compress target assigner

research/object_detection/core/target_assigner.py

@@ -2050,13 +2050,18 @@ class DETRTargetAssigner(object):
       reg_targets = self._create_regression_targets(anchors,
                                                     groundtruth_boxes,
                                                     match)
-      cls_targets = self._create_classification_targets(groundtruth_labels,
-                                                        unmatched_class_label,
-                                                        match)
-      reg_weights = self._create_regression_weights(match, groundtruth_weights)
+      cls_targets = match.gather_based_on_match(
+          groundtruth_labels,
+          unmatched_value=unmatched_class_label,
+          ignored_value=unmatched_class_label)
+      reg_weights = match.gather_based_on_match(groundtruth_weights,
+                                                ignored_value=0.,
+                                                unmatched_value=0.)
+      cls_weights = match.gather_based_on_match(
+          groundtruth_weights,
+          ignored_value=0.,
+          unmatched_value=self._negative_class_weight)
 
-      cls_weights = self._create_classification_weights(match,
-                                                        groundtruth_weights)
       # convert cls_weights from per-anchor to per-class.
       class_label_shape = tf.shape(cls_targets)[1:]
       weights_shape = tf.shape(cls_weights)
@@ -2117,98 +2122,9 @@ class DETRTargetAssigner(object):
 
     # Zero out the unmatched and ignored regression targets.
     unmatched_ignored_reg_targets = tf.tile(
-        self._default_regression_target(), [match_results_shape[0], 1])
+        tf.constant([4 * [0]], tf.float32), [match_results_shape[0], 1])
     matched_anchors_mask = match.matched_column_indicator()
     reg_targets = tf.where(matched_anchors_mask,
                            matched_reg_targets,
                            unmatched_ignored_reg_targets)
     return reg_targets
-
-  def _default_regression_target(self):
-    """Returns the default target for anchors to regress to.
-
-    Default regression targets are set to zero (though in
-    this implementation what these targets are set to should
-    not matter as the regression weight of any box set to
-    regress to the default target is zero).
-
-    Returns:
-      default_target: a float32 tensor with shape [1, box_code_dimension]
-    """
-    return tf.constant([4 * [0]], tf.float32)
-
-  def _create_classification_targets(self, groundtruth_labels,
-                                     unmatched_class_label, match):
-    """Create classification targets for each anchor.
-
-    Assign a classification target of for each anchor to the matching
-    groundtruth label that is provided by match.  Anchors that are not matched
-    to anything are given the target self._unmatched_cls_target
-
-    Args:
-      groundtruth_labels:  a tensor of shape [num_gt_boxes, d_1, ... d_k]
-        with labels for each of the ground_truth boxes. The subshape
-        [d_1, ... d_k] can be empty (corresponding to scalar labels).
-      unmatched_class_label: a float32 tensor with shape [d_1, d_2, ..., d_k]
-        which is consistent with the classification target for each
-        anchor (and can be empty for scalar targets).  This shape must thus be
-        compatible with the groundtruth labels that are passed to the "assign"
-        function (which have shape [num_gt_boxes, d_1, d_2, ..., d_k]).
-      match: a matcher.Match object that provides a matching between anchors
-        and groundtruth boxes.
-
-    Returns:
-      a float32 tensor with shape [num_anchors, d_1, d_2 ... d_k], where the
-      subshape [d_1, ..., d_k] is compatible with groundtruth_labels which has
-      shape [num_gt_boxes, d_1, d_2, ... d_k].
-    """
-    return match.gather_based_on_match(
-        groundtruth_labels,
-        unmatched_value=unmatched_class_label,
-        ignored_value=unmatched_class_label)
-
-  def _create_regression_weights(self, match, groundtruth_weights):
-    """Set regression weight for each anchor.
-
-    Only positive anchors are set to contribute to the regression loss, so this
-    method returns a weight of 1 for every positive anchor and 0 for every
-    negative anchor.
-
-    Args:
-      match: a matcher.Match object that provides a matching between anchors
-        and groundtruth boxes.
-      groundtruth_weights: a float tensor of shape [M] indicating the weight to
-        assign to all anchors match to a particular groundtruth box.
-
-    Returns:
-      a float32 tensor with shape [num_anchors] representing regression weights.
-    """
-    return match.gather_based_on_match(
-        groundtruth_weights, ignored_value=0., unmatched_value=0.)
-
-  def _create_classification_weights(self,
-                                     match,
-                                     groundtruth_weights):
-    """Create classification weights for each anchor.
-
-    Positive (matched) anchors are associated with a weight of
-    positive_class_weight and negative (unmatched) anchors are associated with
-    a weight of negative_class_weight. When anchors are ignored, weights are set
-    to zero. By default, both positive/negative weights are set to 1.0,
-    but they can be adjusted to handle class imbalance (which is almost always
-    the case in object detection).
-
-    Args:
-      match: a matcher.Match object that provides a matching between anchors
-        and groundtruth boxes.
-      groundtruth_weights: a float tensor of shape [M] indicating the weight to
-        assign to all anchors match to a particular groundtruth box.
-
-    Returns:
-      a float32 tensor with shape [num_anchors] representing classification
-      weights.
-    """
-    return match.gather_based_on_match(
-        groundtruth_weights,
-        ignored_value=0.,
-        unmatched_value=self._negative_class_weight)