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"""Script to run experiments."""
from datetime import datetime
import importlib
from pathlib import Path
from typing import Dict, List, NamedTuple, Optional, Union, Type
import click
from loguru import logger
from omegaconf import OmegaConf
import pytorch_lightning as pl
import torch
from torch import nn
from torchsummary import summary
from tqdm import tqdm
import wandb
SEED = 4711
EXPERIMENTS_DIRNAME = Path(__file__).parents[0].resolve() / "experiments"
def _configure_logging(log_dir: Optional[Path], verbose: int = 0) -> None:
"""Configure the loguru logger for output to terminal and disk."""
def _get_level(verbose: int) -> str:
"""Sets the logger level."""
levels = {0: "WARNING", 1: "INFO", 2: "DEBUG"}
verbose = min(verbose, 2)
return levels[verbose]
# Have to remove default logger to get tqdm to work properly.
logger.remove()
# Fetch verbosity level.
level = _get_level(verbose)
logger.add(lambda msg: tqdm.write(msg, end=""), colorize=True, level=level)
if log_dir is not None:
logger.add(
str(log_dir / "train.log"),
format="{time:YYYY-MM-DD at HH:mm:ss} | {level} | {message}",
)
def _import_class(module_and_class_name: str) -> type:
"""Import class from module."""
module_name, class_name = module_and_class_name.rsplit(".", 1)
module = importlib.import_module(module_name)
return getattr(module, class_name)
def _configure_callbacks(
args: List[Union[OmegaConf, NamedTuple]]
) -> List[Type[pl.callbacks.Callback]]:
"""Configures lightning callbacks."""
pl_callbacks = [
getattr(pl.callbacks, callback.type)(**callback.args) for callback in args
]
return pl_callbacks
def _configure_logger(
network: Type[nn.Module], args: Dict, use_wandb: bool
) -> pl.loggers.WandbLogger:
"""Configures lightning logger."""
if use_wandb:
pl_logger = pl.loggers.WandbLogger()
pl_logger.watch(network)
pl_logger.log_hyperparams(vars(args))
else:
pl_logger = pl.logger.TensorBoardLogger("training/logs")
return pl_logger
def _save_best_weights(
callbacks: List[Type[pl.callbacks.Callback]], use_wandb: bool
) -> None:
"""Saves the best model."""
model_checkpoint_callback = next(
callback
for callback in callbacks
if isinstance(callback, pl.callbacks.ModelCheckpoint)
)
best_model_path = model_checkpoint_callback.best_model_path
if best_model_path:
logger.info(f"Best model saved at: {best_model_path}")
if use_wandb:
logger.info("Uploading model to W&B...")
wandb.save(best_model_path)
def _load_lit_model(lit_model_class: type, network: Type[nn.Module], config: OmegaConf) -> Type[pl.LightningModule]:
"""Load lightning model."""
if config.load_checkpoint is not None:
logger.info(
f"Loading network weights from checkpoint: {config.load_checkpoint}"
)
return lit_model_class.load_from_checkpoint(
config.load_checkpoint, network=network, **config.model.args
)
return lit_model_class(network=network, **config.model.args)
def run(path: str, train: bool, test: bool, tune: bool, use_wandb: bool) -> None:
"""Runs experiment."""
logger.info("Starting experiment...")
# Seed everything in the experiment.
logger.info(f"Seeding everthing with seed={SEED}")
pl.utilities.seed.seed_everything(SEED)
# Load config.
logger.info(f"Loading config from: {path}")
config = OmegaConf.load(path)
# Load classes.
data_module_class = _import_class(f"text_recognizer.data.{config.data.type}")
network_class = _import_class(f"text_recognizer.networks.{config.network.type}")
lit_model_class = _import_class(f"text_recognizer.models.{config.model.type}")
# Initialize data object and network.
data_module = data_module_class(**config.data.args)
network = network_class(**data_module.config(), **config.network.args)
# Load callback and logger.
callbacks = _configure_callbacks(config.callbacks)
pl_logger = _configure_logger(network, config, use_wandb)
# Load ligtning model.
lit_model = _load_lit_model(lit_model_class, network, config)
trainer = pl.Trainer(
**config.trainer.args,
callbacks=callbacks,
logger=pl_logger,
weigths_save_path="training/logs",
)
if tune:
logger.info(f"Tuning learning rate and batch size...")
trainer.tune(lit_model, datamodule=data_module)
if train:
logger.info(f"Training network...")
trainer.fit(lit_model, datamodule=data_module)
if test:
logger.info(f"Testing network...")
trainer.test(lit_model, datamodule=data_module)
_save_best_weights(callbacks, use_wandb)
@click.command()
@click.option("-f", "--experiment_config", type=str, help="Path to experiment config.")
@click.option("--use_wandb", is_flag=True, help="If true, do use wandb for logging.")
@click.option(
"--tune", is_flag=True, help="If true, tune hyperparameters for training."
)
@click.option("--train", is_flag=True, help="If true, train the model.")
@click.option("--test", is_flag=True, help="If true, test the model.")
@click.option("-v", "--verbose", count=True)
def cli(
experiment_config: str,
use_wandb: bool,
tune: bool,
train: bool,
test: bool,
verbose: int,
) -> None:
"""Run experiment."""
_configure_logging(None, verbose=verbose)
run(path=experiment_config, train=train, test=test, tune=tune, use_wandb=use_wandb)
if __name__ == "__main__":
cli()
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