def __init__(self, pnet_param="./param_0/pnet.pt", rnet_param="./param_0/rnet.pt",onet_param="./param_0/onet.pt",
isCuda=True):
self.isCuda = isCuda
self.pnet = nets.PNet() # 创建实例变量，实例化P网络
self.rnet = nets.RNet()
self.onet = nets.ONet()
if self.isCuda:
self.pnet.cuda() # 给P网络加速
self.rnet.cuda()
self.onet.cuda()
self.pnet.load_state_dict(torch.load(pnet_param)) # 把训练好的权重加载到P网络中
self.rnet.load_state_dict(torch.load(rnet_param))
self.onet.load_state_dict(torch.load(onet_param))
# self.pnet.eval() # 训练网络里有BN（批归一化时），要调用eval方法，使用是不用BN，dropout方法
# self.rnet.eval()
# self.onet.eval()
self.__image_transform = transforms.Compose([
transforms.ToTensor()
]) # 图片数据类型转换
def __init__(self, pnet_param="./param/pnet.pt", rnet_param="./param/rnet.pt", onet_param="./param/onet.pt",
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()
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()
self.__image_transform = transforms.Compose([
transforms.ToTensor()
])
def __init__(self,
pnet_param=r"./param_0/pnet.pt",
rnet_param=r"./param_0/rnet.pt",
onet_param=r"./param_0/onet.pt",
isCuda=True):
self.isCuda = isCuda
# 创建实例变量，实例化网络
self.pnet = nets.PNet()
self.rnet = nets.RNet()
self.onet = nets.ONet()
# 加入cuda加速训练
if self.isCuda:
self.pnet.cuda()
self.rnet.cuda()
self.onet.cuda()
# 把训练好的权重加载到p网络中
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))
# 调用测试模式，因为网络中含有BN
self.pnet.eval()
self.rnet.eval()
self.onet.eval()
# 图片数据类型转换
self.__image_transform = transforms.Compose([transforms.ToTensor()])
def __init__(self,
pnet_param="./param6/p_net.pth",
rnet_param="./param6/r_net.pth",
onet_param="./param6/o_net.pth",
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()
self.pnet.load_state_dict(torch.load(pnet_param, map_location="cuda"))
self.rnet.load_state_dict(torch.load(rnet_param, map_location="cuda"))
self.onet.load_state_dict(torch.load(onet_param, map_location="cuda"))
self.pnet.eval() # 批归一化, 使用之前训练的Batchnormal
self.rnet.eval()
self.onet.eval()
self._image_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])
def __init__(self,
pnet_param="./param/p_net.pth",
rnet_param="./param/r_net.pth",
onet_param="./param/o_net.pth",
isCuda=False):
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()
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, map_location='cpu'))
self.pnet.eval()
self.rnet.eval()
self.onet.eval()
self.__image_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
def __init__(self, pnet_param="./param/pnet.pt", rnet_param="./param/rnet.pt", onet_param="./param/onet.pt",
isCuda=False):
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()
self.pnet.load_state_dict(torch.load(pnet_param, map_location= "cpu" ))
self.rnet.load_state_dict(torch.load(rnet_param, map_location= "cpu" ))
self.onet.load_state_dict(torch.load(onet_param, map_location= "cpu" ))
self.__image_transform = transforms.Compose([transforms.ToTensor()]) #NCHW and normalized
def __init__(self, pnet_path, rnet_path, onet_path, isCuda=True):
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
self.isCuda = isCuda
self.pnet = nets.PNet().to(self.device)
self.rnet = nets.RNet().to(self.device)
self.onet = nets.ONet().to(self.device)
self.pnet.load_state_dict(torch.load(pnet_path))
self.rnet.load_state_dict(torch.load(rnet_path))
self.onet.load_state_dict(torch.load(onet_path))
self.pnet.eval()
self.rnet.eval()
self.onet.eval()
self.trans = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
])
def __init__(self,
p_net_param=r"./save_params/p_net.pth",
r_net_param=r"./save_params/r_net.pth",
o_net_param="./save_params/o_net.pth",
isCuda=False):
self.isCuda = isCuda
self.p_net = nets.PNet().to(DEVICE)
self.r_net = nets.RNet().to(DEVICE)
self.o_net = nets.ONet().to(DEVICE)
self.p_net.load_state_dict(torch.load(p_net_param))
self.r_net.load_state_dict(torch.load(r_net_param))
self.o_net.load_state_dict(torch.load(o_net_param))
self.p_net.eval()
self.r_net.eval()
self.o_net.eval()
self.__image_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]) # 对于图像这个是有经验值的
])
def __init__(self, pnet_path, rnet_path, onet_path, softnms=False, thresholds=None, factor=0.709):
if thresholds is None:
thresholds = [0.6, 0.6, 0.95]
self.thresholds = thresholds
self.factor = factor
self.softnms = softnms
self.pnet = nets.PNet().to(device)
self.rnet = nets.RNet().to(device)
self.onet = nets.ONet().to(device)
self.pnet.load_state_dict(torch.load(pnet_path))
self.rnet.load_state_dict(torch.load(rnet_path))
self.onet.load_state_dict(torch.load(onet_path))
self.pnet.eval()
self.rnet.eval()
self.onet.eval()
self.img_transfrom = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])
import nets
import train
if __name__ == '__main__':
net = nets.RNet()
trainer = train.Trainer(net, './param/rnet.pt', r"C:\celeba4\24")
trainer.train()
