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https://github.com/The-AI-Summer/self-attention-cv

Implementation of various self-attention mechanisms focused on computer vision. Ongoing repository.
https://github.com/The-AI-Summer/self-attention-cv

artificial-intelligence attention attention-mechanism deep-learning machine-learning machine-learning-algorithms self-attention transformer transformers

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Implementation of various self-attention mechanisms focused on computer vision. Ongoing repository.

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README 

        







# Self-attention building blocks for computer vision applications in PyTorch



Implementation of self attention mechanisms for computer vision in PyTorch with einsum and einops.

Focused on computer vision self-attention modules. 



#### Install it via pip 



```$ pip install self-attention-cv``` 



It would be nice to pre-install pytorch in your environment, in case you don't have a GPU. To run the tests from the terminal 

```$ pytest``` you may need to run ``` export PYTHONPATH=$PATHONPATH:`pwd` ``` before.



## Related articles

- [How Attention works in Deep Learning](https://theaisummer.com/attention/)

- [How Transformers work in deep learning and NLP](https://theaisummer.com/transformer/)

- [How the Vision Transformer (ViT) works in 10 minutes: an image is worth 16x16 words](https://theaisummer.com/vision-transformer/)

- [Understanding einsum for Deep learning: implement a transformer with multi-head self-attention from scratch](https://theaisummer.com/einsum-attention/)

- [How Positional Embeddings work in Self-Attention](https://theaisummer.com/positional-embeddings/)

- [Why multi-head self attention works: math, intuitions and 10+1 hidden insights](https://theaisummer.com/self-attention/)



## Code Examples



#### Multi-head attention



```python

import torch

from self_attention_cv import MultiHeadSelfAttention



model = MultiHeadSelfAttention(dim=64)

x = torch.rand(16, 10, 64)  # [batch, tokens, dim]

mask = torch.zeros(10, 10)  # tokens X tokens

mask[5:8, 5:8] = 1

y = model(x, mask)

```



#### Axial attention



```python

import torch

from self_attention_cv import AxialAttentionBlock

model = AxialAttentionBlock(in_channels=256, dim=64, heads=8)

x = torch.rand(1, 256, 64, 64)  # [batch, tokens, dim, dim]

y = model(x)

```



#### Vanilla Transformer Encoder

```python

import torch

from self_attention_cv import TransformerEncoder

model = TransformerEncoder(dim=64,blocks=6,heads=8)

x = torch.rand(16, 10, 64)  # [batch, tokens, dim]

mask = torch.zeros(10, 10)  # tokens X tokens

mask[5:8, 5:8] = 1

y = model(x,mask)

```

#### Vision Transformer with/without ResNet50 backbone for image classification

```python

import torch

from self_attention_cv import ViT, ResNet50ViT



model1 = ResNet50ViT(img_dim=128, pretrained_resnet=False, 

                        blocks=6, num_classes=10, 

                        dim_linear_block=256, dim=256)

# or

model2 = ViT(img_dim=256, in_channels=3, patch_dim=16, num_classes=10,dim=512)

x = torch.rand(2, 3, 256, 256)

y = model2(x) # [2,10]

```



#### A re-implementation of Unet with the Vision Transformer encoder



```python

import torch

from self_attention_cv.transunet import TransUnet

a = torch.rand(2, 3, 128, 128)

model = TransUnet(in_channels=3, img_dim=128, vit_blocks=8,

vit_dim_linear_mhsa_block=512, classes=5)

y = model(a) # [2, 5, 128, 128]

```



#### Bottleneck Attention block 

```python

import torch

from self_attention_cv.bottleneck_transformer import BottleneckBlock

inp = torch.rand(1, 512, 32, 32)

bottleneck_block = BottleneckBlock(in_channels=512, fmap_size=(32, 32), heads=4, out_channels=1024, pooling=True)

y = bottleneck_block(inp)

```



### Position embeddings are also available



#### 1D Positional Embeddings 



```python

import torch

from self_attention_cv.pos_embeddings import AbsPosEmb1D,RelPosEmb1D



model = AbsPosEmb1D(tokens=20, dim_head=64)

# batch heads tokens dim_head

q = torch.rand(2, 3, 20, 64)

y1 = model(q)



model = RelPosEmb1D(tokens=20, dim_head=64, heads=3)

q = torch.rand(2, 3, 20, 64)

y2 = model(q)

```



#### 2D Positional Embeddings

```python

import torch

from self_attention_cv.pos_embeddings import RelPosEmb2D

dim = 32  # spatial dim of the feat map

model = RelPosEmb2D(

    feat_map_size=(dim, dim),

    dim_head=128)



q = torch.rand(2, 4, dim*dim, 128)

y = model(q)

```



## Acknowledgments

Thanks to Alex Rogozhnikov [@arogozhnikov](https://github.com/arogozhnikov) for the awesome einops package. 

For my re-implementations I have studied and borrowed code from many repositories of Phil Wang [@lucidrains](https://github.com/lucidrains). 

By studying  his code I have managed to grasp self-attention, discover nlp stuff that are never

referred in the papers, and learn from his clean coding style.



### Cited as



```

@article{adaloglou2021transformer,

    title   = "Transformers in Computer Vision",

    author  = "Adaloglou, Nikolas",

    journal = "https://theaisummer.com/",

    year    = "2021",

    howpublished = {https://github.com/The-AI-Summer/self-attention-cv},

  }

```

 

## References



1. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. arXiv preprint arXiv:1706.03762.

2. Wang, H., Zhu, Y., Green, B., Adam, H., Yuille, A., & Chen, L. C. (2020, August). Axial-deeplab: Stand-alone axial-attention for panoptic segmentation. In European Conference on Computer Vision (pp. 108-126). Springer, Cham.

3. Srinivas, A., Lin, T. Y., Parmar, N., Shlens, J., Abbeel, P., & Vaswani, A. (2021). Bottleneck Transformers for Visual Recognition. arXiv preprint arXiv:2101.11605.  

4. Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., ... & Houlsby, N. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929.

5. Ramachandran, P., Parmar, N., Vaswani, A., Bello, I., Levskaya, A., & Shlens, J. (2019). Stand-alone self-attention in vision models. arXiv preprint arXiv:1906.05909.

6. Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., ... & Zhou, Y. (2021). Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306.

7. Wang, S., Li, B., Khabsa, M., Fang, H., & Ma, H. (2020). Linformer: Self-attention with linear complexity. arXiv preprint arXiv:2006.04768.

8. Bertasius, G., Wang, H., & Torresani, L. (2021). Is Space-Time Attention All You Need for Video Understanding?. arXiv preprint arXiv:2102.05095.

9. Shaw, P., Uszkoreit, J., & Vaswani, A. (2018). Self-attention with relative position representations. arXiv preprint arXiv:1803.02155.



## Support

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