1464 lines
63 KiB
C++
1464 lines
63 KiB
C++
// Copyright (c) Facebook, Inc. and its affiliates.
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// All rights reserved.
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//
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// Copyright 2019 Google LLC
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//
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// This source code is licensed under the BSD-style license found in the
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// LICENSE file in the root directory of this source tree.
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#pragma once
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#include <gtest/gtest.h>
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#include <fp16.h>
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#include <algorithm>
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#include <cassert>
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#include <cstddef>
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#include <cstdlib>
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#include <functional>
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#include <limits>
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#include <random>
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#include <vector>
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#include <xnnpack.h>
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class MaxPoolingOperatorTester {
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public:
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inline MaxPoolingOperatorTester& padding_tf_same(bool padding_same) {
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if (padding_same) {
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assert(padding_top() == 0);
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assert(padding_left() == 0);
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assert(padding_bottom() == 0);
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assert(padding_right() == 0);
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}
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this->padding_tf_same_ = padding_same;
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return *this;
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}
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inline bool padding_tf_same() const {
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return this->padding_tf_same_;
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}
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inline MaxPoolingOperatorTester& padding(uint32_t padding) {
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assert(!padding_tf_same());
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this->padding_top_ = padding;
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this->padding_right_ = padding;
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this->padding_bottom_ = padding;
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this->padding_left_ = padding;
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return *this;
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}
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inline MaxPoolingOperatorTester& padding(uint32_t padding_height, uint32_t padding_width) {
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assert(!padding_tf_same());
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this->padding_top_ = padding_height;
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this->padding_right_ = padding_width;
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this->padding_bottom_ = padding_height;
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this->padding_left_ = padding_width;
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return *this;
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}
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inline MaxPoolingOperatorTester& padding_height(uint32_t padding_height) {
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assert(!padding_tf_same());
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this->padding_top_ = padding_height;
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this->padding_bottom_ = padding_height;
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return *this;
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}
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inline MaxPoolingOperatorTester& padding_width(uint32_t padding_width) {
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assert(!padding_tf_same());
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this->padding_right_ = padding_width;
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this->padding_left_ = padding_width;
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return *this;
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}
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inline MaxPoolingOperatorTester& padding_top(uint32_t padding_top) {
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assert(!padding_tf_same());
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this->padding_top_ = padding_top;
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return *this;
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}
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inline uint32_t padding_top() const {
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if (padding_tf_same()) {
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const uint32_t total_padding_height =
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(output_height() - 1) * stride_height() + dilated_pooling_height() - input_height();
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return total_padding_height / 2;
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} else {
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return this->padding_top_;
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}
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}
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inline MaxPoolingOperatorTester& padding_left(uint32_t padding_left) {
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assert(!padding_tf_same());
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this->padding_left_ = padding_left;
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return *this;
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}
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inline uint32_t padding_left() const {
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if (padding_tf_same()) {
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const uint32_t total_padding_width =
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(output_width() - 1) * stride_width() + dilated_pooling_width() - input_width();
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return total_padding_width / 2;
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} else {
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return this->padding_left_;
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}
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}
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inline MaxPoolingOperatorTester& padding_bottom(uint32_t padding_bottom) {
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assert(!padding_tf_same());
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this->padding_bottom_ = padding_bottom;
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return *this;
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}
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inline uint32_t padding_bottom() const {
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if (padding_tf_same()) {
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const uint32_t total_padding_height =
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(output_height() - 1) * stride_height() + dilated_pooling_height() - input_height();
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return total_padding_height - total_padding_height / 2;
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} else {
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return this->padding_bottom_;
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}
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}
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inline MaxPoolingOperatorTester& padding_right(uint32_t padding_right) {
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assert(!padding_tf_same());
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this->padding_right_ = padding_right;
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return *this;
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}
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inline uint32_t padding_right() const {
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if (padding_tf_same()) {
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const uint32_t total_padding_width =
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(output_width() - 1) * stride_width() + dilated_pooling_width() - input_width();
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return total_padding_width - total_padding_width / 2;
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} else {
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return this->padding_right_;
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}
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}
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inline MaxPoolingOperatorTester& input_size(size_t input_height, size_t input_width) {
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assert(input_height >= 1);
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assert(input_width >= 1);
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this->input_height_ = input_height;
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this->input_width_ = input_width;
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return *this;
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}
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inline MaxPoolingOperatorTester& input_height(size_t input_height) {
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assert(input_height >= 1);
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this->input_height_ = input_height;
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return *this;
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}
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inline size_t input_height() const {
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return this->input_height_;
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}
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inline MaxPoolingOperatorTester& input_width(size_t input_width) {
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assert(input_width >= 1);
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this->input_width_ = input_width;
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return *this;
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}
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inline size_t input_width() const {
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return this->input_width_;
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}
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inline MaxPoolingOperatorTester& channels(size_t channels) {
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assert(channels != 0);
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this->channels_ = channels;
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return *this;
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}
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inline size_t channels() const {
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return this->channels_;
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}
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inline MaxPoolingOperatorTester& batch_size(size_t batch_size) {
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assert(batch_size != 0);
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this->batch_size_ = batch_size;
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return *this;
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}
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inline size_t batch_size() const {
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return this->batch_size_;
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}
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inline MaxPoolingOperatorTester& pooling_size(uint32_t pooling_size) {
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assert(pooling_size >= 1);
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this->pooling_height_ = pooling_size;
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this->pooling_width_ = pooling_size;
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return *this;
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}
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inline MaxPoolingOperatorTester& pooling_size(uint32_t pooling_height, uint32_t pooling_width) {
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assert(pooling_height >= 1);
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assert(pooling_width >= 1);
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this->pooling_height_ = pooling_height;
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this->pooling_width_ = pooling_width;
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return *this;
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}
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inline MaxPoolingOperatorTester& pooling_height(uint32_t pooling_height) {
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assert(pooling_height >= 1);
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this->pooling_height_ = pooling_height;
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return *this;
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}
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inline uint32_t pooling_height() const {
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return this->pooling_height_;
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}
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inline MaxPoolingOperatorTester& pooling_width(uint32_t pooling_width) {
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assert(pooling_width >= 1);
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this->pooling_width_ = pooling_width;
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return *this;
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}
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inline uint32_t pooling_width() const {
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return this->pooling_width_;
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}
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inline MaxPoolingOperatorTester& stride(uint32_t stride) {
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assert(stride >= 1);
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this->stride_height_ = stride;
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this->stride_width_ = stride;
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return *this;
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}
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inline MaxPoolingOperatorTester& stride(uint32_t stride_height, uint32_t stride_width) {
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assert(stride_height >= 1);
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assert(stride_width >= 1);
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this->stride_height_ = stride_height;
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this->stride_width_ = stride_width;
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return *this;
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}
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inline MaxPoolingOperatorTester& stride_height(uint32_t stride_height) {
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assert(stride_height >= 1);
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this->stride_height_ = stride_height;
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return *this;
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}
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inline uint32_t stride_height() const {
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return this->stride_height_;
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}
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inline MaxPoolingOperatorTester& stride_width(uint32_t stride_width) {
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assert(stride_width >= 1);
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this->stride_width_ = stride_width;
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return *this;
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}
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inline uint32_t stride_width() const {
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return this->stride_width_;
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}
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inline MaxPoolingOperatorTester& dilation(uint32_t dilation) {
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assert(dilation >= 1);
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this->dilation_height_ = dilation;
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this->dilation_width_ = dilation;
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return *this;
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}
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inline MaxPoolingOperatorTester& dilation(uint32_t dilation_height, uint32_t dilation_width) {
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assert(dilation_height >= 1);
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assert(dilation_width >= 1);
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this->dilation_height_ = dilation_height;
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this->dilation_width_ = dilation_width;
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return *this;
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}
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inline MaxPoolingOperatorTester& dilation_height(uint32_t dilation_height) {
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assert(dilation_height >= 1);
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this->dilation_height_ = dilation_height;
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return *this;
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}
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inline uint32_t dilation_height() const {
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return this->dilation_height_;
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}
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inline MaxPoolingOperatorTester& dilation_width(uint32_t dilation_width) {
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assert(dilation_width >= 1);
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this->dilation_width_ = dilation_width;
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return *this;
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}
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inline uint32_t dilation_width() const {
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return this->dilation_width_;
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}
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inline uint32_t dilated_pooling_height() const {
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return (pooling_height() - 1) * dilation_height() + 1;
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}
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inline uint32_t dilated_pooling_width() const {
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return (pooling_width() - 1) * dilation_width() + 1;
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}
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inline size_t output_height() const {
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if (padding_tf_same()) {
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return (input_height() + stride_height() - 1) / stride_height();
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} else {
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const size_t padded_input_height = padding_top() + input_height() + padding_bottom();
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if (padded_input_height <= dilated_pooling_height()) {
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return 1;
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} else {
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return (padded_input_height - dilated_pooling_height()) / stride_height() + 1;
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}
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}
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}
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inline size_t output_width() const {
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if (padding_tf_same()) {
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return (input_width() + stride_width() - 1) / stride_width();
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} else {
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const size_t padded_input_width = padding_left() + input_width() + padding_right();
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if (padded_input_width <= dilated_pooling_width()) {
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return 1;
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} else {
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return (padded_input_width - dilated_pooling_width()) / stride_width() + 1;
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}
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}
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}
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inline MaxPoolingOperatorTester& input_pixel_stride(size_t input_pixel_stride) {
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assert(input_pixel_stride != 0);
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this->input_pixel_stride_ = input_pixel_stride;
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return *this;
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}
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inline size_t input_pixel_stride() const {
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if (this->input_pixel_stride_ == 0) {
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return channels();
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} else {
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assert(this->input_pixel_stride_ >= channels());
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return this->input_pixel_stride_;
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}
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}
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inline MaxPoolingOperatorTester& output_pixel_stride(size_t output_pixel_stride) {
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assert(output_pixel_stride != 0);
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this->output_pixel_stride_ = output_pixel_stride;
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return *this;
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}
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inline size_t output_pixel_stride() const {
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if (this->output_pixel_stride_ == 0) {
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return channels();
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} else {
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assert(this->output_pixel_stride_ >= channels());
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return this->output_pixel_stride_;
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}
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}
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inline MaxPoolingOperatorTester& next_input_size(uint32_t next_input_height, uint32_t next_input_width) {
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assert(next_input_height >= 1);
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assert(next_input_width >= 1);
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this->next_input_height_ = next_input_height;
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this->next_input_width_ = next_input_width;
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return *this;
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}
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inline MaxPoolingOperatorTester& next_input_height(uint32_t next_input_height) {
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assert(next_input_height >= 1);
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this->next_input_height_ = next_input_height;
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return *this;
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}
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inline uint32_t next_input_height() const {
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if (this->next_input_height_ == 0) {
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return input_height();
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} else {
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return this->next_input_height_;
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}
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}
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inline MaxPoolingOperatorTester& next_input_width(uint32_t next_input_width) {
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assert(next_input_width >= 1);
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this->next_input_width_ = next_input_width;
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return *this;
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}
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inline uint32_t next_input_width() const {
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if (this->next_input_width_ == 0) {
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return input_width();
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} else {
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return this->next_input_width_;
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}
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}
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inline size_t next_output_height() const {
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const size_t padded_next_input_height = padding_top() + next_input_height() + padding_bottom();
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if (padded_next_input_height <= dilated_pooling_height()) {
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return 1;
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} else {
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return (padded_next_input_height - dilated_pooling_height()) / stride_height() + 1;
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}
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}
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inline size_t next_output_width() const {
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const size_t padded_next_input_width = padding_left() + next_input_width() + padding_right();
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if (padded_next_input_width <= dilated_pooling_width()) {
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return 1;
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} else {
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return (padded_next_input_width - dilated_pooling_width()) / stride_width() + 1;
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}
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}
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inline MaxPoolingOperatorTester& next_batch_size(size_t next_batch_size) {
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assert(next_batch_size >= 1);
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this->next_batch_size_ = next_batch_size;
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return *this;
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}
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inline size_t next_batch_size() const {
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if (this->next_batch_size_ == 0) {
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return batch_size();
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} else {
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return this->next_batch_size_;
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}
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}
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inline MaxPoolingOperatorTester& qmin(uint8_t qmin) {
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this->qmin_ = qmin;
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return *this;
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}
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inline uint8_t qmin() const {
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return this->qmin_;
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}
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inline MaxPoolingOperatorTester& qmax(uint8_t qmax) {
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this->qmax_ = qmax;
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return *this;
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}
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inline uint8_t qmax() const {
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return this->qmax_;
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}
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inline MaxPoolingOperatorTester& iterations(size_t iterations) {
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this->iterations_ = iterations;
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return *this;
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}
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inline size_t iterations() const {
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return this->iterations_;
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}
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void TestS8() const {
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std::random_device random_device;
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auto rng = std::mt19937(random_device());
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auto i8rng = std::bind(
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std::uniform_int_distribution<int32_t>(std::numeric_limits<int8_t>::min(), std::numeric_limits<int8_t>::max()),
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std::ref(rng));
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std::vector<int8_t> input((batch_size() * input_height() * input_width() - 1) * input_pixel_stride() + channels() + XNN_EXTRA_BYTES / sizeof(int8_t));
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std::vector<int8_t> output((batch_size() * output_height() * output_width() - 1) * output_pixel_stride() + channels() + XNN_EXTRA_BYTES / sizeof(int8_t));
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std::vector<int8_t> output_ref(batch_size() * output_height() * output_width() * channels());
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for (size_t iteration = 0; iteration < iterations(); iteration++) {
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std::generate(input.begin(), input.end(), std::ref(i8rng));
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std::fill(output.begin(), output.end(), 0xA5);
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// Compute reference results.
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for (size_t i = 0; i < batch_size(); i++) {
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for (size_t oy = 0; oy < output_height(); oy++) {
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for (size_t ox = 0; ox < output_width(); ox++) {
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for (size_t c = 0; c < channels(); c++) {
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int8_t max_value = std::numeric_limits<int8_t>::min();
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for (size_t py = 0; py < pooling_height(); py++) {
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const size_t iy = oy * stride_height() + py * dilation_height() - padding_top();
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for (size_t px = 0; px < pooling_width(); px++) {
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const size_t ix = ox * stride_width() + px * dilation_width() - padding_left();
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if (ix < input_width() && iy < input_height()) {
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max_value = std::max(max_value,
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input[((i * input_height() + iy) * input_width() + ix) * input_pixel_stride() + c]);
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}
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}
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}
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max_value = std::min(max_value, int8_t(qmax() - 0x80));
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max_value = std::max(max_value, int8_t(qmin() - 0x80));
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output_ref[((i * output_height() + oy) * output_width() + ox) * channels() + c] = max_value;
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}
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}
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}
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}
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// Create, setup, run, and destroy Max Pooling operator.
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ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
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xnn_operator_t max_pooling_op = nullptr;
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ASSERT_EQ(xnn_status_success,
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xnn_create_max_pooling2d_nhwc_s8(
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padding_tf_same() ? 0 : padding_top(), padding_tf_same() ? 0 : padding_right(),
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padding_tf_same() ? 0 : padding_bottom(), padding_tf_same() ? 0 : padding_left(),
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pooling_height(), pooling_width(),
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stride_height(), stride_width(),
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dilation_height(), dilation_width(),
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channels(), input_pixel_stride(), output_pixel_stride(),
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int8_t(qmin() - 0x80), int8_t(qmax() - 0x80),
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padding_tf_same() ? XNN_FLAG_TENSORFLOW_SAME_PADDING : 0,
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&max_pooling_op));
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ASSERT_NE(nullptr, max_pooling_op);
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// Smart pointer to automatically delete max_pooling_op.
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std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_max_pooling_op(max_pooling_op, xnn_delete_operator);
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|
ASSERT_EQ(xnn_status_success,
|
|
xnn_setup_max_pooling2d_nhwc_s8(
|
|
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(int32_t(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c]), int32_t(qmax() - 0x80));
|
|
ASSERT_GE(int32_t(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c]), int32_t(qmin() - 0x80));
|
|
ASSERT_EQ(int32_t(output_ref[((i * output_height() + y) * output_width() + x) * channels() + c]),
|
|
int32_t(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c])) <<
|
|
"in batch index " << i << ", pixel (" << y << ", " << x << "), channel " << c;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
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 TestF16() const {
|
|
std::random_device random_device;
|
|
auto rng = std::mt19937(random_device());
|
|
// Note: we need to avoid FP16 denormals in the generated tensor because they might be processed differently in
|
|
// native vs emulated arithmetics, and we use exact comparison to verify the results against reference.
|
|
auto f32rng = std::bind(std::uniform_real_distribution<float>(0.001f, 1.0f), rng);
|
|
auto f16rng = std::bind(fp16_ieee_from_fp32_value, f32rng);
|
|
|
|
std::vector<uint16_t> input((batch_size() * input_height() * input_width() - 1) * input_pixel_stride() + channels() + XNN_EXTRA_BYTES / sizeof(uint16_t));
|
|
std::vector<uint16_t> output((batch_size() * output_height() * output_width() - 1) * output_pixel_stride() + channels() + XNN_EXTRA_BYTES / sizeof(uint16_t));
|
|
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(f16rng));
|
|
std::fill(output.begin(), output.end(), UINT16_C(0x7E00) /* NaN */);
|
|
|
|
// 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,
|
|
fp16_ieee_to_fp32_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;
|
|
float output_min = accumulated_min + accumulated_range / 255.0f * float(qmin());
|
|
float output_max = accumulated_max - accumulated_range / 255.0f * float(255 - qmax());
|
|
output_min = fp16_ieee_to_fp32_value(fp16_ieee_from_fp32_value(output_min));
|
|
output_max = fp16_ieee_to_fp32_value(fp16_ieee_from_fp32_value(output_max));
|
|
if (accumulated_range == 0.0f) {
|
|
output_min = -std::numeric_limits<float>::infinity();
|
|
output_max = +std::numeric_limits<float>::infinity();
|
|
}
|
|
if (qmin() == std::numeric_limits<uint8_t>::min()) {
|
|
output_min = -std::numeric_limits<float>::infinity();
|
|
}
|
|
if (qmax() == std::numeric_limits<uint8_t>::max()) {
|
|
output_max = +std::numeric_limits<float>::infinity();
|
|
}
|
|
|
|
// 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;
|
|
|
|
const xnn_status status = xnn_create_max_pooling2d_nhwc_f16(
|
|
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);
|
|
if (status == xnn_status_unsupported_hardware) {
|
|
GTEST_SKIP();
|
|
}
|
|
ASSERT_EQ(xnn_status_success, status);
|
|
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_f16(
|
|
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(fp16_ieee_to_fp32_value(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c]), output_max);
|
|
ASSERT_GE(fp16_ieee_to_fp32_value(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c]), output_min);
|
|
ASSERT_EQ(
|
|
fp16_ieee_to_fp32_value(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c]),
|
|
output_ref[((i * output_height() + y) * output_width() + x) * channels() + c]) <<
|
|
"in batch index " << i << ", pixel (" << y << ", " << x << "), channel " << c
|
|
<< ", min = " << output_min << ", max = " << output_max;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
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 TestSetupS8() const {
|
|
std::random_device random_device;
|
|
auto rng = std::mt19937(random_device());
|
|
auto i8rng = std::bind(
|
|
std::uniform_int_distribution<int32_t>(std::numeric_limits<int8_t>::min(), std::numeric_limits<int8_t>::max()),
|
|
std::ref(rng));
|
|
|
|
std::vector<int8_t> input(XNN_EXTRA_BYTES / sizeof(int8_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<int8_t> output(XNN_EXTRA_BYTES / sizeof(int8_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(i8rng));
|
|
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++) {
|
|
int8_t max_value = std::numeric_limits<int8_t>::min();
|
|
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, int8_t(qmax() - 0x80));
|
|
max_value = std::max(max_value, int8_t(qmin() - 0x80));
|
|
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_s8(
|
|
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(),
|
|
int8_t(qmin() - 0x80), int8_t(qmax() - 0x80),
|
|
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_s8(
|
|
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(int32_t(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c]), int32_t(qmax() - 0x80));
|
|
ASSERT_GE(int32_t(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c]), int32_t(qmin() - 0x80));
|
|
ASSERT_EQ(int32_t(output_ref[((i * output_height() + y) * output_width() + x) * channels() + c]),
|
|
int32_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(i8rng));
|
|
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++) {
|
|
int8_t max_value = std::numeric_limits<int8_t>::min();
|
|
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, int8_t(qmax() - 0x80));
|
|
max_value = std::max(max_value, int8_t(qmin() - 0x80));
|
|
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_s8(
|
|
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(int32_t(output[((i * next_output_height() + y) * next_output_width() + x) * output_pixel_stride() + c]), int32_t(qmax() - 0x80));
|
|
ASSERT_GE(int32_t(output[((i * next_output_height() + y) * next_output_width() + x) * output_pixel_stride() + c]), int32_t(qmin() - 0x80));
|
|
ASSERT_EQ(int32_t(next_output_ref[((i * next_output_height() + y) * next_output_width() + x) * channels() + c]),
|
|
int32_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 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 TestSetupF16() const {
|
|
std::random_device random_device;
|
|
auto rng = std::mt19937(random_device());
|
|
// Note: we need to avoid FP16 denormals in the generated tensor because they might be processed differently in
|
|
// native vs emulated arithmetics, and we use exact comparison to verify the results against reference.
|
|
auto f32rng = std::bind(std::uniform_real_distribution<float>(0.001f, 1.0f), rng);
|
|
auto f16rng = std::bind(fp16_ieee_from_fp32_value, f32rng);
|
|
|
|
std::vector<uint16_t> input(XNN_EXTRA_BYTES / sizeof(uint16_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<uint16_t> output(XNN_EXTRA_BYTES / sizeof(uint16_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(f16rng));
|
|
std::fill(output.begin(), output.end(), UINT16_C(0x7E00) /* NaN */);
|
|
|
|
// 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,
|
|
fp16_ieee_to_fp32_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;
|
|
float output_min = accumulated_min + accumulated_range / 255.0f * float(qmin());
|
|
float output_max = accumulated_max - accumulated_range / 255.0f * float(255 - qmax());
|
|
output_min = fp16_ieee_to_fp32_value(fp16_ieee_from_fp32_value(output_min));
|
|
output_max = fp16_ieee_to_fp32_value(fp16_ieee_from_fp32_value(output_max));
|
|
if (accumulated_range == 0.0f) {
|
|
output_min = -std::numeric_limits<float>::infinity();
|
|
output_max = +std::numeric_limits<float>::infinity();
|
|
}
|
|
if (qmin() == std::numeric_limits<uint8_t>::min()) {
|
|
output_min = -std::numeric_limits<float>::infinity();
|
|
}
|
|
if (qmax() == std::numeric_limits<uint8_t>::max()) {
|
|
output_max = +std::numeric_limits<float>::infinity();
|
|
}
|
|
|
|
// 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;
|
|
|
|
const xnn_status status = xnn_create_max_pooling2d_nhwc_f16(
|
|
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);
|
|
if (status == xnn_status_unsupported_hardware) {
|
|
GTEST_SKIP();
|
|
}
|
|
ASSERT_EQ(xnn_status_success, status);
|
|
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_f16(
|
|
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(fp16_ieee_to_fp32_value(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c]), output_max);
|
|
ASSERT_GE(fp16_ieee_to_fp32_value(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c]), output_min);
|
|
ASSERT_EQ(
|
|
fp16_ieee_to_fp32_value(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c]),
|
|
output_ref[((i * output_height() + y) * output_width() + x) * channels() + c]) <<
|
|
"in batch index " << i << ", pixel (" << y << ", " << x << "), channel " << c
|
|
<< ", min = " << output_min << ", max = " << output_max;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// Re-generate data for the second run.
|
|
std::generate(input.begin(), input.end(), std::ref(f16rng));
|
|
std::fill(output.begin(), output.end(), UINT16_C(0x7E00) /* NaN */);
|
|
|
|
// 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,
|
|
fp16_ieee_to_fp32_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_f16(
|
|
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(fp16_ieee_to_fp32_value(output[((i * next_output_height() + y) * next_output_width() + x) * output_pixel_stride() + c]), output_max);
|
|
ASSERT_GE(fp16_ieee_to_fp32_value(output[((i * next_output_height() + y) * next_output_width() + x) * output_pixel_stride() + c]), output_min);
|
|
ASSERT_EQ(
|
|
fp16_ieee_to_fp32_value(output[((i * next_output_height() + y) * next_output_width() + x) * output_pixel_stride() + c]),
|
|
next_output_ref[((i * next_output_height() + y) * next_output_width() + x) * channels() + c]) <<
|
|
"in batch index " << i << ", pixel (" << y << ", " << x << "), channel " << c
|
|
<< ", min = " << output_min << ", max = " << output_max;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
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};
|
|
};
|