VGG模型构建与实现

VGG卷积神经网络最大的特点就是由多个vgg_block构成,每个vgg_block包含多个卷积层,除第一个卷积层外,其余卷积层输入输出通道数量保持不变,卷积核大小一般为3*3,填充为1,即卷积层不改变特征图大小,由卷积核为2步长为2的池化层进行特征图缩放。

 vgg_block实现:

def vgg_block(conv_num, in_channel, out_channel):
    layers = []
    for _ in range(conv_num):
        layers.append(nn.Conv2d(in_channel, out_channel, kernel_size=3, padding=1))
        layers.append(nn.ReLU())
        in_channel = out_channel
    layers.append(nn.MaxPool2d(kernel_size=2, stride=2))
    return nn.Sequential(*layers)

         VGG根据不同网络深度分为VGG11、VGG13、VGG16和VGG19几个版本,每个VGG网络都是由5个vgg_block和三个全连接层组成。

VGG网络实现:

def vgg(in_channel, conv_arch):
    conv_blocks = []
    for (conv_num, out_channel) in conv_arch:
        conv_blocks.append(vgg_block(conv_num, in_channel, out_channel))
        in_channel = out_channel
    return nn.Sequential(
        *conv_blocks,
        nn.Flatten(),
        nn.Linear(512 * 7 * 7, 4096),
        nn.ReLU(),
        nn.Dropout(),
        nn.Linear(4096, 4096),
        nn.ReLU(),
        nn.Dropout(),
        nn.Linear(4096, 2)
    )
def vgg11(channel_num):
    conv_arch = ((1, 64), (1, 128), (2, 256), (2, 512), (2, 512))
    model = vgg(channel_num, conv_arch)
    return model


def vgg13(channel_num):
    conv_arch = ((2, 64), (2, 128), (2, 256), (2, 512), (2, 512))
    model = vgg(channel_num, conv_arch)
    return model


def vgg16(channel_num):
    conv_arch = ((2, 64), (2, 128), (3, 256), (3, 512), (3, 512))
    model = vgg(channel_num, conv_arch)
    return model


def vgg19(channel_num):
    conv_arch = ((2, 64), (2, 128), (4, 256), (4, 512), (4, 512))
    model = vgg(channel_num, conv_arch)
    return model

          模型构建后,定义个训练脚本,使用自定义数据集进行训练。

def train():
    # 如有GPU,默认使用第一块GPU
    device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
    print('using device {}'.format(device))
    #数据预处理
    data_transform = {
        "train": transforms.Compose([
            transforms.RandomResizedCrop(224),   #随机缩放裁剪
            transforms.RandomHorizontalFlip(),   #随机水平翻转
            transforms.ToTensor(),               #转换为Tensor
            transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))   #归一化
        ]),
        "val": transforms.Compose([
            transforms.RandomResizedCrop(224),
            transforms.ToTensor(),
            transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
        ])
    }
    #加载数据集
    batch_size = 32
    data_path = 'dataset/dogs'
    assert os.path.exists(data_path), "{} does not exist".format(data_path)
    train_dataset = datasets.ImageFolder(root=os.path.join(data_path, 'train'), transform=data_transform['train'])
    val_dataset = datasets.ImageFolder(root=os.path.join(data_path, 'val'), transform=data_transform['val'])
    train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=8)
    val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=5, shuffle=False, num_workers=8)
    model = vgg11(channel_num=3)
    model.to(device)
    loss_function = nn.CrossEntropyLoss()
    optimizer = torch.optim.Adam(model.parameters(), lr=0.0001)
    epochs = 10
    save_path = 'vgg11.pt'
    best_acc = 0.0
    steps = len(train_loader)
    for epoch in range(epochs):
        model.train()
        running_loss = 0.0
        for step, data in enumerate(train_loader):
            images, labels = data
            optimizer.zero_grad()
            outputs = model(images.to(device))
            loss = loss_function(outputs, labels.to(device))
            loss.backward()
            optimizer.step()
            running_loss += loss.item()
            print('epoch:{},step:{}/{},loss:{}'.format(epoch + 1, step + 1, steps, loss))
        model.eval()
        acc = 0.0
        with torch.no_grad():
            for val_data in val_loader:
                images, labels = val_data
                outputs = model(images.to(device))
                predict = torch.max(outputs, dim=1)[1]
                acc += torch.eq(predict, labels.to(device)).sum().item()
        val_acc = acc / len(val_dataset)
        print('epoch:{}, acc:{}'.format(epoch + 1, val_acc))
        if val_acc > best_acc:
            best_acc = val_acc
            torch.save(model.state_dict(), save_path)
    print("finish train")


if __name__ == '__main__':
    train()

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