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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 | |
| 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) | |