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import abc
import os
import pickle
from argparse import Namespace
import wandb
import os.path
from criteria.localitly_regulizer import Space_Regulizer
import torch
from torchvision import transforms
from lpips import LPIPS
from training.projectors import w_projector
from configs import global_config, paths_config, hyperparameters
from criteria import l2_loss
from models.e4e.psp import pSp
from utils.log_utils import log_image_from_w
from utils.models_utils import toogle_grad, load_old_G
class BaseCoach:
def __init__(self, data_loader, use_wandb):
self.use_wandb = use_wandb
self.data_loader = data_loader
self.w_pivots = {}
self.image_counter = 0
if hyperparameters.first_inv_type == 'w+':
self.initilize_e4e()
self.e4e_image_transform = transforms.Compose([
transforms.ToPILImage(),
transforms.Resize((256, 256)),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])
# Initialize loss
self.lpips_loss = LPIPS(net=hyperparameters.lpips_type).to(global_config.device).eval()
self.restart_training()
# Initialize checkpoint dir
self.checkpoint_dir = paths_config.checkpoints_dir
os.makedirs(self.checkpoint_dir, exist_ok=True)
def restart_training(self):
# Initialize networks
self.G = load_old_G()
toogle_grad(self.G, True)
self.original_G = load_old_G()
self.space_regulizer = Space_Regulizer(self.original_G, self.lpips_loss)
self.optimizer = self.configure_optimizers()
def get_inversion(self, w_path_dir, image_name, image):
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)
if 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.to(global_config.device)
return w_pivot
def load_inversions(self, w_path_dir, image_name):
if image_name in self.w_pivots:
return self.w_pivots[image_name]
if hyperparameters.first_inv_type == 'w+':
w_potential_path = f'{w_path_dir}/{paths_config.e4e_results_keyword}/{image_name}/0.pt'
else:
w_potential_path = f'{w_path_dir}/{paths_config.pti_results_keyword}/{image_name}/0.pt'
if not os.path.isfile(w_potential_path):
return None
w = torch.load(w_potential_path).to(global_config.device)
self.w_pivots[image_name] = w
return w
def calc_inversions(self, image, image_name):
if hyperparameters.first_inv_type == 'w+':
w = self.get_e4e_inversion(image)
else:
id_image = torch.squeeze((image.to(global_config.device) + 1) / 2) * 255
w = w_projector.project(self.G, id_image, device=torch.device(global_config.device), w_avg_samples=600,
num_steps=hyperparameters.first_inv_steps, w_name=image_name,
use_wandb=self.use_wandb)
return w
@abc.abstractmethod
def train(self):
pass
def configure_optimizers(self):
optimizer = torch.optim.Adam(self.G.parameters(), lr=hyperparameters.pti_learning_rate)
return optimizer
def calc_loss(self, generated_images, real_images, log_name, new_G, use_ball_holder, w_batch):
loss = 0.0
if hyperparameters.pt_l2_lambda > 0:
l2_loss_val = l2_loss.l2_loss(generated_images, real_images)
if self.use_wandb:
wandb.log({f'MSE_loss_val_{log_name}': l2_loss_val.detach().cpu()}, step=global_config.training_step)
loss += l2_loss_val * hyperparameters.pt_l2_lambda
if hyperparameters.pt_lpips_lambda > 0:
loss_lpips = self.lpips_loss(generated_images, real_images)
loss_lpips = torch.squeeze(loss_lpips)
if self.use_wandb:
wandb.log({f'LPIPS_loss_val_{log_name}': loss_lpips.detach().cpu()}, step=global_config.training_step)
loss += loss_lpips * hyperparameters.pt_lpips_lambda
if use_ball_holder and hyperparameters.use_locality_regularization:
ball_holder_loss_val = self.space_regulizer.space_regulizer_loss(new_G, w_batch, use_wandb=self.use_wandb)
loss += ball_holder_loss_val
return loss, l2_loss_val, loss_lpips
def forward(self, w):
generated_images = self.G.synthesis(w, noise_mode='const', force_fp32=True)
return generated_images
def initilize_e4e(self):
ckpt = torch.load(paths_config.e4e, map_location='cpu')
opts = ckpt['opts']
opts['batch_size'] = hyperparameters.train_batch_size
opts['checkpoint_path'] = paths_config.e4e
opts = Namespace(**opts)
self.e4e_inversion_net = pSp(opts)
self.e4e_inversion_net.eval()
self.e4e_inversion_net = self.e4e_inversion_net.to(global_config.device)
toogle_grad(self.e4e_inversion_net, False)
def get_e4e_inversion(self, image):
image = (image + 1) / 2
new_image = self.e4e_image_transform(image[0]).to(global_config.device)
_, w = self.e4e_inversion_net(new_image.unsqueeze(0), randomize_noise=False, return_latents=True, resize=False,
input_code=False)
if self.use_wandb:
log_image_from_w(w, self.G, 'First e4e inversion')
return w
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