Method __init__

torchscript/modnet_torchscript.py:174–182

(self, hr_channels, enc_channels)

    """

    def __init__(self, hr_channels, enc_channels):
        super(FusionBranch, self).__init__()
        self.conv_lr4x = Conv2dIBNormRelu(enc_channels[2], hr_channels, 5, stride=1, padding=2)
        
        self.conv_f2x = Conv2dIBNormRelu(2 * hr_channels, hr_channels, 3, stride=1, padding=1)
        self.conv_f = nn.Sequential(
            Conv2dIBNormRelu(hr_channels + 3, int(hr_channels / 2), 3, stride=1, padding=1),
            Conv2dIBNormRelu(int(hr_channels / 2), 1, 1, stride=1, padding=0, with_ibn=False, with_relu=False),
        )

    def forward(self, img, lr8x, hr2x):
        lr4x = F.interpolate(lr8x, scale_factor=2.0, mode='bilinear', align_corners=False)