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""" |
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Modules to compute the matching cost and solve the corresponding LSAP. |
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""" |
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import torch |
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from scipy.optimize import linear_sum_assignment |
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from torch import nn |
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from util.box_ops import box_cxcywh_to_xyxy, generalized_box_iou |
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from models.structures import Instances |
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class HungarianMatcher(nn.Module): |
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"""This class computes an assignment between the targets and the predictions of the network |
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For efficiency reasons, the targets don't include the no_object. Because of this, in general, |
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there are more predictions than targets. In this case, we do a 1-to-1 matching of the best predictions, |
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while the others are un-matched (and thus treated as non-objects). |
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""" |
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def __init__(self, |
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cost_class: float = 1, |
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cost_bbox: float = 1, |
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cost_giou: float = 1): |
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"""Creates the matcher |
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Params: |
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cost_class: This is the relative weight of the classification error in the matching cost |
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cost_bbox: This is the relative weight of the L1 error of the bounding box coordinates in the matching cost |
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cost_giou: This is the relative weight of the giou loss of the bounding box in the matching cost |
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""" |
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super().__init__() |
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self.cost_class = cost_class |
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self.cost_bbox = cost_bbox |
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self.cost_giou = cost_giou |
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assert cost_class != 0 or cost_bbox != 0 or cost_giou != 0, "all costs cant be 0" |
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def forward(self, outputs, targets, use_focal=True): |
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""" Performs the matching |
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Params: |
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outputs: This is a dict that contains at least these entries: |
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"pred_logits": Tensor of dim [batch_size, num_queries, num_classes] with the classification logits |
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"pred_boxes": Tensor of dim [batch_size, num_queries, 4] with the predicted box coordinates |
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targets: This is a list of targets (len(targets) = batch_size), where each target is a dict containing: |
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"labels": Tensor of dim [num_target_boxes] (where num_target_boxes is the number of ground-truth |
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objects in the target) containing the class labels |
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"boxes": Tensor of dim [num_target_boxes, 4] containing the target box coordinates |
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Returns: |
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A list of size batch_size, containing tuples of (index_i, index_j) where: |
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- index_i is the indices of the selected predictions (in order) |
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- index_j is the indices of the corresponding selected targets (in order) |
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For each batch element, it holds: |
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len(index_i) = len(index_j) = min(num_queries, num_target_boxes) |
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""" |
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with torch.no_grad(): |
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bs, num_queries = outputs["pred_logits"].shape[:2] |
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if use_focal: |
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out_prob = outputs["pred_logits"].flatten(0, 1).sigmoid() |
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else: |
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out_prob = outputs["pred_logits"].flatten(0, 1).softmax(-1) |
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out_bbox = outputs["pred_boxes"].flatten(0, 1) |
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if isinstance(targets[0], Instances): |
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tgt_ids = torch.cat([gt_per_img.labels for gt_per_img in targets]) |
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tgt_bbox = torch.cat([gt_per_img.boxes for gt_per_img in targets]) |
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else: |
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tgt_ids = torch.cat([v["labels"] for v in targets]) |
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tgt_bbox = torch.cat([v["boxes"] for v in targets]) |
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if use_focal: |
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alpha = 0.25 |
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gamma = 2.0 |
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neg_cost_class = (1 - alpha) * (out_prob ** gamma) * (-(1 - out_prob + 1e-8).log()) |
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pos_cost_class = alpha * ((1 - out_prob) ** gamma) * (-(out_prob + 1e-8).log()) |
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cost_class = pos_cost_class[:, tgt_ids] - neg_cost_class[:, tgt_ids] |
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else: |
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cost_class = -out_prob[:, tgt_ids] |
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cost_bbox = torch.cdist(out_bbox, tgt_bbox, p=1) |
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cost_giou = -generalized_box_iou(box_cxcywh_to_xyxy(out_bbox), |
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box_cxcywh_to_xyxy(tgt_bbox)) |
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C = self.cost_bbox * cost_bbox + self.cost_class * cost_class + self.cost_giou * cost_giou |
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C = C.view(bs, num_queries, -1).cpu() |
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if isinstance(targets[0], Instances): |
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sizes = [len(gt_per_img.boxes) for gt_per_img in targets] |
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else: |
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sizes = [len(v["boxes"]) for v in targets] |
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indices = [linear_sum_assignment(c[i]) for i, c in enumerate(C.split(sizes, -1))] |
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return [(torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) for i, j in indices] |
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def build_matcher(args): |
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return HungarianMatcher(cost_class=args.set_cost_class, |
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cost_bbox=args.set_cost_bbox, |
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cost_giou=args.set_cost_giou) |
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