Add cnn model

.gitignore

@@ -154,6 +154,7 @@ data/examples/output/
 !/graphnet/src/graphnet/models/pretrained/**/**/**/**/**.pth
 # Exception to geometry tables
 !/data/geometry_tables/**/**.parquet
+!/data/image_mapping_tables/**/**.parquet
 !/data/tests/sqlite/upgrade_genie_step4_140028_000998_first_5_frames/upgrade_genie_step4_140028_000998_first_5_frames.db
 !/data/tests/parquet/oscNext_genie_level7_v02/merged/**
 !data/tests/parquet/oscNext_genie_level7_v02/oscNext_genie_level7_v02_first_5_frames.parquet

examples/04_training/09_train_cnn.py

@@ -0,0 +1,343 @@
+"""Example of training a CNN Model."""
+
+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 TRUTH
+from graphnet.models import StandardModel
+from graphnet.models.cnn import LCSC
+from graphnet.models.data_representation import PercentileClusters
+from graphnet.models.task.reconstruction import EnergyReconstruction
+from graphnet.training.callbacks import PiecewiseLinearLR
+from graphnet.training.loss_functions import LogCoshLoss
+from graphnet.utilities.argparse import ArgumentParser
+from graphnet.utilities.logging import Logger
+from graphnet.data.dataset import SQLiteDataset
+from graphnet.data.dataset import ParquetDataset
+from graphnet.models.detector import ORCA150
+from torch_geometric.data import Batch
+from graphnet.models.data_representation.images import ExamplePrometheusImage
+
+# Constants
+features = ["sensor_id", "sensor_string_id", "t"]
+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,
+        },
+        "dataset_reference": (
+            SQLiteDataset if path.endswith(".db") else ParquetDataset
+        ),
+    }
+
+    archive = os.path.join(EXAMPLE_OUTPUT_DIR, "train_cnn_model")
+    run_name = "lcsc_{}_example".format(config["target"])
+    if wandb:
+        # Log configuration to W&B
+        wandb_logger.experiment.config.update(config)
+
+    # First we need to define how the image is constructed.
+    # This is done using an ImageDefinition.
+
+    # An ImageDefinition combines two components:
+
+    # 1. A pixel definition, which defines how the pixel data is
+    # represented. Since an image has always fixed dimensions this
+    # pixel definition is also responsible to represent the data in
+    # a way such that this fixed dimensions can be achieved.
+    # Normally, this could mean that light pulses that arrive at
+    # the same optical module must be aggregated to a
+    # fixed-dimensional vector.
+    # A pixel definition works exactly the same as the
+    # a node definition in the graph scenerio.
+
+    # 2. A pixel mapping, which defines where each pixel is located
+    # in the final image. This is highly detector specific, as it
+    # depends on the geometry of the detector.
+
+    # An ImageDefinition can be used to create multiple images of
+    # a single event. In the example of IceCube, you can e.g
+    # create three images, one for the so called main array,
+    # one for the upper deep core and one for the lower deep
+    # core. Essentially, these are just different areas in
+    # the detector.
+
+    # Here we use the PercentileClusters pixel definition, which
+    # aggregates the light pulses that arrive at the same optical
+    # module with percentiles.
+    print(features)
+    pixel_definition = PercentileClusters(
+        cluster_on=["sensor_id", "sensor_string_id"],
+        percentiles=[10, 50, 90],
+        add_counts=True,
+        input_feature_names=features,
+    )
+
+    # The final image definition used here is the ExamplePrometheusImage,
+    # which is a detector specific pixel mapping.
+    # It maps optical modules into the image
+    # using the sensor_string_id and sensor_id
+    # (number of the optical module).
+    # The detector class standardizes the input features,
+    # so that the features are in a ML friendly range.
+    # For the mapping of the optical modules to the image it is
+    # essential to not change the value of the sensor_id and
+    # sensor_string_id. Therefore we need to make sure that
+    # these features are not standardized, which is done by the
+    # `replace_with_identity` argument of the detector.
+    image_definition = ExamplePrometheusImage(
+        detector=ORCA150(
+            replace_with_identity=[
+                "sensor_id",
+                "sensor_string_id",
+            ],
+        ),
+        node_definition=pixel_definition,
+        input_feature_names=features,
+        string_label="sensor_string_id",
+        dom_number_label="sensor_id",
+    )
+
+    # Use SQLiteDataset to load in data
+    # The input here depends on the dataset being used,
+    # in this case the Prometheus dataset.
+    dataset = SQLiteDataset(
+        path=config["path"],
+        pulsemaps=config["pulsemap"],
+        truth_table=truth_table,
+        features=features,
+        truth=truth,
+        data_representation=image_definition,
+    )
+
+    # Create the training and validation dataloaders.
+    training_dataloader = torch.utils.data.DataLoader(
+        dataset=dataset,
+        batch_size=config["batch_size"],
+        num_workers=config["num_workers"],
+        collate_fn=Batch.from_data_list,
+    )
+
+    validation_dataloader = torch.utils.data.DataLoader(
+        dataset=dataset,
+        batch_size=config["batch_size"],
+        num_workers=config["num_workers"],
+        collate_fn=Batch.from_data_list,
+    )
+
+    # Building model
+
+    # Define architecture of the backbone, in this example
+    # the LCSC architecture from Alexander Harnisch is used.
+    backbone = LCSC(
+        num_input_features=image_definition.nb_outputs,
+        out_put_dim=2,
+        input_norm=True,
+        num_conv_layers=5,
+        conv_filters=[5, 10, 20, 40, 60],
+        kernel_size=3,
+        image_size=(8, 9, 22),  # dimensions of the example image
+        pooling_type=[
+            "Avg",
+            None,
+            "Avg",
+            None,
+            "Avg",
+        ],
+        pooling_kernel_size=[
+            [1, 1, 2],
+            None,
+            [2, 2, 2],
+            None,
+            [2, 2, 2],
+        ],
+        pooling_stride=[
+            [1, 1, 2],
+            None,
+            [2, 2, 2],
+            None,
+            [2, 2, 2],
+        ],
+        num_fc_neurons=50,
+        norm_list=True,
+        norm_type="Batch",
+    )
+    # Define the task.
+    # Here an energy reconstruction, with a LogCoshLoss function.
+    # The target and prediction are transformed using the log10 function.
+    # When infering the prediction is transformed back to the
+    # original scale using 10^x.
+    task = EnergyReconstruction(
+        hidden_size=backbone.nb_outputs,
+        target_labels=config["target"],
+        loss_function=LogCoshLoss(),
+        transform_prediction_and_target=lambda x: torch.log10(x),
+        transform_inference=lambda x: torch.pow(10, x),
+    )
+    # Define the full model, which includes the backbone, task(s),
+    # along with typical machine learning options such as
+    # learning rate optimizers and schedulers.
+    model = StandardModel(
+        data_representation=image_definition,
+        backbone=backbone,
+        tasks=[task],
+        optimizer_class=Adam,
+        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}/cnn_results.csv")
+
+    # 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}/cnn_state_dict.pth")
+    model.save_config(f"{path}/cnn_model_config.yml")
+
+
+if __name__ == "__main__":
+
+    # Parse command-line arguments
+    parser = ArgumentParser(
+        description="""
+Train GNN model 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", 16),
+        ("num-workers", 2),
+    )
+
+    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,
+    )

src/graphnet/constants.py

@@ -21,6 +21,14 @@
 TEST_PARQUET_DATA = os.path.join(
     TEST_DATA_DIR, "parquet", _test_dataset_name, "merged"
 )
+TEST_IMAGE_DIR = os.path.join(TEST_DATA_DIR, "images")
+TEST_IC86MAIN_IMAGE = os.path.join(TEST_IMAGE_DIR, "IC86main_array_test.npy")
+TEST_IC86LOWERDC_IMAGE = os.path.join(
+    TEST_IMAGE_DIR, "IC86lower_deepcore_test.npy"
+)
+TEST_IC86UPPERDC_IMAGE = os.path.join(
+    TEST_IMAGE_DIR, "IC86upper_deepcore_test.npy"
+)
 
 # Example data
 EXAMPLE_DATA_DIR = os.path.join(DATA_DIR, "examples")
@@ -42,3 +50,12 @@
 PROMETHEUS_GEOMETRY_TABLE_DIR = os.path.join(GEOMETRY_TABLE_DIR, "prometheus")
 LIQUIDO_GEOMETRY_TABLE_DIR = os.path.join(GEOMETRY_TABLE_DIR, "liquid-o")
 MAGIC_GEOMETRY_TABLE_DIR = os.path.join(GEOMETRY_TABLE_DIR, "magic")
+
+# Image Mapping Tables
+IMAGE_MAPPING_TABLE_DIR = os.path.join(DATA_DIR, "image_mapping_tables")
+IC86_CNN_MAPPING = os.path.join(
+    IMAGE_MAPPING_TABLE_DIR, "IC86_CNN_mapping.parquet"
+)
+PROMETHEUS_CNN_MAPPING = os.path.join(
+    IMAGE_MAPPING_TABLE_DIR, "prometheus_CNN_mapping.parquet"
+)

src/graphnet/models/cnn/__init__.py

@@ -0,0 +1,5 @@
+"""CNN-specific modules, for performing the main learnable operations."""
+
+from .cnn import CNN
+from .icecube_dnn import IceCubeDNN
+from .lcsc import LCSC