| // Copyright (c) Facebook, Inc. and its affiliates. |
| // All rights reserved. |
| // |
| // Copyright 2019 Google LLC |
| // |
| // This source code is licensed under the BSD-style license found in the |
| // LICENSE file in the root directory of this source tree. |
| |
| #pragma once |
| |
| #include <gtest/gtest.h> |
| |
| #include <algorithm> |
| #include <cassert> |
| #include <cmath> |
| #include <cstddef> |
| #include <cstdlib> |
| #include <functional> |
| #include <random> |
| #include <vector> |
| |
| #include <xnnpack.h> |
| |
| |
| class DeconvolutionOperatorTester { |
| public: |
| inline DeconvolutionOperatorTester& padding(uint32_t padding) { |
| this->padding_top_ = padding; |
| this->padding_right_ = padding; |
| this->padding_bottom_ = padding; |
| this->padding_left_ = padding; |
| return *this; |
| } |
| |
| inline DeconvolutionOperatorTester& padding(uint32_t padding_height, uint32_t padding_width) { |
| this->padding_top_ = padding_height; |
| this->padding_right_ = padding_width; |
| this->padding_bottom_ = padding_height; |
| this->padding_left_ = padding_width; |
| return *this; |
| } |
| |
| inline DeconvolutionOperatorTester& padding_height(uint32_t padding_height) { |
| this->padding_top_ = padding_height; |
| this->padding_bottom_ = padding_height; |
| return *this; |
| } |
| |
| inline uint32_t padding_height() const { |
| return this->padding_top_ + this->padding_bottom_; |
| } |
| |
| inline DeconvolutionOperatorTester& padding_width(uint32_t padding_width) { |
| this->padding_right_ = padding_width; |
| this->padding_left_ = padding_width; |
| return *this; |
| } |
| |
| inline uint32_t padding_width() const { |
| return this->padding_left_ + this->padding_right_; |
| } |
| |
| inline DeconvolutionOperatorTester& padding_top(uint32_t padding_top) { |
| this->padding_top_ = padding_top; |
| return *this; |
| } |
| |
| inline uint32_t padding_top() const { |
| return this->padding_top_; |
| } |
| |
| inline DeconvolutionOperatorTester& padding_right(uint32_t padding_right) { |
| this->padding_right_ = padding_right; |
| return *this; |
| } |
| |
| inline uint32_t padding_right() const { |
| return this->padding_right_; |
| } |
| |
| inline DeconvolutionOperatorTester& padding_bottom(uint32_t padding_bottom) { |
| this->padding_bottom_ = padding_bottom; |
| return *this; |
| } |
| |
| inline uint32_t padding_bottom() const { |
| return this->padding_bottom_; |
| } |
| |
| inline DeconvolutionOperatorTester& padding_left(uint32_t padding_left) { |
| this->padding_left_ = padding_left; |
| return *this; |
| } |
| |
| inline uint32_t padding_left() const { |
| return this->padding_left_; |
| } |
| |
| inline DeconvolutionOperatorTester& adjustment_height(uint32_t adjustment_height) { |
| this->adjustment_height_ = adjustment_height; |
| return *this; |
| } |
| |
| inline uint32_t adjustment_height() const { |
| return this->adjustment_height_; |
| } |
| |
| inline DeconvolutionOperatorTester& adjustment_width(uint32_t adjustment_width) { |
| this->adjustment_width_ = adjustment_width; |
| return *this; |
| } |
| |
| inline uint32_t adjustment_width() const { |
| return this->adjustment_width_; |
| } |
| |
| inline DeconvolutionOperatorTester& input_size(uint32_t input_height, uint32_t input_width) { |
| assert(input_height >= 1); |
| assert(input_width >= 1); |
| this->input_height_ = input_height; |
| this->input_width_ = input_width; |
| return *this; |
| } |
| |
| inline DeconvolutionOperatorTester& input_height(uint32_t input_height) { |
| assert(input_height >= 1); |
| this->input_height_ = input_height; |
| return *this; |
| } |
| |
| inline uint32_t input_height() const { |
| return this->input_height_; |
| } |
| |
| inline DeconvolutionOperatorTester& input_width(uint32_t input_width) { |
| assert(input_width >= 1); |
| this->input_width_ = input_width; |
| return *this; |
| } |
| |
| inline uint32_t input_width() const { |
| return this->input_width_; |
| } |
| |
| inline DeconvolutionOperatorTester& groups(uint32_t groups) { |
| assert(groups >= 1); |
| this->groups_ = groups; |
| return *this; |
| } |
| |
| inline uint32_t groups() const { |
| return this->groups_; |
| } |
| |
| inline DeconvolutionOperatorTester& group_input_channels(size_t group_input_channels) { |
| assert(group_input_channels >= 1); |
| this->group_input_channels_ = group_input_channels; |
| return *this; |
| } |
| |
| inline size_t group_input_channels() const { |
| return this->group_input_channels_; |
| } |
| |
| inline DeconvolutionOperatorTester& group_output_channels(size_t group_output_channels) { |
| assert(group_output_channels >= 1); |
| this->group_output_channels_ = group_output_channels; |
| return *this; |
| } |
| |
| inline size_t group_output_channels() const { |
| return this->group_output_channels_; |
| } |
| |
| inline DeconvolutionOperatorTester& batch_size(size_t batch_size) { |
| assert(batch_size >= 1); |
| this->batch_size_ = batch_size; |
| return *this; |
| } |
| |
| inline size_t batch_size() const { |
| return this->batch_size_; |
| } |
| |
| inline DeconvolutionOperatorTester& kernel_size(uint32_t kernel_size) { |
| assert(kernel_size >= 1); |
| this->kernel_height_ = kernel_size; |
| this->kernel_width_ = kernel_size; |
| return *this; |
| } |
| |
| inline DeconvolutionOperatorTester& kernel_size(uint32_t kernel_height, uint32_t kernel_width) { |
| assert(kernel_height >= 1); |
| assert(kernel_width >= 1); |
| this->kernel_height_ = kernel_height; |
| this->kernel_width_ = kernel_width; |
| return *this; |
| } |
| |
| inline DeconvolutionOperatorTester& kernel_height(uint32_t kernel_height) { |
| assert(kernel_height >= 1); |
| this->kernel_height_ = kernel_height; |
| return *this; |
| } |
| |
| inline uint32_t kernel_height() const { |
| return this->kernel_height_; |
| } |
| |
| inline DeconvolutionOperatorTester& kernel_width(uint32_t kernel_width) { |
| assert(kernel_width >= 1); |
| this->kernel_width_ = kernel_width; |
| return *this; |
| } |
| |
| inline uint32_t kernel_width() const { |
| return this->kernel_width_; |
| } |
| |
| inline DeconvolutionOperatorTester& dilation(uint32_t dilation) { |
| assert(dilation >= 1); |
| this->dilation_height_ = dilation; |
| this->dilation_width_ = dilation; |
| return *this; |
| } |
| |
| inline DeconvolutionOperatorTester& dilation(uint32_t dilation_height, uint32_t dilation_width) { |
| assert(dilation_height >= 1); |
| assert(dilation_width >= 1); |
| this->dilation_height_ = dilation_height; |
| this->dilation_width_ = dilation_width; |
| return *this; |
| } |
| |
| inline DeconvolutionOperatorTester& dilation_height(uint32_t dilation_height) { |
| assert(dilation_height >= 1); |
| this->dilation_height_ = dilation_height; |
| return *this; |
| } |
| |
| inline uint32_t dilation_height() const { |
| return this->dilation_height_; |
| } |
| |
| inline DeconvolutionOperatorTester& dilation_width(uint32_t dilation_width) { |
| assert(dilation_width >= 1); |
| this->dilation_width_ = dilation_width; |
| return *this; |
| } |
| |
| inline uint32_t dilation_width() const { |
| return this->dilation_width_; |
| } |
| |
| inline DeconvolutionOperatorTester& stride(uint32_t stride) { |
| assert(stride >= 1); |
| this->stride_height_ = stride; |
| this->stride_width_ = stride; |
| return *this; |
| } |
| |
| inline DeconvolutionOperatorTester& stride(uint32_t stride_height, uint32_t stride_width) { |
| assert(stride_height >= 1); |
| assert(stride_width >= 1); |
| this->stride_height_ = stride_height; |
| this->stride_width_ = stride_width; |
| return *this; |
| } |
| |
| inline DeconvolutionOperatorTester& stride_height(uint32_t stride_height) { |
| assert(stride_height >= 1); |
| this->stride_height_ = stride_height; |
| return *this; |
| } |
| |
| inline uint32_t stride_height() const { |
| return this->stride_height_; |
| } |
| |
| inline DeconvolutionOperatorTester& stride_width(uint32_t stride_width) { |
| assert(stride_width >= 1); |
| this->stride_width_ = stride_width; |
| return *this; |
| } |
| |
| inline uint32_t stride_width() const { |
| return this->stride_width_; |
| } |
| |
| inline DeconvolutionOperatorTester& input_pixel_stride(size_t input_pixel_stride) { |
| assert(input_pixel_stride >= 1); |
| this->input_pixel_stride_ = input_pixel_stride; |
| return *this; |
| } |
| |
| inline size_t input_pixel_stride() const { |
| if (this->input_pixel_stride_ == 0) { |
| return group_input_channels() * groups(); |
| } else { |
| assert(this->input_pixel_stride_ >= group_input_channels() * groups()); |
| return this->input_pixel_stride_; |
| } |
| } |
| |
| inline DeconvolutionOperatorTester& output_pixel_stride(size_t output_pixel_stride) { |
| assert(output_pixel_stride >= 1); |
| this->output_pixel_stride_ = output_pixel_stride; |
| return *this; |
| } |
| |
| inline size_t output_pixel_stride() const { |
| if (this->output_pixel_stride_ == 0) { |
| return group_output_channels() * groups(); |
| } else { |
| assert(this->output_pixel_stride_ >= group_output_channels() * groups()); |
| return this->output_pixel_stride_; |
| } |
| } |
| |
| inline uint32_t dilated_kernel_height() const { |
| return (kernel_height() - 1) * dilation_height() + 1; |
| } |
| |
| inline uint32_t dilated_kernel_width() const { |
| return (kernel_width() - 1) * dilation_width() + 1; |
| } |
| |
| inline size_t output_height() const { |
| return stride_height() * (input_height() - 1) + adjustment_height() + dilated_kernel_height() - padding_height(); |
| } |
| |
| inline size_t output_width() const { |
| return stride_width() * (input_width() - 1) + adjustment_width() + dilated_kernel_width() - padding_width(); |
| } |
| |
| inline DeconvolutionOperatorTester& next_input_size(uint32_t next_input_height, uint32_t next_input_width) { |
| assert(next_input_height >= 1); |
| assert(next_input_width >= 1); |
| this->next_input_height_ = next_input_height; |
| this->next_input_width_ = next_input_width; |
| return *this; |
| } |
| |
| inline DeconvolutionOperatorTester& next_input_height(uint32_t next_input_height) { |
| assert(next_input_height >= 1); |
| this->next_input_height_ = next_input_height; |
| return *this; |
| } |
| |
| inline uint32_t next_input_height() const { |
| if (this->next_input_height_ == 0) { |
| return input_height(); |
| } else { |
| return this->next_input_height_; |
| } |
| } |
| |
| inline DeconvolutionOperatorTester& next_input_width(uint32_t next_input_width) { |
| assert(next_input_width >= 1); |
| this->next_input_width_ = next_input_width; |
| return *this; |
| } |
| |
| inline uint32_t next_input_width() const { |
| if (this->next_input_width_ == 0) { |
| return input_width(); |
| } else { |
| return this->next_input_width_; |
| } |
| } |
| |
| inline size_t next_output_height() const { |
| return stride_height() * (next_input_height() - 1) + adjustment_height() + dilated_kernel_height() - padding_height(); |
| } |
| |
| inline size_t next_output_width() const { |
| return stride_width() * (next_input_width() - 1) + adjustment_width() + dilated_kernel_width() - padding_width(); |
| } |
| |
| inline DeconvolutionOperatorTester& next_batch_size(size_t next_batch_size) { |
| assert(next_batch_size >= 1); |
| this->next_batch_size_ = next_batch_size; |
| return *this; |
| } |
| |
| inline size_t next_batch_size() const { |
| if (this->next_batch_size_ == 0) { |
| return batch_size(); |
| } else { |
| return this->next_batch_size_; |
| } |
| } |
| |
| inline DeconvolutionOperatorTester& qmin(uint8_t qmin) { |
| this->qmin_ = qmin; |
| return *this; |
| } |
| |
| inline uint8_t qmin() const { |
| return this->qmin_; |
| } |
| |
| inline DeconvolutionOperatorTester& qmax(uint8_t qmax) { |
| this->qmax_ = qmax; |
| return *this; |
| } |
| |
| inline uint8_t qmax() const { |
| return this->qmax_; |
| } |
| |
| inline DeconvolutionOperatorTester& has_bias(bool has_bias) { |
| this->has_bias_ = has_bias; |
| return *this; |
| } |
| |
| inline bool has_bias() const { |
| return this->has_bias_; |
| } |
| |
| inline DeconvolutionOperatorTester& iterations(size_t iterations) { |
| this->iterations_ = iterations; |
| return *this; |
| } |
| |
| inline size_t iterations() const { |
| return this->iterations_; |
| } |
| |
| void TestQ8() const { |
| std::random_device random_device; |
| auto rng = std::mt19937(random_device()); |
| auto s32rng = std::bind(std::uniform_int_distribution<int32_t>(-10000, 10000), rng); |
| auto u8rng = std::bind(std::uniform_int_distribution<uint8_t>(), rng); |
| |
| std::vector<uint8_t> input(XNN_EXTRA_BYTES / sizeof(uint8_t) + |
| (batch_size() * input_height() * input_width() - 1) * input_pixel_stride() + groups() * group_input_channels()); |
| std::vector<uint8_t> kernel(groups() * group_output_channels() * kernel_height() * kernel_width() * group_input_channels()); |
| std::vector<int32_t> bias(groups() * group_output_channels()); |
| std::vector<uint8_t> output((batch_size() * output_height() * output_width() - 1) * output_pixel_stride() + groups() * group_output_channels()); |
| std::vector<int32_t> accumulators(batch_size() * output_height() * output_width() * groups() * group_output_channels()); |
| std::vector<double> output_ref(batch_size() * output_height() * output_width() * groups() * group_output_channels()); |
| |
| const uint8_t input_zero_point = 127; |
| const uint8_t kernel_zero_point = 127; |
| |
| for (size_t iteration = 0; iteration < iterations(); iteration++) { |
| std::generate(input.begin(), input.end(), std::ref(u8rng)); |
| std::generate(kernel.begin(), kernel.end(), std::ref(u8rng)); |
| std::generate(bias.begin(), bias.end(), std::ref(s32rng)); |
| std::fill(output.begin(), output.end(), 0xA5); |
| |
| // Compute reference results, without renormalization. |
| if (has_bias()) { |
| 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 g = 0; g < groups(); g++) { |
| for (size_t oc = 0; oc < group_output_channels(); oc++) { |
| accumulators[(((i * output_height() + oy) * output_width() + ox) * groups() + g) * group_output_channels() + oc] = |
| bias[g * group_output_channels() + oc]; |
| } |
| } |
| } |
| } |
| } |
| } else { |
| std::fill(accumulators.begin(), accumulators.end(), 0); |
| } |
| 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 ky = 0; ky < kernel_height(); ky++) { |
| const size_t y = oy + padding_top() - ky * dilation_height(); |
| const size_t iy = y / stride_height(); |
| if (iy * stride_height() == y && iy < input_height()) { |
| for (size_t kx = 0; kx < kernel_width(); kx++) { |
| const size_t x = ox + padding_left() - kx * dilation_width(); |
| const size_t ix = x / stride_width(); |
| if (ix * stride_width() == x && ix < input_width()) { |
| for (size_t g = 0; g < groups(); g++) { |
| for (size_t oc = 0; oc < group_output_channels(); oc++) { |
| for (size_t ic = 0; ic < group_input_channels(); ic++) { |
| accumulators[(((i * output_height() + oy) * output_width() + ox) * groups() + g) * group_output_channels() + oc] += |
| (int32_t(input[((i * input_height() + iy) * input_width() + ix) * input_pixel_stride() + g * group_input_channels() + ic]) - int32_t(input_zero_point)) * |
| (int32_t(kernel[(((g * group_output_channels() + oc) * kernel_height() + ky) * kernel_width() + kx) * group_input_channels() + ic]) - int32_t(kernel_zero_point)); |
| } |
| } |
| } |
| } |
| } |
| } |
| } |
| } |
| } |
| } |
| |
| // Compute renormalization parameters. |
| const int32_t accumulated_min = *std::min_element(accumulators.cbegin(), accumulators.cend()); |
| const int32_t accumulated_max = *std::max_element(accumulators.cbegin(), accumulators.cend()); |
| |
| const double output_scale = double(uint32_t(accumulated_max - accumulated_min)) / 255.0; |
| const uint8_t output_zero_point = uint8_t(std::max(std::min( |
| lrint(127.5 - 0.5 * double(accumulated_min + accumulated_max) / output_scale), |
| long(std::numeric_limits<uint8_t>::max())), long(std::numeric_limits<uint8_t>::min()))); |
| |
| // Renormalize reference results. |
| std::transform(accumulators.cbegin(), accumulators.cend(), output_ref.begin(), |
| [this, output_scale, output_zero_point](int32_t x) -> double { |
| return std::max<double>(std::min<double>(double(x) / output_scale, double(qmax()) - output_zero_point), double(qmin()) - output_zero_point); |
| }); |
| |
| // Create, setup, run, and destroy Deconvolution operator. |
| ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */)); |
| xnn_operator_t deconvolution_op = nullptr; |
| |
| ASSERT_EQ(xnn_status_success, |
| xnn_create_deconvolution2d_nhwc_q8( |
| padding_top(), padding_right(), padding_bottom(), padding_left(), |
| kernel_height(), kernel_width(), |
| stride_height(), stride_width(), |
| dilation_height(), dilation_width(), |
| groups(), group_input_channels(), group_output_channels(), |
| input_pixel_stride(), output_pixel_stride(), |
| input_zero_point, 1.0f /* input scale */, |
| kernel_zero_point, 1.0f /* kernel scale */, |
| kernel.data(), has_bias() ? bias.data() : nullptr, |
| output_zero_point, output_scale, qmin(), qmax(), |
| 0, &deconvolution_op)); |
| |
| // Smart pointer to automatically delete deconvolution_op. |
| std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_deconvolution_op(deconvolution_op, xnn_delete_operator); |
| |
| ASSERT_EQ(xnn_status_success, |
| xnn_setup_deconvolution2d_nhwc_q8( |
| deconvolution_op, |
| batch_size(), input_height(), input_width(), |
| adjustment_height(), adjustment_width(), |
| input.data(), output.data(), |
| nullptr /* thread pool */)); |
| |
| ASSERT_EQ(xnn_status_success, |
| xnn_run_operator(deconvolution_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 g = 0; g < groups(); g++) { |
| for (size_t c = 0; c < group_output_channels(); c++) { |
| ASSERT_LE(int32_t(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + g * group_output_channels() + c]), int32_t(qmax())) |
| << "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c; |
| ASSERT_GE(int32_t(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + g * group_output_channels() + c]), int32_t(qmin())) |
| << "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c; |
| ASSERT_NEAR( |
| output_ref[(((i * output_height() + y) * output_width() + x) * groups() + g) * group_output_channels() + c], |
| double(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + g * group_output_channels() + c]) - double(output_zero_point), |
| 0.9) |
| << "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c; |
| } |
| } |
| } |
| } |
| } |
| } |
| } |
| |
| void TestF32() const { |
| std::random_device random_device; |
| auto rng = std::mt19937(random_device()); |
| auto f32rng = std::bind(std::uniform_real_distribution<float>(0.1f, 1.0f), rng); |
| |
| std::vector<float> input(XNN_EXTRA_BYTES / sizeof(float) + |
| (batch_size() * input_height() * input_width() - 1) * input_pixel_stride() + groups() * group_input_channels()); |
| std::vector<float> kernel(groups() * group_output_channels() * kernel_height() * kernel_width() * group_input_channels()); |
| std::vector<float> bias(groups() * group_output_channels()); |
| std::vector<float> output((batch_size() * output_height() * output_width() - 1) * output_pixel_stride() + groups() * group_output_channels()); |
| std::vector<float> output_ref(batch_size() * output_height() * output_width() * groups() * group_output_channels()); |
| |
| for (size_t iteration = 0; iteration < iterations(); iteration++) { |
| std::generate(input.begin(), input.end(), std::ref(f32rng)); |
| std::generate(kernel.begin(), kernel.end(), std::ref(f32rng)); |
| std::generate(bias.begin(), bias.end(), std::ref(f32rng)); |
| std::fill(output.begin(), output.end(), nanf("")); |
| std::fill(output_ref.begin(), output_ref.end(), 0.0f); |
| |
| // Compute reference results, without clamping. |
| if (has_bias()) { |
| 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 g = 0; g < groups(); g++) { |
| for (size_t oc = 0; oc < group_output_channels(); oc++) { |
| output_ref[(((i * output_height() + oy) * output_width() + ox) * groups() + g) * group_output_channels() + oc] = |
| bias[g * group_output_channels() + oc]; |
| } |
| } |
| } |
| } |
| } |
| } else { |
| std::fill(output_ref.begin(), output_ref.end(), 0.0f); |
| } |
| 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 ky = 0; ky < kernel_height(); ky++) { |
| const size_t y = oy + padding_top() - ky * dilation_height(); |
| const size_t iy = y / stride_height(); |
| if (iy * stride_height() == y && iy < input_height()) { |
| for (size_t kx = 0; kx < kernel_width(); kx++) { |
| const size_t x = ox + padding_left() - kx * dilation_width(); |
| const size_t ix = x / stride_width(); |
| if (ix * stride_width() == x && ix < input_width()) { |
| for (size_t g = 0; g < groups(); g++) { |
| for (size_t oc = 0; oc < group_output_channels(); oc++) { |
| for (size_t ic = 0; ic < group_input_channels(); ic++) { |
| output_ref[(((i * output_height() + oy) * output_width() + ox) * groups() + g) * group_output_channels() + oc] += |
| input[((i * input_height() + iy) * input_width() + ix) * input_pixel_stride() + g * group_input_channels() + ic] * |
| kernel[(((g * group_output_channels() + oc) * kernel_height() + ky) * kernel_width() + kx) * group_input_channels() + ic]; |
| } |
| } |
| } |
| } |
| } |
| } |
| } |
| } |
| } |
| } |
| |
| // 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 output_min = accumulated_min + (accumulated_max - accumulated_min) / 255.0f * float(qmin()); |
| const float output_max = accumulated_max - (accumulated_max - accumulated_min) / 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 Deconvolution operator. |
| ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */)); |
| xnn_operator_t deconvolution_op = nullptr; |
| |
| ASSERT_EQ(xnn_status_success, |
| xnn_create_deconvolution2d_nhwc_f32( |
| padding_top(), padding_right(), padding_bottom(), padding_left(), |
| kernel_height(), kernel_width(), |
| stride_height(), stride_width(), |
| dilation_height(), dilation_width(), |
| groups(), group_input_channels(), group_output_channels(), |
| input_pixel_stride(), output_pixel_stride(), |
| kernel.data(), has_bias() ? bias.data() : nullptr, |
| output_min, output_max, |
| 0, &deconvolution_op)); |
| |
| // Smart pointer to automatically delete deconvolution_op. |
| std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_deconvolution_op(deconvolution_op, xnn_delete_operator); |
| |
| ASSERT_EQ(xnn_status_success, |
| xnn_setup_deconvolution2d_nhwc_f32( |
| deconvolution_op, |
| batch_size(), input_height(), input_width(), |
| adjustment_height(), adjustment_width(), |
| input.data(), output.data(), |
| nullptr /* thread pool */)); |
| |
| ASSERT_EQ(xnn_status_success, |
| xnn_run_operator(deconvolution_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 g = 0; g < groups(); g++) { |
| for (size_t c = 0; c < group_output_channels(); c++) { |
| ASSERT_GE(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + g * group_output_channels() + c], output_min) |
| << "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c; |
| ASSERT_LE(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + g * group_output_channels() + c], output_max) |
| << "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c; |
| ASSERT_NEAR( |
| output_ref[(((i * output_height() + y) * output_width() + x) * groups() + g) * group_output_channels() + c], |
| output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + g * group_output_channels() + c], |
| 1.0e-4 * std::abs(output_ref[(((i * output_height() + y) * output_width() + x) * groups() + g) * group_output_channels() + c])) |
| << "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c; |
| } |
| } |
| } |
| } |
| } |
| } |
| } |
| |
| void TestSetupQ8() const { |
| std::random_device random_device; |
| auto rng = std::mt19937(random_device()); |
| auto s32rng = std::bind(std::uniform_int_distribution<int32_t>(-10000, 10000), rng); |
| auto u8rng = std::bind(std::uniform_int_distribution<uint8_t>(), rng); |
| |
| std::vector<uint8_t> input(XNN_EXTRA_BYTES / sizeof(uint8_t) + std::max( |
| (batch_size() * input_height() * input_width() - 1) * input_pixel_stride() + groups() * group_input_channels(), |
| (next_batch_size() * next_input_height() * next_input_width() - 1) * input_pixel_stride() + groups() * group_input_channels())); |
| std::vector<uint8_t> kernel(groups() * group_output_channels() * kernel_height() * kernel_width() * group_input_channels()); |
| std::vector<int32_t> bias(groups() * group_output_channels()); |
| std::vector<uint8_t> output(std::max( |
| (batch_size() * output_height() * output_width() - 1) * output_pixel_stride() + groups() * group_output_channels(), |
| (next_batch_size() * next_output_height() * next_output_width() - 1) * output_pixel_stride() + groups() * group_output_channels())); |
| std::vector<int32_t> accumulators(batch_size() * output_height() * output_width() * groups() * group_output_channels()); |
| std::vector<double> output_ref(batch_size() * output_height() * output_width() * groups() * group_output_channels()); |
| std::vector<int32_t> next_accumulators(next_batch_size() * next_output_height() * next_output_width() * groups() * group_output_channels()); |
| std::vector<double> next_output_ref(next_batch_size() * next_output_height() * next_output_width() * groups() * group_output_channels()); |
| |
| const uint8_t input_zero_point = 127; |
| const uint8_t kernel_zero_point = 127; |
| |
| for (size_t iteration = 0; iteration < iterations(); iteration++) { |
| std::generate(input.begin(), input.end(), std::ref(u8rng)); |
| std::generate(kernel.begin(), kernel.end(), std::ref(u8rng)); |
| std::generate(bias.begin(), bias.end(), std::ref(s32rng)); |
| std::fill(output.begin(), output.end(), 0xA5); |
| |
| // Compute reference results, without renormalization. |
| if (has_bias()) { |
| 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 g = 0; g < groups(); g++) { |
| for (size_t oc = 0; oc < group_output_channels(); oc++) { |
| accumulators[(((i * output_height() + oy) * output_width() + ox) * groups() + g) * group_output_channels() + oc] = |
| bias[g * group_output_channels() + oc]; |
| } |
| } |
| } |
| } |
| } |
| } else { |
| std::fill(accumulators.begin(), accumulators.end(), 0); |
| } |
| 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 ky = 0; ky < kernel_height(); ky++) { |
| const size_t y = oy + padding_top() - ky * dilation_height(); |
| const size_t iy = y / stride_height(); |
| if (iy * stride_height() == y && iy < input_height()) { |
| for (size_t kx = 0; kx < kernel_width(); kx++) { |
| const size_t x = ox + padding_left() - kx * dilation_width(); |
| const size_t ix = x / stride_width(); |
| if (ix * stride_width() == x && ix < input_width()) { |
| for (size_t g = 0; g < groups(); g++) { |
| for (size_t oc = 0; oc < group_output_channels(); oc++) { |
| for (size_t ic = 0; ic < group_input_channels(); ic++) { |
| accumulators[(((i * output_height() + oy) * output_width() + ox) * groups() + g) * group_output_channels() + oc] += |
| (int32_t(input[((i * input_height() + iy) * input_width() + ix) * input_pixel_stride() + g * group_input_channels() + ic]) - int32_t(input_zero_point)) * |
| (int32_t(kernel[(((g * group_output_channels() + oc) * kernel_height() + ky) * kernel_width() + kx) * group_input_channels() + ic]) - int32_t(kernel_zero_point)); |
| } |
| } |
| } |
| } |
| } |
| } |
| } |
| } |
| } |
| } |
| |
| // Compute renormalization parameters. |
| const int32_t accumulated_min = *std::min_element(accumulators.cbegin(), accumulators.cend()); |
| const int32_t accumulated_max = *std::max_element(accumulators.cbegin(), accumulators.cend()); |
| |
| const double output_scale = double(uint32_t(accumulated_max - accumulated_min)) / 255.0; |
| const uint8_t output_zero_point = uint8_t(std::max(std::min( |
| lrint(127.5 - 0.5 * double(accumulated_min + accumulated_max) / output_scale), |
| long(std::numeric_limits<uint8_t>::max())), long(std::numeric_limits<uint8_t>::min()))); |
| |
| // Renormalize reference results. |
| std::transform(accumulators.cbegin(), accumulators.cend(), output_ref.begin(), |
| [this, output_scale, output_zero_point](int32_t x) -> double { |
| return std::max<double>(std::min<double>(double(x) / output_scale, double(qmax()) - output_zero_point), double(qmin()) - output_zero_point); |
| }); |
| |
| // Create, setup, and run Deconvolution operator once. |
| ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */)); |
| xnn_operator_t deconvolution_op = nullptr; |
| |
| ASSERT_EQ(xnn_status_success, |
| xnn_create_deconvolution2d_nhwc_q8( |
| padding_top(), padding_right(), padding_bottom(), padding_left(), |
| kernel_height(), kernel_width(), |
| stride_height(), stride_width(), |
| dilation_height(), dilation_width(), |
| groups(), group_input_channels(), group_output_channels(), |
| input_pixel_stride(), output_pixel_stride(), |
| input_zero_point, 1.0f /* input scale */, |
| kernel_zero_point, 1.0f /* kernel scale */, |
| kernel.data(), has_bias() ? bias.data() : nullptr, |
| output_zero_point, output_scale, qmin(), qmax(), |
| 0, &deconvolution_op)); |
| |
| // Smart pointer to automatically delete deconvolution_op. |
| std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_deconvolution_op(deconvolution_op, xnn_delete_operator); |
| |
| ASSERT_EQ(xnn_status_success, |
| xnn_setup_deconvolution2d_nhwc_q8( |
| deconvolution_op, |
| batch_size(), input_height(), input_width(), |
| adjustment_height(), adjustment_width(), |
| input.data(), output.data(), |
| nullptr /* thread pool */)); |
| |
| ASSERT_EQ(xnn_status_success, |
| xnn_run_operator(deconvolution_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 g = 0; g < groups(); g++) { |
| for (size_t c = 0; c < group_output_channels(); c++) { |
| ASSERT_LE(int32_t(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + g * group_output_channels() + c]), int32_t(qmax())) |
| << "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c; |
| ASSERT_GE(int32_t(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + g * group_output_channels() + c]), int32_t(qmin())) |
| << "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c; |
| ASSERT_NEAR( |
| output_ref[(((i * output_height() + y) * output_width() + x) * groups() + g) * group_output_channels() + c], |
| double(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + g * group_output_channels() + c]) - double(output_zero_point), |
| 0.9) |
| << "(x, y) = (" << x << ", " << y << "), group = " << g << ", 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, including renormalization. |
| if (has_bias()) { |
| 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 g = 0; g < groups(); g++) { |
| for (size_t oc = 0; oc < group_output_channels(); oc++) { |
| next_accumulators[(((i * next_output_height() + oy) * next_output_width() + ox) * groups() + g) * group_output_channels() + oc] = |
| bias[g * group_output_channels() + oc]; |
| } |
| } |
| } |
| } |
| } |
| } else { |
| std::fill(next_accumulators.begin(), next_accumulators.end(), 0); |
| } |
| 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 ky = 0; ky < kernel_height(); ky++) { |
| const size_t y = oy + padding_top() - ky * dilation_height(); |
| const size_t iy = y / stride_height(); |
| if (iy * stride_height() == y && iy < next_input_height()) { |
| for (size_t kx = 0; kx < kernel_width(); kx++) { |
| const size_t x = ox + padding_left() - kx * dilation_width(); |
| const size_t ix = x / stride_width(); |
| if (ix * stride_width() == x && ix < next_input_width()) { |
| for (size_t g = 0; g < groups(); g++) { |
| for (size_t oc = 0; oc < group_output_channels(); oc++) { |
| for (size_t ic = 0; ic < group_input_channels(); ic++) { |
| next_accumulators[(((i * next_output_height() + oy) * next_output_width() + ox) * groups() + g) * group_output_channels() + oc] += |
| (int32_t(input[((i * next_input_height() + iy) * next_input_width() + ix) * input_pixel_stride() + g * group_input_channels() + ic]) - int32_t(input_zero_point)) * |
| (int32_t(kernel[(((g * group_output_channels() + oc) * kernel_height() + ky) * kernel_width() + kx) * group_input_channels() + ic]) - int32_t(kernel_zero_point)); |
| } |
| } |
| } |
| } |
| } |
| } |
| } |
| } |
| } |
| } |
| std::transform(next_accumulators.cbegin(), next_accumulators.cend(), next_output_ref.begin(), |
| [this, output_scale, output_zero_point](int32_t x) -> double { |
| return std::max<double>(std::min<double>(double(x) / output_scale, double(qmax()) - output_zero_point), double(qmin()) - output_zero_point); |
| }); |
| |
| // Setup and run Deconvolution operator the second time, and destroy the operator. |
| ASSERT_EQ(xnn_status_success, |
| xnn_setup_deconvolution2d_nhwc_q8( |
| deconvolution_op, |
| next_batch_size(), next_input_height(), next_input_width(), |
| adjustment_height(), adjustment_width(), |
| input.data(), output.data(), |
| nullptr /* thread pool */)); |
| |
| ASSERT_EQ(xnn_status_success, |
| xnn_run_operator(deconvolution_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 g = 0; g < groups(); g++) { |
| for (size_t c = 0; c < group_output_channels(); c++) { |
| ASSERT_LE(int32_t(output[((i * next_output_height() + y) * next_output_width() + x) * output_pixel_stride() + g * group_output_channels() + c]), int32_t(qmax())) |
| << "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c; |
| ASSERT_GE(int32_t(output[((i * next_output_height() + y) * next_output_width() + x) * output_pixel_stride() + g * group_output_channels() + c]), int32_t(qmin())) |
| << "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c; |
| ASSERT_NEAR( |
| next_output_ref[(((i * next_output_height() + y) * next_output_width() + x) * groups() + g) * group_output_channels() + c], |
| double(output[((i * next_output_height() + y) * next_output_width() + x) * output_pixel_stride() + g * group_output_channels() + c]) - double(output_zero_point), |
| 0.9) |
| << "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c; |
| } |
| } |
| } |
| } |
| } |
| } |
| } |
| |
| void TestSetupF32() const { |
| std::random_device random_device; |
| auto rng = std::mt19937(random_device()); |
| auto f32rng = std::bind(std::uniform_real_distribution<float>(0.1f, 1.0f), rng); |
| |
| std::vector<float> input(XNN_EXTRA_BYTES / sizeof(float) + std::max( |
| (batch_size() * input_height() * input_width() - 1) * input_pixel_stride() + groups() * group_input_channels(), |
| (next_batch_size() * next_input_height() * next_input_width() - 1) * input_pixel_stride() + groups() * group_input_channels())); |
| std::vector<float> kernel(groups() * group_output_channels() * kernel_height() * kernel_width() * group_input_channels()); |
| std::vector<float> bias(groups() * group_output_channels()); |
| std::vector<float> output(std::max( |
| (batch_size() * output_height() * output_width() - 1) * output_pixel_stride() + groups() * group_output_channels(), |
| (next_batch_size() * next_output_height() * next_output_width() - 1) * output_pixel_stride() + groups() * group_output_channels())); |
| std::vector<float> output_ref(batch_size() * output_height() * output_width() * groups() * group_output_channels()); |
| std::vector<float> next_output_ref(next_batch_size() * next_output_height() * next_output_width() * groups() * group_output_channels()); |
| |
| for (size_t iteration = 0; iteration < iterations(); iteration++) { |
| std::generate(input.begin(), input.end(), std::ref(f32rng)); |
| std::generate(kernel.begin(), kernel.end(), std::ref(f32rng)); |
| std::generate(bias.begin(), bias.end(), std::ref(f32rng)); |
| std::fill(output.begin(), output.end(), nanf("")); |
| |
| // Compute reference results, without clamping. |
| if (has_bias()) { |
| 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 g = 0; g < groups(); g++) { |
| for (size_t oc = 0; oc < group_output_channels(); oc++) { |
| output_ref[(((i * output_height() + oy) * output_width() + ox) * groups() + g) * group_output_channels() + oc] = |
| bias[g * group_output_channels() + oc]; |
| } |
| } |
| } |
| } |
| } |
| } else { |
| std::fill(output_ref.begin(), output_ref.end(), 0.0f); |
| } |
| 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 ky = 0; ky < kernel_height(); ky++) { |
| const size_t y = oy + padding_top() - ky * dilation_height(); |
| const size_t iy = y / stride_height(); |
| if (iy * stride_height() == y && iy < input_height()) { |
| for (size_t kx = 0; kx < kernel_width(); kx++) { |
| const size_t x = ox + padding_left() - kx * dilation_width(); |
| const size_t ix = x / stride_width(); |
| if (ix * stride_width() == x && ix < input_width()) { |
| for (size_t g = 0; g < groups(); g++) { |
| for (size_t oc = 0; oc < group_output_channels(); oc++) { |
| for (size_t ic = 0; ic < group_input_channels(); ic++) { |
| output_ref[(((i * output_height() + oy) * output_width() + ox) * groups() + g) * group_output_channels() + oc] += |
| input[((i * input_height() + iy) * input_width() + ix) * input_pixel_stride() + g * group_input_channels() + ic] * |
| kernel[(((g * group_output_channels() + oc) * kernel_height() + ky) * kernel_width() + kx) * group_input_channels() + ic]; |
| } |
| } |
| } |
| } |
| } |
| } |
| } |
| } |
| } |
| } |
| |
| // 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 output_min = accumulated_min + (accumulated_max - accumulated_min) / 255.0f * float(qmin()); |
| const float output_max = accumulated_max - (accumulated_max - accumulated_min) / 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 Deconvolution operator once. |
| ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */)); |
| xnn_operator_t deconvolution_op = nullptr; |
| |
| ASSERT_EQ(xnn_status_success, |
| xnn_create_deconvolution2d_nhwc_f32( |
| padding_top(), padding_right(), padding_bottom(), padding_left(), |
| kernel_height(), kernel_width(), |
| stride_height(), stride_width(), |
| dilation_height(), dilation_width(), |
| groups(), group_input_channels(), group_output_channels(), |
| input_pixel_stride(), output_pixel_stride(), |
| kernel.data(), has_bias() ? bias.data() : nullptr, |
| output_min, output_max, |
| 0, &deconvolution_op)); |
| |
| // Smart pointer to automatically delete deconvolution_op. |
| std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_deconvolution_op(deconvolution_op, xnn_delete_operator); |
| |
| ASSERT_EQ(xnn_status_success, |
| xnn_setup_deconvolution2d_nhwc_f32( |
| deconvolution_op, |
| batch_size(), input_height(), input_width(), |
| adjustment_height(), adjustment_width(), |
| input.data(), output.data(), |
| nullptr /* thread pool */)); |
| |
| ASSERT_EQ(xnn_status_success, |
| xnn_run_operator(deconvolution_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 g = 0; g < groups(); g++) { |
| for (size_t c = 0; c < group_output_channels(); c++) { |
| ASSERT_GE(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + g * group_output_channels() + c], output_min) |
| << "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c; |
| ASSERT_LE(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + g * group_output_channels() + c], output_max) |
| << "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c; |
| ASSERT_NEAR( |
| output_ref[(((i * output_height() + y) * output_width() + x) * groups() + g) * group_output_channels() + c], |
| output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + g * group_output_channels() + c], |
| 1.0e-4 * std::abs(output_ref[(((i * output_height() + y) * output_width() + x) * groups() + g) * group_output_channels() + c])) |
| << "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c; |
| } |
| } |
| } |
| } |
| } |
| |
| // Re-generate data for the second run. |
| std::generate(input.begin(), input.end(), std::ref(f32rng)); |
| std::fill(output.begin(), output.end(), nanf("")); |
| |
| // Compute reference results for the second run, including clamping. |
| if (has_bias()) { |
| 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 g = 0; g < groups(); g++) { |
| for (size_t oc = 0; oc < group_output_channels(); oc++) { |
| next_output_ref[(((i * next_output_height() + oy) * next_output_width() + ox) * groups() + g) * group_output_channels() + oc] = |
| bias[g * group_output_channels() + oc]; |
| } |
| } |
| } |
| } |
| } |
| } else { |
| std::fill(next_output_ref.begin(), next_output_ref.end(), 0.0f); |
| } |
| 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 ky = 0; ky < kernel_height(); ky++) { |
| const size_t y = oy + padding_top() - ky * dilation_height(); |
| const size_t iy = y / stride_height(); |
| if (iy * stride_height() == y && iy < next_input_height()) { |
| for (size_t kx = 0; kx < kernel_width(); kx++) { |
| const size_t x = ox + padding_left() - kx * dilation_width(); |
| const size_t ix = x / stride_width(); |
| if (ix * stride_width() == x && ix < next_input_width()) { |
| for (size_t g = 0; g < groups(); g++) { |
| for (size_t oc = 0; oc < group_output_channels(); oc++) { |
| for (size_t ic = 0; ic < group_input_channels(); ic++) { |
| next_output_ref[(((i * next_output_height() + oy) * next_output_width() + ox) * groups() + g) * group_output_channels() + oc] += |
| input[((i * next_input_height() + iy) * next_input_width() + ix) * input_pixel_stride() + g * group_input_channels() + ic] * |
| kernel[(((g * group_output_channels() + oc) * kernel_height() + ky) * kernel_width() + kx) * group_input_channels() + ic]; |
| } |
| } |
| } |
| } |
| } |
| } |
| } |
| } |
| } |
| } |
| for (float& value : next_output_ref) { |
| value = std::max(std::min(value, output_max), output_min); |
| } |
| |
| // Setup and run Deconvolution operator the second time, and destroy the operator. |
| ASSERT_EQ(xnn_status_success, |
| xnn_setup_deconvolution2d_nhwc_f32( |
| deconvolution_op, |
| next_batch_size(), next_input_height(), next_input_width(), |
| adjustment_height(), adjustment_width(), |
| input.data(), output.data(), |
| nullptr /* thread pool */)); |
| |
| ASSERT_EQ(xnn_status_success, |
| xnn_run_operator(deconvolution_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 g = 0; g < groups(); g++) { |
| for (size_t c = 0; c < group_output_channels(); c++) { |
| ASSERT_GE(output[((i * next_output_height() + y) * next_output_width() + x) * output_pixel_stride() + g * group_output_channels() + c], output_min) |
| << "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c; |
| ASSERT_LE(output[((i * next_output_height() + y) * next_output_width() + x) * output_pixel_stride() + g * group_output_channels() + c], output_max) |
| << "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c; |
| ASSERT_NEAR( |
| next_output_ref[(((i * next_output_height() + y) * next_output_width() + x) * groups() + g) * group_output_channels() + c], |
| output[((i * next_output_height() + y) * next_output_width() + x) * output_pixel_stride() + g * group_output_channels() + c], |
| 1.0e-4 * std::abs(next_output_ref[(((i * next_output_height() + y) * next_output_width() + x) * groups() + g) * group_output_channels() + c])) |
| << "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c; |
| } |
| } |
| } |
| } |
| } |
| } |
| } |
| |
| private: |
| uint32_t padding_top_{0}; |
| uint32_t padding_right_{0}; |
| uint32_t padding_bottom_{0}; |
| uint32_t padding_left_{0}; |
| size_t input_height_{1}; |
| size_t input_width_{1}; |
| uint32_t groups_{1}; |
| size_t group_input_channels_{1}; |
| size_t input_pixel_stride_{0}; |
| size_t group_output_channels_{1}; |
| size_t output_pixel_stride_{0}; |
| size_t batch_size_{1}; |
| uint32_t kernel_height_{1}; |
| uint32_t kernel_width_{1}; |
| uint32_t adjustment_height_{0}; |
| uint32_t adjustment_width_{0}; |
| uint32_t dilation_height_{1}; |
| uint32_t dilation_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}; |
| uint8_t qmin_{0}; |
| uint8_t qmax_{255}; |
| bool has_bias_{true}; |
| size_t iterations_{1}; |
| }; |
