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You are reading the documentation for MMClassification 0.x, which will soon be deprecated at the end of 2022. We recommend you upgrade to MMClassification 1.0 to enjoy fruitful new features and better performance brought by OpenMMLab 2.0. Check the installation tutorial, migration tutorial and changelog for more details.

Source code for mmcls.models.utils.se_layer

# Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule

from .make_divisible import make_divisible


[docs]class SELayer(BaseModule): """Squeeze-and-Excitation Module. Args: channels (int): The input (and output) channels of the SE layer. squeeze_channels (None or int): The intermediate channel number of SElayer. Default: None, means the value of ``squeeze_channels`` is ``make_divisible(channels // ratio, divisor)``. ratio (int): Squeeze ratio in SELayer, the intermediate channel will be ``make_divisible(channels // ratio, divisor)``. Only used when ``squeeze_channels`` is None. Default: 16. divisor(int): The divisor to true divide the channel number. Only used when ``squeeze_channels`` is None. Default: 8. conv_cfg (None or dict): Config dict for convolution layer. Default: None, which means using conv2d. return_weight(bool): Whether to return the weight. Default: False. act_cfg (dict or Sequence[dict]): Config dict for activation layer. If act_cfg is a dict, two activation layers will be configurated by this dict. If act_cfg is a sequence of dicts, the first activation layer will be configurated by the first dict and the second activation layer will be configurated by the second dict. Default: (dict(type='ReLU'), dict(type='Sigmoid')) """ def __init__(self, channels, squeeze_channels=None, ratio=16, divisor=8, bias='auto', conv_cfg=None, act_cfg=(dict(type='ReLU'), dict(type='Sigmoid')), return_weight=False, init_cfg=None): super(SELayer, self).__init__(init_cfg) if isinstance(act_cfg, dict): act_cfg = (act_cfg, act_cfg) assert len(act_cfg) == 2 assert mmcv.is_tuple_of(act_cfg, dict) self.global_avgpool = nn.AdaptiveAvgPool2d(1) if squeeze_channels is None: squeeze_channels = make_divisible(channels // ratio, divisor) assert isinstance(squeeze_channels, int) and squeeze_channels > 0, \ '"squeeze_channels" should be a positive integer, but get ' + \ f'{squeeze_channels} instead.' self.return_weight = return_weight self.conv1 = ConvModule( in_channels=channels, out_channels=squeeze_channels, kernel_size=1, stride=1, bias=bias, conv_cfg=conv_cfg, act_cfg=act_cfg[0]) self.conv2 = ConvModule( in_channels=squeeze_channels, out_channels=channels, kernel_size=1, stride=1, bias=bias, conv_cfg=conv_cfg, act_cfg=act_cfg[1])
[docs] def forward(self, x): out = self.global_avgpool(x) out = self.conv1(out) out = self.conv2(out) if self.return_weight: return out else: return x * out
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