test/argmax-pooling-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 <cstddef>
 #include <cstdlib>
 #include <functional>
 #include <limits>
 #include <random>
 #include <vector>

 #include <xnnpack.h>


 class ArgmaxPoolingOperatorTester {
  public:
   inline ArgmaxPoolingOperatorTester& padding_tf_same(bool padding_same) {
     if (padding_same) {
       assert(padding_top() == 0);
       assert(padding_left() == 0);
       assert(padding_bottom() == 0);
       assert(padding_right() == 0);
     }
     this->padding_tf_same_ = padding_same;
     return *this;
   }

   inline bool padding_tf_same() const {
     return this->padding_tf_same_;
   }

   inline ArgmaxPoolingOperatorTester& padding(uint32_t padding) {
     assert(!padding_tf_same());
     this->padding_top_ = padding;
     this->padding_right_ = padding;
     this->padding_bottom_ = padding;
     this->padding_left_ = padding;
     return *this;
   }

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

   inline ArgmaxPoolingOperatorTester& padding_height(uint32_t padding_height) {
     assert(!padding_tf_same());
     this->padding_top_ = padding_height;
     this->padding_bottom_ = padding_height;
     return *this;
   }

   inline ArgmaxPoolingOperatorTester& padding_width(uint32_t padding_width) {
     assert(!padding_tf_same());
     this->padding_right_ = padding_width;
     this->padding_left_ = padding_width;
     return *this;
   }

   inline ArgmaxPoolingOperatorTester& padding_top(uint32_t padding_top) {
     assert(!padding_tf_same());
     this->padding_top_ = padding_top;
     return *this;
   }

   inline uint32_t padding_top() const {
     if (padding_tf_same()) {
       const uint32_t total_padding_height = output_height() * pooling_height() - input_height();
       return total_padding_height / 2;
     } else {
       return this->padding_top_;
     }
   }

   inline ArgmaxPoolingOperatorTester& padding_left(uint32_t padding_left) {
     assert(!padding_tf_same());
     this->padding_left_ = padding_left;
     return *this;
   }

   inline uint32_t padding_left() const {
     if (padding_tf_same()) {
       const uint32_t total_padding_width = output_width() * pooling_width() - input_width();
       return total_padding_width / 2;
     } else {
       return this->padding_left_;
     }
   }

   inline ArgmaxPoolingOperatorTester& padding_bottom(uint32_t padding_bottom) {
     assert(!padding_tf_same());
     this->padding_bottom_ = padding_bottom;
     return *this;
   }

   inline uint32_t padding_bottom() const {
     if (padding_tf_same()) {
       const uint32_t total_padding_height = output_height() * pooling_height() - input_height();
       return total_padding_height - total_padding_height / 2;
     } else {
       return this->padding_bottom_;
     }
   }

   inline ArgmaxPoolingOperatorTester& padding_right(uint32_t padding_right) {
     assert(!padding_tf_same());
     this->padding_right_ = padding_right;
     return *this;
   }

   inline uint32_t padding_right() const {
     if (padding_tf_same()) {
       const uint32_t total_padding_width = output_width() * pooling_width() - input_width();
       return total_padding_width - total_padding_width / 2;
     } else {
       return this->padding_right_;
     }
   }

   inline ArgmaxPoolingOperatorTester& input_size(size_t input_height, size_t input_width) {
     assert(input_height >= 1);
     assert(input_width >= 1);
     this->input_height_ = input_height;
     this->input_width_ = input_width;
     return *this;
   }

   inline ArgmaxPoolingOperatorTester& input_height(size_t input_height) {
     assert(input_height >= 1);
     this->input_height_ = input_height;
     return *this;
   }

   inline size_t input_height() const {
     return this->input_height_;
   }

   inline ArgmaxPoolingOperatorTester& input_width(size_t input_width) {
     assert(input_width >= 1);
     this->input_width_ = input_width;
     return *this;
   }

   inline size_t input_width() const {
     return this->input_width_;
   }

   inline ArgmaxPoolingOperatorTester& channels(size_t channels) {
     assert(channels != 0);
     this->channels_ = channels;
     return *this;
   }

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

   inline ArgmaxPoolingOperatorTester& 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 ArgmaxPoolingOperatorTester& pooling_size(uint32_t pooling_size) {
     assert(pooling_size >= 1);
     this->pooling_height_ = pooling_size;
     this->pooling_width_ = pooling_size;
     return *this;
   }

   inline ArgmaxPoolingOperatorTester& pooling_size(uint32_t pooling_height, uint32_t pooling_width) {
     assert(pooling_height >= 1);
     assert(pooling_width >= 1);
     this->pooling_height_ = pooling_height;
     this->pooling_width_ = pooling_width;
     return *this;
   }

   inline ArgmaxPoolingOperatorTester& pooling_height(uint32_t pooling_height) {
     assert(pooling_height >= 1);
     this->pooling_height_ = pooling_height;
     return *this;
   }

   inline uint32_t pooling_height() const {
     return this->pooling_height_;
   }

   inline ArgmaxPoolingOperatorTester& pooling_width(uint32_t pooling_width) {
     assert(pooling_width >= 1);
     this->pooling_width_ = pooling_width;
     return *this;
   }

   inline uint32_t pooling_width() const {
     return this->pooling_width_;
   }

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

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

   inline ArgmaxPoolingOperatorTester& input_pixel_stride(size_t input_pixel_stride) {
     assert(input_pixel_stride != 0);
     this->input_pixel_stride_ = input_pixel_stride;
     return *this;
   }

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

   inline ArgmaxPoolingOperatorTester& output_pixel_stride(size_t output_pixel_stride) {
     assert(output_pixel_stride != 0);
     this->output_pixel_stride_ = output_pixel_stride;
     return *this;
   }

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

   inline ArgmaxPoolingOperatorTester& next_input_size(uint32_t next_input_height, uint32_t next_input_width) {
     assert(next_input_height >= 1);
     assert(next_input_width >= 1);
     this->next_input_height_ = next_input_height;
     this->next_input_width_ = next_input_width;
     return *this;
   }

   inline ArgmaxPoolingOperatorTester& next_input_height(uint32_t next_input_height) {
     assert(next_input_height >= 1);
     this->next_input_height_ = next_input_height;
     return *this;
   }

   inline uint32_t next_input_height() const {
     if (this->next_input_height_ == 0) {
       return input_height();
     } else {
       return this->next_input_height_;
     }
   }

   inline ArgmaxPoolingOperatorTester& next_input_width(uint32_t next_input_width) {
     assert(next_input_width >= 1);
     this->next_input_width_ = next_input_width;
     return *this;
   }

   inline uint32_t next_input_width() const {
     if (this->next_input_width_ == 0) {
       return input_width();
     } else {
       return this->next_input_width_;
     }
   }

   inline size_t next_output_height() const {
     const size_t padded_next_input_height = padding_top() + next_input_height() + padding_bottom();
     return padded_next_input_height / pooling_height();
   }

   inline size_t next_output_width() const {
     const size_t padded_next_input_width = padding_left() + next_input_width() + padding_right();
     return padded_next_input_width / pooling_width();
   }

   inline ArgmaxPoolingOperatorTester& next_batch_size(size_t next_batch_size) {
     assert(next_batch_size >= 1);
     this->next_batch_size_ = next_batch_size;
     return *this;
   }

   inline size_t next_batch_size() const {
     if (this->next_batch_size_ == 0) {
       return batch_size();
     } else {
       return this->next_batch_size_;
     }
   }

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

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

   void TestF32() const {
     std::random_device random_device;
     auto rng = std::mt19937(random_device());
     auto f32rng = std::bind(std::uniform_real_distribution<float>(0.0f, 1.0f), rng);

     std::vector<float> input((batch_size() * input_height() * input_width() - 1) * input_pixel_stride() + channels() + XNN_EXTRA_BYTES / sizeof(float));
     std::vector<float> output((batch_size() * output_height() * output_width() - 1) * output_pixel_stride() + channels());
     std::vector<float> output_ref(batch_size() * output_height() * output_width() * channels());
     std::vector<uint32_t> index(batch_size() * output_height() * output_width() * channels());
     std::vector<uint32_t> index_ref(batch_size() * output_height() * output_width() * channels());
     for (size_t iteration = 0; iteration < iterations(); iteration++) {
       std::generate(input.begin(), input.end(), std::ref(f32rng));
       std::fill(output.begin(), output.end(), nanf(""));

       // 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 c = 0; c < channels(); c++) {
               const size_t iy_top_left = std::max<size_t>(oy * pooling_height(), padding_top()) - padding_top();
               const size_t ix_top_left = std::max<size_t>(ox * pooling_width(), padding_left()) - padding_left();
               float max_value =
                 input[((i * input_height() + iy_top_left) * input_width() + ix_top_left) * input_pixel_stride() + c];
               uint32_t max_index = 0;
               for (size_t py = 0; py < pooling_height(); py++) {
                 const size_t iy = oy * pooling_height() + py - padding_top();
                 for (size_t px = 0; px < pooling_width(); px++) {
                   const size_t ix = ox * pooling_width() + px - padding_left();
                   if (ix < input_width() && iy < input_height()) {
                     const float value = input[((i * input_height() + iy) * input_width() + ix) * input_pixel_stride() + c];
                     if (value > max_value) {
                       max_value = value;
                       max_index = uint32_t(px * pooling_height() + py);
                     }
                   }
                 }
               }
               output_ref[((i * output_height() + oy) * output_width() + ox) * channels() + c] = max_value;
               index_ref[((i * output_height() + oy) * output_width() + ox) * channels() + c] = max_index;
             }
           }
         }
       }

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

       ASSERT_EQ(xnn_status_success,
         xnn_create_argmax_pooling2d_nhwc_f32(
           padding_tf_same() ? 0 : padding_top(), padding_tf_same() ? 0 : padding_right(),
           padding_tf_same() ? 0 : padding_bottom(), padding_tf_same() ? 0 : padding_left(),
           pooling_height(), pooling_width(),
           channels(), input_pixel_stride(), output_pixel_stride(),
           padding_tf_same() ? XNN_FLAG_TENSORFLOW_SAME_PADDING : 0,
           &argmax_pooling_op));
       ASSERT_NE(nullptr, argmax_pooling_op);

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

       ASSERT_EQ(xnn_status_success,
         xnn_setup_argmax_pooling2d_nhwc_f32(
           argmax_pooling_op,
           batch_size(), input_height(), input_width(),
           input.data(), output.data(), index.data(),
           nullptr /* thread pool */));

       ASSERT_EQ(xnn_status_success,
         xnn_run_operator(argmax_pooling_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 c = 0; c < channels(); c++) {
               ASSERT_EQ(output_ref[((i * output_height() + y) * output_width() + x) * channels() + c],
                 output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c]) <<
                 "in batch index " << i << ", pixel (" << y << ", " << x << "), channel " << c;
               ASSERT_EQ(index_ref[((i * output_height() + y) * output_width() + x) * channels() + c],
                 index[((i * output_height() + y) * output_width() + x) * channels() + c]) <<
                 "in batch index " << i << ", pixel (" << y << ", " << x << "), channel " << c;
             }
           }
         }
       }
     }
   }

   void TestSetupF32() const {
     std::random_device random_device;
     auto rng = std::mt19937(random_device());
     auto f32rng = std::bind(std::uniform_real_distribution<float>(0.0f, 1.0f), rng);

     std::vector<float> input(XNN_EXTRA_BYTES / sizeof(float) + std::max(
       (batch_size() * input_height() * input_width() - 1) * input_pixel_stride() + channels(),
       (next_batch_size() * next_input_height() * next_input_width() - 1) * input_pixel_stride() + channels()));
     std::vector<float> output(std::max(
       (batch_size() * output_height() * output_width() - 1) * output_pixel_stride() + channels(),
       (next_batch_size() * next_output_height() * next_output_width() - 1) * output_pixel_stride() + channels()));
     std::vector<uint32_t> index(std::max(
       batch_size() * output_height() * output_width() * channels(),
       next_batch_size() * next_output_height() * next_output_width() * channels()));
     std::vector<float> output_ref(batch_size() * output_height() * output_width() * channels());
     std::vector<float> next_output_ref(next_batch_size() * next_output_height() * next_output_width() * channels());
     std::vector<uint32_t> index_ref(batch_size() * output_height() * output_width() * channels());
     std::vector<uint32_t> next_index_ref(next_batch_size() * next_output_height() * next_output_width() * channels());
     for (size_t iteration = 0; iteration < iterations(); iteration++) {
       std::generate(input.begin(), input.end(), std::ref(f32rng));
       std::fill(output.begin(), output.end(), nanf(""));

       // 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 c = 0; c < channels(); c++) {
               const size_t iy_top_left = std::max<size_t>(oy * pooling_height(), padding_top()) - padding_top();
               const size_t ix_top_left = std::max<size_t>(ox * pooling_width(), padding_left()) - padding_left();
               float max_value =
                 input[((i * input_height() + iy_top_left) * input_width() + ix_top_left) * input_pixel_stride() + c];
               uint32_t max_index = 0;
               for (size_t py = 0; py < pooling_height(); py++) {
                 const size_t iy = oy * pooling_height() + py - padding_top();
                 for (size_t px = 0; px < pooling_width(); px++) {
                   const size_t ix = ox * pooling_width() + px - padding_left();
                   if (ix < input_width() && iy < input_height()) {
                     const float value = input[((i * input_height() + iy) * input_width() + ix) * input_pixel_stride() + c];
                     if (value > max_value) {
                       max_value = value;
                       max_index = uint32_t(px * pooling_height() + py);
                     }
                   }
                 }
               }
               output_ref[((i * output_height() + oy) * output_width() + ox) * channels() + c] = max_value;
               index_ref[((i * output_height() + oy) * output_width() + ox) * channels() + c] = max_index;
             }
           }
         }
       }

       // Create, setup, and run Argmax Pooling operator once.
       ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
       xnn_operator_t argmax_pooling_op = nullptr;

       ASSERT_EQ(xnn_status_success,
         xnn_create_argmax_pooling2d_nhwc_f32(
           padding_top(), padding_right(), padding_bottom(), padding_left(),
           pooling_height(), pooling_width(),
           channels(), input_pixel_stride(), output_pixel_stride(),
           0, &argmax_pooling_op));
       ASSERT_NE(nullptr, argmax_pooling_op);

       ASSERT_EQ(xnn_status_success,
         xnn_setup_argmax_pooling2d_nhwc_f32(
           argmax_pooling_op,
           batch_size(), input_height(), input_width(),
           input.data(), output.data(), index.data(),
           nullptr /* thread pool */));

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

       // Verify results of the first run.
       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 c = 0; c < channels(); c++) {
               ASSERT_EQ(
                   output_ref[((i * output_height() + y) * output_width() + x) * channels() + c],
                   output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c])
                 << "in batch index " << i << ", pixel (" << y << ", " << x << "), channel " << c;
               ASSERT_EQ(
                   index_ref[((i * output_height() + y) * output_width() + x) * channels() + c],
                   index[((i * output_height() + y) * output_width() + x) * channels() + c])
                 << "in batch index " << i << ", pixel (" << y << ", " << x << "), channel " << c;
             }
           }
         }
       }

       // Re-generate data for the second run.
       std::generate(input.begin(), input.end(), std::ref(f32rng));
       std::fill(output.begin(), output.end(), 0xA5);

       // Compute reference results for the second run, including clamping.
       for (size_t i = 0; i < next_batch_size(); i++) {
         for (size_t oy = 0; oy < next_output_height(); oy++) {
           for (size_t ox = 0; ox < next_output_width(); ox++) {
             for (size_t c = 0; c < channels(); c++) {
               const size_t iy_top_left = std::max<size_t>(oy * pooling_height(), padding_top()) - padding_top();
               const size_t ix_top_left = std::max<size_t>(ox * pooling_width(), padding_left()) - padding_left();
               float max_value =
                 input[((i * next_input_height() + iy_top_left) * next_input_width() + ix_top_left) * input_pixel_stride() + c];
               uint32_t max_index = 0;
               for (size_t py = 0; py < pooling_height(); py++) {
                 const size_t iy = oy * pooling_height() + py - padding_top();
                 for (size_t px = 0; px < pooling_width(); px++) {
                   const size_t ix = ox * pooling_width() + px - padding_left();
                   if (ix < next_input_width() && iy < next_input_height()) {
                     const float value = input[((i * next_input_height() + iy) * next_input_width() + ix) * input_pixel_stride() + c];
                     if (value > max_value) {
                       max_value = value;
                       max_index = uint32_t(px * pooling_height() + py);
                     }
                   }
                 }
               }
               next_output_ref[((i * next_output_height() + oy) * next_output_width() + ox) * channels() + c] = max_value;
               next_index_ref[((i * next_output_height() + oy) * next_output_width() + ox) * channels() + c] = max_index;
             }
           }
         }
       }

       // Setup and run Argmax Pooling operator the second time, and destroy the operator.
       ASSERT_EQ(xnn_status_success,
         xnn_setup_argmax_pooling2d_nhwc_f32(
           argmax_pooling_op,
           next_batch_size(), next_input_height(), next_input_width(),
           input.data(), output.data(), index.data(),
           nullptr /* thread pool */));

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

       ASSERT_EQ(xnn_status_success,
         xnn_delete_operator(argmax_pooling_op));
       argmax_pooling_op = nullptr;

       // Verify results of the second run.
       for (size_t i = 0; i < next_batch_size(); i++) {
         for (size_t y = 0; y < next_output_height(); y++) {
           for (size_t x = 0; x < next_output_width(); x++) {
             for (size_t c = 0; c < channels(); c++) {
               ASSERT_EQ(
                   next_output_ref[((i * next_output_height() + y) * next_output_width() + x) * channels() + c],
                   output[((i * next_output_height() + y) * next_output_width() + x) * output_pixel_stride() + c])
                 << "in batch index " << i << ", pixel (" << y << ", " << x << "), channel " << c;
               ASSERT_EQ(
                   next_index_ref[((i * next_output_height() + y) * next_output_width() + x) * channels() + c],
                   index[((i * next_output_height() + y) * next_output_width() + x) * output_pixel_stride() + c])
                 << "in batch index " << i << ", pixel (" << y << ", " << x << "), channel " << c;
             }
           }
         }
       }
     }
   }

  private:
   uint32_t padding_top_{0};
   uint32_t padding_right_{0};
   uint32_t padding_bottom_{0};
   uint32_t padding_left_{0};
   bool padding_tf_same_{false};
   size_t input_height_{1};
   size_t input_width_{1};
   size_t channels_{1};
   size_t batch_size_{1};
   size_t input_pixel_stride_{0};
   size_t output_pixel_stride_{0};
   uint32_t pooling_height_{1};
   uint32_t pooling_width_{1};
   size_t next_input_height_{0};
   size_t next_input_width_{0};
   size_t next_batch_size_{0};
   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 <cstddef>
	#include <cstdlib>
	#include <functional>
	#include <limits>
	#include <random>
	#include <vector>

	#include <xnnpack.h>


	class ArgmaxPoolingOperatorTester {
	public:
	inline ArgmaxPoolingOperatorTester& padding_tf_same(bool padding_same) {
	if (padding_same) {
	assert(padding_top() == 0);
	assert(padding_left() == 0);
	assert(padding_bottom() == 0);
	assert(padding_right() == 0);
	}
	this->padding_tf_same_ = padding_same;
	return *this;
	}

	inline bool padding_tf_same() const {
	return this->padding_tf_same_;
	}

	inline ArgmaxPoolingOperatorTester& padding(uint32_t padding) {
	assert(!padding_tf_same());
	this->padding_top_ = padding;
	this->padding_right_ = padding;
	this->padding_bottom_ = padding;
	this->padding_left_ = padding;
	return *this;
	}

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

	inline ArgmaxPoolingOperatorTester& padding_height(uint32_t padding_height) {
	assert(!padding_tf_same());
	this->padding_top_ = padding_height;
	this->padding_bottom_ = padding_height;
	return *this;
	}

	inline ArgmaxPoolingOperatorTester& padding_width(uint32_t padding_width) {
	assert(!padding_tf_same());
	this->padding_right_ = padding_width;
	this->padding_left_ = padding_width;
	return *this;
	}

	inline ArgmaxPoolingOperatorTester& padding_top(uint32_t padding_top) {
	assert(!padding_tf_same());
	this->padding_top_ = padding_top;
	return *this;
	}

	inline uint32_t padding_top() const {
	if (padding_tf_same()) {
	const uint32_t total_padding_height = output_height() * pooling_height() - input_height();
	return total_padding_height / 2;
	} else {
	return this->padding_top_;
	}
	}

	inline ArgmaxPoolingOperatorTester& padding_left(uint32_t padding_left) {
	assert(!padding_tf_same());
	this->padding_left_ = padding_left;
	return *this;
	}

	inline uint32_t padding_left() const {
	if (padding_tf_same()) {
	const uint32_t total_padding_width = output_width() * pooling_width() - input_width();
	return total_padding_width / 2;
	} else {
	return this->padding_left_;
	}
	}

	inline ArgmaxPoolingOperatorTester& padding_bottom(uint32_t padding_bottom) {
	assert(!padding_tf_same());
	this->padding_bottom_ = padding_bottom;
	return *this;
	}

	inline uint32_t padding_bottom() const {
	if (padding_tf_same()) {
	const uint32_t total_padding_height = output_height() * pooling_height() - input_height();
	return total_padding_height - total_padding_height / 2;
	} else {
	return this->padding_bottom_;
	}
	}

	inline ArgmaxPoolingOperatorTester& padding_right(uint32_t padding_right) {
	assert(!padding_tf_same());
	this->padding_right_ = padding_right;
	return *this;
	}

	inline uint32_t padding_right() const {
	if (padding_tf_same()) {
	const uint32_t total_padding_width = output_width() * pooling_width() - input_width();
	return total_padding_width - total_padding_width / 2;
	} else {
	return this->padding_right_;
	}
	}

	inline ArgmaxPoolingOperatorTester& input_size(size_t input_height, size_t input_width) {
	assert(input_height >= 1);
	assert(input_width >= 1);
	this->input_height_ = input_height;
	this->input_width_ = input_width;
	return *this;
	}

	inline ArgmaxPoolingOperatorTester& input_height(size_t input_height) {
	assert(input_height >= 1);
	this->input_height_ = input_height;
	return *this;
	}

	inline size_t input_height() const {
	return this->input_height_;
	}

	inline ArgmaxPoolingOperatorTester& input_width(size_t input_width) {
	assert(input_width >= 1);
	this->input_width_ = input_width;
	return *this;
	}

	inline size_t input_width() const {
	return this->input_width_;
	}

	inline ArgmaxPoolingOperatorTester& channels(size_t channels) {
	assert(channels != 0);
	this->channels_ = channels;
	return *this;
	}

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

	inline ArgmaxPoolingOperatorTester& 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 ArgmaxPoolingOperatorTester& pooling_size(uint32_t pooling_size) {
	assert(pooling_size >= 1);
	this->pooling_height_ = pooling_size;
	this->pooling_width_ = pooling_size;
	return *this;
	}

	inline ArgmaxPoolingOperatorTester& pooling_size(uint32_t pooling_height, uint32_t pooling_width) {
	assert(pooling_height >= 1);
	assert(pooling_width >= 1);
	this->pooling_height_ = pooling_height;
	this->pooling_width_ = pooling_width;
	return *this;
	}

	inline ArgmaxPoolingOperatorTester& pooling_height(uint32_t pooling_height) {
	assert(pooling_height >= 1);
	this->pooling_height_ = pooling_height;
	return *this;
	}

	inline uint32_t pooling_height() const {
	return this->pooling_height_;
	}

	inline ArgmaxPoolingOperatorTester& pooling_width(uint32_t pooling_width) {
	assert(pooling_width >= 1);
	this->pooling_width_ = pooling_width;
	return *this;
	}

	inline uint32_t pooling_width() const {
	return this->pooling_width_;
	}

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

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

	inline ArgmaxPoolingOperatorTester& input_pixel_stride(size_t input_pixel_stride) {
	assert(input_pixel_stride != 0);
	this->input_pixel_stride_ = input_pixel_stride;
	return *this;
	}

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

	inline ArgmaxPoolingOperatorTester& output_pixel_stride(size_t output_pixel_stride) {
	assert(output_pixel_stride != 0);
	this->output_pixel_stride_ = output_pixel_stride;
	return *this;
	}

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

	inline ArgmaxPoolingOperatorTester& next_input_size(uint32_t next_input_height, uint32_t next_input_width) {
	assert(next_input_height >= 1);
	assert(next_input_width >= 1);
	this->next_input_height_ = next_input_height;
	this->next_input_width_ = next_input_width;
	return *this;
	}

	inline ArgmaxPoolingOperatorTester& next_input_height(uint32_t next_input_height) {
	assert(next_input_height >= 1);
	this->next_input_height_ = next_input_height;
	return *this;
	}

	inline uint32_t next_input_height() const {
	if (this->next_input_height_ == 0) {
	return input_height();
	} else {
	return this->next_input_height_;
	}
	}

	inline ArgmaxPoolingOperatorTester& next_input_width(uint32_t next_input_width) {
	assert(next_input_width >= 1);
	this->next_input_width_ = next_input_width;
	return *this;
	}

	inline uint32_t next_input_width() const {
	if (this->next_input_width_ == 0) {
	return input_width();
	} else {
	return this->next_input_width_;
	}
	}

	inline size_t next_output_height() const {
	const size_t padded_next_input_height = padding_top() + next_input_height() + padding_bottom();
	return padded_next_input_height / pooling_height();
	}

	inline size_t next_output_width() const {
	const size_t padded_next_input_width = padding_left() + next_input_width() + padding_right();
	return padded_next_input_width / pooling_width();
	}

	inline ArgmaxPoolingOperatorTester& next_batch_size(size_t next_batch_size) {
	assert(next_batch_size >= 1);
	this->next_batch_size_ = next_batch_size;
	return *this;
	}

	inline size_t next_batch_size() const {
	if (this->next_batch_size_ == 0) {
	return batch_size();
	} else {
	return this->next_batch_size_;
	}
	}

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

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

	void TestF32() const {
	std::random_device random_device;
	auto rng = std::mt19937(random_device());
	auto f32rng = std::bind(std::uniform_real_distribution<float>(0.0f, 1.0f), rng);

	std::vector<float> input((batch_size() * input_height() * input_width() - 1) * input_pixel_stride() + channels() + XNN_EXTRA_BYTES / sizeof(float));
	std::vector<float> output((batch_size() * output_height() * output_width() - 1) * output_pixel_stride() + channels());
	std::vector<float> output_ref(batch_size() * output_height() * output_width() * channels());
	std::vector<uint32_t> index(batch_size() * output_height() * output_width() * channels());
	std::vector<uint32_t> index_ref(batch_size() * output_height() * output_width() * channels());
	for (size_t iteration = 0; iteration < iterations(); iteration++) {
	std::generate(input.begin(), input.end(), std::ref(f32rng));
	std::fill(output.begin(), output.end(), nanf(""));

	// 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 c = 0; c < channels(); c++) {
	const size_t iy_top_left = std::max<size_t>(oy * pooling_height(), padding_top()) - padding_top();
	const size_t ix_top_left = std::max<size_t>(ox * pooling_width(), padding_left()) - padding_left();
	float max_value =
	input[((i * input_height() + iy_top_left) * input_width() + ix_top_left) * input_pixel_stride() + c];
	uint32_t max_index = 0;
	for (size_t py = 0; py < pooling_height(); py++) {
	const size_t iy = oy * pooling_height() + py - padding_top();
	for (size_t px = 0; px < pooling_width(); px++) {
	const size_t ix = ox * pooling_width() + px - padding_left();
	if (ix < input_width() && iy < input_height()) {
	const float value = input[((i * input_height() + iy) * input_width() + ix) * input_pixel_stride() + c];
	if (value > max_value) {
	max_value = value;
	max_index = uint32_t(px * pooling_height() + py);
	}
	}
	}
	}
	output_ref[((i * output_height() + oy) * output_width() + ox) * channels() + c] = max_value;
	index_ref[((i * output_height() + oy) * output_width() + ox) * channels() + c] = max_index;
	}
	}
	}
	}

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

	ASSERT_EQ(xnn_status_success,
	xnn_create_argmax_pooling2d_nhwc_f32(
	padding_tf_same() ? 0 : padding_top(), padding_tf_same() ? 0 : padding_right(),
	padding_tf_same() ? 0 : padding_bottom(), padding_tf_same() ? 0 : padding_left(),
	pooling_height(), pooling_width(),
	channels(), input_pixel_stride(), output_pixel_stride(),
	padding_tf_same() ? XNN_FLAG_TENSORFLOW_SAME_PADDING : 0,
	&argmax_pooling_op));
	ASSERT_NE(nullptr, argmax_pooling_op);

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

	ASSERT_EQ(xnn_status_success,
	xnn_setup_argmax_pooling2d_nhwc_f32(
	argmax_pooling_op,
	batch_size(), input_height(), input_width(),
	input.data(), output.data(), index.data(),
	nullptr /* thread pool */));

	ASSERT_EQ(xnn_status_success,
	xnn_run_operator(argmax_pooling_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 c = 0; c < channels(); c++) {
	ASSERT_EQ(output_ref[((i * output_height() + y) * output_width() + x) * channels() + c],
	output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c]) <<
	"in batch index " << i << ", pixel (" << y << ", " << x << "), channel " << c;
	ASSERT_EQ(index_ref[((i * output_height() + y) * output_width() + x) * channels() + c],
	index[((i * output_height() + y) * output_width() + x) * channels() + c]) <<
	"in batch index " << i << ", pixel (" << y << ", " << x << "), channel " << c;
	}
	}
	}
	}
	}
	}

	void TestSetupF32() const {
	std::random_device random_device;
	auto rng = std::mt19937(random_device());
	auto f32rng = std::bind(std::uniform_real_distribution<float>(0.0f, 1.0f), rng);

	std::vector<float> input(XNN_EXTRA_BYTES / sizeof(float) + std::max(
	(batch_size() * input_height() * input_width() - 1) * input_pixel_stride() + channels(),
	(next_batch_size() * next_input_height() * next_input_width() - 1) * input_pixel_stride() + channels()));
	std::vector<float> output(std::max(
	(batch_size() * output_height() * output_width() - 1) * output_pixel_stride() + channels(),
	(next_batch_size() * next_output_height() * next_output_width() - 1) * output_pixel_stride() + channels()));
	std::vector<uint32_t> index(std::max(
	batch_size() * output_height() * output_width() * channels(),
	next_batch_size() * next_output_height() * next_output_width() * channels()));
	std::vector<float> output_ref(batch_size() * output_height() * output_width() * channels());
	std::vector<float> next_output_ref(next_batch_size() * next_output_height() * next_output_width() * channels());
	std::vector<uint32_t> index_ref(batch_size() * output_height() * output_width() * channels());
	std::vector<uint32_t> next_index_ref(next_batch_size() * next_output_height() * next_output_width() * channels());
	for (size_t iteration = 0; iteration < iterations(); iteration++) {
	std::generate(input.begin(), input.end(), std::ref(f32rng));
	std::fill(output.begin(), output.end(), nanf(""));

	// 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 c = 0; c < channels(); c++) {
	const size_t iy_top_left = std::max<size_t>(oy * pooling_height(), padding_top()) - padding_top();
	const size_t ix_top_left = std::max<size_t>(ox * pooling_width(), padding_left()) - padding_left();
	float max_value =
	input[((i * input_height() + iy_top_left) * input_width() + ix_top_left) * input_pixel_stride() + c];
	uint32_t max_index = 0;
	for (size_t py = 0; py < pooling_height(); py++) {
	const size_t iy = oy * pooling_height() + py - padding_top();
	for (size_t px = 0; px < pooling_width(); px++) {
	const size_t ix = ox * pooling_width() + px - padding_left();
	if (ix < input_width() && iy < input_height()) {
	const float value = input[((i * input_height() + iy) * input_width() + ix) * input_pixel_stride() + c];
	if (value > max_value) {
	max_value = value;
	max_index = uint32_t(px * pooling_height() + py);
	}
	}
	}
	}
	output_ref[((i * output_height() + oy) * output_width() + ox) * channels() + c] = max_value;
	index_ref[((i * output_height() + oy) * output_width() + ox) * channels() + c] = max_index;
	}
	}
	}
	}

	// Create, setup, and run Argmax Pooling operator once.
	ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
	xnn_operator_t argmax_pooling_op = nullptr;

	ASSERT_EQ(xnn_status_success,
	xnn_create_argmax_pooling2d_nhwc_f32(
	padding_top(), padding_right(), padding_bottom(), padding_left(),
	pooling_height(), pooling_width(),
	channels(), input_pixel_stride(), output_pixel_stride(),
	0, &argmax_pooling_op));
	ASSERT_NE(nullptr, argmax_pooling_op);

	ASSERT_EQ(xnn_status_success,
	xnn_setup_argmax_pooling2d_nhwc_f32(
	argmax_pooling_op,
	batch_size(), input_height(), input_width(),
	input.data(), output.data(), index.data(),
	nullptr /* thread pool */));

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

	// Verify results of the first run.
	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 c = 0; c < channels(); c++) {
	ASSERT_EQ(
	output_ref[((i * output_height() + y) * output_width() + x) * channels() + c],
	output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c])
	<< "in batch index " << i << ", pixel (" << y << ", " << x << "), channel " << c;
	ASSERT_EQ(
	index_ref[((i * output_height() + y) * output_width() + x) * channels() + c],
	index[((i * output_height() + y) * output_width() + x) * channels() + c])
	<< "in batch index " << i << ", pixel (" << y << ", " << x << "), channel " << c;
	}
	}
	}
	}

	// Re-generate data for the second run.
	std::generate(input.begin(), input.end(), std::ref(f32rng));
	std::fill(output.begin(), output.end(), 0xA5);

	// Compute reference results for the second run, including clamping.
	for (size_t i = 0; i < next_batch_size(); i++) {
	for (size_t oy = 0; oy < next_output_height(); oy++) {
	for (size_t ox = 0; ox < next_output_width(); ox++) {
	for (size_t c = 0; c < channels(); c++) {
	const size_t iy_top_left = std::max<size_t>(oy * pooling_height(), padding_top()) - padding_top();
	const size_t ix_top_left = std::max<size_t>(ox * pooling_width(), padding_left()) - padding_left();
	float max_value =
	input[((i * next_input_height() + iy_top_left) * next_input_width() + ix_top_left) * input_pixel_stride() + c];
	uint32_t max_index = 0;
	for (size_t py = 0; py < pooling_height(); py++) {
	const size_t iy = oy * pooling_height() + py - padding_top();
	for (size_t px = 0; px < pooling_width(); px++) {
	const size_t ix = ox * pooling_width() + px - padding_left();
	if (ix < next_input_width() && iy < next_input_height()) {
	const float value = input[((i * next_input_height() + iy) * next_input_width() + ix) * input_pixel_stride() + c];
	if (value > max_value) {
	max_value = value;
	max_index = uint32_t(px * pooling_height() + py);
	}
	}
	}
	}
	next_output_ref[((i * next_output_height() + oy) * next_output_width() + ox) * channels() + c] = max_value;
	next_index_ref[((i * next_output_height() + oy) * next_output_width() + ox) * channels() + c] = max_index;
	}
	}
	}
	}

	// Setup and run Argmax Pooling operator the second time, and destroy the operator.
	ASSERT_EQ(xnn_status_success,
	xnn_setup_argmax_pooling2d_nhwc_f32(
	argmax_pooling_op,
	next_batch_size(), next_input_height(), next_input_width(),
	input.data(), output.data(), index.data(),
	nullptr /* thread pool */));

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

	ASSERT_EQ(xnn_status_success,
	xnn_delete_operator(argmax_pooling_op));
	argmax_pooling_op = nullptr;

	// Verify results of the second run.
	for (size_t i = 0; i < next_batch_size(); i++) {
	for (size_t y = 0; y < next_output_height(); y++) {
	for (size_t x = 0; x < next_output_width(); x++) {
	for (size_t c = 0; c < channels(); c++) {
	ASSERT_EQ(
	next_output_ref[((i * next_output_height() + y) * next_output_width() + x) * channels() + c],
	output[((i * next_output_height() + y) * next_output_width() + x) * output_pixel_stride() + c])
	<< "in batch index " << i << ", pixel (" << y << ", " << x << "), channel " << c;
	ASSERT_EQ(
	next_index_ref[((i * next_output_height() + y) * next_output_width() + x) * channels() + c],
	index[((i * next_output_height() + y) * next_output_width() + x) * output_pixel_stride() + c])
	<< "in batch index " << i << ", pixel (" << y << ", " << x << "), channel " << c;
	}
	}
	}
	}
	}
	}

	private:
	uint32_t padding_top_{0};
	uint32_t padding_right_{0};
	uint32_t padding_bottom_{0};
	uint32_t padding_left_{0};
	bool padding_tf_same_{false};
	size_t input_height_{1};
	size_t input_width_{1};
	size_t channels_{1};
	size_t batch_size_{1};
	size_t input_pixel_stride_{0};
	size_t output_pixel_stride_{0};
	uint32_t pooling_height_{1};
	uint32_t pooling_width_{1};
	size_t next_input_height_{0};
	size_t next_input_width_{0};
	size_t next_batch_size_{0};
	uint8_t qmin_{0};
	uint8_t qmax_{255};
	size_t iterations_{1};
	};