pytorch vgg16 猫狗识别

第一次做猫狗识别的任务，遇到点问题记录一下：

主要是数据集的问题：  下载完猫狗数据集后，打开有两个文件夹分别是train和test。这两个文件夹中并没有子文件夹。直接将其导入

train_dataset = torchvision.datasets.ImageFolder(root='/home/qqsdqt/桌面/cat/data/train',transform=transform)

出现了问题是：在文件夹中没有找到文件，真是大写的懵逼，明明是有照片的好不啦。

后来查了查torchvision.datasets.ImageFolder  这个函数发现，这个函数应该读取文件夹，也就是说这个函数中的路径下的应该是文件夹，而不是直接是照片。将这个train下的文件分成dog和cat两个子文件夹。也就是说应该数下面这个样子滴！

百度了一下ImageFolder函数的解释

import torch
import torch.nn as nn
import torchvision
from torch.utils.data import DataLoader
from torch.autograd import Variable
import matplotlib.pyplot as plt
import torchvision.transforms as transformscfg = {'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'],
}class VGG(nn.Module):def __init__(self, vgg_name):super(VGG, self).__init__()self.conv=nn.Sequential(nn.AvgPool2d(8,7))self.features = self._make_layers(cfg[vgg_name])self.classifier = nn.Linear(512, 2)def forward(self, x):out=self.conv(x)out = self.features(out)out = out.view(out.size(0), -1)out = self.classifier(out)return outdef _make_layers(self, cfg):layers = []in_channels = 3for x in cfg:if x == 'M':layers += [nn.MaxPool2d(kernel_size=2, stride=2)]else:layers += [nn.Conv2d(in_channels, x, kernel_size=3, padding=1),nn.BatchNorm2d(x),nn.ReLU(inplace=True)]in_channels = xlayers += [nn.AvgPool2d(kernel_size=1, stride=1)]return nn.Sequential(*layers)BARCH_SIZE=512
LR=0.001
EPOCH=20device= torch.device('cuda' if torch.cuda.is_available() else 'cpu')normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
transform = transforms.Compose([transforms.Resize(size=(227, 227)),transforms.RandomRotation(20),transforms.RandomHorizontalFlip(),transforms.ToTensor(),normalize
])train_dataset = torchvision.datasets.ImageFolder(root='/home/zhouchenglong/cat/data/train',transform=transform)train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=512, shuffle=True)validation_dataset =torchvision.datasets. ImageFolder(root='/home/zhouchenglong/cat/data/test',transform=transform)test_loader = torch.utils.data.DataLoader(validation_dataset, batch_size=512, shuffle=True)vggNet = VGG('VGG16').to(device)
criterion=nn.CrossEntropyLoss().to(device)
opti=torch.optim.Adam(vggNet.parameters(),lr=LR)if __name__=='__main__':Accuracy_list = []Loss_list = []for epoch in range(EPOCH):sum_loss = 0.0correct1 = 0total1 = 0for i,(images,labels)in enumerate(train_loader):print(i)num_images=images.size(0)images=Variable(images.to(device))labels=Variable(labels.to(device))out=vggNet(images)_, predicted = torch.max(out.data, 1)total1 += labels.size(0)print('predict={},labels={}'.format(predicted.size(),labels.size()))correct1 += (predicted == labels).sum().item()loss=criterion(out,labels)sum_loss+=loss.item()opti.zero_grad()loss.backward()opti.step()if i % 10 == 9:print('[%d, %d] loss: %.03f'% (epoch + 1, i + 1, sum_loss / 100))sum_loss = 0.0Accuracy_list.append(100.0 * correct1 / total1)print('accurary={}'.format(100.0 * correct1 / total1))Loss_list.append(loss.item())torch.save(vggNet, './alexNet.pth')x1 = range(0, EPOCH)x2 = range(0, EPOCH)y1 = Accuracy_listy2 = Loss_listplt.subplot(2, 1, 1)plt.plot(x1, y1, 'o-')plt.title('Train accuracy vs. epoches')plt.ylabel('Train accuracy')plt.subplot(2, 1, 2)plt.plot(x2, y2, '.-')plt.xlabel('Train loss vs. epoches')plt.ylabel('Train loss')plt.savefig("accuracy_epoch" + (str)(EPOCH) + ".png")plt.show()

欢迎大家看我的另一篇博文pytorch alexNet 猫狗识别https://blog.csdn.net/qq_38191717/article/details/97160761