def _setup_components()

in training/trainer.py [0:0]

• 34 lines of code
• 11 McCabe index (conditional complexity)

    def _setup_components(self):

        # Get the keys for all the val datasets, if any
        val_phase = Phase.VAL
        val_keys = None
        if self.data_conf.get(val_phase, None) is not None:
            val_keys = collect_dict_keys(self.data_conf[val_phase])
        # Additional checks on the sanity of the config for val datasets
        self._check_val_key_match(val_keys, phase=val_phase)

        logging.info("Setting up components: Model, loss, optim, meters etc.")
        self.epoch = 0
        self.steps = {Phase.TRAIN: 0, Phase.VAL: 0}

        self.logger = Logger(self.logging_conf)

        self.model = instantiate(self.model_conf, _convert_="all")
        print_model_summary(self.model)

        self.loss = None
        if self.loss_conf:
            self.loss = {
                key: el  # wrap_base_loss(el)
                for (key, el) in instantiate(self.loss_conf, _convert_="all").items()
            }
            self.loss = nn.ModuleDict(self.loss)

        self.meters = {}
        self.best_meter_values = {}
        if self.meters_conf:
            self.meters = instantiate(self.meters_conf, _convert_="all")

        self.scaler = torch.amp.GradScaler(
            self.device,
            enabled=self.optim_conf.amp.enabled if self.optim_conf else False,
        )

        self.gradient_clipper = (
            instantiate(self.optim_conf.gradient_clip) if self.optim_conf else None
        )
        self.gradient_logger = (
            instantiate(self.optim_conf.gradient_logger) if self.optim_conf else None
        )

        logging.info("Finished setting up components: Model, loss, optim, meters etc.")