move

tests/callbacks/test_callbacks.py

@@ -1,14 +1,13 @@
+from pathlib import Path
+
 import pytest
 
 import tests.base.utils as tutils
 from pytorch_lightning import Callback
 from pytorch_lightning import Trainer, LightningModule
-from pytorch_lightning.utilities.exceptions import MisconfigurationException
-
-from pytorch_lightning.callbacks import EarlyStopping, LearningRateLogger, ModelCheckpoint
+from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
 from pytorch_lightning.loggers import TensorBoardLogger
 from tests.base import EvalModelTemplate
-from pathlib import Path
 
 
 def test_trainer_callback_system(tmpdir):
@@ -283,97 +282,3 @@ def test_model_checkpoint_path(tmpdir, logger_version, expected):
 
     ckpt_version = Path(trainer.ckpt_path).parent.name
     assert ckpt_version == expected
-
-
-def test_lr_logger_single_lr(tmpdir):
-    """ Test that learning rates are extracted and logged for single lr scheduler"""
-    tutils.reset_seed()
-
-    model = EvalModelTemplate()
-    model.configure_optimizers = model.configure_optimizers__single_scheduler
-
-    lr_logger = LearningRateLogger()
-    trainer = Trainer(
-        default_root_dir=tmpdir,
-        max_epochs=5,
-        val_percent_check=0.1,
-        train_percent_check=0.5,
-        callbacks=[lr_logger]
-    )
-    results = trainer.fit(model)
-
-    assert results == 1
-    assert lr_logger.lrs, 'No learning rates logged'
-    assert len(lr_logger.lrs) == len(trainer.lr_schedulers), \
-        'Number of learning rates logged does not match number of lr schedulers'
-    assert all([k in ['lr-Adam'] for k in lr_logger.lrs.keys()]), \
-        'Names of learning rates not set correctly'
-
-
-def test_lr_logger_no_lr(tmpdir):
-    tutils.reset_seed()
-
-    model = EvalModelTemplate()
-
-    lr_logger = LearningRateLogger()
-    trainer = Trainer(
-        default_root_dir=tmpdir,
-        max_epochs=5,
-        val_percent_check=0.1,
-        train_percent_check=0.5,
-        callbacks=[lr_logger]
-    )
-
-    with pytest.warns(RuntimeWarning):
-        results = trainer.fit(model)
-
-
-def test_lr_logger_multi_lrs(tmpdir):
-    """ Test that learning rates are extracted and logged for multi lr schedulers """
-    tutils.reset_seed()
-
-    model = EvalModelTemplate()
-    model.configure_optimizers = model.configure_optimizers__multiple_schedulers
-
-    lr_logger = LearningRateLogger()
-    trainer = Trainer(
-        default_root_dir=tmpdir,
-        max_epochs=10,
-        val_percent_check=0.1,
-        train_percent_check=0.5,
-        callbacks=[lr_logger]
-    )
-    results = trainer.fit(model)
-
-    assert results == 1
-    assert lr_logger.lrs, 'No learning rates logged'
-    assert len(lr_logger.lrs) == len(trainer.lr_schedulers), \
-        'Number of learning rates logged does not match number of lr schedulers'
-    assert all([k in ['lr-Adam', 'lr-Adam-1'] for k in lr_logger.lrs.keys()]), \
-        'Names of learning rates not set correctly'
-    assert all(len(lr) == trainer.max_epochs for k, lr in lr_logger.lrs.items()), \
-        'Length of logged learning rates exceeds the number of epochs'
-
-
-def test_lr_logger_param_groups(tmpdir):
-    """ Test that learning rates are extracted and logged for single lr scheduler"""
-    tutils.reset_seed()
-
-    model = EvalModelTemplate()
-    model.configure_optimizers = model.configure_optimizers__param_groups
-
-    lr_logger = LearningRateLogger()
-    trainer = Trainer(
-        default_root_dir=tmpdir,
-        max_epochs=5,
-        val_percent_check=0.1,
-        train_percent_check=0.5,
-        callbacks=[lr_logger]
-    )
-    results = trainer.fit(model)
-
-    assert lr_logger.lrs, 'No learning rates logged'
-    assert len(lr_logger.lrs) == 2 * len(trainer.lr_schedulers), \
-        'Number of learning rates logged does not match number of param groups'
-    assert all([k in ['lr-Adam/pg1', 'lr-Adam/pg2'] for k in lr_logger.lrs.keys()]), \
-        'Names of learning rates not set correctly'

tests/callbacks/test_lr.py

@@ -0,0 +1,102 @@
+import pytest
+
+import tests.base.utils as tutils
+from pytorch_lightning import Trainer
+from pytorch_lightning.callbacks import LearningRateLogger
+from tests.base import EvalModelTemplate
+
+
+def test_lr_logger_single_lr(tmpdir):
+    """ Test that learning rates are extracted and logged for single lr scheduler. """
+    tutils.reset_seed()
+
+    model = EvalModelTemplate()
+    model.configure_optimizers = model.configure_optimizers__single_scheduler
+
+    lr_logger = LearningRateLogger()
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        max_epochs=5,
+        val_percent_check=0.1,
+        train_percent_check=0.5,
+        callbacks=[lr_logger]
+    )
+    result = trainer.fit(model)
+    assert result
+
+    assert lr_logger.lrs, 'No learning rates logged'
+    assert len(lr_logger.lrs) == len(trainer.lr_schedulers), \
+        'Number of learning rates logged does not match number of lr schedulers'
+    assert all([k in ['lr-Adam'] for k in lr_logger.lrs.keys()]), \
+        'Names of learning rates not set correctly'
+
+
+def test_lr_logger_no_lr(tmpdir):
+    tutils.reset_seed()
+
+    model = EvalModelTemplate()
+
+    lr_logger = LearningRateLogger()
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        max_epochs=5,
+        val_percent_check=0.1,
+        train_percent_check=0.5,
+        callbacks=[lr_logger]
+    )
+
+    with pytest.warns(RuntimeWarning):
+        result = trainer.fit(model)
+        assert result
+
+
+def test_lr_logger_multi_lrs(tmpdir):
+    """ Test that learning rates are extracted and logged for multi lr schedulers. """
+    tutils.reset_seed()
+
+    model = EvalModelTemplate()
+    model.configure_optimizers = model.configure_optimizers__multiple_schedulers
+
+    lr_logger = LearningRateLogger()
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        max_epochs=10,
+        val_percent_check=0.1,
+        train_percent_check=0.5,
+        callbacks=[lr_logger]
+    )
+    result = trainer.fit(model)
+    assert result
+
+    assert lr_logger.lrs, 'No learning rates logged'
+    assert len(lr_logger.lrs) == len(trainer.lr_schedulers), \
+        'Number of learning rates logged does not match number of lr schedulers'
+    assert all([k in ['lr-Adam', 'lr-Adam-1'] for k in lr_logger.lrs.keys()]), \
+        'Names of learning rates not set correctly'
+    assert all(len(lr) == trainer.max_epochs for k, lr in lr_logger.lrs.items()), \
+        'Length of logged learning rates exceeds the number of epochs'
+
+
+def test_lr_logger_param_groups(tmpdir):
+    """ Test that learning rates are extracted and logged for single lr scheduler. """
+    tutils.reset_seed()
+
+    model = EvalModelTemplate()
+    model.configure_optimizers = model.configure_optimizers__param_groups
+
+    lr_logger = LearningRateLogger()
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        max_epochs=5,
+        val_percent_check=0.1,
+        train_percent_check=0.5,
+        callbacks=[lr_logger]
+    )
+    result = trainer.fit(model)
+    assert result
+
+    assert lr_logger.lrs, 'No learning rates logged'
+    assert len(lr_logger.lrs) == 2 * len(trainer.lr_schedulers), \
+        'Number of learning rates logged does not match number of param groups'
+    assert all([k in ['lr-Adam/pg1', 'lr-Adam/pg2'] for k in lr_logger.lrs.keys()]), \
+        'Names of learning rates not set correctly'