pytorch下载USPS数据集

要在PyTorch中下载USPS数据集，你可以使用torchvision.datasets.USPS()函数。这个函数会返回一个torch.utils.data.Dataset对象，该对象包含USPS数据集的样本和标签。你可以使用torch.utils.data.DataLoader来加载这个数据集。下面是一个例子代码：

import torchvision.datasets as datasets
import torch.utils.data as data

usps_data = datasets.USPS(root='path/to/usps_root', train=True, download=True, transform=transforms.ToTensor())
usps_loader = data.DataLoader(usps_data, batch_size=batch_size, shuffle=True)

这个例子中，'path/to/usps_root'是你想要存储USPS数据集的路径，train=True表示下载训练集，download=True表示如果数据集不存在则会自动下载，transforms.ToTensor()将数据转换为张量形式。你可以根据需要修改参数来满足你的需求。

相关问题

用CNN和领域自适应进行样本迁移pytorch

样本迁移是指在源域和目标域之间进行数据转移的过程，其中CNN和领域自适应是常用的方法之一。下面是一个使用PyTorch实现的样本迁移的示例代码：

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import numpy as np
from torchvision import datasets, transforms

# 定义源域和目标域的数据集
source_dataset = datasets.MNIST(
    './data', train=True, download=True,
    transform=transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize((0.1307,), (0.3081,))
    ])
)
target_dataset = datasets.USPS(
    './data', train=True, download=True,
    transform=transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize((0.5,), (0.5,))
    ])
)

# 定义卷积神经网络(CNN)模型
class CNNModel(nn.Module):
    def __init__(self):
        super(CNNModel, self).__init__()
        self.conv1 = nn.Conv2d(1, 20, kernel_size=5)
        self.conv2 = nn.Conv2d(20, 50, kernel_size=5)
        self.fc1 = nn.Linear(4*4*50, 500)
        self.fc2 = nn.Linear(500, 10)
        
    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.max_pool2d(x, 2)
        x = F.relu(self.conv2(x))
        x = F.max_pool2d(x, 2)
        x = x.view(-1, 4*4*50)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# 定义领域自适应(DA)模型
class DAModel(nn.Module):
    def __init__(self):
        super(DAModel, self).__init__()
        self.fc1 = nn.Linear(500, 500)
        self.fc2 = nn.Linear(500, 2)
        
    def forward(self, x):
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# 定义源域和目标域的数据加载器
source_loader = torch.utils.data.DataLoader(
    source_dataset, batch_size=64, shuffle=True
)
target_loader = torch.utils.data.DataLoader(
    target_dataset, batch_size=64, shuffle=True
)

# 定义CNN和DA的优化器和损失函数
cnn_model = CNNModel()
da_model = DAModel()
cnn_optimizer = optim.SGD(cnn_model.parameters(), lr=0.01, momentum=0.5)
da_optimizer = optim.SGD(da_model.parameters(), lr=0.01, momentum=0.5)
criterion = nn.CrossEntropyLoss()

# 训练CNN和DA模型
for epoch in range(10):
    for i, (source_data, source_target) in enumerate(source_loader):
        target_data, _ = iter(target_loader).next()
        cnn_optimizer.zero_grad()
        da_optimizer.zero_grad()
        source_output = cnn_model(source_data)
        source_loss = criterion(source_output, source_target)
        target_output = cnn_model(target_data)
        da_input = target_output.detach()
        da_output = da_model(da_input)
        domain_target = torch.ones(target_output.size(0)).long()
        domain_target = domain_target.cuda() if torch.cuda.is_available() else domain_target
        domain_loss = criterion(da_output, domain_target)
        loss = source_loss + domain_loss
        loss.backward()
        cnn_optimizer.step()
        da_optimizer.step()
    print('Epoch: {}, Loss: {:.4f}'.format(epoch+1, loss.item()))

在这个示例中，我们使用了一个CNN模型作为源域和目标域之间数据的特征提取器，然后使用一个DA模型来适应不同的数据分布。在训练过程中，我们通过最小化源域和目标域之间的分类误差和领域误差来更新CNN和DA模型的参数。最终，我们可以使用训练好的CNN模型在目标域上进行分类预测。

DANN 自定义数据集 图像分类 python代码

以下是使用Python和PyTorch框架构建DANN模型进行图像分类的代码示例。假设我们的数据集包括两个域：源域和目标域，每个域包含10个类别，每个类别包含100张大小为28x28的灰度图像。

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
from torch.autograd import Function
from torch.utils.data import DataLoader
from torch.utils.data.dataset import Dataset

class CustomDataset(Dataset):
    def __init__(self, data, labels):
        self.data = data
        self.labels = labels
    
    def __getitem__(self, index):
        x = self.data[index]
        y = self.labels[index]
        return x, y
    
    def __len__(self):
        return len(self.data)

class ReverseLayerF(Function):
    @staticmethod
    def forward(ctx, x, alpha):
        ctx.alpha = alpha
        return x
    
    @staticmethod
    def backward(ctx, grad_output):
        output = grad_output.neg() * ctx.alpha
        return output, None

class DANN(nn.Module):
    def __init__(self):
        super(DANN, self).__init__()
        self.feature_extractor = nn.Sequential(
            nn.Conv2d(1, 32, 5),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(32, 48, 5),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Flatten(),
            nn.Linear(48 * 4 * 4, 100),
            nn.ReLU()
        )
        self.class_classifier = nn.Sequential(
            nn.Linear(100, 100),
            nn.ReLU(),
            nn.Linear(100, 10)
        )
        self.domain_classifier = nn.Sequential(
            nn.Linear(100, 100),
            nn.ReLU(),
            nn.Linear(100, 2)
        )
    
    def forward(self, x, alpha):
        features = self.feature_extractor(x)
        class_output = self.class_classifier(features)
        reverse_features = ReverseLayerF.apply(features, alpha)
        domain_output = self.domain_classifier(reverse_features)
        return class_output, domain_output

def train(model, dataloader):
    optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)
    criterion_class = nn.CrossEntropyLoss()
    criterion_domain = nn.CrossEntropyLoss()
    for epoch in range(10):
        for i, (source_data, source_labels) in enumerate(dataloader['source']):
            source_data, source_labels = source_data.to(device), source_labels.to(device)
            target_data, _ = next(iter(dataloader['target']))
            target_data = target_data.to(device)
            source_domain_labels = torch.zeros(source_data.size(0)).long().to(device)
            target_domain_labels = torch.ones(target_data.size(0)).long().to(device)
            optimizer.zero_grad()
            source_class_output, source_domain_output = model(source_data, 0.1)
            source_class_loss = criterion_class(source_class_output, source_labels)
            source_domain_loss = criterion_domain(source_domain_output, source_domain_labels)
            target_class_output, target_domain_output = model(target_data, 0.1)
            target_domain_loss = criterion_domain(target_domain_output, target_domain_labels)
            loss = source_class_loss + source_domain_loss + target_domain_loss
            loss.backward()
            optimizer.step()
            if i % 10 == 0:
                print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch+1, 10, i+1, len(dataloader['source']), loss.item()))

def test(model, dataloader):
    correct = 0
    total = 0
    with torch.no_grad():
        for data, labels in dataloader['target']:
            data, labels = data.to(device), labels.to(device)
            outputs, _ = model(data, 0)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()
    print('Accuracy of the network on the test images: %d %%' % (100 * correct / total))

if __name__ == '__main__':
    device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
    transform = transforms.Compose([
        transforms.Resize((28, 28)),
        transforms.ToTensor(),
        transforms.Normalize((0.5,), (0.5,))
    ])
    source_dataset = torchvision.datasets.MNIST(root='./data', train=True, download=True, transform=transform)
    target_dataset = torchvision.datasets.USPS(root='./data', train=True, download=True, transform=transform)
    source_data = source_dataset.data.unsqueeze(1).float()
    source_labels = source_dataset.targets
    target_data = target_dataset.data.unsqueeze(1).float()
    target_labels = target_dataset.targets
    source_loader = DataLoader(CustomDataset(source_data, source_labels), batch_size=64, shuffle=True)
    target_loader = DataLoader(CustomDataset(target_data, target_labels), batch_size=64, shuffle=True)
    dataloader = {'source': source_loader, 'target': target_loader}
    model = DANN().to(device)
    train(model, dataloader)
    test(model, dataloader)

在这个示例中，我们使用了MNIST和USPS两个数据集作为源域和目标域，分别包含0~9十个数字的手写数字图像。我们使用了PyTorch中的MNIST和USPS数据集类来加载数据，并将图像转换成PyTorch需要的张量格式。同时，我们使用了PyTorch中的DataLoader类来构建数据迭代器，方便进行批量训练和测试。我们使用了交叉熵损失函数来计算分类和域分类的损失，并使用随机梯度下降（SGD）优化器来更新模型参数。在每个epoch开始时，我们从源域数据集中随机选择一批数据，从目标域数据集中选择一批数据，并将其送入模型进行训练。我们使用了Reverse Gradient Layer来实现域适应学习，将特征提取器的梯度反转，从而使得域分类器无法区分源域和目标域之间的特征。在测试阶段，我们将目标域数据集送入训练好的模型中，计算分类准确率。