mmdetection/projects/EfficientDet/efficientdet/tensorflow/trans_max_iou_assigner.py

# Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional

import torch
from mmengine.structures import InstanceData

from mmdet.models.task_modules.assigners.assign_result import AssignResult
from mmdet.models.task_modules.assigners.max_iou_assigner import MaxIoUAssigner
from mmdet.registry import TASK_UTILS


@TASK_UTILS.register_module()
class TransMaxIoUAssigner(MaxIoUAssigner):

    def assign(self,
               pred_instances: InstanceData,
               gt_instances: InstanceData,
               gt_instances_ignore: Optional[InstanceData] = None,
               **kwargs) -> AssignResult:
        """Assign gt to bboxes.

        This method assign a gt bbox to every bbox (proposal/anchor), each bbox
        will be assigned with -1, or a semi-positive number. -1 means negative
        sample, semi-positive number is the index (0-based) of assigned gt.
        The assignment is done in following steps, the order matters.

        1. assign every bbox to the background
        2. assign proposals whose iou with all gts < neg_iou_thr to 0
        3. for each bbox, if the iou with its nearest gt >= pos_iou_thr,
           assign it to that bbox
        4. for each gt bbox, assign its nearest proposals (may be more than
           one) to itself

        Args:
            pred_instances (:obj:`InstanceData`): Instances of model
                predictions. It includes ``priors``, and the priors can
                be anchors or points, or the bboxes predicted by the
                previous stage, has shape (n, 4). The bboxes predicted by
                the current model or stage will be named ``bboxes``,
                ``labels``, and ``scores``, the same as the ``InstanceData``
                in other places.
            gt_instances (:obj:`InstanceData`): Ground truth of instance
                annotations. It usually includes ``bboxes``, with shape (k, 4),
                and ``labels``, with shape (k, ).
            gt_instances_ignore (:obj:`InstanceData`, optional): Instances
                to be ignored during training. It includes ``bboxes``
                attribute data that is ignored during training and testing.
                Defaults to None.

        Returns:
            :obj:`AssignResult`: The assign result.

        Example:
            >>> from mmengine.structures import InstanceData
            >>> self = MaxIoUAssigner(0.5, 0.5)
            >>> pred_instances = InstanceData()
            >>> pred_instances.priors = torch.Tensor([[0, 0, 10, 10],
            ...                                      [10, 10, 20, 20]])
            >>> gt_instances = InstanceData()
            >>> gt_instances.bboxes = torch.Tensor([[0, 0, 10, 9]])
            >>> gt_instances.labels = torch.Tensor([0])
            >>> assign_result = self.assign(pred_instances, gt_instances)
            >>> expected_gt_inds = torch.LongTensor([1, 0])
            >>> assert torch.all(assign_result.gt_inds == expected_gt_inds)
        """
        gt_bboxes = gt_instances.bboxes
        priors = pred_instances.priors
        gt_labels = gt_instances.labels
        if gt_instances_ignore is not None:
            gt_bboxes_ignore = gt_instances_ignore.bboxes
        else:
            gt_bboxes_ignore = None

        assign_on_cpu = True if (self.gpu_assign_thr > 0) and (
            gt_bboxes.shape[0] > self.gpu_assign_thr) else False
        # compute overlap and assign gt on CPU when number of GT is large
        if assign_on_cpu:
            device = priors.device
            priors = priors.cpu()
            gt_bboxes = gt_bboxes.cpu()
            gt_labels = gt_labels.cpu()
            if gt_bboxes_ignore is not None:
                gt_bboxes_ignore = gt_bboxes_ignore.cpu()

        trans_priors = torch.cat([
            priors[..., 1].view(-1, 1), priors[..., 0].view(-1, 1),
            priors[..., 3].view(-1, 1), priors[..., 2].view(-1, 1)
        ],
                                 dim=-1)
        overlaps = self.iou_calculator(gt_bboxes, trans_priors)

        if (self.ignore_iof_thr > 0 and gt_bboxes_ignore is not None
                and gt_bboxes_ignore.numel() > 0 and trans_priors.numel() > 0):
            if self.ignore_wrt_candidates:
                ignore_overlaps = self.iou_calculator(
                    trans_priors, gt_bboxes_ignore, mode='iof')
                ignore_max_overlaps, _ = ignore_overlaps.max(dim=1)
            else:
                ignore_overlaps = self.iou_calculator(
                    gt_bboxes_ignore, trans_priors, mode='iof')
                ignore_max_overlaps, _ = ignore_overlaps.max(dim=0)
            overlaps[:, ignore_max_overlaps > self.ignore_iof_thr] = -1

        assign_result = self.assign_wrt_overlaps(overlaps, gt_labels)
        if assign_on_cpu:
            assign_result.gt_inds = assign_result.gt_inds.to(device)
            assign_result.max_overlaps = assign_result.max_overlaps.to(device)
            if assign_result.labels is not None:
                assign_result.labels = assign_result.labels.to(device)
        return assign_result