完整的项目和所有代码用到的软件和源码和使用教程链接https://pan.quark.cn/s/734a5fcb71cf终于成功这个成功版本必须记录一下好多天的努力。感谢提供帮助的热心群众不知道你是谁但是你的帮助非常有用。这个是步骤在linux系统操作下完成windows可能无法正确的安装nncase会导致遇到问题本人在服务器上运行的问售后问朋友调试安装和处理各种问题整个项目大概花费三天。1.训练pt模型2.转换onnx模型3.转换kmodel模型4.刷入固件替换掉main代码我让AI研究了一下这个转换代码就是pt模型转换到onnx这步说可能有torch的版本的原因如果没有版本问题直接转1.训练pt模型----------------------------------------------------------------------------------------------------------from ultralytics import YOLO model YOLO(/workspace/runs/detect/cccc/weights/best.pt) model.export( formatonnx, imgsz640, opset11, simplifyFalse, )你的环境我看了一下整体没有问题可以导出 ONNX。 不过我发现了几个需要注意的地方。 软件 你的版本 是否有影响 Python 3.9 ✅ 没问题 PyTorch 2.8.0 ⚠️ 需要兼容 weights_only torchvision 0.23.0 ✅ 没问题 ultralytics 8.4.56 ✅ 新版 onnx 1.19.1 ✅ 没问题 nncase 2.9.0 ✅ 后续转换 kmodel 可以直接用 所以你属于 PyTorch 2.8 Ultralytics 8.4 这种环境。 这种环境最大的坑就是 Weights only load failed Unsupported global 所以最好直接写一个兼容版本以后都能用。下面是兼容的转换代码2.转换onnx模型------------------------------------------------------------------------------------------------------# -*- coding: utf-8 -*- YOLOv8 - ONNX 适用于 PyTorch 2.8 Ultralytics 8.4 import os # 必须放在 import torch 前 os.environ[TORCH_LOAD_WEIGHTS_ONLY] 0 import torch from ultralytics import YOLO # ---------------------------------------------------- # PyTorch 2.6 Safe Globals # ---------------------------------------------------- try: from ultralytics.nn.tasks import DetectionModel from ultralytics.nn.modules.conv import ( Conv, Concat, ) from ultralytics.nn.modules.block import ( C2f, Bottleneck, SPPF, DFL, ) from ultralytics.nn.modules.head import Detect from ultralytics.utils.loss import ( v8DetectionLoss, BboxLoss, ) from ultralytics.utils.tal import ( TaskAlignedAssigner, ) from ultralytics.utils import IterableSimpleNamespace from torch.nn import Sequential from torch.nn import Conv2d from torch.nn import BatchNorm2d from torch.nn import SiLU from torch.nn import ModuleList from torch.nn import MaxPool2d from torch.nn import Upsample from torch.nn import BCEWithLogitsLoss torch.serialization.add_safe_globals([ DetectionModel, Sequential, Conv, Conv2d, BatchNorm2d, SiLU, C2f, Bottleneck, SPPF, DFL, ModuleList, MaxPool2d, Upsample, Concat, Detect, v8DetectionLoss, BboxLoss, TaskAlignedAssigner, IterableSimpleNamespace, BCEWithLogitsLoss, ]) except Exception: pass # # 修改这里 # # 训练得到的 best.pt MODEL_PATH /workspace/work/best.pt # 输入尺寸 IMG_SIZE 640 # print( * 60) print(Loading Model...) print(MODEL_PATH) print( * 60) model YOLO(MODEL_PATH) print(Exporting ONNX...) model.export( formatonnx, imgszIMG_SIZE, opset11, simplifyTrue, dynamicFalse, halfFalse, int8False, ) print(\n) print( * 60) print(Export Finished!) print( * 60)转换成onnx后再转换成kmodel,先得把这个什么pip install nncase nncase-kpu然后说需要安装这个# 1. 下载微软官方安装脚本 wget https://dot.net/v1/dotnet-install.sh -O dotnet-install.sh # 2. 赋予执行权限 chmod x dotnet-install.sh # 3. 安装 .NET 7.0 运行时 ./dotnet-install.sh --channel 7.0 --runtime dotnet安装上后输入命令验证。dotnet --list-runtimes# 1. 赋予脚本执行权限 chmod x dotnet-install.sh # 2. 安装 .NET 7.0 运行时 (Runtime) ./dotnet-install.sh --channel 7.0 --runtime dotnet验证export DOTNET_ROOT$HOME/.dotnetexport PATH$HOME/.dotnet:$PATH设置环境变量然后验证dotnet --list-runtimes可以了得是7.几的版本好像才行python - EOF import nncase print(nncase import OK) opts nncase.CompileOptions() opts.target k230 print(CompileOptions OK) compiler nncase.Compiler(opts) print(Compiler OK) EOF执行这个代码验证nncase按照2.8.0安装吧pip install nncase2.8.0 nncase-kpu2.8.0第三步3.转换kmodel模型-----------------------------------------------------------------------------------------------# -*- coding: utf-8 -*- YOLOv8 ONNX - K230 kmodel (nncase 2.8/2.9) 功能 1. 修复 Reshape allowzero 2. LetterBox 校准 3. INT8 PTQ(KLD) 4. 导出 best.kmodel import os import glob import random import onnx import nncase import numpy as np from PIL import Image # # 配置 # ONNX_PATH best.onnx FIXED_ONNX best_fixed.onnx OUTPUT_KMODEL best.kmodel CALIB_DIR /workspace/work/data/valid/images INPUT_SIZE 640 CALIB_NUM 100 # # 修复 allowzero # def fix_reshape(src, dst): model onnx.load(src) fixed 0 for node in model.graph.node: if node.op_type ! Reshape: continue attrs [a for a in node.attribute if a.name ! allowzero] if len(attrs) ! len(node.attribute): del node.attribute[:] node.attribute.extend(attrs) fixed 1 onnx.save(model, dst) print(Fix Reshape:, fixed) # # LetterBox # def letterbox(img): w, h img.size scale min(INPUT_SIZE / w, INPUT_SIZE / h) nw int(w * scale) nh int(h * scale) img img.resize((nw, nh), Image.BILINEAR) canvas Image.new( RGB, (INPUT_SIZE, INPUT_SIZE), (114,114,114) ) dx (INPUT_SIZE - nw) // 2 dy (INPUT_SIZE - nh) // 2 canvas.paste(img, (dx, dy)) return canvas # # PTQ Dataset # def load_dataset(): imgs [] for ext in (*.jpg,*.jpeg,*.png,*.bmp): imgs.extend( glob.glob(os.path.join(CALIB_DIR, ext)) ) random.shuffle(imgs) imgs imgs[:CALIB_NUM] dataset [] print(Calibration Images:, len(imgs)) for path in imgs: img Image.open(path).convert(RGB) img letterbox(img) arr np.array(img, dtypenp.uint8) arr arr.transpose(2,0,1) arr np.expand_dims(arr,0) arr np.ascontiguousarray(arr) dataset.append([arr]) return dataset # # main # print(Step1 Fix ONNX) fix_reshape( ONNX_PATH, FIXED_ONNX ) with open(FIXED_ONNX,rb) as f: model_content f.read() print(Step2 CompileOptions) opts nncase.CompileOptions() opts.targetk230 opts.preprocessTrue opts.input_typeuint8 opts.input_shape[1,3,INPUT_SIZE,INPUT_SIZE] opts.input_layoutNCHW opts.output_layoutNCHW opts.input_range[0,255] opts.mean[0,0,0] opts.std[255,255,255] opts.dump_irFalse opts.dump_asmFalse compiler nncase.Compiler(opts) compiler.import_onnx( model_content, nncase.ImportOptions() ) print(Import ONNX OK) dataset load_dataset() if len(dataset): ptq nncase.PTQTensorOptions() ptq.samples_countlen(dataset) try: ptq.calibrate_methodKld except: pass try: ptq.quant_typeuint8 except: pass ptq.set_tensor_data(dataset) compiler.use_ptq(ptq) print(PTQ Enabled) print(Compiling...) compiler.compile() print(Generate kmodel...) with open( OUTPUT_KMODEL, wb ) as f: try: f.write( compiler.gencode_tobytes() ) except: compiler.gencode(f) print(Done) print( Output:, OUTPUT_KMODEL ) print( %.2f MB % ( os.path.getsize(OUTPUT_KMODEL) / 1024 / 1024 ) )我去真的就是量化的问题或者是nncase的问题把模型放到/sdcard/kmodel/best.kmodel这里第四步4.替换掉main代码--------------------------------------------------------------# -*- coding:utf-8 -*- import sys import os import gc #-------------------------------------------------- # 添加模块路径 #-------------------------------------------------- for p in [/sdcard, /sdcard/F, /sdcard/app]: if p not in sys.path: sys.path.insert(0, p) from libs.PipeLine import PipeLine from libs.YOLO import YOLOv8 #-------------------------------------------------- # 模型路径你的模型放在 /sdcard/kmodel/best.kmodel #-------------------------------------------------- KMODEL /sdcard/kmodel/best.kmodel try: os.stat(KMODEL) except: raise Exception(Cannot find: KMODEL) #-------------------------------------------------- # 参数 #-------------------------------------------------- RGB_SIZE [640, 480] DISPLAY_SIZE [640, 480] # 如果你的模型是320输入就改成[320,320] # 如果是640输入就保持[640,640] MODEL_INPUT [640, 640] LABELS [ Bag, Bottle, Cup ] CONF_THRESH 0.25 NMS_THRESH 0.45 #-------------------------------------------------- # 初始化显示 #-------------------------------------------------- pl PipeLine( rgb888p_sizeRGB_SIZE, display_sizeDISPLAY_SIZE, display_modelcd ) pl.create() #-------------------------------------------------- # 初始化YOLO #-------------------------------------------------- yolo YOLOv8( task_typedetect, modevideo, kmodel_pathKMODEL, labelsLABELS, rgb888p_sizeRGB_SIZE, model_input_sizeMODEL_INPUT, display_sizeDISPLAY_SIZE, conf_threshCONF_THRESH, nms_threshNMS_THRESH, ) yolo.config_preprocess() print() print(YOLOv8 Start) print(Model:, KMODEL) print() frame 0 #-------------------------------------------------- # 主循环 #-------------------------------------------------- try: while True: os.exitpoint() img pl.get_frame() if img is None: continue result yolo.run(img) yolo.draw_result(result, pl.osd_img) pl.show_image() frame 1 if frame % 30 0: gc.collect() except KeyboardInterrupt: pass except Exception as e: sys.print_exception(e) finally: yolo.deinit() pl.destroy()好了这个检测就能够成功了
