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// 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.
#include <algorithm>
#include <cfloat>
#include <cmath>
#include <functional>
#include <limits>
#include <random>
#include <vector>
#include <xnnpack.h>
#include <benchmark/benchmark.h>
#include "bench/utils.h"
void max_pooling_u8(benchmark::State& state, const char* net) {
const size_t batch_size = state.range(0);
const size_t input_height = state.range(1);
const size_t input_width = state.range(2);
const size_t pooling_size = state.range(3);
const size_t padding_size = state.range(4);
const size_t stride = state.range(5);
const size_t channels = state.range(6);
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto u8rng = std::bind(std::uniform_int_distribution<uint32_t>(0, std::numeric_limits<uint8_t>::max()), std::ref(rng));
const size_t output_height = (2 * padding_size + input_height - pooling_size) / stride + 1;
const size_t output_width = (2 * padding_size + input_width - pooling_size) / stride + 1;
std::vector<uint8_t> input(batch_size * input_height * input_width * channels);
std::generate(input.begin(), input.end(), std::ref(u8rng));
std::vector<uint8_t> output(batch_size * output_height * output_width * channels);
std::fill(output.begin(), output.end(), 0xA5);
xnn_status status = xnn_initialize(nullptr /* allocator */);
if (status != xnn_status_success) {
state.SkipWithError("failed to initialize XNNPACK");
return;
}
xnn_operator_t pooling_op = nullptr;
status = xnn_create_max_pooling2d_nhwc_u8(
padding_size, padding_size, padding_size, padding_size,
pooling_size, pooling_size,
stride, stride,
1 /* dilation height */, 1 /* dilation width */,
channels, channels /* input pixel stride */, channels /* output pixel stride */,
0, 255,
0 /* flags */, &pooling_op);
if (status != xnn_status_success) {
state.SkipWithError("failed to create Max Pooling operator");
return;
}
status = xnn_setup_max_pooling2d_nhwc_u8(
pooling_op,
batch_size, input_height, input_width,
input.data(), output.data(),
nullptr /* thread pool */);
if (status != xnn_status_success) {
state.SkipWithError("failed to setup Max Pooling operator");
return;
}
for (auto _ : state) {
status = xnn_run_operator(pooling_op, nullptr /* thread pool */);
if (status != xnn_status_success) {
state.SkipWithError("failed to run Max Pooling operator");
return;
}
}
status = xnn_delete_operator(pooling_op);
if (status != xnn_status_success) {
state.SkipWithError("failed to delete Max Pooling operator");
return;
}
pooling_op = nullptr;
state.counters["Freq"] = benchmark::utils::GetCurrentCpuFrequency();
state.counters["bytes"] = benchmark::Counter(
uint64_t(state.iterations()) *
batch_size * (input_height * input_width + output_height * output_width) * channels * sizeof(uint8_t),
benchmark::Counter::kIsRate);
}
void max_pooling_f32(benchmark::State& state, const char* net) {
const size_t batch_size = state.range(0);
const size_t input_height = state.range(1);
const size_t input_width = state.range(2);
const size_t pooling_size = state.range(3);
const size_t padding_size = state.range(4);
const size_t stride = state.range(5);
const size_t channels = state.range(6);
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto f32rng = std::bind(std::uniform_real_distribution<float>(0.0f, 1.0f), std::ref(rng));
const size_t output_height = (2 * padding_size + input_height - pooling_size) / stride + 1;
const size_t output_width = (2 * padding_size + input_width - pooling_size) / stride + 1;
std::vector<float> input(batch_size * input_height * input_width * channels);
std::generate(input.begin(), input.end(), std::ref(f32rng));
std::vector<float> output(batch_size * output_height * output_width * channels);
std::fill(output.begin(), output.end(), nanf(""));
xnn_status status = xnn_initialize(nullptr /* allocator */);
if (status != xnn_status_success) {
state.SkipWithError("failed to initialize XNNPACK");
return;
}
xnn_operator_t pooling_op = nullptr;
status = xnn_create_max_pooling2d_nhwc_f32(
padding_size, padding_size, padding_size, padding_size,
pooling_size, pooling_size,
stride, stride,
1 /* dilation height */, 1 /* dilation width */,
channels, channels /* input pixel stride */, channels /* output pixel stride */,
-std::numeric_limits<float>::infinity(), +std::numeric_limits<float>::infinity(),
0 /* flags */, &pooling_op);
if (status != xnn_status_success) {
state.SkipWithError("failed to create Max Pooling operator");
return;
}
status = xnn_setup_max_pooling2d_nhwc_f32(
pooling_op,
batch_size, input_height, input_width,
input.data(), output.data(),
nullptr /* thread pool */);
if (status != xnn_status_success) {
state.SkipWithError("failed to setup Max Pooling operator");
return;
}
for (auto _ : state) {
status = xnn_run_operator(pooling_op, nullptr /* thread pool */);
if (status != xnn_status_success) {
state.SkipWithError("failed to run Max Pooling operator");
return;
}
}
status = xnn_delete_operator(pooling_op);
if (status != xnn_status_success) {
state.SkipWithError("failed to delete Max Pooling operator");
return;
}
pooling_op = nullptr;
state.counters["Freq"] = benchmark::utils::GetCurrentCpuFrequency();
state.counters["bytes"] = benchmark::Counter(
uint64_t(state.iterations()) *
batch_size * (input_height * input_width + output_height * output_width) * channels * sizeof(float),
benchmark::Counter::kIsRate);
}
// ShuffleNet v1/v2.
static void ShuffleNet(benchmark::internal::Benchmark* b) {
b->ArgNames({"N", "H", "W", "K", "P", "S", "C"});
/* N H W K P S C */
b->Args({1, 112, 112, 3, 1, 2, 24});
}
// SqueezeNet 1.0
static void SqueezeNetV10(benchmark::internal::Benchmark* b) {
b->ArgNames({"N", "H", "W", "K", "P", "S", "C"});
/*********** MaxPool 1 ************/
/* N H W K P S C */
b->Args({1, 111, 111, 3, 0, 2, 96});
/*********** MaxPool 4 ************/
/* N H W K P S C */
b->Args({1, 27, 27, 3, 0, 2, 256});
/*********** MaxPool 8 ************/
/* N H W K P S C */
b->Args({1, 13, 13, 3, 0, 2, 512});
}
// SqueezeNet 1.1
static void SqueezeNetV11(benchmark::internal::Benchmark* b) {
b->ArgNames({"N", "H", "W", "K", "P", "S", "C"});
/*********** MaxPool 1 ***********/
/* N H W K P S C */
b->Args({1, 111, 111, 3, 0, 2, 64});
/*********** MaxPool 3 ************/
/* N H W K P S C */
b->Args({1, 55, 55, 3, 0, 2, 128});
/*********** MaxPool 5 ************/
/* N H W K P S C */
b->Args({1, 13, 13, 3, 0, 2, 256});
}
static void VGG(benchmark::internal::Benchmark* b) {
b->ArgNames({"N", "H", "W", "K", "P", "S", "C"});
/* N H W K P S C */
b->Args({1, 224, 224, 2, 1, 2, 64});
b->Args({1, 112, 112, 2, 1, 2, 128});
b->Args({1, 56, 56, 2, 1, 2, 256});
b->Args({1, 28, 28, 2, 1, 2, 512});
b->Args({1, 14, 14, 2, 1, 2, 512});
}
BENCHMARK_CAPTURE(max_pooling_f32, shufflenet, "ShuffleNet v1/v2")->Apply(ShuffleNet)->UseRealTime();
BENCHMARK_CAPTURE(max_pooling_f32, squeezenet_v10, "SqueezeNet v1.0")->Apply(SqueezeNetV10)->UseRealTime();
BENCHMARK_CAPTURE(max_pooling_f32, squeezenet_v11, "SqueezeNet v1.1")->Apply(SqueezeNetV11)->UseRealTime();
BENCHMARK_CAPTURE(max_pooling_f32, vgg, "VGG")->Apply(VGG);
BENCHMARK_CAPTURE(max_pooling_u8, shufflenet, "ShuffleNet v1/v2")->Apply(ShuffleNet)->UseRealTime();
BENCHMARK_CAPTURE(max_pooling_u8, squeezenet_v10, "SqueezeNet v1.0")->Apply(SqueezeNetV10)->UseRealTime();
BENCHMARK_CAPTURE(max_pooling_u8, squeezenet_v11, "SqueezeNet v1.1")->Apply(SqueezeNetV11)->UseRealTime();
BENCHMARK_CAPTURE(max_pooling_u8, vgg, "VGG")->Apply(VGG);
#ifndef XNNPACK_BENCHMARK_NO_MAIN
BENCHMARK_MAIN();
#endif