YOLOv5改进 | 低照度检测 | 2024最新改进CPA-Enhancer链式思考网络（适用低照度、图像去雾、雨天、雪天）

一、本文介绍

本文给大家带来的2024.3月份最新改进机制，由CPA-Enhancer: Chain-of-Thought Prompted Adaptive Enhancer for Object Detection under Unknown Degradations论文提出的CPA-Enhancer链式思考网络，CPA-Enhancer通过引入链式思考提示机制，实现了对未知退化条件下图像的自适应增强。该方法的核心在于能够利用CoT提示对图像退化进行动态分析和适应，从而显著提升物体检测性能。其适用的场景非常多低照度、图像去雾、雨天、雪天均有提点效果，本文内容由我独家整理！

欢迎大家订阅我的专栏一起学习YOLO！

专栏回顾：YOLOv5改进专栏——持续复现各种顶会内容——内含100+创新

一、本文介绍

二、原理介绍

三、核心代码

四、手把手教你添加本文机制

4.1 修改一

4.2 修改二

4.3 修改三

五、CPA-Enhancer的yaml文件和运行记录

5.1 CPA-Enhancer的yaml文件

5.2 运行记录

五、本文总结

二、原理介绍

官方论文地址：官方论文地址点击此处即可跳转

官方代码地址：官方代码地址点击此处即可跳转

CPA-Enhancer的创新点和改进机制可以从以下几个方面进行概括：
1. 链式思考（CoT）提示：首次将链式思考（CoT）提示机制应用于物体检测任务中，通过逐步引导的方式处理未知退化图像的问题。
2. 自适应增强策略：提出了一种能够根据CoT提示动态调整其增强策略的自适应增强器，无需事先了解图像的退化类型。
3. 插件式模型设计：CPA-Enhancer设计为一个插件式模块，可以轻松地与任何现有的通用物体检测器集成，提升在退化图像上的检测性能。

改进机制
CoT提示生成模块（CGM）：通过CoT提示生成模块动态生成与图像退化相关的上下文信息，使模型能够识别并适应不同类型的图像退化。
内容驱动提示块（CPB）：利用内容驱动提示块加强输入特征与CoT提示之间的交互，允许模型根据退化的类型调整其增强策略。
端到端训练：CPA-Enhancer能够与目标检测器一起端到端地训练，无需单独的预训练过程或额外的监督信号。

总结
CPA-Enhancer通过引入链式思考提示机制，实现了对未知退化条件下图像的自适应增强。该方法的核心在于能够利用CoT提示对图像退化进行动态分析和适应，从而显著提升物体检测性能。其插件式设计使其可以无缝集成到现有的检测框架中，为处理实际应用中遇到的各种退化条件提供了一种有效的解决方案。通过实验验证，CPA-Enhancer不仅在物体检测任务上设立了新的性能标准，还证明了其对其他下游视觉任务性能的提升作用，展示了广泛的应用潜力。

三、核心代码

核心代码的使用方式看章节四！

import torch
import torch.nn as nn
import torch.nn.functional as F
import numbers
from einops import rearrange
from einops.layers.torch import Rearrange

__all__ = ['CPA_arch']

class RFAConv(nn.Module):  # 基于Group Conv实现的RFAConv
    def __init__(self, in_channel, out_channel, kernel_size=3, stride=1):
        super().__init__()
        self.kernel_size = kernel_size
        self.get_weight = nn.Sequential(nn.AvgPool2d(kernel_size=kernel_size, padding=kernel_size // 2, stride=stride),
                                        nn.Conv2d(in_channel, in_channel * (kernel_size ** 2), kernel_size=1,
                                                  groups=in_channel, bias=False))
        self.generate_feature = nn.Sequential(
            nn.Conv2d(in_channel, in_channel * (kernel_size ** 2), kernel_size=kernel_size, padding=kernel_size // 2,
                      stride=stride, groups=in_channel, bias=False),
            nn.BatchNorm2d(in_channel * (kernel_size ** 2)),
            nn.ReLU())

        self.conv = nn.Sequential(nn.Conv2d(in_channel, out_channel, kernel_size=kernel_size, stride=kernel_size),
                                  nn.BatchNorm2d(out_channel),
                                  nn.ReLU())

    def forward(self, x):
        b, c = x.shape[0:2]
        weight = self.get_weight(x)
        h, w = weight.shape[2:]
        weighted = weight.view(b, c, self.kernel_size ** 2, h, w).softmax(2)  # b c*kernel**2,h,w ->  b c k**2 h w
        feature = self.generate_feature(x).view(b, c, self.kernel_size ** 2, h,
                                                w)  # b c*kernel**2,h,w ->  b c k**2 h w   获得感受野空间特征
        weighted_data = feature * weighted
        conv_data = rearrange(weighted_data, 'b c (n1 n2) h w -> b c (h n1) (w n2)', n1=self.kernel_size,
                              # b c k**2 h w ->  b c h*k w*k
                              n2=self.kernel_size)
        return self.conv(conv_data)

class Downsample(nn.Module):
    def __init__(self, n_feat):
        super(Downsample, self).__init__()

        self.body = nn.Sequential(nn.Conv2d(n_feat, n_feat // 2, kernel_size=3, stride=1, padding=1, bias=False),
                                  nn.PixelUnshuffle(2))

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

class Upsample(nn.Module):
    def __init__(self, n_feat):
        super(Upsample, self).__init__()

        self.body = nn.Sequential(nn.Conv2d(n_feat, n_feat * 2, kernel_size=3, stride=1, padding=1, bias=False),
                                  nn.PixelShuffle(2))

    def forward(self, x):  # (b,c,h,w)
        return self.body(x)  # (b,c/2,h*2,w*2)

class SpatialAttention(nn.Module):
    def __init__(self):
        super(SpatialAttention, self).__init__()
        self.sa = nn.Conv2d(2, 1, 7, padding=3, padding_mode='reflect', bias=True)

    def forward(self, x):  # x:[b,c,h,w]
        x_avg = torch.mean(x, dim=1, keepdim=True)  # (b,1,h,w)
        x_max, _ = torch.max(x, dim=1, keepdim=True)  # (b,1,h,w)
        x2 = torch.concat([x_avg, x_max], dim=1)  # (b,2,h,w)
        sattn = self.sa(x2)  # 7x7conv (b,1,h,w)
        return sattn * x

class ChannelAttention(nn.Module):
    def __init__(self, dim, reduction=8):
        super(ChannelAttention, self).__init__()
        self.gap = nn.AdaptiveAvgPool2d(1)
        self.ca = nn.Sequential(
            nn.Conv2d(dim, dim // reduction, 1, padding=0, bias=True),
            nn.ReLU(inplace=True),  # Relu
            nn.Conv2d(dim // reduction, dim, 1, padding=0, bias=True),
        )

    def forward(self, x):  # x:[b,c,h,w]
        x_gap = self.gap(x)  #  [b,c,1,1]
        cattn = self.ca(x_gap)  # [b,c,1,1]
        return cattn * x

class Channel_Shuffle(nn.Module):
    def __init__(self, num_groups):
        super(Channel_Shuffle, self).__init__()
        self.num_groups = num_groups

    def forward(self, x):
        batch_size, chs, h, w = x.shape
        chs_per_group = chs // self.num_groups
        x = torch.reshape(x, (batch_size, self.num_groups, chs_per_group, h, w))
        # (batch_size, num_groups, chs_per_group, h, w)
        x = x.transpose(1, 2)  # dim_1 and dim_2
        out = torch.reshape(x, (batch_size, -1, h, w))
        return out

class TransformerBlock(nn.Module):
    def __init__(self, dim, num_heads, ffn_expansion_factor, bias, LayerNorm_type):
        super(TransformerBlock, self).__init__()

        self.norm1 = LayerNorm(dim, LayerNorm_type)
        self.attn = Attention(dim, num_heads, bias)
        self.norm2 = LayerNorm(dim, LayerNorm_type)
        self.ffn = FeedForward(dim, ffn_expansion_factor, bias)

    def forward(self, x):
        x = x + self.attn(self.norm1(x))
        x = x + self.ffn(self.norm2(x))
        return x

def to_3d(x):
    return rearrange(x, 'b c h w -> b (h w) c')

def to_4d(x, h, w):
    return rearrange(x, 'b (h w) c -> b c h w', h=h, w=w)

class BiasFree_LayerNorm(nn.Module):
    def __init__(self, normalized_shape):
        super(BiasFree_LayerNorm, self).__init__()
        if isinstance(normalized_shape, numbers.Integral):
            normalized_shape = (normalized_shape,)
        normalized_shape = torch.Size(normalized_shape)

        assert len(normalized_shape) == 1

        self.weight = nn.Parameter(torch.ones(normalized_shape))
        self.normalized_shape = normalized_shape

    def forward(self, x):
        sigma = x.var(-1, keepdim=True, unbiased=False)
        return x / torch.sqrt(sigma + 1e-5) * self.weight

class WithBias_LayerNorm(nn.Module):
    def __init__(self, normalized_shape):
        super(WithBias_LayerNorm, self).__init__()
        if isinstance(normalized_shape, numbers.Integral):
            normalized_shape = (normalized_shape,)
        normalized_shape = torch.Size(normalized_shape)

        assert len(normalized_shape) == 1

        self.weight = nn.Parameter(torch.ones(normalized_shape))
        self.bias = nn.Parameter(torch.zeros(normalized_shape))
        self.normalized_shape = normalized_shape

    def forward(self, x):
        device = x.device
        mu = x.mean(-1, keepdim=True)
        sigma = x.var(-1, keepdim=True, unbiased=False)
        result = (x - mu) / torch.sqrt(sigma + 1e-5) * self.weight.to(device) + self.bias.to(device)
        return result

class LayerNorm(nn.Module):
    def __init__(self, dim, LayerNorm_type):
        super(LayerNorm, self).__init__()
        if LayerNorm_type == 'BiasFree':
            self.body = BiasFree_LayerNorm(dim)
        else:
            self.body = WithBias_LayerNorm(dim)

    def forward(self, x):
        h, w = x.shape[-2:]
        return to_4d(self.body(to_3d(x)), h, w)

class FeedForward(nn.Module):
    def __init__(self, dim, ffn_expansion_factor, bias):
        super(FeedForward, self).__init__()

        hidden_features = int(dim * ffn_expansion_factor)

        self.project_in = nn.Conv2d(dim, hidden_features * 2, kernel_size=1, bias=bias)

        self.dwconv = nn.Conv2d(hidden_features * 2, hidden_features * 2, kernel_size=3, stride=1, padding=1,
                                groups=hidden_features * 2, bias=bias)

        self.project_out = nn.Conv2d(hidden_features, dim, kernel_size=1, bias=bias)

    def forward(self, x):
        device = x.device
        self.project_in = self.project_in.to(device)
        self.dwconv = self.dwconv.to(device)
        self.project_out = self.project_out.to(device)
        x = self.project_in(x)
        x1, x2 = self.dwconv(x).chunk(2, dim=1)
        x = F.gelu(x1) * x2
        x = self.project_out(x)
        return x

class Attention(nn.Module):
    def __init__(self, dim, num_heads, bias):
        super(Attention, self).__init__()
        self.num_heads = num_heads
        self.temperature = nn.Parameter(torch.ones(num_heads, 1, 1, dtype=torch.float32), requires_grad=True)
        self.qkv = nn.Conv2d(dim, dim * 3, kernel_size=1, bias=bias)
        self.qkv_dwconv = nn.Conv2d(dim * 3, dim * 3, kernel_size=3, stride=1, padding=1, groups=dim * 3,
                                    bias=bias)
        self.project_out = nn.Conv2d(dim, dim, kernel_size=1, bias=bias)

    def forward(self, x):
        b, c, h, w = x.shape
        device = x.device
        self.qkv = self.qkv.to(device)
        self.qkv_dwconv = self.qkv_dwconv.to(device)
        self.project_out = self.project_out.to(device)
        qkv = self.qkv(x)
        qkv = self.qkv_dwconv(qkv)
        q, k, v = qkv.chunk(3, dim=1)

        q = rearrange(q, 'b (head c) h w -> b head c (h w)', head=self.num_heads)
        k = rearrange(k, 'b (head c) h w -> b head c (h w)', head=self.num_heads)
        v = rearrange(v, 'b (head c) h w -> b head c (h w)', head=self.num_heads)

        q = torch.nn.functional.normalize(q, dim=-1)
        k = torch.nn.functional.normalize(k, dim=-1)

        attn = (q @ k.transpose(-2, -1)) * self.temperature.to(device)
        attn = attn.softmax(dim=-1)

        out = (attn @ v)

        out = rearrange(out, 'b head c (h w) -> b (head c) h w', head=self.num_heads, h=h, w=w)

        out = self.project_out(out)
        return out

class resblock(nn.Module):
    def __init__(self, dim):
        super(resblock, self).__init__()
        # self.norm = LayerNorm(dim, LayerNorm_type='BiasFree')

        self.body = nn.Sequential(nn.Conv2d(dim, dim, kernel_size=3, stride=1, padding=1, bias=False),
                                  nn.PReLU(),
                                  nn.Conv2d(dim, dim, kernel_size=3, stride=1, padding=1, bias=False))

    def forward(self, x):
        res = self.body((x))
        res += x
        return res

#########################################################################
# Chain-of-Thought Prompt Generation Module (CGM)
class CotPromptParaGen(nn.Module):
    def __init__(self,prompt_inch,prompt_size, num_path=3):
        super(CotPromptParaGen, self).__init__()

        # (128,32,32)->(64,64,64)->(32,128,128)
        self.chain_prompts=nn.ModuleList([
            nn.ConvTranspose2d(
                in_channels=prompt_inch if idx==0 else prompt_inch//(2**idx),
                out_channels=prompt_inch//(2**(idx+1)),
                kernel_size=3, stride=2, padding=1
            ) for idx in range(num_path)
        ])
    def forward(self,x):
        prompt_params = []
        prompt_params.append(x)
        for pe in self.chain_prompts:
            x=pe(x)
            prompt_params.append(x)
        return prompt_params

#########################################################################
# Content-driven Prompt Block (CPB)
class ContentDrivenPromptBlock(nn.Module):
    def __init__(self, dim, prompt_dim, reduction=8, num_splits=4):
        super(ContentDrivenPromptBlock, self).__init__()
        self.dim = dim
        self.num_splits = num_splits
        self.pa2 = nn.Conv2d(2 * dim, dim, 7, padding=3, padding_mode='reflect', groups=dim, bias=True)
        self.sigmoid = nn.Sigmoid()
        self.conv3x3 = nn.Conv2d(prompt_dim, prompt_dim, kernel_size=3, stride=1, padding=1, bias=False)
        self.conv1x1 = nn.Conv2d(dim, prompt_dim, kernel_size=1, stride=1, bias=False)
        self.sa = SpatialAttention()
        self.ca = ChannelAttention(dim, reduction)
        self.myshuffle = Channel_Shuffle(2)
        self.out_conv1 = nn.Conv2d(prompt_dim + dim, dim, kernel_size=1, stride=1, bias=False)
        self.transformer_block = [
            TransformerBlock(dim=dim // num_splits, num_heads=1, ffn_expansion_factor=2.66, bias=False,
                             LayerNorm_type='WithBias') for _ in range(num_splits)]

    def forward(self, x, prompt_param):
        # latent: (b,dim*8,h/8,w/8)  prompt_param3: (1, 256, 16, 16)
        x_ = x
        B, C, H, W = x.shape
        cattn = self.ca(x)  # channel-wise attn
        sattn = self.sa(x)  # spatial-wise attn
        pattn1 = sattn + cattn
        pattn1 = pattn1.unsqueeze(dim=2)  # [b,c,1,h,w]
        x = x.unsqueeze(dim=2)  # [b,c,1,h,w]
        x2 = torch.cat([x, pattn1], dim=2)  #  [b,c,2,h,w]
        x2 = Rearrange('b c t h w -> b (c t) h w')(x2)  # [b,c*2,h,w]
        x2 = self.myshuffle(x2)  # [c1,c1_att,c2,c2_att,...]
        pattn2 = self.pa2(x2)
        pattn2 = self.conv1x1(pattn2)  # [b,prompt_dim,h,w]
        prompt_weight = self.sigmoid(pattn2)  # Sigmod

        prompt_param = F.interpolate(prompt_param, (H, W), mode="bilinear")
        # (b,prompt_dim,prompt_size,prompt_size) -> (b,prompt_dim,h,w)
        prompt = prompt_weight * prompt_param
        prompt = self.conv3x3(prompt)  # (b,prompt_dim,h,w)

        inter_x = torch.cat([x_, prompt], dim=1)  # (b,prompt_dim+dim,h,w)
        inter_x = self.out_conv1(inter_x)  # (b,dim,h,w) dim=64
        splits = torch.split(inter_x, self.dim // self.num_splits, dim=1)

        transformered_splits = []
        for i, split in enumerate(splits):
            transformered_split = self.transformer_block[i](split)
            transformered_splits.append(transformered_split)
        result = torch.cat(transformered_splits, dim=1)
        return result

#########################################################################
# CPA_Enhancer
class CPA_arch(nn.Module):
    def __init__(self, c_in=3, c_out=3, dim=4, prompt_inch=128, prompt_size=32):
        super(CPA_arch, self).__init__()
        self.conv0 = RFAConv(c_in, dim)
        self.conv1 = RFAConv(dim, dim)
        self.conv2 = RFAConv(dim * 2, dim * 2)
        self.conv3 = RFAConv(dim * 4, dim * 4)
        self.conv4 = RFAConv(dim * 8, dim * 8)
        self.conv5 = RFAConv(dim * 8, dim * 4)
        self.conv6 = RFAConv(dim * 4, dim * 2)
        self.conv7 = RFAConv(dim * 2, c_out)

        self.down1 = Downsample(dim)
        self.down2 = Downsample(dim * 2)
        self.down3 = Downsample(dim * 4)

        self.prompt_param_ini = nn.Parameter(torch.rand(1, prompt_inch, prompt_size, prompt_size)) # (b,c,h,w)
        self.myPromptParamGen = CotPromptParaGen(prompt_inch=prompt_inch,prompt_size=prompt_size)
        self.prompt1 = ContentDrivenPromptBlock(dim=dim * 2 ** 1, prompt_dim=prompt_inch // 4, reduction=8)  # !!!!
        self.prompt2 = ContentDrivenPromptBlock(dim=dim * 2 ** 2, prompt_dim=prompt_inch // 2, reduction=8)
        self.prompt3 = ContentDrivenPromptBlock(dim=dim * 2 ** 3, prompt_dim=prompt_inch , reduction=8)

        self.up3 = Upsample(dim * 8)
        self.up2 = Upsample(dim * 4)
        self.up1 = Upsample(dim * 2)

    def forward(self, x):  # (b,c_in,h,w)

        prompt_params = self.myPromptParamGen(self.prompt_param_ini)
        prompt_param1 = prompt_params[2] # [1, 64, 64, 64]
        prompt_param2 = prompt_params[1]  # [1, 128, 32, 32]
        prompt_param3 = prompt_params[0]  # [1, 256, 16, 16]
        x0 = self.conv0(x)  # (b,dim,h,w)
        x1 = self.conv1(x0)  # (b,dim,h,w)
        x1_down = self.down1(x1)  # (b,dim,h/2,w/2)
        x2 = self.conv2(x1_down)  # (b,dim,h/2,w/2)
        x2_down = self.down2(x2)
        x3 = self.conv3(x2_down)
        x3_down = self.down3(x3)
        x4 = self.conv4(x3_down)
        device = x4.device
        self.prompt1 = self.prompt1.to(device)
        self.prompt2 = self.prompt2.to(device)
        self.prompt3 = self.prompt3.to(device)
        x4_prompt = self.prompt3(x4, prompt_param3)
        x3_up = self.up3(x4_prompt)
        x5 = self.conv5(torch.cat([x3_up, x3], 1))
        x5_prompt = self.prompt2(x5, prompt_param2)
        x2_up = self.up2(x5_prompt)
        x2_cat = torch.cat([x2_up, x2], 1)
        x6 = self.conv6(x2_cat)
        x6_prompt = self.prompt1(x6, prompt_param1)
        x1_up = self.up1(x6_prompt)
        x7 = self.conv7(torch.cat([x1_up, x1], 1))
        return x7



if __name__ == "__main__":
    # Generating Sample image
    image_size = (1, 3, 640, 640)
    image = torch.rand(*image_size)
    out = CPA_arch(3, 3, 4)
    out = out(image)
    print(out.size())

四、手把手教你添加本文机制

4.1 修改一

第一还是建立文件，我们找到如下yolov5-master/models文件夹下建立一个目录名字呢就是'modules'文件夹(用群内的文件的话已经有了无需新建)！然后在其内部建立一个新的py文件将核心代码复制粘贴进去即可。

4.2 修改二

第二步我们在该目录下创建一个新的py文件名字为'__init__.py'(用群内的文件的话已经有了无需新建)，然后在其内部导入我们的检测头如下图所示。

4.3 修改三

第三步我门中到如下文件'yolov5-master/models/yolo.py'进行导入和注册我们的模块(用群内的文件的话已经有了无需重新导入直接开始第四步即可)！

从今天开始以后的教程就都统一成这个样子了，因为我默认大家用了我群内的文件来进行修改！！

到此就修改完成了，大家可以复制下面的yaml文件运行，无需修改parse_model方法。。

五、CPA-Enhancer的yaml文件和运行记录

5.1 CPA-Enhancer的yaml文件

注意本文的代码计算量很高但是参数量不高，所以大家可能运行失败因为电脑算力不足而导致！

# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license

# Parameters
nc: 80  # number of classes
depth_multiple: 0.33  # model depth multiple
width_multiple: 0.25  # layer channel multiple
anchors:
  - [10,13, 16,30, 33,23]  # P3/8
  - [30,61, 62,45, 59,119]  # P4/16
  - [116,90, 156,198, 373,326]  # P5/32


# YOLOv5 v6.0 backbone
backbone:
  # [from, number, module, args]
  [[-1, 1, CPA_arch, []],  # 0-P1/2
   [-1, 1, Conv, [64, 6, 2, 2]],  # 1-P1/2
   [-1, 1, Conv, [128, 3, 2]],  # 2-P2/4
   [-1, 3, C3, [128]],
   [-1, 1, Conv, [256, 3, 2]],  # 4-P3/8
   [-1, 6, C3, [256]],
   [-1, 1, Conv, [512, 3, 2]],  # 6-P4/16
   [-1, 9, C3, [512]],
   [-1, 1, Conv, [1024, 3, 2]],  # 8-P5/32
   [-1, 3, C3, [1024]],
   [-1, 1, SPPF, [1024, 5]]  # 10
  ]

# YOLOv5 v6.0 head
head:
  [[-1, 1, Conv, [512, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 7], 1, Concat, [1]],  # cat backbone P4
   [-1, 3, C3, [512, False]],  # 14

   [-1, 1, Conv, [256, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 5], 1, Concat, [1]],  # cat backbone P3
   [-1, 3, C3, [256, False]],  # 18 (P3/8-small)

   [-1, 1, Conv, [256, 3, 2]],
   [[-1, 15], 1, Concat, [1]],  # cat head P4
   [-1, 3, C3, [512, False]],  # 21 (P4/16-medium)

   [-1, 1, Conv, [512, 3, 2]],
   [[-1, 11], 1, Concat, [1]],  # cat head P5
   [-1, 3, C3, [1024, False]],  # 24 (P5/32-large)

   [[18, 21, 24], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
  ]