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import os |
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import torch |
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from tqdm import tqdm |
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from pti.pti_configs import paths_config, hyperparameters, global_config |
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from pti.training.coaches.base_coach import BaseCoach |
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from utils.log_utils import log_images_from_w |
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from torchvision.utils import save_image |
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class SingleIDCoach(BaseCoach): |
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def __init__(self, data_loader, use_wandb): |
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super().__init__(data_loader, use_wandb) |
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def train(self): |
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w_path_dir = f'{paths_config.embedding_base_dir}/{paths_config.input_data_id}' |
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os.makedirs(w_path_dir, exist_ok=True) |
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os.makedirs(f'{w_path_dir}/{paths_config.pti_results_keyword}', exist_ok=True) |
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use_ball_holder = True |
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for fname, image in tqdm(self.data_loader): |
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image_name = fname[0] |
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self.restart_training() |
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if self.image_counter >= hyperparameters.max_images_to_invert: |
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break |
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embedding_dir = f'{w_path_dir}/{paths_config.pti_results_keyword}/{image_name}' |
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os.makedirs(embedding_dir, exist_ok=True) |
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w_pivot = None |
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if hyperparameters.use_last_w_pivots: |
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w_pivot = self.load_inversions(w_path_dir, image_name) |
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elif not hyperparameters.use_last_w_pivots or w_pivot is None: |
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w_pivot = self.calc_inversions(image, image_name) |
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w_pivot = w_pivot.to(global_config.device) |
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torch.save(w_pivot, f'{embedding_dir}/0.pt') |
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log_images_counter = 0 |
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real_images_batch = image.to(global_config.device) |
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for i in range(hyperparameters.max_pti_steps): |
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generated_images = self.forward(w_pivot) |
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loss, l2_loss_val, loss_lpips = self.calc_loss(generated_images, real_images_batch, image_name, |
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self.G, use_ball_holder, w_pivot) |
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if i == 0: |
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tmp1 = torch.clone(generated_images) |
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if i % 10 == 0: |
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print("pti loss: ", i, loss.data, loss_lpips.data) |
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self.optimizer.zero_grad() |
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if loss_lpips <= hyperparameters.LPIPS_value_threshold: |
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break |
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loss.backward() |
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self.optimizer.step() |
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use_ball_holder = global_config.training_step % hyperparameters.locality_regularization_interval == 0 |
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if self.use_wandb and log_images_counter % global_config.image_rec_result_log_snapshot == 0: |
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log_images_from_w([w_pivot], self.G, [image_name]) |
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global_config.training_step += 1 |
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log_images_counter += 1 |
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tmp = torch.cat([real_images_batch, tmp1, generated_images], axis= 3) |
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save_image(tmp, f"{paths_config.experiments_output_dir}/{image_name}.png", normalize=True) |
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self.image_counter += 1 |
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snapshot_data = dict() |
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snapshot_data['G_ema'] = self.G |
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import pickle |
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with open(f'{paths_config.checkpoints_dir}/model_{image_name}.pkl', 'wb') as f: |
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pickle.dump(snapshot_data, f) |
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