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matcher.py

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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,
+        override_multiclass: bool = False,
+        pointmatch: bool = False
+    ):
+        """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"
+        self.override_multiclass = override_multiclass
+        self.pointmatch = pointmatch
+
+    @torch.no_grad()
+    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])
+
+        if self.override_multiclass:
+            tgt_ids = torch.ones(tgt_ids.size()[0], dtype=torch.int)
+        # 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
+        if self.pointmatch:
+            C = cost_point
+        else:
+            C = self.cost_point * cost_point + self.cost_class * cost_class
+        
+        # Reshape to back to [batch_size, num_queries, num_target_points]
+        C = C.view(bs, num_queries, -1).cpu()
+        # compute the matching
+        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, override_multiclass: bool = False):
+    return HungarianMatcher_Crowd(
+        cost_class=args.set_cost_class,
+        cost_point=args.set_cost_point,
+        override_multiclass=override_multiclass,
+        pointmatch=args.pointmatch,
+    )