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| # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved | |
| """ | |
| Mostly copy-paste from DETR (https://github.com/facebookresearch/detr). | |
| """ | |
| import torch | |
| from scipy.optimize import linear_sum_assignment | |
| from torch import nn | |
| class HungarianMatcher_Crowd(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_point: float = 1): | |
| """Creates the matcher | |
| Params: | |
| cost_class: This is the relative weight of the foreground object | |
| cost_point: This is the relative weight of the L1 error of the points coordinates in the matching cost | |
| """ | |
| super().__init__() | |
| self.cost_class = cost_class | |
| self.cost_point = cost_point | |
| assert cost_class != 0 or cost_point != 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 | |
| "points": Tensor of dim [batch_size, num_queries, 2] with the predicted point 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_points] (where num_target_points is the number of ground-truth | |
| objects in the target) containing the class labels | |
| "points": Tensor of dim [num_target_points, 2] containing the target point 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_points) | |
| """ | |
| bs, num_queries = outputs["pred_logits"].shape[:2] | |
| # We flatten to compute the cost matrices in a batch | |
| out_prob = outputs["pred_logits"].flatten(0, 1).softmax(-1) # [batch_size * num_queries, num_classes] | |
| out_points = outputs["pred_points"].flatten(0, 1) # [batch_size * num_queries, 2] | |
| # Also concat the target labels and points | |
| # tgt_ids = torch.cat([v["labels"] for v in targets]) | |
| tgt_ids = torch.cat([v["labels"] for v in targets]) | |
| tgt_points = torch.cat([v["point"] for v in targets]) | |
| # Compute the classification cost. Contrary to the loss, we don't use the NLL, | |
| # but approximate it in 1 - proba[target class]. | |
| # The 1 is a constant that doesn't change the matching, it can be ommitted. | |
| cost_class = -out_prob[:, tgt_ids] | |
| # Compute the L2 cost between point | |
| cost_point = torch.cdist(out_points, tgt_points, p=2) | |
| # Compute the giou cost between point | |
| # Final cost matrix | |
| C = self.cost_point * cost_point + self.cost_class * cost_class | |
| C = C.view(bs, num_queries, -1).cpu() | |
| sizes = [len(v["point"]) 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_crowd(args): | |
| return HungarianMatcher_Crowd(cost_class=args.set_cost_class, cost_point=args.set_cost_point) | |