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Move YOLO Decoder from CPU to GPU

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This commit is contained in:
Marcos Luciano
2022-02-17 15:21:35 -03:00
parent a82f1b8662
commit 91d15dda56
10 changed files with 339 additions and 279 deletions
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154
nvdsinfer_custom_impl_Yolo/nvdsparsebbox_Yolo.cpp
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@@ -143,50 +143,32 @@ static void addBBoxProposal(const float bx, const float by, const float bw, cons
static std::vector<NvDsInferParseObjectInfo>
decodeYoloTensor(
const float* detections, const std::vector<int> &mask, const std::vector<float> &anchors,
const float* detections,
const uint gridSizeW, const uint gridSizeH, const uint stride, const uint numBBoxes,
const uint numOutputClasses, const uint& netW,
const uint& netH,
const float confThresh)
const uint numOutputClasses, const uint& netW, const uint& netH, const float confThresh)
{
std::vector<NvDsInferParseObjectInfo> binfo;
for (uint y = 0; y < gridSizeH; ++y) {
for (uint x = 0; x < gridSizeW; ++x) {
for (uint b = 0; b < numBBoxes; ++b)
{
const float pw = anchors[mask[b] * 2];
const float ph = anchors[mask[b] * 2 + 1];
const int numGridCells = gridSizeH * gridSizeW;
const int bbindex = y * gridSizeW + x;
const float bx
= x + detections[bbindex + numGridCells * (b * (5 + numOutputClasses) + 0)];
const float by
= y + detections[bbindex + numGridCells * (b * (5 + numOutputClasses) + 1)];
const float bw
= pw * detections[bbindex + numGridCells * (b * (5 + numOutputClasses) + 2)];
const float bh
= ph * detections[bbindex + numGridCells * (b * (5 + numOutputClasses) + 3)];
const float objectness
const float bx
= detections[bbindex + numGridCells * (b * (5 + numOutputClasses) + 0)];
const float by
= detections[bbindex + numGridCells * (b * (5 + numOutputClasses) + 1)];
const float bw
= detections[bbindex + numGridCells * (b * (5 + numOutputClasses) + 2)];
const float bh
= detections[bbindex + numGridCells * (b * (5 + numOutputClasses) + 3)];
const float maxProb
= detections[bbindex + numGridCells * (b * (5 + numOutputClasses) + 4)];
float maxProb = 0.0f;
int maxIndex = -1;
for (uint i = 0; i < numOutputClasses; ++i)
{
float prob
= (detections[bbindex
+ numGridCells * (b * (5 + numOutputClasses) + (5 + i))]);
if (prob > maxProb)
{
maxProb = prob;
maxIndex = i;
}
}
maxProb = objectness * maxProb;
const int maxIndex
= detections[bbindex + numGridCells * (b * (5 + numOutputClasses) + 5)];
if (maxProb > confThresh)
{
@@ -200,49 +182,32 @@ decodeYoloTensor(
static std::vector<NvDsInferParseObjectInfo>
decodeYoloV2Tensor(
const float* detections, const std::vector<float> &anchors,
const float* detections,
const uint gridSizeW, const uint gridSizeH, const uint stride, const uint numBBoxes,
const uint numOutputClasses, const uint& netW,
const uint& netH)
const uint numOutputClasses, const uint& netW, const uint& netH)
{
std::vector<NvDsInferParseObjectInfo> binfo;
for (uint y = 0; y < gridSizeH; ++y) {
for (uint x = 0; x < gridSizeW; ++x) {
for (uint b = 0; b < numBBoxes; ++b)
{
const float pw = anchors[b * 2];
const float ph = anchors[b * 2 + 1];
const int numGridCells = gridSizeH * gridSizeW;
const int bbindex = y * gridSizeW + x;
const float bx
= x + detections[bbindex + numGridCells * (b * (5 + numOutputClasses) + 0)];
const float by
= y + detections[bbindex + numGridCells * (b * (5 + numOutputClasses) + 1)];
const float bw
= pw * detections[bbindex + numGridCells * (b * (5 + numOutputClasses) + 2)];
const float bh
= ph * detections[bbindex + numGridCells * (b * (5 + numOutputClasses) + 3)];
const float objectness
const float bx
= detections[bbindex + numGridCells * (b * (5 + numOutputClasses) + 0)];
const float by
= detections[bbindex + numGridCells * (b * (5 + numOutputClasses) + 1)];
const float bw
= detections[bbindex + numGridCells * (b * (5 + numOutputClasses) + 2)] * stride;
const float bh
= detections[bbindex + numGridCells * (b * (5 + numOutputClasses) + 3)] * stride;
const float maxProb
= detections[bbindex + numGridCells * (b * (5 + numOutputClasses) + 4)];
float maxProb = 0.0f;
int maxIndex = -1;
for (uint i = 0; i < numOutputClasses; ++i)
{
float prob
= (detections[bbindex
+ numGridCells * (b * (5 + numOutputClasses) + (5 + i))]);
if (prob > maxProb)
{
maxProb = prob;
maxIndex = i;
}
}
maxProb = objectness * maxProb;
const int maxIndex
= detections[bbindex + numGridCells * (b * (5 + numOutputClasses) + 5)];
addBBoxProposal(bx, by, bw, bh, stride, netW, netH, maxIndex, maxProb, binfo);
}
@@ -270,32 +235,30 @@ static bool NvDsInferParseYolo(
NvDsInferNetworkInfo const& networkInfo,
NvDsInferParseDetectionParams const& detectionParams,
std::vector<NvDsInferParseObjectInfo>& objectList,
const std::vector<float> &anchors,
const std::vector<std::vector<int>> &masks,
const uint &num_classes,
const float &beta_nms)
const uint &numBBoxes,
const uint &numClasses,
const float &betaNMS)
{
if (outputLayersInfo.empty()) {
std::cerr << "ERROR: Could not find output layer in bbox parsing" << std::endl;;
return false;
}
const float kCONF_THRESH = detectionParams.perClassThreshold[0];
const std::vector<const NvDsInferLayerInfo*> sortedLayers =
SortLayers (outputLayersInfo);
if (sortedLayers.size() != masks.size()) {
std::cerr << "ERROR: YOLO output layer.size: " << sortedLayers.size()
<< " does not match mask.size: " << masks.size() << std::endl;
return false;
}
if (num_classes != detectionParams.numClassesConfigured)
if (numClasses != detectionParams.numClassesConfigured)
{
std::cerr << "WARNING: Num classes mismatch. Configured: "
<< detectionParams.numClassesConfigured
<< ", detected by network: " << num_classes << std::endl;
<< ", detected by network: " << numClasses << std::endl;
}
std::vector<NvDsInferParseObjectInfo> objects;
for (uint idx = 0; idx < masks.size(); ++idx) {
for (uint idx = 0; idx < sortedLayers.size(); ++idx) {
const NvDsInferLayerInfo &layer = *sortedLayers[idx]; // 255 x Grid x Grid
assert(layer.inferDims.numDims == 3);
@@ -304,14 +267,13 @@ static bool NvDsInferParseYolo(
const uint stride = DIVUP(networkInfo.width, gridSizeW);
std::vector<NvDsInferParseObjectInfo> outObjs =
decodeYoloTensor((const float*)(layer.buffer), masks[idx], anchors, gridSizeW, gridSizeH, stride, masks[idx].size(),
num_classes, networkInfo.width, networkInfo.height, kCONF_THRESH);
decodeYoloTensor((const float*)(layer.buffer), gridSizeW, gridSizeH, stride, numBBoxes,
numClasses, networkInfo.width, networkInfo.height, kCONF_THRESH);
objects.insert(objects.end(), outObjs.begin(), outObjs.end());
}
objectList.clear();
objectList = nmsAllClasses(beta_nms, objects, num_classes);
objectList = nmsAllClasses(betaNMS, objects, numClasses);
return true;
}
@@ -321,34 +283,31 @@ static bool NvDsInferParseYoloV2(
NvDsInferNetworkInfo const& networkInfo,
NvDsInferParseDetectionParams const& detectionParams,
std::vector<NvDsInferParseObjectInfo>& objectList,
std::vector<float> &anchors,
const uint &num_classes)
const uint &numBBoxes,
const uint &numClasses)
{
if (outputLayersInfo.empty()) {
std::cerr << "Could not find output layer in bbox parsing" << std::endl;;
std::cerr << "ERROR: Could not find output layer in bbox parsing" << std::endl;;
return false;
}
const uint kNUM_BBOXES = anchors.size() / 2;
const NvDsInferLayerInfo &layer = outputLayersInfo[0];
if (num_classes != detectionParams.numClassesConfigured)
if (numClasses != detectionParams.numClassesConfigured)
{
std::cerr << "WARNING: Num classes mismatch. Configured: "
<< detectionParams.numClassesConfigured
<< ", detected by network: " << num_classes << std::endl;
<< ", detected by network: " << numClasses << std::endl;
}
assert(layer.inferDims.numDims == 3);
const uint gridSizeH = layer.inferDims.d[1];
const uint gridSizeW = layer.inferDims.d[2];
const uint stride = DIVUP(networkInfo.width, gridSizeW);
for (auto& anchor : anchors) {
anchor *= stride;
}
std::vector<NvDsInferParseObjectInfo> objects =
decodeYoloV2Tensor((const float*)(layer.buffer), anchors, gridSizeW, gridSizeH, stride, kNUM_BBOXES,
num_classes, networkInfo.width, networkInfo.height);
decodeYoloV2Tensor((const float*)(layer.buffer), gridSizeW, gridSizeH, stride, numBBoxes,
numClasses, networkInfo.width, networkInfo.height);
objectList = objects;
@@ -361,17 +320,18 @@ extern "C" bool NvDsInferParseYolo(
NvDsInferParseDetectionParams const& detectionParams,
std::vector<NvDsInferParseObjectInfo>& objectList)
{
int model_type = kMODEL_TYPE;
int num_bboxes = kNUM_BBOXES;
int num_classes = kNUM_CLASSES;
float beta_nms = kBETA_NMS;
std::vector<float> anchors = kANCHORS;
std::vector<std::vector<int>> mask = kMASK;
if (mask.size() > 0) {
return NvDsInferParseYolo (outputLayersInfo, networkInfo, detectionParams, objectList, anchors, mask, num_classes, beta_nms);
if (model_type != 0) {
return NvDsInferParseYolo (outputLayersInfo, networkInfo, detectionParams, objectList,
num_bboxes, num_classes, beta_nms);
}
else {
return NvDsInferParseYoloV2 (outputLayersInfo, networkInfo, detectionParams, objectList, anchors, num_classes);
return NvDsInferParseYoloV2 (outputLayersInfo, networkInfo, detectionParams, objectList,
num_bboxes, num_classes);
}
}

46
nvdsinfer_custom_impl_Yolo/yolo.cpp
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@@ -31,21 +31,6 @@
#include "calibrator.h"
#endif
void orderParams(std::vector<std::vector<int>> *maskVector) {
std::vector<std::vector<int>> maskinput = *maskVector;
std::vector<int> maskPartial;
for (uint i = 0; i < maskinput.size(); i++) {
for (uint j = i + 1; j < maskinput.size(); j++) {
if (maskinput[i][0] <= maskinput[j][0]) {
maskPartial = maskinput[i];
maskinput[i] = maskinput[j];
maskinput[j] = maskPartial;
}
}
}
*maskVector = maskinput;
}
Yolo::Yolo(const NetworkInfo& networkInfo)
: m_NetworkType(networkInfo.networkType), // YOLO type
m_ConfigFilePath(networkInfo.configFilePath), // YOLO cfg
@@ -71,7 +56,6 @@ nvinfer1::ICudaEngine *Yolo::createEngine (nvinfer1::IBuilder* builder, nvinfer1
m_ConfigBlocks = parseConfigFile(m_ConfigFilePath);
parseConfigBlocks();
orderParams(&m_OutputMasks);
nvinfer1::INetworkDefinition *network = builder->createNetworkV2(0);
if (parseModel(*network) != NVDSINFER_SUCCESS) {
@@ -361,12 +345,12 @@ NvDsInferStatus Yolo::buildYoloNetwork(
}
nvinfer1::IPluginV2* yoloPlugin
= new YoloLayer(curYoloTensor.numBBoxes,
curYoloTensor.numClasses,
curYoloTensor.gridSizeX,
curYoloTensor.gridSizeY,
model_type, new_coords, scale_x_y, beta_nms,
curYoloTensor.anchors,
m_OutputMasks);
curYoloTensor.numClasses,
curYoloTensor.gridSizeX,
curYoloTensor.gridSizeY,
model_type, new_coords, scale_x_y, beta_nms,
curYoloTensor.anchors,
curYoloTensor.masks);
assert(yoloPlugin != nullptr);
nvinfer1::IPluginV2Layer* yolo =
network.addPluginV2(&previous, 1, *yoloPlugin);
@@ -396,15 +380,15 @@ NvDsInferStatus Yolo::buildYoloNetwork(
* (curRegionTensor.numBBoxes * (5 + curRegionTensor.numClasses));
std::string layerName = "region_" + std::to_string(i);
curRegionTensor.blobName = layerName;
std::vector<std::vector<int>> mask;
std::vector<int> mask;
nvinfer1::IPluginV2* regionPlugin
= new YoloLayer(curRegionTensor.numBBoxes,
curRegionTensor.numClasses,
curRegionTensor.gridSizeX,
curRegionTensor.gridSizeY,
0, 0, 1.0, 0,
curRegionTensor.anchors,
mask);
curRegionTensor.numClasses,
curRegionTensor.gridSizeX,
curRegionTensor.gridSizeY,
0, 0, 1.0, 0,
curRegionTensor.anchors,
mask);
assert(regionPlugin != nullptr);
nvinfer1::IPluginV2Layer* region =
network.addPluginV2(&previous, 1, *regionPlugin);
@@ -541,26 +525,22 @@ void Yolo::parseConfigBlocks()
if (block.find("mask") != block.end()) {
std::string maskString = block.at("mask");
std::vector<int> pMASKS;
while (!maskString.empty())
{
int npos = maskString.find_first_of(',');
if (npos != -1)
{
int mask = std::stoul(trim(maskString.substr(0, npos)));
pMASKS.push_back(mask);
outputTensor.masks.push_back(mask);
maskString.erase(0, npos + 1);
}
else
{
int mask = std::stoul(trim(maskString));
pMASKS.push_back(mask);
outputTensor.masks.push_back(mask);
break;
}
}
m_OutputMasks.push_back(pMASKS);
}
outputTensor.numBBoxes = outputTensor.masks.size() > 0

3
nvdsinfer_custom_impl_Yolo/yolo.h
View File

@@ -58,7 +58,7 @@ struct TensorInfo
uint numClasses{0};
uint numBBoxes{0};
uint64_t volume{0};
std::vector<uint> masks;
std::vector<int> masks;
std::vector<float> anchors;
int bindingIndex{-1};
float* hostBuffer{nullptr};
@@ -86,7 +86,6 @@ protected:
const std::string m_DeviceType;
const std::string m_InputBlobName;
std::vector<TensorInfo> m_OutputTensors;
std::vector<std::vector<int>> m_OutputMasks;
std::vector<std::map<std::string, std::string>> m_ConfigBlocks;
uint m_InputH;
uint m_InputW;

50
nvdsinfer_custom_impl_Yolo/yoloForward.cu
View File

@@ -1,15 +1,6 @@
/*
* Copyright (c) 2018-2019 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual property
* and proprietary rights in and to this software, related documentation
* and any modifications thereto. Any use, reproduction, disclosure or
* distribution of this software and related documentation without an express
* license agreement from NVIDIA Corporation is strictly prohibited.
*
* Edited by Marcos Luciano
* Created by Marcos Luciano
* https://www.github.com/marcoslucianops
*
*/
#include <cuda.h>
@@ -21,7 +12,7 @@
inline __device__ float sigmoidGPU(const float& x) { return 1.0f / (1.0f + __expf(-x)); }
__global__ void gpuYoloLayer(const float* input, float* output, const uint gridSizeX, const uint gridSizeY, const uint numOutputClasses,
const uint numBBoxes, const float scale_x_y)
const uint numBBoxes, const float scaleXY, const float* anchors, const int* mask)
{
uint x_id = blockIdx.x * blockDim.x + threadIdx.x;
uint y_id = blockIdx.y * blockDim.y + threadIdx.y;
@@ -35,38 +26,53 @@ __global__ void gpuYoloLayer(const float* input, float* output, const uint gridS
const int numGridCells = gridSizeX * gridSizeY;
const int bbindex = y_id * gridSizeX + x_id;
const float alpha = scale_x_y;
const float beta = -0.5 * (scale_x_y - 1);
const float alpha = scaleXY;
const float beta = -0.5 * (scaleXY - 1);
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 0)]
= sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 0)]) * alpha + beta;
= sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 0)]) * alpha + beta + x_id;
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 1)]
= sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 1)]) * alpha + beta;
= sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 1)]) * alpha + beta + y_id;
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 2)]
= __expf(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 2)]);
= __expf(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 2)]) * anchors[mask[z_id] * 2];
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 3)]
= __expf(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 3)]);
= __expf(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 3)]) * anchors[mask[z_id] * 2 + 1];
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 4)]
const float objectness
= sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 4)]);
float maxProb = 0.0f;
int maxIndex = -1;
for (uint i = 0; i < numOutputClasses; ++i)
{
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + (5 + i))]
float prob
= sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + (5 + i))]);
if (prob > maxProb)
{
maxProb = prob;
maxIndex = i;
}
}
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 4)]
= objectness * maxProb;
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 5)]
= maxIndex;
}
cudaError_t cudaYoloLayer(const void* input, void* output, const uint& batchSize, const uint& gridSizeX, const uint& gridSizeY,
const uint& numOutputClasses, const uint& numBBoxes, uint64_t outputSize, cudaStream_t stream,
const float modelScale);
const float scaleXY, const void* anchors, const void* mask);
cudaError_t cudaYoloLayer(const void* input, void* output, const uint& batchSize, const uint& gridSizeX, const uint& gridSizeY,
const uint& numOutputClasses, const uint& numBBoxes, uint64_t outputSize, cudaStream_t stream,
const float modelScale)
const float scaleXY, const void* anchors, const void* mask)
{
dim3 threads_per_block(16, 16, 4);
dim3 number_of_blocks((gridSizeX / threads_per_block.x) + 1,
@@ -77,7 +83,7 @@ cudaError_t cudaYoloLayer(const void* input, void* output, const uint& batchSize
gpuYoloLayer<<<number_of_blocks, threads_per_block, 0, stream>>>(
reinterpret_cast<const float*>(input) + (batch * outputSize),
reinterpret_cast<float*>(output) + (batch * outputSize), gridSizeX, gridSizeY, numOutputClasses,
numBBoxes, modelScale);
numBBoxes, scaleXY, reinterpret_cast<const float*>(anchors), reinterpret_cast<const int*>(mask));
}
return cudaGetLastError();
}

41
nvdsinfer_custom_impl_Yolo/yoloForward_nc.cu
View File

@@ -9,10 +9,8 @@
#include <stdio.h>
#include <string.h>
inline __device__ float sigmoidGPU(const float& x) { return 1.0f / (1.0f + __expf(-x)); }
__global__ void gpuYoloLayer_nc(const float* input, float* output, const uint gridSizeX, const uint gridSizeY, const uint numOutputClasses,
const uint numBBoxes, const float scale_x_y)
const uint numBBoxes, const float scaleXY, const float* anchors, const int* mask)
{
uint x_id = blockIdx.x * blockDim.x + threadIdx.x;
uint y_id = blockIdx.y * blockDim.y + threadIdx.y;
@@ -26,38 +24,53 @@ __global__ void gpuYoloLayer_nc(const float* input, float* output, const uint gr
const int numGridCells = gridSizeX * gridSizeY;
const int bbindex = y_id * gridSizeX + x_id;
const float alpha = scale_x_y;
const float beta = -0.5 * (scale_x_y - 1);
const float alpha = scaleXY;
const float beta = -0.5 * (scaleXY - 1);
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 0)]
= input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 0)] * alpha + beta;
= input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 0)] * alpha + beta + x_id;
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 1)]
= input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 1)] * alpha + beta;
= input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 1)] * alpha + beta + y_id;
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 2)]
= pow(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 2)] * 2, 2);
= __powf(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 2)] * 2, 2) * anchors[mask[z_id] * 2];
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 3)]
= pow(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 3)] * 2, 2);
= __powf(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 3)] * 2, 2) * anchors[mask[z_id] * 2 + 1];
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 4)]
const float objectness
= input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 4)];
float maxProb = 0.0f;
int maxIndex = -1;
for (uint i = 0; i < numOutputClasses; ++i)
{
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + (5 + i))]
float prob
= input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + (5 + i))];
if (prob > maxProb)
{
maxProb = prob;
maxIndex = i;
}
}
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 4)]
= objectness * maxProb;
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 5)]
= maxIndex;
}
cudaError_t cudaYoloLayer_nc(const void* input, void* output, const uint& batchSize, const uint& gridSizeX, const uint& gridSizeY,
const uint& numOutputClasses, const uint& numBBoxes, uint64_t outputSize, cudaStream_t stream,
const float modelScale);
const float scaleXY, const void* anchors, const void* mask);
cudaError_t cudaYoloLayer_nc(const void* input, void* output, const uint& batchSize, const uint& gridSizeX, const uint& gridSizeY,
const uint& numOutputClasses, const uint& numBBoxes, uint64_t outputSize, cudaStream_t stream,
const float modelScale)
const float scaleXY, const void* anchors, const void* mask)
{
dim3 threads_per_block(16, 16, 4);
dim3 number_of_blocks((gridSizeX / threads_per_block.x) + 1,
@@ -68,7 +81,7 @@ cudaError_t cudaYoloLayer_nc(const void* input, void* output, const uint& batchS
gpuYoloLayer_nc<<<number_of_blocks, threads_per_block, 0, stream>>>(
reinterpret_cast<const float*>(input) + (batch * outputSize),
reinterpret_cast<float*>(output) + (batch * outputSize), gridSizeX, gridSizeY, numOutputClasses,
numBBoxes, modelScale);
numBBoxes, scaleXY, reinterpret_cast<const float*>(anchors), reinterpret_cast<const int*>(mask));
}
return cudaGetLastError();
}

38
nvdsinfer_custom_impl_Yolo/yoloForward_r.cu
View File

@@ -12,7 +12,7 @@
inline __device__ float sigmoidGPU(const float& x) { return 1.0f / (1.0f + __expf(-x)); }
__global__ void gpuYoloLayer_r(const float* input, float* output, const uint gridSizeX, const uint gridSizeY, const uint numOutputClasses,
const uint numBBoxes, const float scale_x_y)
const uint numBBoxes, const float scaleXY, const float* anchors, const int* mask)
{
uint x_id = blockIdx.x * blockDim.x + threadIdx.x;
uint y_id = blockIdx.y * blockDim.y + threadIdx.y;
@@ -26,35 +26,53 @@ __global__ void gpuYoloLayer_r(const float* input, float* output, const uint gri
const int numGridCells = gridSizeX * gridSizeY;
const int bbindex = y_id * gridSizeX + x_id;
const float alpha = scaleXY;
const float beta = -0.5 * (scaleXY - 1);
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 0)]
= sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 0)]) * 2.0 - 0.5;
= sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 0)]) * alpha + beta + x_id;
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 1)]
= sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 1)]) * 2.0 - 0.5;
= sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 1)]) * alpha + beta + y_id;
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 2)]
= pow(sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 2)]) * 2, 2);
= __powf(sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 2)]) * 2, 2) * anchors[mask[z_id] * 2];
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 3)]
= pow(sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 3)]) * 2, 2);
= __powf(sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 3)]) * 2, 2) * anchors[mask[z_id] * 2 + 1];
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 4)]
const float objectness
= sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 4)]);
float maxProb = 0.0f;
int maxIndex = -1;
for (uint i = 0; i < numOutputClasses; ++i)
{
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + (5 + i))]
float prob
= sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + (5 + i))]);
if (prob > maxProb)
{
maxProb = prob;
maxIndex = i;
}
}
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 4)]
= objectness * maxProb;
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 5)]
= maxIndex;
}
cudaError_t cudaYoloLayer_r(const void* input, void* output, const uint& batchSize, const uint& gridSizeX, const uint& gridSizeY,
const uint& numOutputClasses, const uint& numBBoxes, uint64_t outputSize, cudaStream_t stream,
const float modelScale);
const float scaleXY, const void* anchors, const void* mask);
cudaError_t cudaYoloLayer_r(const void* input, void* output, const uint& batchSize, const uint& gridSizeX, const uint& gridSizeY,
const uint& numOutputClasses, const uint& numBBoxes, uint64_t outputSize, cudaStream_t stream,
const float modelScale)
const float scaleXY, const void* anchors, const void* mask)
{
dim3 threads_per_block(16, 16, 4);
dim3 number_of_blocks((gridSizeX / threads_per_block.x) + 1,
@@ -65,7 +83,7 @@ cudaError_t cudaYoloLayer_r(const void* input, void* output, const uint& batchSi
gpuYoloLayer_r<<<number_of_blocks, threads_per_block, 0, stream>>>(
reinterpret_cast<const float*>(input) + (batch * outputSize),
reinterpret_cast<float*>(output) + (batch * outputSize), gridSizeX, gridSizeY, numOutputClasses,
numBBoxes, modelScale);
numBBoxes, scaleXY, reinterpret_cast<const float*>(anchors), reinterpret_cast<const int*>(mask));
}
return cudaGetLastError();
}

85
nvdsinfer_custom_impl_Yolo/yoloForward_v2.cu
View File

@@ -11,8 +11,28 @@
inline __device__ float sigmoidGPU(const float& x) { return 1.0f / (1.0f + __expf(-x)); }
__global__ void gpuRegionLayer(const float* input, float* output, const uint gridSizeX, const uint gridSizeY, const uint numOutputClasses,
const uint numBBoxes)
__device__ void softmaxGPU(const float* input, const int bbindex, const int numGridCells,
uint z_id, const uint numOutputClasses, float temp, float* output)
{
int i;
float sum = 0;
float largest = -INFINITY;
for (i = 0; i < numOutputClasses; ++i) {
int val = input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + (5 + i))];
largest = (val>largest) ? val : largest;
}
for (i = 0; i < numOutputClasses; ++i) {
float e = __expf(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + (5 + i))] / temp - largest / temp);
sum += e;
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + (5 + i))] = e;
}
for (i = 0; i < numOutputClasses; ++i) {
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + (5 + i))] /= sum;
}
}
__global__ void gpuRegionLayer(const float* input, float* output, float* softmax, const uint gridSizeX, const uint gridSizeY, const uint numOutputClasses,
const uint numBBoxes, const float* anchors)
{
uint x_id = blockIdx.x * blockDim.x + threadIdx.x;
uint y_id = blockIdx.y * blockDim.y + threadIdx.y;
@@ -27,43 +47,51 @@ __global__ void gpuRegionLayer(const float* input, float* output, const uint gri
const int bbindex = y_id * gridSizeX + x_id;
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 0)]
= sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 0)]);
= sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 0)]) + x_id;
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 1)]
= sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 1)]);
= sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 1)]) + y_id;
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 2)]
= __expf(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 2)]);
= __expf(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 2)]) * anchors[z_id * 2];
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 3)]
= __expf(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 3)]);
= __expf(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 3)]) * anchors[z_id * 2 + 1];
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 4)]
softmaxGPU(input, bbindex, numGridCells, z_id, numOutputClasses, 1.0, softmax);
const float objectness
= sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 4)]);
float temp = 1.0;
int i;
float sum = 0;
float largest = -INFINITY;
for(i = 0; i < numOutputClasses; ++i){
int val = input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + (5 + i))];
largest = (val>largest) ? val : largest;
}
for(i = 0; i < numOutputClasses; ++i){
float e = exp(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + (5 + i))] / temp - largest / temp);
sum += e;
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + (5 + i))] = e;
}
for(i = 0; i < numOutputClasses; ++i){
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + (5 + i))] /= sum;
float maxProb = 0.0f;
int maxIndex = -1;
for (uint i = 0; i < numOutputClasses; ++i)
{
float prob
= softmax[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + (5 + i))];
if (prob > maxProb)
{
maxProb = prob;
maxIndex = i;
}
}
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 4)]
= objectness * maxProb;
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 5)]
= maxIndex;
}
cudaError_t cudaYoloLayer_v2(const void* input, void* output, const uint& batchSize, const uint& gridSizeX, const uint& gridSizeY,
const uint& numOutputClasses, const uint& numBBoxes, uint64_t outputSize, cudaStream_t stream);
cudaError_t cudaYoloLayer_v2(const void* input, void* output, void* softmax, const uint& batchSize, const uint& gridSizeX, const uint& gridSizeY,
const uint& numOutputClasses, const uint& numBBoxes, uint64_t outputSize, cudaStream_t stream,
const void* anchors);
cudaError_t cudaYoloLayer_v2(const void* input, void* output, const uint& batchSize, const uint& gridSizeX, const uint& gridSizeY,
const uint& numOutputClasses, const uint& numBBoxes, uint64_t outputSize, cudaStream_t stream)
cudaError_t cudaYoloLayer_v2(const void* input, void* output, void* softmax, const uint& batchSize, const uint& gridSizeX, const uint& gridSizeY,
const uint& numOutputClasses, const uint& numBBoxes, uint64_t outputSize, cudaStream_t stream,
const void* anchors)
{
dim3 threads_per_block(16, 16, 4);
dim3 number_of_blocks((gridSizeX / threads_per_block.x) + 1,
@@ -73,8 +101,9 @@ cudaError_t cudaYoloLayer_v2(const void* input, void* output, const uint& batchS
{
gpuRegionLayer<<<number_of_blocks, threads_per_block, 0, stream>>>(
reinterpret_cast<const float*>(input) + (batch * outputSize),
reinterpret_cast<float*>(output) + (batch * outputSize), gridSizeX, gridSizeY, numOutputClasses,
numBBoxes);
reinterpret_cast<float*>(output) + (batch * outputSize),
reinterpret_cast<float*>(softmax) + (batch * outputSize), gridSizeX, gridSizeY, numOutputClasses,
numBBoxes, reinterpret_cast<const float*>(anchors));
}
return cudaGetLastError();
}

160
nvdsinfer_custom_impl_Yolo/yoloPlugins.cpp
View File

@@ -29,10 +29,10 @@
#include <iostream>
#include <memory>
int kMODEL_TYPE;
int kNUM_BBOXES;
int kNUM_CLASSES;
float kBETA_NMS;
std::vector<float> kANCHORS;
std::vector<std::vector<int>> kMASK;
namespace {
template <typename T>
@@ -50,25 +50,28 @@ namespace {
}
}
cudaError_t cudaYoloLayer (
cudaError_t cudaYoloLayer_r (
const void* input, void* output, const uint& batchSize,
const uint& gridSizeX, const uint& gridSizeY, const uint& numOutputClasses,
const uint& numBBoxes, uint64_t outputSize, cudaStream_t stream, const float modelScale);
cudaError_t cudaYoloLayer_v2 (
const void* input, void* output, const uint& batchSize,
const uint& gridSizeX, const uint& gridSizeY, const uint& numOutputClasses,
const uint& numBBoxes, uint64_t outputSize, cudaStream_t stream);
const uint& numBBoxes, uint64_t outputSize, cudaStream_t stream, const float scaleXY,
const void* anchors, const void* mask);
cudaError_t cudaYoloLayer_nc (
const void* input, void* output, const uint& batchSize,
const uint& gridSizeX, const uint& gridSizeY, const uint& numOutputClasses,
const uint& numBBoxes, uint64_t outputSize, cudaStream_t stream, const float modelScale);
const uint& numBBoxes, uint64_t outputSize, cudaStream_t stream, const float scaleXY,
const void* anchors, const void* mask);
cudaError_t cudaYoloLayer_r (
cudaError_t cudaYoloLayer (
const void* input, void* output, const uint& batchSize,
const uint& gridSizeX, const uint& gridSizeY, const uint& numOutputClasses,
const uint& numBBoxes, uint64_t outputSize, cudaStream_t stream, const float modelScale);
const uint& numBBoxes, uint64_t outputSize, cudaStream_t stream, const float scaleXY,
const void* anchors, const void* mask);
cudaError_t cudaYoloLayer_v2 (
const void* input, void* output, void* softmax, const uint& batchSize,
const uint& gridSizeX, const uint& gridSizeY, const uint& numOutputClasses,
const uint& numBBoxes, uint64_t outputSize, cudaStream_t stream, const void* anchors);
YoloLayer::YoloLayer (const void* data, size_t length)
{
@@ -79,10 +82,11 @@ YoloLayer::YoloLayer (const void* data, size_t length)
read(d, m_GridSizeY);
read(d, m_OutputSize);
read(d, m_type);
read(d, m_new_coords);
read(d, m_scale_x_y);
read(d, m_beta_nms);
read(d, m_Type);
read(d, m_NewCoords);
read(d, m_ScaleXY);
read(d, m_BetaNMS);
uint anchorsSize;
read(d, anchorsSize);
for (uint i = 0; i < anchorsSize; i++) {
@@ -90,35 +94,43 @@ YoloLayer::YoloLayer (const void* data, size_t length)
read(d, result);
m_Anchors.push_back(result);
}
uint maskSize;
read(d, maskSize);
for (uint i = 0; i < maskSize; i++) {
uint nMask;
read(d, nMask);
std::vector<int> pMask;
for (uint f = 0; f < nMask; f++) {
int result;
read(d, result);
pMask.push_back(result);
}
m_Mask.push_back(pMask);
int result;
read(d, result);
m_Mask.push_back(result);
}
kMODEL_TYPE = m_Type;
kNUM_BBOXES = m_NumBoxes;
kNUM_CLASSES = m_NumClasses;
kBETA_NMS = m_beta_nms;
kANCHORS = m_Anchors;
kMASK = m_Mask;
kBETA_NMS = m_BetaNMS;
if (m_Anchors.size() > 0) {
float* m_anchors = m_Anchors.data();
CHECK(cudaMallocHost(&mAnchors, m_Anchors.size() * sizeof(float)));
CHECK(cudaMemcpy(mAnchors, m_anchors, m_Anchors.size() * sizeof(float), cudaMemcpyHostToDevice));
}
if (m_Mask.size() > 0) {
int* m_mask = m_Mask.data();
CHECK(cudaMallocHost(&mMask, m_Mask.size() * sizeof(int)));
CHECK(cudaMemcpy(mMask, m_mask, m_Mask.size() * sizeof(int), cudaMemcpyHostToDevice));
}
};
YoloLayer::YoloLayer (
const uint& numBoxes, const uint& numClasses, const uint& gridSizeX, const uint& gridSizeY, const uint model_type, const uint new_coords, const float scale_x_y, const float beta_nms, const std::vector<float> anchors, std::vector<std::vector<int>> mask) :
const uint& numBoxes, const uint& numClasses, const uint& gridSizeX, const uint& gridSizeY, const uint modelType, const uint newCoords, const float scaleXY, const float betaNMS, const std::vector<float> anchors, std::vector<int> mask) :
m_NumBoxes(numBoxes),
m_NumClasses(numClasses),
m_GridSizeX(gridSizeX),
m_GridSizeY(gridSizeY),
m_type(model_type),
m_new_coords(new_coords),
m_scale_x_y(scale_x_y),
m_beta_nms(beta_nms),
m_Type(modelType),
m_NewCoords(newCoords),
m_ScaleXY(scaleXY),
m_BetaNMS(betaNMS),
m_Anchors(anchors),
m_Mask(mask)
{
@@ -127,8 +139,30 @@ YoloLayer::YoloLayer (
assert(m_GridSizeX > 0);
assert(m_GridSizeY > 0);
m_OutputSize = m_GridSizeX * m_GridSizeY * (m_NumBoxes * (4 + 1 + m_NumClasses));
if (m_Anchors.size() > 0) {
float* m_anchors = m_Anchors.data();
CHECK(cudaMallocHost(&mAnchors, m_Anchors.size() * sizeof(float)));
CHECK(cudaMemcpy(mAnchors, m_anchors, m_Anchors.size() * sizeof(float), cudaMemcpyHostToDevice));
}
if (m_Mask.size() > 0) {
int* m_mask = m_Mask.data();
CHECK(cudaMallocHost(&mMask, m_Mask.size() * sizeof(int)));
CHECK(cudaMemcpy(mMask, m_mask, m_Mask.size() * sizeof(int), cudaMemcpyHostToDevice));
}
};
YoloLayer::~YoloLayer()
{
if (m_Anchors.size() > 0) {
CHECK(cudaFreeHost(mAnchors));
}
if (m_Mask.size() > 0) {
CHECK(cudaFreeHost(mMask));
}
}
nvinfer1::Dims
YoloLayer::getOutputDimensions(
int index, const nvinfer1::Dims* inputs, int nbInputDims) noexcept
@@ -159,27 +193,33 @@ int YoloLayer::enqueue(
int batchSize, void const* const* inputs, void* const* outputs, void* workspace,
cudaStream_t stream) noexcept
{
if (m_type == 2) { // YOLOR incorrect param
if (m_Type == 2) { // YOLOR incorrect param: scale_x_y = 2.0
CHECK(cudaYoloLayer_r(
inputs[0], outputs[0], batchSize, m_GridSizeX, m_GridSizeY, m_NumClasses, m_NumBoxes,
m_OutputSize, stream, m_scale_x_y));
inputs[0], outputs[0], batchSize, m_GridSizeX, m_GridSizeY, m_NumClasses, m_NumBoxes,
m_OutputSize, stream, 2.0, mAnchors, mMask));
}
else if (m_type == 1) {
if (m_new_coords) {
else if (m_Type == 1) {
if (m_NewCoords) {
CHECK(cudaYoloLayer_nc(
inputs[0], outputs[0], batchSize, m_GridSizeX, m_GridSizeY, m_NumClasses, m_NumBoxes,
m_OutputSize, stream, m_scale_x_y));
inputs[0], outputs[0], batchSize, m_GridSizeX, m_GridSizeY, m_NumClasses, m_NumBoxes,
m_OutputSize, stream, m_ScaleXY, mAnchors, mMask));
}
else {
CHECK(cudaYoloLayer(
inputs[0], outputs[0], batchSize, m_GridSizeX, m_GridSizeY, m_NumClasses, m_NumBoxes,
m_OutputSize, stream, m_scale_x_y));
inputs[0], outputs[0], batchSize, m_GridSizeX, m_GridSizeY, m_NumClasses, m_NumBoxes,
m_OutputSize, stream, m_ScaleXY, mAnchors, mMask));
}
}
else {
void* softmax;
cudaMallocHost(&softmax, sizeof(outputs[0]));
cudaMemcpy(softmax, outputs[0], sizeof(outputs[0]), cudaMemcpyHostToDevice);
CHECK(cudaYoloLayer_v2(
inputs[0], outputs[0], batchSize, m_GridSizeX, m_GridSizeY, m_NumClasses, m_NumBoxes,
m_OutputSize, stream));
inputs[0], outputs[0], softmax, batchSize, m_GridSizeX, m_GridSizeY, m_NumClasses, m_NumBoxes,
m_OutputSize, stream, mAnchors));
CHECK(cudaFreeHost(softmax));
}
return 0;
}
@@ -193,13 +233,10 @@ size_t YoloLayer::getSerializationSize() const noexcept
int maskSum = 1;
for (uint i = 0; i < m_Mask.size(); i++) {
maskSum += 1;
for (uint f = 0; f < m_Mask[i].size(); f++) {
maskSum += 1;
}
}
return sizeof(m_NumBoxes) + sizeof(m_NumClasses) + sizeof(m_GridSizeX) + sizeof(m_GridSizeY) + sizeof(m_OutputSize) + sizeof(m_type)
+ sizeof(m_new_coords) + sizeof(m_scale_x_y) + sizeof(m_beta_nms) + anchorsSum * sizeof(float) + maskSum * sizeof(int);
return sizeof(m_NumBoxes) + sizeof(m_NumClasses) + sizeof(m_GridSizeX) + sizeof(m_GridSizeY) + sizeof(m_OutputSize) + sizeof(m_Type)
+ sizeof(m_NewCoords) + sizeof(m_ScaleXY) + sizeof(m_BetaNMS) + anchorsSum * sizeof(float) + maskSum * sizeof(int);
}
void YoloLayer::serialize(void* buffer) const noexcept
@@ -211,33 +248,32 @@ void YoloLayer::serialize(void* buffer) const noexcept
write(d, m_GridSizeY);
write(d, m_OutputSize);
write(d, m_type);
write(d, m_new_coords);
write(d, m_scale_x_y);
write(d, m_beta_nms);
write(d, m_Type);
write(d, m_NewCoords);
write(d, m_ScaleXY);
write(d, m_BetaNMS);
uint anchorsSize = m_Anchors.size();
write(d, anchorsSize);
for (uint i = 0; i < anchorsSize; i++) {
write(d, m_Anchors[i]);
}
uint maskSize = m_Mask.size();
write(d, maskSize);
for (uint i = 0; i < maskSize; i++) {
uint pMaskSize = m_Mask[i].size();
write(d, pMaskSize);
for (uint f = 0; f < pMaskSize; f++) {
write(d, m_Mask[i][f]);
}
write(d, m_Mask[i]);
}
kMODEL_TYPE = m_Type;
kNUM_BBOXES = m_NumBoxes;
kNUM_CLASSES = m_NumClasses;
kBETA_NMS = m_beta_nms;
kANCHORS = m_Anchors;
kMASK = m_Mask;
kBETA_NMS = m_BetaNMS;
}
nvinfer1::IPluginV2* YoloLayer::clone() const noexcept
{
return new YoloLayer (m_NumBoxes, m_NumClasses, m_GridSizeX, m_GridSizeY, m_type, m_new_coords, m_scale_x_y, m_beta_nms, m_Anchors, m_Mask);
return new YoloLayer (m_NumBoxes, m_NumClasses, m_GridSizeX, m_GridSizeY, m_Type, m_NewCoords, m_ScaleXY, m_BetaNMS, m_Anchors, m_Mask);
}
REGISTER_TENSORRT_PLUGIN(YoloLayerPluginCreator);

22
nvdsinfer_custom_impl_Yolo/yoloPlugins.h
View File

@@ -57,8 +57,9 @@ class YoloLayer : public nvinfer1::IPluginV2
public:
YoloLayer (const void* data, size_t length);
YoloLayer (const uint& numBoxes, const uint& numClasses, const uint& gridSizeX, const uint& gridSizeY,
const uint model_type, const uint new_coords, const float scale_x_y, const float beta_nms,
const std::vector<float> anchors, const std::vector<std::vector<int>> mask);
const uint modelType, const uint newCoords, const float scaleXY, const float betaNMS,
const std::vector<float> anchors, const std::vector<int> mask);
~YoloLayer ();
const char* getPluginType () const noexcept override { return YOLOLAYER_PLUGIN_NAME; }
const char* getPluginVersion () const noexcept override { return YOLOLAYER_PLUGIN_VERSION; }
int getNbOutputs () const noexcept override { return 1; }
@@ -101,12 +102,15 @@ private:
uint64_t m_OutputSize {0};
std::string m_Namespace {""};
uint m_type {0};
uint m_new_coords {0};
float m_scale_x_y {0};
float m_beta_nms {0};
uint m_Type {0};
uint m_NewCoords {0};
float m_ScaleXY {0};
float m_BetaNMS {0};
std::vector<float> m_Anchors;
std::vector<std::vector<int>> m_Mask;
std::vector<int> m_Mask;
void* mAnchors;
void* mMask;
};
class YoloLayerPluginCreator : public nvinfer1::IPluginCreator
@@ -148,9 +152,9 @@ private:
std::string m_Namespace {""};
};
extern int kMODEL_TYPE;
extern int kNUM_BBOXES;
extern int kNUM_CLASSES;
extern float kBETA_NMS;
extern std::vector<float> kANCHORS;
extern std::vector<std::vector<int>> kMASK;
#endif // __YOLO_PLUGINS__

19
readme.md
View File

@@ -23,7 +23,8 @@ NVIDIA DeepStream SDK 6.0 configuration for YOLO models
* Support for reorg, implicit and channel layers (YOLOR)
* YOLOv5 6.0 native support
* YOLOR native support
* **Models benchmarks**
* Models benchmarks
* **GPU YOLO Decoder (moved from CPU to GPU to get better performance)**
##
@@ -43,6 +44,8 @@ NVIDIA DeepStream SDK 6.0 configuration for YOLO models
### Requirements
#### x86 platform
* [Ubuntu 18.04](https://releases.ubuntu.com/18.04.6/)
* [CUDA 11.4.3](https://developer.nvidia.com/cuda-toolkit)
* [TensorRT 8.0 GA (8.0.1)](https://developer.nvidia.com/tensorrt)
@@ -51,10 +54,22 @@ NVIDIA DeepStream SDK 6.0 configuration for YOLO models
* [NVIDIA DeepStream SDK 6.0](https://developer.nvidia.com/deepstream-sdk)
* [DeepStream-Yolo](https://github.com/marcoslucianops/DeepStream-Yolo)
**For YOLOv5 and YOLOR**:
#### Jetson platform
* [JetPack 4.6](https://developer.nvidia.com/embedded/jetpack)
* [NVIDIA DeepStream SDK 6.0](https://developer.nvidia.com/deepstream-sdk)
* [DeepStream-Yolo](https://github.com/marcoslucianops/DeepStream-Yolo)
### For YOLOv5 and YOLOR
#### x86 platform
* [PyTorch >= 1.7.0](https://pytorch.org/get-started/locally/)
#### Jetson platform
* [PyTorch >= 1.7.0](https://forums.developer.nvidia.com/t/pytorch-for-jetson-version-1-10-now-available/72048)
##
### Tested models