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Fix YOLO kernels

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- Fix YOLO kernels
- Update deprecated functions
This commit is contained in:
unknown
2021-12-12 09:58:23 -03:00
parent ce35e17334
commit 9565254551
11 changed files with 316 additions and 153 deletions
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5
nvdsinfer_custom_impl_Yolo/Makefile
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@@ -63,7 +63,10 @@ SRCFILES:= nvdsinfer_yolo_engine.cpp \
layers/activation_layer.cpp \
utils.cpp \
yolo.cpp \
yoloForward.cu
yoloForward.cu \
yoloForward_v2.cu \
yoloForward_nc.cu \
yoloForward_r.cu
ifeq ($(OPENCV), 1)
SRCFILES+= calibrator.cpp

6
nvdsinfer_custom_impl_Yolo/layers/convolutional_layer.cpp
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@@ -164,13 +164,13 @@ nvinfer1::ILayer* convolutionalLayer(
}
}
nvinfer1::IConvolutionLayer* conv = network->addConvolution(
nvinfer1::IConvolutionLayer* conv = network->addConvolutionNd(
*input, filters, nvinfer1::DimsHW{kernelSize, kernelSize}, convWt, convBias);
assert(conv != nullptr);
std::string convLayerName = "conv_" + std::to_string(layerIdx);
conv->setName(convLayerName.c_str());
conv->setStride(nvinfer1::DimsHW{stride, stride});
conv->setPadding(nvinfer1::DimsHW{pad, pad});
conv->setStrideNd(nvinfer1::DimsHW{stride, stride});
conv->setPaddingNd(nvinfer1::DimsHW{pad, pad});
if (block.find("groups") != block.end())
{

4
nvdsinfer_custom_impl_Yolo/layers/maxpool_layer.cpp
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@@ -19,10 +19,10 @@ nvinfer1::ILayer* maxpoolLayer(
int stride = std::stoi(block.at("stride"));
nvinfer1::IPoolingLayer* pool
= network->addPooling(*input, nvinfer1::PoolingType::kMAX, nvinfer1::DimsHW{size, size});
= network->addPoolingNd(*input, nvinfer1::PoolingType::kMAX, nvinfer1::DimsHW{size, size});
assert(pool);
std::string maxpoolLayerName = "maxpool_" + std::to_string(layerIdx);
pool->setStride(nvinfer1::DimsHW{stride, stride});
pool->setStrideNd(nvinfer1::DimsHW{stride, stride});
pool->setPaddingMode(nvinfer1::PaddingMode::kSAME_UPPER);
pool->setName(maxpoolLayerName.c_str());

2
nvdsinfer_custom_impl_Yolo/layers/upsample_layer.cpp
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@@ -16,7 +16,7 @@ nvinfer1::ILayer* upsampleLayer(
nvinfer1::IResizeLayer* resize_layer = network->addResize(*input);
resize_layer->setResizeMode(nvinfer1::ResizeMode::kNEAREST);
float scale[3] = {1, stride, stride};
float scale[3] = {1, static_cast<float>(stride), static_cast<float>(stride)};
resize_layer->setScales(scale, 3);
std::string layer_name = "upsample_" + std::to_string(layerIdx);
resize_layer->setName(layer_name.c_str());

16
nvdsinfer_custom_impl_Yolo/yolo.cpp
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@@ -75,7 +75,7 @@ nvinfer1::ICudaEngine *Yolo::createEngine (nvinfer1::IBuilder* builder)
nvinfer1::INetworkDefinition *network = builder->createNetworkV2(0);
if (parseModel(*network) != NVDSINFER_SUCCESS) {
network->destroy();
delete network;
return nullptr;
}
@@ -122,7 +122,7 @@ nvinfer1::ICudaEngine *Yolo::createEngine (nvinfer1::IBuilder* builder)
std::cerr << "Building engine failed\n" << std::endl;
}
network->destroy();
delete network;
delete config;
return engine;
}
@@ -232,12 +232,11 @@ NvDsInferStatus Yolo::buildYoloNetwork(
printLayerInfo(layerIndex, layerType, " -", outputVol, " -");
}
else if (m_ConfigBlocks.at(i).at("type") == "dropout") {
else if (m_ConfigBlocks.at(i).at("type") == "dropout") { // Skip dropout layer
assert(m_ConfigBlocks.at(i).find("probability") != m_ConfigBlocks.at(i).end());
//float probability = std::stof(m_ConfigBlocks.at(i).at("probability"));
//nvinfer1::ILayer* out = dropoutLayer(probability, previous, &network);
//previous = out->getOutput(0);
//Skip dropout layer
assert(previous != nullptr);
tensorOutputs.push_back(previous);
printLayerInfo(layerIndex, "dropout", " -", " -", " -");
@@ -300,6 +299,13 @@ NvDsInferStatus Yolo::buildYoloNetwork(
}
else if (m_ConfigBlocks.at(i).at("type") == "yolo") {
uint model_type;
if (m_NetworkType.find("yolor") != std::string::npos) {
model_type = 2;
}
else {
model_type = 1;
}
nvinfer1::Dims prevTensorDims = previous->getDimensions();
TensorInfo& curYoloTensor = m_OutputTensors.at(outputTensorCount);
curYoloTensor.gridSizeY = prevTensorDims.d[1];
@@ -327,7 +333,7 @@ NvDsInferStatus Yolo::buildYoloNetwork(
curYoloTensor.numClasses,
curYoloTensor.gridSizeX,
curYoloTensor.gridSizeY,
1, new_coords, scale_x_y, beta_nms,
model_type, new_coords, scale_x_y, beta_nms,
curYoloTensor.anchors,
m_OutputMasks);
assert(yoloPlugin != nullptr);

121
nvdsinfer_custom_impl_Yolo/yoloForward.cu
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@@ -21,7 +21,7 @@
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 uint new_coords, const float scale_x_y)
const uint numBBoxes, const float scale_x_y)
{
uint x_id = blockIdx.x * blockDim.x + threadIdx.x;
uint y_id = blockIdx.y * blockDim.y + threadIdx.y;
@@ -35,54 +35,9 @@ __global__ void gpuYoloLayer(const float* input, float* output, const uint gridS
const int numGridCells = gridSizeX * gridSizeY;
const int bbindex = y_id * gridSizeX + x_id;
float alpha = scale_x_y;
float beta = -0.5 * (scale_x_y - 1);
const float alpha = scale_x_y;
const float beta = -0.5 * (scale_x_y - 1);
if (new_coords == 1) {
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 0)]
= input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 0)] * alpha + beta;
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 1)]
= input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 1)] * alpha + beta;
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 2)]
= pow(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 2)] * 2, 2);
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 3)]
= pow(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 3)] * 2, 2);
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 4)]
= input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 4)];
for (uint i = 0; i < numOutputClasses; ++i)
{
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + (5 + i))]
= input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + (5 + i))];
}
}
else if (new_coords == 0 && scale_x_y != 1) { // YOLOR incorrect param
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 0)]
= sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 0)]) * 2.0 - 0.5;
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 1)]
= sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 1)]) * 2.0 - 0.5;
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 2)]
= pow(sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 2)]) * 2, 2);
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 3)]
= pow(sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 3)]) * 2, 2);
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 4)]
= sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 4)]);
for (uint i = 0; i < numOutputClasses; ++i)
{
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + (5 + i))]
= sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + (5 + i))]);
}
}
else {
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 0)]
= sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 0)]) * alpha + beta;
@@ -104,85 +59,25 @@ __global__ void gpuYoloLayer(const float* input, float* output, const uint gridS
= sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + (5 + i))]);
}
}
}
__global__ void gpuRegionLayer(const float* input, float* output, const uint gridSizeX, const uint gridSizeY, const uint numOutputClasses,
const uint numBBoxes)
{
uint x_id = blockIdx.x * blockDim.x + threadIdx.x;
uint y_id = blockIdx.y * blockDim.y + threadIdx.y;
uint z_id = blockIdx.z * blockDim.z + threadIdx.z;
if ((x_id >= gridSizeX) || (y_id >= gridSizeY) || (z_id >= numBBoxes))
{
return;
}
const int numGridCells = gridSizeX * gridSizeY;
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)]);
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 1)]
= sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 1)]);
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 2)]
= __expf(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 2)]);
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 3)]
= __expf(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 3)]);
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 4)]
= 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;
}
}
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 uint modelCoords, const float modelScale, const uint modelType);
const uint& numOutputClasses, const uint& numBBoxes, uint64_t outputSize, cudaStream_t stream,
const float modelScale);
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 uint modelCoords, const float modelScale, const uint modelType)
const uint& numOutputClasses, const uint& numBBoxes, uint64_t outputSize, cudaStream_t stream,
const float modelScale)
{
dim3 threads_per_block(16, 16, 4);
dim3 number_of_blocks((gridSizeX / threads_per_block.x) + 1,
(gridSizeY / threads_per_block.y) + 1,
(numBBoxes / threads_per_block.z) + 1);
if (modelType == 1) {
for (unsigned int batch = 0; batch < batchSize; ++batch)
{
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, modelCoords, modelScale);
}
}
else if (modelType == 0) {
for (unsigned int batch = 0; batch < batchSize; ++batch)
{
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);
}
numBBoxes, modelScale);
}
return cudaGetLastError();
}

74
nvdsinfer_custom_impl_Yolo/yoloForward_nc.cu Normal file
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@@ -0,0 +1,74 @@
/*
* Created by Marcos Luciano
* https://www.github.com/marcoslucianops
*/
#include <cuda.h>
#include <cuda_runtime.h>
#include <stdint.h>
#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)
{
uint x_id = blockIdx.x * blockDim.x + threadIdx.x;
uint y_id = blockIdx.y * blockDim.y + threadIdx.y;
uint z_id = blockIdx.z * blockDim.z + threadIdx.z;
if ((x_id >= gridSizeX) || (y_id >= gridSizeY) || (z_id >= numBBoxes))
{
return;
}
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);
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 0)]
= input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 0)] * alpha + beta;
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 1)]
= input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 1)] * alpha + beta;
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 2)]
= pow(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 2)] * 2, 2);
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 3)]
= pow(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 3)] * 2, 2);
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 4)]
= input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 4)];
for (uint i = 0; i < numOutputClasses; ++i)
{
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + (5 + i))]
= input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + (5 + i))];
}
}
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);
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)
{
dim3 threads_per_block(16, 16, 4);
dim3 number_of_blocks((gridSizeX / threads_per_block.x) + 1,
(gridSizeY / threads_per_block.y) + 1,
(numBBoxes / threads_per_block.z) + 1);
for (unsigned int batch = 0; batch < batchSize; ++batch)
{
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);
}
return cudaGetLastError();
}

71
nvdsinfer_custom_impl_Yolo/yoloForward_r.cu Normal file
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@@ -0,0 +1,71 @@
/*
* Created by Marcos Luciano
* https://www.github.com/marcoslucianops
*/
#include <cuda.h>
#include <cuda_runtime.h>
#include <stdint.h>
#include <stdio.h>
#include <string.h>
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)
{
uint x_id = blockIdx.x * blockDim.x + threadIdx.x;
uint y_id = blockIdx.y * blockDim.y + threadIdx.y;
uint z_id = blockIdx.z * blockDim.z + threadIdx.z;
if ((x_id >= gridSizeX) || (y_id >= gridSizeY) || (z_id >= numBBoxes))
{
return;
}
const int numGridCells = gridSizeX * gridSizeY;
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)]) * 2.0 - 0.5;
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 1)]
= sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 1)]) * 2.0 - 0.5;
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 2)]
= pow(sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 2)]) * 2, 2);
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 3)]
= pow(sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 3)]) * 2, 2);
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 4)]
= sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 4)]);
for (uint i = 0; i < numOutputClasses; ++i)
{
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + (5 + i))]
= sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + (5 + i))]);
}
}
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_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)
{
dim3 threads_per_block(16, 16, 4);
dim3 number_of_blocks((gridSizeX / threads_per_block.x) + 1,
(gridSizeY / threads_per_block.y) + 1,
(numBBoxes / threads_per_block.z) + 1);
for (unsigned int batch = 0; batch < batchSize; ++batch)
{
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);
}
return cudaGetLastError();
}

80
nvdsinfer_custom_impl_Yolo/yoloForward_v2.cu Normal file
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@@ -0,0 +1,80 @@
/*
* Created by Marcos Luciano
* https://www.github.com/marcoslucianops
*/
#include <cuda.h>
#include <cuda_runtime.h>
#include <stdint.h>
#include <stdio.h>
#include <string.h>
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)
{
uint x_id = blockIdx.x * blockDim.x + threadIdx.x;
uint y_id = blockIdx.y * blockDim.y + threadIdx.y;
uint z_id = blockIdx.z * blockDim.z + threadIdx.z;
if ((x_id >= gridSizeX) || (y_id >= gridSizeY) || (z_id >= numBBoxes))
{
return;
}
const int numGridCells = gridSizeX * gridSizeY;
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)]);
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 1)]
= sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 1)]);
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 2)]
= __expf(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 2)]);
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 3)]
= __expf(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 3)]);
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 4)]
= 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;
}
}
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, const uint& batchSize, const uint& gridSizeX, const uint& gridSizeY,
const uint& numOutputClasses, const uint& numBBoxes, uint64_t outputSize, cudaStream_t stream)
{
dim3 threads_per_block(16, 16, 4);
dim3 number_of_blocks((gridSizeX / threads_per_block.x) + 1,
(gridSizeY / threads_per_block.y) + 1,
(numBBoxes / threads_per_block.z) + 1);
for (unsigned int batch = 0; batch < batchSize; ++batch)
{
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);
}
return cudaGetLastError();
}

38
nvdsinfer_custom_impl_Yolo/yoloPlugins.cpp
View File

@@ -53,7 +53,22 @@ void read(const char*& buffer, T& val)
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 uint modelCoords, const float modelScale, const uint modelType);
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);
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);
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);
YoloLayer::YoloLayer (const void* data, size_t length)
{
@@ -144,9 +159,28 @@ 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
CHECK(cudaYoloLayer_r(
inputs[0], outputs[0], batchSize, m_GridSizeX, m_GridSizeY, m_NumClasses, m_NumBoxes,
m_OutputSize, stream, m_scale_x_y));
}
else if (m_type == 1) {
if (m_new_coords) {
CHECK(cudaYoloLayer_nc(
inputs[0], outputs[0], batchSize, m_GridSizeX, m_GridSizeY, m_NumClasses, m_NumBoxes,
m_OutputSize, stream, m_scale_x_y));
}
else {
CHECK(cudaYoloLayer(
inputs[0], outputs[0], batchSize, m_GridSizeX, m_GridSizeY, m_NumClasses, m_NumBoxes,
m_OutputSize, stream, m_new_coords, m_scale_x_y, m_type));
m_OutputSize, stream, m_scale_x_y));
}
}
else {
CHECK(cudaYoloLayer_v2(
inputs[0], outputs[0], batchSize, m_GridSizeX, m_GridSizeY, m_NumClasses, m_NumBoxes,
m_OutputSize, stream));
}
return 0;
}

2
readme.md
View File

@@ -16,7 +16,7 @@ NVIDIA DeepStream SDK 6.0 configuration for YOLO models
* Darknet CFG params parser (it doesn't need to edit nvdsparsebbox_Yolo.cpp or another file for native models)
* Support for new_coords, beta_nms and scale_x_y params
* Support for new models
* Support for new layers types
* Support for new layers
* Support for new activations
* Support for convolutional groups
* Support for INT8 calibration