fix pr

research/object_detection/core/target_assigner_test.py

@@ -1923,38 +1923,41 @@ class CenterNetMaskTargetAssignerTest(test_case.TestCase):
         expected_seg_target, segmentation_target)
 
   def test_assign_detr(self):
-    def graph_fn(anchor_means, groundtruth_box_corners):
+    def graph_fn(anchor_means, groundtruth_box_corners,
+                 groundtruth_labels, predicted_labels):
       similarity_calc = region_similarity_calculator.DETRSimilarity()
       matcher = hungarian_matcher.HungarianBipartiteMatcher()
-      box_coder = None
+      box_coder = box
       target_assigner = targetassigner.TargetAssigner(
           similarity_calc, matcher, box_coder)
       anchors_boxlist = box_list.BoxList(anchor_means)
       groundtruth_boxlist = box_list.BoxList(groundtruth_box_corners)
       result = target_assigner.assign(
-          anchors_boxlist, groundtruth_boxlist, unmatched_class_label=None)
+          anchors_boxlist, groundtruth_boxlist, unmatched_class_label=None,
+          groundtruth_labels=groundtruth_labels, class_predictions=predicted_labels)
       (cls_targets, cls_weights, reg_targets, reg_weights, _) = result
       return (cls_targets, cls_weights, reg_targets, reg_weights)
 
-    anchor_means = np.array([[0.0, 0.0, 0.2, 0.2],
+    anchor_means = np.array([[0.0, 0.0, 0.4, 0.2],
                              [0.5, 0.5, 1.0, 0.8],
-                             [0, 0.5, .5, 1.0]], dtype=np.float32)
+                             [0.9, 0.5, 0.1, 1.0]], dtype=np.float32)
     groundtruth_box_corners = np.array([[0.0, 0.0, 0.5, 0.5],
                                         [0.5, 0.5, 0.9, 0.9]],
                                        dtype=np.float32)
-    predicted_labels = np.array([[7, 3], [2, 9], [1, 5]])
-    groundtruth = np.array([[0, 1], [2, 9], [1, 5]])
+    predicted_labels = np.array([[-3, 3], [2, 9], [5, 1]])
+    groundtruth_labels = np.array([[0, 1], [0, 1]])
     
     exp_cls_targets = [[1], [1], [0]]
     exp_cls_weights = [[1], [1], [1]]
-    exp_reg_targets = [[0, 0, 0, 0],
-                       [0, 0, -1, 1],
+    exp_reg_targets = [[0.0, 0.0, 0.5, 0.5],
+                       [0.5, 0.5, 0.9, 0.9],
                        [0, 0, 0, 0]]
     exp_reg_weights = [1, 1, 0]
 
     (cls_targets_out,
      cls_weights_out, reg_targets_out, reg_weights_out) = self.execute(
-         graph_fn, [anchor_means, groundtruth_box_corners])
+         graph_fn, [anchor_means, groundtruth_box_corners,
+                    groundtruth_labels, predicted_labels])
     self.assertAllClose(cls_targets_out, exp_cls_targets)
     self.assertAllClose(cls_weights_out, exp_cls_weights)
     self.assertAllClose(reg_targets_out, exp_reg_targets)