Problems with creation own Logger
🐛 Bug

To Reproduce
Steps to reproduce the behavior:
When creating a custom logger I had a trouble that I need to implement, additional methods and add some parameters to the custom logger class

Code sample

class ReportLogger(LightningLoggerBase):
    def __init__(self, project_dir="./"):
        super(LightningLoggerBase, self).__init__()
        self._metrics_to_agg = False
        self.project_dir = project_dir
        self.exp_dir, self.assets_dir, log_path = self.__setup_final_summary(self.project_dir)
        self.log_file = open(log_path, "w+")
        
    @rank_zero_only
    def log_hyperparams(self, params):
        # params is an argparse.Namespace
        # your code to record hyperparameters goes here
        pass
        
    @rank_zero_only
    def log_metrics(self, metrics, step):
        self.log_file.write('Epoch: {}'.format(step), '\tTraining {}: {:.4f}'.format(metrics[0].key, metrics[0].value))

    def save(self):
        # Optional. Any code necessary to save logger data goes here
        # If you implement this, remember to call `super().save()`
        # at the start of the method (important for aggregation of metrics)
        super().save()

    @rank_zero_only
    def finalize(self, status):
        # Optional. Any code that needs to be run after training
        # finishes goes here
        pass

Expected behavior
From the documentation I understood that it should work without them.

PyTorch Version 0.7.0:
OS Mac OS Catalina:
Installed with conda:
Python version 3.8.3: