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

from torch import nn as nn
from .create_act import get_act_fn


class SEModule(nn.Module):

    def __init__(self, channels, reduction=16, act_layer=nn.ReLU, min_channels=8, reduction_channels=None,
                 gate_fn='sigmoid'):
        super(SEModule, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        reduction_channels = reduction_channels or max(channels // reduction, min_channels)
        self.fc1 = nn.Conv2d(
            channels, reduction_channels, kernel_size=1, padding=0, bias=True)
        self.act = act_layer(inplace=True)
        self.fc2 = nn.Conv2d(
            reduction_channels, channels, kernel_size=1, padding=0, bias=True)
        self.gate_fn = get_act_fn(gate_fn)

    def forward(self, x):
        x_se = self.avg_pool(x)
        x_se = self.fc1(x_se)
        x_se = self.act(x_se)
        x_se = self.fc2(x_se)
        return x * self.gate_fn(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, channel, gate_fn='hard_sigmoid'):
        super(EffectiveSEModule, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.fc = nn.Conv2d(channel, channel, kernel_size=1, padding=0)
        self.gate_fn = get_act_fn(gate_fn)

    def forward(self, x):
        x_se = self.avg_pool(x)
        x_se = self.fc(x_se)
        return x * self.gate_fn(x_se, inplace=True)