import abc import os import pickle from argparse import Namespace import wandb import os.path from .localitly_regulizer import Space_Regulizer, l2_loss import torch from torchvision import transforms from lpips import LPIPS from pti.training.projectors import w_projector from pti.pti_configs import global_config, paths_config, hyperparameters from pti.pti_models.e4e.psp import pSp from utils.log_utils import log_image_from_w from utils.models_utils import toogle_grad, load_old_G class BaseCoach: def __init__(self, data_loader, use_wandb): self.use_wandb = use_wandb self.data_loader = data_loader self.w_pivots = {} self.image_counter = 0 if hyperparameters.first_inv_type == 'w+': self.initilize_e4e() self.e4e_image_transform = transforms.Compose([ transforms.ToPILImage(), transforms.Resize((256, 128)), transforms.ToTensor(), transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]) # Initialize loss self.lpips_loss = LPIPS(net=hyperparameters.lpips_type).to(global_config.device).eval() self.restart_training() # Initialize checkpoint dir self.checkpoint_dir = paths_config.checkpoints_dir os.makedirs(self.checkpoint_dir, exist_ok=True) def restart_training(self): # Initialize networks self.G = load_old_G() toogle_grad(self.G, True) self.original_G = load_old_G() self.space_regulizer = Space_Regulizer(self.original_G, self.lpips_loss) self.optimizer = self.configure_optimizers() def get_inversion(self, w_path_dir, image_name, image): embedding_dir = f'{w_path_dir}/{paths_config.pti_results_keyword}/{image_name}' os.makedirs(embedding_dir, exist_ok=True) w_pivot = None if hyperparameters.use_last_w_pivots: w_pivot = self.load_inversions(w_path_dir, image_name) if not hyperparameters.use_last_w_pivots or w_pivot is None: w_pivot = self.calc_inversions(image, image_name) torch.save(w_pivot, f'{embedding_dir}/0.pt') w_pivot = w_pivot.to(global_config.device) return w_pivot def load_inversions(self, w_path_dir, image_name): if image_name in self.w_pivots: return self.w_pivots[image_name] if hyperparameters.first_inv_type == 'w+': w_potential_path = f'{w_path_dir}/{paths_config.e4e_results_keyword}/{image_name}/0.pt' else: w_potential_path = f'{w_path_dir}/{paths_config.pti_results_keyword}/{image_name}/0.pt' if not os.path.isfile(w_potential_path): return None w = torch.load(w_potential_path).to(global_config.device) self.w_pivots[image_name] = w return w def calc_inversions(self, image, image_name): if hyperparameters.first_inv_type == 'w+': w = self.get_e4e_inversion(image) else: id_image = torch.squeeze((image.to(global_config.device) + 1) / 2) * 255 w = w_projector.project(self.G, id_image, device=torch.device(global_config.device), w_avg_samples=600, num_steps=hyperparameters.first_inv_steps, w_name=image_name, use_wandb=self.use_wandb) return w @abc.abstractmethod def train(self): pass def configure_optimizers(self): optimizer = torch.optim.Adam(self.G.parameters(), lr=hyperparameters.pti_learning_rate) return optimizer def calc_loss(self, generated_images, real_images, log_name, new_G, use_ball_holder, w_batch): loss = 0.0 if hyperparameters.pt_l2_lambda > 0: l2_loss_val = l2_loss(generated_images, real_images) if self.use_wandb: wandb.log({f'MSE_loss_val_{log_name}': l2_loss_val.detach().cpu()}, step=global_config.training_step) loss += l2_loss_val * hyperparameters.pt_l2_lambda if hyperparameters.pt_lpips_lambda > 0: loss_lpips = self.lpips_loss(generated_images, real_images) loss_lpips = torch.squeeze(loss_lpips) if self.use_wandb: wandb.log({f'LPIPS_loss_val_{log_name}': loss_lpips.detach().cpu()}, step=global_config.training_step) loss += loss_lpips * hyperparameters.pt_lpips_lambda if use_ball_holder and hyperparameters.use_locality_regularization: ball_holder_loss_val = self.space_regulizer.space_regulizer_loss(new_G, w_batch, use_wandb=self.use_wandb) loss += ball_holder_loss_val return loss, l2_loss_val, loss_lpips def forward(self, w): generated_images = self.G.synthesis(w, noise_mode='const', force_fp32=True) return generated_images def initilize_e4e(self): ckpt = torch.load(paths_config.e4e, map_location='cpu') opts = ckpt['opts'] opts['batch_size'] = hyperparameters.train_batch_size opts['checkpoint_path'] = paths_config.e4e opts = Namespace(**opts) self.e4e_inversion_net = pSp(opts) self.e4e_inversion_net.eval() self.e4e_inversion_net = self.e4e_inversion_net.to(global_config.device) toogle_grad(self.e4e_inversion_net, False) def get_e4e_inversion(self, image): image = (image + 1) / 2 new_image = self.e4e_image_transform(image[0]).to(global_config.device) _, w = self.e4e_inversion_net(new_image.unsqueeze(0), randomize_noise=False, return_latents=True, resize=False, input_code=False) if self.use_wandb: log_image_from_w(w, self.G, 'First e4e inversion') return w