dgs.models.embedding_generator.pose_based.KeyPointConvolutionPBEG.forward

KeyPointConvolutionPBEG.forward(ds: State) → tuple[torch.Tensor, torch.Tensor]

Forward pass of the custom key point convolution model.

Params:

ds: A State containing the key-points and the corresponding bounding boxes.

Returns:

This modules’ prediction. embeddings is describing the key-points and bounding boxes as a tensor of shape [B x E]. ids is the probability to predict a class. The ids are given as a tensor of shape [B x num_classes] with values in range [0, 1].