有趣的地方

有趣的地方

空间金字塔池化改进 SPP / SPPF / SimSPPF / ASPP / RFB / SPPCSPC / SPPFCSPC / SPPELAN

文章目录


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1 原理

1.1 SPP(Spatial Pyramid Pooling)

SPP模块是何凯明大神在2015年的论文《Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition》中被提出。

SPP全程为空间金字塔池化结构,主要是为了解决两个问题:

  1. 有效避免了对图像区域裁剪、缩放操作导致的图像失真等问题;
  2. 解决了卷积神经网络对图相关重复特征提取的问题,大大提高了产生候选框的速度,且节省了计算成本。

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class SPP(nn.Module):
    # Spatial Pyramid Pooling (SPP) layer https://arxiv.org/abs/1406.4729
    def __init__(self, c1, c2, k=(5, 9, 13)):
        super().__init__()
        c_ = c1 // 2  # hidden channels
        self.cv1 = Conv(c1, c_, 1, 1)
        self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1)
        self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])

    def forward(self, x):
        x = self.cv1(x)
        with warnings.catch_warnings():
            warnings.simplefilter('ignore')  # suppress torch 1.9.0 max_pool2d() warning
            return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1))

1.2 SPPF(Spatial Pyramid Pooling - Fast)

这个是YOLOv5作者Glenn Jocher基于SPP提出的,速度较SPP快很多,所以叫SPP-Fast

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class SPPF(nn.Module):
    # Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher
    def __init__(self, c1, c2, k=5):  # equivalent to SPP(k=(5, 9, 13))
        super().__init__()
        c_ = c1 // 2  # hidden channels
        self.cv1 = Conv(c1, c_, 1, 1)
        self.cv2 = Conv(c_ * 4, c2, 1, 1)
        self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2)

    def forward(self, x):
        x = self.cv1(x)
        with warnings.catch_warnings():
            warnings.simplefilter('ignore')  # suppress torch 1.9.0 max_pool2d() warning
            y1 = self.m(x)
            y2 = self.m(y1)
            return self.cv2(torch.cat((x, y1, y2, self.m(y2)), 1))

1.3 SimSPPF(Simplified SPPF)

美团YOLOv6提出的模块,感觉和SPPF只差了一个激活函数,简单测试了一下,单个ConvBNReLU速度要比ConvBNSiLU18%
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class SimConv(nn.Module):
    '''Normal Conv with ReLU activation'''
    def __init__(self, in_channels, out_channels, kernel_size, stride, groups=1, bias=False):
        super().__init__()
        padding = kernel_size // 2
        self.conv = nn.Conv2d(
            in_channels,
            out_channels,
            kernel_size=kernel_size,
            stride=stride,
            padding=padding,
            groups=groups,
            bias=bias,
        )
        self.bn = nn.BatchNorm2d(out_channels)
        self.act = nn.ReLU()

    def forward(self, x):
        return self.act(self.bn(self.conv(x)))

    def forward_fuse(self, x):
        return self.act(self.conv(x))

class SimSPPF(nn.Module):
    '''Simplified SPPF with ReLU activation'''
    def __init__(self, in_channels, out_channels, kernel_size=5):
        super().__init__()
        c_ = in_channels // 2  # hidden channels
        self.cv1 = SimConv(in_channels, c_, 1, 1)
        self.cv2 = SimConv(c_ * 4, out_channels, 1, 1)
        self.m = nn.MaxPool2d(kernel_size=kernel_size, stride=1, padding=kernel_size // 2)

    def forward(self, x):
        x = self.cv1(x)
        with warnings.catch_warnings():
            warnings.simplefilter('ignore')
            y1 = self.m(x)
            y2 = self.m(y1)
            return self.cv2(torch.cat([x, y1, y2, self.m(y2)], 1))

1.4 ASPP(Atrous Spatial Pyramid Pooling)

受到SPP的启发,语义分割模型DeepLabv2中提出了ASPP模块(空洞空间卷积池化金字塔),该模块使用具有不同采样率的多个并行空洞卷积层。为每个采样率提取的特征在单独的分支中进一步处理,并融合以生成最终结果。该模块通过不同的空洞率构建不同感受野的卷积核,用来获取多尺度物体信息,具体结构比较简单如下图所示:

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ASPP是在DeepLab中提出来的,在后续的DeepLab版本中对其做了改进,如加入BN层、加入深度可分离卷积等,但基本的思路还是没变。

# without BN version
class ASPP(nn.Module):
    def __init__(self, in_channel=512, out_channel=256):
        super(ASPP, self).__init__()
        self.mean = nn.AdaptiveAvgPool2d((1, 1))  # (1,1)means ouput_dim
        self.conv = nn.Conv2d(in_channel,out_channel, 1, 1)
        self.atrous_block1 = nn.Conv2d(in_channel, out_channel, 1, 1)
        self.atrous_block6 = nn.Conv2d(in_channel, out_channel, 3, 1, padding=6, dilation=6)
        self.atrous_block12 = nn.Conv2d(in_channel, out_channel, 3, 1, padding=12, dilation=12)
        self.atrous_block18 = nn.Conv2d(in_channel, out_channel, 3, 1, padding=18, dilation=18)
        self.conv_1x1_output = nn.Conv2d(out_channel * 5, out_channel, 1, 1)

    def forward(self, x):
        size = x.shape[2:]

        image_features = self.mean(x)
        image_features = self.conv(image_features)
        image_features = F.upsample(image_features, size=size, mode='bilinear')

        atrous_block1 = self.atrous_block1(x)
        atrous_block6 = self.atrous_block6(x)
        atrous_block12 = self.atrous_block12(x)
        atrous_block18 = self.atrous_block18(x)

        net = self.conv_1x1_output(torch.cat([image_features, atrous_block1, atrous_block6,
                                              atrous_block12, atrous_block18], dim=1))
        return net

1.5 RFB(Receptive Field Block)

RFB模块是在《ECCV2018:Receptive Field Block Net for Accurate and Fast Object Detection》一文中提出的,该文的出发点是模拟人类视觉的感受野从而加强网络的特征提取能力,在结构上RFB借鉴了Inception的思想,主要是在Inception的基础上加入了空洞卷积,从而有效增大了感受野
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RFBRFB-s的架构。RFB-s用于在浅层人类视网膜主题图中模拟较小的pRF,使用具有较小内核的更多分支。

class BasicConv(nn.Module):

    def __init__(self, in_planes, out_planes, kernel_size, stride=1, padding=0, dilation=1, groups=1, relu=True, bn=True):
        super(BasicConv, self).__init__()
        self.out_channels = out_planes
        if bn:
            self.conv = nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=False)
            self.bn = nn.BatchNorm2d(out_planes, eps=1e-5, momentum=0.01, affine=True)
            self.relu = nn.ReLU(inplace=True) if relu else None
        else:
            self.conv = nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=True)
            self.bn = None
            self.relu = nn.ReLU(inplace=True) if relu else None

    def forward(self, x):
        x = self.conv(x)
        if self.bn is not None:
            x = self.bn(x)
        if self.relu is not None:
            x = self.relu(x)
        return x


class BasicRFB(nn.Module):

    def __init__(self, in_planes, out_planes, stride=1, scale=0.1, map_reduce=8, vision=1, groups=1):
        super(BasicRFB, self).__init__()
        self.scale = scale
        self.out_channels = out_planes
        inter_planes = in_planes // map_reduce

        self.branch0 = nn.Sequential(
            BasicConv(in_planes, inter_planes, kernel_size=1, stride=1, groups=groups, relu=False),
            BasicConv(inter_planes, 2 * inter_planes, kernel_size=(3, 3), stride=stride, padding=(1, 1), groups=groups),
            BasicConv(2 * inter_planes, 2 * inter_planes, kernel_size=3, stride=1, padding=vision, dilation=vision, relu=False, groups=groups)
        )
        self.branch1 = nn.Sequential(
            BasicConv(in_planes, inter_planes, kernel_size=1, stride=1, groups=groups, relu=False),
            BasicConv(inter_planes, 2 * inter_planes, kernel_size=(3, 3), stride=stride, padding=(1, 1), groups=groups),
            BasicConv(2 * inter_planes, 2 * inter_planes, kernel_size=3, stride=1, padding=vision + 2, dilation=vision + 2, relu=False, groups=groups)
        )
        self.branch2 = nn.Sequential(
            BasicConv(in_planes, inter_planes, kernel_size=1, stride=1, groups=groups, relu=False),
            BasicConv(inter_planes, (inter_planes // 2) * 3, kernel_size=3, stride=1, padding=1, groups=groups),
            BasicConv((inter_planes // 2) * 3, 2 * inter_planes, kernel_size=3, stride=stride, padding=1, groups=groups),
            BasicConv(2 * inter_planes, 2 * inter_planes, kernel_size=3, stride=1, padding=vision + 4, dilation=vision + 4, relu=False, groups=groups)
        )

        self.ConvLinear = BasicConv(6 * inter_planes, out_planes, kernel_size=1, stride=1, relu=False)
        self.shortcut = BasicConv(in_planes, out_planes, kernel_size=1, stride=stride, relu=False)
        self.relu = nn.ReLU(inplace=False)

    def forward(self, x):
        x0 = self.branch0(x)
        x1 = self.branch1(x)
        x2 = self.branch2(x)

        out = torch.cat((x0, x1, x2), 1)
        out = self.ConvLinear(out)
        short = self.shortcut(x)
        out = out * self.scale + short
        out = self.relu(out)

        return out



1.6 SPPCSPC

该模块是YOLOv7中使用的SPP结构,表现优于SPPF,但参数量和计算量提升了很多

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class SPPCSPC(nn.Module):
    # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
    def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5, k=(5, 9, 13)):
        super(SPPCSPC, self).__init__()
        c_ = int(2 * c2 * e)  # hidden channels
        self.cv1 = Conv(c1, c_, 1, 1)
        self.cv2 = Conv(c1, c_, 1, 1)
        self.cv3 = Conv(c_, c_, 3, 1)
        self.cv4 = Conv(c_, c_, 1, 1)
        self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])
        self.cv5 = Conv(4 * c_, c_, 1, 1)
        self.cv6 = Conv(c_, c_, 3, 1)
        self.cv7 = Conv(2 * c_, c2, 1, 1)

    def forward(self, x):
        x1 = self.cv4(self.cv3(self.cv1(x)))
        y1 = self.cv6(self.cv5(torch.cat([x1] + [m(x1) for m in self.m], 1)))
        y2 = self.cv2(x)
        return self.cv7(torch.cat((y1, y2), dim=1))
#分组SPPCSPC 分组后参数量和计算量与原本差距不大,不知道效果怎么样
class SPPCSPC_group(nn.Module):
    def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5, k=(5, 9, 13)):
        super(SPPCSPC_group, self).__init__()
        c_ = int(2 * c2 * e)  # hidden channels
        self.cv1 = Conv(c1, c_, 1, 1, g=4)
        self.cv2 = Conv(c1, c_, 1, 1, g=4)
        self.cv3 = Conv(c_, c_, 3, 1, g=4)
        self.cv4 = Conv(c_, c_, 1, 1, g=4)
        self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])
        self.cv5 = Conv(4 * c_, c_, 1, 1, g=4)
        self.cv6 = Conv(c_, c_, 3, 1, g=4)
        self.cv7 = Conv(2 * c_, c2, 1, 1, g=4)

    def forward(self, x):
        x1 = self.cv4(self.cv3(self.cv1(x)))
        y1 = self.cv6(self.cv5(torch.cat([x1] + [m(x1) for m in self.m], 1)))
        y2 = self.cv2(x)
        return self.cv7(torch.cat((y1, y2), dim=1))

1.7 SPPFCSPC🍀

我借鉴了SPPF的思想将SPPCSPC优化了一下,得到了SPPFCSPC,在保持感受野不变的情况下获得速度提升;我把这个模块给v7作者看了,并没有得到否定,详细回答可以看4 Issue

目前这个结构被YOLOv6 3.0版本使用了,效果很不错,大家可以看一下YOLOv6 3.0的论文,里面有详细的实验结果。

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class SPPFCSPC(nn.Module):
    
    def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5, k=5):
        super(SPPFCSPC, self).__init__()
        c_ = int(2 * c2 * e)  # hidden channels
        self.cv1 = Conv(c1, c_, 1, 1)
        self.cv2 = Conv(c1, c_, 1, 1)
        self.cv3 = Conv(c_, c_, 3, 1)
        self.cv4 = Conv(c_, c_, 1, 1)
        self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2)
        self.cv5 = Conv(4 * c_, c_, 1, 1)
        self.cv6 = Conv(c_, c_, 3, 1)
        self.cv7 = Conv(2 * c_, c2, 1, 1)

    def forward(self, x):
        x1 = self.cv4(self.cv3(self.cv1(x)))
        x2 = self.m(x1)
        x3 = self.m(x2)
        y1 = self.cv6(self.cv5(torch.cat((x1,x2,x3, self.m(x3)),1)))
        y2 = self.cv2(x)
        return self.cv7(torch.cat((y1, y2), dim=1))

1.8 SPPELAN

YOLOv9 最新更新的模块,原理很简单,感兴趣可以试试~

import numpy as np
import torch.nn as nn
import torch


def autopad(k, p=None, d=1):  # kernel, padding, dilation
    # Pad to 'same' shape outputs
    if d > 1:
        k = (
            d * (k - 1) + 1 if isinstance(k, int) else [d * (x - 1) + 1 for x in k]
        )  # actual kernel-size
    if p is None:
        p = k // 2 if isinstance(k, int) else [x // 2 for x in k]  # auto-pad
    return p


class Conv(nn.Module):
    # Standard convolution with args(ch_in, ch_out, kernel, stride, padding, groups, dilation, activation)
    default_act = nn.SiLU()  # default activation

    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True):
        super().__init__()
        self.conv = nn.Conv2d(
            c1, c2, k, s, autopad(k, p, d), groups=g, dilation=d, bias=False
        )
        self.bn = nn.BatchNorm2d(c2)
        self.act = (
            self.default_act
            if act is True
            else act
            if isinstance(act, nn.Module)
            else nn.Identity()
        )

    def forward(self, x):
        return self.act(self.bn(self.conv(x)))

    def forward_fuse(self, x):
        return self.act(self.conv(x))
class SP(nn.Module):
    def __init__(self, k=3, s=1):
        super(SP, self).__init__()
        self.m = nn.MaxPool2d(kernel_size=k, stride=s, padding=k // 2)

    def forward(self, x):
        return self.m(x)


class SPPELAN(nn.Module):
    # spp-elan
    def __init__(
        self, c1, c2, c3
    ):  # ch_in, ch_out, number, shortcut, groups, expansion
        super().__init__()
        self.c = c3
        self.cv1 = Conv(c1, c3, 1, 1)
        self.cv2 = SP(5)
        self.cv3 = SP(5)
        self.cv4 = SP(5)
        self.cv5 = Conv(4 * c3, c2, 1, 1)

    def forward(self, x):
        y = [self.cv1(x)]
        y.extend(m(y[-1]) for m in [self.cv2, self.cv3, self.cv4])
        return self.cv5(torch.cat(y, 1))

2 参数量对比

这里我在yolov5s.yaml中使用各个模型替换SPP模块

模型 参数量(parameters) 计算量(GFLOPs)
SPP 7225885 16.5
SPPF 7235389 16.5
SimSPPF 7235389 16.5
ASPP 15485725 23.1
BasicRFB 7895421 17.1
SPPCSPC 13663549 21.7
SPPFCSPC🍀 13663549 21.7
分组SPPCSPC 8355133 17.4

3 改进方式

第一步;各个代码放入common.py
第二步;yolo.py中加入类名
第三步;修改配置文件
yolov5配置文件如下:

# YOLOv5 🚀 by Ultralytics, GPL-3.0 license

# YOLOv5 v6.0 backbone
backbone:
  # [from, number, module, args]
  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
   [-1, 3, C3, [128]],
   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
   [-1, 6, C3, [256]],
   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
   [-1, 9, C3, [512]],
   [-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32
   [-1, 3, C3, [1024]],
   [-1, 1, SPPF, [1024, 5]],  # 9
   # [-1, 1, ASPP, [512]],  # 9
   # [-1, 1, SPP, [1024]],
   # [-1, 1, SimSPPF, [1024, 5]],
   # [-1, 1, BasicRFB, [1024]],
   # [-1, 1, SPPCSPC, [1024]],
   # [-1, 1, SPPFCSPC, [1024, 5]], # 🍀
  ]

4 Issue

Q:Why use SPPCSPC instead of SPPFCSPC? /
yolov5’s SPPF is much faster than SPP.
Why not try to replace SPPCSPC with SPPFCSPC?
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A:
Max pooling uses very few computation, if you programming well, above one could run three max pool layers in parallel, while below one must process three max pool layers sequentially.
By the way, you could replace SPPCSPC by SPPFCSPC at inference time if your hardware is friendly to SPPFCSPC.

感兴趣的可以试一下


本人更多YOLOv5实战内容导航🍀🌟🚀

  1. 手把手带你调参Yolo v5 (v6.2)(推理)🌟强烈推荐

  2. 手把手带你调参Yolo v5 (v6.2)(训练)🚀

  3. 手把手带你调参Yolo v5 (v6.2)(验证)

  4. 如何快速使用自己的数据集训练Yolov5模型

  5. 手把手带你Yolov5 (v6.2)添加注意力机制(一)(并附上30多种顶会Attention原理图)🌟强烈推荐🍀新增8种

  6. 手把手带你Yolov5 (v6.2)添加注意力机制(二)(在C3模块中加入注意力机制)

  7. Yolov5如何更换激活函数?

  8. Yolov5如何更换BiFPN?

  9. Yolov5 (v6.2)数据增强方式解析

  10. Yolov5更换上采样方式( 最近邻 / 双线性 / 双立方 / 三线性 / 转置卷积)

  11. Yolov5如何更换EIOU / alpha IOU / SIoU?

  12. Yolov5更换主干网络之《旷视轻量化卷积神经网络ShuffleNetv2》

  13. YOLOv5应用轻量级通用上采样算子CARAFE

  14. 空间金字塔池化改进 SPP / SPPF / SimSPPF / ASPP / RFB / SPPCSPC / SPPFCSPC🚀

  15. 用于低分辨率图像和小物体的模块SPD-Conv

  16. GSConv+Slim-neck 减轻模型的复杂度同时提升精度🍀

  17. 头部解耦 | 将YOLOX解耦头添加到YOLOv5 | 涨点杀器🍀

  18. Stand-Alone Self-Attention | 搭建纯注意力FPN+PAN结构🍀

  19. YOLOv5模型剪枝实战🚀

  20. YOLOv5知识蒸馏实战🚀

  21. YOLOv7知识蒸馏实战🚀

  22. 改进YOLOv5 | 引入密集连接卷积网络DenseNet思想 | 搭建密集连接模块🍀


请添加图片描述

有问题欢迎大家指正,如果感觉有帮助的话请点赞支持下👍📖🌟


更新日志:2022年8月16日上午9:33分前在图片中增加感受野标注🍀

更新日志:2022年8月29日晚上11点40分在文中增加了SimSPPF模块,并测试了速度

更新日志:2022年8月30日修正了SPPCSPC的结构图

更新日志:2022年8月30日增加了SPPFCSPC的结构

更新日志:2023年5月19日修复了RFB的小错误

更新日志:2023年7月23日修复了RFB的Bug


参考文献:增强感受野SPP、ASPP、RFB、PPM

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