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
It would be nice to install pytorch in your enviroment, in case you don't have a GPU.
pip install self-attention-cv
Related articles
- How Attention works in Deep Learning
- How Transformers work in deep learning and NLP
- How the Vision Transformer (ViT) works in 10 minutes: an image is worth 16x16 words
- Understanding einsum for Deep learning: implement a transformer with multi-head self-attention from scratch
More articles are on the way.
Code Examples
Multi-head attention
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
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
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
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
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
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
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
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)
References
- 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.
- 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.
- Srinivas, A., Lin, T. Y., Parmar, N., Shlens, J., Abbeel, P., & Vaswani, A. (2021). Bottleneck Transformers for Visual Recognition. arXiv preprint arXiv:2101.11605.
- 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.
11 Dec 13, 2022
13 Nov 14, 2022
3 Jun 09, 2021
28 Dec 26, 2022
25 Dec 22, 2022
251 Jan 08, 2023
147 Jan 03, 2023
427 Jan 03, 2023
768 Dec 24, 2022
11 Apr 18, 2022
17 Dec 22, 2022
28 Dec 24, 2022
90 Jan 08, 2023
173 Dec 26, 2022
21 Dec 22, 2022
22 Dec 12, 2022
5 Nov 12, 2021
15 Nov 03, 2022
902 Nov 29, 2022
3 Jun 28, 2022