广州定制网站设计,企业网站托管外包方式,多用户电商平台,360建筑网官网入口官方简述
在最开始的yolov8提供的不同size的版本#xff0c;包括n、s、m、l、x#xff08;模型规模依次增大#xff0c;通过depth, width, max_channels控制大小#xff09;#xff0c;这些都是通过P3、P4和P5提取图片特征#xff1b; 正常的yolov8对象检测模型输出层是P3、…简述
在最开始的yolov8提供的不同size的版本包括n、s、m、l、x模型规模依次增大通过depth, width, max_channels控制大小这些都是通过P3、P4和P5提取图片特征 正常的yolov8对象检测模型输出层是P3、P4、P5三个输出层为了提升对小目标的检测能力新版本的yolov8 已经包含了P2层P2层做的卷积次数少特征图的尺寸分辨率较大更加利于小目标识别有四个输出层。Backbone部分的结果没有改变但是Neck跟Head部分模型结构做了调整。 yolov8-p2 yaml
# Ultralytics YOLO , AGPL-3.0 license
# YOLOv8 object detection model with P2-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect# Parameters
nc: 80 # number of classes
scales: # model compound scaling constants, i.e. modelyolov8n.yaml will call yolov8.yaml with scale n# [depth, width, max_channels]n: [0.33, 0.25, 1024]s: [0.33, 0.50, 1024]m: [0.67, 0.75, 768]l: [1.00, 1.00, 512]x: [1.00, 1.25, 512]# YOLOv8.0 backbone
backbone:# [from, repeats, module, args]- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4- [-1, 3, C2f, [128, True]]- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8- [-1, 6, C2f, [256, True]]- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16- [-1, 6, C2f, [512, True]]- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32- [-1, 3, C2f, [1024, True]]- [-1, 1, SPPF, [1024, 5]] # 9# YOLOv8.0-p2 head
head:- [-1, 1, nn.Upsample, [None, 2, nearest]]- [[-1, 6], 1, Concat, [1]] # cat backbone P4- [-1, 3, C2f, [512]] # 12- [-1, 1, nn.Upsample, [None, 2, nearest]]- [[-1, 4], 1, Concat, [1]] # cat backbone P3- [-1, 3, C2f, [256]] # 15 (P3/8-small)- [-1, 1, nn.Upsample, [None, 2, nearest]]- [[-1, 2], 1, Concat, [1]] # cat backbone P2- [-1, 3, C2f, [128]] # 18 (P2/4-xsmall)- [-1, 1, Conv, [128, 3, 2]]- [[-1, 15], 1, Concat, [1]] # cat head P3- [-1, 3, C2f, [256]] # 21 (P3/8-small)- [-1, 1, Conv, [256, 3, 2]]- [[-1, 12], 1, Concat, [1]] # cat head P4- [-1, 3, C2f, [512]] # 24 (P4/16-medium)- [-1, 1, Conv, [512, 3, 2]]- [[-1, 9], 1, Concat, [1]] # cat head P5- [-1, 3, C2f, [1024]] # 27 (P5/32-large)- [[18, 21, 24, 27], 1, Detect, [nc]] # Detect(P2, P3, P4, P5)yolov8-p2 tensort 实现
参考: https://github.com/wang-xinyu/tensorrtx/tree/master/yolov8 model.cpp 中增加 buildEngineYolov8x_p2 方法. Backbone backbone 和 yolov8 一样 , 无需改动照搬下来就行. /************************************************************************************************************************************************ YOLOV8 BACKBONE ***************************************************************************************************************************************************/nvinfer1::IElementWiseLayer *conv0 convBnSiLU(network, weightMap, *data, 80, 3, 2, 1, model.0);nvinfer1::IElementWiseLayer *conv1 convBnSiLU(network, weightMap, *conv0-getOutput(0), 160, 3, 2, 1, model.1);nvinfer1::IElementWiseLayer *conv2 C2F(network, weightMap, *conv1-getOutput(0), 160, 160, 3, true, 0.5, model.2);nvinfer1::IElementWiseLayer *conv3 convBnSiLU(network, weightMap, *conv2-getOutput(0), 320, 3, 2, 1, model.3);nvinfer1::IElementWiseLayer *conv4 C2F(network, weightMap, *conv3-getOutput(0), 320, 320, 6, true, 0.5, model.4);nvinfer1::IElementWiseLayer *conv5 convBnSiLU(network, weightMap, *conv4-getOutput(0), 640, 3, 2, 1, model.5);nvinfer1::IElementWiseLayer *conv6 C2F(network, weightMap, *conv5-getOutput(0), 640, 640, 6, true, 0.5, model.6);nvinfer1::IElementWiseLayer *conv7 convBnSiLU(network, weightMap, *conv6-getOutput(0), 640, 3, 2, 1, model.7);nvinfer1::IElementWiseLayer *conv8 C2F(network, weightMap, *conv7-getOutput(0), 640, 640, 3, true, 0.5, model.8);nvinfer1::IElementWiseLayer *conv9 SPPF(network, weightMap, *conv8-getOutput(0), 640, 640, 5, model.9);Head 由3个输出层 (P3、P4、P5) 变成4个输出层 (P2、P3、P4、P5) HEAD /************************************************************************************************************************************************* YOLOV8 HEAD ******************************************************************************************************************************************************/float scale[] {1.0, 2.0, 2.0};nvinfer1::IResizeLayer *upsample10 network-addResize(*conv9-getOutput(0));upsample10-setResizeMode(nvinfer1::ResizeMode::kNEAREST);upsample10-setScales(scale, 3);nvinfer1::ITensor *inputTensor11[] {upsample10-getOutput(0), conv6-getOutput(0)};nvinfer1::IConcatenationLayer *cat11 network-addConcatenation(inputTensor11, 2);nvinfer1::IElementWiseLayer *conv12 C2F(network, weightMap, *cat11-getOutput(0), 640, 640, 3, false, 0.5, model.12);nvinfer1::IResizeLayer *upsample13 network-addResize(*conv12-getOutput(0));upsample13-setResizeMode(nvinfer1::ResizeMode::kNEAREST);upsample13-setScales(scale, 3);nvinfer1::ITensor *inputTensor14[] {upsample13-getOutput(0), conv4-getOutput(0)};nvinfer1::IConcatenationLayer *cat14 network-addConcatenation(inputTensor14, 2);nvinfer1::IElementWiseLayer *conv15 C2F(network, weightMap, *cat14-getOutput(0), 320, 320, 3, false, 0.5, model.15);nvinfer1::IResizeLayer *upsample16 network-addResize(*conv15-getOutput(0));upsample16-setResizeMode(nvinfer1::ResizeMode::kNEAREST);upsample16-setScales(scale, 3);nvinfer1::ITensor *inputTensor17[] {upsample16-getOutput(0), conv2-getOutput(0)};nvinfer1::IConcatenationLayer *cat17 network-addConcatenation(inputTensor17, 2);nvinfer1::IElementWiseLayer *conv18 C2F(network, weightMap, *cat17-getOutput(0), 160, 160, 3, false, 0.5, model.18);nvinfer1::IElementWiseLayer *conv19 convBnSiLU(network, weightMap, *conv18-getOutput(0), 160, 3, 2, 1, model.19);nvinfer1::ITensor *inputTensor20[] {conv19-getOutput(0), conv15-getOutput(0)};nvinfer1::IConcatenationLayer *cat20 network-addConcatenation(inputTensor20, 2);nvinfer1::IElementWiseLayer *conv21 C2F(network, weightMap, *cat20-getOutput(0), 320, 320, 3, false, 0.5, model.21);nvinfer1::IElementWiseLayer *conv22 convBnSiLU(network, weightMap, *conv21-getOutput(0), 320, 3, 2, 1, model.22);nvinfer1::ITensor *inputTensor23[] {conv22-getOutput(0), conv12-getOutput(0)};nvinfer1::IConcatenationLayer *cat23 network-addConcatenation(inputTensor23, 2);nvinfer1::IElementWiseLayer *conv24 C2F(network, weightMap, *cat23-getOutput(0), 640, 640, 3, false, 0.5, model.24);nvinfer1::IElementWiseLayer *conv25 convBnSiLU(network, weightMap, *conv24-getOutput(0), 640, 3, 2, 1, model.25);nvinfer1::ITensor *inputTensor26[] {conv25-getOutput(0), conv9-getOutput(0)};nvinfer1::IConcatenationLayer *cat26 network-addConcatenation(inputTensor26, 2);nvinfer1::IElementWiseLayer *conv27 C2F(network, weightMap, *cat26-getOutput(0), 640, 640, 3, false, 0.5, model.27);OUTPUT /**************************************************************************************************************************************************** YOLOV8 OUTPUT *************************************************************************************************************************************************/// output0nvinfer1::IElementWiseLayer *conv28_cv2_0_0 convBnSiLU(network, weightMap, *conv18-getOutput(0), 64, 3, 1, 1, model.28.cv2.0.0);nvinfer1::IElementWiseLayer *conv28_cv2_0_1 convBnSiLU(network, weightMap, *conv28_cv2_0_0-getOutput(0), 64, 3, 1, 1, model.28.cv2.0.1);nvinfer1::IConvolutionLayer *conv28_cv2_0_2 network-addConvolutionNd(*conv28_cv2_0_1-getOutput(0), 64, nvinfer1::DimsHW{1, 1}, weightMap[model.28.cv2.0.2.weight], weightMap[model.28.cv2.0.2.bias]);conv28_cv2_0_2-setStrideNd(nvinfer1::DimsHW{1, 1});conv28_cv2_0_2-setPaddingNd(nvinfer1::DimsHW{0, 0});nvinfer1::IElementWiseLayer *conv28_cv3_0_0 convBnSiLU(network, weightMap, *conv18-getOutput(0), 160, 3, 1, 1, model.28.cv3.0.0);nvinfer1::IElementWiseLayer *conv28_cv3_0_1 convBnSiLU(network, weightMap, *conv28_cv3_0_0-getOutput(0), 160, 3, 1, 1, model.28.cv3.0.1);nvinfer1::IConvolutionLayer *conv28_cv3_0_2 network-addConvolutionNd(*conv28_cv3_0_1-getOutput(0), kNumClass, nvinfer1::DimsHW{1, 1}, weightMap[model.28.cv3.0.2.weight], weightMap[model.28.cv3.0.2.bias]);conv28_cv3_0_2-setStride(nvinfer1::DimsHW{1, 1});conv28_cv3_0_2-setPadding(nvinfer1::DimsHW{0, 0});nvinfer1::ITensor *inputTensor28_0[] {conv28_cv2_0_2-getOutput(0), conv28_cv3_0_2-getOutput(0)};nvinfer1::IConcatenationLayer *cat28_0 network-addConcatenation(inputTensor28_0, 2); // P2// output1nvinfer1::IElementWiseLayer *conv28_cv2_1_0 convBnSiLU(network, weightMap, *conv21-getOutput(0), 64, 3, 1, 1, model.28.cv2.1.0);nvinfer1::IElementWiseLayer *conv28_cv2_1_1 convBnSiLU(network, weightMap, *conv28_cv2_1_0-getOutput(0), 64, 3, 1, 1, model.28.cv2.1.1);nvinfer1::IConvolutionLayer *conv28_cv2_1_2 network-addConvolutionNd(*conv28_cv2_1_1-getOutput(0), 64, nvinfer1::DimsHW{1, 1}, weightMap[model.28.cv2.1.2.weight], weightMap[model.28.cv2.1.2.bias]);conv28_cv2_1_2-setStrideNd(nvinfer1::DimsHW{1, 1});conv28_cv2_1_2-setPaddingNd(nvinfer1::DimsHW{0, 0});nvinfer1::IElementWiseLayer *conv28_cv3_1_0 convBnSiLU(network, weightMap, *conv21-getOutput(0), 160, 3, 1, 1, model.28.cv3.1.0);nvinfer1::IElementWiseLayer *conv28_cv3_1_1 convBnSiLU(network, weightMap, *conv28_cv3_1_0-getOutput(0), 160, 3, 1, 1, model.28.cv3.1.1);nvinfer1::IConvolutionLayer *conv28_cv3_1_2 network-addConvolutionNd(*conv28_cv3_1_1-getOutput(0), kNumClass, nvinfer1::DimsHW{1, 1}, weightMap[model.28.cv3.1.2.weight], weightMap[model.28.cv3.1.2.bias]);conv28_cv3_1_2-setStrideNd(nvinfer1::DimsHW{1, 1});conv28_cv3_1_2-setPaddingNd(nvinfer1::DimsHW{0, 0});nvinfer1::ITensor *inputTensor28_1[] {conv28_cv2_1_2-getOutput(0), conv28_cv3_1_2-getOutput(0)};nvinfer1::IConcatenationLayer *cat28_1 network-addConcatenation(inputTensor28_1, 2);// output2nvinfer1::IElementWiseLayer *conv28_cv2_2_0 convBnSiLU(network, weightMap, *conv24-getOutput(0), 64, 3, 1, 1, model.28.cv2.2.0);nvinfer1::IElementWiseLayer *conv28_cv2_2_1 convBnSiLU(network, weightMap, *conv28_cv2_2_0-getOutput(0), 64, 3, 1, 1, model.28.cv2.2.1);nvinfer1::IConvolutionLayer *conv28_cv2_2_2 network-addConvolution(*conv28_cv2_2_1-getOutput(0), 64, nvinfer1::DimsHW{1, 1}, weightMap[model.28.cv2.2.2.weight], weightMap[model.28.cv2.2.2.bias]);nvinfer1::IElementWiseLayer *conv28_cv3_2_0 convBnSiLU(network, weightMap, *conv24-getOutput(0), 160, 3, 1, 1, model.28.cv3.2.0);nvinfer1::IElementWiseLayer *conv28_cv3_2_1 convBnSiLU(network, weightMap, *conv28_cv3_2_0-getOutput(0), 160, 3, 1, 1, model.28.cv3.2.1);nvinfer1::IConvolutionLayer *conv28_cv3_2_2 network-addConvolution(*conv28_cv3_2_1-getOutput(0), kNumClass, nvinfer1::DimsHW{1, 1}, weightMap[model.28.cv3.2.2.weight], weightMap[model.28.cv3.2.2.bias]);nvinfer1::ITensor *inputTensor28_2[] {conv28_cv2_2_2-getOutput(0), conv28_cv3_2_2-getOutput(0)};nvinfer1::IConcatenationLayer *cat28_2 network-addConcatenation(inputTensor28_2, 2);// output3nvinfer1::IElementWiseLayer *conv28_cv2_3_0 convBnSiLU(network, weightMap, *conv27-getOutput(0), 64, 3, 1, 1, model.28.cv2.3.0);nvinfer1::IElementWiseLayer *conv28_cv2_3_1 convBnSiLU(network, weightMap, *conv28_cv2_3_0-getOutput(0), 64, 3, 1, 1, model.28.cv2.3.1);nvinfer1::IConvolutionLayer *conv28_cv2_3_2 network-addConvolution(*conv28_cv2_3_1-getOutput(0), 64, nvinfer1::DimsHW{1, 1}, weightMap[model.28.cv2.3.2.weight], weightMap[model.28.cv2.3.2.bias]);nvinfer1::IElementWiseLayer *conv28_cv3_3_0 convBnSiLU(network, weightMap, *conv27-getOutput(0), 160, 3, 1, 1, model.28.cv3.3.0);nvinfer1::IElementWiseLayer *conv28_cv3_3_1 convBnSiLU(network, weightMap, *conv28_cv3_3_0-getOutput(0), 160, 3, 1, 1, model.28.cv3.3.1);nvinfer1::IConvolutionLayer *conv28_cv3_3_2 network-addConvolution(*conv28_cv3_3_1-getOutput(0), kNumClass, nvinfer1::DimsHW{1, 1}, weightMap[model.28.cv3.3.2.weight], weightMap[model.28.cv3.3.2.bias]);nvinfer1::ITensor *inputTensor28_3[] {conv28_cv2_3_2-getOutput(0), conv28_cv3_3_2-getOutput(0)};nvinfer1::IConcatenationLayer *cat28_3 network-addConcatenation(inputTensor28_3, 2);DETECT /**************************************************************************************************************************************************** YOLOV8 DETECT *************************************************************************************************************************************************/// P2nvinfer1::IShuffleLayer *shuffle28_0 network-addShuffle(*cat28_0-getOutput(0));shuffle28_0-setReshapeDimensions(nvinfer1::Dims2{64 kNumClass, (kInputH / 4) * (kInputW / 4)});nvinfer1::ISliceLayer *split28_0_0 network-addSlice(*shuffle28_0-getOutput(0), nvinfer1::Dims2{0, 0}, nvinfer1::Dims2{64, (kInputH / 4) * (kInputW / 4)}, nvinfer1::Dims2{1, 1});nvinfer1::ISliceLayer *split28_0_1 network-addSlice(*shuffle28_0-getOutput(0), nvinfer1::Dims2{64, 0}, nvinfer1::Dims2{kNumClass, (kInputH / 4) * (kInputW / 4)}, nvinfer1::Dims2{1, 1});nvinfer1::IShuffleLayer *dfl28_0 DFL(network, weightMap, *split28_0_0-getOutput(0), 4, (kInputH / 4) * (kInputW / 4), 1, 1, 0, model.28.dfl.conv.weight);nvinfer1::ITensor *inputTensor28_dfl_0[] {dfl28_0-getOutput(0), split28_0_1-getOutput(0)};nvinfer1::IConcatenationLayer *cat28_dfl_0 network-addConcatenation(inputTensor28_dfl_0, 2);// P3nvinfer1::IShuffleLayer *shuffle28_1 network-addShuffle(*cat28_1-getOutput(0));shuffle28_1-setReshapeDimensions(nvinfer1::Dims2{64 kNumClass, (kInputH / 8) * (kInputW / 8)});nvinfer1::ISliceLayer *split28_1_0 network-addSlice(*shuffle28_1-getOutput(0), nvinfer1::Dims2{0, 0}, nvinfer1::Dims2{64, (kInputH / 8) * (kInputW / 8)}, nvinfer1::Dims2{1, 1});nvinfer1::ISliceLayer *split28_1_1 network-addSlice(*shuffle28_1-getOutput(0), nvinfer1::Dims2{64, 0}, nvinfer1::Dims2{kNumClass, (kInputH / 8) * (kInputW / 8)}, nvinfer1::Dims2{1, 1});nvinfer1::IShuffleLayer *dfl28_1 DFL(network, weightMap, *split28_1_0-getOutput(0), 4, (kInputH / 8) * (kInputW / 8), 1, 1, 0, model.28.dfl.conv.weight);nvinfer1::ITensor *inputTensor28_dfl_1[] {dfl28_1-getOutput(0), split28_1_1-getOutput(0)};nvinfer1::IConcatenationLayer *cat28_dfl_1 network-addConcatenation(inputTensor28_dfl_1, 2);// P4nvinfer1::IShuffleLayer *shuffle28_2 network-addShuffle(*cat28_2-getOutput(0));shuffle28_2-setReshapeDimensions(nvinfer1::Dims2{64 kNumClass, (kInputH / 16) * (kInputW / 16)});nvinfer1::ISliceLayer *split28_2_0 network-addSlice(*shuffle28_2-getOutput(0), nvinfer1::Dims2{0, 0}, nvinfer1::Dims2{64, (kInputH / 16) * (kInputW / 16)}, nvinfer1::Dims2{1, 1});nvinfer1::ISliceLayer *split28_2_1 network-addSlice(*shuffle28_2-getOutput(0), nvinfer1::Dims2{64, 0}, nvinfer1::Dims2{kNumClass, (kInputH / 16) * (kInputW / 16)}, nvinfer1::Dims2{1, 1});nvinfer1::IShuffleLayer *dfl28_2 DFL(network, weightMap, *split28_2_0-getOutput(0), 4, (kInputH / 16) * (kInputW / 16), 1, 1, 0, model.28.dfl.conv.weight);nvinfer1::ITensor *inputTensor28_dfl_2[] {dfl28_2-getOutput(0), split28_2_1-getOutput(0)};nvinfer1::IConcatenationLayer *cat28_dfl_2 network-addConcatenation(inputTensor28_dfl_2, 2);// P5nvinfer1::IShuffleLayer *shuffle28_3 network-addShuffle(*cat28_3-getOutput(0));shuffle28_3-setReshapeDimensions(nvinfer1::Dims2{64 kNumClass, (kInputH / 32) * (kInputW / 32)});nvinfer1::ISliceLayer *split28_3_0 network-addSlice(*shuffle28_3-getOutput(0), nvinfer1::Dims2{0, 0}, nvinfer1::Dims2{64, (kInputH / 32) * (kInputW / 32)}, nvinfer1::Dims2{1, 1});nvinfer1::ISliceLayer *split28_3_1 network-addSlice(*shuffle28_3-getOutput(0), nvinfer1::Dims2{64, 0}, nvinfer1::Dims2{kNumClass, (kInputH / 32) * (kInputW / 32)}, nvinfer1::Dims2{1, 1});nvinfer1::IShuffleLayer *dfl28_3 DFL(network, weightMap, *split28_3_0-getOutput(0), 4, (kInputH / 32) * (kInputW / 32), 1, 1, 0, model.28.dfl.conv.weight);nvinfer1::ITensor *inputTensor28_dfl_3[] {dfl28_3-getOutput(0), split28_3_1-getOutput(0)};nvinfer1::IConcatenationLayer *cat28_dfl_3 network-addConcatenation(inputTensor28_dfl_3, 2);nvinfer1::IPluginV2Layer *yolo addYoLoLayer(network, std::vectornvinfer1::IConcatenationLayer *{cat28_dfl_0, cat28_dfl_1, cat28_dfl_2, cat28_dfl_3});yolo-getOutput(0)-setName(kOutputTensorName);network-markOutput(*yolo-getOutput(0));修改 yololayer.cu 中 forwardGpu 方法 void YoloLayerPlugin::forwardGpu(const float *const *inputs, float *output, cudaStream_t stream, int mYoloV8netHeight, int mYoloV8NetWidth, int batchSize)
{int outputElem 1 mMaxOutObject * sizeof(Detection) / sizeof(float);cudaMemsetAsync(output, 0, sizeof(float), stream);for (int idx 0; idx batchSize; idx){CUDA_CHECK(cudaMemsetAsync(output idx * outputElem, 0, sizeof(float), stream));}int numElem 0; // int grids[3][2] {{mYoloV8netHeight / 8, mYoloV8NetWidth / 8}, {mYoloV8netHeight / 16, mYoloV8NetWidth / 16}, {mYoloV8netHeight / 32, mYoloV8NetWidth / 32}};// todo int grids[4][2] {{mYoloV8netHeight / 4, mYoloV8NetWidth / 4}, {mYoloV8netHeight / 8, mYoloV8NetWidth / 8}, {mYoloV8netHeight / 16, mYoloV8NetWidth / 16}, {mYoloV8netHeight / 32, mYoloV8NetWidth / 32}};// int strides[] { 8, 16, 32 };// todo int strides[] {4, 8, 16, 32};// for (unsigned int i 0; i 3; i)// todo for (unsigned int i 0; i 4; i){int grid_h grids[i][0];int grid_w grids[i][1];int stride strides[i];numElem grid_h * grid_w * batchSize;if (numElem mThreadCount)mThreadCount numElem;CalDetection(numElem mThreadCount - 1) / mThreadCount, mThreadCount, 0, stream(inputs[i], output, numElem, mMaxOutObject, grid_h, grid_w, stride, mClassCount, outputElem);}
}修改 main.cpp - serialize_engine 增加一个 sub_type ...else if (sub_type x-p2){serialized_engine buildEngineYolov8x_p2(builder, config, DataType::kFLOAT, wts_name);}...参考作者 (https://github.com/wang-xinyu/tensorrtx/tree/master/yolov8) 获取wts 然后生成模型. ./yolov8 -s ./weights/xxx.wts ./weights/xxx.engine x-p2 推理模型测试 ./yolov8 -d xxx.engine ../images g END
官网中没有找到p2的预训练模型所以需要根据自己数据集训练模型自己训练模型需要更改 config.h 中对应的参数.以上纯手工输出若有不对欢迎大佬指正.
参考:
https://github.com/ultralytics/ultralytics/tree/mainhttps://github.com/wang-xinyu/tensorrtx/tree/master/yolov8
