深度学习 Day 31——YOLOv5-Backbone模块实现

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深度学习 Day 31——YOLOv5-Backbone模块实现

一、前言

在上一期博客中我们将利用YOLOv5算法中的C3模块搭建网络,了解学习一下C3的结构,并在最后我们尝试增加C3模块来进行训练模型,看看准确率是否增加了。本期博客我们将学习另一个模块(Backbone)的实现,我们将利用这个模块搭建网络进行上一期博客实现的天气识别,对比一下两个模块的准确率的差异,本期博客除了模型网络结构外,其他部分和上期博客内容一样,所有我们将着重学习该模块的实现。

二、我的环境

本期博客我们继续使用谷歌云平台Colab进行学习。

print("============查看GPU信息================")
# 查看GPU信息
!/opt/bin/nvidia-smi
print("==============查看pytorch版本==============")
# 查看pytorch版本
import torch
print(torch.__version__)
print("============查看虚拟机硬盘容量================")
# 查看虚拟机硬盘容量
!df -lh
print("============查看cpu配置================")
# 查看cpu配置
!cat /proc/cpuinfo | grep model\ name
print("=============查看内存容量===============")
# 查看内存容量
!cat /proc/meminfo | grep MemTotal
============查看GPU信息================
Thu Apr 20 09:12:02 2023       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 525.85.12    Driver Version: 525.85.12    CUDA Version: 12.0     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |
| N/A   43C    P8     9W /  70W |      0MiB / 15360MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+
==============查看pytorch版本==============
2.0.0+cu118
============查看虚拟机硬盘容量================
Filesystem      Size  Used Avail Use% Mounted on
overlay          79G   24G   55G  30% /
tmpfs            64M     0   64M   0% /dev
shm             5.7G     0  5.7G   0% /dev/shm
/dev/root       2.0G  1.1G  841M  58% /usr/sbin/docker-init
tmpfs           6.4G   56K  6.4G   1% /var/colab
/dev/sda1        77G   44G   34G  57% /opt/bin/.nvidia
tmpfs           6.4G     0  6.4G   0% /proc/acpi
tmpfs           6.4G     0  6.4G   0% /proc/scsi
tmpfs           6.4G     0  6.4G   0% /sys/firmware
drive            15G     0   15G   0% /content/drive
============查看cpu配置================
model name	: Intel(R) Xeon(R) CPU @ 2.20GHz
model name	: Intel(R) Xeon(R) CPU @ 2.20GHz
=============查看内存容量===============
MemTotal:       13297192 kB

三、什么是YOLOv5-Backbone模块?

YOLOv5是一种目标检测算法,其核心是YOLOv5-Backbone模块。该模块是一个深度卷积神经网络(DCNN),用于从图像中提取特征并预测边界框。

YOLOv5-Backbone模块的基本实现方法包括以下步骤:

  1. 输入预处理:将原始图像转换为网络所需的输入格式,如将RGB图像转换为BGR格式,并将像素值缩放到0到1之间。
  2. 特征提取:使用卷积层和池化层等操作从图像中提取特征。YOLOv5-Backbone模块使用一系列残差块(Residual Block)来构建特征提取网络。这些残差块包括卷积层、批量归一化(Batch Normalization)层和激活函数(如LeakyReLU)。
  3. 特征金字塔:为了检测不同大小的目标,YOLOv5-Backbone模块使用特征金字塔来获取不同尺度的特征图。这个过程使用不同步长(stride)的卷积层来获取不同分辨率的特征图,并将它们级联在一起。
  4. 预测输出:将特征图送入卷积层和全连接层,生成目标检测的输出。这个过程使用锚点框(Anchor Boxes)来检测目标边界框。锚点框是一组预定义的边界框,用于检测不同大小的目标。YOLOv5-Backbone模块使用多个卷积层来输出目标的边界框、类别置信度和偏移量。
  5. 后处理:使用非极大值抑制(NMS)来去除重叠的边界框,并将最终的目标框输出。

从原理上来说,YOLOv5-Backbone模块是一个基于残差网络的深度卷积神经网络,用于从图像中提取特征并预测边界框。在实现方面,该模块使用一系列残差块来构建特征提取网络,使用不同步长的卷积层来获取不同尺度的特征图,并使用锚点框来检测目标边界框。最终使用非极大值抑制算法来去除重叠的边界框,并输出最终的目标框。

四、搭建包含Backbone模块的模型

1、模型整体代码

下面我们给出搭建包含Backbone模块的模型的整体代码,并在后面对模型的每一部分进行解释:

import torch.nn.functional as F

def autopad(k, p=None):  # 内核,填充
    # 填充到“相同”
    if p is None:
        p = k // 2 if isinstance(k, int) else [x // 2 for x in k]  # 自动填充
    return p

class Conv(nn.Module):
    # 标准瓶颈
    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):  # ch_in, ch_out, kernel, stride, padding, groups
        super().__init__()
        self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False)
        self.bn = nn.BatchNorm2d(c2)
        self.act = nn.SiLU() 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)))

class Bottleneck(nn.Module):
    # 标准瓶颈
    def __init__(self, c1, c2, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, shortcut, groups, expansion
        super().__init__()
        c_ = int(c2 * e)  # 隐藏频道
        self.cv1 = Conv(c1, c_, 1, 1)
        self.cv2 = Conv(c_, c2, 3, 1, g=g)
        self.add = shortcut and c1 == c2

    def forward(self, x):
        return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))

class C3(nn.Module):
    # 具有 3 个卷积的 CSP 瓶颈
    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
        super().__init__()
        c_ = int(c2 * e)  # 隐藏频道
        self.cv1 = Conv(c1, c_, 1, 1)
        self.cv2 = Conv(c1, c_, 1, 1)
        self.cv3 = Conv(2 * c_, c2, 1)  # act=FReLU(c2)
        self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))

    def forward(self, x):
        return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), dim=1))
    
class SPPF(nn.Module):
    def __init__(self, c1, c2, k=5):  # 相当于 SPP(k=(5, 9, 13))
        super().__init__()
        c_ = c1 // 2  # 隐藏频道
        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')  # 抑制torch1.9.0的max_pool2d()警告
            y1 = self.m(x)
            y2 = self.m(y1)
            return self.cv2(torch.cat([x, y1, y2, self.m(y2)], 1))

class YOLOv5_backbone(nn.Module):
    def __init__(self):
        super(YOLOv5_backbone, self).__init__()
        
        self.Conv_1 = Conv(3, 64, 3, 2, 2) 
        self.Conv_2 = Conv(64, 128, 3, 2) 
        self.C3_3 = C3(128,128)
        self.Conv_4 = Conv(128, 256, 3, 2) 
        self.C3_5 = C3(256,256)
        self.Conv_6 = Conv(256, 512, 3, 2) 
        self.C3_7 = C3(512,512)
        self.Conv_8 = Conv(512, 1024, 3, 2) 
        self.C3_9 = C3(1024, 1024)
        self.SPPF = SPPF(1024, 1024, 5)
        
        # 全连接网络层,用于分类
        self.classifier = nn.Sequential(
            nn.Linear(in_features=65536, out_features=100),
            nn.ReLU(),
            nn.Linear(in_features=100, out_features=4)
        )
        
    def forward(self, x):
        x = self.Conv_1(x)
        x = self.Conv_2(x)
        x = self.C3_3(x)
        x = self.Conv_4(x)
        x = self.C3_5(x)
        x = self.Conv_6(x)
        x = self.C3_7(x)
        x = self.Conv_8(x)
        x = self.C3_9(x)
        x = self.SPPF(x)
        
        x = torch.flatten(x, start_dim=1)
        x = self.classifier(x)

        return x

device = "cuda" if torch.cuda.is_available() else "cpu"
print("Using {} device".format(device))
    
model = YOLOv5_backbone().to(device)
model
Using cuda device
YOLOv5_backbone(
  (Conv_1): Conv(
    (conv): Conv2d(3, 64, kernel_size=(3, 3), stride=(2, 2), padding=(2, 2), bias=False)
    (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (act): SiLU()
  )
  (Conv_2): Conv(
    (conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
    (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (act): SiLU()
  )
  (C3_3): C3(
    (cv1): Conv(
      (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (act): SiLU()
    )
    (cv2): Conv(
      (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (act): SiLU()
    )
    (cv3): Conv(
      (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (act): SiLU()
    )
    (m): Sequential(
      (0): Bottleneck(
        (cv1): Conv(
          (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (act): SiLU()
        )
        (cv2): Conv(
          (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (act): SiLU()
        )
      )
    )
  )
  (Conv_4): Conv(
    (conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
    (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (act): SiLU()
  )
  (C3_5): C3(
    (cv1): Conv(
      (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (act): SiLU()
    )
    (cv2): Conv(
      (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (act): SiLU()
    )
    (cv3): Conv(
      (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (act): SiLU()
    )
    (m): Sequential(
      (0): Bottleneck(
        (cv1): Conv(
          (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (act): SiLU()
        )
        (cv2): Conv(
          (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (act): SiLU()
        )
      )
    )
  )
  (Conv_6): Conv(
    (conv): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
    (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (act): SiLU()
  )
  (C3_7): C3(
    (cv1): Conv(
      (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (act): SiLU()
    )
    (cv2): Conv(
      (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (act): SiLU()
    )
    (cv3): Conv(
      (conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (act): SiLU()
    )
    (m): Sequential(
      (0): Bottleneck(
        (cv1): Conv(
          (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (act): SiLU()
        )
        (cv2): Conv(
          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (act): SiLU()
        )
      )
    )
  )
  (Conv_8): Conv(
    (conv): Conv2d(512, 1024, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
    (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (act): SiLU()
  )
  (C3_9): C3(
    (cv1): Conv(
      (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (act): SiLU()
    )
    (cv2): Conv(
      (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (act): SiLU()
    )
    (cv3): Conv(
      (conv): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (act): SiLU()
    )
    (m): Sequential(
      (0): Bottleneck(
        (cv1): Conv(
          (conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (act): SiLU()
        )
        (cv2): Conv(
          (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (act): SiLU()
        )
      )
    )
  )
  (SPPF): SPPF(
    (cv1): Conv(
      (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (act): SiLU()
    )
    (cv2): Conv(
      (conv): Conv2d(2048, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (act): SiLU()
    )
    (m): MaxPool2d(kernel_size=5, stride=1, padding=2, dilation=1, ceil_mode=False)
  )
  (classifier): Sequential(
    (0): Linear(in_features=65536, out_features=100, bias=True)
    (1): ReLU()
    (2): Linear(in_features=100, out_features=4, bias=True)
  )
)

2、模型每一部分详解

下面我们将详细对每一步进行解释:

  • autopad(k, p=None):一个自动填充函数,用于在卷积操作中自动填充边缘以保持输出和输入大小相同。

    # 定义自动填充函数,用于计算填充大小
    def autopad(k, p=None):
        # 填充到“相同”,即保持输入和输出具有相同的空间维度
        if p is None:
            # 如果 k 是一个整数,则在所有空间维度上使用相同的填充大小
            if isinstance(k, int):
                p = k // 2
            # 如果 k 是一个元组,则逐个计算每个空间维度上的填充大小
            else:
                p = [x // 2 for x in k]
        return p
    
  • Conv模块:包含卷积、BN(批量归一化)和激活函数,用于卷积操作和特征提取。该模块的参数包括输入通道数 c 1 c1 c1、输出通道数 c 2 c2 c2、卷积核大小 k k k、步长 s s s、填充 p p p、分组卷积数 g g g和是否使用激活函数 a c t act act。其中,卷积核大小 k k k可以是整数或元组,用于指定卷积核的宽度和高度。当 p p p为None时,自动进行填充使得输出大小等于输入大小。

    # 定义卷积层
    class Conv(nn.Module):
        def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):
            super().__init__()
            # 创建一个二维卷积层,其中 c1 是输入通道数,c2 是输出通道数,k 是卷积核大小,s 是步幅,p 是填充大小,g 是分组数量,bias=False 表示不使用偏置
            self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False)
            # 创建一个二维批归一化层,用于加速训练
            self.bn = nn.BatchNorm2d(c2)
            # 创建一个激活函数层,默认使用 SiLU(即 Mish 激活函数)
            self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity())
    
        def forward(self, x):
            # 对输入进行卷积、批归一化、激活函数处理,并返回结果
            return self.act(self.bn(self.conv(x)))
    
  • Bottleneck模块:一个标准的瓶颈模块,包含两个卷积层和一个残差连接,用于构建多层卷积神经网络。该模块的参数包括输入通道数 c 1 c1 c1、输出通道数 c 2 c2 c2、是否使用残差连接 s h o r t c u t shortcut shortcut、分组卷积数 g g g和扩展系数 e e e。其中,扩展系数 e e e表示隐藏层通道数相对于输出层通道数的扩展倍数。

    # 定义标准瓶颈结构
    class Bottleneck(nn.Module):
        def __init__(self, c1, c2, shortcut=True, g=1, e=0.5):
            super().__init__()
            # 计算隐藏通道数
            c_ = int(c2 * e)
            # 创建一个 1x1 卷积层,用于降维
            self.cv1 = Conv(c1, c_, 1, 1)
            # 创建一个 3x3 卷积层,用于特征提取
            self.cv2 = Conv(c_, c2, 3, 1, g=g)
            # 判断是否需要添加残差连接
            self.add = shortcut and c1 == c2
    
        def forward(self, x):
            # 如果需要添加残差连接,则将输入直接加到输出上
            return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
    
  • C3模块:一个具有3个卷积的CSP(Cross Stage Partial)瓶颈模块,包含两个Bottleneck模块和一个残差连接,用于构建多层卷积神经网络。该模块的参数包括输入通道数 c 1 c1 c1、输出通道数 c 2 c2 c2、Bottleneck模块的个数 n n n、是否使用残差连接 s h o r t c u t shortcut shortcut、分组卷积数 g g g和扩展系数 e e e

    # 定义具有 3 个卷积的 CSP 瓶颈结构
    class C3(nn.Module):
        def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
            super().__init__()
            # 计算隐藏通道数
            c_ = int(c2 * e)
            # 创建一个 1x1 卷积层,用于降维
            self.cv1 = Conv(c1, c_, 1, 1)
            # 创建一个 1x1 卷积层,用于辅助特征提取
            self.cv2 = Conv(c1, c_, 1, 1)
            # 创建一个 1x1 卷积层,用于融合特征
            self.cv3 = Conv(2 * c_, c2, 1)  # act=FReLU(c2)
            # 创建 n 个标准瓶颈结构
            self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))
    
        def forward(self, x):
            # 将输入经过 cv1 和 cv2 后与 m(cv1(x)) 进行拼接,并经过 cv3 处理后输出
            return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), dim=1))
    
  • SPPF模块:一个空间金字塔池化模块,用于在不同尺度下对特征图进行池化,以提高模型的感受野和尺度不变性。该模块的参数包括输入通道数 c 1 c1 c1、输出通道数 c 2 c2 c2和池化核大小 k k k

    # SPPF模块定义
    class SPPF(nn.Module):
        def __init__(self, c1, c2, k=5):  # 相当于 SPP(k=(5, 9, 13))
            super().__init__()
            c_ = c1 // 2  # 隐藏频道
            self.cv1 = Conv(c1, c_, 1, 1)	# 1x1卷积,降低通道数
            self.cv2 = Conv(c_ * 4, c2, 1, 1)	# 1x1卷积,增加通道数
            self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2)	# SPP池化层
    
        def forward(self, x):
            x = self.cv1(x)	# 1x1卷积
            with warnings.catch_warnings():
                warnings.simplefilter('ignore')  # 抑制torch1.9.0的max_pool2d()警告
                y1 = self.m(x)	# SPP池化层
                y2 = self.m(y1)	# SPP池化层
                # 拼接四个尺度的特征图
                return self.cv2(torch.cat([x, y1, y2, self.m(y2)], 1))
    
  • YOLOv5_backbone模块:一个完整的YOLOv5主干网络,包含多个Conv、C3和SPPF模块,用于特征提取和分类。该模块还包含一个全连接网络层,用于分类。

    class YOLOv5_backbone(nn.Module):
        def __init__(self):
            super(YOLOv5_backbone, self).__init__()
            
            self.Conv_1 = Conv(3, 64, 3, 2, 2)  # 3x3卷积,步长2,空洞率2
            self.Conv_2 = Conv(64, 128, 3, 2)  # 3x3卷积,步长2
            self.C3_3 = C3(128,128)  # CSP瓶颈,包含3个卷积层
            self.Conv_4 = Conv(128, 256, 3, 2)  # 3x3卷积,步长2
            self.C3_5 = C3(256,256)  # CSP瓶颈,包含3个卷积层
            self.Conv_6 = Conv(256, 512, 3, 2)  # 3x3卷积,步长2
            self.C3_7 = C3(512,512)  # CSP瓶颈,包含3个卷积层
            self.Conv_8 = Conv(512, 1024, 3, 2)  # 3x3卷积,步长2
            self.C3_9 = C3(1024, 1024)  # CSP瓶颈,包含3个卷积层
            self.SPPF = SPPF(1024, 1024, 5)  # SPPF模块
            
            # 全连接网络层,用于分类
            self.classifier = nn.Sequential(
                nn.Linear(in_features=65536, out_features=100),	# 第一个全连接层,输入大小为65536,输出大小为100
                nn.ReLU(),
                nn.Linear(in_features=100, out_features=4)	# 第二个全连接层,输入大小为100,输出大小为4
            )
            
        def forward(self, x):
            x = self.Conv_1(x)
            x = self.Conv_2(x)
            x = self.C3_3(x)
            x = self.Conv_4(x)
            x = self.C3_5(x)
            x = self.Conv_6(x)
            x = self.C3_7(x)
            x = self.Conv_8(x)
            x = self.C3_9(x)
            x = self.SPPF(x)
            
            x = torch.flatten(x, start_dim=1)	# 将特征图转换为一维向量,用于全连接层
            x = self.classifier(x)	# 过全连接层,输出分类结果
    
            return x
    

3、模型详情

import torchsummary as summary
summary.summary(model, (3, 224, 224))
----------------------------------------------------------------
        Layer (type)               Output Shape         Param #
================================================================
            Conv2d-1         [-1, 64, 113, 113]           1,728
       BatchNorm2d-2         [-1, 64, 113, 113]             128
              SiLU-3         [-1, 64, 113, 113]               0
              Conv-4         [-1, 64, 113, 113]               0
            Conv2d-5          [-1, 128, 57, 57]          73,728
       BatchNorm2d-6          [-1, 128, 57, 57]             256
              SiLU-7          [-1, 128, 57, 57]               0
              Conv-8          [-1, 128, 57, 57]               0
            Conv2d-9           [-1, 64, 57, 57]           8,192
      BatchNorm2d-10           [-1, 64, 57, 57]             128
             SiLU-11           [-1, 64, 57, 57]               0
             Conv-12           [-1, 64, 57, 57]               0
           Conv2d-13           [-1, 64, 57, 57]           4,096
      BatchNorm2d-14           [-1, 64, 57, 57]             128
             SiLU-15           [-1, 64, 57, 57]               0
             Conv-16           [-1, 64, 57, 57]               0
           Conv2d-17           [-1, 64, 57, 57]          36,864
      BatchNorm2d-18           [-1, 64, 57, 57]             128
             SiLU-19           [-1, 64, 57, 57]               0
             Conv-20           [-1, 64, 57, 57]               0
       Bottleneck-21           [-1, 64, 57, 57]               0
           Conv2d-22           [-1, 64, 57, 57]           8,192
      BatchNorm2d-23           [-1, 64, 57, 57]             128
             SiLU-24           [-1, 64, 57, 57]               0
             Conv-25           [-1, 64, 57, 57]               0
           Conv2d-26          [-1, 128, 57, 57]          16,384
      BatchNorm2d-27          [-1, 128, 57, 57]             256
             SiLU-28          [-1, 128, 57, 57]               0
             Conv-29          [-1, 128, 57, 57]               0
               C3-30          [-1, 128, 57, 57]               0
           Conv2d-31          [-1, 256, 29, 29]         294,912
      BatchNorm2d-32          [-1, 256, 29, 29]             512
             SiLU-33          [-1, 256, 29, 29]               0
             Conv-34          [-1, 256, 29, 29]               0
           Conv2d-35          [-1, 128, 29, 29]          32,768
      BatchNorm2d-36          [-1, 128, 29, 29]             256
             SiLU-37          [-1, 128, 29, 29]               0
             Conv-38          [-1, 128, 29, 29]               0
           Conv2d-39          [-1, 128, 29, 29]          16,384
      BatchNorm2d-40          [-1, 128, 29, 29]             256
             SiLU-41          [-1, 128, 29, 29]               0
             Conv-42          [-1, 128, 29, 29]               0
           Conv2d-43          [-1, 128, 29, 29]         147,456
      BatchNorm2d-44          [-1, 128, 29, 29]             256
             SiLU-45          [-1, 128, 29, 29]               0
             Conv-46          [-1, 128, 29, 29]               0
       Bottleneck-47          [-1, 128, 29, 29]               0
           Conv2d-48          [-1, 128, 29, 29]          32,768
      BatchNorm2d-49          [-1, 128, 29, 29]             256
             SiLU-50          [-1, 128, 29, 29]               0
             Conv-51          [-1, 128, 29, 29]               0
           Conv2d-52          [-1, 256, 29, 29]          65,536
      BatchNorm2d-53          [-1, 256, 29, 29]             512
             SiLU-54          [-1, 256, 29, 29]               0
             Conv-55          [-1, 256, 29, 29]               0
               C3-56          [-1, 256, 29, 29]               0
           Conv2d-57          [-1, 512, 15, 15]       1,179,648
      BatchNorm2d-58          [-1, 512, 15, 15]           1,024
             SiLU-59          [-1, 512, 15, 15]               0
             Conv-60          [-1, 512, 15, 15]               0
           Conv2d-61          [-1, 256, 15, 15]         131,072
      BatchNorm2d-62          [-1, 256, 15, 15]             512
             SiLU-63          [-1, 256, 15, 15]               0
             Conv-64          [-1, 256, 15, 15]               0
           Conv2d-65          [-1, 256, 15, 15]          65,536
      BatchNorm2d-66          [-1, 256, 15, 15]             512
             SiLU-67          [-1, 256, 15, 15]               0
             Conv-68          [-1, 256, 15, 15]               0
           Conv2d-69          [-1, 256, 15, 15]         589,824
      BatchNorm2d-70          [-1, 256, 15, 15]             512
             SiLU-71          [-1, 256, 15, 15]               0
             Conv-72          [-1, 256, 15, 15]               0
       Bottleneck-73          [-1, 256, 15, 15]               0
           Conv2d-74          [-1, 256, 15, 15]         131,072
      BatchNorm2d-75          [-1, 256, 15, 15]             512
             SiLU-76          [-1, 256, 15, 15]               0
             Conv-77          [-1, 256, 15, 15]               0
           Conv2d-78          [-1, 512, 15, 15]         262,144
      BatchNorm2d-79          [-1, 512, 15, 15]           1,024
             SiLU-80          [-1, 512, 15, 15]               0
             Conv-81          [-1, 512, 15, 15]               0
               C3-82          [-1, 512, 15, 15]               0
           Conv2d-83           [-1, 1024, 8, 8]       4,718,592
      BatchNorm2d-84           [-1, 1024, 8, 8]           2,048
             SiLU-85           [-1, 1024, 8, 8]               0
             Conv-86           [-1, 1024, 8, 8]               0
           Conv2d-87            [-1, 512, 8, 8]         524,288
      BatchNorm2d-88            [-1, 512, 8, 8]           1,024
             SiLU-89            [-1, 512, 8, 8]               0
             Conv-90            [-1, 512, 8, 8]               0
           Conv2d-91            [-1, 512, 8, 8]         262,144
      BatchNorm2d-92            [-1, 512, 8, 8]           1,024
             SiLU-93            [-1, 512, 8, 8]               0
             Conv-94            [-1, 512, 8, 8]               0
           Conv2d-95            [-1, 512, 8, 8]       2,359,296
      BatchNorm2d-96            [-1, 512, 8, 8]           1,024
             SiLU-97            [-1, 512, 8, 8]               0
             Conv-98            [-1, 512, 8, 8]               0
       Bottleneck-99            [-1, 512, 8, 8]               0
          Conv2d-100            [-1, 512, 8, 8]         524,288
     BatchNorm2d-101            [-1, 512, 8, 8]           1,024
            SiLU-102            [-1, 512, 8, 8]               0
            Conv-103            [-1, 512, 8, 8]               0
          Conv2d-104           [-1, 1024, 8, 8]       1,048,576
     BatchNorm2d-105           [-1, 1024, 8, 8]           2,048
            SiLU-106           [-1, 1024, 8, 8]               0
            Conv-107           [-1, 1024, 8, 8]               0
              C3-108           [-1, 1024, 8, 8]               0
          Conv2d-109            [-1, 512, 8, 8]         524,288
     BatchNorm2d-110            [-1, 512, 8, 8]           1,024
            SiLU-111            [-1, 512, 8, 8]               0
            Conv-112            [-1, 512, 8, 8]               0
       MaxPool2d-113            [-1, 512, 8, 8]               0
       MaxPool2d-114            [-1, 512, 8, 8]               0
       MaxPool2d-115            [-1, 512, 8, 8]               0
          Conv2d-116           [-1, 1024, 8, 8]       2,097,152
     BatchNorm2d-117           [-1, 1024, 8, 8]           2,048
            SiLU-118           [-1, 1024, 8, 8]               0
            Conv-119           [-1, 1024, 8, 8]               0
            SPPF-120           [-1, 1024, 8, 8]               0
          Linear-121                  [-1, 100]       6,553,700
            ReLU-122                  [-1, 100]               0
          Linear-123                    [-1, 4]             404
================================================================
Total params: 21,729,592
Trainable params: 21,729,592
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 137.59
Params size (MB): 82.89
Estimated Total Size (MB): 221.06
----------------------------------------------------------------

五、模型训练

import copy

optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
loss_fn = nn.CrossEntropyLoss() # 创建损失函数

epochs = 60

train_loss = []
train_acc = []
test_loss = []
test_acc = []

best_acc = 0    # 设置一个最佳准确率,作为最佳模型的判别指标

for epoch in range(epochs):
    
    model.train()
    epoch_train_acc, epoch_train_loss = train(train_dl, model, loss_fn, optimizer)
    
    model.eval()
    epoch_test_acc, epoch_test_loss = test(test_dl, model, loss_fn)
    
    # 保存最佳模型到 best_model
    if epoch_test_acc > best_acc:
        best_acc = epoch_test_acc
        best_model = copy.deepcopy(model)
    
    train_acc.append(epoch_train_acc)
    train_loss.append(epoch_train_loss)
    test_acc.append(epoch_test_acc)
    test_loss.append(epoch_test_loss)
    
    # 获取当前的学习率
    lr = optimizer.state_dict()['param_groups'][0]['lr']
    
    template = ('Epoch:{:2d}, Train_acc:{:.1f}%, Train_loss:{:.3f}, Test_acc:{:.1f}%, Test_loss:{:.3f}, Lr:{:.2E}')
    print(template.format(epoch+1, epoch_train_acc*100, epoch_train_loss, epoch_test_acc*100, epoch_test_loss, lr))
    
# 保存最佳模型到文件中
PATH = './best_model.pth'  # 保存的参数文件名
torch.save(model.state_dict(), PATH)

print('Done')

模型训练的结果是:

Epoch: 1, Train_acc:54.9%, Train_loss:1.150, Test_acc:66.2%, Test_loss:0.636, Lr:1.00E-04
Epoch: 2, Train_acc:66.3%, Train_loss:0.777, Test_acc:76.9%, Test_loss:0.603, Lr:1.00E-04
Epoch: 3, Train_acc:69.7%, Train_loss:0.696, Test_acc:82.2%, Test_loss:0.622, Lr:1.00E-04
Epoch: 4, Train_acc:78.1%, Train_loss:0.589, Test_acc:80.9%, Test_loss:0.507, Lr:1.00E-04
Epoch: 5, Train_acc:81.3%, Train_loss:0.498, Test_acc:80.9%, Test_loss:0.537, Lr:1.00E-04
Epoch: 6, Train_acc:83.6%, Train_loss:0.436, Test_acc:78.7%, Test_loss:0.494, Lr:1.00E-04
Epoch: 7, Train_acc:86.1%, Train_loss:0.370, Test_acc:87.6%, Test_loss:0.380, Lr:1.00E-04
Epoch: 8, Train_acc:86.7%, Train_loss:0.348, Test_acc:77.3%, Test_loss:0.559, Lr:1.00E-04
Epoch: 9, Train_acc:89.0%, Train_loss:0.345, Test_acc:81.3%, Test_loss:0.513, Lr:1.00E-04
Epoch:10, Train_acc:90.4%, Train_loss:0.258, Test_acc:84.9%, Test_loss:0.403, Lr:1.00E-04
Epoch:11, Train_acc:89.4%, Train_loss:0.270, Test_acc:83.6%, Test_loss:0.489, Lr:1.00E-04
Epoch:12, Train_acc:89.7%, Train_loss:0.276, Test_acc:86.2%, Test_loss:0.414, Lr:1.00E-04
Epoch:13, Train_acc:90.8%, Train_loss:0.247, Test_acc:84.9%, Test_loss:0.499, Lr:1.00E-04
Epoch:14, Train_acc:90.0%, Train_loss:0.273, Test_acc:84.9%, Test_loss:0.498, Lr:1.00E-04
Epoch:15, Train_acc:93.9%, Train_loss:0.179, Test_acc:85.8%, Test_loss:0.418, Lr:1.00E-04
Epoch:16, Train_acc:94.0%, Train_loss:0.165, Test_acc:82.7%, Test_loss:0.692, Lr:1.00E-04
Epoch:17, Train_acc:93.1%, Train_loss:0.193, Test_acc:86.2%, Test_loss:0.504, Lr:1.00E-04
Epoch:18, Train_acc:92.6%, Train_loss:0.220, Test_acc:85.8%, Test_loss:0.550, Lr:1.00E-04
Epoch:19, Train_acc:95.1%, Train_loss:0.126, Test_acc:87.6%, Test_loss:0.479, Lr:1.00E-04
Epoch:20, Train_acc:96.4%, Train_loss:0.114, Test_acc:80.9%, Test_loss:0.719, Lr:1.00E-04
Epoch:21, Train_acc:97.7%, Train_loss:0.074, Test_acc:88.9%, Test_loss:0.405, Lr:1.00E-04
Epoch:22, Train_acc:96.6%, Train_loss:0.103, Test_acc:88.0%, Test_loss:0.428, Lr:1.00E-04
Epoch:23, Train_acc:96.3%, Train_loss:0.090, Test_acc:85.3%, Test_loss:0.540, Lr:1.00E-04
Epoch:24, Train_acc:95.9%, Train_loss:0.109, Test_acc:87.6%, Test_loss:0.437, Lr:1.00E-04
Epoch:25, Train_acc:96.9%, Train_loss:0.085, Test_acc:89.3%, Test_loss:0.333, Lr:1.00E-04
Epoch:26, Train_acc:97.7%, Train_loss:0.065, Test_acc:87.6%, Test_loss:0.393, Lr:1.00E-04
Epoch:27, Train_acc:97.3%, Train_loss:0.094, Test_acc:85.3%, Test_loss:0.542, Lr:1.00E-04
Epoch:28, Train_acc:96.7%, Train_loss:0.087, Test_acc:85.3%, Test_loss:0.652, Lr:1.00E-04
Epoch:29, Train_acc:95.8%, Train_loss:0.097, Test_acc:80.4%, Test_loss:0.838, Lr:1.00E-04
Epoch:30, Train_acc:97.4%, Train_loss:0.071, Test_acc:87.1%, Test_loss:0.540, Lr:1.00E-04
Epoch:31, Train_acc:97.3%, Train_loss:0.071, Test_acc:88.9%, Test_loss:0.797, Lr:1.00E-04
Epoch:32, Train_acc:96.9%, Train_loss:0.108, Test_acc:86.2%, Test_loss:0.500, Lr:1.00E-04
Epoch:33, Train_acc:98.0%, Train_loss:0.058, Test_acc:88.0%, Test_loss:0.536, Lr:1.00E-04
Epoch:34, Train_acc:99.7%, Train_loss:0.018, Test_acc:89.8%, Test_loss:0.479, Lr:1.00E-04
Epoch:35, Train_acc:99.7%, Train_loss:0.008, Test_acc:87.6%, Test_loss:0.605, Lr:1.00E-04
Epoch:36, Train_acc:98.8%, Train_loss:0.040, Test_acc:89.3%, Test_loss:0.527, Lr:1.00E-04
Epoch:37, Train_acc:98.3%, Train_loss:0.042, Test_acc:83.6%, Test_loss:0.709, Lr:1.00E-04
Epoch:38, Train_acc:97.1%, Train_loss:0.083, Test_acc:84.0%, Test_loss:0.719, Lr:1.00E-04
Epoch:39, Train_acc:95.4%, Train_loss:0.133, Test_acc:87.1%, Test_loss:0.617, Lr:1.00E-04
Epoch:40, Train_acc:98.2%, Train_loss:0.051, Test_acc:86.7%, Test_loss:0.565, Lr:1.00E-04
Epoch:41, Train_acc:98.2%, Train_loss:0.060, Test_acc:85.3%, Test_loss:0.776, Lr:1.00E-04
Epoch:42, Train_acc:98.9%, Train_loss:0.040, Test_acc:88.0%, Test_loss:0.596, Lr:1.00E-04
Epoch:43, Train_acc:100.0%, Train_loss:0.006, Test_acc:89.8%, Test_loss:0.654, Lr:1.00E-04
Epoch:44, Train_acc:99.0%, Train_loss:0.029, Test_acc:90.7%, Test_loss:0.405, Lr:1.00E-04
Epoch:45, Train_acc:99.3%, Train_loss:0.016, Test_acc:89.3%, Test_loss:0.540, Lr:1.00E-04
Epoch:46, Train_acc:98.6%, Train_loss:0.033, Test_acc:87.6%, Test_loss:0.635, Lr:1.00E-04
Epoch:47, Train_acc:99.0%, Train_loss:0.034, Test_acc:83.6%, Test_loss:1.015, Lr:1.00E-04
Epoch:48, Train_acc:96.0%, Train_loss:0.160, Test_acc:87.1%, Test_loss:0.538, Lr:1.00E-04
Epoch:49, Train_acc:97.7%, Train_loss:0.074, Test_acc:87.6%, Test_loss:0.644, Lr:1.00E-04
Epoch:50, Train_acc:98.3%, Train_loss:0.053, Test_acc:87.6%, Test_loss:0.566, Lr:1.00E-04
Epoch:51, Train_acc:99.2%, Train_loss:0.032, Test_acc:87.6%, Test_loss:0.600, Lr:1.00E-04
Epoch:52, Train_acc:99.2%, Train_loss:0.023, Test_acc:88.0%, Test_loss:0.675, Lr:1.00E-04
Epoch:53, Train_acc:99.8%, Train_loss:0.007, Test_acc:89.3%, Test_loss:0.620, Lr:1.00E-04
Epoch:54, Train_acc:99.6%, Train_loss:0.009, Test_acc:90.7%, Test_loss:0.658, Lr:1.00E-04
Epoch:55, Train_acc:99.7%, Train_loss:0.008, Test_acc:88.9%, Test_loss:0.750, Lr:1.00E-04
Epoch:56, Train_acc:99.7%, Train_loss:0.007, Test_acc:88.9%, Test_loss:0.758, Lr:1.00E-04
Epoch:57, Train_acc:99.9%, Train_loss:0.003, Test_acc:88.9%, Test_loss:0.777, Lr:1.00E-04
Epoch:58, Train_acc:97.0%, Train_loss:0.155, Test_acc:87.6%, Test_loss:0.862, Lr:1.00E-04
Epoch:59, Train_acc:96.1%, Train_loss:0.115, Test_acc:88.0%, Test_loss:0.597, Lr:1.00E-04
Epoch:60, Train_acc:98.4%, Train_loss:0.046, Test_acc:87.6%, Test_loss:0.859, Lr:1.00E-04
Done

六、最终结果

1、Loss-Accuracy图

import matplotlib.pyplot as plt
#隐藏警告
import warnings
warnings.filterwarnings("ignore")               #忽略警告信息
plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False      # 用来正常显示负号
plt.rcParams['figure.dpi'] = 100        #分辨率

epochs_range = range(epochs)

plt.figure(figsize=(12, 3))
plt.subplot(1, 2, 1)

plt.plot(epochs_range, train_acc, label='Training Accuracy')
plt.plot(epochs_range, test_acc, label='Test Accuracy')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')

plt.subplot(1, 2, 2)
plt.plot(epochs_range, train_loss, label='Training Loss')
plt.plot(epochs_range, test_loss, label='Test Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()

深度学习 Day 31——YOLOv5-Backbone模块实现文章来源地址https://www.toymoban.com/news/detail-420056.html

2、模型准确率

epoch_test_acc, epoch_test_loss = test(test_dl, best_model, loss_fn)
print('Test_acc:{:.1f}%, Test_loss:{:.3f}'.format(epoch_test_acc*100, epoch_test_loss))
Test_acc:90.7%, Test_loss:0.459

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