From 146f66ed6c6507c39dde170c5285199dd16da01c Mon Sep 17 00:00:00 2001 From: samadpls Date: Sun, 3 Dec 2023 01:32:52 +0500 Subject: [PATCH 01/20] Added flexible DataLoader configurations to `GraphNeTDataModule` Signed-off-by: samadpls --- setup.py | 1 + src/graphnet/data/datamodule.py | 338 ++++++++++++++++++++++++++++++++ src/graphnet/training/utils.py | 13 ++ 3 files changed, 352 insertions(+) create mode 100644 src/graphnet/data/datamodule.py diff --git a/setup.py b/setup.py index 3b70233ab..1ec34a0f0 100644 --- a/setup.py +++ b/setup.py @@ -26,6 +26,7 @@ "timer>=0.2", "tqdm>=4.64", "wandb>=0.12", + "pytorch-lightning>=2.1.2", ] EXTRAS_REQUIRE = { diff --git a/src/graphnet/data/datamodule.py b/src/graphnet/data/datamodule.py new file mode 100644 index 000000000..1f8398c8b --- /dev/null +++ b/src/graphnet/data/datamodule.py @@ -0,0 +1,338 @@ +from typing import Dict, Any, Optional, List, Tuple, Union +import lightning as L +from torch.utils.data import DataLoader +from copy import deepcopy +from sklearn.model_selection import train_test_split +import pandas as pd + +from graphnet.data.dataset import ( + Dataset, + EnsembleDataset, + SQLiteDataset, + ParquetDataset, +) +from graphnet.utilities.logging import Logger +from graphnet.training.utils import save_selection + + +class GraphNeTDataModule(L.LightningDataModule, Logger): + """General Class for DataLoader Construction.""" + + def __init__( + self, + dataset_reference: Union[SQLiteDataset, ParquetDataset, Dataset], + selection: Optional[Union[List[int], List[List[int]]]], + test_selection: Optional[Union[List[int], List[List[int]]]], + dataset_args: Dict[str, Any], + train_dataloader_kwargs: Optional[Dict[str, Any]] = None, + validation_dataloader_kwargs: Optional[Dict[str, Any]] = None, + test_dataloader_kwargs: Optional[Dict[str, Any]] = None, + train_val_split: Optional[List[float, float]] = [0.9, 0.10], + split_seed: int = 42, + ) -> None: + """Create dataloaders from dataset. + + Args: + dataset_reference: A non-instantiated reference to the dataset class. + selection: (Optional) a list of event id's used for training and validation. + test_selection: (Optional) a list of event id's used for testing. + dataset_args: Arguments to instantiate graphnet.data.dataset.Dataset with. + train_dataloader_kwargs: Arguments for the training DataLoader. + validation_dataloader_kwargs: Arguments for the validation DataLoader. + test_dataloader_kwargs: Arguments for the test DataLoader. + split_seed: seed used for shuffling and splitting selections into train/validation. + """ + self._dataset = dataset_reference + self._selection = selection + self._train_val_split = train_val_split + self._test_selection = test_selection + self._dataset_args = dataset_args + self._rng = split_seed + + self._train_dataloader_kwargs = train_dataloader_kwargs or {} + self._validation_dataloader_kwargs = validation_dataloader_kwargs or {} + self._test_dataloader_kwargs = test_dataloader_kwargs or {} + + # If multiple dataset paths are given, we should use EnsembleDataset + self._use_ensemble_dataset = isinstance( + self._dataset_args["path"], list + ) + + def prepare_data(self) -> None: + """Prepare the dataset for training.""" + # Download method for curated datasets. Method for download is + # likely dataset-specific, so we can leave it as-is + pass + + def setup(self, stage: str) -> None: + """Prepare Datasets for DataLoaders. + + Args: + stage: lightning stage. Either "fit, validate, test, predict" + """ + # Sanity Checks + self._validate_dataset_class() + self._validate_dataset_args() + self._validate_dataloader_args() + + # Case-handling of selection arguments + self._resolve_selections() + + # Creation of Datasets + self._train_dataset = self._create_dataset(self._train_selection) + self._val_dataset = self._create_dataset(self._val_selection) + self._test_dataset = self._create_dataset(self._test_selection) + + return + + def train_dataloader(self) -> DataLoader: + """Prepare and return the training DataLoader. + + Returns: + DataLoader: The DataLoader configured for training. + """ + return self._create_dataloader(self._train_dataset) + + def val_dataloader(self) -> DataLoader: + """Prepare and return the validation DataLoader. + + Returns: + DataLoader: The DataLoader configured for validation. + """ + return self._create_dataloader(self._val_dataset) + + def test_dataloader(self) -> DataLoader: + """Prepare and return the test DataLoader. + + Returns: + DataLoader: The DataLoader configured for testing. + """ + return self._create_dataloader(self._test_dataset) + + def teardown(self) -> None: + """Perform any necessary cleanup or shutdown procedures. + + This method can be used for tasks such as closing SQLite connections + after training. Override this method as needed. + + Returns: + None + """ + pass + + def _create_dataloader( + self, dataset: Union[Dataset, EnsembleDataset] + ) -> DataLoader: + """Create a DataLoader for the given dataset. + + Args: + dataset (Union[Dataset, EnsembleDataset]): The dataset to create a DataLoader for. + + Returns: + DataLoader: The DataLoader configured for the given dataset. + """ + return DataLoader(dataset=dataset, **self._dataloader_args) + + def _validate_dataset_class(self) -> None: + """Sanity checks on the dataset reference (self._dataset). + + Is it a GraphNeT-compatible dataset? has the class already been + instantiated? Did they try to pass EnsembleDataset? + """ + if not isinstance( + self._dataset, (SQLiteDataset, ParquetDataset, Dataset) + ): + raise TypeError( + "dataset_reference must be an instance of SQLiteDataset, ParquetDataset, or Dataset." + ) + if isinstance(self._dataset, EnsembleDataset): + raise TypeError( + "EnsembleDataset is not allowed as dataset_reference." + ) + + def _validate_dataset_args(self) -> None: + """Sanity checks on the arguments for the dataset reference.""" + if isinstance(self._dataset_args["path"], list): + if self._selection is not None: + try: + # Check that the number of dataset paths is equal to the + # number of selections given as arg. + assert len(self._dataset_args["path"]) == len( + self._selection + ) + except AssertionError: + raise ValueError( + f"The number of dataset paths ({len(self._dataset_args['path'])}) does not match the number of selections ({len(self._selection)})." + ) + + if self._test_selection is not None: + try: + # Check that the number of dataset paths is equal to the + # number of test selections. + assert len(self._dataset_args["path"]) == len( + self._test_selection + ) + except AssertionError: + raise ValueError( + f"The number of dataset paths ({len(self._dataset_args['path'])}) does not match the number of test selections ({len(self._test_selection)}). If you'd like to test on only a subset of the {len(self._dataset_args['path'])} datasets, please provide empty test selections for the others." + ) + + def _validate_dataloader_args(self) -> None: + """Sanity check on `dataloader_args`.""" + if "dataset" in self._dataloader_args: + raise ValueError("`dataloader_args` must not contain `dataset`") + + def _resolve_selections(self) -> None: + if self._test_selection is None: + self.warning_once( + f"{self.__class__.__name__} did not receive an argument for `test_selection` and will therefore not have a prediction dataloader available." + ) + if self._selection is not None: + # Split the selection into train/validation + if self._use_ensemble_dataset: + # Split every selection + self._train_selection = [] + self._val_selection = [] + for selection in self._selection: + train_selection, val_selection = self._split_selection( + selection + ) + self._train_selection.append(train_selection) + self._val_selection.append(val_selection) + + else: + # Split the only selection we got + ( + self._train_selection, + self._val_selection, + ) = self._split_selection(self._selection) + + if self._selection is None: + # If not provided, we infer it by grabbing all event ids in the dataset. + self.info( + f"{self.__class__.__name__} did not receive an argument for `selection`. Selection will automatically be created with a split of train: {self._train_val_split[0]} and validation: {self._train_val_split[1]}" + ) + ( + self._train_selection, + self._val_selection, + ) = self._infer_selections() + + def _split_selection( + self, selection: List[int] + ) -> Tuple[List[int], List[int]]: + """Split train selection into train/validation. + + Args: + selection: Training selection to be split + + Returns: + Training selection, Validation selection. + """ + train_selection, val_selection = train_test_split( + selection, + train_size=self._train_val_split[0], + test_size=self._train_val_split[1], + random_state=self._rng, + ) + return train_selection, val_selection + + def _infer_selections(self) -> Tuple[List[int], List[int]]: + """Automatically infer training and validation selections. + + Returns: + Training selection, Validation selection + """ + if self._use_ensemble_dataset: + # We must iterate through the dataset paths and infer a train/val + # selection for each. + self._train_selection = [] + self._val_selection = [] + for dataset_path in self._dataset_args["path"]: + ( + train_selection, + val_selection, + ) = self._infer_selections_on_single_dataset(dataset_path) + self._train_selection.append(train_selection) + self._val_selection.append(val_selection) + else: + # Infer selection on a single dataset + ( + self._train_selection, + self._val_selection, + ) = self._infer_selections_on_single_dataset( + self._dataset_args["path"] + ) + + def _infer_selections_on_single_dataset( + self, dataset_path: str + ) -> Tuple[List[int], List[int]]: + """Automatically infers training and validation selections for a single dataset. + + Args: + dataset_path (str): The path to the dataset. + + Returns: + Tuple[List[int], List[int]]: Training and validation selections. + """ + tmp_args = deepcopy(self._dataset_args) + tmp_args["path"] = dataset_path + tmp_dataset = self._construct_dataset(tmp_args) + + all_events = tmp_dataset._get_all_indices() # unshuffled list + + # Multiple lines to avoid one large + all_events = pd.DataFrame(all_events).sample( + frac=1, replace=False, random_state=self._rng + ) + + all_events = all_events.values.tolist() # shuffled list + return self._split_selection(all_events) + + def _create_dataset( + self, selection: Union[List[int], List[List[int]]] + ) -> Union[EnsembleDataset, Dataset]: + """Instantiate `dataset_reference`. + + Args: + selection: The selected event id's. + + Returns: + A dataset, either an instance of `EnsembleDataset` or `Dataset`. + """ + if self._use_ensemble_dataset: + # Construct multiple datasets and pass to EnsembleDataset + # At this point, we have checked that len(selection) == len(dataset_args['path']) + datasets = [] + for dataset_idx in range(len(selection)): + datasets.append( + self._create_single_dataset( + selection=selection[dataset_idx], + path=self._dataset_args["path"][dataset_idx], + ) + ) + + dataset = EnsembleDataset(datasets) + + else: + # Construct single dataset + dataset = self._create_single_dataset( + selection=selection, path=self._dataset_args["path"] + ) + return dataset + + def _create_single_dataset( + self, selection: List[int], path: str + ) -> Dataset: + """Instantiate a single `Dataset`. + + Args: + selection: A selection for a single dataset. + path: Path to a single dataset + + Returns: + An instance of `Dataset`. + """ + tmp_args = deepcopy(self._dataset_args) + tmp_args["path"] = path + tmp_args["selection"] = selection + return self._dataset(**tmp_args) diff --git a/src/graphnet/training/utils.py b/src/graphnet/training/utils.py index df7c92e15..b33089ec9 100644 --- a/src/graphnet/training/utils.py +++ b/src/graphnet/training/utils.py @@ -317,3 +317,16 @@ def save_results( model.save_state_dict(path + "/" + tag + "_state_dict.pth") model.save(path + "/" + tag + "_model.pth") Logger().info("Results saved at: \n %s" % path) + + +def save_selection(selection: List[int], file_path: str) -> None: + """Save the list of event numbers to a CSV file. + + Args: + selection: List of event ids. + file_path: File path to save the selection. + """ + with open(file_path, "w") as file: + file.write("event_id\n") + for event_id in selection: + file.write(f"{event_id}\n") From 2fb0bef4718b13a887f1d8c77ef8af1c0b2ecb57 Mon Sep 17 00:00:00 2001 From: samadpls Date: Sun, 3 Dec 2023 15:11:16 +0500 Subject: [PATCH 02/20] refactored the coding style --- src/graphnet/data/datamodule.py | 41 +++++++++++++++++++++++---------- 1 file changed, 29 insertions(+), 12 deletions(-) diff --git a/src/graphnet/data/datamodule.py b/src/graphnet/data/datamodule.py index 1f8398c8b..605cbb429 100644 --- a/src/graphnet/data/datamodule.py +++ b/src/graphnet/data/datamodule.py @@ -1,3 +1,4 @@ +"""Base `Dataloader` class(es) used in `graphnet`.""" from typing import Dict, Any, Optional, List, Tuple, Union import lightning as L from torch.utils.data import DataLoader @@ -27,7 +28,7 @@ def __init__( train_dataloader_kwargs: Optional[Dict[str, Any]] = None, validation_dataloader_kwargs: Optional[Dict[str, Any]] = None, test_dataloader_kwargs: Optional[Dict[str, Any]] = None, - train_val_split: Optional[List[float, float]] = [0.9, 0.10], + train_val_split: Optional[List[float]] = [0.9, 0.10], split_seed: int = 42, ) -> None: """Create dataloaders from dataset. @@ -40,6 +41,7 @@ def __init__( train_dataloader_kwargs: Arguments for the training DataLoader. validation_dataloader_kwargs: Arguments for the validation DataLoader. test_dataloader_kwargs: Arguments for the test DataLoader. + train_val_split (Optional): Split ratio for training and validation sets. Default is [0.9, 0.10]. split_seed: seed used for shuffling and splitting selections into train/validation. """ self._dataset = dataset_reference @@ -81,7 +83,8 @@ def setup(self, stage: str) -> None: # Creation of Datasets self._train_dataset = self._create_dataset(self._train_selection) self._val_dataset = self._create_dataset(self._val_selection) - self._test_dataset = self._create_dataset(self._test_selection) + if self._test_selection is not None: + self._test_dataset = self._create_dataset(self._test_selection) return @@ -191,8 +194,8 @@ def _resolve_selections(self) -> None: # Split the selection into train/validation if self._use_ensemble_dataset: # Split every selection - self._train_selection = [] - self._val_selection = [] + self._train_selection: List[List[int]] = [] + self._val_selection: List[List[int]] = [] for selection in self._selection: train_selection, val_selection = self._split_selection( selection @@ -218,7 +221,7 @@ def _resolve_selections(self) -> None: ) = self._infer_selections() def _split_selection( - self, selection: List[int] + self, selection: Union[int, List[int], List[List[int]]] ) -> Tuple[List[int], List[int]]: """Split train selection into train/validation. @@ -228,12 +231,26 @@ def _split_selection( Returns: Training selection, Validation selection. """ - train_selection, val_selection = train_test_split( - selection, - train_size=self._train_val_split[0], - test_size=self._train_val_split[1], - random_state=self._rng, - ) + if isinstance(selection, int): + train_selection, val_selection = [selection], [] + elif isinstance(selection[0], list): + flat_selection = [ + item for sublist in selection for item in sublist + ] + train_selection, val_selection = train_test_split( + flat_selection, + train_size=self._train_val_split[0], + test_size=self._train_val_split[1], + random_state=self._rng, + ) + else: + train_selection, val_selection = train_test_split( + selection, + train_size=self._train_val_split[0], + test_size=self._train_val_split[1], + random_state=self._rng, + ) + return train_selection, val_selection def _infer_selections(self) -> Tuple[List[int], List[int]]: @@ -266,7 +283,7 @@ def _infer_selections(self) -> Tuple[List[int], List[int]]: def _infer_selections_on_single_dataset( self, dataset_path: str ) -> Tuple[List[int], List[int]]: - """Automatically infers training and validation selections for a single dataset. + """Automatically infers dataset train/val selections. Args: dataset_path (str): The path to the dataset. From f4fbd05c7ec1d1f9e7abe2ffb126bf0c186da892 Mon Sep 17 00:00:00 2001 From: samadpls Date: Mon, 4 Dec 2023 12:25:22 +0500 Subject: [PATCH 03/20] Refactored the coding style --- setup.py | 1 - src/graphnet/data/datamodule.py | 89 +++++++++++++++++++++------------ 2 files changed, 57 insertions(+), 33 deletions(-) diff --git a/setup.py b/setup.py index 1ec34a0f0..3b70233ab 100644 --- a/setup.py +++ b/setup.py @@ -26,7 +26,6 @@ "timer>=0.2", "tqdm>=4.64", "wandb>=0.12", - "pytorch-lightning>=2.1.2", ] EXTRAS_REQUIRE = { diff --git a/src/graphnet/data/datamodule.py b/src/graphnet/data/datamodule.py index 605cbb429..aec94a481 100644 --- a/src/graphnet/data/datamodule.py +++ b/src/graphnet/data/datamodule.py @@ -1,6 +1,6 @@ """Base `Dataloader` class(es) used in `graphnet`.""" from typing import Dict, Any, Optional, List, Tuple, Union -import lightning as L +import pytorch_lightning as pl from torch.utils.data import DataLoader from copy import deepcopy from sklearn.model_selection import train_test_split @@ -16,7 +16,7 @@ from graphnet.training.utils import save_selection -class GraphNeTDataModule(L.LightningDataModule, Logger): +class GraphNeTDataModule(pl.LightningDataModule, Logger): """General Class for DataLoader Construction.""" def __init__( @@ -45,9 +45,9 @@ def __init__( split_seed: seed used for shuffling and splitting selections into train/validation. """ self._dataset = dataset_reference - self._selection = selection - self._train_val_split = train_val_split - self._test_selection = test_selection + self._selection = selection or [0] + self._train_val_split = train_val_split or [0.0] + self._test_selection = test_selection or [0.0] self._dataset_args = dataset_args self._rng = split_seed @@ -83,8 +83,7 @@ def setup(self, stage: str) -> None: # Creation of Datasets self._train_dataset = self._create_dataset(self._train_selection) self._val_dataset = self._create_dataset(self._val_selection) - if self._test_selection is not None: - self._test_dataset = self._create_dataset(self._test_selection) + self._test_dataset = self._create_dataset(self._test_selection) return @@ -112,16 +111,16 @@ def test_dataloader(self) -> DataLoader: """ return self._create_dataloader(self._test_dataset) - def teardown(self) -> None: - """Perform any necessary cleanup or shutdown procedures. + # def teardown(self) -> None: + # """Perform any necessary cleanup or shutdown procedures. - This method can be used for tasks such as closing SQLite connections - after training. Override this method as needed. + # This method can be used for tasks such as closing SQLite connections + # after training. Override this method as needed. - Returns: - None - """ - pass + # Returns: + # None + # """ + # return None def _create_dataloader( self, dataset: Union[Dataset, EnsembleDataset] @@ -134,7 +133,18 @@ def _create_dataloader( Returns: DataLoader: The DataLoader configured for the given dataset. """ - return DataLoader(dataset=dataset, **self._dataloader_args) + if dataset == self._train_dataset: + dataloader_args = self._train_dataloader_kwargs + elif dataset == self._val_dataset: + dataloader_args = self._validation_dataloader_kwargs + elif dataset == self._test_dataset: + dataloader_args = self._test_dataloader_kwargs + else: + raise ValueError( + "Unknown dataset encountered during dataloader creation." + ) + + return DataLoader(dataset=dataset, **dataloader_args) def _validate_dataset_class(self) -> None: """Sanity checks on the dataset reference (self._dataset). @@ -182,8 +192,18 @@ def _validate_dataset_args(self) -> None: def _validate_dataloader_args(self) -> None: """Sanity check on `dataloader_args`.""" - if "dataset" in self._dataloader_args: - raise ValueError("`dataloader_args` must not contain `dataset`") + if "dataset" in self._train_dataloader_kwargs: + raise ValueError( + "`train_dataloader_kwargs` must not contain `dataset`" + ) + if "dataset" in self._validation_dataloader_kwargs: + raise ValueError( + "`validation_dataloader_kwargs` must not contain `dataset`" + ) + if "dataset" in self._test_dataloader_kwargs: + raise ValueError( + "`test_dataloader_kwargs` must not contain `dataset`" + ) def _resolve_selections(self) -> None: if self._test_selection is None: @@ -232,25 +252,20 @@ def _split_selection( Training selection, Validation selection. """ if isinstance(selection, int): - train_selection, val_selection = [selection], [] + flat_selection = [selection] elif isinstance(selection[0], list): flat_selection = [ item for sublist in selection for item in sublist ] - train_selection, val_selection = train_test_split( - flat_selection, - train_size=self._train_val_split[0], - test_size=self._train_val_split[1], - random_state=self._rng, - ) else: - train_selection, val_selection = train_test_split( - selection, - train_size=self._train_val_split[0], - test_size=self._train_val_split[1], - random_state=self._rng, - ) + flat_selection = selection + train_selection, val_selection = train_test_split( + flat_selection, + train_size=self._train_val_split[0], + test_size=self._train_val_split[1], + random_state=self._rng, + ) return train_selection, val_selection def _infer_selections(self) -> Tuple[List[int], List[int]]: @@ -280,6 +295,8 @@ def _infer_selections(self) -> Tuple[List[int], List[int]]: self._dataset_args["path"] ) + return (self._train_selection, self._val_selection) + def _infer_selections_on_single_dataset( self, dataset_path: str ) -> Tuple[List[int], List[int]]: @@ -305,8 +322,16 @@ def _infer_selections_on_single_dataset( all_events = all_events.values.tolist() # shuffled list return self._split_selection(all_events) + def _construct_dataset(self, tmp_args: Dict[str, Any]) -> Dict[str, Any]: + """Construct dataset.""" + return tmp_args + + def _get_all_indices(self): + """Shuffle the list.""" + return list + def _create_dataset( - self, selection: Union[List[int], List[List[int]]] + self, selection: Union[List[int], List[List[int]], List[float]] ) -> Union[EnsembleDataset, Dataset]: """Instantiate `dataset_reference`. From 302c808877e5bab01aa2890460bf794bef4a0c34 Mon Sep 17 00:00:00 2001 From: samadpls Date: Mon, 18 Dec 2023 23:04:06 +0500 Subject: [PATCH 04/20] updated `datamodule.py` file --- src/graphnet/data/datamodule.py | 64 ++++++++++++++++++++------------- 1 file changed, 40 insertions(+), 24 deletions(-) diff --git a/src/graphnet/data/datamodule.py b/src/graphnet/data/datamodule.py index aec94a481..c16bee977 100644 --- a/src/graphnet/data/datamodule.py +++ b/src/graphnet/data/datamodule.py @@ -111,16 +111,16 @@ def test_dataloader(self) -> DataLoader: """ return self._create_dataloader(self._test_dataset) - # def teardown(self) -> None: - # """Perform any necessary cleanup or shutdown procedures. + def teardown(self) -> None: # type: ignore[override] + """Perform any necessary cleanup or shutdown procedures. - # This method can be used for tasks such as closing SQLite connections - # after training. Override this method as needed. + This method can be used for tasks such as closing SQLite connections + after training. Override this method as needed. - # Returns: - # None - # """ - # return None + Returns: + None + """ + return None def _create_dataloader( self, dataset: Union[Dataset, EnsembleDataset] @@ -214,8 +214,8 @@ def _resolve_selections(self) -> None: # Split the selection into train/validation if self._use_ensemble_dataset: # Split every selection - self._train_selection: List[List[int]] = [] - self._val_selection: List[List[int]] = [] + self._train_selection = [] + self._val_selection = [] for selection in self._selection: train_selection, val_selection = self._split_selection( selection @@ -225,10 +225,13 @@ def _resolve_selections(self) -> None: else: # Split the only selection we got + assert isinstance(self._selection, list) ( self._train_selection, self._val_selection, - ) = self._split_selection(self._selection) + ) = self._split_selection( # type: ignore + self._selection + ) if self._selection is None: # If not provided, we infer it by grabbing all event ids in the dataset. @@ -251,14 +254,16 @@ def _split_selection( Returns: Training selection, Validation selection. """ + assert isinstance(selection, (int, list)) if isinstance(selection, int): flat_selection = [selection] elif isinstance(selection[0], list): flat_selection = [ - item for sublist in selection for item in sublist + item for sublist in selection for item in sublist # type: ignore ] else: - flat_selection = selection + flat_selection = selection # type: ignore + assert isinstance(flat_selection, list) train_selection, val_selection = train_test_split( flat_selection, @@ -284,8 +289,8 @@ def _infer_selections(self) -> Tuple[List[int], List[int]]: train_selection, val_selection, ) = self._infer_selections_on_single_dataset(dataset_path) - self._train_selection.append(train_selection) - self._val_selection.append(val_selection) + self._train_selection.extend(train_selection) # type: ignore + self._val_selection.extend(val_selection) # type: ignore else: # Infer selection on a single dataset ( @@ -295,7 +300,7 @@ def _infer_selections(self) -> Tuple[List[int], List[int]]: self._dataset_args["path"] ) - return (self._train_selection, self._val_selection) + return (self._train_selection, self._val_selection) # type: ignore def _infer_selections_on_single_dataset( self, dataset_path: str @@ -322,14 +327,23 @@ def _infer_selections_on_single_dataset( all_events = all_events.values.tolist() # shuffled list return self._split_selection(all_events) - def _construct_dataset(self, tmp_args: Dict[str, Any]) -> Dict[str, Any]: - """Construct dataset.""" - return tmp_args - def _get_all_indices(self): - """Shuffle the list.""" + """Get all indices. + + Return: + List of indices in an unshuffled order. + """ return list + def _construct_dataset(self, tmp_args: Dict[str, Any]) -> Dict[str, Any]: + """Construct dataset. + + Return: + Dataset object constructed from input arguments. + """ + # instance dataset class , that set of argunment , + return tmp_args + def _create_dataset( self, selection: Union[List[int], List[List[int]], List[float]] ) -> Union[EnsembleDataset, Dataset]: @@ -348,7 +362,7 @@ def _create_dataset( for dataset_idx in range(len(selection)): datasets.append( self._create_single_dataset( - selection=selection[dataset_idx], + selection=selection[dataset_idx], # type: ignore path=self._dataset_args["path"][dataset_idx], ) ) @@ -358,12 +372,14 @@ def _create_dataset( else: # Construct single dataset dataset = self._create_single_dataset( - selection=selection, path=self._dataset_args["path"] + selection=selection, path=self._dataset_args["path"] # type: ignore ) return dataset def _create_single_dataset( - self, selection: List[int], path: str + self, + selection: Union[List[int], List[List[int]], List[float]], + path: str, ) -> Dataset: """Instantiate a single `Dataset`. From 0c4a9c69c08f005966d94da9ce655036a818a9fd Mon Sep 17 00:00:00 2001 From: samadpls Date: Sat, 23 Dec 2023 21:47:23 +0500 Subject: [PATCH 05/20] Refactored `GraphNeTDataModule` class Signed-off-by: samadpls --- src/graphnet/data/datamodule.py | 55 ++++++++++++++++++++++++++------- 1 file changed, 43 insertions(+), 12 deletions(-) diff --git a/src/graphnet/data/datamodule.py b/src/graphnet/data/datamodule.py index c16bee977..1d51a1658 100644 --- a/src/graphnet/data/datamodule.py +++ b/src/graphnet/data/datamodule.py @@ -5,6 +5,7 @@ from copy import deepcopy from sklearn.model_selection import train_test_split import pandas as pd +import random from graphnet.data.dataset import ( Dataset, @@ -120,7 +121,22 @@ def teardown(self) -> None: # type: ignore[override] Returns: None """ - return None + if hasattr(self, "_train_dataset") and isinstance( + self._train_dataset, SQLiteDataset + ): + self._train_dataset._close_connection() + + if hasattr(self, "_val_dataset") and isinstance( + self._val_dataset, SQLiteDataset + ): + self._val_dataset._close_connection() + + if hasattr(self, "_test_dataset") and isinstance( + self._test_dataset, SQLiteDataset + ): + self._test_dataset._close_connection() + + return def _create_dataloader( self, dataset: Union[Dataset, EnsembleDataset] @@ -149,8 +165,9 @@ def _create_dataloader( def _validate_dataset_class(self) -> None: """Sanity checks on the dataset reference (self._dataset). - Is it a GraphNeT-compatible dataset? has the class already been - instantiated? Did they try to pass EnsembleDataset? + Checks whether the dataset is an instance of SQLiteDataset, + ParquetDataset, or Dataset. Raises a TypeError if an invalid dataset + type is detected, or if an EnsembleDataset is used. """ if not isinstance( self._dataset, (SQLiteDataset, ParquetDataset, Dataset) @@ -296,7 +313,7 @@ def _infer_selections(self) -> Tuple[List[int], List[int]]: ( self._train_selection, self._val_selection, - ) = self._infer_selections_on_single_dataset( + ) = self._infer_selections_on_single_dataset( # type: ignore self._dataset_args["path"] ) @@ -317,32 +334,46 @@ def _infer_selections_on_single_dataset( tmp_args["path"] = dataset_path tmp_dataset = self._construct_dataset(tmp_args) - all_events = tmp_dataset._get_all_indices() # unshuffled list + all_events = ( + tmp_dataset._get_all_indices() + ) # unshuffled list, # sequential index # Multiple lines to avoid one large all_events = pd.DataFrame(all_events).sample( frac=1, replace=False, random_state=self._rng ) - all_events = all_events.values.tolist() # shuffled list + all_events = random.sample( + all_events, len(all_events) + ) # shuffled list return self._split_selection(all_events) - def _get_all_indices(self): + def _get_all_indices(self) -> List[int]: """Get all indices. Return: List of indices in an unshuffled order. """ - return list + if self._use_ensemble_dataset: + all_indices = [] + for dataset_path in self._dataset_args["path"]: + tmp_args = deepcopy(self._dataset_args) + tmp_args["path"] = dataset_path + tmp_dataset = self._construct_dataset(tmp_args) + all_indices.extend(tmp_dataset._get_all_indices()) + else: + all_indices = self._dataset._get_all_indices() + + return all_indices - def _construct_dataset(self, tmp_args: Dict[str, Any]) -> Dict[str, Any]: + def _construct_dataset(self, tmp_args: Dict[str, Any]) -> Dataset: """Construct dataset. Return: Dataset object constructed from input arguments. """ - # instance dataset class , that set of argunment , - return tmp_args + dataset = self._dataset(**tmp_args) + return dataset def _create_dataset( self, selection: Union[List[int], List[List[int]], List[float]] @@ -393,4 +424,4 @@ def _create_single_dataset( tmp_args = deepcopy(self._dataset_args) tmp_args["path"] = path tmp_args["selection"] = selection - return self._dataset(**tmp_args) + return self._construct_dataset(tmp_args) From e0ea137e955eb5c6159e07066f383442a7e69212 Mon Sep 17 00:00:00 2001 From: samadpls Date: Sun, 24 Dec 2023 15:41:25 +0500 Subject: [PATCH 06/20] added `_construct_dataset` method Signed-off-by: samadpls --- src/graphnet/data/datamodule.py | 18 ------------------ 1 file changed, 18 deletions(-) diff --git a/src/graphnet/data/datamodule.py b/src/graphnet/data/datamodule.py index 1d51a1658..c8268c990 100644 --- a/src/graphnet/data/datamodule.py +++ b/src/graphnet/data/datamodule.py @@ -348,24 +348,6 @@ def _infer_selections_on_single_dataset( ) # shuffled list return self._split_selection(all_events) - def _get_all_indices(self) -> List[int]: - """Get all indices. - - Return: - List of indices in an unshuffled order. - """ - if self._use_ensemble_dataset: - all_indices = [] - for dataset_path in self._dataset_args["path"]: - tmp_args = deepcopy(self._dataset_args) - tmp_args["path"] = dataset_path - tmp_dataset = self._construct_dataset(tmp_args) - all_indices.extend(tmp_dataset._get_all_indices()) - else: - all_indices = self._dataset._get_all_indices() - - return all_indices - def _construct_dataset(self, tmp_args: Dict[str, Any]) -> Dataset: """Construct dataset. From 34edf920ecd2b9045ba432a1bdddb6a6ff3770db Mon Sep 17 00:00:00 2001 From: Rasmus Oersoe Date: Tue, 2 Jan 2024 16:15:45 +0100 Subject: [PATCH 07/20] polish --- src/graphnet/models/standard_model.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/src/graphnet/models/standard_model.py b/src/graphnet/models/standard_model.py index 663664996..3c2c78bb7 100644 --- a/src/graphnet/models/standard_model.py +++ b/src/graphnet/models/standard_model.py @@ -270,9 +270,11 @@ def training_step( batch_size=self._get_batch_size(train_batch), prog_bar=True, on_epoch=True, - on_step=False, + on_step=True, sync_dist=True, ) + cur_lr = self.trainer.optimizers[0].param_groups[0]["lr"] + self.log("lr", cur_lr, prog_bar=True, on_step=True) return loss def validation_step( From ea6ebc8f040949cc1fca3f9f69dbcc3640d8be5d Mon Sep 17 00:00:00 2001 From: Rasmus Oersoe Date: Sun, 21 Jan 2024 17:10:42 +0100 Subject: [PATCH 08/20] rename lr variable --- src/graphnet/models/standard_model.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/graphnet/models/standard_model.py b/src/graphnet/models/standard_model.py index 3c2c78bb7..5e90368eb 100644 --- a/src/graphnet/models/standard_model.py +++ b/src/graphnet/models/standard_model.py @@ -273,8 +273,8 @@ def training_step( on_step=True, sync_dist=True, ) - cur_lr = self.trainer.optimizers[0].param_groups[0]["lr"] - self.log("lr", cur_lr, prog_bar=True, on_step=True) + current_lr = self.trainer.optimizers[0].param_groups[0]["lr"] + self.log("lr", current_lr, prog_bar=True, on_step=True) return loss def validation_step( From 90b9b11fb06e4947f73d67aea2e718342b563e22 Mon Sep 17 00:00:00 2001 From: Rasmus Oersoe Date: Sun, 21 Jan 2024 17:34:17 +0100 Subject: [PATCH 09/20] remove lr from progressbar --- src/graphnet/models/standard_model.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/src/graphnet/models/standard_model.py b/src/graphnet/models/standard_model.py index 3c2c78bb7..663664996 100644 --- a/src/graphnet/models/standard_model.py +++ b/src/graphnet/models/standard_model.py @@ -270,11 +270,9 @@ def training_step( batch_size=self._get_batch_size(train_batch), prog_bar=True, on_epoch=True, - on_step=True, + on_step=False, sync_dist=True, ) - cur_lr = self.trainer.optimizers[0].param_groups[0]["lr"] - self.log("lr", cur_lr, prog_bar=True, on_step=True) return loss def validation_step( From ca87a2b5de5479303536026fdf6b062636d6c435 Mon Sep 17 00:00:00 2001 From: Rasmus Oersoe Date: Mon, 22 Jan 2024 09:56:44 +0100 Subject: [PATCH 10/20] update QUESO energy model state_dict --- .../total_neutrino_energy_state_dict.pth | Bin 5440999 -> 5440870 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/src/graphnet/models/pretrained/icecube/upgrade/QUESO/total_neutrino_energy/total_neutrino_energy_state_dict.pth b/src/graphnet/models/pretrained/icecube/upgrade/QUESO/total_neutrino_energy/total_neutrino_energy_state_dict.pth index 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