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Fix minor typos 

1. bechmark -> benchmark
2. MMN -> MNN
3. nomalize -> normalize
4. paramater -> parameter
5. tflie -> tflite
This commit is contained in:
Sungmann Cho 2019-05-14 08:29:37 +09:00
parent ac335b377f
commit 455786f0dc
11 changed files with 65 additions and 65 deletions
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2
benchmark/bench_android.sh
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@ -74,7 +74,7 @@ function bench_android() {
adb shell "echo Build Flags: ABI=$ABI OpenMP=$OPENMP Vulkan=$VULKAN OpenCL=$OPENCL >> $ANDROID_DIR/benchmark.txt" adb shell "echo Build Flags: ABI=$ABI OpenMP=$OPENMP Vulkan=$VULKAN OpenCL=$OPENCL >> $ANDROID_DIR/benchmark.txt"
#benchmark CPU #benchmark CPU
adb shell "LD_LIBRARY_PATH=$ANDROID_DIR $ANDROID_DIR/benchmark.out $ANDROID_DIR/benchmark_models $RUN_LOOP $FORWARD_TYPE 2>$ANDROID_DIR/benchmark.err >> $ANDROID_DIR/benchmark.txt" adb shell "LD_LIBRARY_PATH=$ANDROID_DIR $ANDROID_DIR/benchmark.out $ANDROID_DIR/benchmark_models $RUN_LOOP $FORWARD_TYPE 2>$ANDROID_DIR/benchmark.err >> $ANDROID_DIR/benchmark.txt"
#bechmark Vulkan #benchmark Vulkan
adb shell "LD_LIBRARY_PATH=$ANDROID_DIR $ANDROID_DIR/benchmark.out $ANDROID_DIR/benchmark_models $RUN_LOOP 7 2>$ANDROID_DIR/benchmark.err >> $ANDROID_DIR/benchmark.txt" adb shell "LD_LIBRARY_PATH=$ANDROID_DIR $ANDROID_DIR/benchmark.out $ANDROID_DIR/benchmark_models $RUN_LOOP 7 2>$ANDROID_DIR/benchmark.err >> $ANDROID_DIR/benchmark.txt"
#benchmark OpenGL #benchmark OpenGL
#adb shell "LD_LIBRARY_PATH=$ANDROID_DIR $ANDROID_DIR/benchmark.out $ANDROID_DIR/benchmark_models 10 6 2>$ANDROID_DIR/benchmark.err >> $ANDROID_DIR/benchmark.txt" #adb shell "LD_LIBRARY_PATH=$ANDROID_DIR $ANDROID_DIR/benchmark.out $ANDROID_DIR/benchmark_models 10 6 2>$ANDROID_DIR/benchmark.err >> $ANDROID_DIR/benchmark.txt"

10
source/backend/cpu/CPUInnerProduct.cpp
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@ -18,19 +18,19 @@ namespace MNN {
class CPUInnerProductExecutor : public Execution { class CPUInnerProductExecutor : public Execution {
public: public:
CPUInnerProductExecutor(Backend *bn, const MNN::Op *op) : Execution(bn) { CPUInnerProductExecutor(Backend *bn, const MNN::Op *op) : Execution(bn) {
auto paramater = op->main_as_InnerProduct(); auto parameter = op->main_as_InnerProduct();
int outputCount = paramater->outputCount(); int outputCount = parameter->outputCount();
int srcCount = paramater->weight()->size() / outputCount; int srcCount = parameter->weight()->size() / outputCount;
mWeight.reset(CPUConvolution::reorderWeightSize(srcCount, outputCount, 1, 4)); mWeight.reset(CPUConvolution::reorderWeightSize(srcCount, outputCount, 1, 4));
if (mWeight.get() == nullptr) { if (mWeight.get() == nullptr) {
mValid = false; mValid = false;
return; return;
} }
mWeight.clear(); mWeight.clear();
CPUConvolution::reorderWeight(mWeight.get(), paramater->weight()->data(), srcCount, outputCount, 1, 4); CPUConvolution::reorderWeight(mWeight.get(), parameter->weight()->data(), srcCount, outputCount, 1, 4);
mBias.reset(ALIGN_UP4(outputCount)); mBias.reset(ALIGN_UP4(outputCount));
mBias.clear(); mBias.clear();
::memcpy(mBias.get(), paramater->bias()->data(), paramater->bias()->size() * sizeof(float)); ::memcpy(mBias.get(), parameter->bias()->data(), parameter->bias()->size() * sizeof(float));
mInputPad.reset(new Tensor(2)); mInputPad.reset(new Tensor(2));
mOutputPad.reset(new Tensor(2)); mOutputPad.reset(new Tensor(2));
} }

8
source/backend/vulkan/execution/VulkanGroupConvolution.cpp
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@ -13,7 +13,7 @@
namespace MNN { namespace MNN {
VulkanGroupConvolution::VulkanGroupConvolution(const Op *op, Backend *backend) VulkanGroupConvolution::VulkanGroupConvolution(const Op *op, Backend *backend)
: Execution(backend), mTempSrc(4), mTempDst(4) { : Execution(backend), mTempSrc(4), mTempDst(4) {
mConvParamater = op->main_as_Convolution2D(); mConvParameter = op->main_as_Convolution2D();
mBackend = static_cast<VulkanBackend *>(backend); mBackend = static_cast<VulkanBackend *>(backend);
} }
@ -31,12 +31,12 @@ ErrorCode VulkanGroupConvolution::onExecute(const std::vector<Tensor *> &inputs,
ErrorCode VulkanGroupConvolution::onResize(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) { ErrorCode VulkanGroupConvolution::onResize(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
auto input = inputs[0]; auto input = inputs[0];
auto output = outputs[0]; auto output = outputs[0];
const int group = mConvParamater->common()->group(); const int group = mConvParameter->common()->group();
mTempInputs = std::vector<Tensor *>{&mTempSrc}; mTempInputs = std::vector<Tensor *>{&mTempSrc};
mTempOutputs = std::vector<Tensor *>{&mTempDst}; mTempOutputs = std::vector<Tensor *>{&mTempDst};
if (mSubConvolutions.empty()) { if (mSubConvolutions.empty()) {
mSubConvolutions.resize(group); mSubConvolutions.resize(group);
const auto convReal = mConvParamater; const auto convReal = mConvParameter;
const auto common = convReal->common(); const auto common = convReal->common();
const auto outputCount = common->outputCount(); const auto outputCount = common->outputCount();
const int fh = common->kernelY(); const int fh = common->kernelY();
@ -61,7 +61,7 @@ ErrorCode VulkanGroupConvolution::onResize(const std::vector<Tensor *> &inputs,
const float *curWeightPtr = source + i * groupWeightSize; const float *curWeightPtr = source + i * groupWeightSize;
const float *curBiasPtr = convReal->bias()->data() + i * groupCO; const float *curBiasPtr = convReal->bias()->data() + i * groupCO;
std::shared_ptr<Execution> subConvolution(VulkanConvolutionImpl::create( std::shared_ptr<Execution> subConvolution(VulkanConvolutionImpl::create(
mBackend, mConvParamater->common(), input, output, curWeightPtr, curBiasPtr, groupCI, groupCO)); mBackend, mConvParameter->common(), input, output, curWeightPtr, curBiasPtr, groupCI, groupCO));
std::get<1>(mSubConvolutions[i]) = subConvolution; std::get<1>(mSubConvolutions[i]) = subConvolution;
} }
} }

2
source/backend/vulkan/execution/VulkanGroupConvolution.hpp
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@ -24,7 +24,7 @@ private:
Tensor mTempDst; Tensor mTempDst;
std::vector<Tensor *> mTempInputs; std::vector<Tensor *> mTempInputs;
std::vector<Tensor *> mTempOutputs; std::vector<Tensor *> mTempOutputs;
const Convolution2D *mConvParamater; const Convolution2D *mConvParameter;
std::vector<std::tuple<std::shared_ptr<VulkanCommandPool::Buffer>, std::shared_ptr<Execution>, std::vector<std::tuple<std::shared_ptr<VulkanCommandPool::Buffer>, std::shared_ptr<Execution>,
std::shared_ptr<VulkanCommandPool::Buffer>>> std::shared_ptr<VulkanCommandPool::Buffer>>>
mSubConvolutions; mSubConvolutions;

72
source/backend/vulkan/execution/VulkanNormlize.cpp → source/backend/vulkan/execution/VulkanNormalize.cpp
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@ -1,12 +1,12 @@
// //
// VulkanNormlize.cpp // VulkanNormalize.cpp
// MNN // MNN
// //
// Created by MNN on 2019/01/31. // Created by MNN on 2019/01/31.
// Copyright © 2018, Alibaba Group Holding Limited // Copyright © 2018, Alibaba Group Holding Limited
// //
#include "VulkanNormlize.hpp" #include "VulkanNormalize.hpp"
#include "Macro.h" #include "Macro.h"
#include "TensorUtils.hpp" #include "TensorUtils.hpp"
@ -17,11 +17,11 @@ struct GpuParam {
float eps; float eps;
}; };
VulkanNormlize::VulkanNormlize(const Op* op, Backend* bn) : VulkanBasicExecution(bn) { VulkanNormalize::VulkanNormalize(const Op* op, Backend* bn) : VulkanBasicExecution(bn) {
auto normlizeParam = op->main_as_Normalize(); auto normalizeParam = op->main_as_Normalize();
mEps = normlizeParam->eps(); mEps = normalizeParam->eps();
std::vector<VkDescriptorType> VulkanNormlizeTypes{ std::vector<VkDescriptorType> VulkanNormalizeTypes{
VK_DESCRIPTOR_TYPE_STORAGE_IMAGE, VK_DESCRIPTOR_TYPE_STORAGE_IMAGE,
VK_DESCRIPTOR_TYPE_COMBINED_IMAGE_SAMPLER, VK_DESCRIPTOR_TYPE_COMBINED_IMAGE_SAMPLER,
VK_DESCRIPTOR_TYPE_UNIFORM_BUFFER, VK_DESCRIPTOR_TYPE_UNIFORM_BUFFER,
@ -33,29 +33,29 @@ VulkanNormlize::VulkanNormlize(const Op* op, Backend* bn) : VulkanBasicExecution
mVkBackend = static_cast<VulkanBackend*>(bn); mVkBackend = static_cast<VulkanBackend*>(bn);
mSampler = mVkBackend->getCommonSampler(); mSampler = mVkBackend->getCommonSampler();
// normlize // normalize
mVulkanNormlizePipeline = mVulkanNormalizePipeline =
mVkBackend->getPipeline("glsl_normalizeChannel_comp", mVkBackend->getPipeline("glsl_normalizeChannel_comp",
/*glsl_normalizeChannel_comp, glsl_normalizeChannel_comp_len,*/ VulkanNormlizeTypes); /*glsl_normalizeChannel_comp, glsl_normalizeChannel_comp_len,*/ VulkanNormalizeTypes);
mParamBuffer.reset(new VulkanBuffer(mVkBackend->getMemoryPool(), false, sizeof(GpuParam), nullptr, mParamBuffer.reset(new VulkanBuffer(mVkBackend->getMemoryPool(), false, sizeof(GpuParam), nullptr,
VK_BUFFER_USAGE_UNIFORM_BUFFER_BIT)); VK_BUFFER_USAGE_UNIFORM_BUFFER_BIT));
MNN_ASSERT(normlizeParam->channelShared() == false); MNN_ASSERT(normalizeParam->channelShared() == false);
// scale // scale
mVulkanScalePipeline = mVulkanScalePipeline =
mVkBackend->getPipeline("glsl_scale_comp", /*glsl_scale_comp, glsl_scale_comp_len,*/ VulkanScaleTypes); mVkBackend->getPipeline("glsl_scale_comp", /*glsl_scale_comp, glsl_scale_comp_len,*/ VulkanScaleTypes);
mScale.reset(new VulkanBuffer(mVkBackend->getMemoryPool(), false, sizeof(float) * normlizeParam->scale()->size(), mScale.reset(new VulkanBuffer(mVkBackend->getMemoryPool(), false, sizeof(float) * normalizeParam->scale()->size(),
normlizeParam->scale()->data(), VK_BUFFER_USAGE_STORAGE_BUFFER_BIT)); normalizeParam->scale()->data(), VK_BUFFER_USAGE_STORAGE_BUFFER_BIT));
mBias.reset(new VulkanBuffer(mVkBackend->getMemoryPool(), false, sizeof(float) * normlizeParam->scale()->size(), mBias.reset(new VulkanBuffer(mVkBackend->getMemoryPool(), false, sizeof(float) * normalizeParam->scale()->size(),
nullptr, VK_BUFFER_USAGE_STORAGE_BUFFER_BIT)); nullptr, VK_BUFFER_USAGE_STORAGE_BUFFER_BIT));
auto biasPtr = reinterpret_cast<float*>(mBias->map()); auto biasPtr = reinterpret_cast<float*>(mBias->map());
::memset(biasPtr, 0, sizeof(float) * normlizeParam->scale()->size()); ::memset(biasPtr, 0, sizeof(float) * normalizeParam->scale()->size());
mBias->unmap(); mBias->unmap();
} }
VulkanNormlize::~VulkanNormlize() { VulkanNormalize::~VulkanNormalize() {
} }
ErrorCode VulkanNormlize::onEncode(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs, ErrorCode VulkanNormalize::onEncode(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs,
const VulkanCommandPool::Buffer* cmdBuffer) { const VulkanCommandPool::Buffer* cmdBuffer) {
auto input = inputs[0]; auto input = inputs[0];
auto output = outputs[0]; auto output = outputs[0];
const int channelDiv4 = UP_DIV(input->channel(), 4); const int channelDiv4 = UP_DIV(input->channel(), 4);
@ -67,28 +67,28 @@ ErrorCode VulkanNormlize::onEncode(const std::vector<Tensor*>& inputs, const std
auto tempTensorImage = mVkBackend->findTensor(mTempTensor.deviceId())->image(); auto tempTensorImage = mVkBackend->findTensor(mTempTensor.deviceId())->image();
MNN_ASSERT(nullptr != tempTensorImage); MNN_ASSERT(nullptr != tempTensorImage);
auto VulkanNormlizeParam = reinterpret_cast<GpuParam*>(mParamBuffer->map()); auto VulkanNormalizeParam = reinterpret_cast<GpuParam*>(mParamBuffer->map());
::memset(VulkanNormlizeParam, 0, sizeof(GpuParam)); ::memset(VulkanNormalizeParam, 0, sizeof(GpuParam));
VulkanNormlizeParam->imgSize[0] = input->width(); VulkanNormalizeParam->imgSize[0] = input->width();
VulkanNormlizeParam->imgSize[1] = input->height(); VulkanNormalizeParam->imgSize[1] = input->height();
VulkanNormlizeParam->imgSize[2] = channelDiv4; VulkanNormalizeParam->imgSize[2] = channelDiv4;
VulkanNormlizeParam->imgSize[3] = 0; VulkanNormalizeParam->imgSize[3] = 0;
VulkanNormlizeParam->channelDiv4 = channelDiv4; VulkanNormalizeParam->channelDiv4 = channelDiv4;
VulkanNormlizeParam->eps = mEps; VulkanNormalizeParam->eps = mEps;
mParamBuffer->flush(true, 0, sizeof(GpuParam)); mParamBuffer->flush(true, 0, sizeof(GpuParam));
mParamBuffer->unmap(); mParamBuffer->unmap();
// normlize // normalize
mNormlizeDescriptorSet.reset(mVulkanNormlizePipeline->createSet()); mNormalizeDescriptorSet.reset(mVulkanNormalizePipeline->createSet());
mNormlizeDescriptorSet->writeImage(reinterpret_cast<VkImageView>(mTempTensor.deviceId()), mSampler->get(), mNormalizeDescriptorSet->writeImage(reinterpret_cast<VkImageView>(mTempTensor.deviceId()), mSampler->get(),
VK_IMAGE_LAYOUT_GENERAL, 0); VK_IMAGE_LAYOUT_GENERAL, 0);
mNormlizeDescriptorSet->writeImage(reinterpret_cast<VkImageView>(input->deviceId()), mSampler->get(), mNormalizeDescriptorSet->writeImage(reinterpret_cast<VkImageView>(input->deviceId()), mSampler->get(),
VK_IMAGE_LAYOUT_SHADER_READ_ONLY_OPTIMAL, 1); VK_IMAGE_LAYOUT_SHADER_READ_ONLY_OPTIMAL, 1);
mNormlizeDescriptorSet->writeBuffer(mParamBuffer->buffer(), 2, mParamBuffer->size()); mNormalizeDescriptorSet->writeBuffer(mParamBuffer->buffer(), 2, mParamBuffer->size());
mVulkanNormlizePipeline->bind(cmdBuffer->get(), mNormlizeDescriptorSet->get()); mVulkanNormalizePipeline->bind(cmdBuffer->get(), mNormalizeDescriptorSet->get());
vkCmdDispatch(cmdBuffer->get(), UP_DIV(input->width(), 8), UP_DIV(input->height(), 8), input->batch()); vkCmdDispatch(cmdBuffer->get(), UP_DIV(input->width(), 8), UP_DIV(input->height(), 8), input->batch());
@ -111,15 +111,15 @@ ErrorCode VulkanNormlize::onEncode(const std::vector<Tensor*>& inputs, const std
return NO_ERROR; return NO_ERROR;
} }
class VulkanNormlizeCreator : public VulkanBackend::Creator { class VulkanNormalizeCreator : public VulkanBackend::Creator {
public: public:
virtual Execution* onCreate(const std::vector<Tensor*>& inputs, const MNN::Op* op, Backend* bn) const override { virtual Execution* onCreate(const std::vector<Tensor*>& inputs, const MNN::Op* op, Backend* bn) const override {
return new VulkanNormlize(op, bn); return new VulkanNormalize(op, bn);
} }
}; };
static bool gResistor = []() { static bool gResistor = []() {
VulkanBackend::addCreator(OpType_Normalize, new VulkanNormlizeCreator); VulkanBackend::addCreator(OpType_Normalize, new VulkanNormalizeCreator);
return true; return true;
}(); }();

16
source/backend/vulkan/execution/VulkanNormlize.hpp → source/backend/vulkan/execution/VulkanNormalize.hpp
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@ -1,28 +1,28 @@
// //
// VulkanNormlize.hpp // VulkanNormalize.hpp
// MNN // MNN
// //
// Created by MNN on 2019/01/31. // Created by MNN on 2019/01/31.
// Copyright © 2018, Alibaba Group Holding Limited // Copyright © 2018, Alibaba Group Holding Limited
// //
#ifndef VulkanNormlize_hpp #ifndef VulkanNormalize_hpp
#define VulkanNormlize_hpp #define VulkanNormalize_hpp
#include "VulkanBasicExecution.hpp" #include "VulkanBasicExecution.hpp"
namespace MNN { namespace MNN {
class VulkanNormlize : public VulkanBasicExecution { class VulkanNormalize : public VulkanBasicExecution {
public: public:
VulkanNormlize(const Op* op, Backend* bn); VulkanNormalize(const Op* op, Backend* bn);
virtual ~VulkanNormlize(); virtual ~VulkanNormalize();
ErrorCode onEncode(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs, ErrorCode onEncode(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs,
const VulkanCommandPool::Buffer* cmdBuffer) override; const VulkanCommandPool::Buffer* cmdBuffer) override;
private: private:
std::shared_ptr<VulkanBuffer> mParamBuffer; std::shared_ptr<VulkanBuffer> mParamBuffer;
const VulkanPipeline* mVulkanNormlizePipeline; const VulkanPipeline* mVulkanNormalizePipeline;
const VulkanPipeline* mVulkanScalePipeline; const VulkanPipeline* mVulkanScalePipeline;
std::shared_ptr<VulkanPipeline::DescriptorSet> mNormlizeDescriptorSet; std::shared_ptr<VulkanPipeline::DescriptorSet> mNormalizeDescriptorSet;
std::shared_ptr<VulkanPipeline::DescriptorSet> mScaleDescriptorSet; std::shared_ptr<VulkanPipeline::DescriptorSet> mScaleDescriptorSet;
std::shared_ptr<VulkanBuffer> mScale; std::shared_ptr<VulkanBuffer> mScale;
std::shared_ptr<VulkanBuffer> mBias; std::shared_ptr<VulkanBuffer> mBias;

10
source/backend/vulkan/execution/glsl/normalizeChannel.comp
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@ -7,7 +7,7 @@ layout(set = 0, binding = 2) uniform constBuffer{
ivec4 imgSize; ivec4 imgSize;
int channelDiv4; int channelDiv4;
float eps; float eps;
}uNormlizeParam; }uNormalizeParam;
layout(local_size_x = 8, local_size_y = 8) in; layout(local_size_x = 8, local_size_y = 8) in;
@ -15,19 +15,19 @@ void main()
{ {
ivec3 pos = ivec3(gl_GlobalInvocationID); ivec3 pos = ivec3(gl_GlobalInvocationID);
if(all(lessThan(pos, uNormlizeParam.imgSize.xyz))) if(all(lessThan(pos, uNormalizeParam.imgSize.xyz)))
{ {
vec4 color = texelFetch(uInput, ivec3(pos.x, pos.y, 0), 0); vec4 color = texelFetch(uInput, ivec3(pos.x, pos.y, 0), 0);
vec4 sum = color * color; vec4 sum = color * color;
for(int i = 1; i < uNormlizeParam.channelDiv4; ++i) for(int i = 1; i < uNormalizeParam.channelDiv4; ++i)
{ {
color = texelFetch(uInput, ivec3(pos.x, pos.y, i), 0); color = texelFetch(uInput, ivec3(pos.x, pos.y, i), 0);
sum += color * color; sum += color * color;
} }
float summerResult = inversesqrt((sum.x + sum.y + sum.z + sum.w) + uNormlizeParam.eps); float summerResult = inversesqrt((sum.x + sum.y + sum.z + sum.w) + uNormalizeParam.eps);
for(int i = 0; i < uNormlizeParam.channelDiv4; ++i) for(int i = 0; i < uNormalizeParam.channelDiv4; ++i)
{ {
vec4 tempSum = vec4(summerResult); vec4 tempSum = vec4(summerResult);
ivec3 curPos = ivec3(pos.x, pos.y, i); ivec3 curPos = ivec3(pos.x, pos.y, i);

2
source/cv/SkNx.h
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@ -5,7 +5,7 @@
* found in the LICENSE file. * found in the LICENSE file.
*/ */
/* /*
Modified by MMN Modified by MNN
2018.9.19 2018.9.19
*/ */

2
source/cv/SkNx_neon.h
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@ -5,7 +5,7 @@
* found in the LICENSE file. * found in the LICENSE file.
*/ */
/* /*
Modified by MMN Modified by MNN
2018.9.19 2018.9.19
*/ */

4
source/shape/ShapeInnerProduct.cpp
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@ -19,13 +19,13 @@ public:
auto output = outputs[0]; auto output = outputs[0];
auto input = inputs[0]; auto input = inputs[0];
auto paramater = op->main_as_InnerProduct(); auto parameter = op->main_as_InnerProduct();
MNN_ASSERT(2 == input->buffer().dimensions); MNN_ASSERT(2 == input->buffer().dimensions);
output->buffer().dimensions = input->buffer().dimensions; output->buffer().dimensions = input->buffer().dimensions;
output->buffer().dim[0].extent = input->buffer().dim[0].extent; output->buffer().dim[0].extent = input->buffer().dim[0].extent;
output->buffer().dim[0].flags = 0; output->buffer().dim[0].flags = 0;
output->buffer().dim[1].extent = paramater->outputCount(); output->buffer().dim[1].extent = parameter->outputCount();
output->buffer().dim[1].flags = 0; output->buffer().dim[1].flags = 0;
return true; return true;

2
tools/converter/source/tflite/liteConverter.cpp
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@ -24,7 +24,7 @@ int tflite2MNNNet(const std::string inputModel, const std::string bizCode, std::
const auto subGraphsSize = tfliteModel->subgraphs.size(); const auto subGraphsSize = tfliteModel->subgraphs.size();
const auto& tfliteModelBuffer = tfliteModel->buffers; const auto& tfliteModelBuffer = tfliteModel->buffers;
// check whether this tflie model is quantization model // check whether this tflite model is quantization model
// use the weight's data type of Conv2D|DepthwiseConv2D to decide quantizedModel mode // use the weight's data type of Conv2D|DepthwiseConv2D to decide quantizedModel mode
bool quantizedModel = true; bool quantizedModel = true;
for (int i = 0; i < subGraphsSize; ++i) { for (int i = 0; i < subGraphsSize; ++i) {