Updated the postprocess function in CenterNetMetaArch such that the

true_image_shape is not required and can be inferred by the prediction tensors.

PiperOrigin-RevId: 390421432

research/object_detection/meta_architectures/center_net_meta_arch.py

@@ -3742,6 +3742,16 @@ class CenterNetMetaArch(model.DetectionModel):
     """
     object_center_prob = tf.nn.sigmoid(prediction_dict[OBJECT_CENTER][-1])
 
+    if true_image_shapes is None:
+      # If true_image_shapes is not provided, we assume the whole image is valid
+      # and infer the true_image_shapes from the object_center_prob shape.
+      batch_size, strided_height, strided_width, _ = _get_shape(
+          object_center_prob, 4)
+      true_image_shapes = tf.stack(
+          [strided_height * self._stride, strided_width * self._stride,
+           tf.constant(len(self._feature_extractor._channel_means))])   # pylint: disable=protected-access
+      true_image_shapes = tf.stack([true_image_shapes] * batch_size, axis=0)
+    else:
       # Mask object centers by true_image_shape. [batch, h, w, 1]
       object_center_mask = mask_from_true_image_shape(
           _get_shape(object_center_prob, 4), true_image_shapes)

research/object_detection/meta_architectures/center_net_meta_arch_tf2_test.py

@@ -2056,10 +2056,11 @@ class CenterNetMetaArchTest(test_case.TestCase, parameterized.TestCase):
                                    cnma.TEMPORAL_OFFSET)])
 
   @parameterized.parameters(
-      {'target_class_id': 1},
-      {'target_class_id': 2},
+      {'target_class_id': 1, 'with_true_image_shape': True},
+      {'target_class_id': 2, 'with_true_image_shape': True},
+      {'target_class_id': 1, 'with_true_image_shape': False},
   )
-  def test_postprocess(self, target_class_id):
+  def test_postprocess(self, target_class_id, with_true_image_shape):
     """Test the postprocess function."""
     model = build_center_net_meta_arch()
     max_detection = model._center_params.max_box_predictions
@@ -2140,8 +2141,11 @@ class CenterNetMetaArchTest(test_case.TestCase, parameterized.TestCase):
     }
 
     def graph_fn():
+      if with_true_image_shape:
         detections = model.postprocess(prediction_dict,
                                        tf.constant([[128, 128, 3]]))
+      else:
+        detections = model.postprocess(prediction_dict, None)
       return detections
 
     detections = self.execute_cpu(graph_fn, [])