#计算Dice系数 多分类 def meandice(pred, label): sumdice = 0 smooth = 1e-6 for i in range(1, 5): pred_bin = (pred==i)*1 label_bin = (label==i)*1 pred_bin = pred_bin.contiguous().view(pred_bin.shape[0], -1) label_bin = label_bin.contiguous().view(label_bin.shape[...
Mean IoU是在所有类别的IoU上取平均值。示例 以三分类语义分割为例,使用ConfusionMeter计算混淆矩阵并绘制假设图片大小为2x2像素,模型输出num_classes=3通道的矩阵,利用torchnet.meter中的ConfusionMeter计算多分类模型的混淆矩阵,acc和iu。1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22...
reduction="mean", get_not_nans=False) post_trans = Compose([EnsureType(), Activations(sigmoid=...
Albert, K. Hoang DucMarc, ModatKelvin, K. LeungM., Jorge CardosoJosephine, BarnesTimor, KadirSebastien, Ourselin
one way is to use the mean absolute deviation from the median as a measure of relative difficulty of changing a continuous feature. By default, DiCE computes this internally and divides the distance between continuous features by the MAD of the feature's values in the training set. We can al...
perishmean to relinquish life. Todieis to become dead from any cause and in any circumstances. It is the simplest, plainest, and most direct word for this idea, and is used figuratively of anything that has once displayed activity:An echo, flame, storm, rumor dies.Passaway(orpasson) is...
reduce_mean(dice) return dice 4 多分类 假设是一个10分类的任务,那么我们应该会有一个这样的模型预测结果:[batch_size,10,width,height],然后我们的ground truth需要改成one hot的形式,也变成[batch_size,10,width,height]。剩下的和二分类的代码基本相同了,先ground truth和预测结果对应元素相乘,然后对相乘...
[targets].permute(0,3,1,2).to(predictions.device)# 计算每个类别的 Dice Lossintersection=torch.sum(predictions*targets,dim=(0,2,3))dice=(2.*intersection+self.smooth)/(torch.sum(predictions,dim=(0,2,3))+torch.sum(targets,dim=(0,2,3))+self.smooth)dice_loss=1-dice.mean()returndice_...
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) #统一尺寸 self.crop_size = crop_size #读取所有voc图像并标注 features, labels = read_voc_images(voc_dir, is_train=is_train) #对图像和标签做标准化,移除不符合尺寸的图像 self.features = [self.normalize_image(feature) for feature in...
mAP(mean average precision):对各个类别的AP取平均,即 MAP = 各类别的AP求和/类别数量 实际上,不同数据集对AP的计算方式不同,介绍如下: PASCAL Voc 2008的AP计算: (1)IOU阈值为0.5 (2)一个GT只有一个正例,冗余检测框为负例 (3)在平滑之后的pr曲线上进行计算,即将横轴均分十段(recall=0也是一个点,共...