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mmedit.models.losses.face_id_loss 源代码

# Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional

import torch
import torch.nn as nn

from mmedit.registry import MODELS


@MODELS.register_module()
[文档]class FaceIdLoss(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: .. code-block:: python :linenos: data_info = dict( gt='real_imgs', pred='fake_imgs') Then, the module will automatically construct this mapping from the input data dictionary. Args: 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, device='cuda'). 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'. """ def __init__(self, loss_weight: float = 1.0, data_info: Optional[dict] = None, facenet: dict = dict(type='ArcFace', ir_se50_weights=None), loss_name: str = 'loss_id') -> None: super(FaceIdLoss, self).__init__() self.loss_weight = loss_weight self.data_info = data_info self.net = MODELS.build(facenet) self._loss_name = loss_name
[文档] def forward(self, pred: torch.Tensor, gt: torch.Tensor) -> torch.Tensor: """Forward function.""" # NOTE: only return the loss term return self.net(pred, gt)[0] * self.loss_weight
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