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)