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Update Benchmarks + Add YOLOv7-u6 + Fixes

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This commit is contained in:
Marcos Luciano
2023-05-21 02:12:09 -03:00
parent af20c2f72c
commit 79d22283c1
13 changed files with 176 additions and 87 deletions
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34
nvdsinfer_custom_impl_Yolo/nvdsparsebbox_Yolo.cpp
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@@ -73,22 +73,22 @@ addBBoxProposal(const float bx1, const float by1, const float bx2, const float b
}
static std::vector<NvDsInferParseObjectInfo>
decodeTensorYolo(const float* detection, const uint& outputSize, const uint& count, const uint& netW, const uint& netH,
decodeTensorYolo(const float* detection, const uint& outputSize, const uint& netW, const uint& netH,
const std::vector<float>& preclusterThreshold)
{
std::vector<NvDsInferParseObjectInfo> binfo;
for (uint b = 0; b < outputSize; ++b) {
float maxProb = count == 6 ? detection[b * count + 4] : detection[b * count + 4] * detection[b * count + 6];
int maxIndex = (int) detection[b * count + 5];
float maxProb = detection[b * 6 + 4];
int maxIndex = (int) detection[b * 6 + 5];
if (maxProb < preclusterThreshold[maxIndex])
continue;
float bxc = detection[b * count + 0];
float byc = detection[b * count + 1];
float bw = detection[b * count + 2];
float bh = detection[b * count + 3];
float bxc = detection[b * 6 + 0];
float byc = detection[b * 6 + 1];
float bw = detection[b * 6 + 2];
float bh = detection[b * 6 + 3];
float bx1 = bxc - bw / 2;
float by1 = byc - bh / 2;
@@ -102,22 +102,22 @@ decodeTensorYolo(const float* detection, const uint& outputSize, const uint& cou
}
static std::vector<NvDsInferParseObjectInfo>
decodeTensorYoloE(const float* detection, const uint& outputSize, const uint& count, const uint& netW, const uint& netH,
decodeTensorYoloE(const float* detection, const uint& outputSize, const uint& netW, const uint& netH,
const std::vector<float>& preclusterThreshold)
{
std::vector<NvDsInferParseObjectInfo> binfo;
for (uint b = 0; b < outputSize; ++b) {
float maxProb = count == 6 ? detection[b * count + 4] : detection[b * count + 4] * detection[b * count + 6];
int maxIndex = (int) detection[b * count + 5];
float maxProb = detection[b * 6 + 4];
int maxIndex = (int) detection[b * 6 + 5];
if (maxProb < preclusterThreshold[maxIndex])
continue;
float bx1 = detection[b * count + 0];
float by1 = detection[b * count + 1];
float bx2 = detection[b * count + 2];
float by2 = detection[b * count + 3];
float bx1 = detection[b * 6 + 0];
float by1 = detection[b * 6 + 1];
float bx2 = detection[b * 6 + 2];
float by2 = detection[b * 6 + 3];
addBBoxProposal(bx1, by1, bx2, by2, netW, netH, maxIndex, maxProb, binfo);
}
@@ -139,9 +139,8 @@ NvDsInferParseCustomYolo(std::vector<NvDsInferLayerInfo> const& outputLayersInfo
const NvDsInferLayerInfo& layer = outputLayersInfo[0];
const uint outputSize = layer.inferDims.d[0];
const uint count = layer.inferDims.d[1];
std::vector<NvDsInferParseObjectInfo> outObjs = decodeTensorYolo((const float*) (layer.buffer), outputSize, count,
std::vector<NvDsInferParseObjectInfo> outObjs = decodeTensorYolo((const float*) (layer.buffer), outputSize,
networkInfo.width, networkInfo.height, detectionParams.perClassPreclusterThreshold);
objects.insert(objects.end(), outObjs.begin(), outObjs.end());
@@ -165,9 +164,8 @@ NvDsInferParseCustomYoloE(std::vector<NvDsInferLayerInfo> const& outputLayersInf
const NvDsInferLayerInfo& layer = outputLayersInfo[0];
const uint outputSize = layer.inferDims.d[0];
const uint count = layer.inferDims.d[1];
std::vector<NvDsInferParseObjectInfo> outObjs = decodeTensorYoloE((const float*) (layer.buffer), outputSize, count,
std::vector<NvDsInferParseObjectInfo> outObjs = decodeTensorYoloE((const float*) (layer.buffer), outputSize,
networkInfo.width, networkInfo.height, detectionParams.perClassPreclusterThreshold);
objects.insert(objects.end(), outObjs.begin(), outObjs.end());