decoder update

official/vision/beta/projects/yolo/modeling/decoders/yolo_decoder.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 # Lint as: python3
-"""Feature Pyramid Network and Path Aggregation variants used in YOLO."""
+"""Feature Pyramid Network and Path Aggregation variants used in YOLO"""
 
 import tensorflow as tf
 from official.vision.beta.projects.yolo.modeling.layers import nn_blocks
@@ -22,7 +22,7 @@ from official.vision.beta.projects.yolo.modeling.layers import nn_blocks
 @tf.keras.utils.register_keras_serializable(package='yolo')
 class _IdentityRoute(tf.keras.layers.Layer):
 
-  def call(self, inputs):
+  def call(self, inputs):  # pylint: disable=arguments-differ
     return None, inputs
 
 
@@ -39,7 +39,7 @@ class YoloFPN(tf.keras.layers.Layer):
                use_sync_bn=False,
                norm_momentum=0.99,
                norm_epsilon=0.001,
-               kernel_initializer='glorot_uniform',
+               kernel_initializer='VarianceScaling',
                kernel_regularizer=None,
                bias_regularizer=None,
                **kwargs):
@@ -172,7 +172,7 @@ class YoloFPN(tf.keras.layers.Layer):
 
 @tf.keras.utils.register_keras_serializable(package='yolo')
 class YoloPAN(tf.keras.layers.Layer):
-  """YOLO Path Aggregation Network."""
+  """YOLO Path Aggregation Network"""
 
   def __init__(self,
                path_process_len=6,
@@ -184,7 +184,7 @@ class YoloPAN(tf.keras.layers.Layer):
                use_sync_bn=False,
                norm_momentum=0.99,
                norm_epsilon=0.001,
-               kernel_initializer='glorot_uniform',
+               kernel_initializer='VarianceScaling',
                kernel_regularizer=None,
                bias_regularizer=None,
                fpn_input=True,
@@ -323,7 +323,7 @@ class YoloPAN(tf.keras.layers.Layer):
 
     Args:
       minimum_depth: `int` depth of the smallest branch of the FPN.
-      inputs: `dict[str, tf.InputSpec]` of the shape of input args as a
+      inputs: `dict[str, tf.InputSpec]` of the shape of input args as a 
         dictionary of lists.
 
     Returns:
@@ -374,14 +374,14 @@ class YoloDecoder(tf.keras.Model):
                use_sync_bn=False,
                norm_momentum=0.99,
                norm_epsilon=0.001,
-               kernel_initializer='glorot_uniform',
+               kernel_initializer='VarianceScaling',
                kernel_regularizer=None,
                bias_regularizer=None,
                **kwargs):
-    """Yolo Decoder initialization function.
-
-    A unified model that ties all decoder components into a conditionally build
-    YOLO decoder.
+    """Yolo Decoder initialization function. 
+    
+    A unified model that ties all decoder components into a conditionally build 
+    YOLO decder.
 
     Args:
       input_specs: `dict[str, tf.InputSpec]`: input specs of each of the inputs
@@ -403,7 +403,7 @@ class YoloDecoder(tf.keras.Model):
         zero.
       kernel_initializer: kernel_initializer for convolutional layers.
       kernel_regularizer: tf.keras.regularizers.Regularizer object for Conv2D.
-      bias_regularizer: tf.keras.regularizers.Regularizer object for Conv2D.
+      bias_regularizer: tf.keras.regularizers.Regularizer object for Conv2d.
       **kwargs: keyword arguments to be passed.
     """