xrosliang / vit-pytorch

Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

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

Update: A new paper proposes using distillation tokens for distilling conv nets into efficient image transformers. I'll offer a way to toggle the use of distillation tokens in the coming days.

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 = 16,
    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)

Parameters

• image_size: int.
  Image size.
• patch_size: int.
  Number of patches. image_size must be divisible by patch_size.
  The number of patches is: n = (image_size // patch_size) ** 2 and n must be greater than 16.
• num_classes: int.
  Number of classes to classify.
• dim: int.
  Last dimension of output tensor after linear transformation nn.Linear(..., dim).
• depth: int.
  Number of Transformer blocks.
• heads: int.
  Number of heads in Multi-head Attention layer.
• mlp_dim: int.
  Dimension of the MLP (FeedForward) layer.
• channels: int, default 3.
  Number of image's channels.
• dropout: float between [0, 1], default 0..
  Dropout rate.
• emb_dropout: float between [0, 1], default 0.
  Embedding dropout rate.
• pool: string, either cls token pooling or mean pooling

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_latent'
)

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

@misc{dosovitskiy2020image,
    title   = {An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
    author  = {Alexey Dosovitskiy and Lucas Beyer and Alexander Kolesnikov and Dirk Weissenborn and Xiaohua Zhai and Thomas Unterthiner and Mostafa Dehghani and Matthias Minderer and Georg Heigold and Sylvain Gelly and Jakob Uszkoreit and Neil Houlsby},
    year    = {2020},
    eprint  = {2010.11929},
    archivePrefix = {arXiv},
    primaryClass = {cs.CV}
}

@misc{vaswani2017attention,
    title   = {Attention Is All You Need},
    author  = {Ashish Vaswani and Noam Shazeer and Niki Parmar and Jakob Uszkoreit and Llion Jones and Aidan N. Gomez and Lukasz Kaiser and Illia Polosukhin},
    year    = {2017},
    eprint  = {1706.03762},
    archivePrefix = {arXiv},
    primaryClass = {cs.CL}
}