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miniImageNet100.py
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miniImageNet100.py
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import numpy as np
import torch
import copy
import warnings
import argparse
from core import Masking, train, evaluate, DeathCosineDecay
from utils import set_args, print_and_log, set_optimizer, set_mask, create_labels, load_dataset,\
get_network, get_loader, freeze_used_params, save_mask, save_checkpoint
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--dataset', type=str, default='miniImagenet100',
help='dataset to use')
parser.add_argument('--num_classes', type=int, default=100,
help='number of classes')
parser.add_argument('--num_tasks', type=int, default=10,
help='number of tasks')
parser.add_argument('--num_classes_per_task', type=int, default=10,
help='number of classes per task')
parser.add_argument('--net_name', type=str, default='resnet18',
help='network architecture to use')
parser.add_argument('--optimizer', type=str, default='adam',
help='optimizer. options: adam, sgd')
parser.add_argument('--lr', type=float, default=0.001,
help='initial learning rate')
parser.add_argument('--momentum', type=float, default=0.9,
help='sgd momentum')
parser.add_argument('--l2', type=float, default=0,
help='weight decay coefficient')
parser.add_argument('--epochs', type=int, default=100,
help='number training epochs')
parser.add_argument('--batch_size', type=int, default=128,
help='number of examples per training batch')
parser.add_argument('--sparse', type=bool, default=True,
help='enable sparse mode. Default: True.')
parser.add_argument('--density', type=float, default=0.1,
help='density of the overall sparse network.')
parser.add_argument('--init', type=str, default='uniform',
help='sparse initialization. options: ERK, uniform')
parser.add_argument('--death', type=str, default='magnitude',
help='pruning mode. options: magnitude, SET, Taylor_FO.')
parser.add_argument('--growth', type=str, default='random',
help='rewiring mode. options: random, random_unfired, gradient and momentum.')
parser.add_argument('--death_rate', type=float, default=0.50,
help='pruning rate.')
parser.add_argument('--death_growth_ratio', type=float, default=1.0,
help='ratio between pruning and rewiring.')
parser.add_argument('--redistribution', type=str, default='none',
help='redistribution mode. options: momentum, magnitude, nonzeros, or none.')
parser.add_argument('--update_frequency', type=int, default=50,
help='number of batches to train between parameter exploration')
parser.add_argument('--mask_selection', type=str, default='max',
help='mask selection method. options: max')
parser.add_argument('--isolate', type=bool, default=True,
help='freeze weights of the previous tasks')
parser.add_argument('--regularize', type=bool, default=False,
help='regularize weights of the previous tasks')
parser.add_argument('--seed', type=int, default=0,
help='random seed')
parser.add_argument('--train', type=bool, default=True,
help='train the model; if False inference mode only')
parser.add_argument('--test', type=bool, default=True,
help='test the model; if False train mode only')
args = parser.parse_args()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
warnings.filterwarnings("ignore", category=UserWarning)
set_args(args)
task_list = create_labels(args.num_classes, args.num_tasks, args.num_classes_per_task)
train_dataset = load_dataset(args.dataset, train=True, tasks=task_list)
test_dataset = load_dataset(args.dataset, train=False, tasks=task_list)
net, NET = get_network(args.net_name, args.num_classes, device)
if args.train:
used_params = {}
mask = None
for task_id in range(args.num_tasks):
train_loader = get_loader(train_dataset, task_id=task_id, batch=args.batch_size)
optimizer, lr_scheduler = set_optimizer(args.optimizer, args.lr, args.momentum, args.l2,
args.epochs, net)
if args.sparse:
if task_id != 0:
net.load_state_dict(
torch.load('./models/{0}_{1}_DENSE.pt'.format(args.dataset, args.net_name)))
if args.isolate:
used_params = freeze_used_params(used_params, args.dataset, task_id,
args.net_name)
death_rate_decay = DeathCosineDecay(args.death_rate,
len(train_loader) * args.epochs)
mask = Masking(args.init, args.density, args.death, args.growth,
args.death_rate, args.epochs, args.update_frequency,
args.death_growth_ratio,
death_rate_decay, optimizer, device)
mask.add_module(net)
print_and_log('TRAINING...\n')
best_acc = -1.0
for epoch in range(args.epochs):
train_loss, train_acc = train(net, optimizer, task_id, train_loader,
args.num_classes_per_task, args.regularize,
args.isolate,
args.batch_size, epoch, device, mask, used_params)
lr_scheduler.step()
if train_acc > best_acc:
save_checkpoint(net, args.dataset, args.net_name)
best_acc = train_acc
if args.sparse:
save_mask(mask, args.dataset, task_id, args.net_name)
if args.sparse:
net.load_state_dict(
torch.load('./models/{0}_{1}.pt'.format(args.dataset, args.net_name)))
if task_id == 0:
NET = copy.deepcopy(net)
else:
for (name, param), (old_name, old_param) in zip(net.named_parameters(),
NET.named_parameters()):
param.data[param == 0] = old_param.data[param == 0]
NET = copy.deepcopy(net)
torch.save(NET.state_dict(),
'./models/{0}_{1}_DENSE.pt'.format(args.dataset, args.net_name))
test_loader = get_loader(test_dataset, task_id=task_id, batch=args.batch_size)
masked_net = set_mask(args.dataset, args.net_name, NET, task_id)
_, _, test_loss, test_accuracy = evaluate(masked_net, device, test_loader,
args.num_classes_per_task, task_id)
if args.test:
print_and_log('TESTING...\n')
acc_matrix = np.zeros((args.num_tasks, args.num_tasks))
bwt = np.zeros(args.num_tasks)
fwt = np.zeros(args.num_tasks)
if args.sparse:
NET.load_state_dict(
torch.load('./models/{0}_{1}_DENSE.pt'.format(args.dataset, args.net_name)))
else:
net.load_state_dict(
torch.load('./models/{0}_{1}.pt'.format(args.dataset, args.net_name)))
for task_id in range(args.num_tasks):
for t in range(args.num_tasks):
test_loader = get_loader(test_dataset, task_id=t, batch=args.batch_size)
if args.sparse:
if task_id >= t:
masked_net = set_mask(args.dataset, args.net_name, NET, t)
_, _, test_loss, test_accuracy = evaluate(masked_net, device, test_loader,
args.num_classes_per_task,
task_id)
else:
masked_net = set_mask(args.dataset, args.net_name, NET, task_id)
_, _, test_loss, test_accuracy = evaluate(masked_net, device, test_loader,
args.num_classes_per_task,
task_id, t,
is_fwt=True)
acc_matrix[task_id, t] = test_accuracy * 100
for task_id in range(1, args.num_tasks):
for t in range(task_id):
bwt[task_id] += (acc_matrix[task_id, t] - acc_matrix[t, t]) / task_id
for task_id in range(args.num_tasks):
for t in range(task_id + 1, args.num_tasks):
fwt[task_id] = (np.mean(acc_matrix[task_id, t:args.num_tasks]))
break
final_acc = acc_matrix[-1]
inc_acc = np.zeros_like(final_acc)
for i in range(len(final_acc)):
if i == 0:
inc_acc[i] = final_acc[i]
else:
current_values = final_acc[:i + 1]
inc_acc[i] = np.mean(current_values)
print_and_log('ACCURACY MATRIX:\n {}\n'.format(acc_matrix))
print_and_log('INCREMENTAL ACCURACY:\n {}\n'.format(inc_acc))
print_and_log('BWT:\n {}\n'.format(bwt))
print_and_log('FWT:\n {}\n'.format(fwt))
print_and_log('Average BWT:\n {}\n'.format(bwt[-1]))
print_and_log('Average FWT:\n {}\n'.format(np.mean(fwt[:-1])))
if __name__ == '__main__':
main()