2

想象一下我有模型（tf.keras.Model）：

class ContextExtractor(tf.keras.Model):
    def __init__(self):
        super().__init__()
        self.model = self.__get_model()

    def call(self, x, training=False, **kwargs):
        features = self.model(x, training=training)
        return features

    def __get_model(self):
        return self.__get_small_conv()

    def __get_small_conv(self):
        model = tf.keras.Sequential()
        model.add(layers.Conv2D(32, (3, 3), strides=(2, 2), padding='same'))
        model.add(layers.LeakyReLU(alpha=0.2))

        model.add(layers.Conv2D(32, (3, 3), strides=(2, 2), padding='same'))
        model.add(layers.LeakyReLU(alpha=0.2))

        model.add(layers.Conv2D(64, (3, 3), strides=(2, 2), padding='same'))
        model.add(layers.LeakyReLU(alpha=0.2))

        model.add(layers.Conv2D(128, (3, 3), strides=(2, 2), padding='same'))
        model.add(layers.LeakyReLU(alpha=0.2))

        model.add(layers.Conv2D(256, (3, 3), strides=(2, 2), padding='same'))
        model.add(layers.LeakyReLU(alpha=0.2))


        model.add(layers.GlobalAveragePooling2D())

        return model

我对其进行了训练并使用以下方法保存了它：

   checkpoint = tf.train.Checkpoint(
                model=self.model,
                global_step=tf.train.get_or_create_global_step())
   checkpoint.save(weights_path / f'epoch_{epoch}')

这意味着我有两个保存的文件：epoch_10-2.index和epoch_10-2.data-00000-of-00001

现在我想部署我的模型。我想获取 .pb 文件。我怎么才能得到它？我想我需要以图形模式打开我的模型，加载我的权重并将其保存在 pb.file 中。实际上怎么做呢？

4

2 回答 2

2

感谢@BCJuan 提供信息，我找到了解决方案。

请大家在我的问题上寻找答案，请往下看。

注意：我想您已经保存了模型checkpoint_dir并希望以图形模式获取此模型，以便您可以将其保存为.pb文件。

model = ContextExtractor()

predictions = model(images, training=False)

checkpoint = tf.train.Checkpoint(model=model, global_step=tf.train.get_or_create_global_step())
status = checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))
status.assert_consumed()

with tf.Session() as sess:
    status.initialize_or_restore(sess) # this is the main line for loading

    # Actually, I don't know it is necessary to pass one batch for creating graph or not   
    img_batch = get_image(...) 
    ans = sess.run(predictions, feed_dict={images: img_batch})

    frozen_graph = freeze_session(sess, output_names=[out.op.name for out in model.outputs])

# save your model
tf.train.write_graph(frozen_graph, "where/to/save", "tf_model.pb", as_text=False)
于 2019-04-05T14:21:57.057 回答
2

你应该得到会话：

tf.keras.backend.get_session()

然后冻结模型，例如这里https://www.dlology.com/blog/how-to-convert-trained-keras-model-to-tensorflow-and-make-prediction/

def freeze_session(session, keep_var_names=None, output_names=None, clear_devices=True):
    """
    Freezes the state of a session into a pruned computation graph.

    Creates a new computation graph where variable nodes are replaced by
    constants taking their current value in the session. The new graph will be
    pruned so subgraphs that are not necessary to compute the requested
    outputs are removed.
    @param session The TensorFlow session to be frozen.
    @param keep_var_names A list of variable names that should not be frozen,
                          or None to freeze all the variables in the graph.
    @param output_names Names of the relevant graph outputs.
    @param clear_devices Remove the device directives from the graph for better portability.
    @return The frozen graph definition.
    """
    from tensorflow.python.framework.graph_util import convert_variables_to_constants
    graph = session.graph
    with graph.as_default():
        freeze_var_names = list(set(v.op.name for v in tf.global_variables()).difference(keep_var_names or []))
        output_names = output_names or []
        output_names += [v.op.name for v in tf.global_variables()]
        # Graph -> GraphDef ProtoBuf
        input_graph_def = graph.as_graph_def()
        if clear_devices:
            for node in input_graph_def.node:
                node.device = ""
        frozen_graph = convert_variables_to_constants(session, input_graph_def,
                                                      output_names, freeze_var_names)
        return frozen_graph


frozen_graph = freeze_session(K.get_session(),
                              output_names=[out.op.name for out in model.outputs])

然后将模型另存为.pb（也显示在链接中）：

tf.train.write_graph(frozen_graph, "model", "tf_model.pb", as_text=False)

如果这太麻烦，请尝试将 keras 模型保存为.h5（HDF5 类型文件），然后按照提供的链接中的说明进行操作。

来自张量流文档：

编写兼容的代码 为急切执行编写的相同代码也将在图形执行期间构建图形。为此，只需在未启用 Eager Execution 的新 Python 会话中运行相同的代码即可。

同样来自同一页面：

为了保存和加载模型，tf.train.Checkpoint 存储对象的内部状态，不需要隐藏变量。要记录模型、优化器和全局步骤的状态，请将它们传递给 tf.train.Checkpoint：

checkpoint_dir = tempfile.mkdtemp()
checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt")
root = tf.train.Checkpoint(optimizer=optimizer,
                           model=model,
                           optimizer_step=tf.train.get_or_create_global_step())

root.save(checkpoint_prefix)
root.restore(tf.train.latest_checkpoint(checkpoint_dir))

我向您推荐本页的最后一部分：https ://www.tensorflow.org/guide/eager

希望这可以帮助。

于 2019-04-05T08:29:11.700 回答