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mmcls.models.heads.multi_label_csra_head 源代码

# Copyright (c) OpenMMLab. All rights reserved.
# Modified from https://github.com/Kevinz-code/CSRA
from typing import Tuple

import torch
import torch.nn as nn
from mmengine.model import BaseModule, ModuleList

from mmcls.registry import MODELS
from .multi_label_cls_head import MultiLabelClsHead


[文档]@MODELS.register_module() class CSRAClsHead(MultiLabelClsHead): """Class-specific residual attention classifier head. Please refer to the `Residual Attention: A Simple but Effective Method for Multi-Label Recognition (ICCV 2021) <https://arxiv.org/abs/2108.02456>`_ for details. Args: num_classes (int): Number of categories. in_channels (int): Number of channels in the input feature map. num_heads (int): Number of residual at tensor heads. loss (dict): Config of classification loss. lam (float): Lambda that combines global average and max pooling scores. init_cfg (dict, optional): The extra init config of layers. Defaults to use ``dict(type='Normal', layer='Linear', std=0.01)``. """ temperature_settings = { # softmax temperature settings 1: [1], 2: [1, 99], 4: [1, 2, 4, 99], 6: [1, 2, 3, 4, 5, 99], 8: [1, 2, 3, 4, 5, 6, 7, 99] } def __init__(self, num_classes: int, in_channels: int, num_heads: int, lam: float, init_cfg=dict(type='Normal', layer='Linear', std=0.01), **kwargs): assert num_heads in self.temperature_settings.keys( ), 'The num of heads is not in temperature setting.' assert lam > 0, 'Lambda should be between 0 and 1.' super(CSRAClsHead, self).__init__(init_cfg=init_cfg, **kwargs) self.temp_list = self.temperature_settings[num_heads] self.csra_heads = ModuleList([ CSRAModule(num_classes, in_channels, self.temp_list[i], lam) for i in range(num_heads) ])
[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 ``CSRAClsHead``, we just obtain the feature of the last stage. """ # The CSRAClsHead 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) logit = sum([head(pre_logits) for head in self.csra_heads]) return logit
class CSRAModule(BaseModule): """Basic module of CSRA with different temperature. Args: num_classes (int): Number of categories. in_channels (int): Number of channels in the input feature map. T (int): Temperature setting. lam (float): Lambda that combines global average and max pooling scores. init_cfg (dict | optional): The extra init config of layers. Defaults to use dict(type='Normal', layer='Linear', std=0.01). """ def __init__(self, num_classes: int, in_channels: int, T: int, lam: float, init_cfg=None): super(CSRAModule, self).__init__(init_cfg=init_cfg) self.T = T # temperature self.lam = lam # Lambda self.head = nn.Conv2d(in_channels, num_classes, 1, bias=False) self.softmax = nn.Softmax(dim=2) def forward(self, x): score = self.head(x) / torch.norm( self.head.weight, dim=1, keepdim=True).transpose(0, 1) score = score.flatten(2) base_logit = torch.mean(score, dim=2) if self.T == 99: # max-pooling att_logit = torch.max(score, dim=2)[0] else: score_soft = self.softmax(score * self.T) att_logit = torch.sum(score * score_soft, dim=2) return base_logit + self.lam * att_logit
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