Reorganize unit tests

official/vision/beta/projects/yolo/modeling/tests/darknet_test.py → official/vision/beta/projects/yolo/modeling/backbones/darknet_test.py

@@ -22,7 +22,7 @@ import tensorflow as tf
 
 from tensorflow.python.distribute import combinations
 from tensorflow.python.distribute import strategy_combinations
-from official.vision.beta.projects.yolo.modeling.backbones import Darknet
+from official.vision.beta.projects.yolo.modeling.backbones import darknet
 
 
 class DarkNetTest(parameterized.TestCase, tf.test.TestCase):
@@ -37,8 +37,7 @@ class DarkNetTest(parameterized.TestCase, tf.test.TestCase):
     """Test creation of ResNet family models."""
     tf.keras.backend.set_image_data_format('channels_last')
 
-    network = Darknet.Darknet(model_id=model_id, min_level=3, max_level=5)
-    print(network.model_id)
+    network = darknet.Darknet(model_id=model_id, min_level=3, max_level=5)
     self.assertEqual(network.model_id, model_id)
 
     inputs = tf.keras.Input(shape=(input_size, input_size, 3), batch_size=1)
@@ -70,7 +69,7 @@ class DarkNetTest(parameterized.TestCase, tf.test.TestCase):
     tf.keras.backend.set_image_data_format('channels_last')
 
     with strategy.scope():
-      network = Darknet.Darknet(model_id="darknet53", min_size=3, max_size=5)
+      network = darknet.Darknet(model_id="darknet53", min_size=3, max_size=5)
       _ = network(inputs)
 
   @parameterized.parameters(1, 3, 4)
@@ -79,7 +78,7 @@ class DarkNetTest(parameterized.TestCase, tf.test.TestCase):
     tf.keras.backend.set_image_data_format('channels_last')
 
     input_specs = tf.keras.layers.InputSpec(shape=[None, None, None, input_dim])
-    network = Darknet.Darknet(model_id="darknet53", min_level=3, max_level=5, input_specs=input_specs)
+    network = darknet.Darknet(model_id="darknet53", min_level=3, max_level=5, input_specs=input_specs)
 
     inputs = tf.keras.Input(shape=(224, 224, input_dim), batch_size=1)
     _ = network(inputs)
@@ -88,8 +87,8 @@ class DarkNetTest(parameterized.TestCase, tf.test.TestCase):
     # Create a network object that sets all of its config options.
     kwargs = dict(
         model_id="darknet53",
-        min_level = 3, 
-        max_level = 5, 
+        min_level = 3,
+        max_level = 5,
         use_sync_bn=False,
         activation='relu',
         norm_momentum=0.99,
@@ -98,13 +97,13 @@ class DarkNetTest(parameterized.TestCase, tf.test.TestCase):
         kernel_regularizer=None,
         bias_regularizer=None,
     )
-    network = Darknet.Darknet(**kwargs)
+    network = darknet.Darknet(**kwargs)
 
     expected_config = dict(kwargs)
     self.assertEqual(network.get_config(), expected_config)
 
     # Create another network object from the first object's config.
-    new_network = Darknet.Darknet.from_config(network.get_config())
+    new_network = darknet.Darknet.from_config(network.get_config())
 
     # Validate that the config can be forced to JSON.
     _ = new_network.to_json()

official/vision/beta/projects/yolo/modeling/building_blocks/csp_connect.py

 import tensorflow as tf
 import tensorflow.keras as ks
-from ._DarkConv import DarkConv
+from .dark_conv import DarkConv
 
 
 @ks.utils.register_keras_serializable(package='yolo')

official/vision/beta/projects/yolo/modeling/building_blocks/csp_downsample.py

 import tensorflow as tf
 import tensorflow.keras as ks
-from ._DarkConv import DarkConv
+from .dark_conv import DarkConv
 
 
 @ks.utils.register_keras_serializable(package='yolo')

official/vision/beta/projects/yolo/modeling/building_blocks/csp_tiny.py

 """Contains common building blocks for yolo neural networks."""
 import tensorflow as tf
 import tensorflow.keras as ks
-from ._DarkConv import DarkConv
+from .dark_conv import DarkConv
 
 
 @ks.utils.register_keras_serializable(package='yolo')

official/vision/beta/projects/yolo/modeling/building_blocks/dark_conv.py

@@ -4,7 +4,7 @@ from functools import partial
 import tensorflow as tf
 import tensorflow.keras as ks
 import tensorflow.keras.backend as K
-from ._Identity import Identity
+from .identity import Identity
 
 from official.vision.beta.projects.yolo.modeling.functions.mish_activation import mish
 

official/vision/beta/projects/yolo/modeling/building_blocks/dark_residual.py

 """Contains common building blocks for yolo neural networks."""
 import tensorflow as tf
 import tensorflow.keras as ks
-from ._DarkConv import DarkConv
-from ._Identity import Identity
+from .dark_conv import DarkConv
+from .identity import Identity
 
 
 @ks.utils.register_keras_serializable(package='yolo')

official/vision/beta/projects/yolo/modeling/building_blocks/dark_tiny.py

 """Contains common building blocks for yolo neural networks."""
 import tensorflow as tf
 import tensorflow.keras as ks
-from ._DarkConv import DarkConv
+from .dark_conv import DarkConv
 
 
 @ks.utils.register_keras_serializable(package='yolo')

official/vision/beta/projects/yolo/modeling/tests/test_DarkTiny.py → official/vision/beta/projects/yolo/modeling/building_blocks/dark_tiny_test.py

@@ -14,8 +14,6 @@ class DarkTinyTest(tf.test.TestCase, parameterized.TestCase):
     x = ks.Input(shape=(width, height, filters))
     test_layer = DarkTiny(filters=filters, strides=strides)
     outx = test_layer(x)
-    print(outx)
-    print(outx.shape.as_list())
     self.assertEqual(width % strides, 0, msg="width % strides != 0")
     self.assertEqual(height % strides, 0, msg="height % strides != 0")
     self.assertAllEqual(outx.shape.as_list(),

official/vision/beta/projects/yolo/modeling/tests/README.md

-# Unit Tests for Yolo and Darknet models