train.py

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, mults_amount=mult_number)
    print("Train Dataset Length:", len(train_dataset))  # Debug print
    train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
    
    if fine_tuning:
        finetuning_dataset = FineTuningDataset(data_path, transforms, mult_amount=mult_number)
        print("FineTuning Dataset Length:", len(finetuning_dataset))  # Debug print
        finetuning_dataloader = torch.utils.data.DataLoader(finetuning_dataset, batch_size=batch_size, shuffle=True)
        return train_dataloader, finetuning_dataloader

    return train_dataloader, None


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 = data_iter.next()
        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, _ = data_iter.next()
    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, data_iter, 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:
            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()))