if self.nt_per_class.sum() == 0: LOGGER.warning( f"WARNING ⚠️ no labels found in {self.args.task} set, can not compute metrics without labels") # Print results per class if self.args.verbose and not self.training and self.nc > 1 and len(self.stats): for i, c in enumera...
warning( f'警告 ⚠️ 边界框和分割计数应相等,但得到了len(segments) = {len_segments}, ' f'len(boxes) = {len_boxes}。将只使用边界框,所有分割将被移除。') for lb in labels: lb['segments'] = [] # 移除分割 if len_cls == 0: LOGGER.warning(f'警告 ⚠️ 在{cache_path}中未...
im_files) # 检查哈希值 except (FileNotFoundError, AssertionError): cache, exists = self.cache_labels(cache_path), False # 如果加载失败,则缓存标签 # 处理缓存结果 labels = cache['labels'] if not labels: LOGGER.warning(f'警告 ⚠️ 在 {cache_path} 中未找到图像,训练可能无法正常工作。'...
import contextlib import json import cv2 import pandas as pd from PIL import Image from collections import defaultdict from utils import * # Convert INFOLKS JSON file into YOLO-format labels --- def convert_infolks_json(name, files, img_path): # Create folders path = make_dirs() # Import...
PackageNotFoundError as e: if hard: raise ModuleNotFoundError(emojis(f"WARNING ⚠️ {current} package is required but not installed")) from e else: return False if not required: # 如果要求的版本号约束为空或None,则视为版本号符合要求 return True op = "" version = "" result = True...
{trainer.save_dir}', view at http://localhost:6006/")# 捕获可能发生的异常exceptExceptionase:# 记录警告日志,指示 TensorBoard 初始化失败,当前运行未记录日志LOGGER.warning(f"{PREFIX}WARNING ⚠️ TensorBoard not initialized correctly, not logging this run.{e}")# 在训练开始时调用的回调函数,用于...
cls=batch["cls"].view(-1),# warning: use .view(), not .squeeze() for Classify modelsfname=self.save_dir /f"val_batch{ni}_labels.jpg", names=self.names, on_plot=self.on_plot, )# 绘制输入图像上的预测边界框并保存结果defplot_predictions(self, batch, preds, ni):"""Plots predicted...
debug(f"COMET WARNING: Image: {image_path} has no bounding boxes labels") return None # 获取类别标签 cls_labels = batch["cls"][indices].squeeze(1).tolist() if class_name_map: cls_labels = [str(class_name_map[label]) for label in cls_labels] # 获取原始图像形状、调整大小的图像...
label_name=Path(file_name[i]).stem+'.txt'withopen(path+'/labels/'+label_name,'a')asfile:forainx['output']['objects']:# if a['classTitle'] == 'Missing product':# continue # skipcategory_id=names.index(a['classTitle'].lower())# The INFOLKS bounding box format is [x-min, ...
assert mode in ['train', 'val'] with torch_distributed_zero_first(rank): dataset = self.build_dataset(dataset_path, mode, batch_size) shuffle = mode == 'train' if getattr(dataset, 'rect', False) and shuffle: LOGGER.warning("WARNING ⚠️ 'rect=True' is incompatible with DataLoader...