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general_main.py
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general_main.py
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import argparse
import random
import numpy as np
import torch
from experiment.run import multiple_run
from utils.utils import boolean_string
def main(args):
print(args)
# set up seed
np.random.seed(args.seed)
random.seed(args.seed)
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
args.trick = {'labels_trick': args.labels_trick, 'separated_softmax': args.separated_softmax,
'kd_trick': args.kd_trick, 'kd_trick_star': args.kd_trick_star, 'review_trick': args.review_trick,
'ncm_trick': args.ncm_trick}
multiple_run(args, store=args.store, save_path=args.save_path)
if __name__ == "__main__":
# Commandline arguments
parser = argparse.ArgumentParser(description="Online Continual Learning PyTorch")
########################General#########################
parser.add_argument('--num_runs', dest='num_runs', default=1, type=int,
help='Number of runs (default: %(default)s)')
parser.add_argument('--seed', dest='seed', default=0, type=int,
help='Random seed')
########################Misc#########################
parser.add_argument('--val_size', dest='val_size', default=0.1, type=float,
help='val_size (default: %(default)s)')
parser.add_argument('--num_val', dest='num_val', default=3, type=int,
help='Number of batches used for validation (default: %(default)s)')
parser.add_argument('--num_runs_val', dest='num_runs_val', default=3, type=int,
help='Number of runs for validation (default: %(default)s)')
parser.add_argument('--error_analysis', dest='error_analysis', default=False, type=boolean_string,
help='Perform error analysis (default: %(default)s)')
parser.add_argument('--verbose', type=boolean_string, default=True,
help='print information or not (default: %(default)s)')
parser.add_argument('--store', type=boolean_string, default=False,
help='Store result or not (default: %(default)s)')
parser.add_argument('--save-path', dest='save_path', default=None)
########################Agent#########################
parser.add_argument('--agent', dest='agent', default='ER',
choices=['ER', 'EWC', 'AGEM', 'CNDPM', 'LWF', 'ICARL', 'GDUMB', 'ASER', 'SCR'],
help='Agent selection (default: %(default)s)')
parser.add_argument('--update', dest='update', default='random', choices=['random', 'GSS', 'ASER'],
help='Update method (default: %(default)s)')
parser.add_argument('--retrieve', dest='retrieve', default='random', choices=['MIR', 'random', 'ASER', 'match', 'mem_match'],
help='Retrieve method (default: %(default)s)')
########################Optimizer#########################
parser.add_argument('--optimizer', dest='optimizer', default='SGD', choices=['SGD', 'Adam'],
help='Optimizer (default: %(default)s)')
parser.add_argument('--learning_rate', dest='learning_rate', default=0.1,
type=float,
help='Learning_rate (default: %(default)s)')
parser.add_argument('--epoch', dest='epoch', default=1,
type=int,
help='The number of epochs used for one task. (default: %(default)s)')
parser.add_argument('--batch', dest='batch', default=10,
type=int,
help='Batch size (default: %(default)s)')
parser.add_argument('--test_batch', dest='test_batch', default=128,
type=int,
help='Test batch size (default: %(default)s)')
parser.add_argument('--weight_decay', dest='weight_decay', type=float, default=0,
help='weight_decay')
########################Data#########################
parser.add_argument('--num_tasks', dest='num_tasks', default=10,
type=int,
help='Number of tasks (default: %(default)s), OpenLORIS num_tasks is predefined')
parser.add_argument('--fix_order', dest='fix_order', default=False,
type=boolean_string,
help='In NC scenario, should the class order be fixed (default: %(default)s)')
parser.add_argument('--plot_sample', dest='plot_sample', default=False,
type=boolean_string,
help='In NI scenario, should sample images be plotted (default: %(default)s)')
parser.add_argument('--data', dest='data', default="cifar10",
help='Path to the dataset. (default: %(default)s)')
parser.add_argument('--cl_type', dest='cl_type', default="nc", choices=['nc', 'ni'],
help='Continual learning type: new class "nc" or new instance "ni". (default: %(default)s)')
parser.add_argument('--ns_factor', dest='ns_factor', nargs='+',
default=(0.0, 0.4, 0.8, 1.2, 1.6, 2.0, 2.4, 2.8, 3.2, 3.6), type=float,
help='Change factor for non-stationary data(default: %(default)s)')
parser.add_argument('--ns_type', dest='ns_type', default='noise', type=str, choices=['noise', 'occlusion', 'blur'],
help='Type of non-stationary (default: %(default)s)')
parser.add_argument('--ns_task', dest='ns_task', nargs='+', default=(1, 1, 2, 2, 2, 2), type=int,
help='NI Non Stationary task composition (default: %(default)s)')
parser.add_argument('--online', dest='online', default=True,
type=boolean_string,
help='If False, offline training will be performed (default: %(default)s)')
########################ER#########################
parser.add_argument('--mem_size', dest='mem_size', default=10000,
type=int,
help='Memory buffer size (default: %(default)s)')
parser.add_argument('--eps_mem_batch', dest='eps_mem_batch', default=10,
type=int,
help='Episode memory per batch (default: %(default)s)')
########################EWC##########################
parser.add_argument('--lambda', dest='lambda_', default=100, type=float,
help='EWC regularization coefficient')
parser.add_argument('--alpha', dest='alpha', default=0.9, type=float,
help='EWC++ exponential moving average decay for Fisher calculation at each step')
parser.add_argument('--fisher_update_after', dest='fisher_update_after', type=int, default=50,
help="Number of training iterations after which the Fisher will be updated.")
########################MIR#########################
parser.add_argument('--subsample', dest='subsample', default=50,
type=int,
help='Number of subsample to perform MIR(default: %(default)s)')
########################GSS#########################
parser.add_argument('--gss_mem_strength', dest='gss_mem_strength', default=10, type=int,
help='Number of batches randomly sampled from memory to estimate score')
parser.add_argument('--gss_batch_size', dest='gss_batch_size', default=10, type=int,
help='Random sampling batch size to estimate score')
########################ASER########################
parser.add_argument('--k', dest='k', default=5,
type=int,
help='Number of nearest neighbors (K) to perform ASER (default: %(default)s)')
parser.add_argument('--aser_type', dest='aser_type', default="asvm", type=str, choices=['neg_sv', 'asv', 'asvm'],
help='Type of ASER: '
'"neg_sv" - Use negative SV only,'
' "asv" - Use extremal values of Adversarial SV and Cooperative SV,'
' "asvm" - Use mean values of Adversarial SV and Cooperative SV')
parser.add_argument('--n_smp_cls', dest='n_smp_cls', default=2.0,
type=float,
help='Maximum number of samples per class for random sampling (default: %(default)s)')
########################CNDPM#########################
parser.add_argument('--stm_capacity', dest='stm_capacity', default=1000, type=int, help='Short term memory size')
parser.add_argument('--classifier_chill', dest='classifier_chill', default=0.01, type=float,
help='NDPM classifier_chill')
parser.add_argument('--log_alpha', dest='log_alpha', default=-300, type=float, help='Prior log alpha')
########################GDumb#########################
parser.add_argument('--minlr', dest='minlr', default=0.0005, type=float, help='Minimal learning rate')
parser.add_argument('--clip', dest='clip', default=10., type=float,
help='value for gradient clipping')
parser.add_argument('--mem_epoch', dest='mem_epoch', default=70, type=int, help='Epochs to train for memory')
#######################Tricks#########################
parser.add_argument('--labels_trick', dest='labels_trick', default=False, type=boolean_string,
help='Labels trick')
parser.add_argument('--separated_softmax', dest='separated_softmax', default=False, type=boolean_string,
help='separated softmax')
parser.add_argument('--kd_trick', dest='kd_trick', default=False, type=boolean_string,
help='Knowledge distillation with cross entropy trick')
parser.add_argument('--kd_trick_star', dest='kd_trick_star', default=False, type=boolean_string,
help='Improved knowledge distillation trick')
parser.add_argument('--review_trick', dest='review_trick', default=False, type=boolean_string,
help='Review trick')
parser.add_argument('--ncm_trick', dest='ncm_trick', default=False, type=boolean_string,
help='Use nearest class mean classifier')
parser.add_argument('--mem_iters', dest='mem_iters', default=1, type=int,
help='mem_iters')
####################Early Stopping######################
parser.add_argument('--min_delta', dest='min_delta', default=0., type=float,
help='A minimum increase in the score to qualify as an improvement')
parser.add_argument('--patience', dest='patience', default=0, type=int,
help='Number of events to wait if no improvement and then stop the training.')
parser.add_argument('--cumulative_delta', dest='cumulative_delta', default=False, type=boolean_string,
help='If True, `min_delta` defines an increase since the last `patience` reset, '
'otherwise, it defines an increase after the last event.')
####################SupContrast######################
parser.add_argument('--temp', type=float, default=0.07,
help='temperature for loss function')
parser.add_argument('--buffer_tracker', type=boolean_string, default=False,
help='Keep track of buffer with a dictionary')
parser.add_argument('--warmup', type=int, default=4,
help='warmup of buffer before retrieve')
parser.add_argument('--head', type=str, default='mlp',
help='projection head')
args = parser.parse_args()
args.cuda = torch.cuda.is_available()
main(args)