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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,235 @@ | ||
| """Example of training a conditional NormalizingFlow.""" | ||
|
|
||
| import os | ||
| from typing import Any, Dict, List, Optional | ||
|
|
||
| from pytorch_lightning.loggers import WandbLogger | ||
| import torch | ||
| from torch.optim.adam import Adam | ||
|
|
||
| from graphnet.constants import EXAMPLE_DATA_DIR, EXAMPLE_OUTPUT_DIR | ||
| from graphnet.data.constants import FEATURES, TRUTH | ||
| from graphnet.models.detector.prometheus import Prometheus | ||
| from graphnet.models.gnn import DynEdge | ||
| from graphnet.models.graphs import KNNGraph | ||
| from graphnet.training.callbacks import PiecewiseLinearLR | ||
| from graphnet.training.utils import make_train_validation_dataloader | ||
| from graphnet.utilities.argparse import ArgumentParser | ||
| from graphnet.utilities.logging import Logger | ||
| from graphnet.utilities.imports import has_jammy_flows_package | ||
|
|
||
| # Make sure the jammy flows is installed | ||
| try: | ||
| assert has_jammy_flows_package() | ||
| from graphnet.models import NormalizingFlow | ||
| except AssertionError: | ||
| raise AssertionError( | ||
| "This example requires the package`jammy_flow` " | ||
| " to be installed. It appears that the package is " | ||
| " not installed. Please install the package." | ||
| ) | ||
|
|
||
| # Constants | ||
| features = FEATURES.PROMETHEUS | ||
| truth = TRUTH.PROMETHEUS | ||
|
|
||
|
|
||
| def main( | ||
| path: str, | ||
| pulsemap: str, | ||
| target: str, | ||
| truth_table: str, | ||
| gpus: Optional[List[int]], | ||
| max_epochs: int, | ||
| early_stopping_patience: int, | ||
| batch_size: int, | ||
| num_workers: int, | ||
| wandb: bool = False, | ||
| ) -> None: | ||
| """Run example.""" | ||
| # Construct Logger | ||
| logger = Logger() | ||
|
|
||
| # Initialise Weights & Biases (W&B) run | ||
| if wandb: | ||
| # Make sure W&B output directory exists | ||
| wandb_dir = "./wandb/" | ||
| os.makedirs(wandb_dir, exist_ok=True) | ||
| wandb_logger = WandbLogger( | ||
| project="example-script", | ||
| entity="graphnet-team", | ||
| save_dir=wandb_dir, | ||
| log_model=True, | ||
| ) | ||
|
|
||
| logger.info(f"features: {features}") | ||
| logger.info(f"truth: {truth}") | ||
|
|
||
| # Configuration | ||
| config: Dict[str, Any] = { | ||
| "path": path, | ||
| "pulsemap": pulsemap, | ||
| "batch_size": batch_size, | ||
| "num_workers": num_workers, | ||
| "target": target, | ||
| "early_stopping_patience": early_stopping_patience, | ||
| "fit": { | ||
| "gpus": gpus, | ||
| "max_epochs": max_epochs, | ||
| }, | ||
| } | ||
|
|
||
| archive = os.path.join(EXAMPLE_OUTPUT_DIR, "train_model_without_configs") | ||
| run_name = "dynedge_{}_example".format(config["target"]) | ||
| if wandb: | ||
| # Log configuration to W&B | ||
| wandb_logger.experiment.config.update(config) | ||
|
|
||
| # Define graph representation | ||
| graph_definition = KNNGraph(detector=Prometheus()) | ||
|
|
||
| ( | ||
| training_dataloader, | ||
| validation_dataloader, | ||
| ) = make_train_validation_dataloader( | ||
| db=config["path"], | ||
| graph_definition=graph_definition, | ||
| pulsemaps=config["pulsemap"], | ||
| features=features, | ||
| truth=truth, | ||
| batch_size=config["batch_size"], | ||
| num_workers=config["num_workers"], | ||
| truth_table=truth_table, | ||
| selection=None, | ||
| ) | ||
|
|
||
| # Building model | ||
|
|
||
| backbone = DynEdge( | ||
| nb_inputs=graph_definition.nb_outputs, | ||
| global_pooling_schemes=["min", "max", "mean", "sum"], | ||
| ) | ||
|
|
||
| model = NormalizingFlow( | ||
| graph_definition=graph_definition, | ||
| backbone=backbone, | ||
| optimizer_class=Adam, | ||
| target_labels=config["target"], | ||
| optimizer_kwargs={"lr": 1e-03, "eps": 1e-03}, | ||
| scheduler_class=PiecewiseLinearLR, | ||
| scheduler_kwargs={ | ||
| "milestones": [ | ||
| 0, | ||
| len(training_dataloader) / 2, | ||
| len(training_dataloader) * config["fit"]["max_epochs"], | ||
| ], | ||
| "factors": [1e-2, 1, 1e-02], | ||
| }, | ||
| scheduler_config={ | ||
| "interval": "step", | ||
| }, | ||
| ) | ||
|
|
||
| # Training model | ||
| model.fit( | ||
| training_dataloader, | ||
| validation_dataloader, | ||
| early_stopping_patience=config["early_stopping_patience"], | ||
| logger=wandb_logger if wandb else None, | ||
| **config["fit"], | ||
| ) | ||
|
|
||
| # Get predictions | ||
| additional_attributes = model.target_labels | ||
| assert isinstance(additional_attributes, list) # mypy | ||
|
|
||
| results = model.predict_as_dataframe( | ||
| validation_dataloader, | ||
| additional_attributes=additional_attributes + ["event_no"], | ||
| gpus=config["fit"]["gpus"], | ||
| ) | ||
|
|
||
| # Save predictions and model to file | ||
| db_name = path.split("/")[-1].split(".")[0] | ||
| path = os.path.join(archive, db_name, run_name) | ||
| logger.info(f"Writing results to {path}") | ||
| os.makedirs(path, exist_ok=True) | ||
|
|
||
| # Save results as .csv | ||
| results.to_csv(f"{path}/results.csv") | ||
|
|
||
| # Save full model (including weights) to .pth file - not version safe | ||
| # Note: Models saved as .pth files in one version of graphnet | ||
| # may not be compatible with a different version of graphnet. | ||
| model.save(f"{path}/model.pth") | ||
|
|
||
| # Save model config and state dict - Version safe save method. | ||
| # This method of saving models is the safest way. | ||
| model.save_state_dict(f"{path}/state_dict.pth") | ||
| model.save_config(f"{path}/model_config.yml") | ||
|
|
||
|
|
||
| if __name__ == "__main__": | ||
|
|
||
| # Parse command-line arguments | ||
| parser = ArgumentParser( | ||
| description=""" | ||
| Train conditional NormalizingFlow without the use of config files. | ||
| """ | ||
| ) | ||
|
|
||
| parser.add_argument( | ||
| "--path", | ||
| help="Path to dataset file (default: %(default)s)", | ||
| default=f"{EXAMPLE_DATA_DIR}/sqlite/prometheus/prometheus-events.db", | ||
| ) | ||
|
|
||
| parser.add_argument( | ||
| "--pulsemap", | ||
| help="Name of pulsemap to use (default: %(default)s)", | ||
| default="total", | ||
| ) | ||
|
|
||
| parser.add_argument( | ||
| "--target", | ||
| help=( | ||
| "Name of feature to use as regression target (default: " | ||
| "%(default)s)" | ||
| ), | ||
| default="total_energy", | ||
| ) | ||
|
|
||
| parser.add_argument( | ||
| "--truth-table", | ||
| help="Name of truth table to be used (default: %(default)s)", | ||
| default="mc_truth", | ||
| ) | ||
|
|
||
| parser.with_standard_arguments( | ||
| "gpus", | ||
| ("max-epochs", 1), | ||
| "early-stopping-patience", | ||
| ("batch-size", 50), | ||
| "num-workers", | ||
| ) | ||
|
|
||
| parser.add_argument( | ||
| "--wandb", | ||
| action="store_true", | ||
| help="If True, Weights & Biases are used to track the experiment.", | ||
| ) | ||
|
|
||
| args, unknown = parser.parse_known_args() | ||
|
|
||
| main( | ||
| args.path, | ||
| args.pulsemap, | ||
| args.target, | ||
| args.truth_table, | ||
| args.gpus, | ||
| args.max_epochs, | ||
| args.early_stopping_patience, | ||
| args.batch_size, | ||
| args.num_workers, | ||
| args.wandb, | ||
| ) |
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