Spaces:
Runtime error
Runtime error
| import os | |
| import torch | |
| from tqdm import tqdm | |
| from PTI.configs import paths_config, hyperparameters, global_config | |
| from PTI.training.coaches.base_coach import BaseCoach | |
| from PTI.utils.log_utils import log_images_from_w | |
| class SingleIDCoach(BaseCoach): | |
| def __init__(self, data_loader, use_wandb): | |
| super().__init__(data_loader, use_wandb) | |
| def train(self): | |
| w_path_dir = f"{paths_config.embedding_base_dir}/{paths_config.input_data_id}" | |
| os.makedirs(w_path_dir, exist_ok=True) | |
| os.makedirs(f"{w_path_dir}/{paths_config.pti_results_keyword}", exist_ok=True) | |
| use_ball_holder = True | |
| w_pivot = None | |
| fname, image = next(iter(self.data_loader)) | |
| print("NANANAN", fname) | |
| image_name = fname[0] | |
| self.restart_training() | |
| embedding_dir = f"{w_path_dir}/{paths_config.pti_results_keyword}/{image_name}" | |
| os.makedirs(embedding_dir, exist_ok=True) | |
| if hyperparameters.use_last_w_pivots: | |
| w_pivot = self.load_inversions(w_path_dir, image_name) | |
| elif 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.detach().clone().to(global_config.device) | |
| w_pivot = w_pivot.to(global_config.device) | |
| log_images_counter = 0 | |
| real_images_batch = image.to(global_config.device) | |
| for i in tqdm(range(hyperparameters.max_pti_steps)): | |
| generated_images = self.forward(w_pivot) | |
| loss, l2_loss_val, loss_lpips = self.calc_loss( | |
| generated_images, | |
| real_images_batch, | |
| image_name, | |
| self.G, | |
| use_ball_holder, | |
| w_pivot, | |
| ) | |
| self.optimizer.zero_grad() | |
| if loss_lpips <= hyperparameters.LPIPS_value_threshold: | |
| break | |
| loss.backward() | |
| self.optimizer.step() | |
| use_ball_holder = ( | |
| global_config.training_step | |
| % hyperparameters.locality_regularization_interval | |
| == 0 | |
| ) | |
| if ( | |
| self.use_wandb | |
| and log_images_counter % global_config.image_rec_result_log_snapshot | |
| == 0 | |
| ): | |
| log_images_from_w([w_pivot], self.G, [image_name]) | |
| global_config.training_step += 1 | |
| log_images_counter += 1 | |
| torch.save( | |
| self.G, | |
| f"{paths_config.checkpoints_dir}/model_{global_config.run_name}_{image_name}.pt", | |
| ) | |
| return self.G, w_pivot | |