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| # ------------------------------------------------------------------------ | |
| # Deformable DETR | |
| # Copyright (c) 2020 SenseTime. All Rights Reserved. | |
| # Licensed under the Apache License, Version 2.0 [see LICENSE for details] | |
| # ------------------------------------------------------------------------ | |
| # Modified from DETR (https://github.com/facebookresearch/detr) | |
| # 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 | |
| from .util.box_ops import box_cxcywh_to_xyxy, generalized_box_iou | |
| import einops | |
| def cdist(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: | |
| if (not (x.dtype is torch.float)) and x.is_cuda: | |
| x = einops.rearrange(x, "b r -> b () r") | |
| y = einops.rearrange(y, "b r -> () b r") | |
| return (x - y).norm(dim=-1, p=1) | |
| return torch.cdist(x, y, p=1) | |
| 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_bbox: float = 1, | |
| cost_giou: float = 1): | |
| """Creates the matcher | |
| Params: | |
| cost_class: This is the relative weight of the classification error in the matching cost | |
| cost_bbox: This is the relative weight of the L1 error of the bounding box coordinates in the matching cost | |
| cost_giou: This is the relative weight of the giou loss of the bounding box in the matching cost | |
| """ | |
| super().__init__() | |
| self.cost_class = cost_class | |
| self.cost_bbox = cost_bbox | |
| self.cost_giou = cost_giou | |
| assert cost_class != 0 or cost_bbox != 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_logits": Tensor of dim [batch_size, num_queries, num_classes] with the classification logits | |
| "pred_boxes": Tensor of dim [batch_size, num_queries, 4] with the predicted box 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_boxes] (where num_target_boxes is the number of ground-truth | |
| objects in the target) containing the class labels | |
| "boxes": Tensor of dim [num_target_boxes, 4] containing the target box 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_boxes) | |
| """ | |
| with torch.no_grad(): | |
| 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).sigmoid() | |
| out_bbox = outputs["pred_boxes"].flatten(0, 1) # [batch_size * num_queries, 4] | |
| # Also concat the target labels and boxes | |
| tgt_ids = torch.cat([v["labels"] for v in targets]) | |
| tgt_bbox = torch.cat([v["boxes"] for v in targets]) | |
| # Compute the classification cost. | |
| alpha = 0.25 | |
| gamma = 2.0 | |
| neg_cost_class = (1 - alpha) * (out_prob ** gamma) * (-(1 - out_prob + 1e-8).log()) | |
| pos_cost_class = alpha * ((1 - out_prob) ** gamma) * (-(out_prob + 1e-8).log()) | |
| cost_class = pos_cost_class[:, tgt_ids] - neg_cost_class[:, tgt_ids] | |
| # Compute the L1 cost between boxes | |
| cost_bbox = cdist(out_bbox, tgt_bbox) | |
| # Compute the giou cost betwen boxes | |
| cost_giou = -generalized_box_iou(box_cxcywh_to_xyxy(out_bbox), | |
| box_cxcywh_to_xyxy(tgt_bbox)) | |
| # Final cost matrix | |
| C = self.cost_bbox * cost_bbox + self.cost_class * cost_class + self.cost_giou * cost_giou | |
| C = C.view(bs, num_queries, -1).cpu() | |
| sizes = [len(v["boxes"]) 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_class=args.set_cost_class, | |
| cost_bbox=args.set_cost_bbox, | |
| cost_giou=args.set_cost_giou) | |