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# Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmengine.model import BaseModule
from mmpretrain.registry import MODELS
@MODELS.register_module()
class SimMIMHead(BaseModule):
"""Head for SimMIM Pre-training.
Args:
patch_size (int): Patch size of each token.
loss (dict): The config for loss.
"""
def __init__(self, patch_size: int, loss: dict) -> None:
super().__init__()
self.patch_size = patch_size
self.loss_module = MODELS.build(loss)
def loss(self, pred: torch.Tensor, target: torch.Tensor,
mask: torch.Tensor) -> torch.Tensor:
"""Generate loss.
This method will expand mask to the size of the original image.
Args:
pred (torch.Tensor): The reconstructed image (B, C, H, W).
target (torch.Tensor): The target image (B, C, H, W).
mask (torch.Tensor): The mask of the target image.
Returns:
torch.Tensor: The reconstruction loss.
"""
mask = mask.repeat_interleave(self.patch_size, 1).repeat_interleave(
self.patch_size, 2).unsqueeze(1).contiguous()
loss = self.loss_module(pred, target, mask)
return loss
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