Spaces:
Sleeping
Sleeping
| # ------------------------------------------------------------------------ | |
| # DINO | |
| # Copyright (c) 2022 IDEA. All Rights Reserved. | |
| # Licensed under the Apache License, Version 2.0 [see LICENSE for details] | |
| # ------------------------------------------------------------------------ | |
| # Modules to compute the matching cost and solve the corresponding LSAP. | |
| # Copyright (c) 2021 Microsoft. 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. | |
| # ------------------------------------------------------------------------ | |
| # Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR) | |
| # Copyright (c) 2020 SenseTime. All Rights Reserved. | |
| # ------------------------------------------------------------------------ | |
| import torch, os | |
| from torch import nn | |
| from scipy.optimize import linear_sum_assignment | |
| from util.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). | |
| """ | |
| def __init__(self, cost_class: float = 1, cost_bbox: float = 1, cost_giou: float = 1, focal_alpha = 0.25): | |
| """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" | |
| self.focal_alpha = focal_alpha | |
| def forward(self, outputs, targets, label_map): | |
| """ 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 | |
| out_prob = outputs["pred_logits"].flatten(0, 1).sigmoid() # [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. | |
| alpha = self.focal_alpha | |
| gamma = 2.0 | |
| new_label_map=label_map[tgt_ids.cpu()] | |
| 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()) | |
| new_label_map=new_label_map.to(pos_cost_class.device) | |
| cost_bbox = torch.cdist(out_bbox[:, :2], tgt_bbox[:, :2], p=1) | |
| # cost_class=(pos_cost_class @ new_label_map.T - neg_cost_class@ new_label_map.T) | |
| cost_class=[] | |
| for idx_map in new_label_map: | |
| idx_map = idx_map / idx_map.sum() | |
| cost_class.append(pos_cost_class @ idx_map - neg_cost_class@ idx_map) | |
| if cost_class: | |
| cost_class=torch.stack(cost_class,dim=0).T | |
| else: | |
| cost_class=torch.zeros_like(cost_bbox) | |
| # Compute the L1 cost between boxes | |
| # Compute the giou cost betwen boxes | |
| cost_giou = -generalized_box_iou(box_cxcywh_to_xyxy(out_bbox), box_cxcywh_to_xyxy(tgt_bbox)) | |
| # import pdb;pdb.set_trace() | |
| # 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() | |
| C[torch.isnan(C)] = 0.0 | |
| C[torch.isinf(C)] = 0.0 | |
| sizes = [len(v["boxes"]) for v in targets] | |
| try: | |
| indices = [linear_sum_assignment(c[i]) for i, c in enumerate(C.split(sizes, -1))] | |
| except: | |
| print("warning: use SimpleMinsumMatcher") | |
| indices = [] | |
| device = C.device | |
| for i, (c, _size) in enumerate(zip(C.split(sizes, -1), sizes)): | |
| weight_mat = c[i] | |
| idx_i = weight_mat.min(0)[1] | |
| idx_j = torch.arange(_size).to(device) | |
| indices.append((idx_i, idx_j)) | |
| return [(torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) for i, j in indices] | |
| class SimpleMinsumMatcher(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, focal_alpha = 0.25): | |
| """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" | |
| self.focal_alpha = focal_alpha | |
| 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) | |
| """ | |
| 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() # [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. | |
| alpha = self.focal_alpha | |
| 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 = 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 | |
| C = self.cost_bbox * cost_bbox + self.cost_class * cost_class + self.cost_giou * cost_giou | |
| C = C.view(bs, num_queries, -1) | |
| sizes = [len(v["boxes"]) for v in targets] | |
| indices = [] | |
| device = C.device | |
| for i, (c, _size) in enumerate(zip(C.split(sizes, -1), sizes)): | |
| weight_mat = c[i] | |
| idx_i = weight_mat.min(0)[1] | |
| idx_j = torch.arange(_size).to(device) | |
| indices.append((idx_i, idx_j)) | |
| return [(torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) for i, j in indices] | |
| def build_matcher(args): | |
| assert args.matcher_type in ['HungarianMatcher', 'SimpleMinsumMatcher'], "Unknown args.matcher_type: {}".format(args.matcher_type) | |
| if args.matcher_type == 'HungarianMatcher': | |
| return HungarianMatcher( | |
| cost_class=args.set_cost_class, cost_bbox=args.set_cost_bbox, cost_giou=args.set_cost_giou, | |
| focal_alpha=args.focal_alpha | |
| ) | |
| elif args.matcher_type == 'SimpleMinsumMatcher': | |
| return SimpleMinsumMatcher( | |
| cost_class=args.set_cost_class, cost_bbox=args.set_cost_bbox, cost_giou=args.set_cost_giou, | |
| focal_alpha=args.focal_alpha | |
| ) | |
| else: | |
| raise NotImplementedError("Unknown args.matcher_type: {}".format(args.matcher_type)) | |