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training.py
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training.py
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import os
import sys
import time
import hydra
import numpy as np
from PIL import Image
from omegaconf import OmegaConf
import torch
import torch.nn.functional as F
from torch.optim import Adam
from torch.utils.data import DataLoader
from utils.logger import Logger
from utils.dataset import CharacterDataset
from utils.function import plot_sample
from model.generator import SynthesisGenerator
from model.discriminator import MultiscaleDiscriminator
@hydra.main(version_base=None, config_path='./config', config_name='training')
def main(config):
# load configuration
dataset_path = str(config.parameter.dataset_path)
checkpoint_path = str(config.parameter.checkpoint_path)
device = torch.device('cuda') if config.parameter.device == 'gpu' else torch.device('cpu')
batch_size = int(config.parameter.batch_size)
num_workers = int(config.parameter.num_workers)
reference_count = int(config.parameter.reference_count)
num_iterations = int(config.parameter.num_iterations)
report_interval = int(config.parameter.report_interval)
save_interval = int(config.parameter.save_interval)
# create logger
sys.stdout = Logger(os.path.join(checkpoint_path, 'training.log'))
config.parameter.checkpoint_path = checkpoint_path
config.parameter.device = str(device)
print(OmegaConf.to_yaml(config))
# load dataset
dataset = CharacterDataset(dataset_path, reference_count=reference_count)
dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers, pin_memory=True, drop_last=True)
print('image number: {}\n'.format(len(dataset)))
# create model
generator_model = SynthesisGenerator(reference_count=reference_count).to(device)
generator_model.train()
discriminator_model = MultiscaleDiscriminator(dataset.writer_count, dataset.character_count).to(device)
discriminator_model.train()
# create optimizer
generator_optimizer = Adam(generator_model.parameters(), lr=config.parameter.generator.learning_rate, betas=(0, 0.999), weight_decay=1e-4)
discriminator_optimizer = Adam(discriminator_model.parameters(), lr=config.parameter.discriminator.learning_rate, betas=(0, 0.999), weight_decay=1e-4)
# start training
current_iteration = 0
current_time = time.time()
while current_iteration < num_iterations:
for reference_image, writer_label, template_image, character_label, script_image in dataloader:
current_iteration += 1
reference_image, writer_label, template_image, character_label, script_image = reference_image.to(device), writer_label.to(device), template_image.to(device), character_label.to(device), script_image.to(device)
# generator
generator_optimizer.zero_grad()
result_image, template_structure, reference_style = generator_model(reference_image, template_image)
loss_generator_adversarial = 0
loss_generator_classification = 0
for prediction_reality, prediction_writer, prediction_character in discriminator_model(result_image):
loss_generator_adversarial += F.binary_cross_entropy(prediction_reality, torch.ones_like(prediction_reality))
loss_generator_classification += F.cross_entropy(prediction_writer, writer_label) + F.cross_entropy(prediction_character, character_label)
result_structure = generator_model.structure(result_image)
loss_generator_structure = 0
for i in range(len(result_structure)):
loss_generator_structure += 0.5 * torch.mean(torch.square(template_structure[i] - result_structure[i]))
result_style = generator_model.style(result_image.repeat_interleave(reference_count, dim=1))
loss_generator_style = 0.5 * torch.mean(torch.square(reference_style - result_style))
loss_generator_reconstruction = F.l1_loss(result_image, script_image)
loss_generator = config.parameter.generator.loss_function.weight_adversarial * loss_generator_adversarial + config.parameter.generator.loss_function.weight_classification * loss_generator_classification + config.parameter.generator.loss_function.weight_structure * loss_generator_structure + config.parameter.generator.loss_function.weight_style * loss_generator_style + config.parameter.generator.loss_function.weight_reconstruction * loss_generator_reconstruction
loss_generator.backward()
generator_optimizer.step()
# discriminator
discriminator_optimizer.zero_grad()
loss_discriminator_adversarial = 0
loss_discriminator_classification = 0
for prediction_reality, prediction_writer, prediction_character in discriminator_model(result_image.detach()):
loss_discriminator_adversarial += F.binary_cross_entropy(prediction_reality, torch.zeros_like(prediction_reality))
loss_discriminator_classification += F.cross_entropy(prediction_writer, writer_label) + F.cross_entropy(prediction_character, character_label)
for prediction_reality, prediction_writer, prediction_character in discriminator_model(script_image):
loss_discriminator_adversarial += F.binary_cross_entropy(prediction_reality, torch.ones_like(prediction_reality))
loss_discriminator_classification += F.cross_entropy(prediction_writer, writer_label) + F.cross_entropy(prediction_character, character_label)
loss_discriminator = config.parameter.discriminator.loss_function.weight_adversarial * loss_discriminator_adversarial + config.parameter.discriminator.loss_function.weight_classification * loss_discriminator_classification
loss_discriminator.backward()
discriminator_optimizer.step()
# report
if current_iteration % report_interval == 0:
last_time = current_time
current_time = time.time()
iteration_time = (current_time - last_time) / report_interval
print('iteration {} / {}:'.format(current_iteration, num_iterations))
print('time: {:.6f} seconds per iteration'.format(iteration_time))
print('generator loss: {:.6f}, adversarial loss: {:.6f}, classification loss: {:.6f}, structure loss: {:.6f}, style loss: {:.6f}, reconstruction loss: {:.6f}'.format(loss_generator.item(), loss_generator_adversarial.item(), loss_generator_classification.item(), loss_generator_structure.item(), loss_generator_style.item(), loss_generator_reconstruction.item()))
print('discriminator loss: {:.6f}, adversarial loss: {:.6f}, classification loss: {:.6f}\n'.format(loss_discriminator.item(), loss_discriminator_adversarial.item(), loss_discriminator_classification.item()))
# save
if current_iteration % save_interval == 0:
save_path = os.path.join(checkpoint_path, 'iteration_{}'.format(current_iteration))
os.makedirs(save_path, exist_ok=True)
image_path = os.path.join(save_path, 'sample.png')
generator_path = os.path.join(save_path, 'generator.pth')
discriminator_path = os.path.join(save_path, 'discriminator.pth')
image = plot_sample(reference_image, template_image, script_image, result_image)[0]
Image.fromarray((255 * image).astype(np.uint8)).save(image_path)
torch.save(generator_model.state_dict(), generator_path)
torch.save(discriminator_model.state_dict(), discriminator_path)
print('save sample image in: {}'.format(image_path))
print('save generator model in: {}'.format(generator_path))
print('save discriminator model in: {}\n'.format(discriminator_path))
if current_iteration >= num_iterations:
break
if __name__ == '__main__':
main()