├── data ├── .gitkeep └── .DS_Store ├── plots ├── .gitkeep ├── .DS_Store ├── per_class_analysis │ ├── .DS_Store │ ├── opp │ │ ├── .DS_Store │ │ ├── dccae │ │ │ ├── .DS_Store │ │ │ └── acce_gyro │ │ │ │ ├── class_accuracy_A0_B0_AB30_label_AB_test_A.pdf │ │ │ │ ├── class_accuracy_A0_B0_AB30_label_AB_test_B.pdf │ │ │ │ ├── class_accuracy_A0_B0_AB30_label_A_test_B.pdf │ │ │ │ ├── class_accuracy_A0_B0_AB30_label_B_test_A.pdf │ │ │ │ ├── class_accuracy_A0_B10_AB30_label_A_test_B.pdf │ │ │ │ ├── class_accuracy_A0_B10_AB30_label_B_test_A.pdf │ │ │ │ ├── class_accuracy_A0_B30_AB0_label_B_test_B.pdf │ │ │ │ ├── class_accuracy_A10_B0_AB30_label_A_test_B.pdf │ │ │ │ ├── class_accuracy_A10_B0_AB30_label_B_test_A.pdf │ │ │ │ ├── class_accuracy_A30_B0_AB0_label_A_test_A.pdf │ │ │ │ ├── class_accuracy_A10_B10_AB30_label_A_test_B.pdf │ │ │ │ └── class_accuracy_A10_B10_AB30_label_B_test_A.pdf │ │ └── ablation │ │ │ ├── .DS_Store │ │ │ └── acce_gyro │ │ │ ├── class_accuracy_A30_B30_AB0_label_A_test_B.pdf │ │ │ └── class_accuracy_A30_B30_AB0_label_B_test_A.pdf │ ├── mhealth │ │ ├── .DS_Store │ │ ├── split_ae │ │ │ ├── .DS_Store │ │ │ ├── acce_gyro │ │ │ │ ├── class_accuracy_A0_B0_AB30_label_AB_test_A.pdf │ │ │ │ ├── class_accuracy_A0_B0_AB30_label_AB_test_B.pdf │ │ │ │ ├── class_accuracy_A0_B0_AB30_label_A_test_B.pdf │ │ │ │ ├── class_accuracy_A0_B0_AB30_label_B_test_A.pdf │ │ │ │ ├── class_accuracy_A0_B10_AB30_label_A_test_B.pdf │ │ │ │ ├── class_accuracy_A0_B10_AB30_label_B_test_A.pdf │ │ │ │ ├── class_accuracy_A0_B30_AB0_label_B_test_B.pdf │ │ │ │ ├── class_accuracy_A10_B0_AB30_label_A_test_B.pdf │ │ │ │ ├── class_accuracy_A10_B0_AB30_label_B_test_A.pdf │ │ │ │ ├── class_accuracy_A30_B0_AB0_label_A_test_A.pdf │ │ │ │ ├── class_accuracy_A10_B10_AB30_label_A_test_B.pdf │ │ │ │ └── class_accuracy_A10_B10_AB30_label_B_test_A.pdf │ │ │ ├── acce_mage │ │ │ │ ├── class_accuracy_A0_B0_AB30_label_AB_test_A.pdf │ │ │ │ ├── class_accuracy_A0_B0_AB30_label_AB_test_B.pdf │ │ │ │ ├── class_accuracy_A0_B0_AB30_label_A_test_B.pdf │ │ │ │ ├── class_accuracy_A0_B0_AB30_label_B_test_A.pdf │ │ │ │ ├── class_accuracy_A0_B10_AB30_label_A_test_B.pdf │ │ │ │ ├── class_accuracy_A0_B10_AB30_label_B_test_A.pdf │ │ │ │ ├── class_accuracy_A0_B30_AB0_label_B_test_B.pdf │ │ │ │ ├── class_accuracy_A10_B0_AB30_label_A_test_B.pdf │ │ │ │ ├── class_accuracy_A10_B0_AB30_label_B_test_A.pdf │ │ │ │ ├── class_accuracy_A30_B0_AB0_label_A_test_A.pdf │ │ │ │ ├── class_accuracy_A10_B10_AB30_label_A_test_B.pdf │ │ │ │ └── class_accuracy_A10_B10_AB30_label_B_test_A.pdf │ │ │ └── gyro_mage │ │ │ │ ├── class_accuracy_A0_B0_AB30_label_AB_test_A.pdf │ │ │ │ ├── class_accuracy_A0_B0_AB30_label_AB_test_B.pdf │ │ │ │ ├── class_accuracy_A0_B0_AB30_label_A_test_B.pdf │ │ │ │ ├── class_accuracy_A0_B0_AB30_label_B_test_A.pdf │ │ │ │ ├── class_accuracy_A0_B10_AB30_label_A_test_B.pdf │ │ │ │ ├── class_accuracy_A0_B10_AB30_label_B_test_A.pdf │ │ │ │ ├── class_accuracy_A0_B30_AB0_label_B_test_B.pdf │ │ │ │ ├── class_accuracy_A10_B0_AB30_label_A_test_B.pdf │ │ │ │ ├── class_accuracy_A10_B0_AB30_label_B_test_A.pdf │ │ │ │ ├── class_accuracy_A30_B0_AB0_label_A_test_A.pdf │ │ │ │ ├── class_accuracy_A10_B10_AB30_label_A_test_B.pdf │ │ │ │ └── class_accuracy_A10_B10_AB30_label_B_test_A.pdf │ │ └── ablation │ │ │ ├── acce_gyro │ │ │ ├── class_accuracy_A30_B30_AB0_label_A_test_B.pdf │ │ │ └── class_accuracy_A30_B30_AB0_label_B_test_A.pdf │ │ │ ├── acce_mage │ │ │ ├── class_accuracy_A30_B30_AB0_label_A_test_B.pdf │ │ │ └── class_accuracy_A30_B30_AB0_label_B_test_A.pdf │ │ │ └── gyro_mage │ │ │ ├── class_accuracy_A30_B30_AB0_label_A_test_B.pdf │ │ │ └── class_accuracy_A30_B30_AB0_label_B_test_A.pdf │ └── ur_fall │ │ ├── .DS_Store │ │ ├── ablation │ │ ├── .DS_Store │ │ ├── acce_rgb │ │ │ ├── class_accuracy_A30_B30_AB0_label_A_test_B.pdf │ │ │ └── class_accuracy_A30_B30_AB0_label_B_test_A.pdf │ │ ├── rgb_depth │ │ │ ├── class_accuracy_A30_B30_AB0_label_A_test_B.pdf │ │ │ └── class_accuracy_A30_B30_AB0_label_B_test_A.pdf │ │ └── acce_depth │ │ │ ├── class_accuracy_A30_B30_AB0_label_A_test_B.pdf │ │ │ └── class_accuracy_A30_B30_AB0_label_B_test_A.pdf │ │ └── split_ae │ │ ├── .DS_Store │ │ ├── acce_rgb │ │ ├── class_accuracy_A0_B0_AB30_label_A_test_B.pdf │ │ ├── class_accuracy_A0_B0_AB30_label_B_test_A.pdf │ │ ├── class_accuracy_A0_B30_AB0_label_B_test_B.pdf │ │ ├── class_accuracy_A30_B0_AB0_label_A_test_A.pdf │ │ ├── class_accuracy_A0_B0_AB30_label_AB_test_A.pdf │ │ ├── class_accuracy_A0_B0_AB30_label_AB_test_B.pdf │ │ ├── class_accuracy_A0_B10_AB30_label_A_test_B.pdf │ │ ├── class_accuracy_A0_B10_AB30_label_B_test_A.pdf │ │ ├── class_accuracy_A10_B0_AB30_label_A_test_B.pdf │ │ ├── class_accuracy_A10_B0_AB30_label_B_test_A.pdf │ │ ├── class_accuracy_A10_B10_AB30_label_A_test_B.pdf │ │ └── class_accuracy_A10_B10_AB30_label_B_test_A.pdf │ │ ├── acce_depth │ │ ├── class_accuracy_A0_B0_AB30_label_A_test_B.pdf │ │ ├── class_accuracy_A0_B0_AB30_label_B_test_A.pdf │ │ ├── class_accuracy_A0_B30_AB0_label_B_test_B.pdf │ │ ├── class_accuracy_A30_B0_AB0_label_A_test_A.pdf │ │ ├── class_accuracy_A0_B0_AB30_label_AB_test_A.pdf │ │ ├── class_accuracy_A0_B0_AB30_label_AB_test_B.pdf │ │ ├── class_accuracy_A0_B10_AB30_label_A_test_B.pdf │ │ ├── class_accuracy_A0_B10_AB30_label_B_test_A.pdf │ │ ├── class_accuracy_A10_B0_AB30_label_A_test_B.pdf │ │ ├── class_accuracy_A10_B0_AB30_label_B_test_A.pdf │ │ ├── class_accuracy_A10_B10_AB30_label_A_test_B.pdf │ │ └── class_accuracy_A10_B10_AB30_label_B_test_A.pdf │ │ └── rgb_depth │ │ ├── class_accuracy_A0_B0_AB30_label_AB_test_A.pdf │ │ ├── class_accuracy_A0_B0_AB30_label_AB_test_B.pdf │ │ ├── class_accuracy_A0_B0_AB30_label_A_test_B.pdf │ │ ├── class_accuracy_A0_B0_AB30_label_B_test_A.pdf │ │ ├── class_accuracy_A0_B10_AB30_label_A_test_B.pdf │ │ ├── class_accuracy_A0_B10_AB30_label_B_test_A.pdf │ │ ├── class_accuracy_A0_B30_AB0_label_B_test_B.pdf │ │ ├── class_accuracy_A10_B0_AB30_label_A_test_B.pdf │ │ ├── class_accuracy_A10_B0_AB30_label_B_test_A.pdf │ │ ├── class_accuracy_A30_B0_AB0_label_A_test_A.pdf │ │ ├── class_accuracy_A10_B10_AB30_label_A_test_B.pdf │ │ └── class_accuracy_A10_B10_AB30_label_B_test_A.pdf ├── cross_modality_comparison_Opp_dccae_acce_gyro.pdf ├── cross_modality_comparison_UR_Fall_split_ae_rgb_depth.pdf ├── cross_modality_comparison_mHealth_split_ae_acce_gyro.pdf ├── cross_modality_comparison_mHealth_split_ae_acce_mage.pdf ├── cross_modality_comparison_mHealth_split_ae_gyro_mage.pdf ├── single_multi_modality_comparison_Opp_dccae_acce_gyro.pdf ├── cross_modality_comparison_UR_Fall_split_ae_acce_depth.pdf ├── single_multi_modality_comparison_UR_Fall_split_ae_acce_depth.pdf ├── single_multi_modality_comparison_UR_Fall_split_ae_rgb_depth.pdf ├── single_multi_modality_comparison_mHealth_split_ae_acce_gyro.pdf ├── single_multi_modality_comparison_mHealth_split_ae_acce_mage.pdf ├── single_multi_modality_comparison_mHealth_split_ae_gyro_mage.pdf └── per_class_chart │ ├── opp │ ├── dccae │ │ └── acce_gyro │ │ │ └── class_occurances_A0_B0_AB30_label_A_test_B.pdf │ └── ablation │ │ └── acce_gyro │ │ └── class_occurances_A30_B30_AB0_label_B_test_A.pdf │ ├── mhealth │ ├── split_ae │ │ ├── acce_gyro │ │ │ └── class_occurances_A0_B0_AB30_label_A_test_B.pdf │ │ ├── acce_mage │ │ │ └── class_occurances_A0_B0_AB30_label_A_test_B.pdf │ │ └── gyro_mage │ │ │ └── class_occurances_A0_B0_AB30_label_A_test_B.pdf │ └── ablation │ │ ├── acce_gyro │ │ └── class_occurances_A30_B30_AB0_label_B_test_A.pdf │ │ ├── acce_mage │ │ └── class_occurances_A30_B30_AB0_label_B_test_A.pdf │ │ └── gyro_mage │ │ └── class_occurances_A30_B30_AB0_label_B_test_A.pdf │ └── ur_fall │ ├── ablation │ ├── acce_rgb │ │ └── class_occurances_A30_B30_AB0_label_B_test_A.pdf │ ├── acce_depth │ │ └── class_occurances_A30_B30_AB0_label_B_test_A.pdf │ └── rgb_depth │ │ └── class_occurances_A30_B30_AB0_label_B_test_A.pdf │ └── split_ae │ ├── acce_rgb │ └── class_occurances_A0_B0_AB30_label_A_test_B.pdf │ ├── rgb_depth │ └── class_occurances_A0_B0_AB30_label_A_test_B.pdf │ └── acce_depth │ └── class_occurances_A0_B0_AB30_label_A_test_B.pdf ├── results ├── .gitkeep ├── .DS_Store ├── opp │ ├── .DS_Store │ ├── dccae │ │ ├── .DS_Store │ │ └── acce_gyro │ │ │ ├── .DS_Store │ │ │ └── A0_B0_AB30_label_B_test_A │ │ │ └── .DS_Store │ └── ablation │ │ ├── .DS_Store │ │ └── acce_gyro │ │ └── .DS_Store ├── mhealth │ ├── .DS_Store │ ├── ablation │ │ ├── .DS_Store │ │ └── acce_gyro │ │ │ └── .DS_Store │ └── split_ae │ │ └── .DS_Store └── ur_fall │ ├── .DS_Store │ ├── ablation │ └── .DS_Store │ └── split_ae │ └── .DS_Store ├── .DS_Store ├── src ├── .DS_Store ├── __pycache__ │ ├── fl.cpython-310.pyc │ ├── utils.cpython-310.pyc │ ├── client.cpython-310.pyc │ ├── losses.cpython-310.pyc │ ├── models.cpython-310.pyc │ └── server.cpython-310.pyc ├── test.py └── main.py ├── config ├── .DS_Store ├── opp │ ├── .DS_Store │ ├── dccae │ │ ├── A0_B0_AB30_label_A_test_B │ │ ├── A0_B0_AB30_label_B_test_A │ │ ├── A0_B30_AB0_label_B_test_B │ │ ├── A30_B0_AB0_label_A_test_A │ │ ├── A0_B0_AB30_label_AB_test_A │ │ ├── A0_B0_AB30_label_AB_test_B │ │ ├── A0_B10_AB30_label_A_test_B │ │ ├── A0_B10_AB30_label_B_test_A │ │ ├── A10_B0_AB30_label_A_test_B │ │ ├── A10_B0_AB30_label_B_test_A │ │ ├── A10_B10_AB30_label_A_test_B │ │ └── A10_B10_AB30_label_B_test_A │ └── ablation │ │ ├── A30_B30_AB0_label_A_test_B │ │ └── A30_B30_AB0_label_B_test_A ├── mhealth │ ├── .DS_Store │ ├── ablation │ │ ├── .DS_Store │ │ ├── acce_gyro │ │ │ ├── A30_B30_AB0_label_A_test_B │ │ │ └── A30_B30_AB0_label_B_test_A │ │ ├── acce_mage │ │ │ ├── A30_B30_AB0_label_A_test_B │ │ │ └── A30_B30_AB0_label_B_test_A │ │ └── gyro_mage │ │ │ ├── A30_B30_AB0_label_A_test_B │ │ │ └── A30_B30_AB0_label_B_test_A │ └── split_ae │ │ ├── .DS_Store │ │ ├── acce_gyro │ │ ├── A0_B0_AB30_label_A_test_B │ │ ├── A0_B0_AB30_label_B_test_A │ │ ├── A30_B0_AB0_label_A_test_A │ │ ├── A0_B0_AB30_label_AB_test_A │ │ ├── A0_B0_AB30_label_AB_test_B │ │ ├── A0_B10_AB30_label_A_test_B │ │ ├── A0_B10_AB30_label_B_test_A │ │ ├── A0_B30_AB0_label_B_test_B │ │ ├── A10_B0_AB30_label_A_test_B │ │ ├── A10_B0_AB30_label_B_test_A │ │ ├── A10_B10_AB30_label_A_test_B │ │ └── A10_B10_AB30_label_B_test_A │ │ ├── acce_mage │ │ ├── A0_B0_AB30_label_A_test_B │ │ ├── A0_B0_AB30_label_B_test_A │ │ ├── A30_B0_AB0_label_A_test_A │ │ ├── A0_B0_AB30_label_AB_test_A │ │ ├── A0_B0_AB30_label_AB_test_B │ │ ├── A0_B10_AB30_label_A_test_B │ │ ├── A0_B10_AB30_label_B_test_A │ │ ├── A0_B30_AB0_label_B_test_B │ │ ├── A10_B0_AB30_label_A_test_B │ │ ├── A10_B0_AB30_label_B_test_A │ │ ├── A10_B10_AB30_label_A_test_B │ │ └── A10_B10_AB30_label_B_test_A │ │ └── gyro_mage │ │ ├── A0_B0_AB30_label_A_test_B │ │ ├── A0_B0_AB30_label_B_test_A │ │ ├── A30_B0_AB0_label_A_test_A │ │ ├── A0_B0_AB30_label_AB_test_A │ │ ├── A0_B0_AB30_label_AB_test_B │ │ ├── A0_B10_AB30_label_A_test_B │ │ ├── A0_B10_AB30_label_B_test_A │ │ ├── A0_B30_AB0_label_B_test_B │ │ ├── A10_B0_AB30_label_A_test_B │ │ ├── A10_B0_AB30_label_B_test_A │ │ ├── A10_B10_AB30_label_A_test_B │ │ └── A10_B10_AB30_label_B_test_A ├── ur_fall │ ├── .DS_Store │ ├── ablation │ │ ├── .DS_Store │ │ ├── acce_rgb │ │ │ ├── A30_B30_AB0_label_A_test_B │ │ │ └── A30_B30_AB0_label_B_test_A │ │ ├── acce_depth │ │ │ ├── A30_B30_AB0_label_A_test_B │ │ │ └── A30_B30_AB0_label_B_test_A │ │ └── rgb_depth │ │ │ ├── A30_B30_AB0_label_A_test_B │ │ │ └── A30_B30_AB0_label_B_test_A │ └── split_ae │ │ ├── .DS_Store │ │ ├── acce_rgb │ │ ├── A0_B0_AB30_label_AB_test_A │ │ ├── A0_B0_AB30_label_AB_test_B │ │ ├── A0_B0_AB30_label_A_test_B │ │ ├── A0_B0_AB30_label_B_test_A │ │ ├── A0_B10_AB30_label_A_test_B │ │ ├── A0_B10_AB30_label_B_test_A │ │ ├── A0_B30_AB0_label_B_test_B │ │ ├── A10_B0_AB30_label_A_test_B │ │ ├── A10_B0_AB30_label_B_test_A │ │ ├── A30_B0_AB0_label_A_test_A │ │ ├── A10_B10_AB30_label_A_test_B │ │ └── A10_B10_AB30_label_B_test_A │ │ ├── rgb_depth │ │ ├── A0_B0_AB30_label_A_test_B │ │ ├── A0_B0_AB30_label_B_test_A │ │ ├── A0_B30_AB0_label_B_test_B │ │ ├── A30_B0_AB0_label_A_test_A │ │ ├── A0_B0_AB30_label_AB_test_A │ │ ├── A0_B0_AB30_label_AB_test_B │ │ ├── A0_B10_AB30_label_A_test_B │ │ ├── A0_B10_AB30_label_B_test_A │ │ ├── A10_B0_AB30_label_A_test_B │ │ ├── A10_B0_AB30_label_B_test_A │ │ ├── A10_B10_AB30_label_A_test_B │ │ └── A10_B10_AB30_label_B_test_A │ │ └── acce_depth │ │ ├── A0_B0_AB30_label_AB_test_A │ │ ├── A0_B0_AB30_label_AB_test_B │ │ ├── A0_B0_AB30_label_A_test_B │ │ ├── A0_B0_AB30_label_B_test_A │ │ ├── A0_B10_AB30_label_A_test_B │ │ ├── A0_B10_AB30_label_B_test_A │ │ ├── A0_B30_AB0_label_B_test_B │ │ ├── A10_B0_AB30_label_A_test_B │ │ ├── A10_B0_AB30_label_B_test_A │ │ ├── A30_B0_AB0_label_A_test_A │ │ ├── A10_B10_AB30_label_A_test_B │ │ └── A10_B10_AB30_label_B_test_A ├── config_debug └── config_example ├── sub ├── opp │ ├── dccae │ │ ├── A0_B0_AB30_label_AB_test_A.pbs │ │ ├── A0_B0_AB30_label_AB_test_B.pbs │ │ ├── A0_B0_AB30_label_A_test_B.pbs │ │ ├── A0_B0_AB30_label_B_test_A.pbs │ │ ├── A0_B10_AB30_label_A_test_B.pbs │ │ ├── A0_B10_AB30_label_B_test_A.pbs │ │ ├── A0_B30_AB0_label_B_test_B.pbs │ │ ├── A10_B0_AB30_label_A_test_B.pbs │ │ ├── A10_B0_AB30_label_B_test_A.pbs │ │ ├── A30_B0_AB0_label_A_test_A.pbs │ │ ├── A10_B10_AB30_label_A_test_B.pbs │ │ └── A10_B10_AB30_label_B_test_A.pbs │ └── ablation │ │ ├── A30_B30_AB0_label_A_test_B.pbs │ │ └── A30_B30_AB0_label_B_test_A.pbs ├── mhealth │ ├── split_ae │ │ ├── acce_gyro │ │ │ ├── A0_B0_AB30_label_A_test_B.pbs │ │ │ ├── A0_B0_AB30_label_B_test_A.pbs │ │ │ ├── A0_B30_AB0_label_B_test_B.pbs │ │ │ ├── A30_B0_AB0_label_A_test_A.pbs │ │ │ ├── A0_B0_AB30_label_AB_test_A.pbs │ │ │ ├── A0_B0_AB30_label_AB_test_B.pbs │ │ │ ├── A0_B10_AB30_label_A_test_B.pbs │ │ │ ├── A0_B10_AB30_label_B_test_A.pbs │ │ │ ├── A10_B0_AB30_label_A_test_B.pbs │ │ │ ├── A10_B0_AB30_label_B_test_A.pbs │ │ │ ├── A10_B10_AB30_label_A_test_B.pbs │ │ │ └── A10_B10_AB30_label_B_test_A.pbs │ │ ├── acce_mage │ │ │ ├── A0_B0_AB30_label_A_test_B.pbs │ │ │ ├── A0_B0_AB30_label_B_test_A.pbs │ │ │ ├── A0_B30_AB0_label_B_test_B.pbs │ │ │ ├── A30_B0_AB0_label_A_test_A.pbs │ │ │ ├── A0_B0_AB30_label_AB_test_A.pbs │ │ │ ├── A0_B0_AB30_label_AB_test_B.pbs │ │ │ ├── A0_B10_AB30_label_A_test_B.pbs │ │ │ ├── A0_B10_AB30_label_B_test_A.pbs │ │ │ ├── A10_B0_AB30_label_A_test_B.pbs │ │ │ ├── A10_B0_AB30_label_B_test_A.pbs │ │ │ ├── A10_B10_AB30_label_A_test_B.pbs │ │ │ └── A10_B10_AB30_label_B_test_A.pbs │ │ └── gyro_mage │ │ │ ├── A0_B0_AB30_label_A_test_B.pbs │ │ │ ├── A0_B0_AB30_label_B_test_A.pbs │ │ │ ├── A0_B30_AB0_label_B_test_B.pbs │ │ │ ├── A30_B0_AB0_label_A_test_A.pbs │ │ │ ├── A0_B0_AB30_label_AB_test_A.pbs │ │ │ ├── A0_B0_AB30_label_AB_test_B.pbs │ │ │ ├── A0_B10_AB30_label_A_test_B.pbs │ │ │ ├── A0_B10_AB30_label_B_test_A.pbs │ │ │ ├── A10_B0_AB30_label_A_test_B.pbs │ │ │ ├── A10_B0_AB30_label_B_test_A.pbs │ │ │ ├── A10_B10_AB30_label_A_test_B.pbs │ │ │ └── A10_B10_AB30_label_B_test_A.pbs │ └── ablation │ │ ├── acce_gyro │ │ ├── A30_B30_AB0_label_A_test_B.pbs │ │ └── A30_B30_AB0_label_B_test_A.pbs │ │ ├── acce_mage │ │ ├── A30_B30_AB0_label_A_test_B.pbs │ │ └── A30_B30_AB0_label_B_test_A.pbs │ │ └── gyro_mage │ │ ├── A30_B30_AB0_label_A_test_B.pbs │ │ └── A30_B30_AB0_label_B_test_A.pbs └── ur_fall │ ├── ablation │ ├── acce_rgb │ │ ├── A30_B30_AB0_label_A_test_B.pbs │ │ └── A30_B30_AB0_label_B_test_A.pbs │ ├── rgb_depth │ │ ├── A30_B30_AB0_label_A_test_B.pbs │ │ └── A30_B30_AB0_label_B_test_A.pbs │ └── acce_depth │ │ ├── A30_B30_AB0_label_A_test_B.pbs │ │ └── A30_B30_AB0_label_B_test_A.pbs │ └── split_ae │ ├── acce_rgb │ ├── A0_B0_AB30_label_AB_test_A.pbs │ ├── A0_B0_AB30_label_AB_test_B.pbs │ ├── A0_B0_AB30_label_A_test_B.pbs │ ├── A0_B0_AB30_label_B_test_A.pbs │ ├── A0_B10_AB30_label_A_test_B.pbs │ ├── A0_B10_AB30_label_B_test_A.pbs │ ├── A0_B30_AB0_label_B_test_B.pbs │ ├── A10_B0_AB30_label_A_test_B.pbs │ ├── A10_B0_AB30_label_B_test_A.pbs │ ├── A30_B0_AB0_label_A_test_A.pbs │ ├── A10_B10_AB30_label_A_test_B.pbs │ └── A10_B10_AB30_label_B_test_A.pbs │ ├── rgb_depth │ ├── A0_B0_AB30_label_A_test_B.pbs │ ├── A0_B0_AB30_label_B_test_A.pbs │ ├── A0_B30_AB0_label_B_test_B.pbs │ ├── A30_B0_AB0_label_A_test_A.pbs │ ├── A0_B0_AB30_label_AB_test_A.pbs │ ├── A0_B0_AB30_label_AB_test_B.pbs │ ├── A0_B10_AB30_label_A_test_B.pbs │ ├── A0_B10_AB30_label_B_test_A.pbs │ ├── A10_B0_AB30_label_A_test_B.pbs │ ├── A10_B0_AB30_label_B_test_A.pbs │ ├── A10_B10_AB30_label_A_test_B.pbs │ └── A10_B10_AB30_label_B_test_A.pbs │ └── acce_depth │ ├── A0_B0_AB30_label_AB_test_A.pbs │ ├── A0_B0_AB30_label_AB_test_B.pbs │ ├── A0_B0_AB30_label_A_test_B.pbs │ ├── A0_B0_AB30_label_B_test_A.pbs │ ├── A0_B10_AB30_label_A_test_B.pbs │ ├── A0_B10_AB30_label_B_test_A.pbs │ ├── A0_B30_AB0_label_B_test_B.pbs │ ├── A10_B0_AB30_label_A_test_B.pbs │ ├── A10_B0_AB30_label_B_test_A.pbs │ ├── A30_B0_AB0_label_A_test_A.pbs │ ├── A10_B10_AB30_label_A_test_B.pbs │ └── 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select=2:ncpus=32:mem=124gb 3 | #PBS -N opp_dc_A0_B0_AB30_label_AB_test_B 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/opp/dccae/A0_B0_AB30_label_AB_test_B 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/opp/dccae/A0_B0_AB30_label_A_test_B.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N opp_dc_A0_B0_AB30_label_A_test_B 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/opp/dccae/A0_B0_AB30_label_A_test_B 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/opp/dccae/A0_B0_AB30_label_B_test_A.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N opp_dc_A0_B0_AB30_label_B_test_A 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/opp/dccae/A0_B0_AB30_label_B_test_A 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/opp/dccae/A0_B10_AB30_label_A_test_B.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N opp_dc_A0_B10_AB30_label_A_test_B 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/opp/dccae/A0_B10_AB30_label_A_test_B 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/opp/dccae/A0_B10_AB30_label_B_test_A.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N opp_dc_A0_B10_AB30_label_B_test_A 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/opp/dccae/A0_B10_AB30_label_B_test_A 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/opp/dccae/A0_B30_AB0_label_B_test_B.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N opp_dc_A0_B30_AB0_label_B_test_B 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/opp/dccae/A0_B30_AB0_label_B_test_B 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/opp/dccae/A10_B0_AB30_label_A_test_B.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N opp_dc_A10_B0_AB30_label_A_test_B 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/opp/dccae/A10_B0_AB30_label_A_test_B 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/opp/dccae/A10_B0_AB30_label_B_test_A.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N opp_dc_A10_B0_AB30_label_B_test_A 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/opp/dccae/A10_B0_AB30_label_B_test_A 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/opp/dccae/A30_B0_AB0_label_A_test_A.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N opp_dc_A30_B0_AB0_label_A_test_A 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/opp/dccae/A30_B0_AB0_label_A_test_A 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/opp/dccae/A10_B10_AB30_label_A_test_B.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N opp_dc_A10_B10_AB30_label_A_test_B 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/opp/dccae/A10_B10_AB30_label_A_test_B 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/opp/dccae/A10_B10_AB30_label_B_test_A.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N opp_dc_A10_B10_AB30_label_B_test_A 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/opp/dccae/A10_B10_AB30_label_B_test_A 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/opp/ablation/A30_B30_AB0_label_A_test_B.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N opp_abl_A30_B30_AB0_label_A_test_B 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/opp/ablation/A30_B30_AB0_label_A_test_B 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/opp/ablation/A30_B30_AB0_label_B_test_A.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N opp_abl_A30_B30_AB0_label_B_test_A 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/opp/ablation/A30_B30_AB0_label_B_test_A 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/mhealth/split_ae/acce_gyro/A0_B0_AB30_label_A_test_B.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N mhealth_sp_acce_gyro_A0_B0_AB30_label_A_test_B 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/mhealth/split_ae/acce_gyro/A0_B0_AB30_label_A_test_B 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/mhealth/split_ae/acce_gyro/A0_B0_AB30_label_B_test_A.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N mhealth_sp_acce_gyro_A0_B0_AB30_label_B_test_A 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/mhealth/split_ae/acce_gyro/A0_B0_AB30_label_B_test_A 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/mhealth/split_ae/acce_gyro/A0_B30_AB0_label_B_test_B.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N mhealth_sp_acce_gyro_A0_B30_AB0_label_B_test_B 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/mhealth/split_ae/acce_gyro/A0_B30_AB0_label_B_test_B 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/mhealth/split_ae/acce_gyro/A30_B0_AB0_label_A_test_A.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N mhealth_sp_acce_gyro_A30_B0_AB0_label_A_test_A 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/mhealth/split_ae/acce_gyro/A30_B0_AB0_label_A_test_A 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/mhealth/split_ae/acce_mage/A0_B0_AB30_label_A_test_B.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N mhealth_sp_acce_mage_A0_B0_AB30_label_A_test_B 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/mhealth/split_ae/acce_mage/A0_B0_AB30_label_A_test_B 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/mhealth/split_ae/acce_mage/A0_B0_AB30_label_B_test_A.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N mhealth_sp_acce_mage_A0_B0_AB30_label_B_test_A 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/mhealth/split_ae/acce_mage/A0_B0_AB30_label_B_test_A 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/mhealth/split_ae/acce_mage/A0_B30_AB0_label_B_test_B.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N mhealth_sp_acce_mage_A0_B30_AB0_label_B_test_B 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/mhealth/split_ae/acce_mage/A0_B30_AB0_label_B_test_B 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/mhealth/split_ae/acce_mage/A30_B0_AB0_label_A_test_A.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N mhealth_sp_acce_mage_A30_B0_AB0_label_A_test_A 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/mhealth/split_ae/acce_mage/A30_B0_AB0_label_A_test_A 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/mhealth/split_ae/gyro_mage/A0_B0_AB30_label_A_test_B.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N mhealth_sp_gyro_mage_A0_B0_AB30_label_A_test_B 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/mhealth/split_ae/gyro_mage/A0_B0_AB30_label_A_test_B 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/mhealth/split_ae/gyro_mage/A0_B0_AB30_label_B_test_A.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N mhealth_sp_gyro_mage_A0_B0_AB30_label_B_test_A 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/mhealth/split_ae/gyro_mage/A0_B0_AB30_label_B_test_A 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/mhealth/split_ae/gyro_mage/A0_B30_AB0_label_B_test_B.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N mhealth_sp_gyro_mage_A0_B30_AB0_label_B_test_B 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/mhealth/split_ae/gyro_mage/A0_B30_AB0_label_B_test_B 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/mhealth/split_ae/gyro_mage/A30_B0_AB0_label_A_test_A.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N mhealth_sp_gyro_mage_A30_B0_AB0_label_A_test_A 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/mhealth/split_ae/gyro_mage/A30_B0_AB0_label_A_test_A 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/ur_fall/ablation/acce_rgb/A30_B30_AB0_label_A_test_B.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N ur_fall_abl_acce_rgb_A30_B30_AB0_label_A_test_B 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/ur_fall/ablation/acce_rgb/A30_B30_AB0_label_A_test_B 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/ur_fall/ablation/acce_rgb/A30_B30_AB0_label_B_test_A.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N ur_fall_abl_acce_rgb_A30_B30_AB0_label_B_test_A 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/ur_fall/ablation/acce_rgb/A30_B30_AB0_label_B_test_A 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/ur_fall/split_ae/acce_rgb/A0_B0_AB30_label_AB_test_A.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N ur_fall_sp_acce_rgb_A0_B0_AB30_label_AB_test_A 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/ur_fall/split_ae/acce_rgb/A0_B0_AB30_label_AB_test_A 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/ur_fall/split_ae/acce_rgb/A0_B0_AB30_label_AB_test_B.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N ur_fall_sp_acce_rgb_A0_B0_AB30_label_AB_test_B 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/ur_fall/split_ae/acce_rgb/A0_B0_AB30_label_AB_test_B 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/ur_fall/split_ae/acce_rgb/A0_B0_AB30_label_A_test_B.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N ur_fall_sp_acce_rgb_A0_B0_AB30_label_A_test_B 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/ur_fall/split_ae/acce_rgb/A0_B0_AB30_label_A_test_B 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/ur_fall/split_ae/acce_rgb/A0_B0_AB30_label_B_test_A.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N ur_fall_sp_acce_rgb_A0_B0_AB30_label_B_test_A 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/ur_fall/split_ae/acce_rgb/A0_B0_AB30_label_B_test_A 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/ur_fall/split_ae/acce_rgb/A0_B10_AB30_label_A_test_B.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N ur_fall_sp_acce_rgb_A0_B10_AB30_label_A_test_B 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/ur_fall/split_ae/acce_rgb/A0_B10_AB30_label_A_test_B 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/ur_fall/split_ae/acce_rgb/A0_B10_AB30_label_B_test_A.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N ur_fall_sp_acce_rgb_A0_B10_AB30_label_B_test_A 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/ur_fall/split_ae/acce_rgb/A0_B10_AB30_label_B_test_A 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/ur_fall/split_ae/acce_rgb/A0_B30_AB0_label_B_test_B.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N ur_fall_sp_acce_rgb_A0_B30_AB0_label_B_test_B 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/ur_fall/split_ae/acce_rgb/A0_B30_AB0_label_B_test_B 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/ur_fall/split_ae/acce_rgb/A10_B0_AB30_label_A_test_B.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N ur_fall_sp_acce_rgb_A10_B0_AB30_label_A_test_B 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/ur_fall/split_ae/acce_rgb/A10_B0_AB30_label_A_test_B 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/ur_fall/split_ae/acce_rgb/A10_B0_AB30_label_B_test_A.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N ur_fall_sp_acce_rgb_A10_B0_AB30_label_B_test_A 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/ur_fall/split_ae/acce_rgb/A10_B0_AB30_label_B_test_A 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/ur_fall/split_ae/acce_rgb/A30_B0_AB0_label_A_test_A.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N ur_fall_sp_acce_rgb_A30_B0_AB0_label_A_test_A 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/ur_fall/split_ae/acce_rgb/A30_B0_AB0_label_A_test_A 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/ur_fall/split_ae/rgb_depth/A0_B0_AB30_label_A_test_B.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N ur_fall_sp_rgb_depth_A0_B0_AB30_label_A_test_B 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/ur_fall/split_ae/rgb_depth/A0_B0_AB30_label_A_test_B 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/ur_fall/split_ae/rgb_depth/A0_B0_AB30_label_B_test_A.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N ur_fall_sp_rgb_depth_A0_B0_AB30_label_B_test_A 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/ur_fall/split_ae/rgb_depth/A0_B0_AB30_label_B_test_A 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/ur_fall/split_ae/rgb_depth/A0_B30_AB0_label_B_test_B.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N ur_fall_sp_rgb_depth_A0_B30_AB0_label_B_test_B 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/ur_fall/split_ae/rgb_depth/A0_B30_AB0_label_B_test_B 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/ur_fall/split_ae/rgb_depth/A30_B0_AB0_label_A_test_A.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N ur_fall_sp_rgb_depth_A30_B0_AB0_label_A_test_A 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/ur_fall/split_ae/rgb_depth/A30_B0_AB0_label_A_test_A 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/mhealth/ablation/acce_gyro/A30_B30_AB0_label_A_test_B.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N mhealth_abl_acce_gyro_A30_B30_AB0_label_A_test_B 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/mhealth/ablation/acce_gyro/A30_B30_AB0_label_A_test_B 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/mhealth/ablation/acce_gyro/A30_B30_AB0_label_B_test_A.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N mhealth_abl_acce_gyro_A30_B30_AB0_label_B_test_A 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/mhealth/ablation/acce_gyro/A30_B30_AB0_label_B_test_A 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/mhealth/ablation/acce_mage/A30_B30_AB0_label_A_test_B.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N mhealth_abl_acce_mage_A30_B30_AB0_label_A_test_B 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/mhealth/ablation/acce_mage/A30_B30_AB0_label_A_test_B 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/mhealth/ablation/acce_mage/A30_B30_AB0_label_B_test_A.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N mhealth_abl_acce_mage_A30_B30_AB0_label_B_test_A 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/mhealth/ablation/acce_mage/A30_B30_AB0_label_B_test_A 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/mhealth/ablation/gyro_mage/A30_B30_AB0_label_A_test_B.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N mhealth_abl_gyro_mage_A30_B30_AB0_label_A_test_B 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/mhealth/ablation/gyro_mage/A30_B30_AB0_label_A_test_B 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/mhealth/ablation/gyro_mage/A30_B30_AB0_label_B_test_A.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N mhealth_abl_gyro_mage_A30_B30_AB0_label_B_test_A 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/mhealth/ablation/gyro_mage/A30_B30_AB0_label_B_test_A 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/mhealth/split_ae/acce_gyro/A0_B0_AB30_label_AB_test_A.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N mhealth_sp_acce_gyro_A0_B0_AB30_label_AB_test_A 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/mhealth/split_ae/acce_gyro/A0_B0_AB30_label_AB_test_A 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/mhealth/split_ae/acce_gyro/A0_B0_AB30_label_AB_test_B.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N mhealth_sp_acce_gyro_A0_B0_AB30_label_AB_test_B 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/mhealth/split_ae/acce_gyro/A0_B0_AB30_label_AB_test_B 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/mhealth/split_ae/acce_gyro/A0_B10_AB30_label_A_test_B.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N mhealth_sp_acce_gyro_A0_B10_AB30_label_A_test_B 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/mhealth/split_ae/acce_gyro/A0_B10_AB30_label_A_test_B 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/mhealth/split_ae/acce_gyro/A0_B10_AB30_label_B_test_A.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N mhealth_sp_acce_gyro_A0_B10_AB30_label_B_test_A 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/mhealth/split_ae/acce_gyro/A0_B10_AB30_label_B_test_A 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/mhealth/split_ae/acce_gyro/A10_B0_AB30_label_A_test_B.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N mhealth_sp_acce_gyro_A10_B0_AB30_label_A_test_B 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/mhealth/split_ae/acce_gyro/A10_B0_AB30_label_A_test_B 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/mhealth/split_ae/acce_gyro/A10_B0_AB30_label_B_test_A.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N mhealth_sp_acce_gyro_A10_B0_AB30_label_B_test_A 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/mhealth/split_ae/acce_gyro/A10_B0_AB30_label_B_test_A 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/mhealth/split_ae/acce_gyro/A10_B10_AB30_label_A_test_B.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N mhealth_sp_acce_gyro_A10_B10_AB30_label_A_test_B 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/mhealth/split_ae/acce_gyro/A10_B10_AB30_label_A_test_B 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/mhealth/split_ae/acce_gyro/A10_B10_AB30_label_B_test_A.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N mhealth_sp_acce_gyro_A10_B10_AB30_label_B_test_A 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/mhealth/split_ae/acce_gyro/A10_B10_AB30_label_B_test_A 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/mhealth/split_ae/acce_mage/A0_B0_AB30_label_AB_test_A.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N mhealth_sp_acce_mage_A0_B0_AB30_label_AB_test_A 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/mhealth/split_ae/acce_mage/A0_B0_AB30_label_AB_test_A 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/mhealth/split_ae/acce_mage/A0_B0_AB30_label_AB_test_B.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N mhealth_sp_acce_mage_A0_B0_AB30_label_AB_test_B 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/mhealth/split_ae/acce_mage/A0_B0_AB30_label_AB_test_B 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/mhealth/split_ae/acce_mage/A0_B10_AB30_label_A_test_B.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N mhealth_sp_acce_mage_A0_B10_AB30_label_A_test_B 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/mhealth/split_ae/acce_mage/A0_B10_AB30_label_A_test_B 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/mhealth/split_ae/acce_mage/A0_B10_AB30_label_B_test_A.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N mhealth_sp_acce_mage_A0_B10_AB30_label_B_test_A 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/mhealth/split_ae/acce_mage/A0_B10_AB30_label_B_test_A 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/mhealth/split_ae/acce_mage/A10_B0_AB30_label_A_test_B.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N mhealth_sp_acce_mage_A10_B0_AB30_label_A_test_B 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/mhealth/split_ae/acce_mage/A10_B0_AB30_label_A_test_B 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/mhealth/split_ae/acce_mage/A10_B0_AB30_label_B_test_A.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N mhealth_sp_acce_mage_A10_B0_AB30_label_B_test_A 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/mhealth/split_ae/acce_mage/A10_B0_AB30_label_B_test_A 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/mhealth/split_ae/acce_mage/A10_B10_AB30_label_A_test_B.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N mhealth_sp_acce_mage_A10_B10_AB30_label_A_test_B 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/mhealth/split_ae/acce_mage/A10_B10_AB30_label_A_test_B 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/mhealth/split_ae/acce_mage/A10_B10_AB30_label_B_test_A.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N mhealth_sp_acce_mage_A10_B10_AB30_label_B_test_A 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/mhealth/split_ae/acce_mage/A10_B10_AB30_label_B_test_A 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/mhealth/split_ae/gyro_mage/A0_B0_AB30_label_AB_test_A.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N mhealth_sp_gyro_mage_A0_B0_AB30_label_AB_test_A 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/mhealth/split_ae/gyro_mage/A0_B0_AB30_label_AB_test_A 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/mhealth/split_ae/gyro_mage/A0_B0_AB30_label_AB_test_B.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N mhealth_sp_gyro_mage_A0_B0_AB30_label_AB_test_B 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/mhealth/split_ae/gyro_mage/A0_B0_AB30_label_AB_test_B 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/mhealth/split_ae/gyro_mage/A0_B10_AB30_label_A_test_B.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N mhealth_sp_gyro_mage_A0_B10_AB30_label_A_test_B 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/mhealth/split_ae/gyro_mage/A0_B10_AB30_label_A_test_B 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/mhealth/split_ae/gyro_mage/A0_B10_AB30_label_B_test_A.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N mhealth_sp_gyro_mage_A0_B10_AB30_label_B_test_A 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/mhealth/split_ae/gyro_mage/A0_B10_AB30_label_B_test_A 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/mhealth/split_ae/gyro_mage/A10_B0_AB30_label_A_test_B.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N mhealth_sp_gyro_mage_A10_B0_AB30_label_A_test_B 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/mhealth/split_ae/gyro_mage/A10_B0_AB30_label_A_test_B 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/mhealth/split_ae/gyro_mage/A10_B0_AB30_label_B_test_A.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N mhealth_sp_gyro_mage_A10_B0_AB30_label_B_test_A 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/mhealth/split_ae/gyro_mage/A10_B0_AB30_label_B_test_A 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/mhealth/split_ae/gyro_mage/A10_B10_AB30_label_A_test_B.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N mhealth_sp_gyro_mage_A10_B10_AB30_label_A_test_B 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/mhealth/split_ae/gyro_mage/A10_B10_AB30_label_A_test_B 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/mhealth/split_ae/gyro_mage/A10_B10_AB30_label_B_test_A.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N mhealth_sp_gyro_mage_A10_B10_AB30_label_B_test_A 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/mhealth/split_ae/gyro_mage/A10_B10_AB30_label_B_test_A 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/ur_fall/ablation/rgb_depth/A30_B30_AB0_label_A_test_B.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N ur_fall_abl_rgb_depth_A30_B30_AB0_label_A_test_B 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/ur_fall/ablation/rgb_depth/A30_B30_AB0_label_A_test_B 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/ur_fall/ablation/rgb_depth/A30_B30_AB0_label_B_test_A.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N ur_fall_abl_rgb_depth_A30_B30_AB0_label_B_test_A 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/ur_fall/ablation/rgb_depth/A30_B30_AB0_label_B_test_A 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/ur_fall/split_ae/acce_depth/A0_B0_AB30_label_AB_test_A.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N ur_fall_sp_acce_depth_A0_B0_AB30_label_AB_test_A 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/ur_fall/split_ae/acce_depth/A0_B0_AB30_label_AB_test_A 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/ur_fall/split_ae/acce_depth/A0_B0_AB30_label_AB_test_B.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N ur_fall_sp_acce_depth_A0_B0_AB30_label_AB_test_B 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/ur_fall/split_ae/acce_depth/A0_B0_AB30_label_AB_test_B 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/ur_fall/split_ae/acce_depth/A0_B0_AB30_label_A_test_B.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N ur_fall_sp_acce_depth_A0_B0_AB30_label_A_test_B 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/ur_fall/split_ae/acce_depth/A0_B0_AB30_label_A_test_B 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/ur_fall/split_ae/acce_depth/A0_B0_AB30_label_B_test_A.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N ur_fall_sp_acce_depth_A0_B0_AB30_label_B_test_A 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/ur_fall/split_ae/acce_depth/A0_B0_AB30_label_B_test_A 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/ur_fall/split_ae/acce_depth/A0_B10_AB30_label_A_test_B.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N ur_fall_sp_acce_depth_A0_B10_AB30_label_A_test_B 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/ur_fall/split_ae/acce_depth/A0_B10_AB30_label_A_test_B 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/ur_fall/split_ae/acce_depth/A0_B10_AB30_label_B_test_A.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N ur_fall_sp_acce_depth_A0_B10_AB30_label_B_test_A 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/ur_fall/split_ae/acce_depth/A0_B10_AB30_label_B_test_A 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/ur_fall/split_ae/acce_depth/A0_B30_AB0_label_B_test_B.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N ur_fall_sp_acce_depth_A0_B30_AB0_label_B_test_B 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/ur_fall/split_ae/acce_depth/A0_B30_AB0_label_B_test_B 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/ur_fall/split_ae/acce_depth/A10_B0_AB30_label_A_test_B.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N ur_fall_sp_acce_depth_A10_B0_AB30_label_A_test_B 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/ur_fall/split_ae/acce_depth/A10_B0_AB30_label_A_test_B 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/ur_fall/split_ae/acce_depth/A10_B0_AB30_label_B_test_A.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N ur_fall_sp_acce_depth_A10_B0_AB30_label_B_test_A 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/ur_fall/split_ae/acce_depth/A10_B0_AB30_label_B_test_A 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/ur_fall/split_ae/acce_depth/A30_B0_AB0_label_A_test_A.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N ur_fall_sp_acce_depth_A30_B0_AB0_label_A_test_A 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/ur_fall/split_ae/acce_depth/A30_B0_AB0_label_A_test_A 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/ur_fall/split_ae/acce_rgb/A10_B10_AB30_label_A_test_B.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N ur_fall_sp_acce_rgb_A10_B10_AB30_label_A_test_B 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/ur_fall/split_ae/acce_rgb/A10_B10_AB30_label_A_test_B 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/ur_fall/split_ae/acce_rgb/A10_B10_AB30_label_B_test_A.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N ur_fall_sp_acce_rgb_A10_B10_AB30_label_B_test_A 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/ur_fall/split_ae/acce_rgb/A10_B10_AB30_label_B_test_A 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/ur_fall/split_ae/rgb_depth/A0_B0_AB30_label_AB_test_A.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N ur_fall_sp_rgb_depth_A0_B0_AB30_label_AB_test_A 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/ur_fall/split_ae/rgb_depth/A0_B0_AB30_label_AB_test_A 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/ur_fall/split_ae/rgb_depth/A0_B0_AB30_label_AB_test_B.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N ur_fall_sp_rgb_depth_A0_B0_AB30_label_AB_test_B 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/ur_fall/split_ae/rgb_depth/A0_B0_AB30_label_AB_test_B 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/ur_fall/split_ae/rgb_depth/A0_B10_AB30_label_A_test_B.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N ur_fall_sp_rgb_depth_A0_B10_AB30_label_A_test_B 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/ur_fall/split_ae/rgb_depth/A0_B10_AB30_label_A_test_B 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/ur_fall/split_ae/rgb_depth/A0_B10_AB30_label_B_test_A.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N ur_fall_sp_rgb_depth_A0_B10_AB30_label_B_test_A 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/ur_fall/split_ae/rgb_depth/A0_B10_AB30_label_B_test_A 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/ur_fall/split_ae/rgb_depth/A10_B0_AB30_label_A_test_B.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N ur_fall_sp_rgb_depth_A10_B0_AB30_label_A_test_B 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/ur_fall/split_ae/rgb_depth/A10_B0_AB30_label_A_test_B 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/ur_fall/split_ae/rgb_depth/A10_B0_AB30_label_B_test_A.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N ur_fall_sp_rgb_depth_A10_B0_AB30_label_B_test_A 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/ur_fall/split_ae/rgb_depth/A10_B0_AB30_label_B_test_A 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/ur_fall/split_ae/rgb_depth/A10_B10_AB30_label_A_test_B.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N ur_fall_sp_rgb_depth_A10_B10_AB30_label_A_test_B 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/ur_fall/split_ae/rgb_depth/A10_B10_AB30_label_A_test_B 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/ur_fall/split_ae/rgb_depth/A10_B10_AB30_label_B_test_A.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N ur_fall_sp_rgb_depth_A10_B10_AB30_label_B_test_A 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/ur_fall/split_ae/rgb_depth/A10_B10_AB30_label_B_test_A 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/ur_fall/ablation/acce_depth/A30_B30_AB0_label_A_test_B.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N ur_fall_abl_acce_depth_A30_B30_AB0_label_A_test_B 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/ur_fall/ablation/acce_depth/A30_B30_AB0_label_A_test_B 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/ur_fall/ablation/acce_depth/A30_B30_AB0_label_B_test_A.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N ur_fall_abl_acce_depth_A30_B30_AB0_label_B_test_A 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/ur_fall/ablation/acce_depth/A30_B30_AB0_label_B_test_A 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/ur_fall/split_ae/acce_depth/A10_B10_AB30_label_A_test_B.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N ur_fall_sp_acce_depth_A10_B10_AB30_label_A_test_B 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/ur_fall/split_ae/acce_depth/A10_B10_AB30_label_A_test_B 12 | 13 | 14 | -------------------------------------------------------------------------------- /sub/ur_fall/split_ae/acce_depth/A10_B10_AB30_label_B_test_A.pbs: -------------------------------------------------------------------------------- 1 | #PBS -l walltime=24:00:00 2 | #PBS -l select=2:ncpus=32:mem=124gb 3 | #PBS -N ur_fall_sp_acce_depth_A10_B10_AB30_label_B_test_A 4 | 5 | module load anaconda3/personal 6 | source activate deep-learning 7 | module load mpi 8 | 9 | cd $PBS_O_WORKDIR 10 | 11 | mpirun -n 64 python3 src/main.py --config ./config/ur_fall/split_ae/acce_depth/A10_B10_AB30_label_B_test_A 12 | 13 | 14 | -------------------------------------------------------------------------------- /exp_opp.sh: -------------------------------------------------------------------------------- 1 | qsub sub/opp/dccae/A0_B0_AB30_label_AB_test_A.pbs 2 | qsub sub/opp/dccae/A0_B0_AB30_label_AB_test_B.pbs 3 | qsub sub/opp/dccae/A0_B0_AB30_label_A_test_B.pbs 4 | qsub sub/opp/dccae/A0_B0_AB30_label_B_test_A.pbs 5 | qsub sub/opp/dccae/A0_B10_AB30_label_A_test_B.pbs 6 | qsub sub/opp/dccae/A0_B10_AB30_label_B_test_A.pbs 7 | qsub sub/opp/dccae/A0_B30_AB0_label_B_test_B.pbs 8 | qsub sub/opp/dccae/A10_B0_AB30_label_A_test_B.pbs 9 | qsub sub/opp/dccae/A10_B0_AB30_label_B_test_A.pbs 10 | qsub sub/opp/dccae/A10_B10_AB30_label_A_test_B.pbs 11 | qsub sub/opp/dccae/A10_B10_AB30_label_B_test_A.pbs 12 | qsub sub/opp/dccae/A30_B0_AB0_label_A_test_A.pbs -------------------------------------------------------------------------------- /config/config_debug: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = opp 3 | data_path = data 4 | results_path = results/opp/debug 5 | modality_A = acce 6 | modality_B = gyro 7 | model_ae = DCCAE_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.25 12 | train_supervised_ratio = 0.25 13 | num_clients_A = 10 14 | num_clients_B = 10 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 14 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = B 29 | # test_modality can be A or B 30 | test_modality = A 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.001 35 | optimizer = Adam 36 | criterion = DCCAELoss -------------------------------------------------------------------------------- /config/opp/dccae/A0_B0_AB30_label_A_test_B: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = opp 3 | data_path = data 4 | results_path = results/opp/dccae/acce_gyro/A0_B0_AB30_label_A_test_B 5 | modality_A = acce 6 | modality_B = gyro 7 | model_ae = DCCAE_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.07 12 | train_supervised_ratio = 0.07 13 | num_clients_A = 0 14 | num_clients_B = 0 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 10 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = A 29 | # test_modality can be A or B 30 | test_modality = B 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.001 35 | optimizer = Adam 36 | criterion = DCCAELoss -------------------------------------------------------------------------------- /config/opp/dccae/A0_B0_AB30_label_B_test_A: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = opp 3 | data_path = data 4 | results_path = results/opp/dccae/acce_gyro/A0_B0_AB30_label_B_test_A 5 | modality_A = acce 6 | modality_B = gyro 7 | model_ae = DCCAE_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.07 12 | train_supervised_ratio = 0.07 13 | num_clients_A = 0 14 | num_clients_B = 0 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 10 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = B 29 | # test_modality can be A or B 30 | test_modality = A 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.001 35 | optimizer = Adam 36 | criterion = DCCAELoss -------------------------------------------------------------------------------- /config/opp/dccae/A0_B30_AB0_label_B_test_B: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = opp 3 | data_path = data 4 | results_path = results/opp/dccae/acce_gyro/A0_B30_AB0_label_B_test_B 5 | modality_A = acce 6 | modality_B = gyro 7 | model_ae = DCCAE_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.07 12 | train_supervised_ratio = 0.07 13 | num_clients_A = 0 14 | num_clients_B = 30 15 | num_clients_AB = 0 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 10 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = B 29 | # test_modality can be A or B 30 | test_modality = B 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.001 35 | optimizer = Adam 36 | criterion = DCCAELoss -------------------------------------------------------------------------------- /config/opp/dccae/A30_B0_AB0_label_A_test_A: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = opp 3 | data_path = data 4 | results_path = results/opp/dccae/acce_gyro/A30_B0_AB0_label_A_test_A 5 | modality_A = acce 6 | modality_B = gyro 7 | model_ae = DCCAE_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.07 12 | train_supervised_ratio = 0.07 13 | num_clients_A = 30 14 | num_clients_B = 0 15 | num_clients_AB = 0 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 10 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = A 29 | # test_modality can be A or B 30 | test_modality = A 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.001 35 | optimizer = Adam 36 | criterion = DCCAELoss -------------------------------------------------------------------------------- /exp_ablation.sh: -------------------------------------------------------------------------------- 1 | qsub sub/opp/ablation/A30_B30_AB0_label_A_test_B.pbs 2 | qsub sub/opp/ablation/A30_B30_AB0_label_B_test_A.pbs 3 | qsub sub/ur_fall/ablation/acce_depth/A30_B30_AB0_label_A_test_B.pbs 4 | qsub sub/ur_fall/ablation/acce_depth/A30_B30_AB0_label_B_test_A.pbs 5 | qsub sub/ur_fall/ablation/rgb_depth/A30_B30_AB0_label_A_test_B.pbs 6 | qsub sub/ur_fall/ablation/rgb_depth/A30_B30_AB0_label_B_test_A.pbs 7 | qsub sub/mhealth/ablation/acce_gyro/A30_B30_AB0_label_A_test_B.pbs 8 | qsub sub/mhealth/ablation/acce_gyro/A30_B30_AB0_label_B_test_A.pbs 9 | qsub sub/mhealth/ablation/acce_mage/A30_B30_AB0_label_A_test_B.pbs 10 | qsub sub/mhealth/ablation/acce_mage/A30_B30_AB0_label_B_test_A.pbs 11 | qsub sub/mhealth/ablation/gyro_mage/A30_B30_AB0_label_A_test_B.pbs 12 | qsub sub/mhealth/ablation/gyro_mage/A30_B30_AB0_label_B_test_A.pbs -------------------------------------------------------------------------------- /config/opp/dccae/A0_B0_AB30_label_AB_test_A: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = opp 3 | data_path = data 4 | results_path = results/opp/dccae/acce_gyro/A0_B0_AB30_label_AB_test_A 5 | modality_A = acce 6 | modality_B = gyro 7 | model_ae = DCCAE_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.07 12 | train_supervised_ratio = 0.07 13 | num_clients_A = 0 14 | num_clients_B = 0 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 10 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = AB 29 | # test_modality can be A or B 30 | test_modality = A 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.001 35 | optimizer = Adam 36 | criterion = DCCAELoss -------------------------------------------------------------------------------- /config/opp/dccae/A0_B0_AB30_label_AB_test_B: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = opp 3 | data_path = data 4 | results_path = results/opp/dccae/acce_gyro/A0_B0_AB30_label_AB_test_B 5 | modality_A = acce 6 | modality_B = gyro 7 | model_ae = DCCAE_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.07 12 | train_supervised_ratio = 0.07 13 | num_clients_A = 0 14 | num_clients_B = 0 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 10 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = AB 29 | # test_modality can be A or B 30 | test_modality = B 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.001 35 | optimizer = Adam 36 | criterion = DCCAELoss -------------------------------------------------------------------------------- /config/opp/dccae/A0_B10_AB30_label_A_test_B: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = opp 3 | data_path = data 4 | results_path = results/opp/dccae/acce_gyro/A0_B10_AB30_label_A_test_B 5 | modality_A = acce 6 | modality_B = gyro 7 | model_ae = DCCAE_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.07 12 | train_supervised_ratio = 0.07 13 | num_clients_A = 0 14 | num_clients_B = 10 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 10 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = A 29 | # test_modality can be A or B 30 | test_modality = B 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.001 35 | optimizer = Adam 36 | criterion = DCCAELoss -------------------------------------------------------------------------------- /config/opp/dccae/A0_B10_AB30_label_B_test_A: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = opp 3 | data_path = data 4 | results_path = results/opp/dccae/acce_gyro/A0_B10_AB30_label_B_test_A 5 | modality_A = acce 6 | modality_B = gyro 7 | model_ae = DCCAE_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.07 12 | train_supervised_ratio = 0.07 13 | num_clients_A = 0 14 | num_clients_B = 10 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 10 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = B 29 | # test_modality can be A or B 30 | test_modality = A 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.001 35 | optimizer = Adam 36 | criterion = DCCAELoss -------------------------------------------------------------------------------- /config/opp/dccae/A10_B0_AB30_label_A_test_B: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = opp 3 | data_path = data 4 | results_path = results/opp/dccae/acce_gyro/A10_B0_AB30_label_A_test_B 5 | modality_A = acce 6 | modality_B = gyro 7 | model_ae = DCCAE_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.07 12 | train_supervised_ratio = 0.07 13 | num_clients_A = 10 14 | num_clients_B = 0 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 10 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = A 29 | # test_modality can be A or B 30 | test_modality = B 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.001 35 | optimizer = Adam 36 | criterion = DCCAELoss -------------------------------------------------------------------------------- /config/opp/dccae/A10_B0_AB30_label_B_test_A: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = opp 3 | data_path = data 4 | results_path = results/opp/dccae/acce_gyro/A10_B0_AB30_label_B_test_A 5 | modality_A = acce 6 | modality_B = gyro 7 | model_ae = DCCAE_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.07 12 | train_supervised_ratio = 0.07 13 | num_clients_A = 10 14 | num_clients_B = 0 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 10 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = B 29 | # test_modality can be A or B 30 | test_modality = A 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.001 35 | optimizer = Adam 36 | criterion = DCCAELoss -------------------------------------------------------------------------------- /config/opp/dccae/A10_B10_AB30_label_A_test_B: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = opp 3 | data_path = data 4 | results_path = results/opp/dccae/acce_gyro/A10_B10_AB30_label_A_test_B 5 | modality_A = acce 6 | modality_B = gyro 7 | model_ae = DCCAE_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.07 12 | train_supervised_ratio = 0.07 13 | num_clients_A = 10 14 | num_clients_B = 10 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 10 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = A 29 | # test_modality can be A or B 30 | test_modality = B 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.001 35 | optimizer = Adam 36 | criterion = DCCAELoss -------------------------------------------------------------------------------- /config/opp/dccae/A10_B10_AB30_label_B_test_A: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = opp 3 | data_path = data 4 | results_path = results/opp/dccae/acce_gyro/A10_B10_AB30_label_B_test_A 5 | modality_A = acce 6 | modality_B = gyro 7 | model_ae = DCCAE_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.07 12 | train_supervised_ratio = 0.07 13 | num_clients_A = 10 14 | num_clients_B = 10 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 10 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = B 29 | # test_modality can be A or B 30 | test_modality = A 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.001 35 | optimizer = Adam 36 | criterion = DCCAELoss -------------------------------------------------------------------------------- /config/opp/ablation/A30_B30_AB0_label_A_test_B: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = opp 3 | data_path = data 4 | results_path = results/opp/ablation/acce_gyro/A30_B30_AB0_label_A_test_B 5 | modality_A = acce 6 | modality_B = gyro 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.07 12 | train_supervised_ratio = 0.07 13 | num_clients_A = 30 14 | num_clients_B = 30 15 | num_clients_AB = 0 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 10 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = A 29 | # test_modality can be A or B 30 | test_modality = B 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.001 35 | optimizer = Adam 36 | criterion = MSELoss 37 | -------------------------------------------------------------------------------- /config/opp/ablation/A30_B30_AB0_label_B_test_A: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = opp 3 | data_path = data 4 | results_path = results/opp/ablation/acce_gyro/A30_B30_AB0_label_B_test_A 5 | modality_A = acce 6 | modality_B = gyro 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.07 12 | train_supervised_ratio = 0.07 13 | num_clients_A = 30 14 | num_clients_B = 30 15 | num_clients_AB = 0 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 10 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = B 29 | # test_modality can be A or B 30 | test_modality = A 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.001 35 | optimizer = Adam 36 | criterion = MSELoss 37 | -------------------------------------------------------------------------------- /config/mhealth/split_ae/acce_gyro/A0_B0_AB30_label_A_test_B: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = mhealth 3 | data_path = data 4 | results_path = results/mhealth/split_ae/acce_gyro/A0_B0_AB30_label_A_test_B 5 | modality_A = acce 6 | modality_B = gyro 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 0 14 | num_clients_B = 0 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 4 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = A 29 | # test_modality can be A or B 30 | test_modality = B 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/mhealth/split_ae/acce_gyro/A0_B0_AB30_label_B_test_A: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = mhealth 3 | data_path = data 4 | results_path = results/mhealth/split_ae/acce_gyro/A0_B0_AB30_label_B_test_A 5 | modality_A = acce 6 | modality_B = gyro 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 0 14 | num_clients_B = 0 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 4 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = B 29 | # test_modality can be A or B 30 | test_modality = A 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/mhealth/split_ae/acce_gyro/A30_B0_AB0_label_A_test_A: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = mhealth 3 | data_path = data 4 | results_path = results/mhealth/split_ae/acce_gyro/A30_B0_AB0_label_A_test_A 5 | modality_A = acce 6 | modality_B = gyro 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 30 14 | num_clients_B = 0 15 | num_clients_AB = 0 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 4 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = A 29 | # test_modality can be A or B 30 | test_modality = A 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/mhealth/split_ae/acce_mage/A0_B0_AB30_label_A_test_B: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = mhealth 3 | data_path = data 4 | results_path = results/mhealth/split_ae/acce_mage/A0_B0_AB30_label_A_test_B 5 | modality_A = acce 6 | modality_B = mage 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 0 14 | num_clients_B = 0 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 4 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = A 29 | # test_modality can be A or B 30 | test_modality = B 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/mhealth/split_ae/acce_mage/A0_B0_AB30_label_B_test_A: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = mhealth 3 | data_path = data 4 | results_path = results/mhealth/split_ae/acce_mage/A0_B0_AB30_label_B_test_A 5 | modality_A = acce 6 | modality_B = mage 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 0 14 | num_clients_B = 0 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 4 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = B 29 | # test_modality can be A or B 30 | test_modality = A 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/mhealth/split_ae/acce_mage/A30_B0_AB0_label_A_test_A: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = mhealth 3 | data_path = data 4 | results_path = results/mhealth/split_ae/acce_mage/A30_B0_AB0_label_A_test_A 5 | modality_A = acce 6 | modality_B = mage 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 30 14 | num_clients_B = 0 15 | num_clients_AB = 0 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 4 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = A 29 | # test_modality can be A or B 30 | test_modality = A 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/mhealth/split_ae/gyro_mage/A0_B0_AB30_label_A_test_B: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = mhealth 3 | data_path = data 4 | results_path = results/mhealth/split_ae/gyro_mage/A0_B0_AB30_label_A_test_B 5 | modality_A = gyro 6 | modality_B = mage 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 0 14 | num_clients_B = 0 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 4 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = A 29 | # test_modality can be A or B 30 | test_modality = B 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/mhealth/split_ae/gyro_mage/A0_B0_AB30_label_B_test_A: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = mhealth 3 | data_path = data 4 | results_path = results/mhealth/split_ae/gyro_mage/A0_B0_AB30_label_B_test_A 5 | modality_A = gyro 6 | modality_B = mage 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 0 14 | num_clients_B = 0 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 4 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = B 29 | # test_modality can be A or B 30 | test_modality = A 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/mhealth/split_ae/gyro_mage/A30_B0_AB0_label_A_test_A: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = mhealth 3 | data_path = data 4 | results_path = results/mhealth/split_ae/gyro_mage/A30_B0_AB0_label_A_test_A 5 | modality_A = gyro 6 | modality_B = mage 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 30 14 | num_clients_B = 0 15 | num_clients_AB = 0 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 4 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = A 29 | # test_modality can be A or B 30 | test_modality = A 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/ur_fall/split_ae/acce_rgb/A0_B0_AB30_label_AB_test_A: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = ur_fall 3 | data_path = data 4 | results_path = results/ur_fall/split_ae/acce_rgb/A0_B0_AB30_label_AB_test_A 5 | modality_A = acce 6 | modality_B = rgb 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 0 14 | num_clients_B = 0 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 2 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = AB 29 | # test_modality can be A or B 30 | test_modality = A 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.001 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/ur_fall/split_ae/acce_rgb/A0_B0_AB30_label_AB_test_B: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = ur_fall 3 | data_path = data 4 | results_path = results/ur_fall/split_ae/acce_rgb/A0_B0_AB30_label_AB_test_B 5 | modality_A = acce 6 | modality_B = rgb 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 0 14 | num_clients_B = 0 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 2 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = AB 29 | # test_modality can be A or B 30 | test_modality = B 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.001 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/ur_fall/split_ae/acce_rgb/A0_B0_AB30_label_A_test_B: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = ur_fall 3 | data_path = data 4 | results_path = results/ur_fall/split_ae/acce_rgb/A0_B0_AB30_label_A_test_B 5 | modality_A = acce 6 | modality_B = rgb 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 0 14 | num_clients_B = 0 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 2 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = A 29 | # test_modality can be A or B 30 | test_modality = B 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.001 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/ur_fall/split_ae/acce_rgb/A0_B0_AB30_label_B_test_A: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = ur_fall 3 | data_path = data 4 | results_path = results/ur_fall/split_ae/acce_rgb/A0_B0_AB30_label_B_test_A 5 | modality_A = acce 6 | modality_B = rgb 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 0 14 | num_clients_B = 0 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 2 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = B 29 | # test_modality can be A or B 30 | test_modality = A 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.001 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/ur_fall/split_ae/acce_rgb/A0_B10_AB30_label_A_test_B: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = ur_fall 3 | data_path = data 4 | results_path = results/ur_fall/split_ae/acce_rgb/A0_B10_AB30_label_A_test_B 5 | modality_A = acce 6 | modality_B = rgb 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 0 14 | num_clients_B = 10 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 2 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = A 29 | # test_modality can be A or B 30 | test_modality = B 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.001 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/ur_fall/split_ae/acce_rgb/A0_B10_AB30_label_B_test_A: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = ur_fall 3 | data_path = data 4 | results_path = results/ur_fall/split_ae/acce_rgb/A0_B10_AB30_label_B_test_A 5 | modality_A = acce 6 | modality_B = rgb 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 0 14 | num_clients_B = 10 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 2 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = B 29 | # test_modality can be A or B 30 | test_modality = A 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.001 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/ur_fall/split_ae/acce_rgb/A0_B30_AB0_label_B_test_B: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = ur_fall 3 | data_path = data 4 | results_path = results/ur_fall/split_ae/acce_rgb/A0_B30_AB0_label_B_test_B 5 | modality_A = acce 6 | modality_B = rgb 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 0 14 | num_clients_B = 30 15 | num_clients_AB = 0 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 2 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = B 29 | # test_modality can be A or B 30 | test_modality = B 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.001 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/ur_fall/split_ae/acce_rgb/A10_B0_AB30_label_A_test_B: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = ur_fall 3 | data_path = data 4 | results_path = results/ur_fall/split_ae/acce_rgb/A10_B0_AB30_label_A_test_B 5 | modality_A = acce 6 | modality_B = rgb 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 10 14 | num_clients_B = 0 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 2 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = A 29 | # test_modality can be A or B 30 | test_modality = B 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.001 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/ur_fall/split_ae/acce_rgb/A10_B0_AB30_label_B_test_A: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = ur_fall 3 | data_path = data 4 | results_path = results/ur_fall/split_ae/acce_rgb/A10_B0_AB30_label_B_test_A 5 | modality_A = acce 6 | modality_B = rgb 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 10 14 | num_clients_B = 0 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 2 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = B 29 | # test_modality can be A or B 30 | test_modality = A 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.001 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/ur_fall/split_ae/acce_rgb/A30_B0_AB0_label_A_test_A: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = ur_fall 3 | data_path = data 4 | results_path = results/ur_fall/split_ae/acce_rgb/A30_B0_AB0_label_A_test_A 5 | modality_A = acce 6 | modality_B = rgb 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 30 14 | num_clients_B = 0 15 | num_clients_AB = 0 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 2 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = A 29 | # test_modality can be A or B 30 | test_modality = A 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.001 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/ur_fall/split_ae/rgb_depth/A0_B0_AB30_label_A_test_B: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = ur_fall 3 | data_path = data 4 | results_path = results/ur_fall/split_ae/rgb_depth/A0_B0_AB30_label_A_test_B 5 | modality_A = rgb 6 | modality_B = depth 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 0 14 | num_clients_B = 0 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 4 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = A 29 | # test_modality can be A or B 30 | test_modality = B 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/ur_fall/split_ae/rgb_depth/A0_B0_AB30_label_B_test_A: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = ur_fall 3 | data_path = data 4 | results_path = results/ur_fall/split_ae/rgb_depth/A0_B0_AB30_label_B_test_A 5 | modality_A = rgb 6 | modality_B = depth 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 0 14 | num_clients_B = 0 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 4 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = B 29 | # test_modality can be A or B 30 | test_modality = A 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/ur_fall/split_ae/rgb_depth/A0_B30_AB0_label_B_test_B: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = ur_fall 3 | data_path = data 4 | results_path = results/ur_fall/split_ae/rgb_depth/A0_B30_AB0_label_B_test_B 5 | modality_A = rgb 6 | modality_B = depth 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 0 14 | num_clients_B = 30 15 | num_clients_AB = 0 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 4 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = B 29 | # test_modality can be A or B 30 | test_modality = B 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/ur_fall/split_ae/rgb_depth/A30_B0_AB0_label_A_test_A: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = ur_fall 3 | data_path = data 4 | results_path = results/ur_fall/split_ae/rgb_depth/A30_B0_AB0_label_A_test_A 5 | modality_A = rgb 6 | modality_B = depth 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 30 14 | num_clients_B = 0 15 | num_clients_AB = 0 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 4 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = A 29 | # test_modality can be A or B 30 | test_modality = A 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/mhealth/split_ae/acce_gyro/A0_B0_AB30_label_AB_test_A: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = mhealth 3 | data_path = data 4 | results_path = results/mhealth/split_ae/acce_gyro/A0_B0_AB30_label_AB_test_A 5 | modality_A = acce 6 | modality_B = gyro 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 0 14 | num_clients_B = 0 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 4 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = AB 29 | # test_modality can be A or B 30 | test_modality = A 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/mhealth/split_ae/acce_gyro/A0_B0_AB30_label_AB_test_B: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = mhealth 3 | data_path = data 4 | results_path = results/mhealth/split_ae/acce_gyro/A0_B0_AB30_label_AB_test_B 5 | modality_A = acce 6 | modality_B = gyro 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 0 14 | num_clients_B = 0 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 4 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = AB 29 | # test_modality can be A or B 30 | test_modality = B 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/mhealth/split_ae/acce_gyro/A0_B10_AB30_label_A_test_B: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = mhealth 3 | data_path = data 4 | results_path = results/mhealth/split_ae/acce_gyro/A0_B10_AB30_label_A_test_B 5 | modality_A = acce 6 | modality_B = gyro 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 0 14 | num_clients_B = 10 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 4 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = A 29 | # test_modality can be A or B 30 | test_modality = B 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/mhealth/split_ae/acce_gyro/A0_B10_AB30_label_B_test_A: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = mhealth 3 | data_path = data 4 | results_path = results/mhealth/split_ae/acce_gyro/A0_B10_AB30_label_B_test_A 5 | modality_A = acce 6 | modality_B = gyro 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 0 14 | num_clients_B = 10 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 4 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = B 29 | # test_modality can be A or B 30 | test_modality = A 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/mhealth/split_ae/acce_gyro/A0_B30_AB0_label_B_test_B: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = mhealth 3 | data_path = data 4 | results_path = results/mhealth/split_ae/acce_gyro/A0_B30_AB0_label_B_test_B 5 | modality_A = acce 6 | modality_B = gyro 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 0 14 | num_clients_B = 30 15 | num_clients_AB = 0 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 4 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = B 29 | # test_modality can be A or B 30 | test_modality = B 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss 37 | -------------------------------------------------------------------------------- /config/mhealth/split_ae/acce_gyro/A10_B0_AB30_label_A_test_B: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = mhealth 3 | data_path = data 4 | results_path = results/mhealth/split_ae/acce_gyro/A10_B0_AB30_label_A_test_B 5 | modality_A = acce 6 | modality_B = gyro 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 10 14 | num_clients_B = 0 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 4 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = A 29 | # test_modality can be A or B 30 | test_modality = B 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/mhealth/split_ae/acce_gyro/A10_B0_AB30_label_B_test_A: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = mhealth 3 | data_path = data 4 | results_path = results/mhealth/split_ae/acce_gyro/A10_B0_AB30_label_B_test_A 5 | modality_A = acce 6 | modality_B = gyro 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 10 14 | num_clients_B = 0 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 4 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = B 29 | # test_modality can be A or B 30 | test_modality = A 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/mhealth/split_ae/acce_gyro/A10_B10_AB30_label_A_test_B: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = mhealth 3 | data_path = data 4 | results_path = results/mhealth/split_ae/acce_gyro/A10_B10_AB30_label_A_test_B 5 | modality_A = acce 6 | modality_B = gyro 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 10 14 | num_clients_B = 10 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 4 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = A 29 | # test_modality can be A or B 30 | test_modality = B 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/mhealth/split_ae/acce_gyro/A10_B10_AB30_label_B_test_A: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = mhealth 3 | data_path = data 4 | results_path = results/mhealth/split_ae/acce_gyro/A10_B10_AB30_label_B_test_A 5 | modality_A = acce 6 | modality_B = gyro 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 10 14 | num_clients_B = 10 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 4 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = B 29 | # test_modality can be A or B 30 | test_modality = A 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/mhealth/split_ae/acce_mage/A0_B0_AB30_label_AB_test_A: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = mhealth 3 | data_path = data 4 | results_path = results/mhealth/split_ae/acce_mage/A0_B0_AB30_label_AB_test_A 5 | modality_A = acce 6 | modality_B = mage 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 0 14 | num_clients_B = 0 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 4 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = AB 29 | # test_modality can be A or B 30 | test_modality = A 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/mhealth/split_ae/acce_mage/A0_B0_AB30_label_AB_test_B: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = mhealth 3 | data_path = data 4 | results_path = results/mhealth/split_ae/acce_mage/A0_B0_AB30_label_AB_test_B 5 | modality_A = acce 6 | modality_B = mage 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 0 14 | num_clients_B = 0 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 4 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = AB 29 | # test_modality can be A or B 30 | test_modality = B 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/mhealth/split_ae/acce_mage/A0_B10_AB30_label_A_test_B: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = mhealth 3 | data_path = data 4 | results_path = results/mhealth/split_ae/acce_mage/A0_B10_AB30_label_A_test_B 5 | modality_A = acce 6 | modality_B = mage 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 0 14 | num_clients_B = 10 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 4 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = A 29 | # test_modality can be A or B 30 | test_modality = B 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/mhealth/split_ae/acce_mage/A0_B10_AB30_label_B_test_A: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = mhealth 3 | data_path = data 4 | results_path = results/mhealth/split_ae/acce_mage/A0_B10_AB30_label_B_test_A 5 | modality_A = acce 6 | modality_B = mage 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 0 14 | num_clients_B = 10 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 4 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = B 29 | # test_modality can be A or B 30 | test_modality = A 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/mhealth/split_ae/acce_mage/A0_B30_AB0_label_B_test_B: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = mhealth 3 | data_path = data 4 | results_path = results/mhealth/split_ae/acce_mage/A0_B30_AB0_label_B_test_B 5 | modality_A = acce 6 | modality_B = mage 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 0 14 | num_clients_B = 30 15 | num_clients_AB = 0 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 4 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = B 29 | # test_modality can be A or B 30 | test_modality = B 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss 37 | -------------------------------------------------------------------------------- /config/mhealth/split_ae/acce_mage/A10_B0_AB30_label_A_test_B: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = mhealth 3 | data_path = data 4 | results_path = results/mhealth/split_ae/acce_mage/A10_B0_AB30_label_A_test_B 5 | modality_A = acce 6 | modality_B = mage 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 10 14 | num_clients_B = 0 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 4 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = A 29 | # test_modality can be A or B 30 | test_modality = B 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/mhealth/split_ae/acce_mage/A10_B0_AB30_label_B_test_A: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = mhealth 3 | data_path = data 4 | results_path = results/mhealth/split_ae/acce_mage/A10_B0_AB30_label_B_test_A 5 | modality_A = acce 6 | modality_B = mage 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 10 14 | num_clients_B = 0 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 4 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = B 29 | # test_modality can be A or B 30 | test_modality = A 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/mhealth/split_ae/acce_mage/A10_B10_AB30_label_A_test_B: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = mhealth 3 | data_path = data 4 | results_path = results/mhealth/split_ae/acce_mage/A10_B10_AB30_label_A_test_B 5 | modality_A = acce 6 | modality_B = mage 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 10 14 | num_clients_B = 10 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 4 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = A 29 | # test_modality can be A or B 30 | test_modality = B 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/mhealth/split_ae/acce_mage/A10_B10_AB30_label_B_test_A: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = mhealth 3 | data_path = data 4 | results_path = results/mhealth/split_ae/acce_mage/A10_B10_AB30_label_B_test_A 5 | modality_A = acce 6 | modality_B = mage 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 10 14 | num_clients_B = 10 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 4 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = B 29 | # test_modality can be A or B 30 | test_modality = A 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/mhealth/split_ae/gyro_mage/A0_B0_AB30_label_AB_test_A: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = mhealth 3 | data_path = data 4 | results_path = results/mhealth/split_ae/gyro_mage/A0_B0_AB30_label_AB_test_A 5 | modality_A = gyro 6 | modality_B = mage 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 0 14 | num_clients_B = 0 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 4 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = AB 29 | # test_modality can be A or B 30 | test_modality = A 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/mhealth/split_ae/gyro_mage/A0_B0_AB30_label_AB_test_B: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = mhealth 3 | data_path = data 4 | results_path = results/mhealth/split_ae/gyro_mage/A0_B0_AB30_label_AB_test_B 5 | modality_A = gyro 6 | modality_B = mage 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 0 14 | num_clients_B = 0 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 4 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = AB 29 | # test_modality can be A or B 30 | test_modality = B 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/mhealth/split_ae/gyro_mage/A0_B10_AB30_label_A_test_B: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = mhealth 3 | data_path = data 4 | results_path = results/mhealth/split_ae/gyro_mage/A0_B10_AB30_label_A_test_B 5 | modality_A = gyro 6 | modality_B = mage 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 0 14 | num_clients_B = 10 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 4 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = A 29 | # test_modality can be A or B 30 | test_modality = B 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/mhealth/split_ae/gyro_mage/A0_B10_AB30_label_B_test_A: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = mhealth 3 | data_path = data 4 | results_path = results/mhealth/split_ae/gyro_mage/A0_B10_AB30_label_B_test_A 5 | modality_A = gyro 6 | modality_B = mage 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 0 14 | num_clients_B = 10 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 4 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = B 29 | # test_modality can be A or B 30 | test_modality = A 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/mhealth/split_ae/gyro_mage/A0_B30_AB0_label_B_test_B: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = mhealth 3 | data_path = data 4 | results_path = results/mhealth/split_ae/gyro_mage/A0_B30_AB0_label_B_test_B 5 | modality_A = gyro 6 | modality_B = mage 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 0 14 | num_clients_B = 30 15 | num_clients_AB = 0 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 4 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = B 29 | # test_modality can be A or B 30 | test_modality = B 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss 37 | -------------------------------------------------------------------------------- /config/mhealth/split_ae/gyro_mage/A10_B0_AB30_label_A_test_B: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = mhealth 3 | data_path = data 4 | results_path = results/mhealth/split_ae/gyro_mage/A10_B0_AB30_label_A_test_B 5 | modality_A = gyro 6 | modality_B = mage 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 10 14 | num_clients_B = 0 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 4 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = A 29 | # test_modality can be A or B 30 | test_modality = B 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/mhealth/split_ae/gyro_mage/A10_B0_AB30_label_B_test_A: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = mhealth 3 | data_path = data 4 | results_path = results/mhealth/split_ae/gyro_mage/A10_B0_AB30_label_B_test_A 5 | modality_A = gyro 6 | modality_B = mage 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 10 14 | num_clients_B = 0 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 4 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = B 29 | # test_modality can be A or B 30 | test_modality = A 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/mhealth/split_ae/gyro_mage/A10_B10_AB30_label_A_test_B: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = mhealth 3 | data_path = data 4 | results_path = results/mhealth/split_ae/gyro_mage/A10_B10_AB30_label_A_test_B 5 | modality_A = gyro 6 | modality_B = mage 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 10 14 | num_clients_B = 10 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 4 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = A 29 | # test_modality can be A or B 30 | test_modality = B 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/mhealth/split_ae/gyro_mage/A10_B10_AB30_label_B_test_A: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = mhealth 3 | data_path = data 4 | results_path = results/mhealth/split_ae/gyro_mage/A10_B10_AB30_label_B_test_A 5 | modality_A = gyro 6 | modality_B = mage 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 10 14 | num_clients_B = 10 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 4 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = B 29 | # test_modality can be A or B 30 | test_modality = A 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/ur_fall/ablation/acce_rgb/A30_B30_AB0_label_A_test_B: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = ur_fall 3 | data_path = data 4 | results_path = results/ur_fall/ablation/acce_rgb/A30_B30_AB0_label_A_test_B 5 | modality_A = acce 6 | modality_B = rgb 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 30 14 | num_clients_B = 30 15 | num_clients_AB = 0 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 2 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = A 29 | # test_modality can be A or B 30 | test_modality = B 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.001 35 | optimizer = Adam 36 | criterion = MSELoss 37 | -------------------------------------------------------------------------------- /config/ur_fall/ablation/acce_rgb/A30_B30_AB0_label_B_test_A: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = ur_fall 3 | data_path = data 4 | results_path = results/ur_fall/ablation/acce_rgb/A30_B30_AB0_label_B_test_A 5 | modality_A = acce 6 | modality_B = rgb 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 30 14 | num_clients_B = 30 15 | num_clients_AB = 0 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 2 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = B 29 | # test_modality can be A or B 30 | test_modality = A 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.001 35 | optimizer = Adam 36 | criterion = MSELoss 37 | -------------------------------------------------------------------------------- /config/ur_fall/split_ae/acce_depth/A0_B0_AB30_label_AB_test_A: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = ur_fall 3 | data_path = data 4 | results_path = results/ur_fall/split_ae/acce_depth/A0_B0_AB30_label_AB_test_A 5 | modality_A = acce 6 | modality_B = depth 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 0 14 | num_clients_B = 0 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 2 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = AB 29 | # test_modality can be A or B 30 | test_modality = A 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/ur_fall/split_ae/acce_depth/A0_B0_AB30_label_AB_test_B: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = ur_fall 3 | data_path = data 4 | results_path = results/ur_fall/split_ae/acce_depth/A0_B0_AB30_label_AB_test_B 5 | modality_A = acce 6 | modality_B = depth 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 0 14 | num_clients_B = 0 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 2 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = AB 29 | # test_modality can be A or B 30 | test_modality = B 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/ur_fall/split_ae/acce_depth/A0_B0_AB30_label_A_test_B: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = ur_fall 3 | data_path = data 4 | results_path = results/ur_fall/split_ae/acce_depth/A0_B0_AB30_label_A_test_B 5 | modality_A = acce 6 | modality_B = depth 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 0 14 | num_clients_B = 0 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 2 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = A 29 | # test_modality can be A or B 30 | test_modality = B 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/ur_fall/split_ae/acce_depth/A0_B0_AB30_label_B_test_A: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = ur_fall 3 | data_path = data 4 | results_path = results/ur_fall/split_ae/acce_depth/A0_B0_AB30_label_B_test_A 5 | modality_A = acce 6 | modality_B = depth 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 0 14 | num_clients_B = 0 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 2 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = B 29 | # test_modality can be A or B 30 | test_modality = A 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/ur_fall/split_ae/acce_depth/A0_B10_AB30_label_A_test_B: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = ur_fall 3 | data_path = data 4 | results_path = results/ur_fall/split_ae/acce_depth/A0_B10_AB30_label_A_test_B 5 | modality_A = acce 6 | modality_B = depth 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 0 14 | num_clients_B = 10 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 2 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = A 29 | # test_modality can be A or B 30 | test_modality = B 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/ur_fall/split_ae/acce_depth/A0_B10_AB30_label_B_test_A: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = ur_fall 3 | data_path = data 4 | results_path = results/ur_fall/split_ae/acce_depth/A0_B10_AB30_label_B_test_A 5 | modality_A = acce 6 | modality_B = depth 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 0 14 | num_clients_B = 10 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 2 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = B 29 | # test_modality can be A or B 30 | test_modality = A 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/ur_fall/split_ae/acce_depth/A0_B30_AB0_label_B_test_B: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = ur_fall 3 | data_path = data 4 | results_path = results/ur_fall/split_ae/acce_depth/A0_B30_AB0_label_B_test_B 5 | modality_A = acce 6 | modality_B = depth 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 0 14 | num_clients_B = 30 15 | num_clients_AB = 0 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 2 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = B 29 | # test_modality can be A or B 30 | test_modality = B 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/ur_fall/split_ae/acce_depth/A10_B0_AB30_label_A_test_B: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = ur_fall 3 | data_path = data 4 | results_path = results/ur_fall/split_ae/acce_depth/A10_B0_AB30_label_A_test_B 5 | modality_A = acce 6 | modality_B = depth 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 10 14 | num_clients_B = 0 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 2 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = A 29 | # test_modality can be A or B 30 | test_modality = B 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/ur_fall/split_ae/acce_depth/A10_B0_AB30_label_B_test_A: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = ur_fall 3 | data_path = data 4 | results_path = results/ur_fall/split_ae/acce_depth/A10_B0_AB30_label_B_test_A 5 | modality_A = acce 6 | modality_B = depth 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 10 14 | num_clients_B = 0 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 2 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = B 29 | # test_modality can be A or B 30 | test_modality = A 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/ur_fall/split_ae/acce_depth/A30_B0_AB0_label_A_test_A: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = ur_fall 3 | data_path = data 4 | results_path = results/ur_fall/split_ae/acce_depth/A30_B0_AB0_label_A_test_A 5 | modality_A = acce 6 | modality_B = depth 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 30 14 | num_clients_B = 0 15 | num_clients_AB = 0 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 2 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = A 29 | # test_modality can be A or B 30 | test_modality = A 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/ur_fall/split_ae/acce_rgb/A10_B10_AB30_label_A_test_B: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = ur_fall 3 | data_path = data 4 | results_path = results/ur_fall/split_ae/acce_rgb/A10_B10_AB30_label_A_test_B 5 | modality_A = acce 6 | modality_B = rgb 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 10 14 | num_clients_B = 10 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 2 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = A 29 | # test_modality can be A or B 30 | test_modality = B 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.001 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/ur_fall/split_ae/acce_rgb/A10_B10_AB30_label_B_test_A: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = ur_fall 3 | data_path = data 4 | results_path = results/ur_fall/split_ae/acce_rgb/A10_B10_AB30_label_B_test_A 5 | modality_A = acce 6 | modality_B = rgb 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 10 14 | num_clients_B = 10 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 2 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = B 29 | # test_modality can be A or B 30 | test_modality = A 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.001 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/ur_fall/split_ae/rgb_depth/A0_B0_AB30_label_AB_test_A: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = ur_fall 3 | data_path = data 4 | results_path = results/ur_fall/split_ae/rgb_depth/A0_B0_AB30_label_AB_test_A 5 | modality_A = rgb 6 | modality_B = depth 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 0 14 | num_clients_B = 0 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 4 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = AB 29 | # test_modality can be A or B 30 | test_modality = A 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/ur_fall/split_ae/rgb_depth/A0_B0_AB30_label_AB_test_B: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = ur_fall 3 | data_path = data 4 | results_path = results/ur_fall/split_ae/rgb_depth/A0_B0_AB30_label_AB_test_B 5 | modality_A = rgb 6 | modality_B = depth 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 0 14 | num_clients_B = 0 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 4 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = AB 29 | # test_modality can be A or B 30 | test_modality = B 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/ur_fall/split_ae/rgb_depth/A0_B10_AB30_label_A_test_B: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = ur_fall 3 | data_path = data 4 | results_path = results/ur_fall/split_ae/rgb_depth/A0_B10_AB30_label_A_test_B 5 | modality_A = rgb 6 | modality_B = depth 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 0 14 | num_clients_B = 10 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 4 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = A 29 | # test_modality can be A or B 30 | test_modality = B 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/ur_fall/split_ae/rgb_depth/A0_B10_AB30_label_B_test_A: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = ur_fall 3 | data_path = data 4 | results_path = results/ur_fall/split_ae/rgb_depth/A0_B10_AB30_label_B_test_A 5 | modality_A = rgb 6 | modality_B = depth 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 0 14 | num_clients_B = 10 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 4 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = B 29 | # test_modality can be A or B 30 | test_modality = A 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/ur_fall/split_ae/rgb_depth/A10_B0_AB30_label_A_test_B: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = ur_fall 3 | data_path = data 4 | results_path = results/ur_fall/split_ae/rgb_depth/A10_B0_AB30_label_A_test_B 5 | modality_A = rgb 6 | modality_B = depth 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 10 14 | num_clients_B = 0 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 4 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = A 29 | # test_modality can be A or B 30 | test_modality = B 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/ur_fall/split_ae/rgb_depth/A10_B0_AB30_label_B_test_A: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = ur_fall 3 | data_path = data 4 | results_path = results/ur_fall/split_ae/rgb_depth/A10_B0_AB30_label_B_test_A 5 | modality_A = rgb 6 | modality_B = depth 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 10 14 | num_clients_B = 0 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 4 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = B 29 | # test_modality can be A or B 30 | test_modality = A 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/ur_fall/split_ae/rgb_depth/A10_B10_AB30_label_A_test_B: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = ur_fall 3 | data_path = data 4 | results_path = results/ur_fall/split_ae/rgb_depth/A10_B10_AB30_label_A_test_B 5 | modality_A = rgb 6 | modality_B = depth 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 10 14 | num_clients_B = 10 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 4 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = A 29 | # test_modality can be A or B 30 | test_modality = B 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/ur_fall/split_ae/rgb_depth/A10_B10_AB30_label_B_test_A: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = ur_fall 3 | data_path = data 4 | results_path = results/ur_fall/split_ae/rgb_depth/A10_B10_AB30_label_B_test_A 5 | modality_A = rgb 6 | modality_B = depth 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 10 14 | num_clients_B = 10 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 4 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = B 29 | # test_modality can be A or B 30 | test_modality = A 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/mhealth/ablation/acce_gyro/A30_B30_AB0_label_A_test_B: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = mhealth 3 | data_path = data 4 | results_path = results/mhealth/ablation/acce_gyro/A30_B30_AB0_label_A_test_B 5 | modality_A = acce 6 | modality_B = gyro 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 30 14 | num_clients_B = 30 15 | num_clients_AB = 0 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 4 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = A 29 | # test_modality can be A or B 30 | test_modality = B 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss 37 | -------------------------------------------------------------------------------- /config/mhealth/ablation/acce_gyro/A30_B30_AB0_label_B_test_A: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = mhealth 3 | data_path = data 4 | results_path = results/mhealth/ablation/acce_gyro/A30_B30_AB0_label_B_test_A 5 | modality_A = acce 6 | modality_B = gyro 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 30 14 | num_clients_B = 30 15 | num_clients_AB = 0 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 4 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = B 29 | # test_modality can be A or B 30 | test_modality = A 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss 37 | -------------------------------------------------------------------------------- /config/mhealth/ablation/acce_mage/A30_B30_AB0_label_A_test_B: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = mhealth 3 | data_path = data 4 | results_path = results/mhealth/ablation/acce_mage/A30_B30_AB0_label_A_test_B 5 | modality_A = acce 6 | modality_B = mage 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 30 14 | num_clients_B = 30 15 | num_clients_AB = 0 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 4 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = A 29 | # test_modality can be A or B 30 | test_modality = B 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss 37 | -------------------------------------------------------------------------------- /config/mhealth/ablation/acce_mage/A30_B30_AB0_label_B_test_A: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = mhealth 3 | data_path = data 4 | results_path = results/mhealth/ablation/acce_mage/A30_B30_AB0_label_B_test_A 5 | modality_A = acce 6 | modality_B = mage 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 30 14 | num_clients_B = 30 15 | num_clients_AB = 0 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 4 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = B 29 | # test_modality can be A or B 30 | test_modality = A 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss 37 | -------------------------------------------------------------------------------- /config/mhealth/ablation/gyro_mage/A30_B30_AB0_label_A_test_B: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = mhealth 3 | data_path = data 4 | results_path = results/mhealth/ablation/gyro_mage/A30_B30_AB0_label_A_test_B 5 | modality_A = gyro 6 | modality_B = mage 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 30 14 | num_clients_B = 30 15 | num_clients_AB = 0 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 4 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = A 29 | # test_modality can be A or B 30 | test_modality = B 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss 37 | -------------------------------------------------------------------------------- /config/mhealth/ablation/gyro_mage/A30_B30_AB0_label_B_test_A: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = mhealth 3 | data_path = data 4 | results_path = results/mhealth/ablation/gyro_mage/A30_B30_AB0_label_B_test_A 5 | modality_A = gyro 6 | modality_B = mage 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 30 14 | num_clients_B = 30 15 | num_clients_AB = 0 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 4 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = B 29 | # test_modality can be A or B 30 | test_modality = A 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss 37 | -------------------------------------------------------------------------------- /config/ur_fall/ablation/acce_depth/A30_B30_AB0_label_A_test_B: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = ur_fall 3 | data_path = data 4 | results_path = results/ur_fall/ablation/acce_depth/A30_B30_AB0_label_A_test_B 5 | modality_A = acce 6 | modality_B = depth 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 30 14 | num_clients_B = 30 15 | num_clients_AB = 0 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 2 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = A 29 | # test_modality can be A or B 30 | test_modality = B 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss 37 | -------------------------------------------------------------------------------- /config/ur_fall/ablation/acce_depth/A30_B30_AB0_label_B_test_A: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = ur_fall 3 | data_path = data 4 | results_path = results/ur_fall/ablation/acce_depth/A30_B30_AB0_label_B_test_A 5 | modality_A = acce 6 | modality_B = depth 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 30 14 | num_clients_B = 30 15 | num_clients_AB = 0 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 2 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = B 29 | # test_modality can be A or B 30 | test_modality = A 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss 37 | -------------------------------------------------------------------------------- /config/ur_fall/ablation/rgb_depth/A30_B30_AB0_label_A_test_B: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = ur_fall 3 | data_path = data 4 | results_path = results/ur_fall/ablation/rgb_depth/A30_B30_AB0_label_A_test_B 5 | modality_A = rgb 6 | modality_B = depth 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 30 14 | num_clients_B = 30 15 | num_clients_AB = 0 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 4 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = A 29 | # test_modality can be A or B 30 | test_modality = B 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss 37 | -------------------------------------------------------------------------------- /config/ur_fall/ablation/rgb_depth/A30_B30_AB0_label_B_test_A: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = ur_fall 3 | data_path = data 4 | results_path = results/ur_fall/ablation/rgb_depth/A30_B30_AB0_label_B_test_A 5 | modality_A = rgb 6 | modality_B = depth 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 30 14 | num_clients_B = 30 15 | num_clients_AB = 0 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 4 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = B 29 | # test_modality can be A or B 30 | test_modality = A 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss 37 | -------------------------------------------------------------------------------- /config/ur_fall/split_ae/acce_depth/A10_B10_AB30_label_A_test_B: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = ur_fall 3 | data_path = data 4 | results_path = results/ur_fall/split_ae/acce_depth/A10_B10_AB30_label_A_test_B 5 | modality_A = acce 6 | modality_B = depth 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 10 14 | num_clients_B = 10 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 2 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = A 29 | # test_modality can be A or B 30 | test_modality = B 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /config/ur_fall/split_ae/acce_depth/A10_B10_AB30_label_B_test_A: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | data = ur_fall 3 | data_path = data 4 | results_path = results/ur_fall/split_ae/acce_depth/A10_B10_AB30_label_B_test_A 5 | modality_A = acce 6 | modality_B = depth 7 | model_ae = split_LSTM 8 | model_sv = MLP 9 | 10 | [FL] 11 | train_ratio = 0.11 12 | train_supervised_ratio = 0.11 13 | num_clients_A = 10 14 | num_clients_B = 10 15 | num_clients_AB = 30 16 | rounds = 100 17 | eval_interval = 2 18 | rep_size = 2 19 | DCCAE_lamda = 0.01 20 | 21 | [SERVER] 22 | frac = 0.10 23 | num_epochs = 5 24 | lr = 0.001 25 | criterion = CrossEntropyLoss 26 | optimizer = Adam 27 | # label_modality can be A, B, or AB 28 | label_modality = B 29 | # test_modality can be A or B 30 | test_modality = A 31 | 32 | [CLIENT] 33 | num_epochs = 2 34 | lr = 0.01 35 | optimizer = Adam 36 | criterion = MSELoss -------------------------------------------------------------------------------- /exp_mhealth_acce_gyro.sh: -------------------------------------------------------------------------------- 1 | qsub sub/mhealth/split_ae/acce_gyro/A0_B0_AB30_label_AB_test_A.pbs 2 | qsub sub/mhealth/split_ae/acce_gyro/A0_B0_AB30_label_AB_test_B.pbs 3 | qsub sub/mhealth/split_ae/acce_gyro/A0_B0_AB30_label_A_test_B.pbs 4 | qsub sub/mhealth/split_ae/acce_gyro/A0_B0_AB30_label_B_test_A.pbs 5 | qsub sub/mhealth/split_ae/acce_gyro/A0_B10_AB30_label_A_test_B.pbs 6 | qsub sub/mhealth/split_ae/acce_gyro/A0_B10_AB30_label_B_test_A.pbs 7 | qsub sub/mhealth/split_ae/acce_gyro/A0_B30_AB0_label_B_test_B.pbs 8 | qsub sub/mhealth/split_ae/acce_gyro/A10_B0_AB30_label_A_test_B.pbs 9 | qsub sub/mhealth/split_ae/acce_gyro/A10_B0_AB30_label_B_test_A.pbs 10 | qsub sub/mhealth/split_ae/acce_gyro/A10_B10_AB30_label_A_test_B.pbs 11 | qsub sub/mhealth/split_ae/acce_gyro/A10_B10_AB30_label_B_test_A.pbs 12 | qsub sub/mhealth/split_ae/acce_gyro/A30_B0_AB0_label_A_test_A.pbs -------------------------------------------------------------------------------- /exp_mhealth_acce_mage.sh: -------------------------------------------------------------------------------- 1 | qsub sub/mhealth/split_ae/acce_mage/A0_B0_AB30_label_AB_test_A.pbs 2 | qsub sub/mhealth/split_ae/acce_mage/A0_B0_AB30_label_AB_test_B.pbs 3 | qsub sub/mhealth/split_ae/acce_mage/A0_B0_AB30_label_A_test_B.pbs 4 | qsub sub/mhealth/split_ae/acce_mage/A0_B0_AB30_label_B_test_A.pbs 5 | qsub sub/mhealth/split_ae/acce_mage/A0_B10_AB30_label_A_test_B.pbs 6 | qsub sub/mhealth/split_ae/acce_mage/A0_B10_AB30_label_B_test_A.pbs 7 | qsub sub/mhealth/split_ae/acce_mage/A0_B30_AB0_label_B_test_B.pbs 8 | qsub sub/mhealth/split_ae/acce_mage/A10_B0_AB30_label_A_test_B.pbs 9 | qsub sub/mhealth/split_ae/acce_mage/A10_B0_AB30_label_B_test_A.pbs 10 | qsub sub/mhealth/split_ae/acce_mage/A10_B10_AB30_label_A_test_B.pbs 11 | qsub sub/mhealth/split_ae/acce_mage/A10_B10_AB30_label_B_test_A.pbs 12 | qsub sub/mhealth/split_ae/acce_mage/A30_B0_AB0_label_A_test_A.pbs -------------------------------------------------------------------------------- /exp_mhealth_gyro_mage.sh: -------------------------------------------------------------------------------- 1 | qsub sub/mhealth/split_ae/gyro_mage/A0_B0_AB30_label_AB_test_A.pbs 2 | qsub sub/mhealth/split_ae/gyro_mage/A0_B0_AB30_label_AB_test_B.pbs 3 | qsub sub/mhealth/split_ae/gyro_mage/A0_B0_AB30_label_A_test_B.pbs 4 | qsub sub/mhealth/split_ae/gyro_mage/A0_B0_AB30_label_B_test_A.pbs 5 | qsub sub/mhealth/split_ae/gyro_mage/A0_B10_AB30_label_A_test_B.pbs 6 | qsub sub/mhealth/split_ae/gyro_mage/A0_B10_AB30_label_B_test_A.pbs 7 | qsub sub/mhealth/split_ae/gyro_mage/A0_B30_AB0_label_B_test_B.pbs 8 | qsub sub/mhealth/split_ae/gyro_mage/A10_B0_AB30_label_A_test_B.pbs 9 | qsub sub/mhealth/split_ae/gyro_mage/A10_B0_AB30_label_B_test_A.pbs 10 | qsub sub/mhealth/split_ae/gyro_mage/A10_B10_AB30_label_A_test_B.pbs 11 | qsub sub/mhealth/split_ae/gyro_mage/A10_B10_AB30_label_B_test_A.pbs 12 | qsub sub/mhealth/split_ae/gyro_mage/A30_B0_AB0_label_A_test_A.pbs -------------------------------------------------------------------------------- /exp_ur_fall_rgb_depth.sh: -------------------------------------------------------------------------------- 1 | qsub sub/ur_fall/split_ae/rgb_depth/A0_B0_AB30_label_AB_test_A.pbs 2 | qsub sub/ur_fall/split_ae/rgb_depth/A0_B0_AB30_label_AB_test_B.pbs 3 | qsub sub/ur_fall/split_ae/rgb_depth/A0_B0_AB30_label_A_test_B.pbs 4 | qsub sub/ur_fall/split_ae/rgb_depth/A0_B0_AB30_label_B_test_A.pbs 5 | qsub sub/ur_fall/split_ae/rgb_depth/A0_B10_AB30_label_A_test_B.pbs 6 | qsub sub/ur_fall/split_ae/rgb_depth/A0_B10_AB30_label_B_test_A.pbs 7 | qsub sub/ur_fall/split_ae/rgb_depth/A0_B30_AB0_label_B_test_B.pbs 8 | qsub sub/ur_fall/split_ae/rgb_depth/A10_B0_AB30_label_A_test_B.pbs 9 | qsub sub/ur_fall/split_ae/rgb_depth/A10_B0_AB30_label_B_test_A.pbs 10 | qsub sub/ur_fall/split_ae/rgb_depth/A10_B10_AB30_label_A_test_B.pbs 11 | qsub sub/ur_fall/split_ae/rgb_depth/A10_B10_AB30_label_B_test_A.pbs 12 | qsub sub/ur_fall/split_ae/rgb_depth/A30_B0_AB0_label_A_test_A.pbs -------------------------------------------------------------------------------- /exp_ur_fall_acce_depth.sh: -------------------------------------------------------------------------------- 1 | qsub sub/ur_fall/split_ae/acce_depth/A0_B0_AB30_label_AB_test_A.pbs 2 | qsub sub/ur_fall/split_ae/acce_depth/A0_B0_AB30_label_AB_test_B.pbs 3 | qsub sub/ur_fall/split_ae/acce_depth/A0_B0_AB30_label_A_test_B.pbs 4 | qsub sub/ur_fall/split_ae/acce_depth/A0_B0_AB30_label_B_test_A.pbs 5 | qsub sub/ur_fall/split_ae/acce_depth/A0_B10_AB30_label_A_test_B.pbs 6 | qsub sub/ur_fall/split_ae/acce_depth/A0_B10_AB30_label_B_test_A.pbs 7 | qsub sub/ur_fall/split_ae/acce_depth/A0_B30_AB0_label_B_test_B.pbs 8 | qsub sub/ur_fall/split_ae/acce_depth/A10_B0_AB30_label_A_test_B.pbs 9 | qsub sub/ur_fall/split_ae/acce_depth/A10_B0_AB30_label_B_test_A.pbs 10 | qsub sub/ur_fall/split_ae/acce_depth/A10_B10_AB30_label_A_test_B.pbs 11 | qsub sub/ur_fall/split_ae/acce_depth/A10_B10_AB30_label_B_test_A.pbs 12 | qsub sub/ur_fall/split_ae/acce_depth/A30_B0_AB0_label_A_test_A.pbs -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2022 Yuchen Zhao 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 | -------------------------------------------------------------------------------- /config/config_example: -------------------------------------------------------------------------------- 1 | [SIMULATION] 2 | # Used dataset (opp/mhealth/ur_fall) 3 | data = ur_fall 4 | # Path of datasets 5 | data_path = data 6 | # Path of output results 7 | results_path = results/ur_fall/example 8 | # Modality A and B (acce/gyro for opp, acce/gyro/mage for mhealth, acce/rgb/depth for ur_fall) 9 | modality_A = rgb 10 | modality_B = depth 11 | # Local autoencoder (split_LSTM for SplitAE, DCCAE_LSTM for DCCAE) 12 | model_ae = split_LSTM 13 | # Supervised classifier (only supports MLP) 14 | model_sv = MLP 15 | 16 | [FL] 17 | # Ratio of the training dataset to generate a client's data 18 | train_ratio = 0.11 19 | # Ratio of the training dataset to generate a labelled dataset on the server 20 | train_supervised_ratio = 0.11 21 | # Number of unimodal clients for modality A 22 | num_clients_A = 10 23 | # Number of unimodal clients for modality B 24 | num_clients_B = 30 25 | # Number of multimodal clients 26 | num_clients_AB = 10 27 | # Communication rounds of FL 28 | rounds = 100 29 | # Round interval for evaluating the global classifier 30 | eval_interval = 2 31 | # size of the hidden representation h 32 | rep_size = 4 33 | # lamda factor for DCCAE 34 | DCCAE_lamda = 0.01 35 | 36 | [SERVER] 37 | # Fraction of clients randomly selected by the server 38 | frac = 0.10 39 | # Number of epochs for supervised learning on the server 40 | num_epochs = 5 41 | # Learning rate for supervised learning on the server 42 | lr = 0.001 43 | # Loss function for supervised learning (only supports CrossEntropyLoss) 44 | criterion = CrossEntropyLoss 45 | # Optimizer for supervised learning (only supports Adam) 46 | optimizer = Adam 47 | # Modality of the labelled dataset(s) on the server (A/B/AB) 48 | label_modality = B 49 | # Modality of the testing dataset on the server (A/B) 50 | test_modality = A 51 | 52 | [CLIENT] 53 | # Number of epochs for unsupervised local training on a client 54 | num_epochs = 2 55 | # Learning rate for unsupervised local training on a client 56 | lr = 0.01 57 | # Optimizer for unsupervised local training (only supports Adam) 58 | optimizer = Adam 59 | # Loss function for unsupervised local training (MSELoss for split_LSTM, DCCAELoss for DCCAE_LSTM) 60 | criterion = DCCAELoss 61 | -------------------------------------------------------------------------------- /src/test.py: -------------------------------------------------------------------------------- 1 | 2 | # data = [] 3 | # for x in range(57): 4 | # data.append(x) 5 | # num_rounds, local_ae_loss, train_loss, train_accuracy, test_loss, test_accuracy, test_f1, class_occurances_correct, class_occurances_total = [], [], [], [], [], [], [], [], [] 6 | 7 | # print("data.shape", len(data)) 8 | # #class_occurances_total = [0] * 25 9 | # #class_occurances_correct = [0] * 25 10 | # for x in range(len(data)): 11 | # print(data[x]) 12 | # num_rounds.append(data[x]) 13 | # local_ae_loss.append(data[x]) 14 | # train_loss.append(data[x]) 15 | # train_accuracy.append(data[x]) 16 | # test_loss.append(data[x]) 17 | # test_accuracy.append(data[x]) 18 | # test_f1.append(data[x]) 19 | # class_occurances_correct.append(data[7:32]) 20 | # class_occurances_total.append(data[32:]) 21 | 22 | # print("class_occurances_correct", len(class_occurances_correct), len(class_occurances_correct[0])) 23 | # print("class_occurances_total", len(class_occurances_total), len(class_occurances_total[0])) 24 | 25 | # data = [] 26 | # for x in range(57): 27 | # data.append(x) 28 | 29 | # #print("data", len(data), data) 30 | 31 | 32 | # class_occurances_correct = [] 33 | # class_occurances_total = [] 34 | # class_occurances_correct.append(data[7:32]) 35 | # class_occurances_total.append(data[32:]) 36 | 37 | 38 | # print("\nclass_occurances_correct", len(class_occurances_correct), len(class_occurances_correct[0])) 39 | 40 | # print("class_occurances_total", len(class_occurances_total), len(class_occurances_total[0])) 41 | 42 | 43 | 44 | import matplotlib.pyplot as plt 45 | 46 | # Sample data for the table 47 | data = [ 48 | ['Name', 'Age', 'Gender'], 49 | ['Alice', 25, 'Female'], 50 | ['Bob', 30, 'Male'], 51 | ['Charlie', 28, 'Male'], 52 | ['Diana', 35, 'Female'] 53 | ] 54 | 55 | # Create a figure and axis 56 | fig, ax = plt.subplots() 57 | 58 | # Hide the axes to display only the table 59 | ax.axis('off') 60 | 61 | # Create the table and add it to the plot 62 | table = ax.table(cellText=data, loc='center', cellLoc='center', colLabels=None) 63 | 64 | # Modify table properties (optional) 65 | table.auto_set_font_size(False) 66 | table.set_fontsize(10) 67 | table.scale(1.2, 1.2) # Adjust the table size 68 | 69 | plt.title('Sample Table') # Optional: Set a title for the plot 70 | plt.show() 71 | -------------------------------------------------------------------------------- /src/main.py: -------------------------------------------------------------------------------- 1 | import configparser 2 | import argparse 3 | import logging 4 | import os 5 | import warnings 6 | import torch 7 | #from mpi4py import MPI 8 | from fl import FL 9 | 10 | 11 | # script_path = os.path.abspath(__file__) 12 | # script_dir = os.path.dirname(script_path) 13 | # os.chdir(script_dir) 14 | # print("cwd: ", os.getcwd()) 15 | # print("script_dir: ", script_dir) 16 | 17 | 18 | 19 | # config_list.remove("A0_B0_AB30_label_AB_test_B") 20 | # config_list.remove("A0_B0_AB30_label_AB_test_A") 21 | 22 | 23 | #print("# of configs in dir: ", len(config_list)) 24 | #config_list.remove("A0_B0_AB30_label_A_test_B") 25 | # config_list.remove("A0_B0_AB30_label_AB_test_A") 26 | 27 | 28 | 29 | def read_config(path, file): 30 | config = configparser.ConfigParser() 31 | config.read(path + file) 32 | return config 33 | 34 | 35 | if __name__ == "__main__": 36 | # add all files from path to list 37 | dataset_path = '/Users/zach/Desktop/School/Fall2023/ECE535/ECE535-FederatedLearning/config/ur_fall/' 38 | dataset_type = ['split_ae/', 'ablation/'] 39 | paths = ['acce_depth/', 'acce_rgb/', 'rgb_depth/'] 40 | config_list = [] 41 | count=0 42 | for y in dataset_type: 43 | print(y) 44 | for x in paths: 45 | print(x) 46 | for file in os.listdir(dataset_path+y+x): 47 | count+=1 48 | print("count", count) 49 | print("config: ", dataset_path+y+x+file) 50 | config = read_config(dataset_path+y+x, file) 51 | fl = FL(config) 52 | fl.start() 53 | 54 | 55 | 56 | 57 | 58 | # if file == 'A30_B30_AB0_label_B_test_A' and y == 'ablation/' and x == 'acce_gyro/': 59 | # # continue 60 | # config_list.append(file) 61 | # print(file) 62 | 63 | # print("# of configs in dir: ", len(config_list)) 64 | 65 | # for file in config_list: 66 | # print("\n\nrunning config file", file) 67 | # config = read_config(path, file) 68 | # fl = FL(config) 69 | # fl.start() 70 | 71 | 72 | 73 | 74 | 75 | 76 | 77 | 78 | 79 | # for file in config_list: 80 | # config = read_config(file) 81 | # print(file) 82 | # fl = FL(config) 83 | # fl.start() 84 | 85 | 86 | # def read_config(): 87 | # config = configparser.ConfigParser() 88 | # config.read('/Users/zach/Desktop/School/Fall2023/ECE535/ECE535-FederatedLearning/config/opp/dccae/A0_B0_AB30_label_AB_test_A') 89 | # return config 90 | 91 | # config = read_config() 92 | # fl = FL(config) 93 | # fl.start() 94 | 95 | # # For MPI experiments 96 | # COMM = MPI.COMM_WORLD 97 | # RANK = COMM.Get_rank() 98 | 99 | 100 | # def main(): 101 | # is_mpi = COMM.Get_size() != 1 102 | # config = read_config() 103 | # fl = FL(config, is_mpi, RANK) 104 | # fl.start() 105 | 106 | 107 | # def read_config(): 108 | # arg_parser = argparse.ArgumentParser() 109 | # arg_parser.add_argument("--config", type=str, 110 | # help="name of the config file of simulation") 111 | # args = arg_parser.parse_args() 112 | # config = configparser.ConfigParser() 113 | # config.read(args.config) 114 | # return config 115 | 116 | 117 | # if __name__ == "__main__": 118 | # main() 119 | --------------------------------------------------------------------------------