def __init__(self, config: Config, model: torch.nn.Module):
optimizer: torch.optim.Optimizer = create_optimizer(
config.optimizer, model)
self.scheduler: torch.optim.lr_scheduler = (create_scheduler(
config.scheduler, optimizer) if config.scheduler else Scheduler())
self.sparsifier: Sparsifier = (create_sparsifier(config.sparsifier)
if config.sparsifier else Sparsifier())
model, self.optimizer = precision.initialize(model, optimizer)
self.config = config
def __init__(self, config: Config, model: torch.nn.Module):
if config.early_stop_after > 0:
assert config.do_eval, "can't do early stopping when not running evalution"
optimizer: torch.optim.Optimizer = create_optimizer(
config.optimizer, model)
self.scheduler: torch.optim.lr_scheduler = (create_scheduler(
config.scheduler, optimizer) if config.scheduler else Scheduler())
self.sparsifier: Sparsifier = (create_sparsifier(config.sparsifier)
if config.sparsifier else Sparsifier())
model, self.optimizer = precision.initialize(model, optimizer)
self.config = config
def __init__(self, config: Config, model: torch.nn.Module):
if config.early_stop_after > 0:
assert config.do_eval, "can't do early stopping when not running evalution"
if precision.FP16_ENABLED:
self.optimizer: torch.optim.Optimizer = create_optimizer(
config.fp16_args,
model,
config.optimizer,
config.num_accumulated_batches,
)
else:
self.optimizer: torch.optim.Optimizer = create_optimizer(
config.optimizer, model)
self.scheduler: torch.optim.lr_scheduler = (create_scheduler(
config.scheduler, self.optimizer) if config.scheduler else
Scheduler())
self.sparsifier: Sparsifier = (create_sparsifier(config.sparsifier)
if config.sparsifier else Sparsifier())
self.config = config