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| """ |
| Modules to compute the matching cost and solve the corresponding LSAP. |
| """ |
| import torch |
| from scipy.optimize import linear_sum_assignment |
| from torch import nn |
|
|
| from util.box_ops import box_cxcywh_to_xyxy, generalized_box_iou |
|
|
|
|
| class HungarianMatcher(nn.Module): |
| """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). |
| """ |
|
|
| def __init__(self, |
| cost_class: float = 1, |
| cost_bbox: float = 1, |
| cost_giou: float = 1): |
| """Creates the matcher |
| |
| Params: |
| cost_class: This is the relative weight of the classification error in the matching cost |
| cost_bbox: This is the relative weight of the L1 error of the bounding box coordinates in the matching cost |
| cost_giou: This is the relative weight of the giou loss of the bounding box in the matching cost |
| """ |
| super().__init__() |
| self.cost_class = cost_class |
| self.cost_bbox = cost_bbox |
| self.cost_giou = cost_giou |
| assert cost_class != 0 or cost_bbox != 0 or cost_giou != 0, "all costs cant be 0" |
|
|
| def forward(self, 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: |
| A list of size batch_size, containing tuples of (index_i, index_j) where: |
| - 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) |
| """ |
| with torch.no_grad(): |
| bs, num_queries = outputs["pred_logits"].shape[:2] |
|
|
| |
| out_prob = outputs["pred_logits"].flatten(0, 1).sigmoid() |
| out_bbox = outputs["pred_boxes"].flatten(0, 1) |
|
|
| |
| tgt_ids = torch.cat([v["labels"] for v in targets]) |
| tgt_bbox = torch.cat([v["boxes"] for v in targets]) |
|
|
| |
| alpha = 0.25 |
| gamma = 2.0 |
| neg_cost_class = (1 - alpha) * (out_prob ** gamma) * (-(1 - out_prob + 1e-8).log()) |
| pos_cost_class = alpha * ((1 - out_prob) ** gamma) * (-(out_prob + 1e-8).log()) |
| cost_class = pos_cost_class[:, tgt_ids] - neg_cost_class[:, tgt_ids] |
|
|
| |
| cost_bbox = torch.cdist(out_bbox, tgt_bbox, p=1) |
|
|
| |
| cost_giou = -generalized_box_iou(box_cxcywh_to_xyxy(out_bbox), |
| box_cxcywh_to_xyxy(tgt_bbox)) |
|
|
| |
| C = self.cost_bbox * cost_bbox + self.cost_class * cost_class + self.cost_giou * cost_giou |
| C = C.view(bs, num_queries, -1).cpu() |
|
|
| sizes = [len(v["boxes"]) for v in targets] |
| indices = [linear_sum_assignment(c[i]) for i, c in enumerate(C.split(sizes, -1))] |
| return [(torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) for i, j in indices] |
|
|
|
|
| def build_matcher(args): |
| return HungarianMatcher(cost_class=args.set_cost_class, |
| cost_bbox=args.set_cost_bbox, |
| cost_giou=args.set_cost_giou) |
|
|
