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include include include include include include include include include
static const int NUM_CLASSES_YOLO = 80;
extern “C” bool NvDsInferParseCustomYoloV3(
extern “C” bool NvDsInferParseCustomYoloV3Tiny(
extern “C” bool NvDsInferParseCustomYoloV2(
extern “C” bool NvDsInferParseCustomYoloV2Tiny(
/* This is a sample bounding box parsing function for the sample YoloV3 detector model */
x0 = clamp(x0, 0, netW);
y0 = clamp(y0, 0, netH);
x1 = clamp(x1, 0, netW);
y1 = clamp(y1, 0, netH);
b.left = x0;
b.width = clamp(x1 - x0, 0, netW);
b.top = y0;
b.height = clamp(y1 - y0, 0, netH);
return b;
}
static void addBBoxProposal(const float bx, const float by, const float bw, const float bh,
bbi.detectionConfidence = maxProb;
bbi.classId = maxIndex;
binfo.push_back(bbi);
}
static std::vector
const int numGridCells = gridSizeH * gridSizeW;
const int bbindex = y * gridSizeW + x;
const float bx
= x + detections[bbindex + numGridCells * (b * (5 + numOutputClasses) + 0)];
const float by
= y + detections[bbindex + numGridCells * (b * (5 + numOutputClasses) + 1)];
const float bw
= pw * exp (detections[bbindex + numGridCells * (b * (5 + numOutputClasses) + 2)]);
const float bh
= ph * exp (detections[bbindex + numGridCells * (b * (5 + numOutputClasses) + 3)]);
const float objectness
= detections[bbindex + numGridCells * (b * (5 + numOutputClasses) + 4)];
float maxProb = 0.0f;
int maxIndex = -1;
for (uint i = 0; i < numOutputClasses; ++i)
{
float prob
= (detections[bbindex
+ numGridCells * (b * (5 + numOutputClasses) + (5 + i))]);
if (prob > maxProb)
{
maxProb = prob;
maxIndex = i;
}
}
maxProb = objectness * maxProb;
addBBoxProposal(bx, by, bw, bh, stride, netW, netH, maxIndex, maxProb, binfo);
}
}
}
return binfo;
}
static std::vector
const int numGridCells = gridSizeH * gridSizeW;
const int bbindex = y * gridSizeW + x;
const float bx
= x + detections[bbindex + numGridCells * (b * (5 + numOutputClasses) + 0)];
const float by
= y + detections[bbindex + numGridCells * (b * (5 + numOutputClasses) + 1)];
const float bw
= pw * detections[bbindex + numGridCells * (b * (5 + numOutputClasses) + 2)];
const float bh
= ph * detections[bbindex + numGridCells * (b * (5 + numOutputClasses) + 3)];
const float objectness
= detections[bbindex + numGridCells * (b * (5 + numOutputClasses) + 4)];
float maxProb = 0.0f;
int maxIndex = -1;
for (uint i = 0; i < numOutputClasses; ++i)
{
float prob
= (detections[bbindex
+ numGridCells * (b * (5 + numOutputClasses) + (5 + i))]);
if (prob > maxProb)
{
maxProb = prob;
maxIndex = i;
}
}
maxProb = objectness * maxProb;
addBBoxProposal(bx, by, bw, bh, stride, netW, netH, maxIndex, maxProb, binfo);
}
}
}
return binfo;
}
static inline std::vector<const NvDsInferLayerInfo*>
static bool NvDsInferParseYoloV3(
const std::vector<const NvDsInferLayerInfo*> sortedLayers =
SortLayers (outputLayersInfo);
if (sortedLayers.size() != masks.size()) {
std::cerr << "ERROR: yoloV3 output layer.size: " << sortedLayers.size()
<< " does not match mask.size: " << masks.size() << std::endl;
return false;
}
if (NUM_CLASSES_YOLO != detectionParams.numClassesConfigured)
{
std::cerr << "WARNING: Num classes mismatch. Configured:"
<< detectionParams.numClassesConfigured
<< ", detected by network: " << NUM_CLASSES_YOLO << std::endl;
}
std::vector<NvDsInferParseObjectInfo> objects;
for (uint idx = 0; idx < masks.size(); ++idx) {
const NvDsInferLayerInfo &layer = *sortedLayers[idx]; // 255 x Grid x Grid
assert(layer.inferDims.numDims == 3);
const uint gridSizeH = layer.inferDims.d[1];
const uint gridSizeW = layer.inferDims.d[2];
const uint stride = DIVUP(networkInfo.width, gridSizeW);
assert(stride == DIVUP(networkInfo.height, gridSizeH));
std::vector<NvDsInferParseObjectInfo> outObjs =
decodeYoloV3Tensor((const float*)(layer.buffer), masks[idx], anchors, gridSizeW, gridSizeH, stride, kNUM_BBOXES,
NUM_CLASSES_YOLO, networkInfo.width, networkInfo.height);
objects.insert(objects.end(), outObjs.begin(), outObjs.end());
}
objectList = objects;
return true;
}
/* C-linkage to prevent name-mangling */
extern “C” bool NvDsInferParseCustomYoloV3Tiny(
return NvDsInferParseYoloV3 (
outputLayersInfo, networkInfo, detectionParams, objectList,
kANCHORS, kMASKS);
}
static bool NvDsInferParseYoloV2(
if (outputLayersInfo.empty()) {
std::cerr << "Could not find output layer in bbox parsing" << std::endl;;
return false;
}
const NvDsInferLayerInfo &layer = outputLayersInfo[0];
if (NUM_CLASSES_YOLO != detectionParams.numClassesConfigured)
{
std::cerr << "WARNING: Num classes mismatch. Configured:"
<< detectionParams.numClassesConfigured
<< ", detected by network: " << NUM_CLASSES_YOLO << std::endl;
}
assert(layer.inferDims.numDims == 3);
const uint gridSizeH = layer.inferDims.d[1];
const uint gridSizeW = layer.inferDims.d[2];
const uint stride = DIVUP(networkInfo.width, gridSizeW);
assert(stride == DIVUP(networkInfo.height, gridSizeH));
for (auto& anchor : anchors) {
anchor *= stride;
}
std::vector<NvDsInferParseObjectInfo> objects =
decodeYoloV2Tensor((const float*)(layer.buffer), anchors, gridSizeW, gridSizeH, stride, kNUM_BBOXES,
NUM_CLASSES_YOLO, networkInfo.width, networkInfo.height);
objectList = objects;
return true;
}
extern “C” bool NvDsInferParseCustomYoloV2(
extern “C” bool NvDsInferParseCustomYoloV2Tiny(
/* Check that the custom function has been defined correctly */
what it must be changed in this code for an ultra_light_320.onnx model and what i have to remove this code could tell me these things for me