关于数据集
数据集选择的是Kaggle上的Cat and Dog，猫狗图片数量上达到了上万张。你可以通过这里进入Kaggle下载数据集Cat and Dog | Kaggle。
在我的Github仓库当中也放了猫狗图片各666张。
VGG网络
VGG的主要特点是使用了一系列具有相同尺寸 3×3 大小的卷积核进行多次卷积操作。这种结构的一个优势是可以堆叠更多的卷积层，使得网络能够学习到更复杂的特征。
详情请看此篇VGG16模型详解_夏天是冰红茶的博客-CSDN博客。
今天让我们来探究一下在2014年的ImageNet图像分类竞赛中取得显著成绩的VGG模型效果如何。
# net.pyimport torchimport torchvisionimport torch.nn as nnimport torchsummaryfrom torch.hub import load_state_dict_from_url# model = torchvision.models.vgg16()model_urls = {"vgg16": "https://download.pytorch.org/models/vgg16-397923af.pth","vgg19": "https://download.pytorch.org/models/vgg19-dcbb9e9d.pth"}cfgs = {"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"],}class VGG(nn.Module):def __init__(self, features, num_classes = 1000, init_weights= True, dropout = 0.5):super(VGG,self).__init__()self.features = featuresself.avgpool = nn.AdaptiveAvgPool2d((7, 7))self.classifier = nn.Sequential(nn.Linear(512 * 7 * 7, 4096),nn.ReLU(True),nn.Dropout(p=dropout),nn.Linear(4096, 4096),nn.ReLU(True),nn.Dropout(p=dropout),nn.Linear(4096, num_classes),)if init_weights:for m in self.modules():if isinstance(m, nn.Conv2d):nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")if m.bias is not None:nn.init.constant_(m.bias, 0)elif isinstance(m, nn.BatchNorm2d):nn.init.constant_(m.weight, 1)nn.init.constant_(m.bias, 0)elif isinstance(m, nn.Linear):nn.init.normal_(m.weight, 0, 0.01)nn.init.constant_(m.bias, 0)def forward(self, x):x = self.features(x)x = self.avgpool(x)x = torch.flatten(x, 1)x = self.classifier(x)return xdef make_layers_with_vgg(cfg, batch_norm = False):layers = []in_channels = 3for v in cfg:if v == "M":layers += [nn.MaxPool2d(kernel_size=2, stride=2)]else:conv2d = nn.Conv2d(in_channels, v, kernel_size=(3,3), padding=1)if batch_norm:layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]else:layers += [conv2d, nn.ReLU(inplace=True)]in_channels = vreturn nn.Sequential(*layers)def vgg(mode='vgg16',pretrained=False, progress=True, num_classes=2):model = VGG(make_layers_with_vgg(cfgs[mode]))if pretrained:state_dict = load_state_dict_from_url(model_urls[mode], model_dir='./model', progress=progress)#预训练模型地址model.load_state_dict(state_dict)if num_classes != 1000:model.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),)return modelif __name__=='__main__':in_data = torch.ones(2, 3, 224, 224)net = vgg(mode='vgg16', pretrained=False, progress=True, num_classes=2)# 使用默认的 VGG-16 架构# net = vgg(mode='vgg19', pretrained=False, progress=True, num_classes=2)# 使用 VGG-19 架构out = net(in_data)print(out)torchsummary.summary(net, input_size=(3, 224, 224))与前面纯手打的VGG16网络不同，这里还添加了VGG19网络结构以及预训练权重。
import torchvisionmodel = torchvision.models.vgg16()你可以通过这里来查看VGG16的模型结构与预训练权重的url，上面也是从pytorch实现的网络中更改过的，所有你也可以去pytorch的官网查找。
创建分类数据列表
将指定路径中的图像文件的类别和类型信息写入到名为 class_data.txt的文件中，以便后续用于分类任务或其他需要这些信息的应用。
清华源安装
pip install pyzjr==1.1.1 –user -i https://pypi.tuna.tsinghua.edu.cn/simple
猫狗分类任务的数据列表的脚本
# annotation_txt.pyimport osimport pyzjr as pzclasses = ['cat', 'dog']path = 'train'if __name__ == '__main__':with open('class_data.txt', 'w') as txt_file:# 打开文件，注意使用 'w' 模式file_list = [os.path.join(path, i) for i in os.listdir(path)]for data_path in file_list:types_name, _ = pz.getPhotopath(data_path, True)cls_id = classes.index(os.path.basename(data_path))for type_name in types_name:line = f"{str(cls_id)};{str(type_name)}"txt_file.write(line + '\n')# 追加写入数据txt文件大致内容如下：
0;D:/deeplearning/VGGnet/train/cat/cat000.jpg
0;D:/deeplearning/VGGnet/train/cat/cat001.jpg
0;D:/deeplearning/VGGnet/train/cat/cat002.jpg
……
1;D:/deeplearning/VGGnet/train/dog/dog198.jpg
1;D:/deeplearning/VGGnet/train/dog/dog199.jpg
1;D:/deeplearning/VGGnet/train/dog/dog200.jpg
由于我本人的笔记本类型不是很好，所以就仅仅各自取了200张进行一个测试。
文件批量重命名（可选）
才下载的数据，它是这样的：
import pyzjr as pzimport osimport shutil# 原始图片所在路径、保存指定图片路径image_folder_path = r"D:\pythonprojects\deeplabv3_pytorch\img"save_image_folder_path = pz.CreateFolder(r"D:\pythonprojects\deeplabv3_pytorch\imgs")newbasename = 'Crack'if __name__=="__main__":imglist,allist=pz.getPhotopath(image_folder_path,debug=False)print(imglist)for i,file in enumerate(imglist):print(i,file)properties = pz.ImageAttribute(file)name, ext = os.path.splitext(properties['name'])# -----------------------------------------------# 格式可以在这里修改 i:03d ——> 001# 扩展名也可以自己定义,默认采用原本的ext(.png,.jpg这种)#newname = f"{newbasename}{i:03d}{ext}"## -----------------------------------------------new_path = os.path.join(save_image_folder_path, newname)shutil.copy(file, new_path)print("文件批量重命名和保存完成")只需要修改newbasename以及具体的格式即可，而扩展名我是默认使用的原本的ext，但要记住的是，修改扩展名时候要把“ . ”加上。
你也可以调用pyzjr.RenameFile进行批量化的重命名。
数据预处理与损失历史记录
这两个功能均在dataoperation.py文件当中，为深度学习模型的训练提供了一些辅助功能。可以在深度学习模型的训练过程中使用，以便更好地监控训练的进展和效果。
# dataoperation.pyimport cv2import numpy as npimport torch.utils.data as dataimport matplotlibimport torchmatplotlib.use('Agg')from matplotlib import pyplot as pltimport scipy.signalfrom PIL import Imagefrom torch.utils.tensorboard import SummaryWriterimport osdef preprocess_input(x):x/=127.5x-=1.return xdef cvtColor(image):if len(np.shape(image))==3 and np.shape(image)[-2]==3:return imageelse:image=image.convert('RGB')return imageclass DataGenerator(data.Dataset):def __init__(self,annotation_lines,inpt_shape,random=True):self.annotation_lines=annotation_linesself.input_shape=inpt_shapeself.random=randomdef __len__(self):return len(self.annotation_lines)def __getitem__(self, index):annotation_path=self.annotation_lines[index].split(';')[1].split()[0]image=Image.open(annotation_path)image=self.get_random_data(image,self.input_shape,random=self.random)image=np.transpose(preprocess_input(np.array(image).astype(np.float32)),[2,0,1])y=int(self.annotation_lines[index].split(';')[0])return image,ydef rand(self,a=0.,b=1.):return np.random.rand()*(b-a)+adef get_random_data(self,image,inpt_shape,jitter=.3,hue=.1,sat=1.5,val=1.5,random=True):image=cvtColor(image)iw,ih=image.sizeh,w=inpt_shapeif not random:scale=min(w/iw,h/ih)nw=int(iw*scale)nh=int(ih*scale)dx=(w-nw)//2dy=(h-nh)//2image=image.resize((nw,nh),Image.BICUBIC)new_image=Image.new('RGB',(w,h),(128,128,128))new_image.paste(image,(dx,dy))image_data=np.array(new_image,np.float32)return image_datanew_ar=w/h*self.rand(1-jitter,1+jitter)/self.rand(1-jitter,1+jitter)scale=self.rand(.75,1.25)if new_ar<1:nh=int(scale*h)nw=int(nh*new_ar)else:nw=int(scale*w)nh=int(nw/new_ar)image=image.resize((nw,nh),Image.BICUBIC)dx=int(self.rand(0,w-nw))dy=int(self.rand(0,h-nh))new_image=Image.new('RGB',(w,h),(128,128,128))new_image.paste(image,(dx,dy))image=new_imageflip=self.rand()<.5if flip: image=image.transpose(Image.FLIP_LEFT_RIGHT)rotate=self.rand()<.5if rotate:angle=np.random.randint(-15,15)a,b=w/2,h/2M=cv2.getRotationMatrix2D((a,b),angle,1)image=cv2.warpAffine(np.array(image),M,(w,h),borderValue=[128,128,128])hue=self.rand(-hue,hue)sat=self.rand(1,sat) if self.rand()<.5 else 1/self.rand(1,sat)val=self.rand(1,val) if self.rand() 360, 0] = 360x[:, :, 1:][x[:, :, 1:] > 1] = 1x[x < 0] = 0image_data=cv2.cvtColor(x,cv2.COLOR_HSV2RGB)*255return image_dataclass LossHistory():def __init__(self, log_dir, model, input_shape):self.log_dir = log_dirself.losses = []self.val_loss = []os.makedirs(self.log_dir,True)self.writer = SummaryWriter(self.log_dir)try:dummy_input = torch.randn(2, 3, input_shape[0], input_shape[1])self.writer.add_graph(model, dummy_input)except:passdef append_loss(self, epoch, loss, val_loss):if not os.path.exists(self.log_dir):os.makedirs(self.log_dir)self.losses.append(loss)self.val_loss.append(val_loss)with open(os.path.join(self.log_dir, "epoch_loss.txt"), 'a') as f:f.write(str(loss))f.write("\n")with open(os.path.join(self.log_dir, "epoch_val_loss.txt"), 'a') as f:f.write(str(val_loss))f.write("\n")self.writer.add_scalar('loss', loss, epoch)self.writer.add_scalar('val_loss', val_loss, epoch)self.loss_plot()def loss_plot(self):iters = range(len(self.losses))plt.figure()# plt.plot(iters, self.losses, 'red', linewidth=2, label='train loss')# plt.plot(iters, self.val_loss, 'coral', linewidth=2, label='val loss')plt.plot(iters, [loss.item() for loss in self.losses], 'red', linewidth=2, label='train loss')plt.plot(iters, [loss.item() for loss in self.val_loss], 'coral', linewidth=2, label='val loss')try:if len(self.losses) < 25:num = 5else:num = 15plt.plot(iters, scipy.signal.savgol_filter(self.losses, num, 3), 'green', linestyle='--', linewidth=2, label='smooth train loss')plt.plot(iters, scipy.signal.savgol_filter(self.val_loss, num, 3), '#8B4513', linestyle='--', linewidth=2, label='smooth val loss')except:passplt.grid(True)plt.xlabel('Epoch')plt.ylabel('Loss')plt.legend(loc="upper right")plt.savefig(os.path.join(self.log_dir, "epoch_loss.png"))plt.cla()plt.close("all")训练主文件
import torch.nn as nnfrom net import vggfrom torch.utils.data import DataLoaderfrom tqdm import tqdmimport datetimefrom dataoperation import *if __name__=="__main__":#---------------------------------## Cuda 是否使用Cuda#没有GPU可以设置成False#---------------------------------#Cuda = False# ---------------------------------## 'vgg16' and'vgg19'# ---------------------------------#Net = 'vgg16'# ---------------------------------## 先运行annotation_txt脚本# ---------------------------------#annotation_path='class_data.txt'# ---------------------------------## 输入图片尺寸# ---------------------------------#input_shape = [224, 224]# ---------------------------------##分类个数,比如这里只要猫和狗两类# ---------------------------------#num_classes = 2# -------------------------------------------------------## lr 模型的最大学习率#当使用Adam优化器时建议设置lr=5e-4#当使用SGD优化器时建议设置 lr=7e-3# -------------------------------------------------------#lr = 0.0001# ---------------------------------## 优化器选择 SGD 与 Adam# ---------------------------------#optimizer_type = "Adam"# ---------------------------------## 验证集所占百分比# ---------------------------------#percentage = 0.2# ---------------------------------## 训练轮次# ---------------------------------#epochs = 80# ---------------------------------## save_period 多少个epoch保存一次权值# ---------------------------------#save_period = 1# ------------------------------------------------------------------## save_dir权值与日志文件保存的文件夹# ------------------------------------------------------------------#save_dir = 'log'if not os.path.exists(save_dir):os.makedirs(save_dir)time_str = datetime.datetime.strftime(datetime.datetime.now(), '%Y_%m_%d_%H_%M_%S')log_dir = os.path.join(save_dir, "loss_" + str(time_str))loss_history = LossHistory(log_dir=log_dir, model=Net, input_shape=input_shape)with open(annotation_path,'r') as f:lines=f.readlines()np.random.seed(10101)np.random.shuffle(lines)np.random.seed(None)num_val=int(len(lines) * percentage)num_train=len(lines) - num_valtrain_data=DataGenerator(lines[:num_train],input_shape,True)val_data=DataGenerator(lines[num_train:],input_shape,False)val_len=len(val_data)print(val_len)gen_train=DataLoader(train_data,batch_size=4)gen_test=DataLoader(val_data,batch_size=4)device=torch.device('cuda'if torch.cuda.is_available() and Cuda else "cpu")net=vgg(mode=Net, pretrained=True, progress=True, num_classes=num_classes)net.to(device)if optimizer_type == 'Adam':optim = torch.optim.Adam(net.parameters(), lr=lr)elif optimizer_type == 'SGD':optim = torch.optim.SGD(net.parameters(), lr=lr, momentum=0.9)else:raise ValueError("Unsupported optimizer type: {}".format(optimizer_type))sculer=torch.optim.lr_scheduler.StepLR(optim,step_size=1)for epoch in range(epochs):total_train=0for data in tqdm(gen_train, desc=f"Epoch{epoch + 1}/Train"):img,label=datawith torch.no_grad():img =img.to(device)label=label.to(device)optim.zero_grad()output=net(img)train_loss=nn.CrossEntropyLoss()(output,label).to(device)train_loss.backward()optim.step()total_train+=train_losssculer.step()total_test=0total_accuracy=0for data in tqdm(gen_test, desc=f"Epoch{epoch + 1}/Test"):img,label =datawith torch.no_grad():img=img.to(device)label=label.to(device)optim.zero_grad()out=net(img)test_loss=nn.CrossEntropyLoss()(out,label).to(device)total_test+=test_lossaccuracy=((out.argmax(1)==label).sum()).clone().detach().cpu().numpy()total_accuracy += accuracyprint("训练集上的损失：{}".format(total_train))print("测试集上的损失：{}".format(total_test))print("测试集上的精度：{:.1%}".format(total_accuracy/val_len))loss_history.append_loss(epoch + 1, total_train, total_test)if (epoch+1) % save_period == 0:modepath = os.path.join(log_dir,"DogandCat{}.pth".format(epoch+1))torch.save(net.state_dict(),modepath)print("模型已保存")设置相关参数：
Cuda: 是否使用GPU加速，默认为False。Net: 选择要使用的VGG网络版本，可以是'vgg16'或'vgg19'。annotation_path: 数据集的注释文件路径，这是一个包含图像路径和标签的文本文件。input_shape: 输入图像的尺寸。num_classes: 分类的类别数量。lr: 学习率。optimizer_type: 选择优化器，可以是'Adam'或'SGD'。percentage: 验证集所占百分比。epochs: 训练轮次。save_period: 多少个epoch保存一次模型权重。save_dir: 模型权重和日志文件保存的目录。
接下来是进行数据准备将数据随机打乱并划分为训练集和验证集，创建训练集和验证集的数据生成器，然后实例化VGG模型，并根据选择的网络版本加载预训练权重，根据选择的优化器类型创建优化器，并设置学习率调度器，最后，每个epoch中计算训练集和验证集上的损失和精度，并记录到损失历史记录器中。
由于比较的费时间，这里我仅仅就进行了猫狗图片各自200张进行训练，主要是看看VGG的一个分类效果，所以就尽可能的快点。
模型预测
# predict.pyfrom torchvision import transformsfrom PIL import Imageimport matplotlib.pyplot as pltimport torchimport torch.nn.functional as Ffrom VGGnet.net import vggif __name__=="__main__":# ---------------------------------## Cuda 是否使用Cuda#没有GPU可以设置成False# ---------------------------------#Cuda = False# ---------------------------------## 分类类型# ---------------------------------#num_classes = ['cat', 'dog']# ---------------------------------## 'vgg16' and'vgg19'# ---------------------------------#Netmode = 'vgg16'# ------------------------------------------------------------------------------## detection_mode用于指定测试的模式:## 'predict' 表示单张图片预测# 'dir_predict' 表示遍历文件夹进行检测并保存。默认遍历img文件夹，保存img_out文件夹# ------------------------------------------------------------------------------#detection_mode = "dir_predict"# -------------------------------------------------------## model_path指向log文件夹下的权值文件# 训练好后log文件夹下存在多个权值文件，选择验证集损失较低的即可。# -------------------------------------------------------#model_path = r"log\loss_2023_08_16_13_52_51\DogandCat30.pth"#-------------------------------------------------------------------------## dir_origin_path 指定了用于检测的图片的文件夹路径# dir_save_path 指定了检测完图片的保存路径## dir_origin_path和dir_save_path仅在 detection_mode='dir_predict'时有效#-------------------------------------------------------------------------#dir_origin_path = "img/"dir_save_path = "img_out/"device = torch.device("cuda" if torch.cuda.is_available() and Cuda else "cpu")model = vgg(mode=Netmode,num_classes=len(num_classes))model.load_state_dict(torch.load(model_path, map_location=device))model.to(device)model.eval()transform = transforms.Compose([transforms.Resize((224, 224)),transforms.ToTensor(),transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),])def predict_single_image(image_path):image = Image.open(image_path)image = transform(image).unsqueeze(0).to(device)with torch.no_grad():model.eval()output = model(image)probabilities = F.softmax(output, dim=1)predicted_class = torch.argmax(probabilities).item()predicted_label = num_classes[predicted_class]predicted_prob = probabilities[0][predicted_class].item()print("Output tensor:", output)print("Probabilities tensor:", probabilities)print(f"Predicted class: {predicted_label}, Probability: {predicted_prob:.2f}")plt.imshow(Image.open(image_path))plt.title(f"Predicted class: {predicted_label}, Probability: {predicted_prob:.2f}")plt.axis('off')plt.show()def predict_images_in_directory(origin_path, save_path):import osos.makedirs(save_path, exist_ok=True)image_files = [f for f in os.listdir(origin_path) if f.lower().endswith(('.jpg', '.jpeg', '.png', '.gif'))]for image_file in image_files:image_path = os.path.join(origin_path, image_file)result_image_path = os.path.join(save_path, image_file)image = Image.open(image_path)image = transform(image).unsqueeze(0).to(device)with torch.no_grad():model.eval()output = model(image)probabilities = F.softmax(output, dim=1)predicted_class = torch.argmax(probabilities).item()predicted_label = num_classes[predicted_class]predicted_prob = probabilities[0][predicted_class].item()print("Predicted class:", predicted_label)print("Predicted probability:", predicted_prob)plt.imshow(Image.open(image_path))plt.title(f"Predicted class: {predicted_label}, Probability: {predicted_prob:.2f}")plt.axis('off')plt.savefig(result_image_path)# plt.show()print("Prediction and saving complete.")if detection_mode == "predict":while True:image_path = input('Input image filename (or "exit" to quit): ')if image_path.lower() == "exit":breakpredict_single_image(image_path)elif detection_mode == "dir_predict":predict_images_in_directory(dir_origin_path, dir_save_path)else:raise ValueError("Invalid detection_mode")单张检测模式
文件夹检测模式
资源链接
Auorui/VGG16-CatandDog: Explore the effectiveness of the VGG model, which achieved significant results in the ImageNet image classification competition in 2014, and use VGG for cat and dog classification (github.com)