File "/home/optobot-01/anaconda3/envs/cc/lib/python3.8/site-packages/torch/utils/data/sampler.py", line 107, in __init__ raise ValueError("num_samples should be a positive integer " ValueError: num_samples should be a positive integer value, but got num_samples=0
时间: 2023-10-04 11:07:25 浏览: 276
这个错误通常是由于数据集中的样本数量为0导致的。在使用PyTorch的DataLoader时,如果num_samples参数被设置为0,就会出现这个错误。请检查你的数据集是否为空或者是否正确加载了数据。你可以在代码中添加一些调试语句来检查数据集是否正确加载。另外,你也可以尝试在创建DataLoader对象时设置一个较小的batch_size,例如1,以便更好地调试。
相关问题
File "/Users/liudianhewen/Desktop/气象局实践/ganomaly-master/lib/data.py", line 354, in load_data dataloader = {x: torch.utils.data.DataLoader(dataset=dataset[x], File "/Users/liudianhewen/Desktop/气象局实践/ganomaly-master/lib/data.py", line 354, in <dictcomp> dataloader = {x: torch.utils.data.DataLoader(dataset=dataset[x], File "/opt/anaconda3/envs/python3812/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 277, in __init__ sampler = RandomSampler(dataset, generator=generator) # type: ignore[arg-type] File "/opt/anaconda3/envs/python3812/lib/python3.8/site-packages/torch/utils/data/sampler.py", line 96, in __init__ if not isinstance(self.num_samples, int) or self.num_samples <= 0: File "/opt/anaconda3/envs/python3812/lib/python3.8/site-packages/torch/utils/data/sampler.py", line 104, in num_samples return len(self.data_source) TypeError: object of type 'Dataset' has no len()
根据错误信息来看,代码中存在一个 `TypeError` 错,错误信息显示为 `object of type 'Dataset' has no len()`。该错误发生在试图获取数据集的长度时出现问题。
这个错误通常是由于数据集对象(`Dataset`)没有实现 `len()` 方法导致的。在使用 `torch.utils.data.DataLoader` 创建数据加载器时,需要传入一个数据集对象,并且数据集对象需要实现 `len()` 方法来返回数据集的长度。
解决这个问题的方法是确保你使用的数据集对象具有 `len()` 方法。如果你使用的是 PyTorch 提供的内置数据集类(如 `torchvision.datasets.ImageFolder`),那么通常不会遇到这个问题。但如果你自定义了一个数据集类,你需要确保在该类中实现了 `len()` 方法。
以下是一个自定义数据集类的示例,展示了如何实现 `len()` 方法:
```python
import torch
from torch.utils.data import Dataset
class MyDataset(Dataset):
def __init__(self, data):
self.data = data
def __getitem__(self, index):
return self.data[index]
def __len__(self):
return len(self.data)
```
在上面的示例中,我们创建了一个名为 `MyDataset` 的自定义数据集类。它接受一个数据列表作为参数,并在 `__getitem__` 方法中实现了获取单个样本的逻辑。同时,我们在 `__len__` 方法中返回了数据列表的长度。这样,我们就可以在使用 `torch.utils.data.DataLoader` 创建数据加载器时,传入 `MyDataset` 对象,而不会再出现 `TypeError` 错误。
06/06/2023-16:31:47] [TRT] [I] [MemUsageChange] TensorRT-managed allocation in IExecutionContext creation: CPU +0, GPU +0, now: CPU 0, GPU 0 (MiB) /home/sniper/anaconda3/envs/labelme/lib/python3.8/site-packages/tensorrt/__init__.py:166: FutureWarning: In the future `np.bool` will be defined as the corresponding NumPy scalar. bool: np.bool, Traceback (most recent call last): File "/home/sniper/anaconda3/envs/labelme/bin/yolo", line 8, in <module> sys.exit(entrypoint()) File "/home/sniper/anaconda3/envs/labelme/lib/python3.8/site-packages/ultralytics/yolo/cfg/__init__.py", line 398, in entrypoint getattr(model, mode)(**overrides) # default args from model File "/home/sniper/anaconda3/envs/labelme/lib/python3.8/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context return func(*args, **kwargs) File "/home/sniper/anaconda3/envs/labelme/lib/python3.8/site-packages/ultralytics/yolo/engine/model.py", line 302, in val validator(model=self.model) File "/home/sniper/anaconda3/envs/labelme/lib/python3.8/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context return func(*args, **kwargs) File "/home/sniper/anaconda3/envs/labelme/lib/python3.8/site-packages/ultralytics/yolo/engine/validator.py", line 114, in __call__ model = AutoBackend(model, device=self.device, dnn=self.args.dnn, data=self.args.data, fp16=self.args.half) File "/home/sniper/anaconda3/envs/labelme/lib/python3.8/site-packages/ultralytics/nn/autobackend.py", line 174, in __init__ dtype = trt.nptype(model.get_binding_dtype(i)) File "/home/sniper/anaconda3/envs/labelme/lib/python3.8/site-packages/tensorrt/__init__.py", line 166, in nptype bool: np.bool, File "/home/sniper/anaconda3/envs/labelme/lib/python3.8/site-packages/numpy/__init__.py", line 305, in __getattr__ raise AttributeError(__former_attrs__[attr]) AttributeError: module 'numpy' has no attribute 'bool'. `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` 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 如何修复
这个错误是由于使用了在NumPy 1.20中被废弃的`np.bool`别名所引起的。可以将`np.bool`替换为`bool`来避免这个错误。可以在代码中查找`np.bool`并将其替换为`bool`。如果你确实需要使用NumPy的布尔类型,可以使用`np.bool_`。你可以查看NumPy 1.20的发布说明以获取更多详细信息和指导。
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