refactor unit tests

pytorch_lightning/trainer/trainer.py

@@ -1239,7 +1239,7 @@ def _log_hyperparams(self) -> None:
         elif datamodule_log_hyperparams:
             hparams_initial = self.datamodule.hparams_initial
 
-        for logger in trainer.loggers:
+        for logger in self.loggers:
             if hparams_initial is not None:
                 self.logger.log_hyperparams(hparams_initial)
             self.logger.log_graph(self.lightning_module)

tests/loggers/test_base.py

@@ -186,10 +186,11 @@ def test_multiple_loggers_pickle(tmpdir):
     trainer = Trainer(logger=[logger1, logger2])
     pkl_bytes = pickle.dumps(trainer)
     trainer2 = pickle.loads(pkl_bytes)
-    trainer2.logger.log_metrics({"acc": 1.0}, 0)
+    for logger in trainer2.loggers:
+        logger.log_metrics({"acc": 1.0}, 0)
 
-    assert trainer2.logger[0].metrics_logged == {"acc": 1.0}
-    assert trainer2.logger[1].metrics_logged == {"acc": 1.0}
+    for logger in trainer2.loggers:
+        assert logger.metrics_logged == {"acc": 1.0}
 
 
 def test_adding_step_key(tmpdir):

tests/profiler/test_profiler.py

@@ -24,8 +24,7 @@
 
 from pytorch_lightning import Callback, Trainer
 from pytorch_lightning.callbacks import EarlyStopping, StochasticWeightAveraging
-from pytorch_lightning.loggers.base import DummyLogger, LoggerCollection
-from pytorch_lightning.loggers.tensorboard import TensorBoardLogger
+from pytorch_lightning.loggers import CSVLogger, LoggerCollection, TensorBoardLogger
 from pytorch_lightning.profiler import AdvancedProfiler, PassThroughProfiler, PyTorchProfiler, SimpleProfiler
 from pytorch_lightning.profiler.pytorch import RegisterRecordFunction, warning_cache
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
@@ -494,7 +493,7 @@ def look_for_trace(trace_dir):
 
     model = BoringModel()
     # Wrap the logger in a list so it becomes a LoggerCollection
-    logger = [TensorBoardLogger(save_dir=tmpdir), DummyLogger()]
+    logger = [TensorBoardLogger(save_dir=tmpdir), CSVLogger(tmpdir)]
     trainer = Trainer(default_root_dir=tmpdir, profiler="pytorch", logger=logger, limit_train_batches=5, max_epochs=1)
     assert isinstance(trainer.logger, LoggerCollection)
     trainer.fit(model)

tests/trainer/properties/test_log_dir.py

@@ -15,8 +15,7 @@
 
 from pytorch_lightning import Trainer
 from pytorch_lightning.callbacks import ModelCheckpoint
-from pytorch_lightning.loggers import LoggerCollection, TensorBoardLogger
-from pytorch_lightning.loggers.base import DummyLogger
+from pytorch_lightning.loggers import CSVLogger, LoggerCollection, TensorBoardLogger
 from tests.helpers.boring_model import BoringModel
 
 
@@ -118,7 +117,7 @@ def test_logdir_logger_collection(tmpdir):
     trainer = Trainer(
         default_root_dir=default_root_dir,
         max_steps=2,
-        logger=[TensorBoardLogger(save_dir=save_dir, name="custom_logs"), DummyLogger()],
+        logger=[TensorBoardLogger(save_dir=save_dir, name="custom_logs"), CSVLogger(tmpdir)],
     )
     assert isinstance(trainer.logger, LoggerCollection)
     assert trainer.log_dir == default_root_dir

tests/trainer/properties/test_loggers.py

@@ -30,18 +30,20 @@ def test_trainer_loggers_property():
     # trainer.loggers should create a list of size 1
     trainer = Trainer(logger=logger1)
 
+    assert trainer.logger == logger1
     assert trainer.loggers == [logger1]
 
     # trainer.loggers should be an empty list
     trainer = Trainer(logger=False)
 
+    assert trainer.logger is None
     assert trainer.loggers == []
 
     # trainer.loggers should be a list of size 1 holding the default logger
     trainer = Trainer(logger=True)
 
     assert trainer.loggers == [trainer.logger]
-    assert type(trainer.loggers[0]) == TensorBoardLogger
+    assert isinstance(trainer.logger, TensorBoardLogger)
 
 
 def test_trainer_loggers_setters():