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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的输出结果是什幺
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
class IOUloss(nn.Module): def __init__(self, reduction="none", loss_type="iou"): super(IOUloss, self).__init__() self.reduction = reduction 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 # 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 # 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
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