def main():
log_out(conf)
print(banner(conf.Program_name))
args = cmdLineParser()
conf.update(cmdLineParser().parse_args()[0].__dict__)
if conf.version:
print conf.version
exit()
if conf.url is None:
args.print_help()
exit()
try:
attack = conf.mode_list[conf.mode]
except KeyError:
errmsg = 'mode {name} is not exist'.format(name=conf)
raise ArgERROR(errmsg)
Log.info('attack mode {name}'.format(name=conf.mode))
try:
data = conf.payloads[conf.payload]
except KeyError as e:
errmsg = 'payload {name} is not exist'.format(name=e)
raise ArgERROR(message=errmsg)
payload = data.get_payload(conf)
fuzz = attack.get_waf(conf, payload)
fuzz.check(conf.url)
Log.info('test finish, result file {name}'.format(name=conf.output_file))
logging.info(f'training conf is {conf2}')
from exargs import parser
if __name__ == '__main__':
args = parser.parse_args()
logging.info(f'args is {args}')
if args.work_path:
conf.work_path = Path(args.work_path)
conf.model_path = conf.work_path / 'models'
conf.log_path = conf.work_path / 'log'
conf.save_path = conf.work_path / 'save'
else:
args.work_path = conf.work_path
conf.update(args.__dict__)
if conf.local_rank is not None:
torch.cuda.set_device(conf.local_rank)
torch.distributed.init_process_group(backend='nccl',
init_method="env://")
if torch.distributed.get_rank() != 0:
set_stream_logger(logging.WARNING)
from Learner import *
# decs = msgpack_load('decs.pk')
# conf.decs = None#decs
# conf.net_mode = 'sglpth'
learner = face_learner(conf, )
ttl_params = (sum(p.numel() for p in learner.model.parameters()) / 1000000.0)
from thop import profile
def main():
args = parser.parse_args()
print('args.data_url', args.data_url)
if conf.cloud:
mox.file.copy_parallel(args.data_url, '/cache/face_train/')
args.data_url = '/cache/face_train/'
conf.use_data_folder = args.data_url
if args.work_path:
conf.work_path = Path(args.work_path)
conf.model_path = conf.work_path / 'models'
conf.log_path = conf.work_path / 'log'
conf.save_path = conf.work_path / 'save'
else:
args.work_path = conf.work_path
conf.update(args.__dict__)
if conf.local_rank is not None:
torch.cuda.set_device(conf.local_rank)
torch.distributed.init_process_group(backend='nccl',
init_method="env://")
if torch.distributed.get_rank() != 0:
set_stream_logger(logging.WARNING)
# if osp.exists(conf.save_path):
# logging.info('ok')
# exit(1)
# simplify_conf(conf)
# exit(0)
from Learner import face_learner
# decs = msgpack_load('decs.pk')
# conf.decs = decs
learner = face_learner(conf, )
# fstrs = learner.list_fixed_strs('work_space/sglpth.casia/models')
# stps = learner.list_steps('work_space/sglpth.casia/models')
# fstr = fstrs[np.argmax(stps)]
# stt_dct = torch.load('work_space/sglpth.casia/models/model_' + fstr)
# learner.model.module.load_state_dict_sglpth(stt_dct)
# print(fstrs, stps, fstr, )
if conf.get('load_from'):
# p= 'r100.128.retina.clean.arc',
# 'hrnet.retina.arc.3',
# 'mbv3.retina.arc',
# 'mbfc.lrg.retina.arc.s48',
# 'effnet.casia.arc',
# 'mbfc.retina.cl.distill.cont2',
# 'mbfc2',
# 'r18.l2sft',
# 'r18.adamrg',
# 'mbfc.se.elu.ms1m.radam.1',
# 'mbfc.se.elu.specnrm.allbutdw.ms1m.adam.1',
# 'mbfc.se.prelu.specnrm.ms1m.cesigsft.1',
# 'irse.elu.ms1m',
# 'irse.elu.casia.arc.2048',
p = Path(conf.load_from)
print(
'try to load from ',
p,
)
learner.load_state(
resume_path=p,
load_optimizer=False,
load_head=conf.head_load, # todo note!
load_imp=False,
latest=True,
strict=False,
)
# simplify_conf(conf)
learner.cloud_sync_log()
# res = learner.validate_ori(conf, valds_names=('cfp_fp', ))
# exit(0)
# learner.calc_img_feas(out='work_space/mbfc.crash.h5')
# log_lrs, losses = learner.find_lr(
# num=999,
# bloding_scale=1000)
# losses[np.isnan(losses)] = 999
# best_lr = 10 ** (log_lrs[np.argmin(losses)])
# print('best lr is ', best_lr)
# conf.lr = best_lr
# exit(0)
# learner.init_lr()
# conf.tri_wei = 0
# log_conf(conf)
# learner.train(conf, 1, name='xent')
learner.init_lr()
simplify_conf(conf)
if conf.head_init:
learner.head_initialize()
if conf.warmup:
learner.warmup(conf, conf.warmup)
learner.train_simple(conf, conf.epochs)
# learner.train_dist(conf, conf.epochs)
if conf.net_mode == 'sglpth':
decs = learner.model.module.get_decisions()
msgpack_dump(decs, 'decs.pk')
# learner.train_cotching(conf, conf.epochs)
# learner.train_cotching_accbs(conf, conf.epochs)
# learner.train_ghm(conf, conf.epochs)
# learner.train_with_wei(conf, conf.epochs)
# learner.train_use_test(conf, conf.epochs)
# res = learner.validate_ori(conf, )
if not conf.cloud:
from tools.test_ijbc3 import test_ijbc3
res = test_ijbc3(conf, learner)
tpr6, tpr4, tpr3 = res[0][1], res[1][1], res[2][1]
learner.writer.add_scalar('ijbb/6', tpr6, learner.step)
learner.writer.add_scalar('ijbb/4', tpr4, learner.step)
learner.writer.add_scalar('ijbb/3', tpr3, learner.step)
learner.writer.close()
if conf.never_stop:
img = torch.randn((conf.batch_size // 2, 3, conf.input_size,
conf.input_size)).cuda()
learner.model.eval()
logging.info('never stop')
while True:
_ = learner.model(img)