关于yolov8的output0

关于yolov8的output0

// output0
    nvinfer1::IElementWiseLayer* conv22_cv2_0_0 = convBnSiLU(network, weightMap, *conv15->getOutput(0), base_in_channel, 3, 1, 1, "model.22.cv2.0.0");
    nvinfer1::IElementWiseLayer* conv22_cv2_0_1 = convBnSiLU(network, weightMap, *conv22_cv2_0_0->getOutput(0), base_in_channel, 3, 1, 1, "model.22.cv2.0.1");
    nvinfer1::IConvolutionLayer* conv22_cv2_0_2 = network->addConvolutionNd(*conv22_cv2_0_1->getOutput(0), 64, nvinfer1::DimsHW{1, 1}, weightMap["model.22.cv2.0.2.weight"], weightMap["model.22.cv2.0.2.bias"]);
    conv22_cv2_0_2->setStrideNd(nvinfer1::DimsHW{1, 1});
    conv22_cv2_0_2->setPaddingNd(nvinfer1::DimsHW{0, 0});
    nvinfer1::IElementWiseLayer* conv22_cv3_0_0 = convBnSiLU(network, weightMap, *conv15->getOutput(0),base_out_channel, 3, 1, 1, "model.22.cv3.0.0");
    nvinfer1::IElementWiseLayer* conv22_cv3_0_1 = convBnSiLU(network, weightMap, *conv22_cv3_0_0->getOutput(0), base_out_channel, 3, 1, 1, "model.22.cv3.0.1");
    nvinfer1::IConvolutionLayer* conv22_cv3_0_2 = network->addConvolutionNd(*conv22_cv3_0_1->getOutput(0), kNumClass, nvinfer1::DimsHW{1, 1}, weightMap["model.22.cv3.0.2.weight"], weightMap["model.22.cv3.0.2.bias"]);
    conv22_cv3_0_2->setStride(nvinfer1::DimsHW{1, 1});
    conv22_cv3_0_2->setPadding(nvinfer1::DimsHW{0, 0});
    nvinfer1::ITensor* inputTensor22_0[] = {conv22_cv2_0_2->getOutput(0), conv22_cv3_0_2->getOutput(0)};
    nvinfer1::IConcatenationLayer* cat22_0 = network->addConcatenation(inputTensor22_0, 2);

onnx图为