fix BatchNorm+fp16 in inference mode

tensorpack/models/batch_norm.py

@@ -183,6 +183,11 @@ def BatchNorm(inputs, axis=None, training=None, momentum=0.9, epsilon=1e-5,
                 tf_args['virtual_batch_size'] = virtual_batch_size
             else:
                 assert virtual_batch_size is None, "Feature not supported in this version of TF!"
+            use_fp16 = inputs.dtype == tf.float16
+            if use_fp16:
+                # non-fused does not support fp16; fused does not support all layouts.
+                # we made our best guess here
+                tf_args['fused'] = True
             layer = tf.layers.BatchNormalization(**tf_args)
             xn = layer.apply(inputs, training=training, scope=tf.get_variable_scope())