def __init__(self, in_channels, res_channels, channels, n_filters):
    super().__init__()
    self.up = nn.Upsample(scale_factor=2, mode='bilinear')
    self.conv1 = nn.Conv2d((in_channels + res_channels), channels, 3, padding=1)
    self.norm1 = nn.BatchNorm2d(channels)
    self.relu1 = nonlinearity(inplace=True)
    self.conv2 = nn.Conv2d(channels, n_filters, 3, padding=1)
    self.norm2 = nn.BatchNorm2d(n_filters)
    self.relu2 = nonlinearity(inplace=True)
    self.SCSE = SCSEBlock(n_filters)
def __init__(self, in_channels, res_channels, channels, n_filters):
    super().__init__()
    self.up = nn.Upsample(scale_factor=2, mode='bilinear')
    self.conv1 = nn.Conv2d((in_channels + res_channels), channels, 3, padding=1)
    self.norm1 = nn.BatchNorm2d(channels)
    self.relu1 = nonlinearity(inplace=True)
    self.conv2 = nn.Conv2d(channels, n_filters, 3, padding=1)
    self.norm2 = nn.BatchNorm2d(n_filters)
    self.relu2 = nonlinearity(inplace=True)
    self.SCSE = SCSEBlock(n_filters)