[vision] Add GIOU https://giou.stanford.edu/GIoU.pdf.

PiperOrigin-RevId: 388547681

official/vision/beta/ops/box_ops.py

@@ -624,6 +624,76 @@ def bbox_overlap(boxes, gt_boxes):
     return iou
 
 
+def bbox_generalized_overlap(boxes, gt_boxes):
+  """Calculates the GIOU between proposal and ground truth boxes.
+
+  The generalized intersection of union is an adjustment of the traditional IOU
+  metric which provides continuous updates even for predictions with no overlap.
+  This metric is defined in https://giou.stanford.edu/GIoU.pdf. Note, some
+  `gt_boxes` may have been padded. The returned `giou` tensor for these boxes
+  will be -1.
+
+  Args:
+    boxes: a `Tensor` with a shape of [batch_size, N, 4]. N is the number of
+      proposals before groundtruth assignment (e.g., rpn_post_nms_topn). The
+      last dimension is the pixel coordinates in [ymin, xmin, ymax, xmax] form.
+    gt_boxes: a `Tensor` with a shape of [batch_size, max_num_instances, 4].
+      This tensor may have paddings with a negative value and will also be in
+      the [ymin, xmin, ymax, xmax] format.
+
+  Returns:
+    giou: a `Tensor` with as a shape of [batch_size, N, max_num_instances].
+  """
+  with tf.name_scope('bbox_generalized_overlap'):
+    assert boxes.shape.as_list(
+    )[-1] == 4, 'Boxes must be defined by 4 coordinates.'
+    assert gt_boxes.shape.as_list(
+    )[-1] == 4, 'Groundtruth boxes must be defined by 4 coordinates.'
+
+    bb_y_min, bb_x_min, bb_y_max, bb_x_max = tf.split(
+        value=boxes, num_or_size_splits=4, axis=2)
+    gt_y_min, gt_x_min, gt_y_max, gt_x_max = tf.split(
+        value=gt_boxes, num_or_size_splits=4, axis=2)
+
+    # Calculates the hull area for each pair of boxes, with one from
+    # boxes and the other from gt_boxes.
+    # Outputs for coordinates are of shape [batch_size, N, max_num_instances]
+    h_xmin = tf.minimum(bb_x_min, tf.transpose(gt_x_min, [0, 2, 1]))
+    h_xmax = tf.maximum(bb_x_max, tf.transpose(gt_x_max, [0, 2, 1]))
+    h_ymin = tf.minimum(bb_y_min, tf.transpose(gt_y_min, [0, 2, 1]))
+    h_ymax = tf.maximum(bb_y_max, tf.transpose(gt_y_max, [0, 2, 1]))
+    h_area = tf.maximum((h_xmax - h_xmin), 0) * tf.maximum((h_ymax - h_ymin), 0)
+    # Add a small epsilon to avoid divide-by-zero.
+    h_area = h_area + 1e-8
+
+    # Calculates the intersection area.
+    i_xmin = tf.maximum(bb_x_min, tf.transpose(gt_x_min, [0, 2, 1]))
+    i_xmax = tf.minimum(bb_x_max, tf.transpose(gt_x_max, [0, 2, 1]))
+    i_ymin = tf.maximum(bb_y_min, tf.transpose(gt_y_min, [0, 2, 1]))
+    i_ymax = tf.minimum(bb_y_max, tf.transpose(gt_y_max, [0, 2, 1]))
+    i_area = tf.maximum((i_xmax - i_xmin), 0) * tf.maximum((i_ymax - i_ymin), 0)
+
+    # Calculates the union area.
+    bb_area = (bb_y_max - bb_y_min) * (bb_x_max - bb_x_min)
+    gt_area = (gt_y_max - gt_y_min) * (gt_x_max - gt_x_min)
+
+    # Adds a small epsilon to avoid divide-by-zero.
+    u_area = bb_area + tf.transpose(gt_area, [0, 2, 1]) - i_area + 1e-8
+
+    # Calculates IoU.
+    iou = i_area / u_area
+    # Calculates GIoU.
+    giou = iou - (h_area - u_area) / h_area
+
+    # Fills -1 for GIoU entries between the padded ground truth boxes.
+    gt_invalid_mask = tf.less(
+        tf.reduce_max(gt_boxes, axis=-1, keepdims=True), 0.0)
+    padding_mask = tf.broadcast_to(
+        tf.transpose(gt_invalid_mask, [0, 2, 1]), tf.shape(giou))
+    giou = tf.where(padding_mask, -tf.ones_like(giou), giou)
+    return giou
+
+
 def box_matching(boxes, gt_boxes, gt_classes):
   """Match boxes to groundtruth boxes.