yolov8 添加S2-MLP

原创已于 2023-12-28 12:38:31 修改 · 479 阅读

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#YOLO #深度学习 #机器学习

于 2023-12-28 12:37:52 首次发布

本文介绍了如何在Ultralytics的YOLOv8模型中集成S2Attention模块，包括SpatialShift操作和SplitAttention层，以提升对象检测的性能。作者提供了必要的代码实现和配置文件修改说明。

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我没有实验效果保证，只是代码能跑通。

1.在ultralytics/nn/modules/下新建文件saattention.py 代码如下

import numpy as np
import torch
from torch import nn
from torch.nn import init
__all__ = 'S2Attention'

# https://arxiv.org/abs/2108.01072
def spatial_shift1(x):
    b, w, h, c = x.size()
    x[:, 1:, :, :c // 4] = x[:, :w - 1, :, :c // 4]
    x[:, :w - 1, :, c // 4:c // 2] = x[:, 1:, :, c // 4:c // 2]
    x[:, :, 1:, c // 2:c * 3 // 4] = x[:, :, :h - 1, c // 2:c * 3 // 4]
    x[:, :, :h - 1, 3 * c // 4:] = x[:, :, 1:, 3 * c // 4:]
    return x


def spatial_shift2(x):
    b, w, h, c = x.size()
    x[:, :, 1:, :c // 4] = x[:, :, :h - 1, :c // 4]
    x[:, :, :h - 1, c // 4:c // 2] = x[:, :, 1:, c // 4:c // 2]
    x[:, 1:, :, c // 2:c * 3 // 4] = x[:, :w - 1, :, c // 2:c * 3 // 4]
    x[:, :w - 1, :, 3 * c // 4:] = x[:, 1:, :, 3 * c // 4:]
    return x


class SplitAttention(nn.Module):
    def __init__(self, channel=512, k=3):
        super().__init__()
        self.channel = channel
        self.k = k
        self.mlp1 = nn.Linear(channel, channel, bias=False)
        self.gelu = nn.GELU()
        self.mlp2 = nn.Linear(channel, channel * k, bias=False)
        self.softmax = nn.Softmax(1)

    def forward(self, x_all):
        b, k, h, w, c = x_all.shape
        x_all = x_all.reshape(b, k, -1, c)
        a = torch.sum(torch.sum(x_all, 1), 1)
        hat_a = self.mlp2(self.gelu(self.mlp1(a)))
        hat_a = hat_a.reshape(b, self.k, c)
        bar_a = self.softmax(hat_a)
        attention = bar_a.unsqueeze(-2)
        out = attention * x_all
        out = torch.sum(out, 1).reshape(b, h, w, c)
        return out


class S2Attention(nn.Module):

    def __init__(self, channels=512):
        super().__init__()
        self.mlp1 = nn.Linear(channels, channels * 3)
        self.mlp2 = nn.Linear(channels, channels)
        self.split_attention = SplitAttention(channels)

    def forward(self, x):
        b, c, w, h = x.size()
        x = x.permute(0, 2, 3, 1)
        x = self.mlp1(x)
        x1 = spatial_shift1(x[:, :, :, :c])
        x2 = spatial_shift2(x[:, :, :, c:c * 2])
        x3 = x[:, :, :, c * 2:]
        x_all = torch.stack([x1, x2, x3], 1)
        a = self.split_attention(x_all)
        x = self.mlp2(a)
        x = x.permute(0, 3, 1, 2)
        return x

2.常规操作：修改ultralytics/nn/modules/__init__.py 文件，

3.tasks.py文件中parse_model函数中添加

        elif m is S2Attention:
            c2 = ch[f]
            args = [c2, *args]

4.yaml文件如下

# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect

# Parameters
nc: 80  # 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, S2Attention, []] #修改

# 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)