Fix logger bug and prepare data bug (#1933)

* tests, fix logger bug and prepare data bug

* add CHANGELOG.md

Co-authored-by: Nicki Skafte <nugginea@gmail.com>

CHANGELOG.md

@@ -32,6 +32,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Fixed user warning when apex was used together with learning rate schedulers ([#1873](https://github.com/PyTorchLightning/pytorch-lightning/pull/1873))
 
+- Fix bug related to logger not being reset correctly for model after tuner algorithms ([#1933](https://github.com/PyTorchLightning/pytorch-lightning/pull/1933))
+
 ## [0.7.6] - 2020-05-16
 
 ### Added

pytorch_lightning/trainer/trainer.py

@@ -401,6 +401,7 @@ class Trainer(
 
         self.auto_lr_find = auto_lr_find
         self.auto_scale_batch_size = auto_scale_batch_size
+        self._is_data_prepared = False
         self.replace_sampler_ddp = replace_sampler_ddp
 
         self.truncated_bptt_steps = truncated_bptt_steps
@@ -823,17 +824,21 @@ class Trainer(
         # download the data and do whatever transforms we need
         # do before any spawn calls so that the model can assign properties
         # only on proc 0 because no spawn has happened yet
+        if not self._is_data_prepared:
             model.prepare_data()
+            self._is_data_prepared = True
 
         # Run auto batch size scaling
         if self.auto_scale_batch_size:
             if isinstance(self.auto_scale_batch_size, bool):
                 self.auto_scale_batch_size = 'power'
             self.scale_batch_size(model, mode=self.auto_scale_batch_size)
+            model.logger = self.logger  # reset logger binding
 
         # Run learning rate finder:
         if self.auto_lr_find:
             self._run_lr_finder_internally(model)
+            model.logger = self.logger  # reset logger binding
 
         # route to appropriate start method
         # when using multi-node or DDP within a node start each module in a separate process

tests/trainer/test_lr_finder.py

@@ -199,3 +199,26 @@ def test_suggestion_with_non_finite_values(tmpdir):
 
     assert before_lr == after_lr, \
         'Learning rate was altered because of non-finite loss values'
+
+
+def test_logger_reset_correctly(tmpdir):
+    """ Test that logger is updated correctly """
+    tutils.reset_seed()
+
+    hparams = EvalModelTemplate.get_default_hparams()
+    model = EvalModelTemplate(hparams)
+
+    trainer = Trainer(
+        default_save_path=tmpdir,
+        max_epochs=10,
+        auto_lr_find=True
+    )
+    logger1 = trainer.logger
+    trainer.fit(model)
+    logger2 = trainer.logger
+    logger3 = model.logger
+
+    assert logger1 == logger2, \
+        'Learning rate finder altered the logger of trainer'
+    assert logger2 == logger3, \
+        'Learning rate finder altered the logger of model'

tests/trainer/test_trainer_tricks.py

@@ -128,3 +128,26 @@ def test_error_on_dataloader_passed_to_fit(tmpdir):
 
     with pytest.raises(MisconfigurationException):
         trainer.fit(model, **fit_options)
+
+
+def test_logger_reset_correctly(tmpdir):
+    """ Test that logger is updated correctly """
+    tutils.reset_seed()
+
+    hparams = EvalModelTemplate.get_default_hparams()
+    model = EvalModelTemplate(hparams)
+
+    trainer = Trainer(
+        default_save_path=tmpdir,
+        max_epochs=1,
+        auto_scale_batch_size=True
+    )
+    logger1 = trainer.logger
+    trainer.fit(model)
+    logger2 = trainer.logger
+    logger3 = model.logger
+
+    assert logger1 == logger2, \
+        'Batch size finder altered the logger of trainer'
+    assert logger2 == logger3, \
+        'Batch size finder altered the logger of model'