├── .cirrus.yml ├── .github └── workflows │ ├── automatic_rebase.yml │ └── pre-commit.yml ├── .gitignore ├── .isort.cfg ├── .pre-commit-config.yaml ├── AUTHORS.rst ├── LICENSE ├── Pipfile.lock ├── Poster_Slide ├── CVPR-2021 │ ├── ProSelfLC_Poster.pdf │ ├── ProSelfLC_Poster.pptx │ └── ProSelfLC_Slide.pdf └── Talks │ ├── 2022-05-17-XW-Loughborough.pdf │ ├── 2022-08-12-XW-SUSTECH-Poster.pdf │ ├── 2022-08-12-XW-SUSTECH.pdf │ └── 2022-12-23-ShanghaiDianJiUniversity.pdf ├── README.md ├── Reviews ├── CVPR2021 │ ├── Rebuttal.pdf │ ├── final_reviews-Meta.pdf │ ├── final_reviews.pdf │ └── initial_reviews.pdf └── NeurIPS2020 │ ├── CMT_Review.pdf │ └── ProSelfLC_personal_response.pdf ├── dataset ├── CASP12_HHblits.csv ├── CB513_HHblits.csv ├── TS115_HHblits.csv ├── Train_HHblits.csv ├── Validation_HHblits.csv ├── deeploc_eda_reports │ ├── MS-with-unknown_train.pdf │ ├── MS-with-unknown_valid or test: the same dataset.pdf │ ├── MS_train.pdf │ ├── MS_valid or test: the same dataset.pdf │ ├── deeploc_prottransMS-with-unknown_classnum_2_maxseqlen_434_ratio_539_1087_val.pdf │ ├── deeploc_prottransMS-with-unknown_classnum_2_train.pdf │ ├── deeploc_prottransMS_classnum_2_maxseqlen_1022_ratio_960_1087_val.pdf │ ├── deeploc_prottransMS_classnum_2_maxseqlen_1534_ratio_1046_1087_val.pdf │ ├── deeploc_prottransMS_classnum_2_maxseqlen_2046_ratio_1073_1087_val.pdf │ ├── deeploc_prottransMS_classnum_2_maxseqlen_434_ratio_539_1087_val.pdf │ ├── deeploc_prottransMS_classnum_2_maxseqlen_510_ratio_644_1087_val.pdf │ └── deeploc_prottransMS_classnum_2_train.pdf ├── deeploc_per_protein_test.csv ├── deeploc_per_protein_train.csv └── eda_reports │ ├── deeploc_prottransMS_classnum_2_train.pdf │ └── deeploc_prottransMS_classnum_2_val.pdf ├── demos_jupyter_notebooks ├── bert_deeploc │ ├── dataset │ │ └── deeploc_eda_reports │ │ │ └── MS-with-unknown_train.pdf │ ├── trainer_2MSwithunknown_bert_proselflc.ipynb │ └── trainer_2MSwithunknown_bert_proselflc.pdf └── convnets_cifar100 │ ├── trainer_cifar100_resnet18_proselflc.ipynb │ ├── trainer_cifar100_shufflenetv2_labelsmoothing.ipynb │ └── trainer_cifar100_shufflenetv2_proselflc.ipynb ├── docs ├── Makefile ├── _static │ └── .gitignore ├── authors.rst ├── changelog.rst ├── conf.py ├── index.rst └── license.rst ├── experiments_records ├── cifar100_symmetric_noise_rate_0.4 │ ├── resnet18 │ │ ├── 002_crossentropy_warm0_20220402-223020 │ │ │ ├── accuracy.pdf │ │ │ ├── accuracy_loss_normalised_entropy_max_p_metadata.pdf │ │ │ ├── accuracy_loss_normalised_entropy_max_p_metadata.xlsx │ │ │ ├── loss.pdf │ │ │ ├── max_p.pdf │ │ │ ├── metadata.pdf │ │ │ ├── normalised_entropy.pdf │ │ │ └── params.csv │ │ ├── 008_confidencepenalty_warm0_20220328-130116 │ │ │ ├── accuracy.pdf │ │ │ ├── accuracy_loss_normalised_entropy_max_p_metadata.pdf │ │ │ ├── accuracy_loss_normalised_entropy_max_p_metadata.xlsx │ │ │ ├── loss.pdf │ │ │ ├── max_p.pdf │ │ │ ├── metadata.pdf │ │ │ ├── normalised_entropy.pdf │ │ │ └── params.csv │ │ ├── 008_labelcorrection_warm0_20220328-125437 │ │ │ ├── accuracy.pdf │ │ │ ├── accuracy_loss_normalised_entropy_max_p_metadata.pdf │ │ │ ├── accuracy_loss_normalised_entropy_max_p_metadata.xlsx │ │ │ ├── loss.pdf │ │ │ ├── max_p.pdf │ │ │ ├── metadata.pdf │ │ │ ├── normalised_entropy.pdf │ │ │ └── params.csv │ │ ├── 008_labelsmoothing_warm0_20220328-125731 │ │ │ ├── accuracy.pdf │ │ │ ├── accuracy_loss_normalised_entropy_max_p_metadata.pdf │ │ │ ├── accuracy_loss_normalised_entropy_max_p_metadata.xlsx │ │ │ ├── loss.pdf │ │ │ ├── max_p.pdf │ │ │ ├── metadata.pdf │ │ │ ├── normalised_entropy.pdf │ │ │ └── params.csv │ │ └── 138_proselflc_warm0_20220402-173610 │ │ │ ├── accuracy.pdf │ │ │ ├── accuracy_loss_normalised_entropy_max_p_metadata.pdf │ │ │ ├── accuracy_loss_normalised_entropy_max_p_metadata.xlsx │ │ │ ├── loss.pdf │ │ │ ├── max_p.pdf │ │ │ ├── metadata.pdf │ │ │ ├── normalised_entropy.pdf │ │ │ └── params.csv │ └── shufflenetv2 │ │ ├── 003_crossentropy_warm0_20220403-011907 │ │ ├── accuracy.pdf │ │ ├── accuracy_loss_normalised_entropy_max_p_metadata.pdf │ │ ├── accuracy_loss_normalised_entropy_max_p_metadata.xlsx │ │ ├── loss.pdf │ │ ├── max_p.pdf │ │ ├── metadata.pdf │ │ ├── normalised_entropy.pdf │ │ └── params.csv │ │ ├── 012_confidencepenalty_warm0_20220328-184933 │ │ ├── accuracy.pdf │ │ ├── accuracy_loss_normalised_entropy_max_p_metadata.pdf │ │ ├── accuracy_loss_normalised_entropy_max_p_metadata.xlsx │ │ ├── loss.pdf │ │ ├── max_p.pdf │ │ ├── metadata.pdf │ │ ├── normalised_entropy.pdf │ │ └── params.csv │ │ ├── 012_labelcorrection_warm0_20220328-184721 │ │ ├── accuracy.pdf │ │ ├── accuracy_loss_normalised_entropy_max_p_metadata.pdf │ │ ├── accuracy_loss_normalised_entropy_max_p_metadata.xlsx │ │ ├── loss.pdf │ │ ├── max_p.pdf │ │ ├── metadata.pdf │ │ ├── normalised_entropy.pdf │ │ └── params.csv │ │ ├── 012_labelsmoothing_warm0_20220328-184409 │ │ ├── accuracy.pdf │ │ ├── accuracy_loss_normalised_entropy_max_p_metadata.pdf │ │ ├── accuracy_loss_normalised_entropy_max_p_metadata.xlsx │ │ ├── loss.pdf │ │ ├── max_p.pdf │ │ ├── metadata.pdf │ │ ├── normalised_entropy.pdf │ │ └── params.csv │ │ └── 502_proselflc_warm0_20220606-150113 │ │ ├── accuracy.pdf │ │ ├── accuracy_loss_normalised_entropy_max_p_metadata.pdf │ │ ├── accuracy_loss_normalised_entropy_max_p_metadata.xlsx │ │ ├── loss.pdf │ │ ├── max_p.pdf │ │ ├── metadata.pdf │ │ ├── normalised_entropy.pdf │ │ └── params.csv ├── clothing1m_withbs_symmetric_noise_rate_0.0 │ └── resnet50_tv │ │ └── 790_proselflc_warm0.18_20220618-070235 │ │ ├── .~lock.accuracy_loss_normalised_entropy_max_p_metadata.xlsx# │ │ ├── accuracy.pdf │ │ ├── accuracy_loss_normalised_entropy_max_p_metadata.pdf │ │ ├── accuracy_loss_normalised_entropy_max_p_metadata.xlsx │ │ ├── loss.pdf │ │ ├── max_p.pdf │ │ ├── metadata.pdf │ │ ├── normalised_entropy.pdf │ │ └── params.csv ├── deeploc_prottrans_symmetric_noise_rate_0.0 │ └── Rostlab_prot_bert_bfd_seq │ │ ├── MS-with-unknown │ │ ├── 001_labelsmoothing_warm0_20220501-145915 │ │ │ ├── accuracy.pdf │ │ │ ├── accuracy_loss_normalised_entropy_max_p_metadata.pdf │ │ │ ├── accuracy_loss_normalised_entropy_max_p_metadata.xlsx │ │ │ ├── loss.pdf │ │ │ ├── max_p.pdf │ │ │ ├── metadata.pdf │ │ │ ├── normalised_entropy.pdf │ │ │ └── params.csv │ │ ├── 002_labelsmoothing_warm0_20220501-145917 │ │ │ ├── accuracy.pdf │ │ │ ├── accuracy_loss_normalised_entropy_max_p_metadata.pdf │ │ │ ├── accuracy_loss_normalised_entropy_max_p_metadata.xlsx │ │ │ ├── loss.pdf │ │ │ ├── max_p.pdf │ │ │ ├── metadata.pdf │ │ │ ├── normalised_entropy.pdf │ │ │ └── params.csv │ │ ├── 005_proselflc_warm0_20220430-142316 │ │ │ ├── accuracy.pdf │ │ │ ├── accuracy_loss_normalised_entropy_max_p_metadata.pdf │ │ │ ├── accuracy_loss_normalised_entropy_max_p_metadata.xlsx │ │ │ ├── loss.pdf │ │ │ ├── max_p.pdf │ │ │ ├── metadata.pdf │ │ │ ├── normalised_entropy.pdf │ │ │ └── params.csv │ │ ├── 009_confidencepenalty_warm0_20220501-145913 │ │ │ ├── accuracy.pdf │ │ │ ├── accuracy_loss_normalised_entropy_max_p_metadata.pdf │ │ │ ├── accuracy_loss_normalised_entropy_max_p_metadata.xlsx │ │ │ ├── loss.pdf │ │ │ ├── max_p.pdf │ │ │ ├── metadata.pdf │ │ │ ├── normalised_entropy.pdf │ │ │ └── params.csv │ │ └── 057_labelcorrection_warm0_20220619-032243 │ │ │ ├── accuracy.pdf │ │ │ ├── accuracy_loss_normalised_entropy_max_p_metadata.pdf │ │ │ ├── accuracy_loss_normalised_entropy_max_p_metadata.xlsx │ │ │ ├── loss.pdf │ │ │ ├── max_p.pdf │ │ │ ├── metadata.pdf │ │ │ ├── normalised_entropy.pdf │ │ │ └── params.csv │ │ └── MS │ │ ├── 010_proselflc_warm0_20220430-215151 │ │ ├── accuracy.pdf │ │ ├── accuracy_loss_normalised_entropy_max_p_metadata.pdf │ │ ├── accuracy_loss_normalised_entropy_max_p_metadata.xlsx │ │ ├── loss.pdf │ │ ├── max_p.pdf │ │ ├── metadata.pdf │ │ ├── normalised_entropy.pdf │ │ └── params.csv │ │ ├── 021_labelsmoothing_warm0_20220501-195700 │ │ ├── accuracy.pdf │ │ ├── accuracy_loss_normalised_entropy_max_p_metadata.pdf │ │ ├── accuracy_loss_normalised_entropy_max_p_metadata.xlsx │ │ ├── loss.pdf │ │ ├── max_p.pdf │ │ ├── metadata.pdf │ │ ├── normalised_entropy.pdf │ │ └── params.csv │ │ ├── 022_labelsmoothing_warm0_20220501-222438 │ │ ├── accuracy.pdf │ │ ├── accuracy_loss_normalised_entropy_max_p_metadata.pdf │ │ ├── accuracy_loss_normalised_entropy_max_p_metadata.xlsx │ │ ├── loss.pdf │ │ ├── max_p.pdf │ │ ├── metadata.pdf │ │ ├── normalised_entropy.pdf │ │ └── params.csv │ │ ├── 029_confidencepenalty_warm0_20220501-210635 │ │ ├── accuracy.pdf │ │ ├── accuracy_loss_normalised_entropy_max_p_metadata.pdf │ │ ├── accuracy_loss_normalised_entropy_max_p_metadata.xlsx │ │ ├── loss.pdf │ │ ├── max_p.pdf │ │ ├── metadata.pdf │ │ ├── normalised_entropy.pdf │ │ └── params.csv │ │ └── 039_labelcorrection_warm0_20220501-233934 │ │ ├── accuracy.pdf │ │ ├── accuracy_loss_normalised_entropy_max_p_metadata.pdf │ │ ├── accuracy_loss_normalised_entropy_max_p_metadata.xlsx │ │ ├── loss.pdf │ │ ├── max_p.pdf │ │ ├── metadata.pdf │ │ ├── normalised_entropy.pdf │ │ └── params.csv └── food101n_symmetric_noise_rate_0.0 │ └── resnet50_tv │ └── 014_proselflc_warm2_20220515-005356 │ ├── accuracy.pdf │ ├── accuracy_loss_normalised_entropy_max_p_metadata.pdf │ ├── accuracy_loss_normalised_entropy_max_p_metadata.xlsx │ ├── loss.pdf │ ├── max_p.pdf │ ├── metadata.pdf │ ├── normalised_entropy.pdf │ └── params.csv ├── pyproject.toml ├── script_tools └── html2pdf.ipynb ├── setup.cfg ├── setup.py ├── src ├── __init__.py └── proselflc │ ├── __init__.py │ ├── exceptions.py │ ├── optim │ └── sgd_multistep.py │ ├── slicegetter │ ├── __init__.py │ ├── get_dataloader.py │ ├── get_lossfunction.py │ └── get_network.py │ ├── slices │ ├── __init__.py │ ├── datain │ │ ├── __init__.py │ │ ├── dataloaders │ │ │ ├── cifar100dataloader.py │ │ │ ├── clothing1mdataloader.py │ │ │ ├── deeplocdataloaders.py │ │ │ └── food101n.py │ │ ├── datasets │ │ │ ├── __init__.py │ │ │ ├── cifar100dataset.py │ │ │ ├── clothing1m.py │ │ │ ├── clothing1m_getlabels.py │ │ │ ├── deeplocdatasets.py │ │ │ ├── food101n.py │ │ │ └── food101n_utils.py │ │ ├── transforms │ │ │ ├── __init__.py │ │ │ ├── cifar100transforms.py │ │ │ └── clothing1mtransforms.py │ │ └── utils.py │ ├── losses │ │ ├── __init__.py │ │ ├── confidencepenalty.py │ │ ├── crossentropy.py │ │ ├── labelcorrection.py │ │ ├── lablesmoothing.py │ │ ├── mean_absolute_error.py │ │ └── proselflc.py │ └── networks │ │ ├── PreResNet.py │ │ ├── __init__.py │ │ ├── mobilenetv2.py │ │ ├── nasnet.py │ │ ├── resnet.py │ │ ├── resnet50_tv.py │ │ ├── resnet_plc.py │ │ ├── senet.py │ │ ├── shufflenet.py │ │ ├── shufflenetv2.py │ │ ├── stochasticdepth.py │ │ ├── transformers │ │ ├── prot_bert_bfd_seqlevel.py │ │ └── prot_bert_bfd_tokenlevel.py │ │ ├── vit_modelconfig │ │ ├── configs.py │ │ ├── modeling.py │ │ └── modeling_resnet.py │ │ └── vit_models.py │ └── trainer │ ├── __init__.py │ ├── trainer_cnn_vision_derivedgrad.py │ ├── trainer_cnn_vision_derivedgrad_adaptedfordeeploc.py 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