Improve comments and code formatting (#296)

src/datamodules/mnist_datamodule.py

@@ -11,11 +11,22 @@ class MNISTDataModule(LightningDataModule):
     """Example of LightningDataModule for MNIST dataset.
 
     A DataModule implements 5 key methods:
-        - prepare_data (things to do on 1 GPU/TPU, not on every GPU/TPU in distributed mode)
-        - setup (things to do on every accelerator in distributed mode)
-        - train_dataloader (the training dataloader)
-        - val_dataloader (the validation dataloader(s))
-        - test_dataloader (the test dataloader(s))
+
+        def prepare_data(self):
+            # things to do on 1 GPU/TPU (not on every GPU/TPU in DDP)
+            # download data, split, etc...
+        def setup(self, stage):
+            # things to do on every process in DDP
+            # load data, split, set variables, etc...
+        def train_dataloader(self):
+            # return train dataloader
+        def val_dataloader(self):
+            # return validation dataloader
+        def test_dataloader(self):
+            # return test dataloader
+        def teardown(self):
+            # called on every process in DDP
+            # clean up after fit or test
 
     This allows you to share a full dataset without explaining how to download,
     split, transform and process the data.
@@ -35,6 +46,7 @@ def __init__(
         super().__init__()
 
         # this line allows to access init params with 'self.hparams' attribute
+        # it also ensures init params will be stored in ckpt
         self.save_hyperparameters(logger=False)
 
         # data transformations
@@ -51,23 +63,17 @@ def num_classes(self) -> int:
         return 10
 
     def prepare_data(self):
-        """Download data if needed.
-
-        This method is called only from a single GPU.
-        Do not use it to assign state (self.x = y).
-        """
+        """Download data if needed."""
         MNIST(self.hparams.data_dir, train=True, download=True)
         MNIST(self.hparams.data_dir, train=False, download=True)
 
     def setup(self, stage: Optional[str] = None):
         """Load data. Set variables: `self.data_train`, `self.data_val`, `self.data_test`.
 
-        This method is called by lightning when doing `trainer.fit()` and `trainer.test()`,
-        so be careful not to execute the random split twice! The `stage` can be used to
-        differentiate whether it's called before trainer.fit()` or `trainer.test()`.
+        This method is called by Lightning with both `trainer.fit()` and `trainer.test()`,
+        so be careful not to execute the random split twice!
         """
-
-        # load datasets only if they're not loaded already
+        # load and split datasets only if not loaded already
         if not self.data_train and not self.data_val and not self.data_test:
             trainset = MNIST(self.hparams.data_dir, train=True, transform=self.transforms)
             testset = MNIST(self.hparams.data_dir, train=False, transform=self.transforms)
@@ -104,3 +110,6 @@ def test_dataloader(self):
             pin_memory=self.hparams.pin_memory,
             shuffle=False,
         )
+
+    def teardown(self):
+        pass

src/models/mnist_module.py

@@ -11,12 +11,13 @@
 class MNISTLitModule(LightningModule):
     """Example of LightningModule for MNIST classification.
 
-    A LightningModule organizes your PyTorch code into 5 sections:
+    A LightningModule organizes your PyTorch code into 6 sections:
         - Computations (init).
         - Train loop (training_step)
         - Validation loop (validation_step)
         - Test loop (test_step)
-        - Optimizers (configure_optimizers)
+        - Prediction Loop (predict_step)
+        - Optimizers and LR Schedulers (configure_optimizers)
 
     Read the docs:
         https://pytorch-lightning.readthedocs.io/en/latest/common/lightning_module.html
@@ -118,7 +119,7 @@ def configure_optimizers(self):
         """Choose what optimizers and learning-rate schedulers to use in your optimization.
         Normally you'd need one. But in the case of GANs or similar you might have multiple.
 
-        See examples here:
+        Examples:
             https://pytorch-lightning.readthedocs.io/en/latest/common/lightning_module.html#configure-optimizers
         """
         return torch.optim.Adam(

src/testing_pipeline.py

@@ -23,29 +23,26 @@ def test(config: DictConfig) -> None:
 
     assert config.ckpt_path
 
-    # Init lightning datamodule
+    # init lightning datamodule
     log.info(f"Instantiating datamodule <{config.datamodule._target_}>")
     datamodule: LightningDataModule = hydra.utils.instantiate(config.datamodule)
 
-    # Init lightning model
+    # init lightning model
     log.info(f"Instantiating model <{config.model._target_}>")
     model: LightningModule = hydra.utils.instantiate(config.model)
 
-    # Init lightning loggers
+    # init lightning loggers
     logger: List[LightningLoggerBase] = []
     if "logger" in config:
         for _, lg_conf in config.logger.items():
             if "_target_" in lg_conf:
                 log.info(f"Instantiating logger <{lg_conf._target_}>")
                 logger.append(hydra.utils.instantiate(lg_conf))
 
-    # Init lightning trainer
+    # init lightning trainer
     log.info(f"Instantiating trainer <{config.trainer._target_}>")
     trainer: Trainer = hydra.utils.instantiate(config.trainer, logger=logger)
 
-    # Log hyperparameters
-    if trainer.logger:
-        trainer.logger.log_hyperparams({"ckpt_path": config.ckpt_path})
-
+    # test the model
     log.info("Starting testing!")
     trainer.test(model=model, datamodule=datamodule, ckpt_path=config.ckpt_path)

src/training_pipeline.py

@@ -22,37 +22,37 @@ def train(config: DictConfig) -> Optional[float]:
         Optional[float]: Metric score for hyperparameter optimization.
     """
 
-    # Init lightning datamodule
+    # init lightning datamodule
     log.info(f"Instantiating datamodule <{config.datamodule._target_}>")
     datamodule: LightningDataModule = hydra.utils.instantiate(config.datamodule)
 
-    # Init lightning model
+    # init lightning model
     log.info(f"Instantiating model <{config.model._target_}>")
     model: LightningModule = hydra.utils.instantiate(config.model)
 
-    # Init lightning callbacks
+    # init lightning callbacks
     callbacks: List[Callback] = []
     if "callbacks" in config:
         for _, cb_conf in config.callbacks.items():
             if "_target_" in cb_conf:
                 log.info(f"Instantiating callback <{cb_conf._target_}>")
                 callbacks.append(hydra.utils.instantiate(cb_conf))
 
-    # Init lightning loggers
+    # init lightning loggers
     logger: List[LightningLoggerBase] = []
     if "logger" in config:
         for _, lg_conf in config.logger.items():
             if "_target_" in lg_conf:
                 log.info(f"Instantiating logger <{lg_conf._target_}>")
                 logger.append(hydra.utils.instantiate(lg_conf))
 
-    # Init lightning trainer
+    # init lightning trainer
     log.info(f"Instantiating trainer <{config.trainer._target_}>")
     trainer: Trainer = hydra.utils.instantiate(
         config.trainer, callbacks=callbacks, logger=logger, _convert_="partial"
     )
 
-    # Send some parameters from config to all lightning loggers
+    # send hyperparameters to loggers
     log.info("Logging hyperparameters!")
     utils.log_hyperparameters(
         config=config,
@@ -63,24 +63,22 @@ def train(config: DictConfig) -> Optional[float]:
         logger=logger,
     )
 
-    # Train the model
+    # train the model
     if config.get("train"):
         log.info("Starting training!")
         trainer.fit(model=model, datamodule=datamodule, ckpt_path=config.get("ckpt_path"))
 
-    # Get metric score for hyperparameter optimization
+    # get metric score for hyperparameter optimization
     metric_name = config.get("optimized_metric")
     score = utils.get_metric_value(metric_name, trainer) if metric_name else None
 
-    # Test the model
+    # test the model
+    ckpt_path = "best" if config.get("train") and not config.trainer.get("fast_dev_run") else None
     if config.get("test"):
-        ckpt_path = "best"
-        if not config.get("train") or config.trainer.get("fast_dev_run"):
-            ckpt_path = None
         log.info("Starting testing!")
         trainer.test(model=model, datamodule=datamodule, ckpt_path=ckpt_path)
 
-    # Make sure everything closed properly
+    # make sure everything closed properly
     log.info("Finalizing!")
     utils.finish(
         config=config,
@@ -91,9 +89,9 @@ def train(config: DictConfig) -> Optional[float]:
         logger=logger,
     )
 
-    # Print path to best checkpoint
+    # print path to best checkpoint
     if not config.trainer.get("fast_dev_run") and config.get("train"):
         log.info(f"Best model ckpt at {trainer.checkpoint_callback.best_model_path}")
 
-    # Return metric score for hyperparameter optimization
+    # return metric score for hyperparameter optimization
     return score

src/utils/__init__.py

@@ -18,15 +18,8 @@ def get_logger(name=__name__) -> logging.Logger:
 
     # this ensures all logging levels get marked with the rank zero decorator
     # otherwise logs would get multiplied for each GPU process in multi-GPU setup
-    for level in (
-        "debug",
-        "info",
-        "warning",
-        "error",
-        "exception",
-        "fatal",
-        "critical",
-    ):
+    logging_levels = ("debug", "info", "warning", "error", "exception", "fatal", "critical")
+    for level in logging_levels:
         setattr(logger, level, rank_zero_only(getattr(logger, level)))
 
     return logger
@@ -94,7 +87,7 @@ def print_config(
 
     for field in print_order:
         queue.append(field) if field in config else log.info(
-            f"Field '{field}' not found in config."
+            f"Field '{field}' not found in config. Skipping '{field}' config printing..."
         )
 
     for field in config:
@@ -157,6 +150,8 @@ def log_hyperparameters(
         hparams["seed"] = config["seed"]
     if "callbacks" in config:
         hparams["callbacks"] = config["callbacks"]
+    if "ckpt_path" in config:
+        hparams["ckpt_path"] = config.ckpt_path
 
     # send hparams to all loggers
     trainer.logger.log_hyperparams(hparams)

test.py

@@ -10,15 +10,15 @@
 @hydra.main(config_path="configs/", config_name="test.yaml")
 def main(config: DictConfig):
 
-    # Imports can be nested inside @hydra.main to optimize tab completion
+    # imports can be nested inside @hydra.main to optimize tab completion
     # https://github.com/facebookresearch/hydra/issues/934
     from src import utils
     from src.testing_pipeline import test
 
-    # Applies optional utilities
+    # applies optional utilities
     utils.extras(config)
 
-    # Evaluate model
+    # evaluate model
     return test(config)
 
 

train.py

@@ -10,15 +10,15 @@
 @hydra.main(config_path="configs/", config_name="train.yaml")
 def main(config: DictConfig):
 
-    # Imports can be nested inside @hydra.main to optimize tab completion
+    # imports can be nested inside @hydra.main to optimize tab completion
     # https://github.com/facebookresearch/hydra/issues/934
     from src import utils
     from src.training_pipeline import train
 
-    # Applies optional utilities
+    # applies optional utilities
     utils.extras(config)
 
-    # Train model
+    # train model
     return train(config)