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37 lines
1.4 KiB
37 lines
1.4 KiB
from torch import nn as nn
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from .create_act import create_act_layer
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class SEModule(nn.Module):
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def __init__(self, channels, reduction=16, act_layer=nn.ReLU, min_channels=8, reduction_channels=None,
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gate_layer='sigmoid'):
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super(SEModule, self).__init__()
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reduction_channels = reduction_channels or max(channels // reduction, min_channels)
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self.fc1 = nn.Conv2d(channels, reduction_channels, kernel_size=1, bias=True)
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self.act = act_layer(inplace=True)
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self.fc2 = nn.Conv2d(reduction_channels, channels, kernel_size=1, bias=True)
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self.gate = create_act_layer(gate_layer)
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def forward(self, x):
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x_se = x.mean((2, 3), keepdim=True)
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x_se = self.fc1(x_se)
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x_se = self.act(x_se)
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x_se = self.fc2(x_se)
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return x * self.gate(x_se)
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class EffectiveSEModule(nn.Module):
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""" 'Effective Squeeze-Excitation
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From `CenterMask : Real-Time Anchor-Free Instance Segmentation` - https://arxiv.org/abs/1911.06667
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"""
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def __init__(self, channels, gate_layer='hard_sigmoid'):
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super(EffectiveSEModule, self).__init__()
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self.fc = nn.Conv2d(channels, channels, kernel_size=1, padding=0)
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self.gate = create_act_layer(gate_layer, inplace=True)
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def forward(self, x):
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x_se = x.mean((2, 3), keepdim=True)
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x_se = self.fc(x_se)
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return x * self.gate(x_se)
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