mmpretrain.models.heads.deit_head 源代码

# 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

[文档]@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)
[文档] 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
[文档] def forward(self, feats: Tuple[List[torch.Tensor]]) -> torch.Tensor: """The forward process.""" if 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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