GraFEIModule Class Reference

Inheritance diagram for GraFEIModule:

Public Member Functions
def	__init__ (self, particle_list, cfg_path=None, param_file=None, sig_side_lcas=None, sig_side_masses=None, gpu=False, payload_config_name="graFEIConfigFile", payload_model_name="graFEIModelFile")
def	initialize (self)
def	event (self)

Public Attributes
	particle_list
	Input particle list.
	cfg_path
	Config yaml file path.
	param_file
	PyTorch parameter file path.
	sig_side_lcas
	Chosen sig-side LCAS.
	sig_side_masses
	Chosen sig-side mass hypotheses.
	gpu
	If running on GPU.
	payload_config_name
	Config file name in the payload.
	payload_model_name
	Model file name in the payload.
	storeTrueInfo
	Figure out if we re running on data or MC.
	device
	Figure out which device all this is running on - CPU or GPU.
	configs
	Config file.
	mc_particle
	Top MC particle.
	max_level
	Max LCAS level.
	normalize
	Normalize features.
	use_amp
	Mixed precision.
	node_features
	Node features.
	edge_features
	Edge features.
	glob_features
	Global features.
	discarded_features
	Discarded node features.
	model
	The model The correct edge_classes is taken from the config file.

Detailed Description

Applies graFEI model to a particle list in basf2.
GraFEI information is stored as extraInfos.

Args:
    particle_list (str): Name of particle list.
    cfg_path (str): Path to config file. If `None` the config file in the global tag is used.
    param_file (str): Path to parameter file containing the model. If `None` the parameter file in the global tag is used.
    sig_side_lcas (list): List containing LCAS matrix of signal-side.
    sig_side_masses (list): List containing mass hypotheses of signal-side.
    gpu (bool): Whether to run on a GPU.
    payload_config_name (str): Name of config file payload. The default should be kept, except in basf2 examples.
    payload_model_name (str): Name of model file payload. The default should be kept, except in basf2 examples.

Definition at line 31 of file GraFEIModule.py.

Constructor & Destructor Documentation

__init__()

def __init__	(		self,
			particle_list,
			cfg_path = None,
			param_file = None,
			sig_side_lcas = None,
			sig_side_masses = None,
			gpu = False,
			payload_config_name = "graFEIConfigFile",
			payload_model_name = "graFEIModelFile"
	)

Initialization.

Definition at line 47 of file GraFEIModule.py.

    ):
        """
        Initialization.
        """
        super().__init__()
        
        self.particle_list = particle_list
        
        self.cfg_path = cfg_path
        
        self.param_file = param_file
        
        self.sig_side_lcas = torch.tensor(sig_side_lcas) if sig_side_lcas else None
        
        self.sig_side_masses = sig_side_masses
        
        self.gpu = gpu
        
        self.payload_config_name = payload_config_name
        
        self.payload_model_name = payload_model_name
 

Member Function Documentation

event()

def event

(

self

)

Called at the beginning of each event.

Definition at line 173 of file GraFEIModule.py.

    def event(self):
        """
        Called at the beginning of each event.
        """
        b2.B2DEBUG(10, "---- Processing new event ----")
 
        # Get the B candidate list
        candidate_list = get_object_list(Belle2.PyStoreObj(self.particle_list).obj())
 
        # Get the particle candidate(s)
        for candidate in candidate_list:
            # Get FSPs
            p_list = get_object_list(candidate.getFinalStateDaughters())
 
            # Number of FSPs
            n_nodes = len(p_list)
 
            # Particle nature
            masses = np.array([abs(p.getPDGCode()) for p in p_list])
 
            # Number of charged and photons
            graFEI_nFSP = n_nodes
            graFEI_nPhotons_preFit = (masses == 22).sum()
            graFEI_nCharged_preFit = graFEI_nFSP - graFEI_nPhotons_preFit
            graFEI_nElectrons_preFit = (masses == 11).sum()
            graFEI_nMuons_preFit = (masses == 13).sum()
            graFEI_nPions_preFit = (masses == 211).sum()
            graFEI_nKaons_preFit = (masses == 321).sum()
            graFEI_nProtons_preFit = (masses == 2212).sum()
            graFEI_nLeptons_preFit = graFEI_nElectrons_preFit + graFEI_nMuons_preFit
            graFEI_nOthers_preFit = graFEI_nCharged_preFit - \
                (graFEI_nLeptons_preFit + graFEI_nPions_preFit + graFEI_nKaons_preFit + graFEI_nProtons_preFit)
 
            candidate.addExtraInfo("graFEI_nFSP", graFEI_nFSP)
            candidate.addExtraInfo("graFEI_nCharged_preFit", graFEI_nCharged_preFit)
            candidate.addExtraInfo("graFEI_nPhotons_preFit", graFEI_nPhotons_preFit)
            candidate.addExtraInfo("graFEI_nElectrons_preFit", graFEI_nElectrons_preFit)
            candidate.addExtraInfo("graFEI_nMuons_preFit", graFEI_nMuons_preFit)
            candidate.addExtraInfo("graFEI_nPions_preFit", graFEI_nPions_preFit)
            candidate.addExtraInfo("graFEI_nKaons_preFit", graFEI_nKaons_preFit)
            candidate.addExtraInfo("graFEI_nProtons_preFit", graFEI_nProtons_preFit)
            candidate.addExtraInfo("graFEI_nLeptons_preFit", graFEI_nLeptons_preFit)
            candidate.addExtraInfo("graFEI_nOthers_preFit", graFEI_nOthers_preFit)
 
            # Trivial decay tree
            if n_nodes < 2:
                b2.B2WARNING(
                    f"Skipping candidate with {n_nodes} reconstructed FSPs"
                )
 
                continue
 
            # Initialize node features array
            x_nodes = np.empty((n_nodes, len(self.node_features)))
            x_dis = np.empty((n_nodes, len(self.discarded_features)))
 
            # Fill node features array
            for p, particle in enumerate(p_list):
                for f, feat in enumerate(self.node_features):
                    feat = feat[feat.find("feat_") + 5:]
                    x_nodes[p, f] = vm.evaluate(feat, particle)
                for f, feat in enumerate(self.discarded_features):
                    feat = feat[feat.find("feat_") + 5:]
                    x_dis[p, f] = vm.evaluate(feat, particle)
            b2.B2DEBUG(11, "Node features:\n", x_nodes)
 
            # Fill edge features array
            x_edges = (compute_edge_features(self.edge_features, self.node_features + self.discarded_features,
                                             np.concatenate([x_nodes, x_dis], axis=1)) if self.edge_features != [] else [])
            edge_index = torch.tensor(list(itertools.permutations(range(n_nodes), 2)), dtype=torch.long)
            b2.B2DEBUG(11, "Edge features:\n", x_edges)
 
            # Fill global features # TODO: get them from basf2
            x_global = (
                np.array([[n_nodes]], dtype=float)
                if self.glob_features != []
                else []
            )
            b2.B2DEBUG(11, "Global features:\n", x_global)
 
            # Fill tensor to assign each node to a graph (trivial since we have only one graph per decay)
            torch_batch = torch.zeros(size=[n_nodes], dtype=torch.long)
 
            # Set nans to zero, this is a surrogate value, may change in future
            np.nan_to_num(x_nodes, copy=False)
            np.nan_to_num(x_edges, copy=False)
            np.nan_to_num(x_global, copy=False)
 
            # Normalize any features that should be
            if self.normalize is not None:
                normalize_features(
                    self.normalize,
                    self.node_features,
                    x_nodes,
                    self.edge_features,
                    x_edges,
                    self.glob_features,
                    x_global,
                )
 
            # Convert everything to torch tensors and/or send to some device in case
            x = torch.tensor(x_nodes, dtype=torch.float).to(self.device)
            edge_index = edge_index.t().contiguous().to(self.device)
            edge_attr = torch.tensor(x_edges, dtype=torch.float).to(self.device)
            u = torch.tensor(x_global, dtype=torch.float).to(self.device)
            torch_batch = torch_batch.to(self.device)
 
            # Create Batch object to be passed to model
            batch = Batch(
                x=x, edge_index=edge_index, edge_attr=edge_attr, u=u, batch=torch_batch
            )
 
            # Evaluate model
            with torch.no_grad():
                x_pred, e_pred, u_pred = self.model(batch)
                # if self.use_amp:
                #    with autocast(enabled=True):
                #        x_pred, e_pred, u_pred = self.model(batch)
                # else:
                #    x_pred, e_pred, u_pred = self.model(batch)
 
            # Select edges from predictions
            edge_probs = torch.softmax(e_pred, dim=1)
            edge_probability, predicted_LCA = edge_probs.max(dim=1)
 
            # Select masses from predictions
            mass_probs = torch.softmax(x_pred, dim=1)
            mass_probability, predicted_masses = mass_probs.max(dim=1)
            b2.B2DEBUG(10, "Predicted mass classes:\n", predicted_masses)
            b2.B2DEBUG(11, "Mass class probabilities:\n", mass_probability)
 
            # Count number of predicted particles for each mass hypothesis
            graFEI_nPhotons_postFit = (predicted_masses == 6).sum()
            graFEI_nCharged_postFit = graFEI_nFSP - graFEI_nPhotons_postFit
            graFEI_nElectrons_postFit = (predicted_masses == 1).sum()
            graFEI_nMuons_postFit = (predicted_masses == 2).sum()
            graFEI_nPions_postFit = (predicted_masses == 3).sum()
            graFEI_nKaons_postFit = (predicted_masses == 4).sum()
            graFEI_nProtons_postFit = (predicted_masses == 5).sum()
            graFEI_nLeptons_postFit = graFEI_nElectrons_postFit + graFEI_nMuons_postFit
            graFEI_nOthers_postFit = (predicted_masses == 0).sum()
 
            # Assign new mass hypotheses as extraInfo
            for i, p in enumerate(p_list):
                p.addExtraInfo("graFEI_massHypothesis", predicted_masses[i])
 
            # Get square matrices
            edge_probability_square = torch.sparse_coo_tensor(
                edge_index, edge_probability
            ).to_dense()
            predicted_LCA_square = torch.sparse_coo_tensor(
                edge_index, predicted_LCA, dtype=int
            ).to_dense()
            b2.B2DEBUG(10, "Predicted LCA:\n", predicted_LCA_square)
            b2.B2DEBUG(11, "Edge class probabilities:\n", edge_probability_square)
 
            # Remove symmetric elements from probability
            edge_probability_unique = edge_probability_square[
                edge_probability_square.tril(diagonal=-1) > 0
            ]
 
            # Get particles predicted as matched by the model
            predicted_matched = np.array(
                [False if torch.all(i == 0) else True for i in predicted_LCA_square]
            )
            b2.B2DEBUG(10, "Predicted matched particles:\n", predicted_matched)
            # Same but ignoring photons
            predicted_matched_noPhotons = predicted_matched[masses != 22]
 
            # Get number of predicted as unmatched
            graFEI_nPredictedUnmatched = (~predicted_matched).sum()
            graFEI_nPredictedUnmatched_noPhotons = (
                (~predicted_matched_noPhotons).sum()
                if predicted_matched_noPhotons.size != 0
                else 0
            )
 
            # Get LCA of predicted matched only
            predicted_LCA_square_matched = predicted_LCA_square[predicted_matched]
            predicted_LCA_square_matched = predicted_LCA_square_matched[:, predicted_matched]
 
            # Get predicted masses of predicted matched only
            predicted_masses_matched = predicted_masses[predicted_matched]
 
            # Check if LCA describes a tree graph
            graFEI_validTree = 0
            if not torch.all(predicted_LCA_square == 0):
                try:
                    adjacency = lca_to_adjacency(predicted_LCA_square_matched)
                    graFEI_validTree = 1
                except InvalidLCAMatrix:
                    pass
 
            # Check if event is good, depending on the chosen sig-side LCA matrix/masses
            graFEI_goodEvent = 0
            if graFEI_validTree:
                # Check if the event is good
                good_decay, root_level, sig_side_fsps = select_good_decay(predicted_LCA_square_matched,
                                                                          predicted_masses_matched,
                                                                          self.sig_side_lcas,
                                                                          self.sig_side_masses)
                graFEI_goodEvent = int((self.max_level == root_level) and good_decay)
 
                if graFEI_goodEvent:
                    # Find sig- and tag-side FSPs (1 = sig-side, 0 = tag-side)
                    p_list_matched = list(np.array(p_list)[predicted_matched])
                    for i, particle in enumerate(p_list_matched):
                        if i in sig_side_fsps:
                            particle.addExtraInfo("graFEI_sigSide", 1)
                        else:
                            particle.addExtraInfo("graFEI_sigSide", 0)
 
                b2.B2DEBUG(11, "This LCA describes a valid tree")
                b2.B2DEBUG(
                    11,
                    "Predicted LCA on matched particles:\n",
                    predicted_LCA_square_matched,
                )
                b2.B2DEBUG(11, "Adjacency matrix:\n", adjacency)
 
            # Particles not assigned to B decays get -1
            for particle in p_list:
                if not particle.hasExtraInfo("graFEI_sigSide"):
                    particle.addExtraInfo("graFEI_sigSide", -1)
 
            # Define B probabilities
            graFEI_probEdgeProd = edge_probability_unique.prod().item()
            graFEI_probEdgeMean = edge_probability_unique.mean().item()
            graFEI_probEdgeGeom = torch.pow(edge_probability_unique.prod(), 1/n_nodes).item()
 
            # Add extra info for each B candidate
            candidate.addExtraInfo("graFEI_probEdgeProd", graFEI_probEdgeProd)
            candidate.addExtraInfo("graFEI_probEdgeMean", graFEI_probEdgeMean)
            candidate.addExtraInfo("graFEI_probEdgeGeom", graFEI_probEdgeGeom)
            candidate.addExtraInfo("graFEI_validTree", graFEI_validTree)
            candidate.addExtraInfo("graFEI_goodEvent", graFEI_goodEvent)
            candidate.addExtraInfo("graFEI_nPhotons_postFit", graFEI_nPhotons_postFit)
            candidate.addExtraInfo("graFEI_nCharged_postFit", graFEI_nCharged_postFit)
            candidate.addExtraInfo("graFEI_nElectrons_postFit", graFEI_nElectrons_postFit)
            candidate.addExtraInfo("graFEI_nMuons_postFit", graFEI_nMuons_postFit)
            candidate.addExtraInfo("graFEI_nPions_postFit", graFEI_nPions_postFit)
            candidate.addExtraInfo("graFEI_nKaons_postFit", graFEI_nKaons_postFit)
            candidate.addExtraInfo("graFEI_nProtons_postFit", graFEI_nProtons_postFit)
            candidate.addExtraInfo("graFEI_nLeptons_postFit", graFEI_nLeptons_postFit)
            candidate.addExtraInfo("graFEI_nOthers_postFit", graFEI_nOthers_postFit)
            candidate.addExtraInfo("graFEI_nPredictedUnmatched", graFEI_nPredictedUnmatched)
            candidate.addExtraInfo("graFEI_nPredictedUnmatched_noPhotons", graFEI_nPredictedUnmatched_noPhotons)
 
            # Add MC truth information
            if self.storeTrueInfo:
                # Get the true IDs of the ancestors (if it's a B)
                parentID = np.array([vm.evaluate("ancestorBIndex", p) for p in p_list], dtype=int)
                b2.B2DEBUG(10, "Ancestor true ID:\n", parentID)
 
                # Get particle indices
                p_indices = np.array(
                    [
                        p.getMCParticle().getArrayIndex() if parentID[i] >= 0 else -1
                        for (i, p) in enumerate(p_list)
                    ]
                )
                # Get particle masses
                p_masses = masses_to_classes(
                    np.array(
                        [
                            p.getMCParticle().getPDG() if parentID[i] >= 0 else -1
                            for (i, p) in enumerate(p_list)
                        ]
                    )
                )
                b2.B2DEBUG(10, "True mass classes:\n", p_masses)
                # And primary information
                evt_primary = np.array(
                    [
                        p.getMCParticle().isPrimaryParticle()
                        if parentID[i] >= 0
                        else False
                        for (i, p) in enumerate(p_list)
                    ]
                )
                b2.B2DEBUG(10, "Is primary particle:\n", evt_primary)
 
                # Get unique B indices associated to each predicted matched particle which is also a primary
                # The idea is that if a primary particle coming from the other B is categorized as unmatched,
                # then it's ok and the decay could still have a perfectLCA
                B_indices = parentID[np.logical_and(evt_primary, predicted_matched)]
                b2.B2DEBUG(
                    10, "Ancestor ID of predicted matched particles:\n", B_indices
                )
                B_indices = list(set(B_indices))
 
                # Initialize truth-matching variables
                graFEI_truth_perfectLCA = 0  # 1 if LCA perfectly reconstructed
                graFEI_truth_isSemileptonic = -1  # 0 if hadronic, 1 is semileptonic, -1 if not matched
                graFEI_truth_nFSP = -1  # Number of true FSPs
                graFEI_truth_perfectMasses = int((predicted_masses.numpy() == p_masses).all()
                                                 )  # Check if all the masses are predicted correctly
                graFEI_truth_nPhotons = (p_masses == 6).sum()
                graFEI_truth_nElectrons = (p_masses == 1).sum()
                graFEI_truth_nMuons = (p_masses == 2).sum()
                graFEI_truth_nPions = (p_masses == 3).sum()
                graFEI_truth_nKaons = (p_masses == 4).sum()
                graFEI_truth_nProtons = (p_masses == 5).sum()
                graFEI_truth_nOthers = (p_masses == 0).sum()
 
                # Get the generated B's
                gen_list = Belle2.PyStoreObj(self.mc_particle)
 
                # Iterate over generated Ups
                if self.mc_particle == "Upsilon(4S):MC" and gen_list.getListSize() > 1:
                    b2.B2WARNING(
                        f"Found {gen_list.getListSize()} true Upsilon(4S) in the generated MC (??)")
 
                if gen_list.getListSize() > 0:
                    # Here we look if the candidate has a perfectly reconstructed LCA
                    for genP in gen_list.obj():
                        mcp = genP.getMCParticle()
                        # If storing true info on B decays and we have matched particles coming
                        # from different Bs the decay will not have a perfectLCA
                        if self.mc_particle != "Upsilon(4S):MC" and len(B_indices) != 1:
                            break
 
                        # Get array index of MC particle
                        array_index = mcp.getArrayIndex()
 
                        # If we are reconstructing Bs, skip the other in the event
                        if self.mc_particle != "Upsilon(4S):MC" and array_index != B_indices[0]:
                            continue
 
                        # Write leaf history
                        (
                            leaf_hist,
                            levels,
                            _,
                            _,
                            semilep_flag,
                        ) = write_hist(
                            particle=mcp,
                            leaf_hist={},
                            levels={},
                            hist=[],
                            pdg={},
                            leaf_pdg={},
                            semilep_flag=False,
                            )
 
                        # Skip B decays with trivial LCA (should be always false except for B -> nunu ?)
                        if len(leaf_hist) < 2:
                            continue
 
                        # Initialize LCA...
                        true_LCA_square = np.zeros(
                            [len(leaf_hist), len(leaf_hist)], dtype=int
                        )
 
                        # Number of true FSPs
                        graFEI_truth_nFSP = len(leaf_hist)
 
                        # ... and fill it!
                        for x, y in itertools.combinations(enumerate(leaf_hist), 2):
                            intersection = [
                                i for i in leaf_hist[x[1]] if i in leaf_hist[y[1]]
                            ]
                            true_LCA_square[x[0], y[0]] = levels[intersection[-1]]
                            true_LCA_square[y[0], x[0]] = levels[intersection[-1]]
 
                        x_leaves = p_indices
                        y_leaves = list(leaf_hist.keys())
 
                        # Get LCA indices in order that the leaves appear in reconstructed particles
                        # Secondaries aren't in the LCA leaves list so they get a 0
                        locs = np.array(
                            [
                                np.where(y_leaves == i)[0].item()
                                if (i in y_leaves)
                                else 0
                                for i in x_leaves
                            ],
                            dtype=int,
                        )
 
                        # Insert dummy rows for secondaries
                        true_LCA_square = true_LCA_square[locs, :][:, locs]
 
                        # Set everything that's not primary (unmatched and secondaries) rows.cols to 0
                        # Note we only consider the subset of leaves that made it into x_rows
                        x_rows = np.array(
                            [
                                vm.evaluate("ancestorBIndex", p) == array_index
                                for p in p_list
                            ]
                        ) if self.mc_particle != "Upsilon(4S):MC" else evt_primary
 
                        primaries_from_right_cand = np.logical_and(evt_primary, x_rows)
 
                        # Set the rows
                        true_LCA_square = np.where(
                            primaries_from_right_cand, true_LCA_square, 0
                        )
                        # Set the columns
                        true_LCA_square = np.where(
                            primaries_from_right_cand[:, None], true_LCA_square, 0
                        )
 
                        # Convert LCA to tensor
                        true_LCA_square = torch.tensor(true_LCA_square, dtype=int)
                        b2.B2DEBUG(10, "True LCA:\n", true_LCA_square)
 
                        # Check if perfect LCA
                        if (true_LCA_square == predicted_LCA_square).all():
                            graFEI_truth_perfectLCA = 1
                            b2.B2DEBUG(10, "LCA perfectly reconstructed!")
 
                        # Assign semileptonic flag
                        graFEI_truth_isSemileptonic = int(semilep_flag)
 
                # Perfect event = perfectLCA and perfectMasses
                graFEI_truth_perfectEvent = int(graFEI_truth_perfectLCA and graFEI_truth_perfectMasses)
 
                # Write extra info
                candidate.addExtraInfo("graFEI_truth_perfectLCA", graFEI_truth_perfectLCA)
                candidate.addExtraInfo("graFEI_truth_perfectMasses", graFEI_truth_perfectMasses)
                candidate.addExtraInfo("graFEI_truth_perfectEvent", graFEI_truth_perfectEvent)
                candidate.addExtraInfo("graFEI_truth_isSemileptonic", graFEI_truth_isSemileptonic)
                candidate.addExtraInfo("graFEI_truth_nFSP", graFEI_truth_nFSP)
                candidate.addExtraInfo("graFEI_truth_nPhotons", graFEI_truth_nPhotons)
                candidate.addExtraInfo("graFEI_truth_nElectrons", graFEI_truth_nElectrons)
                candidate.addExtraInfo("graFEI_truth_nMuons", graFEI_truth_nMuons)
                candidate.addExtraInfo("graFEI_truth_nPions", graFEI_truth_nPions)
                candidate.addExtraInfo("graFEI_truth_nKaons", graFEI_truth_nKaons)
                candidate.addExtraInfo("graFEI_truth_nProtons", graFEI_truth_nProtons)
                candidate.addExtraInfo("graFEI_truth_nOthers", graFEI_truth_nOthers)

initialize()

def initialize

(

self

)

Called at the beginning.

Definition at line 79 of file GraFEIModule.py.

    def initialize(self):
        """
        Called at the beginning.
        """
        # Get weights and configs from the DB if they are not provided from the user
        if not self.cfg_path:
            config = Belle2.DBAccessorBase(
                Belle2.DBStoreEntry.c_RawFile, self.payload_config_name, True
            )
            self.cfg_path = config.getFilename()
        if not self.param_file:
            model = Belle2.DBAccessorBase(
                Belle2.DBStoreEntry.c_RawFile, self.payload_model_name, True
            )
            self.param_file = model.getFilename()
 
        
        self.storeTrueInfo = Belle2.Environment.Instance().isMC()
 
        
        self.device = torch.device(
            "cuda" if (self.gpu and torch.cuda.is_available()) else "cpu"
        )
 
        # Load configs
        cfg_file = open(self.cfg_path, "r")
        
        self.configs = yaml.safe_load(cfg_file)
 
        
        self.mc_particle = None
        
        self.max_level = None
        # B or Ups reco? 0 = Ups, 1 = B0, 2 = B+
        if self.configs["model"]["B_reco"] == 0:
            self.mc_particle = "Upsilon(4S):MC"
            self.max_level = 6
        elif self.configs["model"]["B_reco"] == 1:
            self.mc_particle = "B0:MC"
            self.max_level = 5
        elif self.configs["model"]["B_reco"] == 2:
            self.mc_particle = "B+:MC"
            self.max_level = 5
        else:
            b2.B2FATAL("The B_reco setting in the config file is incorrect.")
 
        
        self.normalize = self.configs["dataset"]["config"]["normalize"]
 
        
        self.use_amp = self.configs["train"][
            "mixed_precision"
        ] and self.device == torch.device("cuda")
 
        
        self.node_features = self.configs["dataset"]["config"]["features"]
        
        self.edge_features = self.configs["dataset"]["config"]["edge_features"]
        
        self.glob_features = self.configs["dataset"]["config"]["global_features"]
 
        # Naming convention
        self.node_features = [f"feat_{name}" for name in self.node_features] if self.node_features else []
        self.edge_features = [f"edge_{name}" for name in self.edge_features] if self.edge_features else []
        self.glob_features = [f"glob_{name}" for name in self.glob_features] if self.glob_features else []
        
        self.discarded_features = ["feat_x", "feat_y", "feat_z", "feat_px", "feat_py", "feat_p"]
 
        # Extract the number of features
        n_infeatures = len(self.node_features)
        e_infeatures = len(self.edge_features)
        g_infeatures = len(self.glob_features)
 
        
        self.model = GraFEIModel(
            nfeat_in_dim=n_infeatures,
            efeat_in_dim=e_infeatures,
            gfeat_in_dim=g_infeatures,
            **self.configs["model"],
        )
 
        # Load paramaters' values
        self.model.load_state_dict(
            torch.load(self.param_file, map_location=self.device)["model"]
        )
 
        # Activate evaluation mode
        self.model.eval()
        # Push model to GPU in case
        self.model.to(self.device)
 
        b2.B2DEBUG(10, "Model structure:\n", {self.model})
 

Member Data Documentation

cfg_path

cfg_path

Config yaml file path.

Definition at line 65 of file GraFEIModule.py.

configs

configs

Config file.

Definition at line 106 of file GraFEIModule.py.

device

device

Figure out which device all this is running on - CPU or GPU.

Definition at line 99 of file GraFEIModule.py.

discarded_features

discarded_features

Discarded node features.

Definition at line 145 of file GraFEIModule.py.

edge_features

edge_features

Edge features.

Definition at line 136 of file GraFEIModule.py.

glob_features

glob_features

Global features.

Definition at line 138 of file GraFEIModule.py.

gpu

gpu

If running on GPU.

Definition at line 73 of file GraFEIModule.py.

max_level

max_level

Max LCAS level.

Definition at line 111 of file GraFEIModule.py.

mc_particle

mc_particle

Top MC particle.

Definition at line 109 of file GraFEIModule.py.

model

model

The model The correct edge_classes is taken from the config file.

Definition at line 154 of file GraFEIModule.py.

node_features

node_features

Node features.

Definition at line 134 of file GraFEIModule.py.

normalize

normalize

Normalize features.

Definition at line 126 of file GraFEIModule.py.

param_file

param_file

PyTorch parameter file path.

Definition at line 67 of file GraFEIModule.py.

particle_list

particle_list

Input particle list.

Definition at line 63 of file GraFEIModule.py.

payload_config_name

payload_config_name

Config file name in the payload.

Definition at line 75 of file GraFEIModule.py.

payload_model_name

payload_model_name

Model file name in the payload.

Definition at line 77 of file GraFEIModule.py.

sig_side_lcas

sig_side_lcas

Chosen sig-side LCAS.

Definition at line 69 of file GraFEIModule.py.

sig_side_masses

sig_side_masses

Chosen sig-side mass hypotheses.

Definition at line 71 of file GraFEIModule.py.

storeTrueInfo

storeTrueInfo

Figure out if we re running on data or MC.

Definition at line 96 of file GraFEIModule.py.

use_amp

use_amp

Mixed precision.

Definition at line 129 of file GraFEIModule.py.

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The documentation for this class was generated from the following file:

• analysis/scripts/grafei/modules/GraFEIModule.py