mmpretrain.models.heads.multi_label_linear_head 源代码

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

import torch
import torch.nn as nn

from mmpretrain.registry import MODELS
from .multi_label_cls_head import MultiLabelClsHead

[文档]@MODELS.register_module() class MultiLabelLinearClsHead(MultiLabelClsHead): """Linear classification head for multilabel task. Args: loss (dict): Config of classification loss. Defaults to dict(type='CrossEntropyLoss', use_sigmoid=True). thr (float, optional): Predictions with scores under the thresholds are considered as negative. Defaults to None. topk (int, optional): Predictions with the k-th highest scores are considered as positive. Defaults to None. init_cfg (dict, optional): The extra init config of layers. Defaults to use dict(type='Normal', layer='Linear', std=0.01). Notes: If both ``thr`` and ``topk`` are set, use ``thr` to determine positive predictions. If neither is set, use ``thr=0.5`` as default. """ def __init__(self, num_classes: int, in_channels: int, loss: Dict = dict(type='CrossEntropyLoss', use_sigmoid=True), thr: Optional[float] = None, topk: Optional[int] = None, init_cfg: Optional[dict] = dict( type='Normal', layer='Linear', std=0.01)): super(MultiLabelLinearClsHead, self).__init__( loss=loss, thr=thr, topk=topk, init_cfg=init_cfg) assert num_classes > 0, f'num_classes ({num_classes}) must be a ' \ 'positive integer.' self.in_channels = in_channels self.num_classes = num_classes self.fc = nn.Linear(self.in_channels, self.num_classes)
[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 ``MultiLabelLinearClsHead``, we just obtain the feature of the last stage. """ # The obtain the MultiLabelLinearClsHead doesn't have other module, # just return after unpacking. return feats[-1]
[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.fc(pre_logits) return cls_score
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