* Replaced the deprecated `to_int/to_float` in `center_net_meta_arch.py`...

  * Replaced the deprecated `to_int/to_float` in `center_net_meta_arch.py` with `tf.cast()` (following the warning).
  * Added `max_norm_coord` argument to CenterNet source code to allow controlling the maximum coordinate value to be considered, to allow for the wrap-around special case for RxR data.

PiperOrigin-RevId: 391319766

research/object_detection/core/target_assigner.py

@@ -961,7 +961,8 @@ class CenterNetCenterHeatmapTargetAssigner(object):
                                        width,
                                        gt_boxes_list,
                                        gt_classes_list,
-                                       gt_weights_list=None):
+                                       gt_weights_list=None,
+                                       maximum_normalized_coordinate=1.1):
     """Computes the object center heatmap target.
 
     Args:
@@ -977,6 +978,9 @@ class CenterNetCenterHeatmapTargetAssigner(object):
         in the gt_boxes_list.
       gt_weights_list: A list of float tensors with shape [num_boxes]
         representing the weight of each groundtruth detection box.
+      maximum_normalized_coordinate: Maximum coordinate value to be considered
+        as normalized, default to 1.1. This is used to check bounds during
+        converting normalized coordinates to absolute coordinates.
 
     Returns:
       heatmap: A Tensor of size [batch_size, output_height, output_width,
@@ -1002,7 +1006,8 @@ class CenterNetCenterHeatmapTargetAssigner(object):
       boxes = box_list_ops.to_absolute_coordinates(
           boxes,
           tf.maximum(height // self._stride, 1),
-          tf.maximum(width // self._stride, 1))
+          tf.maximum(width // self._stride, 1),
+          maximum_normalized_coordinate=maximum_normalized_coordinate)
       # Get the box center coordinates. Each returned tensors have the shape of
       # [num_instances]
       (y_center, x_center, boxes_height,

research/object_detection/meta_architectures/center_net_meta_arch.py

@@ -2714,7 +2714,8 @@ class CenterNetMetaArch(model.DetectionModel):
     return target_assigners
 
   def _compute_object_center_loss(self, input_height, input_width,
-                                  object_center_predictions, per_pixel_weights):
+                                  object_center_predictions, per_pixel_weights,
+                                  maximum_normalized_coordinate=1.1):
     """Computes the object center loss.
 
     Args:
@@ -2726,6 +2727,9 @@ class CenterNetMetaArch(model.DetectionModel):
       per_pixel_weights: A float tensor of shape [batch_size,
         out_height * out_width, 1] with 1s in locations where the spatial
         coordinates fall within the height and width in true_image_shapes.
+      maximum_normalized_coordinate: Maximum coordinate value to be considered
+        as normalized, default to 1.1. This is used to check bounds during
+        converting normalized coordinates to absolute coordinates.
 
     Returns:
       A float scalar tensor representing the object center loss per instance.
@@ -2752,7 +2756,8 @@ class CenterNetMetaArch(model.DetectionModel):
           width=input_width,
           gt_classes_list=gt_classes_list,
           gt_keypoints_list=gt_keypoints_list,
-          gt_weights_list=gt_weights_list)
+          gt_weights_list=gt_weights_list,
+          maximum_normalized_coordinate=maximum_normalized_coordinate)
     else:
       gt_boxes_list = self.groundtruth_lists(fields.BoxListFields.boxes)
       heatmap_targets = assigner.assign_center_targets_from_boxes(
@@ -2760,7 +2765,8 @@ class CenterNetMetaArch(model.DetectionModel):
           width=input_width,
           gt_boxes_list=gt_boxes_list,
           gt_classes_list=gt_classes_list,
-          gt_weights_list=gt_weights_list)
+          gt_weights_list=gt_weights_list,
+          maximum_normalized_coordinate=maximum_normalized_coordinate)
 
     flattened_heatmap_targets = _flatten_spatial_dimensions(heatmap_targets)
     num_boxes = _to_float32(get_num_instances_from_weights(gt_weights_list))
@@ -3577,7 +3583,9 @@ class CenterNetMetaArch(model.DetectionModel):
     self._batched_prediction_tensor_names = predictions.keys()
     return predictions
 
-  def loss(self, prediction_dict, true_image_shapes, scope=None):
+  def loss(
+      self, prediction_dict, true_image_shapes, scope=None,
+      maximum_normalized_coordinate=1.1):
     """Computes scalar loss tensors with respect to provided groundtruth.
 
     This function implements the various CenterNet losses.
@@ -3589,6 +3597,9 @@ class CenterNetMetaArch(model.DetectionModel):
         the form [height, width, channels] indicating the shapes of true images
         in the resized images, as resized images can be padded with zeros.
       scope: Optional scope name.
+      maximum_normalized_coordinate: Maximum coordinate value to be considered
+        as normalized, default to 1.1. This is used to check bounds during
+        converting normalized coordinates to absolute coordinates.
 
     Returns:
       A dictionary mapping the keys [
@@ -3616,7 +3627,7 @@ class CenterNetMetaArch(model.DetectionModel):
 
     # TODO(vighneshb) Explore whether using floor here is safe.
     output_true_image_shapes = tf.ceil(
-        tf.to_float(true_image_shapes) / self._stride)
+        tf.cast(true_image_shapes, tf.float32) / self._stride)
     valid_anchor_weights = get_valid_anchor_weights_in_flattened_image(
         output_true_image_shapes, output_height, output_width)
     valid_anchor_weights = tf.expand_dims(valid_anchor_weights, 2)
@@ -3625,7 +3636,8 @@ class CenterNetMetaArch(model.DetectionModel):
         object_center_predictions=prediction_dict[OBJECT_CENTER],
         input_height=input_height,
         input_width=input_width,
-        per_pixel_weights=valid_anchor_weights)
+        per_pixel_weights=valid_anchor_weights,
+        maximum_normalized_coordinate=maximum_normalized_coordinate)
     losses = {
         OBJECT_CENTER:
             self._center_params.object_center_loss_weight * object_center_loss
@@ -3765,8 +3777,8 @@ class CenterNetMetaArch(model.DetectionModel):
             k=self._center_params.max_box_predictions))
     multiclass_scores = tf.gather_nd(
         object_center_prob, tf.stack([y_indices, x_indices], -1), batch_dims=1)
-
-    num_detections = tf.reduce_sum(tf.to_int32(detection_scores > 0), axis=1)
+    num_detections = tf.reduce_sum(
+        tf.cast(detection_scores > 0, tf.int32), axis=1)
     postprocess_dict = {
         fields.DetectionResultFields.detection_scores: detection_scores,
         fields.DetectionResultFields.detection_multiclass_scores: