Today’s deep learning applications require loading and pre-processing data efficiently to achieve high processing throughput. This requires creating efficient processing pipelines fully utilizing the underlying hardware capabilities. Some examples are load and decode data, do a variety of augmentations, color-format conversions, etc. Deep learning frameworks require supporting multiple data formats and augmentations to adapt to a variety of data-sets and models.
AMD ROCm Augmentation Library (rocAL) is designed to efficiently do such processing pipelines from both images and video as well as from a variety of storage formats. These pipelines are programmable by the user using both C++ and Python APIs.
- Chapter 1 - Overview
- Chapter 2 - Architecture Components
- Chapter 3 - Installation
- Chapter 4 - Using with Python API
- Chapter 5 - Framework Integration
- Chapter 6 - Using with C++ API
- Full processing pipeline support for data_loading, meta-data loading, augmentations, and data-format conversions for training and inference.
- Being able to do processing on CPU or Radeon GPU (with OpenCL or HIP backend)
- Ease of integration with framework plugins in Python
- Support variety of augmentation operations through AMD’s Radeon Performance Primitives (RPP).
- All available public and open-sourced under ROCm.
Refer rocAL Prerequisites
Refer rocAL build instructions
- rocAL Python package has been created using Pybind11 which enables data transfer between rocAL C++ API and Python API.
- rocal Python Bindings has both PyTorch and TensorFlow framework support.
- Various reader format support including FileReader, COCOReader, and TFRecordReader.
- examples folder has sample implementations for PyTorch and Tensorflow training and inference pipeline.
- Contains the image augmentations & file read and decode operations which are linked to rocAL C++ API
- All ops (listed below) are supported for the single input image and batched inputs.
| Image Augmentation | Reader and Decoder | Geometric Ops | Audio Augmentation |
|---|---|---|---|
| Color Twist | Image File Reader | Crop Mirror Normalize | PreEmphasis Filter |
| Color Temperature | Caffe Reader | Crop Resize | Non-Silent Region |
| Brightness | Caffe2 Reader | Resize | Resample |
| Gamma Correction | CIFAR10 Reader | Random Crop | Spectrogram |
| Snow | COCO Reader | Warp Affine | Mel-Filter Bank |
| Rain | TF Record Reader | Fish Eye | ToDecibels |
| Blur | MXNet Record Reader | Lens Correction | Normalize |
| Jitter | Video File Reader | Rotate | |
| Hue | Image Decoder | Crop | |
| Saturation | Image Decoder Random Crop | Flip | |
| Fog | Video Decoder | Resize Crop Mirror | |
| Contrast | Audio Decoder | Resize Crop Mirror Normalize | |
| Vignette | Slice | ||
| SNP Noise | |||
| Pixelate | |||
| Blend | |||
| Exposure |
- Contains Pipeline class which has all the data needed to build and run the rocAL graph.
- Contains support for context/graph creation, verify and run the graph.
- Has data transfer functions to exchange data between frameworks and rocAL
- define_graph functionality has been implemented to add nodes to build a pipeline graph.
amd.rocal.types are enums exported from C++ API to python. Some examples include CPU, GPU, FLOAT, FLOAT16, RGB, GRAY, etc.
- Contains ROCALGenericIterator for Pytorch.
- ROCALClassificationIterator class implements iterator for image classification and return images with corresponding labels.
- ROCALAudioIterator class for audio tasks and returns audio data, corresponding labels and its roi.
- From the above classes, any hybrid iterator pipeline can be created by adding augmentations.
- See example PyTorch Simple Example. Requires PyTorch.
- Contains ROCALIterator for TensorFlow.
- Any hybrid iterator pipeline can be created by adding augmentations.
- See example Tensorflow Simple Example. Requires TensorFlow.
- Build and install RPP
- Build and install MIVisionX
- Build and install rocAL