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train_esrgan.py
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train_esrgan.py
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from absl import app, flags, logging
from absl.flags import FLAGS
import os
import tensorflow as tf
from modules.models import RRDB_Model, RRDB_Model_16x, RFB_Model_16x, DiscriminatorVGG128, DiscriminatorVGG512
from modules.lr_scheduler import MultiStepLR
from modules.losses import (PixelLoss, ContentLoss, DiscriminatorLoss, gradient_penalty,
GeneratorLoss, PixelLossDown)
from modules.utils import (load_yaml, load_dataset, ProgressBar,
set_memory_growth, load_val_dataset)
from evaluate import evaluate_dataset
flags.DEFINE_string('cfg_path', './configs/esrgan.yaml', 'config file path')
flags.DEFINE_string('gpu', '0', 'which gpu to use')
def main(_):
# init
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
os.environ['CUDA_VISIBLE_DEVICES'] = FLAGS.gpu
logger = tf.get_logger()
logger.disabled = True
logger.setLevel(logging.FATAL)
set_memory_growth()
cfg = load_yaml(FLAGS.cfg_path)
# define network
if cfg['network_G']['name']=='RRDB': # ESRGAN 4x
generator = RRDB_Model(None, cfg['ch_size'], cfg['network_G'])
elif cfg['network_G']['name']=='RRDB_CIPLAB':
generator = RRDB_Model_16x(None, cfg['ch_size'], cfg['network_G'])
elif cfg['network_G']['name']=='RFB_ESRGAN':
generator = RFB_Model_16x(None, cfg['ch_size'], cfg['network_G'])
generator.summary(line_length=80)
if cfg['network_G']['name']=='RRDB':
discriminator = DiscriminatorVGG128(cfg['gt_size'], cfg['ch_size'], scale=cfg['scale'], refgan=cfg['refgan'])
elif cfg['network_G']['name']=='RRDB_CIPLAB':
pass
elif cfg['network_G']['name']=='RFB_ESRGAN':
discriminator = DiscriminatorVGG512(cfg['gt_size'], cfg['ch_size'], scale=cfg['scale'], refgan=cfg['refgan'])
discriminator.summary(line_length=80)
# load dataset
train_dataset = load_dataset(cfg, 'train_dataset', shuffle=False)
set5_dataset = load_val_dataset(cfg, 'set5')
set14_dataset = load_val_dataset(cfg, 'set14')
if 'DIV8K' in cfg['test_dataset']:
print('[*] Loading test dataset.')
DIV8K_val = load_val_dataset(cfg, 'DIV8K', crop_centor=cfg['test_dataset']['DIV8K_crop_centor'])
# define optimizer
learning_rate_G = MultiStepLR(cfg['lr_G'], cfg['lr_steps'], cfg['lr_rate'])
learning_rate_D = MultiStepLR(cfg['lr_D'], cfg['lr_steps'], cfg['lr_rate'])
optimizer_G = tf.keras.optimizers.Adam(learning_rate=learning_rate_G,
beta_1=cfg['adam_beta1_G'],
beta_2=cfg['adam_beta2_G'])
optimizer_D = tf.keras.optimizers.Adam(learning_rate=learning_rate_D,
beta_1=cfg['adam_beta1_D'],
beta_2=cfg['adam_beta2_D'])
# define losses function
if cfg['cycle_mse']:
pixel_loss_fn = PixelLossDown(criterion=cfg['pixel_criterion'],
lr_size=(cfg['gt_size']//cfg['scale'], cfg['gt_size']//cfg['scale']))
else:
pixel_loss_fn = PixelLoss(criterion=cfg['pixel_criterion'])
fea_loss_fn = ContentLoss(criterion=cfg['feature_criterion'])
gen_loss_fn = GeneratorLoss(gan_type=cfg['gan_type'])
dis_loss_fn = DiscriminatorLoss(gan_type=cfg['gan_type'])
# load checkpoint
checkpoint_dir = cfg['log_dir'] + '/checkpoints/'
checkpoint = tf.train.Checkpoint(step=tf.Variable(0, name='step'),
optimizer_G=optimizer_G,
optimizer_D=optimizer_D,
model=generator,
discriminator=discriminator)
manager = tf.train.CheckpointManager(checkpoint=checkpoint,
directory=checkpoint_dir,
max_to_keep=3)
if manager.latest_checkpoint:
checkpoint.restore(manager.latest_checkpoint)
print('[*] load ckpt from {} at step {}.'.format(
manager.latest_checkpoint, checkpoint.step.numpy()))
else: # if checkpoint file doesn't exist
if cfg['pretrain_dir'] is not None: # load from pretrained model
pretrain_dir = cfg['pretrain_dir'] + '/checkpoints/'
if tf.train.latest_checkpoint(pretrain_dir):
checkpoint.restore(tf.train.latest_checkpoint(pretrain_dir))
checkpoint.step.assign(0)
print("[*] training from pretrain model {}.".format(
pretrain_dir))
else:
print("[*] cannot find pretrain model {}.".format(
pretrain_dir))
else:
print("[*] training from scratch.")
# define training step function
@tf.function
def train_step(lr, hr):
with tf.GradientTape(persistent=True) as tape:
sr = generator(lr, training=True)
if cfg['refgan']:
hr_output = discriminator([hr, lr], training=True)
sr_output = discriminator([sr, lr], training=True)
else:
hr_output = discriminator(hr, training=True)
sr_output = discriminator(sr, training=True)
losses_G = {}
losses_D = {}
losses_G['reg'] = tf.reduce_sum(generator.losses)
losses_D['reg'] = tf.reduce_sum(discriminator.losses)
if cfg['w_gan'] > 0.0 and cfg['gan_type']=='wgan-gp': # add GP loss
losses_D['gp'] = gradient_penalty(discriminator, hr, sr, lr, refgan=cfg['refgan']) * cfg['gp_weight']
if cfg['w_pixel'] > 0.0:
losses_G['pixel'] = cfg['w_pixel'] * pixel_loss_fn(hr, sr)
if cfg['w_feature'] > 0.0:
losses_G['feature'] = cfg['w_feature'] * fea_loss_fn(hr, sr)
if cfg['w_gan'] > 0.0:
losses_G['gan'] = cfg['w_gan'] * gen_loss_fn(hr_output, sr_output)
losses_D['gan'] = dis_loss_fn(hr_output, sr_output)
total_loss_G = tf.add_n([l for l in losses_G.values()])
total_loss_D = tf.add_n([l for l in losses_D.values()])
grads_G = tape.gradient(
total_loss_G, generator.trainable_variables)
grads_D = tape.gradient(
total_loss_D, discriminator.trainable_variables)
optimizer_G.apply_gradients(
zip(grads_G, generator.trainable_variables))
optimizer_D.apply_gradients(
zip(grads_D, discriminator.trainable_variables))
return total_loss_G, total_loss_D, losses_G, losses_D
# training loop
summary_writer = tf.summary.create_file_writer(cfg['log_dir']+'/logs')
prog_bar = ProgressBar(cfg['niter'], checkpoint.step.numpy())
remain_steps = max(cfg['niter'] - checkpoint.step.numpy(), 0)
for _ in range(remain_steps):
lr, hr = train_dataset()
checkpoint.step.assign_add(1)
steps = checkpoint.step.numpy()
total_loss_G, total_loss_D, losses_G, losses_D = train_step(lr, hr)
prog_bar.update(
"loss_G={:.4f}, loss_D={:.4f}, lr_G={:.1e}, lr_D={:.1e}".format(
total_loss_G.numpy(), total_loss_D.numpy(),
optimizer_G.lr(steps).numpy(), optimizer_D.lr(steps).numpy()))
if steps % 10 == 0:
with summary_writer.as_default():
tf.summary.scalar(
'loss_G/total_loss', total_loss_G, step=steps)
tf.summary.scalar(
'loss_D/total_loss', total_loss_D, step=steps)
for k, l in losses_G.items():
tf.summary.scalar('loss_G/{}'.format(k), l, step=steps)
for k, l in losses_D.items():
tf.summary.scalar('loss_D/{}'.format(k), l, step=steps)
tf.summary.scalar(
'learning_rate_G', optimizer_G.lr(steps), step=steps)
tf.summary.scalar(
'learning_rate_D', optimizer_D.lr(steps), step=steps)
if steps % cfg['save_steps'] == 0:
manager.save()
print("\n[*] save ckpt file at {}".format(
manager.latest_checkpoint))
# log results on test data
set5_logs = evaluate_dataset(set5_dataset, generator, cfg)
set14_logs = evaluate_dataset(set14_dataset, generator, cfg)
if 'DIV8K' in cfg['test_dataset']:
DIV8K_logs = evaluate_dataset(DIV8K_val, generator, cfg)
with summary_writer.as_default():
if cfg['logging']['psnr']:
tf.summary.scalar('set5/psnr', set5_logs['psnr'], step=steps)
tf.summary.scalar('set14/psnr', set14_logs['psnr'], step=steps)
if 'DIV8K' in cfg['test_dataset']:
tf.summary.scalar('DIV8K/psnr', DIV8K_logs['psnr'], step=steps)
if cfg['logging']['ssim']:
tf.summary.scalar('set5/ssim', set5_logs['ssim'], step=steps)
tf.summary.scalar('set14/ssim', set14_logs['ssim'], step=steps)
if 'DIV8K' in cfg['test_dataset']:
tf.summary.scalar('DIV8K/psnr', DIV8K_logs['psnr'], step=steps)
if cfg['logging']['lpips']:
tf.summary.scalar('set5/lpips', set5_logs['lpips'], step=steps)
tf.summary.scalar('set14/lpips', set14_logs['lpips'], step=steps)
if 'DIV8K' in cfg['test_dataset']:
tf.summary.scalar('DIV8K/lpips', DIV8K_logs['lpips'], step=steps)
if cfg['logging']['plot_samples']:
tf.summary.image("set5/samples", [set5_logs['samples']], step=steps)
tf.summary.image("set14/samples", [set14_logs['samples']], step=steps)
if 'DIV8K' in cfg['test_dataset']:
tf.summary.image("DIV8K/samples", [DIV8K_logs['samples']], step=steps)
print("\n [*] training done!")
if __name__ == '__main__':
app.run(main)