def main():
lecun_fix()
parser = ArgumentParser(description='mammoth', allow_abbrev=False)
parser.add_argument('--model',
type=str,
required=True,
help='Model name.',
choices=get_all_models())
parser.add_argument('--load_best_args',
action='store_true',
help='Loads the best arguments for each method, '
'dataset and memory buffer.')
add_management_args(parser)
args = parser.parse_known_args()[0]
mod = importlib.import_module('models.' + args.model)
if args.load_best_args:
parser.add_argument('--dataset',
type=str,
required=True,
choices=DATASET_NAMES,
help='Which dataset to perform experiments on.')
if hasattr(mod, 'Buffer'):
parser.add_argument('--buffer_size',
type=int,
required=True,
help='The size of the memory buffer.')
args = parser.parse_args()
if args.model == 'joint':
best = best_args[args.dataset]['sgd']
else:
best = best_args[args.dataset][args.model]
if args.model == 'joint' and args.dataset == 'mnist-360':
args.model = 'joint_gcl'
if hasattr(args, 'buffer_size'):
best = best[args.buffer_size]
else:
best = best[-1]
for key, value in best.items():
setattr(args, key, value)
else:
get_parser = getattr(mod, 'get_parser')
parser = get_parser()
args = parser.parse_args()
if args.seed is not None:
set_random_seed(args.seed)
if args.model == 'mer': setattr(args, 'batch_size', 1)
dataset = get_dataset(args)
backbone = dataset.get_backbone()
loss = dataset.get_loss()
model = get_model(args, backbone, loss, dataset.get_transform())
if isinstance(dataset, ContinualDataset):
train(model, dataset, args)
else:
assert not hasattr(model, 'end_task') or model.NAME == 'joint_gcl'
ctrain(args)
print('done')
print('arguments for train:')
print(train_args)
print('rebuilding model...')
model = Set2Seq(voc.num_words).to(args.device)
model.load_state_dict(checkpoint['model'])
model_optimizer = train_args.optimiser(model.parameters(), lr=args.learning_rate)
model_optimizer.load_state_dict(checkpoint['opt'])
print('done')
print('loading test data...')
test_set = PairDataset(voc, dataset_file_path=args.test_file, reverse=True)
print('done')
test_seq_acc, test_tok_acc, test_loss = eval_model(model, test_set)
print("[TEST]Loss: {:.4f}; Seq-level Accuracy: {:.4f}; Tok-level Accuracy: {:.4f}".format(
test_loss, test_seq_acc * 100, test_tok_acc * 100)
)
if __name__ == '__main__':
set_random_seed(args.seed)
with autograd.detect_anomaly():
print('with detect_anomaly')
if args.test:
test()
else:
train()
def main():
if torch.cuda.device_count() > 1:
torch.set_num_threads(6 * torch.cuda.device_count())
else:
torch.set_num_threads(2)
parser = ArgumentParser(description='mammoth', allow_abbrev=False)
parser.add_argument('--model',
type=str,
required=True,
help='Model name.',
choices=get_all_models())
parser.add_argument('--load_best_args',
action='store_true',
help='Loads the best arguments for each method, '
'dataset and memory buffer.')
add_management_args(parser)
args = parser.parse_known_args()[0]
mod = importlib.import_module('models.' + args.model)
if args.load_best_args:
parser.add_argument('--dataset',
type=str,
required=True,
choices=DATASET_NAMES,
help='Which dataset to perform experiments on.')
if hasattr(mod, 'Buffer'):
parser.add_argument('--buffer_size',
type=int,
required=True,
help='The size of the memory buffer.')
args = parser.parse_args()
model = args.model
if model == 'joint':
model = 'sgd'
best = best_args[args.dataset][model]
if hasattr(args, 'buffer_size'):
best = best[args.buffer_size]
else:
best = best[-1]
for key, value in best.items():
setattr(args, key, value)
else:
get_parser = getattr(mod, 'get_parser')
parser = get_parser()
args = parser.parse_args()
if args.seed is not None:
set_random_seed(args.seed)
off_joint = False
if args.model == 'joint' and args.dataset == 'seq-core50':
args.dataset = 'seq-core50j'
args.model = 'sgd'
off_joint = True
dataset = get_dataset(args)
# continual learning
backbone = dataset.get_backbone()
loss = dataset.get_loss()
model = get_model(args, backbone, loss, dataset.get_transform())
if off_joint:
print('BEGIN JOINT TRAINING')
jtrain(model, dataset, args)
else:
print('BEGIN CONTINUAL TRAINING')
train(model, dataset, args)
checkpoint = torch.load(args.param_file, map_location=args.device)
train_args = checkpoint['args']
voc = checkpoint['voc']
print('done')
print('arguments for train:')
print(train_args)
print('rebuilding model...')
model = Set2Seq2Choice(voc.num_words).to(args.device)
model.load_state_dict(checkpoint['model'])
print('done')
print('loading test data...')
test_set = ChooseDataset(voc, dataset_file_path=args.test_file)
print('done')
test_acc, test_loss = eval_model(model, test_set)
print("[TEST]Loss: {:.4f}; Accuracy: {:.4f};".format(
test_loss, test_acc * 100))
if __name__ == '__main__':
set_random_seed(1234)
with autograd.detect_anomaly():
print('with detect_anomaly')
if args.test:
test()
else:
train()
def main():
parser = ArgumentParser(description='mammoth', allow_abbrev=False)
args = parser.parse_known_args()[0]
args.model = 'ocilfast'
args.seed = None
args.validation = True
args.img_dir = 'img/test' # 打印图片存储路径
args.print_file = open('../'+args.img_dir+'/result.txt', mode='w')
args.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
"""
# seq-tinyimagenet
args.dataset = 'seq-tinyimg'
args.lr = 2e-3
args.batch_size = 32
args.buffer_size = 0
args.minibatch_size = 32
args.n_epochs = 100
args.nu = 0.7
args.eta = 0.04
args.eps = 1
args.embedding_dim = 250
args.weight_decay = 1e-2
args.margin = 1
args.r = 0.01
args.nf = 32
"""
# seq-cifar10
args.dataset = 'seq-cifar10'
args.lr = 1e-3
args.batch_size = 32
args.buffer_size = 0
args.minibatch_size = 32
args.n_epochs = 50
args.nu = 0.7
args.eta = 0.8
args.eps = 1
args.embedding_dim = 250
args.weight_decay = 1e-2
args.margin = 1
args.r = 0.1
args.nf = 32
# seq-mnist
# args.dataset = 'seq-mnist'
# args.buffer_size = 0
# args.lr = 1e-3
# args.batch_size = 128
# args.minibatch_size = 128
# args.n_epochs = 10
#
# args.nu = 0.8
# args.eta = 1
# args.eps = 0.1
# args.embedding_dim = 150
# args.weight_decay = 0
# args.margin = 5
# args.r = 0.1 # 半径
# args.nf = 32
if args.seed is not None:
set_random_seed(args.seed)
train(args)
