test/unpooling-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 UnpoolingOperatorTester {
  public:
   inline UnpoolingOperatorTester& padding(uint32_t padding) {
     this->padding_top_ = padding;
     this->padding_right_ = padding;
     this->padding_bottom_ = padding;
     this->padding_left_ = padding;
     return *this;
   }

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

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

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

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

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

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

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

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

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

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

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

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

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

   inline UnpoolingOperatorTester& 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 UnpoolingOperatorTester& 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 UnpoolingOperatorTester& 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 {
     const size_t padding_height = padding_top() + padding_bottom();
     return std::max<size_t>(input_height() * pooling_height(), padding_height) - padding_height;
   }

   inline size_t output_width() const {
     const size_t padding_width = padding_left() + padding_right();
     return std::max<size_t>(input_width() * pooling_width(), padding_width) - padding_width;
   }

   inline UnpoolingOperatorTester& 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 UnpoolingOperatorTester& 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 UnpoolingOperatorTester& 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 UnpoolingOperatorTester& 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 UnpoolingOperatorTester& 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 padding_height = padding_top() + padding_bottom();
     return std::max<size_t>(next_input_height() * pooling_height(), padding_height) - padding_height;
   }

   inline size_t next_output_width() const {
     const size_t padding_width = padding_left() + padding_right();
     return std::max<size_t>(next_input_width() * pooling_width(), padding_width) - padding_width;
   }

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

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

   void TestX32() const {
     std::random_device random_device;
     auto rng = std::mt19937(random_device());
     auto u32rng = std::bind(std::uniform_int_distribution<uint32_t>(), std::ref(rng));
     auto idx_rng = std::bind(std::uniform_int_distribution<uint32_t>(0, pooling_height() * pooling_width() - 1), std::ref(rng));

     std::vector<uint32_t> input((batch_size() * input_height() * input_width() - 1) * input_pixel_stride() + channels());
     std::vector<uint32_t> index(batch_size() * input_height() * input_width() * channels());
     std::vector<uint32_t> output((batch_size() * output_height() * output_width() - 1) * output_pixel_stride() + channels());
     std::vector<uint32_t> output_ref(batch_size() * output_height() * output_width() * channels());
     for (size_t iteration = 0; iteration < iterations(); iteration++) {
       std::generate(input.begin(), input.end(), std::ref(u32rng));
       std::generate(index.begin(), index.end(), std::ref(idx_rng));
       std::generate(output.begin(), output.end(), std::ref(u32rng));

       // Compute reference results.
       std::fill(output_ref.begin(), output_ref.end(), 0);
       for (size_t i = 0; i < batch_size(); i++) {
         for (size_t iy = 0; iy < input_height(); iy++) {
           for (size_t ix = 0; ix < input_width(); ix++) {
             for (size_t c = 0; c < channels(); c++) {
               const uint32_t pooling_index = index[((i * input_height() + iy) * input_width() + ix) * channels() + c];
               const uint32_t py = pooling_index % pooling_height();
               const uint32_t px = pooling_index / pooling_height();
               const size_t oy = std::min(std::max<size_t>(iy * pooling_height() + py, padding_top()) - padding_top(), output_height() - 1);
               const size_t ox = std::min(std::max<size_t>(ix * pooling_width() + px, padding_left()) - padding_left(), output_width() - 1);
               output_ref[((i * output_height() + oy) * output_width() + ox) * channels() + c] =
                 input[((i * input_height() + iy) * input_width() + ix) * input_pixel_stride() + c];
             }
           }
         }
       }

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

       ASSERT_EQ(xnn_status_success,
         xnn_create_unpooling2d_nhwc_x32(
           padding_top(), padding_right(), padding_bottom(), padding_left(),
           pooling_height(), pooling_width(),
           channels(), input_pixel_stride(), output_pixel_stride(),
           0, &unpooling_op));
       ASSERT_NE(nullptr, unpooling_op);

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

       ASSERT_EQ(xnn_status_success,
         xnn_setup_unpooling2d_nhwc_x32(
           unpooling_op,
           batch_size(), input_height(), input_width(),
           input.data(), index.data(), output.data(),
           nullptr /* thread pool */));

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

       // Verify results.
       for (size_t i = 0; i < batch_size(); i++) {
         for (size_t c = 0; c < channels(); c++) {
           for (size_t y = 0; y < output_height(); y++) {
             for (size_t x = 0; x < output_width(); x++) {
               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;
             }
           }
         }
       }
     }
   }

   void TestSetupX32() const {
     std::random_device random_device;
     auto rng = std::mt19937(random_device());
     auto u32rng = std::bind(std::uniform_int_distribution<uint32_t>(), std::ref(rng));
     auto idx_rng = std::bind(std::uniform_int_distribution<uint32_t>(0, pooling_height() * pooling_width() - 1), std::ref(rng));

     std::vector<uint32_t> input(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<uint32_t> index(std::max(
       batch_size() * input_height() * input_width() * channels(),
       next_batch_size() * next_input_height() * next_input_width() * channels()));
     std::vector<uint32_t> 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> output_ref(batch_size() * output_height() * output_width() * channels());
     std::vector<uint32_t> next_output_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(u32rng));
       std::generate(index.begin(), index.end(), std::ref(idx_rng));
       std::generate(output.begin(), output.end(), std::ref(u32rng));

       // Compute reference results.
       std::fill(output_ref.begin(), output_ref.end(), 0);
       for (size_t i = 0; i < batch_size(); i++) {
         for (size_t iy = 0; iy < input_height(); iy++) {
           for (size_t ix = 0; ix < input_width(); ix++) {
             for (size_t c = 0; c < channels(); c++) {
               const uint32_t pooling_index = index[((i * input_height() + iy) * input_width() + ix) * channels() + c];
               const uint32_t py = pooling_index % pooling_height();
               const uint32_t px = pooling_index / pooling_height();
               const size_t oy = std::min(std::max<size_t>(iy * pooling_height() + py, padding_top()) - padding_top(), output_height() - 1);
               const size_t ox = std::min(std::max<size_t>(ix * pooling_width() + px, padding_left()) - padding_left(), output_width() - 1);
               output_ref[((i * output_height() + oy) * output_width() + ox) * channels() + c] =
                 input[((i * input_height() + iy) * input_width() + ix) * input_pixel_stride() + c];
             }
           }
         }
       }

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

       ASSERT_EQ(xnn_status_success,
         xnn_create_unpooling2d_nhwc_x32(
           padding_top(), padding_right(), padding_bottom(), padding_left(),
           pooling_height(), pooling_width(),
           channels(), input_pixel_stride(), output_pixel_stride(),
           0, &unpooling_op));
       ASSERT_NE(nullptr, unpooling_op);

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

       ASSERT_EQ(xnn_status_success,
         xnn_setup_unpooling2d_nhwc_x32(
           unpooling_op,
           batch_size(), input_height(), input_width(),
           input.data(), index.data(), output.data(),
           nullptr /* thread pool */));

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

       // Verify results of the first run.
       for (size_t i = 0; i < batch_size(); i++) {
         for (size_t c = 0; c < channels(); c++) {
           for (size_t y = 0; y < output_height(); y++) {
             for (size_t x = 0; x < output_width(); x++) {
               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;
             }
           }
         }
       }

       // Re-generate data for the second run.
       std::generate(input.begin(), input.end(), std::ref(u32rng));
       std::generate(index.begin(), index.end(), std::ref(idx_rng));
       std::generate(output.begin(), output.end(), std::ref(u32rng));

       // Compute reference results for the second run, including clamping.
       std::fill(next_output_ref.begin(), next_output_ref.end(), 0);
       for (size_t i = 0; i < next_batch_size(); i++) {
         for (size_t iy = 0; iy < next_input_height(); iy++) {
           for (size_t ix = 0; ix < next_input_width(); ix++) {
             for (size_t c = 0; c < channels(); c++) {
               const uint32_t pooling_index = index[((i * next_input_height() + iy) * next_input_width() + ix) * channels() + c];
               const uint32_t py = pooling_index % pooling_height();
               const uint32_t px = pooling_index / pooling_height();
               const size_t oy = std::min(std::max<size_t>(iy * pooling_height() + py, padding_top()) - padding_top(), next_output_height() - 1);
               const size_t ox = std::min(std::max<size_t>(ix * pooling_width() + px, padding_left()) - padding_left(), next_output_width() - 1);
               next_output_ref[((i * next_output_height() + oy) * next_output_width() + ox) * channels() + c] =
                 input[((i * next_input_height() + iy) * next_input_width() + ix) * input_pixel_stride() + c];
             }
           }
         }
       }

       // Setup and run Max Pooling operator the second time, and destroy the operator.
       ASSERT_EQ(xnn_status_success,
         xnn_setup_unpooling2d_nhwc_x32(
           unpooling_op,
           next_batch_size(), next_input_height(), next_input_width(),
           input.data(), index.data(), output.data(),
           nullptr /* thread pool */));

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

       // Verify results of the second run.
       for (size_t i = 0; i < next_batch_size(); i++) {
         for (size_t c = 0; c < channels(); c++) {
           for (size_t y = 0; y < next_output_height(); y++) {
             for (size_t x = 0; x < next_output_width(); x++) {
               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;
             }
           }
         }
       }
     }
   }

  private:
   uint32_t padding_top_{0};
   uint32_t padding_right_{0};
   uint32_t padding_bottom_{0};
   uint32_t padding_left_{0};
   size_t input_height_{1};
   size_t input_width_{1};
   size_t 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};
   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 UnpoolingOperatorTester {
	public:
	inline UnpoolingOperatorTester& padding(uint32_t padding) {
	this->padding_top_ = padding;
	this->padding_right_ = padding;
	this->padding_bottom_ = padding;
	this->padding_left_ = padding;
	return *this;
	}

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

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

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

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

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

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

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

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

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

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

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

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

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

	inline UnpoolingOperatorTester& 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 UnpoolingOperatorTester& 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 UnpoolingOperatorTester& 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 {
	const size_t padding_height = padding_top() + padding_bottom();
	return std::max<size_t>(input_height() * pooling_height(), padding_height) - padding_height;
	}

	inline size_t output_width() const {
	const size_t padding_width = padding_left() + padding_right();
	return std::max<size_t>(input_width() * pooling_width(), padding_width) - padding_width;
	}

	inline UnpoolingOperatorTester& 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 UnpoolingOperatorTester& 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 UnpoolingOperatorTester& 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 UnpoolingOperatorTester& 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 UnpoolingOperatorTester& 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 padding_height = padding_top() + padding_bottom();
	return std::max<size_t>(next_input_height() * pooling_height(), padding_height) - padding_height;
	}

	inline size_t next_output_width() const {
	const size_t padding_width = padding_left() + padding_right();
	return std::max<size_t>(next_input_width() * pooling_width(), padding_width) - padding_width;
	}

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

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

	void TestX32() const {
	std::random_device random_device;
	auto rng = std::mt19937(random_device());
	auto u32rng = std::bind(std::uniform_int_distribution<uint32_t>(), std::ref(rng));
	auto idx_rng = std::bind(std::uniform_int_distribution<uint32_t>(0, pooling_height() * pooling_width() - 1), std::ref(rng));

	std::vector<uint32_t> input((batch_size() * input_height() * input_width() - 1) * input_pixel_stride() + channels());
	std::vector<uint32_t> index(batch_size() * input_height() * input_width() * channels());
	std::vector<uint32_t> output((batch_size() * output_height() * output_width() - 1) * output_pixel_stride() + channels());
	std::vector<uint32_t> output_ref(batch_size() * output_height() * output_width() * channels());
	for (size_t iteration = 0; iteration < iterations(); iteration++) {
	std::generate(input.begin(), input.end(), std::ref(u32rng));
	std::generate(index.begin(), index.end(), std::ref(idx_rng));
	std::generate(output.begin(), output.end(), std::ref(u32rng));

	// Compute reference results.
	std::fill(output_ref.begin(), output_ref.end(), 0);
	for (size_t i = 0; i < batch_size(); i++) {
	for (size_t iy = 0; iy < input_height(); iy++) {
	for (size_t ix = 0; ix < input_width(); ix++) {
	for (size_t c = 0; c < channels(); c++) {
	const uint32_t pooling_index = index[((i * input_height() + iy) * input_width() + ix) * channels() + c];
	const uint32_t py = pooling_index % pooling_height();
	const uint32_t px = pooling_index / pooling_height();
	const size_t oy = std::min(std::max<size_t>(iy * pooling_height() + py, padding_top()) - padding_top(), output_height() - 1);
	const size_t ox = std::min(std::max<size_t>(ix * pooling_width() + px, padding_left()) - padding_left(), output_width() - 1);
	output_ref[((i * output_height() + oy) * output_width() + ox) * channels() + c] =
	input[((i * input_height() + iy) * input_width() + ix) * input_pixel_stride() + c];
	}
	}
	}
	}

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

	ASSERT_EQ(xnn_status_success,
	xnn_create_unpooling2d_nhwc_x32(
	padding_top(), padding_right(), padding_bottom(), padding_left(),
	pooling_height(), pooling_width(),
	channels(), input_pixel_stride(), output_pixel_stride(),
	0, &unpooling_op));
	ASSERT_NE(nullptr, unpooling_op);

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

	ASSERT_EQ(xnn_status_success,
	xnn_setup_unpooling2d_nhwc_x32(
	unpooling_op,
	batch_size(), input_height(), input_width(),
	input.data(), index.data(), output.data(),
	nullptr /* thread pool */));

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

	// Verify results.
	for (size_t i = 0; i < batch_size(); i++) {
	for (size_t c = 0; c < channels(); c++) {
	for (size_t y = 0; y < output_height(); y++) {
	for (size_t x = 0; x < output_width(); x++) {
	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;
	}
	}
	}
	}
	}
	}

	void TestSetupX32() const {
	std::random_device random_device;
	auto rng = std::mt19937(random_device());
	auto u32rng = std::bind(std::uniform_int_distribution<uint32_t>(), std::ref(rng));
	auto idx_rng = std::bind(std::uniform_int_distribution<uint32_t>(0, pooling_height() * pooling_width() - 1), std::ref(rng));

	std::vector<uint32_t> input(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<uint32_t> index(std::max(
	batch_size() * input_height() * input_width() * channels(),
	next_batch_size() * next_input_height() * next_input_width() * channels()));
	std::vector<uint32_t> 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> output_ref(batch_size() * output_height() * output_width() * channels());
	std::vector<uint32_t> next_output_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(u32rng));
	std::generate(index.begin(), index.end(), std::ref(idx_rng));
	std::generate(output.begin(), output.end(), std::ref(u32rng));

	// Compute reference results.
	std::fill(output_ref.begin(), output_ref.end(), 0);
	for (size_t i = 0; i < batch_size(); i++) {
	for (size_t iy = 0; iy < input_height(); iy++) {
	for (size_t ix = 0; ix < input_width(); ix++) {
	for (size_t c = 0; c < channels(); c++) {
	const uint32_t pooling_index = index[((i * input_height() + iy) * input_width() + ix) * channels() + c];
	const uint32_t py = pooling_index % pooling_height();
	const uint32_t px = pooling_index / pooling_height();
	const size_t oy = std::min(std::max<size_t>(iy * pooling_height() + py, padding_top()) - padding_top(), output_height() - 1);
	const size_t ox = std::min(std::max<size_t>(ix * pooling_width() + px, padding_left()) - padding_left(), output_width() - 1);
	output_ref[((i * output_height() + oy) * output_width() + ox) * channels() + c] =
	input[((i * input_height() + iy) * input_width() + ix) * input_pixel_stride() + c];
	}
	}
	}
	}

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

	ASSERT_EQ(xnn_status_success,
	xnn_create_unpooling2d_nhwc_x32(
	padding_top(), padding_right(), padding_bottom(), padding_left(),
	pooling_height(), pooling_width(),
	channels(), input_pixel_stride(), output_pixel_stride(),
	0, &unpooling_op));
	ASSERT_NE(nullptr, unpooling_op);

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

	ASSERT_EQ(xnn_status_success,
	xnn_setup_unpooling2d_nhwc_x32(
	unpooling_op,
	batch_size(), input_height(), input_width(),
	input.data(), index.data(), output.data(),
	nullptr /* thread pool */));

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

	// Verify results of the first run.
	for (size_t i = 0; i < batch_size(); i++) {
	for (size_t c = 0; c < channels(); c++) {
	for (size_t y = 0; y < output_height(); y++) {
	for (size_t x = 0; x < output_width(); x++) {
	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;
	}
	}
	}
	}

	// Re-generate data for the second run.
	std::generate(input.begin(), input.end(), std::ref(u32rng));
	std::generate(index.begin(), index.end(), std::ref(idx_rng));
	std::generate(output.begin(), output.end(), std::ref(u32rng));

	// Compute reference results for the second run, including clamping.
	std::fill(next_output_ref.begin(), next_output_ref.end(), 0);
	for (size_t i = 0; i < next_batch_size(); i++) {
	for (size_t iy = 0; iy < next_input_height(); iy++) {
	for (size_t ix = 0; ix < next_input_width(); ix++) {
	for (size_t c = 0; c < channels(); c++) {
	const uint32_t pooling_index = index[((i * next_input_height() + iy) * next_input_width() + ix) * channels() + c];
	const uint32_t py = pooling_index % pooling_height();
	const uint32_t px = pooling_index / pooling_height();
	const size_t oy = std::min(std::max<size_t>(iy * pooling_height() + py, padding_top()) - padding_top(), next_output_height() - 1);
	const size_t ox = std::min(std::max<size_t>(ix * pooling_width() + px, padding_left()) - padding_left(), next_output_width() - 1);
	next_output_ref[((i * next_output_height() + oy) * next_output_width() + ox) * channels() + c] =
	input[((i * next_input_height() + iy) * next_input_width() + ix) * input_pixel_stride() + c];
	}
	}
	}
	}

	// Setup and run Max Pooling operator the second time, and destroy the operator.
	ASSERT_EQ(xnn_status_success,
	xnn_setup_unpooling2d_nhwc_x32(
	unpooling_op,
	next_batch_size(), next_input_height(), next_input_width(),
	input.data(), index.data(), output.data(),
	nullptr /* thread pool */));

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

	// Verify results of the second run.
	for (size_t i = 0; i < next_batch_size(); i++) {
	for (size_t c = 0; c < channels(); c++) {
	for (size_t y = 0; y < next_output_height(); y++) {
	for (size_t x = 0; x < next_output_width(); x++) {
	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;
	}
	}
	}
	}
	}
	}

	private:
	uint32_t padding_top_{0};
	uint32_t padding_right_{0};
	uint32_t padding_bottom_{0};
	uint32_t padding_left_{0};
	size_t input_height_{1};
	size_t input_width_{1};
	size_t 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};
	size_t iterations_{1};
	};