df93c4e61e7ca1db25f1ee346ac0516685498b02,keras_rcnn/backend/tensorflow_backend.py,,bbox_transform_inv,#Any#Any#,12

Before Change

def bbox_transform_inv(shifted, boxes):
    if boxes.shape[0] == 0:
        return keras.backend.zeros((0, boxes.shape[1]), dtype=boxes.dtype)

    a = shifted[:, 2] - shifted[:, 0] + 1.0
    b = shifted[:, 3] - shifted[:, 1] + 1.0

After Change

        return keras.backend.concatenate(prediction)

    zero_boxes = keras.backend.equal(keras.backend.shape(boxes)[0], 0)

    pred_boxes = tensorflow.cond(zero_boxes, shape_zero, shape_non_zero)

    return pred_boxes


def non_maximum_suppression(boxes, scores, maximum, threshold=0.5):
    return tensorflow.image.non_max_suppression(

Instances