Method _default_iteration_print

monai/handlers/stats_handler.py:239–291

Execute iteration log operation based on Ignite `engine.state.output` data. Print the values from `self.output_transform(engine.state.output)`. Since `engine.state.output` is a decollated list and we replicated the loss value for every item of the decollated list, th

(self, engine: Engine)

            self.logger.info(out_str)

    def _default_iteration_print(self, engine: Engine) -> None:
        """
        Execute iteration log operation based on Ignite `engine.state.output` data.
        Print the values from `self.output_transform(engine.state.output)`.
        Since `engine.state.output` is a decollated list and we replicated the loss value for every item
        of the decollated list, the default behavior is to print the loss from `output[0]`.

        Args:
            engine: Ignite Engine, it can be a trainer, validator or evaluator.

        """
        loss = self.output_transform(engine.state.output)
        if loss is None:
            return  # no printing if the output is empty

        out_str = ""
        if isinstance(loss, dict):  # print dictionary items
            for name in sorted(loss):
                value = loss[name]
                if not is_scalar(value):
                    warnings.warn(
                        "ignoring non-scalar output in StatsHandler,"
                        " make sure `output_transform(engine.state.output)` returns"
                        " a scalar or dictionary of key and scalar pairs to avoid this warning."
                        f" {name}:{type(value)}"
                    )
                    continue  # not printing multi dimensional output
                out_str += self.key_var_format.format(name, value.item() if isinstance(value, torch.Tensor) else value)
        elif is_scalar(loss):  # not printing multi dimensional output
            out_str += self.key_var_format.format(
                self.tag_name, loss.item() if isinstance(loss, torch.Tensor) else loss
            )
        else:
            warnings.warn(
                "ignoring non-scalar output in StatsHandler,"
                " make sure `output_transform(engine.state.output)` returns"
                " a scalar or a dictionary of key and scalar pairs to avoid this warning."
                f" {type(loss)}"
            )

        if not out_str:
            return  # no value to print

        num_iterations = engine.state.epoch_length
        current_iteration = engine.state.iteration
        if num_iterations is not None:
            current_iteration = (current_iteration - 1) % num_iterations + 1
        current_epoch = engine.state.epoch
        num_epochs = engine.state.max_epochs

        base_str = f"Epoch: {current_epoch}/{num_epochs}, Iter: {current_iteration}/{num_iterations} --"

        self.logger.info(" ".join([base_str, out_str]))