def __init__(self, opt):
super(DddLoss, self).__init__()
self.crit = torch.nn.MSELoss() if opt.mse_loss else FocalLoss()
self.crit_reg = L1Loss()
self.crit_rot = BinRotLoss()
self.opt = opt
self.emb_scale = 1
def __init__(self, opt):
super(DddLoss, self).__init__()
self.crit = torch.nn.MSELoss() if opt.mse_loss else FocalLoss()
self.crit_reg = L1Loss()
self.crit_kp = RegWeightedL1Loss()
self.crit_rot = BinRotLoss()
self.opt = opt
def __init__(self, opt):
super(DddLoss, self).__init__()
self.crit = torch.nn.MSELoss() if opt.mse_loss else FocalLoss()
self.crit_reg = L1Loss()
self.crit_rot = BinRotLoss()
self.dept_reg = ConfidenceLoss()
self.opt = opt
def __init__(self, opt):
super(DddLoss, self).__init__()
self.crit = torch.nn.MSELoss() if opt.mse_loss else FocalLoss()
self.crit_reg = L1Loss()
self.crit_rot = BinRotLoss()
self.depth_reg = L2Loss()
self.dim_reg = L2Loss()
self.vec_reg = L1Loss_ver()
self.opt = opt
def __init__(self, opt):
super(CarPoseLoss, self).__init__()
self.crit = FocalLoss()
self.crit_hm_hp = torch.nn.MSELoss() if opt.mse_loss else FocalLoss()
self.crit_kp = RegWeightedL1Loss() if not opt.dense_hp else \
torch.nn.L1Loss(reduction='sum')
self.crit_reg = RegL1Loss() if opt.reg_loss == 'l1' else \
RegLoss() if opt.reg_loss == 'sl1' else None
self.crit_rot = BinRotLoss()
self.opt = opt
self.position_loss = Position_loss(opt)
def __init__(self, opt):
super(DddLoss, self).__init__()
# 定义损失函数
self.crit = torch.nn.MSELoss() if opt.mse_loss else FocalLoss()
# L1损失函数
self.crit_reg = L1Loss()
# 混合连续-离散损失：multiBin损失
self.crit_rot = BinRotLoss()
self.opt = opt
def __init__(self, opt):
super(RodetLoss, self).__init__()
self.crit = torch.nn.MSELoss() if opt.mse_loss else FocalLoss()
self.crit_reg = RegL1Loss() if opt.reg_loss == 'l1' else \
RegLoss() if opt.reg_loss == 'sl1' else None
self.crit_wh = torch.nn.L1Loss(reduction='sum') if opt.dense_wh else \
NormRegL1Loss() if opt.norm_wh else \
RegWeightedL1Loss() if opt.cat_spec_wh else self.crit_reg
self.crit_angle = BinRotLoss() if opt.rotate_binloss else RegMSELoss()
self.crit_dense_angle = DenseRegL1Loss() if opt.dense_angle else None
self.opt = opt