.rst .pdf

mlreco.models.layers.gnn.encoders.mixed module

class mlreco.models.layers.gnn.encoders.mixed.ClustMixNodeEncoder(model_config, **kwargs)

Bases: torch.nn.modules.module.Module

Produces node features using both geometric and cnn encoder based feature extraction

__init__(model_config, **kwargs)

Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(data, clusts)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

__module__ = 'mlreco.models.layers.gnn.encoders.mixed'

training: bool

class mlreco.models.layers.gnn.encoders.mixed.ClustMixEdgeEncoder(model_config, **kwargs)

Bases: torch.nn.modules.module.Module

Produces edge features using both geometric and cnn encoder based feature extraction

__init__(model_config, **kwargs)

Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(data, clusts, edge_index)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

__module__ = 'mlreco.models.layers.gnn.encoders.mixed'

training: bool

mlreco.models.layers.gnn.encoders.geometric module mlreco.models.layers.gnn.losses package