Attach version_ to checkpoint path only if version is int (#1748)

CHANGELOG.md

@@ -15,7 +15,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Added type hints in `Trainer.fit()` and `Trainer.test()` to reflect that also a list of dataloaders can be passed in ([#1723](https://github.com/PyTorchLightning/pytorch-lightning/pull/1723)).
 
 ### Changed
- 
+
 - Reduction when `batch_size < num_gpus` ([#1609](https://github.com/PyTorchLightning/pytorch-lightning/pull/1609))
 
 - Updated LightningTemplateModel to look more like Colab example ([#1577](https://github.com/PyTorchLightning/pytorch-lightning/pull/1577))
@@ -38,6 +38,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Fixed bugs that prevent lr finder to be used together with early stopping and validation dataloaders ([#1676](https://github.com/PyTorchLightning/pytorch-lightning/pull/1676))
 
+- Fixed a bug in Trainer that prepended the checkpoint path with `version_` when it shouldn't ([#1748](https://github.com/PyTorchLightning/pytorch-lightning/pull/1748))
+
 ## [0.7.5] - 2020-04-27
 
 ### Changed

pytorch_lightning/trainer/callback_config.py

@@ -49,10 +49,12 @@ class TrainerCallbackConfigMixin(ABC):
                 if self.weights_save_path is not None:
                     save_dir = self.weights_save_path
 
+                version = self.logger.version if isinstance(
+                    self.logger.version, str) else f'version_{self.logger.version}'
                 ckpt_path = os.path.join(
                     save_dir,
                     self.logger.name,
-                    f'version_{self.logger.version}',
+                    version,
                     "checkpoints"
                 )
             else:

tests/callbacks/test_callbacks.py

@@ -3,7 +3,9 @@ import tests.base.utils as tutils
 from pytorch_lightning import Callback
 from pytorch_lightning import Trainer, LightningModule
 from pytorch_lightning.callbacks import EarlyStopping, LearningRateLogger, ModelCheckpoint
+from pytorch_lightning.loggers import TensorBoardLogger
 from tests.base import EvalModelTemplate
+from pathlib import Path
 
 
 def test_trainer_callback_system(tmpdir):
@@ -258,6 +260,28 @@ def test_model_checkpoint_with_non_string_input(tmpdir, save_top_k):
     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(tutils.get_default_hparams())
+    logger = TensorBoardLogger(str(tmpdir), version=logger_version)
+
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        overfit_pct=0.2,
+        max_epochs=5,
+        logger=logger
+    )
+    trainer.fit(model)
+
+    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()