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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 | |