不允许迭代 tf.Tensor:此函数中禁用 AutoGraph [英] Iterating over tf.Tensor is not allowed: AutoGraph is disabled in this function

问题描述

我正在使用 tensorflow 2.1 和 python 3.7

I am using tensorflow 2.1 along with python 3.7

以下代码片段用于构建张量流图.当作为独立的 python 脚本执行时，代码运行没有错误.(可能 tensorflow 正在以 Eager 模式运行?我不确定.)

The following snippet of code is being used to build a tensorflow graph. The code runs without errors when executed as a standalone python script. (Probably tensorflow is running in eager mode? I am not sure.)

import tensorflow as tf
patches = tf.random.uniform(shape=(1, 10, 50, 300), dtype=tf.dtypes.float32)
s = tf.shape(patches)
patches = [patches[0][x][y] - tf.reduce_mean(patches[0][x][y]) for y in tf.range(s[2]) for x in tf.range(s[1])]

但是，当这是张量流图的一部分时，代码会失败.我收到以下错误:张量流.

However, the code fails when this is part of a tensorflow graph. I receive the following error: tensorflow.

python.framework.errors_impl.OperatorNotAllowedInGraphError:迭代不允许超过 tf.Tensor:在此功能中禁用 AutoGraph.尝试直接用@tf.function 装饰它.

python.framework.errors_impl.OperatorNotAllowedInGraphError: iterating over tf.Tensor is not allowed: AutoGraph is disabled in this function. Try decorating it directly with @tf.function.

我还在包装上述代码行的方法中添加了装饰器 @tf.function.它没有帮助.我不确定我是否完全理解用 @tf.function 装饰的含义.我还检查了这可能是在张量流图中使用 python 列表理解的问题.我不确定如何使用 tf.map_fn 或 tf.while_loop 就我而言，因为我有嵌套循环.

I also added the decorator @tf.function to the method which wraps the above lines of code. It didn't help. I am not sure if I fully understand the meaning of decorating with @tf.function. I also checked that this could be a problem with using python list comprehension inside the tensorflow graph. I am not sure how to use tf.map_fn or tf.while_loop for my case, since I have nested loops.

提前致谢！

推荐答案

签名尚不支持列表推导式.提出的错误也需要改进.堆积在 https://github.com/tensorflow/tensorflow/issues/32546 应该帮助尽快解决.

List comprehensions are not yet supported in autograph. The error that's raised needs to be improved, too. Piling up on https://github.com/tensorflow/tensorflow/issues/32546 should help resolve it sooner.

在支持理解之前，您必须使用 map_fn，在这种情况下，它看起来像这样:

Until comprehensions are supported, you have to use map_fn, which in this case would look something like this:

def outer_comp(x):
  def inner_comp(y):
    return patches[0][x][y] - tf.reduce_mean(patches[0][x][y])
  return tf.map_fn(inner_comp, tf.range(s[2]), dtype=tf.float32)
patches = tf.map_fn(outer_comp, tf.range(s[1]), dtype=tf.float32)

也就是说，我相信你可以直接使用reduce_mean:

That said, I believe you can just use reduce_mean directly:

patches = patches - tf.expand_dims(tf.reduce_mean(patches, axis=3), -1)

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