clean up and improve tests

research/object_detection/core/box_list_ops.py

@@ -327,7 +327,7 @@ def l1(boxlist1, boxlist2, scope=None):
         tf.transpose(w1), axis=1))
     return ycenters + xcenters + heights + widths
 
-def giou_loss(boxlist1, boxlist2, scope=None):
+def giou(boxlist1, boxlist2, scope=None):
   """
   Computes generalized IOU loss between two boxlists pairwise,
   as described at giou.stanford.edu.
@@ -345,7 +345,7 @@ def giou_loss(boxlist1, boxlist2, scope=None):
     M = boxlist2.num_boxes()
     boxes1 = tf.repeat(boxlist1.get(), repeats=M, axis=0)
     boxes2 = tf.tile(boxlist2.get(), multiples=[N, 1])
-    return tf.reshape(1.0 - ops.giou(boxes1, boxes2), [N, M])
+    return tf.reshape(ops.giou(boxes1, boxes2), [N, M])
 
 def matched_iou(boxlist1, boxlist2, scope=None):
   """Compute intersection-over-union between corresponding boxes in boxlists.

research/object_detection/core/box_list_ops_test.py

@@ -247,9 +247,9 @@ class BoxListOpsTest(test_case.TestCase):
       corners2 = tf.constant([[5.0, 7.0, 7.0, 9.0], [5.0, 11.0, 7.0, 13.0]])
       boxes1 = box_list.BoxList(corners1)
       boxes2 = box_list.BoxList(corners2)
-      giou = box_list_ops.giou_loss(boxes1, boxes2)
+      giou = box_list_ops.giou(boxes1, boxes2)
       return giou
-    exp_output = [[0.0, 4.0 / 3.0]]
+    exp_output = [[1.0, -1.0 / 3.0]]
     giou_output = self.execute(graph_fn, [])
     self.assertAllClose(giou_output, exp_output)
 

research/object_detection/utils/ops_test.py

@@ -1631,19 +1631,19 @@ class TestGatherWithPaddingValues(test_case.TestCase):
 
 
 class TestGIoU(test_case.TestCase):
-  def test_giou_general(self):
+  def test_giou_with_no_overlap(self):
     expected_giou_tensor = [
-        0, -1/3, 1/25, -3/4, 0, -98/100
+        0, -1/3, -3/4, 0, -98/100
     ]
 
     def graph_fn():
       boxes1 = tf.constant([[3, 4, 5, 6], [3, 3, 5, 5],
-                            [2, 1, 7, 6], [0, 0, 0, 0],
-                            [3, 3, 5, 5], [9, 9, 10, 10]],
+                            [0, 0, 0, 0], [3, 3, 5, 5],
+                            [9, 9, 10, 10]],
                             dtype=tf.float32)
       boxes2 = tf.constant([[3, 2, 5, 4], [3, 7, 5, 9],
-                            [4, 3, 5, 4], [5, 5, 10, 10],
-                            [3, 5, 5, 7], [0, 0, 1, 1]], dtype=tf.float32)
+                            [5, 5, 10, 10], [3, 5, 5, 7],
+                            [0, 0, 1, 1]], dtype=tf.float32)
 
       giou = ops.giou(boxes1, boxes2)
       self.assertEqual(giou.dtype, tf.float32)
@@ -1653,15 +1653,50 @@ class TestGIoU(test_case.TestCase):
     giou = self.execute(graph_fn, [])
     self.assertAllClose(expected_giou_tensor, giou)
 
-  def test_giou_edge_cases(self):
+  def test_giou_with_overlaps(self):
     expected_giou_tensor = [
-        1, 0
+        1/25, 1/4, 1/3, 1/7 - 2/9
     ]
 
     def graph_fn():
-      boxes1 = tf.constant([[3, 3, 5, 5], [1, 1, 1, 1]],
+      boxes1 = tf.constant([[2, 1, 7, 6], [2, 2, 4, 4],
+                            [2, 2, 4, 4], [2, 2, 4, 4]],
                             dtype=tf.float32)
-      boxes2 = tf.constant([[3, 3, 5, 5], [1, 1, 1, 1]],
+      boxes2 = tf.constant([[4, 3, 5, 4], [3, 3, 4, 4],
+                            [2, 3, 4, 5], [3, 3, 5, 5]], dtype=tf.float32)
+
+      giou = ops.giou(boxes1, boxes2)
+      self.assertEqual(giou.dtype, tf.float32)
+
+      return giou
+
+    giou = self.execute(graph_fn, [])
+    self.assertAllClose(expected_giou_tensor, giou)
+
+  def test_giou_with_perfect_overlap(self):
+    expected_giou_tensor = [1]
+
+    def graph_fn():
+      boxes1 = tf.constant([[3, 3, 5, 5]],
+          dtype=tf.float32)
+      boxes2 = tf.constant([[3, 3, 5, 5]],
+          dtype=tf.float32)
+
+      giou = ops.giou(boxes1, boxes2)
+      self.assertEqual(giou.dtype, tf.float32)
+
+      return giou
+
+    giou = self.execute(graph_fn, [])
+    self.assertAllClose(expected_giou_tensor, giou)
+
+  def test_giou_with_zero_area_boxes(self):
+    expected_giou_tensor = [0]
+
+    def graph_fn():
+      boxes1 = tf.constant([[1, 1, 1, 1]],
+          dtype=tf.float32)
+      boxes2 = tf.constant([[1, 1, 1, 1]],
           dtype=tf.float32)
 
       giou = ops.giou(boxes1, boxes2)
@@ -1672,7 +1707,7 @@ class TestGIoU(test_case.TestCase):
     giou = self.execute(graph_fn, [])
     self.assertAllClose(expected_giou_tensor, giou)
 
-  def test_giou_l1_same(self):
+  def test_giou_correlates_with_same_l1(self):
     expected_giou_tensor = [
         2/3, 3/5
     ]