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# Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Tuple
import torch
import torch.nn as nn
from mmcls.registry import MODELS
from mmcls.structures import ClsDataSample
from .cls_head import ClsHead
@MODELS.register_module()
class EfficientFormerClsHead(ClsHead):
"""EfficientFormer classifier head.
Args:
num_classes (int): Number of categories excluding the background
category.
in_channels (int): Number of channels in the input feature map.
distillation (bool): Whether use a additional distilled head.
Defaults to True.
init_cfg (dict): The extra initialization configs. Defaults to
``dict(type='Normal', layer='Linear', std=0.01)``.
"""
def __init__(self,
num_classes,
in_channels,
distillation=True,
init_cfg=dict(type='Normal', layer='Linear', std=0.01),
*args,
**kwargs):
super(EfficientFormerClsHead, self).__init__(
init_cfg=init_cfg, *args, **kwargs)
self.in_channels = in_channels
self.num_classes = num_classes
self.dist = distillation
if self.num_classes <= 0:
raise ValueError(
f'num_classes={num_classes} must be a positive integer')
self.head = nn.Linear(self.in_channels, self.num_classes)
if self.dist:
self.dist_head = nn.Linear(self.in_channels, self.num_classes)
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.head(pre_logits)
if self.dist:
cls_score = (cls_score + self.dist_head(pre_logits)) / 2
return cls_score
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 :obj`EfficientFormerClsHead`, we just
obtain the feature of the last stage.
"""
# The EfficientFormerClsHead doesn't have other module, just return
# after unpacking.
return feats[-1]
def loss(self, feats: Tuple[torch.Tensor],
data_samples: List[ClsDataSample], **kwargs) -> dict:
"""Calculate losses from the classification score.
Args:
feats (tuple[Tensor]): The features extracted from the backbone.
Multiple stage inputs are acceptable but only the last stage
will be used to classify. The shape of every item should be
``(num_samples, num_classes)``.
data_samples (List[ClsDataSample]): The annotation data of
every samples.
**kwargs: Other keyword arguments to forward the loss module.
Returns:
dict[str, Tensor]: a dictionary of loss components
"""
if self.dist:
raise NotImplementedError(
"MMClassification doesn't support to train"
' the distilled version EfficientFormer.')
else:
return super().loss(feats, data_samples, **kwargs)
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