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Running
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Zero
| # Copyright 3D-Speaker (https://github.com/alibaba-damo-academy/3D-Speaker). All Rights Reserved. | |
| # Licensed under the Apache License, Version 2.0 (http://www.apache.org/licenses/LICENSE-2.0) | |
| import torch | |
| import torch.nn as nn | |
| class AFF(nn.Module): | |
| def __init__(self, channels=64, r=4): | |
| super(AFF, self).__init__() | |
| inter_channels = int(channels // r) | |
| self.local_att = nn.Sequential( | |
| nn.Conv2d(channels * 2, inter_channels, kernel_size=1, stride=1, padding=0), | |
| nn.BatchNorm2d(inter_channels), | |
| nn.SiLU(inplace=True), | |
| nn.Conv2d(inter_channels, channels, kernel_size=1, stride=1, padding=0), | |
| nn.BatchNorm2d(channels), | |
| ) | |
| def forward(self, x, ds_y): | |
| xa = torch.cat((x, ds_y), dim=1) | |
| x_att = self.local_att(xa) | |
| x_att = 1.0 + torch.tanh(x_att) | |
| xo = torch.mul(x, x_att) + torch.mul(ds_y, 2.0 - x_att) | |
| return xo | |