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.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_rot = BinRotLoss()
self.dept_reg = ConfidenceLoss()
self.opt = opt
def __init__(self, opt):
super(Det3dLoss, self).__init__()
print("Using MSE loss for clasification = ", opt.mse_loss)
self.crit = torch.nn.MSELoss() if opt.mse_loss else FocalLoss()
self.crit_view_side = CrossEntropyLossWMask()
self.crit_view_front_rear = CrossEntropyLossWMask()
self.crit_reg = L1Loss()
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 build_loss_criterion(cfg):
losses = cfg.TRAIN.losses
weight_classes = cfg.DATASET.weight_classes
img_loss = L1Loss(losses.il) if losses.il else None
per_loss = PerceptualLoss(
{'conv5_4': 1}, perceptual_weight=losses.per) if losses.per else None
adv_loss = GANLoss('vanilla',
loss_weight=losses.adv) if losses.adv else None
tv_loss = WeightedTVLoss(losses.tv) if losses.tv else None
seg_loss = SegLoss(losses.seg, weight_classes) if losses.seg else None
return (img_loss, per_loss, adv_loss, tv_loss, seg_loss)
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(CarPose6DoFLoss, self).__init__()
self.crit = torch.nn.MSELoss() if opt.mse_loss else FocalLoss()
self.crit_reg = L1Loss()
self.crit_xyz = DenseLocL1Loss()
self.opt = opt
def __init__(self, opt):
self.opt = opt
self.crit = FocalLoss()
self.crit_reg = L1Loss()
self.crit_wh = self.crit_reg
