detach().cpu() 杀死内核
detach().cpu() kills kernel
背景
我正在尝试使用 pytorch 绘制图像噪声，但是，当我达到这一点时，内核就会死掉。我正在 Google Colab 尝试使用相同的代码，但我确实得到了结果
结果在 Google Colab
Jupyter 的结果
我不认为它与代码本身有关，但我发布了绘制网格的函数：
def Exec_ShowImgGrid(ObjTensor, NumCh=1, NumSizeData=(28,28), NumImgs=16):
#tensor: 128(pictures at the time ) * 784 (28*28)
Objdata= ObjTensor.detach().cpu().view(-1,NumCh,*NumSizeData) #128 *1 *28*28
Objgrid= make_grid(Objdata[:NumCh],nrow=4).permute(1,2,0) #1*28*28 = 28*28*1 #Mathplot library isnt the saame as pytorch, we are accomodating the args
Objpyplot.imshow(Objgrid)
Objpyplot.show()
我设置了一个 pdb，我注意到它没有 运行 Objdata 行，所以我假设它与 detach() 有关。cpu()
这些是所用环境中的库，我认为这可能是罪魁祸首
name: GPUBase
channels:
- pytorch
- defaults
dependencies:
- argon2-cffi=21.3.0=pyhd3eb1b0_0
- argon2-cffi-bindings=21.2.0=py39h2bbff1b_0
- async_generator=1.10=pyhd3eb1b0_0
- attrs=21.4.0=pyhd3eb1b0_0
- backcall=0.2.0=pyhd3eb1b0_0
- blas=1.0=mkl
- bleach=4.1.0=pyhd3eb1b0_0
- ca-certificates=2021.10.26=haa95532_4
- certifi=2021.10.8=py39haa95532_2
- cffi=1.15.0=py39h2bbff1b_1
- colorama=0.4.4=pyhd3eb1b0_0
- cpuonly=2.0=0
- cudatoolkit=11.3.1=h59b6b97_2
- debugpy=1.5.1=py39hd77b12b_0
- decorator=5.1.1=pyhd3eb1b0_0
- defusedxml=0.7.1=pyhd3eb1b0_0
- entrypoints=0.3=py39haa95532_0
- freetype=2.10.4=hd328e21_0
- importlib_metadata=4.8.2=hd3eb1b0_0
- intel-openmp=2021.4.0=haa95532_3556
- ipykernel=6.4.1=py39haa95532_1
- ipython=7.31.1=py39haa95532_0
- ipython_genutils=0.2.0=pyhd3eb1b0_1
- jedi=0.18.1=py39haa95532_1
- jinja2=3.0.2=pyhd3eb1b0_0
- jpeg=9d=h2bbff1b_0
- jsonschema=3.2.0=pyhd3eb1b0_2
- jupyter_client=7.1.2=pyhd3eb1b0_0
- jupyter_core=4.9.1=py39haa95532_0
- jupyterlab_pygments=0.1.2=py_0
- libpng=1.6.37=h2a8f88b_0
- libtiff=4.2.0=hd0e1b90_0
- libuv=1.40.0=he774522_0
- libwebp=1.2.0=h2bbff1b_0
- lz4-c=1.9.3=h2bbff1b_1
- markupsafe=2.0.1=py39h2bbff1b_0
- matplotlib-inline=0.1.2=pyhd3eb1b0_2
- mistune=0.8.4=py39h2bbff1b_1000
- mkl=2021.4.0=haa95532_640
- mkl-service=2.4.0=py39h2bbff1b_0
- mkl_fft=1.3.1=py39h277e83a_0
- mkl_random=1.2.2=py39hf11a4ad_0
- nbclient=0.5.3=pyhd3eb1b0_0
- nbconvert=6.1.0=py39haa95532_0
- nbformat=5.1.3=pyhd3eb1b0_0
- nest-asyncio=1.5.1=pyhd3eb1b0_0
- notebook=6.4.8=py39haa95532_0
- numpy-base=1.21.5=py39hc2deb75_0
- olefile=0.46=pyhd3eb1b0_0
- openssl=1.1.1m=h2bbff1b_0
- packaging=21.3=pyhd3eb1b0_0
- pandocfilters=1.5.0=pyhd3eb1b0_0
- parso=0.8.3=pyhd3eb1b0_0
- pickleshare=0.7.5=pyhd3eb1b0_1003
- pip=21.2.4=py39haa95532_0
- prometheus_client=0.13.1=pyhd3eb1b0_0
- prompt-toolkit=3.0.20=pyhd3eb1b0_0
- pycparser=2.21=pyhd3eb1b0_0
- pygments=2.11.2=pyhd3eb1b0_0
- pyrsistent=0.18.0=py39h196d8e1_0
- python=3.9.7=h6244533_1
- python-dateutil=2.8.2=pyhd3eb1b0_0
- pytorch-mutex=1.0=cpu
- pywin32=302=py39h827c3e9_1
- pywinpty=2.0.2=py39h5da7b33_0
- pyzmq=22.3.0=py39hd77b12b_2
- send2trash=1.8.0=pyhd3eb1b0_1
- setuptools=58.0.4=py39haa95532_0
- six=1.16.0=pyhd3eb1b0_1
- sqlite=3.37.2=h2bbff1b_0
- terminado=0.13.1=py39haa95532_0
- testpath=0.5.0=pyhd3eb1b0_0
- tk=8.6.11=h2bbff1b_0
- tornado=6.1=py39h2bbff1b_0
- traitlets=5.1.1=pyhd3eb1b0_0
- typing_extensions=3.10.0.2=pyh06a4308_0
- tzdata=2021e=hda174b7_0
- vc=14.2=h21ff451_1
- vs2015_runtime=14.27.29016=h5e58377_2
- wcwidth=0.2.5=pyhd3eb1b0_0
- wheel=0.37.1=pyhd3eb1b0_0
- wincertstore=0.2=py39haa95532_2
- winpty=0.4.3=4
- xz=5.2.5=h62dcd97_0
- zipp=3.7.0=pyhd3eb1b0_0
- zlib=1.2.11=h8cc25b3_4
- zstd=1.4.9=h19a0ad4_0
- pip:
- absl-py==1.0.0
- astunparse==1.6.3
- cachetools==5.0.0
- charset-normalizer==2.0.12
- cycler==0.11.0
- docutils==0.18.1
- flatbuffers==2.0
- fonttools==4.29.1
- gast==0.5.3
- google-auth==2.6.0
- google-auth-oauthlib==0.4.6
- google-pasta==0.2.0
- grpcio==1.44.0
- h5py==3.6.0
- htmlmin==0.1.12
- idna==3.3
- imagehash==4.2.1
- importlib-metadata==4.11.1
- ipywidgets==7.6.5
- joblib==1.0.1
- jupyterlab-widgets==1.0.2
- keras==2.8.0
- keras-preprocessing==1.1.2
- keyring==23.5.0
- kiwisolver==1.3.2
- libclang==13.0.0
- markdown==3.3.6
- matplot==0.1.9
- matplotlib==3.5.1
- missingno==0.5.0
- multimethod==1.7
- networkx==2.6.3
- numpy==1.22.2
- oauthlib==3.2.0
- opt-einsum==3.3.0
- pandas==1.4.1
- pandas-profiling==3.1.0
- phik==0.12.0
- pillow==9.0.1
- pkginfo==1.8.2
- protobuf==3.19.4
- pyasn1==0.4.8
- pyasn1-modules==0.2.8
- pydantic==1.9.0
- pyloco==0.0.139
- pyparsing==3.0.7
- pytz==2021.3
- pywavelets==1.2.0
- pywin32-ctypes==0.2.0
- pyyaml==6.0
- readme-renderer==32.0
- requests==2.27.1
- requests-oauthlib==1.3.1
- requests-toolbelt==0.9.1
- rfc3986==2.0.0
- rsa==4.8
- scikit-learn==1.0.2
- scipy==1.8.0
- seaborn==0.11.2
- simplewebsocketserver==0.1.1
- tangled-up-in-unicode==0.1.0
- tensorboard==2.8.0
- tensorboard-data-server==0.6.1
- tensorboard-plugin-wit==1.8.1
- tensorflow==2.8.0
- tensorflow-gpu==2.8.0
- tensorflow-io-gcs-filesystem==0.24.0
- termcolor==1.1.0
- tf-estimator-nightly==2.8.0.dev2021122109
- threadpoolctl==3.1.0
- torch==1.10.2
- torchaudio==0.7.2
- torchutils==0.0.4
- torchvision==0.8.2+cu110
- tqdm==4.62.3
- twine==3.8.0
- typing==3.7.4.3
- typing-extensions==4.1.1
- urllib3==1.26.8
- ushlex==0.99.1
- visions==0.7.4
- webencodings==0.5.1
- websocket-client==1.2.3
- werkzeug==2.0.3
- widgetsnbextension==3.5.2
- wrapt==1.13.3
- xlwings==0.26.3
问题
我怎样才能像 google colab 中那样为所述函数绘图？
几天后我找到了解决方案
首先，我的代码需要修复才能使用正确的名称正确调用所需的参数
def Exec_ShowImgGrid(ObjTensor, ch=1, size=(28,28), num=16):
#tensor: 128(pictures at the time ) * 784 (28*28)
Objdata= ObjTensor.detach().cpu().view(-1,ch,*size) #128 *1 *28*28
Objgrid= make_grid(Objdata[:num],nrow=4).permute(1,2,0) #1*28*28 = 28*28*1 #Mathplot library isnt the saame as pytorch, we are accomodating the args
Objpyplot.imshow(Objgrid)
Objpyplot.show()
内核还在死机，唯一的解决办法是在 状态的环境中安装 numba。我只需要在我的声明中附加 from numba import cuda 声明。
背景
我正在尝试使用 pytorch 绘制图像噪声，但是，当我达到这一点时，内核就会死掉。我正在 Google Colab 尝试使用相同的代码，但我确实得到了结果
结果在 Google Colab
Jupyter 的结果
我不认为它与代码本身有关，但我发布了绘制网格的函数：
def Exec_ShowImgGrid(ObjTensor, NumCh=1, NumSizeData=(28,28), NumImgs=16):
#tensor: 128(pictures at the time ) * 784 (28*28)
Objdata= ObjTensor.detach().cpu().view(-1,NumCh,*NumSizeData) #128 *1 *28*28
Objgrid= make_grid(Objdata[:NumCh],nrow=4).permute(1,2,0) #1*28*28 = 28*28*1 #Mathplot library isnt the saame as pytorch, we are accomodating the args
Objpyplot.imshow(Objgrid)
Objpyplot.show()
我设置了一个 pdb，我注意到它没有 运行 Objdata 行，所以我假设它与 detach() 有关。cpu()
这些是所用环境中的库，我认为这可能是罪魁祸首
name: GPUBase
channels:
- pytorch
- defaults
dependencies:
- argon2-cffi=21.3.0=pyhd3eb1b0_0
- argon2-cffi-bindings=21.2.0=py39h2bbff1b_0
- async_generator=1.10=pyhd3eb1b0_0
- attrs=21.4.0=pyhd3eb1b0_0
- backcall=0.2.0=pyhd3eb1b0_0
- blas=1.0=mkl
- bleach=4.1.0=pyhd3eb1b0_0
- ca-certificates=2021.10.26=haa95532_4
- certifi=2021.10.8=py39haa95532_2
- cffi=1.15.0=py39h2bbff1b_1
- colorama=0.4.4=pyhd3eb1b0_0
- cpuonly=2.0=0
- cudatoolkit=11.3.1=h59b6b97_2
- debugpy=1.5.1=py39hd77b12b_0
- decorator=5.1.1=pyhd3eb1b0_0
- defusedxml=0.7.1=pyhd3eb1b0_0
- entrypoints=0.3=py39haa95532_0
- freetype=2.10.4=hd328e21_0
- importlib_metadata=4.8.2=hd3eb1b0_0
- intel-openmp=2021.4.0=haa95532_3556
- ipykernel=6.4.1=py39haa95532_1
- ipython=7.31.1=py39haa95532_0
- ipython_genutils=0.2.0=pyhd3eb1b0_1
- jedi=0.18.1=py39haa95532_1
- jinja2=3.0.2=pyhd3eb1b0_0
- jpeg=9d=h2bbff1b_0
- jsonschema=3.2.0=pyhd3eb1b0_2
- jupyter_client=7.1.2=pyhd3eb1b0_0
- jupyter_core=4.9.1=py39haa95532_0
- jupyterlab_pygments=0.1.2=py_0
- libpng=1.6.37=h2a8f88b_0
- libtiff=4.2.0=hd0e1b90_0
- libuv=1.40.0=he774522_0
- libwebp=1.2.0=h2bbff1b_0
- lz4-c=1.9.3=h2bbff1b_1
- markupsafe=2.0.1=py39h2bbff1b_0
- matplotlib-inline=0.1.2=pyhd3eb1b0_2
- mistune=0.8.4=py39h2bbff1b_1000
- mkl=2021.4.0=haa95532_640
- mkl-service=2.4.0=py39h2bbff1b_0
- mkl_fft=1.3.1=py39h277e83a_0
- mkl_random=1.2.2=py39hf11a4ad_0
- nbclient=0.5.3=pyhd3eb1b0_0
- nbconvert=6.1.0=py39haa95532_0
- nbformat=5.1.3=pyhd3eb1b0_0
- nest-asyncio=1.5.1=pyhd3eb1b0_0
- notebook=6.4.8=py39haa95532_0
- numpy-base=1.21.5=py39hc2deb75_0
- olefile=0.46=pyhd3eb1b0_0
- openssl=1.1.1m=h2bbff1b_0
- packaging=21.3=pyhd3eb1b0_0
- pandocfilters=1.5.0=pyhd3eb1b0_0
- parso=0.8.3=pyhd3eb1b0_0
- pickleshare=0.7.5=pyhd3eb1b0_1003
- pip=21.2.4=py39haa95532_0
- prometheus_client=0.13.1=pyhd3eb1b0_0
- prompt-toolkit=3.0.20=pyhd3eb1b0_0
- pycparser=2.21=pyhd3eb1b0_0
- pygments=2.11.2=pyhd3eb1b0_0
- pyrsistent=0.18.0=py39h196d8e1_0
- python=3.9.7=h6244533_1
- python-dateutil=2.8.2=pyhd3eb1b0_0
- pytorch-mutex=1.0=cpu
- pywin32=302=py39h827c3e9_1
- pywinpty=2.0.2=py39h5da7b33_0
- pyzmq=22.3.0=py39hd77b12b_2
- send2trash=1.8.0=pyhd3eb1b0_1
- setuptools=58.0.4=py39haa95532_0
- six=1.16.0=pyhd3eb1b0_1
- sqlite=3.37.2=h2bbff1b_0
- terminado=0.13.1=py39haa95532_0
- testpath=0.5.0=pyhd3eb1b0_0
- tk=8.6.11=h2bbff1b_0
- tornado=6.1=py39h2bbff1b_0
- traitlets=5.1.1=pyhd3eb1b0_0
- typing_extensions=3.10.0.2=pyh06a4308_0
- tzdata=2021e=hda174b7_0
- vc=14.2=h21ff451_1
- vs2015_runtime=14.27.29016=h5e58377_2
- wcwidth=0.2.5=pyhd3eb1b0_0
- wheel=0.37.1=pyhd3eb1b0_0
- wincertstore=0.2=py39haa95532_2
- winpty=0.4.3=4
- xz=5.2.5=h62dcd97_0
- zipp=3.7.0=pyhd3eb1b0_0
- zlib=1.2.11=h8cc25b3_4
- zstd=1.4.9=h19a0ad4_0
- pip:
- absl-py==1.0.0
- astunparse==1.6.3
- cachetools==5.0.0
- charset-normalizer==2.0.12
- cycler==0.11.0
- docutils==0.18.1
- flatbuffers==2.0
- fonttools==4.29.1
- gast==0.5.3
- google-auth==2.6.0
- google-auth-oauthlib==0.4.6
- google-pasta==0.2.0
- grpcio==1.44.0
- h5py==3.6.0
- htmlmin==0.1.12
- idna==3.3
- imagehash==4.2.1
- importlib-metadata==4.11.1
- ipywidgets==7.6.5
- joblib==1.0.1
- jupyterlab-widgets==1.0.2
- keras==2.8.0
- keras-preprocessing==1.1.2
- keyring==23.5.0
- kiwisolver==1.3.2
- libclang==13.0.0
- markdown==3.3.6
- matplot==0.1.9
- matplotlib==3.5.1
- missingno==0.5.0
- multimethod==1.7
- networkx==2.6.3
- numpy==1.22.2
- oauthlib==3.2.0
- opt-einsum==3.3.0
- pandas==1.4.1
- pandas-profiling==3.1.0
- phik==0.12.0
- pillow==9.0.1
- pkginfo==1.8.2
- protobuf==3.19.4
- pyasn1==0.4.8
- pyasn1-modules==0.2.8
- pydantic==1.9.0
- pyloco==0.0.139
- pyparsing==3.0.7
- pytz==2021.3
- pywavelets==1.2.0
- pywin32-ctypes==0.2.0
- pyyaml==6.0
- readme-renderer==32.0
- requests==2.27.1
- requests-oauthlib==1.3.1
- requests-toolbelt==0.9.1
- rfc3986==2.0.0
- rsa==4.8
- scikit-learn==1.0.2
- scipy==1.8.0
- seaborn==0.11.2
- simplewebsocketserver==0.1.1
- tangled-up-in-unicode==0.1.0
- tensorboard==2.8.0
- tensorboard-data-server==0.6.1
- tensorboard-plugin-wit==1.8.1
- tensorflow==2.8.0
- tensorflow-gpu==2.8.0
- tensorflow-io-gcs-filesystem==0.24.0
- termcolor==1.1.0
- tf-estimator-nightly==2.8.0.dev2021122109
- threadpoolctl==3.1.0
- torch==1.10.2
- torchaudio==0.7.2
- torchutils==0.0.4
- torchvision==0.8.2+cu110
- tqdm==4.62.3
- twine==3.8.0
- typing==3.7.4.3
- typing-extensions==4.1.1
- urllib3==1.26.8
- ushlex==0.99.1
- visions==0.7.4
- webencodings==0.5.1
- websocket-client==1.2.3
- werkzeug==2.0.3
- widgetsnbextension==3.5.2
- wrapt==1.13.3
- xlwings==0.26.3
问题
我怎样才能像 google colab 中那样为所述函数绘图？
几天后我找到了解决方案
首先，我的代码需要修复才能使用正确的名称正确调用所需的参数
def Exec_ShowImgGrid(ObjTensor, ch=1, size=(28,28), num=16):
#tensor: 128(pictures at the time ) * 784 (28*28)
Objdata= ObjTensor.detach().cpu().view(-1,ch,*size) #128 *1 *28*28
Objgrid= make_grid(Objdata[:num],nrow=4).permute(1,2,0) #1*28*28 = 28*28*1 #Mathplot library isnt the saame as pytorch, we are accomodating the args
Objpyplot.imshow(Objgrid)
Objpyplot.show()
内核还在死机，唯一的解决办法是在 from numba import cuda 声明。