make fixes

research/object_detection/meta_architectures/context_rcnn_lib.py

@@ -61,10 +61,11 @@ class AttentionBlock(tf.keras.layers.Layer):
 
   def build(self, input_shapes):
     self.feature_proj = ContextProjection(input_shapes[0][-1], self.freeze_batchnorm)
-    self.key_proj.build(input_shapes[0])
-    self.val_proj.build(input_shapes[0])
-    self.query_proj.build(input_shapes[0])
-    self.feature_proj.build(input_shapes[0])
+    #self.key_proj.build(input_shapes[0])
+    #self.val_proj.build(input_shapes[0])
+    #self.query_proj.build(input_shapes[0])
+    #self.feature_proj.build(input_shapes[0])
+    pass
 
   def filter_weight_value(self, weights, values, valid_mask):
     """Filters weights and values based on valid_mask.
@@ -140,7 +141,7 @@ class AttentionBlock(tf.keras.layers.Layer):
 
     projected_features = layer(features, is_training)
 
-    projected_features = tf.reshape((batch_size, -1, bottleneck_dimension))(projected_features)
+    projected_features = tf.reshape(projected_features, [batch_size, -1, bottleneck_dimension])
     print(projected_features.shape)
 
     if normalize: