thebears/deepstream_yolo
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New optimized NMS

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This commit is contained in:
Marcos Luciano
2022-08-15 02:48:23 -03:00
parent 8ad99d3f8d
commit c8a4a49f16
24 changed files with 206 additions and 394 deletions
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43
nvdsinfer_custom_impl_Yolo/yoloForward_e.cu
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@@ -7,9 +7,9 @@
#include <stdio.h>
__global__ void gpuYoloLayer_e(
const float* cls, const float* reg, 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 numOutputClasses,
const uint64_t outputSize)
const float* cls, const float* reg, int* num_detections, float* detection_boxes, float* detection_scores,
int* detection_classes, const float scoreThreshold, const uint netWidth, const uint netHeight,
const uint numOutputClasses, const uint64_t outputSize)
{
uint x_id = blockIdx.x * blockDim.x + threadIdx.x;
@@ -34,39 +34,38 @@ __global__ void gpuYoloLayer_e(
if (maxProb < scoreThreshold)
return;
int count = (int)atomicAdd(countData, 1);
int count = (int)atomicAdd(num_detections, 1);
d_indexes[count] = count;
d_scores[count] = maxProb + 1.f;
d_boxes[count * 4 + 0] = reg[x_id * 4 + 0];
d_boxes[count * 4 + 1] = reg[x_id * 4 + 1];
d_boxes[count * 4 + 2] = reg[x_id * 4 + 2];
d_boxes[count * 4 + 3] = reg[x_id * 4 + 3];
d_classes[count] = maxIndex;
detection_boxes[count * 4 + 0] = reg[x_id * 4 + 0];
detection_boxes[count * 4 + 1] = reg[x_id * 4 + 1];
detection_boxes[count * 4 + 2] = reg[x_id * 4 + 2];
detection_boxes[count * 4 + 3] = reg[x_id * 4 + 3];
detection_scores[count] = maxProb;
detection_classes[count] = maxIndex;
}
cudaError_t cudaYoloLayer_e(
const void* cls, const void* reg, void* d_indexes, void* d_scores, void* d_boxes, void* d_classes, void* countData,
const uint& batchSize, uint64_t& outputSize, const float& scoreThreshold, const uint& netWidth, const uint& netHeight,
const uint& numOutputClasses, cudaStream_t stream);
const void* cls, const void* reg, void* num_detections, void* detection_boxes, void* detection_scores,
void* detection_classes, const uint& batchSize, uint64_t& outputSize, const float& scoreThreshold, const uint& netWidth,
const uint& netHeight, const uint& numOutputClasses, cudaStream_t stream);
cudaError_t cudaYoloLayer_e(
const void* cls, const void* reg, void* d_indexes, void* d_scores, void* d_boxes, void* d_classes, void* countData,
const uint& batchSize, uint64_t& outputSize, const float& scoreThreshold, const uint& netWidth, const uint& netHeight,
const uint& numOutputClasses, cudaStream_t stream)
const void* cls, const void* reg, void* num_detections, void* detection_boxes, void* detection_scores,
void* detection_classes, const uint& batchSize, uint64_t& outputSize, const float& scoreThreshold, const uint& netWidth,
const uint& netHeight, const uint& numOutputClasses, cudaStream_t stream)
{
int threads_per_block = 16;
int number_of_blocks = 525;
int number_of_blocks = (outputSize / threads_per_block) + 1;
for (unsigned int batch = 0; batch < batchSize; ++batch)
{
gpuYoloLayer_e<<<number_of_blocks, threads_per_block, 0, stream>>>(
reinterpret_cast<const float*>(cls) + (batch * numOutputClasses * outputSize),
reinterpret_cast<const float*>(reg) + (batch * 4 * outputSize),
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),
reinterpret_cast<int*>(num_detections) + (batch),
reinterpret_cast<float*>(detection_boxes) + (batch * 4 * outputSize),
reinterpret_cast<float*>(detection_scores) + (batch * outputSize),
reinterpret_cast<int*>(detection_classes) + (batch * outputSize),
scoreThreshold, netWidth, netHeight, numOutputClasses, outputSize);
}
return cudaGetLastError();