wypr/evaluation/ap_helper.py [172:198]:
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    def __init__(self, ap_iou_thresh=0.25, class2type_map=None):
        """
            Args:
                ap_iou_thresh: float between 0 and 1.0
                    IoU threshold to judge whether a prediction is positive.
                class2type_map: [optional] dict {class_int:class_name}
        """
        self.ap_iou_thresh = ap_iou_thresh
        self.class2type_map = class2type_map
        self.reset()
        
    def step(self, batch_pred_map_cls, batch_gt_map_cls):
        """ Accumulate one batch of prediction and groundtruth.
            Args:
                batch_pred_map_cls: a list of lists [[(pred_cls, pred_box_params, score),...],...]
                batch_gt_map_cls: a list of lists [[(gt_cls, gt_box_params),...],...]
                    should have the same length with batch_pred_map_cls (batch_size)
        """
        
        bsize = len(batch_pred_map_cls)
        assert(bsize == len(batch_gt_map_cls))
        for i in range(bsize):
            self.gt_map_cls[self.scan_cnt] = batch_gt_map_cls[i] 
            self.pred_map_cls[self.scan_cnt] = batch_pred_map_cls[i] 
            self.scan_cnt += 1
    
    def compute_metrics(self):
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wypr/evaluation/ar_helper.py [16:43]:
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    def __init__(self, ap_iou_thresh=0.25, class2type_map=None):
        """
        Args:
            ap_iou_thresh: float between 0 and 1.0
                IoU threshold to judge whether a prediction is positive.
            class2type_map: [optional] dict {class_int:class_name}
        """
        self.ap_iou_thresh = ap_iou_thresh
        self.class2type_map = class2type_map
        self.reset()
        
    def step(self, batch_pred_map_cls, batch_gt_map_cls):
        """ Accumulate one batch of prediction and groundtruth.
        
        Args:
            batch_pred_map_cls: a list of lists [[(pred_cls, pred_box_params),...],...]
            batch_gt_map_cls: a list of lists [[(gt_cls, gt_box_params),...],...]
                should have the same length with batch_pred_map_cls (batch_size)
        """
        
        bsize = len(batch_pred_map_cls)
        assert(bsize == len(batch_gt_map_cls))
        for i in range(bsize):
            self.gt_map_cls[self.scan_cnt] = batch_gt_map_cls[i] 
            self.pred_map_cls[self.scan_cnt] = batch_pred_map_cls[i] 
            self.scan_cnt += 1
    
    def compute_metrics(self):
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