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417 lines
16 KiB
417 lines
16 KiB
// Copyright (c) Facebook, Inc. and its affiliates.
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// All rights reserved.
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//
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// Copyright 2019 Google LLC
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//
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// This source code is licensed under the BSD-style license found in the
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// LICENSE file in the root directory of this source tree.
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#include <algorithm>
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#include <cfloat>
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#include <cmath>
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#include <functional>
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#include <limits>
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#include <random>
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#include <vector>
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#include <xnnpack.h>
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#include <benchmark/benchmark.h>
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#ifdef BENCHMARK_TENSORFLOW_LITE
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#include "flatbuffers/include/flatbuffers/flatbuffers.h"
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#include "tensorflow/lite/interpreter.h"
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#include "tensorflow/lite/kernels/register.h"
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#include "tensorflow/lite/model.h"
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#include "tensorflow/lite/schema/schema_generated.h"
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#include "tensorflow/lite/version.h"
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#endif // BENCHMARK_TENSORFLOW_LITE
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#include "bench/utils.h"
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#ifndef XNN_NO_QU8_OPERATORS
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static void xnnpack_average_pooling_qu8(benchmark::State& state, const char* net) {
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const size_t batch_size = state.range(0);
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const size_t input_height = state.range(1);
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const size_t input_width = state.range(2);
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const size_t pooling_size = state.range(3);
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const size_t padding_size = state.range(4);
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const size_t stride = state.range(5);
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const size_t channels = state.range(6);
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std::random_device random_device;
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auto rng = std::mt19937(random_device());
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auto u8rng = std::bind(std::uniform_int_distribution<uint32_t>(0, std::numeric_limits<uint8_t>::max()), std::ref(rng));
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const size_t output_height = (2 * padding_size + input_height - pooling_size) / stride + 1;
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const size_t output_width = (2 * padding_size + input_width - pooling_size) / stride + 1;
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std::vector<uint8_t> input(batch_size * input_height * input_width * channels + XNN_EXTRA_BYTES / sizeof(uint8_t));
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std::generate(input.begin(), input.end(), std::ref(u8rng));
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std::vector<uint8_t> output(batch_size * output_height * output_width * channels);
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std::fill(output.begin(), output.end(), 0xA5);
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xnn_status status = xnn_initialize(nullptr /* allocator */);
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if (status != xnn_status_success) {
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state.SkipWithError("failed to initialize XNNPACK");
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return;
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}
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xnn_operator_t pooling_op = nullptr;
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status = xnn_create_average_pooling2d_nhwc_qu8(
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padding_size, padding_size, padding_size, padding_size,
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pooling_size, pooling_size,
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stride, stride,
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channels, channels /* input pixel stride */, channels /* output pixel stride */,
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127 /* input zero point */, 0.75f /* input scale */,
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127 /* output zero point */, 1.25f /* output scale */,
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0, 255,
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0 /* flags */, &pooling_op);
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if (status != xnn_status_success) {
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state.SkipWithError("failed to create Average Pooling operator");
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return;
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}
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status = xnn_setup_average_pooling2d_nhwc_qu8(
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pooling_op,
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batch_size, input_height, input_width,
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input.data(), output.data(),
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nullptr /* thread pool */);
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if (status != xnn_status_success) {
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state.SkipWithError("failed to setup Average Pooling operator");
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return;
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}
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for (auto _ : state) {
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status = xnn_run_operator(pooling_op, nullptr /* thread pool */);
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if (status != xnn_status_success) {
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state.SkipWithError("failed to run Average Pooling operator");
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return;
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}
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}
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status = xnn_delete_operator(pooling_op);
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if (status != xnn_status_success) {
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state.SkipWithError("failed to delete Average Pooling operator");
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return;
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}
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pooling_op = nullptr;
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const uint64_t cpu_frequency = benchmark::utils::GetCurrentCpuFrequency();
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if (cpu_frequency != 0) {
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state.counters["cpufreq"] = cpu_frequency;
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}
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state.counters["bytes"] = benchmark::Counter(
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uint64_t(state.iterations()) *
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batch_size * (input_height * input_width + output_height * output_width) * channels * sizeof(uint8_t),
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benchmark::Counter::kIsRate);
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}
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#endif // XNN_NO_QU8_OPERATORS
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static void xnnpack_average_pooling_f32(benchmark::State& state, const char* net) {
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const size_t batch_size = state.range(0);
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const size_t input_height = state.range(1);
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const size_t input_width = state.range(2);
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const size_t pooling_size = state.range(3);
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const size_t padding_size = state.range(4);
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const size_t stride = state.range(5);
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const size_t channels = state.range(6);
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std::random_device random_device;
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auto rng = std::mt19937(random_device());
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auto f32rng = std::bind(std::uniform_real_distribution<float>(), std::ref(rng));
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const size_t output_height = (2 * padding_size + input_height - pooling_size) / stride + 1;
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const size_t output_width = (2 * padding_size + input_width - pooling_size) / stride + 1;
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std::vector<float> input(batch_size * input_height * input_width * channels + XNN_EXTRA_BYTES / sizeof(float));
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std::generate(input.begin(), input.end(), std::ref(f32rng));
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std::vector<float> output(batch_size * output_height * output_width * channels);
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std::fill(output.begin(), output.end(), std::nanf(""));
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xnn_status status = xnn_initialize(nullptr /* allocator */);
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if (status != xnn_status_success) {
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state.SkipWithError("failed to initialize XNNPACK");
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return;
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}
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xnn_operator_t pooling_op = nullptr;
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status = xnn_create_average_pooling2d_nhwc_f32(
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padding_size, padding_size, padding_size, padding_size,
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pooling_size, pooling_size,
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stride, stride,
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channels, channels /* input pixel stride */, channels /* output pixel stride */,
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-std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(),
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0 /* flags */, &pooling_op);
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if (status != xnn_status_success) {
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state.SkipWithError("failed to create Average Pooling operator");
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return;
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}
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status = xnn_setup_average_pooling2d_nhwc_f32(
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pooling_op,
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batch_size, input_height, input_width,
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input.data(), output.data(),
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nullptr /* thread pool */);
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if (status != xnn_status_success) {
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state.SkipWithError("failed to setup Average Pooling operator");
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return;
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}
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for (auto _ : state) {
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status = xnn_run_operator(pooling_op, nullptr /* thread pool */);
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if (status != xnn_status_success) {
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state.SkipWithError("failed to run Average Pooling operator");
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return;
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}
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}
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status = xnn_delete_operator(pooling_op);
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if (status != xnn_status_success) {
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state.SkipWithError("failed to delete Average Pooling operator");
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return;
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}
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pooling_op = nullptr;
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const uint64_t cpu_frequency = benchmark::utils::GetCurrentCpuFrequency();
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if (cpu_frequency != 0) {
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state.counters["cpufreq"] = cpu_frequency;
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}
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state.counters["bytes"] = benchmark::Counter(
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uint64_t(state.iterations()) *
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batch_size * (input_height * input_width + output_height * output_width) * channels * sizeof(float),
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benchmark::Counter::kIsRate);
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}
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#ifdef BENCHMARK_TENSORFLOW_LITE
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void tflite_average_pooling_f32(benchmark::State& state, const char* net) {
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const size_t batch_size = state.range(0);
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const size_t input_height = state.range(1);
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const size_t input_width = state.range(2);
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const size_t pooling_size = state.range(3);
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const size_t padding_size = state.range(4);
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const size_t stride = state.range(5);
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const size_t channels = state.range(6);
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std::random_device random_device;
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auto rng = std::mt19937(random_device());
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auto f32rng = std::bind(std::uniform_real_distribution<float>(), std::ref(rng));
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tflite::Padding padding = tflite::Padding_VALID;
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if (2 * padding_size == (pooling_size - 1)) {
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padding = tflite::Padding_SAME;
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} else if (padding_size == 0) {
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padding = tflite::Padding_VALID;
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} else {
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state.SkipWithError("unsupported padding");
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return;
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}
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const size_t output_height = (2 * padding_size + input_height - pooling_size) / stride + 1;
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const size_t output_width = (2 * padding_size + input_width - pooling_size) / stride + 1;
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std::vector<float> input(batch_size * input_height * input_width * channels + XNN_EXTRA_BYTES / sizeof(float));
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std::generate(input.begin(), input.end(), std::ref(f32rng));
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std::vector<float> output(batch_size * output_height * output_width * channels);
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std::fill(output.begin(), output.end(), std::nanf(""));
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flatbuffers::FlatBufferBuilder builder;
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flatbuffers::Offset<tflite::OperatorCode> operator_code =
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CreateOperatorCode(builder, tflite::BuiltinOperator_AVERAGE_POOL_2D);
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flatbuffers::Offset<tflite::Pool2DOptions> pool2d_options = CreatePool2DOptions(
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builder, padding,
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stride /* stride_w */, stride /* stride_h */,
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pooling_size /* filter_width */, pooling_size /* filter_height */,
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tflite::ActivationFunctionType_NONE);
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flatbuffers::Offset<tflite::Buffer> buffers[1] = {
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tflite::CreateBuffer(builder, builder.CreateVector({})),
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};
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const int32_t input_shape[4] = {
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static_cast<int32_t>(batch_size),
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static_cast<int32_t>(input_height),
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static_cast<int32_t>(input_width),
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static_cast<int32_t>(channels)
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};
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const int32_t output_shape[4] = {
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static_cast<int32_t>(batch_size),
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static_cast<int32_t>(output_height),
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static_cast<int32_t>(output_width),
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static_cast<int32_t>(channels)
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};
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flatbuffers::Offset<tflite::Tensor> tensors[2] = {
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tflite::CreateTensor(builder,
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builder.CreateVector<int32_t>(input_shape, 4),
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tflite::TensorType_FLOAT32),
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tflite::CreateTensor(builder,
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builder.CreateVector<int32_t>(output_shape, 4),
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tflite::TensorType_FLOAT32),
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};
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const int32_t op_inputs[1] = { 0 };
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const int32_t op_outputs[1] = { 1 };
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flatbuffers::Offset<tflite::Operator> op = CreateOperator(
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builder,
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0 /* opcode_index */,
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builder.CreateVector<int32_t>(op_inputs, 1),
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builder.CreateVector<int32_t>(op_outputs, 1),
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tflite::BuiltinOptions_Pool2DOptions,
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pool2d_options.Union());
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const int32_t graph_inputs[1] = { 0 };
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const int32_t graph_outputs[1] = { 1 };
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flatbuffers::Offset<tflite::SubGraph> subgraph = CreateSubGraph(
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builder,
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builder.CreateVector(tensors, 2),
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builder.CreateVector<int32_t>(graph_inputs, 1),
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builder.CreateVector<int32_t>(graph_outputs, 1),
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builder.CreateVector(&op, 1));
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flatbuffers::Offset<tflite::Model> model_buffer = tflite::CreateModel(builder,
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TFLITE_SCHEMA_VERSION,
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builder.CreateVector(&operator_code, 1),
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builder.CreateVector(&subgraph, 1),
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builder.CreateString("AVERAGE_POOL_2D model"),
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builder.CreateVector(buffers, 1));
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builder.Finish(model_buffer);
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const tflite::Model* model = tflite::GetModel(builder.GetBufferPointer());
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tflite::ops::builtin::BuiltinOpResolver resolver;
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tflite::InterpreterBuilder interpreterBuilder(model, resolver);
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std::unique_ptr<tflite::Interpreter> interpreter;
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if (interpreterBuilder(&interpreter) != kTfLiteOk) {
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state.SkipWithError("failed to create TFLite interpreter");
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return;
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}
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if (interpreter == nullptr) {
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state.SkipWithError("TFLite interpreter is null");
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return;
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}
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interpreter->SetNumThreads(1);
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if (interpreter->AllocateTensors() != kTfLiteOk) {
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state.SkipWithError("failed to allocate tensors");
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return;
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}
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std::generate(
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interpreter->typed_tensor<float>(0),
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interpreter->typed_tensor<float>(0) + batch_size * input_height * input_width * channels,
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std::ref(f32rng));
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for (auto _ : state) {
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if (interpreter->Invoke() != kTfLiteOk) {
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state.SkipWithError("failed to invoke TFLite interpreter");
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return;
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}
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}
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const uint64_t cpu_frequency = benchmark::utils::GetCurrentCpuFrequency();
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if (cpu_frequency != 0) {
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state.counters["cpufreq"] = cpu_frequency;
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}
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state.counters["bytes"] = benchmark::Counter(
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uint64_t(state.iterations()) *
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batch_size * (input_height * input_width + output_height * output_width) * channels * sizeof(float),
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benchmark::Counter::kIsRate);
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}
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#endif // BENCHMARK_TENSORFLOW_LITE
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// Final global average pooling in ImageNet classification models.
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static void ImageNet(benchmark::internal::Benchmark* b) {
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b->ArgNames({"N", "H", "W", "K", "P", "S", "C"});
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/* N H W K P S C */
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b->Args({1, 13, 13, 13, 0, 1, 1000});
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b->Args({1, 7, 7, 7, 0, 1, 1000});
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}
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// ShuffleNet v1 with 1 group.
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static void ShuffleNetV1G1(benchmark::internal::Benchmark* b) {
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b->ArgNames({"N", "H", "W", "K", "P", "S", "C"});
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/* N H W K P S C */
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b->Args({1, 56, 56, 3, 1, 2, 24});
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b->Args({1, 28, 28, 3, 1, 2, 144});
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b->Args({1, 14, 14, 3, 1, 2, 288});
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b->Args({1, 7, 7, 3, 1, 2, 576});
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}
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// ShuffleNet v1 with 2 groups.
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static void ShuffleNetV1G2(benchmark::internal::Benchmark* b) {
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b->ArgNames({"N", "H", "W", "K", "P", "S", "C"});
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/* N H W K P S C */
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b->Args({1, 56, 56, 3, 1, 2, 24});
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b->Args({1, 28, 28, 3, 1, 2, 200});
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b->Args({1, 14, 14, 3, 1, 2, 400});
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b->Args({1, 7, 7, 3, 1, 2, 800});
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}
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// ShuffleNet v1 with 3 groups.
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static void ShuffleNetV1G3(benchmark::internal::Benchmark* b) {
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b->ArgNames({"N", "H", "W", "K", "P", "S", "C"});
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/* N H W K P S C */
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b->Args({1, 56, 56, 3, 1, 2, 24});
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b->Args({1, 28, 28, 3, 1, 2, 240});
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b->Args({1, 14, 14, 3, 1, 2, 480});
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b->Args({1, 7, 7, 3, 1, 2, 960});
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}
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// ShuffleNet v1 with 4 groups.
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static void ShuffleNetV1G4(benchmark::internal::Benchmark* b) {
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b->ArgNames({"N", "H", "W", "K", "P", "S", "C"});
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/* N H W K P S C */
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b->Args({1, 56, 56, 3, 1, 2, 24});
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b->Args({1, 28, 28, 3, 1, 2, 272});
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b->Args({1, 14, 14, 3, 1, 2, 576});
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b->Args({1, 7, 7, 3, 1, 2, 1088});
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}
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// ShuffleNet v1 with 8 groups.
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static void ShuffleNetV1G8(benchmark::internal::Benchmark* b) {
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b->ArgNames({"N", "H", "W", "K", "P", "S", "C"});
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/* N H W K P S C */
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b->Args({1, 56, 56, 3, 1, 2, 24});
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b->Args({1, 28, 28, 3, 1, 2, 384});
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b->Args({1, 14, 14, 3, 1, 2, 768});
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b->Args({1, 7, 7, 3, 1, 2, 1536});
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}
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BENCHMARK_CAPTURE(xnnpack_average_pooling_f32, imagenet, "ImageNet")->Apply(ImageNet)->UseRealTime();
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BENCHMARK_CAPTURE(xnnpack_average_pooling_f32, shufflenet_v1_g1, "ShuffleNet v1 (1 group)")->Apply(ShuffleNetV1G1)->UseRealTime();
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BENCHMARK_CAPTURE(xnnpack_average_pooling_f32, shufflenet_v1_g2, "ShuffleNet v1 (2 groups)")->Apply(ShuffleNetV1G2)->UseRealTime();
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BENCHMARK_CAPTURE(xnnpack_average_pooling_f32, shufflenet_v1_g3, "ShuffleNet v1 (3 groups)")->Apply(ShuffleNetV1G3)->UseRealTime();
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BENCHMARK_CAPTURE(xnnpack_average_pooling_f32, shufflenet_v1_g4, "ShuffleNet v1 (4 groups)")->Apply(ShuffleNetV1G4)->UseRealTime();
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BENCHMARK_CAPTURE(xnnpack_average_pooling_f32, shufflenet_v1_g8, "ShuffleNet v1 (8 groups)")->Apply(ShuffleNetV1G8)->UseRealTime();
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#ifdef BENCHMARK_TENSORFLOW_LITE
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BENCHMARK_CAPTURE(tflite_average_pooling_f32, imagenet, "ImageNet")->Apply(ImageNet)->UseRealTime();
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BENCHMARK_CAPTURE(tflite_average_pooling_f32, shufflenet_v1_g1, "ShuffleNet v1 (1 group)")->Apply(ShuffleNetV1G1)->UseRealTime();
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BENCHMARK_CAPTURE(tflite_average_pooling_f32, shufflenet_v1_g2, "ShuffleNet v1 (2 groups)")->Apply(ShuffleNetV1G2)->UseRealTime();
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BENCHMARK_CAPTURE(tflite_average_pooling_f32, shufflenet_v1_g3, "ShuffleNet v1 (3 groups)")->Apply(ShuffleNetV1G3)->UseRealTime();
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BENCHMARK_CAPTURE(tflite_average_pooling_f32, shufflenet_v1_g4, "ShuffleNet v1 (4 groups)")->Apply(ShuffleNetV1G4)->UseRealTime();
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BENCHMARK_CAPTURE(tflite_average_pooling_f32, shufflenet_v1_g8, "ShuffleNet v1 (8 groups)")->Apply(ShuffleNetV1G8)->UseRealTime();
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#endif // BENCHMARK_TENSORFLOW_LITE
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#ifndef XNN_NO_QU8_OPERATORS
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BENCHMARK_CAPTURE(xnnpack_average_pooling_qu8, imagenet, "ImageNet")->Apply(ImageNet)->UseRealTime();
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BENCHMARK_CAPTURE(xnnpack_average_pooling_qu8, shufflenet_v1_g1, "ShuffleNet v1 (1 group)")->Apply(ShuffleNetV1G1)->UseRealTime();
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BENCHMARK_CAPTURE(xnnpack_average_pooling_qu8, shufflenet_v1_g2, "ShuffleNet v1 (2 groups)")->Apply(ShuffleNetV1G2)->UseRealTime();
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BENCHMARK_CAPTURE(xnnpack_average_pooling_qu8, shufflenet_v1_g3, "ShuffleNet v1 (3 groups)")->Apply(ShuffleNetV1G3)->UseRealTime();
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BENCHMARK_CAPTURE(xnnpack_average_pooling_qu8, shufflenet_v1_g4, "ShuffleNet v1 (4 groups)")->Apply(ShuffleNetV1G4)->UseRealTime();
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BENCHMARK_CAPTURE(xnnpack_average_pooling_qu8, shufflenet_v1_g8, "ShuffleNet v1 (8 groups)")->Apply(ShuffleNetV1G8)->UseRealTime();
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#endif // XNN_NO_QU8_OPERATORS
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#ifndef XNNPACK_BENCHMARK_NO_MAIN
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BENCHMARK_MAIN();
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#endif
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