def setUpClass(cls):
cls.setup = yaml.load(
open(os.path.join('tests', 'data', 'config.yml'), 'r'))
cls.RRDN = RRDN(arch_params=cls.setup['rrdn'],
patch_size=cls.setup['patch_size'])
cls.f_ext = Cut_VGG19(patch_size=cls.setup['patch_size'],
layers_to_extract=[1, 2])
cls.discr = Discriminator(patch_size=cls.setup['patch_size'])
cls.weights_path = {
'generator':
os.path.join(cls.setup['weights_dir'], 'test_gen_weights.hdf5'),
'discriminator':
os.path.join(cls.setup['weights_dir'], 'test_dis_weights.hdf5'),
}
cls.TH = TrainerHelper(
generator=cls.RRDN,
weights_dir=cls.setup['weights_dir'],
logs_dir=cls.setup['log_dir'],
lr_train_dir=cls.setup['lr_input'],
feature_extractor=cls.f_ext,
discriminator=cls.discr,
dataname='TEST',
pretrained_weights_path={},
fallback_save_every_n_epochs=2,
)
def setUpClass(cls):
cls.setup = yaml.load(Path('./tests/data/config.yml').read_text())
cls.RRDN = RRDN(arch_params=cls.setup['rrdn'],
patch_size=cls.setup['patch_size'])
cls.f_ext = Cut_VGG19(patch_size=cls.setup['patch_size'],
layers_to_extract=[1, 2])
cls.discr = Discriminator(patch_size=cls.setup['patch_size'])
cls.weights_path = {
'generator':
Path(cls.setup['weights_dir']) / 'test_gen_weights.hdf5',
'discriminator':
Path(cls.setup['weights_dir']) / 'test_dis_weights.hdf5',
}
cls.TH = TrainerHelper(
generator=cls.RRDN,
weights_dir=cls.setup['weights_dir'],
logs_dir=cls.setup['log_dir'],
lr_train_dir=cls.setup['lr_input'],
feature_extractor=cls.f_ext,
discriminator=cls.discr,
dataname='TEST',
weights_generator='',
weights_discriminator='',
fallback_save_every_n_epochs=2,
)
cls.TH.session_id = '0000'
cls.TH.logger.setLevel(50)
def __init__(
self,
generator,
discriminator,
feature_extractor,
lr_train_dir,
hr_train_dir,
lr_valid_dir,
hr_valid_dir,
loss_weights={
'generator': 1.0,
'discriminator': 0.003,
'feature_extractor': 1 / 12
},
log_dirs={
'logs': 'logs',
'weights': 'weights'
},
fallback_save_every_n_epochs=2,
dataname=None,
weights_generator=None,
weights_discriminator=None,
n_validation=None,
flatness={
'min': 0.0,
'increase_frequency': None,
'increase': 0.0,
'max': 0.0
},
learning_rate={
'initial_value': 0.0004,
'decay_frequency': 100,
'decay_factor': 0.5
},
adam_optimizer={
'beta1': 0.9,
'beta2': 0.999,
'epsilon': None
},
losses={
'generator': 'mae',
'discriminator': 'binary_crossentropy',
'feature_extractor': 'mse',
},
metrics={'generator': 'PSNR_Y'},
):
self.generator = generator
self.discriminator = discriminator
self.feature_extractor = feature_extractor
self.scale = generator.scale
self.lr_patch_size = generator.patch_size
self.learning_rate = learning_rate
self.loss_weights = loss_weights
self.weights_generator = weights_generator
self.weights_discriminator = weights_discriminator
self.adam_optimizer = adam_optimizer
self.dataname = dataname
self.flatness = flatness
self.n_validation = n_validation
self.losses = losses
self.log_dirs = log_dirs
self.metrics = metrics
if self.metrics['generator'] == 'PSNR_Y':
self.metrics['generator'] = PSNR_Y
elif self.metrics['generator'] == 'PSNR':
self.metrics['generator'] = PSNR
self._parameters_sanity_check()
self.model = self._combine_networks()
self.settings = {}
self.settings['training_parameters'] = locals()
self.settings['training_parameters'][
'lr_patch_size'] = self.lr_patch_size
self.settings = self.update_training_config(self.settings)
self.logger = get_logger(__name__)
self.helper = TrainerHelper(
generator=self.generator,
weights_dir=log_dirs['weights'],
logs_dir=log_dirs['logs'],
lr_train_dir=lr_train_dir,
feature_extractor=self.feature_extractor,
discriminator=self.discriminator,
dataname=dataname,
weights_generator=self.weights_generator,
weights_discriminator=self.weights_discriminator,
fallback_save_every_n_epochs=fallback_save_every_n_epochs,
)
self.train_dh = DataHandler(
lr_dir=lr_train_dir,
hr_dir=hr_train_dir,
patch_size=self.lr_patch_size,
scale=self.scale,
n_validation_samples=None,
)
self.valid_dh = DataHandler(
lr_dir=lr_valid_dir,
hr_dir=hr_valid_dir,
patch_size=self.lr_patch_size,
scale=self.scale,
n_validation_samples=n_validation,
)
def __init__(
self,
generator,
discriminator,
feature_extractor,
lr_train_dir,
hr_train_dir,
lr_valid_dir,
hr_valid_dir,
learning_rate=0.0004,
loss_weights={'MSE': 1.0},
logs_dir='logs',
weights_dir='weights',
dataname=None,
weights_generator=None,
weights_discriminator=None,
n_validation=None,
T=0.01,
lr_decay_frequency=100,
lr_decay_factor=0.5,
fallback_save_every_n_epochs=2,
beta_1=0.9,
beta_2=0.999,
epsilon=0.00001,
):
if discriminator:
assert generator.patch_size * generator.scale == discriminator.patch_size
if feature_extractor:
assert generator.patch_size * generator.scale == feature_extractor.patch_size
self.generator = generator
self.discriminator = discriminator
self.feature_extractor = feature_extractor
self.scale = generator.scale
self.lr_patch_size = generator.patch_size
self.learning_rate = learning_rate
self.loss_weights = loss_weights
self.weights_generator = weights_generator
self.weights_discriminator = weights_discriminator
self.lr_decay_factor = lr_decay_factor
self.lr_decay_frequency = lr_decay_frequency
self.beta_1 = beta_1
self.beta_2 = beta_2
self.epsilon = epsilon
self.dataname = dataname
self.T = T
self.n_validation = n_validation
self.helper = TrainerHelper(
generator=self.generator,
weights_dir=weights_dir,
logs_dir=logs_dir,
lr_train_dir=lr_train_dir,
feature_extractor=self.feature_extractor,
discriminator=self.discriminator,
dataname=dataname,
weights_generator=self.weights_generator,
weights_discriminator=self.weights_discriminator,
fallback_save_every_n_epochs=fallback_save_every_n_epochs,
)
self.model = self._combine_networks()
self.train_dh = DataHandler(
lr_dir=lr_train_dir,
hr_dir=hr_train_dir,
patch_size=self.lr_patch_size,
scale=self.scale,
n_validation_samples=None,
T=T,
)
self.valid_dh = DataHandler(
lr_dir=lr_valid_dir,
hr_dir=hr_valid_dir,
patch_size=self.lr_patch_size,
scale=self.scale,
n_validation_samples=n_validation,
T=0.0,
)
self.logger = get_logger(__name__)
self.settings = self.get_training_config()
def __init__(
self,
generator,
discriminator,
feature_extractor,
lr_train_dir,
hr_train_dir,
lr_valid_dir,
hr_valid_dir,
learning_rate=0.0004,
loss_weights={'MSE': 1.0},
logs_dir='logs',
weights_dir='weights',
dataname=None,
weights_generator=None,
weights_discriminator=None,
n_validation=None,
T=0.01,
lr_decay_frequency=100,
lr_decay_factor=0.5,
):
if discriminator:
assert generator.patch_size * generator.scale == discriminator.patch_size
if feature_extractor:
assert generator.patch_size * generator.scale == feature_extractor.patch_size
self.generator = generator
self.discriminator = discriminator
self.feature_extractor = feature_extractor
self.scale = generator.scale
self.lr_patch_size = generator.patch_size
self.learning_rate = learning_rate
self.loss_weights = loss_weights
self.best_metrics = {}
self.pretrained_weights_path = {
'generator': weights_generator,
'discriminator': weights_discriminator,
}
self.lr_decay_factor = lr_decay_factor
self.lr_decay_frequency = lr_decay_frequency
self.helper = TrainerHelper(
generator=self.generator,
weights_dir=weights_dir,
logs_dir=logs_dir,
lr_train_dir=lr_train_dir,
feature_extractor=self.feature_extractor,
discriminator=self.discriminator,
dataname=dataname,
pretrained_weights_path=self.pretrained_weights_path,
)
self.model = self._combine_networks()
self.train_dh = DataHandler(
lr_dir=lr_train_dir,
hr_dir=hr_train_dir,
patch_size=self.lr_patch_size,
scale=self.scale,
n_validation_samples=None,
T=T,
)
self.valid_dh = DataHandler(
lr_dir=lr_valid_dir,
hr_dir=hr_valid_dir,
patch_size=self.lr_patch_size,
scale=self.scale,
n_validation_samples=n_validation,
T=0.01,
)
self.logger = get_logger(__name__)