def __init__(self, pnet_param, rnet_param, onet_param, isCuda=True):
self.isCuda = isCuda
self.pnet = Nets.PNet()
self.rnet = Nets.RNet()
self.onet = Nets.ONet()
if self.isCuda:
self.pnet.cuda()
self.rnet.cuda()
self.onet.cuda()
# 加载网络参数
self.pnet.load_state_dict(torch.load(pnet_param))
self.rnet.load_state_dict(torch.load(rnet_param))
self.onet.load_state_dict(torch.load(onet_param))
# 网络是测试
self.pnet.eval()
self.rnet.eval()
self.onet.eval()
# 定义transform为ToTensor
self.__image_transform = transforms.Compose([transforms.ToTensor()])
def __init__(self, pnet_param, rnet_param, onet_param, isCuda=False):
self.isCuda = isCuda
# 实例化网络
self.pnet = Nets.PNet()
self.rnet = Nets.RNet()
self.onet = Nets.ONet()
# CUDA加速网络
if self.isCuda:
self.pnet().cuda()
self.rnet().cuda()
self.onet().cuda()
# 装载网络训练结果
self.pnet.load_state_dict(torch.load(pnet_param))
self.rnet.load_state_dict(torch.load(rnet_param))
self.onet.load_state_dict(torch.load(onet_param))
self.pnet.eval()
self.rnet.eval()
self.onet.eval()
# 将图片数据转换成NCHW
self.__image_transform = transforms.Compose(transforms.ToTensor())
import Nets
import train_nets
if __name__ == '__main__':
net = Nets.PNet()
trainer = train_nets.Trainer(net, './param/pnet1.pth', r'E:\data\data\12')
trainer.train()
if __name__ == '__main__':
pnet = PNet(test=True)
pnet.load_state_dict(
torch.load('models/pnet_2019_09_04', map_location='cpu')['weights'])
rnet = RNet(test=True)
rnet.load_state_dict(
torch.load('models/rnet_2019_09_10', map_location='cpu')['weights'])
onet = ONet(test=True)
onet.load_state_dict(
torch.load('models/onet_2019_09_11', map_location='cpu')['weights'])
pnet2 = PNet(test=True)
pnet2.load_state_dict(
torch.load('models/pnet_2019_09_08_18_07',
map_location='cpu')['weights'])
pnet3 = Nets.PNet(test=True)
pnet3.load_state_dict(
torch.load('../mtcnn-pytorch/scripts/models/pnet_20190908_final.pkl',
map_location='cpu')['weights'])
detector = mtcnn(pnet, rnet, onet, mini_face_size=20, name="1", show=True)
detector2 = mtcnn(pnet2,
rnet,
onet,
mini_face_size=20,
name="2",
show=True)
detector3 = mtcnn(pnet3,
rnet,
onet,
mini_face_size=20,
name="3",