BigGAN_PyTorch/BigGAN.py [183:209]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        if self.G_param == "SN":
            self.which_conv = functools.partial(
                layers.SNConv2d,
                kernel_size=3,
                padding=1,
                num_svs=num_G_SVs,
                num_itrs=num_G_SV_itrs,
                eps=self.SN_eps,
            )
            self.which_linear = functools.partial(
                layers.SNLinear,
                num_svs=num_G_SVs,
                num_itrs=num_G_SV_itrs,
                eps=self.SN_eps,
            )
        else:
            self.which_conv = functools.partial(nn.Conv2d, kernel_size=3, padding=1)
            self.which_linear = nn.Linear

        # We use a non-spectral-normed embedding here regardless;
        # For some reason applying SN to G's embedding seems to randomly cripple G
        self.which_embedding = nn.Embedding
        bn_linear = (
            functools.partial(self.which_linear, bias=False)
            if self.G_shared
            else self.which_embedding
        )
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



BigGAN_PyTorch/BigGANdeep.py [212:238]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        if self.G_param == "SN":
            self.which_conv = functools.partial(
                layers.SNConv2d,
                kernel_size=3,
                padding=1,
                num_svs=num_G_SVs,
                num_itrs=num_G_SV_itrs,
                eps=self.SN_eps,
            )
            self.which_linear = functools.partial(
                layers.SNLinear,
                num_svs=num_G_SVs,
                num_itrs=num_G_SV_itrs,
                eps=self.SN_eps,
            )
        else:
            self.which_conv = functools.partial(nn.Conv2d, kernel_size=3, padding=1)
            self.which_linear = nn.Linear

        # We use a non-spectral-normed embedding here regardless;
        # For some reason applying SN to G's embedding seems to randomly cripple G
        self.which_embedding = nn.Embedding
        bn_linear = (
            functools.partial(self.which_linear, bias=False)
            if self.G_shared
            else self.which_embedding
        )
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -