def prepare_all():
print("loading faceboxes model!!")
faceboxes = FaceBoxesDetect(cfg)
faceboxes.prepare()
print('faceboxes loaded!!\n')
print("prepare Arc model!!!")
conf = get_config(False)
conf.use_mobilfacenet = True
learner = face_learner(conf, inference=True)
learner.threshold = conf.threshold
if conf.device.type == 'cpu':
learner.load_state(conf, 'cpu_final.pth', True, True)
else:
learner.load_state(conf,
'0.884.pth',
from_save_folder=False,
model_only=True)
learner.model.eval()
print('Arc loaded\n')
if conf.update:
targets, names = prepare_facebank(conf,
learner.model,
faceboxes,
tta=conf.tta)
print('facebank updated')
else:
targets, names = load_facebank(conf)
print('facebank loaded')
return conf, faceboxes, learner, targets, names
def prepare_arc():
conf = get_config(False)
conf.use_mobilfacenet = True
learner = face_learner(conf, inference=True)
learner.threshold = conf.threshold
learner.load_state(conf, conf.arc_model, from_save_folder=False, model_only=True)
learner.model.eval()
print('Arc loaded\n')
print('begin load facebank!')
targets, names = load_facebank(conf)
print('facebank loaded')
return conf, learner, targets, names
default=96,
type=int)
parser.add_argument("-w",
"--num_workers",
help="workers number",
default=3,
type=int)
parser.add_argument("-d",
"--data_mode",
help="use which database, [vgg, ms1m, emore, concat]",
default='emore',
type=str)
args = parser.parse_args()
conf = get_config()
if args.net_mode == 'mobilefacenet':
conf.use_mobilfacenet = True
else:
conf.net_mode = args.net_mode
conf.net_depth = args.net_depth
conf.lr = args.lr
conf.batch_size = args.batch_size
conf.num_workers = args.num_workers
conf.data_mode = args.data_mode
learner = face_learner(conf)
log_lrs, losses = learner.find_lr(conf)
learner.train(conf, args.epochs)
"--update",
help="whether perform update the facebank",
action="store_true")
parser.add_argument("-tta",
"--tta",
help="whether test time augmentation",
action="store_true")
args = parser.parse_args()
conf = get_config(False)
mtcnn = MTCNN()
print('mtcnn loaded')
learner = face_learner(conf, True)
learner.threshold = args.threshold
if conf.device.type == 'cpu':
learner.load_state(conf, 'cpu_final.pth', False, True)
else:
learner.load_state(conf, 'final.pth', False, True)
learner.model.eval()
print('learner loaded')
if args.update:
# for path in conf.facebank_path.iterdir():
targets, names = prepare_facebank(conf,
learner.model,
mtcnn,
tta=args.tta)
print('facebank updated')