File "/root/autodl-tmp/ultralytics-main/ultralytics/main.py", line 2 model = YOLO("yolov8-att.yaml").train(**('cfg':'/root/autodl-tmp/ultralytics-main/ultralytics/yolo/cfg/1.yaml') ) # 训练模型 ^ SyntaxError: invalid syntax

这是一个 `SyntaxError` 错误，它提示在 `main.py` 文件的第 2 行存在语法错误，具体是在下面这行代码：

model = YOLO("yolov8-att.yaml").train(**('cfg':'/root/autodl-tmp/ultralytics-main/ultralytics/yolo/cfg/1.yaml') )

这行代码中的语法错误在于使用了 `**` 标记，但是后面的内容不符合字典的格式。如果你想传递一个字典作为参数，应该使用如下的方式：

model = YOLO("yolov8-att.yaml").train(cfg='/root/autodl-tmp/ultralytics-main/ultralytics/yolo/cfg/1.yaml')

这样就可以将参数以字典的形式传递给 `train()` 函数了。

相关问题

Traceback (most recent call last): File "/root/autodl-tmp/ultralytics-main/run.py", line 4, in <module> model = YOLO('/root/autodl-tmp/ultralytics-main/ultralytics/models/v8/yolov8.yaml').load('/root/autodl-tmp/ultralytics-main/yolov8x.pt') # build from YAML and transfer weights File "/root/autodl-tmp/ultralytics-main/ultralytics/yolo/engine/model.py", line 105, in __init__ self._new(model, task) File "/root/autodl-tmp/ultralytics-main/ultralytics/yolo/engine/model.py", line 138, in _new self.model = TASK_MAP[self.task][0](cfg_dict, verbose=verbose and RANK == -1) # build model File "/root/autodl-tmp/ultralytics-main/ultralytics/nn/tasks.py", line 194, in __init__ self.model, self.save = parse_model(deepcopy(self.yaml), ch=ch, verbose=verbose) # model, savelist File "/root/autodl-tmp/ultralytics-main/ultralytics/nn/tasks.py", line 496, in parse_model m = getattr(torch.nn, m[3:]) if 'nn.' in m else globals()[m] # get module KeyError: 'CBAM'

这个错误通常发生在使用YOLOv8时，因为YOLOv8中使用了CBAM模块，而您的环境中没有安装CBAM模块。

解决这个问题的方法是安装CBAM模块。您可以使用以下命令安装CBAM模块：

pip install cbam

或者，您可以在运行YOLOv8之前将CBAM模块的源代码下载到您的环境中，并将其添加到您的PYTHONPATH中。

git clone https://github.com/Jongchan/attention-module.git
export PYTHONPATH=$PYTHONPATH:/path/to/attention-module

如果您已经安装了CBAM模块，但仍然遇到此错误，请确保您的PYTHONPATH包含CBAM模块的路径。

Traceback (most recent call last): File "/root/autodl-tmp/ultralytics-main/run.py", line 7, in <module> model.train(data='/root/autodl-tmp/ultralytics-main/traindata3/data.yaml') File "/root/autodl-tmp/ultralytics-main/ultralytics/yolo/engine/model.py", line 371, in train self.trainer.train() File "/root/autodl-tmp/ultralytics-main/ultralytics/yolo/engine/trainer.py", line 192, in train self._do_train(world_size) File "/root/autodl-tmp/ultralytics-main/ultralytics/yolo/engine/trainer.py", line 328, in _do_train preds = self.model(batch['img']) File "/root/miniconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl return forward_call(*args, **kwargs) File "/root/autodl-tmp/ultralytics-main/ultralytics/nn/tasks.py", line 219, in forward return self._forward_once(x, profile, visualize) # single-scale inference, train File "/root/autodl-tmp/ultralytics-main/ultralytics/nn/tasks.py", line 70, in _forward_once x = m(x) # run File "/root/miniconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl return forward_call(*args, **kwargs) File "/root/autodl-tmp/ultralytics-main/ultralytics/nn/modules/block.py", line 183, in forward return self.cv2(torch.cat(y, 1)) torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 100.00 MiB (GPU 0; 23.65 GiB total capacity; 6.18 GiB already allocated; 98.56 MiB free; 6.21 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF terminate called without an active exception Aborted (core dumped)

这是一个CUDA out of memory错误，说明你的GPU显存已经不足以支持你的模型在训练时的计算了。你可以尝试减小batch size或者使用更高显存的GPU来解决这个问题。另外，你也可以尝试使用PyTorch提供的一些内存管理的工具，比如设置max_split_size_mb参数来避免内存碎片化。