Keras HDF5 ImageDataGenerator

A blazing fast HDF5 Image Generator for Keras ⚡️

Overview

Sometimes you'd like to work with large scale image datasets that cannot fit into the memory. Luckily, Keras provides various data generators to feed your network with mini-batch of data directly from a directory, simply by passing the source path. But this method is terribly inefficient. During training, the model has to deal with massive I/Os operations on disk which introduces huge latency.

A more efficient way is to take advantage of HDF5 data structure which is optimized for I/O operations. The idea is to (1) store your raw images and their labels to an HDF5 file, and to (2) create a generator that will load and preprocess mini-batches in real-time.

This image generator is built on top of Keras Sequence class and it's safe for multiprocessing. It's also using the super-fast image-processing albumentations library.

Installation / Usage

To install use pip:

$ pip install h5imagegenerator

Dependencies

• Keras
• Numpy
• Albumentations
• h5py

Contributing

Feel free to PR any change/request. 😁

Example

First, import the image generator class:

from h5imagegenerator import HDF5ImageGenerator

Then, create a new image generator:

train_generator = HDF5ImageGenerator(
        src='path/to/train.h5',
        X_key='images,
        y_key='labels,
        scaler=True,
        labels_encoding='hot',
        batch_size=32,
        mode='train')

• src: the source HDF5 file
• X_key: the key of the image tensors dataset (default is images)
• y_key: the key of the labels dataset (default is labels)
• scaler: scale inputs to the range [0, 1] (basic normalization) (default is True)
• labels_encoding: set it to hot to convert integers labels to binary matrix (one hot encoding), set it to smooth to perform smooth encoding (default is hot)
• batch_size: the number of samples to be generated at each iteration (default is 32)
• mode: 'train' to generate tuples of image samples and labels, 'test' to generate image samples only (default is 'train')

Note:

(1) When using smooth labels_encoding, you should provides a smooth_factor (defaults to 0.1).

(2) Labels stored in the HDF5 file must be integers or list of lists/tuples of integers in case you're doing multi-labels classification. ie: labels=[1, 2, 3, 6, 9] or labels=[(1, 2), (5, 9), (3, 9)]...

Sometimes you'd like to perform some data augmentation on-the-fly, to flip, zoom, rotate or scale images. You can pass to the generator an albumentations transformation pipeline:

my_augmenter = Compose([
        HorizontalFlip(p=0.5),
        RandomContrast(limit=0.2, p=0.5),
        RandomGamma(gamma_limit=(80, 120), p=0.5),
        RandomBrightness(limit=0.2, p=0.5),
        Resize(227, 227, cv2.INTER_AREA)])
    
train_generator = HDF5ImageGenerator(
        src='path/to/train.h5',
        X_key='images,
        y_key='labels,
        scaler=True,
        labels_encoding='hot',
        batch_size=32,
        augmenter=my_augmenter)

Note:

(1) albumentations offers a ToFloat(max_value=255) transformation which scales pixel intensities from [0, 255] to [0, 1]. Thus, when using it, you must turn off scaling: scaler=False.

(2) If you want to apply standardization (mean/std), you may want to use albumentations Normalize instead.

(3) Make sure to turn off data augmentation (augmenter=False) when using evaluate_generator() and predict_generator().

Finally, pass the generator to your model:

model.compile(
        loss='categorical_crossentropy',
        metrics=['accuracy'],
        optimizer='rmsprop')

# Example with fit:
model.fit_generator(
    train_generator,
    validation_data=val_generator,
    workers=10,
    use_multiprocessing=True,
    verbose=1,
    epochs=1)
    
    
# Example with evaluate:
model.evaluate_generator(
    eval_generator,
    workers=10,
    use_multiprocessing=True,
    verbose=1,
    epochs=1)