107 lines
5.1 KiB
Python
107 lines
5.1 KiB
Python
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# ------------------------------------------------------------------------
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# Deformable DETR
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# Copyright (c) 2020 SenseTime. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
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# ------------------------------------------------------------------------
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# Modified from DETR (https://github.com/facebookresearch/detr)
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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
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# ------------------------------------------------------------------------
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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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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):
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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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# We flatten to compute the cost matrices in a batch
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out_prob = outputs["pred_logits"].flatten(0, 1).sigmoid() # [batch_size * num_queries, num_classes]
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out_bbox = outputs["pred_boxes"].flatten(0, 1) # [batch_size * num_queries, 4]
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# Also concat the target labels and boxes
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tgt_ids = torch.cat([v["labels"] for v in targets])
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# print("tgt_ids_shape", tgt_ids.shape)
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tgt_bbox = torch.cat([v["boxes"] for v in targets])
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# Compute the classification cost.
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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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#print("pos_cost_class_shape", pos_cost_class.shape)
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cost_class = pos_cost_class[:, tgt_ids] - neg_cost_class[:, tgt_ids]
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#print("cost_class_shape", cost_class.shape)
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# Compute the L1 cost between boxes
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cost_bbox = torch.cdist(out_bbox, tgt_bbox, p=1)
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# Compute the giou cost betwen boxes
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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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# Final cost matrix
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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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sizes = [len(v["boxes"]) for v in targets]
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#print("size", sizes)
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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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