progress on pr

research/object_detection/core/box_list_ops.py

@@ -34,7 +34,6 @@ from object_detection.core import box_list
 from object_detection.utils import ops
 from object_detection.utils import shape_utils
 
-
 class SortOrder(object):
   """Enum class for sort order.
 
@@ -303,6 +302,53 @@ def iou(boxlist1, boxlist2, scope=None):
         tf.equal(intersections, 0.0),
         tf.zeros_like(intersections), tf.truediv(intersections, unions))
 
+def l1(boxlist1, boxlist2, scope=None):
+  """Computes l1 loss (pairwise) between two boxlists.
+
+  Args:
+    boxlist1: BoxList holding N boxes
+    boxlist2: BoxList holding M boxes
+    scope: name scope.
+
+  Returns:
+    a tensor with shape [N, M] representing the pairwise L1 loss.
+  """
+  with tf.name_scope(scope, 'PairwiseL1'):
+    ycenter1, xcenter1, h1, w1 = boxlist1.get_center_coordinates_and_sizes()
+    ycenter2, xcenter2, h2, w2 = boxlist2.get_center_coordinates_and_sizes()
+    ycenters = tf.abs(tf.expand_dims(ycenter2, axis=0) - \
+        tf.expand_dims(tf.transpose(ycenter1), axis=1))
+    xcenters = tf.abs(tf.expand_dims(xcenter2, axis=0) - \
+        tf.expand_dims(tf.transpose(xcenter1), axis=1))
+    heights = tf.abs(tf.expand_dims(h2, axis=0) - \
+        tf.expand_dims(tf.transpose(h1), axis=1))
+    widths = tf.abs(tf.expand_dims(w2, axis=0) - \
+        tf.expand_dims(tf.transpose(w1), axis=1))
+    return ycenters + xcenters + heights + widths
+
+def giou_loss(boxlist1, boxlist2, scope=None):
+  """
+  Computes generalized IOU loss between two boxlists pairwise,
+  as described at giou.stanford.edu.
+  
+  Args:
+    boxlist1: BoxList holding N boxes
+    boxlist2: BoxList holding M boxes
+    scope: name scope.
+
+  Returns:
+    a tensor with shape [N, M] representing the pairwise GIoU loss.
+  """
+  # Import done internally so this dependency is not required for
+  # the OD API as a whole.
+  import tensorflow_addons as tfa
+
+  with tf.name_scope(scope, "PairwiseGIoU"):
+    N = boxlist1.num_boxes()
+    M = boxlist2.num_boxes()
+    boxes1 = tf.repeat(boxlist1.get(), repeats=M, axis=0)
+    boxes2 = tf.tile(boxlist2.get(), multiples=[N, 1])
+    return tf.reshape(tfa.losses.giou_loss(boxes1, boxes2), [N, M])
 
 def matched_iou(boxlist1, boxlist2, scope=None):
   """Compute intersection-over-union between corresponding boxes in boxlists.
@@ -319,11 +365,15 @@ def matched_iou(boxlist1, boxlist2, scope=None):
     intersections = matched_intersection(boxlist1, boxlist2)
     areas1 = area(boxlist1)
     areas2 = area(boxlist2)
+    print("AREAS AND INTERSECTION", areas1, areas2, intersections)
     unions = areas1 + areas2 - intersections
     return tf.where(
         tf.equal(intersections, 0.0),
         tf.zeros_like(intersections), tf.truediv(intersections, unions))
 
+def matched_giou(boxlist1, boxlist2, scope=None):
+  with tf.name_scope(scope, 'MatchedGIOU'):
+    pass
 
 def ioa(boxlist1, boxlist2, scope=None):
   """Computes pairwise intersection-over-area between box collections.

research/object_detection/core/region_similarity_calculator.py

@@ -31,11 +31,12 @@ import tensorflow.compat.v1 as tf
 from object_detection.core import box_list_ops
 from object_detection.core import standard_fields as fields
 
+EPSILON = 1e-8
 
 class RegionSimilarityCalculator(six.with_metaclass(ABCMeta, object)):
   """Abstract base class for region similarity calculator."""
 
-  def compare(self, boxlist1, boxlist2, scope=None):
+  def compare(self, boxlist1, boxlist2, scope=None, groundtruth_labels=None, predicted_labels=None):
     """Computes matrix of pairwise similarity between BoxLists.
 
     This op (to be overridden) computes a measure of pairwise similarity between
@@ -53,10 +54,10 @@ class RegionSimilarityCalculator(six.with_metaclass(ABCMeta, object)):
       a (float32) tensor of shape [N, M] with pairwise similarity score.
     """
     with tf.name_scope(scope, 'Compare', [boxlist1, boxlist2]) as scope:
-      return self._compare(boxlist1, boxlist2)
+      return self._compare(boxlist1, boxlist2, groundtruth_labels, predicted_labels)
 
   @abstractmethod
-  def _compare(self, boxlist1, boxlist2):
+  def _compare(self, boxlist1, boxlist2, groundtruth_labels=None, predicted_labels=None):
     pass
 
 
@@ -78,6 +79,27 @@ class IouSimilarity(RegionSimilarityCalculator):
     """
     return box_list_ops.iou(boxlist1, boxlist2)
 
+class DETRSimiliarity(RegionSimilarityCalculator):
+  """Class to compute similarity based on Intersection over Union (IOU) metric.
+
+  This class computes pairwise similarity between two BoxLists based on IOU.
+  """
+
+  def _compare(self, boxlist1, boxlist2, groundtruth_labels=None, predicted_labels=None):
+    """Compute pairwise IOU similarity between the two BoxLists.
+
+    Args:
+      boxlist1: BoxList holding N boxes.
+      boxlist2: BoxList holding M boxes.
+
+    Returns:
+      A tensor with shape [N, M] representing pairwise iou scores.
+    """
+    classification_scores = tf.matmul(groundtruth_labels,
+        tf.nn.softmax(predicted_labels), transpose_b=True)
+    return -5 * box_list_ops.l1(boxlist1, boxlist2) + \
+           classification_scores + \
+           2 * (1 - box_list_ops.giou_loss(boxlist1, boxlist2))
 
 class NegSqDistSimilarity(RegionSimilarityCalculator):
   """Class to compute similarity based on the squared distance metric.
@@ -86,7 +108,7 @@ class NegSqDistSimilarity(RegionSimilarityCalculator):
   negative squared distance metric.
   """
 
-  def _compare(self, boxlist1, boxlist2):
+  def _compare(self, boxlist1, boxlist2, groundtruth_labels=None, predicted_labels=None):
     """Compute matrix of (negated) sq distances.
 
     Args:
@@ -98,7 +120,6 @@ class NegSqDistSimilarity(RegionSimilarityCalculator):
     """
     return -1 * box_list_ops.sq_dist(boxlist1, boxlist2)
 
-
 class IoaSimilarity(RegionSimilarityCalculator):
   """Class to compute similarity based on Intersection over Area (IOA) metric.
 
@@ -106,7 +127,7 @@ class IoaSimilarity(RegionSimilarityCalculator):
   pairwise intersections divided by the areas of second BoxLists.
   """
 
-  def _compare(self, boxlist1, boxlist2):
+  def _compare(self, boxlist1, boxlist2, groundtruth_labels=None, predicted_labels=None):
     """Compute pairwise IOA similarity between the two BoxLists.
 
     Args:
@@ -138,7 +159,7 @@ class ThresholdedIouSimilarity(RegionSimilarityCalculator):
     super(ThresholdedIouSimilarity, self).__init__()
     self._iou_threshold = iou_threshold
 
-  def _compare(self, boxlist1, boxlist2):
+  def _compare(self, boxlist1, boxlist2, groundtruth_labels=None, predicted_labels=None):
     """Compute pairwise IOU similarity between the two BoxLists and score.
 
     Args: