def test_saver_no_file(self, mock_isfile):
mock_isfile.return_value = False
self.saver = Saver("test", "./", 3, self.lock)
read_data = ""
mockOpen = mock_open(read_data=read_data)
with patch('builtins.open', mockOpen):
self.saver.save(self.model, condition="Test", result= self.result)
def test_saver_file_empty(self, mock_isfile):
mock_isfile.return_value = True
self.saver = Saver("test", "./", 3, self.lock)
read_data = pickle.dumps({})
mockOpen = mock_open(read_data=read_data)
with patch('builtins.open', mockOpen):
self.saver.save(self.model, condition="Test", result=self.result)
def test_load_no_file(self, mock_isfile):
mock_isfile.return_value = False
self.saver = Saver("test", "./", 3, self.lock)
read_data = pickle.dumps({"LSTM": {3:self.model.state_dict()}})
mockOpen = mock_open(read_data=read_data)
with self.assertRaises(Exception):
with patch('builtins.open', mockOpen):
self.saver.load(self.model)
def test_load_file(self, mock_isfile):
mock_isfile.return_value = True
self.saver = Saver("test", "./", 3, self.lock)
read_data = pickle.dumps({"LSTM": {3:self.model.state_dict()}})
mockOpen = mock_open(read_data=read_data)
with patch('builtins.open', mockOpen):
model = self.saver.load(self.model)
self.assertIsInstance(model, LSTMLayer)
def __init__(self,
cardinality: Cardinality,
lock: threading.Lock,
batch_size=128,
path_model="",
name_dataset="",
batch_result=20000,
exclude_test=False,
stoppingcondition="earlystopping",
condition_value=0.005,
condition_step=3,
duration=5,
condition_epoch=3):
self.dataset = cardinality
self.cardinality = self.dataset.cardinality
self.batch_size = batch_size
self.model = -1
# self.stopping_condition = StoppingCondition(method="earlystopping", condition_value = 0.005, condition_step=3)
if stoppingcondition == "earlystopping":
self.stopping_condition = StoppingCondition(
method=stoppingcondition,
condition_value=condition_value,
condition_step=condition_step)
elif stoppingcondition == "timer":
self.stopping_condition = StoppingCondition(
method=stoppingcondition, duration=duration)
elif stoppingcondition == "epoch":
self.stopping_condition = StoppingCondition(
method=stoppingcondition, condition_epoch=condition_epoch)
else:
raise Exception(
"Stopping condition method is not implemented. Please use 'earlystopping', 'timer', or 'epoch'"
)
self.path_model = path_model
self.name_dataset = name_dataset
self.lock = lock
self.exlude_test = exclude_test
self.saver = Saver(path_model=self.path_model,
name_model=self.name_dataset,
cardinality=self.cardinality,
lock=self.lock)
self.batch_result = batch_result
if torch.cuda.is_available():
self.device = torch.device('cuda')
logger.info("Starting learning on GPU")
else:
self.device = torch.device('cpu')
logger.info("Starting learning on CPU")