Add encode_background_as_zeros option to the SSDMetaArch class --- now clients...

Add encode_background_as_zeros option to the SSDMetaArch class --- now clients have the option of encoding background targets as an all zeros vector or a one-hot vector with the 0th dimension corresponding to a background prediction.

PiperOrigin-RevId: 185228281

research/object_detection/builders/model_builder.py

@@ -152,6 +152,7 @@ def _build_ssd_model(ssd_config, is_training, add_summaries):
   matcher = matcher_builder.build(ssd_config.matcher)
   region_similarity_calculator = sim_calc.build(
       ssd_config.similarity_calculator)
+  encode_background_as_zeros = ssd_config.encode_background_as_zeros
   ssd_box_predictor = box_predictor_builder.build(hyperparams_builder.build,
                                                   ssd_config.box_predictor,
                                                   is_training, num_classes)
@@ -173,6 +174,7 @@ def _build_ssd_model(ssd_config, is_training, add_summaries):
       feature_extractor,
       matcher,
       region_similarity_calculator,
+      encode_background_as_zeros,
       image_resizer_fn,
       non_max_suppression_fn,
       score_conversion_fn,

research/object_detection/meta_architectures/ssd_meta_arch.py

@@ -121,6 +121,7 @@ class SSDMetaArch(model.DetectionModel):
                feature_extractor,
                matcher,
                region_similarity_calculator,
+               encode_background_as_zeros,
                image_resizer_fn,
                non_max_suppression_fn,
                score_conversion_fn,
@@ -147,6 +148,9 @@ class SSDMetaArch(model.DetectionModel):
       matcher: a matcher.Matcher object.
       region_similarity_calculator: a
         region_similarity_calculator.RegionSimilarityCalculator object.
+      encode_background_as_zeros: boolean determining whether background
+        targets are to be encoded as an all zeros vector or a one-hot
+        vector (where background is the 0th class).
       image_resizer_fn: a callable for image resizing.  This callable always
         takes a rank-3 image tensor (corresponding to a single image) and
         returns a rank-3 image tensor, possibly with new spatial dimensions and
@@ -190,7 +194,12 @@ class SSDMetaArch(model.DetectionModel):
     # TODO: handle agnostic mode and positive/negative class
     # weights
     unmatched_cls_target = None
-    unmatched_cls_target = tf.constant([1] + self.num_classes * [0], tf.float32)
+    unmatched_cls_target = tf.constant([1] + self.num_classes * [0],
+                                       tf.float32)
+    if encode_background_as_zeros:
+      unmatched_cls_target = tf.constant((self.num_classes + 1) * [0],
+                                         tf.float32)
+
     self._target_assigner = target_assigner.TargetAssigner(
         self._region_similarity_calculator,
         self._matcher,

research/object_detection/meta_architectures/ssd_meta_arch_test.py

@@ -84,7 +84,7 @@ class SsdMetaArchTest(test_case.TestCase):
     fake_feature_extractor = FakeSSDFeatureExtractor()
     mock_matcher = test_utils.MockMatcher()
     region_similarity_calculator = sim_calc.IouSimilarity()
-
+    encode_background_as_zeros = False
     def image_resizer_fn(image):
       return [tf.identity(image), tf.shape(image)]
 
@@ -111,10 +111,10 @@ class SsdMetaArchTest(test_case.TestCase):
     model = ssd_meta_arch.SSDMetaArch(
         is_training, mock_anchor_generator, mock_box_predictor, mock_box_coder,
         fake_feature_extractor, mock_matcher, region_similarity_calculator,
-        image_resizer_fn, non_max_suppression_fn, tf.identity,
-        classification_loss, localization_loss, classification_loss_weight,
-        localization_loss_weight, normalize_loss_by_num_matches,
-        hard_example_miner, add_summaries=False)
+        encode_background_as_zeros, image_resizer_fn, non_max_suppression_fn,
+        tf.identity, classification_loss, localization_loss,
+        classification_loss_weight, localization_loss_weight,
+        normalize_loss_by_num_matches, hard_example_miner, add_summaries=False)
     return model, num_classes, mock_anchor_generator.num_anchors(), code_size
 
   def test_preprocess_preserves_shapes_with_dynamic_input_image(self):

research/object_detection/protos/ssd.proto

@@ -32,6 +32,10 @@ message Ssd {
   // Region similarity calculator to compute similarity of boxes.
   optional RegionSimilarityCalculator similarity_calculator = 6;
 
+  // Whether background targets are to be encoded as an all
+  // zeros vector or a one-hot vector (where background is the 0th class).
+  optional bool encode_background_as_zeros = 12 [default=false];
+
   // Box predictor to attach to the features.
   optional BoxPredictor box_predictor = 7;