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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"
@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])
# 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)
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