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box_loss.py
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# borrow from https://github.com/Zzh-tju/CIoU/blob/master/layers/modules/multibox_loss.py
import torch, math
def ciou(bboxes1, bboxes2):
bboxes1 = torch.sigmoid(bboxes1)
bboxes2 = torch.sigmoid(bboxes2)
rows = bboxes1.shape[0]
cols = bboxes2.shape[0]
cious = torch.zeros((rows, cols))
if rows * cols == 0:
return cious
exchange = False
if bboxes1.shape[0] > bboxes2.shape[0]:
bboxes1, bboxes2 = bboxes2, bboxes1
cious = torch.zeros((cols, rows))
exchange = True
w1 = torch.exp(bboxes1[:, 2])
h1 = torch.exp(bboxes1[:, 3])
w2 = torch.exp(bboxes2[:, 2])
h2 = torch.exp(bboxes2[:, 3])
area1 = w1 * h1
area2 = w2 * h2
center_x1 = bboxes1[:, 0]
center_y1 = bboxes1[:, 1]
center_x2 = bboxes2[:, 0]
center_y2 = bboxes2[:, 1]
inter_l = torch.max(center_x1 - w1 / 2,center_x2 - w2 / 2)
inter_r = torch.min(center_x1 + w1 / 2,center_x2 + w2 / 2)
inter_t = torch.max(center_y1 - h1 / 2,center_y2 - h2 / 2)
inter_b = torch.min(center_y1 + h1 / 2,center_y2 + h2 / 2)
inter_area = torch.clamp((inter_r - inter_l),min=0) * torch.clamp((inter_b - inter_t),min=0)
c_l = torch.min(center_x1 - w1 / 2,center_x2 - w2 / 2)
c_r = torch.max(center_x1 + w1 / 2,center_x2 + w2 / 2)
c_t = torch.min(center_y1 - h1 / 2,center_y2 - h2 / 2)
c_b = torch.max(center_y1 + h1 / 2,center_y2 + h2 / 2)
inter_diag = (center_x2 - center_x1)**2 + (center_y2 - center_y1)**2
c_diag = torch.clamp((c_r - c_l),min=0)**2 + torch.clamp((c_b - c_t),min=0)**2
union = area1+area2-inter_area
u = (inter_diag) / c_diag
iou = inter_area / union
v = (4 / (math.pi ** 2)) * torch.pow((torch.atan(w2 / h2) - torch.atan(w1 / h1)), 2)
with torch.no_grad():
S = (iou>0.5).float()
alpha= S*v/(1-iou+v)
cious = iou - u - alpha * v
cious = torch.clamp(cious,min=-1.0,max = 1.0)
if exchange:
cious = cious.T
return 1-cious
def diou(bboxes1, bboxes2):
bboxes1 = torch.sigmoid(bboxes1)
bboxes2 = torch.sigmoid(bboxes2)
rows = bboxes1.shape[0]
cols = bboxes2.shape[0]
cious = torch.zeros((rows, cols))
if rows * cols == 0:
return cious
exchange = False
if bboxes1.shape[0] > bboxes2.shape[0]:
bboxes1, bboxes2 = bboxes2, bboxes1
cious = torch.zeros((cols, rows))
exchange = True
w1 = torch.exp(bboxes1[:, 2])
h1 = torch.exp(bboxes1[:, 3])
w2 = torch.exp(bboxes2[:, 2])
h2 = torch.exp(bboxes2[:, 3])
area1 = w1 * h1
area2 = w2 * h2
center_x1 = bboxes1[:, 0]
center_y1 = bboxes1[:, 1]
center_x2 = bboxes2[:, 0]
center_y2 = bboxes2[:, 1]
inter_l = torch.max(center_x1 - w1 / 2,center_x2 - w2 / 2)
inter_r = torch.min(center_x1 + w1 / 2,center_x2 + w2 / 2)
inter_t = torch.max(center_y1 - h1 / 2,center_y2 - h2 / 2)
inter_b = torch.min(center_y1 + h1 / 2,center_y2 + h2 / 2)
inter_area = torch.clamp((inter_r - inter_l),min=0) * torch.clamp((inter_b - inter_t),min=0)
c_l = torch.min(center_x1 - w1 / 2,center_x2 - w2 / 2)
c_r = torch.max(center_x1 + w1 / 2,center_x2 + w2 / 2)
c_t = torch.min(center_y1 - h1 / 2,center_y2 - h2 / 2)
c_b = torch.max(center_y1 + h1 / 2,center_y2 + h2 / 2)
inter_diag = (center_x2 - center_x1)**2 + (center_y2 - center_y1)**2
c_diag = torch.clamp((c_r - c_l),min=0)**2 + torch.clamp((c_b - c_t),min=0)**2
union = area1+area2-inter_area
u = (inter_diag) / c_diag
iou = inter_area / union
dious = iou - u
dious = torch.clamp(dious,min=-1.0,max = 1.0)
if exchange:
dious = dious.T
return 1-dious
if __name__ == "__main__":
x = torch.rand(10, 4)
y = torch.rand(10,4)
import ipdb;ipdb.set_trace()
cxy = ciou(x, y)
dxy = diou(x, y)
print(cxy.shape, dxy.shape)