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# Copyright (c) OpenMMLab. All rights reserved.
import warnings
from typing import List, Tuple
import torch
import torch.nn as nn
from mmpretrain.registry import MODELS
from .vision_transformer_head import VisionTransformerClsHead
@MODELS.register_module()
class DeiTClsHead(VisionTransformerClsHead):
"""Distilled Vision Transformer classifier head.
Comparing with the :class:`VisionTransformerClsHead`, this head adds an
extra linear layer to handle the dist token. The final classification score
is the average of both linear transformation results of ``cls_token`` and
``dist_token``.
Args:
num_classes (int): Number of categories excluding the background
category.
in_channels (int): Number of channels in the input feature map.
hidden_dim (int, optional): Number of the dimensions for hidden layer.
Defaults to None, which means no extra hidden layer.
act_cfg (dict): The activation config. Only available during
pre-training. Defaults to ``dict(type='Tanh')``.
init_cfg (dict): The extra initialization configs. Defaults to
``dict(type='Constant', layer='Linear', val=0)``.
"""
def _init_layers(self):
""""Init extra hidden linear layer to handle dist token if exists."""
super(DeiTClsHead, self)._init_layers()
if self.hidden_dim is None:
head_dist = nn.Linear(self.in_channels, self.num_classes)
else:
head_dist = nn.Linear(self.hidden_dim, self.num_classes)
self.layers.add_module('head_dist', head_dist)
def pre_logits(self,
feats: Tuple[List[torch.Tensor]]) -> Tuple[torch.Tensor]:
"""The process before the final classification head.
The input ``feats`` is a tuple of list of tensor, and each tensor is
the feature of a backbone stage. In ``DeiTClsHead``, we obtain the
feature of the last stage and forward in hidden layer if exists.
"""
feat = feats[-1] # Obtain feature of the last scale.
# For backward-compatibility with the previous ViT output
if len(feat) == 3:
_, cls_token, dist_token = feat
else:
cls_token, dist_token = feat
if self.hidden_dim is None:
return cls_token, dist_token
else:
cls_token = self.layers.act(self.layers.pre_logits(cls_token))
dist_token = self.layers.act(self.layers.pre_logits(dist_token))
return cls_token, dist_token
def forward(self, feats: Tuple[List[torch.Tensor]]) -> torch.Tensor:
"""The forward process."""
if self.training:
warnings.warn('MMPretrain cannot train the '
'distilled version DeiT.')
cls_token, dist_token = self.pre_logits(feats)
# The final classification head.
cls_score = (self.layers.head(cls_token) +
self.layers.head_dist(dist_token)) / 2
return cls_score
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