Python tensorflow.contrib.slim 模块，assign_from_checkpoint() 实例源码

def _create_image_encoder(preprocess_fn, factory_fn, image_shape, batch_size=32,
                         session=None, checkpoint_path=None,
                         loss_mode="cosine"):
    image_var = tf.placeholder(tf.uint8, (None, ) + image_shape)

    preprocessed_image_var = tf.map_fn(
        lambda x: preprocess_fn(x, is_training=False),
        tf.cast(image_var, tf.float32))

    l2_normalize = loss_mode == "cosine"
    feature_var, _ = factory_fn(
        preprocessed_image_var, l2_normalize=l2_normalize, reuse=None)
    feature_dim = feature_var.get_shape().as_list()[-1]

    if session is None:
        session = tf.Session()
    if checkpoint_path is not None:
        slim.get_or_create_global_step()
        init_assign_op, init_feed_dict = slim.assign_from_checkpoint(
            checkpoint_path, slim.get_variables_to_restore())
        session.run(init_assign_op, feed_dict=init_feed_dict)

    def encoder(data_x):
        out = np.zeros((len(data_x), feature_dim), np.float32)
        _run_in_batches(
            lambda x: session.run(feature_var, feed_dict=x),
            {image_var: data_x}, out, batch_size)
        return out

    return encoder

def load_ckpt(self, sess, ckpt='ckpts/vgg_16.ckpt'):
        variables = slim.get_variables(scope='vgg_16')
        init_assign_op, init_feed_dict = slim.assign_from_checkpoint(ckpt, variables)
        sess.run(init_assign_op, init_feed_dict)