Pytorch自定义模型实现猫狗分类

谁践踏了优雅 2022-12-25 09:54 259阅读 0赞

# 摘要 #

本例采用猫狗大战的部分数据作为数据集，模型是自定义的模型。

# 训练 #

*  ## 1、构建数据集 ##

在data文件夹下面新家train和val文件夹，分别在train和val文件夹下面新家cat和dog文件夹，并将图片放进去。如图：

![20201201131856311.png][]

*  ## 2、导入库 ##

*  ## 3、设置超参数 ##

# 设置超参数
    
    BATCH_SIZE = 20
    
    EPOCHS = 10
    
    DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

*  ## 4、图像处理与图像增强 ##

# 数据预处理
    
    transform = transforms.Compose([
    
        transforms.Resize(100),
    
        transforms.RandomVerticalFlip(),
    
        transforms.RandomCrop(50),
    
        transforms.RandomResizedCrop(150),
    
        transforms.ColorJitter(brightness=0.5, contrast=0.5, hue=0.5),
    
        transforms.ToTensor(),
    
        transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
    
    ])

*  ## 5、读取数据和导入数据 ##

# 读取数据
    
    dataset_train = datasets.ImageFolder('data/train', transform)
    
    print(dataset_train.imgs)
    
    # 对应文件夹的label
    
    print(dataset_train.class_to_idx)
    
    dataset_test = datasets.ImageFolder('data/val', transform)
    
    # 对应文件夹的label
    
    print(dataset_test.class_to_idx)
    
    # 导入数据
    
    train_loader = torch.utils.data.DataLoader(dataset_train, batch_size=BATCH_SIZE, shuffle=True)
    
    test_loader = torch.utils.data.DataLoader(dataset_test, batch_size=BATCH_SIZE, shuffle=True)

*  ## 6、定义网络模型 ##

# 定义网络
    
    class ConvNet(nn.Module):
    
        def __init__(self):
    
            super(ConvNet, self).__init__()
    
            self.conv1 = nn.Conv2d(3, 32, 3)
    
            self.max_pool1 = nn.MaxPool2d(2)
    
            self.conv2 = nn.Conv2d(32, 64, 3)
    
            self.max_pool2 = nn.MaxPool2d(2)
    
            self.conv3 = nn.Conv2d(64, 64, 3)
    
            self.conv4 = nn.Conv2d(64, 64, 3)
    
            self.max_pool3 = nn.MaxPool2d(2)
    
            self.conv5 = nn.Conv2d(64, 128, 3)
    
            self.conv6 = nn.Conv2d(128, 128, 3)
    
            self.max_pool4 = nn.MaxPool2d(2)
    
            self.fc1 = nn.Linear(4608, 512)
    
            self.fc2 = nn.Linear(512, 1)
    
    
    
        def forward(self, x):
    
            in_size = x.size(0)
    
            x = self.conv1(x)
    
            x = F.relu(x)
    
            x = self.max_pool1(x)
    
            x = self.conv2(x)
    
            x = F.relu(x)
    
            x = self.max_pool2(x)
    
            x = self.conv3(x)
    
            x = F.relu(x)
    
            x = self.conv4(x)
    
            x = F.relu(x)
    
            x = self.max_pool3(x)
    
            x = self.conv5(x)
    
            x = F.relu(x)
    
            x = self.conv6(x)
    
            x = F.relu(x)
    
            x = self.max_pool4(x)
    
            # 展开
    
            x = x.view(in_size, -1)
    
            x = self.fc1(x)
    
            x = F.relu(x)
    
            x = self.fc2(x)
    
            x = torch.sigmoid(x)
    
            return x
    
    
    
    
    
    modellr = 1e-4
    
    # 实例化模型并且移动到GPU
    
    model = ConvNet().to(DEVICE)
    
    # 选择简单暴力的Adam优化器，学习率调低
    
    optimizer = optim.Adam(model.parameters(), lr=modellr)

*  ## 7、调整学习率 ##

def adjust_learning_rate(optimizer, epoch):
    
        """Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
    
        modellrnew = modellr * (0.1 ** (epoch // 5))
    
        print("lr:",modellrnew)
    
        for param_group in optimizer.param_groups:
    
            param_group['lr'] = modellrnew

*  ## 8、定义训练与验证方法 ##

# 定义训练过程
    
    def train(model, device, train_loader, optimizer, epoch):
    
        model.train()
    
        for batch_idx, (data, target) in enumerate(train_loader):
    
            data, target = data.to(device), target.to(device).float().unsqueeze(1)
    
            optimizer.zero_grad()
    
            output = model(data)
    
            # print(output)
    
            loss = F.binary_cross_entropy(output, target)
    
            loss.backward()
    
            optimizer.step()
    
            if (batch_idx + 1) % 10 == 0:
    
                print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
    
                    epoch, (batch_idx + 1) * len(data), len(train_loader.dataset),
    
                           100. * (batch_idx + 1) / len(train_loader), loss.item()))
    
    
    
    
    
    # 定义测试过程
    
    def val(model, device, test_loader):
    
        model.eval()
    
        test_loss = 0
    
        correct = 0
    
        with torch.no_grad():
    
            for data, target in test_loader:
    
                data, target = data.to(device), target.to(device).float().unsqueeze(1)
    
                output = model(data)
    
                # print(output)
    
                test_loss += F.binary_cross_entropy(output, target, reduction='mean').item()  # 将一批的损失相加
    
                pred = torch.tensor([[1] if num[0] >= 0.5 else [0] for num in output]).to(device)
    
                correct += pred.eq(target.long()).sum().item()
    
            print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
    
                test_loss, correct, len(test_loader.dataset),
    
                100. * correct / len(test_loader.dataset)))

## 9、训练并保存模型 ##

# 训练
    
    for epoch in range(1, EPOCHS + 1):
    
        adjust_learning_rate(optimizer, epoch)
    
        train(model, DEVICE, train_loader, optimizer, epoch)
    
        val(model, DEVICE, test_loader)
    
    torch.save(model, 'model.pth')

## 完整代码： ##

# 导入库
    
    import torch.nn.functional as F
    
    import torch.optim as optim
    
    import torch
    
    import torch.nn as nn
    
    import torch.nn.parallel
    
    
    
    import torch.optim
    
    import torch.utils.data
    
    import torch.utils.data.distributed
    
    import torchvision.transforms as transforms
    
    import torchvision.datasets as datasets
    
    
    
    # 设置超参数
    
    BATCH_SIZE = 20
    
    EPOCHS = 10
    
    DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    
    
    
    # 数据预处理
    
    transform = transforms.Compose([
    
        transforms.Resize(100),
    
        transforms.RandomVerticalFlip(),
    
        transforms.RandomCrop(50),
    
        transforms.RandomResizedCrop(150),
    
        transforms.ColorJitter(brightness=0.5, contrast=0.5, hue=0.5),
    
        transforms.ToTensor(),
    
        transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
    
    ])
    
    
    
    # 读取数据
    
    dataset_train = datasets.ImageFolder('data/train', transform)
    
    print(dataset_train.imgs)
    
    # 对应文件夹的label
    
    print(dataset_train.class_to_idx)
    
    dataset_test = datasets.ImageFolder('data/val', transform)
    
    # 对应文件夹的label
    
    print(dataset_test.class_to_idx)
    
    # 导入数据
    
    train_loader = torch.utils.data.DataLoader(dataset_train, batch_size=BATCH_SIZE, shuffle=True)
    
    test_loader = torch.utils.data.DataLoader(dataset_test, batch_size=BATCH_SIZE, shuffle=True)
    
    
    
    
    
    # 定义网络
    
    class ConvNet(nn.Module):
    
        def __init__(self):
    
            super(ConvNet, self).__init__()
    
            self.conv1 = nn.Conv2d(3, 32, 3)
    
            self.max_pool1 = nn.MaxPool2d(2)
    
            self.conv2 = nn.Conv2d(32, 64, 3)
    
            self.max_pool2 = nn.MaxPool2d(2)
    
            self.conv3 = nn.Conv2d(64, 64, 3)
    
            self.conv4 = nn.Conv2d(64, 64, 3)
    
            self.max_pool3 = nn.MaxPool2d(2)
    
            self.conv5 = nn.Conv2d(64, 128, 3)
    
            self.conv6 = nn.Conv2d(128, 128, 3)
    
            self.max_pool4 = nn.MaxPool2d(2)
    
            self.fc1 = nn.Linear(4608, 512)
    
            self.fc2 = nn.Linear(512, 1)
    
    
    
        def forward(self, x):
    
            in_size = x.size(0)
    
            x = self.conv1(x)
    
            x = F.relu(x)
    
            x = self.max_pool1(x)
    
            x = self.conv2(x)
    
            x = F.relu(x)
    
            x = self.max_pool2(x)
    
            x = self.conv3(x)
    
            x = F.relu(x)
    
            x = self.conv4(x)
    
            x = F.relu(x)
    
            x = self.max_pool3(x)
    
            x = self.conv5(x)
    
            x = F.relu(x)
    
            x = self.conv6(x)
    
            x = F.relu(x)
    
            x = self.max_pool4(x)
    
            # 展开
    
            x = x.view(in_size, -1)
    
            x = self.fc1(x)
    
            x = F.relu(x)
    
            x = self.fc2(x)
    
            x = torch.sigmoid(x)
    
            return x
    
    
    
    
    
    modellr = 1e-4
    
    # 实例化模型并且移动到GPU
    
    model = ConvNet().to(DEVICE)
    
    # 选择简单暴力的Adam优化器，学习率调低
    
    optimizer = optim.Adam(model.parameters(), lr=modellr)
    
    
    
    
    
    def adjust_learning_rate(optimizer, epoch):
    
        """Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
    
        modellrnew = modellr * (0.1 ** (epoch // 5))
    
        print("lr:",modellrnew)
    
        for param_group in optimizer.param_groups:
    
            param_group['lr'] = modellrnew
    
    
    
    
    
    # 定义训练过程
    
    def train(model, device, train_loader, optimizer, epoch):
    
        model.train()
    
        for batch_idx, (data, target) in enumerate(train_loader):
    
            data, target = data.to(device), target.to(device).float().unsqueeze(1)
    
            optimizer.zero_grad()
    
            output = model(data)
    
            # print(output)
    
            loss = F.binary_cross_entropy(output, target)
    
            loss.backward()
    
            optimizer.step()
    
            if (batch_idx + 1) % 10 == 0:
    
                print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
    
                    epoch, (batch_idx + 1) * len(data), len(train_loader.dataset),
    
                           100. * (batch_idx + 1) / len(train_loader), loss.item()))
    
    
    
    
    
    # 定义测试过程
    
    def val(model, device, test_loader):
    
        model.eval()
    
        test_loss = 0
    
        correct = 0
    
        with torch.no_grad():
    
            for data, target in test_loader:
    
                data, target = data.to(device), target.to(device).float().unsqueeze(1)
    
                output = model(data)
    
                # print(output)
    
                test_loss += F.binary_cross_entropy(output, target, reduction='mean').item()  # 将一批的损失相加
    
                pred = torch.tensor([[1] if num[0] >= 0.5 else [0] for num in output]).to(device)
    
                correct += pred.eq(target.long()).sum().item()
    
            print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
    
                test_loss, correct, len(test_loader.dataset),
    
                100. * correct / len(test_loader.dataset)))
    
    
    
    
    
    # 训练
    
    for epoch in range(1, EPOCHS + 1):
    
        adjust_learning_rate(optimizer, epoch)
    
        train(model, DEVICE, train_loader, optimizer, epoch)
    
        val(model, DEVICE, test_loader)
    
    torch.save(model, 'model.pth')

# 测试 #

## 完整代码: ##

from __future__ import print_function, division
    
    from PIL import Image
    
    from torchvision import transforms
    import torch.nn.functional as F
    
    import torch
    import torch.nn as nn
    import torch.nn.parallel
    # 定义网络
    class ConvNet(nn.Module):
        def __init__(self):
            super(ConvNet, self).__init__()
            self.conv1 = nn.Conv2d(3, 32, 3)
            self.max_pool1 = nn.MaxPool2d(2)
            self.conv2 = nn.Conv2d(32, 64, 3)
            self.max_pool2 = nn.MaxPool2d(2)
            self.conv3 = nn.Conv2d(64, 64, 3)
            self.conv4 = nn.Conv2d(64, 64, 3)
            self.max_pool3 = nn.MaxPool2d(2)
            self.conv5 = nn.Conv2d(64, 128, 3)
            self.conv6 = nn.Conv2d(128, 128, 3)
            self.max_pool4 = nn.MaxPool2d(2)
            self.fc1 = nn.Linear(4608, 512)
            self.fc2 = nn.Linear(512, 1)
    
        def forward(self, x):
            in_size = x.size(0)
            x = self.conv1(x)
            x = F.relu(x)
            x = self.max_pool1(x)
            x = self.conv2(x)
            x = F.relu(x)
            x = self.max_pool2(x)
            x = self.conv3(x)
            x = F.relu(x)
            x = self.conv4(x)
            x = F.relu(x)
            x = self.max_pool3(x)
            x = self.conv5(x)
            x = F.relu(x)
            x = self.conv6(x)
            x = F.relu(x)
            x = self.max_pool4(x)
            # 展开
            x = x.view(in_size, -1)
            x = self.fc1(x)
            x = F.relu(x)
            x = self.fc2(x)
            x = torch.sigmoid(x)
            return x
    # 模型存储路径
    model_save_path = 'model.pth'
    
    # ------------------------ 加载数据 --------------------------- #
    # Data augmentation and normalization for training
    # Just normalization for validation
    # 定义预训练变换
    # 数据预处理
    transform_test = transforms.Compose([
        transforms.Resize(100),
        transforms.RandomVerticalFlip(),
        transforms.RandomCrop(50),
        transforms.RandomResizedCrop(150),
        transforms.ColorJitter(brightness=0.5, contrast=0.5, hue=0.5),
        transforms.ToTensor(),
        transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
    ])
    
    
    class_names = ['cat', 'dog']  # 这个顺序很重要，要和训练时候的类名顺序一致
    
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    
    # ------------------------ 载入模型并且训练 --------------------------- #
    model = torch.load(model_save_path)
    model.eval()
    # print(model)
    
    image_PIL = Image.open('dog.12.jpg')
    #
    image_tensor = transform_test(image_PIL)
    # 以下语句等效于 image_tensor = torch.unsqueeze(image_tensor, 0)
    image_tensor.unsqueeze_(0)
    # 没有这句话会报错
    image_tensor = image_tensor.to(device)
    
    out = model(image_tensor)
    pred = torch.tensor([[1] if num[0] >= 0.5 else [0] for num in out]).to(device)
    print(class_names[pred])

运行结果:

##  ##

![20201201195255883.png][]

[20201201131856311.png]: /images/20221120/f1c4c3bcba2d4301b3fa13e25d30b1d2.png
[20201201195255883.png]: https://img-blog.csdnimg.cn/20201201195255883.png