def test_05_load_model(self):
path = ROOT_PATH
manager = InstanceManager(path)
model = DummyModel
input_shape = (32, 32, 3)
manager.gen_instance(model, input_shape)
meta_data_path = manager.metadata_path
visualizer_1 = DummyVisualizer_1
visualizer_2 = DummyVisualizer_2
visualizers = [visualizer_1, visualizer_2]
manager.load_visualizer(visualizers)
dataset = DummyDataset()
epoch_time = 10
check_point_interval = 2
manager.train_model(dataset, epoch_time, check_point_interval)
#
manager = InstanceManager(path)
manager.load_model(meta_data_path, input_shape)
visualizer_1 = DummyVisualizer_1
visualizer_2 = DummyVisualizer_2
visualizers = [visualizer_1, visualizer_2]
manager.load_visualizer(visualizers)
dataset = DummyDataset()
epoch_time = 10
check_point_interval = 2
manager.train_model(dataset, epoch_time, check_point_interval)
def test_01_gen_model(self):
path = ROOT_PATH
manager = InstanceManager(path)
model = DummyModel
input_shape = (32, 32, 3)
manager.gen_instance(model, input_shape)
def gen_model_and_train(model=None, input_shapes=None, dataset=None, visualizers=None, env_path=None, epoch_time=50,
check_point_interval_per_iter=5000):
manager = InstanceManager(env_path)
manager.gen_instance(model, input_shapes)
manager.load_visualizer(visualizers)
manager.open_tensorboard()
manager.train_model(dataset, epoch_time, check_point_interval_per_iter)
manager.close_tensorboard()
del manager
def test_02_load_visualizer(self):
path = ROOT_PATH
manager = InstanceManager(path)
model = DummyModel
input_shape = (32, 32, 3)
manager.gen_instance(model, input_shape)
visualizer_1 = DummyVisualizer_1
visualizer_2 = DummyVisualizer_2
visualizers = [visualizer_1, visualizer_2]
manager.load_visualizer(visualizers)
def test_03_train_model(self):
path = ROOT_PATH
manager = InstanceManager(path)
model = DummyModel
input_shape = (32, 32, 3)
manager.gen_instance(model, input_shape)
visualizer_1 = DummyVisualizer_1
visualizer_2 = DummyVisualizer_2
visualizers = [visualizer_1, visualizer_2]
manager.load_visualizer(visualizers)
dataset = DummyDataset()
epoch_time = 10
manager.train_model(dataset, epoch_time)
def f():
path = ROOT_PATH
manager = InstanceManager(path)
model = DummyModel
input_shape = (32, 32, 3)
manager.gen_instance(model, input_shape)
visualizers = [
(dummy_log, 10),
]
manager.load_visualizer(visualizers)
dataset = DummyDataset()
epoch_time = 1
check_point_interval = 500
manager.train_model(dataset, epoch_time, check_point_interval)
import os
class dummy_log(AbstractPrintLog):
def task(self, sess=None, iter_num=None, model=None, dataset=None):
super().task(sess, iter_num, model, dataset)
self.log('this is dummy log')
if __name__ == '__main__':
root_path = ROOT_PATH
LLD_PATH = os.path.join(root_path, 'dataset', 'LLD')
lld_data = LLD()
lld_data.load(LLD_PATH)
manager = InstanceManager(ROOT_PATH)
input_shape = [32, 32, 3]
model = GAN
manager.gen_instance(model, input_shape)
metadata_path = manager.metadata_path
iter_cycle = 10
visualizers = [(dummy_log, iter_cycle)]
manager.load_visualizer(visualizers)
epoch_time = 10
check_point_interval_per_iter = 5000
manager.train_model(lld_data, epoch_time, check_point_interval_per_iter)
def test_models(model_list=None, input_shapes=None, env_setting=None):
for model in model_list:
manager = InstanceManager(env_setting)
manager.gen_instance(model, input_shapes)
del manager