def _compare(self, boxes1, boxes2):
"""Compute matrix of (negated) sq distances.
Args:
boxlist1: BoxList holding N boxes.
boxlist2: BoxList holding M boxes.
Returns:
A tensor with shape [N, M] representing negated pairwise squared distance.
"""
boxes1_bv = box_np_ops.rbbox2d_to_near_bbox(boxes1)
boxes2_bv = box_np_ops.rbbox2d_to_near_bbox(boxes2)
ret = box_np_ops.iou_jit(boxes1_bv, boxes2_bv, eps=0.0)
return ret
def __call__(self, res, info):
class_names_by_task = [t.classes for t in self.target_assigners]
# Calculate output featuremap size
grid_size = res["lidar"]["voxels"]["shape"]
feature_map_size = grid_size[:2] // self.out_size_factor
feature_map_size = [*feature_map_size, 1][::-1]
anchors_by_task = [
t.generate_anchors(feature_map_size) for t in self.target_assigners
]
anchor_dicts_by_task = [
t.generate_anchors_dict(feature_map_size)
for t in self.target_assigners
]
reshaped_anchors_by_task = [
t["anchors"].reshape([-1, t["anchors"].shape[-1]])
for t in anchors_by_task
]
matched_by_task = [t["matched_thresholds"] for t in anchors_by_task]
unmatched_by_task = [
t["unmatched_thresholds"] for t in anchors_by_task
]
bv_anchors_by_task = [
box_np_ops.rbbox2d_to_near_bbox(anchors[:, [0, 1, 3, 4, -1]])
for anchors in reshaped_anchors_by_task
]
anchor_caches_by_task = dict(
anchors=reshaped_anchors_by_task,
anchors_bv=bv_anchors_by_task,
matched_thresholds=matched_by_task,
unmatched_thresholds=unmatched_by_task,
anchors_dict=anchor_dicts_by_task,
)
if res["mode"] == "train":
gt_dict = res["lidar"]["annotations"]
task_masks = []
flag = 0
for class_name in class_names_by_task:
task_masks.append([
np.where(gt_dict["gt_classes"] == class_name.index(i) + 1 +
flag) for i in class_name
])
flag += len(class_name)
task_boxes = []
task_classes = []
task_names = []
flag2 = 0
for idx, mask in enumerate(task_masks):
task_box = []
task_class = []
task_name = []
for m in mask:
task_box.append(gt_dict["gt_boxes"][m])
task_class.append(gt_dict["gt_classes"][m] - flag2)
task_name.append(gt_dict["gt_names"][m])
task_boxes.append(np.concatenate(task_box, axis=0))
task_classes.append(np.concatenate(task_class))
task_names.append(np.concatenate(task_name))
flag2 += len(mask)
for task_box in task_boxes:
# limit rad to [-pi, pi]
task_box[:, -1] = box_np_ops.limit_period(task_box[:, -1],
offset=0.5,
period=np.pi * 2)
# print(gt_dict.keys())
gt_dict["gt_classes"] = task_classes
gt_dict["gt_names"] = task_names
gt_dict["gt_boxes"] = task_boxes
res["lidar"]["annotations"] = gt_dict
anchorss = anchor_caches_by_task["anchors"]
anchors_bvs = anchor_caches_by_task["anchors_bv"]
anchors_dicts = anchor_caches_by_task["anchors_dict"]
example = {}
example["anchors"] = anchorss
if self.anchor_area_threshold >= 0:
example["anchors_mask"] = []
for idx, anchors_bv in enumerate(anchors_bvs):
anchors_mask = None
# slow with high resolution. recommend disable this forever.
coors = coordinates
dense_voxel_map = box_np_ops.sparse_sum_for_anchors_mask(
coors, tuple(grid_size[::-1][1:]))
dense_voxel_map = dense_voxel_map.cumsum(0)
dense_voxel_map = dense_voxel_map.cumsum(1)
anchors_area = box_np_ops.fused_get_anchors_area(
dense_voxel_map, anchors_bv, voxel_size, pc_range,
grid_size)
anchors_mask = anchors_area > anchor_area_threshold
example["anchors_mask"].append(anchors_mask)
if res["mode"] == "train":
targets_dicts = []
for idx, target_assigner in enumerate(self.target_assigners):
if "anchors_mask" in example:
anchors_mask = example["anchors_mask"][idx]
else:
anchors_mask = None
targets_dict = target_assigner.assign_v2(
anchors_dicts[idx],
gt_dict["gt_boxes"][idx],
anchors_mask,
gt_classes=gt_dict["gt_classes"][idx],
gt_names=gt_dict["gt_names"][idx],
)
targets_dicts.append(targets_dict)
example.update({
"labels":
[targets_dict["labels"] for targets_dict in targets_dicts],
"reg_targets": [
targets_dict["bbox_targets"]
for targets_dict in targets_dicts
],
"reg_weights": [
targets_dict["bbox_outside_weights"]
for targets_dict in targets_dicts
],
})
res["lidar"]["targets"] = example
return res, info
def __call__(self, res, info):
# 需要检测的物体类别
class_names_by_task = [t.classes for t in self.target_assigners]
# Calculate output featuremap size
grid_size = res["lidar"]["voxels"]["shape"]
feature_map_size = grid_size[:2] // self.out_size_factor
feature_map_size = [*feature_map_size, 1][::-1] # z轴的size是1
# 生成不同类的不同anchor
# anchors list
anchors_by_task = [
t.generate_anchors(feature_map_size) for t in self.target_assigners
]
# anchors 类别有dict
anchor_dicts_by_task = [
t.generate_anchors_dict(feature_map_size)
for t in self.target_assigners
]
# reshape乘n*7的形式
reshaped_anchors_by_task = [
t["anchors"].reshape([-1, t["anchors"].shape[-1]])
for t in anchors_by_task
]
matched_by_task = [t["matched_thresholds"] for t in anchors_by_task]
unmatched_by_task = [
t["unmatched_thresholds"] for t in anchors_by_task
]
# bev下的anchor
bv_anchors_by_task = [
box_np_ops.rbbox2d_to_near_bbox(anchors[:, [0, 1, 3, 4, -1]])
for anchors in reshaped_anchors_by_task
]
# 整合为字典
anchor_caches_by_task = dict(
anchors=reshaped_anchors_by_task,
anchors_bv=bv_anchors_by_task,
matched_thresholds=matched_by_task,
unmatched_thresholds=unmatched_by_task,
anchors_dict=anchor_dicts_by_task,
)
if res["mode"] == "train":
# 得到当前帧的标注
gt_dict = res["lidar"]["annotations"]
task_masks = []
flag = 0
# 不同任务的不同类gt，得到mask,task_id:[]
for class_name in class_names_by_task:
task_masks.append([
np.where(gt_dict["gt_classes"] == class_name.index(i) + 1 +
flag) for i in class_name
])
flag += len(class_name)
# task存储
task_boxes = []
task_boxes_1 = []
task_boxes_2 = []
task_classes = []
task_names = []
flag2 = 0
for idx, mask in enumerate(task_masks):
task_box = []
task_box_1 = []
task_box_2 = []
task_class = []
task_name = []
for m in mask:
# mask 应用
# TODO: 在这里应用mask时需要把前后两帧的label加入
task_box.append(gt_dict["gt_boxes"][m])
task_box_1.append(gt_dict["gt_boxes_1"][m])
task_box_2.append(gt_dict["gt_boxes_2"][m])
task_class.append(gt_dict["gt_classes"][m] - flag2)
task_name.append(gt_dict["gt_names"][m])
task_boxes.append(np.concatenate(task_box, axis=0))
task_boxes_1.append(np.concatenate(task_box_1, axis=0))
task_boxes_2.append(np.concatenate(task_box_2, axis=0))
task_classes.append(np.concatenate(task_class))
task_names.append(np.concatenate(task_name))
flag2 += len(mask)
for task_box, task_box_1, task_box_2 in zip(
task_boxes, task_boxes_1, task_boxes_2):
# TODO:将朝向角限制在-pi~pi
# limit rad to [-pi, pi]
task_box[:, -1] = box_np_ops.limit_period(task_box[:, -1],
offset=0.5,
period=np.pi * 2)
task_box_1[:, -1] = box_np_ops.limit_period(task_box_1[:, -1],
offset=0.5,
period=np.pi * 2)
task_box_2[:, -1] = box_np_ops.limit_period(task_box_2[:, -1],
offset=0.5,
period=np.pi * 2)
# print(gt_dict.keys())
# 对应的gt_boxes_1
gt_dict["gt_classes"] = task_classes
gt_dict["gt_names"] = task_names
gt_dict["gt_boxes"] = task_boxes
gt_dict["gt_boxes_1"] = task_boxes_1
gt_dict["gt_boxes_2"] = task_boxes_2
res["lidar"]["annotations"] = gt_dict
anchorss = anchor_caches_by_task["anchors"]
anchors_bvs = anchor_caches_by_task["anchors_bv"]
anchors_dicts = anchor_caches_by_task["anchors_dict"]
example = {}
# anchor是reshape出来的D*H*W*7
example["anchors"] = anchorss
if self.anchor_area_threshold >= 0:
# 暂时没有用到
example["anchors_mask"] = []
for idx, anchors_bv in enumerate(anchors_bvs):
anchors_mask = None
# slow with high resolution. recommend disable this forever.
coors = coordinates
dense_voxel_map = box_np_ops.sparse_sum_for_anchors_mask(
coors, tuple(grid_size[::-1][1:]))
dense_voxel_map = dense_voxel_map.cumsum(0)
dense_voxel_map = dense_voxel_map.cumsum(1)
anchors_area = box_np_ops.fused_get_anchors_area(
dense_voxel_map, anchors_bv, voxel_size, pc_range,
grid_size)
anchors_mask = anchors_area > anchor_area_threshold
example["anchors_mask"].append(anchors_mask)
if res["mode"] == "train":
targets_dicts = []
# 主体部分，利用anchors和gt_boxes得到target
# TODO: 修改的核心部分
for idx, target_assigner in enumerate(self.target_assigners):
if "anchors_mask" in example:
anchors_mask = example["anchors_mask"][idx]
else:
anchors_mask = None
targets_dict = target_assigner.assign_v3(
anchors_dicts[idx],
gt_dict["gt_boxes"][idx],
gt_dict["gt_boxes_1"][idx],
gt_dict["gt_boxes_2"][idx],
anchors_mask,
gt_classes=gt_dict["gt_classes"][idx],
gt_names=gt_dict["gt_names"][idx],
)
targets_dicts.append(targets_dict)
example.update({
"labels":
[targets_dict["labels"] for targets_dict in targets_dicts],
"reg_targets": [
targets_dict["bbox_targets"]
for targets_dict in targets_dicts
],
"reg_targets_1": [
targets_dict["bbox_targets_1"]
for targets_dict in targets_dicts
],
"reg_targets_2": [
targets_dict["bbox_targets_2"]
for targets_dict in targets_dicts
],
"reg_weights": [
targets_dict["bbox_outside_weights"]
for targets_dict in targets_dicts
],
})
res["lidar"]["targets"] = example
return res, info