prune tests (#564)

* format docstring in tests

* prune unused vars

* optimize imports

* drop duplicated var

tests/debug.py

@@ -52,54 +52,3 @@ class CoolModel(pl.LightningModule):
     @pl.data_loader
     def test_dataloader(self):
         return DataLoader(MNIST('path/to/save', train=False), batch_size=32)
-
-#
-# def main():
-#     reset_seed()
-#     set_random_master_port()
-#
-#     hparams = get_hparams()
-#     model = LightningTestModel(hparams)
-#
-#     save_dir = init_save_dir()
-#
-#     # exp file to get meta
-#     logger = get_test_tube_logger(False)
-#
-#     print(logger.debug)
-#
-#     # exp file to get weights
-#     checkpoint = init_checkpoint_callback(logger)
-#
-#     trainer_options = dict(
-#         show_progress_bar=False,
-#         max_nb_epochs=1,
-#         train_percent_check=0.4,
-#         val_percent_check=0.2,
-#         checkpoint_callback=checkpoint,
-#         logger=logger,
-#         gpus=[0, 1],
-#         distributed_backend='ddp'
-#     )
-#
-#     # fit model
-#     trainer = Trainer(**trainer_options)
-#     result = trainer.fit(model)
-#
-#     exp = logger.experiment
-#     print(os.listdir(exp.get_data_path(exp.name, exp.version)))
-#
-#     # correct result and ok accuracy
-#     assert result == 1, 'training failed to complete'
-#     pretrained_model = load_model(logger.experiment, save_dir,
-#                                   module_class=LightningTestModel)
-#
-#     # run test set
-#     new_trainer = Trainer(**trainer_options)
-#     new_trainer.test(pretrained_model)
-#
-#     # test we have good test accuracy
-#     clear_save_dir()
-#
-# if __name__ == '__main__':
-#     main()