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| # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved | |
| """ | |
| Modules to compute the matching cost and solve the corresponding LSAP. | |
| """ | |
| import torch | |
| from scipy.optimize import linear_sum_assignment | |
| from torch import nn | |
| import torch.nn.functional as F | |
| from utils.span_utils import generalized_temporal_iou, span_cxw_to_xx | |
| 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_span: float = 1, cost_giou: float = 1, | |
| span_loss_type: str = "l1", max_v_l: int = 75): | |
| """Creates the matcher | |
| Params: | |
| cost_span: This is the relative weight of the L1 error of the span coordinates in the matching cost | |
| cost_giou: This is the relative weight of the giou loss of the spans in the matching cost | |
| """ | |
| super().__init__() | |
| self.cost_class = cost_class | |
| self.cost_span = cost_span | |
| self.cost_giou = cost_giou | |
| self.span_loss_type = span_loss_type | |
| self.max_v_l = max_v_l | |
| self.foreground_label = 0 | |
| assert cost_class != 0 or cost_span != 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_spans": Tensor of dim [batch_size, num_queries, 2] with the predicted span coordinates, | |
| in normalized (cx, w) format | |
| ""pred_logits": Tensor of dim [batch_size, num_queries, num_classes] with the classification logits | |
| targets: This is a list of targets (len(targets) = batch_size), where each target is a dict containing: | |
| "spans": Tensor of dim [num_target_spans, 2] containing the target span coordinates. The spans are | |
| in normalized (cx, w) format | |
| 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_spans) | |
| """ | |
| bs, num_queries = outputs["pred_spans"].shape[:2] | |
| targets = targets["span_labels"] | |
| # Also concat the target labels and spans | |
| out_prob = outputs["pred_logits"].flatten(0, 1).softmax(-1) # [batch_size * num_queries, num_classes] | |
| tgt_spans = torch.cat([v["spans"] for v in targets]) # [num_target_spans in batch, 2] | |
| tgt_ids = torch.full([len(tgt_spans)], self.foreground_label) # [total #spans in the batch] | |
| # Compute the classification cost. Contrary to the loss, we don't use the NLL, | |
| # but approximate it in 1 - prob[target class]. | |
| # The 1 is a constant that doesn't change the matching, it can be omitted. | |
| cost_class = -out_prob[:, tgt_ids] # [batch_size * num_queries, total #spans in the batch] | |
| if self.span_loss_type == "l1": | |
| # We flatten to compute the cost matrices in a batch | |
| out_spans = outputs["pred_spans"].flatten(0, 1) # [batch_size * num_queries, 2] | |
| # Compute the L1 cost between spans | |
| cost_span = torch.cdist(out_spans, tgt_spans, p=1) # [batch_size * num_queries, total #spans in the batch] | |
| # Compute the giou cost between spans | |
| # [batch_size * num_queries, total #spans in the batch] | |
| cost_giou = - generalized_temporal_iou(span_cxw_to_xx(out_spans), span_cxw_to_xx(tgt_spans)) | |
| else: | |
| pred_spans = outputs["pred_spans"] # (bsz, #queries, max_v_l * 2) | |
| pred_spans = pred_spans.view(bs * num_queries, 2, self.max_v_l).softmax(-1) # (bsz * #queries, 2, max_v_l) | |
| cost_span = - pred_spans[:, 0][:, tgt_spans[:, 0]] - \ | |
| pred_spans[:, 1][:, tgt_spans[:, 1]] # (bsz * #queries, #spans) | |
| # pred_spans = pred_spans.repeat(1, n_spans, 1, 1).flatten(0, 1) # (bsz * #queries * #spans, max_v_l, 2) | |
| # tgt_spans = tgt_spans.view(1, n_spans, 2).repeat(bs * num_queries, 1, 1).flatten(0, 1) # (bsz * #queries * #spans, 2) | |
| # cost_span = pred_spans[tgt_spans] | |
| # cost_span = cost_span.view(bs * num_queries, n_spans) | |
| # giou | |
| cost_giou = 0 | |
| # Final cost matrix | |
| # import ipdb; ipdb.set_trace() | |
| C = self.cost_span * cost_span + self.cost_giou * cost_giou + self.cost_class * cost_class | |
| C = C.view(bs, num_queries, -1).cpu() | |
| sizes = [len(v["spans"]) 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_span=args.set_cost_span, cost_giou=args.set_cost_giou, | |
| cost_class=args.set_cost_class, span_loss_type=args.span_loss_type, max_v_l=args.max_v_l | |
| ) | |