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>>> model.fit(x_train, y_train, epochs=10, callbacks=[tensorboard_callback]) Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/keras/engine/training.py", line 705, in fit self._assert_compile_was_called() File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/keras/engine/training.py", line 2874, in _assert_compile_was_called raise RuntimeError('You must compile your model before ' RuntimeError: You must compile your model before training/testing. Use `model.compile(optimizer, loss)`.

时间: 2024-01-17 20:06:10 浏览: 211
这个错误提示是因为在调用 `model.fit()` 方法之前,你需要先使用 `model.compile()` 方法编译模型。`model.compile()` 方法指定了模型的优化器、损失函数和评估指标等参数,这些参数在模型训练时起着重要的作用。例如: ``` import tensorflow as tf # 定义 x_train 和 y_train x_train = ... y_train = ... model = tf.keras.models.Sequential([ tf.keras.layers.Dense(64, activation='relu'), tf.keras.layers.Dense(10, activation='softmax') ]) tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir="./logs") # 编译模型 model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) # 训练模型 model.fit(x_train, y_train, epochs=10, callbacks=[tensorboard_callback]) ``` 在 `model.compile()` 方法中,我们指定了优化器为 `adam`,损失函数为 `categorical_crossentropy`,评估指标为 `accuracy`。你需要根据你的具体任务选择合适的优化器、损失函数和评估指标。在模型编译完成后,你就可以调用 `model.fit()` 方法来训练模型了。
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import tkinter as tk from tkinter import ttk, filedialog, messagebox import pandas as pd import numpy as np import matplotlib as mpl import matplotlib.pyplot as plt from matplotlib.font_manager import FontProperties from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg from sklearn.preprocessing import MinMaxScaler import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import LSTM, Dense,Layer,Dropout from tensorflow.keras.optimizers import Adam from tensorflow.keras.callbacks import EarlyStopping,ModelCheckpoint,ReduceLROnPlateau import os plt.rcParams['font.sans-serif'] = ['SimHei'] # 使用黑体 plt.rcParams['axes.unicode_minus'] = False class DamSeepageModel: def __init__(self, root): self.root = root self.root.title("大坝渗流预测模型") self.root.geometry("1200x800") # 初始化数据 self.train_df = None self.test_df = None self.model = None self.scaler = MinMaxScaler(feature_range=(0, 1)) # 创建主界面 self.create_widgets() def create_widgets(self): # 创建主框架 main_frame = ttk.Frame(self.root, padding=10) main_frame.pack(fill=tk.BOTH, expand=True) # 左侧控制面板 control_frame = ttk.LabelFrame(main_frame, text="模型控制", padding=10) control_frame.pack(side=tk.LEFT, fill=tk.Y, padx=5, pady=5) # 文件选择部分 file_frame = ttk.LabelFrame(control_frame, text="数据文件", padding=10) file_frame.pack(fill=tk.X, pady=5) # 训练集选择 ttk.Label(file_frame, text="训练集:").grid(row=0, column=0, sticky=tk.W, pady=5) self.train_file_var = tk.StringVar() ttk.Entry(file_frame, textvariable=self.train_file_var, width=30, state='readonly').grid(row=0, column=1, padx=5) ttk.Button(file_frame, text="选择文件", command=lambda: self.select_file("train")).grid(row=0, column=2) # 测试集选择 ttk.Label(file_frame, text="测试集:").grid(row=1, column=0, sticky=tk.W, pady=5) self.test_file_var = tk.StringVar() ttk.Entry(file_frame, textvariable=self.test_file_var, width=30, state='readonly').grid(row=1, column=1, padx=5) ttk.Button(file_frame, text="选择文件", command=lambda: self.select_file("test")).grid(row=1, column=2) # 参数设置部分 param_frame = ttk.LabelFrame(control_frame, text="模型参数", padding=10) param_frame.pack(fill=tk.X, pady=10) ttk.Label(param_frame, text="Dropout率:").grid(row=5, column=0, sticky=tk.W, pady=5) self.dropout_rate_var = tk.DoubleVar(value=0.2) ttk.Spinbox(param_frame, from_=0.0, to=0.5, increment=0.05, format="%.2f", textvariable=self.dropout_rate_var, width=10).grid(row=5, column=1, padx=5) # 时间窗口大小 ttk.Label(param_frame, text="时间窗口大小:").grid(row=0, column=0, sticky=tk.W, pady=5) self.window_size_var = tk.IntVar(value=60) ttk.Spinbox(param_frame, from_=10, to=200, increment=5, textvariable=self.window_size_var, width=10).grid(row=0, column=1, padx=5) # LSTM单元数量 ttk.Label(param_frame, text="LSTM单元数:").grid(row=1, column=0, sticky=tk.W, pady=5) self.lstm_units_var = tk.IntVar(value=50) ttk.Spinbox(param_frame, from_=10, to=200, increment=10, textvariable=self.lstm_units_var, width=10).grid(row=1, column=1, padx=5) # 训练轮次 ttk.Label(param_frame, text="训练轮次:").grid(row=2, column=0, sticky=tk.W, pady=5) self.epochs_var = tk.IntVar(value=100) ttk.Spinbox(param_frame, from_=10, to=500, increment=10, textvariable=self.epochs_var, width=10).grid(row=2, column=1, padx=5) # 批处理大小 ttk.Label(param_frame, text="批处理大小:").grid(row=3, column=0, sticky=tk.W, pady=5) self.batch_size_var = tk.IntVar(value=32) ttk.Spinbox(param_frame, from_=16, to=128, increment=16, textvariable=self.batch_size_var, width=10).grid(row=3, column=1, padx=5) # 控制按钮 btn_frame = ttk.Frame(control_frame) btn_frame.pack(fill=tk.X, pady=10) ttk.Button(btn_frame, text="训练模型", command=self.train_model).pack(side=tk.LEFT, padx=5) ttk.Button(btn_frame, text="预测结果", command=self.predict).pack(side=tk.LEFT, padx=5) ttk.Button(btn_frame, text="保存结果", command=self.save_results).pack(side=tk.LEFT, padx=5) ttk.Button(btn_frame, text="重置", command=self.reset).pack(side=tk.RIGHT, padx=5) # 状态栏 self.status_var = tk.StringVar(value="就绪") status_bar = ttk.Label(control_frame, textvariable=self.status_var, relief=tk.SUNKEN, anchor=tk.W) status_bar.pack(fill=tk.X, side=tk.BOTTOM) # 右侧结果显示区域 result_frame = ttk.Frame(main_frame) result_frame.pack(side=tk.RIGHT, fill=tk.BOTH, expand=True, padx=5, pady=5) # 创建标签页 self.notebook = ttk.Notebook(result_frame) self.notebook.pack(fill=tk.BOTH, expand=True) # 损失曲线标签页 self.loss_frame = ttk.Frame(self.notebook) self.notebook.add(self.loss_frame, text="训练损失") # 预测结果标签页 self.prediction_frame = ttk.Frame(self.notebook) self.notebook.add(self.prediction_frame, text="预测结果") # 初始化绘图区域 self.fig, self.ax = plt.subplots(figsize=(10, 6)) self.canvas = FigureCanvasTkAgg(self.fig, master=self.prediction_frame) self.canvas.get_tk_widget().pack(fill=tk.BOTH, expand=True) self.loss_fig, self.loss_ax = plt.subplots(figsize=(10, 4)) self.loss_canvas = FigureCanvasTkAgg(self.loss_fig, master=self.loss_frame) self.loss_canvas.get_tk_widget().pack(fill=tk.BOTH, expand=True) # 文件选择 def select_file(self, file_type): """选择Excel文件""" file_path = filedialog.askopenfilename( title=f"选择{file_type}集Excel文件", filetypes=[("Excel文件", "*.xlsx *.xls"), ("所有文件", "*.*")] ) if file_path: try: # 读取Excel文件 df = pd.read_excel(file_path) # 时间特征列 time_features = ['year', 'month', 'day'] missing_time_features = [feat for feat in time_features if feat not in df.columns] if '水位' not in df.columns: messagebox.showerror("列名错误", "Excel文件必须包含'水位'列") return if missing_time_features: messagebox.showerror("列名错误", f"Excel文件缺少预处理后的时间特征列: {', '.join(missing_time_features)}\n" "请确保已使用预处理功能添加这些列") return # 创建完整的时间戳列 # 处理可能缺失的小时、分钟、秒数据 if 'hour' in df.columns and 'minute' in df.columns and 'second' in df.columns: df['datetime'] = pd.to_datetime( df[['year', 'month', 'day', 'hour', 'minute', 'second']] ) elif 'hour' in df.columns and 'minute' in df.columns: df['datetime'] = pd.to_datetime( df[['year', 'month', 'day', 'hour', 'minute']].assign(second=0) ) else: df['datetime'] = pd.to_datetime(df[['year', 'month', 'day']]) # 设置时间索引 df = df.set_index('datetime') # 保存数据 if file_type == "train": self.train_df = df self.train_file_var.set(os.path.basename(file_path)) self.status_var.set(f"已加载训练集: {len(self.train_df)}条数据") else: self.test_df = df self.test_file_var.set(os.path.basename(file_path)) self.status_var.set(f"已加载测试集: {len(self.test_df)}条数据") except Exception as e: messagebox.showerror("文件错误", f"读取文件失败: {str(e)}") def create_dataset(self, data, window_size): """创建时间窗口数据集""" X, y = [], [] for i in range(len(data) - window_size): X.append(data[i:(i + window_size), 0]) y.append(data[i + window_size, 0]) return np.array(X), np.array(y) def train_model(self): """训练LSTM模型""" if self.train_df is None: messagebox.showwarning("警告", "请先选择训练集文件") return try: self.status_var.set("正在预处理数据...") self.root.update() # 数据预处理 train_scaled = self.scaler.fit_transform(self.train_df[['水位']]) # 创建时间窗口数据集 window_size = self.window_size_var.get() X_train, y_train = self.create_dataset(train_scaled, window_size) # 调整LSTM输入格式 X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1)) # 构建LSTM模型 self.model = Sequential() self.model.add(LSTM( self.lstm_units_var.get(), return_sequences=True, input_shape=(window_size, 1) )) self.model.add(Dropout(self.dropout_rate_var.get())) # 添加Dropout # 添加额外的LSTM层 for _ in range(self.lstm_layers_var.get() - 1): self.model.add(LSTM(self.lstm_units_var.get())) self.model.add(Dropout(self.dropout_rate_var.get())) # 添加Dropout self.model.add(Dense(1)) self.model.compile( optimizer=Adam(learning_rate=0.001), # 优化器 loss='binary_crossentropy', # 损失函数 metrics=['accuracy'] # 评估指标 ) # 添加更完善的回调机制 early_stopping = EarlyStopping( monitor='val_loss', patience=30, # 增加耐心值 min_delta=0.0001, restore_best_weights=True, verbose=1 ) model_checkpoint = ModelCheckpoint( 'best_model.h5', monitor='val_loss', save_best_only=True, verbose=1 ) reduce_lr = ReduceLROnPlateau( monitor='val_loss', factor=0.4, # 更激进的学习率衰减 patience=20, min_lr=1e-6, verbose=1 ) # 训练模型(添加更多回调) history = self.model.fit( X_train, y_train, epochs=self.epochs_var.get(), batch_size=self.batch_size_var.get(), validation_split=0.2, callbacks=[early_stopping, model_checkpoint, reduce_lr], verbose=0 ) # 绘制损失曲线 self.loss_ax.clear() self.loss_ax.plot(history.history['loss'], label='训练损失') self.loss_ax.plot(history.history['val_loss'], label='验证损失') self.loss_ax.set_title('模型训练损失') self.loss_ax.set_xlabel('轮次') self.loss_ax.set_ylabel('损失',rotation=0) self.loss_ax.legend() self.loss_ax.grid(True) self.loss_canvas.draw() # 根据早停情况更新状态信息 if early_stopping.stopped_epoch > 0: stopped_epoch = early_stopping.stopped_epoch best_epoch = early_stopping.best_epoch final_loss = history.history['loss'][-1] best_loss = min(history.history['val_loss']) self.status_var.set( f"训练在{stopped_epoch + 1}轮提前终止 | " f"最佳模型在第{best_epoch + 1}轮 | " f"最终损失: {final_loss:.6f} | " f"最佳验证损失: {best_loss:.6f}" ) messagebox.showinfo( "训练完成", f"模型训练提前终止!\n" f"最佳模型在第{best_epoch + 1}轮\n" f"最佳验证损失: {best_loss:.6f}" ) else: final_loss = history.history['loss'][-1] self.status_var.set(f"模型训练完成 | 最终损失: {final_loss:.6f}") messagebox.showinfo("训练完成", "模型训练成功完成!") except Exception as e: messagebox.showerror("训练错误", f"模型训练失败:\n{str(e)}") self.status_var.set("训练失败") def predict(self): """使用模型进行预测""" if self.model is None: messagebox.showwarning("警告", "请先训练模型") return if self.test_df is None: messagebox.showwarning("警告", "请先选择测试集文件") return try: self.status_var.set("正在生成预测...") self.root.update() # 预处理测试数据 test_scaled = self.scaler.transform(self.test_df[['水位']]) # 创建测试集时间窗口 window_size = self.window_size_var.get() X_test, y_test = self.create_dataset(test_scaled, window_size) X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1)) # 进行预测 test_predict = self.model.predict(X_test) # 反归一化 test_predict = self.scaler.inverse_transform(test_predict) y_test_orig = self.scaler.inverse_transform([y_test]).T # 创建时间索引 test_time = self.test_df.index[window_size:window_size + len(test_predict)] # 绘制结果 self.ax.clear() self.ax.plot(self.train_df.index, self.train_df['水位'], 'b-', label='训练集数据') self.ax.plot(test_time, self.test_df['水位'][window_size:window_size + len(test_predict)], 'g-', label='测试集数据') self.ax.plot(test_time, test_predict, 'r--', label='模型预测') # 添加分隔线 split_point = test_time[0] self.ax.axvline(x=split_point, color='k', linestyle='--', alpha=0.5) self.ax.text(split_point, self.ax.get_ylim()[0] * 0.9, ' 训练/测试分界', rotation=90) self.ax.set_title('大坝渗流水位预测结果') self.ax.set_xlabel('时间') self.ax.set_ylabel('测压管水位',rotation=0) self.ax.legend() self.ax.grid(True) self.ax.tick_params(axis='x', rotation=45) self.fig.tight_layout() self.canvas.draw() self.status_var.set("预测完成,结果已显示") except Exception as e: messagebox.showerror("预测错误", f"预测失败:\n{str(e)}") self.status_var.set("预测失败") def save_results(self): """保存预测结果""" if not hasattr(self, 'test_predict') or self.test_predict is None: messagebox.showwarning("警告", "请先生成预测结果") return save_path = filedialog.asksaveasfilename( defaultextension=".xlsx", filetypes=[("Excel文件", "*.xlsx"), ("所有文件", "*.*")] ) if not save_path: return try: # 创建包含预测结果的DataFrame window_size = self.window_size_var.get() test_time = self.test_df.index[window_size:window_size + len(self.test_predict)] result_df = pd.DataFrame({ '时间': test_time, '实际水位': self.test_df['水位'][window_size:window_size + len(self.test_predict)].values, '预测水位': self.test_predict.flatten() }) # 保存到Excel result_df.to_excel(save_path, index=False) # 保存图表 chart_path = os.path.splitext(save_path)[0] + "_chart.png" self.fig.savefig(chart_path, dpi=300) self.status_var.set(f"结果已保存至: {os.path.basename(save_path)}") messagebox.showinfo("保存成功", f"预测结果和图表已保存至:\n{save_path}\n{chart_path}") except Exception as e: messagebox.showerror("保存错误", f"保存结果失败:\n{str(e)}") def reset(self): """重置程序状态""" self.train_df = None self.test_df = None self.model = None self.train_file_var.set("") self.test_file_var.set("") self.ax.clear() self.loss_ax.clear() self.canvas.draw() self.loss_canvas.draw() self.data_text.delete(1.0, tk.END) self.status_var.set("已重置,请选择新数据") messagebox.showinfo("重置", "程序已重置,可以开始新的分析") if __name__ == "__main__": root = tk.Tk() app = DamSeepageModel(root) root.mainloop() 这个代码怎么改进来降低训练和验证损失

# YOLOv5 🚀 by Ultralytics, GPL-3.0 license """ Train a YOLOv5 model on a custom dataset. Models and datasets download automatically from the latest YOLOv5 release. Usage - Single-GPU training: $ python train.py --data coco128.yaml --weights yolov5s.pt --img 640 # from pretrained (recommended) $ python train.py --data coco128.yaml --weights '' --cfg yolov5s.yaml --img 640 # from scratch Usage - Multi-GPU DDP training: $ python -m torch.distributed.run --nproc_per_node 4 --master_port 1 train.py --data coco128.yaml --weights yolov5s.pt --img 640 --device 0,1,2,3 Models: https://github.com/ultralytics/yolov5/tree/master/models Datasets: https://github.com/ultralytics/yolov5/tree/master/data Tutorial: https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data """ import argparse import math import os os.environ["GIT_PYTHON_REFRESH"] = "quiet" # add there import random import sys import time from copy import deepcopy from datetime import datetime from pathlib import Path import numpy as np import torch import torch.distributed as dist import torch.nn as nn import yaml from torch.optim import lr_scheduler from tqdm import tqdm # import numpy # import torch.serialization # torch.serialization.add_safe_globals([numpy._core.multiarray._reconstruct]) FILE = Path(__file__).resolve() ROOT = FILE.parents[0] # YOLOv5 root directory if str(ROOT) not in sys.path: sys.path.append(str(ROOT)) # add ROOT to PATH ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative import val as validate # for end-of-epoch mAP from models.experimental import attempt_load from models.yolo import Model from utils.autoanchor import check_anchors from utils.autobatch import check_train_batch_size from utils.callbacks import Callbacks from utils.dataloaders import create_dataloader from utils.downloads import attempt_download, is_url from utils.general import (LOGGER, TQDM_BAR_FORMAT, check_amp, check_dataset, check_file, check_git_info, check_git_status, check_img_size, check_requirements, check_suffix, check_yaml, colorstr, get_latest_run, increment_path, init_seeds, intersect_dicts, labels_to_class_weights, labels_to_image_weights, methods, one_cycle, print_args, print_mutation, strip_optimizer, yaml_save) from utils.loggers import Loggers from utils.loggers.comet.comet_utils import check_comet_resume from utils.loss import ComputeLoss from utils.metrics import fitness from utils.plots import plot_evolve from utils.torch_utils import (EarlyStopping, ModelEMA, de_parallel, select_device, smart_DDP, smart_optimizer, smart_resume, torch_distributed_zero_first) LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1)) # https://pytorch.org/docs/stable/elastic/run.html RANK = int(os.getenv('RANK', -1)) WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1)) GIT_INFO = check_git_info() def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictionary save_dir, epochs, batch_size, weights, single_cls, evolve, data, cfg, resume, noval, nosave, workers, freeze = \ Path(opt.save_dir), opt.epochs, opt.batch_size, opt.weights, opt.single_cls, opt.evolve, opt.data, opt.cfg, \ opt.resume, opt.noval, opt.nosave, opt.workers, opt.freeze callbacks.run('on_pretrain_routine_start') # Directories w = save_dir / 'weights' # weights dir (w.parent if evolve else w).mkdir(parents=True, exist_ok=True) # make dir last, best = w / 'last.pt', w / 'best.pt' # Hyperparameters if isinstance(hyp, str): with open(hyp, errors='ignore') as f: hyp = yaml.safe_load(f) # load hyps dict LOGGER.info(colorstr('hyperparameters: ') + ', '.join(f'{k}={v}' for k, v in hyp.items())) opt.hyp = hyp.copy() # for saving hyps to checkpoints # Save run settings if not evolve: yaml_save(save_dir / 'hyp.yaml', hyp) yaml_save(save_dir / 'opt.yaml', vars(opt)) # Loggers data_dict = None if RANK in {-1, 0}: loggers = Loggers(save_dir, weights, opt, hyp, LOGGER) # loggers instance # Register actions for k in methods(loggers): callbacks.register_action(k, callback=getattr(loggers, k)) # Process custom dataset artifact link data_dict = loggers.remote_dataset if resume: # If resuming runs from remote artifact weights, epochs, hyp, batch_size = opt.weights, opt.epochs, opt.hyp, opt.batch_size # Config plots = not evolve and not opt.noplots # create plots cuda = device.type != 'cpu' init_seeds(opt.seed + 1 + RANK, deterministic=True) with torch_distributed_zero_first(LOCAL_RANK): data_dict = data_dict or check_dataset(data) # check if None train_path, val_path = data_dict['train'], data_dict['val'] nc = 1 if single_cls else int(data_dict['nc']) # number of classes names = {0: 'item'} if single_cls and len(data_dict['names']) != 1 else data_dict['names'] # class names is_coco = isinstance(val_path, str) and val_path.endswith('coco/val2017.txt') # COCO dataset # Model check_suffix(weights, '.pt') # check weights pretrained = weights.endswith('.pt') if pretrained: with torch_distributed_zero_first(LOCAL_RANK): weights = attempt_download(weights) # download if not found locally ckpt = torch.load(weights, map_location='cpu', weights_only=False) # load checkpoint to CPU to avoid CUDA memory leak model = Model(cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create exclude = ['anchor'] if (cfg or hyp.get('anchors')) and not resume else [] # exclude keys csd = ckpt['model'].float().state_dict() # checkpoint state_dict as FP32 csd = intersect_dicts(csd, model.state_dict(), exclude=exclude) # intersect model.load_state_dict(csd, strict=False) # load LOGGER.info(f'Transferred {len(csd)}/{len(model.state_dict())} items from {weights}') # report else: model = Model(cfg, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create amp = check_amp(model) # check AMP # Freeze freeze = [f'model.{x}.' for x in (freeze if len(freeze) > 1 else range(freeze[0]))] # layers to freeze for k, v in model.named_parameters(): v.requires_grad = True # train all layers # v.register_hook(lambda x: torch.nan_to_num(x)) # NaN to 0 (commented for erratic training results) if any(x in k for x in freeze): LOGGER.info(f'freezing {k}') v.requires_grad = False # Image size gs = max(int(model.stride.max()), 32) # grid size (max stride) imgsz = check_img_size(opt.imgsz, gs, floor=gs * 2) # verify imgsz is gs-multiple # Batch size if RANK == -1 and batch_size == -1: # single-GPU only, estimate best batch size batch_size = check_train_batch_size(model, imgsz, amp) loggers.on_params_update({"batch_size": batch_size}) # Optimizer nbs = 64 # nominal batch size accumulate = max(round(nbs / batch_size), 1) # accumulate loss before optimizing hyp['weight_decay'] *= batch_size * accumulate / nbs # scale weight_decay optimizer = smart_optimizer(model, opt.optimizer, hyp['lr0'], hyp['momentum'], hyp['weight_decay']) # Scheduler if opt.cos_lr: lf = one_cycle(1, hyp['lrf'], epochs) # cosine 1->hyp['lrf'] else: lf = lambda x: (1 - x / epochs) * (1.0 - hyp['lrf']) + hyp['lrf'] # linear scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf) # plot_lr_scheduler(optimizer, scheduler, epochs) # EMA ema = ModelEMA(model) if RANK in {-1, 0} else None # Resume best_fitness, start_epoch = 0.0, 0 if pretrained: if resume: best_fitness, start_epoch, epochs = smart_resume(ckpt, optimizer, ema, weights, epochs, resume) del ckpt, csd # DP mode if cuda and RANK == -1 and torch.cuda.device_count() > 1: LOGGER.warning('WARNING ⚠️ DP not recommended, use torch.distributed.run for best DDP Multi-GPU results.\n' 'See Multi-GPU Tutorial at https://github.com/ultralytics/yolov5/issues/475 to get started.') model = torch.nn.DataParallel(model) # SyncBatchNorm if opt.sync_bn and cuda and RANK != -1: model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device) LOGGER.info('Using SyncBatchNorm()') # Trainloader train_loader, dataset = create_dataloader(train_path, imgsz, batch_size // WORLD_SIZE, gs, single_cls, hyp=hyp, augment=True, cache=None if opt.cache == 'val' else opt.cache, rect=opt.rect, rank=LOCAL_RANK, workers=workers, image_weights=opt.image_weights, quad=opt.quad, prefix=colorstr('train: '), shuffle=True) labels = np.concatenate(dataset.labels, 0) mlc = int(labels[:, 0].max()) # max label class assert mlc < nc, f'Label class {mlc} exceeds nc={nc} in {data}. Possible class labels are 0-{nc - 1}' # Process 0 if RANK in {-1, 0}: val_loader = create_dataloader(val_path, imgsz, batch_size // WORLD_SIZE * 2, gs, single_cls, hyp=hyp, cache=None if noval else opt.cache, rect=True, rank=-1, workers=workers * 2, pad=0.5, prefix=colorstr('val: '))[0] if not resume: if not opt.noautoanchor: check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz) # run AutoAnchor model.half().float() # pre-reduce anchor precision callbacks.run('on_pretrain_routine_end', labels, names) # DDP mode if cuda and RANK != -1: model = smart_DDP(model) # Model attributes nl = de_parallel(model).model[-1].nl # number of detection layers (to scale hyps) hyp['box'] *= 3 / nl # scale to layers hyp['cls'] *= nc / 80 * 3 / nl # scale to classes and layers hyp['obj'] *= (imgsz / 640) ** 2 * 3 / nl # scale to image size and layers hyp['label_smoothing'] = opt.label_smoothing model.nc = nc # attach number of classes to model model.hyp = hyp # attach hyperparameters to model model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc # attach class weights model.names = names # Start training t0 = time.time() nb = len(train_loader) # number of batches nw = max(round(hyp['warmup_epochs'] * nb), 100) # number of warmup iterations, max(3 epochs, 100 iterations) # nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training last_opt_step = -1 maps = np.zeros(nc) # mAP per class results = (0, 0, 0, 0, 0, 0, 0) # P, R, [email protected], [email protected], val_loss(box, obj, cls) scheduler.last_epoch = start_epoch - 1 # do not move scaler = torch.cuda.amp.GradScaler(enabled=amp) stopper, stop = EarlyStopping(patience=opt.patience), False compute_loss = ComputeLoss(model) # init loss class callbacks.run('on_train_start') LOGGER.info(f'Image sizes {imgsz} train, {imgsz} val\n' f'Using {train_loader.num_workers * WORLD_SIZE} dataloader workers\n' f"Logging results to {colorstr('bold', save_dir)}\n" f'Starting training for {epochs} epochs...') for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------ callbacks.run('on_train_epoch_start') model.train() # Update image weights (optional, single-GPU only) if opt.image_weights: cw = model.class_weights.cpu().numpy() * (1 - maps) ** 2 / nc # class weights iw = labels_to_image_weights(dataset.labels, nc=nc, class_weights=cw) # image weights dataset.indices = random.choices(range(dataset.n), weights=iw, k=dataset.n) # rand weighted idx # Update mosaic border (optional) # b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs) # dataset.mosaic_border = [b - imgsz, -b] # height, width borders mloss = torch.zeros(3, device=device) # mean losses if RANK != -1: train_loader.sampler.set_epoch(epoch) pbar = enumerate(train_loader) LOGGER.info(('\n' + '%11s' * 7) % ('Epoch', 'GPU_mem', 'box_loss', 'obj_loss', 'cls_loss', 'Instances', 'Size')) if RANK in {-1, 0}: pbar = tqdm(pbar, total=nb, bar_format=TQDM_BAR_FORMAT) # progress bar optimizer.zero_grad() for i, (imgs, targets, paths, _) in pbar: # batch ------------------------------------------------------------- callbacks.run('on_train_batch_start') ni = i + nb * epoch # number integrated batches (since train start) imgs = imgs.to(device, non_blocking=True).float() / 255 # uint8 to float32, 0-255 to 0.0-1.0 # Warmup if ni <= nw: xi = [0, nw] # x interp # compute_loss.gr = np.interp(ni, xi, [0.0, 1.0]) # iou loss ratio (obj_loss = 1.0 or iou) accumulate = max(1, np.interp(ni, xi, [1, nbs / batch_size]).round()) for j, x in enumerate(optimizer.param_groups): # bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0 x['lr'] = np.interp(ni, xi, [hyp['warmup_bias_lr'] if j == 0 else 0.0, x['initial_lr'] * lf(epoch)]) if 'momentum' in x: x['momentum'] = np.interp(ni, xi, [hyp['warmup_momentum'], hyp['momentum']]) # Multi-scale if opt.multi_scale: sz = random.randrange(imgsz * 0.5, imgsz * 1.5 + gs) // gs * gs # size sf = sz / max(imgs.shape[2:]) # scale factor if sf != 1: ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]] # new shape (stretched to gs-multiple) imgs = nn.functional.interpolate(imgs, size=ns, mode='bilinear', align_corners=False) # Forward # with torch.cuda.amp.autocast(amp): with torch.amp.autocast(device_type='cuda'): pred = model(imgs) # forward loss, loss_items = compute_loss(pred, targets.to(device)) # loss scaled by batch_size if RANK != -1: loss *= WORLD_SIZE # gradient averaged between devices in DDP mode if opt.quad: loss *= 4. # Backward scaler.scale(loss).backward() # Optimize - https://pytorch.org/docs/master/notes/amp_examples.html if ni - last_opt_step >= accumulate: scaler.unscale_(optimizer) # unscale gradients torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=10.0) # clip gradients scaler.step(optimizer) # optimizer.step scaler.update() optimizer.zero_grad() if ema: ema.update(model) last_opt_step = ni # Log if RANK in {-1, 0}: mloss = (mloss * i + loss_items) / (i + 1) # update mean losses mem = f'{torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0:.3g}G' # (GB) pbar.set_description(('%11s' * 2 + '%11.4g' * 5) % (f'{epoch}/{epochs - 1}', mem, *mloss, targets.shape[0], imgs.shape[-1])) callbacks.run('on_train_batch_end', model, ni, imgs, targets, paths, list(mloss)) if callbacks.stop_training: return # end batch ------------------------------------------------------------------------------------------------ # Scheduler lr = [x['lr'] for x in optimizer.param_groups] # for loggers scheduler.step() if RANK in {-1, 0}: # mAP callbacks.run('on_train_epoch_end', epoch=epoch) ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'names', 'stride', 'class_weights']) final_epoch = (epoch + 1 == epochs) or stopper.possible_stop if not noval or final_epoch: # Calculate mAP results, maps, _ = validate.run(data_dict, batch_size=batch_size // WORLD_SIZE * 2, imgsz=imgsz, half=amp, model=ema.ema, single_cls=single_cls, dataloader=val_loader, save_dir=save_dir, plots=False, callbacks=callbacks, compute_loss=compute_loss) # Update best mAP fi = fitness(np.array(results).reshape(1, -1)) # weighted combination of [P, R, [email protected], [email protected]] stop = stopper(epoch=epoch, fitness=fi) # early stop check if fi > best_fitness: best_fitness = fi log_vals = list(mloss) + list(results) + lr callbacks.run('on_fit_epoch_end', log_vals, epoch, best_fitness, fi) # Save model if (not nosave) or (final_epoch and not evolve): # if save ckpt = { 'epoch': epoch, 'best_fitness': best_fitness, 'model': deepcopy(de_parallel(model)).half(), 'ema': deepcopy(ema.ema).half(), 'updates': ema.updates, 'optimizer': optimizer.state_dict(), 'opt': vars(opt), 'git': GIT_INFO, # {remote, branch, commit} if a git repo 'date': datetime.now().isoformat()} # Save last, best and delete torch.save(ckpt, last) if best_fitness == fi: torch.save(ckpt, best) if opt.save_period > 0 and epoch % opt.save_period == 0: torch.save(ckpt, w / f'epoch{epoch}.pt') del ckpt callbacks.run('on_model_save', last, epoch, final_epoch, best_fitness, fi) # EarlyStopping if RANK != -1: # if DDP training broadcast_list = [stop if RANK == 0 else None] dist.broadcast_object_list(broadcast_list, 0) # broadcast 'stop' to all ranks if RANK != 0: stop = broadcast_list[0] if stop: break # must break all DDP ranks # end epoch ---------------------------------------------------------------------------------------------------- # end training ----------------------------------------------------------------------------------------------------- if RANK in {-1, 0}: LOGGER.info(f'\n{epoch - start_epoch + 1} epochs completed in {(time.time() - t0) / 3600:.3f} hours.') for f in last, best: if f.exists(): strip_optimizer(f) # strip optimizers if f is best: LOGGER.info(f'\nValidating {f}...') results, _, _ = validate.run( data_dict, batch_size=batch_size // WORLD_SIZE * 2, imgsz=imgsz, model=attempt_load(f, device).half(), iou_thres=0.65 if is_coco else 0.60, # best pycocotools at iou 0.65 single_cls=single_cls, dataloader=val_loader, save_dir=save_dir, save_json=is_coco, verbose=True, plots=plots, callbacks=callbacks, compute_loss=compute_loss) # val best model with plots if is_coco: callbacks.run('on_fit_epoch_end', list(mloss) + list(results) + lr, epoch, best_fitness, fi) callbacks.run('on_train_end', last, best, epoch, results) torch.cuda.empty_cache() return results def parse_opt(known=False): parser = argparse.ArgumentParser() parser.add_argument('--weights', type=str, default='./weights/yolov5s.pt', help='initial weights path') parser.add_argument('--cfg', type=str, default='./models/yolov5s.yaml', help='model.yaml path') parser.add_argument('--data', type=str, default=r'C:data/AAAA.yaml', help='data.yaml path') parser.add_argument('--hyp', type=str, default=ROOT / 'data/hyps/hyp.scratch-low.yaml', help='hyperparameters path') parser.add_argument('--epochs', type=int, default=100, help='total training epochs') parser.add_argument('--batch-size', type=int, default=1, help='total batch size for all GPUs, -1 for autobatch') parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='train, val image size (pixels)') parser.add_argument('--rect', action='store_true', help='rectangular training') parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training') parser.add_argument('--nosave', action='store_true', help='only save final checkpoint') parser.add_argument('--noval', action='store_true', help='only validate final epoch') parser.add_argument('--noautoanchor', action='store_true', help='disable AutoAnchor') parser.add_argument('--noplots', action='store_true', help='save no plot files') parser.add_argument('--evolve', type=int, nargs='?', const=300, help='evolve hyperparameters for x generations') parser.add_argument('--bucket', type=str, default='', help='gsutil bucket') parser.add_argument('--cache', type=str, nargs='?', const='ram', help='image --cache ram/disk') parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training') parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%') parser.add_argument('--single-cls', action='store_true', help='train multi-class data as single-class') parser.add_argument('--optimizer', type=str, choices=['SGD', 'Adam', 'AdamW'], default='SGD', help='optimizer') parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode') parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)') parser.add_argument('--project', default=ROOT / 'runs/train', help='save to project/name') parser.add_argument('--name', default='welding_defect_yolov5s_20241101_300', help='save to project/name') parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') parser.add_argument('--quad', action='store_true', help='quad dataloader') parser.add_argument('--cos-lr', action='store_true', help='cosine LR scheduler') parser.add_argument('--label-smoothing', type=float, default=0.0, help='Label smoothing epsilon') parser.add_argument('--patience', type=int, default=100, help='EarlyStopping patience (epochs without improvement)') parser.add_argument('--freeze', nargs='+', type=int, default=[0], help='Freeze layers: backbone=10, first3=0 1 2') parser.add_argument('--save-period', type=int, default=5, help='Save checkpoint every x epochs (disabled if < 1)') parser.add_argument('--seed', type=int, default=0, help='Global training seed') parser.add_argument('--local_rank', type=int, default=-1, help='Automatic DDP Multi-GPU argument, do not modify') # Logger arguments parser.add_argument('--entity', default=None, help='Entity') parser.add_argument('--upload_dataset', nargs='?', const=True, default=False, help='Upload data, "val" option') parser.add_argument('--bbox_interval', type=int, default=-1, help='Set bounding-box image logging interval') parser.add_argument('--artifact_alias', type=str, default='latest', help='Version of dataset artifact to use') return parser.parse_known_args()[0] if known else parser.parse_args() def main(opt, callbacks=Callbacks()): # Checks if RANK in {-1, 0}: print_args(vars(opt)) check_git_status() check_requirements() # Resume (from specified or most recent last.pt) if opt.resume and not check_comet_resume(opt) and not opt.evolve: last = Path(check_file(opt.resume) if isinstance(opt.resume, str) else get_latest_run()) opt_yaml = last.parent.parent / 'opt.yaml' # train options yaml opt_data = opt.data # original dataset if opt_yaml.is_file(): with open(opt_yaml, errors='ignore') as f: d = yaml.safe_load(f) else: d = torch.load(last, map_location='cpu')['opt'] opt = argparse.Namespace(**d) # replace opt.cfg, opt.weights, opt.resume = '', str(last), True # reinstate if is_url(opt_data): opt.data = check_file(opt_data) # avoid HUB resume auth timeout else: opt.data, opt.cfg, opt.hyp, opt.weights, opt.project = \ check_file(opt.data), check_yaml(opt.cfg), check_yaml(opt.hyp), str(opt.weights), str(opt.project) # checks assert len(opt.cfg) or len(opt.weights), 'either --cfg or --weights must be specified' if opt.evolve: if opt.project == str(ROOT / 'runs/train'): # if default project name, rename to runs/evolve opt.project = str(ROOT / 'runs/evolve') opt.exist_ok, opt.resume = opt.resume, False # pass resume to exist_ok and disable resume if opt.name == 'cfg': opt.name = Path(opt.cfg).stem # use model.yaml as name opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) # DDP mode device = select_device(opt.device, batch_size=opt.batch_size) if LOCAL_RANK != -1: msg = 'is not compatible with YOLOv5 Multi-GPU DDP training' assert not opt.image_weights, f'--image-weights {msg}' assert not opt.evolve, f'--evolve {msg}' assert opt.batch_size != -1, f'AutoBatch with --batch-size -1 {msg}, please pass a valid --batch-size' assert opt.batch_size % WORLD_SIZE == 0, f'--batch-size {opt.batch_size} must be multiple of WORLD_SIZE' assert torch.cuda.device_count() > LOCAL_RANK, 'insufficient CUDA devices for DDP command' torch.cuda.set_device(LOCAL_RANK) device = torch.device('cuda', LOCAL_RANK) dist.init_process_group(backend="nccl" if dist.is_nccl_available() else "gloo") # Train if not opt.evolve: train(opt.hyp, opt, device, callbacks) # Evolve hyperparameters (optional) else: # Hyperparameter evolution metadata (mutation scale 0-1, lower_limit, upper_limit) meta = { 'lr0': (1, 1e-5, 1e-1), # initial learning rate (SGD=1E-2, Adam=1E-3) 'lrf': (1, 0.01, 1.0), # final OneCycleLR learning rate (lr0 * lrf) 'momentum': (0.3, 0.6, 0.98), # SGD momentum/Adam beta1 'weight_decay': (1, 0.0, 0.001), # optimizer weight decay 'warmup_epochs': (1, 0.0, 5.0), # warmup epochs (fractions ok) 'warmup_momentum': (1, 0.0, 0.95), # warmup initial momentum 'warmup_bias_lr': (1, 0.0, 0.2), # warmup initial bias lr 'box': (1, 0.02, 0.2), # box loss gain 'cls': (1, 0.2, 4.0), # cls loss gain 'cls_pw': (1, 0.5, 2.0), # cls BCELoss positive_weight 'obj': (1, 0.2, 4.0), # obj loss gain (scale with pixels) 'obj_pw': (1, 0.5, 2.0), # obj BCELoss positive_weight 'iou_t': (0, 0.1, 0.7), # IoU training threshold 'anchor_t': (1, 2.0, 8.0), # anchor-multiple threshold 'anchors': (2, 2.0, 10.0), # anchors per output grid (0 to ignore) 'fl_gamma': (0, 0.0, 2.0), # focal loss gamma (efficientDet default gamma=1.5) 'hsv_h': (1, 0.0, 0.1), # image HSV-Hue augmentation (fraction) 'hsv_s': (1, 0.0, 0.9), # image HSV-Saturation augmentation (fraction) 'hsv_v': (1, 0.0, 0.9), # image HSV-Value augmentation (fraction) 'degrees': (1, 0.0, 45.0), # image rotation (+/- deg) 'translate': (1, 0.0, 0.9), # image translation (+/- fraction) 'scale': (1, 0.0, 0.9), # image scale (+/- gain) 'shear': (1, 0.0, 10.0), # image shear (+/- deg) 'perspective': (0, 0.0, 0.001), # image perspective (+/- fraction), range 0-0.001 'flipud': (1, 0.0, 1.0), # image flip up-down (probability) 'fliplr': (0, 0.0, 1.0), # image flip left-right (probability) 'mosaic': (1, 0.0, 1.0), # image mixup (probability) 'mixup': (1, 0.0, 1.0), # image mixup (probability) 'copy_paste': (1, 0.0, 1.0)} # segment copy-paste (probability) with open(opt.hyp, errors='ignore') as f: hyp = yaml.safe_load(f) # load hyps dict if 'anchors' not in hyp: # anchors commented in hyp.yaml hyp['anchors'] = 3 if opt.noautoanchor: del hyp['anchors'], meta['anchors'] opt.noval, opt.nosave, save_dir = True, True, Path(opt.save_dir) # only val/save final epoch # ei = [isinstance(x, (int, float)) for x in hyp.values()] # evolvable indices evolve_yaml, evolve_csv = save_dir / 'hyp_evolve.yaml', save_dir / 'evolve.csv' if opt.bucket: os.system(f'gsutil cp gs://{opt.bucket}/evolve.csv {evolve_csv}') # download evolve.csv if exists for _ in range(opt.evolve): # generations to evolve if evolve_csv.exists(): # if evolve.csv exists: select best hyps and mutate # Select parent(s) parent = 'single' # parent selection method: 'single' or 'weighted' x = np.loadtxt(evolve_csv, ndmin=2, delimiter=',', skiprows=1) n = min(5, len(x)) # number of previous results to consider x = x[np.argsort(-fitness(x))][:n] # top n mutations w = fitness(x) - fitness(x).min() + 1E-6 # weights (sum > 0) if parent == 'single' or len(x) == 1: # x = x[random.randint(0, n - 1)] # random selection x = x[random.choices(range(n), weights=w)[0]] # weighted selection elif parent == 'weighted': x = (x * w.reshape(n, 1)).sum(0) / w.sum() # weighted combination # Mutate mp, s = 0.8, 0.2 # mutation probability, sigma npr = np.random npr.seed(int(time.time())) g = np.array([meta[k][0] for k in hyp.keys()]) # gains 0-1 ng = len(meta) v = np.ones(ng) while all(v == 1): # mutate until a change occurs (prevent duplicates) v = (g * (npr.random(ng) < mp) * npr.randn(ng) * npr.random() * s + 1).clip(0.3, 3.0) for i, k in enumerate(hyp.keys()): # plt.hist(v.ravel(), 300) hyp[k] = float(x[i + 7] * v[i]) # mutate # Constrain to limits for k, v in meta.items(): hyp[k] = max(hyp[k], v[1]) # lower limit hyp[k] = min(hyp[k], v[2]) # upper limit hyp[k] = round(hyp[k], 5) # significant digits # Train mutation results = train(hyp.copy(), opt, device, callbacks) callbacks = Callbacks() # Write mutation results keys = ('metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95', 'val/box_loss', 'val/obj_loss', 'val/cls_loss') print_mutation(keys, results, hyp.copy(), save_dir, opt.bucket) # Plot results plot_evolve(evolve_csv) LOGGER.info(f'Hyperparameter evolution finished {opt.evolve} generations\n' f"Results saved to {colorstr('bold', save_dir)}\n" f'Usage example: $ python train.py --hyp {evolve_yaml}') def run(**kwargs): # Usage: import train; train.run(data='coco128.yaml', imgsz=320, weights='yolov5m.pt') opt = parse_opt(True) for k, v in kwargs.items(): setattr(opt, k, v) main(opt) return opt if __name__ == "__main__": opt = parse_opt() main(opt) 为什么训练之后,他的runs里面并没有显示best.pt跟last.pt 请查找原因

Traceback (most recent call last): File "D:\ultralytics-8.3.28\yolov11_train.py", line 87, in <module> model.train( File "D:\ultralytics-8.3.28\ultralytics\engine\model.py", line 796, in train self.trainer = (trainer or self._smart_load("trainer"))(overrides=args, _callbacks=self.callbacks) File "D:\ultralytics-8.3.28\ultralytics\engine\trainer.py", line 101, in __init__ self.args = get_cfg(cfg, overrides) File "D:\ultralytics-8.3.28\ultralytics\cfg\__init__.py", line 296, in get_cfg check_dict_alignment(cfg, overrides) File "D:\ultralytics-8.3.28\ultralytics\cfg\__init__.py", line 481, in check_dict_alignment raise SyntaxError(string + CLI_HELP_MSG) from e SyntaxError: 'callbacks' is not a valid YOLO argument. Arguments received: ['yolo']. Ultralytics 'yolo' commands use the following syntax: yolo TASK MODE ARGS Where TASK (optional) is one of {'obb', 'classify', 'detect', 'segment', 'pose'} MODE (required) is one of {'track', 'predict', 'val', 'train', 'benchmark', 'export'} ARGS (optional) are any number of custom 'arg=value' pairs like 'imgsz=320' that override defaults. See all ARGS at https://docs.ultralytics.com/usage/cfg or with 'yolo cfg' 1. Train a detection model for 10 epochs with an initial learning_rate of 0.01 yolo train data=coco8.yaml model=yolo11n.pt epochs=10 lr0=0.01 2. Predict a YouTube video using a pretrained segmentation model at image size 320: yolo predict model=yolo11n-seg.pt source='https://youtu.be/LNwODJXcvt4' imgsz=320 3. Val a pretrained detection model at batch-size 1 and image size 640: yolo val model=yolo11n.pt data=coco8.yaml batch=1 imgsz=640 4. Export a YOLO11n classification model to ONNX format at image size 224 by 128 (no TASK required) yolo export model=yolo11n-cls.pt format=onnx imgsz=224,128 5. Streamlit real-time webcam inference GUI yolo streamlit-pre

E:\Anaconda\envs\yolo\python.exe "D:\New study\yolo\train3.py" WARNING no model scale passed. Assuming scale='n'. Transferred 490/541 items from pretrained weights Traceback (most recent call last): File "D:\New study\yolo\train3.py", line 43, in <module> main() File "D:\New study\yolo\train3.py", line 10, in main results = model.train( File "E:\Anaconda\envs\yolo\lib\site-packages\ultralytics\engine\model.py", line 791, in train self.trainer = (trainer or self._smart_load("trainer"))(overrides=args, _callbacks=self.callbacks) File "E:\Anaconda\envs\yolo\lib\site-packages\ultralytics\models\yolo\pose\train.py", line 66, in __init__ super().__init__(cfg, overrides, _callbacks) File "E:\Anaconda\envs\yolo\lib\site-packages\ultralytics\engine\trainer.py", line 119, in __init__ self.args = get_cfg(cfg, overrides) File "E:\Anaconda\envs\yolo\lib\site-packages\ultralytics\cfg\__init__.py", line 305, in get_cfg check_dict_alignment(cfg, overrides) File "E:\Anaconda\envs\yolo\lib\site-packages\ultralytics\cfg\__init__.py", line 498, in check_dict_alignment raise SyntaxError(string + CLI_HELP_MSG) from e SyntaxError: 'kpt_weight' is not a valid YOLO argument. 'obj_weight' is not a valid YOLO argument. 'kpt_shape' is not a valid YOLO argument. Arguments received: ['yolo']. Ultralytics 'yolo' commands use the following syntax: yolo TASK MODE ARGS Where TASK (optional) is one of ['detect', 'pose', 'obb', 'classify', 'segment'] MODE (required) is one of ['train', 'track', 'predict', 'val', 'benchmark', 'export'] ARGS (optional) are any number of custom 'arg=value' pairs like 'imgsz=320' that override defaults. See all ARGS at https://docs.ultralytics.com/usage/cfg or with 'yolo cfg' 1. Train a detection model for 10 epochs with an initial learning_rate of 0.01 yolo train data=coco8.yaml model=yolo11n.pt epochs=10 lr0=0.01 2. Predict a YouTube video using a pretrained segmentation model at image size 320: yolo predict model=yolo11n-seg.pt source='https://youtu.be/LNwODJXcvt4' imgsz=320 3. Val a pretrained detection model at batch-size 1 and image size 640: yolo val model=yolo11n.pt data=coco8.yaml batch=1 imgsz=640 4. Export a YOLO11n classification model to ONNX format at image size 224 by 128 (no TASK required) yolo export model=yolo11n-cls.pt format=onnx imgsz=224,128 5. Ultralytics solutions usage yolo solutions count or in ['crop', 'blur', 'workout', 'heatmap', 'isegment', 'visioneye', 'speed', 'queue', 'analytics', 'inference', 'trackzone'] source="path/to/video.mp4" 6. Run special commands: yolo help yolo checks yolo version yolo settings yolo copy-cfg yolo cfg yolo solutions help Docs: https://docs.ultralytics.com Solutions: https://docs.ultralytics.com/solutions/ Community: https://community.ultralytics.com GitHub: https://github.com/ultralytics/ultralytics 进程已结束,退出代码为 1 出现这种错误何解

Traceback (most recent call last): File "c:\Users\lenvo\Desktop\train.py", line 4, in <module> results=model.train(data=r"C:\Users\lenvo\Desktop\yolob8源码\YOLOV8\ultralytics-main\datasets\管道腐蚀\data.yaml",epochs=10,batch=4) ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\lenvo\AppData\Local\Programs\Python\Python313\Lib\site-packages\ultralytics\engine\model.py", line 810, in train self.trainer.train() ~~~~~~~~~~~~~~~~~~^^ File "C:\Users\lenvo\AppData\Local\Programs\Python\Python313\Lib\site-packages\ultralytics\engine\trainer.py", line 208, in train self._do_train(world_size) ~~~~~~~~~~~~~~^^^^^^^^^^^^ File "C:\Users\lenvo\AppData\Local\Programs\Python\Python313\Lib\site-packages\ultralytics\engine\trainer.py", line 381, in _do_train self.loss, self.loss_items = self.model(batch) ~~~~~~~~~~^^^^^^^ File "C:\Users\lenvo\AppData\Local\Programs\Python\Python313\Lib\site-packages\torch\nn\modules\module.py", line 1739, in _wrapped_call_impl return self._call_impl(*args, **kwargs) ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^ File "C:\Users\lenvo\AppData\Local\Programs\Python\Python313\Lib\site-packages\torch\nn\modules\module.py", line 1750, in _call_impl return forward_call(*args, **kwargs) File "C:\Users\lenvo\AppData\Local\Programs\Python\Python313\Lib\site-packages\ultralytics\nn\tasks.py", line 113, in forward return self.loss(x, *args, **kwargs) ~~~~~~~~~^^^^^^^^^^^^^^^^^^^^ File "C:\Users\lenvo\AppData\Local\Programs\Python\Python313\Lib\site-packages\ultralytics\nn\tasks.py", line 292, in loss return self.criterion(preds, batch) ~~~~~~~~~~~~~~^^^^^^^^^^^^^^ File "C:\Users\lenvo\AppData\Local\Programs\Python\Python313\Lib\site-packages\ultralytics\utils\loss.py", line 297, in __call__ raise TypeError( ...<5 lines>... ) from e TypeError: ERROR

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