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# Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmpretrain.registry import MODELS
from .mae_head import MAEPretrainHead
@MODELS.register_module()
class MixMIMPretrainHead(MAEPretrainHead):
"""Head for MixMIM Pre-training.
Args:
loss (dict): Config of loss.
norm_pix_loss (bool): Whether or not normalize target.
Defaults to False.
patch_size (int): Patch size. Defaults to 16.
"""
def __init__(self,
loss: dict,
norm_pix: bool = False,
patch_size: int = 16) -> None:
super().__init__(loss=loss, norm_pix=norm_pix, patch_size=patch_size)
def loss(self, x_rec: torch.Tensor, target: torch.Tensor,
mask: torch.Tensor) -> torch.Tensor:
"""Generate loss.
Args:
pred (torch.Tensor): The reconstructed image.
target (torch.Tensor): The target image.
mask (torch.Tensor): The mask of the target image.
Returns:
torch.Tensor: The reconstruction loss.
"""
target = self.construct_target(target)
B, L, C = x_rec.shape
# unmix tokens
x1_rec = x_rec[:B // 2]
x2_rec = x_rec[B // 2:]
unmix_x_rec = x1_rec * mask + x2_rec.flip(0) * (1 - mask)
loss_rec = self.loss_module(unmix_x_rec, target)
return loss_rec
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