1，本文介绍

全新的聚焦线性注意力模块（Focused Linear Attention）是一种旨在提高计算效率和准确性的注意力机制。传统的自注意力机制在处理长序列数据时通常计算复杂度较高，限制了其在大规模数据上的应用。聚焦线性注意力模块则通过优化注意力计算的方式，显著降低了计算复杂度。

核心特点：

1. 线性时间复杂度：与传统的自注意力机制不同，聚焦线性注意力模块采用了线性时间复杂度的计算方法，这使得处理长序列数据时更加高效。

2. 聚焦机制：该模块专注于关键的上下文信息，通过聚焦策略来提高注意力计算的准确性，同时减少不必要的计算开销。

3. 改进的性能：通过优化注意力计算，聚焦线性注意力模块能够在保持高性能的同时，显著提升模型的计算效率，尤其适用于大规模数据处理。

应用场景：

• 自然语言处理：在长文本或大规模语料库的处理上，聚焦线性注意力模块能够提供更高的效率和更低的延迟。
• 计算机视觉：在处理高分辨率图像或视频数据时，能够加速计算过程，提升模型的实时性。

总体而言，聚焦线性注意力模块为处理大规模和长序列数据提供了一种高效且精确的解决方案，适用于各种需要高效注意力机制的应用场景。

关于Focused Linear Attention的详细介绍可以看论文：https://arxiv.org/pdf/2308.00442

本文将讲解如何将Focused Linear Attention融合进yolov8

话不多说，上代码！

2， 将Focused Linear Attention融合进yolov8

2.1 步骤一

找到如下的目录'ultralytics/nn/modules'，然后在这个目录下创建一个FLA.py文件，文件名字可以根据你自己的习惯起，然后将Focused Linear Attention的核心代码复制进去

import torch
import torch.nn as nn
from einops import rearrange
 
 
class FocusedLinearAttention(nn.Module):
    r""" Window based multi-head self attention (W-MSA) module with relative position bias.
    It supports both of shifted and non-shifted window.
    Args:
        dim (int): Number of input channels.
        window_size (tuple[int]): The height and width of the window.
        num_heads (int): Number of attention heads.
        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
    """
 
    def __init__(self, dim, window_size=[20, 20], num_heads=8, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.,
                 focusing_factor=3, kernel_size=5):
 
        super().__init__()
        device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
        self.dim = dim
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.focusing_factor = focusing_factor
        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)
        self.window_size = window_size
        self.positional_encoding = nn.Parameter(torch.zeros(size=(1, window_size[0] * window_size[1], dim)))
 
        self.softmax = nn.Softmax(dim=-1)
 
        self.dwc = nn.Conv2d(in_channels=head_dim, out_channels=head_dim, kernel_size=kernel_size,
                             groups=head_dim, padding=kernel_size // 2)
        self.scale = nn.Parameter(torch.zeros(size=(1, 1, dim)))
 
    def forward(self, x, mask=None):
        """
        Args:
            x: input features with shape of (num_windows*B, N, C)
            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
        """
        # flatten: [B, C, H, W] -> [B, C, HW]
        # transpose: [B, C, HW] -> [B, HW, C]
        x = x.flatten(2).transpose(1, 2)
        B, N, C = x.shape
        qkv = self.qkv(x).reshape(B, N, 3, C).permute(2, 0, 1, 3)
        q, k, v = qkv.unbind(0)
        k = k + self.positional_encoding[:, :k.shape[1], :]
        focusing_factor = self.focusing_factor
        kernel_function = nn.ReLU()
        q = kernel_function(q) + 1e-6
        k = kernel_function(k) + 1e-6
        scale = nn.Softplus()(self.scale)
        q = q / scale
        k = k / scale
        q_norm = q.norm(dim=-1, keepdim=True)
        k_norm = k.norm(dim=-1, keepdim=True)
        if float(focusing_factor) <= 6:
            q = q ** focusing_factor
            k = k ** focusing_factor
        else:
            q = (q / q.max(dim=-1, keepdim=True)[0]) ** focusing_factor
            k = (k / k.max(dim=-1, keepdim=True)[0]) ** focusing_factor
        q = (q / q.norm(dim=-1, keepdim=True)) * q_norm
        k = (k / k.norm(dim=-1, keepdim=True)) * k_norm
        q, k, v = (rearrange(x, "b n (h c) -> (b h) n c", h=self.num_heads) for x in [q, k, v])
 
        i, j, c, d = q.shape[-2], k.shape[-2], k.shape[-1], v.shape[-1]
 
        z = 1 / (torch.einsum("b i c, b c -> b i", q, k.sum(dim=1)) + 1e-6)
        if i * j * (c + d) > c * d * (i + j):
            kv = torch.einsum("b j c, b j d -> b c d", k, v)
            x = torch.einsum("b i c, b c d, b i -> b i d", q, kv, z)
        else:
            qk = torch.einsum("b i c, b j c -> b i j", q, k)
            x = torch.einsum("b i j, b j d, b i -> b i d", qk, v, z)
 
        num = int(v.shape[1] ** 0.5)
 
        feature_map = rearrange(v, "b (w h) c -> b c w h", w=num, h=num)
        feature_map = rearrange(self.dwc(feature_map), "b c w h -> b (w h) c")
        x = x + feature_map
        x = rearrange(x, "(b h) n c -> b n (h c)", h=self.num_heads)
        x = self.proj(x)
        x = self.proj_drop(x)
        x = rearrange(x, "b (w h) c -> b c w h", b=B, c=self.dim, w=num, h=num)
        return x

2.2 步骤二

在task.py导入我们的模块

2.3 步骤三

在task.py的parse_model方法里面注册我们的模块

到此注册成功,复制后面的yaml文件直接运行即可

yaml文件

# Ultralytics YOLO 🚀, GPL-3.0 license
# YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
 
# Parameters
nc: 1  # number of classes
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
  # [depth, width, max_channels]
  n: [0.33, 0.25, 1024]  # YOLOv8n summary: 225 layers,  3157200 parameters,  3157184 gradients,   8.9 GFLOPs
  s: [0.33, 0.50, 1024]  # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients,  28.8 GFLOPs
  m: [0.67, 0.75, 768]   # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients,  79.3 GFLOPs
  l: [1.00, 1.00, 512]   # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs
  x: [1.00, 1.25, 512]   # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs
 
# YOLOv8.0n backbone
backbone:
  # [from, repeats, module, args]
  - [-1, 1, Conv, [64, 3, 2]]  # 0-P1/2
  - [-1, 1, Conv, [128, 3, 2]]  # 1-P2/4
  - [-1, 3, C2f, [128, True]]
  - [-1, 1, Conv, [256, 3, 2]]  # 3-P3/8
  - [-1, 6, C2f, [256, True]]
  - [-1, 1, Conv, [512, 3, 2]]  # 5-P4/16
  - [-1, 6, C2f, [512, True]]
  - [-1, 1, Conv, [1024, 3, 2]]  # 7-P5/32
  - [-1, 3, C2f, [1024, True]]
  - [-1, 1, SPPF, [1024, 5]]  # 9
  - [-1, 1, FocusedLinearAttention, [256]]  # 10
 
# YOLOv8.0n head
head:
  - [-1, 1, nn.Upsample, [None, 2, 'nearest']]
  - [[-1, 6], 1, Concat, [1]]  # cat backbone P4
  - [-1, 3, C2f, [512]]  # 13
 
  - [-1, 1, nn.Upsample, [None, 2, 'nearest']]
  - [[-1, 4], 1, Concat, [1]]  # cat backbone P3
  - [-1, 3, C2f, [256]]  # 16 (P3/8-small)
 
  - [-1, 1, Conv, [256, 3, 2]]
  - [[-1, 13], 1, Concat, [1]]  # cat head P4
  - [-1, 3, C2f, [512]]  # 19 (P4/16-medium)
 
  - [-1, 1, Conv, [512, 3, 2]]
  - [[-1, 10], 1, Concat, [1]]  # cat head P5
  - [-1, 3, C2f, [1024]]  # 22 (P5/32-large)
 
  - [[16, 19, 22], 1, Detect, [nc]]  # Detect(P3, P4, P5)

可能会出现如下所示报错

 raise EinopsError(message + "\n {}".format(e))
einops.EinopsError:  Error while processing rearrange-reduction pattern "b (w h) c -> b c w h".
 Input tensor shape: torch.Size([128, 294, 32]). Additional info: {'w': 17, 'h': 17}.
 Shape mismatch, 294 != 289

则按以下方法进行修改

找到ultralytics/models/yolo/detect/train.py的如下所示代码

 def build_dataset(self, img_path, mode='train', batch=None):
        """
        Build YOLO Dataset.
        Args:
            img_path (str): Path to the folder containing images.
            mode (str): `train` mode or `val` mode, users are able to customize different augmentations for each mode.
            batch (int, optional): Size of batches, this is for `rect`. Defaults to None.
        """
        gs = max(int(de_parallel(self.model).stride.max() if self.model else 0), 32)
     
        return build_yolo_dataset(self.args, img_path, batch, self.data, mode=mode, rect=mode == 'val', stride=gs)

将该方法的最后一句代码修改为下面的代码

return build_yolo_dataset(self.args, img_path, batch, self.data, mode=mode, rect=(False if mode == 'val' else False), stride=gs)

不知不觉已经看完了哦，动动小手留个点赞收藏吧--_--