The rocAL pipeline

rocAL pipelines are used to load, decode, and augment audio, video, and image files that will be used in training and inference.

Audio, video, and image data is passed through the pipeline in batches.

Pipelines are created from graph definition functions written by the user that have been decorated with @pipeline_def. The @pipeline_def decorator converts a graph definition function into a pipeline factory.

Graph definition functions need to load a file, decode it, and augment it. The return value is the result of the augmentation.

For example, the my_pipe() function defines a graph that flips an image:

@pipeline_def
def my_pipe(flip_vertical, flip_horizontal):
    data, _ = fn.readers.file(file_root=images_dir)
    img = fn.decoders.image(data, device="mixed")
    flipped = fn.flip(img, horizontal=flip_horizontal, vertical=flip_vertical)
    return flipped, img

For more information on the @pipeline_def decorator, see the pipeline API reference.

In addition to the graph, parameters such as the batch size, device ID, and tensor layout need to be set to build the pipeline. For example:

pipe = my_pipe(True, False, batch_size=32, num_threads=1, device_id=0)

See the pipeline API reference for more information on the parameters that are needed to build a pipeline.

The pipeline is built using pipe.build() before being run.

There are two ways to run a pipeline. The first is using the pipeline.run() function. This function will run the pipeline exactly once on a single batch of files. The second way is to use an iterator that prefetches and loads the next batch of files while the initial batch is being processed.

pipe.build()
data_loader = ROCALClassificationIterator(pipe, device=device)
images = next(iter(data_loader))

The output of the pipeline is the output of the graph definition function.