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# Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Optional, Union
import torch
import torch.nn as nn
from mmengine.model import BaseModule
from mmpretrain.registry import MODELS
@MODELS.register_module()
class BEiTV2Head(BaseModule):
"""Head for BEiT v2 Pre-training.
Compute the logits and the cross entropy loss.
Args:
embed_dims (int): The dimension of embedding.
num_embed (int): The number of classification types.
loss (dict): The config of loss.
init_cfg (dict or List[dict], optional): Initialization config dict.
Defaults to None.
"""
def __init__(
self,
embed_dims: int,
num_embed: int,
loss: dict,
init_cfg: Optional[Union[dict, List[dict]]] = dict(
type='TruncNormal', layer='Linear', std=0.02, bias=0)
) -> None:
super().__init__(init_cfg=init_cfg)
self.cls_head = nn.Linear(embed_dims, num_embed)
self.loss_module = MODELS.build(loss)
def loss(self, feats: torch.Tensor, feats_cls_pt: torch.Tensor,
target: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
"""Generate loss.
Args:
feats (torch.Tensor): Features from backbone.
feats_cls_pt (torch.Tensor) : Features from class late layers for
pretraining.
target (torch.Tensor): Target generated by target_generator.
mask (torch.Tensor): Generated mask for pretraing.
"""
mask = mask.flatten(1).to(torch.bool)
target = target[mask]
# shared cls head
logits = self.cls_head(feats[mask])
logits_cls_pt = self.cls_head(feats_cls_pt[mask])
loss_1 = self.loss_module(logits, target)
loss_2 = self.loss_module(logits_cls_pt, target)
return loss_1, loss_2
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