AttributeError: 'Tensor' object has no attribute 'numpy' in Tensorflow 2.1

Question

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'

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

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)

This throws the error:

dataset.map(transform, num_parallel_calls=1)

While this perfecly works:

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

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

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

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

is the shape of img completely defined? If its shape contains None in any one of the dimensions, then this could happen — learner
– learner, Commented Feb 22, 2020 at 5:37
I edited my post to add a bit more context. I'm using tf.io.decode_png to parse img so I'm guessing that the shape is known, isn't it? Also calling numpy()on img instead of it shape give me the same behavior... The weird thing is that all of this does not result in an error if I do this in elements from dataset.take() instead of inside the map... — Nick Skywalker
– Nick Skywalker, Commented Feb 22, 2020 at 10:27

Timbus Calin · Accepted Answer · 2022-12-20 08:56:36Z

30

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.

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

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

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.

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?

edited Dec 20, 2022 at 8:56

answered Feb 24, 2020 at 15:10

Timbus Calin

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4 Comments

Nick Skywalker Over a year ago

Thanks! I think I can't convert everything from my pipeline to native tensorflow operations as I need to dynamically populate a list based on tensor values.... Do you think I can still gain performance by mapping everything I can in pure tensorflow ops and then doing the dynamic part in another map calling tf.py_func?

Timbus Calin Over a year ago

You can still gain some performance, but I would benchmark to clearly see the differences/gains/losses in time performance.

Mattia Surricchio Over a year ago

This is what i was looking for. Is there a way to inspect the value of those tensors inside the function? For debug purposes

Timbus Calin Over a year ago

Yes. There is tf.print() instead of pure Python print(), one easy solution.

Aelius · Accepted Answer · 2022-12-20 10:53:11Z

1

To expand the answer of Timbus Calin further, here's an implementation example for your use case using the tf.py_function(). Keeping the transform function you have as it is, you should change the dataset.map like this:

dataset.map(lambda img, labels: tf.py_function(transform, 
                                               inp=[img, labels], 
                                               Tout=[tf.float64, tf.float32]))

Change the output types in Tout according to your data.

edited Dec 20, 2022 at 10:53

answered Dec 19, 2022 at 16:53

Aelius

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