def main(arguments):
model_parameters = {
'scale': arguments['scale'],
'learning_rate': 1e-5,
'D': arguments['D'],
'C': arguments['C'],
'G': arguments['G'],
'kernel_size': 3,
'c_dim': 3,
'G0': arguments['G0'],
}
model = load_model(model_parameters, arguments['vgg'], verbose=arguments['verbose'])
if arguments['summary'] is True:
model.rdn.summary()
if arguments['train'] is True:
from trainer.train import Trainer
trainer = Trainer(train_arguments=arguments)
trainer.train_model(model)
if arguments['test'] is True:
from predict import Predictor
predictor = Predictor(test_arguments=arguments)
predictor.get_predictions(model)
def test_if_trainable_weights_update_with_one_step(self):
self.scale = self.model_params['scale']
self.img_size = {'HR': 10 * self.scale, 'LR': 10}
self.dataset_size = 8
self.create_random_dataset(type='correct')
before_step = []
for layer in self.model.rdn.layers:
if len(layer.trainable_weights) > 0:
before_step.append(layer.get_weights()[0])
train_arguments = {
'validation_labels': self.dataset_folder['correct']['HR'],
'validation_input': self.dataset_folder['correct']['LR'],
'training_labels': self.dataset_folder['correct']['HR'],
'training_input': self.dataset_folder['correct']['LR'],
}
cl_args = ['--pytest', '--no_verbose']
parser = get_parser()
cl_args = parser.parse_args(cl_args)
cl_args = vars(cl_args)
load_configuration(cl_args, '../config.json')
cl_args.update(train_arguments)
trainer = Trainer(train_arguments=cl_args)
i = 0
for layer in self.model.rdn.layers:
if len(layer.trainable_weights) > 0:
self.assertTrue(
np.all(before_step[i] == layer.get_weights()[0]))
i += 1
trainer.train_model(self.model)
i = 0
for layer in self.model.rdn.layers:
if len(layer.trainable_weights) > 0:
self.assertFalse(
np.all(before_step[i] == layer.get_weights()[0]))
i += 1