def __init__()

in pytext/trainers/trainer.py [0:0]

• 83 lines of code
• 21 McCabe index (conditional complexity)

    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 (
            config.discriminative_lr is not None
            or config.freeze_params_pattern is not None
        ):
            optimizer_grouped_parameters = []
            optimizer_parameters_covered = []
            if config.freeze_params_pattern is not None:
                tmp_param = {
                    n: p
                    for n, p in model.named_parameters()
                    if any(nd in n for nd in config.freeze_params_pattern)
                }
                if len(tmp_param) > 0:
                    optimizer_parameters_covered.extend(list(tmp_param.keys()))
                    optimizer_grouped_parameters.append(
                        {
                            "params": list(tmp_param.values()),
                            "lr": 0.0,
                        }
                    )
            if config.discriminative_lr is not None:
                assert (
                    config.discriminative_lr_params_pattern is not None
                ), "Missing discriminative_lr_params_pattern"
                tmp_param = {
                    n: p
                    for n, p in model.named_parameters()
                    if any(nd in n for nd in config.discriminative_lr_params_pattern)
                    and n not in optimizer_parameters_covered
                }
                if len(tmp_param) > 0:
                    optimizer_parameters_covered.extend(list(tmp_param.keys()))
                    optimizer_grouped_parameters.append(
                        {
                            "params": list(tmp_param.values()),
                            "lr": config.discriminative_lr,
                        }
                    )
            optimizer_grouped_parameters.append(
                {
                    "params": [
                        p
                        for n, p in model.named_parameters()
                        if n not in optimizer_parameters_covered
                    ]
                }
            )
            if precision.FP16_ENABLED:
                self.optimizer: torch.optim.Optimizer = create_optimizer(
                    config.fp16_args,
                    model,
                    config.optimizer,
                    config.num_accumulated_batches,
                    optimizer_grouped_parameters,
                )
            else:
                self.optimizer: torch.optim.Optimizer = create_optimizer(
                    config.optimizer, model, optimizer_grouped_parameters
                )
        else:
            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