Hi, I am so interested in your work and want to generate new datasets of mine, yet the segmentation fault always occurs when I run the curate.py, for example, in the cell [15] of demo.ipynb, two results of the same codes are listed below:

from mmcv import Config
cfg = Config.fromfile('../configs/curators/lbap_core_ec50_assay.py')
cfg.path.source_root = '/data/ly03/DrugOOD/CHEMBL_SQLLITE/chembl_29_sqlite/chembl_29.db'
cfg.path.target_root = '/data/ly03/DrugOOD/dataset/'
cfg.noise_filter.assay.molecules_number=[50, 100]
cfg.path.task.subset ="lbap_core_ec50_assay_custom"
print(f'Built-in Config:\n{cfg.pretty_text}')
from drugood.apis.curate import curate_data
curate_data(cfg)

conda clean -a is used already, with no help for this problem. I think the segmentation fault may occur when the addresses of vars are not defined with constants, for instance, the dicts of the 'sample' are not assumed here. But I notice the codes in demo.ipynb doesn't point to these vars, either. So might this error occur because of the CPU/GPU limitations? I'm using the Ubuntu 16.04 with a memory of 94GB, and Gpu: 12 GB of TITAN V. Are these suitable to generate the datasets?

Looking forward to your advice! Thank you for your gorgeous work.

P.S.

1. with ulimit -a the info is:

core file size          (blocks, -c) unlimited
data seg size           (kbytes, -d) unlimited
scheduling priority             (-e) 0
file size               (blocks, -f) unlimited
pending signals                 (-i) 384842
max locked memory       (kbytes, -l) 64
max memory size         (kbytes, -m) unlimited
open files                      (-n) 1024
pipe size            (512 bytes, -p) 8
POSIX message queues     (bytes, -q) 819200
real-time priority              (-r) 0
stack size              (kbytes, -s) unlimited
cpu time               (seconds, -t) unlimited
max user processes              (-u) 384842
virtual memory          (kbytes, -v) unlimited
file locks                      (-x) unlimited

2. my env info is listed below(CUDA 10.2 CUDNN 7.6.5)

name: drugood
channels:
  - brown-data-science
  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch
  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/bioconda/
  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/
  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2/
  - bioconda
  - conda-forge
  - defaults
  - r
dependencies:
  - _libgcc_mutex=0.1=main
  - _openmp_mutex=4.5=1_gnu
  - blas=1.0=mkl
  - bzip2=1.0.8=h7b6447c_0
  - ca-certificates=2021.10.8=ha878542_0
  - certifi=2021.10.8=py38h578d9bd_2
  - cudatoolkit=10.2.89=hfd86e86_1
  - ffmpeg=4.3=hf484d3e_0
  - freetype=2.10.4=h5ab3b9f_0
  - gcc=5.4.0=0
  - gmp=6.2.1=h2531618_2
  - gnutls=3.6.15=he1e5248_0
  - intel-openmp=2021.3.0=h06a4308_3350
  - jpeg=9b=h024ee3a_2
  - lame=3.100=h7b6447c_0
  - lcms2=2.12=h3be6417_0
  - ld_impl_linux-64=2.35.1=h7274673_9
  - libffi=3.3=he6710b0_2
  - libgcc-ng=9.3.0=h5101ec6_17
  - libgomp=9.3.0=h5101ec6_17
  - libiconv=1.15=h63c8f33_5
  - libidn2=2.3.1=h27cfd23_0
  - libpng=1.6.37=hbc83047_0
  - libstdcxx-ng=9.3.0=hd4cf53a_17
  - libtasn1=4.16.0=h27cfd23_0
  - libtiff=4.2.0=h85742a9_0
  - libunistring=0.9.10=h27cfd23_0
  - libuv=1.40.0=h7b6447c_0
  - libwebp-base=1.2.0=h27cfd23_0
  - lz4-c=1.9.3=h2531618_0
  - mkl=2021.3.0=h06a4308_520
  - mkl-service=2.4.0=py38h7f8727e_0
  - mkl_fft=1.3.0=py38h42c9631_2
  - mkl_random=1.2.2=py38h51133e4_0
  - ncurses=6.2=he6710b0_1
  - nettle=3.7.3=hbbd107a_1
  - ninja=1.10.2=hff7bd54_1
  - numpy=1.20.3=py38hf144106_0
  - numpy-base=1.20.3=py38h74d4b33_0
  - olefile=0.46=py_0
  - openh264=2.1.0=hd408876_0
  - openjpeg=2.3.0=h05c96fa_1
  - openssl=1.1.1k=h7f98852_0
  - pillow=8.3.1=py38h2c7a002_0
  - pip=21.1.3=py38h06a4308_0
  - python=3.8.5=h7579374_1
  - python_abi=3.8=2_cp38
  - pytorch=1.7.1=py3.8_cuda10.2.89_cudnn7.6.5_0
  - readline=8.1=h27cfd23_0
  - setuptools=52.0.0=py38h06a4308_0
  - six=1.16.0=pyhd3eb1b0_0
  - sqlite=3.36.0=hc218d9a_0
  - tk=8.6.10=hbc83047_0
  - torchaudio=0.7.2=py38
  - torchvision=0.8.2=py38_cu102
  - tree=1.8.0=h7f98852_2
  - typing_extensions=3.10.0.0=pyh06a4308_0
  - wheel=0.36.2=pyhd3eb1b0_0
  - xz=5.2.5=h7b6447c_0
  - zlib=1.2.11=h7b6447c_3
  - zstd=1.4.9=haebb681_0
  - pip:
    - absl-py==1.0.0
    - addict==2.4.0
    - attrs==21.4.0
    - cachetools==5.0.0
    - charset-normalizer==2.0.12
    - click==8.0.4
    - cloudpickle==2.0.0
    - codecov==2.1.12
    - colorama==0.4.4
    - coverage==6.3.2
    - cycler==0.11.0
    - cython==0.29.28
    - dgl-cu102==0.6.1
    - dgl-cu110==0.6.1
    - dgllife==0.2.9
    - drugood==0.0.1
    - filelock==3.6.0
    - flake8==4.0.1
    - fonttools==4.29.1
    - future==0.18.2
    - fuzzywuzzy==0.18.0
    - google-auth==2.6.0
    - google-auth-oauthlib==0.4.6
    - googledrivedownloader==0.4
    - grpcio==1.44.0
    - huggingface-hub==0.4.0
    - hyperopt==0.2.7
    - idna==3.3
    - importlib-metadata==4.11.1
    - iniconfig==1.1.1
    - interrogate==1.5.0
    - isodate==0.6.1
    - isort==4.3.21
    - jinja2==3.0.3
    - joblib==1.1.0
    - kiwisolver==1.3.2
    - littleutils==0.2.2
    - markdown==3.3.6
    - markupsafe==2.1.0
    - matplotlib==3.5.1
    - mccabe==0.6.1
    - mmcv==1.4.5
    - networkx==2.6.3
    - oauthlib==3.2.0
    - ogb==1.3.2
    - opencv-python==4.5.5.62
    - outdated==0.2.1
    - packaging==21.3
    - pandas==1.4.1
    - pluggy==1.0.0
    - prettytable==3.2.0
    - protobuf==3.19.4
    - py==1.11.0
    - py4j==0.10.9.3
    - pyasn1==0.4.8
    - pyasn1-modules==0.2.8
    - pycodestyle==2.8.0
    - pyflakes==2.4.0
    - pyparsing==3.0.7
    - pytdc==0.3.6
    - pytest==7.0.1
    - python-dateutil==2.8.2
    - pytz==2021.3
    - pyyaml==6.0
    - rdflib==6.1.1
    - rdkit-pypi==2021.9.4
    - regex==2022.1.18
    - requests==2.27.1
    - requests-oauthlib==1.3.1
    - rsa==4.8
    - sacremoses==0.0.47
    - scikit-learn==1.0.2
    - scipy==1.8.0
    - seaborn==0.11.2
    - tabulate==0.8.9
    - tensorboard==2.8.0
    - tensorboard-data-server==0.6.1
    - tensorboard-plugin-wit==1.8.1
    - threadpoolctl==3.1.0
    - tokenizers==0.11.5
    - toml==0.10.2
    - tomli==2.0.1
    - torch-cluster==1.5.9
    - torch-geometric==2.0.3
    - torch-scatter==2.0.7
    - torch-sparse==0.6.9
    - torch-spline-conv==1.2.1
    - tqdm==4.62.3
    - transformers==4.16.2
    - urllib3==1.26.8
    - wcwidth==0.2.5
    - werkzeug==2.0.3
    - wilds==2.0.0
    - xdoctest==0.15.10
    - yacs==0.1.8
    - yapf==0.32.0
    - zipp==3.7.0
prefix: /home/ly03/anaconda3/envs/drugood

Hello, I noticed that in some of the configuration python files of curators, there exist nested definitions of assay.

For example:

After removing a layer of assay dictionary, the data curation process runs well.

Patching CVE-2007-4559

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According to the original paper of IRM and its provided code, the value of "scale" is always 1 and will not be updated, which means it should not be add to parameters of the model. Also you forget to multiply the irm_penalty by irm_lambda while calculating losses

In the provided code of orginal IRM article, the value of dummy_w (the self.scale in irm.py of your codes) will not be updated. However, In your code, this value serves as a parameter of the model, which means it will be updated via the optimizer

Hi,

I'm testing the dataset curated from protein family, e.g., configs/curators/sbap_core_ki_protein_family.py. And I get the following exception:

Traceback (most recent call last):
  File "xxx/drugood/apis/curate.py", line 14, in curate_data
    data = curator.data_splitting(data)
  File "xxx/drugood/curators/curator.py", line 206, in data_splitting
    domain_value = domain_func(value_for_generating_domain)
  File "xxx/drugood/curators/get_domain_info.py", line 75, in protein_family
    class_id = self.protein_family_getter(protein_seq)
  File "xxx/drugood/curators/chembl/protein_family.py", line 48, in __call__
    target_level_class_id = self.get_target_level_class_id(class_id)
  File "xxx/drugood/curators/chembl/protein_family.py", line 37, in get_target_level_class_id
    class_id_cur_level = self.dict_id_to_parent_level[class_id_cur_level][0]
KeyError: None

It turns out that protein_family_level is None.

A quick update: this line fails to pass in the protein_family_level.

Dear authors, First, thank you for the excellent benchmark. I have one question about the protein target of LBAP task. In your paper, on page 12, you said "In LBAP, we follow the common practice and do not involve any protein target information, which is usually used in the activity prediction for one specific protein target." To my understanding, it should keep the protein target the same in both the training set and testing set. Is that correct? But I cannot find the code to ensure this requirement. If I missed it, could you point out where it is? Thanks advance.

Every provided backbone will call the function "move_to_device", moving the data to device "cuda:0". This means the model must be defined on "cuda:0" and "cuda:0" must be included in gpu-ids. This is quite inconvenient.