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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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38
nvdsinfer_custom_impl_Yolo/yoloForward_r.cu
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@@ -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();
}