def _model_init():
p_model = pcn.Pnet()
#p_model.apply(weight_init)
p_model.cuda(DEVICE_IDS[0])
p_model.train()
return p_model
import tools_matrix_cpu as tools
threshold = [0.6, 0.6, 0.7]
EPS = 0.001
if __name__ == "__main__":
#image_name = "images/25.jpg"
#image_name = "images/yueyu.jpg"
image_name = "images/qingxie.jpg"
#image_name = "images/9.jpg"
#image_name = "images/17.jpg"
#image_name = "images/24.jpg"
#image_name = "images/5.jpg"
#image_name = "images/7.jpg"
#image_name = "images/daozhi.jpg"
pnet = pcn.Pnet()
pnet.load_state_dict(
torch.load("pnet/pnet_190310_iter_1238000_.pth",
map_location=lambda storage, loc: storage))
pnet.eval()
rnet = pcn.Rnet()
rnet.load_state_dict(
torch.load("rnet/pnet_190312_iter_979000_.pth",
map_location=lambda storage, loc: storage))
rnet.eval()
onet = pcn.Onet()
onet.load_state_dict(
torch.load("onet/onet_190227_iter_1499000_.pth",
map_location=lambda storage, loc: storage))