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import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import albumentations as albu
import argparse
import datetime
from utils.utils import open_json, weights_init, weights_init_spectr, generate_mask
from model.models import Colorizer, Generator, Content, Discriminator
from model.extractor import get_seresnext_extractor
from dataset.datasets import TrainDataset, FineTuningDataset
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("-p", "--path", required=True, help = "dataset path")
parser.add_argument('-ft', '--fine_tuning', dest = 'fine_tuning', action = 'store_true')
parser.add_argument('-g', '--gpu', dest = 'gpu', action = 'store_true')
parser.set_defaults(fine_tuning = False)
parser.set_defaults(gpu = False)
args = parser.parse_args()
return args
def get_transforms():
return albu.Compose([albu.RandomCrop(512, 512, always_apply = True), albu.HorizontalFlip(p = 0.5)], p = 1.)
def get_dataloaders(data_path, transforms, batch_size, fine_tuning, mult_number):
train_dataset = TrainDataset(data_path, transforms, mult_number)
train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size = batch_size, shuffle = True)
if fine_tuning:
finetuning_dataset = FineTuningDataset(data_path, transforms)
finetuning_dataloader = torch.utils.data.DataLoader(finetuning_dataset, batch_size = batch_size, shuffle = True)
return train_dataloader, finetuning_dataloader
def get_models(device):
generator = Generator()
extractor = get_seresnext_extractor()
colorizer = Colorizer(generator, extractor)
colorizer.extractor_eval()
colorizer = colorizer.to(device)
discriminator = Discriminator().to(device)
content = Content('model/vgg16-397923af.pth').eval().to(device)
for param in content.parameters():
param.requires_grad = False
return colorizer, discriminator, content
def set_weights(colorizer, discriminator):
colorizer.generator.apply(weights_init)
colorizer.load_extractor_weights(torch.load('model/extractor.pth'))
discriminator.apply(weights_init_spectr)
def generator_loss(disc_output, true_labels, main_output, guide_output, real_image, content_gen, content_true, dist_loss = nn.L1Loss(), content_dist_loss = nn.MSELoss(), class_loss = nn.BCEWithLogitsLoss()):
sim_loss_full = dist_loss(main_output, real_image)
sim_loss_guide = dist_loss(guide_output, real_image)
adv_loss = class_loss(disc_output, true_labels)
content_loss = content_dist_loss(content_gen, content_true)
sum_loss = 10 * (sim_loss_full + 0.9 * sim_loss_guide) + adv_loss + content_loss
return sum_loss
def get_optimizers(colorizer, discriminator, generator_lr, discriminator_lr):
optimizerG = optim.Adam(colorizer.generator.parameters(), lr = generator_lr, betas=(0.5, 0.9))
optimizerD = optim.Adam(discriminator.parameters(), lr = discriminator_lr, betas=(0.5, 0.9))
return optimizerG, optimizerD
def generator_step(inputs, colorizer, discriminator, content, loss_function, optimizer, device, white_penalty = True):
for p in discriminator.parameters():
p.requires_grad = False
for p in colorizer.generator.parameters():
p.requires_grad = True
colorizer.generator.zero_grad()
bw, color, hint, dfm = inputs
bw, color, hint, dfm = bw.to(device), color.to(device), hint.to(device), dfm.to(device)
fake, guide = colorizer(torch.cat([bw, dfm, hint], 1))
logits_fake = discriminator(fake)
y_real = torch.ones((bw.size(0), 1), device = device)
content_fake = content(fake)
with torch.no_grad():
content_true = content(color)
generator_loss = loss_function(logits_fake, y_real, fake, guide, color, content_fake, content_true)
if white_penalty:
mask = (~((color > 0.85).float().sum(dim = 1) == 3).unsqueeze(1).repeat((1, 3, 1, 1 ))).float()
white_zones = mask * (fake + 1) / 2
white_penalty = (torch.pow(white_zones.sum(dim = 1), 2).sum(dim = (1, 2)) / (mask.sum(dim = (1, 2, 3)) + 1)).mean()
generator_loss += white_penalty
generator_loss.backward()
optimizer.step()
return generator_loss.item()
def discriminator_step(inputs, colorizer, discriminator, optimizer, device, loss_function = nn.BCEWithLogitsLoss()):
for p in discriminator.parameters():
p.requires_grad = True
for p in colorizer.generator.parameters():
p.requires_grad = False
discriminator.zero_grad()
bw, color, hint, dfm = inputs
bw, color, hint, dfm = bw.to(device), color.to(device), hint.to(device), dfm.to(device)
y_real = torch.full((bw.size(0), 1), 0.9, device = device)
y_fake = torch.zeros((bw.size(0), 1), device = device)
with torch.no_grad():
fake_color, _ = colorizer(torch.cat([bw, dfm, hint], 1))
fake_color.detach()
logits_fake = discriminator(fake_color)
logits_real = discriminator(color)
fake_loss = loss_function(logits_fake, y_fake)
real_loss = loss_function(logits_real, y_real)
discriminator_loss = real_loss + fake_loss
discriminator_loss.backward()
optimizer.step()
return discriminator_loss.item()
def decrease_lr(optimizer, rate):
for group in optimizer.param_groups:
group['lr'] /= rate
def set_lr(optimizer, value):
for group in optimizer.param_groups:
group['lr'] = value
def train(colorizer, discriminator, content, dataloader, epochs, colorizer_optimizer, discriminator_optimizer, lr_decay_epoch = -1, device = 'cpu'):
colorizer.generator.train()
discriminator.train()
disc_step = True
for epoch in range(epochs):
if (epoch == lr_decay_epoch):
decrease_lr(colorizer_optimizer, 10)
decrease_lr(discriminator_optimizer, 10)
sum_disc_loss = 0
sum_gen_loss = 0
for n, inputs in enumerate(dataloader):
if n % 5 == 0:
print(datetime.datetime.now().time())
print('Step : %d Discr loss: %.4f Gen loss : %.4f \n'%(n, sum_disc_loss / (n // 2 + 1), sum_gen_loss / (n // 2 + 1)))
if disc_step:
step_loss = discriminator_step(inputs, colorizer, discriminator, discriminator_optimizer, device)
sum_disc_loss += step_loss
else:
step_loss = generator_step(inputs, colorizer, discriminator, content, generator_loss, colorizer_optimizer, device)
sum_gen_loss += step_loss
disc_step = disc_step ^ True
print(datetime.datetime.now().time())
print('Epoch : %d Discr loss: %.4f Gen loss : %.4f \n'%(epoch, sum_disc_loss / (n // 2 + 1), sum_gen_loss / (n // 2 + 1)))
def fine_tuning_step(data_iter, colorizer, discriminator, gen_optimizer, disc_optimizer, device, loss_function = nn.BCEWithLogitsLoss()):
for p in discriminator.parameters():
p.requires_grad = True
for p in colorizer.generator.parameters():
p.requires_grad = False
for cur_disc_step in range(5):
discriminator.zero_grad()
bw, dfm, color_for_real = next(data_iter)
bw, dfm, color_for_real = bw.to(device), dfm.to(device), color_for_real.to(device)
y_real = torch.full((bw.size(0), 1), 0.9, device = device)
y_fake = torch.zeros((bw.size(0), 1), device = device)
empty_hint = torch.zeros(bw.shape[0], 4, bw.shape[2] , bw.shape[3] ).float().to(device)
with torch.no_grad():
fake_color_manga, _ = colorizer(torch.cat([bw, dfm, empty_hint ], 1))
fake_color_manga.detach()
logits_fake = discriminator(fake_color_manga)
logits_real = discriminator(color_for_real)
fake_loss = loss_function(logits_fake, y_fake)
real_loss = loss_function(logits_real, y_real)
discriminator_loss = real_loss + fake_loss
discriminator_loss.backward()
disc_optimizer.step()
for p in discriminator.parameters():
p.requires_grad = False
for p in colorizer.generator.parameters():
p.requires_grad = True
colorizer.generator.zero_grad()
bw, dfm, _ = next(data_iter)
bw, dfm = bw.to(device), dfm.to(device)
y_real = torch.ones((bw.size(0), 1), device = device)
empty_hint = torch.zeros(bw.shape[0], 4, bw.shape[2] , bw.shape[3]).float().to(device)
fake_manga, _ = colorizer(torch.cat([bw, dfm, empty_hint], 1))
logits_fake = discriminator(fake_manga)
adv_loss = loss_function(logits_fake, y_real)
generator_loss = adv_loss
generator_loss.backward()
gen_optimizer.step()
def fine_tuning(colorizer, discriminator, content, dataloader, iterations, colorizer_optimizer, discriminator_optimizer, ft_dataloader, device = 'cpu'):
colorizer.generator.train()
discriminator.train()
disc_step = True
for n, inputs in enumerate(dataloader):
if n == iterations:
return
if disc_step:
discriminator_step(inputs, colorizer, discriminator, discriminator_optimizer, device)
else:
generator_step(inputs, colorizer, discriminator, content, generator_loss, colorizer_optimizer, device)
disc_step = disc_step ^ True
if n % 10 == 5:
data_iter = iter(ft_dataloader)
fine_tuning_step(data_iter, colorizer, discriminator, colorizer_optimizer, discriminator_optimizer, device)
if __name__ == '__main__':
args = parse_args()
config = open_json('configs/train_config.json')
if args.gpu:
device = 'cuda'
else:
device = 'cpu'
augmentations = get_transforms()
train_dataloader, ft_dataloader = get_dataloaders(args.path, augmentations, config['batch_size'], args.fine_tuning, config['number_of_mults'])
colorizer, discriminator, content = get_models(device)
set_weights(colorizer, discriminator)
gen_optimizer, disc_optimizer = get_optimizers(colorizer, discriminator, config['generator_lr'], config['discriminator_lr'])
train(colorizer, discriminator, content, train_dataloader, config['epochs'], gen_optimizer, disc_optimizer, config['lr_decrease_epoch'], device)
if args.fine_tuning:
set_lr(gen_optimizer, config["finetuning_generator_lr"])
fine_tuning(colorizer, discriminator, content, train_dataloader, config['finetuning_iterations'], gen_optimizer, disc_optimizer, iter(ft_dataloader), device)
torch.save(colorizer.generator.state_dict(), str(datetime.datetime.now().time())) |