dgs.models.combine.dynamic.DynamicAlphaCombine.forward

DynamicAlphaCombine.forward(*tensors: torch.Tensor, s: State | None = None, **_kwargs) → torch.Tensor[source]

The forward call of this module combines an arbitrary number of similarity matrices using an importance weight \(\alpha\).

\(\alpha_i\) describes how important the similarity \(s_i\) is. The sum of all \(\alpha_i\) should be 1 by definition given the last layer is a softmax layer. \(\alpha\) is computed using the respective BaseAlphaModule and the given State.

All tensors should be on the same device and should have the same shape.

Parameters:

  • tensors – A tuple of tensors describing similarities between the detections and tracks. All S similarity matrices of this iterable should have values in range [0,1], be of the same shape [D x T], and be on the same device. If tensors is a single tensor, it should have the shape [S x D x T]. S can be any number of similarity matrices greater than 0, even though only values greater than 1 really make sense.

  • s – A State containing the batched input data for the alpha models. The state should be on the same device as tensors.

Returns:

The weighted similarity matrix as tensor of shape [D x T].

Return type:

torch.Tensor

Raises:

  • ValueError – If alpha or the matrices have invalid shapes.

  • RuntimeError – If one of the tensors is not on the correct device.

  • TypeError – If one of the tensors or one of the alpha inputs is not of type class:torch.Tensor.