為 pytorch 安裝預訓練模型時出錯

Question

我正在使用 Windows 10 機器（是的，我知道，別笑，）。 並使用 python 3,7: 我正在嘗試在此處安裝預訓練模型：

https://github.com/meliketoy/fine-tuning.pytorch

該網站建議的命令是：

$ git clone https://github.com/Cadene/pretrained-models.pytorch.git
$ pretrained-models.pytorch
$ python setup.py install

雖然網站上說這是針對 Python 3.5，而我有 3.7，但我認為 3.7 版本應該是向后兼容的，對吧？

我成功運行了git clone ，而pretrained-models.pytorch實際上是一個cd命令（這讓我陷入了一個循環。）。 然后我遇到了python setup.py install的麻煩

我得到的錯誤是：

[Errno 2] No such file or directory: 'build\\bdist.win-amd64\\egg\\pretrainedmodels\\models\\resnext_features\\__pycache__\\resnext101_32x4d_features.cpython-37.pyc.1702181039952'

我該如何解決這個錯誤？

編輯（回應評論）：有人要求完整的追溯。 這里是！

(base) G:\>python setup.py install
running install
running bdist_egg
running egg_info
creating pretrainedmodels.egg-info
writing pretrainedmodels.egg-info\PKG-INFO
writing dependency_links to pretrainedmodels.egg-info\dependency_links.txt
writing requirements to pretrainedmodels.egg-info\requires.txt
writing top-level names to pretrainedmodels.egg-info\top_level.txt
writing manifest file 'pretrainedmodels.egg-info\SOURCES.txt'
reading manifest file 'pretrainedmodels.egg-info\SOURCES.txt'
writing manifest file 'pretrainedmodels.egg-info\SOURCES.txt'
installing library code to build\bdist.win-amd64\egg
running install_lib
running build_py
creating build
creating build\lib
creating build\lib\pretrainedmodels
copying pretrainedmodels\utils.py -> build\lib\pretrainedmodels
copying pretrainedmodels\version.py -> build\lib\pretrainedmodels
copying pretrainedmodels\__init__.py -> build\lib\pretrainedmodels
creating build\lib\pretrainedmodels\datasets
copying pretrainedmodels\datasets\utils.py -> build\lib\pretrainedmodels\datasets
copying pretrainedmodels\datasets\voc.py -> build\lib\pretrainedmodels\datasets
copying pretrainedmodels\datasets\__init__.py -> build\lib\pretrainedmodels\datasets
creating build\lib\pretrainedmodels\models
copying pretrainedmodels\models\bninception.py -> build\lib\pretrainedmodels\models
copying pretrainedmodels\models\cafferesnet.py -> build\lib\pretrainedmodels\models
copying pretrainedmodels\models\dpn.py -> build\lib\pretrainedmodels\models
copying pretrainedmodels\models\fbresnet.py -> build\lib\pretrainedmodels\models
copying pretrainedmodels\models\inceptionresnetv2.py -> build\lib\pretrainedmodels\models
copying pretrainedmodels\models\inceptionv4.py -> build\lib\pretrainedmodels\models
copying pretrainedmodels\models\nasnet.py -> build\lib\pretrainedmodels\models
copying pretrainedmodels\models\nasnet_mobile.py -> build\lib\pretrainedmodels\models
copying pretrainedmodels\models\pnasnet.py -> build\lib\pretrainedmodels\models
copying pretrainedmodels\models\polynet.py -> build\lib\pretrainedmodels\models
copying pretrainedmodels\models\resnext.py -> build\lib\pretrainedmodels\models
copying pretrainedmodels\models\senet.py -> build\lib\pretrainedmodels\models
copying pretrainedmodels\models\torchvision_models.py -> build\lib\pretrainedmodels\models
copying pretrainedmodels\models\utils.py -> build\lib\pretrainedmodels\models
copying pretrainedmodels\models\vggm.py -> build\lib\pretrainedmodels\models
copying pretrainedmodels\models\wideresnet.py -> build\lib\pretrainedmodels\models
copying pretrainedmodels\models\xception.py -> build\lib\pretrainedmodels\models
copying pretrainedmodels\models\__init__.py -> build\lib\pretrainedmodels\models
creating build\lib\pretrainedmodels\models\resnext_features
copying pretrainedmodels\models\resnext_features\resnext101_32x4d_features.py -> build\lib\pretrainedmodels\models\resnext_features
copying pretrainedmodels\models\resnext_features\resnext101_64x4d_features.py -> build\lib\pretrainedmodels\models\resnext_features
copying pretrainedmodels\models\resnext_features\__init__.py -> build\lib\pretrainedmodels\models\resnext_features
creating build\bdist.win-amd64
creating build\bdist.win-amd64\egg
creating build\bdist.win-amd64\egg\pretrainedmodels
creating build\bdist.win-amd64\egg\pretrainedmodels\datasets
copying build\lib\pretrainedmodels\datasets\utils.py -> build\bdist.win-amd64\egg\pretrainedmodels\datasets
copying build\lib\pretrainedmodels\datasets\voc.py -> build\bdist.win-amd64\egg\pretrainedmodels\datasets
copying build\lib\pretrainedmodels\datasets\__init__.py -> build\bdist.win-amd64\egg\pretrainedmodels\datasets
creating build\bdist.win-amd64\egg\pretrainedmodels\models
copying build\lib\pretrainedmodels\models\bninception.py -> build\bdist.win-amd64\egg\pretrainedmodels\models
copying build\lib\pretrainedmodels\models\cafferesnet.py -> build\bdist.win-amd64\egg\pretrainedmodels\models
copying build\lib\pretrainedmodels\models\dpn.py -> build\bdist.win-amd64\egg\pretrainedmodels\models
copying build\lib\pretrainedmodels\models\fbresnet.py -> build\bdist.win-amd64\egg\pretrainedmodels\models
copying build\lib\pretrainedmodels\models\inceptionresnetv2.py -> build\bdist.win-amd64\egg\pretrainedmodels\models
copying build\lib\pretrainedmodels\models\inceptionv4.py -> build\bdist.win-amd64\egg\pretrainedmodels\models
copying build\lib\pretrainedmodels\models\nasnet.py -> build\bdist.win-amd64\egg\pretrainedmodels\models
copying build\lib\pretrainedmodels\models\nasnet_mobile.py -> build\bdist.win-amd64\egg\pretrainedmodels\models
copying build\lib\pretrainedmodels\models\pnasnet.py -> build\bdist.win-amd64\egg\pretrainedmodels\models
copying build\lib\pretrainedmodels\models\polynet.py -> build\bdist.win-amd64\egg\pretrainedmodels\models
copying build\lib\pretrainedmodels\models\resnext.py -> build\bdist.win-amd64\egg\pretrainedmodels\models
creating build\bdist.win-amd64\egg\pretrainedmodels\models\resnext_features
copying build\lib\pretrainedmodels\models\resnext_features\resnext101_32x4d_features.py -> build\bdist.win-amd64\egg\pretrainedmodels\models\resnext_features
copying build\lib\pretrainedmodels\models\resnext_features\resnext101_64x4d_features.py -> build\bdist.win-amd64\egg\pretrainedmodels\models\resnext_features
copying build\lib\pretrainedmodels\models\resnext_features\__init__.py -> build\bdist.win-amd64\egg\pretrainedmodels\models\resnext_features
copying build\lib\pretrainedmodels\models\senet.py -> build\bdist.win-amd64\egg\pretrainedmodels\models
copying build\lib\pretrainedmodels\models\torchvision_models.py -> build\bdist.win-amd64\egg\pretrainedmodels\models
copying build\lib\pretrainedmodels\models\utils.py -> build\bdist.win-amd64\egg\pretrainedmodels\models
copying build\lib\pretrainedmodels\models\vggm.py -> build\bdist.win-amd64\egg\pretrainedmodels\models
copying build\lib\pretrainedmodels\models\wideresnet.py -> build\bdist.win-amd64\egg\pretrainedmodels\models
copying build\lib\pretrainedmodels\models\xception.py -> build\bdist.win-amd64\egg\pretrainedmodels\models
copying build\lib\pretrainedmodels\models\__init__.py -> build\bdist.win-amd64\egg\pretrainedmodels\models
copying build\lib\pretrainedmodels\utils.py -> build\bdist.win-amd64\egg\pretrainedmodels
copying build\lib\pretrainedmodels\version.py -> build\bdist.win-amd64\egg\pretrainedmodels
copying build\lib\pretrainedmodels\__init__.py -> build\bdist.win-amd64\egg\pretrainedmodels
byte-compiling build\bdist.win-amd64\egg\pretrainedmodels\datasets\utils.py to utils.cpython-37.pyc
byte-compiling build\bdist.win-amd64\egg\pretrainedmodels\datasets\voc.py to voc.cpython-37.pyc
byte-compiling build\bdist.win-amd64\egg\pretrainedmodels\datasets\__init__.py to __init__.cpython-37.pyc
byte-compiling build\bdist.win-amd64\egg\pretrainedmodels\models\bninception.py to bninception.cpython-37.pyc
byte-compiling build\bdist.win-amd64\egg\pretrainedmodels\models\cafferesnet.py to cafferesnet.cpython-37.pyc
byte-compiling build\bdist.win-amd64\egg\pretrainedmodels\models\dpn.py to dpn.cpython-37.pyc
byte-compiling build\bdist.win-amd64\egg\pretrainedmodels\models\fbresnet.py to fbresnet.cpython-37.pyc
byte-compiling build\bdist.win-amd64\egg\pretrainedmodels\models\inceptionresnetv2.py to inceptionresnetv2.cpython-37.pyc
byte-compiling build\bdist.win-amd64\egg\pretrainedmodels\models\inceptionv4.py to inceptionv4.cpython-37.pyc
byte-compiling build\bdist.win-amd64\egg\pretrainedmodels\models\nasnet.py to nasnet.cpython-37.pyc
byte-compiling build\bdist.win-amd64\egg\pretrainedmodels\models\nasnet_mobile.py to nasnet_mobile.cpython-37.pyc
byte-compiling build\bdist.win-amd64\egg\pretrainedmodels\models\pnasnet.py to pnasnet.cpython-37.pyc
byte-compiling build\bdist.win-amd64\egg\pretrainedmodels\models\polynet.py to polynet.cpython-37.pyc
byte-compiling build\bdist.win-amd64\egg\pretrainedmodels\models\resnext.py to resnext.cpython-37.pyc
byte-compiling build\bdist.win-amd64\egg\pretrainedmodels\models\resnext_features\resnext101_32x4d_features.py to resnext101_32x4d_features.cpython-37.pyc
error: [Errno 2] No such file or directory: 'build\\bdist.win-amd64\\egg\\pretrainedmodels\\models\\resnext_features\\__pycache__\\resnext101_32x4d_features.cpython-37.pyc.1702181039952'

Answer 1

一種選擇是使用 docker 圖像，我經常使用的是來自datascience-notebook圖像。

為了這：

• 1. 為 Windows 安裝 docker 桌面，參考此鏈接。
• 2. 在 Docker Destop Settings 中啟用文件共享

正如您從C:users\amtre中看到的那樣，我可以將任何子目錄掛載到容器中，例如Documents文件夾中的所有子目錄。

• 3. 一旦 docker 可以訪問容器的掛載目錄，我們將使用 jupyter datascience-notebook ，因為它已經默認附帶了一些軟件包。 在終端上輸入

docker run -it -e GRANT_SUDO=yes --user root --rm -p 8888:8888 -p 4040:4040 -v C:/users/amtre/Documents:/home/jovyan/work jupyter/datascience-notebook

拉動 docker 映像需要一段時間，但最后您將獲得 URL 來訪問筆記本，如上圖所示。

4. 在 Jupyter 中，打開一個終端並輸入

git clone https://github.com/Cadene/pretrained-models.pytorch.git
cd pretrained-models.pytorch
python setup.py install

這也將安裝'torch', 'torchvision', 'munch', 'tqdm' ，因為它在setup.py的install_requires中。 安裝完成后，您應該可以開始使用預訓練模型了