i * output_channels() + c]))
	<< "batch index = " << i << ", channel = " << c;
	}
	}
	}
	}

	private:
	size_t input_channels_{1};
	size_t input_stride_{0};
	size_t output_channels_{1};
	size_t output_stride_{0};
	size_t batch_size_{1};
	uint8_t qmin_{0};
	uint8_t qmax_{255};
	bool transpose_weights_{false};
	bool has_bias_{true};
	size_t iterations_{1};
	};