deformable attention

変形可能な注意

この論文の Deformable Attendance を Pytorch に実装したもので、DETR で提案されたものを改良したものと思われます。相対位置埋め込みも、SwinV2 で提案されている連続位置埋め込みを使用して、外挿を改善するために変更されました。

インストール

$ pip install deformable-attention

使用法

 import torch
from deformable_attention import DeformableAttention

attn = DeformableAttention (
    dim = 512 ,                   # feature dimensions
    dim_head = 64 ,               # dimension per head
    heads = 8 ,                   # attention heads
    dropout = 0. ,                # dropout
    downsample_factor = 4 ,       # downsample factor (r in paper)
    offset_scale = 4 ,            # scale of offset, maximum offset
    offset_groups = None ,        # number of offset groups, should be multiple of heads
    offset_kernel_size = 6 ,      # offset kernel size
)

x = torch . randn ( 1 , 512 , 64 , 64 )
attn ( x ) # (1, 512, 64, 64)

3D変形可能注意

 import torch
from deformable_attention import DeformableAttention3D

attn = DeformableAttention3D (
    dim = 512 ,                          # feature dimensions
    dim_head = 64 ,                      # dimension per head
    heads = 8 ,                          # attention heads
    dropout = 0. ,                       # dropout
    downsample_factor = ( 2 , 8 , 8 ),      # downsample factor (r in paper)
    offset_scale = ( 2 , 8 , 8 ),           # scale of offset, maximum offset
    offset_kernel_size = ( 4 , 10 , 10 ),   # offset kernel size
)

x = torch . randn ( 1 , 512 , 10 , 32 , 32 ) # (batch, dimension, frames, height, width)
attn ( x ) # (1, 512, 10, 32, 32)

1D変形可能に十分注意

 import torch
from deformable_attention import DeformableAttention1D

attn = DeformableAttention1D (
    dim = 128 ,
    downsample_factor = 4 ,
    offset_scale = 2 ,
    offset_kernel_size = 6
)

x = torch . randn ( 1 , 128 , 512 )
attn ( x ) # (1, 128, 512) 

引用

 @misc { xia2022vision ,
    title   = { Vision Transformer with Deformable Attention } , 
    author  = { Zhuofan Xia and Xuran Pan and Shiji Song and Li Erran Li and Gao Huang } ,
    year    = { 2022 } ,
    eprint  = { 2201.00520 } ,
    archivePrefix = { arXiv } ,
    primaryClass = { cs.CV }
}

 @misc { liu2021swin ,
    title   = { Swin Transformer V2: Scaling Up Capacity and Resolution } ,
    author  = { Ze Liu and Han Hu and Yutong Lin and Zhuliang Yao and Zhenda Xie and Yixuan Wei and Jia Ning and Yue Cao and Zheng Zhang and Li Dong and Furu Wei and Baining Guo } ,
    year    = { 2021 } ,
    eprint  = { 2111.09883 } ,
    archivePrefix = { arXiv } ,
    primaryClass = { cs.CV }
}