thebears/deepstream_yolo
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Move YOLO Decoder from CPU to GPU

Browse Source
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
Download Patch File Download Diff File Expand all files Collapse all files

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();
}