第P9周-YOLOv5Backbone模块

CSP Bottleneck块和C3 类的设计使其非常适合目标检测任务，充分考虑了多尺度特征融合、梯度流动和计算效率等因素。C3 类以及CSP（Cross Stage Partial） Bottleneck块作为YOLOv5中的一部分，具有以下优势，相对于传统的普通神经网络：

特定任务定制：C3 类和CSP Bottleneck块是专门为目标检测任务设计的。它们在特征提取和融合方面进行了特定的优化，有助于提高目标检测性能。传统的神经网络结构通常用于通用的图像分类任务，不一定适用于目标检测。
多尺度特征融合：CSP Bottleneck块具有特殊的结构，允许多尺度的特征信息进行有效融合。这对于目标检测非常关键，因为目标可能具有不同尺寸和比例，需要在不同尺度下进行检测。
快捷连接：CSP Bottleneck块允许引入快捷连接，这对于信息流动和梯度传播至关重要。传统的神经网络通常没有这种结构，而YOLOv5中的C3块通过快捷连接促进了梯度的传递，减轻了梯度消失问题，有助于训练的稳定性。
高效性能：尽管CSP Bottleneck块具有更复杂的结构，但它们经过有效的设计，不会引入过多的计算复杂性。这使得YOLOv5能够在相对较短的时间内完成目标检测任务。

一、前期工作

1.1 导入数据集


import torch
import torch.nn as nn
import torchvision.transforms as transforms
import torchvision
from torchvision import transforms, datasets
import os,PIL,pathlib,warnings
 
warnings.filterwarnings("ignore")             #忽略警告信息
 
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(device)
 
import os,PIL,random,pathlib
 
data_dir = 'D:/T6star/'
data_dir = pathlib.Path(data_dir)
 
data_paths  = list(data_dir.glob('*'))
classeNames = [str(path).split("\\")[2] for path in data_paths]
print(classeNames)

1.2 标准化处理


# 关于transforms.Compose的更多介绍可以参考：https://blog.csdn.net/qq_38251616/article/details/124878863
train_transforms = transforms.Compose([
    transforms.Resize([224, 224]),  # 将输入图片resize成统一尺寸
    # transforms.RandomHorizontalFlip(), # 随机水平翻转
    transforms.ToTensor(),          # 将PIL Image或numpy.ndarray转换为tensor，并归一化到[0,1]之间
    transforms.Normalize(           # 标准化处理-->转换为标准正太分布（高斯分布），使模型更容易收敛
        mean=[0.485, 0.456, 0.406], 
        std=[0.229, 0.224, 0.225])  # 其中 mean=[0.485,0.456,0.406]与std=[0.229,0.224,0.225] 从数据集中随机抽样计算得到的。
])
 
test_transform = transforms.Compose([
    transforms.Resize([224, 224]),  # 将输入图片resize成统一尺寸
    transforms.ToTensor(),          # 将PIL Image或numpy.ndarray转换为tensor，并归一化到[0,1]之间
    transforms.Normalize(           # 标准化处理-->转换为标准正太分布（高斯分布），使模型更容易收敛
        mean=[0.485, 0.456, 0.406], 
        std=[0.229, 0.224, 0.225])  # 其中 mean=[0.485,0.456,0.406]与std=[0.229,0.224,0.225] 从数据集中随机抽样计算得到的。
])
 
total_data = datasets.ImageFolder("D:/P8/weather_photos/",transform=train_transforms)
print(total_data)

1.3 划分数据集


train_size = int(0.8 * len(total_data))
test_size  = len(total_data) - train_size
train_dataset, test_dataset = torch.utils.data.random_split(total_data, [train_size, test_size])
print(train_dataset, test_dataset)

1.4 设置数据加载器


batch_size = 4
 
train_dl = torch.utils.data.DataLoader(train_dataset,
                                           batch_size=batch_size,
                                           shuffle=True,
                                           num_workers=1)
test_dl = torch.utils.data.DataLoader(test_dataset,
                                          batch_size=batch_size,
                                          shuffle=True,

二、搭建包含CSP Bottleneck块和`C3` 类的YOLOv5的主干网络


import torch.nn.functional as F
 
def autopad(k, p=None):  # kernel, padding
    # Pad to 'same'
    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
    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)# 定义卷积层
        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):
    # Standard bottleneck
    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)  # hidden channels
        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):
    # CSP Bottleneck with 3 convolutions
    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)  # hidden channels
        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):
    # 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))
"""
这个是YOLOv5, 6.0版本的主干网络，这里进行复现
（注：有部分删改，详细讲解将在后续进行展开）
"""
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)
print(model)

autopad(k, p) 函数：

这个函数用于计算卷积操作中的自动填充大小。
如果没有提供填充参数 p，则它会根据卷积核大小 k 的情况自动计算填充大小，以实现“same”填充，即输入和输出特征图具有相同的空间维度。

Conv 类：

这个类定义了标准的卷积层，包括卷积、批量归一化和激活函数等。
构造函数接受参数 c1（输入通道数）、c2（输出通道数）、k（卷积核大小）、s（步幅）、p（填充）、g（分组卷积）、act（激活函数类型）等。
forward 方法执行卷积、批量归一化和激活函数操作，并返回输出。

Bottleneck 类：

这个类定义了标准的瓶颈块，用于深层网络中的特征提取。
构造函数接受参数 c1（输入通道数）、c2（输出通道数）、shortcut（是否包含快捷连接）、g（分组卷积）、e（扩展系数）、groups 卷积操作中的分组参数，通常为1，表示标准卷积。在深度可分离卷积等操作中，该值可能不为1。 bias 设置为False，表示卷积操作不使用偏置项。
forward 方法执行一系列卷积操作，包括卷积核为1x1和3x3的卷积，以及可选的快捷连接操作。

C3 类：

这个类定义了CSP（Cross Stage Partial） Bottleneck块，它包含3个卷积操作。
forward 方法执行一系列卷积操作，包括卷积核为1x1的卷积、2个不同的卷积核为1x1的卷积和可选的快捷连接操作。

SPPF 类：

SPPF层是指"Spatial Pyramid Pooling - Fast"层，它是YOLOv5中的一种特殊层，用于多尺度特征融合。SPPF层的作用是对输入特征图进行空间金字塔池化，以捕获不同尺度的特征信息，从而提高目标检测性能。

self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2)

nn.MaxPool2d 是PyTorch中的最大池化层，用于进行池化操作。kernel_size 参数设定了池化核的大小，这里使用了k 作为参数，表示池化核的大小为k x k。stride 参数设置为1，表示池化操作的滑动步幅为1。padding 参数设置为k // 2，表示对输入特征图进行填充以保持输出特征图的尺寸与输入相同。

nn.MaxPool2d 层被用于执行空间金字塔池化，其中kernel_size 的不同取值对应于不同尺度的池化区域。这有助于在不同尺度上捕获特征信息，从而提高目标检测性能。在这个特定的情况下，k 的取值是5，9 和13，相应地表示了3个不同尺度的池化操作，从而构成了空间金字塔池化。

这个类定义了SPPF（Spatial Pyramid Pooling - Fast）层，用于多尺度特征融合。
forward 方法执行空间金字塔池化操作，将不同尺度的特征图拼接在一起。

YOLOv5_backbone 类：

定义主干网络的各个层（Conv_1、Conv_2、C3_3、Conv_4、C3_5、Conv_6、C3_7、Conv_8、C3_9 和 SPPF）用于逐步提取输入图像的特征。。
classifier 是一个全连接网络层，用于分类任务。
forward 方法定义了整个主干网络的前向传播过程，包括各个层次的卷积和特征融合。

小结：

在上述代码中，forward 函数在不同类中的作用如下：

forward 函数在 Conv 类中的作用：
- Conv 类代表标准的卷积层，其 forward 函数用于执行卷积操作。
- 输入 x 是卷积层的输入特征图。
- 通过卷积操作、批量归一化（Batch Normalization）、激活函数等一系列操作，将输入特征图 x 转化为经过卷积层处理后的输出。
forward 函数在 Bottleneck 类中的作用：
- Bottleneck 类代表标准的瓶颈块，其 forward 函数用于执行瓶颈块的前向传播。
- 输入 x 是瓶颈块的输入特征图。
- 瓶颈块包括一系列卷积层和残差连接（如果 shortcut 为 True），将输入特征图 x 转化为经过瓶颈块处理后的输出。
forward 函数在 C3 类中的作用：
- C3 类代表一种特殊的 CSP（Cross-Stage-Partial）瓶颈块，包括三个卷积操作。
- 输入 x 是 C3 瓶颈块的输入特征图。
- forward 函数首先执行两个不同卷积操作 self.cv1 和 self.cv2，然后将它们的输出与输入 x 进行拼接（Concatenate），并传递给第三个卷积操作 self.cv3。
- 最后，通过一系列堆叠的 Bottleneck 块（self.m 中的循环），将输入特征图 x 通过多次瓶颈块的处理，生成经过 C3 块处理后的输出。
forward 函数在 SPPF 类中的作用：
- SPPF 类代表空间金字塔池化（Spatial Pyramid Pooling - Fast）层。
- 输入 x 是SPPF层的输入特征图。
- forward 函数执行空间金字塔池化操作，将输入特征图 x 通过最大池化操作 self.m 在不同尺度上池化，然后将池化后的结果进行拼接，生成SPPF层的输出特征。
forward 函数在 YOLOv5_backbone 类中的作用：
- YOLOv5_backbone 类代表整个YOLOv5的主干网络，包括多个卷积层和瓶颈块，以及SPPF层。
- forward 函数按照网络的顺序将输入特征图 x 通过各个组件，包括卷积层、瓶颈块、SPPF层，然后通过全连接网络层 self.classifier 进行分类。
- 返回经过主干网络处理后的特征表示，用于目标检测的进一步处理。

运行结果：


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)
  )
)

查看模型详情：


# 统计模型参数量以及其他指标
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
----------------------------------------------------------------
None

三、训练函数

3.1 编写训练函数


# 训练循环
def train(dataloader, model, loss_fn, optimizer):
    size = len(dataloader.dataset)  # 训练集的大小
    num_batches = len(dataloader)   # 批次数目, (size/batch_size，向上取整)
 
    train_loss, train_acc = 0, 0  # 初始化训练损失和正确率
    
    for X, y in dataloader:  # 获取图片及其标签
        X, y = X.to(device), y.to(device)
        
        # 计算预测误差
        pred = model(X)          # 网络输出
        loss = loss_fn(pred, y)  # 计算网络输出和真实值之间的差距，targets为真实值，计算二者差值即为损失
        
        # 反向传播
        optimizer.zero_grad()  # grad属性归零
        loss.backward()        # 反向传播
        optimizer.step()       # 每一步自动更新
        
        # 记录acc与loss
        train_acc  += (pred.argmax(1) == y).type(torch.float).sum().item()
        train_loss += loss.item()
            
    train_acc  /= size
    train_loss /= num_batches
 
    return train_acc, train_loss

3.2 编写测试函数


def test (dataloader, model, loss_fn):
    size        = len(dataloader.dataset)  # 测试集的大小
    num_batches = len(dataloader)          # 批次数目, (size/batch_size，向上取整)
    test_loss, test_acc = 0, 0
    
    # 当不进行训练时，停止梯度更新，节省计算内存消耗
    with torch.no_grad():
        for imgs, target in dataloader:
            imgs, target = imgs.to(device), target.to(device)
            
            # 计算loss
            target_pred = model(imgs)
            loss        = loss_fn(target_pred, target)
            
            test_loss += loss.item()
            test_acc  += (target_pred.argmax(1) == target).type(torch.float).sum().item()
 
    test_acc  /= size
    test_loss /= num_batches
 
    return test_acc, test_loss

3.3 正式训练


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(best_model.state_dict(), PATH)
 
print('Done')

运行结果：


cpu
cpu
Epoch: 1, Train_acc:53.0%, Train_loss:1.110, Test_acc:64.4%, Test_loss:0.690, Lr:1.00E-04
cpu
cpu
Epoch: 2, Train_acc:60.8%, Train_loss:0.851, Test_acc:60.4%, Test_loss:0.783, Lr:1.00E-04
cpu
cpu
Epoch: 3, Train_acc:68.3%, Train_loss:0.723, Test_acc:74.7%, Test_loss:0.628, Lr:1.00E-04
cpu
cpu
Epoch: 4, Train_acc:73.4%, Train_loss:0.634, Test_acc:75.6%, Test_loss:0.455, Lr:1.00E-04
cpu
cpu
Epoch: 5, Train_acc:74.4%, Train_loss:0.598, Test_acc:76.0%, Test_loss:0.554, Lr:1.00E-04
cpu
cpu
Epoch: 6, Train_acc:76.8%, Train_loss:0.578, Test_acc:81.3%, Test_loss:0.403, Lr:1.00E-04
cpu
cpu
Epoch: 7, Train_acc:80.4%, Train_loss:0.480, Test_acc:83.6%, Test_loss:0.359, Lr:1.00E-04
cpu
cpu
Epoch: 8, Train_acc:82.4%, Train_loss:0.450, Test_acc:82.7%, Test_loss:0.423, Lr:1.00E-04
cpu
cpu
Epoch: 9, Train_acc:82.6%, Train_loss:0.403, Test_acc:89.3%, Test_loss:0.275, Lr:1.00E-04
cpu
cpu
Epoch:10, Train_acc:86.8%, Train_loss:0.345, Test_acc:87.6%, Test_loss:0.373, Lr:1.00E-04
cpu
cpu
Epoch:11, Train_acc:87.1%, Train_loss:0.319, Test_acc:91.6%, Test_loss:0.240, Lr:1.00E-04
cpu
cpu
Epoch:12, Train_acc:88.3%, Train_loss:0.296, Test_acc:84.0%, Test_loss:0.408, Lr:1.00E-04
cpu
cpu
Epoch:13, Train_acc:88.6%, Train_loss:0.284, Test_acc:77.8%, Test_loss:0.519, Lr:1.00E-04
cpu
cpu
Epoch:14, Train_acc:91.9%, Train_loss:0.242, Test_acc:89.8%, Test_loss:0.246, Lr:1.00E-04
cpu
cpu
Epoch:15, Train_acc:93.0%, Train_loss:0.193, Test_acc:89.8%, Test_loss:0.316, Lr:1.00E-04
cpu
cpu
Epoch:16, Train_acc:92.2%, Train_loss:0.201, Test_acc:85.3%, Test_loss:0.451, Lr:1.00E-04
cpu
cpu
Epoch:17, Train_acc:90.6%, Train_loss:0.229, Test_acc:86.7%, Test_loss:0.535, Lr:1.00E-04
cpu
cpu
Epoch:18, Train_acc:92.3%, Train_loss:0.198, Test_acc:80.4%, Test_loss:0.586, Lr:1.00E-04
cpu
cpu
Epoch:19, Train_acc:92.6%, Train_loss:0.196, Test_acc:90.2%, Test_loss:0.251, Lr:1.00E-04
cpu
cpu
Epoch:20, Train_acc:94.8%, Train_loss:0.155, Test_acc:90.7%, Test_loss:0.223, Lr:1.00E-04
cpu
cpu
Epoch:21, Train_acc:95.2%, Train_loss:0.132, Test_acc:90.7%, Test_loss:0.282, Lr:1.00E-04
cpu
cpu
Epoch:22, Train_acc:95.9%, Train_loss:0.121, Test_acc:79.6%, Test_loss:0.744, Lr:1.00E-04
cpu
cpu
Epoch:23, Train_acc:96.8%, Train_loss:0.102, Test_acc:92.9%, Test_loss:0.183, Lr:1.00E-04
cpu
cpu
Epoch:24, Train_acc:97.1%, Train_loss:0.083, Test_acc:87.6%, Test_loss:0.380, Lr:1.00E-04
cpu
cpu
Epoch:25, Train_acc:95.8%, Train_loss:0.133, Test_acc:88.9%, Test_loss:0.350, Lr:1.00E-04
cpu
cpu
Epoch:26, Train_acc:97.4%, Train_loss:0.090, Test_acc:91.1%, Test_loss:0.378, Lr:1.00E-04
cpu
cpu
Epoch:27, Train_acc:96.1%, Train_loss:0.118, Test_acc:88.9%, Test_loss:0.420, Lr:1.00E-04
cpu
cpu
Epoch:28, Train_acc:97.7%, Train_loss:0.075, Test_acc:88.4%, Test_loss:0.343, Lr:1.00E-04
cpu
cpu
Epoch:29, Train_acc:97.1%, Train_loss:0.073, Test_acc:89.8%, Test_loss:0.308, Lr:1.00E-04
cpu
cpu
Epoch:30, Train_acc:98.6%, Train_loss:0.048, Test_acc:90.7%, Test_loss:0.283, Lr:1.00E-04
cpu
cpu
Epoch:31, Train_acc:98.4%, Train_loss:0.056, Test_acc:89.8%, Test_loss:0.340, Lr:1.00E-04
cpu
cpu
Epoch:32, Train_acc:97.6%, Train_loss:0.077, Test_acc:90.7%, Test_loss:0.278, Lr:1.00E-04
cpu
cpu
Epoch:33, Train_acc:98.3%, Train_loss:0.051, Test_acc:82.7%, Test_loss:0.511, Lr:1.00E-04
cpu
cpu
Epoch:34, Train_acc:97.6%, Train_loss:0.069, Test_acc:91.6%, Test_loss:0.416, Lr:1.00E-04
cpu
cpu
Epoch:35, Train_acc:97.1%, Train_loss:0.089, Test_acc:89.8%, Test_loss:0.352, Lr:1.00E-04
cpu
cpu
Epoch:36, Train_acc:97.0%, Train_loss:0.078, Test_acc:90.2%, Test_loss:0.337, Lr:1.00E-04
cpu
cpu
Epoch:37, Train_acc:98.2%, Train_loss:0.060, Test_acc:80.0%, Test_loss:0.723, Lr:1.00E-04
cpu
cpu
Epoch:38, Train_acc:98.0%, Train_loss:0.060, Test_acc:91.6%, Test_loss:0.285, Lr:1.00E-04
cpu
cpu
Epoch:39, Train_acc:99.1%, Train_loss:0.024, Test_acc:92.0%, Test_loss:0.376, Lr:1.00E-04
cpu
cpu
Epoch:40, Train_acc:99.2%, Train_loss:0.032, Test_acc:80.0%, Test_loss:0.669, Lr:1.00E-04
cpu
cpu

四、完整代码


import torch
import torch.nn as nn
import torchvision.transforms as transforms
import torchvision
from torchvision import transforms, datasets
import os,PIL,pathlib,warnings
 
warnings.filterwarnings("ignore")             #忽略警告信息
 
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(device)
 
import os,PIL,random,pathlib
 
 
def main():
    data_dir = 'D:/P8/weather_photos/'
    data_dir = pathlib.Path(data_dir)
 
    data_paths  = list(data_dir.glob('*'))
    classeNames = [str(path).split("\\")[2] for path in data_paths]
    print(classeNames)
 
    # 关于transforms.Compose的更多介绍可以参考：https://blog.csdn.net/qq_38251616/article/details/124878863
    train_transforms = transforms.Compose([
        transforms.Resize([224, 224]),  # 将输入图片resize成统一尺寸
        # transforms.RandomHorizontalFlip(), # 随机水平翻转
        transforms.ToTensor(),          # 将PIL Image或numpy.ndarray转换为tensor，并归一化到[0,1]之间
        transforms.Normalize(           # 标准化处理-->转换为标准正太分布（高斯分布），使模型更容易收敛
            mean=[0.485, 0.456, 0.406], 
            std=[0.229, 0.224, 0.225])  # 其中 mean=[0.485,0.456,0.406]与std=[0.229,0.224,0.225] 从数据集中随机抽样计算得到的。
    ])
 
    test_transform = transforms.Compose([
        transforms.Resize([224, 224]),  # 将输入图片resize成统一尺寸
        transforms.ToTensor(),          # 将PIL Image或numpy.ndarray转换为tensor，并归一化到[0,1]之间
        transforms.Normalize(           # 标准化处理-->转换为标准正太分布（高斯分布），使模型更容易收敛
            mean=[0.485, 0.456, 0.406], 
            std=[0.229, 0.224, 0.225])  # 其中 mean=[0.485,0.456,0.406]与std=[0.229,0.224,0.225] 从数据集中随机抽样计算得到的。
    ])
 
    total_data = datasets.ImageFolder("D:/P8/weather_photos/",transform=train_transforms)
    print(total_data)
 
    train_size = int(0.8 * len(total_data))
    test_size  = len(total_data) - train_size
    train_dataset, test_dataset = torch.utils.data.random_split(total_data, [train_size, test_size])
    print(train_dataset, test_dataset)
 
    batch_size = 4
 
    train_dl = torch.utils.data.DataLoader(train_dataset,
                                            batch_size=batch_size,
                                            shuffle=True,
                                            num_workers=1)
    test_dl = torch.utils.data.DataLoader(test_dataset,
                                            batch_size=batch_size,
                                            shuffle=True,
                                            num_workers=1)
 
    import torch.nn.functional as F
 
    def autopad(k, p=None):  # kernel, padding
        # Pad to 'same'
        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
        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)# 定义卷积层
            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):
        # Standard bottleneck
        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)  # hidden channels
            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):
        # CSP Bottleneck with 3 convolutions
        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)  # hidden channels
            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):
        # 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))
    """
    这个是YOLOv5, 6.0版本的主干网络，这里进行复现
    （注：有部分删改，详细讲解将在后续进行展开）
    """
    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)
    print(model)
 
    # 统计模型参数量以及其他指标
    import torchsummary as summary
    print(summary.summary(model, (3, 224, 224)))
 
    # 训练循环
    def train(dataloader, model, loss_fn, optimizer):
        size = len(dataloader.dataset)  # 训练集的大小
        num_batches = len(dataloader)   # 批次数目, (size/batch_size，向上取整)
 
        train_loss, train_acc = 0, 0  # 初始化训练损失和正确率
        
        for X, y in dataloader:  # 获取图片及其标签
            X, y = X.to(device), y.to(device)
            
            # 计算预测误差
            pred = model(X)          # 网络输出
            loss = loss_fn(pred, y)  # 计算网络输出和真实值之间的差距，targets为真实值，计算二者差值即为损失
            
            # 反向传播
            optimizer.zero_grad()  # grad属性归零
            loss.backward()        # 反向传播
            optimizer.step()       # 每一步自动更新
            
            # 记录acc与loss
            train_acc  += (pred.argmax(1) == y).type(torch.float).sum().item()
            train_loss += loss.item()
                
        train_acc  /= size
        train_loss /= num_batches
 
        return train_acc, train_loss
 
    def test (dataloader, model, loss_fn):
        size        = len(dataloader.dataset)  # 测试集的大小
        num_batches = len(dataloader)          # 批次数目, (size/batch_size，向上取整)
        test_loss, test_acc = 0, 0
        
        # 当不进行训练时，停止梯度更新，节省计算内存消耗
        with torch.no_grad():
            for imgs, target in dataloader:
                imgs, target = imgs.to(device), target.to(device)
                
                # 计算loss
                target_pred = model(imgs)
                loss        = loss_fn(target_pred, target)
                
                test_loss += loss.item()
                test_acc  += (target_pred.argmax(1) == target).type(torch.float).sum().item()
 
        test_acc  /= size
        test_loss /= num_batches
 
        return test_acc, test_loss
 
    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(best_model.state_dict(), PATH)
 
    print('Done')
 
if __name__ == '__main__':
    main()

torch.save(model.state_dict(), PATH) 和 torch.save(model, PATH) 之间有很大的区别：

torch.save(model.state_dict(), PATH) 保存的是模型的参数字典（state_dict），而不是整个模型对象。这意味着只有模型的权重和偏置等参数会被保存，而模型的结构、图层和其他属性不会被保存。这种方式通常用于保存和加载模型的参数，而不包括模型的结构。
torch.save(model, PATH) 保存的是整个模型对象，包括模型的结构、图层、参数和其他属性。这意味着整个模型的状态都会被保存，包括模型的结构和权重。这种方式通常用于保存和加载完整的模型，包括模型的结构和参数。

copy.deepcopy(model)

copy.deepcopy(model): 这部分代码使用 Python 的 copy.deepcopy 函数创建了模型的深度拷贝。这意味着它会复制整个模型对象，包括模型的架构、权重、参数等。通常情况下，这用于创建一个独立的模型副本，以便进一步的处理或修改，而不会影响原始模型或其他引用。

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原文地址：https://blog.csdn.net/qq_60245590/article/details/133782541

第P9周-YOLOv5Backbone模块

一、前期工作

1.1 导入数据集

1.2 标准化处理

二、搭建包含CSP Bottleneck块和C3 类的YOLOv5的主干网络

小结：

运行结果：

三、训练函数

3.1 编写训练函数

3.2 编写测试函数

3.3 正式训练

四、完整代码

二、搭建包含CSP Bottleneck块和`C3` 类的YOLOv5的主干网络