Tensorboard log_hyperparams(params, metrics) seems not to have effect
🐛 Bug
Calling self.logger.log_hyperparams(hparams_dicts, metrics_dicts) in test_epoch_end doesn't have the desired effect. It should show the entries in the Hparams section with hparams and metrics specified but it shows nothing instead.
Looking at the code it seems to be caused by self.hparams and the pre-logging of the hyperparameters at the start of the training. In this way, calls to log_hyperparams won't be able to log the hyperparameters AND the metrics properly since they will clash with the previous log, hence, showing nothing.
To Reproduce
Try to log metrics with self.logger.log_hyperparams.

Code sample
def test_epoch_end(self, outputs):
    avg_recall = np.concatenate([x['recall'] for x in outputs]).mean()
    tensorboard_logs = {'test/avg_ndcg': avg_ndcg, "test/avg_recall": avg_recall}
    ## Log metrics
    self.logger.log_hyperparams(vars(self.params),tensorboard_logs)
    return tensorboard_logs

Expected behavior
Tensorboard should show me the section of Hparams with each entry composed by hyperparameters and metrics.

Environment

CUDA:
- GPU:
- available:         False
- version:           10.2
Packages:
- numpy:             1.18.1
- pyTorch_debug:     False
- pyTorch_version:   1.5.0
- pytorch-lightning: 0.7.5
- tensorboard:       2.2.1
- tqdm:              4.46.0
System:
- OS:                Linux
- architecture:
- 64bit
- ELF
- processor:         x86_64
- python:            3.8.2
- version:           #34~18.04.1-Ubuntu SMP Fri Feb 28 13:42:26 UTC 2020

Additional context