tmpdir

tests/callbacks/test_callbacks.py

@@ -160,6 +160,7 @@ def on_test_end(self, trainer, pl_module):
     test_callback = TestCallback()
 
     trainer_options = dict(
+        default_root_dir=tmpdir,
         callbacks=[test_callback],
         max_epochs=1,
         limit_val_batches=0.1,
@@ -261,3 +262,86 @@ def on_test_end(self, trainer, pl_module):
     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

@@ -32,7 +32,7 @@ def on_train_start(self, trainer, pl_module):
         default_root_dir=tmpdir,
         checkpoint_callback=checkpoint_callback,
         early_stop_callback=early_stop_callback,
-        max_epochs=4
+        max_epochs=4,
     )
     trainer.fit(model)
     early_stop_callback_state = early_stop_callback.state_dict()
@@ -44,6 +44,7 @@ def on_train_start(self, trainer, pl_module):
     # ensure state is reloaded properly (assertion in the callback)
     early_stop_callback = EarlyStoppingTestRestore(early_stop_callback_state)
     new_trainer = Trainer(
+        default_root_dir=tmpdir,
         max_epochs=2,
         resume_from_checkpoint=checkpoint_filepath,
         early_stop_callback=early_stop_callback,

tests/callbacks/test_lr_logger.py

@@ -19,7 +19,7 @@ def test_lr_logger_single_lr(tmpdir):
         max_epochs=2,
         limit_val_batches=0.1,
         limit_train_batches=0.5,
-        callbacks=[lr_logger]
+        callbacks=[lr_logger],
     )
     result = trainer.fit(model)
     assert result
@@ -42,7 +42,7 @@ def test_lr_logger_no_lr(tmpdir):
         max_epochs=2,
         limit_val_batches=0.1,
         limit_train_batches=0.5,
-        callbacks=[lr_logger]
+        callbacks=[lr_logger],
     )
 
     with pytest.warns(RuntimeWarning):
@@ -63,7 +63,7 @@ def test_lr_logger_multi_lrs(tmpdir):
         max_epochs=2,
         limit_val_batches=0.1,
         limit_train_batches=0.5,
-        callbacks=[lr_logger]
+        callbacks=[lr_logger],
     )
     result = trainer.fit(model)
     assert result
@@ -90,7 +90,7 @@ def test_lr_logger_param_groups(tmpdir):
         max_epochs=2,
         limit_val_batches=0.1,
         limit_train_batches=0.5,
-        callbacks=[lr_logger]
+        callbacks=[lr_logger],
     )
     result = trainer.fit(model)
     assert result

tests/callbacks/test_model_checkpoint.py

@@ -22,7 +22,7 @@ def test_model_checkpoint_with_non_string_input(tmpdir, save_top_k):
         default_root_dir=tmpdir,
         checkpoint_callback=checkpoint,
         overfit_pct=0.20,
-        max_epochs=5
+        max_epochs=5,
     )
     trainer.fit(model)
 
@@ -44,7 +44,7 @@ def test_model_checkpoint_path(tmpdir, logger_version, expected):
         default_root_dir=tmpdir,
         overfit_pct=0.2,
         max_epochs=5,
-        logger=logger
+        logger=logger,
     )
     trainer.fit(model)
 

tests/callbacks/test_progress_bar.py

@@ -107,10 +107,11 @@ def test_progress_bar_totals(tmpdir):
     assert bar.test_batch_idx == k
 
 
-def test_progress_bar_fast_dev_run():
+def test_progress_bar_fast_dev_run(tmpdir):
     model = EvalModelTemplate()
 
     trainer = Trainer(
+        default_root_dir=tmpdir,
         fast_dev_run=True,
     )