fix dirpath in log_dir for CSVLogger

src/pytorch_lightning/callbacks/model_checkpoint.py

@@ -369,7 +369,8 @@ def _save_checkpoint(self, trainer: "pl.Trainer", filepath: str) -> None:
         # notify loggers
         if trainer.is_global_zero:
             for logger in trainer.loggers:
-                logger.after_save_checkpoint(proxy(self))
+                if hasattr(logger, "after_save_checkpoint"):
+                    logger.after_save_checkpoint(proxy(self))
 
     def _should_skip_saving_checkpoint(self, trainer: "pl.Trainer") -> bool:
         from pytorch_lightning.trainer.states import TrainerFn
@@ -582,14 +583,11 @@ def __resolve_ckpt_dir(self, trainer: "pl.Trainer") -> _PATH:
             return self.dirpath
 
         if len(trainer.loggers) > 0:
-            if trainer.loggers[0].save_dir is not None:
-                save_dir = trainer.loggers[0].save_dir
-            else:
-                save_dir = trainer.default_root_dir
-            name = trainer.loggers[0].name
-            version = trainer.loggers[0].version
+            logger_ = trainer.loggers[0]
+            save_dir = getattr(logger_, "save_dir") or trainer.default_root_dir
+            version = logger_.version
             version = version if isinstance(version, str) else f"version_{version}"
-            ckpt_path = os.path.join(save_dir, str(name), version, "checkpoints")
+            ckpt_path = os.path.join(save_dir, str(logger_.name), version, "checkpoints")
         else:
             # if no loggers, use default_root_dir
             ckpt_path = os.path.join(trainer.default_root_dir, "checkpoints")

src/pytorch_lightning/loggers/csv_logs.py

@@ -39,7 +39,7 @@ class ExperimentWriter(_FabricExperimentWriter):
     r"""
     Experiment writer for CSVLogger.
 
-    Currently supports to log hyperparameters and metrics in YAML and CSV
+    Currently, supports to log hyperparameters and metrics in YAML and CSV
     format, respectively.
 
     Args:

src/pytorch_lightning/trainer/trainer.py

@@ -1807,10 +1807,8 @@ def model(self, model: torch.nn.Module) -> None:
     @property
     def log_dir(self) -> Optional[str]:
         if len(self.loggers) > 0:
-            if not isinstance(self.loggers[0], TensorBoardLogger):
-                dirpath = self.loggers[0].save_dir
-            else:
-                dirpath = self.loggers[0].log_dir
+            logger_ = self.loggers[0]
+            dirpath = getattr(logger_, "log_dir", getattr(logger_, "save_dir", None))
         else:
             dirpath = self.default_root_dir