Shortcuts

mmdet.core.bbox.assigners.region_assigner 源代码

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

from mmdet.core import anchor_inside_flags
from ..builder import BBOX_ASSIGNERS
from .assign_result import AssignResult
from .base_assigner import BaseAssigner


def calc_region(bbox, ratio, stride, featmap_size=None):
    """Calculate region of the box defined by the ratio, the ratio is from the
    center of the box to every edge."""
    # project bbox on the feature
    f_bbox = bbox / stride
    x1 = torch.round((1 - ratio) * f_bbox[0] + ratio * f_bbox[2])
    y1 = torch.round((1 - ratio) * f_bbox[1] + ratio * f_bbox[3])
    x2 = torch.round(ratio * f_bbox[0] + (1 - ratio) * f_bbox[2])
    y2 = torch.round(ratio * f_bbox[1] + (1 - ratio) * f_bbox[3])
    if featmap_size is not None:
        x1 = x1.clamp(min=0, max=featmap_size[1])
        y1 = y1.clamp(min=0, max=featmap_size[0])
        x2 = x2.clamp(min=0, max=featmap_size[1])
        y2 = y2.clamp(min=0, max=featmap_size[0])
    return (x1, y1, x2, y2)


def anchor_ctr_inside_region_flags(anchors, stride, region):
    """Get the flag indicate whether anchor centers are inside regions."""
    x1, y1, x2, y2 = region
    f_anchors = anchors / stride
    x = (f_anchors[:, 0] + f_anchors[:, 2]) * 0.5
    y = (f_anchors[:, 1] + f_anchors[:, 3]) * 0.5
    flags = (x >= x1) & (x <= x2) & (y >= y1) & (y <= y2)
    return flags


[文档]@BBOX_ASSIGNERS.register_module() class RegionAssigner(BaseAssigner): """Assign a corresponding gt bbox or background to each bbox. Each proposals will be assigned with `-1`, `0`, or a positive integer indicating the ground truth index. - -1: don't care - 0: negative sample, no assigned gt - positive integer: positive sample, index (1-based) of assigned gt Args: center_ratio: ratio of the region in the center of the bbox to define positive sample. ignore_ratio: ratio of the region to define ignore samples. """ def __init__(self, center_ratio=0.2, ignore_ratio=0.5): self.center_ratio = center_ratio self.ignore_ratio = ignore_ratio
[文档] def assign(self, mlvl_anchors, mlvl_valid_flags, gt_bboxes, img_meta, featmap_sizes, anchor_scale, anchor_strides, gt_bboxes_ignore=None, gt_labels=None, allowed_border=0): """Assign gt to anchors. This method assign a gt bbox to every bbox (proposal/anchor), each bbox will be assigned with -1, 0, or a positive number. -1 means don't care, 0 means negative sample, positive number is the index (1-based) of assigned gt. The assignment is done in following steps, and the order matters. 1. Assign every anchor to 0 (negative) 2. (For each gt_bboxes) Compute ignore flags based on ignore_region then assign -1 to anchors w.r.t. ignore flags 3. (For each gt_bboxes) Compute pos flags based on center_region then assign gt_bboxes to anchors w.r.t. pos flags 4. (For each gt_bboxes) Compute ignore flags based on adjacent anchor level then assign -1 to anchors w.r.t. ignore flags 5. Assign anchor outside of image to -1 Args: mlvl_anchors (list[Tensor]): Multi level anchors. mlvl_valid_flags (list[Tensor]): Multi level valid flags. gt_bboxes (Tensor): Ground truth bboxes of image img_meta (dict): Meta info of image. featmap_sizes (list[Tensor]): Feature mapsize each level anchor_scale (int): Scale of the anchor. anchor_strides (list[int]): Stride of the anchor. 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 (k, ). allowed_border (int, optional): The border to allow the valid anchor. Defaults to 0. Returns: :obj:`AssignResult`: The assign result. """ if gt_bboxes_ignore is not None: raise NotImplementedError num_gts = gt_bboxes.shape[0] num_bboxes = sum(x.shape[0] for x in mlvl_anchors) if num_gts == 0 or num_bboxes == 0: # No ground truth or boxes, return empty assignment max_overlaps = gt_bboxes.new_zeros((num_bboxes, )) assigned_gt_inds = gt_bboxes.new_zeros((num_bboxes, ), dtype=torch.long) if gt_labels is None: assigned_labels = None else: assigned_labels = gt_bboxes.new_full((num_bboxes, ), -1, dtype=torch.long) return AssignResult( num_gts, assigned_gt_inds, max_overlaps, labels=assigned_labels) num_lvls = len(mlvl_anchors) r1 = (1 - self.center_ratio) / 2 r2 = (1 - self.ignore_ratio) / 2 scale = torch.sqrt((gt_bboxes[:, 2] - gt_bboxes[:, 0]) * (gt_bboxes[:, 3] - gt_bboxes[:, 1])) min_anchor_size = scale.new_full( (1, ), float(anchor_scale * anchor_strides[0])) target_lvls = torch.floor( torch.log2(scale) - torch.log2(min_anchor_size) + 0.5) target_lvls = target_lvls.clamp(min=0, max=num_lvls - 1).long() # 1. assign 0 (negative) by default mlvl_assigned_gt_inds = [] mlvl_ignore_flags = [] for lvl in range(num_lvls): h, w = featmap_sizes[lvl] assert h * w == mlvl_anchors[lvl].shape[0] assigned_gt_inds = gt_bboxes.new_full((h * w, ), 0, dtype=torch.long) ignore_flags = torch.zeros_like(assigned_gt_inds) mlvl_assigned_gt_inds.append(assigned_gt_inds) mlvl_ignore_flags.append(ignore_flags) for gt_id in range(num_gts): lvl = target_lvls[gt_id].item() featmap_size = featmap_sizes[lvl] stride = anchor_strides[lvl] anchors = mlvl_anchors[lvl] gt_bbox = gt_bboxes[gt_id, :4] # Compute regions ignore_region = calc_region(gt_bbox, r2, stride, featmap_size) ctr_region = calc_region(gt_bbox, r1, stride, featmap_size) # 2. Assign -1 to ignore flags ignore_flags = anchor_ctr_inside_region_flags( anchors, stride, ignore_region) mlvl_assigned_gt_inds[lvl][ignore_flags] = -1 # 3. Assign gt_bboxes to pos flags pos_flags = anchor_ctr_inside_region_flags(anchors, stride, ctr_region) mlvl_assigned_gt_inds[lvl][pos_flags] = gt_id + 1 # 4. Assign -1 to ignore adjacent lvl if lvl > 0: d_lvl = lvl - 1 d_anchors = mlvl_anchors[d_lvl] d_featmap_size = featmap_sizes[d_lvl] d_stride = anchor_strides[d_lvl] d_ignore_region = calc_region(gt_bbox, r2, d_stride, d_featmap_size) ignore_flags = anchor_ctr_inside_region_flags( d_anchors, d_stride, d_ignore_region) mlvl_ignore_flags[d_lvl][ignore_flags] = 1 if lvl < num_lvls - 1: u_lvl = lvl + 1 u_anchors = mlvl_anchors[u_lvl] u_featmap_size = featmap_sizes[u_lvl] u_stride = anchor_strides[u_lvl] u_ignore_region = calc_region(gt_bbox, r2, u_stride, u_featmap_size) ignore_flags = anchor_ctr_inside_region_flags( u_anchors, u_stride, u_ignore_region) mlvl_ignore_flags[u_lvl][ignore_flags] = 1 # 4. (cont.) Assign -1 to ignore adjacent lvl for lvl in range(num_lvls): ignore_flags = mlvl_ignore_flags[lvl] mlvl_assigned_gt_inds[lvl][ignore_flags] = -1 # 5. Assign -1 to anchor outside of image flat_assigned_gt_inds = torch.cat(mlvl_assigned_gt_inds) flat_anchors = torch.cat(mlvl_anchors) flat_valid_flags = torch.cat(mlvl_valid_flags) assert (flat_assigned_gt_inds.shape[0] == flat_anchors.shape[0] == flat_valid_flags.shape[0]) inside_flags = anchor_inside_flags(flat_anchors, flat_valid_flags, img_meta['img_shape'], allowed_border) outside_flags = ~inside_flags flat_assigned_gt_inds[outside_flags] = -1 if gt_labels is not None: assigned_labels = torch.zeros_like(flat_assigned_gt_inds) pos_flags = assigned_gt_inds > 0 assigned_labels[pos_flags] = gt_labels[ flat_assigned_gt_inds[pos_flags] - 1] else: assigned_labels = None return AssignResult( num_gts, flat_assigned_gt_inds, None, labels=assigned_labels)
Read the Docs v: latest
Versions
latest
stable
v2.19.1
v2.19.0
v2.18.1
v2.18.0
v2.17.0
v2.16.0
v2.15.1
v2.15.0
v2.14.0
Downloads
pdf
html
epub
On Read the Docs
Project Home
Builds

Free document hosting provided by Read the Docs.