models.matcher

Modules to compute the matching cost and solve the corresponding LSAP.

Classes

HungarianMatcher

This class computes an assignment between the targets and the predictions of the network.

Module Contents

class models.matcher.HungarianMatcher(cost_class: float = 1, cost_bbox: float = 1, cost_giou: float = 1)

This class computes an assignment between the targets and the predictions of the network.

For efficiency reasons, the targets don’t include the no_object. Because of this, in general, there are more predictions than targets. In this case, we do a 1-to-1 matching of the best predictions, while the others are un-matched (and thus treated as non-objects).

forward(outputs, targets)

Performs the matching.

Params:

outputs: This is a dict that contains at least these entries:

“pred_logits”: Tensor of dim [batch_size, num_queries, num_classes] with the classification logits “pred_boxes”: Tensor of dim [batch_size, num_queries, 4] with the predicted box coordinates

targets: This is a list of targets (len(targets) = batch_size), where each target is a dict containing:

“labels”: Tensor of dim [num_target_boxes] (where num_target_boxes is the number of ground-truth

objects in the target) containing the class labels

“boxes”: Tensor of dim [num_target_boxes, 4] containing the target box coordinates

Returns:

• index_i is the indices of the selected predictions (in order)

  • index_j is the indices of the corresponding selected targets (in order)

For each batch element, it holds:

len(index_i) = len(index_j) = min(num_queries, num_target_boxes)

Return type:

A list of size batch_size, containing tuples of (index_i, index_j) where