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android / platform / external / XNNPACK / refs/tags/android-11.0.0_r33 / . / bench / convolution.cc
blob: 3d6a587c7572d31ed0c246695051cd4589ddfa22 [file] [log] [blame]
// 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 <ostream>
#include <random>
#include <string>
#include <vector>
#include <cpuinfo.h>
#include <xnnpack.h>
#ifdef BENCHMARK_ARM_COMPUTE_LIBRARY
#include "arm_compute/core/Types.h"
#include "arm_compute/runtime/Tensor.h"
#include "arm_compute/runtime/CPP/CPPScheduler.h"
#include "arm_compute/runtime/NEON/functions/NEDepthwiseConvolutionLayer.h"
#include "arm_compute/runtime/NEON/functions/NEConvolutionLayer.h"
#endif // BENCHMARK_ARM_COMPUTE_LIBRARY
#include <benchmark/benchmark.h>
#ifdef BENCHMARK_TENSORFLOW_LITE
#include "flatbuffers/include/flatbuffers/flatbuffers.h"
#include "tensorflow/lite/interpreter.h"
#include "tensorflow/lite/kernels/register.h"
#include "tensorflow/lite/model.h"
#include "tensorflow/lite/schema/schema_generated.h"
#include "tensorflow/lite/version.h"
#endif // BENCHMARK_TENSORFLOW_LITE
#include "bench/utils.h"
void xnnpack_convolution_q8(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 kernel_height = state.range(3);
const size_t kernel_width = state.range(4);
const size_t padding_height = state.range(5);
const size_t padding_width = state.range(6);
const size_t subsampling = state.range(7);
const size_t dilation = state.range(8);
const size_t groups = state.range(9);
const size_t group_input_channels = state.range(10);
const size_t group_output_channels = state.range(11);
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);
const size_t output_pixel_stride = groups * group_output_channels;
const size_t input_pixel_stride = groups * group_input_channels;
const size_t effective_kernel_height = (kernel_height - 1) * dilation + 1;
const size_t effective_kernel_width = (kernel_width - 1) * dilation + 1;
const size_t padding_left = padding_width / 2;
const size_t padding_top = padding_height / 2;
const size_t padding_right = padding_width - padding_left;
const size_t padding_bottom = padding_height - padding_top;
const size_t output_height = (input_height + padding_height - effective_kernel_height) / subsampling + 1;
const size_t output_width = (input_width + padding_width - effective_kernel_width) / subsampling + 1;
std::vector<uint8_t> input(batch_size * input_height * input_width * input_pixel_stride);
std::generate(input.begin(), input.end(), std::ref(u8rng));
std::vector<uint8_t> kernel(groups * group_output_channels * kernel_height * kernel_width * group_input_channels);
std::generate(kernel.begin(), kernel.end(), std::ref(u8rng));
std::vector<int32_t> bias(groups * group_output_channels);
std::generate(bias.begin(), bias.end(), std::ref(s32rng));
const size_t output_elements = batch_size * output_height * output_width * output_pixel_stride;
xnn_status status = xnn_initialize(nullptr /* allocator */);
if (status != xnn_status_success) {
state.SkipWithError("failed to initialize XNNPACK");
return;
}
if (!cpuinfo_initialize()) {
state.SkipWithError("cpuinfo initialization failed");
return;
}
const size_t num_buffers = 1 +
benchmark::utils::DivideRoundUp<size_t>(benchmark::utils::GetMaxCacheSize(),
sizeof(uint8_t) * kernel.size() + sizeof(int32_t) * bias.size() + sizeof(uint8_t) * output_elements);
std::vector<uint8_t> output(output_elements * num_buffers);
std::vector<xnn_operator_t> convolution_operators(num_buffers);
for (xnn_operator_t& convolution_op : convolution_operators) {
status = xnn_create_convolution2d_nhwc_q8(
padding_top, padding_right, padding_bottom, padding_left,
kernel_height, kernel_width,
subsampling, subsampling,
dilation, dilation,
groups, group_input_channels, group_output_channels,
input_pixel_stride, output_pixel_stride,
127, 0.5f,
127, 0.5f,
kernel.data(), bias.data(),
127, 0.5f, 0, 255,
0 /* flags */, &convolution_op);
if (status != xnn_status_success) {
state.SkipWithError("failed to create QINT8 Convolution operator");
return;
}
}
for (size_t i = 0; i < convolution_operators.size(); i++) {
status = xnn_setup_convolution2d_nhwc_q8(
convolution_operators[i],
batch_size, input_height, input_width,
input.data(), output.data() + i * output_elements,
nullptr /* thread pool */);
if (status != xnn_status_success) {
state.SkipWithError("failed to setup QINT8 Convolution operator");
return;
}
}
size_t buffer_index = 0;
for (auto _ : state) {
state.PauseTiming();
benchmark::utils::PrefetchToL1(input.data(), input.size() * sizeof(uint8_t));
buffer_index = (buffer_index + 1) % num_buffers;
state.ResumeTiming();
status = xnn_run_operator(convolution_operators[buffer_index],
nullptr /* thread pool */);
if (status != xnn_status_success) {
state.SkipWithError("failed to run QINT8 Convolution operator");
return;
}
}
for (xnn_operator_t& convolution_op : convolution_operators) {
status = xnn_delete_operator(convolution_op);
if (status != xnn_status_success) {
state.SkipWithError("failed to delete QINT8 Convolution operator");
return;
}
convolution_op = nullptr;
}
state.counters["Freq"] = benchmark::utils::GetCurrentCpuFrequency();
state.counters["OPS"] = benchmark::Counter(
uint64_t(state.iterations()) * 2 *
batch_size * output_height * output_width *
groups * group_input_channels * group_output_channels *
kernel_height * kernel_width,
benchmark::Counter::kIsRate);
}
void xnnpack_convolution_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 kernel_height = state.range(3);
const size_t kernel_width = state.range(4);
const size_t padding_height = state.range(5);
const size_t padding_width = state.range(6);
const size_t subsampling = state.range(7);
const size_t dilation = state.range(8);
const size_t groups = state.range(9);
const size_t group_input_channels = state.range(10);
const size_t group_output_channels = state.range(11);
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto f32rng = std::bind(std::uniform_real_distribution<float>(0.0f, 1.0f), rng);
const size_t output_pixel_stride = groups * group_output_channels;
const size_t input_pixel_stride = groups * group_input_channels;
const size_t effective_kernel_height = (kernel_height - 1) * dilation + 1;
const size_t effective_kernel_width = (kernel_width - 1) * dilation + 1;
const size_t padding_left = padding_width / 2;
const size_t padding_top = padding_height / 2;
const size_t padding_right = padding_width - padding_left;
const size_t padding_bottom = padding_height - padding_top;
const size_t output_height = (input_height + padding_height - effective_kernel_height) / subsampling + 1;
const size_t output_width = (input_width + padding_width - effective_kernel_width) / subsampling + 1;
std::vector<float> input(batch_size * input_height * input_width * input_pixel_stride + XNN_EXTRA_BYTES / sizeof(float));
std::generate(input.begin(), input.end(), std::ref(f32rng));
std::vector<float> kernel(groups * group_output_channels * kernel_height * kernel_width * group_input_channels);
std::generate(kernel.begin(), kernel.end(), std::ref(f32rng));
std::vector<float> bias(groups * group_output_channels);
std::generate(bias.begin(), bias.end(), std::ref(f32rng));
const size_t output_elements = batch_size * output_height * output_width * output_pixel_stride;
xnn_status status = xnn_initialize(nullptr /* allocator */);
if (status != xnn_status_success) {
state.SkipWithError("failed to initialize XNNPACK");
return;
}
if (!cpuinfo_initialize()) {
state.SkipWithError("cpuinfo initialization failed");
return;
}
const size_t num_buffers = 1 +
benchmark::utils::DivideRoundUp<size_t>(benchmark::utils::GetMaxCacheSize(),
sizeof(float) * (kernel.size() + bias.size() + output_elements));
std::vector<float> output(output_elements * num_buffers);
std::vector<xnn_operator_t> convolution_operators(num_buffers);
for (xnn_operator_t& convolution_op : convolution_operators) {
status = xnn_create_convolution2d_nhwc_f32(
padding_top, padding_right, padding_bottom, padding_left,
kernel_height, kernel_width,
subsampling, subsampling,
dilation, dilation,
groups, group_input_channels, group_output_channels,
input_pixel_stride, output_pixel_stride,
kernel.data(), bias.data(),
-std::numeric_limits<float>::infinity(), +std::numeric_limits<float>::infinity(),
0 /* flags */, &convolution_op);
if (status != xnn_status_success) {
state.SkipWithError("failed to create FP32 Convolution operator");
return;
}
}
for (size_t i = 0; i < convolution_operators.size(); i++) {
status = xnn_setup_convolution2d_nhwc_f32(
convolution_operators[i],
batch_size, input_height, input_width,
input.data(), output.data() + i * output_elements,
nullptr /* thread pool */);
if (status != xnn_status_success) {
state.SkipWithError("failed to setup FP32 Convolution operator");
return;
}
}
size_t buffer_index = 0;
for (auto _ : state) {
state.PauseTiming();
benchmark::utils::PrefetchToL1(input.data(), input.size() * sizeof(float));
buffer_index = (buffer_index + 1) % num_buffers;
state.ResumeTiming();
status = xnn_run_operator(convolution_operators[buffer_index], nullptr /* thread pool */);
if (status != xnn_status_success) {
state.SkipWithError("failed to run FP32 Convolution operator");
return;
}
}
for (xnn_operator_t& convolution_op : convolution_operators) {
status = xnn_delete_operator(convolution_op);
if (status != xnn_status_success) {
state.SkipWithError("failed to delete FP32 Convolution operator");
return;
}
convolution_op = nullptr;
}
state.counters["Freq"] = benchmark::utils::GetCurrentCpuFrequency();
state.counters["FLOPS"] = benchmark::Counter(
uint64_t(state.iterations()) * 2 *
batch_size * output_height * output_width *
groups * group_input_channels * group_output_channels *
kernel_height * kernel_width,
benchmark::Counter::kIsRate);
}
#ifdef BENCHMARK_TENSORFLOW_LITE
void tflite_convolution_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 kernel_height = state.range(3);
const size_t kernel_width = state.range(4);
const size_t padding_height = state.range(5);
const size_t padding_width = state.range(6);
const size_t subsampling = state.range(7);
const size_t dilation = state.range(8);
const size_t groups = state.range(9);
const size_t group_input_channels = state.range(10);
const size_t group_output_channels = state.range(11);
bool is_depthwise = false;
if (groups != 1) {
if (group_input_channels == 1) {
is_depthwise = true;
} else {
state.SkipWithError("grouped convolution is not supported");
return;
}
}
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto f32rng = std::bind(std::uniform_real_distribution<float>(0.0f, 1.0f), rng);
const size_t effective_kernel_height = (kernel_height - 1) * dilation + 1;
const size_t effective_kernel_width = (kernel_width - 1) * dilation + 1;
tflite::Padding padding = tflite::Padding_VALID;
if (padding_width == (effective_kernel_width - 1) && padding_height == (effective_kernel_height - 1)) {
padding = tflite::Padding_SAME;
} else if (padding_width == 0 && padding_height == 0) {
padding = tflite::Padding_VALID;
} else {
state.SkipWithError("unsupported padding");
return;
}
const size_t output_height = (input_height + padding_height - effective_kernel_height) / subsampling + 1;
const size_t output_width = (input_width + padding_width - effective_kernel_width) / subsampling + 1;
std::vector<float> kernel(groups * group_output_channels * kernel_height * kernel_width * group_input_channels);
std::generate(kernel.begin(), kernel.end(), std::ref(f32rng));
std::vector<float> bias(groups * group_output_channels);
std::generate(bias.begin(), bias.end(), std::ref(f32rng));
flatbuffers::FlatBufferBuilder builder;
flatbuffers::Offset<tflite::OperatorCode> operator_code =
CreateOperatorCode(
builder,
is_depthwise ? tflite::BuiltinOperator_DEPTHWISE_CONV_2D : tflite::BuiltinOperator_CONV_2D,
0);
flatbuffers::Offset<tflite::Conv2DOptions> conv2d_options = CreateConv2DOptions(
builder,
padding,
static_cast<int32_t>(subsampling), static_cast<int32_t>(subsampling),
tflite::ActivationFunctionType_NONE,
static_cast<int32_t>(dilation), static_cast<int32_t>(dilation));
flatbuffers::Offset<tflite::DepthwiseConv2DOptions> dwconv2d_options = CreateDepthwiseConv2DOptions(
builder,
padding,
static_cast<int32_t>(subsampling), static_cast<int32_t>(subsampling),
static_cast<int32_t>(group_output_channels),
tflite::ActivationFunctionType_NONE,
static_cast<int32_t>(dilation), static_cast<int32_t>(dilation));
flatbuffers::Offset<tflite::Buffer> buffers[3] = {
tflite::CreateBuffer(builder, builder.CreateVector({})),
tflite::CreateBuffer(builder, builder.CreateVector(
reinterpret_cast<const uint8_t*>(kernel.data()),
sizeof(float) * kernel.size())),
tflite::CreateBuffer(builder, builder.CreateVector(
reinterpret_cast<const uint8_t*>(bias.data()),
sizeof(float) * bias.size())),
};
const int32_t input_shape[4] = {
static_cast<int32_t>(batch_size),
static_cast<int32_t>(input_height),
static_cast<int32_t>(input_width),
static_cast<int32_t>(groups * group_input_channels)
};
const int32_t output_shape[4] = {
static_cast<int32_t>(batch_size),
static_cast<int32_t>(output_height),
static_cast<int32_t>(output_width),
static_cast<int32_t>(groups * group_output_channels)
};
const int32_t filter_shape[4] = {
static_cast<int32_t>(group_output_channels),
static_cast<int32_t>(kernel_height),
static_cast<int32_t>(kernel_width),
static_cast<int32_t>(groups * group_input_channels)
};
const int32_t bias_shape[1] = {
static_cast<int32_t>(groups * group_output_channels)
};
flatbuffers::Offset<tflite::Tensor> tensors[4] = {
tflite::CreateTensor(builder,
builder.CreateVector<int32_t>(input_shape, 4),
tflite::TensorType_FLOAT32,
0 /* buffer id */,
builder.CreateString("input")),
tflite::CreateTensor(builder,
builder.CreateVector<int32_t>(filter_shape, 4),
tflite::TensorType_FLOAT32,
1 /* buffer id */,
builder.CreateString("filter")),
tflite::CreateTensor(builder,
builder.CreateVector<int32_t>(bias_shape, 1),
tflite::TensorType_FLOAT32,
2 /* buffer id */,
builder.CreateString("bias")),
tflite::CreateTensor(builder,
builder.CreateVector<int32_t>(output_shape, 4),
tflite::TensorType_FLOAT32,
0 /* buffer id */,
builder.CreateString("output")),
};
const int32_t op_inputs[3] = { 0, 1, 2 };
const int32_t op_outputs[1] = { 3 };
flatbuffers::Offset<tflite::Operator> op = CreateOperator(
builder,
0 /* opcode_index */,
builder.CreateVector<int32_t>(op_inputs, 3),
builder.CreateVector<int32_t>(op_outputs, 1),
is_depthwise ? tflite::BuiltinOptions_DepthwiseConv2DOptions : tflite::BuiltinOptions_Conv2DOptions,
is_depthwise ? dwconv2d_options.Union() : conv2d_options.Union(),
/*custom_options */ 0,
tflite::CustomOptionsFormat_FLEXBUFFERS);
const int32_t graph_inputs[1] = { 0 };
const int32_t graph_outputs[1] = { 3 };
flatbuffers::Offset<tflite::SubGraph> subgraph = CreateSubGraph(
builder,
builder.CreateVector(tensors, 4),
builder.CreateVector<int32_t>(graph_inputs, 1),
builder.CreateVector<int32_t>(graph_outputs, 1),
builder.CreateVector(&op, 1),
builder.CreateString("Conv2D subgraph"));
flatbuffers::Offset<flatbuffers::String> description = builder.CreateString("Conv2D model");
flatbuffers::Offset<tflite::Model> model_buffer = tflite::CreateModel(builder,
TFLITE_SCHEMA_VERSION,
builder.CreateVector(&operator_code, 1),
builder.CreateVector(&subgraph, 1),
description,
builder.CreateVector(buffers, 3));
builder.Finish(model_buffer);
const tflite::Model* model = tflite::GetModel(builder.GetBufferPointer());
tflite::ops::builtin::BuiltinOpResolver resolver;
tflite::InterpreterBuilder interpreterBuilder(model, resolver);
std::unique_ptr<tflite::Interpreter> interpreter;
if (interpreterBuilder(&interpreter) != kTfLiteOk) {
state.SkipWithError("failed to create TFLite interpreter");
return;
}
if (interpreter == nullptr) {
state.SkipWithError("TFLite interpreter is null");
return;
}
interpreter->SetNumThreads(1);
if (interpreter->AllocateTensors() != kTfLiteOk) {
state.SkipWithError("failed to allocate tensors");
return;
}
std::generate(
interpreter->typed_tensor<float>(0),
interpreter->typed_tensor<float>(0) + batch_size * groups * group_input_channels * input_height * input_width,
std::ref(f32rng));
for (auto _ : state) {
state.PauseTiming();
benchmark::utils::WipeCache();
benchmark::utils::PrefetchToL1(
interpreter->typed_tensor<float>(0),
batch_size * groups * group_input_channels * input_height * input_width * sizeof(float));
state.ResumeTiming();
if (interpreter->Invoke() != kTfLiteOk) {
state.SkipWithError("failed to invoke TFLite interpreter");
return;
}
}
state.counters["Freq"] = benchmark::utils::GetCurrentCpuFrequency();
state.counters["FLOPS"] = benchmark::Counter(
uint64_t(state.iterations()) * 2 *
batch_size * output_height * output_width *
groups * group_input_channels * group_output_channels *
kernel_height * kernel_width,
benchmark::Counter::kIsRate);
interpreter.reset();
}
#endif // BENCHMARK_TENSORFLOW_LITE
#ifdef BENCHMARK_ARM_COMPUTE_LIBRARY
static std::string compare_with_convolution_f32_reference_output(
const benchmark::State& state, const float* input, size_t input_size,
const float* kernel, size_t kernel_size, const float* bias, size_t bias_size,
const float* output, size_t output_size)
{
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 kernel_height = state.range(3);
const size_t kernel_width = state.range(4);
const size_t padding_height = state.range(5);
const size_t padding_width = state.range(6);
const size_t subsampling = state.range(7);
const size_t dilation = state.range(8);
const size_t groups = state.range(9);
const size_t group_input_channels = state.range(10);
const size_t group_output_channels = state.range(11);
const size_t effective_kernel_height = (kernel_height - 1) * dilation + 1;
const size_t effective_kernel_width = (kernel_width - 1) * dilation + 1;
const size_t output_height = (input_height + padding_height - effective_kernel_height) / subsampling + 1;
const size_t output_width = (input_width + padding_width - effective_kernel_width) / subsampling + 1;
const size_t input_pixel_stride = groups * group_input_channels;
const size_t padding_left = padding_width / 2;
const size_t padding_top = padding_height / 2;
assert(input_size == batch_size * input_height * input_width * groups * group_input_channels);
assert(kernel_size == group_output_channels * kernel_height * kernel_width * groups * group_input_channels);
assert(bias_size == groups * group_output_channels);
assert(output_size == batch_size * output_height * output_width * groups * group_output_channels);
std::vector<float> output_ref(output_size);
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];
}
}
}
}
}
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 iy = oy * subsampling + ky * dilation - padding_top;
if (iy < input_height) {
for (size_t kx = 0; kx < kernel_width; kx++) {
const size_t ix = ox * subsampling + kx * dilation - padding_left;
if (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[(((oc * kernel_height + ky) * kernel_width + kx) * groups + g) * group_input_channels + ic];
} // group_input_channels loop
} // group_output_channels loop
} // groups loop
}
} // kernel_width loop
}
} // kernel_height loop
} // output_width loop
} // output_height loop
} // batch_size loop
const float relative_error_tolerance = 1e-4;
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++) {
const size_t idx = (((i * output_height + y) * output_width + x) * groups + g) * group_output_channels + c;
const float value_ref = output_ref[idx];
const float value = output[idx];
if (std::abs(value - value_ref) > std::max(std::abs(value_ref) * relative_error_tolerance, std::numeric_limits<float>::epsilon())) {
std::ostringstream error_stream;
error_stream << "(x, y) = (" << x << ", " << y << "), group = " << g
<< ", channel = " << c << ", refValue = " << value_ref
<< ", actualValue = " << value
<< ", absDiff=" << std::abs(value - value_ref);
return error_stream.str();
}
}
}
}
}
}
return "";
}
void armcl_convolution_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 kernel_height = state.range(3);
const size_t kernel_width = state.range(4);
const size_t padding_height = state.range(5);
const size_t padding_width = state.range(6);
const size_t subsampling = state.range(7);
const size_t dilation = state.range(8);
const size_t groups = state.range(9);
const size_t group_input_channels = state.range(10);
const size_t group_output_channels = state.range(11);
const size_t effective_kernel_height = (kernel_height - 1) * dilation + 1;
const size_t effective_kernel_width = (kernel_width - 1) * dilation + 1;
const size_t padding_left = padding_width / 2;
const size_t padding_top = padding_height / 2;
const size_t padding_right = padding_width - padding_left;
const size_t padding_bottom = padding_height - padding_top;
const size_t output_height = (input_height + padding_height - effective_kernel_height) / subsampling + 1;
const size_t output_width = (input_width + padding_width - effective_kernel_width) / subsampling + 1;
arm_compute::PadStrideInfo pad_stride_info(
subsampling /* stride height */,
subsampling /* stride width */,
padding_left, padding_right, padding_top, padding_bottom,
arm_compute::DimensionRoundingType::FLOOR);
arm_compute::Size2D dilation_info(dilation, dilation);
// Note: activation is disabled by default.
arm_compute::ActivationLayerInfo activation_info;
// Note: no batch size and reverse order of dimensions, i.e. CWHN for NHWC.
arm_compute::TensorShape input_shape(
/* C */ groups * group_input_channels,
/* W */ input_width,
/* H */ input_height,
/* N */ batch_size);
arm_compute::TensorInfo input_info(
input_shape,
1 /* number of channels per element (!) */,
arm_compute::DataType::F32);
input_info.set_data_layout(arm_compute::DataLayout::NHWC);
arm_compute::Tensor input_tensor;
input_tensor.allocator()->init(input_info);
input_tensor.allocator()->allocate();
// Note: reverse order of dimensions, i.e. for IWHO for OHWI.
arm_compute::TensorShape kernel_shape(
/* I */ groups * group_input_channels,
/* W */ kernel_width,
/* H */ kernel_height,
/* O */ group_output_channels);
arm_compute::TensorInfo kernel_info(
kernel_shape,
1 /* number of channels per element (!) */,
arm_compute::DataType::F32);
kernel_info.set_data_layout(arm_compute::DataLayout::NHWC);
arm_compute::Tensor kernelTensor;
kernelTensor.allocator()->init(kernel_info);
kernelTensor.allocator()->allocate();
arm_compute::TensorShape bias_shape(groups * group_output_channels);
arm_compute::TensorInfo bias_info(
bias_shape,
1 /* number of channels per element (!) */,
arm_compute::DataType::F32);
bias_info.set_data_layout(arm_compute::DataLayout::NHWC);
arm_compute::Tensor bias_tensor;
bias_tensor.allocator()->init(bias_info);
bias_tensor.allocator()->allocate();
// Note: no batch size and reverse order of dimensions, i.e. CWHN for NHWC.
arm_compute::TensorShape output_shape(
/* C */ groups * group_output_channels,
/* W */ output_width,
/* H */ output_height,
/* N */ batch_size);
arm_compute::TensorInfo output_info(
output_shape,
1 /* number of channels per element (!) */,
arm_compute::DataType::F32);
output_info.set_data_layout(arm_compute::DataLayout::NHWC);
arm_compute::Tensor output_tensor;
output_tensor.allocator()->init(output_info);
output_tensor.allocator()->allocate();
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto f32rng = std::bind(std::uniform_real_distribution<float>(0.0f, 1.0f), rng);
std::generate(
reinterpret_cast<float*>(input_tensor.buffer()),
reinterpret_cast<float*>(input_tensor.buffer()) + input_shape.total_size(),
std::ref(f32rng));
std::generate(
reinterpret_cast<float*>(kernelTensor.buffer()),
reinterpret_cast<float*>(kernelTensor.buffer()) + kernel_shape.total_size(),
std::ref(f32rng));
std::generate(
reinterpret_cast<float*>(bias_tensor.buffer()),
reinterpret_cast<float*>(bias_tensor.buffer()) + bias_shape.total_size(),
std::ref(f32rng));
std::generate(
reinterpret_cast<float*>(output_tensor.buffer()),
reinterpret_cast<float*>(output_tensor.buffer()) + output_shape.total_size(),
std::ref(f32rng));
bool is_depthwise = false;
if (groups != 1) {
// NEConvolutionLayer uses NEGEMMConvolutionLayer by default, which doesn't support grouped convolution.
// However, depthwise convolution is supported via NEDepthwiseConvolutionLayer.
if (group_input_channels == 1) {
is_depthwise = true;
} else {
state.SkipWithError("grouped convolution is not supported");
return;
}
}
std::shared_ptr<arm_compute::IFunction> layer;
if (is_depthwise) {
if (dilation != 1) {
state.SkipWithError("dilated depthwise convolution is not supported");
return;
}
// Avoid NEDepthwiseConvolutionLayer3x3 when stride isn't 2 in order to pass the output verification.
// TODO(b/130206370) This looks like a bug and needs further investigation.
if (kernel_height == 3 && kernel_width == 3 && subsampling == 2) {
auto* depthwise_3x3_convolution_layer = new arm_compute::NEDepthwiseConvolutionLayer3x3();
layer.reset(depthwise_3x3_convolution_layer);
depthwise_3x3_convolution_layer->configure(
&input_tensor, &kernelTensor, &bias_tensor, &output_tensor,
pad_stride_info, group_output_channels, activation_info);
if (!depthwise_3x3_convolution_layer->validate(
&input_info, &kernel_info, &bias_info, &output_info,
pad_stride_info, group_output_channels, activation_info))
{
state.SkipWithError("validation failed");
return;
}
} else {
auto* depthwise_convolution_layer = new arm_compute::NEDepthwiseConvolutionLayer();
layer.reset(depthwise_convolution_layer);
depthwise_convolution_layer->configure(
&input_tensor, &kernelTensor, &bias_tensor, &output_tensor,
pad_stride_info, group_output_channels, activation_info);
if (!depthwise_convolution_layer->validate(
&input_info, &kernel_info, &bias_info, &output_info,
pad_stride_info, group_output_channels, activation_info))
{
state.SkipWithError("validation failed");
return;
}
}
} else {
auto* convolution_layer = new arm_compute::NEConvolutionLayer();
layer.reset(convolution_layer);
convolution_layer->configure(
&input_tensor, &kernelTensor, &bias_tensor, &output_tensor,
pad_stride_info, arm_compute::WeightsInfo(), dilation_info, activation_info,
true /* enable fast math */, groups);
if (!convolution_layer->validate(
&input_info, &kernel_info, &bias_info, &output_info,
pad_stride_info, arm_compute::WeightsInfo(), dilation_info, activation_info,
true /* enable fast math */, groups))
{
state.SkipWithError("validation failed");
return;
}
}
// Dry run to let ACL do one-time initializations.
arm_compute::CPPScheduler::get().set_num_threads(1);
layer->run();
for (auto _ : state) {
state.PauseTiming();
benchmark::utils::WipeCache();
benchmark::utils::PrefetchToL1(
input_tensor.buffer(),
batch_size * groups * group_input_channels * input_height * input_width * sizeof(float));
state.ResumeTiming();
layer->run();
}
// Validate outputs.
const std::string error_string = compare_with_convolution_f32_reference_output(
state, reinterpret_cast<const float*>(input_tensor.buffer()),
input_shape.total_size(),
reinterpret_cast<const float*>(kernelTensor.buffer()),
kernel_shape.total_size(),
reinterpret_cast<const float*>(bias_tensor.buffer()),
bias_shape.total_size(),
reinterpret_cast<const float*>(output_tensor.buffer()),
output_shape.total_size());
if (!error_string.empty()) {
state.SkipWithError(("validation failed: " + error_string).c_str());
return;
}
input_tensor.allocator()->free();
kernelTensor.allocator()->free();
bias_tensor.allocator()->free();
output_tensor.allocator()->free();
state.counters["Freq"] = benchmark::utils::GetCurrentCpuFrequency();
state.counters["FLOPS"] = benchmark::Counter(
uint64_t(state.iterations()) * 2 *
batch_size * output_height * output_width *
groups * group_input_channels * group_output_channels *
kernel_height * kernel_width,
benchmark::Counter::kIsRate);
}
#endif // BENCHMARK_ARM_COMPUTE_LIBRARY
// ShuffleNet v1 with 1 group.
static void ShuffleNetV1G1(benchmark::internal::Benchmark* b) {
b->ArgNames({"N", "H", "W", "KH", "KW", "PH", "PW", "S", "D", "G", "GCin", "GCout"});
/*************************** Conv 1 **************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 224, 224, 3, 3, 2, 2, 2, 1, 1, 3, 24});
/******************* Stage 2: stride-2 unit ******************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 56, 56, 1, 1, 0, 0, 1, 1, 1, 24, 36});
b->Args({1, 56, 56, 3, 3, 2, 2, 2, 1, 36, 1, 1});
b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 36, 120});
/******************* Stage 2: stride-1 units *****************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 144, 36});
b->Args({1, 28, 28, 3, 3, 2, 2, 2, 1, 36, 1, 1});
b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 36, 144});
/******************* Stage 3: stride-2 unit ******************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 144, 72});
b->Args({1, 28, 28, 3, 3, 2, 2, 2, 1, 72, 1, 1});
b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 72, 144});
/******************* Stage 3: stride-1 units *****************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 288, 72});
b->Args({1, 14, 14, 3, 3, 2, 2, 2, 1, 72, 1, 1});
b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 72, 288});
/******************* Stage 4: stride-2 unit ******************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 288, 144});
b->Args({1, 14, 14, 3, 3, 2, 2, 2, 1, 144, 1, 1});
b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 144, 288});
/******************* Stage 4: stride-1 units *****************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 576, 144});
b->Args({1, 7, 7, 3, 3, 2, 2, 2, 1, 144, 1, 1});
b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 144, 576});
}
// ShuffleNet v1 with 2 groups.
static void ShuffleNetV1G2(benchmark::internal::Benchmark* b) {
b->ArgNames({"N", "H", "W", "KH", "KW", "PH", "PW", "S", "D", "G", "GCin", "GCout"});
/*************************** Conv 1 **************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 224, 224, 3, 3, 2, 2, 2, 1, 1, 3, 24});
/******************* Stage 2: stride-2 unit ******************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 56, 56, 1, 1, 0, 0, 1, 1, 1, 24, 50});
b->Args({1, 56, 56, 3, 3, 2, 2, 2, 1, 50, 1, 1});
b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 2, 25, 88});
/******************* Stage 2: stride-1 units *****************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 2, 100, 25});
b->Args({1, 28, 28, 3, 3, 2, 2, 2, 1, 50, 1, 1});
b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 2, 25, 100});
/******************* Stage 3: stride-2 unit ******************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 2, 100, 50});
b->Args({1, 28, 28, 3, 3, 2, 2, 2, 1, 100, 1, 1});
b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 2, 50, 100});
/******************* Stage 3: stride-1 units *****************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 2, 200, 50});
b->Args({1, 14, 14, 3, 3, 2, 2, 2, 1, 100, 1, 1});
b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 2, 50, 200});
/******************* Stage 4: stride-2 unit ******************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 2, 200, 100});
b->Args({1, 14, 14, 3, 3, 2, 2, 2, 1, 200, 1, 1});
b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 2, 100, 200});
/******************* Stage 4: stride-1 units *****************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 2, 400, 100});
b->Args({1, 7, 7, 3, 3, 2, 2, 2, 1, 200, 1, 1});
b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 2, 100, 400});
}
// ShuffleNet v1 with 3 groups.
static void ShuffleNetV1G3(benchmark::internal::Benchmark* b) {
b->ArgNames({"N", "H", "W", "KH", "KW", "PH", "PW", "S", "D", "G", "GCin", "GCout"});
/*************************** Conv 1 **************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 224, 224, 3, 3, 2, 2, 2, 1, 1, 3, 24});
/******************* Stage 2: stride-2 unit ******************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 56, 56, 1, 1, 0, 0, 1, 1, 1, 24, 60});
b->Args({1, 56, 56, 3, 3, 2, 2, 2, 1, 60, 1, 1});
b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 3, 20, 72});
/******************* Stage 2: stride-1 units *****************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 3, 80, 20});
b->Args({1, 28, 28, 3, 3, 2, 2, 2, 1, 60, 1, 1});
b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 3, 20, 80});
/******************* Stage 3: stride-2 unit ******************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 3, 80, 40});
b->Args({1, 28, 28, 3, 3, 2, 2, 2, 1, 120, 1, 1});
b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 3, 40, 80});
/******************* Stage 3: stride-1 units *****************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 3, 160, 40});
b->Args({1, 14, 14, 3, 3, 2, 2, 2, 1, 120, 1, 1});
b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 3, 40, 160});
/******************* Stage 4: stride-2 unit ******************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 3, 160, 80});
b->Args({1, 14, 14, 3, 3, 2, 2, 2, 1, 240, 1, 1});
b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 3, 80, 160});
/******************* Stage 4: stride-1 units *****************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 3, 320, 80});
b->Args({1, 7, 7, 3, 3, 2, 2, 2, 1, 240, 1, 1});
b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 3, 80, 320});
}
// ShuffleNet v1 with 4 groups.
static void ShuffleNetV1G4(benchmark::internal::Benchmark* b) {
b->ArgNames({"N", "H", "W", "KH", "KW", "PH", "PW", "S", "D", "G", "GCin", "GCout"});
/*************************** Conv 1 **************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 224, 224, 3, 3, 2, 2, 2, 1, 1, 3, 24});
/******************* Stage 2: stride-2 unit ******************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 56, 56, 1, 1, 0, 0, 1, 1, 1, 24, 68});
b->Args({1, 56, 56, 3, 3, 2, 2, 2, 1, 68, 1, 1});
b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 4, 17, 62});
/******************* Stage 2: stride-1 units *****************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 4, 68, 17});
b->Args({1, 28, 28, 3, 3, 2, 2, 2, 1, 68, 1, 1});
b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 4, 17, 68});
/******************* Stage 3: stride-2 unit ******************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 4, 68, 34});
b->Args({1, 28, 28, 3, 3, 2, 2, 2, 1, 136, 1, 1});
b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 4, 34, 68});
/******************* Stage 3: stride-1 units *****************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 4, 136, 34});
b->Args({1, 14, 14, 3, 3, 2, 2, 2, 1, 136, 1, 1});
b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 4, 34, 136});
/******************* Stage 4: stride-2 unit ******************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 4, 136, 68});
b->Args({1, 14, 14, 3, 3, 2, 2, 2, 1, 272, 1, 1});
b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 4, 68, 136});
/******************* Stage 4: stride-1 units *****************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 4, 272, 68});
b->Args({1, 7, 7, 3, 3, 2, 2, 2, 1, 272, 1, 1});
b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 4, 68, 272});
}
// ShuffleNet v1 with 8 groups.
static void ShuffleNetV1G8(benchmark::internal::Benchmark* b) {
b->ArgNames({"N", "H", "W", "KH", "KW", "PH", "PW", "S", "D", "G", "GCin", "GCout"});
/*************************** Conv 1 **************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 224, 224, 3, 3, 2, 2, 2, 1, 1, 3, 24});
/******************* Stage 2: stride-2 unit ******************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 56, 56, 1, 1, 0, 0, 1, 1, 1, 24, 96});
b->Args({1, 56, 56, 3, 3, 2, 2, 2, 1, 96, 1, 1});
b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 8, 12, 45});
/******************* Stage 2: stride-1 units *****************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 8, 48, 12});
b->Args({1, 28, 28, 3, 3, 2, 2, 2, 1, 96, 1, 1});
b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 8, 12, 48});
/******************* Stage 3: stride-2 unit ******************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 8, 48, 24});
b->Args({1, 28, 28, 3, 3, 2, 2, 2, 1, 192, 1, 1});
b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 8, 24, 48});
/******************* Stage 3: stride-1 units *****************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 8, 96, 24});
b->Args({1, 14, 14, 3, 3, 2, 2, 2, 1, 192, 1, 1});
b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 8, 24, 96});
/******************* Stage 4: stride-2 unit ******************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 8, 96, 48});
b->Args({1, 14, 14, 3, 3, 2, 2, 2, 1, 384, 1, 1});
b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 8, 48, 96});
/******************* Stage 4: stride-1 units *****************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 8, 192, 48});
b->Args({1, 7, 7, 3, 3, 2, 2, 2, 1, 384, 1, 1});
b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 8, 48, 192});
}
// ShuffleNet v2 (0.5X scale)
static void ShuffleNetV2X05(benchmark::internal::Benchmark* b) {
b->ArgNames({"N", "H", "W", "KH", "KW", "PH", "PW", "S", "D", "G", "GCin", "GCout"});
/*************************** Conv 1 **************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 224, 224, 3, 3, 2, 2, 2, 1, 1, 3, 24});
/************************** Stage 2 **************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 56, 56, 3, 3, 2, 2, 2, 1, 24, 1, 1});
b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 24, 24});
b->Args({1, 56, 56, 1, 1, 0, 0, 1, 1, 1, 24, 24});
b->Args({1, 28, 28, 3, 3, 2, 2, 1, 1, 24, 1, 1});
/************************** Stage 3 **************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 28, 28, 3, 3, 2, 2, 2, 1, 48, 1, 1});
b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 48, 48});
b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 48, 48});
b->Args({1, 14, 14, 3, 3, 2, 2, 1, 1, 48, 1, 1});
/************************** Stage 4 **************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 14, 14, 3, 3, 2, 2, 2, 1, 96, 1, 1});
b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 96, 96});
b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 96, 96});
b->Args({1, 7, 7, 3, 3, 2, 2, 1, 1, 96, 1, 1});
/*************************** Conv 5 **************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 192, 1024});
}
// ShuffleNet v2 (1.0X scale)
static void ShuffleNetV2X10(benchmark::internal::Benchmark* b) {
b->ArgNames({"N", "H", "W", "KH", "KW", "PH", "PW", "S", "D", "G", "GCin", "GCout"});
/*************************** Conv 1 **************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 224, 224, 3, 3, 2, 2, 2, 1, 1, 3, 24});
/************************** Stage 2 **************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 56, 56, 3, 3, 2, 2, 2, 1, 24, 1, 1});
b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 24, 58});
b->Args({1, 56, 56, 1, 1, 0, 0, 1, 1, 1, 24, 58});
b->Args({1, 56, 56, 3, 3, 2, 2, 2, 1, 58, 1, 1});
b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 58, 58});
b->Args({1, 28, 28, 3, 3, 2, 2, 1, 1, 58, 1, 1});
/************************** Stage 3 **************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 28, 28, 3, 3, 2, 2, 2, 1, 116, 1, 1});
b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 116, 116});
b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 116, 116});
b->Args({1, 14, 14, 3, 3, 2, 2, 1, 1, 116, 1, 1});
/************************** Stage 4 **************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 14, 14, 3, 3, 2, 2, 2, 1, 232, 1, 1});
b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 232, 232});
b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 232, 232});
b->Args({1, 7, 7, 3, 3, 2, 2, 1, 1, 232, 1, 1});
/*************************** Conv 5 **************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 464, 1024});
}
// ShuffleNet v2 (1.5X scale)
static void ShuffleNetV2X15(benchmark::internal::Benchmark* b) {
b->ArgNames({"N", "H", "W", "KH", "KW", "PH", "PW", "S", "D", "G", "GCin", "GCout"});
/*************************** Conv 1 **************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 224, 224, 3, 3, 2, 2, 2, 1, 1, 3, 24});
/************************** Stage 2 **************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 56, 56, 3, 3, 2, 2, 2, 1, 24, 1, 1});
b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 24, 88});
b->Args({1, 56, 56, 1, 1, 0, 0, 1, 1, 1, 24, 88});
b->Args({1, 56, 56, 3, 3, 2, 2, 2, 1, 88, 1, 1});
b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 88, 88});
b->Args({1, 28, 28, 3, 3, 2, 2, 1, 1, 88, 1, 1});
/************************** Stage 3 **************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 28, 28, 3, 3, 2, 2, 2, 1, 176, 1, 1});
b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 176, 176});
b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 176, 176});
b->Args({1, 14, 14, 3, 3, 2, 2, 1, 1, 176, 1, 1});
/************************** Stage 4 **************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 14, 14, 3, 3, 2, 2, 2, 1, 352, 1, 1});
b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 352, 352});
b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 352, 352});
b->Args({1, 7, 7, 3, 3, 2, 2, 1, 1, 352, 1, 1});
/*************************** Conv 5 **************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 704, 1024});
}
// ShuffleNet v2 (2.0X scale)
static void ShuffleNetV2X20(benchmark::internal::Benchmark* b) {
b->ArgNames({"N", "H", "W", "KH", "KW", "PH", "PW", "S", "D", "G", "GCin", "GCout"});
/*************************** Conv 1 **************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 224, 224, 3, 3, 2, 2, 2, 1, 1, 3, 24});
/************************** Stage 2 **************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 56, 56, 3, 3, 2, 2, 2, 1, 24, 1, 1});
b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 24, 122});
b->Args({1, 56, 56, 1, 1, 0, 0, 1, 1, 1, 24, 122});
b->Args({1, 56, 56, 3, 3, 2, 2, 2, 1, 122, 1, 1});
b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 122, 122});
b->Args({1, 28, 28, 3, 3, 2, 2, 1, 1, 122, 1, 1});
/************************** Stage 3 **************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 28, 28, 3, 3, 2, 2, 2, 1, 244, 1, 1});
b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 244, 244});
b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 244, 244});
b->Args({1, 14, 14, 3, 3, 2, 2, 1, 1, 244, 1, 1});
/************************** Stage 4 **************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 14, 14, 3, 3, 2, 2, 2, 1, 488, 1, 1});
b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 488, 488});
b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 488, 488});
b->Args({1, 7, 7, 3, 3, 2, 2, 1, 1, 488, 1, 1});
/*************************** Conv 5 **************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 976, 2048});
}
static void MobileNetV1(benchmark::internal::Benchmark* b) {
b->ArgNames({"N", "H", "W", "KH", "KW", "PH", "PW", "S", "D", "G", "GCin", "GCout"});
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 224, 224, 3, 3, 2, 2, 2, 1, 1, 3, 32});
b->Args({1, 112, 112, 3, 3, 2, 2, 1, 1, 32, 1, 1});
b->Args({1, 112, 112, 1, 1, 0, 0, 1, 1, 1, 32, 64});
b->Args({1, 112, 112, 3, 3, 2, 2, 2, 1, 64, 1, 1});
b->Args({1, 56, 56, 1, 1, 0, 0, 1, 1, 1, 64, 128});
b->Args({1, 56, 56, 3, 3, 2, 2, 1, 1, 128, 1, 1});
b->Args({1, 56, 56, 1, 1, 0, 0, 1, 1, 1, 128, 128});
b->Args({1, 56, 56, 3, 3, 2, 2, 2, 1, 128, 1, 1});
b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 128, 256});
b->Args({1, 28, 28, 3, 3, 2, 2, 1, 1, 256, 1, 1});
b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 256, 256});
b->Args({1, 28, 28, 3, 3, 2, 2, 2, 1, 256, 1, 1});
b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 256, 512});
b->Args({1, 14, 14, 3, 3, 2, 2, 1, 1, 512, 1, 1});
b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 512, 512});
b->Args({1, 14, 14, 3, 3, 2, 2, 2, 1, 512, 1, 1});
b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 512, 1024});
b->Args({1, 7, 7, 3, 3, 2, 2, 1, 1, 1024, 1, 1});
b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 1024, 1024});
}
static void MobileNetV2(benchmark::internal::Benchmark* b) {
b->ArgNames({"N", "H", "W", "KH", "KW", "PH", "PW", "S", "D", "G", "GCin", "GCout"});
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 224, 224, 3, 3, 2, 2, 2, 1, 1, 3, 32});
/************************ Bottleneck 1 ***********************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 112, 112, 3, 3, 2, 2, 1, 1, 32, 1, 1});
b->Args({1, 112, 112, 1, 1, 0, 0, 1, 1, 1, 32, 16});
/************************ Bottleneck 2 ***********************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 112, 112, 1, 1, 0, 0, 1, 1, 1, 16, 96});
b->Args({1, 112, 112, 3, 3, 2, 2, 2, 1, 96, 1, 1});
b->Args({1, 56, 56, 1, 1, 0, 0, 1, 1, 1, 96, 24});
b->Args({1, 56, 56, 1, 1, 0, 0, 1, 1, 1, 24, 144});
b->Args({1, 56, 56, 3, 3, 2, 2, 1, 1, 144, 1, 1});
b->Args({1, 56, 56, 1, 1, 0, 0, 1, 1, 1, 144, 24});
/************************ Bottleneck 3 ***********************/
/* N H W KH KW PH PW S D G GCin GCout */
//b->Args({1, 56, 56, 1, 1, 0, 0, 1, 1, 1, 24, 144});
b->Args({1, 56, 56, 3, 3, 2, 2, 2, 1, 144, 1, 1});
b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 144, 32});
b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 32, 192});
b->Args({1, 28, 28, 3, 3, 2, 2, 1, 1, 192, 1, 1});
b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 192, 32});
//b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 32, 192});
//b->Args({1, 28, 28, 3, 3, 2, 2, 1, 1, 192, 1, 1});
//b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 192, 32});
/************************ Bottleneck 4 ***********************/
/* N H W KH KW PH PW S D G GCin GCout */
//b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 32, 192});
b->Args({1, 28, 28, 3, 3, 2, 2, 2, 1, 192, 1, 1});
b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 192, 64});
b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 64, 384});
b->Args({1, 14, 14, 3, 3, 2, 2, 1, 1, 384, 1, 1});
b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 384, 64});
//b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 64, 384});
//b->Args({1, 14, 14, 3, 3, 2, 2, 1, 1, 384, 1, 1});
//b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 384, 64});
//b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 64, 384});
//b->Args({1, 14, 14, 3, 3, 2, 2, 1, 1, 384, 1, 1});
//b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 384, 64});
/************************ Bottleneck 5 ***********************/
/* N H W KH KW PH PW S D G GCin GCout */
//b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 64, 384});
//b->Args({1, 14, 14, 3, 3, 2, 2, 1, 1, 384, 1, 1});
b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 384, 96});
b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 96, 576});
b->Args({1, 14, 14, 3, 3, 2, 2, 1, 1, 576, 1, 1});
b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 576, 96});
//b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 96, 576});
//b->Args({1, 14, 14, 3, 3, 2, 2, 1, 1, 576, 1, 1});
//b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 576, 96});
/************************ Bottleneck 6 ***********************/
/* N H W KH KW PH PW S D G GCin GCout */
//b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 96, 576});
b->Args({1, 14, 14, 3, 3, 2, 2, 2, 1, 576, 1, 1});
b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 576, 160});
b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 160, 960});
b->Args({1, 7, 7, 3, 3, 2, 2, 1, 1, 960, 1, 1});
b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 960, 160});
//b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 160, 960});
//b->Args({1, 7, 7, 3, 3, 2, 2, 1, 1, 960, 1, 1});
//b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 960, 160});
/************************ Bottleneck 7 ***********************/
/* N H W KH KW PH PW S D G GCin GCout */
//b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 160, 960});
//b->Args({1, 7, 7, 3, 3, 2, 2, 1, 1, 960, 1, 1});
b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 960, 320});
/******************** Pre-pooling Conv2D *********************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 320, 1280});
/******************** Post-pooling Conv2D ********************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1280, 1000});
}
static void MobileNetV3Small(benchmark::internal::Benchmark* b) {
b->ArgNames({"N", "H", "W", "KH", "KW", "PH", "PW", "S", "D", "G", "GCin", "GCout"});
/*********************** Initial Stage ***********************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 224, 224, 3, 3, 2, 2, 2, 1, 1, 3, 16});
/*********************** Bottleneck 1 ************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 112, 112, 3, 3, 2, 2, 2, 1, 16, 1, 1});
b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 16, 8});
b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 8, 16});
b->Args({1, 56, 56, 1, 1, 0, 0, 1, 1, 1, 16, 16});
/*********************** Bottleneck 2 ************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 56, 56, 1, 1, 0, 0, 1, 1, 1, 16, 72});
b->Args({1, 56, 56, 3, 3, 2, 2, 2, 1, 72, 1, 1});
b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 72, 24});
/*********************** Bottleneck 3 ************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 24, 88});
b->Args({1, 28, 28, 3, 3, 2, 2, 1, 1, 88, 1, 1});
b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 88, 24});
/*********************** Bottleneck 4 ************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 24, 96});
b->Args({1, 28, 28, 5, 5, 4, 4, 2, 1, 96, 1, 1});
b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 96, 24});
b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 24, 96});
b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 96, 40});
/*********************** Bottleneck 5 ************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 40, 240});
b->Args({1, 14, 14, 5, 5, 4, 4, 1, 1, 240, 1, 1});
b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 240, 64});
b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 64, 240});
b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 240, 40});
/*********************** Bottleneck 6 ************************/
/* N H W KH KW PH PW S D G GCin GCout */
//b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 40, 240});
//b->Args({1, 14, 14, 5, 5, 4, 4, 1, 1, 240, 1, 1});
//b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 240, 64});
//b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 64, 240});
//b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 240, 40});
/*********************** Bottleneck 7 ************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 40, 120});
b->Args({1, 14, 14, 5, 5, 4, 4, 1, 1, 120, 1, 1});
b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 120, 32});
b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 32, 120});
b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 120, 48});
/*********************** Bottleneck 8 ************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 48, 144});
b->Args({1, 14, 14, 5, 5, 4, 4, 1, 1, 144, 1, 1});
b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 144, 40});
b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 40, 144});
b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 144, 48});
/*********************** Bottleneck 9 ************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 48, 288});
b->Args({1, 14, 14, 5, 5, 4, 4, 2, 1, 288, 1, 1});
b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 288, 72});
b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 72, 288});
b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 288, 96});
/*********************** Bottleneck 10 ***********************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 96, 576});
b->Args({1, 7, 7, 5, 5, 4, 4, 1, 1, 576, 1, 1});
b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 576, 144});
b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 144, 576});
b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 576, 96});
/*********************** Bottleneck 11 ***********************/
/* N H W KH KW PH PW S D G GCin GCout */
//b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 96, 576});
//b->Args({1, 7, 7, 5, 5, 4, 4, 1, 1, 576, 1, 1});
//b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 576, 144});
//b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 144, 576});
//b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 576, 96});
/************************ Last Stage ************************/
/* N H W KH KW PH PW S D G GCin GCout */
//b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 96, 576});
b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 576, 1024});
b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1024, 1001});
}
static void MobileNetV3Large(benchmark::internal::Benchmark* b) {
b->ArgNames({"N", "H", "W", "KH", "KW", "PH", "PW", "S", "D", "G", "GCin", "GCout"});
/*********************** Initial Stage ***********************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 224, 224, 3, 3, 2, 2, 2, 1, 1, 3, 16});
/*********************** Bottleneck 1 ************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 112, 112, 3, 3, 2, 2, 1, 1, 16, 1, 1});
b->Args({1, 112, 112, 1, 1, 0, 0, 1, 1, 1, 16, 16});
/*********************** Bottleneck 2 ************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 112, 112, 1, 1, 0, 0, 1, 1, 1, 16, 64});
b->Args({1, 112, 112, 3, 3, 2, 2, 2, 1, 64, 1, 1});
b->Args({1, 56, 56, 1, 1, 0, 0, 1, 1, 1, 64, 24});
/*********************** Bottleneck 3 ************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 56, 56, 1, 1, 0, 0, 1, 1, 1, 24, 72});
b->Args({1, 56, 56, 3, 3, 2, 2, 1, 1, 72, 1, 1});
b->Args({1, 56, 56, 1, 1, 0, 0, 1, 1, 1, 72, 24});
/*********************** Bottleneck 4 ************************/
/* N H W KH KW PH PW S D G GCin GCout */
//b->Args({1, 56, 56, 1, 1, 0, 0, 1, 1, 1, 24, 72});
b->Args({1, 56, 56, 5, 5, 4, 4, 2, 1, 72, 1, 1});
b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 72, 24});
b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 24, 72});
b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 72, 40});
/*********************** Bottleneck 5 ************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 40, 120});
b->Args({1, 28, 28, 5, 5, 4, 4, 1, 1, 120, 1, 1});
b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 120, 32});
b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 32, 120});
b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 120, 40});
/*********************** Bottleneck 6 ************************/
/* N H W KH KW PH PW S D G GCin GCout */
//b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 40, 120});
//b->Args({1, 28, 28, 5, 5, 4, 4, 1, 1, 120, 1, 1});
//b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 120, 32});
//b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 32, 120});
//b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 120, 40});
/*********************** Bottleneck 7 ************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 40, 240});
b->Args({1, 28, 28, 3, 3, 2, 2, 2, 1, 240, 1, 1});
b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 240, 80});
/*********************** Bottleneck 8 ************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 80, 200});
b->Args({1, 14, 14, 3, 3, 2, 2, 1, 1, 200, 1, 1});
b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 200, 80});
/*********************** Bottleneck 9 ************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 80, 184});
b->Args({1, 14, 14, 3, 3, 2, 2, 1, 1, 184, 1, 1});
b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 184, 80});
/********************** Bottleneck 10 ***********************/
/* N H W KH KW PH PW S D G GCin GCout */
//b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 80, 184});
//b->Args({1, 14, 14, 3, 3, 2, 2, 1, 1, 184, 1, 1});
//b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 184, 80});
/********************** Bottleneck 11 ***********************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 80, 480});
b->Args({1, 14, 14, 3, 3, 2, 2, 1, 1, 480, 1, 1});
b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 480, 120});
b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 120, 480});
b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 480, 112});
/********************** Bottleneck 12 ***********************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 112, 672});
b->Args({1, 14, 14, 3, 3, 2, 2, 1, 1, 672, 1, 1});
b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 672, 168});
b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 168, 672});
b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 672, 112});
/********************** Bottleneck 13 ***********************/
/* N H W KH KW PH PW S D G GCin GCout */
//b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 112, 672});
b->Args({1, 14, 14, 5, 5, 4, 4, 2, 1, 672, 1, 1});
b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 672, 160});
/********************** Bottleneck 14 ***********************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 160, 960});
b->Args({1, 7, 7, 5, 5, 4, 4, 1, 1, 960, 1, 1});
b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 960, 240});
b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 240, 960});
b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 960, 160});
/********************** Bottleneck 15 ***********************/
/* N H W KH KW PH PW S D G GCin GCout */
//b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 160, 960});
//b->Args({1, 7, 7, 5, 5, 4, 4, 1, 1, 960, 1, 1});
//b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 960, 240});
//b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 240, 960});
//b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 960, 160});
/************************ Last Stage ***********************/
/* N H W KH KW PH PW S D G GCin GCout */
//b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 160, 960});
b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 960, 1280});
b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1280, 1001});
}
// SqueezeNet 1.0
static void SqueezeNetV10(benchmark::internal::Benchmark* b) {
b->ArgNames({"N", "H", "W", "KH", "KW", "PH", "PW", "S", "D", "G", "GCin", "GCout"});
/************************** Conv 1 *************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 224, 224, 7, 7, 6, 6, 2, 1, 1, 3, 96});
/************************** Fire 2 *************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 55, 55, 1, 1, 0, 0, 1, 1, 1, 96, 16});
b->Args({1, 55, 55, 1, 1, 0, 0, 1, 1, 1, 16, 64});
b->Args({1, 55, 55, 3, 3, 2, 2, 1, 1, 1, 16, 64});
/************************** Fire 3 *************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 56, 55, 1, 1, 0, 0, 1, 1, 1, 128, 16});
//b->Args({1, 55, 55, 1, 1, 0, 0, 1, 1, 1, 16, 64});
//b->Args({1, 55, 55, 3, 3, 2, 2, 1, 1, 1, 16, 64});
/************************** Fire 4 *************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 55, 55, 1, 1, 0, 0, 1, 1, 1, 128, 32});
b->Args({1, 55, 55, 1, 1, 0, 0, 1, 1, 1, 32, 128});
b->Args({1, 55, 55, 3, 3, 2, 2, 1, 1, 1, 32, 128});
/************************** Fire 5 *************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 27, 27, 1, 1, 0, 0, 1, 1, 1, 256, 32});
b->Args({1, 27, 27, 1, 1, 0, 0, 1, 1, 1, 32, 128});
b->Args({1, 27, 27, 3, 3, 2, 2, 1, 1, 1, 32, 128});
/************************** Fire 6 *************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 27, 27, 1, 1, 0, 0, 1, 1, 1, 256, 48});
b->Args({1, 27, 27, 1, 1, 0, 0, 1, 1, 1, 48, 192});
b->Args({1, 27, 27, 3, 3, 2, 2, 1, 1, 1, 48, 192});
/************************** Fire 7 *************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 27, 27, 1, 1, 0, 0, 1, 1, 1, 384, 48});
//b->Args({1, 27, 27, 1, 1, 0, 0, 1, 1, 1, 48, 192});
//b->Args({1, 27, 27, 3, 3, 2, 2, 1, 1, 1, 48, 192});
/************************** Fire 8 *************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 27, 27, 1, 1, 0, 0, 1, 1, 1, 384, 64});
b->Args({1, 27, 27, 1, 1, 0, 0, 1, 1, 1, 64, 256});
b->Args({1, 27, 27, 3, 3, 2, 2, 1, 1, 1, 64, 256});
/************************** Fire 9 *************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 13, 13, 1, 1, 0, 0, 1, 1, 1, 512, 64});
b->Args({1, 13, 13, 1, 1, 0, 0, 1, 1, 1, 64, 256});
b->Args({1, 13, 13, 3, 3, 2, 2, 1, 1, 1, 64, 256});
/************************* Conv 10 *************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 13, 13, 1, 1, 0, 0, 1, 1, 1, 512, 1000});
}
// SqueezeNet 1.1
static void SqueezeNetV11(benchmark::internal::Benchmark* b) {
b->ArgNames({"N", "H", "W", "KH", "KW", "PH", "PW", "S", "D", "G", "GCin", "GCout"});
/************************** Conv 1 *************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 224, 224, 3, 3, 2, 2, 2, 1, 1, 3, 64});
/************************** Fire 2 *************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 55, 55, 1, 1, 0, 0, 1, 1, 1, 64, 16});
b->Args({1, 55, 55, 1, 1, 0, 0, 1, 1, 1, 16, 64});
b->Args({1, 55, 55, 3, 3, 2, 2, 1, 1, 1, 16, 64});
/************************** Fire 3 *************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 55, 55, 1, 1, 0, 0, 1, 1, 1, 128, 16});
//b->Args({1, 55, 55, 1, 1, 0, 0, 1, 1, 1, 16, 64});
//b->Args({1, 55, 55, 3, 3, 2, 2, 1, 1, 1, 16, 64});
/************************** Fire 4 *************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 27, 27, 1, 1, 0, 0, 1, 1, 1, 128, 32});
b->Args({1, 27, 27, 1, 1, 0, 0, 1, 1, 1, 32, 128});
b->Args({1, 27, 27, 3, 3, 2, 2, 1, 1, 1, 32, 128});
/************************** Fire 5 *************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 27, 27, 1, 1, 0, 0, 1, 1, 1, 256, 32});
//b->Args({1, 27, 27, 1, 1, 0, 0, 1, 1, 1, 32, 128});
//b->Args({1, 27, 27, 3, 3, 2, 2, 1, 1, 1, 32, 128});
/************************** Fire 6 *************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 13, 13, 1, 1, 0, 0, 1, 1, 1, 256, 48});
b->Args({1, 13, 13, 1, 1, 0, 0, 1, 1, 1, 48, 192});
b->Args({1, 13, 13, 3, 3, 2, 2, 1, 1, 1, 48, 192});
/************************** Fire 7 *************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 13, 13, 1, 1, 0, 0, 1, 1, 1, 384, 48});
//b->Args({1, 13, 13, 1, 1, 0, 0, 1, 1, 1, 48, 192});
//b->Args({1, 13, 13, 3, 3, 2, 2, 1, 1, 1, 48, 192});
/************************** Fire 8 *************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 13, 13, 1, 1, 0, 0, 1, 1, 1, 384, 64});
b->Args({1, 13, 13, 1, 1, 0, 0, 1, 1, 1, 64, 256});
b->Args({1, 13, 13, 3, 3, 2, 2, 1, 1, 1, 64, 256});
/************************** Fire 9 *************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 13, 13, 1, 1, 0, 0, 1, 1, 1, 512, 64});
//b->Args({1, 13, 13, 1, 1, 0, 0, 1, 1, 1, 64, 256});
//b->Args({1, 13, 13, 3, 3, 2, 2, 1, 1, 1, 64, 256});
/************************* Conv 10 *************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 13, 13, 1, 1, 0, 0, 1, 1, 1, 512, 1000});
}
static void InceptionV3(benchmark::internal::Benchmark* b) {
b->ArgNames({"N", "H", "W", "KH", "KW", "PH", "PW", "S", "D", "G", "GCin", "GCout"});
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 299, 299, 3, 3, 0, 0, 2, 1, 1, 3, 32});
b->Args({1, 149, 149, 3, 3, 0, 0, 1, 1, 1, 32, 32});
b->Args({1, 147, 147, 3, 3, 2, 2, 1, 1, 1, 32, 64});
b->Args({1, 73, 73, 1, 1, 0, 0, 1, 1, 1, 64, 80});
b->Args({1, 73, 73, 3, 3, 0, 0, 1, 1, 1, 80, 192});
b->Args({1, 35, 35, 1, 1, 0, 0, 1, 1, 1, 192, 64});
b->Args({1, 35, 35, 1, 1, 0, 0, 1, 1, 1, 192, 48});
b->Args({1, 35, 35, 5, 5, 4, 4, 1, 1, 1, 48, 64});
b->Args({1, 35, 35, 3, 3, 2, 2, 1, 1, 1, 64, 96});
b->Args({1, 35, 35, 3, 3, 2, 2, 1, 1, 1, 96, 96});
b->Args({1, 35, 35, 1, 1, 0, 0, 1, 1, 1, 192, 32});
b->Args({1, 35, 35, 1, 1, 0, 0, 1, 1, 1, 256, 64});
b->Args({1, 35, 35, 1, 1, 0, 0, 1, 1, 1, 256, 48});
b->Args({1, 35, 35, 1, 1, 0, 0, 1, 1, 1, 288, 64});
b->Args({1, 35, 35, 1, 1, 0, 0, 1, 1, 1, 288, 48});
b->Args({1, 35, 35, 3, 3, 0, 0, 2, 1, 1, 288, 384});
b->Args({1, 35, 35, 3, 3, 0, 0, 2, 1, 1, 96, 96});
b->Args({1, 17, 17, 1, 1, 0, 0, 1, 1, 1, 768, 192});
b->Args({1, 17, 17, 1, 1, 0, 0, 1, 1, 1, 768, 128});
b->Args({1, 17, 17, 1, 7, 0, 6, 1, 1, 1, 128, 128});
b->Args({1, 17, 17, 7, 1, 6, 0, 1, 1, 1, 128, 192});
b->Args({1, 17, 17, 7, 1, 6, 0, 1, 1, 1, 128, 128});
b->Args({1, 17, 17, 1, 7, 0, 6, 1, 1, 1, 128, 192});
b->Args({1, 17, 17, 1, 1, 0, 0, 1, 1, 1, 768, 160});
b->Args({1, 17, 17, 1, 7, 0, 6, 1, 1, 1, 160, 160});
b->Args({1, 17, 17, 7, 1, 6, 0, 1, 1, 1, 160, 192});
b->Args({1, 17, 17, 7, 1, 6, 0, 1, 1, 1, 160, 160});
b->Args({1, 17, 17, 1, 7, 0, 6, 1, 1, 1, 160, 192});
b->Args({1, 17, 17, 1, 7, 0, 6, 1, 1, 1, 192, 192});
b->Args({1, 17, 17, 7, 1, 6, 0, 1, 1, 1, 192, 192});
b->Args({1, 17, 17, 3, 3, 0, 0, 2, 1, 1, 192, 320});
b->Args({1, 17, 17, 3, 3, 0, 0, 2, 1, 1, 192, 192});
b->Args({1, 8, 8, 1, 1, 0, 0, 1, 1, 1, 1280, 320});
b->Args({1, 8, 8, 1, 1, 0, 0, 1, 1, 1, 1280, 384});
b->Args({1, 8, 8, 1, 3, 0, 2, 1, 1, 1, 384, 384});
b->Args({1, 8, 8, 3, 1, 2, 0, 1, 1, 1, 384, 384});
b->Args({1, 8, 8, 1, 1, 0, 0, 1, 1, 1, 1280, 448});
b->Args({1, 8, 8, 3, 3, 2, 2, 1, 1, 1, 448, 384});
b->Args({1, 8, 8, 1, 1, 0, 0, 1, 1, 1, 1280, 192});
b->Args({1, 8, 8, 1, 1, 0, 0, 1, 1, 1, 2048, 320});
b->Args({1, 8, 8, 1, 1, 0, 0, 1, 1, 1, 2048, 384});
b->Args({1, 8, 8, 1, 1, 0, 0, 1, 1, 1, 2048, 448});
b->Args({1, 8, 8, 1, 1, 0, 0, 1, 1, 1, 2048, 192});
b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 2048, 1001});
}
static void ResNet18(benchmark::internal::Benchmark* b) {
b->ArgNames({"N", "H", "W", "KH", "KW", "PH", "PW", "S", "D", "G", "GCin", "GCout"});
/************************* Conv 1 *************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 224, 224, 7, 7, 6, 6, 2, 1, 1, 3, 64});
/************************ Conv 2.X ************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 56, 56, 3, 3, 2, 2, 1, 1, 1, 64, 64});
/************************ Conv 3.X ************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 56, 56, 3, 3, 2, 2, 2, 1, 1, 64, 128});
b->Args({1, 28, 28, 3, 3, 2, 2, 1, 1, 1, 128, 128});
b->Args({1, 56, 56, 1, 1, 0, 0, 2, 1, 1, 64, 128});
/************************ Conv 4.X ************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 28, 28, 3, 3, 2, 2, 2, 1, 1, 128, 256});
b->Args({1, 14, 14, 3, 3, 2, 2, 1, 1, 1, 256, 256});
b->Args({1, 28, 28, 1, 1, 0, 0, 2, 1, 1, 128, 256});
/************************ Conv 5.X ************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 14, 14, 3, 3, 2, 2, 2, 1, 1, 256, 512});
b->Args({1, 7, 7, 3, 3, 2, 2, 1, 1, 1, 512, 512});
b->Args({1, 14, 14, 1, 1, 0, 0, 2, 1, 1, 256, 512});
}
static void ResNet50(benchmark::internal::Benchmark* b) {
b->ArgNames({"N", "H", "W", "KH", "KW", "PH", "PW", "S", "D", "G", "GCin", "GCout"});
/************************* Conv 1 *************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 224, 224, 7, 7, 6, 6, 2, 1, 1, 3, 64});
/************************ Conv 2.1 ************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 56, 56, 1, 1, 0, 0, 1, 1, 1, 64, 64});
b->Args({1, 56, 56, 3, 3, 2, 2, 1, 1, 1, 64, 64});
b->Args({1, 56, 56, 1, 1, 0, 0, 1, 1, 1, 64, 256});
//b->Args({1, 56, 56, 1, 1, 0, 0, 1, 1, 1, 64, 256});
/************************ Conv 2.X ************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 56, 56, 1, 1, 0, 0, 1, 1, 1, 256, 64});
//b->Args({1, 56, 56, 3, 3, 2, 2, 1, 1, 1, 64, 64});
//b->Args({1, 56, 56, 1, 1, 0, 0, 1, 1, 1, 64, 256});
/************************ Conv 3.1 ************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 56, 56, 1, 1, 0, 0, 1, 1, 1, 256, 128});
b->Args({1, 56, 56, 3, 3, 2, 2, 2, 1, 1, 128, 128});
b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 128, 512});
b->Args({1, 56, 56, 1, 1, 0, 0, 2, 1, 1, 256, 512});
/************************ Conv 3.X ************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 512, 128});
b->Args({1, 28, 28, 3, 3, 2, 2, 1, 1, 1, 128, 128});
//b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 128, 512});
/************************ Conv 4.1 ************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 512, 256});
b->Args({1, 28, 28, 3, 3, 2, 2, 2, 1, 1, 256, 256});
b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 256, 1024});
b->Args({1, 28, 28, 1, 1, 0, 0, 2, 1, 1, 512, 1024});
/************************ Conv 4.X ************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 1024, 256});
b->Args({1, 14, 14, 3, 3, 2, 2, 1, 1, 1, 256, 256});
//b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 256, 1024});
/************************ Conv 5.1 ************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 1024, 512});
b->Args({1, 14, 14, 3, 3, 2, 2, 2, 1, 1, 512, 512});
b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 512, 2048});
b->Args({1, 14, 14, 1, 1, 0, 0, 2, 1, 1, 1024, 2048});
/************************ Conv 5.X ************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 2048, 512});
b->Args({1, 7, 7, 3, 3, 2, 2, 1, 1, 1, 512, 512});
//b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 512, 2048});
}
static void VGG(benchmark::internal::Benchmark* b) {
b->ArgNames({"N", "H", "W", "KH", "KW", "PH", "PW", "S", "D", "G", "GCin", "GCout"});
/************************* Conv 1.1 ************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 224, 224, 3, 3, 2, 2, 1, 1, 1, 3, 64});
/************************* Conv 1.2 ************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 224, 224, 3, 3, 2, 2, 1, 1, 1, 64, 64});
/************************* Conv 2.1 ************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 112, 112, 3, 3, 2, 2, 1, 1, 1, 64, 128});
/************************* Conv 2.2 ************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 112, 112, 3, 3, 2, 2, 1, 1, 1, 128, 128});
/************************* Conv 3.1 ************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 56, 56, 3, 3, 2, 2, 1, 1, 1, 128, 256});
/************************* Conv 3.2 ************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 56, 56, 3, 3, 2, 2, 1, 1, 1, 256, 256});
/************************* Conv 3.3 ************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 56, 56, 1, 1, 0, 0, 1, 1, 1, 256, 256});
/************************* Conv 4.1 ************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 28, 28, 3, 3, 2, 2, 1, 1, 1, 256, 512});
/************************* Conv 4.2 ************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 28, 28, 3, 3, 2, 2, 1, 1, 1, 512, 512});
/************************* Conv 4.3 ************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 512, 512});
/************************* Conv 5.X ************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 14, 14, 3, 3, 2, 2, 1, 1, 1, 512, 512});
/************************* Conv 5.3 ************************/
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 512, 512});
}
// SRCNN (9-1-5)
static void SRCNN915(benchmark::internal::Benchmark* b) {
b->ArgNames({"N", "H", "W", "KH", "KW", "PH", "PW", "S", "D", "G", "GCin", "GCout"});
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 384, 384, 9, 9, 0, 0, 1, 1, 1, 1, 64});
b->Args({1, 376, 376, 1, 1, 0, 0, 1, 1, 1, 64, 32});
b->Args({1, 376, 376, 5, 5, 0, 0, 1, 1, 1, 32, 1});
}
// SRCNN (9-3-5)
static void SRCNN935(benchmark::internal::Benchmark* b) {
b->ArgNames({"N", "H", "W", "KH", "KW", "PH", "PW", "S", "D", "G", "GCin", "GCout"});
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 384, 384, 9, 9, 0, 0, 1, 1, 1, 1, 64});
b->Args({1, 376, 376, 3, 3, 0, 0, 1, 1, 1, 64, 32});
b->Args({1, 374, 374, 5, 5, 0, 0, 1, 1, 1, 32, 1});
}
// SRCNN (9-5-5)
static void SRCNN955(benchmark::internal::Benchmark* b) {
b->ArgNames({"N", "H", "W", "KH", "KW", "PH", "PW", "S", "D", "G", "GCin", "GCout"});
/* N H W KH KW PH PW S D G GCin GCout */
b->Args({1, 384, 384, 9, 9, 0, 0, 1, 1, 1, 1, 64});
b->Args({1, 376, 376, 5, 5, 0, 0, 1, 1, 1, 64, 32});
b->Args({1, 372, 372, 5, 5, 0, 0, 1, 1, 1, 32, 1});
}
BENCHMARK_CAPTURE(xnnpack_convolution_f32, mobilenet_v1, "MobileNet v1")->Apply(MobileNetV1)->UseRealTime();
BENCHMARK_CAPTURE(xnnpack_convolution_f32, mobilenet_v2, "MobileNet v2")->Apply(MobileNetV2)->UseRealTime();
BENCHMARK_CAPTURE(xnnpack_convolution_f32, mobilenet_v3_small, "MobileNet v3 Small")->Apply(MobileNetV3Small)->UseRealTime();
BENCHMARK_CAPTURE(xnnpack_convolution_f32, mobilenet_v3_large, "MobileNet v3 Large")->Apply(MobileNetV3Large)->UseRealTime();
BENCHMARK_CAPTURE(xnnpack_convolution_f32, shufflenet_v1_g1, "ShuffleNet v1 (1 group)")->Apply(ShuffleNetV1G1)->UseRealTime();
BENCHMARK_CAPTURE(xnnpack_convolution_f32, shufflenet_v1_g2, "ShuffleNet v1 (2 groups)")->Apply(ShuffleNetV1G2)->UseRealTime();
BENCHMARK_CAPTURE(xnnpack_convolution_f32, shufflenet_v1_g3, "ShuffleNet v1 (3 groups)")->Apply(ShuffleNetV1G3)->UseRealTime();
BENCHMARK_CAPTURE(xnnpack_convolution_f32, shufflenet_v1_g4, "ShuffleNet v1 (4 groups)")->Apply(ShuffleNetV1G4)->UseRealTime();
BENCHMARK_CAPTURE(xnnpack_convolution_f32, shufflenet_v1_g8, "ShuffleNet v1 (8 groups)")->Apply(ShuffleNetV1G8)->UseRealTime();
BENCHMARK_CAPTURE(xnnpack_convolution_f32, shufflenet_v2_x05, "ShuffleNet v2 0.5X")->Apply(ShuffleNetV2X05)->UseRealTime();
BENCHMARK_CAPTURE(xnnpack_convolution_f32, shufflenet_v2_x10, "ShuffleNet v2 1.0X")->Apply(ShuffleNetV2X10)->UseRealTime();
BENCHMARK_CAPTURE(xnnpack_convolution_f32, shufflenet_v2_x15, "ShuffleNet v2 1.5X")->Apply(ShuffleNetV2X15)->UseRealTime();
BENCHMARK_CAPTURE(xnnpack_convolution_f32, shufflenet_v2_x20, "ShuffleNet v2 2.0X")->Apply(ShuffleNetV2X20)->UseRealTime();
BENCHMARK_CAPTURE(xnnpack_convolution_f32, squeezenet_v10, "SqueezeNet 1.0")->Apply(SqueezeNetV10)->UseRealTime();
BENCHMARK_CAPTURE(xnnpack_convolution_f32, squeezenet_v11, "SqueezeNet 1.1")->Apply(SqueezeNetV11)->UseRealTime();
BENCHMARK_CAPTURE(xnnpack_convolution_f32, inception_v3, "Inception v3")->Apply(InceptionV3)->UseRealTime();
BENCHMARK_CAPTURE(xnnpack_convolution_f32, resnet18, "ResNet-18")->Apply(ResNet18)->UseRealTime();
BENCHMARK_CAPTURE(xnnpack_convolution_f32, resnet50, "ResNet-50")->Apply(ResNet50)->UseRealTime();
BENCHMARK_CAPTURE(xnnpack_convolution_f32, vgg, "VGG")->Apply(VGG)->UseRealTime();
BENCHMARK_CAPTURE(xnnpack_convolution_f32, srcnn915, "SRCNN (9-1-5)")->Apply(SRCNN915)->UseRealTime();
BENCHMARK_CAPTURE(xnnpack_convolution_f32, srcnn935, "SRCNN (9-3-5)")->Apply(SRCNN935)->UseRealTime();
BENCHMARK_CAPTURE(xnnpack_convolution_f32, srcnn955, "SRCNN (9-5-5)")->Apply(SRCNN955)->UseRealTime();
BENCHMARK_CAPTURE(xnnpack_convolution_q8, mobilenet_v1, "MobileNet v1")->Apply(MobileNetV1)->UseRealTime();
BENCHMARK_CAPTURE(xnnpack_convolution_q8, mobilenet_v2, "MobileNet v2")->Apply(MobileNetV2)->UseRealTime();
BENCHMARK_CAPTURE(xnnpack_convolution_q8, mobilenet_v3_small, "MobileNet v3 Small")->Apply(MobileNetV3Small)->UseRealTime();
BENCHMARK_CAPTURE(xnnpack_convolution_q8, mobilenet_v3_large, "MobileNet v3 Large")->Apply(MobileNetV3Large)->UseRealTime();
BENCHMARK_CAPTURE(xnnpack_convolution_q8, shufflenet_v1_g1, "ShuffleNet v1 (1 group)")->Apply(ShuffleNetV1G1)->UseRealTime();
BENCHMARK_CAPTURE(xnnpack_convolution_q8, shufflenet_v1_g2, "ShuffleNet v1 (2 groups)")->Apply(ShuffleNetV1G2)->UseRealTime();
BENCHMARK_CAPTURE(xnnpack_convolution_q8, shufflenet_v1_g3, "ShuffleNet v1 (3 groups)")->Apply(ShuffleNetV1G3)->UseRealTime();
BENCHMARK_CAPTURE(xnnpack_convolution_q8, shufflenet_v1_g4, "ShuffleNet v1 (4 groups)")->Apply(ShuffleNetV1G4)->UseRealTime();
BENCHMARK_CAPTURE(xnnpack_convolution_q8, shufflenet_v1_g8, "ShuffleNet v1 (8 groups)")->Apply(ShuffleNetV1G8)->UseRealTime();
BENCHMARK_CAPTURE(xnnpack_convolution_q8, shufflenet_v2_x05, "ShuffleNet v2 0.5X")->Apply(ShuffleNetV2X05)->UseRealTime();
BENCHMARK_CAPTURE(xnnpack_convolution_q8, shufflenet_v2_x10, "ShuffleNet v2 1.0X")->Apply(ShuffleNetV2X10)->UseRealTime();
BENCHMARK_CAPTURE(xnnpack_convolution_q8, shufflenet_v2_x15, "ShuffleNet v2 1.5X")->Apply(ShuffleNetV2X15)->UseRealTime();
BENCHMARK_CAPTURE(xnnpack_convolution_q8, shufflenet_v2_x20, "ShuffleNet v2 2.0X")->Apply(ShuffleNetV2X20)->UseRealTime();
BENCHMARK_CAPTURE(xnnpack_convolution_q8, squeezenet_v10, "SqueezeNet 1.0")->Apply(SqueezeNetV10)->UseRealTime();
BENCHMARK_CAPTURE(xnnpack_convolution_q8, squeezenet_v11, "SqueezeNet 1.1")->Apply(SqueezeNetV11)->UseRealTime();
BENCHMARK_CAPTURE(xnnpack_convolution_q8, inception_v3, "Inception v3")->Apply(InceptionV3)->UseRealTime();
BENCHMARK_CAPTURE(xnnpack_convolution_q8, resnet18, "ResNet-18")->Apply(ResNet18)->UseRealTime();
BENCHMARK_CAPTURE(xnnpack_convolution_q8, resnet50, "ResNet-50")->Apply(ResNet50)->UseRealTime();
BENCHMARK_CAPTURE(xnnpack_convolution_q8, vgg, "VGG")->Apply(VGG)->UseRealTime();
BENCHMARK_CAPTURE(xnnpack_convolution_q8, srcnn915, "SRCNN (9-1-5)")->Apply(SRCNN915)->UseRealTime();
BENCHMARK_CAPTURE(xnnpack_convolution_q8, srcnn935, "SRCNN (9-3-5)")->Apply(SRCNN935)->UseRealTime();
BENCHMARK_CAPTURE(xnnpack_convolution_q8, srcnn955, "SRCNN (9-5-5)")->Apply(SRCNN955)->UseRealTime();
#ifdef BENCHMARK_TENSORFLOW_LITE
BENCHMARK_CAPTURE(tflite_convolution_f32, mobilenet_v1, "MobileNet v1")->Apply(MobileNetV1)->UseRealTime();
BENCHMARK_CAPTURE(tflite_convolution_f32, mobilenet_v2, "MobileNet v2")->Apply(MobileNetV2)->UseRealTime();
BENCHMARK_CAPTURE(tflite_convolution_f32, mobilenet_v3_small, "MobileNet v3 Small")->Apply(MobileNetV3Small)->UseRealTime();
BENCHMARK_CAPTURE(tflite_convolution_f32, mobilenet_v3_large, "MobileNet v3 Large")->Apply(MobileNetV3Large)->UseRealTime();
BENCHMARK_CAPTURE(tflite_convolution_f32, shufflenet_v1_g1, "ShuffleNet v1 (1 group)")->Apply(ShuffleNetV1G1)->UseRealTime();
BENCHMARK_CAPTURE(tflite_convolution_f32, shufflenet_v1_g2, "ShuffleNet v1 (2 groups)")->Apply(ShuffleNetV1G2)->UseRealTime();
BENCHMARK_CAPTURE(tflite_convolution_f32, shufflenet_v1_g3, "ShuffleNet v1 (3 groups)")->Apply(ShuffleNetV1G3)->UseRealTime();
BENCHMARK_CAPTURE(tflite_convolution_f32, shufflenet_v1_g4, "ShuffleNet v1 (4 groups)")->Apply(ShuffleNetV1G4)->UseRealTime();
BENCHMARK_CAPTURE(tflite_convolution_f32, shufflenet_v1_g8, "ShuffleNet v1 (8 groups)")->Apply(ShuffleNetV1G8)->UseRealTime();
BENCHMARK_CAPTURE(tflite_convolution_f32, shufflenet_v2_x05, "ShuffleNet v2 0.5X")->Apply(ShuffleNetV2X05)->UseRealTime();
BENCHMARK_CAPTURE(tflite_convolution_f32, shufflenet_v2_x10, "ShuffleNet v2 1.0X")->Apply(ShuffleNetV2X10)->UseRealTime();
BENCHMARK_CAPTURE(tflite_convolution_f32, shufflenet_v2_x15, "ShuffleNet v2 1.5X")->Apply(ShuffleNetV2X15)->UseRealTime();
BENCHMARK_CAPTURE(tflite_convolution_f32, shufflenet_v2_x20, "ShuffleNet v2 2.0X")->Apply(ShuffleNetV2X20)->UseRealTime();
BENCHMARK_CAPTURE(tflite_convolution_f32, squeezenet_v10, "SqueezeNet 1.0")->Apply(SqueezeNetV10)->UseRealTime();
BENCHMARK_CAPTURE(tflite_convolution_f32, squeezenet_v11, "SqueezeNet 1.1")->Apply(SqueezeNetV11)->UseRealTime();
BENCHMARK_CAPTURE(tflite_convolution_f32, inception_v3, "Inception v3")->Apply(InceptionV3)->UseRealTime();
BENCHMARK_CAPTURE(tflite_convolution_f32, resnet18, "ResNet-18")->Apply(ResNet18)->UseRealTime();
BENCHMARK_CAPTURE(tflite_convolution_f32, resnet50, "ResNet-50")->Apply(ResNet50)->UseRealTime();
BENCHMARK_CAPTURE(tflite_convolution_f32, vgg, "VGG")->Apply(VGG)->UseRealTime();
BENCHMARK_CAPTURE(tflite_convolution_f32, srcnn915, "SRCNN (9-1-5)")->Apply(SRCNN915)->UseRealTime();
BENCHMARK_CAPTURE(tflite_convolution_f32, srcnn935, "SRCNN (9-3-5)")->Apply(SRCNN935)->UseRealTime();
BENCHMARK_CAPTURE(tflite_convolution_f32, srcnn955, "SRCNN (9-5-5)")->Apply(SRCNN955)->UseRealTime();
#endif // BENCHMARK_TENSORFLOW_LITE
#ifdef BENCHMARK_ARM_COMPUTE_LIBRARY
BENCHMARK_CAPTURE(armcl_convolution_f32, mobilenet_v1, "MobileNet v1")->Apply(MobileNetV1)->UseRealTime();
BENCHMARK_CAPTURE(armcl_convolution_f32, mobilenet_v2, "MobileNet v2")->Apply(MobileNetV2)->UseRealTime();
BENCHMARK_CAPTURE(armcl_convolution_f32, shufflenet_v1_g1, "ShuffleNet v1 (1 group)")->Apply(ShuffleNetV1G1)->UseRealTime();
BENCHMARK_CAPTURE(armcl_convolution_f32, shufflenet_v1_g2, "ShuffleNet v1 (2 groups)")->Apply(ShuffleNetV1G2)->UseRealTime();
BENCHMARK_CAPTURE(armcl_convolution_f32, shufflenet_v1_g3, "ShuffleNet v1 (3 groups)")->Apply(ShuffleNetV1G3)->UseRealTime();
BENCHMARK_CAPTURE(armcl_convolution_f32, shufflenet_v1_g4, "ShuffleNet v1 (4 groups)")->Apply(ShuffleNetV1G4)->UseRealTime();
BENCHMARK_CAPTURE(armcl_convolution_f32, shufflenet_v1_g8, "ShuffleNet v1 (8 groups)")->Apply(ShuffleNetV1G8)->UseRealTime();
BENCHMARK_CAPTURE(armcl_convolution_f32, shufflenet_v2_x05, "ShuffleNet v2 0.5X")->Apply(ShuffleNetV2X05)->UseRealTime();
BENCHMARK_CAPTURE(armcl_convolution_f32, shufflenet_v2_x10, "ShuffleNet v2 1.0X")->Apply(ShuffleNetV2X10)->UseRealTime();
BENCHMARK_CAPTURE(armcl_convolution_f32, shufflenet_v2_x15, "ShuffleNet v2 1.5X")->Apply(ShuffleNetV2X15)->UseRealTime();
BENCHMARK_CAPTURE(armcl_convolution_f32, shufflenet_v2_x20, "ShuffleNet v2 2.0X")->Apply(ShuffleNetV2X20)->UseRealTime();
BENCHMARK_CAPTURE(armcl_convolution_f32, squeezenet_v10, "SqueezeNet 1.0")->Apply(SqueezeNetV10)->UseRealTime();
BENCHMARK_CAPTURE(armcl_convolution_f32, squeezenet_v11, "SqueezeNet 1.1")->Apply(SqueezeNetV11)->UseRealTime();
BENCHMARK_CAPTURE(armcl_convolution_f32, inception_v3, "Inception v3")->Apply(InceptionV3)->UseRealTime();
BENCHMARK_CAPTURE(armcl_convolution_f32, resnet18, "ResNet-18")->Apply(ResNet18)->UseRealTime();
BENCHMARK_CAPTURE(armcl_convolution_f32, resnet50, "ResNet-50")->Apply(ResNet50)->UseRealTime();
BENCHMARK_CAPTURE(armcl_convolution_f32, vgg, "VGG")->Apply(VGG)->UseRealTime();
BENCHMARK_CAPTURE(armcl_convolution_f32, srcnn915, "SRCNN (9-1-5)")->Apply(SRCNN915)->UseRealTime();
BENCHMARK_CAPTURE(armcl_convolution_f32, srcnn935, "SRCNN (9-3-5)")->Apply(SRCNN935)->UseRealTime();
BENCHMARK_CAPTURE(armcl_convolution_f32, srcnn955, "SRCNN (9-5-5)")->Apply(SRCNN955)->UseRealTime();
#endif // BENCHMARK_ARM_COMPUTE_LIBRARY
#ifndef XNNPACK_BENCHMARK_NO_MAIN
BENCHMARK_MAIN();
#endif