argmax-pooling-operator-tester.h

// 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 <algorithm>
#include <cassert>
#include <cmath>
#include <cstddef>
#include <cstdint>
#include <cstdlib>
#include <memory>
#include <random>
#include <vector>

#include <gtest/gtest.h>
#include "xnnpack.h"
#include "xnnpack/aligned-allocator.h"
#include "xnnpack/common.h"
#include "replicable_random_device.h"
#include "pthreadpool.h"

class ArgmaxPoolingOperatorTester {
 public:
  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;
  }

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

  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;
  }

  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;
  }

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

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

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

  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_;
    }
  }

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

  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_;
    }
  }

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

  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_;
    }
  }

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

  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_;
    }
  }

  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;
  }

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

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

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

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

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

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

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

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

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

  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;
  }

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

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

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

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

  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();
    }
  }

  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();
    }
  }

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

  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_;
    }
  }

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

  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_;
    }
  }

  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;
  }

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

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

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

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

  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();
  }

  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();
  }

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

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

  ArgmaxPoolingOperatorTester& multithreaded(size_t multithreaded) {
    this->multithreaded_ = multithreaded;
    return *this;
  }

  size_t multithreaded() const {
    return this->multithreaded_;
  }

  size_t num_threads() const {
    // Do not spin up excessive number of threads for tests.
    return multithreaded() ? 5 : 1;
  }

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

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

  void TestF32() const {
    xnnpack::ReplicableRandomDevice rng;
    std::uniform_real_distribution<float> f32dist;

    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::unique_ptr<pthreadpool, decltype(&pthreadpool_destroy)> auto_threadpool{nullptr, pthreadpool_destroy};
      if (multithreaded()) {
        const pthreadpool_t threadpool = pthreadpool_create(num_threads());
        if (pthreadpool_get_threads_count(threadpool) <= 1) {
          GTEST_SKIP();
        } else {
          auto_threadpool.reset(threadpool);
        }
      }

      std::generate(input.begin(), input.end(), [&]() { return f32dist(rng); });
      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(),
          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);

      size_t workspace_size = SIZE_MAX;
      size_t workspace_alignment = SIZE_MAX;
      ASSERT_EQ(xnn_status_success,
        xnn_reshape_argmax_pooling2d_nhwc_f32(
          argmax_pooling_op,
          batch_size(), input_height(), input_width(),
          channels(), input_pixel_stride(), output_pixel_stride(),
          &workspace_size, &workspace_alignment,
          /*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
          auto_threadpool.get()));

      ASSERT_NE(workspace_size, SIZE_MAX);
      ASSERT_LE(workspace_alignment, XNN_ALLOCATION_ALIGNMENT);
      std::vector<char, AlignedAllocator<char, XNN_ALLOCATION_ALIGNMENT>> workspace(workspace_size);

      ASSERT_EQ(xnn_status_success,
        xnn_setup_argmax_pooling2d_nhwc_f32(
          argmax_pooling_op,
          workspace.data(),
          input.data(), output.data(), index.data()));

      ASSERT_EQ(xnn_status_success,
        xnn_run_operator(argmax_pooling_op, auto_threadpool.get()));

      // 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++) {
              EXPECT_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;
              EXPECT_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 {
    xnnpack::ReplicableRandomDevice rng;
    std::uniform_real_distribution<float> f32dist;

    std::vector<float> input(XNN_EXTRA_BYTES / sizeof(float) + std::max<size_t>(
      (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<size_t>(
      (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<size_t>(
      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::unique_ptr<pthreadpool, decltype(&pthreadpool_destroy)> auto_threadpool{nullptr, pthreadpool_destroy};
      if (multithreaded()) {
        const pthreadpool_t threadpool = pthreadpool_create(num_threads());
        if (pthreadpool_get_threads_count(threadpool) <= 1) {
          GTEST_SKIP();
        } else {
          auto_threadpool.reset(threadpool);
        }
      }

      std::generate(input.begin(), input.end(), [&]() { return f32dist(rng); });
      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(),
          0, &argmax_pooling_op));
      ASSERT_NE(nullptr, argmax_pooling_op);

      size_t workspace_size = SIZE_MAX;
      size_t workspace_alignment = SIZE_MAX;
      ASSERT_EQ(xnn_status_success,
        xnn_reshape_argmax_pooling2d_nhwc_f32(
          argmax_pooling_op,
          batch_size(), input_height(), input_width(),
          channels(), input_pixel_stride(), output_pixel_stride(),
          &workspace_size, &workspace_alignment,
          /*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
          auto_threadpool.get()));

      ASSERT_NE(workspace_size, SIZE_MAX);
      ASSERT_LE(workspace_alignment, XNN_ALLOCATION_ALIGNMENT);
      std::vector<char, AlignedAllocator<char, XNN_ALLOCATION_ALIGNMENT>> workspace(workspace_size);

      ASSERT_EQ(xnn_status_success,
        xnn_setup_argmax_pooling2d_nhwc_f32(
          argmax_pooling_op,
          workspace.data(),
          input.data(), output.data(), index.data()));

      ASSERT_EQ(xnn_status_success,
        xnn_run_operator(argmax_pooling_op, auto_threadpool.get()));

      // 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++) {
              EXPECT_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;
              EXPECT_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(), [&]() { return f32dist(rng); });
      std::fill(output.begin(), output.end(), std::nanf(""));

      // 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;
            }
          }
        }
      }

      size_t next_workspace_size = SIZE_MAX;
      size_t next_workspace_alignment = SIZE_MAX;
      ASSERT_EQ(xnn_status_success,
        xnn_reshape_argmax_pooling2d_nhwc_f32(
          argmax_pooling_op,
          next_batch_size(), next_input_height(), next_input_width(),
          channels(), input_pixel_stride(), output_pixel_stride(),
          &next_workspace_size, &next_workspace_alignment,
          /*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
          auto_threadpool.get()));

      ASSERT_NE(workspace_size, SIZE_MAX);
      ASSERT_LE(next_workspace_alignment, XNN_ALLOCATION_ALIGNMENT);
      std::vector<char, AlignedAllocator<char, XNN_ALLOCATION_ALIGNMENT>> next_workspace(next_workspace_size);

      // 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_workspace.data(),
          input.data(), output.data(), index.data()));

      ASSERT_EQ(xnn_status_success,
        xnn_run_operator(argmax_pooling_op, auto_threadpool.get()));

      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++) {
              EXPECT_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;
              EXPECT_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};
  bool multithreaded_{false};
  size_t iterations_{1};
};