mmdet.core.bbox.assigners.center_region_assigner 源代码

# Copyright (c) OpenMMLab. All rights reserved.
import torch

from ..builder import BBOX_ASSIGNERS
from ..iou_calculators import build_iou_calculator
from .assign_result import AssignResult
from .base_assigner import BaseAssigner

def scale_boxes(bboxes, scale):
    """Expand an array of boxes by a given scale.

        bboxes (Tensor): Shape (m, 4)
        scale (float): The scale factor of bboxes

        (Tensor): Shape (m, 4). Scaled bboxes
    assert bboxes.size(1) == 4
    w_half = (bboxes[:, 2] - bboxes[:, 0]) * .5
    h_half = (bboxes[:, 3] - bboxes[:, 1]) * .5
    x_c = (bboxes[:, 2] + bboxes[:, 0]) * .5
    y_c = (bboxes[:, 3] + bboxes[:, 1]) * .5

    w_half *= scale
    h_half *= scale

    boxes_scaled = torch.zeros_like(bboxes)
    boxes_scaled[:, 0] = x_c - w_half
    boxes_scaled[:, 2] = x_c + w_half
    boxes_scaled[:, 1] = y_c - h_half
    boxes_scaled[:, 3] = y_c + h_half
    return boxes_scaled

def is_located_in(points, bboxes):
    """Are points located in bboxes.

      points (Tensor): Points, shape: (m, 2).
      bboxes (Tensor): Bounding boxes, shape: (n, 4).

      Tensor: Flags indicating if points are located in bboxes, shape: (m, n).
    assert points.size(1) == 2
    assert bboxes.size(1) == 4
    return (points[:, 0].unsqueeze(1) > bboxes[:, 0].unsqueeze(0)) & \
           (points[:, 0].unsqueeze(1) < bboxes[:, 2].unsqueeze(0)) & \
           (points[:, 1].unsqueeze(1) > bboxes[:, 1].unsqueeze(0)) & \
           (points[:, 1].unsqueeze(1) < bboxes[:, 3].unsqueeze(0))

def bboxes_area(bboxes):
    """Compute the area of an array of bboxes.

        bboxes (Tensor): The coordinates ox bboxes. Shape: (m, 4)

        Tensor: Area of the bboxes. Shape: (m, )
    assert bboxes.size(1) == 4
    w = (bboxes[:, 2] - bboxes[:, 0])
    h = (bboxes[:, 3] - bboxes[:, 1])
    areas = w * h
    return areas

[文档]@BBOX_ASSIGNERS.register_module() class CenterRegionAssigner(BaseAssigner): """Assign pixels at the center region of a bbox as positive. Each proposals will be assigned with `-1`, `0`, or a positive integer indicating the ground truth index. - -1: negative samples - semi-positive numbers: positive sample, index (0-based) of assigned gt Args: pos_scale (float): Threshold within which pixels are labelled as positive. neg_scale (float): Threshold above which pixels are labelled as positive. min_pos_iof (float): Minimum iof of a pixel with a gt to be labelled as positive. Default: 1e-2 ignore_gt_scale (float): Threshold within which the pixels are ignored when the gt is labelled as shadowed. Default: 0.5 foreground_dominate (bool): If True, the bbox will be assigned as positive when a gt's kernel region overlaps with another's shadowed (ignored) region, otherwise it is set as ignored. Default to False. """ def __init__(self, pos_scale, neg_scale, min_pos_iof=1e-2, ignore_gt_scale=0.5, foreground_dominate=False, iou_calculator=dict(type='BboxOverlaps2D')): self.pos_scale = pos_scale self.neg_scale = neg_scale self.min_pos_iof = min_pos_iof self.ignore_gt_scale = ignore_gt_scale self.foreground_dominate = foreground_dominate self.iou_calculator = build_iou_calculator(iou_calculator)
[文档] def get_gt_priorities(self, gt_bboxes): """Get gt priorities according to their areas. Smaller gt has higher priority. Args: gt_bboxes (Tensor): Ground truth boxes, shape (k, 4). Returns: Tensor: The priority of gts so that gts with larger priority is \ more likely to be assigned. Shape (k, ) """ gt_areas = bboxes_area(gt_bboxes) # Rank all gt bbox areas. Smaller objects has larger priority _, sort_idx = gt_areas.sort(descending=True) sort_idx = sort_idx.argsort() return sort_idx
[文档] def assign(self, bboxes, gt_bboxes, gt_bboxes_ignore=None, gt_labels=None): """Assign gt to bboxes. This method assigns gts 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. Args: bboxes (Tensor): Bounding boxes to be assigned, shape(n, 4). gt_bboxes (Tensor): Groundtruth boxes, shape (k, 4). gt_bboxes_ignore (tensor, optional): Ground truth bboxes that are labelled as `ignored`, e.g., crowd boxes in COCO. gt_labels (tensor, optional): Label of gt_bboxes, shape (num_gts,). Returns: :obj:`AssignResult`: The assigned result. Note that \ shadowed_labels of shape (N, 2) is also added as an \ `assign_result` attribute. `shadowed_labels` is a tensor \ composed of N pairs of anchor_ind, class_label], where N \ is the number of anchors that lie in the outer region of a \ gt, anchor_ind is the shadowed anchor index and class_label \ is the shadowed class label. Example: >>> self = CenterRegionAssigner(0.2, 0.2) >>> bboxes = torch.Tensor([[0, 0, 10, 10], [10, 10, 20, 20]]) >>> gt_bboxes = torch.Tensor([[0, 0, 10, 10]]) >>> assign_result = self.assign(bboxes, gt_bboxes) >>> expected_gt_inds = torch.LongTensor([1, 0]) >>> assert torch.all(assign_result.gt_inds == expected_gt_inds) """ # There are in total 5 steps in the pixel assignment # 1. Find core (the center region, say inner 0.2) # and shadow (the relatively ourter part, say inner 0.2-0.5) # regions of every gt. # 2. Find all prior bboxes that lie in gt_core and gt_shadow regions # 3. Assign prior bboxes in gt_core with a one-hot id of the gt in # the image. # 3.1. For overlapping objects, the prior bboxes in gt_core is # assigned with the object with smallest area # 4. Assign prior bboxes with class label according to its gt id. # 4.1. Assign -1 to prior bboxes lying in shadowed gts # 4.2. Assign positive prior boxes with the corresponding label # 5. Find pixels lying in the shadow of an object and assign them with # background label, but set the loss weight of its corresponding # gt to zero. assert bboxes.size(1) == 4, 'bboxes must have size of 4' # 1. Find core positive and shadow region of every gt gt_core = scale_boxes(gt_bboxes, self.pos_scale) gt_shadow = scale_boxes(gt_bboxes, self.neg_scale) # 2. Find prior bboxes that lie in gt_core and gt_shadow regions bbox_centers = (bboxes[:, 2:4] + bboxes[:, 0:2]) / 2 # The center points lie within the gt boxes is_bbox_in_gt = is_located_in(bbox_centers, gt_bboxes) # Only calculate bbox and gt_core IoF. This enables small prior bboxes # to match large gts bbox_and_gt_core_overlaps = self.iou_calculator( bboxes, gt_core, mode='iof') # The center point of effective priors should be within the gt box is_bbox_in_gt_core = is_bbox_in_gt & ( bbox_and_gt_core_overlaps > self.min_pos_iof) # shape (n, k) is_bbox_in_gt_shadow = ( self.iou_calculator(bboxes, gt_shadow, mode='iof') > self.min_pos_iof) # Rule out center effective positive pixels is_bbox_in_gt_shadow &= (~is_bbox_in_gt_core) num_gts, num_bboxes = gt_bboxes.size(0), bboxes.size(0) if num_gts == 0 or num_bboxes == 0: # If no gts exist, assign all pixels to negative assigned_gt_ids = \ is_bbox_in_gt_core.new_zeros((num_bboxes,), dtype=torch.long) pixels_in_gt_shadow = assigned_gt_ids.new_empty((0, 2)) else: # Step 3: assign a one-hot gt id to each pixel, and smaller objects # have high priority to assign the pixel. sort_idx = self.get_gt_priorities(gt_bboxes) assigned_gt_ids, pixels_in_gt_shadow = \ self.assign_one_hot_gt_indices(is_bbox_in_gt_core, is_bbox_in_gt_shadow, gt_priority=sort_idx) if gt_bboxes_ignore is not None and gt_bboxes_ignore.numel() > 0: # No ground truth or boxes, return empty assignment gt_bboxes_ignore = scale_boxes( gt_bboxes_ignore, scale=self.ignore_gt_scale) is_bbox_in_ignored_gts = is_located_in(bbox_centers, gt_bboxes_ignore) is_bbox_in_ignored_gts = is_bbox_in_ignored_gts.any(dim=1) assigned_gt_ids[is_bbox_in_ignored_gts] = -1 # 4. Assign prior bboxes with class label according to its gt id. assigned_labels = None shadowed_pixel_labels = None if gt_labels is not None: # Default assigned label is the background (-1) assigned_labels = assigned_gt_ids.new_full((num_bboxes, ), -1) pos_inds = torch.nonzero( assigned_gt_ids > 0, as_tuple=False).squeeze() if pos_inds.numel() > 0: assigned_labels[pos_inds] = gt_labels[assigned_gt_ids[pos_inds] - 1] # 5. Find pixels lying in the shadow of an object shadowed_pixel_labels = pixels_in_gt_shadow.clone() if pixels_in_gt_shadow.numel() > 0: pixel_idx, gt_idx =\ pixels_in_gt_shadow[:, 0], pixels_in_gt_shadow[:, 1] assert (assigned_gt_ids[pixel_idx] != gt_idx).all(), \ 'Some pixels are dually assigned to ignore and gt!' shadowed_pixel_labels[:, 1] = gt_labels[gt_idx - 1] override = ( assigned_labels[pixel_idx] == shadowed_pixel_labels[:, 1]) if self.foreground_dominate: # When a pixel is both positive and shadowed, set it as pos shadowed_pixel_labels = shadowed_pixel_labels[~override] else: # When a pixel is both pos and shadowed, set it as shadowed assigned_labels[pixel_idx[override]] = -1 assigned_gt_ids[pixel_idx[override]] = 0 assign_result = AssignResult( num_gts, assigned_gt_ids, None, labels=assigned_labels) # Add shadowed_labels as assign_result property. Shape: (num_shadow, 2) assign_result.set_extra_property('shadowed_labels', shadowed_pixel_labels) return assign_result
[文档] def assign_one_hot_gt_indices(self, is_bbox_in_gt_core, is_bbox_in_gt_shadow, gt_priority=None): """Assign only one gt index to each prior box. Gts with large gt_priority are more likely to be assigned. Args: is_bbox_in_gt_core (Tensor): Bool tensor indicating the bbox center is in the core area of a gt (e.g. 0-0.2). Shape: (num_prior, num_gt). is_bbox_in_gt_shadow (Tensor): Bool tensor indicating the bbox center is in the shadowed area of a gt (e.g. 0.2-0.5). Shape: (num_prior, num_gt). gt_priority (Tensor): Priorities of gts. The gt with a higher priority is more likely to be assigned to the bbox when the bbox match with multiple gts. Shape: (num_gt, ). Returns: tuple: Returns (assigned_gt_inds, shadowed_gt_inds). - assigned_gt_inds: The assigned gt index of each prior bbox \ (i.e. index from 1 to num_gts). Shape: (num_prior, ). - shadowed_gt_inds: shadowed gt indices. It is a tensor of \ shape (num_ignore, 2) with first column being the \ shadowed prior bbox indices and the second column the \ shadowed gt indices (1-based). """ num_bboxes, num_gts = is_bbox_in_gt_core.shape if gt_priority is None: gt_priority = torch.arange( num_gts, device=is_bbox_in_gt_core.device) assert gt_priority.size(0) == num_gts # The bigger gt_priority, the more preferable to be assigned # The assigned inds are by default 0 (background) assigned_gt_inds = is_bbox_in_gt_core.new_zeros((num_bboxes, ), dtype=torch.long) # Shadowed bboxes are assigned to be background. But the corresponding # label is ignored during loss calculation, which is done through # shadowed_gt_inds shadowed_gt_inds = torch.nonzero(is_bbox_in_gt_shadow, as_tuple=False) if is_bbox_in_gt_core.sum() == 0: # No gt match shadowed_gt_inds[:, 1] += 1 # 1-based. For consistency issue return assigned_gt_inds, shadowed_gt_inds # The priority of each prior box and gt pair. If one prior box is # matched bo multiple gts. Only the pair with the highest priority # is saved pair_priority = is_bbox_in_gt_core.new_full((num_bboxes, num_gts), -1, dtype=torch.long) # Each bbox could match with multiple gts. # The following codes deal with this situation # Matched bboxes (to any gt). Shape: (num_pos_anchor, ) inds_of_match = torch.any(is_bbox_in_gt_core, dim=1) # The matched gt index of each positive bbox. Length >= num_pos_anchor # , since one bbox could match multiple gts matched_bbox_gt_inds = torch.nonzero( is_bbox_in_gt_core, as_tuple=False)[:, 1] # Assign priority to each bbox-gt pair. pair_priority[is_bbox_in_gt_core] = gt_priority[matched_bbox_gt_inds] _, argmax_priority = pair_priority[inds_of_match].max(dim=1) assigned_gt_inds[inds_of_match] = argmax_priority + 1 # 1-based # Zero-out the assigned anchor box to filter the shadowed gt indices is_bbox_in_gt_core[inds_of_match, argmax_priority] = 0 # Concat the shadowed indices due to overlapping with that out side of # effective scale. shape: (total_num_ignore, 2) shadowed_gt_inds = (shadowed_gt_inds, torch.nonzero( is_bbox_in_gt_core, as_tuple=False)), dim=0) # `is_bbox_in_gt_core` should be changed back to keep arguments intact. is_bbox_in_gt_core[inds_of_match, argmax_priority] = 1 # 1-based shadowed gt indices, to be consistent with `assigned_gt_inds` if shadowed_gt_inds.numel() > 0: shadowed_gt_inds[:, 1] += 1 return assigned_gt_inds, shadowed_gt_inds
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