mmagic.models.losses.loss_comps.face_id_loss_comps

Module Contents

Classes

FaceIdLossComps

Face similarity loss. Generally this loss is used to keep the id

class mmagic.models.losses.loss_comps.face_id_loss_comps.FaceIdLossComps(loss_weight: float = 1.0, data_info: Optional[dict] = None, facenet: dict = dict(type='ArcFace', ir_se50_weights=None), loss_name: str = 'loss_id')

Bases: torch.nn.Module

Face similarity loss. Generally this loss is used to keep the id consistency of the input face image and output face image.

In this loss, we may need to provide gt, pred and x. Thus, an example of the data_info is:

data_info = dict(
    gt='real_imgs',
    pred='fake_imgs')

Then, the module will automatically construct this mapping from the input data dictionary.

Parameters

• loss_weight (float, optional) – Weight of this loss item. Defaults to 1..

• data_info (dict, optional) – Dictionary contains the mapping between loss input args and data dictionary. If None, this module will directly pass the input data to the loss function. Defaults to None.

• facenet (dict, optional) – Config dict for facenet. Defaults to dict(type=’ArcFace’, ir_se50_weights=None).

• loss_name (str, optional) – Name of the loss item. If you want this loss item to be included into the backward graph, loss_ must be the prefix of the name. Defaults to ‘loss_id’.

forward(*args, **kwargs) → torch.Tensor

Forward function.

If self.data_info is not None, a dictionary containing all of the data and necessary modules should be passed into this function. If this dictionary is given as a non-keyword argument, it should be offered as the first argument. If you are using keyword argument, please name it as outputs_dict.

If self.data_info is None, the input argument or key-word argument will be directly passed to loss function, third_party_net_loss.

loss_name() → str

Loss Name.

This function must be implemented and will return the name of this loss function. This name will be used to combine different loss items by simple sum operation. In addition, if you want this loss item to be included into the backward graph, loss_ must be the prefix of the name.

Returns

The name of this loss item.

Return type

str