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Starting
on
T4
Starting
on
T4
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 | |