def _train(args):
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s [%(levelname)s] => %(message)s',
handlers=[
logging.FileHandler(filename=args['prefix'] +
'_{}_{}_{}_{}.log'.format(
args['model_name'], args['convnet_type'],
args['init_cls'], args['increment'])),
logging.StreamHandler(sys.stdout)
])
logging.info('Seed: {}'.format(args['seed']))
logging.info('Model: {}'.format(args['model_name']))
logging.info('Convnet: {}'.format(args['convnet_type']))
_set_device(args)
data_manager = DataManager(args['dataset'], args['shuffle'], args['seed'],
args['init_cls'], args['increment'])
model = factory.get_model(args['model_name'], args)
curve = []
for task in range(data_manager.nb_tasks):
logging.info('All params: {}'.format(count_parameters(model._network)))
logging.info('Trainable params: {}'.format(
count_parameters(model._network, True)))
model.incremental_train(data_manager)
accy = model.eval_task()
model.after_task()
logging.info(accy)
curve.append(accy['total'])
logging.info('Curve: {}\n'.format(curve))
def _train(args):
logfilename = '{}_{}_{}_{}_{}_{}_{}'.format(
args['prefix'], args['seed'], args['model_name'], args['convnet_type'],
args['dataset'], args['init_cls'], args['increment'])
logging.basicConfig(level=logging.INFO,
format='%(asctime)s [%(filename)s] => %(message)s',
handlers=[
logging.FileHandler(filename=logfilename + '.log'),
logging.StreamHandler(sys.stdout)
])
_set_random()
_set_device(args)
print_args(args)
data_manager = DataManager(args['dataset'], args['shuffle'], args['seed'],
args['init_cls'], args['increment'])
model = factory.get_model(args['model_name'], args)
cnn_curve, nme_curve = {'top1': [], 'top5': []}, {'top1': [], 'top5': []}
for task in range(data_manager.nb_tasks):
logging.info('All params: {}'.format(count_parameters(model._network)))
logging.info('Trainable params: {}'.format(
count_parameters(model._network, True)))
model.incremental_train(data_manager)
cnn_accy, nme_accy = model.eval_task()
model.after_task()
if nme_accy is not None:
logging.info('CNN: {}'.format(cnn_accy['grouped']))
logging.info('NME: {}'.format(nme_accy['grouped']))
cnn_curve['top1'].append(cnn_accy['top1'])
cnn_curve['top5'].append(cnn_accy['top5'])
nme_curve['top1'].append(nme_accy['top1'])
nme_curve['top5'].append(nme_accy['top5'])
logging.info('CNN top1 curve: {}'.format(cnn_curve['top1']))
logging.info('CNN top5 curve: {}'.format(cnn_curve['top5']))
logging.info('NME top1 curve: {}'.format(nme_curve['top1']))
logging.info('NME top5 curve: {}\n'.format(nme_curve['top5']))
else:
logging.info('No NME accuracy.')
logging.info('CNN: {}'.format(cnn_accy['grouped']))
cnn_curve['top1'].append(cnn_accy['top1'])
cnn_curve['top5'].append(cnn_accy['top5'])
logging.info('CNN top1 curve: {}'.format(cnn_curve['top1']))
logging.info('CNN top5 curve: {}\n'.format(cnn_curve['top5']))
def _train(args):
logfilename = '{}_{}_{}_{}_{}_{}_{}'.format(
args['prefix'], args['seed'], args['model_name'], args['convnet_type'],
args['dataset'], args['init_cls'], args['increment'])
'''@Author:defeng
{
"prefix": "reproduce",
"dataset": "cifar100",
"memory_size": 2000,
"memory_per_class": 20,
"fixed_memory": true,
"shuffle": true,
"init_cls": 50,
"increment": 10, #increase $increment classes each task. see "# Grouped accuracy" in toolkit.py
"model_name": "UCIR",
"convnet_type": "cosine_resnet32",
"device": ["0"],
"seed": [30]
}
'''
logging.basicConfig(level=logging.INFO,
format='%(asctime)s [%(filename)s] => %(message)s',
handlers=[
logging.FileHandler(filename=logfilename + '.log'),
logging.StreamHandler(sys.stdout)
])
'''@Author:defeng
see for details: https://www.cnblogs.com/xianyulouie/p/11041777.html
26 May 2021 (Wednesday)
format: %(filename)s enables output like this "2021-05-26 22:01:34,371 [*ucir.py*]" and we can know which file \
a certain output come from.
'''
'''@Author:defeng
set random seed and cuda devices
'''
_set_random()
_set_device(args)
print_args(args)
'''@Author:defeng
*set: dataset and model.*
'''
data_manager = DataManager(args['dataset'], args['shuffle'], args['seed'],
args['init_cls'], args['increment'])
model = factory.get_model(args['model_name'], args)
'''@Author:defeng
the actual work for getting model ready is done by the .py files in the "models" folder.
'''
'''@Author:defeng
cnn: softmax prediction
nme: nearest-mean-of-neightbors prediction
see ucir paper "Baselines" for detail.
'''
cnn_curve, nme_curve = {'top1': [], 'top5': []}, {'top1': [], 'top5': []}
for task in range(data_manager.nb_tasks):
logging.info('All params: {}'.format(count_parameters(model._network)))
logging.info('Trainable params: {}'.format(
count_parameters(model._network, True)))
model.incremental_train(data_manager) #train
cnn_accy, nme_accy = model.eval_task() #val
model.after_task() #post-processing
if nme_accy is not None:
logging.info('CNN: {}'.format(cnn_accy['grouped']))
logging.info('NME: {}'.format(nme_accy['grouped']))
cnn_curve['top1'].append(cnn_accy['top1'])
cnn_curve['top5'].append(cnn_accy['top5'])
nme_curve['top1'].append(nme_accy['top1'])
nme_curve['top5'].append(nme_accy['top5'])
logging.info('CNN top1 curve: {}'.format(cnn_curve['top1']))
logging.info('CNN top5 curve: {}'.format(cnn_curve['top5']))
logging.info('NME top1 curve: {}'.format(nme_curve['top1']))
logging.info('NME top5 curve: {}\n'.format(nme_curve['top5']))
else:
logging.info('No NME accuracy.')
logging.info('CNN: {}'.format(cnn_accy['grouped']))
cnn_curve['top1'].append(cnn_accy['top1'])
cnn_curve['top5'].append(cnn_accy['top5'])
logging.info('CNN top1 curve: {}'.format(cnn_curve['top1']))
logging.info('CNN top5 curve: {}\n'.format(cnn_curve['top5']))
def __init__(self, cfg):
super().__init__()
self.cfg = cfg
self.net = get_model(self.cfg.model)
self.loss = get_loss(self.cfg.loss)