DistillationDilaDBLoss question

[csj3684]

Hi, thanks for your great works.

I did OCR detection training with 'ch_PP-OCRv4_det_cml.yml' on my custom dataset.

And I just found there is a something wrong with 'DistillationDilaDBLoss'

It inherits 'DBLoss' and it should send some parameters but in the definition in DistillationDilaDBLoss.init, it does not.

Any reason for this ?

Thank you.

class DBLoss(nn.Layer):
"""
Differentiable Binarization (DB) Loss Function
args:
param (dict): the super paramter for DB Loss
"""

def __init__(
    self,
    balance_loss=True,
    main_loss_type="DiceLoss",
    alpha=5,
    beta=10,
    ohem_ratio=3,
    eps=1e-6,
    **kwargs,
):
    super(DBLoss, self).__init__()
    self.alpha = alpha
    self.beta = beta
    self.dice_loss = DiceLoss(eps=eps)
    self.l1_loss = MaskL1Loss(eps=eps)
    self.bce_loss = BalanceLoss(
        balance_loss=balance_loss,
        main_loss_type=main_loss_type,
        negative_ratio=ohem_ratio,
    )

def forward(self, predicts, labels):
    ...

class DistillationDBLoss(DBLoss):
def init(
self,
model_name_list=[],
balance_loss=True,
main_loss_type="DiceLoss",
alpha=5,
beta=10,
ohem_ratio=3,
eps=1e-6,
name="db",
**kwargs,
):
super().init()
self.model_name_list = model_name_list
self.name = name
self.key = None

def forward(self, predicts, batch):
    loss_dict = {}
    for idx, model_name in enumerate(self.model_name_list):
        out = predicts[model_name]
        if self.key is not None:
            out = out[self.key]
        **loss = super().forward(out, batch)**

        if isinstance(loss, dict):
            for key in loss.keys():
                if key == "loss":
                    continue
                name = "{}_{}_{}".format(self.name, model_name, key)
                loss_dict[name] = loss[key]
        else:
            loss_dict["{}_{}".format(self.name, model_name)] = loss

    loss_dict = _sum_loss(loss_dict)
    return loss_dict