CODEWITHSUNDEEP
tensorflow2.0tensorflow-datasetstensorflow2.x
Phys
Phys

Reputation: 518

Tensorflow 2 - OperatorNotAllowedInGraphError: using a `tf.Tensor` as a Python `bool` is not allowed: AutoGraph is disabled in this function

I am writing a mapping function for a dataset in Tensorflow 2. The dataset contains several images and the corresponding labels, more specifically there are only three possible values for the labels, 13, 17 and 34. The mapping function is supposed to take the labels and convert them into categorical labels.

There might be better ways to implement this function (please feel free to suggest), but this is my implementation:

def map_labels(dataset):

    def convert_labels_to_categorical(image, labels):
        labels = [1.0, 0., 0.] if labels == 13 else [0., 1.0, 0.] if labels == 17 else [0., 0., 1.0]

        return image, labels

        categorical_dataset = dataset.map(convert_labels_to_categorical)

    return categorical_dataset

The main issue is that I get the error below:

OperatorNotAllowedInGraphError: using a `tf.Tensor` as a Python `bool` is not allowed: AutoGraph is disabled in this function. Try decorating it directly with @tf.function.

I really have no idea about what this error means, and there are not so many other sources on the internet documenting the same error. Any idea?

EDIT (new non-working implementation):

def map_labels(dataset):

    def convert_labels_to_categorical(image, labels):
        labels = tf.Variable([1.0, 0., 0.]) if tf.reduce_any(tf.math.equal(labels, tf.constant(0,dtype=tf.int64))) \
        else tf.Variable([0., 1.0, 0.]) if tf.reduce_any(tf.math.equal(labels, tf.constant(90,dtype=tf.int64))) \
        else tf.Variable([0., 0., 1.0])

        return image, labels

    categorical_dataset = dataset.map(convert_labels_to_categorical)

    return categorical_dataset

Upvotes: 2

Views: 918

Answers (2)

Phys
Phys

Reputation: 518

I found a working solution. First, I created a dictionary, then the dataset:

dictionary = {"data":data, "labels":labels.astype('int32')}
dataset = tf.data.Dataset.from_tensor_slices(dict(dictionary))

This allows me to easily access the data and the labels inside dataset. There might be other ways that do not require using a dictionary, but this one works for me. For the mapping I used:

def map_labels(dataset):

    def convert_labels_to_categorical(dataset):
        if dataset['labels'] ==  tf.constant(13):
            dataset['labels'] = tf.constant([1, 0, 0]) 

        elif dataset['labels'] == tf.constant(17):
            dataset['labels'] =  tf.constant([0, 1, 0])

        elif dataset['labels'] == tf.constant(34):
            dataset['labels'] = tf.constant([0, 0, 1])

        return dataset

    categorical_dataset = dataset.map(convert_labels_to_categorical)

    return categorical_dataset

After mapping the dataset, if I inspect it with categorical_dataset.element_spec, I get:

{'data': TensorSpec(shape=(32, 32, 3), dtype=tf.uint8, name=None), 'labels': TensorSpec(shape=(None,), dtype=tf.int32, name=None)}

and if I print the elements, the new categorical labels are correctly assigned to the corresponding image. In summary, == and = are still working for tf variables.

Upvotes: 1

Timbus Calin
Timbus Calin

Reputation: 15053

Your problem comes from the fact that you intermix Python code with TensorFlow code.

Practically, in your map function you use arbitrary Python code, and not exclusively optimized TensorFlow code.

In a map function, you may only use functions that belong to the tf* category. If you still want to use arbitrary Python code, you need to use the tf.py_function() library.

You may want to consult this thread to get a better overview:

Is there an alternative to tf.py_function() for custom Python code?

To solve your problem, you need to exclusively use functions from the tf module, such as tf.strings, tf.bool etc.

Upvotes: 0

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