o oi @sZddlZddlmZddejdejdeded ejf d d Zd ejd ejd ejfddZdS)N)confusion_matrixư>predtarget num_classesepsreturnc Cst|s|jtjurtdt|t|s(|jtjur(tdt||j|jks:td|jd|j|j|jksLtd|jd|jt |t rU|dkr\td|t |||}tj |d d }tj |dd }tj |d d d }|||}||||} | S)aCalculate mean Intersection-Over-Union (mIOU). The function internally computes the confusion matrix. Args: pred : tensor with estimated targets returned by a classifier. The shape can be :math:`(B, *)` and must contain integer values between 0 and K-1. target: tensor with ground truth (correct) target values. The shape can be :math:`(B, *)` and must contain integer values between 0 and K-1, where targets are assumed to be provided as one-hot vectors. num_classes: total possible number of classes in target. eps: epsilon for numerical stability. Returns: a tensor representing the mean intersection-over union with shape :math:`(B, K)` where K is the number of classes. Example: >>> logits = torch.tensor([[0, 1, 0]]) >>> target = torch.tensor([[0, 1, 0]]) >>> mean_iou(logits, target, num_classes=3) tensor([[1., 1., 1.]]) zBInput pred type is not a torch.Tensor with torch.int64 dtype. Got zDInput target type is not a torch.Tensor with torch.int64 dtype. Got z6Inputs pred and target must have the same shape. Got: z and z(Inputs must be in the same device. Got: z - z?The number of classes must be an integer bigger than two. Got: r)dim)dim1dim2)torch is_tensordtypeint64 TypeErrortypeshape ValueErrordevice isinstanceintrsumdiagonal) rrrrconf_mat sum_over_row sum_over_col conf_mat_diag denominatoriousr#K/home/ubuntu/.local/lib/python3.10/site-packages/kornia/metrics/mean_iou.pymean_ious"    r%boxes_1boxes_2c Cs|dddf|dddfdks,|dddf|dddfdks,td|dddf|dddfdksX|dddf|dddfdksXtdt|ddddfd|ddddfd}t|ddddfd|ddddfd}tj||dd}|dddddf|dddddf}|dddf|dddf|dddf|dddf}|dddf|dddf|dddf|dddf}|d|d|}||S) aCompute the IoU of the cartesian product of two sets of boxes. Each box in each set shall be (x1, y1, x2, y2). Args: boxes_1: a tensor of bounding boxes in :math:`(B1, 4)`. boxes_2: a tensor of bounding boxes in :math:`(B2, 4)`. Returns: a tensor in dimensions :math:`(B1, B2)`, representing the intersection of each of the boxes in set 1 with respect to each of the boxes in set 2. Example: >>> boxes_1 = torch.tensor([[40, 40, 60, 60], [30, 40, 50, 60]]) >>> boxes_2 = torch.tensor([[40, 50, 60, 70], [30, 40, 40, 50]]) >>> mean_iou_bbox(boxes_1, boxes_2) tensor([[0.3333, 0.0000], [0.1429, 0.2500]]) Nr rrz0Boxes_1 does not follow (x1, y1, x2, y2) format.z0Boxes_2 does not follow (x1, y1, x2, y2) format.)min)allAssertionErrorrmax unsqueezer)clamp) r&r' lower_bounds upper_boundsintersection_dims intersection areas_set_1 areas_set_2unionr#r#r$ mean_iou_bboxNsPP88,@@r6)r)rrTensorrfloatr%r6r#r#r#r$s & 7