ModuleNotFoundError: No module named ‘ultralytics.utils‘_modulenotfounderror: no module named 'ultralytics

一、错误提示

二、原因分析

使用多个yolov5项目，或者yolo项目，相互调用时，下级推理程序会直接调用上级（即根目录下）的程序文件，例如：下级推理文件会直接调用上级模型项目中utils包中的plots.py,而不会调用同级目录下的utils包。当上级项目目录下的程序文件发生变化更改时，就会发生调用错误。

三、解决方法

    使用onnx的权重格式，代替.pt格式

1）使用export.py文件导出best.onnx文件

2)推理文件

import argparse
import os
import platform
import sys
from pathlib import Path
 
import torch
from models.common import DetectMultiBackend
from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams
from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2,
                           increment_path, non_max_suppression, print_args, scale_boxes, strip_optimizer, xyxy2xywh)
from utils.plots import Annotator, colors, save_one_box
from utils.torch_utils import select_device, smart_inference_mode
import onnxruntime
 
 
def my_letter_box(img,size=(640,640)):
    h,w,c = img.shape
    r = min(size[0]/h,size[1]/w)
    new_h,new_w = int(h*r),int(w*r)
    top = int((size[0]-new_h)/2)
    left = int((size[1]-new_w)/2)
    
    bottom = size[0]-new_h-top
    right = size[1]-new_w-left
    img_resize = cv2.resize(img,(new_w,new_h))
    img = cv2.copyMakeBorder(img_resize,top,bottom,left,right,borderType=cv2.BORDER_CONSTANT,value=(114,114,114))
    return img,r,left,top
 
def xywh2xyxy(boxes):
    xywh =copy.deepcopy(boxes)
    xywh[:,0]=boxes[:,0]-boxes[:,2]/2
    xywh[:,1]=boxes[:,1]-boxes[:,3]/2
    xywh[:,2]=boxes[:,0]+boxes[:,2]/2
    xywh[:,3]=boxes[:,1]+boxes[:,3]/2
    return xywh
 
def my_nms(boxes,iou_thresh):
    index = np.argsort(boxes[:,4])[::-1]
    keep = []
    while index.size >0:
        i = index[0]
        keep.append(i)
        x1=np.maximum(boxes[i,0],boxes[index[1:],0])
        y1=np.maximum(boxes[i,1],boxes[index[1:],1])
        x2=np.minimum(boxes[i,2],boxes[index[1:],2])
        y2=np.minimum(boxes[i,3],boxes[index[1:],3])
        
        w = np.maximum(0,x2-x1)
        h = np.maximum(0,y2-y1)
 
        inter_area = w*h
        union_area = (boxes[i,2]-boxes[i,0])*(boxes[i,3]-boxes[i,1])+(boxes[index[1:],2]-boxes[index[1:],0])*(boxes[index[1:],3]-boxes[index[1:],1])
        iou = inter_area/(union_area-inter_area)
        idx = np.where(iou<=iou_thresh)[0]
        index = index[idx+1]
    return keep
 
def restore_box(boxes,r,left,top):
    boxes[:,[0,2,5,7,9,11]]-=left
    boxes[:,[1,3,6,8,10,12]]-=top
 
    boxes[:,[0,2,5,7,9,11]]/=r
    boxes[:,[1,3,6,8,10,12]]/=r
    return boxes
    
 
def post_precessing(dets,r,left,top,conf_thresh=0.3,iou_thresh=0.5):
    choice = dets[:,:,4]>conf_thresh
    dets=dets[choice]
    dets[:,13:15]*=dets[:,4:5]
    box = dets[:,:4]
    boxes = xywh2xyxy(box)
    score= np.max(dets[:,13:15],axis=-1,keepdims=True)
    index = np.argmax(dets[:,13:15],axis=-1).reshape(-1,1)
    output = np.concatenate((boxes,score,dets[:,5:13],index),axis=1) 
    reserve_=my_nms(output,iou_thresh) 
    output=output[reserve_]
    output = restore_box(output,r,left,top)
    return output
 
 
 
weight='./best.onnx'
 
providers =  ['CPUExecutionProvider']
session = onnxruntime.InferenceSession(wheelweight, providers=providers)
img,r,left,top=my_letter_box(img)
 
img =img[:,:,::-1].transpose(2,0,1).copy().astype(np.float32)
img=img.reshape(1,*img.shape)
 
y_onnx = session.run([session.get_outputs()[0].name], {session.get_inputs()[0].name: img})[0]
outputs = post_precessing(y_onnx,r,left,top)
 
if len(outputs)>1:
    continue
for output in outputs:
    output = output.tolist()
    rect=output[:4]
    cv2.rectangle(img,(int(rect[0]),int(rect[1])),(int(rect[2]),int(rect[3])),(0,255,255),thickness=3)