test/convolution-spnchw-operator-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 <random>
 #include <vector>

 #include <xnnpack.h>


 class ConvolutionSpNCHWOperatorTester {
  public:
   inline ConvolutionSpNCHWOperatorTester& padding(uint32_t padding) {
     this->padding_top_ = padding;
     this->padding_right_ = padding;
     this->padding_bottom_ = padding;
     this->padding_left_ = padding;
     return *this;
   }

   inline ConvolutionSpNCHWOperatorTester& padding(uint32_t padding_height, uint32_t padding_width) {
     this->padding_top_ = padding_height;
     this->padding_right_ = padding_width;
     this->padding_bottom_ = padding_height;
     this->padding_left_ = padding_width;
     return *this;
   }

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

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

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

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

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

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

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

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

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

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

   inline ConvolutionSpNCHWOperatorTester& 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 ConvolutionSpNCHWOperatorTester& 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 ConvolutionSpNCHWOperatorTester& 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 ConvolutionSpNCHWOperatorTester& groups(uint32_t groups) {
     assert(groups >= 1);
     this->groups_ = groups;
     return *this;
   }

   inline uint32_t groups() const {
     return this->groups_;
   }

   inline ConvolutionSpNCHWOperatorTester& group_input_channels(size_t group_input_channels) {
     assert(group_input_channels >= 1);
     this->group_input_channels_ = group_input_channels;
     return *this;
   }

   inline size_t group_input_channels() const {
     return this->group_input_channels_;
   }

   inline ConvolutionSpNCHWOperatorTester& group_output_channels(size_t group_output_channels) {
     assert(group_output_channels >= 1);
     this->group_output_channels_ = group_output_channels;
     return *this;
   }

   inline size_t group_output_channels() const {
     return this->group_output_channels_;
   }

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

   inline ConvolutionSpNCHWOperatorTester& kernel_size(uint32_t kernel_height, uint32_t kernel_width) {
     assert(kernel_height >= 1);
     assert(kernel_width >= 1);
     this->kernel_height_ = kernel_height;
     this->kernel_width_ = kernel_width;
     return *this;
   }

   inline ConvolutionSpNCHWOperatorTester& 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 ConvolutionSpNCHWOperatorTester& 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 ConvolutionSpNCHWOperatorTester& dilation(uint32_t dilation) {
     assert(dilation >= 1);
     this->dilation_height_ = dilation;
     this->dilation_width_ = dilation;
     return *this;
   }

   inline ConvolutionSpNCHWOperatorTester& dilation(uint32_t dilation_height, uint32_t dilation_width) {
     assert(dilation_height >= 1);
     assert(dilation_width >= 1);
     this->dilation_height_ = dilation_height;
     this->dilation_width_ = dilation_width;
     return *this;
   }

   inline ConvolutionSpNCHWOperatorTester& dilation_height(uint32_t dilation_height) {
     assert(dilation_height >= 1);
     this->dilation_height_ = dilation_height;
     return *this;
   }

   inline uint32_t dilation_height() const {
     return this->dilation_height_;
   }

   inline ConvolutionSpNCHWOperatorTester& dilation_width(uint32_t dilation_width) {
     assert(dilation_width >= 1);
     this->dilation_width_ = dilation_width;
     return *this;
   }

   inline uint32_t dilation_width() const {
     return this->dilation_width_;
   }

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

   inline ConvolutionSpNCHWOperatorTester& subsampling(uint32_t subsampling_height, uint32_t subsampling_width) {
     assert(subsampling_height >= 1);
     assert(subsampling_width >= 1);
     this->subsampling_height_ = subsampling_height;
     this->subsampling_width_ = subsampling_width;
     return *this;
   }

   inline ConvolutionSpNCHWOperatorTester& 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 ConvolutionSpNCHWOperatorTester& 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 ConvolutionSpNCHWOperatorTester& input_batch_stride(size_t input_batch_stride) {
     assert(input_batch_stride >= 1);
     this->input_batch_stride_ = input_batch_stride;
     return *this;
   }

   inline size_t input_batch_stride() const {
     if (this->input_batch_stride_ == 0) {
       return groups() * group_input_channels() * input_height() * input_width();
     } else {
       assert(this->input_batch_stride_ >= groups() * group_input_channels() * input_height() * input_width());
       return this->input_batch_stride_;
     }
   }

   inline ConvolutionSpNCHWOperatorTester& output_batch_stride(size_t output_batch_stride) {
     assert(output_batch_stride >= 1);
     this->output_batch_stride_ = output_batch_stride;
     return *this;
   }

   inline size_t output_batch_stride() const {
     if (this->output_batch_stride_ == 0) {
       return groups() * group_output_channels() * output_height() * output_width();
     } else {
       assert(this->output_batch_stride_ >= groups() * group_output_channels() * output_height() * output_width());
       return this->output_batch_stride_;
     }
   }

   inline uint32_t dilated_kernel_height() const {
     return (kernel_height() - 1) * dilation_height() + 1;
   }

   inline uint32_t dilated_kernel_width() const {
     return (kernel_width() - 1) * dilation_width() + 1;
   }

   inline size_t output_height() const {
     const size_t padded_input_height = padding_top() + input_height() + padding_bottom();
     if (padded_input_height <= dilated_kernel_height()) {
       return 1;
     } else {
       return (padded_input_height - dilated_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 <= dilated_kernel_width()) {
       return 1;
     } else {
       return (padded_input_width - dilated_kernel_width()) / subsampling_width() + 1;
     }
   }

   inline ConvolutionSpNCHWOperatorTester& nhwc_input(bool nhwc_input) {
     this->nhwc_input_ = nhwc_input;
     return *this;
   }

   inline bool nhwc_input() const {
     return this->nhwc_input_;
   }

   inline ConvolutionSpNCHWOperatorTester& sparsity(float sparsity) {
     this->sparsity_ = sparsity;
     return *this;
   }

   inline float sparsity() const {
     return this->sparsity_;
   }

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

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

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

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

   inline ConvolutionSpNCHWOperatorTester& depthwise_layout(bool depthwise_layout) {
     this->depthwise_layout_ = depthwise_layout;
     return *this;
   }

   inline bool depthwise_layout() const {
     return this->depthwise_layout_;
   }

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

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

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

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

   void TestF32() const {
     ASSERT_FALSE(depthwise_layout());

     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);
     auto prng = std::bind(std::uniform_real_distribution<float>(), rng);

     std::vector<float> input(XNN_EXTRA_BYTES / sizeof(float) +
       batch_size() * input_batch_stride() + groups() * group_input_channels() * input_height() * input_width());
     std::vector<float> kernel(
       groups() * group_output_channels() * kernel_height() * kernel_width() * group_input_channels());
     std::vector<float> bias(groups() * group_output_channels());
     std::vector<float> output(
       batch_size() * output_batch_stride() + groups() * group_output_channels() * output_height() * output_width());
     std::vector<float> output_ref(batch_size() * groups() * group_output_channels() * output_height() * output_width());

     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));
       for (float& k : kernel) {
         if (prng() <= sparsity()) {
           k = 0.0f;
         }
       }
       std::generate(bias.begin(), bias.end(), std::ref(f32rng));
       std::fill(output.begin(), output.end(), nanf(""));

       // Compute reference results, without clamping.
       if (has_bias()) {
         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 g = 0; g < groups(); g++) {
                 for (size_t oc = 0; oc < group_output_channels(); oc++) {
                   output_ref[(((i * groups() + g) * group_output_channels() + oc) * output_height() + oy) * output_width() + ox] =
                     bias[g * group_output_channels() + oc];
                 }
               }
             }
           }
         }
       } else {
         std::fill(output_ref.begin(), output_ref.end(), 0.0f);
       }
       if (nhwc_input()) {
         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 ky = 0; ky < kernel_height(); ky++) {
                 const size_t iy = oy * subsampling_height() + ky * dilation_height() - padding_top();
                 if (iy < input_height()) {
                   for (size_t kx = 0; kx < kernel_width(); kx++) {
                     const size_t ix = ox * subsampling_width() + kx * dilation_width() - padding_left();
                     if (ix < input_width()) {
                       for (size_t g = 0; g < groups(); g++) {
                         for (size_t oc = 0; oc < group_output_channels(); oc++) {
                           for (size_t ic = 0; ic < group_input_channels(); ic++) {
                             output_ref[(((i * groups() + g) * group_output_channels() + oc) * output_height() + oy) * output_width() + ox] +=
                               input[((((i * input_height() + iy) * input_width() + ix) * groups() + g) * group_input_channels() + ic)] *
                               kernel[(((g * group_output_channels() + oc) * kernel_height() + ky) * kernel_width() + kx) * group_input_channels() + ic];
                           }
                         }
                       }
                     }
                   }
                 }
               }
             }
           }
         }
       } else {
         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 ky = 0; ky < kernel_height(); ky++) {
                 const size_t iy = oy * subsampling_height() + ky * dilation_height() - padding_top();
                 if (iy < input_height()) {
                   for (size_t kx = 0; kx < kernel_width(); kx++) {
                     const size_t ix = ox * subsampling_width() + kx * dilation_width() - padding_left();
                     if (ix < input_width()) {
                       for (size_t g = 0; g < groups(); g++) {
                         for (size_t oc = 0; oc < group_output_channels(); oc++) {
                           for (size_t ic = 0; ic < group_input_channels(); ic++) {
                             output_ref[(((i * groups() + g) * group_output_channels() + oc) * output_height() + oy) * output_width() + ox] +=
                               input[i * input_batch_stride() +
                                     ((g * group_input_channels() + ic) * input_height() + iy) * input_width() + ix] *
                               kernel[(((g * group_output_channels() + oc) * kernel_height() + ky) * kernel_width() + kx) * group_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 Convolution operator.
       ASSERT_EQ(xnn_status_success, xnn_initialize());
       xnn_operator_t convolution_op = nullptr;

       xnn_status status = xnn_create_convolution2d_spnchw_f32(
         padding_top(), padding_right(), padding_bottom(), padding_left(),
         kernel_height(), kernel_width(),
         subsampling_height(), subsampling_width(),
         dilation_height(), dilation_width(),
         groups(), group_input_channels(), group_output_channels(),
         kernel.data(), has_bias() ? bias.data() : nullptr,
         output_min, output_max,
         (depthwise_layout() ? XNN_FLAG_DEPTHWISE_CONVOLUTION : 0) | (nhwc_input() ? XNN_FLAG_INPUT_NHWC : 0),
         &convolution_op);
       if (status == xnn_status_unsupported_parameter) {
         GTEST_SKIP();
       }
       ASSERT_EQ(xnn_status_success, status);

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

       ASSERT_EQ(xnn_status_success,
         xnn_setup_convolution2d_spnchw_f32(
           convolution_op,
           batch_size(), input_batch_stride(), output_batch_stride(), input_height(), input_width(),
           input.data(), output.data(),
           nullptr /* thread pool */));

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

       // Verify results.
       for (size_t i = 0; i < batch_size(); i++) {
         for (size_t y = 0; y < output_height(); y++) {
           for (size_t x = 0; x < output_width(); x++) {
             for (size_t g = 0; g < groups(); g++) {
               for (size_t c = 0; c < group_output_channels(); c++) {
                 ASSERT_GE(output[i * output_batch_stride() + ((g * group_output_channels() + c) * output_height() + y) * output_width() + x], output_min)
                   << "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c << ", image = " << i;
                 ASSERT_LE(output[i * output_batch_stride() + ((g * group_output_channels() + c) * output_height() + y) * output_width() + x], output_max)
                   << "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c << ", image = " << i;
                 ASSERT_NEAR(
                     output_ref[(((i * groups() + g) * group_output_channels() + c) * output_height() + y) * output_width() + x],
                     output[i * output_batch_stride() + ((g * group_output_channels() + c) * output_height() + y) * output_width() + x],
                     1.0e-4 * std::abs(output_ref[(((i * groups() + g) * group_output_channels() + c) * output_height() + y) * output_width() + x]))
                   << "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c << ", image = " << i;
               }
             }
           }
         }
       }
     }
   }

  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};
   uint32_t groups_{1};
   size_t group_input_channels_{1};
   size_t input_batch_stride_{0};
   size_t group_output_channels_{1};
   size_t output_batch_stride_{0};
   size_t batch_size_{1};
   uint32_t kernel_height_{1};
   uint32_t kernel_width_{1};
   uint32_t dilation_height_{1};
   uint32_t dilation_width_{1};
   uint32_t subsampling_height_{1};
   uint32_t subsampling_width_{1};
   bool nhwc_input_{false};
   float sparsity_{0.5f};
   uint8_t qmin_{0};
   uint8_t qmax_{255};
   bool depthwise_layout_{false};
   bool has_bias_{true};
   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 <random>
	#include <vector>

	#include <xnnpack.h>


	class ConvolutionSpNCHWOperatorTester {
	public:
	inline ConvolutionSpNCHWOperatorTester& padding(uint32_t padding) {
	this->padding_top_ = padding;
	this->padding_right_ = padding;
	this->padding_bottom_ = padding;
	this->padding_left_ = padding;
	return *this;
	}

	inline ConvolutionSpNCHWOperatorTester& padding(uint32_t padding_height, uint32_t padding_width) {
	this->padding_top_ = padding_height;
	this->padding_right_ = padding_width;
	this->padding_bottom_ = padding_height;
	this->padding_left_ = padding_width;
	return *this;
	}

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

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

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

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

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

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

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

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

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

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

	inline ConvolutionSpNCHWOperatorTester& 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 ConvolutionSpNCHWOperatorTester& 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 ConvolutionSpNCHWOperatorTester& 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 ConvolutionSpNCHWOperatorTester& groups(uint32_t groups) {
	assert(groups >= 1);
	this->groups_ = groups;
	return *this;
	}

	inline uint32_t groups() const {
	return this->groups_;
	}

	inline ConvolutionSpNCHWOperatorTester& group_input_channels(size_t group_input_channels) {
	assert(group_input_channels >= 1);
	this->group_input_channels_ = group_input_channels;
	return *this;
	}

	inline size_t group_input_channels() const {
	return this->group_input_channels_;
	}

	inline ConvolutionSpNCHWOperatorTester& group_output_channels(size_t group_output_channels) {
	assert(group_output_channels >= 1);
	this->group_output_channels_ = group_output_channels;
	return *this;
	}

	inline size_t group_output_channels() const {
	return this->group_output_channels_;
	}

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

	inline ConvolutionSpNCHWOperatorTester& kernel_size(uint32_t kernel_height, uint32_t kernel_width) {
	assert(kernel_height >= 1);
	assert(kernel_width >= 1);
	this->kernel_height_ = kernel_height;
	this->kernel_width_ = kernel_width;
	return *this;
	}

	inline ConvolutionSpNCHWOperatorTester& 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 ConvolutionSpNCHWOperatorTester& 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 ConvolutionSpNCHWOperatorTester& dilation(uint32_t dilation) {
	assert(dilation >= 1);
	this->dilation_height_ = dilation;
	this->dilation_width_ = dilation;
	return *this;
	}

	inline ConvolutionSpNCHWOperatorTester& dilation(uint32_t dilation_height, uint32_t dilation_width) {
	assert(dilation_height >= 1);
	assert(dilation_width >= 1);
	this->dilation_height_ = dilation_height;
	this->dilation_width_ = dilation_width;
	return *this;
	}

	inline ConvolutionSpNCHWOperatorTester& dilation_height(uint32_t dilation_height) {
	assert(dilation_height >= 1);
	this->dilation_height_ = dilation_height;
	return *this;
	}

	inline uint32_t dilation_height() const {
	return this->dilation_height_;
	}

	inline ConvolutionSpNCHWOperatorTester& dilation_width(uint32_t dilation_width) {
	assert(dilation_width >= 1);
	this->dilation_width_ = dilation_width;
	return *this;
	}

	inline uint32_t dilation_width() const {
	return this->dilation_width_;
	}

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

	inline ConvolutionSpNCHWOperatorTester& subsampling(uint32_t subsampling_height, uint32_t subsampling_width) {
	assert(subsampling_height >= 1);
	assert(subsampling_width >= 1);
	this->subsampling_height_ = subsampling_height;
	this->subsampling_width_ = subsampling_width;
	return *this;
	}

	inline ConvolutionSpNCHWOperatorTester& 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 ConvolutionSpNCHWOperatorTester& 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 ConvolutionSpNCHWOperatorTester& input_batch_stride(size_t input_batch_stride) {
	assert(input_batch_stride >= 1);
	this->input_batch_stride_ = input_batch_stride;
	return *this;
	}

	inline size_t input_batch_stride() const {
	if (this->input_batch_stride_ == 0) {
	return groups() * group_input_channels() * input_height() * input_width();
	} else {
	assert(this->input_batch_stride_ >= groups() * group_input_channels() * input_height() * input_width());
	return this->input_batch_stride_;
	}
	}

	inline ConvolutionSpNCHWOperatorTester& output_batch_stride(size_t output_batch_stride) {
	assert(output_batch_stride >= 1);
	this->output_batch_stride_ = output_batch_stride;
	return *this;
	}

	inline size_t output_batch_stride() const {
	if (this->output_batch_stride_ == 0) {
	return groups() * group_output_channels() * output_height() * output_width();
	} else {
	assert(this->output_batch_stride_ >= groups() * group_output_channels() * output_height() * output_width());
	return this->output_batch_stride_;
	}
	}

	inline uint32_t dilated_kernel_height() const {
	return (kernel_height() - 1) * dilation_height() + 1;
	}

	inline uint32_t dilated_kernel_width() const {
	return (kernel_width() - 1) * dilation_width() + 1;
	}

	inline size_t output_height() const {
	const size_t padded_input_height = padding_top() + input_height() + padding_bottom();
	if (padded_input_height <= dilated_kernel_height()) {
	return 1;
	} else {
	return (padded_input_height - dilated_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 <= dilated_kernel_width()) {
	return 1;
	} else {
	return (padded_input_width - dilated_kernel_width()) / subsampling_width() + 1;
	}
	}

	inline ConvolutionSpNCHWOperatorTester& nhwc_input(bool nhwc_input) {
	this->nhwc_input_ = nhwc_input;
	return *this;
	}

	inline bool nhwc_input() const {
	return this->nhwc_input_;
	}

	inline ConvolutionSpNCHWOperatorTester& sparsity(float sparsity) {
	this->sparsity_ = sparsity;
	return *this;
	}

	inline float sparsity() const {
	return this->sparsity_;
	}

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

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

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

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

	inline ConvolutionSpNCHWOperatorTester& depthwise_layout(bool depthwise_layout) {
	this->depthwise_layout_ = depthwise_layout;
	return *this;
	}

	inline bool depthwise_layout() const {
	return this->depthwise_layout_;
	}

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

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

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

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

	void TestF32() const {
	ASSERT_FALSE(depthwise_layout());

	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);
	auto prng = std::bind(std::uniform_real_distribution<float>(), rng);

	std::vector<float> input(XNN_EXTRA_BYTES / sizeof(float) +
	batch_size() * input_batch_stride() + groups() * group_input_channels() * input_height() * input_width());
	std::vector<float> kernel(
	groups() * group_output_channels() * kernel_height() * kernel_width() * group_input_channels());
	std::vector<float> bias(groups() * group_output_channels());
	std::vector<float> output(
	batch_size() * output_batch_stride() + groups() * group_output_channels() * output_height() * output_width());
	std::vector<float> output_ref(batch_size() * groups() * group_output_channels() * output_height() * output_width());

	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));
	for (float& k : kernel) {
	if (prng() <= sparsity()) {
	k = 0.0f;
	}
	}
	std::generate(bias.begin(), bias.end(), std::ref(f32rng));
	std::fill(output.begin(), output.end(), nanf(""));

	// Compute reference results, without clamping.
	if (has_bias()) {
	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 g = 0; g < groups(); g++) {
	for (size_t oc = 0; oc < group_output_channels(); oc++) {
	output_ref[(((i * groups() + g) * group_output_channels() + oc) * output_height() + oy) * output_width() + ox] =
	bias[g * group_output_channels() + oc];
	}
	}
	}
	}
	}
	} else {
	std::fill(output_ref.begin(), output_ref.end(), 0.0f);
	}
	if (nhwc_input()) {
	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 ky = 0; ky < kernel_height(); ky++) {
	const size_t iy = oy * subsampling_height() + ky * dilation_height() - padding_top();
	if (iy < input_height()) {
	for (size_t kx = 0; kx < kernel_width(); kx++) {
	const size_t ix = ox * subsampling_width() + kx * dilation_width() - padding_left();
	if (ix < input_width()) {
	for (size_t g = 0; g < groups(); g++) {
	for (size_t oc = 0; oc < group_output_channels(); oc++) {
	for (size_t ic = 0; ic < group_input_channels(); ic++) {
	output_ref[(((i * groups() + g) * group_output_channels() + oc) * output_height() + oy) * output_width() + ox] +=
	input[((((i * input_height() + iy) * input_width() + ix) * groups() + g) * group_input_channels() + ic)] *
	kernel[(((g * group_output_channels() + oc) * kernel_height() + ky) * kernel_width() + kx) * group_input_channels() + ic];
	}
	}
	}
	}
	}
	}
	}
	}
	}
	}
	} else {
	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 ky = 0; ky < kernel_height(); ky++) {
	const size_t iy = oy * subsampling_height() + ky * dilation_height() - padding_top();
	if (iy < input_height()) {
	for (size_t kx = 0; kx < kernel_width(); kx++) {
	const size_t ix = ox * subsampling_width() + kx * dilation_width() - padding_left();
	if (ix < input_width()) {
	for (size_t g = 0; g < groups(); g++) {
	for (size_t oc = 0; oc < group_output_channels(); oc++) {
	for (size_t ic = 0; ic < group_input_channels(); ic++) {
	output_ref[(((i * groups() + g) * group_output_channels() + oc) * output_height() + oy) * output_width() + ox] +=
	input[i * input_batch_stride() +
	((g * group_input_channels() + ic) * input_height() + iy) * input_width() + ix] *
	kernel[(((g * group_output_channels() + oc) * kernel_height() + ky) * kernel_width() + kx) * group_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 Convolution operator.
	ASSERT_EQ(xnn_status_success, xnn_initialize());
	xnn_operator_t convolution_op = nullptr;

	xnn_status status = xnn_create_convolution2d_spnchw_f32(
	padding_top(), padding_right(), padding_bottom(), padding_left(),
	kernel_height(), kernel_width(),
	subsampling_height(), subsampling_width(),
	dilation_height(), dilation_width(),
	groups(), group_input_channels(), group_output_channels(),
	kernel.data(), has_bias() ? bias.data() : nullptr,
	output_min, output_max,
	(depthwise_layout() ? XNN_FLAG_DEPTHWISE_CONVOLUTION : 0) \| (nhwc_input() ? XNN_FLAG_INPUT_NHWC : 0),
	&convolution_op);
	if (status == xnn_status_unsupported_parameter) {
	GTEST_SKIP();
	}
	ASSERT_EQ(xnn_status_success, status);

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

	ASSERT_EQ(xnn_status_success,
	xnn_setup_convolution2d_spnchw_f32(
	convolution_op,
	batch_size(), input_batch_stride(), output_batch_stride(), input_height(), input_width(),
	input.data(), output.data(),
	nullptr /* thread pool */));

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

	// Verify results.
	for (size_t i = 0; i < batch_size(); i++) {
	for (size_t y = 0; y < output_height(); y++) {
	for (size_t x = 0; x < output_width(); x++) {
	for (size_t g = 0; g < groups(); g++) {
	for (size_t c = 0; c < group_output_channels(); c++) {
	ASSERT_GE(output[i * output_batch_stride() + ((g * group_output_channels() + c) * output_height() + y) * output_width() + x], output_min)
	<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c << ", image = " << i;
	ASSERT_LE(output[i * output_batch_stride() + ((g * group_output_channels() + c) * output_height() + y) * output_width() + x], output_max)
	<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c << ", image = " << i;
	ASSERT_NEAR(
	output_ref[(((i * groups() + g) * group_output_channels() + c) * output_height() + y) * output_width() + x],
	output[i * output_batch_stride() + ((g * group_output_channels() + c) * output_height() + y) * output_width() + x],
	1.0e-4 * std::abs(output_ref[(((i * groups() + g) * group_output_channels() + c) * output_height() + y) * output_width() + x]))
	<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c << ", image = " << i;
	}
	}
	}
	}
	}
	}
	}

	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};
	uint32_t groups_{1};
	size_t group_input_channels_{1};
	size_t input_batch_stride_{0};
	size_t group_output_channels_{1};
	size_t output_batch_stride_{0};
	size_t batch_size_{1};
	uint32_t kernel_height_{1};
	uint32_t kernel_width_{1};
	uint32_t dilation_height_{1};
	uint32_t dilation_width_{1};
	uint32_t subsampling_height_{1};
	uint32_t subsampling_width_{1};
	bool nhwc_input_{false};
	float sparsity_{0.5f};
	uint8_t qmin_{0};
	uint8_t qmax_{255};
	bool depthwise_layout_{false};
	bool has_bias_{true};
	size_t iterations_{1};
	};