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101 lines
3.3 KiB
101 lines
3.3 KiB
""" CBAM (sort-of) Attention
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Experimental impl of CBAM: Convolutional Block Attention Module: https://arxiv.org/abs/1807.06521
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WARNING: Results with these attention layers have been mixed. They can significantly reduce performance on
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some tasks, especially fine-grained it seems. I may end up removing this impl.
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Hacked together by Ross Wightman
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"""
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import torch
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from torch import nn as nn
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from .conv_bn_act import ConvBnAct
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class ChannelAttn(nn.Module):
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""" Original CBAM channel attention module, currently avg + max pool variant only.
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"""
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def __init__(self, channels, reduction=16, act_layer=nn.ReLU):
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super(ChannelAttn, self).__init__()
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self.avg_pool = nn.AdaptiveAvgPool2d(1)
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self.max_pool = nn.AdaptiveMaxPool2d(1)
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self.fc1 = nn.Conv2d(channels, channels // reduction, 1, bias=False)
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self.act = act_layer(inplace=True)
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self.fc2 = nn.Conv2d(channels // reduction, channels, 1, bias=False)
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def forward(self, x):
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x_avg = self.avg_pool(x)
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x_max = self.max_pool(x)
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x_avg = self.fc2(self.act(self.fc1(x_avg)))
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x_max = self.fc2(self.act(self.fc1(x_max)))
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x_attn = x_avg + x_max
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return x * x_attn.sigmoid()
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class LightChannelAttn(ChannelAttn):
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"""An experimental 'lightweight' that sums avg + max pool first
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"""
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def __init__(self, channels, reduction=16):
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super(LightChannelAttn, self).__init__(channels, reduction)
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def forward(self, x):
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x_pool = 0.5 * self.avg_pool(x) + 0.5 * self.max_pool(x)
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x_attn = self.fc2(self.act(self.fc1(x_pool)))
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return x * x_attn.sigmoid()
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class SpatialAttn(nn.Module):
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""" Original CBAM spatial attention module
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"""
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def __init__(self, kernel_size=7):
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super(SpatialAttn, self).__init__()
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self.conv = ConvBnAct(2, 1, kernel_size, act_layer=None)
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def forward(self, x):
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x_avg = torch.mean(x, dim=1, keepdim=True)
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x_max = torch.max(x, dim=1, keepdim=True)[0]
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x_attn = torch.cat([x_avg, x_max], dim=1)
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x_attn = self.conv(x_attn)
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return x * x_attn.sigmoid()
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class LightSpatialAttn(nn.Module):
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"""An experimental 'lightweight' variant that sums avg_pool and max_pool results.
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"""
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def __init__(self, kernel_size=7):
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super(LightSpatialAttn, self).__init__()
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self.conv = ConvBnAct(1, 1, kernel_size, act_layer=None)
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def forward(self, x):
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x_avg = torch.mean(x, dim=1, keepdim=True)
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x_max = torch.max(x, dim=1, keepdim=True)[0]
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x_attn = 0.5 * x_avg + 0.5 * x_max
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x_attn = self.conv(x_attn)
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return x * x_attn.sigmoid()
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class ConvBlockAttn(nn.Module):
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def __init__(self, channels, spatial_kernel_size=7):
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super(ConvBlockAttn, self).__init__()
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self.channel = ChannelAttn(channels)
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self.spatial = SpatialAttn(spatial_kernel_size)
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def forward(self, x):
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x = self.channel(x)
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x = self.spatial(x)
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return x
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class LightConvBlockAttn(nn.Module):
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def __init__(self, channels, spatial_kernel_size=7):
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super(LightConvBlockAttn, self).__init__()
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self.channel = LightChannelAttn(channels)
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self.spatial = LightSpatialAttn(spatial_kernel_size)
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def forward(self, x):
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x = self.channel(x)
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x = self.spatial(x)
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return x
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