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A10G
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"""
Feature Fusion for Varible-Length Data Processing
AFF/iAFF is referred and modified from https://github.com/YimianDai/open-aff/blob/master/aff_pytorch/aff_net/fusion.py
According to the paper: Yimian Dai et al, Attentional Feature Fusion, IEEE Winter Conference on Applications of Computer Vision, WACV 2021
"""
import torch
import torch.nn as nn
class DAF(nn.Module):
"""
直接相加 DirectAddFuse
"""
def __init__(self):
super(DAF, self).__init__()
def forward(self, x, residual):
return x + residual
class iAFF(nn.Module):
"""
多特征融合 iAFF
"""
def __init__(self, channels=64, r=4, type="2D"):
super(iAFF, self).__init__()
inter_channels = int(channels // r)
if type == "1D":
# 本地注意力
self.local_att = nn.Sequential(
nn.Conv1d(channels, inter_channels, kernel_size=1, stride=1, padding=0),
nn.BatchNorm1d(inter_channels),
nn.ReLU(inplace=True),
nn.Conv1d(inter_channels, channels, kernel_size=1, stride=1, padding=0),
nn.BatchNorm1d(channels),
)
# 全局注意力
self.global_att = nn.Sequential(
nn.AdaptiveAvgPool1d(1),
nn.Conv1d(channels, inter_channels, kernel_size=1, stride=1, padding=0),
nn.BatchNorm1d(inter_channels),
nn.ReLU(inplace=True),
nn.Conv1d(inter_channels, channels, kernel_size=1, stride=1, padding=0),
nn.BatchNorm1d(channels),
)
# 第二次本地注意力
self.local_att2 = nn.Sequential(
nn.Conv1d(channels, inter_channels, kernel_size=1, stride=1, padding=0),
nn.BatchNorm1d(inter_channels),
nn.ReLU(inplace=True),
nn.Conv1d(inter_channels, channels, kernel_size=1, stride=1, padding=0),
nn.BatchNorm1d(channels),
)
# 第二次全局注意力
self.global_att2 = nn.Sequential(
nn.AdaptiveAvgPool1d(1),
nn.Conv1d(channels, inter_channels, kernel_size=1, stride=1, padding=0),
nn.BatchNorm1d(inter_channels),
nn.ReLU(inplace=True),
nn.Conv1d(inter_channels, channels, kernel_size=1, stride=1, padding=0),
nn.BatchNorm1d(channels),
)
elif type == "2D":
# 本地注意力
self.local_att = nn.Sequential(
nn.Conv2d(channels, inter_channels, kernel_size=1, stride=1, padding=0),
nn.BatchNorm2d(inter_channels),
nn.ReLU(inplace=True),
nn.Conv2d(inter_channels, channels, kernel_size=1, stride=1, padding=0),
nn.BatchNorm2d(channels),
)
# 全局注意力
self.global_att = nn.Sequential(
nn.AdaptiveAvgPool2d(1),
nn.Conv2d(channels, inter_channels, kernel_size=1, stride=1, padding=0),
nn.BatchNorm2d(inter_channels),
nn.ReLU(inplace=True),
nn.Conv2d(inter_channels, channels, kernel_size=1, stride=1, padding=0),
nn.BatchNorm2d(channels),
)
# 第二次本地注意力
self.local_att2 = nn.Sequential(
nn.Conv2d(channels, inter_channels, kernel_size=1, stride=1, padding=0),
nn.BatchNorm2d(inter_channels),
nn.ReLU(inplace=True),
nn.Conv2d(inter_channels, channels, kernel_size=1, stride=1, padding=0),
nn.BatchNorm2d(channels),
)
# 第二次全局注意力
self.global_att2 = nn.Sequential(
nn.AdaptiveAvgPool2d(1),
nn.Conv2d(channels, inter_channels, kernel_size=1, stride=1, padding=0),
nn.BatchNorm2d(inter_channels),
nn.ReLU(inplace=True),
nn.Conv2d(inter_channels, channels, kernel_size=1, stride=1, padding=0),
nn.BatchNorm2d(channels),
)
else:
raise f"the type is not supported"
self.sigmoid = nn.Sigmoid()
def forward(self, x, residual):
flag = False
xa = x + residual
if xa.size(0) == 1:
xa = torch.cat([xa, xa], dim=0)
flag = True
xl = self.local_att(xa)
xg = self.global_att(xa)
xlg = xl + xg
wei = self.sigmoid(xlg)
xi = x * wei + residual * (1 - wei)
xl2 = self.local_att2(xi)
xg2 = self.global_att(xi)
xlg2 = xl2 + xg2
wei2 = self.sigmoid(xlg2)
xo = x * wei2 + residual * (1 - wei2)
if flag:
xo = xo[0].unsqueeze(0)
return xo
class AFF(nn.Module):
"""
多特征融合 AFF
"""
def __init__(self, channels=64, r=4, type="2D"):
super(AFF, self).__init__()
inter_channels = int(channels // r)
if type == "1D":
self.local_att = nn.Sequential(
nn.Conv1d(channels, inter_channels, kernel_size=1, stride=1, padding=0),
nn.BatchNorm1d(inter_channels),
nn.ReLU(inplace=True),
nn.Conv1d(inter_channels, channels, kernel_size=1, stride=1, padding=0),
nn.BatchNorm1d(channels),
)
self.global_att = nn.Sequential(
nn.AdaptiveAvgPool1d(1),
nn.Conv1d(channels, inter_channels, kernel_size=1, stride=1, padding=0),
nn.BatchNorm1d(inter_channels),
nn.ReLU(inplace=True),
nn.Conv1d(inter_channels, channels, kernel_size=1, stride=1, padding=0),
nn.BatchNorm1d(channels),
)
elif type == "2D":
self.local_att = nn.Sequential(
nn.Conv2d(channels, inter_channels, kernel_size=1, stride=1, padding=0),
nn.BatchNorm2d(inter_channels),
nn.ReLU(inplace=True),
nn.Conv2d(inter_channels, channels, kernel_size=1, stride=1, padding=0),
nn.BatchNorm2d(channels),
)
self.global_att = nn.Sequential(
nn.AdaptiveAvgPool2d(1),
nn.Conv2d(channels, inter_channels, kernel_size=1, stride=1, padding=0),
nn.BatchNorm2d(inter_channels),
nn.ReLU(inplace=True),
nn.Conv2d(inter_channels, channels, kernel_size=1, stride=1, padding=0),
nn.BatchNorm2d(channels),
)
else:
raise f"the type is not supported."
self.sigmoid = nn.Sigmoid()
def forward(self, x, residual):
flag = False
xa = x + residual
if xa.size(0) == 1:
xa = torch.cat([xa, xa], dim=0)
flag = True
xl = self.local_att(xa)
xg = self.global_att(xa)
xlg = xl + xg
wei = self.sigmoid(xlg)
xo = 2 * x * wei + 2 * residual * (1 - wei)
if flag:
xo = xo[0].unsqueeze(0)
return xo
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