mmcls/models/heads/linear_head.py

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

import torch
import torch.nn as nn

from mmcls.registry import MODELS
from .cls_head import ClsHead


@MODELS.register_module()
class LinearClsHead(ClsHead):
    """Linear classifier head.

    Args:
        num_classes (int): Number of categories excluding the background
            category.
        in_channels (int): Number of channels in the input feature map.
        loss (dict): Config of classification loss. Defaults to
            ``dict(type='CrossEntropyLoss', loss_weight=1.0)``.
        topk (int | Tuple[int]): Top-k accuracy. Defaults to ``(1, )``.
        cal_acc (bool): Whether to calculate accuracy during training.
            If you use batch augmentations like Mixup and CutMix during
            training, it is pointless to calculate accuracy.
            Defaults to False.
        init_cfg (dict, optional): the config to control the initialization.
            Defaults to ``dict(type='Normal', layer='Linear', std=0.01)``.
    """

    def __init__(self,
                 num_classes: int,
                 in_channels: int,
                 init_cfg: Optional[dict] = dict(
                     type='Normal', layer='Linear', std=0.01),
                 **kwargs):
        super(LinearClsHead, self).__init__(init_cfg=init_cfg, **kwargs)

        self.in_channels = in_channels
        self.num_classes = num_classes

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

        self.fc = nn.Linear(self.in_channels, self.num_classes)

    def pre_logits(self, feats: Tuple[torch.Tensor]) -> torch.Tensor:
        """The process before the final classification head.

        The input ``feats`` is a tuple of tensor, and each tensor is the
        feature of a backbone stage. In ``LinearClsHead``, we just obtain the
        feature of the last stage.
        """
        # The LinearClsHead doesn't have other module, just return after
        # unpacking.
        return feats[-1]

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