| from torch import nn | |
| class SELayer(nn.Module): | |
| def __init__(self, channel, reduction=16): | |
| super(SELayer, self).__init__() | |
| self.avg_pool = nn.AdaptiveAvgPool2d(1) | |
| self.fc = nn.Sequential( | |
| nn.Linear(channel, channel // reduction, bias=False), | |
| nn.ReLU(inplace=True), | |
| nn.Linear(channel // reduction, channel, bias=False), | |
| nn.Sigmoid() | |
| ) | |
| def forward(self, x): | |
| b, c, _, _ = x.size() | |
| y = self.avg_pool(x).view(b, c) | |
| y = self.fc(y).view(b, c, 1, 1) | |
| return x * y.expand_as(x) | |
| class SEBlock(nn.Module): | |
| def __init__(self, channels, reduction=16, | |
| use_conv=True, mid_activation=nn.ReLU(inplace=True), out_activation=nn.Sigmoid()): | |
| super(SEBlock, self).__init__() | |
| self.use_conv = use_conv | |
| mid_channels = channels // reduction | |
| self.pool = nn.AdaptiveAvgPool2d(output_size=1) | |
| if use_conv: | |
| self.conv1 = nn.Conv2d(channels, mid_channels, kernel_size=1, bias=True) | |
| else: | |
| self.fc1 = nn.Linear(channels, mid_channels) | |
| self.activ = mid_activation | |
| if use_conv: | |
| self.conv2 = nn.Conv2d(mid_channels, channels, kernel_size=1, bias=True) | |
| else: | |
| self.fc2 = nn.Linear(mid_channels, channels) | |
| self.sigmoid = out_activation | |
| def forward(self, x): | |
| w = self.pool(x) | |
| if not self.use_conv: | |
| w = w.view(x.size(0), -1) | |
| w = self.conv1(w) if self.use_conv else self.fc1(w) | |
| w = self.activ(w) | |
| w = self.conv2(w) if self.use_conv else self.fc2(w) | |
| w = self.sigmoid(w) | |
| if not self.use_conv: | |
| w = w.unsqueeze(2).unsqueeze(3) | |
| x = x * w | |
| return x |