| // Copyright (c) Facebook, Inc. and its affiliates. |
| // All rights reserved. |
| // |
| // 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 MaxPoolingOperatorTester { |
| public: |
| inline MaxPoolingOperatorTester& 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 MaxPoolingOperatorTester& 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 MaxPoolingOperatorTester& 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 MaxPoolingOperatorTester& padding_height(uint32_t padding_height) { |
| assert(!padding_tf_same()); |
| this->padding_top_ = padding_height; |
| this->padding_bottom_ = padding_height; |
| return *this; |
| } |
| |
| inline MaxPoolingOperatorTester& padding_width(uint32_t padding_width) { |
| assert(!padding_tf_same()); |
| this->padding_right_ = padding_width; |
| this->padding_left_ = padding_width; |
| return *this; |
| } |
| |
| inline MaxPoolingOperatorTester& 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() - 1) * stride_height() + dilated_pooling_height() - input_height(); |
| return total_padding_height / 2; |
| } else { |
| return this->padding_top_; |
| } |
| } |
| |
| inline MaxPoolingOperatorTester& 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() - 1) * stride_width() + dilated_pooling_width() - input_width(); |
| return total_padding_width / 2; |
| } else { |
| return this->padding_left_; |
| } |
| } |
| |
| inline MaxPoolingOperatorTester& 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() - 1) * stride_height() + dilated_pooling_height() - input_height(); |
| return total_padding_height - total_padding_height / 2; |
| } else { |
| return this->padding_bottom_; |
| } |
| } |
| |
| inline MaxPoolingOperatorTester& 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() - 1) * stride_width() + dilated_pooling_width() - input_width(); |
| return total_padding_width - total_padding_width / 2; |
| } else { |
| return this->padding_right_; |
| } |
| } |
| |
| inline MaxPoolingOperatorTester& 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 MaxPoolingOperatorTester& 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 MaxPoolingOperatorTester& 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 MaxPoolingOperatorTester& channels(size_t channels) { |
| assert(channels != 0); |
| this->channels_ = channels; |
| return *this; |
| } |
| |
| inline size_t channels() const { |
| return this->channels_; |
| } |
| |
| inline MaxPoolingOperatorTester& 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 MaxPoolingOperatorTester& pooling_size(uint32_t pooling_size) { |
| assert(pooling_size >= 1); |
| this->pooling_height_ = pooling_size; |
| this->pooling_width_ = pooling_size; |
| return *this; |
| } |
| |
| inline MaxPoolingOperatorTester& 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 MaxPoolingOperatorTester& 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 MaxPoolingOperatorTester& 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 MaxPoolingOperatorTester& stride(uint32_t stride) { |
| assert(stride >= 1); |
| this->stride_height_ = stride; |
| this->stride_width_ = stride; |
| return *this; |
| } |
| |
| inline MaxPoolingOperatorTester& stride(uint32_t stride_height, uint32_t stride_width) { |
| assert(stride_height >= 1); |
| assert(stride_width >= 1); |
| this->stride_height_ = stride_height; |
| this->stride_width_ = stride_width; |
| return *this; |
| } |
| |
| inline MaxPoolingOperatorTester& stride_height(uint32_t stride_height) { |
| assert(stride_height >= 1); |
| this->stride_height_ = stride_height; |
| return *this; |
| } |
| |
| inline uint32_t stride_height() const { |
| return this->stride_height_; |
| } |
| |
| inline MaxPoolingOperatorTester& stride_width(uint32_t stride_width) { |
| assert(stride_width >= 1); |
| this->stride_width_ = stride_width; |
| return *this; |
| } |
| |
| inline uint32_t stride_width() const { |
| return this->stride_width_; |
| } |
| |
| inline MaxPoolingOperatorTester& dilation(uint32_t dilation) { |
| assert(dilation >= 1); |
| this->dilation_height_ = dilation; |
| this->dilation_width_ = dilation; |
| return *this; |
| } |
| |
| inline MaxPoolingOperatorTester& dilation(uint32_t dilation_height, uint32_t dilation_width) { |
| assert(dilation_height >= 1); |
| assert(dilation_width >= 1); |
| this->dilation_height_ = dilation_height; |
| this->dilation_width_ = dilation_width; |
| return *this; |
| } |
| |
| inline MaxPoolingOperatorTester& dilation_height(uint32_t dilation_height) { |
| assert(dilation_height >= 1); |
| this->dilation_height_ = dilation_height; |
| return *this; |
| } |
| |
| inline uint32_t dilation_height() const { |
| return this->dilation_height_; |
| } |
| |
| inline MaxPoolingOperatorTester& dilation_width(uint32_t dilation_width) { |
| assert(dilation_width >= 1); |
| this->dilation_width_ = dilation_width; |
| return *this; |
| } |
| |
| inline uint32_t dilation_width() const { |
| return this->dilation_width_; |
| } |
| |
| inline uint32_t dilated_pooling_height() const { |
| return (pooling_height() - 1) * dilation_height() + 1; |
| } |
| |
| inline uint32_t dilated_pooling_width() const { |
| return (pooling_width() - 1) * dilation_width() + 1; |
| } |
| |
| inline size_t output_height() const { |
| if (padding_tf_same()) { |
| return (input_height() + stride_height() - 1) / stride_height(); |
| } else { |
| const size_t padded_input_height = padding_top() + input_height() + padding_bottom(); |
| if (padded_input_height <= dilated_pooling_height()) { |
| return 1; |
| } else { |
| return (padded_input_height - dilated_pooling_height()) / stride_height() + 1; |
| } |
| } |
| } |
| |
| inline size_t output_width() const { |
| if (padding_tf_same()) { |
| return (input_width() + stride_width() - 1) / stride_width(); |
| } else { |
| const size_t padded_input_width = padding_left() + input_width() + padding_right(); |
| if (padded_input_width <= dilated_pooling_width()) { |
| return 1; |
| } else { |
| return (padded_input_width - dilated_pooling_width()) / stride_width() + 1; |
| } |
| } |
| } |
| |
| inline MaxPoolingOperatorTester& 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 MaxPoolingOperatorTester& 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 MaxPoolingOperatorTester& 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 MaxPoolingOperatorTester& 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 MaxPoolingOperatorTester& 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(); |
| if (padded_next_input_height <= dilated_pooling_height()) { |
| return 1; |
| } else { |
| return (padded_next_input_height - dilated_pooling_height()) / stride_height() + 1; |
| } |
| } |
| |
| inline size_t next_output_width() const { |
| const size_t padded_next_input_width = padding_left() + next_input_width() + padding_right(); |
| if (padded_next_input_width <= dilated_pooling_width()) { |
| return 1; |
| } else { |
| return (padded_next_input_width - dilated_pooling_width()) / stride_width() + 1; |
| } |
| } |
| |
| inline MaxPoolingOperatorTester& 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 MaxPoolingOperatorTester& qmin(uint8_t qmin) { |
| this->qmin_ = qmin; |
| return *this; |
| } |
| |
| inline uint8_t qmin() const { |
| return this->qmin_; |
| } |
| |
| inline MaxPoolingOperatorTester& qmax(uint8_t qmax) { |
| this->qmax_ = qmax; |
| return *this; |
| } |
| |
| inline uint8_t qmax() const { |
| return this->qmax_; |
| } |
| |
| inline MaxPoolingOperatorTester& iterations(size_t iterations) { |
| this->iterations_ = iterations; |
| return *this; |
| } |
| |
| inline size_t iterations() const { |
| return this->iterations_; |
| } |
| |
| void TestU8() const { |
| std::random_device random_device; |
| auto rng = std::mt19937(random_device()); |
| auto u8rng = std::bind(std::uniform_int_distribution<uint32_t>(0, std::numeric_limits<uint8_t>::max()), rng); |
| |
| std::vector<uint8_t> input((batch_size() * input_height() * input_width() - 1) * input_pixel_stride() + channels() + XNN_EXTRA_BYTES / sizeof(uint8_t)); |
| std::vector<uint8_t> output((batch_size() * output_height() * output_width() - 1) * output_pixel_stride() + channels() + XNN_EXTRA_BYTES / sizeof(uint8_t)); |
| std::vector<uint8_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(u8rng)); |
| std::fill(output.begin(), output.end(), 0xA5); |
| |
| // Compute reference results. |
| 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++) { |
| uint8_t max_value = 0; |
| for (size_t py = 0; py < pooling_height(); py++) { |
| const size_t iy = oy * stride_height() + py * dilation_height() - padding_top(); |
| for (size_t px = 0; px < pooling_width(); px++) { |
| const size_t ix = ox * stride_width() + px * dilation_width() - padding_left(); |
| if (ix < input_width() && iy < input_height()) { |
| max_value = std::max(max_value, |
| input[((i * input_height() + iy) * input_width() + ix) * input_pixel_stride() + c]); |
| } |
| } |
| } |
| max_value = std::min(max_value, qmax()); |
| max_value = std::max(max_value, qmin()); |
| output_ref[((i * output_height() + oy) * output_width() + ox) * channels() + c] = max_value; |
| } |
| } |
| } |
| } |
| |
| // Create, setup, run, and destroy Max Pooling operator. |
| ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */)); |
| xnn_operator_t max_pooling_op = nullptr; |
| |
| ASSERT_EQ(xnn_status_success, |
| xnn_create_max_pooling2d_nhwc_u8( |
| 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(), |
| stride_height(), stride_width(), |
| dilation_height(), dilation_width(), |
| channels(), input_pixel_stride(), output_pixel_stride(), |
| qmin(), qmax(), |
| padding_tf_same() ? XNN_FLAG_TENSORFLOW_SAME_PADDING : 0, |
| &max_pooling_op)); |
| ASSERT_NE(nullptr, max_pooling_op); |
| |
| // Smart pointer to automatically delete max_pooling_op. |
| std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_max_pooling_op(max_pooling_op, xnn_delete_operator); |
| |
| ASSERT_EQ(xnn_status_success, |
| xnn_setup_max_pooling2d_nhwc_u8( |
| max_pooling_op, |
| batch_size(), input_height(), input_width(), |
| input.data(), output.data(), |
| nullptr /* thread pool */)); |
| |
| ASSERT_EQ(xnn_status_success, |
| xnn_run_operator(max_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_LE(uint32_t(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c]), uint32_t(qmax())); |
| ASSERT_GE(uint32_t(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c]), uint32_t(qmin())); |
| ASSERT_EQ(uint32_t(output_ref[((i * output_height() + y) * output_width() + x) * channels() + c]), |
| uint32_t(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c])) << |
| "in batch index " << i << ", pixel (" << y << ", " << x << "), channel " << c; |
| } |
| } |
| } |
| } |
| } |
| } |
| |
| 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() + XNN_EXTRA_BYTES / sizeof(float)); |
| std::vector<float> 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(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++) { |
| float max_value = -std::numeric_limits<float>::infinity(); |
| for (size_t py = 0; py < pooling_height(); py++) { |
| const size_t iy = oy * stride_height() + py * dilation_height() - padding_top(); |
| for (size_t px = 0; px < pooling_width(); px++) { |
| const size_t ix = ox * stride_width() + px * dilation_width() - padding_left(); |
| if (ix < input_width() && iy < input_height()) { |
| max_value = std::max(max_value, |
| input[((i * input_height() + iy) * input_width() + ix) * input_pixel_stride() + c]); |
| } |
| } |
| } |
| output_ref[((i * output_height() + oy) * output_width() + ox) * channels() + c] = max_value; |
| } |
| } |
| } |
| } |
| |
| // Compute clamping parameters. |
| const float accumulated_min = *std::min_element(output_ref.cbegin(), output_ref.cend()); |
| const float accumulated_max = *std::max_element(output_ref.cbegin(), output_ref.cend()); |
| const float accumulated_range = accumulated_max - accumulated_min; |
| const float output_min = accumulated_range == 0.0f ? |
| -std::numeric_limits<float>::infinity() : |
| accumulated_min + accumulated_range / 255.0f * float(qmin()); |
| const float output_max = accumulated_range == 0.0f ? |
| +std::numeric_limits<float>::infinity() : |
| accumulated_max - accumulated_range / 255.0f * float(255 - qmax()); |
| |
| // Clamp reference results. |
| for (float& value : output_ref) { |
| value = std::max(std::min(value, output_max), output_min); |
| } |
| |
| // Create, setup, run, and destroy Max Pooling operator. |
| ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */)); |
| xnn_operator_t max_pooling_op = nullptr; |
| |
| ASSERT_EQ(xnn_status_success, |
| xnn_create_max_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(), |
| stride_height(), stride_width(), |
| dilation_height(), dilation_width(), |
| channels(), input_pixel_stride(), output_pixel_stride(), |
| output_min, output_max, |
| padding_tf_same() ? XNN_FLAG_TENSORFLOW_SAME_PADDING : 0, |
| &max_pooling_op)); |
| ASSERT_NE(nullptr, max_pooling_op); |
| |
| // Smart pointer to automatically delete max_pooling_op. |
| std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_max_pooling_op(max_pooling_op, xnn_delete_operator); |
| |
| ASSERT_EQ(xnn_status_success, |
| xnn_setup_max_pooling2d_nhwc_f32( |
| max_pooling_op, |
| batch_size(), input_height(), input_width(), |
| input.data(), output.data(), |
| nullptr /* thread pool */)); |
| |
| ASSERT_EQ(xnn_status_success, |
| xnn_run_operator(max_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_LE(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c], output_max); |
| ASSERT_GE(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c], output_min); |
| 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 |
| << ", min = " << output_min << ", max = " << output_max; |
| } |
| } |
| } |
| } |
| } |
| } |
| |
| void TestSetupU8() const { |
| std::random_device random_device; |
| auto rng = std::mt19937(random_device()); |
| auto u8rng = std::bind(std::uniform_int_distribution<uint32_t>(0, std::numeric_limits<uint8_t>::max()), rng); |
| |
| std::vector<uint8_t> input(XNN_EXTRA_BYTES / sizeof(uint8_t) + 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<uint8_t> output(XNN_EXTRA_BYTES / sizeof(uint8_t) + 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<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()); |
| for (size_t iteration = 0; iteration < iterations(); iteration++) { |
| std::generate(input.begin(), input.end(), std::ref(u8rng)); |
| std::fill(output.begin(), output.end(), 0xA5); |
| |
| // Compute reference results. |
| 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++) { |
| uint8_t max_value = 0; |
| for (size_t py = 0; py < pooling_height(); py++) { |
| const size_t iy = oy * stride_height() + py * dilation_height() - padding_top(); |
| for (size_t px = 0; px < pooling_width(); px++) { |
| const size_t ix = ox * stride_width() + px * dilation_width() - padding_left(); |
| if (ix < input_width() && iy < input_height()) { |
| max_value = std::max(max_value, |
| input[((i * input_height() + iy) * input_width() + ix) * input_pixel_stride() + c]); |
| } |
| } |
| } |
| max_value = std::min(max_value, qmax()); |
| max_value = std::max(max_value, qmin()); |
| output_ref[((i * output_height() + oy) * output_width() + ox) * channels() + c] = max_value; |
| } |
| } |
| } |
| } |
| |
| // Create, setup, and run Max Pooling operator once. |
| ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */)); |
| xnn_operator_t max_pooling_op = nullptr; |
| |
| ASSERT_EQ(xnn_status_success, |
| xnn_create_max_pooling2d_nhwc_u8( |
| padding_top(), padding_right(), padding_bottom(), padding_left(), |
| pooling_height(), pooling_width(), |
| stride_height(), stride_width(), |
| dilation_height(), dilation_width(), |
| channels(), input_pixel_stride(), output_pixel_stride(), |
| qmin(), qmax(), |
| 0, &max_pooling_op)); |
| ASSERT_NE(nullptr, max_pooling_op); |
| |
| // Smart pointer to automatically delete max_pooling_op. |
| std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_max_pooling_op(max_pooling_op, xnn_delete_operator); |
| |
| ASSERT_EQ(xnn_status_success, |
| xnn_setup_max_pooling2d_nhwc_u8( |
| max_pooling_op, |
| batch_size(), input_height(), input_width(), |
| input.data(), output.data(), |
| nullptr /* thread pool */)); |
| |
| ASSERT_EQ(xnn_status_success, |
| xnn_run_operator(max_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_LE(uint32_t(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c]), uint32_t(qmax())); |
| ASSERT_GE(uint32_t(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c]), uint32_t(qmin())); |
| ASSERT_EQ(uint32_t(output_ref[((i * output_height() + y) * output_width() + x) * channels() + c]), |
| uint32_t(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(u8rng)); |
| std::fill(output.begin(), output.end(), 0xA5); |
| |
| // Compute reference results for the second run. |
| 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++) { |
| uint8_t max_value = 0; |
| for (size_t py = 0; py < pooling_height(); py++) { |
| const size_t iy = oy * stride_height() + py * dilation_height() - padding_top(); |
| for (size_t px = 0; px < pooling_width(); px++) { |
| const size_t ix = ox * stride_width() + px * dilation_width() - padding_left(); |
| if (ix < next_input_width() && iy < next_input_height()) { |
| max_value = std::max(max_value, |
| input[((i * next_input_height() + iy) * next_input_width() + ix) * input_pixel_stride() + c]); |
| } |
| } |
| } |
| max_value = std::min(max_value, qmax()); |
| max_value = std::max(max_value, qmin()); |
| next_output_ref[((i * next_output_height() + oy) * next_output_width() + ox) * channels() + c] = max_value; |
| } |
| } |
| } |
| } |
| |
| // Setup and run Max Pooling operator the second time, and destroy the operator. |
| ASSERT_EQ(xnn_status_success, |
| xnn_setup_max_pooling2d_nhwc_u8( |
| max_pooling_op, |
| next_batch_size(), next_input_height(), next_input_width(), |
| input.data(), output.data(), |
| nullptr /* thread pool */)); |
| |
| ASSERT_EQ(xnn_status_success, |
| xnn_run_operator(max_pooling_op, nullptr /* thread pool */)); |
| |
| // 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_LE(uint32_t(output[((i * next_output_height() + y) * next_output_width() + x) * output_pixel_stride() + c]), uint32_t(qmax())); |
| ASSERT_GE(uint32_t(output[((i * next_output_height() + y) * next_output_width() + x) * output_pixel_stride() + c]), uint32_t(qmin())); |
| ASSERT_EQ(uint32_t(next_output_ref[((i * next_output_height() + y) * next_output_width() + x) * channels() + c]), |
| uint32_t(output[((i * next_output_height() + y) * next_output_width() + x) * output_pixel_stride() + 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(XNN_EXTRA_BYTES / sizeof(float) + 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<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()); |
| 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++) { |
| float max_value = -std::numeric_limits<float>::infinity(); |
| for (size_t py = 0; py < pooling_height(); py++) { |
| const size_t iy = oy * stride_height() + py * dilation_height() - padding_top(); |
| for (size_t px = 0; px < pooling_width(); px++) { |
| const size_t ix = ox * stride_width() + px * dilation_width() - padding_left(); |
| if (ix < input_width() && iy < input_height()) { |
| max_value = std::max(max_value, |
| input[((i * input_height() + iy) * input_width() + ix) * input_pixel_stride() + c]); |
| } |
| } |
| } |
| output_ref[((i * output_height() + oy) * output_width() + ox) * channels() + c] = max_value; |
| } |
| } |
| } |
| } |
| |
| // Compute clamping parameters. |
| const float accumulated_min = *std::min_element(output_ref.cbegin(), output_ref.cend()); |
| const float accumulated_max = *std::max_element(output_ref.cbegin(), output_ref.cend()); |
| const float accumulated_range = accumulated_max - accumulated_min; |
| const float output_min = accumulated_range == 0.0f ? |
| -std::numeric_limits<float>::infinity() : |
| accumulated_min + accumulated_range / 255.0f * float(qmin()); |
| const float output_max = accumulated_range == 0.0f ? |
| +std::numeric_limits<float>::infinity() : |
| accumulated_max - accumulated_range / 255.0f * float(255 - qmax()); |
| |
| // Clamp reference results. |
| for (float& value : output_ref) { |
| value = std::max(std::min(value, output_max), output_min); |
| } |
| |
| // Create, setup, and run Max Pooling operator once. |
| ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */)); |
| xnn_operator_t max_pooling_op = nullptr; |
| |
| ASSERT_EQ(xnn_status_success, |
| xnn_create_max_pooling2d_nhwc_f32( |
| padding_top(), padding_right(), padding_bottom(), padding_left(), |
| pooling_height(), pooling_width(), |
| stride_height(), stride_width(), |
| dilation_height(), dilation_width(), |
| channels(), input_pixel_stride(), output_pixel_stride(), |
| output_min, output_max, |
| 0, &max_pooling_op)); |
| ASSERT_NE(nullptr, max_pooling_op); |
| |
| // Smart pointer to automatically delete max_pooling_op. |
| std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_max_pooling_op(max_pooling_op, xnn_delete_operator); |
| |
| ASSERT_EQ(xnn_status_success, |
| xnn_setup_max_pooling2d_nhwc_f32( |
| max_pooling_op, |
| batch_size(), input_height(), input_width(), |
| input.data(), output.data(), |
| nullptr /* thread pool */)); |
| |
| ASSERT_EQ(xnn_status_success, |
| xnn_run_operator(max_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_LE(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c], output_max); |
| ASSERT_GE(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c], output_min); |
| 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; |
| } |
| } |
| } |
| } |
| |
| // 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++) { |
| float max_value = -std::numeric_limits<float>::infinity(); |
| for (size_t py = 0; py < pooling_height(); py++) { |
| const size_t iy = oy * stride_height() + py * dilation_height() - padding_top(); |
| for (size_t px = 0; px < pooling_width(); px++) { |
| const size_t ix = ox * stride_width() + px * dilation_width() - padding_left(); |
| if (ix < next_input_width() && iy < next_input_height()) { |
| max_value = std::max(max_value, |
| input[((i * next_input_height() + iy) * next_input_width() + ix) * input_pixel_stride() + c]); |
| } |
| } |
| } |
| max_value = std::min(max_value, output_max); |
| max_value = std::max(max_value, output_min); |
| next_output_ref[((i * next_output_height() + oy) * next_output_width() + ox) * channels() + c] = max_value; |
| } |
| } |
| } |
| } |
| |
| // Setup and run Max Pooling operator the second time, and destroy the operator. |
| ASSERT_EQ(xnn_status_success, |
| xnn_setup_max_pooling2d_nhwc_f32( |
| max_pooling_op, |
| next_batch_size(), next_input_height(), next_input_width(), |
| input.data(), output.data(), |
| nullptr /* thread pool */)); |
| |
| ASSERT_EQ(xnn_status_success, |
| xnn_run_operator(max_pooling_op, nullptr /* thread pool */)); |
| |
| // 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_LE(output[((i * next_output_height() + y) * next_output_width() + x) * output_pixel_stride() + c], output_max); |
| ASSERT_GE(output[((i * next_output_height() + y) * next_output_width() + x) * output_pixel_stride() + c], output_min); |
| 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; |
| } |
| } |
| } |
| } |
| } |
| } |
| |
| 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}; |
| uint32_t stride_height_{1}; |
| uint32_t stride_width_{1}; |
| uint32_t dilation_height_{1}; |
| uint32_t dilation_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}; |
| }; |