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beb7843 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 | import numpy as np
import torch
from torchvision.ops.boxes import box_area
def get_ious(bboxes1,
bboxes2,
box_mode="xyxy",
iou_type="iou"):
"""
Compute iou loss of type ['iou', 'giou', 'linear_iou']
Args:
inputs (tensor): pred values
targets (tensor): target values
weight (tensor): loss weight
box_mode (str): 'xyxy' or 'ltrb', 'ltrb' is currently supported.
loss_type (str): 'giou' or 'iou' or 'linear_iou'
reduction (str): reduction manner
Returns:
loss (tensor): computed iou loss.
"""
if box_mode == "ltrb":
bboxes1 = torch.cat((-bboxes1[..., :2], bboxes1[..., 2:]), dim=-1)
bboxes2 = torch.cat((-bboxes2[..., :2], bboxes2[..., 2:]), dim=-1)
elif box_mode != "xyxy":
raise NotImplementedError
eps = torch.finfo(torch.float32).eps
bboxes1_area = (bboxes1[..., 2] - bboxes1[..., 0]).clamp_(min=0) \
* (bboxes1[..., 3] - bboxes1[..., 1]).clamp_(min=0)
bboxes2_area = (bboxes2[..., 2] - bboxes2[..., 0]).clamp_(min=0) \
* (bboxes2[..., 3] - bboxes2[..., 1]).clamp_(min=0)
w_intersect = (torch.min(bboxes1[..., 2], bboxes2[..., 2])
- torch.max(bboxes1[..., 0], bboxes2[..., 0])).clamp_(min=0)
h_intersect = (torch.min(bboxes1[..., 3], bboxes2[..., 3])
- torch.max(bboxes1[..., 1], bboxes2[..., 1])).clamp_(min=0)
area_intersect = w_intersect * h_intersect
area_union = bboxes2_area + bboxes1_area - area_intersect
ious = area_intersect / area_union.clamp(min=eps)
if iou_type == "iou":
return ious
elif iou_type == "giou":
g_w_intersect = torch.max(bboxes1[..., 2], bboxes2[..., 2]) \
- torch.min(bboxes1[..., 0], bboxes2[..., 0])
g_h_intersect = torch.max(bboxes1[..., 3], bboxes2[..., 3]) \
- torch.min(bboxes1[..., 1], bboxes2[..., 1])
ac_uion = g_w_intersect * g_h_intersect
gious = ious - (ac_uion - area_union) / ac_uion.clamp(min=eps)
return gious
else:
raise NotImplementedError
# modified from torchvision to also return the union
def box_iou(boxes1, boxes2):
area1 = box_area(boxes1)
area2 = box_area(boxes2)
lt = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2]
rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2]
wh = (rb - lt).clamp(min=0) # [N,M,2]
inter = wh[:, :, 0] * wh[:, :, 1] # [N,M]
union = area1[:, None] + area2 - inter
iou = inter / union
return iou, union
def rescale_bboxes(bboxes, orig_size):
orig_w, orig_h = orig_size[0], orig_size[1]
bboxes[..., [0, 2]] = np.clip(
bboxes[..., [0, 2]] * orig_w, a_min=0., a_max=orig_w
)
bboxes[..., [1, 3]] = np.clip(
bboxes[..., [1, 3]] * orig_h, a_min=0., a_max=orig_h
)
return bboxes
if __name__ == '__main__':
box1 = torch.tensor([[10, 10, 20, 20]])
box2 = torch.tensor([[15, 15, 25, 25]])
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