Pytorch实现鲜花分类(102 Category Flower Dataset)

以你之姓@ 2023-07-12 14:25 13阅读 0赞

# 本文主要讲解该算法的实现过程 #

### 实验环境 ###

python3.6+pytorch1.2+cuda10.1

### 数据集 ###

102 Category Flower Dataset数据集由102类产自英国的花卉组成，每类由40-258张图片组成

下边使用的数据集看好多人私信要，我就上传到CSDN了：[鲜花分类集（已划分）-深度学习文档类资源-CSDN下载][-_-CSDN]

## 下边是代码实现过程及讲解 ##

### 数据加载 ###

#选择设备
    device = torch.device("cuda:0")
    #对三种数据集进行不同预处理，对训练数据进行加强
    data_transforms = {
        'train': transforms.Compose([
            transforms.RandomRotation(30),
            transforms.RandomResizedCrop(224),
            transforms.RandomHorizontalFlip(),
            transforms.ToTensor(),
            transforms.Normalize([0.485, 0.456, 0.406],
                                 [0.229, 0.224, 0.225])
        ]),
        'valid': transforms.Compose([
            transforms.Resize(256),
            transforms.CenterCrop(224),
            transforms.ToTensor(),
            transforms.Normalize([0.485, 0.456, 0.406],
                                 [0.229, 0.224, 0.225])
        ]),
        'test': transforms.Compose([
            transforms.Resize(256),
            transforms.CenterCrop(224),
            transforms.ToTensor(),
            transforms.Normalize([0.485, 0.456, 0.406],
                                 [0.229, 0.224, 0.225])
        ])
    }
    
    #数据目录
    data_dir = "./data"
    
    #获取三个数据集
    image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),
                     data_transforms[x]) for x in ['train', 'valid','test']}
    traindataset = image_datasets['train']
    validdataset = image_datasets['valid']
    testdataset = image_datasets['test']
    
    batch_size = 8
    dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=batch_size,
                 shuffle=True, num_workers=4) for x in ['train', 'valid','test']}
    print(dataloaders)
    traindataloader = dataloaders['train']
    validdataloader = dataloaders['valid']
    testdataloader = dataloaders['test']
    
    dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'valid','test']}

### 定义网络结构 ###

###  使用ResNet152 ###

#使用resnet152的网络结构，最后一层全连接重写输出102
    class Net(nn.Module):
        def __init__(self,model):
            super(Net,self).__init__()
            self.resnet = nn.Sequential(*list(model.children())[:-1])
            #可以选择冻结卷积层
            # for p in self.parameters():
            #     p.requires_grad = False
            self.fc = nn.Linear(in_features=2048,out_features=102)
    
    
        def forward(self,x):
            x = self.resnet(x)
            x = x.view(x.shape[0], -1)
            x = self.fc(x)
            return x
    
    resnet152 = models.resnet152(pretrained=True)
    net = Net(resnet152)

###  使用VGG19 ###

class Net(nn.Module):
        def __init__(self,model):
            super(Net,self).__init__()
            self.features = model.features
            # for p in self.parameters():
            #     p.requires_grad = False
            self.classifier = nn.Sequential(
                nn.Linear(25088, 4096,bias=True),
                nn.ReLU(inplace=True),
                nn.Dropout(p=0.5,inplace=False),
                nn.Linear(4096, 4096,bias=True),
                nn.ReLU(inplace=True),
                nn.Dropout(p=0.5,inplace=False),
                nn.Linear(4096, 102,bias=True)
            )
    
        def forward(self,x):
            x = self.features(x)
            x = x.view(x.shape[0], -1)
            x = self.classifier(x)
            return x
    
    
    vgg = models.vgg19(pretrained=True)
    net = Net(vgg)

### 参数设定 ###

criterion = nn.CrossEntropyLoss()
    optimizer = torch.optim.SGD(filter(lambda p: p.requires_grad, net.parameters()),lr=0.0001,momentum=0.9)

### 测试集检验 ###

def valid_model(model, criterion):
        best_acc = 0.0
        print('-' * 10)
    
        running_loss = 0.0
        running_corrects = 0
        model = model.to(device)
        for inputs, labels in validdataloader:
            inputs = inputs.to(device)
            labels = labels.to(device)
            model.eval()
            with torch.no_grad():
                outputs = model(inputs)
            loss = criterion(outputs, labels)
    
            _, preds = torch.max(outputs, 1)
            running_loss += loss.item()
            running_corrects += torch.sum(preds == labels)
        epoch_loss = running_loss / dataset_sizes['valid']
        print(running_corrects.double())
        epoch_acc = running_corrects.double() / dataset_sizes['valid']
        print('{} Loss: {:.4f} Acc: {:.4f}'.format(
                'valid', epoch_loss, epoch_acc))
        print('-' * 10)
        print()

### 验证集检验 ###

def test_model(model, criterion):
        best_acc = 0.0
        print('-' * 10)
    
        running_loss = 0.0
        running_corrects = 0
        model = model.to(device)
        for inputs, labels in testdataloader:
            inputs = inputs.to(device)
            labels = labels.to(device)
            model.eval()
            with torch.no_grad():
                outputs = model(inputs)
            loss = criterion(outputs, labels)
    
            _, preds = torch.max(outputs, 1)
            running_loss += loss.item()
            running_corrects += torch.sum(preds == labels)
        epoch_loss = running_loss / dataset_sizes['test']
        print(running_corrects.double())
        epoch_acc = running_corrects.double() / dataset_sizes['test']
        print('{} Loss: {:.4f} Acc: {:.4f}'.format(
                'test', epoch_loss, epoch_acc))
        print('-' * 10)
        print()

### 训练模型 ###

def train_model(model, criterion, optimizer, num_epochs=5):
        since = time.time()
        best_acc = 0.0
        for epoch in range(num_epochs):
            if (epoch+1)%5==0:
                test_model(model, criterion)
            print('-' * 10)
            print('Epoch {}/{}'.format(epoch+1, num_epochs))
    
            running_loss = 0.0
            running_corrects = 0
            model = model.to(device)
            for inputs, labels in traindataloader:
                inputs = inputs.to(device)
                labels = labels.to(device)
                model.train()
                optimizer.zero_grad()
                outputs = model(inputs)
                loss = criterion(outputs, labels)
                loss.backward()
                optimizer.step()
    
                _, preds = torch.max(outputs, 1)
                running_loss += loss.item()
                running_corrects += torch.sum(preds == labels)
            epoch_loss = running_loss / dataset_sizes['train']
            print(dataset_sizes['train'])
            print(running_corrects.double())
            epoch_acc = running_corrects.double() / dataset_sizes['train']
            best_acc = max(best_acc,epoch_acc)
            print('{} Loss: {:.4f} Acc: {:.4f}'.format(
                    'train', epoch_loss, epoch_acc))
    
            print()
    
        time_elapsed = time.time() - since
        print('Training complete in {:.0f}m {:.0f}s'.format(
            time_elapsed // 60, time_elapsed % 60))
        print('Best val Acc: {:4f}'.format(best_acc))
    
        return model

### 开始训练 ###

epochs = 48
    model = train_model(net, criterion, optimizer, epochs)
    
    valid_model(model,criterion)
    
    torch.save(model, 'model.pkl')

### 输出结果 ###

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxNjg1MjY1_size_16_color_FFFFFF_t_70][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxNjg1MjY1_size_16_color_FFFFFF_t_70 1][]

在最后，训练集的精度达到了97.01%，测试集上达到了97.45%

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxNjg1MjY1_size_16_color_FFFFFF_t_70 2][]

[-_-CSDN]: https://download.csdn.net/download/qq_41685265/13460143
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxNjg1MjY1_size_16_color_FFFFFF_t_70]: https://img-blog.csdnimg.cn/20200309093509254.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxNjg1MjY1,size_16,color_FFFFFF,t_70
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxNjg1MjY1_size_16_color_FFFFFF_t_70 1]: https://img-blog.csdnimg.cn/20200309093521765.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxNjg1MjY1,size_16,color_FFFFFF,t_70
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxNjg1MjY1_size_16_color_FFFFFF_t_70 2]: https://img-blog.csdnimg.cn/20200309093653851.jpg?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxNjg1MjY1,size_16,color_FFFFFF,t_70