Source code for mmselfsup.models.heads.beitv2_head
# 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 mmselfsup.registry import MODELS
[docs]@MODELS.register_module()
class BEiTV2Head(BaseModule):
"""Pretrain Head for BEiT.
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 = MODELS.build(loss)
[docs] def forward(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 = self.loss((logits, logits_cls_pt), target)
return loss