Tests: refactor cleanup (#1744)

* wip

* cleaning

* optim imports

* -

* default hparams

* fix restore

* fix imports

tests/callbacks/test_callbacks.py

@@ -1,4 +1,5 @@
 import pytest
+
 import tests.base.utils as tutils
 from pytorch_lightning import Callback
 from pytorch_lightning import Trainer, LightningModule
@@ -11,7 +12,7 @@ from pathlib import Path
 def test_trainer_callback_system(tmpdir):
     """Test the callback system."""
 
-    hparams = tutils.get_default_hparams()
+    hparams = EvalModelTemplate.get_default_hparams()
     model = EvalModelTemplate(hparams)
 
     def _check_args(trainer, pl_module):
@@ -209,7 +210,7 @@ def test_early_stopping_no_val_step(tmpdir):
             output.update({'my_train_metric': output['loss']})  # could be anything else
             return output
 
-    model = CurrentModel(tutils.get_default_hparams())
+    model = CurrentModel()
     model.validation_step = None
     model.val_dataloader = None
 
@@ -245,7 +246,7 @@ def test_pickling(tmpdir):
 def test_model_checkpoint_with_non_string_input(tmpdir, save_top_k):
     """ Test that None in checkpoint callback is valid and that chkp_path is set correctly """
     tutils.reset_seed()
-    model = EvalModelTemplate(tutils.get_default_hparams())
+    model = EvalModelTemplate()
 
     checkpoint = ModelCheckpoint(filepath=None, save_top_k=save_top_k)
 
@@ -267,7 +268,7 @@ def test_model_checkpoint_with_non_string_input(tmpdir, save_top_k):
 def test_model_checkpoint_path(tmpdir, logger_version, expected):
     """Test that "version_" prefix is only added when logger's version is an integer"""
     tutils.reset_seed()
-    model = EvalModelTemplate(tutils.get_default_hparams())
+    model = EvalModelTemplate()
     logger = TensorBoardLogger(str(tmpdir), version=logger_version)
 
     trainer = Trainer(
@@ -286,7 +287,7 @@ def test_lr_logger_single_lr(tmpdir):
     """ Test that learning rates are extracted and logged for single lr scheduler"""
     tutils.reset_seed()
 
-    model = EvalModelTemplate(tutils.get_default_hparams())
+    model = EvalModelTemplate()
     model.configure_optimizers = model.configure_optimizers__single_scheduler
 
     lr_logger = LearningRateLogger()
@@ -311,7 +312,7 @@ def test_lr_logger_multi_lrs(tmpdir):
     """ Test that learning rates are extracted and logged for multi lr schedulers """
     tutils.reset_seed()
 
-    model = EvalModelTemplate(tutils.get_default_hparams())
+    model = EvalModelTemplate()
     model.configure_optimizers = model.configure_optimizers__multiple_schedulers
 
     lr_logger = LearningRateLogger()

tests/callbacks/test_progress_bar.py

@@ -58,7 +58,7 @@ def test_progress_bar_misconfiguration():
 def test_progress_bar_totals():
     """Test that the progress finishes with the correct total steps processed."""
 
-    model = EvalModelTemplate(tutils.get_default_hparams())
+    model = EvalModelTemplate()
 
     trainer = Trainer(
         progress_bar_refresh_rate=1,
@@ -107,7 +107,7 @@ def test_progress_bar_totals():
 
 
 def test_progress_bar_fast_dev_run():
-    model = EvalModelTemplate(tutils.get_default_hparams())
+    model = EvalModelTemplate()
 
     trainer = Trainer(
         fast_dev_run=True,
@@ -140,7 +140,7 @@ def test_progress_bar_fast_dev_run():
 def test_progress_bar_progress_refresh(refresh_rate):
     """Test that the three progress bars get correctly updated when using different refresh rates."""
 
-    model = EvalModelTemplate(tutils.get_default_hparams())
+    model = EvalModelTemplate()
 
     class CurrentProgressBar(ProgressBar):