`torch_mist.utils.train.model`

Module Contents

`instantiate_optimizer`(→ Tuple[torch.optim.Optimizer, ...)
`train_epoch`(model, train_loader, opt, device[, ...])
`validate`(→ float)
`is_early_stopping_possible`(→ bool)
`compute_training_time`(→ Tuple[int, int, int])
`train_model`(, max_epochs, max_iterations, ...)

torch_mist.utils.train.model.instantiate_optimizer(model: torch.nn.Module, max_iterations: int, warmup_iterations: int = 0, optimizer_class: Type[torch.optim.Optimizer] = Adam, optimizer_params: Dict[str, Any] | None = None, lr_annealing: bool = False) → Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LRScheduler]

torch_mist.utils.train.model.train_epoch(model: torch.nn.Module, train_loader: torch.utils.data.DataLoader, opt: torch.optim.Optimizer, device: str | torch.device, lr_scheduler: torch.optim.lr_scheduler.LRScheduler | None = None, train_method: str = 'loss', eval_method: str | None = None, logger: torch_mist.utils.logging.logger.base.Logger | None = None, tqdm_iteration: tqdm.autonotebook.tqdm | None = None, train_logged_methods: List[str | Tuple[str, Callable]] | None = None, eval_logged_methods: List[str | Tuple[str, Callable]] | None = None, max_iterations: int | None = None)

torch_mist.utils.train.model.validate(model: torch.nn.Module, eval_method: str, valid_loader: torch.utils.data.DataLoader | None, device: str | torch.device, logger: torch_mist.utils.logging.logger.base.Logger, eval_logged_methods: List[str | Tuple[str, Callable]] | None = None) → float

torch_mist.utils.train.model.is_early_stopping_possible(valid_loader_missing: bool, eval_method_missing: bool, bound: bool) → bool

torch_mist.utils.train.model.compute_training_time(iterations_per_epoch: int, max_epochs: int, max_iterations: int, warmup_percentage: float) → Tuple[int, int, int]

torch_mist.utils.train.model.train_model(model: torch_mist.nn.Model, train_data: torch_mist.utils.data.utils.TensorDictLike, train_method: str = 'loss', eval_method: str | None = None, valid_data: torch_mist.utils.data.utils.TensorDictLike | None = None, valid_percentage: float = 0.1, batch_size: int | None = None, num_workers: int = 0, device: torch.device | str = torch.device('cpu'), max_epochs: int | None = None, max_iterations: int | None = None, optimizer_class: Type[torch.optim.Optimizer] = Adam, optimizer_params: Dict[str, Any] | None = None, lr_annealing: bool = False, warmup_percentage: float = 0, verbose: bool = True, logger: torch_mist.utils.logging.logger.base.Logger | bool | None = None, early_stopping: bool = False, patience: int | None = None, tolerance: float = 0.001, fast_train: bool = False, train_logged_methods: List[str | Tuple[str, Callable]] | None = None, eval_logged_methods: List[str | Tuple[str, Callable]] | None = None) → Any | None