feature extractor and model builder update.

research/object_detection/models/ssd_mobilenet_v1_feature_extractor_test.py

@@ -20,75 +20,120 @@ import tensorflow as tf
 from object_detection.models import ssd_feature_extractor_test
 from object_detection.models import ssd_mobilenet_v1_feature_extractor
 
+slim = tf.contrib.slim
+
 
 class SsdMobilenetV1FeatureExtractorTest(
     ssd_feature_extractor_test.SsdFeatureExtractorTestBase, tf.test.TestCase):
 
-  def _create_feature_extractor(self, depth_multiplier):
+  def _create_feature_extractor(self, depth_multiplier, pad_to_multiple,
+                                is_training=True, batch_norm_trainable=True):
     """Constructs a new feature extractor.
 
     Args:
       depth_multiplier: float depth multiplier for feature extractor
+      pad_to_multiple: the nearest multiple to zero pad the input height and
+        width dimensions to.
+      is_training: whether the network is in training mode.
+      batch_norm_trainable: Whether to update batch norm parameters during
+        training or not.
     Returns:
       an ssd_meta_arch.SSDFeatureExtractor object.
     """
     min_depth = 32
-    conv_hyperparams = {}
+    with slim.arg_scope([slim.conv2d], normalizer_fn=slim.batch_norm) as sc:
+      conv_hyperparams = sc
     return ssd_mobilenet_v1_feature_extractor.SSDMobileNetV1FeatureExtractor(
-        depth_multiplier, min_depth, conv_hyperparams)
+        is_training, depth_multiplier, min_depth, pad_to_multiple,
+        conv_hyperparams, batch_norm_trainable)
 
   def test_extract_features_returns_correct_shapes_128(self):
     image_height = 128
     image_width = 128
     depth_multiplier = 1.0
+    pad_to_multiple = 1
     expected_feature_map_shape = [(4, 8, 8, 512), (4, 4, 4, 1024),
                                   (4, 2, 2, 512), (4, 1, 1, 256),
                                   (4, 1, 1, 256), (4, 1, 1, 128)]
     self.check_extract_features_returns_correct_shape(
-        image_height, image_width, depth_multiplier, expected_feature_map_shape)
+        image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape)
 
   def test_extract_features_returns_correct_shapes_299(self):
     image_height = 299
     image_width = 299
     depth_multiplier = 1.0
+    pad_to_multiple = 1
     expected_feature_map_shape = [(4, 19, 19, 512), (4, 10, 10, 1024),
                                   (4, 5, 5, 512), (4, 3, 3, 256),
                                   (4, 2, 2, 256), (4, 1, 1, 128)]
     self.check_extract_features_returns_correct_shape(
-        image_height, image_width, depth_multiplier, expected_feature_map_shape)
+        image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape)
 
   def test_extract_features_returns_correct_shapes_enforcing_min_depth(self):
     image_height = 299
     image_width = 299
     depth_multiplier = 0.5**12
+    pad_to_multiple = 1
     expected_feature_map_shape = [(4, 19, 19, 32), (4, 10, 10, 32),
                                   (4, 5, 5, 32), (4, 3, 3, 32),
                                   (4, 2, 2, 32), (4, 1, 1, 32)]
     self.check_extract_features_returns_correct_shape(
-        image_height, image_width, depth_multiplier, expected_feature_map_shape)
+        image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape)
+
+  def test_extract_features_returns_correct_shapes_with_pad_to_multiple(self):
+    image_height = 299
+    image_width = 299
+    depth_multiplier = 1.0
+    pad_to_multiple = 32
+    expected_feature_map_shape = [(4, 20, 20, 512), (4, 10, 10, 1024),
+                                  (4, 5, 5, 512), (4, 3, 3, 256),
+                                  (4, 2, 2, 256), (4, 1, 1, 128)]
+    self.check_extract_features_returns_correct_shape(
+        image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape)
 
   def test_extract_features_raises_error_with_invalid_image_size(self):
     image_height = 32
     image_width = 32
     depth_multiplier = 1.0
+    pad_to_multiple = 1
     self.check_extract_features_raises_error_with_invalid_image_size(
-        image_height, image_width, depth_multiplier)
+        image_height, image_width, depth_multiplier, pad_to_multiple)
 
   def test_preprocess_returns_correct_value_range(self):
     image_height = 128
     image_width = 128
     depth_multiplier = 1
+    pad_to_multiple = 1
     test_image = np.random.rand(4, image_height, image_width, 3)
-    feature_extractor = self._create_feature_extractor(depth_multiplier)
+    feature_extractor = self._create_feature_extractor(depth_multiplier,
+                                                       pad_to_multiple)
     preprocessed_image = feature_extractor.preprocess(test_image)
     self.assertTrue(np.all(np.less_equal(np.abs(preprocessed_image), 1.0)))
 
   def test_variables_only_created_in_scope(self):
     depth_multiplier = 1
+    pad_to_multiple = 1
     scope_name = 'MobilenetV1'
-    self.check_feature_extractor_variables_under_scope(depth_multiplier,
-                                                       scope_name)
+    self.check_feature_extractor_variables_under_scope(
+        depth_multiplier, pad_to_multiple, scope_name)
 
+  def test_nofused_batchnorm(self):
+    image_height = 40
+    image_width = 40
+    depth_multiplier = 1
+    pad_to_multiple = 1
+    image_placeholder = tf.placeholder(tf.float32,
+                                       [1, image_height, image_width, 3])
+    feature_extractor = self._create_feature_extractor(depth_multiplier,
+                                                       pad_to_multiple)
+    preprocessed_image = feature_extractor.preprocess(image_placeholder)
+    _ = feature_extractor.extract_features(preprocessed_image)
+    self.assertFalse(any(op.type == 'FusedBatchNorm'
+                         for op in tf.get_default_graph().get_operations()))
 
 if __name__ == '__main__':
   tf.test.main()