AttributeError: 'Tensor' 对象在 Tensorflow 2.1 中没有属性 'numpy' [英] AttributeError: 'Tensor' object has no attribute 'numpy' in Tensorflow 2.1

问题描述

我正在尝试在 Tensorflow 2.1 中转换 Tensor 的 shape 属性，但出现此错误:

I am trying to convert the shape property of a Tensor in Tensorflow 2.1 and I get this error:

AttributeError: 'Tensor' object has no attribute 'numpy'

我已经检查了 tf.executing early() 的输出是 True，

I already checked that the output of tf.executing eagerly() is True,

一点上下文:我从 TFRecords 加载 tf.data.Dataset，然后应用 map.映射函数正在尝试将数据集示例 Tensor 之一的 shape 属性转换为 numpy:

A bit of context: I load a tf.data.Dataset from a TFRecords, then I apply a map. The maping function is trying to convert the shape property of one of the dataset sample Tensor to numpy:

def _parse_and_decode(serialized_example):
    """ parse and decode each image """
    features = tf.io.parse_single_example(
        serialized_example,
        features={
            'encoded_image': tf.io.FixedLenFeature([], tf.string),
            'kp_flat': tf.io.VarLenFeature(tf.int64),
            'kp_shape': tf.io.FixedLenFeature([3], tf.int64),
        }
    )
    image = tf.io.decode_png(features['encoded_image'], dtype=tf.uint8)
    image = tf.cast(image, tf.float32)

    kp_shape = features['kp_shape']

    kp_flat = tf.sparse.to_dense(features['kp_flat'])
    kp = tf.reshape(kp_flat, kp_shape)

    return image, kp


def read_tfrecords(records_dir, batch_size=1):
    # Read dataset from tfrecords
    tfrecords_files = glob.glob(os.path.join(records_dir, '*'))
    dataset = tf.data.TFRecordDataset(tfrecords_files)
    dataset = dataset.map(_parse_and_decode, num_parallel_calls=batch_size)
    return dataset


def transform(img, labels):
    img_shape = img.shape  # type: <class 'tensorflow.python.framework.ops.Tensor'>`
    img_shape = img_shape.numpy()  # <-- Throws the error
    # ...    

dataset = read_tfrecords(records_dir)

这会引发错误:

dataset.map(transform, num_parallel_calls=1)

虽然这非常有效:

for img, labels in dataset.take(1):
    print(img.shape.numpy())

尝试访问 img.numpy() 而不是 img.shape.numpy() 会导致相同的行为变压器和代码就在上面.

trying to access the img.numpy() instead of img.shape.numpy() results in the same behavior in the tranformer and the codde just above.

我检查了 img_shape 的类型，它是 .

I checked the type of img_shape and it is <class 'tensorflow.python.framework.ops.Tensor'>.

有没有人在新版本的 Tensorflow 中解决过这类问题?

Has anyone solved this sort of issue in new versions of Tensorflow?

推荐答案

你的代码中的问题是你不能在映射到 tf.data 的函数中使用 .numpy().Datasets，因为 .numpy() 是 Python 代码，而不是纯 TensorFlow 代码.

The problem in your code is that you cannot use .numpy() inside functions that are mapped onto tf.data.Datasets, because .numpy() is Python code not pure TensorFlow code.

当您使用像 my_dataset.map(my_function) 这样的函数时，您只能在 my_function 函数中使用 tf.* 函数.

When you use a function like my_dataset.map(my_function), you can only use tf.* functions inside your my_function function.

这不是 TensorFlow 2.x 版本的错误，而是出于性能目的在幕后如何生成静态图.

This is not a bug of TensorFlow 2.x versions, but rather on how static graphs are generated behind the scenes for performance purposes.

如果您想在映射到数据集的函数中使用自定义 Python 代码，则必须使用 tf.py_function()，文档:https://www.tensorflow.org/api_docs/python/tf/py_function.在数据集上映射时，真的没有其他方法可以混合 Python 代码和 TensorFlow 代码.

If you want to use custom Python code inside a function which you map on your dataset, you have to use tf.py_function(), docs: https://www.tensorflow.org/api_docs/python/tf/py_function. There is really no other way to mix Python code and TensorFlow code when mapping on a dataset.

您也可以咨询此问题以获取更多信息；这是我几个月前问的确切问题:是否有用于自定义 Python 代码的 tf.py_function() 替代方法?

You can also consult this question for further information; it's the exact question that I asked a couple of months ago: Is there an alternative to tf.py_function() for custom Python code?

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