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import os
import shutil
from pathlib import Path
from typing import List
import torch
import lightning as L
from lightning.pytorch.loggers import Logger, TensorBoardLogger, CSVLogger
from lightning.pytorch.callbacks import (
ModelCheckpoint,
EarlyStopping,
RichModelSummary,
RichProgressBar,
)
from dotenv import load_dotenv, find_dotenv
import hydra
from omegaconf import DictConfig, OmegaConf
from src.datamodules.catdog_datamodule import CatDogImageDataModule
from src.utils.logging_utils import setup_logger, task_wrapper
from loguru import logger
import rootutils
# Load environment variables
load_dotenv(find_dotenv(".env"))
# Setup root directory
root = rootutils.setup_root(__file__, indicator=".project-root")
def initialize_callbacks(cfg: DictConfig) -> List[L.Callback]:
"""Initialize callbacks based on configuration."""
callback_classes = {
"model_checkpoint": ModelCheckpoint,
"early_stopping": EarlyStopping,
"rich_model_summary": RichModelSummary,
"rich_progress_bar": RichProgressBar,
}
return [callback_classes[name](**params) for name, params in cfg.callbacks.items()]
def initialize_loggers(cfg: DictConfig) -> List[Logger]:
"""Initialize loggers based on configuration."""
logger_classes = {
"tensorboard": TensorBoardLogger,
"csv": CSVLogger,
}
return [logger_classes[name](**params) for name, params in cfg.logger.items()]
def load_checkpoint_if_available(ckpt_path: str) -> str:
"""Return the checkpoint path if available, else None."""
if ckpt_path and Path(ckpt_path).exists():
logger.info(f"Using checkpoint: {ckpt_path}")
return ckpt_path
logger.warning(f"Checkpoint not found at {ckpt_path}. Using current model weights.")
return None
def clear_checkpoint_directory(ckpt_dir: str):
"""Clear checkpoint directory contents without removing the directory."""
ckpt_dir_path = Path(ckpt_dir)
if not ckpt_dir_path.exists():
logger.info(f"Creating checkpoint directory: {ckpt_dir}")
ckpt_dir_path.mkdir(parents=True, exist_ok=True)
else:
logger.info(f"Clearing checkpoint directory: {ckpt_dir}")
for item in ckpt_dir_path.iterdir():
try:
item.unlink() if item.is_file() else shutil.rmtree(item)
except Exception as e:
logger.error(f"Failed to delete {item}: {e}")
@task_wrapper
def train_module(
data_module: L.LightningDataModule, model: L.LightningModule, trainer: L.Trainer
):
"""Train the model and log metrics."""
logger.info("Starting training")
trainer.fit(model, data_module)
train_metrics = trainer.callback_metrics
train_acc = train_metrics.get("train_acc")
val_acc = train_metrics.get("val_acc")
logger.info(
f"Training completed. Metrics - train_acc: {train_acc}, val_acc: {val_acc}"
)
return train_metrics
@task_wrapper
def run_test_module(
cfg: DictConfig,
datamodule: L.LightningDataModule,
model: L.LightningModule,
trainer: L.Trainer,
):
"""Test the model using the best checkpoint or current model weights."""
logger.info("Starting testing")
datamodule.setup(stage="test")
test_metrics = trainer.test(
model, datamodule, ckpt_path=load_checkpoint_if_available(cfg.ckpt_path)
)
logger.info(f"Test metrics: {test_metrics}")
return test_metrics[0] if test_metrics else {}
@hydra.main(config_path="../configs", config_name="train", version_base="1.1")
def setup_run_trainer(cfg: DictConfig):
"""Set up and run the Trainer for training and testing."""
# Display configuration
logger.info(f"Config:\n{OmegaConf.to_yaml(cfg)}")
# Initialize logger
log_path = Path(cfg.paths.log_dir) / (
"train.log" if cfg.task_name == "train" else "eval.log"
)
setup_logger(log_path)
# Display key paths
for path_name in [
"root_dir",
"data_dir",
"log_dir",
"ckpt_dir",
"artifact_dir",
"output_dir",
]:
logger.info(
f"{path_name.replace('_', ' ').capitalize()}: {cfg.paths[path_name]}"
)
# Initialize DataModule and Model
logger.info(f"Instantiating datamodule <{cfg.data._target_}>")
datamodule: L.LightningDataModule = hydra.utils.instantiate(cfg.data)
logger.info(f"Instantiating model <{cfg.model._target_}>")
model: L.LightningModule = hydra.utils.instantiate(cfg.model)
# Check GPU availability and set seed for reproducibility
logger.info("GPU available" if torch.cuda.is_available() else "No GPU available")
L.seed_everything(cfg.seed, workers=True)
# Set up callbacks, loggers, and Trainer
callbacks = initialize_callbacks(cfg)
logger.info(f"Callbacks: {callbacks}")
loggers = initialize_loggers(cfg)
logger.info(f"Loggers: {loggers}")
trainer: L.Trainer = hydra.utils.instantiate(
cfg.trainer, callbacks=callbacks, logger=loggers
)
# Training phase
train_metrics = {}
if cfg.get("train"):
clear_checkpoint_directory(cfg.paths.ckpt_dir)
train_metrics = train_module(datamodule, model, trainer)
(Path(cfg.paths.ckpt_dir) / "train_done.flag").write_text(
"Training completed.\n"
)
# Testing phase
test_metrics = {}
if cfg.get("test"):
test_metrics = run_test_module(cfg, datamodule, model, trainer)
# Combine metrics and extract optimization metric
all_metrics = {**train_metrics, **test_metrics}
optimization_metric = all_metrics.get(cfg.get("optimization_metric"), 0.0)
(
logger.warning(
f"Optimization metric '{cfg.get('optimization_metric')}' not found. Defaulting to 0."
)
if optimization_metric == 0.0
else logger.info(f"Optimization metric: {optimization_metric}")
)
return optimization_metric
if __name__ == "__main__":
setup_run_trainer()
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