Add YOLO-NAS and ONNX support
This commit is contained in:
Marcos Luciano
@@ -45,6 +45,8 @@ ifeq ($(OPENCV), 1)
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LIBS+= $(shell pkg-config --libs opencv4 2> /dev/null || pkg-config --libs opencv)
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endif
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CUFLAGS:= -I/opt/nvidia/deepstream/deepstream/sources/includes -I/usr/local/cuda-$(CUDA_VER)/include
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LIBS+= -lnvinfer_plugin -lnvinfer -lnvparsers -L/usr/local/cuda-$(CUDA_VER)/lib64 -lcudart -lcublas -lstdc++fs
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LFLAGS:= -shared -Wl,--start-group $(LIBS) -Wl,--end-group
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@@ -70,7 +72,7 @@ all: $(TARGET_LIB)
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$(CC) -c $(COMMON) -o $@ $(CFLAGS) $<
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%.o: %.cu $(INCS) Makefile
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$(NVCC) -c -o $@ --compiler-options '-fPIC' $<
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$(NVCC) -c -o $@ --compiler-options '-fPIC' $(CUFLAGS) $<
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$(TARGET_LIB) : $(TARGET_OBJS)
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$(CC) -o $@ $(TARGET_OBJS) $(LFLAGS)
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38
nvdsinfer_custom_impl_Yolo/nvdsinitinputlayers_Yolo.cpp
Normal file
38
nvdsinfer_custom_impl_Yolo/nvdsinitinputlayers_Yolo.cpp
Normal file
@@ -0,0 +1,38 @@
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/*
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* Copyright (c) 2018-2019, NVIDIA CORPORATION. All rights reserved.
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*
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* Permission is hereby granted, free of charge, to any person obtaining a
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* copy of this software and associated documentation files (the "Software"),
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* to deal in the Software without restriction, including without limitation
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* the rights to use, copy, modify, merge, publish, distribute, sublicense,
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* and/or sell copies of the Software, and to permit persons to whom the
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* Software is furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in
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* all copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
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* THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
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* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
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* DEALINGS IN THE SOFTWARE.
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*
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* Edited by Marcos Luciano
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* https://www.github.com/marcoslucianops
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*/
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#include "nvdsinfer_custom_impl.h"
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bool
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NvDsInferInitializeInputLayers(std::vector<NvDsInferLayerInfo> const &inputLayersInfo,
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NvDsInferNetworkInfo const &networkInfo, unsigned int maxBatchSize)
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{
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float *scaleFactor = (float *) inputLayersInfo[0].buffer;
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for (unsigned int i = 0; i < maxBatchSize; i++) {
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scaleFactor[i * 2 + 0] = 1.0;
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scaleFactor[i * 2 + 1] = 1.0;
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}
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return true;
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}
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469
nvdsinfer_custom_impl_Yolo/nvdsparsebbox_Yolo_cuda.cu
Normal file
469
nvdsinfer_custom_impl_Yolo/nvdsparsebbox_Yolo_cuda.cu
Normal file
@@ -0,0 +1,469 @@
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/*
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* Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
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*
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* Permission is hereby granted, free of charge, to any person obtaining a
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* copy of this software and associated documentation files (the "Software"),
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* to deal in the Software without restriction, including without limitation
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* the rights to use, copy, modify, merge, publish, distribute, sublicense,
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* and/or sell copies of the Software, and to permit persons to whom the
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* Software is furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in
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* all copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
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* THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
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* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
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* DEALINGS IN THE SOFTWARE.
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*
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* Edited by Marcos Luciano
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* https://www.github.com/marcoslucianops
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*/
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#include <thrust/host_vector.h>
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#include <thrust/device_vector.h>
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#include "nvdsinfer_custom_impl.h"
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#include "utils.h"
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#include "yoloPlugins.h"
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__global__ void decodeTensorYolo_ONNX(NvDsInferParseObjectInfo *binfo, const float* detections, const int numClasses,
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const int outputSize, float netW, float netH)
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{
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uint x_id = blockIdx.x * blockDim.x + threadIdx.x;
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if (x_id >= outputSize)
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return;
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float maxProb = 0.0f;
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int maxIndex = -1;
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for (uint i = 0; i < numClasses; ++i) {
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float prob = detections[x_id * (5 + numClasses) + 5 + i];
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if (prob > maxProb) {
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maxProb = prob;
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maxIndex = i;
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}
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}
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const float objectness = detections[x_id * (5 + numClasses) + 4];
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const float bxc = detections[x_id * (5 + numClasses) + 0];
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const float byc = detections[x_id * (5 + numClasses) + 1];
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const float bw = detections[x_id * (5 + numClasses) + 2];
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const float bh = detections[x_id * (5 + numClasses) + 3];
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float x0 = bxc - bw / 2;
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float y0 = byc - bh / 2;
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float x1 = x0 + bw;
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float y1 = y0 + bh;
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x0 = fminf(float(netW), fmaxf(float(0.0), x0));
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y0 = fminf(float(netH), fmaxf(float(0.0), y0));
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x1 = fminf(float(netW), fmaxf(float(0.0), x1));
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y1 = fminf(float(netH), fmaxf(float(0.0), y1));
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binfo[x_id].left = x0;
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binfo[x_id].top = y0;
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binfo[x_id].width = fminf(float(netW), fmaxf(float(0.0), x1 - x0));
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binfo[x_id].height = fminf(float(netH), fmaxf(float(0.0), y1 - y0));
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binfo[x_id].detectionConfidence = objectness * maxProb;
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binfo[x_id].classId = maxIndex;
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}
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__global__ void decodeTensorYoloV8_ONNX(NvDsInferParseObjectInfo *binfo, const float* detections, const int numClasses,
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const int outputSize, float netW, float netH)
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{
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uint x_id = blockIdx.x * blockDim.x + threadIdx.x;
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if (x_id >= outputSize)
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return;
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float maxProb = 0.0f;
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int maxIndex = -1;
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for (uint i = 0; i < numClasses; ++i) {
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float prob = detections[x_id + outputSize * (i + 4)];
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if (prob > maxProb) {
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maxProb = prob;
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maxIndex = i;
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}
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}
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const float bxc = detections[x_id + outputSize * 0];
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const float byc = detections[x_id + outputSize * 1];
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const float bw = detections[x_id + outputSize * 2];
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const float bh = detections[x_id + outputSize * 3];
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float x0 = bxc - bw / 2;
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float y0 = byc - bh / 2;
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float x1 = x0 + bw;
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float y1 = y0 + bh;
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x0 = fminf(float(netW), fmaxf(float(0.0), x0));
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y0 = fminf(float(netH), fmaxf(float(0.0), y0));
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x1 = fminf(float(netW), fmaxf(float(0.0), x1));
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y1 = fminf(float(netH), fmaxf(float(0.0), y1));
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binfo[x_id].left = x0;
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binfo[x_id].top = y0;
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binfo[x_id].width = fminf(float(netW), fmaxf(float(0.0), x1 - x0));
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binfo[x_id].height = fminf(float(netH), fmaxf(float(0.0), y1 - y0));
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binfo[x_id].detectionConfidence = maxProb;
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binfo[x_id].classId = maxIndex;
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}
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__global__ void decodeTensorYoloX_ONNX(NvDsInferParseObjectInfo *binfo, const float* detections, const int numClasses,
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const int outputSize, float netW, float netH, const int *grid0, const int *grid1, const int *strides)
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{
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uint x_id = blockIdx.x * blockDim.x + threadIdx.x;
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if (x_id >= outputSize)
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return;
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float maxProb = 0.0f;
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int maxIndex = -1;
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for (uint i = 0; i < numClasses; ++i) {
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float prob = detections[x_id * (5 + numClasses) + 5 + i];
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if (prob > maxProb) {
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maxProb = prob;
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maxIndex = i;
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}
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}
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const float objectness = detections[x_id * (5 + numClasses) + 4];
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const float bxc = (detections[x_id * (5 + numClasses) + 0] + grid0[x_id]) * strides[x_id];
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const float byc = (detections[x_id * (5 + numClasses) + 1] + grid1[x_id]) * strides[x_id];
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const float bw = __expf(detections[x_id * (5 + numClasses) + 2]) * strides[x_id];
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const float bh = __expf(detections[x_id * (5 + numClasses) + 3]) * strides[x_id];
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float x0 = bxc - bw / 2;
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float y0 = byc - bh / 2;
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float x1 = x0 + bw;
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float y1 = y0 + bh;
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x0 = fminf(float(netW), fmaxf(float(0.0), x0));
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y0 = fminf(float(netH), fmaxf(float(0.0), y0));
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x1 = fminf(float(netW), fmaxf(float(0.0), x1));
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y1 = fminf(float(netH), fmaxf(float(0.0), y1));
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binfo[x_id].left = x0;
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binfo[x_id].top = y0;
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binfo[x_id].width = fminf(float(netW), fmaxf(float(0.0), x1 - x0));
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binfo[x_id].height = fminf(float(netH), fmaxf(float(0.0), y1 - y0));
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binfo[x_id].detectionConfidence = objectness * maxProb;
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binfo[x_id].classId = maxIndex;
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}
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__global__ void decodeTensor_YOLO_NAS_ONNX(NvDsInferParseObjectInfo *binfo, const float* scores, const float* boxes,
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const int numClasses, const int outputSize, float netW, float netH)
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{
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uint x_id = blockIdx.x * blockDim.x + threadIdx.x;
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if (x_id >= outputSize)
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return;
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float maxProb = 0.0f;
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int maxIndex = -1;
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for (uint i = 0; i < numClasses; ++i) {
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float prob = scores[x_id * numClasses + i];
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if (prob > maxProb) {
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maxProb = prob;
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maxIndex = i;
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}
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}
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float x0 = boxes[x_id * 4 + 0];
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float y0 = boxes[x_id * 4 + 1];
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float x1 = boxes[x_id * 4 + 2];
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float y1 = boxes[x_id * 4 + 3];
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x0 = fminf(float(netW), fmaxf(float(0.0), x0));
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y0 = fminf(float(netH), fmaxf(float(0.0), y0));
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x1 = fminf(float(netW), fmaxf(float(0.0), x1));
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y1 = fminf(float(netH), fmaxf(float(0.0), y1));
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binfo[x_id].left = x0;
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binfo[x_id].top = y0;
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binfo[x_id].width = fminf(float(netW), fmaxf(float(0.0), x1 - x0));
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binfo[x_id].height = fminf(float(netH), fmaxf(float(0.0), y1 - y0));
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binfo[x_id].detectionConfidence = maxProb;
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binfo[x_id].classId = maxIndex;
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}
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__global__ void decodeTensor_PPYOLOE_ONNX(NvDsInferParseObjectInfo *binfo, const float* scores, const float* boxes,
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const int numClasses, const int outputSize, float netW, float netH)
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{
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uint x_id = blockIdx.x * blockDim.x + threadIdx.x;
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if (x_id >= outputSize)
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return;
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float maxProb = 0.0f;
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int maxIndex = -1;
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for (uint i = 0; i < numClasses; ++i) {
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float prob = scores[x_id + outputSize * i];
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if (prob > maxProb) {
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maxProb = prob;
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maxIndex = i;
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}
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}
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float x0 = boxes[x_id * 4 + 0];
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float y0 = boxes[x_id * 4 + 1];
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float x1 = boxes[x_id * 4 + 2];
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float y1 = boxes[x_id * 4 + 3];
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x0 = fminf(float(netW), fmaxf(float(0.0), x0));
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y0 = fminf(float(netH), fmaxf(float(0.0), y0));
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x1 = fminf(float(netW), fmaxf(float(0.0), x1));
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y1 = fminf(float(netH), fmaxf(float(0.0), y1));
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binfo[x_id].left = x0;
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binfo[x_id].top = y0;
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binfo[x_id].width = fminf(float(netW), fmaxf(float(0.0), x1 - x0));
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binfo[x_id].height = fminf(float(netH), fmaxf(float(0.0), y1 - y0));
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binfo[x_id].detectionConfidence = maxProb;
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binfo[x_id].classId = maxIndex;
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}
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static bool
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NvDsInferParseCustomYolo_ONNX(std::vector<NvDsInferLayerInfo> const& outputLayersInfo,
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NvDsInferNetworkInfo const& networkInfo, NvDsInferParseDetectionParams const& detectionParams,
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std::vector<NvDsInferParseObjectInfo>& objectList)
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{
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if (outputLayersInfo.empty()) {
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std::cerr << "ERROR: Could not find output layer in bbox parsing" << std::endl;
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return false;
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}
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const NvDsInferLayerInfo& layer = outputLayersInfo[0];
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const uint outputSize = layer.inferDims.d[0];
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const uint numClasses = layer.inferDims.d[1] - 5;
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if (numClasses != detectionParams.numClassesConfigured) {
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std::cerr << "WARNING: Number of classes mismatch, make sure to set num-detected-classes=" << numClasses
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<< " in config_infer file\n" << std::endl;
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}
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thrust::device_vector<NvDsInferParseObjectInfo> objects(outputSize);
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int threads_per_block = 1024;
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int number_of_blocks = ((outputSize - 1) / threads_per_block) + 1;
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decodeTensorYolo_ONNX<<<threads_per_block, number_of_blocks>>>(
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thrust::raw_pointer_cast(objects.data()), (const float*) (layer.buffer), numClasses, outputSize,
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static_cast<float>(networkInfo.width), static_cast<float>(networkInfo.height));
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objectList.resize(outputSize);
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thrust::copy(objects.begin(), objects.end(), objectList.begin());
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return true;
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}
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static bool
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NvDsInferParseCustomYoloV8_ONNX(std::vector<NvDsInferLayerInfo> const& outputLayersInfo,
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NvDsInferNetworkInfo const& networkInfo, NvDsInferParseDetectionParams const& detectionParams,
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std::vector<NvDsInferParseObjectInfo>& objectList)
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{
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if (outputLayersInfo.empty()) {
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std::cerr << "ERROR: Could not find output layer in bbox parsing" << std::endl;
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return false;
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}
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const NvDsInferLayerInfo& layer = outputLayersInfo[0];
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const uint numClasses = layer.inferDims.d[0] - 4;
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const uint outputSize = layer.inferDims.d[1];
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if (numClasses != detectionParams.numClassesConfigured) {
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std::cerr << "WARNING: Number of classes mismatch, make sure to set num-detected-classes=" << numClasses
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<< " in config_infer file\n" << std::endl;
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}
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thrust::device_vector<NvDsInferParseObjectInfo> objects(outputSize);
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int threads_per_block = 1024;
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int number_of_blocks = ((outputSize - 1) / threads_per_block) + 1;
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decodeTensorYoloV8_ONNX<<<threads_per_block, number_of_blocks>>>(
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thrust::raw_pointer_cast(objects.data()), (const float*) (layer.buffer), numClasses, outputSize,
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static_cast<float>(networkInfo.width), static_cast<float>(networkInfo.height));
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objectList.resize(outputSize);
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thrust::copy(objects.begin(), objects.end(), objectList.begin());
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return true;
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}
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static bool
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NvDsInferParseCustomYoloX_ONNX(std::vector<NvDsInferLayerInfo> const& outputLayersInfo,
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NvDsInferNetworkInfo const& networkInfo, NvDsInferParseDetectionParams const& detectionParams,
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std::vector<NvDsInferParseObjectInfo>& objectList)
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{
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if (outputLayersInfo.empty()) {
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std::cerr << "ERROR: Could not find output layer in bbox parsing" << std::endl;
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return false;
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}
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const NvDsInferLayerInfo& layer = outputLayersInfo[0];
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const uint outputSize = layer.inferDims.d[0];
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const uint numClasses = layer.inferDims.d[1] - 5;
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if (numClasses != detectionParams.numClassesConfigured) {
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std::cerr << "WARNING: Number of classes mismatch, make sure to set num-detected-classes=" << numClasses
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<< " in config_infer file\n" << std::endl;
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}
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thrust::device_vector<NvDsInferParseObjectInfo> objects(outputSize);
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std::vector<int> strides = {8, 16, 32};
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std::vector<int> grid0;
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std::vector<int> grid1;
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std::vector<int> grid_strides;
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for (uint s = 0; s < strides.size(); ++s) {
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int num_grid_y = networkInfo.height / strides[s];
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int num_grid_x = networkInfo.width / strides[s];
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for (int g1 = 0; g1 < num_grid_y; ++g1) {
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for (int g0 = 0; g0 < num_grid_x; ++g0) {
|
||||
grid0.push_back(g0);
|
||||
grid1.push_back(g1);
|
||||
grid_strides.push_back(strides[s]);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
thrust::device_vector<int> d_grid0(grid0);
|
||||
thrust::device_vector<int> d_grid1(grid1);
|
||||
thrust::device_vector<int> d_grid_strides(grid_strides);
|
||||
|
||||
int threads_per_block = 1024;
|
||||
int number_of_blocks = ((outputSize - 1) / threads_per_block) + 1;
|
||||
|
||||
decodeTensorYoloX_ONNX<<<threads_per_block, number_of_blocks>>>(
|
||||
thrust::raw_pointer_cast(objects.data()), (const float*) (layer.buffer), numClasses, outputSize,
|
||||
static_cast<float>(networkInfo.width), static_cast<float>(networkInfo.height),
|
||||
thrust::raw_pointer_cast(d_grid0.data()), thrust::raw_pointer_cast(d_grid1.data()),
|
||||
thrust::raw_pointer_cast(d_grid_strides.data()));
|
||||
|
||||
objectList.resize(outputSize);
|
||||
thrust::copy(objects.begin(), objects.end(), objectList.begin());
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
static bool
|
||||
NvDsInferParseCustom_YOLO_NAS_ONNX(std::vector<NvDsInferLayerInfo> const& outputLayersInfo,
|
||||
NvDsInferNetworkInfo const& networkInfo, NvDsInferParseDetectionParams const& detectionParams,
|
||||
std::vector<NvDsInferParseObjectInfo>& objectList)
|
||||
{
|
||||
if (outputLayersInfo.empty()) {
|
||||
std::cerr << "ERROR: Could not find output layer in bbox parsing" << std::endl;
|
||||
return false;
|
||||
}
|
||||
|
||||
const NvDsInferLayerInfo& scores = outputLayersInfo[0];
|
||||
const NvDsInferLayerInfo& boxes = outputLayersInfo[1];
|
||||
|
||||
const uint outputSize = scores.inferDims.d[0];
|
||||
const uint numClasses = scores.inferDims.d[1];
|
||||
|
||||
if (numClasses != detectionParams.numClassesConfigured) {
|
||||
std::cerr << "WARNING: Number of classes mismatch, make sure to set num-detected-classes=" << numClasses
|
||||
<< " in config_infer file\n" << std::endl;
|
||||
}
|
||||
|
||||
thrust::device_vector<NvDsInferParseObjectInfo> objects(outputSize);
|
||||
|
||||
int threads_per_block = 1024;
|
||||
int number_of_blocks = ((outputSize - 1) / threads_per_block) + 1;
|
||||
|
||||
decodeTensor_YOLO_NAS_ONNX<<<threads_per_block, number_of_blocks>>>(
|
||||
thrust::raw_pointer_cast(objects.data()), (const float*) (scores.buffer), (const float*) (boxes.buffer), numClasses,
|
||||
outputSize, static_cast<float>(networkInfo.width), static_cast<float>(networkInfo.height));
|
||||
|
||||
objectList.resize(outputSize);
|
||||
thrust::copy(objects.begin(), objects.end(), objectList.begin());
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
static bool
|
||||
NvDsInferParseCustom_PPYOLOE_ONNX(std::vector<NvDsInferLayerInfo> const& outputLayersInfo,
|
||||
NvDsInferNetworkInfo const& networkInfo, NvDsInferParseDetectionParams const& detectionParams,
|
||||
std::vector<NvDsInferParseObjectInfo>& objectList)
|
||||
{
|
||||
if (outputLayersInfo.empty()) {
|
||||
std::cerr << "ERROR: Could not find output layer in bbox parsing" << std::endl;
|
||||
return false;
|
||||
}
|
||||
|
||||
const NvDsInferLayerInfo& scores = outputLayersInfo[0];
|
||||
const NvDsInferLayerInfo& boxes = outputLayersInfo[1];
|
||||
|
||||
const uint numClasses = scores.inferDims.d[0];
|
||||
const uint outputSize = scores.inferDims.d[1];
|
||||
|
||||
if (numClasses != detectionParams.numClassesConfigured) {
|
||||
std::cerr << "WARNING: Number of classes mismatch, make sure to set num-detected-classes=" << numClasses
|
||||
<< " in config_infer file\n" << std::endl;
|
||||
}
|
||||
|
||||
thrust::device_vector<NvDsInferParseObjectInfo> objects(outputSize);
|
||||
|
||||
int threads_per_block = 1024;
|
||||
int number_of_blocks = ((outputSize - 1) / threads_per_block) + 1;
|
||||
|
||||
decodeTensor_PPYOLOE_ONNX<<<threads_per_block, number_of_blocks>>>(
|
||||
thrust::raw_pointer_cast(objects.data()), (const float*) (scores.buffer), (const float*) (boxes.buffer), numClasses,
|
||||
outputSize, static_cast<float>(networkInfo.width), static_cast<float>(networkInfo.height));
|
||||
|
||||
objectList.resize(outputSize);
|
||||
thrust::copy(objects.begin(), objects.end(), objectList.begin());
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
extern "C" bool
|
||||
NvDsInferParseYolo_ONNX(std::vector<NvDsInferLayerInfo> const& outputLayersInfo, NvDsInferNetworkInfo const& networkInfo,
|
||||
NvDsInferParseDetectionParams const& detectionParams, std::vector<NvDsInferParseObjectInfo>& objectList)
|
||||
{
|
||||
return NvDsInferParseCustomYolo_ONNX(outputLayersInfo, networkInfo, detectionParams, objectList);
|
||||
}
|
||||
|
||||
extern "C" bool
|
||||
NvDsInferParseYoloV8_ONNX(std::vector<NvDsInferLayerInfo> const& outputLayersInfo, NvDsInferNetworkInfo const& networkInfo,
|
||||
NvDsInferParseDetectionParams const& detectionParams, std::vector<NvDsInferParseObjectInfo>& objectList)
|
||||
{
|
||||
return NvDsInferParseCustomYoloV8_ONNX(outputLayersInfo, networkInfo, detectionParams, objectList);
|
||||
}
|
||||
|
||||
extern "C" bool
|
||||
NvDsInferParseYoloX_ONNX(std::vector<NvDsInferLayerInfo> const& outputLayersInfo, NvDsInferNetworkInfo const& networkInfo,
|
||||
NvDsInferParseDetectionParams const& detectionParams, std::vector<NvDsInferParseObjectInfo>& objectList)
|
||||
{
|
||||
return NvDsInferParseCustomYoloX_ONNX(outputLayersInfo, networkInfo, detectionParams, objectList);
|
||||
}
|
||||
|
||||
extern "C" bool
|
||||
NvDsInferParse_YOLO_NAS_ONNX(std::vector<NvDsInferLayerInfo> const& outputLayersInfo, NvDsInferNetworkInfo const& networkInfo,
|
||||
NvDsInferParseDetectionParams const& detectionParams, std::vector<NvDsInferParseObjectInfo>& objectList)
|
||||
{
|
||||
return NvDsInferParseCustom_YOLO_NAS_ONNX(outputLayersInfo, networkInfo, detectionParams, objectList);
|
||||
}
|
||||
|
||||
extern "C" bool
|
||||
NvDsInferParse_PPYOLOE_ONNX(std::vector<NvDsInferLayerInfo> const& outputLayersInfo, NvDsInferNetworkInfo const& networkInfo,
|
||||
NvDsInferParseDetectionParams const& detectionParams, std::vector<NvDsInferParseObjectInfo>& objectList)
|
||||
{
|
||||
return NvDsInferParseCustom_PPYOLOE_ONNX(outputLayersInfo, networkInfo, detectionParams, objectList);
|
||||
}
|
||||
Reference in New Issue
Block a user