YOLOV8进行resume接续训练_yolov8 resume

01 修改train脚本

        修改train中模型的加载方式

路径在ultralytics/yolo/engine/model.py中

01找到

 def train(self, **kwargs):

02注释掉下面代码

self.trainer.model = self.model

03修改完整体的代码为

    # region def train-模型训练过程
    def train(self, **kwargs):
        """
        Trains the model on a given dataset.
        Args:
            **kwargs (Any): Any number of arguments representing the training configuration.
        """
        self._check_is_pytorch_model()
        if self.session:  # Ultralytics HUB session
            if any(kwargs):
                LOGGER.warning('WARNING ⚠️ using HUB training arguments, ignoring local training arguments.')
            kwargs = self.session.train_args
        check_pip_update_available()
        overrides = self.overrides.copy()
        overrides.update(kwargs)
        if kwargs.get('cfg'):
            LOGGER.info(f"cfg file passed. Overriding default params with {kwargs['cfg']}.")
 
            # region 参数改为False
            overrides = yaml_load(check_yaml(kwargs['cfg']),append_filename=False)
 
            # endregion
        overrides['mode'] = 'train'
        if not overrides.get('data'):
            raise AttributeError("Dataset required but missing, i.e. pass 'data=coco128.yaml'")
        if overrides.get('resume'):
            overrides['resume'] = self.ckpt_path
        self.task = overrides.get('task') or self.task
        self.trainer = TASK_MAP[self.task][1](overrides=overrides, _callbacks=self.callbacks)
 
 
        # region 从新初始化模型
        # if not overrides.get('resume'):  # manually set model only if not resuming
        #     self.trainer.model = self.trainer.get_model(weights=self.model if self.ckpt else None, cfg=self.model.yaml)
        #     self.model = self.trainer.model
        # endregion
        # region #自己修改的 增加
 
        # self.trainer.model = self.model
 
        # endregion
 
        self.trainer.hub_session = self.session  # attach optional HUB session
 
        # region #开始训练
        self.trainer.train()
        # Update model and cfg after training
        if RANK in (-1, 0):
            self.model, _ = attempt_load_one_weight(str(self.trainer.best))
            self.overrides = self.model.args
            self.metrics = getattr(self.trainer.validator, 'metrics', None)  # TODO: no metrics returned by DDP
        # endregion
    # endregion

02 修改check_resume代码

        路径在ultralytics/yolo/engine/trainer.py

01找到check_resume方法

def check_resume(self):

02 注释掉下面代码

resume = self.args.resume

03替换为直接加载路径

resume = r'D:\learn\sdxx\mbjc\ultralytics\weights\last.pt'

03修改完整体的代码为

    def check_resume(self):
        """Check if resume checkpoint exists and update arguments accordingly."""
        # region #注释与新增
        # resume = self.args.resume
        resume = r'D:\learn\sdxx\mbjc\ultralytics\weights\last.pt'
        # endregion
 
        if resume:
            try:
                exists = isinstance(resume, (str, Path)) and Path(resume).exists()
                last = Path(check_file(resume) if exists else get_latest_run())
 
                # Check that resume data YAML exists, otherwise strip to force re-download of dataset
                ckpt_args = attempt_load_weights(last).args
                if not Path(ckpt_args['data']).exists():
                    ckpt_args['data'] = self.args.data
 
                self.args = get_cfg(ckpt_args)
                self.args.model, resume = str(last), True  # reinstate
            except Exception as e:
                raise FileNotFoundError('Resume checkpoint not found. Please pass a valid checkpoint to resume from, '
                                        "i.e. 'yolo train resume model=path/to/last.pt'") from e
        self.resume = resume

 03 修改def resume_training代码

          路径在ultralytics/yolo/engine/trainer.py

01找到

def resume_training(self, ckpt):

在下面加入

ckpt=torch.load('D:\learn\sdxx\mbjc\ultralytics\weights\last.pt')

整体的代码为

    def resume_training(self, ckpt):
 
        # region #新增加的
        ckpt=torch.load(r'D:\learn\sdxx\mbjc\ultralytics\weights\last.pt')
        # endregion
        
        
        """Resume YOLO training from given epoch and best fitness."""
        if ckpt is None:
            return
        best_fitness = 0.0
        start_epoch = ckpt['epoch'] + 1
        if ckpt['optimizer'] is not None:
            self.optimizer.load_state_dict(ckpt['optimizer'])  # optimizer
            best_fitness = ckpt['best_fitness']
        if self.ema and ckpt.get('ema'):
            self.ema.ema.load_state_dict(ckpt['ema'].float().state_dict())  # EMA
            self.ema.updates = ckpt['updates']
        if self.resume:
            assert start_epoch > 0, \
                f'{self.args.model} training to {self.epochs} epochs is finished, nothing to resume.\n' \
                f"Start a new training without resuming, i.e. 'yolo train model={self.args.model}'"
            LOGGER.info(
                f'Resuming training from {self.args.model} from epoch {start_epoch + 1} to {self.epochs} total epochs')
        if self.epochs < start_epoch:
            LOGGER.info(
                f"{self.model} has been trained for {ckpt['epoch']} epochs. Fine-tuning for {self.epochs} more epochs.")
            self.epochs += ckpt['epoch']  # finetune additional epochs
        self.best_fitness = best_fitness
        self.start_epoch = start_epoch
        if start_epoch > (self.epochs - self.args.close_mosaic):
            LOGGER.info('Closing dataloader mosaic')
            if hasattr(self.train_loader.dataset, 'mosaic'):
                self.train_loader.dataset.mosaic = False
            if hasattr(self.train_loader.dataset, 'close_mosaic'):
                self.train_loader.dataset.close_mosaic(hyp=self.args)

04 复制最后的权重到相应路径

我的是last.pt复制到D:\learn\sdxx\mbjc\ultralytics\weights\last.pt

05 点击训练即可开始正常训练