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#pragma once |
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#include <gtest/gtest.h> |
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#include <fp16/fp16.h> |
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#include <algorithm> |
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#include <cmath> |
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#include <cassert> |
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#include <cstddef> |
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#include <cstdlib> |
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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 AveragePoolingOperatorTester { |
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public: |
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inline AveragePoolingOperatorTester& 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 AveragePoolingOperatorTester& 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 AveragePoolingOperatorTester& 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 AveragePoolingOperatorTester& 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 AveragePoolingOperatorTester& 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 AveragePoolingOperatorTester& 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() + 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 AveragePoolingOperatorTester& 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() + 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 AveragePoolingOperatorTester& 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() + 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 AveragePoolingOperatorTester& 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() + 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 AveragePoolingOperatorTester& 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 AveragePoolingOperatorTester& 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 AveragePoolingOperatorTester& 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 AveragePoolingOperatorTester& 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 AveragePoolingOperatorTester& 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 AveragePoolingOperatorTester& 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 AveragePoolingOperatorTester& 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 AveragePoolingOperatorTester& 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 AveragePoolingOperatorTester& 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 AveragePoolingOperatorTester& 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 AveragePoolingOperatorTester& 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 AveragePoolingOperatorTester& 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 AveragePoolingOperatorTester& 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 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 <= pooling_height()) { |
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return 1; |
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} else { |
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return (padded_input_height - 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 <= pooling_width()) { |
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return 1; |
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} else { |
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return (padded_input_width - pooling_width()) / stride_width() + 1; |
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} |
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} |
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} |
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inline AveragePoolingOperatorTester& 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 AveragePoolingOperatorTester& 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 AveragePoolingOperatorTester& 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 AveragePoolingOperatorTester& 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 AveragePoolingOperatorTester& 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 <= pooling_height()) { |
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return 1; |
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} else { |
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return (padded_next_input_height - 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 <= pooling_width()) { |
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return 1; |
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} else { |
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return (padded_next_input_width - pooling_width()) / stride_width() + 1; |
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} |
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} |
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inline AveragePoolingOperatorTester& 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 AveragePoolingOperatorTester& input_scale(float input_scale) { |
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assert(input_scale > 0.0f); |
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assert(std::isnormal(input_scale)); |
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this->input_scale_ = input_scale; |
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return *this; |
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} |
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inline float input_scale() const { |
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return this->input_scale_; |
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} |
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inline AveragePoolingOperatorTester& input_zero_point(uint8_t input_zero_point) { |
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this->input_zero_point_ = input_zero_point; |
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return *this; |
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} |
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inline uint8_t input_zero_point() const { |
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return this->input_zero_point_; |
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} |
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inline AveragePoolingOperatorTester& output_scale(float output_scale) { |
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assert(output_scale > 0.0f); |
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assert(std::isnormal(output_scale)); |
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this->output_scale_ = output_scale; |
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return *this; |
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} |
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inline float output_scale() const { |
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return this->output_scale_; |
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} |
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inline AveragePoolingOperatorTester& output_zero_point(uint8_t output_zero_point) { |
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this->output_zero_point_ = output_zero_point; |
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return *this; |
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} |
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inline uint8_t output_zero_point() const { |
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return this->output_zero_point_; |
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} |
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inline AveragePoolingOperatorTester& 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 AveragePoolingOperatorTester& 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 AveragePoolingOperatorTester& 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 TestF16() const { |
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std::random_device random_device; |
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auto rng = std::mt19937(random_device()); |
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std::uniform_real_distribution<float> f32dist; |
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std::vector<uint16_t> input((batch_size() * input_height() * input_width() - 1) * input_pixel_stride() + channels() + XNN_EXTRA_BYTES / sizeof(uint16_t)); |
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std::vector<uint16_t> output((batch_size() * output_height() * output_width() - 1) * output_pixel_stride() + channels()); |
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std::vector<float> 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(), [&]() { return fp16_ieee_from_fp32_value(f32dist(rng)); }); |
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std::fill(output.begin(), output.end(), UINT16_C(0x7E00) ); |
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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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float acc = 0.0f; |
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int32_t n = 0; |
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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 - 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 - padding_left(); |
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if (ix < input_width() && iy < input_height()) { |
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acc += fp16_ieee_to_fp32_value(input[((i * input_height() + iy) * input_width() + ix) * input_pixel_stride() + c]); |
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n += 1; |
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} |
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} |
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} |
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output_ref[((i * output_height() + oy) * output_width() + ox) * channels() + c] = acc / float(n); |
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} |
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} |
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} |
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} |
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const float accumulated_min = *std::min_element(output_ref.cbegin(), output_ref.cend()); |
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const float accumulated_max = *std::max_element(output_ref.cbegin(), output_ref.cend()); |
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const float accumulated_range = accumulated_max - accumulated_min; |
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float output_min = accumulated_min + accumulated_range / 255.0f * float(qmin()); |
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float output_max = accumulated_max - accumulated_range / 255.0f * float(255 - qmax()); |
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output_min = fp16_ieee_to_fp32_value(fp16_ieee_from_fp32_value(output_min)); |
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output_max = fp16_ieee_to_fp32_value(fp16_ieee_from_fp32_value(output_max)); |
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if (accumulated_range == 0.0f) { |
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output_min = -std::numeric_limits<float>::infinity(); |
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output_max = +std::numeric_limits<float>::infinity(); |
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} |
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if (qmin() == std::numeric_limits<uint8_t>::min()) { |
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output_min = -std::numeric_limits<float>::infinity(); |
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} |
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if (qmax() == std::numeric_limits<uint8_t>::max()) { |
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output_max = +std::numeric_limits<float>::infinity(); |
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} |
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for (float& value : output_ref) { |
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value = std::max(std::min(value, output_max), output_min); |
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} |
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ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr )); |
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xnn_operator_t average_pooling_op = nullptr; |
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const xnn_status status = xnn_create_average_pooling2d_nhwc_f16( |
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padding_top(), padding_right(), padding_bottom(), padding_left(), |
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pooling_height(), pooling_width(), |
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stride_height(), stride_width(), |
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channels(), input_pixel_stride(), output_pixel_stride(), |
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output_min, output_max, |
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0, &average_pooling_op); |
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if (status == xnn_status_unsupported_hardware) { |
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GTEST_SKIP(); |
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} |
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ASSERT_EQ(xnn_status_success, status); |
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ASSERT_NE(nullptr, average_pooling_op); |
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std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_average_pooling_op(average_pooling_op, xnn_delete_operator); |
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ASSERT_EQ(xnn_status_success, |
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xnn_reshape_average_pooling2d_nhwc_f16( |
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average_pooling_op, |
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batch_size(), input_height(), input_width(), |
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nullptr, nullptr, |
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nullptr )); |
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ASSERT_EQ(xnn_status_success, |
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xnn_setup_average_pooling2d_nhwc_f16( |
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average_pooling_op, |
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input.data(), output.data())); |
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ASSERT_EQ(xnn_status_success, |
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xnn_run_operator(average_pooling_op, nullptr )); |
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for (size_t i = 0; i < batch_size(); i++) { |
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for (size_t y = 0; y < output_height(); y++) { |
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for (size_t x = 0; x < output_width(); x++) { |
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for (size_t c = 0; c < channels(); c++) { |
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EXPECT_LE(fp16_ieee_to_fp32_value(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c]), output_max); |
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EXPECT_GE(fp16_ieee_to_fp32_value(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c]), output_min); |
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EXPECT_NEAR( |
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fp16_ieee_to_fp32_value(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c]), |
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output_ref[((i * output_height() + y) * output_width() + x) * channels() + c], |
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std::max(1.0e-3f, std::abs(output_ref[((i * output_height() + y) * output_width() + x) * channels() + c]) * 1.0e-2f)) << |
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"in batch index " << i << ", pixel (" << y << ", " << x << "), channel " << c; |
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} |
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} |
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} |
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} |
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} |
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} |
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void TestF32() const { |
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std::random_device random_device; |
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auto rng = std::mt19937(random_device()); |
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std::uniform_real_distribution<float> f32dist; |
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|
|
std::vector<float> input((batch_size() * input_height() * input_width() - 1) * input_pixel_stride() + channels() + XNN_EXTRA_BYTES / sizeof(float)); |
|
std::vector<float> output((batch_size() * output_height() * output_width() - 1) * output_pixel_stride() + channels()); |
|
std::vector<float> output_ref(batch_size() * output_height() * output_width() * channels()); |
|
for (size_t iteration = 0; iteration < iterations(); iteration++) { |
|
std::generate(input.begin(), input.end(), [&]() { return f32dist(rng); }); |
|
std::fill(output.begin(), output.end(), std::nanf("")); |
|
|
|
|
|
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 acc = 0.0f; |
|
int32_t n = 0; |
|
for (size_t py = 0; py < pooling_height(); py++) { |
|
const size_t iy = oy * stride_height() + py - padding_top(); |
|
for (size_t px = 0; px < pooling_width(); px++) { |
|
const size_t ix = ox * stride_width() + px - padding_left(); |
|
if (ix < input_width() && iy < input_height()) { |
|
acc += input[((i * input_height() + iy) * input_width() + ix) * input_pixel_stride() + c]; |
|
n += 1; |
|
} |
|
} |
|
} |
|
output_ref[((i * output_height() + oy) * output_width() + ox) * channels() + c] = acc / float(n); |
|
} |
|
} |
|
} |
|
} |
|
|
|
|
|
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()); |
|
|
|
|
|
for (float& value : output_ref) { |
|
value = std::max(std::min(value, output_max), output_min); |
|
} |
|
|
|
|
|
ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr )); |
|
xnn_operator_t average_pooling_op = nullptr; |
|
|
|
ASSERT_EQ(xnn_status_success, |
|
xnn_create_average_pooling2d_nhwc_f32( |
|
padding_top(), padding_right(), padding_bottom(), padding_left(), |
|
pooling_height(), pooling_width(), |
|
stride_height(), stride_width(), |
|
channels(), input_pixel_stride(), output_pixel_stride(), |
|
output_min, output_max, |
|
0, &average_pooling_op)); |
|
ASSERT_NE(nullptr, average_pooling_op); |
|
|
|
|
|
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_average_pooling_op(average_pooling_op, xnn_delete_operator); |
|
|
|
ASSERT_EQ(xnn_status_success, |
|
xnn_reshape_average_pooling2d_nhwc_f32( |
|
average_pooling_op, |
|
batch_size(), input_height(), input_width(), |
|
nullptr, nullptr, |
|
nullptr )); |
|
|
|
ASSERT_EQ(xnn_status_success, |
|
xnn_setup_average_pooling2d_nhwc_f32( |
|
average_pooling_op, |
|
input.data(), output.data())); |
|
|
|
ASSERT_EQ(xnn_status_success, |
|
xnn_run_operator(average_pooling_op, nullptr )); |
|
|
|
|
|
for (size_t i = 0; i < batch_size(); i++) { |
|
for (size_t y = 0; y < output_height(); y++) { |
|
for (size_t x = 0; x < output_width(); x++) { |
|
for (size_t c = 0; c < channels(); c++) { |
|
EXPECT_LE(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c], output_max); |
|
EXPECT_GE(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c], output_min); |
|
EXPECT_NEAR(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c], |
|
output_ref[((i * output_height() + y) * output_width() + x) * channels() + c], |
|
std::abs(output_ref[((i * output_height() + y) * output_width() + x) * channels() + c]) * 1.0e-6f) << |
|
"in batch index " << i << ", pixel (" << y << ", " << x << "), channel " << c; |
|
} |
|
} |
|
} |
|
} |
|
} |
|
} |
|
|
|
void TestQU8() const { |
|
std::random_device random_device; |
|
auto rng = std::mt19937(random_device()); |
|
std::uniform_int_distribution<int32_t> u8dist( |
|
std::numeric_limits<uint8_t>::min(), std::numeric_limits<uint8_t>::max()); |
|
|
|
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()); |
|
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(), [&]() { return u8dist(rng); }); |
|
std::fill(output.begin(), output.end(), UINT8_C(0xA5)); |
|
|
|
|
|
const double scale = double(input_scale()) / (double(output_scale()) * double(pooling_height() * pooling_width())); |
|
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++) { |
|
double acc = 0.0f; |
|
for (size_t py = 0; py < pooling_height(); py++) { |
|
const size_t iy = oy * stride_height() + py - padding_top(); |
|
for (size_t px = 0; px < pooling_width(); px++) { |
|
const size_t ix = ox * stride_width() + px - padding_left(); |
|
if (ix < input_width() && iy < input_height()) { |
|
acc += double(int32_t(input[((i * input_height() + iy) * input_width() + ix) * input_pixel_stride() + c]) - int32_t(input_zero_point())); |
|
} |
|
} |
|
} |
|
output_ref[((i * output_height() + oy) * output_width() + ox) * channels() + c] = float(acc * scale + double(output_zero_point())); |
|
output_ref[((i * output_height() + oy) * output_width() + ox) * channels() + c] = |
|
std::min<float>(output_ref[((i * output_height() + oy) * output_width() + ox) * channels() + c], float(qmax())); |
|
output_ref[((i * output_height() + oy) * output_width() + ox) * channels() + c] = |
|
std::max<float>(output_ref[((i * output_height() + oy) * output_width() + ox) * channels() + c], float(qmin())); |
|
} |
|
} |
|
} |
|
} |
|
|
|
|
|
ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr )); |
|
xnn_operator_t average_pooling_op = nullptr; |
|
|
|
ASSERT_EQ(xnn_status_success, |
|
xnn_create_average_pooling2d_nhwc_qu8( |
|
padding_top(), padding_right(), padding_bottom(), padding_left(), |
|
pooling_height(), pooling_width(), |
|
stride_height(), stride_width(), |
|
channels(), input_pixel_stride(), output_pixel_stride(), |
|
input_zero_point(), input_scale(), |
|
output_zero_point(), output_scale(), |
|
qmin(), qmax(), |
|
0, &average_pooling_op)); |
|
ASSERT_NE(nullptr, average_pooling_op); |
|
|
|
|
|
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_average_pooling_op(average_pooling_op, xnn_delete_operator); |
|
|
|
ASSERT_EQ(xnn_status_success, |
|
xnn_reshape_average_pooling2d_nhwc_qu8( |
|
average_pooling_op, |
|
batch_size(), input_height(), input_width(), |
|
nullptr, nullptr, |
|
nullptr )); |
|
|
|
ASSERT_EQ(xnn_status_success, |
|
xnn_setup_average_pooling2d_nhwc_qu8( |
|
average_pooling_op, |
|
input.data(), output.data())); |
|
|
|
ASSERT_EQ(xnn_status_success, |
|
xnn_run_operator(average_pooling_op, nullptr )); |
|
|
|
|
|
for (size_t i = 0; i < batch_size(); i++) { |
|
for (size_t y = 0; y < output_height(); y++) { |
|
for (size_t x = 0; x < output_width(); x++) { |
|
for (size_t c = 0; c < channels(); c++) { |
|
EXPECT_LE(uint32_t(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c]), uint32_t(qmax())); |
|
EXPECT_GE(uint32_t(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c]), uint32_t(qmin())); |
|
EXPECT_NEAR(float(int32_t(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c])), |
|
output_ref[((i * output_height() + y) * output_width() + x) * channels() + c], 0.80f) << |
|
"in batch index " << i << ", pixel (" << y << ", " << x << "), channel " << c; |
|
} |
|
} |
|
} |
|
} |
|
} |
|
} |
|
|
|
void TestSetupF16() const { |
|
std::random_device random_device; |
|
auto rng = std::mt19937(random_device()); |
|
std::uniform_real_distribution<float> f32dist; |
|
|
|
std::vector<uint16_t> input(XNN_EXTRA_BYTES / sizeof(uint16_t) + std::max<size_t>( |
|
(batch_size() * input_height() * input_width() - 1) * input_pixel_stride() + channels(), |
|
(next_batch_size() * next_input_height() * next_input_width() - 1) * input_pixel_stride() + channels())); |
|
std::vector<uint16_t> output(std::max<size_t>( |
|
(batch_size() * output_height() * output_width() - 1) * output_pixel_stride() + channels(), |
|
(next_batch_size() * next_output_height() * next_output_width() - 1) * output_pixel_stride() + channels())); |
|
std::vector<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(), [&]() { return fp16_ieee_from_fp32_value(f32dist(rng)); }); |
|
std::fill(output.begin(), output.end(), UINT16_C(0x7E00) ); |
|
|
|
|
|
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 acc = 0.0f; |
|
size_t n = 0; |
|
for (size_t py = 0; py < pooling_height(); py++) { |
|
const size_t iy = oy * stride_height() + py - padding_top(); |
|
for (size_t px = 0; px < pooling_width(); px++) { |
|
const size_t ix = ox * stride_width() + px - padding_left(); |
|
if (ix < input_width() && iy < input_height()) { |
|
acc += fp16_ieee_to_fp32_value(input[((i * input_height() + iy) * input_width() + ix) * input_pixel_stride() + c]); |
|
n += 1; |
|
} |
|
} |
|
} |
|
output_ref[((i * output_height() + oy) * output_width() + ox) * channels() + c] = acc / float(n); |
|
} |
|
} |
|
} |
|
} |
|
|
|
|
|
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(); |
|
} |
|
|
|
|
|
for (float& value : output_ref) { |
|
value = std::max(std::min(value, output_max), output_min); |
|
} |
|
|
|
|
|
ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr )); |
|
xnn_operator_t average_pooling_op = nullptr; |
|
|
|
const xnn_status status = xnn_create_average_pooling2d_nhwc_f16( |
|
padding_top(), padding_right(), padding_bottom(), padding_left(), |
|
pooling_height(), pooling_width(), |
|
stride_height(), stride_width(), |
|
channels(), input_pixel_stride(), output_pixel_stride(), |
|
output_min, output_max, |
|
0, &average_pooling_op); |
|
if (status == xnn_status_unsupported_hardware) { |
|
GTEST_SKIP(); |
|
} |
|
ASSERT_EQ(xnn_status_success, status); |
|
ASSERT_NE(nullptr, average_pooling_op); |
|
|
|
ASSERT_EQ(xnn_status_success, |
|
xnn_reshape_average_pooling2d_nhwc_f16( |
|
average_pooling_op, |
|
batch_size(), input_height(), input_width(), |
|
nullptr, nullptr, |
|
nullptr )); |
|
|
|
ASSERT_EQ(xnn_status_success, |
|
xnn_setup_average_pooling2d_nhwc_f16( |
|
average_pooling_op, |
|
input.data(), output.data())); |
|
|
|
ASSERT_EQ(xnn_status_success, |
|
xnn_run_operator(average_pooling_op, nullptr )); |
|
|
|
|
|
for (size_t i = 0; i < batch_size(); i++) { |
|
for (size_t y = 0; y < output_height(); y++) { |
|
for (size_t x = 0; x < output_width(); x++) { |
|
for (size_t c = 0; c < channels(); c++) { |
|
EXPECT_LE(fp16_ieee_to_fp32_value(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c]), output_max); |
|
EXPECT_GE(fp16_ieee_to_fp32_value(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c]), output_min); |
|
EXPECT_NEAR( |
|
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], |
|
std::max(1.0e-3f, std::abs(output_ref[((i * output_height() + y) * output_width() + x) * channels() + c]) * 1.0e-2f)) << |
|
"in batch index " << i << ", pixel (" << y << ", " << x << "), channel " << c; |
|
} |
|
} |
|
} |
|
} |
|
|
|
|
|
std::generate(input.begin(), input.end(), [&]() { return fp16_ieee_from_fp32_value(f32dist(rng)); }); |
|
std::fill(output.begin(), output.end(), UINT16_C(0x7E00) ); |
|
|
|
|
|
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 acc = 0.0f; |
|
int32_t n = 0; |
|
for (size_t py = 0; py < pooling_height(); py++) { |
|
const size_t iy = oy * stride_height() + py - padding_top(); |
|
for (size_t px = 0; px < pooling_width(); px++) { |
|
const size_t ix = ox * stride_width() + px - padding_left(); |
|
if (ix < next_input_width() && iy < next_input_height()) { |
|
acc += fp16_ieee_to_fp32_value(input[((i * next_input_height() + iy) * next_input_width() + ix) * input_pixel_stride() + c]); |
|
n += 1; |
|
} |
|
} |
|
} |
|
next_output_ref[((i * next_output_height() + oy) * next_output_width() + ox) * channels() + c] = |
|
std::max(std::min(acc / float(n), output_max), output_min); |
|
} |
|
} |
|
} |
|
} |
|
|
|
|
|
ASSERT_EQ(xnn_status_success, |
|
xnn_reshape_average_pooling2d_nhwc_f16( |
|
average_pooling_op, |
|
next_batch_size(), next_input_height(), next_input_width(), |
|
nullptr, nullptr, |
|
nullptr )); |
|
ASSERT_EQ(xnn_status_success, |
|
xnn_setup_average_pooling2d_nhwc_f16( |
|
average_pooling_op, |
|
input.data(), output.data())); |
|
|
|
ASSERT_EQ(xnn_status_success, |
|
xnn_run_operator(average_pooling_op, nullptr )); |
|
|
|
ASSERT_EQ(xnn_status_success, |
|
xnn_delete_operator(average_pooling_op)); |
|
average_pooling_op = nullptr; |
|
|
|
|
|
for (size_t i = 0; i < next_batch_size(); i++) { |
|
for (size_t y = 0; y < next_output_height(); y++) { |
|
for (size_t x = 0; x < next_output_width(); x++) { |
|
for (size_t c = 0; c < channels(); c++) { |
|
EXPECT_LE(fp16_ieee_to_fp32_value(output[((i * next_output_height() + y) * next_output_width() + x) * output_pixel_stride() + c]), output_max); |
|
EXPECT_GE(fp16_ieee_to_fp32_value(output[((i * next_output_height() + y) * next_output_width() + x) * output_pixel_stride() + c]), output_min); |
|
EXPECT_NEAR( |
|
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], |
|
std::max(1.0e-3f, std::abs(next_output_ref[((i * next_output_height() + y) * next_output_width() + x) * channels() + c]) * 1.0e-2f)) << |
|
"in batch index " << i << ", pixel (" << y << ", " << x << "), channel " << c; |
|
} |
|
} |
|
} |
|
} |
|
} |
|
} |
|
|
|
void TestSetupF32() const { |
|
std::random_device random_device; |
|
auto rng = std::mt19937(random_device()); |
|
std::uniform_real_distribution<float> f32dist; |
|
|
|
std::vector<float> input(XNN_EXTRA_BYTES / sizeof(float) + std::max<size_t>( |
|
(batch_size() * input_height() * input_width() - 1) * input_pixel_stride() + channels(), |
|
(next_batch_size() * next_input_height() * next_input_width() - 1) * input_pixel_stride() + channels())); |
|
std::vector<float> output(std::max<size_t>( |
|
(batch_size() * output_height() * output_width() - 1) * output_pixel_stride() + channels(), |
|
(next_batch_size() * next_output_height() * next_output_width() - 1) * output_pixel_stride() + channels())); |
|
std::vector<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(), [&]() { return f32dist(rng); }); |
|
std::fill(output.begin(), output.end(), std::nanf("")); |
|
|
|
|
|
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 acc = 0.0f; |
|
size_t n = 0; |
|
for (size_t py = 0; py < pooling_height(); py++) { |
|
const size_t iy = oy * stride_height() + py - padding_top(); |
|
for (size_t px = 0; px < pooling_width(); px++) { |
|
const size_t ix = ox * stride_width() + px - padding_left(); |
|
if (ix < input_width() && iy < input_height()) { |
|
acc += input[((i * input_height() + iy) * input_width() + ix) * input_pixel_stride() + c]; |
|
n += 1; |
|
} |
|
} |
|
} |
|
output_ref[((i * output_height() + oy) * output_width() + ox) * channels() + c] = acc / float(n); |
|
} |
|
} |
|
} |
|
} |
|
|
|
|
|
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()); |
|
|
|
|
|
for (float& value : output_ref) { |
|
value = std::max(std::min(value, output_max), output_min); |
|
} |
|
|
|
|
|
ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr )); |
|
xnn_operator_t average_pooling_op = nullptr; |
|
|
|
ASSERT_EQ(xnn_status_success, |
|
xnn_create_average_pooling2d_nhwc_f32( |
|
padding_top(), padding_right(), padding_bottom(), padding_left(), |
|
pooling_height(), pooling_width(), |
|
stride_height(), stride_width(), |
|
channels(), input_pixel_stride(), output_pixel_stride(), |
|
output_min, output_max, |
|
0, &average_pooling_op)); |
|
ASSERT_NE(nullptr, average_pooling_op); |
|
|
|
ASSERT_EQ(xnn_status_success, |
|
xnn_reshape_average_pooling2d_nhwc_f32( |
|
average_pooling_op, |
|
batch_size(), input_height(), input_width(), |
|
nullptr, nullptr, |
|
nullptr )); |
|
|
|
ASSERT_EQ(xnn_status_success, |
|
xnn_setup_average_pooling2d_nhwc_f32( |
|
average_pooling_op, |
|
input.data(), output.data())); |
|
|
|
ASSERT_EQ(xnn_status_success, |
|
xnn_run_operator(average_pooling_op, nullptr )); |
|
|
|
|
|
for (size_t i = 0; i < batch_size(); i++) { |
|
for (size_t y = 0; y < output_height(); y++) { |
|
for (size_t x = 0; x < output_width(); x++) { |
|
for (size_t c = 0; c < channels(); c++) { |
|
EXPECT_LE(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c], output_max); |
|
EXPECT_GE(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c], output_min); |
|
EXPECT_NEAR(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c], |
|
output_ref[((i * output_height() + y) * output_width() + x) * channels() + c], |
|
std::abs(output_ref[((i * output_height() + y) * output_width() + x) * channels() + c]) * 1.0e-6f) << |
|
"in batch index " << i << ", pixel (" << y << ", " << x << "), channel " << c; |
|
} |
|
} |
|
} |
|
} |
|
|
|
|
|
std::generate(input.begin(), input.end(), [&]() { return f32dist(rng); }); |
|
std::fill(output.begin(), output.end(), std::nanf("")); |
|
|
|
|
|
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 acc = 0.0f; |
|
int32_t n = 0; |
|
for (size_t py = 0; py < pooling_height(); py++) { |
|
const size_t iy = oy * stride_height() + py - padding_top(); |
|
for (size_t px = 0; px < pooling_width(); px++) { |
|
const size_t ix = ox * stride_width() + px - padding_left(); |
|
if (ix < next_input_width() && iy < next_input_height()) { |
|
acc += input[((i * next_input_height() + iy) * next_input_width() + ix) * input_pixel_stride() + c]; |
|
n += 1; |
|
} |
|
} |
|
} |
|
next_output_ref[((i * next_output_height() + oy) * next_output_width() + ox) * channels() + c] = |
|
std::max(std::min(acc / float(n), output_max), output_min); |
|
} |
|
} |
|
} |
|
} |
|
|
|
|
|
ASSERT_EQ(xnn_status_success, |
|
xnn_reshape_average_pooling2d_nhwc_f32( |
|
average_pooling_op, |
|
next_batch_size(), next_input_height(), next_input_width(), |
|
nullptr, nullptr, |
|
nullptr )); |
|
ASSERT_EQ(xnn_status_success, |
|
xnn_setup_average_pooling2d_nhwc_f32( |
|
average_pooling_op, |
|
input.data(), output.data())); |
|
|
|
ASSERT_EQ(xnn_status_success, |
|
xnn_run_operator(average_pooling_op, nullptr )); |
|
|
|
ASSERT_EQ(xnn_status_success, |
|
xnn_delete_operator(average_pooling_op)); |
|
average_pooling_op = nullptr; |
|
|
|
|
|
for (size_t i = 0; i < next_batch_size(); i++) { |
|
for (size_t y = 0; y < next_output_height(); y++) { |
|
for (size_t x = 0; x < next_output_width(); x++) { |
|
for (size_t c = 0; c < channels(); c++) { |
|
EXPECT_LE(output[((i * next_output_height() + y) * next_output_width() + x) * output_pixel_stride() + c], output_max); |
|
EXPECT_GE(output[((i * next_output_height() + y) * next_output_width() + x) * output_pixel_stride() + c], output_min); |
|
EXPECT_NEAR(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], |
|
std::abs(next_output_ref[((i * next_output_height() + y) * next_output_width() + x) * channels() + c]) * 1.0e-6f) << |
|
"in batch index " << i << ", pixel (" << y << ", " << x << "), channel " << c; |
|
} |
|
} |
|
} |
|
} |
|
} |
|
} |
|
|
|
void TestSetupQU8() const { |
|
std::random_device random_device; |
|
auto rng = std::mt19937(random_device()); |
|
std::uniform_int_distribution<int32_t> u8dist( |
|
std::numeric_limits<uint8_t>::min(), std::numeric_limits<uint8_t>::max()); |
|
|
|
std::vector<uint8_t> input(XNN_EXTRA_BYTES / sizeof(uint8_t) + std::max<size_t>( |
|
(batch_size() * input_height() * input_width() - 1) * input_pixel_stride() + channels(), |
|
(next_batch_size() * next_input_height() * next_input_width() - 1) * input_pixel_stride() + channels())); |
|
std::vector<uint8_t> output(std::max<size_t>( |
|
(batch_size() * output_height() * output_width() - 1) * output_pixel_stride() + channels(), |
|
(next_batch_size() * next_output_height() * next_output_width() - 1) * output_pixel_stride() + channels())); |
|
std::vector<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(), [&]() { return u8dist(rng); }); |
|
std::fill(output.begin(), output.end(), INT8_C(0xA5)); |
|
|
|
|
|
const double scale = double(input_scale()) / (double(output_scale()) * double(pooling_height() * pooling_width())); |
|
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++) { |
|
double acc = 0.0f; |
|
for (size_t py = 0; py < pooling_height(); py++) { |
|
const size_t iy = oy * stride_height() + py - padding_top(); |
|
for (size_t px = 0; px < pooling_width(); px++) { |
|
const size_t ix = ox * stride_width() + px - padding_left(); |
|
if (ix < input_width() && iy < input_height()) { |
|
acc += double(int32_t(input[((i * input_height() + iy) * input_width() + ix) * input_pixel_stride() + c]) - int32_t(input_zero_point())); |
|
} |
|
} |
|
} |
|
output_ref[((i * output_height() + oy) * output_width() + ox) * channels() + c] = float(acc * scale + double(output_zero_point())); |
|
output_ref[((i * output_height() + oy) * output_width() + ox) * channels() + c] = |
|
std::min<float>(output_ref[((i * output_height() + oy) * output_width() + ox) * channels() + c], float(qmax())); |
|
output_ref[((i * output_height() + oy) * output_width() + ox) * channels() + c] = |
|
std::max<float>(output_ref[((i * output_height() + oy) * output_width() + ox) * channels() + c], float(qmin())); |
|
} |
|
} |
|
} |
|
} |
|
|
|
|
|
ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr )); |
|
xnn_operator_t average_pooling_op = nullptr; |
|
|
|
ASSERT_EQ(xnn_status_success, |
|
xnn_create_average_pooling2d_nhwc_qu8( |
|
padding_top(), padding_right(), padding_bottom(), padding_left(), |
|
pooling_height(), pooling_width(), |
|
stride_height(), stride_width(), |
|
channels(), input_pixel_stride(), output_pixel_stride(), |
|
input_zero_point(), input_scale(), |
|
output_zero_point(), output_scale(), |
|
qmin(), qmax(), |
|
0, &average_pooling_op)); |
|
ASSERT_NE(nullptr, average_pooling_op); |
|
|
|
ASSERT_EQ(xnn_status_success, |
|
xnn_reshape_average_pooling2d_nhwc_qu8( |
|
average_pooling_op, |
|
batch_size(), input_height(), input_width(), |
|
nullptr, nullptr, |
|
nullptr )); |
|
|
|
ASSERT_EQ(xnn_status_success, |
|
xnn_setup_average_pooling2d_nhwc_qu8( |
|
average_pooling_op, |
|
input.data(), output.data())); |
|
|
|
ASSERT_EQ(xnn_status_success, |
|
xnn_run_operator(average_pooling_op, nullptr )); |
|
|
|
|
|
for (size_t i = 0; i < batch_size(); i++) { |
|
for (size_t y = 0; y < output_height(); y++) { |
|
for (size_t x = 0; x < output_width(); x++) { |
|
for (size_t c = 0; c < channels(); c++) { |
|
EXPECT_LE(uint32_t(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c]), uint32_t(qmax())); |
|
EXPECT_GE(uint32_t(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c]), uint32_t(qmin())); |
|
EXPECT_NEAR(float(int32_t(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c])), |
|
output_ref[((i * output_height() + y) * output_width() + x) * channels() + c], 0.80f) << |
|
"in batch index " << i << ", pixel (" << y << ", " << x << "), channel " << c; |
|
} |
|
} |
|
} |
|
} |
|
|
|
|
|
std::generate(input.begin(), input.end(), [&]() { return u8dist(rng); }); |
|
std::fill(output.begin(), output.end(), UINT8_C(0xA5)); |
|
|
|
|
|
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++) { |
|
double acc = 0.0f; |
|
for (size_t py = 0; py < pooling_height(); py++) { |
|
const size_t iy = oy * stride_height() + py - padding_top(); |
|
for (size_t px = 0; px < pooling_width(); px++) { |
|
const size_t ix = ox * stride_width() + px - padding_left(); |
|
if (ix < next_input_width() && iy < next_input_height()) { |
|
acc += double(int32_t(input[((i * next_input_height() + iy) * next_input_width() + ix) * input_pixel_stride() + c]) - int32_t(input_zero_point())); |
|
} |
|
} |
|
} |
|
next_output_ref[((i * next_output_height() + oy) * next_output_width() + ox) * channels() + c] = float(acc * scale + double(output_zero_point())); |
|
next_output_ref[((i * next_output_height() + oy) * next_output_width() + ox) * channels() + c] = |
|
std::min<float>(next_output_ref[((i * next_output_height() + oy) * next_output_width() + ox) * channels() + c], float(qmax())); |
|
next_output_ref[((i * next_output_height() + oy) * next_output_width() + ox) * channels() + c] = |
|
std::max<float>(next_output_ref[((i * next_output_height() + oy) * next_output_width() + ox) * channels() + c], float(qmin())); |
|
} |
|
} |
|
} |
|
} |
|
|
|
|
|
ASSERT_EQ(xnn_status_success, |
|
xnn_reshape_average_pooling2d_nhwc_qu8( |
|
average_pooling_op, |
|
next_batch_size(), next_input_height(), next_input_width(), |
|
nullptr, nullptr, |
|
nullptr )); |
|
ASSERT_EQ(xnn_status_success, |
|
xnn_setup_average_pooling2d_nhwc_qu8( |
|
average_pooling_op, |
|
input.data(), output.data())); |
|
|
|
ASSERT_EQ(xnn_status_success, |
|
xnn_run_operator(average_pooling_op, nullptr )); |
|
|
|
ASSERT_EQ(xnn_status_success, |
|
xnn_delete_operator(average_pooling_op)); |
|
average_pooling_op = nullptr; |
|
|
|
|
|
for (size_t i = 0; i < next_batch_size(); i++) { |
|
for (size_t y = 0; y < next_output_height(); y++) { |
|
for (size_t x = 0; x < next_output_width(); x++) { |
|
for (size_t c = 0; c < channels(); c++) { |
|
EXPECT_LE(uint32_t(output[((i * next_output_height() + y) * next_output_width() + x) * output_pixel_stride() + c]), uint32_t(qmax())); |
|
EXPECT_GE(uint32_t(output[((i * next_output_height() + y) * next_output_width() + x) * output_pixel_stride() + c]), uint32_t(qmin())); |
|
EXPECT_NEAR(float(int32_t(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], 0.80f) << |
|
"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}; |
|
size_t next_input_height_{0}; |
|
size_t next_input_width_{0}; |
|
size_t next_batch_size_{0}; |
|
float input_scale_{1.0f}; |
|
float output_scale_{1.0f}; |
|
uint8_t input_zero_point_{121}; |
|
uint8_t output_zero_point_{133}; |
|
uint8_t qmin_{0}; |
|
uint8_t qmax_{255}; |
|
size_t iterations_{1}; |
|
}; |
|
|