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EIoU SIoU alpha
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“iscyy” committed Aug 8, 2022
1 parent 110be26 commit 7515630
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4 changes: 3 additions & 1 deletion utils/loss.py
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Expand Up @@ -6,7 +6,7 @@
import torch
import torch.nn as nn

from utils.metrics import bbox_iou, box_iou
from utils.metrics import bbox_iou, box_iou, bbox_alpha_iou
from utils.torch_utils import de_parallel, is_parallel
from utils.general import xywh2xyxy
import torch.nn.functional as F
Expand Down Expand Up @@ -135,7 +135,9 @@ def __call__(self, p, targets): # predictions, targets, model
pxy = ps[:, :2].sigmoid() * 2 - 0.5
pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i]
pbox = torch.cat((pxy, pwh), 1) # predicted box
# IoU update add EIoU, SIoU
iou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, CIoU=True) # iou(prediction, target)
# iou = bbox_alpha_iou(pbox.T, tbox[i], x1y1x2y2=False, alpha=3, CIoU=True) # use alpha IoU
lbox += (1.0 - iou).mean() # iou loss

# Objectness
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91 changes: 83 additions & 8 deletions utils/metrics.py
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Expand Up @@ -198,8 +198,58 @@ def print(self):
for i in range(self.nc + 1):
print(' '.join(map(str, self.matrix[i])))

def bbox_alpha_iou(box1, box2, x1y1x2y2=False, GIoU=False, DIoU=False, CIoU=False, alpha=2, eps=1e-9):
# Returns tsqrt_he IoU of box1 to box2. box1 is 4, box2 is nx4
box2 = box2.T

# Get the coordinates of bounding boxes
if x1y1x2y2: # x1, y1, x2, y2 = box1
b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3]
b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3]
else: # transform from xywh to xyxy
b1_x1, b1_x2 = box1[0] - box1[2] / 2, box1[0] + box1[2] / 2
b1_y1, b1_y2 = box1[1] - box1[3] / 2, box1[1] + box1[3] / 2
b2_x1, b2_x2 = box2[0] - box2[2] / 2, box2[0] + box2[2] / 2
b2_y1, b2_y2 = box2[1] - box2[3] / 2, box2[1] + box2[3] / 2

# Intersection area
inter = (torch.min(b1_x2, b2_x2) - torch.max(b1_x1, b2_x1)).clamp(0) * \
(torch.min(b1_y2, b2_y2) - torch.max(b1_y1, b2_y1)).clamp(0)

def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7):
# Union Area
w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps
w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps
union = w1 * h1 + w2 * h2 - inter + eps

# change iou into pow(iou+eps)
# iou = inter / union
iou = torch.pow(inter/union + eps, alpha)
# beta = 2 * alpha
if GIoU or DIoU or CIoU:
cw = torch.max(b1_x2, b2_x2) - torch.min(b1_x1, b2_x1) # convex (smallest enclosing box) width
ch = torch.max(b1_y2, b2_y2) - torch.min(b1_y1, b2_y1) # convex height
if CIoU or DIoU: # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1
c2 = (cw ** 2 + ch ** 2) ** alpha + eps # convex diagonal
rho_x = torch.abs(b2_x1 + b2_x2 - b1_x1 - b1_x2)
rho_y = torch.abs(b2_y1 + b2_y2 - b1_y1 - b1_y2)
rho2 = ((rho_x ** 2 + rho_y ** 2) / 4) ** alpha # center distance
if DIoU:
return iou - rho2 / c2 # DIoU
elif CIoU: # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47
v = (4 / math.pi ** 2) * torch.pow(torch.atan(w2 / h2) - torch.atan(w1 / h1), 2)
with torch.no_grad():
alpha_ciou = v / ((1 + eps) - inter / union + v)
# return iou - (rho2 / c2 + v * alpha_ciou) # CIoU
return iou - (rho2 / c2 + torch.pow(v * alpha_ciou + eps, alpha)) # CIoU
else: # GIoU https://arxiv.org/pdf/1902.09630.pdf
# c_area = cw * ch + eps # convex area
# return iou - (c_area - union) / c_area # GIoU
c_area = torch.max(cw * ch + eps, union) # convex area
return iou - torch.pow((c_area - union) / c_area + eps, alpha) # GIoU
else:
return iou # torch.log(iou+eps) or iou

def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=False, EIoU=False, SIoU=False, eps=1e-7):
# Returns the IoU of box1 to box2. box1 is 4, box2 is nx4
box2 = box2.T

Expand All @@ -223,21 +273,46 @@ def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=False, eps=
union = w1 * h1 + w2 * h2 - inter + eps

iou = inter / union
if CIoU or DIoU or GIoU:
if CIoU or DIoU or GIoU or EIoU or SIoU:
cw = torch.max(b1_x2, b2_x2) - torch.min(b1_x1, b2_x1) # convex (smallest enclosing box) width
ch = torch.max(b1_y2, b2_y2) - torch.min(b1_y1, b2_y1) # convex height
if CIoU or DIoU: # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1
if CIoU or DIoU or EIoU or SIoU: # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1
c2 = cw ** 2 + ch ** 2 + eps # convex diagonal squared
rho2 = ((b2_x1 + b2_x2 - b1_x1 - b1_x2) ** 2 +
(b2_y1 + b2_y2 - b1_y1 - b1_y2) ** 2) / 4 # center distance squared
if CIoU: # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47
if DIoU: #DIoU
return iou - rho2 / c2 # DIoU
elif CIoU: #CIoU https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47
v = (4 / math.pi ** 2) * torch.pow(torch.atan(w2 / h2) - torch.atan(w1 / h1), 2)
with torch.no_grad():
alpha = v / (v - iou + (1 + eps))
return iou - (rho2 / c2 + v * alpha) # CIoU
return iou - rho2 / c2 # DIoU
c_area = cw * ch + eps # convex area
return iou - (c_area - union) / c_area # GIoU https://arxiv.org/pdf/1902.09630.pdf
return iou - (rho2 / c2 + v * alpha) # CIoU
elif SIoU:# SIoU
s_cw = (b2_x1 + b2_x2 - b1_x1 - b1_x2) * 0.5
s_ch = (b2_y1 + b2_y2 - b1_y1 - b1_y2) * 0.5
sigma = torch.pow(s_cw ** 2 + s_ch ** 2, 0.5)
sin_alpha_1 = torch.abs(s_cw) / sigma
sin_alpha_2 = torch.abs(s_ch) / sigma
threshold = pow(2, 0.5) / 2
sin_alpha = torch.where(sin_alpha_1 > threshold, sin_alpha_2, sin_alpha_1)
angle_cost = torch.cos(torch.arcsin(sin_alpha) * 2 - math.pi / 2)
rho_x = (s_cw / cw) ** 2
rho_y = (s_ch / ch) ** 2
gamma = angle_cost - 2
distance_cost = 2 - torch.exp(gamma * rho_x) - torch.exp(gamma * rho_y)
omiga_w = torch.abs(w1 - w2) / torch.max(w1, w2)
omiga_h = torch.abs(h1 - h2) / torch.max(h1, h2)
shape_cost = torch.pow(1 - torch.exp(-1 * omiga_w), 4) + torch.pow(1 - torch.exp(-1 * omiga_h), 4)
return iou - 0.5 * (distance_cost + shape_cost)
else:# EIoU
w_dis=torch.pow(b1_x2-b1_x1-b2_x2+b2_x1, 2)
h_dis=torch.pow(b1_y2-b1_y1-b2_y2+b2_y1, 2)
cw2=torch.pow(cw , 2)+eps
ch2=torch.pow(ch , 2)+eps
return iou-(rho2/c2+w_dis/cw2+h_dis/ch2)
else:
c_area = cw * ch + eps # convex area
return iou - (c_area - union) / c_area # GIoU https://arxiv.org/pdf/1902.09630.pdf
return iou # IoU


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