>- **🍨 本文为[🔗365天深度学习训练营]中的学习记录博客**
>- **🍖 原作者：[K同学啊]**

 本人往期文章可查阅： 深度学习总结

本周任务：

1.请根据本文TensorFlow代码，编写出相应的pytorch代码

2.了解残差结构

3.是否可以将残差模块融入到C3当中（自由探索）

🏡 我的环境：

• 语言环境：Python3.8

• 编译器：Jupyter Notebook

• 深度学习环境：Pytorch

• • torch==2.3.1+cu118

• • torchvision==0.18.1+cu118

       本文完全根据 第J1周：ResNet-50算法实战与解析（TensorFlow版）中的内容转换为pytorch版本，所以前述性的内容不在一一重复，仅就pytorch版本中的内容进行叙述。

一、 前期准备

1. 设置GPU

如果设备上支持GPU就使用GPU,否则使用CPU

import warnings
warnings.filterwarnings("ignore") #忽略警告信息

import torch
device=torch.device("cuda" if torch.cuda.is_available() else "CPU")
device

运行结果：

device(type='cuda')

2. 导入数据

同时查看数据集中图片的数量

import pathlib

data_dir=r"D:\THE MNIST DATABASE\J-series\J1\bird_photos"
data_dir=pathlib.Path(data_dir)

image_count=len(list(data_dir.glob('*/*')))
print("图片总数为：",image_count)

运行结果：

图片总数为： 565

3. 查看数据集分类

data_paths=list(data_dir.glob('*'))
classeNames=[str(path).split("\\")[5] for path in data_paths]
classeNames

运行结果：

['Bananaquit', 'Black Skimmer', 'Black Throated Bushtiti', 'Cockatoo']

4. 随机查看图片

随机抽取数据集中的20张图片进行查看

import random,PIL
import matplotlib.pyplot as plt
from PIL import Image

data_paths2=list(data_dir.glob('*/*'))
plt.figure(figsize=(20,4))
for i in range(20):
    plt.subplot(2,10,i+1)
    plt.axis('off')
    image=random.choice(data_paths2)  #随机选择一个图片
    plt.title(image.parts[-2]) #通过glob对象取出他的文件夹名称，即分类名
    plt.imshow(Image.open(str(image)))  #显示图片

运行结果：

5. 图片预处理  

import torchvision.transforms as transforms
from torchvision import transforms,datasets

train_transforms=transforms.Compose([
    transforms.Resize([224,224]), #将图片统一尺寸
    transforms.RandomHorizontalFlip(), #将图片随机水平翻转
    transforms.ToTensor(), #将图片转换为tensor
    transforms.Normalize(  #标准化处理—>转换为正态分布，使模型更容易收敛
        mean=[0.485,0.456,0.406],
        std=[0.229,0.224,0.225]
    )
])
test_transforms=transforms.Compose([
    transforms.Resize([224,224]), #将图片统一尺寸
    transforms.RandomHorizontalFlip(), #将图片随机水平翻转
    transforms.ToTensor(), #将图片转换为tensor
    transforms.Normalize(  #标准化处理—>转换为正态分布，使模型更容易收敛
        mean=[0.485,0.456,0.406],
        std=[0.229,0.224,0.225]
    )
])

total_data=datasets.ImageFolder(
    r"D:\THE MNIST DATABASE\J-series\J1\bird_photos",
    transform=train_transforms
)
total_data

运行结果：

Dataset ImageFolder
    Number of datapoints: 565
    Root location: D:\THE MNIST DATABASE\J-series\J1\bird_photos
    StandardTransform
Transform: Compose(
               Resize(size=[224, 224], interpolation=bilinear, max_size=None, antialias=True)
               RandomHorizontalFlip(p=0.5)
               ToTensor()
               Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
           )

---

将数据集分类情况进行映射输出：

total_data.class_to_idx

运行结果：

{'Bananaquit': 0,
 'Black Skimmer': 1,
 'Black Throated Bushtiti': 2,
 'Cockatoo': 3}

6. 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]
)
train_dataset,test_dataset

运行结果：

(<torch.utils.data.dataset.Subset at 0x2195b60dd50>,
 <torch.utils.data.dataset.Subset at 0x219508d5910>)

---

查看训练集和测试集的数据数量：

train_size,test_size

运行结果：

(452, 113)

7. 加载数据集

batch_size=8
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
)

查看测试集的情况：

for x,y in train_dl:
    print("Shape of x [N,C,H,W]:",x.shape)
    print("Shape of y:",y.shape,y.dtype)
    break

运行结果：

Shape of x [N,C,H,W]: torch.Size([8, 3, 224, 224])
Shape of y: torch.Size([8]) torch.int64

二、手动搭建ResNet50模型

ResNet-50有两个基本的块，分别名为Conv Block和Identity Block

Conv Block结构：

1. 搭建模型 

import torch.nn as nn

#定义使得网络前后一致的padding
def autopad(k,p=None):
    if p is None:
        p=k//2 if isinstance(k,int) else [x//2 for x in k]
    return p

class IdentityBlock(nn.Module):
    def __init__(self,in_channel,kernel_size,filters):
        super(IdentityBlock,self).__init__()
        
        filters1,filters2,filters3=filters
        #卷积块1
        self.conv1=nn.Sequential(
            nn.Conv2d(in_channel,filters1,1,stride=1,padding=0,bias=False),
            nn.BatchNorm2d(filters1),
            nn.ReLU(True)
        )
        #卷积块2
        self.conv2=nn.Sequential(
            nn.Conv2d(filters1,filters2,kernel_size,stride=1,padding=autopad(kernel_size),bias=False),
            nn.BatchNorm2d(filters2),
            nn.ReLU(True)            
        )
        #卷积块3
        self.conv3=nn.Sequential(
            nn.Conv2d(filters2,filters3,1,stride=1,padding=0,bias=False),
            nn.BatchNorm2d(filters3)
        )
        self.relu=nn.ReLU(True)
        
    def forward(self,x):
        x1=self.conv1(x)
        x1=self.conv2(x1)
        x1=self.conv3(x1)
        x=x+x1
        self.relu(x)
        
        return x
    
class ConvBlock(nn.Module):
    def __init__(self,in_channel,kernel_size,filters,stride=2):
        super(ConvBlock,self).__init__()
        
        filters1,filters2,filters3=filters
        #卷积块1
        self.conv1=nn.Sequential(
            nn.Conv2d(in_channel,filters1,1,stride=stride,padding=0,bias=False),
            nn.BatchNorm2d(filters1),
            nn.ReLU(True)
        )
        #卷积块2
        self.conv2=nn.Sequential(
            nn.Conv2d(filters1,filters2,kernel_size,stride=1,padding=autopad(kernel_size),bias=False),
            nn.BatchNorm2d(filters2),
            nn.ReLU(True)
        )
        #卷积块3
        self.conv3=nn.Sequential(
            nn.Conv2d(filters2,filters3,1,stride=1,padding=0,bias=False),
            nn.BatchNorm2d(filters3)
        )
        #短路卷积块
        self.shortcut=nn.Sequential(
            nn.Conv2d(in_channel,filters3,1,stride=stride,padding=0,bias=False),
            nn.BatchNorm2d(filters3)
        )
        self.relu=nn.ReLU(True)
        
    def forward(self,x):
        x1=self.conv1(x)
        x1=self.conv2(x1)
        x1=self.conv3(x1)
        x_shortcut=self.shortcut(x)
        x=x1+x_shortcut
        self.relu(x)
        
        return x
    
class ResNet50(nn.Module):
    def __init__(self,classes=4):
        super(ResNet50,self).__init__()
        #初始操作
        self.first=nn.Sequential(
            nn.Conv2d(3,64,7,stride=2,padding=3,bias=False,padding_mode='zeros'),
            nn.BatchNorm2d(64),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=3,stride=2,padding=0)
        )
        #第一部分
        self.block1=nn.Sequential(
            ConvBlock(64,3,[64,64,256],stride=1),
            IdentityBlock(256,3,[64,64,256]),
            IdentityBlock(256,3,[64,64,256])
        )
        #第二部分
        self.block2=nn.Sequential(
            ConvBlock(256,3,[128,128,512]),
            IdentityBlock(512,3,[128,128,512]),
            IdentityBlock(512,3,[128,128,512]),
            IdentityBlock(512,3,[128,128,512])
        )
        #第三部分
        self.block3=nn.Sequential(
            ConvBlock(512,3,[256,256,1024]),
            IdentityBlock(1024,3,[256,256,1024]),
            IdentityBlock(1024,3,[256,256,1024]),
            IdentityBlock(1024,3,[256,256,1024]),
            IdentityBlock(1024,3,[256,256,1024]),
            IdentityBlock(1024,3,[256,256,1024])
        )
        #第四部分
        self.block4=nn.Sequential(
            ConvBlock(1024,3,[512,512,2048]),
            IdentityBlock(2048,3,[512,512,2048]),
            IdentityBlock(2048,3,[512,512,2048])
        )
        #结尾部分
        self.pool=nn.AvgPool2d(kernel_size=7,stride=7,padding=0)
        self.fc=nn.Linear(2048,classes)
        
    def forward(self,x):
        x=self.first(x)
        x=self.block1(x)
        x=self.block2(x)
        x=self.block3(x)
        x=self.block4(x)
        x=self.pool(x)
        x=torch.flatten(x,start_dim=1)
        x=self.fc(x)
        
        return x
    
model=ResNet50().to(device)
model

运行结果：

ResNet50(
  (first): Sequential(
    (0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): ReLU()
    (3): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
  )
  (block1): Sequential(
    (0): ConvBlock(
      (conv1): Sequential(
        (0): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU(inplace=True)
      )
      (conv2): Sequential(
        (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU(inplace=True)
      )
      (conv3): Sequential(
        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (shortcut): Sequential(
        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (1): IdentityBlock(
      (conv1): Sequential(
        (0): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU(inplace=True)
      )
      (conv2): Sequential(
        (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU(inplace=True)
      )
      (conv3): Sequential(
        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (2): IdentityBlock(
      (conv1): Sequential(
        (0): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU(inplace=True)
      )
      (conv2): Sequential(
        (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU(inplace=True)
      )
      (conv3): Sequential(
        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
  )
  (block2): Sequential(
    (0): ConvBlock(
      (conv1): Sequential(
        (0): Conv2d(256, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)
        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU(inplace=True)
      )
      (conv2): Sequential(
        (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU(inplace=True)
      )
      (conv3): Sequential(
        (0): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (shortcut): Sequential(
        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (1): IdentityBlock(
      (conv1): Sequential(
        (0): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU(inplace=True)
      )
      (conv2): Sequential(
        (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU(inplace=True)
      )
      (conv3): Sequential(
        (0): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (2): IdentityBlock(
      (conv1): Sequential(
        (0): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU(inplace=True)
      )
      (conv2): Sequential(
        (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU(inplace=True)
      )
      (conv3): Sequential(
        (0): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (3): IdentityBlock(
      (conv1): Sequential(
        (0): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU(inplace=True)
      )
      (conv2): Sequential(
        (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU(inplace=True)
      )
      (conv3): Sequential(
        (0): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
  )
  (block3): Sequential(
    (0): ConvBlock(
      (conv1): Sequential(
        (0): Conv2d(512, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)
        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU(inplace=True)
      )
      (conv2): Sequential(
        (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU(inplace=True)
      )
      (conv3): Sequential(
        (0): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (shortcut): Sequential(
        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)
        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (1): IdentityBlock(
      (conv1): Sequential(
        (0): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU(inplace=True)
      )
      (conv2): Sequential(
        (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU(inplace=True)
      )
      (conv3): Sequential(
        (0): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (2): IdentityBlock(
      (conv1): Sequential(
        (0): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU(inplace=True)
      )
      (conv2): Sequential(
        (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU(inplace=True)
      )
      (conv3): Sequential(
        (0): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (3): IdentityBlock(
      (conv1): Sequential(
        (0): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU(inplace=True)
      )
      (conv2): Sequential(
        (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU(inplace=True)
      )
      (conv3): Sequential(
        (0): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (4): IdentityBlock(
      (conv1): Sequential(
        (0): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU(inplace=True)
      )
      (conv2): Sequential(
        (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU(inplace=True)
      )
      (conv3): Sequential(
        (0): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (5): IdentityBlock(
      (conv1): Sequential(
        (0): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU(inplace=True)
      )
      (conv2): Sequential(
        (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU(inplace=True)
      )
      (conv3): Sequential(
        (0): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
  )
  (block4): Sequential(
    (0): ConvBlock(
      (conv1): Sequential(
        (0): Conv2d(1024, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU(inplace=True)
      )
      (conv2): Sequential(
        (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU(inplace=True)
      )
      (conv3): Sequential(
        (0): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (shortcut): Sequential(
        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)
        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (1): IdentityBlock(
      (conv1): Sequential(
        (0): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU(inplace=True)
      )
      (conv2): Sequential(
        (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU(inplace=True)
      )
      (conv3): Sequential(
        (0): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (2): IdentityBlock(
      (conv1): Sequential(
        (0): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU(inplace=True)
      )
      (conv2): Sequential(
        (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU(inplace=True)
      )
      (conv3): Sequential(
        (0): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
  )
  (pool): AvgPool2d(kernel_size=7, stride=7, padding=0)
  (fc): Linear(in_features=2048, out_features=4, bias=True)
)

2. 查看模型详情

#显示网络结构
import torchsummary
torchsummary.summary(model,(3,224,224))

运行结果：

----------------------------------------------------------------
        Layer (type)               Output Shape         Param #
================================================================
            Conv2d-1         [-1, 64, 112, 112]           9,408
       BatchNorm2d-2         [-1, 64, 112, 112]             128
              ReLU-3         [-1, 64, 112, 112]               0
         MaxPool2d-4           [-1, 64, 55, 55]               0
            Conv2d-5           [-1, 64, 55, 55]           4,096
       BatchNorm2d-6           [-1, 64, 55, 55]             128
              ReLU-7           [-1, 64, 55, 55]               0
            Conv2d-8           [-1, 64, 55, 55]          36,864
       BatchNorm2d-9           [-1, 64, 55, 55]             128
             ReLU-10           [-1, 64, 55, 55]               0
           Conv2d-11          [-1, 256, 55, 55]          16,384
      BatchNorm2d-12          [-1, 256, 55, 55]             512
           Conv2d-13          [-1, 256, 55, 55]          16,384
      BatchNorm2d-14          [-1, 256, 55, 55]             512
             ReLU-15          [-1, 256, 55, 55]               0
        ConvBlock-16          [-1, 256, 55, 55]               0
           Conv2d-17           [-1, 64, 55, 55]          16,384
      BatchNorm2d-18           [-1, 64, 55, 55]             128
             ReLU-19           [-1, 64, 55, 55]               0
           Conv2d-20           [-1, 64, 55, 55]          36,864
      BatchNorm2d-21           [-1, 64, 55, 55]             128
             ReLU-22           [-1, 64, 55, 55]               0
           Conv2d-23          [-1, 256, 55, 55]          16,384
      BatchNorm2d-24          [-1, 256, 55, 55]             512
             ReLU-25          [-1, 256, 55, 55]               0
    IdentityBlock-26          [-1, 256, 55, 55]               0
           Conv2d-27           [-1, 64, 55, 55]          16,384
      BatchNorm2d-28           [-1, 64, 55, 55]             128
             ReLU-29           [-1, 64, 55, 55]               0
           Conv2d-30           [-1, 64, 55, 55]          36,864
      BatchNorm2d-31           [-1, 64, 55, 55]             128
             ReLU-32           [-1, 64, 55, 55]               0
           Conv2d-33          [-1, 256, 55, 55]          16,384
      BatchNorm2d-34          [-1, 256, 55, 55]             512
             ReLU-35          [-1, 256, 55, 55]               0
    IdentityBlock-36          [-1, 256, 55, 55]               0
           Conv2d-37          [-1, 128, 28, 28]          32,768
      BatchNorm2d-38          [-1, 128, 28, 28]             256
             ReLU-39          [-1, 128, 28, 28]               0
           Conv2d-40          [-1, 128, 28, 28]         147,456
      BatchNorm2d-41          [-1, 128, 28, 28]             256
             ReLU-42          [-1, 128, 28, 28]               0
           Conv2d-43          [-1, 512, 28, 28]          65,536
      BatchNorm2d-44          [-1, 512, 28, 28]           1,024
           Conv2d-45          [-1, 512, 28, 28]         131,072
      BatchNorm2d-46          [-1, 512, 28, 28]           1,024
             ReLU-47          [-1, 512, 28, 28]               0
        ConvBlock-48          [-1, 512, 28, 28]               0
           Conv2d-49          [-1, 128, 28, 28]          65,536
      BatchNorm2d-50          [-1, 128, 28, 28]             256
             ReLU-51          [-1, 128, 28, 28]               0
           Conv2d-52          [-1, 128, 28, 28]         147,456
      BatchNorm2d-53          [-1, 128, 28, 28]             256
             ReLU-54          [-1, 128, 28, 28]               0
           Conv2d-55          [-1, 512, 28, 28]          65,536
      BatchNorm2d-56          [-1, 512, 28, 28]           1,024
             ReLU-57          [-1, 512, 28, 28]               0
    IdentityBlock-58          [-1, 512, 28, 28]               0
           Conv2d-59          [-1, 128, 28, 28]          65,536
      BatchNorm2d-60          [-1, 128, 28, 28]             256
             ReLU-61          [-1, 128, 28, 28]               0
           Conv2d-62          [-1, 128, 28, 28]         147,456
      BatchNorm2d-63          [-1, 128, 28, 28]             256
             ReLU-64          [-1, 128, 28, 28]               0
           Conv2d-65          [-1, 512, 28, 28]          65,536
      BatchNorm2d-66          [-1, 512, 28, 28]           1,024
             ReLU-67          [-1, 512, 28, 28]               0
    IdentityBlock-68          [-1, 512, 28, 28]               0
           Conv2d-69          [-1, 128, 28, 28]          65,536
      BatchNorm2d-70          [-1, 128, 28, 28]             256
             ReLU-71          [-1, 128, 28, 28]               0
           Conv2d-72          [-1, 128, 28, 28]         147,456
      BatchNorm2d-73          [-1, 128, 28, 28]             256
             ReLU-74          [-1, 128, 28, 28]               0
           Conv2d-75          [-1, 512, 28, 28]          65,536
      BatchNorm2d-76          [-1, 512, 28, 28]           1,024
             ReLU-77          [-1, 512, 28, 28]               0
    IdentityBlock-78          [-1, 512, 28, 28]               0
           Conv2d-79          [-1, 256, 14, 14]         131,072
      BatchNorm2d-80          [-1, 256, 14, 14]             512
             ReLU-81          [-1, 256, 14, 14]               0
           Conv2d-82          [-1, 256, 14, 14]         589,824
      BatchNorm2d-83          [-1, 256, 14, 14]             512
             ReLU-84          [-1, 256, 14, 14]               0
           Conv2d-85         [-1, 1024, 14, 14]         262,144
      BatchNorm2d-86         [-1, 1024, 14, 14]           2,048
           Conv2d-87         [-1, 1024, 14, 14]         524,288
      BatchNorm2d-88         [-1, 1024, 14, 14]           2,048
             ReLU-89         [-1, 1024, 14, 14]               0
        ConvBlock-90         [-1, 1024, 14, 14]               0
           Conv2d-91          [-1, 256, 14, 14]         262,144
      BatchNorm2d-92          [-1, 256, 14, 14]             512
             ReLU-93          [-1, 256, 14, 14]               0
           Conv2d-94          [-1, 256, 14, 14]         589,824
      BatchNorm2d-95          [-1, 256, 14, 14]             512
             ReLU-96          [-1, 256, 14, 14]               0
           Conv2d-97         [-1, 1024, 14, 14]         262,144
      BatchNorm2d-98         [-1, 1024, 14, 14]           2,048
             ReLU-99         [-1, 1024, 14, 14]               0
   IdentityBlock-100         [-1, 1024, 14, 14]               0
          Conv2d-101          [-1, 256, 14, 14]         262,144
     BatchNorm2d-102          [-1, 256, 14, 14]             512
            ReLU-103          [-1, 256, 14, 14]               0
          Conv2d-104          [-1, 256, 14, 14]         589,824
     BatchNorm2d-105          [-1, 256, 14, 14]             512
            ReLU-106          [-1, 256, 14, 14]               0
          Conv2d-107         [-1, 1024, 14, 14]         262,144
     BatchNorm2d-108         [-1, 1024, 14, 14]           2,048
            ReLU-109         [-1, 1024, 14, 14]               0
   IdentityBlock-110         [-1, 1024, 14, 14]               0
          Conv2d-111          [-1, 256, 14, 14]         262,144
     BatchNorm2d-112          [-1, 256, 14, 14]             512
            ReLU-113          [-1, 256, 14, 14]               0
          Conv2d-114          [-1, 256, 14, 14]         589,824
     BatchNorm2d-115          [-1, 256, 14, 14]             512
            ReLU-116          [-1, 256, 14, 14]               0
          Conv2d-117         [-1, 1024, 14, 14]         262,144
     BatchNorm2d-118         [-1, 1024, 14, 14]           2,048
            ReLU-119         [-1, 1024, 14, 14]               0
   IdentityBlock-120         [-1, 1024, 14, 14]               0
          Conv2d-121          [-1, 256, 14, 14]         262,144
     BatchNorm2d-122          [-1, 256, 14, 14]             512
            ReLU-123          [-1, 256, 14, 14]               0
          Conv2d-124          [-1, 256, 14, 14]         589,824
     BatchNorm2d-125          [-1, 256, 14, 14]             512
            ReLU-126          [-1, 256, 14, 14]               0
          Conv2d-127         [-1, 1024, 14, 14]         262,144
     BatchNorm2d-128         [-1, 1024, 14, 14]           2,048
            ReLU-129         [-1, 1024, 14, 14]               0
   IdentityBlock-130         [-1, 1024, 14, 14]               0
          Conv2d-131          [-1, 256, 14, 14]         262,144
     BatchNorm2d-132          [-1, 256, 14, 14]             512
            ReLU-133          [-1, 256, 14, 14]               0
          Conv2d-134          [-1, 256, 14, 14]         589,824
     BatchNorm2d-135          [-1, 256, 14, 14]             512
            ReLU-136          [-1, 256, 14, 14]               0
          Conv2d-137         [-1, 1024, 14, 14]         262,144
     BatchNorm2d-138         [-1, 1024, 14, 14]           2,048
            ReLU-139         [-1, 1024, 14, 14]               0
   IdentityBlock-140         [-1, 1024, 14, 14]               0
          Conv2d-141            [-1, 512, 7, 7]         524,288
     BatchNorm2d-142            [-1, 512, 7, 7]           1,024
            ReLU-143            [-1, 512, 7, 7]               0
          Conv2d-144            [-1, 512, 7, 7]       2,359,296
     BatchNorm2d-145            [-1, 512, 7, 7]           1,024
            ReLU-146            [-1, 512, 7, 7]               0
          Conv2d-147           [-1, 2048, 7, 7]       1,048,576
     BatchNorm2d-148           [-1, 2048, 7, 7]           4,096
          Conv2d-149           [-1, 2048, 7, 7]       2,097,152
     BatchNorm2d-150           [-1, 2048, 7, 7]           4,096
            ReLU-151           [-1, 2048, 7, 7]               0
       ConvBlock-152           [-1, 2048, 7, 7]               0
          Conv2d-153            [-1, 512, 7, 7]       1,048,576
     BatchNorm2d-154            [-1, 512, 7, 7]           1,024
            ReLU-155            [-1, 512, 7, 7]               0
          Conv2d-156            [-1, 512, 7, 7]       2,359,296
     BatchNorm2d-157            [-1, 512, 7, 7]           1,024
            ReLU-158            [-1, 512, 7, 7]               0
          Conv2d-159           [-1, 2048, 7, 7]       1,048,576
     BatchNorm2d-160           [-1, 2048, 7, 7]           4,096
            ReLU-161           [-1, 2048, 7, 7]               0
   IdentityBlock-162           [-1, 2048, 7, 7]               0
          Conv2d-163            [-1, 512, 7, 7]       1,048,576
     BatchNorm2d-164            [-1, 512, 7, 7]           1,024
            ReLU-165            [-1, 512, 7, 7]               0
          Conv2d-166            [-1, 512, 7, 7]       2,359,296
     BatchNorm2d-167            [-1, 512, 7, 7]           1,024
            ReLU-168            [-1, 512, 7, 7]               0
          Conv2d-169           [-1, 2048, 7, 7]       1,048,576
     BatchNorm2d-170           [-1, 2048, 7, 7]           4,096
            ReLU-171           [-1, 2048, 7, 7]               0
   IdentityBlock-172           [-1, 2048, 7, 7]               0
       AvgPool2d-173           [-1, 2048, 1, 1]               0
          Linear-174                    [-1, 4]           8,196
================================================================
Total params: 23,516,228
Trainable params: 23,516,228
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 270.43
Params size (MB): 89.71
Estimated Total Size (MB): 360.71
----------------------------------------------------------------

三、 训练模型

1. 编写训练函数

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
    
    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，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

2. 编写测试函数

测试函数和训练函数大致相同，但是由于不进行梯度下降对网络权重进行更新，所以不需要传入优化器

#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
    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. 正式训练

import copy
optimizer=torch.optim.Adam(model.parameters(),lr=1e-4)  #创建优化器，并设置学习率
loss_fn=nn.CrossEntropyLoss()  #创建损失函数 

epochs=10

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J1_model
    if epoch_test_acc>best_acc:
        best_acc=epoch_test_acc
        J1_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=r'D:\THE MNIST DATABASE\J-series\J1_model.pth'
torch.save(model.state_dict(),PATH)

运行结果：

Epoch: 1,Train_acc:52.0%,Train_loss:1.178,Test_acc:43.4%,Test_loss:2.180,Lr:1.00E-04
Epoch: 2,Train_acc:68.1%,Train_loss:0.836,Test_acc:76.1%,Test_loss:0.952,Lr:1.00E-04
Epoch: 3,Train_acc:78.5%,Train_loss:0.664,Test_acc:77.9%,Test_loss:0.635,Lr:1.00E-04
Epoch: 4,Train_acc:80.5%,Train_loss:0.513,Test_acc:58.4%,Test_loss:1.794,Lr:1.00E-04
Epoch: 5,Train_acc:84.7%,Train_loss:0.416,Test_acc:75.2%,Test_loss:0.755,Lr:1.00E-04
Epoch: 6,Train_acc:82.5%,Train_loss:0.555,Test_acc:78.8%,Test_loss:0.734,Lr:1.00E-04
Epoch: 7,Train_acc:85.0%,Train_loss:0.399,Test_acc:64.6%,Test_loss:1.196,Lr:1.00E-04
Epoch: 8,Train_acc:88.1%,Train_loss:0.372,Test_acc:43.4%,Test_loss:4.219,Lr:1.00E-04
Epoch: 9,Train_acc:87.8%,Train_loss:0.319,Test_acc:90.3%,Test_loss:0.375,Lr:1.00E-04
Epoch:10,Train_acc:95.1%,Train_loss:0.166,Test_acc:92.0%,Test_loss:0.321,Lr:1.00E-04
Epoch:11,Train_acc:90.5%,Train_loss:0.263,Test_acc:84.1%,Test_loss:0.422,Lr:1.00E-04
Epoch:12,Train_acc:88.9%,Train_loss:0.310,Test_acc:90.3%,Test_loss:0.404,Lr:1.00E-04
Epoch:13,Train_acc:93.1%,Train_loss:0.190,Test_acc:89.4%,Test_loss:0.489,Lr:1.00E-04
Epoch:14,Train_acc:90.5%,Train_loss:0.282,Test_acc:81.4%,Test_loss:0.456,Lr:1.00E-04
Epoch:15,Train_acc:93.6%,Train_loss:0.181,Test_acc:85.8%,Test_loss:0.512,Lr:1.00E-04
Epoch:16,Train_acc:96.9%,Train_loss:0.100,Test_acc:92.0%,Test_loss:0.256,Lr:1.00E-04
Epoch:17,Train_acc:97.8%,Train_loss:0.096,Test_acc:89.4%,Test_loss:0.294,Lr:1.00E-04
Epoch:18,Train_acc:91.4%,Train_loss:0.260,Test_acc:85.8%,Test_loss:0.641,Lr:1.00E-04
Epoch:19,Train_acc:95.8%,Train_loss:0.139,Test_acc:90.3%,Test_loss:0.534,Lr:1.00E-04
Epoch:20,Train_acc:95.4%,Train_loss:0.157,Test_acc:89.4%,Test_loss:0.459,Lr:1.00E-04

四、 结果可视化

1. Loss与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']   #正常显示中文标签
plt.rcParams['axes.unicode_minus']=False   #正常显示负号
plt.rcParams['figure.dpi']=300   #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()

运行结果：

可以看出中间有明显的起伏波动，再次修改 batch_size=16，尝试后结果如下所示：

再次修改 batch_size=32，结果如下所示：

之后，又采取了数据增强的方法对图片进行旋转等操作，结果均不如人意，猜测可能是由于样本数量过少引起测试集结果的震荡。 

2. 指定图片进行预测 

from PIL import Image
 
classes=list(total_data.class_to_idx)
 
def predict_one_image(image_path,model,transform,classes):
    
    test_img=Image.open(image_path).convert('RGB')
    plt.imshow(test_img)   #展示预测的图片
    
    test_img=transform(test_img)
    img=test_img.to(device).unsqueeze(0)
    
    model.eval()
    output=model(img)
    
    _,pred=torch.max(output,1)
    pred_class=classes[pred]
    print(f'预测结果是：{pred_class}')

 预测图片：

#预测训练集中的某张照片
predict_one_image(image_path=r'D:\THE MNIST DATABASE\J-series\J1\bird_photos\Black Skimmer\001.jpg',
                  model=model,transform=train_transforms,classes=classes)

运行结果：

预测结果是：Black Skimmer

3. 模型评估

J1_model.eval()
epoch_test_acc,epoch_test_loss=test(test_dl,J1_model,loss_fn)
epoch_test_acc,epoch_test_loss

运行结果：

(0.7787610619469026, 0.8548152595758438)

五、心得体会

本周项目训练中，在pytorch环境下手动搭建了resnet50模型，与上节课相比，更加深层的理解了该模型的构造原理，对该模型有了更深层次的感悟。但模型的训练结果中，测试集的acc和loss都出现了较大的震荡，虽然多次修改batch_size，也通过图形旋转、翻转等方法对数据进行增强，但结果仍然不尽人意。通过查询学习笔记44-分析acc/loss上下波动不稳定的原因？，猜测引起该结果可能是由于数据集过小造成的，留待今后验证。