context R-CNN bugfix and documentation fix

research/object_detection/meta_architectures/context_rcnn_lib_tf2.py

@@ -35,14 +35,15 @@ class ContextProjection(tf.keras.layers.Layer):
     self.projection = tf.keras.layers.Dense(units=projection_dimension,
                                             activation=tf.nn.relu6,
                                             use_bias=True)
+    self.projection_dimension = projection_dimension
     super(ContextProjection, self).__init__(**kwargs)
 
   def build(self, input_shape):
-    self.batch_norm.build(input_shape)
     self.projection.build(input_shape)
+    self.batch_norm.build(input_shape[:1] + [self.projection_dimension])
 
   def call(self, input_features, is_training=False):
-    return self.projection(self.batch_norm(input_features, is_training))
+    return self.batch_norm(self.projection(input_features), is_training)
 
 
 class AttentionBlock(tf.keras.layers.Layer):
@@ -92,8 +93,8 @@ class AttentionBlock(tf.keras.layers.Layer):
     """Handles a call by performing attention.
 
     Args:
-      box_features: A float Tensor of shape [batch_size, input_size,
-        num_input_features].
+      box_features: A float Tensor of shape [batch_size, input_size, height,
+        width, num_input_features].
       context_features: A float Tensor of shape [batch_size, context_size,
         num_context_features].
       valid_context_size: A int32 Tensor of shape [batch_size].