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 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(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'] * 2, layers_to_extract=[1, 2])
cls.discr = Discriminator(patch_size=cls.setup['patch_size'] * 2)
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.temp_data = Path('tests/temporary_test_data')
cls.not_matching_hr = cls.temp_data / 'not_matching_hr'
cls.not_matching_hr.mkdir(parents=True)
for item in ['data2.gif', 'data1.png', 'data0.jpeg']:
(cls.not_matching_hr / item).touch()
cls.not_matching_lr = cls.temp_data / 'not_matching_lr'
cls.not_matching_lr.mkdir(parents=True)
for item in ['data1.png']:
(cls.not_matching_lr / item).touch()
cls.matching_hr = cls.temp_data / 'matching_hr'
cls.matching_hr.mkdir(parents=True)
for item in ['data2.gif', 'data1.png', 'data0.jpeg']:
(cls.matching_hr / item).touch()
cls.matching_lr = cls.temp_data / 'matching_lr'
cls.matching_lr.mkdir(parents=True)
for item in ['data1.png', 'data0.jpeg']:
(cls.matching_lr / item).touch()
with patch('ISR.utils.datahandler.DataHandler._check_dataset', return_value=True):
cls.trainer = Trainer(
generator=cls.RRDN,
discriminator=cls.discr,
feature_extractor=cls.f_ext,
lr_train_dir=str(cls.matching_lr),
hr_train_dir=str(cls.matching_hr),
lr_valid_dir=str(cls.matching_lr),
hr_valid_dir=str(cls.matching_hr),
learning_rate={'initial_value': 0.0004, 'decay_factor': 0.5, 'decay_frequency': 5},
log_dirs={
'logs': './tests/temporary_test_data/logs',
'weights': './tests/temporary_test_data/weights',
},
dataname='TEST',
weights_generator=None,
weights_discriminator=None,
n_validation=2,
flatness={'min': 0.01, 'max': 0.3, 'increase': 0.01, 'increase_frequency': 5},
adam_optimizer={'beta1': 0.9, 'beta2': 0.999, 'epsilon': None},
losses={'generator': 'mae', 'discriminator': 'mse', 'feature_extractor': 'mse'},
loss_weights={'generator': 1.0, 'discriminator': 1.0, 'feature_extractor': 0.5},
)
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'] * 2, layers_to_extract=[1, 2])
cls.discr = Discriminator(patch_size=cls.setup['patch_size'] * 2)
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'),
}
def fake_folders(kind):
if kind['matching'] == False:
if kind['res'] == 'hr':
return ['data2.gif', 'data1.png', 'data0.jpeg']
elif kind['res'] == 'lr':
return ['data1.png']
else:
raise
if kind['matching'] == True:
if kind['res'] == 'hr':
return ['data2.gif', 'data1.png', 'data0.jpeg']
elif kind['res'] == 'lr':
return ['data1.png', 'data0.jpeg']
else:
raise
with patch('os.listdir', side_effect=fake_folders):
with patch('ISR.utils.datahandler.DataHandler._check_dataset', return_value=True):
cls.trainer = Trainer(
generator=cls.RRDN,
discriminator=cls.discr,
feature_extractor=cls.f_ext,
lr_train_dir={'res': 'lr', 'matching': True},
hr_train_dir={'res': 'hr', 'matching': True},
lr_valid_dir={'res': 'lr', 'matching': True},
hr_valid_dir={'res': 'hr', 'matching': True},
learning_rate=0.0004,
loss_weights={'MSE': 1.0, 'discriminator': 1.0, 'feat_extr': 1.0},
logs_dir='./tests/temporary_test_data/logs',
weights_dir='./tests/temporary_test_data/weights',
dataname='TEST',
weights_generator=None,
weights_discriminator=None,
n_validation=2,
lr_decay_factor=0.5,
lr_decay_frequency=5,
T=0.01,
)
def setUpClass(cls):
cls.setup = yaml.load(open(os.path.join('tests', 'data', 'config.yml'), 'r'))
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.hr_shape = (cls.setup['patch_size'] * 2,) * 2 + (3,)
cls.RRDN = RRDN(arch_params=cls.setup['rrdn'], patch_size=cls.setup['patch_size'])
cls.RRDN.model.compile(optimizer=Adam(), loss=['mse'])
cls.RDN = RDN(arch_params=cls.setup['rdn'], patch_size=cls.setup['patch_size'])
cls.RDN.model.compile(optimizer=Adam(), loss=['mse'])
cls.f_ext = Cut_VGG19(patch_size=cls.setup['patch_size'] * 2, layers_to_extract=[1, 2])
cls.f_ext.model.compile(optimizer=Adam(), loss=['mse', 'mse'])
cls.discr = Discriminator(patch_size=cls.setup['patch_size'] * 2)
cls.discr.model.compile(optimizer=Adam(), loss=['mse'])
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'] * 2,
layers_to_extract=[1, 2])
cls.discr = Discriminator(patch_size=cls.setup['patch_size'] * 2)
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.temp_data = Path('tests/temporary_test_data')
cls.not_matching_hr = cls.temp_data / 'not_matching_hr'
cls.not_matching_hr.mkdir(parents=True)
for item in ['data2.gif', 'data1.png', 'data0.jpeg']:
(cls.not_matching_hr / item).touch()
cls.not_matching_lr = cls.temp_data / 'not_matching_lr'
cls.not_matching_lr.mkdir(parents=True)
for item in ['data1.png']:
(cls.not_matching_lr / item).touch()
cls.matching_hr = cls.temp_data / 'matching_hr'
cls.matching_hr.mkdir(parents=True)
for item in ['data2.gif', 'data1.png', 'data0.jpeg']:
(cls.matching_hr / item).touch()
cls.matching_lr = cls.temp_data / 'matching_lr'
cls.matching_lr.mkdir(parents=True)
for item in ['data1.png', 'data0.jpeg']:
(cls.matching_lr / item).touch()
with patch('ISR.utils.datahandler.DataHandler._check_dataset',
return_value=True):
cls.trainer = Trainer(
generator=cls.RRDN,
discriminator=cls.discr,
feature_extractor=cls.f_ext,
lr_train_dir=str(cls.matching_lr),
hr_train_dir=str(cls.matching_hr),
lr_valid_dir=str(cls.matching_lr),
hr_valid_dir=str(cls.matching_hr),
learning_rate=0.0004,
loss_weights={
'MSE': 1.0,
'discriminator': 1.0,
'feat_extr': 1.0
},
logs_dir='./tests/temporary_test_data/logs',
weights_dir='./tests/temporary_test_data/weights',
dataname='TEST',
weights_generator=None,
weights_discriminator=None,
n_validation=2,
lr_decay_factor=0.5,
lr_decay_frequency=5,
T=0.01,
)