我在Google Colab中预先训练了对象检测模型(Google Colab + TensorFlow),我每周运行两到三次,用于我的新图像,去年到本周一切都很好。现在,当我尝试运行模型时,我有这样的消息:
Graph execution error:
2 root error(s) found.
(0) UNIMPLEMENTED: DNN library is not found.
[[{{node functional_1/conv1_conv/Conv2D}}]]
[[StatefulPartitionedCall/SecondStagePostprocessor/BatchMultiClassNonMaxSuppression/MultiClassNonMaxSuppression/Reshape_5/_126]]
(1) UNIMPLEMENTED: DNN library is not found.
[[{{node functional_1/conv1_conv/Conv2D}}]]
0 successful operations.
0 derived errors ignored. [Op:__inference_restored_function_body_27380] ***
以前从没发生过。
在运行模型之前,我必须使用以下命令安装Tensor Flow对象检测API:
import os
os.chdir('/project/models/research')
!protoc object_detection/protos/*.proto --python_out=.
!cp object_detection/packages/tf2/setup.py .
!python -m pip install .
这是命令的输出:
Processing /content/gdrive/MyDrive/models/research
DEPRECATION: A future pip version will change local packages to be built in-place without first copying to a temporary directory. We recommend you use --use-feature=in-tree-build to test your packages with this new behavior before it becomes the default.
pip 21.3 will remove support for this functionality. You can find discussion regarding this at https://github.com/pypa/pip/issues/7555.
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ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
yellowbrick 1.3.post1 requires numpy<1.20,>=1.16.0, but you have numpy 1.20.3 which is incompatible.
multiprocess 0.70.12.2 requires dill>=0.3.4, but you have dill 0.3.1.1 which is incompatible.
google-colab 1.0.0 requires requests~=2.23.0, but you have requests 2.27.1 which is incompatible.
datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.
albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.
Successfully installed apache-beam-2.35.0 avro-python3-1.10.2 colorama-0.4.4 dill-0.3.1.1 fastavro-1.4.9 hdfs-2.6.0 keras-2.8.0 lvis-0.5.3 numpy-1.20.3 object-detection-0.1 opencv-python-headless-4.5.5.62 orjson-3.6.6 portalocker-2.3.2 proto-plus-1.19.9 protobuf-3.19.4 py-cpuinfo-8.0.0 pymongo-3.12.3 pyyaml-5.4.1 requests-2.27.1 sacrebleu-2.0.0 sentencepiece-0.1.96 seqeval-1.2.2 tensorboard-2.8.0 tensorflow-2.8.0 tensorflow-addons-0.15.0 tensorflow-io-0.24.0 tensorflow-io-gcs-filesystem-0.24.0 tensorflow-model-optimization-0.7.0 tensorflow-text-2.8.1 tf-estimator-nightly-2.8.0.dev2021122109 tf-models-official-2.8.0 tf-slim-1.1.0
我注意到这个命令卸载了tensorflow 2.7并安装了tensorflow 2.8。我不确定它以前是否发生过。也许这就是DNN库链接丢失的原因?
我可以证实这些:
1.预训练模型或已经安装的模型或我一年前下载的object_detection源文件中没有任何变化。
1.我试过执行命令!pip install dnn -不工作
1.我试图重新启动运行时(不断开连接)-不工作
有人能帮忙吗谢谢.
6条答案
按热度按时间fsi0uk1n1#
这对我很有效(Colab)
x759pob22#
如果您从Github克隆Tensorflow Repo,请将..\models\research\object_detection\packages\tf2中的setup.py更改为
因此,当您安装TFOD时,它将安装Tensorflow 2.7而不是2.8。但是请确保在从github克隆repo之后进行更改。否则您的更改将消失
fquxozlt3#
这看起来像是Tensorflow、cuda和models repo代码的兼容性问题。
尝试在colab开始时安装下面的TensorFlow版本和cuda版本。应该能行
omqzjyyz4#
对于有同样错误的人:安装object_detection后,只需运行以下命令重新安装tensorflow=2.7.0:
在此之后,您需要重新启动运行时(菜单->重新启动->重新启动)
这将解决“(0)未实现:DNN library is not found”问题
xn1cxnb45#
我也面临着同样的问题。尝试了所有的解决方案,但结果是python进程仍然在我的GPU上运行。我必须使用
nvidia-smi
查找进程id,然后使用从那以后,一切都很顺利。
8qgya5xd6#
在执行训练模型之前,只需在colab中执行以下命令,它将解决tensorflow 2.8.2中找不到的DNN库
!apt install --allow-change-held-packages libcudnn8=8.1.0.77-1+cuda11.2