Pytorch搭建ResNet_pytorch resnet

Pytorch搭建ResNet

1、网络架构

ResNet的网络架构这里就不做过多解释，论文原文网络结构如下图，详细可以参照你必须要知道CNN模型：ResNet

2、环境搭建

pytorch版本：1.10.2

python版本：3.6.15

pytorch的安装教程可以参照pytorch的安装和入门使用

3、模型搭建

3.1 定义ResNet[18,34]基础残差块BasicBlock

1. expansion用来区分残差结构中不同层卷积核的个数，（50，101，152）的残差块中的第三层卷积和个数时是第一层和第二层的4倍。

   class BasicBlock(nn.Module):
       expansion = 1
       # 用来区分残差结构中不同层卷积核的个数
       # （50，101，152的残差块中的第三层卷积和个数时是第一层和第二层的4倍，这里就应该写4）
   1
   2
   3
   4

2. 在init函数中初始化残差块需要用到的结构

   • in_channel：残差块输入的通道数

   • out_channel：残差块输出的通道数

   • stride：卷积核移动的步长

   • downsample：下采样方法，默认为空（例如：网络架构中conv2.x的输出为[56,56,64]，但是conv3.x中需要的输入为[28,28,128]，所以需要下采样，对应下图虚线处的残差结构）

     

   def __init__(self, in_channel, out_channel, stride=1, downsample=None):
           super(BasicBlock, self).__init__()
           self.conv1 = nn.Conv2d(in_channels=in_channel, out_channels=out_channel, kernel_size=3, stride=stride,
                                  padding=1,
                                  bias=False)
           self.bn1 = nn.BatchNorm2d(out_channel)
           self.relu = nn.ReLU()
           self.conv2 = nn.Conv2d(in_channels=out_channel, out_channels=out_channel, kernel_size=3, stride=1, padding=1,
                                  bias=False)
           self.bn2 = nn.BatchNorm2d(out_channel)
           self.downsample = downsample
   
   1
   2
   3
   4
   5
   6
   7
   8
   9
   10
   11
   12

3. 编写forward函数，定义模型的前向传输过程

   • identity用来表示残差结构的支线
   • out表示残差结构的主线

   def forward(self, x):
           identity = x
           if self.downsample is not None:
               identity = self.downsample(x)
   
           out = self.conv1(x)
           out = self.bn1(out)
           out = self.relu(out)
   
           out = self.conv2(out)
           out = self.bn2(out)
   
           out += identity
           out = self.relu(out)
   
           return out
   1
   2
   3
   4
   5
   6
   7
   8
   9
   10
   11
   12
   13
   14
   15
   16

3.2 定义ResNet[50,101,152]的基础残差块Bottleneck

1. 50，101，152的残差块中的第三层卷积和个数时是第一层和第二层的4倍，因此定义expansion为4

   class Bottleneck(nn.Module):
       expansion = 4
   1
   2

2. 在init函数中初始化残差块需要用到的结构

       def __init__(self, in_channel, out_channel, stride=1, downsample=None):
           super(Bottleneck, self).__init__()
           self.conv1 = nn.Conv2d(in_channels=in_channel, out_channels=out_channel, kernel_size=1, stride=stride,
                                  bias=False)
           self.bn1 = nn.BatchNorm2d(out_channel)
   
           self.conv2 = nn.Conv2d(in_channels=out_channel, out_channels=out_channel, kernel_size=3, stride=1, padding=1,
                                  bias=False)
           self.bn2 = nn.BatchNorm2d(out_channel)
   
           self.conv3 = nn.Conv2d(in_channels=out_channel, out_channels=out_channel * self.expansion, kernel_size=1,
                                  stride=1, bias=False)
           self.bn3 = nn.BatchNorm2d(out_channel * self.expansion)
           self.relu = nn.ReLU(inplace=True)
   
           self.downsample = downsample
   1
   2
   3
   4
   5
   6
   7
   8
   9
   10
   11
   12
   13
   14
   15
   16

3. 编写forward函数，定义模型的前向传输过程

       def forward(self, x):
           identity = x
           if self.downsample is not None:
               identity = self.downsample(x)
   
           out = self.conv1(x)
           out = self.bn1(out)
           out = self.relu(out)
   
           out = self.conv2(out)
           out = self.bn2(out)
           out = self.relu(out)
   
           out = self.conv3(out)
           out = self.bn3(out)
   
           out += identity
           out = self.relu(out)
   
           return out
   1
   2
   3
   4
   5
   6
   7
   8
   9
   10
   11
   12
   13
   14
   15
   16
   17
   18
   19
   20

3.3 定义ResNet整体网络结构

1. 定义_make_layer函数：用来构建网络架构中的（conv2.x，conv3.x，conv4.x，conv5.x）

   • block：基础残差结构（根据定义的网络层数不同，传入不同的残差结构，[18,34]传BasicBlock，[50,101,152]传Bottleneck）
   • channel：残差结构中第一个卷积层使用的卷积核的个数
   • block_num：该层包含多少个残差结构
   • stride：卷积和移动的步距
   • 什么时候分支需要进行下采样：
     • 对于所有的resnet结构，在conv2.x—>conv3.x—>conv4.x—>conv5.x的途中分支都需要进行下采样(stride=2)
     • 对于resnet[50,101,152]网络结构来说，从maxpool层进入conv2.x时分支也需要进行一次“下采样”，这次“下采样”只改变深度，不改变高度和宽度（stride=1）。

       def __make_layer(self, block, channel, block_num, stride=1):
           downsample = None
           if stride != 1 or self.in_channel != channel * block.expansion:  # 判断是否进行下采样
               downsample = nn.Sequential(
                   nn.Conv2d(self.in_channel, channel * block.expansion, kernel_size=1, stride=stride, bias=False),
                   nn.BatchNorm2d(channel * block.expansion)
               )
           layers = []
           layers.append(block(self.in_channel, channel, downsample=downsample, stride=stride))  # 添加第一个残差基础块
           self.in_channel = channel * block.expansion
           # 对于18，34层的网络，经过第一个残差块后，输出的特征矩阵通道数与第一层的卷积层个数一样
           # 对于50，101，152层的网络，经过第一个残差块后，输出的特征矩阵通道数时第一个卷积层的4倍,因此要将后续残差块的输入特征矩阵通道数调整过来
           for _ in range(1, block_num):  # 添加后续的基础残差模块,后续的基础模块都不需要进行下采样操作
               layers.append(block(self.in_channel, channel))
   
           return nn.Sequential(*layers)
   1
   2
   3
   4
   5
   6
   7
   8
   9
   10
   11
   12
   13
   14
   15
   16

2. 在init函数中初始化网络需要用到的结构

   • block：基础残差结构
   • block_num：列表参数，标注所使用残差结构的数目，对应网络架构图中的数目
   • num_classes：训练集分类个数
   • include_top：resnet网络上接其他网络，构成更复杂的网络

       def __init__(self, block, block_num, num_classes=1000, include_top=True):
           super(ResNet, self).__init__()
           self.include_top = include_top
           self.in_channel = 64  # maxpooling之后得到的特征矩阵的深度
   
           self.conv1 = nn.Conv2d(in_channels=3, out_channels=self.in_channel, kernel_size=7, stride=2, padding=3,
                                  bias=False)
           self.bn1 = nn.BatchNorm2d(self.in_channel)
           self.relu = nn.ReLU(inplace=True)
           self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
   
           self.layer1 = self.__make_layer(block, 64, block_num[0])
           self.layer2 = self.__make_layer(block, 128, block_num[1], stride=2)
           self.layer3 = self.__make_layer(block, 256, block_num[2], stride=2)
           self.layer4 = self.__make_layer(block, 512, block_num[3], stride=2)
           if self.include_top:
               self.avgpool = nn.AdaptiveAvgPool2d(1, 1)
               self.fc = nn.Linear(512 * block.expansion, num_classes)
   
           for m in self.modules():  # 初始化模型参数
               if isinstance(m, nn.Conv2d):
                   nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
   1
   2
   3
   4
   5
   6
   7
   8
   9
   10
   11
   12
   13
   14
   15
   16
   17
   18
   19
   20
   21
   22

3. 编写forward函数，定义模型的前向传输过程

       def forward(self, x):
           x = self.conv1(x)
           x = self.bn1(x)
           x = self.relu(x)
           x = self.maxpool(x)
   
           x = self.layer1(x)
           x = self.layer2(x)
           x = self.layer3(x)
           x = self.layer4(x)
   
           if self.include_top:
               x = self.avgpool(x)
               x = torch.flatten(x, 1)
               x = self.fc(x)
   
           return x
   1
   2
   3
   4
   5
   6
   7
   8
   9
   10
   11
   12
   13
   14
   15
   16
   17

4. 定义resnet不同层数的网络

   def resnet18(num_classes=1000, include_top=True):
       return ResNet(BasicBlock, [2, 2, 2, 2], num_classes=num_classes, include_top=include_top)
   
   
   def resnet34(num_classes=1000, include_top=True):
       return ResNet(BasicBlock, [3, 4, 6, 3], num_classes=num_classes, include_top=include_top)
   
   
   def resnet50(num_classes=1000, include_top=True):
       return ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes, include_top=include_top)
   
   
   def resnet101(num_classes=1000, include_top=True):
       return ResNet(Bottleneck, [3, 4, 23, 3], num_classes=num_classes, include_top=include_top)
   
   
   def resnet34(num_classes=1000, include_top=True):
       return ResNet(Bottleneck, [3, 8, 26, 3], num_classes=num_classes, include_top=include_top)
   1
   2
   3
   4
   5
   6
   7
   8
   9
   10
   11
   12
   13
   14
   15
   16
   17
   18

4、训练

4.1 下载官方提供的ResNet网络的与训练模型参数

model_urls = {
    'resnet18': 'https://download.pytorch.org/models/resnet18-f37072fd.pth',
    'resnet34': 'https://download.pytorch.org/models/resnet34-b627a593.pth',
    'resnet50': 'https://download.pytorch.org/models/resnet50-0676ba61.pth',
    'resnet101': 'https://download.pytorch.org/models/resnet101-63fe2227.pth',
    'resnet152': 'https://download.pytorch.org/models/resnet152-394f9c45.pth',
    'resnext50_32x4d': 'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth',
    'resnext101_32x8d': 'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth',
    'wide_resnet50_2': 'https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth',
    'wide_resnet101_2': 'https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth',
}
1
2
3
4
5
6
7
8
9
10
11

4.2 数据集准备

1. 获取花分类数据集，放在data_set/flower_data文件夹下

2. 使用split.py划分训练集和测试集

   import os
   from shutil import copy, rmtree
   import random
   
   
   def mk_file(file_path: str):
       if os.path.exists(file_path):
           # 如果文件夹存在，则先删除原文件夹在重新创建
           rmtree(file_path)
       os.makedirs(file_path)
   
   
   def main():
       # 保证随机可复现
       random.seed(0)
   
       # 将数据集中10%的数据划分到验证集中
       split_rate = 0.1
   
       # 指向你解压后的flower_photos文件夹
       cwd = os.getcwd()
       data_root = os.path.join(cwd, "flower_data")
       origin_flower_path = os.path.join(data_root, "flower_photos")
       assert os.path.exists(origin_flower_path), "path '{}' does not exist.".format(origin_flower_path)
   
       flower_class = [cla for cla in os.listdir(origin_flower_path)
                       if os.path.isdir(os.path.join(origin_flower_path, cla))]
   
       # 建立保存训练集的文件夹
       train_root = os.path.join(data_root, "train")
       mk_file(train_root)
       for cla in flower_class:
           # 建立每个类别对应的文件夹
           mk_file(os.path.join(train_root, cla))
   
       # 建立保存验证集的文件夹
       val_root = os.path.join(data_root, "val")
       mk_file(val_root)
       for cla in flower_class:
           # 建立每个类别对应的文件夹
           mk_file(os.path.join(val_root, cla))
   
       for cla in flower_class:
           cla_path = os.path.join(origin_flower_path, cla)
           images = os.listdir(cla_path)
           num = len(images)
           # 随机采样验证集的索引
           eval_index = random.sample(images, k=int(num*split_rate))
           for index, image in enumerate(images):
               if image in eval_index:
                   # 将分配至验证集中的文件复制到相应目录
                   image_path = os.path.join(cla_path, image)
                   new_path = os.path.join(val_root, cla)
                   copy(image_path, new_path)
               else:
                   # 将分配至训练集中的文件复制到相应目录
                   image_path = os.path.join(cla_path, image)
                   new_path = os.path.join(train_root, cla)
                   copy(image_path, new_path)
               print("\r[{}] processing [{}/{}]".format(cla, index+1, num), end="")  # processing bar
           print()
   
       print("processing done!")
   
   
   if __name__ == '__main__':
       main()
   1
   2
   3
   4
   5
   6
   7
   8
   9
   10
   11
   12
   13
   14
   15
   16
   17
   18
   19
   20
   21
   22
   23
   24
   25
   26
   27
   28
   29
   30
   31
   32
   33
   34
   35
   36
   37
   38
   39
   40
   41
   42
   43
   44
   45
   46
   47
   48
   49
   50
   51
   52
   53
   54
   55
   56
   57
   58
   59
   60
   61
   62
   63
   64
   65
   66
   67

   

4.3 构建train.py文件

1. 定义数据标准化处理方式（这里的normalize的参数为官方提供的参数）

       data_transform = {
           "train": transforms.Compose([transforms.RandomResizedCrop(224),
                                        transforms.RandomHorizontalFlip(),
                                        transforms.ToTensor(),
                                        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]),
           "val": transforms.Compose([transforms.Resize(256),# 最小边缩放
                                      transforms.CenterCrop(224),# 中心裁剪
                                      transforms.ToTensor(),
                                      transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])}
   1
   2
   3
   4
   5
   6
   7
   8
   9

2. 载入数据集

       data_root = os.path.abspath(os.path.join(os.getcwd(), "../.."))  # get data root path
       image_path = os.path.join(data_root, "data_set", "flower_data")  # flower data set path
       assert os.path.exists(image_path), "{} path does not exist.".format(image_path)
       train_dataset = datasets.ImageFolder(root=os.path.join(image_path, "train"),
                                            transform=data_transform["train"])
       train_num = len(train_dataset)
       
       # {'daisy':0, 'dandelion':1, 'roses':2, 'sunflower':3, 'tulips':4}
       flower_list = train_dataset.class_to_idx
       cla_dict = dict((val, key) for key, val in flower_list.items())
       # write dict into json file
       json_str = json.dumps(cla_dict, indent=4)
       with open('class_indices.json', 'w') as json_file:
           json_file.write(json_str)
           
       batch_size = 16
   
       train_loader = torch.utils.data.DataLoader(train_dataset,
                                                  batch_size=batch_size, shuffle=True,
                                                  num_workers=0)
   
       validate_dataset = datasets.ImageFolder(root=os.path.join(image_path, "val"),
                                               transform=data_transform["val"])
       val_num = len(validate_dataset)
       validate_loader = torch.utils.data.DataLoader(validate_dataset,
                                                     batch_size=batch_size, shuffle=False,
                                                     num_workers=0)
   
       print("using {} images for training, {} images for validation.".format(train_num,
                                                                              val_num))
   1
   2
   3
   4
   5
   6
   7
   8
   9
   10
   11
   12
   13
   14
   15
   16
   17
   18
   19
   20
   21
   22
   23
   24
   25
   26
   27
   28
   29
   30

3. 初始化网络模型并载入预训练参数（由于使用cpu进行训练，为了节省时间使用了迁移学习的方法）

   	net = resnet34()
       model_weight_path = "./pth/resnet34-pre.pth"
       assert os.path.exists(model_weight_path), "file {} does not exist.".format(model_weight_path)
       net.load_state_dict(torch.load(model_weight_path, map_location='cpu'))
       # for param in net.parameters():
       #     param.requires_grad = False
   1
   2
   3
   4
   5
   6

4. 修改最后全连接层的输出类别数量（这里只预测5类）

       # change fc layer structure
       in_channel = net.fc.in_features
       net.fc = nn.Linear(in_channel, 5)
       net.to(device)
   1
   2
   3
   4

5. 定义损失函数和优化器

   
       # define loss function
       loss_function = nn.CrossEntropyLoss()
   
       # construct an optimizer
       params = [p for p in net.parameters() if p.requires_grad]
       optimizer = optim.Adam(params, lr=0.0001)
   
   1
   2
   3
   4
   5
   6
   7
   8

6. 定义一些初始化参数（epoch，模型参数保存路径等）

       epochs = 3
       best_acc = 0.0
       save_path = './resNet34.pth'
       train_steps = len(train_loader)
   1
   2
   3
   4

7. 开始训练，打印训练结果

       for epoch in range(epochs):
           # train
           net.train()
           running_loss = 0.0
           train_bar = tqdm(train_loader, file=sys.stdout)
           for step, data in enumerate(train_bar):
               images, labels = data
               optimizer.zero_grad()
               logits = net(images.to(device))
               loss = loss_function(logits, labels.to(device))
               loss.backward()
               optimizer.step()
   
               # print statistics
               running_loss += loss.item()
   
               train_bar.desc = "train epoch[{}/{}] loss:{:.3f}".format(epoch + 1,
                                                                        epochs,
                                                                        loss)
   
           # validate
           net.eval()
           acc = 0.0  # accumulate accurate number / epoch
           with torch.no_grad():
               val_bar = tqdm(validate_loader, file=sys.stdout)
               for val_data in val_bar:
                   val_images, val_labels = val_data
                   outputs = net(val_images.to(device))
                   # loss = loss_function(outputs, test_labels)
                   predict_y = torch.max(outputs, dim=1)[1]
                   acc += torch.eq(predict_y, val_labels.to(device)).sum().item()
   
                   val_bar.desc = "valid epoch[{}/{}]".format(epoch + 1,
                                                              epochs)
   
           val_accurate = acc / val_num
           print('[epoch %d] train_loss: %.3f  val_accuracy: %.3f' %
                 (epoch + 1, running_loss / train_steps, val_accurate))
   
           if val_accurate > best_acc:
               best_acc = val_accurate
               torch.save(net.state_dict(), save_path)
   
       print('Finished Training')
   1
   2
   3
   4
   5
   6
   7
   8
   9
   10
   11
   12
   13
   14
   15
   16
   17
   18
   19
   20
   21
   22
   23
   24
   25
   26
   27
   28
   29
   30
   31
   32
   33
   34
   35
   36
   37
   38
   39
   40
   41
   42
   43
   44

   

5、预测

注意：预测时，对于数据的标准化处理方式要采用和训练时的一致的处理方式

python
import os
import json

import torch
from PIL import Image
from torchvision import transforms
import matplotlib.pyplot as plt

from model import resnet34


def main():
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

    data_transform = transforms.Compose(
        [transforms.Resize(256),
         transforms.CenterCrop(224),
         transforms.ToTensor(),
         transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])

    # load image
    img_path = "./tulipa.jpg"
    assert os.path.exists(img_path), "file: '{}' dose not exist.".format(img_path)
    img = Image.open(img_path)
    plt.imshow(img)
    # [N, C, H, W]
    img = data_transform(img)
    # expand batch dimension
    img = torch.unsqueeze(img, dim=0)

    # read class_indict
    json_path = './class_indices.json'
    assert os.path.exists(json_path), "file: '{}' dose not exist.".format(json_path)

    json_file = open(json_path, "r")
    class_indict = json.load(json_file)

    # create model
    model = resnet34(num_classes=5).to(device)

    # load model weights
    weights_path = "./resNet34.pth"
    assert os.path.exists(weights_path), "file: '{}' dose not exist.".format(weights_path)
    model.load_state_dict(torch.load(weights_path, map_location=device))

    # prediction
    model.eval()
    with torch.no_grad():
        # predict class
        output = torch.squeeze(model(img.to(device))).cpu()
        predict = torch.softmax(output, dim=0)
        predict_cla = torch.argmax(predict).numpy()

    print_res = "class: {}   prob: {:.3}".format(class_indict[str(predict_cla)],
                                                 predict[predict_cla].numpy())
    plt.title(print_res)
    for i in range(len(predict)):
        print("class: {:10}   prob: {:.3}".format(class_indict[str(i)],
                                                  predict[i].numpy()))
    plt.show()


if __name__ == '__main__':
    main()
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65