用pytorch写一个分类模型用于手势识别

以下是一个用PyTorch编写的手势识别分类模型的示例代码。

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 torchvision.datasets import ImageFolder

然后，我们可以定义一些超参数，例如批量大小、学习率等：

batch_size = 32
learning_rate = 0.001
num_epochs = 10

接下来，我们可以定义数据集和数据增强的转换：

data_transforms = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406],
                         std=[0.229, 0.224, 0.225])
])

train_dataset = ImageFolder(root='./train/', transform=data_transforms)
valid_dataset = ImageFolder(root='./valid/', transform=data_transforms)

接下来，我们可以定义模型。在这个例子中，我们使用了预训练的ResNet18模型：

model = nn.Sequential(
    nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False),
    nn.BatchNorm2d(64),
    nn.ReLU(inplace=True),
    nn.MaxPool2d(kernel_size=3, stride=2, padding=1),

    nn.Sequential(*list(torchvision.models.resnet18(pretrained=True).children())[4:-1]),

    nn.AdaptiveAvgPool2d((1, 1)),
    nn.Flatten(),
    nn.Linear(512, 6)
)

然后，我们可以定义损失函数和优化器：

criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

最后，我们可以训练模型：

train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
valid_loader = torch.utils.data.DataLoader(valid_dataset, batch_size=batch_size, shuffle=False)

for epoch in range(num_epochs):
    model.train()
    for images, labels in train_loader:
        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

    model.eval()
    with torch.no_grad():
        total = 0
        correct = 0
        for images, labels in valid_loader:
            outputs = model(images)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()

        accuracy = 100 * correct / total
        print(f'Epoch [{epoch + 1}/{num_epochs}], Validation Accuracy: {accuracy:.2f}%')

希望这个示例代码能够帮助你开始构建自己的手势识别分类模型。