remove duplicate tests (#2685)

* remove duplicate test

* remove duplicated tests

tests/callbacks/test_callbacks.py

@@ -1,38 +1,8 @@
-from pathlib import Path
-
-import pytest
-import torch
-
-import tests.base.develop_utils as tutils
 from pytorch_lightning import Callback
 from pytorch_lightning import Trainer, LightningModule
-from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
-from pytorch_lightning.loggers import TensorBoardLogger
 from tests.base import EvalModelTemplate
 
 
-def test_early_stopping_functionality(tmpdir):
-
-    class CurrentModel(EvalModelTemplate):
-        def validation_epoch_end(self, outputs):
-            losses = [8, 4, 2, 3, 4, 5, 8, 10]
-            val_loss = losses[self.current_epoch]
-            return {'val_loss': torch.tensor(val_loss)}
-
-    model = CurrentModel()
-
-    trainer = Trainer(
-        default_root_dir=tmpdir,
-        early_stop_callback=True,
-        overfit_batches=0.20,
-        max_epochs=20,
-    )
-    result = trainer.fit(model)
-    print(trainer.current_epoch)
-
-    assert trainer.current_epoch == 5, 'early_stopping failed'
-
-
 def test_trainer_callback_system(tmpdir):
     """Test the callback system."""
 
@@ -262,86 +232,3 @@ def test_trainer_callback_system(tmpdir):
     assert not test_callback.on_validation_end_called
     assert not test_callback.on_validation_batch_end_called
     assert not test_callback.on_validation_batch_start_called
-
-
-def test_early_stopping_no_val_step(tmpdir):
-    """Test that early stopping callback falls back to training metrics when no validation defined."""
-
-    class CurrentModel(EvalModelTemplate):
-        def training_step(self, *args, **kwargs):
-            output = super().training_step(*args, **kwargs)
-            output.update({'my_train_metric': output['loss']})  # could be anything else
-            return output
-
-    model = CurrentModel()
-    model.validation_step = None
-    model.val_dataloader = None
-
-    stopping = EarlyStopping(monitor='my_train_metric', min_delta=0.1)
-    trainer = Trainer(
-        default_root_dir=tmpdir,
-        early_stop_callback=stopping,
-        overfit_batches=0.20,
-        max_epochs=2,
-    )
-    result = trainer.fit(model)
-
-    assert result == 1, 'training failed to complete'
-    assert trainer.current_epoch < trainer.max_epochs
-
-
-def test_pickling(tmpdir):
-    import pickle
-    early_stopping = EarlyStopping()
-    ckpt = ModelCheckpoint(tmpdir)
-
-    early_stopping_pickled = pickle.dumps(early_stopping)
-    ckpt_pickled = pickle.dumps(ckpt)
-
-    early_stopping_loaded = pickle.loads(early_stopping_pickled)
-    ckpt_loaded = pickle.loads(ckpt_pickled)
-
-    assert vars(early_stopping) == vars(early_stopping_loaded)
-    assert vars(ckpt) == vars(ckpt_loaded)
-
-
-@pytest.mark.parametrize('save_top_k', [-1, 0, 1, 2])
-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()
-
-    checkpoint = ModelCheckpoint(filepath=None, save_top_k=save_top_k)
-
-    trainer = Trainer(
-        default_root_dir=tmpdir,
-        checkpoint_callback=checkpoint,
-        overfit_batches=0.20,
-        max_epochs=2,
-    )
-    trainer.fit(model)
-
-    # These should be different if the dirpath has be overridden
-    assert trainer.ckpt_path != trainer.default_root_dir
-
-
-@pytest.mark.parametrize(
-    'logger_version,expected',
-    [(None, 'version_0'), (1, 'version_1'), ('awesome', 'awesome')],
-)
-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()
-    logger = TensorBoardLogger(str(tmpdir), version=logger_version)
-
-    trainer = Trainer(
-        default_root_dir=tmpdir,
-        overfit_batches=0.2,
-        max_epochs=2,
-        logger=logger,
-    )
-    trainer.fit(model)
-
-    ckpt_version = Path(trainer.ckpt_path).parent.name
-    assert ckpt_version == expected

tests/callbacks/test_early_stopping.py

@@ -130,3 +130,49 @@ def test_pickling(tmpdir):
     early_stopping_pickled = cloudpickle.dumps(early_stopping)
     early_stopping_loaded = cloudpickle.loads(early_stopping_pickled)
     assert vars(early_stopping) == vars(early_stopping_loaded)
+
+
+def test_early_stopping_no_val_step(tmpdir):
+    """Test that early stopping callback falls back to training metrics when no validation defined."""
+
+    class CurrentModel(EvalModelTemplate):
+        def training_step(self, *args, **kwargs):
+            output = super().training_step(*args, **kwargs)
+            output.update({'my_train_metric': output['loss']})  # could be anything else
+            return output
+
+    model = CurrentModel()
+    model.validation_step = None
+    model.val_dataloader = None
+
+    stopping = EarlyStopping(monitor='my_train_metric', min_delta=0.1)
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        early_stop_callback=stopping,
+        overfit_batches=0.20,
+        max_epochs=2,
+    )
+    result = trainer.fit(model)
+
+    assert result == 1, 'training failed to complete'
+    assert trainer.current_epoch < trainer.max_epochs
+
+
+def test_early_stopping_functionality(tmpdir):
+
+    class CurrentModel(EvalModelTemplate):
+        def validation_epoch_end(self, outputs):
+            losses = [8, 4, 2, 3, 4, 5, 8, 10]
+            val_loss = losses[self.current_epoch]
+            return {'val_loss': torch.tensor(val_loss)}
+
+    model = CurrentModel()
+
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        early_stop_callback=True,
+        overfit_batches=0.20,
+        max_epochs=20,
+    )
+    trainer.fit(model)
+    assert trainer.current_epoch == 5, 'early_stopping failed'

tests/callbacks/test_model_checkpoint.py

@@ -15,9 +15,7 @@ from tests.base import EvalModelTemplate
 
 @pytest.mark.parametrize('save_top_k', [-1, 0, 1, 2])
 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
-    """
+    """ Test that None in checkpoint callback is valid and that chkp_path is set correctly """
     tutils.reset_seed()
     model = EvalModelTemplate()
 
@@ -26,8 +24,8 @@ def test_model_checkpoint_with_non_string_input(tmpdir, save_top_k):
     trainer = Trainer(
         default_root_dir=tmpdir,
         checkpoint_callback=checkpoint,
-        overfit_pct=0.20,
-        max_epochs=(save_top_k + 2),
+        overfit_batches=0.20,
+        max_epochs=2,
     )
     trainer.fit(model)
 
@@ -47,8 +45,8 @@ def test_model_checkpoint_path(tmpdir, logger_version, expected):
 
     trainer = Trainer(
         default_root_dir=tmpdir,
-        overfit_pct=0.2,
-        max_epochs=5,
+        overfit_batches=0.2,
+        max_epochs=2,
         logger=logger,
     )
     trainer.fit(model)