Updated the model builder and feature extractor such that the upsampling

interpolation method is configurable.

PiperOrigin-RevId: 372198977

research/object_detection/builders/model_builder.py

@@ -1130,11 +1130,18 @@ def _build_center_net_feature_extractor(feature_extractor_config, is_training):
       feature_extractor_config.use_separable_conv or
       feature_extractor_config.type == 'mobilenet_v2_fpn_sep_conv')
   kwargs = {
-      'channel_means': list(feature_extractor_config.channel_means),
-      'channel_stds': list(feature_extractor_config.channel_stds),
-      'bgr_ordering': feature_extractor_config.bgr_ordering,
-      'depth_multiplier': feature_extractor_config.depth_multiplier,
-      'use_separable_conv': use_separable_conv,
+      'channel_means':
+          list(feature_extractor_config.channel_means),
+      'channel_stds':
+          list(feature_extractor_config.channel_stds),
+      'bgr_ordering':
+          feature_extractor_config.bgr_ordering,
+      'depth_multiplier':
+          feature_extractor_config.depth_multiplier,
+      'use_separable_conv':
+          use_separable_conv,
+      'upsampling_interpolation':
+          feature_extractor_config.upsampling_interpolation,
   }
 
 

research/object_detection/builders/model_builder_tf2_test.py

@@ -398,7 +398,7 @@ class ModelBuilderTF2Test(
       }
     """
     # Set up the configuration proto.
-    config = text_format.Merge(proto_txt, model_pb2.DetectionModel())
+    config = text_format.Parse(proto_txt, model_pb2.DetectionModel())
     # Only add object center and keypoint estimation configs here.
     config.center_net.object_center_params.CopyFrom(
         self.get_fake_object_center_from_keypoints_proto())
@@ -422,6 +422,50 @@ class ModelBuilderTF2Test(
     self.assertEqual(kp_params.keypoint_labels,
                      ['nose', 'left_shoulder', 'right_shoulder', 'hip'])
 
+  def test_create_center_net_model_mobilenet(self):
+    """Test building a CenterNet model using bilinear interpolation."""
+    proto_txt = """
+      center_net {
+        num_classes: 10
+        feature_extractor {
+          type: "mobilenet_v2_fpn"
+          depth_multiplier: 1.0
+          use_separable_conv: true
+          upsampling_interpolation: "bilinear"
+        }
+        image_resizer {
+          keep_aspect_ratio_resizer {
+            min_dimension: 512
+            max_dimension: 512
+            pad_to_max_dimension: true
+          }
+        }
+      }
+    """
+    # Set up the configuration proto.
+    config = text_format.Parse(proto_txt, model_pb2.DetectionModel())
+    # Only add object center and keypoint estimation configs here.
+    config.center_net.object_center_params.CopyFrom(
+        self.get_fake_object_center_from_keypoints_proto())
+    config.center_net.keypoint_estimation_task.append(
+        self.get_fake_keypoint_proto())
+    config.center_net.keypoint_label_map_path = (
+        self.get_fake_label_map_file_path())
+
+    # Build the model from the configuration.
+    model = model_builder.build(config, is_training=True)
+
+    feature_extractor = model._feature_extractor
+    # Verify the upsampling layers in the FPN use 'bilinear' interpolation.
+    fpn = feature_extractor.get_layer('model_1')
+    num_up_sampling2d_layers = 0
+    for layer in fpn.layers:
+      if 'up_sampling2d' in layer.name:
+        num_up_sampling2d_layers += 1
+        self.assertEqual('bilinear', layer.interpolation)
+    # Verify that there are up_sampling2d layers.
+    self.assertGreater(num_up_sampling2d_layers, 0)
+
 
 if __name__ == '__main__':
   tf.test.main()

research/object_detection/models/center_net_mobilenet_v2_fpn_feature_extractor.py

@@ -39,7 +39,8 @@ class CenterNetMobileNetV2FPNFeatureExtractor(
                channel_means=(0., 0., 0.),
                channel_stds=(1., 1., 1.),
                bgr_ordering=False,
-               use_separable_conv=False):
+               use_separable_conv=False,
+               upsampling_interpolation='nearest'):
     """Intializes the feature extractor.
 
     Args:
@@ -52,6 +53,9 @@ class CenterNetMobileNetV2FPNFeatureExtractor(
         [blue, red, green] order.
       use_separable_conv: If set to True, all convolutional layers in the FPN
         network will be replaced by separable convolutions.
+      upsampling_interpolation: A string (one of 'nearest' or 'bilinear')
+        indicating which interpolation method to use for the upsampling ops in
+        the FPN.
     """
 
     super(CenterNetMobileNetV2FPNFeatureExtractor, self).__init__(
@@ -84,7 +88,8 @@ class CenterNetMobileNetV2FPNFeatureExtractor(
     for i, num_filters in enumerate(num_filters_list):
       level_ind = len(num_filters_list) - 1 - i
       # Upsample.
-      upsample_op = tf.keras.layers.UpSampling2D(2, interpolation='nearest')
+      upsample_op = tf.keras.layers.UpSampling2D(
+          2, interpolation=upsampling_interpolation)
       top_down = upsample_op(top_down)
 
       # Residual (skip-connection) from bottom-up pathway.
@@ -144,7 +149,8 @@ class CenterNetMobileNetV2FPNFeatureExtractor(
 
 
 def mobilenet_v2_fpn(channel_means, channel_stds, bgr_ordering,
-                     use_separable_conv=False, depth_multiplier=1.0, **kwargs):
+                     use_separable_conv=False, depth_multiplier=1.0,
+                     upsampling_interpolation='nearest', **kwargs):
   """The MobileNetV2+FPN backbone for CenterNet."""
   del kwargs
 
@@ -159,4 +165,5 @@ def mobilenet_v2_fpn(channel_means, channel_stds, bgr_ordering,
       channel_means=channel_means,
       channel_stds=channel_stds,
       bgr_ordering=bgr_ordering,
-      use_separable_conv=use_separable_conv)
+      use_separable_conv=use_separable_conv,
+      upsampling_interpolation=upsampling_interpolation)

research/object_detection/models/center_net_mobilenet_v2_fpn_feature_extractor_tf2_test.py

@@ -103,6 +103,30 @@ class CenterNetMobileNetV2FPNFeatureExtractorTest(test_case.TestCase):
     # a depth multiplier of 2.
     self.assertEqual(64, first_conv.filters)
 
+  def test_center_net_mobilenet_v2_fpn_feature_extractor_interpolation(self):
+
+    channel_means = (0., 0., 0.)
+    channel_stds = (1., 1., 1.)
+    bgr_ordering = False
+    model = (
+        center_net_mobilenet_v2_fpn_feature_extractor.mobilenet_v2_fpn(
+            channel_means, channel_stds, bgr_ordering, use_separable_conv=True,
+            upsampling_interpolation='bilinear'))
+
+    def graph_fn():
+      img = np.zeros((8, 224, 224, 3), dtype=np.float32)
+      processed_img = model.preprocess(img)
+      return model(processed_img)
+
+    outputs = self.execute(graph_fn, [])
+    self.assertEqual(outputs.shape, (8, 56, 56, 24))
+
+    # Verify the upsampling layers in the FPN use 'bilinear' interpolation.
+    fpn = model.get_layer('model_1')
+    for layer in fpn.layers:
+      if 'up_sampling2d' in layer.name:
+        self.assertEqual('bilinear', layer.interpolation)
+
 
 if __name__ == '__main__':
   tf.test.main()

research/object_detection/protos/center_net.proto

@@ -440,11 +440,17 @@ message CenterNetFeatureExtractor {
   optional bool use_depthwise = 5 [default = false];
 
 
-  // Depth multiplier. Only valid for specific models (e.g. MobileNet). See subclasses of `CenterNetFeatureExtractor`.
+  // Depth multiplier. Only valid for specific models (e.g. MobileNet). See
+  // subclasses of `CenterNetFeatureExtractor`.
   optional float depth_multiplier = 9 [default = 1.0];
 
   // Whether to use separable convolutions. Only valid for specific
   // models. See subclasses of `CenterNetFeatureExtractor`.
   optional bool use_separable_conv = 10 [default = false];
+
+  // Which interpolation method to use for the upsampling ops in the FPN.
+  // Currently only valid for CenterNetMobileNetV2FPNFeatureExtractor. The value
+  // can be on of 'nearest' or 'bilinear'.
+  optional string upsampling_interpolation = 11 [default = 'nearest'];
 }