def load_loss(self, loss):
"""
Initialize PyTorch loss function
:param loss: class name of loss
:return: initialized loss
"""
Loss = import_class(loss)
loss = Loss()
self.logger.print_log(f'Loss: {Loss.__name__} initialized')
return loss
def load_sampler(self, sampler, **sampler_args):
"""
Initialize data sampler
:param sampler: class name of sampler
:param sampler_args: arguments for sampler
:return: initialized sampler
"""
Sampler = import_class(sampler)
sampler = Sampler(**sampler_args)
self.logger.print_log(f'Sampler: {Sampler.__name__} initialized')
return sampler
def load_feeder(self, feeder, **feeder_args):
"""
Initialize data feeder
:param feeder: class name of feeder
:param feeder_args: arguments for feeder
:return: initialized feeder
"""
Feeder = import_class(feeder)
feeder = Feeder(**feeder_args)
self.logger.print_log(f'Feeder: {Feeder.__name__} initialized')
return feeder
def load_scheduler(self, scheduler, optimizer, **scheduler_args):
"""
Initialize PyTorch scheduler
:param scheduler: class name of scheduler
:param optimizer: initialized optimizer
:param scheduler_args: arguments for scheduler
:return: initialized scheduler
"""
Scheduler = import_class(scheduler)
scheduler = Scheduler(optimizer, **scheduler_args)
self.logger.print_log(f'Scheduler: {Scheduler.__name__} initialized')
return scheduler
def load_optimizer(self, optimizer, model, **optimizer_args):
"""
Initialize PyTorch optimizer for model training
:param optimizer: class name of optimizer
:param model: model to optimize
:param optimizer_args: arguments for optimizer
:return: initialized optimizer
"""
Optimizer = import_class(optimizer)
optimizer = Optimizer(model.parameters(), **optimizer_args)
self.logger.print_log(f'Optimizer: {Optimizer.__name__} initialized')
return optimizer
def load_model(self, model, **model_args):
"""
Load PyTorch model
:param model: class name of model
:param model_args: arguments for model
:return: initialized model
"""
Model = import_class(model)
model = Model(**model_args)
self.model_text += '\n\n' + str(model)
self.logger.print_log(f'Model: {Model.__name__} initialized')
return model