| // 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 <cmath> |
| #include <cassert> |
| #include <cstddef> |
| #include <cstdlib> |
| #include <functional> |
| #include <random> |
| #include <vector> |
| |
| #include <xnnpack.h> |
| |
| |
| class ResizeBilinearOperatorTester { |
| public: |
| inline ResizeBilinearOperatorTester& input_size(size_t input_height, size_t input_width) { |
| assert(input_height >= 1); |
| assert(input_width >= 1); |
| this->input_height_ = input_height; |
| this->input_width_ = input_width; |
| return *this; |
| } |
| |
| inline ResizeBilinearOperatorTester& input_height(size_t input_height) { |
| assert(input_height >= 1); |
| this->input_height_ = input_height; |
| return *this; |
| } |
| |
| inline size_t input_height() const { |
| return this->input_height_; |
| } |
| |
| inline ResizeBilinearOperatorTester& input_width(size_t input_width) { |
| assert(input_width >= 1); |
| this->input_width_ = input_width; |
| return *this; |
| } |
| |
| inline size_t input_width() const { |
| return this->input_width_; |
| } |
| |
| inline ResizeBilinearOperatorTester& output_size(size_t output_height, size_t output_width) { |
| assert(output_height >= 1); |
| assert(output_width >= 1); |
| this->output_height_ = output_height; |
| this->output_width_ = output_width; |
| return *this; |
| } |
| |
| inline ResizeBilinearOperatorTester& output_height(size_t output_height) { |
| assert(output_height >= 1); |
| this->output_height_ = output_height; |
| return *this; |
| } |
| |
| inline size_t output_height() const { |
| return this->output_height_; |
| } |
| |
| inline ResizeBilinearOperatorTester& output_width(size_t output_width) { |
| assert(output_width >= 1); |
| this->output_width_ = output_width; |
| return *this; |
| } |
| |
| inline size_t output_width() const { |
| return this->output_width_; |
| } |
| |
| inline float height_scale() const { |
| if (align_corners() && output_height() > 1) { |
| return float(input_height() - 1) / float(output_height() - 1); |
| } else { |
| return float(input_height()) / float(output_height()); |
| } |
| } |
| |
| inline float width_scale() const { |
| if (align_corners() && output_width() > 1) { |
| return float(input_width() - 1) / float(output_width() - 1); |
| } else { |
| return float(input_width()) / float(output_width()); |
| } |
| } |
| |
| inline ResizeBilinearOperatorTester& channels(size_t channels) { |
| assert(channels != 0); |
| this->channels_ = channels; |
| return *this; |
| } |
| |
| inline size_t channels() const { |
| return this->channels_; |
| } |
| |
| inline ResizeBilinearOperatorTester& batch_size(size_t batch_size) { |
| assert(batch_size != 0); |
| this->batch_size_ = batch_size; |
| return *this; |
| } |
| |
| inline size_t batch_size() const { |
| return this->batch_size_; |
| } |
| |
| inline ResizeBilinearOperatorTester& input_pixel_stride(size_t input_pixel_stride) { |
| assert(input_pixel_stride != 0); |
| this->input_pixel_stride_ = input_pixel_stride; |
| return *this; |
| } |
| |
| inline size_t input_pixel_stride() const { |
| if (this->input_pixel_stride_ == 0) { |
| return channels(); |
| } else { |
| assert(this->input_pixel_stride_ >= channels()); |
| return this->input_pixel_stride_; |
| } |
| } |
| |
| inline ResizeBilinearOperatorTester& output_pixel_stride(size_t output_pixel_stride) { |
| assert(output_pixel_stride != 0); |
| this->output_pixel_stride_ = output_pixel_stride; |
| return *this; |
| } |
| |
| inline size_t output_pixel_stride() const { |
| if (this->output_pixel_stride_ == 0) { |
| return channels(); |
| } else { |
| assert(this->output_pixel_stride_ >= channels()); |
| return this->output_pixel_stride_; |
| } |
| } |
| |
| inline ResizeBilinearOperatorTester& 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 ResizeBilinearOperatorTester& 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 ResizeBilinearOperatorTester& 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 ResizeBilinearOperatorTester& 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 ResizeBilinearOperatorTester& align_corners(bool align_corners) { |
| this->align_corners_ = align_corners; |
| return *this; |
| } |
| |
| inline bool align_corners() const { |
| return this->align_corners_; |
| } |
| |
| inline ResizeBilinearOperatorTester& tf_legacy_mode(bool tf_legacy_mode) { |
| this->tf_legacy_mode_ = tf_legacy_mode; |
| return *this; |
| } |
| |
| inline bool tf_legacy_mode() const { |
| return this->tf_legacy_mode_; |
| } |
| |
| inline ResizeBilinearOperatorTester& iterations(size_t iterations) { |
| this->iterations_ = iterations; |
| return *this; |
| } |
| |
| inline size_t iterations() const { |
| return this->iterations_; |
| } |
| |
| void TestNHWCxF32() const { |
| if (align_corners()) { |
| ASSERT_FALSE(tf_legacy_mode()); |
| } |
| |
| std::random_device random_device; |
| auto rng = std::mt19937(random_device()); |
| auto f32rng = std::bind(std::uniform_real_distribution<float>(), rng); |
| |
| std::vector<float> input((batch_size() * input_height() * input_width() - 1) * input_pixel_stride() + channels() + XNN_EXTRA_BYTES / sizeof(float)); |
| std::vector<float> output((batch_size() * output_height() * output_width() - 1) * output_pixel_stride() + channels()); |
| std::vector<float> output_ref(batch_size() * output_height() * output_width() * channels()); |
| for (size_t iteration = 0; iteration < iterations(); iteration++) { |
| std::generate(input.begin(), input.end(), std::ref(f32rng)); |
| std::fill(output.begin(), output.end(), std::nanf("")); |
| |
| // Compute reference results. |
| const float offset = (tf_legacy_mode() || align_corners()) ? 0.0f : 0.5f; |
| for (size_t batch_index = 0; batch_index < batch_size(); batch_index++) { |
| for (size_t output_y = 0; output_y < output_height(); output_y++) { |
| const float input_y = (float(output_y) + offset) * height_scale() - offset; |
| const int64_t input_y_top = std::max<int64_t>(int64_t(std::floor(input_y)), 0); |
| const int64_t input_y_bottom = std::min<int64_t>(int64_t(std::ceil(input_y)), input_height() - 1); |
| const float y_alpha = input_y - std::floor(input_y); |
| for (size_t output_x = 0; output_x < output_width(); output_x++) { |
| const float input_x = (float(output_x) + offset) * width_scale() - offset; |
| const int64_t input_x_left = std::max<int64_t>(int64_t(std::floor(input_x)), 0); |
| const int64_t input_x_right = std::min<int64_t>(int64_t(std::ceil(input_x)), input_width() - 1); |
| const float x_alpha = input_x - std::floor(input_x); |
| for (size_t c = 0; c < channels(); c++) { |
| output_ref[((batch_index * output_height() + output_y) * output_width() + output_x) * channels() + c] = |
| input[((batch_index * input_height() + input_y_top) * input_width() + input_x_left) * input_pixel_stride() + c] * (1.0f - y_alpha) * (1.0f - x_alpha) + |
| input[((batch_index * input_height() + input_y_top) * input_width() + input_x_right) * input_pixel_stride() + c] * (1.0f - y_alpha) * x_alpha + |
| input[((batch_index * input_height() + input_y_bottom) * input_width() + input_x_left) * input_pixel_stride() + c] * y_alpha * (1.0f - x_alpha) + |
| input[((batch_index * input_height() + input_y_bottom) * input_width() + input_x_right) * input_pixel_stride() + c] * y_alpha * x_alpha; |
| } |
| } |
| } |
| } |
| |
| // Create, setup, run, and destroy Resize Bilinear operator. |
| ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */)); |
| xnn_operator_t resize_bilinear_op = nullptr; |
| |
| ASSERT_EQ(xnn_status_success, |
| xnn_create_resize_bilinear2d_nhwc_f32( |
| channels(), input_pixel_stride(), output_pixel_stride(), |
| (align_corners() ? XNN_FLAG_ALIGN_CORNERS : 0) | (tf_legacy_mode() ? XNN_FLAG_TENSORFLOW_LEGACY_MODE : 0), |
| &resize_bilinear_op)); |
| ASSERT_NE(nullptr, resize_bilinear_op); |
| |
| // Smart pointer to automatically delete resize_bilinear_op. |
| std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_resize_bilinear_op(resize_bilinear_op, xnn_delete_operator); |
| |
| ASSERT_EQ(xnn_status_success, |
| xnn_setup_resize_bilinear2d_nhwc_f32( |
| resize_bilinear_op, |
| batch_size(), input_height(), input_width(), |
| output_height(), output_width(), |
| input.data(), output.data(), |
| nullptr /* thread pool */)); |
| |
| ASSERT_EQ(xnn_status_success, |
| xnn_run_operator(resize_bilinear_op, nullptr /* thread pool */)); |
| |
| // Verify results. |
| for (size_t i = 0; i < batch_size(); i++) { |
| for (size_t y = 0; y < output_height(); y++) { |
| for (size_t x = 0; x < output_width(); x++) { |
| for (size_t c = 0; c < channels(); c++) { |
| ASSERT_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-5f) << |
| "in batch index " << i << ", pixel (" << y << ", " << x << "), channel " << c; |
| } |
| } |
| } |
| } |
| } |
| } |
| |
| void TestNCHWxF32() const { |
| if (align_corners()) { |
| ASSERT_FALSE(tf_legacy_mode()); |
| } |
| |
| std::random_device random_device; |
| auto rng = std::mt19937(random_device()); |
| auto f32rng = std::bind(std::uniform_real_distribution<float>(), rng); |
| |
| std::vector<float> input((batch_size() * input_height() * input_width() - 1) * input_pixel_stride() + channels() + XNN_EXTRA_BYTES / sizeof(float)); |
| std::vector<float> output((batch_size() * output_height() * output_width() - 1) * output_pixel_stride() + channels()); |
| std::vector<float> output_ref(batch_size() * output_height() * output_width() * channels()); |
| for (size_t iteration = 0; iteration < iterations(); iteration++) { |
| std::generate(input.begin(), input.end(), std::ref(f32rng)); |
| std::fill(output.begin(), output.end(), std::nanf("")); |
| |
| // Compute reference results. |
| const float offset = (tf_legacy_mode() || align_corners()) ? 0.0f : 0.5f; |
| const int64_t input_num_pixels = input_height() * input_width(); |
| const int64_t input_num_elements = input_num_pixels * input_pixel_stride(); |
| const int64_t output_num_pixels = output_height() * output_width(); |
| const int64_t output_num_elements = output_num_pixels * channels(); |
| for (size_t batch_index = 0; batch_index < batch_size(); batch_index++) { |
| for (size_t output_y = 0; output_y < output_height(); output_y++) { |
| const float input_y = (float(output_y) + offset) * height_scale() - offset; |
| const int64_t input_y_top = std::max<int64_t>(int64_t(std::floor(input_y)), 0); |
| const int64_t input_y_bottom = std::min<int64_t>(int64_t(std::ceil(input_y)), input_height() - 1); |
| const float y_alpha = input_y - std::floor(input_y); |
| for (size_t output_x = 0; output_x < output_width(); output_x++) { |
| const float input_x = (float(output_x) + offset) * width_scale() - offset; |
| const int64_t input_x_left = std::max<int64_t>(int64_t(std::floor(input_x)), 0); |
| const int64_t input_x_right = std::min<int64_t>(int64_t(std::ceil(input_x)), input_width() - 1); |
| const float x_alpha = input_x - std::floor(input_x); |
| for (size_t c = 0; c < channels(); c++) { |
| output_ref[batch_index * output_num_elements + c * output_num_pixels + output_y * output_width() + output_x] = |
| input[batch_index * input_num_elements + c * input_num_pixels + input_y_top * input_width() + input_x_left] * (1.0f - y_alpha) * (1.0f - x_alpha) + |
| input[batch_index * input_num_elements + c * input_num_pixels + input_y_top * input_width() + input_x_right] * (1.0f - y_alpha) * x_alpha + |
| input[batch_index * input_num_elements + c * input_num_pixels + input_y_bottom * input_width() + input_x_left] * y_alpha * (1.0f - x_alpha) + |
| input[batch_index * input_num_elements + c * input_num_pixels + input_y_bottom * input_width() + input_x_right] * y_alpha * x_alpha; |
| } |
| } |
| } |
| } |
| |
| // Create, setup, run, and destroy Resize Bilinear operator. |
| ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */)); |
| xnn_operator_t resize_bilinear_op = nullptr; |
| |
| ASSERT_EQ(xnn_status_success, |
| xnn_create_resize_bilinear2d_nchw_f32( |
| channels(), input_pixel_stride(), output_pixel_stride(), |
| (align_corners() ? XNN_FLAG_ALIGN_CORNERS : 0) | (tf_legacy_mode() ? XNN_FLAG_TENSORFLOW_LEGACY_MODE : 0), |
| &resize_bilinear_op)); |
| ASSERT_NE(nullptr, resize_bilinear_op); |
| |
| // Smart pointer to automatically delete resize_bilinear_op. |
| std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_resize_bilinear_op(resize_bilinear_op, xnn_delete_operator); |
| |
| ASSERT_EQ(xnn_status_success, |
| xnn_setup_resize_bilinear2d_nchw_f32( |
| resize_bilinear_op, |
| batch_size(), input_height(), input_width(), |
| output_height(), output_width(), |
| input.data(), output.data(), |
| nullptr /* thread pool */)); |
| |
| ASSERT_EQ(xnn_status_success, |
| xnn_run_operator(resize_bilinear_op, nullptr /* thread pool */)); |
| |
| // Verify results. |
| for (size_t i = 0; i < batch_size(); i++) { |
| for (size_t y = 0; y < output_height(); y++) { |
| for (size_t x = 0; x < output_width(); x++) { |
| for (size_t c = 0; c < channels(); c++) { |
| ASSERT_NEAR(output[i * output_num_elements + c * output_num_pixels + y * output_width() + x], |
| output_ref[i * output_num_elements + c * output_num_pixels + y * output_width() + x], |
| 1.0e-6f) << |
| "in batch index " << i << ", pixel (" << y << ", " << x << "), channel " << c; |
| } |
| } |
| } |
| } |
| } |
| } |
| |
| // void TestSetupF32() const { |
| // std::random_device random_device; |
| // auto rng = std::mt19937(random_device()); |
| // auto f32rng = std::bind(std::uniform_real_distribution<float>(), rng); |
| |
| // std::vector<float> input(XNN_EXTRA_BYTES / sizeof(float) + std::max( |
| // (batch_size() * input_height() * input_width() - 1) * input_pixel_stride() + channels(), |
| // (next_batch_size() * next_input_height() * next_input_width() - 1) * input_pixel_stride() + channels())); |
| // std::vector<float> output(std::max( |
| // (batch_size() * output_height() * output_width() - 1) * output_pixel_stride() + channels(), |
| // (next_batch_size() * next_output_height() * next_output_width() - 1) * output_pixel_stride() + channels())); |
| // std::vector<float> output_ref(batch_size() * output_height() * output_width() * channels()); |
| // std::vector<float> next_output_ref(next_batch_size() * next_output_height() * next_output_width() * channels()); |
| // for (size_t iteration = 0; iteration < iterations(); iteration++) { |
| // std::generate(input.begin(), input.end(), std::ref(f32rng)); |
| // std::fill(output.begin(), output.end(), std::nanf("")); |
| |
| // // Compute reference results, without clamping. |
| // for (size_t batch_index = 0; batch_index < batch_size(); batch_index++) { |
| // for (size_t output_y = 0; output_y < output_height(); output_y++) { |
| // for (size_t output_x = 0; output_x < output_width(); output_x++) { |
| // 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 = output_y * stride_height() + py - padding_top(); |
| // for (size_t px = 0; px < pooling_width(); px++) { |
| // const size_t input_x = output_x * stride_width() + px - padding_left(); |
| // if (input_x < input_width() && iy < input_height()) { |
| // acc += input[((batch_index * input_height() + iy) * input_width() + input_x) * input_pixel_stride() + c]; |
| // n += 1; |
| // } |
| // } |
| // } |
| // output_ref[((batch_index * output_height() + output_y) * output_width() + output_x) * channels() + c] = acc / float(n); |
| // } |
| // } |
| // } |
| // } |
| |
| // // Compute clamping parameters. |
| // const float accumulated_min = *std::min_element(output_ref.cbegin(), output_ref.cend()); |
| // const float accumulated_max = *std::max_element(output_ref.cbegin(), output_ref.cend()); |
| // const float accumulated_range = accumulated_max - accumulated_min; |
| // const float output_min = accumulated_range == 0.0f ? |
| // -std::numeric_limits<float>::infinity() : |
| // accumulated_min + accumulated_range / 255.0f * float(qmin()); |
| // const float output_max = accumulated_range == 0.0f ? |
| // +std::numeric_limits<float>::infinity() : |
| // accumulated_max - accumulated_range / 255.0f * float(255 - qmax()); |
| |
| // // Clamp reference results. |
| // for (float& value : output_ref) { |
| // value = std::max(std::min(value, output_max), output_min); |
| // } |
| |
| // // Create, setup, and run Average Pooling operator once. |
| // ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */)); |
| // xnn_operator_t resize_bilinear_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, &resize_bilinear_op)); |
| // ASSERT_NE(nullptr, resize_bilinear_op); |
| |
| // ASSERT_EQ(xnn_status_success, |
| // xnn_setup_average_pooling2d_nhwc_f32( |
| // resize_bilinear_op, |
| // batch_size(), input_height(), input_width(), |
| // input.data(), output.data(), |
| // nullptr /* thread pool */)); |
| |
| // ASSERT_EQ(xnn_status_success, |
| // xnn_run_operator(resize_bilinear_op, nullptr /* thread pool */)); |
| |
| // // Verify results of the first run. |
| // for (size_t batch_index = 0; batch_index < batch_size(); batch_index++) { |
| // for (size_t y = 0; y < output_height(); y++) { |
| // for (size_t x = 0; x < output_width(); x++) { |
| // for (size_t c = 0; c < channels(); c++) { |
| // ASSERT_LE(output[((batch_index * output_height() + y) * output_width() + x) * output_pixel_stride() + c], output_max); |
| // ASSERT_GE(output[((batch_index * output_height() + y) * output_width() + x) * output_pixel_stride() + c], output_min); |
| // ASSERT_NEAR(output[((batch_index * output_height() + y) * output_width() + x) * output_pixel_stride() + c], |
| // output_ref[((batch_index * output_height() + y) * output_width() + x) * channels() + c], |
| // std::abs(output_ref[((batch_index * output_height() + y) * output_width() + x) * channels() + c]) * 1.0e-6f) << |
| // "in batch index " << batch_index << ", pixel (" << y << ", " << x << "), channel " << c; |
| // } |
| // } |
| // } |
| // } |
| |
| // // Re-generate data for the second run. |
| // std::generate(input.begin(), input.end(), std::ref(f32rng)); |
| // std::fill(output.begin(), output.end(), std::nanf("")); |
| |
| // // Compute reference results for the second run. |
| // for (size_t batch_index = 0; batch_index < next_batch_size(); batch_index++) { |
| // for (size_t output_y = 0; output_y < next_output_height(); output_y++) { |
| // for (size_t output_x = 0; output_x < next_output_width(); output_x++) { |
| // 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 = output_y * stride_height() + py - padding_top(); |
| // for (size_t px = 0; px < pooling_width(); px++) { |
| // const size_t input_x = output_x * stride_width() + px - padding_left(); |
| // if (input_x < next_input_width() && iy < next_input_height()) { |
| // acc += input[((batch_index * next_input_height() + iy) * next_input_width() + input_x) * input_pixel_stride() + c]; |
| // n += 1; |
| // } |
| // } |
| // } |
| // next_output_ref[((batch_index * next_output_height() + output_y) * next_output_width() + output_x) * channels() + c] = |
| // std::max(std::min(acc / float(n), output_max), output_min); |
| // } |
| // } |
| // } |
| // } |
| |
| // // Setup and run Average Pooling operator the second time, and destroutput_y the operator. |
| // ASSERT_EQ(xnn_status_success, |
| // xnn_setup_average_pooling2d_nhwc_f32( |
| // resize_bilinear_op, |
| // next_batch_size(), next_input_height(), next_input_width(), |
| // input.data(), output.data(), |
| // nullptr /* thread pool */)); |
| |
| // ASSERT_EQ(xnn_status_success, |
| // xnn_run_operator(resize_bilinear_op, nullptr /* thread pool */)); |
| |
| // ASSERT_EQ(xnn_status_success, |
| // xnn_delete_operator(resize_bilinear_op)); |
| // resize_bilinear_op = nullptr; |
| |
| // // Verify results of the second run. |
| // for (size_t batch_index = 0; batch_index < next_batch_size(); batch_index++) { |
| // for (size_t y = 0; y < next_output_height(); y++) { |
| // for (size_t x = 0; x < next_output_width(); x++) { |
| // for (size_t c = 0; c < channels(); c++) { |
| // ASSERT_LE(output[((batch_index * next_output_height() + y) * next_output_width() + x) * output_pixel_stride() + c], output_max); |
| // ASSERT_GE(output[((batch_index * next_output_height() + y) * next_output_width() + x) * output_pixel_stride() + c], output_min); |
| // ASSERT_NEAR(output[((batch_index * next_output_height() + y) * next_output_width() + x) * output_pixel_stride() + c], |
| // next_output_ref[((batch_index * next_output_height() + y) * next_output_width() + x) * channels() + c], |
| // std::abs(next_output_ref[((batch_index * next_output_height() + y) * next_output_width() + x) * channels() + c]) * 1.0e-6f) << |
| // "in batch index " << batch_index << ", pixel (" << y << ", " << x << "), channel " << c; |
| // } |
| // } |
| // } |
| // } |
| // } |
| // } |
| |
| private: |
| size_t input_height_{1}; |
| size_t input_width_{1}; |
| size_t output_height_{1}; |
| size_t output_width_{1}; |
| size_t channels_{1}; |
| size_t batch_size_{1}; |
| size_t input_pixel_stride_{0}; |
| size_t output_pixel_stride_{0}; |
| size_t next_input_height_{0}; |
| size_t next_input_width_{0}; |
| size_t next_batch_size_{0}; |
| bool align_corners_{false}; |
| bool tf_legacy_mode_{false}; |
| size_t iterations_{1}; |
| }; |