68 lines
2.7 KiB
Python
68 lines
2.7 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
|
|
import warnings
|
|
from typing import List, Tuple
|
|
|
|
import torch
|
|
import torch.nn as nn
|
|
|
|
from mmcls.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.
|
|
"""
|
|
_, cls_token, dist_token = feats[-1]
|
|
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('MMClassification 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
|