raise KeyError( KeyError: 'CustomNuScenesDataset is not in the dataset registry'这个报错怎么解决
时间: 2024-05-14 11:18:25 浏览: 20
这个报错通常是由于数据集注册表中没有包含你使用的数据集名称而导致的。你可以通过以下几个步骤解决这个问题:
1. 确认你的数据集名称是正确的,可能是大小写或拼写错误导致了这个问题。
2. 确认你的数据集已经被正确地注册到数据集注册表中,你可以通过以下代码检查:
```
import torchvision
# 查看数据集注册表中的所有数据集名称
print(torchvision.datasets.__all__)
```
如果你的数据集不在列表中,你需要将它注册到数据集注册表中。你可以通过在你的数据集类中添加以下代码来实现:
```
import torchvision.datasets as datasets
# 注册自定义数据集
datasets.CUSTOM_DATASET = CustomDataset
```
这里,`CustomDataset`是你自定义的数据集类名称。
注意:如果你使用的是 PyTorch 的早期版本,你可能需要使用 `torchvision.datasets.folder` 而不是 `torchvision.datasets` 来注册数据集。
3. 如果你的数据集已经被正确地注册到数据集注册表中,但是仍然遇到了这个问题,你可以尝试重新安装 PyTorch 和 torchvision 库。有时候这个问题可能会出现在库版本不兼容的情况下。
相关问题
KeyError: 'BSDSDataset is not in the dataset registry'
This error message suggests that the dataset you are trying to access or use in your code, namely BSDSDataset, is not registered in the dataset registry.
To fix this error, you may need to check the dataset registry and ensure that the BSDSDataset is properly registered. Alternatively, you may need to install or import the necessary packages or modules that are required for accessing or using the BSDSDataset.
If you are not familiar with the dataset registry or the required packages, you may need to consult the documentation or seek help from the community or your colleagues who are knowledgeable in this area.
KeyError: 'arr_1 is not a file in the archive'
This error is typically encountered when trying to access a file or data that is not present in the archive or dataset being used. The error message specifically suggests that the file "arr_1" is missing from the archive.
To resolve this issue, you can try checking the contents of the archive or dataset to ensure that the required file is included. You can also try re-downloading the dataset or archive to ensure that all necessary files are present.
If you are still encountering the error, it may be helpful to review the code or script you are using to access the data and ensure that it is correctly referencing the file or data you are trying to access. Additionally, you can consult documentation or seek assistance from other users or developers who have worked with the same dataset or archive.