import torchvision.transforms as transformers 是什么意思

import torchvision.transforms as transformers 是导入 PyTorch 中 torchvision.transforms 模块，并将它重命名为 transformers，使得可以使用其中的图像处理函数进行图像预处理。

相关问题

transformers 库进行图像分割的例子

以下是使用 transformers 库进行图像分割的例子：

1. 安装必要的库和模块：

!pip install transformers
!pip install torch torchvision

2. 导入必要的库和模块：

import torch
import torchvision
import matplotlib.pyplot as plt
from transformers import ViTFeatureExtractor, ViTForImageSegmentation

3. 加载数据集：

transform = torchvision.transforms.Compose([
    torchvision.transforms.Resize((224, 224)),
    torchvision.transforms.ToTensor(),
])

train_dataset = torchvision.datasets.CocoDetection(
    root='./data/train2017',
    annFile='./data/annotations/instances_train2017.json',
    transform=transform
)

test_dataset = torchvision.datasets.CocoDetection(
    root='./data/val2017',
    annFile='./data/annotations/instances_val2017.json',
    transform=transform
)

4. 加载模型和特征提取器：

feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224')
model = ViTForImageSegmentation.from_pretrained('google/vit-base-patch16-224')

5. 定义训练函数：

def train(model, train_dataloader, optimizer, criterion, device):
    model.train()
    train_loss = 0
    for i, (inputs, targets) in enumerate(train_dataloader):
        inputs = inputs.to(device)
        targets = targets.to(device)
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, targets)
        loss.backward()
        optimizer.step()
        train_loss += loss.item()
    return train_loss / len(train_dataloader)

6. 定义测试函数：

def test(model, test_dataloader, criterion, device):
    model.eval()
    test_loss = 0
    with torch.no_grad():
        for i, (inputs, targets) in enumerate(test_dataloader):
            inputs = inputs.to(device)
            targets = targets.to(device)
            outputs = model(inputs)
            loss = criterion(outputs, targets)
            test_loss += loss.item()
    return test_loss / len(test_dataloader)

7. 训练模型：

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)

optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
criterion = torch.nn.CrossEntropyLoss()

train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=16, shuffle=True)
test_dataloader = torch.utils.data.DataLoader(test_dataset, batch_size=16, shuffle=False)

num_epochs = 10

train_losses = []
test_losses = []

for epoch in range(num_epochs):
    train_loss = train(model, train_dataloader, optimizer, criterion, device)
    test_loss = test(model, test_dataloader, criterion, device)
    train_losses.append(train_loss)
    test_losses.append(test_loss)
    print(f'Epoch {epoch+1}/{num_epochs}, Train Loss: {train_loss:.4f}, Test Loss: {test_loss:.4f}')

plt.plot(train_losses, label='Train Loss')
plt.plot(test_losses, label='Test Loss')
plt.legend()
plt.show()

8. 对单个图像进行预测：

image = torchvision.io.read_image('image.jpg')
inputs = transform(image).unsqueeze(0).to(device)
outputs = model(inputs)
segmentation_map = torch.argmax(outputs, dim=1).squeeze().cpu().numpy()
plt.imshow(segmentation_map)
plt.show()

以上是使用 transformers 库进行图像分割的例子，其中使用了 ViT 模型和 COCO 数据集。

Transformers做cifar-10图像分类任务pytorch版本的代码

下面是一个基于PyTorch实现的Transformers对cifar-10图像分类的代码，使用了BERT作为Transformers的模型。

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
from transformers import BertModel

# 超参数设置
input_size = 32
hidden_size = 768
num_classes = 10
batch_size = 100
num_epochs = 10
learning_rate = 0.001

# CIFAR-10数据集下载和预处理
transform = transforms.Compose(
    [transforms.Resize((input_size, input_size)),
     transforms.ToTensor(),
     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

train_dataset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                             download=True, transform=transform)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size,
                                           shuffle=True, num_workers=2)

test_dataset = torchvision.datasets.CIFAR10(root='./data', train=False,
                                            download=True, transform=transform)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size,
                                          shuffle=False, num_workers=2)

# BERT模型定义
class BERT(nn.Module):
    def __init__(self, hidden_size, num_classes):
        super(BERT, self).__init__()
        self.bert = BertModel.from_pretrained('bert-base-uncased')
        self.fc = nn.Linear(hidden_size, num_classes)

    def forward(self, x):
        # 前向传播 BERT
        out = self.bert(x)[0][:, 0, :]
        out = self.fc(out)
        return out

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = BERT(hidden_size, num_classes).to(device)

# 损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

# 模型训练
for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):
        # 将图像转换成BERT需要的形状
        images = images.reshape(-1, input_size * input_size * 3).to(device)
        labels = labels.to(device)

        # 前向传播
        outputs = model(images)
        loss = criterion(outputs, labels)

        # 反向传播和优化
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        if (i+1) % 100 == 0:
            print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'
                  .format(epoch+1, num_epochs, i+1, len(train_loader), loss.item()))

# 模型测试
with torch.no_grad():
    correct = 0
    total = 0
    for images, labels in test_loader:
        # 将图像转换成BERT需要的形状
        images = images.reshape(-1, input_size * input_size * 3).to(device)
        labels = labels.to(device)

        # 前向传播
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

    print('Test Accuracy of the model on the 10000 test images: {} %'.format(100 * correct / total))

需要注意的是，这个代码中使用了一个将图像转换成BERT需要的形状的过程，即将每个图像的像素展平成一维向量。在这个例子中，我们使用了预训练的BERT模型来提取图像的特征，并将特征向量输入到一个全连接层中进行分类。