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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
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