def __call__(self, gt_boxes, gt_labels):
if type(gt_boxes) is np.ndarray:
gt_boxes = torch.from_numpy(gt_boxes)
if type(gt_labels) is np.ndarray:
gt_labels = torch.from_numpy(gt_labels)
boxes, labels = box_utils.assign_priors(gt_boxes, gt_labels,
self.corner_form_priors, self.iou_threshold)
boxes = box_utils.corner_form_to_center_form(boxes)
locations = box_utils.convert_boxes_to_locations(boxes, self.center_form_priors, self.center_variance, self.size_variance)
return locations, labels
def __call__(self, gt_boxes, gt_labels):
if type(gt_boxes) is np.ndarray:
gt_boxes = torch.from_numpy(gt_boxes)
if type(gt_labels) is np.ndarray:
gt_labels = torch.from_numpy(gt_labels)
if (gt_boxes.shape[0] == 0):
raise ValueError("Error in ground truth box size: " +
str(gt_boxes.shape))
boxes, labels = box_utils.assign_priors(gt_boxes, gt_labels,
self.corner_form_priors,
self.iou_threshold)
boxes = box_utils.corner_form_to_center_form(boxes)
locations = box_utils.convert_boxes_to_locations(
boxes, self.center_form_priors, self.center_variance,
self.size_variance)
return locations, labels
