Perceiver [1] is a general architecture that works on many kinds of data, including images, video, audio, 3D point clouds, language and symbolic inputs, multimodal combinations, etc. Perceivers can handle new types of data with only minimal modifications. Perceivers process inputs using domain-agnostic Transformer-style attention. Unlike Transformers, Perceivers first map inputs to a small latent space where processing is cheap and doesn't depend on the input size. This makes it possible to build very deep networks even when using large inputs like images or videos.
Perceiver IO [2] is a generalization of Perceiver to handle arbitrary outputs in addition to arbitrary inputs. The original Perceiver only produced a single classification label. In addition to classification labels, Perceiver IO can produce (for example) language, optical flow, and multimodal videos with audio. This is done using the same building blocks as the original Perceiver. The computational complexity of Perceiver IO is linear in the input and output size and the bulk of the processing occurs in the latent space, allowing us to process inputs and outputs that are much larger than can be handled by standard Transformers. This means, for example, Perceiver IO can do BERT-style masked language modeling directly using bytes instead of tokenized inputs.
This directory contains our implementation of Perceiver IO
(encompassing the original Perceiver as a special case).
The perceiver.py file contains our implementation of Perceiver IO,
and io_processors.py contains domain-specific input and output processors
for the experiments we ran.
We provide example colabs in the colabs directory to demonstrate
how our models can be used and show the qualitative performance of Perceiver IO
on a diverse collection of tasks.
First, install dependencies following these instructions:
- Create a virtual env:
python3 -m venv ~/.venv/perceiver - Switch to the virtual env:
source ~/.venv/perceiver/bin/activate - Follow instructions for installing JAX on your platform: https://github.com/google/jax#installation
- Install other dependencies:
pip install -f requirements.txt
After installing dependencies, you can open the notebooks in the colabs directory
using Jupyter or Colab, and you can run our example training script.
Our colabs and training script assume that you are running from the
deepmind_research directory.
We provide the following colabs:
- colabs/masked_language_modelling.ipynb: Colab for running a pre-trained Perceiver IO masked-language model (Section 4.1 in [2]).
- colabs/optical_flow.ipynb: Colab for running a pre-trained optical flow Perceiver IO model and visualizing the output flow (Section 4.2 in [2]).
- colabs/video_autoencoding.ipynb: Colab for running a pre-trained video autoencoding Perceiver IO model and visualizing video reconstructions (Section 4.3 in [2]).
- colabs/imagenet_classification.ipynb: Colab for running three pre-trained ImageNet classification Perceiver IO models (Section 4.5 in [2]).
We also provide an example training script to train a Perceiver IO model for ImageNet classification. The provided hyperparameters are the settings used to train Perceiver IO with 2D Fourier position encodings, as described in section 4.5 and supplemental section H of the paper [2].
To run the script locally and train a miniature Perceiver model,
run: perceiver/train/launch_local.sh.
The script would need to be adapted to run on a distributed training setup
in order to train a full-scale model with the full batch size.
The file perceiver/train/autoaugment.py originates from the tensorflow/tpu
repository (https://github.com/tensorflow/tpu/blob/b24729de804fdb751b06467d3dce0637fa652060/models/official/efficientnet/autoaugment.py),
copyright (c) The Tensorflow Authors.
Sintel data is provided by and available from Sintel.org (https://durian.blender.org/), copyright (c) Blender Foundation/www.sintel.org.
The sample image in imagenet_classification.ipynb is obtained from
Getty Images under license (https://www.gettyimages.co.uk/eula#RF).
Video content may include clips provided as part of the THUMOS Challenge datasets, which may be accessed at http://crcv.ucf.edu/THUMOS14/download.html, copyrights held by the creators.
All data and parameters included with Perceiver are made available under the terms of the CC BY 4.0 license, available at https://creativecommons.org/licenses/by/4.0/legalcode.
This is not an officially supported Google product.
[1] Andrew Jaegle, Felix Gimeno, Andrew Brock, Andrew Zisserman, Oriol Vinyals, João Carreira. Perceiver: General Perception with Iterative Attention. ICML 2021. https://arxiv.org/abs/2103.03206
[2] Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier Hénaff, Matthew M. Botvinick, Andrew Zisserman, Oriol Vinyals, João Carreira. Perceiver IO: A General Architecture for Structured Inputs & Outputs. arXiv, 2021. https://arxiv.org/abs/2107.14795