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authorGustaf Rydholm <gustaf.rydholm@gmail.com>2021-07-05 18:12:23 +0200
committerGustaf Rydholm <gustaf.rydholm@gmail.com>2021-07-05 18:12:23 +0200
commitf0481decdad9afb52494e9e95996deef843ef233 (patch)
treebe3993c3df71c3d0b52e10a8cc5b3b01f6bb23b3 /training/run.py
parent80e9bed0dd9840ac0cc9de1c6c1be3b6fed90cf9 (diff)
Finalizing training loop with hydra
Diffstat (limited to 'training/run.py')
-rw-r--r--training/run.py37
1 files changed, 8 insertions, 29 deletions
diff --git a/training/run.py b/training/run.py
index ed1b372..5f7c927 100644
--- a/training/run.py
+++ b/training/run.py
@@ -14,35 +14,12 @@ from pytorch_lightning import (
from pytorch_lightning.loggers import LightningLoggerBase
from torch import nn
-from utils import configure_logging
-
-
-def configure_callbacks(
- config: DictConfig,
-) -> List[Type[Callback]]:
- """Configures lightning callbacks."""
- callbacks = []
- if config.get("callbacks"):
- for callback_config in config.callbacks.values():
- if config.get("_target_"):
- log.info(f"Instantiating callback <{callback_config._target_}>")
- callbacks.append(hydra.utils.instantiate(callback_config))
- return callbacks
-
-
-def configure_logger(config: DictConfig) -> List[Type[LightningLoggerBase]]:
- logger = []
- if config.get("logger"):
- for logger_config in config.logger.values():
- if config.get("_target_"):
- log.info(f"Instantiating callback <{logger_config._target_}>")
- logger.append(hydra.utils.instantiate(logger_config))
- return logger
+import utils
def run(config: DictConfig) -> Optional[float]:
"""Runs experiment."""
- configure_logging(config.logging)
+ utils.configure_logging(config.logging)
log.info("Starting experiment...")
if config.get("seed"):
@@ -65,8 +42,8 @@ def run(config: DictConfig) -> Optional[float]:
)
# Load callback and logger.
- callbacks = configure_callbacks(config)
- logger = configure_logger(config)
+ callbacks: List[Type[Callback]] = utils.configure_callbacks(config)
+ logger: List[Type[LightningLoggerBase]] = utils.configure_logger(config)
log.info(f"Instantiating trainer <{config.trainer._target_}>")
trainer: Trainer = hydra.utils.instantiate(
@@ -74,6 +51,7 @@ def run(config: DictConfig) -> Optional[float]:
)
# Log hyperparameters
+ utils.log_hyperparameters(config=config, model=model, trainer=trainer)
if config.debug:
log.info("Fast development run...")
@@ -81,7 +59,7 @@ def run(config: DictConfig) -> Optional[float]:
return None
if config.tune:
- log.info("Tuning learning rate and batch size...")
+ log.info("Tuning hyperparameters...")
trainer.tune(model, datamodule=datamodule)
if config.train:
@@ -92,4 +70,5 @@ def run(config: DictConfig) -> Optional[float]:
log.info("Testing network...")
trainer.test(model, datamodule=datamodule)
- # Make sure everything closes properly
+ log.info(f"Best checkpoint path:\n{trainer.checkpoint_callback.best_model_path}")
+ utils.finish(trainer)