(self, bbox_target_data, labels)
return bbox_targets, bbox_inside_weights
Bounding-box regression targets (bbox_target_data) are stored in a
        compact form N x (tx, ty, tw, th)
        This function expands those targets into the 4-of-4*K representation used
        by the network (i.e. only one class has non-zero targets).
        Returns:
            bbox_targets (ndarray): N x 4K blob of regression targets
            bbox_inside_weights (ndarray): N x 4K blob of loss weights
Bounding-box regression targets (bbox_target_data) are stored in a
        compact form N x (tx, ty, tw, th)
        This function expands those targets into the 4-of-4*K representation used
        by the network (i.e. only one class has non-zero targets).
        Returns:
            bbox_targets (ndarray): N x 4K blob of regression targets
            bbox_inside_weights (ndarray): N x 4K blob of loss weights
Bounding - box regression targets ( bbox_target_data ) are stored in a compact form N x ( tx ty tw th ) This function expands those targets into the 4 - of - 4 * K representation used by the network ( i . e . only one class has non - zero targets ) . Returns : bbox_targets ( ndarray ) : N x 4K blob of regression targets bbox_inside_weights ( ndarray ) : N x 4K blob of loss weights
def _get_bbox_regression_labels(self, bbox_target_data, labels):
        """Bounding-box regression targets (bbox_target_data) are stored in a
        compact form N x (tx, ty, tw, th)
        This function expands those targets into the 4-of-4*K representation used
        by the network (i.e. only one class has non-zero targets).
        Returns:
            bbox_targets (ndarray): N x 4K blob of regression targets
            bbox_inside_weights (ndarray): N x 4K blob of loss weights
        """
        bbox_targets = np.zeros((self.num_rois, 4 * self.num_classes), dtype=np.float32)
        bbox_inside_weights = np.zeros(bbox_targets.shape, dtype=np.float32)
        inds = np.where(labels > 0)[0]
        for ind in inds:
            l = labels[ind]
            start = int(4 * l)
            end = int(start + 4)
            bbox_targets[ind, start:end] = bbox_target_data[ind]
            bbox_inside_weights[ind, start:end] = 1.0
        return bbox_targets, bbox_inside_weights
def _get_bbox_regression_labels ( self , bbox_target_data , labels ) : bbox_targets = np . zeros ( ( self . num_rois , 4 * self . num_classes ) , dtype = np . float32 ) bbox_inside_weights = np . zeros ( bbox_targets . shape , dtype = np . float32 ) inds = np . where ( labels > 0 ) [ 0 ] for ind in inds : l = labels [ ind ] start = int ( 4 * l ) end = int ( start + 4 ) bbox_targets [ ind , start : end ] = bbox_target_data [ ind ] bbox_inside_weights [ ind , start : end ] = 1.0 return bbox_targets , bbox_inside_weights