File size: 5,755 Bytes
b6068b4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 |
import abc
import os
import pickle
from argparse import Namespace
import wandb
import os.path
from .localitly_regulizer import Space_Regulizer, l2_loss
import torch
from torchvision import transforms
from lpips import LPIPS
from pti.training.projectors import w_projector
from pti.pti_configs import global_config, paths_config, hyperparameters
from pti.pti_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, 128)),
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(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
|