'Provide groundtruth tensors.
Args:
groundtruth_boxes_list: a list of 2-D tf.float32 tensors of shape
[num_boxes, 4] containing coordinates of the groundtruth boxes.
Groundtruth boxes are provided in [y_min, x_min, y_max, x_max]
format and assumed to be normalized and clipped
relative to the image window with y_min <= y_max and x_min <= x_max.
groundtruth_classes_list: a list of 2-D tf.float32 one-hot (or k-hot)
tensors of shape [num_boxes, num_classes] containing the class targets
with the 0th index assumed to map to the first non-background class.
groundtruth_masks_list: a list of 2-D tf.float32 tensors of
shape [max_detections, height_in, width_in] containing instance
masks with values in {0, 1}.  If None, no masks are provided.
Mask resolution `height_in`x`width_in` must agree with the resolution
of the input image tensor provided to the `preprocess` function.
groundtruth_keypoints_list: a list of 2-D tf.float32 tensors of
shape [batch, max_detections, num_keypoints, 2] containing keypoints.
Keypoints are assumed to be provided in normalized coordinates and
missing keypoints should be encoded as NaN.'

def provide_groundtruth(self, groundtruth_boxes_list, groundtruth_classes_list, groundtruth_masks_list=None, groundtruth_keypoints_list=None):

    self._groundtruth_lists[fields.BoxListFields.boxes] = groundtruth_boxes_list
    self._groundtruth_lists[fields.BoxListFields.classes] = groundtruth_classes_list
    if groundtruth_masks_list:
        self._groundtruth_lists[fields.BoxListFields.masks] = groundtruth_masks_list
    if groundtruth_keypoints_list:
        self._groundtruth_lists[fields.BoxListFields.keypoints] = groundtruth_keypoints_list
