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# 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
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