AttributeError: Can't get attribute '_rebuild_parameter' on module 'torch._utils'

本文详细介绍了在使用PyTorch进行深度学习项目时遇到的torch.load()错误,并提供了具体的解决方案,包括如何卸载和重新安装与环境匹配的torch版本。
部署运行你感兴趣的模型镜像

调用 torch.load()会出现下面的错误

Can't get attribute '_rebuild_parameter' on <module 'torch._utils

可以查看是由于torch版本的不匹配造成的,如果是版本问题先卸载torch

pip uninstall torch

然后在https://www.lfd.uci.edu/~gohlke/pythonlibs/?%20?上下载与自己环境相匹配的版本

我的环境是python36版本,win64,使用的torch‑1.0.0‑cp36‑cp36m‑win_amd64.whl,下载torch‑1.0.0‑cp36‑cp36m‑win_amd64.whl,随意放到一个路径,例如放到d盘,则

pip install d:/torch‑1.0.0‑cp36‑cp36m‑win_amd64.whl

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PyTorch 是一个开源的 Python 机器学习库,基于 Torch 库,底层由 C++ 实现,应用于人工智能领域,如计算机视觉和自然语言处理

(yolov8) PS C:\Users\10556\Downloads> # 2. 验证安装结果 (yolov8) PS C:\Users\10556\Downloads> python -c "import torchvision; print(f'TorchVision版本: {torchvision.__version__}, CUDA状态: {torchvision._C._cuda_available}')" A module that was compiled using NumPy 1.x cannot be run in NumPy 2.0.2 as it may crash. To support both 1.x and 2.x versions of NumPy, modules must be compiled with NumPy 2.0. Some module may need to rebuild instead e.g. with 'pybind11>=2.12'. If you are a user of the module, the easiest solution will be to downgrade to 'numpy<2' or try to upgrade the affected module. We expect that some modules will need time to support NumPy 2. Traceback (most recent call last): File "<string>", line 1, in <module> File "C:\mini\envs\yolov8\lib\site-packages\torchvision\__init__.py", line 5, in <module> import torch File "C:\mini\envs\yolov8\lib\site-packages\torch\__init__.py", line 1471, in <module> from .functional import * # noqa: F403 File "C:\mini\envs\yolov8\lib\site-packages\torch\functional.py", line 9, in <module> import torch.nn.functional as F File "C:\mini\envs\yolov8\lib\site-packages\torch\nn\__init__.py", line 1, in <module> from .modules import * # noqa: F403 File "C:\mini\envs\yolov8\lib\site-packages\torch\nn\modules\__init__.py", line 35, in <module> from .transformer import TransformerEncoder, TransformerDecoder, \ File "C:\mini\envs\yolov8\lib\site-packages\torch\nn\modules\transformer.py", line 20, in <module> device: torch.device = torch.device(torch._C._get_default_device()), # torch.device('cpu'), C:\mini\envs\yolov8\lib\site-packages\torch\nn\modules\transformer.py:20: UserWarning: Failed to initialize NumPy: _ARRAY_API not found (Triggered internally at C:\actions-runner\_work\pytorch\pytorch\builder\windows\pytorch\torch\csrc\utils\tensor_numpy.cpp:84.) device: torch.device = torch.device(torch._C._get_default_device()), # torch.device('cpu'), Traceback (most recent call last): File "<string>", line 1, in <module> AttributeError: module 'torchvision' has no attribute '_C' (yolov8) PS C:\Users\10556\Downloads> (yolov8) PS C:\Users\10556\Downloads> # 3. 检查PyTorch整体环境 (yolov8) PS C:\Users\10556\Downloads> python -c "import torch; print(f'PyTorch版本: {torch.__version__}, CUDA可用: {torch.cuda.is_available()}')" A module that was compiled using NumPy 1.x cannot be run in NumPy 2.0.2 as it may crash. To support both 1.x and 2.x versions of NumPy, modules must be compiled with NumPy 2.0. Some module may need to rebuild instead e.g. with 'pybind11>=2.12'. If you are a user of the module, the easiest solution will be to downgrade to 'numpy<2' or try to upgrade the affected module. We expect that some modules will need time to support NumPy 2. Traceback (most recent call last): File "<string>", line 1, in <module> File "C:\mini\envs\yolov8\lib\site-packages\torch\__init__.py", line 1471, in <module> from .functional import * # noqa: F403 File "C:\mini\envs\yolov8\lib\site-packages\torch\functional.py", line 9, in <module> import torch.nn.functional as F File "C:\mini\envs\yolov8\lib\site-packages\torch\nn\__init__.py", line 1, in <module> from .modules import * # noqa: F403 File "C:\mini\envs\yolov8\lib\site-packages\torch\nn\modules\__init__.py", line 35, in <module> from .transformer import TransformerEncoder, TransformerDecoder, \ File "C:\mini\envs\yolov8\lib\site-packages\torch\nn\modules\transformer.py", line 20, in <module> device: torch.device = torch.device(torch._C._get_default_device()), # torch.device('cpu'), C:\mini\envs\yolov8\lib\site-packages\torch\nn\modules\transformer.py:20: UserWarning: Failed to initialize NumPy: _ARRAY_API not found (Triggered internally at C:\actions-runner\_work\pytorch\pytorch\builder\windows\pytorch\torch\csrc\utils\tensor_numpy.cpp:84.) device: torch.device = torch.device(torch._C._get_default_device()), # torch.device('cpu'), PyTorch版本: 2.2.0+cu118, CUDA可用: True
最新发布
09-10
>>> import torch A module that was compiled using NumPy 1.x cannot be run in NumPy 2.0.1 as it may crash. To support both 1.x and 2.x versions of NumPy, modules must be compiled with NumPy 2.0. Some module may need to rebuild instead e.g. with 'pybind11>=2.12'. If you are a user of the module, the easiest solution will be to downgrade to 'numpy<2' or try to upgrade the affected module. We expect that some modules will need time to support NumPy 2. Traceback (most recent call last): File "<stdin>", line 1, in <module> File "E:\Anaconda_backup\envs\pytorch2.2.2\lib\site-packages\torch\__init__.py", line 1477, in <module> from .functional import * # noqa: F403 File "E:\Anaconda_backup\envs\pytorch2.2.2\lib\site-packages\torch\functional.py", line 9, in <module> import torch.nn.functional as F File "E:\Anaconda_backup\envs\pytorch2.2.2\lib\site-packages\torch\nn\__init__.py", line 1, in <module> from .modules import * # noqa: F403 File "E:\Anaconda_backup\envs\pytorch2.2.2\lib\site-packages\torch\nn\modules\__init__.py", line 35, in <module> from .transformer import TransformerEncoder, TransformerDecoder, \ File "E:\Anaconda_backup\envs\pytorch2.2.2\lib\site-packages\torch\nn\modules\transformer.py", line 20, in <module> device: torch.device = torch.device(torch._C._get_default_device()), # torch.device('cpu'), E:\Anaconda_backup\envs\pytorch2.2.2\lib\site-packages\torch\nn\modules\transformer.py:20: UserWarning: Failed to initialize NumPy: _ARRAY_API not found (Triggered internally at C:\cb\pytorch_1000000000000\work\torch\csrc\utils\tensor_numpy.cpp:84.) device: torch.device = torch.device(torch._C._get_default_device()), # torch.device('cpu'), >>> torch.cuda.is_avaiable() Traceback (most recent call last): File "<stdin>", line 1, in <module> AttributeError: module 'torch.cuda' has no attribute 'is_avaiable'
07-19
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