save progress

research/object_detection/core/losses.py

@@ -239,7 +239,7 @@ class WeightedGIOULocalizationLoss(Loss):
                           #                               #1.0 - box_list_ops.matched_iou(predicted_boxes,
                           #                               target_boxes)
     #print("Weights", weights)
-    return tf.reshape(weights, [-1]) * per_anchor_iou_loss # * per_anchor_iou_loss
+    return tf.reshape(weights, [-1]) * per_anchor_iou_loss
 
 
 class WeightedSigmoidClassificationLoss(Loss):

research/object_detection/core/region_similarity_calculator.py

@@ -67,12 +67,17 @@ class IouSimilarity(RegionSimilarityCalculator):
   This class computes pairwise similarity between two BoxLists based on IOU.
   """
 
-  def _compare(self, boxlist1, boxlist2):
+  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.
+      groundtruth_labels: a Tensor of shape [num_boxes, num_classes]
+        containing groundtruth labels.
+      predicted_labels: a Tensor of shape [num_boxes, num_classes]
+        containing predicted labels.
 
     Returns:
       A tensor with shape [N, M] representing pairwise iou scores.
@@ -80,17 +85,23 @@ class IouSimilarity(RegionSimilarityCalculator):
     return box_list_ops.iou(boxlist1, boxlist2)
 
 class DETRSimiliarity(RegionSimilarityCalculator):
-  """Class to compute similarity based on Intersection over Union (IOU) metric.
+  """Class to compute similarity for the Detection Transformer model.
 
-  This class computes pairwise similarity between two BoxLists based on IOU.
+  This class computes pairwise similarity between two BoxLists using a weighted
+  combination of IOU, classification scores, and the L1 loss.
   """
 
-  def _compare(self, boxlist1, boxlist2, groundtruth_labels=None, predicted_labels=None):
+  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.
+      groundtruth_labels: a Tensor of shape [num_boxes, num_classes]
+        containing groundtruth labels.
+      predicted_labels: a Tensor of shape [num_boxes, num_classes]
+        containing predicted labels.
 
     Returns:
       A tensor with shape [N, M] representing pairwise iou scores.
@@ -108,12 +119,17 @@ class NegSqDistSimilarity(RegionSimilarityCalculator):
   negative squared distance metric.
   """
 
-  def _compare(self, boxlist1, boxlist2, groundtruth_labels=None, predicted_labels=None):
+  def _compare(self, boxlist1, boxlist2,
+               groundtruth_labels=None, predicted_labels=None):
     """Compute matrix of (negated) sq distances.
 
     Args:
       boxlist1: BoxList holding N boxes.
       boxlist2: BoxList holding M boxes.
+      groundtruth_labels: a Tensor of shape [num_boxes, num_classes]
+        containing groundtruth labels.
+      predicted_labels: a Tensor of shape [num_boxes, num_classes]
+        containing predicted labels.
 
     Returns:
       A tensor with shape [N, M] representing negated pairwise squared distance.
@@ -127,12 +143,17 @@ class IoaSimilarity(RegionSimilarityCalculator):
   pairwise intersections divided by the areas of second BoxLists.
   """
 
-  def _compare(self, boxlist1, boxlist2, groundtruth_labels=None, predicted_labels=None):
+  def _compare(self, boxlist1, boxlist2,
+               groundtruth_labels=None, predicted_labels=None):
     """Compute pairwise IOA similarity between the two BoxLists.
 
     Args:
       boxlist1: BoxList holding N boxes.
       boxlist2: BoxList holding M boxes.
+      groundtruth_labels: a Tensor of shape [num_boxes, num_classes]
+        containing groundtruth labels.
+      predicted_labels: a Tensor of shape [num_boxes, num_classes]
+        containing predicted labels.
 
     Returns:
       A tensor with shape [N, M] representing pairwise IOA scores.
@@ -159,12 +180,17 @@ class ThresholdedIouSimilarity(RegionSimilarityCalculator):
     super(ThresholdedIouSimilarity, self).__init__()
     self._iou_threshold = iou_threshold
 
-  def _compare(self, boxlist1, boxlist2, groundtruth_labels=None, predicted_labels=None):
+  def _compare(self, boxlist1, boxlist2,
+               groundtruth_labels=None, predicted_labels=None):
     """Compute pairwise IOU similarity between the two BoxLists and score.
 
     Args:
       boxlist1: BoxList holding N boxes. Must have a score field.
       boxlist2: BoxList holding M boxes.
+      groundtruth_labels: a Tensor of shape [num_boxes, num_classes]
+        containing groundtruth labels.
+      predicted_labels: a Tensor of shape [num_boxes, num_classes]
+        containing predicted labels.
 
     Returns:
       A tensor with shape [N, M] representing scores threholded by pairwise

research/object_detection/core/region_similarity_calculator_test.py

@@ -93,6 +93,20 @@ class RegionSimilarityCalculatorTest(test_case.TestCase):
     iou_output = self.execute(graph_fn, [])
     self.assertAllClose(iou_output, exp_output)
 
+  def test_detr_similarity(self):
+    def graph_fn():
+      corners1 = tf.constant([[4.0, 3.0, 7.0, 5.0], [5.0, 6.0, 10.0, 7.0]])
+      corners2 = tf.constant([[3.0, 4.0, 6.0, 8.0], [14.0, 14.0, 15.0, 15.0],
+                              [0.0, 0.0, 20.0, 20.0]])
+      groundtruth_labels = tf.constant([[]])
+      boxes1 = box_list.BoxList(corners1)
+      boxes2 = box_list.BoxList(corners2)
+      iou_similarity_calculator = region_similarity_calculator.IouSimilarity()
+      iou_similarity = iou_similarity_calculator.compare(boxes1, boxes2)
+      return iou_similarity
+    exp_output = [[2.0 / 16.0, 0, 6.0 / 400.0], [1.0 / 16.0, 0.0, 5.0 / 400.0]]
+    iou_output = self.execute(graph_fn, [])
+    self.assertAllClose(iou_output, exp_output)
 
 if __name__ == '__main__':
   tf.test.main()