【Plan&Summary】2018.4.3 一周计划

本周学习计划回顾,包括提交代码到GitHub的过程、JavaScript学习进展、计算机网络与组成原理复习情况等,同时反思了时间管理和任务优先级设定的问题。

这次不是从周一开始,下周要记得不可以落周一。

4月3日

hint:下午的毛概记得坐在最外面,要不听不到。

//结果没坐最外面,也没听太清楚,背了一个lesson单词,做掉了计算机组成原理的作业,听了一题听力,但是至今还没有剪辑成短的,手机内存卡太小了qwq

上午:

1.研究怎么提交代码到github,已经拖了一个月了

2.在Chrome开始学习JavaScript,试着把书上的都实现一遍

3.找书

完成:

1.成功了,高估了提交难度了,原来git这么难QAQ

2.有空再做吧...反正这两天还没还书

中午:

1.单词

2.听力视频

(总之中午绝对不能吃高淀粉的东西了,尤其是什么粉之类的)

完成:

1.半个小时背完了

2.听力视频看了一半...后面找了半小时东西orz,还做了啥我真的不记得了

下午:

见上。

晚上:

1.图书馆完成了计算机网络&组成原理的复习

2.单词

3.JavaScript看了3章


回寝室:

1.今晚要至少做一题java

2.继续看JavaScript

3.课设搜一下

完成:1.做了一题java,但只得了20分

2.舍友非拉我打游戏...答应的不好拒绝,搞到十二点快,之后整个人状态回不来了。下次只能双休日打游戏。

3.问了一下别人。


4月4日

上午:

1.JavaScript换了本写的清楚的,看了一章。

2.找了田老师,找到地方了。

3.单词一个lesson

中午:

1.单词两个lesson复习

2.找同届同学,未完。

下午:

1.网络课下课后写总结

2.单词背掉

3.直接去寝室,今晚看尽量多的JavaScript


C:\Users\wangs>nvidia-smi Sun Nov 23 18:46:42 2025 +-----------------------------------------------------------------------------------------+ | NVIDIA-SMI 556.19 Driver Version: 556.19 CUDA Version: 12.5 |这是我的电脑,在anaconda中操作, (base) C:\Users\wangs>conda activate Resnet (Resnet) C:\Users\wangs>conda install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121 usage: conda-script.py [-h] [-v] [--no-plugins] [-V] COMMAND ... conda-script.py: error: unrecognized arguments: --index-url https://download.pytorch.org/whl/cu121 (Resnet) C:\Users\wangs>pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121 Looking in indexes: https://download.pytorch.org/whl/cu121 Collecting torch Using cached https://download.pytorch.org/whl/cu121/torch-2.5.1%2Bcu121-cp39-cp39-win_amd64.whl (2449.3 MB) Collecting torchvision Using cached https://download.pytorch.org/whl/cu121/torchvision-0.20.1%2Bcu121-cp39-cp39-win_amd64.whl (6.1 MB) Collecting torchaudio Using cached https://download.pytorch.org/whl/cu121/torchaudio-2.5.1%2Bcu121-cp39-cp39-win_amd64.whl (4.1 MB) Collecting filelock (from torch) Downloading https://download.pytorch.org/whl/filelock-3.19.1-py3-none-any.whl.metadata (2.1 kB) Requirement already satisfied: typing-extensions>=4.8.0 in c:\users\wangs\anaconda3\envs\resnet\lib\site-packages (from torch) (4.14.1) Collecting networkx (from torch) Downloading https://download.pytorch.org/whl/networkx-3.5-py3-none-any.whl.metadata (6.3 kB) Requirement already satisfied: jinja2 in c:\users\wangs\anaconda3\envs\resnet\lib\site-packages (from torch) (3.1.6) Collecting fsspec (from torch) Downloading https://download.pytorch.org/whl/fsspec-2025.9.0-py3-none-any.whl.metadata (10 kB) Collecting sympy==1.13.1 (from torch) Using cached https://download.pytorch.org/whl/sympy-1.13.1-py3-none-any.whl (6.2 MB) Collecting mpmath<1.4,>=1.1.0 (from sympy==1.13.1->torch) Downloading https://download.pytorch.org/whl/mpmath-1.3.0-py3-none-any.whl (536 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 536.2/536.2 kB 1.8 MB/s 0:00:00 Requirement already satisfied: numpy in c:\users\wangs\anaconda3\envs\resnet\lib\site-packages (from torchvision) (2.0.2) Collecting pillow!=8.3.*,>=5.3.0 (from torchvision) Downloading https://download.pytorch.org/whl/pillow-11.3.0-cp39-cp39-win_amd64.whl.metadata (9.2 kB) Requirement already satisfied: MarkupSafe>=2.0 in c:\users\wangs\anaconda3\envs\resnet\lib\site-packages (from jinja2->torch) (3.0.2) INFO: pip is looking at multiple versions of networkx to determine which version is compatible with other requirements. This could take a while. Collecting networkx (from torch) Downloading https://download.pytorch.org/whl/networkx-3.2.1-py3-none-any.whl.metadata (5.2 kB) Downloading https://download.pytorch.org/whl/pillow-11.3.0-cp39-cp39-win_amd64.whl (7.0 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 7.0/7.0 MB 2.9 MB/s 0:00:02 Downloading https://download.pytorch.org/whl/filelock-3.19.1-py3-none-any.whl (15 kB) Downloading https://download.pytorch.org/whl/fsspec-2025.9.0-py3-none-any.whl (199 kB) Downloading https://download.pytorch.org/whl/networkx-3.2.1-py3-none-any.whl (1.6 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.6/1.6 MB 2.3 MB/s 0:00:00 Installing collected packages: mpmath, sympy, pillow, networkx, fsspec, filelock, torch, torchvision, torchaudio Successfully installed filelock-3.19.1 fsspec-2025.9.0 mpmath-1.3.0 networkx-3.2.1 pillow-11.3.0 sympy-1.13.1 torch-2.5.1+cu121 torchaudio-2.5.1+cu121 torchvision-0.20.1+cu121 (Resnet) C:\Users\wangs>conda install monai Channels: - conda-forge - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/msys2 - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/r - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/pro - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free - defaults Platform: win-64 Collecting package metadata (repodata.json): done Solving environment: done ## Package Plan ## environment location: C:\Users\wangs\anaconda3\envs\Resnet added / updated specs: - monai The following packages will be downloaded: package | build ---------------------------|----------------- _openmp_mutex-4.5 | 2_gnu 48 KB conda-forge cuda-cudart-12.9.79 | he0c23c2_0 167 KB conda-forge cuda-cudart_win-64-12.9.79 | he0c23c2_0 23 KB conda-forge cuda-cupti-12.9.79 | hac47afa_1 3.8 MB conda-forge cuda-nvrtc-12.9.86 | hac47afa_1 55.8 MB conda-forge cuda-version-12.9 | h4f385c5_3 21 KB conda-forge cudnn-9.10.2.21 | h32ff316_0 19 KB conda-forge filelock-3.19.1 | pyhd8ed1ab_0 18 KB conda-forge fsspec-2025.7.0 | pyhd8ed1ab_0 142 KB conda-forge intel-openmp-2024.2.1 | h57928b3_1083 1.8 MB conda-forge libblas-3.9.0 | 35_h5709861_mkl 64 KB conda-forge libcblas-3.9.0 | 35_h2a3cdd5_mkl 65 KB conda-forge libcublas-12.9.1.4 | hac47afa_1 439.8 MB conda-forge libcudnn-9.10.2.21 | hca898b4_0 486.1 MB conda-forge libcudnn-dev-9.10.2.21 | hca898b4_0 153 KB conda-forge libcudss-0.6.0.5 | hca898b4_0 33.7 MB conda-forge libcufft-11.4.1.4 | hac47afa_1 154.7 MB conda-forge libcurand-10.3.10.19 | hac47afa_1 46.8 MB conda-forge libcusolver-11.7.5.82 | hac47afa_2 189.0 MB conda-forge libcusparse-12.5.10.65 | hac47afa_2 196.9 MB conda-forge liblapack-3.9.0 | 35_hf9ab0e9_mkl 77 KB conda-forge libmagma-2.9.0 | hb6a17ea_3 441.8 MB conda-forge libnvjitlink-12.9.86 | hac47afa_2 26.1 MB conda-forge libtorch-2.7.1 |cuda128_mkl_h2cc4d28_304 517.5 MB conda-forge libuv-1.51.0 | hfd05255_1 290 KB conda-forge llvm-openmp-20.1.8 | h29ce207_0 329 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/man mkl-2024.2.2 | h57928b3_16 98.3 MB conda-forge monai-1.5.1 | pyhd8ed1ab_0 1.2 MB conda-forge mpmath-1.3.0 | pyhd8ed1ab_1 429 KB conda-forge networkx-3.2.1 | pyhd8ed1ab_0 1.1 MB conda-forge optree-0.17.0 | py39h9da4e41_0 326 KB conda-forge pybind11-3.0.1 | pyh7a1b43c_0 227 KB conda-forge pybind11-global-3.0.1 | pyh5e4992e_0 222 KB conda-forge pytorch-2.7.1 |cuda128_mkl_py39_hf237e59_304 22.6 MB conda-forge sleef-3.9.0 | h67fd636_0 2.2 MB conda-forge sympy-1.14.0 | pyh04b8f61_5 4.4 MB conda-forge tbb-2021.13.0 | hd094cb3_4 146 KB conda-forge ------------------------------------------------------------ Total: 2.66 GB The following NEW packages will be INSTALLED: _openmp_mutex conda-forge/win-64::_openmp_mutex-4.5-2_gnu cuda-cudart conda-forge/win-64::cuda-cudart-12.9.79-he0c23c2_0 cuda-cudart_win-64 conda-forge/noarch::cuda-cudart_win-64-12.9.79-he0c23c2_0 cuda-cupti conda-forge/win-64::cuda-cupti-12.9.79-hac47afa_1 cuda-nvrtc conda-forge/win-64::cuda-nvrtc-12.9.86-hac47afa_1 cuda-version conda-forge/noarch::cuda-version-12.9-h4f385c5_3 cudnn conda-forge/win-64::cudnn-9.10.2.21-h32ff316_0 filelock conda-forge/noarch::filelock-3.19.1-pyhd8ed1ab_0 fsspec conda-forge/noarch::fsspec-2025.7.0-pyhd8ed1ab_0 intel-openmp conda-forge/win-64::intel-openmp-2024.2.1-h57928b3_1083 libabseil conda-forge/win-64::libabseil-20250512.1-cxx17_habfad5f_0 libcublas conda-forge/win-64::libcublas-12.9.1.4-hac47afa_1 libcudnn conda-forge/win-64::libcudnn-9.10.2.21-hca898b4_0 libcudnn-dev conda-forge/win-64::libcudnn-dev-9.10.2.21-hca898b4_0 libcudss conda-forge/win-64::libcudss-0.6.0.5-hca898b4_0 libcufft conda-forge/win-64::libcufft-11.4.1.4-hac47afa_1 libcurand conda-forge/win-64::libcurand-10.3.10.19-hac47afa_1 libcusolver conda-forge/win-64::libcusolver-11.7.5.82-hac47afa_2 libcusparse conda-forge/win-64::libcusparse-12.5.10.65-hac47afa_2 libgomp conda-forge/win-64::libgomp-15.2.0-h1383e82_7 libmagma conda-forge/win-64::libmagma-2.9.0-hb6a17ea_3 libnvjitlink conda-forge/win-64::libnvjitlink-12.9.86-hac47afa_2 libprotobuf conda-forge/win-64::libprotobuf-6.31.1-hdcda5b4_2 libtorch conda-forge/win-64::libtorch-2.7.1-cuda128_mkl_h2cc4d28_304 libuv conda-forge/win-64::libuv-1.51.0-hfd05255_1 monai conda-forge/noarch::monai-1.5.1-pyhd8ed1ab_0 mpmath conda-forge/noarch::mpmath-1.3.0-pyhd8ed1ab_1 networkx conda-forge/noarch::networkx-3.2.1-pyhd8ed1ab_0 optree conda-forge/win-64::optree-0.17.0-py39h9da4e41_0 pybind11 conda-forge/noarch::pybind11-3.0.1-pyh7a1b43c_0 pybind11-global conda-forge/noarch::pybind11-global-3.0.1-pyh5e4992e_0 pytorch conda-forge/win-64::pytorch-2.7.1-cuda128_mkl_py39_hf237e59_304 sleef conda-forge/win-64::sleef-3.9.0-h67fd636_0 sympy conda-forge/noarch::sympy-1.14.0-pyh04b8f61_5 The following packages will be SUPERSEDED by a higher-priority channel: llvm-openmp conda-forge::llvm-openmp-21.1.6-h4fa8~ --> anaconda/pkgs/main::llvm-openmp-20.1.8-h29ce207_0 The following packages will be DOWNGRADED: libblas 3.11.0-2_hf2e6a31_mkl --> 3.9.0-35_h5709861_mkl libcblas 3.11.0-2_h2a3cdd5_mkl --> 3.9.0-35_h2a3cdd5_mkl liblapack 3.11.0-2_hf9ab0e9_mkl --> 3.9.0-35_hf9ab0e9_mkl mkl 2025.3.0-hac47afa_454 --> 2024.2.2-h57928b3_16 tbb 2022.3.0-hd094cb3_1 --> 2021.13.0-hd094cb3_4 Proceed ([y]/n)? y done (Resnet) C:\Users\wangs>conda install pandas numpy scikit-learn -y Channels: - conda-forge - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/msys2 - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/r - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/pro done (Resnet) C:\Users\wangs>pip install tensorboard Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple Collecting tensorboard Using cached https://pypi.tuna.tsinghua.edu.cn/packages/9c/d9/a5db55f88f258ac669a92858b70a714bbbd5acd993820b41ec4a96a4d77f/tensorboard-2.20.0-py3-none-any.whl (5.5 MB) Collecting absl-py>=0.4 (from tensorboard) Using cached https://pypi.tuna.tsinghua.edu.cn/packages/8f/aa/ba0014cc4659328dc818a28827be78e6d97312ab0cb98105a770924dc11e/absl_py-2.3.1-py3-none-any.whl (135 kB) Collecting grpcio>=1.48.2 (from tensorboard) Using cached https://pypi.tuna.tsinghua.edu.cn/packages/de/d1/fb90564a981eedd3cd87dc6bfd7c249e8a515cfad1ed8e9af73be223cd3b/grpcio-1.76.0-cp39-cp39-win_amd64.whl (4.7 MB) Collecting markdown>=2.6.8 (from tensorboard) Using cached https://pypi.tuna.tsinghua.edu.cn/packages/70/ae/44c4a6a4cbb496d93c6257954260fe3a6e91b7bed2240e5dad2a717f5111/markdown-3.9-py3-none-any.whl (107 kB) Requirement already satisfied: numpy>=1.12.0 in c:\users\wangs\anaconda3\envs\resnet\lib\site-packages (from tensorboard) (2.0.2) Requirement already satisfied: packaging in c:\users\wangs\anaconda3\envs\resnet\lib\site-packages (from tensorboard) (25.0) Requirement already satisfied: pillow in c:\users\wangs\anaconda3\envs\resnet\lib\site-packages (from tensorboard) (11.3.0) Collecting protobuf!=4.24.0,>=3.19.6 (from tensorboard) Using cached https://pypi.tuna.tsinghua.edu.cn/packages/a2/0f/77b5a12825d59af2596634f062eb1a472f44494965a05dcd97cb5daf3ae5/protobuf-6.33.1-cp39-cp39-win_amd64.whl (436 kB) Requirement already satisfied: setuptools>=41.0.0 in c:\users\wangs\anaconda3\envs\resnet\lib\site-packages (from tensorboard) (80.9.0) Collecting tensorboard-data-server<0.8.0,>=0.7.0 (from tensorboard) Using cached https://pypi.tuna.tsinghua.edu.cn/packages/7a/13/e503968fefabd4c6b2650af21e110aa8466fe21432cd7c43a84577a89438/tensorboard_data_server-0.7.2-py3-none-any.whl (2.4 kB) Collecting werkzeug>=1.0.1 (from tensorboard) Using cached https://pypi.tuna.tsinghua.edu.cn/packages/52/24/ab44c871b0f07f491e5d2ad12c9bd7358e527510618cb1b803a88e986db1/werkzeug-3.1.3-py3-none-any.whl (224 kB) Requirement already satisfied: typing-extensions~=4.12 in c:\users\wangs\anaconda3\envs\resnet\lib\site-packages (from grpcio>=1.48.2->tensorboard) (4.14.1) Requirement already satisfied: importlib-metadata>=4.4 in c:\users\wangs\anaconda3\envs\resnet\lib\site-packages (from markdown>=2.6.8->tensorboard) (8.7.0) Requirement already satisfied: zipp>=3.20 in c:\users\wangs\anaconda3\envs\resnet\lib\site-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard) (3.23.0) Requirement already satisfied: MarkupSafe>=2.1.1 in c:\users\wangs\anaconda3\envs\resnet\lib\site-packages (from werkzeug>=1.0.1->tensorboard) (3.0.2) Installing collected packages: werkzeug, tensorboard-data-server, protobuf, grpcio, absl-py, markdown, tensorboard Successfully installed absl-py-2.3.1 grpcio-1.76.0 markdown-3.9 protobuf-6.33.1 tensorboard-2.20.0 tensorboard-data-server-0.7.2 werkzeug-3.1.3 (Resnet) C:\Users\wangs>pip install nibabel Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple Collecting nibabel Downloading https://pypi.tuna.tsinghua.edu.cn/packages/43/b2/dc384197be44e2a640bb43311850e23c2c30f3b82ce7c8cdabbf0e53045e/nibabel-5.3.2-py3-none-any.whl (3.3 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 3.3/3.3 MB 10.2 MB/s 0:00:00 Collecting importlib-resources>=5.12 (from nibabel) Downloading https://pypi.tuna.tsinghua.edu.cn/packages/a4/ed/1f1afb2e9e7f38a545d628f864d562a5ae64fe6f7a10e28ffb9b185b4e89/importlib_resources-6.5.2-py3-none-any.whl (37 kB) Requirement already satisfied: numpy>=1.22 in c:\users\wangs\anaconda3\envs\resnet\lib\site-packages (from nibabel) (2.0.2) Requirement already satisfied: packaging>=20 in c:\users\wangs\anaconda3\envs\resnet\lib\site-packages (from nibabel) (25.0) Requirement already satisfied: typing-extensions>=4.6 in c:\users\wangs\anaconda3\envs\resnet\lib\site-packages (from nibabel) (4.14.1) Requirement already satisfied: zipp>=3.1.0 in c:\users\wangs\anaconda3\envs\resnet\lib\site-packages (from importlib-resources>=5.12->nibabel) (3.23.0) Installing collected packages: importlib-resources, nibabel Successfully installed importlib-resources-6.5.2 nibabel-5.3.2 (Resnet) C:\Users\wangs>pip install chardet Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple Collecting chardet Using cached https://pypi.tuna.tsinghua.edu.cn/packages/38/6f/f5fbc992a329ee4e0f288c1fe0e2ad9485ed064cac731ed2fe47dcc38cbf/chardet-5.2.0-py3-none-any.whl (199 kB) Installing collected packages: chardet Successfully installed chardet-5.2.0 (Resnet) C:\Users\wangs>conda install tqdm -y Channels: done (Resnet) C:\Users\wangs>python -c "import torch; print(&#39;PyTorch:&#39;, torch.__version__); print(&#39;CUDA可用:&#39;, torch.cuda.is_available())" Traceback (most recent call last): File "<string>", line 1, in <module> File "C:\Users\wangs\anaconda3\envs\Resnet\lib\site-packages\torch\__init__.py", line 2126, in <module> from torch import _VF as _VF, functional as functional # usort: skip File "C:\Users\wangs\anaconda3\envs\Resnet\lib\site-packages\torch\functional.py", line 8, in <module> import torch.nn.functional as F File "C:\Users\wangs\anaconda3\envs\Resnet\lib\site-packages\torch\nn\__init__.py", line 8, in <module> from torch.nn.modules import * # usort: skip # noqa: F403 File "C:\Users\wangs\anaconda3\envs\Resnet\lib\site-packages\torch\nn\modules\__init__.py", line 1, in <module> from .module import Module # usort: skip File "C:\Users\wangs\anaconda3\envs\Resnet\lib\site-packages\torch\nn\modules\module.py", line 17, in <module> from torch.utils._python_dispatch import is_traceable_wrapper_subclass File "C:\Users\wangs\anaconda3\envs\Resnet\lib\site-packages\torch\utils\__init__.py", line 8, in <module> from torch.utils import ( File "C:\Users\wangs\anaconda3\envs\Resnet\lib\site-packages\torch\utils\data\__init__.py", line 1, in <module> from torch.utils.data.dataloader import ( File "C:\Users\wangs\anaconda3\envs\Resnet\lib\site-packages\torch\utils\data\dataloader.py", line 21, in <module> import torch.distributed as dist File "C:\Users\wangs\anaconda3\envs\Resnet\lib\site-packages\torch\distributed\__init__.py", line 122, in <module> from .device_mesh import DeviceMesh, init_device_mesh File "C:\Users\wangs\anaconda3\envs\Resnet\lib\site-packages\torch\distributed\device_mesh.py", line 40, in <module> from torch.distributed.distributed_c10d import ( File "C:\Users\wangs\anaconda3\envs\Resnet\lib\site-packages\torch\distributed\distributed_c10d.py", line 236, in <module> class Backend(str): # noqa: SLOT000 File "C:\Users\wangs\anaconda3\envs\Resnet\lib\site-packages\torch\distributed\distributed_c10d.py", line 287, in Backend XCCL: ProcessGroup.BackendType.XCCL, AttributeError: type object &#39;torch._C._distributed_c10d.BackendType&#39; has no attribute &#39;XCCL&#39; (Resnet) C:\Users\wangs>pip uninstall torch torchvision torchaudio -y Found existing installation: torch 2.5.1+cu121 Uninstalling torch-2.5.1+cu121: Successfully uninstalled torch-2.5.1+cu121 WARNING: Failed to remove contents in a temporary directory &#39;C:\Users\wangs\anaconda3\envs\Resnet\Lib\site-packages\torch\~ib&#39;. You can safely remove it manually. Found existing installation: torchvision 0.20.1+cu121 Uninstalling torchvision-0.20.1+cu121: Successfully uninstalled torchvision-0.20.1+cu121 Found existing installation: torchaudio 2.5.1+cu121 Uninstalling torchaudio-2.5.1+cu121: Successfully uninstalled torchaudio-2.5.1+cu121 (Resnet) C:\Users\wangs>conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia Channels: - pytorch - nvidia - conda-forge - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/msys2 - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/r - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/pro - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free - defaults Platform: win-64 Collecting package metadata (repodata.json): done Solving environment: done ## Package Plan ## environment location: C:\Users\wangs\anaconda3\envs\Resnet added / updated specs: - pytorch - pytorch-cuda=11.8 - torchaudio - torchvision The following packages will be downloaded: package | build ---------------------------|----------------- cuda-cccl-12.9.27 | 0 16 KB nvidia cuda-cccl_win-64-12.9.27 | 0 1.1 MB nvidia cuda-cudart-11.8.89 | 0 1.4 MB nvidia cuda-cudart-dev-11.8.89 | 0 723 KB nvidia cuda-cupti-11.8.87 | 0 11.5 MB nvidia cuda-libraries-11.8.0 | 0 1 KB nvidia cuda-libraries-dev-11.8.0 | 0 1 KB nvidia cuda-nvrtc-11.8.89 | 0 72.1 MB nvidia cuda-nvrtc-dev-11.8.89 | 0 16.1 MB nvidia cuda-nvtx-11.8.86 | 0 43 KB nvidia cuda-profiler-api-12.9.79 | 0 19 KB nvidia cuda-runtime-11.8.0 | 0 1 KB nvidia imath-3.1.12 | hbb528cf_0 157 KB conda-forge libabseil-20250127.1 | cxx17_h4eb7d71_0 1.8 MB conda-forge libcublas-11.11.3.6 | 0 33 KB nvidia libcublas-dev-11.11.3.6 | 0 375.9 MB nvidia libcufft-10.9.0.58 | 0 6 KB nvidia libcufft-dev-10.9.0.58 | 0 144.6 MB nvidia libcurand-dev-10.3.5.147 | 0 49.7 MB nvidia libcusolver-11.4.1.48 | 0 29 KB nvidia libcusolver-dev-11.4.1.48 | 0 94.1 MB nvidia libcusparse-11.7.5.86 | 0 13 KB nvidia libcusparse-dev-11.7.5.86 | 0 175.7 MB nvidia libnpp-11.8.0.86 | 0 294 KB nvidia libnpp-dev-11.8.0.86 | 0 143.2 MB nvidia libnvjpeg-11.9.0.86 | 0 4 KB nvidia libnvjpeg-dev-11.9.0.86 | 0 1.9 MB nvidia libopencv-4.12.0 |qt6_py39h45bf879_600 32.6 MB conda-forge libopenvino-2025.0.0 | hb1d9b14_3 3.3 MB conda-forge libopenvino-auto-batch-plugin-2025.0.0| h04f32e0_3 99 KB conda-forge libopenvino-auto-plugin-2025.0.0| h04f32e0_3 189 KB conda-forge libopenvino-hetero-plugin-2025.0.0| hb61b842_3 157 KB conda-forge libopenvino-intel-cpu-plugin-2025.0.0| hb1d9b14_3 8.3 MB conda-forge libopenvino-intel-gpu-plugin-2025.0.0| hb1d9b14_3 7.7 MB conda-forge libopenvino-ir-frontend-2025.0.0| hb61b842_3 156 KB conda-forge libopenvino-onnx-frontend-2025.0.0| hf9c6bd6_3 1013 KB conda-forge libopenvino-paddle-frontend-2025.0.0| hf9c6bd6_3 418 KB conda-forge libopenvino-pytorch-frontend-2025.0.0| he0c23c2_3 677 KB conda-forge libopenvino-tensorflow-frontend-2025.0.0| hd51e7bd_3 864 KB conda-forge libopenvino-tensorflow-lite-frontend-2025.0.0| he0c23c2_3 329 KB conda-forge libprotobuf-5.29.3 | hd33f5f0_2 6.7 MB conda-forge libtorch-2.5.1 |cpu_mkl_hf54a72f_117 31.6 MB conda-forge opencv-4.12.0 |qt6_py39hb5cec6c_600 27 KB conda-forge openexr-3.3.5 | h4750f91_0 1.0 MB conda-forge pthread-stubs-0.4 | h0e40799_1002 9 KB conda-forge py-opencv-4.12.0 |qt6_py39h54bbc76_600 1.1 MB conda-forge pytorch-2.5.1 |cpu_mkl_py39_hab059a2_117 20.8 MB conda-forge pytorch-cuda-11.8 | h24eeafa_6 7 KB pytorch pytorch-mutex-1.0 | cuda 3 KB pytorch setuptools-75.8.2 | pyhff2d567_0 760 KB conda-forge torchaudio-2.5.1 | py39_cu118 7.1 MB pytorch torchvision-0.20.1 | py39_cu118 7.7 MB pytorch ------------------------------------------------------------ Total: 1.19 GB The following NEW packages will be INSTALLED: cuda-cccl nvidia/win-64::cuda-cccl-12.9.27-0 cuda-cccl_win-64 nvidia/win-64::cuda-cccl_win-64-12.9.27-0 cuda-cudart-dev nvidia/win-64::cuda-cudart-dev-11.8.89-0 cuda-libraries nvidia/win-64::cuda-libraries-11.8.0-0 cuda-libraries-dev nvidia/win-64::cuda-libraries-dev-11.8.0-0 cuda-nvrtc-dev nvidia/win-64::cuda-nvrtc-dev-11.8.89-0 cuda-nvtx nvidia/win-64::cuda-nvtx-11.8.86-0 cuda-profiler-api nvidia/win-64::cuda-profiler-api-12.9.79-0 cuda-runtime nvidia/win-64::cuda-runtime-11.8.0-0 lcms2 conda-forge/win-64::lcms2-2.17-hbcf6048_0 libcublas-dev nvidia/win-64::libcublas-dev-11.11.3.6-0 libcufft-dev nvidia/win-64::libcufft-dev-10.9.0.58-0 libcurand-dev nvidia/win-64::libcurand-dev-10.3.5.147-0 libcusolver-dev nvidia/win-64::libcusolver-dev-11.4.1.48-0 libcusparse-dev nvidia/win-64::libcusparse-dev-11.7.5.86-0 libgcc conda-forge/win-64::libgcc-15.2.0-h1383e82_7 libnpp nvidia/win-64::libnpp-11.8.0.86-0 libnpp-dev nvidia/win-64::libnpp-dev-11.8.0.86-0 libnvjpeg nvidia/win-64::libnvjpeg-11.9.0.86-0 libnvjpeg-dev nvidia/win-64::libnvjpeg-dev-11.9.0.86-0 libwebp conda-forge/win-64::libwebp-1.6.0-h4d5522a_0 libxcb conda-forge/win-64::libxcb-1.17.0-h0e4246c_0 openjpeg conda-forge/win-64::openjpeg-2.5.4-h24db6dd_0 pillow conda-forge/win-64::pillow-11.3.0-py39hbad85af_0 pthread-stubs conda-forge/win-64::pthread-stubs-0.4-h0e40799_1002 pytorch-cuda pytorch/win-64::pytorch-cuda-11.8-h24eeafa_6 pytorch-mutex pytorch/noarch::pytorch-mutex-1.0-cuda torchaudio pytorch/win-64::torchaudio-2.5.1-py39_cu118 torchvision pytorch/win-64::torchvision-0.20.1-py39_cu118 xorg-libxau conda-forge/win-64::xorg-libxau-1.0.12-hba3369d_1 xorg-libxdmcp conda-forge/win-64::xorg-libxdmcp-1.1.5-hba3369d_1 The following packages will be REMOVED: libmagma-2.9.0-hb6a17ea_3 The following packages will be SUPERSEDED by a higher-priority channel: cuda-cudart conda-forge::cuda-cudart-12.9.79-he0c~ --> nvidia::cuda-cudart-11.8.89-0 cuda-cupti conda-forge::cuda-cupti-12.9.79-hac47~ --> nvidia::cuda-cupti-11.8.87-0 cuda-nvrtc conda-forge::cuda-nvrtc-12.9.86-hac47~ --> nvidia::cuda-nvrtc-11.8.89-0 libcublas conda-forge::libcublas-12.9.1.4-hac47~ --> nvidia::libcublas-11.11.3.6-0 libcufft conda-forge::libcufft-11.4.1.4-hac47a~ --> nvidia::libcufft-10.9.0.58-0 libcusolver conda-forge::libcusolver-11.7.5.82-ha~ --> nvidia::libcusolver-11.4.1.48-0 libcusparse conda-forge::libcusparse-12.5.10.65-h~ --> nvidia::libcusparse-11.7.5.86-0 The following packages will be DOWNGRADED: imath 3.2.1-h1608b31_0 --> 3.1.12-hbb528cf_0 libabseil 20250512.1-cxx17_habfad5f_0 --> 20250127.1-cxx17_h4eb7d71_0 libopencv 4.12.0-qt6_py39hee57e69_602 --> 4.12.0-qt6_py39h45bf879_600 libopenvino 2025.2.0-hbf28c98_1 --> 2025.0.0-hb1d9b14_3 libopenvino-auto-~ 2025.2.0-hdd9a157_1 --> 2025.0.0-h04f32e0_3 libopenvino-auto-~ 2025.2.0-hdd9a157_1 --> 2025.0.0-h04f32e0_3 libopenvino-heter~ 2025.2.0-hc39e7c6_1 --> 2025.0.0-hb61b842_3 libopenvino-intel~ 2025.2.0-hbf28c98_1 --> 2025.0.0-hb1d9b14_3 libopenvino-intel~ 2025.2.0-hbf28c98_1 --> 2025.0.0-hb1d9b14_3 libopenvino-ir-fr~ 2025.2.0-hc39e7c6_1 --> 2025.0.0-hb61b842_3 libopenvino-onnx-~ 2025.2.0-hee3bb10_1 --> 2025.0.0-hf9c6bd6_3 libopenvino-paddl~ 2025.2.0-hee3bb10_1 --> 2025.0.0-hf9c6bd6_3 libopenvino-pytor~ 2025.2.0-hac47afa_1 --> 2025.0.0-he0c23c2_3 libopenvino-tenso~ 2025.2.0-h293fe96_1 --> 2025.0.0-hd51e7bd_3 libopenvino-tenso~ 2025.2.0-hac47afa_1 --> 2025.0.0-he0c23c2_3 libprotobuf 6.31.1-hdcda5b4_2 --> 5.29.3-hd33f5f0_2 libtorch 2.7.1-cuda128_mkl_h2cc4d28_304 --> 2.5.1-cpu_mkl_hf54a72f_117 opencv 4.12.0-qt6_py39h5bb9280_602 --> 4.12.0-qt6_py39hb5cec6c_600 openexr 3.3.5-hed76565_1 --> 3.3.5-h4750f91_0 py-opencv 4.12.0-qt6_py39h58a0e84_602 --> 4.12.0-qt6_py39h54bbc76_600 pytorch 2.7.1-cuda128_mkl_py39_hf237e59_304 --> 2.5.1-cpu_mkl_py39_hab059a2_117 setuptools 80.9.0-pyhff2d567_0 --> 75.8.2-pyhff2d567_0 Proceed ([y]/n)? y done (Resnet) C:\Users\wangs>nvcc --version &#39;nvcc&#39; 不是内部或外部命令,也不是可运行的程序 或批处理文件。 (Resnet) C:\Users\wangs>conda remove pytorch torchvision torchaudio pytorch-cuda libtorch "libopenvino*" -y PackagesNotFoundError: The following packages are missing from the target environment: - libopenvino* (Resnet) C:\Users\wangs>conda clean --packages --tarballs -y Will remove 199 (4.00 GB) tarball(s). Will remove 167 (7.13 GB) package(s). WARNING: cannot remove, file permissions: C:\Users\wangs\anaconda3\pkgs\pytorch-2.7.1-cuda128_mkl_py39_hf237e59_304 (Resnet) C:\Users\wangs>conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia --override-channels Channels: - pytorch - nvidia Platform: win-64 Collecting package metadata (repodata.json): done Solving environment: done # All requested packages already installed. (Resnet) C:\Users\wangs>conda list | findstr /I "torch cuda nvidia" cuda-cccl 12.9.27 0 nvidia cuda-cccl_win-64 12.9.27 0 nvidia cuda-cudart 11.8.89 0 nvidia cuda-cudart-dev 11.8.89 0 nvidia cuda-cudart_win-64 12.9.79 he0c23c2_0 conda-forge cuda-cupti 11.8.87 0 nvidia cuda-libraries 11.8.0 0 nvidia cuda-libraries-dev 11.8.0 0 nvidia cuda-nvrtc 11.8.89 0 nvidia cuda-nvrtc-dev 11.8.89 0 nvidia cuda-nvtx 11.8.86 0 nvidia cuda-profiler-api 12.9.79 0 nvidia cuda-runtime 11.8.0 0 nvidia cuda-version 12.9 h4f385c5_3 conda-forge libcublas 11.11.3.6 0 nvidia libcublas-dev 11.11.3.6 0 nvidia libcufft 10.9.0.58 0 nvidia libcufft-dev 10.9.0.58 0 nvidia libcurand-dev 10.3.5.147 0 nvidia libcusolver 11.4.1.48 0 nvidia libcusolver-dev 11.4.1.48 0 nvidia libcusparse 11.7.5.86 0 nvidia libcusparse-dev 11.7.5.86 0 nvidia libnpp 11.8.0.86 0 nvidia libnpp-dev 11.8.0.86 0 nvidia libnvjpeg 11.9.0.86 0 nvidia libnvjpeg-dev 11.9.0.86 0 nvidia libopenvino-pytorch-frontend 2025.0.0 he0c23c2_3 conda-forge libtorch 2.5.1 cpu_mkl_hf54a72f_117 conda-forge pytorch 2.5.1 cpu_mkl_py39_hab059a2_117 conda-forge pytorch-cuda 11.8 h24eeafa_6 pytorch pytorch-mutex 1.0 cuda pytorch torchaudio 2.5.1 pypi_0 pypi torchvision 0.20.1 pypi_0 pypi (Resnet) C:\Users\wangs>conda remove pytorch torchvision torchaudio pytorch-cuda libtorch "libopenvino*" "cuda-cccl*" cuda-cccl cuda-cccl_win-64 cuda-cudart_win-64 cuda-profiler-api cuda-version -y PackagesNotFoundError: The following packages are missing from the target environment: - cuda-cccl* (Resnet) C:\Users\wangs>conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia --override-channels Channels: - pytorch - nvidia Platform: win-64 Collecting package metadata (repodata.json): done Solving environment: done # All requested packages already installed. (Resnet) C:\Users\wangs>conda list | findstr /I "torch cuda nvidia" cuda-cccl 12.9.27 0 nvidia cuda-cccl_win-64 12.9.27 0 nvidia cuda-cudart 11.8.89 0 nvidia cuda-cudart-dev 11.8.89 0 nvidia cuda-cudart_win-64 12.9.79 he0c23c2_0 conda-forge cuda-cupti 11.8.87 0 nvidia cuda-libraries 11.8.0 0 nvidia cuda-libraries-dev 11.8.0 0 nvidia cuda-nvrtc 11.8.89 0 nvidia cuda-nvrtc-dev 11.8.89 0 nvidia cuda-nvtx 11.8.86 0 nvidia cuda-profiler-api 12.9.79 0 nvidia cuda-runtime 11.8.0 0 nvidia cuda-version 12.9 h4f385c5_3 conda-forge libcublas 11.11.3.6 0 nvidia libcublas-dev 11.11.3.6 0 nvidia libcufft 10.9.0.58 0 nvidia libcufft-dev 10.9.0.58 0 nvidia libcurand-dev 10.3.5.147 0 nvidia libcusolver 11.4.1.48 0 nvidia libcusolver-dev 11.4.1.48 0 nvidia libcusparse 11.7.5.86 0 nvidia libcusparse-dev 11.7.5.86 0 nvidia libnpp 11.8.0.86 0 nvidia libnpp-dev 11.8.0.86 0 nvidia libnvjpeg 11.9.0.86 0 nvidia libnvjpeg-dev 11.9.0.86 0 nvidia libopenvino-pytorch-frontend 2025.0.0 he0c23c2_3 conda-forge libtorch 2.5.1 cpu_mkl_hf54a72f_117 conda-forge pytorch 2.5.1 cpu_mkl_py39_hab059a2_117 conda-forge pytorch-cuda 11.8 h24eeafa_6 pytorch pytorch-mutex 1.0 cuda pytorch torchaudio 2.5.1 pypi_0 pypi torchvision 0.20.1 pypi_0 pypi (Resnet) C:\Users\wangs>conda remove -y \ PackagesNotFoundError: The following packages are missing from the target environment: - \ (Resnet) C:\Users\wangs> "pytorch" "libtorch" "pytorch-cuda" "pytorch-mutex" \ &#39;"pytorch"&#39; 不是内部或外部命令,也不是可运行的程序 或批处理文件。 (Resnet) C:\Users\wangs> "libopenvino*" \ &#39;"libopenvino*"&#39; 不是内部或外部命令,也不是可运行的程序 或批处理文件。 (Resnet) C:\Users\wangs> "cuda-cccl*" "cuda-cudart_win-64" "cuda-profiler-api" "cuda-version" \ &#39;"cuda-cccl*"&#39; 不是内部或外部命令,也不是可运行的程序 或批处理文件。 (Resnet) C:\Users\wangs> conda remove -y \ PackagesNotFoundError: The following packages are missing from the target environment: - \ (Resnet) C:\Users\wangs> "pytorch" "libtorch" "pytorch-cuda" "pytorch-mutex" \ &#39;"pytorch"&#39; 不是内部或外部命令,也不是可运行的程序 或批处理文件。 (Resnet) C:\Users\wangs> "libopenvino*" \ &#39;"libopenvino*"&#39; 不是内部或外部命令,也不是可运行的程序 或批处理文件。 (Resnet) C:\Users\wangs> "cuda-cccl*" "cuda-cudart_win-64" "cuda-profiler-api" "cuda-version" \ &#39;"cuda-cccl*"&#39; 不是内部或外部命令,也不是可运行的程序 或批处理文件。 (Resnet) C:\Users\wangs>conda remove -y pytorch libtorch pytorch-cuda pytorch-mutex libopenvino-pytorch-frontend libopenvino libopenvino-auto-batch libopenvino-dev libopenvino-hetero-plugin libopenvino-ir-frontend libopenvino-onnx-frontend libopenvino-paddle-frontend libopenvino-pytorch-frontend libopenvino-tensorflow-frontend libopenvino-tensorflow-lite-frontend cuda-cccl cuda-cccl_win-64 cuda-cudart_win-64 cuda-profiler-api cuda-version PackagesNotFoundError: The following packages are missing from the target environment: - libopenvino-dev - libopenvino-auto-batch (Resnet) C:\Users\wangs>conda install -y pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia --override-channels Channels: - pytorch - nvidia Platform: win-64 Collecting package metadata (repodata.json): done Solving environment: done # All requested packages already installed. (Resnet) C:\Users\wangs>conda list | findstr /I "torch cuda nvidia openvino" cuda-cccl 12.9.27 0 nvidia cuda-cccl_win-64 12.9.27 0 nvidia cuda-cudart 11.8.89 0 nvidia cuda-cudart-dev 11.8.89 0 nvidia cuda-cudart_win-64 12.9.79 he0c23c2_0 conda-forge cuda-cupti 11.8.87 0 nvidia cuda-libraries 11.8.0 0 nvidia cuda-libraries-dev 11.8.0 0 nvidia cuda-nvrtc 11.8.89 0 nvidia cuda-nvrtc-dev 11.8.89 0 nvidia cuda-nvtx 11.8.86 0 nvidia cuda-profiler-api 12.9.79 0 nvidia cuda-runtime 11.8.0 0 nvidia cuda-version 12.9 h4f385c5_3 conda-forge libcublas 11.11.3.6 0 nvidia libcublas-dev 11.11.3.6 0 nvidia libcufft 10.9.0.58 0 nvidia libcufft-dev 10.9.0.58 0 nvidia libcurand-dev 10.3.5.147 0 nvidia libcusolver 11.4.1.48 0 nvidia libcusolver-dev 11.4.1.48 0 nvidia libcusparse 11.7.5.86 0 nvidia libcusparse-dev 11.7.5.86 0 nvidia libnpp 11.8.0.86 0 nvidia libnpp-dev 11.8.0.86 0 nvidia libnvjpeg 11.9.0.86 0 nvidia libnvjpeg-dev 11.9.0.86 0 nvidia libopenvino 2025.0.0 hb1d9b14_3 conda-forge libopenvino-auto-batch-plugin 2025.0.0 h04f32e0_3 conda-forge libopenvino-auto-plugin 2025.0.0 h04f32e0_3 conda-forge libopenvino-hetero-plugin 2025.0.0 hb61b842_3 conda-forge libopenvino-intel-cpu-plugin 2025.0.0 hb1d9b14_3 conda-forge libopenvino-intel-gpu-plugin 2025.0.0 hb1d9b14_3 conda-forge libopenvino-ir-frontend 2025.0.0 hb61b842_3 conda-forge libopenvino-onnx-frontend 2025.0.0 hf9c6bd6_3 conda-forge libopenvino-paddle-frontend 2025.0.0 hf9c6bd6_3 conda-forge libopenvino-pytorch-frontend 2025.0.0 he0c23c2_3 conda-forge libopenvino-tensorflow-frontend 2025.0.0 hd51e7bd_3 conda-forge libopenvino-tensorflow-lite-frontend 2025.0.0 he0c23c2_3 conda-forge libtorch 2.5.1 cpu_mkl_hf54a72f_117 conda-forge pytorch 2.5.1 cpu_mkl_py39_hab059a2_117 conda-forge pytorch-cuda 11.8 h24eeafa_6 pytorch pytorch-mutex 1.0 cuda pytorch torchaudio 2.5.1 pypi_0 pypi torchvision 0.20.1 pypi_0 pypi (Resnet) C:\Users\wangs>conda list | findstr /I "torch cuda nvidia openvino" cuda-cccl 12.9.27 0 nvidia cuda-cccl_win-64 12.9.27 0 nvidia cuda-cudart 11.8.89 0 nvidia cuda-cudart-dev 11.8.89 0 nvidia cuda-cudart_win-64 12.9.79 he0c23c2_0 conda-forge cuda-cupti 11.8.87 0 nvidia cuda-libraries 11.8.0 0 nvidia cuda-libraries-dev 11.8.0 0 nvidia cuda-nvrtc 11.8.89 0 nvidia cuda-nvrtc-dev 11.8.89 0 nvidia cuda-nvtx 11.8.86 0 nvidia cuda-profiler-api 12.9.79 0 nvidia cuda-runtime 11.8.0 0 nvidia cuda-version 12.9 h4f385c5_3 conda-forge libcublas 11.11.3.6 0 nvidia libcublas-dev 11.11.3.6 0 nvidia libcufft 10.9.0.58 0 nvidia libcufft-dev 10.9.0.58 0 nvidia libcurand-dev 10.3.5.147 0 nvidia libcusolver 11.4.1.48 0 nvidia libcusolver-dev 11.4.1.48 0 nvidia libcusparse 11.7.5.86 0 nvidia libcusparse-dev 11.7.5.86 0 nvidia libnpp 11.8.0.86 0 nvidia libnpp-dev 11.8.0.86 0 nvidia libnvjpeg 11.9.0.86 0 nvidia libnvjpeg-dev 11.9.0.86 0 nvidia libopenvino 2025.0.0 hb1d9b14_3 conda-forge libopenvino-auto-batch-plugin 2025.0.0 h04f32e0_3 conda-forge libopenvino-auto-plugin 2025.0.0 h04f32e0_3 conda-forge libopenvino-hetero-plugin 2025.0.0 hb61b842_3 conda-forge libopenvino-intel-cpu-plugin 2025.0.0 hb1d9b14_3 conda-forge libopenvino-intel-gpu-plugin 2025.0.0 hb1d9b14_3 conda-forge libopenvino-ir-frontend 2025.0.0 hb61b842_3 conda-forge libopenvino-onnx-frontend 2025.0.0 hf9c6bd6_3 conda-forge libopenvino-paddle-frontend 2025.0.0 hf9c6bd6_3 conda-forge libopenvino-pytorch-frontend 2025.0.0 he0c23c2_3 conda-forge libopenvino-tensorflow-frontend 2025.0.0 hd51e7bd_3 conda-forge libopenvino-tensorflow-lite-frontend 2025.0.0 he0c23c2_3 conda-forge libtorch 2.5.1 cpu_mkl_hf54a72f_117 conda-forge pytorch 2.5.1 cpu_mkl_py39_hab059a2_117 conda-forge pytorch-cuda 11.8 h24eeafa_6 pytorch pytorch-mutex 1.0 cuda pytorch torchaudio 2.5.1 pypi_0 pypi torchvision 0.20.1 pypi_0 pypi (Resnet) C:\Users\wangs>where python C:\Users\wangs\anaconda3\envs\Resnet\python.exe C:\Users\wangs\AppData\Local\Programs\Python\Python313\python.exe C:\Users\wangs\AppData\Local\Microsoft\WindowsApps\python.exe (Resnet) C:\Users\wangs>where pip C:\Users\wangs\anaconda3\envs\Resnet\Scripts\pip.exe C:\Users\wangs\AppData\Local\Programs\Python\Python313\Scripts\pip.exe (Resnet) C:\Users\wangs>where torch 信息: 用提供的模式无法找到文件。 (Resnet) C:\Users\wangs>pip show torch Name: torch Version: 2.5.1 Summary: Tensors and Dynamic neural networks in Python with strong GPU acceleration Home-page: https://pytorch.org/ Author: PyTorch Team Author-email: packages@pytorch.org License: BSD-3-Clause Location: c:\users\wangs\anaconda3\envs\resnet\lib\site-packages Requires: filelock, fsspec, jinja2, networkx, sympy, typing-extensions Required-by: monai, torchaudio, torchvision (Resnet) C:\Users\wangs>python -m torch.utils.collect_env Traceback (most recent call last): File "C:\Users\wangs\anaconda3\envs\Resnet\lib\runpy.py", line 188, in _run_module_as_main mod_name, mod_spec, code = _get_module_details(mod_name, _Error) File "C:\Users\wangs\anaconda3\envs\Resnet\lib\runpy.py", line 111, in _get_module_details __import__(pkg_name) File "C:\Users\wangs\anaconda3\envs\Resnet\lib\site-packages\torch\__init__.py", line 367, in <module> from torch._C import * # noqa: F403 ImportError: DLL load failed while importing _C: 找不到指定的程序。
11-24
内容概要:本文是一篇关于使用RandLANet模型对SensatUrban数据集进行点云语义分割的实战教程,系统介绍了从环境搭建、数据准备、模型训练与测试到精度评估的完整流程。文章详细说明了在Ubuntu系统下配置TensorFlow 2.2、CUDA及cuDNN等深度学习环境的方法,并指导用户下载和预处理SensatUrban数据集。随后,逐步讲解RandLANet代码的获取与运行方式,包括训练、测试命令的执行与参数含义,以及如何监控训练过程中的关键指标。最后,教程涵盖测试结果分析、向官方平台提交结果、解读评估报告及可视化效果等内容,并针对常见问题提供解决方案。; 适合人群:具备一定深度学习基础,熟悉Python编程和深度学习框架,从事计算机视觉或三维点云相关研究的学生、研究人员及工程师;适合希望动手实践点云语义分割项目的初学者与进阶者。; 使用场景及目标:①掌握RandLANet网络结构及其在点云语义分割任务中的应用;②学会完整部署一个点云分割项目,包括数据处理、模型训练、测试与性能评估;③为参与相关竞赛或科研项目提供技术支撑。; 阅读建议:建议读者结合提供的代码链接和密码访问完整资料,在本地或云端环境中边操作边学习,重点关注数据格式要求与训练参数设置,遇到问题时参考“常见问题与解决技巧”部分及时排查。
内容概要:本文详细介绍了三相异步电机SVPWM-DTC(空间矢量脉宽调制-直接转矩控制)的Simulink仿真实现方法,结合DTC响应快与SVPWM谐波小的优点,构建高性能电机控制系统。文章系统阐述了控制原理,包括定子磁链观测、转矩与磁链误差滞环比较、扇区判断及电压矢量选择,并通过SVPWM技术生成固定频率PWM信号,提升系统稳态性能。同时提供了完整的Simulink建模流程,涵盖电机本体、磁链观测器、误差比较、矢量选择、SVPWM调制、逆变器驱动等模块的搭建与参数设置,给出了仿真调试要点与预期结果,如电流正弦性、转矩响应快、磁链轨迹趋圆等,并提出了模型优化与扩展方向,如改进观测器、自适应滞环、弱磁控制和转速闭环等。; 适合人群:电气工程、自动化及相关专业本科生、研究生,从事电机控制算法开发的工程师,具备一定MATLAB/Simulink和电机控制理论基础的技术人员。; 使用场景及目标:①掌握SVPWM-DTC控制策略的核心原理与实现方式;②在Simulink中独立完成三相异步电机高性能控制系统的建模与仿真;③通过仿真验证控制算法有效性,为实际工程应用提供设计依据。; 阅读建议:学习过程中应结合文中提供的电机参数和模块配置逐步搭建模型,重点关注磁链观测、矢量选择表和SVPWM调制的实现细节,仿真时注意滞环宽度与开关频率的调试,建议配合MATLAB官方工具箱文档进行参数校准与结果分析。
已经博主授权,源码转载自 https://pan.quark.cn/s/bf1e0d5b9490 本文重点阐述了Vue2.0多Tab切换组件的封装实践,详细说明了通过封装Tab切换组件达成多Tab切换功能,从而满足日常应用需求。 知识点1:Vue2.0多Tab切换组件的封装* 借助封装Tab切换组件,达成多Tab切换功能* 支持tab切换、tab定位、tab自动化仿React多Tab实现知识点2:TabItems组件的应用* 在index.vue文件中应用TabItems组件,借助name属性设定tab的标题* 通过:isContTab属性来设定tab的内容* 能够采用子组件作为tab的内容知识点3:TabItems组件的样式* 借助index.less文件来设定TabItems组件的样式* 设定tab的标题样式、背景色彩、边框样式等* 使用animation达成tab的切换动画知识点4:Vue2.0多Tab切换组件的构建* 借助运用Vue2.0框架,达成多Tab切换组件的封装* 使用Vue2.0的组件化理念,达成TabItems组件的封装* 通过运用Vue2.0的指令和绑定机制,达成tab的切换功能知识点5:Vue2.0多Tab切换组件的优势* 达成多Tab切换功能,满足日常应用需求* 支持tab切换、tab定位、tab自动化仿React多Tab实现* 能够满足多样的业务需求,具备良好的扩展性知识点6:Vue2.0多Tab切换组件的应用场景* 能够应用于多样的业务场景,例如:管理系统、电商平台、社交媒体等* 能够满足不同的业务需求,例如:多Tab切换、数据展示、交互式操作等* 能够与其它Vue2.0组件结合运用,达成复杂的业务逻辑Vue2.0多Tab切换组件的封装实例提供了...
代码下载地址: https://pan.quark.cn/s/41cd695ddf65 `htmldiff` 是一个以 Ruby 语言为基础构建的库,其主要功能是在 HTML 文档中展示文本之间的差异。 该库的一个显著特点在于它不仅能够识别出不同之处,还会借助 HTML 标签来呈现这些差异,从而让用户能够直观地观察到文本的变化情况。 这种特性使得 `htmldiff` 在版本控制、文档对比或任何需要展示文本变动场景的应用中显得尤为有用。 `htmldiff` 的核心作用是对比两个字符串,并生成一个 HTML 输出结果,这个结果会明确地指出哪些部分被添加、哪些部分被删除以及哪些部分被修改。 此外,通过运用 CSS,用户可以进一步调整差异展示的样式,使其与项目或网站的现有设计风格相协调。 在使用 `htmldiff` 之前,需要先完成该库的安装。 如果项目已经配置了 Ruby 环境和 Gemfile,可以在 Gemfile 文件中添加 `gem htmldiff` 语句,随后执行 `bundle install` 命令进行安装。 如果没有 Gemfile 文件,也可以直接采用 `gem install htmldiff` 命令来进行全局安装。 在编程实现时,可以通过调用 `Htmldiff.diff` 方法来对比两个字符串,并获取相应的 HTML 输出。 例如:```rubyrequire htmldiffstr1 = "这是一个示例文本。 "str2 = "这是一个示例文本,现在有更多内容。 "diff_html = Htmldiff.diff(str1, str2)puts diff_html```上述代码将会输出两个字符串之间的差异,其中新增的内容会被 `<ins>` 标签所包围,而...
源码地址: https://pan.quark.cn/s/4b03c5611266 依据所提供的资料,可以判定这份资料是关于《电子技术基础模拟部分》第五版教科书第七章节的习题解析,由湖南人文科技学院通信与控制工程系的田汉平教师提供。 尽管具体内容未予展示,但能够围绕模拟电子技术的基础理论、第七章节所涉及的核心概念以及潜在的习题种类等方面来展开相关知识点的阐述。 ### 模拟电子技术概述模拟电子技术是电子工程学科中的一个关键领域,主要探讨模拟信号的产生、转换、传输和处理等议题。 模拟信号是指时间与幅度上均呈现连续变化的电信号。 模拟电路的设计与剖析是模拟电子技术的核心,它涵盖了放大器、振荡器、滤波器等电路的设计原理及其应用。 ### 第七章核心知识点猜测#### 1. 放大电路分析与设计- **基本放大电路**:共射极、共基极和共集电极放大电路的特性及其应用场景。 - **多级放大电路**:掌握如何将多个放大电路串联,以提升增益或优化频率响应。 - **差分放大电路**:用于抑制共模信号,放大差模信号,是精密仪器和测量设备中的关键构成部分。 #### 2. 反馈电路与稳定性- **反馈的基本概念**:正反馈与负反馈的区分,以及它们在电路中的应用场景。 - **深度负反馈**:解析深度负反馈状态下的放大器性能改进,包括增益稳定性和带宽的拓宽。 - **振荡电路**:理解LC振荡器、RC振荡器的工作机制及应用领域。 #### 3. 功率放大器- **A类、B类、AB类功率放大器**:熟练掌握不同类型功率放大器的特性、效率及其适用环境。 - **热效应与保护措施**:讨论在功率放大器设计过程中需要关注的散热问题及相应的防护措施。 #### 4. 集成运算放大器的应用- **理想运放模型**:熟...
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