D:\Anaconda3\lib\site-packages\ruamel_yaml\constructor.py:283: DuplicateKeyFutureWarning: while constructing a mapping in "C:\Users\25436\.condarc", line 1, column 1 found duplicate key "channels" with value "['https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/', 'https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/', 'defaults']" (original value: "['https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/', 'defaults']") in "C:\Users\25436\.condarc", line 7, column 1 To suppress this check see: http://yaml.readthedocs.io/en/latest/api.html#duplicate-keys Duplicate keys will become an error in future releases, and are errors by default when using the new API.
时间: 2024-04-23 20:24:41 浏览: 301
这个问题看起来是在解析 YAML 文件时出现了重复的键值。根据报错信息,您的"C:\Users\25436\.condarc"文件中第一行第一个键"channels"的值已经被定义为"['https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/', 'defaults']",但是在第七行第一个键"channels"的值重复定义为"['https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/', 'https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/', 'defaults']",这个值包含了第一个键值。
为了解决这个问题,您可以尝试删除第七行的"channels"键值对,或者将第七行的"channels"键值对的值修改为不包含第一个键值的内容。另外,为了避免未来的重复键问题,您可以查看链接 http://yaml.readthedocs.io/en/latest/api.html#duplicate-keys,了解如何在新的 API 中禁止重复键检查。
相关问题
(GCC) PS G:\python\github\GCC-master> python end2end.py --config_env configs/env.yml --config_exp configs/end2end/end2end_cifar10.yml Traceback (most recent call last): File "end2end.py", line 181, in <module> main() File "end2end.py", line 37, in main p = create_config(args.config_env, args.config_exp) File "G:\python\github\GCC-master\utils\config.py", line 16, in create_config config = yaml.safe_load(stream) File "G:\anaconda\envs\GCC\lib\site-packages\yaml\__init__.py", line 162, in safe_load return load(stream, SafeLoader) File "G:\anaconda\envs\GCC\lib\site-packages\yaml\__init__.py", line 112, in load loader = Loader(stream) File "G:\anaconda\envs\GCC\lib\site-packages\yaml\loader.py", line 34, in __init__ Reader.__init__(self, stream) File "G:\anaconda\envs\GCC\lib\site-packages\yaml\reader.py", line 85, in __init__ self.determine_encoding() File "G:\anaconda\envs\GCC\lib\site-packages\yaml\reader.py", line 124, in determine_encoding self.update_raw() File "G:\anaconda\envs\GCC\lib\site-packages\yaml\reader.py", line 178, in update_raw data = self.stream.read(size) UnicodeDecodeError: 'gbk' codec can't decode byte 0xab in position 11: illegal multibyte sequence
这个错误是由于在读取配置文件时使用了错误的编码导致的。在你的代码中,使用了`gbk`编码来读取文件,但是文件中包含了非法的多字节序列,导致解码错误。
要解决这个问题,你可以尝试以下几种方法:
1. 使用正确的编码:确定配置文件的编码格式,然后在读取文件时指定正确的编码。例如,如果配置文件是使用UTF-8编码保存的,你可以将文件读取语句修改为:
```python
with open(file_path, 'r', encoding='utf-8') as file:
config = yaml.safe_load(file)
```
2. 修复配置文件:打开配置文件并尝试修复其中的非法字符。你可以使用文本编辑器打开文件并将非法字符替换为合法的字符。
3. 修改系统默认编码:如果以上方法无效,你可以尝试修改系统默认编码为UTF-8。这样,在读取文件时就会使用UTF-8编码,而不会再出现解码错误。请注意,修改系统默认编码可能会对其他应用程序产生影响,所以请谨慎操作。
请根据你的具体情况选择适合的解决方法。希望能帮到你!如果还有其他问题,请随时提问。
Traceback (most recent call last): File "D:/LPRNet_Pytorch-master/LPRNet_Pytorch-master/train_LPRNet.py", line 268, in <module> train() File "D:/LPRNet_Pytorch-master/LPRNet_Pytorch-master/train_LPRNet.py", line 107, in train lprnet.load_state_dict(torch.load(args.pretrained_model)) File "D:\Anaconda\lib\site-packages\torch\serialization.py", line 608, in load return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args) File "D:\Anaconda\lib\site-packages\torch\serialization.py", line 787, in _legacy_load result = unpickler.load() File "D:\Anaconda\lib\site-packages\torch\serialization.py", line 743, in persistent_load deserialized_objects[root_key] = restore_location(obj, location) File "D:\Anaconda\lib\site-packages\torch\serialization.py", line 175, in default_restore_location result = fn(storage, location) File "D:\Anaconda\lib\site-packages\torch\serialization.py", line 151, in _cuda_deserialize device = validate_cuda_device(location) File "D:\Anaconda\lib\site-packages\torch\serialization.py", line 135, in validate_cuda_device raise RuntimeError('Attempting to deserialize object on a CUDA ' RuntimeError: Attempting to deserialize object on a CUDA device but torch.cuda.is_available() is False. If you are running on a CPU-only machine, please use torch.load with map_location=torch.device('cpu') to map your storages to the CPU.
这个错误是因为你在尝试从一个CUDA设备上加载模型,但是当前的机器上没有GPU。你需要使用`torch.load`函数时传入`map_location=torch.device('cpu')`的参数来将模型加载到CPU上。 例如:
```python
lprnet.load_state_dict(torch.load(args.pretrained_model, map_location=torch.device('cpu')))
```
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