├── .gitignore ├── LICENSE ├── ProtoPNet_LICENSE ├── README.md ├── automatic_extract_confound.py ├── conf ├── config.yaml └── experiment │ ├── artificial_aggregation.yaml │ ├── artificial_base.yaml │ ├── artificial_iaiabl.yaml │ ├── artificial_lower.yaml │ ├── artificial_upper.yaml │ ├── covid_aggregation.yaml │ ├── covid_base.yaml │ ├── natural_aggregation.yaml │ ├── natural_base.yaml │ ├── natural_base_upper_lower.yaml │ ├── natural_iaiabl.yaml │ ├── natural_upper.yaml │ └── natural_upper1.yaml ├── copy_top_20.sh ├── data ├── bird_add_confound.py ├── bird_initial_images_processing.py ├── cdatasets │ ├── __init__.py │ ├── bimcv_covid_manual_projection_labels.yml │ ├── bimcv_covid_unknown_labels.txt │ ├── bimcvcovid.py │ ├── bimcvnegative.py │ ├── chestxray14dataset.py │ ├── chestxray14h5.py │ ├── cxrdataset.py │ ├── domainconfoundeddatasets.py │ ├── githubcovid.py │ ├── padchestdataset.py │ ├── padchesth5.py │ └── padchestmap.py ├── data_loader.py ├── generate_synthetic_images.py ├── make_csv_bimcv_positive.py ├── make_h5.py └── shuffle_test_background.py ├── data_preprocessing.py ├── debugging-loop.png ├── environment.yml ├── extract_confound.py ├── features ├── custom_features.py ├── densenet_features.py ├── resnet_features.py └── vgg_features.py ├── find_nearest.py ├── global_analysis.py ├── helpers.py ├── local_analysis.py ├── main.py ├── model.py ├── plot.sh ├── plot_repetitions.sh ├── plot_stat.py ├── plots └── .gitignore ├── preprocess.py ├── pretrained_models └── .gitignore ├── push.py ├── ranking_class_performance.py ├── receptive_field.py ├── run_artificial_confound.sh ├── run_covid_confound.sh ├── run_natural_confound.sh ├── saved_models └── .gitignore ├── settings.py ├── slides ├── poster_iclr23.pdf └── protopdebug___iclr23.pdf ├── telegram_notification.py ├── termination_error.py ├── tests ├── covid_test.py └── dataset_test.py ├── train_and_test.py ├── user_experiment ├── google_script_form.js ├── round_1_answers.csv ├── round_1_questions │ ├── cuts │ │ ├── c=0_p=0_i=1_a.png │ │ ├── c=0_p=0_i=1_b.png │ │ ├── c=0_p=0_i=2_a.png │ │ ├── c=0_p=0_i=2_b.png │ │ ├── c=0_p=0_i=2_c.png │ │ ├── c=0_p=0_i=2_d.png │ │ ├── c=0_p=0_i=3_a.png │ │ ├── c=0_p=0_i=3_b.png │ │ ├── c=0_p=0_i=3_c.png │ │ ├── c=0_p=0_i=4_a.png │ │ ├── c=0_p=0_i=4_b.png │ │ ├── c=0_p=0_i=5_a.png │ │ ├── c=0_p=0_i=5_b.png │ │ ├── c=0_p=0_i=5_c.png │ │ ├── c=0_p=1_i=1_b.png │ │ ├── c=0_p=1_i=1_c.png │ │ ├── c=0_p=1_i=2_a.png │ │ ├── c=0_p=1_i=2_b.png │ │ ├── c=0_p=1_i=2_c.png │ │ ├── c=0_p=1_i=3_b.png │ │ ├── c=0_p=1_i=3_c.png │ │ ├── c=0_p=1_i=4_a.png │ │ ├── c=0_p=1_i=4_c.png │ │ ├── c=0_p=1_i=4_d.png │ │ ├── c=0_p=1_i=5_a.png │ │ ├── c=0_p=1_i=5_b.png │ │ ├── c=0_p=1_i=5_c.png │ │ ├── c=14_p=28_i=1_a.png │ │ ├── c=14_p=28_i=2_a.png │ │ ├── c=14_p=28_i=2_b.png │ │ ├── c=14_p=28_i=3_a.png │ │ ├── c=14_p=28_i=3_b.png │ │ ├── c=14_p=28_i=3_c.png │ │ ├── c=14_p=28_i=4_a.png │ │ ├── c=14_p=28_i=5_a.png │ │ ├── c=14_p=28_i=5_b.png │ │ ├── c=14_p=29_i=1_a.png │ │ ├── c=14_p=29_i=2_a.png │ │ ├── c=14_p=29_i=3_a.png │ │ ├── c=14_p=29_i=4_a.png │ │ ├── c=14_p=29_i=4_b.png │ │ ├── c=14_p=29_i=5_a.png │ │ ├── c=15_p=30_i=1_a.png │ │ ├── c=15_p=30_i=2_a.png │ │ ├── c=15_p=30_i=3_a.png │ │ ├── c=15_p=30_i=3_b.png │ │ ├── c=15_p=30_i=4_a.png │ │ ├── c=15_p=30_i=4_b.png │ │ ├── c=15_p=30_i=5_a.png │ │ ├── c=15_p=30_i=5_b.png │ │ ├── c=15_p=30_i=5_c.png │ │ ├── c=15_p=31_i=1_a.png │ │ ├── c=15_p=31_i=2_a.png │ │ ├── c=15_p=31_i=2_b.png │ │ ├── c=15_p=31_i=2_c.png │ │ ├── c=15_p=31_i=3_a.png │ │ ├── c=15_p=31_i=4_a.png │ │ ├── c=15_p=31_i=4_b.png │ │ ├── c=15_p=31_i=5_b.png │ │ ├── c=6_p=12_i=1_a.png │ │ ├── c=6_p=12_i=1_b.png │ │ ├── c=6_p=12_i=2_a.png │ │ ├── c=6_p=12_i=2_b.png │ │ ├── c=6_p=12_i=3_a.png │ │ ├── c=6_p=12_i=3_b.png │ │ ├── c=6_p=12_i=4_a.png │ │ ├── c=6_p=12_i=5_a.png │ │ ├── c=6_p=12_i=5_b.png │ │ ├── c=6_p=12_i=5_c.png │ │ ├── c=6_p=13_i=1_a.png │ │ ├── c=6_p=13_i=1_b.png │ │ ├── c=6_p=13_i=2_a.png │ │ ├── c=6_p=13_i=3_a.png │ │ ├── c=6_p=13_i=4_a.png │ │ ├── c=6_p=13_i=5_a.png │ │ ├── c=8_p=16_i=1_a.png │ │ ├── c=8_p=16_i=1_b.png │ │ ├── c=8_p=16_i=2_a.png │ │ ├── c=8_p=16_i=2_c.png │ │ ├── c=8_p=16_i=3_a.png │ │ ├── c=8_p=16_i=3_b.png │ │ ├── c=8_p=16_i=4_a.png │ │ ├── c=8_p=16_i=4_b.png │ │ ├── c=8_p=16_i=4_c.png │ │ ├── c=8_p=16_i=4_d.png │ │ ├── c=8_p=16_i=5_a.png │ │ ├── c=8_p=16_i=5_c.png │ │ ├── c=8_p=16_i=5_d.png │ │ ├── c=8_p=17_i=1_a.png │ │ ├── c=8_p=17_i=1_b.png │ │ ├── c=8_p=17_i=2_a.png │ │ ├── c=8_p=17_i=2_b.png │ │ ├── c=8_p=17_i=3_a.png │ │ ├── c=8_p=17_i=3_b.png │ │ ├── c=8_p=17_i=4_a.png │ │ ├── c=8_p=17_i=5_a.png │ │ └── c=8_p=17_i=5_b.png │ └── random_form_figures │ │ ├── 00Hexp_c=0_p=1_i=5_a.png │ │ ├── 01Hexp_c=0_p=0_i=2_c.png │ │ ├── 02Hexp_c=14_p=28_i=3_b.png │ │ ├── 03Hexp_c=14_p=28_i=1_a.png │ │ ├── 04Hexp_c=14_p=29_i=2_a.png │ │ ├── 05Hexp_c=6_p=13_i=4_a.png │ │ ├── 06Hexp_c=14_p=28_i=5_b.png │ │ ├── 07Hexp_c=15_p=30_i=2_a.png │ │ ├── 08Hexp_c=14_p=29_i=4_a.png │ │ ├── 09Hexp_c=6_p=12_i=5_a.png │ │ ├── 10Hexp_c=8_p=16_i=1_a.png │ │ ├── 11Hexp_c=8_p=16_i=4_c.png │ │ ├── 12Hexp_c=0_p=0_i=3_a.png │ │ ├── 13Hexp_c=14_p=29_i=4_b.png │ │ ├── 14Hexp_c=15_p=30_i=5_b.png │ │ ├── 15Hexp_c=8_p=16_i=2_c.png │ │ ├── 16Hexp_c=6_p=12_i=1_b.png │ │ ├── 17Hexp_c=8_p=16_i=5_a.png │ │ ├── 18Hexp_c=8_p=17_i=3_a.png │ │ ├── 19Hexp_c=14_p=28_i=2_b.png │ │ ├── 20Hexp_c=15_p=31_i=1_a.png │ │ ├── 21Hexp_c=8_p=16_i=4_d.png │ │ ├── 22Hexp_c=6_p=12_i=3_b.png │ │ ├── 23Hexp_c=8_p=16_i=4_b.png │ │ ├── 24Hexp_c=8_p=16_i=4_a.png │ │ ├── 25Hexp_c=8_p=17_i=5_a.png │ │ ├── 26Hexp_c=8_p=17_i=4_a.png │ │ ├── 27Hexp_c=0_p=0_i=4_a.png │ │ ├── 28Hexp_c=8_p=16_i=2_a.png │ │ ├── 29Hexp_c=0_p=0_i=2_a.png │ │ ├── 30Hexp_c=0_p=1_i=3_c.png │ │ ├── 31Hexp_c=8_p=17_i=1_b.png │ │ ├── 32Hexp_c=15_p=30_i=4_a.png │ │ ├── 33Hexp_c=15_p=31_i=3_a.png │ │ ├── 34Hexp_c=15_p=31_i=2_c.png │ │ ├── 35Hexp_c=6_p=13_i=3_a.png │ │ ├── 36Hexp_c=15_p=30_i=5_a.png │ │ ├── 37Hexp_c=6_p=12_i=3_a.png │ │ ├── 38Hexp_c=6_p=12_i=2_a.png │ │ ├── 39Hexp_c=6_p=12_i=5_b.png │ │ ├── 40Hexp_c=8_p=17_i=3_b.png │ │ ├── 41Hexp_c=0_p=0_i=4_b.png │ │ ├── 42Hexp_c=0_p=0_i=5_c.png │ │ ├── 43Hexp_c=6_p=12_i=1_a.png │ │ ├── 44Hexp_c=0_p=1_i=2_a.png │ │ ├── 45Hexp_c=8_p=16_i=1_b.png │ │ ├── 46Hexp_c=6_p=12_i=5_c.png │ │ ├── 47Hexp_c=8_p=16_i=5_c.png │ │ ├── 48Hexp_c=15_p=31_i=2_a.png │ │ ├── 49Hexp_c=15_p=31_i=4_a.png │ │ ├── 50Hexp_c=8_p=16_i=5_d.png │ │ ├── 51Hexp_c=0_p=1_i=4_d.png │ │ ├── 52Hexp_c=0_p=1_i=2_c.png │ │ ├── 53Hexp_c=0_p=1_i=3_b.png │ │ ├── 54Hexp_c=14_p=28_i=2_a.png │ │ ├── 55Hexp_c=15_p=31_i=2_b.png │ │ ├── 56Hexp_c=0_p=0_i=1_b.png │ │ ├── 57Hexp_c=0_p=0_i=2_b.png │ │ ├── 58Hexp_c=0_p=0_i=2_d.png │ │ ├── 59Hexp_c=15_p=30_i=4_b.png │ │ ├── 60Hexp_c=8_p=17_i=2_a.png │ │ ├── 61Hexp_c=14_p=28_i=3_a.png │ │ ├── 62Hexp_c=8_p=16_i=3_a.png │ │ ├── 63Hexp_c=0_p=0_i=5_a.png │ │ ├── 64Hexp_c=14_p=29_i=1_a.png │ │ ├── 65Hexp_c=6_p=13_i=5_a.png │ │ ├── 66Hexp_c=0_p=1_i=1_b.png │ │ ├── 67Hexp_c=0_p=1_i=5_c.png │ │ ├── 68Hexp_c=0_p=0_i=5_b.png │ │ ├── 69Hexp_c=14_p=29_i=5_a.png │ │ ├── 70Hexp_c=15_p=31_i=5_b.png │ │ ├── 71Hexp_c=8_p=17_i=5_b.png │ │ ├── 72Hexp_c=15_p=30_i=3_b.png │ │ ├── 73Hexp_c=8_p=17_i=2_b.png │ │ ├── 74Hexp_c=8_p=17_i=1_a.png │ │ ├── 75Hexp_c=0_p=1_i=4_c.png │ │ ├── 76Hexp_c=0_p=1_i=2_b.png │ │ ├── 77Hexp_c=0_p=0_i=3_c.png │ │ ├── 78Hexp_c=6_p=13_i=1_b.png │ │ ├── 79Hexp_c=15_p=30_i=1_a.png │ │ ├── 80Hexp_c=6_p=13_i=1_a.png │ │ ├── 81Hexp_c=15_p=31_i=4_b.png │ │ ├── 82Hexp_c=0_p=0_i=1_a.png │ │ ├── 83Hexp_c=6_p=13_i=2_a.png │ │ ├── 84Hexp_c=8_p=16_i=3_b.png │ │ ├── 85Hexp_c=6_p=12_i=4_a.png │ │ ├── 86Hexp_c=0_p=1_i=1_c.png │ │ ├── 87Hexp_c=14_p=28_i=5_a.png │ │ ├── 88Hexp_c=0_p=0_i=3_b.png │ │ ├── 89Hexp_c=0_p=1_i=4_a.png │ │ ├── 90Hexp_c=0_p=1_i=5_b.png │ │ ├── 91Hexp_c=14_p=28_i=3_c.png │ │ ├── 92Hexp_c=14_p=28_i=4_a.png │ │ ├── 93Hexp_c=15_p=30_i=3_a.png │ │ ├── 94Hexp_c=15_p=30_i=5_c.png │ │ ├── 95Hexp_c=6_p=12_i=2_b.png │ │ └── 96Hexp_c=14_p=29_i=3_a.png ├── round_2_answers.csv ├── round_2_questions │ ├── cuts │ │ ├── c=0_p=0_i=1_a.png │ │ ├── c=0_p=0_i=2_a.png │ │ ├── c=0_p=0_i=2_b.png │ │ ├── c=0_p=0_i=2_c.png │ │ ├── c=0_p=0_i=3_a.png │ │ ├── c=0_p=0_i=3_b.png │ │ ├── c=0_p=0_i=4_a.png │ │ ├── c=0_p=0_i=4_b.png │ │ ├── c=0_p=0_i=5_a.png │ │ ├── c=0_p=1_i=1_a.png │ │ ├── c=0_p=1_i=1_b.png │ │ ├── c=0_p=1_i=1_c.png │ │ ├── c=0_p=1_i=1_d.png │ │ ├── c=0_p=1_i=2_b.png │ │ ├── c=0_p=1_i=3_b.png │ │ ├── c=0_p=1_i=4_a.png │ │ ├── c=0_p=1_i=4_b.png │ │ ├── c=0_p=1_i=5_a.png │ │ ├── c=0_p=1_i=5_b.png │ │ ├── c=14_p=28_i=1_a.png │ │ ├── c=14_p=28_i=2_b.png │ │ ├── c=14_p=28_i=3_a.png │ │ ├── c=14_p=28_i=4_a.png │ │ ├── c=14_p=28_i=5_a.png │ │ ├── c=14_p=29_i=1_a.png │ │ ├── c=14_p=29_i=2_a.png │ │ ├── c=14_p=29_i=3_a.png │ │ ├── c=14_p=29_i=3_b.png │ │ ├── c=14_p=29_i=4_a.png │ │ ├── c=14_p=29_i=5_a.png │ │ ├── c=15_p=30_i=1_a.png │ │ ├── c=15_p=30_i=1_b.png │ │ ├── c=15_p=30_i=2_a.png │ │ ├── c=15_p=30_i=3_a.png │ │ ├── c=15_p=30_i=3_b.png │ │ ├── c=15_p=30_i=4_a.png │ │ ├── c=15_p=30_i=5_a.png │ │ ├── c=15_p=30_i=5_b.png │ │ ├── c=15_p=31_i=1_a.png │ │ ├── c=15_p=31_i=1_b.png │ │ ├── c=15_p=31_i=2_a.png │ │ ├── c=15_p=31_i=2_b.png │ │ ├── c=15_p=31_i=3_b.png │ │ ├── c=15_p=31_i=4_a.png │ │ ├── c=15_p=31_i=5_b.png │ │ ├── c=6_p=12_i=1_a.png │ │ ├── c=6_p=12_i=2_a.png │ │ ├── c=6_p=12_i=3_a.png │ │ ├── c=6_p=12_i=3_b.png │ │ ├── c=6_p=12_i=4_a.png │ │ ├── c=6_p=12_i=4_b.png │ │ ├── c=6_p=12_i=5_a.png │ │ ├── c=6_p=12_i=5_b.png │ │ ├── c=6_p=13_i=1_a.png │ │ ├── c=6_p=13_i=2_a.png │ │ ├── c=6_p=13_i=3_a.png │ │ ├── c=6_p=13_i=3_b.png │ │ ├── c=6_p=13_i=4_a.png │ │ ├── c=6_p=13_i=5_a.png │ │ ├── c=6_p=13_i=5_c.png │ │ ├── c=8_p=16_i=1_a.png │ │ ├── c=8_p=16_i=2_a.png │ │ ├── c=8_p=16_i=3_a.png │ │ ├── c=8_p=16_i=3_b.png │ │ ├── c=8_p=16_i=4_a.png │ │ ├── c=8_p=16_i=4_c.png │ │ ├── c=8_p=16_i=5_a.png │ │ ├── c=8_p=17_i=1_a.png │ │ ├── c=8_p=17_i=1_b.png │ │ ├── c=8_p=17_i=2_a.png │ │ ├── c=8_p=17_i=3_a.png │ │ ├── c=8_p=17_i=3_b.png │ │ ├── c=8_p=17_i=4_a.png │ │ └── c=8_p=17_i=5_a.png │ └── random_form_figures │ │ ├── 00Hexp_c=14_p=29_i=1_a.png │ │ ├── 01Hexp_c=0_p=0_i=3_a.png │ │ ├── 02Hexp_c=15_p=30_i=1_b.png │ │ ├── 03Hexp_c=14_p=29_i=3_b.png │ │ ├── 04Hexp_c=15_p=30_i=5_b.png │ │ ├── 05Hexp_c=15_p=30_i=4_a.png │ │ ├── 06Hexp_c=15_p=31_i=4_a.png │ │ ├── 07Hexp_c=15_p=31_i=1_b.png │ │ ├── 08Hexp_c=0_p=0_i=4_a.png │ │ ├── 09Hexp_c=15_p=31_i=2_a.png │ │ ├── 10Hexp_c=6_p=12_i=4_a.png │ │ ├── 11Hexp_c=8_p=16_i=5_a.png │ │ ├── 12Hexp_c=15_p=30_i=1_a.png │ │ ├── 13Hexp_c=15_p=31_i=3_b.png │ │ ├── 14Hexp_c=14_p=28_i=2_b.png │ │ ├── 15Hexp_c=0_p=1_i=5_b.png │ │ ├── 16Hexp_c=8_p=16_i=3_b.png │ │ ├── 17Hexp_c=6_p=12_i=5_b.png │ │ ├── 18Hexp_c=0_p=1_i=3_b.png │ │ ├── 19Hexp_c=6_p=12_i=3_a.png │ │ ├── 20Hexp_c=14_p=29_i=2_a.png │ │ ├── 21Hexp_c=0_p=0_i=2_c.png │ │ ├── 22Hexp_c=15_p=30_i=2_a.png │ │ ├── 23Hexp_c=8_p=16_i=4_a.png │ │ ├── 24Hexp_c=8_p=17_i=2_a.png │ │ ├── 25Hexp_c=8_p=16_i=1_a.png │ │ ├── 26Hexp_c=8_p=17_i=5_a.png │ │ ├── 27Hexp_c=6_p=13_i=5_a.png │ │ ├── 28Hexp_c=0_p=0_i=2_b.png │ │ ├── 29Hexp_c=8_p=16_i=3_a.png │ │ ├── 30Hexp_c=6_p=12_i=4_b.png │ │ ├── 31Hexp_c=0_p=1_i=4_a.png │ │ ├── 32Hexp_c=8_p=17_i=4_a.png │ │ ├── 33Hexp_c=6_p=13_i=3_a.png │ │ ├── 34Hexp_c=0_p=0_i=2_a.png │ │ ├── 35Hexp_c=0_p=1_i=1_b.png │ │ ├── 36Hexp_c=14_p=28_i=3_a.png │ │ ├── 37Hexp_c=14_p=28_i=5_a.png │ │ ├── 38Hexp_c=8_p=17_i=3_a.png │ │ ├── 39Hexp_c=6_p=12_i=1_a.png │ │ ├── 40Hexp_c=8_p=17_i=1_b.png │ │ ├── 41Hexp_c=15_p=31_i=1_a.png │ │ ├── 42Hexp_c=0_p=1_i=2_b.png │ │ ├── 43Hexp_c=0_p=0_i=3_b.png │ │ ├── 44Hexp_c=0_p=0_i=5_a.png │ │ ├── 45Hexp_c=6_p=13_i=1_a.png │ │ ├── 46Hexp_c=8_p=17_i=1_a.png │ │ ├── 47Hexp_c=6_p=13_i=3_b.png │ │ ├── 48Hexp_c=14_p=29_i=5_a.png │ │ ├── 49Hexp_c=8_p=17_i=3_b.png │ │ ├── 50Hexp_c=6_p=12_i=5_a.png │ │ ├── 51Hexp_c=0_p=1_i=1_c.png │ │ ├── 52Hexp_c=0_p=1_i=1_a.png │ │ ├── 53Hexp_c=6_p=13_i=4_a.png │ │ ├── 54Hexp_c=15_p=30_i=3_a.png │ │ ├── 55Hexp_c=8_p=16_i=2_a.png │ │ ├── 56Hexp_c=0_p=0_i=1_a.png │ │ ├── 57Hexp_c=15_p=31_i=5_b.png │ │ ├── 58Hexp_c=15_p=30_i=3_b.png │ │ ├── 59Hexp_c=8_p=16_i=4_c.png │ │ ├── 60Hexp_c=14_p=28_i=4_a.png │ │ ├── 61Hexp_c=14_p=29_i=4_a.png │ │ ├── 62Hexp_c=0_p=0_i=4_b.png │ │ ├── 63Hexp_c=0_p=1_i=5_a.png │ │ ├── 64Hexp_c=6_p=12_i=3_b.png │ │ ├── 65Hexp_c=6_p=13_i=2_a.png │ │ ├── 66Hexp_c=6_p=12_i=2_a.png │ │ ├── 67Hexp_c=14_p=29_i=3_a.png │ │ ├── 68Hexp_c=14_p=28_i=1_a.png │ │ ├── 69Hexp_c=6_p=13_i=5_c.png │ │ ├── 70Hexp_c=15_p=31_i=2_b.png │ │ ├── 71Hexp_c=0_p=1_i=1_d.png │ │ ├── 72Hexp_c=15_p=30_i=5_a.png │ │ └── 73Hexp_c=0_p=1_i=4_b.png ├── round_3_answers.csv └── round_3_questions │ ├── cuts │ ├── c=0_p=0_i=1_a.png │ ├── c=0_p=0_i=2_a.png │ ├── c=0_p=0_i=3_a.png │ ├── c=0_p=0_i=4_a.png │ ├── c=0_p=0_i=5_a.png │ ├── c=0_p=0_i=5_b.png │ ├── c=0_p=0_i=5_c.png │ ├── c=0_p=0_i=5_d.png │ ├── c=0_p=0_i=5_e.png │ ├── c=0_p=1_i=1_a.png │ ├── c=0_p=1_i=1_b.png │ ├── c=0_p=1_i=1_d.png │ ├── c=0_p=1_i=1_e.png │ ├── c=0_p=1_i=2_b.png │ ├── c=0_p=1_i=2_c.png │ ├── c=0_p=1_i=3_a.png │ ├── c=0_p=1_i=3_b.png │ ├── c=0_p=1_i=4_a.png │ ├── c=0_p=1_i=4_b.png │ ├── c=0_p=1_i=5_a.png │ ├── c=0_p=1_i=5_b.png │ ├── c=14_p=28_i=1_a.png │ ├── c=14_p=28_i=2_a.png │ ├── c=14_p=28_i=3_a.png │ ├── c=14_p=28_i=4_a.png │ ├── c=14_p=28_i=4_b.png │ ├── c=14_p=28_i=5_b.png │ ├── c=14_p=29_i=1_a.png │ ├── c=14_p=29_i=2_a.png │ ├── c=14_p=29_i=2_b.png │ ├── c=14_p=29_i=3_a.png │ ├── c=14_p=29_i=4_a.png │ ├── c=14_p=29_i=5_a.png │ ├── c=15_p=30_i=1_a.png │ ├── c=15_p=30_i=2_a.png │ ├── c=15_p=30_i=3_a.png │ ├── c=15_p=30_i=3_b.png │ ├── c=15_p=30_i=4_a.png │ ├── c=15_p=30_i=5_a.png │ ├── c=15_p=31_i=1_a.png │ ├── c=15_p=31_i=2_a.png │ ├── c=15_p=31_i=3_a.png │ ├── c=15_p=31_i=3_b.png │ ├── c=15_p=31_i=4_a.png │ ├── c=15_p=31_i=5_a.png │ ├── c=6_p=12_i=1_a.png │ ├── c=6_p=12_i=2_a.png │ ├── c=6_p=12_i=3_a.png │ ├── c=6_p=12_i=4_a.png │ ├── c=6_p=12_i=5_a.png │ ├── c=6_p=13_i=1_a.png │ ├── c=6_p=13_i=2_a.png │ ├── c=6_p=13_i=2_b.png │ ├── c=6_p=13_i=2_c.png │ ├── c=6_p=13_i=2_d.png │ ├── c=6_p=13_i=3_a.png │ ├── c=6_p=13_i=4_a.png │ ├── c=6_p=13_i=5_a.png │ ├── c=8_p=16_i=1_a.png │ ├── c=8_p=16_i=2_a.png │ ├── c=8_p=16_i=2_b.png │ ├── c=8_p=16_i=2_c.png │ ├── c=8_p=16_i=3_a.png │ ├── c=8_p=16_i=3_b.png │ ├── c=8_p=16_i=4_a.png │ ├── c=8_p=16_i=5_a.png │ ├── c=8_p=16_i=5_b.png │ ├── c=8_p=16_i=5_c.png │ ├── c=8_p=17_i=1_a.png │ ├── c=8_p=17_i=2_a.png │ ├── c=8_p=17_i=2_b.png │ ├── c=8_p=17_i=2_c.png │ ├── c=8_p=17_i=3_a.png │ ├── c=8_p=17_i=3_b.png │ ├── c=8_p=17_i=4_a.png │ ├── c=8_p=17_i=5_a.png │ ├── c=8_p=17_i=5_b.png │ └── c=8_p=17_i=5_c.png │ └── random_form_figures │ ├── 00Hexp_c=14_p=28_i=4_a.png │ ├── 01Hexp_c=0_p=0_i=5_a.png │ ├── 02Hexp_c=14_p=29_i=4_a.png │ ├── 03Hexp_c=14_p=29_i=1_a.png │ ├── 04Hexp_c=15_p=30_i=4_a.png │ ├── 05Hexp_c=15_p=30_i=3_a.png │ ├── 06Hexp_c=15_p=31_i=4_a.png │ ├── 07Hexp_c=15_p=31_i=1_a.png │ ├── 08Hexp_c=8_p=16_i=5_b.png │ ├── 09Hexp_c=0_p=0_i=5_c.png │ ├── 10Hexp_c=15_p=31_i=2_a.png │ ├── 11Hexp_c=6_p=12_i=5_a.png │ ├── 12Hexp_c=8_p=16_i=2_b.png │ ├── 13Hexp_c=8_p=16_i=5_c.png │ ├── 14Hexp_c=8_p=17_i=3_b.png │ ├── 15Hexp_c=8_p=17_i=2_a.png │ ├── 16Hexp_c=8_p=16_i=1_a.png │ ├── 17Hexp_c=6_p=13_i=2_b.png │ ├── 18Hexp_c=0_p=1_i=2_c.png │ ├── 19Hexp_c=6_p=12_i=3_a.png │ ├── 20Hexp_c=14_p=28_i=4_b.png │ ├── 21Hexp_c=0_p=0_i=4_a.png │ ├── 22Hexp_c=14_p=29_i=5_a.png │ ├── 23Hexp_c=8_p=16_i=4_a.png │ ├── 24Hexp_c=8_p=16_i=3_b.png │ ├── 25Hexp_c=15_p=31_i=3_b.png │ ├── 26Hexp_c=8_p=17_i=5_c.png │ ├── 27Hexp_c=0_p=1_i=4_b.png │ ├── 28Hexp_c=0_p=0_i=3_a.png │ ├── 29Hexp_c=8_p=17_i=2_b.png │ ├── 30Hexp_c=6_p=13_i=2_d.png │ ├── 31Hexp_c=0_p=1_i=3_a.png │ ├── 32Hexp_c=8_p=17_i=5_b.png │ ├── 33Hexp_c=8_p=16_i=2_a.png │ ├── 34Hexp_c=0_p=0_i=2_a.png │ ├── 35Hexp_c=0_p=1_i=1_b.png │ ├── 36Hexp_c=6_p=12_i=4_a.png │ ├── 37Hexp_c=14_p=28_i=1_a.png │ ├── 38Hexp_c=14_p=28_i=3_a.png │ ├── 39Hexp_c=8_p=17_i=4_a.png │ ├── 40Hexp_c=6_p=13_i=2_c.png │ ├── 41Hexp_c=8_p=16_i=3_a.png │ ├── 42Hexp_c=15_p=30_i=5_a.png │ ├── 43Hexp_c=8_p=16_i=5_a.png │ ├── 44Hexp_c=0_p=1_i=2_b.png │ ├── 45Hexp_c=15_p=30_i=2_a.png │ ├── 46Hexp_c=0_p=1_i=3_b.png │ ├── 47Hexp_c=6_p=13_i=5_a.png │ ├── 48Hexp_c=15_p=31_i=5_a.png │ ├── 49Hexp_c=6_p=13_i=4_a.png │ ├── 50Hexp_c=0_p=0_i=5_e.png │ ├── 51Hexp_c=15_p=30_i=1_a.png │ ├── 52Hexp_c=6_p=13_i=3_a.png │ ├── 53Hexp_c=0_p=1_i=1_d.png │ ├── 54Hexp_c=0_p=1_i=1_a.png │ ├── 55Hexp_c=15_p=31_i=3_a.png │ ├── 56Hexp_c=14_p=29_i=2_b.png │ ├── 57Hexp_c=0_p=0_i=5_b.png │ ├── 58Hexp_c=0_p=0_i=1_a.png │ ├── 59Hexp_c=8_p=17_i=2_c.png │ ├── 60Hexp_c=15_p=30_i=3_b.png │ ├── 61Hexp_c=14_p=28_i=5_b.png │ ├── 62Hexp_c=6_p=12_i=2_a.png │ ├── 63Hexp_c=0_p=1_i=5_a.png │ ├── 64Hexp_c=0_p=1_i=4_a.png │ ├── 65Hexp_c=0_p=0_i=5_d.png │ ├── 66Hexp_c=0_p=1_i=5_b.png │ ├── 67Hexp_c=0_p=1_i=1_e.png │ ├── 68Hexp_c=8_p=17_i=1_a.png │ ├── 69Hexp_c=6_p=13_i=2_a.png │ ├── 70Hexp_c=8_p=17_i=3_a.png │ ├── 71Hexp_c=14_p=28_i=2_a.png │ ├── 72Hexp_c=14_p=29_i=3_a.png │ ├── 73Hexp_c=6_p=13_i=1_a.png │ ├── 74Hexp_c=8_p=16_i=2_c.png │ ├── 75Hexp_c=14_p=29_i=2_a.png │ ├── 76Hexp_c=6_p=12_i=1_a.png │ └── 77Hexp_c=8_p=17_i=5_a.png └── visualization ├── plot_global_analysis.py ├── plot_local_analysis.py └── plot_prototypes.py /.gitignore: -------------------------------------------------------------------------------- 1 | wandb 2 | .idea 3 | datasets/* 4 | #!datasets/*.dvc 5 | .dvc 6 | 7 | # Byte-compiled / optimized / DLL files 8 | __pycache__/ 9 | *.py[cod] 10 | *$py.class 11 | *.sw[op] 12 | *.pkl 13 | *pkl.gz 14 | results/ 15 | *.pth 16 | *.DS_Store 17 | *.Rhistory 18 | 19 | # C extensions 20 | *.so 21 | 22 | # Distribution / packaging 23 | .Python 24 | build/ 25 | develop-eggs/ 26 | dist/ 27 | downloads/ 28 | eggs/ 29 | .eggs/ 30 | lib/ 31 | lib64/ 32 | parts/ 33 | sdist/ 34 | var/ 35 | wheels/ 36 | *.egg-info/ 37 | .installed.cfg 38 | *.egg 39 | MANIFEST 40 | 41 | # PyInstaller 42 | # Usually these files are written by a python script from a template 43 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 44 | *.manifest 45 | *.spec 46 | 47 | # Installer logs 48 | pip-log.txt 49 | pip-delete-this-directory.txt 50 | 51 | # Unit test / coverage reports 52 | htmlcov/ 53 | .tox/ 54 | .coverage 55 | .coverage.* 56 | .cache 57 | nosetests.xml 58 | coverage.xml 59 | *.cover 60 | .hypothesis/ 61 | .pytest_cache/ 62 | 63 | # Translations 64 | *.mo 65 | *.pot 66 | 67 | # Django stuff: 68 | *.log 69 | local_settings.py 70 | db.sqlite3 71 | 72 | # Flask stuff: 73 | instance/ 74 | .webassets-cache 75 | 76 | # Scrapy stuff: 77 | .scrapy 78 | 79 | # Sphinx documentation 80 | docs/_build/ 81 | 82 | # PyBuilder 83 | target/ 84 | 85 | # Jupyter Notebook 86 | .ipynb_checkpoints 87 | 88 | # pyenv 89 | .python-version 90 | 91 | # celery beat schedule file 92 | celerybeat-schedule 93 | 94 | # SageMath parsed files 95 | *.sage.py 96 | 97 | # Environments 98 | .env 99 | .venv 100 | env/ 101 | venv/ 102 | ENV/ 103 | env.bak/ 104 | venv.bak/ 105 | 106 | # Spyder project settings 107 | .spyderproject 108 | .spyproject 109 | 110 | # Rope project settings 111 | .ropeproject 112 | 113 | # mkdocs documentation 114 | /site 115 | 116 | # mypy 117 | .mypy_cache/ 118 | 119 | my_old_code/ 120 | 121 | old_code/ 122 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2022 abonte 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /ProtoPNet_LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2019 Chaofan Chen (cfchen-duke), Oscar Li (OscarcarLi), 4 | Chaofan Tao, Alina Jade Barnett, Cynthia Rudin 5 | 6 | Permission is hereby granted, free of charge, to any person obtaining a copy 7 | of this software and associated documentation files (the "Software"), to deal 8 | in the Software without restriction, including without limitation the rights 9 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 10 | copies of the Software, and to permit persons to whom the Software is 11 | furnished to do so, subject to the following conditions: 12 | 13 | The above copyright notice and this permission notice shall be included in all 14 | copies or substantial portions of the Software. 15 | 16 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 17 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 18 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 19 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 20 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 21 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 22 | SOFTWARE. 23 | -------------------------------------------------------------------------------- /conf/config.yaml: -------------------------------------------------------------------------------- 1 | defaults: 2 | - myconfig 3 | # - my_run_dir 4 | - _self_ 5 | 6 | 7 | hydra: 8 | run: 9 | dir: 'saved_models/${data.name}/${experiment_name}__${model.base_architecture}__${data.name}__e=${epochs}__we=${warm_epochs}__λfix=${model.coefs.debug}__${hydra.job.override_dirname}' 10 | sweep: 11 | dir: 'saved_models/${data.name}/${experiment_name}__${model.base_architecture}__${data.name}__e=${epochs}__we=${warm_epochs}__loss=${debug.loss}_fa=${debug.fine_annotation}' 12 | subdir: ${hydra.job.override_dirname} 13 | job: 14 | chdir: True 15 | config: 16 | override_dirname: 17 | item_sep: '__' 18 | exclude_keys: 19 | - experiment_name 20 | - warm_epochs 21 | - epochs 22 | - push_epochs 23 | - push_start 24 | - last_layer_iterations 25 | - data 26 | - cpu 27 | - gpuid 28 | - wandb 29 | - model.base_architecture 30 | - debug.path_to_model 31 | - debug.auto_path_to_model 32 | - debug.classes 33 | - debug.protos 34 | - data.train_push_batch_size 35 | - data.train_batch_size 36 | - data.test_batch_size 37 | -------------------------------------------------------------------------------- /conf/experiment/artificial_aggregation.yaml: -------------------------------------------------------------------------------- 1 | # @package _global_ 2 | defaults: 3 | - artificial_base 4 | - /data/cub200_artificial 5 | - _self_ 6 | 7 | debug: 8 | loss: aggregation 9 | act_place: center 10 | class_specific_penalization: true 11 | classes: 12 | - 0 13 | - 1 14 | - 4 15 | 16 | model: 17 | coefs: 18 | debug: 100 -------------------------------------------------------------------------------- /conf/experiment/artificial_base.yaml: -------------------------------------------------------------------------------- 1 | # @package _global_ 2 | defaults: 3 | - _self_ 4 | 5 | wandb: true 6 | warm_epochs: 15 7 | epochs: 15 8 | 9 | model: 10 | base_architecture: vgg16 11 | topk_k: 1 -------------------------------------------------------------------------------- /conf/experiment/artificial_iaiabl.yaml: -------------------------------------------------------------------------------- 1 | # @package _global_ 2 | defaults: 3 | - artificial_base 4 | - /data/cub200_artificial 5 | - _self_ 6 | 7 | debug: 8 | loss: iaiabl 9 | 10 | model: 11 | coefs: 12 | debug: 0.001 -------------------------------------------------------------------------------- /conf/experiment/artificial_lower.yaml: -------------------------------------------------------------------------------- 1 | # @package _global_ 2 | defaults: 3 | - artificial_base 4 | - /data/cub200_artificial 5 | - _self_ -------------------------------------------------------------------------------- /conf/experiment/artificial_upper.yaml: -------------------------------------------------------------------------------- 1 | # @package _global_ 2 | defaults: 3 | - artificial_base 4 | - /data/cub200_clean_5 5 | - _self_ -------------------------------------------------------------------------------- /conf/experiment/covid_aggregation.yaml: -------------------------------------------------------------------------------- 1 | # @package _global_ 2 | defaults: 3 | - covid_base 4 | - /data/covid 5 | - _self_ 6 | 7 | 8 | epochs: 2 9 | warm_epochs: 2 10 | 11 | debug: 12 | class_specific_penalization: False 13 | loss: aggregation 14 | act_place: all 15 | 16 | model: 17 | coefs: 18 | debug: 100 19 | rem: 10 20 | clst: 0.1 21 | sep: 0.001 22 | crs_ent: 0.4 -------------------------------------------------------------------------------- /conf/experiment/covid_base.yaml: -------------------------------------------------------------------------------- 1 | # @package _global_ 2 | defaults: 3 | - /data/covid 4 | - _self_ 5 | 6 | wandb: False 7 | warm_epochs: 15 8 | epochs: 15 9 | last_layer_iterations: 0 10 | 11 | model: 12 | num_prototypes_per_class: 2 13 | base_architecture: vgg19 14 | topk_k: 0.05 -------------------------------------------------------------------------------- /conf/experiment/natural_aggregation.yaml: -------------------------------------------------------------------------------- 1 | # @package _global_ 2 | defaults: 3 | - natural_base 4 | - _self_ 5 | 6 | warm_epochs: 5 7 | epochs: 5 8 | last_layer_iterations: 2 9 | 10 | debug: 11 | loss: aggregation 12 | act_place: all 13 | class_specific_penalization: false 14 | 15 | model: 16 | coefs: 17 | debug: 100 -------------------------------------------------------------------------------- /conf/experiment/natural_base.yaml: -------------------------------------------------------------------------------- 1 | # @package _global_ 2 | defaults: 3 | - /data/cub200_clean_top20 4 | - _self_ 5 | 6 | wandb: False 7 | last_layer_iterations: 10 8 | 9 | model: 10 | num_prototypes_per_class: 2 11 | base_architecture: vgg16 12 | topk_k: 0.05 -------------------------------------------------------------------------------- /conf/experiment/natural_base_upper_lower.yaml: -------------------------------------------------------------------------------- 1 | # @package _global_ 2 | defaults: 3 | - natural_base 4 | - _self_ 5 | 6 | warm_epochs: 45 7 | epochs: 45 8 | push_epochs: 9 | - 14 10 | - 29 11 | - 44 -------------------------------------------------------------------------------- /conf/experiment/natural_iaiabl.yaml: -------------------------------------------------------------------------------- 1 | # @package _global_ 2 | defaults: 3 | - natural_base_upper_lower 4 | - _self_ 5 | 6 | debug: 7 | loss: iaiabl 8 | fine_annotation: 0.20 9 | 10 | model: 11 | coefs: 12 | debug: 0.001 -------------------------------------------------------------------------------- /conf/experiment/natural_upper.yaml: -------------------------------------------------------------------------------- 1 | # @package _global_ 2 | defaults: 3 | - natural_base_upper_lower 4 | - _self_ 5 | 6 | data: 7 | train_on_segmented_image: True -------------------------------------------------------------------------------- /conf/experiment/natural_upper1.yaml: -------------------------------------------------------------------------------- 1 | # @package _global_ 2 | defaults: 3 | - natural_base_upper_lower 4 | - _self_ 5 | 6 | data: 7 | train_on_segmented_image: True 8 | test_on_segmented_image: True -------------------------------------------------------------------------------- /copy_top_20.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | classes=( 4 | 113.Baird_Sparrow 5 | 200.Common_Yellowthroat 6 | 076.Dark_eyed_Junco 7 | 086.Pacific_Loon 8 | 040.Olive_sided_Flycatcher 9 | 037.Acadian_Flycatcher 10 | 026.Bronzed_Cowbird 11 | 001.Black_footed_Albatross 12 | 128.Seaside_Sparrow 13 | 070.Green_Violetear 14 | 137.Cliff_Swallow 15 | 161.Blue_winged_Warbler 16 | 004.Groove_billed_Ani 17 | 043.Yellow_bellied_Flycatcher 18 | 183.Northern_Waterthrush 19 | 188.Pileated_Woodpecker 20 | 056.Pine_Grosbeak 21 | 122.Harris_Sparrow 22 | 006.Least_Auklet 23 | 085.Horned_Lark 24 | ) 25 | 26 | dest="clean_top_20" 27 | 28 | for cls in "${classes[@]}"; do 29 | for folder in test_cropped_shuffled train_cropped_augmented test_cropped_shuffled_segmentation train_cropped_augmented_segmentation train_cropped train_cropped_segmentation; do 30 | echo $cls $folder 31 | mkdir -p "datasets/cub200_cropped/$dest/$folder" 32 | cmd="cp -r datasets/cub200_cropped/clean_all/$folder/$cls datasets/cub200_cropped/$dest/$folder" 33 | $cmd 34 | done 35 | done -------------------------------------------------------------------------------- /data/cdatasets/__init__.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # __init__.py 3 | from .chestxray14dataset import ChestXray14Dataset 4 | from .chestxray14h5 import ChestXray14H5Dataset 5 | 6 | from .padchestdataset import PadChestDataset 7 | from .padchesth5 import PadChestH5Dataset 8 | 9 | from .githubcovid import GitHubCOVIDDataset 10 | from .bimcvcovid import BIMCVCOVIDDataset 11 | from .bimcvnegative import BIMCVNegativeDataset 12 | 13 | from .domainconfoundeddatasets import DomainConfoundedDataset 14 | -------------------------------------------------------------------------------- /data/cdatasets/bimcvnegative.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | from .bimcvcovid import * 3 | import os 4 | import pandas 5 | 6 | 7 | class BIMCVNegativeDataset(BIMCVCOVIDDataset): 8 | def __init__(self, fold, random_state=30493, include_lateral=False, 9 | include_unknown_projections=False, include_ap_supine=False, 10 | include_unknown_labels=False, initialize_h5=False, 11 | covid_labels='molecular', 12 | labels='chexpert'): 13 | if include_unknown_projections: 14 | raise NotImplementedError('not implemented for bimcv negative dataset') 15 | super().__init__(fold, random_state=random_state, 16 | include_lateral=include_lateral, 17 | include_unknown_projections=include_unknown_projections, 18 | include_ap_supine=include_ap_supine, 19 | include_unknown_labels=include_unknown_labels, 20 | initialize_h5=initialize_h5, covid_labels=covid_labels, 21 | labels=labels) 22 | 23 | def _set_datapaths(self): 24 | self.datapath = 'data/bimcv-' 25 | self.labelpath = 'derivatives/labels/labels_SARS-cov-2_nega.tsv' 26 | # leave the same as bimcv-covid-19+ for now 27 | self.unknown_label_path = 'datasets/bimcv_covid_unknown_labels.txt' 28 | self.labeldf = pandas.read_csv(os.path.join(self.datapath, self.labelpath), 29 | delimiter='\t') 30 | self.h5path = 'data/bimcv-/bimcv-.h5' 31 | self.df = pandas.read_csv( 32 | os.path.join(self.datapath, 'BIMCV-COVID-19-negative.csv')) 33 | # Not available for now 34 | self.manual_projection_label_path = "datasets/bimcv_covid_manual_projection_labels.yml" 35 | 36 | def add_covid_label(self, findings): 37 | # NO samples are covid-19+ 38 | return findings 39 | -------------------------------------------------------------------------------- /data/cdatasets/chestxray14h5.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | import io 3 | import os 4 | 5 | import h5py 6 | import numpy 7 | from PIL import Image 8 | 9 | from settings import ROOT_DIR 10 | from .chestxray14dataset import ChestXray14Dataset 11 | from .cxrdataset import H5Dataset 12 | 13 | 14 | class ChestXray14H5Dataset(ChestXray14Dataset, H5Dataset): 15 | """ 16 | HDF5 dataset for ChestX-ray14 images. 17 | """ 18 | 19 | def __init__( 20 | self, 21 | fold, 22 | random_state=30493, 23 | labels='CheXpert', 24 | initialize_h5=False, 25 | pneumo=None, 26 | normalize=False, 27 | subsample_test=True): 28 | """ 29 | initialize_h5: (bool) If true, open a handle for the HDF5 file when the 30 | class is instantiated. Use `False` when the dataset will be wrapped 31 | by a PyTorch dataloader with num_workers > 0, and True otherwise. 32 | """ 33 | self.h5path = os.path.join(ROOT_DIR, "datasets/covid/ChestX-ray14/chestxray14.h5") 34 | if initialize_h5: 35 | self.h5 = h5py.File(self.h5path, 'r', swmr=True) 36 | super().__init__(fold, 37 | random_state=random_state, 38 | labels=labels, 39 | pneumo=pneumo, 40 | normalize=normalize, 41 | subsample_test=subsample_test) 42 | 43 | def init_worker(self, worker_id): 44 | self.h5 = h5py.File(self.h5path, 'r', swmr=True) 45 | 46 | def _raw_image_from_disk(self, idx): 47 | """ 48 | Retrieve the raw PIL image from storage. 49 | """ 50 | imagename = self.df.index[idx] 51 | data = self.h5['images']['images'].get(imagename) 52 | image = Image.open(io.BytesIO(numpy.array(data))) 53 | image = image.convert('RGB') 54 | return image, os.path.join(ROOT_DIR, "datasets/covid/ChestX-ray14/images/", imagename) 55 | -------------------------------------------------------------------------------- /data/cdatasets/domainconfoundeddatasets.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | import numpy 3 | import torch 4 | 5 | 6 | class DomainConfoundedDataset(torch.utils.data.Dataset): 7 | def __init__(self, datasetclass1, datasetclass2): 8 | self.ds1 = datasetclass1 9 | self.ds2 = datasetclass2 10 | self.len1 = len(self.ds1) 11 | self.len2 = len(self.ds2) 12 | self.labels = self.ds1.labels 13 | 14 | def init_worker(self, worker_id): 15 | self.ds1.init_worker(worker_id) 16 | self.ds2.init_worker(worker_id) 17 | 18 | def __getitem__(self, idx): 19 | if idx < self.len1: 20 | item = self.ds1[idx] 21 | else: # if idx >= self.len1 22 | item = self.ds2[idx - self.len1] 23 | return item[0], item[1], item[2], item[3] 24 | 25 | def __len__(self): 26 | return self.len1 + self.len2 27 | 28 | def get_all_labels(self): 29 | labels1 = self.ds1.get_all_labels() 30 | labels2 = self.ds2.get_all_labels() 31 | if labels1.shape[0] == 1: 32 | return numpy.concatenate((self.ds1.get_all_labels(), 33 | self.ds2.get_all_labels()), axis=1) 34 | else: 35 | return numpy.concatenate((self.ds1.get_all_labels(), 36 | self.ds2.get_all_labels()), axis=0) 37 | -------------------------------------------------------------------------------- /data/cdatasets/padchesth5.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | import io 3 | import os 4 | 5 | import h5py 6 | import numpy 7 | from PIL import Image 8 | 9 | from settings import ROOT_DIR 10 | from .padchestdataset import PadChestDataset 11 | from .cxrdataset import H5Dataset 12 | 13 | 14 | class PadChestH5Dataset(PadChestDataset, H5Dataset): 15 | """ 16 | HDF5 dataset for PadChest images. 17 | """ 18 | 19 | def __init__( 20 | self, 21 | fold, 22 | include_lateral=False, 23 | random_state=30493, 24 | labels='CheXpert', 25 | initialize_h5=False, 26 | pneumo=None): 27 | """ 28 | initialize_h5: (bool) If true, open a handle for the HDF5 file when the 29 | class is instantiated. Use `False` when the dataset will be wrapped 30 | by a PyTorch dataloader with num_workers > 0, and True otherwise. 31 | """ 32 | self.h5path = os.path.join(ROOT_DIR, "datasets/covid/PadChest/padchest.h5") 33 | if initialize_h5: 34 | self.h5 = h5py.File(self.h5path, 'r', swmr=True) 35 | super().__init__(fold, 36 | include_lateral=include_lateral, 37 | random_state=random_state, 38 | labels=labels, 39 | pneumo=pneumo) 40 | 41 | def init_worker(self, worker_id): 42 | self.h5 = h5py.File(self.h5path, 'r', swmr=True) 43 | 44 | def _raw_image_from_disk(self, idx): 45 | """ 46 | Retrieve the raw PIL image from storage. 47 | """ 48 | imagename = self.df.ImageID.iloc[idx] 49 | data = self.h5['images']['images'].get(imagename) 50 | image = Image.open(io.BytesIO(numpy.array(data))) 51 | return image 52 | -------------------------------------------------------------------------------- /data/make_h5.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # make_h5.py 3 | # 4 | import argparse 5 | import h5py 6 | import io 7 | import numpy 8 | import os 9 | import torchvision.transforms 10 | import tqdm 11 | from PIL import Image 12 | 13 | import sys 14 | 15 | sys.path.append(os.path.join(os.path.dirname(__file__), '..')) 16 | import cdatasets.padchestdataset 17 | 18 | PADCHEST_CORRUPTED = set(cdatasets.padchestdataset.CORRUPTED) 19 | 20 | 21 | def is_image(path): 22 | lower = path.lower() 23 | if lower.startswith('.') or lower.endswith('fake.jpg'): 24 | return False 25 | if lower.endswith('.jpg') or lower.endswith('png'): 26 | return True 27 | else: 28 | return False 29 | 30 | 31 | def find_images(parentpath): 32 | """ 33 | Find images in ``parentpath`` and all subdirectories. Return list of paths 34 | (strings). 35 | """ 36 | paths = [] 37 | for triple in os.walk(parentpath, followlinks=True): 38 | for path in triple[-1]: 39 | if is_image(path): 40 | paths.append(os.path.join(triple[0], path)) 41 | # remove path prefix 42 | for ipath, path in enumerate(paths): 43 | paths[ipath] = path[len(parentpath):] 44 | return paths 45 | 46 | 47 | def save_png_bytes(h5handle, path, image): 48 | image_byte_array = io.BytesIO() 49 | image.save(image_byte_array, format='PNG') 50 | image_bytes = image_byte_array.getvalue() 51 | # magic 52 | image_raw_np = numpy.asarray(image_bytes) 53 | h5handle['images'].create_dataset(path, data=image_raw_np) 54 | 55 | 56 | def convert_dataset(): 57 | transform = torchvision.transforms.Compose([ 58 | torchvision.transforms.Resize(224), 59 | torchvision.transforms.CenterCrop(224) 60 | ]) 61 | 62 | parser = argparse.ArgumentParser() 63 | parser.add_argument("-i", dest='imagedir') 64 | parser.add_argument("-o", dest='outpath') 65 | args = parser.parse_args() 66 | 67 | if not os.path.exists(args.imagedir): 68 | raise ValueError(f'{args.imagedir} does not exist!') 69 | h5handle = h5py.File(args.outpath, 'w', libver='latest') 70 | h5handle.swmr_mode = True 71 | 72 | h5handle.create_group("images") 73 | 74 | # directory structure is
/ 75 | print("\n") 76 | img_paths = find_images(args.imagedir) 77 | for ip in tqdm.tqdm(img_paths): 78 | if not os.path.basename(ip) in PADCHEST_CORRUPTED: 79 | path = os.path.join(os.path.abspath(args.imagedir), ip.strip('/')) 80 | image = Image.open(path) 81 | # downsample 82 | image = transform(image) 83 | save_png_bytes(h5handle, ip.strip('/'), image) 84 | 85 | 86 | if __name__ == "__main__": 87 | convert_dataset() 88 | -------------------------------------------------------------------------------- /debugging-loop.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/abonte/protopdebug/b43848b10f14bb5bb6477db2f788e0acf91ca3f3/debugging-loop.png -------------------------------------------------------------------------------- /environment.yml: -------------------------------------------------------------------------------- 1 | name: protopnet 2 | channels: 3 | - defaults 4 | - conda-forge 5 | - pytorch 6 | dependencies: 7 | - opencv 8 | - cudatoolkit=11.3 9 | - python-dotenv 10 | - pytorch 11 | - torchvision 12 | - prettytable 13 | - requests 14 | - torchaudio 15 | - wandb 16 | - tqdm 17 | - scikit-learn 18 | - matplotlib 19 | - pillow 20 | - python=3.9 21 | - pandas 22 | - hydra-core 23 | 24 | 25 | -------------------------------------------------------------------------------- /features/custom_features.py: -------------------------------------------------------------------------------- 1 | import torch.nn as nn 2 | 3 | 4 | def custom_features(pretrained=False): 5 | model = Custom_features() 6 | return model 7 | 8 | 9 | class Custom_features(nn.Module): 10 | 11 | def __init__(self): 12 | super(Custom_features, self).__init__() 13 | self.kernel_sizes = [] 14 | self.strides = [] 15 | self.paddings = [] 16 | self.features = nn.Sequential( 17 | nn.Conv2d(in_channels=3, out_channels=8, kernel_size=3, stride=2, padding=1), 18 | nn.ReLU(), 19 | nn.MaxPool2d(kernel_size=(3, 3), stride=2, padding=1), 20 | nn.Conv2d(in_channels=8, out_channels=16, kernel_size=(3, 3), padding=1), 21 | nn.ReLU(), 22 | #nn.Dropout(0.2) 23 | ) 24 | 25 | self.kernel_sizes.extend([3, 3, 3]) 26 | self.strides.extend([2, 2, 1]) 27 | self.paddings.extend([1, 1, 1]) 28 | 29 | self.n_layers = 3 30 | 31 | def forward(self, x): 32 | return self.features(x) 33 | 34 | def conv_info(self): 35 | return self.kernel_sizes, self.strides, self.paddings 36 | 37 | def num_layers(self): 38 | ''' 39 | the number of conv layers in the network 40 | ''' 41 | return self.n_layers 42 | -------------------------------------------------------------------------------- /plot.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | model=$(echo $1 | rev | cut -d '/' -f 1 | rev) 4 | modeldir=$(echo $1 | rev | cut -d '/' -f 2- | rev) 5 | 6 | classes=$2 7 | 8 | # classes="0 8 14 6 15" # Cub200 9 | # classes="0 1" # COVID 10 | 11 | python plot_stat.py -o $modeldir comparison -modeldir $modeldir 12 | python visualization/plot_prototypes.py -modeldir $modeldir -classes $classes 13 | python global_analysis.py $1 && \ 14 | python visualization/plot_global_analysis.py $1 -classes $classes 15 | -------------------------------------------------------------------------------- /plot_repetitions.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | find $1 -maxdepth 1 -mindepth 1 -type d | while read line; do 4 | echo $line 5 | python plot_stat.py -o $line comparison -modeldir $line 6 | #python visualization/plot_prototypes.py -modeldir $line 7 | 8 | #modelpath=$(find $line -maxdepth 1 -type f -name "*nopush*.pth.tar" | tail -n 1) 9 | #echo $modelpath 10 | #python global_analysis.py $modelpath 11 | done 12 | -------------------------------------------------------------------------------- /plots/.gitignore: -------------------------------------------------------------------------------- 1 | * 2 | !.gitignore -------------------------------------------------------------------------------- /preprocess.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | mean = (0.485, 0.456, 0.406) 4 | std = (0.229, 0.224, 0.225) 5 | 6 | 7 | def preprocess(x, mean, std): 8 | assert x.size(1) == 3 9 | y = torch.zeros_like(x) 10 | for i in range(3): 11 | y[:, i, :, :] = (x[:, i, :, :] - mean[i]) / std[i] 12 | return y 13 | 14 | 15 | def preprocess_input_function(x): 16 | """ 17 | allocate new tensor like x and apply the normalization used in the 18 | pretrained model 19 | """ 20 | return preprocess(x, mean=mean, std=std) 21 | 22 | 23 | def undo_preprocess(x, mean, std): 24 | assert x.size(1) == 3 25 | y = torch.zeros_like(x) 26 | for i in range(3): 27 | y[:, i, :, :] = x[:, i, :, :] * std[i] + mean[i] 28 | return y 29 | 30 | 31 | def undo_preprocess_input_function(x): 32 | """ 33 | allocate new tensor like x and undo the normalization used in the 34 | pretrained model 35 | """ 36 | return undo_preprocess(x, mean=mean, std=std) 37 | -------------------------------------------------------------------------------- /pretrained_models/.gitignore: -------------------------------------------------------------------------------- 1 | * 2 | !.gitignore -------------------------------------------------------------------------------- /ranking_class_performance.py: -------------------------------------------------------------------------------- 1 | import os 2 | 3 | from plot_stat import load_experiments, _get_f1_per_class 4 | import numpy as np 5 | 6 | dataset_path = 'datasets/cub200_cropped/clean_top_20' 7 | 8 | path_upper = "saved_models/cub200_clean_top20/237_upperbound__vgg16__cub200_clean_top20__e=45__we=5__λfix=0.0__+experiment=natural_upper" 9 | path_lower = "saved_models/cub200_clean_top20/237_lowerbound__vgg16__cub200_clean_top20__e=45__we=5__λfix=0.0__+experiment=natural_base_upper_lower" 10 | 11 | print('Load...') 12 | trace_upper = load_experiments([path_upper]) 13 | trace_lower = load_experiments([path_lower]) 14 | 15 | assert len(trace_lower) == 1 16 | assert len(trace_upper) == 1 17 | 18 | classes = sorted(os.listdir(os.path.join(dataset_path, 'test_cropped_shuffled'))) 19 | classes.remove('.DS_Store') 20 | 21 | f1_lower = _get_f1_per_class(trace_lower[0][0], 'test') 22 | f1_upper = _get_f1_per_class(trace_upper[0][0], 'test') 23 | assert np.array(list(f1_lower.values())).flatten().shape == np.array(list(f1_upper.values())).flatten().shape 24 | print(f'f1 lower: {np.mean(np.array(list(f1_lower.values())).flatten())} f1 upper {np.mean(np.array(list(f1_upper.values())).flatten())}') 25 | 26 | difference = {} 27 | 28 | assert len(f1_upper.keys()) == len(f1_lower.keys()) == len(classes) 29 | 30 | for cl in f1_upper.keys(): 31 | difference[classes[int(cl)]] = (f1_upper[cl][-1] - f1_lower[cl][-1], int(cl)) 32 | 33 | sorted_classes = dict(sorted(difference.items(), key=lambda x: x[1][0])[::-1]) 34 | 35 | for k, v in sorted_classes.items(): 36 | print(k, v[0], 'idx=', v[1]) 37 | -------------------------------------------------------------------------------- /run_artificial_confound.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | ############################################## 4 | # EXP 1: bird dataset with ARTIFICIAL confound 5 | ############################################## 6 | 7 | repetition="seed=range(5,20)" 8 | 9 | 10 | cmd_aggregation="python main.py --multirun $repetition 11 | experiment_name=forgetting_loss 12 | +experiment=artificial_aggregation" 13 | echo $cmd_aggregation && $cmd_aggregation 14 | 15 | 16 | 17 | 18 | cmd_lower="python main.py --multirun $repetition 19 | experiment_name=lowerbound 20 | +experiment=artificial_lower" 21 | echo $cmd_lower && $cmd_lower 22 | 23 | 24 | 25 | 26 | cmd_upper="python main.py --multirun $repetition 27 | experiment_name=upperbound 28 | +experiment=artificial_upper" 29 | echo $cmd_upper && $cmd_upper 30 | 31 | 32 | 33 | 34 | cmd_iaiabl="python main.py --multirun $repetition 35 | experiment_name=iaiabl 36 | +experiment=artificial_iaiabl 37 | debug.fine_annotation=3 38 | model.coefs.debug=0.01" 39 | echo $cmd_iaiabl && $cmd_iaiabl 40 | 41 | 42 | 43 | cmd_iaiabl="python main.py --multirun $repetition 44 | experiment_name=iaiabl 45 | +experiment=artificial_iaiabl 46 | debug.fine_annotation=0.05" 47 | echo $cmd_iaiabl && $cmd_iaiabl 48 | 49 | 50 | cmd_iaiabl="python main.py --multirun $repetition 51 | experiment_name=iaiabl 52 | +experiment=artificial_iaiabl 53 | debug.fine_annotation=0.2" 54 | echo $cmd_iaiabl && $cmd_iaiabl 55 | 56 | 57 | cmd_iaiabl="python main.py --multirun $repetition 58 | experiment_name=iaiabl 59 | +experiment=artificial_iaiabl 60 | debug.fine_annotation=1.0" 61 | echo $cmd_iaiabl && $cmd_iaiabl 62 | 63 | 64 | date 65 | echo 'Done!' 66 | -------------------------------------------------------------------------------- /run_covid_confound.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | ############################################## 4 | # EXP 3: COVID dataset 5 | ############################################## 6 | 7 | 8 | cmd="python main.py experiment_name=first_round +experiment=covid_base" 9 | $cmd 10 | 11 | #debug 200 12 | #rem 10 13 | # clst 0.1 14 | # sep 0.001 15 | # crs_ent 0.4 16 | 17 | cmd="python main.py experiment_name=\"second_round\" 18 | +experiment=covid_aggregation 19 | debug.path_to_model='$1'" 20 | $cmd 21 | 22 | date 23 | echo 'Done!' 24 | -------------------------------------------------------------------------------- /run_natural_confound.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | ############################################## 4 | # EXP 2: bird dataset with NATURAL confound 5 | ############################################## 6 | 7 | 8 | # ========================================== 9 | # ProtoPNet (LOWER BOUND) 10 | # ========================================== 11 | 12 | cmd_lower="python main.py experiment_name=\"lowerbound\" +experiment=natural_base_upper_lower" 13 | echo $cmd_lower && $cmd_lower 14 | 15 | 16 | # ========================================== 17 | # ProtoPNet clean (UPPER BOUND) 18 | # ========================================== 19 | 20 | cmd_upper="python main.py experiment_name=\"upperbound\" +experiment=natural_upper" 21 | echo $cmd_upper && $cmd_upper 22 | 23 | 24 | # ========================================== 25 | # IAIA-BL 26 | # ========================================== 27 | cmd_iaia="python main.py experiment_name=\"iaia\" +experiment=natural_iaiabl" 28 | echo $cmd_iaia && $cmd_iaia 29 | 30 | 31 | date 32 | echo "Done!" -------------------------------------------------------------------------------- /saved_models/.gitignore: -------------------------------------------------------------------------------- 1 | * 2 | !.gitignore -------------------------------------------------------------------------------- /slides/poster_iclr23.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/abonte/protopdebug/b43848b10f14bb5bb6477db2f788e0acf91ca3f3/slides/poster_iclr23.pdf -------------------------------------------------------------------------------- /slides/protopdebug___iclr23.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/abonte/protopdebug/b43848b10f14bb5bb6477db2f788e0acf91ca3f3/slides/protopdebug___iclr23.pdf -------------------------------------------------------------------------------- /telegram_notification.py: -------------------------------------------------------------------------------- 1 | import dotenv 2 | import os 3 | 4 | import requests 5 | 6 | 7 | dotenv.load_dotenv() 8 | 9 | 10 | def send_telegram_notification(text): 11 | payload = { 12 | 'chat_id': os.getenv('TELEGRAM_CHAT_ID'), 13 | 'text': text, 14 | 'parse_mode': 'HTML' 15 | } 16 | return requests.post( 17 | f'https://api.telegram.org/bot{os.getenv("TELEGRAM_TOKEN")}/sendMessage', 18 | data=payload).content 19 | -------------------------------------------------------------------------------- /termination_error.py: -------------------------------------------------------------------------------- 1 | import signal 2 | 3 | 4 | class TerminationError(Exception): 5 | """ 6 | Error raised when a termination signal is received 7 | """ 8 | 9 | def __init__(self): 10 | super().__init__("External signal received: forcing termination") 11 | 12 | 13 | def __handle_signal(signum, frame): 14 | raise TerminationError() 15 | 16 | 17 | def register_termination_handlers(): 18 | """ 19 | Makes this process catch SIGINT and SIGTERM. 20 | When the process receives such a signal after this call, a TerminationError is raised. 21 | """ 22 | 23 | signal.signal(signal.SIGINT, __handle_signal) 24 | signal.signal(signal.SIGTERM, __handle_signal) 25 | -------------------------------------------------------------------------------- /user_experiment/google_script_form.js: -------------------------------------------------------------------------------- 1 | var main = '1utOGo9u1ck14CY_8tk-HrDAx1gPOThjy8oL9lxxq9q4' 2 | 3 | function cleanMainForm() { 4 | var f = FormApp.openById(main) 5 | 6 | var items = f.getItems(); 7 | for (var e = 0; e < items.length; e++) { 8 | if (items[e].getType() == FormApp.ItemType.PAGE_BREAK) { 9 | var pb = items[e].asPageBreakItem() 10 | if (pb.getTitle().startsWith('Figure')) { 11 | Logger.log(pb.getTitle()) 12 | f.deleteItem(items[e]); 13 | } 14 | } 15 | if (items[e].getType() == FormApp.ItemType.MULTIPLE_CHOICE) { 16 | if (items[e].getTitle() === 'The highlighted overlay covers') { 17 | Logger.log(items[e].getTitle()) 18 | f.deleteItem(items[e]); 19 | } 20 | } 21 | if (items[e].getType() == FormApp.ItemType.IMAGE) { 22 | Logger.log(items[e].getTitle()) 23 | f.deleteItem(items[e]); 24 | } 25 | } 26 | } 27 | function _question(form, img, qid) { 28 | form.addPageBreakItem().setTitle("Figure " + qid); 29 | 30 | form.addImageItem() 31 | .setImage(img); 32 | 33 | var item = form.addMultipleChoiceItem(); 34 | item.setTitle('The highlighted overlay covers') 35 | .setRequired(true) 36 | .setChoices([ 37 | item.createChoice('some part of the bird'), 38 | item.createChoice('exclusively (or very nearly so) the background') 39 | ]) 40 | } 41 | 42 | function addToMainForm() { 43 | var form = FormApp.openById(main); 44 | 45 | var i = 0; 46 | var items = form.getItems(); 47 | for (var e = 0; e < items.length; e++) { 48 | if (items[e].getType() == FormApp.ItemType.PAGE_BREAK) { 49 | i = i + 1; 50 | } 51 | if (i == 3) { // add question after three sections (excluded the title) 52 | break 53 | } 54 | } 55 | _add_questions(form); 56 | } 57 | 58 | function 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os.listdir(BASE_PATH): 13 | if ".DS_Store" in cls: 14 | continue 15 | print(f'Class: {cls}') 16 | cls_path = os.path.join(BASE_PATH, cls) 17 | 18 | for img in os.listdir(cls_path): 19 | if ".DS_Store" in img: 20 | continue 21 | print(f' img: {img}') 22 | fig, axs = plt.subplots(2, n_higly_activated_patches, figsize=(15, 7)) 23 | 24 | path = os.path.join(cls_path, img) 25 | obj = load(os.path.join(path, 'most_activated_prototypes', 'stats.pickle')) 26 | activation_value = obj['activation_value'] 27 | prototype_class_identity = obj['class_identity'] 28 | 29 | prototype_class_identity = np.load( 30 | os.path.join(path, 'most_activated_prototypes', 'class_identity.npy'), 31 | allow_pickle=True) 32 | activation_value = np.load( 33 | os.path.join(path, 'most_activated_prototypes', 'activation_value.npy'), 34 | allow_pickle=True) 35 | 36 | axs = axs.flatten() 37 | counter = 0 38 | 39 | for i in range(1, n_higly_activated_patches + 1): 40 | filename = f'prototype_activation_map_by_top-{i}_prototype.png' 41 | image = plt.imread( 42 | os.path.join(path, 'most_activated_prototypes', filename)) 43 | ax = axs[counter] 44 | ax.imshow(image) 45 | ax.axis('off') 46 | counter += 1 47 | 48 | last_layer_weight = np.load(os.path.join(path, 'most_activated_prototypes', 49 | 'last_layer_weight.npy')) 50 | for i, cl, act in zip(range(1, n_higly_activated_patches + 1), 51 | prototype_class_identity, activation_value): 52 | filename = f'top-{i}_activated_prototype_in_original_pimg.png' 53 | image = plt.imread( 54 | os.path.join(path, 'most_activated_prototypes', filename)) 55 | ax = axs[counter] 56 | ax.set_title(f'cl={cl}, act_value={act:.2f} w={last_layer_weight[i-1]:.2f}') 57 | ax.imshow(image) 58 | ax.axis('off') 59 | counter += 1 60 | 61 | output_path = os.path.join(PLOT_PATH, cls) 62 | os.makedirs(output_path, exist_ok=True) 63 | plt.savefig(os.path.join(output_path, img)) 64 | 65 | 66 | if __name__ == "__main__": 67 | import argparse 68 | 69 | parser = argparse.ArgumentParser() 70 | parser.add_argument('-model', help='path to model', type=str) 71 | parser.add_argument('-o', dest='output', type=str, help="Output path") 72 | args = parser.parse_args() 73 | 74 | BASE_PATH = args.model.split('.pth')[0] 75 | PLOT_PATH = args.output if args.output is not None else os.path.join(os.path.dirname(args.model), 'local_analysis') 76 | model =os.path.basename(BASE_PATH) 77 | n_higly_activated_patches = 4 78 | 79 | main() 80 | --------------------------------------------------------------------------------