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Support for YOLOv5 3.0/3.1

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Added support for YOLOv5 3.0/3.1
This commit is contained in:
Marcos Luciano
2021-05-03 21:31:22 -03:00
parent 18d33d78fd
commit b4d1b43600
9 changed files with 1042 additions and 0 deletions
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52
external/yolov5-3.X/nvdsinfer_custom_impl_Yolo/Makefile vendored Normal file
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#
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
CUDA_VER?=
ifeq ($(CUDA_VER),)
$(error "CUDA_VER is not set")
endif
CC:= g++
NVCC:=/usr/local/cuda-$(CUDA_VER)/bin/nvcc
CFLAGS:= -Wall -std=c++11 -shared -fPIC -Wno-error=deprecated-declarations
CFLAGS+= -I../../includes -I/usr/local/cuda-$(CUDA_VER)/include
LIBS:= -lnvinfer_plugin -lnvinfer -lnvparsers -L/usr/local/cuda-$(CUDA_VER)/lib64 -lcudart -lcublas -lstdc++fs
LFLAGS:= -shared -Wl,--start-group $(LIBS) -Wl,--end-group
INCS:= $(wildcard *.h)
SRCFILES:= nvdsparsebbox_Yolo.cpp \
yololayer.cu
TARGET_LIB:= libnvdsinfer_custom_impl_Yolo.so
TARGET_OBJS:= $(SRCFILES:.cpp=.o)
TARGET_OBJS:= $(TARGET_OBJS:.cu=.o)
all: $(TARGET_LIB)
%.o: %.cpp $(INCS) Makefile
$(CC) -c -o $@ $(CFLAGS) $<
%.o: %.cu $(INCS) Makefile
$(NVCC) -c -o $@ --compiler-options '-fPIC' $<
$(TARGET_LIB) : $(TARGET_OBJS)
$(CC) -o $@ $(TARGET_OBJS) $(LFLAGS)
clean:
rm -rf $(TARGET_LIB)
rm -rf $(TARGET_OBJS)

122
external/yolov5-3.X/nvdsinfer_custom_impl_Yolo/nvdsparsebbox_Yolo.cpp vendored Normal file
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/*
* Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include <algorithm>
#include <cassert>
#include <cmath>
#include <cstring>
#include <fstream>
#include <iostream>
#include <unordered_map>
#include "nvdsinfer_custom_impl.h"
#include <map>
#define kNMS_THRESH 0.45
static constexpr int LOCATIONS = 4;
struct alignas(float) Detection{
//center_x center_y w h
float bbox[LOCATIONS];
float conf; // bbox_conf * cls_conf
float class_id;
};
float iou(float lbox[4], float rbox[4]) {
float interBox[] = {
std::max(lbox[0] - lbox[2]/2.f , rbox[0] - rbox[2]/2.f), //left
std::min(lbox[0] + lbox[2]/2.f , rbox[0] + rbox[2]/2.f), //right
std::max(lbox[1] - lbox[3]/2.f , rbox[1] - rbox[3]/2.f), //top
std::min(lbox[1] + lbox[3]/2.f , rbox[1] + rbox[3]/2.f), //bottom
};
if(interBox[2] > interBox[3] || interBox[0] > interBox[1])
return 0.0f;
float interBoxS =(interBox[1]-interBox[0])*(interBox[3]-interBox[2]);
return interBoxS/(lbox[2]*lbox[3] + rbox[2]*rbox[3] -interBoxS);
}
bool cmp(Detection& a, Detection& b) {
return a.conf > b.conf;
}
void nms(std::vector<Detection>& res, float *output, float conf_thresh, float nms_thresh) {
int det_size = sizeof(Detection) / sizeof(float);
std::map<float, std::vector<Detection>> m;
for (int i = 0; i < output[0] && i < 1000; i++) {
if (output[1 + det_size * i + 4] <= conf_thresh) continue;
Detection det;
memcpy(&det, &output[1 + det_size * i], det_size * sizeof(float));
if (m.count(det.class_id) == 0) m.emplace(det.class_id, std::vector<Detection>());
m[det.class_id].push_back(det);
}
for (auto it = m.begin(); it != m.end(); it++) {
auto& dets = it->second;
std::sort(dets.begin(), dets.end(), cmp);
for (size_t m = 0; m < dets.size(); ++m) {
auto& item = dets[m];
res.push_back(item);
for (size_t n = m + 1; n < dets.size(); ++n) {
if (iou(item.bbox, dets[n].bbox) > nms_thresh) {
dets.erase(dets.begin()+n);
--n;
}
}
}
}
}
/* This is a sample bounding box parsing function for the sample YoloV5 detector model */
static bool NvDsInferParseYoloV5(
std::vector<NvDsInferLayerInfo> const& outputLayersInfo,
NvDsInferNetworkInfo const& networkInfo,
NvDsInferParseDetectionParams const& detectionParams,
std::vector<NvDsInferParseObjectInfo>& objectList)
{
const float kCONF_THRESH = detectionParams.perClassThreshold[0];
std::vector<Detection> res;
nms(res, (float*)(outputLayersInfo[0].buffer), kCONF_THRESH, kNMS_THRESH);
for(auto& r : res) {
NvDsInferParseObjectInfo oinfo;
oinfo.classId = r.class_id;
oinfo.left = static_cast<unsigned int>(r.bbox[0]-r.bbox[2]*0.5f);
oinfo.top = static_cast<unsigned int>(r.bbox[1]-r.bbox[3]*0.5f);
oinfo.width = static_cast<unsigned int>(r.bbox[2]);
oinfo.height = static_cast<unsigned int>(r.bbox[3]);
oinfo.detectionConfidence = r.conf;
objectList.push_back(oinfo);
}
return true;
}
extern "C" bool NvDsInferParseCustomYoloV5(
std::vector<NvDsInferLayerInfo> const &outputLayersInfo,
NvDsInferNetworkInfo const &networkInfo,
NvDsInferParseDetectionParams const &detectionParams,
std::vector<NvDsInferParseObjectInfo> &objectList)
{
return NvDsInferParseYoloV5(
outputLayersInfo, networkInfo, detectionParams, objectList);
}
/* Check that the custom function has been defined correctly */
CHECK_CUSTOM_PARSE_FUNC_PROTOTYPE(NvDsInferParseCustomYoloV5);

94
external/yolov5-3.X/nvdsinfer_custom_impl_Yolo/utils.h vendored Normal file
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#ifndef __TRT_UTILS_H_
#define __TRT_UTILS_H_
#include <iostream>
#include <vector>
#include <algorithm>
#include <cudnn.h>
#ifndef CUDA_CHECK
#define CUDA_CHECK(callstr) \
{ \
cudaError_t error_code = callstr; \
if (error_code != cudaSuccess) { \
std::cerr << "CUDA error " << error_code << " at " << __FILE__ << ":" << __LINE__; \
assert(0); \
} \
}
#endif
namespace Tn
{
class Profiler : public nvinfer1::IProfiler
{
public:
void printLayerTimes(int itrationsTimes)
{
float totalTime = 0;
for (size_t i = 0; i < mProfile.size(); i++)
{
printf("%-40.40s %4.3fms\n", mProfile[i].first.c_str(), mProfile[i].second / itrationsTimes);
totalTime += mProfile[i].second;
}
printf("Time over all layers: %4.3f\n", totalTime / itrationsTimes);
}
private:
typedef std::pair<std::string, float> Record;
std::vector<Record> mProfile;
virtual void reportLayerTime(const char* layerName, float ms)
{
auto record = std::find_if(mProfile.begin(), mProfile.end(), [&](const Record& r){ return r.first == layerName; });
if (record == mProfile.end())
mProfile.push_back(std::make_pair(layerName, ms));
else
record->second += ms;
}
};
//Logger for TensorRT info/warning/errors
class Logger : public nvinfer1::ILogger
{
public:
Logger(): Logger(Severity::kWARNING) {}
Logger(Severity severity): reportableSeverity(severity) {}
void log(Severity severity, const char* msg) override
{
// suppress messages with severity enum value greater than the reportable
if (severity > reportableSeverity) return;
switch (severity)
{
case Severity::kINTERNAL_ERROR: std::cerr << "INTERNAL_ERROR: "; break;
case Severity::kERROR: std::cerr << "ERROR: "; break;
case Severity::kWARNING: std::cerr << "WARNING: "; break;
case Severity::kINFO: std::cerr << "INFO: "; break;
default: std::cerr << "UNKNOWN: "; break;
}
std::cerr << msg << std::endl;
}
Severity reportableSeverity{Severity::kWARNING};
};
template<typename T>
void write(char*& buffer, const T& val)
{
*reinterpret_cast<T*>(buffer) = val;
buffer += sizeof(T);
}
template<typename T>
void read(const char*& buffer, T& val)
{
val = *reinterpret_cast<const T*>(buffer);
buffer += sizeof(T);
}
}
#endif

270
external/yolov5-3.X/nvdsinfer_custom_impl_Yolo/yololayer.cu vendored Normal file
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#include <assert.h>
#include "yololayer.h"
#include "utils.h"
using namespace Yolo;
namespace nvinfer1
{
YoloLayerPlugin::YoloLayerPlugin()
{
mClassCount = CLASS_NUM;
mYoloKernel.clear();
mYoloKernel.push_back(yolo1);
mYoloKernel.push_back(yolo2);
mYoloKernel.push_back(yolo3);
mKernelCount = mYoloKernel.size();
CUDA_CHECK(cudaMallocHost(&mAnchor, mKernelCount * sizeof(void*)));
size_t AnchorLen = sizeof(float)* CHECK_COUNT*2;
for(int ii = 0; ii < mKernelCount; ii ++)
{
CUDA_CHECK(cudaMalloc(&mAnchor[ii],AnchorLen));
const auto& yolo = mYoloKernel[ii];
CUDA_CHECK(cudaMemcpy(mAnchor[ii], yolo.anchors, AnchorLen, cudaMemcpyHostToDevice));
}
}
YoloLayerPlugin::~YoloLayerPlugin()
{
}
// create the plugin at runtime from a byte stream
YoloLayerPlugin::YoloLayerPlugin(const void* data, size_t length)
{
using namespace Tn;
const char *d = reinterpret_cast<const char *>(data), *a = d;
read(d, mClassCount);
read(d, mThreadCount);
read(d, mKernelCount);
mYoloKernel.resize(mKernelCount);
auto kernelSize = mKernelCount*sizeof(YoloKernel);
memcpy(mYoloKernel.data(),d,kernelSize);
d += kernelSize;
CUDA_CHECK(cudaMallocHost(&mAnchor, mKernelCount * sizeof(void*)));
size_t AnchorLen = sizeof(float)* CHECK_COUNT*2;
for(int ii = 0; ii < mKernelCount; ii ++)
{
CUDA_CHECK(cudaMalloc(&mAnchor[ii],AnchorLen));
const auto& yolo = mYoloKernel[ii];
CUDA_CHECK(cudaMemcpy(mAnchor[ii], yolo.anchors, AnchorLen, cudaMemcpyHostToDevice));
}
assert(d == a + length);
}
void YoloLayerPlugin::serialize(void* buffer) const
{
using namespace Tn;
char* d = static_cast<char*>(buffer), *a = d;
write(d, mClassCount);
write(d, mThreadCount);
write(d, mKernelCount);
auto kernelSize = mKernelCount*sizeof(YoloKernel);
memcpy(d,mYoloKernel.data(),kernelSize);
d += kernelSize;
assert(d == a + getSerializationSize());
}
size_t YoloLayerPlugin::getSerializationSize() const
{
return sizeof(mClassCount) + sizeof(mThreadCount) + sizeof(mKernelCount) + sizeof(Yolo::YoloKernel) * mYoloKernel.size();
}
int YoloLayerPlugin::initialize()
{
return 0;
}
Dims YoloLayerPlugin::getOutputDimensions(int index, const Dims* inputs, int nbInputDims)
{
//output the result to channel
int totalsize = MAX_OUTPUT_BBOX_COUNT * sizeof(Detection) / sizeof(float);
return Dims3(totalsize + 1, 1, 1);
}
// Set plugin namespace
void YoloLayerPlugin::setPluginNamespace(const char* pluginNamespace)
{
mPluginNamespace = pluginNamespace;
}
const char* YoloLayerPlugin::getPluginNamespace() const
{
return mPluginNamespace;
}
// Return the DataType of the plugin output at the requested index
DataType YoloLayerPlugin::getOutputDataType(int index, const nvinfer1::DataType* inputTypes, int nbInputs) const
{
return DataType::kFLOAT;
}
// Return true if output tensor is broadcast across a batch.
bool YoloLayerPlugin::isOutputBroadcastAcrossBatch(int outputIndex, const bool* inputIsBroadcasted, int nbInputs) const
{
return false;
}
// Return true if plugin can use input that is broadcast across batch without replication.
bool YoloLayerPlugin::canBroadcastInputAcrossBatch(int inputIndex) const
{
return false;
}
void YoloLayerPlugin::configurePlugin(const PluginTensorDesc* in, int nbInput, const PluginTensorDesc* out, int nbOutput)
{
}
// Attach the plugin object to an execution context and grant the plugin the access to some context resource.
void YoloLayerPlugin::attachToContext(cudnnContext* cudnnContext, cublasContext* cublasContext, IGpuAllocator* gpuAllocator)
{
}
// Detach the plugin object from its execution context.
void YoloLayerPlugin::detachFromContext() {}
const char* YoloLayerPlugin::getPluginType() const
{
return "YoloLayer_TRT";
}
const char* YoloLayerPlugin::getPluginVersion() const
{
return "1";
}
void YoloLayerPlugin::destroy()
{
delete this;
}
// Clone the plugin
IPluginV2IOExt* YoloLayerPlugin::clone() const
{
YoloLayerPlugin *p = new YoloLayerPlugin();
p->setPluginNamespace(mPluginNamespace);
return p;
}
__device__ float Logist(float data){ return 1.0f / (1.0f + expf(-data)); };
__global__ void CalDetection(const float *input, float *output,int noElements,
int yoloWidth,int yoloHeight,const float anchors[CHECK_COUNT*2],int classes,int outputElem) {
int idx = threadIdx.x + blockDim.x * blockIdx.x;
if (idx >= noElements) return;
int total_grid = yoloWidth * yoloHeight;
int bnIdx = idx / total_grid;
idx = idx - total_grid*bnIdx;
int info_len_i = 5 + classes;
const float* curInput = input + bnIdx * (info_len_i * total_grid * CHECK_COUNT);
for (int k = 0; k < 3; ++k) {
float box_prob = Logist(curInput[idx + k * info_len_i * total_grid + 4 * total_grid]);
if (box_prob < IGNORE_THRESH) continue;
int class_id = 0;
float max_cls_prob = 0.0;
for (int i = 5; i < info_len_i; ++i) {
float p = Logist(curInput[idx + k * info_len_i * total_grid + i * total_grid]);
if (p > max_cls_prob) {
max_cls_prob = p;
class_id = i - 5;
}
}
float *res_count = output + bnIdx*outputElem;
int count = (int)atomicAdd(res_count, 1);
if (count >= MAX_OUTPUT_BBOX_COUNT) return;
char* data = (char *)res_count + sizeof(float) + count * sizeof(Detection);
Detection* det = (Detection*)(data);
int row = idx / yoloWidth;
int col = idx % yoloWidth;
//Location
det->bbox[0] = (col - 0.5f + 2.0f * Logist(curInput[idx + k * info_len_i * total_grid + 0 * total_grid])) * INPUT_W / yoloWidth;
det->bbox[1] = (row - 0.5f + 2.0f * Logist(curInput[idx + k * info_len_i * total_grid + 1 * total_grid])) * INPUT_H / yoloHeight;
det->bbox[2] = 2.0f * Logist(curInput[idx + k * info_len_i * total_grid + 2 * total_grid]);
det->bbox[2] = det->bbox[2] * det->bbox[2] * anchors[2*k];
det->bbox[3] = 2.0f * Logist(curInput[idx + k * info_len_i * total_grid + 3 * total_grid]);
det->bbox[3] = det->bbox[3] * det->bbox[3] * anchors[2*k + 1];
det->conf = box_prob * max_cls_prob;
det->class_id = class_id;
}
}
void YoloLayerPlugin::forwardGpu(const float *const * inputs, float* output, cudaStream_t stream, int batchSize) {
int outputElem = 1 + MAX_OUTPUT_BBOX_COUNT * sizeof(Detection) / sizeof(float);
for(int idx = 0 ; idx < batchSize; ++idx) {
CUDA_CHECK(cudaMemset(output + idx*outputElem, 0, sizeof(float)));
}
int numElem = 0;
for (unsigned int i = 0; i < mYoloKernel.size(); ++i)
{
const auto& yolo = mYoloKernel[i];
numElem = yolo.width*yolo.height*batchSize;
if (numElem < mThreadCount)
mThreadCount = numElem;
CalDetection<<< (yolo.width*yolo.height*batchSize + mThreadCount - 1) / mThreadCount, mThreadCount, 0, stream>>>
(inputs[i], output, numElem, yolo.width, yolo.height, (float *)mAnchor[i], mClassCount, outputElem);
}
}
int YoloLayerPlugin::enqueue(int batchSize, const void*const * inputs, void** outputs, void* workspace, cudaStream_t stream)
{
forwardGpu((const float *const *)inputs, (float*)outputs[0], stream, batchSize);
return 0;
}
PluginFieldCollection YoloPluginCreator::mFC{};
std::vector<PluginField> YoloPluginCreator::mPluginAttributes;
YoloPluginCreator::YoloPluginCreator()
{
mPluginAttributes.clear();
mFC.nbFields = mPluginAttributes.size();
mFC.fields = mPluginAttributes.data();
}
const char* YoloPluginCreator::getPluginName() const
{
return "YoloLayer_TRT";
}
const char* YoloPluginCreator::getPluginVersion() const
{
return "1";
}
const PluginFieldCollection* YoloPluginCreator::getFieldNames()
{
return &mFC;
}
IPluginV2IOExt* YoloPluginCreator::createPlugin(const char* name, const PluginFieldCollection* fc)
{
YoloLayerPlugin* obj = new YoloLayerPlugin();
obj->setPluginNamespace(mNamespace.c_str());
return obj;
}
IPluginV2IOExt* YoloPluginCreator::deserializePlugin(const char* name, const void* serialData, size_t serialLength)
{
// This object will be deleted when the network is destroyed, which will
// call MishPlugin::destroy()
YoloLayerPlugin* obj = new YoloLayerPlugin(serialData, serialLength);
obj->setPluginNamespace(mNamespace.c_str());
return obj;
}
}

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external/yolov5-3.X/nvdsinfer_custom_impl_Yolo/yololayer.h vendored Normal file
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#ifndef _YOLO_LAYER_H
#define _YOLO_LAYER_H
#include <vector>
#include <string>
#include "NvInfer.h"
namespace Yolo
{
static constexpr int CHECK_COUNT = 3;
static constexpr float IGNORE_THRESH = 0.1f;
static constexpr int MAX_OUTPUT_BBOX_COUNT = 1000;
static constexpr int CLASS_NUM = 80;
static constexpr int INPUT_H = 608;
static constexpr int INPUT_W = 608;
struct YoloKernel
{
int width;
int height;
float anchors[CHECK_COUNT*2];
};
static constexpr YoloKernel yolo1 = {
INPUT_W / 32,
INPUT_H / 32,
{116,90, 156,198, 373,326}
};
static constexpr YoloKernel yolo2 = {
INPUT_W / 16,
INPUT_H / 16,
{30,61, 62,45, 59,119}
};
static constexpr YoloKernel yolo3 = {
INPUT_W / 8,
INPUT_H / 8,
{10,13, 16,30, 33,23}
};
static constexpr int LOCATIONS = 4;
struct alignas(float) Detection{
//center_x center_y w h
float bbox[LOCATIONS];
float conf; // bbox_conf * cls_conf
float class_id;
};
}
namespace nvinfer1
{
class YoloLayerPlugin: public IPluginV2IOExt
{
public:
explicit YoloLayerPlugin();
YoloLayerPlugin(const void* data, size_t length);
~YoloLayerPlugin();
int getNbOutputs() const override
{
return 1;
}
Dims getOutputDimensions(int index, const Dims* inputs, int nbInputDims) override;
int initialize() override;
virtual void terminate() override {};
virtual size_t getWorkspaceSize(int maxBatchSize) const override { return 0;}
virtual int enqueue(int batchSize, const void*const * inputs, void** outputs, void* workspace, cudaStream_t stream) override;
virtual size_t getSerializationSize() const override;
virtual void serialize(void* buffer) const override;
bool supportsFormatCombination(int pos, const PluginTensorDesc* inOut, int nbInputs, int nbOutputs) const override {
return inOut[pos].format == TensorFormat::kLINEAR && inOut[pos].type == DataType::kFLOAT;
}
const char* getPluginType() const override;
const char* getPluginVersion() const override;
void destroy() override;
IPluginV2IOExt* clone() const override;
void setPluginNamespace(const char* pluginNamespace) override;
const char* getPluginNamespace() const override;
DataType getOutputDataType(int index, const nvinfer1::DataType* inputTypes, int nbInputs) const override;
bool isOutputBroadcastAcrossBatch(int outputIndex, const bool* inputIsBroadcasted, int nbInputs) const override;
bool canBroadcastInputAcrossBatch(int inputIndex) const override;
void attachToContext(
cudnnContext* cudnnContext, cublasContext* cublasContext, IGpuAllocator* gpuAllocator) override;
void configurePlugin(const PluginTensorDesc* in, int nbInput, const PluginTensorDesc* out, int nbOutput) override;
void detachFromContext() override;
private:
void forwardGpu(const float *const * inputs,float * output, cudaStream_t stream,int batchSize = 1);
int mClassCount;
int mKernelCount;
std::vector<Yolo::YoloKernel> mYoloKernel;
int mThreadCount = 256;
void** mAnchor;
const char* mPluginNamespace;
};
class YoloPluginCreator : public IPluginCreator
{
public:
YoloPluginCreator();
~YoloPluginCreator() override = default;
const char* getPluginName() const override;
const char* getPluginVersion() const override;
const PluginFieldCollection* getFieldNames() override;
IPluginV2IOExt* createPlugin(const char* name, const PluginFieldCollection* fc) override;
IPluginV2IOExt* deserializePlugin(const char* name, const void* serialData, size_t serialLength) override;
void setPluginNamespace(const char* libNamespace) override
{
mNamespace = libNamespace;
}
const char* getPluginNamespace() const override
{
return mNamespace.c_str();
}
private:
std::string mNamespace;
static PluginFieldCollection mFC;
static std::vector<PluginField> mPluginAttributes;
};
REGISTER_TENSORRT_PLUGIN(YoloPluginCreator);
};
#endif