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GPU Batched NMS

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This commit is contained in:
Marcos Luciano
2022-06-19 03:25:50 -03:00
parent 2300e3b44b
commit f621c0f429
24 changed files with 835 additions and 654 deletions
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63
nvdsinfer_custom_impl_Yolo/yoloForward_nc.cu
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@@ -3,16 +3,12 @@
* https://www.github.com/marcoslucianops
*/
#include <cuda.h>
#include <cuda_runtime.h>
#include <stdint.h>
#include <stdio.h>
#include <string.h>
__global__ void gpuYoloLayer_nc(
const float* input, float* output, const uint netWidth, const uint netHeight, const uint gridSizeX,
const uint gridSizeY, const uint numOutputClasses, const uint numBBoxes, const float scaleXY,
const float* anchors, const int* mask)
const float* input, int* d_indexes, float* d_scores, float* d_boxes, int* d_classes, int* countData,
const float scoreThreshold, const uint netWidth, const uint netHeight, const uint gridSizeX, const uint gridSizeY,
const uint numOutputClasses, 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;
@@ -26,28 +22,32 @@ __global__ void gpuYoloLayer_nc(
const int numGridCells = gridSizeX * gridSizeY;
const int bbindex = y_id * gridSizeX + x_id;
const float objectness
= input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 4)];
if (objectness < scoreThreshold) return;
int count = (int)atomicAdd(&countData[0], 1);
const float alpha = scaleXY;
const float beta = -0.5 * (scaleXY - 1);
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 0)]
float x
= (input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 0)]
* alpha + beta + x_id) * netWidth / gridSizeX;
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 1)]
float y
= (input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 1)]
* alpha + beta + y_id) * netHeight / gridSizeY;
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 2)]
float w
= __powf(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 2)] * 2, 2)
* anchors[mask[z_id] * 2];
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 3)]
float h
= __powf(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 3)] * 2, 2)
* anchors[mask[z_id] * 2 + 1];
const float objectness
= input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 4)];
float maxProb = 0.0f;
int maxIndex = -1;
@@ -63,22 +63,26 @@ __global__ void gpuYoloLayer_nc(
}
}
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 4)]
= objectness * maxProb;
output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 5)]
= maxIndex;
d_indexes[count] = count;
d_scores[count] = objectness * maxProb + 1.f;
d_boxes[count * 4 + 0] = x - 0.5 * w;
d_boxes[count * 4 + 1] = y - 0.5 * h;
d_boxes[count * 4 + 2] = x + 0.5 * w;
d_boxes[count * 4 + 3] = y + 0.5 * h;
d_classes[count] = maxIndex;
}
cudaError_t cudaYoloLayer_nc(
const void* input, void* output, const uint& batchSize, const uint& netWidth, const uint& netHeight,
const uint& gridSizeX, const uint& gridSizeY, const uint& numOutputClasses, const uint& numBBoxes,
uint64_t& outputSize, const float& scaleXY, const void* anchors, const void* mask, cudaStream_t stream);
const void* input, void* d_indexes, void* d_scores, void* d_boxes, void* d_classes, void* countData,
const uint& batchSize, uint64_t& inputSize, uint64_t& outputSize, const float& scoreThreshold, const uint& netWidth,
const uint& netHeight, const uint& gridSizeX, const uint& gridSizeY, const uint& numOutputClasses, const uint& numBBoxes,
const float& scaleXY, const void* anchors, const void* mask, cudaStream_t stream);
cudaError_t cudaYoloLayer_nc(
const void* input, void* output, const uint& batchSize, const uint& netWidth, const uint& netHeight,
const uint& gridSizeX, const uint& gridSizeY, const uint& numOutputClasses, const uint& numBBoxes,
uint64_t& outputSize, const float& scaleXY, const void* anchors, const void* mask, cudaStream_t stream)
const void* input, void* d_indexes, void* d_scores, void* d_boxes, void* d_classes, void* countData,
const uint& batchSize, uint64_t& inputSize, uint64_t& outputSize, const float& scoreThreshold, const uint& netWidth,
const uint& netHeight, const uint& gridSizeX, const uint& gridSizeY, const uint& numOutputClasses, const uint& numBBoxes,
const float& scaleXY, const void* anchors, const void* mask, cudaStream_t stream)
{
dim3 threads_per_block(16, 16, 4);
dim3 number_of_blocks((gridSizeX / threads_per_block.x) + 1,
@@ -88,9 +92,12 @@ cudaError_t cudaYoloLayer_nc(
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),
netWidth, netHeight, gridSizeX, gridSizeY, numOutputClasses, numBBoxes, scaleXY,
reinterpret_cast<const float*>(input) + (batch * inputSize),
reinterpret_cast<int*>(d_indexes) + (batch * outputSize),
reinterpret_cast<float*>(d_scores) + (batch * outputSize),
reinterpret_cast<float*>(d_boxes) + (batch * 4 * outputSize),
reinterpret_cast<int*>(d_classes) + (batch * outputSize), reinterpret_cast<int*>(countData) + (batch),
scoreThreshold, netWidth, netHeight, gridSizeX, gridSizeY, numOutputClasses, numBBoxes, scaleXY,
reinterpret_cast<const float*>(anchors), reinterpret_cast<const int*>(mask));
}
return cudaGetLastError();