VGG图像分类网络（Pytorch）_vgg 图像分类-优快云博客

VGG是2014年由牛津大学研究组提出的，获得了2014年ImageNet比赛中定位任务的第一名和分类任务的第二名。
根据不同的需要，VGG网络可以有很多不同的结构，下图是论文中给出的几种搭建方式。
在这里插入图片描述我们选择其中的ABDE四种结构来搭建，下面是模型的代码：

import torch.nn as nn
import torch
class VGG(nn.Module):
    def __init__(self, features, class_num=1000, init_weights=False):
        super(VGG, self).__init__()
        self.features = features
        self.classifier = nn.Sequential(      #分类网络结构
            nn.Dropout(p=0.5),                 #50%失活，减少过拟合
            nn.Linear(512*7*7, 2048),          #第一层全连接层，原论文是4096
            nn.ReLU(True),
            nn.Dropout(p=0.5),
            nn.Linear(2048, 2048),
            nn.ReLU(True),
            nn.Linear(2048, class_num)
        )
        if init_weights:      #是否初始化
            self._initialize_weights()
    def forward(self, x):     #前向传播
        # N x 3 x 224 x 224
        x = self.features(x)      #先提取特征
        # N x 512 x 7 x 7
        x = torch.flatten(x, start_dim=1)   #展平处理
        # N x 512*7*7
        x = self.classifier(x)    #分类网络
        return x
    def _initialize_weights(self):    #初始化权重函数
        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)   #xavier初始化方法
                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)
def make_features(cfg: list):    #提取特征的函数
    layers = []   #定义一个空列表
    in_channels = 3
    for v in cfg:     #遍历配置列表
        if v == "M":
            layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
        else:
            conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
            layers += [conv2d, nn.ReLU(True)]
            in_channels = v   #改变深度
    return nn.Sequential(*layers)     #非关键字参数传入
cfgs = {    #对应不同配置的网络
    #数字代表卷积核个数，字母代表池化层参数
    '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'],
}
def vgg(model_name="vgg16", **kwargs):    #实例化模型
    try:
        cfg = cfgs[model_name]    #传入字典
    except:
        print("Warning: model number {} not in cfgs dict!".format(model_name))
        exit(-1)
    model = VGG(make_features(cfg), **kwargs)   #1特征，2可变长度的字典变量，包含分类个数和是否初始化
    return model

然后是训练的代码：

import torch.nn as nn
from torchvision import transforms, datasets
import json
import os
import torch.optim as optim
from model import vgg
import torch
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(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 = data_root + "/data/flower_data/"  # flower data set path，得到花文件的路径
train_dataset = datasets.ImageFolder(root=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 = 32
train_loader = torch.utils.data.DataLoader(train_dataset,
                                           batch_size=batch_size, shuffle=True,
                                           num_workers=0)
validate_dataset = datasets.ImageFolder(root=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)
# test_data_iter = iter(validate_loader)
# test_image, test_label = test_data_iter.next()
model_name = "vgg16"
net = vgg(model_name=model_name, class_num=5, init_weights=True)  #实例化vgg模型
net.to(device)
loss_function = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=0.0001)
best_acc = 0.0
save_path = './{}Net.pth'.format(model_name)
for epoch in range(30):
    # train
    net.train()
    running_loss = 0.0
    for step, data in enumerate(train_loader, start=0):
        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()
        # print train process
        rate = (step + 1) / len(train_loader)
        a = "*" * int(rate * 50)
        b = "." * int((1 - rate) * 50)
        print("\rtrain loss: {:^3.0f}%[{}->{}]{:.3f}".format(int(rate * 100), a, b, loss), end="")
    print()
    # validate
    net.eval()
    acc = 0.0  # accumulate accurate number / epoch
    with torch.no_grad():
        for data_test in validate_loader:
            test_images, test_labels = data_test
            optimizer.zero_grad()
            outputs = net(test_images.to(device))
            predict_y = torch.max(outputs, dim=1)[1]
            acc += (predict_y == test_labels.to(device)).sum().item()
        accurate_test = acc / val_num
        if accurate_test > best_acc:
            best_acc = accurate_test
            torch.save(net.state_dict(), save_path)
        print('[epoch %d] train_loss: %.3f  test_accuracy: %.3f' %
              (epoch + 1, running_loss / step, accurate_test))
print('Finished Training')

我这里选择训练最常用的VGG16网络，迭代30次之后可以达到0.7以上的准确率。
在这里插入图片描述

用训练好的网络来预测一张玫瑰的图片：

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

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 = Image.open("./data/predict/roses1.jpg")
plt.imshow(img)
# [N, C, H, W]
img = data_transform(img)
# expand batch dimension
img = torch.unsqueeze(img, dim=0)

# read class_indict
try:
    json_file = open('./class_indices.json', 'r')
    class_indict = json.load(json_file)
except Exception as e:
    print(e)
    exit(-1)

# create model
model = vgg(model_name="vgg16", class_num=5)
# load model weights
model_weight_path = "./vgg16Net.pth"
model.load_state_dict(torch.load(model_weight_path))
model.eval()
with torch.no_grad():
    # predict class
    output = torch.squeeze(model(img))
    predict = torch.softmax(output, dim=0)
    predict_cla = torch.argmax(predict).numpy()
print(class_indict[str(predict_cla)],predict[predict_cla].item())
plt.show()