def test_05_load_model(self): path = ROOT_PATH manager = InstanceManager(path) model = DummyModel input_shape = (32, 32, 3) manager.build_instance(model) 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_instance(dataset, epoch_time, check_point_interval) # manager = InstanceManager(path) manager.load_instance(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_instance(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 load_and_sampling(input_shapes=None, visualizers=None, metadata_path=None, env_setting=None): manager = InstanceManager(env_setting) manager.load_instance(instance_path=metadata_path, input_shapes=input_shapes) manager.load_visualizer(visualizers) manager.sampling_instance(is_restore=True)
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 manager.build_instance(model) visualizer_1 = DummyVisualizer_1 visualizer_2 = DummyVisualizer_2 visualizers = [visualizer_1, visualizer_2] manager.load_visualizer(visualizers)
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 manager.build_instance(model) visualizer_1 = DummyVisualizer_1 visualizer_2 = DummyVisualizer_2 visualizers = [visualizer_1, visualizer_2] manager.load_visualizer(visualizers) dataset = DummyDataset() epoch_time = 10 manager.train_instance(dataset, epoch_time)
def load_model_and_train(input_shapes=None, visualizers=None, metadata_path=None, env_setting=None, dataset=None, epoch_time=50, check_point_interval_per_iter=5000): manager = None try: manager = InstanceManager(env_setting) manager.load_model(metadata_path=metadata_path, input_shapes=input_shapes) manager.load_visualizer(visualizers) manager.open_tensorboard() manager.train_model(dataset, epoch_time, check_point_interval_per_iter, is_restore=True) except Exception as e: print(e) finally: manager.close_tensorboard() del manager
def build_and_train(model=None, input_shapes=None, dataset=None, visualizers=None, epoch_time=50, check_point_interval=5000): manager = InstanceManager() metadata_path = manager.build_instance(model) manager.load_instance(metadata_path, input_shapes) for visualizer, interval in visualizers: manager.load_visualizer(visualizer, interval) manager.train_instance(epoch_time, dataset=dataset, check_point_interval=check_point_interval) del manager
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)
def tf_model_train(model=None, dataset=None, visuliziers=None, epoch=None): dataset = DatasetLoader().load_dataset(dataset) input_shapes = dataset.train_set.input_shapes model = ModelClassLoader.load_model_class(model) manager = InstanceManager() metadata_path = manager.build_instance(model, input_shapes) manager.load_instance(metadata_path) for v_fun, i in visuliziers: manager.load_visualizer(VisualizerClassLoader.load(v_fun), i) manager.train_instance(epoch=epoch, dataset=dataset, check_point_interval=5000, with_tensorboard=True) del manager
# build model from model.ModelClassLoader import ModelClassLoader from InstanceManger import InstanceManager model = ModelClassLoader.load_model_class("model") manager = InstanceManager() model_metadata_path = manager.build_instance(model) # load model # after build model manager.load_instance(model_metadata_path, input_shapes_from_dataset) # load visualizer # feed visualizer_class from visualizer.VisualizerClassLoader import VisualizerClassLoader visualizer = VisualizerClassLoader.load("visualizer_name") manager.load_visualizer(visualizer, execute_interval=10) # train model # after load instance into manager train model manager.train_instance( epoch_time, dataset=dataset, check_point_interval=check_point_interval, # if need to start train from checkpoint # set is_restore to True # is_restore=True # default to start with tensorboard sub process while train instance and after train instance close tensorboard # if does not need tensorboard set with_tensorboard to False
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
def build_test_model(model_list=None, env_setting=None): for model in model_list: manager = InstanceManager(env_setting) manager.build_instance(model) del manager
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_instance(dataset, epoch_time, check_point_interval) if __name__ == '__main__': path = ROOT_PATH manager = InstanceManager(path) model = DummyModel input_shape = (32, 32, 3) manager.build_instance(model) visualizers = [ (dummy_log, 10), ] manager.load_visualizer(visualizers) dataset = DummyDataset() epoch_time = 1 check_point_interval = 500 manager.train_instance(dataset, epoch_time, check_point_interval)
def test_01_gen_model(self): path = ROOT_PATH manager = InstanceManager(path) model = DummyModel manager.build_instance(model)
def test_00_init(self): path = ROOT_PATH InstanceManager(path)