def optimistic_remap_restore()

in utils.py [0:0]

• 22 lines of code
• 5 McCabe index (conditional complexity)

def optimistic_remap_restore(session, save_file, v_prefix):
    reader = tf.train.NewCheckpointReader(save_file)
    saved_shapes = reader.get_variable_to_shape_map()

    vars_list = tf.get_collection(
        tf.GraphKeys.GLOBAL_VARIABLES,
        scope='context_{}'.format(v_prefix))
    var_names = sorted([(var.name.split(':')[0], var) for var in vars_list if (
        (var.name.split(':')[0]).replace('context_{}'.format(v_prefix), 'context_0') in saved_shapes)])
    restore_vars = []

    v_map = {}
    with tf.variable_scope('', reuse=True):
        for saved_var_name, curr_var in var_names:
            var_shape = curr_var.get_shape().as_list()
            saved_var_name = saved_var_name.replace(
                'context_{}'.format(v_prefix), 'context_0')
            if var_shape == saved_shapes[saved_var_name]:
                v_map[saved_var_name] = curr_var
            else:
                print(saved_var_name)
                print(var_shape, saved_shapes[saved_var_name])

    saver = tf.train.Saver(v_map)
    saver.restore(session, save_file)