Why I suffer from endless OPENBLAS warning.

[tzjtatata]

Thank you for your exciting work.

But when I use your code, it suffers from endless warning by openblas, like following:

After find candidates and before 'Number of samples that are misclassified...'.

I use a brand new environment via conda and pip. Details like following: name: ALFA_Mix channels:

• defaults dependencies:
• _libgcc_mutex=0.1=main
• _openmp_mutex=5.1=1_gnu
• ca-certificates=2023.01.10=h06a4308_0
• certifi=2022.12.7=py38h06a4308_0
• ld_impl_linux-64=2.38=h1181459_1
• libffi=3.3=he6710b0_2
• libgcc-ng=11.2.0=h1234567_1
• libgomp=11.2.0=h1234567_1
• libstdcxx-ng=11.2.0=h1234567_1
• ncurses=6.4=h6a678d5_0
• openssl=1.1.1t=h7f8727e_0
• pip=23.0.1=py38h06a4308_0
• python=3.8.3=hcff3b4d_2
• readline=8.2=h5eee18b_0
• setuptools=65.6.3=py38h06a4308_0
• sqlite=3.41.1=h5eee18b_0
• tk=8.6.12=h1ccaba5_0
• wheel=0.38.4=py38h06a4308_0
• xz=5.2.10=h5eee18b_1
• zlib=1.2.13=h5eee18b_0
• pip:
  • absl-py==1.4.0
  • asttokens==2.2.1
  • backcall==0.2.0
  • cachetools==4.2.4
  • charset-normalizer==3.1.0
  • click==8.1.3
  • cycler==0.11.0
  • decorator==5.1.1
  • executing==1.2.0
  • fonttools==4.39.3
  • future==0.18.3
  • google-auth==1.35.0
  • google-auth-oauthlib==0.4.6
  • grpcio==1.53.0
  • idna==3.4
  • importlib-metadata==6.3.0
  • ipdb==0.13.3
  • ipython==8.12.0
  • jedi==0.18.2
  • joblib==1.2.0
  • kiwisolver==1.4.4
  • liac-arff==2.5.0
  • markdown==3.4.3
  • markupsafe==2.1.2
  • matplotlib==3.5.3
  • matplotlib-inline==0.1.6
  • nltk==3.5
  • numpy==1.18.5
  • oauthlib==3.2.2
  • openml==0.11.0
  • packaging==23.0
  • pandas==1.4.4
  • parso==0.8.3
  • pexpect==4.8.0
  • pickleshare==0.7.5
  • pillow==9.5.0
  • prompt-toolkit==3.0.38
  • protobuf==3.20.0
  • ptyprocess==0.7.0
  • pure-eval==0.2.2
  • pyasn1==0.4.8
  • pyasn1-modules==0.2.8
  • pygments==2.14.0
  • pyparsing==3.0.9
  • python-dateutil==2.8.2
  • pytz==2023.3
  • regex==2023.3.23
  • requests==2.28.2
  • requests-oauthlib==1.3.1
  • rsa==4.9
  • scikit-learn==0.23.1
  • scipy==1.9.3
  • seaborn==0.10.1
  • six==1.16.0
  • sklearn==0.0
  • stack-data==0.6.2
  • tensorboard==2.2.2
  • tensorboard-plugin-wit==1.8.1
  • threadpoolctl==3.1.0
  • torch==1.13.1+cu117
  • torchattacks==2.13.2
  • torchaudio==0.13.1+cu117
  • torchvision==0.14.1+cu117
  • tqdm==4.48.0
  • traitlets==5.9.0
  • typing-extensions==4.5.0
  • urllib3==1.26.15
  • wcwidth==0.2.6
  • werkzeug==2.2.3
  • xmltodict==0.13.0
  • zipp==3.15.0

And I run the code like README.md: python main.py
--data_name CIFAR100 --data_dir /data2/dataset --log_dir ./logs
--n_init_lb 100 --n_query 100 --n_round 10 --learning_rate 0.001 --n_epoch 200 --model resnet18
--strategy AlphaMixSampling --alpha_opt --choose_best_val_model

And I suspect that this problem may influence accuracy, because the results are low: 0.041 for 100 samples, 0.056 for 400 samples.