如何将急切执行中的模型转换为静态图并保存在 .pb 文件中？

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

我训练它并使用以下方法保存它：

   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 and epoch_10-2.data-00000-of-00001

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

您应该获得会话：

tf.keras.backend.get_session()

然后冻结模型，例如此处所做的https://www.dlology.com/blog/how-to-convert-trained-keras-model-to-tensorflow-and-make-prediction/ 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（也在链接中显示）：

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

如果这太麻烦，请尝试将 keras 模型另存为.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。只需在未启用急切执行的新 Python 会话中运行相同的代码即可实现此目的。

也来自同一页面：

为了保存和加载模型，tf.train.Checkpoint 存储对象的内部状态，而不需要隐藏变量。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 https://www.tensorflow.org/guide/eager

希望这可以帮助。