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https://github.com/lucidrains/perceiver-pytorch
Implementation of Perceiver, General Perception with Iterative Attention, in Pytorch
https://github.com/lucidrains/perceiver-pytorch
artificial-intelligence attention-mechanism deep-learning
Last synced: about 24 hours ago
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Implementation of Perceiver, General Perception with Iterative Attention, in Pytorch
- Host: GitHub
- URL: https://github.com/lucidrains/perceiver-pytorch
- Owner: lucidrains
- License: mit
- Created: 2021-03-05T02:58:52.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2023-08-22T18:46:05.000Z (over 1 year ago)
- Last Synced: 2024-10-29T17:49:29.418Z (about 2 months ago)
- Topics: artificial-intelligence, attention-mechanism, deep-learning
- Language: Python
- Homepage:
- Size: 108 KB
- Stars: 1,087
- Watchers: 31
- Forks: 134
- Open Issues: 31
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
## Perceiver - Pytorch
Implementation of Perceiver, General Perception with Iterative Attention, in Pytorch
## Install
```bash
$ pip install perceiver-pytorch
```
## Usage
```python
import torch
from perceiver_pytorch import Perceiver
model = Perceiver(
input_channels = 3, # number of channels for each token of the input
input_axis = 2, # number of axis for input data (2 for images, 3 for video)
num_freq_bands = 6, # number of freq bands, with original value (2 * K + 1)
max_freq = 10., # maximum frequency, hyperparameter depending on how fine the data is
depth = 6, # depth of net. The shape of the final attention mechanism will be:
# depth * (cross attention -> self_per_cross_attn * self attention)
num_latents = 256, # number of latents, or induced set points, or centroids. different papers giving it different names
latent_dim = 512, # latent dimension
cross_heads = 1, # number of heads for cross attention. paper said 1
latent_heads = 8, # number of heads for latent self attention, 8
cross_dim_head = 64, # number of dimensions per cross attention head
latent_dim_head = 64, # number of dimensions per latent self attention head
num_classes = 1000, # output number of classes
attn_dropout = 0.,
ff_dropout = 0.,
weight_tie_layers = False, # whether to weight tie layers (optional, as indicated in the diagram)
fourier_encode_data = True, # whether to auto-fourier encode the data, using the input_axis given. defaults to True, but can be turned off if you are fourier encoding the data yourself
self_per_cross_attn = 2 # number of self attention blocks per cross attention
)
img = torch.randn(1, 224, 224, 3) # 1 imagenet image, pixelized
model(img) # (1, 1000)
```
For the backbone of Perceiver IO, the follow up paper that allows for flexible number of output sequence length, just import `PerceiverIO` instead
```python
import torch
from perceiver_pytorch import PerceiverIO
model = PerceiverIO(
dim = 32, # dimension of sequence to be encoded
queries_dim = 32, # dimension of decoder queries
logits_dim = 100, # dimension of final logits
depth = 6, # depth of net
num_latents = 256, # number of latents, or induced set points, or centroids. different papers giving it different names
latent_dim = 512, # latent dimension
cross_heads = 1, # number of heads for cross attention. paper said 1
latent_heads = 8, # number of heads for latent self attention, 8
cross_dim_head = 64, # number of dimensions per cross attention head
latent_dim_head = 64, # number of dimensions per latent self attention head
weight_tie_layers = False, # whether to weight tie layers (optional, as indicated in the diagram)
seq_dropout_prob = 0.2 # fraction of the tokens from the input sequence to dropout (structured dropout, for saving compute and regularizing effects)
)
seq = torch.randn(1, 512, 32)
queries = torch.randn(128, 32)
logits = model(seq, queries = queries) # (1, 128, 100) - (batch, decoder seq, logits dim)
```
As an example, using PerceiverIO as a language model
```python
import torch
from perceiver_pytorch import PerceiverLM
model = PerceiverLM(
num_tokens = 20000, # number of tokens
dim = 32, # dimension of sequence to be encoded
depth = 6, # depth of net
max_seq_len = 2048, # maximum sequence length
num_latents = 256, # number of latents, or induced set points, or centroids. different papers giving it different names
latent_dim = 512, # latent dimension
cross_heads = 1, # number of heads for cross attention. paper said 1
latent_heads = 8, # number of heads for latent self attention, 8
cross_dim_head = 64, # number of dimensions per cross attention head
latent_dim_head = 64, # number of dimensions per latent self attention head
weight_tie_layers = False # whether to weight tie layers (optional, as indicated in the diagram)
)
seq = torch.randint(0, 20000, (1, 512))
mask = torch.ones(1, 512).bool()
logits = model(seq, mask = mask) # (1, 512, 20000)
```
## Experimental
I have also included a version of Perceiver that includes bottom-up (in addition to top-down) attention, using the same scheme as presented in the original Set Transformers paper as the Induced Set Attention Block.
You simply have to change the above import to
```python
from perceiver_pytorch.experimental import Perceiver
```
## Citations
```bibtex
@misc{jaegle2021perceiver,
title = {Perceiver: General Perception with Iterative Attention},
author = {Andrew Jaegle and Felix Gimeno and Andrew Brock and Andrew Zisserman and Oriol Vinyals and Joao Carreira},
year = {2021},
eprint = {2103.03206},
archivePrefix = {arXiv},
primaryClass = {cs.CV}
}
```
```bibtex
@misc{jaegle2021perceiver,
title = {Perceiver IO: A General Architecture for Structured Inputs & Outputs},
author = {Andrew Jaegle and Sebastian Borgeaud and Jean-Baptiste Alayrac and Carl Doersch and Catalin Ionescu and David Ding and Skanda Koppula and Andrew Brock and Evan Shelhamer and Olivier Hénaff and Matthew M. Botvinick and Andrew Zisserman and Oriol Vinyals and João Carreira},
year = {2021},
eprint = {2107.14795},
archivePrefix = {arXiv},
primaryClass = {cs.LG}
}
```