.rst .pdf

mlreco.models.layers.cluster_cnn.losses.gs_embeddings module

class mlreco.models.layers.cluster_cnn.losses.gs_embeddings.WeightedEdgeLoss(loss_type='BCE', reduction='mean', invert=False)

Bases: torch.nn.modules.module.Module

__init__(loss_type='BCE', reduction='mean', invert=False)

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

forward(logits, targets)

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.cluster_cnn.losses.gs_embeddings'

training: bool

mlreco.models.layers.cluster_cnn.losses.gs_embeddings.compute_edge_weight(sp_emb: torch.Tensor, ft_emb: torch.Tensor, cov: torch.Tensor, edge_indices: torch.Tensor, occ=None, eps=0.001)

class mlreco.models.layers.cluster_cnn.losses.gs_embeddings.GraphSPICEEmbeddingLoss(cfg, name='graph_spice_loss')

Bases: torch.nn.modules.module.Module

Loss function for Sparse Spatial Embeddings Model, with fixed centroids and symmetric gaussian kernels.

__init__(cfg, name='graph_spice_loss')

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

feature_embedding_loss(ft_emb, groups, ft_centroids)

Compute discriminative feature embedding loss.

INPUTS:

• ft_emb (N x F)

• groups (N)

• ft_centroids (N_c X F)

spatial_embedding_loss(sp_emb, groups, sp_centroids)

Compute spatial centroid regression loss.

INPUTS:

• sp_emb (N x D)

• groups (N)

• ft_centroids (N_c X F)

covariance_loss(sp_emb, ft_emb, cov, groups, sp_centroids, ft_centroids, eps=0.001)

occupancy_loss(occ, groups)

INPUTS:

• occ (N x 1)

• groups (N)

combine_multiclass(sp_embeddings, ft_embeddings, covariance, occupancy, slabels, clabels)

Wrapper function for combining different components of the loss, in particular when clustering must be done PER SEMANTIC CLASS.

NOTE: When there are multiple semantic classes, we compute the DLoss by first masking out by each semantic segmentation (ground-truth/prediction) and then compute the clustering loss over each masked point cloud.

INPUTS:

features (torch.Tensor): pixel embeddings slabels (torch.Tensor): semantic labels clabels (torch.Tensor): group/instance/cluster labels

OUTPUT

• loss_segs (list) – list of computed loss values for each semantic class.

• loss[i] = computed DLoss for semantic class <i>.

• acc_segs (list) – list of computed clustering accuracy for each semantic class.

forward(out, segment_label, cluster_label)

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.cluster_cnn.losses.gs_embeddings'

training: bool

class mlreco.models.layers.cluster_cnn.losses.gs_embeddings.NodeEdgeHybridLoss(cfg, name='graph_spice_loss')

Bases: torch.nn.modules.loss._Loss

Combined Node + Edge Loss

__init__(cfg, name='graph_spice_loss')

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

__module__ = 'mlreco.models.layers.cluster_cnn.losses.gs_embeddings'

reduction: str

forward(result, segment_label, cluster_label)

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.

mlreco.models.layers.cluster_cnn.losses package mlreco.models.layers.cluster_cnn.losses.lovasz module