pipeline package#
This package handles the various steps of the GNN-based pipeline, from preprocessing to track-finding evaluation.
- pipeline.get_model(path_or_config, step)[source]#
Get the model class of a given step for a given pipeline configuration.
- Parameters:
path_or_config (
str
|dict
) – pipeline configurationstep (
str
) – model step (embedding
orgnn
)
- Return type:
Type
[ModelBase
]- Returns:
Model class that can be later instantiated.
- pipeline.instantiate_model_for_training(path_or_config, step)[source]#
Instantiate a new model. The model can then be trained.
The function auto-detects the the model type. The latter can also be instantiated from a trained model (transfer learning) using the parameter
from
in the configuration file.- Parameters:
path_or_config (
str
|dict
) – pipeline configuraitonstep (
str
) – model step (embedding
orgnn
)
- Return type:
- Returns:
Model that was not trained.
- pipeline.load_trained_model(path_or_config, step, checkpoint_path=None, **kwargs)[source]#
Load a model that was already trained.
- Parameters:
path_or_config (
str
|dict
) – pipeline configurationstep (
str
) – model step (embedding
orgnn
)checkpoint_path (
Optional
[str
]) – path to a checkpoint
- Return type:
- Returns:
Trained model
Subpackages#
- pipeline.Preprocessing package
- pipeline.Preprocessing.balancing module
- pipeline.Preprocessing.hit_filtering module
- pipeline.Preprocessing.inputloader module
- pipeline.Preprocessing.line_metrics module
- pipeline.Preprocessing.particle_fitting_metrics module
- pipeline.Preprocessing.poly_metrics module
- pipeline.Preprocessing.preprocessing module
- pipeline.Preprocessing.preprocessing_paths module
- pipeline.Preprocessing.process_custom module
SelectionFunction
apply_mask()
at_least_1_hit_on_scifi()
at_least_7_planes()
compute_n_particles_per_hit()
compute_n_unique_planes()
cut_long_tracks()
default_old_training_for_rta_presentation()
everything_but_electrons()
everything_but_long_electrons()
less_than_3_hits_on_same_plane()
only_keep_hits_on_particles()
only_long_electrons()
reconstructible_scifi()
remove_curved_particles()
remove_particle_not_poly_enough()
remove_particles_too_scattered_on_plane()
track_weighting_selection()
triplets_first_selection()
- pipeline.Preprocessing.run_preprocessing module
- pipeline.Processing package
- pipeline.Embedding package
- Subpackages
- pipeline.Embedding.embedding_base module
EmbeddingBase
EmbeddingBase.append_true_pairs()
EmbeddingBase.build_edges()
EmbeddingBase.edgedir
EmbeddingBase.get_hnm_pairs()
EmbeddingBase.get_lazy_dataset()
EmbeddingBase.get_lazy_dataset_partition()
EmbeddingBase.get_loss()
EmbeddingBase.get_query_points()
EmbeddingBase.get_random_pairs()
EmbeddingBase.get_squared_distances()
EmbeddingBase.get_training_edges()
EmbeddingBase.get_truth()
EmbeddingBase.inference()
EmbeddingBase.input_kwargs
EmbeddingBase.input_to_dynamic_axes
EmbeddingBase.last_plane
EmbeddingBase.n_planes
EmbeddingBase.n_total_planes
EmbeddingBase.query_planes
EmbeddingBase.remove_planes()
EmbeddingBase.subnetwork_to_outputs
EmbeddingBase.to_onnx()
EmbeddingBase.training_step()
EmbeddingBase.training_validation_step()
EmbeddingBase.validate_edges()
EmbeddingBase.validation_step()
EmbeddingLazyDataSet
get_example_data()
- pipeline.Embedding.build_embedding module
- pipeline.Embedding.embedding_plots module
- pipeline.Embedding.embedding_validation module
- pipeline.Embedding.process_custom module
- pipeline.GNN package
- Subpackages
- pipeline.GNN.triplet_gnn_base module
TripletGNNBase
TripletGNNBase.common_training_validation_step()
TripletGNNBase.compute_normalised_loss()
TripletGNNBase.edge_checkpointing
TripletGNNBase.filter_edges()
TripletGNNBase.forward()
TripletGNNBase.forward_edges()
TripletGNNBase.forward_triplets()
TripletGNNBase.get_lazy_dataset()
TripletGNNBase.get_lazy_dataset_partition()
TripletGNNBase.inference()
TripletGNNBase.input_kwargs
TripletGNNBase.input_to_dynamic_axes
TripletGNNBase.log_metrics_gen()
TripletGNNBase.loss
TripletGNNBase.n_hiddens
TripletGNNBase.shared_evaluation()
TripletGNNBase.subnetwork_to_outputs
TripletGNNBase.test_step()
TripletGNNBase.to_onnx()
TripletGNNBase.training_step()
TripletGNNBase.triplet_checkpointing
TripletGNNBase.triplet_output_step()
TripletGNNBase.triplet_output_step_articulation()
TripletGNNBase.triplet_output_step_elbow_left()
TripletGNNBase.triplet_output_step_elbow_right()
TripletGNNBase.validation_step()
TripletGNNBase.with_triplets
TripletGNNLazyDataset
get_df_edges_from_batch_only()
- pipeline.GNN.perfect_gnn module
- pipeline.GNN.gnn_validation module
- pipeline.GNN.gnn_plots module
- pipeline.TrackBuilding package
- pipeline.Evaluation package
- pipeline.utils package
- Subpackages
- pipeline.utils.commonutils package
- pipeline.utils.graphutils package
- pipeline.utils.graphutils.batch2df module
- pipeline.utils.graphutils.edgebuilding module
- pipeline.utils.graphutils.edgeutils module
- pipeline.utils.graphutils.knn module
- pipeline.utils.graphutils.torchutils module
- pipeline.utils.graphutils.tripletbuilding module
- pipeline.utils.graphutils.truths module
- pipeline.utils.loaderutils package
- pipeline.utils.modelutils package
- pipeline.utils.modelutils.basemodel module
- pipeline.utils.modelutils.batches module
- pipeline.utils.modelutils.build module
- pipeline.utils.modelutils.checkpoint_utils module
- pipeline.utils.modelutils.exploration module
- pipeline.utils.modelutils.export module
- pipeline.utils.modelutils.metrics module
- pipeline.utils.modelutils.mlp module
- pipeline.utils.plotutils package
- pipeline.utils.scriptutils package
- pipeline.utils.tools package
- Subpackages