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| # Copyright (c) SenseTime Research. All rights reserved. | |
| import os | |
| import torch | |
| from tqdm import tqdm | |
| from pti.pti_configs import paths_config, hyperparameters, global_config | |
| from pti.training.coaches.base_coach import BaseCoach | |
| from utils.log_utils import log_images_from_w | |
| from torchvision.utils import save_image | |
| 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 | |
| for fname, image in tqdm(self.data_loader): | |
| image_name = fname[0] | |
| self.restart_training() | |
| if self.image_counter >= hyperparameters.max_images_to_invert: | |
| break | |
| 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) | |
| # Copyright (c) SenseTime Research. All rights reserved. | |
| elif not hyperparameters.use_last_w_pivots or w_pivot is None: | |
| w_pivot = self.calc_inversions(image, image_name) | |
| # w_pivot = w_pivot.detach().clone().to(global_config.device) | |
| w_pivot = w_pivot.to(global_config.device) | |
| torch.save(w_pivot, f'{embedding_dir}/0.pt') | |
| log_images_counter = 0 | |
| real_images_batch = image.to(global_config.device) | |
| for i in 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) | |
| if i == 0: | |
| tmp1 = torch.clone(generated_images) | |
| if i % 10 == 0: | |
| print("pti loss: ", i, loss.data, loss_lpips.data) | |
| 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 | |
| # save output image | |
| tmp = torch.cat( | |
| [real_images_batch, tmp1, generated_images], axis=3) | |
| save_image( | |
| tmp, f"{paths_config.experiments_output_dir}/{image_name}.png", normalize=True) | |
| self.image_counter += 1 | |
| # torch.save(self.G, | |
| # f'{paths_config.checkpoints_dir}/model_{image_name}.pt') #'.pt' | |
| snapshot_data = dict() | |
| snapshot_data['G_ema'] = self.G | |
| import pickle | |
| with open(f'{paths_config.checkpoints_dir}/model_{image_name}.pkl', 'wb') as f: | |
| pickle.dump(snapshot_data, f) | |