python - 失败前提条件错误: GetNext() failed after loading a Tensorflow Saved_Model

Question

我建立了一个专门的类来构建、训练、保存然后加载我的模型。保存是通过 tf.saved_model.simple_save 完成的，然后通过 tf.saved_model.loader.load 恢复的。

训练和推理是使用数据集 API 完成的。使用经过训练的模型时一切正常。

但是，如果我恢复保存的模型，则推理会中断并引发此错误:

FailedPreconditionError (see above for traceback): GetNext() failed because the iterator has not been initialized. Ensure that you have run the initializer operation for this iterator before getting the next element.

[[Node: datasets/cond/IteratorGetNext_1 = IteratorGetNextoutput_shapes=[[?,?,30], [?,5]], output_types=[DT_INT32, DT_INT32], _device="/job:localhost/replica:0/task:0/device:CPU:0"]]

我确定迭代器已初始化(print 按预期显示，请参阅下面的代码)。这可能与图表变量所属有关吗？还有其他想法吗？我有点被困在这里

(简化)代码:

class Model():
    def __init__(self):
        self.graph = tf.Graph()
        self.sess = tf.Session(graph=self.graph)
        with self.graph.as_default():
            model.features_data_ph = tf.Placeholder(...)
            model.labels_data_ph = tf.Placeholder(...)

    def build(self):
        with self.graph.as_default():
            self.logits = my_model(self.input_tensor)
            self.loss = my_loss(self.logits, self.labels_tensor)

    def train(self):
        my_training_procedure()

    def set_datasets(self):
        with self.graph.as_default():
            with tf.variable_scope('datasets'):
                self.dataset = tf.data.Dataset.from_tensor_slices((self.features_data_ph, self.labels_data_ph))
                self.iter = self.dataset.make_initializable_iterator()
                self.input_tensor, self.labels_tensor = self.iter.get_next

    def initialize_iterators(self, inference_data):
        with self.graph.as_default():
            feats = inference_data
            labs = np.zeros((len(feats), self.hp.num_classes))
            self.sess.run(self.iter.initializer,
                feed_dict={self.features_data_ph: feats,
                    self.labels_data_ph: labs})
            print('Iterator ready to infer')

    def infer(self, inference_data):
        self.initialize_iterators(inference_data)
        return sess.run(self.logits)

    def save(self, path):
        inputs = {"features_data_ph": self.features_data_ph,
            "labels_data_ph": self.labels_data_ph}
        outputs = {"logits": self.model.logits}
        tf.saved_model.simple_save(self.sess, path)

    @staticmethod
    def restore(path):
        model = Model()
        tf.saved_model.loader.load(model.sess, [tag_constants.SERVING], path)
        model.features_data_ph = model.graph.get_tensor_by_name("features_data_ph:0")
        model.labels_data_ph = model.graph.get_tensor_by_name("labels_data_ph:0")
        model.logits = model.graph.get_tensor_by_name("model/classifier/dense/BiasAdd:0")
        model.set_datasets()
        return model

失败例程:

model1 = Model()
model1.build()
model1.train()
model1.save(model1_path)

...

model2 = Model.restore(model1_path)
model2.infer(some_numpy_array) # Error here, after print, at sess.run()

(恢复模型有效，原始模型和恢复模型之间的张量值匹配)

Answer 1

我遇到了同样的问题，我相信问题是您正在初始化一个新的数据集对象，而不是初始化与模型一起保存的迭代器。

尝试:

make_iter = model.get_operation_by_name("YOURPREFIX/MakeIterator")
sess.run(make_iter, feed_dict)
model.infer(some_numpy_array)