Fix logger, tensorboard (#610)

* fix logger tests

* fix missing flush

* fix tensorboard

* fix namespace

* fix flush

* fix add_hparams

pytorch_lightning/logging/__init__.py

@@ -168,7 +168,7 @@ Every k batches, lightning will write the new logs to disk
 from os import environ
 from .base import LightningLoggerBase, rank_zero_only
 
-from .tensorboard import TensorboardLogger
+from .tensorboard import TensorBoardLogger
 
 try:
     from .test_tube import TestTubeLogger

pytorch_lightning/logging/tensorboard.py

@@ -8,8 +8,8 @@ from torch.utils.tensorboard import SummaryWriter
 from .base import LightningLoggerBase, rank_zero_only
 
 
-class TensorboardLogger(LightningLoggerBase):
-    r"""Log to local file system in Tensorboard format
+class TensorBoardLogger(LightningLoggerBase):
+    r"""Log to local file system in TensorBoard format
 
     Implemented using :class:`torch.utils.tensorboard.SummaryWriter`. Logs are saved to
     `os.path.join(save_dir, name, version)`
@@ -18,7 +18,7 @@ class TensorboardLogger(LightningLoggerBase):
 
     .. code-block:: python
 
-        logger = TensorboardLogger("tb_logs", name="my_model")
+        logger = TensorBoardLogger("tb_logs", name="my_model")
         trainer = Trainer(logger=logger)
         trainer.train(model)
 
@@ -35,7 +35,7 @@ class TensorboardLogger(LightningLoggerBase):
         super().__init__()
         self.save_dir = save_dir
         self._name = name
-        self._version = version if version is not None else None
+        self._version = version
 
         self._experiment = None
         self.kwargs = kwargs
@@ -59,23 +59,35 @@ class TensorboardLogger(LightningLoggerBase):
     def log_hyperparams(self, params):
         if parse_version(torch.__version__) < parse_version("1.3.0"):
             warn(
-                f"Hyperparameter logging is not available for Torch version {torch.__version__}. "
-                "Skipping log_hyperparams. Upgrade to Torch 1.3.0 or above to enable "
-                "hyperparameter logging"
+                f"Hyperparameter logging is not available for Torch version {torch.__version__}."
+                " Skipping log_hyperparams. Upgrade to Torch 1.3.0 or above to enable"
+                " hyperparameter logging."
             )
+            # TODO: some alternative should be added
             return
-        self.experiment.add_hparams(hparam_dict=vars(params))
+        try:
+            # in case converting from namespace, todo: rather test if it is namespace
+            params = vars(params)
+        except TypeError:
+            pass
+        if params is not None:
+            # `add_hparams` requires both - hparams and metric
+            self.experiment.add_hparams(hparam_dict=dict(params), metric_dict={})
 
     @rank_zero_only
-    def log_metrics(self, metrics, step_idx=None):
+    def log_metrics(self, metrics, step=None):
         for k, v in metrics.items():
             if isinstance(v, torch.Tensor):
                 v = v.item()
-            self.experiment.add_scalar(k, v, step_idx)
+            self.experiment.add_scalar(k, v, step)
 
     @rank_zero_only
     def save(self):
-        self.experiment.flush()
+        try:
+            self.experiment.flush()
+        except AttributeError:
+            # you are using PT version (<v1.2) which does not have implemented flush
+            self.experiment._get_file_writer().flush()
 
     @rank_zero_only
     def finalize(self, status):

pytorch_lightning/trainer/logging.py

@@ -21,7 +21,7 @@ class TrainerLoggingMixin(ABC):
         self.use_ddp2 = None
         self.num_gpus = None
 
-    def log_metrics(self, metrics, grad_norm_dic):
+    def log_metrics(self, metrics, grad_norm_dic, step=None):
         """Logs the metric dict passed in.
 
         :param metrics:
@@ -41,9 +41,10 @@ class TrainerLoggingMixin(ABC):
         # turn all tensors to scalars
         scalar_metrics = self.metrics_to_scalars(metrics)
 
+        step = step if step is not None else self.global_step
         # log actual metrics
         if self.proc_rank == 0 and self.logger is not None:
-            self.logger.log_metrics(scalar_metrics, step=self.global_step)
+            self.logger.log_metrics(scalar_metrics, step=step)
             self.logger.save()
 
     def add_tqdm_metrics(self, metrics):

tests/test_logging.py

@@ -9,7 +9,7 @@ from pytorch_lightning import Trainer
 from pytorch_lightning.logging import (
     LightningLoggerBase,
     rank_zero_only,
-    TensorboardLogger,
+    TensorBoardLogger,
 )
 from pytorch_lightning.testing import LightningTestModel
 
@@ -169,8 +169,8 @@ def test_comet_pickle(tmpdir, monkeypatch):
     except ModuleNotFoundError:
         return
 
-    hparams = tutils.get_hparams()
-    model = LightningTestModel(hparams)
+    # hparams = tutils.get_hparams()
+    # model = LightningTestModel(hparams)
 
     comet_dir = os.path.join(tmpdir, "cometruns")
 
@@ -199,9 +199,9 @@ def test_tensorboard_logger(tmpdir):
     hparams = tutils.get_hparams()
     model = LightningTestModel(hparams)
 
-    logger = TensorboardLogger(save_dir=tmpdir, name="tensorboard_logger_test")
+    logger = TensorBoardLogger(save_dir=tmpdir, name="tensorboard_logger_test")
 
-    trainer_options = dict(max_num_epochs=1, train_percent_check=0.01, logger=logger)
+    trainer_options = dict(max_epochs=1, train_percent_check=0.01, logger=logger)
 
     trainer = Trainer(**trainer_options)
     result = trainer.fit(model)
@@ -213,14 +213,12 @@ def test_tensorboard_logger(tmpdir):
 def test_tensorboard_pickle(tmpdir):
     """Verify that pickling trainer with Tensorboard logger works."""
 
-    hparams = tutils.get_hparams()
-    model = LightningTestModel(hparams)
+    # hparams = tutils.get_hparams()
+    # model = LightningTestModel(hparams)
 
-    comet_dir = os.path.join(tmpdir, "cometruns")
+    logger = TensorBoardLogger(save_dir=tmpdir, name="tensorboard_pickle_test")
 
-    logger = TensorboardLogger(save_dir=tmpdir, name="tensorboard_pickle_test")
-
-    trainer_options = dict(max_num_epochs=1, logger=logger)
+    trainer_options = dict(max_epochs=1, logger=logger)
 
     trainer = Trainer(**trainer_options)
     pkl_bytes = pickle.dumps(trainer)
@@ -235,7 +233,7 @@ def test_tensorboard_automatic_versioning(tmpdir):
     root_dir.mkdir("0")
     root_dir.mkdir("1")
 
-    logger = TensorboardLogger(save_dir=tmpdir, name="tb_versioning")
+    logger = TensorBoardLogger(save_dir=tmpdir, name="tb_versioning")
 
     assert logger.version == 2
 
@@ -248,14 +246,14 @@ def test_tensorboard_manual_versioning(tmpdir):
     root_dir.mkdir("1")
     root_dir.mkdir("2")
 
-    logger = TensorboardLogger(save_dir=tmpdir, name="tb_versioning", version=1)
+    logger = TensorBoardLogger(save_dir=tmpdir, name="tb_versioning", version=1)
 
     assert logger.version == 1
 
 
 @pytest.mark.parametrize("step_idx", [10, None])
 def test_tensorboard_log_metrics(tmpdir, step_idx):
-    logger = TensorboardLogger(tmpdir)
+    logger = TensorBoardLogger(tmpdir)
     metrics = {
         "float": 0.3,
         "int": 1,
@@ -266,7 +264,7 @@ def test_tensorboard_log_metrics(tmpdir, step_idx):
 
 
 def test_tensorboard_log_hyperparams(tmpdir):
-    logger = TensorboardLogger(tmpdir)
+    logger = TensorBoardLogger(tmpdir)
     hparams = {
         "float": 0.3,
         "int": 1,