add unit test for get_proposal_feature_extractor_model smaller input size

research/object_detection/models/faster_rcnn_resnet_v1_fpn_keras_feature_extractor_tf2_test.py

@@ -53,6 +53,19 @@ class FasterRCNNResnetV1FPNKerasFeatureExtractorTest(tf.test.TestCase):
         weight_decay=0.0)
   
   def test_extract_proposal_features_returns_expected_size(self):
+    feature_extractor = self._build_feature_extractor()
+    preprocessed_inputs = tf.random_uniform(
+        [2, 320, 320, 3], maxval=255, dtype=tf.float32)
+    rpn_feature_maps = feature_extractor.get_proposal_feature_extractor_model(
+        name='TestScope')(preprocessed_inputs)
+    features_shapes = [tf.shape(rpn_feature_map) 
+        for name, rpn_feature_map in rpn_feature_maps.items()]
+    
+    self.assertAllEqual(features_shapes[0].numpy(), [2, 40, 40, 256])
+    self.assertAllEqual(features_shapes[1].numpy(), [2, 20, 20, 256])
+    self.assertAllEqual(features_shapes[2].numpy(), [2, 10, 10, 256])
+
+  def test_extract_proposal_features_half_size_input(self):
     feature_extractor = self._build_feature_extractor()
     preprocessed_inputs = tf.random_uniform(
         [2, 160, 160, 3], maxval=255, dtype=tf.float32)