def main(args):
lr_train_patch_size = args.patch_size
layers_to_extract = [5, 9]
hr_train_patch_size = lr_train_patch_size * args.scale
if args.model == 'rdn':
model = RDN(arch_params={'C':4, 'D':3, 'G':64, 'G0':64, 'T':10, 'x':args.scale}, patch_size=lr_train_patch_size)
else:
model = RRDN(arch_params={'C':4, 'D':3, 'G':64, 'G0':64, 'T':10, 'x':args.scale}, patch_size=lr_train_patch_size)
f_ext = Cut_VGG19(patch_size=hr_train_patch_size, layers_to_extract=layers_to_extract)
discr = Discriminator(patch_size=hr_train_patch_size, kernel_size=3)
loss_weights = {
'generator': 0.0,
'feature_extractor': 0.0833,
'discriminator': 0.01,
}
trainer = Trainer(
generator=model,
discriminator=discr,
feature_extractor=f_ext,
lr_train_dir='low_res/training/images',
hr_train_dir='high_res/training/images',
lr_valid_dir='low_res/validation/images',
hr_valid_dir='high_res/validation/images',
loss_weights=loss_weights,
dataname=args.name,
logs_dir='./logs',
weights_dir='./weights',
weights_generator=None,
weights_discriminator=None,
n_validation=40,
lr_decay_frequency=30,
lr_decay_factor=0.5,
)
trainer.train(epochs=args.num_epochs,
steps_per_epoch=args.epoch_steps,
batch_size,args.batch_size)
learning_rate = {
'initial_value': 0.0004,
'decay_factor': 0.5,
'decay_frequency': 30
}
flatness = {'min': 0.0, 'max': 0.15, 'increase': 0.01, 'increase_frequency': 5}
trainer = Trainer(
generator=rrdn,
discriminator=discr,
feature_extractor=f_ext,
lr_train_dir='/home/sandeeppanku/Public/Code/superres/data/trainImageslr',
hr_train_dir='/home/sandeeppanku/Public/Code/superres/data/trainImageshr',
lr_valid_dir='/home/sandeeppanku/Public/Code/superres/data/testImageslr',
hr_valid_dir='/home/sandeeppanku/Public/Code/superres/data/testImageshr',
loss_weights=loss_weights,
learning_rate=learning_rate,
flatness=flatness,
log_dirs=log_dirs,
weights_generator=None,
weights_discriminator=None,
n_validation=40,
)
trainer.train(epochs=80,
steps_per_epoch=500,
batch_size=16,
monitored_metrics={'val_PSNR_Y': 'max'})
##################################################################################################################
learning_rate = {
'initial_value': 0.0004,
'decay_factor': 0.5,
'decay_frequency': 30
}
flatness = {'min': 0.0, 'max': 0.15, 'increase': 0.01, 'increase_frequency': 5}
trainer = Trainer(
generator=rdn,
discriminator=discr,
feature_extractor=f_ext,
hr_train_dir='galaxy_zoo/individuals_2blend_train/',
lr_train_dir='galaxy_zoo/merged_2blend_train/',
hr_valid_dir='galaxy_zoo/individuals_2blend_valid/',
lr_valid_dir='galaxy_zoo/merged_2blend_valid/',
loss_weights=loss_weights,
learning_rate=learning_rate,
flatness=flatness,
dataname='div2k',
log_dirs=log_dirs,
weights_generator=None,
weights_discriminator=None,
n_validation=5, #to be modified
)
trainer.train(epochs=200,
steps_per_epoch=5000,
batch_size=16,
monitored_metrics={'val_generator_PSNR_Y': 'max'})
learning_rate = {
'initial_value': 0.0004,
'decay_factor': 0.5,
'decay_frequency': 30
}
flatness = {'min': 0.0, 'max': 0.15, 'increase': 0.01, 'increase_frequency': 5}
trainer = Trainer(
generator=rrdn,
discriminator=discr,
feature_extractor=f_ext,
hr_train_dir='images/face_train/',
lr_train_dir='images/musk_train/',
hr_valid_dir='images/face_valid/',
lr_valid_dir='images/musk_valid/',
loss_weights=loss_weights,
learning_rate=learning_rate,
flatness=flatness,
dataname='div2k',
log_dirs=log_dirs,
weights_generator=None,
weights_discriminator=None,
n_validation=5, #to be modified
)
trainer.train(epochs=200,
steps_per_epoch=1000,
batch_size=4,
monitored_metrics={'val_generator_PSNR_Y': 'max'})
'initial_value': 0.0004,
'decay_factor': 0.5,
'decay_frequency': 30
}
flatness = {'min': 0.0, 'max': 0.15, 'increase': 0.01, 'increase_frequency': 5}
trainer = Trainer(
generator=rrdn,
discriminator=discr,
feature_extractor=f_ext,
lr_train_dir='training_lr/3x/',
hr_train_dir='training/',
lr_valid_dir='training_lr/3x/',
hr_valid_dir='training/',
loss_weights=loss_weights,
learning_rate=learning_rate,
flatness=flatness,
dataname='training',
log_dirs=log_dirs,
weights_generator=None,
weights_discriminator=None,
n_validation=291,
)
trainer.train(
epochs=100,
steps_per_epoch=500,
batch_size=16,
monitored_metrics={'val_generator_PSNR_Y': 'max'}
)
'initial_value': 0.0004,
'decay_factor': 0.5,
'decay_frequency': 30
}
flatness = {'min': 0.0, 'max': 0.15, 'increase': 0.01, 'increase_frequency': 5}
trainer = Trainer(
generator=rrdn,
discriminator=discr,
feature_extractor=f_ext,
lr_train_dir='data/DIV2K/DIV2K_train_LR_bicubic/X2',
hr_train_dir='data/DIV2K/DIV2K_train_HR',
lr_valid_dir='data/DIV2K/DIV2K_valid_LR_bicubic/X2',
hr_valid_dir='data/DIV2K/DIV2K_valid_HR',
loss_weights=loss_weights,
learning_rate=learning_rate,
flatness=flatness,
dataname='image_dataset',
log_dirs=log_dirs,
weights_generator=None,
weights_discriminator=None,
n_validation=40,
)
trainer.train(epochs=80,
steps_per_epoch=500,
batch_size=16,
monitored_metrics={'val_PSNR_Y': 'max'})
rrdn.model.save_weights('ex.hdf5')
}
flatness = {'min': 0.0, 'max': 0.15, 'increase': 0.01, 'increase_frequency': 5}
adam_optimizer = {'beta1': 0.9, 'beta2': 0.999, 'epsilon': None}
trainer = Trainer(
generator=rrdn,
discriminator=discr,
feature_extractor=f_ext,
lr_train_dir='/media/jhonatan/Data/h6f86gl(2)/final/lr_train',
hr_train_dir='/media/jhonatan/Data/h6f86gl(2)/final/hr_train',
lr_valid_dir='/media/jhonatan/Data/h6f86gl(2)/final/lr_val',
hr_valid_dir='/media/jhonatan/Data/h6f86gl(2)/final/hr_val',
loss_weights=loss_weights,
losses=losses,
learning_rate=learning_rate,
flatness=flatness,
log_dirs=log_dirs,
adam_optimizer=adam_optimizer,
metrics={'generator': 'PSNR_Y'},
dataname='aolp',
weights_generator=None,
weights_discriminator=None,
n_validation=40,
)
trainer.train(epochs=100,
steps_per_epoch=20,
batch_size=4,
monitored_metrics={'val_generator_loss': 'min'})
learning_rate = {
'initial_value': 0.0004,
'decay_factor': 0.5,
'decay_frequency': 30
}
flatness = {'min': 0.0, 'max': 0.15, 'increase': 0.01, 'increase_frequency': 5}
trainer = Trainer(
generator=rrdn,
discriminator=discr,
feature_extractor=f_ext,
lr_train_dir='train/low',
hr_train_dir='train/high',
lr_valid_dir='test/low',
hr_valid_dir='test/high',
loss_weights=loss_weights,
learning_rate=learning_rate,
flatness=flatness,
dataname='image_dataset',
log_dirs=log_dirs,
weights_generator=None,
weights_discriminator=None,
n_validation=40,
)
trainer.train(epochs=1,
steps_per_epoch=11,
batch_size=16,
monitored_metrics={'val_PSNR_Y': 'max'})
'decay_factor': 0.5,
'decay_frequency': 30
}
flatness = {'min': 0.0, 'max': 0.15, 'increase': 0.01, 'increase_frequency': 5}
trainer = Trainer(
generator=rrdn,
discriminator=discr,
feature_extractor=f_ext,
lr_train_dir=
'/home/minhhoang/image-super-resolution/data/training/div2k/DIV2K_train_LR_bicubic/X2/',
hr_train_dir=
'/home/minhhoang/image-super-resolution/data/training/div2k/DIV2K_train_HR/',
lr_valid_dir=
'/home/minhhoang/image-super-resolution/data/training/div2k/DIV2K_train_LR_bicubic/X2/',
hr_valid_dir=
'/home/minhhoang/image-super-resolution/data/training/div2k/DIV2K_train_HR/',
loss_weights=loss_weights,
learning_rate=learning_rate,
flatness=flatness,
dataname='div2k',
log_dirs=log_dirs,
weights_generator=None,
weights_discriminator=None,
n_validation=40,
)
trainer.train(epochs=1,
steps_per_epoch=20,
batch_size=4,
monitored_metrics={'val_generator_PSNR_Y': 'max'})