下面是纯diou代码
-
'''
-
计算两个box的中心点距离d
-
'''
-
# d = math.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2)
-
d = math.sqrt((pred[:, -
1] - target[:, -
1]) **
2 + (pred[:, -
2] - target[:, -
2]) **
2)
-
-
# 左边x
-
pred_l = pred[:, -
1] - pred[:, -
1] /
2
-
target_l = target[:, -
1] - target[:, -
1] /
2
-
# 上边y
-
pred_t = pred[:, -
2] - pred[:, -
2] /
2
-
target_t = target[:, -
2] - target[:, -
2] /
2
-
# 右边x
-
pred_r = pred[:, -
1] + pred[:, -
1] /
2
-
target_r = target[:, -
1] + target[:, -
1] /
2
-
# 下边y
-
pred_b = pred[:, -
2] + pred[:, -
2] /
2
-
target_b = target[:, -
2] + target[:, -
2] /
2
-
-
'''
-
计算两个box的bound的对角线距离
-
'''
-
bound_l = torch.
min(pred_l, target_l)
# left
-
bound_r = torch.
max(pred_r, target_r)
# right
-
bound_t = torch.
min(pred_t, target_t)
# top
-
bound_b = torch.
max(pred_b, target_b)
# bottom
-
c = math.sqrt((bound_r - bound_l) **
2 + (bound_b - bound_t) **
2)
-
dloss = iou - (d **
2) / (c **
2)
-
loss =
1 - dloss.clamp(
min=-
1.0,
max=
1.0)
第一步 计算两个box的中心点距离d
首先要知道pred和target的输出结果是什么
pred[:,:2]第一个：表示多个图片，第二个:2表示前两个数值，代表矩形框中心点(Y，X)
pred[:,2:]第一个：表示多个图片，第二个2:表示后两个数值，代表矩形框长宽(H，W)
target[:,:2]同理，
d =
根据上面的分析来计算左右上下坐标lrtb
然后计算内部2个矩形的最小外接矩形的对角线长度c
d是两个预测矩形中心点的距离
下面接受各种极端情况
A 两个框中心对齐时候，d/c=0，iou可能0-1
A 两个框相距很远时，d/c=1，iou=0
所以d/c属于0-1
dloss=iou-d/c属于-1到1
因此设置loss=1-dloss属于0-2
展示iou\giou\diou代码，这是YOLOX自带的损失函数，其中dloss是我自己写的
YOLOX是下载自
GitHub - Megvii-BaseDetection/YOLOX: YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/ - GitHub - Megvii-BaseDetection/YOLOX: YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/
https://github.com/Megvii-BaseDetection/YOLOX
-
class
IOUloss(nn.Module):
-
def
__init__(
self, reduction="none", loss_type="iou"):
-
super(IOUloss, self).__init__()
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self.reduction = reduction
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self.loss_type = loss_type
-
-
def
forward(
self, pred, target):
-
assert pred.shape[
0] == target.shape[
0]
-
-
pred = pred.view(-
1,
4)
-
target = target.view(-
1,
4)
-
tl = torch.
max(
-
(pred[:, :
2] - pred[:,
2:] /
2), (target[:, :
2] - target[:,
2:] /
2)
-
)
-
# pred target都是[H,W,Y,X]
-
# (Y,X)-(H,W) 左上角
-
br = torch.
min(
-
(pred[:, :
2] + pred[:,
2:] /
2), (target[:, :
2] + target[:,
2:] /
2)
-
)
-
# (X,Y)+(H,W) 右下角
-
-
-
area_p = torch.prod(pred[:,
2:],
1)
# HxW
-
area_g = torch.prod(target[:,
2:],
1)
-
-
en = (tl < br).
type(tl.
type()).prod(dim=
1)
-
area_i = torch.prod(br - tl,
1) * en
-
area_u = area_p + area_g - area_i
-
iou = (area_i) / (area_u +
1e-16)
-
-
if self.loss_type ==
"iou":
-
loss =
1 - iou **
2
-
elif self.loss_type ==
"giou":
-
c_tl = torch.
min(
-
(pred[:, :
2] - pred[:,
2:] /
2), (target[:, :
2] - target[:,
2:] /
2)
-
)
-
c_br = torch.
max(
-
(pred[:, :
2] + pred[:,
2:] /
2), (target[:, :
2] + target[:,
2:] /
2)
-
)
-
area_c = torch.prod(c_br - c_tl,
1)
-
giou = iou - (area_c - area_u) / area_c.clamp(
1e-16)
-
loss =
1 - giou.clamp(
min=-
1.0,
max=
1.0)
-
-
# pred[:, :2] pred[:, 2:]
-
# (Y,X) (H,W)
-
# target[:, :2] target[:, 2:]
-
# (Y,X) (H,W)
-
-
elif self.loss_type ==
"diou":
-
'''
-
计算两个box的中心点距离d
-
'''
-
# d = math.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2)
-
d = math.sqrt((pred[:, -
1] - target[:, -
1]) **
2 + (pred[:, -
2] - target[:, -
2]) **
2)
-
-
# 左边x
-
pred_l = pred[:, -
1] - pred[:, -
1] /
2
-
target_l = target[:, -
1] - target[:, -
1] /
2
-
# 上边y
-
pred_t = pred[:, -
2] - pred[:, -
2] /
2
-
target_t = target[:, -
2] - target[:, -
2] /
2
-
# 右边x
-
pred_r = pred[:, -
1] + pred[:, -
1] /
2
-
target_r = target[:, -
1] + target[:, -
1] /
2
-
# 下边y
-
pred_b = pred[:, -
2] + pred[:, -
2] /
2
-
target_b = target[:, -
2] + target[:, -
2] /
2
-
-
'''
-
计算两个box的bound的对角线距离
-
'''
-
bound_l = torch.
min(pred_l, target_l)
# left
-
bound_r = torch.
max(pred_r, target_r)
# right
-
bound_t = torch.
min(pred_t, target_t)
# top
-
bound_b = torch.
max(pred_b, target_b)
# bottom
-
c = math.sqrt((bound_r - bound_l) **
2 + (bound_b - bound_t) **
2)
-
dloss = iou - (d **
2) / (c **
2)
-
loss =
1 - dloss.clamp(
min=-
1.0,
max=
1.0)
-
-
# Step1
-
# def DIoU(a, b):
-
# d = a.center_distance(b)
-
# c = a.bound_diagonal_distance(b)
-
# return IoU(a, b) - (d ** 2) / (c ** 2)
-
-
# Step2-1
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# def center_distance(self, other):
-
# '''
-
# 计算两个box的中心点距离
-
# '''
-
# return euclidean_distance(self.center, other.center)
-
-
# Step2-2
-
# def euclidean_distance(p1, p2):
-
# '''
-
# 计算两个点的欧式距离
-
# '''
-
# x1, y1 = p1
-
# x2, y2 = p2
-
# return math.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2)
-
-
# Step3
-
# def bound_diagonal_distance(self, other):
-
# '''
-
# 计算两个box的bound的对角线距离
-
# '''
-
# bound = self.boundof(other)
-
# return euclidean_distance((bound.x, bound.y), (bound.r, bound.b))
-
-
# Step3-2
-
# def boundof(self, other):
-
# '''
-
# 计算box和other的边缘外包框，使得2个box都在框内的最小矩形
-
# '''
-
# xmin = min(self.x, other.x)
-
# ymin = min(self.y, other.y)
-
# xmax = max(self.r, other.r)
-
# ymax = max(self.b, other.b)
-
# return BBox(xmin, ymin, xmax, ymax)
-
-
# Step3-3
-
# def euclidean_distance(p1, p2):
-
# '''
-
# 计算两个点的欧式距离
-
# '''
-
# x1, y1 = p1
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# x2, y2 = p2
-
# return math.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2)
-
-
-
-
-
if self.reduction ==
"mean":
-
loss = loss.mean()
-
elif self.reduction ==
"sum":
-
loss = loss.
sum()
-
-
return loss
转载：https://blog.csdn.net/zjc910997316/article/details/125500138
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