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mmpretrain.models.heads.efficientformer_head 源代码

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

import torch
import torch.nn as nn

from mmpretrain.registry import MODELS
from mmpretrain.structures import DataSample
from .cls_head import ClsHead


[文档]@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)
[文档] 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
[文档] 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]
[文档] def loss(self, feats: Tuple[torch.Tensor], data_samples: List[DataSample], **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[DataSample]): 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( "MMPretrain doesn't support to train" ' the distilled version EfficientFormer.') else: return super().loss(feats, data_samples, **kwargs)
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