addressing ocmments

official/vision/beta/projects/yolo/modeling/backbones/darknet.py

@@ -665,8 +665,7 @@ def build_darknet(
     l2_regularizer: tf.keras.regularizers.Regularizer = None) -> tf.keras.Model:
   """Builds darknet."""
 
-  backbone_cfg = backbone_config.backbone.get()
-
+  backbone_cfg = backbone_config.get()
   model = Darknet(
       model_id=backbone_cfg.model_id,
       min_level=backbone_cfg.min_level,

official/vision/beta/projects/yolo/modeling/factory.py

@@ -65,7 +65,6 @@ def build_yolo_decoder(input_specs, model_config: yolo.Yolo, l2_regularization):
       kernel_regularizer=l2_regularization)
 
   base_model.update(base_dict)
-  print(base_model)
 
   model = YoloDecoder(input_specs, **base_model)
 
@@ -74,14 +73,14 @@ def build_yolo_decoder(input_specs, model_config: yolo.Yolo, l2_regularization):
 
 def build_yolo_filter(model_config: yolo.Yolo, decoder: YoloDecoder, masks,
                       xy_scales, path_scales):
-
   def _build(values):
-    print(values)
+    """Used to make model cfg shorter when different FPN lvls require 
+    different hyper paramters while others do not. The term all will 
+    blanket set the same hyper paramter for all FPN levels."""
     if "all" in values and values["all"] is not None:
       for key in values:
         if key != 'all':
           values[key] = values["all"]
-    print(values)
     return values
 
   model = YoloLayer(
@@ -125,12 +124,7 @@ def build_yolo_head(input_specs, model_config: yolo.Yolo, l2_regularization):
 
 def build_yolo(input_specs, model_config, l2_regularization, masks, xy_scales,
                path_scales):
-  print(model_config.as_dict())
-  print(input_specs)
-  print(l2_regularization)
 
-  # backbone = factory.build_backbone(input_specs, model_config,
-  #                                   l2_regularization)
   backbone = build_darknet(input_specs, model_config,
                                     l2_regularization)
   decoder = build_yolo_decoder(backbone.output_specs, model_config,

official/vision/beta/projects/yolo/modeling/layers/detection_generator.py

-"""Contains common building blocks for yolo neural networks."""
+"""Contains common building blocks for yolo neural networtf.keras."""
 import tensorflow as tf
-import tensorflow.keras as ks
 import tensorflow.keras.backend as K
 
 from official.vision.beta.projects.yolo.ops import box_ops
 
 
-@ks.utils.register_keras_serializable(package='yolo')
-class YoloLayer(ks.Model):
+@tf.keras.utils.register_keras_serializable(package='yolo')
+class YoloLayer(tf.keras.Model):
 
   def __init__(self,
                masks,
@@ -134,9 +133,6 @@ class YoloLayer(ks.Model):
 
     self._nms_type = self._nms_types[nms_type]
 
-    if self._nms_type >= 2 and self._nms_type <= 5:
-      self._nms = nms_ops.TiledNMS(iou_type=nms_type)
-
     self._scale_xy = scale_xy or {key: 1.0 for key, _ in masks.items()}
 
     self._generator = {}

official/vision/beta/projects/yolo/modeling/layers/detection_generator_test.py

@@ -15,17 +15,14 @@
 # ==============================================================================
 """Tests for yolo."""
 
-# Import libraries
 from absl.testing import parameterized
 import numpy as np
 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.layers import detection_generator as dg
 
-
 class YoloDecoderTest(parameterized.TestCase, tf.test.TestCase):
 
   @parameterized.parameters(
@@ -61,6 +58,6 @@ class YoloDecoderTest(parameterized.TestCase, tf.test.TestCase):
 
 
 if __name__ == '__main__':
-  from yolo.utils.run_utils import prep_gpu
-  prep_gpu()
+  # from yolo.utils.run_utils import prep_gpu
+  # prep_gpu()
   tf.test.main()