test/conv-hwc-microkernel-tester.h - platform/external/XNNPACK - Gitiles

 // 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/AlignedAllocator.h>
 #include <xnnpack/pack.h>
 #include <xnnpack/params.h>
 #include <xnnpack/requantization.h>
 #include <xnnpack.h>


 class ConvHWCMicrokernelTester {
 public:
   enum class Variant {
     Native,
     Scalar,
   };

   inline ConvHWCMicrokernelTester& output_channels_tile(uint32_t output_channels_tile) {
     this->output_channels_tile_ = output_channels_tile;
     return *this;
   }

   inline uint32_t output_channels_tile() const {
     return this->output_channels_tile_;
   }

   inline ConvHWCMicrokernelTester& padding(uint32_t padding) {
     this->padding_top_ = padding;
     this->padding_right_ = padding;
     this->padding_bottom_ = padding;
     this->padding_left_ = padding;
     return *this;
   }

   inline ConvHWCMicrokernelTester& padding_height(uint32_t padding_height) {
     this->padding_top_ = padding_height;
     this->padding_bottom_ = padding_height;
     return *this;
   }

   inline ConvHWCMicrokernelTester& padding_width(uint32_t padding_width) {
     this->padding_right_ = padding_width;
     this->padding_left_ = padding_width;
     return *this;
   }

   inline ConvHWCMicrokernelTester& padding_top(uint32_t padding_top) {
     this->padding_top_ = padding_top;
     return *this;
   }

   inline uint32_t padding_top() const {
     return this->padding_top_;
   }

   inline ConvHWCMicrokernelTester& padding_right(uint32_t padding_right) {
     this->padding_right_ = padding_right;
     return *this;
   }

   inline uint32_t padding_right() const {
     return this->padding_right_;
   }

   inline ConvHWCMicrokernelTester& padding_bottom(uint32_t padding_bottom) {
     this->padding_bottom_ = padding_bottom;
     return *this;
   }

   inline uint32_t padding_bottom() const {
     return this->padding_bottom_;
   }

   inline ConvHWCMicrokernelTester& padding_left(uint32_t padding_left) {
     this->padding_left_ = padding_left;
     return *this;
   }

   inline uint32_t padding_left() const {
     return this->padding_left_;
   }

   inline ConvHWCMicrokernelTester& 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 ConvHWCMicrokernelTester& 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 ConvHWCMicrokernelTester& 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 ConvHWCMicrokernelTester& 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 ConvHWCMicrokernelTester& 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 size_t packed_output_channels() const {
     return output_channels() % output_channels_tile() == 0 ? output_channels() : output_channels() / output_channels_tile() * output_channels_tile() + output_channels_tile();
   }

   inline ConvHWCMicrokernelTester& 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 ConvHWCMicrokernelTester& kernel_size(uint32_t kernel_size) {
     assert(kernel_size >= 1);
     this->kernel_height_ = kernel_size;
     this->kernel_width_ = kernel_size;
     return *this;
   }

   inline ConvHWCMicrokernelTester& 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 ConvHWCMicrokernelTester& 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 ConvHWCMicrokernelTester& subsampling(uint32_t subsampling) {
     assert(subsampling >= 1);
     this->subsampling_height_ = subsampling;
     this->subsampling_width_ = subsampling;
     return *this;
   }

   inline ConvHWCMicrokernelTester& subsampling_height(uint32_t subsampling_height) {
     assert(subsampling_height >= 1);
     this->subsampling_height_ = subsampling_height;
     return *this;
   }

   inline uint32_t subsampling_height() const {
     return this->subsampling_height_;
   }

   inline ConvHWCMicrokernelTester& subsampling_width(uint32_t subsampling_width) {
     assert(subsampling_width >= 1);
     this->subsampling_width_ = subsampling_width;
     return *this;
   }

   inline uint32_t subsampling_width() const {
     return this->subsampling_width_;
   }

   inline ConvHWCMicrokernelTester& output_y_start(uint32_t output_y_start) {
     this->output_y_start_ = output_y_start;
     return *this;
   }

   inline uint32_t output_y_start() const {
     return this->output_y_start_;
   }

   inline ConvHWCMicrokernelTester& output_y_end(uint32_t output_y_end) {
     this->output_y_end_ = output_y_end;
     return *this;
   }

   inline uint32_t output_y_end() const {
     if (this->output_y_end_ == std::numeric_limits<uint32_t>::max()) {
       return output_height();
     } else {
       return this->output_y_end_;
     }
   }

   inline size_t input_pixel_stride() const {
     return input_channels();
   }

   inline size_t output_pixel_stride() const {
     return output_channels();
   }

   inline size_t output_height() const {
     const size_t padded_input_height = padding_top() + input_height() + padding_bottom();
     if (padded_input_height <= kernel_height()) {
       return 1;
     } else {
       return (padded_input_height - kernel_height()) / subsampling_height() + 1;
     }
   }

   inline size_t output_width() const {
     const size_t padded_input_width = padding_left() + input_width() + padding_right();
     if (padded_input_width <= kernel_width()) {
       return 1;
     } else {
       return (padded_input_width - kernel_width()) / subsampling_width() + 1;
     }
   }

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

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

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

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

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

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

   void Test(xnn_f32_conv_hwc_ukernel_function conv, Variant variant = Variant::Native) const {
     ASSERT_LT(output_y_start(), output_height());
     ASSERT_LE(output_y_end(), output_height());
     ASSERT_GT(output_y_end(), output_y_start());

     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() * ((input_height() * input_width() - 1) * input_pixel_stride() + input_channels()));
     std::vector<float> zero(XNN_EXTRA_BYTES / sizeof(float) + input_width() * input_channels());
     std::vector<float> kernel(output_channels() * kernel_height() * kernel_width() * input_channels());
     std::vector<float> bias(output_channels());
     std::vector<float> output(batch_size() * ((output_height() * output_width() - 1) * output_pixel_stride() + output_channels()));
     std::vector<float> output_ref(batch_size() * output_height() * output_width() * output_channels());
     std::vector<float, AlignedAllocator<float, 32>> packed_weights((input_channels() * kernel_height() * kernel_width() + 1) * packed_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(packed_weights.begin(), packed_weights.end(), 0.0f);

       xnn_pack_f32_dconv_oki_w(
         output_channels(),
         input_channels(),
         output_channels_tile(),
         kernel_height(), kernel_width(),
         kernel.data(), bias.data(), packed_weights.data());

       // Compute reference results, without clamping.
       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 oc = 0; oc < output_channels(); oc++) {
               float acc = bias[oc];
               for (size_t ky = 0; ky < kernel_height(); ky++) {
                 const size_t iy = oy * subsampling_height() + ky - padding_top();
                 if (iy < input_height()) {
                   for (size_t kx = 0; kx < kernel_width(); kx++) {
                     const size_t ix = ox * subsampling_width() + kx - padding_left();
                     if (ix < input_width()) {
                       for (size_t ic = 0; ic < input_channels(); ic++) {
                         acc +=
                           input[((i * input_height() + iy) * input_width() + ix) * input_pixel_stride() + ic] *
                           kernel[((oc * kernel_height() + ky) * kernel_width() + kx) * input_channels() + ic];
                       }
                     }
                   }
                 }
               }
               output_ref[((i * output_height() + oy) * output_width() + ox) * output_channels() + oc] = acc;
             }
           }
         }
       }

       // 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);
       }

       // Prepare output parameters.
       xnn_f32_output_params output_params = { };
       switch (variant) {
         case Variant::Native:
           output_params = xnn_compute_f32_output_params(output_min, output_max);
           break;
         case Variant::Scalar:
           output_params = xnn_compute_scalar_f32_output_params(output_min, output_max);
           break;
       }

       // Call optimized micro-kernel.
       conv(
         input_height(), input_width(),
         output_y_start(), output_y_end(),
         input.data(), zero.data(), packed_weights.data(), output.data(),
         padding_top(), output_channels(),
         output_pixel_stride() * output_width() * sizeof(float),
         output_pixel_stride() * sizeof(float),
         &output_params);

       // Verify results.
       for (size_t i = 0; i < batch_size(); i++) {
         for (size_t y = output_y_start(); y < output_y_end(); y++) {
           for (size_t x = 0; x < output_width(); x++) {
             for (size_t c = 0; c < output_channels(); c++) {
               ASSERT_GE(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c], output_min)
                 << "(x, y) = (" << x << ", " << y << "), channel = " << c;
               ASSERT_LE(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c], output_max)
                 << "(x, y) = (" << x << ", " << y << "), channel = " << c;
               ASSERT_NEAR(
                   output_ref[((i * output_height() + y) * output_width() + x) * output_channels() + c],
                   output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c],
                   1.0e-4 * std::abs(output_ref[((i * output_height() + y) * output_width() + x) * output_channels() + c]))
                 << "(x, y) = (" << x << ", " << y << "), 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};
   size_t input_channels_{1};
   size_t output_channels_{1};
   uint32_t output_channels_tile_{1};
   size_t batch_size_{1};
   uint32_t kernel_height_{1};
   uint32_t kernel_width_{1};
   uint32_t subsampling_height_{1};
   uint32_t subsampling_width_{1};
   uint32_t output_y_start_{0};
   uint32_t output_y_end_{std::numeric_limits<uint32_t>::max()};
   uint8_t qmin_{0};
   uint8_t qmax_{255};
   size_t iterations_{1};
 };
	// 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/AlignedAllocator.h>
	#include <xnnpack/pack.h>
	#include <xnnpack/params.h>
	#include <xnnpack/requantization.h>
	#include <xnnpack.h>


	class ConvHWCMicrokernelTester {
	public:
	enum class Variant {
	Native,
	Scalar,
	};

	inline ConvHWCMicrokernelTester& output_channels_tile(uint32_t output_channels_tile) {
	this->output_channels_tile_ = output_channels_tile;
	return *this;
	}

	inline uint32_t output_channels_tile() const {
	return this->output_channels_tile_;
	}

	inline ConvHWCMicrokernelTester& padding(uint32_t padding) {
	this->padding_top_ = padding;
	this->padding_right_ = padding;
	this->padding_bottom_ = padding;
	this->padding_left_ = padding;
	return *this;
	}

	inline ConvHWCMicrokernelTester& padding_height(uint32_t padding_height) {
	this->padding_top_ = padding_height;
	this->padding_bottom_ = padding_height;
	return *this;
	}

	inline ConvHWCMicrokernelTester& padding_width(uint32_t padding_width) {
	this->padding_right_ = padding_width;
	this->padding_left_ = padding_width;
	return *this;
	}

	inline ConvHWCMicrokernelTester& padding_top(uint32_t padding_top) {
	this->padding_top_ = padding_top;
	return *this;
	}

	inline uint32_t padding_top() const {
	return this->padding_top_;
	}

	inline ConvHWCMicrokernelTester& padding_right(uint32_t padding_right) {
	this->padding_right_ = padding_right;
	return *this;
	}

	inline uint32_t padding_right() const {
	return this->padding_right_;
	}

	inline ConvHWCMicrokernelTester& padding_bottom(uint32_t padding_bottom) {
	this->padding_bottom_ = padding_bottom;
	return *this;
	}

	inline uint32_t padding_bottom() const {
	return this->padding_bottom_;
	}

	inline ConvHWCMicrokernelTester& padding_left(uint32_t padding_left) {
	this->padding_left_ = padding_left;
	return *this;
	}

	inline uint32_t padding_left() const {
	return this->padding_left_;
	}

	inline ConvHWCMicrokernelTester& 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 ConvHWCMicrokernelTester& 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 ConvHWCMicrokernelTester& 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 ConvHWCMicrokernelTester& 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 ConvHWCMicrokernelTester& 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 size_t packed_output_channels() const {
	return output_channels() % output_channels_tile() == 0 ? output_channels() : output_channels() / output_channels_tile() * output_channels_tile() + output_channels_tile();
	}

	inline ConvHWCMicrokernelTester& 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 ConvHWCMicrokernelTester& kernel_size(uint32_t kernel_size) {
	assert(kernel_size >= 1);
	this->kernel_height_ = kernel_size;
	this->kernel_width_ = kernel_size;
	return *this;
	}

	inline ConvHWCMicrokernelTester& 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 ConvHWCMicrokernelTester& 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 ConvHWCMicrokernelTester& subsampling(uint32_t subsampling) {
	assert(subsampling >= 1);
	this->subsampling_height_ = subsampling;
	this->subsampling_width_ = subsampling;
	return *this;
	}

	inline ConvHWCMicrokernelTester& subsampling_height(uint32_t subsampling_height) {
	assert(subsampling_height >= 1);
	this->subsampling_height_ = subsampling_height;
	return *this;
	}

	inline uint32_t subsampling_height() const {
	return this->subsampling_height_;
	}

	inline ConvHWCMicrokernelTester& subsampling_width(uint32_t subsampling_width) {
	assert(subsampling_width >= 1);
	this->subsampling_width_ = subsampling_width;
	return *this;
	}

	inline uint32_t subsampling_width() const {
	return this->subsampling_width_;
	}

	inline ConvHWCMicrokernelTester& output_y_start(uint32_t output_y_start) {
	this->output_y_start_ = output_y_start;
	return *this;
	}

	inline uint32_t output_y_start() const {
	return this->output_y_start_;
	}

	inline ConvHWCMicrokernelTester& output_y_end(uint32_t output_y_end) {
	this->output_y_end_ = output_y_end;
	return *this;
	}

	inline uint32_t output_y_end() const {
	if (this->output_y_end_ == std::numeric_limits<uint32_t>::max()) {
	return output_height();
	} else {
	return this->output_y_end_;
	}
	}

	inline size_t input_pixel_stride() const {
	return input_channels();
	}

	inline size_t output_pixel_stride() const {
	return output_channels();
	}

	inline size_t output_height() const {
	const size_t padded_input_height = padding_top() + input_height() + padding_bottom();
	if (padded_input_height <= kernel_height()) {
	return 1;
	} else {
	return (padded_input_height - kernel_height()) / subsampling_height() + 1;
	}
	}

	inline size_t output_width() const {
	const size_t padded_input_width = padding_left() + input_width() + padding_right();
	if (padded_input_width <= kernel_width()) {
	return 1;
	} else {
	return (padded_input_width - kernel_width()) / subsampling_width() + 1;
	}
	}

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

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

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

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

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

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

	void Test(xnn_f32_conv_hwc_ukernel_function conv, Variant variant = Variant::Native) const {
	ASSERT_LT(output_y_start(), output_height());
	ASSERT_LE(output_y_end(), output_height());
	ASSERT_GT(output_y_end(), output_y_start());

	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() * ((input_height() * input_width() - 1) * input_pixel_stride() + input_channels()));
	std::vector<float> zero(XNN_EXTRA_BYTES / sizeof(float) + input_width() * input_channels());
	std::vector<float> kernel(output_channels() * kernel_height() * kernel_width() * input_channels());
	std::vector<float> bias(output_channels());
	std::vector<float> output(batch_size() * ((output_height() * output_width() - 1) * output_pixel_stride() + output_channels()));
	std::vector<float> output_ref(batch_size() * output_height() * output_width() * output_channels());
	std::vector<float, AlignedAllocator<float, 32>> packed_weights((input_channels() * kernel_height() * kernel_width() + 1) * packed_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(packed_weights.begin(), packed_weights.end(), 0.0f);

	xnn_pack_f32_dconv_oki_w(
	output_channels(),
	input_channels(),
	output_channels_tile(),
	kernel_height(), kernel_width(),
	kernel.data(), bias.data(), packed_weights.data());

	// Compute reference results, without clamping.
	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 oc = 0; oc < output_channels(); oc++) {
	float acc = bias[oc];
	for (size_t ky = 0; ky < kernel_height(); ky++) {
	const size_t iy = oy * subsampling_height() + ky - padding_top();
	if (iy < input_height()) {
	for (size_t kx = 0; kx < kernel_width(); kx++) {
	const size_t ix = ox * subsampling_width() + kx - padding_left();
	if (ix < input_width()) {
	for (size_t ic = 0; ic < input_channels(); ic++) {
	acc +=
	input[((i * input_height() + iy) * input_width() + ix) * input_pixel_stride() + ic] *
	kernel[((oc * kernel_height() + ky) * kernel_width() + kx) * input_channels() + ic];
	}
	}
	}
	}
	}
	output_ref[((i * output_height() + oy) * output_width() + ox) * output_channels() + oc] = acc;
	}
	}
	}
	}

	// 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);
	}

	// Prepare output parameters.
	xnn_f32_output_params output_params = { };
	switch (variant) {
	case Variant::Native:
	output_params = xnn_compute_f32_output_params(output_min, output_max);
	break;
	case Variant::Scalar:
	output_params = xnn_compute_scalar_f32_output_params(output_min, output_max);
	break;
	}

	// Call optimized micro-kernel.
	conv(
	input_height(), input_width(),
	output_y_start(), output_y_end(),
	input.data(), zero.data(), packed_weights.data(), output.data(),
	padding_top(), output_channels(),
	output_pixel_stride() * output_width() * sizeof(float),
	output_pixel_stride() * sizeof(float),
	&output_params);

	// Verify results.
	for (size_t i = 0; i < batch_size(); i++) {
	for (size_t y = output_y_start(); y < output_y_end(); y++) {
	for (size_t x = 0; x < output_width(); x++) {
	for (size_t c = 0; c < output_channels(); c++) {
	ASSERT_GE(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c], output_min)
	<< "(x, y) = (" << x << ", " << y << "), channel = " << c;
	ASSERT_LE(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c], output_max)
	<< "(x, y) = (" << x << ", " << y << "), channel = " << c;
	ASSERT_NEAR(
	output_ref[((i * output_height() + y) * output_width() + x) * output_channels() + c],
	output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c],
	1.0e-4 * std::abs(output_ref[((i * output_height() + y) * output_width() + x) * output_channels() + c]))
	<< "(x, y) = (" << x << ", " << y << "), 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};
	size_t input_channels_{1};
	size_t output_channels_{1};
	uint32_t output_channels_tile_{1};
	size_t batch_size_{1};
	uint32_t kernel_height_{1};
	uint32_t kernel_width_{1};
	uint32_t subsampling_height_{1};
	uint32_t subsampling_width_{1};
	uint32_t output_y_start_{0};
	uint32_t output_y_end_{std::numeric_limits<uint32_t>::max()};
	uint8_t qmin_{0};
	uint8_t qmax_{255};
	size_t iterations_{1};
	};