diff --git a/ciou_test.py b/ciou_test.py
deleted file mode 100644
index 50fa6d945dce67cee4c7f62f9a27a72b1149b157..0000000000000000000000000000000000000000
--- a/ciou_test.py
+++ /dev/null
@@ -1,59 +0,0 @@
-import math
-
-import numpy as np
-import torch
-
-
-def box_ciou(b1, b2):
-    """
-    输入为：
-    ----------
-    b1: tensor, shape=(batch, feat_w, feat_h, anchor_num, 4), xywh
-    b2: tensor, shape=(batch, feat_w, feat_h, anchor_num, 4), xywh
-
-    返回为：
-    -------
-    ciou: tensor, shape=(batch, feat_w, feat_h, anchor_num, 1)
-    """
-    # 求出预测框左上角右下角
-    b1_xy = b1[..., :2]
-    b1_wh = b1[..., 2:4]
-    b1_wh_half = b1_wh/2.
-    b1_mins = b1_xy - b1_wh_half
-    b1_maxes = b1_xy + b1_wh_half
-    # 求出真实框左上角右下角
-    b2_xy = b2[..., :2]
-    b2_wh = b2[..., 2:4]
-    b2_wh_half = b2_wh/2.
-    b2_mins = b2_xy - b2_wh_half
-    b2_maxes = b2_xy + b2_wh_half
-
-    # 求真实框和预测框所有的iou
-    intersect_mins = torch.max(b1_mins, b2_mins)
-    intersect_maxes = torch.min(b1_maxes, b2_maxes)
-    intersect_wh = torch.max(intersect_maxes - intersect_mins, torch.zeros_like(intersect_maxes))
-    intersect_area = intersect_wh[..., 0] * intersect_wh[..., 1]
-    b1_area = b1_wh[..., 0] * b1_wh[..., 1]
-    b2_area = b2_wh[..., 0] * b2_wh[..., 1]
-    union_area = b1_area + b2_area - intersect_area
-    iou = intersect_area / (union_area + 1e-7)
-
-    # 计算中心的差距
-    center_distance = torch.sum(torch.pow((b1_xy - b2_xy), 2), axis=-1)
-    # 找到包裹两个框的最小框的左上角和右下角
-    enclose_mins = torch.min(b1_mins, b2_mins)
-    enclose_maxes = torch.max(b1_maxes, b2_maxes)
-    enclose_wh = torch.max(enclose_maxes - enclose_mins, torch.zeros_like(intersect_maxes))
-    # 计算对角线距离
-    enclose_diagonal = torch.sum(torch.pow(enclose_wh,2), axis=-1)
-    ciou = iou - 1.0 * (center_distance) / (enclose_diagonal + 1e-7)
-
-    v = (4 / (math.pi ** 2)) * torch.pow((torch.atan(b1_wh[..., 0]/b1_wh[..., 1]) - torch.atan(b2_wh[..., 0]/b2_wh[..., 1])), 2)
-    alpha = v / (1.0 - iou + v)
-    ciou = ciou - alpha * v
-    return ciou
-
-box1 = torch.from_numpy(np.array([[25,25,40,40]])).type(torch.FloatTensor)
-box2 = torch.from_numpy(np.array([[25,25,30,40]])).type(torch.FloatTensor)
-
-print(box_ciou(box1,box2))