def load_trainer(self):
"""
AUTHORS:
--------
:author: Alix Leroy
:author: Samuel Westlake
DESCRIPTION:
------------
Load a trainer
PARAMETERS:
-----------
None
RETURN:
-------
:return None
"""
# If the train step is enabled
if self.config.data.enabled.train:
self.loading_message("Trainer")
# Input transform manager
transform_manager = TransformManager(
**self.config.transform.train.get(ignore="outputs"))
# Output Transformer
output_transform_manager = OutputTransformer(
transform_files=self.config.transform.train.get("outputs"))
# Initialise training dataset
i = self.get_dataset_index(
DEEP_DATASET_TRAIN) # Get index of train dataset
dataset = Dataset(**self.config.data.datasets[i].get(
ignore=["batch_size"]),
transform_manager=transform_manager)
# Initialise scheduler
if self.optimizer is not None and self.config.training.scheduler.enabled:
self.load_scheduler()
# Initialise trainer
self.trainer = Trainer(
dataset,
**self.config.data.dataloader.get(),
batch_size=self.config.data.datasets[i].batch_size,
model=self.model,
metrics=self.metrics,
losses=self.losses,
optimizer=self.optimizer,
scheduler=self.scheduler,
**self.config.training.get(
ignore=["overwatch", "saver", "scheduler"]),
validator=self.validator,
transform_manager=output_transform_manager)
else:
Notification(DEEP_NOTIF_INFO, "Trainer disabled")
def load_predictor(self):
# If the predict step is enabled
if self.config.data.enabled.predict:
self.loading_message("Predictor")
i = self.get_dataset_index(DEEP_DATASET_PREDICTION)
Notification(
DEEP_NOTIF_INFO,
DEEP_NOTIF_DATA_LOADING % self.config.data.datasets[i].name)
# Input Transform Manager
transform_manager = TransformManager(
**self.config.transform.predict.get(ignore="outputs"))
# Output Transform Manager
output_transform_manager = OutputTransformer(
transform_files=self.config.transform.predict.get("outputs"))
# Initialise prediction dataset
dataset = Dataset(**self.config.data.datasets[i].get(
ignore=["batch_size"]),
transform_manager=transform_manager)
# Initialise predictor
#self.predictor = Predictor(
# **self.config.data.dataloader.get(),
# batch_size = self.config.data.datasets[i].batch_size,
# name="Predictor",
# model=self.model,
# dataset=dataset,
# transform_manager=output_transform_manager
#)
else:
Notification(DEEP_NOTIF_INFO,
DEEP_MSG_DATA_DISABLED % DEEP_DATASET_PREDICTION.name)
def load_predictor(self):
# If the predict step is enabled
if self.config.data.enabled.predict:
predict_index = self.get_dataset_index("predict")
Notification(
DEEP_NOTIF_INFO, DEEP_NOTIF_DATA_LOADING %
self.config.data.datasets[predict_index].name)
# Input Transform Manager
transform_manager = TransformManager(
**self.config.transform.predict.get(ignore="outputs"))
# Output Transform Manager
output_transform_manager = OutputTransformer(
transform_files=self.config.transform.predict.get("outputs"))
# Dataset
dataset = Dataset(**self.config.data.datasets[predict_index].get(
ignore="type"),
transform_manager=transform_manager)
# Predictor
self.predictor = Predictor(
**self.config.data.dataloader.get(),
model=self.model,
dataset=dataset,
transform_manager=output_transform_manager)
else:
Notification(DEEP_NOTIF_INFO,
DEEP_MSG_DATA_DISABLED % "Prediction set")
def load_validator(self):
"""
AUTHORS:
--------
:author: Alix Leroy
:author: Samuel Westlake
DESCRIPTION:
------------
Load the validation inferer in memory
PARAMETERS:
-----------
None
RETURN:
-------
:return: None
"""
# If the validation step is enabled
if self.config.data.enabled.validation:
self.loading_message("Validator")
# Transform Manager
transform_manager = TransformManager(
**self.config.transform.validation.get(ignore="outputs"))
# Output Transformer
output_transform_manager = OutputTransformer(
transform_files=self.config.transform.validation.get(
"outputs"))
# Initialise validation dataset
i = self.get_dataset_index(DEEP_DATASET_VAL)
dataset = Dataset(**self.config.data.datasets[i].get(
ignore=["batch_size"]),
transform_manager=transform_manager)
# Initialise validator
self.validator = Tester(
**self.config.data.dataloader.get(),
batch_size=self.config.data.datasets[i].batch_size,
model=self.model,
dataset=dataset,
metrics=self.metrics,
losses=self.losses,
transform_manager=output_transform_manager,
name="Validator")
# Update trainer.tester with this new validator
if self.trainer is not None:
self.trainer.tester = self.validator
else:
Notification(DEEP_NOTIF_INFO,
DEEP_MSG_DATA_DISABLED % DEEP_DATASET_VAL.name)
def load_validator(self):
"""
AUTHORS:
--------
:author: Alix Leroy
:author: Samuel Westlake
DESCRIPTION:
------------
Load the validation inferer in memory
PARAMETERS:
-----------
None
RETURN:
-------
:return: None
"""
# If the validation step is enabled
if self.config.data.enabled.validation:
validation_index = self.get_dataset_index("validation")
Notification(
DEEP_NOTIF_INFO, DEEP_NOTIF_DATA_LOADING %
self.config.data.datasets[validation_index].name)
# Transform Manager
transform_manager = TransformManager(
**self.config.transform.validation.get(ignore="outputs"))
# Output Transformer
output_transform_manager = OutputTransformer(
transform_files=self.config.transform.validation.get(
"outputs"))
# output_transformer.summary()
# Dataset
dataset = Dataset(**self.config.data.datasets[validation_index].
get(ignore="type"),
transform_manager=transform_manager)
# Validator
self.validator = Tester(**self.config.data.dataloader.get(),
model=self.model,
dataset=dataset,
metrics=self.metrics,
losses=self.losses,
transform_manager=output_transform_manager)
else:
Notification(DEEP_NOTIF_INFO,
DEEP_MSG_DATA_DISABLED % "Validation set")
def load_tester(self):
"""
AUTHORS:
--------
:author: Alix Leroy
:author: Samuel Westlake
DESCRIPTION:
------------
Load the test inferer in memory
PARAMETERS:
-----------
None
RETURN:
-------
:return: None
"""
# If the test step is enabled
if self.config.data.enabled.test:
self.loading_message("Tester")
i = self.get_dataset_index(DEEP_DATASET_TEST)
Notification(
DEEP_NOTIF_INFO,
DEEP_NOTIF_DATA_LOADING % self.config.data.datasets[i].name)
# Input Transform Manager
transform_manager = TransformManager(
**self.config.transform.test.get(ignore="outputs"))
# Output Transformer
output_transform_manager = OutputTransformer(
transform_files=self.config.transform.test.get("outputs"))
# Initialise test dataset
dataset = Dataset(**self.config.data.datasets[i].get(
ignore=["batch_size"]),
transform_manager=transform_manager)
# Initialise tester
self.tester = Tester(
**self.config.data.dataloader.get(),
batch_size=self.config.data.datasets[i].batch_size,
model=self.model,
dataset=dataset,
metrics=self.metrics,
losses=self.losses,
transform_manager=output_transform_manager)
else:
Notification(DEEP_NOTIF_INFO,
DEEP_MSG_DATA_DISABLED % DEEP_DATASET_TEST.name)
def load_trainer(self):
"""
AUTHORS:
--------
:author: Alix Leroy
:author: Samuel Westlake
DESCRIPTION:
------------
Load a trainer
PARAMETERS:
-----------
None
RETURN:
-------
:return None
"""
# If the train step is enabled
if self.config.data.enabled.train:
train_index = self.get_dataset_index("train")
Notification(
DEEP_NOTIF_INFO, DEEP_NOTIF_DATA_LOADING %
self.config.data.datasets[train_index].name)
# Input Transform Manager
transform_manager = TransformManager(
**self.config.transform.train.get(ignore="outputs"))
# Output Transformer
output_transform_manager = OutputTransformer(
transform_files=self.config.transform.train.get("outputs"))
# output_transformer.summary()
# Dataset
dataset = Dataset(**self.config.data.datasets[train_index].get(
ignore="type"),
transform_manager=transform_manager)
# Trainer
self.trainer = Trainer(
**self.config.data.dataloader.get(),
model=self.model,
dataset=dataset,
metrics=self.metrics,
losses=self.losses,
optimizer=self.optimizer,
num_epochs=self.config.training.num_epochs,
initial_epoch=self.config.training.initial_epoch,
shuffle_method=self.config.training.shuffle,
verbose=self.config.history.verbose,
tester=self.validator,
transform_manager=output_transform_manager)
else:
Notification(DEEP_NOTIF_INFO,
DEEP_MSG_DATA_DISABLED % "Training set")