本文分享自華爲雲社區《昇騰 CANN YOLOV8 和 YOLOV9 適配》，作者：jackwangcumt。
1 概述
華爲昇騰 CANN YOLOV8 推理示例 C++樣例 , 是基於Ascend CANN Samples官方示例中的sampleYOLOV7進行的YOLOV8適配。一般來說，YOLOV7模型輸出的數據大小爲[1,25200,85]，而YOLOV8模型輸出的數據大小爲[1,84,8400]，因此，需要對sampleYOLOV7中的後處理部分進行修改，從而做到YOLOV8/YOLOV9模型的適配。因項目研發需要，公司購置了一臺 Atlas 500 Pro 智能邊緣服務器， 安裝的操作系統爲Ubuntu 20.04 LTS Server，並按照官方說明文檔，安裝的Ascend-cann-toolkit_7.0.RC1_linux-aarch64.run等軟件。具體可以參考另外一篇博文【Atlas 500 Pro 智能邊緣服務器推理環境搭建】，這裏不再贅述。
2 YOLOV8模型準備
在進行YOLOV8模型適配工作之前，首先需要獲取YOLOV8的模型文件，這裏以官方的 YOLOV8n.pt模型爲例，在Windows操作系統上可以安裝YOLOV8環境，並執行如下python腳本（pth2onnx.py）將.pt模型轉化成.onnx模型：
import argparse
from ultralytics import YOLO
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--pt', default="yolov8n", help='.pt file')
args = parser.parse_args()
model = YOLO(args.pt)
onnx_model = model.export(format="onnx", dynamic=False, simplify=True, opset=11)
if __name__ == '__main__':
main()
具體的YOLOV8環境搭建步驟，可以參考 https://github.com/ultralytics/ultralytics 網站。當成功執行後，會生成yolov8n.onnx模型。輸出內容示例如下所示：
(base) I:\yolov8\Yolov8_for_PyTorch>python pth2onnx.py --pt=yolov8n.pt Ultralytics YOLOv8.0.229 🚀 Python-3.11.5 torch-2.1.2 CPU (Intel Core(TM) i7-10700K 3.80GHz) YOLOv8n summary (fused): 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs PyTorch: starting from 'yolov8n.pt' with input shape (1, 3, 640, 640) BCHW and output shape(s) (1, 84, 8400) (6.2 MB) ONNX: starting export with onnx 1.15.0 opset 11... ONNX: simplifying with onnxsim 0.4.36... ONNX: export success ✅ 1.0s, saved as 'yolov8n.onnx' (12.2 MB) Export complete (3.2s) Results saved to I:\yolov8\Yolov8_for_PyTorch Predict: yolo predict task=detect model=yolov8n.onnx imgsz=640 Validate: yolo val task=detect model=yolov8n.onnx imgsz=640 data=coco.yaml Visualize: https://netron.app
從輸出信息中可以看出， yolov8n.pt原始模型的輸出尺寸爲 (1, 3, 640, 640)，格式爲 BCHW ，輸出尺寸爲 (1, 84, 8400) 。這個模型的更多信息，可以用 netron 工具進行可視化查看，在安裝了netron後，可以執行如下命令打開yolov8n.onnx模型進行Web網絡結構的查看:
(base) I:\yolov8\Yolov8_for_PyTorch>netron yolov8n.onnx Serving 'yolov8x.onnx' at http://localhost:8080
可以看到，轉化後的yolov8n.onnx模型輸入的節點名稱爲images，輸入張量的大小爲[1,3,640,640] 。在將yolov8n.onnx模型上傳到Atlas 500 Pro服務器上，執行如下命令進行模型轉換：
atc --model=yolov8n.onnx --framework=5 --output=yolov8n --input_shape="images:1,3,640,640" --soc_version=Ascend310P3 --insert_op_conf=aipp.cfg
其中的:
--soc_version=Ascend310P3可以通過npu-smi info命令進行查看，我這裏打印的是 310P3 則，--soc_version 爲 Ascend前綴加上310P3，即Ascend310P3。
--input_shape="images:1,3,640,640" 表示NCHW，即批處理爲1，通道爲3，圖片大小爲640x640，這與onnx模型的輸入節點一致 。
--insert_op_conf=aipp.cfg 中的aipp.cfg來自官網sampleYOLOV7示例。由於原始輸入圖片的大小可能不符合要求，需要縮放到640x640的尺寸。aipp.cfg內容如下：
aipp_op{
aipp_mode:static
input_format : YUV420SP_U8
src_image_size_w : 640
src_image_size_h : 640
csc_switch : true
rbuv_swap_switch : false
matrix_r0c0 : 256
matrix_r0c1 : 0
matrix_r0c2 : 359
matrix_r1c0 : 256
matrix_r1c1 : -88
matrix_r1c2 : -183
matrix_r2c0 : 256
matrix_r2c1 : 454
matrix_r2c2 : 0
input_bias_0 : 0
input_bias_1 : 128
input_bias_2 : 128
crop: true
load_start_pos_h : 0
load_start_pos_w : 0
crop_size_w : 640
crop_size_h : 640
min_chn_0 : 0
min_chn_1 : 0
min_chn_2 : 0
var_reci_chn_0: 0.0039215686274509803921568627451
var_reci_chn_1: 0.0039215686274509803921568627451
var_reci_chn_2: 0.0039215686274509803921568627451
}
生執行成功後，會生成 yolov8n.om 離線模型
3 適配代碼
根據官網sampleYOLOV7示例適配的YOLOV8示例，代碼已經開源，地址爲：https://gitee.com/cumt/ascend-yolov8-sample 。核心代碼sampleYOLOV8.cpp中的後處理方法GetResult爲：
Result SampleYOLOV8::GetResult(std::vector<InferenceOutput> &inferOutputs,
string imagePath, size_t imageIndex, bool release)
{
uint32_t outputDataBufId = 0;
float *classBuff = static_cast<float *>(inferOutputs[outputDataBufId].data.get());
// confidence threshold
float confidenceThreshold = 0.35;
// class number
size_t classNum = 80;
//// number of (x, y, width, hight)
size_t offset = 4;
// total number of boxs yolov8 [1,84,8400]
size_t modelOutputBoxNum = 8400;
// read source image from file
cv::Mat srcImage = cv::imread(imagePath);
int srcWidth = srcImage.cols;
int srcHeight = srcImage.rows;
// filter boxes by confidence threshold
vector<BoundBox> boxes;
size_t yIndex = 1;
size_t widthIndex = 2;
size_t heightIndex = 3;
// size_t all_num = 1 * 84 * 8400 ; // 705,600
for (size_t i = 0; i < modelOutputBoxNum; ++i)
{
float maxValue = 0;
size_t maxIndex = 0;
for (size_t j = 0; j < classNum; ++j)
{
float value = classBuff[(offset + j) * modelOutputBoxNum + i];
if (value > maxValue)
{
// index of class
maxIndex = j;
maxValue = value;
}
}
if (maxValue > confidenceThreshold)
{
BoundBox box;
box.x = classBuff[i] * srcWidth / modelWidth_;
box.y = classBuff[yIndex * modelOutputBoxNum + i] * srcHeight / modelHeight_;
box.width = classBuff[widthIndex * modelOutputBoxNum + i] * srcWidth / modelWidth_;
box.height = classBuff[heightIndex * modelOutputBoxNum + i] * srcHeight / modelHeight_;
box.score = maxValue;
box.classIndex = maxIndex;
box.index = i;
if (maxIndex < classNum)
{
boxes.push_back(box);
}
}
}
ACLLITE_LOG_INFO("filter boxes by confidence threshold > %f success, boxes size is %ld", confidenceThreshold,boxes.size());
// filter boxes by NMS
vector<BoundBox> result;
result.clear();
float NMSThreshold = 0.45;
int32_t maxLength = modelWidth_ > modelHeight_ ? modelWidth_ : modelHeight_;
std::sort(boxes.begin(), boxes.end(), sortScore);
BoundBox boxMax;
BoundBox boxCompare;
while (boxes.size() != 0)
{
size_t index = 1;
result.push_back(boxes[0]);
while (boxes.size() > index)
{
boxMax.score = boxes[0].score;
boxMax.classIndex = boxes[0].classIndex;
boxMax.index = boxes[0].index;
// translate point by maxLength * boxes[0].classIndex to
// avoid bumping into two boxes of different classes
boxMax.x = boxes[0].x + maxLength * boxes[0].classIndex;
boxMax.y = boxes[0].y + maxLength * boxes[0].classIndex;
boxMax.width = boxes[0].width;
boxMax.height = boxes[0].height;
boxCompare.score = boxes[index].score;
boxCompare.classIndex = boxes[index].classIndex;
boxCompare.index = boxes[index].index;
// translate point by maxLength * boxes[0].classIndex to
// avoid bumping into two boxes of different classes
boxCompare.x = boxes[index].x + boxes[index].classIndex * maxLength;
boxCompare.y = boxes[index].y + boxes[index].classIndex * maxLength;
boxCompare.width = boxes[index].width;
boxCompare.height = boxes[index].height;
// the overlapping part of the two boxes
float xLeft = max(boxMax.x, boxCompare.x);
float yTop = max(boxMax.y, boxCompare.y);
float xRight = min(boxMax.x + boxMax.width, boxCompare.x + boxCompare.width);
float yBottom = min(boxMax.y + boxMax.height, boxCompare.y + boxCompare.height);
float width = max(0.0f, xRight - xLeft);
float hight = max(0.0f, yBottom - yTop);
float area = width * hight;
float iou = area / (boxMax.width * boxMax.height + boxCompare.width * boxCompare.height - area);
// filter boxes by NMS threshold
if (iou > NMSThreshold)
{
boxes.erase(boxes.begin() + index);
continue;
}
++index;
}
boxes.erase(boxes.begin());
}
ACLLITE_LOG_INFO("filter boxes by NMS threshold > %f success, result size is %ld", NMSThreshold,result.size());
// opencv draw label params
const double fountScale = 0.5;
const uint32_t lineSolid = 2;
const uint32_t labelOffset = 11;
const cv::Scalar fountColor(0, 0, 255); // BGR
const vector<cv::Scalar> colors{
cv::Scalar(255, 0, 0), cv::Scalar(0, 255, 0),
cv::Scalar(0, 0, 255)};
int half = 2;
for (size_t i = 0; i < result.size(); ++i)
{
cv::Point leftUpPoint, rightBottomPoint;
leftUpPoint.x = result[i].x - result[i].width / half;
leftUpPoint.y = result[i].y - result[i].height / half;
rightBottomPoint.x = result[i].x + result[i].width / half;
rightBottomPoint.y = result[i].y + result[i].height / half;
cv::rectangle(srcImage, leftUpPoint, rightBottomPoint, colors[i % colors.size()], lineSolid);
string className = label[result[i].classIndex];
string markString = to_string(result[i].score) + ":" + className;
ACLLITE_LOG_INFO("object detect [%s] success", markString.c_str());
cv::putText(srcImage, markString, cv::Point(leftUpPoint.x, leftUpPoint.y + labelOffset),
cv::FONT_HERSHEY_COMPLEX, fountScale, fountColor);
}
string savePath = "out_" + to_string(imageIndex) + ".jpg";
cv::imwrite(savePath, srcImage);
if (release)
{
free(classBuff);
classBuff = nullptr;
}
return SUCCESS;
}
YOLOV8的輸出尺寸爲 (1, 84, 8400)，其中的8400代表模型原始預測的對象檢測框信息，即代碼中用 size_t modelOutputBoxNum = 8400 ; 進行表示。而 84 代表 4個位的邊界框預測值（x,y,w,h）位置信息和80個檢測類別數，即 84 = 4 + 80 。由於模型檢測結果是用內存連續的一維數組進行表示的，因此，需要根據yolov8輸出尺寸的實際含義，來訪問需要的數組內存地址來獲取需要的值。根據資料顯示，yolov8模型不另外對置信度進行預測， 而是採用類別裏面最大的概率作爲置信度的值，8400是yolov8模型各尺度輸出特徵圖疊加之後的結果，一般推理不需要處理。下面給出模型尺寸和內存數組的映射示意圖 ：
即除首行外，將其他83行的每一行依次變換到首行的末尾構成一維數組，一維數組的大小位 8400 x 84 。遍歷數組時，首先將8400個預測信息中的置信度獲取到，即偏移offset=4個後，獲取80個類別位中最大的值以及索引轉化爲置信度和類別ID。前4個代表x,y,w,預測框信息。對於個性化定製的模型，則需要修改 size_t classNum = 80; 即可，參考onnx輸出尺寸[1,84,8400]中的84-4 = 80 , 比如自定義的模型輸出爲[1,26,8400]，則 size_t classNum = 22 （26-4）.
4 編譯運行
下載開源代碼，上傳服務器，並解壓，然後執行如下命令進行代碼編譯：
unzip ascend-yolov8-sample-master.zip -d ./kztech cd ascend-yolov8-sample-master/src # src目錄下 cmake . make #如果正確執行，則會在../out目錄中生成 main 可執行文件,在src目錄中運行示例 ../out/main #如果報如下錯誤： ../out/main: error while loading shared libraries: libswresample.so.3: cannot open shared object file: No such file or directory 則嘗試設置如下環境變量後重試: export LD_LIBRARY_PATH=/usr/local/Ascend/thirdpart/aarch64/lib:$LD_LIBRARY_PATH #正確執行後，會在當前目錄中生成out_0.jpg文件
執行成功，控制檯打印如下信息：
root@atlas500ai:/home/kztech/ascend-yolov8-sample-master/src# ../out/main [INFO] Acl init ok [INFO] Open device 0 ok [INFO] Use default context currently [INFO] dvpp init resource ok [INFO] Load model ../model/yolov8n.om success [INFO] Create model description success [INFO] Create model(../model/yolov8n.om) output success [INFO] Init model ../model/yolov8n.om success [INFO] filter boxes by confidence threshold > 0.350000 success, boxes size is 10 [INFO] filter boxes by NMS threshold > 0.450000 success, result size is 1 [INFO] object detect [0.878906:dog] success [INFO] Inference elapsed time : 0.038817 s , fps is 25.761685 [INFO] Unload model ../model/yolov8n.om success [INFO] destroy context ok [INFO] Reset device 0 ok [INFO] Finalize acl ok
5 總結
YOLO各系列的適配過程，大部分都是處理輸入格式和輸出格式的變換上，參考YOLOV7，可以進行YOLOV8模型的適配，同理，YOLOV9的模型適配也是一樣的。目前YOLOV9和YOLOV8模型輸出格式一致，因此，只需要進行yolov9xx.om模型的生成工作即可。yolov9-c-converted.pt模型(https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-c-converted.pt)轉換如下： 在windows操作系統上可以安裝YOLOV9環境，並執行如下python腳本將.pt模型轉化成.onnx模型:
#從base環境創建新的環境yolov9 conda create -n yolov9 --clone base #激活虛擬環境yolov9 conda activate yolov9 #克隆yolov9代碼 git clone https://github.com/WongKinYiu/yolov9 # 安裝yolov9項目的依賴 (yolov9) I:\yolov9-main>pip install -r requirements.txt # 模型轉換導出onnx (yolov9) I:\yolov9-main>python export.py --weights yolov9-c-converted.pt --include onnx
(yolov9) I:\yolov9-main>python export.py --weights yolov9-c-converted.pt --include onnx
export: data=I:\yolov9-main\data\coco.yaml, weights=['yolov9-c-converted.pt'], imgsz=[640, 640], batch_size=1, device=cpu, half=False, inplace=False,
keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=12, verbose=False, workspace=4, nms=False, agnostic_nms=False, topk_per_class=100, topk_all=100, iou_thres=0.45, conf_thres=0.25, include=['onnx']
YOLO 2024-3-13 Python-3.11.5 torch-2.1.2 CPU
Fusing layers...
gelan-c summary: 387 layers, 25288768 parameters, 64944 gradients, 102.1 GFLOPs
PyTorch: starting from yolov9-c-converted.pt with output shape (1, 84, 8400) (49.1 MB)
ONNX: starting export with onnx 1.15.0...
ONNX: export success 2.9s, saved as yolov9-c-converted.onnx (96.8 MB)
Export complete (4.4s)
Results saved to I:\yolov9-main
Detect: python detect.py --weights yolov9-c-converted.onnx
Validate: python val.py --weights yolov9-c-converted.onnx
PyTorch Hub: model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov9-c-converted.onnx')
Visualize: https://netron.app
atc --model=yolov9-c-converted.onnx --framework=5 --output=yolov9-c-converted --input_shape="images:1,3,640,640" --soc_version=Ascend310P3 --insert_op_conf=aipp.cfg