Spaces:
Running
on
Zero
Running
on
Zero
| """ | |
| Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved | |
| Modules to compute the matching cost and solve the corresponding LSAP. | |
| Copyright (c) 2024 The D-FINE Authors All Rights Reserved. | |
| """ | |
| from typing import Dict | |
| import numpy as np | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from scipy.optimize import linear_sum_assignment | |
| from ...core import register | |
| from .box_ops import box_cxcywh_to_xyxy, generalized_box_iou | |
| 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). | |
| """ | |
| __share__ = [ | |
| "use_focal_loss", | |
| ] | |
| def __init__(self, weight_dict, use_focal_loss=False, alpha=0.25, gamma=2.0): | |
| """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 = weight_dict["cost_class"] | |
| self.cost_bbox = weight_dict["cost_bbox"] | |
| self.cost_giou = weight_dict["cost_giou"] | |
| self.use_focal_loss = use_focal_loss | |
| self.alpha = alpha | |
| self.gamma = gamma | |
| assert ( | |
| self.cost_class != 0 or self.cost_bbox != 0 or self.cost_giou != 0 | |
| ), "all costs cant be 0" | |
| def forward(self, outputs: Dict[str, torch.Tensor], targets, return_topk=False): | |
| """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) | |
| """ | |
| bs, num_queries = outputs["pred_logits"].shape[:2] | |
| # We flatten to compute the cost matrices in a batch | |
| if self.use_focal_loss: | |
| out_prob = F.sigmoid(outputs["pred_logits"].flatten(0, 1)) | |
| else: | |
| out_prob = ( | |
| outputs["pred_logits"].flatten(0, 1).softmax(-1) | |
| ) # [batch_size * num_queries, num_classes] | |
| 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. 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. | |
| if self.use_focal_loss: | |
| out_prob = out_prob[:, tgt_ids] | |
| neg_cost_class = ( | |
| (1 - self.alpha) * (out_prob**self.gamma) * (-(1 - out_prob + 1e-8).log()) | |
| ) | |
| pos_cost_class = ( | |
| self.alpha * ((1 - out_prob) ** self.gamma) * (-(out_prob + 1e-8).log()) | |
| ) | |
| cost_class = pos_cost_class - neg_cost_class | |
| else: | |
| cost_class = -out_prob[:, tgt_ids] | |
| # Compute the L1 cost between boxes | |
| cost_bbox = torch.cdist(out_bbox, tgt_bbox, p=1) | |
| # 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 3 * self.cost_bbox + 2 * self.cost_class + self.cost_giou | |
| 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] | |
| C = torch.nan_to_num(C, nan=1.0) | |
| indices_pre = [linear_sum_assignment(c[i]) for i, c in enumerate(C.split(sizes, -1))] | |
| indices = [ | |
| (torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) | |
| for i, j in indices_pre | |
| ] | |
| # Compute topk indices | |
| if return_topk: | |
| return { | |
| "indices_o2m": self.get_top_k_matches( | |
| C, sizes=sizes, k=return_topk, initial_indices=indices_pre | |
| ) | |
| } | |
| return {"indices": indices} # , 'indices_o2m': C.min(-1)[1]} | |
| def get_top_k_matches(self, C, sizes, k=1, initial_indices=None): | |
| indices_list = [] | |
| # C_original = C.clone() | |
| for i in range(k): | |
| indices_k = ( | |
| [linear_sum_assignment(c[i]) for i, c in enumerate(C.split(sizes, -1))] | |
| if i > 0 | |
| else initial_indices | |
| ) | |
| indices_list.append( | |
| [ | |
| (torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) | |
| for i, j in indices_k | |
| ] | |
| ) | |
| for c, idx_k in zip(C.split(sizes, -1), indices_k): | |
| idx_k = np.stack(idx_k) | |
| c[:, idx_k] = 1e6 | |
| indices_list = [ | |
| ( | |
| torch.cat([indices_list[i][j][0] for i in range(k)], dim=0), | |
| torch.cat([indices_list[i][j][1] for i in range(k)], dim=0), | |
| ) | |
| for j in range(len(sizes)) | |
| ] | |
| # C.copy_(C_original) | |
| return indices_list | |