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_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_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)
class UtilTest(unittest.TestCase):
def setUp(self):
self.lock = torch.multiprocessing.get_context('spawn').Lock()
self.model = LSTMLayer(num_classes=5)
cardinality = Cardinality(3, "", "")
cardinality.list_classes = [1,1,1,2,2,3,4,5,6]
cardinality.counter= {1:10, 2:100, 3:100, 4:100, 6:1000, 5:1000}
cardinality.compute_position()
self.result = Result(cardinality)
@patch('os.path.isfile')
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)
@patch('os.path.isfile')
def test_saver_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):
self.saver.save(self.model, condition="Test", result= self.result)
@patch('os.path.isfile')
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)
@patch('os.path.isfile')
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)
@patch('os.path.isfile')
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 __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")
class Worker_single():
"""A single worker is responsible for the creation of the dataloader, the learning/testing step and for saving files of one cardinality.
Args:
cardinality (Cardinality): the cardinality object containing the data.
lock (threading.Lock): lock used for saving files in the same file for all cardinalities.
batch_size (int, optional): size of the batch. Defaults to 128.
path_model (str, optional): path to the model to save. Defaults to "".
name_dataset (str, optional): name of the dataset. Defaults to "".
batch_result (int, optional): show results each batch_result number of batchs. Defaults to 2000.
exclude_test (boolean, optional): exlude the testing step during the learning step. Can be use with the timer as stopping condition to have an exact duration.
stoppingcondition (str, optional): condition to stop the learning step (timer, earlystopping, epoch). Defaults to earlystopping.
condition_value (float, optional): stoppingcondition option. Value of the increase. Defaults to 0.005.
condition_step (int, optional): stoppingcondition option. Number of steps. Defaults to 3.
duration (int, optional): stoppingcondition option. Duration of the learning step in minute. Defaults to 60.
condition_epoch (int, optional): stoppingcondition option. Number of epochs to be done. Defaults to 3.
"""
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")
def create_dataloader(self,
validation_split=0.6,
condition="Test",
subsample=False,
subsample_split=0.01) -> DataLoader:
"""Create the dataloader for the learning/testing step.
Args:
validation_split (float, optional): ratio between the learning and the testing set. Defaults to 0.6.
condition (str, optional): if Test the dataloader contains the test data. Else it contains the learning data. Defaults to "Test".
subsample (bool, optional): use only a subsample of the data. Can be used for the learning and/or the testing step. Defaults to False.
subsample_split (float, optional): ratio of the data to use. Defaults to 0.01.
Returns:
DataLoader: PyTorch dataloader corresponding to the previous features.
"""
if not self.dataset.loaded:
self.dataset.load_files()
self.dataset.compute_position()
self.size = len(self.dataset)
logger.info("Cardinality: " + str(self.dataset.cardinality) +
" size of dataset: " + str(self.size))
logger.info("Nb of classes: " +
str(self.dataset.number_of_classes))
# Set the random seed to have always the same random value.
random_seed = 42
np.random.seed(random_seed)
split = int(np.floor(validation_split * self.size))
indices = list(range(self.size))
np.random.shuffle(indices)
if condition == "Test":
indices = indices[:split]
else:
indices = indices[split:]
if subsample:
split = int(np.floor(subsample_split * len(indices)))
np.random.shuffle(indices)
indices = indices[:split]
sampler = SubsetRandomSampler(indices)
dataloader = DataLoader(self.dataset,
batch_size=self.batch_size,
pin_memory=True,
drop_last=True,
num_workers=5,
sampler=sampler) # type: ignore
return dataloader
def load_model(self):
"""Load the learned model from a previous state
Raises:
e: file is not found
"""
self.model = LSTMLayer(num_classes=self.dataset.number_of_classes).to(
self.device)
try:
self.model = self.saver.load(model=self.model)
except FileNotFoundError as e:
logger.critical("No such file: " + self.path_model +
self.name_dataset + "_model.lf" + ".torch")
print("Raising: ", e)
raise e
def train(self, validation_split=0.6, resuming=False):
"""Train the model
Args:
validation_split (float, optional): ratio between testing and learning set. Defaults to 0.6.
resuming (bool, optional): resume the learning from a previous step. Not implemented yet. Defaults to False.
"""
# Create the dataloader
dataloader_train = self.create_dataloader(
validation_split=validation_split, condition="train")
if resuming:
self.load_model()
else:
self.model = LSTMLayer(num_classes=self.dataset.number_of_classes,
batch_size=self.batch_size).to(self.device)
# Create the results
result = Result(self.dataset, condition="Train")
optimizer = optim.Adam(self.model.parameters())
loss_fn = nn.CrossEntropyLoss()
self.model.train()
logger.info("Cardinality: " + str(self.cardinality) +
" Starting the learning step")
# Start the learning
while not self.stopping_condition.stop():
for index_batch, batch in enumerate(dataloader_train):
optimizer.zero_grad()
label = batch['output']
input_data = batch['input'].to(self.device)
prediction = self.model(input_data)
loss = loss_fn(prediction, label.to(self.device))
loss.backward()
optimizer.step()
result.update(prediction, label)
# Compute the results each 2000 batchs.
if index_batch % self.batch_result == 0 and index_batch != 0:
result.computing_result(progress=index_batch /
len(dataloader_train))
self.saver.save(model=self.model,
result=result,
condition="temp")
if not self.exlude_test:
# Test only on a subsample
self.test(subsample=True, subsample_split=0.1)
print(self.stopping_condition)
# Test if we stop only for the timer method at each batch
if self.stopping_condition.method == "timer" and self.stopping_condition.stop(
):
logger.debug("[Stopping] Cardinality: " +
str(self.cardinality) + " " +
str(self.stopping_condition) +
" stopping learning step.")
break
# At the end of one epoch, use the all testing test and update the condition
if not self.exlude_test:
self.test()
result.computing_result(reinit=True, progress=1)
if self.stopping_condition.stop():
logger.debug("[Stopping] Cardinality: " +
str(self.cardinality) + " " +
str(self.stopping_condition) +
" stopping learning step.")
self.saver.save(model=self.model, result=result, condition="Train")
# logger.info("[Test] Cardinality: " + str(self.cardinality) + " " + str(self.stopping_condition) + " stopping learning step.")
# self.saver.save(model=self.model.state_dict())
def test(self,
validation_split=0.6,
subsample=False,
subsample_split=0.01):
"""Test the model
Args:
validation_split (float, optional): ratio between testing and learning set. Defaults to 0.6.
subsample (bool, optional): if False, use all the available data, if True, use only a ratio of the data (subsample_split*data). Defaults to False.
subsample_split (float, optional): ratio of the data to use. Defaults to 0.01.
"""
dataloader_test = self.create_dataloader(
validation_split=validation_split,
condition="Test",
subsample=subsample,
subsample_split=0.01)
result = Result(self.dataset, condition="Test", subsample=subsample)
if self.model == -1:
self.load_model()
self.model.eval()
self.conf_matrix = torch.zeros(self.dataset.number_of_classes,
self.dataset.number_of_classes)
for index_batch, batch in enumerate(dataloader_test):
label = batch['output']
input_data = batch['input'].to(self.device)
prediction = self.model(input_data)
result.update(prediction, label)
if index_batch % self.batch_result == 0:
result.computing_result(reinit=False,
progress=index_batch /
len(dataloader_test))
self.model.train()
self.saver.save(model=self.model, result=result, condition="Test")
result.computing_result(reinit=True, progress=1)
self.stopping_condition.update(result.microf1)
self.stopping_condition.stop()