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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 | |