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# Copyright (c) OpenMMLab. All rights reserved.
import math
from typing import Sequence, Union
import torch
from mmdet.structures.bbox.transforms import get_box_tensor
from torch import Tensor
def make_divisible(x: float,
widen_factor: float = 1.0,
divisor: int = 8) -> int:
"""Make sure that x*widen_factor is divisible by divisor."""
return math.ceil(x * widen_factor / divisor) * divisor
def make_round(x: float, deepen_factor: float = 1.0) -> int:
"""Make sure that x*deepen_factor becomes an integer not less than 1."""
return max(round(x * deepen_factor), 1) if x > 1 else x
def gt_instances_preprocess(batch_gt_instances: Union[Tensor, Sequence],
batch_size: int) -> Tensor:
"""Split batch_gt_instances with batch size.
From [all_gt_bboxes, box_dim+2] to [batch_size, number_gt, box_dim+1].
For horizontal box, box_dim=4, for rotated box, box_dim=5
If some shape of single batch smaller than
gt bbox len, then using zeros to fill.
Args:
batch_gt_instances (Sequence[Tensor]): Ground truth
instances for whole batch, shape [all_gt_bboxes, box_dim+2]
batch_size (int): Batch size.
Returns:
Tensor: batch gt instances data, shape
[batch_size, number_gt, box_dim+1]
"""
if isinstance(batch_gt_instances, Sequence):
max_gt_bbox_len = max(
[len(gt_instances) for gt_instances in batch_gt_instances])
# fill zeros with length box_dim+1 if some shape of
# single batch not equal max_gt_bbox_len
batch_instance_list = []
for index, gt_instance in enumerate(batch_gt_instances):
bboxes = gt_instance.bboxes
labels = gt_instance.labels
box_dim = get_box_tensor(bboxes).size(-1)
batch_instance_list.append(
torch.cat((labels[:, None], bboxes), dim=-1))
if bboxes.shape[0] >= max_gt_bbox_len:
continue
fill_tensor = bboxes.new_full(
[max_gt_bbox_len - bboxes.shape[0], box_dim + 1], 0)
batch_instance_list[index] = torch.cat(
(batch_instance_list[index], fill_tensor), dim=0)
return torch.stack(batch_instance_list)
else:
# faster version
# format of batch_gt_instances: [img_ind, cls_ind, (box)]
# For example horizontal box should be:
# [img_ind, cls_ind, x1, y1, x2, y2]
# Rotated box should be
# [img_ind, cls_ind, x, y, w, h, a]
# sqlit batch gt instance [all_gt_bboxes, box_dim+2] ->
# [batch_size, max_gt_bbox_len, box_dim+1]
assert isinstance(batch_gt_instances, Tensor)
box_dim = batch_gt_instances.size(-1) - 2
if len(batch_gt_instances) > 0:
gt_images_indexes = batch_gt_instances[:, 0]
max_gt_bbox_len = gt_images_indexes.unique(
return_counts=True)[1].max()
# fill zeros with length box_dim+1 if some shape of
# single batch not equal max_gt_bbox_len
batch_instance = torch.zeros(
(batch_size, max_gt_bbox_len, box_dim + 1),
dtype=batch_gt_instances.dtype,
device=batch_gt_instances.device)
for i in range(batch_size):
match_indexes = gt_images_indexes == i
gt_num = match_indexes.sum()
if gt_num:
batch_instance[i, :gt_num] = batch_gt_instances[
match_indexes, 1:]
else:
batch_instance = torch.zeros((batch_size, 0, box_dim + 1),
dtype=batch_gt_instances.dtype,
device=batch_gt_instances.device)
return batch_instance
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