def __init__(self, in_channels, n_filters):
    super().__init__()
    self.conv1 = nn.Conv2d(in_channels, (in_channels // 4), 1)
    self.norm1 = nn.BatchNorm2d((in_channels // 4))
    self.relu1 = nonlinearity(inplace=True)
    self.deconv2 = ConvUp((in_channels // 4), (in_channels // 4))
    self.norm2 = nn.BatchNorm2d((in_channels // 4))
    self.relu2 = nonlinearity(inplace=True)
    self.conv3 = nn.Conv2d((in_channels // 4), n_filters, 1)
    self.norm3 = nn.BatchNorm2d(n_filters)
    self.relu3 = nonlinearity(inplace=True)
    self.cSE = ChannelSE(n_filters)
    self.sSE = SpatialSE(n_filters)
def __init__(self, in_channels, n_filters):
    super().__init__()
    self.conv1 = nn.Conv2d(in_channels, (in_channels // 4), 1)
    self.norm1 = nn.BatchNorm2d((in_channels // 4))
    self.relu1 = nonlinearity(inplace=True)
    self.deconv2 = ConvUp((in_channels // 4), (in_channels // 4))
    self.norm2 = nn.BatchNorm2d((in_channels // 4))
    self.relu2 = nonlinearity(inplace=True)
    self.conv3 = nn.Conv2d((in_channels // 4), n_filters, 1)
    self.norm3 = nn.BatchNorm2d(n_filters)
    self.relu3 = nonlinearity(inplace=True)
    self.cSE = ChannelSE(n_filters)
    self.sSE = SpatialSE(n_filters)