Vision Transformer - Pytorch

Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch. Significance is further explained in Yannic Kilcher's video. There's really not much to code here, but may as well lay it out for everyone so we expedite the attention revolution.

For a Pytorch implementation with pretrained models, please see Ross Wightman's repository here

Install

$ pip install vit-pytorch

Usage

import torch
from vit_pytorch import ViT

v = ViT(
    image_size = 256,
    patch_size = 32,
    num_classes = 1000,
    dim = 1024,
    depth = 6,
    heads = 8,
    mlp_dim = 2048,
    dropout = 0.1,
    emb_dropout = 0.1
)

img = torch.randn(1, 3, 256, 256)
mask = torch.ones(1, 8, 8).bool() # optional mask, designating which patch to attend to

preds = v(img, mask = mask) # (1, 1000)

Research Ideas

Self Supervised Training

You can train this with a near SOTA self-supervised learning technique, BYOL, with the following code.

(1)

$ pip install byol-pytorch

(2)

import torch
from vit_pytorch import ViT
from byol_pytorch import BYOL

model = ViT(
    image_size = 256,
    patch_size = 32,
    num_classes = 1000,
    dim = 1024,
    depth = 6,
    heads = 8,
    mlp_dim = 2048
)

learner = BYOL(
    model,
    image_size = 256,
    hidden_layer = 'to_cls_token'
)

opt = torch.optim.Adam(learner.parameters(), lr=3e-4)

def sample_unlabelled_images():
    return torch.randn(20, 3, 256, 256)

for _ in range(100):
    images = sample_unlabelled_images()
    loss = learner(images)
    opt.zero_grad()
    loss.backward()
    opt.step()
    learner.update_moving_average() # update moving average of target encoder

# save your improved network
torch.save(model.state_dict(), './pretrained-net.pt')

A pytorch-lightning script is ready for you to use at the repository link above.

Efficient Attention

There may be some coming from computer vision who think attention still suffers from quadratic costs. Fortunately, we have a lot of new techniques that may help. This repository offers a way for you to plugin your own sparse attention transformer.

An example with Linformer

$ pip install linformer

import torch
from vit_pytorch.efficient import ViT
from linformer import Linformer

efficient_transformer = Linformer(
    dim = 512,
    seq_len = 4096 + 1,  # 64 x 64 patches + 1 cls token
    depth = 12,
    heads = 8,
    k = 256
)

v = ViT(
    dim = 512,
    image_size = 2048,
    patch_size = 32,
    num_classes = 1000,
    transformer = efficient_transformer
)

img = torch.randn(1, 3, 2048, 2048) # your high resolution picture
v(img) # (1, 1000)

Other sparse attention frameworks I would highly recommend is Routing Transformer or Sinkhorn Transformer

Citations

@inproceedings{
    anonymous2021an,
    title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
    author={Anonymous},
    booktitle={Submitted to International Conference on Learning Representations},
    year={2021},
    url={https://openreview.net/forum?id=YicbFdNTTy},
    note={under review}
}