conv_split_awa_hybrid.py [125:166]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - parser.add_argument("--cross-validate-mode", action="store_true", help="If option is chosen then snapshoting after each batch is disabled") parser.add_argument("--online-cross-val", action="store_true", help="If option is chosen then enable the online cross validation of the learning rate") parser.add_argument("--train-single-epoch", action="store_true", help="If option is chosen then train for single epoch") parser.add_argument("--set-hybrid", action="store_true", help="If option is chosen then train using hybrid model") parser.add_argument("--eval-single-head", action="store_true", help="If option is chosen then evaluate on a single head setting.") parser.add_argument("--arch", type=str, default=ARCH, help="Network Architecture for the experiment.\ \n \nSupported values: %s"%(VALID_ARCHS)) parser.add_argument("--num-runs", type=int, default=NUM_RUNS, help="Total runs/ experiments over which accuracy is averaged.") parser.add_argument("--train-iters", type=int, default=TRAIN_ITERS, help="Number of training iterations for each task.") parser.add_argument("--batch-size", type=int, default=BATCH_SIZE, help="Mini-batch size for each task.") parser.add_argument("--random-seed", type=int, default=RANDOM_SEED, help="Random Seed.") parser.add_argument("--learning-rate", type=float, default=LEARNING_RATE, help="Starting Learning rate for each task.") parser.add_argument("--optim", type=str, default=OPTIM, help="Optimizer for the experiment. \ \n \nSupported values: %s"%(VALID_OPTIMS)) parser.add_argument("--imp-method", type=str, default=IMP_METHOD, help="Model to be used for LLL. \ \n \nSupported values: %s"%(MODELS)) parser.add_argument("--synap-stgth", type=float, default=SYNAP_STGTH, help="Synaptic strength for the regularization.") parser.add_argument("--fisher-ema-decay", type=float, default=FISHER_EMA_DECAY, help="Exponential moving average decay for Fisher calculation at each step.") parser.add_argument("--fisher-update-after", type=int, default=FISHER_UPDATE_AFTER, help="Number of training iterations after which the Fisher will be updated.") parser.add_argument("--do-sampling", action="store_true", help="Whether to do sampling") parser.add_argument("--mem-size", type=int, default=SAMPLES_PER_CLASS, help="Number of samples per class from previous tasks.") parser.add_argument("--is-herding", action="store_true", help="Herding based sampling") parser.add_argument("--data-dir", type=str, default=DATA_DIR, - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - conv_split_cub_hybrid.py [119:160]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - parser.add_argument("--cross-validate-mode", action="store_true", help="If option is chosen then snapshoting after each batch is disabled") parser.add_argument("--online-cross-val", action="store_true", help="If option is chosen then enable the online cross validation of the learning rate") parser.add_argument("--train-single-epoch", action="store_true", help="If option is chosen then train for single epoch") parser.add_argument("--set-hybrid", action="store_true", help="If option is chosen then train using hybrid model") parser.add_argument("--eval-single-head", action="store_true", help="If option is chosen then evaluate on a single head setting.") parser.add_argument("--arch", type=str, default=ARCH, help="Network Architecture for the experiment.\ \n \nSupported values: %s"%(VALID_ARCHS)) parser.add_argument("--num-runs", type=int, default=NUM_RUNS, help="Total runs/ experiments over which accuracy is averaged.") parser.add_argument("--train-iters", type=int, default=TRAIN_ITERS, help="Number of training iterations for each task.") parser.add_argument("--batch-size", type=int, default=BATCH_SIZE, help="Mini-batch size for each task.") parser.add_argument("--random-seed", type=int, default=RANDOM_SEED, help="Random Seed.") parser.add_argument("--learning-rate", type=float, default=LEARNING_RATE, help="Starting Learning rate for each task.") parser.add_argument("--optim", type=str, default=OPTIM, help="Optimizer for the experiment. \ \n \nSupported values: %s"%(VALID_OPTIMS)) parser.add_argument("--imp-method", type=str, default=IMP_METHOD, help="Model to be used for LLL. \ \n \nSupported values: %s"%(MODELS)) parser.add_argument("--synap-stgth", type=float, default=SYNAP_STGTH, help="Synaptic strength for the regularization.") parser.add_argument("--fisher-ema-decay", type=float, default=FISHER_EMA_DECAY, help="Exponential moving average decay for Fisher calculation at each step.") parser.add_argument("--fisher-update-after", type=int, default=FISHER_UPDATE_AFTER, help="Number of training iterations after which the Fisher will be updated.") parser.add_argument("--do-sampling", action="store_true", help="Whether to do sampling") parser.add_argument("--mem-size", type=int, default=SAMPLES_PER_CLASS, help="Number of samples per class from previous tasks.") parser.add_argument("--is-herding", action="store_true", help="Herding based sampling") parser.add_argument("--data-dir", type=str, default=DATA_DIR, - 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