def fn(x):
boxes, num_boxes = x
boxes = boxes[:num_boxes]
# if the images are padded we need to rescale groundtruth boxes:
boxes = boxes * self.box_scaler
reg_targets, matches = get_training_targets(
self.anchors, boxes, threshold=MATCHING_THRESHOLD)
return reg_targets, matches
def fn(x):
boxes, labels, num_boxes = x
boxes, labels = boxes[:num_boxes], labels[:num_boxes]
labels = tf.one_hot(labels,
self.num_classes,
axis=1,
dtype=tf.float32)
cls_targets, reg_targets, matches = get_training_targets(
self.anchors,
boxes,
labels,
self.num_classes,
threshold=MATCHING_THRESHOLD)
return cls_targets, reg_targets, matches
def fn(x):
boxes, num_boxes = x
boxes = boxes[:num_boxes]
reg_targets, matches = get_training_targets(
self.anchors, boxes, threshold=MATCHING_THRESHOLD)
return reg_targets, matches