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I use the data augmentation according to the official TensorFlow tutorial. First, I create a sequential model with augmenting layers:

def _getAugmentationFunction(self):
    if not self.augmentation:
        return None
    pipeline = []
    
    pipeline.append(layers.RandomFlip('horizontal_and_vertical'))
    pipeline.append(layers.RandomRotation(30))
    pipeline.append(layers.RandomTranslation(0.1, 0.1, fill_mode='nearest'))
    pipeline.append(layers.RandomBrightness(0.1, value_range=(0.0, 1.0)))

    model =  Sequential(pipeline)
    return lambda x, y: (model(x, training=True), y)

Then, I use the map function on the dataset:

data_augmentation = self._getAugmentationFunction()
self.train_data = self.train_data.map(data_augmentation,
                                      num_parallel_calls=AUTOTUNE)

The code works as expected but I get the following warning:

WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip
WARNING:tensorflow:Using a while_loop for converting Bitcast
WARNING:tensorflow:Using a while_loop for converting Bitcast
WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2
WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip
WARNING:tensorflow:Using a while_loop for converting Bitcast
WARNING:tensorflow:Using a while_loop for converting Bitcast
WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2
WARNING:tensorflow:Using a while_loop for converting ImageProjectiveTransformV3
WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip
WARNING:tensorflow:Using a while_loop for converting Bitcast
WARNING:tensorflow:Using a while_loop for converting Bitcast
WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2
WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip
WARNING:tensorflow:Using a while_loop for converting Bitcast
WARNING:tensorflow:Using a while_loop for converting Bitcast
WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2
WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip
WARNING:tensorflow:Using a while_loop for converting Bitcast
WARNING:tensorflow:Using a while_loop for converting Bitcast
WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2
WARNING:tensorflow:Using a while_loop for converting ImageProjectiveTransformV3
WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip
WARNING:tensorflow:Using a while_loop for converting Bitcast
WARNING:tensorflow:Using a while_loop for converting Bitcast
WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2

What is the reason of the warnings and how to fix it? I'm using TF v2.9.1

Shubham Panchal
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Karol Borkowski
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4 Answers4

6

It's not only warnings - these layers are extremly slow! In my case, the time for one epoch went up from 30 seconds to several minutes.

This seems to be a bug in keras version 2.9 and 2.10 (which is included in tensorflow): https://github.com/keras-team/keras-cv/issues/581

It works correctly with TF v2.8.3 - no error messages, and training is fast.

On my arch system – I have had installed TF by installing the python-tensorflow-opt-cuda package using pacman – I issued the following command which solved the issue:

python -m pip install tensorflow-gpu==2.8.3
AndrzejO
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4

Just updating this answer 6 months after, the bug is still in keras 2.11

i tried for like 3 days changing my input pipeline to apply the layers directly or switch it to tf.image or something but i will save a lot of headches to people saying: just downgrade to tensorflow 2.8.3

as previously said, it will save you a lot of time, plus there's something wrong with newer versions of tensorflow as well not only in the keras layers but using image_dataset_from_directory in tf 2.11 is taking 3.30 mins to load a dataset that in version 2.8.3 takes 33 seconds

here's the root cause of those inefficiencys so i suggest waiting for 2.12 or next implementation if isn't resolved yet:

https://github.com/keras-team/keras-cv/issues/291

Emir Bernal
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2

The warnings in TensorFlow could be managed by tf.get_logger().setLevel(). To turn off the warnings you can use

tf.get_logger().setLevel('ERROR')

I tried to replicate it in the gist, please find it here. Thank you!

1

the downgrade works nicely except some of my augmentation is not available.
'tensorflow.keras.layers' has no attribute 'RandomBrightness'

and GPU support has vanished. Was included in 2.10 and beyond. will have to reinstall tensorflow-gpu

Nick
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