pytorch-image-models/timm/models/layers/se.py

from torch import nn as nn
from .create_act import create_act_layer


class SEModule(nn.Module):

    def __init__(self, channels, reduction=16, act_layer=nn.ReLU, min_channels=8, reduction_channels=None,
                 gate_layer='sigmoid'):
        super(SEModule, self).__init__()
        reduction_channels = reduction_channels or max(channels // reduction, min_channels)
        self.fc1 = nn.Conv2d(channels, reduction_channels, kernel_size=1, bias=True)
        self.act = act_layer(inplace=True)
        self.fc2 = nn.Conv2d(reduction_channels, channels, kernel_size=1, bias=True)
        self.gate = create_act_layer(gate_layer)

    def forward(self, x):
        x_se = x.mean((2, 3), keepdim=True)
        x_se = self.fc1(x_se)
        x_se = self.act(x_se)
        x_se = self.fc2(x_se)
        return x * self.gate(x_se)


class EffectiveSEModule(nn.Module):
    """ 'Effective Squeeze-Excitation
    From `CenterMask : Real-Time Anchor-Free Instance Segmentation` - https://arxiv.org/abs/1911.06667
    """
    def __init__(self, channels, gate_layer='hard_sigmoid'):
        super(EffectiveSEModule, self).__init__()
        self.fc = nn.Conv2d(channels, channels, kernel_size=1, padding=0)
        self.gate = create_act_layer(gate_layer, inplace=True)

    def forward(self, x):
        x_se = x.mean((2, 3), keepdim=True)
        x_se = self.fc(x_se)
        return x * self.gate(x_se)