mirror of
https://github.com/YifanXu74/MQ-Det.git
synced 2025-06-03 15:03:07 +08:00
52 lines
1.8 KiB
Python
52 lines
1.8 KiB
Python
from torch import nn
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class SELayer(nn.Module):
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def __init__(self, channel, reduction=16):
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super(SELayer, self).__init__()
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self.avg_pool = nn.AdaptiveAvgPool2d(1)
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self.fc = nn.Sequential(
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nn.Linear(channel, channel // reduction, bias=False),
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nn.ReLU(inplace=True),
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nn.Linear(channel // reduction, channel, bias=False),
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nn.Sigmoid()
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)
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def forward(self, x):
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b, c, _, _ = x.size()
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y = self.avg_pool(x).view(b, c)
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y = self.fc(y).view(b, c, 1, 1)
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return x * y.expand_as(x)
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class SEBlock(nn.Module):
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def __init__(self, channels, reduction=16,
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use_conv=True, mid_activation=nn.ReLU(inplace=True), out_activation=nn.Sigmoid()):
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super(SEBlock, self).__init__()
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self.use_conv = use_conv
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mid_channels = channels // reduction
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self.pool = nn.AdaptiveAvgPool2d(output_size=1)
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if use_conv:
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self.conv1 = nn.Conv2d(channels, mid_channels, kernel_size=1, bias=True)
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else:
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self.fc1 = nn.Linear(channels, mid_channels)
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self.activ = mid_activation
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if use_conv:
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self.conv2 = nn.Conv2d(mid_channels, channels, kernel_size=1, bias=True)
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else:
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self.fc2 = nn.Linear(mid_channels, channels)
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self.sigmoid = out_activation
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def forward(self, x):
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w = self.pool(x)
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if not self.use_conv:
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w = w.view(x.size(0), -1)
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w = self.conv1(w) if self.use_conv else self.fc1(w)
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w = self.activ(w)
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w = self.conv2(w) if self.use_conv else self.fc2(w)
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w = self.sigmoid(w)
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if not self.use_conv:
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w = w.unsqueeze(2).unsqueeze(3)
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x = x * w
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return x |