Source code for mmpretrain.models.heads.vision_transformer_head

# Copyright (c) OpenMMLab. All rights reserved.
import math
from collections import OrderedDict
from typing import List, Optional, Tuple

import torch
import torch.nn as nn
from mmcv.cnn import build_activation_layer
from mmengine.model import Sequential
from mmengine.model.weight_init import trunc_normal_

from mmpretrain.registry import MODELS
from .cls_head import ClsHead


@MODELS.register_module()
class VisionTransformerClsHead(ClsHead):
    """Vision Transformer classifier head.

    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__(self,
                 num_classes: int,
                 in_channels: int,
                 hidden_dim: Optional[int] = None,
                 act_cfg: dict = dict(type='Tanh'),
                 init_cfg: dict = dict(type='Constant', layer='Linear', val=0),
                 **kwargs):
        super(VisionTransformerClsHead, self).__init__(
            init_cfg=init_cfg, **kwargs)
        self.in_channels = in_channels
        self.num_classes = num_classes
        self.hidden_dim = hidden_dim
        self.act_cfg = act_cfg

        if self.num_classes <= 0:
            raise ValueError(
                f'num_classes={num_classes} must be a positive integer')

        self._init_layers()

    def _init_layers(self):
        """"Init hidden layer if exists."""
        if self.hidden_dim is None:
            layers = [('head', nn.Linear(self.in_channels, self.num_classes))]
        else:
            layers = [
                ('pre_logits', nn.Linear(self.in_channels, self.hidden_dim)),
                ('act', build_activation_layer(self.act_cfg)),
                ('head', nn.Linear(self.hidden_dim, self.num_classes)),
            ]
        self.layers = Sequential(OrderedDict(layers))

    def init_weights(self):
        """"Init weights of hidden layer if exists."""
        super(VisionTransformerClsHead, self).init_weights()
        # Modified from ClassyVision
        if hasattr(self.layers, 'pre_logits'):
            # Lecun norm
            trunc_normal_(
                self.layers.pre_logits.weight,
                std=math.sqrt(1 / self.layers.pre_logits.in_features))
            nn.init.zeros_(self.layers.pre_logits.bias)

    def pre_logits(self, feats: Tuple[List[torch.Tensor]]) -> 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 ``VisionTransformerClsHead``, 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
        cls_token = feat[-1] if isinstance(feat, list) else feat
        if self.hidden_dim is None:
            return cls_token
        else:
            x = self.layers.pre_logits(cls_token)
            return self.layers.act(x)

    def forward(self, feats: Tuple[List[torch.Tensor]]) -> torch.Tensor:
        """The forward process."""
        pre_logits = self.pre_logits(feats)
        # The final classification head.
        cls_score = self.layers.head(pre_logits)
        return cls_score