跟2d的FCOS差不太多， 主要是依靠图片坐标系来分配target：
代码语言：javascript复制def _get_target_single(self, gt_bboxes, gt_labels, gt_bboxes_3d,
gt_labels_3d, centers2d, depths, attr_labels,
points, regress_ranges, num_points_per_lvl):
"""Compute regression and classification targets for a single image."""
num_points = points.size(0)
num_gts = gt_labels.size(0)
if not isinstance(gt_bboxes_3d, torch.Tensor):
gt_bboxes_3d = gt_bboxes_3d.tensor.to(gt_bboxes.device)
if num_gts == 0:
return gt_labels.new_full((num_points,), self.background_label),
gt_bboxes.new_zeros((num_points, 4)),
gt_labels_3d.new_full(
(num_points,), self.background_label),
gt_bboxes_3d.new_zeros((num_points, self.bbox_code_size)),
gt_bboxes_3d.new_zeros((num_points,)),
attr_labels.new_full(
(num_points,), self.attr_background_label)
# change orientation to local yaw
gt_bboxes_3d[..., 6] = -torch.atan2(
gt_bboxes_3d[..., 0], gt_bboxes_3d[..., 2]) gt_bboxes_3d[..., 6]
areas = (gt_bboxes[:, 2] - gt_bboxes[:, 0]) * (
gt_bboxes[:, 3] - gt_bboxes[:, 1]) # [tl_x, tl_y, br_x, br_y]--> S_areas
areas = areas[None].repeat(num_points, 1) # [2] --> [30929, 2]
regress_ranges = regress_ranges[:, None, :].expand(
num_points, num_gts, 2) # [30929, 2] --> [30929, 2, 2]
gt_bboxes = gt_bboxes[None].expand(num_points, num_gts, 4)
centers2d = centers2d[None].expand(num_points, num_gts, 2)
gt_bboxes_3d = gt_bboxes_3d[None].expand(num_points, num_gts,
self.bbox_code_size)
depths = depths[None, :, None].expand(num_points, num_gts, 1)
# 每个points的坐标(xs,ys)
xs, ys = points[:, 0], points[:, 1]
xs = xs[:, None].expand(num_points, num_gts)
ys = ys[:, None].expand(num_points, num_gts)
# gt center --> offsets
## centers2d: 每个gt在2d image上的坐标
delta_xs = (xs - centers2d[..., 0])[..., None]
delta_ys = (ys - centers2d[..., 1])[..., None]
# 0. 前面的操作是主要是为了这里，获得跟网络输出相同的target_box
bbox_targets_3d = torch.cat(
(delta_xs, delta_ys, depths, gt_bboxes_3d[..., 3:]), dim=-1)
left = xs - gt_bboxes[..., 0]
right = gt_bboxes[..., 2] - xs
top = ys - gt_bboxes[..., 1]
bottom = gt_bboxes[..., 3] - ys
bbox_targets = torch.stack((left, top, right, bottom), -1)
assert self.center_sampling is True, 'Setting center_sampling to '
'False has not been implemented for FCOS3D.'
# condition1: inside a `center bbox`
radius = self.center_sample_radius # 1.5
center_xs = centers2d[..., 0]
center_ys = centers2d[..., 1]
center_gts = torch.zeros_like(gt_bboxes)
stride = center_xs.new_zeros(center_xs.shape)
# project the points on current lvl back to the `original` sizes
# 1. 将各层特征点位置映射回输入图像中
lvl_begin = 0
for lvl_idx, num_points_lvl in enumerate(num_points_per_lvl): # [23200, 5800, 1450, 375, 104]
lvl_end = lvl_begin num_points_lvl
stride[lvl_begin:lvl_end] = self.strides[lvl_idx] * radius # [8, 16, 32, 64, 128] * 1.5
# 每个point的缩放系数 * 半径
lvl_begin = lvl_end
# 2. 位于物体框内的位置点作为正样本候选
## 边长1.5的框 -->
center_gts[..., 0] = center_xs - stride
center_gts[..., 1] = center_ys - stride
center_gts[..., 2] = center_xs stride
center_gts[..., 3] = center_ys stride
cb_dist_left = xs - center_gts[..., 0] # points中心点到
cb_dist_right = center_gts[..., 2] - xs
cb_dist_top = ys - center_gts[..., 1]
cb_dist_bottom = center_gts[..., 3] - ys
center_bbox = torch.stack(
(cb_dist_left, cb_dist_top, cb_dist_right, cb_dist_bottom), -1)
inside_gt_bbox_mask = center_bbox.min(-1)[0] > 0 # anchor_box中心点落在gt_box中心点1.5单位的正方形内才有效
# condition2: limit the regression range for each location
# 3. 某位置点到物体边框的距离只有位于一定范围内才可作为正样本(每层有各自的范围)
max_regress_distance = bbox_targets.max(-1)[0]
# 确保在每层level的回归范围内
inside_regress_range = (
(max_regress_distance >= regress_ranges[..., 0])
& (max_regress_distance <= regress_ranges[..., 1]))
# center-based criterion to deal with ambiguity
# 4. 基于中心准则的模糊处理
## 4.1选出偏移量最小的gt gt_inds
dists = torch.sqrt(torch.sum(bbox_targets_3d[..., :2]**2, dim=-1)) # offsets的欧式距离 [30929, 2]
dists[inside_gt_bbox_mask == 0] = INF # 筛选anchor
dists[inside_regress_range == 0] = INF
min_dist, min_dist_inds = dists.min(dim=1)
labels = gt_labels[min_dist_inds] # 筛选gt
labels_3d = gt_labels_3d[min_dist_inds]
attr_labels = attr_labels[min_dist_inds]
labels[min_dist == INF] = self.background_label # set as BG 10
labels_3d[min_dist == INF] = self.background_label # set as BG
attr_labels[min_dist == INF] = self.attr_background_label
## 4.2 每个point上选出对应的box_target
bbox_targets = bbox_targets[range(num_points), min_dist_inds] # [30929, 2, 4] --> [30929, 4]
bbox_targets_3d = bbox_targets_3d[range(num_points), min_dist_inds]
## 4.3 筛选centerness_targets
## 偏移量--> 斜边 / 边长1.5scale到实际三角形的边长 == 相对距离
relative_dists = torch.sqrt(
torch.sum(bbox_targets_3d[..., :2]**2,
dim=-1)) / (1.414 * stride[:, 0])
# [N, 1] / [N, 1]
centerness_targets = torch.exp(-self.centerness_alpha * relative_dists) # exp(-2.5 * relative_dists) todo?
return labels, bbox_targets, labels_3d, bbox_targets_3d,
centerness_targets, attr_labels
