VGG网络结构详解与代码复现，感受野计算

前言：

参考内容来自up：4.1 VGG网络详解及感受野的计算_哔哩哔哩_bilibili

up的代码和ppt：GitHub - WZMIAOMIAO/deep-learning-for-image-processing: deep learning for image processing including classification and object-detection etc.

一、简介

VGG 在2014年由牛津大学著名研究组 VGG（Visual Geometry Group）提出，斩获该年 ImageNet 竞赛中 Localization Task（定位任务）第一名和 Classification Task（分类任务）第二名。

原论文地址：[1409.1556] Very Deep Convolutional Networks for Large-Scale Image Recognition (arxiv.org)

VGG网络的创新点：通过堆叠多个小卷积核来替代大尺度卷积核，可以减少训练参数，同时能保证相同的感受野。

论文中提到，可以通过堆叠两个3×3的卷积核替代5x5的卷积核，堆叠三个3×3的卷积核替代7x7的卷积核，这样做可以拥有相同的感受野 。

二、详解

1. CNN感受野

在卷积神经网络中，决定某一层输出结果中一个元素所对应的输入层的区域大小，被称作感受野（receptive field）。

通俗的解释是，输出feature map上的一个单元 对应输入层上的区域大小。

以下图为例，输出层 layer3 中一个单元 对应 输入层 layer2 上区域大小为2×2（池化操作），对应输入层 layer1 上大小为5×5

（可以这么理解，layer2中 2×2区域中的每一块对应一个3×3的卷积核，又因为 stride=2，所以layer1的感受野为5×5）

感受野的计算公式为：

F(i)=( F (i +1)−1) × Stride +Ksize

• F(i) 为第 i 层感受野
• Stride 为第 i 层的步距
• Ksize 为 卷积核或池化核尺寸

以上图为例：

Feature map: F(3) = 1

Pool1：F ( 2 ) = ( 1 − 1 ) × 2 + 2 = 2

Conv1: F ( 1 ) = ( 2 − 1 ) × 2 + 3 = 5

2. 多个小卷积核

验证一下VGG论文中的两点结论：

1.堆叠两个3×3的卷积核替代5x5的卷积核，堆叠三个3×3的卷积核替代7x7的卷积核。替代前后感受野是否相同？

（注：VGG网络中卷积的Stride默认为1）

Feature map: F = 1

Conv3x3(3): F = ( 1 − 1 ) × 1 + 3 = 3

Conv3x3(2): F = ( 3 − 1 ) × 1 + 3 = 5 （5×5卷积核感受野）

Conv3x3(1): F = ( 5 − 1 ) × 1 + 3 = 7 （7×7卷积核感受野）

2.堆叠3×3卷积核后训练参数是否真的减少了？

注：CNN参数个数 = 卷积核尺寸×卷积核深度 × 卷积核组数 = 卷积核尺寸 × 输入特征矩阵深度 × 输出特征矩阵深度

现假设:输入特征矩阵深度 = 输出特征矩阵深度 = C

使用7×7卷积核所需参数个数：

堆叠三个3×3的卷积核所需参数个数：

3. VGG-16

VGG网络有多个版本，一般常用的是VGG-16模型，其网络结构如下如所示

16层分别为：13个卷积层和3个全连接层

经3×3卷积的特征矩阵的尺寸是不改变的：

因为maxpool的池化和大小和步距为2，所以图像缩小为原来的一半

如下图所示，channel会根据上一层的卷积核个数变化，每经过一次maxpool，图像的长宽都会缩小为原来的一半

全连接层3有1000个节点，因为分类任务有一千类别，最后一层不需要Relu激活函数，因为最后要经过一次softmax函数。

三、代码复现

接下来搭建A,B,D,E四个配置模型

将VGG网络分为两个部分：提取特征网络结构和分类网络结构

 

1. model.py

定义一个字典文件，字典的每个key代表一个模型的配置文件

cfgs = {        
    'vgg11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
    'vgg13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
    'vgg16': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],
    'vgg19': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],
}

如：VGG11，对应的值是一个列表，数字代表卷积核的个数，M代表池化层的结构，与上图中的结构一一对应

Sequential类的使用示例：

1. #通过将一个个非关键参数输入到Sequential类中，从而生成新的网络层结构

model = nn.Sequential(   
                  nn.Conv2d(1,20,5),
                  nn.ReLU(),
                  nn.Conv2d(20,64,5),
                  nn.ReLU()
                )

1. 通过有序字典的方式输入

        model = nn.Sequential(OrderedDict([
                  ('conv1', nn.Conv2d(1,20,5)),
                  ('relu1', nn.ReLU()),
                  ('conv2', nn.Conv2d(20,64,5)),
                  ('relu2', nn.ReLU())
                ]))

参考：Python 函数参数前面一个星号（*）和两个星号（**）的区别

完整代码：

import torch.nn as nn
import torch

# official pretrain weights
model_urls = {
    'vgg11': 'https://download.pytorch.org/models/vgg11-bbd30ac9.pth',
    'vgg13': 'https://download.pytorch.org/models/vgg13-c768596a.pth',
    'vgg16': 'https://download.pytorch.org/models/vgg16-397923af.pth',
    'vgg19': 'https://download.pytorch.org/models/vgg19-dcbb9e9d.pth'
}


class VGG(nn.Module):
    #传入提取特征网络结构，init_weights表示是否对权重进行初始化
    def __init__(self, features, num_classes=1000, init_weights=False):     
        super(VGG, self).__init__()
        self.features = features

        #全连接层分类，提取特征得到的时7*7*512，要先进行展平处理才能进行分类
        self.classifier = nn.Sequential(        
            nn.Linear(512*7*7, 4096),
            nn.ReLU(True),
            nn.Dropout(p=0.5),
            nn.Linear(4096, 4096),
            nn.ReLU(True),
            nn.Dropout(p=0.5),
            nn.Linear(4096, num_classes)
        )
        #是否对网络参数初始化
        if init_weights:
            self._initialize_weights()

    #正向传播
    def forward(self, x):
        # N x 3 x 224 x 224
        x = self.features(x)
        # N x 512 x 7 x 7，展平操作，从第一个维度开始
        x = torch.flatten(x, start_dim=1)
        # N x 512*7*7
        x = self.classifier(x)
        return x

    #初始化参数
    def _initialize_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
                nn.init.xavier_uniform_(m.weight)
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear):
                nn.init.xavier_uniform_(m.weight)
                # nn.init.normal_(m.weight, 0, 0.01)
                nn.init.constant_(m.bias, 0)


# 卷积层提取特征
def make_features(cfg: list):       #提取特征的函数，使用时只需要传入对应的列表
    layers = []     #定义一个空列表存放我们定义的每一层结构
    in_channels = 3     #彩色图像，channel为3                                                  
    for v in cfg:       #遍历配置列表
        if v == "M":
            layers += [nn.MaxPool2d(kernel_size=2, stride=2)]       #创建最大池化下采样层 
        else:       #卷积层
            conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)        
                #第一层时彩色图像in_channels=3，v=64个卷积核
            layers += [conv2d, nn.ReLU(True)]       #将刚刚定义的卷积层和Relu激活函数拼接在一起，添加到layers列表中
            in_channels = v     #特征矩阵经过该层卷积之后，输出的深度变成v
    return nn.Sequential(*layers)       #将列表通过非关键字的形式传入


#定义一个字典文件，字典的每个key代表一个模型的配置文件
cfgs = {        
    'vgg11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
    'vgg13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
    'vgg16': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],
    'vgg19': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],
}


#实例化vgg网络
def vgg(model_name="vgg16", **kwargs):
    assert model_name in cfgs, "Warning: model number {} not in cfgs dict!".format(model_name)
    cfg = cfgs[model_name]      #得到cfg

    model = VGG(make_features(cfg), **kwargs)       #**kwargs可变长度的字典变量
    return model

2. train.py

import os
import sys
import json

import torch
torch.cuda.current_device()
import torch.nn as nn
from torchvision import transforms, datasets
import torch.optim as optim
from tqdm import tqdm

from model import vgg


def main():
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    print("using {} device.".format(device))

    data_transform = {
        "train": transforms.Compose([transforms.RandomResizedCrop(224),
                                     transforms.RandomHorizontalFlip(),
                                     transforms.ToTensor(),
                                     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]),
        "val": transforms.Compose([transforms.Resize((224, 224)),
                                   transforms.ToTensor(),
                                   transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])}

    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 = 4
    nw = min([os.cpu_count(), batch_size if batch_size > 1 else 0, 8])  # number of workers
    print('Using {} dataloader workers every process'.format(nw))

    train_loader = torch.utils.data.DataLoader(train_dataset,
                                               batch_size=batch_size, shuffle=True,
                                               num_workers=nw)

    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=nw)
    print("using {} images for training, {} images for validation.".format(train_num,
                                                                           val_num))

    # test_data_iter = iter(validate_loader)
    # test_image, test_label = test_data_iter.next()

    model_name = "vgg16"
    net = vgg(model_name=model_name, num_classes=5, init_weights=True)
    net.to(device)
    loss_function = nn.CrossEntropyLoss()
    optimizer = optim.Adam(net.parameters(), lr=0.0001)

    epochs = 10
    best_acc = 0.0
    save_path = './{}Net.pth'.format(model_name)
    train_steps = len(train_loader)
    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()
            outputs = net(images.to(device))
            loss = loss_function(outputs, 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))
                predict_y = torch.max(outputs, dim=1)[1]
                acc += torch.eq(predict_y, val_labels.to(device)).sum().item()

        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')


if __name__ == '__main__':
    main()

训练脚本跟上一篇AlexNet基本一致

函数调用过程：

net = vgg(model_name="vgg16", num_classes=5, init_weights=True)

cfg = cfgs[model_name]

  = cfgs[vgg16] = [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M']

model = VGG(make_features(cfg), num_classes=5, init_weights=True)

make_features(cfg: list)

3. predict.py

import os
import json

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

from model import vgg


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

    data_transform = transforms.Compose(
        [transforms.Resize((224, 224)),
         transforms.ToTensor(),
         transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

    # load image
    img_path = "../tulip.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)

    with open(json_path, "r") as f:
        class_indict = json.load(f)
    
    # create model
    model = vgg(model_name="vgg16", num_classes=5).to(device)
    # load model weights
    weights_path = "./vgg16Net.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))

    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()

我这里减少图片数量进行训练，得到很低的准确率