def process_latents()

in models/generator.py [0:0]

• 11 lines of code
• 6 McCabe index (conditional complexity)

    def process_latents(self, z):
        # output should be list with separate latent code for each conditional layer in the model

        if isinstance(z, list):  # latents already in proper format
            pass
        elif z.ndim == 2:  # standard training, shape [B, ch]
            z = self.latent_normalization(z)
            z = self.mapping_network(z)
            z = [z] * self.n_layers
        elif z.ndim == 3:  # latent optimization, shape [B, n_latent_layers, ch]
            n_latents = z.shape[1]
            z = [self.latent_normalization(self.mapping_network(z[:, i])) for i in range(n_latents)]
        return z