separate_vae/models/pix2pixhd_networks.py [146:161]:
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        model = [nn.ReflectionPad2d(3), nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0), norm_layer(ngf), activation]
        ### downsample
        for i in range(n_downsampling):
            mult = 2**i
            # Clip at 1024
            if mult >= max_mult:
                model += [nn.Conv2d(ngf * max_mult, ngf * max_mult, kernel_size=3, stride=2, padding=1),
                          norm_layer(ngf * max_mult), activation]
            else:
                model += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1),
                          norm_layer(ngf * mult * 2), activation]

        ### resnet blocks
        mult = min(2**n_downsampling, max_mult)
        for i in range(n_blocks):
            model += [ResnetBlock(ngf * mult, padding_type=padding_type, activation=activation, norm_layer=norm_layer)]
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separate_vae/models/pix2pixhd_networks.py [241:256]:
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        model = [nn.ReflectionPad2d(3), nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0), norm_layer(ngf), activation]
        ### downsample
        for i in range(n_downsampling):
            mult = 2**i
            # Clip at 1024
            if mult >= max_mult:
                model += [nn.Conv2d(ngf * max_mult, ngf * max_mult, kernel_size=3, stride=2, padding=1),
                          norm_layer(ngf * max_mult), activation]
            else:
                model += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1),
                          norm_layer(ngf * mult * 2), activation]

        ### resnet blocks
        mult = min(2**n_downsampling, max_mult)
        for i in range(n_blocks):
            model += [ResnetBlock(ngf * mult, padding_type=padding_type, activation=activation, norm_layer=norm_layer)]
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