Logger tests and fixes

CHANGELOG.md

@@ -47,6 +47,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed a bug where the model checkpointer didn't write to the same directory as the logger ([#771](https://github.com/PyTorchLightning/pytorch-lightning/pull/771))
 - Fixed a bug where the `TensorBoardLogger` class would create an additional empty log file during fitting ([#777](https://github.com/PyTorchLightning/pytorch-lightning/pull/777))
 - Fixed a bug where `global_step` was advanced incorrectly when using `accumulate_grad_batches > 1` ([#832](https://github.com/PyTorchLightning/pytorch-lightning/pull/832))
+- Fixed a bug when calling `self.logger.experiment` with multiple loggers ([#1009](https://github.com/PyTorchLightning/pytorch-lightning/pull/1009))
+- Fixed a bug when calling `logger.append_tags` on a `NeptuneLogger` with a single tag ([#1009](https://github.com/PyTorchLightning/pytorch-lightning/pull/1009))
 
 ## [0.6.0] - 2020-01-21
 

pytorch_lightning/loggers/base.py

@@ -105,7 +105,7 @@ def __getitem__(self, index: int) -> LightningLoggerBase:
 
     @property
     def experiment(self) -> List[Any]:
-        return [logger.experiment() for logger in self._logger_iterable]
+        return [logger.experiment for logger in self._logger_iterable]
 
     def log_metrics(self, metrics: Dict[str, float], step: Optional[int] = None):
         [logger.log_metrics(metrics, step) for logger in self._logger_iterable]
@@ -122,11 +122,7 @@ def finalize(self, status: str):
     def close(self):
         [logger.close() for logger in self._logger_iterable]
 
-    @property
-    def rank(self) -> int:
-        return self._rank
-
-    @rank.setter
+    @LightningLoggerBase.rank.setter
     def rank(self, value: int):
         self._rank = value
         for logger in self._logger_iterable:

pytorch_lightning/loggers/comet.py

@@ -7,7 +7,7 @@
 """
 import argparse
 from logging import getLogger
-from typing import Optional, Union, Dict
+from typing import Optional, Dict, Union
 
 try:
     from comet_ml import Experiment as CometExperiment
@@ -20,8 +20,10 @@
         # For more information, see: https://www.comet.ml/docs/python-sdk/releases/#release-300
         from comet_ml.papi import API
 except ImportError:
-    raise ImportError('Missing comet_ml package.')
+    raise ImportError('You want to use `comet_ml` logger which is not installed yet,'
+                      ' install it with `pip install comet-ml`.')
 
+import torch
 from torch import is_tensor
 
 from pytorch_lightning.utilities.debugging import MisconfigurationException
@@ -87,11 +89,7 @@ def __init__(self, api_key: Optional[str] = None, save_dir: Optional[str] = None
         self._experiment = None
 
         # Determine online or offline mode based on which arguments were passed to CometLogger
-        if save_dir is not None and api_key is not None:
-            # If arguments are passed for both save_dir and api_key, preference is given to online mode
-            self.mode = "online"
-            self.api_key = api_key
-        elif api_key is not None:
+        if api_key is not None:
             self.mode = "online"
             self.api_key = api_key
         elif save_dir is not None:
@@ -168,7 +166,11 @@ def log_hyperparams(self, params: argparse.Namespace):
         self.experiment.log_parameters(vars(params))
 
     @rank_zero_only
-    def log_metrics(self, metrics: Dict[str, float], step: Optional[int] = None):
+    def log_metrics(
+            self,
+            metrics: Dict[str, Union[torch.Tensor, float]],
+            step: Optional[int] = None
+    ):
         # Comet.ml expects metrics to be a dictionary of detached tensors on CPU
         for key, val in metrics.items():
             if is_tensor(val):

pytorch_lightning/loggers/mlflow.py

@@ -31,7 +31,8 @@ def any_lightning_module_function_or_hook(...):
 try:
     import mlflow
 except ImportError:
-    raise ImportError('Missing mlflow package.')
+    raise ImportError('You want to use `mlflow` logger which is not installed yet,'
+                      ' install it with `pip install mlflow`.')
 
 from .base import LightningLoggerBase, rank_zero_only
 
@@ -79,7 +80,7 @@ def run_id(self):
         if expt:
             self._expt_id = expt.experiment_id
         else:
-            logger.warning(f"Experiment with name {self.experiment_name} not found. Creating it.")
+            logger.warning(f'Experiment with name {self.experiment_name} not found. Creating it.')
             self._expt_id = self._mlflow_client.create_experiment(name=self.experiment_name)
 
         run = self._mlflow_client.create_run(experiment_id=self._expt_id, tags=self.tags)
@@ -96,17 +97,15 @@ def log_metrics(self, metrics: Dict[str, float], step: Optional[int] = None):
         timestamp_ms = int(time() * 1000)
         for k, v in metrics.items():
             if isinstance(v, str):
-                logger.warning(
-                    f"Discarding metric with string value {k}={v}"
-                )
+                logger.warning(f'Discarding metric with string value {k}={v}.')
                 continue
             self.experiment.log_metric(self.run_id, k, v, timestamp_ms, step)
 
     def save(self):
         pass
 
     @rank_zero_only
-    def finalize(self, status: str = "FINISHED"):
+    def finalize(self, status: str = 'FINISHED'):
         if status == 'success':
             status = 'FINISHED'
         self.experiment.set_terminated(self.run_id, status)

pytorch_lightning/loggers/neptune.py

@@ -15,11 +15,11 @@
     from neptune.experiments import Experiment
 except ImportError:
     raise ImportError('You want to use `neptune` logger which is not installed yet,'
-                      ' please install it e.g. `pip install neptune-client`.')
+                      ' install it with `pip install neptune-client`.')
 
+import torch
 from torch import is_tensor
 
-# from .base import LightningLoggerBase, rank_zero_only
 from pytorch_lightning.loggers.base import LightningLoggerBase, rank_zero_only
 
 logger = getLogger(__name__)
@@ -130,15 +130,15 @@ def any_lightning_module_function_or_hook(...):
         self._kwargs = kwargs
 
         if offline_mode:
-            self.mode = "offline"
+            self.mode = 'offline'
             neptune.init(project_qualified_name='dry-run/project',
                          backend=neptune.OfflineBackend())
         else:
-            self.mode = "online"
+            self.mode = 'online'
             neptune.init(api_token=self.api_key,
                          project_qualified_name=self.project_name)
 
-        logger.info(f"NeptuneLogger was initialized in {self.mode} mode")
+        logger.info(f'NeptuneLogger was initialized in {self.mode} mode')
 
     @property
     def experiment(self) -> Experiment:
@@ -166,53 +166,58 @@ def experiment(self) -> Experiment:
     @rank_zero_only
     def log_hyperparams(self, params: argparse.Namespace):
         for key, val in vars(params).items():
-            self.experiment.set_property(f"param__{key}", val)
+            self.experiment.set_property(f'param__{key}', val)
 
     @rank_zero_only
-    def log_metrics(self, metrics: Dict[str, float], step: Optional[int] = None):
+    def log_metrics(
+            self,
+            metrics: Dict[str, Union[torch.Tensor, float]],
+            step: Optional[int] = None
+    ):
         """Log metrics (numeric values) in Neptune experiments
 
         Args:
             metrics: Dictionary with metric names as keys and measured quantities as values
             step: Step number at which the metrics should be recorded, must be strictly increasing
         """
-
         for key, val in metrics.items():
-            if is_tensor(val):
-                val = val.cpu().detach()
-
-            if step is None:
-                self.experiment.log_metric(key, val)
-            else:
-                self.experiment.log_metric(key, x=step, y=val)
+            self.log_metric(key, val, step=step)
 
     @rank_zero_only
     def finalize(self, status: str):
         self.experiment.stop()
 
     @property
     def name(self) -> str:
-        if self.mode == "offline":
-            return "offline-name"
+        if self.mode == 'offline':
+            return 'offline-name'
         else:
             return self.experiment.name
 
     @property
     def version(self) -> str:
-        if self.mode == "offline":
-            return "offline-id-1234"
+        if self.mode == 'offline':
+            return 'offline-id-1234'
         else:
             return self.experiment.id
 
     @rank_zero_only
-    def log_metric(self, metric_name: str, metric_value: float, step: Optional[int] = None):
+    def log_metric(
+            self,
+            metric_name: str,
+            metric_value: Union[torch.Tensor, float, str],
+            step: Optional[int] = None
+    ):
         """Log metrics (numeric values) in Neptune experiments
 
         Args:
             metric_name:  The name of log, i.e. mse, loss, accuracy.
             metric_value: The value of the log (data-point).
             step: Step number at which the metrics should be recorded, must be strictly increasing
         """
+        if is_tensor(metric_value):
+            metric_value = metric_value.cpu().detach()
+
         if step is None:
             self.experiment.log_metric(metric_name, metric_value)
         else:
@@ -227,10 +232,7 @@ def log_text(self, log_name: str, text: str, step: Optional[int] = None):
             text: The value of the log (data-point).
             step: Step number at which the metrics should be recorded, must be strictly increasing
         """
-        if step is None:
-            self.experiment.log_metric(log_name, text)
-        else:
-            self.experiment.log_metric(log_name, x=step, y=text)
+        self.log_metric(log_name, text, step=step)
 
     @rank_zero_only
     def log_image(self, log_name: str, image: Union[str, Any], step: Optional[int] = None):
@@ -277,6 +279,6 @@ def append_tags(self, tags: Union[str, Iterable[str]]):
                 If multiple - comma separated - str are passed, all of them are added as tags.
                 If list of str is passed, all elements of the list are added as tags.
         """
-        if not isinstance(tags, Iterable):
+        if str(tags) == tags:
             tags = [tags]  # make it as an iterable is if it is not yet
         self.experiment.append_tags(*tags)

pytorch_lightning/loggers/tensorboard.py

@@ -44,7 +44,10 @@ class TensorBoardLogger(LightningLoggerBase):
     """
     NAME_CSV_TAGS = 'meta_tags.csv'
 
-    def __init__(self, save_dir: str, name: str = "default", version: Optional[Union[int, str]] = None, **kwargs):
+    def __init__(
+            self, save_dir: str, name: Optional[str] = "default",
+            version: Optional[Union[int, str]] = None, **kwargs
+    ):
         super().__init__()
         self.save_dir = save_dir
         self._name = name

pytorch_lightning/loggers/test_tube.py

@@ -4,7 +4,8 @@
 try:
     from test_tube import Experiment
 except ImportError:
-    raise ImportError('Missing test-tube package.')
+    raise ImportError('You want to use `test_tube` logger which is not installed yet,'
+                      ' install it with `pip install test-tube`.')
 
 from .base import LightningLoggerBase, rank_zero_only
 

pytorch_lightning/loggers/wandb.py

@@ -9,12 +9,14 @@
 import os
 from typing import Optional, List, Dict
 
+import torch.nn as nn
+
 try:
     import wandb
     from wandb.wandb_run import Run
 except ImportError:
     raise ImportError('You want to use `wandb` logger which is not installed yet,'
-                      ' please install it e.g. `pip install wandb`.')
+                      ' install it with `pip install wandb`.')
 
 from .base import LightningLoggerBase, rank_zero_only
 
@@ -50,7 +52,7 @@ def __init__(self, name: Optional[str] = None, save_dir: Optional[str] = None,
         super().__init__()
         self._name = name
         self._save_dir = save_dir
-        self._anonymous = "allow" if anonymous else None
+        self._anonymous = 'allow' if anonymous else None
         self._id = version or id
         self._tags = tags
         self._project = project
@@ -79,27 +81,25 @@ def experiment(self) -> Run:
           """
         if self._experiment is None:
             if self._offline:
-                os.environ["WANDB_MODE"] = "dryrun"
+                os.environ['WANDB_MODE'] = 'dryrun'
             self._experiment = wandb.init(
                 name=self._name, dir=self._save_dir, project=self._project, anonymous=self._anonymous,
-                id=self._id, resume="allow", tags=self._tags, entity=self._entity)
+                id=self._id, resume='allow', tags=self._tags, entity=self._entity)
         return self._experiment
 
-    def watch(self, model, log="gradients", log_freq=100):
-        wandb.watch(model, log, log_freq)
+    def watch(self, model: nn.Module, log: str = 'gradients', log_freq: int = 100):
+        wandb.watch(model, log=log, log_freq=log_freq)
 
     @rank_zero_only
     def log_hyperparams(self, params: argparse.Namespace):
         self.experiment.config.update(params)
 
     @rank_zero_only
     def log_metrics(self, metrics: Dict[str, float], step: Optional[int] = None):
-        metrics["global_step"] = step
+        if step is not None:
+            metrics['global_step'] = step
         self.experiment.log(metrics)
 
-    def save(self):
-        pass
-
     @rank_zero_only
     def finalize(self, status: str = 'success'):
         try: