def test_that_the_output_is_of_the_correct_scale(self):
# create random weights
self.make_model()
self.weights_path = os.path.join(self.weights_folder,
'testweights_E001_.hdf5')
self.rdn.save_weights(self.weights_path)
# test the network with random weights
self.create_random_dataset('correct', dataset_size=1)
cl_args = ['--no_verbose', '--test']
test_args = {
'test_folder': self.dataset_folder['correct']['LR'],
'results_folder': self.results_folder,
'weights_path': self.weights_path,
}
parser = get_parser()
cl_args = parser.parse_args(cl_args)
cl_args = vars(cl_args)
load_configuration(cl_args, '../config.json')
cl_args.update(test_args)
cl_args.update(self.model_params)
run.main(cl_args)
for file in os.listdir(self.results_folder):
img = imageio.imread(os.path.join(self.results_folder, file))
self.assertTrue(img.shape == (self.img_size['HR'],
self.img_size['HR'], 3))
def test_that_configuration_is_loaded_correctly(self):
parser = get_parser()
cl_args = parser.parse_args(['--train', '--custom-data'])
cl_args = vars(cl_args)
load_configuration(cl_args, '../config.json')
self.assertTrue(isinstance(cl_args['G'], int))
self.assertTrue(cl_args['log_dir'] == './logs')
self.assertTrue(cl_args['weights_dir'] == './weights')
self.assertTrue(cl_args['data_name'] == 'CUSTOM')
def test_if_train_is_executed(self):
self.create_random_dataset('correct', dataset_size=14)
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)
run.main(cl_args)
self.assertTrue(1 != 0)
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
sm_inp.active = False
vm_nrn.active = False
for vm in vm_br:
vm.active = False
core.kernel.run_chunk(simulation_time - 40, 0, simulation_time)
sm_inp.active = True
vm_nrn.active = True
for vm in vm_br:
vm.active = True
core.kernel.run_chunk(20.0, 0, simulation_time)
if __name__ == '__main__':
import copy
from scoop import futures
# Load the configuration file.
config = utils.load_configuration("config_rewiring_ex1.yaml")
configs = []
num_trials = 25
for trial in range(num_trials):
config["master_seed"] = 10 * (trial + 1)
configs.append(copy.deepcopy(config))
r = list(
futures.map(main,
[[trial, config] for trial, config in enumerate(configs)]))
'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.predict import Predictor
predictor = Predictor(test_arguments=arguments)
predictor.get_predictions(model)
if __name__ == '__main__':
parser = get_parser()
cl_args = parser.parse_args()
cl_args = vars(cl_args)
load_configuration(cl_args)
main(cl_args)