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_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_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 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 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
def test_01_gen_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
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 build_test_model(model_list=None, env_setting=None):
for model in model_list:
manager = InstanceManager(env_setting)
manager.build_instance(model)
del manager