model = Model_LA().to(device=DEVICE,dtype=torch.float) AttributeError: 'Model_LA' object has no attribute 'to'

这个错误可能是因为 `Model_LA` 类没有继承自 `nn.Module`，而 `to()` 方法是 `nn.Module` 类的方法。所以，你需要将 `Model_LA` 类定义为继承自 `nn.Module`，例如：

import torch.nn as nn

class Model_LA(nn.Module):
    def __init__(self):
        super(Model_LA, self).__init__()
        # 在这里定义模型层

    def forward(self, x):
        # 在这里定义前向传播逻辑
        return x

这样，你就可以使用 `to()` 方法将模型移动到指定的设备上了。例如：

model = Model_LA()
model.to(device='cuda', dtype=torch.float)

请注意，你需要将 `device` 参数设置为字符串类型的设备名称（如 `'cuda'` 或 `'cpu'`），而不是 `torch.device` 对象。

相关问题

real_labels = torch.ones(real_images.size(0), dtype=torch.float, device=device) AttributeError: 'list' object has no attribute 'size'

这个错误是因为你试图在一个 Python 列表上调用 `size()` 方法，但是该方法只能在 PyTorch 张量上调用。要解决这个问题，你需要将 `real_images` 转换为 PyTorch 张量，例如通过 `torch.tensor()` 或者 `torch.from_numpy()` 方法。

如何解决Loading and preparing results... DONE (t=0.01s) creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.53s). Accumulating evaluation results... Traceback (most recent call last): File "tools/train.py", line 133, in <module> main() File "tools/train.py", line 129, in main runner.train() File "/home/wangbei/anaconda3/envs/Object_mmdetection/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1721, in train model = self.train_loop.run() # type: ignore File "/home/wangbei/anaconda3/envs/Object_mmdetection/lib/python3.8/site-packages/mmengine/runner/loops.py", line 102, in run self.runner.val_loop.run() File "/home/wangbei/anaconda3/envs/Object_mmdetection/lib/python3.8/site-packages/mmengine/runner/loops.py", line 366, in run metrics = self.evaluator.evaluate(len(self.dataloader.dataset)) File "/home/wangbei/anaconda3/envs/Object_mmdetection/lib/python3.8/site-packages/mmengine/evaluator/evaluator.py", line 79, in evaluate _results = metric.evaluate(size) File "/home/wangbei/anaconda3/envs/Object_mmdetection/lib/python3.8/site-packages/mmengine/evaluator/metric.py", line 133, in evaluate _metrics = self.compute_metrics(results) # type: ignore File "/home/wangbei/mmdetection(coco)/mmdet/evaluation/metrics/coco_metric.py", line 512, in compute_metrics coco_eval.accumulate() File "/home/wangbei/anaconda3/envs/Object_mmdetection/lib/python3.8/site-packages/pycocotools-2.0-py3.8-linux-x86_64.egg/pycocotools/cocoeval.py", line 378, in accumulate tp_sum = np.cumsum(tps, axis=1).astype(dtype=np.float) File "/home/wangbei/anaconda3/envs/Object_mmdetection/lib/python3.8/site-packages/numpy/__init__.py", line 305, in __getattr__ raise AttributeError(__former_attrs__[attr]) AttributeError: module 'numpy' has no attribute 'float'. `np.float` was a deprecated alias for the builtin `float`. To avoid this error in existing code, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here. The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 30235 closing signal SIGTERM ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 0 (pid: 30234) of binary: /home/wangbei/anaconda3/envs/Object_mmdetection/bin/python

这是一个错误日志，看起来是在运行目标检测模型时出现的。错误信息显示在使用numpy库时出现了问题，建议检查代码中是否有使用到numpy库的地方，尤其是是否有使用了np.float这个别名，如果有的话，应该使用float代替。此外，还建议检查代码中是否有使用到torch.distributed.elastic.multiprocessing.api，如果有，也需要检查相关的代码是否有问题。最后，可以尝试搜索相关错误信息，看看是否有其他开发者遇到过类似的问题并给出了解决方案。