OperatorNotAllowedInGraphError:不允许将'tf.Tensor'用作Python'bool'：AutoGraph未转换此函数

Question

我正在尝试按其索引筛选tensorflow.dataset：

    dataset = tf.data.Dataset.from_tensor_slices((sequences_matrix, label_data.astype(np.int8)))
    dataset = dataset.cache()
    dataset = dataset.enumerate()

    @tf.function
    def filter_function(i, data):
        return i in train_index # train_index is a list of integers

    train_dataset = dataset.filter(filter_function)

但我得到的错误如下：

Traceback (most recent call last):
  File "/home/marzi/workspace/nlp_classification/src/main.py", line 355, in <module>
    if __name__ == '__main__': main()
  File "/home/marzi/workspace/nlp_classification/src/main.py", line 320, in main
    deep_learning_algo(THE_DATA, HYPER_DICT)
  File "/home/marzi/workspace/nlp_classification/src/main.py", line 226, in deep_learning_algo
    tokenizer_name=tokenizer_name
  File "/home/marzi/workspace/nlp_classification/src/train.py", line 118, in fit_normal
    train_dataset = dataset.filter(filter_function)
  File "/home/marzi/anaconda3/envs/nlp_classification/lib/python3.7/site-packages/tensorflow/python/data/ops/dataset_ops.py", line 1862, in filter
    return FilterDataset(self, predicate)
  File "/home/marzi/anaconda3/envs/nlp_classification/lib/python3.7/site-packages/tensorflow/python/data/ops/dataset_ops.py", line 4264, in __init__
    use_legacy_function=use_legacy_function)
  File "/home/marzi/anaconda3/envs/nlp_classification/lib/python3.7/site-packages/tensorflow/python/data/ops/dataset_ops.py", line 3371, in __init__
    self._function = wrapper_fn.get_concrete_function()
  File "/home/marzi/anaconda3/envs/nlp_classification/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 2939, in get_concrete_function
    *args, **kwargs)
  File "/home/marzi/anaconda3/envs/nlp_classification/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 2906, in _get_concrete_function_garbage_collected
    graph_function, args, kwargs = self._maybe_define_function(args, kwargs)
  File "/home/marzi/anaconda3/envs/nlp_classification/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3213, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File "/home/marzi/anaconda3/envs/nlp_classification/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3075, in _create_graph_function
    capture_by_value=self._capture_by_value),
  File "/home/marzi/anaconda3/envs/nlp_classification/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 986, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "/home/marzi/anaconda3/envs/nlp_classification/lib/python3.7/site-packages/tensorflow/python/data/ops/dataset_ops.py", line 3364, in wrapper_fn
    ret = _wrapper_helper(*args)
  File "/home/marzi/anaconda3/envs/nlp_classification/lib/python3.7/site-packages/tensorflow/python/data/ops/dataset_ops.py", line 3299, in _wrapper_helper
    ret = autograph.tf_convert(func, ag_ctx)(*nested_args)
  File "/home/marzi/anaconda3/envs/nlp_classification/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 780, in __call__
    result = self._call(*args, **kwds)
  File "/home/marzi/anaconda3/envs/nlp_classification/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 823, in _call
    self._initialize(args, kwds, add_initializers_to=initializers)
  File "/home/marzi/anaconda3/envs/nlp_classification/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 697, in _initialize
    *args, **kwds))
  File "/home/marzi/anaconda3/envs/nlp_classification/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 2855, in _get_concrete_function_internal_garbage_collected
    graph_function, _, _ = self._maybe_define_function(args, kwargs)
  File "/home/marzi/anaconda3/envs/nlp_classification/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3213, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File "/home/marzi/anaconda3/envs/nlp_classification/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3075, in _create_graph_function
    capture_by_value=self._capture_by_value),
  File "/home/marzi/anaconda3/envs/nlp_classification/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 986, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "/home/marzi/anaconda3/envs/nlp_classification/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 600, in wrapped_fn
    return weak_wrapped_fn().__wrapped__(*args, **kwds)
  File "/home/marzi/anaconda3/envs/nlp_classification/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 973, in wrapper
    raise e.ag_error_metadata.to_exception(e)
tensorflow.python.framework.errors_impl.OperatorNotAllowedInGraphError: in user code:

    /home/marzi/workspace/nlp_classification/src/train.py:116 filter_function  *
        return i in train_index
    /home/marzi/anaconda3/envs/nlp_classification/lib/python3.7/site-packages/tensorflow/python/framework/ops.py:877 __bool__
        self._disallow_bool_casting()
    /home/marzi/anaconda3/envs/nlp_classification/lib/python3.7/site-packages/tensorflow/python/framework/ops.py:487 _disallow_bool_casting
        "using a `tf.Tensor` as a Python `bool`")
    /home/marzi/anaconda3/envs/nlp_classification/lib/python3.7/site-packages/tensorflow/python/framework/ops.py:474 _disallow_when_autograph_enabled
        " indicate you are trying to use an unsupported feature.".format(task))

    OperatorNotAllowedInGraphError: using a `tf.Tensor` as a Python `bool` is not allowed: AutoGraph did convert this function. This might indicate you are trying to use an unsupported feature.

但是如果我将filter函数中的条件从i in train_index更改为i > 10，它就可以正常工作。我不明白这两个条件之间有什么区别，这两个条件使其中一个产生错误，而另一个没有

Answer 1

使用@tf.function将把操作转换为图形模式，并在图形模式下列出理解is not supported。您可以改为使用tf.map_fn或tf.py_function：

@tf.function
def filter_function(i, data):
    return tf.py_function(lambda x: x in train_index, inp=[i], Tout=tf.bool)

例如：

import tensorflow as tf

train_index = [i for i in range(25) if i > 10]

dataset = tf.data.Dataset.from_tensor_slices(list(range(25)))
dataset = dataset.cache()
dataset = dataset.enumerate()


@tf.function
def filter_function(i, data):
    return tf.py_function(lambda x: x in train_index, inp=[i], Tout=tf.bool)


train_dataset = dataset.filter(filter_function)

for i in train_dataset:
    print(i[0].numpy())

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