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113 lines
4.3 KiB
113 lines
4.3 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 / Copyright 2020 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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import torch.nn.functional as F
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from .conv_bn_act import ConvNormAct
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from .create_act import create_act_layer, get_act_layer
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from .helpers import make_divisible
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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__(
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self, channels, rd_ratio=1./16, rd_channels=None, rd_divisor=1,
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act_layer=nn.ReLU, gate_layer='sigmoid', mlp_bias=False):
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super(ChannelAttn, self).__init__()
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if not rd_channels:
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rd_channels = make_divisible(channels * rd_ratio, rd_divisor, round_limit=0.)
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self.fc1 = nn.Conv2d(channels, rd_channels, 1, bias=mlp_bias)
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self.act = act_layer(inplace=True)
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self.fc2 = nn.Conv2d(rd_channels, channels, 1, bias=mlp_bias)
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self.gate = create_act_layer(gate_layer)
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def forward(self, x):
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x_avg = self.fc2(self.act(self.fc1(x.mean((2, 3), keepdim=True))))
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x_max = self.fc2(self.act(self.fc1(x.amax((2, 3), keepdim=True))))
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return x * self.gate(x_avg + x_max)
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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__(
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self, channels, rd_ratio=1./16, rd_channels=None, rd_divisor=1,
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act_layer=nn.ReLU, gate_layer='sigmoid', mlp_bias=False):
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super(LightChannelAttn, self).__init__(
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channels, rd_ratio, rd_channels, rd_divisor, act_layer, gate_layer, mlp_bias)
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def forward(self, x):
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x_pool = 0.5 * x.mean((2, 3), keepdim=True) + 0.5 * x.amax((2, 3), keepdim=True)
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x_attn = self.fc2(self.act(self.fc1(x_pool)))
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return x * F.sigmoid(x_attn)
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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, gate_layer='sigmoid'):
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super(SpatialAttn, self).__init__()
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self.conv = ConvNormAct(2, 1, kernel_size, apply_act=False)
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self.gate = create_act_layer(gate_layer)
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def forward(self, x):
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x_attn = torch.cat([x.mean(dim=1, keepdim=True), x.amax(dim=1, keepdim=True)], dim=1)
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x_attn = self.conv(x_attn)
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return x * self.gate(x_attn)
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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, gate_layer='sigmoid'):
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super(LightSpatialAttn, self).__init__()
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self.conv = ConvNormAct(1, 1, kernel_size, apply_act=False)
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self.gate = create_act_layer(gate_layer)
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def forward(self, x):
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x_attn = 0.5 * x.mean(dim=1, keepdim=True) + 0.5 * x.amax(dim=1, keepdim=True)
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x_attn = self.conv(x_attn)
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return x * self.gate(x_attn)
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class CbamModule(nn.Module):
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def __init__(
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self, channels, rd_ratio=1./16, rd_channels=None, rd_divisor=1,
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spatial_kernel_size=7, act_layer=nn.ReLU, gate_layer='sigmoid', mlp_bias=False):
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super(CbamModule, self).__init__()
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self.channel = ChannelAttn(
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channels, rd_ratio=rd_ratio, rd_channels=rd_channels,
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rd_divisor=rd_divisor, act_layer=act_layer, gate_layer=gate_layer, mlp_bias=mlp_bias)
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self.spatial = SpatialAttn(spatial_kernel_size, gate_layer=gate_layer)
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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 LightCbamModule(nn.Module):
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def __init__(
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self, channels, rd_ratio=1./16, rd_channels=None, rd_divisor=1,
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spatial_kernel_size=7, act_layer=nn.ReLU, gate_layer='sigmoid', mlp_bias=False):
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super(LightCbamModule, self).__init__()
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self.channel = LightChannelAttn(
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channels, rd_ratio=rd_ratio, rd_channels=rd_channels,
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rd_divisor=rd_divisor, act_layer=act_layer, gate_layer=gate_layer, mlp_bias=mlp_bias)
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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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