TensorFlow 2.12: Issues with tf.data.Dataset.map() for Image Preprocessing and Performance
I'm maintaining legacy code that I'm getting frustrated with I've looked through the documentation and I'm still confused about Quick question that's been bugging me - I'm working on a TensorFlow 2.12 image classification project and I've run into a performance bottleneck when using `tf.data.Dataset.map()` for preprocessing images..... I'm trying to apply a series of transformations such as resizing, normalization, and data augmentation within the `map()` function, but I notice that the training throughput drops significantly, making it much slower than expected. Here's the relevant part of my code: ```python import tensorflow as tf from tensorflow.keras.preprocessing.image import ImageDataGenerator # Define a function to preprocess images def preprocess_image(image, label): image = tf.image.resize(image, [224, 224]) image = tf.cast(image, tf.float32) / 255.0 # Normalize to [0, 1] return image, label # Create a dataset from a directory train_ds = tf.keras.preprocessing.image_dataset_from_directory( 'path/to/train', image_size=(224, 224), batch_size=32 ) # Apply preprocessing using map train_ds = train_ds.map(preprocess_image, num_parallel_calls=tf.data.AUTOTUNE) ``` While this approach works, the performance is much lower than when I do preprocessing with `ImageDataGenerator` instead. I also tried using `tf.data.experimental.AUTOTUNE` but didn't see any improvement. When I check the time taken per epoch, it is taking almost double the time compared to before. Additionally, I get warnings like `Warning: The map transformation is not optimizing the dataset pipeline as expected`, which indicates that my transformations might not be efficient. Is there a better way to structure the dataset pipeline to improve performance? Should I be utilizing `tf.data` features differently or is there a more optimal approach to preprocess images in TensorFlow? Any suggestions would be greatly appreciated! For context: I'm using Python on Ubuntu. I'm working on a application that needs to handle this. This issue appeared after updating to Python LTS. How would you solve this? I'm on Ubuntu 22.04 using the latest version of Python. I'd love to hear your thoughts on this.