aot/utils/metric.py

import torch


def pytorch_iou(pred, target, obj_num, epsilon=1e-6):
    '''
    pred: [bs, h, w]
    target: [bs, h, w]
    obj_num: [bs]
    '''
    bs = pred.size(0)
    all_iou = []
    for idx in range(bs):
        now_pred = pred[idx].unsqueeze(0)
        now_target = target[idx].unsqueeze(0)
        now_obj_num = obj_num[idx]

        obj_ids = torch.arange(0, now_obj_num + 1,
                               device=now_pred.device).int().view(-1, 1, 1)
        if obj_ids.size(0) == 1:  # only contain background
            continue
        else:
            obj_ids = obj_ids[1:]
            now_pred = (now_pred == obj_ids).float()
            now_target = (now_target == obj_ids).float()

            intersection = (now_pred * now_target).sum((1, 2))
            union = ((now_pred + now_target) > 0).float().sum((1, 2))

            now_iou = (intersection + epsilon) / (union + epsilon)

            all_iou.append(now_iou.mean())
    if len(all_iou) > 0:
        all_iou = torch.stack(all_iou).mean()
    else:
        all_iou = torch.ones((1), device=pred.device)
    return all_iou