Method __init__

models/styleheat/base_function.py:311–321

(self, input_nc, output_nc, norm_layer=nn.BatchNorm2d, nonlinearity=nn.LeakyReLU(), use_spect=False)

class DownBlock2d(nn.Module):
    def __init__(self, input_nc, output_nc, norm_layer=nn.BatchNorm2d, nonlinearity=nn.LeakyReLU(), use_spect=False):
        super(DownBlock2d, self).__init__()

        kwargs = {'kernel_size': 3, 'stride': 1, 'padding': 1}
        conv = spectral_norm(nn.Conv2d(input_nc, output_nc, **kwargs), use_spect)
        pool = nn.AvgPool2d(kernel_size=(2, 2))

        if type(norm_layer) == type(None):
            self.model = nn.Sequential(conv, nonlinearity, pool)
        else:
            self.model = nn.Sequential(conv, norm_layer(output_nc), nonlinearity, pool)

    def forward(self, x):
        out = self.model(x)