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parser.py
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parser.py
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# coding=utf-8
import os
import argparse
def get_parser():
parser = argparse.ArgumentParser(description='Continual learning')
parser.add_argument('--expt_name', type=str, default='test_lamaml',
help='name of the experiment')
# model details
parser.add_argument('--model', type=str, default='single',
help='algo to train')
parser.add_argument('--arch', type=str, default='linear',
help='arch to use for training', choices = ['linear', 'pc_cnn'])
parser.add_argument('--n_hiddens', type=int, default=100,
help='number of hidden neurons at each layer')
parser.add_argument('--n_layers', type=int, default=2,
help='number of hidden layers')
parser.add_argument('--xav_init', default=False , action='store_true',
help='Use xavier initialization')
# optimizer parameters influencing all models
parser.add_argument("--glances", default=1, type=int,
help="Number of times the model is allowed to train over a set of samples in the single pass setting")
parser.add_argument('--n_epochs', type=int, default=1,
help='Number of epochs per task')
parser.add_argument('--batch_size', type=int, default=1,
help='the amount of items received by the algorithm at one time (set to 1 across all ' +
'experiments). Variable name is from GEM project.')
parser.add_argument('--replay_batch_size', type=float, default=20,
help='The batch size for experience replay.')
parser.add_argument('--memories', type=int, default=5120,
help='number of total memories stored in a reservoir sampling based buffer')
parser.add_argument('--lr', type=float, default=1e-3,
help='learning rate (For baselines)')
# experiment parameters
parser.add_argument('--cuda', default=False , action='store_true',
help='Use GPU')
parser.add_argument('--seed', type=int, default=0,
help='random seed of model')
parser.add_argument('--log_every', type=int, default=1000,
help='frequency of checking the validation accuracy, in minibatches')
parser.add_argument('--log_dir', type=str, default='logs/',
help='the directory where the logs will be saved')
parser.add_argument('--tf_dir', type=str, default='',
help='(not set by user)')
parser.add_argument('--calc_test_accuracy', default=False , action='store_true',
help='Calculate test accuracy along with val accuracy')
# data parameters
parser.add_argument('--data_path', default='data/',
help='path where data is located')
parser.add_argument('--loader', type=str, default='task_incremental_loader',
help='data loader to use')
parser.add_argument('--samples_per_task', type=int, default=-1,
help='training samples per task (all if negative)')
parser.add_argument('--shuffle_tasks', default=False, action='store_true',
help='present tasks in order')
parser.add_argument('--classes_per_it', type=int, default=4,
help='number of classes in every batch')
parser.add_argument('--iterations', type=int, default=5000,
help='number of classes in every batch')
parser.add_argument("--dataset", default="mnist_rotations", type=str,
help="Dataset to train and test on.")
parser.add_argument("--workers", default=3, type=int,
help="Number of workers preprocessing the data.")
parser.add_argument("--validation", default=0., type=float,
help="Validation split (0. <= x <= 1.).")
parser.add_argument("-order", "--class_order", default="old", type=str,
help="define classes order of increment ",
choices = ["random", "chrono", "old", "super"])
parser.add_argument("-inc", "--increment", default=5, type=int,
help="number of classes to increment by in class incremental loader")
parser.add_argument('--test_batch_size', type=int, default=100000 ,
help='batch size to use during testing.')
# La-MAML parameters
parser.add_argument('--opt_lr', type=float, default=1e-1,
help='learning rate for LRs')
parser.add_argument('--opt_wt', type=float, default=1e-1,
help='learning rate for weights')
parser.add_argument('--alpha_init', type=float, default=1e-3,
help='initialization for the LRs')
parser.add_argument('--learn_lr', default=False, action='store_true',
help='model should update the LRs during learning')
parser.add_argument('--sync_update', default=False , action='store_true',
help='the LRs and weights should be updated synchronously')
parser.add_argument('--grad_clip_norm', type=float, default=2.0,
help='Clip the gradients by this value')
parser.add_argument("--cifar_batches", default=3, type=int,
help="Number of batches in inner trajectory")
parser.add_argument('--use_old_task_memory', default=False, action='store_true',
help='Use only old task samples for replay buffer data')
parser.add_argument('--second_order', default=False , action='store_true',
help='use second order MAML updates')
# memory parameters for GEM | AGEM | ICARL
parser.add_argument('--n_memories', type=int, default=0,
help='number of memories per task')
parser.add_argument('--memory_strength', default=0, type=float,
help='memory strength (meaning depends on memory)')
parser.add_argument('--steps_per_sample', default=1, type=int,
help='training steps per batch')
# parameters specific to MER
parser.add_argument('--gamma', type=float, default=1.0,
help='gamma learning rate parameter')
parser.add_argument('--beta', type=float, default=1.0,
help='beta learning rate parameter')
parser.add_argument('--s', type=float, default=1,
help='current example learning rate multiplier (s)')
parser.add_argument('--batches_per_example', type=float, default=1,
help='the number of batch per incoming example')
# parameters specific to Meta-BGD
parser.add_argument('--bgd_optimizer', type=str, default="bgd", choices=["adam", "adagrad", "bgd", "sgd"],
help='Optimizer.')
parser.add_argument('--optimizer_params', default="{}", type=str, nargs='*',
help='Optimizer parameters')
parser.add_argument('--train_mc_iters', default=5, type=int,
help='Number of MonteCarlo samples during training(default 10)')
parser.add_argument('--std_init', default=5e-2, type=float,
help='STD init value (default 5e-2)')
parser.add_argument('--mean_eta', default=1, type=float,
help='Eta for mean step (default 1)')
parser.add_argument('--fisher_gamma', default=0.95, type=float,
help='')
return parser