def create_mtcnn_net(p_model_path=None,
r_model_path=None,
o_model_path=None,
use_cuda=True):
pnet, rnet, onet = None, None, None
if p_model_path is not None:
pnet = PNet(use_cuda=use_cuda)
if (use_cuda):
print('p_model_path:{0}'.format(p_model_path))
pnet.load_state_dict(torch.load(p_model_path))
pnet.cuda()
else:
# forcing all GPU tensors to be in CPU while loading
pnet.load_state_dict(
torch.load(p_model_path,
map_location=lambda storage, loc: storage))
pnet.eval()
if r_model_path is not None:
rnet = RNet(use_cuda=use_cuda)
if (use_cuda):
print('r_model_path:{0}'.format(r_model_path))
rnet.load_state_dict(torch.load(r_model_path))
rnet.cuda()
else:
rnet.load_state_dict(
torch.load(r_model_path,
map_location=lambda storage, loc: storage))
rnet.eval()
if o_model_path is not None:
onet = ONet(use_cuda=use_cuda)
if (use_cuda):
print('o_model_path:{0}'.format(o_model_path))
onet.load_state_dict(torch.load(o_model_path))
onet.cuda()
else:
onet.load_state_dict(
torch.load(o_model_path,
map_location=lambda storage, loc: storage))
onet.eval()
return pnet, rnet, onet
def create_mtcnn_net(self):
''' Create the mtcnn model '''
pnet, rnet, onet = None, None, None
if len(self.args.pnet_file) > 0:
pnet = PNet(use_cuda=self.args.use_cuda)
if self.args.use_cuda:
pnet.load_state_dict(torch.load(self.args.pnet_file))
pnet = torch.nn.DataParallel(
pnet, device_ids=self.args.gpu_ids).cuda()
else:
pnet.load_state_dict(torch.load(self.args.pnet_file,\
map_location=lambda storage, loc: storage))
pnet.eval()
if len(self.args.rnet_file) > 0:
rnet = RNet(use_cuda=self.args.use_cuda)
if self.args.use_cuda:
rnet.load_state_dict(torch.load(self.args.rnet_file))
rnet = torch.nn.DataParallel(
rnet, device_ids=self.args.gpu_ids).cuda()
else:
rnet.load_state_dict(torch.load(self.args.rnet_file,\
map_location=lambda storage, loc: storage))
rnet.eval()
if len(self.args.onet_file) > 0:
onet = ONet(use_cuda=self.args.use_cuda)
if self.args.use_cuda:
onet.load_state_dict(torch.load(self.args.onet_file))
onet = torch.nn.DataParallel(
onet, device_ids=self.args.gpu_ids).cuda()
else:
onet.load_state_dict(torch.load(self.args.onet_file, \
map_location=lambda storage, loc: storage))
onet.eval()
self.pnet_detector = pnet
self.rnet_detector = rnet
self.onet_detector = onet