import torch import numpy as np from criteria import l2_loss from configs import hyperparameters from configs import global_config class Space_Regulizer: def __init__(self, original_G, lpips_net): self.original_G = original_G self.morphing_regulizer_alpha = hyperparameters.regulizer_alpha self.lpips_loss = lpips_net def get_morphed_w_code(self, new_w_code, fixed_w): interpolation_direction = new_w_code - fixed_w interpolation_direction_norm = torch.norm(interpolation_direction, p=2) direction_to_move = hyperparameters.regulizer_alpha * interpolation_direction / interpolation_direction_norm result_w = fixed_w + direction_to_move self.morphing_regulizer_alpha * fixed_w + (1 - self.morphing_regulizer_alpha) * new_w_code return result_w def get_image_from_ws(self, w_codes, G): return torch.cat([G.synthesis(w_code, noise_mode='none', force_fp32=True) for w_code in w_codes]) def ball_holder_loss_lazy(self, new_G, num_of_sampled_latents, w_batch, use_wandb=False): loss = 0.0 z_samples = np.random.randn(num_of_sampled_latents, self.original_G.z_dim) w_samples = self.original_G.mapping(torch.from_numpy(z_samples).to(global_config.device), None, truncation_psi=0.5) territory_indicator_ws = [self.get_morphed_w_code(w_code.unsqueeze(0), w_batch) for w_code in w_samples] for w_code in territory_indicator_ws: new_img = new_G.synthesis(w_code, noise_mode='none', force_fp32=True) with torch.no_grad(): old_img = self.original_G.synthesis(w_code, noise_mode='none', force_fp32=True) if hyperparameters.regulizer_l2_lambda > 0: l2_loss_val = l2_loss.l2_loss(old_img, new_img) if use_wandb: wandb.log({f'space_regulizer_l2_loss_val': l2_loss_val.detach().cpu()}, step=global_config.training_step) loss += l2_loss_val * hyperparameters.regulizer_l2_lambda if hyperparameters.regulizer_lpips_lambda > 0: loss_lpips = self.lpips_loss(old_img, new_img) loss_lpips = torch.mean(torch.squeeze(loss_lpips)) if use_wandb: wandb.log({f'space_regulizer_lpips_loss_val': loss_lpips.detach().cpu()}, step=global_config.training_step) loss += loss_lpips * hyperparameters.regulizer_lpips_lambda return loss / len(territory_indicator_ws) def space_regulizer_loss(self, new_G, w_batch, use_wandb): ret_val = self.ball_holder_loss_lazy(new_G, hyperparameters.latent_ball_num_of_samples, w_batch, use_wandb) return ret_val