徐州住房和城乡建设部网站,wordpress 主题设计,图纸设计平面图软件,农业信息网站建设概念关于数据集
数据集选择的是Kaggle上的Cat and Dog#xff0c;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 3x3 大小的卷积核进…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 3x3 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 torch
import torchvision
import torch.nn as nn
import 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(pdropout),nn.Linear(4096, 4096),nn.ReLU(True),nn.Dropout(pdropout),nn.Linear(4096, num_classes),)if init_weights:for m in self.modules():if isinstance(m, nn.Conv2d):nn.init.kaiming_normal_(m.weight, modefan_out, nonlinearityrelu)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_size2, stride2)]else:conv2d nn.Conv2d(in_channels, v, kernel_size(3,3), padding1)if batch_norm:layers [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplaceTrue)]else:layers [conv2d, nn.ReLU(inplaceTrue)]in_channels vreturn nn.Sequential(*layers)def vgg(modevgg16,pretrainedFalse, progressTrue, num_classes2):model VGG(make_layers_with_vgg(cfgs[mode]))if pretrained:state_dict load_state_dict_from_url(model_urls[mode], model_dir./model, progressprogress)#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(p0.5),nn.Linear(4096, 4096),nn.ReLU(True),nn.Dropout(p0.5),nn.Linear(4096, num_classes),)return modelif __name____main__:in_data torch.ones(2, 3, 224, 224)net vgg(modevgg16, pretrainedFalse, progressTrue, num_classes2) # 使用默认的 VGG-16 架构# net vgg(modevgg19, pretrainedFalse, progressTrue, num_classes2) # 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 pyzjr1.1.1 --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 os
import pyzjr as pzclasses [cat, dog]
path trainif __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 pz
import os
import shutil
# 原始图片所在路径、保存指定图片路径
image_folder_path rD:\pythonprojects\deeplabv3_pytorch\img
save_image_folder_path pz.CreateFolder(rD:\pythonprojects\deeplabv3_pytorch\imgs)newbasename Crackif __name____main__:imglist,allistpz.getPhotopath(image_folder_path,debugFalse)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 cv2
import numpy as np
import torch.utils.data as data
import matplotlib
import torch
matplotlib.use(Agg)
from matplotlib import pyplot as plt
import scipy.signal
from PIL import Image
from torch.utils.tensorboard import SummaryWriter
import osdef preprocess_input(x):x/127.5x-1.return x
def cvtColor(image):if len(np.shape(image))3 and np.shape(image)[-2]3:return imageelse:imageimage.convert(RGB)return imageclass DataGenerator(data.Dataset):def __init__(self,annotation_lines,inpt_shape,randomTrue):self.annotation_linesannotation_linesself.input_shapeinpt_shapeself.randomrandomdef __len__(self):return len(self.annotation_lines)def __getitem__(self, index):annotation_pathself.annotation_lines[index].split(;)[1].split()[0]imageImage.open(annotation_path)imageself.get_random_data(image,self.input_shape,randomself.random)imagenp.transpose(preprocess_input(np.array(image).astype(np.float32)),[2,0,1])yint(self.annotation_lines[index].split(;)[0])return image,ydef rand(self,a0.,b1.):return np.random.rand()*(b-a)adef get_random_data(self,image,inpt_shape,jitter.3,hue.1,sat1.5,val1.5,randomTrue):imagecvtColor(image)iw,ihimage.sizeh,winpt_shapeif not random:scalemin(w/iw,h/ih)nwint(iw*scale)nhint(ih*scale)dx(w-nw)//2dy(h-nh)//2imageimage.resize((nw,nh),Image.BICUBIC)new_imageImage.new(RGB,(w,h),(128,128,128))new_image.paste(image,(dx,dy))image_datanp.array(new_image,np.float32)return image_datanew_arw/h*self.rand(1-jitter,1jitter)/self.rand(1-jitter,1jitter)scaleself.rand(.75,1.25)if new_ar1:nhint(scale*h)nwint(nh*new_ar)else:nwint(scale*w)nhint(nw/new_ar)imageimage.resize((nw,nh),Image.BICUBIC)dxint(self.rand(0,w-nw))dyint(self.rand(0,h-nh))new_imageImage.new(RGB,(w,h),(128,128,128))new_image.paste(image,(dx,dy))imagenew_imageflipself.rand().5if flip: imageimage.transpose(Image.FLIP_LEFT_RIGHT)rotateself.rand().5if rotate:anglenp.random.randint(-15,15)a,bw/2,h/2Mcv2.getRotationMatrix2D((a,b),angle,1)imagecv2.warpAffine(np.array(image),M,(w,h),borderValue[128,128,128])hueself.rand(-hue,hue)satself.rand(1,sat) if self.rand().5 else 1/self.rand(1,sat)valself.rand(1,val) if self.rand().5 else 1/self.rand(1,val)xcv2.cvtColor(np.array(image,np.float32)/255,cv2.COLOR_RGB2HSV)#颜色空间转换x[..., 1] * satx[..., 2] * valx[x[:, :, 0] 360, 0] 360x[:, :, 1:][x[:, :, 1:] 1] 1x[x 0] 0image_datacv2.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, linewidth2, labeltrain loss)# plt.plot(iters, self.val_loss, coral, linewidth2, labelval loss)plt.plot(iters, [loss.item() for loss in self.losses], red, linewidth2, labeltrain loss)plt.plot(iters, [loss.item() for loss in self.val_loss], coral, linewidth2, labelval loss)try:if len(self.losses) 25:num 5else:num 15plt.plot(iters, scipy.signal.savgol_filter(self.losses, num, 3), green, linestyle--, linewidth2,labelsmooth train loss)plt.plot(iters, scipy.signal.savgol_filter(self.val_loss, num, 3), #8B4513, linestyle--, linewidth2,labelsmooth val loss)except:passplt.grid(True)plt.xlabel(Epoch)plt.ylabel(Loss)plt.legend(locupper 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 nn
from net import vgg
from torch.utils.data import DataLoader
from tqdm import tqdm
import datetime
from 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_pathclass_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 lr5e-4# 当使用SGD优化器时建议设置 lr7e-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 logif 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_dirlog_dir, modelNet, input_shapeinput_shape)with open(annotation_path,r) as f:linesf.readlines()np.random.seed(10101)np.random.shuffle(lines)np.random.seed(None)num_valint(len(lines) * percentage)num_trainlen(lines) - num_valtrain_dataDataGenerator(lines[:num_train],input_shape,True)val_dataDataGenerator(lines[num_train:],input_shape,False)val_lenlen(val_data)print(val_len)gen_trainDataLoader(train_data,batch_size4)gen_testDataLoader(val_data,batch_size4)devicetorch.device(cudaif torch.cuda.is_available() and Cuda else cpu)netvgg(modeNet, pretrainedTrue, progressTrue, num_classesnum_classes)net.to(device)if optimizer_type Adam:optim torch.optim.Adam(net.parameters(), lrlr)elif optimizer_type SGD:optim torch.optim.SGD(net.parameters(), lrlr, momentum0.9)else:raise ValueError(Unsupported optimizer type: {}.format(optimizer_type))sculertorch.optim.lr_scheduler.StepLR(optim,step_size1)for epoch in range(epochs):total_train0for data in tqdm(gen_train, descfEpoch{epoch 1}/Train):img,labeldatawith torch.no_grad():img img.to(device)labellabel.to(device)optim.zero_grad()outputnet(img)train_lossnn.CrossEntropyLoss()(output,label).to(device)train_loss.backward()optim.step()total_traintrain_losssculer.step()total_test0total_accuracy0for data in tqdm(gen_test, descfEpoch{epoch 1}/Test):img,label datawith torch.no_grad():imgimg.to(device)labellabel.to(device)optim.zero_grad()outnet(img)test_lossnn.CrossEntropyLoss()(out,label).to(device)total_testtest_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 (epoch1) % save_period 0:modepath os.path.join(log_dir,DogandCat{}.pth.format(epoch1))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 transforms
from PIL import Image
import matplotlib.pyplot as plt
import torch
import torch.nn.functional as F
from 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 rlog\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_modedir_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(modeNetmode,num_classeslen(num_classes))model.load_state_dict(torch.load(model_path, map_locationdevice))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, dim1)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(fPredicted class: {predicted_label}, Probability: {predicted_prob:.2f})plt.imshow(Image.open(image_path))plt.title(fPredicted 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_okTrue)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, dim1)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(fPredicted 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)
