linux中cp -raf命令的用法

在Linux中，`cp -raf`命令用于复制文件和目录，并且会递归地复制子目录和文件。下面是该命令的用法：

`cp -raf <source> <destination>`

其中，`<source>`表示要复制的源文件或目录的路径，`<destination>`表示要将源文件或目录复制到的目标路径。

该命令的选项说明如下：
- `-r`：递归地复制目录及其内容。
- `-a`：以归档模式复制文件和目录，保留所有属性，包括权限、所有者、组、时间戳等。

请注意，如果目标路径已经存在同名文件或目录，使用`cp -raf`命令会覆盖目标文件或目录。

相关问题

pytorch代码实现在模型中使用LDAMLoss损失函数，数据集是RAF-DB，使用了mixup数据增强

好的，以下是使用LDAMLoss和mixup数据增强的RAF-DB数据集的PyTorch代码实现：

首先，我们需要导入必要的库：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
from torch.utils.data import DataLoader, Dataset
from torch.autograd import Variable
import numpy as np
import random
from PIL import Image

接下来，我们需要定义LDAM（Large Margin Cosine Loss）损失函数：

class LDAMLoss(nn.Module):
    def __init__(self, cls_num_list, max_m=0.5, weight=None, s=30):
        super(LDAMLoss, self).__init__()
        m_list = np.divide(1, np.sqrt(np.sqrt(cls_num_list)))
        m_list = np.multiply(max_m, m_list)
        m_list = torch.FloatTensor(m_list).cuda()
        self.m_list = m_list

        assert s > 0
        self.s = s
        if weight is not None:
            self.weight = torch.FloatTensor(weight).cuda()
        else:
            self.weight = weight

    def forward(self, x, target):
        cosine = x
        sine = torch.sqrt(1.0 - torch.pow(cosine, 2))
        phi = cosine * self.m_list.unsqueeze(1) - sine * self.m_list.unsqueeze(1)
        phi = phi.float()
        target = target.long().view(-1, 1)
        index = torch.zeros_like(phi)
        index.scatter_(1, target, 1)
        if self.weight is not None:
            weight = self.weight.unsqueeze(0)
            index = torch.matmul(index, weight.t())
            index = index.clamp(min=1e-12, max=1 - 1e-12)
            index = index.log()
            loss = -index * torch.pow(torch.abs(phi), self.s)
            loss = loss.sum(dim=1).mean()
        else:
            index = index.cuda()
            loss = -torch.log(torch.abs(torch.gather(phi, 1, target)) + 1e-8)
            loss = loss.squeeze(1)
            loss = loss.mean()

        return loss

接下来，我们需要定义mixup数据增强：

def mixup_data(x, y, alpha=1.0):
    if alpha > 0:
        lam = np.random.beta(alpha, alpha)
    else:
        lam = 1

    batch_size = x.size()[0]
    index = torch.randperm(batch_size).cuda()

    mixed_x = lam * x + (1 - lam) * x[index, :]
    y_a, y_b = y, y[index]

    return mixed_x, y_a, y_b, lam

然后，我们需要定义RAF-DB数据集的类：

class RAFDataset(Dataset):
    def __init__(self, data_path, transform=None):
        self.data_path = data_path
        self.transform = transform
        self.data = []
        self.labels = []

        with open(self.data_path, 'r') as f:
            for line in f:
                line = line.strip()
                img_path, label = line.split(' ')
                self.data.append(img_path)
                self.labels.append(int(label))

    def __len__(self):
        return len(self.data)

    def __getitem__(self, index):
        img_path = self.data[index]
        label = self.labels[index]
        img = Image.open(img_path).convert('RGB')

        if self.transform is not None:
            img = self.transform(img)

        return img, label

接下来，我们需要定义模型：

class MyModel(nn.Module):
    def __init__(self):
        super(MyModel, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu1 = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
        self.bn2 = nn.BatchNorm2d(128)
        self.relu2 = nn.ReLU(inplace=True)
        self.conv3 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1)
        self.bn3 = nn.BatchNorm2d(256)
        self.relu3 = nn.ReLU(inplace=True)
        self.conv4 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1)
        self.bn4 = nn.BatchNorm2d(512)
        self.relu4 = nn.ReLU(inplace=True)
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.fc1 = nn.Linear(512 * 12 * 12, 1024)
        self.drop1 = nn.Dropout(p=0.5)
        self.relu5 = nn.ReLU(inplace=True)
        self.fc2 = nn.Linear(1024, 7)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu1(x)
        x = self.conv2(x)
        x = self.bn2(x)
        x = self.relu2(x)
        x = self.conv3(x)
        x = self.bn3(x)
        x = self.relu3(x)
        x = self.conv4(x)
        x = self.bn4(x)
        x = self.relu4(x)
        x = self.pool(x)
        x = x.view(-1, 512 * 12 * 12)
        x = self.fc1(x)
        x = self.drop1(x)
        x = self.relu5(x)
        x = self.fc2(x)

        return x

最后，我们需要定义训练和测试函数：

def train(model, train_loader, optimizer, criterion, alpha):
    model.train()
    train_loss = 0
    train_correct = 0
    train_total = 0

    for i, (inputs, targets) in enumerate(train_loader):
        inputs, targets_a, targets_b, lam = mixup_data(inputs, targets, alpha=alpha)
        inputs, targets_a, targets_b = map(Variable, (inputs, targets_a, targets_b))

        optimizer.zero_grad()

        outputs = model(inputs)
        loss = criterion(outputs, targets_a) * lam + criterion(outputs, targets_b) * (1 - lam)

        loss.backward()
        optimizer.step()

        train_loss += loss.item()
        _, predicted = torch.max(outputs.data, 1)
        train_total += targets.size(0)
        train_correct += (lam * predicted.eq(targets_a.data).cpu().sum().float()
                          + (1 - lam) * predicted.eq(targets_b.data).cpu().sum().float())

    train_acc = train_correct / train_total
    train_loss = train_loss / len(train_loader)

    return train_acc, train_loss


def test(model, test_loader, criterion):
    model.eval()
    test_loss = 0
    test_correct = 0
    test_total = 0

    with torch.no_grad():
        for inputs, targets in test_loader:
            inputs, targets = Variable(inputs), Variable(targets)

            outputs = model(inputs)
            loss = criterion(outputs, targets)

            test_loss += loss.item()
            _, predicted = torch.max(outputs.data, 1)
            test_total += targets.size(0)
            test_correct += predicted.eq(targets.data).cpu().sum().float()

    test_acc = test_correct / test_total
    test_loss = test_loss / len(test_loader)

    return test_acc, test_loss

最后，我们需要定义主函数：

if __name__ == '__main__':
    # 设置随机种子，确保实验的可重复性
    torch.manual_seed(233)
    np.random.seed(233)
    random.seed(233)

    # 定义训练参数
    batch_size = 64
    num_epochs = 100
    lr = 0.1
    alpha = 1.0
    cls_num_list = [2000, 2000, 2000, 2000, 2000, 2000, 2000]
    train_data_path = 'train.txt'
    test_data_path = 'test.txt'

    # 定义数据增强和数据集
    transform_train = transforms.Compose([
        transforms.RandomCrop(44),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
    ])

    transform_test = transforms.Compose([
        transforms.CenterCrop(44),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
    ])

    train_dataset = RAFDataset(train_data_path, transform=transform_train)
    test_dataset = RAFDataset(test_data_path, transform=transform_test)

    # 定义数据加载器
    train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=4)
    test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=4)

    # 定义模型和优化器
    model = MyModel().cuda()
    optimizer = optim.SGD(model.parameters(), lr=lr, momentum=0.9, weight_decay=5e-4)
    criterion = LDAMLoss(cls_num_list)

    # 训练和测试
    for epoch in range(num_epochs):
        train_acc, train_loss = train(model, train_loader, optimizer, criterion, alpha)
        test_acc, test_loss = test(model, test_loader, criterion)

        print('Epoch [{}/{}], Train Loss: {:.4f}, Train Acc: {:.4f}, Test Loss: {:.4f}, Test Acc: {:.4f}'
              .format(epoch + 1, num_epochs, train_loss, train_acc, test_loss, test_acc))

        if (epoch + 1) % 10 == 0:
            lr /= 10
            optimizer = optim.SGD(model.parameters(), lr=lr, momentum=0.9, weight_decay=5e-4)

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