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)
pnet.load_state_dict(torch.load(p_model_path))
if (use_cuda):
pnet.cuda()
pnet.eval()
if r_model_path is not None:
rnet = RNet(use_cuda=use_cuda)
rnet.load_state_dict(torch.load(r_model_path))
if (use_cuda):
rnet.cuda()
rnet.eval()
if o_model_path is not None:
onet = ONet(use_cuda=use_cuda)
onet.load_state_dict(torch.load(o_model_path))
if (use_cuda):
onet.cuda()
onet.eval()
return pnet, rnet, onet
def create_mtcnn_net(self, use_cuda=True):
self.device = torch.device(
"cuda" if use_cuda and torch.cuda.is_available() else "cpu")
pnet = PNet()
pnet.load_state_dict(model_zoo.load_url(model_urls['pnet']))
pnet.to(self.device).eval()
onet = ONet()
onet.load_state_dict(model_zoo.load_url(model_urls['onet']))
onet.to(self.device).eval()
rnet = RNet()
rnet.load_state_dict(model_zoo.load_url(model_urls['rnet']))
rnet.to(self.device).eval()
return pnet, rnet, onet
def create_mtcnn_net(self, use_cuda=True):
self.device = torch.device(
"cuda" if use_cuda and torch.cuda.is_available() else "cpu")
pnet = PNet()
pnet.load_state_dict(model_zoo.load_url(model_urls['pnet']))
pnet.to(self.device).eval()
onet = ONet()
onet.load_state_dict(model_zoo.load_url(model_urls['onet']))
onet.to(self.device).eval()
rnet = RNet()
rnet.load_state_dict(model_zoo.load_url(model_urls['rnet']))
rnet.to(self.device).eval()
return pnet, rnet, onet
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, use_cuda=True):
self.device = torch.device(
"cuda" if use_cuda and torch.cuda.is_available() else "cpu")
pnet = PNet()
#pnet.load_state_dict(torch.load(r'.\results\pnet\log_bs512_lr0.010_072402\check_point\model_050.pth'))
pnet.load_state_dict(model_zoo.load_url(model_urls['pnet']))
pnet.to(self.device).eval()
onet = ONet()
#onet.load_state_dict(torch.load(r'.\results\onet\log_bs512_lr0.010_072602\check_point\model_050.pth'))
onet.load_state_dict(model_zoo.load_url(model_urls['onet']))
onet.to(self.device).eval()
rnet = RNet()
#rnet.load_state_dict(torch.load(r'.\results\rnet\log_bs512_lr0.001_072502\check_point\model_050.pth'))
rnet.load_state_dict(model_zoo.load_url(model_urls['rnet']))
rnet.to(self.device).eval()
return pnet, rnet, onet